Science, Irving K. Barber Faculty of (Okanagan)
Biology, Department of (Okanagan)
DSpace
UBCO
Hassanpour Farshour, Nazli
Master of Science - MSc
University of British Columbia
The cabbage looper, Trichoplusia ni (T. ni) is a generalist insect (Lepidoptera: Noctuidae) that is an agricultural pest of crucifers and other crops of economic importance. In fact, over 70% of all agricultural pest insects are Lepidopterans. Although insecticides are effective at controlling insect populations by targeting the nervous system, they can have negative off-target effects. Cytochrome P450s (CYPs), glutathione S-transferases (GSTs), UDP-glucuronosyltransferases (UGTs), and carboxylesterases (CEs) are metabolic enzymes frequently implicated in insecticide resistance. These enzymes work in concert with transport genes such as ATP-binding cassette transporters (ABCs), organic cation transporters (OCTs) and multidrug resistance proteins (MRPs). Upregulation of detoxification enzymes and inhibited binding of insecticides due to target-site mutations are the main mechanisms causing insecticide resistance. Enriched metabolic detoxification of xenobiotics along with the subsequent development of insecticide resistance have been linked to the overexpression of CYPs in insects. Previous studies have suggested that generalist insect herbivores’ (e.g. T. ni) growth and development are inhibited by exposure to the plant alkaloid nicotine, whereas specialist insect herbivores (e.g. tobacco hornworm) are not affected. However, it has been shown that the Malpighian (renal) tubules of T. ni actively excrete nicotine. Recent research has shown that T. ni do in fact detoxify nicotine into the three major metabolites; cotinine, cotinine-N- oxide, and nicotine-N-oxide. The objective of this thesis was to utilize next-generation sequencing methods to establish a greater understanding of how nicotine, a model plant alkaloid, is detoxified by a generalist insect such as T. ni. This thesis showed that dietary nicotine exposure in T. ni resulted in the increased expression of a number of metabolic detoxification and transport-related gene candidates. CYPs in particular showed increased expression in response to nicotine in both midgut and renal tissues. A number of these showed high sequence similarity to previously published CYPs in other insect pests. Taken together these data provide further insights on how nicotine is metabolized and excreted by T. ni. This research will increase opportunities for the development of new biochemical and physiological targets for the control of insect agricultural pests and disease vectors which are economically, environmentally, and medically significant.
NICOTINE METABOLISM IN THE CABBAGE LOOPER TRICHOPLUSIA NI (HÜBNER) – A TOXICOGENOMIC APPROACH by Nazli Hassanpour Farshour B.Sc., The University of British Columbia, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) June 2021 © Nazli Hassanpour Farshour, 2021 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: NICOTINE METABOLISM IN THE CABBAGE LOOPER TRICHOPLUSIA NI (HÜBNER) – A TOXICOGENOMIC APPROACH submitted by Nazli Hassanpour Farshour in partial fulfillment of the requirements of the degree of Master of Science. Mark Rheault, Biology, Irving K. Barber Faculty of Science Supervisor Michael Deyholos, Biology, Irving K. Barber Faculty of Science Supervisory Committee Member David Theilmann, Agriculture and Agri-Food Canada Supervisory Committee Member Isaac Li, Biochemistry and Molecular Biology, Chemistry, Irving K. Barber Faculty of Science University Examiner iii Abstract The cabbage looper, Trichoplusia ni (T. ni) is a generalist insect (Lepidoptera: Noctuidae) that is an agricultural pest of crucifers and other crops of economic importance. In fact, over 70% of all agricultural pest insects are Lepidopterans. Although insecticides are effective at controlling insect populations by targeting the nervous system, they can have negative off-target effects. Cytochrome P450s (CYPs), glutathione S-transferases (GSTs), UDP-glucuronosyltransferases (UGTs), and carboxylesterases (CEs) are metabolic enzymes frequently implicated in insecticide resistance. These enzymes work in concert with transport genes such as ATP-binding cassette transporters (ABCs), organic cation transporters (OCTs) and multidrug resistance proteins (MRPs). Upregulation of detoxification enzymes and inhibited binding of insecticides due to target-site mutations are the main mechanisms causing insecticide resistance. Enriched metabolic detoxification of xenobiotics along with the subsequent development of insecticide resistance have been linked to the overexpression of CYPs in insects. Previous studies have suggested that generalist insect herbivores’ (e.g. T. ni) growth and development are inhibited by exposure to the plant alkaloid nicotine, whereas specialist insect herbivores (e.g. tobacco hornworm) are not affected. However, it has been shown that the Malpighian (renal) tubules of T. ni actively excrete nicotine. Recent research has shown that T. ni do in fact detoxify nicotine into the three major metabolites; cotinine, cotinine-N- oxide, and nicotine-N-oxide. The objective of this thesis was to utilize next-generation sequencing methods to establish a greater understanding of how nicotine, a model plant alkaloid, is detoxified by a generalist insect such as T. ni. This thesis showed that dietary nicotine exposure in T. ni resulted in the increased expression of a number of metabolic detoxification and transport-related gene candidates. CYPs in particular showed increased expression in response to nicotine in both midgut and renal tissues. A number of these showed high sequence similarity to previously published CYPs in other insect pests. Taken together these data provide further insights on how nicotine is metabolized and excreted by T. ni. This research will increase opportunities for the development of new biochemical and physiological targets for the control of insect agricultural pests and disease vectors which are economically, environmentally, and medically significant. iv Lay Summary Insects play an important role in ecosystems through pollination, decomposition, and the control of pest populations by predation. Agricultural insect pests cause significant damage to plants, leading to costly reductions in agricultural production. Insect pests are often exposed to toxic compounds present in the various plants they consume. Synthetic pesticides are most commonly used to control insect pest populations by targeting the nervous system and while they are effective, they have negative off-target effects. Therefore, more effective and environmentally friendly pest control measures are needed. To develop more targeted controls, we need a comprehensive understanding of how insects eliminate toxic plant compounds. We will study how a caterpillar breaks down and eliminates the toxic plant compound nicotine. The first objective of this work is to compare gene expression in caterpillars exposed to diets with and without nicotine. The second objective is to determine gene targets for use in further studies. v Preface Chapters 2, 3, and 4 are based on work completed in the Laboratory for Insect Physiology and Biochemistry at UBC Okanagan under the supervision of Dr. Mark Rheault. Method development was completed by Nazli Hassanpour Farshour with assistance by Mark R. Rheault. NHF completed all experimental work and data analysis. A version of this thesis is in preparation for publication as the following manuscript: Hassanpour N, Kolosov D, Rheault MR. Using RNAseq to evaluate nicotine metabolism in the cabbage looper (Trichoplusia ni Hübner). Journal of Insect Biochemistry and Molecular Biology. vi Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of Contents ......................................................................................................................... vi List of Tables ................................................................................................................................ ix List of Figures .................................................................................................................................x List of Abbreviations .................................................................................................................. xii Acknowledgements .................................................................................................................... xiv Dedication .....................................................................................................................................xv Chapter 1: Introduction ................................................................................................................1 1.1 Agricultural Insect Pests ……………………………………………………………... 1 1.1.1 Trichoplusia ni (Hübner) ........................................................................................ 1 1.2 Synthetic Pesticides and Insecticides ………………………………………………… 4 1.3 Plant Secondary Metabolites ………………………………………………………….5 1.4 Effect of Nicotine in Insects ………………………………………………………. ... 7 1.4.1 Nicotine’s Mode-of-Action ..................................................................................... 9 1.4.2 Nicotine Metabolism ............................................................................................. 10 1.4.3 Effects of Nicotine in Different Insect Species ..................................................... 12 1.4.3.1 Gypsy Moth (Lymantria dispar) ................................................................... 12 1.4.3.2 Green Peach Aphid (Myzus persicae) ........................................................... 12 1.4.3.3 Honey Bee (Apis mellifera) .......................................................................... 12 1.4.3.4 Parasitic Wasp (Cotesia congregata and Hyposoter annulipes) .................. 13 1.4.3.5 Southern Armyworm (Spodoptera eridania) ................................................ 14 1.4.3.6 Tobacco Hornworm (Manduca sexta) .......................................................... 14 1.4.4 Effect of Nicotine in the Cabbage Looper (Trichoplusia ni) ................................ 15 1.5 Nicotine Resistance in Insects ……………………………………………………… 15 1.5.1 Excretion of Nicotine in Different Insect Species ................................................ 15 1.5.2 Oxidative Detoxification of Nicotine .................................................................... 16 1.5.2.1 Cytochrome P450 Genes ............................................................................... 19 1.5.3 Oxidative Detoxification of Nicotine in Different Insect Species ........................ 20 vii 1.5.3.1 Green Peach Aphid (Myzus persicae) ........................................................... 20 1.5.3.2 Tobacco Whitefly (Bemisia tabaci) .............................................................. 20 1.5.3.3 Fruit Fly (Drosophila melanogaster) ............................................................ 21 1.5.3.4 Tobacco Hornworm (Manduca sexta) .......................................................... 21 1.5.3.5 Honey Bee (Apis mellifera) .......................................................................... 22 1.5.4 Other Xenobiotic Resistance Strategies ................................................................ 22 1.6 Excretion and Oxidative Detoxification of Nicotine in T. ni ……………………….. 23 1.7 Hypothesis and Objectives …………………………………………………………..26 Chapter 2: Materials and Methods ............................................................................................27 2.1 Experimental Animals ……………………………………………………………… 27 2.2 Dietary Nicotine Exposure and Sample Collection ………………………………… 27 2.3 RNA Extraction and TURBO-DNase Clean Up …………………………………….30 2.4 Next Generation Transcriptome Sequencing ……………………………………….. 31 2.5 Next Generation Transcriptome Analysis Pipeline ………………………………….32 2.5.1 Quality Control ..................................................................................................... 32 2.5.2 Mapping to Transcriptome .................................................................................... 32 2.5.3 Differential Gene Expression Analysis ................................................................. 33 2.5.4 Transcriptome Data Analysis ................................................................................ 33 2.6 RT-qPCR Validation ………………………………………………………………...33 2.6.1 Complementary cDNA Synthesis ......................................................................... 33 2.6.2 Primer Design and Optimization .......................................................................... 34 2.6.3 Primer Efficiency Test: Standard Curve ............................................................... 36 2.6.4 Quantitative Polymerase Chain Reaction ............................................................. 36 2.6.5 Statistical Analysis ................................................................................................ 39 Chapter 3: Results ........................................................................................................................40 3.1 RNA Yields, Purity, and Integrity ………………………………………………….. 40 3.2 Next Generation Transcriptome Sequencing ……………………………………….. 46 3.3 Next Generation Transcriptome Sequencing Analysis ……………………………... 48 3.3.1 Quality of NGS Reads .......................................................................................... 48 3.3.2 Mapping to Transcriptome .................................................................................... 48 3.3.3 Differential Gene Expression ................................................................................ 48 viii 3.3.3.1 Comparison of DEGs in the midgut and Malpighian tubules of T. ni after nicotine treatment .......................................................................................................... 53 3.3.4 Transcriptome Data Analysis ................................................................................ 55 3.4 RT-qPCR Validation…………………………………………………………………58 3.4.1 cDNA Synthesis and Primer Selection ................................................................. 58 3.4.2 Primer Efficiency Test: Standard Curve and Melt Curve ..................................... 60 3.4.3 RT-qPCR Analysis for Validation of DEGs observed in RNA-seq data .............. 64 Chapter 4: Discussion ..................................................................................................................70 Chapter 5: Conclusion .................................................................................................................78 5.1 Objective 1: To use RNA-seq to produce transcriptomes for both the midgut and Malpighian tubule tissues of T. ni exposed to diets containing 0% nicotine and 1% nicotine.………………………………………………………………………………..……78 5.2 Objective 2: Determine the differential expression of genes in the Midgut and Malpighian tubule tissues of T. ni following 1% dietary nicotine exposure ……………… 78 5.3 Objective 3: Use quantitative PCR to validate the results of differential gene expression from RNAseq …………………………………………………………………..79 5.4 Limitations ………………………………………………………………………….. 79 5.5 Future Directions …………………………………………………………………… 80 Bibliography .................................................................................................................................82 ix List of Tables Table 2.1 The modified McMorran Grisdale insect diet. .............................................................. 29 Table 2.2 Primers used in real-time quantitative PCR (RT-qPCR) .............................................. 35 Table 3.1 RNA yield concentrations (µg/mL), 260/280 ratio, tissue mass, RQI, and classification for all T. ni larvae biological replicate Midgut and Malpighian tubule tissue samples exposed to 0% (control) and 1.0% nicotine diets for 24 hours ....................................................................... 43 Table 3.2 BGI sample testing results of total RNA using the Agilent 2100 Bioanalyzer ............ 45 Table 3.3 Summary of raw and clean reads of T. ni RNA-seq data ............................................. 47 Table 3.4 Nicotine-detoxification related transcripts .................................................................... 57 Table 3.5 Summary of efficiencies (E value), R2, standard curve slope, and y-intercept values of primer sets tested to be used in RT-qPCR analysis ...................................................................... 63 Table 3.6 The nicotine related CYP genes detected in the T. ni transcriptome chosen for RT-qPCR validation based on log2FC and adjusted p values obtained from RNA-seq. ..................... 66 Table 3.7 Target stability of reference genes used in RT-qPCR geNorm analysis. ..................... 67 x List of Figures Figure 1.1 Distribution map of T. ni generated from CABI, 2021 ................................................. 3 Figure 1.2 Chemical structure of nicotine, a common plant alkaloid produced by plants of the Solanaceae family. Created in Chemdraw Direct (Perkin Elmer) .................................................. 8 Figure 1.3 Three major biochemical pathways for the enzymatic modification of nicotine to its common metabolites: cotinine, nicotine-N-oxide, cotinine-N-oxide, and 4-hydroxy-4-(3-pyridyl) butanoic acid ................................................................................................................................. 11 Figure 1.4 General pathways for oxidative detoxification ............................................................ 18 Figure 1.5 Generalized model for metabolism and excretion in T. ni .......................................... 25 Figure 2.1 24-well culture plate set-up for dietary exposure of T. ni control and nicotine-containing diets ............................................................................................................................. 28 Figure 2.2 Sample plate design for genes A through D using sample maximization ................... 38 Figure 3.1 1% agarose gel displaying RNA samples to be used in sequencing and subsequent RT-qPCR validation...................................................................................................................... 42 Figure 3.2 Virtual gel images of total RNA samples generated from Experion Automated Gel Electrophoresis system .................................................................................................................. 44 Figure 3.3 Differential expression analysis using DESeq2 in R of transcripts expressed in the midgut (A) and Malpighian tubules (B) of larval T. ni fed diets containing 1% nicotine compared to control diet ................................................................................................................................ 45 Figure 3.4 Principal component analysis of gene expression across midgut samples .................. 51 xi Figure 3.5 Principal component analysis of gene expression across Malpighian tubule samples 52 Figure 3.6 Venn diagram displaying the number of differentially expressed transcripts in the Malpighian tubules an Midgut of T. ni ......................................................................................... 54 Figure 3.7 2% agarose gel displaying PCR products of candidate primers to be used in RT-qPCR gene expression analysis ............................................................................................................... 59 Figure 3.8 Standard curves for XM_026873420.1 (CYP9e2-like) in panel A and XM_026884877.1 (CYP6B2-like) in panel B from T. ni larval tissue samples ........................... 61 Figure 3.9 Sample melt curve analysis for XM_026873420.1 (CYP9e2-like) in panel A and XM_026884877.1 (CYP6B2-like) in panel B from T. ni larval tissue samples ........................... 62 Figure 3.10 Expression levels of CYPs in midgut samples of T. ni larvae obtained from RT-qPCR (A) and RNA-seq (B) ......................................................................................................... 68 Figure 3.11 Expression levels of CYPs in Malpighian tubule samples of T. ni larvae obtained from RT-qPCR (A) and RNA-seq (B) .......................................................................................... 69 xii List of Abbreviations ABC ATP Binding Cassette AChE Acetylcholinesterase MT Malpighian tubules cDNA Complementary DNA CE Carboxylesterase CNO Cotinine-N-oxide CYP Cytochrome P450 (protein) cyp Cytochrome P450 (gene) DDT Dichlorodiphenyltrichloroethane DEG Differentially expressed gene DNA Deoxyribonucleic acid ECD Efficiency of conversion of digested food ED50 Effective dose for 50 % of a population FDR False discovery rate FMO Flavin-containing monooxygenase (protein) fmo Flavin-containing monooxidase genes (gene) FW % Wet food weight GABA gamma-aminobutyric acid GST Glutathione-S-transferase HPLC High performance liquid chromatography HYB 4-hydroxy-4-(3-pyridyl) butanoic acid INDEL Insertion/deletion polymorphism IPM Integrated pest management LD50 Lethal dose for 50 % of a population mAChR Muscarinic acetylcholine receptor MDR Multidrug resistance transporter MRP Multi-drug resistance protein MS Mass spectrometry MS/MS Tandem mass spectrometry xiii nAChR Nicotinic acetylcholine receptor NGS Next generation sequencing NNO Nicotine-N-oxide NRT No reverse transcription NTC No template control OAT Organic anion tranporter OATp Organic anion transporting polypeptide OC Organochloride OCT Organic cation transporter OP Organophosphate PCA Principal components analysis PCR Polymerase chain reaction RIN RNA integrity number RNA Ribonucleic acid RPK Reads per kilobase RQI RNA quality indicator rRNA Ribosomal RNA RT-qPCR Reverse-transcription quantitative polymerase chain reaction SNP Single nucleotide polymorphism TYLCV Tomato yellow leaf curl virus UGT UDP-glucuronosyltransferase UPLC Ultra high-performance liquid chromatography xiv Acknowledgements Thank you to The University of British Columbia and the Natural Sciences and Engineering Council of Canada for financial support of this research. I would also like to thank the Finch family for their financial support of my studies. I am immensely grateful for my supervisor Dr. Mark Rheault for his endless advice, both professional and personal, and for all of his valuable guidance on this project. Thank you to my committee members Dr. Michael Deyholos and Dr. David Theilmann for their support and expertise. Thank you to all my lab mates, in particular Sophia Russo, Madison Dirks, and Eric McGinn, in the Facility for Insect Physiology and Biochemistry and the Plant Secondary Metabolites Laboratory. Our coffee breaks, wing nights, and trivia nights were some of the most memorable times during my graduate school journey. My friends and family have supported me from start to finish with their advice and encouragement. Special thanks to my closest friends Christina Horst and Mac Repovs for our endless adventures, hikes, and road trips. My time in graduate school would not have been as adventurous without them. I am eternally grateful for my family for their love and for always believing in me. xv Dedication This thesis is dedicated to my family and closest friends who supported me tremendously along the way and to myself for never giving up. 1 Chapter 1: Introduction 1.1 Agricultural Insect Pests Insects play an important role in ecosystems through pollination, decomposition, and the control of pest populations by predation. Current estimates of the number of insect species are between 2 – 8 million, which represents 80% of all species identified worldwide. Of these species, approximately 1 million have been classified to date (Chapman 2009). Although most insects are highly beneficial, some can have deleterious effects on human, animal, and plant health. Agricultural insect pests cause significant damage to plants through herbivory or as vectors carrying pathogens to healthy plants, leading to reduced agricultural production. For example, tomato production worldwide is greatly diminished by up to 100% due to the Tomato yellow leaf curl virus (TYLCV), which is transmitted by the sweetpotato whitefly Bemisia tabaci (Yan et al. 2018; Liu et al. 2013). Herbivorous insects causing damage to plants are classified as either generalists (polyphagous) or specialists (monophagous). As their classification suggests, generalists have a wider range of plants from various families within their dietary range, whereas specialists are limited to plants in one botanical family (Zhu et al. 2016). Insect herbivores exhibit a diversity of feeding strategies and can be categorized as leaf chewers, stem borers, sap suckers, root pruners, leaf miners, gall makers, and pollen or nectar collectors. Agricultural insect pests are typically ones that cause damage to plant tissue. The larvae of Plutella xylostella (Lepidoptera: Plutellidae), a major pest of canola worldwide, causes damage costing $1.3 – 5 billion US dollars annually. Costs for agricultural insect pests are calculated based on estimates for both management and crop yield loss (Zalucki et al. 2012). 1.1.1 Trichoplusia ni (Hübner) The cabbage looper, Trichoplusia ni (T. ni), is a globally distributed generalist insect (Lepidoptera: Noctuidae) with a diverse diet composed of over 160 plant species. T. ni is a major agricultural pest of crucifers and other crops of economic importance such as cotton and tomatoes (Franklin et al. 2011; Kain et. al 2015). T. ni lacks a diapause, rendering it incapable of 2 overwintering in areas of prolonged cold temperatures. As such, places like the Southern US and South-East Asia with moderate daily temperatures are inhabited by T. ni during winter months. T. ni can also be found in Northern areas due to seasonal migration patterns and can affect growing crops during summer months. As temperature increases from 21 to 32 °C, development from egg to adult takes less time, 25 to 18 days, respectively (Toba et al. 1973). Development and survival are hindered or stop at temperatures below 10°C and above 40°C. T. ni progresses through five larval stages or instars, a pupal stage, and finally emerges as a greyish-brown adult moth distinguished by silvery white spots on its forewings. Feeding is most prominent in the larval stage and causes significant damage to the leaves of crops, classifying the larvae of T. ni as an agricultural insect pest (Dedes 2014). The broad diet of cabbage looper larvae is primarily composed of crucifers, but can also include field crops, flower crops, and weeds (Soo et al. 1984). The damage caused by T. ni larvae is extensive due to their ability to eat three times their body weight in plant material daily (Dedes 2014). Furthermore, resistance to common pest control strategies has been demonstrated in cabbage looper populations. Recent research has indicated the evolution of resistance in T. ni to the chemical insecticide, Dichlorodiphenyltrichloroethane (DDT), and to the biological insecticide, Bacillus thuringiensis toxin (Fu et al. 2017). In addition, T. ni populations are able to resist the natural chemical defenses of the invasive Hemlock species, Conium maculatum, which are highly toxic to vertebrates (Castells and Berenbaum 2008). T. ni is an ideal model organism for agricultural insect pest studies as it represents the pest-rich Lepidoptera order (Simmons et al. 2013), is easily cultured in the lab, and very recently has had a draft of its genome released (Fu et al. 2017). 3 Figure 1.1 Distribution map of T. ni generated from CABI, 2021. Invasive Species Compendium. Wallingford, UK: CAB International. www.cabi.org/isc. Areas shaded in green indicate countries or their first level subdivisions (provinces, state, counties) where the presence of T. ni has been recorded. The map was created using the WorldMap:subdivisions online tool at https://mapchart.net/world-subdivisions.html. Figure 1.1 Distribution map of T. ni generated from CABI, 2021. Invasive Species Compendium. Wallingford, UK: CAB International. www.cabi.org/isc. Areas shaded in green indicate countries or their first level subdivisions (provinces, state, counties) where the presence of T. ni has been recorded. The map was created using the WorldMap:subdivisions online tool at https://mapchart.net/world-subdivisions.html. 4 1.2 Synthetic Pesticides and Insecticides Synthetic pesticides are the most common component of current pest control strategies and are divided into many classes such as organochlorides, organophosphates, carbamates, pyrethroids, and neonicotinoids. These chemical compounds target plants, fungi, bacteria, vertebrate pests, and invertebrate pests. Organochlorides (OCs) such as DDT and lindane were first used as insecticides extensively in the 1940s (Bertomeu-Sánchez 2019). Lindane became popular for its use against lice and in agriculture, but was banned in the 2000s due its negative effects on the environment which are still observed even after its ban. During World War II, insects responsible for spreading malaria and typhus in troops were combatted primarily using DDT. However, toxicological studies following the widespread use of DDT revealed its substantial danger to mammals and the environment (Bertomeu-Sánchez 2019). This led the banning and restriction of DDT and other organochlorine insecticides in most countries after the 1960s (Aktar et al. 2019). The first organophosphate (OP) insecticide marketed was parathion in the 1940s with its use peaking in the 1970s following the ban of OCs (Costa 2018). In comparison to OCs, OPs are rapidly hydrolysed and degraded. These properties limit risks of river and soil contamination along with food poisoning (Bertomeu-Sánchez 2019). Despite these benefits, OPs are less widely used due to their negative effects on the nervous system. OPs inhibit acetylcholinesterase (AChE), leading to an accumulation of acetylcholine. This can eventually lead to flaccid paralysis and neuropsychiatric deficits with long-term exposure (Robb & Baker 2020). Carbamates share many functional and structural similarities with OPs, but their key difference is reversible binding to AChE (Silberman & Taylor 2020). Carbazyl was introduced in 1956 as the first carbamate insecticide (Chavoshani et al. 2020). The effectiveness of these compounds combined with short lifetime and broad range make them still widely used in pest control (E et al. 2019). The first pyrethroid to be synthesized and marketed was allethrin in 1949 (Bradberry et al. 2005). In addition to residential and commercial uses, pyrethroids are also used medicinally to treat headlice and scabies (Bradberry et al. 2005). The poor absorption of these chemicals through the skin combined with rapid metabolism to less toxic compounds reduce their risk to mammals. The small size and lower body temperature of insects along with increased sodium channel sensitivity make them 2250 times more susceptible to the toxicity of pyrethroids (Bradberry et al. 2005). Neonicotinoids are the most widely used insecticide worldwide, with imidacloprid as the top-selling chemical compound in this class since its 5 development in the early 1990s (Yamamoto, 1999). Insecticides are chemical compounds used to harm, repel, or kill insects. Due to their associations with insect resistance and human toxicity, previously common pesticides like carbamate and organophosphate have been replaced by neonicotinoids (Craddock et al. 2019). The water-soluble properties of neonicotinoids allow them to persist in plant vascular tissue and foliage resulting in enhanced protection against herbivorous insects over previously used methods (Wood et. al 2017). Furthermore, neonicotinoids are more persistent, target a broader range of insects, and can enter all parts of treated plants (Craddock et al. 2019). Although synthetic pesticides and insecticides are effective at controlling insect populations by targeting the nervous system, they can have negative off-target effects (Casida et al. 2013). These off-target effects are due to the fact that 95% of the active ingredient in neonicotinoids is predominantly distributed into the environment while only 5% is taken up by plants (Wood et. al 2017). The roles of nicotinic acetylcholine receptors (nAChRs) in learning and memory make them ideal targets of neonicotinoids, which act as agonists at postsynaptic insect nAChRs to cause hyper-excitability of neurons (further discussed in section 1.4.1). Neonicotinoids are predicted to be less toxic to mammals and birds than other synthetic insecticide classes due to their selectivity for insect nAChRs over mammalian nAChRs (Craddock et al. 2019). However, the widespread use of neonicotinoid insecticides has led to a decrease in their effectiveness due to increasing resistance in various insect species (Taillebois et al. 2018). Off-target effects include environmental concerns linked to the accumulation of pesticides and their effects on non-target insects such as the colony collapse of pollinators (Taillebois et al. 2018). 1.3 Plant Secondary Metabolites Plants synthesize many compounds that play no direct role in their primary functions of growth, reproduction, and photosynthesis. These compounds, collectively termed plant secondary metabolites, aid plants in numerous ways; one example is that the tobacco plants in the Solanaceae family produce the alkaloid nicotine as a defense mechanism against insect herbivory (Steppuhn et al. 2004). These compounds have made botanical derived insecticides an attractive alternative to synthetic pesticides for their apparent greater safety and lower cost (Amoabeng et al. 2014). Plant secondary metabolites can be classified into three main categories: 6 terpenes, phenolics, and nitrogen-containing compounds. Terpenes are derived from the hydrocarbon isoprene which consists of 5 carbons and 8 hydrogens and as such are categorized based on the number of these isoprene units in the molecule (Hussein and El-Ansarry 2018). Monoterpenes in particular are commonly found in plants used in the production of essential oils. Terpenes also have medicinal, anti-fungal, anti-itching, anti-inflammatory, and antibacterial uses. Furthermore, quassinoids are a group of terpenes used as an insecticide (Hussein and El-Ansarry 2018). Phenolics consist of one or more phenol groups and are commonly associated with aroma, taste, and flavour. These compounds are regarded for their anti-inflammatory and antihepatoxic properties (Hussein and El-Ansarry 2018). The nitrogen-containing group includes alkaloids such as nicotine, which have insecticidal properties (reviewed in Chowański et al. 2016). Abundant amounts of these alkaloids are produced in nearly all plant tissues of various plants and their effects on insects have been widely examined (reviewed in Chowański et al. 2016). The desirability of plants to insects is often reduced by production of such alkaloids. Plants often synthesize higher concentrations of these compounds in response to stress induced by insect pests and the effects can be toxic, repelling, or antidigestive (Wu and Baldwin, 2010). In order to be used effectively, insecticidal plant compounds must be better understood molecularly before they can be implemented as pest control measures. Pest control using plant secondary metabolites possesses advantageous features such as increased difficulty in the development of resistance, rapid degradation, relatively high selectivity, low mammalian toxicity, and minimal environmental impact (Buss and Brown, 2014). Most plant secondary metabolites cause eventual death of insects by disrupting regular function at the cellular and physiological levels, rendering them incapable of maintaining homeostasis (Chowański et al. 2016). Alternatively, these compounds can destabilize insect populations through non-lethal changes to key structures and tissues. In contrast to synthetic pesticides, acute mortality is not the result of all plant secondary metabolites; sub-lethal effects include but are not limited to reduction in organism fitness, fecundity, and increased energetic cost associated with detoxification (Kliot and Ghanim, 2012). The ability of these compounds to reduce the risk of resistance emerging in a pest population and limit off-target effects in surrounding environments makes them potentially valuable in insect pest control. Integrated pest management programs (IPM) can benefit greatly from the inclusion of plant secondary metabolites as a component. For example, a reduction in settlement and 7 oviposition of the silverleaf whitefly B. tabaci is observed when using essential oils derived from five different plant species: Piper callosum, Adenocalymma alliaceum, Pelargoium graveolens, and Plectranthus neochlius (Fanela et al. 2016). Biological control agents have also emerged as an alternative strategy in IPM to accomplish their goal of using fewer toxic synthetic pesticides. Examples of these agents include natural predators, microbial agents, sterilization of male insects through gamma radiation, and luring chemical attractants (Stewart 2018). Perry et al. (2011) have argued that a greater understanding of the mechanisms responsible for insecticide resistance at the molecular level must be established before plant secondary metabolites can be implemented effectively into IPM strategies. Consequently, the following sections will review literature pertaining the physical, biological, and molecular responses of various insects to the model plant alkaloid, nicotine. 1.4 Effect of Nicotine in Insects Nicotine (Fig. 1.2) is an alkaloid produced by plants of the Solanaceae family and acts as a defensive mechanism against herbivorous insects (Castells and Berenbaum, 2008). In addition to making plants less desirable for consumption by many insect herbivores, it is also toxic to higher vertebrates (Krischik et al. 1991). Post-synaptic nicotinic acetylcholine receptors in the central nervous system of insects are targeted by nicotine in an agonistic manner (du Rand et al. 2017). By mimicking acetylcholine, nicotine leads to paralysis and death (Millar and Denholm 2007). The large amounts of solid waste created during tobacco processing are non-recyclable and hazardous due to high levels of nicotine. The disposal of this waste cannot occur in sanitary landfills, making nicotine a significant environmental pollutant (Briški et al. 2003). The cabbage looper T. ni in particular is susceptible to the toxic effects of dietary nicotine as it cannot survive on diets containing 0.125% nicotine or greater (Krischik et al. 1991). The dose of nicotine required to induce 50% mortality (LD50) is used to measure its toxicity, which is relatively higher in insects than vertebrates. For example, nicotine has a LD50 of 315 mg/kg in the honeybee Apis mellifera in contrast to a much lower LD50 of 55 mg/kg in rats and 50 mg/kg in rabbits (Soloway 1976). The ability of insects to survive high levels of nicotine makes them suitable models for use in studies of nicotine metabolism and resistance. 8 Figure 2.2 Chemical structure of nicotine, a common plant alkaloid produced by plants of the Solanaceae family. Created in Chemdraw Direct (Perkin Elmer). Figure 1.2 Chemical structure of nicotine, a common plant alkaloid produced by plants of the Solanaceae family. Created in Chemdraw Direct (Perkin Elmer). 9 1.4.1 Nicotine’s Mode-of-Action The insect nervous system relies heavily on the cholinergic pathway and its primary neurotransmitter acetylcholine for excitatory neurotransmission. The receptors for acetylcholine are muscarinic (mAChRs) and nicotinic (nAChRs) (Thany 2010). While nAChRs are ionotropic (ligand-gated ion channels), mAChRs are metabotropic (G-protein-coupled). Furthermore, mAChRs use acetylcholine as secondary messengers whereas nAChRs do not use secondary messengers (Verma et al. 2018). These secondary messengers are molecules that transmit signals from the receptor to the target. Thus, mAChRs are slow acting as they don’t transmit signals directly. The mAChRs are divided into five subtypes: M1, M2, M3, M4, and M5. M1, M2, M4, and M5 mAChRs are mainly expressed in the central nervous system whereas M3 mAChRs are more highly expressed peripherally in smooth muscle (Habibi 2017). However, nAChRs play a larger role in the central nervous due to their higher expression and fast-acting mechanisms (Thany 2010). The nAChRs in particular are targets for insecticides such as neonicotinoids due to their high expression in the central and peripheral nervous system, and role in excitatory neurotransmission. Excitatory neurotransmission begins with the release of acetylcholine from the presynaptic membrane and its subsequent agonistic interaction with the binding site of the nicotinic acetylcholine receptor ion channel complex (Thany 2010). Consequently, the receptor undergoes a conformational change which causes the non-selective cation channel to open, typically leading to an influx of extracellular Na+ and efflux of intracellular K+. Nicotine itself can act as an agonist and modifies neural signaling by mimicking acetylcholine at nAChRs. Although acetylcholine is normally removed from synapses through hydrolyzation by acetylcholine esterase, nicotine is able to remain at these synapses longer due its nonhydrolyzable nature (Matsuda et al. 2009). Structural differences in sub-units of the nAChRs are responsible for the lower toxicity of nicotine observed in insects relative to mammals (Tomizawa et al. 2000). Nicotine facilitates acute toxicity through paralysis of nerve centres. Furthermore, chronic exposure may hinder growth and reduce fertility (Gordon 1961). Despite its low efficiency and high toxicity to mammals, nicotine was used for many years to control insect pests. Major insecticides, particularly neonicotinoids, are a more current control and act as agonists at nAChRs or by inhibiting acetylcholinesterase. In order to be deemed as safe and effective, insecticides must demonstrate selective toxicity which is defined as a higher potency to pests and low hazard to mammals (Tomizawa and Casida, 2009, 2005, 2003, 1999). The 10 neurotoxicity of nicotine can be minimized by its metabolism into substrates that don’t act as agonists for nAChRs. The following sections will explore nicotine metabolism and the effects of nicotine in various insects. 1.4.2 Nicotine Metabolism Enriched metabolic detoxification of xenobiotics by insects is most commonly facilitated by Cytochrome P450 (CYP) enzymes in insects (Liu et al. 2015). In most animals, the common metabolites of nicotine are cotinine and nicotine-N-oxide. In vertebrates, additional metabolites of nicotine include glucuronide, nornicotine, and 2’ hydroxynicotine (Hukkanen et al., 2005). Insects are capable of further metabolizing cotinine into products such as cotinine-N-oxide, 3’-hydroxycotinine, norcotinine, and 4-hydroxy-4-(3-pyridyl) butanoic acid (du Rand et al. 2017). The three major biochemical pathways for the enzymatic modification of nicotine to its common metabolites are summarized in Figure 1.3. The metabolism of nicotine is mediated by various independent pathways: 5’ C-oxidation to cotinine and further oxidative metabolites, N-oxidation to nicotine-N-oxide, and 2’ C-oxidation to 4-hydroxy-4-(3-pyridyl) butanoic acid (Fig 1.3). CYPs mediate the metabolism of nicotine to cotinine, nornicotine, and 2 hydroxynicotine, whereas flavin-containing monooxygenases (FMOs) mediate the metabolism of nicotine to nicotine-N-oxide (Hukkanen et al. 2005). The metabolic enzymes responsible for the final production of 4-hydroxy-4-(3-pyridyl) butanoic acid from nicotine have not been determined (du Rand et al. 2017). A wide variation exists in the metabolism of nicotine between different species, which will be highlighted in the following sections. 11 2 3 4 5 Nicotine Aminoketone 4-Oxo-4-(3-pyridyl) butanoic acid Cotinine Cotinine-N-oxide 2’ C-oxidation 5’ C-oxidation Oxidation N-oxidation Nicotine-N-oxide 4-Hydroxy-4-(3-pyridyl) butanoic acid Figure 4.3 Three major biochemical pathways for the enzymatic modification of nicotine to its common metabolites: cotinine, nicotine-N-oxide, cotinine-N-oxide, and 4-hydroxy-4-(3-pyridyl) butanoic acid. The major pathway in honey bees is the 2’ C-oxidation (green). In mammals and tobacco hornworm the major pathway is 5’ C-oxidation (blue). N-oxidation of nicotine is a minor pathway in many organisms (purple). Figure 1.3 Three major biochemical pathways for the enzymatic modification of nicotine to its common metabolites: cotinine, nicotine-N-oxide, cotinine-N-oxide, and 4-hydroxy-4-(3-pyridyl) butanoic acid. The major pathway in honey bees is the 2’C-oxidation (green). In mammals and tobacco hornworm the major pathway is 5’C-oxidation (blue). N-oxidation of nicotine is a minor pathway in many organisms (purple). 12 1.4.3 Effects of Nicotine in Different Insect Species 1.4.3.1 Gypsy Moth (Lymantria dispar) In a study by Shields et al. (2008), gypsy moth larvae (Lymantria dispar) were exposed to nicotine and eight other alkaloids to determine the effects of such metabolites on the insects’ feeding. Upon application of compounds at concentrations ranging from 0.001 mM to 300 mM, results showed feeding deterrence threshold (DT) concentrations ranging from 0.1 mM to 10 mM from all alkaloids tested. The effective dose (ED50), representing concentrations that reduce feeding by 50%, was used to compare the effects on feeding of the various alkaloids. Each alkaloid’s ED50 was estimated using the DT concentration obtained from concentration-response curves. Nicotine in particular exhibited an ED50 of approximately 15.6 mM and was estimated to be the third most potent alkaloid tested, following berberine and aristolochic acid. Despite its ability to detoxify nicotine, L. dispar displayed sensitivity to low levels of nicotine and typically avoided plant material containing nicotine. 1.4.3.2 Green Peach Aphid (Myzus persicae) The green peach aphid, Myzus persicae, is globally distributed insect pest and has a plant host range spanning over 400 different species. Tobacco-adapted lineages of the green peach aphid, M. persicae nicotianae, exhibit increased nicotine tolerance relative to M. persicae stricto (s.s.) (Bass et al., 2013). Neonicotinoids, which are a commonly used insecticidal control and chemically similar to nicotine, are also less effective against M. persicae nicotianae. Clones of M. persicae nicotianae and M. persicae s.s. were also collected from various plants in a number of countries at different periods of time. Clones of M. persicae nicotianae exhibited survival for 144h on an artificial feeding bioassay of 30 ppm nicotine whereas clones of M. persicae s.s. had 0% survival at the same time point (Bass et al., 2013). 1.4.3.3 Honey Bee (Apis mellifera) Honeybees (A. mellifera) encounter nicotine in some plant species at trace amounts of 0.1 to 5 ppm (0.6 to 31 µM) (Singaravelan et al. 2006). Adult foraging bees exposed to >50 ppm (308 µM) nicotine for 15 days exhibited decreased flight activity and food uptake, their survival 13 was not significantly affected. Although naturally occurring concentrations of nicotine (0.5 – 5 ppm or 3 to 31 µM) had no effect on hatching success or survival of larvae, 50 ppm nicotine resulted in a 30% reduction of larval survival (Singaravelan et al. 2006). Larvae were not adversely affected by natural concentrations (3 – 31 µM) of nectar-nicotine, but greater concentrations increased mortality (Köhler et al. 2012). The high nutritional requirements of A. mellifera larvae is responsible for their heightened sensitivity to ingested nicotine and suggests a threshold for nicotine detoxification by the species (Köhler et al. 2012). Secondary metabolites like nicotine can be both beneficial and unfavourable to bees. Beneficial effects from secondary metabolites include reduction of parasitic infections, improved memory, and increased foraging efficiency. However, high concentrations of metabolites are toxic and may reduce the production and size of offspring, negatively altering colony fitness (du Rand et al. 2017). 1.4.3.4 Parasitic Wasp (Cotesia congregata and Hyposoter annulipes) Cotesia congregata and Hyposoter annulipes are parasitic wasps that deposit their eggs on the larvae of their insect hosts Manduca sexta and Spodoptera frugiperda, respectively (Barbosa et al. 1986). The tobacco hornworm (M. sexta) is a specialist insect herbivore which feeds on nicotine-producing Nicotania species, while the fall armyworm (S. frugiperda) is a generalist insect herbivore which feeds on rice and maize (Steppuhn et al. 2004; Qi et al. 2018). M. sexta and S. frugiperda were reared on diets containing various levels of nicotine and the effects on larval emergence of their respective parasitoids in response to diet were compared. In the M. sexta populations, nicotine diets resulted in significantly reduced larval emergence of the parasitoid relative to the nicotine-free diet. Of these larvae, those emerging from hornworms fed nicotine-free diets were more successful at forming cocoons than those exposed to nicotine. The survival of pupae, larval and pupal size and development, adult size, and average number of emerging larvae or total of number of adults per host were not significantly affected by nicotine (Barbosa et al. 1986). Larval emergence of H. annulipes in response to nicotine was also tested in S. frugiperda populations, which showed similar effects to those on C. congregata. However, a 30% increase in parasitoid mortality was observed in armyworms fed nicotine-containing diets. H. annulipes of nicotine-fed hosts also demonstrated prolonged larval development and reduction in adult size relative to those of hosts fed a nicotine-free diet (Barbosa et al. 1986). These findings demonstrating that host diets can negatively affect parasitoids suggest that 14 insecticidal compounds such as nicotine can be stored and repurposed by common insect pests for their own benefit (See section 1.5.3 Other resistance strategies). 1.4.3.5 Southern Armyworm (Spodoptera eridania) The southern armyworm Spodoptera eridania exhibits a number of physical and physiological responses upon exposure to dietary nicotine. When reared on artificial diets rich with nutrients and 0.5% nicotine content, fourth instar S. eridania larvae displayed reductions in food ingestion, weight gain, relative growth rate, and efficiency of conversion of digested food (ECD). The negative effects of nicotine on net growth efficiency are attributed to a substantial energetic cost associated with detoxification of such insecticidal compounds. The response of S. eridania to dietary α-(+)-pinene, a plant monoterpene used by insects in chemical signaling for communication, was also examined. Despite being a substrate of the same detoxification system as nicotine, dietary exposure to α-(+)-pinene did not cause any significant changes to the health of S. eridania larvae (Cresswell et al. 1992). 1.4.3.6 Tobacco Hornworm (Manduca sexta) As a specialist insect herbivore, the tobacco hornworm’s diet is limited to plants of the Solanaceae family with tobacco plants being predominately consumed. High levels of tobacco plant consumption paired with the lack of change in developmental period upon dietary nicotine exposure make M. sexta the most nicotine-tolerant insect (Harvey et al. 2007; Wink and Theile, 2002). Thus, M. sexta is an ideal subject for uncovering the physiological effects of nicotine and tolerance in insects. An artificial diet containing 0.1% nicotine does not affect mortality, but leads to decreased larval mass in field caught M. sexta (Kumar et al. 2014). Moreover, consumption of diets high in nicotine is linked to slowed growth in M. sexta (Steppuhn et al. 2004). Despite the physical effects of nicotine on M. sexta, the absence of a change in mortality suggests that M. sexta larvae have the capability to efficiently detoxify and excrete nicotine. The significant energetic costs associated with such detoxification and excretion may attribute the observed reduction in larval mass. 15 1.4.4 Effect of Nicotine in the Cabbage Looper (Trichoplusia ni) The effects of nicotine in cabbage looper larvae have not been explored as extensively as in previously reviewed insects. Previous studies have suggested that generalist insect herbivore (e.g. T. ni) growth and development are inhibited by exposure to the plant alkaloid nicotine, whereas specialist insect herbivores (e.g. tobacco hornworm) are not affected (Krischik et al., 1991). T. ni larvae were unable to survive on a diet containing > 0.125% nicotine by wet weight (Krischik et al., 1991). The length of exposure to sub-lethal doses (< 0.125%) of nicotine ranged from 15 to 22 days with results indicating a higher sensitivity to nicotine in T. ni relative to other Lepidoptera. While larvae exposed to chronic sub-lethal levels of dietary nicotine below 0.125% survived, they exhibited a longer duration to pupation and decreased pupal weight (Krischik et al., 1991). In contrast, significant changes in development were not observed in larvae reared on artificial diets with nicotine content up to 0.064% (640 ppm or 400 µmol l-1). A more recent study demonstrated a high mortality of 93% in T. ni larvae reared for 120h on diets containing high doses of nicotine (1.0% FW) (Saremba et al., 2018). Following 24h exposure, however, no significant differences in mortality were observed across diets containing various acute doses of nicotine from 0.1 to 1.0% (Saremba et al., 2018). Despite high mortality observed in T. ni in response to high levels of nicotine, a detailed explanation of their tolerance to chronic nicotine at lower doses is needed. 1.5 Nicotine Resistance in Insects 1.5.1 Excretion of Nicotine in Different Insect Species Insects often defend against xenobiotic compounds, such as nicotine, through the mechanism of excretion to limit their accumulation and access to target receptors. Insects make use of their renal organ, the Malpighian tubules, to facilitate excretion. The Malpighian tubules function similarly to the mammalian kidney, relying on membrane-bound multidrug resistance (MDR) transporters for excretion. MDRs are members of the ubiquitous ATP-binding cassette (ABC) transporter superfamily. ABC transporters have been frequently implicated in insecticide resistance due to their ability to transport many structurally diverse xenobiotics in a non-specific manner (reviewed in Merzendorfer, 2014). The significant role of these proteins in the transport 16 of various xenobiotics has been demonstrated in insects such as Helicoverpa armigera, M. sexta, Drosophila melanogaster, Daphnis nerii, T. ni and Bombyx mori (Bretschneider et al., 2016a; Dermauw and Van Leeuwen, 2014; Labbé et al., 2011). Xenobiotics and their metabolites have the potential to be rapidly eliminated through excretion. The degree of excretion of unmetabolized and metabolized xenobiotics can be investigated using chemical analysis of insect excretions. Following direct injection into the haemolymph, chemical analysis detected unmetabolized nicotine in the feces of M. sexta (Maddrell and Gardiner, 1975). Nicotine transport by the MTs of M. sexta has been demonstrated by measuring the concentration of 14C-labeled nicotine secreted into primary urine following its addition of the radiolabeled substrate to the saline bathing these tubules. The ability of M. sexta to actively transport from the haemolymph to the urine was suggested by a higher concentration of 14C-labeled nicotine in the secreted fluid than in the initial bathing medium. Studies examining MTs and related organs have demonstrated that both phytophagous and hematophagous insects across many orders have the capability to excrete plant alkaloids at multiple stages of their life cycles. Nicotine transport has been demonstrated in a number of insect species in diverse orders such as Oncopeltus. fasciatus, Rhodnius prolixus, D. melanogaster, Aedes aegyptii, Acheta domesticus, Locusta migratoria, Tenebrio molitor, Periplaneta Americana, and T. ni (Rheault et al., 2006). This suggests that the mechanism of excretion for nicotine is highly conserved across many species, making it a valuable option when considering the implementation of plant secondary metabolites as pesticides. The physiological state of an organism is highly dependent on excretion due to its role in osmoregulation, ion regulation, and toxin removal. Investigating the role of excretion in coping with plant derived compounds can provide valuable information for the design of novel control measures (Ruiz-sanchez and O'Donnell, 2015). For example, the important role of ABC transporters in excretion make them a promising target for increasing the efficiency of insecticidal compounds (Merzendorfer 2014). 1.5.2 Oxidative Detoxification of Nicotine A common strategy among insects in detoxifying xenobiotics and synthetic insecticides is their enzymatic conversion into by-products that are less toxic or more easily excreted (Li et al., 2007; Xu et al., 2005). Oxidative detoxification can generally be categorized into Phase I, II, and III reactions (Fig 1.4). Phase I reactions are primarily carried out by Cytochrome P450 proteins 17 (CYP) that are largely found in the lung, gastrointestinal tract, kidney, and liver (Xu et al., 2005). Many superfamilies of enzymes are responsible for mediating Phase II reactions, including UDP-glucuronosyltransferases (UGT) and glutathione-S-transferases (GST) which are of particular interest in insect xenobiotic detoxification. Conjugation with these enzymes aids in easier excretion by increasing hydrophilicity of xenobiotic compounds and/or their by-products from Phase I reactions (Xu et al., 2005). The final step in oxidative detoxification is the transport of Phase I and/or Phase II products across epithelia for later excretion. These Phase III reactions are mediated by various membrane bound xenobiotics transporters; such as organic cation transporters (OCT), the multi-drug resistance protein (MDR) family, also known as P-glycoproteins, the multidrug resistance-associated proteins (MRP) family, and the organic anion tranporter (OAT) and organic anion transporting polypeptide (OATp) families. Despite a considerable amount of knowledge on the mechanism of xenobiotic detoxification in mammals and the identification of hundreds of similar genes in insects (Chung et al., 2009; You et al., 2013), there is limited experimental evidence regarding the specific insect Phase I, II, and III proteins that mediate detoxification of xenobiotics in vivo. Oxidative detoxification of nicotine is most commonly associated with CYPs in insects and has been demonstrated to be highly efficient (reviewed in Li et al., 2007). Through 5’ C-oxidation via CYPs, nicotine can be oxidized to cotinine and further oxidized to cotinine N-oxide. When applied to the cuticle of two fly species, Luciliu caesar and Culliphoru vomitoria, nicotine exhibited high toxicity while its oxidative derivatives cotinine and cotinine N-oxide exhibited ≤ 3% toxicity (Riah et al., 1997). A significant energetic cost is linked to oxidative detoxification in many insect species. For example, the honeybee A. mellifera’s detoxification of nicotine via induction of Phase I oxidative enzymes is energetically demanding. This energy is predominantly linked to increased expression of genes, proteins and substrates associated with the glycolytic pathway (Du Rand et al., 2015). 18 Figure 5.4 General pathways for oxidative detoxification. The coloured circles represent enzymes: cytochrome P450s (CYP), carboxylesterases (CE), UDP-glucuronosyltransferases (UGT), glutathione-s-transferases (GST), multidrug resistance transporters (MDR), multidrug resistance proteins (MRP), organic cation transporters (OCT), and organic anion transporters (OAT). Figure 1.4 General pathways for oxidative detoxification. The coloured circles represent enzymes: cytochrome P450s (CYP), carboxylesterases (CE), UDP-glucuronosyltransferases (UGT), glutathione-s-transferases (GST), multidrug resistance transporters (MDR), multidrug resistance proteins (MRP), organic cation transporters (OCT), and organic anion transporters (OAT). 19 1.5.2.1 Cytochrome P450 Genes Cytochrome P450s (CYP) are abundantly present in almost all living organisms and range from 57 cyp genes in humans to 170 cyp genes in mosquitos (Gilbert 2012). Enriched metabolic detoxification of xenobiotics, such as plant toxins and pesticides, along with the subsequent development of insecticide resistance have been linked to the overexpression of various cyps in insects (Liu et al. 2015). Transcriptional upregulation of cyps is responsible for an increase in protein production and activities of these CYP enzymes, which are linked to enhanced metabolic detoxification (Liu et al. 2015). These genes are primarily found in tissues that have a digestive function (Cui et al. 2016). CYP enzymes also play a role in insect growth, nutrition, and development (Liang et al. 2015). The four main clades into which insect cytochrome P450s are organized are; CYP2, CYP3, CYP4, and Mitochondrial CYPs, with genes in the CYP3 clan being most commonly linked to xenobiotic metabolism (Liu et al. 2015). The CYP3 clan includes both insect CYP6 and CYP9 genes, which are commonly linked to alkaloid metabolism and insecticide resistance (Liang et al. 2015) and is most closely related to vertebrate CYP3 families (Gilbert 2012). The CYP3 family includes genes that fit the category of environmental response genes (Gilbert 2012). The emergence of tolerance to alkaloids such as nicotine has been a growing area of interest in the field of insect research. In the black swallowtail, Papilio polyxenes, CYP6B1 and CYP6B3 have been implicated in the resistance against furanocoumarins found in plants (Hung et al. 1995). Recent research has also discovered the role of particular CYPs in nicotine metabolism. For example, M. persicae tolerate nicotine through overexpression of a nicotine oxidizing gene known as CYP6CY3 (Bass et al. 2013). In Lepidopteran insects such as Spodoptera litura and S. frugiperda, CYP6AB and CYP321AF are induced by allelochemicals, respectively (Wang et al. 2014). In B. tabaci, CYP6CM1 is overexpressed in nicotine-resistant lineages (Kliot et al. 2014). Furthermore, CYP6B46 is associated with nicotine metabolism in the tobacco hornworm, M. sexta (Kumar et al. 2014). Studies in honeybees have indicated the development of tolerance through oxidative detoxification of nicotine (du Rand et al. 2017). DDT resistance is associated with CYP6G1 in D. melanogaster Meigen and CYP6Z1 in the Anopheles gambiae Giles malaria vector (Liu et al. 2015). In another agricultural pest, Erthesina fullo, there have been a number of genes in the CYP3 clan connected to alkaloid metabolism (Liu et al. 2015). A study on the red flour beetle, Tribolium castaneum, has demonstrated rapid 20 up-regulation of CYPs in the presence of insecticides. This study revealed that cypermethrin, permethrin and lambda-cyhalothrin may induce up-regulation of CYP4G7 and CYP345A1, while imidacloprid may induce up-regulation of CYP4BR3 and CYP345A1. Additionally, CYP4G7, CYP4BR3, and CYP345A1 were up-regulated in just six hours by all four insecticides at one fourth of LC20. These results demonstrate that up-regulation of these genes does indeed occur even at low concentrations of insecticides (Liang et al. 2015). In humans, CYP2A6 has been shown to be primarily responsible for nicotine metabolism (Nakajima et al. 2006). These studies highlight the vast diversity and importance of CYPs in metabolic detoxification and insecticide resistance. 1.5.3 Oxidative Detoxification of Nicotine in Different Insect Species 1.5.3.1 Green Peach Aphid (Myzus persicae) The generalist green peach aphid, M. persicae, consumes over 400 plant species, many of which are economically significant crops. Some M. persicae populations have lineage-specific adaptations making them resistant to novel host plants. For example, lineages adapted to tobacco have been discovered in countries growing tobacco which exhibit resistance to neonicotinoids and nicotine fumigation. Nicotine detoxification in M. persicae nicotianae has been linked to the increased copy number of the cytochrome P450, CYP6CY3 (Bass et al., 2013). Bass et al. used recombinant technology to express CYP6CY3, and insect P450 homolog, and CYP2A6, a human P450, in microsomes. They then observed the rate of nicotine depletion along with the corresponding increase of its major metabolite cotinine. These metabolism assays demonstrated that CYP6CY3 is more efficient at nicotine metabolism than CYP2A6 which has been shown to metabolize nicotine in humans (Bass et al., 2013). These findings suggest the potential for increased metabolic capacity of nicotine in insects and thus increased resistance. 1.5.3.2 Tobacco Whitefly (Bemisia tabaci) The sweet potato or tobacco whitefly (B. tabaci) is a generalist herbivore with a diet comprising over 600 plant species. Cytochrome P450s have been implicated in B. tabaci’s resistance to neonicotinoids (Kliot et al. 2014). Imidacloprid, the most commonly used 21 neonicotinoid, is strongly resisted by two different biotypes of B. tabaci which exhibit overexpression of CYP6CM1 (Karunker et al. 2008). Furthermore, increased copy number of CYP6CM1 was shown in B. tabaci populations exposed to nicotine, suggesting its role in nicotine detoxification (Kliot et al. 2014). Increased expression of CYPs upon exposure to natural plant secondary metabolites in insects can in turn lead to subsequent resistance against commercial pesticides. B. tabaci populations with higher cyp gene expression in particular show enhanced tolerance to a wide range of insecticides and these populations are linked to negatively impacted agricultural systems (Guo et al., 2014). Thus, an insect herbivore’s success in the agricultural setting can be associated with its ability to induce and utilize the generalized CYP detoxification system. 1.5.3.3 Fruit Fly (Drosophila melanogaster) Nicotine resistance in D. melanogaster is strongly associated with increased expression of ornithine aminotransferases. High ratios of GABA to glutamate prior to nicotine exposure may be partially associated with nicotine resistance due to inhibitory actions of GABAergic signals on dopaminergic neurons. This inhibition reduces activation of nicotinic acetylcholine receptors and subsequently lessens the potency of nicotine (Passador-Gurgel et al. 2007). Nicotine tolerance in both resistant and susceptible populations was also linked to CYPs, specifically CYP6a20 (Passador-Gurgel et al. 2007). In addition, the overexpression of CYP6G1 has been implicated in the resistance of D. melanogaster to DDT and imidacloprid (Yu et al. 2015). Mapping the loci of D. melanogaster nicotine resistance revealed that nicotine is metabolized by fruit fly larvae into less toxic, more excretable compounds (Marriage et al., 2014). 1.5.3.4 Tobacco Hornworm (Manduca sexta) Nicotine detoxification has been extensively studied in the tobacco hornworm and has been identified to be primarily associated with the CYP detoxification system (Self et al., 1964; Snyder et al., 1993, 1994, 1995). It has been demonstrated that M. sexta metabolizes nicotine into primarily cotinine-N-oxide, where dietary nicotine reversibly induces this metabolism, and that rapid excretion of alkaloids occurs after metabolism (Snyder et al. 1994). A particular 22 cytochrome P450, CYP6B46, has been associated with the metabolism of nicotine in the midgut of M. sexta (Kumar et al. 2014). However, nicotine may also be excreted by the Malpighian tubules prior to chemical modification (Kumar et al. 2014). 1.5.3.5 Honey Bee (Apis mellifera) Recent studies have led to the discovery that the honeybee A. mellifera is capable of metabolizing nectar nicotine primarily into 4-hydroxy-4-(3-pyridyl) butanoic acid via the 2’C oxidation pathway (du Rand et al. 2017). The first intermediate of this pathway is aminoketone (Fig 1.2) In most insect classes, the 2’C-oxidation pathway is not typically linked to nicotine tolerance. Interestingly, the 2’C-oxidation pathway is also used by the aphid M. persicae to metabolize nicotine primarily into aminoketone (Bass et al., 2013; Wink and Theile, 2002). This metabolism is associated with the upregulation of CYP6CY3 in M. persicae (Bass et al., 2013; Wink and Theile, 2002). In contrast, insects in the Lepidoptera class often use the 5’C-oxidation pathway, which converts nicotine to cotinine and further derivatives. For example, the previously mentioned tobacco hornworm utilizes 5’C-oxidation via CYP6B46 upregulation in nicotine detoxification (Kumar et al., 2014). Nicotine tolerance in A. mellifera was not significantly linked to the rapid extraction of unmetabolized nicotine (du Rand et al., 2017). The specific detoxification enzymes involved in nicotine metabolism of honeybees are yet to be determined. 1.5.4 Other Xenobiotic Resistance Strategies The co-evolution of insects with their host plants has led to the development of many specialized strategies other than metabolism and excretion for defense against xenobiotics (reviewed in Aniszewski 2007). Such strategies include sequestration in which toxic compounds are stored in an isolated compartment to allow for reproduction to occur before death and avoidance in which insects use their senses to avoid consuming toxic compounds altogether. Other unique strategies have been documented in insects such as repurposing xenobiotics for defense and subsequent increase in overall fitness. For example, the Lepidopteran pest M. sexta which solely feeds on tobacco plants defends against its natural predator the wolf spider using ingested nicotine through nicotine halitosis (Kumar et al., 2014b). The spiracles in the outer cuticle are the primary component to this defense as they connect M. sexta’s respiratory system 23 to the outside air. Unmetabolized nicotine circulating in the haemolymph is released into the headspace around the caterpillar through these spiracles. Predation by wolf spiders had a higher occurrence in M. sexta larvae fed nicotine-free diets than those fed nicotine-containing diets. The link between xenobiotic detoxification and gut microbiota has become a growing area of interest for recent research in insects. The gut microbes of several insect species have been associated with the detoxification of xenobiotics (Hammer and Bowers 2015). The coffee berry borer pest Hypothenemus hampei for example has been demonstrated to utilize gut microbiota in its defense against caffeine (Ceja-Navarro et al., 2015). Lepidopterans however have not been well studied in this area due to their guts being a poor environment for common insect gut microbiota. The caterpillar’s gut environment is made by unhospitable due to the gut’s simple structure, elevated alkaline pH, and rapid passage time. When researchers investigated the gut microbiota in a number of caterpillar species, transient plant bacteria were identified as the majority of microbe species. The caterpillars also lacked resident, host-specific gut microbes (Hammer et al., 2017). Therefore, gut microbiota may not be of particular interest or significance in uncovering the detoxification of nicotine and other xenobiotics in Lepidopteran pests such as the cabbage looper. 1.6 Excretion and Oxidative Detoxification of Nicotine in T. ni Extensive research on various Lepidopteran insects has demonstrated their capability to utilize Malpighian tubules in the excretion of xenobiotic compounds and their metabolic derivatives present in haemolymph (Maddrell and Gardiner 1975). Although previous studies have suggested that generalist herbivore growth and development are inhibited by nicotine exposure, one study identifying nicotine in the feces of generalist cabbage looper larvae indicates their ability to excrete nicotine (Krischik et al. 1991; Self et al., 1964). Furthermore, rapid induction of nicotine flux in cabbage looper larvae via the Malpighian tubules was confirmed in vitro (Rheault et al., 2006). The secretion rate of nicotine by the Malpighian tubules of 4th instar T. ni exhibited the second highest rate relative to eleven other species tested from nine insect orders (~48 pmol-1 h-1 mm-1). A more recent study by Saremba et al. in 2018 has more closely examined the metabolism and excretion of nicotine in T. ni. It has been discovered that T. ni metabolize nicotine into three major metabolites; cotinine, cotinine-N-oxide, and nicotine-N-oxide (Saremba et al. 2018). Over a 24 h sampling period, cotinine represents over 80% of 24 detected nicotine and metabolites and is the major metabolite in all tissue samples. When placed on a nicotine-free diet following nicotine exposure, T. ni larvae are capable of clearing 99% of nicotine and its metabolites in just 6 h. Over a 24 h sampling period, 98% of total nicotine and metabolites were detected in frass samples, indicating efficient detoxification and elimination. Frass samples primarily contained cotinine (84%), nicotine (16%), and trace levels of nicotine-N-oxide (0.001 %) and cotinine-N-oxide (0.04 %). Figure 1.5 shows a generalized model for metabolism and excretion in T. ni in the three basic cavities of the insect body: the gut, the haemocoel, and the Malpighian tubules. Following ingestion, unmetabolized nicotine enters the midgut after passing the mouth and foregut. In the midgut, nicotine is metabolized into cotinine and cotinine-N-oxide (Saremba et al. 2018). These metabolites are then transported to the Malpighian tubules for excretion. Alternatively, nicotine is transported in its unmetabolized form into the haemolymph via the midgut epithelium. From the haemolymph, unmetabolized nicotine is transported to the Malpighian tubules for direct excretion or is metabolized into nicotine-N-oxide in the Malpighian tubules (Saremba et al. 2018). It is suggested that nicotine and its metabolites may be further metabolized and secreted by the Malpighian tubules from the haemolymph before being excreted in frass (Saremba et al. 2018). T. ni have recently been shown to utilize the same 5’ C-oxidation pathway as M. sexta seen in Figure 1.3 (Saremba et al. 2018). Moreover, T. ni do not use the 2’ C-oxidation pathway used by honeybees (du Rand et al. 2017). The product of this pathway, 4-hydroxy-4-(3-pyridyl)-butanoic acid was not detected in any sample types of T. ni following nicotine exposure (Saremba et al. 2018). The cabbage looper genome has also been recently released, enabling the discovery of enzymes responsible for nicotine metabolism and resistance (Yu et al. 2017). 25 Figure 6.5 Generalized model for metabolism and excretion in T. ni. The model shows the three basic cavities of the insect body: the gut, the haemocoel, and the Malpighian tubules. The colored acronyms are compounds: nicotine (NIC), nicotine N-oxide (NNO), cotinine (COT), and cotinine N-oxide (CNO). The yellow circle represents cytochrome P450 enzymes (CYP). The blue circle represents the Multidrug resistance transporter (MDR). Dashed lines represent passive diffusion, dotted lines represent metabolism, and solid lines represent active transport. Figure 1.5 Generalized model for metabolism and excretion in T. ni. The model shows the three basic cavities of the insect body: the gut, the haemocoel, and the Malpighian tubules. The colored acronyms are compounds: nicotine (NIC), nicotine N-oxide (NNO), cotinine (COT), and cotinine N-oxide (CNO). The yellow circle represents cytochrome P450 enzymes (CYP). The blue circle represents the Multidrug resistance transporter (MDR). Dashed lines represent passive diffusion, dotted lines represent metabolism, and solid lines represent active transport. 26 1.7 Hypothesis and Objectives Given the recent discovery of nicotine metabolism in T. ni, more research is required to determine the exact mechanisms through which this detoxification occurs. The proposed research aims to reveal the genetic mechanisms through which the plant alkaloid nicotine is metabolized and excreted by T. ni, a model agricultural pest with the following hypothesis proposed: The cabbage looper Trichoplusia ni metabolizes nicotine in the midgut and Malpighian tubules through increased expression of cytochrome P450 transcripts and their resultant proteins. We hypothesize that 4th instar T. ni larvae metabolize nicotine primarily in the midgut and thus will exhibit increased expression of cyp genes in response to a diet containing 1% nicotine. We also hypothesize that nicotine is partially metabolized and excreted by the Malpighian tubules, leading to increased expression of cyp genes and transport genes. To test this hypothesis, I propose the following three objectives: Objective 1) To use RNA-seq to produce transcriptomes for both the midgut and Malpighian tubule tissues of T. ni exposed to diets containing 0% nicotine and 1% nicotine. Objective 2) To analyze the transcriptomes generated in objective 1 to determine the differential expression of genes in the Midgut and Malpighian tubule tissues of T. ni following 1% dietary nicotine exposure. Objective 3) To validate results from objective 2 using real-time quantitative PCR (RT- qPCR). 27 Chapter 2: Materials and Methods 2.1 Experimental Animals Cabbage looper (T. ni) eggs were purchased from Insect Production Services at the Great Lakes Forestry Centre (Sault St. Marie, ON, CA). Approximately 20-30 eggs and 5 mL of insect diet were received in plastic rearing cups with cardboard lids. Cups were immediately placed food side up, lid side down in an incubator (CU-36L4, Percival®; Perry, IA, US) at 27 °C, 60% RH, and 16:8 L:d. Following seven days of incubation, larvae were transferred from original cups to new cups containing 5 mL of revised McMorran diet (Hervet et al. 2016) with a density of 3 larvae per cup to prevent cannibalism. The larvae were incubated for an additional five days. 2.2 Dietary Nicotine Exposure and Sample Collection Diets containing 1% nicotine were prepared by adding the appropriate volume of nicotine to the prepared Modified McMorran Grisdale artificial diet. Exposures of 4th instar larvae were carried out in 24-well culture plates (VWR, Radnor, PA). Fine netting and rubber bands were used to prevent the movement of larvae between adjacent wells (Figure 2.1). At a density of 1 insect per well, larvae from the same cohort were fed 1.5 mL control or nicotine-containing (1% FW) McMorran diet for 24 h. The control diet used was the revised McMorran diet. Midgut and Malpighian tubule samples were dissected under Drosophila saline made in RNase-free water consisting of (in mmol l–1): 117.5 NaCl, 20 KCl, 10.2 NaHCO3, 8.5 MgCl2, 2 CaCl2, 8.6 Hepes, and 4.3 NaH2PO4 (Rheault et al. 2006). Briefly, the midgut was separated from the hindgut, attached tissues including Malpighian tubules and connective tissue were removed, and the gut contents were discarded using forceps. Gut contents were removed with the peritrophic membrane to limit contamination from microbial or ingested food sources in downstream experiments. All free sections of Malpighian tubule tissue from the ampulla to those not under the cryptonephric complex were pooled together and collected into a separate tube. Prior to collection, tissues were rinsed with sterilized saline. Tissues from five caterpillars were pooled into one biological replicate. Tissues were collected in tubes cooled on liquid nitrogen and were stored at -80°C until further RNA extraction. 28 Figure 2.2 24-well culture plate set-up for dietary exposure of T. ni control and nicotine-containing diets. 1 29 Table 2.1 The modified McMorran Grisdale insect diet. Ingredient Volume (mL) Mass (g) E-pure H2O 840 Agar 17.36 Alphacel 5 Casein 35 Wesson’s Salt Mix 10 Sugar 35 Toasted wheat germ 30.69 Choline chloride 1 Ascorbic acid 4 Formalin (37 % formaldehyde) 0.5 Methyl paraben 1.5 Aureomycin 2.1 Raw linseed oil 5 Vitamin solution 10 Anti-fungal spray - - Potassium hydroxide 5 30 2.3 RNA Extraction and TURBO-DNase Clean Up Midgut and Malpighian tubule tissues were homogenized in 1.5 mL and 1.0 mL of Purezol (Bio-Rad, Hercules, California, USA), respectively using a homogenizer (PT1200E, Polytron®; Kinematica). RNA was extracted following the manufacturer’s protocol. Briefly, molecular grade chloroform (100 μL) was added to each sample and mixed vigorously by hand for 15 seconds, and further incubated at room temperature for 5 minutes with periodic mixing. Samples were then centrifuged at 12,000 x g for 15 minutes at 4°C to cause phase separation. Without disturbing the interphase, the clear aqueous phase containing total RNA was immediately transferred into a new 1.5 mL RNAse-free Eppendorf tubes and kept on ice. Molecular grade isopropanol (250 μL) was added to each sample to precipitate RNA. Tubes were mixed thoroughly and incubated for 5 minutes at room temperature, after which they were centrifuged at 12,000 x g for 10 minutes at 4°C until a white pellet containing RNA appeared at the bottom of each tube. The supernatant was discarded and the RNA pellet was washed with 500 μL of 75% molecular grade RNAse-free ethanol. The samples were vortexed and centrifuged at 7,500 x g for 5 minutes at 4°C. The supernatant was discarded and the pellets were allowed to air-dry for 5 minutes at room temperature. The pellets were resuspended in 40 μL and 160 μL of DEPC-treated water for Malpighian tubules and midguts, respectively. If the RNA pellet did not completely resuspend, the samples were placed on a heat block and incubated at 55°C for 10 minutes. RNA yields and purity were determined using a spectrophotometer. Total RNA was purified using the TURBO DNA-free kit according to the manufacturers protocol (Invitrogen, Carlsbad, California, USA). Briefly, all samples were diluted to 200 μg/mL to make 50 μL volumes of RNA for treatment. First, 0.1 volume RNA of 10X TURBO DNase Buffer was added to each tube of RNA. 1 μL of TURBO DNase was added and the tube was incubated at 37°C for 30 minutes. 0.1 volumes RNA plus reagents of DNase Inactivation Reagent was then added and incubated at room temperature for 5 minutes with periodic mixing. The samples were centrifuged at 10,000 x g for 1.5 minutes at 4°C. The supernatant containing RNA was transferred into a fresh RNAse-free tube. RNA yields and purity were initially assessed by analysis on a spectrophotometer. RNA quality was further examined using gel electrophoresis on a 1% agarose gel containing Invitrogen SYBR® Safe. Subsequently all samples sent for further sequencing were analysed using the Experion RNA StdSens Analysis Kit on an Experion Automated Electrophoresis 31 System (Bio-Rad, Hercules, California, USA) using a modification of the manufacturer’s protocol. In contrast to the manufacturer’s recommendation, RNA samples were not denatured prior to analysis. Preliminary studies have demonstrated that insect RNAs do not yield quantifiable RIN numbers if denatured (Fabrick & Hull 2017). 2.4 Next Generation Transcriptome Sequencing Total RNA samples were submitted to BGI Genomics (Shenzhen, China) for further quality control using the Agilent 2100 Bioanalyzer (Agilent RNA 6000 Nano Kit), library preparation and sequencing. Although five biological replicates were collected, only the best three replicates were submitted for sequencing based on RNA quality analysis. Three replicates were sent for each of midgut control (MGCON), midgut nicotine (MGNIC), Malpighian tubule control (MTCON), and Malpighian tubule nicotine (MTNIC). The samples chosen based on quality analysis were as follows: MG1con, MG3con, MG4con, MG2nic, MG3nic, MG5nic, MT3con, MT4con, MT5con, MT2nic, MT4nic, MT5nic. Sequencing was carried out on the BGISEQ-500 platform with 100 paired-end reads and an output of 30 million clean reads. The BIGSEQ-500 platform combines DNA nanoball-based nanoarrays and stepwise sequencing using the Combinational Probe-Anchor Synthesis Sequencing Method. Purification of mRNA was performed using poly-T oligo-attached magnetic beads. Fragmentation of mRNA was completed using divalent cations under high temperature. First strand cDNA was synthesized from cleaved RNA fragments using reverse transcriptase and random primers, followed by second strand cDNA synthesis using DNA Polymerase I and RNAse H. A single ‘A’ base was added to cDNA fragments before adapters were ligated. PCR amplification was used for purification and enrichment of products. PCR yield was quantified by Qubit and samples were pooled to create the final library containing a single strand DNA circle (ssDNA circle). Rolling circle replication was carried out to generate DNA nanoballs (DNBs) with the ssDNA circle. Three biological replicates per tissue (midgut and Malpighian tubule) and treatment (non-nicotine and nicotine-containing diets) were sequenced for a total of twelve libraries. Samples were quality checked, sequenced, and analyzed using bioinformatics. Standard bioinformatics analysis was conducted by BGI, which included sequencing reads filtering, genome mapping, novel transcript prediction, SNP and INDEL detection, differentially splicing gene detection, 32 gene expression calculation, differentially expressed gene detection, hierarchical clustering analysis of DEG, gene ontology analysis of DEGs, and pathway analysis of DEGs. 2.5 Next Generation Transcriptome Analysis Pipeline Bioinformatics was carried out on sequencing data for mapping to the transcriptome and differential gene expression analysis. The overall workflow involved mapping clean reads with SNP/INDEL analysis and differentially splicing gene detection, followed by novel transcript prediction, gene expression analysis, differentially expressed gene analysis, and pathway analysis with functional annotation. Bioinformatics analysis was conducted using methods adapted from previous work on transcriptomic analysis of T. ni (Kolosov et al. 2019). 2.5.1 Quality Control Initial transcriptome sequencing reads were subjected to filtering by BGI Genomics using SOAPnuke to remove reads with adapters, remove reads containing over 10% unknown bases (N), and to remove low quality reads. Resultant reads were stored in FASTQ format. Subsequently, we took reads of exactly 100 bp and subjected them to further quality control trimming using Trimmomatic v0.38 with a 4-bp sliding window and Phred quality score greater than 30 (usegalaxy.org; Goecks et al., 2010). 2.5.2 Mapping to Transcriptome Clean, paired reads were mapped to the T. ni transcriptome from the ovarian cell line Hi5 (BTI-Tn-5B1-4) RefSeq assembly (ID: 18285) accessed on NCBI. Mapping was performed using Salmon (Galaxy 0.9.1) mapping software with default parameters on usegalaxy.org (Patro et al., 2017) using annotations from the same Hi5 (BTI-Tn-5B1-4) RefSeq assembly. To obtain relative expression data, the reads from control samples of each tissue were mapped and quantified as stated above. These values were normalized by calculating reads per kilobase (RPK), the “per million” scaling factor (RPK divided by 1,000,000), and sample sequencing depth (RPK divided by the “per million” scaling factor). To avoid single-sample bias, these 33 normalized abundance values in transcripts per kilobase per million mapped reads (TPM) were calculated for each control sample and averaged (N=3). The same process was carried out for samples exposed to a nicotine-containing diet. 2.5.3 Differential Gene Expression Analysis Differentially expressed genes (DEGs) were analyzed in R (version 3.6.0) using the DESeq2 package (release 3.9) (Love et al. 2014). Nicotine groups for each tissue were compared independently to their respective control groups. The resulting P-values were adjusted by the Benjamini-Hochberg FDR (false discovery rate) correction. Only transcripts with ≥ 2-fold change in expression levels (log2FC ≥ 1) and adjusted p-values below 0.05 were considered to be differentially expressed. The generated transcripts were annotated using the information contained in the same Hi5 (BTI-Tn-5B1-4) RefSeq assembly mentioned above. 2.5.4 Transcriptome Data Analysis The DEGs of nicotine-treated and control samples in the T. ni midgut and Malpighian tubules were sorted on Microsoft Excel and those that were considered to confer nicotine resistance were divided into ones pertaining to metabolism and transport. Metabolism-related DEGs were categorized into cytochrome P450s (CYPs), carboxylesterases (CEs), glutathione S-transferases (GSTs), and UDP-glucuronosyltransferases (UGTs). Transport-related DEGs were categorized into ATP-binding cassette transporters (ABCs), organic cation transporters (OCTs) and multi-drug resistance proteins (MRPs). 2.6 RT-qPCR Validation 2.6.1 Complementary cDNA Synthesis Complementary DNA (cDNA) was synthesized from RNA using the iScriptTM cDNA synthesis kit (Bio-Rad, Hercules, California, USA). The same RNA samples were used for sequencing and RT-qPCR validation. 1 μg of RNA was combined with 8 μL of 5X iScript reaction mix, 2 μL of iScript reverse transcriptase, and enough nuclease-free water to make a 40 34 μL solution. The complete reaction mix was incubated in a thermal cycler under the following conditions: priming at 25°C for 5 minutes, reverse transcription at 46°C for 20 minutes, RT inactivation at 95°C for 1 minute, and then held indefinitely at 4°C. No reverse transcription controls (NRTs) where iScript reverse transcriptase was replaced with nuclease-free water were also created using the same conditions for all sample RNAs. All cDNAs were stored long term at -80°C for future analysis. 2.6.2 Primer Design and Optimization Primer sets for both genes of interest and reference genes were designed using the Geneious R11.1.5 (https://www.geneious.com). Primers were designed for 10 differentially expressed CYPs and five candidate reference genes and ordered from Integrated DNA Technologies Inc (Coralville, IA) (Table 2.2). The CYPs chosen were prominent DEGs showing significantly increased expression in response to a diet supplemented with nicotine. Primers were designed to have a product size of 75 to 200 base pairs, melting temperature (Tm) of 55-61°C, a max Tm difference of 3°C, 50% GC content, and no hairpins. We accepted self-dimers of less than 26°C. 10 uM working stocks of each primer set were made from 100 uM stock solutions. All primer sets were tested via standard PCR to determine the most efficient primer set. A mixed cDNA sample was created by combining 10 uL of each of the midgut and Malpighian cDNA samples previously made to serve as a source template for primer testing. Subsequently a standard PCR reaction was conducted for each primer pair. Each PCR reaction tube contained 12.5 uL GoTaq® Green Master Mix, 1 uL of the forward primer, 1 uL of the reverse primer, 9.5 uL nuclease-free water, and 1 uL of the mixed cDNA template. PCR cycling was carried out in a Bio-Rad C1000 Thermal Cycler. The cycling procedure consisted of an initial denaturation step for 2 minutes at 95°C, followed by 29 additional cycles of denaturation at 95°C for 30 seconds followed by an annealing step at 58°C for 30 seconds, an elongation step at 72°C for 2 minutes, and a final elongation step at 72°C for 5 minutes. The resultant PCR products were stored at 4°C. PCR products were examined via horizontal gel electrophoresis on a 2% agarose gel containing Invitrogen SYBR® Safe in 0.5X TBE buffer. 35 Table 2.2 Primers used in real-time quantitative PCR (RT-qPCR) RefSeq Accession# Sequence (F, R) Predicted Product Size Tm (°C) Description XM_026870730 TGAAGTTTTCGACCCGGAAC ACCAGACCAGCCATCATTTG 132 58.3 cytochrome P450 6B5-like XM_026870731 TGACCCCACTATTCACAACG CACAAACAGCAGCGTTACAG 156 57.8 cytochrome P450 6B5-like XM_026871767 AGTGGATGCTTTGATGGCTC ACCTCCCGACTGATCATAGG 132 58.1 cytochrome P450 6B2-like XM_026873420 CCAAGAAGTTCATCCCCGAG GGTACAGCATCACCTTCACC 141 58.2 cytochrome P450 9e2-like XM_026874091 CTTAGATGCAGTTGTCCGGG GACCAGAACTCCACAACCAG 127 58.2 cytochrome P450 4C1-like XM_026884809 AATGGTGATGATGGCTGGTC CCCATCCCTCTCGTTCAATG 155 58 cytochrome P450 6B2-like XM_026884877 GGCTGGATTCGAGACATCAG AACTTATTTTTCTGGCGCGC 126 58.1 cytochrome P450 6B2-like XM_026885476 TCATGGACTTGATGTTGGGC ATCCACCGACGTAGAAGACA 132 58.2 cytochrome P450 6B6-like XM_026885477 CTGTGCTTACTTGCCGTTTG CTCGGTTTAACGGGGAGATC 153 57.9 cytochrome P450 6B6-like XM_026889335 TAGATGCCTGCCTCTACG GAATATGGACACGAACGCC 126 56 cytochrome P450 6j1-like XM_026868736 AGTTCACCGTCATGTTCAGG AGGTCGTTCATGTTGCTCTC 103 57.9 tubulin beta chain-like XM_026892236 TTCACAACCACAGAGAAGGC TATGAGGGGTCGTAGGCTTC 128 58.2 glyceraldehyde-3-phosphate dehydrogenase XR_003402039 TTCATACCACGTTGTCTACACC GGTCACCCATCCAAGTACTG 85 57.9 5S ribosomal RNA XM_026889426 ACAACATGGAGCGAATCTGG ACTATGGGTGACAAGGAACG 140 57.8 actin-like XM_026887549 CTCATCTCTGCCACACTTCC CACGTTCCACAGCAACAAAG 136 58.1 eukaryotic initiation factor 4A-III 36 2.6.3 Primer Efficiency Test: Standard Curve Primer efficiencies were obtained from standard curves using serial dilutions and the presence of single amplicons were confirmed using analysis of standard melting curves. A stock solution of cDNA was created by mixing equal parts of cDNA from all 12 tissue samples previously used for the RNA sequencing analysis. From this stock a 5-fold dilution series at 5 different concentrations of cDNA template diluted in nuclease-free water was created. All subsequent RT-qPCR reactions for calculation of primer efficiencies contained 5 uL of SsofastTM EvaGreen® Supermix (Bio-Rad, Hercules, California, USA), 1 uL of dilution series template, 0.5 uL each of 10 uM stock forward and reverse primers for a final concentration of 500 nM, and 3 uL of nuclease-free water for a total of 10 uL. Three technical replicates were included for each sample along with three No Template Controls (NTC) for each primer set. No Reverse Transcriptase (NRT) controls were made previously during cDNA synthesis using the same protocol without the reverse transcriptase enzyme. Standard curve cycling was carried out in a C1000 Thermal Cycler (Bio-Rad, Hercules, California, USA). Temperature cycling conditions were 95 °C for 2 min (enzyme activation), followed by 40 cycles of 95 °C for 30 s (denaturation), 58 °C for 30 s (annealing), and 72 °C for 2 min (extension), followed by a final extension of 72 °C for 5 min. Amplified fragments were visualized on a 2% agarose gel. 2.6.4 Quantitative Polymerase Chain Reaction Gene expression of T. ni samples was validated through RT-qPCR. To avoid inter-run variations and assure a simpler and more efficient RT-qPCR run, plates were designed using the sample maximization technique (Ruijter et al. 2015). A sample plate design is displayed in Figure 2.2. cDNA samples were taken from storage at -80°C and diluted 5-fold by combining 8 uL template with 32 uL nuclease-free water. Each reaction mixture contained 5 uL of SsofastTM EvaGreen® Supermix (Bio-Rad, Hercules, California, USA), 1 uL template, 0.5 uL each of 10 uM stock forward and reverse primers for a final concentration of 500 nM, and 3 uL of nuclease-free water for a total of 10 uL. Four genes were tested on each plate. Two technical replicates were included for each sample and three NTCs were included for each primer set. Two technical replicates for each NRT were run on the same plate to test for genomic DNA contamination. RT-qPCR reactions were carried out in a C1000 Thermal Cycler with the CFX96 module (Bio-Rad, Hercules, California, USA). Temperature cycling conditions were 95 °C for 30 s (enzyme 37 activation), followed by 40 cycles of 95 °C for 5 s (denaturation) and 58 °C for 5 s (annealing), followed by a final extension of 95 °C for 10 s. Following the final cycle, a standard melt curve was constructed by increasing temperature from 65 °C to 95 °C by 0.5 °C every 5 s. The ΔΔCt method was used to quantify relative expression for each gene using the global arithmetic mean of suitable reference genes (Livak & Schmittgen 2001). The suitability of reference genes was predicted using a geNorm analysis (Vandesompele et al. 2002). 38 Figure 3.2 Sample plate design for genes A through D using sample maximization. Three biological replicates and two technical replicates were plated for each experimental condition. Dietary treatments were control (midgut ctrl) and 1.0% nicotine (midgut nic). No reverse transcription (NRT) controls were placed on the same plate. igure 2.2 Sa ple plate design for genes through using sa ple axi ization. hree biological replicates and two technical replicates w re plated for each experimental condition. Dietary treatments were control (midgut ctrl) and 1.0% nicotine (midgut nic). No reverse transcription (NRT) controls were plac d on th same plate. 39 2.6.5 Statistical Analysis Statistical analysis was performed on all RT-qPCR experimental data using Prism 6 (GraphPad Software, Inc). An unpaired t-test was used to compare the means of RT-qPCR results of larvae exposed to a control diet to the means of those exposed to dietary nicotine. 40 Chapter 3: Results 3.1 RNA Yields, Purity, and Integrity Genomic contamination was successfully removed from all RNA samples with the TURBO DNase clean up kit. Figure 3.1 shows RNA samples on an agarose gel before TURBO DNase clean up. The quality of RNA samples is typically determined on an agarose gel by the presence of two bands corresponding to 28S and 18S rRNA. In contrast to mammalian samples, the 28S rRNA in insects is composed of two hydrogen-bonded fragments, which is typically referred to as an endogenous “hidden break” (Winnebeck et al. 2010). This bond can be spontaneously or purposely broken by heat-denaturing samples, splitting the 28S rRNA into two fragments. Since these fragments are similar in size to the 18S rRNA, they all co-migrate and appear as one band. RNA samples are typically heat-denatured to cause this nick in rRNA, resulting in an rRNA profile with a single distinct band. Under the non-denaturing conditions of this study, one band is typically seen with high intensity. This intense band corresponds to 18S rRNA. The 28S rRNA can still be seen as a faint band above the 18S rRNA in Figure 3.1. The 18S band is normally observed at approximately 2000 bp. The faster migration of RNA than DNA combined with the use of a DNA gel and DNA ladders resulted in the observation of intense bands at 1000 bp. Figure 3.2 shows RNA samples tested using the Experion Automated Electrophoresis System. The results are clearer on the virtual gels produced using this method as the 28S band is more clearly observed at 4000 bp and the more intense 18S band is observed at 2000 bp. Table 3.1 displays the concentrations of RNA samples with their 260/280 ratios after clean up along with results obtained from the Experion system. Purity of nucleic acid samples is indicated by the 260/280 ratio, where nucleic acids absorb at 260 nm and proteins absorb at 280 nm. A value of approximately 2.0 indicates pure RNA (ThermoScientific). An average 260/280 ratio of 1.91 was observed across all samples. cDNA synthesis requires a minimum of 1 ug RNA and all samples had more than 1 ug RNA left after clean up. Intact RNA is usually indicated by a 28S/18S ratio of 2:1 (Bio-Rad, Hercules, California, USA). Since insect samples typically show a single peak in the Experion system due to the natural nick in the 28S rRNA, this ratio is usually close to zero. Furthermore, an RQI value of 1 indicates highly degraded RNA while a value of 10 indicates intact RNA. Samples were also tested by BGI Genomics on the Agilent 2100 41 Bioanalyzer platform prior to RNA sequencing, the results for which are displayed in Table 3.2. RIN values are comparable to RQI values as a value of 10 corresponds to intact RNA and 1 corresponds to degraded RNA. However, differences in these values are observed since samples were heat-denatured by BGI eliminating the value of their reported RIN values. All samples were considered to be qualified for library construction and sequencing. 42 MGcontrol1 MGcontrol2 MGcontrol3 MGcontrol4 MGcontrol5 MTcontrol1 MTcontrol2 MTcontrol3 MTcontrol4 MTcontrol5 MGnicotine1 MGnicotine2 MGnicotine3 MGnicotine4 MGnicotine5 MTnicotine1 MTnicotine2 MTnicotine3 MTnicotine4 MTnicotine5 1kb ladder 100bp ladder Figure 3.2 1% agarose gel displaying RNA samples to be used in sequencing and subsequent RT-qPCR validation. Quickload® 100bp and 1kb DNA ladders (Biolabs, New England) were used as references to confirm product size. 1,000 bp 1,517 bp 1,200 bp 500/517 bp 28S RNA 18S RNA 2000 bp 3000 bp Figure 3.1 1% agarose gel displaying RNA samples to be used in sequencing and subsequent RT-qPCR validation. Quickload® 100bp and 1kb DNA ladders (Biolabs, New England) were used as references to confirm product size. 43 Table 3.1 RNA yield concentrations (µg/mL), 260/280 ratio, tissue mass, RQI, and classification for all T. ni larvae biological replicate Midgut and Malpighian tubule tissue samples exposed to 0% (control) and 1.0% nicotine diets for 24 hours. Measurements were taken on a spectrophotometer following Bio-Rad Purezol RNA extraction and TURBO DNase treatment (spec). Measurements were also taken on the Experion Automated Electrophoresis System (experion). Samples chosen for sequencing are indicated in bold. Sample Tissue Mass (mg) Concentration (µg/mL) (spec) 260/280 (spec) Concentration (µg/mL) (experion) Ratio 28S:18S (experion) RQI (experion) Mgcontrol1 139.1 179.4 1.97 190.79 0.14 10 Mgcontrol2 145.1 160.6 1.90 194.37 0.16 9.6 Mgcontrol3 179.2 240.6 2.00 181.17 0.12 10 Mgcontrol4 109.6 170 1.84 174.32 0.14 10 Mgcontrol5 150.5 162 1.91 235.52 0.02 9.4 Mtcontrol1 46.4 175 2.00 47.84 0.03 10 Mtcontrol2 50.5 187.5 1.99 0 0 0 Mtcontrol3 51.5 159.4 1.71 156.94 0.05 10 Mtcontrol4 46.4 337 1.85 65.24 0.02 10 Mtcontrol5 58.8 145.5 1.89 88.01 0.05 10 Mgnicotine1 108.8 162.1 1.97 175.21 0.09 N/A Mgnicotine2 99.3 170.9 1.90 187.03 0.03 8.4 Mgnicotine3 89.9 185.5 2.00 196.91 0.02 8.4 Mgnicotine4 98.1 171.9 1.84 215.79 0.02 7 Mgnicotine5 68.6 158 1.91 211.44 0.03 7.6 Mtnicotine1 47.9 176.8 2.00 74.64 0.02 10 Mtnicotine2 48.2 134.2 1.99 91.08 0.03 10 Mtnicotine3 49 93.7 1.71 93.8 0.03 10 Mtnicotine4 30.8 180.4 1.85 81.81 0.03 10 Mtnicotine5 40.3 178 1.89 88.97 0.03 0.62 44 MGcontrol1 MGcontrol2 MGcontrol3 MGcontrol4 MGcontrol5 MTcontrol1 MTcontrol2 MTcontrol3 MTcontrol4 MTcontrol5 MGnicotine1 MGnicotine2 MGnicotine3 MGnicotine4 MGnicotine5 MTnicotine1 MTnicotine2 MTnicotine3 MTnicotine4 MTnicotine5 DEPC water DEPC water DEPC water DEPC water Figure 3.3 Virtual gel images of total RNA samples generated from Experion Automated Gel Electrophoresis system for midgut (A) and Malpighian tubule (B) isolated from 4th instar T. ni tissues. A B Figure 3.2 Virtual gel images of total RNA samples generated from Experion Automated Gel Electrophoresis system for midgut (A) and Malpighian tubule (B) isolated from 4th instar T. ni tissues. 45 Table 3.2 BGI sample testing results of total RNA using the Agilent 2100 Bioanalyzer. Qualified means the sample meets the requirements for library construction and sequencing. Sample Tissue Mass (µg) Volume (µL) Concentration (ng/µL) RIN 28S/18S Test Result Mgcontrol1 1.25 12 104 6.8 0.7 Qualified Mgcontrol3 1.13 12 94 2.8 0.1 Qualified Mgcontrol4 1.33 12 111 5.7 0.6 Qualified Mtcontrol3 2.08 12 173 6.9 0.1 Qualified Mtcontrol4 1.46 17 86 6.9 0.1 Qualified Mtcontrol5 1.83 15 122 6.8 0.1 Qualified Mgnicotine2 1.20 12 100 5.7 0.1 Qualified Mgnicotine3 1.22 12 102 5.6 0.00 Qualified Mgnicotine5 1.16 12 97 5.6 0.1 Qualified Mtnicotine2 1.26 12 105 6.8 0.00 Qualified Mtnicotine4 1.62 17 95 6.8 0.00 Qualified Mtnicotine5 1.85 15 123 6.9 0.00 Qualified 46 3.2 Next Generation Transcriptome Sequencing To investigate the gene expression changes in response to a diet containing 1% nicotine, we sent twelve samples of T. ni midgut and Malpighian tubule tissues to BGI Genomics in Shenzhen, China for RNA sequencing on the BGISEQ-500 platform, with three replicates for each tissue and treatment. Based on our RNA quality analysis, the subset of samples chosen for full sequencing were Mgcontrol1, Mgcontrol3, Mgcontrol4, Mgnicotine2, Mgnicotine3, Mgnicotine5, Mtcontrol3, Mtcontrol4, Mtcontrol5, Mtnicotine2, Mtnicotine4, and Mtnicotine5. An average of 7.37 Gb per sample for a total of 88.42 Gb of data was obtained which contained twelve strand-specific libraries (3 control midgut, 3 nicotine midgut, 3 control Malpighian tubule, and 3 nicotine Malpighian tubule). Sequencing yielded raw reads per library ranging from 36 to 37.4 million reads with an average of 36.8 ± 0.2 million reads per library and a total of 442,105,168 reads across all libraries (Table 3.3). 47 Table 3.3 Summary of raw and clean reads of T. ni RNA-seq data Sample Total Raw Reads Total Clean Reads GC (%) Clean Reads Ratio (%) Mapped Reads Mapped Ratio (%) Mgcontrol1 36124602 32452097 44 90 26688445 82 Mgcontrol2 36070630 32064834 43 89 23874406 74 Mgcontrol3 36138493 32681411 43 90 25652500 78 Mtcontrol1 37345653 34176856 40 92 24900784 73 Mtcontrol2 37371095 34233240 41 92 26120388 76 Mtcontrol3 37352319 34221114 40 92 25748905 75 Mgnicotine1 36106107 32532124 43 90 22662255 70 Mgnicotine2 36032982 32391790 43 90 22559447 70 Mgnicotine3 37386424 34389294 43 92 23857436 69 Mtnicotine1 37401859 34475612 41 92 25707801 75 Mtnicotine2 37382766 34331524 41 92 25698424 75 Mtnicotine3 37392238 34768080 41 93 26217190 75 48 3.3 Next Generation Transcriptome Sequencing Analysis 3.3.1 Quality of NGS Reads Following Trimmomatic quality control, 91% ± 0.3 of the clean data on average was retained and used in further analysis (Table 3.3). Trimmomatic quality control yielded clean reads per library ranging from 32.1 to 34.8 million reads for an average of 33.6 ± 0.3 million clean reads per sample and a total of 402,717,976 clean reads across all samples. The cleaned sequencing data has an average GC content of 42% ± 0.4 and all reads have a Phred score above Q30. Phred quality scores are a measure of sequencing accuracy based on base-calling probabilities (Ewing & Green 1998). A Q score of 30 (Q30) corresponds to a base call with 99.9% accuracy or conversely, a 1 in 1000 chance of an incorrect base call. A Q score of 20 indicates 99% accuracy or 1 in 100 chance of error, which increases the likelihood of error in every 100 bp sequencing read. Therefore, Q30 is regarded as a standard for quality in next-generation sequencing. 3.3.2 Mapping to Transcriptome An average of 74% ± 1 of the reads could be mapped to the T. ni transcriptome from the ovarian cell line Hi5 (BTI-Tn-5B1-4) RefSeq assembly (ID: 18285) accessed on NCBI (Table 3.3). Of the 25,279 transcripts in the T. ni transcriptome, RNAseq detected 99% (24,947) of these in the midgut. In the Malpighian tubules, RNA-seq detected 99% (24,947) transcripts of the 25,279 transcripts in the T. ni transcriptome. 3.3.3 Differential Gene Expression We compared the differentially expressed genes (DEGs) between treatments within individual tissues. We used a false discovery rate (FDR) < 0.05 and |log2FC| ≧ 1.0 as a threshold to filter out transcripts (Figure 3.3). Of the detected transcripts in the midgut, a total of 9796 transcripts were differentially expressed in response to nicotine diet treatment. Out of the differentially expressed transcripts in the midgut, 60% (5831) showed increased expression in response to the nicotine diet treatment and 40% (3965) showed decreased expression in response 49 to the nicotine diet treatment. Of the detected transcripts in the Malpighian tubules, a total of 7202 transcripts were differentially expressed in response to the nicotine diet treatment. Out of the differentially expressed transcripts in the midgut, 59% (4217) showed increased expression in response to the nicotine diet treatment and 41% (2985) showed decreased expression in response to the nicotine exposure. Principal components analysis (PCA) also confirmed that control samples and nicotine treated samples with different treatments are not significantly related (Figure 3.4, Figure 3.5). The PCA plot measures variance between experimental conditions (control vs. nicotine-containing diets) as well as variation between samples in each experimental condition. The x-axis displays the first principal component (PC1) which measures variability between treatments. The y-axis displays the second principal component (PC2) which measures variability between samples within a single treatment. Figure 3.4 displays more variance in the nicotine-treated midgut samples than control samples as observed by the clustering of points. However, there is a clear separation overall between the two treatments. Of the three nicotine-treated Malpighian tubule samples Figure 3.5 shows that one sample is highly variable from the other two samples. However, all of the nicotine-treated samples cluster further away from the control samples indicating that the samples between treatments are distinct. 50 Figure 3.5 Differential expression analysis using DESeq2 in R of transcripts expressed in the midgut (A) and Malpighian tubules (B) of larval T. ni fed diets containing 1% nicotine compared to control diet. Volcano plots show the fold change in abundance of individual transcripts plotted against the adjusted p-value (FDR) of the change. Changes in green indicate |log2FC| ≧ 1 and p < 0.05. Changes in orange indicate |log2FC| ≧ 1 and p > 0.05. Changes in red indicate |log2FC| < 1 and p < 0.05. Changes in black indicate |log2FC| < 1 and p > 0.05. A B Figure 3.3 Differential expression analysis using DESeq2 in R of transcripts expressed in the midgut (A) and Malpighian tubules (B) of larval T. ni fed diets containing 1% nicotine compared to control diet. Volcano plots show the fold change in abundance of individual transcripts plotted against the adjusted p-value (FDR) of the change. Changes in green indicate |log2FC| ≧ 1 and p < 0.05. Changes in orange indicate |log2FC| ≧ 1 and p > 0.05. Changes in red indicate |log2FC| < 1 and p < 0.05. Changes in black indicate |log2FC| < 1 and p > 0.05. 51 Figure 3.6 Principal component analysis of gene expression across midgut samples. Each point represents a sample and each colour represents the treatment. The distance between points reflects the variance in gene expression between samples. Figure 3.4 Principal component analysis of gene expression across midgut samples. Each point represents a sample and each colour represents the treatment. The distance between points reflects the variance in gene expression between samples. 52 Figure 3.7 Principal component analysis of gene expression across Malpighian tubule samples. Each point represents a sample and each colour represents the treatment. The distance between points reflects the variance in gene expression between samples. Figure 3.5 Principal co ponent analysis of gene expression across alpighian tubule sa ples. ach point represents a sa ple and each colour represents the treat ent. he distance bet een points reflects the variance in gene expression bet een sa ples. 53 3.3.3.1 Comparison of DEGs in the midgut and Malpighian tubules of T. ni after nicotine treatment Differences in gene expression in the midgut and Malpighian tubule tissues of T. ni exposed to nicotine were investigated by RNA-seq to uncover the potential molecular mechanisms involved in nicotine detoxification. Nicotine exposures were conducted on T. ni larvae at a concentration of 1% FW for 24 h, after which the midgut and Malpighian tubules were dissected for DEG analysis. While the volcano plots produced from DEseq2 provide a lot of information, they are restricted to each individual tissue and don’t show areas of overlap between the two tissues. Figure 3.6 highlights how many DEGs do or don’t overlap between the midgut and Malpighian tubules. Of the 25,279 transcripts in the T. ni transcriptome, 2590 upregulated transcripts and 2045 downregulated transcripts were observed only in the Malpighian tubules (Figure 3.6A), whereas 4205 upregulated transcripts and 3025 downregulated transcripts were observed only in the midgut (Figure 3.6B). In addition, 1626 transcripts were upregulated and 940 transcripts were downregulated in both the midgut and Malpighian tubules respectively (Figure 3.6, log2FC ≧ 1 and padj < 0.05). 54 1626 4205 2590 3025 940 2045 Malpighian Tubules Midgut Up regulated DEGs Down regulated DEGs Malpighian Tubules Malpighian Midgut Figure 3.8 Venn diagram displaying the number of differentially expressed transcripts in the Malpighian tubules an Midgut of T. ni subjected to diets containing 1% FW nicotine compared to control diets. Transcripts with increased expression in response to nicotine treatment are shown in (A). Transcripts with decreased expression in response to nicotine treatment are shown in (B). The p-values were adjusted to control the false discovery rate (FDR). Log2FC ≧ 1 and padj < 0.05 were used as thresholds to asses differentially expressed transcripts between nicotine treatment and control groups. A A B B Figure 3.6 Venn diagram displaying the number of differentially expressed transcripts in the alpighian tubules an idgut of T. ni subjected to diets containing 1 F nicotine compared to control diets. Transcripts with increased expression in response to nicotine treatment are shown in (A). Transcripts with decreased expression in response to nicotine treatment are shown in (B). The p-values were adjusted to control the false discovery rate (FDR). Log2FC ≧ 1 and padj < 0.05 were used as thresholds to asses differentially expressed transcripts between nicotine treatment and control groups. 55 3.3.4 Transcriptome Data Analysis A subset of putative transcripts responsible for the metabolism and transport of nicotine which were differentially expressed in nicotine-treated samples of T. ni midgut and Malpighian tubules were identified in Table 3. Metabolism-related DEGs were categorized into CYPs, CEs, GSTs, and UGTs. A similar number of upregulated CYPs were observed in the midgut and Malpighian tubules, whereas a higher number of upregulated CEs, GSTs, and UGTs were observed in the Malpighian tubules. In response to a diet containing 1% FW nicotine, 65 DEGs encoding CYPs were detected in the midgut. Among these transcripts, 10 were significantly upregulated and 14 significantly downregulated. In the Malpighian tubules, 55 DEGs encoding CYPs were detected in response to nicotine. Among these transcripts, 9 were significantly upregulated in the Malpighian tubules whereas 8 were significantly downregulated. Of the 56 DEGs encoding CEs in the midgut, 4 were significantly upregulated and 19 were significantly downregulated. Of the 56 DEGs encoding CEs in the Malpighian tubules, 11 were significantly upregulated and 3 were significantly downregulated. Of the 21 DEGs encoding GSTs detected in the midgut, 1 was significantly upregulated and 5 were significantly downregulated. Of the 19 DEGs encoding GSTs detected in the Malpighian tubules, 3 were significantly upregulated and 5 were significantly downregulated. Finally, 1 UGT was significantly upregulated and 18 were significantly downregulated of the 33 DEGs detected in the midgut. In the Malpighian tubules, 35 DEGs encoding UGTs were detected of which 13 were significantly upregulated and 4 were significantly downregulated. Transport-related DEGs were categorized into ABCs, OCTs and MRPs. A similar number of significantly upregulated DEGs related to xenobiotic transport were observed in the midgut and Malpighian tubules in response to nicotine treatment. A total of 27 DEGS encoding ABCs were detected in the midgut. Of these DEGs, 3 were significantly upregulated and 9 significantly downregulated. In the Malpighian tubules, 25 DEGs encoding ABCs were detected. Of these DEGs, 4 were significantly upregulated and 2 significantly downregulated. Of the 21 DEGs encoding OCTs in the midgut, 1 was significantly upregulated and 8 were significantly downregulated. In the Malpighian tubules, 23 DEGs encoding OCTs were detected of which 1 was significantly upregulated and 9 were significantly downregulated. Finally, 17 DEGs encoding MRPs were detected in midgut. Of these DEGs, none were significantly upregulated 56 but 4 were significantly downregulated. Only 4 DEGs encoding MRPs were detected in the Malpighian tubules. Of these DEGs, none were significantly upregulated or downregulated. 57 Table 3.4 Nicotine-detoxification related transcripts. CYPs, cytochrome P450s; CEs, carboxylesterases; GSTs, glutathione S-transferases; UGTs, UDP-glucuronosyltransferases; ABCs, ATP-binding cassette transporters; OCTs, organic cation transporters; MRPs, multi-drug resistance proteins (MRPs). *Bolded numbers in brackets indicate how many of the non-bolded upregulated or downregulated DEGs were determined to be significant by a p-value of < 0.05 adjusted by the Benjamini-Hochberg FDR (false discovery rate) correction. Classification Candidate Transcripts DEGs in Midgut DEGs in Malpighian Tubules Up Down Total Up Down Total Metabolism CYPs 39(10) 26(14) 65 39(9) 16(8) 55 CEs 22(4) 34(19) 56 47(11) 16(3) 63 GSTs 6(1) 15(5) 21 8(3) 11(5) 19 UGTs 9(1) 24(18) 33 24(13) 11(4) 35 Transport ABCs 10(3) 17(9) 27 15(4) 10(2) 25 OCTs MRPs 8(1) 8(0) 13(8) 9(4) 21 17 7(1) 1(0) 16(9) 3(0) 23 4 58 3.4 RT-qPCR Validation 3.4.1 cDNA Synthesis and Primer Selection The product length and efficiency of each primer set ordered from Integrated DNA Technologies was verified on an agarose gel using a 100 kb DNA ladder, shown in Figure 3.7. All primers produced bands corresponding to their respective predicted product sizes indicated in Table 2.2. Where two primer sets were constructed for one gene, the primer set chosen for further analysis was based on which one showed the brightest band on the gel. Interestingly, the primer set tested XM_026889335.1 (B) shows multiple bands. These multiple bands are due to non-specific amplification and as a result this primer was not used in further analysis. Primer pairs that produced the brightest bands and the correct predicted product size were chosen for efficiency testing via RT-qPCR. 59 1,000 bp 1,517 bp ¯ XM_026870730.1(A) XM_026870731.1(A) 100bp ladder XM_026870730.1(B) XM_026870731.1(B) XM_026871767.1(A) XM_026871767.1(B) XM_026873420.1(B) XM_026873420.1(A) XM_026874091.1(A) XM_026874091.1(B) XM_026884809.1(B) XM_026884877.1(A) XM_026868736.1(B) XM_026884809.1(A) XM_026885476.1(A) XM_026889335.1(A) XM_026782236.1(B) XM_026884877.1(B) XM_026885476.1(B) XM_026889335.1(B) XM_026885477.1(A) XM_026885477.1(B) XM_026868736.1(A) XM_026887549.1 XM_026778426.1 XM_003402039.1 XM_026782236.1(A) ¯ ¯ ¯ ¯ ¯ ¯ ¯ ¯ ¯ ¯ ¯ ¯ ¯ ¯ Figure 3.10 2% agarose gel displaying PCR products of candidate primers to be used in RT-qPCR gene expression analysis. Quickload® 100kb and 1mb DNA ladders (Biolabs, New England) were used as references to confirm product size of primer sets in the far left and right lanes, respectively. For primers where two sets were ordered and tested, A and B indicate different sets constructed for the same gene. Red arrows indicate the primer sets chosen for use in final RT-qPCR validation. 500/517 bp 300 bp 200 bp 100 bp 1kb ladder igure 3.7 2% agarose gel displaying PCR products of candidate primers to be used in RT-qPCR gene expression analysis. uickload 100kb and 1 b ladders ( iolabs, e ngland) ere used as references to confir product size of pri er sets in the far left and right lanes, respectively. or pri ers here t o sets ere ordered and tested, and indicate different sets constructed for the sa e gene. ed arro s indicate the pri er sets chosen for use in final -q validation. 60 3.4.2 Primer Efficiency Test: Standard Curve and Melt Curve A standard curve was created to obtain primer efficiencies, concentration gradient slopes, y-intercepts and R2 values for all selected primer sets. One cytochrome P450 gene, XM_026885477, was determined to be inefficient even when the second primer set was tested. This gene was omitted from further analysis. Based on the geNorm analysis, reference genes XM_026868736 (tubulin beta-chain), XM_026892236 (glyceraldehyde-3-phosphate), XM_026889426 (actin), and XM_026887549 (eukaryotic initiation factor 4A-III) were determined to be stable whereas XR_003402039 (5S ribosomal RNA) was not determined to be stable and was omitted from the study. Examples of the standard curves for XM_026873420.1 (CYP9e2-like) and XM_026884877.1 (CYP6B2-like) are shown in Figure 3.8. The results from these standard curves are summarized in Table 3.3. All primer sets have an efficiency between 98-110%. An ideal efficiency is 100%, which indicates the amount of amplified product doubling every cycle. All primer sets have an R2 between 0.9-0.99. A value of R2 >0.98 indicates a regression line that fits the series dilution points well with minimal scatter between points. All primer sets have concentration grades close to -3.32, which is the value most ideal for a primer efficiency of 100%. The theoretical maximum detection of the reaction is indicated by the y-intercept, which is approximately 23 in Figure 3.8A and 19 in Figure 3.8B. Two sample melt curves are shown in Figure 3.9. The melt curve displays a change in fluorescence in response to increasing temperature. This change in fluorescence corresponds to the release of dye following dissociation of double-stranded DNA (dsDNA) into single-stranded DNA (ssDNA). Once all dsDNA has dissociated at the melting point of the primers, the fluorescence signal drops. Melt curves are used to check primer-dimer amplification and non-specific amplification. Thus, a single peak observed in the melt curves indicates specific binding and amplification. All primers displayed similar melt curves with a single peak to those observed in Figure 3.9. 61 Figure 3.11 Standard curves for XM_026873420.1 (CYP9e2-like) in panel A and XM_026884877.1 (CYP6B2-like) in panel B from T. ni larval tissue samples. A 5-fold 5 series dilution of a mixed cDNA sample from both midgut and Malpighian tubule tissues was used to obtain primer efficiencies (E), R2 values, concentration gradient slopes, and y-intercept values. The values are represented as the threshold cycle (Cq) by the logarithmic value of template starting quantity. A B Figure 3.8 Standard curves for XM_026873420.1 (CYP9e2-like) in panel A and XM_026884877.1 (CYP6B2-like) in panel B from T. ni larval tissue samples. A 5-fold 5 series dilution of a mixed cDNA sample from both midgut and Malpighian tubule tissues was used to obtain primer efficiencies (E), R2 values, concentration gradient slopes, and y-intercept values. The values are represented as the threshold cycle (Cq) by the logarithmic value of template starting quantity. 62 Figure 3.12 Sample melt curve analysis for XM_026873420.1 (CYP9e2-like) in panel A and XM_026884877.1 (CYP6B2-like) in panel B from T. ni larval tissue samples. A 5-fold 5 series dilution of a mixed cDNA sample from both midgut and Malpighian tubule tissues was used to obtain melt curves. A single SYBR® fluorescence amplicon is present at 75-85°C, indicating a single PCR product and the absence of primer-dimers or genomic contamination. A B Figure 3.9 Sample melt curve analysis for XM_026873420.1 (CYP9e2-like) in panel A and XM_026884877.1 (CYP6B2-like) in panel B from T. ni larval tissue samples. A 5-fold 5 series dilution of a mixed cDNA sample from both midgut and Malpighian tubule tissues was used to obtain melt curves. A single SYBR® fluorescence amplicon is present at 75-85°C, indicating a single PCR product and the absence of primer-dimers or genomic contamination. 63 Table 3.5 Summary of efficiencies (E value), R2, standard curve slope, and y-intercept values of primer sets tested to be used in RT-qPCR analysis. Values were obtained from standard curves made from A 5-fold 5 series dilution of a mixed cDNA sample. Genes indicated in red were deemed inefficient and omitted from further analysis. Gene Description E value (%) R2 Slope Y-int XM_026870730 cytochrome p450 6B5-like 92.3 0.975 -3.522 21.783 XM_026870731 cytochrome p450 6B5-like 102.9 0.988 -3.254 21.907 XM_026871767 cytochrome p450 6B2-like 98.5 0.982 -3.357 22.399 XM_026873420 cytochrome p450 9e2-like 103.7 0.990 -3.237 22.725 XM_026874091 cytochrome p450 4C1-like 106.1 0.986 -3.183 27.371 XM_026884809 cytochrome p450 6B2-like 102.0 0.975 -3.275 25.766 XM_026884877 cytochrome p450 6B2-like 102.2 0.992 -3.269 18.647 XM_026885476 cytochrome p450 6B6-like 99.7 0.989 -3.330 20.713 XM_026885477 cytochrome p450 6B6-like 127.5 0.981 -2.802 24.546 XM_026889335 cytochrome p450 6j1-like 105.9 0.986 -3.189 23.920 XM_026868736 tubulin beta chain-like 95.2 0.987 -3.443 17.837 XM_026892236 glyceraldehyde-3-phosphate dehydrogenase 97.9 0.991 -3.374 17.919 XR_003402039 5S ribosomal RNA 102.4 0.960 -3.265 20.091 XM_026889426 actin-like 100.0 1.000 0.149 37.203 XM_026887549 eukaryotic initiation factor 4A-III 95.5 0.990 -3.436 22.080 64 3.4.3 RT-qPCR Analysis for Validation of DEGs observed in RNA-seq data For validation of DEGs observed in RNA-seq data, expression levels of ten CYPs in two tissues (midgut and Malpighian tubule) were determined by RT-qPCR. The CYPs chosen were prominent DEGs showing increased expression in response to a diet supplemented with nicotine (Table 3.6). Based on geNorm analysis, four reference (housekeeping) genes exhibiting stable expression in all samples were used in the final analysis. The results of the geNorm analysis indicating stability of these reference genes is displayed in Table 3.7. An average coefficient variance of 0.2038 and average M-value of 0.4868 were observed in midgut samples. An average coefficient variance of 0.1874 and average M-value of 0.4719 were observed in Malpighian tubule samples. For heterogeneous samples like those of this study, M values below 1 and low coefficients of variance indicate reference gene stability (Hellemans et al. 2017). The patterns of expression observed by RT-qPCR were similar to those observed in RNA-seq with differences in magnitude (Figure 3.10, Figure 3.11). The obtained data showed that all genes were expressed in both tissues, but expression patterns varied depending on the tissue. Figure 3.10 shows similar expression patterns in the midgut samples when RT-qPCR results are compared to those of RNA-seq. Genes with increased expression in RNA-seq data also displayed increased expression in RT-qPCR. More specifically, 6 of these 9 genes showed significantly increased expression in RNA-seq. However, only 5 of these genes showed significantly increased expression in RT-qPCR where the RT-qPCR results for XM_026873420.1 were not significant. More variation between methods can be observed in Figure 3.11 for the Malpighian tubule samples. While 6 out of 8 genes showed similar expression patterns between RT-qPCR and RNA-seq data, 2 genes (XM_026873420.1 and XM_026884809.1) showed different expression patterns. These two genes showed increased expression in RT-qPCR while they did not show a particular change in expression in RNA-seq. In response to nicotine, genes XM_026870730.1, XM_026870731.1, and XM_026871767.1 showed increased expression in both RT-qPCR and RNA-seq data in both tissues. Results from RT-qPCR showed that genes XM_026870730.1 and XM_026870731.1 were more highly expressed in the Malpighian tubules by about 1.7-fold and 2.7-fold, respectively, while gene XM_026871767.1 was more highly expressed in the midgut by about 4.1-fold. Genes XM_026873420.1 and XM_026885476.1 also showed increased expression in both datasets with RT-qPCR data showing higher expression levels in the Malpighian tubules by 65 about 2.2-fold and 4-fold, respectively. Genes XM_026874091.1 and XM_026889335.1 displayed increased expression in both datasets with RT-qPCR results showing slightly higher expression in the midgut by about 3.4-fold and 1.6-fold, respectively. Gene XM_026884877.1 was stable in both tissues across treatments, while the primer set for gene XM_026884809.1 failed to produce results in the Malpighian tubules. Gene XM_026884809.1 showed high expression levels in the midgut in both RT-qPCR and RNA-seq datasets. 66 Table 3.6 The nicotine related CYP genes detected in the T. ni transcriptome chosen for RT-qPCR validation based on log2FC and adjusted p values obtained from RNA-seq. Unigene ID Annotation Midgut Malpighian Tubules Log2FC P value Log2FC P value XM_026871767 cytochrome P450 6B2-like 5.495 2.698E-40 2.988 4.001E-18 XM_026884809 cytochrome P450 6B2-like 4.174 0.03604 10.984 NA XM_026874091 cytochrome P450 4C1-like 3.135 6.276E-05 -0.262 NA XM_026873420 cytochrome P450 9e2-like 2.167 0.01015 0.238 NA XM_026885476 cytochrome P450 6B6-like 1.564 0.02598 5.162 1.037E-09 XM_026889335 cytochrome P450 6j1-like 1.075 0.01448 1.671 0.15279 XM_026885477 cytochrome P450 6B6-like -0.055 0.98026 6.418 1.375E-05 XM_026870731 cytochrome P450 6B5-like -0.274 0.87641 1.268 0.00021 XM_026870730 cytochrome P450 6B5-like -0.705 0.55407 1.212 0.02058 XM_026884877 cytochrome P450 6B2-like -0.209 0.82295 1.155 0.00051 67 Table 3.7 Target stability of reference genes used in RT-qPCR geNorm analysis. Gene Description Midgut Malpighian Tubules Coefficient Variance M-value Coefficient Variance M-value XM_026868736 tubulin beta chain-like 0.3585 0.7050 0.3032 0.5925 XM_026892236 glyceraldehyde-3-phosphate dehydrogenase 0.0466 0.3973 0.0713 0.3649 XM_026889426 actin-like 0.1795 0.3973 0.2897 0.5653 XM_026887549 eukaryotic initiation factor 4A-III 0.2306 0.4476 0.0854 0.3649 68 Figure 3.13 Expression levels of CYPs in midgut samples of T. ni larvae obtained from RT-qPCR (A) and RNA-seq (B). Fold expression is displayed for RT-qPCR with controls normalized as 1.0. RNA-seq data is displayed on a log2FC scale. Statistical analysis was conducted to compare expression levels between the control and nicotine-treated insects using the unpaired t-test. Asterisk above the standard error bars indicates significant difference (p<0.05: *; p<0.01: **; p<0.001:***). A B Figure 3.10 xpression levels of Ps in idgut sa les of T. ni larvae obtained fro -qP ( ) and RNA-seq (B). Fold expression is displayed for RT-qPCR with controls normalized as 1.0. RNA-seq data is displayed on a log2FC scale. Statistical analysis was conducted to compare expression levels between the control and nicotine-treated insects using the unpaired t-test. Asterisk above the standard error bars indicates significant difference (p<0.05: *; p<0.01: **; p<0.001:***). 69 Figure 3.14 Expression levels of CYPs in Malpighian tubule samples of T. ni larvae obtained from RT-qPCR (A) and RNA-seq (B). Fold expression is displayed for RT-qPCR with controls normalized as 1.0. RNA-seq data is displayed on a log2FC scale. Statistical analysis was conducted to compare expression levels between the control and nicotine-treated insects using the unpaired t-test. Asterisk above the standard error bars indicates significant difference (p<0.05: *; p<0.01: **; p<0.001:***). A B Figure 3.11 Expression levels of CYPs in Malpighian tubule samples of T. ni larvae obtained from RT-qPCR (A) and RNA-seq (B). Fold expression is displayed for RT-qPCR with controls normalized as 1.0. RNA-seq data is displayed on a log2FC scale. Statistical analysis was conducted to compare expression levels between the control and nicotine-treated insects using the unpaired t-test. Asterisk above the standard error bars indicates significant difference (p<0.05: *; p<0.01: **; p<0.001:***). 70 Chapter 4: Discussion This study was the first to investigate the changes in gene expression of the T. ni midgut and Malpighian tubules in response to short-term dietary exposure to nicotine. A T. ni genome was published just recently (Yu et al. 2017), allowing for investigation of the genetic mechanisms through which this agricultural pest metabolizes nicotine. The RNAseq approach was chosen for its particular advantages to detect all gene transcripts in the tissues along with any associated alteration in transcript expression in response to dietary nicotine treatments. This study has successfully made T. ni tissue and diet-specific transcriptomes available for the first time. Further data mining of these transcriptomes can pave the way for numerous hypothesis-driven studies beyond our current study. Analyzing the effects of xenobiotics, such as nicotine, on the T. ni midgut and Malpighian tubule transcriptome may enable development of novel insecticides which target other essential homeostatic mechanisms outside the nervous system. This may limit the chance of resistance and off-target side effects associated with current pesticides and their targets. Transcriptomic analysis may lead to the discovery of novel resistance genes and their expression during metabolism. Literature on xenobiotic resistance and detoxification in generalist insect pests like T. ni is limited. It has long been suggested that generalist herbivore growth and development are inhibited by nicotine exposure, whereas specialists are not affected. Specialist herbivores are thought to use only a few fine-tuned detoxification mechanisms that are highly efficient in adapting to host plants (Crava et al. 2016). In comparison, the detoxification genes used by generalist herbivores vary greatly, covering a greater range of efficiency and substrate specificity (Govind et al. 2010; Vogel eat al. 2014). To discover genes related to nicotine detoxification in T. ni, 4th instar larvae were exposed to a nicotine-free control diet and a diet containing 1% FW nicotine for 24 h. The midgut and Malpighian tubule tissues of these larvae were collected separately for RNA extraction and sequencing by BGI genomics. The resultant reads were subjected to quality control and mapped to the T. ni transcriptome from the Hi5 (BTI-Tn-5B1-4) RefSeq assembly (ID: 18285) accessed on NCBI. The reads proved to be of high quality as 91.07% of the clean data was retained following trimming for a total of 402,717,976 reads with an average GC content of 41.83% and Phred score above Q30. A total of 24,947 transcripts were detected in 71 both tissues, comprising 98.69% of transcripts in the current T. ni transcriptome. Differentially expressed genes (DEGs) between midgut and Malpighian tubule tissues of nicotine-exposed larva were compared to control larva using a false discovery rate (FDR) < 0.05 and |log2FC| ≧ 1.0 as a threshold to filter out transcripts. The main focus of this project was to search for transcripts showing increased expression following nicotine exposure as these may be key players in nicotine detoxification. A total of 5831 (59.5%) transcripts showed increased expression in the midgut while 4217 (58.6%) transcripts showed increased expression in the Malpighian tubules. Of the DEGs, 4205 upregulated transcripts and 3025 downregulated transcripts were observed only in the midgut, whereas 2590 upregulated transcripts and 2045 downregulated transcripts were observed only in the Malpighian tubules. In addition, 940 transcripts were downregulated and 1626 transcripts were upregulated in both the midgut and Malpighian tubules. The presence of upregulated genes overlapping between both tissues indicates that they may work together during metabolism but through different pathways. Knowing which DEGs overlap between the tissues is valuable information in the development of novel insect pest management strategies. For example, it would be valuable to determine if a gene involved in nicotine metabolism is only present in the midgut or Malpighian tubules. Therefore, knockdown strategies can be developed accordingly to target tissues individually or both together. To pinpoint the nicotine detoxification pathways in which the observed DEGs are involved, the potential metabolic pathways and metabolites of nicotine in T. ni must be understood. A recent study in our lab by Saremba et al. in 2018 uncovered these pathways and metabolites in T. ni larvae. Over a 24 h sampling period, T. ni larvae are able to eliminate over 98% of total nicotine and metabolites in the frass. Frass samples of nicotine-exposed larvae are primarily composed of cotinine (84%), nicotine (16%), and trace levels of nicotine-N-oxide (0.001 %) and cotinine-N-oxide (0.04 %). Cotinine is the major metabolite detected in all tissues of T. ni larvae post nicotine exposure, representing over 80% of the total nicotine and derived metabolites. The major metabolite detected in honeybees fed nectar nicotine is 4-hydroxy-4-(3-pyridyl)-butanoic acid, which is a product of the 2’C oxidation pathway (du Rand et al. 2017). However, this metabolite was not detected in any samples of T. ni larvae fed nicotine. When taken together this strongly suggests that the major pathway used by T. ni is the 5’C oxidation of nicotine. The 5’C oxidation pathway metabolizes nicotine into cotinine, which can then be 72 further oxidized into cotinine-n-oxide. The N-oxidation pathway which converts nicotine into nicotine-N-oxide is also likely used, but only trace levels of this metabolite have been detected in T. ni (Saremba et al. 2018). Previous literature has suggested various genes play roles in the metabolism and transport of xenobiotics in insects. Our results showed that among the up-regulated transcripts, a number were annotated which were similar in identity to those previously associated with metabolism and transport. Enzymes frequently implicated in xenobiotic metabolism are CYPs, CEs, GSTs, and UGTs while those associated with xenobiotic transport are ABCs, OCTs, and MRPs. Xenobiotic detoxification involves two metabolic and one transport phase in which these enzymes work cooperatively (Xu et al. 2005). Phase I reactions are mediated by enzymes such as CYPs and CEs which work to increase hydrophilicity of a molecule by oxidation, reduction, or hydrolysis (Xu et al. 2005). Phase II reactions are mediated by conjugating enzymes such as GSTs and UGTs which couple a molecule to the xenobiotic or its metabolite for increased size and hydrophilicity (Xu et al. 2005). The hydrophilic molecules generated from phase I and II reactions can then be readily excreted in phase III reactions mediated by transport enzymes such as ABCs, OCTs, and MRPs (Xu et al. 2005). Our results identified several of these metabolism and transport-related transcripts whose expression was increased in response to nicotine. A similar number of upregulated CYPs were observed in the midgut and Malpighian tubules, whereas a higher number of upregulated CEs, GSTs, and UGTs were observed in the Malpighian tubules. This suggests that metabolism is likely occurring in the Malpighian tubules as well, possibly even to a higher degree than in the midgut. These results correspond to the detection of the metabolite nicotine-N-oxide solely in frass samples (Saremba et al. 2018). Thus, in addition to transport the tubules may be responsible for some metabolism of nicotine. A similar number of significantly upregulated DEGs related to xenobiotic transport were observed in the midgut and Malpighian tubules in response to nicotine treatment. These findings were expected as nicotine and its metabolites are likely transported through both tissues (Rheault et al. 2006; Saremba et al. 2018). Cabbage looper larvae have numerous pathways to eliminate nicotine. The midgut is thought to be the location of nicotine detoxification in M. sexta (Snyder et al., 1995, 1993; Stevens et al., 2000). Nicotine detoxification in the midgut of 4th instar T. ni larvae has recently been discovered (Saremba et al. 2018). Nicotine and its metabolites were detected in midgut 73 samples of larvae exposed to a nicotine-containing diet. Cotinine, the product of 5’C oxidation of nicotine, comprised 97% of the total nicotine and metabolites detected in midgut samples while trace levels (0.0004%) of cotinine-N-oxide were detected. Multidrug or ABC transporters play a key role in nicotine detoxification and are highly present in insect midgut tissues (Bretschneider et al., 2016a; Dermauw and Van Leeuwen, 2014; Hawthorne and Dively, 2011; Labbé et al., 2011; Lanning et al., 1996; Liu, 2011; Merzendorfer, 2014). For oxidative metabolism to occur, organic compounds like nicotine must enter midgut epithelial cells with the help of these transporters. Transporters can then move nicotine and its metabolites further along to the haemocoel via the basal membrane. Alternatively, nicotine can diffuse back into the midgut. The decreased level of nicotine and its metabolites in midgut samples over the 24 h sampling period suggests transport out of the midgut for further metabolism and/or subsequent excretion. This transport across the apical and basal membranes of the midgut epithelium is also supported by the detection of nicotine and its metabolites in haemolymph samples. Diffusion across the insect midgut epithelium is also a possibility as it’s permeable to small organic compounds owing to a lack of a cuticle (Huang et al., 2015; Klein et al., 1996; Sacchi and Wolfersberger, 1996; Turunen and Crailsheim, 1996). Neutral nicotine likely moves from the midgut to haemocoel by transcellular diffusion or via the paracellular route (Huang et al. 2015). Sequestration is another detoxification strategy used by many Lepidopteran insects in which xenobiotics are removed from haemolymph for storage in other tissues (Dixon et al., 1978; Frick and Wink, 1995; Nishida, 2002; Seiber et al., 1980). Moth larvae are capable of using sequestration to make themselves less appetizing to natural predators by reducing content of plant secondary metabolites such as pyrrolizidine alkaloids in their haemolymph (Nickisch-Rosenegk et al., 1990; Wink and Nickisch-Rosenegk, 1993). Carrier-mediated sequestration has been indicated in Lepidopteran insects (Frick and Wink, 1995). T. ni larvae are likely capable of some short-term sequestration, suggested by the increasing relative proportion of nicotine and metabolites in carcass samples within the first 6 h of samples (Saremba et al. 2018). This finding combined with rapid clearance from haemolymph indicate passive and carrier-mediated movement of nicotine and its metabolites from haemolymph to other tissues. Thus, T. ni do not appear to use sequestration as a long-term strategy for detoxification. The focus of this thesis was CYPs as they are frequently implicated in the detoxification of nicotine and other xenobiotics. Insect growth, nutrition, and development are also influenced 74 by CYP enzymes (Liang et al. 2015). Insect CYPs are divided into four main categories: CYP2, CYP3, CYP4, and Mitochondrial CYP with genes in the CYP3 clan being most commonly linked to metabolizing xenobiotics (Liu et al. 2015). Alkaloid metabolism and insecticide resistance is specifically associated with CYP6 and CYP9 genes which are members of the CYP3 clan. Recent studies examining resistance strategies in other Lepidopteran insects such as the tobacco hornworm, M. sexta, have assisted in highlighting the importance of cytochrome P450 enzymes in detoxification (Kumar et al. 2014). Nicotine tolerance in M. sexta has been linked to the overexpression of CYP6B46 (Kumar et al. 2014). Furthermore, overexpression of CYP6CM1 and CYP6CY3 have been implicated in nicotine tolerance of B. tabaci and M. persicae, respectively (Bass et al. 2013; Kliot et al. 2014). Nicotine metabolism in humans has been primarily associated with CYP2A6 (Nakajima et al. 2006). For validation of RNA-seq data, expression levels of ten CYPs in two tissues (midgut and Malpighian tubule) were determined by RT-qPCR. The CYPs chosen were prominent DEGs showing significantly increased expression in response to a diet supplemented with nicotine. Of the ten CYPs, eight belong to the CYP6 family, one belongs to the CYP4 family, and one belongs to the CYP9 family. The patterns of expression observed by RT-qPCR were similar to those observed in RNA-seq with differences in magnitude. Successful validation of RNA-seq data suggests that the datasets generated in this thesis are reliable to use in the basis of future studies. A BLAST search on Geneious software was also conducted on all of the protein sequences for these CYPs in search of related CYPs in other insects. The goal was to determine if any of the T. ni upregulated CYPs are similar in sequence to CYPs identified in xenobiotic detoxification of other insects. In the midgut, the transcripts showing significantly increased expression in both RNA-seq and RT-qPCR in response to nicotine were XM_026871767 (CYP6B2-like), XM_026874091 (CYP4C1-like), XM_026884809 (CYP6B2-like), XM_026885476 (CYP6B6-like), and XM_026889335 (CYP 6j1-like). XM_026871767 did not match to any particular CYPs of interest, but its expression was highly increased in response to nicotine. Data from RNA-seq indicated a log2FC of 5.49 (fold change of 45) in response to nicotine while RT-qPCR data indicated a fold change in expression of approximately 73. This dramatic increase in expression and the known role of CYPs in the CYP6 family in xenobiotic metabolism suggests that this P450 may play a large role in nicotine metabolism in T. ni (Liang et al. 2015). 75 XM_026874091 also did not match to any CYPs of interest in other insects. Data from RNA-seq indicated a log2FC of 3.135 (fold change of 8.8) in response to nicotine while RT-qPCR data indicated a fold change in expression of approximately 8.5. Although genes in the CYP6 family are more commonly linked to insecticides metabolism, insecticides such as imidacloprid have induced upregulation of genes in the CYP4 family (Liang et al. 2015). This finding along with our results suggest that XM_026874091 may also be involved in metabolism of nicotine of T. ni. XM_026884809 did not match up to any CYPs of interest either. Data from RNA-seq indicated a log2FC of 4.174 (fold change of 18) in response to nicotine while RT-qPCR data indicated a fold change in expression of approximately 116. This is also a member of the CYP6 family and its highly increased expression in response to nicotine suggests its involvement in metabolism. XM_026885476 showed sequence similarity to a number of published CYPs in other insects. A similarity of 62.10% was observed when compared to CYP6B46 in M. sexta which has shown upregulation in the midgut in response to dietary nicotine (Kumar et al. 2014). XM_026885476 also showed 69.90% similarity to CYP6B6 in H. armigera and 69.10% similarity to CYP6B8 in Helicoverpa zea. CYP6B6 is thought to be involved in the metabolism of esfenvalerate in H. armigera (Tian et al. 2017). In H. zea, metabolism of the plant allelochemicals xanthotoxin, quercetin, flavone, chlorogenic acid, indole-3- carbinol, and rutin is mediated by CYP6B8 as well as the metabolism of insecticides diazinon, cypermethrin, and aldrin (Li et al. 2004). Data from RNA-seq indicated a log2FC of 1.564 (fold change of 3) of XM_026885476 in response to nicotine while RT-qPCR data indicated a fold change in expression of approximately 8.8. These findings along with XM_026885476 being a member of the CYP6 family strongly point to its involvement in nicotine metabolism. XM_026889335 showed 61% sequence similarity to CYP6AE19 in H. armigera, which is highly expressed in response to xanthotoxin (Wang et al. 2018). Data from RNA-seq indicated a log2FC of 1.075 (fold change of 2) of XM_026885476 in response to nicotine while RT-qPCR data indicated a fold change in expression of approximately 2.4. These results together suggest that there are a number of CYPs involved in nicotine metabolism in the midgut, but XM_026885476 may be the most interesting due to its similarity to published CYPs in other insects. The Malpighian tubules are responsible for rapid excretion of nicotine from haemolymph (Rheault et al. 2006). The same multidrug transporters facilitating xenobiotic detoxification in the midgut are abundant in insect Malpighian tubules and are responsible for removing organic 76 compounds like nicotine (Dermauw and Van Leeuwen, 2014). The Malpighian tubules are also the prominent site of xenobiotic detoxification in insects such as D. melanogaster (Yang et al., 2007). Interestingly, nicotine-N-oxide has only been detected in frass samples of T. ni exposed to nicotine. This finding suggests that the metabolism of nicotine to nicotine-N-oxide by N-oxidation only occurs in the Malpighian tubules. In the Malpighian tubules, the transcripts showing significantly increased expression in both RNA-seq and RT-qPCR in response to nicotine were XM_026870730 (CYP6B5-like), XM_026870731 (CYP6B5-like), XM_026871767 (CYP6B2-like), and XM_026885476 (CYP6B6-like). XM_026870730 showed a sequence similarity of 61.10% with CYP321A7 in S. litura, 59.80% similarity to CYP321A1 in H. zea, and 62.90% similarity to CYP9A28 in S. frugiperda. XM_026870731 also showed similarity degrees of sequence of similarity to the same three CYPs. In S. litura, CYP321A7 has shown increased expression in the midgut and fat body of larvae fed a diet containing xanthotoxin or coumarin (Wang et al. 2017). CYP321A1 is thought to metabolize xanthotoxin and angelicin in H. zea (Sasabe et al. 2004). CYP9A28 demonstrates increased expression in response to methoprene and fipronil treatment in S. frugiperda (Giraudo et al. 2015). For XM_026870730, data from RNA-seq indicated a log2FC of 1.212 (fold change of 2.3) in response to nicotine while RT-qPCR data indicated a fold change in expression of approximately 2.8. For XM_026870731, data from RNA-seq indicated a log2FC of 1.268 (fold change of 2.4) in response to nicotine while RT-qPCR data indicated a fold change in expression of approximately 3.2. As the increased expression of these two CYPs is unique to the Malpighian tubules and not shown in the midgut, these may be possible candidates for the hypothesized N-oxidation of nicotine to NNO in the tubules which was observed in Saremba et al. Further studies would be required to evaluate this possibility. XM_026871767 and XM_026885476 showed significantly increased expression in both the midgut and Malpighian tubules. For XM_026871767 in the tubules, data from RNA-seq indicated a log2FC of 2.988 (fold change of 7.9) in response to nicotine while RT-qPCR data indicated a fold change in expression of approximately 17.8. For XM_026885476, data from RNA-seq indicated a log2FC of 5.162 (fold change of 35.8) in response to nicotine while RT-qPCR data indicated a fold change in expression of approximately 35.3. These findings and the sequence similarity of XM_026885476 to other important CYPs discovered in insect pests suggests the crucial role of this gene in nicotine metabolism. Furthermore, the increased 77 expression observed in both tissues of this gene along with XM_026871767 make them appealing targets in further studies. Knocking down a gene present in both tissues could prove to detrimental to T. ni’s defense against nicotine. Our knowledge of molecular genetics in insecticide resistance has been overwhelmingly advanced by RNA-seq techniques. In this thesis, RNA-seq demonstrated an increase in the expression of cytochrome P450s in the midgut and Malpighian tubules of T. ni larvae in response to dietary nicotine. Increasing awareness of the importance of cyp genes in alkaloid detoxification will allow for more efficient development and application of insecticides in the future. Furthermore, the overall trends in RT-qPCR expression results were consistent with those of RNA-seq data. This valuable information from the transcriptome and DEG analysis can be supplemented by further gene function experiments to more closely examine nicotine detoxification in T. ni. 78 Chapter 5: Conclusion 5.1 Objective 1: To use RNA-seq to produce transcriptomes for both the midgut and Malpighian tubule tissues of T. ni exposed to diets containing 0% nicotine and 1% nicotine. In this thesis I successfully used RNA-seq to produce tissue- and diet-specific transcriptomes for T. ni. Two tissues, the midgut and Malpighian tubules, were sequenced from 4th-instar T. ni larvae exposed to a control diet containing no nicotine and a diet containing 1% nicotine (FW). Four high-quality datasets are now available for T. ni. Following Trimmomatic quality control, 91.07% of the clean data was retained with raw reads per library ranging from 32.1 to 34.8 million reads for an average of 33.6 million and a total of 402,717,976 reads. The cleaned sequencing data is of high quality, based on an average GC content 41.83% and a Phred score above Q30 in all reads. The speed and high-throughput properties of next-generation sequencing (NGS) techniques make them a cost-effective and valuable method in genomics research. To date, the only genomic databases for T. ni are based on the Hi5 (BTI-Tn-5B1-4) germ cell line (Fu et al. 2018) and the carcass of a homozygous Cornell Strain of T. ni (Chen et al. 2019). The high-quality databases developed from this thesis provide a valuable resource for research pertaining to T. ni. Areas of research that can benefit from NGS data can range from basic insect biology to the development of more effective insect pest management programs based on information related to the mechanisms of insect resistance to various pesticides. NGS data can also benefit studies on insect-plant interactions as well as their co-evolution. Furthermore, the results from such research can supplement similar research in other insects leading to comparative studies as well. 5.2 Objective 2: Determine the differential expression of genes in the Midgut and Malpighian tubule tissues of T. ni following 1% dietary nicotine exposure. The first step in completing objective 2 was to map clean reads to the T. ni transcriptome from the ovarian cell line Hi5 (BTI-Tn-5B1-4) RefSeq assembly and annotate them. Next, differentially expressed genes (DEGs) were determined and the results were analyzed. We used a false discovery rate (FDR) < 0.05 and |log2FC|≧ 1.0 as a threshold to classify transcripts as 79 differentially expressed. This thesis specifically focused on DEGs that may confer nicotine resistance through their roles in metabolism and transport. CYPs in particular were the main focus of this thesis. However, many more research questions can arise from the data provided in this thesis such as the roles of other DEGs (CEs, GSTs, UGTs, ABCs, OCTs, MRPs) in nicotine metabolism and transport. I hypothesized that T. ni metabolizes nicotine in the midgut and Malpighian tubules through increased expression of cytochrome P450 transcripts and the resultant proteins. This hypothesis was supported as numerous CYPs showed increased expression in response to nicotine exposure in both the midgut and Malpighian tubules. I also hypothesized that nicotine exposure would lead to increased expression of transport genes. This hypothesis was also supported by the increased expression of ABCs, OCTs, and MRPs observed following nicotine exposure. The control samples for both samples also serve as tissue-specific transcriptomes. These can be used to identify relevant transcripts in each tissue and can serve as a tool for other researchers in investigations of other tissue-specific transcripts. 5.3 Objective 3: Use quantitative PCR to validate the results of differential gene expression from RNAseq. The final step was to validate the RNA-seq results using RT-qPCR. For validation of RNA-seq data, expression levels of ten CYPs in both tissues (midgut and Malpighian tubule) were determined by RT-qPCR. The CYPs chosen were prominent DEGs showing increased expression in response to a diet supplemented with nicotine. The overall trends observed in RNA-seq data were also observed by RT-qPCR, indicating that the RNA-seq data is a high-quality and reliable resource that can be applied to future studies on T. ni. 5.4 Limitations The genomic resources available for T. ni proved to be a limitation in this study. The datasets available are based on the Hi5 germ cell line (Fu et al. 2018) and the carcass of an inbred Cornell Strain of T. ni (Chen et al. 2019). The transcriptome based on the carcass would be inapplicable to this study as it does not contain the midgut and Malpighian tubule tissues. Therefore, the transcriptome created from the Hi5 (BTI-Tn-5B1-4) cell line was used. The cell 80 line is developed from the ovary cells of T. ni which may not be representative of all transcripts in all tissues. Since it is a transcriptome of an isolated subset of cells, it may not reflect the full transcriptome of the ovary tissue and other tissues. Although using cell culture is a rapid and cost-effective method in biological research, cell line research is often associated with inconsistent results, contamination, and misidentification. The function and behaviour of a cell is highly influenced by its microenvironment, suggesting that results observed using a cell culture may be physiologically irrelevant. This may cause issues with reproducibility in biological research. The dataset chosen also caused confusion when processing RNA-seq data due to differences in annotation by the research group and those of the data deposited on NCBI. Furthermore, many transcripts in the dataset lack annotation and many others are only predictions. These transcripts lacking annotation could be genes of interest in our study. Therefore, de novo sequence assembly and tools such as Blast2GO can be used to create an accurate assembly with complete annotation. The cost of sequencing also limited this study. Ideally, additional replicates could be sequenced to obtain a higher sample size and may improve the significance of the results. For example, if the number of reads sequenced in one sample was zero or varied greatly from the other two samples the results would be statistically insignificant. Additional samples could limit the data lost from this issue. However, the results of this thesis still provide valuable information for further studies. Methods such as RT-qPCR and droplet digital PCR can be used to validate even more DEGs observed in this study. Furthermore, larvae can be reared on different concentrations of nicotine and samples at varying timepoints for comparison to the observations of this study. Other instars could also be tested. Studies using other xenobiotics or insecticides can also be conducted and compared to the control transcriptomes from this study. 5.5 Future Directions Transcriptomic analysis allows for the discovery of novel resistance genes and their pathways in xenobiotic metabolism. This study has provided some promising results in showing that there is a significant increase in metabolism- and excretion-related genes in T. ni larvae exposed to a diet containing 1% nicotine. This information can be applied to the development of gene editing technologies such as CRISPR/Cas9 or RNAi to evaluate the functional significance 81 of gene knockdown on xenobiotic metabolism. However, T. ni is a non-model organism and these methods have not been thoroughly developed for this insect species and other Lepidopterans. Studies using CRISPR/Cas9 editing in Lepidopterans have revealed difficulties. For example, early stage eggs of the codling moth pest, Cydia pomonella, were successfully injected to introduce mutations (Garczynski et al. 2017). However, females produced nonviable eggs demonstrating a negative impact on fecundity and fertility. There are minimal studies to date showing successful CRISPR/Cas9 editing in Lepidopterans. However, no studies demonstrating successful CRISPR/Cas9 editing in Trichoplusia ni have been published to date. The CRISPR/Cas9 system has been successfully applied to only a handful of insects such as Drosophila, silkworm, mosquito, and butterfly (Chen et al. 2016). Further studies should focus on long-term nicotine exposure to determine whether the changes in transcript levels observed in this study are maintained over time. This study is one of the first of its kind to be conducted using the cabbage looper, making it an important step in the path to finding more suitable controls for generalist agricultural pests like T. ni. 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Thesis/Dissertation
eng
Biology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Rheault, Mark
Nicotine metabolism in the cabbage looper Trichoplusia ni (Hübner) - a toxicogenomic approach
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