Science, Irving K. Barber Faculty of (Okanagan)
Chemistry, Department of (Okanagan)
DSpace
UBCO
Solonenka, Julia Tekla
Master of Science - MSc
University of British Columbia
β-N-Methylamino-L-alanine is a neurotoxic non-protein amino acid that is naturally produced by all taxonomic orders of cyanobacteria. Alongside BMAA, there are three isomers of interest: N-(2-aminoethyl)glycine (AEG); 2,4-diaminobutyric acid (DAB); and β-aminomethyl-L-alanine (BAMA), that have also been detected in cyanobacteria. While BMAA has been detected in a number of plant-cyanobacterial symbioses, there have been no explicit investigations into whether plants natively contain and/or produce BMAA and its isomers. However, considering that these non-protein amino acids have been detected in dinoflagellates, diatoms, and other zooplankton, it suggests that their biosynthesis was evolutionarily conserved in plastid lineages. In this thesis, I hypothesized that BMAA biosynthesis was conserved along Chlorophyte evolution. My objectives were to first ascertain that BMAA and its isomers could be detected in isolated monocultures of a variety of cyanobacterial genera, then determine whether BMAA and its isomers could be detected in plants representing the "tree of life". To accomplish these objectives, I established a collection of monocyanobacterial cultures from field samples of cyanobacteria through repeated subculturing over the course of 16 months, then detected and quantified BMAA, AEG, DAB, and BAMA using a previously published and validated ultrapure liquid chromatography-tandem mass spectrometry method. Through this work, I confirmed that isolated cyanobacteria collected from Lake Winnipeg, Peru and Chile, and Nunavut all produced BMAA and its isomers. Next, I sampled plants representing 10 clades across the “tree of life” and detected BMAA, AEG, DAB, and BAMA in more than half of the samples, demonstrating the occurrence of these NPAAs in green plants. Combined, these data provide the first evidence for the potential conservation of BMAA biosynthesis from cyanobacteria to the Chlorophyte plastid lineage and introduce plants as a novel model system to elucidate the function of BMAA and its isomers.
Characterizing β-N-Methylamino-L-alanine and its naturally occurring isomers in cyanobacteria and plants by Julia Tekla Solonenka B.Sc., University of Waterloo, 2021 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Chemistry) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) July 2023 © Julia Tekla Solonenka, 2023 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: Characterizing β-N-Methylamino-L-alanine and its naturally occurring isomers in cyanobacteria and plants submitted by Julia Tekla Solonenka in partial fulfillment of the requirements of the degree of Master of Science in Chemistry . Dr. Susan J. Murch, Irving K. Barber School of Arts and Sciences, Department of Chemistry Supervisor Dr. Isaac T. S. Li, Irving K. Barber School of Arts and Sciences, Department of Chemistry Supervisory Committee Member Dr. Michael K. Deyholos, Irving K. Barber School of Arts and Sciences, Department of Biology Supervisory Committee Member Dr. Sanjoy Ghosh, Irving K. Barber School of Arts and Sciences, Department of Biology University Examiner formatting iii Abstract β-N-Methylamino-L-alanine is a neurotoxic non-protein amino acid that is naturally produced by all taxonomic orders of cyanobacteria. Alongside BMAA, there are three isomers of interest: N-(2-aminoethyl)glycine (AEG); 2,4-diaminobutyric acid (DAB); and β-aminomethyl-L-alanine (BAMA), that have also been detected in cyanobacteria. While BMAA has been detected in a number of plant-cyanobacterial symbioses, there have been no explicit investigations into whether plants natively contain and/or produce BMAA and its isomers. However, considering that these non-protein amino acids have been detected in dinoflagellates, diatoms, and other zooplankton, it suggests that their biosynthesis was evolutionarily conserved in plastid lineages. In this thesis, I hypothesized that BMAA biosynthesis was conserved along Chlorophyte evolution. My objectives were to first ascertain that BMAA and its isomers could be detected in isolated monocultures of a variety of cyanobacterial genera, then determine whether BMAA and its isomers could be detected in plants representing the "tree of life". To accomplish these objectives, I established a collection of monocyanobacterial cultures from field samples of cyanobacteria through repeated subculturing over the course of 16 months, then detected and quantified BMAA, AEG, DAB, and BAMA using a previously published and validated ultrapure liquid chromatography-tandem mass spectrometry method. Through this work, I confirmed that isolated cyanobacteria collected from Lake Winnipeg, Peru and Chile, and Nunavut all produced BMAA and its isomers. Next, I sampled plants representing 10 clades across the “tree of life” and detected BMAA, AEG, DAB, and BAMA in more than half of the samples, demonstrating the occurrence of these NPAAs in green plants. Combined, these data provide the first evidence for the potential conservation of BMAA biosynthesis from cyanobacteria to the Chlorophyte plastid lineage and introduce plants as a novel model system to elucidate the function of BMAA and its isomers. iv Lay Summary β-N-Methylamino-L-alanine (BMAA) is an amino acid made by blue-green algae known as cyanobacteria. The goal of this work was to isolate different kinds of cyanobacteria and to determine if BMAA and related amino acids are synthesized in all of them. Since cyanobacteria were the evolutionary ancestor to plants, it is possible that plants are also capable of producing BMAA and its related chemicals. I investigated a variety of plants representing the “tree of life” to determine if they contained these amino acids. Through this research, I found that all cyanobacteria produced similar amounts of BMAA, and that more than half of the plants studied contained BMAA and related amino acids. This suggests that the production of these amino acids has been conserved in plant evolution, and provides another set of organisms to study to learn how and why these amino acids are made. v Preface Chapters 2, 3, and 4 are based on work I conducted in Dr. Susan Murch’s lab at UBC Okanagan. The field samples of cyanobacteria and preliminary cultures were originally collected by the Lake Winnipeg Research Consortium, and field collections from Dr. Susan Murch and Dr. Stephanie Bishop. I performed the monocyanobacterial isolation, experimental work, and data analysis. Data from this work was presented as: “Determining the Impacts of Microbial Diversity on Non-Protein Amino Acid Biosynthesis in Lab-cultured Cyanobacteria” at the Canadian Chemistry Conference and Exhibition (2023) in Vancouver, British Columbia. The results of this work are being prepared for publication in: Solonenka JT, Bishop SL, Giebelhaus RT, Li ITS, and Murch SJ (2023) Microbial community composition affects non-protein amino acid production in cyanobacteria and freshwater organisms collected from Lake Winnipeg. Solonenka JT, Erland LAE, and Murch SJ (2023) Cyanobacteria impacts on ecosystems in the Inuit Nunangat. Chapters 5 and 6 are based on work I conducted at Dr. Susan Murch’s lab at UBC Okanagan. Plants were originally collected from field collections from: Sandra A. Banack, Stephanie L. Bishop, Paula N. Brown, Ian B. Cole, Paul Alan Cox, A. Maxwell P. Jones, Lauren A.E. Erland, Ryland T. Giebelhaus, Susan J. Murch, Diane Ragone, Josh Schneider, and Barry Tomlinson. Corresponding vouchers are deposited at the Beaty Biodiversity Center (UBC) for rare and endangered species. Sources of my new plant collections are indicated in the Methods sections. Plants grown at the UBCO Plant Growth Facility are maintained by Dr. Samantha Olivier. The results of this work are being prepared for publication in: vi Solonenka JT, Erland LAE, and Murch SJ (2023) Conservation of Non-Protein Amino Acid Metabolism Across the Tree-of-Life in Green Plants. vii Table of Contents Abstract...................................................................................................................................... iii Lay Summary ............................................................................................................................. iv Preface ....................................................................................................................................... v Table of Contents ...................................................................................................................... vii List of Tables ........................................................................................................................... xiv List of Figures .......................................................................................................................... xvi List of Abbreviations ................................................................................................................xxvi Acknowledgements ............................................................................................................... xxviii Dedication ...............................................................................................................................xxix Chapter 1: Introduction and Literature Review ............................................................................ 1 1.1 History and Discovery of β-N-Methylamino-L-alanine ............................................... 1 1.2 Naturally Occurring Isomers of BMAA....................................................................... 2 1.3 Research Interest in BMAA ....................................................................................... 3 1.4 Methods for the Detection and Quantification of BMAA ............................................ 5 1.5 The Ubiquity of BMAA in Cyanobacteria ................................................................... 8 1.5.1 Reported Detections of BMAA in Cyanobacteria ................................................. 8 1.5.2 Proposed Pathways for the Biosynthesis of BMAA ............................................10 1.5.2.1 The First Pathway; Transamination and Methylation ..................................10 1.5.2.2 The Nitrogen Starvation Paradox ...............................................................11 1.5.2.3 Revisiting 2,3-DAP; the Steer towards Non-Ribosomal Peptide Synthesis .11 1.5.2.4 L-Amino Acid Oxidase and Glutamine Oxoglutarate Aminotransferase ......13 1.5.2.5 Current State of Research on the Biosynthetic Route to BMAA ..................13 1.6 Is BMAA found in Plants? ........................................................................................15 1.6.1 Reported Detections of BMAA in Plants .............................................................15 1.6.2 Rationale for the Proposed Conservation of BMAA in Plants .............................17 viii 1.7 Hypothesis and Objectives ......................................................................................19 Chapter 2: Non-Protein Amino Acids in Cyanobacteria from Lake Winnipeg ............................. 20 2.1 Synopsis ..................................................................................................................20 2.2 Introduction ..............................................................................................................20 2.3 Hypothesis and Objectives ......................................................................................21 2.4 Experimental Methods .............................................................................................21 2.4.1 Cyanobacterial Culture Isolation ........................................................................21 2.4.1.1 Subculturing Protocols ...............................................................................21 2.4.1.2 Generation of New Isolates ........................................................................23 2.4.2 Metagenomic Analysis .......................................................................................24 2.4.2.1 Genomic DNA Extraction ...........................................................................25 2.4.2.2 16s rRNA Metagenomic Sequencing ..........................................................26 2.4.3 Bioinformatic Analysis and Species Identification ...............................................26 2.4.3.1 DADA2 Taxonomic Identification ................................................................27 2.4.3.2 Microscope Imaging and Visual Identification .............................................27 2.4.4 Quantification of Non-Protein Amino Acids.........................................................28 2.4.4.1 Sample Collection and Derivatization .........................................................28 2.4.4.2 Preparation of Standards ...........................................................................29 2.4.4.3 UPLC-MS/MS Parameters .........................................................................30 2.4.5 Quantification of Total Protein ............................................................................32 2.4.6 Statistics ............................................................................................................32 2.5 Experimental Results ..............................................................................................34 2.5.1 Cyanobacterial Culture Isolation ........................................................................34 2.5.2 Metagenomic and Bioinformatic Analyses ..........................................................37 2.5.2.1 Genomic DNA Quality ................................................................................37 2.5.2.2 Metagenomes from Illumina MiSeq ............................................................40 ix 2.5.2.3 Metagenomes from DADA2 ........................................................................41 2.5.2.4 Phylogenetic Diversity ................................................................................42 2.5.3 Species Identification .........................................................................................44 2.5.4 UPLC-MS/MS Quantification of Non-Protein Amino Acids .................................45 2.5.4.1 UPLC-MS/MS Method Performance Metrics ..............................................45 2.5.4.2 UPLC-MS/MS Method Accuracy ................................................................48 2.5.4.3 Method Detection Limits .............................................................................50 2.5.4.4 Quantification of Non-Protein Amino Acids in Crude and Isolate Cultures ..50 2.5.4.5 Quantification of Bound and Free Non-Protein Amino Acids .......................54 2.6 Discussion of Results .............................................................................................57 2.6.1 Culture Isolation .................................................................................................57 2.6.2 Sequencing and Bioinformatics ..........................................................................57 2.6.3 UPLC-MS/MS Method Performance ..................................................................59 2.6.4 Non-Protein Amino Acids in Crude and Isolate Cyanobacteria ...........................60 2.6.5 BMAA in Lake Winnipeg ....................................................................................62 Chapter 3: Non-Protein Amino Acids in Cyanobacteria from Peru and Chile ............................. 64 3.1 Synopsis ..................................................................................................................64 3.2 Introduction ..............................................................................................................64 3.3 Hypothesis and Objectives ......................................................................................65 3.4 Experimental Methods .............................................................................................65 3.5 Experimental Results ..............................................................................................65 3.5.1 Cyanobacterial Culture Isolation ........................................................................65 3.5.2 Metagenomic and Bioinformatic Analyses .........................................................68 3.5.2.1 Genomic DNA Quality ................................................................................68 3.5.2.2 Metagenomes from Illumina MiSeq ............................................................71 3.5.2.3 Metagenomes from DADA2 ........................................................................72 x 3.5.2.4 Phylogenetic Diversity ................................................................................73 3.5.3 Species Identification .........................................................................................74 3.5.4 UPLC-MS/MS Quantification of Non-Protein Amino Acids .................................75 3.5.4.1 Quantification of Non-Protein Amino Acids in Field Samples and Isolate Cultures 75 3.5.4.2 Quantification of Bound and Free Non-Protein Amino Acids .......................79 3.6 Discussion of Results .............................................................................................82 3.6.1 Culture Isolation .................................................................................................82 3.6.2 Sequencing and Bioinformatics ..........................................................................82 3.6.3 Non-Protein Amino Acids in Field Samples and Isolate Cultures ........................83 Chapter 4: Non-Protein Amino Acids in Arctic Cyanobacteria ................................................... 86 4.1 Synopsis ..................................................................................................................86 4.2 Introduction ..............................................................................................................86 4.3 Hypothesis and Objectives ......................................................................................87 4.4 Experimental Methods .............................................................................................87 4.4.1 Culture Isolation .................................................................................................87 4.5 Experimental Results ..............................................................................................88 4.5.1 Cyanobacterial Culture Collection and Isolation .................................................88 4.5.2 Metagenomic and Bioinformatic Analyses .........................................................91 4.5.2.1 Genomic DNA Quality ................................................................................91 4.5.2.2 Metagenomes from Illumina MiSeq ............................................................94 4.5.2.3 Metagenomes from DADA2 ........................................................................95 4.5.2.4 Phylogenetic Diversity ................................................................................96 4.5.3 Species Identification .........................................................................................97 4.5.4 UPLC-MS/MS Quantification of Non-Protein Amino Acids .................................98 4.5.4.1 Quantification of Non-Protein Amino Acids in Crude and Isolate Cultures ..99 xi 4.5.4.2 Quantification of Bound and Free Non-Protein Amino Acids ..................... 103 4.5.4.3 Non-Protein Amino Acids as a Fraction of Total Protein Content .............. 105 4.6 Discussion of Results ........................................................................................... 106 4.6.1 Culture Isolation ............................................................................................... 106 4.6.2 Sequencing and Bioinformatics ........................................................................ 107 4.6.3 Non-Protein Amino Acids in Arctic Cyanobacteria ............................................ 107 Chapter 5: Conservation of Non-Protein Amino Acids Biosynthesis in Plants .......................... 109 5.1 Synopsis ................................................................................................................ 109 5.2 Introduction ............................................................................................................ 109 5.3 Hypothesis and Objectives .................................................................................... 110 5.4 Experimental Methods ........................................................................................... 110 5.4.1 Plant Sources and Growing Conditions ............................................................ 110 5.4.1.1 Plants Grown In vivo ................................................................................ 112 5.4.1.2 Aquatic Plants .......................................................................................... 113 5.4.1.3 Plants Grown In vitro ................................................................................ 113 5.4.2 Quantification of Non-Protein Amino Acids in Plants by UPLC-MS/MS Analysis 114 5.4.2.1 Sample Collection and Derivatization ....................................................... 114 5.4.2.2 UPLC-MS/MS Analysis ............................................................................ 115 5.4.3 Statistics .......................................................................................................... 115 5.4.4 Phylogenetic Trees .......................................................................................... 115 5.5 Experimental Results ............................................................................................. 115 5.5.1 Non-Protein Amino Acids in Plants .................................................................. 115 5.5.2 Impact of Growth Conditions on NPAA Production in Plants ............................ 119 5.6 Discussion of Results ............................................................................................ 121 Chapter 6: Sources of Non-Protein Amino Acids in Plants ...................................................... 123 xii 6.1 Synopsis ................................................................................................................ 123 6.2 Hypothesis and Objectives .................................................................................... 123 6.3 Experimental Methods ........................................................................................... 123 6.3.1 Methods for Experiment 1: Time-Course BMAA Uptake................................... 123 6.3.1.1 Preparation of Solutions ........................................................................... 124 6.3.1.2 Experimental Design ................................................................................ 125 6.3.1.3 Sample Preparation and UPLC-MS/MS Analysis ..................................... 126 6.3.1.4 Statistics ................................................................................................... 127 6.3.2 Methods for Experiment 2: Quantum Dot-labelled BMAA Uptake ..................... 127 6.3.2.1 Preparation of Quantum Dot-labelled BMAA (QD-BMAA) ........................ 127 6.3.2.2 Spectrophotometric Quantification of QD-BMAA ...................................... 128 6.3.2.3 Experimental Design ................................................................................ 129 6.3.3 Methods for Experiment 3: NPAAs in Soil ........................................................ 129 6.3.3.3 Soil Sample Collection ............................................................................. 129 6.3.2.3 Soil Sample Preparation and UPLC-MS/MS Analysis ............................... 130 6.3.2.4 Statistics ................................................................................................... 130 6.4 Experimental Results ............................................................................................. 130 6.4.1 Experiment 1 Results: Time-Course BMAA Uptake ......................................... 130 6.4.2 Experiment 2 Results: QD-BMAA Uptake ........................................................ 132 6.4.3 Experiment 3 Results: NPAAs in Soil ............................................................... 135 6.5 Discussion of Results ............................................................................................ 137 6.5.1 Experiment 1 Discussion .................................................................................. 137 6.5.2 Experiment 2 Discussion .................................................................................. 137 6.5.3 Experiment 3 Discussion .................................................................................. 138 Chapter 7: Conclusions ........................................................................................................... 141 7.1 Non-Protein Amino Acids in Cyanobacteria ........................................................... 141 xiii 7.1.1 Chapter 2 Conclusions: NPAAs in Cyanobacteria in Lake Winnipeg ................ 141 7.1.2 Chapter 3 Conclusions: NPAAs in Cyanobacteria from Peru and Chile ............ 142 7.1.3 Chapter 4 Conclusions: NPAAs in Arctic Cyanobacteria .................................. 143 7.2 Non-Protein Amino Acids in Plants ........................................................................ 143 7.2.1 Chapter 5 Conclusions: NPAAs in Plants ......................................................... 143 7.2.2 Chapter 6 Conclusions: Sources of NPAAs in Plants ....................................... 144 7.3 Summary of Findings & Significance of Work ........................................................ 145 7.4 Future Studies ....................................................................................................... 146 Bibliography ............................................................................................................................ 147 Appendices ............................................................................................................................. 173 A. Illumina MiSeq Metagenomic Results .................................................................... 173 B. DADA2 Bootstrap Comparison .............................................................................. 182 xiv List of Tables Table 1. Distribution of 55 literature articles on the presence of BMAA in various cyanobacterial genera ....................................................................................................................................... 9 Table 2. MRM transitions for NPAAs using validated UPLC-MS/MS method with AQC derivatization. ............................................................................................................................31 Table 3. Descriptions of crude Lake Winnipeg cyanobacterial cultures analyzed in 2019. .........34 Table 4. Descriptions of isolate cultures established from crude cyanobacterial cultures collected from Lake Winnipeg and analyzed in 2022. ................................................................35 Table 5. Genomic DNA quality metrics and Illumina MiSeq metrics of analyzed isolate cultures in 2022. .....................................................................................................................................38 Table 6. Pearson correlation coefficient of alpha diversity measures and NPAA concentration in cultures. ....................................................................................................................................54 Table 7. Concentrations of protein-bound and free BMAA, AEG, DAB, and BAMA measured in isolate cultures. “D” indicates NPAA detected but fell below the limit of quantification. “ND” indicates NPAA not detected. “STE” refers to standard error of the mean. ................................56 Table 8. Collection information and descriptions for field samples collected in Peru and Chile in 2019 and corresponding isolate cultures that were analyzed by UPLC-MS/MS. (“PNL” = Parque Nacional Lauca). .......................................................................................................................66 Table 9. Genomic DNA quality metrics and Illumina MiSeq metrics of analyzed Peru/Chile isolate cultures ..........................................................................................................................69 Table 10. Concentrations of protein-bound and free BMAA, AEG, DAB, and BAMA measured in isolate cultures. “D” indicates NPAA detected but fell below the limit of quantification. “ND” indicates NPAA not detected. “STE” refers to standard error of the mean. ................................81 Table 11. Collection information and descriptions for crude cyanobacterial cultures collected in Nunavut in 2019 and corresponding isolate cultures that were analyzed by UPLCS-MS/MS. ...89 xv Table 12. Genomic DNA quality metrics and Illumina MiSeq metrics of analyzed Peru/Chile isolate cultures ..........................................................................................................................92 Table 13. Concentrations of protein-bound and free BMAA, AEG, DAB, and BAMA measured in select crude and isolate Arctic cultures. “D” indicates NPAA detected but fell below the limit of quantification. “ND” indicates NPAA not detected. “STE” refers to standard error of the mean. ............................................................................................................................................... 104 Table 14. Total protein and non-protein amino acids in cultures isolated from Nunavut collections ............................................................................................................................... 106 Table 15. Source, clade, growing conditions, and names of plant species employed for the current study. .......................................................................................................................... 110 xvi List of Figures Figure 1. β-N-Methlyamino-L-alanine (BMAA) and its naturally occurring isomers: N-(2-aminoethyl)glycine (AEG); 2,4-diaminobutyric acid (DAB); and β-aminomethyl-L-alanine (BAMA). ..................................................................................................................................... 2 Figure 2. Overview of Web of Science™ search results for BMAA and synonyms. Top image shows distribution of scientific fields reporting on BMAA, while the bottom image is the publication and citation record for BMAA spanning 1988 – 2023. Figures generated using Web of Science™. ............................................................................................................................. 3 Figure 3. Overview of the categorization process of BMAA-related research articles, beginning with the Web of Science™ search to the breakdown of research topics. Articles on detection and quantification (green) were categorized by sample type; articles on physiological properties (blue) were categorized by model system. ................................................................................. 4 Figure 4. Overview of methods used for BMAA detection and quantification; a) Percentage of reported methods used to detect and quantify BMAA in the literature; colours correspond to those shown in: b) Percentage of positive BMAA detections per method; c) Types and distribution of RPLC-based methods used to detect BMAA in the literature. .............................. 6 Figure 5. Global distribution of 50 studies detecting BMAA in environmental cyanobacterial samples in 28 countries between 2005 and 2022. (Lab strains with no specified source, symbionts, and food sources not considered) Colour indicates number of published studies including samples from the country; (XX%) indicates percentage of studies with positive BMAA detections. Figure generated with Datawrapper. ........................................................................ 8 Figure 6. Mantas et al. searched for genes encoding the above enzymes in pathways to 2,3-DAP (Albizia, Lathrus, VioKB, and SbnAB) and BMAA (pam) in cyanobacteria, Paenibacillus spp. and diatoms; (n/a indicates that these genomes were not searched). ................................12 Figure 7 Currently proposed and known pathways to BMAA: a) Proposed two-step pathway to BMAA, beginning with the transamination of a β-substituted alanine to 2,3-DAP and subsequent xvii methylation to BMAA; b) Proposed pathway to BMAA involving the hydrolysis of cyclic BMAA-β-NCO2; c) Structure of paenilamicin B1 with BMAA indicated in red (known pathway). ...............15 Figure 8. Species tree showing the characterization of BMAA across green plants. Species in blue were identified as containing detectable BMAA via literature review. Major clades are represented by colour. Species symbolled with a green circle indicate that the plant contains a reported cyanobacterial symbiont; species symbolled with a yellow diamond are analyzed in this thesis. The phylogenetic tree was generated using phyloT v2 and visualized using iTOL v6.7.6. .................................................................................................................................................17 Figure 9. Phylogenetic tree showing evolutionary divergence of cyanobacteria from plastid lineages; adapted from Moore et al.115 Red box indicates cyanobacteria and red algae with confirmed detections of BMAA reported in the literature. ...........................................................18 Figure 10. Most common types of contamination present in cyanobacterial cultures; from left to right, top to bottom: a) Grey burst-like appearance, common in early culture growth; b) Grey globular colonies, often interacting with cyanobacteria; c) Grey, fungal-like spread from cyanobacterial colonies; d) Small, spherical blue-tinged bacteria that distributed across the culture; e) Reddish to yellow globular colonies, also often interacting with cyanobacteria; and f) Reddish, brownish, or blueish tendrils intertwined with cyanobacterial colonies ........................23 Figure 11. Overview of the procedure for isolating monocyanobacterial cultures from environmental samples. Figure created using Biorender. ..........................................................24 Figure 12. Sample collection and 16S rRNA metagenomic sequencing. Figure generated with Biorender. .................................................................................................................................25 Figure 13. Structures of BMAA, AEG, DAB, and BAMA prior to and post-derivatization with AQC, and respective quantifier and qualifier ion transitions generated after fragmentation. ......30 Figure 14. Overview of the NPAA quantification experiment described in this section. Figure generated using BioRender.......................................................................................................33 xviii Figure 15. Culture quality tracking spanning a period of 445 days. The lines are normalized to dates of subculturing to show progression over time. The colours used correspond to the qualitative 1 – 5 ranking (1=magenta, 2=orange, 3=yellow, 4=lime green, 5=green). Grey indicates that a subculture was created, but no growth of any kind was observed. White circle denotes subculture used for NPAA quantification and 16S rRNA metagenomic sequencing. ....36 Figure 16. Alluvial diagram showing median culture ranking during four 111-day periods over the course of the cyanobacterial culture purification process. “Good” scores defined as qualitative rankings of 4 or 5; “Poor” scores defined as qualitative rankings of 1 or 2. Dashed lines follow the ratio of “poor” (dark blue) and “good” (light blue) scores across the time periods. Diagram generated with RAWGraphs.io....................................................................................37 Figure 17. a) gDNA concentration of isolate cultures with detected (green) and not-detected (white) cyanobacteria; b) A260/A280 and A260/A230 ratios of samples with detected (green) and not-detected (white) cyanobacteria; c) Number of reads generated and number of reads passing quality check for samples with detected (green) and not-detected (white) cyanobacteria. No statistically significant differences in any metric detected by unpaired t-test (α=0.05). .........39 Figure 18. Metagenomic profiles of a) crude and b) isolate cultures computed via the Illumina MiSeq bioinformatic pipeline. Barplots show relative abundance of identified orders from the Proteobacteria (purple-yellow gradient), Firmicutes (blue), Actinobacteria (mahoghany), Cyanobacteria (green to teal gradient), Sphingobacteria (brown), Bacteroidota (orange), Spirochaetes (magenta), other bacteria (light grey), and unidentified species (dark grey). ........40 Figure 19. Metagenomic profiles of crude and isolate cultures, computed using the DADA2 bioinformatic pipeline and taxonomy assigned using SILVA v138.1 and CyanoSeq v1.2 databases (bootstrap level=50). ................................................................................................42 Figure 20. a) Alpha diversity measures (Shannon, Simpson) of crude cultures compared to isolate cultures, coloured by lake location (orange = lake bloom, green = north basin, blue = south basin). Statistically significant differences calculated by unpaired t-test (α=0.05). b) Beta xix diversity measure (Bray-Curtis distances) of crude cultures (circles) compared to isolate cultures (triangles). Clustering of crude cultures outlined in yellow. ........................................................43 Figure 21. 40X magnification images of crude (greyscale, cyanobacteria highlighted in yellow) and isolate (colour) cyanobacteria. Putative assignments based on morphological characteristics; confirmed assignments based on 16S rRNA taxonomic identification. Lake Winnipeg map data acquired from Lake Winnipeg Basin Information Network and processed in QGIS v3.28.0. Station map acquired from LWRC. ....................................................................44 Figure 22. Chromatograms for the quantifier (blue; 459>289, 459>119, 459>171) NPAA transitions and qualifier (purple, DAB (459>188); pink, AEG (459>214); and red, BMAA (459>258)) NPAA transitions. ...................................................................................................45 Figure 23. Example standard curves of the 459>171 transitions of a) BMAA, b) AEG, c) DAB, and d) BAMA, demonstrating excellent linearity (R2>0.99) ........................................................46 Figure 24. Linearity of the standard curves used for quantification (459>171) of a) BMAA, b) AEG, c) DAB, and d) BAMA over the course of the data acquisition period (August – November 2022) ........................................................................................................................................47 Figure 25. Peak area for the 459>171 transition of a) BMAA, b) AEG, c) DAB, and d) BAMA of Standard 4 over the course of the data acquisition period (August – November 2022) ..............48 Figure 26. Overlaid spiked and non-spiked chromatograms of a) Cyanobacterial free amino acid matrix; b) Cyanobacterial bound amino acid matrix; and c) Plant total amino acid matrix analyzed in this thesis work; spike recovery in % matrix. ..........................................................49 Figure 27. Comparison of a) BMAA, b) AEG, c) DAB, and d) BAMA measurements between crude (brown) and isolate (green) cultures. Crosses (+) indicate sample means. ND values estimated with 1/10 MDL of each NPAA for calculation of statistically significant differences via unpaired t-test (α=0.05). ............................................................................................................51 Figure 28. Comparison of combined a) BMAA and b) AEG between crude (brown) and isolate (green) cultures from the same field sample. Error bars indicate standard error of the mean (n=3 xx for crude cultures, n=4 for isolate cultures). Missing bars indicate NPAA not detected. Statistically significant differences by unpaired t-test (α=0.05) indicated with: *(p<0.05); **(p<0.01); ***(p<0.001). ...........................................................................................................52 Figure 29. Comparison of combined a) DAB and b) BAMA between crude (brown) and isolate (green) cultures from the same field sample. Error bars indicate standard error of the mean (n=3 for crude cultures, n=4 for isolate cultures). Missing bars indicate NPAA not detected. Statistically significant differences by unpaired t-test (α=0.05) indicated with: *(p<0.05); **(p<0.01); ***(p<0.001). ...........................................................................................................53 Figure 30. Comparison of protein-bound (dark green) vs. free (turquoise) a) BMAA, b) AEG, c) DAB, and d) BAMA. Crosses (+) indicate sample means. ND values estimated with 1/10 LOD for calculation of statistically significant differences via unpaired t-test (α=0.05). .......................55 Figure 31. Culture quality tracking spanning a period of 464 days. The lines are normalized to dates of subculturing to show progression over time. The colours used correspond to the qualitative 1 – 5 ranking. White circle denotes subculture used for NPAA quantification and 16S rRNA metagenomic sequencing. ...............................................................................................67 Figure 32. Alluvial diagram showing median culture ranking during four 116-day periods over the course of the cyanobacterial culture purification process. “Good” scores defined as qualitative rankings of 4 or 5; “Poor” scores defined as qualitative rankings of 1 or 2. Diagram generated with RAWGraphs.io. .................................................................................................68 Figure 33. a) gDNA concentration of samples with detected (green) and not-detected (white) cyanobacteria; b) A260/A280 and A260/A230 ratios of samples with detected (green) and not-detected (white) cyanobacteria; c) Number of reads generated and number of reads passing quality check for samples with detected (green) and not-detected (white) cyanobacteria. No statistically significant differences in any metric detected by unpaired t-test (α=0.05). ...............70 Figure 34. Metagenomic profiles of crude and isolate cultures computed via the Illumina MiSeq bioinformatic pipeline. Barplots show relative abundance of identified orders from the xxi Proteobacteria (purple-pink gradient), Firmicutes (blue), Actinobacteria (red), Cyanobacteria (green), Bacteroidota (orange), Gemmatales (brown), other bacteria (light grey), and unidentified species (dark grey). ...............................................................................................71 Figure 35. Metagenomic profiles of crude and isolate cultures, computed using the DADA2 bioinformatic pipeline and taxonomy assigned using SILVA v138.1 and CyanoSeq v1.2 databases (bootstrap level=50). ................................................................................................72 Figure 36. a) Alpha diversity measures (Shannon, Simpson) of crude cultures compared to isolate cultures, coloured by country of collection (orange = Chile, teal = Peru). Statistically significant differences calculated by unpaired t-test (α=0.05). b) Beta diversity measure (Bray-Curtis distances) of crude cultures (circles) compared to isolate cultures (triangles). Clustering of crude cultures outlined in yellow. ...........................................................................................73 Figure 37. 40X magnification images of crude (greyscale, cyanobacteria highlighted in teal) and isolate (colour) cyanobacteria. Putative assignments based on morphological characteristics; confirmed assignments based on 16S rRNA taxonomic identification. Map data acquired as a custom GeoJSON file from geojson-maps.ash.ms and processed in QGIS v.3.28.0 .................74 Figure 38. Comparison of a) BMAA, b) AEG, c) DAB, and d) BAMA measurements between field (brown) and isolate (green) cultures. Crosses (+) indicate sample means. ND values estimated with 1/10 MDL for each NPAA for calculation of statistically significant differences via unpaired t-test (α=0.05). ............................................................................................................76 Figure 39. Combined a) BMAA and b) AEG in parent field samples (brown) and corresponding isolate culture pair (green). Error bars represent standard error of the mean (n=3 for field, n=4 for isolate). Missing bars indicate NPAA not detected. Statistically significant differences by unpaired t-test (α=0.05) indicated by: *(p<0.05); **(p<0.01); ***(p<0.001). ................................77 Figure 40. Combined a) DAB and b) BAMA in parent field samples (brown) and corresponding isolate culture pair (green). Error bars represent standard error of the mean (n=3 for field, n=4 xxii for isolate). Missing bars indicate NPAA not detected. Statistically significant differences by unpaired t-test (α=0.05) indicated by: *(p<0.05); **(p<0.01); ***(p<0.001). ................................78 Figure 41. Comparison of protein-bound (dark green) vs. free (turquoise) a) BMAA, b) AEG, c) DAB, and d) BAMA. Crosses (+) indicate sample means. ND values estimated with 1/10 MDL of each NPAA for calculation of statistically significant differences via unpaired t-test (α=0.05). ...80 Figure 42. Culture quality tracking spanning a period of 464 days. The lines are normalized to dates of subculturing to show progression over time. The colours used correspond to the qualitative 1 – 5 ranking. White circle denotes subculture used for NPAA quantification and 16S rRNA metagenomic sequencing. ...............................................................................................90 Figure 43. Alluvial diagram showing median culture ranking during four 116-day periods over the course of the cyanobacterial culture purification process. “Good” scores defined as qualitative rankings of 4 or 5; “Poor” scores defined as qualitative rankings of 1 or 2. Diagram generated with RAWGraphs.io. .................................................................................................91 Figure 44. a) gDNA concentration of samples with detected (green) and not-detected (white) cyanobacteria; b) A260/A280 and A260/A230 ratios of samples with detected (green) and not-detected (white) cyanobacteria; c) Number of reads generated and number of reads passing quality check for samples with detected (green) and not-detected (white) cyanobacteria. Statistically significant differences in any metric detected by unpaired t-test (α=0.05). ..............93 Figure 45. Metagenomic profiles of crude “cold” and isolate “warm” cultures computed via the Illumina MiSeq bioinformatic pipeline. Barplots show relative abundance of identified orders from the Proteobacteria (pink-yellow gradient), Firmicutes (blue), Actinobacteria (red), Cyanobacteria (green), Bacteroidota (orange), other bacteria (light grey), and unidentified species (dark grey). ..................................................................................................................94 Figure 46. Metagenomic profiles of crude and isolate Arctic cultures, computed using the DADA2 bioinformatic pipeline and taxonomy assigned using SILVA v138.1 and CyanoSeq v1.2 databases (bootstrap level=50). ................................................................................................95 xxiii Figure 47. a) Alpha diversity measures (Shannon, Simpson) of crude cultures compared to isolate cultures, coloured by location of collection (orange = Iqaluit, teal = Resolute). Statistically significant differences calculated by unpaired t-test (α=0.05). b) Beta diversity measure (Bray-Curtis distances) of crude cultures (circles) compared to isolate cultures (triangles). ................96 Figure 48. 40X magnification images of isolate Arctic cyanobacteria. Putative assignments based on morphological characteristics; confirmed assignments based on 16S rRNA taxonomic identification. Map of Nunavut generated with datawrapper.de .................................................98 Figure 49. Comparison of combined a) BMAA, b) AEG, c) DAB, and d) BAMA measurements between crude “cold” (blue) and isolate “warm” (pink) cultures. Crosses (+) indicate sample means. ND values estimated with 1/10 MDL for calculation of statistically significant differences via unpaired t-test (α=0.05). ......................................................................................................99 Figure 50. Combined a) BMAA and b) AEG in crude “cold” culture(s) (blue) and corresponding “warm” isolate culture(s) (pink). Error bars represent standard error of the mean (n=4 or 6 for crude, n=4 for isolate). Missing bars indicate NPAA not detected. Statistically significant differences by unpaired t-test (α=0.05) indicated by: *(p<0.05); **(p<0.01); ***(p<0.001). ....... 100 Figure 51. Combined a) DAB and b) BAMA in crude “cold” culture(s) (blue) and corresponding “warm” isolate culture(s) (pink). Error bars represent standard error of the mean (n=4 or 6 for crude, n=4 for isolate). Missing bars indicate NPAA not detected. Statistically significant differences by unpaired t-test (α=0.05) indicated by: *(p<0.05); **(p<0.01); ***(p<0.001). ....... 101 Figure 52. Comparison of protein-bound (dark green) vs. free (turquoise) a) BMAA, b) AEG, c) DAB, and d) BAMA in crude and isolate cultures. Crosses (+) indicate sample means. ND values estimated with 1/10 LOD for calculation of statistically significant differences via unpaired t-test (α=0.05). ........................................................................................................................ 103 Figure 53. Percentage of AEG (orange), DAB (blue), and BAMA (green) composing the protein pellet for analyzed cyanobacterial samples. Total protein quantified via BCA assay. .............. 105 xxiv Figure 54. Concentration of total BMAA (ng/g dry weight) in the plant species sampled for the study. Error bars represent standard error (n=3) of the mean. ................................................ 116 Figure 55. Chromatograms showing the 459>171 quantifier transitions, with BMAA peaks highlighted in pink. Plant species as indicated; Std indicates NPAA standard mix showing retention time of BMAA. .......................................................................................................... 117 Figure 56. Concentration of total AEG (ng/g dry weight) in the plant species sampled for the study. Error bars represent standard error (n=3) of the mean. ................................................ 118 Figure 57. Concentration of total DAB (ng/g dry weight) in the plant species sampled for the study. Error bars represent standard error (n=3) of the mean. ................................................ 118 Figure 58. Concentration of total BAMA (ng/g dry weight) in the plant species sampled for the study. Error bars represent standard error (n=3) of the mean. ................................................ 119 Figure 59. Comparison of detected concentrations of a) BMAA, b) AEG, c) DAB, and d) BAMA in plants grown in vivo (light brown) versus in vitro (dark brown). All values reported as ng/g dry weight. .................................................................................................................................... 120 Figure 60. Concentrations of BMAA, AEG, DAB, and BAMA in Eleocharis acicularis (light blue), Sedum acre (dark blue), and Vesicularia montagneii (orange), grown NIC and IC. Error bars represent standard error of the mean (n=3)............................................................................. 121 Figure 61. Overview of experimental design for time-course St. John’s wort BMAA uptake study. Figure generated with BioRender. ........................................................................................... 124 Figure 62. Overview of the quantum dot conjugation process. Figure created using BioRender. ............................................................................................................................................... 128 Figure 63. Concentration of BMAA detected in root samples after incubation with 50 μM BMAA for 0, 2, 6, and 24 hours. Bars represent average concentrations; error bars represent standard error of the mean. ................................................................................................................... 131 xxv Figure 64. Concentration of BMAA detected in shoot samples after incubation with 50 μM BMAA for 0, 2, 6, and 24 hours. Bars represent average concentrations; error bars represent standard error of the mean. ................................................................................................................... 132 Figure 65. Emission spectra of the QD-BMAA standard curve, excited at 300 nm. Maximum emission wavelength was confirmed to be around 545 nm. .................................................... 133 Figure 66. Fluorescence measurements of QD-Mel (black) and QD-BMAA (grey) standard solution curves, measured at λex/λem 300nm/545nm. ............................................................... 133 Figure 67. Fluorescence measurements of a) plant solutions, b) ground root, and c) ground leaf samples before-and-after 48 hours of exposure to QD-Mel, QD-BMAA, or water. Error bars represent standard deviations of the mean (n=3). ................................................................... 134 Figure 68. Concentrations of free a) AEG and b) DAB in the soil mix and soils containing Norfolk pine, Wollemi pine, and cycad. Error bars represent standard error of the mean (n=3). .......... 136 Figure 69. Cyanobacterial order classification of all samples at default bootstrep level (50). ... 182 Figure 70. Cyanobacterial order classification of all samples at bootstrap levels 80 (top) and 95 (bottom) .................................................................................................................................. 183 xxvi List of Abbreviations 2,3-DAP: 2,3-diaminopropanoic acid AEG: N-(2-aminoethyl)glycine ALS: Amyotrophic lateral sclerosis ALS/PDC: Amyotrophic lateral sclerosis and Parkisonism dementia complex AQC: 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate BAMA: β-aminomethyl-L-alanine BCA: Bicinchoninic acid BMAA: β-N-Methylamino-L-alanine BSC: Biological safety cabinet CAS: Chemical abstracts service CE: Capillary electrophoresis CZE: Chip-based capillary zone electrophoresis DAB: 2,4-diaminobutyric acid DIC: Differential interference contrast EDC: N-ethyl-N’-dimethylaminopropyl-carbodiimide ELISA: Enzyme-linked immunosorbent assay EPA: Environmental Protection Agency ESI: Electrospray ionization EST: Expressed sequence tag FD: Fluorescence detection Fmoc: Fluorenylmethoxycarbonyl GC: Gas chromatography HCl: Hydrochloric acid HEPA: High-efficiency particulate absorbing HILIC: Hydrophilic interaction liquid chromatography xxvii IUPAC: International Union of Pure and Applied Chemistry L-AAO: L-amino acid oxidase LC: Liquid chromatography LWRC: Lake Winnipeg Research Consortium MDL: Method detection limit MRM: Multiple reaction monitoring MS: Mass spectrometry MS/MS: Tandem mass spectrometry NPAA: Non-protein amino acid PBS: Phosphate-buffered saline PVDF: Polyvinylidene fluoride RPLC: Reverse-phase liquid chromatography STE: Standard error TCA: Trichloroacetic acid UV: Ultraviolet xxviii Acknowledgements I would like to acknowledge the following individuals for their assistance and support in carrying out this project: Firstly, I would like to express my gratitude to my supervisor, Dr. Susan Murch, for her patience and guidance, both academically and personally. As well, I would like to acknowledge my committee members, Dr. Isaac Li and Dr. Michael Deyholos, for their support and insights, and Dr. Paul Shipley for his valuable feedback when preparing for presentations. Thank you to my university examiner, Dr. Sanjoy Ghosh, and neutral chair, Dr. Trudy Kavanagh, for their time and assistance with my defense of this thesis. I am eternally grateful to the members of the PlantSMART lab, present and previous, who have assisted this project. To former members Dr. Lauren Erland, Dr. Stephanie Bishop, and Ryland Giebelhaus, for their patience with my endless e-mails and questions; and to current members Pawanjit Sandu, Alisha Greene, and Adeyemi Adebowale, who have provided feedback, assistance, and encouragement when I needed it most. As well, I am grateful to former undergraduate students Georgia Chalmers for her help in maintaining the plant inventory, and Simona Mastroianni for her support in my early months. I would further like to acknowledge David Bakker and Adam Yasunaga from Dr. Li’s lab for their aid in acquiring microscopic images of the cyanobacterial cultures, and Dr. Samantha Olivier from the Plant Growth Facility for assisting in my plant sampling process. Finally, I would like to thank the funding agencies that supported this work: the Canada Research Chair Program, the Natural Sciences and Engineering Research Council of Canada, and internal funding from the University of British Columbia Okanagan, including a scholarship received in memoriam of Florence M. Myers. xxix Dedication I am dearly grateful for my family and friends for their never-ending support. To put my gratitude into words would take another thesis entirely. Thank you for carrying this little tiger this far. © Microsoft Office 365 1 Chapter 1: Introduction and Literature Review Click or tap here to enter text. 1.1 History and Discovery of β-N-Methylamino-L-alanine β-N-Methylamino-L-alanine (BMAA) was initially discovered during medical investigations to the cause of an endemic amyotrophic lateral sclerosis (ALS) cluster in the Micronesian island of Guam in the 1950s.1 After the territory was ceded to the United States following World War II, American medical personnel noted the rate of ALS among the inhabitants was 50 – 100 times higher than the global average.2 The indigenous Chamarros of the island referred to the disease as lytico, with the full medical archetype – amyotrophic lateral sclerosis and Parkisonism dementia complex (ALS/PDC) – known as lytico-bodig. The ethnobotanist Marjorie G. Whiting did extensive field studies in Guam, focusing primarily on the diet of the Chamarros and their historical usage of cycad (Cycas spp., Zamia spp., Dioon spp., Encephalartos spp.) as medicine and foodstuffs.3 Through her work, she uncovered repeated occurrences of illness and fatalities associated with consumption of cycad, noting that they appeared during “periods of extreme food shortage” and that they could often be traced back to “inadequate preparation of cycad products”. Dr. Whiting’s work led chemists A. Vega and E. Arthur Bell to investigate chemical constituents in the cycad that could account for its latent neurotoxicity, and they isolated BMAA from Cycas circinalis seeds in 1967.4 In their report, they write that the non-protein amino acid (NPAA) is “markedly neurotoxic to chicks”, thereby proposing BMAA as a causative agent for lytico-bodig for the first time. Further studies showed continuous oral exposure to BMAA induced excitotoxin-like neurodegeneration in macaques.5 However, direct studies on cycad flours after traditional washing and preparation techniques indicated that more than 85% of the amino acid was removed prior to consumption, and so appeared unlikely to be causing such widespread illness among the inhabitants of Guam.6 2 After another decade with no new answers, BMAA was revisited with a renewed perspective. In 2002, ethnobotanist Paul A. Cox proposed a hypothesis in tandem with Dr. Oliver W. Sacks that BMAA accumulated within the food web.7 Their hypothesis proposed that flying foxes (Pteropus mariannus) fed on cycad seeds and stored BMAA in their bodies; as flying foxes were another traditional food of the Chamarros, this introduced a new means of accumulating the amino acid through their diets. Consistent with the hypothesis, BMAA was subsequently detected in the flying foxes,8–11 and was shown to be present in human brains of patients who died from either ALS/PDC and Alzheimer’s.12 Subsequent research determined that BMAA was produced by a cyanobacterium of the genus Nostoc sp. which symbiotically associates with cycads, creating specialized nitrogen-fixing coralloid roots, and BMAA was soon determined to be present in cultures of all five classes of cyanobacteria.13 1.2 Naturally Occurring Isomers of BMAA Alongside BMAA, there are three isomers of interest: N-(2-aminoethyl)glycine (AEG); 2,4-diaminobutyric acid (DAB); and β-aminomethyl-L-alanine (BAMA) (Figure 1). Both AEG and DAB have shown neurotoxic effects in vivo,14 but little is known about the activity and prevalence of BAMA. In 2012, Jiang et al. performed a database search for structural isomers and listed seven possible compounds which were expected to be identifiable via tandem mass spectrometry; other than AEG, DAB, and BAMA, this list included 2,3-diaminobutanoic acid, 3,4-diaminobutanoic acid, 3-amino-2-(aminomethyl)propanoic acid, and 2,3-diamino-2-methylpropanoic acid.15 These latter four compounds have not been assessed in environmental samples. Figure 1. β-N-Methlyamino-L-alanine (BMAA) and its naturally occurring isomers: N-(2-aminoethyl)glycine (AEG); 2,4-diaminobutyric acid (DAB); and β-aminomethyl-L-alanine (BAMA). 3 1.3 Research Interest in BMAA A Web of Science™ search for the terms: “BMAA”, “N-β-methylamino-L-alanine”, “β-N-methylamino-L-alanine”, and “β-methylamino-L-alanine” was performed on March 24th, 2023. This search yielded 498 article results, with a total of 15 265 citations and an h-index of 62. Figure 2. Overview of Web of Science™ search results for BMAA and synonyms. Top image shows distribution of scientific fields reporting on BMAA, while the bottom image is the publication and citation record for BMAA spanning 1988 – 2023. Figures generated using Web of Science™. 4 The list of articles was manually curated to remove duplicates, review articles, and unrelated articles. In total, 116 articles were removed, leaving 382 articles for analysis. These articles were then grouped into three categories: (1) Research related to the physiological and toxicological effects of BMAA; (2) Research on the biosynthesis, metabolism, and chemical reactivity of BMAA; and (3) Research on the analytical detection and quantification of BMAA. An overview of this process is shown in Figure 3. Figure 3. Overview of the categorization process of BMAA-related research articles, beginning with the Web of Science™ search to the breakdown of research topics. Articles on detection and quantification (green) were categorized by sample type; articles on physiological properties (blue) were categorized by model system. 5 The majority of studies (187/382; 49%) were related to determining physiological effects of BMAA exposure. Most of these studies were conducted using cell lines or in vitro experiments to observe outcomes on targeted enzymes. Approximately half of the studies were performed on animals, of which primarily rodent models were utilized. The remaining studies examined consequences of BMAA exposure in insect, plant, phytoplankton, bacterial, and fungal models. The next bulk of studies (168/382; 44%) focused on the analytical detection and quantification of BMAA in the environment. Most articles examined the presence of BMAA in animals (including humans) and phytoplankton, with these groups making up 70% of all detection studies. About 16% of the studies looked at the presence of BMAA in plants, 11% examined water samples, and the remaining research detailed method development without additional samples. Only 27/382 (7%) of studies explored the biochemical and chemical characterization of BMAA; of these, 41% fell into a recent interest on degradation mechanisms that began around 2017. Four of the studies revolved around the BMAA carbamate adduct. Finally, only 12 studies focused on biosynthesis and metabolism of BMAA, highlighting the large gap in the current understanding of the function and synthesis of this non-protein amino acid. Uncovering species-and condition-specific differences in BMAA production is anticipated to clarify its biological role. 1.4 Methods for the Detection and Quantification of BMAA Various methods for the detection and quantification of BMAA have been reported in the literature, falling into one of six approaches: (1) reverse-phase liquid chromatography (RPLC); (2) hydrophilic interaction liquid chromatography (HILIC); (3) gas chromatography (GC); (4) enzyme-linked immunosorbent assay (ELISA); (5) capillary electrophoresis (CE); and (6) chip-based capillary zone electrophoresis (CZE). RPLC is the most commonly used method, applied in 68% of studies (Figure 4a), and has the highest relevant positive detection rate for BMAA (Figure 4b) – capillary electrophoresis has only been used in a single study.16 6 Figure 4. Overview of methods used for BMAA detection and quantification; a) Percentage of reported methods used to detect and quantify BMAA in the literature; colours correspond to those shown in: b) Percentage of positive BMAA detections per method; c) Types and distribution of RPLC-based methods used to detect BMAA in the literature. Prior to 2003, few methods were available for the detection and quantification of BMAA. Early studies examining BMAA in the Cycadales and cycad flour samples used fluorescence-based methods (-FD) to detect BMAA by derivatizing samples with fluorenylmethoxycarbonyl (Fmoc) protecting groups prior to RPLC.17,18 Another frequently used approach was gas 7 chromatography; involving conjugation with groups such as N, N-dipentafluoropropionyl and 2-methyl- l-propanol,19 N,N'-bis-pentafluoroacyl,20 N-ethoxycarbonyl ethyl ester,21 and tert-butyldimethylsilyl chloride.22 In 2003, 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC) was introduced as a derivatization agent for BMAA, and was applied in both LC-FD23 approaches and also in tandem mass-spectrometry (-MS/MS)1,10 approaches. All of the characterization studies published between 2003-2004 used AQC and successfully detected both naturally occurring BMAA in cycad, and accumulated BMAA in flying foxes and human brain tissue samples.1,10,23–25 Since then, AQC derivatization has been the most commonly employed procedure for BMAA analysis; 50% of studies employ this agent to great success, as BMAA is positively detected >90% of the time. This method was validated in a single laboratory validation26 in 2015 by Glover et al., and was followed with a second laboratory validation in 2019.27 Other derivatization methods that have been frequently used include propylchloroformate (EZ:faast)28–33 and dansyl chloride.34–36 Other protocols that have been applied in the literature have included HILIC, with ZIC-HILIC-MS/MS being employed in approximately half of these studies. However, ZIC-HILIC-MS/MS was recently determined to be not fit-for-purpose37 as it failed to meet criteria for selectivity and had poor precision and variable sensitivity. Another new method involved enzyme-linked immunosorbent assay (ELISA) kits developed for BMAA, introduced by Abraxis, but its high limit of detection can increase the variability of the calibration curve, resulting in potentially inflated values when compared to LC-MS/MS methods.38 In Faassen et al.’s evaluation of the kit in 2013, they routinely yielded spike recoveries of >100% and noted that the kit frequently returned positive detections of BMAA that could not be corroborated by LC-MS/MS.39 A novel rapid test kit for BMAA is currently in development between Arlington Scientific and Dr. Paul Cox’s Brain Chemistry Labs as of March 2022.40 8 1.5 The Ubiquity of BMAA in Cyanobacteria 1.5.1 Reported Detections of BMAA in Cyanobacteria BMAA was first reported to be produced in all five groups of cyanobacteria in 2005.13 Since then, 54 articles have been published detailing their detections of BMAA or lack thereof in cyanobacteria (Table 1). These studies covered 35 cyanobacterial genera across 8 orders; BMAA was positively detected in all orders studied and at least once in 31/35 genera reported. Of the four remaining (Arthospira sp., Nodosilinea sp., Romeria sp., and Woronichinia sp.), all were analyzed in singular studies. Currently, the prevailing belief in this field of study is that most, if not all, cyanobacteria are capable of producing BMAA,13,41 although controversy continues due to several studies reporting failures to detect the NPAA in their samples.16,39,42–53 While some non-detects can be arguably attributed to methods that are not fit-for-purpose37 or poor in sensitivity,39 the mechanisms driving BMAA production in cyanobacteria also have yet to be elucidated, complicating the assertion of whether any particular species are non-producers, or simply did not contain detectable levels of BMAA at the time of sampling. Figure 5. Global distribution of 50 studies detecting BMAA in environmental cyanobacterial samples in 28 countries between 2005 and 2022. (Lab strains with no specified source, symbionts, and food sources not considered) Colour indicates number of published studies including samples from the country; (XX%) indicates percentage of studies with positive BMAA detections. Figure generated with Datawrapper. 9 Table 1. Distribution of 55 literature articles on the presence of BMAA in various cyanobacterial genera Order Genus Number of Studies BMAA Detected References Chroococcales Chroococcus sp. 1 1 (100%) 29 Chroococcales Microcystis spp. 12 5 (42%) 13,29,41,45,47,48,52,54–58 Chroococcidiopsidales Chroococcidiopsis spp. 2 1 (50%) 58 Nostocales Anabaena spp. 9 6 (67%) 13,29,41,42,45,47,54,56,59 Nostocales Aphanizomenon spp. 5 1 (20%) 13,44,47,51,54 Nostocales Calothrix spp. 5 3 (60%) 13,41,47,54,56 Nostocales Chlorogloeopsis sp. 1 1 (100%) 13 Nostocales Cylindrospermopsis spp. 5 1 (20%) 13,44,45,48,51 Nostocales Dolichospermum spp. 2 2 (100%) 54,55 Nostocales Fischerella sp. 1 1 (100%) 13 Nostocales Nodularia spp. 6 5 (83%) 13,45,54,60–62 Nostocales Nostoc spp. 14 10 (71%) 13,43,47,49,52,57,58,60,62–67 Nostocales Scytonema spp. 2 1 (50%) 13,47 Oscillatoriales Arthospira sp. 1 0 (0%) 53 Oscillatoriales Lyngbya spp. 3 2 (67%) 13,29,47 Oscillatoriales Microcoleus spp. 3 2 (67%) 58,60,62 Oscillatoriales Oscillatoria spp. 3 3 (100%) 29,41,56 Oscillatoriales Phormidium spp. 5 4 (80%) 13,41,54,60,62 Oscillatoriales Planktothrix spp. 4 3 (75%) 13,41,47,54 Oscillatoriales Plectonema spp. 2 1 (50%) 13,47 Oscillatoriales Symploca sp. 1 1 (100%) 13 Oscillatoriales Trichodesmium sp. 1 1 (100%) 13 Pleurocapsales Myxosarcina spp. 2 2 (100%) 13,58 Pseudanabaenales Nodosilinea sp. 1 0 (0%) 58 Pseudanabaenales Pseudanabaena spp. 4 2 (50%) 41,54,56,58 Pseudanabaenales Romeria sp. 1 0 (0%) 58 Spirulinales Spirulina sp. 1 1 (100%) 54 Synechococcales Cyanobium spp. 4 3 (75%) 58,60,62,68 Synechococcales Leptolyngbya spp. 11 8 (73%) 29,41–43,54,58–60,62,69,70 Synechococcales Limnothrix spp. 2 2 (100%) 41,54 Synechococcales Merismopedia sp. 1 1 (100%) 29 Synechococcales Prochlorococcus sp. 1 1 (100%) 13 Synechococcales Synechococcus spp. 5 3 (60%) 13,47,58,62,68 Synechococcales Synechocystis spp. 9 5 (56%) 13,41,45,54,56,58–60,62 Synechococcales Woronichinia sp. 1 0 (0%) 54 Stigonematales 1 1 (100%) 71 Unspecified cyanobacteria 22 17 (77%) 16,25,27,32–34,46,50,55,58,61,70,72–81 Environmental samples containing cyanobacteria 6 4 (67%) 82–87 10 Verifying BMAA presence in cyanobacteria has been a global effort; as of 2023, 55 studies have been published testing environmental cyanobacterial samples collected from more than 28 different countries (Figure 5). 1.5.2 Proposed Pathways for the Biosynthesis of BMAA 1.5.2.1 The First Pathway; Transamination and Methylation The first investigation of a possible biosynthetic pathway for BMAA was conducted by Brenner et al. in 2003 using the cycad Cycas rumphii Miq. (related to Cycas micronesica Hill)88 as a model organism.89 The seed of this species has been found to contain BMAA in quantities ranging from 1.25 – 1.56 mg/g dry weight.20 To identify putative enzymes, mRNA was extracted from immature leaves of C. rumphii and used to construct a cDNA library, from which expressed sequence tags (ESTs) were generated. Brenner hypothesized that BMAA would be synthesized in a similar manner to other β-substituted alanines and suggested a two-step mechanism in which a β-substituted alanine is transaminated to 2,3-diaminopropanoic acid (2,3-DAP) and subsequently methylated to BMAA. (Figure 7a) The authors identified genes encoding enzymes predicted to be capable to performing these steps within the ESTs and defended their conclusion by claiming that they made their cDNA library from tissues that produced relatively large amounts of BMAA. However, the 1989 paper by Duncan et al. that is referenced clearly states their BMAA analysis was restricted to the seed endosperm, while Brenner’s cDNA library was constructed with immature leaf mRNA. Notably, there was no evidence at the time to suggest that either the seed or leaf tissue of cycad produced BMAA in situ, or even contained 2,3-DAP, so concrete conclusions regarding the biosynthetic pathway could not be drawn from this approach. It was not until 2010 that Marler et al. measured BMAA in cycad seeds grown purportedly without endophytic cyanobacteria,90 showing for the first time that the plant appeared to be capable of producing the compound on its own. However, the proof supplied for the omission of the endophyte was solely visual, based on the disappearance of the corraloid 11 root system, and was not corroborated with other biological or biochemical methods. Botanist Milindasuta previously published observations of corraloid roots in various cycads with missing distinctive green algal cortex layers,91 which other researchers have noted complicates in vitro studies distinguishing wild type and cyanosymbiont-free cycads.92 Marler’s study has not been replicated. 1.5.2.2 The Nitrogen Starvation Paradox Evidence seemingly contradicting Brenner’s hypothesized pathway was revealed in Downing et al.’s 2011 study in which 15N was used to track BMAA biosynthesis in cyanobacteria.93 The major conclusion of this work was that BMAA biosynthesis increased in cyanobacteria when they were depleted of nitrogen, but the transamination reaction proposed by Brenner would require high ammonium concentrations. Moreover, Downing found that when ammonium and nitrate were reintroduced to nitrogen-starved cyanobacteria, free BMAA concentrations decreased significantly. Experiments conducted on field phytoplankton samples and other strains of cyanobacteria corroborated that BMAA production negatively correlated with exogenous combined-nitrogen concentrations.94,95 These findings suggested that cyanobacteria likely do not use the simplified two-step pathway for synthesizing BMAA, but it remains possible that cycads employ a different mechanism. Likewise, experiments conducted in diatoms showed species and strain-specific responses to varying nitrogen conditions, suggesting that there could be different routes to BMAA production and that it may be a by-product of degradation pathways activated by environmental stress.96 1.5.2.3 Revisiting 2,3-DAP; the Steer towards Non-Ribosomal Peptide Synthesis In 2022, Mantas et al. used a bioinformatics approach to explore mechanisms for the 2,3-DAP-dependent biosynthesis of BMAA in plants, bacteria, and cyanobacteria.97 To date, four biosynthetic routes to 2,3-DAP have been elucidated in the plants Albizia julibrissin and Lathyrus sativus, and in bacteria from Streptomyces spp. and Staphylococcus spp.. Additionally, BMAA believed to be derived from 2,3-DAP has been identified in the non-ribosomal peptides 12 galantin and paenilamicin produced by Paenibacillus larvae.98 (Figure 7c) Genes encoding enzymes in these pathways were searched for in 130 cyanobacterial genomes, 103 Paenibacillus spp. genomes, and 2 BMAA-producing diatom genomes, summarized in Figure 6. Six cyanobacterial species contained both dihydropyrimidinase and β-ureidopropionase, but without dihydrouracil dehydrogenase this pathway seemed unlikely to lead to 2,3-DAP. The enzyme theorized to lead to 2,3-DAP in Lathyrus spp. has no deposited amino acid or nucleotide sequence, so although 98% of cyanobacteria contained variants of cysteine synthase, the relevance of this pathway could not be confirmed. Figure 6. Mantas et al. searched for genes encoding the above enzymes in pathways to 2,3-DAP (Albizia, Lathrus, VioKB, and SbnAB) and BMAA (pam) in cyanobacteria, Paenibacillus spp. and diatoms; (n/a indicates that these genomes were not searched). VioK was found in two species of cyanobacteria, but without adjoining VioB and not in an apparent metabolic cluster. In contrast, SbnA and SbnB were both found in Coleofasciculus chthonoplastes and Moorea producens; in the former, SbnAB were located in a metabolic cluster analogous to a known siderophore-producer, suggesting that 2,3-DAP may be incorporated into a siderophore structure in this species. In all four cases, no nearby 0/130 10/103 0/2 13 methyltransferases were located to indicate that BMAA would be produced. Finally, pam was found to be constricted to P. larvae and was not identified in 93 other Paenibacillus spp. genomes by the researchers; the genes involved in 2,3-DAP biosynthesis in pam are expected to be analogous to SbnAB since the active sites are conserved. 1.5.2.4 L-Amino Acid Oxidase and Glutamine Oxoglutarate Aminotransferase A 1991 paper by Hashmi & Anders showed that BMAA could be converted to its α-keto acid form (β-N-methylaminopyruvate) by L-amino acid oxidase (L-AAO) in vitro, after which it was rapidly decarboxylated to N-methylglycine using hydrogen peroxide.99 However, these reactions were not at all indicative of cellular conditions; BMAA was used at a 20 mM concentration and no competing amino acids were present. Enzymatic studies were not attempted again until 2016, when Downing et al. used isotope-labelled BMAA to continue investigating its role in cyanobacterial metabolism.100 By employing inhibitors of glutamine synthetase (GS) and glutamine oxoglutarate aminotransferase (GOGAT), they discovered that BMAA was a substrate of the latter in Synechocystis PC6803, again forming β-N-methylaminopyruvate as a product (Figure 7b). Confirming that BMAA acted as a substrate in the GS-GOGAT cycle and its nitrogen could be shuffled into downstream products is a useful puzzle piece, although it raises questions about how and why BMAA is accumulated in cyanobacteria. As well, it is unknown if all cyanobacterial GS-GOGAT enzymes have this capability, or if it is species-specific. 1.5.2.5 Current State of Research on the Biosynthetic Route to BMAA One of the key issues with the current state of research was aptly pointed out by Mantas et al.: without certainty of axenic phytoplankton cultures (only one-quarter of detection studies reported “axenic” cultures101 in their analyses) and growing knowledge that exogenous BMAA has allelopathic tendencies,102,103 the belief that the NPAA is ubiquitously produced by most or all phytoplankton may require re-examination. Further complicating the matter, it has been recently called into question whether truly axenic cyanobacterial cultures are even obtainable.101 14 As a result, it is possible that BMAA may be truly produced by only a subset of phytoplankton species, but is released and taken up by other members of the microbial community. A 2023 paper by Li et al. applied a transcriptomics approach to an axenic culture of the diatom Thalassiosira minima, discovering that BMAA production greatly increased under nitrogen-depleted conditions and when co-cultured with Cyanobacterium aponium.104 The mixing of species was additionally found to induce genes related to ubiquitination, autophagy, and COPII transport in the diatom, suggesting it initiates metabolic turnover processes to shift resources towards its defensive mechanisms. Likewise, Li et al. demonstrated multiple mechanisms by which exogenous BMAA disrupted the growth of microalga Isochrysis galbana, including G1 arrest, down-regulation of nitrogen metabolism, and induced protein misfolding through transcriptional interference.105 These recent data support an allelopathic function for BMAA. Little is known about the metabolic fate of BMAA in organisms; Downing’s work was the first to suggest that it could be metabolized to the keto acid form in vivo, but no other downstream products have been discovered. In vitro and in vivo studies in humans suggest that the amino acid is mostly unmodified by the body, as indicated by a previous study showing human hepatocyte and intestinal epithelial cells were unable to metabolize the amino acid, which is consistent with its models of toxicity.106 This implies that L-AAO is not likely to be involved in BMAA metabolism under physiological conditions. These findings suggest that phytoplankton have specific adaptations allowing them to metabolize the amino acid. Interestingly, cyanobacteria and photosynthetic eukaryotes contain a ferredoxin-dependent GOGAT isoenzyme which is not found in non-photosynthetic organisms,107 which could suggest that a mechanism for metabolizing BMAA has been conserved in algae and plants. 15 Figure 7 Currently proposed and known pathways to BMAA: a) Proposed two-step pathway to BMAA, beginning with the transamination of a β-substituted alanine to 2,3-DAP and subsequent methylation to BMAA; b) Proposed pathway to BMAA involving the hydrolysis of cyclic BMAA-β-NCO2; c) Structure of paenilamicin B1 with BMAA indicated in red (known pathway). 1.6 Is BMAA found in Plants? 1.6.1 Reported Detections of BMAA in Plants The occurrence and accumulation of BMAA and its isomers in the environment has been a growing field of interest ever since evidence for the biomagnification of these NPAAs was described in 2003. BMAA has been detected in one plant species— water milfoil (Myriophyllum spp.)108— and in cyanobacterial-plant symbioses such as water fern (Azolla filiculoides) and Gunnera kauaiensis.1 As well, studies have demonstrated that plants are able to accumulate BMAA from soil and culture media, such as alfalfa,28 wheat,109 and Chinese cabbage.110 The uptake of all other isomers of BMAA has not yet been examined in plants. Moreover, there have 16 been few investigations into the endogenous production of BMAA in plants, although DAB has been found in seeds from the Lathyrus genus45,111 and its biosynthesis has been documented in higher plants and bacteria.112 Preliminary work in this field focused on characterizing BMAA in Cycadales as all species in this order form symbiotic relationships with cyanobacteria.92 Unsurprisingly, 38/41 (93%) of the cycad species analyzed contained BMAA (Figure 8). Results from the aforementioned uptake studies indicated that controls (with no exposure to BMAA) did not have any detectable amounts of the amino acid, except for small amounts present in the roots of alfalfa plants, totalling 2/11 (18%) positive detections in green plants without known cyanobacterial associations. One study measuring BMAA production in C. micronesica seedlings without their endophytic cyanobacteria indicated that the plant endogenously produced the metabolite.90 In contrast, a similar study analyzing BMAA in the cyanobacteria-plant symbiont Hydrilla verticillata could not detect BMAA in the plant sample lacking the epiphyte.71 Outside of the select symbiotic model systems studied, there have been no explicit investigations into whether green plants natively contain and/or produce BMAA and its isomers. 17 Figure 8. Species tree showing the characterization of BMAA across green plants. Species in blue were identified as containing detectable BMAA via literature review. Major clades are represented by colour. Species symbolled with a green circle indicate that the plant contains a reported cyanobacterial symbiont; species symbolled with a yellow diamond are analyzed in this thesis. The phylogenetic tree was generated using phyloT v2 and visualized using iTOL v6.7.6. 1.6.2 Rationale for the Proposed Conservation of BMAA in Plants ` It is widely accepted that chloroplasts, the photosynthetic organelle responsible for photosynthesis in rhodophytes, green algae, and plants, are derived from an endosymbiotic event in which an early eukaryotic cell engulfed a cyanobacterium.113 While pinpointing the exact 18 time in Earth’s evolutionary history this plastid-forming event occurred remains elusive– estimates range from 600 million114 to 2.1 billion years ago113– the cyanobacterium sister to the plastid lineage has been confirmed to be Gloeomargarita sp. by phylogenetic analyses, with all other modern orders of cyanobacteria (the Pseudanabaenales, Synechococcales, Chroococcales, Nostocales, and Oscillatoriales) diverging after this event.115 Three distinct plastid lineages arose from the primary engulfment: (1) the Glaucophyte lineage, comprised of fifteen named species;116 (2) the Rhodophyte lineage, including red algae such as zooplankton, dinoflagellates, and diatoms; and (3) the Chlorophyte lineage, conserved in all green plants.117 BMAA and its isomers have been previously detected in red algae,72,96,118–122 including in axenic cultures,104 suggesting the conservation of BMAA biosynthesis in this plastid lineage. Therefore, as the closest diverging branch to the Rhodophytes, it is possible that bioysynthesis of this NPAA may have also been conserved in green plants. Figure 9. Phylogenetic tree showing evolutionary divergence of cyanobacteria from plastid lineages; adapted from Moore et al.115 Red box indicates cyanobacteria and red algae with confirmed detections of BMAA reported in the literature. 19 1.7 Hypothesis and Objectives The overall goal of this thesis is to determine whether BMAA and/or its naturally occurring isomers are synthesized by plants. I hypothesized that BMAA biosynthesis was conserved along chlorophyte evolution. To investigate this hypothesis, I had the following objectives: (1) To determine whether BMAA, AEG, DAB, and BAMA biosynthesis in cyanobacteria is influenced by microbial community dynamics. (2) To determine whether BMAA, AEG, DAB, and BAMA are present in plant species representing the evolutionary “tree of life”. (3) To determine whether BMAA could be taken up by plants from their environment. (4) To determine whether environmental BMAA could account for its presence in plants. 20 Chapter 2: Non-Protein Amino Acids in Cyanobacteria from Lake Winnipeg Blank space for formatting 2.1 Synopsis Recent literature has identified two obstacles to understanding the presence of BMAA and its naturally occurring isomers in cyanobacteria. Firstly, only 25% of detection studies in phytoplankton employed “axenic” cultures,97 and more crucially, the ability to isolate axenic cyanobacterial cultures is considered near-impossible.101 Therefore, the impact of contaminant microbial species on NPAA biosynthesis in cyanobacterial cultures still requires determination. Chapters 2, 3, and 4 of this thesis will explore the differences in NPAA biosynthesis between crude and isolate cyanobacterial cultures. This chapter describes the methods used to purify crude cyanobacterial cultures, to characterize the complexity of the culture communities, to putatively identify the organisms and to quantify BMAA, AEG, DAB, and BAMA using UPLC-MS/MS in cyanobacterial cultures collected from Lake Winnipeg, Canada. 2.2 Introduction The PlantSMART lab has an ongoing partnership with the Lake Winnipeg Research Consortium (LWRC), which regularly collects water and bloom samples at its 65 research stations (Figure 21) every spring, summer, and fall. In 2018, Bishop et al. reported the presence of BMAA and its isomers in bloom samples collected from Lake Winnipeg and in in vitro cultures rapidly established after sampling.78 The PlantSMART lab maintained a collection of crude in vitro cultures of bloom samples collected between 2016 – 2019 that were previously analyzed for NPAAs in 2019.123 Additionally, in October 2021, water samples collected by the research 21 vessel “Namao” from the LWRC were acquired. These crude cultures and new field samples were purified to establish isolate cyanobacteria cultures for analysis in this thesis. 2.3 Hypothesis and Objectives I hypothesized that crude and isolate cyanobacterial cultures from Lake Winnipeg would produce different amounts of BMAA, AEG, DAB, and BAMA. To test this hypothesis, my objectives were: (1) To establish isolate cyanobacterial cultures from crude and bloom culture samples collected from Lake Winnipeg. (2) To characterize the microbial communities of the crude and isolate cultures via bioinformatic analysis of their 16s rRNA metagenomes. (3) To quantify the BMAA, AEG, DAB, and BAMA produced in the isolate cultures using UPLC-MS/MS for comparison with previously analyzed crude cultures. 2.4 Experimental Methods 2.4.1 Cyanobacterial Culture Isolation 2.4.1.1 Subculturing Protocols Cyanobacterial cultures were maintained on solid BG11 media (Blue-green media; Phytotechnology Laboratories #B1511, lot #AKR1511004A) in sterile Petri dishes (VWR, Mississauga, Ontario) solidified with 1% (w/v) agar (Fisher Scientific, Ottawa, Ontario). All media were sterilized by autoclaving to 121 °C and 15 psi for 20 minutes (Steris, Mentor, OH, USA). Stock cultures were grown in a sterile controlled growth environment room (25 °C, 16:8 photoperiod) illuminated with cool white fluorescent lamps (Lumenari Horticulture, Richmond, British Columbia) at a light intensity of 25 – 45 µmol·m2·s-1 and positive pressure (+2.9 – 3.7 Pa) under HEPA-filtered air. Light intensity was measured with a Li-Cor Biosciences LI-250A Light Meter. 22 Cyanobacterial subculturing was performed in a biological safety cabinet (LabConco Purifier Class II Biosafety Cabinet, Delta Series #3621204, Kansas City, Missouri) to ensure sterility. To prepare for subculturing, the BSC was irradiated with UV-C light for at least 15 minutes to decontaminate interior surfaces. The interior along with all tools and objects were surface-sterilized with 70% ethanol. A sterile inoculation loop (Nunc™ Disposable Loops and Needles; Thermo Scientific) was used to collect a pinpoint amount of cyanobacterial cells from the previous culture, which was then spread across a fresh agar plate. As few cells as possible were transferred in order to minimize the number of contaminant bacteria. After subculture, the Petri dishes were securely wrapped with Parafilm™ M Wrapping Film (Fisher Scientific) to prevent external contamination and retain moisture. To assess culture quality, cyanobacterial cultures were routinely checked under a Nikon SMZ745 stereo microscope at 3X magnification to monitor the presence of concurrently growing organisms. Images of common contaminants are shown in Figure 10. Culture quality was ranked qualitatively on a scale of 1 – 5, with 1 indicating heavy contamination (i.e., no regions of uncontaminated cyanobacterial growth) and 5 meaning no visible contamination. Once the cultures were fully established and cyanobacterial growth appeared to plateau, after approximately three weeks, they were checked under the stereoscope and clean regions were marked for subculturing to gradually purify the samples of contaminants. Cyanobacteria were subcultured approximately every 2 months or as needed. 23 Figure 10. Most common types of contamination present in cyanobacterial cultures; from left to right, top to bottom: a) Grey burst-like appearance, common in early culture growth; b) Grey globular colonies, often interacting with cyanobacteria; c) Grey, fungal-like spread from cyanobacterial colonies; d) Small, spherical blue-tinged bacteria that distributed across the culture; e) Reddish to yellow globular colonies, also often interacting with cyanobacteria; and f) Reddish, brownish, or blueish tendrils intertwined with cyanobacterial colonies 2.4.1.2 Generation of New Isolates In October 2021, water samples collected by the research vessel “Namao” from the Lake Winnipeg Research Consortium were acquired. In order to establish new isolate cultures, the procedure shown in Figure 11 was followed. In brief, the water samples arrived in 50 mL Falcon tubes (Corning CentriStar; Corning, New York, USA) and were acclimatized to the controlled growth environment for 1 week. To generate crude cultures, the tubes were vortexed to resuspend organic matter, then 100 μL aliquots were transferred onto BG11 agar plates and spread to form a thin film on the plate surface. After around three weeks, colonies began to appear. The crude cultures were inspected stereoscopically to identify the number of unique 24 cyanobacterial colony morphologies; these were then marked and transferred onto new agar plates and subject to the same subculturing protocols described in the previous section. Figure 11. Overview of the procedure for isolating monocyanobacterial cultures from environmental samples. Figure created using Biorender. 2.4.2 Metagenomic Analysis Prokaryotic organisms contain a 16S ribosomal RNA (rRNA) gene, which is approximately 1500 base pairs long and is conserved except for nine variable regions which can be used for phylogenetic classification.124 In this experiment, the aim was to identify the microbial communities of the cyanobacterial cultures by amplifying the V3 and V4 regions of the 16S rRNA genes following the Illumina MiSeq protocol and subjecting the results to bioinformatic analysis. 25 2.4.2.1 Genomic DNA Extraction Cyanobacterial samples for metagenomic sequencing were collected directly from the BG11 agar plates. Colonies were collected from the center regions of the plate in order to sample the representative community as accurately as possible with minimal bias. Samples for BCA and UPLC-MS/MS were collected at the same time from the sample culture plates to ensure that the biological interpretations of the results would be relevant. The samples were stored at -20 °C until analysis. The process is shown in briefly in Error! Reference source not found.. Figure 12. Sample collection and 16S rRNA metagenomic sequencing. Figure generated with Biorender. Genomic DNA (gDNA) was extracted from the fifty samples using the Soil DNA Isolation Kit (Norgen Biotek #64000) with some additional modifications to increase disruption of the cyanobacterial membranes. The frozen cell pellets were resuspended in 750 μL kit-provided Lysis Buffer G and vortexed for several seconds; the resuspensions were then transferred to kit-provided bead tubes. 200 μL of kit-provided Lysis Additive A and 50 μL of 0.2 μg/μL lysozyme (Acros Organics, egg white lysozyme #61193-0050) were then added, and the samples were vortexed at maximum speed for 10 minutes, followed by sonication for 5 minutes. The samples were pelleted (16 000 x g, 2 min) and the supernatant transferred to a fresh tube. 100 μL of Binding Buffer I was added and the tubes were inverted 8x and incubated at 4 °C for 5 minutes. 26 They were centrifuged (16 000 x g, 4 min) and the supernatant again transferred. 50 μL of OSR solution was added and the tubes were inverted 8x and incubated at 4 °C for 5 minutes. They were centrifuged (16 000 x g, 4 min) and 700 μL of the supernatant was transferred to fresh tubes. 400 μL of Lysis Buffer QP and 550 μL of ethanol were added and the samples were briefly vortexed and then passed through kit-provided spin columns (3 x 11 000 x g, 30s). 550 μL Binding Buffer B was passed through the spin columns (11 000g, 1 min), then they were washed with 500 μL Wash Solution A (2 x 11 000 x g, 1 min). The columns were dried by centrifugation (16 000 x g, 4 min) and the columns transferred to the elution tubes, to which 50 μL Elution Buffer B was added. The elution tubes were incubated for 1 minute at room temperature, and finally the gDNA was collected by elution (11 000 x g, 1 min). Eluted gDNA was stored at -20 °C until further analysis. The concentration and quality (A260/A280, and A260/A230) of gDNA in the samples were analyzed using an Implen NanoPhotometer NP80. The kit-provided Elution Buffer B was used to blank the NanoPhotometer for all measurements. 2.4.2.2 16s rRNA Metagenomic Sequencing Illumina MiSeq 16S rRNA sequencing of the samples was performed at the Sequencing + Bioinformatics Consortium at the University of British Columbia in September 2022. A 25 μL aliquot of each gDNA sample was submitted. The V3 and V4 regions of the 16S rRNA genes were PCR amplified using literature-derived bacterial primers and then modified with Illumina sequencing adaptors and dual-index barcodes with the Nextera XT DNA Index kit. The libraries were then pooled, normalized, and sequenced with a 2 x 250 bp read length. 2.4.3 Bioinformatic Analysis and Species Identification Illumina MiSeq Reporter (v2.6.2.3) was used to generate bioinformatic reports with the taxonomic information for each sample. The complete MiSeq taxonomic report for all samples can be found in Appendix A. Illumina MiSeq Metagenomic Results. In addition, the 27 sequences were processed using DADA2125 and phyloseq.126 Micrscope images of all analyzed cultures were taken for morphological identification. 2.4.3.1 DADA2 Taxonomic Identification The DADA2 Pipeline (v1.16)125 was followed to process the Illumina paired-end fastq files. In brief, the read qualities of the samples were inspected and filtered; the first 20 and final 10 nucleotides from each sequence were trimmed to remove primer sequences and poor-quality regions. The forward and reverse reads were then merged and chimeras removed. Taxonomy was assigned using CyanoSeq v1.1.2, a curated cyanobacterial 16s rRNA database containing 5411 cyanobacterial sequences,127 and SILVA v138.1, a database containing more than 2 million 16s/18s rRNA bacterial, archaiac, and eukaryotic sequences.128 Taxonomic assignments were performed at bootstrap levels of 50, 80, and 95. Cyanobacterial amplicon sequence variants (ASVs) were further extracted and subjected to taxonomic alignment using NCBI MOLE-BLAST. Shannon and Simpson indices of alpha diversity, and Bray-Curtis distances for beta diversity were calculated using phyloseq v1.44.126 2.4.3.2 Microscope Imaging and Visual Identification For microscopic imaging, small samples of each culture were resuspended in phosphate-buffered saline [PBS; 8 g/L sodium chloride (Fisher Scientific); 0.2 g/L potassium chloride (Fisher Scientific); 1.44 g/L sodium phosphate dibasic (Sigma Aldrich); 0.245 g/L potassium phosphate monobasic (Fisher Scientific); pH 7.4] buffer and mounted onto microscope slides fitted with cover slips. Each culture was imaged on an Olympus IX83 Inverted microscope at 40x magnification with colour and differential interference contrast (DIC); resultant images were processed in ImageJ v1.51. Putative cyanobacterial orders for samples unidentified by metagenomic sequencing were assigned by visual inspection of the microscopic images using the morphological characteristics described by Komárek et al. in 2014,129 the Cyanobacterial Key developed by 28 Robin A. Matthews and Geoffrey B. Matthews at Western Washington University in 2018 (http://snoringcat.net/cyanobacteria_key/index.html), the cyanobacterial gallery maintained by Dr. Jason Oyadomari at Finlandia University (https://www.keweenawalgae.mtu.edu/index.htm), and the taxonomic key in the 2018 “A Guide to Cyanobacteria; Identification and Impact” by Mark A. Nienaber and Miriam Steinitz-Kannan.130 The primary features used to assign the orders are as follows: (1) Nostocales: monolithic filamentous chains, sometimes enclosed in mucilaginous colonies. (2) Pseudanabaenales: medium-length filamentous chains with distinct crosswalls, lacking aerotopes and heterocysts. (3) Synechococcales: small (<10 micron) spherical cells, mostly solitary. (4) Chroococccales: large (8-15 micron) spherical cells, persist in colonies of 2, 4, or more (5) Plants/Algae: cultures that did not appear to fit in any cyanobacterial orders; consisted of plant-like structures or large (15-25 micron) crescent-shaped cells with some visible inner structures. 2.4.4 Quantification of Non-Protein Amino Acids Methods for the detection and quantification of BMAA, AEG, DAB, and BAMA have been previously published and validated.12,13,24,26,78 These methods are described below including all minor modifications. 2.4.4.1 Sample Collection and Derivatization Approximately 10-20 mg of fresh cells were collected directly from each Petri dish into pre-weighed microcentrifuge tubes and analyzed without lyophilization. Samples for UPLC-MS/MS analysis and metagenomic analysis were collected concurrently from the same culture plates to ensure biological relevancy of the results. The free amino acid portion was extracted by pipetting 200 μL of 0.1 N trichloroacetic acid (TCA; Sigma Aldrich) to each sample and vortexing at full speed for 30 seconds. The samples were then pelleted for 5 min at 13,000 rpm 29 and the supernatant was pipetted into a centrifuge filter tube (Ultrafree®-MC, 0.22 µm PVDF membrane; Millipore) and centrifuged for 5 min at 13,000 rpm to yield the filtered free amino acid portion. The pellet was resuspended in 200 μL 0.1X phosphate-buffered saline (PBS) (pH 7.4); a 100 μL aliquot was saved for protein quantification via bicinchoninic acid (BCA) assay, and the remaining 100 μL were transferred to a glass hydrolysis vial (15 x 45 mm; Fisher Scientific). 900 µL of 6 N hydrochloric acid (HCl; Fisher Scientific) were added to the hydrolysis vial, purged with nitrogen gas for 30 sec, then hydrolyzed at 110 °C for 18 – 22 hours on a heating block (VWR Standard Dry Block Heater). After cooling, 500 µL of sample was pipetted into a centrifuge filter tube (Ultrafree®-MC, 0.22 µm PVDF membrane; Millipore) and centrifuged for 5 min at 13,000 rpm to remove debris. A 20 µL aliquot of the filtrate was placed into a microfuge tube and dried to complete dryness in a Speedvac (Labconco Centrivap, VWR). The dried filtrate was then reconstituted with 80 μL 0.2 M borate buffer (pH 10). For AQC derivatization, 10 μL of each reconstituted sample was diluted with 30 μL 0.2 M borate buffer (pH 10) and derivatized with 10 µL AccQ-Fluor reagent (Waters Corp., Mississauga, ON, Canada; reconstituted as per the kit instructions). Complete reaction was ensured by vortex mixing (Vortex Genie 2; Scientific Industries, Bohemia, USA) and incubation for 10 minutes at 55 °C prior to analysis. The 10 µL aliquot of derivatized sample was then transferred to an autosampler vial (2 mL amber glass with pre-slit Teflon-coated caps; Waters Corp.) fitted with a conical bottom spring insert (250 µL glass; Canadian Life Science, Peterborough, ON, Canada) for analysis by UPLC-MS/MS. 2.4.4.2 Preparation of Standards Samples were quantified by comparison to authentic standards (BMAA: Sigma Aldrich, CAS No. 16012-55-8; AEG: Sigma Aldrich, CAS No. 24123-14-6; DAB: TCI America, CAS No. 1883-09-06; and a custom synthesis of BAMA; see Bishop et al. 2020)87. NPAA structures and fragments used for quantification and qualification are shown on the following page. 30 Figure 13. Structures of BMAA, AEG, DAB, and BAMA prior to and post-derivatization with AQC, and respective quantifier and qualifier ion transitions generated after fragmentation. Standards were prepared using initial stock solutions made at 65 µmol/mL NPAA in 0.1 N TCA. Three 10-fold dilutions were performed, preparing concentrations of stock solutions at 0.065 µmol/mL for each isomer. Standard 1 was prepared by combining 100 μL of each 0.065 µmol/mL NPAA stock solution together with 600 μL of 0.1 N TCA. From this, a 6-step serial dilution series (Standards 2 – 7) was prepared from the higher concentration stock by taking 250 µL of the previous standard and combining it with 750 µL of 0.1 N TCA to create the next standard. These stock standards were stored at -20 °C. 2.4.4.3 UPLC-MS/MS Parameters 10 μL aliquots of all samples were injected and analyzed by UPLC-MS/MS (Waters Acquity I-Class BSM, SM-FTN, column heater/cooler; Waters Xevo TQ-S with ESCi probe, MassLynx 4.1) with electrospray ionization (ESI) and multiple reaction monitoring (MRM) on a reverse phase Waters Acquity UPLC BEH C18 (2.1 x 100 mm; 1.7 µm) column. A 12-minute gradient-elution of 20 mM ammonium formate at pH of 5.0 adjusted with formic acid (Fisher) 31 (Solvent A) and methanol (Fisher, Optima™ LC/MS grade) (Solvent B) at a flow rate of 0.3 mL/min as follows: Initial: 90% A: 10% B; 7.0 min: 50% A: 50% B; 7.5 min: 25% A: 75% B; 9.6 min: 90% A: 10% B. The needle and seal wash solvent consisted of 50% methanol (Fisher, Optima™ LC/MS grade) and 50% water (Millipore Ultrapure, 18.2 MΩ cm). The capillary voltage was 4.75 kV*, cone voltage was 8.00 V with a source offset of 20.0 V. The source temperature was 150 °C and the desolvation temperature was 550°C. The cone and desolvation gas (N2) were set to 150 L/hr and 800 L/hr, respectively. Argon gas was used in the collision cell and the flow was set to 0.30 mL/min. The nebulizer gas flow was set to 7.00 bar. Data were acquired using multiple reaction monitoring (MRM) with previously optimized transitions (Table 2) with MassLynx v4.1 and acquired data was processed using TargetLynx v4.1 (Waters). Table 2. MRM transitions for NPAAs using validated UPLC-MS/MS method with AQC derivatization. Analytes Electrospray Ionization Mode Retention Time (min) % RSD Transition (m/z) Cone Voltage (V) Collision Voltage (V) BAMA + 5.04 0.30% 459 > 171 459 > 119 459 > 289 16 16 16 30 18 15 AEG + 5.47 0.27% 459 > 171 459 > 119 459 > 289 459 > 214 (qualifier) 16 16 16 16 30 18 15 20 BMAA + 5.70 0.36% 459 > 171 459 > 119 459 > 289 459 > 258 (qualifier) 16 16 16 16 30 18 15 20 DAB + 5.98 0.23% 459 > 171 459 > 119 459 > 289 459 > 188 (qualifier) 16 16 16 16 30 18 15 20 *Due to damage to the power supply of the mass spectrometer during the data acquisition period, a higher capillary voltage was necessary to maintain expected sensitivity. Typically when this method is used, a capillary voltage of 0.75 – 1.50 kV is applied. 32 2.4.5 Quantification of Total Protein To quantify total protein content, bicinchoninic acid (BCA) assays were performed using the Thermo Scientific PierceTM BCA Protein Assay Kit (#23225). Eight bovine serum albumin (BSA) standards were prepared, in concentrations of 2000, 1500, 1000, 750, 500, 250, 125, and 25 μg/mL. The Working Reagent was prepared by mixing the kit-supplied Reagents A and B in a ratio of 50:1. The total protein in the pellet (bound fraction) was quantified; the reserved 100 μL aliquots were resuspended in 900 μL E-pure H2O and vortexed until fully recombined. Cyanobacterial pellets with more resilient membranes (i.e., the pellet did not break apart by simple vortexing) were subjected to the following procedure: (1) auto-vortexing at the highest setting for 5 minutes; (2) sonication for 10 minutes; (3) auto-vortexing at the highest setting for an additional 10 minutes; (4) crushing with disposable pestles (Kontes Pellet Pestle; Fisher Scientific). Once pellets were fully disrupted and resuspended, 25 μL of standard or samples were pipetted into the wells of clear-bottom white 96-well microplates in triplicate. 200 μL of Working Reagent was then pipetted into each well, and the plate gently mixed for 30 seconds prior to covering with aluminum foil and incubation at 37 °C for 30 minutes. After incubation, the plate was allowed to cool to room temperature, and then the absorbance of each well was read at 562 nm on a Biotek Epoch Microplate Reader using the Gen5 (v3.04) software. Data were processed in Microsoft Excel®. 2.4.6 Statistics Data from the chromatographic analysis of NPAAs were collated in Excel® to calculate concentrations of each isomer in each sample. Graphs were generated using ggplot2 and Excel®. Statistically significant differences between crude and isolate, and bound and free NPAA concentrations were calculated using unpaired t-tests in Excel® (α=0.05). Values falling below the lowest standard (LLOQ) but with signal-to-noise ratios ≥ 3 were considered Detected (“D”). Values with signal-to-noise ratios <3 were considered Not Detected (“ND”). All values are 33 expressed as averages ± standard error. Not detected (ND) values were replaced with 1/10th of the MDL to calculate statistical differences. Combined NPAA values were calculated by summing the bound and free NPAA portion for each replicate measurement. A visual summary of the experiment described in this section is shown below. Crude cultures analyzed in 2019 were subject to the same experimental methodology as described herein for isolate cultures analyzed in 2022. Figure 14. Overview of the NPAA quantification experiment described in this section. Figure generated using BioRender. 34 2.5 Experimental Results 2.5.1 Cyanobacterial Culture Isolation An overview of previously analyzed crude cultures is tabled below: Table 3. Descriptions of crude Lake Winnipeg cyanobacterial cultures analyzed in 2019. Sample Name Month Collected Lake Winnipeg Station Location in Lake Bloom_2017_crude1 Oct 2017 Bloom n/a Bloom_2019_crude1 Sept 2019 Bloom n/a Bloom_2019_crude2 Sept 2019 Bloom n/a North_2018_crude1 Aug 2018 North Basin North Basin North_2018_crude2 Aug 2018 North Basin North Basin Stn1_2017_crude1 Aug 2017 Station 1 South Basin Stn12B_2019_crude1 Sept 2019 Station 12B South Basin Stn12B_2019_crude2 Sept 2019 Station 12B South Basin Stn19_2018_crude1 Oct 2018 Station 19 North Basin Stn20_2018_crude1 Aug 2018 Station 20 North Basin Stn20_2018_crude2 Aug 2018 Station 20 North Basin Stn3C_2017_crude1 Aug 2017 Station 3C South Basin Stn53_2019_crude1 Sept 2019 Station 53 North Basin Stn53_2019_crude2 Sept 2019 Station 53 North Basin Stn5NS_2017_crude1 Aug 2017 Station 5 NS South Basin Stn5NS_2017_crude2 Aug 2017 Station 5 NS South Basin Stn7NS_2017_crude1 Aug 2017 Station 7 NS South Basin Stn7NS_2017_crude2 Aug 2017 Station 7 NS South Basin Stn9_2019_crude1 Sept 2019 Station 9 South Basin StnW1_2018_crude1 Aug 2018 Station W1 North Basin StnW1_2018_crude2 Aug 2018 Station W1 North Basin StnW7_2018_crude1 Sept 2018 Station W7 North Basin StnW7_2018_crude2 Sept 2018 Station W7 North Basin StnW9_2018_crude1 Sept 2018 Station W9 South Basin StnW9_2018_crude2 Sept 2018 Station W9 South Basin StnW9_2019_crude1 Sept 2019 Station W9 South Basin StnW9_2019_crude2 Sept 2019 Station W9 South Basin 35 Descriptions of isolate cyanobacterial cultures generated and analyzed in this experiment are provided below: Table 4. Descriptions of isolate cultures established from crude cyanobacterial cultures collected from Lake Winnipeg and analyzed in 2022. Culture Name Month Collected Lake Winnipeg Station Location in Lake Culture Morphology Isolate cultures established from previously maintained crude Lake Winnipeg cyanobacterial cultures Bloom_2019_isolate1 Sept 2019 Bloom n/a Small to medium bright green colonies Stn1_2017_isolate1 Aug 2017 Station 1 South Basin Small to medium dark green colonies Stn19_2018_isolate1 Oct 2018 Station 19 North Basin Medium to large dark green colonies Stn20_2018_isolate1 Aug 2018 Station 20 North Basin Small to medium olive-green colonies StnW1_2016_isolate1 July 2016 Station W1 North Basin Small dark green colonies StnW4_2016_isolate1 July 2016 Station W4 North Basin Small light to dark green colonies StnW9_2018_isolate1 Sept 2018 Station W9 South Basin Splotchy green colonies Isolate cultures established from newly generated crude Lake Winnipeg cyanobacterial cultures Stn19_2021_isolate1 Sept 2021 Station 19 North Basin Bright green colonies Stn19_2021_isolate2 Sept 2021 Station 19 North Basin Bright green cauliflower-like colonies Stn19_2021_isolate3 Sept 2021 Station 19 North Basin Thick medium olive-green colonies Stn19_2021_isolate4 Sept 2021 Station 19 North Basin Fuzzy bright green spread Stn5NS_2021_isolate1 Oct 2021 Station 5 NS South Basin Small to medium turquoise colonies Stn5NS_2021_isolate2 Oct 2021 Station 5 NS South Basin Turquoise green with spreading Stn5NS_2021_isolate3 Oct 2021 Station 5 NS South Basin Small light green colonies StnW4_2021_isolate1 Oct 2021 Station W4 North Basin Pale green bubbly colonies StnW4_2021_isolate2 Oct 2021 Station W4 North Basin Medium dark green colonies The records of isolate culture quality tracking over the course of the experimental period are visualized in Figure 15. Cyanobacterial cultures were subcultured an average of 6.4 times in the 445 day period, corresponding to a subculture period of approximately every 70 days. 36 Figure 15. Culture quality tracking spanning a period of 445 days. The lines are normalized to dates of subculturing to show progression over time. The colours used correspond to the qualitative 1 – 5 ranking (1=magenta, 2=orange, 3=yellow, 4=lime green, 5=green). Grey indicates that a subculture was created, but no growth of any kind was observed. White circle denotes subculture used for NPAA quantification and 16S rRNA metagenomic sequencing. An alluvial diagram illustrating the flow of culture quality across the four 111-day periods can be seen in Figure 16. For each time period, the median culture ranking was used, rounded down to the nearest integer value to avoid overinflating the result. 37 Figure 16. Alluvial diagram showing median culture ranking during four 111-day periods over the course of the cyanobacterial culture purification process. “Good” scores defined as qualitative rankings of 4 or 5; “Poor” scores defined as qualitative rankings of 1 or 2. Dashed lines follow the ratio of “poor” (dark blue) and “good” (light blue) scores across the time periods. Diagram generated with RAWGraphs.io. From Fall 2021 (Period 1) to Winter 2022 (Period 2), a decrease in culture quality is observed during the establishment of new isolates. From Winter 2022 to Summer 2022 (Period 3), the proportion of high culture rank (4 and 5) increased in a linear pattern during the purification stage. Samples were collected for analysis in Period 3. Between Summer 2022 and Fall 2022 (Period 4), the culture quality appeared to plateau. 2.5.2 Metagenomic and Bioinformatic Analyses 2.5.2.1 Genomic DNA Quality The gDNA concentration and quality, and Illumina MiSeq quality metrics for isolate cultures analyzed in 2022 are shown in Table 5 on the following page. 38 Table 5. Genomic DNA quality metrics and Illumina MiSeq metrics of analyzed isolate cultures in 2022. Sample Name gDNA Quality Metrics Illumina MiSeq Metrics gDNA (ng/µL) A260/ A280 A260/ A230 # of Reads # of Reads Passing Quality Check % of Reads Passing Quality Check Cyano-bacteria detected? Bloom_2019_isolate1 15.6 2.182 0.984 21538 16528 76.7 No Stn1_2017_isolate1 50.2 1.760 0.501 27217 20798 76.4 No Stn19_2018_isolate1 82.0 1.864 0.723 15632 11894 76.1 No Stn19_2021_isolate1 81.0 1.841 0.401 21497 16841 78.3 Yes Stn19_2021_isolate2 41.0 2.152 1.771 21439 16759 78.2 Yes Stn19_2021_isolate3 100.4 1.732 0.620 21121 16662 78.9 No Stn19_2021_isolate4 61.2 1.712 0.557 23059 17689 76.7 Yes Stn20_2018_isolate1 18.5 2.056 0.804 13842 10564 76.3 No Stn5NS_2021_isolate1 206.7 1.911 0.861 23406 18657 79.7 Yes Stn5NS_2021_isolate2 51.5 1.712 0.667 14871 11557 77.7 Yes Stn5NS_2021_isolate3 29.7 1.650 0.476 13230 10457 79 Yes StnW1_2016_isolate1 43.4 2.109 0.371 17041 13410 78.7 Yes StnW4_2016_isolate1 14.1 2.043 0.313 20261 15547 76.7 Yes StnW4_2021_isolate1 10.1 1.853 0.732 20545 15651 76.2 Yes StnW4_2021_isolate2 98.4 1.662 0.524 20913 16370 78.3 Yes StnW9_2018_isolate1 11.7 1.887 0.258 18415 14451 78.5 No Of the 16 isolate samples submitted, cyanobacteria was detected in 10 samples (63%) by Illumina MiSeq software. The results were further analyzed to determine whether any quality metrics correlated with positive cyanobacterial detection by Illumina MiSeq taxonomic identification. This analysis is visualized in Figure 17. None of the DNA quality metrics (gDNA concentration, A260/A280, A260/A230) or Illumina MiSeq metrics (Reads generated, reads passing quality check, % of reads passing quality check) were significantly different between samples with positive or negative detections of cyanobacteria (α=0.05) by unpaired t-test. 39 Figure 17. a) gDNA concentration of isolate cultures with detected (green) and not-detected (white) cyanobacteria; b) A260/A280 and A260/A230 ratios of samples with detected (green) and not-detected (white) cyanobacteria; c) Number of reads generated and number of reads passing quality check for samples with detected (green) and not-detected (white) cyanobacteria. No statistically significant differences in any metric detected by unpaired t-test (α=0.05). 40 2.5.2.2 Metagenomes from Illumina MiSeq Figure 18. Metagenomic profiles of a) crude and b) isolate cultures computed via the Illumina MiSeq bioinformatic pipeline. Barplots show relative abundance of identified orders from the Proteobacteria (purple-yellow gradient), Firmicutes (blue), Actinobacteria (mahoghany), Cyanobacteria (green to teal gradient), Sphingobacteria (brown), Bacteroidota (orange), Spirochaetes (magenta), other bacteria (light grey), and unidentified species (dark grey). 41 The metagenomic composition of crude cyanobacterial cultures analyzed in 2019 and isolate cultures analyzed in 2022 as determined by Illumina MiSeq taxonomic analysis are visualized in Figure 18. On average, 34 classifications to at least class level were made for the crude cultures, while 11 classifications were determined for the isolate cultures. See Appendix A. Illumina MiSeq Metagenomic Results for full metagenomic results. 2.5.2.3 Metagenomes from DADA2 The metagenomic composition of crude cyanobacterial cultures analyzed in 2019 and isolate cultures analyzed in 2022 as computed using DADA2 is visualized on the following page. The assignment of cyanobacteria was performed at three bootstrap levels- 50 (default), 80, and 95; however, no difference in cyanobacterial order classification was observed with increased bootstrap level (see Appendix B. DADA2 Bootstrap Comparison) The isolate cultures contained relatively higher proportions of species from the Cyanobacterial phylum compared to the crude cultures. Furthermore, no species from the Firmicutes were detected in the isolate cultures, while they were present in 100% of the crude cultures. However, Proteobacteria were persistent in 11 of the isolates. The phylogenetic classification using NCBI MOLE-BLAST yielded no matches for any of the extracted ASVs. 42 Figure 19. Metagenomic profiles of crude and isolate cultures, computed using the DADA2 bioinformatic pipeline and taxonomy assigned using SILVA v138.1 and CyanoSeq v1.2 databases (bootstrap level=50). 2.5.2.4 Phylogenetic Diversity Alpha and beta diversity measures of the crude and isolate cyanobacterial cultures computed with phyloseq v1.44 are shown on the following page. The Shannon and Simpson measures of alpha diversity were both significantly lower in the isolate cultures compared to the crude cultures (p<0.001). The ordination plot (Figure 20b) revealed a clustering of the crude cultures indicating similarity in species composition. 43 Figure 20. a) Alpha diversity measures (Shannon, Simpson) of crude cultures compared to isolate cultures, coloured by lake location (orange = lake bloom, green = north basin, blue = south basin). Statistically significant differences calculated by unpaired t-test (α=0.05). b) Beta diversity measure (Bray-Curtis distances) of crude cultures (circles) compared to isolate cultures (triangles). Clustering of crude cultures outlined in yellow. 44 2.5.3 Species Identification Microscope images of crude and isolate Lake Winnipeg cyanobacteria are below: Figure 21. 40X magnification images of crude (greyscale, cyanobacteria highlighted in yellow) and isolate (colour) cyanobacteria. Putative assignments based on morphological characteristics; confirmed assignments based on 16S rRNA taxonomic identification. Lake Winnipeg map data acquired from Lake Winnipeg Basin Information Network and processed in QGIS v3.28.0. Station map acquired from LWRC. 45 Of the 16 isolate cultures analyzed, two cultures (Stn5NS_isolate1 and Stn5NS_isolate2) were successfully identified as Prochlorococcus marinus. This assignment was consistent between Illumina MiSeq taxonomic identification, DADA2 taxonomic identification, and morphology of the cyanobacterium. The rest of cultures were tentatively identified; StnW4_isolate1 and Stn1_isolate1 resemble Gloeocapsa sp., StnW4_isolate2 and Stn5NS_isolate 3 resemble eukaryotic alga Chlorogonium sp., while the rest were putatively assigned to either the Synechococcales or Chroococcales as per previously described methods 2.5.4 UPLC-MS/MS Quantification of Non-Protein Amino Acids 2.5.4.1 UPLC-MS/MS Method Performance Metrics Chromatograms of quantifier and qualifier transition ion peaks for Standard 3 are below: Figure 22. Chromatograms for the quantifier (blue; 459>289, 459>119, 459>171) NPAA transitions and qualifier (purple, DAB (459>188); pink, AEG (459>214); and red, BMAA (459>258)) NPAA transitions. The peaks for BAMA, AEG, BMAA, and DAB have complete separation and are symmetrical with minimal peak tailing and no splitting. All four non-protein amino acids showed similar peak widths, eluting over the course of approximately 0.2 min. 46 All four NPAA standards showed excellent linearity (R2>0.99) during the course of the data acquisition period. Example standard curves for BMAA, AEG, DAB, and BAMA are given in Figure 23 and plots demonstrating linearity-over-time for the data acquisition period are provided in Figure 24. Figure 23. Example standard curves of the 459>171 transitions of a) BMAA, b) AEG, c) DAB, and d) BAMA, demonstrating excellent linearity (R2>0.99) 47 Figure 24. Linearity of the standard curves used for quantification (459>171) of a) BMAA, b) AEG, c) DAB, and d) BAMA over the course of the data acquisition period (August – November 2022) The primary data acquisition period occurred from August 26, 2022 to October 21, 2022. During this eight-week period, thirteen sample sets were run on the instrument. The sensitivity of the instrument was found to increase with regular use of the instrument over the course of the data acquisition period, as seen on the following page. 48 Figure 25. Peak area for the 459>171 transition of a) BMAA, b) AEG, c) DAB, and d) BAMA of Standard 4 over the course of the data acquisition period (August – November 2022) 2.5.4.2 UPLC-MS/MS Method Accuracy Accuracy was measured by the % Recovery of the spiked NPAA standards in the three different matrices analyzed in this thesis: (1) Cyanobacterial free amino acid matrix; (2) Cyanobacterial bound amino acid matrix; and (3) Plant total amino acid matrix. The latter matrix is analyzed in Chapters 5 and 6 of this work. For spike recovery experiments, representative samples of each matrix were spiked with a final concentration of 0.78 ng/mL of each NPAA prior to derivatization. Chromatograms and spike recoveries in each matrix can be found in Figure 26. 49 Figure 26. Overlaid spiked and non-spiked chromatograms of a) Cyanobacterial free amino acid matrix; b) Cyanobacterial bound amino acid matrix; and c) Plant total amino acid matrix analyzed in this thesis work; spike recovery in % matrix. Spike recoveries were found to be strongly impacted by matrix effects. The lowest spike recoveries (47 – 60%) were observed in the cyanobacterial free amino acid matrix (Figure 26a). Some of the native peaks were also obscured in the original sample by the spike (peaks at 4.87, 50 5.37, 5.50, 6.46 min), suggesting complex matrix interactions. Spike recoveries in the bound amino acid matrix (80 – 127%) generally fell within acceptable analytical standards (80 – 120%), while the plant total amino acid matrix was below the acceptable standard (59 – 77%). However, the recovery of the four NPAAs was relatively similar within each matrix, with %RSDs of 10%, 18%, and 12%, respectively. 2.5.4.3 Method Detection Limits The Method Detection Limit (MDL) was calculated by the U.S. Environmental Protection Agency (EPA) guidelines, defined as the minimum measured concentration of a substance that can be reported with 99% confidence that the measured concentration is distinguishable from method blank results. The MDLs using the spiked blank method for β-N-methylamino-L-alanine, N-(2-aminoethyl)glycine, 2,4-diaminobutyric acid, and β-aminomethyl-L-alanine were determined to be 0.48, 0.34, 1.19, and 0.67 pg on column, respectively. 2.5.4.4 Quantification of Non-Protein Amino Acids in Crude and Isolate Cultures All four non-protein amino acids were detected in both crude and isolate cyanobacterial cultures (Figure 27). The isolate cultures were found to contain significantly lower concentrations of combined BMAA and combined AEG compared to the crude cultures. The isolate cultures contained significantly more combined BAMA compared to the crude cultures. No statistically significant difference was observed in the concentration of combined DAB between the crude and isolate cultures. 51 Figure 27. Comparison of a) BMAA, b) AEG, c) DAB, and d) BAMA measurements between crude (brown) and isolate (green) cultures. Crosses (+) indicate sample means. ND values estimated with 1/10 MDL of each NPAA for calculation of statistically significant differences via unpaired t-test (α=0.05). Four individual crude-isolate culture pairs were analyzed in the experiment: Bloom_2019, StnW9_2018, Stn20_2018, and Stn19_2018. The comparison of combined NPAAs in the individual pairs is illustrated below: 52 Figure 28. Comparison of combined a) BMAA and b) AEG between crude (brown) and isolate (green) cultures from the same field sample. Error bars indicate standard error of the mean (n=3 for crude cultures, n=4 for isolate cultures). Missing bars indicate NPAA not detected. Statistically significant differences by unpaired t-test (α=0.05) indicated with: *(p<0.05); **(p<0.01); ***(p<0.001). BMAA was detected in 3/4 the isolate cultures but only 2/4 of the crude counterparts. It was not detected in the isolate of Stn20_2018, although the crude culture contained a relatively high amount of combined BMAA. Combined AEG was significantly decreased in all 4 crude-isolate pairs. 53 Figure 29. Comparison of combined a) DAB and b) BAMA between crude (brown) and isolate (green) cultures from the same field sample. Error bars indicate standard error of the mean (n=3 for crude cultures, n=4 for isolate cultures). Missing bars indicate NPAA not detected. Statistically significant differences by unpaired t-test (α=0.05) indicated with: *(p<0.05); **(p<0.01); ***(p<0.001). Similar concentrations of combined DAB were observed in StnW9_2018, while significantly decreased DAB was apparent in the rest of the samples. No significant differences in combined BAMA were observed in any of the crude-isolate pairs. 54 The correlation between alpha diversity and combined NPAA concentrations was calculated to infer possible relationships between culture complexity and NPAA production in cyanobacteria: Table 6. Pearson correlation coefficient of alpha diversity measures and NPAA concentration in cultures. Shannon Simpson BMAA 0.285 0.216 AEG 0.437 0.394 DAB -0.239 -0.166 BAMA -0.028 -0.017 BMAA, DAB, and BAMA showed negligible to weak correlations to the alpha diversity measures (r<0.3), while AEG was found to be moderately correlated to alpha diversity (0.3<r<0.5). 2.5.4.5 Quantification of Bound and Free Non-Protein Amino Acids Within the isolate cultures, BMAA was only detected in the free amino acid form. Of the 16 isolate cultures analyzed, free BMAA was detectable in 9 samples (56%). Bound AEG was quantifiable in 2/16 (13%) samples and detected under the LLOQ in 12 samples. Free AEG was detected in 14/16 (88%) isolate cultures. No significant difference in concentrations of bound AEG and free AEG were found. Both bound and free DAB were detected in 100% of isolate cultures, but no there was no significant difference in the concentrations. Finally, bound BAMA was quantifiable in 2/16 (13%) of cultures and detected under the LLOQ in 6 samples. Free BAMA was detected in 12/16 (75%) isolates. The concentration of bound BAMA was found to be significantly higher (p<0.01). 55 Figure 30. Comparison of protein-bound (dark green) vs. free (turquoise) a) BMAA, b) AEG, c) DAB, and d) BAMA. Crosses (+) indicate sample means. ND values estimated with 1/10 LOD for calculation of statistically significant differences via unpaired t-test (α=0.05). A table providing the concentrations of bound and free NPAAs in each isolate culture is outlined on the following page. 56 Table 7. Concentrations of protein-bound and free BMAA, AEG, DAB, and BAMA measured in isolate cultures. “D” indicates NPAA detected but fell below the limit of quantification. “ND” indicates NPAA not detected. “STE” refers to standard error of the mean. Sample Name Average BMAA (ng/g) Average AEG (ng/g) Bound STE Free STE Bound STE Free STE Bloom_2019_isolate1 ND D D 8.70 0.87 Stn1_2017_isolate1 ND 14.70 7.09 ND 4.40 1.69 Stn19_2018_isolate1 ND 43.94 9.61 D ND Stn19_2021_isolate1 ND 29.91 19.03 ND 7.32 1.87 Stn19_2021_isolate2 ND 45.99 26.77 D D Stn19_2021_isolate3 ND ND D ND Stn19_2021_isolate4 ND 18.00 9.19 D 19.51 3.30 Stn20_2018_isolate1 ND ND D D Stn5NS_2021_isolate1 ND ND D 5.27 2.73 Stn5NS_2021_isolate2 ND ND 56.57 17.00 191.08 55.17 Stn5NS_2021_isolate3 ND ND D D StnW1_2016_isolate1 ND 70.80 57.85 44.15 18.02 9.82 2.78 StnW4_2016_isolate1 ND ND D 30.41 16.84 StnW4_2021_isolate1 ND 5.23 2.64 D 4.23 1.33 StnW4_2021_isolate2 ND ND D D StnW9_2018_isolate1 ND 39.32 19.35 D 11.32 4.24 Sample Name Average DAB (ng/g) Average BAMA (ng/g) Bound STE Free STE Bound STE Free STE Bloom_2019_isolate1 87.93 24.70 220.13 52.20 ND 7.88 4.11 Stn1_2017_isolate1 107.90 13.68 123.46 23.90 ND 5.44 2.59 Stn19_2018_isolate1 334.73 102.76 48.47 14.40 ND 16.67 3.43 Stn19_2021_isolate1 140.52 59.73 482.00 101.17 D 28.10 11.54 Stn19_2021_isolate2 100.60 14.63 1110.83 96.67 ND 10.50 3.87 Stn19_2021_isolate3 D 178.75 25.01 ND ND Stn19_2021_isolate4 40.31 7.53 112.72 24.78 22.78 13.25 64.02 3.65 Stn20_2018_isolate1 520.41 211.38 283.67 42.01 ND 4.94 2.87 Stn5NS_2021_isolate1 1626.39 525.74 3152.46 366.45 D 8.94 2.63 Stn5NS_2021_isolate2 4814.30 1105.64 6208.95 1096.22 D D Stn5NS_2021_isolate3 7578.00 1172.41 11070.53 1716.80 ND 64.57 26.96 StnW1_2016_isolate1 115.62 11.58 243.69 46.50 ND ND StnW4_2016_isolate1 73.92 18.98 39.20 2.74 185.02 95.14 ND StnW4_2021_isolate1 71.96 2.84 34.16 4.11 D 3.67 1.86 StnW4_2021_isolate2 76.83 10.51 428.00 30.62 D ND StnW9_2018_isolate1 418.55 72.41 275.02 72.25 D D 57 2.6 Discussion of Results 2.6.1 Culture Isolation As seen in Figure 16, the qualitative ranking of isolate cultures improved over the course of the experimental period, indicating that the visual purity of the cultures progressed with repeated subculturing. Furthermore, the plateau between Periods 3 and 4 suggests no more contaminants could be removed from the cultures, signaling that minimum community compositions were achieved. Since cyanobacteria exist in tandem with various micro-organisms, the flux of these communities is expected as they compete for nutrients. Therefore, the results reveal the desired and most realistic outcome- a marked improvement in quality, followed by a plateau and cycling of the culture quality as the microbial populations fluctuate over time. 2.6.2 Sequencing and Bioinformatics The quality of DNA extracted using kits is evaluated by three metrics- DNA concentration (i.e., extraction yield), the A260/A280 ratio and the A260/A230 ratio. In particular, the two ratios refer to the absorbance at 260 nm (DNA) relative to the absorbances at 280 nm (protein) and 230 nm (RNA). For DNA to be considered high purity, an ideal A260/A280 ranges from 1.8 – 2.0, while A260/A230 is best >2.0, with values <1.5 indicating possible contaminants in the sample.131 The sequencing performance can be evaluated using the number of generated reads. For Illumina 16S metagenomic analysis, 15 000 – 100 000 reads are recommended. The bioinformatics reports detail the number of generated reads as well as the proportion that passed the quality check. None of the analyzed samples had both ideal absorbance ratios; however, based on reports from the extraction kit manufacturer, low A260/A230 ratios in their DNA samples did not affect downstream PCR performance which is consistent with the results of this study, as there 58 were no statistically significant differences in A260/A280 and A260/A230 between samples with positive and negative detections of cyanobacteria (Figure 17). Moreover, there was no correlation found between the number of reads generated and positive and negative detections of cyanobacteria. Nonetheless, there were some limitations apparent: many of the classifications by the Illumina MiSeq reporter were inconsistent with the morphology of the cyanobacteria from the microscope images. For example, six of the isolate cultures were identified as Calothrix parietina, a filamentous cyanobacterium, but all of the samples in this study were spherical cells. Similarly, while the taxonomic assignment performed using DADA2 and the CyanoSeq and SILVA databases indicated that 100% of crude and isolate cultures contained sequences belonging to Cyanobacteria (Figure 19), the majority of samples could not be classified past order level, so only 2/16 isolates were consistently and correctly assigned from both bioinformatic pipelines. Two probable reasons for these results are the limitations of the Illumina MiSeq sequencing process and the lack of reported cyanobacterial genomes. Firstly, the primers used in this study cover only two of the nine variable regions in the 16S rRNA gene, therefore only a small portion of the gene is actually covered. Recent literature argues full coverage of the nine variable regions should be used for accurate phylogenetic assessment since advances in sequencing technology have made the process more accessible and efficient.132 Moreover, these primers were evaluated for bacteria and archaea,133 so for more effective cyanobacterial metagenomic studies, primer optimization should be considered. Secondly, fewer cyanobacterial genomes have been reported in comparison to bacteria; only 0.6% of publicly available prokaryotic genomes are for cyanobacteria.134 This difference is also apparent in the size of the databases; CyanoSeq v1.2 is up-to-date as of April 2023 with approximately 5400 cyanobacterial sequences,127 while SILVA v138.1 contains more than 2 million bacterial and archaean sequences.128 In tandem, these results indicate that the cyanobacteria employed in this study are likely uncharacterized species. 59 The alpha diversity of the isolate cultures was found to be significantly decreased relative to the crude cultures, indicating a reduction in the number of contaminant species (Figure 20a). This provides quantitative evidence for the successful semi-purification of cyanobacterial cultures. Interestingly, the cluster of crude cultures found in the ordination plot (Figure 20b) highlight the basal microbial community present within Lake Winnipeg, which has recently been seasonally characterized.135 The separation of isolate cultures indicate that as these ubiquitous species were removed, the cultures became more distinct in terms of their community compositions, again evidencing their relative purification. 2.6.3 UPLC-MS/MS Method Performance The UPLC-MS/MS method utilized in this experiment showed excellent separation of NPAAs, linearity, and linearity-over-time, consistent with previous method validations.26,27 During the primary data acquisition period between August – October 2022, the sensitivity of the instrument appeared to increase linearly for all four NPAAs (Figure 25). This change in sensitivity over time is consistent with both Glover and Banack’s laboratory validation of this method. The former reports higher inter-day variability in detector response for BMAA, AEG, and DAB compared to intra-day variability,26 while the latter reports generally higher between-month RSD compared to within-day RSD for BMAA, AEG, and DAB in standards 1 – 7.27 The routine running of samples appeared to be correlated with increased sensitivity of the instrument. The phenomenon of increased detector response over time has been observed in ESI-MS analysis previously,136 albeit over the course of three hours, but suggested that electrospray ionization efficiency increases over time. Anecdotally, the capillary voltage was observed to gain stability over the course of the data collection period. Due to electronic damage to the mass spectrometer power supply, the capillary regularly experienced shorts during early sample runs; however, the frequency of these shorts decreased over time. 60 After the primary data collection period, the instrument was left in Standby mode until a final sample collection in November 2022, in which the capillary was relatively destabilized, resulting in the reoccurrence of shorts. This destabilization possibly accounts for the approximate ~30 000 decrease in peak area observed in the NPAAs during this sample run. Spike recoveries were found to be strongly impacted by matrix effects. Some of the native peaks were also obscured in the original samples by the spike (Figure 26a, peaks at 4.87, 5.37, 5.50, 6.46 min), suggesting complex matrix interactions. Less than 80% recoveries of the NPAAs in the free amino acid and total plant matrices indicate that the concentrations calculated in these samples are likely to be understated rather than overstated and provide more credence to the notion that failure-to-detect NPAAs is not truly indicative of their absence. 2.6.4 Non-Protein Amino Acids in Crude and Isolate Cyanobacteria As seen in Figure 27, combined BMAA and combined AEG were significantly different between crude and isolate cultures at the bulk level. Of the 4 direct crude-isolate culture pairs, 3/4 isolate cultures contained combined BMAA while only 2 crude cultures produced the NPAA. Interestingly, samples Bloom2019 and StnW9_2018 appear to be the same species (Figure 18, Figure 21) and showed identical trends in NPAA production between crude and isolate: no BMAA present in crude; BMAA present in isolate; decreased AEG in isolate; and increased DAB and BAMA in isolate. This co-occurrence supports a species-specific pattern of NPAA production. As well, the correlation between alpha diversity measures and BMAA was not strong, suggesting a relatively neutral relationship between general culture complexity and BMAA production by cyanobacteria. Relatively lower amounts of combined DAB were detected in the isolate cultures, including in all of the paired samples, but there was no strong correlation found to alpha diversity nor a significant difference at the bulk level. As well, while the crude cultures generally contained significantly more BAMA, there were no significant differences found in indivually 61 analyzed pairs and no strong correlations found to alpha diversity. All together, these findings suggest microbial community composition uniquely influences BMAA, DAB, and BAMA biosynthesis via species-specific interactions rather than general culture diversity. In contrast, combined AEG was detected in all 4 crude and 4 isolate cultures, with significantly lower concentrations detected in the isolates, and this observation was consistent in the overall comparison. AEG also showed the most meaningful correlation to alpha diversity, indicating that this NPAA may be the most influenced by contaminating bacteria in culture of these isomers studied. Metagenomic analyses of the isolate cultures provided interesting glimpses of species-specific NPAA production. Two samples were identified as Prochlorococcus marinus, but this species is not well characterized in terms of its capacity of produce NPAAs. Despite being possibly the most abundant cyanobacterium on Earth,137 it has only been examined for BMAA in one previous report.13 Although BMAA was not detected in this species in this analysis, relatively high levels of DAB were noted, the role of which could warrant investigation. There was also a marked difference in free DAB between StnW4_isolate2 and Stn5NS_isolate 3, which were putatively identified as eukaryotic alga Chlorogonium sp.. The microbial population for the latter was found to contain a high proportion of bacteria from the genus Yonghaparkia sp., which reportedly contains DAB in the peptides composing its cell wall138 and so could be an environmental source of the NPAA in lake ecosystems. The four paired crude-isolate cultures were all putatively identified as Chroococcales via morphological inspection; samples Bloom_2019, StnW9_2018, and Stn20_2018 contained oblong cells forming stacks of 2-4, while sample Stn19_2018 formed three-dimensional clusters of spherical cells. Samples Bloom_2019 and StnW9_2018 appeared identical in size (approximately 5-6 microns), while the cells in sample Stn20_2018 were similar, albeit slightly larger in size (approximately 6-8 microns). These cultures contained residual bacteria from the genera Dietzia spp. and Alkalibacterium spp., both of which are chemoorganotrophic species. 62 139,140 BMAA, AEG, and BAMA are not well-characterized in bacteria, but some microbial species are known producers of DAB.121,141 Overall, these data indicate that NPAA biosynthesis in in vitro cultures of cyanobacteria is not strongly correlated to microbial diversity on a bulk scale but varies more at the species level. 2.6.5 BMAA in Lake Winnipeg Field samples studied directly from Lake Winnipeg in 2016 contained BMAA levels more than 600x higher (average of 4.05 µg/g) than the same samples once cultured in vitro (6.5 ng/g).78 Other studies analyzing algal bloom samples in Australia and the Netherlands report similar concentrations of 12-47 µg/g and 4-42 µg/g, respectively.77,142 However, the crude cultures studied in 2019 and the isolate cultures studied in 2022 produced comparably similar amounts of BMAA (averages of 136 ng/g versus 24.7 ng/g). My findings suggest that in vitro, cyanobacteria produce a relatively small level of BMAA that fluctuates depending on the particular species or conditions, whereas in the environment, additional factors may induce BMAA biosynthesis. Recent studies of field blooms have indicated that BMAA concentrations in cyanobacteria and lakewater differ during the three stages of algal bloom development (emergence, outbreak, and collapse). BMAA is highest within cyanobacteria during bloom outbreak.33,73 A previous study of whole water samples in Lake Winnipeg indicated that lakewater BMAA concentrations heightened during bloom collapse.143 Put together, these studies suggest that cyanobacteria may release stored intracellular BMAA as one mechanism to initiate bloom collapse and is consistent with previous hypotheses that BMAA is involved in competitive species-species interactions, and/or nutrient-scavenging in the environment.102,144,145 Consequently, concentrations of BMAA and its isomers detected in cultured cyanobacteria may not reveal the total capacity of these organisms to produce NPAAs, 63 as experiments in isolated laboratory contexts are not comparable to the complex ecological conditions that occur during algal blooms. For example, Pip et al.’s field study in Lake Winnipeg observed correlations between preceding total nitrate concentrations and BMAA, seemingly in contrast with Downing’s work demonstrating increased BMAA production in near-axenic cyanobacteria under nitrogen-starved conditions.93,94 Further studies attempting to replicate bloom conditions in the laboratory may serve to branch variations between field and in vitro samples. By comparison, in vitro conditions are more favourable and stable than environmental ones; researchers have pointed out that laboratory conditions may alter the ability of some cyanobacterial strains to produce detectable BMAA.57 Esterhuizen & Downing noted similar fluctuations of BMAA concentrations in their analysis of South African cyanobacterial isolates as seen in this work, suggesting that they “may be attributed to a number of factors such as growth conditions, culture age and history, or nutritional or environmental stresses.”41 In summary, while BMAA is significantly decreased in vitro compared to environmental samples affected by highly variable nutrient and light conditions, my findings appear to indicate that BMAA production in cyanobacteria is not strongly correlated to the presence of contaminant bacteria in culture. This inference upholds most previously published studies analyzing cyanobacteria in vitro by showing that lack of culture axenicity is not anticipated to significantly affect characterization of BMAA and other NPAAs in these species. 64 Chapter 3: Non-Protein Amino Acids in Cyanobacteria from Peru and Chile 3.1 Synopsis Cyanobacterial cultures were collected in Peru and Chile in 2019 and maintained in an in vitro collection in the PlantSMART laboratory. This chapter describes the methods used to purify crude cyanobacterial cultures, to characterize the complexity of the culture communities, to putatively identify the organisms and quantify BMAA, AEG, DAB, and BAMA using UPLC-MS/MS. 3.2 Introduction The Quechua and Aymara are indigenous peoples living in Peru and Chile who consume cyanobacteria in the form of traditional foods known as llullucha or Llayta. They consist of large, globular colonies of Nostoc commune Vaucher ex Bornet et Flahault with associated cyanobacteria such as Cyanocohniella sp.146 and are collected from ephermal pools in the Peruvian highlands. BMAA was detected in Nostoc commune previously147 and undetected in Llaya in a separate study.50 A 2019 analysis by the PlantSMART laboratory measured all four NPAAs in environmental and market (“field”) cyanobacterial samples collected from Peru and Chile.123 After NPAA analysis, crude cultures were initiated from the field samples for metagenomic sequencing and maintained by the PlantSMART laboratory. This chapter will describe the purification of the crude cultures to isolate cyanobacterial cultures and the follow-up analysis. 65 3.3 Hypothesis and Objectives I hypothesized that isolate cyanobacterial cultures from Peru and Chile would produce different amounts of BMAA, AEG, DAB, and BAMA compared to their respective field sample. To test this hypothesis, my objectives were: (1) To establish isolate cyanobacterial cultures from crude cultures generated from field samples collected in Peru and Chile. (2) To characterize the microbial communities of the crude and isolate cultures via bioinformatic analysis of their 16s rRNA metagenomes. (3) To quantify the BMAA, AEG, DAB, and BAMA produced in the isolate cultures using UPLC-MS/MS for comparison with previously analyzed field samples. 3.4 Experimental Methods The methods used for the experiments carried out in this chapter are identical to those described previously in Section 2.4 Experimental Methods. No modifications were made. Specific details regarding the field collections of the cyanobacterial cultures employed in this study, including ethnobotanical survey design, permits, and collection co-ordinates, can be found in Bishop 2020.123 3.5 Experimental Results 3.5.1 Cyanobacterial Culture Isolation A summarized compilation of the collection notes for field samples analyzed in 2019 and corresponding isolate cultures generated in this work from previously initiated crude cultures is outlined in Table 8 on the following page. 66 In total, 18 field samples were analyzed for NPAAs in 2019. Of these, 15 crude cultures were initiated by the PlantSMART laboratory (not listed in Table 8) for metagenomic sequencing and microscope imaging in 2019. Fifteen corresponding isolate cultures were purified from the crude cultures in the present experiment following the methods described in Section 2.4.1.1 Subculturing Protocols. Table 8. Collection information and descriptions for field samples collected in Peru and Chile in 2019 and corresponding isolate cultures that were analyzed by UPLC-MS/MS. (“PNL” = Parque Nacional Lauca). Cyanobacterial Sample Name Location of Collection Date of Collection Morphological Description Field Sample Isolate Culture C_Lauca_field1 C_Lauca_isolate1 PNL, Chile Jan 2019 Small to medium light green colonies C_Lauca_field2 C_Lauca_isolate2 PNL, Chile Jan 2019 Medium green, thick, and spiralling C_Lauca_field3 C_Lauca_isolate3 PNL, Chile Jan 2019 Large thick dark green colonies C_Lauca_field4 C_Lauca_isolate4 PNL, Chile Jan 2019 Medium green, thick, and spiralling C_Lauca_field5 C_Lauca_isolate5 PNL, Chile Jan 2019 Light to medium green colonies C_Lauca_field6 n/a PNL, Chile Jan 2019 Dark brown-green algae C_Lauca_field7 n/a PNL, Chile Jan 2019 Dark brown-green algae P_Cusco_field1 P_Cusco_isolate1 Cusco, Peru Feb 2019 Medium to large dark green colonies P_Cusco_field2 P_Cusco_isolate2 Cusco, Peru Feb 2019 Medium green colonies with clusters P_Cusco_field3 P_Cusco_isolate3 Cusco, Peru Feb 2019 Large dark green colonies P_Cusco_field4 P_Cusco_isolate4 Cusco, Peru Feb 2019 Small medium green colonies P_Cusco_field5 P_Cusco_isolate5 Cusco, Peru Feb 2019 Large thick dark green colonies P_Cusco_field6 n/a Cusco, Peru Feb 2019 Medium green, thin, and spiralling P_Pisac_field1 P_Pisac_isolate1 Pisac, Peru Feb 2019 Small medium green colonies P_Pisac_field2 P_Pisac_isolate2 Pisac, Peru Feb 2019 Light green spreading colonies P_Pisac_field3 P_Pisac_isolate3 Pisac, Peru Feb 2019 Light green spreading colonies P_Pisac_field4 P_Pisac_isolate4 Pisac, Peru Feb 2019 Thick dark green colonies P_Urubamba_field1 P_Urubamba_isolate1 Urubamba, Peru Feb 2019 Large dark green colonies 67 The records of isolate culture quality tracking over the course of the experimental period are visualized in Figure 31. Cyanobacterial cultures were subcultured an average of 6.9 times in the 464 day period, corresponding to a subculture period of approximately every 70 days. An alluvial diagram illustrating the flow of culture quality across the four 116-day periods can be seen in Figure 32. For each time period, the median culture ranking was used, rounded down to the nearest integer value to avoid overexaggerating the result. Figure 31. Culture quality tracking spanning a period of 464 days. The lines are normalized to dates of subculturing to show progression over time. The colours used correspond to the qualitative 1 – 5 ranking. White circle denotes subculture used for NPAA quantification and 16S rRNA metagenomic sequencing. 68 Figure 32. Alluvial diagram showing median culture ranking during four 116-day periods over the course of the cyanobacterial culture purification process. “Good” scores defined as qualitative rankings of 4 or 5; “Poor” scores defined as qualitative rankings of 1 or 2. Diagram generated with RAWGraphs.io. From Fall 2021 (Period 1) to Winter 2022 (Period 2), the culture quality showed a rapid linear increase as the crude cultures were purified. From Winter 2022 to Fall 2022 (Period 4), the proportion of poor and good scores relatively plateaued, indicating that minimum microbial communities were achieved and the population flux between cyanobacteria and bacteria had initiated. Samples were collected for analysis in Period 3. 3.5.2 Metagenomic and Bioinformatic Analyses 3.5.2.1 Genomic DNA Quality The gDNA concentration and quality, and Illumina MiSeq quality metrics for isolate cultures analyzed in 2022 are shown on the following page. 69 Table 9. Genomic DNA quality metrics and Illumina MiSeq metrics of analyzed Peru/Chile isolate cultures Sample Name gDNA Quality Metrics Illumina MiSeq Metrics gDNA (ng/µL) A260/ A280 A260/ A230 # of Reads # of Reads Passing Quality Check % of Reads Passing Quality Check Cyano-bacteria detected? C_Lauca_isolate1 24.2 2.013 0.523 17864 14073 78.8 No C_Lauca_isolate2 91.4 1.735 0.346 19403 15578 80.3 Yes C_Lauca_isolate3 87.6 2.117 1.369 3311 2459 74.3 No C_Lauca_isolate4 15.6 2.019 0.540 18252 14286 78.3 Yes C_Lauca_isolate5 141.9 1.888 0.740 16234 12865 79.2 No P_Cusco_isolate1 202.6 2.158 2.156 16029 12478 77.8 No P_Cusco_isolate2 15.5 1.914 0.455 19075 14590 76.5 No P_Cusco_isolate3 70.0 2.107 1.026 18922 14901 78.7 No P_Cusco_isolate4 200.1 2.187 1.867 19057 15312 80.3 No P_Cusco_isolate5 92.3 2.070 1.410 18462 14482 78.4 No P_Pisac_isolate1 26.6 2.066 0.834 16065 12534 78.0 No P_Pisac_isolate2 41.6 2.049 1.323 18019 13648 75.7 Yes P_Pisac_isolate3 69.2 2.149 1.702 17018 13514 79.4 No P_Pisac_isolate4 12.9 1.977 0.599 15548 11900 76.5 No P_Urubamba_isolate1 55.4 2.113 1.203 19747 15235 77.2 No Of the 15 isolate samples submitted, cyanobacteria was detected in 3 samples (20%). The results were further analyzed to determine whether any quality metrics correlated with positive cyanobacterial detection by Illumina MiSeq taxonomic identification. This analysis is visualized in Figure 33. None of the DNA quality metrics (gDNA concentration, A260/A280, A260-/A230) or Illumina MiSeq metrics (Reads generated, reads passing quality check, % of reads passing quality check) were significantly different between samples with positive or negative detections of cyanobacteria (α=0.05) by unpaired t-test. 70 Figure 33. a) gDNA concentration of samples with detected (green) and not-detected (white) cyanobacteria; b) A260/A280 and A260/A230 ratios of samples with detected (green) and not-detected (white) cyanobacteria; c) Number of reads generated and number of reads passing quality check for samples with detected (green) and not-detected (white) cyanobacteria. No statistically significant differences in any metric detected by unpaired t-test (α=0.05). 71 3.5.2.2 Metagenomes from Illumina MiSeq The metagenomic composition of crude cyanobacterial cultures initiated from field samples in 2019 and isolate cultures analyzed in 2022 as determined by Illumina MiSeq taxonomic analysis are visualized below. Figure 34. Metagenomic profiles of crude and isolate cultures computed via the Illumina MiSeq bioinformatic pipeline. Barplots show relative abundance of identified orders from the Proteobacteria (purple-pink gradient), Firmicutes (blue), Actinobacteria (red), Cyanobacteria (green), Bacteroidota (orange), Gemmatales (brown), other bacteria (light grey), and unidentified species (dark grey). 72 3.5.2.3 Metagenomes from DADA2 The metagenomic composition of crude cyanobacterial cultures analyzed in 2019 and isolate cultures analyzed in 2022 as computed using DADA2 is visualized below. The assignment of cyanobacteria was performed at three bootstrap levels- 50 (default), 80, and 95; no difference in classification was observed with increased bootstrap level. Figure 35. Metagenomic profiles of crude and isolate cultures, computed using the DADA2 bioinformatic pipeline and taxonomy assigned using SILVA v138.1 and CyanoSeq v1.2 databases (bootstrap level=50). 73 Most of the isolate cultures contained relatively higher proportions of species from the Cyanobacterial phylum compared to the crude cultures. Cyanobacteria was not detected in cultures C_Lauca_isolate1, C_Lauca_isolate5, or P_Cusco_isolate2. The isolate cultures were largely purified of contaminating Proteobacteria, but Firmicutes and Actinobacteria were residually present in the isolates. NCBI MOLE-BLAST yielded no matches for any ASVs. 3.5.2.4 Phylogenetic Diversity Alpha and beta diversity measures of the crude and isolate cyanobacterial cultures computed with phyloseq v1.44 are revealed below: Figure 36. a) Alpha diversity measures (Shannon, Simpson) of crude cultures compared to isolate cultures, coloured by country of collection (orange = Chile, teal = Peru). Statistically significant differences calculated by unpaired t-test (α=0.05). b) Beta diversity measure (Bray-Curtis distances) of crude cultures (circles) compared to isolate cultures (triangles). Clustering of crude cultures outlined in yellow. The Shannon and Simpson measures of alpha diversity were both significantly lower in the isolate cultures compared to the crude cultures (p<0.00001). The ordination plot (Figure 36a) showed clustering of the crude cultures, indicating some analogous species were present. 74 3.5.3 Species Identification Microscope images of crude and isolate cultures collected from Peru and Chile follow: Figure 37. 40X magnification images of crude (greyscale, cyanobacteria highlighted in teal) and isolate (colour) cyanobacteria. Putative assignments based on morphological characteristics; confirmed assignments based on 16S rRNA taxonomic identification. Map data acquired as a custom GeoJSON file from geojson-maps.ash.ms and processed in QGIS v.3.28.0 75 Two of the fifteen isolates (C_Lauca_isolate2 and C_Lauca_isolate4) were identified as Nodularia sp. by both Illumina MiSeq and DADA2/CyanoSeq metagenomic analyses. This identification is consistent with the morphology of the species. The rest of the cultures were putatively assigned to the Chroococcales except for P_Pisac_isolate2, which was tentatively identified as Synechococcales using the approaches described in Section 2.4.3.2 Microscope Imaging and Visual Identification. 3.5.4 UPLC-MS/MS Quantification of Non-Protein Amino Acids An overview of UPLC-MS/MS method performance and accuracy is provided in Sections 2.5.4.1 UPLC-MS/MS Method Performance Metrics and 2.5.4.2 UPLC-MS/MS Method Accuracy. The MDLs were calculated using the EPA method; for β-N-methylamino-L-alanine, N-(2-aminoethyl)glycine, 2,4-diaminobutyric acid, and β-aminomethyl-L-alanine were determined to be 0.48, 0.34, 1.19, and 0.67 pg on column, respectively. 3.5.4.1 Quantification of Non-Protein Amino Acids in Field Samples and Isolate Cultures The concentrations of combined BMAA, AEG and BAMA were significantly decreased in the isolate cultures compared to the field samples, while no significant difference was observed for combined DAB (Figure 38). Fifteen field sample-isolate culture pairs were analyzed in this study. The pairwise comparisons of combined NPAAs are visualized in figures found in the following pages. 76 Figure 38. Comparison of a) BMAA, b) AEG, c) DAB, and d) BAMA measurements between field (brown) and isolate (green) cultures. Crosses (+) indicate sample means. ND values estimated with 1/10 MDL for each NPAA for calculation of statistically significant differences via unpaired t-test (α=0.05). Less or no combined BMAA was detected in 13/15 isolate cultures compared to their parent field sample, significantly in 8 of the pairs (Figure 39a). BMAA was not detected in neither the field nor isolate of sample C_Lauca3, but was present in at least the field version of all other samples. Combined BMAA was similar in the field and isolates of sample P_Cusco5. 77 Figure 39. Combined a) BMAA and b) AEG in parent field samples (brown) and corresponding isolate culture pair (green). Error bars represent standard error of the mean (n=3 for field, n=4 for isolate). Missing bars indicate NPAA not detected. Statistically significant differences by unpaired t-test (α=0.05) indicated by: *(p<0.05); **(p<0.01); ***(p<0.001). 78 Figure 40. Combined a) DAB and b) BAMA in parent field samples (brown) and corresponding isolate culture pair (green). Error bars represent standard error of the mean (n=3 for field, n=4 for isolate). Missing bars indicate NPAA not detected. Statistically significant differences by unpaired t-test (α=0.05) indicated by: *(p<0.05); **(p<0.01); ***(p<0.001). 79 Less combined AEG was detected in the isolate cultures in just over two-thirds of the field-isolate pairs (11/15); significantly in 7 pairs (Figure 39b). Higher AEG concentrations were noticed in the remaining isolates; two of the parent field samples did not contain detectable AEG. Higher amounts of combined DAB were quantified in the isolate cultures for 6/15 pairs; this difference was significant for three samples: C_Lauca_4, P_Pisac_4, and P_Urubamba_1. In the remaining sets, the isolates contained less DAB. Less combined BAMA was detected in all of the isolate cultures compared to their parent samples; significant differences were noted in 13/15 of the pairs. 3.5.4.2 Quantification of Bound and Free Non-Protein Amino Acids Significantly higher concentrations of free BMAA were measured in the isolate cultures compared to the bound forms of the NPAA (Figure 41). Bound BMAA was detectable in 2 of the samples, whereas the free amino acid was observed in just over half the cultures (8/15). 80 Figure 41. Comparison of protein-bound (dark green) vs. free (turquoise) a) BMAA, b) AEG, c) DAB, and d) BAMA. Crosses (+) indicate sample means. ND values estimated with 1/10 MDL of each NPAA for calculation of statistically significant differences via unpaired t-test (α=0.05). Bound AEG was detected in all samples and quantifiable in 3/15 of the isolates analyzed. Likewise, free AEG was detected in 12/15 samples and was also measurable in 3/15 isolates. Bound and free DAB were quantifiable in all 15 cultures in similar concentrations. Finally, bound BAMA was detected in 8/15 samples, while the free form was present in all but 1 culture. A table providing the concentrations of bound and free NPAAs in each isolate culture can be found on the following page. 81 Table 10. Concentrations of protein-bound and free BMAA, AEG, DAB, and BAMA measured in isolate cultures. “D” indicates NPAA detected but fell below the limit of quantification. “ND” indicates NPAA not detected. “STE” refers to standard error of the mean. Sample Name Average BMAA (ng/g) Average AEG (ng/g) Bound STE Free STE Bound STE Free STE C_Lauca_isolate1 ND ND D 7.49 2.07 C_Lauca_isolate2 ND D D 3.20 0.86 C_Lauca_isolate3 ND ND D D C_Lauca_isolate4 ND ND D D C_Lauca_isolate5 ND 15.82 5.33 D D P_Cusco_isolate1 ND ND D D P_Cusco_isolate2 18.00 10.13 6.87 0.99 D D P_Cusco_isolate3 D ND 37.24 15.34 D P_Cusco_isolate4 ND 24.93 10.58 D ND P_Cusco_isolate5 ND 67.36 6.94 19.90 5.47 ND P_Pisac_isolate1 ND ND D 2.84 1.30 P_Pisac_isolate2 ND 69.38 38.76 D ND P_Pisac_isolate3 ND ND D D P_Pisac_isolate4 ND 8.35 5.34 D D P_Urubamba_isolate1 ND D 106.15 24.56 D Sample Name Average DAB (ng/g) Average BAMA (ng/g) Bound STE Free STE Bound STE Free STE C_Lauca_isolate1 157.64 31.58 213.77 16.94 D 7.07 2.50 C_Lauca_isolate2 236.24 68.69 44.11 9.34 11.82 9.61 2.47 1.00 C_Lauca_isolate3 65.67 19.42 120.80 20.76 ND 13.31 2.27 C_Lauca_isolate4 844.35 220.72 1125.51 282.63 ND 1.43 0.67 C_Lauca_isolate5 262.47 88.65 365.72 33.22 ND 7.67 3.03 P_Cusco_isolate1 558.74 327.59 70.99 7.52 D 7.59 2.81 P_Cusco_isolate2 820.89 412.12 56.52 15.18 60.27 41.53 2.33 1.01 P_Cusco_isolate3 51.81 20.77 105.90 31.90 52.81 31.02 D P_Cusco_isolate4 171.62 68.32 270.85 59.05 D ND P_Cusco_isolate5 145.34 62.27 42.03 12.22 D 4.41 1.27 P_Pisac_isolate1 102.72 18.83 202.18 35.93 ND D P_Pisac_isolate2 156.19 56.85 99.08 32.20 ND 59.46 20.33 P_Pisac_isolate3 77.21 14.95 107.64 15.36 ND 6.16 2.13 P_Pisac_isolate4 150.62 56.48 803.89 141.67 ND 7.56 3.22 P_Urubamba_isolate1 1068.19 497.40 527.53 86.82 75.75 30.49 D 82 3.6 Discussion of Results 3.6.1 Culture Isolation As seen in Figure 31, the qualitative ranking of isolate cultures improved rapidly and settled into a plateau relatively early in the experimental period. These results are consistent with those seen in Chapter 2 with the isolation of Lake Winnipeg cyanobacteria; however, since there was no generation of new cultures from field samples in this experiment, the purification began in an earlier period and reached the minimum community compositions more rapidly. 3.6.2 Sequencing and Bioinformatics Like the Lake Winnipeg samples, none of the analyzed Peru and Chile cultures had both ideal absorbance ratios and similarly there were no statistically significant differences in A260/A280 and A260/A230 between samples with positive and negative detections of cyanobacteria (Figure 33). There was also no significant difference found between the number of reads generated and positive and negative detections of cyanobacteria; however, the average number of reads passing the quality check for both categories were 12 715 and 14 499, respectively, both which fell under the recommended lower threshold of 15 000 reads for the Illumina MiSeq platform. Consequently, Illumina MiSeq platform was able to detect cyanobacteria in only two of the isolates, which were corroborated using the DADA2 pipeline to be Nodularia sp., and suggests that primer inefficiency may be at fault for the inability to generate sufficient reads. Results from microbial ecology experiments may be indeterminate for several reasons, such as DNA extraction, sequencing protocol, and bioinformatic processing. In 2015, the Microbiome Quality Control project was carried out by researchers in the United States to characterize the sources of variations in microbiota profiling.148 Their results showed that experimentally, the choice of 16S rRNA amplicon primer had potentially the greatest impact on generated data. Darwish et al. expanded on this study in 2021, testing eight different bacterial 83 primer pairs in chicken cecal samples.149 They showed that the primers significantly affected alpha and beta diversities and taxonomic composition of the samples. The same conclusion has been drawn in cyanobacterial studies: in 2017, Lee et al. designed two novel cyanobacterial-specific primer sets and compared them to a standard universal NGS 16S rRNA primer set using freshwater samples. The universal 16S primer gave an average relative cyanobacterial abundance of 14.2%, whereas the two new primers indicated relative abundances of 34.1% and 38.3%, respectively. Therefore, the non-detection of cyanobacteria via the bioinformatic pipelines may possibly be attributed to failing to amplify cyanobacterial 16S rRNA during the sequencing process. The alpha diversity of the isolate cultures was found to be significantly decreased relative to the crude cultures, indicating successful semi-purification of the cultures. The ordination plot showed some clustering of the crude cultures but it was not as concise as observed for the Lake Winnipeg samples, likely owing to the more diverse geographical regions of collection for these samples. 3.6.3 Non-Protein Amino Acids in Field Samples and Isolate Cultures A previous study of NPAAs in South America reported no BMAA detected in Llayta collected from northern Chile,50 but it has been previously detected in Nostoc commune147 and earlier analyses in the PlantSMART laboratory detected BMAA, AEG, DAB, and BAMA in fresh field samples collected from Peru and Chile.123 The results of Chapter 2 indicated that the differences in NPAA production between crude and isolate cyanobacterial cultures was not as major as the difference between field samples and in vitro samples. Therefore, I had hypothesized that cultured South American cyanobacteria would have significantly different NPAA concentrations compared to their parent field samples. The results indicated that BMAA, AEG, and BAMA were both significantly decreased in cultured cyanobacteria compared to the field samples (Figure 38). Individually, less or no 84 combined BMAA was detected in 13/15 isolate cultures compared to their parent field sample, less AEG was detected in 11/15 isolates, and less combined BAMA was detected in all of the isolates. The concentration difference of BMAA between field samples and cultured samples was one order of magnitude apart (averages of 159 ng/g versus 18.5 ng/g), similar to the findings of Chapter 2 (averages of 136 ng/g versus 24.7 ng/g). These findings indicate that the field conditions under which these samples were collected were largely similar to crude in vitro microbial communities, and suggest that algal bloom conditions may specifically influence BMAA biosynthesis in cyanobacteria. The pattern of AEG production was found to be consistent with the results of the Lake Winnipeg chapter, again indicating that the biosynthesis of this NPAA is possibly downregulated by cyanobacterial isolation. Finally, BAMA was significantly higher in the field samples by 2-4 orders of magnitude, in contrast to the findings of the previous chapter. Cyanobacterial BAMA is rarely characterized and has been detected at low concentrations (<0.5 ng/g) previously;87 a possible explanation for these findings may be that BAMA behaves as an environment-specific adaptation. Consistent with the findings in Chapter 2, combined DAB was not significantly different in the isolate cultures compared to the field samples. DAB is believed to play a defensive role in phytoplankton150 and is hypothesized to act as an iron-scavenger,141 two functions that would suggest that higher amounts should be produced under environmental conditions. However, as in the case of most NPAA studies, it has also been considered strain- and species-specific and the exact stressors influencing its biosynthesis remain undetermined.96 AEG is thought to be an evolutionary remnant of peptide nucleic acids,151 and likewise factors contributing to its production are unknown. Overall, the findings of this study highlight the impact of environmental stress on BMAA production and agree with the results seen in the previous chapter and reports in the literature. The significantly higher concentrations of BMAA in the field samples pose potential hazard to human health; one serving of about 50 g DW of the cyanobacteria foodstuffs could 85 contain 6 – 23 µg BMAA.123 It is possible that cultivation of the cyanobacteria under more controlled conditions for ingestion may help reduce the intake of BMAA in populations that regularly consume Llayta as a food source, but the value of such a project would require further study. 86 Chapter 4: Non-Protein Amino Acids in Arctic Cyanobacteria 4.1 Synopsis Field samples of cyanobacteria were collected in Resolute and Iqaluit, Nunavut, Canada, by the PlantSMART lab in 2019. Situated on an island, the climate is extremely cold and dry, with an average temperature of -15.7 °C and yearly precipitation of approximately 16.2 cm. The impact of extreme environmental conditions on NPAA production in cyanobacteria has not yet been explored. Crude cultures from field collections were initiated and maintained in the PlantSMART laboratory at 10 °C (“cold” conditions) to mimic summer Arctic environments. These cultures were previously subjected to metagenomic analysis and NPAA quantification (unpublished). This chapter will detail the purification of these crude cultures to isolates for follow-up analysis to determine whether temperature and light availability impact NPAA production in cold-adapted cyanobacteria. 4.2 Introduction Cyanobacteria are the dominant organisms inhabiting extreme cold environments; to exist in such locations, they have garnered ecophysiological adaptations including tolerances to large temperature fluctuations, dessication, freezing, salinity, solar radiation, and lack of light.152 BMAA, AEG, and DAB were previously reported in 100-year old cyanobacterial mats collected from Antarctica, providing evidence that biosynthesis of these NPAAs is also conserved in extremely isolated polar cyanobacteria.80 The effect of harsh environmental conditions on the production of BMAA and its isomers is unexplored, and the capacity to produce these NPAAs in cyanobacteria collected from the Arctic has not yet been reported. 87 4.3 Hypothesis and Objectives I hypothesized that crude cyanobacterial cultures grown under cold conditions and isolate cyanobacterial cultures grown at room temperature (“warm” conditions) would show altered production of BMAA, AEG, DAB, and BAMA. To test this hypothesis, my objectives were: (1) To establish isolate cyanobacterial cultures from crude cultures generated from field samples collected in Resolute and Iqaluit, Canada to be grown at warm conditions. (2) To characterize the microbial communities of the crude and isolate cultures via bioinformatic analysis of their 16s rRNA metagenomes. (3) To quantify the BMAA, AEG, DAB, and BAMA produced in the cold-grown crude and warm-grown isolate cultures using UPLC-MS/MS for comparison with previously analyzed cold-grown crude cyanobacterial cultures. 4.4 Experimental Methods The methods used for the experiments carried out in this chapter are identical to those described previously in Section 2.4 Experimental Methods. Modifications are outlined below. 4.4.1 Culture Isolation Crude Arctic cultures were grown at 10 °C (VWR Commercial Fridge, Cat#89209-840, Model#MH72PA-GAEE-VW) without illumination (0.14 µmol·m2·s-1) to mimic Arctic climates. The subculturing protocols and methods used for purification of crude cultures to isolate cyanobacterial cultures were followed as described in Sections 2.4.1.1 Subculturing Protocols and 2.4.1.2 Generation of New Isolates, and isolate Arctic cultures were maintained at previously described conditions (25 °C, 16:8 photoperiod) in the sterile controlled growth environment. 88 4.5 Experimental Results 4.5.1 Cyanobacterial Culture Collection and Isolation A compilation of crude and isolate Arctic cyanobacterial cultures and their collection notes is provided in Table 11. Field samples were collected from sites around Resolute and Iqaluit, Nunavut, Canada, in August 2019. Samples were collected under Wildlife Research Permit #2019-047 granted by the Nunavut Department of Environment pursuant to the Wildlife Act. Logistical support was provided by the Polar Continental Shelf Program (PCSP) of the National Research Council of Canada (Project: 63319 – “ARCTIC Change - Arctic Research and Conservation Team Investigating Climate Change”) including access to the Roy “Fritz” Koerner Scientific Lab facilities, vehicles— including ATVs, bikes, and a pick-up truck—housing, fresh water, and food. Guidance for this project was provided by: staff and researchers at the PCSP facility in Resolute; the Nunavut Department of Environment in Iqaluit; and Inuit guides, collaborators, and knowledge keepers in Resolute Bay and Iqaluit. The question that was asked was: “Do you know a location where the impacts of climate change are apparent?”. The field samples included algae, plants, seeds, and vouchers from sites that were identified by local and indigenous guides as areas that were undergoing rapid ecosystem change. The subset of these collections that had associated cyanobacteria and algae are investigated in this chapter. In total, 12 crude cold-grown samples were analyzed (7 in 2019; 5 in 2022) and 9 isolate warm-grown cultures were generated. 89 Table 11. Collection information and descriptions for crude cyanobacterial cultures collected in Nunavut in 2019 and corresponding isolate cultures that were analyzed by UPLCS-MS/MS. Cyanobacterial Sample Name Location of Collection Date of Collection Morphological Description Crude “cold” culture Isolate “warm” culture Iqaluit_crude1a Iqaluit_crude1b Iqaluit_isolate1 Apex Beach, Iqaluit N 63° 44' 28.29'' W 68° 28' 51.55" Elevation 10 m Aug 12, 2019 Small medium green colonies Resolute_crude1* n/a 3 Mile Lake, Resolute N 74° 44' 54.48" W 95° 3' 30.84" Elevation 3 m Aug 3, 2019 Stringy, moss-like Resolute_crude2a Resolute_crude2b Resolute_isolate2 Crash plane, Resolute N 74° 10' 59.76" W 95° 45' 15" Elevation 43 m Aug 6, 2019 Clumpy vivid green colonies Resolute_crude3 Resolute_isolate3 Crash plane, Resolute N 74° 10' 59.76" W 95° 45' 15" Elevation 43 m Aug 6, 2019 Small vivid green colonies Resolute_crude4a Resolute_crude4b Resolute_isolate4 Airport, Resolute N 75° 10' 43.01″ W 95° 58′ 10″ Elevation 65 m Aug 8, 2019 Small vivid green colonies Resolute_crude5a* Resolute_crude5b Resolute_isolate5 Merretta Lake, Resolute N 74° 41' 54.93" W 94° 58' 51.98" Elevation 124 m Aug 8, 2019 Clumpy medium green colonies Resolute_crude6 Resolute_isolate6a Resolute_isolate6b Resolute_isolate6c Merretta Lake, Resolute N 74° 41' 54.93" W 94° 58' 51.98" Elevation 124 m Aug 8, 2019 Emerald green, vine-like Resolute_crude7 Resolute_isolate7 Crash plane, Resolute N 75° 10' 43.74" W 95° 37' 43.02" Elevation 43 m Aug 6, 2019 Moss-like (plant) *Cultures not analyzed by UPLC-MS/MS; only metagenomic data was available for analysis 90 The records of isolate culture quality tracking over the course of the experimental period are visualized in Figure 42. Isolate cyanobacterial cultures were subcultured an average of 5.7 times in the 464 day period, corresponding to a subculture period of approximately every 82 days. An alluvial diagram showing the flow of culture quality across the four 116-day periods is illustrated in Figure 43. For each time period, the median culture ranking was used, rounded down to the nearest integer value to avoid overexaggerating the result. Figure 42. Culture quality tracking spanning a period of 464 days. The lines are normalized to dates of subculturing to show progression over time. The colours used correspond to the qualitative 1 – 5 ranking. White circle denotes subculture used for NPAA quantification and 16S rRNA metagenomic sequencing. 91 Figure 43. Alluvial diagram showing median culture ranking during four 116-day periods over the course of the cyanobacterial culture purification process. “Good” scores defined as qualitative rankings of 4 or 5; “Poor” scores defined as qualitative rankings of 1 or 2. Diagram generated with RAWGraphs.io. The culture quality increased rapidly between Fall 2021 (Period 1) and Winter 2022 (Period 2) as the isolate Arctic cultures were established. Between Winter 2022 and Summer 2022 (Period 3), a decrease in qualitative ranking was observed during the first purification period, but the cultures were readily purified by Fall 2022 (Period 4). Samples were collected during Period 3. No plateau period was observed, indicating that minimal microbial populations were not yet established. However, 0/9 isolate cultures were ranked as “poor” during the final period, indicating that a high level of purity was achieved. 4.5.2 Metagenomic and Bioinformatic Analyses 4.5.2.1 Genomic DNA Quality The gDNA concentration and quality, and Illumina MiSeq quality metrics for select crude and all isolate Arctic cultures analyzed in 2022 is shown on the following page: 92 Table 12. Genomic DNA quality metrics and Illumina MiSeq metrics of analyzed Arctic isolate cultures Sample Name gDNA Quality Metrics Illumina MiSeq Metrics gDNA (ng/µL) A260/ A280 A260/ A230 # of Reads # of Reads Passing Quality Check % of Reads Passing Quality Check Cyano-bacteria detected? Iqaluit_crude1b 3.4 2.792 0.114 24451 19445 0.795 Yes Iqaluit_isolate1 44.4 2.193 1.407 20106 15634 0.778 Yes Resolute_crude2b 21.4 2.173 0.740 17804 13932 0.783 Yes Resolute_crude4b 50.8 1.908 0.865 23054 18153 0.787 Yes Resolute_crude5b 25.5 2.335 0.628 8620 7158 0.83 No Resolute_crude7 75.2 1.790 0.589 19767 15359 0.777 Yes Resolute_isolate2 49.0 1.970 1.159 23074 18048 0.782 Yes Resolute_isolate3 91.4 2.092 1.773 24110 18037 0.748 Yes Resolute_isolate4 23.9 1.817 0.469 21430 17050 0.796 Yes Resolute_isolate5 55.7 2.187 1.319 9363 7539 0.805 No Resolute_isolate6a 99.3 2.096 1.824 18215 13469 0.739 Yes Resolute_isolate6b 14.4 2.05 0.870 20071 16057 0.8 Yes Resolute_isolate6c 118.9 2.167 1.711 19439 15679 0.807 Yes Resolute_isolate7 51.1 1.771 0.567 22723 18027 0.793 Yes Of the 5 crude and 9 isolate Arctic samples submitted, cyanobacteria were detected in 12 samples (86%). No cyanobacteria were detected in the crude or isolate version of sample Resolute_5. The results were further analyzed to determine whether any quality metrics correlated with positive cyanobacterial detection by Illumina MiSeq taxonomic identification. This analysis is shown in Figure 44. None of the DNA quality metrics (gDNA concentration, A260/A280, A260/A230) were significantly different between samples with positive or negative detections of cyanobacteria (α=0.05) by unpaired t-test. However, the two samples without detected cyanobacteria contained significantly fewer generated reads, and consequently significantly fewer reads passing the quality check. Samples Resolute_crude5b and Resolute_isolate5 had 7158 and 7539 passing reads, respectively, while the remaining samples averaged 16 574. 93 Figure 44. a) gDNA concentration of samples with detected (green) and not-detected (white) cyanobacteria; b) A260/A280 and A260/A230 ratios of samples with detected (green) and not-detected (white) cyanobacteria; c) Number of reads generated and number of reads passing quality check for samples with detected (green) and not-detected (white) cyanobacteria. Statistically significant differences in any metric detected by unpaired t-test (α=0.05). 94 4.5.2.2 Metagenomes from Illumina MiSeq The metagenomic composition of crude “cold” and isolate “warm” Arctic cyanobacterial cultures as determined using the data obtained from Illumina MiSeq is provided below. Figure 45. Metagenomic profiles of crude “cold” and isolate “warm” cultures computed via the Illumina MiSeq bioinformatic pipeline. Barplots show relative abundance of identified orders from the Proteobacteria (pink-yellow gradient), Firmicutes (blue), Actinobacteria (red), Cyanobacteria (green), Bacteroidota (orange), other bacteria (light grey), and unidentified species (dark grey). 95 4.5.2.3 Metagenomes from DADA2 The metagenomic composition of crude “cold” Arctic cultures and isolate “warm” Arctic cultures as computed using DADA2 is visualized below. Figure 46. Metagenomic profiles of crude and isolate Arctic cultures, computed using the DADA2 bioinformatic pipeline and taxonomy assigned using SILVA v138.1 and CyanoSeq v1.2 databases (bootstrap level=50). Three of the isolate cultures showed relatively high purity (Iqaluit_isolate1, Resolute_isolate3, and Resolute_isolate6a). The Bacteroidotes were not detected in any isolate samples, suggesting purification of this contaminant. Samples Resolute_crude5b and Resolute_isolate5 had noticeably low to no cyanobacterial species present, consistent with the findings in the Illumina MiSeq bioinformatic report. 96 4.5.2.4 Phylogenetic Diversity Alpha and beta diversity measures of the crude and isolate Arctic cyanobacterial cultures computed with phyloseq v1.44 are given in the following figure. Figure 47. a) Alpha diversity measures (Shannon, Simpson) of crude cultures compared to isolate cultures, coloured by location of collection (orange = Iqaluit, teal = Resolute). Statistically significant differences calculated by unpaired t-test (α=0.05). b) Beta diversity measure (Bray-Curtis distances) of crude cultures (circles) compared to isolate cultures (triangles). 97 The Shannon and Simpson measures of alpha diversity were both significantly lower in the isolate cultures compared to the crude cultures (p<0.01). No clustering was observed in the ordination plot (Figure 47b), indicating dissimilarity in microbial compositions between cultures. 4.5.3 Species Identification Microscope images of isolate “warm” Arctic cultures and the locations in Nunavut the samples were acquired from are shown on the following page. The genera of 4/9 isolate Arctic cultures were successfully identified by Illumina MiSeq and DADA2/CyanoSeq bioinformatic analysis and validated with cyanobacterial morphology. Culture Resolute_isolate2 was determined to belong to the genus Microcoleus sp., while replicate cultures Resolute_isolate6a, 6b, and 6c were confirmed to be Pseudanabaena sp. The remaining cultures were putatively assigned with the previously described approach. Resolute_isolate3 and Resolute_isolate4 were classed as Synechococcales and resemble identical species. Iqaluit_isolate1 was tentatively listed as Chroococcales. Finally, Resolute_isolate5 and Resolute_isolate7 are believed to be eukaryotic organisms, with the former resembling unicellular algae belonging to the Glaucophytes, and the latter sample resembling a mossy plant. 98 Figure 48. 40X magnification images of isolate Arctic cyanobacteria. Putative assignments based on morphological characteristics; confirmed assignments based on 16S rRNA taxonomic identification. Map of Nunavut generated with datawrapper.de 4.5.4 UPLC-MS/MS Quantification of Non-Protein Amino Acids An overview of UPLC-MS/MS method performance and accuracy is provided in Sections 2.5.4.1 UPLC-MS/MS Method Performance Metrics and 2.5.4.2 UPLC-MS/MS Method Accuracy. The MDLs for this chapter were calculated using the International Union of Pure and Applied Chemistry (IUPAC) method, defined as the smallest concentration of analyte that has a signal significantly larger than the signal from a suitable blank, equivalent to 3 standard deviations of the blank concentrations. The MDLs using the blank method for β-N-methylamino-L-alanine, N-(2-aminoethyl)glycine, 2,4-diaminobutyric acid, and β-aminomethyl-L-alanine were determined to be 0.05, 0.21, 0.21, and 0.06 pg on column, respectively. 99 4.5.4.1 Quantification of Non-Protein Amino Acids in Crude and Isolate Cultures Figure 49. Comparison of combined a) BMAA, b) AEG, c) DAB, and d) BAMA measurements between crude “cold” (blue) and isolate “warm” (pink) cultures. Crosses (+) indicate sample means. ND values estimated with 1/10 MDL for calculation of statistically significant differences via unpaired t-test (α=0.05). The isolate Arctic cultures contained significantly different levels AEG and DAB (Figure 49). Combined BMAA and BAMA were similar between the culture types, whereas the crude Arctic cultures contained significantly higher concentrations of combined AEG and DAB. Combined NPAAs in grouped crude-isolate culture sets are shown in the following pages. 100 Figure 50. Combined a) BMAA and b) AEG in crude “cold” culture(s) (blue) and corresponding “warm” isolate culture(s) (pink). Error bars represent standard error of the mean (n=4 or 6 for crude, n=4 for isolate). Missing bars indicate NPAA not detected. Statistically significant differences by unpaired t-test (α=0.05) indicated by: *(p<0.05); **(p<0.01); ***(p<0.001). 101 Figure 51. Combined a) DAB and b) BAMA in crude “cold” culture(s) (blue) and corresponding “warm” isolate culture(s) (pink). Error bars represent standard error of the mean (n=4 or 6 for crude, n=4 for isolate). Missing bars indicate NPAA not detected. Statistically significant differences by unpaired t-test (α=0.05) indicated by: *(p<0.05); **(p<0.01); ***(p<0.001). 102 The levels of combined BMAA did not appear to significantly differ in any of the culture pairs except for Reoslute_7. BMAA was detected in the highest concentrations in sample Resolute_3 and in the lowest apparent amounts in sample Resolute_4. The concentration of AEG was decreased in the isolate culture(s) relative to the crude counterparts in 5/7 sets; this difference was significant in the Resolute_3, Resolute_4, Resolute_5, and Iqaluit_1 culture groups. Levels of combined DAB appeared to vary greatly between cultures; significant decreases in the isolates were observed for sample sets Resolute_2, Resolute_3, and Resolute_6. In the case of Resolute_4, the isolate culture had significantly more DAB than one crude measurement, and significantly less than the other crude measurement. The concentrations of combined BAMA were similar among the grouped sets; no significant differences were calculated. Combined BAMA appeared in the isolate versions of Resolute_3 and Resolute_6, where it was not detected in the crude parents. 103 4.5.4.2 Quantification of Bound and Free Non-Protein Amino Acids Figure 52. Comparison of protein-bound (dark green) vs. free (turquoise) a) BMAA, b) AEG, c) DAB, and d) BAMA in crude and isolate cultures. Crosses (+) indicate sample means. ND values estimated with 1/10 LOD for calculation of statistically significant differences via unpaired t-test (α=0.05). BMAA was not detected in the protein-bound form in any of the cultures analyzed in this experiment. It was detected in the free amino acid form in 12/14 (86%) of the samples. There was significantly less free AEG compared to bound AEG. The bound form was detected in 13/14 cultures, while free AEG was detected in 11/14 cultures. Free DAB was detected in 104 relatively similar concentrations to bound DAB. 100% of the cultures contained both bound and free DAB. The concentrations of bound and free BAMA were also not significantly different. The protein-bound form was detected in 10/14 cultures whereas the free amino acid form was detected in 13/14 samples. Table 13. Concentrations of protein-bound and free BMAA, AEG, DAB, and BAMA measured in select crude and isolate Arctic cultures. “D” indicates NPAA detected but fell below the limit of quantification. “ND” indicates NPAA not detected. “STE” refers to standard error of the mean. Sample Name Average BMAA (ng/g) Average AEG (ng/g) Bound STE Free STE Bound STE Free STE Iqaluit_crude1b ND 35.55 11.91 ND D Iqaluit_isolate1 ND ND D D Resolute_crude2b ND 15.46 5.70 D 31.47 29.91 Resolute_isolate2 ND 34.12 26.43 D 10.02 4.60 Resolute_isolate3 ND 137.67 52.45 108.32 44.45 14.78 10.25 Resolute_crude4b ND 1.53 0.30 86.95 46.56 0.68 1.04 Resolute_isolate4 ND ND D 13.48 6.86 Resolute_crude5b ND 20.53 8.84 D ND Resolute_isolate5 ND 14.16 6.39 D ND Resolute_isolate6a ND 4.44 1.56 D ND Resolute_isolate6b ND 16.09 5.81 D 29.41 33.51 Resolute_isolate6c ND 47.68 21.81 23.82 10.84 2.49 2.77 Resolute_crude7 ND 0.44 0.28 D 0.71 0.48 Resolute_isolate7 ND 60.03 7.18 D 1.67 1.45 Sample Name Average DAB (ng/g) Average BAMA (ng/g) Bound STE Free STE Bound STE Free STE Iqaluit_crude1b 337.97 193.12 28.30 8.26 D 1.06 0.72 Iqaluit_isolate1 776.90 291.05 444.37 113.81 D D Resolute_crude2b 51.89 23.26 124.23 46.43 D 4.55 4.76 Resolute_isolate2 41.42 12.50 113.70 19.69 91.64 82.29 29.12 18.68 Resolute_isolate3 136.61 30.57 89.04 12.29 315.44 252.36 30.68 25.12 Resolute_crude4b 144.35 37.52 6.09 1.60 52.17 44.15 D Resolute_isolate4 205.16 44.21 392.69 51.96 D 11.45 7.62 Resolute_crude5b 30.28 12.35 3.24 1.11 ND 3.20 4.21 Resolute_isolate5 126.69 25.82 422.87 80.35 ND ND Resolute_isolate6a 77.72 17.17 17.95 3.34 ND 24.39 10.73 Resolute_isolate6b 110.17 23.05 17.37 5.21 D 24.33 15.81 Resolute_isolate6c 97.89 25.64 36.06 1.69 ND 12.85 7.70 Resolute_crude7 448.43 139.55 244.99 38.87 19.65 10.43 D Resolute_isolate7 84.63 27.37 440.13 25.62 D D 105 4.5.4.3 Non-Protein Amino Acids as a Fraction of Total Protein Content The percentage of protein-bound NPAAs in the protein pellets of analyzed cyanobacteria is shown below: Figure 53. Percentage of AEG (orange), DAB (blue), and BAMA (green) composing the protein pellet for analyzed cyanobacterial samples. Total protein quantified via BCA assay. In general, the isolate cultures grown at warm temperatures contained approximately 5x as much total protein as the crude, cold-grown cultures (Table 14). Interestingly, BMAA was not found to be sequestered in the protein pellet but AEG, DAB, and BAMA were all detected at low levels. 106 Table 14. Total protein and non-protein amino acids in cultures isolated from Nunavut collections Sample Name Total Protein (mg/g) % BMAA % AEG % DAB % BAMA Iqaluit_crude1b 6.190 ND 0.0001 0.0048 0.0007 Resolute_crude2b 3.656 ND 0.0003 0.0012 0.0001 Resolute_crude4b 2.761 ND 0.0026 0.0060 0.0016 Resolute_crude5b 0.920 ND 0.0004 0.0031 0.0001 Resolute_crude7 2.872 ND 0.0002 0.0145 0.0006 Average in Crudes 3.280 ± 0.86 --- 0.0007 ± 0.0005 0.0059 ± 0.0023 0.0006 ± 0.0003 Iqaluit_isolate1 25.795 ND <0.0001 0.0031 0.0002 Resolute_isolate2 8.155 ND 0.0001 0.0005 0.0008 Resolute_isolate3 30.162 ND 0.0003 0.0005 0.0009 Resolute_isolate4 36.570 ND 0.0001 0.0006 0.0001 Resolute_isolate5 19.748 ND 0.0001 0.0007 <0.0001 Resolute_isolate6a 11.107 ND 0.0001 0.0007 <0.0001 Resolute_isolate6b 9.510 ND 0.0063 0.0012 0.0002 Resolute_isolate6c 15.095 ND 0.0051 0.0008 <0.0001 Resolute_isolate7 1.559 ND 0.0007 0.0058 0.0004 Average in Isolates 17.522 ± 3.82 --- 0.0014 ± 0.0008 0.0015 ± 0.0006 0.0003 ± 0.0001 4.6 Discussion of Results 4.6.1 Culture Isolation The diagram illustrated in Figure 43 shows a continual progression in culture quality over the course of the experimental period, indicating that unlike the cultures in Chapters 2 and 3, minimum community populations were not achieved. However, the qualitative rankings of the cultures by the end of the experimental period were relatively high, with no heavily contaminated cultures remaining. 107 4.6.2 Sequencing and Bioinformatics Most of the cultures in this sample set contained detectable cyanobacteria by Illumina MiSeq sequencing and with the corroboration by DADA2, several cultures were successfully identified as Pseudanabaena sp. and Microcoleus sp.. Two of the cultures analyzed that did not contain detectable cyanobacteria were the Resolute_5 crude and isolate samples analyzed in 2022. Both of these samples contained less than 10 000 generated reads, resulting in the statistically significant differences in reads between samples with positive and negative detections of cyanobacteria. With these discussion of Chapter 3 in mind (see Section 3.6.2 Sequencing and Bioinformatics) the significantly decreased generated reads in the Resolute_5 crude and isolate samples can be possibly attributed to inoptimal primers. The gDNA quality of either sample was within the range of the other cultures analyzed (Table 12) indicating that the DNA extraction protocol was not an issue. 4.6.3 Non-Protein Amino Acids in Arctic Cyanobacteria The concentrations of AEG and DAB were significantly different in the warm isolate cultures compared to the cold crude cyanobacterial cultures. Combined AEG has been consistently decreased in isolated cyanobacteria compared to crude cultures or field samples as per the results of Chapters 2 and 3 of this work; however, these results demonstrate the first significant difference in combined DAB concentrations between culture types, indicating that environmental conditions including temperature may influence the biosynthesis of this NPAA. No significant difference was observed in BAMA concentrations between cold and warm cyanobacteria at a bulk level. Likewise, individual crude-isolate groups did not show any significant differences. Interestingly, no significant difference was found in the concentration of BMAA between culture types, in contrast to the results of the previous chapters. The warm-grown isolates 108 produced similar levels as seen for the Lake Winnipeg and South American cyanobacteria (averaging 50.2 ng/g), but the cold, crude cultures similarly produced an average of 43.7 ng/g BMAA. Cyanobacteria grow optimally under conditions of 25 – 30 °C153 and light intensities between 10 – 100 µmol·m2·s-1,154 and BMAA is believed to accumulate in cultured cyanobacteria, releasing more free amino acid at cell decline compared to logarithmic or plateau periods,95 which suggests that this relative decrease may be connected to the decreased productivity of the crude cultures in their cold conditions. The growth conditions were found to significantly affect the total protein produced in the cyanobacteria in a similar fashion. No protein-bound BMAA was detected in any of the cultures studied, indicating that all of the amino acid was freely circulating under both types of growing conditions. This further suggests that under cold conditions, cyanobacteria are still actively utilizing BMAA. This experiment demonstrates for the first time that polar cyanobacteria are capable of biosynthesizing BMAA, AEG, DAB, and BAMA and further indicates that environmental growth factors including temperature and light availability may impact the production of these NPAAs in vitro. With climate change resulting in growing temperatures in Arctic regions, the accumulation of NPAA-producing cyanobacteria would be expected to heighten. 109 Chapter 5: Conservation of Non-Protein Amino Acids Biosynthesis in Plants 5.1 Synopsis Despite nearly two decades of continuous research into BMAA, its connection to plants is faintly understood. There is evidence that plants with cyanobacterial symbioses contain BMAA, but whether or not plants endogenously produce it remains uncertain. Cyanobacteria and plants are evolutionarily connected by their chloroplast lineage; therefore, the goal of this chapter is to determine whether the biosynthetic capability for BMAA and its isomers has been conserved in plants. 5.2 Introduction BMAA has been detected in cyanobacterial-plant symbioses, including cycad (Cycas spp.),7,20,22,155 water milfoil (Myriophyllum spp.),108 and water fern (Azolla filiculoides) and Gunnera kauaiensis.1 There have been few investigations into the endogenous production of BMAA in plants: one study measured BMAA production in C. micronesica seedlings without their endophytic cyanobacteria and indicated that the plant endogenously produced the metabolite.90 In contrast, a similar study analyzing BMAA in the cyanobacteria-plant symbiont Hydrilla verticillata could not detect BMAA in the plant sample lacking the epiphyte.71 Outside of the select symbiontic model systems studied, there have been no explicit investigations into whether plants natively contain and/or produce BMAA and its isomers. Since BMAA biosynthesis has been conserved to the Rhodophyte chloroplast lineage, it is possible that it has been conserved in green plants as well. Therefore, this chapter aims to detect and quantify BMAA and its isomers in plants through a phylogenetic perspective, by specifically sampling plants representative of different clades across the “tree of life”, to determine whether the biosynthesis of these NPAAs has been evolutionarily conserved. 110 5.3 Hypothesis and Objectives I hypothesized that BMAA, AEG, DAB, and BAMA biosynthesis have been conserved across plant evolution. To test this hypothesis, my objectives were: (1) To sample representative plant species from major phylogenetic clades. (2) To quantify levels of BMAA, AEG, DAB, and BAMA across the “tree of life” in plants using UPLC-MS/MS. 5.4 Experimental Methods 5.4.1 Plant Sources and Growing Conditions A total of 24 different plant species grown at the University of British Columbia Okanagan campus were employed for the study, representing ten established phylogenetic clades.156 The plant names, representative clades, and growing conditions can be found in Table 15. Table 15. Source, clade, growing conditions, and names of plant species employed for the current study. Plant species Clade Source Growing Conditions Chara longifolia C.B.Rob. Streptophytes Collection from Tompson Lake, British Columbia (50°10'10.65"N 119°24'19.13"W) Aquatic Vesicularia montagnei (Bel.) Fleisch. Bryophytes 1-2-Grow! (Egå, Denmark) Aquatic and In vitro Marchantia polymorpha L. Bryophytes Merlan Scientific (Toronto, Canada) In vivo (original peat moss) Azolla filiculoides Lam. Ferns Water Plants (Latvia) Aquatic Cycas revoluta Thunb. Gymnosperms Home Depot (Kelowna, Canada) In vivo (soil mix) Araucaria heterophylla (Salisb.) Franco Gymnosperms Home Depot (Kelowna, Canada) 157 In vivo (soil mix) 111 Plant species Clade Source Growing Conditions Wollemia nobilis W.G.Jones, K.D.Hill & J.M.Allen, 1995 Gymnosperms Department of Primary Industries Forestry facility (Queensland, Australia). Transported to Canada in a certified, soil-less shipping medium under permit and inspection from the Canadian Food Inspection Agency (CFIA) 157,158 In vivo (soil mix) Piper methysticum G.Forst. cv. ‘Hawaiian Black Stem’ Magnoliids National Tropical Botanical Garden, Kahanu Garden (Maui, Hawai’i) (Accession no. 900061) In vivo (soil mix) Chlorophytum comosum (Thunb.) Jacques Monocots The Greenery (Kelowna, Canada) In vivo (soil mix) Eleocharis acicularis (L.) Roem. & Schult. Monocots 1-2-Grow! (Egå, Denmark) Aquatic and In vitro Nepenthes spectabilis Danser (1928) x veitchii Hook.f. (1859) Caryophyllales Brad’s Greenhouse Carnivorous Plant Nursery (Vancouver Island, Canada) In vivo (soil mix) Saxifraga bryoides L. Saxifragales The Greenery (Kelowna, Canada) In vivo (soil mix) Myriophyllum aquaticum (Vell.) Verdc. Saxifragales Swimming Jewels Aquatic Gardens (Lake Country, Canada) Aquatic Sedum acre L. Saxifragales The Greenery (Kelowna, Canada) In vivo (soil mix) and In vitro Hypericum perforatum L. cv. ‘Anthos’ Rosids Richter’s Herbs (Goodwood, Canada) In vitro Humulus lupulus L. Rosids Skagit Horticulture (Mt. Vernon, Washington, USA) In vitro Cannabis sativa L. (Purple Kush) Rosids Aurora Cannabis Inc. (Vancouver, Canada) In vitro 112 Plant species Clade Source Growing Conditions Vaccinium macrocarpon L. 'Stevens' Asterids Ocean Spray Ltd, Canada In vivo (soil mix) Neurolaena lobata (L.) R.Br. ex Cass. Asterids Collection from Pacifico Central Conservation Area (San Pablo, Turrubares) and transported to Canada as surface-sterilized plant material under a CFIA permit and phytosanitary permit issued by the government of Costa Rica In vitro Hedera helix L. Asterids Home Depot (Kelowna, Canada) In vivo (soil mix) Mitragyna speciosa (Korth.) Havil. Asterids Booman Floral (Vista, California, USA) In vivo (soil mix) Nicotiana benthamiana Domin Asterids Agriculture and Agrifood Canada (Saskatoon, Canada) In vitro Streptocarpus ionanthus (H.Wendl.) Christenh. Asterids Harster Greenhouses Inc. (Dundas, Canada) 159 In vivo (soil mix) Trachelospermum jasminoides (Lindl.) Lem. Asterids Skagit Horticulture (Mt. Vernon, Washington, USA) In vivo (soil mix) 5.4.1.1 Plants Grown In vivo In vivo rooted plants were grown under standard greenhouse conditions in original substrates or Sunshine Mix #4 Professional Growing Mix soil (Sungro Horticulture, Agawam, Massachusetts). This soil contains sphagnum peat moss, perlite, dolomite lime, wetting agents, and proprietary RESiLIENCE® silicon additives. Notably, this soil mix is free of mycorrhizal fungi. The carniverous Nepenthes spectabilis x veitchii plants were further supplemented with sphagnum moss purchased online via Amazon from the seller YYGTGG Store (YYQTGG 6L Garden Sphagnum Moss, Water Retention and Dry Moss for Moisturizing Organic Fertilizer Phalaenopsis Orchid) to support their additional nutrient requirements. 113 Greenhouse conditions maintained a constant controlled temperature at 24 °C by chilled water cooling, shade curtains, and hot water piping depending on season. The photosynthetic active radiation (PAR) was maintained by High Pressure Sodium lights active during daylight hours at 330 µmol/m2/s at bench height. Humidity was maintained from 45 – 75% by overhead irrigation and hand watering. Standard fertilizer (NPK 20:20:20 + Micro) was applied every 1-2 months. Integrated Pest Management (IPM) techniques were used to control pests with minimal pesticide sprays. 5.4.1.2 Aquatic Plants Plants requiring an aquatic environment were grown in glass terrariums with collection water supplemented with deionized water. The room temperature averaged 26 °C and tanks did not receive supplemental lighting. Ambient light was provided by overhead fluorescent bulbs and sunlight through window glass. 5.4.1.3 Plants Grown In vitro Plants were cultured in Magenta boxes (Sigma) containing approximately 100 mL solidified media consisting of Murashige & Skoog Modified Basal Medium with Gamborg B5 Vitamins (Phytotechnology Laboratories, Lenexa, Kansas), 3% sucrose at pH 5.7, and solidified with 0.3% Phytagel™ (Sigma-Aldrich) prior to autoclaving at 121 °C and 15 psi for 20 minutes (Steris, Mississauga, ON), hereafter referred to as MSO. Plants were maintained by nodal subculture every 3-6 months. When needed, new cultures or contaminated cultures were surface-sterilized with a solution of 20% bleach (Clorox) with ~1% Head & ShouldersTM shampoo for 15 – 20 minutes and rinsed with sterile water for 15 – 20 minutes; nodal segments including the apical node were then excised from the treated plants and transferred to sterile culture boxes containing the solidified MSO media. In vitro plant cultures were maintained in a sterile controlled growth environment room at 25 °C, under a 16:8 photoperiod illuminated with 114 daylight fluorescent bulbs (SunBlaster™) at a light intensity of 25 – 45 µmol/m2/s at bench level, and at a positive pressure (+2.9 – 3.7 Pa) under HEPA-filtered air. 5.4.2 Quantification of Non-Protein Amino Acids in Plants by UPLC-MS/MS Analysis 5.4.2.1 Sample Collection and Derivatization The procedures used for sample collection and preparation are similar to those described in Section 2.4.4.1 Sample Collection and Derivatization. For this experiment, total NPAAs were measured. The procedure is described briefly with key modifications below; all equipment and tools are identical to those described in previous method sections. Three 50 – 100 mg wet shoot and/or leaf samples were collected from each plant species studied. All samples collected were biological pseudoreplicates, i.e., three samples were collected from the same plant, culture, or container/tank for each species. All samples were collected into pre-weighed microcentrifuge tubes. Samples were dried in a 45 °C oven until fully dry. The dried plant material from each sample was then transferred to glass hydrolysis vials. 1 – 1.5 mL of 6 N HCl were transferred to the vials, which were then vortexed and the plant material fully crushed with disposable pestles. Samples were purged with nitrogen gas for 30 seconds, then hydrolyzed at 110 °C for 18 – 22 hours. After cooling, 800 µL of sample was pipetted into a centrifuge filter tube and centrifuged for 5 min at 13,000 rpm. A 20 µL aliquot of the filtrate was placed into a microfuge tube and dried to complete dryness in a Speedvac. The dried filtrate was then reconstituted with 100 μL 0.2 M borate buffer (pH 10). For derivatization, 20 μL of each reconstituted sample was diluted with 60 μL 0.2 M borate buffer (pH 10) and derivatized with 20 µL AccQ-Fluor reagent. Complete reaction was ensured by vortex mixing and incubation for 10 minutes at 55 °C prior to analysis. The 10 µL aliquot of derivatized sample was then transferred to an autosampler vial fitted with a conical bottom spring insert for analysis by UPLC-MS/MS. 115 5.4.2.2 UPLC-MS/MS Analysis The preparation of standards and parameters for the UPLC-MS/MS quantification of NPAAs were identical to the protocols described in Sections 2.4.4.2 Preparation of Standards and 2.4.4.3 UPLC-MS/MS Parameters. 5.4.3 Statistics Data from the chromatographic analysis of NPAAs were collated in Excel® to calculate concentrations of each isomer in each sample. Graphs were generated using Excel®. Values falling below the lowest standard (LLOQ) but with signal-to-noise ratios ≥ 3 were considered Detected (“D”). Values with signal-to-noise ratios <3 were considered Not Detected (“ND”). All values are expressed as averages ± standard error. All concentrations in this chapter are reported as ng/g dry weight. 5.4.4 Phylogenetic Trees Phylogenetic trees of the selected plants were generated with phyloT v2 (https://phylot.biobyte.de/) using the NCBI taxonomies (database v2022.3) of the named species. Phylogenetic trees were visualized using iTOL v6.7.6.160 5.5 Experimental Results 5.5.1 Non-Protein Amino Acids in Plants BMAA was detected in 6/10 clades and in 10/24 plant samples studied (Figure 54). It was measured in the highest concentration in the positive control cycad tissue (29.3 ± 1.4 μg/g dw) as expected; the other eight plants with quantifiable BMAA contained much lower but relatively similar concentrations (ranging from 145.4 – 322.6 ng/g dw). 116 Figure 54. Concentration of total BMAA (ng/g dry weight) in the plant species sampled for the study. Error bars represent standard error (n=3) of the mean. BMAA was putatively identified in V. montagnei, A. filiculoides, W. nobilis, E. acicularis, M. aquaticum, S. acre, H. helix, and M. speciosa. Chromatograms of the plant samples with putative BMAA peaks are given in Figure 55. BMAA was also detected in M. polymorpha under the LLOQ. All peaks have consistent retention times with the BMAA standard. Fragmentation patterns were also comparable with positive controls in the cyanobacteria experiments. 117 Figure 55. Chromatograms showing the 459>171 quantifier transitions, with BMAA peaks highlighted in pink. Plant species as indicated; Std indicates NPAA standard mix showing retention time of BMAA. AEG was detected in all clades and in 23/24 (96%) of the plant species studied (Figure 56). It was not detected in Myriophyllum aquaticum. Concentrations of AEG varied quite widely; it fell below the LLOQ in one-third of the plants and ranged in concentration from 70.3 ng/g dw in Kratom to 27.0 μg/g dw in St. John’s wort. AEG appeared to be found in highest concentration in the Saxifragales and Asterids. Like AEG, DAB appeared to be ubiquitous in plants (Figure 57). It was detected in 9/10 clades (excluding the Ferns) and in 22/24 plants. All of the detections fell well above the LLOQ, and the concentrations ranged from 102 ng/g dw – 6 μg/g dw. BAMA was detected in half of the clades and 13/24 plants (Figure 58). It was not found in any of the early clades, and occurred beginning with the Gymnosperms. BAMA concentrations appeared less varied than AEG or DAB, and ranged from 43.0 – 436.8 ng/g dw. 118 Figure 56. Concentration of total AEG (ng/g dry weight) in the plant species sampled for the study. Error bars represent standard error (n=3) of the mean. Figure 57. Concentration of total DAB (ng/g dry weight) in the plant species sampled for the study. Error bars represent standard error (n=3) of the mean. 119 Figure 58. Concentration of total BAMA (ng/g dry weight) in the plant species sampled for the study. Error bars represent standard error (n=3) of the mean. 5.5.2 Impact of Growth Conditions on NPAA Production in Plants When the in vitro and in vivo plants were compared, significant differences in concentration of all four NPAAs were noticed (Figure 59). Firstly, BMAA was only detected in plants grown in vivo. Conversely, AEG, DAB, and BAMA were all significantly higher in concentration in plants grown in vitro (p=6.58E-05, p=0.0184, p=0.0365, respectively). 120 Figure 59. Comparison of detected concentrations of a) BMAA, b) AEG, c) DAB, and d) BAMA in plants grown in vivo (light brown) versus in vitro (dark brown). All values reported as ng/g dry weight. To specifically assess the impact of growing conditions on non-protein amino acid metabolism, in vitro cultures of E. acicularis, V. montagnei, and S. acre were initiated for comparative analysis. Interestingly, the results showed consistent differences in BMAA, AEG, and DAB production in the three model systems. Like the bulk analysis, BMAA was only detected in the tissues of the in vivo plants, while AEG and DAB appeared to increase in concentration in all three plants when grown in tissue culture On average, the concentration of AEG increased by more than six-fold in vitro, while DAB increased by around two-fold. Finally, BAMA was not detected in any of these plants except for the S. acre when grown in vitro. 121 Figure 60. Concentrations of BMAA, AEG, DAB, and BAMA in Eleocharis acicularis (light blue), Sedum acre (dark blue), and Vesicularia montagneii (orange), grown NIC and IC. Error bars represent standard error of the mean (n=3). 5.6 Discussion of Results The results of this analysis show the putative conservation of BMAA, AEG, DAB, and BAMA biosynthesis in plants. All four NPAAs were present in at least 50% of the plant species sampled and in at least half of the clades studied. Of the plants sampled, BMAA was also previously detected in Cycas revoluta,155 Azolla filiculoides,1 and Myriophyllum aquaticum.108 This experiment putatively identifies BMAA in the plant species Sedum acre, Eleocharis acicularis, Vesicularia montagnei, Wollemia nobilis, Hedera helix, and Mitragyna speciosa for the first time. Although most plants contain bacterial or fungal endophytes,161 these plants did not have any reported cyanobacterial endophytes in the literature to the best of my knowledge. 122 BMAA was also detected in the liverwort Marchantia polymorpha in small quantities (<100 ng/g dw) and this species is reportedly capable of forming endophytic and epiphytic relationships with cyanobacteria.162 DAB has been found in seeds from the Lathyrus genus45,111 and its biosynthesis has been documented in higher plants, including Vicia aurantica and Polygonatum multiflorum.112 My findings suggest that DAB may be ubiquitously biosynthesized in plants. AEG or BAMA have not yet been reported in plants, but AEG biosynthesis also appears to be conserved. It was observed that variations in detection rate and concentration may be related to the varying growth conditions of the plants; for instance, BMAA was detected only in plants grown in vivo. This finding suggests that growth conditions significantly affect BMAA production in plants, and it is possible that certain nutrient availability or lack thereof drives its biosynthesis. However, these results also might conversely indicate that plants are only capable of accumulating BMAA through their environments or from associated cyanobacteria. The detection of AEG, DAB, and BAMA in plants grown in vitro provides evidence for the inherent production of these NPAAs in plants and suggests that they are produced more readily when nutrient availability is high. However, since BMAA was not detected in any plants grown in vitro, it is possible that the NPAA was accumulated from the environment in plants with positive detections. Therefore, it will be necessary to test the capability of plants to take up BMAA externally, and also analyze the soil these plants have been grown in to see whether their environment could be accountable for the apparent BMAA. These objectives will be explored in Chapter 6 of this thesis. 123 Chapter 6: Sources of Non-Protein Amino Acids in Plants 6.1 Synopsis The results in Chapter 5 indicated that plants grown in vitro did not contain detectable BMAA. This chapter describes three experiments that were carried out to determine whether the presence of BMAA in plants could be attributed to uptake from the environment. 6.2 Hypothesis and Objectives I hypothesized that plants are capable of obtaining BMAA from the environment. To test this hypothesis, my objectives were: (1) Using St. John’s wort (Hypericum perforatum) as a model system, to quantify BMAA uptake in a time-course exposure study using UPLC-MS/MS. (2) To determine whether the uptake of quantum dot-labelled BMAA in St. John’s wort could be quantified using spectrophotometric methods. I also hypothesized that the presence of NPAAs in plants can be explained by the uptake of those NPAAs from their growth medium. To test this hypothesis, my objective was to: (1) Quantify free NPAAs in the soil medium used in the Plant Growth Facility using UPLC-MS/MS. 6.3 Experimental Methods 6.3.1 Methods for Experiment 1: Time-Course BMAA Uptake The growth and subculture conditions for St. John’s wort (Hypericum perforatum) are described in 5.4.1.3 Plants Grown In vitro. In preparation for the experiment, 30 shoots were subcultured on November 11th, 2022 and grown for 4 weeks in the controlled growth room. After 4 weeks, 11/30 of the plants had rooted, and a time-course experiment was designed with 11 samples. 124 Figure 61. Overview of experimental design for time-course St. John’s wort BMAA uptake study. Figure generated with BioRender. 6.3.1.1 Preparation of Solutions A 10 mM BMAA•HCl stock solution was prepared by weighing 0.0161 g of BMAA•HCl (L-BMAA Hydrochloride: Sigma Aldrich, CAS No. 16012-55-8) and making up the solution to 10 mL with sterile E-pure water. The solution was filtered using 0.2 μm nylon membrane filters (Nalgene™ Sterile Syringe Filters: Fisher Scientific, #726-2520) and stored as 0.5 mL aliquots in autoclave-sterilized microfuge tubes at -20 °C until further use. Sterile MSO media was prepared as described previously (5.4.1.3 Plants Grown In vitro). 125 6.3.1.2 Experimental Design Four time points were selected to monitor the uptake of BMAA over the course of one day: 0h, 2h, 6h, and 24h after inoculation with BMAA. For each time point, 1 plant was designated as a control. At the 0h timepoint, 1 plant was treated; at all other time points, 2 plants were treated. For the control treatment, each sterile polypropylene culture tube (Simport; #T406-2A) was filled with 5 mL MSO media. For the experimental treatment, each sterile polypropylene culture tube was filled initially with 4.5 mL MSO media. Rooting St. John’s wort plants were removed from their gel growth media, rinsed in sterile water, and transferred to the prepared culture tubes at random. Care was taken to ensure all roots were fully immersed in liquid and minimal shoot was in solution. The plants were left to acclimatize to the liquid media for 12 hours in the controlled growth environment. After 12 hours, the tubes designated for the experimental treatment were inoculated with BMAA. First, 0.5 mL of 10 mM BMAA•HCl was diluted with 9.5 mL MSO to a final concentration of 0.5 mM (20X dilution). Then, 0.5 mL of the 0.5 mM BMAA•HCl solution was pipetted into each culture tube designated for experimental treatment, giving a final BMAA concentration of 50 μM (10X dilution). Once all the tubes were inoculated, the 0h timepoint samples were collected. The rest of the samples were collected after the indicated incubation time had passed. The following procedure was meticulously performed to minimize residual BMAA remaining on the plant surfaces after collection. (1) Plants were transferred to 50 mL conical centrifuge tubes filled with deionized water. The tubes were capped and shaken vigorously for 10 seconds (“dirty dunk”). (2) The plants were then transferred to clean 50 mL conical centrifuge tubes filled with deionized water. The tubes were again capped and shaken vigorously for 10 seconds (“clean dunk”). (3) The plants were pat completely dry with paper towel. 126 (4) The roots and shoots of the plants were snipped and transferred to pre-weighed microfuge tubes. The wet weights were recorded, and the samples stored at 4 °C until further analysis. (5) Between plants, forceps and scissors were thoroughly rinsed with 70% EtOH. All control samples were rinsed and collected before treated samples. All rinsewater was discarded into the appropriate waste venue; conical tubes used for the “dirty dunk” were rinsed and re-used, but new tubes were used for the “clean dunks” for each plant. Once all samples were collected, they were placed in a 40 °C oven until fully dry. Dry weights were recorded and the samples stored at -20 °C until further analysis. 6.3.1.3 Sample Preparation and UPLC-MS/MS Analysis In this experiment, total BMAA was measured in each root and shoot sample. In general, the sample derivatization procedure and analysis by UPLC-MS/MS followed closely with other methods described in this thesis (see Sections 2.4.4 Quantification of Non-Protein Amino Acids and 5.4.2.1 Sample Collection and Derivatization). In brief, the dried samples were transferred to glass hydrolysis vials after weighing and hydrolyzed with 0.5 mL or 1 mL of 6 N HCl for the root and shoot samples, respectively. The samples were broken apart with plastic pestles and vortexed until fully homogenized, then purged with nitrogen gas for 30 seconds. The samples were heated at 110 °C for 22 – 22.5 hours until fully hydrolyzed. After hydrolysis, 500 μL aliquots of the samples were filtered as previously described, then 2 x 100 μL aliquots of the roots and 2 x 50 μL aliquots of the shoot samples were dried in the Centrivap to complete dryness. The dried samples were resuspended in 100 μL kit-provided borate buffer and derivatized with AQC as described previously and subject to chromatographic analysis. The parameters of the UPLC-MS/MS method were identical to those described in Sections 2.4.4.3 UPLC-MS/MS Parameters. 127 6.3.1.4 Statistics Data from the chromatographic analysis of NPAAs were collated in Excel® to calculate concentrations of BMAA in each sample. Graphs were generated using Excel®. Values falling below the lowest standard (LLOQ) but with signal-to-noise ratios ≥3 were considered Detected (“D”). Values with signal-to-noise ratios <3 were considered Not Detected (“ND”). All values are expressed as averages ± standard error. Concentrations are reported as ng/g wet weight. 6.3.2 Methods for Experiment 2: Quantum Dot-labelled BMAA Uptake 6.3.2.1 Preparation of Quantum Dot-labelled BMAA (QD-BMAA) Fresh borate buffer solutions were prepared from 1 M buffer stock: first, 2 mL of stock solution were diluted with 38 mL E-pure water to yield 50 mM borate buffer (20X dilution). Then, 2 mL of the 50 mM buffer were diluted with 8 mL E-pure water for a final 10 mM borate buffer (5X dilution). The 50 mM and 10 mM buffers were buffered to pH 8.3 and 7.4 respectively using 1 N HCl and 1 N NaOH. Quantum dots (Qdot™ 545 ITK™ Organic Quantum Dots; Fisher Scientific, #Q21791MP) were shipped at a concentration of 8 μM. 50 μL aliquots were pipetted into glass vials. 350 μL of 10 mM borate buffer was added to each vial and swirled to mix; the solution turned a clear yellow. 96 μL of 10 mg/mL melatonin or 10 mg/mL BMAA•HCl were added to the vials, causing the solutions to turn cloudy. 5 μL of 1 N NaOH were added at a time to each vial until the cloudiness subsided. N-ethyl-N’-dimethylaminopropyl-carbodiimide (EDC) was quickly prepared by mixing 5 mg in 500 μL water (10 mg/mL); immediately after, 11.4 μL of this solution was added to each vial and swirled to mix. The vials were incubated at room temperature at 69 rpm for two hours. After incubation, the solutions were transferred to centrifuge filter tubes and spun down to concentrate solutions to 100 μL (2 x 5 min, 13 000 rpm, cooled on ice between centrifugations). 128 The buffer was exchanged by pipetting 500 μL of 50 mM borate buffer to each tube, and again solutions were concentrated (2 x 5 min, 13 000 rpm, cooled on ice between centrifugations). The buffer exchange step was repeated twice for a total of 3 exchanges. Finally, the 4 μM solutions were transferred to fresh microfuge tubes and stored at 4 °C. Figure 62. Overview of the quantum dot conjugation process. Figure created using BioRender. 6.3.2.2 Spectrophotometric Quantification of QD-BMAA Standard curve solutions of quantum dot-labelled melatonin (QD-Mel) and quantum dot-labelled BMAA (QD-BMAA) were prepared by creating a 100X stock of each solution (5 μL of 4 μM QD-conjugate in 495 μL water), and then diluting the stock to yield 200X, 400X, 600X, 800X, and 1000X dilutions. For measurement, 100 μL of each solution was transferred to the wells of white clear-bottom 96-well Greiner plates. Solutions were measured on a TECAN Spark® Multifunctional Plate Reader equipped with SparkControl software (v3.1 SP1). An emission scan ranging from 500 – 600 nm was performed at an excitation wavelength of 300 nm to confirm the maximum emission wavelength. 129 6.3.2.3 Experimental Design The growth and subculture conditions for St. John’s wort (Hypericum perforatum) are described in Section 5.4.1.3 Plants Grown In vitro. Three rooting plants were used for the experiment: one for treatment with QD-Mel (positive control), one for treatment with QD-BMAA (experimental treatment), and one without treatment (negative control). Three sterile polypropylene culture tubes were prepared with 5 mL sterile E-pure water. 10 μL of 4 μM QD-Mel or QD-BMAA were added to the positive control and experimental treatments, respectively, to yield final concentrations of approximately 8 nM. A 300 μL aliquot of each solution was collected prior to adding the rooting plants. Additionally, three root (~1 cm white root) and three leaf (~1-2 cm young leaf) samples were collected from each plant and stored in 100 μL water prior to transferring the plants to the culture tubes. All of the pre-incubation samples were stored covered in aluminum foil at 4 °C. The plants were incubated in the controlled growth environment with gentle shaking at room temperature for 48 hours. After incubation, 300 μL aliquots of the solutions were collected. Three root and leaf samples were collected from each plant into 100 μL of water. All tissue samples were ground up using plastic pestles and then centrifuged (1 minute, 10 000 rpm). 100 μL of the supernatant from each sample was then transferred to the wells of a clear-bottom white 96-well Greiner plate and the fluorescence measured as described in the previous section. 6.3.3 Methods for Experiment 3: NPAAs in Soil 6.3.3.3 Soil Sample Collection Four soil samples were analyzed in this study. First, approximately 5 g of fresh potting soil (Section 5.4.1.1 Plants Grown In vivo) was collected from the Plant Growth Facility. Additionally, approximate 5 g samples were collected from the growing soils of the cycad, Wollemi pine, and Norfolk pine plants. 50 – 100 mg of soil were subsampled in triplicate from 130 the collected samples into pre-weighed microfuge tubes and their wet weights were recorded before further analysis. 6.3.2.3 Soil Sample Preparation and UPLC-MS/MS Analysis For this analysis, only the free amino acid portion was quantified. After weighing, 500 μL of 0.1 N TCA was pipetted to each sample, followed by vortexing at maximum speed under acidic conditions (pH 1) for 5 minutes to extract free amino acids from nutrient scaffolds in the soil. The samples were centrifuged briefly to pellet the soil, then 200 μL of the supernatant was collected and filtered as previously described. The filtrates were derivatized and analyzed via UPLC-MS/MS with the same parameters as written in Section 2.4.4 Quantification of Non-Protein Amino Acids. 6.3.2.4 Statistics Data from the chromatographic analysis of NPAAs were collated in Excel® to calculate concentrations of free BMAA in each sample. Graphs were generated using Excel®. Values falling below the lowest standard (LLOQ) but with signal-to-noise ratios ≥3 were considered Detected (“D”). Values with signal-to-noise ratios <3 were considered Not Detected (“ND”). All values are expressed as averages ± standard error and are reported as ng/g wet weight. 6.4 Experimental Results 6.4.1 Experiment 1 Results: Time-Course BMAA Uptake BMAA was not detected in the roots of any of the control samples, but was detected in the roots of the treated plants at all four timepoints (Figure 63). At the 0h timepoint (approximately 5 minutes after inoculation), the concentration of BMAA found in the root was 23.9 ± 3.4 ng/g ww. After 2 and 6 hours of BMAA exposure, the concentrations were quite 131 similar: 2.76 ± 1.4 μg/g ww and 2.16 ± 1.1 μg/g ww, respectively. The concentration detected fell to 0.97 ± 0.3 μg/g ww after 24 hours of incubation. Figure 63. Concentration of BMAA detected in root samples after incubation with 50 μM BMAA for 0, 2, 6, and 24 hours. Bars represent average concentrations; error bars represent standard error of the mean. These values indicate that the roots contained averages of 0.12 ng, 14.2 ng, 11.1 ng, and 4.98 ng of BMAA at 0, 2, 6, and 24 hours, based on the average masses of the wet root samples. BMAA was not detected in any of the control shoot samples, consistent with previous analyses of St. John’s wort shoots in this work (Figure 64). No BMAA was detected in the plants up to 2 hours of exposure, but BMAA began to appear at the 6h timepoint (21.21 ± 4.2 ng/g ww) and increased after 24 hours (66.10 ± 4.0 ng/g ww). These values indicate that the shoots contained an average of 0.5 and 1.7 ng of BMAA after 6 and 24 hours, based on the average masses of the wet shoot samples. 132 Figure 64. Concentration of BMAA detected in shoot samples after incubation with 50 μM BMAA for 0, 2, 6, and 24 hours. Bars represent average concentrations; error bars represent standard error of the mean. 6.4.2 Experiment 2 Results: QD-BMAA Uptake The maximum emission wavelength was confirmed to be approximately 545 nm (Figure 65) consistent with kit descriptions. Fluorescence measurements of the QD-Mel and QD-BMAA standard curves (Figure 66) showed excellent linearity (R2>0.99) when the data was log-transformed. 133 Figure 65. Emission spectra of the QD-BMAA standard curve, excited at 300 nm. Maximum emission wavelength was confirmed to be around 545 nm. Figure 66. Fluorescence measurements of QD-Mel (black) and QD-BMAA (grey) standard solution curves, measured at λex/λem 300nm/545nm. 020000400006000080000100000120000140000160000500505510515520525530535540545550555560565570575580585590595600RFUEmission wavelength (nm)1000X800X600X400X200X100X134 Figure 67. Fluorescence measurements of a) plant solutions, b) ground root, and c) ground leaf samples before-and-after 48 hours of exposure to QD-Mel, QD-BMAA, or water. Error bars represent standard deviations of the mean (n=3). 135 The fluorescence measurements for the ground root (Figure 67b) and ground leaf samples (Figure 67c) do not appear to show meaningful results; the fluorescence decreased in both the positive control (QD-Mel) and experimental samples (QD-BMAA), but seemingly increased for the negative controls. Moreover, the fluorescence detected in the quantum dot-treated plants was not significantly different from the plant grown in water by single-factor ANOVA (p=0.256 and p=0.0523 for the ground root and ground leaf samples, respectively). There was a marked decrease in fluorescence in the quantum dot-containing growth solutions before-and-after incubation, with the values after 48 hours resembling that of the control. The water control also showed increased fluorescence in the growth solution after 48 hours. 6.4.3 Experiment 3 Results: NPAAs in Soil Free BMAA and free BAMA were not detected in any of the soil samples analyzed. In contrast, free AEG and free DAB were detected in all of the samples (Figure 68). The Norfolk pine and cycad soil contained similar levels of free AEG as the soil mix (ranging from 0.15 – 0.75 ng/g). The Wollemi pine soil was found to contain significantly more free AEG (40.3 ± 22 ng/g) than the other soils (p<0.01 by single factor ANOVA). Relatively higher levels of free DAB compared to AEG were discovered in the soils; the control contained significantly more free DAB (35.7 ± 1.9 ng/g) than the plant-containing soils (p<0.01 by single factor ANOVA) which ranged from approximately 2.5 – 15 ng/g. 136 Figure 68. Concentrations of free a) AEG and b) DAB in the soil mix and soils containing Norfolk pine, Wollemi pine, and cycad. Error bars represent standard error of the mean (n=3). 137 6.5 Discussion of Results 6.5.1 Experiment 1 Discussion The results indicate that St. John’s wort is capable of taking up BMAA from its growth media. Interestingly, the roots appeared to hit an upper limit of uptake relatively quickly, as the concentration of BMAA rose sharply to approximately 2.5 μg/g after two hours, then steadily decreased over the course of exposure. Moreover, the results show that St. John’s wort was capable of transporting BMAA up into the shoots of the plants. The differences in physical amounts of BMAA in the plant tissue indicate that St. John’s wort may be able to metabolize BMAA; the roots contained approximately 12-14 ng between 2 – 6 hours, which decreased by ~8 ng after 24 hours. However, at 6 and 24 hours, the shoots contained less than 2 ng of BMAA, less than 25% of what was displaced in the roots. One explanation for these data is that St. John’s wort has a mechanism to metabolize the amino acid, which could be confirmed using labelling studies. It is currently unknown whether plants have the capacity to metabolize BMAA, but given that they contain ferrodoxin-dependent GOGAT enzymes similar to cyanobacteria,107 it appears plausible that they may have mechanisms to shuffle nitrogen from BMAA to other amino acids. 6.5.2 Experiment 2 Discussion The results of the ground root and ground leaf samples did not show clear evidence of QD-conjugate uptake, with similar results between the positive control and experimental treatment. These data suggest that the fluorescence of the quantum dots is too heavily obfuscated by the biological matrix to be meaningfully detected, which is corroborated by the negative control tissue having nearly identical fluorescence signals as the treated plants. It is possible that higher concentrations of QD-conjugate could yield more visible results; the quantum dot solutions herein were applied at final concentrations of 4 nM compared to the 138 previous study which applied BMAA at a concentration of 50 μM and showed definitive uptake. Future experiments could optimize these parameters to expand on the results. The decrease in fluorescence of the plant solutions pre- and post- incubation could be the result of either uptake in the plant, or by degradation of the quantum dots. This could be confirmed through an additional sample containing unconjugated quantum dots as another negative control, since St. John’s wort lacks the ability to take up unconjugated quantum dots.163 At the time of the experiment, further studies were not conducted since analysis of the plant tissues proved inconclusive; however, if future attempts were to be made to optimize the experiment, the use of unconjugated quantum dots as a negative control would be helpful to indicate uptake by analysis of the plant solutions. Ideally, a chemical QD-conjugate that is known to be not taken up by St. John’s wort would serve as the negative control in this experiment, since its difference in signal could be used to differentiate between uptake and degradation of the actual EDC coupling. 6.5.3 Experiment 3 Discussion Relatively high levels of free AEG were detected in the soil of the Wollemi plant, although only trace amounts were detected in the plant tissue. Although the soil mix itself is ensured to contain no mycorrhizal fungi, the Wollemi plants did appear to have a moss growing on the surface of their soil. After discussion with the Plant Growth Facility manager, it was proposed that since the Wollemi were grown alongside the Nepenthes spectabilis x veitchii for some time, some of the supplementary sphagnum moss may have transferred to the Wollemi plants. Therefore, additional free AEG may have been released by the moss tissues during the extraction process. Interestingly, although sphagnum moss symbiotically associates with Nostoc sp. cyanobacteria,164 no BMAA was detected in this soil nor in the Nepenthes sp. plant. 139 There was significantly more free DAB detected in the soil mix compared to the soil of any three plants, indicating that it is possible for the soil to be a source of the compound in plants. However, the analysis of plants grown in vivo versus in vitro demonstrated that nonetheless, plants produce more DAB in culture, so the significance of this finding is ultimately overshadowed. Failure to detect BMAA in the soil suggests that it would not be an exogenous source of the NPAA for the plants. Although typically a failure to detect is not evidence for the lack of presence of BMAA, considering the sensitivity with which AEG was detected (in sub-ng/g concentrations), the concentrations at which it could be present at in the soil would not explain the amount that was detected in the plants. It is also interesting that no BMAA was detected in the soil of the cycad plant, which purports that the plant does not release BMAA into its environment. However, since this experiment only analyzed free amino acids, it is possible that BMAA and/or its isomers are sequestered in the soil within peptides. Many oligopeptides function as signalling molecules in plants and can be taken up by a number of various peptide transporters.165,166 Since trichloroacetic acid is too weak to hydrolyze peptide bonds, the extraction process used in this experiment could not have revealed the presence of bound NPAAs in the soil.167 It is critical to note these data do not definitively conclude that the BMAA detected in the plants was endogenously produced, as there may be other mechanisms of uptake. To validate the hypotheses presented in this thesis, BMAA would need to be detected in plants grown in vitro. Recalling Chapters 2 and 4, two of the analyzed cyanobacterial isolate cultures contained organisms with plant-like structures (Chapter 2: Stn19_2021_isolate4 and Chapter 4: Resolute_isolate7) The primary difference in the experiments conducted with the cyanobacterial cultures and the plants were the growth conditions- all the cultures in the cyanobacteria experiments were grown on algae-oriented BG11 media, which contains very little ammonium (0.021 mM ferric ammonium citrate) compared to the plants grown on MSO, which necessitates 140 an ammonium nitrate concentration of 20.6 mM since nitrogen is a limiting nutrient for plant growth. The current notion for cyanobacteria is that BMAA production is inversely correlated to nitrogen concentrations;93,94,96 the same may hold true for plants. As such, further studies on BMAA content in plants grown under varying nitrogen conditions should provide a more conclusive answer as to whether they are capable of biosynthesizing this NPAA. To the best of my knowledge, this work describes the first attempt at characterizing BMAA biosynthesis in plants without known cyanobacterial endophytes. By establishing green plants as other biological entities potentially capable of BMAA production, novel studies can be carried out to elucidate the biosynthesis and function of this amino acid in vitro that are not complicated by the issues observed with cyanobacteria and endophyte model systems. 141 Chapter 7: Conclusions Click or tap her e to enter text. 7.1 Non-Protein Amino Acids in Cyanobacteria Chapters 2, 3, and 4 of this thesis explored NPAA production in cyanobacteria. Conclusions drawn from each chapter are summarized below. 7.1.1 Chapter 2 Conclusions: NPAAs in Cyanobacteria in Lake Winnipeg The overlying goal of this chapter was to establish isolate monocyanobacterial cultures and determine whether BMAA, AEG, DAB, and/or BAMA biosynthesis are impacted by the presence of contaminating bacteria in cultures. I successfully semi-purified crude cultures and field-collected cyanobacterial samples to minimal communities and identified dominant cyanobacterial and bacterial species with metagenomic and morphological approaches. Previous studies detecting BMAA and its isomers in cyanobacteria have typically invoked only the latter technique to classify their samples; however, as new light is shed on the complex composition and dynamics of cyanobacterial communities, characterizing them metagenomically can provide insight on species-specific interactions influencing BMAA biosynthesis in cyanobacteria or reveal novel microbial producers of these NPAAs. This study utilized a qualitative approach to cyanobacterial culture purification that was corroborated by bioinformatic analysis of the metagenomes, showing that isolates contained significantly less microbial diversity compared to initial crude cultures. Moreover, this suggests that for simple laboratory purposes, visual inspection can suffice to qualitatively track microbial diversity and cleanliness of cyanobacterial cultures when sequencing is too prohibitive. Analysis of the results revealed that BMAA, AEG, DAB, and BAMA were all present in cyanobacterial isolates, but there were no strong correlations between NPAA concentrations and the microbial diversity of the cultures. These findings suggested that in vitro, cyanobacteria 142 produce a small level of BMAA and other NPAAs that fluctuate depending on the particular species or other conditions, but the concentrations were largely unrelated to the diversity of contaminating bacteria in the culture. The two challenges presented at the start of this chapter referenced the difficulty of obtaining axenic cyanobacterial cultures and the lack of published data quantifying NPAAs in axenic cultures. Through my work, I discovered that a lack of culture axenicity does not significantly affect characterization of BMAA and other NPAAs in cyanobacteria, meaning that the obstacle of culture purification is no longer a bottleneck for future studies in this field. Furthermore, this conclusion supports previous research in non-axenic cyanobacterial cultures and indicates that they can be generally compared to studies with near-axenic cultures. 7.1.2 Chapter 3 Conclusions: NPAAs in Cyanobacteria from Peru and Chile The goal of this chapter was to establish isolate monocyanobacterial cultures from parent field samples collected in South America and determine whether BMAA, AEG, DAB, and/or BAMA biosynthesis differs between field-collected cyanobacteria and daughter in vitro cultures. Following the protocols described in Chapter 2, I successfully isolated monocultures and detected BMAA, AEG, DAB, and BAMA in the cyanobacterial isolates. Since comparison of the Chapter 2 results to previous literature indicated that field samples contained significantly more BMAA, I hypothesized that I would observe similar findings when comparing field samples and their isolated cultures. In support of my hypothesis, I detected significantly less BMAA in the isolated cultures. Overall, the findings of this study highlight the impact of environmental stress on BMAA production and support the conclusion that the concentrations of BMAA and its isomers detected in cultured cyanobacteria are not indicative of the total capacity of these organisms to produce these NPAAs in the environment, and findings in isolated laboratory contexts may not be applicable to complex ecological systems. 143 7.1.3 Chapter 4 Conclusions: NPAAs in Arctic Cyanobacteria The goal of this chapter was to establish monocyanobacterial cultures from crude cyanobacterial cultures generated from field samples collected in Nunavut, Canada and compare the effects of growth conditions on Arctic cyanobacteria. Following the protocols described in Chapter 2, I successfully purified isolate cultures that were grown at “warm” conditions compared to “cold”-grown crude cultures. BMAA, AEG, DAB, and BAMA were detected in the cyanobacterial isolates. The patterns of NPAA biosynthesis between the cultures grown under different conditions varied from those observed in Chapters 2 and 3, indicating that environmental conditions such as temperature and light availability influence the production of BMAA, AEG, DAB, and BAMA. Lower temperatures used to grow the crude cultures were found to hinder the biosynthesis of BMAA in some cyanobacteria. Interestingly, DAB was significantly associated with the cold-grown cultures, suggesting possible roles in cold and dark adaptations in certain species. As transcriptomic-based approaches become more common for the study of BMAA and its isomers in cyanobacteria,104,105 further study into growth conditions may help reveal mechanisms of biosynthesis for these amino acids. 7.2 Non-Protein Amino Acids in Plants Chapters 5 and 6 of this thesis explored NPAA production in plants. Conclusions drawn from each chapter are summarized below. 7.2.1 Chapter 5 Conclusions: NPAAs in Plants The primary objective of this thesis was to determine whether the capacity for BMAA biosynthesis was conserved in plants. All four NPAAs – BMAA, AEG, DAB, and BAMA – were detected in at least half of the samples studied, and covering the “tree of life”. This experiment has led to several discoveries that will be invaluable for future research in this field. First and foremost, the detection of BMAA in non-symbiotic plants provides the first putative evidence that 144 biosynthesis of this NPAA was conserved in the Chlorophyte plastid lineage. This opens up avenues for genetically exploring the basis for BMAA biosynthesis, as it must be contained not only within Chlorophytes, but also in cyanobacteria and Rhodophytes. Furthermore, my work also reports near-ubiquity of AEG and DAB in plants for the first time. While DAB has been described in some Lathyrus spp. previously45,111 and is believed to be biosynthesized by higher plants,141 AEG is currently completely uncharacterized in plants to the best of my knowledge, and the function of both these NPAAs is undetermined. Analysis of my results also showed that AEG, DAB, and BAMA increased in concentration significantly when grown in culture, suggesting that their presence is highly tied to nutrient availability, while the inverse appeared to be true for BMAA. Interestingly, BMAA was not detected in the plants grown in vitro whatsoever, which necessitated additional studies regarding plant capacity to uptake the NPAA and determining whether it was exogenously sourced in the plants with positive detections. 7.2.2 Chapter 6 Conclusions: Sources of NPAAs in Plants Through a time-course exposure study, I confirmed that St. John’s wort is capable of taking up BMAA from its growth medium through its roots and transporting it into its shoots. Moreover, the displacement of BMAA between the roots and shoots over the duration of exposure suggests that St. John’s wort may also have mechanisms for metabolizing the NPAA. I also attempted to design an orthogonal method of quantifying BMAA uptake using quantum dot conjugates of the amino acid; however, this approach was complicated by the complex plant matrix and may require higher concentrations of the quantum dot conjugate in solution. It may be possible to indirectly quantify uptake via measurement of the culture medium itself if the method is optimized in the future. The soil experiment showed that there was no free BMAA present in the growing soil used for the plants in the Plant Growth Facility, suggesting that there is likely no exogenous 145 source of the amino acid and the plants were capable of biosynthesizing BMAA. The soil was found to contain free AEG and free DAB, again highlighting the omnipresence of these NPAAs in environmental systems. 7.3 Summary of Findings & Significance of Work Ultimately, two key findings were determined through this thesis. Firstly, my findings indicated that isolated field collections of cyanobacteria, crude in vitro cultures of cyanobacteria, and semi-pure in vitro cultures of cyanobacteria produce relatively similar amounts of BMAA, differing at most by approximately one order of magnitude, and with no strong correlations to microbial diversity in culture. In contrast, cyanobacteria collected from algal blooms in Lake Winnipeg produced BMAA 2-3 orders of magnitude higher, meaning that specific ecological stressors may heighten production of this NPAA. Since BMAA has established neurotoxic properties, it will be necessary going forward to monitor its presence in algal bloom-affected waters for the health of nearby communities. This risk is most pertinent for remote communities which rely on their local aquatic ecosystems for foodstuffs, as increased bloom frequency and climate change may drive the biomagnification of BMAA in their resources. A better understanding of the conditions that drive the production of BMAA and its naturally occurring isomers will be helpful to assess the potential hazards of these compounds in the environment. Secondly, my results indicate that green plants may have the potential to biosynthesize BMAA, AEG, DAB, and BAMA, with the proposed hypothesis being that the genetic capacity was conserved in plastids. Future studies that either corroborate or dispute this hypothesis will hopefully reveal new insights about the biosynthetic space for these non-protein amino acids, and may lead to discoveries about their roles and functions in the organisms that produce them. 146 7.4 Future Studies It will be necessary to detect BMAA in plants grown in vitro to validate the claim that they are capable of biosynthesizing the amino acid. Like cyanobacteria, truly confirming production ability will be difficult until the exact enzymatic mechanisms are deduced and the relevant genes identified; however, plant cultures can be grown axenically with less difficulty, so detecting BMAA in tissue cultured plants will indicate biosynthetic capacity beyond reasonable doubt. Future studies can take the form of determining environmental conditions that can induce BMAA production in plants; for instance, growth in nitrogen-depleted media. My results preliminarily indicated that plants in BG11 media contained BMAA whereas plants grown in MSO did not. Recently, more researchers have started investigating the metabolome and proteome of isolated chloroplasts from green plants, such as the crop plant Solanum lycopersicum.168 An experiment quantifying BMAA and its naturally occurring isomers in isolated chloroplasts of green plants could also provide relevant information about whether biosynthetic capacity has been conserved. Moreover, since a large portion of plastid genes are believed to have been transferred to the plant nucleus,169 the results of this study could potentially narrow down the genetic space for the biosynthesis of these non-protein amino acids. Another interesting facet of these findings was that plants potentially had the capacity to metabolize BMAA. The metabolism of BMAA is not well characterized even in cyanobacteria, but it is confirmed to be a substrate of GS-GOGAT.100 Since photosynthetic eukaryotes contain a unique ferrodoxin-dependent isoenzyme not present in other prokaryotes, it is reasonable that plants would be able to metabolize the amino acid in hindsight. This hypothesis can be tested using purified Fd-GOGAT from cyanobacteria and various plants and solutions of BMAA; since the keto-acid form is missing an -NH3 group, it would only be singly derivatized with AQC and subsequently would be easy to differentiate from BMAA by tandem mass spectrometry. As well, it would be possible to feed plants with 15N-labelled BMAA, similar to Downing et al.’s study, and determine whether the same keto-acid product is formed. 147 Bibliography (1) Cox, P. A.; Banack, S. A.; Murch, S. J. Biomagnification of Cyanobacterial Neurotoxins and Neurodegenerative Disease among the Chamorro People of Guam. 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Thesis/Dissertation
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Chemistry
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Graduate
Murch, S. J. (Susan J.)
Characterizing β-N-Methylamino-L-alanine and its naturally occurring isomers in cyanobacteria and plants
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