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The physiological and proteomic response of two strains of the diatom Thalassiosira oceanica to copper… Hippmann, Anna Angelika 2017

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The Physiological and Proteomic Response of Two Strains of the Diatom  Thalassiosira oceanica to Copper and/or Iron Limitation  by  Anna Angelika Hippmann  Dipl. Biol., Johannes Gutenberg University of Mainz, Germany, 2005  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Oceanography)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  December 2017  © Anna Angelika Hippmann, 2017 ii  Abstract  Diatoms are responsible for over 20% of the Earth's photosynthetic productivity, thus impact global fisheries, biogeochemical cycles and climate. However, marine primary productivity is limited by the micronutrient iron (Fe) in ~40% of the ocean. Diatoms inhabiting these regions have evolved unique physiological strategies to survive under these extremely low Fe conditions. Several physiological adaptations to Fe-limitation in diatoms require an increased dependency on copper (Cu), suggesting an interaction between Fe and Cu nutrition and the potential for Fe-Cu co-limitation in these regions. Though some published work has illustrated transcriptomic and proteomic adaptations of some diatoms to low Fe, there is limited knowledge on diatoms' acclimation strategies to low Cu or Fe-Cu co-limitation.  My thesis research focused on elucidating concomitant physiological and proteomic responses of two strains of the open ocean diatom Thalassiosira oceanica (CCMP1003 and CCMP1005) acclimated to Fe, Cu, and Fe-Cu limiting conditions. I measured over 20 physiological parameters, including carbon assimilation rate, oxygen production, respiration rate, an array of photosynthetic parameters [such as electron transport rate of photosystem II (ETRPSII), non-photochemical quenching (NPQ), and antenna absorption cross section of photosystem II, PSII], and photosynthetic rates as a function of light intensity. Moreover, I investigated the differential expression of proteins in T. oceanica in response to these three metal limitations, using stable isotope dimethyl labelling proteomics. I first describe the physiological and proteomic responses to Cu limitation in T. oceanica, focusing on the changes to and the interplay among proteins and pathways involved in the light reactions of photosynthesis, the carbon and nitrogen metabolisms, and the prevention of iii  oxidative stress. I then compare unique changes to the photosynthetic apparatus induced by each metal limitation (Fe, Cu and Fe-Cu) vs. changes induced by general cellular stress. Furthermore, given that my research investigated two strains of T. oceanica, I uncovered stunning intraspecific differences in their proteomic and physiological responses to trace metal limitation. My research unveiled a comprehensive restructuring of the photosynthetic apparatus, and a sophisticated interaction among metabolic pathways in T. oceanica (CCMP1003) in response to low metal availability (especially Cu), demonstrating exceptional adaptations to low trace metal availabilities.   iv  Lay Summary  Diatoms are one of the most important groups of algae in our oceans. Indeed, a quarter of the oxygen we breathe is produced by diatoms. These microorganisms support global fisheries and modulate nutrient cycles and climate. However, their growth is limited by iron (Fe) availability in over one-third of the global ocean. Diatoms have adapted to survive in these Fe-limited regions using a higher number of copper (Cu) containing proteins and thus diatoms may potentially experience co-limitation by Fe and Cu in situ. On-going anthropogenic changes to our oceans, such as ocean acidification and higher temperature, are expected to affect Fe and Cu bioavailabilities.  Thus, to predict future changes in marine primary productivity, we need to understand how diatoms may adapt to low trace metal conditions. My research provides insights on different physiological and proteomic strategies employed by two open ocean diatoms in response to three different metal limitation scenarios.   v  Preface  Chapter 2 is the Methods chapter for Chapters 3-5. This chapter is a modified version (i.e. more detailed, and including the Fe and Fe-Cu co-limiting conditions) of the method section published in: Hippmann, A.A., Schuback, N., Moon, K.-M., McCrow, J.P., Allen, A.E., Foster, L.J., Green, B.R., and Maldonado, M.T. (2017). Contrasting effects of copper limitation on the photosynthetic apparatus in two strains of the open ocean diatom Thalassiosira oceanica. PLoS ONE 12, e0181753. The experiments were designed by AAH with help from MTM, BRG, LJF, and AAE. AAH wrote most of the chapter. The section on FRRf parameters was originally drafted by NS, the section on differential proteomics was originally drafted by KMM, and the section on the EST library generation was drafted by AAH and AEA.  Chapter 3 has been published as:  Hippmann, A.A., Schuback, N., Moon, K.-M., McCrow, J.P., Allen, A.E., Foster, L.J., Green, B.R., and Maldonado, M.T. (2017). Contrasting effects of copper limitation on the photosynthetic apparatus in two strains of the open ocean diatom Thalassiosira oceanica. PLoS ONE 12, e0181753. The experiments were designed by AAH with help from MTM, BRG, LJF, and AAE. AAH was responsible for the culturing and harvesting of the diatoms, the determination and extraction of cellular proteins and their separation into soluble and insoluble fractions. AAH also determined all physiological parameters, except the FRRf measurements, which were conducted by NS.  vi  MKM was responsible for stable isotope dimethylation and LC-MS/MS, as well as running the MASCOT® software. Cells for the EST library were grown by AAH, while the DNA extraction, sequencing, and EST library assembly were conducted by JPM and AEA. AAH interpreted the data and wrote the manuscript, with help from MTM, BRG, and LJF.  Chapters 4 and 5 are in preparation for submission to peer-reviewed scientific journals. The co-authors are: Hippmann, A.A., Schuback, N., Moon, K.-M., McCrow, J.P., Allen, A.E., Foster, L.J., Green, B.R., and Maldonado, M.T. The experiments were designed by AAH with help from MTM, BRG, LJF, and AAE. AAH was responsible for the culturing and harvesting of the diatoms, the determination and extraction of cellular proteins and their separation into soluble and insoluble fractions. AAH also determined all physiological parameters, except the FRRf measurements, which were conducted by NS.  MKM was responsible for stable isotope dimethylation and LC-MS/MS, as well as running the MASCOT® software. Cells for the EST library were grown by AAH, while the DNA extraction, sequencing, and EST library assembly were conducted by JPM and AEA. AAH interpreted the data and wrote the chapters, with help from MTM, BRG, and LJF. vii  Table of Contents  Abstract .......................................................................................................................................... ii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of Contents ........................................................................................................................ vii List of Tables .............................................................................................................................. xvi List of Figures ...............................................................................................................................xx List of Symbols ......................................................................................................................... xxiii List of Abbreviations ............................................................................................................... xxiv Acknowledgements ....................................................................................................................xxx Dedication ................................................................................................................................ xxxii Chapter 1: Introduction ................................................................................................................1 1.1 Global ocean, phytoplankton, and climate ...................................................................... 1 1.2 Diatoms ........................................................................................................................... 2 1.3 Photosynthesis................................................................................................................. 3 1.4 Phytoplankton productivity ............................................................................................. 3 1.5 Iron in the ocean.............................................................................................................. 5 1.6 Iron - cellular demand ..................................................................................................... 5 1.7 Copper in the ocean ........................................................................................................ 7 1.8 Copper – cellular demand ............................................................................................... 8 1.9 Algal trace-metal physiology – iron, copper and their interaction ............................... 10 1.10 T. oceanica - a model species for open ocean diatoms adapted to low Fe ................... 11 viii  1.11 Diatoms – their phylogenetic ancestry as key to their success ..................................... 13 1.12 Studying the response to environmental factors in the era of 'OMICS' ........................ 15 1.13 Thesis objective ............................................................................................................ 18 1.14 Thesis structure ............................................................................................................. 19 1.15 Tables and figures ......................................................................................................... 21 Chapter 2: Methods .....................................................................................................................26 2.1 General .......................................................................................................................... 26 2.1.1 Study organism ......................................................................................................... 26 2.1.2 ITS region ................................................................................................................. 26 2.1.3 Culture media and growth conditions ....................................................................... 27 2.2 Whole cell physiology .................................................................................................. 28 2.2.1 Growth measurements .............................................................................................. 28 2.2.2 Chla measurements ................................................................................................... 29 2.2.3 Cellular protein concentration................................................................................... 29 2.2.4 Oxygen evolution, respiration  and alternative oxydase (AOX) essay ..................... 29 2.2.5 Chla fluorescence parameters and ETRRCII using Fast Repetition Rate fluorometry (FRRf)  .................................................................................................................................. 31 2.2.6 14C carbon assimilation ............................................................................................. 32 2.2.7 PE curves .................................................................................................................. 33 2.2.8 Derivation of conversion factor ................................................................................ 33 2.2.9 Statistical analysis of physiological experiments ..................................................... 34 2.3 Transcriptomics............................................................................................................. 35 2.3.1 RNA-Seq library preparation .................................................................................... 35 ix  2.3.2 RNA-Seq data analysis ............................................................................................. 35 2.3.3 T. oceanica strain comparison .................................................................................. 36 2.4 Proteomics..................................................................................................................... 36 2.4.1 Protein purification ................................................................................................... 36 2.4.2 Protein preparation for mass spectrometry ............................................................... 37 2.4.3 Liquid chromatography-tandem mass spectrometry – LC-MS/MS.......................... 38 2.4.4 Analysis of mass spectrometry data .......................................................................... 40 2.4.5 Statistical analysis of proteomic differential expression .......................................... 40 2.4.6 Protein annotation ..................................................................................................... 42 2.5 Phylogenetic trees ......................................................................................................... 42 2.5.1 Light  harvesting complex (LHC) ............................................................................. 42 2.5.2 ITS region ................................................................................................................. 43 2.6 Tables and figures ......................................................................................................... 44 Chapter 3: Contrasting Effects of Copper Limitation on the Photosynthetic Apparatus in Two Strains of the Open Ocean Diatom Thalassiosira oceanica .............................................49 3.1 Summary ....................................................................................................................... 49 3.2 Introduction ................................................................................................................... 50 3.3 Results ........................................................................................................................... 53 3.3.1 Effect of chronic copper limitation on whole cell physiological parameters ........... 54 3.3.2 Effects of Cu limitation on photophysiology, ETRRCII and C-assimilation .............. 55 3.3.3 Overview of the two proteomic datasets for the two strains – original vs. EST included ................................................................................................................................. 56 3.3.4 Proteomic shift in the antennae and photosynthetic transport chain ......................... 57 x  3.3.4.1 Identity and expression of LHC proteins .......................................................... 57 3.3.4.2 Proteomic shift within the chloroplast electron transport chain (ETC) ............ 58 3.4 Discussion ..................................................................................................................... 59 3.4.1 Differences between T. oceanica CCMP 1003 and CCMP 1005 ............................. 59 3.4.2 Proteomic shift within the photosynthetic apparatus in TO03: electron transport chain and antennae ................................................................................................................ 64 3.4.2.1 Electron transport chain - ETC ......................................................................... 64 3.4.2.2 Antennae ........................................................................................................... 66 3.4.3 PvsE curves (ETRPSII & 14C), conversion factor ....................................................... 67 3.4.3.1 PvsE curve – ETRRCII and carbon uptake ......................................................... 68 3.4.3.2 Conversion factor .............................................................................................. 70 3.5 Conclusion .................................................................................................................... 71 3.6 Tables and figures ......................................................................................................... 74 Chapter 4: Proteomic Analysis Revealed the Intricate Interaction Between the Carbon and Nitrogen Metabolisms in Copper-Limited T. oceanica ............................................................90 4.1 Summary ....................................................................................................................... 90 4.2 Introduction ................................................................................................................... 92 4.3 Results ........................................................................................................................... 95 4.3.1 Overview of proteomic datasets................................................................................ 96 4.3.2 Calvin Benson Bassham cycle .................................................................................. 96 4.3.3 Glycolysis ................................................................................................................. 97 4.3.4 Citrate (TCA) cycle................................................................................................... 98 4.3.5 Nitrogen metabolism ................................................................................................. 99 xi  4.3.6 Glutathione metabolism .......................................................................................... 100 4.3.7 Malate shunt ............................................................................................................ 102 4.3.8 Pyruvate metabolism ............................................................................................... 102 4.3.9 Respiration .............................................................................................................. 103 4.4 Discussion ................................................................................................................... 104 4.4.1 Carbon metabolism – the Calvin-Benson-Bassham cycle is down-regulated via its activase and the reactions of glycolysis are used to redistribute ATP and NAD(P)H within the cell ................................................................................................................................. 105 4.4.2 Nitrogen metabolism is essential for Fdred oxidation and glutamate synthesis to fight ROS ................................................................................................................................. 109 4.4.2.1 Counteracting reactive oxygen species – glutathione, thioredoxin, and superoxide dismutases .................................................................................................... 111 4.4.3 The malate shunt drains reducing equivalents from the chloroplast to the mitochondria, thus integrating the nitrogen and carbon metabolisms ................................ 114 4.4.4 Contrasting adaptations to Cu limitation in TO03 vs. TO05 .................................. 117 4.5 Conclusion .................................................................................................................. 119 4.6 Tables and figures ....................................................................................................... 121 Chapter 5: Comparative Proteomic Analysis of the Photosynthetic Apparatus of Copper and/or Iron Limited T. oceanica ...............................................................................................145 5.1 Summary ..................................................................................................................... 145 5.2 Introduction ................................................................................................................. 146 5.3 Results and discussion ................................................................................................ 148 5.3.1 Status of limitation .................................................................................................. 148 xii  5.3.1.1 LowCu status .................................................................................................. 149 5.3.1.2 LowFe status ................................................................................................... 149 5.3.1.3 LowFeCu status .............................................................................................. 151 5.3.2 Changes in physiology ............................................................................................ 151 5.3.3 Changes to the photosynthetic apparatus to balance maximum growth and minimal photodamage under low metal conditions .......................................................................... 153 5.3.3.1 LowCu – changes to photosynthetic apparatus ............................................... 154 5.3.3.2 LowFe – changes to photosynthetic apparatus ............................................... 158 5.3.3.3 LowFeCu – changes to the photosynthetic apparatus ..................................... 160 5.3.4 Comparisons of the changes to the photosynthetic apparatus between strains and among trace metal limiting treatments ................................................................................ 162 5.3.4.1 Iron-Cu co-limitation in T. oceanica – more than just Type III ..................... 165 5.4 Conclusion .................................................................................................................. 167 5.5 Tables and figures ....................................................................................................... 169 Chapter 6: Conclusion ...............................................................................................................183 6.1 General thoughts on elemental limitation ................................................................... 183 6.2 My research ................................................................................................................. 184 6.3 Challenges of my research .......................................................................................... 187 6.4 Implications of my findings ........................................................................................ 188 6.5 Outlook for future research ......................................................................................... 189 6.6 Tables and figures ....................................................................................................... 193 References ...................................................................................................................................196 Appendices ..................................................................................................................................233 xiii  Appendix A −   Supplementary material for Chapter 3: Contrasting Effects of Copper Limitation on the Photosynthetic Apparatus in Two Strains of the Open Ocean Diatom Thalassiosira oceanica ........................................................................................................... 233 A.1 ITS fragment visualization ...................................................................................... 233 A.2 ITS sequence alignment .......................................................................................... 234 A.3 ITS phylogenetic tree .............................................................................................. 235 A.4 ITS sequences used for phylogenetic tree ............................................................... 236 A.5 Summary table of effects of low Cu on physiological parameters in Thalassiosira oceanica (CCMP 1003 and CCMP 1005) .......................................................................... 237 A.6 Table of raw physiological data for all three biological replicates of each treatment (control and low Cu) and strain (CCMP 1003 and CCMP 1005) ....................................... 240 A.7 Differential expression of all 48 predicted LHCs in T. oceanica (CCMP 1003 and CCMP 1005) across all four datasets .................................................................................. 242 A.8 Differential expression of all identified proteins involved in the photosynthetic ETC in T. oceanica (CCMP 1003 and CCMP 1005) across all four datasets ............................. 244 A.9 Overview of the number of identified proteins in the four different proteomic datasets ................................................................................................................................ 246 Appendix B −   Supplementary material for Chapter 4: Proteomic Analysis Revealed the Intricate Interaction Between the Carbon and Nitrogen Metabolisms in Copper-Limited T. oceanica .................................................................................................................................. 247 B.1 The Calvin-Benson-Bassham cycle - Differential expression of proteins involved in Calvin-Benson-Bassham cycle in T. oceanica CCMP 1003 (TO03) and 1005 (TO05) grown under Cu-limiting conditions .............................................................................................. 247 xiv  B.2 Putative triose-phosphate transporters and their differential expression in T. oceanica CCMP 1003 (TO03) and 1005 (TO05) grown under Cu-limiting conditions ..... 249 B.3 Glycolysis - Differential expression of proteins involved in glycolysis in T. oceanica CCMP 1003 (TO03) and 1005 (TO05) grown under Cu-limiting conditions .................... 250 B.4 Citrate Cycle (TCA) - Differential expression of proteins involved in glycolysis in T. oceanica CCMP 1003 (TO03) and 1005 (TO05) grown under Cu-limiting conditions ..... 253 B.5 Urea and nitrogen metabolism - Differential expression of proteins involved in urea and nitrogen metabolism in T. oceanica CCMP 1003 (TO03) and 1005 (TO05) grown under Cu-limiting conditions ........................................................................................................ 255 B.6 Glutathione metabolism - Differential expression of proteins involved in glutathione metabolism in T. oceanica CCMP 1003 (TO03) and 1005 (TO05) grown under Cu-limiting conditions ............................................................................................................................ 259 B.7 Malate Shunt - Differential expression of proteins involved in the putative malate shunt in T. oceanica CCMP 1003 (TO03) and 1005 (TO05) grown under Cu-limiting conditions ............................................................................................................................ 262 B.8 Pyruvate Hub - Differential expression of proteins involved in the pyruvate hub in T. oceanica CCMP 1003 (TO03) and 1005 (TO05) grown under Cu-limiting conditions ..... 264 B.9 Respiration - Differential expression of proteins involved in respiration in T. oceanica CCMP 1003 (TO03) and 1005 (TO05) grown under Cu-limiting conditions ..... 266 Appendix C −   Supplementary material for Chapter 4: Comparative Proteomic Analysis of the Photosynthetic Apparatus of Copper and/or Iron Limited T. oceanica .................................. 267 C.1 Intraspecific differences in the effects of chronic metal limitation on physiology in two strains of the open ocean diatom Thalassiosira oceanica. .......................................... 267 xv  C.2 Effect of all three low metal treatments (lowCu, lowFe, lowFeCu) on physiology in the open ocean diatom Thalassiosira oceanica (CCMP 1003, TO03). .............................. 269 C.3 Effect of all three low metal treatments (lowCu, lowFe, lowFeCu) on physiology in the open ocean diatom Thalassiosira oceanica (CCMP 1005, TO05). .............................. 271 C.4 All gene models of iron-responsive proteins and their expression across all three metal limitation treatments in two strains of T. oceanica. .................................................. 273 C.5 Expression of light-harvesting complex (LHC) proteins across all three metal limitation treatments in two strains of T. oceanica ............................................................. 275  xvi  List of Tables  Table 2.1: Overview of the Fe[total] and Cu[total] concentrations added to our media for each T.oceanica strain. .......................................................................................................................... 44 Table 3.1. Overview of expression of all putative LHCs in both strains of T. oceanica. ............. 83 Table 3.2. Significantly differentially expressed LHCs under chronic low Cu conditions. ......... 84 Table 3.3. Differential expression of proteins involved in the photosynthetic electron transport chain (ETC) in response to chronic Cu limitation in T. oceanica. ............................................... 86 Table 4.1: Comparison of cellular localization of various carbon metabolic pathways. (modified from Gruber and Kroth 2017) ..................................................................................................... 121 Table 4.2: Fructose-bisphosphate aldolase (FBA) isoenzymes in P. tricornutum (Pt) and homologs in T. oceanica (To, CCMP 1003). .............................................................................. 138 Table 5.1 Expression of iron-responsive proteins in two strains of T. oceanica, CCMP 1003 (TO03) and 1005 (TO05), grown under low Cu (lowCu), low Fe (lowFe), and low Fe and Cu conditions (lowFeCu).................................................................................................................. 173 Table 5.2 Significantly differentially expressed LHCs in two strains of T. oceanica, CCMP 1003 (TO03) and 1005 (TO05), grown under low Cu (lowCu), low Fe (lowFe), and low Fe and Cu conditions (lowFeCu).................................................................................................................. 174 Table 5.3: Differential expression of proteins involved in the photosynthetic electron transport chain (ETC) in two strains of T. oceanica, CCMP 1003 (TO03) and 1005 (TO05), in response to chronic metal limitation induced by low Cu (lowCu), low Fe (lowFe), and low Fe and Cu concentrations (lowFeCu). .......................................................................................................... 176 xvii  Table 5.4: Overview of all highly regulated LHC in two strains of T. oceanica (CCMP 1003, TO03, and 1005, TO05) in response to low Cu (lowCu), low Fe (lowFe), and low Fe and Cu conditions (lowFeCu).................................................................................................................. 181 Table 5.5: Changes in stoichiometry in the ETC normalized to the change seen in cytb6f in two strains of T. oceanica (CCMP 1003, TO03, and 1005, TO05) in response to low Cu (lowCu), low Fe (lowFe), and low Fe and Cu conditions (lowFeCu). .............................................................. 182 Table 6.1: Overview of Fe and Cu concentrations used in various studies on Fe and Cu physiology in T. oceanica and their induced changes in growth rate (%max) ........................... 195 Table A.1: Sequence information used to generate phylogenetic tree of various diatom ITS sequences .................................................................................................................................... 236 Table A.3: Physiological raw data of all three biological replicates for both strains (CCMP 1003 and CCMP 1005) and treatments (control and lowCu) .............................................................. 240 Table A.4: Expression of all 48 predicted LHC in TO03 and TO05 across all four datasets ..... 242 Table A.5: Differential expression of all identified proteins involved in photosynthetic ETC across all datasets ........................................................................................................................ 244 Table A.6 Overview of the number of identified proteins in the four different proteomic datasets...................................................................................................................................................... 246 Table B.1 Differential expression and predicted cellular location of proteins involved in the Calvin-Benson-Bassham cycle in TO03 and TO05 cultured in low Cu conditions vs. control. . 247 Table B.2: Proteins with triose-phosphate transporter PFAM and their expression in TO03 and TO05 in response to Cu limitation vs. control ............................................................................ 249 Table B.3: Differential expression and predicted cellular location of proteins involved in glycolysis in TO03 and TO05 cultured in low Cu conditions vs. control................................... 250 xviii  Table B.4: Differential expression and predicted cellular location of proteins involved in the tricarboxylic acid (TCA) / citrate cycle in TO03 and TO05 cultured in low Cu conditions vs. control. (for diagram, see   Figure 4.4) ....................................................................................... 253 Table B.5: Differential expression and predicted cellular location of proteins involved in nitrogen metabolism including the urea cycle in TO03 and TO05 cultured in low Cu conditions vs. control. ........................................................................................................................................ 255 Table B.6: Differential expression and predicted cellular location of proteins involved in glutathione metabolism in TO03 and TO05 cultured in low Cu conditions vs. control. ............ 259 Table B.7: Differential expression and predicted cellular location of proteins involved in the putative malate shunt in TO03 and TO05 cultured in low Cu conditions vs. control................. 262 Table B.8: Differential expression and predicted cellular location of proteins involved in pyruvate metabolism in TO03 and TO05 cultured in low Cu conditions vs. control. ................ 264 Table B.9: Differential expression and predicted cellular location of proteins involved in respiration in TO03 and TO05 cultured in low Cu conditions vs. control. ................................. 266 Table C.1 Intraspecific differences in the effects of chronic metal limitation on physiology in 2 strains of the open ocean diatom Thalassiosira oceanica. ......................................................... 268 Table C.2: Effect of all three low metal treatments (lowCu, lowFe, lowFeCu) on physiology in the open ocean diatom Thalassiosira oceanica (CCMP 1003, TO03). ...................................... 269 Table C.3: Effect of all three low metal treatments (lowCu, lowFe, lowFeCu) on physiology in the open ocean diatom Thalassiosira oceanica(CCMP 1005, TO05). ....................................... 271 Table C.4: All gene models of iron-responsive proteins and their expression across all three metal limitation treatments in two strains of T. oceanica. .......................................................... 273 xix  Table C.5: Expression of light-harvesting complex (LHC) proteins across all three metal limitation treatments in two strains of T. oceanica ..................................................................... 275  xx  List of Figures  Figure 1.1: Light microscopy images of Thalassiosira oceanica (CCMP 1003 and CCMP 1005). A) CCMP 1003 in side (girdle) view (A. Hippmann); B) CCMP 1005 in top (valve) view; C) CCMP 1005 from side (girdle) view (Lommer et al. 2012). ........................................................ 21 Figure 1.2: Schematic overview of the photosynthetic electron transport chain (ETC) in the chloroplast thylakoid membrane. .................................................................................................. 22 Figure 1.3 Schematic overview of the phylogenetic ancestry of diatoms (based on (Finazzi et al. 2010). ............................................................................................................................................ 23 Figure 1.4 Overview of experimental set-up. ............................................................................... 25 Figure 2.3 Diagram, illustrating how the differential proteomic data was analyzed .................... 48 Figure 3.1. Copper-dependent growth rates of Thalassiosira oceanica TO03 and TO05. ........... 74 Figure 3.2. The effects of Cu limitation on growth rate, cell diameter, protein content and a series of photophysiological parameters in two strains of T. oceanica. ................................................. 76 Figure 3.3. Comparison between EST-mapped proteomics datasets of TO03 and TO05, emphasizing the similarity in their genomes. A) Results for TO03 (plusEST). B) Results for TO05 (plusEST). ........................................................................................................................... 78 Figure 3.4. A) Representative sequence alignment of predicted LHCs from Thalassiosira oceanica (THAOC, CCMP 1005) and T. pseudonana (Tp, CCMP 1335). .................................. 80 Figure 3.5. Phylogenetic tree of 48 predicted LHCs from the T. oceanica genome (CCMP 1005) aligned with 41 LHCs from T. pseudonana (CCMP 1335).. ........................................................ 82 Figure 3.6. Model of the changes of the photosynthetic apparatus in T. oceanica (CCMP 1003) in response to growth in the presence of low Cu compared to optimal Cu levels (control). ............ 89 xxi  Figure 4.1: A) Overview of original proteomics data for T. oceanica CCMP 1003 (TO03, pink) and 1005 (TO05, blue) grown in Cu-limiting conditions. .......................................................... 123 Figure 4.2: Pie charts of the first level KeggOrthology (KO) identifiers associated with proteins identified in the original T. oceanica CCMP 1003 (TO03) proteomic dataset:.......................... 125 Figure 4.3: Diagram of proteins involved in the Calvin Benson Bassham cycle and citrate (TCA) cycle. ........................................................................................................................................... 127 Figure 4.4: Diagram of proteins involved in glycolysis in the three compartments (chloroplast, mitochondrion, cytosol), including Entner-Dourdoroff pathway. .............................................. 129 Figure 4.5: Diagram of proteins involved in nitrogen metabolism. ............................................ 131 Figure 4.6 Diagram of proteins involved in glutathione metabolism. ........................................ 133 Figure 4.7: Diagram of proteins involved in the malate shunt. .................................................. 135 Figure 4.8: Diagram of proteins involved in pyruvate metabolism. ........................................... 137 Figure 4.9. The regulated proteins involved in glycolysis. A) Up-regulated proteins in TO03 under Cu limitation. B) significantly regulated proteins in T. pseudonana under acute N limitation (Hockin et al. 2012). ................................................................................................... 140 Figure 4.10: An overview of the proteomic response in the nitrogen and carbon metabolisms in T. oceanica (CCMP 1003) grown under Cu-limiting conditions. .............................................. 142 Figure 4.11: An overview of the proteomic response in the nitrogen and carbon metabolisms in T. oceanica (CCMP 1005) grown under Cu-limiting conditions. .............................................. 144 Figure 5.1 The effects of all three low metal treatments (lowCu, lowFe, lowFeCu) on various physiological parameters in two strains of T. oceanica, CCMP 1003 (TO03) and 1005 (TO05)...................................................................................................................................................... 171 xxii  Figure 5.2 Percent growth rates for the Fe-Cu co-limited cultures (lowFeCu) of T. oceanica CCMP 1003 (TO03) and 1005 (TO05) relative to their respective Fe-limited growth rates (lowFe).. ...................................................................................................................................... 172 Figure 5.3 Model of the proteomic and selected physiological changes observed in the photosynthetic electron transport chain (ETC) of two strains of T. oceanica (CCMP 1003, TO03, and 1005, TO05) in response to low Cu (lowCu), low Fe (lowFe), and low Fe and Cu conditions (lowFeCu) compared to optimal metal levels (control). ............................................................. 180 Figure 6.1: Concept of how metal homeostasis on cellular level helps increase resilience towards metal fluctuations. ....................................................................................................................... 193  Figure A.1: ITS fragment visualization. ..................................................................................... 233 Figure A.2: ITS Sequence alignment of  T. oceanica (CCMP 1003 and CCMP 1005). ............ 234 Figure A.3: Phylogenetic tree of 14 diatom ITS regions. ........................................................... 235  xxiii  List of Symbols  1/nPSII  mol of chla associated with each RCII (mol chla mol RCII-1) initial slope of the PvsE curve, quantifies the linear light-dependent increase of the rate of photosynthesis under sub-saturating light conditions (mol e-/RCII)/(µmol quanta / m2 * s) e-  electron  F‘ steady-state chla fluorescence yield in the light-regulated state  Fm  maximum chla fluorescence yield in the dark-regulated state  Fm′  maximum fluorescence in actinic light  Fo  minimum chla fluorescence yield in the dark-regulated state  Fo′  minimum chla fluorescence yield in the light-regulated state  Fq′  (= Fm′ - F′), ‗quenched‘ variable chla fluorescence yield in the light-regulated state  Fv  (= Fm - Fo), variable chla fluorescence yield in the dark-regulated state  Fv/Fm  maximum quantum yield of PSII photochemistry  Fv′  (= Fm′ - Fo′), variable chla fluorescence yield in the light-regulated state  PSII functional PSII absorption cross section (Å2 RCII-1) Φe:C  electron requirement for carbon fixation (mol e- mol  C-1), note that the more recent symbol for this parameter is ΚC  xxiv  List of Abbreviations  1,3BPG 1,3-bisphosphateglycerate 2K3D-PG 2-keto-3-deoxyphosphogluconate 2PG 2-phosphoglycerate 3PG 3-phosphoglycerate 6PG 6-phosphogluconate AA amino acid AAT aspartate aminotransferase ACAS acetyl-CoA synthase ACC acetyl-CoA carboxylase ACO aconitase hydratase ADP  adenosine diphosphate  Agm agmatinase Allox  alloxanthin  AMT ammonium transporter APX ascorbate peroxidase Arg arginase argD n-acetylornithine aminotransferase AsL argininosuccinate lyase AsuS argininosuccinate synthase ATCase aspartate carbamoyltransferase ATP  adenosine triphosphate  C  carbon  cbbX rubisco expression protein CCMP  culture collection of marine phytoplankton  CET  cyclic electron transport  Chl  chlorophyll  Chla  chlorophyll a  xxv  Chlb  chlorophyll b  ChlF  chlorophyll a fluorescence  CO2  carbon dioxide  CS citrate synthase Cu  copper Cys cysteine CYS cysteine synthase Dd  diadinoxanthin  DHAP dihydroxyacetone phosphate DHAR dehydroascorbate reductase DLDH dihydrolipoamide dehydrogenase DLHC  disconnected light-harvesting complexes  Dt  diatoxanthin  EDA 2-keto-3-deoxyphosphogluconatealdolase EDD 6-phosphogluconate dehydratase EK  light-saturation parameter (from PvsE curves) ENO enolase  ETC  electron transport chain  ETR  electron transport rate  ETRRCII  electron transport rate (RCII-specific rate of charge separation)  F2BP fructose-1,6-bisphosphatase FA fatty acid FBA I fructose-bisphosphate aldolase class-I FBA II fructose-bisphosphate aldolase class-II Fe  iron  Fe-NiR Nitrite reductase (ferredoxin-dependent) FH fumarate hydratase FNR ferredoxin:NAD(P)+ reductase FRRF  Fast repetition rate fluorometer /fast repetition rate fluorometry  xxvi  Fru-1,6-bis-P fructose-1,6-bisphosphate Fru-6P fructose 6-phosphate Fx fucoxanthin GAP glyceraldehyde 3-phosphate GAPDH glyceraldehyde 3-phosphate dehydrogenase GCL glutamate cysteine ligase GDCP glycine decarboxylase p-protein GDCT glycine decarboxylase t-protein GDH glutamate dehydrogenase GF/F  glass fibre filter  glu glutamate Glu 6-P glucose 6-phosphate GOGAT glutamate synthase GPI glucose 6-phosphate isomerase GR glutathione reductase GRX glutaredoxin GSI glutamine synthetase I GSII glutamine synthetase II GSIII glutamine synthetase III GSS glutathione synthetase GTR glutathione transporter HNLC  high-nutrient low chlorophyll  HPLC  high pressure liquid chromatography / high performance liquid chromatography  IDH  isocitrate dehydrogenase LC-MS/MS liquid chromatography - tandem mass spectrometry LDH L-lactate dehydrogenase LED  light emitting diode  LET  linear electron transfer chain  LHCs  light-harvesting complexes  xxvii  MDH malate dehydrogenase ME malic enzyme NAD(P)H-NiR nitrite reductase (NAD(P)H-dependent) NAD(P)H  nicotinamide adenine dinucleotide (phosphate), reduced  NiRT formate/nitrite transporter NPQ  non-photochemical quenching (Stern-Volmer quenching)  NPQNSV  non-photochemical quenching (normalized Stern-Volmer quenching)  NR nitrate reductase NRT nitrate/nitrite transporter O2  oxygen  OAA oxaloacetate OCD ornithine cyclodeaminase OdC ornithine decarboxylase OGD 2-oxoglutarate dehydrogenase OTC ornithine carbamoyltransferase P680  chlorophyll molecule in RCII  PAR  photosynthetically available radiation PC pyruvate carboxylase pc plastocyanin PCR polymerase chain reaction PDH-E1 pyruvate dehydrogenase-E1 component PDH-E2 pyruvate dehydrogenase E2 component (dihydrolipoamide acetyltransferase) PEPC phosphoenolpyruvate carboxylase PEPCK phosphoenolpyruvate carboxykinase PEPS phosphoenolpyruvate synthase PFK phosphofructokinase PGAM phosphoglycerate mutase pgCPSII carbamoyl-phosphate synthase II PGK phosphoglycerate kinase xxviii  PGM phosphoglucomutase PK pyruvate kinase Pmax maximum photosynthetic rate PPDK pyruvate, phosphate dikinase PQ  plastoquinone  PSI  photosystem I  PSII  photosystem II  PTOX  plastid terminal oxidase  PvsE  productivity /photosynthesis vs. light curve  QA  primary stable electron acceptor after charge separation in RCII  QB  secondary stable electron acceptor after charge separation in RCII qPCR real-time quantitative PCR rbcL ribulose-bisphosphate carboxylase large chain rbcS ribulose-bisphosphate carboxylase small chain RCI  reaction center I  RCII  reaction center II  ROS  reactive oxygen species  RPE ribulose-5-phosphate epimerase RPI ribose-5-Phosphate-isomerase RT-PCR reverse-transcript PCR RuBisCO ribulose-bisphosphate carboxylase RuBisP ribulose-bisphosphate SD  standard deviation  SSLC  steady-state light curve  ST  single turnover  SUCLA succinate CoA synthetase TPI triose-phosphate isomerase TPT putative location of triose-phosphate-transporter TXN thioredoxin xxix  unCPS (CPSase III) carbamoyl-phosphate synthase Ure urease  URT Na/urea-polyamine transporter γ-glu-cys γ-glutamylcysteine xxx  Acknowledgements I want to express my sincere gratitude for the guidance and mentorship provided throughout my PhD by my supervisor Maite Maldonado. Your door was always open, and the time discussing trace metal physiology, concentrations and interactions was always stimulating. Especially your eye for detail without losing sight of the big picture implications was and is inspiring. It has been a long journey, but your support was unwavering. Maite, when I thought I wouldn‘t even start my PhD because I was pregnant, your exuberant reaction was ―Anna, this is great!!! Both motherhood and a scientific career are wonderful!!!! If you want to do it, you can do it.‖ And you were right! However, just as the saying goes ―It takes a village to raise a child‖, the same is true for a PhD.  Beverley Green, who has become my unofficial co-supervisor, especially in everything related to LHCs and metabolic pathways. Thank you for many hours of insightful discussions and digging deeper into the photosynthetic apparatus, diatom phylogeny, and sequence homologies. Our trip to Seattle will not be forgotten! Both you and Maite are true role models, scientifically and personally. Thank you to my whole advisory committee: Maite Maldonado, Beverley Green, Leonard Foster and Michael Murphy: The fruitful discussions, your different perspectives and insights were challenging and motivating. I could not have imagined a more supportive committee. You never caged me in, but instead listened to my ideas and gave valuable feedback and guidance.   I would also like to thank our collaborators in San Diego. I learned so much in the week I spent at JCVI. Andy Allen, John McCrow, James McCarthy, Jeff McQuaid, and Talina Konotchick: the time we spent discussing iron physiology in diatoms −ISIPs in particular− and how to best map the transcriptome to the genome, opened yet another window for me.  Thank you to Jenny Moon of the Foster Lab and Angele Arrieta of the Murphy Lab for patiently guiding me through and answering all my questions related to differential proteomics and PCR, respectively. Our group, the Tornadoes! Your friendship, camaraderie, understanding, support and advice throughout the years will be treasured: Alyssia Herr, Ania Posacka, Carolyn Duckham, Chen Zeng, Dave Capelle, Dave Semeniuk, David Cassis, Eva Jordison, Florian Baier, xxxi  Genevieve Savard, Iselle Flores Ruiz, Jade Schiller, Jian Guo, Kang Wang, Kristina Brown, Lauren Mocchia, Lindsey Fenwick, Mirkko Flecken, Nina Schuback, Robert Izett, Tereza Jarnikova, Will Burt, Xinjuan, Yuanji Sun.  In particular, Dave Semeniuk – for challenging scientific, educational, political and artistic views, not for the mere challenge but to really try to get to the bottom of it (btw, still so sorry about the desk!!!); Nina – my photosynthesis-FRRF-yoga-bike guru, your calm and funny dry humor in really any situation is awe-inspiring; Ania – for moral, R, and chocolate support in the lab and beyond; Robert – for meeting-menthos and your re-freshening unruffledness; Pia – thanks for care packages and postcards and just being you and there; Randolf – for funny PhD wisdom exactly when needed on the other end of the email and insights from the chemistry perspective; Carlos – for never questioning supplying yet another laptop, printer, screen, and other office supplies as soon as I needed them. This thesis would not have been possible without coffee, dark chocolate, and the ceaseless love and unwavering support of my family. My parents, Mama und Papa (Nana und Bopa), even an ocean apart, really, just a phone call away −or even just a 10-hour flight when really needed− you supported me and us throughout these years; similarly, Tim‘s parents Dieter and Martina (Oma und Opa); Ben, Max, und Johanna, my fearless warriors, you were often so patient and even made breakfast, when mom was so tired in the mornings; Tim, no words can express my gratitude, love, and amazement at how you have supported our family throughout these years. Finally: ―I‘ll be finished by Christmas!‖ I love you. 2248   xxxii  Dedication To my family     “Think left and think right and think low and think high.  Oh, the things you can think up if only you try!”  (Dr. Seuss)   “just use bleach” (Carlos Chiu)  “NO!” (Angelina)           1  Chapter 1: Introduction Here I provide a brief unifying context to the experimental work presented in Chapters 3-5, discussing the significance of diatoms in global nutrient cycles and their dependency on the two trace metals of interest in this thesis; iron (Fe) and copper (Cu). I describe the phylogenetic ancestry of diatoms and the resulting chimaeric genomic makeup that enables them to develop unique strategies to adapt to multiple environmental conditions. I also introduce my model diatom species, Thalassiosira oceanica (Figure 1.1). This chapter concludes with a summary of the overall research objectives and structure of this work.  1.1 Global ocean, phytoplankton, and climate The global ocean covers over 70% of our planet. Besides containing many diverse ecosystems that support humans economically and ecologically, it has an intricate relationship with our atmosphere and ultimately our climate. This relationship is due to differential gas solubility constants and resulting gas exchange between the liquid phase of the ocean and the gaseous phase of the atmosphere. The concentrations of gases, such as carbon dioxide (CO2) or oxygen (O2), in the ocean-atmosphere boundary layer, are, thus, dependent on physical and biological processes in the surface ocean, such as mixing, photosynthesis and respiration.  Marine phytoplankton are unicellular, microscopic algae that are able to use the energy of the sun to reduce CO2 to organic carbon. The fate of this organic carbon varies. It can be 1) used as a storage, structural, or other metabolic component in the cell, thus temporally removing CO2 from the atmosphere, 2) reoxidized by respiration within the mitochondria to supply cellular energy, thereby releasing CO2 back to the environment, 3) remineralized by heterotrophic bacteria once the cell has died (which also releases CO2), 4) ingested by zooplankton after the 2  cell has been grazed, or 5) exported to the deep ocean as dead cells sink, most likely, inside zooplankton fecal pellets. The latter is called the biological carbon pump as it effectively removes CO2 from the atmosphere sequestering it into the deep ocean as organic carbon (de la Rocha, 2007).  Marine phytoplankton account for approximately 40% of the global primary productivity and are responsible for the export of 16 gigaton (1012 kg) of fixed CO2 to the deep ocean (Nelson et al. 1995; Field et al. 1998). Variations in the magnitude of this organic C export affect the CO2 content of the upper ocean which in turn regulates atmospheric CO2 levels (Archer 2003). To fully understand the global carbon cycle, it is thus essential to establish the factors controlling oceanic phytoplankton productivity.  1.2 Diatoms Diatoms are eukaryotic, single-celled microalgae found in almost every aqueous environment, both freshwater and marine. Diatoms have a very complex highly ornamented, silicified cell wall called the frustule. Despite evolving relatively recently, diatoms are among the most successful phytoplankton groups (Falkowski et al. 2004) and are ubiquitous in the well-lit surface layer (i.e. euphotic zone) of the ocean. They are estimated to account for ~ 40% of the ocean‘s net primary production and for ~ 50% of the carbon sequestration into the deep ocean (Falkowski et al. 2004). In other words, the oxygen in every 4th breath that we take has been produced by diatoms. Furthermore, given their productivity, diatoms largely support global fisheries and influence biogeochemical cycles of both macronutrients (e.g. carbon, nitrogen, phosphorous, and silica) and micronutrients (e.g. Fe, Mn, and Cu). 3  1.3 Photosynthesis Photosynthesis is arguably the most important metabolic pathway on our planet. In it, the energy of the sun is absorbed to create the cellular reducing equivalent NAD(P)H and energy equivalent ATP, while releasing oxygen (O2). NAD(P)H and ATP can then be used to fix CO2 into organic compounds. This organic carbon can fuel phytoplankton growth, as well as organisms in higher trophic levels. A schematic overview of the photosynthetic electron transport chain is illustrated in Figure 1.2. Light is absorbed by special pigmented light-harvesting complexes (LHCs). Some of the absorbed energy is channelled towards photosystem II (PSII) in which a special pigment pair (Chla) is excited in such a way that induces charge separation. The electron is then transferred via several proteins (plastoquinone, cytb6f complex, plastocyanin/cytc6) to the second photosystem (PSI). Photosystem I (PSI) also has its own LHC antennae complex and special pigment pair (Chla), where charge separation also occurs and the electron is transported via a ferredoxin (Fdx) and ferredoxin:NADP+-reductase (FNR) to NADP+ thus generating the cellular reducing equivalent NAD(P)H. During several of these steps, protons (H+) are pumped into the lumen generating a pH gradient that in turn is used for ATP synthesis, the cellular energy currency. The two photosynthetic reactants, ATP and NAD(P)H, can then be used in the Calvin-Benson-Bassham cycle to fix CO2 into carbohydrates. Thus, in photosynthesis, light energy is transformed into chemical energy (carbohydrates) that is accessible to all life forms. This process is called primary productivity.  1.4 Phytoplankton productivity  Phytoplankton primary productivity varies among oceanic regions. As observed in terrestrial systems (Longhurst 1995), there are highly productive regions (i.e. eutrophic) such as coastal and 4  open ocean upwelling regions, where more than 1 mg C m-3 are fixed annually. In contrast, low productivity regions (i.e.oligotrophic) convert less than 0.1 mg C m-3 every year (Field et al. 1998). However, even though carbon fixation per m3 is over an order of magnitude larger in eutrophic than oligotrophic waters, the vast regions of oligotrophic waters in the ocean account for over 23% of the total ocean net primary productivity (NPP) compared to 19% for the eutrophic waters (mesotrophic waters accounting for 56 % and macrophytes for 1%).   Phytoplankton productivity depends on physical, chemical, and biological parameters e.g. light, temperature, mixing, nutrients and predator assemblages. These parameters have a direct influence on planktonic organisms, populations, and community structure. In addition to carbon, macronutrients such as nitrate and phosphate are needed by phytoplankton in a conserved molar stoichiometry, 106 mol C:16 mol N:1 mol P, the so-called Redfield ratio (Redfield 1934, 1958). Trace elements, such as iron (Fe), zinc (Zn), manganese (Mn), cobalt (Co), or copper (Cu) occur in extremely low concentrations in the open ocean (~10-9 M), but are essential for phytoplankton growth. These trace elements are required in minuscule amounts (0.5-100 µmol mol C-1) and are so-called essential micronutrients. Ho et al. (2003) measured the elemental composition of representative phytoplankton phyla and derived an extended Redfield ratio including trace elements (subscript indicates mol; note that macronutrient values are multiplied by 1,000):    (C124 N16 P1 S1.3 K1.7 Mg0.56 Ca0.5)1,000 -  Sr5.0 Fe7.5 Zn0.80 Cu0.38 Co0.19 Cd0.21 Mo0.03 (Ho et al. 2003). If the concentration of any one of these micronutrients is too low, growth and hence primary productivity will be limited.  Even though nitrate was thought to be the main limiting resource for marine primary productivity, in at least 30% of the ocean, nitrate concentrations are not depleted, suggesting that another nutrient limits phytoplankton. The regions with excess nitrate are the subarctic and 5  equatorial Pacific, as well as the Southern Ocean. Since the 1990s, extensive research, including several mesoscale iron fertilization experiments (Boyd et al. 2007), has indicated that 30-40% of the global ocean is indeed Fe limited (Moore et al. 2004). Furthermore, more recent work has suggested the possibility of Fe-Cu co-limitation in some of these areas (Coale 1991; Peers and Price 2006).  1.5 Iron in the ocean In the modern, oxygenated ocean, Fe is stable as its oxidized species, ferric ion (Fe3+), which tends to form insoluble Fe hydroxides, such as Fe(OH)2- (Millero 1998). Iron concentrations in the ocean vary by two orders of magnitude, with higher concentrations at the coast relative to those in the open ocean. The mean dissolved Fe concentration in open ocean waters (> 50 km away from the coast) is 0.07 nM (Johnson et al. 1997). In surface waters, approximately 99.9% of the dissolved Fe is bound to strong organic ligands, resulting in 0.07 pM dissolved inorganic Fe (Rue and Bruland 1995; Wu and Luther III 1995).   1.6 Iron - cellular demand There are two compelling reasons supporting the important role of Fe in essential cellular redox systems. First, Fe was readily available in the reducing Archaean ocean when pathways such as respiration and later photosynthesis evolved (Catling and Claire 2005). Second, the standard electrochemical potential E0 (0.77 V) between Fe (II) and Fe (III) gives Fe a wide applicability to catalyze metabolic redox reactions (Hunter and Boyd 2007). Furthermore, depending on the protein matrix to which the Fe is bound, the redox potential can be modulated from +550 mV to -700 mV (Capozzi et al. 1998). Therefore, Fe is the best redox catalyst in nature. 6  The two main classes of Fe-containing electron transfer proteins are the heme-containing cytochromes (cyt) and iron-sulphur (Fe-S) proteins. In heme proteins, the Fe is bound to one of nine porphyrin systems and can be used to channel electrons, bind oxygen, or both, depending on the localization of the heme (or multiple hemes) within the protein. Most prominent heme-containing protein classes include cytochromes (cyt), oxygenases, peroxidases, and catalases. Iron-sulphur (Fe-S) proteins contain one or more Fe ions that are coordinated by thiol ligands from cysteine residues provided by the protein, and bridged by sulphide ions. Besides some representatives that are involved in enzymatic activity (e.g. mitochondrial aconitases (Jordan et al. 1999); nitrite reductase (Adhikari and Rahman 2017); sulphite reductase (Hermann et al. 2015)), the vast majority of Fe-S proteins are ferredoxins and rubredoxins that participate solely in electron transfer reactions. Important Fe-S containing proteins include Rieske-Proteins (involved in mitochondrial cytbc1 and plastidial cytb6f complex, (Schmidt and Shaw 2001)), ferredoxins (in electron transfer e.g. in photosynthesis, (Hanke and Mulo 2013)), and nitrogenases (globally important as the only nitrogen-fixing proteins (Smith 1999; Hoffman et al. 2011)). Several essential metabolic pathways have a high Fe demand; for example, the respiratory electron transport chain in the mitochondrion (~28 Fe atoms), the photosynthetic electron transport chain in the chloroplast (~22 Fe atoms) (Raven et al. 1999), and nitrate assimilation (~7 Fe atoms). Furthermore, Fe is needed in several proteins involved in the detoxification of reactive oxygen species (ROS), such as ascorbate peroxidases and Fe-containing representatives of superoxide dismutase (Alscher et al. 1997).  Cellular Fe:C quotas can differ among species and depend on the nutritional status of the cells. For example, under Fe replete conditions, Fe:C quotas in diatoms can range from 26-102 7  mol Fe:mol C, while under Fe-limiting conditions these decrease to 0.65 - 18 mol Fe:mol C (Maldonado and Price 1996).  1.7 Copper in the ocean In the modern ocean, Cu is stable as cupric ion (Cu2+) and forms inorganic complexes, such as CuCO3, CuOH+, and CuOH2 (van den Berg 1984). Photochemical processes in surface waters may also produce significant amounts of transient Cu+ (Moffett and Zika 1983). Interestingly, as with Fe, the speciation of dissolved Cu is dominated by organic complexation with strong and weak ligands that bind > 97% of dissolved Cu in the ocean (van den Berg 1984; Coale and Bruland 1988). Moffet and Brand (1996) have provided strong evidence that these ligands are biogenic. However, in contrast to Fe-binding ligands that are thought to be released by bacteria in order to acquire Fe more efficiently (Trick 1989), Cu-binding ligands are released in response to high Cu levels to lower the free cupric ion concentration, and thus its toxicity (Moffett and Brand 1996; Moffett et al. 1997).  Concentration gradients of Cu in marine surface waters are not as distinct as those for Fe. In the open ocean, Cu concentrations range from 0.5 nM to 1.4 nM in the Pacific (Bruland 1980; Boyle et al. 1981) and 0.6 to 1 nM in the Atlantic (Rutgers et al. 1997). In coastal waters, due to anthropogenic sources, Cu concentrations may range from 4-6 nM in non-polluted areas (Boyle et al. 1981; Moffett et al. 1997) to ~100 nM in moderately polluted estuaries, such as San Francisco Bay (Buck et al. 2006).  8  1.8 Copper – cellular demand Why is there a need for Cu when Fe is the best redox catalyst for metabolic pathways? As mentioned above, Fe was readily available in the Archaean ocean (> 2.5 Gyr ago) when photosynthesis and respiration evolved. These two features, suitable physicochemical characteristics and bioavailability, were crucial in determining the metal of choice incorporated into a protein. Approximately 2.32 Gyr ago, atmospheric oxygen levels rose considerably (Bekker et al. 2004), changing the solubilities of a variety of metals. As summarized by Kaim and Rall (1996), the oxidizing atmosphere had two distinct advantages for the incorporation of Cu into proteins: while the formerly soluble Fe(II) was now oxidized to insoluble Fe(III) hydroxides, the former Cu (I), trapped in insoluble sulfides, was now oxidized to soluble Cu (II).  Depending on the protein matrix, Cu+/Cu2+ enzymes may have redox potentials ranging from 180 - 790 mV. In addition to redox reactions, Cu proteins are key in numerous enzymatic reactions involving dioxygen, such as transport, oxidases, oxygenases, and detoxification (Fetherolf et al. 2017). However, even though Cu is essential for eukaryotes and prokaryotes, it is also potentially toxic due to its high reactivity with oxygen and tendency to generate reactive oxygen species (ROS, Knauert and Knauer 2008). Therefore, Cu homeostasis is essential for any living cell. Strikingly, Rae et al. (1999) have deduced that the free Cu ion concentration in yeast is maintained below 1 Cu atom per cell (Rae et al. 1999). Indeed, within the cell, Cu is transported and delivered to various destinations by specialized Cu-binding chaperones and metallothioneins (Blaby-Haas et al. 2014). The majority of Cu proteins are involved in either electron transfer reactions or binding and activation of small molecules such as oxygen or nitrous oxide (as reviewed by Kaim and Rall 1996). The group of Cu proteins almost exclusively responsible for electron transfer 9  reactions are blue Cu proteins, with plastocyanin as the most prominent representative (De Rienzo et al. 2000). Plastocyanin can be found in photosynthetic bacteria, green algae and plant chloroplasts. It ferries electrons in the photosynthetic electron transport chain from cytb6f to the reaction centre of photosystem I (P700) thus being an analogue to cytc6, which is used by more ancient photosynthetically active phytoplankton (reviewed by Navarro et al. 1997). Depending on Cu availability, some algae (e.g. the green alga Chlamydomonas reinhardtii and others) have the option to switch between the Cu-containing plastocyanin and the Fe-containing cytc6 (Sandmann and Böger 1980; Merchant and Bogorad 1986).  The second group of abundant Cu proteins involved in dioxygen reactions, includes blue Cu oxidases, cytc oxidases and superoxide dismutases. Multicopper oxidases are widespread in bacteria, fungi, plants and animals and catalyze sequential substrate oxidation by transferring 4 electrons to O2, resulting in 2 H2O (Messerschmidt 1998). Copper- (and Fe-) containing cytc oxidases are of global importance as they catalyze the final reduction of dioxygen to water in the respiratory chain.  In the well-studied freshwater green algae Chlamydomonas reinhardii, the most abundant Cu proteins are cytc oxidase, plastocyanin, and a multicopper oxidase, which is involved in Fe acquisition (La Fontaine et al. 2002; Merchant et al. 2006). Interestingly, until recently, plastocyanin was thought to be only used by plants and eukaryotic marine phytoplankton in the green lineage. Surprisingly, 10 years ago, the oceanic diatom, Thalassiosira oceanica, was shown to use plastocyanin instead of cytc6 in its photosynthetic apparatus (Peers and Price 2006). Comparable to Fe:C quotas, Cu:C quotas vary as well, depending on species and nutritional status. In coastal isolates, Cu:C quotas range from 0.4 to 2.67 mol Cu:mol C under Cu replete conditions and from 0.32 to 1.96 mol Cu:mol C under Cu limiting conditions. In 10  general, in oceanic isolates, Cu:C quotas are higher than those in coastal phytoplankton, ranging from 1.84 to 6.32 mol Cu : mol C under Cu replete conditions and from 0.32 to 2.58 mol Cu : mol C under Cu-limiting conditions (Annett et al. 2008).  1.9 Algal trace-metal physiology – iron, copper and their interaction Trace metal requirements of oceanic and coastal phytoplankton vary, reflecting the trace metal conditions in their respective habitats (Brand et al. 1983; Sunda and Huntsman 1995). Most prominently, oceanic species have to adapt to 100 times lower Fe concentrations compared to coastal species. As mentioned above (section 1.5; Iron in the ocean), Fe concentrations in vast ocean regions were deemed too low to support the growth of even the smallest photosynthetic cell (Raven 1988; Sunda et al. 1991). However, oceanic phytoplankton have evolved unique mechanisms to lower their trace metal demands while optimizing trace metal transport in environments with limiting trace metals such as Fe, Mn, and Zn (Brand et al. 1983). For example, to reduce its Fe requirement, the model open ocean diatom T. oceanica has altered the stoichiometry of its photosynthetic apparatus by reducing the concentration of the Fe-rich photosystem I (12 atoms Fe) by 5-fold and that of the cytb6f complex (5 atoms Fe) by 7-fold relative to the model coastal species T. weisfloggii (Strzepek and Harrison 2004). Furthermore, T. oceanica has been shown to use the Cu-containing plastocyanin instead of the Fe-containing cytc6, lowering the cellular Fe requirement by 10% (Peers and Price 2006). This protein substitution is particularly interesting as plastocyanin was only known to exist in Chlb containing photosynthetic organisms (e.g. green algae and plants) and cyanobacteria. In photosynthesis, two additional atoms of Fe can be saved by substituting ferredoxin with flavodoxin, even though the latter has a lower specific reaction rate (Raven et al. 1999). 11   However, the full mosaic of phytoplankton adaptations to various trace metal deficiencies is not well understood. Historically, investigations on Cu and phytoplankton interactions have focused on Cu as a toxic metal (Rueter et al. 1979; Brand et al. 1986; Croot et al. 2000; Davis et al. 2006). More recently, several studies acknowledged the importance of Cu as a micronutrient for phytoplankton physiology, and the intricate relationship between Fe and Cu nutrition in oceanic phytoplankton (Wells et al. 2005; Peers and Price 2006; Annett et al. 2008). Relative to their coastal counterparts, oceanic phytoplankton require more Cu for growth (Peers et al. 2005), and have a higher cellular Cu content (Annett et al. 2008). Even though Peers and Price (2006) argue that the higher Cu requirement of T. oceanica can be solely accounted for by their use of plastocyanin, other studies suggest higher Cu demands due to the involvement of additional Cu-containing proteins in other metabolic pathways. Maldonado et al. (2006) for example showed an up to 60 fold up-regulation of a putative multi Cu-containing oxidase (MCO), which is believed to be involved in the high-affinity Fe uptake system of marine diatoms.   1.10 T. oceanica - a model species for open ocean diatoms adapted to low Fe  For several decades, Thalassiosira pseudonana (also known as Cyclotella nana) strains have been isolated and successfully grown for various morphological and physiological experiments (Guillard and Ryther 1962). The coastal strain 3-H (CCMP 1335) was isolated in coastal waters off the coast of Long Island and was the first diatom whose whole genome was sequenced (Armbrust et al. 2004). The oceanic strain 13-1, which was regarded as a typical oceanic diatom, was isolated in the oligotrophic Sargasso Sea and was only identified as the distinct species T. oceanica in 1983 (Hasle 1983). Since then, other strains have been isolated from the Sargasso Sea and identified as T. oceanica, including strains CCMP 1003 and CCMP 1005, isolated in 12  1977 and 1958, respectively. These two latter strains are the T. oceanica strains investigated in the present study (Figure 1.1).  For decades, both strains have been used as model open ocean diatoms and important traits in their trace metal physiology have been revealed compared to their coastal counterparts. Most importantly, these oceanic strains have succeeded in adapting to their low Fe environment by both lowering their Fe requirements and increasing the efficiency of Fe acquisition mechanisms. Interestingly, both strategies required a higher physiological Cu dependency (Peers et al. 2005; Peers and Price 2006; Maldonado et al. 2006).  T. oceanica has a 25% lower cellular Fe demand (2 µmol Fe:mol C) than T. pseudonana to achieve maximal growth rates (Sunda et al. 1991). Under Fe limitation, both species engage an inducible, Cu-dependent, high-affinity uptake system which allows them to access Fe bound to strong organic ligands (Maldonado et al. 2006). However, under the same conditions, T. oceanica shows faster Fe uptake rates and higher Cu dependency compared to its coastal counterpart. Copper dependency is so severe that Cu limitation might, in fact, induce Fe limitation in this species (Maldonado et al. 2006). Incidentally, and in contrast to its lower cellular Fe content, T. oceanica has a 50% higher Cu demand than T. pseudonana (2.93 vs. 1.9 µmol Cu: mol C, respectively, (Annett et al. 2008). In accordance with its more efficient Fe uptake system, T. oceanica has 2-fold faster Cu uptake rates than T. pseudonana at identical Cu concentration. In addition, the Km of the proposed high-affinity Cu uptake system in T. oceanica is three times lower than its coastal counterpart ( 48.3 nM vs. 188.4 nM) (Guo et al. 2010).   The alteration of PSII: PSI ratio (Strzepek and Harrison 2004), and the use of Cu-containing plastocyanin instead of Fe-containing cytc6 in the photosynthetic electron transport chain (Peers and Price 2006) explain the lower Fe requirement and higher Cu-demand in oceanic 13  species. It has been suggested that this high Cu-dependency of Fe-limited phytoplankton may lead to Fe-Cu co-limitation in the field (Coale 1991; Peers and Price 2006). It is thus surprising that no study has examined the proteomic effects of Cu limitation or Fe-Cu co-limitation in these diatoms.  1.11 Diatoms – their phylogenetic ancestry as key to their success Humans are part of one phylum −chordate−, which includes dinosaurs, birds, and fish. In contrast, aquatic phytoplankton are part of at least 12 eukaryotic and 2 prokaryotic phyla, highlighting the huge diversity of these microbes. After some debate, strong evidence suggested that eukaryotic phytoplankton were derived from a single endosymbiotic event in which an ancient cyanobacterium was engulfed by a eukaryote and eventually became a totally dependent endosymbiont, the chloroplast (Rodríguez-Ezpeleta et al. 2005; Reyes-Prieto et al. 2007) (see Figure 1.3).   Three distinct lineages evolved from this primary endosymbiosis event: glaucophytes, red algae, and green algae. From the latter, land plants evolved. However, species from both red and green algae went through secondary endosymbiosis events in which they were engulfed by a heterotrophic eukaryote (Keeling 2004; Archibald 2005). The red algal endosymbiont gave rise to a diverse group of organisms referred to as chromalveolates, although they may have originated via several separate secondary endosymbiosis events (Burki et al. 2016). They include dinoflagellates, haptophytes, heterokontophytes (including bacillariophyta = diatoms), cryptophytes and others. In the evolution of the organisms derived from secondary endosymbiosis, what is left from the original red and green algae are the chloroplast and the outer membranes resulting generally in 4 chloroplast membranes. The inner two membranes 14  belong to the original cyanobacterium (plastid envelope, inner and outer membrane), the third is the outer membrane of the primary host (i.e. red algae), and the fourth membrane represents the phagosomal membrane of the secondary host (i.e. heterotrophic eukaryote) (McFadden 1999; Archibald and Keeling 2002). This fourth, outermost plastid membrane is part of the host's endoplasmatic reticulum. Genes from both the nucleus and chloroplast have been transferred to the host nucleus, leaving only a small number of genes in the chloroplast genome itself (McFadden 1999).   Importantly, the proposed sequential secondary endosymbiosis of both a green and red alga (Moustafa et al. 2009), combined with bacterial horizontal gene transfer, resulted in an unprecedented chimaeric genome in diatoms. There are genes derived from their green algal, red algal and original heterotrophic ancestor as well as bacterial genes. This has been exemplified in the diverse ancestry of genes involved in the heme biosynthesis pathway (Oborník and Green 2005) and the five isoenzymes of fructose-bisphosphate aldolases (Allen et al. 2012).  Other surprising traits were revealed when the first two diatom genomes were sequenced (T. pseudonana (Armbrust et al. 2004); Phaeodactylum tricornutum (Bowler et al. 2008)), including 1) a high amount of transposable elements that appear to self-prime reverse transcription in a novel manner, 2) proteins responsible for a complete urea cycle (previously only found in heterotrophic eukaryotes), 3) proteins possibly involved in C4-like carbon concentrating mechanisms (common in certain land plants), and 4) an unusual polyunsaturated fatty acid metabolism resulting in the storage compound chrysolaminarin. However, still half of the genes of T. pseudonana are of unknown function, mainly because there are no homologous proteins in other organisms. For example, the genes encoding for the proteins involved in silicate 15  polymerization, which is required for frustule synthesis, or plastid envelope transporters (Armbrust et al. 2004) have not been fully identified.  One of the striking surprises though arose from the genomic comparison of the centric (T. pseudonana) and the pennate diatom (P. tricornutum). Even though diatoms are a relatively young phylum, and pennates diverged from centric diatoms only 90 million years ago, they share only ~60 % of their genes (Bowler et al. 2008). Still puzzling is that many of the genes that are shared between centric and pennate diatoms are often differently regulated, potentially underlining the adaptability to their respective environmental niches (Allen et al. 2011; Hockin et al. 2012; Bender et al. 2014).  1.12 Studying the response to environmental factors in the era of 'OMICS' Given the important role of diatoms in many processes affecting global biogeochemical cycles, the sequencing of the diatom genomes was awaited with keen anticipation. The first diatom to be sequenced was T. pseudonana (Armbrust et al. 2004). Thereafter, the field of diatom sequencing grew exponentially, with the genome of P. tricornutum completed in 2008 (Bowler et al. 2008), that of the open ocean T. oceanica (CCMP1005) in 2012 (Lommer et al. 2012), those of the two oleaginous diatoms Fistulifera solaris and Cyclotella cryptic in 2015 and 2016, respectively (Tanaka et al. 2015; Traller et al. 2016), and that of the pennate, cold-water adapted Fragilariopsis cylindricus in 2017 (Mock et al. 2017). Though meaningful information can be retrieved from the genome of an organism, to elucidate cellular adaptations to certain environmental conditions, physiological, proteomic and transcriptomic data are crucial (Parker et al. 2008; Mock et al. 2008). The genome encodes all possible proteins of an organism to survive in an array of environmental conditions. However, 16  like a colour palette of an artist, not every colour is used for every picture. By subjecting the organism to a certain stress, one can monitor the cellular response (up- or down-regulation of proteins or genes) in comparison to a control. Two widely used approaches include monitoring either the proteomic content, e.g. via high-throughput mass spectrometric analysis (Washburn et al. 2001), or monitoring the genomic expression levels, e.g. via EST libraries (Maheswari et al. 2005) or genomic microarrays (Mock et al. 2008). All of these methods have advantages and disadvantages. For example, proteomic MS data is often biased towards abundant proteins and against membrane proteins (Ong and Mann 2005). Microarray data are highly dependent on the selected microarray and statistical analysis (Baldi and Long 2001).  To date, it is not possible to predict the regulation of all genes in a sequenced genome. For example, a whole-genome expression profiling for T. pseudonana generated extensive genomic microarray and EST data, as well as some differential proteomic data (Mock et al. 2008). This study found evidence for ~ 3,500 new genes which had not been predicted by the ~ 11,400 existing gene models originally used in the sequencing of the T. pseudonana genome (Armbrust et al. 2004). Of the new genes in Mock et al. (2008), 86% had no blast match, while 64% had EST support (with and without blast matches). Of the original gene models (Armbrust et al. 2004), only 28 % were predicted solely through homology searches, whereas the rest were supported by arrays and/or ESTs. Studies examining acclimation strategies to metal limitation in diatoms have focussed on Fe limitation, with only two studies investigating physical and chemical parameter interactions, such as iron/light (Smith et al. 2016) or iron/temperature (Zhu et al. 2016b). Studies employed either transcriptomics  (Mock et al. 2008; Allen et al. 2008), whole cell proteomics (Lommer et al. 2012; Nunn et al. 2013; Luo et al. 2014) or qRT-PCR of a limited set of genes of interest 17  (Kustka et al. 2007; Whitney et al. 2011; Chappell et al. 2014). When comparing results, it is imperative to be aware of the kind of Fe limitation that has been imposed, i.e. some studies investigate acute Fe limitation, most likely experienced in coastal areas (e.g. Lommer et al. 2012), whereas others grow acclimated cultures resembling a scenario more prevalent in open ocean environments (e.g. Nunn et al. 2013). Growing phytoplankton cultures with trace metal clean techniques, extracting DNA, RNA, or proteins and subjecting them further to down-stream analysis is a very time-consuming and costly process. Importantly, much more information can be obtained from whole-cell proteomic or transcriptomic data when it is complemented with a substantial and relevant set of whole-cell physiological parameters. Unfortunately, the standard physiological parameters supporting proteomic or transcriptomic data are usually limited to growth rate, Fv/Fm and maybe cell size and Chla concentration, although Lommer et al. (2012) included additional information on chloroplast concentration and protein content. So far, Allen et al. (2008) have provided the most extensive list of physiological parameters (up to 13), including some enzyme essays and estimates of PSII, cytb6f, and PSI concentration in their study of Fe limitation.  For the present study, we focused on the differential whole-cell proteomic responses of two strains of the open ocean, centric diatom T. oceanica (CCMP1003 and 1005), to trace metal limitation. This proteomic data was combined with an unprecedented array of physiological parameters, more than 20, including light response curves, carbon uptake, FRRF measurements of the photosynthetic apparatus, oxygen production, alternative oxidase enzymatic activity, cellular protein content, and the standard growth rate, cell size, and Chla concentration measurements. Furthermore, we generated the first genomic information available for the T. oceanica strain CCMP1003, as an extensive EST library. Thus, we mapped our differential 18  proteomic peptide data to both the publicly available genome of T. oceanica (CCMP1005) and to our own transcriptomic data (CCMP1003).  1.13 Thesis objective  In my thesis I have two overarching questions to address: 1) what are the physiological and proteomic adaptations of open ocean diatoms to limiting concentrations of Cu and/or Fe, and 2) what are the differences in these adaptations between the two strains of T. oceanica, CCMP1003 and CCMP1005 (i.e. differential intraspecific responses to trace metal limitation)? In order to answer these questions, I employed an integrative approach that combined measurements of a large number of physiological parameters with the most up-to-date molecular techniques (transcriptomics and proteomics), facilitated by the availability of a draft genome sequence of the T. oceanica strain CCMP1005 (Lommer et al. 2012). In brief, I grew triplicate 10 L acclimated cultures for each of the two strains of T. oceanica (CCMP1003, referred to as TO03; and CCMP1005, referred to as TO05) under each of our 4 treatments (i.e. control; Cu-limiting, referred to as lowCu; Fe-limiting conditions, referred to as lowFe, and Fe-Cu co-limiting conditions, referred to as lowFeCu; see overview Figure 1.4).  Slower growth rates were used as a proxy for their trace metal limited status. On the day of harvest, when the cultures were still in exponential phase, a part of the culture was used to extract proteins (both from the soluble and insoluble fractions). These protein extracts were the subject of differential proteomic analysis. The rest of the culture was used to investigate in vivo and in vitro physiological parameters such as cell size, Chla content, rates of gross oxygen production, carbon uptake and electron transport in photosystem II (ETRPSII), as well as photosynthesis vs. irradiance (PE) response curves, and activity of alternative oxidases. Having 19  concomitant proteomic and physiological data from the same cultures allowed me to draw conclusions regarding proteomic changes responsible for physiological adaptations to trace metal limitation. To this end, I was able to describe in detail very different adaptations by the two strains in response to low Cu. Furthermore, by comparing these different metal limitations (lowCu, lowFe, and lowFeCu), I was able to draw conclusions on physiological and proteomic responses to general cellular stress, versus those specific to trace metal limitation. My work furthers our knowledge on how diatoms adapt to metal limitation and opens up new avenues for future research.  1.14 Thesis structure The physiological and proteomic datasets that I generated and analyzed for Chapters 3, 4, and 5 were derived through the same methodological approach, which I describe in the Method Chapter (Chapter 2). The importance of Fe limitation in controlling global primary productivity and therefore climate has been known for decades. Many phytoplankton adaptations to Fe limitation have been described using physiological, transcriptomic and proteomic approaches. However, the physiological interaction between Cu and Fe in open ocean diatoms, although known for about 10 years, has not received much attention. As little is known about Cu as a limiting nutrient and how marine phytoplankton respond to low Cu availability, I dedicated two chapters of my thesis to an in-depth description of the effects of low Cu on the photosynthetic apparatus (Chapter 3) and on the enzymes involved in the nitrogen, carbon, and oxidative stress metabolism (Chapter 4). In Chapter 5, I compare the effects of Cu and Fe limitation with each other and with co-limitation by both Cu and Fe on the photosynthetic apparatus. The challenges 20  of my research, the implications of my findings, as well as my outlook on future research are discussed in Chapter 6.   21  1.15 Tables and figures       Figure 1.1: Light microscopy images of Thalassiosira oceanica (CCMP 1003 and CCMP 1005). A) CCMP 1003 in side (girdle) view (A. Hippmann); B) CCMP 1005 in top (valve) view; C) CCMP 1005 from side (girdle) view (Lommer et al. 2012). B and C from (Lommer et al. 2012). Note that the chloroplasts are in their native brownish colour in A and B, and in red colour in C, from overlay of chlorophyll autofluorescence.    A      B         C                                              22   Figure 1.2: Schematic overview of the photosynthetic electron transport chain (ETC) in the chloroplast thylakoid membrane. Light energy is absorbed by light-harvesting complexes (LHCs) in the antenna and then channelled towards the reaction centres, PSII and PSI, respectively, where charge separation occurs and electrons are channelled through the electron transport chain (ETC) from PSII via plastoquinone (PQ Pool), cytochromeb6f (cytb6f) and plastocyanin (PC) to PSI, where electrons are used to reduce first ferredoxin (Fdx) and subsequently via ferredoxin:NADP+ reductase (FNR) NADP+ to NAD(P)H. The electrons are replenished via the splitting of water (at PSII) into oxygen, electrons and H+. Throughout the ETC, protons are pumped into the thylakoid lumen, thereby creating a proton gradient that is used to synthesise ATP. Figure used with permission from Dr Jon Nield, Queen Margaret University of London (for further information on all subunits see http://macromol.sbcs.qmul.ac.uk/).  23    Figure 1.3 Schematic overview of the phylogenetic ancestry of diatoms. 1) in the primary endosymbiosis event an ancient cyanobacterium is engulfed by a heterotrophic eukaryote. 2) three main algae lineages develop from this primary endosymbiosis; glaucophytes, red, and green algae. 3) in a 1st secondary endosymbiosis, a green alga is engulfed by a heterotrophic eukaryote. Gene transfer occurs from the green algal nucleus and green algal chloroplast genome into the host nucleus. 4) in a 2nd secondary endosymbiosis, the same organism from 3) engulfs a red alga. Subsequent gene transfer from the red algal nucleus and red algal chloroplast to the host nucleus and between the green and red algal chloroplasts takes place. Eventually, all remnants from the green algal cell including the plastid are lost. 5) throughout diatom history, bacterial gene transfers have occurred (based on Finazzi et al. 2010).     1 3 2 5 4 24   25  Figure 1.4 Overview of experimental set-up. Triplicate 10 L cultures were grown in 4 different trace metal regimes (control, replete trace metal conditions; Fe-limiting conditions, lowFe; Cu-limiting conditions, lowCu; and Fe-Cu co-limiting conditions, lowFeCu) for 2 strains of  T. oceanica (CCMP1003, TO03; and CCMP1005, TO05). While still in exponential growth phase, each 10 L culture was divided into 7-8 L for differential proteomic analysis and 1-2 L for the determination of an array of in vivo and in vitro physiological parameters. For a more in-depth description of the stable-isotope labeling used for our differential proteomics, please see (Chapter 2, Figure 2.1).   26  Chapter 2: Methods  2.1 General Experimental set-up and analysis for all three data chapters were identical. Different subsets of the data are discussed in each data chapter, with some overlap in Chapters 3 and 5 (i.e. the low Cu photosynthetic protein response).  2.1.1 Study organism Two strains of the centric diatom species Thalassiosira oceanica were used in this investigation, CCMP 1003 and CCMP 1005. Species and strain were confirmed based on the ITS region sequence (encompassing the ITS1–5.8S – ITS2, (Moniz and Kaczmarska 2009, 2010); Figure A.2). All isolates were obtained from the Provasoli-Guillard Center for Culture of Marine Phytoplankton, now National Centre for Marine Algae and Biota (NCMA) at Bigelow Laboratory for Ocean Sciences.  2.1.2 ITS region For DNA extraction, the DNeasy® Plant Mini Kit (cat. nos. 69104 and 69106) from QIAGEN was used following its manual with one modification: cells from 40 mL of late exponential culture were concentrated via centrifugation, then mixed with 400 µL AP buffer, 2 µL RNase and 100 µL 0.2-0.5mm silica microbeads. The microbeads increase the DNA yield by breaking open the silica frustules. The 700bp long ITS fragment, comprised of ITS1, 5.8S rDNA gene and part of ITS2, as described by Moniz and Kaszmarska (Moniz and Kaczmarska 2009), was amplified using primers ITS1 and ITS5 and a PCR reaction mix as described by White et al. 27  (White et al. 1990) with an amplification regime as per Amato et al. (Amato et al. 2007). PCR products were visualized on a 1.5% agarose gel (Figure A.1). For PCR product cleanup, Promega Wizard ® SV Gel and PCR Clean-Up System (#A9282) was used. Sequencing was conducted by Genewiz© (Seattle, USA), following their standard procedure. The resulting sequences were aligned with other published Thalassiosira ITS regions using the multiple sequence alignment algorithm MAFFT (Katoh and Standley 2013, 2016) to create a phylogenetic tree (see 2.5.2 ).  2.1.3 Culture media and growth conditions Cultures were grown in the artificial seawater medium Aquil (Price et al. 1988), which consists of a salt mixture (synthetic ocean water, SOW) at pH 8.2, enriched with standard additions of nitrate (300 µmol L-1 NO3-), phosphate (10 µmol L-1 PO43-), and silicic acid (100 µmol L-1  SiO32-), as well as vitamins. The trace elements Mn, Zn, Co, Mo, and Se were added bound to EDTA to attain a final EDTA concentration of 100 µmol L-1 in the growth medium (Maldonado and Price 1996). Iron and Cu were added separately as a premixed FeEDTA or CuEDTA complex (1:1.05) (for an overview of the Fe and Cu concentrations used in our media, see Table 2.1). Iron was added at a total concentration of 1.37 µmol L-1 (pFe 19) (speciation calculated using MINEQL) (Westall et al. 1976). Copper was added to give total concentrations of 10.2 nmol L-1 (pCu 14) and 14.32 nmol L-1 (pCu 13.5) in the Cu-sufficient media for TO03 and TO05, respectively. Copper-limited media had just background Cu contamination for TO03 and was enriched with 6.08 nmol L-1 for TO05. For the Fe- and Fe-Cu limiting media, Fe was added at a total concentration of 12.5 nmol L-1  (pFe 20.5) for both strains. In the Fe-Cu co-limiting media, Cu, however, was added at a total concentration of 1.96 nmol L-1 (pCu 15) for TO03 and 6.08 nmol L-1 (pCu 14.5) for TO05. Background Cu contamination in the media was <1 nmol L-1 28  and was determined in parallel medium preparations by chemiluminescence detection using a flow injection analysis system (Zamzow et al. 1998; Semeniuk 2014). Sterile, trace metal-clean techniques were used during all manipulations, and the metal-EDTA reactions in the media were allowed to equilibrate overnight.   2.2 Whole cell physiology 2.2.1 Growth measurements Both strains were cultured in 28 mL acid-cleaned polycarbonate tubes at 19 ±1°C in continuous light (ca. 155 μmol quanta m-2 s-1). Growth was monitored by daily measurements of in vivo Chla fluorescence using a Turner10-AU Fluorometer, and cultures were kept in exponential growth phase using semi-continuous batch culturing (Brand et al. 1983). The cultures were considered acclimated when growth rates during approximately 40 cell divisions (five successive transfers), varied by <15% (Brand et al. 1983). Acclimated, exponentially growing cells were used to inoculate triplicate 10 L cultures grown in polycarbonate carboys with gentle stirring. Growth in the 10 L cultures was determined by monitoring cell density with a Coulter Counter (model Z2). During early to mid-exponential phase, each biological replicate culture was sampled in duplicate for Chla concentration, total cellular protein, oxygen evolution, Fe uptake, 14C uptake, 14C light response (PvsE, PE) curves, electron transport rate in the reaction centre of PSII (ETRRCII) -PE curves and other FRRF derived parameters, as described below. The remainder of the culture (7 – 8 L) was used for differential proteomic analyses. Sterile, trace metal clean techniques were used at all times.   29  2.2.2 Chla measurements Duplicate 10 mL subsamples of the 10 L cultures were filtered onto a 25 mm glass fibre filter (GF/F, pore size 3 µm), flash-frozen in liquid N2 and stored at -20°C until analysis. Chlorophyll was extracted overnight in 8 mL ice-cold 90% acetone and the Chla concentration was determined fluorometrically following the method of Arar and Collins (Arar and Collins 1997).  2.2.3 Cellular protein concentration Cellular protein concentration was measured following a method adapted from Lommer et al. (2012). Briefly, 1.5 x 106 cells were concentrated by filtering 10-20 mL of culture onto a 25 mm, 3 µm pore-sized polycarbonate (PC) filter. Filters were flash frozen in liquid N2 and stored at -80°C until analysis. For protein determination, cells were resuspended and lysed in 250 µL SDS/CO3 buffer [4% w/v SDS, 68mM Na2CO3, Halt™ protease inhibitor cocktail (Thermo Fisher Scientific, Waltham, MA, USA, #78430) ] with application of ultrasonication. Cell debris was pelleted via centrifugation at 16,000 x g, at room temperature, for 4 min; protein concentration was determined with technical duplicates for each of the three biological replicates, using 50 µl of the supernatant in a bicinchoninic acid (BCA) protein assay (Thermo Fisher Scientific, Waltham, MA, USA, # 23227).  2.2.4 Oxygen evolution, respiration  and alternative oxidase (AOX) assay Oxygen evolution was measured using a Clark-type oxygen electrode (Hansatech). Before each experiment, the electrode was calibrated with sterile SOW bubbled with filtered compressed air or N2 gas for O2 saturated and O2 zero standard, respectively. Cells were concentrated 15-fold via gentle filtration onto a 3 µm pore-sized PC filter, resuspended in fresh media, and duplicate 30  subsamples from each biological replicate were introduced to the sample chamber. Samples were briefly bubbled with N2 before illumination at growth irradiance while O2 evolution was recorded. Subsequently, samples were exposed to complete darkness, in order to record the rate of respiration. Rates of oxygen evolution and respiration were derived from the slope of the linear increase and decrease of dissolved dioxygen (O2) over time. The raw data were analyzed using the software Oxypeak supplied by the manufacturer.   Alternative oxidase (AOX) is an enzyme that can oxidize NADH to NAD+ instead of the usual cytochrome c oxidase (COX) in the mitochondrial respiratory electron transport chain. Both enzymes transfer electrons to oxygen, thereby producing H2O. However, in contrast to COX, AOX does not contribute to the proton gradient, thus it does not increase ATP production. However, AOX has been shown to be wildly used in both plants and some phytoplankton species to counteract an overreduction of the mitochondrial electron transport chain (Eriksen and Lewitus 1999; Allen et al. 2008; Zhang et al. 2012). In order to test the activity of AOX vs COX, two inhibitors were used. After 30-60 sec of recording the rate of respiration, in one subsample, the AOX inhibitor SHAM (salicylhydroxamic acid) was added into the sample chamber at a final concentration of 0.1 mM. The resulting change in respiration rate was due to the decrease in AOX activity. Accordingly, in the second subsample, the COX inhibitor Antimycin A was added at a final concentration of 1 mM. Here, the change in respiration rate was due to the decrease in COX activity.  31  2.2.5 Chla fluorescence parameters and ETRRCII using Fast Repetition Rate fluorometry (FRRf) A bench-top FRRf instrument (Soliense Inc. ) was used for all active chlorophyll fluorescence (ChlF) measurements as described in detail in Schuback et al. (Schuback et al. 2015). Briefly, a single turnover (ST) protocol was designed and used to derive the ChlF yields Fo and Fm in the dark-regulated state, as well as F′ and Fm′ in the light-regulated state while subjected to 10 ‗background‘ irradiances ranging from 0 to 1,000 μmol quanta m-2 s-1. Fo′ was calculated as Fo′ = Fo/ (Fv/Fm + Fo/Fm′) (Oxborough and Baker 1997). The five ChlF yields, Fo, Fm, F′, Fm′ and Fo′ were used to calculate ChlF parameters following Kitajima and Butler (1975), as described in detail in Schuback et al. (2015). For the dark-regulated state, we derived the commonly used Fv/Fm ratio as Fv/Fm = (Fm-Fo) / Fm  (Kolber and Falkowski 1993). Furthermore, for each ‗background‘ light level we derived: (1) The photochemical quenching of variable fluorescence, Fq′/Fv′ = (Fm′-F′) / (Fm′-Fo′), which quantifies the fraction of functional RCII in the open state (i.e. primary quinone acceptor QA in the oxidized state); (2) The maximum quantum yield of PSII photochemistry, Fv′/Fm′ = (Fm′-Fo′) / Fm′, which can be used to quantify the extent to which photochemistry in PSII is limited by competition with thermal decay of excitation energy (Oxborough and Baker 1997); (3) The overall quantum efficiency of photochemical energy conversion in PSII at a given light intensity Fq′/Fm′ = (Fm′-F′) / Fm′ = ФPSII′ (the product of Fq′/Fv′ and Fv′/Fm′). The functional absorption cross section of PSII PSII (Å2 RCII-1), was derived from the rate of closure of RCII in the dark-regulated and at each light-regulated state (Kolber and Falkowski 1993; Kolber et al. 1998). Rates of charge separation (ETRRCII) in functional RCII (mol e- mol RCII-1 s-1) were estimated as the product of incident irradiance (E, µmol quanta m-2 s-1), the 32  fraction of irradiance absorbed by PSII (σPSII, Å2 RCII-1) and the efficiency with which charge separation occurs in RCII (Fq′/Fv′):  𝐸𝑇𝑅𝑅𝐶𝐼𝐼  = 𝐸 ×  𝜎𝑃𝑆𝐼𝐼 ′ ×  𝐹𝑞′ Fv′ ×  6.022 ×  10−3        (1)  The number 6.022 x 10-3 converts μmol quanta to quanta, Å2 to m2, and RC to mol RC.   Non-photochemical quenching (NPQ) at each light level was estimated as the normalized Stern-Volmer quenching coefficient, defined as NPQNSV = (Fm′/Fv′) -1 = Fo′/Fv′ (McKew et al. 2013; Schuback et al. 2015, 2016).   2.2.6 14C carbon assimilation Light response curves (photosynthesis, P vs. irradiance, E: PE curves) for C fixation were generated by measuring rates of carbon assimilation at various light intensities as described in Schuback et al. (2015), using a custom built photosynthetron (Lewis and Smith 1983). A photosynthetron is a temperature controlled apparatus in which small aliquots of the same culture are subjected to different light intensities simultaneously. Briefly, on the day of harvest, an 80 mL subsample of exponentially growing culture was spiked with 40 μCi NaH14CO3 and 3 mL aliquots were incubated in glass vials in the photosynthetron for 60 minutes. Each subsample was subjected to a different light intensity provided by high power light emitting diodes (LEDs) located under each glass vial. Each PE curve consisted of 11 light levels spanning intensities from 2 to 700 μmol quanta m-2 s-1. Actual light intensities were measured before and after each 33  experiment using a 4π quantum sensor (QSL-2100, Biospherical Instruments) immersed in water inside a scintillation vial. The temperature was kept within 1°C of growth temperature by circulating water from a water-bath through an aluminium cooling jacket. Duplicate curves were measured for each sample. The incubation was terminated by adding 1 mL of 1 M HCl to each vial. The samples were then completely dried, and subsequently re-suspended in 1 mL MilliQ water.   2.2.7 PE curves Measurements of CO2-assimilation and ETRRCII were plotted against irradiance, and the model of Jassby and Platt (1976) was fit to the data using the ‗phytotool‘ package (Silsbe and Malkin 2015) in R and RStudio (R Core Team 2013; RStudio Team 2015). For both rates of productivity, we derived the light utilization efficiency α [ETRα = (mol e- RCII-) / (µmol quanta m-2s-1); 14Cα = (g C g Chla-1 h-1) / (µmol quanta m-2 s-1) ], the light-saturated maximum rate Pmax (ETRPmax = mol e- RCII-; 14CPmax = g C g Chla-1 h-1), and the light saturation point Ek (µmol quanta m-2 s-1). When photoinhibition was observed at high irradiances, the data points were excluded from the fitting procedure.   2.2.8 Derivation of conversion factor Because we derived ETRRCII in units of mol e- mol RCII-1 s-1 and CO2-assimilation in units of mol C mol Chla-1 s-1, the conversion factor between the two rates accounts for changes in Chla functionally associated with each RCII (1/nPSII, mol Chla mol RCII-1) and the number of charge separations in RCII needed per CO2-assimilated into organic carbon products (Φe:C, mol e- mol C-1). 34  ETR RCII  mol  e− mol  RC−1s−1 CO 2  assimilation   mol  C mol  Chl  𝑎−1s−1 =  Φe:C  mol  e−mol  C ×  1nPSII mol  Chl  𝑎mol  RCII      (2)  In this approach, we attribute the observed decoupling between ETRRCII and CO2-assimilation to changes in both 1/nPSII and Φe:C.  2.2.9 Statistical analysis of physiological experiments Statistical analysis to test for significant and interacting effects was done on the complete dataset, even for Chapters 3 and 4 where only lowCu vs. control was discussed. This is to decrease the number of false positives that would ensue when examining each of the three treatment pairs separately (ctrl vs. low Cu, ctrl vs. low Fe, ctrl vs. low FeCu). The three main factors included in the linear model were: 1) Strain (TO03 vs. TO05), 2) Fe level (high vs. low), and 3) Cu level (high vs. low). We used the phia package (De Rosario-Martinez 2015) in R and RStudio (R Core Team 2013; RStudio Team 2015) to fit the linear model (including all three main effects) as well as for the post-hoc analysis of interactions (phia) in the factorial ANOVA. To focus the analysis on the low Cu data presented in Chapters 3 and 4, we then tested simple main effects for interactions, which consisted of evaluating contrasts across the levels of one factor, while the values of the other interaction factors were fixed at certain levels. For example, to test if the low Cu response in TO03 is significantly different from the control treatment, we set the fixed factors ―Strain‖ = TO03, and ―Fe level‖ = high. The variable factor to be tested would be ―Cu level‖ (high vs. low). To test for differences between strains under low Cu, the factors of variable ―Strain‖ (contrasts of TO03 vs. TO05) would be evaluated with fixed ―Fe level‖ = high and ―Cu level‖ = low. 35   2.3 Transcriptomics 2.3.1 RNA-Seq library preparation Total RNA was purified from cell pellets using the Trizol reagent (Life Technologies; Carlsbad, CA), treated with DNase (Qiagen, Valencia, CA, USA), and cleaned with the RNeasy MinElute Kit (Qiagen, Valencia, CA, USA). RNA-Seq libraries were constructed with TruSeq RNA Sample Preparation Kits (Illumina; San Diego, CA). 0.8 µg of total RNA was used as input followed by the manufacturer‘s TruSeq RNA Sample Preparation Low Throughput protocol. A high sensitivity DNA Assay chip was used to assess quality (Agilent; Santa Clara, CA, USA). The mean sizes of the libraries were around 430-480bp.   2.3.2 RNA-Seq data analysis Transcriptomes were sequenced on the Illumina HiSeq. Reads were trimmed of primer sequences, and rRNA sequences removed using Ribopicker v.0.4.3 (Schmieder et al. 2012). Contigs were assembled de novo using CLC Assembly Cell (CLC Assembly Cell, Quiagen Bioinformatics; http://www.clcbio.com, version 3. 22.55708, accessed 2013). Putative open reading frames on the assembled contigs were called using FragGeneScan (Rho et al. 2010). Predicted protein sequences were annotated by hidden Markov models for Pfam and TIGRfam, and by Blastp against PhyloDB 1.06 with an E-value threshold of 1e-3. PhyloDB is a comprehensive in-house database at JCVI consisting of proteins from many public sequence databases, including KEGG (Kanehisa et al. 2012), Pfam (Punta et al. 2012), GenBank (Benson et al. 2013), Ensembl (Flicek et al. 2011) and several in-house assemblies of algal uniculture transcriptomic sequences. The database PhyloDB is available for download at the following 36  website: https://scripps. ucsd. edu/labs/aallen/data/ (accessed 2017). The RNA-seq data reported here have been deposited in the NCBI sequence read archive (https://www.ncbi.nlm.nih.gov/sra/; BioProject accession no. PRJNA382002; BioSample accession nos. SAMN06698917- SAMN06698931, accessed 2017).   2.3.3 T. oceanica strain comparison Filtered transcriptome reads from TO03 (CCMP1003) were mapped to the following sources: predicted ORFs resulting from the de novo assembly of TO03, assembled contigs of the publicly available TO05 genome (CCMP 1005, https://www.ncbi.nlm. nih.gov/bioproject/PRJNA36595, downloaded 12/1/2015), nuclear genes of TO05, and chloroplast genes of TO05. Read mapping was performed using BWA MEM (Li 2013) with default parameters. Mapped reads from each sample were counted and reads per kilobase of transcript per million mapped reads (RPKM) values were calculated for all genes.   2.4 Proteomics 2.4.1 Protein purification From the 10 L triplicate cultures, 7 – 8 L in early to mid-exponential growth phase were harvested via gentle filtration onto 47 mm, 3 µm pore-sized PC filters. Depending on cell density and culture treatment, filters clogged after varying amounts of culture volume and had to be replaced. Cells washed from all filters were combined and resuspended in 10 mL ice-cold SOW and concentrated via centrifugation at 2,500 x g for 10 min. The pellet was resuspended in 2 mL ice-cold SOW and spun at 13,000 x g for 2 min. The resulting cell pellet was flash frozen in liquid N2 and stored at -80ºC until final processing. During protein extraction, all samples and 37  buffers were kept on ice. To disrupt the cells, approximately 0.8 g of glass beads (212 - 300 µm diameter, Sigma, #G1277-100g) were added to the cell pellet in the Eppendorf tube, as well as ~ 1.5 mL lysis buffer (0.05 M Hepes, 0.02 M KCl, 0.001 M EDTA, 0.0002 M DTT, 0.15 sorbitol plus freshly added Protease Inhibitor, one Roche® Tablet per 50 mL lysis buffer). Complete cell lysis was achieved with three 1 min vortexing intervals, interrupted by 1 min cooling periods on ice. The samples were then settled by 10 seconds centrifugation in a tabletop centrifuge and the supernatant was transferred to a new 2 mL Eppendorf tube. The remaining cell debris and beads were repeatedly washed with lysis buffer and the supernatants were combined until the wash steps resulted in an almost clear supernatant (approximately 5-10 times). To pellet any potentially contaminating cell debris or beads, the combined supernatant was centrifuged at 2,500 x g, at 4ºC, for 15 min. The resulting supernatant was transferred to 12 mL ultracentrifuge tubes. Soluble and insoluble protein fractions were separated by ultracentrifugation at 100,000 x g, at 4ºC, for 1 h.   2.4.2 Protein preparation for mass spectrometry Proteins were reduced with dithiothreitol (1:50 ratio of dithiothreitol: protein), alkylated using iodoacetamide (1:10 ratio of iodoacetamide:protein) and Trypsin digested (1:50 ratio of enzyme: protein) as described in Foster et al. (2003). Digested peptides were purified and concentrated on C18 STAGE-tips (Rappsilber et al. 2003) and eluted in 80% acetonitrile, 0.5% acetic acid, and dried in a vacuum concentrator (Eppendorf). Dried peptides were resuspended in 100 mM triethylammonium bicarbonate and labelled via chemical dimethylation using light (CH2O, for control treatment), medium (CD2O, for low Cu treatment), and heavy (13CD2O for low FeCu treatment) isotopologues of formaldehyde as described previously (Boersema et al. 2009). 38  Labeled samples were finally combined together in equal amounts for STAGE-tip purification and subsequently eluted in 80% acetonitrile, 0.5 % acetic acid. Eluted samples were dried and resuspended in 0.5% acetic acid for the TO03 set and 1% TFA for the TO05 set (Boersema et al. 2009) (Figure 2.1).  2.4.3 Liquid chromatography-tandem mass spectrometry – LC-MS/MS TO03‘s purified peptides were analyzed on the linear-trapping quadrupole-Orbitrap mass spectrometer (LTQ-Orbitrap Velos; ThermoFisher Scientific) on-line coupled to an Agilent 1290 Series HPLC using a nanospray ionization source (ThermoFisher Scientific) including a 2 cm long, 100 μm-inner diameter fused silica trap column, 50 μm-inner diameter fused silica fritted analytical column and a 20 μm-inner diameter fused silica gold coated spray tip (6 μm-diameter opening, pulled on a P-2000 laser puller from Sutter Instruments, coated on Leica EM SCD005 Super Cool Sputtering Device). The trap column was packed with 5 μm-diameter Aqua C-18 beads (Phenomenex, www.phenomenex.com, accessed 2015) while the analytical column was packed with 3 μm-diameter Reprosil-Pur C-18-AQ beads (Dr Maisch, www.Dr-Maisch.com, accessed 2015). Buffer A consisted of 0.5% aqueous acetic acid, and buffer B consisted of 0.5% acetic acid and 80% acetonitrile in water. Samples were run on a gradient method where buffer B was from 10% to 25% over 120 min, from 25% to 60% over 20 min, from 60% to 100% B over 7 min, kept at 100% for 2.5 min and then the column was reconditioned for 20 min with buffer A. The HPLC system included Agilent 1290 series Pump and Autosampler with Thermostat set at 6°C. The sample was loaded onto the trap column at 5 μL min-1 and the analysis was performed at 0.1 µL min-1. The LTQ-Orbitrap was set to acquire a full-range scan at 60,000 resolution from 350 to 1600 Th in the Orbitrap to simultaneously fragment the top fifteen peptide 39  ions by CID in each cycle in the LTQ (minimum intensity 200 counts). Parent ions were then excluded from MS/MS for the next 30 sec. Singly charged ions were excluded since in ESI mode peptides usually carry multiple charges. The Orbitrap was continuously recalibrated using the lock-mass function. The mass error measurement was typically within 5 ppm and was not allowed to exceed 10 ppm. TO05‘s purified peptides were analyzed using a quadrupole – time of flight mass spectrometer (Impact II; Bruker Daltonics) on-line coupled to an Easy nano LC 1000 HPLC (ThermoFisher Scientific) using a Captive nanospray ionization source (Bruker Daltonics) including a column setup identical to that for TO03. Buffer A consisted of 0.1% aqueous formic acid, and buffer B consisted of 0.1% formic acid and 80% acetonitrile in water. Samples were run on a gradient method where buffer B was from 5% to 20% over 45 min, from 20% to 40% over 45 min then to 100% over 2 min, held at 100% for 15 min. Re-equilibration back to 5% buffer B was done separately by the LC automatically. The LC thermostat temperature was set at 7°C. The sample was loaded onto the trap column at 800 Bar and the analysis was performed at 0.25 μL min-1. The Impact II was set to acquire in a data-dependent auto-MS/MS mode fragmenting the 17 most abundant ions (one at the time) after each full-range scan from m/z 200 Th to m/z 2000 Th. The isolation window for MS/MS was 2 to 3 Th depending on parent ion mass to charge ratio. Parent ions were then excluded from MS/MS for the next 0.4 min. Singly charged ions were excluded since in ESI mode peptides usually carry multiple charges. The error of mass measurement was typically within 5 ppm and was not allowed to exceed 10 ppm.   40  2.4.4 Analysis of mass spectrometry data Analysis of mass spectrometry data was performed using MaxQuant 1.5.1.0. Originally, the search was performed against a database comprised of the protein sequences from the source organism (TO05, publicly available) plus common contaminants using the default MaxQuant parameters with match between run and re-quantification options turned on. Only those peptides exceeding the individually calculated 99% confidence limit (as opposed to the average limit for the whole experiment) were considered as accurately identified. Another search was performed with identical search parameters but against a larger database that combined protein sequences from both the TO05 genome and our own TO03 transcriptome (predicted proteins on assembled EST contigs). If not noted otherwise, differential expression data is given from the original search. Tables that include both differential expression data from the original and the EST mapped search are found in the respective supplementary tables. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (Vizcaíno et al. 2016) partner repository with the dataset identifier PXD006237.   2.4.5 Statistical analysis of proteomic differential expression As described above (Protein preparation for mass spectrometry), peptides were labelled with different isotopologues of formaldehyde depending on their growth regime (i.e. control, lowCu, lowFeCu). Once the peptides of the three treatments were labelled, they were mixed together in a 1:1:1 ratio and analyzed by LC-MS/MS. The differential expression of proteins is then derived from the ratio of the intensities (area under the curve) of the light, medium and heavy peaks for each peptide (for a schematic of the method see Figure 2.1). 41  For the published manuscript (Hippmann et al. 2017), we set the criteria to determine statistical significance in the differential expression of a protein as: 1) an observed differential expression ratio in at least two of the three biological replicates (see Figure 2.2 legend for details); 2) an average of the ratios > ±2-fold for significantly up- and down-regulated proteins; and 3) a p-value of <0.05 (p<0.05) for the z.test, determining significant difference of the average ratios between treatments, taking the variance into account. Additionally, any protein that had a ratio of >10 (up-regulated) or <0.1 (down-regulated) in at least one biological replicate was also considered to be an all-or-nothing response and was included in the ‗significantly changed‘ set. For Chapters 4 and 5, we also included in the ‗significantly changed‘ set, proteins that were more moderately regulated with average ratios between ±1.3- to 2-fold, as long as they fulfilled the other criteria (i.e. the protein was detected in at least two of the three biological replicates and p<0.05).   Separating the proteins into soluble and insoluble fractions has both advantages (e.g. deeper probing of the proteome), and disadvantages (e.g. more complex results when differential expression values are in disagreement between the two fractions). The expression values of the soluble and insoluble proteins are given in the respective tables. However, for the interpretation of the results, only one of them was discussed. For many proteins, the dominant cellular fraction (e.g. soluble or insoluble) where the protein should be found is known; in this case, the respective value is used. Some proteins, however, are loosely associated with the membrane and thus are more likely than others to be detected in both the soluble and insoluble fractions. For these, the following strategy was used: 1) if one is not significantly regulated and the other one is significantly regulated, the significant value is discussed; 2) if both are diametrically, but 42  significantly regulated, the one in the most likely fraction was discussed. For example, the usually membrane-bound LHC, THAOC_05777 (a TpFCP10 homolog) had a -2.24-fold change in the soluble fraction and +2.28 in the insoluble fraction, therefore, I discussed the  +2.28-fold change observed in the insoluble fraction (Table 5.2), but data for both fractions was reported in the summary table (Table C.5).  2.4.6 Protein annotation Predicted proteins from both the publicly available genome of TO05 (CCMP 1005) and our transcriptome of TO03 (CCMP 1003) were searched against the JCVI in-house curated database PhyloDB for functional annotation using BlastP. To determine gene localization for proteins involved in photosynthesis, three strategies were followed: 1) sequences of candidate genes were compared to the publicly available chloroplast genomes of T. oceanica (CCMP 1005) (Lommer et al. 2010) and T. pseudonana (Armbrust et al. 2004; Oudot-Le Secq et al. 2007), 2) the diatom-specific chloroplast targeting sequence software ASAFind (Gruber et al. 2015) was used in conjunction with SignalP (Petersen et al. 2011) to find nuclear encoded, chloroplast targeted proteins, and 3) the remaining proteins were blasted against NCBI to find the closest homologs.   2.5 Phylogenetic trees 2.5.1 Light  harvesting complex (LHC)  Blasting of the publicly available genome of TO05 against the JCVI in-house curated database PhyloDB resulted in 68 predicted proteins containing Chla binding sites typical for family members. Alignment with other known heterokont LHCs showed that 48 of these predicted T. oceanica LHCs had both the expected three trans-membrane helices as well as eight conserved 43  residues (Green and Durnford 1996; Green 2003; Liu et al. 2004). For phylogenetic tree analysis, the amino acid sequences of these 48 predicted T. oceanica gene models were aligned with 33 T. pseudonana gene models using the multiple sequence alignment algorithm MAFFT (Katoh and Standley 2013, 2016). Alignments were edited using the software package Bioedit (Hall 2013) resulting in 81 aligned sequences consisting of 118 characters, including 23 gaps. The phylogenetic tree was inferred using PhyML 3.0 (100 bootstrap replicates) (Guindon et al. 2010). Branch support was determined by three different methods: SH-aLRT, aBayes and standard bootstrap (Anisimova et al. 2011). The phylogenetic tree was visualized using TreeView (Page 1996, 2001, 2002).  2.5.2 ITS region  The ITS sequences of my diatoms (CCMP1003 and CCMP1005) were aligned with the publicly available sequences of 12 other diatoms (8 other T. oceanica, 2 T. pseudonana, 2 T. weisfloggii, see Table A.1) using the multiple sequence alignment algorithm MAFFT(Katoh and Standley 2013, 2016). Alignments were edited using the software package Bioedit (Hall 2013) resulting in 14 aligned sequences consisting of 640 characters, including 7 gaps. The phylogenetic tree was inferred using PhyML 3.0 (100 bootstrap replicates) (Guindon et al. 2010). Branch support was determined by standard bootstrap (Anisimova et al. 2011). The phylogenetic tree was visualized using TreeView (Page 1996, 2001, 2002).    44  2.6 Tables and figures  Table 2.1: Overview of the Fe[total] and Cu[total] concentrations added to our media for each T.oceanica strain. Treatment TO03 TO05 Fe[total] Cu[total] Fe[total] Cu[total] control 1.37 µM 10.2 nM 1.37 µM 14.32nM lowCu 1.37 µM <1 nM 1.37 µM 6.08 nM lowFe 12.5 nM 10.2 nM 12.5 nM 14.32 nM lowFeCu 12.5 nM 1.96 nM 12.5 nM 6.08 nM 45   Figure 2.1: Overview of Proteomic Method. A) Workflow: First the extracted proteins are trypsin digested. The resulting peptides are then labelled via chemical dimethylation using light (CH2O, for control treatment; green), medium (CD2O, for low Cu treatment; blue), and heavy (13CD2O for low FeCu treatment; red) isotopologues of formaldehyde as described previously (Boersema et al. 2009). After peptides of the respective treatments are labelled, they are mixed together in a 1:1:1 ratio and then analyzed together by LC-MS/MS. The differential expression of proteins is then derived from the ratio of the intensities (area under the curve, here depicted as the height of bars) of the light (green), medium (blue) and heavy (red) peaks for each peptide. B) Table of preparation and mixing of samples analyzed by LC-MS/MS. Each biological replicate (three per treatment) is labelled 46  individually. Then one labelled sample of each treatment is mixed together in a 1:1:1 ratio, resulting in three separate biological replicate mixes to be analyzed by LC-MS/MS. Each of these three biological replicate mixes was then analyzed in technical duplicates (TO03) or triplicates (TO05).   47   48  Figure 2.2 Diagram, illustrating how the differential proteomic data were analyzed. If a protein was only detected in one treatment but not in another, it would not be part of the differential expression data set (e.g. Protein A: replicate 2, lowCu data). Furthermore, if only one of the three biological replicates resulted in a differential expression ratio, the protein would be part of the dataset, but the expression ratio would not have a statistical measure (pval) associated with it (e.g.Protein A: only detected in replicate 1, part of both lowCu and lowFeCu differnetial expression data sets). If two or three differential expression ratios were detected, the protein would be part of the dataset, this time with an associated pval (e.g. Protein B: lowCu differential expression data, replicate 2 and 3; Protein C: lowCu data, replicate 1, 2, and 3; Protein C: lowFeCu data, replicate 1 and 2).  49  Chapter 3: Contrasting Effects of Copper Limitation on the Photosynthetic Apparatus in Two Strains of the Open Ocean Diatom Thalassiosira oceanica  3.1 Summary There is an intricate interaction between iron (Fe) and copper (Cu) physiology in diatoms. However, strategies to cope with low Cu are largely unknown. This study unveils the comprehensive restructuring of the photosynthetic apparatus in the diatom Thalassiosira oceanica (CCMP1003) in response to low Cu, at the physiological and proteomic level. The restructuring results in a shift from light harvesting for photochemistry —and ultimately for carbon fixation—to photoprotection, reducing carbon fixation and oxygen evolution. The observed decreases in the physiological parameters Fv/Fm, carbon fixation, and oxygen evolution, concomitant with increases in the antennae absorption cross section (σPSII), non-photochemical quenching (NPQ) and the conversion factor (φe:C/ηPSII) are in agreement with well-documented cellular responses to low Fe. However, the underlying proteomic changes due to low Cu are very different from those elicited by low Fe. Low Cu induces a significant four-fold reduction in the Cu-containing photosynthetic electron carrier plastocyanin. The decrease in plastocyanin causes a bottleneck within the photosynthetic electron transport chain (ETC), ultimately leading to substantial stoichiometric changes. Namely, a 2-fold reduction in both cytochrome b6f complex (cytb6f) and photosystem II (PSII), no change in the Fe-rich PSI and a 40- and a 2-fold increase in proteins potentially involved in detoxification of reactive oxygen species (ferredoxin and ferredoxin: NADP+ reductase, respectively). Furthermore, we identify 48 light-harvesting complex (LHC) proteins in the publicly available genome of T. oceanica and provide proteomic 50  evidence for 33 of these. The change in the LHC composition within the antennae in response to low Cu underlines the shift from photochemistry to photoprotection in T. oceanica (CCMP1003). Interestingly, we also reveal very significant intra-specific strain differences. Another strain of T. oceanica (CCMP 1005) requires significantly higher Cu concentrations to sustain both its maximal and minimal growth rate compared to CCMP 1003. Under low Cu, CCMP 1005 decreases its growth rate, cell size, Chla and total protein per cell. We argue that the reduction in protein per cell is the main strategy to decrease its cellular Cu requirement, as none of the other parameters tested are affected. Differences between the two strains, as well as differences between the well-documented responses to low Fe and those presented here in response to low Cu, are discussed.   3.2 Introduction Diatoms account for almost a quarter of global primary productivity, thus contributing significantly to the transfer of CO2 from the atmosphere to the ocean interior (Nelson et al. 1995; Field et al. 1998; Raven and Waite 2004). Yet in large oceanic regions, optimal growth of diatoms is constrained by iron (Fe) supply (Moore et al. 2004). However, in these low Fe regions, diatoms and other phytoplankton persist, and have evolved unique physiological strategies to grow at chronically low Fe levels (Maldonado and Price 1999, 2001; Strzepek and Harrison 2004; Maldonado et al. 2006; Allen et al. 2008; Zhu et al. 2010), even though they also respond rapidly to sporadic Fe inputs (Jickells et al. 2005; Marchetti et al. 2009a). In phytoplankton, the process of photosynthesis has the highest requirement for Fe. Therefore, some of the underlying cellular adaptations to low Fe include a) reducing the number of 51  chloroplasts, and the volume of individual chloroplasts (Lommer et al. 2012), b) restructuring the photosynthetic apparatus, such that the Fe intensive PSI is down-regulated relative to PSII (Strzepek and Harrison 2004), and c) replacing the electron carrier ferredoxin with the non-metal equivalent flavodoxin (LaRoche et al. 1996). In addition to these physiological adaptations under low Fe, open ocean diatoms enhance their demand for copper (Cu) ― another essential redox active metal ― by eliminating the Fe-containing cytc6 in photosynthesis and replacing it with plastocyanin, a Cu-containing counterpart (Peers and Price 2006), and by up-regulating a high-affinity Fe uptake system (HAFeT) that depends on a multi-Cu oxidase (Peers et al. 2005; Maldonado et al. 2006). These physiological adaptations result in a higher Cu demand in Fe-limited than in Fe-sufficient diatoms and suggest an intricate link between Fe and Cu physiology (Annett et al. 2008).  Open ocean Fe concentrations are significantly lower (average ~0.6 nM) than those at the coast (average ~ 2nM) (Moore and Braucher 2008). Diatoms in oligotrophic regions, such as the Sargasso Sea, although not primarily chronically Fe limited, often may experience low Fe availability (surface waters [Fe]diss ranging from 0.2-0.8 nM (Wu and Boyle 2002), relative to the average half-saturation constant for growth for Fe (Ku) for field populations, 0.32 nM Fe (Marchetti and Maldonado 2016) in addition to macronutrient limitation and might, as a result, exhibit the physiological adaptations mentioned above. Indeed, several laboratory and field studies have shown that open ocean phytoplankton usually have higher Cu:C demands and thus are more easily limited by low Cu than their coastal counterparts (Peers et al. 2005; Annett et al. 2008; Guo et al. 2012). The literature on adaptations of phytoplankton to cope with Cu limitation is scarce. In the model freshwater green alga Chlamydomonas reinhardtii, Cu limitation induces a tight 52  transcriptional regulatory mechanism (Kropat et al. 2005; Castruita et al. 2011). Most notably, it uses the Cu-containing electron carrier plastocyanin when grown under Cu replete conditions and switches to the Fe-containing cytc6 under Cu limiting conditions (Merchant and Bogorad 1986; Merchant et al. 1991). This decreases its cellular photosynthetic Cu demand and enables adequate Cu supply to cytochrome oxidase in respiration (Kropat et al. 2015). An increase in unsaturated fatty acids in the thylakoid membrane also occurs in response to Cu limitation (Castruita et al. 2011). Changes in thylakoid fatty acid composition can have far-reaching consequences in a photosynthetic cell (Kern et al. 2009; Goss et al. 2009; Gao et al. 2014). An increase in the fatty acid MGDG (monogalactosyldiacylglycerol) might result in an increase in non-photochemical quenching (NPQ, an estimate of how much excess excitation energy is dissipated as heat) by facilitating both recruitment of diadinoxanthin and its interaction with light-harvesting complexes (LHCs) (Goss et al. 2005, 2007, 2009). One study examined the effects of low Cu on the photosynthetic apparatus of the prymnesiophyte Phaeocystis. Photosystem II reaction centres seemed to be damaged but no effects were observed downstream in the electron transport chain (Lombardi and Maldonado 2011). In addition to photosynthesis, Cu limitation affects diatom physiology by a) inducing a high-affinity Cu uptake system, which has different characteristics and regulation in oceanic and coastal centric species (Guo et al. 2010), and b) increasing the cellular Chla and lipid content in pennate diatoms (Lelong et al. 2013). Most recently, the genes encoding various Cu transporters and chaperones have been identified in silico in the genome of the diatom T. pseudonana (Guo et al. 2015). Many of the physiological adaptations to Fe limitation described above were investigated using the model open ocean centric diatom, T. oceanica, strain CCMP 1003 (hereafter referred to 53  as TO03), whose genome has not been sequenced. However, recently, the genome of a different strain of T. oceanica (CCMP 1005; here TO05) was sequenced (Lommer et al. 2012). Given the importance of Cu nutrition for T. oceanica (Peers et al. 2005; Peers and Price 2006; Annett et al. 2008; Guo et al. 2010, 2012), we aimed to investigate the physiological and proteomic response to Cu limitation in both strains. Since TO03 has not been sequenced, we grew this strain under various Cu conditions and used transcriptomics to create an expressed sequence tag (EST) library with assembled contigs. Our preliminary growth experiments showed very contrasting Cu demands between TO05 and TO03 and inspired further comparative investigations. The present study focuses on the physiological and proteomic response of the photosynthetic apparatus to Cu limitation in both strains. Here, we also discuss similarities and differences between the well-known photosynthetic response of diatoms to low Fe and their understudied response to low Cu.  3.3 Results The two strains of the oceanic diatom T. oceanica studied were both isolated from the Sargasso Sea, twenty-seven years apart (TO05 in 1958, TO03 in 1985). Despite being the same  species, they have exceptionally different Cu requirements. The data presented below illustrate the contrasting physiology of these strains in response to Cu limitation. We structure our results in four sections; first, we describe the effects of Cu limitation on whole cell physiology, followed by a more focused description of the effects on photophysiology. We then present a brief overview of the two proteomic datasets. We conclude with a portrayal of the nature and expression patterns of the underlying light-harvesting complexes and proteins involved in photosynthesis, responsible for the changes we see in the photophysiological results. 54  3.3.1 Effect of chronic copper limitation on whole cell physiological parameters  TO03 achieved a maximum specific growth rate (µmax) of 1.05 per day (d-1) in the presence of 10.2 nM [Cu]total (Figure 3.1). The background Cu concentration in the media (<1 nM [Cu]total) allowed TO03 to sustain a growth rate of 0.51 d-1, equivalent to 48% of µmax (± 6.45% std error). In contrast, TO05 could not survive at this lowCu level (<1 nM [Cu]total) and achieved its maximum growth rate (1.26 d-1 ) at higher Cu concentrations (14.3 nM [Cu]total) than TO03. In TO05, Cu limitation was induced at 6.08 nM [Cu]total (0.82 d-1, or 65% µmax). Even though the absolute growth rates under Cu replete and deplete conditions are statistically different for the two strains, their relative Cu-limited growth rates (µ/µmax) are indistinguishable (p>0.1, Figure 3.2A, Table A.2). Therefore, comparing the effect of Cu limitation between the strains is appropriate.  Cell diameter decreased 10% in both strains in response to low Cu (Figure 3.2B, Table A.2). Chla concentration per cell was constant in TO03, while it decreased somewhat in TO05 (Figure 3.2C). However, as cell diameter decreased in both strains, when normalized to cell volume, Chla concentration increased in response to low Cu in TO03 (4.3±0.1 to 7.4 ±0.5 fg Chla fL-1, p<0.001) and stayed the same in TO05 (4.3±0.8 and 3.8 ±0.4 fg Chla fL-1, Table A.2). Cellular protein content remained constant in TO03. However, TO05 significantly decreased its cellular protein content in response to low Cu by 50% (Figure 3.2O). Gross oxygen production decreased in TO03 in response to Cu limitation, regardless of the parameter used to normalize the rates (Table A.2): normalized to Chla, it decreased by 72% (Figure 3.2D), and normalized to cell, it decreased by 70% (123±11.5 to 36 ±4.3 fmol O2 cell-1 h-1, p<0.001). In contrast, in TO05, O2 production was only significantly decreased when normalized to cell (139±10.4 to 69.4 ±2.0 fmol O2 cell-1 h-1, p<0.01, Table A.2) but not when normalized to Chla. Respiration, on the other 55  hand, was not affected by Cu concentration in either of the two strains, independent of normalization (Table A.2).   3.3.2 Effects of Cu limitation on photophysiology, ETRRCII and C-assimilation Chronic Cu limitation had profound effects on the photophysiology of TO03, but not TO05. In fact, none of the following photophysiological, ETRRCII, or C-assimilation parameters changed in TO05 in response to low Cu. In TO03, Cu limitation resulted in a decrease of Fv/Fm from 0.6±0.01 to 0.3±0.01 A.U., p<0.001 (Figure 3.2E). The functional absorption cross-section of PSII (σPSII) increased by 30% (Figure 3.2F) and the plastoquinone (PQ) pool size increased by 37% (4.3 ±0.5 to 6.0±0.4 PQ mol QB-1, p<0.1) (Table A.2). To gain more insight into photophysiological changes, we generated PE curves for short-term C-assimilation and ETRRCII. From these curves, we derived the respective photosynthetic parameters Pmax, α, and Ek. For short-term C-assimilation, normalized to Chla, all three parameters decreased in TO03: Pmax decreased by 75%, α decreased by 63% and Ek decreased by 54% (Figure 3.2G-1). For ETRRCII normalized to RC (Figure 3.2J-L), α increased by 18% in response to low Cu, while Pmax remained constant and Ek decreased by 25%. For both strains, the light saturation point Ek was higher for carbon fixation than ETRRCII (Figure 3.2I, L), implying that ETRRCII saturated at lower light intensities than carbon fixation. At the growth irradiance (155 µmol quanta m-2 s-1), the rates of ETRRCII relative to those of C-assimilation (i.e. the conversion factor φe:C/ηPSII) increased two-fold in response to low Cu in TO03 (Table 3.2M). In line with the study by Schuback et al. (2015), we examined FRRF derived parameters at the growth irradiance to further elucidate the photophysiological mechanisms. The following parameters changed only in TO03 and not in TO05. Low Cu decreased the efficiency of 56  excitation energy capture by the fraction of open RCII (Fv′/Fm′) by 53% in TO03 (0.6±0.01 to 0.3±0.01 A.U., p<0.0001, Table A.2). In line with the decrease in Fv′/Fm′, we observed a four-fold increase in non-photochemical quenching, estimated as the normalized Stern Vollmer NPQNSV (Figure 3.2N). The overall quantum efficiency of photochemical energy conversion in PSII (Fq′/Fm′, φ′PSII) decreased by 63% in response to low Cu (0.4±0.03 to 0.2±0.01 A.U., p<0.001, Table A.2). In contrast, photochemical quenching (Fq′/Fv′), an indicator of the efficiency of charge separation in functional RCII was only slightly reduced by 17% (0.7 ±0.04 to 0.6±0.02 A.U., p<0.05, Table A.2).  3.3.3 Overview of the two proteomic datasets for the two strains – original vs. EST included For each strain, we mapped the peptides generated by the LC-MS/MS to two databases (see methods). In the original search (thereafter called 'original'), peptides were mapped to proteins from the TO05 genome. To ensure that we did not miss any important information regarding the unique response of TO03 due to possible genomic differences between the two strains, we added the second search (thereafter called 'plusEST'), were the same peptides were mapped to a larger database composed of the original protein sequences of the TO05 genome plus the protein sequences of our own TO03 transcriptome. The 'plusEST' search leads to a moderate increase of identified proteins in both strains when comparing the results with the respective original search (15.6% in TO03 and 13.4 % in TO05, see Table A.6). As illustrated in Figure 3.3, the distribution of the identified proteins in both strains is as follows: approximately 83% of all identified proteins had best hits in the TO05 genome part and 17% (e. g. TO03: 139 ESTs) in the EST part of the database. Examining these 139 hits, we also observe a similar proportion of EST 57  sub-groups represented in both strains: 75% of these ESTs can themselves be mapped to predicted proteins of the TO05 genome, a little over 19% map to putatively non-coding regions of the TO05 genome, and 1.25% map to the chloroplast genome, whereas 6% do not map anywhere on the TO05 genome.   3.3.4 Proteomic shift in the antennae and photosynthetic transport chain 3.3.4.1 Identity and expression of LHC proteins In silico probing of the publicly available genome of Thalassiosira oceanica (CCMP 1005) for proteins containing a predicted Chla/b binding site, revealed 69 potential candidate genes coding for light harvesting complex (LHC) proteins. Aligning the predicted protein sequences with those of 41 predicted LHCs from the closely related diatom Thalassiosira pseudonana (CCMP 1335) revealed that only 48 LHCs from T. oceanica feature all three helices with conserved residues deemed essential for their proper functioning (Figure 3.4, Table A.3) (Green and Durnford 1996; Green 2003; Liu et al. 2004). Of the 48 predicted proteins in T. oceanica, 33 LHCs (69%) were identified at the protein level in this study (Figure 3.5, Table 3.1). The overall expressed inventory of LHCs was very similar between the two strains.  Of the 48 predicted gene models, we found evidence at the protein level for 33 LHCs (29 found in both, 1 only in TO03, and 3 only in TO05, Table A.3). Note that the number of proteins expressed per LHC clade is similar between the two strains. Considerable numbers of significantly differentially expressed LHCs are only found in TO03 with 9 up- and 7 down-regulated, whereas there is only one up-regulated in TO05 (Group III, Table 3.1, Table 3.2). Phylogenetic analysis of these 48 putative LHCs in T. oceanica (CCMP 1005) led to their classification into the four previously described clades (Figure 3.5, Table 3.1, Table A.4) 58  (Lefebvre et al. 2010; Goss and Lepetit 2015; Mock et al. 2017): the main Chla/c Lhcf (26 predicted proteins), the red algal-like Lhcr (10 predicted proteins), the related clade Lhcz (3 predicted proteins), and the LI818-like clade Lhcx (8 predicted proteins). The additional clade around Tp17531 has been added, given that at least 5 different diatoms have a protein that clusters here (Mock et al. 2017), including T. oceanica (this study). Furthermore, the Lhcf clade can be divided into three subclades: Lhcf- Group I-III.  Of the 33 LHCs identified at the protein level, 88% (29 LHCs) are expressed in both strains. However, whereas TO05 has only one up-regulated LHC in response to low Cu concentrations (THAOC_08587, Lhcf-group III, TpFCP7 homolog) and none down-regulated, TO03 demonstrates a well-orchestrated response (Figure 3.5, Table 3.2). Approximately 55% of its expressed LHCs were significantly regulated in response to Cu limitation. Eight LHCs were down-regulated: four proteins in the Lhcf- Group I, one in the Lhcf – Group III and two in the Lhcr clade. Nine LHCs were up-regulated: one protein in the diatom-specific Lhcf- Group II, four in Lhcf - Group III, one in the Lhcr clade and three in the Lhcx clade.   3.3.4.2 Proteomic shift within the chloroplast electron transport chain (ETC)  In both strains, many of the proteins involved in the photosynthetic electron transport chain (ETC) were identified (23 in TO03, 24 in TO05) (Table 3.3); 17 of these were shared, whereas five and six were only found in TO03 and TO05, respectively. Acclimation to low Cu has a profound impact on the stoichiometry of the complexes involved in the ETC in strain TO03. Here, protein levels of PSII (psbC, psbD, psbE, psbQ), cytb6f (petB), and the Cu-containing electron carrier plastocyanin (petE) are all significantly reduced (2-fold, 2-fold, and 4-fold, respectively) (Table 3). Protein levels for PSI are unchanged in response to low Cu (psaA, psaB, 59  psaC, psaD, psaF) whereas ferredoxin (petF) and FNR (petH) are both induced (40-fold and 2.4-fold, respectively). TO05 shows no significant differential expression of any of the proteins involved in the photosynthetic ETC. The EST mapped data support these findings (see Table A.4). We present a model of the photosynthetic electron transport chain in TO03, visualizing the stoichiometric changes of its major components in response to chronic Cu limitation (Figure 3.6).  3.4 Discussion Low copper (Cu) concentrations in the media elicited a remarkably different cellular response in the two strains of the oceanic diatom T. oceanica (CCMP 1003 and CCMP 1005). This in itself is striking and will be discussed below. However, the main body of the discussion will focus on the effect of low Cu on TO03 (T. oceanica, CCMP 1003), showing an extensive restructuring of the photosynthetic apparatus, shifting from a primarily light harvesting to a photoprotective role. This restructuring is reflected in a) the composition of the light-harvesting antennae, b) the stoichiometry of components of the photosynthetic electron transport chain, and c) the difference in the ratio of electron transport and carbon fixation rate. Differences between the well-documented response to low Fe and that to low Cu presented here are discussed.   3.4.1 Differences between T. oceanica CCMP 1003 and CCMP 1005 The two strains of the open ocean diatom T. oceanica, CCMP 1003 (TO03) and CCMP 1005 (TO05), were isolated from the Sargasso Sea 27 years apart, and are considered the same species based on morphology. In our study, the species identity of the two T. oceanica strains was confirmed, using the comparison of the diatom barcoding region encompassing the ITS1–5.8S – 60  ITS2 (Moniz and Kaczmarska 2009, 2010). The phylogenetic tree of 14 Thalassiosira ITS sequences, including the sequences of the two T.oceanica strains used in this study (CCMP1003 and CCMP1005) plus the publicly available ITS sequences of eight other T. oceanica strains, two T. pseudonana strains and two T. weisfloggii strains, clearly supports the assumption that these are indeed strains of the same species (Fig A.3, p235). Aligning the ITS region of both strains (Figure A.2) shows a 99.66% identical overlap in their ITS sequence. Further evidence of their close genomic relationship is given when we compare the additional proteomic datasets of TO03 (EST) and TO05 (EST) (Figure 3.3). In both strains, peptides were mapped to a combined database comprised of predicted protein sequences derived from the TO05 genome and our TO03 transcriptome. We predicted that TO03 would have a higher proportion of identified proteins coming from the mapping of the peptides to its own transcriptome relative to mapping them to the TO05 genome. In contrast, for TO05 we predicted the opposite: a higher proportion of identified proteins coming from the mapping of the peptides to its own genome. However, the distribution of identified proteins using the TO05 genome plus TO03 transcriptome database is practically the same in both strains. Furthermore, even the fraction of EST subsets is the same in both strains (Figure 3.3). Thus, even though these two strains are the same species, have been isolated from the same oceanic region, and have a similar genomic makeup, it is striking that their physiological responses to Cu limitation are so different (Figure 3.1 and Figure 3.2, Table A.2). Strain TO03 was able to sustain growth at a Cu concentration ([Cu]total) of <1 nM, whereas strain TO05 required 6 times more Cu ([Cu]total = 6.08 nM). Moreover, to achieve maximal growth rate, TO03 required only 10.2 nM [Cu]total, while TO05 required 40% more Cu (14.32 nM Cu; Figure 3.1, Table A.2). Although different Cu concentrations were used for the 61  two strains to reach their respective µmax and Cu-limited growth rate, they achieved identical relative growth rate reduction under low Cu (~55% µmax), allowing comparison between their respective physiological and proteomic responses (Figure 3.2A, Table A.2). In addition to their matching growth rate reduction (Figure 3.2A), both also decreased their cell size by 10% (Figure 3.2B). Cell size reduction is a common strategy in unicellular organisms in response to nutrient limitation. Decreasing cell size increases surface-area-to-volume ratio, allowing a greater number of nutrient transporters at the cell surface, relative to the cellular nutrient demand, which is proportional to cell volume. Furthermore, smaller cells have a thinner surface boundary layer, facilitating the diffusion of nutrients to the cell membrane (Marchetti and Maldonado 2016). Interestingly, of the 23 parameters presented here, TO05 changed only four significantly, whereas TO03 adjusted 18.  Nutrient limitation usually results in chlorosis, a decrease in cellular Chla concentration (Collier and Grossman 1992; Geider et al. 1993). This common strategy is only seen in TO05 but not in TO03 (Figure 3.2C). As described in more detail below, TO03 restructured its whole photosynthetic apparatus under low Cu, changing its Chla content (normalized to cell volume, it actually increases, Table A.2), PSII, Fv/Fm, NPQNSV, ETR, and 14C uptake. TO05 did not vary any of these parameters (p> 0.05) (Figure 3.2, Table A.2). Furthermore, of the most prominent cellular Cu-containing proteins (i.e. plastocyanin, cytochrome c oxidase), none are down-regulated in TO05.  We speculate that the dominant strategy used by TO05 to deal with Cu limitation is a reduction in cell size (Figure 3.2B) and overall protein content (Figure 3.2O). A comparable decrease has been seen in this strain in response to low Fe (Lommer et al. 2012). An overall reduction in protein content could be expected to result in a decrease in growth rate. However, as 62  the majority of proteins are reduced to the same extent, this would not be detected in our proteomic analysis, because we mixed equal absolute amounts of protein (5 ng control: 5 ng low Cu) independent of changes in cellular protein content. Even though we did not measure Cu quotas, TO05, just by decreasing its overall cellular protein content by 50%, will decrease its Cu-containing proteins by 50%. Strikingly, this agrees well with the 57% decrease in [Cu]total concentration in the media and the resulting 45% decrease in growth rate we observed for TO05.  The gross oxygen production data provide additional evidence for the singular strategy of TO05 to reduce its protein content by 50%. Gross oxygen production, when normalized to cell, decreased by 50% (Table A.2), while when normalized to Chla (Figure 3.2D) stays constant. This indicates that the photosynthetic unit itself in TO05 was not compromised, while it was greatly affected in TO03. Theoretically, when Cu is extremely low in the medium, the cell would be forced to decrease the proportion of Cu-containing proteins, as we observed for plastocyanin in TO03. However, it seems that TO05 is unable to decrease the Cu-containing proteins (i.e. plastocyanin) relative to other proteins, and thus is unable to grow at extremely low Cu. It is possible that TO05 has lost (or TO03 has gained) a Cu responsive transcription factor able to orchestrate a broad response when Cu concentrations are declining, similar to the one identified in Chlamydomonas (Sommer et al. 2010). TO03 is able to decrease plastocyanin significantly relative to other proteins and has to cope with potentially increased electron pressure and reactive oxygen species. As discussed below, TO03 rearranges its whole photosynthetic apparatus, and consequently, is able to sustain a decent growth rate (~0.5 d-1) at extremely low [Cu]total concentrations (<1 nM Cu). In recent years, multiple intra-specific physiological variations have been demonstrated in various phytoplankton taxa, such as the harmful algal bloom (HAB) forming raphidophyte 63  Heterosigma (Fredrickson et al. 2011; Lebret et al. 2012; Harvey et al. 2015), and diatoms such as Skeletonema (Kremp et al. 2012), Ditylum (Rynearson et al. 2006), Thalassiosira rotula (Chen and Rynearson 2016) and the HAB forming Pseudo-nitzschia (Orsini et al. 2004; Kim et al. 2015). These intra-specific differences can manifest themselves in spatial and/or temporal distinct occurrences of one strain over the other. However, four distinct strains of T. rotula co-existed in the North Atlantic spring bloom in 2008 (Chen and Rynearson 2016). This co-existence of various strains over large periods of time might facilitate niche-adaptations (Menden-Deuer and Rowlett 2014).  However, bearing in mind that diatoms are one of the faster-evolving phytoplankton phyla (Bowler et al. 2008), we must still consider the possibility of changes in the genome or epigenome of organisms kept in long-term culture collections. For example, the green alga C. reinhardtii took less than 500 generations in nutrient-rich media to lose its ability to synthesize cobalamin (Vit B12) (Helliwell et al. 2015). To address this issue, we contacted the curator at NCMA; their culture transfer regime (every 2-3 weeks for >20 years without inverting the tubes) would indeed allow the cells to experience nutrient rich and limiting conditions during each transfer cycle, hopefully preventing changes in genome and epigenome. In support of this, all the studies investigating trace metal requirements of open ocean and coastal species have used isolates that had been in culture for over 20 years, under trace metal replete conditions. These studies still revealed striking differences in the metal requirements in the open ocean and coastal isolates (Sunda et al. 1991; Sunda and Huntsman 1995; Annett et al. 2008; Guo et al. 2010). We believe that the responses of TO05 (i.e. decrease of the whole complement of proteome to decrease Cu requirement) and TO03 (i.e. differential expression of proteins) are both viable, but represent very different strategies to cope with Cu limitation.  64   3.4.2 Proteomic shift within the photosynthetic apparatus in TO03: electron transport chain and antennae  3.4.2.1 Electron transport chain - ETC In the plant photosynthetic electron transport chain (ETC), electrons are channelled from PSII via the PQ pool to cytb6f, then via the small Cu-containing plastocyanin to PSI, from where they are transferred by way of ferredoxin (petF, Fdx ) and FNR to NADP+, thereby producing the reducing equivalent NAD(P)H (Nelson and Ben-Shem 2004). In addition to the supply of reducing equivalents as NAD(P)H, the ETC generates a proton gradient across the thylakoid membrane ultimately resulting in ATP synthesis. Both ATP and NAD(P)H are needed in the carbon fixing Calvin-Benson-Bassham cycle. Since more ATP is needed for carbon fixation than the amount of ATP that can be generated via the proton gradient generated by this linear electron transport (LET) chain (Allen 2002, 2003), additional protons can be pumped into the lumen via cyclic electron transport (CET) either around PSII (Prasil et al. 1996; Lavaud et al. 2002; Ruban et al. 2004) or PSI (Yamori and Shikanai 2016). Keeping an adequate ATP/ NAD(P)H ratio is imperative for any cell to maximize growth and minimize cell damage. As shown by Bailleul et al. (2015), in diatoms, additional ATP and NAD(P)H can also be supplied to the chloroplast by the mitochondria.  Acclimation to low Cu has a profound impact on the stoichiometry of the complexes involved in the ETC in strain TO03 (Table 3). Changes in ETC stoichiometry have also been observed in TO03 in response to low Fe (Strzepek and Harrison 2004), when Fe-rich complexes, such as PSI and cytb6f, are significantly reduced. However, low Cu concentrations induce a 65  different change in stoichiometry, such that the concentrations of some of the proteins associated with photosystem II, and with the photosynthetic electron transport chain (cytb6f, and plastocyanin) are significantly reduced (Table 3.3, Figure 3.6). Protein levels for PSI, however, are unchanged in response to low Cu. The reduction in plastocyanin is expected given that this protein is thought to be the dominant Cu pool in T. oceanica (Peers and Price 2006). Indeed, our proteomic data showed a significant down-regulation of plastocyanin (4-fold) —a mandatory component in the ETC in T. oceanica—in response to low Cu (Table 3). This is accompanied by the 2-fold reduction of upstream components of the ETC (i.e. cytb6f and PSII), thus avoiding over reduction and ensuing photodamage of PSII. In contrast to the low Fe response, the Fe-rich PSI complex located down-stream of plastocyanin is not affected by Cu limitation. Furthermore, ferredoxin and FNR are both induced under low Cu (Table 3.3). FNR is up-regulated by 2.4-fold and has been shown to increase the ability to deal with oxidative stress in tobacco plants (Rodriguez et al. 2007). Ferredoxin (Fdx, petF) is the most up-regulated protein in our dataset (>40-fold). In the green alga Chlamydomonas, one of six different ferredoxin genes is also strongly up-regulated under low Cu and might facilitate chlorophyll biosynthesis (Terauchi et al. 2009). However, Lin et al. (2013), using transgenic cell lines overexpressing petF, showed an increased ability to deal with reactive oxygen species (ROS) in heat stressed chloroplasts, most likely due to ferredoxin‘s ability to donate electrons in ascorbate-mediated ROS scavenging mechanisms. In stark contrast to the proteomic response to low Cu in TO03, TO05 shows no significant differential expression of any of the proteins involved in the photosynthetic ETC (Table 3.3). Considering that TO03 can grow relatively well at <1 nM Cu, while TO05 cannot even survive at 2 nM Cu, our results seem to indicate that TO05 is unable to restructure its photosynthetic apparatus to deal with low Cu. 66   3.4.2.2 Antennae Under low Cu, strain TO03 exhibits another striking shift within the light-harvesting antennae. While cellular Chla significantly increased when normalized to cell volume (Table A.2), the functional absorption cross section of PSII, σPSII (Å2 RCII-1) also increased by 30% (Figure 3.2F). An increase in σPSII has also been observed in cultures acclimated to low Fe (Petrou et al. 2014; Schuback et al. 2015), which is believed to compensate for decreased Chla content and/or PSII abundance. I hypothesize that the unexpected trend of an increase in cellular Chla (p<0.1) under low Cu could be associated with disconnected LHCs (DLHCs). DLHCs are not energetically connected to photosystems, hence they elevate Fo without changing Fm, thereby decreasing the apparent Fv/Fm (see review by Behrenfeld and Milligan (2013) and references therein). They may provide a fast pigment pool in the early stages of Fe recovery. Furthermore, DLHCs have been proclaimed to play a vital role in non-photochemical quenching (Miloslavina et al. 2009; Goss and Lepetit 2015). In summary, the proposed presence of DLHCs in TO03 in response to low Cu can explain increased Chla per cell volume (Table A.2), decreased Fv/Fm and increased NPQNSV (Figure 3.2E, N). The restructuring of the light-harvesting antenna of PSII is further reflected in changes of protein composition in TO03 (Table 3.1, Table 3.2). Indeed, the protein abundance of 55% of the 29 LHCs changed significantly under low Cu (Table 3.1, Table 3.2). As shown in the phylogenetic tree (Figure 3.5, summarized in Table 2), the down-regulated proteins are phylogenetically related to known LHCs that are particularly involved in light harvesting reactions in both PSII (Nagao et al. 2013) and PSI (Grouneva et al. 2011; Ikeda et al. 2013). Of the nine up-regulated LHCs, four are part of Lhcf-group III, which is most closely related to the 67  major Lhcf cluster in the haptophyte Emiliania (McKew et al. 2013). To our knowledge, this group has not been associated with stress response in diatoms in any other study and was actually down-regulated by high light stress in Emiliania. One up-regulated LHC is part of the diatom-specific Lhcf-group II. The most notable set of up-regulated LHCs are the Lhcx homologs. Lhcx1, in particular, has been shown to play an important role in stress responses by maintaining thylakoid membrane stability (Zhu and Green 2010) and supporting the antenna's ability for NPQ (Miloslavina et al. 2009; Zhu et al. 2010; Goss and Lepetit 2015; Taddei et al. 2016). This rearrangement supports a general switch within the antennae from light harvesting (for photochemistry) to photoprotection in TO03. This switch is not seen in TO05 which only regulated one LHC (TpFCP7 homolog THAOC_08587: up-regulated).  3.4.3 PvsE curves (ETRPSII & 14C), conversion factor For decades, PE curves have played a central role in elucidating strategies of photoacclimation in different photosynthetically active organisms (Richardson et al. 1983; Zhu et al. 2016a). Various parameters can be derived to obtain a measure of change in photosynthesis (P) per incident light (E, µmol quanta m-2 s-1). The initial slope of the curve, α, quantifies the linear light-dependent increase of the rate of photosynthesis under sub-saturating light conditions (Jassby and Platt 1976). When irradiance increases beyond the light saturation point, Ek, photosynthesis starts to become light-saturated until the cell reaches its maximum photosynthetic rate Pmax. In our study, we generated PE curves for both short-term C assimilation normalized to Chla and electron transport rate in PSII [ETRRCII, normalized to reaction centres (RC)]. The two curves respond differently to Cu limitation, demonstrating that low Cu decouples these two rates of photosynthesis.  68   3.4.3.1 PvsE curve – ETRRCII and carbon uptake At growth irradiance, in response to low Cu, TO03 reduced CO2 uptake per chlorophyll by 68%. Furthermore, all PE parameters significantly decreased in response to low Cu: α14C by 63%, Ek14C by 32%, and Pmax14C by 75% (Figure 3.2G-I). This indicates that under the same irradiance, Cu limited cells cannot provide the necessary amount of ATP and/or NAD(P)H to sustain the same rate of carbon fixation as the Cu replete cultures. Counter-intuitively—considering the 68% decrease in carbon uptake—the rate of charge separation per reaction centre in PSII (ETRRCII) is not impacted by low Cu. However, as the number of reaction centres in the cell decreases by 50% under low Cu (as per 2-fold decrease in PSII proteins), the overall cellular ETC performance is impaired. Under Cu limitation, the functional absorption cross section of PSII (σPSII) increased by 30%, whereas PSII concentration decreased 2-fold (Table 3.3, Figure 3.6). These changes result in more efficient light harvesting per RCII at low light levels, reflected in both an increase in αETR and a decrease in the light saturation point EkETR (Figure 3.2J, L). EkETR is reduced to such an extent that cells were effectively growing under light saturating conditions (lowCu EkETR = 105 µmol quanta m-2 s-1 vs. growth irradiance = 155 µmol quanta m-2 s-1). Maintaining the same maximal rate of charge separation per functional RC, even under severe Cu limitation, assists in preventing accumulation of excess excitation energy which could result in an increase in ROS. Even though protein levels of both PSII and cytb6f decreased to the same extent (~2x), plastocyanin levels decreased even more (>4x) in response to low Cu (Table 3.3). This disproportionate decrease of a key electron carrier downstream of PSII ultimately restricts the flow of electrons away from PSII. This bottleneck in the linear electron transport chain, if not safely dissipated, could 69  ultimately lead to a built-up of excitation energy and photoinhibition. The increased PQ pool size (mol PQ mol QB-1) (Table A.2, Figure 3.6) is an indication of its needed buffer function. Furthermore, numerous alternative electron sinks have been suggested to alleviate excess excitation pressure. Of these alternative electron sinks, the Mehler reaction (Claquin et al. 2004), photorespiration (Parker and Armbrust 2005), nitrate reduction (Lomas and Glibert 2000) and cyclic electron transport around PSI (CETPSI) occur after plastocyanin, whereas the short water-water cycle involving plastoquinone terminal oxidase (PTOX) (Bailey et al. 2008; Mackey et al. 2008) channels electrons away from the linear electron transport chain before plastocyanin. Cyclic electron transport around PSII (CETPSII)(Prasil et al. 1996; Lavaud et al. 2002; Ruban et al. 2004) could also reduce electron pressure. In CETPSII, the e- in RCPSII that has been injected into the ETC, is resupplied by a recycled e- from the reduced plastoquinone pool, thus no additional new e- from the oxygen-evolving complex (OEC) is added. This could also explain, in part, the decreased O2 production in TO03 in response to low Cu (Table A.2). We identified putative candidate genes involved in these alternative electron flow pathways in the T. oceanica genome (i.e. the two PTOX homologues THAOC_16363, THAOC_20783). However, none of these were found expressed in any of the four proteomic datasets (2x TO03, 2x TO05). It is possible that these proteins are present but at a concentration below the detection limit of our method. If this is the case in only one of the two treatments, the protein would not be seen in our dataset (see method section). For example, the abundance of PTOX in a non-stressed cell is as low as 1 per 100 PSII complexes (Lennon et al. 2003).  In summary, up to 50% of the decrease in both carbon uptake and oxygen production in TO03 can be explained by the overall cellular reduction of PSII reaction centres (as seen in the proteomic data), as the ETR in each of those reaction centres is not impaired. The extra reduction 70  (17%) in carbon uptake could be due to the diversion of electrons away from the Calvin-Benson-Bassham cycle as suggested above. Cyclic electron transport around PSII, in particular, could rationalize the additional decrease in oxygen production (extra 5%).  3.4.3.2 Conversion factor The conversion factor (mol e- mol RCII-1 / mol C mol Chla-1) as a measure of how many charge separations occur per RCPSII in the light reaction of photosynthesis per carbon fixed per Chla in the dark reaction of photosynthesis, has recently been shown to increase in response to low Fe in both natural phytoplankton assemblages and monoclonal T. oceanica (CCMP1003) cultures (Schuback et al. 2015). This factor is of particular interest to oceanographers estimating global primary productivity using fluorescence data (Jassby and Platt 1976; Schuback et al. 2015). However, whereas the low Fe-mediated increase is due to an increase in ETRRCII compared to a stable Chla normalized C-assimilation, the low Cu-mediated increase is due to a stable ETRRCII coupled to a substantially decreased Chla normalized C-assimilation rate (Figure 3.2M, Table A.2). Why is carbon fixation so impaired in TO03, if cellular Chla and rates of initial charge separation (ETRRCII) are not affected by Cu limitation? The answer to this question has multiple components: first, the ETR per reaction centre (ETRRCII) is not impaired, but given that the overall concentration of PSII decreases 2-fold, overall cellular ETRRCII also decreases; second, a higher fraction of absorbed light energy is dissipated as heat, as suggested by a) the 4-fold increase in NPQNSV (as an estimate of how much excess excitation energy is dissipated as heat, ultimately relieving excitation energy from PSII), b) the 53% decrease in the efficiency of excitation energy capture by the fraction of open RCII (Fv′/Fm′) and c) the 63% decrease in overall quantum efficiency of photochemical energy conversion in PSII (Fq′/Fm′, φ′PSII, is the 71  fraction of the primary stable electron acceptor QA in the oxidized state, hence ready to participate directly in ETR) (Table A.2). So even though the abundance of cellular RCPSII is decreased, the cells are able to modulate the amount of excitation energy reaching the functional RCPSII in a way that ETRRCII is not greatly impaired. The slight 17% reduction in photochemical quenching (Fq′/Fv′) indicates a small increase in the reduction level of the photosynthetic electron transport chain, pointing towards the potential of increased ROS production and the need for other electron sinks (as discussed above). In summary, this suggests that TO03 copes with low Cu by dissipating an increased fraction of the absorbed light as heat, thus modulating the amount of excitation energy reaching functional RCII, such that the ETRRCII is not greatly impaired.   3.5 Conclusion The physiological adaptations of diatoms to cope with Cu limitation are largely unknown. In the present study, we investigated the response to Cu limitation in two strains of the model open ocean diatom T. oceanica (CCMP 1003 and CCMP 1005), focusing on physiological and proteomic changes in the photosynthetic apparatus. Our results show remarkable differences between the adaptations of TO05 and TO03 to low Cu, highlighting significant intraspecific variations. In essence, TO03 seems to be able to grow more efficiently under extremely low Cu by decreasing its most abundant Cu-containing protein, plastocyanin, an essential electron carrier between its PSII and PSI. However, decreasing plastocyanin levels promotes a bottleneck in the linear electron transport between PSII and PSI, which increases excitation energy in the pigment antennae and enhances susceptibility to photodamage by the production of ROS. The increased susceptibility to photodamage is counteracted by a) reconstructing the light harvesting antennae 72  (i.e. increasing the ability for photoprotective heat dissipation of absorbed light energy in the pigment antenna), b) decreasing the ETC components prior to plastocyanin (i.e. PSII, cytb6f) to balance the flow of electrons through the ETC, c) increasing the ETC components counteracting ROS (i.e. Fdx, FNR), and d) using other sinks and pathways for excess e- (i.e. CETPSII, PTOX, photorespiration). Together, these biochemical shifts explain the observed changes in physiology, such as a) decreased oxygen evolution, carbon fixation, photosynthetic quotient, Fv/Fm and b) increased cellular Chla, PSII, and the conversion factor between ETRPSII and carbon fixation. Therefore, even though the physiological response of TO03 (e.g. C-fixation rate or growth rate) to low Fe is similar to that of low Cu, this study shows that the underlying restructuring of the photosynthetic apparatus is markedly different for diatoms exposed to low Cu versus low Fe.  One has to be cautious, though, when trying to draw conclusions from one diatom to all. As presented here, a different strain of the same species, TO05, has a vastly different response to low Cu: it reduces its cellular protein content by half, without changing the relative concentration of certain expected proteins, such as the Cu-containing ones. Thus, TO05 seems unable to modulate a concerted response to low Cu and is unable to grow at Cu concentrations where TO03 thrives.  Strong evidence for Cu limitation of phytoplankton in the ocean is presently lacking. However, based on our data, the concentrations of Cu in some oceanic regions are sufficiently low (e.g. 0.5 nM in the North Pacific Bruland and Franks 1983) to result in sporadic Cu limitation of phytoplankton growth. For example, in regions with Cu concentrations less than 1 nM, large phytoplankton may experience Cu limitation (due to their low surface area to volume ratio). In addition, phytoplankton in Fe limited regions may experience co-limitation by Fe and 73  Cu, as shown by Semeniuk et al. (2009). Given our results, similarly to Fe, Cu limitation is likely to negatively affect C export to the ocean interior. Additionally, in response to low Cu, the potentially significant increase in the proposed DLHC, which do not participate in energy capture for carbon fixation, might bias global estimates of primary productivity based on satellite-derived Chla fluorescence.   74  3.6 Tables and figures   Figure 3.1. Copper-dependent growth rates of Thalassiosira oceanica TO03 and TO05. Mean values ± standard error are shown; numbers of biological replicates (n) are indicated in brackets. Note that TO05 was not able to grow under 1.96 nM Cu in the medium (indicated by X in the graph). Given the scope of this study, TO03 was not grown under 1.96 nM or 6.08 nM, as it was able to grow under Cu concentrations <1 nM.    75   76   Figure 3.2. The effects of Cu limitation on growth rate, cell diameter, protein content and a series of photophysiological parameters in two strains of T. oceanica. 2A-C, growth rate, cell diameter, and Chla per cell, respectively; 2D-F, gross oxygen production, Fv/Fm, and the absorption cross section of PS II antennae, respectively; 2G-I, 14C PE curve parameters α, Pmax, and EK, respectively; 2J-L, ETRPSII PE curve parameters α, Pmax, and EK, respectively; 2M-O, conversion factor, NPQNSV, and cellular protein content, respectively. The values are mean ± std. error of three biological replicates. Differing letters above bars represent statistically significant changes (p<0.05) using a 2-way ANOVA with post-hoc interaction analysis (see methods for details). Note that both strains have the same physiological responses under metal replete conditions. Under low Cu conditions, only growth rate and cell size are significantly reduced in the same manner in both strains.      77     78  Figure 3.3. Comparison between EST-mapped proteomics datasets of TO03 and TO05, emphasizing the similarity in their genomes. A) Results for TO03 (plusEST). B) Results for TO05 (plusEST). The left side of the panel shows the proportion of identified predicted proteins coming from the TO05 genome vs. the TO03 transcriptome (ESTs). The right side of the panel shows the proportion of the different types of ESTs that have been mapped by the peptides coming from the LC-MS/MS. Even though we used the combined database including all known TO05 and TO03 predicted proteins, neither strain shows bias towards its own subset of proteins. 79  80  Figure 3.4. A) Representative sequence alignment of predicted LHCs from Thalassiosira oceanica (THAOC, CCMP 1005) and T. pseudonana (Tp, CCMP 1335): boxes indicate conserved residues; dotted lines show linkage between helix I and III; bold residues are predicted binding sites for Chl molecules. B) Cartoon of the predicted general LHC structure (grey) comprised of three membrane-spanning helices within the thylakoid membrane (orange) and the lumenal N-terminus and stromal C-terminus (Liu et al. 2004).81   82  Figure 3.5. Phylogenetic tree of 48 predicted LHCs from the T. oceanica genome (CCMP 1005) aligned with 41 LHCs from T. pseudonana (CCMP 1335). Boxed THAOC LHCs have been identified at the protein level in this study. LHCs in grey shaded boxes with bold, black letters are significantly down-regulated and those in black shaded boxes with bold, white letters are significantly up-regulated in response to low Cu in TO03. The only significantly regulated LHC in TO05 is THAOC_08587 (up), which aligns closest to TpFCP7 (Table 2). Numbers on nodes are based on PhyML alertSH, aBayes, and standard bootstrap (100 replicates) and are expressed as percentages. Bootstrap values below 65% are not shown.    83  Table 3.1. Overview of expression of all putative LHCs in both strains of T. oceanica.  clade  TO03 expressedb TO05 expressedb TO03 upc TO03 downc predicted  All 48 30 32 9 8 Lhcf - Group I 17 10 10 - 5 Lhcf - Group II 1 1 1 1 - Lhcf - Group III 9 7 8 4 1 Lhcr 10 9 9 1 2 Lhcx 8 3 3 3 - Lhcz 2 - - - - 17531 1 - 1 - - aclade assignment as per phylogenetic tree (Figure 3.5)  bfor full list of expressed LHCs see Table A.3 cup/down refers to significant differential expression as defined in methods  84  Table 3.2. Significantly differentially expressed LHCs under chronic low Cu conditions. cladea gene name (NCBI)  closest homolog in Tpa TO03 sig regulatedb TO05 sig regulatedb Evidence for role in other diatoms Lhcf - Group I THAOC_06305 TpLhcf -10. 34c   light harvesting  THAOC_20854 TpLhcf -5. 56  light harvesting  THAOC_01208 TpLhcf -2. 79    THAOC_32932 TpLhcf8 -2. 12  light harvesting, trimers, oligomersd   THAOC_37647 TpLhcf -2. 04   Lhcf - Group II THAOC_09684 TpLhcf11 4. 83   Lhcf - Group III THAOC_06968 TpFCP2 -2.52  in TP tightly bound to PSIe   THAOC_04469 TpFCP4 2. 06    THAOC_05777 TpFCP10 2. 40    THAOC_16345 TpFCP4 2. 68    THAOC_08587 TpFCP7 2. 79 2. 07 close to haptophyte LHCse Lhcr THAOC_07036 TpLhcr13 -6. 57   PS I light harvestingf, g, h   THAOC_35518 TpLhcr10 -2.55  PS I light harvestingg, i  THAOC_00550 TpLhcr11 2. 39c  PS I light harvestingg, i  Lhcx THAOC_08095 TpLhcx7 2. 76c    THAOC_09937 TpLhcx1 3. 29  photoprotection, stress response, associated with both PSI + II, FCP trimers, oligo, or only loosely associated with the membrane, facilitates NPQ g, i, j, k, l   THAOC_31988 TpLhcx1  3. 29   Tp, Thalassiosira pseudonana aas per phylogenetic tree (Figure 3.5)  bsignificant differential expression in original dataset as defined in methods, given in fold-change 85  csignificant differential expression in EST dataset as defined in methods, for Table showing all expressed LHCs and including differential expression in both original and EST datasets, see Table A.3  d(Nagao et al. 2013) e(Hoffman et al. 2011) f(Zhu and Green 2010) g(Grouneva et al. 2011) h(Ikeda et al. 2013) i(Gundermann and Büchel 2014) j(Zhu and Green 2010) k(Green 2007) l(Koziol et al. 2007)  86  Table 3.3. Differential expression of proteins involved in the photosynthetic electron transport chain (ETC) in response to chronic Cu limitation in T. oceanica.      differential expressiona,b  Part of gene name (NCBI)a Protein Descriptiona TO03 TO05 where encoded  PS II THAOC_34020 psb27-like, involved in Mn cluster formation  1. 73 1 Nuc  psbA psbA, photosystem II protein D1 -1. 92 -1. 03 C  psbB psbB, photosystem II CP47 reaction center protein -1. 61 -1. 1 C  psbC, THAOC_26185 psbC, photosystem II CP43 reaction centre protein -2. 1 1. 05 C  psbD, THAOC_24371 psbD, photosystem II D2 protein -2. 1 -1. 01 C  psbE, THAOC_24363 psbE, cytochrome b559 subunit alpha -2. 25 -1. 21 C  psbH psbH, photosystem II reaction centre protein H  -1. 49 C  THAOC_03193 psbO, Mn-stabilizing protein -1. 41 Nuc  THAOC_15373 psbP, oxygen-evolving enhancer protein 2 (OEE2)   -1. 03 Nuc  THAOC_08500 psbQ, oxygen-evolving enhancer protein 3 (OEE3)  -2. 96  Nuc  THAOC_09685 psbU-like, small extrinsic protein 1. 62 1. 32 Nuc  psbV, THAOC_30541 psbV, cytochrome c-550  1. 07 1. 18 C  psbY psbY, photosystem II protein Y  -1. 11 C       PET petA petA, cytochrome f -1. 63 -1. 11 C  petB, THAOC_26188 petB, cytochrome b6 -2. 33  C  THAOC_33417 petC, Fe-S subunit (Rieske protein)  -1. 51  Nuc  petD, THAOC_24366 petD, cytochrome b6-f complex subunit 4  1. 1 C  THAOC_29732 petE, plastocyanin -4. 41 1. 31 Nuc       PS I psaA psaA, photosystem I P700 chlorophyll a apoprotein A1 -1. 21 1. 14 C  psaB psaB, photosystem I P700 chlorophyll a apoprotein A2 1. 11 1. 01 C 87     differential expressiona,b  Part of gene name (NCBI)a Protein Descriptiona TO03 TO05 where encoded  psaC psaC, photosystem I iron-sulfur center  -1. 18 1. 2 C  psaD, THAOC_24369 psaD, photosystem I reaction centre subunit II 1. 2 1 C  psaF psaF, photosystem I reaction centre subunit III -1. 2 1. 04 C  THAOC_24361 psaL, photosystem I reaction centre subunit XI -1. 56 -1. 02 C       PET THAOC_25559 petF, ferredoxin 43. 79 -1. 1 Nuc  THAOC_36724 petH, FNR - ferredoxin--NADP+ reductase  2. 47  Nuc  THAOC_06509 petH - FNR - ferredoxin--NADP+ reductase  -1. 13 Nuc PSII, photosystem II; PET, photosynthetic electron transport; PSI, photosystem I, Nuc, nucleus; C, chloroplast acontent in bold indicates significantly differentially expressed proteins in TO03, as defined in methods bdifferential expression given in fold-change of the original dataset; for Table including differential expression in EST dataset, see Table A.4  88      89  Figure 3.6. Model of the changes of the photosynthetic apparatus in T. oceanica (CCMP 1003) in response to growth in the presence of low Cu compared to optimal Cu levels (control). In the process of acclimation to low Cu concentration, several structural rearrangements take place in the photosynthetic apparatus allowing maximal photosynthetic efficiency and minimal photoinhibition. In our model, the represented changes in the numbers of LHCs, protein complexes and electron carriers are a reflection of the changes we observed in the proteomic data (see Figure 3.3 for details): Overall cellular concentrations of proteins associated with PSII, and the photosynthetic electron transport chain (PSII, cytb6f complex and plastocyanin) decrease, whereas those associated with PSI, remain unchanged, and the size of the plastoquinone pool increases. In general, cellular Chla concentration stays constant with an increase in the absorption cross section σPSII. NPQNSV increases dramatically as reflected in both a) quenching sites Q1 and Q2 located in detached oligomers (DLHCs) and b) trimers with diatoxanthin (Dt) binding Lhcx that are bound to PSII, respectively (NPQ response adapted from Goss and Lepetit 2015). Approximate pigment content per LHC monomer is 8 Fucoxanthin, 4-6 Chla, 2 Chlc (as per Premvardhan et al. 2010). For more details see ―3.4.2 Proteomic shift within the photosynthetic apparatus in TO03‖ in the discussion.    90  Chapter 4: Proteomic Analysis Revealed the Intricate Interaction Between the Carbon and Nitrogen Metabolisms in Copper-Limited T. oceanica  4.1 Summary Diatoms are one of the most successful phytoplankton groups in our oceans. Their phylogenetic history lead to a chimaeric genome with genes derived from the green lineage, red lineage, bacteria and heterotrophs. Furthermore, the sequential endosymbiosis events lead to an increased set of isoenzymes that can be regulated in very distinct ways and that are targeted to either the same or different cellular compartments. It gave also rise to novel interactions between metabolic pathways. In Chapter 3, I presented how Cu limitation affected the photosynthetic apparatus in T. oceanica (CCMP 1003), leading to the up-regulation of ferredoxin (Fdx) and ferredoxin:NAD(P)+ reductase (FNR) and to a potential overreduction of the photosynthetic electron transport chain. In order to elucidate how the rest of the cellular metabolism changes to help alleviate the stresses incurred by low Cu (i.e. overreduction and the potentially ensuing reactive oxygen species), I probed the proteomic datasets and identified proteins involved in various carbon, nitrogen and stress-related metabolic pathways. Furthermore, due to the high amount of isoenzymes, I predicted their respective cellular localization by a combination of a) their homologies to known proteins in other diatoms and b) the use of various targeting softwares. I created metabolic pathway maps illustrating changes in their regulation and highlighting their integrated response to Cu limitation, including 1) nitrogen metabolism – photosynthesis: preferential up-regulation of ferredoxin-dependent isoenzymes in nitrogen metabolism; 2) nitrogen metabolism – reactive oxygen species (ROS) fighting: up-regulation of glutamate and cysteine synthase to support glutathione production to counteract ROS; 3) 91  photosynthesis – malate shunt: up-regulation of the malate-shunt to channel excess electrons from the chloroplast to the mitochondria; 4) photosynthesis – glycolysis: differential regulation of glycolytic enzymes to channel electrons from the chloroplast to the mitochondria and increase ATP levels in the chloroplast; and 5) malate shunt – citrate cycle – urea cycle – glycolysis: the interconnectedness among these metabolic pathways is enhanced under lowCu, whereby carbon skeletons and reducing equivalents are channeled in a directed manner to meet the cellular needs. Differences between the two strains and other published nutrient limitations are discussed.     92  4.2 Introduction Diatoms form an integral part of our oceans, influencing nutrient cycling and productivity of many marine food webs (Armbrust 2009). Annually, marine diatoms fix as much carbon dioxide through photosynthesis as all terrestrial rainforests combined  (Nelson et al. 1995; Field et al. 1998), thus having a significant impact on atmospheric CO2 levels and global climate. Diatoms have a complex evolutionary history, resulting in a mosaic genome with genes derived from the original heterotrophic eukaryotic host cell, the engulfed green and red algal endosymbionts, as well as genes from bacterial horizontal gene transfer (Armbrust et al. 2004; Bowler et al. 2008; Finazzi et al. 2010). Their complex genomic make-up is reflected in several key features, including four membranes surrounding their chloroplast and having multiple isoenzymes in various metabolic pathways, especially in their carbon metabolism (Kroth et al. 2008; Gruber et al. 2009; Smith et al. 2012; Gruber and Kroth 2014). It is not yet well understood how the gene products of the acquired genes from various organisms are targeted into the various cellular compartments of the new host cell, especially for those targeted to the chloroplast with its four membranes.  In diatoms, multiple isoenzymes have also led to new locations for these enzymes and novel interactions among metabolic pathways, compared to green algal and animal ancestors (Allen et al. 2011; Gruber and Kroth 2017). For example, in animals, the complete set of proteins involved in glycolysis is located in the cytosol, whereas in green algae, the first half of glycolysis (glucose to glyceraldehyde-3-phosphate, GAP) is located in the chloroplast and the second half (GAP to pyruvate) in the cytosol. In diatoms, an almost complete set of glycolytic proteins is found in both the cytosol and the chloroplast, with an additional set of proteins from the second half of glycolysis located in the mitochondria (Kroth et al. 2008; Smith et al. 2012). Furthermore, 93  proteins involved in the ancient Entner-Dourodoff pathway, which is predominantly restricted to prokaryotes and catabolizes glucose to pyruvate using distinct enzymes, have also been identified in diatom genomes and are targeted to the mitochondria (Fabris et al. 2012). The expanded repertoire of isoenzymes in diatoms, both within one and/or across compartments, enables them to meet their metabolic needs (Allen et al. 2012; Smith et al. 2012; Gruber and Kroth 2014). Underlining the mosaic nature of diatom genomes, Allen et al. (2012) showed that the genome of P. tricornutum encodes no less than five different fructose-bisphosphate aldolase (FBA) isoenzymes (three targeted to the chloroplast and two to the cytosol). Each FBA has its own phylogenetic history and is potentially involved in either carbon fixation in the Calvin-Benson-Bassham cycle or glycolysis. These FBAs are derived from a) their green and red algal ancestors, as well as the heterotrophic eukaryotic host cell, b) from bacteria through horizontal gene transfer, and c) from a chromalveolate-specific gene duplication event (Allen et al. 2012). The expression pattern of these five isoenzymes changes depending on the nutritional status of the cell.  One of the most surprising revelations from the sequencing of diatom genomes was the discovery of a complete set of proteins involved in the urea cycle (Armbrust et al. 2004; Allen et al. 2011). In contrast to the catabolic nature of the urea cycle in animals, in diatoms, it is an anabolic process. The urea cycle is an integral part of cellular metabolism and a hub of nitrogen and carbon redistribution within the cell. It is involved in amino acid synthesis, cell wall formation, carbon and nitrogen recycling, and interacts with the citric acid cycle (Armbrust et al. 2004; Allen et al. 2011). 94  Proteins and/or transcripts of genes involved in nitrogen and carbon metabolism are often differentially regulated under nutrient limitation. So far, studies have focused on adaptations to macronutrient limitation, such as nitrogen, phosphate or the essential micronutrient Fe. In 50% of the ocean, diatoms are limited by nitrate (Moore et al. 2002). However, in over 30% of the ocean, Fe is the limiting resource for phytoplankton (Moore et al. 2004). An intricate relationship between Fe and Cu physiology in diatoms has been shown (Maldonado et al. 2002; Peers and Price 2006; Maldonado et al. 2006; Annett et al. 2008; Guo et al. 2012, Semeniuk et al. 2015) and the possibility of  Fe-Cu co-limitation of in situ diatoms in low Fe regions has been suggested (Coale 1991; Peers et al. 2005). Despite the importance of Cu in Fe-limited diatoms, our understanding of physiological adaptations to low Cu is limited to a handful of studies (Semeniuk et al. 2015), Semeniuk et al. 2015).  My recent comprehensive investigation on the physiological and proteomic changes to the photosynthetic apparatus in response to chronic Cu limitation in T. oceanica revealed both similar and inherently different strategies compared to those observed in response to low Fe (Chapter 3). To better understand how carbon and nitrogen metabolisms are affected and may interact when Cu is limiting, we expanded our probing, using both of our proteomic datasets, as well as the publicly available T. oceanica genome. I identified proteins involved in various carbon and nitrogen metabolic pathways (e.g. Calvin-Benson-Bassham cycle, glycolysis, nitrogen acquisition and assimilation, urea cycle, malate shunt, glutathione metabolism), predicted their targeted cellular compartments, and created respective metabolic pathway maps documenting their differential protein expression. In this chapter, I show a striking interaction between carbon and nitrogen metabolisms that potentially alleviates detrimental effects in the photosynthetic apparatus imposed by Cu limitation. This interaction is mediated by the 95  differential expression of isoenzymes both within and across cellular compartments, ensuring that excess, and possibly damaging, reducing equivalents in the chloroplast are safely removed and/or oxidized.  4.3 Results I present a brief overview of the two original proteomic datasets (original TO03 and original TO05, as per methods), in which the peptides derived from the mass spectrometry were mapped against the TO05 genome. In order to elucidate the effect of Cu limitation on the carbon and nitrogen metabolisms, I then present all detected proteins involved in the respective key metabolic pathways: Calvin Benson Bassham cycle, glycolysis, citrate cycle, and nitrogen uptake and metabolism, including the urea cycle and proteins involved in glutathione metabolism, redox sensing and protection against ROS. Furthermore, I present data on proteins involved in the putative malate shunt, as well as an overview of the proteins directly involved in pyruvate metabolism and respiration. In line with the effects of Cu limitation on the photosynthetic apparatus described in Chapter 3, TO03 exhibits a far more comprehensive response in carbon and nitrogen metabolism compared with that in TO05. Thus, if not otherwise noted, I will focus on the visualization and description of the TO03 results. Tables will give the information on all relevant proteins across both strains (TO03, TO05), both proteomic searches (original and +EST, as per methods), as well as soluble and insoluble cellular fractions. When referring to proteins, either the official gene names are given, or the organism prefix ―THAOC_‖ is abbreviated to ―To‖. For example, the mitochondrial malate dehydrogenase (MDH) protein, THAOC_03405, will be referred to as To03405. All differential expression data discussed in the text are significantly up- or down-regulated (p<0.05), unless otherwise noted.  96   4.3.1 Overview of proteomic datasets The proteomic dataset of TO03 encompasses 724 distinctive proteins, whereas in TO05 1,431 were identified. As seen in Figure 4.1, 644 proteins were identified in the datasets of both strains. T. oceanica (TO03) has three times as many significantly up-regulated proteins (78) than TO05 (28), of which four are shared. T. oceanica (TO03) has 45 proteins significantly down-regulated, whereas TO05 has only 5, of which one is shared.  Of the 724 distinctive proteins in TO03, 525 have associated Kegg Orthology (KO) identifiers (Figure 4.2). Overall, half of these (52%) are related to metabolism, 30% to genetic information processing, 10% cellular processes, and 8% to environmental information processing (Figure 4.2A). The proteins involved in metabolism are particularly affected by Cu limitation (Figure 4.2B and C). The general trend of down-regulation of energy metabolism, up-regulation of carbohydrate metabolism, and regulation of amino acid metabolism is illustrated in Figure 4.2D.  4.3.2 Calvin Benson Bassham cycle Twelve proteins involved in the carbon fixing Calvin Benson Bassham cycle in the chloroplast were identified (Figure 4.3, Table B.1). Glyceraldehyde 3-phosphate dehydrogenase (GAPDH, To13085) and the essential Rubisco activator protein cbbX (To24360) were the only proteins that were down-regulated under Cu limitation (4.7- and 2.3-fold, respectively). In contrast, the Rubisco subunits were not affected (rbcS, rbcL). Similarly, fructose-bisphosphate aldolase, class I (FBA I, To02112), ribose 5-phosphate isomerase (RPI, To31290) and ribulose 5-phosphate epimerase (RPE, To09031) were unaffected. Five proteins were up-regulated: phosphoglycerate 97  kinase (PGK, To07617) by 6.8-fold, the three triose phosphate isomerase proteins (TPI, To02438, To35826, To32006) by 3.3-, 1.9- and 1.5-fold, respectively, and fructose-bisphosphate aldolase, class II (FBA II, To12069) by 2-fold. In TO05, cbbX and rbcS were 1.3- and 1.6-fold down-regulated, respectively, and only PGK was up-regulated by 3-fold.   4.3.3 Glycolysis In diatoms, proteins involved in glycolysis are found in all three major cellular compartments. Of the 21 glycolytic proteins identified, seven are cytosolic, nine are targeted to the chloroplast, and five are targeted to the mitochondria (  Figure 4.4, Table B.3). As described earlier (see methods), targeting was predicted via a combination of targeting software programs (TargetP, ASAFind) and the predicted targeting of the closest diatom homologous proteins (i.e. T. pseudonana, Tp; P. tricornutum, Pt; and/or F. cylindricus, Fc). Of the seven expressed cytosolic proteins, four were not affected by Cu limitation: glucose-6-phosphate isomerase (GPI, To25871), fructose-1,6-bisphosphatase (F2BP, To05494), triose phosphate isomerase (TPI, To21096), and fructose-bisphosphate aldolase, class II (FBA II, To24977). Three proteins were down-regulated: phosphoglucomutase (PGM, To06412) by 3.2-fold, phosphofructokinase (PFK, To16559) by 1.8-fold, and fructose-bisphosphate aldolase, class I (FBA I, To24978) by 1.6-fold (or 2.7-fold considering expression of a contig associated with the same gene). The only cytosolic protein involved in glycolysis that was up-regulated was pyruvate kinase (PK, To34937, by 1.6-fold). In TO05, GPI was up-regulated by 1.5-fold, whereas FBA II and FBA I were down-regulated by 1.3- and 2.4-fold, respectively. Of the nine expressed proteins targeted to the chloroplast, only fructose-bisphosphate aldolase, class I (FBA I, To02112) was not affected by Cu limitation. Two fructose-bisphosphate 98  aldolase, class II proteins were up-regulated by 1.4- and 2-fold (FBA II, To00388 and To12069), three triosephosphate isomerase isoenzymes were up-regulated by 3.3-, 1.9-, and 1.5-fold (TPI; To02438, To35926, and To32006, respectively), and phosphoglycerate kinase (PGK, To07617) was up-regulated by 6.9-fold. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH, To13085) and phosphoglycerate mutase (PGAM, To21902) were both down-regulated, by 4- and 2.3-fold, respectively. The up-regulation of TPI combined with a down-regulation of GAPDH could lead to an increase in and subsequent export of triose-compounds out of the chloroplast. Probing the genome for gene models containing a triose phosphate transporter Pfam domain identified seven candidate genes (Table B.2) of which only two were found expressed in TO03 and four in TO05. Of all chloroplast targeted glycolytic proteins in TO05, only FBA I and PGAM were down-regulated by 1.4- and 1.5-fold, respectively, whereas PGK was up-regulated by 3-fold. Five proteins involved in glycolysis were found to be targeted to the mitochondria. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH, To33331) and enolase (ENO, To34936) were both up-regulated by 3.8- and 1.5-fold, respectively. Phosphoglycerate mutase (PGAM, To08963) was not affected, whereas pyruvate kinase (PK, To07097) was down-regulated by 1.3-fold. The mitochondrial aldolase involved in the ancient Entner-Dourodoff glycolytic pathway, 2-keto-3-deoxy phosphogluconate aldolase (EDA, To10009) was not affected. In TO05 GAPDH was 1.6-fold up-regulated.  4.3.4 Citrate (TCA) cycle Nine expressed proteins involved in the citrate cycle in the mitochondria were identified (Figure 4.4, Table B.4). Citrate synthase (CS, To19912) abundance did not change in response to low Cu concentrations. Aconitase hydratase (ACO, To20545) and two isocitrate proteins (IDH, 99  To37807, To34595) were all down-regulated by 4.7-, 3.0-, and 1.6-fold, respectively. Oxoglutarate dehydrogenase E1 component (OGD1, To28027), succinate CoA synthetase (SUCLA, To13425), and fumarate hydratase (FH, To24310) were not affected, whereas malate dehydrogenase (MDH, To03405) was 1.6-fold up-regulated. In TO05, two proteins were up-regulated: IDH by 1.5-fold and SUCLA by 1.6-fold.  4.3.5 Nitrogen metabolism Twenty-two proteins involved in the urea cycle, nitrogen acquisition and assimilation, as well as four membrane transporters were identified at the proteomic level (Figure 4.5, Table B.5). Of these four transporters, the expression of the formate/nitrate (NiRT, To00240) and the ammonium transporter (AMT, To07247), both putatively located in the chloroplast envelope, were not affected by Cu limitation. However, the urea (URT, To31656) and nitrate/nitrite transporters (NRT, To04919), at the plasma membrane, were both significantly up-regulated (6.9 and 11-fold, respectively). In TO05, both URT and NiRT were up-regulated by 1.3-fold. Interestingly and in contrast to TO03, TO05 had two different isoenzymes of NRT up-regulated in response to low Cu (To01055 and To01056, by 1.7- and 1.6-fold, respectively). Within the chloroplast, three different nitrite reductases were identified. Of these, the NAD(P)H-dependent isoenzyme was not differentially expressed (NAD(P)H-NiR, To35252), whereas two proteins containing ferredoxin-dependent nitrite reductase domains (Fe-NiR, To00016 and To02363) were up-regulated by 1.3- and 2.6-fold, respectively. Glutamine (GSII, To31900) and glutamate synthases (GOGAT, To13288) were both up-regulated (3.8 and 1.6-fold, respectively). In contrast, aspartate aminotransferase (AAT, To16827) was the only down-100  regulated (2.3-fold) protein directly involved in the core nitrogen metabolism within the chloroplast. In TO05, only GS was mildly up-regulated (by 1.3-fold). In the mitochondria, glutamine synthase (GSIII, To06032) was 5.3-fold down-regulated while glutamate synthase (GOGAT, TO04828) expression did not change. The mitochondrial aspartate aminotransferase (AAT, To15049) was up-regulated by 3.6-fold. Glycine decarboxylase t- and p-proteins (GDCT/P, To17688 and To36273), involved in photorespiration, were not affected by Cu limitation. In TO05, GS was again mildly up-regulated (by 1.4-fold).  In the urea cycle, six proteins were expressed. Five of these were not affected by Cu limitation: carbamoyl phosphate synthase (unCPSIII, To01996), argininosuccinate synthase (AsuS, To37170), argininosuccinate ligase (AsL, To12984), ornithine cyclodeaminase (OCD, To04380), and aspartate carbamoyltransferase (ATCase, To22587). In contrast, ornithine carbamoyltransferase (OTC, To05385) was up-regulated by 1.7-fold. In TO05 AsuS was 1.4-fold up-regulated. Cytosolic glutamate dehydrogenase (GDH, To06254) was up-regulated by 2.09-fold (p = 0.05) and nitrate reductase (NR, To34460) was up-regulated by 1.5-fold. In TO05, GDH was up-regulated by 1.5-fold, while NR was down-regulated by 1.4-fold.  4.3.6 Glutathione metabolism  Glutathione is a small tripeptide (Glu-Cys-Gly) that is involved in redox sensing and counteracting ROS. Twenty-one expressed proteins involved in glutathione metabolism and other antioxidant agents (eg. thioredoxins, glutaredoxins, and superoxide dismutases, SOD) were identified (Figure 4.6, Table B.6). Nine proteins are predicted to be targeted to the chloroplast. The only down-regulated proteins were two chloroplast glutaredoxins (GRX; To07269, 101  To18234; both by 1.3-fold). In contrast, two chloroplast isoenzymes for cysteine synthase were up-regulated (CYS, To27524 by 2.5-fold, To10442 by 1.5-fold). As mentioned above (section 4.3.5), chloroplast glutamate synthase (GOGAT, To13288) was also up-regulated (by 1.6-fold). Additionally, glutathione synthase (GSS, To04546), glutathione reductase (GR, To07268) and thioredoxin (TXN, To31425) were all up-regulated (by 1.5-, 2.5-, and 1.5-fold, respectively). In addition, the Mn-containing SOD was also up-regulated (MnSOD, To02860 by 1.8-fold). In TO05, only two CYS isoenzymes were up-regulated (To27524 by 1.4-fold; To10442 by 1.3-fold), as well as a TXN (To31425 by 1.6-fold). Of the seven cytosolic proteins, glutathione-S-transferase (GST, To09062) and TXN (To05213) were significantly up-regulated (by 7.3- and 4.2-fold, respectively). One of the two nickel-dependent SOD isoenzymes was also up-regulated (NiSOD, To10112, by 1.4-fold). Glutamate-cysteine ligase (GCL, To2335) was only identified in one pairing of the biological triplicate, where a differential expression of 4.5-fold was observed. The expression of cytosolic CYS (To05931) did not change upon Cu limitation, whereas the expression of glutamate dehydrogenase (GDH, To06254) increased by 2.1-fold (p=0.05, see also section 4.3.5). Interestingly, in TO05, a different isoenzyme of glutathione-S-transferase was significantly up-regulated (GST, To24526, by 2.3-fold). The latter has homologs to other diatom GSTs, whereas the up-regulated GST in TO03 (To09062) has no homolog in diatoms, but closest homologs in the polyp Hydra vulgaris, the anemone Nematostella, and the brachiopod Lingula. Cytosolic TXN and GDH were also up-regulated in TO05 (1.4- and 1.5-fold, respectively). Only two of the 18 expressed proteins involved in glutathione metabolism are predicted to be targeted to the mitochondria: GRX and TXN. Of these, GRX (To02323) was not differentially 102  expressed under Cu limitation in TO03, whereas TXN (To13864) was up-regulated by 1.8-fold. In TO05, neither was differentially expressed.  4.3.7 Malate shunt Six proteins were expressed in TO03 that are involved in the putative malate shunt (Figure 4.7, Table B.7). Three of these are located in the chloroplast and were all differentially expressed in response to low Cu. Whereas both pyruvate carboxylase (PC, To31413) and malate dehydrogenase (MDH, To30817) were up-regulated by 2.6-fold, aspartate aminotransferase (AAT, To16827) was down-regulated by 2.3-fold. In the mitochondria, PC (To19570) was not differentially expressed. Both MDH (To03405) and AAT (To15049) were up-regulated by 1.6- and 3.6-fold, respectively. None of these six proteins were affected in TO05. In fact, neither chloroplast AAT nor mitochondrial MDH have been identified in TO05. However, two putative malate/2-oxoglutarate antiporters have been identified in the TO05 proteome (To27515, To25255) and not in that of TO03. Their expression was not affected by Cu limitation.  4.3.8 Pyruvate metabolism Pyruvate is a key metabolite at the intersection of, among others, glycolysis/gluconeogenesis, the citrate cycle and fatty acid metabolism. Sixteen proteins involved in pyruvate metabolism were identified (Figure 4.8, Table B.8). In the chloroplast, pyruvate carboxylase (PC, To31413) and malate dehydrogenase (MDH, To30817) were both 2.6-fold up-regulated. Pyruvate dehydrogenase E1 (PDH-E1, To34740) as well dihydrolipoamide dehydrogenase (DLDH, To19934) were not affected by low Cu. None were affected in TO05. 103  In the cytosol, four of the six expressed proteins were not differentially expressed: PDH-E1 (To08200), PDH-E2 (TO03499), acetyl-CoA synthase (ACAS, To05626), and lactate dehydrogenase (LDH, To33506). Phosphoenolpyruvate carboxylase (PEPC, To00276) was down-regulated by 4.3-fold. Pyruvate kinase (PK, To34937) was up-regulated by 1.6-fold. None were affected in TO05. In the mitochondria, PC (To19570), DLDH (To30825), and PEPC (To33731) were all unaffected. However, PK (To07097) was 1.3-fold down-regulated and MDH was 1.6-fold up-regulated. In TO05, mitochondrial PEPC (To33731) was the only differentially expressed protein involved in pyruvate metabolism (1.4-fold up-regulated).  4.3.9 Respiration Five proteins involved in mitochondrial respiration were identified (Table B.9). In TO03, each one was only identified in one of the paired biological triplicates (low Cu / control mix, see Chapter 2) and thus did not result in a significant differential expression ratio (i.e. no p-value as illustrated in Figure 2.2). However, NAD(P)H dehydrogenase (NAD(P)H-DH), as well as the three subunits of cytochrome c oxidase (cytc) had fold increases indicative of up-regulation: NAD(P)H-DH (To05862) by 1.9-fold, cytc oxidase subunit IV (To04852) by 1.5-fold, the subunit Vb (To22544) by 2.6-fold, and the subunit VIb (To12811) by 4.0-fold. The latter three membrane-bound subunits were identified in the insoluble fraction, as expected. However, the subunits Vb and VIb were also found in the soluble fraction, albeit with down-regulated expression (by 2.2 and 1.2-fold, respectively). Ubiquinol-cytc reductase, in the insoluble fraction, was down-regulated by 1.4-fold in the low Cu treatment. 104  In TO05, subunit IV and Vb were both significantly up-regulated (by 2.0- and 2.1-fold, respectively) in the insoluble fraction. Subunit VIb was only found in the soluble fraction in one of the paired biological triplicates and was not differentially regulated (-1.1-fold). NAD(P)H-DH was up-regulated by 1.4-fold (p<0.05) and ubiquinol-cytc reductase by 1.2-fold (p<0.05).  4.4 Discussion In Chapter 3, I presented data implying that in response to low Cu, T. oceanica (CCMP1003) restructures the proteins in its photosynthetic electron transport chain, which results in a decrease in carbon assimilation, and an overreduction of the photosynthetic apparatus. Evidence for overreduction of photosynthesis was seen in an increase of the PQ pool size, as well as a reduction in photochemical quenching (Fq′/Fv′). The latter, in particular, indicates an excess of reducing power in the photosynthetic electron transport chain, pointing towards a potential increase in reactive oxygen species (ROS) and the need for additional electron sinks. Furthermore, an ~40-fold increase in ferredoxin (Fd, petF) and 2.5-fold increase in ferredoxin:NAD(P)H oxidoreductase (FNR) was observed. These proteins are potentially important in detoxifying reactive oxygen species. Thus, these combined results suggest that under Cu limitation there is a surplus of both reduced ferredoxin (Fdred) and reducing equivalents in the form of NAD(P)H within the chloroplast. In order for photosynthesis to proceed safely, the cell needs to find a way to re-oxidize these two compounds, as well as counteract the production of potentially harmful ROS.  In this chapter, I describe the interaction between various metabolic pathways and subcellular compartments to facilitate the re-oxidation of Fdred, as well as NAD(P)H in the chloroplast. The interaction among metabolic pathways (e.g. nitrogen assimilation, glycolysis, 105  citrate and the urea cycle), and the sophisticated coordination between the chloroplast and the mitochondria in TO03, underlines a physiological versatility that is most likely responsible for the environmental success of this genera. At the end of the chapter, I will contrast the response of TO03 and TO05. In this study, the cells were acclimated to low Cu concentrations for many generations. Hence, the acclimation strategies in their physiology and proteome are not sudden, short-lived stress responses, but rather another ‗normal‘ state for the cell to sustain continual growth under low Cu conditions.   4.4.1 Carbon metabolism – the Calvin-Benson-Bassham cycle is down-regulated via its activase and the reactions of glycolysis are used to redistribute ATP and NAD(P)H within the cell One of the striking features of diatom genomes is the number of isoenzymes, particularly those involved in carbon metabolism. The differential expression of these isoenzymes is thought to direct the carbon flow within the cell. In this section, I argue that the Calvin-Benson-Bassham cycle is indeed down-regulated, whereas glycolysis is modulated in TO03 to promote ATP and reducing equivalent redistribution among cellular compartments to overcome Cu limitation.  The three most thoroughly annotated diatom genomes to date [T. pseudonana (Tp), Armbrust et al., 2004; P. tricornutum (Pt), Bowler et al., 2008; F. cylindricus (Fc), Mock et al., 2017] revealed a high number of isoenzymes in their respective sequences. However, studies comparing carbon metabolisms among these diatoms have found (Kroth et al. 2008; Smith et al. 2012) that even though there is a big overlap of homologous isoenzymes among them, due to gene loss, duplication, and/or horizontal gene transfer, there are still a significant number of 106  proteins that are only found in T. pseudonana (7), P. tricornutum (8), or F. cylindricus (11), or are shared between only two of these diatoms (Smith et al. 2012). For example, in T. pseudonana, the glycolytic enzyme enolase (ENO) is found in both the mitochondria and the cytosol, whereas P. tricornutum and F. cylindricus have enolases targeted only to the mitochondria and chloroplasts. In my study, T. oceanica has also two enolases but I predicted that both are targeted to the mitochondria (To23624, To34936, Figure 4.4, Table B.3). Within the chloroplast of diatoms, depending on the specific species, five of the 8-10 proteins involved in glycolysis are also part of the Calvin-Benson-Bassham cycle (Smith et al. 2012). Different isoenzymes might be preferentially involved in glycolysis over carbon fixation, thereby regulating carbon flow. For example, the three plastidial fructose-bisphosphate aldolases (FBAs) in P. tricornutum are regulated differently under low vs. high Fe conditions (Allen et al. 2012). Strikingly, these FBAs are also targeted to different parts of the chloroplast (Allen et al., 2012, Table 4.2). The two FBAs closely associated with the pyrenoid and β-carbonic anhydrase (FbaC1 and C5) are up-regulated under low Fe in P. tricornutum. It is not yet resolved how the segregation of parts of the Calvin Benson Bassham cycle to the pyrenoid is advantageous for the cells (Allen et al. 2012). The third plastidial FBA, FbaC2, on the other hand, is diffusely distributed throughout the chloroplast and is down-regulated (Allen et al. 2012). In contrast, in response to low Cu, the regulation of the three corresponding FBA homologs in TO03 unfolds differently: one of the pyrenoid-associated FBAs is only mildly up-regulated (1.4-fold, FbaC1 homolog, To00388), whereas the expression of the other one remains unchanged (FbaC5 homolog, To02112). On the other hand, the FBA found throughout the chloroplast (FbaC2 homolog, To12069) is up-regulated by 2-fold.  107  I propose that FbaC2 is preferentially involved in glycolysis over carbon assimilation. Four lines of evidence support this conclusion: (1) carbon assimilation decreased by 66% compared to that in the control conditions (Chapter 3, Table A.2), making it less likely for the proteins involved in this metabolic pathway to be up-regulated; (2) in line with this reasoning, each of the three significantly up-regulated proteins involved in the Calvin-Benson-Bassham cycle can also be part of glycolysis, i.e. PGK, TPI, and FBA; (3) none of the three proteins that are distinct Calvin-Benson-Bassham cycle proteins, i.e. Rubisco, RPI, and RPE, were differentially expressed; (4) the red algal-type Rubisco activase, cbbX, was down-regulated by 2.25-fold. Thus, due to the down-regulation of its activase, even though Rubisco levels are unchanged, half of the Rubisco proteins are not actively involved in carbon fixation (Mueller-Cajar et al. 2011). With a fraction of 2-6% of the total cellular protein content, Rubisco is one of the most abundant proteins in the cell (Losh et al. 2013).  This physiological adaptation might seem as a waste of cellular resources. However, as the abundance of RPI and RPE remains constant, it can be expected that these Rubisco proteins have ribulose-bisphosphate already bound and are thereby poised to engage in carbon assimilation anytime. Thus, once nutrient concentrations are more favourable, only the activase, cbbX, needs to be up-regulated and carbon fixation can proceed immediately. This strategy might be advantageous to outcompete other organisms that need more time to respond, especially in environments that are usually nutrient limited and where nutrient-rich conditions are only sporadic and short-lived. The Calvin-Benson-Bassham cycle is dependent on the balance of ATP/NAD(P)H within the chloroplast and is thus linked to the state of the photosynthetic apparatus. Under Cu limitation, there is a significant electron pressure in the photosynthetic electron transport chain, resulting in excess reducing equivalents in the form of NAD(P)H in the chloroplast. Due to its 108  dependence on a defined ATP/NAD(P)H ratio, these additional reducing equivalents cannot be re-oxidized in the Calvin cycle. However, I argue that the differential expression of proteins involved in glycolysis can help channel NAD(P)H away from the chloroplast to the mitochondria, while generating ATP in the chloroplast. In animals, glycolysis is restricted to the cytosol, whereas in green algae, the first half is located in the chloroplast and the second half is found in the cytosol. Thus, finding a subset of glycolytic proteins targeted to the mitochondria in diatoms was unexpected (Bowler et al. 2008). To date, the only known eukaryotes to share this trait are non-photosynthetic, heterokont oomycetes. In general, most reactions facilitated by proteins in glycolysis can proceed in either direction, i.e. glycolysis or gluconeogenesis. Smith et al. (2012) suggested that gluconeogenesis prevails in the mitochondria. However, assuming that the required metabolite transporters are present in the mitochondria (e.g. aspartate/glutamate shuttle, malate/2–oxoglutarate shuttle, citrate/malate shuttle, and fumarate/succinate shuttle), modelling flux balances in P. tricornutum predicted that glycolysis would indeed be more favorable in the mitochondria than gluconeogenesis (Kim et al. 2016). Another surprising finding in diatoms was the discovery of the ancient glycolytic Entner-Dourodoff pathway (EDP) in P. tricornutum (Fabris et al. 2012). This pathway, predominantly restricted to prokaryotes, generates less ATP than the usual Embden-Meyerhof-Parnas glycolysis pathway (EMPP). But, the EDP requires only two enzymes. Thus, the metabolic cost to sustain this pathway is less than that of EMPP. The EDP pathway generates pyruvate and GAP. The latter can then be metabolized in the mitochondria via the lower part of EMPP glycolysis. One hypothesis is that having multiple glycolytic pathways enables the cells to adapt faster to changing energy status in response to varying nutrient conditions (Fabris et al. 2012). It is worth noting that the involved 2-keto-3-deoxy 109  phosphogluconate aldolase (EDA, To10009) in the EDP was expressed constitutively in T. oceanica (Table B.3) and thus might not be an active component in the adaptation to chronic Cu limitation.  In TO03, in each cellular compartment, different subsets of glycolytic proteins were up- or down-regulated (Figure 4.4). Focusing on the up-regulated proteins only (Figure 4.9A), a pattern emerges revealing a regulation that leads to ATP formation in the chloroplast and cytosol, as well as NAD(P)H release in the mitochondria. In TO03, under low Cu, I have shown that a decreased ATP/NAD(P)H ratio in the plastid is likely. By reducing chloroplast (To13085) and increasing mitochondrial GAPDH (To33331), reducing equivalents are generated in the mitochondria, whereas increasing PGK (To07617) in the chloroplast will increase ATP in this compartment. Interestingly, Hoeckin et al. (2012) postulated for T. pseudonana an overall increase in the glycolytic activity when exposed to acute nitrogen starvation. However, when I mapped the involved proteins in T. pseudonana to their cellular target compartments (Figure 4.9B), I revealed a targeted regulation of these isoenzymes similar to the response of TO03 grown under chronic Cu limitation (i.e. PK down-regulated in mitochondria and up-regulated in the cytosol). Thus, the coordinated regulation of particular isoenzymes within one metabolic pathway seems to be indeed a general trait in diatoms.  4.4.2 Nitrogen metabolism is essential for Fdred oxidation and glutamate synthesis to fight ROS Another striking feature in the response to Cu limitation in TO03 was the up-regulation of nitrogen acquisition and assimilation (nitrogen metabolism, Figure 4.5, Table B.5; general overview, Figure 4.10). In contrast, short-term Fe limitation in P. tricornutum induced down-110  regulation of the transcript levels of various genes involved in nitrogen metabolism: cytosolic nitrate reductase (NR), chloroplast nitrite transporter, chloroplast nitrite reductase, NiR, and chloroplast glutamine synthase, GSII (Allen et al. 2008) and resulted in a 50-fold decrease in NR activity in T. weissfloggii (Milligan and Harrison 2000). Furthermore, it has been shown that most of the proteins involved in nitrogen assimilation are redox-sensitive and are down-regulated upon short-term nitrogen limitation. In Chlamydomonas, NR has been shown to be redox-regulated in tandem with carbon fixation via the reductive state of the PQ-pool, with a higher reducing state of the PQ-pool linked to faster carbon and nitrogen assimilation (Giordano et al. 2005). However, the authors suggest that the redox-dependent activity of NR might be one of several components in the complex regulatory network of nitrate assimilation. In plants, nitrogen assimilation is an important sink for excess reducing equivalents (Hoefnagel et al. 1998). I argue that the up-regulation of nitrate assimilation in TO03 helps the cell to alleviate the stress incurred by low Cu, namely by re-oxidizing Fdred in the chloroplast. This is achieved via differential regulation of only those isoenzymes that use Fdred as their cofactor: of the three chloroplast nitrite reductases (NiR), the expression of the NAD(P)H-dependent NiR abundance stayed constant (To35252), whereas the two Fdred dependent-NiRs were up-regulated (To00016, To02363). Glutamine synthase (GSII, To31900) and the Fdred-dependent glutamate synthase (GOGAT, To13288) were also up-regulated, thereby easing the electron pressure in the chloroplast. The importance of this strategy for TO03 is further emphasized by the fact that both the urea (To31656) and nitrate (To 04919) transporters at the cell membrane are part of the 15 highest up-regulated proteins in our dataset (6.9- and 11.0-fold, respectively). In light of the decrease in carbon fixation (14C data) and proposed increase in 111  nitrogen assimilation (proteomic data) in TO03, a decrease in the cellular C:N ratio would be expected. However, we did not measure particulate organic carbon or nitrogen. Interestingly, in TO05, based on unchanged carbon uptake rates and only a mild increase in nitrogen assimilation, we would assume no change, or only a slight change in its C:N ratio. This is in line with the constant carbon:nitrogen ratio documented for TO05 under Cu limitation by Kim and Price (2017).  4.4.2.1 Counteracting reactive oxygen species – glutathione, thioredoxin, and superoxide dismutases An additional benefit of enhanced nitrogen assimilation is an increase of cellular glutamate levels. Glutamate and its aminated form, glutamine, may 1) play a central role in amino acid biosynthesis and metabolism (Bromke 2013); 2) be decarboxylated into the signaling molecule γ-aminobutyric acid (GABA) (Allen et al. 2006; Port et al. 2013); 3) be deaminated to 2-oxoglutarate, thus supplying carbon skeletons to the TCA; and 4) be incorporated into glutathione (GSH). Glutathione is a small sulphur containing tripeptide (γ-L-glutamyl-L-cysteine-glycine) able to detoxify ROS via either direct scavenging or the ascorbate-glutathione cycle (Foyer and Noctor 2011). Furthermore, it is involved in redox signaling, modulating gene expression and enzymatic activity (see review Noctor et al. 2012). Two proteins are involved in glutathione biosynthesis: (1) glutamate cysteine ligase (GCL, also known as γ-glutamylcysteine synthase, GCS) which combines glutamate and cysteine to γ-glutamylcysteine and (2) glutathione synthase (GSS) which adds glycine. Both genes are present in single copies in T. pseudonana, P. tricornutum and T. oceanica. Glutamate cysteine ligase is a cytosolic enzyme, while GSS is targeted to the chloroplast. Both proteins were up-regulated under Cu limitation in 112  TO03, but only in one pairing of the biological triplicates (GCL up by 4.5-fold and GSS up by 1.48-fold, no p-value). Interestingly, in plants, the rate-limiting process for glutathione production is cysteine biosynthesis (Zechmann 2014). In TO03, two chloroplast cysteine synthase isoenzymes were up-regulated (CS, To27524 and To10442, by 1.6- and 2.5-fold, respectively; Figure 4.6, Table B.6) indicating an increase in glutathione production. Furthermore, glutathione-S-transferase is one of the most highly up-regulated proteins in our dataset (GST, To09062, by 7.3-fold). It can add glutathione to nucleophilic groups as part of the cellular detoxification system, which might be related to oxidative stress (Gallogly and Mieyal 2007). Strikingly, of the six GSTs identified in the T. oceanica genome, the one that is significantly up-regulated in TO03 (To09062) has no homologs in the three other diatoms (T.pseudonana, P. tricornutum and F. cylindricus), but its three closest homologs are found in multicellular organisms (e.g. the polyp Hydra vulgaris, the anemone Nematostella, and the brachiopod Lingula). In contrast, in TO05 a different GST is significantly up-regulated (To24526, by 2.3-fold) with homologs in all three other diatoms. Further evidence supporting that glutathione is used by TO03 to counteract ROS is the up-regulation of glutathione reductase (GR, To07268, by 2.5-fold). Over-expressing glutathione reductase has the additional advantage of oxidizing the over-abundant NAD(P)H in the chloroplast. Nevertheless, it was not possible to establish whether the glutathione-ascorbate cycle is up-regulated under Cu limitation, given that two of the required enzymes were not identified in our dataset (chloroplast dehydroascorbate reductase, DHAR; chloroplast ascorbate peroxidase, APX). However, two APX isoenzymes were constitutively expressed in the cytosol (To29696 and To37364). 113  Thioredoxins (TXN) are important redox regulators in plants, specifically in the chloroplast (Balmer et al. 2003), although their role in diatoms is still elusive (Weber et al. 2009). In TO03, three thioredoxins were up-regulated, and each one was targeted to either the chloroplast (TXN, To31425 by 1.5-fold), the cytosol (To05213 by 4.2-fold), or the mitochondria (To31425 by 1.5-fold). The P. tricornutum chloroplast TXN homolog (Pt46280) has been suggested to be involved in the activation of pyrenoid β-carbonic anhydrase (β-CA), thus primarily regulating carbon acquisition instead of carbon fixation (Kikutani et al. 2012). However, it is problematic to translate this functional regulation to homologs in Thalassiosirales, as the cellular allocation of CAs in diatoms differs greatly between Thalassiosirales and Naviculales (e.g. T. pseudonana and P. tricornutum) ((Samukawa et al. 2014). Most importantly in this context, T. pseudonana lacks a pyrenoid associated CA. Hence, even though the Pt TXN homolog regulates pyrenoid β-CA (Kikutani et al. 2012), it is challenging to confidently predict the exact regulatory target of the corresponding To TXN homolog without any biochemical evidence. Another defence mechanism against ROS is the production of superoxide dismutases (SOD). These proteins catalyze the conversion of superoxide radicals into hydrogen peroxide and oxygen. Of the three SODs identified in TO03 under Cu limitation, two were up-regulated: chloroplast Mn/Fe-SOD (To02860) by1.8-fold and cytosolic Ni-SOD (To10112) by 1.4-fold. Thus, under Cu limitation, TO03 is able to control ROS levels by increasing the expression of both glutathione and superoxide dismutases. The increase of thioredoxin isoenzymes in all three major cellular compartments (i.e. cytosol, chloroplast, and mitochondria), suggests that they may be involved in sensing the cellular redox state and regulating the cellular response to excessive reducing equivalents.  114   4.4.3 The malate shunt drains reducing equivalents from the chloroplast to the mitochondria, thus integrating the nitrogen and carbon metabolisms The efficiency of photosynthesis (both electron transport and carbon fixation) relies on having an adequate supply of ATP/ADP and NAD(P)H/NAD(P)+ (Allen 2002). In plants, the malate shunt channels excessive reducing equivalents from the chloroplast to other compartments in the cell, via the differential regulation of malate dehydrogenase (MDH) isoenzymes (Heineke et al. 1991; Scheibe 2004). In the chloroplast, NAD(P)H is used to reduce oxaloacetate to malate, a compound that can be transported across membranes and can be reoxidized, resulting in the production of NAD(P)H in the target compartment. Furthermore, NAD(P)H can be used in reactions such as nitrate reduction in the cytosol or ATP production in the mitochondria (e.g. through oxidative phosphorylation in respiration). In circumstances where the mitochondrial cytochrome-dependent respiration is comprised, such as under Fe limitation, excessive NAD(P)H can be dissipated via the mitochondrial alternative oxidase (AOX) (Yoshida et al. 2007). In diatoms, the interaction between the chloroplast and the mitochondria has been suggested to be even more significant with possibly direct exchange of ATP/ADP and indirect exchange of reducing equivalents via the malate/aspartate shunt (Prihoda et al. 2012; Bailleul et al. 2015). Neither the suggested overlap of plastidial and mitochondrial membranes nor the targeting of potential transporters to this intersection (e.g. malate/2-oxoglutarate antiporter, glutamate/aspartate antiporter ) has been conclusively proven to exist (Kroth et al. 2008; Bailleul et al. 2015; Kim et al. 2016). However, an increase of AOX in response to low Fe, as demonstrated by Yoshida in plants, has been documented in diatoms (Allen et al. 2008). Probing the T. oceanica genome, two homologs were 115  found to T. pseudonana mitochondrial AOX (AOX_1, To08807; AOX_2, To08808), and one homolog to an F. cylindricus mitochondrial AOX (AOX, To33762). However, these were not detected in our proteomic datasets (TO03, TO05, orig/+EST). Furthermore, our physiological data showed no changes in AOX activity due to Cu limitation (Chapter 3, Table A.2). This could be due to species-specific differences between T. oceanica and P. tricornutum (Allen et al. 2008 investigated P. tricornutum), or the fact that cytc oxidase is not affected by low Cu concentrations in TO03 (Table B.9). The latter would imply that any additional NAD(P)H―resulting from channelling electrons from the chloroplast to the mitochondria via the malate/aspartate shunt―could be oxidized via cytochrome-dependent respiration. One line of evidence in support of excess reducing equivalents being channelled towards respiration, as opposed to being used in the Calvin-Benson-Bassham cycle, is that carbon fixation decreased by 66%, while respiration rates remained constant (Chapter 3, Table A.2) Another indication for an activated malate/aspartate shunt in TO03 in response to low Cu is the up-regulation of chloroplast and mitochondrial MDH (To 30817, To03405), as well as mitochondrial aspartate aminotransferase (AAT, To15049) (malate shunt, Figure 4.7; complete overview, Figure 4.10). Chloroplast oxaloacetate (OAA) is reduced to malate via MDH. Malate is then transported into the mitochondria via a putative malate/2-oxoglutarate antiporter (Kim et al. 2016). Reducing equivalents in the form of NAD(P)H are released in the mitochondria via the re-oxidation of malate to OAA by mitochondrial MDH. In turn, mitochondrial AAT transfers an amine group from glutamate to OAA, thereby releasing aspartate and 2-oxoglutarate into the mitochondria. To close the cycle, aspartate would be transported back, via a glutamate/aspartate antiporter, into the chloroplast where the plastidial AAT isoenzyme would resupply OAA. However, chloroplast AAT was significantly down-regulated. I suggest that chloroplast OAA, 116  the substrate for MDH, is resupplied in the chloroplast via the ATP-dependent carboxylation of pyruvate due to the significant up-regulation of pyruvate carboxylase (PC). This would lead to a net decrease of NAD(P)H in the chloroplast and a net increase of NAD(P)H in the mitochondria.  The increase in 2-oxoglutarate and aspartate in the mitochondria, as a result of the up-regulation of mitochondrial AAT, can be helpful for the cell in various ways. If the putative malate/2-oxoglutarate antiporter is indeed involved in the malate shunt, 2-oxoglutarate will be transported back into the chloroplast. As chloroplast AAT is down-regulated, 2-oxoglutarate can be used as a substrate for the up-regulated Fd-dependent glutamate synthase (GOGAT) in nitrogen assimilation (see 4.3.5 Nitrogen metabolism). Any surplus 2-oxoglutarate can feed into the citrate cycle. Fittingly, aconitase (To20545) and isocitrate dehydrogenase (To34595), the two proteins involved in the citrate cycle, immediately before 2-oxoglutarate, are both significantly down-regulated. Mitochondrial aspartate can be channelled into the urea cycle, where it will produce argininosuccinate, which can then be channelled back into the mitochondrial citrate cycle as fumarate via the aspartate/argininosuccinate shunt (Allen et al. 2008). Thus, even though two of the first three steps in the citrate cycle were down-regulated, the malate shunt, in combination with the urea cycle, ensures the continuation of this vital metabolic pathway by supplying it with essential carbon skeletons, i.e. 2-oxoglutarate and fumarate. However, the involved antiporters were only identified in the TO05 proteomic data, and not in TO03. This might be due to the differential sequencing depth in the two strains, as TO03 had 50% less sequencing depth than TO05 (due to the different instruments utilized, see Chapter 2). Furthermore, membrane-bound proteins are generally less prominent in proteomic data. As a reminder, if a protein is not identified in one of the treatments, it will not be included in the differentially expressed protein dataset. 117  Recently, another shuttle system has been proposed in transferring excess reducing equivalents from the chloroplast to the mitochondria in P. tricornutum; the glutamine-ornithine shunt (Levering et al. 2016). However, none of the homologs involved in this newly proposed shunt were identified or up-regulated in T. oceanica under Cu limitation (e.g. n-acetyl-γ-glutamyl-phosphate reductase, AGRP, To01956, To25145, neither identified; n-acetylornithine aminotransferase, ACOAT, chloroplast, To09237, -1.4, p<0.001; cytosolic, To32466, -3.0, p = 0.17).  4.4.4 Contrasting adaptations to Cu limitation in TO03 vs. TO05  The response to Cu limitation is strikingly different between TO03 and TO05. Thalassiosira oceanica TO03 is able to decrease its Cu requirement drastically by restructuring its complete photosynthetic apparatus (Chapter 3) combined with complex changes in its carbon and nitrogen metabolism. These changes allow TO03 to alleviate ensued electron and ROS stress to sustain ongoing growth (this chapter). Strikingly, the main coping mechanism in TO05 to deal with Cu limitation lies in a 50% decrease of its entire proteome. Indeed, in response to low Cu, the only physiological parameters significantly affected in TO05 were growth rate, cell size and protein content (Chapter 3, Figure 3.2).  In contrast to the response in TO03, none of the proteins involved in nitrogen acquisition and assimilation were significantly up-regulated in TO05 (Figure 4.11). The only proteins that were somewhat up-regulated (between 1.3- and 2-fold with p<0.5) were the involved transporters (URT, NRT, NiRT) and glutamine synthases (GSII/GSII). As neither NAD(P)H, Fdred oxidizing enzymes, or thioredoxins were up-regulated, it is unlikely that TO05 experienced any significant excess of reducing equivalents within the chloroplast, for which an enhanced 118  nitrogen metabolism would be helpful. At this point, it is unclear how a potential increase in glutamine (both chloroplast and mitochondrial glutamine synthase are up-regulated) would be beneficial for the cell. It is also puzzling, why the Calvin-Benson-Bassham cycle influencing Rubisco activase, cbbX, and Rubisco itself were both somewhat down-regulated. This differs from the unchanged carbon assimilation rate in Cu limited TO05. I acknowledge, though, that I have no information on metabolites in my study, so the hypothesis would be more robust if it were derived from combined results of significant changes in protein expressions and matching physiological data (e.g. inferred increased reduction state of chloroplast Fered/Fedox in TO03 under Cu limitation).  The malate shunt was not induced under low Cu in TO05. Two homologs of a putative malate/oxoglutarate antiporter were expressed constitutively. However, chloroplast ATT and mitochondrial MDH were not even identified, suggesting levels below detection in at least one treatment (see Chapter 2). The two only indications of an active response to Cu limitation in TO05 are the significant up-regulation of glutathione-S-transferase and two glycolytic enzymes. The differential expression of various isozymes involved in glycolysis is a central strategy in TO03 to direct energy and reducing equivalent flow within the cell (Section 4.4.1 Carbon metabolism). TO05 does not exhibit the same level of sophistication in metabolic control. However, the only two significantly up-regulated proteins in carbon metabolism are chloroplast PGK and mitochondrial GAPDH, which would increase NADH in the mitochondria and ATP in the chloroplast. These two proteins might thus be the first line of defense in TO05 against small energy or reducing equivalent imbalances in the cell.  119  4.5 Conclusion The success of diatoms in the modern ocean is due to their complex genomic makeup, and their successful integration and versatility of metabolic pathways among cellular compartments. This was exemplified in the acclimatization of T. oceanica (CCMP 1003) to severe Cu limiting growth conditions, where the interaction among metabolic pathways ensured their continuous growth. For example: (1) Photosynthesis – N metabolism (4.4.2 Nitrogen metabolism): the overreduced state of the photosynthetic apparatus resulted in a proposed increase in Fdred, which was then oxidized by an N-assimilatory, Fdred isoenzyme located in the chloroplast; (2) Nitrogen metabolism and ROS fighting (4.4.2 Nitrogen metabolism): the increase in glutamate and cysteine synthase, as well as other key proteins in the glutathione metabolism, ensures the ability to counteract ROS; (3) Photosynthesis and Malate Shunt (4.4.1 Malate Shunt): excess reducing equivalents generated by the photosynthetic apparatus are channeled from the chloroplast to the mitochondria via the malate shunt. (4) Photosynthesis – glycolysis – carbon fixation (4.4.1 Carbon Metabolism): to counteract the imbalance between ATP/NAD(P)H in the chloroplast, specific reactions in glycolysis occur in different compartments, where reducing equivalents or ATP are needed (i.e. NAD(P)H generation in mitochondria, ATP production in cytosol and chloroplast). (5) Malate shunt – urea cycle– citrate cycle – glycolysis (4.4.3 Malate Shunt, 4.4.2 Nitrogen Metabolism, 4.4.1 Carbon Metabolism): the products of the malate shunt can feed into the TCA cycle, and the master cellular C and N redistribution hub, the urea cycle. Furthermore, pyruvate can be carboxylated in the chloroplast to feed into the malate shunt, again transferring both reducing equivalents and carbon skeletons from the chloroplast to the mitochondria. The up-regulation of N assimilation in response to chronic low Cu in TO03 contrasts the response of 120  TO03 to acute Fe limitation, as well as the response of P. tricornutum to N limitation. Whether this is due to differences in species, nutrients or level of stress remains an intriguing question.    121  4.6 Tables and figures Table 4.1: Comparison of cellular localization of various carbon metabolic pathways. (modified from Gruber and Kroth 2017) Pathway Function Chlamydomonas (green lineage) Diatoms Animals Comment regarding diatoms Calvin Cycle (Reductive Pentose P Way) Carbon fixation 3RuBisP + 3 CO2 -> GAP Plastid Plastid -- Rubsico  has higher CO2 affinity than other organisms Oxidative Pentose P way NAD(P)H, ribose (C5 sugars) for nt/ FA, AA Plastid,    Cytosol Cytosol only (Kroth et al. 2008) Cytosol Cytosol only -> needed re-import of nts into plastid -> lots of nt transporters Glycolysis 1st half (GAP) Glucose -> GAP Plastid Plastid, Cytosol Cytosol  Glycolysis 2nd half (pyruvate) GAP -> pyruvate Cytosol (Johnson and Alric 2013) Plastid, Cytosol, Mitochondria Cytosol Maybe also in mito to use Entner-Douderoff pathway generated pyruvate? Entner-Douderoff pathway 6PG ->pyruvate + GAP -- Mitochondria -- Fewer proteins involved (cost for cell), but also less ATP generated, but then again, faster as well, another tool to be adaptable to fast changing environment Phosphoketolase pathway Fructose-6-P -> acetyl-phosphate and GAP -- -- (only in Phaeodactylum) --  Citrate Cycle Carbon hub, reducing equivalents for respiration Mitochondria Mitochondria Mitochondria Integrated with urea cycle / Aspartate shunt Urea Cycle Animals: catabolic nitrogen excretion Diatoms: anabolic nitrogen/carbon redistribution hub -- Mitochondria Cytosol Mitochondria Incorporation of recycled N and C; in combination with bacterial HGT proteins, production of polyamines involved in cell wall formation and the osmolyte proline Malate Shunt Redistribution of reducing equivalents and C4/5-compounds Plastid, Mitochondria Plastid, Mitochondria Mitochondria Often mentioned/proclaimed as part of cross-talk between chloroplast and Mitochondria; no proteins found so far in Tp, only Mitochondrial membrane proteins from Oxaloacetate transporter in Pt. Pyrimidine biosynthesis  Plastid Cytosol Cytosol They need to be transported across all four membranes Carbon Storage  Plastid, starch Vacuole, Chrysolaminaran, lipids Cytosol, lipid droplets (triglyceride)  122   123  Figure 4.1: A) Overview of original proteomics data for T. oceanica CCMP 1003 (TO03, pink) and 1005 (TO05, blue) grown in Cu-limiting conditions. Dotted red lines indicate the significance threshold of two-fold difference. B-D) Venn diagrams of distinct identified proteins in the original datasets of TO03 and TO05: B) All proteins found; C) significantly up-regulated proteins; D) significantly down-regulated proteins. Note that even though only 50% of the proteins identified in TO05 were identified in TO03, in TO03 there were three times more significantly up-regulated proteins and five times more significantly down-regulated proteins than in TO05. 124   125  Figure 4.2: Pie charts of the first level KeggOrthology (KO) identifiers associated with proteins identified in the original T. oceanica CCMP 1003 (TO03) proteomic dataset: A) All proteins found; B) significantly up-regulated proteins; C) significantly down-regulated proteins. D) Bar chart of second level KO identifiers associated with proteins in the original TO03 dataset, comparing proportions between all proteins and those that are significantly up- or down-regulated. Numbers in brackets indicate absolute protein numbers in each subset. Note that proteins involved in metabolism seem to be the most highly affected: those involved in energy metabolism seemed down-regulated, those in carbohydrate metabolism seem to be mainly up-regulated, whereas those involved in amino acid metabolism are both up- and down-regulated. 126   127  Figure 4.3: Diagram of proteins involved in the Calvin Benson Bassham cycle and citrate (TCA) cycle. Boxes indicate proteins with their abbreviated name and known T. pseudonana (Tp) and T. oceanica (To) homologs. The colors of the boxes indicate expression in TO03 under low Cu: dark red, highly up-regulated (>2-fold, p<0.05); light pink, up-regulated by 1.3 to 2-fold (p<0.05); dark blue, highly down-regulated (>2-fold, p<0.05); light blue, down-regulated by 1.3 to 2-fold (p<0.05); white, expressed in TO03; grey border around box, found in T. oceanica genome but not expressed in TO03 proteomic data; grey.  Protein abbreviations: ACO, aconitase hydratase; cbbX, rubisco expression protein; CS, citrate synthase; DLDH, dihydrolipoamide dehydrogenase; FBA I, fructose-bisphosphate aldolase class-I; FBA II, fructose-bisphosphate aldolase class-II; FH, fumarate hydratase; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; GPI, glucose-6-phosphate isomerase; IDH, isocitrate dehydrogenase; MDH, malate dehydrogenase; OGD, 2-oxoglutarate dehydrogenase; PC, pyruvate carboxylase; PDH, pyruvate dehydrogenase; PFK, phosphofructokinase; PGAM, phosphoglycerate mutase; PGK, phosphoglycerate kinase; PGM, phosphoglucomutasePK, pyruvate kinase; rbcL,  ribulose-bisphosphate carboxylase large chain; rbcS,  ribulose-bisphosphate carboxylase small chain; RPE, ribulose-5-phosphate epimerase; RPI, ribose-5-Phosphate-isomerase; SUCLA, succinate CoA synthetase; TPI, triose-phosphate isomerase;   Compound abbreviations: 1,3- bisPG, 1,3-bisphosphateglycerate; G3P, glucose-3-phosphate; Ery-4-P, erythrose 4 phosphate; Sedo-1,7-bisP, sedoheptulose 1 7-bisphosphate; Rib-1,5-bisP, ribulose-1,5-bisphosphate; DHAP, dihydroxyacetone phosphate; Fru-1,6-bis-P, fructose 1,6-bisphosphate; Fru-6-P, fructose 6-phosphate; GAP, glyceraldehyde 3-phosphate; HCO3-, bicarbonate; OAA, oxaloacetate; TP, triose phosphate   128   129    Figure 4.4: Diagram of proteins involved in glycolysis in the three compartments (chloroplast, mitochondrion, cytosol), including Entner-Dourdoroff pathway. Boxes indicate proteins with their abbreviated name and known T. pseudonana (Tp) and T. oceanica (To) homologs. The colors of the boxes indicate expression in TO03 under low Cu: dark red, highly up-regulated (>2-fold, p<0.05); light pink, up-regulated by 1.3 to 2-fold (p<0.05); dark blue, highly down-regulated (>2-fold, p<0.05); light blue, down-regulated by 1.3 to 2-fold (p<0.05); white, expressed in TO03; grey border around box, found in T. oceanica genome but not expressed in TO03 proteomic data; grey, dashed border around box, no putative homologs in the T. oceanica genome.  Protein abbreviations: CBB cycle, Calvin Benson Bassham cycle, EDA, 2-keto-3-deoxy phosphogluconate aldolase; EDD, 6-phosphogluconate dehydratase; ENO, enolase; F2BP, fructose-1-6-bisphosphatase; FBA I, fructose-bisphosphate aldolase class-I; FBA II, fructose-bisphosphate aldolase class-II; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; GPI, glucose-6-phosphate isomerase; PC, pyruvate carboxylase; PDH, pyruvate dehydrogenase; PFK, phosphofructokinase; PGAM, phosphoglycerate mutase; PGK, phosphoglycerate kinase; PGM, phosphoglucomutase; PK, pyruvate kinase; TCA, tricarboxylic acid cycle;TP, triose phosphate; TPI, triose-phosphate isomerase, TPT, triose phosphate transporter   Compound abbreviations: 1,3- bisPG, 1,3-bisphosphateglycerate; 2K3D-PG, 2-keto-3-deoxyphosphogluconate; 2PG, 2-phosphoglycerate; 3PG, 3-phosphoglycerate; 6PG, 6-phosphogluconate; DHAP, dihydroxyacetone phosphate; Fru-1,6-bis-P, fructose 1,6-bisphosphate; Fru-6P, fructose 6-phosphate; GAP, glyceraldehyde 3-phosphate; Glu 6-P, glucose 6-phosphate; HCO3-, bicarbonate; OAA, oxaloacetate; PEP, phosphoenolpyruvate;    130   131  Figure 4.5: Diagram of proteins involved in nitrogen metabolism. Boxes indicate proteins with their abbreviated name and known T. pseudonana (Tp) and T. oceanica (To) homologs. The colours of the boxes indicate expression in TO03 under low Cu: dark red, highly up-regulated (>2-fold, p<0.05); light pink, up-regulated by 1.3 to 2-fold (p<0.05); dark blue, highly down-regulated (>2-fold, p<0.05); light blue, down-regulated by 1.3 to 2-fold (p<0.05); white, expressed in TO03; grey border around box, found in T. oceanica genome but not expressed in TO03 proteomic data; grey, dashed border around box, no putative homologs in the T. oceanica genome.  Protein abbreviations: AAT, aspartate aminotransferase; Agm, agmatinase; AMT, ammonium transporter; Arg, arginase; argD, n-acetylornithine aminotransferase; AsL, argininosuccinate lyase; AsuS, argininosuccinate synthase; ATCase, aspartate carbamoyltransferase; Fe-NiR, nitrite reductase (ferredoxin-dependent); GDCP, glycine decarboxylase p-protein; GDCT, glycine decarboxylase t-protein; GDH, glutamate dehydrogenase; GOGAT, glutamate synthase; GSI, glutamine synthase; GSII, glutamine synthetase; GSIII, glutamine synthetase; NAD(P)H-NiR, nitrite reductase (NAD(P)H dependent); NiRT, formate/nitrite transporter; NR, nitrate reductase; NRT, nitrate/nitrite transporter; OCD, ornithine cyclodeaminase; OdC, ornithine decarboxylase; OTC, ornithine carbamoyltransferase; pgCPSII, carbamoyl-phosphate synthase II; unCPS (CPSase III), carbamoyl-phosphate synthase; Ure, urease; URT, Na/urea-polyamine transporter.  132   133  Figure 4.6 Diagram of proteins involved in glutathione metabolism. Boxes indicate proteins with their abbreviated name and known T. pseudonana (Tp) and T. oceanica (To) homologs. The colors of the boxes indicate expression in TO03 under low Cu: dark red, highly up-regulated (>2-fold, p<0.05); light pink, up-regulated by 1.3 to 2-fold (p<0.05); dark blue, highly down-regulated (>2-fold, p<0.05); light blue, down-regulated by 1.3 to 2-fold (p<0.05); white, expressed in TO03; grey border around box, found in T. oceanica genome but not expressed in TO03 proteomic data; grey, dashed border around box, no putative homologs in the T. oceanica genome.  Protein and compound abbreviations: APX, ascorbate peroxidase; Cys, cysteine;  CYS, cysteine synthase; DHAR, dehydroascorbate reductase; γ-glu-cys, γ-glutamylcysteine;  GCL, glutamate cysteine ligase; GDH, glutamate dehydrogenase, glu, glutamate; NADP dependent; GOGAT, glutamate synthase; GR, glutathione reductase; GRX, glutaredoxin; GSS, glutathione synthetase; GTR, glutathione transporter; TXN, thioredoxin    134   135  Figure 4.7: Diagram of proteins involved in the malate shunt. Boxes indicate proteins with their abbreviated name and known T. pseudonana (Tp) and T. oceanica (To) homologs. The colors of the boxes indicate expression in TO03 under low Cu: dark red, highly up-regulated (>2-fold, p<0.05); light pink, up-regulated by 1.3 to 2-fold (p<0.05); dark blue, highly down-regulated (>2-fold, p<0.05); light blue, down-regulated by 1.3 to 2-fold (p<0.05); white, expressed in TO03; grey border around box, found in T. oceanica genome but not expressed in TO03 proteomic data; grey, dashed border around box, no putative homologs in the T. oceanica genome.  Abbreviations: AAT, aspartate aminotransferase; MDH, malate dehydrogenase; ME, malic enzyme; OAA, oxaloacetate; PC, pyruvate carboxylase; PK, pyruvate kinase    136   137  Figure 4.8: Diagram of proteins involved in pyruvate metabolism. Boxes indicate proteins with their abbreviated name and known T. pseudonana (Tp) and T. oceanica (To) homologs. The colours of the boxes indicate expression in TO03 under low Cu: dark red, highly up-regulated (>2-fold, p<0.05); light pink, up-regulated by 1.3 to 2-fold (p<0.05); dark blue, highly down-regulated (>2-fold, p<0.05); light blue, down-regulated by 1.3 to 2-fold (p<0.05); white, expressed in TO03; grey border around box, found in T. oceanica genome but not expressed in TO03 proteomic data; grey, dashed border around box, no putative homologs in the T. oceanica genome. aTp and To model are not homologs  Abbreviations: ACAS, acetyl-CoA synthase; ACC, acetyl-CoA carboxylase; DLDH, dihydrolipoamide dehydrogenase; LDH, L-lactate dehydrogenase; MDH, malate dehydrogenase; ME, malic enzyme; PC, pyruvate carboxylase; PDH-E1, pyruvate dehydrogenase-E1 component; PDH-E2, pyruvate dehydrogenase E2 component (dihydrolipoamide acetyltransferase); PEPC, phosphoenolpyruvate carboxylase; PEPCK, phosphoenolpyruvate carboxykinase; PEPS, phosphoenolpyruvate synthase; PK, pyruvate kinase; PPDK, pyruvate, phosphate dikinase.  138  Table 4.2: Fructose-bisphosphate aldolase (FBA) isoenzymes in P. tricornutum (Pt) and homologs in T. oceanica (To, CCMP 1003). Information on P. tricornutum as per Allen et al. (2012). Gene name (Pt)a FBA classb Phylogenetic ancestryc Location in Ptd Pt ide Pt mRNA lowFef To homologg protein ratio lowCuh FbaC1 Class II Chromalveolate specific gene duplication of FbaC2 prior to diversification Chloroplast, Pyrenoid Bd825 ↑ >25 To00388 ↑ (1.4 ) FbaC2 Class II Endosymbiotic gene transfer from prasinophyte-like green algal ancestor Chloroplast, diffuse Pt22993 ↓ <20 To12069 ↑ (2.0 ) Fba3 Class II Heterokont host of secondary endosymbiosis Cytosol Pt29014 ↑ >10 To24977 ± Fba4 Class I Bacterial like (unknown in non-diatom eukaryotes) Cytosol, putative cytoskeletal interaction Pt42447 ~1 To24978 ↓ (-2.8 ) FbaC5 Class I Endosymbiotic gene transfer from red algal ancestor (with selective gene loss in some centric diatoms) Chloroplast, Pyrenoid Pt51289 ↑ >80 To02112 ± aas per Allen et al. (2012) bClass I uses a metal co-factor, Class II uses a Schiff base cas per Allen et al. (2012) das per Allen et al. (2012) using GFP-fusion proteins eNCBI identifier ffold-change of mRNA transcript levels in acute Fe limited vs. Fe replete cultures; arrows indicating up- and down-regulation gas per blastP search hfold change of protein levels in chronic Cu limited vs. Cu replete cultures FBA, fructose-bisphosphate aldolase; Pt, Phaeodactylum tricornutum; To, Thalassiosira oceanica 139   140  Figure 4.9. The regulated proteins involved in glycolysis. A) Up-regulated proteins in TO03 under Cu limitation. B) significantly regulated proteins in T. pseudonana under acute N limitation (Hockin et al. 2012). Dark red boxes indicate highly up-regulated proteins (TO03: > 2-fold, p<0.05; Tp: as per Hockin et al. 2012), light red boxes indicate 1.3- to 2-fold up-regulated proteins (p<0.05), white boxes indicate no significant regulation, dark blue boxes indicate highly down-regulated proteins (TO03: > 2-fold, p<0.05; Tp: as per Hockin et al. 2012), light blue boxes indicate 1.3 to 2-fold down-regulated proteins (p<0.05). Abbreviations: EDA, 2-keto-3-deoxy phosphogluconate aldolase; EDD, 6-phosphogluconate dehydratase; ENO, enolase; F2BP, fructose-1-6-bisphosphatase; FBA I, fructose-bisphosphate aldolase class-I; FBA II, fructose-bisphosphate aldolase class-II; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; GPI, glucose-6-phosphate isomerase; PFK, phosphofructokinase; PGAM, phosphoglycerate mutase; PGK, phosphoglycerate kinase; PGM, phosphoglucomutase; PK, pyruvate kinase; TPI, triose-phosphate isomerase.  141   142  Figure 4.10: An overview of the proteomic response in the nitrogen and carbon metabolisms in T. oceanica (CCMP 1003) grown under Cu-limiting conditions. Boxes indicate proteins with their abbreviated name. The colors of the boxes indicate expression in TO03 under low Cu: dark red, highly up-regulated (> 2-fold, p<0.05); light pink, up-regulated by 1.3 to 2-fold (p<0.05); dark blue, highly down-regulated (> 2-fold, p<0.05); light blue, down-regulated by 1.3 to 2-fold (p<0.05); white, expressed in TO03; grey border around box, found in T. oceanica genome but not detected in TO03 proteomic data; grey, dashed border around box, no putative homologs in the T. oceanica genome.  Abbreviations: AAT, aspartate aminotransferase; ACO, aconitase hydratase; Arg, Arginase; AsL, argininosuccinate lyase; AsuS, argininosuccinate synthase; cbbX, rubisco expression protein; CS, citrate synthase; DLDH, dihydrolipoamide dehydrogenase; EDA, 2-keto-3-deoxy phosphogluconate aldolase; EDD, 6-phosphogluconate dehydratase; ENO, Enolase; F2BP, Fructose-1-6-bisphosphatase; FBA I, fructose-bisphosphate aldolase class-I; FBA II, fructose-bisphosphate aldolase class-II; Fe-NiR, nitrite reductase (ferredoxin-dependent); FH, fumarate hydratase; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; GDH, glutamate dehydrogenase; GOGAT, glutamate synthase; GPI, glucose-6-phosphate isomerase; GSII, glutamine synthetase; GSIII, glutamine synthetase; IDH, isocitrate dehydrogenase; MDH, malate dehydrogenase; NR, nitrate reductase; NRT, nitrate/nitrite transporter; OGD, 2-oxoglutarate dehydrogenase; OTC, ornithine carbamoyltransferase; PC, pyruvate carboxylase; PEPC, phosphoenolpyruvate carboxylase; PFK, phosphofructokinase; PGAM, phosphoglycerate mutase; PGK, phosphoglycerate kinase; PGM, phosphoglucomutase; PK, pyruvate kinase; RPE, ribulose-5-phosphate epimerase; RPI, ribose-5-Phosphate-isomerase; RuBisCO,  ribulose-bisphosphate carboxylase; SUCLA, Succinate CoA synthetase; TPI, triose-phosphate isomerase; TPI, triose-phosphate isomerase; unCPS (CPSase III), carbamoyl-phosphate synthase; URT, Na/urea-polyamine transporter.  143   144  Figure 4.11: An overview of the proteomic response in the nitrogen and carbon metabolisms in T. oceanica (CCMP 1005) grown under Cu-limiting conditions. Boxes indicate proteins with their abbreviated name. The colors of the boxes indicate expression in TO03 under low Cu: dark red, highly up-regulated (> 2-fold, p<0.05); light red, up-regulated by 1.3 to 2-fold (p<0.05); dark blue, highly down-regulated (> 2-fold, p<0.05); light blue, down-regulated by 1.3 to 2-fold (p<0.05); white, expressed in TO03; grey border around box, found in T. oceanica genome but not detected in TO03 proteomic data; grey, dashed border around box, no putative homologs in the T. oceanica genome.  Abbreviations: AAT, aspartate aminotransferase; ACO, aconitase hydratase; Arg, Arginase; AsL, argininosuccinate lyase; AsuS, argininosuccinate synthase; cbbX, rubisco expression protein; CS, citrate synthase; DLDH, dihydrolipoamide dehydrogenase; EDA, 2-keto-3-deoxy phosphogluconate aldolase; EDD, 6-phosphogluconate dehydratase; ENO, Enolase; F2BP, Fructose-1-6-bisphosphatase; FBA I, fructose-bisphosphate aldolase class-I; FBA II, fructose-bisphosphate aldolase class-II; Fe-NiR, nitrite reductase (ferredoxin-dependent); FH, fumarate hydratase; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; GDH, glutamate dehydrogenase; GOGAT, glutamate synthase; GPI, glucose-6-phosphate isomerase; GSII, glutamine synthetase; GSIII, glutamine synthetase; IDH, isocitrate dehydrogenase; MDH, malate dehydrogenase; NR, nitrate reductase; NRT, nitrate/nitrite transporter; OGD, 2-oxoglutarate dehydrogenase; OTC, ornithine carbamoyltransferase; PC, pyruvate carboxylase; PEPC, phosphoenolpyruvate carboxylase; PFK, phosphofructokinase; PGAM, phosphoglycerate mutase; PGK, phosphoglycerate kinase; PGM, phosphoglucomutase; PK, pyruvate kinase; RPE, ribulose-5-phosphate epimerase; RPI, ribose-5-Phosphate-isomerase; RuBisCO,  ribulose-bisphosphate carboxylase; SUCLA, Succinate CoA synthetase; TPI, triose-phosphate isomerase; TPI, triose-phosphate isomerase; unCPS (CPSase III), carbamoyl-phosphate synthase; URT, Na/urea-polyamine transporter. 145  Chapter 5: Comparative Proteomic Analysis of the Photosynthetic Apparatus of Copper and/or Iron Limited T. oceanica  5.1 Summary Iron (Fe) and copper (Cu) are co-factors in proteins involved in photosynthesis, thus are essential micronutrients for photosynthetic organisms. Iron availability limits productivity in large oceanic regions and some studies have suggested the possibility of Fe-Cu co-limitation in these areas. Diatoms are responsible for almost half of the marine carbon fixation, are ubiquitous in Fe-limited regions, and are known to have interactive Fe and Cu physiologies. However, little is known about the underlying biochemical and proteomic changes in response to Cu- and Fe-Cu co-limitation. To gain a better understanding of the mechanics of low metal adaptation, I investigated and evaluated the physiological and proteomic response of two strains of the open ocean diatom Thalassiosira oceanica (CCMP1003 and CCMP1005) grown under three different limiting metal regimes: low Fe, low Cu and low Fe and Cu. Here, I show that the differential regulation of the photosynthetic components is both metal- and strain-dependent. Even though both strains were isolated from the oligotrophic Sargasso Sea, CCMP1003 seems to be better adapted to low metal concentrations than CCMP1005. Most strikingly, T. oceanica CCMP1003 modulated levels of the Cu-containing plastocyanin depending on the metal stress, i.e. decreasing it under low Cu, and to a lesser extent under low Fe and Cu, but increasing it under low Fe. Furthermore, TO03 up-regulated the expression of ferredoxin: NADP+-reductase and an unknown flavodoxin isoform in response to all three metal limiting conditions. Both proteins are potentially important in detoxifying reactive oxygen species. Interestingly, in TO05 only the Fe-146  responsive flavodoxin FldA was detected. Further differences and similarities between conditions and strains are discussed.  5.2 Introduction Iron (Fe) and copper (Cu) are important trace metals for photosynthetic cells. Their redox potential makes them excellent co-factors in proteins involved in electron transfer reactions, such as the photosynthetic and respiratory electron transport chain in the chloroplast and mitochondria, respectively. Photosynthesis is particularly rich in Fe-containing proteins: PSII, 3-4 Fe atoms (depending on species); cytb6f, 5 Fe atoms; PSI, 12 Fe atoms; and Fdx, 2 Fe atoms. Also depending on algal species, the electron carrier between cytb6f and PSI may either be Fe- containing cytc6, (1 Fe atom) or Cu-containing plastocyanin (1 Cu atom) (Raven et al. 1999). Even though these elements are only needed in trace amounts, Fe concentrations in the open ocean are so low that they limit primary productivity in 30-40% of the global ocean (Moore et al. 2004; Boyd et al. 2007). However, phytoplankton have partially adapted to these low Fe conditions by lowering their Fe requirements and increasing their ability for Fe uptake and storage. Strategies employed by some open ocean diatoms, like Thalassiosira oceanica, include lowering their PSII:PSI ratio and cytb6f concentrations (Strzepek and Harrison 2004), substituting Fe-containing proteins with non-Fe-containing counterparts − such as Fdx with FldA (LaRoche et al. 1996; McKay et al. 1999) or cytc6 with plastocyanin (Peers and Price 2006), and employing a high-affinity Fe uptake system (HAFeT) containing multi-Cu oxidases (MCO) (Maldonado et al. 2006). The higher Cu requirements in some oceanic phytoplankton―specifically under low Fe―(Annett et al. 2008; Guo et al. 2012), and the identification of plastocyanin (Peers et al. 147  2005) and MCO  (Peers et al. 2005; Maldonado et al. 2006; Guo et al. 2015) inspired further experiments to elucidate the interaction between Fe and Cu nutrition in diatoms. In the last 10 years, some open ocean diatoms (e.g. T. oceanica) were shown to have higher Cu:C quotas and faster steady-state Cu uptake rates than their coastal counterparts (e.g. T. pseudonana) (Annett et al. 2008; Guo et al. 2012). Furthermore, both T. oceanica and T. pseudonana have low- and high-affinity Cu uptake systems, but as expected, T. oceanica has a more efficient high-affinity Cu uptake system (Guo et al. 2010). Surprisingly, the high-affinity Cu transporters in T. pseudonana (TpCTR |9391 and TpCTR |24275) are up-regulated under low Fe, and down-regulated when Cu-limited cells are resupplied with Cu (Guo et al. 2015).  In summary, the complex interaction between Fe and Cu physiology in open ocean diatoms enables them to survive in low Fe environments. However, their higher Cu requirements might exceed supply in some Fe-limited open ocean settings, thus making diatoms susceptible to Fe-Cu co-limitation (Peers et al. 2005). Iron and Cu enrichment experiments in both the Bering Sea and the North East Pacific suggested that some phytoplankton groups could indeed experience Fe-Cu co-limitation in situ (Coale 1991; Peers et al. 2005). Despite the potential importance of Fe and Cu nutrition in controlling primary productivity, little is known about biochemical changes in the cell in response to Cu or Fe-Cu co-limitation. To investigate the biological effects of these limiting trace metal conditions in diatoms, I grew two strains of the model open ocean diatom T. oceanica (TO03 and TO05) under low Cu conditions (Chapter 3), and also under low Fe and Fe-Cu co-limiting concentrations and evaluated cellular changes at the physiological and proteomic level. This Chapter focuses on the photosynthetic modifications, and compares the adaptive strategies a) of each strain to the three low metal conditions, and b) of both strains to each trace metal limiting treatment. 148   5.3 Results and discussion In Chapter 3, I presented the contrasting effects of Cu limitation (lowCu) on the photosynthetic apparatus of both strains at the physiological and proteomic level. I analyzed the same parameters for two additional low metal treatments: Fe-limitation and Fe-Cu co-limitation, henceforth called lowFe, and lowFeCu, respectively. Below, I have focused the results and discussion on the most important differences elicited by lowFe, lowCu and lowFeCu in order to highlight a) the important interaction between Fe and Cu physiology in diatoms, and b) the intraspecific differences and similarities. I will first discuss the trace metal limitation achieved in the different treatments and strains, in order to determine the comparability of the respective findings. Then I will present the changes to the photosynthetic apparatus, including the light-harvesting antennae, in each treatment. Finally, comparisons between the strains will be made and conclusions will be drawn. Note, that the lowCu data presented here is a summary of Chapter 3. All up- and down-regulated proteins discussed in the present chapter are significantly regulated as defined in Chapter 2 (Methods) (i.e. highly up-regulated, ± 2-fold, p<0.05; up-regulated, ± 1.3- to 2-fold, p<0.05) unless otherwise noted.   5.3.1 Status of limitation Before comparing proteomic and physiological responses to low metal availability between the strains, or different metal limitation responses in one strain, it is paramount to ensure that the comparisons are valid. For example, for comparisons between diatoms exposed to low trace metal conditions, either the degree of a trace metal limitation experienced is similar (Bucciarelli 149  et al. 2010), or the limiting trace metal concentration used is the same for all studied organisms (Strzepek and Harrison 2004). These two approaches are different but equally valid. Given the varying Fe and Cu requirements among diatoms (Annett et al. 2008), for comparisons among different trace metal limiting conditions within a given strain, the degree of limitation (relative decrease in growth rate relative to maximum growth rate, /max) achieved in each treatment should be identified.   5.3.1.1 LowCu status For both strains, we used the same Fe replete concentration (1.37 µM Fe[total]) as that in previous studies from our laboratory (Maldonado et al. 2006; Guo et al. 2012). However, the Cu replete concentration used in these studies (10.2 nM [Cu]total) only supported maximum growth rate for TO03. Hence, we increased the Cu replete concentration for TO05 to 14.3 nM [Cu]total thus enabling it to achieve its maximum growth rate under Fe replete concentration (for a more in-depth discussion of TO05 growth rates, see Chapter 3; for an overview of metal concentrations in our media, see Chapter 2, Table 2.1). Therefore, even though we used different Cu concentrations in the control media for TO03 and TO05, comparisons between the strains are valid, as both achieved maximal growth rates (max) in the control treatment and statistically the same percentage of growth rate reduction under lowCu conditions (TO03, 48% of max; TO05, 65% of max; Figure 5.1A, Table C.1, Table C.2, Table C.3).  5.3.1.2 LowFe status Under lowFe, we used their respective control Cu concentrations (TO03, 10.2 nM [Cu]total; TO05, 14.3 nM [Cu]total) but added the same lowFe concentration (12.5 nM [Fe]total, as in Guo et 150  al. (2012). This low Fe level induced a significant growth rate reduction in both strains (Table 5.1): a 15% reduction of max in TO03, and a 33% reduction of max in TO05. Even though the % reduction of max is greater for TO05, TO03 was also experiencing significant Fe limitation. Four lines of evidence support this conclusion.  Firstly, while the reduction in /max in TO03 was not as severe as in TO05, the reduction was still significant (Figure 5.1). In addition, several well-established proxies of Fe-limitation were observed in low Fe TO03 cultures, including 1) lower Fv/Fm (reviewed by (Behrenfeld and Milligan 2013), 2) down-regulation of ferredoxin (Fdx) combined with an up-regulation of flavodoxin (FldA) (Ferreira and Straus 1994; LaRoche et al. 1996; McKay et al. 1999; Whitney et al. 2011), and 3) up-regulation of a set of iron starvation induced proteins (ISIPs) (Allen et al. 2008; Whitney et al. 2011; Lommer et al. 2012; Morrissey et al. 2015). Specifically, in TO03, each of these proxies behaved as expected for a Fe-limited culture: Fv/Fm was reduced from 0.6 to 0.35 (Figure 5.1 E), Fdx was down-regulated by 1.5-fold (no p-value), while FldA was up-regulated by 2.4-fold, and ISIP1A was up-regulated by 2.6-fold (Table 5.1, Table C.4). Thus, we can conclude that TO03 was indeed Fe limited.  However, given that under identical Fe concentrations, TO05 achieved slower relative growth rates than TO03, the comparisons between strains should be restricted to adaptations to Fe limitation induced by this low Fe concentration.  Compared to the control, the growth rate of TO05 under lowFe was decreased to the same extent as that under lowCu. Thus, for TO05 comparisons between these two limiting trace metal conditions are sound.   151  5.3.1.3 LowFeCu status Iron-Cu co-limitation is considered a Type III co-limitation (Saito et al. 2008), where uptake of one nutrient is dependent on sufficient cellular concentration of the other. To induce Fe-Cu co-limitation, we used the same lowFe concentration that was used to induce the lowFe response (12.5 nM [Fe]total). In TO05, this lowFe concentration was combined with the same lowCu concentration that was used to induce its lowCu response (6.1 nM [Cu]total), leading to a lowFeCu growth rate of 43% of µmax. In contrast, the lowCu concentration supplied to TO03 (background Cu, <1 nM [Cu]total) was insufficient to support growth when combined with the lowFe concentration. Thus, to induce measurable Fe-Cu co-limitation, TO03 had to be cultured with a higher Cu concentration (2.0 nM [Cu]total) than its respective lowCu treatment (i.e. TO03: lowCu, <1.0 nM). Relative to the growth rate achieved in the lowFe condition, the extra growth rate reduction due to Fe-Cu co-limitation was almost identical between the two strains (Figure 5.2, TO03, 67% of lowFe; TO05, 63% of lowFe). Thus, even though we used different Cu concentrations in the respective lowFeCu media, compared to their respective lowFe growth rate, their additional reduction in growth rate (~34% in both strains) in response to limiting levels of another metal, Cu, is practically the same between the two strains.  5.3.2 Changes in physiology Chla concentrations are often used as a proxy for phytoplankton biomass. However, the cellular pigment content can change depending on light and nutrient concentrations. Whereas TO03 decreased its cellular Chla concentration drastically (50%) only under lowFeCu conditions, TO05 had the same reduction in cellular Chla under lowFe and lowFeCu. 152  Furthermore, TO05 also decreased its Chla concentration under lowCu (35% reduction, p<0.1) (Figure 5.1 B). Carbon fixation (normalized to Chla) in TO03 was decreased greatly by both lowCu treatments (lowCu and lowFeCu) (Figure 5.1C). In contrast, carbon fixation was unaffected by lowFe alone, as previously shown for this strain when severely Fe limited (growth rate = 32% of µmax, 0.13 nM [Fe]total, Schuback et al. 2015). Similarly, TO05 did not change its ability to fix carbon per Chla under any metal limitation.  Gross oxygen production (normalized to Chla) in TO03 was reduced most severely by lowCu (73%), followed by lowFe (by 42%), and then lowFeCu (by 30%, Figure 5.1D). The severe decrease in oxygen production under lowCu could be due to an increase of cyclic e- transport around PSII (for further discussion see Chapter 3). TO05, on the other hand, did not modulate its gross oxygen production per Chla in response to low metal availabilities. Fv/Fm describes the maximum quantum yield of PSII photochemistry. It decreased significantly in all three metal limiting conditions in TO03 (Figure 5.1E). In TO05, Fv/Fm was only decreased in the two lowFe conditions (lowFe and lowFeCu). A constant Fv/Fm under Cu limitation has been previously documented for TO05 (supplementary data in Peers and Price 2006). NPQ(NSV), non-photochemical quenching, usually increased under all low metal conditions. Relative to the control, TO03 increased NPQ(NSV) in lowCu and lowFeCu conditions (Figure 5.1G). Unfortunately, no results are available for our lowFe treatment. However, in Schuback et al. (2015), NPQNSV increased significantly in Fe-limited TO03. In TO05, NPQNSV increased to some extent in lowFe and lowFeCu (p>0.05), but remained constant under lowCu. 153  PSII, the antennae absorption cross section in PSII, increased in TO03 by 30% in both lowCu treatments (lowCu and lowFeCu), but remained constant in lowFe (Figure 5.1F). Interestingly, under severe Fe limitation, Schuback et al. (2015) observed in TO03 an increase in PSII, thus this parameter could be dependent on the degree of limitation experienced by TO03. In contrast, TO05 increased its antenna size by 20% in the two lowFe treatments (lowFe and lowFeCu) and kept it constant under lowCu, even though its growth rate was limited to a similar extent in the lowCu and lowFe treatments. This means that the degree of growth rate reduction alone is a poor predictor for PSII. However, increasing PSII may be one strategy of photoacclimation when PSII concentrations are decreased (Moore et al. 2006). In agreement, the treatments with increased PSII are indeed the treatments where a decrease in PSII concentration was observed (see schematic visualization of all changes in photosynthetic electron transport chain in Figure 5.3). Protein per cell concentrations in TO03 remained constant under lowCu but increased under lowFe (2-fold) and lowFeCu (2.5-fold) (Figure 5.1H). In contrast, TO05 protein content decreased when grown under all metal limitations: by 50% in the lowCu and by 75% in the lowFe and the lowFeCu treatments. In TO05, the decrease in overall protein content seems to be its main strategy to reduce its metal requirements (for further discussion Chapter 3).  5.3.3 Changes to the photosynthetic apparatus to balance maximum growth and minimal photodamage under low metal conditions  Limitation by Fe, Cu or Fe-Co poses different challenges for a photosynthetic cell. Trace metal(s) limitation is likely to alter the ideal stoichiometry of the electron transport chain (ETC). Changes in the ETC components will then require further adjustments to proteins up-stream (i.e. 154  antennae) and downstream (i.e. Fdx/FldA) of the ETC. Each of these changes, in turn, ensures that 1) sufficient light energy is absorbed to support maximal electron transport rates in PSII (ETRPSII) while 2) maximum amounts of ATP and NAD(P)H are generated for cellular metabolism and growth and 3) these changes are finely tuned so that not too much light energy is absorbed, as this would lead to the production of harmful reactive oxygen species (ROS) (Nishiyama et al. 2006; Mackey et al. 2008). As expected, stoichiometric changes to the ETC differ between strains and among the metal limitation imparted to the diatoms (Figure 5.3). However, it is worth noting that ETRPSII rates did not vary among treatments or strains (Table C.1, Table C.2, Table C.3). This suggests that the acclimation strategies were efficient and allowed the photosystems to remain undamaged. In other words, the new stoichiometry of the electron transport chain was the new ideal under these trace metal limiting conditions. Changes in growth rate in these acclimated diatoms were due to decreases in the number of photosystems and/or alternative metabolic uses (e.g. fighting ROS) of photosynthetic reactants (ATP/NAD(P)H) besides carbon assimilation.  5.3.3.1 LowCu – changes to photosynthetic apparatus In cyanobacteria and the green lineage of photosynthetic eukaryotes (i.e. chlorophytes, plants), Cu limitation leads to the substitution of the Cu-containing electron carrier plastocyanin with the equivalent Fe-containing cytc6. Diatoms usually possess only cytc6 (Groussman et al. 2015), but T. oceanica uses plastocyanin, potentially even constitutively  (Peers and Price 2006). Even though its genome encodes two putative cytc6 isoforms (cytc6 isoformA, THAOC_06916; isoformB, THAOC_24412 (Lommer et al. 2012)), neither was detected in our proteomic dataset nor in any other published work (Strzepek and Harrison 2004; Lommer et al. 2012). It is possible 155  that cytc6 was not detected in our data set because cytc6 is generally only used under Cu limiting conditions (e.g. in Chlamydomonas, Quinn and Merchant 1998), thus cytc6 levels in our control could have been below the detection limit (thus they would not be part of our dataset). The other published studies investigated responses to Fe limitation, making detection of the Fe-containing cytc6 unlikely, given that plastocyanin is usually considered to be the preferred electron carrier in species that are able to express both (unless Cu is limiting) (Quinn and Merchant 1998). Traditional PCR can be used to detect qualitatively if a gene of interest is present in the genome. To determine if for example cytc6 encoding genes are actually transcribed under Cu limitation, reverse transcript PCR (RT-PCR, using mRNA as template) might be a suitable option. To see if the expression level of cytc6 mRNA changes under varying metal concentrations, qualitative reverse transcript real-time PCR (RT-qPCR) would be used, in which a fluorescent dye is used to monitor the amplification reaction in real-time. As discussed in detail in Chapter 3, the primary response of TO03 to lowCu is a decrease in plastocyanin, thereby generating an 'electron bottleneck' in the ETC just before PSI. In order to accommodate the observed 4-fold decrease in plastocyanin, complexes before plastocyanin were also down-regulated (cytb6f, 2.3-fold; PSII, ~ 2-fold; Table 5.3, Figure 5.3). Even though F‘q/F‘v, an estimate of the open PSII (i.e. the fraction of oxidized primary electron acceptor QA), was decreased by 17%, electron transport rates in PSII (ETRPSII) were not impaired (Table C.2). Indeed, LHC composition and antennae structure had changed successfully to channel an adequate amount of the absorbed light energy towards the PSII core for photochemistry while also dissipating enough excess energy as heat (see increase of NPQNSV, Figure 5.1G).  Differential expression of LHCs in response to lowCu further confirms my hypothesis [for an overview of highly regulated LHCs (> ±2-fold, p<0.05) see Table 5.4; for complete list, 156  see Table C.5]. Lhcf-Group I proteins are usually associated with light harvesting for photochemistry in PSII. Five of these proteins were strongly down-regulated. Furthermore, three of four known PSI LHCs were also down-regulated (TpFCP2-, TpLhcr10-,  and TpLhcr13-homolog) (Grouneva et al. 2011). Energy channelled towards the reaction centres was thereby decreased. In addition, TpLhcf11 and TpLhcr11 were up-regulated. There is evidence that neither of these two LHCs is tightly associated with the thylakoid membrane in T. pseudonana, a closely related centric diatom (Grouneva et al. 2011), suggesting that TpLhcf11 and TpLhcr11 might be involved in energy dissipation. A set of four TpFCP homologs (Lhcf-Group III) were also up-regulated. This group is closely related to haptophyte LHCs which are down-regulated in Emiliania under high light conditions (McKew et al. 2013), implying a general involvement in stress response or an induction by an overreduction of the ETC. Our study is the first to show differential regulation of this group of LHCs in diatoms (Chapter 3, Hippmann et al. 2017). A well-anticipated LHC group to be up-regulated under stress is the Lhcx clade. Lhcx1 and Lhcx7 were the up-regulated representatives. Lhcx1, a well-studied protein, is often up-regulated under various nutritional stresses and is thought to be involved in increasing NPQNSV and giving further membrane stability (Green 2007; Koziol et al. 2007; Zhu and Green 2010; Grouneva et al. 2011; Gundermann and Büchel 2014). It can also be associated with both photosystems (Grouneva et al. 2011). Overall, in TO03 grown under lowCu, the antennae appear to decrease the amount of absorbed light energy channelled to photochemistry in exchange for an increase in the amount of absorbed light energy re-emitted as heat to diminish photodamage (see Chapter 3, overview model Figure 3.6).  At the downstream side of PSI, Fdx and FNR were up-regulated (Figure 5.3, Table 5.3). Both have been previously associated with alleviating stress related to ROS (Rodriguez et al. 157  2007; Lin et al. 2013). Interestingly, two isoforms of flavodoxin were also up-regulated: FldA (THAOC_31152), which is known to be Fe-responsive (LaRoche et al. 1996), by 1.5-fold and another Fld (THAOC_05010) by 3-fold. These two flavodoxins are likely up-regulated in addition to their Fe-containing counterpart Fdx, because of their ability to counteract a variety of cellular stresses. The stress response function of these proteins has been demonstrated in transgenic tobacco plants (Tognetti et al. 2006; Zurbriggen et al. 2008). The changes to the photosynthetic components in TO05 were much more subtle. The growth rate reduction is mainly attributed to a decrease in overall protein content. Similarly, Chla per cell decreased but so did the cellular volume. Furthermore, the absorption cross section of PSII, as well as the stoichiometry of almost all ETC components stayed constant (Figure 5.3, Table 5.3). Only FldA was up-regulated by 1.4-fold, and one putative stress-related LHC (TpFCP7) by 2.1-fold, thus mirroring the response in TO03 for these parameters. Surprisingly, one other protein was slightly up-regulated under Cu limitation: plastocyanin, by 1.3-fold. Counter-intuitively, this would not have increased cellular Cu levels, considering that the overall cellular protein decreased by 50% and plastocyanin being considered the main Cu-pool in T. oceanica (Peers and Price 2006). Thus cellular Cu requirements in TO05 would still be approximately 45% less in the lowCu treatment compared to those in the control. The only potentially down-regulated protein in the ETR in response to lowCu was FNR. No definite answer can be given, as there is only one data point (i.e. no p-value for lowCu). However, given that both lowFe and lowFeCu induced a decrease in FNR, this could also be the case under lowCu in TO05.  158  5.3.3.2 LowFe – changes to photosynthetic apparatus Under lowFe, a decrease in Fe-containing proteins is expected, in addition to substitutions with non-Fe containing counterparts. Furthermore, proteins involved in Fe-uptake would be anticipated to be up-regulated (Maldonado and Price 1996; Lommer et al. 2010; Morrissey et al. 2015). Yet, neither Fe-transporters nor members of the high-affinity Fe transport systems (HAFeT) (e.g. multi-Cu containing oxidase) were readily identified in our dataset. This is not necessarily surprising, as these proteins a) are membrane-bound, which are notoriously underrepresented in proteomic datasets (Ong and Mann 2005), and b) may be expressed at very low levels, (i.e. below detection) under our control conditions. However, one putative membrane-associated iron-responsive protein (ISIP1A) was found in Fe-limited TO03. ISIP1A has been suggested to be involved in siderophore uptake, delivering the Fe-siderophore complex to the chloroplast (Bowler et al., submitted).  The Fe-containing proteins involved in the ETC stayed either constant (PSII and PSI) or were down-regulated: cytb6f complex by 1.7-fold and Fdx potentially (i.e. one data point;  no p-value) by 1.5-fold, thereby decreasing cellular Fe requirements. However, having the Fe-responsive FldA and ISIP1A up-regulated, suggests Fdx was likely, and truly down-regulated, as expected in a standard lowFe response. The abundance of both PSII and PSI stayed constant. This seems surprising at first, especially for PSI, being such a Fe-rich complex (with 12 Fe atoms). However, while coastal Thalassiosira species indeed lower their PSI:PSII ratio upon Fe limitation, the open ocean adapted T. oceanica has already a very low PSI: PSII ratio even under Fe replete conditions (Strzepek and Harrison 2004). I hypothesize that decreasing this ratio further might only be executed under severe Fe limitation.  Under the present milder Fe limiting 159  conditions (85% /max), a decrease in cytb6f (5 Fe) and Fdx (1 Fe) seem to be sufficient to lower the photosynthetic Fe demand. The non-Fe containing proteins in the ETC were all up-regulated. The Fe-responsive FldA, which is thought to act as a substitute for the Fe-containing Fdx, was up-regulated by 2.3-fold. Furthermore, the two proteins that are potentially involved in alleviating ROS and other stressors, FNR and the second Fld isoform (see lowCu response) were up-regulated by 2.7- and 2.3-fold, respectively. Interestingly, the Cu-containing plastocyanin was also up-regulated (by 2-fold). Usually, plastoquinone (PQ) pool oxidation is considered the rate-limiting step in the ETC. However, in barley, a 2- to 3-fold increase in plastocyanin has been shown to support electron flow under high light conditions; most likely due to a higher plastocyanin to PSI ratio (Burkey 1993). Maybe, in diatoms, the up-regulation of plastocyanin might help to ensure that cytb6f complexes (now in lower abundance) stay oxidized, preventing an electron flow bottleneck in ETC, which would lead to the overreduction of the PQ pool. Unfortunately, we do not have the complete set of FRRF parameters that would allow us to verify changes in the PQ pool size or the oxidation state of the primary electron acceptor at PSII. Another scenario where increasing the plastocyanin content would be advantageous could be if the diffusional motion of PC is inhibited under lowFe and thus a higher pool of PC could buffer the slower movement. However, given our results, there is no obvious physiological explanation for the increased plastocyanin content under lowFe.  The functional absorption cross section (PSII) stayed constant under lowFe. However, the LHC composition changed in a very similar fashion as that in lowCu (Table 5.4). Light harvesting for photochemistry in both PSII and PSI was down-regulated (5 Lhcf-Group I and 2 Lhcr LHCs were down-regulated), whereas known (Lhcx) and potentially stress-induced LHCs 160  (Lhcf-Group III, and the two less tightly membrane-associated TpLhcr11 and TpLhcf11homologs) were up-regulated. Interestingly, two more Lhcr were up-regulated (TpLhcr2, TpLhcr13) demonstrating a finely tuned differentiation between the various LHCs depending on cellular needs. Contrary to its lowCu response, the Fe-limited TO05 showed a remarkable stoichiometric shift in its photosynthetic apparatus, resembling a textbook response to Fe limitation: all Fe-containing complexes were down-regulated (PSII, 1.5-fold; cytb6f, 1.8-fold; PSI, 1.9-fold), whereas plastocyanin and FNR remained constant, and FldA was up-regulated by 8-fold. Changes in the regulation of Fdx were not definitive due to insufficient data, but the one single data point we have indicates the expected down-regulation (5.3-fold, no p-value).  The increased PSII is also expected, given the lower abundance of PSII (Moore et al. 2006), and was accompanied by a lowering of Fv/Fm. Generally, most LHCs seemed to be uniformly down-regulated by 1.2- to 1.5-fold (see Table C.5, underlined expression values have an associated p<0.05). The three LHCs that were highly regulated (> ± 2-fold, p<0.05) were: another TpFCP4 homolog (up-regulated), TpFCP10 homolog (down-regulated), and TpLhcr13 homolog (down-regulated). The diatom-specific LHC (THAOC_Tp17531 homolog, 17531-clade) was down-regulated by 1.8-fold. The 17531-homolog in  P. tricornutum has been found associated with PSI (Grouneva et al. 2011), but no putative role has been suggested for this elusive LHC. Surprisingly, no Lhcx was up-regulated.  5.3.3.3 LowFeCu – changes to the photosynthetic apparatus Iron and Cu are important co-factors in a series of photosynthetic ETC proteins and thus the growth of open ocean diatoms might be affected by co-limitation of Fe and Cu. Both strains of T. 161  oceanica were Fe-Cu co-limited to a similar extent (as discussed in 5.3.1.3). When TO03 was grown in lowFeCu, all metal-containing components were down-regulated: PSII by 2.3-fold, cytb6f by 3-fold, plastocyanin by 1.5-fold, PSI by 3-fold and Fdx by 11-fold. All three photosynthetic non-metal containing proteins were up-regulated: the Fe-responsive FldA by 218-fold, the supporting Fld isoform by 4-fold, and FNR by 2.9-fold. As PSII decreased, PSII increased again. The composition of LHCs changed drastically as well, but some unique features appeared compared to the single metal limitations: three of the four Lhcf-Group III LHCs that were up-regulated under single metal limitation were down-regulated under Fe-Cu co-limitation (two TpFCP4 and one TpFCP10 homologs). Maybe lowFeCu poses an even more severe over-reduction of the ETC, approaching a turning point to substitute for more suitable LHCs. In line with this hypothesis, the change in LHCs did dissipate enough energy to even increase the portion of open PSII (Fq/Fv) under lowFeCu. This adaptation suggests that the cells are extremely well equipped to dissipate excess energy as heat. Fittingly, NPQNSV was also highest under lowFeCu (Figure 5.1G). The Fe-responsive TpLhcr2 homolog that was up-regulated under lowFe was likewise up-regulated under lowFeCu. Curiously, TpLhcx7 homolog was down-regulated under lowFeCu, whereas it was up-regulated under lowCu.  Under lowFeCu, the regulation of the ETC components in TO05 was very similar to those in TO03. Specifically, all metal-containing proteins were down-regulated (PSII, 1.8-fold; cytb6f, 2.1-fold; plastocyanin, 1.4-fold; PSI, 1.9-fold; Fdx, 1.9-fold). The Fe-responsive FldA was highly up-regulated, too (by 17-fold). However, FNR was down-regulated by 1.6-fold. Sigma and NPQNSV, on the other hand, followed the same trend as in TO03 (i.e. an increase in absorption cross section PSII and NPQNSV). LHCs, however, were only down-regulated 162  (TpFCP4 homolog and two Lhcr homologs). Interestingly though, the same TpLhcr3 homolog was down-regulated under lowFeCu in TO05 and TO03. Under lowFeCu, a lower concentration of FNR and/or insufficient regulation of LHCs in TO05 may be responsible for a somewhat less efficient ETC compared with that in TO03, as indicated by a lower fraction of open PSII in TO05. However, as mentioned above, the maximal electron transport rate in PSII (ETRPSII) was constant in both strains (Table C.1, Table C.2, Table C.3).   5.3.4 Comparisons of the changes to the photosynthetic apparatus between strains and among trace metal limiting treatments  Comparisons of the changes in the photosynthetic apparatus in response to lowCu, lowFe, and lowFeCu reveal trends and differences between strains and amongst treatments. Most importantly, under trace metal limitation(s), the diatoms changed their photosynthetic protein inventories to prevent photodamage while retaining maximal rates of electron transport in PSII. There are two main themes that stand out regarding regulation of proteins in TO03: 1) metal-dependent modulation (i.e. protein regulation depends on the limiting metal) and 2) metal-independent modulation (i.e. regulation seems related to a more general stress response). I will discuss these first in TO03 and will proceed with comparing them with those in TO05.  Metal-dependent regulation. The regulation of certain proteins seemed to be determined by whether Fe or Cu concentrations were limiting growth. For example, under lowCu, PSII was decreased to accommodate lower levels of essential downstream PSII components, such as plastocyanin. In contrast, in lowFe, PSII remained constant, possibly because under mild Fe limitation a decrease in cytb6f and Fdx was sufficient to lower cellular Fe requirements while 163  supporting optimal photosystem activity. However, under Fe-Cu co-limitation (lowFeCu), lower Cu concentrations might have caused slower rates of Fe transport, thus intensifying Fe limitation, and resulting in a need for reduction of all photosynthetic Fe-containing proteins. A similar chain of explanations is possible for PSI. However, in contrast to the lowCu induced down-regulation of PSII, PSI was unaffected by lowCu. This might be because PSI is down-stream of the electron transport bottleneck generated by a decrease in plastocyanin. Thus, regulation of PSII and PSI seems to vary depending on the unique stoichiometric pattern imposed by the distinct metal limitations. The regulation of plastocyanin seems to be mainly Cu dependent. At the lowest Cu concentration, plastocyanin was down-regulated by 4-fold. Similarly, under lowFeCu (with slightly higher lowCu concentrations), it was decreased by 1.5-fold. Puzzlingly, lowFe induced up-regulation of plastocyanin by 2-fold.  Whether plastocyanin might fulfil a similar buffer capacity for electrons between cytb6f and PSI, as the plastoquinone pool between PSII and cytb6f, remains to be seen.  Ferredoxin seems to be used to alleviate electron pressure under lowCu and was hence highly up-regulated (more on the use of Fdx to alleviate electron pressure, Chapter 4, 4.4.2). However, under both lowFe conditions, this Fe-containing carrier was replaced with the non-Fe-containing alternative FldA.  In general, TO05 only modulated its protein levels moderately. Plastocyanin levels, even though also changing in a metal-dependent manner, did so differently compared to TO03: it increased somewhat under lowCu (by 1.3-fold), stayed constant under lowFe and increased under lowFeCu (by 1.4-fold). As discussed under 5.3.3.1, the counter-intuitive slight increase under lowCu, would not impact cellular Cu levels greatly, but, taking the overall 50% decrease 164  in cellular protein content into account (Figure 5.1H), it would still result in an overall decrease in cellular Cu content of ~ 45%. Under lowFe, plastocyanin remains constant, whereas it is decreased under lowFeCu. The main strategy used by TO05 to decrease its cellular metal requirements was decreasing its overall proteome, by 50% in lowCu and by 75% in each lowFe and lowFeCu treatment (relative to the control). Additional modulation of ETC components occurred in TO05 only under lowFe and lowFeCu, in which Fe-containing proteins were down-regulated and FldA was up-regulated. Interestingly, the lowFeCu response of TO05 seems to be a more pronounced lowFe response (PSII from 1.5-fold down-regulation to 1.8-fold; cytb6f from 1.8-fold to 2.1-fold; plastocyanin from 1 to 1.4-fold, PSI from 1.7-fold to 3-fold), thus underlining the Type III co-limitation in this strain.  Metal-independent regulation might be more closely connected to a holistic cellular stress-response, such as excess intracellular reducing equivalents or suboptimal pH. This was observed in TO03 in the expression of FNR and both Fld isoforms, where protein levels were enhanced in all trace metal limiting treatments. These proteins are generally associated with alleviating various cellular stresses (Rodriguez et al. 2007; Zurbriggen et al. 2008). Interestingly, TO05 did not express the second flavodoxin isoform. Furthermore, in contrast to TO03, the expression of FNR was either maintained or down-regulated. The inability to use the second isoform of Fld and increase FNR, both proteins potentially involved in alleviating ROS, might be one reason why TO05 is more susceptible to low metal concentrations compared to TO03.  165  5.3.4.1 Iron-Cu co-limitation in T. oceanica – more than just Type III Co-limitation can occur in three theoretical forms (see Saito et al., 2008 and references therein).  Type I co-limitation occurs when both nutrients are potentially limiting on their own, independent of each other (i.e. ―independent‖ in the strict sense that they are neither dependent as described for Type II or Type III). In Type II, one nutrient can substitute for the other either through 1) direct substitution of co-factors in the same protein or 2) as co-factors of proteins that fulfil the same task (e.g. plastocyanin and cytc6 in Chlamydomonas). In Type III, uptake of one nutrient is dependent on the other. Iron-Cu co-limitation has been deemed ―Type III – biochemically dependent‖ as Cu is needed in the high-affinity uptake of Fe (Maldonado et al. 2006). However, given that in the open ocean diatom T. oceanica, plastocyanin accounts for the vast majority of its intracellular Cu-pool (Peers and Price 2006), the interaction between Fe and Cu in photosynthesis might be even more intriguing and interesting.  I propose an integrated Fe-Cu co-limitation in the form of a combined Type III and Type I limitation. Evidence for the classic Type III Fe-Cu co-limitation is observed in both strains: even though the same Fe concentration (42 nM Fe) was used for lowFe and lowFeCu media, all Fe-containing proteins were reduced further in the lowFeCu compared to the lowFe only media (Figure 5.3). When Cu concentration was decreased in the lowFeCu media to the lowCu-only concentration (i.e. no Cu added), TO03 could not survive. In a Type III co-limitation, this would be predominantly due to the need for Cu in Fe-uptake. My data suggest that this is only part of the story, as in TO03, Cu requirements are expected to be higher under low Fe because of the increase in plastocyanin (i.e. it is up-regulated by 2-fold under lowFe). Perhaps, the extra plastocyanin helps to compensate for the decrease in cytb6f in maintaining electron flow in the ETC.  166  Thus, under lowFeCu, plastocyanin levels could not be down-regulated to the same extent as in lowCu (by 4-fold), but only by 1.5-fold. This decrease could be explained in two ways. First, the higher Cu concentration in lowFeCu (2 nM Cu) relative to that in lowCu (<1 nM Cu) will permit higher plastocyanin expression. Secondly, in the lowFe medium, the Cu concentrations (10.2 nM) might have been sufficient to support the higher plastocyanin levels observed in order to alleviate the stress incurred by lowFe, as well as the Cu required for the high-affinity Fe uptake system. In contrast, under lowFeCu, the Cu concentration was almost an order of magnitude lower (1.96 nM Cu) than that under lowFe, thus the cell would have had a much lower intracellular Cu pool to fulfil the Cu demand of both photosynthetic electron and cellular Fe transport. Without data on TO03 grown with 2 nM Cu under Fe replete conditions, or maybe even qPCR data on MCO expression under lowCu vs. lowFe vs. lowFeCu, it is speculative to select which scenario is correct. However, either one of these scenarios would further support a Type I Fe-Cu co-limitation. It is possible that the ratio between cytb6f and plastocyanin might play a bigger role in sustaining an efficient electron flow under Fe-limitation than previously thought. In Table 5.5, I normalized the differential expression of the four major ETC components (PSII, cytb6f, plastocyanin, PSI) to the differential expression of cytb6f. Under lowCu, cytb6f: plastocyanin is 1:0.6, reflecting the 2.3-fold decrease in cytb6f and the 4-fold decrease in plastocyanin. Under lowFe, the ratio is 1:3.5 (reflecting the 1.7-fold decrease in cytb6f and the 2-fold increase is plastocyanin). As discussed above, the plastocyanin pool might have some electron buffering capacity to ensure that cytb6f is oxidized at all times. Under lowFeCu, this putative buffer capacity would still be needed, thus the cytb6f: plastocyanin ratio (1:2, reflecting the 3-fold 167  decrease in cytb6f and the 1.5-fold decrease in plastocyanin) resembles more the lowFe response (1:3.5) than the lowCu one (1:0.6).  5.4 Conclusion Overall, open ocean diatoms seem to be well equipped to adapt to chronic metal limitation (days/weeks), which they might experience often in their natural habitat (Behrenfeld and Milligan 2013). In general, TO03 is ultimately capable of withstanding lower Fe and Cu concentrations than TO05. Indeed, TO03 is able to make significant and complex changes to its light-harvesting components and photosynthetic ETC proteins, including plastocyanin, depending on the trace metal limitation experienced by the cell and its general needs. Furthermore, TO03 can enlist in the up-regulation of proteins potentially involved in the alleviation of ROS, such as FNR and two types of flavodoxins.  According to the results presented here, the interaction between Fe and Cu, as trace metals potentially limiting primary productivity, is synergistic. Importantly, this means that we cannot predict changes in the stoichiometry of proteins or their absolute levels by examining the response to a single metal limitation in the laboratory. Furthermore, the proteomic response to trace metal limitation(s) by these two closely related T. oceanica strains was extremely different. These differences couldn't have been inferred from genomic data. In general, intraspecific strain differences have become the focus of many recent investigations, both in natural assemblages and model species (Von Dassow et al. 2008; Kremp et al. 2012; Kim et al. 2015; Luxem et al. 2017; Hippmann et al. 2017). Ultimately, predicting which strain will be more successful in its original habitat will be difficult, as environmental conditions in the sea are very dynamic. However, a deeper understanding of the possible stoichiometries of a fully operational 168  photosynthetic apparatus under various trace metal limiting conditions is useful for interpreting in situ findings.   169  5.5 Tables and figures 170   171  Figure 5.1 The effects of all three low metal treatments (lowCu, lowFe, lowFeCu) on various physiological parameters in two strains of T. oceanica, CCMP 1003 (TO03) and 1005 (TO05). A) relative growth rate, B) Chla per cell, C) carbon uptake at growth irradiance, D) gross oxygen production, E) Fv/Fm, F) absorption cross section of PSII antennae, PSII, G) NPQNSV at growth irradiance, H) cellular protein content. The values are means ± standard error of three biological replicates. Differing signs above columns indicate statistically significant changes (p<0.05) using a 2-way ANOVA with post-hoc interaction analysis (see methods for details): * indicates significant difference between strains in the same treatment (intraspecific), (*) indicates differences with p<0.01, ˅ indicates significant difference between one specific treatment of one strain vs. its respective control (e.g. TO03 lowFeCu vs.TO03 control).   172   Figure 5.2 Percent growth rates for the Fe-Cu co-limited cultures (lowFeCu) of T. oceanica CCMP 1003 (TO03) and 1005 (TO05) relative to their respective Fe-limited growth rates (lowFe). The values are means ± standard error of three biological replicates.˅ indicates significant difference between lowFeCu and lowFe.    % lowFe173  Table 5.1 Expression of iron-responsive proteins in two strains of T. oceanica, CCMP 1003 (TO03) and 1005 (TO05), grown under low Cu (lowCu), low Fe (lowFe), and low Fe and Cu conditions (lowFeCu).   TO03  lowCua TO03  lowFea TO03  lowFeCua TO05  lowCua TO05  lowFea TO05  lowFeCua Gene name Name sol insol sol insol sol insol sol insol sol insol sol insol ISIPS,  iron starvation induced proteins                   THAOC_30995 ISIP1A, isoform A 1.56   2.57 1.52 9.41 16.19 1.51 1.41 7.14 34.1 6.64 9.04 THAOC_34758, contig_117647_681_1617_- ISIP3         1.01 1.37 18.3 19.2 15.33 18.94 THAOC_36410 ISIP2A, isoform A               1   -1.37 -1.58 -1.15 ferredoxin                   THAOC_25559 petF 43.79   -1.46   -11.02   -1.48   -5.31   -1.21 -1.85 flavodoxin                  THAOC_05010 fld 2.95   2.32   3.82               THAOC_31152 fldA, isoform A 1.44   2.35   218.8   1.4   8.09   17.24 20.77 FNR, ferredoxin-NADP+ reductase                   THAOC_06509 FNR -1.46   -1.27   -1.05   -1.14 -1.14 1.27   1.34 1.35 THAOC_36724 FNR 2.47  2.71   2.88  -1.34  -1.32   -1.62 -1.83 THAOC_36724, contig_64183_1_637_+ FNR 5.5   4.15   3.92              aexpression of proteins given in fold-change as per original proteomic analysis (see methods for details); bold indicates highly differential expression (>± 2-fold, p<0.05); underlined indicates moderate differential expression (>±1.3- to 2-fold, p<0.05), italic indicates expression value is derived from EST proteomic analysis   174  Table 5.2 Significantly differentially expressed LHCs in two strains of T. oceanica, CCMP 1003 (TO03) and 1005 (TO05), grown under low Cu (lowCu), low Fe (lowFe), and low Fe and Cu conditions (lowFeCu). Gene namea closest homologb TO03 lowCuc TO03 lowFec TO03 lowFeCuc TO05 lowCuc TO05 lowFec TO05 lowFeCuc Evidence for role in other diatoms Lhcf - Group I         THAOC_01208 TpLhcf -2.79       THAOC_01927 TpLhcf  -2.04  -2.04      THAOC_06305 TpLhcf -10.34 -8.21 -2.07       light harvesting THAOC_20854 TpLhcf -5.56 -5.1 -2.79    light harvesting THAOC_20855 TpLhcf    -3.9         THAOC_25270, contig_123884_1_219_- TpLhcf   -2.23      THAOC_32932 TpLhcf8 -2.12 -2.05 -2.23       light harvesting, trimers, oligomersd,e THAOC_36248 TpLhcf   3.55     Lhcf - Group II         THAOC_09684 TpLhcf11 4.83 3.7 2.52     Lhcf - Group III         THAOC_04469 TpFCP4 2.06 2.09 -5.65     THAOC_05777 TpFCP10 2.4 2.28 -2.22  -2.14   THAOC_06968 TpFCP2 -2.52   -2.37      in TP tightly bound to PSIf,g THAOC_08587 TpFCP7 2.79 2.54 -2.72 2.07   close to haptophyte LHCsf THAOC_16345 TpFCP4  2.68  2.17         THAOC_18994 TpFCP4     2.48 2.87           175  Gene namea closest homologb TO03 lowCuc TO03 lowFec TO03 lowFeCuc TO05 lowCuc TO05 lowFec TO05 lowFeCuc Evidence for role in other diatoms Lhcr         THAOC_00550 TpLhcr11 2.39 2.33 -6.64    PS I light harvestingg, h THAOC_05707 TpLhcr4  -9.33      THAOC_05708 TpLhcr14    -2.21         THAOC_07034 TpLhcr13   2.08      THAOC_07036 TpLhcr13 -6.57 -5.79 -2.75    -2.09 -2.18 PS I light harvestingg, h, i THAOC_16963 TpLhcr3    -6.51   -2.23  THAOC_34573 TpLhcr2   3.06  3.12         THAOC_35518 TpLhcr10 -2.55  -4.93    PS I light harvestingg, h Lhcx         THAOC_08095 TpLhcx7 2.76  -2.78     THAOC_08095, contig_119183_41_788_+ TpLhcx7   -2.39     THAOC_09937 TpLhcx1 3.29  3.09  12.57        photoprotection, stress response, associated with both PSI + II, FCP trimers, oligo, or only loosely associated with membrane, facilitates NPQ g, h, i,j, k THAOC_31988 TpLhcx1  3.29  3.14  12.41     agrouped into different LHC clades as per phylogenetic analysis (Chapter 3, Figure 3.5) bclosest homolog as per phylogenetic analysis (Chapter 3, Figure 3.5) cexpression of proteins given in fold-change as per original proteomic analysis (see methods for details); italic indicates that expression value is derived from EST proteomic analysis d(Ikeda et al. 2013) e(Nagao et al. 2013) f(Hoffman et al. 2011) g(Grouneva et al. 2011) h(Gundermann and Büchel 2014)  i(Green 2007)  j(Zhu and Green 2010) k(Koziol et al. 2007)  176  Table 5.3: Differential expression of proteins involved in the photosynthetic electron transport chain (ETC) in two strains of T. oceanica, CCMP 1003 (TO03) and 1005 (TO05), in response to chronic metal limitation induced by low Cu (lowCu), low Fe (lowFe), and low Fe and Cu concentrations (lowFeCu).  TO03 lowCub TO03 lowFeb TO03 lowFeCub TO05 lowCub TO05 lowFeb TO05 lowFeCub Gene namea Protein name sol insol sol insol sol insol sol insol sol insol sol insol PSII                    THAOC_34020 psb27-like, involved in Mn cluster formation  1.73 1.13 1.43  1.14 1.84 -1.21 1 -1.6 -1.4 -1.42 -1.51 psbA psbA, PSII protein D1  -1.92  1.09  -1.63  -1.03  -1.41 -1.69 -1.71 psbA, contig_8044_1_175_+ psbA, PSII protein D1  -2.45  -1.06  -2.05  -1.2  -1.61  -1.99 psbB psbB, PSII CP47 RC protein  -1.61  -1.28  -2.18 -1.61 -1.1  -1.43 -1.53 -1.43 psbC, THAOC_26185 psbC, PSII CP43 RC protein        1.05  -1.41 -1.53 -1.36 psbC, THAOC_26185 psbC, PSII CP43 RC protein  -2.1  -1.22  -3       psbD, THAOC_24371 psbD, PSII D2 protein  -2.01  -1.14  -2.48       psbD, THAOC_24371 psbD, PSII D2 protein  -2.1  -1.08  -2.02  -1.01  -1.34 -1.57 -1.39 psbE, THAOC_24363 psbE, cytb559 subunit alpha  -2.28  -1.1  -2.21  -1.2  -1.42  -1.91 psbE, THAOC_24363 psbE, cyt b559 subunit alpha  -2.25 4.15 -1.1 4.38 -2.19  -1.22  -1.38 -1.92 -1.71 psbH psbH, PSII RC protein H    -1.23  -1.37  -1.61  -1.61 -2.47 -2.77 psbL psbL, PSII reaction center protein L  -1.63  -1.48  -2.97  -1.12    -2.62 THAOC_03193 psbO, Mn-stabilizing protein -1.41  1.15  -1.09  -1.27 -1.03 -1.42 -1.26 -1.18 -1.06 THAOC_15373 psbP, oxygen-evolving enhancer protein 2 (OEE2)       -1.06  -1.16  1.03 -1.26 177   TO03 lowCub TO03 lowFeb TO03 lowFeCub TO05 lowCub TO05 lowFeb TO05 lowFeCub Gene namea Protein name sol insol sol insol sol insol sol insol sol insol sol insol THAOC_08500 psbQ, oxygen-evolving enhancer protein 3 (OEE3) -1.15 -2.96 1.34 1.06 -1.86 -2.04 -1.44 1.34 -1.71 -1.48 -1.66 -2.13 THAOC_09685 psbU-like, small extrinsic protein 1.62  3.14 -1.29 1.45 -2.68 -1.19 1.31 -1.22 -1.58 -1.19 -1.19 psbV, THAOC_30541 psbV, cytc550   1.56 1.1  -1.89 4.92 1.06 1.18 -1.2 1.6 -1.33 1.48 psbV, THAOC_30541 psbV, cytc550 1.07 1.56 1.11  -1.73 4.92 1.04 1.18 -1.26 1.6 1.09 1.13 psbY psbY, PSII protein Y        -1.12  -1.33 -1.68 -1.87 PET                    petA petA, cytf  -1.65  -1.49  -2.93  -1.12  -1.99 -2.02 -2.04 petB, THAOC_26188 petB, cytb6  -2.33  -1.92  -4.69  -1.16  -1.64 -1.91 -2.15 THAOC_33417 petC, Fe-S subunit (Rieske protein)  -1.51  -2.83  -3.97  -1.48  -1.93 -2.15 -2.21 THAOC_33417, contig_35113_1_741_+ petC, Fe-S subunit (Rieske protein)  -1.01    -1.47  -1.2    -1.86 petD, THAOC_24366 petD, cytb6f complex subunit 4        -1.15  -1.86  -2.27 petD, THAOC_24366 petD, cytb6f complex subunit 4        -1.1  -1.88 -2.34 -2.01 THAOC_06915, contig_97569_1_258_- petE, plastocyanin       -1.92  1.12    THAOC_29732 petE, plastocyanin -4.41  2.23  -1.49  1.16 1.32 -1.46 -1.14 -1.3 -1.41 PS I                    psaA psaA, PSI P700 chla apoprotein A1  -1.21  -1.14  -3.03  1.14  -1.54 -1.74 -1.7 psaB psaB, PSI P700 chla apoprotein A2  1.11  -1.07  -2.18  1.01  -1.73 -2.14 -2.08 psaC psaC, PSI Fe-S center   -1.18  -1.65  -6.95  1.2  -1.55 -2.05 -1.98 psaD, THAOC_24369 psaD, PSI RC subunit II  1.2  1.12  -2.71 -1.02 -1.02 1.09 -1.83 1.02 -2.17 178   TO03 lowCub TO03 lowFeb TO03 lowFeCub TO05 lowCub TO05 lowFeb TO05 lowFeCub Gene namea Protein name sol insol sol insol sol insol sol insol sol insol sol insol psaD, THAOC_24369 psaD, PSI RC subunit II  1.2  1.16  -2.59 1 -1.01 1.21 -1.88 -1.91 -1.95 psaF psaF, PSI RC subunit III  -1.2  -1.03  -2.3  1.04  -1.7 -1.86 -1.75 psaL, THAOC_24361 psaL, PSI RC subunit XI  -2.08  -1.56  -4.09  1.31  -1.56  -1.96 psaL, THAOC_24361 psaL, PSI RC subunit XI  -1.56  -1.56    -1.05  -1.76 -2.83 -2.23 PET                   THAOC_05010 fld, flavodoxin 2.95  2.32  3.82           THAOC_31152 fldA, isoform A 1.44  2.35  218.8  1.4  8.09  7.36 20.77 THAOC_25559 petF, ferredoxin 43.79  -1.46  -11.02  -1.48  -5.31  -1.21 -1.85 THAOC_36724 petH, FNR  2.47  2.71  2.88  -1.34  -1.32  -1.62 -1.83 THAOC_36724, contig_64183_1_637_+ petH, FNR    4.15  3.92        THAOC_36724 FNR 2.47  2.71  2.88  -1.34  -1.32  -1.62 -1.83 THAOC_36724, contig_64183_1_637_+ FNR 5.5   4.15   3.92         THAOC_06509 FNR -1.46  -1.27  -1.05  -1.14 -1.14 1.27  1.3 1.35 agrouped into core photosynthetic protein complexes (PSII, PSI) and proteins involved in photosynthetic electron transfer (PET)  bexpression of proteins given in fold-change as per original proteomic analysis unless in italics, then as per +EST dataset (see methods for details); bold indicates highly regulated changes in expression as defined in methods (>± 2-fold, p<0.05); underlined indicates moderately regulated changes in expression (>±1.3-fold p<0.05).  chla, chlorophylla; cyt, cytochrome; FNR, ferredoxin: NADP+ reductase; PS, photosystem; RC, reaction centre179    180  Figure 5.3 Model of the proteomic and selected physiological changes observed in the photosynthetic electron transport chain (ETC) of two strains of T. oceanica (CCMP 1003, TO03, and 1005, TO05) in response to low Cu (lowCu), low Fe (lowFe), and low Fe and Cu conditions (lowFeCu) compared to optimal metal levels (control). Depending on the strain and the limiting metal(s) treatment, acclimation results in the up- and down-regulation of different components of the ETC. (?) indicates that this ratio was only detected in one of the three biological replicates, thus it has no p-value associated; C, Chla concentration per cell (Figure 5.1); Ctrl, control; cytb6f, cytochromeb6f complex; Fdx, ferredoxin; Fld, flavodoxin; FldA, flavodoxin isoform A; FNR, ferredoxin-NADP+- reductase; P, Protein concentrations per cell (Figure 5.1); Pc, plastocyanin; PSI, photosystem I; PSII, photosystem II; , antenna absorption cross section of PSII, PSII (Figure 5.1).    181   Table 5.4: Overview of all highly regulated LHC in two strains of T. oceanica (CCMP 1003, TO03, and 1005, TO05) in response to low Cu (lowCu), low Fe (lowFe), and low Fe and Cu conditions (lowFeCu). Strain and Treatment Lhcf –  Group Ia Lhcf –  Group IIa Lhcf –  Group IIIa Lhcra Lhcxa TO03            lowCu       Lhcf8-32932 Lhcf_01208 Lhcf-01927 Lhcf-06305 Lhcf-20854 Lhcf11-09684 FCP4-04469 FCP4-16345 FCP7-08587 FCP10-05777  FCP2-06968 Lhcr11-00550     Lhcr10-35513 Lhcr13-07036 Lhcx1-09937 Lhcx1-31988 Lhcx7-08095       lowFe      Lhcf8-32932 Lhcf-01927 Lhcf-06305 Lhcf-20854 Lhcf-25270 Lhcf11-09684 FCP4-04469 FCP4-16345 FCP7-08587 FCP10-05777 Lhcr2-34573 Lhcr11-00550 Lhcr13-07034  Lhcr4-05707 Lhcr13-07036 Lhcx1-09937 Lhcx1-31988        lowFeCu  Lhcf-36248   Lhcf8-32932 Lhcf-06305 Lhcf-20854 Lhcf-20855 Lhcf11-09684    FCP2-06968 FCP4-04469 FCP4-16345 FCP10-05777 Lhcr2-34573   Lhcr3-16963 Lhcr10-35513 Lhcr11-00550 Lhcr14-05708 Lhcx1-09937 Lhcx1-31988  Lhcx7-08095 TO05            lowCu    FCP7-08587         lowFe    FCP4-18994  FCP10-05777   Lhcr13-07036        lowFeCu    FCP4-18994 Lhcr3-16963 Lhcr13-07036  abold: name of Tp homolog as per phylogenetic tree; number after name indicates THAOC_ gene model; arrows indicate up- or down-regulation (>±2-fold, p<0.05) 182  Table 5.5: Changes in stoichiometry in the ETC normalized to the change seen in cytb6f in two strains of T. oceanica (CCMP 1003, TO03, and 1005, TO05) in response to low Cu (lowCu), low Fe (lowFe), and low Fe and Cu conditions (lowFeCu).  Treatment PSII cytb6f plastocyanin PSI TO03     lowCu  1.2 1 0.6 2.3 lowFe 1.7 1 3.5 1.7 lowFeCu 1.3 1 2 1  TO05     lowCu 1 1 1.3 1 lowFe 1.2 1 1.8 1 lowFeCu 1.2 1 1.5 0.7  183  Chapter 6: Conclusion  6.1 General thoughts on elemental limitation  Sabeeha Merchant‘s group has studied the interplay between nutrient demand and supply in the green alga Chlamydomonas extensively (Merchant et al. 2006; Glaesener et al. 2013; Blaby-Haas and Merchant 2014, 2017) and has coined the terms ―metal sparing‖ and ―metal economy‖. Metal sparing refers to the reduced use of the limiting nutrient. It may consist of repressing the synthesis of non-essential proteins or macromolecules containing the limiting nutrient, or substituting the protein or macromolecule for another one (Merchant and Helmann 2012). Metal economy (Figure 6.1 A and B) is related to the concept of metal homeostasis. There is only a very narrow set of circumstances in which nutrient demand and supply are equal. Most of the time, the supply is either lower or higher than the demand. The cell has numerous ways to counteract this imbalance. In case of a limiting supply, the cell can increase nutrient acquisition capacity via an increase in transporters or a change to high-affinity transporters (e.g. HAFeT in various phytoplankton including diatoms, Maldonado et al., 2006). Furthermore, internal stores can be released (e.g. Fe stored in ferritin in Phaeodactylum (Marchetti et al. 2009b)) or, in case of metals, other non-metal containing alternative proteins can be used (one form of metal sparing: e.g. flavodoxin for Fe- containing ferredoxin, LaRoche et al., 1995). In case of a surplus of nutrients, uptake can be decreased, efflux increased, or nutrients can be stored in vacuoles or specialized proteins for later consumption. With this metal economy, the cell is able to support its maximal growth rate over an extended nutrient concentration gradient and optimize it under nutrient limitation or toxicity. 184  When we investigate adaptations to limiting conditions, we need to be aware of the physiological state of our model species. This will help when we compare the different responses of various species to metal limiting conditions. For that reason, I made extensive physiological measurements in parallel with acquiring proteomics data.  6.2 My research In this thesis, my main goal was to describe in detail the acclimation strategies used by two strains of Thalassiosira oceanica (CCMP1003 and CCMP1005) when grown under Cu-limiting conditions (Chapters 3 and 4). So far Cu-limitation in diatoms has received little attention. The uniqueness of my project is that I combined differential proteomic data with an array of physiological measurements. This enabled me to investigate the interplay between various metabolic pathways, as well as how changes in the stoichiometry of a given metabolic pathway affect cellular physiology. For example, in Cu-limited T. oceanica (CCMP 1003), I was able to document alterations in proteins within the photosynthetic apparatus, and concomitantly determine the resulting effects on photophysiology. I also revealed an intricate interaction between the N and C metabolisms, allowing T. oceanica (CCMP 1003) to counteract the excess of reducing power within the chloroplast. Namely, that nitrogen acquisition and assimilation are up-regulated with an emphasis on plastid-located, ferredoxin-dependent isoenzymes, thus helping to alleviate the proposed increased reduced plastidial ferredoxin pool. Furthermore, the differential expression of isoenzymes of both malate dehydrogenase and aspartate aminotransferase supports the previously proposed malate shunt (Prihoda et al. 2012; Bailleul et al. 2015), channelling both electrons and carbon skeletons from the chloroplast to the mitochondria. I also uncovered the modulation of glycolytic isoenzymes (located in the cytosol, 185  chloroplast and mitochondria), directing a) reducing equivalents to the mitochondria, and b) ATP as well as pyruvate (needed to resupply oxaloacetate involved in the malate shunt) to the chloroplast.  Hockin et al. (2012) showed that glycolysis is up-regulated under acute nitrogen limitation in T. pseudonana.  When I mapped these up-regulated glycolytic proteins to their target locations in T. pseudonana, I revealed a close resemblance of the directed modulation seen in TO03 under chronic Cu limitation. This suggests that compartmentalized modulation of specific isoenzymes in glycolysis might be a general first line of defence in diatoms to redistribute reducing equivalents and ATP to meet their new cellular requirements.  When I compared the response of TO03 and TO05, I showed significant differences between the two strains (intraspecific differences) in their acclimation strategies to limiting metal conditions. As a result of these distinct acclimation strategies, TO05 has higher Cu requirements and lower tolerance to Cu limitation than TO03. The impaired capability of TO05 to deal with Cu-limitation stems from its inability to reduce plastocyanin content and/or to counteract excess reducing power in the photosynthetic ETC. Instead, TO05 decreases its Cu requirement mainly by a general 50% reduction of its overall proteome. In addition, I collected the same set of physiological and differential proteomic data for T. oceanica cultures grown under Fe-limiting concentrations (a well-documented in situ environmental condition) as well as Fe-Cu co-limiting levels (a possible in situ condition in Fe-limited regions). Photosynthesis is the most important metabolic pathway in phytoplankton. Thus, I focused on comparing changes to the photosynthetic apparatus as the cells acclimated to the three metal limiting conditions. I uncovered specific metal-dependent changes in the photosynthetic apparatus and general stress-dependent ones.  186  The most important finding when comparing the three metal limiting conditions relates to the regulation of Fe- and Cu-containing proteins involved in the ETC in TO03. Namely, plastocyanin is down-regulated (by 4-fold) under lowCu, accompanied by the down-regulation of the up-stream cytb6f and PSII but not the down-stream PSI. Under low Fe, an unforeseen up-regulation of plastocyanin (by 2-fold) seems to support the efficiency of the ETC under stoichiometric changes involving a decrease in the Fe-containing cytb6f. Up-regulation of plastocyanin under these conditions was surprising as increased Cu requirements under low Fe have been attributed to increased activity of multi-Cu oxidases in the high-affinity Fe transport system (HAFeT, Maldonado et al. 2006). Given that plastocyanin is down-regulated under lowCu and up-regulated under lowFe, it is difficult to predict its regulation under lowFeCu. Based on our data, under lowFeCu, plastocyanin is somewhat down-regulated (by 1.5-fold), suggesting a dual effect of growth limitation by Cu and Fe on plastocyanin (decrease by 4-fold combined with an increase by 2-fold results in a decrease by ~ 2-fold). However, the actual down-regulation of plastocyanin under lowFeCu was slightly less (by 1.5 fold) than the expected 2-fold. This could be due to the slightly higher Cu concentrations in the lowFeCu media relative to that in lowCu (1.96 vs. < 1 nM Cu, respectively) or the process of Cu-sparing. In this process, the cell represses the synthesis of the least essential Cu-proteins (e.g. plastocyanin) and enhances the synthesis of the most essential ones (e.g. MCO). Therefore, in the lowFeCu case, we could predict an increased amount of Cu allocated to the HAFeT system to increase Fe uptake, and thus a limiting availability of Cu allocated to plastocyanin in the photosynthetic ETC.   187  6.3 Challenges of my research The two T. oceanica strains have different Fe and Cu requirements. The differential trace metal demands made it challenging to determine comparable growth conditions for culturing them. In previous studies using T. oceanica CCMP 1003 and 1005, control (replete) concentrations ranged from 10.2 to 21.4 nM for Cu in both strains, 42 nM to 8.6 M for Fe in TO03 and 1.29 M to 1.37 M for Fe in TO05 (Table 6.1). In my study, I determined that TO05 needed higher Cu concentrations (14.32 nM) to support its maximum growth rate than that in our laboratory‘s standard Cu-sufficient medium (10.2 nM Cu, Figure 6.1C). To deal with this problem, I first determined the Fe and Cu concentrations to support the maximum growth rate of TO03 and TO05, respectively. Secondly, I determined a Cu concentration that limited the growth of TO03. Thirdly, I established the comparable limiting Cu concentration for TO05, so that identical growth rate reductions were observed for TO05 and TO03 under the lowCu conditions (Figure 6.1D). To induce Fe limitation, I decided to use the same Fe concentration for both strains and focused on their response to this specific Fe-limiting condition (Figure 6.1E). Culturing phytoplankton under trace metal limiting conditions is challenging, even though every precaution to avoid contamination is taken. Comparisons among published studies clearly show different growth responses to identical trace metal limiting conditions, or identical growth rate responses to different trace metal limiting conditions. This ultimately suggests different levels of trace metal contamination in each laboratory. For example, in three TO03 studies, Fe concentrations ranged from 4 to 42 nM Fe but resulted in the same relative growth rate of ~ 85 % max (Table 6.1). Similarly, in the three TO03 studies using 42 nM Fe to induce Fe-limitation, the relative growth rates ranged from 58% max to 100% max. Kim and Price (2017) conceded that, even though Cu concentration in their media was below ICPMS detection, 188  Cu contamination was likely in their zero-Cu-media, as TO05 was able to grow. Thus, I suggest that it is critical to report growth rate reductions relative to max to compare the results of studies investigating the responses of phytoplankton to metal limitation.  The remarkable intraspecific differences that I discovered questioned whether my two strains were truly the same species. I thus compared their ITS regions and confirmed that they were the same species. Specifically, the sequenced CCMP1003 ITS region  was 99.66% identical to the sequence of CCMP1005. Further evidence of their overall matching genomes was found when I mapped their respective proteomes to both the sequenced TO05 genome and our TO03 EST database. There was no bias in either direction in either of the strains. At this point, there is no conclusive explanation for the underlying differential regulation of proteins in these two strains. Ongoing research into epigenomics and transcriptional regulation in diatoms points to transcriptional modulation strategies acquired from plants and animals, including RNA directed DNA methylation and posttranslational modifications of histones (see review by Tirichine et al. 2017). However, the exact mechanisms underlying the differential expression of proteins I observed in TO03 and TO05 remains elusive.   6.4 Implications of my findings Plastocyanin concentration plays a more important role in Thalassiosira oceanica’s response to Fe and Cu limitation than previously thought. Its up-regulation under low Fe was surprising, given the general assumption that the release of oxidized plastoquinone from the cytb6f complex is the rate-limiting step in the ETC (Rochaix 2011). This opens up the interesting question: how could an increase in plastocyanin support more efficient photosynthetic electron transport? I hypothesized that the increase in plastocyanin might support rapid oxidation of cytb6f in order to  189  (as suggested in barley, Burkey 1993) limit the possibility of cytb6f becoming an ETC bottleneck due to its reduced state. Another possibility is that the ratio between plastocyanin and its downside partner, PSI, is the more important one. Here, again, the release of the electron carrier (plastocyanin from PSI) is thought to be the rate-limiting step. Monitoring the ratio of these three ETC components (cytb6f, plastocyanin, PSI) across varying Fe, Cu and Fe-Cu co-limiting conditions might help to answer this question. The intraspecific strain differences that I described here could not have been predicted from their genomic information. This means that caution should be exercised when gathering detailed information from only one strain and extrapolating the findings to an ―open ocean population‖. Thus, we need to a) verify prediction through in situ experiments and b) broaden our laboratory experiments to include more than one strain of a species. The recent review on intraspecific strain differences in diatoms (Godhe and Rynearson 2017) also stressed the importance of including several strains in future experiments.   6.5 Outlook for future research Copper physiology in diatoms, especially in the context of co-limitation, either with Fe (Wells et al. 2005; Lelong et al. 2013) or light (Kim and Price 2017) has been acknowledged to be relevant in the ocean in recent years. I hope that the results of this thesis will promote further investigations on the interacting effects of Fe and Cu on phytoplankton growth.  As a result of my work, a number of research questions remain to be addressed. I also have some recommendations for future work. For example, when investigating metal limitation in any species, it would be beneficial to generate a growth rate curve as a function of metal concentration, as seen for TO 1005 in Kim and Price (2017) for Cu and in Bucciarelli et al. 190  (2010) for Fe. This will provide valuable information on each strain‘s ability to adapt to low metal concentrations. Furthermore, this will allow better comparability between studies. For example, if we find a direct relationship between plastocyanin levels, Cu concentrations and growth rate reduction, the corresponding physiological data collected can be more easily used in other studies. However, depending on the species investigated, other metals might also be important contributors to growth rate modifications. Wells et al. (2005) had to decrease nickel concentrations in their control media to achieve higher growth rates when investigating Fe and Cu physiology in Pseudo-nitzschia. For years, flavodoxin (FldA) alone or the expression ratio of ferredoxin / (ferredoxin + FldA) have been used as a proxy for Fe-limitation in diatoms both in the laboratory and in situ (Roche et al. 1995; LaRoche et al. 1996; McKay et al. 1999; Whitney et al. 2011; Suzuki et al. 2014; Chappell et al. 2014). A next goal would be to find a suitable proxy to determine the degree of Cu or even Fe-Cu co-limitation of laboratory and in situ phytoplankton. Several potential candidate proteins or protein ratios could be investigated. As a first step, I suggest investigating the relationship or metal sparing between plastocyanin, multicopper oxidase, chloroplast (ETC) cytc, and mitochondrial cytc oxidase. As neither MCO nor chloroplast cytc were detected in our proteomic dataset, qPCR or an immunological approach could be useful to monitor the expression levels of these proteins. As I hypothesized that the ratio of plastocyanin: cytb6f or plastocyanin:PSI could be relevant to ensure efficient electron transport in the ETC under lowFe and lowFeCu conditions, I would also monitor both cytb6f and PSI levels. Uncovering a suitable proxy for Cu limitation might be a useful tool during in situ Cu amendment experiments to determine whether an increase in phytoplankton biomass is due to a 191  toxic Cu effect on the grazers of phytoplankton or a true response in phytoplankton biomass due to their Cu limiting conditions in situ (Coale 1991; Peers et al. 2005; Semeniuk et al. 2009).  There are large areas in the open ocean in which reported dissolved Cu concentrations are as low as those that are used to induce Cu limitation in the laboratory (e.g. Jacquot and Moffett 2015; Heller and Croot 2015; Roshan and Wu 2015). However, we do not expect to find exclusively Cu-limited phytoplankton in the open ocean. This hypothesis is based on two facts. First, dissolved Cu oncentrations in the open ocean are relatively high, ranging from 0.5-3 nM Cu (Jacquot and Moffett 2015), and thus are not expected to limit phytoplankton productivity. Second, even though a few numbers of studies have suggested that low inorganic Cu2+ levels may limit primary productivity in some oceanic regions (eg. Moffet and Dupont 2007), this hypothesis assumes that only inorganic Cu can be used by phytoplankton. Recent studies have demonstrated that Cu bound to model and in situ strong organic complexes is taken up by indigenous phytoplankton (Semeniuk et al. 2009, 2015). Thus, phytoplankton are not expected to be Cu limited as they are able to access Cu within strong organic ligands from a relatively large dissolved Cu pool. I mainly envision the possibility of co-limitation of Cu and Fe in the open ocean―rather than Cu-limitation alone―given the higher Cu quotas of some oceanic phytoplankton and the higher Cu requirements documented for some Fe-limited phytoplankton (Annett et al. 2008; Guo et al. 2012), and the co-occuring low Fe concentrations in the open ocean. As suggested above, more in situ bottle, metal amendment experiments are needed to determine which phytoplankton groups might benefit from increased Cu concentrations in the ocean.  192  The projected increasing acidification and stratification of our global oceans due to climate change will alter Fe and Cu bioavailabilities. Thus, in order to predict how the phytoplankton community might react to these changing conditions, it is of importance to understand their Fe and Cu physiology. More research on a variety of phytoplankton species and strains will uncover unifying and unique phytoplankton responses to trace metal limitation that reflect genomic and metabolic diversity of the in situ population. This will be useful for understanding phytoplankton population dynamics in a changing ocean. 193  6.6 Tables and figures  Figure 6.1: Concept of how metal homeostasis on cellular level helps increase resilience towards metal fluctuations. A) The absence of controlled metal economy. A cell can only achieve maximum growth if inner demand equals outer supply. B) Controlled economy. Cellular metabolism can accommodate fluctuation in metal supply to some extent. C-E) Limitation status achieved in T. oceanica cultures. ―OLD‖ denotes the growth rate of 194  TO05 achieved in the standard Cu-concentration medium used in our lab (10.2 nM Cu), relative to the maximum growth rate achieved in my new control medium (14.3 nM Cu).  195  Table 6.1: Overview of Fe and Cu concentrations used in various studies on Fe and Cu physiology in T. oceanica and their induced changes in growth rate (%max)  TO03 TO05   Treatment [Fe]total [Cu]total µ/µmax [Fe]total [Cu]total µ/µmax Ref    Control 1.37 µM   10.2 nM  1.37µM   14.32 nM  (1) Hippmann et al., 2017   42 nM  10.2 nM      (2) Schuback et al., 2015   1.37µM  11.2 nM      (3) Guo et al., 2010  1.37 µM   10.2 nM  1.37µM   10.2 nM  (4) Annett et al., 2008   1.37µM  10.2 nM      (5) Maldonado et al., 2006       1.37 µM?  21.4 nM  (6) Peers and Price, 2006   1.29 µM  21.4 nM   1.29 µM  21.4 nM  (7) Peers et al., 2005   59 nM  10.2 nM ?      (8) Strzepek and Harrison, 2004   8.6 µM  10.2 nM ?         (9) Maldonado and Price, 2001 lowCu 1.37µM  < 1.0 nM 48% 1.37 µM   6.08 nM 65% (1) Hippmann et al., 2017     1.37 µM  1.96 nM 0% (1) Hippmann et al., 2017  1.37 µM    1.0 nM 91%     (3) Guo, 2010  1.37 µM   1.96 nM      (4) Annett et al., 2008  1.37 µM   1.96 nM 49%     (5) Maldonado et al., 2006      1.37 µM   2.5 nM 58% (6) Peers and Price, 2006      1.37 µM   1.0 nM 38% (6) Peers and Price, 2006   1.29 µM   1.0 nM 65%   1.29 µM   1.0 nM 65% (7) Peers et al., 2005 lowFe 42 nM   10.2 nM 85% 42 nM   14.32 nM 68% (1) Hippmann et al., 2017   0.13 nM   10.2 nM 32%     (2) Schuback et al., 2015   1.28 nM  11.2 nM 64%     (3) Guo et al., 2010  42 nM   10.2 nM 100% 42 nM   10.2 nM 100% (4) Annett et al., 2008  4.2 nM   10.2 nM 62%     (4) Annett et al., 2008  42 nM   10.2 nM 58% (42-95%, n=6)     (5) Maldonado et al., 2006   4 nM   21.4 nM 82%  4 nM   21.4 nM 78% (7) Peers et al., 2005   7.8 nM   10.2 nM ? 100%     (8) Strzepek and Harrison, 2004  4.7 nM  10.2 nM? 83%     (8) Strzepek and Harrison, 2004   12.9 nM   10.2 nM 1.27 - 1.8 dd-1 (n=5)     (9) Maldonado and Price, 2001 lowFeCu 42 nM   1.96 nM 56% 42 nM   6.08 nM 43% (1) Hippmann et al., 2017  42 nM  < 1.0 nM 0%    (1) Hippmann et al., 2017   1.28 nM   2.96 nM 60%     (3) Guo, 2010  42 nM   1.96 nM 82% 42 nM   1.96 nM 68% (4) Annett et al., 2008  4.2 nM   1.96 nM 65%     (4) Annett et al., 2008  42 nM   1 nM 70%     (5) Maldonado et al., 2006   4 nM   1.0 nM 41% 40.8 nM   1.0 nM 66% (7) Peers et al., 2005  4 nM   0.0 nM 0%  4 nM   1.0 nM 44% (7) Peers et al., 2005 196   References Adhikari, U. 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TO03, T. oceanica (CCMP1003); TO05, T. oceanica (CCMP1005); TP, T. pseudonana; TW, T. weisfloggii   234  A.2 ITS sequence alignment  TO1003_R-       cggaaggatcatttcacaccatccaacttcatccccgggaaacgacgagcggagctcgag TO1005_18S      cggaaggatcatttcacaccatccaacttcatccccgggaaacgacgagcggagctcgag                 ************************************************************  TO1003_R-       cgccgatacactggccgggcttgtcctcctaacataatcataaaaaacaaaaccaggtgc TO1005_18S      cgccgatacactggccgggcttgtcctcctaacataatcataaaaaacaaaaccaggtgc                 ************************************************************  TO1003_R-       tactaccagcaatggatgtagtgccgcccaatactaagtgacaaaataactgaacataac TO1005_18S      tactaccagcaatggatgtagtgccgcccaatactaagtgacaaaataactgaacataac                 ************************************************************  TO1003_R-       ggtctcttggagcgagagctcctctaggctgcacaattatttacaactttcagcaatgga TO1005_18S      ggtctcttggagcgagagctcctctaggctgcacaattatttacaactttcagcaatgga                 ************************************************************  TO1003_R-       tgtcttggatcccacaacgatgaagaacgcagcgaactgcgatacgtaatacgaactgca TO1005_18S      tgtcttggatcccacaacgatgaagaacgcagcgaactgcgatacgtaatacgaactgca                 ************************************************************  TO1003_R-       gaacctcgtgaatcatcaaaattttgaacgcacattgcgcttttgggtaattcccaagag TO1005_18S      gaacctcgtgaatcatcaaaattttgaacgcacattgcgcttttgggtaattcccaagag                 ************************************************************  TO1003_R-       catgcttatctgagtgtccctgaaccccactcagccactgctgcggttgcaagaccccgg TO1005_18S      catgcttatctgagtgtccctgaaccccactcagccactgctgcggttgcaagactccgg                 *******************************************************.****  TO1003_R-       agtgagctggactgtgacggtgcaaggccctgccgagctccgttcaaatggttagcctgt TO1005_18S      agtgagctggactgtgacggtgcaaggccctgccgagctccgttcaaatggttagcctgt                 ************************************************************  TO1003_R-       gcactgacccctcctggtgagcgcagtgatggacttggtagatgcactgatgtgtgtttg TO1005_18S      gcactgacccctcctggtgagcgcagtgatggacttggtagatgcactgatgtgtgtttg                 ************************************************************  TO1003_R-       agtcttgacccgtgcgagcctaggtaggacggtgcctcatggtgcatgccgtgtgcatgc TO1005_18S      agtcttgacccgtgcgagcctaggtaggacggtgcctcatggtgcatgccgtgtgtatgc                 *******************************************************.****  TO1003_R-       ccatccaatcctatacttggatctcagatta TO1005_18S      ccatccaatcctatacttggatctcagatta                 *******************************    Figure A.2: CLUSTAL format alignment by MAFFT (v7.372) of the two T. oceanica ITS sequences (CCMP 1003 and CCMP 1005). Of the 597 nucleotides, only two (yellow) are different (99.66% identical)  235  A.3 ITS phylogenetic tree   Figure A.3: Phylogenetic tree of 14 diatom ITS regions (10 Thalassiosira oceanica, 2 T. pseudonana, and 2 T. weisfloggii), supporting the close genetic relationship between the T. oceanica strains used in this thesis (CCMP1003 and CCMP1005).  Only bootstrap values (100 replicates) above 90 are shown.  236  A.4 ITS sequences used for phylogenetic tree  Table A.1: Sequence information used to generate phylogenetic tree of various diatom ITS sequences Strain abbreviation  genbank ITS sequence accession number  TO999  EF134953.1  TO1001 clone A  EF134955.1  TO1001 clone B  EF134954.1  TO1004 clone1  EF208794.1  TO1004 clone3  EF208795.1  TO1006 clone1  EF362631.1  TO1006 clone2  EF362632.1  TO1006 clone3  EF362633.1  TP1015  EF208792.1  TP1335  EF208793.1  TW1047  EF208779.1  TW1051  EF208787.1     237  A.5 Summary table of effects of low Cu on physiological parameters in Thalassiosira oceanica (CCMP 1003 and CCMP 1005)   Table A.2: Effect of chronic copper limitation on physiology in two strains of the open ocean diatom Thalassiosira oceanica: CCMP 1003 and CCMP 1005 abbreviated to TO03 and TO05, respectively. Arrows indicate if the response to low Cu statistically increases ↑ or decreases ↓. Shown are means with standard errors for parameters derived from three biological replicates (^ indicates measurements have only been done on two of the three biological triplicates, see raw data in Table A.2). Low Cu results in bold indicate statistically significant differences compared to the respective control treatment. Stars (*, **, ***) indicate the level of significance of a 2-way ANOVA with post-hoc interaction analysis (see methods for details). The right panel indicates whether the physiological response differs between the two strains; ↓ and ↑ mean the result is significantly lower or higher in TO03 compared to TO05. Note that there is hardly any difference in their physiology under their respective replete metal concentrations. However, when Cu limited, roughly half of the tested parameters show significantly different results.238   TO 1003 TO 1005  TO03  TO05  TO03 TO05  Parameter tested control   low Cu control   low Cu  control lowCu [Fe] (nmol) 1370.00   1370.00 1370.00   1370.00      [Cu] (nmol) 10.20   0.20 14.32   6.08      µ (dd-1) 1.52 ± 0.06  ↓ 0.73 ± 0.10 *** 1.82 ± 0.02 ↓ 1.19 ± 0.03 ***  ↓ ↓ µ (d-1) 1.05 ± 0.07 ↓ 0.51 ± 0.10*** 1.26 ± 0.02 ↓ 0.82 ± 0.03***  ↓ ↓ µ (% µmax) 100.50 ± 4.21  ↓ 47.86 ± 6.45 **** 99.82 ± 1.19 ↓ 65.46 ± 1.61 ****      cell diameter (μm) 5.22 ± 0.05  ↓ 4.72 ± 0.04 **** 5.42 ± 0.16 ↓ 4.82 ± 0.23 ****      cell vol (fl) 74.68 ± 1.93  ↓ 55.16 ± 1.43 **** 83.69 ± 7.74 ↓ 59.54 ± 8.78 ****    . cell surface area (μm^2) 85.73 ± 1.52 ↓ 70.1 ± 1.23 ** 92.34 ± 5.65 ↓ 73.31 ± 7.13 **      cell SA / V (μm^2 / fL) 1.15 ± 0.01 ↑ 1.27 ± 0.01 ** 1.11 ± 0.03 ↑ 1.25 ± 0.06 **      Chl a conc (pg/cell) 0.30 ± 0.01    0.34 ± 0.05  0.35 ± 0.05   0.23 ± 0.05 .    . Chl a conc (fg/fl) cell vol 4.34 ± 0.11  ↑ 7.40 ± 0.46 *** 4.28 ± 0.80   3.79 ± 0.41     ↑ Fv/Fm (A.U.) 0.60 ± 0.01^ ↓ 0.29 ± 0.01 **** 0.61 ± 0.01   0.58 ± 0.02 .  ↓ ↓ Sigma (Å / RCII) 584.40 ± 4.95^ ↑ 764.01 ± 12.50 *** 579.15 ± 3.58^   572.76 ± 24.94^    ↑ PQ-Pool size (mol PQ / mol Qb) 4.34 ± 0.47^   5.98 ± 0.35 . 5.65 ± 0.18^   5.12 ± 0.19^      sigma ' @ growth irradiance (Å / RCII) 583.09 ± 18.30 ↑ 664.04 ± 10.95 ** 585 ± 3.23^   575.61 ± 2.47^    ↑ Fq'/Fv' @ growth irradiance (A.U.) 0.7 ± 0.04 ↓ 0.58 ± 0.02 * 0.69 ± 0.02^   0.66 ± 0.01^      Fv'/Fm' @ growth irradiance (A.U.) 0.61 ± 0.01 ↓ 0.29 ± 0.01 **** 0.60 ± 0.01^   0.62 ± 0.00^    ↓ Fq'/Fm' @ growth irradiance (A.U.) 0.43 ± 0.03 ↓ 0.16 ± 0.01 *** 0.41 ± 0.01^   0.41 ± 0.01^    ↓ ETR @ growth irradiance (mol e-/RCII * s) 398.33 ± 31.51   373.65 ± 16.77 390.88 ± 7.34^   368.45 ± 5.97^      NPQ (nsv) @ growth irradiance 0.63 ± 0.03 ↑ 2.43 ± 0.15 **** 0.66 ± 0.01^   0.59 ± 0.01^    ↑ ETR - PE curve - alpha (mol e-/RCII)/(µmol quanta / m^2 * s) 3.49 ± 0.11 ↑ 4.04 ± 0.11 * 3.29 ± 0.01^   3.46 ± 0.03^    ↑ ETR - PE curve - pmax pmax (mol e-/RCII * s) 493.72 ± 54.98   425.67 ± 23.75  502.11 ± 23.46^   431.17 ± 4.69^      ETR - PE curve - ek (µmol quanta / m^2 * s) 140.79 ± 12.48 ↓ 105.34 ± 5.07 * 152.55 ± 7.65^   124.6 ± 0.031^      Conversion factor (ETR / 14C uptake)  192.24 ± 22.23 ↑ 406.85 ± 20.43 * 117.55 ± 61.89^   111.99 ± 56.03^    ↑ 14C - PvsE -  curve - α (g C / g Chla * h)/(µmol quanta / m^2 * s) 0.0151 ± 1.69E-03  ↓ 5.66E-03 ± 1.75E-03 * 1.41E-02 ± 3.32E-03   2.06E-02 ± 4.70E-03     ↓ 14C - PvsE -  curve - eK (µmol quanta / m^2 * s) 251.97 ± 23.73  ↓ 172.00 ± 2.51 ** 249.05 ± 10.46   223.04 ± 20.76     ↓ 14C - PvsE -  curve - Pmax (g C / g Chla * h) 3.89 ± 0.81  ↓ 0.97 ± 0.29 ** 3.46 ± 0.70   4.40 ± 0.54     ↓ 14C uptake @ 155 uEinstein (g C / g Chla * h )  2.09 ± 0.27  ↓ 0.70 ± 0.21 * 1.94 ± 0.44   2.71 ± 0.53     ↓ 239   TO 1003 TO 1005  TO03  TO05  TO03 TO05  Parameter tested control   low Cu control   low Cu  control lowCu FeDFB uptake (zmol / μm^2 * h) 4.23 ± 1.62    1.03 ± 0.24  5.47 ± 2.83   2.01 ± 2.00 .      Gross O2 Prod (mol O2 / mol Chla * h) 366.59 ± 28.16 ↓ 101.02 ± 26.53*** 362.04 ± 33.22   295.11 ± 58.21    . Gross O2 Prod (μmol O2 / cell * h) 1.23E-07 ± 1.15E-08 ↓ 3.60E-08 ± 4.27E-09**** 1.39E-07 ± 1.04E-08 ↓ 6.94E-08 ± 1.99E-09**      Respiration (mol O2 / mol Chla * h) 0.07 ± 0.03   0.04 ± 0.01 0.05 ± 0.01   0.06 ± 0.00    . Respiration (μmol O2 / cell * h) 1.16E-08 ± 2.32E-9   9.10E-09 ± 1.16E-9 1.79E-08 ± 3.79E-9   1.60E-08 ± 3.76E-9      14C uptake @ 155 uE (mol C / mol Chla * h )  0.16 ± 0.03 ↓ 0.05 ± 0.02* 0.15 ± 0.04   0.20 ± 0.04      Protein content (pg/cell) 12.61 ± 1.47   15.44 ± 2.32 31.15 ± 2.18 ↓ 15.36 ± 1.22****  ↑   AOX activity 48.84 ± 0.9    53.60 ± 7.05  60.56 ± 6.23   63.69 ± 7.12       . p-value< 0.1          * p-value< 0.05          ** p-value< 0.01          *** p-value< 0.001          **** p-value< 0.0001                     240  A.6 Table of raw physiological data for all three biological replicates of each treatment (control and low Cu) and strain (CCMP 1003 and CCMP 1005)  Table A.3: Physiological raw data of all three biological replicates for both strains (CCMP 1003 and CCMP 1005) and treatments (control and lowCu)  Culture TO1003 control TO1003 lowCu TO1005 control TO1005 lowCu µ (dd-1) 1.43 1.48 1.65 0.59 0.92 0.67 1.85 1.77 1.83 1.19 1.14 1.24 µ (d-1) 0.99 1.03 1.14 0.41 0.64 0.47 1.28 1.23 1.27 0.82 0.79 0.86 % µ/µmax 94.26 97.64 108.24 38.89 60.37 44.32 101.41 97.50 100.56 65.34 62.74 68.30 cell size (µm) 5.13 5.27 5.27 4.64 4.76 4.77 5.30 5.74 5.21 4.59 4.59 5.28 cell vol (fl/cell) 70.83 76.73 76.48 52.29 56.53 56.66 77.92 99.01 74.15 50.79 50.72 77.10 cell SA (µm2) 82.68 87.25 87.25 67.64 71.18 71.48 88.25 103.51 85.28 66.19 66.19 87.58 Chla per cell (pg/ cell) 0.29 0.31 0.30 0.26 0.33 0.44 0.46 0.32 0.28 0.22 0.15 0.32 Chla per cell vol (fg/ fL) 4.52 4.35 4.14 6.49 7.92 7.80 5.85 3.27 3.71 4.28 2.97 4.12 FvFm 0.59 0.60   0.27 0.29 0.31 0.63 0.59 0.62 0.53 0.61 0.59 Sig 590.00 578.00   786.00 763.00 743.00   575.00 584.00 542.00 603.00   PQ_Siz 4.90 3.80   6.70 5.50 5.80   5.90 5.40 4.90 5.30   sig' 609.97 548.13 591.17 685.03 659.00 648.11   588.95 581.05 578.64 572.58   F'q/F'v 0.71 0.64 0.77 0.61 0.59 0.55   0.67 0.71 0.67 0.65   F'v/F'm 0.60 0.60 0.64 0.27 0.29 0.32   0.60 0.61 0.63 0.62   F'q/F'm 0.43 0.38 0.49 0.16 0.17 0.17   0.40 0.43 0.43 0.41   ETR @ growth irradiance 417.45 336.77 440.78 400.61 377.47 342.89   381.89 399.87 375.76 361.14   NPQ(nsv) 0.66 0.66 0.57 2.70 2.46 2.16   0.67 0.65 0.58 0.61   ETR alpha  3.47 3.31 3.70 4.25 3.91 3.96   3.31 3.28 3.51 3.42   ETR pmax  542.03 384.02 555.13 456.14 442.02 378.88   473.39 530.84 436.92 425.44   ETR ek 156.20 116.10 150.10 107.27 113.01 95.76   143.18 161.93 124.64 124.57   241  Culture TO1003 control TO1003 lowCu TO1005 control TO1005 lowCu Conversion factor @ growth irradiance 155.98 188.12 232.64 1513.34 431.88 381.83   209.90 142.78 164.41 171.56   14C per Chla alpha 0.02 0.01 0.01 0.00 0.01 0.01 0.01 0.01 0.02 0.02 0.02 0.03 14C per Chla ek 297.00 216.47 242.44 176.98 170.12 168.91 269.71 241.60 235.84 248.97 238.15 181.99 14C per Chla pmax 5.50 2.89 3.28 0.38 1.25 1.27 2.42 3.16 4.79 4.07 3.67 5.45 14C per Chla @ growth irradiance 2.64 1.78 1.85 0.27 0.90 0.92 1.25 1.79 2.76 2.25 2.10 3.77 FeDFB (zmol um 2 h)  3.00 2.20 7.40 1.40 0.60 1.10 6.87 0.01 9.52 4.01 0.01   Gross Productivity (mol O2/ ( mol Chla  * h) ) 370.23 413.44 316.10 153.16 83.46 66.43 296.68 405.11 384.32 274.01 404.81 206.50 Gross Productivity / cell  (mol O2 / cell * h)  0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Respiration (mol O2 / mol Chla * h) 22.40 34.84 45.92 34.79 18.53 21.04 25.73 42.29 82.41 63.88 57.45 64.01 Respiration (µmol O2 / cell * h) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 14C (molC / mol Chla * h) 0.14 0.13 0.21 0.02 0.07 0.07 0.09 0.13 0.22 0.17 0.16 0.28 cellular protein (pg / cell) 11.71 15.48 10.64 15.97 11.18 19.17 28.97 33.33   16.58 14.14   AOX activity (%) 47.05 49.74 49.73 53.14 41.62 66.04 57.93 72.43 51.33 50.77 75.35 64.94 Cell Density 1.65E+05 1.10E+05 3.12E+05 7.99E+04 1.11E+05 9.67E+04 1.37E+05 1.21E+05 9.95E+04 3.16E+05 2.95E+05 2.50E+05    242  A.7 Differential expression of all 48 predicted LHCs in T. oceanica (CCMP 1003 and CCMP 1005) across all four datasets Table A.4: Expression of all 48 predicted LHC in TO03 and TO05 across all four datasets Expression of all 48 predicted LHCs in T. oceanica (CCMP 1003 and CCMP 1005) across all four datasets     differential expressionh) cladea gene nameb closest homolog in Tpc  exprd TO03 I (original)e sigg TO03 II (+EST)f sigg TO05 I (original)e sigg TO05 II (+EST)f sigg 17531 THAOC_01290 Tp17531 y     -1.06  -1.05  Lhcf - Group I THAOC_02510 TpLhcf y -1.18  -1.20  -1.13  -1.17   THAOC_06305 TpLhcf y -4.2  -10.34 y -1.21  -1.23   THAOC_17964 TpLhcf y 1.19  1.06  -1.07  -1.10   THAOC_17964, contig_83951_1_204_+ TpLhcf y       -1.07   THAOC_20854 TpLhcf y -5.56 y -5.56 y -1.28  -1.27   THAOC_20855 TpLhcf y -1.63  -1.63  -1.21  -1.20   THAOC_25270 TpLhcf y 1.23  1.24  -1.13  -1.06   THAOC_25270, contig_119270_60_376_- TpLhcf y       -1.32   THAOC_25270, contig_123884_1_219_- TpLhcf y   -1.70       THAOC_25270, contig_70472_60_300_- TpLhcf y   1.52    -1.07   THAOC_25963 TpLhcf           THAOC_33280 TpLhcf           THAOC_36248 TpLhcf y -1.38  -1.31  -1.09  -1.12   THAOC_37647 TpLhcf y. -2.04 y        THAOC_08131 TpLhcf8 y     -1.32  -1.31   THAOC_08131, contig_91916_1_273_+ TpLhcf8 y       1.04   THAOC_32932 TpLhcf8 y -2.12 y -2.16 y -1.15  -1.15   THAOC_01207 TpLhcf           THAOC_01208 TpLhcf y -2.79 y -2.81 y -1.13  -1.10   THAOC_16761 TpLhcf           THAOC_25269 TpLhcf           THAOC_25594 TpLhcf          Lhcf - Group II THAOC_09684 TpLhcf11 y 4.83 y 3.89 y -1.23  -1.24  Lhcf - Group III THAOC_05777 TpFCP10 y 2.40 y 2.32 y -1.16  -1.16   THAOC_18180 TpFCP11 y 2.02    1.09  -1.16   THAOC_06968 TpFCP2 y -2.52 y -2.52 y -1.09  -1.10   THAOC_04469 TpFCP4 y 2.06 y 2.16  -1.26  -1.24   THAOC_16345 TpFCP4 y 2.68 y 2.68 y -1.30  -1.29   THAOC_18994 TpFCP4           THAOC_08587 TpFCP7 y 2.79 y 2.90  2.07 y 2.00   THAOC_16412 TpFCP9 y 1.97  1.97  -1.09  -1.09  243      differential expressionh) cladea gene nameb closest homolog in Tpc  exprd TO03 I (original)e sigg TO03 II (+EST)f sigg TO05 I (original)e sigg TO05 II (+EST)f sigg Lhcr THAOC_06811 TpLhcr           THAOC_35171 TpLhcr1 y 3.48  3.48  -1.39  -1.37   THAOC_35518 TpLhcr10 y -2.55 y -2.56 y -1.04  1.03   THAOC_00550 TpLhcr11 y 2.75  2.39 y -1.14  -1.19   THAOC_07034 TpLhcr13 y 1.66  1.63  -1.11  -1.12   THAOC_07036 TpLhcr13 y -6.57 y -6.32 y -1.19  -1.12   THAOC_05708 TpLhcr14 y -1.06  1.04  1.13  1.12   THAOC_34573 TpLhcr2 y 4.44  4.42  -1.32  -1.44   THAOC_16963 TpLhcr3 y -1.56  -1.45  -1.13  -1.14   THAOC_05707 TpLhcr4 y 1.24  1.24  -1.21  -1.15  Lhcx THAOC_09937 TpLhcx1 y 3.29 y   -1.222     THAOC_12734 TpLhcx1           THAOC_32497 TpLhcx1           THAOC_31988 TpLhcx1  y   3.29 y   -1.22   THAOC_23429 TpLhcx4           THAOC_14109 TpLhcx5           THAOC_31495 TpLhcx6           THAOC_08095 TpLhcx7 y 1.14  2.76 y -1.15  -1.13   THAOC_08095, contig_119183_41_788_+ TpLhcx7 y   -1.41      Lhcz THAOC_09862 TpLhcr5           THAOC_09825 TpLhcr8           THAOC_37813 TpFCP5 y     -1.46  -1.44  aclades as per phylogenetic tree in Figure 4 bcontent in bold indicates significantly differentially expressed proteins in TO03, as defined in methods; blue, significantly down-regulated; red, significantly up-regulated cbold names indicate proteins are found expressed in at least one dataset d―y‖ indicates proteins are found expressed in at least one dataset eoriginal dataset, peptides from LC-MS/MS were mapped against database of predicted proteins of TO05 genome,  fEST dataset, peptides from LC-MS/MS were mapped against predicted proteins of combined database consisting of both TO05 genome and our own TO03 Transcriptome (assembled EST contigs) g―y‖ indicates that the differential expression in the column left of it is significantly regulated as per methods hdifferential expression given in fold-change, bold content indicates significantly regulated as per methods Tp, Thalassiosira pseudonana; expr, expressed; sig, significant differential expression   244  A.8 Differential expression of all identified proteins involved in the photosynthetic ETC in T. oceanica (CCMP 1003 and CCMP 1005) across all four datasets  Table A.5: Differential expression of all identified proteins involved in photosynthetic ETC across all datasets    differential expression  space differential expression  Part_of gene name (NCBI)a Protein Descriptiona TO03 I (original) a,b TO05 I (original) a,b where encoded space TO03 II (EST) a,c TO05 II (EST) c  PS II THAOC_34020 psb27-like, involved in Mn cluster formation  1.73 1 Nuc  1.73 1.05  psbA psbA, photosystem II protein D1 -1.92 -1.03 C  -1.63 1.07  psbB psbB, photosystem II CP47 reaction center protein -1.61 -1.1 C  -1.65 -1.09  psbC, THAOC_26185 psbC, photosystem II CP43 reaction center protein -2.1 1.05 C   1.01  psbD, THAOC_24371 psbD, photosystem II D2 protein -2.1 -1.01 C  -2.01   psbE, THAOC_24363 psbE, cytochrome b559 subunit alpha -2.25 -1.21 C  -2.27 -1.20  psbH psbH, photosystem II reaction center protein H  -1.49 C   -1.56  THAOC_03193 psbO, Mn-stabilizing protein -1.41  Nuc  -1.40 1.04  THAOC_15373 psbP, oxygen-evolving enhancer protein 2 (OEE2)  -1.03 Nuc   1.04  THAOC_08500 psbQ, oxygen-evolving enhancer protein 3 (OEE3) -2.96  Nuc  -3.94 1.37  THAOC_09685 psbU-like, small extrinsic protein 1.62 1.32 Nuc  1.63 1.32  psbV, THAOC_30541 psbV, cytochrome c-550  1.07 1.18 C  1.03 1.18  psbY psbY, photosystem II protein Y  -1.11 C   -1.18          PET   petA petA, cytochrome f -1.63 -1.11 C  -1.65   petB, THAOC_26188 petB, cytochrome b6 -2.33  C  -2.33 -1.14  THAOC_33417 petC, Fe-S subunit (Rieske protein) -1.51  Nuc   -1.48  petD, THAOC_24366 petD, cytochrome b6-f complex subunit 4  1.1 C   -1.15  THAOC_29732 petE, plastocyanin -4.41 1.31 Nuc  -4.71 1.52          245     differential expression  space differential expression  Part_of gene name (NCBI)a Protein Descriptiona TO03 I (original) a,b TO05 I (original) a,b where encoded space TO03 II (EST) a,c TO05 II (EST) c  PS I psaA psaA, photosystem I P700 chlorophyll a apoprotein A1 -1.21 1.14 C  -1.17 1.09  psaB psaB, photosystem I P700 chlorophyll a apoprotein A2 1.11 1.01 C  1.46 1.02  psaC psaC, photosystem I iron-sulfur center  -1.18 1.2 C  1.15   psaD, THAOC_24369 psaD, photosystem I reaction centre subunit II 1.2 1 C  1.20 -1.02  psaF psaF, photosystem I reaction centre subunit III -1.2 1.04 C  -1.14 1.01  THAOC_24361 psaL, photosystem I reaction centre subunit XI -1.56 -1.02 C  -2.08 1.31           PET   THAOC_25559 petF, ferredoxin 43.79 -1.1 Nuc  43.79 -2.52  THAOC_36724 petH, FNR - ferredoxin--NADP+ reductase  2.47  Nuc  2.44 -1.09  THAOC_36724,  contig_64183_1_637_+  petH, FNR - ferredoxin--NADP+ reductase      5.50    THAOC_06509 petH - FNR - ferredoxin--NADP+ reductase   -1.13 Nuc  -1.49 -1.17 a) content in bold indicates significantly differentially expressed proteins in TO03, as defined in methods b) significant differential expression in original dataset as defined in methods, given in fold-change c) significant differential expression in EST dataset as defined in methods, given in fold-change     246  A.9 Overview of the number of identified proteins in the four different proteomic datasets  Table A.6 Overview of the number of identified proteins in the four different proteomic datasets.  TO03 (original)e TO03 (EST)f TO03II (%) TO05 (original)e TO05 (EST)f TO05II (%) Diff TO03 (EST and original) % Diff TO05 (EST) and (original) % peptides identified by LC-MS/MSa 4,055 4,055   8,967 8,967        proteins identifiedb 730 844 100% 1431 1622 100% 114 16% 191 13% proteins mapped to TO05 genomec  705 84%  1327 82%     proteins mapped to TO03 ESTd   139 16%   295 18%                               proteins mapped to TO03 ESTd   139 100%   295 100%         TO03 ESTs mapped to TO05 chloroplast proteins   2 1%   1 0%      TO03 ESTs mapped to TO05 nuclear proteins  100 72%  207 70%     TO03 ESTs mapped to TO05 genome (excluding predicted proteins)   28 20%  69 23%     TO03 ESTs NOT mapped to any published TO5 sequences   9 6%   18 6%         apeptides of the initial LC-MS/Ms were used in searches against  ball proteins identified call proteins identified that map to the TO05 genome dall proteins identified that map to the TO03 transcriptome eoriginal dataset in which peptides were mapped against database containing predicted proteins of TO05 genome fsecond dataset in which peptides were mapped against a combined database of TO05 proteins and predicted proteins in TO03 transcriptome247  Appendix B  −   Supplementary material for Chapter 4: Proteomic Analysis Revealed the Intricate Interaction Between the Carbon and Nitrogen Metabolisms in Copper-Limited T. oceanica  B.1 The Calvin-Benson-Bassham cycle - Differential expression of proteins involved in Calvin-Benson-Bassham cycle in T. oceanica CCMP 1003 (TO03) and 1005 (TO05) grown under Cu-limiting conditions   Table B.1 Differential expression and predicted cellular location of proteins involved in the Calvin-Benson-Bassham cycle in TO03 and TO05 cultured in low Cu conditions vs. control.  Calvin-Benson-Bassham Cycle  TO03c TO03c    original +EST original +EST Gene namea Diatom homologb Name sol insol sol insol sol insol sol insol cbbX, THAOC_24360 Tp24123, Pt 36139 cbbX, rubisco expression protein -2.25   -2.25   -1.34 -1.03 -1.28 -1.08 rbcS   rbcS,  ribulose-bisphosphate carboxylase small chain 1.12 1.23 1.14 1.2 -1.63 -1.49 -1.62 -1.44 rbcL   rbcL, ribulose-bisphosphate carboxylase large chain 1.03 1.33 1.01 1.39 -1.09 -1.34 -1.08 -1.38 THAOC_07617 Tp270304 PGK, phosphoglycerate kinase 1.54 -1.08     2.98 -1.71     THAOC_07617 contig_117075_1_571_+        6.85       1.02   THAOC_07617 contig_55250_138_1396_+        1.89 -1.16     1.17 1.05 THAOC_13085 Tp270231 GAPDH, glyceraldehyde 3-phosphate dehydrogenase -1.29 -4.16 -1.43 -4 1.19 -1.11 1.17 -1.09 248  Calvin-Benson-Bassham Cycle  TO03c TO03c    original +EST original +EST Gene namea Diatom homologb Name sol insol sol insol sol insol sol insol THAOC_02438 Tp30380, Pt54738, Fc274452 TPI2, triose-phosphate isomerase 3.3   3.13   -1.06   -1.12   THAOC_35826 Tp8590 TPI1, triose-phosphate isomerase 1.94   1.91   1   1   THAOC_32006 Tp8590 TPI3, triose-phosphate isomerase 1.45   1.43   1.09   1.14   THAOC_00388 Tp270396, PtBd825 FBA II, fructose-bisphosphate aldolase class-II 1.36       -1.23       THAOC_12069 Tp21748, Pt22993 FBA II, fructose-bisphosphate aldolase class-II 2   1.99   1.15   1.2   THAOC_02112 no Tp, Pt51289 FBA I, fructose-bisphosphate aldolase class-I -1.73   -1.73   -1.18 1.08 -1.35 1.18 THAOC_31290   RPI, ribose-5-phosphate-isomerase -1.12   -1.12   -1.09   -1.09   THAOC_31290 contig_19155_129_974_+        4.9         THAOC_09031   RPE, ribulose-5-phosphate epimerase 1.22   -1.19   -1.1   -1.1    aGene name as per Lommer et al., 2012; if identified based on EST data, the respective contig identifier is given as well  busing blastp, this is the closest diatom homolog  caverage fold-change in Cu-limited compared to control cultures, bold indicates highly differentially expressed (> ± 2-fold, p<0.05), underlined indicates differential expression ratio of ±1.3- to 2-fold (p<0.05). original, original proteomics dataset in which peptides where mapped to the TO05 genome; +EST, second proteomics dataset in which peptides where mapped to both the TO05 genome and our EST library; sol, soluble protein fraction; insol, insoluble protein fraction    249  B.2 Putative triose-phosphate transporters and their differential expression in T. oceanica CCMP 1003 (TO03) and 1005 (TO05) grown under Cu-limiting conditions   Table B.2: Proteins with triose-phosphate transporter PFAM and their expression in TO03 and TO05 in response to Cu limitation vs. control  Putative triose-P-transporters  TO03c TO05c    original +EST original +EST Gene namea Diatom homologb TargetP/ASAFind sol insol sol insol sol insol sol insol Cytosol                   THAOC_31045 triose-phosphate transporter _(3) / Not plastid           -1.08   -1.07 THAOC_33538 triose-phosphate transporter _(2) / Not plastid                 Chloroplast           THAOC_05244 triose-phosphate transporter S(2) / Plastid       -1.82   -1.06   -1.06 THAOC_11183 triose-phosphate transporter S(1) / Plastid                 Mitochondria           THAOC_35890 triose-phosphate transporter M(3) / Not plastid           1.36   1.36 Signal Sequence           THAOC_04741 triose-phosphate transporter S(5) / Not plastid   -1.31   -1.31   1.34   1.35 THAOC_13820 triose-phosphate transporter S(3) / Not plastid                 aGene name as per Lommer et al., 2012; if identified based on EST data, the respective contig identifier is given as well  busing blastp, this is the closest diatom homolog  caverage fold-change in Cu-limited compared to control cultures, bold indicates highly differentially expressed (> ± 2-fold, p<0.05), underlined indicates differential expression ratio of ±1.3- to 2-fold (p<0.05). original, original proteomics dataset in which peptides where mapped to the TO05 genome; +EST, second proteomics dataset in which peptides where mapped to both the TO05 genome and our EST library; sol, soluble protein fraction; insol, insoluble protein fraction    250  B.3 Glycolysis - Differential expression of proteins involved in glycolysis in T. oceanica CCMP 1003 (TO03) and 1005 (TO05) grown under Cu-limiting conditions   Table B.3: Differential expression and predicted cellular location of proteins involved in glycolysis in TO03 and TO05 cultured in low Cu conditions vs. control  Glycolysis  TO03c TO05c    original +EST original +EST Gene namea Diatom homologb Name sol insol sol insol sol insol sol insol Cytosol           THAOC_06412 Pt50444 PGM, phosphoglucomutase -1.79   -3.17   -1.01 1.13 -1.01 1.13 THAOC_06412 contig_11427_139_3309_+        -1.71           THAOC_25871 Tp38266, Pt23924  GPI, glucose-6-phosphate isomerase -1.27   -1.26   1.45   1.34   THAOC_16559 Tp22213 PFK, phosphofructokinase -1.8   -1.75   -1.03   1.02   THAOC_05494 Tp256250 F2BP, fructose-1-6-bisphosphatase 1.23   1.21   -1.18   -1.21   THAOC_24977 Tp270288, Pt29014 FBA II, fructose-bisphosphate aldolase class-II -1.18   -1.22   -1.32   -1.18   THAOC_24978 Tp11761 FBA I, fructose-bisphosphate aldolase class-I -1.6   -1.53   -1.06 -2.37 -1.09 -2.26 THAOC_24978 contig_128882_77_667_-        -2.79          THAOC_21096 Tp30380, Pt54738 TPI2, triose-phosphate isomerase -1.01   1.04   1.13   1.25   THAOC_21095 Pt54738 TPI2, triose-phosphate isomerase -1.25   -1.49   1.01   -1.07   THAOC_34937 Tp40393 PK, pyruvate kinase 1.58   1.58   1.28 -1.61 1.25 -1.61            251  Glycolysis  TO03c TO05c    original +EST original +EST Gene namea Diatom homologb Name sol insol sol insol sol insol sol insol Chloroplast           THAOC_12069 Tp21748, Pt22993 FBA II, Fructose-bisphosphate aldolase class-II 2   1.99   1.15   1.2   THAOC_00388 Tp270396, PtBd825 FBA II, Fructose-bisphosphate aldolase class-II 1.36       -1.23       THAOC_02112 no Tp, Pt51289 FBA I, Fructose-bisphosphate aldolase class-I -1.73   -1.73   -1.18 1.08 -1.35 1.18 THAOC_02438 Tp30380, Pt54738, Fc274452 TPI2, triose-phosphate isomerase 3.3   3.13   -1.06   -1.12   THAOC_35826 Tp8590 TPI1, triose-phosphate isomerase 1.94   1.91   1   1   THAOC_32006 Tp8590 TPI3, triose-phosphate isomerase 1.45   1.43   1.09   1.14   THAOC_13085 Tp270231 GAPD1, glyceraldehyde 3-phosphate dehydrogenase -1.29 -4.16 -1.43 -4 1.19 -1.11 1.17 -1.09 THAOC_07617 Tp270304 PGK, phosphoglycerate kinase 1.54 -1.08     2.98 -1.71     THAOC_07617 contig_117075_1_571_+        6.85       1.02   THAOC_07617 contig_55250_138_1396_+        1.89 -1.16     1.17 1.05 THAOC_21902 Tp28350  PGAM2, phosphoglycerate mutase -2.25   -2.25   -1.46   -1.19   Mitochondria           THAOC_33331 Tp28334 GAPDH, glyceraldehyde 3-phosphate dehydrogenase 2 -2.15 1.75   1.42 1.19 1.29 1.32 THAOC_33331 contig_105735_1_207_+        3.79     1.55 1.3 THAOC_33331 contig_105693_1_204_+              1.34   THAOC_07275 Tp28241 GAPDH, glyceraldehyde 3-phosphate dehydrogenase 1.06   1.04   1.14 1.06 1.07 1.09 THAOC_08963 Tp27850 PGAM, phosphoglycerate mutase -1.03   -1.06   1.12   1.06   THAOC_34936 Tp40391 ENO, Enolase 1.44   1.54   1.2 1.04 1.26 1.04 THAOC_07097 Pt561725 PK, pyruvate kinase -1.31   -1.31   -1.24 -1.34 -1.25 -1.34 252  Glycolysis  TO03c TO05c    original +EST original +EST Gene namea Diatom homologb Name sol insol sol insol sol insol sol insol THAOC_10009 Pt34120, Fc267632, Tp38807 EDA, 2-keto-3-deoxy phosphogluconate aldolase 1.06   1.06   1.26   1.26 1.32 aGene name as per Lommer et al., 2012; if identified based on EST data, the respective contig identifier is given as well  busing blastp, this is the closest diatom homolog  caverage fold-change in Cu-limited compared to control cultures, bold indicates highly differentially expressed (> ± 2-fold, p<0.05), underlined indicates differential expression ratio of ±1.3- to 2-fold (p<0.05). original, original proteomics dataset in which peptides where mapped to the TO05 genome; +EST, second proteomics dataset in which peptides where mapped to both the TO05 genome and our EST library; sol, soluble protein fraction; insol, insoluble protein fraction     253  B.4 Citrate Cycle (TCA) - Differential expression of proteins involved in glycolysis in T. oceanica CCMP 1003 (TO03) and 1005 (TO05) grown under Cu-limiting conditions   Table B.4: Differential expression and predicted cellular location of proteins involved in the tricarboxylic acid (TCA) / citrate cycle in TO03 and TO05 cultured in low Cu conditions vs. control. (for diagram, see   Figure 4.4) Citrate / TCA Cycle  TO03c TO05c    original +EST original +EST NCBI protein identifiera Diatom homologb Name sol insol sol insol sol insol sol insol Mitochondria           THAOC_19912 Tp11411, Pt30145 CS, citrate synthase 1.06   -1.05   1.15   1.07 1.14 THAOC_20545 Tp268965 ACO, aconitase hydratase -4.73   -4.17   1.26   1.3   THAOC_37807 Tp21640, Pt14762, Fc168262 IDH, isocitrate/ isopropylmalate dehydrogenase -1.64   -1.66   1.14   1.14   THAOC_34595 Tp1456, Pt45017 IDH1, isocitrate dehydrogenase (monomeric) -3.02   -2.79   1.41   1.45   THAOC_34595 contig_117709_1_1294_+         -5.5   1.41   -1.23   THAOC_28027 Tp269718, Pt29016 OGD1, 2-oxoglutarate dehydrogenase E1 component 1.08   1.08   1.2   1.03   THAOC_28027 contig_117377_84_1088_-                 1.34   THAOC_13425 Fc226444 SUCLA, succinate CoA synthetase, beta chain 1.29   1.29   1.43   1.57   THAOC_24310 Tp24123, Pt 36139 FH, fumarate hydratase, class II -1.09   -1.09   -1.23   -1.18   THAOC_03405 Tp20726, Pt51297 MDH1, malate dehydrogenase 1.6   1.54   1.15 1.05 1.15 1.05 aGene name as per Lommer et al., 2012; if identified based on EST data, the respective contig identifier is given as well  254  busing blastp, this is the closest diatom homolog  caverage fold-change in Cu-limited compared to control cultures, bold indicates highly differentially expressed (> ± 2-fold, p<0.05), underlined indicates differential expression ratio of ±1.3- to 2-fold (p<0.05). original, original proteomics dataset in which peptides where mapped to the TO05 genome; +EST, second proteomics dataset in which peptides where mapped to both the TO05 genome and our EST library; sol, soluble protein fraction; insol, insoluble protein fraction    255  B.5 Urea and nitrogen metabolism - Differential expression of proteins involved in urea and nitrogen metabolism in T. oceanica CCMP 1003 (TO03) and 1005 (TO05) grown under Cu-limiting conditions   Table B.5: Differential expression and predicted cellular location of proteins involved in nitrogen metabolism including the urea cycle in TO03 and TO05 cultured in low Cu conditions vs. control.  Nitrogen Metabolism  TO03c TO05c    original +EST original +EST Gene namea Diatom homologb Name sol insol sol insol sol insol sol insol Transporters            THAOC_04919 Tp, Pt, Fc NRT, nitrate/nitrite transporter   11   11         THAOC_01055 Tp, Pt, Fc NRT, nitrate/nitrite transporter            1.69     THAOC_01056 Tp, Pt, Fc NRT, nitrate/nitrite transporter            1.36    1.56 THAOC_31656 Tp24250, Pt20424, Fc214292 URT, Na/urea-polyamine transporter   6.93   6.93   1.66   1.32 THAOC_07247 Tp13996, Pt27877, Fc275907 AMT, ammonium transporter   -1.39   -1.39   -1.07   -1.11 THAOC_00240 Tp3393, Pt46427, Fc262069 NiRT, formate/nitrite transporter   -1.14   -1.14   1.29   1.14 Cytosol           THAOC_34460 Tp25299, Pt54983, Fc206583 NR, nitrate reductase 1.49   1.45   -1.05 -1.32 -1.05 -1.32 256  Nitrogen Metabolism  TO03c TO05c    original +EST original +EST Gene namea Diatom homologb Name sol insol sol insol sol insol sol insol THAOC_37170 Tp42719, Pt, Fc207847 AsuS, argininosuccinate synthase 2.59   -1.28   1.39   1.37   THAOC_12984 Tp29075, Pt34526, Fc262171 AsL, argininosuccinate lyase -1.51   -1.51   1.08   1.12   THAOC_04380 Tp260953, Pt260953, Fc187231 OCD, ornithine cyclodeaminase 1.24   1.24   1.04   1.22   THAOC_22587 Tp34595, Pt25183, Fc190753 ATCase, aspartate carbamoyltransferase -1.05   -1.05   1.2   1.2   THAOC_06254 Tp38359, Pt13951, Fc192073 GDH, glutamate dehydrogenase, NADP dependent 2.09   2.77   1.38   1.5   THAOC_06254 contig_96116_1_202_-         1.07       1.34   Chloroplast           THAOC_00016 Tp262125, Pt27757, Fc185143 NR, nitrite reductase 1.34   1.34   1.07   1.08   THAOC_02363 Tp270365, Pt, Fc172194 Fe-NiR, nitrite/sulfite reductase ferredoxin-like half-domain 2.27   2.32   -1.13   -1.1   THAOC_35252 Tp26941, Pt, Fc200565 NAD(P)H-NiR, nitrite reductase -1.63   -1.65   1.06   1.07   THAOC_31900 Tp26051, Pt51092,   Fc228642 GSII, glutamine synthetase 1.71 1.76 1.59 1.81 1.27 1.46 1.28 1.5 THAOC_31900 contig_127491_284_455_+        3.83      257  Nitrogen Metabolism  TO03c TO05c    original +EST original +EST Gene namea Diatom homologb Name sol insol sol insol sol insol sol insol THAOC_13288 Tp269900, Pt, Fc225787 GOGAT, glutamate synthase 1.55   1.57   1.05   1.1   THAOC_16827 Tp31394, Pt22909, Fc273803 AAT, aspartate aminotransferase -2.33   -2.33   1.04   1.03   THAOC_32466 Tp270136, Pt50577, Fc277974 argD, n-acetylornithine aminotransferase -3.01   -2.97   1.09   1.09   Mitochondria           THAOC_17688 Tp36208, Pt, Fc224733 GDCT, glycine decarboxylase t-protein -1.23   -1.23           THAOC_36273 Tp39799, Pt22187, Fc276117 GDCP, glycine decarboxylase p-protein -1.86   -1.43   -1.17   -1.17   THAOC_01996 Tp40323, Pt24195, Fc169332 unCPS (CPSase III), carbamoyl-phosphate synthase -1.14   -1.11   -1.06   -1.05   THAOC_05385 Tp997, Pt30514, Fc268473 OTC, ornithine carbamoyltransferase 1.67   1.67   -1.05   -1.05   THAOC_06032 Tp270138, Pt22357, Fc277211 GSIII, glutamine synthetase -5.29 -2.62 -7.01 -2.62 1.34 1.16 1.18   THAOC_06032 contig_17263_419_2602_-                 1.37 1.22 THAOC_15049 Tp31424, Pt23059, Fc170472 AAT, aspartate aminotransferase 3.57   3.57           aGene name as per Lommer et al., 2012; if identified based on EST data, the respective contig identifier is given as well  busing blastp, this is the closest diatom homolog  258  caverage fold-change in Cu-limited compared to control cultures, bold indicates highly differentially expressed (> ± 2-fold, p<0.05), underlined indicates differential expression ratio of ±1.3- to 2-fold (p<0.05). original, original proteomics dataset in which peptides where mapped to the TO05 genome; +EST, second proteomics dataset in which peptides where mapped to both the TO05 genome and our EST library; sol, soluble protein fraction; insol, insoluble protein fraction    259   B.6 Glutathione metabolism - Differential expression of proteins involved in glutathione metabolism in T. oceanica CCMP 1003 (TO03) and 1005 (TO05) grown under Cu-limiting conditions  Table B.6: Differential expression and predicted cellular location of proteins involved in glutathione metabolism in TO03 and TO05 cultured in low Cu conditions vs. control. Glutathione  TO03c TO05c    original +EST original +EST Gene namea Diatom homologb Name sol insol sol insol sol insol sol insol Cytosol           THAOC_29696 Tp262753 , Pt47395 , Fc173923 APX, ascorbate peroxidase -1.17   -1.17   -1.27   -1.31   THAOC_37364 Tp38724 , Pt___ , Fc___, Ehux444342 APX, ascorbate peroxidase 1.42   1.37   1.11 1.75 1.08 1.75 THAOC_05931 Tp267987 , Pt___ , Fc172515 CYS2, cysteine synthase -1.01   -1.01   1.1   1.06   THAOC_05931, contig_111591_1_205_-                 1.16   THAOC_05931, contig_68507_1_219_+         -1.26       -1.02   THAOC_23355 Tp13064 , Pt27240 , Fc291479 GCS, γ-glutamyl-cysteine synthase 4.53   4.53           THAOC_09062 Tp___ , Pt__ , Fc___, (Hydra, polyp) GST, glutathione-S-transferase 7.28   12.4           260  Glutathione  TO03c TO05c    original +EST original +EST Gene namea Diatom homologb Name sol insol sol insol sol insol sol insol THAOC_24526 Tp269866  Pt37658 , Fc196320 GST, glutathione-S-transferase         2.11   2.33 0.03 THAOC_10112 Tp40669 , Pt49037 , Fc267154 NiSOD, nickel-dependent superoxide dismutase 1.04 1.38 1.15 1.38 1.08 1.25 1.15 1.24 THAOC_14269 Tp___ , Pt49037 , Fc267154  NiSOD, nickel-dependent superoxide dismutase -2.68   -2.79   1.54 1.15 1.54 1.15 THAOC_05213 Tp41697 , Pt56471 , Fc231810 TXN, thioredoxin 2.62   4.16   1.33   1.35   THAOC_06254 Tp38359, Pt13951, Fc192073 GDH, glutamate dehydrogenase, NADP dependent 2.09   2.77   1.38   1.5   THAOC_06254, contig_96116_1_202_-         1.07      1.34   Chloroplast           THAOC_27524 Tp270338 , Pt_bd542 , Fc168185 CYS, cysteine synthase 2.43   2.5   1.38 -1.44 1.28   THAOC_10442 Tp267987 , Pt__ , Fc172515 CYS2, cysteine synthase 1.51       -1.3   -1.32   THAOC_10442, contig_37614_97_1300_+         1.61      1.6   THAOC_19132 Tp23356 , Pt36641 , Fc229440 DHAR, dehydroascorbate reductase                 THAOC_13288 Tp269900 , Pt , Fc GOGAT, glutamate synthase 1.55   1.57   1.05   1.1   THAOC_07268 Tp26457 , Pt__ , Fc228584 GR, glutathione reductase 2.47   2.4   1.16   1.34   261  Glutathione  TO03c TO05c    original +EST original +EST Gene namea Diatom homologb Name sol insol sol insol sol insol sol insol THAOC_07269 Tp11178 , Pt , Fc259160, Ehux58907 GRX, glutaredoxin -1.3   -1.3   1.1   1.1   THAOC_07269, contig_70896_7_411_-         -1.4       1.01   THAOC_18234 Tp270300 , Pt9035 , Fc225878 GRX, glutaredoxin -1.29   -1.4   1.01   -1.24   THAOC_04546 Tp29212 , Pt25876 , Fc291481 GSS, glutathione synthetase 1.48   1.48   -1.18   -1.27   THAOC_08875   GST, glutathione-S-transferase         1.29   1.29   THAOC_02860 Tp32874 , Pt12583 , Fc 269011 MnSOD, Mn/Fe binding superoxide dismutase 1.77   1.77   1.07   1.27   THAOC_31425 Tp270230 , Pt46280 , Fc221387 TXN, thioredoxin 1.47   1.47   1.54   1.64   Mitochondria           THAOC_02323 Tpbd718 , Pt38124 , Fc182589 GRX, glutaredoxin 1.01   1.01   1.05   -1.11   THAOC_13865 Tp19991 , Pt , Fc235520 TXN, thioredoxin 1.79   1.77   1.27   1.26   aGene name as per Lommer et al., 2012; if identified based on EST data, the respective contig identifier is given as well  busing blastp, this is the closest diatom homolog  caverage fold-change in Cu-limited compared to control cultures, bold indicates highly differentially expressed (> ± 2-fold, p<0.05), underlined indicates differential expression ratio of ±1.3- to 2-fold (p<0.05). original, original proteomics dataset in which peptides where mapped to the TO05 genome; +EST, second proteomics dataset in which peptides where mapped to both the TO05 genome and our EST library; sol, soluble protein fraction; insol, insoluble protein fraction   262  B.7 Malate Shunt - Differential expression of proteins involved in the putative malate shunt in T. oceanica CCMP 1003 (TO03) and 1005 (TO05) grown under Cu-limiting conditions  Table B.7: Differential expression and predicted cellular location of proteins involved in the putative malate shunt in TO03 and TO05 cultured in low Cu conditions vs. control.  Malate-Shunt  TO03c TO05c    original +EST original +EST Gene namea Diatom homologb Name sol insol sol insol sol insol sol insol Transporter           THAOC_27515 Tp26366, Pt23908, Fc180618 malate- 2-oxoglutarate antiporter           1.24   1.24 THAOC_25255 Tp16746, Pt__, Fc226900 malate- 2-oxoglutarate antiporter           1.09   1.09 Mitochondria                     THAOC_03405 Tp20726, Pt51297 MDH1, malate dehydrogenase 1.6   1.54   1.15 1.05 1.15 1.05 THAOC_19570 no Tp homolog, Pt30519, Fc183259 PC(1), pyruvate carboxylase -1.63   -1.63   -1.14   -1.1   THAOC_15049 Tp31424, Pt23059, Fc170472 AAT, aspartate aminotransferase 3.57   3.57           THAOC_37124, contig_65096_132_669_+ Tp34030, Pt56501, Fc277202 ME, malic enzyme           -1.15     Chloroplast           THAOC_30817 Tp25953, Fc291625 MDH2, malate dehydrogenase  2.64   2.64           THAOC_31413 Tp269908, Pt49339, Fc186955 PC(2), pyruvate carboxylase 2.64   2.7   1.02   1.01   263  Malate-Shunt  TO03c TO05c    original +EST original +EST Gene namea Diatom homologb Name sol insol sol insol sol insol sol insol THAOC_31413 contig_117322_1_422_-         2.43   1.02   1.01   THAOC_16827 Tp31394, Pt22909, Fc273803 AAT, aspartate aminotransferase -2.33   -2.33   1.04   1.03   aGene name as per Lommer et al., 2012; if identified based on EST data, the respective contig identifier is given as well  busing blastp, this is the closest diatom homolog  caverage fold-change in Cu-limited compared to control cultures, bold indicates highly differentially expressed (> ± 2-fold, p<0.05), underlined indicates differential expression ratio of ±1.3- to 2-fold (p<0.05). original, original proteomics dataset in which peptides where mapped to the TO05 genome; +EST, second proteomics dataset in which peptides where mapped to both the TO05 genome and our EST library; sol, soluble protein fraction; insol, insoluble protein fraction     264  B.8 Pyruvate Hub - Differential expression of proteins involved in the pyruvate hub in T. oceanica CCMP 1003 (TO03) and 1005 (TO05) grown under Cu-limiting conditions  Table B.8: Differential expression and predicted cellular location of proteins involved in pyruvate metabolism in TO03 and TO05 cultured in low Cu conditions vs. control.  Pyruvate-HUB  TO03c TO05c    original +EST original +EST Gene namea Diatom homologb Name sol insol sol insol sol insol sol insol Chloroplast           THAOC_31413 Tp269908, Pt49339, Fc186955 PC, pyruvate carboxylase 2.64   2.7   1.02   1.01   THAOC_31413 contig_117322_1_422_-         2.43           THAOC_30817 Tp25953, Fc291625 MDH, malate dehydrogenase 2.64   2.64           THAOC_34740 Tp16169, Pt20360, Fc263399 PDH-E1, pyruvate dehydrogenase-E1 component -1.12   -1.14   -1.06   -1.07   THAOC_19934 Tp24399, Pt30113, Fc186500 DLDH, dihydrolipoamide dehydrogenase -1.23   -1.4   -1.18 1.1 -1.08 1.56 Cytosol           THAOC_34937 Tp 40393, Pt49098 PK, pyruvate kinase 1.58   1.58   1.28 -1.61 1.25 -1.61 THAOC_00276 Tp34543, Pt 51136, Fc259279 PEPC, phosphoenolpyruvate carboxylase -4.26       1.04 -1.17     265  Pyruvate-HUB  TO03c TO05c    original +EST original +EST Gene namea Diatom homologb Name sol insol sol insol sol insol sol insol THAOC_08200 Tp8778, Pt20183 PDH-E1, pyruvate dehydrogenase-E1 component -1.29 1.48 -1.29 1.48 1.37 1.22 -1.07 1.21 THAOC_03499 Tp547, Pt, Fc166615 PDH-E2, pyruvatedehydrogenase E2 component (dihydrolipoamideacetyltransferase) 0.29   0.06   0.26 0.3 0.33 0.36 THAOC_33506 Tp3353, Pt50804, Fc274468 LDH, L-lactate dehydrogenase   -3.62   -3.62   1.06   1.03 THAOC_05626 Tp34187, PT22974, FC239808 ACAS, acetyl-CoA synthase -1.3   -1.3   1.04 1.25 1.07 1.35 Mitochondria           THAOC_07097 Tp31810, Pt56172 PK, pyruvate kinase -1.31   -1.31   -1.24 -1.34 -1.25 -1.34 THAOC_33731 Tp268546, Pt27976, Fc241035 PEPC, phosphoenolpyruvate carboxylase -1.13   -1.13   1.39   1.37   THAOC_19570 Pt30519, Fc183259 PC, pyruvate carboxylase -1.63   -1.63   -1.14   -1.1   THAOC_03405 Tp20726, Pt51297 MDH, malate dehydrogenase 1.6   1.54   1.15 1.05 1.15 1.05 THAOC_30825 Tp36716, Pt26432, Fc271375 DLDH, dihydrolipoamide dehydrogenase -1.4   -1.4   -1.2 1.27 -1.2 1.42 aGene name as per Lommer et al., 2012; if identified based on EST data, the respective contig identifier is given as well  busing blastp, this is the closest diatom homolog  caverage fold-change in Cu-limited compared to control cultures, bold indicates highly differentially expressed (> ± 2-fold, p<0.05), underlined indicates differential expression ratio of ±1.3- to 2-fold (p<0.05). original, original proteomics dataset in which peptides where mapped to the TO05 genome; +EST, second proteomics dataset in which peptides where mapped to both the TO05 genome and our EST library; sol, soluble protein fraction; insol, insoluble protein fraction    266  B.9 Respiration - Differential expression of proteins involved in respiration in T. oceanica CCMP 1003 (TO03) and 1005 (TO05) grown under Cu-limiting conditions  Table B.9: Differential expression and predicted cellular location of proteins involved in respiration in TO03 and TO05 cultured in low Cu conditions vs. control.  Respiration  TO03c TO05c    original +EST original +EST Gene namea Diatom homologb Name sol insol sol insol sol insol sol insol THAOC_05862   NAD(P)H dehydrogenase (ubiquinone), alpha/beta subcomplex I 1.91   1.91   1.21 1.42 1.47 1.48 THAOC_04072   ubiquinol-cytochrome c reductase subunit VII   -1.42   -1.34 0 1.24   1.27 THAOC_04852   cytochrome c oxidase subunit IV   1.52   1.52 0 2.06   2.06 THAOC_22544   cytochrome c oxidase subunit Vb -2.21 2.59 -2.21 2.59 -1.19 2.1 -1.19 1.84 THAOC_12811   cytochrome c oxidase subunit VIb -1.15 4.03 -1.15 4.03 -1.12   -1.12   aGene name as per Lommer et al., 2012; if identified based on EST data, the respective contig identifier is given as well  busing blastp, this is the closest diatom homolog  caverage fold-change in Cu-limited compared to control cultures, bold indicates highly differentially expressed (> ± 2-fold, p<0.05), underlined indicates differential expression ratio of ±1.3- to 2-fold (p<0.05). original, original proteomics dataset in which peptides where mapped to the TO05 genome; +EST, second proteomics dataset in which peptides where mapped to both the TO05 genome and our EST library; sol, soluble protein fraction; insol, insoluble protein fraction    267  Appendix C  −   Supplementary material for Chapter 4: Comparative Proteomic Analysis of the Photosynthetic Apparatus of Copper and/or Iron Limited T. oceanica  C.1 Intraspecific differences in the effects of chronic metal limitation on physiology in two strains of the open ocean diatom Thalassiosira oceanica.    268  Table C.1 Intraspecific differences in the effects of chronic metal limitation on physiology in 2 strains of the open ocean diatom Thalassiosira oceanica. Arrows (―↓‖ and ―↑‖ ) indicate a significantly lower or higher response in TO03 compared to TO05 (p<0.05). Note that only three parameters −absolute growth rate, Fv/Fm, and total cellular protein− are significantly different in the control, whereas responses differ greatly under the three metal limiting conditions. ―∙‖ indicates p<0.1  TO03  TO05  TO03 TO05  TO03  TO05  TO03 TO05  Treatment control lowCu lowFe lowFeCu [Fe] (nmol)         [Cu] (nmol)         µ (dd-1) ↓ ↓     µ (d-1) ↓ ↓     µ (% µmax)     ↑ . cell diameter (μm)     ↑   Chl a conc (pg/cell)   . ↑   Chl a conc (fg/fl) cell vol   ↑ ↑   Fv/Fm (A.U.) ↓ ↓   ↓ Sigma (Å / RCII)   ↑ ↓ ↑ PQ-Pool size (mol PQ / mol Qb)         sigma ' @ growth irradiance (Å / RCII)   ↑   ↓ Fq'/Fv' @ growth irradiance (A.U.)       ↑ Fv'/Fm' @ growth irradiance (A.U.)   ↓   ↓ Fq'/Fm' @ growth irradiance (A.U.)   ↓   ↓ ETR @ growth irradiance (mol e-/RCII * s)         NPQ (nsv) @ growth irradiance   ↑   ↑ ETR - PE curve - alpha (mol e-/RCII)/(µmol quanta / m^2 * s)   ↑     ETR - PE curve - pmax pmax (mol e-/RCII * s)         ETR - PE curve - ek (µmol quanta / m^2 * s)       . Conversion factor (ETR / 14C uptake)    ↑     14C - PvsE -  curve - α (g C / g Chla * h)/(µmol quanta / m^2 * s)   ↓     14C - PvsE -  curve - eK (µmol quanta / m^2 * s)   ↓ ↓ . 14C - PvsE -  curve - Pmax (g C / g Chla * h)   ↓     14C uptake @ 155 uEinstein (g C / g Chla * h )    ↓     Gross O2 Prod (mol O2 / mol Chla * h)   .     Respiration (mol O2 / mol Chla * h)   .     Respiration (μmol O2 / cell * h)     .   AOX activity (%)         protein concentration (pg/cell ) ↓   ↑ ↑ 269  C.2 Effect of all three low metal treatments (lowCu, lowFe, lowFeCu) on physiology in the open ocean diatom Thalassiosira oceanica (CCMP 1003, TO03).  Table C.2: Effect of all three low metal treatments (lowCu, lowFe, lowFeCu) on physiology in the open ocean diatom Thalassiosira oceanica (CCMP 1003, TO03). Arrows indicate if the response to low Cu statistically increases ↑ or decreases ↓ compared to the control. Shown are means with standard errors for parameters derived from three biological replicates (^ indicates measurements have only been done on two of the three biological triplicates, see raw data). Results in bold indicate statistically significant differences compared to the respective control treatment. Stars (., *, **, ***, ****) indicate the level of significance of a 2-way ANOVA with post-hoc interaction analysis (see methods for details).    TO03 Treatment control   low Cu   lowFe   low FeCu [Fe] (nmol) 1370.00   1370.00   12.5   12.5 [Cu] (nmol) 10.20   0.20   10.20   1.96 µ (dd-1) 1.52 ± 0.06  ↓ 0.73 ± 0.10 *** ↓ 1.28 ±0.03* ↓ 0.86 ±0.08**** µ (d-1) 1.05 ± 0.07 ↓ 0.51 ± 0.10*** ↓ 0.89 ±0.02* ↓ 0.59 ±0.05**** µ (% µmax) 100.50 ± 4.21  ↓ 47.86 ± 6.45 **** ↓ 84.04 ±1.98* ↓ 56.17 ±4.91**** cell diameter (μm) 5.22 ± 0.05  ↓ 4.72 ± 0.04 ****   5.36 ±0.02 ↓ 4.7 ±0.03** Chl a conc (pg/cell) 0.30 ± 0.01    0.34 ± 0.05    0.37 ±0.01 ↓ 0.16 ±0.01** Chl a conc (fg/fl) cell vol 4.34 ± 0.11  ↑ 7.40 ± 0.46 ***   4.61 ±0.04 ↓ 3.02 ±0.17* Fv/Fm (A.U.) 0.60 ± 0.01^ ↓ 0.29 ± 0.01 **** ↓ 0.35 ±0.03**** ↓ 0.28 ±0.02**** Sigma (Å / RCII) 584.40 ± 4.95^ ↑ 764.01 ± 12.50 ***   606 ±21.94 ↑ 796.67 ±28.04**** PQ-Pool size (mol PQ / mol Qb) 4.34 ± 0.47^   5.98 ± 0.35 .   2.9 ±1.48   5.67 ±0.77 sigma ' @ growth irradiance (Å / RCII) 583.09 ± 18.30 ↑ 664.04 ± 10.95 **       549.79 ±9.1 Fq'/Fv' @ growth irradiance (A.U.) 0.7 ± 0.04 ↓ 0.58 ± 0.02 *     ↑ 0.81 ±0.01* Fv'/Fm' @ growth irradiance (A.U.) 0.61 ± 0.01 ↓ 0.29 ± 0.01 ****     ↓ 0.2 ±0.01**** Fq'/Fm' @ growth irradiance (A.U.) 0.43 ± 0.03 ↓ 0.16 ± 0.01 ***     ↓ 0.17 ±0.01**** ETR @ growth irradiance (mol e-/RCII * s) 398.33 ± 31.51   373.65 ± 16.77       431.34 ±8.44 270   TO03 Treatment control   low Cu   lowFe   low FeCu NPQ (nsv) @ growth irradiance 0.63 ± 0.03 ↑ 2.43 ± 0.15 ****     ↑ 3.93 ±0.27**** ETR - PE curve - alpha (mol e-/RCII)/(µmol quanta / m^2 * s) 3.49 ± 0.11 ↑ 4.04 ± 0.11 *       3.72 ±0.03. ETR - PE curve - pmax pmax (mol e-/RCII * s) 493.72 ± 54.98   425.67 ± 23.75        585.25 ±38.13. ETR - PE curve - ek (µmol quanta / m^2 * s) 140.79 ± 12.48 ↓ 105.34 ± 5.07 *       157.18 ±10.3 Conversion factor (ETR / 14C uptake)  192.24 ± 22.23 ↑ 406.85 ± 20.43 *     ↑ 378.09 ±32.2* 14C - PvsE -  curve - α (g C / g Chla * h)/(µmol quanta / m^2 * s) 0.0151 ± 1.69E-03  ↓ 5.66E-03 ± 1.75E-03 *   0.02 ±0   0.01 ±0. 14C - PvsE -  curve - eK (µmol quanta / m^2 * s) 251.97 ± 23.73  ↓ 172.00 ± 2.51 ** ↓ 162.51 ±3.9*** ↓ 201.03 ±6.51* 14C - PvsE -  curve - Pmax (g C / g Chla * h) 3.89 ± 0.81  ↓ 0.97 ± 0.29 **   2.59 ±0.19 ↓ 1.82 ±0.13** 14C uptake @ 155 uEinstein (g C / g Chla * h )  2.09 ± 0.27  ↓ 0.70 ± 0.21 *   1.91 ±0.12 ↓ 1.18 ±0.07* Gross O2 Prod (mol O2 / mol Chla * h) 366.59 ± 28.16 ↓ 101.02 ± 26.53*** ↓ 210.53 ±36.49* ↓ 258.21 ±37.02** Respiration (mol O2 / mol Chla * h) 0.07 ± 0.03   0.04 ± 0.01   0.08 ±0.02 ↓ 0.06 ±0.02* Respiration (μmol O2 / cell * h) 1.16E-08 ± 2.32E-9   9.10E-09 ± 1.16E-9   0 ±0.   0 ±0 AOX activity 48.84 ± 0.9    53.60 ± 7.05    68.55 ±0.79   63.45 ±6.01 protein concentration (pg/cell ) 12.61 ± 1.47   15.44 ± 2.32 ↑ 23.21 ±5.14* ↑ 31.46 ±1.57**** . p-value< 0.1  * p-value< 0.5  ** p-value< 0.01  *** p-value< 0.001  **** p-value< 0.0001      271  C.3 Effect of all three low metal treatments (lowCu, lowFe, lowFeCu) on physiology in the open ocean diatom Thalassiosira oceanica (CCMP 1005, TO05).  Table C.3: Effect of all three low metal treatments (lowCu, lowFe, lowFeCu) on physiology in the open ocean diatom Thalassiosira oceanica(CCMP 1005, TO05). Arrows indicate if the response to low Cu statistically increases ↑ or decreases ↓ compared to the control. Shown are means with standard errors for parameters derived from three biological replicates (^ indicates measurements have only been done on two of the three biological triplicates, see raw data). Results in bold indicate statistically significant differences compared to the respective control treatment. Stars (., *, **, ***, ****) indicate the level of significance of a 2-way ANOVA with post-hoc interaction analysis (see methods for details).  TO05 Treatment control   low Cu   lowFe   low FeCu [Fe] (nmol) 1370.00  1370.00 ↓ 12.5 ↓ 12.5 [Cu] (nmol) 14.32   6.08 ↑ 14.32 ↑ 6.08 µ (dd-1) 1.82 ± 0.02 ↓ 1.19 ± 0.03 *** ↓ 1.24 ±0.08**** ↓ 0.77 ±0.08**** µ (d-1) 1.26 ± 0.02 ↓ 0.82 ± 0.03*** ↓ 0.86 ±0.06**** ↓ 0.54 ±0.05**** µ (% µmax) 99.82 ± 1.19 ↓ 65.46 ± 1.61 **** ↓ 67.96 ±4.4*** ↓ 42.46 ±4.25**** cell diameter (μm) 5.42 ± 0.16 ↓ 4.82 ± 0.23 **** ↓ 4.92 ±0.03* ↓ 4.56 ±0.03**** Chl a conc (pg/cell) 0.35 ± 0.05  0.23 ± 0.05 . ↓ 0.15 ±0** ↓ 0.15 ±0*** Chl a conc (fg/fl) cell vol 4.28 ± 0.80   3.79 ± 0.41  ↓ 2.32 ±0.02** ↓ 2.95 ±0.03* Fv/Fm (A.U.) 0.61 ± 0.01  0.58 ± 0.02 . ↓ 0.5 ±0.05* ↓ 0.47 ±0.02** Sigma (Å / RCII) 579.15 ± 3.58^   572.76 ± 24.94^ ↑ 695 ±7.35** ↑ 706 ±10.6*** PQ-Pool size (mol PQ / mol Qb) 5.65 ± 0.18^  5.12 ± 0.19^  4.25 ±1.18 ↓ 2.9 ±0.17* sigma ' @ growth irradiance (Å / RCII) 585 ± 3.23^   575.61 ± 2.47^ ↑ 760.92 ±48.58*** ↑ 763.33 ±8.57**** Fq'/Fv' @ growth irradiance (A.U.) 0.69 ± 0.02^  0.66 ± 0.01^  0.56 ±0.06. ↓ 0.57 ±0.01* Fv'/Fm' @ growth irradiance (A.U.) 0.60 ± 0.01^   0.62 ± 0.00^ ↓ 0.47 ±0.05** ↓ 0.47 ±0.02** Fq'/Fm' @ growth irradiance (A.U.) 0.41 ± 0.01^  0.41 ± 0.01^ ↓ 0.27 ±0.06** ↓ 0.27 ±0.02** ETR @ growth irradiance (mol e-/RCII * s) 390.88 ± 7.34^   368.45 ± 5.97^   409.66 ±16.65   416.48 ±6.32 NPQ (nsv) @ growth irradiance 0.66 ± 0.01^  0.59 ± 0.01^  1.15 ±0.22  1.15 ±0.1 272   TO05 Treatment control   low Cu   lowFe   low FeCu ETR - PE curve - alpha (mol e-/RCII)/(µmol quanta / m^2 * s) 3.29 ± 0.01^   3.46 ± 0.03^ ↑ 3.93 ±0.11** ↑ 3.91 ±0.04*** ETR - PE curve - pmax pmax (mol e-/RCII * s) 502.11 ± 23.46^  431.17 ± 4.69^  491.99 ±22.08  500.07 ±10.97 ETR - PE curve - ek (µmol quanta / m^2 * s) 152.55 ± 7.65^   124.6 ± 0.031^   125.15 ±2.14   127.76 ±1.93. Conversion factor (ETR / 14C uptake)  117.55 ± 61.89^  111.99 ± 56.03^  132.5 ±66.25  238.43 ±9.45 14C - PvsE -  curve - α (g C / g Chla * h)/(µmol quanta / m^2 * s) 1.41E-02 ± 3.32E-03   2.06E-02 ± 4.70E-03    0.02 ±0   0.01 ±0 14C - PvsE -  curve - eK (µmol quanta / m^2 * s) 249.05 ± 10.46  223.04 ± 20.76   229 ±5.47 ↓ 162.82 ±4.52*** 14C - PvsE -  curve - Pmax (g C / g Chla * h) 3.46 ± 0.70   4.40 ± 0.54    4 ±0.52   2.38 ±0.09 14C uptake @ 155 uEinstein (g C / g Chla * h )  1.94 ± 0.44  2.71 ± 0.53   2.35 ±0.27  1.76 ±0.09 Gross O2 Prod (mol O2 / mol Chla * h) 362.04 ± 33.22   295.11 ± 58.21   284.81 ±17.91   352.28 ±28.73 Respiration (mol O2 / mol Chla * h) 0.05 ± 0.01  0.06 ± 0.00  0.04 ±0.02  0.07 ±0.01 Respiration (μmol O2 / cell * h) 1.79E-08 ± 3.79E-9   1.60E-08 ± 3.76E-9   0 ±0   0 ±0. AOX activity 60.56 ± 6.23  63.69 ± 7.12   59.2 ±6.25  81.43 ±20.89 protein concentration (pg/cell ) 31.15 ± 2.18 ↓ 15.36 ± 1.22**** ↓ 7.97 ±1.43*** ↓ 7.38 ±0.77**** . p-value< 0.1  * p-value< 0.5  ** p-value< 0.01  *** p-value< 0.001  **** p-value< 0.0001        273  C.4 All gene models of iron-responsive proteins and their expression across all three metal limitation treatments in two strains of T. oceanica.  Table C.4: All gene models of iron-responsive proteins and their expression across all three metal limitation treatments in two strains of T. oceanica.    TO03 lowCua TO03 lowFea TO03 lowFeCua TO05 lowCua TO05 lowFea TO05 lowFeCua Gene name Protein name sol insol sol insol sol insol sol insol sol insol sol insol ISIPS (iron starvation induced proteins)                   THAOC_21195 ISIP2                         THAOC_21560 ISIP2A                  THAOC_24851 ISIP2A                         THAOC_24852 ISIP2A                  THAOC_27288 ISIP1B                         THAOC_30995 ISIP1A 1.56  2.57 1.52 9.41 16.19 1.51 1.41 7.14 34.12 6.64 9.04 THAOC_32281 ISIP2B                         THAOC_34562 ISIP2A                  THAOC_34758, contig_117647_681_1617_- ISIP3           1.01 1.37 18.32 19.16 15.33 18.94 THAOC_36410 ISIP2A           1   -1.37 -1.58 -1.15 ferredoxin                 THAOC_13269 fdx                         THAOC_20937 fdx                  THAOC_25559 petF 43.79   -1.46  -11.02   -1.48   -5.31  -1.21 -1.85 THAOC_27092 fdx                  THAOC_32402 fdx                         274    TO03 lowCua TO03 lowFea TO03 lowFeCua TO05 lowCua TO05 lowFea TO05 lowFeCua Gene name Protein name sol insol sol insol sol insol sol insol sol insol sol insol flavodoxin                  THAOC_05010 fld 2.95   2.32   3.82               THAOC_09647 fld                THAOC_16198 fld                  THAOC_16623 fld                         THAOC_19008 fldB,  isoform B                  THAOC_31152 fldA,  isoform A 1.44   2.35   218.8   1.4   8.09   17.24 20.77 FNR, ferredoxin-NADP+ reductase                   THAOC_06509 FNR -1.46   -1.27   -1.05   -1.14 -1.14 1.27   1.34 1.35 THAOC_34226 FNR                  THAOC_34645 FNR                         THAOC_36724 FNR 2.47  2.71  2.88  -1.34  -1.32  -1.62 -1.83 THAOC_36724, contig_64183_1_637_+ FNR 5.5   4.15   3.92               aexpression of proteins given in fold-change as per original proteomic analysis (see methods for details); bold highlights significant changes in expression as defined in Methods (>± 2-fold, p<0.05); underlined highlights significant changes in expression (±1.3- to 2-fold, p<0.05), italic indicates that expression value is derived from EST proteomic analysis.   275  C.5 Expression of light-harvesting complex (LHC) proteins across all three metal limitation treatments in two strains of T. oceanica   Table C.5: Expression of light-harvesting complex (LHC) proteins across all three metal limitation treatments in two strains of T. oceanica    TO03 lowCuc TO03 lowFec TO03 lowFeCuc TO05 lowCuc TO05 lowFec TO05 lowFeCuc Gene namea closest homologb sol insol sol insol sol insol sol insol sol insol sol insol Lhcf - Group I                    THAOC_01208 TpLhcf -2.79 2.05 -3.49 1.07 1.55 1.22  -1.13  -1.36 -1.43 -1.39 THAOC_01927 TpLhcf  -2.04  -2.04  -1.61       THAOC_02510 TpLhcf -1.41 -1.18 -1.54 -1.06 1.23 -1.37 -1.19 -1.13 -1.17 -1.19 1.05 -1.26 THAOC_06305 TpLhcf -10.34 1.04 -8.21 1.02 -5.15 -2.07 1.54 -1.21  -1.29 -1.24 -1.24 THAOC_08131 TpLhcf8        -1.31  -1.32 -1.13 1.08 THAOC_08131, contig_91916_1_273_+ TpLhcf8       -1.14 -1.32  -1.46  -1.1 THAOC_17964 TpLhcf  1.19  1.05  1.27  -1.07  -1.02 1.03 1.05 THAOC_17964, contig_83951_1_204_+ TpLhcf        -1.07  -1.37  -1.49 THAOC_20854 TpLhcf -5.56 -1.49 -5.1 -1.49 -2.79 -1.47 1.32 -1.28 -1.58 1.21 -1.96 1.27 THAOC_20855 TpLhcf  -1.63  -1.66  -3.9  -1.21  -1.08 -1.25 -1.22 THAOC_25270 TpLhcf  1.23  1.31  1.74  -1.13  -1.16 -1.23 -1.2 THAOC_25270, contig_119270_60_376_- TpLhcf        -1.32  -1.2  -1.36 THAOC_25270, contig_123884_1_219_- TpLhcf  -1.7  -2.23  -1.67       THAOC_25270, contig_70472_60_300_- TpLhcf  1.52  1.1  1.33  -1.07  -1.39  -1.36 THAOC_32932 TpLhcf8 -5 -2.12 -3.59 -2.05 -2.23 -1.92  -1.15  -1.22 -1.23 -1.18 THAOC_36248 TpLhcf 1.45 -1.38 -1.01 -1.5 3.55 1.15  -1.09  1.19 1.32 1.35 276    TO03 lowCuc TO03 lowFec TO03 lowFeCuc TO05 lowCuc TO05 lowFec TO05 lowFeCuc Gene namea closest homologb sol insol sol insol sol insol sol insol sol insol sol insol Lhcf - Group II                   THAOC_09684 TpLhcf11 -1.07 4.83 9.06 3.7 1.18 2.52 -1.05 -1.23 -1.64 -1.6 -1.62 -1.52 Lhcf - Group III                   THAOC_04469 TpFCP4 -8.68 2.06 -5.94 2.09 -5.65 1.24 1.53 -1.26 1.2 -1.64 -1.56 -1.73 THAOC_05777 TpFCP10 -1.04 2.4 -2.24 2.28 -2.22 -1.38 1.33 -1.16 -1.15 -2.14 -1.76 -1.67 THAOC_06968 TpFCP2 -2.52 1.4 -1.43 1.7 -2.37 1.06  -1.09  -1.36 -1.3 -1.11 THAOC_08587 TpFCP7 -2.73 2.79 -2.81 2.54 -2.72 1.83 2.07 -1.22  -1.34 -1.09 -1.12 THAOC_16345 TpFCP4  2.68  2.17  1.61  -1.3  -1.25 -1.13 -1.34 THAOC_16412 TpFCP9 -3.31 1.97  1.2 -3.79 -1.99 1.54 -1.09 -1.11 1.01 1.04 -1.16 THAOC_18180 TpFCP11  2.02    1.06 1.31 1.09  -1.7 -1.6 -1.33 THAOC_18994 TpFCP4         1.93 2.48 2.58 2.87 Lhcr                   THAOC_00550 TpLhcr11 -6.06 2.39 -7.12 2.33 -6.64 1.02 1.34 -1.14 -1.42 -1.26 -1.83 -1.49 THAOC_05707 TpLhcr4  1.24 -9.33 1.2 -2.8 -1.49 1.7 -1.21 1 -1.35 -1.51 -1.37 THAOC_05708 TpLhcr14  -1.06  -1.47  -2.21  1.13  -1.69 -1.98 -1.51 THAOC_07034 TpLhcr13  1.66  2.08  -1.26 1.83 -1.11 1.38 -1.2 -1.19 -1.26 THAOC_07036 TpLhcr13 -6.57 1.06 -5.79 1.09 -7.66 -2.75 1.24 -1.19  -2.09 -2.18 -1.76 THAOC_16963 TpLhcr3  -1.56  1.73  -6.51 -1.21 -1.13  -1.96 -2.28 -2.23 THAOC_34573 TpLhcr2  4.44  3.06  3.12 1.34 -1.32 -1.25 -1.6 -1.62 -1.72 THAOC_35171 TpLhcr1  3.48  1.52  1.18  -1.39  -1.42 -1.3 -1.26 THAOC_35518 TpLhcr10 -2.55 -1.26  1.21 -4.93 -1.89  -1.04 1.02 -1.64 -1.47 -1.86               277    TO03 lowCuc TO03 lowFec TO03 lowFeCuc TO05 lowCuc TO05 lowFec TO05 lowFeCuc Gene namea closest homologb sol insol sol insol sol insol sol insol sol insol sol insol Lhcx                   THAOC_08095 TpLhcx7 1.26 2.76 -2.1 1.34 -2.78 -1.53  -1.15  -1.32 -1.25 -1.4 THAOC_08095, contig_119183_41_788_+ TpLhcx7 -1.86 -1.41 -4.31 1.12 -3.01 -2.39 -1.16 -1.13 -1.24 -1.51 -1.53 -1.36 THAOC_09937 TpLhcx1 3.29  3.09  12.57  -1.22  -2.25  -1.77 -1.24 THAOC_31988 TpLhcx1  3.29  3.14  12.41  -1.22    -1.24  17531                   THAOC_01290 Tp17531        -1.06  -1.81 -1.87 -1.81 Lhcz                   THAOC_37813 TpFCP5        -1.46  -1.26 -1.27 -1.34 agrouped into different LHC clades as per phylogenetic analysis (Chapter 3) bclosest homolog as per phylogenetic analysis (Chapter 3) cexpression of proteins given in fold-change as per original proteomic analysis (see methods for details); bold highlights significant changes in expression as defined in Methods (>± 2-fold, p<0.05); underlined highlights significant changes in expression (±1.3- to 2-fold, p<0.05), italic indicates that expression value is derived from EST proteomic analysis. 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