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Using Molecular Biomarkers to Determine Possible Effects of Shellfish Aquaculture on Native Populations of Crassostrea Virginica

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Using Molecular Biomarkers to Determine Possible Effects of Shellfish Aquaculture on Native Populations of Crassostrea Virginica USING MOLECULAR BIOMARKERS TO DETERMINE POSSIBLE EFFECTS OF SHELLFISH AQUACULTURE ON NATIVE POPULATIONS OF CRASSOSTREA VIRGINICA Kelsey S. Billet A Dissertation Submitted to the University of North Carolina Wilmington in Partial Fulfillment of the Requirements for the Degree of Master of Biology Department of Biology and Marine Biology University of North Carolina Wilmington 2020 Approved by Advisory Committee Susanne Brander Susanna Lopez-Legentil Martin Posey Chair Accepted by Dean, Graduate School TABLE OF CONTENTS ABSTRACT……………………………………………………………………………………. iv ACKNOWLEDGEMENTS……………………………………………………………………. v LISTS OF TABLES……………………………….…………………………………………… vii LISTS OF FIGURES…………………………..……………………………………………….. viii INTRODUCTION……………………………………………………………………………… 1 1.1 Aquaculture 1.2 Oysters as a Model 1.3 Possible Benefits of Aquaculture Oysters 1.4 Possible DraWbacks of Aquaculture Oysters GENES………………………………………………………………………………………… 9 2.1 Stress Response 2.2 Immune Response 2.3 Metabolic Response METHODS……………………………………………………………………………………. 12 3.1 Specimen Selection and Preservation 3.2 RNA Extraction 3.3 cDNA Synthesis 3.4 Quantitative PCR 3.5 Statistical Analysis RESULTS……………………………………………………………………………………… 16 4.1 Quantitative PCR 4.2 Quantitative PCR Principal Components Analysis Body Tissue 4.3 Quantitative PCR ANOVA Body Tissue 4.4 Quantitative PCR Principal Components Analysis Gill Tissue 4.5 Quantitative PCR ANOVA Gill Tissue DISCUSSION…………………………………………………………………………………... 21 5.1 Stress Response Genes in Gill Tissue 5.2 Immune Response Genes in Gill Tissue 5.3 Metabolic Response Genes in Gill Tissue 5.4 Gene Responses in Body Tissue ii CONCLUSIONS……………………………………………………………………………….. 29 REFERENCES…………………………………………………………………………………. 30 APPENDIX…………………………………………………………………………………….. 67 iii ABSTRACT Oysters are a keystone ecological species that generate numerous direct and indirect ecosystem services. They are also an important species economically, generating over 150 million dollars in 2014, which has led to the rapid expansion of aquaculture facilities and sites throughout the World. However, oyster aquaculture reefs my pose specific and/or novel difficulties for native oyster populations in surrounding areas. The change in regulation of genes within the Crassostrea virginica genus relating to metabolism, immunity, and cellular stress can be used as indicators, or molecular biomarkers, to determine whether the oyster is being exposed to harmful biotic or abiotic factors. The aim of this study is to use these established biomarkers to determine if possible changes to wild oyster gene expression can be attributed to the presence of shellfish aquaculture operations. In a study conducted in Big Bay, North Carolina, the expression levels of 7 genes were used as biomarkers of stress (AS6, KCrec, EDL, HSP70, PRDX6, GS, and SUP) their expression levels were then analyzed using qPCR. Differences in gene expression betWeen control and potentially impacted sites, in gill tissue, was found for the EDL, HSP70, PRDX6 and GS genes. Differences in gene expression betWeen seasons, in gill tissue, was found for the AS6, and KCrec genes. Body tissue was less sensitive, with only tWo genes showing differential expression (PRDX6 and SUP). This research shows that there is a difference in gene expression betWeen naturally occurring reefs that are closer to aquaculture sites and naturally occurring reefs that are farther from aquaculture sites, supporting their continued use as molecular biomarkers of stress in oysters. iv ACKNOWLEDGEMENTS I would first like to thank Dr. Susanne Brander, I am so grateful that you took me on as a masters student in your lab. I would also like to thank Dr. Martin Posey and Dr. Susanna Lopez- Legentil, along with Dr. Brander you all have stuck with me through distance, time changes, pregnancies, natural disasters, and pandemics. I appreciate that you were all Willing to work with me every step of the way through this research project and degree. I would also like to thank the entire ShARES team including, Dr. Beth Darrow, Troy Alphin, Jessica Kinsella and Madison Lytle. Whether it was providing gear or a vessel to perform oyster sampling, a body to help with dissections, or someone to help share the work load of early data analysis and ShARES meeting presentations you all were invaluable assets to my research and I could not have completed this project without your assistance. Last but most definitely not least I would like to thank my family and friends. A special shout out to Julie Neurohr, Bethany DeCourten, and Cheyenne Stienbarger, I cannot thank the three of you enough for supporting me through the past three years. Having you all as friends, confidants, workout buddies, mentors, and lab mates means the world to me, you are three of the strongest, smartest women I know, and I am so lucky to have met you. Mom, I don’t know how to thank you enough for all that you have done for me. You have supported me financially and emotionally through every step of the circuitous, crazy path that has been my masters degree journey. You have been there as a shoulder to cry on, a cheerleader, v an editor, a mentor and more. You believed in me when I didn’t believe in myself, I am the Woman I am today because of you and I love you so very much. Daniel, I don’t even know how to put into words how much you mean to me, I am so lucky that you asked me to marry you and that you stuck by me, supporting me and my dreams. Who would have guessed when I got out of the Marine Corps that 11 years later we would have a beautiful son, be living in OkinaWa, Japan, and that I would have a masters degree in biology?! You kneW I could accomplish this from the beginning and your faith in me never faltered, you mean everything to me and none of this would have been possible without your love, support, and sacrifice. Cash, my love for you knows no bounds, as much as this was my dream it was for you too. I hope that one day you are proud of me. vi LIST OF TABLES Table Page 1. Characteristics of primers and efficiency percentages of genes used……………………... 38 2. Summary of all principal components analysis data………………………………………. 39 vii LIST OF FIGURES Figure Page 1. Principal components analysis biplots showing mean expression values from genes analyzed via qPCR in body tissue from control and possible impacted sites……………. 40 2. Principal components analysis biplots showing mean expression values from genes analyzed via qPCR in body tissue from control sites 1, 2, and 6…………………………. 41 3. Principal components analysis biplots showing mean expression values from genes analyzed via qPCR in body tissue from impacted sites 1, 2, and 4……………………….. 42 4. Principal components analysis biplots showing mean expression values from genes analyzed via qPCR in body tissue from fall, spring, and………………………………….. 43 5. Principal components analysis biplots showing mean expression values from genes analyzed via qPCR in gill tissue from control sites and possible impacted sites………….. 44 6. Principal components analysis biplots showing mean expression values from genes analyzed via qPCR in gill tissue from control sites 1, 2, and 6……………………………. 45 7. Principal components analysis biplots showing mean expression values from genes analyzed via qPCR in gill tissue from Possibly impacted site 1, 2, and 4…………………. 46 8. Principal components analysis biplots shoWing mean expression values from genes analyzed via qPCR in gill tissue from fall, spring, and summer…………………………… 47 9. One way analysis of variance of the KCrec biomarker in gill tissue betWeen all sites from fall…………………………………………………………………………………….. 48 10. One way analysis of variance of the AS6 biomarker in gill tissue betWeen three seasons fall, spring, and summer……………………………………………………………………. 49 11. One way analysis of variance of the EDL biomarker in gill tissue betWeen control and possibly impacted sites from spring………………………………………………………... 50 12. One way analysis of variance of the EDL biomarker in gill tissue betWeen control and possibly impacted sites from summer……………………………………………………… 51 13. One way analysis of variance of the EDL biomarker in gill tissue betWeen control and possbily impacted sites from fall…………………………………………………………… 52 14. One way analysis of variance of the EDL biomarker in gill tissue of betWeen all control and all possibly impacted sites…………………………………………………………….. 53 viii LIST OF FIGURES Figure Page 15. One way analysis of variance of the EDL biomarker in gill tissue bettWeen all sites………………………………………………………………………………………... 54 16. One way analysis of variance of the HSP70 biomarker in gill tissue betWeen all control and possibly impacted sites……………………………………………………….. 55 17. One way analysis of variance of the PRDX6 biomarker in body tissue betWeen control and possibly impacted sites from fall……………………………………………... 56 18. One way analysis of variance of the PRDX6 biomarker in body tissue betWeen control and possibly impacted sites from spring………………………………………….. 57 19. One way analysis of variance of the PRDX6 biomarker in body tissue betWeen fall, spring, and summer……………………………………………………………........... 58 20. One way analysis of variance of the PRDX6 biomarker in gill tissue betWeen all sites….. 59 21. One way analysis of variance of the PRDX6 biomarker in gill tissue betWeen all sites within the fall………………………………………………………………………… 60 22. One way analysis of variance of the PRDX6 biomarker in gill tissue betWeen all control and possibly impacted sites…………………………………………………….. 61 23. One way analysis of variance of the PRDX6 biomarker in gill tissue
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