README FILE TITLE: Population-specific responses in eastern oysters exposed to low salinity in the northern Gulf of Mexico CONTACT INFORMATION: Author(s) University Email 1. Kyle Sirovy Louisiana State University kyleasirovy@gmail.com 2. Sandra Casas Louisiana State University SCasasListe@agcenter.lsu.edu 3. Jerome La Peyre Louisiana State University JLaPeyre@agcenter.lsu.edu 4. Morgan Kelly Louisiana State University morgankelly@lsu.edu CONTACT PERSON FOR QUESTIONS: Kyle Sirovy (kyleasirovy@gmail.com) DATE OF DATA COLLECTION: July, 2020 INFORMATION ABOUT GEOGRAPHIC LOCATION: In December 2017 and January 2018, subtidal adult C. virginica oysters were collected from two estuaries in Louisiana, USA; Calcasieu Lake (CL; 29° 50′ 58′′ N, 93° 17′ 1′′ W) and Vermilion Bay (VB; 29° 34′ 47′′ N, 92° 2′ 4′′ W). Additionally, in August 2018 oysters were collected from two estuaries in Texas, USA; Packery Channel (PC; 27° 37′ 38′′ N, 97° 13′ 59′′ W) and Aransas Bay (AB; 28° 7′ 38′′ N, 96° 59′ 8′′ W). In August 2018, all four broodstock populations were naturally induced to spawn at the Auburn University Shellfish Laboratory (AUSL) in Dauphin Island, Alabama, as described in Marshall et al. (2021). Oyster spat were maintained in upwelling nursery systems until ~6mm in shell height, and subsequently deployed in bags at an AUSL-permitted grow-out site at Bayou Sullivan, Alabama (30° 21′ 52′′ N, 88° 12′ 57′′ W) before being moved in March 2019 to the Grand Bay Oyster Park (GBOP), Alabama (30˚ 22′ 15′′ N, 88˚ 19’ 0” W) for further growth (see Marshall et al., 2021 for environmental data). In February 2020, we transferred the oysters (~18 months old) from each stock from GBOP to the static systems at Louisiana State University Agricultural Center Animal and Food Sciences laboratory building (AFL) in Baton Rouge, Louisiana. KEYWORDS: Transcriptomics, Plasticity, Genotype-by-Environment Interaction, Common Garden FUNDING RESOURCES This research was supported by NSF-BioOCE 1731710 to MK and JL, Louisiana Sea Grant award NA14OAR4170099 to MK and JL, and the Alfred P. Sloan Foundation research fellowship awarded to MK. During the project, KS was supported by an NSF Graduate Research Fellowship under Grant No. 00001414. DATA AND FILE OVERVIEW: Below is a list of the provided files and a brief explanation of what is contained in them. Gene_Expression_Count_Matrix.txt # The raw count data produced from the ReadsPerGene.out.tab output from STAR. This is the input matrix for edgeR, PERMANOVA, or WGCNA analysis. Scripts.txt # Scripts used to perform gene expression analysis on the count matrix provided using edgeR, PERMANOVA tests, or WGCNA analysis. Table_S1.xlsx # A list of attributes (i.e., population, salinity, shell height, clearance rates) for all oysters sampled. Table_S3.xlsx # List of GO terms with the response category (i.e., environment, infection) and analysis used (i.e., edgeR, PERMANOVA) reported. METHODOLOGICAL INFORMATION See "Information about geographic location of data collection" above for experimental conditions. Sample collection: In July 2020 we measured the final shell height, shell width, shell weight, and dry meat weight of the surviving oysters for each population at each salinity (Table S1). Shell height was measured from shell umbo to distal edge and shell width was measured as the maximum distance between the two valves when closed (Galtsoff, 1964). Dry meat weight was measured after drying at 70 °C for 48 h. For gene expression analysis, we sampled 5 oysters per population per salinity (n=40). Approximately 0.5 cm2 piece of gill tissue was dissected and immediately frozen in liquid nitrogen and placed in a -80˚C freezer until later extraction. Clearance Rates: The clearance rate, defined as the volume of water completely cleared of suspended particles per unit of time, was quantified using a static system (Widdows, 1985). Oysters were individually placed in 1 L beakers filled with 1 L of 0.5-μm filtered seawater and left undisturbed for ~ 60 min. Shellfish Diet 1800® was added to each beaker to give an initial concentration of 3 × 104 cells mL−1. Particle concentrations (>5 μm) were collected every 30 seconds until particle count declined to 50% of original values and was measured using a PAMAS Model S4031GO particle counter (PAMAS Partikelmess-und Analysesysteme GMBH, Rutesheim, Germany). We used air stones to reduce algal sedimentation. Beakers with algal cells and oyster shells inside were used as controls. Individual oyster clearance rate (CRi) was calculated according to the equation CRi = [(b – b') × Vol(L) × 60 min h−1], where b is the slope of the linear regression between the natural logarithm of cell concentration (cells mL−1) and time (min) in a beaker, b′ is the slope for the control beaker, and Vol is the volume of seawater in the beaker (1 L). This method follows Cranford et al. (2016), in which a generalization of Coughlan’s (1969) method is developed to integrate the values of intermediate particle concentration samplings, which aims to reduce the impact of outliers. Accordingly, only linear regressions with an r2 > 0.90 were included in these calculations. CRi measurements are in units of L h−1. Clearance rates were also standardized by shell height, specifically to a standard oyster of 80 mm (i.e., average shell height for the oysters) using the equation CRh =(𝐻std/𝐻exp)𝑏×CRi, where CRh is the clearance rate standardized by shell height, Hstd is the shell height of the standard oyster (80 mm), Hexp is the shell height of the experimental oyster, CRi is the individual oyster clearance rate, and b is the allometric exponent for shell height set to 1.78 (Cranford et al. 2011). CRh measurements are in units of L h−1 80 mm−1. Oxygen Consumption Rates: To measure the oxygen consumption rate of fed oysters, known as the routine oxygen consumption rate (Bayne, 2017; Gosling, 2015), individual oysters were placed in 915-mL acrylic chambers filled with 0.5-μm filtered seawater from their respective tank. We used a ProOBOD probe (YSI Incorporated, Yellow Springs, OH, USA) to measure dissolved oxygen and temperature recorded every 30 s by a YSI Multilab 4010-3 m (YSI Inc.) connected to the probe. The readings started once the oyster’s valves opened and ran continuously for up to 90 min or until dissolved oxygen fell below 70% saturation. Individual oyster oxygen consumption rate (OCRi) was calculated as OCRi = [(b – b') × Vol (L) × 60 min h−1], where b is the slope of the linear regression of oxygen concentration (mg L−1) versus time, b′ is the slope for the control, and Vol is the volume of water (in L) in the chamber (chamber minus oyster volume). OCRi measurements are in units of mg O2 h−1. Only linear regressions with an r2 > 0.95 were included in the calculations. Oxygen consumption rates were also standardized by dry meat weight (OCRw), specifically to a standard oyster of 1 g dry meat weight. We used the equation OCRw =(𝑊std/𝑊exp)𝑏 ×OCRi, where Wstd is the dry meat weight of the standard oyster (1 g), Wexp is the dry meat weight of the experimental oyster, and b is the allometric exponent, 0.58 (Casas et al., 2018). OCRw measurements are in units of mg O2 h−1 g−1. RNAseq analysis: Total RNA was extracted using an E.Z.N.A.® Total RNA Kit I (Omega BIO-TEK Inc., Norcross, GA, USA; VWR) following the manufacturer's instructions. The yield and quantity were initially assessed using a NanoDrop 2000 spectrophotometer. Total RNA extracted from the 40 individuals was sent to the University of Texas at Austin’s Genomic Sequencing and Analysis Facility where RNA quality was confirmed using a 2100 Agilent Bioanalyzer on a Eukaryote Total RNA Nano chip, and libraries were produced using the Tag-Sequencing approach (Meyer et al., 2011). The resulting 40 libraries were pooled at equimolar ratios and sequenced across two lanes of an Illumina NovaSeq platform, with 100 base pair single-end reads. Sequencing reads were trimmed of adapter sequences and base pairs with quality scores below 20 were removed using Trimmomatic (version 0.39) (Bolger et al., 2014). The trimmed reads were mapped to the C. virginica reference genome (Gómez-Chiarri et al., 2015) with known haplotigs removed (https://github.com/jpuritz/OysterGenomeProject/tree/master/Haplotig_Masked_Genome) using the single pass option for STAR RNA-seq aligner (version 2.6.0a) (Dobin et al., 2013). Three samples displayed poor sequencing quality (i.e., the lowest number of reads, poorest mapping rates, and isolated in PCA) and were subsequently removed for all downstream analyses. A count matrix was generated from the ReadsPerGene.out.tab output from STAR. To ensure statistical robustness, we used three different approaches to examine gene expression: edgeR, a PERMANOVA test, and a Weighted Gene Co-Expression Network Analysis (WGCNA). PAPERS CITED IN ORGINIAL ARTICLE Anderson, J. D., Karel, W. J., Mace, C. E., Bartram, B. L., & Hare, M. P. (2014). Spatial genetic features of eastern oysters (Crassostrea virginica Gmelin) in the Gulf of Mexico: northward movement of a secondary contact zone. Ecology and Evolution, 4(9), 1671-1685. Bates, D., Mächler, M., Bolker, B. M., & Walker, S. C. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1). doi: 10.18637/jss.v067.i01 Bayne, B. L. (2017). Biology of oysters. Academic Press. Benjamini, Y., & Hochberg, Y. (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289–300. doi: 10.1111/j.2517-6161.1995.tb02031.x Bishop, D. A., Williams, A. P., & Seager, R. (2019). Increased Fall Precipitation in the Southeastern United States Driven by Higher‐Intensity, Frontal Precipitation. Geophysical Research Letters, 46(14), 8300–8309. doi: 10.1029/2019GL083177 Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 30(15), 2114–2120. doi: 10.1093/bioinformatics/btu170 Breitburg, D. L., Salisbury, J., Bernhard, J. M., Cai, W. J., Dupont, S., Doney, S. C., ... & Tarrant, A. M. (2015). And on top of all that… Coping with ocean acidification in the midst of many stressors. Oceanography, 28(2), 48-61. Casas, S. M., Lavaud, R., La Peyre, M. K., Comeau, L. A., Filgueira, R., & La Peyre, J. F. (2018). Quantifying salinity and season effects on eastern oyster clearance and oxygen consumption rates. Marine Biology, 165(5), 1-13. Cayan, D. R., Das, T., Pierce, D. W., Barnett, T. P., Tyree, M., & Gershunova, A. (2010). Future dryness in the Southwest US and the hydrology of the early 21st century drought. Proceedings of the National Academy of Sciences of the United States of America, 107(50), 21271–21276. doi: 10.1073/pnas.0912391107 Clements, J. C., Carver, C. E., Mallet, M. A., Comeau, L. A., & Mallet, A. L. (2021). CO2-induced low pH in an eastern oyster (Crassostrea virginica) hatchery positively affects reproductive development and larval survival but negatively affects larval shape and size, with no intergenerational linkages. ICES Journal of Marine Science, 78(1), 349-359. Coughlan, J. (1969). The estimation of filtering rate from the clearance of suspensions. Marine biology, 2(4), 356-358. Cranford, P. J., Strohmeier, T., Filgueira, R., & Strand, Ø. (2016). Potential methodological influences on the determination of particle retention efficiency by suspension feeders: Mytilus edulis and Ciona intestinalis. Aquatic Biology, 25, 61-73. Das, A., Justic, D., Inoue, M., Hoda, A., Huang, H., & Park, D. (2012). Impacts of Mississippi River diversions on salinity gradients in a deltaic Louisiana estuary: Ecological and management implications. Estuarine, Coastal and Shelf Science, 111, 17–26. doi: 10.1016/j.ecss.2012.06.005 DeBiasse, M. B., & Kelly, M. W. (2016). Plastic and evolved responses to global change: What can we learn from comparative transcriptomics? Journal of Heredity, 107(1), 71–81. doi: 10.1093/jhered/esv073 DeWitt, T. J., & Scheiner, S. M. (2004). Phenotypic Plasticity: Functional and Conceptual Approaches. New York, USA: Oxford University Press. Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., … Gingeras, T. R. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29(1), 15–21. doi: 10.1093/bioinformatics/bts635 Doney, S. C., Ruckelshaus, M., Emmett Duffy, J., Barry, J. P., Chan, F., English, C. A., ... & Talley, L. D. (2012). Climate change impacts on marine ecosystems. Annual review of marine science, 4, 11-37. Forsman, A. (2015). Rethinking phenotypic plasticity and its consequences for individuals, populations and species. Heredity, 115(4), 276-284. Fox, J., & Weisberg, S. (2011). An R companion to applied regression (2nd ed.). London, UK: Sage. Galtsoff, P.S., 1964. The American Oyster, Crassostrea virginica (Gmelin). U.S. Fish and Wildlife Service, 64. Fishery Bulletin. Ghalamnor, C. K., McKay, J. K., Carroll, S. P., & Reznick, D. N. (2007). Adaptive versus non-adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments. Functional Ecology, 21(3), 394–407. Gómez-Chiarri, M., Warren, W. C., Guo, X., & Proestou, D. (2015). Developing tools for the study of molluscan immunity: The sequencing of the genome of the eastern oyster, Crassostrea virginica. Fish & Shellfish Immunology, 46(1), 2–4. doi: 10.1016/J.FSI.2015.05.004 Gosling, E. (2015). Marine bivalve molluscs. John Wiley & Sons. Gunderson, A. R., Armstrong, E. J., & Stillman, J. H. (2016). Multiple stressors in a changing world: the need for an improved perspective on physiological responses to the dynamic marine environment. Annual review of marine science, 8, 357-378. Hajovsky, P., Beseres Pollack, J., & Anderson, J. (2021). Morphological Assessment of the Eastern Oyster Crassostrea virginica throughout the Gulf of Mexico. Marine and Coastal Fisheries, 13(4), 309-319. Heilmayer, O., Digialleonardo, J., Qian, L., & Roesijadi, G. (2008). Stress tolerance of a subtropical Crassostrea virginica population to the combined effects of temperature and salinity. Estuarine, Coastal and Shelf Science, 79(1), 179-185. Hendry, A. P. (2016). Key questions on the role of phenotypic plasticity in eco-evolutionary dynamics. Journal of Heredity, 107(1), 25-41. Hoffmann, A. A., & Sgro, C. M. (2011). Climate change and evolutionary adaptation. Nature, 470(7335), 479-485. doi: 10.1038/nature09670 Hughes, A. R., Hanley, T. C., Byers, J. E., Grabowski, J. H., Malek, J. C., Piehler, M. F., & Kimbro, D. L. (2017). Genetic by environmental variation but no local adaptation in oysters (Crassostrea virginica). Ecology and evolution, 7(2), 697-709. Jong, G. De. (2005). Evolution of phenotypic plasticity: patterns of plasticity and the emergence of ecotypes. New Phytologist, 166(1), 101–118. Koch, E. L., & Guillaume, F. (2020). Additive and mostly adaptive plastic responses of gene expression to multiple stress in Tribolium castaneum. PLoS Genetics, 16(5), e1008768. doi: 10.1371/journal.pgen.1008768 La Peyre, M. K., Aguilar Marshall, D., Miller, L. S., & Humphries, A. T. (2019). Oyster reefs in northern Gulf of Mexico estuaries harbor diverse fish and decapod crustacean assemblages: a meta-synthesis. Frontiers in Marine Science, 6, 666. La Peyre, M. K., Gossman, B., & La Peyre, J. F. (2009). Defining optimal freshwater flow for oyster production: effects of freshet rate and magnitude of change and duration on eastern oysters and Perkinsus marinus infection. Estuaries and Coasts, 32(3), 522-534. Langfelder, P., & Horvath, S. (2008). WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics, 9(1), 1–13. doi: 10.1186/1471-2105-9-559 Lavaud, R., La Peyre, M. K., Justic, D., & La Peyre, J. F. (2021). Dynamic Energy Budget modelling to predict eastern oyster growth, reproduction, and mortality under river management and climate change scenarios. Estuarine, Coastal and Shelf Science, 251, 107188. Leonhardt, J. M., Casas, S., Supan, J. E., & La Peyre, J. F. (2017). Stock assessment for eastern oyster seed production and field grow-out in Louisiana. Aquaculture, 466, 9-19. Levine, M. T., Eckert, M. L., & Begun, D. J. (2011). Whole-genome expression plasticity across tropical and temperate Drosophila melanogaster populations from eastern Australia. Molecular Biology and Evolution, 28(1), 249–256. doi: 10.1093/molbev/msq197 Marshall, D. A., Coxe, N. C., La Peyre, M. K., Walton, W. C., Rikard, F. S., Pollack, J. B., ... & La Peyre, J. F. (2021). Tolerance of northern Gulf of Mexico eastern oysters to chronic warming at extreme salinities. Journal of Thermal Biology, 100, 103072. Marshall, D. A., Casas, S. M., Walton, W. C., Rikard, F. S., Palmer, T. A., Breaux, N., ... & La Peyre, J. F. (2021). Divergence in salinity tolerance of northern Gulf of Mexico eastern oysters under field and laboratory exposure. Conservation physiology, 9(1), coab065. Meyer, E., Aglyamova, G. V., & Matz, M. V. (2011). Profiling gene expression responses of coral larvae (Acropora millepora) to elevated temperature and settlement inducers using a novel RNA-Seq procedure. Molecular Ecology, 20(17), 3599–3616. Nevins, J. A., Pollack, J. B., & Stunz, G. W. (2014). Characterizing nekton use of the largest unfished oyster reef in the United States compared with adjacent estuarine habitats. Journal of Shellfish Research, 33(1), 227-238. Parker, R. H. (1960). Ecology and Distributional Patterns of Marine Macro-Invertebrates, Northern Gulf of Mexico. In F.B. Phleger & T.H. Van Andel (Eds.), Recent Sediments, Northwest Gulf of Mexico (pp. 302-337). Tulsa, Okla.: Tulsa, American Association of Petroleum Geologists Parmesan, C. (2006). Ecological and Evolutionary Responses to Recent Climate Change. Annual Review of Ecology, Evolution, and Systematics, 37(1), 637–669. doi: Schmidtko, S., Stramma, L., & Visbeck, M. (2017). Decline in global oceanic oxygen content during the past five decades. Nature, 542(7641), 335-339. Pickett, S. T. A. (1989). Space-for-Time Substitution as an Alternative to Long-Term Studies. In Long-Term Studies in Ecology (pp. 110–135). New York: Springer-Verlag. doi: 10.1007/978-1-4615-7358-6_5 Price, T. D., Qvarnström, A., & Irwin, D. E. (2003). The role of phenotypic plasticity in driving genetic evolution. Proceedings of the Royal Society of London. Series B: Biological Sciences, 270(1523), 1433–1440. doi: 10.1098/rspb.2003.2372 Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139–140. Robinson, Mark D, & Oshlack, A. (2010). A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biology, 11(3), 1-9. Rockman, M. V. (2008). Reverse engineering the genotype–phenotype map with natural genetic variation. Nature, 456(7223), 738-744. Royston, J. P. (1982). An Extension of Shapiro and Wilk’s W Test for Normality to Large Samples. Applied Statistics, 31(2), 115. doi: 10.2307/2347973 Rybovich, M., Peyre, M. K. La, Hall, S. G., & Peyre, J. F. La. (2016). Increased Temperatures Combined with Lowered Salinities Differentially Impact Oyster Size Class Growth and Mortality. Journal of Shellfish Research, 35(1), 101–113. doi: 10.2983/035.035.0112 Sanford, E., & Kelly, M. W. (2011). Local adaptation in marine invertebrates. Annual review of marine science, 3, 509-535. Seebacher, F., White, C. R., & Franklin, C. E. (2015). Physiological plasticity increases resilience of ectothermic animals to climate change. Nature Climate Change, 5(1), 61–66. doi: 10.1038/nclimate2457 Sirovy, K. A., Johnson, K. M., Casas, S. M., La Peyre, J. F., & Kelly, M. W. (2021). Lack of genotype‐by‐environment interaction suggests limited potential for evolutionary changes in plasticity in the eastern oyster, Crassostrea virginica. Molecular Ecology, 30(22), 5721-5734. Swam, L. M., Couvillion, B., Callam, B., La Peyre, J. F., & La Peyre, M. K. (2022). Defining oyster resource zones across coastal Louisiana for restoration and aquaculture. Ocean & Coastal Management, 225, 106178. Thongda, W., Zhao, H., Zhang, D., Jescovitch, L. N., Liu, M., Guo, X., ... & Peatman, E. (2018). Development of SNP panels as a new tool to assess the genetic diversity, population structure, and parentage analysis of the eastern oyster (Crassostrea virginica). Marine biotechnology, 20(3), 385-395. Via, S., & Lande, R. (1985). Genotype-environment interaction and the evolution of phenotypic plasticity. Evolution, 39(3), 505–522. doi: 10.1111/j.1558-5646.1985.tb00391.x Volety, A. K., Haynes, L., Goodman, P., & Gorman, P. (2014). Ecological condition and value of oyster reefs of the Southwest Florida shelf ecosystem. Ecological Indicators, 44, 108-119. West-Eberhard, M. J. (2003). Developmental plasticity and evolution. New York: Oxford University Press. Widdows, J. (1985). Physiological procedures In: Bayne BL, Brown DA, Burns K, Dixon DR, Ivanovici A, Livingstone DR, et al., editors. The Effects of Stress and Pollution on Marine Animals. Wilson, C., Scotto, L., Scarpa, J., Volety, A., Laramore, S., & Haunert, D. (2005). Survey of water quality, oyster reproduction and oyster health status in the St. Lucie Estuary. Journal of Shellfish Research, 24(1), 157-165. Witkop, E. M., Wikfors, G. H., Proestou, D. A., Lundgren, K. M., Sullivan, M., & Gomez-Chiarri, M. (2022). Perkinsus marinus suppresses in vitro eastern oyster apoptosis via IAP-dependent and caspase-independent pathways involving TNFR, NF-kB, and oxidative pathway crosstalk. Developmental & Comparative Immunology, 104339. Wright, R. M., Aglyamova, G. V., Meyer, E., & Matz, M. V. (2015). Gene expression associated with white syndromes in a reef building coral, Acropora hyacinthus. BMC genomics, 16(1), 371. Wund, M. A. (2012). Assessing the impacts of phenotypic plasticity on evolution. Integrative and Comparative Biology, 52(1), 5–15. doi: 10.1093/icb/ics050 Xiong, X., Cao, Y., Li, Z., Jiao, Y., Du, X., & Zheng, Z. (2021). Transcriptome analysis reveals the transition and crosslinking of immune response and biomineralization in shell damage repair in pearl oyster. Aquaculture Reports, 21, 100851. Zhou, S., Campbell, T. G., Stone, E. A., Mackay, T. F. C., & Anholt, R. R. H. (2012). Phenotypic Plasticity of the Drosophila Transcriptome. PLoS Genetics, 8(3), e1002593. doi: 10.1371/journal.pgen.1002593 Zhou, C., Song, H., Feng, J., Hu, Z., Yu, Z. L., Yang, M. J., ... & Zhang, T. (2021). RNA-Seq analysis and WGCNA reveal dynamic molecular responses to air exposure in the hard clam Mercenaria mercenaria. Genomics, 113(4), 2847-2859.