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A multi-omics investigation into breeding shellfish for ocean acidification resilience in the California current system

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A multi-omics investigation into breeding shellfish for ocean acidification resilience in the California current system

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Content A Multi-omics Investigation into Breeding Shellfish for Ocean Acidification Resilience in the
California Current System
by
Jordan Lynn Chancellor
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(MARINE BIOLOGY AND BIOLOGICAL
OCEANOGRAPHY)
December 2024
Copyright 2024 Jordan Lynn Chancellor

ii
TABLE OF CONTENTS
Acknowledgements........................................................................................................................ v
List of Tables................................................................................................................................. vi
List of Figures............................................................................................................................. viii
Abstract ........................................................................................................................................ xii
Introduction.................................................................................................................................... 1
Chapter 1. Differential gene expression reveals different molecular mechanisms for
physiological resilience to ocean acidification stress in native and introduced oyster species .... 10
1.1 Abstract ............................................................................................................................... 10
1.2 Introduction..........................................................................................................................11
1.3 Methods............................................................................................................................... 16
1.3.1 Experimental ocean acidification exposures design .................................................... 16
1.3.2 RNAseq library construction and sequencing.............................................................. 17
1.3.3 Sequencing and read processing .................................................................................. 18
1.3.4 Differential Gene Expression Analysis........................................................................ 19
1.3.5 Biomineralization expression patterns......................................................................... 20
1.3.6 Reference re-annotation ............................................................................................... 21
1.3.7 Oyster Shell Dissolution .............................................................................................. 21
1.4 Results................................................................................................................................. 22
1.4.1 Reference genome and transcriptome mapping........................................................... 22
1.4.2 PCA.............................................................................................................................. 23
1.4.3 Differential gene expression ........................................................................................ 25
1.4.4 Gene set enrichment analysis....................................................................................... 31
1.4.5 Biomineralization gene expression patterns ................................................................ 32
1.4.6 Shell dissolution........................................................................................................... 35
1.5 Discussion........................................................................................................................... 36
1.5.1 Effects of experimental ocean acidification vary between oyster species................... 36
1.5.2 Differential gene expression and enriched GO term patterns between species reflect
differences in demographic history and adaptation to low pH environments....................... 37
1.5.3 Physiological responses to ocean acidification do not vary between species despite
differences in molecular expression...................................................................................... 40
1.5.4 Conclusion ................................................................................................................... 41
1.6 Author Contributions .......................................................................................................... 43
Chapter 2. Assessing effects of ocean acidification on Mediterranean (Mytilus galloprovincialis)
and California (Mytilus californianus) mussel larval rearing in a small-scale experimental
hatchery system............................................................................................................................. 44
2.1 Abstract ............................................................................................................................... 44
2.2 Introduction......................................................................................................................... 45
2.3 Methods............................................................................................................................... 46
2.3.1 Broodstock collection and spawning ........................................................................... 46
2.3.2 Experimental tank design and monitoring ................................................................... 47

iii
2.3.3 Larval husbandry and ocean acidification experimental exposure .............................. 50
2.3.4 Adult mussel phenotyping............................................................................................ 52
2.4 Results................................................................................................................................. 53
2.4.1 Tank pH, temperature, and salinity conditions across treatments and species............. 53
2.4.2 Larval survival and growth throughout high PCO2 exposure ..................................... 56
2.4.3 Adult mussel phenotype at harvest .............................................................................. 58
2.5 Discussion........................................................................................................................... 59
2.5.1 Tank parameters and experimental repeatability can be maintained in small-scale,
inexpensive experimental systems........................................................................................ 59
2.5.2 Larval growth is hindered while survival is stable under experimental ocean
acidification conditions......................................................................................................... 62
2.5.3 Larval ocean acidification exposure during development carries over into adult stage
............................................................................................................................................... 63
2.6 Author Contributions .......................................................................................................... 64
Chapter 3: Experimental ocean acidification results in allele frequency divergence in larval
Mediterranean mussels Mytilus galloprovincialis ........................................................................ 65
3.1 Abstract ............................................................................................................................... 65
3.2 Introduction......................................................................................................................... 65
3.3 Methods............................................................................................................................... 68
3.3.1 Larval ocean acidification exposure ............................................................................ 68
3.3.2 DNA extraction and shotgun pool-sequencing ........................................................... 68
3.3.3 SNP calling and filtration............................................................................................. 69
3.3.4 SNP effect annotation .................................................................................................. 70
3.3.5 Identification of genes under selective pressure .......................................................... 70
3.3.6 Biomineralization gene set orthologs and annotations ................................................ 71
3.3.7 Gene set enrichment analysis....................................................................................... 71
3.3.8 Analyzing SNP effects of enriched genes.................................................................... 72
3.4 Results................................................................................................................................. 73
3.4.1 Reference genome mapping, SNP calling, and SNP effect annotation........................ 73
3.4.2 Genes with significant allele frequency differences between control and ocean
acidification cohorts.............................................................................................................. 74
3.4.3 Identification of genes highly involved in biomineralization under selective pressure75
3.4.4 Gene set enrichment analysis....................................................................................... 76
3.4.5 Investigation of genes highly involved in enriched GO terms with high allele
frequency differences............................................................................................................ 77
3.5 Discussion........................................................................................................................... 80
3.5.1 Mussels show a genetic signature of selection in response to ocean acidification within
a single generation ................................................................................................................ 80
3.5.2 Ocean acidification results in selection of biological pathways involved in immune
defense, biomineralization, and protein regulation............................................................... 81
3.5.3 Standing variation and moderate impact mutations provide the basis for adaptation to
ocean acidification in mussels .............................................................................................. 82
3.6 Author contributions ........................................................................................................... 83
Chapter 4: Ocean acidification shapes larval mussel microbiome community composition ...... 84
4.1 Abstract ............................................................................................................................... 84

iv
4.2 Introduction......................................................................................................................... 84
4.3 Methods............................................................................................................................... 87
4.3.1 MAG assembly, classification, and functional annotation of Mytilus galloprovincialisassociated microbes .............................................................................................................. 87
4.3.2 16S classification of Mytilus galloprovincialis shotgun sequenced reads................... 89
4.3.3 16S rDNA sequencing of Mytilus galloprovincialis and Mytilus californianus larvae
across developmental timepoints.......................................................................................... 89
4.3.4 Relative abundance, ordination, and diversity analysis of microbial community
composition........................................................................................................................... 90
4.4 Results................................................................................................................................. 91
4.4.1 MAG, classification, relative abundance, and predicted metabolic function .............. 91
4.4.2 16S ribosomal classification and abundance of shotgun sequenced reads .................. 93
4.4.3 16S region V3-V4 amplicon sequencing throughout larval development................... 95
4.5 Discussion......................................................................................................................... 104
4.5.1 MAG diversity and predictive function ..................................................................... 104
4.5.2 Core microbiome membership varies between M. californianus and M.
galloprovincialis larvae....................................................................................................... 106
4.5.3 Ocean acidification of seawater is reflected in M. galloprovincialis microbiome
community membership...................................................................................................... 107
4.5.4 Ocean acidification diminishes microbial diversity in M. galloprovincialis and may
compromise immunity in M. californianus......................................................................... 108
4.5.5 OA effects on diversity vary between mussel species ............................................... 108
4.6 Author Contributions ........................................................................................................ 109
Conclusion ..................................................................................................................................110
References...................................................................................................................................112
Appendix A: Supplementary Tables........................................................................................... 146
Appendix B: Supplementary Figures......................................................................................... 181

v
Acknowledgements
I would like to first and foremost thank my two advisors, Dr. Andrew Gracey and Dr. Sergey
Nuzhdin, for over five years of continuous support. I would also like to thank my other project
collaborators and co-authors for their many contributions to this work: Dr. Nina Bednaršek, Dr.
Nathan Chruches, Dr. Diane Y Kim, Maxim Kovalev, Dr. Emily Aguirre, and Ian Jacobson. This
work could not have been completed without the support of Bernard Friedman of Santa Barbara
Mariculture, Holdfast Aquaculture, Carlsbad Aquafarms, the Berelson Lab at USC, and Altasea
at the Port of Los Angeles, all of whom contribute valuable expertise and resources. I would like
to thank Dr. Julien Vignier and the Cawthron Institute for generously hosting and mentoring me
in New Zealand and for continuous collaboration and support. Finally, I would like to thank all
of my fellow lab members, as well as my friends and family for unwavering support, friendship,
and love over the course of my PhD.

vi
List of Tables
Table 1.1. Average total number and percent reads mapped per read type (PE, SF, SR) for
each species................................................................................................................................... 23
Table 1.2. Number of differentially expressed genes for each treatment and time point for
both species................................................................................................................................... 28
Table 1.3. Number of unique enriched GO terms for each treatment and time point for both
species. .......................................................................................................................................... 32
Table 2.1. Mean and standard deviation for all measured tank parameters.................................. 55
Table 3.1. Chi-squared test results of each SNP effect type and direction of allele frequency
change from the intial to final timepoint....................................................................................... 78
Table 3.2. Logistic regression results of the full model and reduced models for our selected
genes (56) against all other genes (~44,000). ............................................................................... 79
Table 3.3. Logistic regression results of the frequency (%) of significant results of each SNP
effect type for the full model and reduced models for our selected genes against and equal
number of random genes for 10,000 permutations. ...................................................................... 80
Table 4.1. MAG taxonomic classifications as determined by GTDB-TK.................................... 92
Supplementary Table 1. List of genes involved in biomineralization from Chandra Rajan et
al. (2021) identified in Magallana hongkongensis used to identify biomineralization genes
within this study following genome/transcriptome re-annotation. ............................................. 146
Supplementary Table 2. Fastq read filtering and mapping statistics for Pacific and Olympia
oysters.. ....................................................................................................................................... 147
Supplementary Table 3. Kruskall-Wallis test results for determining best PC describing
treatment grouping for M. gigas ................................................................................................. 151
Supplementary Table 4. Dunn test results for significant differences between treatment
groups for M. gigas..................................................................................................................... 151
Supplementary Table 5. Wilcoxon rank sum test results for determining best PC describing
pH type and time for M. gigas .................................................................................................... 151
Supplementary Table 6. Kruskall-Wallis test results for determining best PC describing
treatment grouping for O. lurida................................................................................................. 153
Supplementary Table 7. Dunn test results for significant differences between treatment
groups for O. lurida .................................................................................................................... 154
Supplementary Table 8. Wilcoxon rank sum test results for determining best PC describing
pH type and time for O. lurida.................................................................................................... 154
Supplementary Table 9. Top 20 differentially expressed genes and their products for each
treatment and timepoint for M. gigas.......................................................................................... 155
Supplementary Table 10. Top 20 differentially expressed genes and their products for each
treatment and timepoint for O. lurida ......................................................................................... 158
Supplementary Table 11. Genes shared between conditions out of top 20 differentially
expressed genes for each treatment for M. gigas........................................................................ 161
Supplementary Table 12. Genes shared between treatment 7.7C week 2 and week 6 for M.
gigas............................................................................................................................................ 161
Supplementary Table 13. Genes shared between treatment 7.7A0.2 week 2 and week 6 for M.
gigas............................................................................................................................................ 162

vii
Supplementary Table 14. Genes shared between treatment 7.7A0.5 week 2 and week 6 for M.
gigas............................................................................................................................................ 162
Supplementary Table 15. Genes shared between conditions out of top 20 differentially
expressed genes for each treatment for O. lurida ....................................................................... 164
Supplementary Table 16. Genes shared between treatment 7.7C week 2 and week 6 for O.
lurida........................................................................................................................................... 165
Supplementary Table 17. Genes shared between treatment 7.7A0.2 week 2 and week 6 for O.
lurida........................................................................................................................................... 165
Supplementary Table 18. Genes shared between treatment 7.7A0.5 week 2 and week 6 for O.
lurida........................................................................................................................................... 165
Supplementary Table 19. Two-way ANOVA results for pH for species and treatment group .. 165
Supplementary Table 20. Two-way ANOVA results for temperature for species and
treatment group ........................................................................................................................... 166
Supplementary Table 21. Tukey post-hoc test results for temperature ...................................... 166
Supplementary Table 22. Two-way ANOVA results for salinity for species and treatment
group ........................................................................................................................................... 166
Supplementary Table 23. Tukey post-hoc test results for salinity.............................................. 167
Supplementary Table 24. Two-way ANOVA results for DIC for species and treatment group 167
Supplementary Table 25. Two-way ANOVA results for TA for species and treatment group.. 168
Supplementary Table 26. Two-way ANOVA results for pCO2for species and treatment group168
Supplementary Table 27. Tukey post-hoc test results for pCO2................................................. 168
Supplementary Table 28. Two-way ANOVA results for Ωar saturation for species and
treatment group ........................................................................................................................... 169
Supplementary Table 29. Tukey post-hoc test results for Ωar saturation .................................... 169
Supplementary Table 30. Generalized linear model fit results for log-transformed larval size . 170
Supplementary Table 31. Two-sample t-test results for M. galloprovincialis phenotypes ........ 170
Supplementary Table 32. Trimgalore! read filtering statistics for M. galloprovincialis WGS
reads............................................................................................................................................ 171
Supplementary Table 33. BWA mem mapping statistics of quality filtered and trimmed reads
to the M. galloprovincialis reference genome ............................................................................ 172
Supplementary Table 34. GSEA results of top enriched GO terms. .......................................... 173
Supplementary Table 35. Descriptions of 56 selected genes which appear > 1 across selected
enriched GO terms. ..................................................................................................................... 176
Supplementary Table 36. DASTool statistics of highest quality bins from Metabat,
CONCOCT, and MaxBin from Megahit metagenome assembly. .............................................. 178
Supplementary Table 37. CheckM statistics of bins selected by DASTool. .............................. 179
Supplementary Table 38. CheckM statistics of filtered and dereplicated bins used in final
metagenome analysis in Chapter 4: Ocean acidification shapes larval mussel microbiome
community composition.............................................................................................................. 180
Supplementary Table 39. coverm mapping statistics of WGS M. galloprovincialis reads to
the 16S rRNA subunit................................................................................................................. 180

viii
List of Figures
Figure 1. Long-term means of RFR-LME mapped OA indicators. Mapped averages of
(a) pCO2(RFR-LME), (b) CT(RFR-LME), (c) pHT(RFR-LME), (d) [H+
]T(RFR-LME), (e) Ωar(RFR-LME), (f)
Ωca(RFR-LME), (g) [CO3
2−]T(RFR-LME), and (h) RF(RFR-LME) over the timeseries (1998–2022)
within each LME. Figure from Sharp et al., 2024. ......................................................................... 4
Figure 2. Seasonally detrended observations of (d) pCO2 (μatm) and (e) pH from Southern
California CalCOFI Line 90, Station 90. Figure adapted from Wolfe et al., 2023......................... 5
Figure 3. The mussel life cycle. Figure created with BioRender.................................................... 7
Figure 1.1. One-week schematic of ocean acidification experimental conditions across the
four treatments. Experimental treatments included a control, static treatment (8.0C), a static,
ocean acidification treatment (7.7C), and two dynamic ocean acidification treatments of
varying testing amplitudes (7.7A0.2, 7.7A0.5). Figure adapted from Bednarsek et al. (2022).... 17
Figure 1.2. Number of read pairs (second from top), forward reads (second from bottom), and
reverse reads (bottom) surviving each step of pre-processing. Total reads (top, “Total”) were
first trimmed and quality filtered with trimmomatic, and the three libraries with the highest
combined number of reads were retained (“Selection”) and mapped against their respective
reference (“Mapping”). ................................................................................................................. 23
Figure 1.3. PCA results for treatment (left) and pH type (constant or fluctuating, right)
grouping for M. gigas (top) and O. lurida (bottom). The x-axis is determined by PC1, while
the y-axis reflects the most significant (lowest p-value) PC and best describe the respective
data type. Colors represent different treatments and shapes represent week................................ 25
Figure 1.4. Volcano plot of genes identified by DESeq2 for M. gigas. Red circles represent
differentially expressed genes defined as |log2FoldChange| >= 1 and p-value <= 0.01. All
treatments are compared with treatment 8.0C as the control........................................................ 27
Figure 1.5. Volcano plot of genes identified by DESeq2 for O. lurida. Red circles represent
differentially expressed genes defined as |log2FoldChange| >= 1 and p-value <= 0.01. All
treatments are compared with treatment 8.0C as the control........................................................ 28
Figure 1.6. Venn diagram of differentially expressed genes from M. gigas (left) and O. lurida
(right) shared between each treatment at weeks two (top) and six (bottom). Differential
expression for a treatment was defined as a gene with |log2FoldChange| >= 1 and p-value <=
0.01 as compared to the control treatment (8.0C)......................................................................... 29
Figure 1.7. GSEA results of GO terms which were statistically significant in at least one
treatment, time point, or species. Color is representative of degree and direction of
expression (LFC), and triangles represent statistical significance (p-value  0.01). .................... 32
Figure 1.8. Heatmap of biomineralization genes for M. gigas (top) and O. lurida (bottom)
within our dataset. Color gradient describes degree and direction of expression and asterisk
denote statistically significant DEGs. Rows were hierarchically clustered separately for each
species. .......................................................................................................................................... 34
Figure 1.9. Heatmap of biomineralization genes found in both M. gigas (top) and O. lurida
(bottom) datasets. Color gradient describes degree and direction of expression and asterisk
denote statistically significant DEGs. ........................................................................................... 35
Figure 1.10. Mean dissolution of both M. gigas and O. lurida throughout experimental ocean
acidification exposure across various static and fluctuating pH treatments. Mean dissolution
was scored on a scale of 1-3 based on the type of dissolution observed from SEM of oyster
shells (see Bednaršek et al., 2022). A linear model fit of mean dissolution revealed a

ix
statistically significant effect of treatment on mean dissolution, however, a two-way ANOVA
revealed no significant difference of species on shell of dissolution............................................ 36
Figure 2.1. Final constructed tank system utilized in this study. Identical control and
treatment tanks with triplicate buckets were built and placed side-by-side and elevated off the
ground in order to streamline husbandry and minimize tank footprint. Each tank system was
composed of particulate filters, UV filter, chiller, and pump. ...................................................... 50
Figure 2.2. Schematic of experimental ocean acidification exposure. The experiment was
carried out separately, but identically, for each species, Mytilus galloprovincialis and Mytilus
californianus. All environmental parameters, target stocking densities, feeding regimens,
sample collection, and animal husbandry remained the same, except for the pH of the control
(8.1) and treatment (7.7) tanks. Figure created in BioRender (biorender.com)............................ 52
Figure 2.3. pH (top), salinity (ppm, middle), and temperature (C , bottom) data of control
and treatment tanks throughout the course of ocean acidification exposure experiments for
M. galloprovincialis (left) and M. californianus (right) species. .................................................. 54
Figure 2.4. DIC (uM/kg), TA (ueq/kg, second from top), pCO2 (µatm, second from bottom),
and aragonite saturation (Ωar, bottom) data of control and treatment tanks throughout the
course of ocean acidification exposure experiments for M. galloprovincialis (left) and M.
californianus (right) species. ........................................................................................................ 55
Figure 2.5. Survival probability curves of treatment and control larval mussels throughout the
ocean acidification exposure experiments for M. galloprovincialis (left) and M. californianus
(right) species. Dashed lines represent mean survival time for control (black) and treatment
(grey) cohorts. ............................................................................................................................... 57
Figure 2.6. Growth of treatment and control larval mussels throughout the ocean acidification
exposure experiments for M. galloprovincialis (left) and M. californianus (right) species. ........ 58
Figure 2.7. Length (left) and wet weight (right) of control and treatment adult M.
galloprovincialis mussels after ten months of grow-out on longlines at Santa Barbara
Mariculture shellfish farm............................................................................................................. 58
Figure 3.1. Genome-wide depth histograms of mpileup files generated from BAM files
outputted from BWA mem mapping of pooled larval M. galloprovincialis samples to the
reference genome. ......................................................................................................................... 74
Figure 3.2. Absolute allele frequency difference distribution of genes passing filter #1 (left)
as well as filter #2 in (right) control and OA treatments. ............................................................. 75
Figure 3.3. GSEA results of the 32 top enriched gene sets within our filtered dataset. ............... 77
Figure 3.4. Genes (56) which appear in more than one enriched GO term and which terms
they appear in................................................................................................................................ 78
Figure 4.1. Relative abundance of reads mapped to assembled MAGs across taxa levels preexposure (left) and post-exposure for control (middle) and OA (right) M. galloprovincialis
cohorts........................................................................................................................................... 93
Figure 4.2. Relative abundance of 16S-classified shotgun sequencing reads across taxa levels
pre-exposure (left) and post-exposure for control (middle) and OA (right) M.
galloprovincialis cohorts. ............................................................................................................. 95
Figure 4.3. Relative abundance of 16S rDNA V3-V4 amplicon sequenced reads
agglomerated at the Order level for M. californianus (left) and M. galloprovincialis (right)...... 97
Figure 4.4. Relative abundance of 16S rDNA V3-V4 amplicon sequenced reads
agglomerated at the Family level for M. californianus (left) and M. galloprovincialis (right).... 98

x
Figure 4.5. Relative abundance of 16S rDNA V3-V4 amplicon sequenced reads
agglomerated at the Genus level for M. californianus (left) and M. galloprovincialis (right). .... 99
Figure 4.6. PCA of clr-normalized 16S rDNA V3-V4 amplicon sequenced reads for M.
californianus and M. galloprovincialis....................................................................................... 102
Figure 4.7. Alpha diversity metrics of 16S rDNA V3-V4 amplicon sequenced reads for M.
californianus (left) and M. galloprovincialis (right)................................................................... 104
Supplemental Figure 1. Percent of genes remaining after filtering low-count genes prior to
running the DESeq2 function. x-axis values correspond to minimum count requirement per
gene. ............................................................................................................................................ 181
Supplemental Figure 2. BUSCO results of the M. gigas reference genome, the O. lurida
transcriptome, and the CD-HIT thinned O. lurida transcriptome using in this study using the
eukaryota (top), metazoa (middle), and mollusca (bottom) datasets. ......................................... 183
Supplemental Figure 3. Cellular component gene set enrichment analysis results for all
treatments and timepoints in both M. gigas and O. lurida. Color is representative of the
degree and direction of expression (LFC), and triangles denote statistical significance (pvalue  0.01). .............................................................................................................................. 184
Supplemental Figure 4. Biological process gene set enrichment analysis results for all
treatments and timepoints in both M. gigas and O. lurida. Color is representative of the
degree of normalized enrichment score, and significant gene sets (p-value  0.01) are
denoted by a triangle shape......................................................................................................... 185
Supplemental Figure 5. Molecular function gene set enrichment analysis results for all
treatments and timepoints in both M. gigas and O. lurida. Color is representative of the
degree of normalized enrichment score, and significant gene sets (p-value  0.01) are
denoted by a triangle shape......................................................................................................... 186
Supplemental Figure 6. Density histogram of the number of SNPs and number of genes used
to select filter #1 values. ............................................................................................................. 187
Supplemental Figure 7. Absolute allele frequency difference distribution of 28
biomineralization genes passing both filter #1 as well as filter #2 in control and OA
treatments.................................................................................................................................... 188
Supplemental Figure 8. Example of custom scoring metric used to rank each of the 930 genes
found to have high absolute differences in allele frequencies for input into GSEA. The panels
show genes with high (top), medium (middle), and low (bottom) scores based on maximum,
mode, and interquartile range of the SNP ∆𝑇𝐶 distribution within each gene. .......................... 189
Supplemental Figure 9. Gene length (Kbp) of selected genes from enriched GO terms (56)
versus all other genes in the dataset (43779). ............................................................................. 190
Supplemental Figure 10. Relative abundance of 16S rDNA V3-V4 amplicon sequenced reads
agglomerated at the Phylum level for M. californianus (left) and M. galloprovincialis (right). 191
Supplemental Figure 11. Relative abundance of 16S rDNA V3-V4 amplicon sequenced reads
agglomerated at the Class level for M. californianus (left) and M. galloprovincialis (right)..... 192
Supplemental Figure 12. PERMDISP2 within-group dispersion and group distance from
centroid beta diversity results for treatment for M. californianus .............................................. 193
Supplemental Figure 13. PERMDISP2 within-group dispersion and group distance from
centroid beta diversity results for time for M. californianus...................................................... 194
Supplemental Figure 14. ANOSIM group dissimilarity beta diversity results for treatment
(top) and time (bottom) for M. californianus.............................................................................. 195

xi
Supplemental Figure 15. PERMDISP2 within-group dispersion and group distance from
centroid beta diversity results for treatment for M. galloprovincialis ........................................ 196
Supplemental Figure 16. PERMDISP2 within-group dispersion and group distance from
centroid beta diversity results for time for M. galloprovincialis ................................................ 197
Supplemental Figure 17. ANOSIM group dissimilarity beta diversity results for treatment
(top) and time (bottom) for M. galloprovincialis........................................................................ 198
Supplemental Figure 18. PERMDISP2 within-group dispersion (top), group distance from
centroid (middle), and ANOSIM beta diversity results for species............................................ 199

xii
Abstract
Aquaculture, the cultivation of aquatic organisms, is a sector of growing global demand and
commercial interest, particularly within the context of climate change and sustainability.
Aquaculture products have been developed and utilized across various industries, though most
notably for consumption. Of aquaculture species farmed for food, shellfish are a multifaceted
product, providing both ecosystem services and supporting the food industry. Shellfish farming
has been highlighted as an avenue for sustainable food production due to its small environmental
footprint. The West Coast of the United States has many commercial shellfish farms, however,
the California Current, which drives the upwelling ecosystem along this coast, has experienced
rapid ocean acidification (OA) over recent decades as a result of increased atmospheric carbon
dioxide (CO2). The absorption of CO2 by seawater decreases pH levels, resulting in a myriad of
physiological effects on marine species. Shellfish are particularly susceptible to these effects due
to the nature of their calcium carbonate shells and early larval life stages, which struggle under
low pH. It is therefore imperative to understand the effects of OA to ensure continued
production. Molecular biology has been utilized in crop development to improve yields, disease
resistance, and desirable traits, however, these methods have only recently been applied to
aquaculture. The present study employed multi-omics approaches to investigate OA effects on
local oyster and mussel species in order to characterize and disentangle effects of OA and inform
future farming and restoration. The results provide valuable insights into selectively breeding
local shellfish species for OA resilience.

1
Introduction
Human population is on track to reach 10 billion by the year 2050, and with this rise in
population is an ever-growing need for food and nutrition security (Gephart et al., 2020). In order
to feed the planet, global food production and distributions will require massive transformation
and scale-up. However, increased food production and efficiency is and will continue to be
thwarted by global climate change, which is continuously exacerbated by traditional resourceintensive farming and fishing practices. Aquaculture has been identified as a solution to meet
both rising demand and lesson the pressure on wild fish stocks, which have suffered due to
overfishing, pollution, and habitat degradation in recent decades (Smith et al., 2010). Seafood is
a rich source of protein and micronutrients, and is constituting an increasingly larger portion of
the global diet, more than tripling in live-weight volume from 1996 to 2017 (Béné et al., 2015;
Gephart et al., 2020; Naylor et al., 2021). Although aquaculture takes the pressure off of wild
fisheries and is considered more sustainable than terrestrial agriculture, many aquaculture
practices, particularly finfish aquaculture production, still result in negative environmental
problems, including pollution, habitat degradation, introduction of invasive species, and others
(Martinez-Porchas & Martinez-Cordova, 2012). For these reasons, bivalve aquaculture, which
includes clams, oysters, mussels, abalone, scallops, amongst others, has received increased
attention, research, expansion, and funding in recent years.
Shellfish aquaculture, alongside algal aquaculture, is considered “extractive” species due
to the fact that they act as living filters and do not require supplemental feed, and therefore do
not present the same environmental issues as other aquaculture species (Buck et al., 2017).
Shellfish aquaculture therefore has the capacity to fill nutritional gaps as well as support healthy
ecosystems as they provide invaluable ecosystem services. Bivalves provide benthic ecosystem

2
stability by forming reef structures, but most notably they support healthy aquatic ecosystems via
filtration. By filtering large volumes of water, bivalves assimilate and remove phytoplankton and
excess nutrients, such as nitrogen and phosphorous from agricultural runoff, from the
environment, effectively cleaning coastal waters and mitigating the effects of eutrophication,
harmful algal blooms, and other pollutants (Naylor et al., 2021). While this fact raises more food
safety concerns surrounding bivalves, it is generally not a major issue in commercial production,
as larvae are reared on onshore hatchery facilities and out-planted to farms in suitable
environments for safe growing. Bivalve aquaculture has increased steadily over the past twenty
years, however, its potential to support nutritional security and mitigate negative environmental
conditions is under exploited (Naylor et al., 2021)
As with agriculture, aquaculture is being substantially affected by climate change
(Froehlich et al., 2018; Maulu et al., 2021). This includes direct effects, such as physiological
effects on organisms and structures of ecosystems, and indirect effects, whereby production is
hindered by effects on other required goods and services. The aquaculture sector is rapidly
expanding mitigation practices, however, much larger efforts will be required for the industry to
meet both rising demand and adapt to a rapidly changing climate. In particular, an exponential
increase in global emissions over the past century has resulted in irreversible effects on climate
warming, extreme weather events, global weather patterns, biodiversity, and ecosystem
degradation, all of which will continue to challenge future agriculture and aquaculture
production alike (IPCC, 2014).
The global oceans are estimated to have taken up a third of all anthropogenic carbon
emissions over the past 200 years, and this uptake of excess atmospheric CO2 by the ocean
results in a drop in pH and carbonate ion (CO3
2-
) concentrations in a phenomenon known as

3
ocean acidification (OA) (Figure 1, Canadell et al., 2007; Chris & Feely, 2007; Doney et al.,
2009). This decrease in available carbonate ions similarly results in a decrease in calcium
carbonate (CaCO3) saturation states, which negatively affects marine calcifiers such as
pteropods, corals, and molluscs that utilize calcium carbonate, specifically aragonite, to calcify
shells. It has recently been reported that the anthropogenic carbon accumulation rates measured
over the past decade correspond to a total pH decrease of about 0.002 per year and an aragonite
saturation rate decrease of -0.006 to 0.011 per year (Richard A Feely et al., 2010; Ma et al., 2023;
Sharp et al., 2024; Wolfe et al., 2023). The saturation state of carbonate minerals is defined by
Ω =
[𝐶𝑎2+] × [𝐶𝑂3
2−]
𝐾𝑠𝑝
When the saturation state of aragonite is Ωar > 1, precipitation is favored, but when aragonite
saturation drops so that Ωar < 1, dissolution is favored and marine calcifiers struggle to secrete
and maintain calcium carbonate shell structures (Li-Qing Jiang et al., 2015). In addition to
struggling to maintain shells, OA causes a wide range of negative physiological effects on
organisms such as hypercapnia, increased susceptibility to disease, and an overall increase
mortality and abnormal development (Clements & Chopin, 2017; Doney et al., 2020; Fabry et
al., 2008; Frédéric Gazeau et al., 2013; Kurihara et al., 2008; Orr et al., 2005; Vargas et al.,
2022).

4
Figure 1. Long-term means of RFR-LME mapped OA indicators. Mapped averages of (a) pCO2(RFR-LME), (b) CT(RFRLME), (c) pHT(RFR-LME), (d) [H+
]T(RFR-LME), (e) Ωar(RFR-LME), (f) Ωca(RFR-LME), (g) [CO3
2−]T(RFR-LME), and (h) RF(RFR-LME) over
the timeseries (1998–2022) within each LME. Figure from Sharp et al., 2024.

5
Upwelling, a natural phenomenon by which carbon-rich waters are transported to the
upper ocean as a result of ocean currents, is often times associated with a drop in aragonite
saturation state, and these events are further are exacerbated by OA (Richard A Feely et al., 2008;
Hickey, 1979; Jacox et al., 2014; Turi et al., 2014). The California Current, which runs along the
West Coast of the North America from British Columbia to Baja California Sur, produces a large
upwelling region along the coast which has been subject to rapid-intensification of OA in the
past several decades (Figure 2, Richard A Feely et al., 2024; Gruber et al., 2012; C Hauri et al.,
2013; Claudine Hauri et al., 2009; L.-Q. Jiang et al., 2024; Wolfe et al., 2023). Calcifying
organisms residing within this region are disproportionately affected by OA, and thereby at risk
of substantial losses in species numbers in coming years. These losses could have large ecologic
and economic repercussions, as commercial shellfish species such as oysters, mussels, and
scallops are marine calcifiers and support the vast aquaculture industry along the West Coast of
North America (Clements & Chopin, 2017; Cooley & Doney, 2009; Frédéric Gazeau et al.,
2013).
Figure 2. Seasonally detrended observations of (d) pCO2 (μatm) and (e) pH from Southern California CalCOFI Line
90, Station 90. Figure adapted from Wolfe et al., 2023.

6
Bivalves are particularly sensitive to ocean acidification at early life stages, where they
spend 1-3 months, depending on the species, in a planktonic larval phase (E. Gosling, 2008;
Kurihara, 2008). Figure 3 depicts a schematic of the mussel life cycle for example. Bivalves
reproduce via broadcast spawning, where individuals release gametes into the water column in
synchronization in response to environmental stimuli (Morton, 1960). Once in the water column,
gametes undergo fertilization and form a zygote, indicated by the appearance of two polar
bodies. Cell division begins within thirty minutes following fertilization and within 24-36 hours
the egg has passed through blastula and gastrula stages and developed into a motile trochophore.
At approximate 48 hours post-fertilization, the egg has reached the “D-hinge” stage, and begins
feeding on phytoplankton. Larvae continue to swim, feed, and grow for three to four weeks,
during which they increase in size and mobility and pass through the veliger (and pediveliger, in
the case of bivalves which grow a foot) stages. At approximately 21-25 days post-fertilization,
mature larvae begin to settle on substrate, where they metamorphose into the juvenile “spat”
stage, where they now have fully-formed internal organs and external calcium carbonate shells.
Juveniles reach sexual maturation anywhere from 10 months to four years, depending on the
species (E. Gosling, 2008).
Throughout the larval stage, organisms are absorbing chemicals from the surrounding
seawater and utilizing it to secrete calcium carbonate from the internal mantle structure. Due to
the sensitivity and energetic demands of this life stage, ocean acidification can result in slow or
abnormal growth, changes in gene expression, and increased mortality (De Wit et al., 2018;
Kurihara et al., 2008; Y. Zhang et al., 2019).

7
Figure 3. The mussel life cycle. Figure created with BioRender.
Climate change is happening at an unprecedented rate, and it has been predicted that this
rate is faster than the rate of adaptation for many organisms (Fujita et al., 2023). Selective
breeding, or domestication for particular traits or environments, are direct methods for increasing
the rate of adaptation, as well as desired traits, in species of interest. Agriculture has long
employed observational domestication methods to develop cultivars with specific phenotypes,
however, breakthroughs in genetics have allowed for an understanding in crop development that
has resulted in increased selection efficiency, increased yields, and improved characteristics of
crops (Doebley et al., 2006). Genetics-driven domestication has great potential to aid in the
development of climate-resilient and sustainable crops in an ever-changing global climate and
increased demand in food production (Jian et al., 2022). While many of these methods have

8
already been widely applied to land-cultivars, aquaculture has the potential to experience similar
benefits. Due to the substantial growth in both demand and production of aquaculture products,
domestication of these species will be hugely beneficial in both breeding for commercial traits of
interest, such as size or nutrient content, as well as for adaptive traits for climate change
(Hedgecock, 2011). The domestication of shellfish for resilience to climate-driven effects such as
ocean acidification will support continued commercial production, as well as guide future
restoration and conservation practices.
This thesis aimed to investigate the effects of ocean acidification on commercially and
ecologically important bivalve species within the Southern California Current System by
implementing long-term ocean acidification exposure experiments and characterizing organismal
responses utilizing a variety of genomics methods. Chapter one investigated transcriptomic
responses of two oyster species: the native Olympia oyster Ostrea lurida and the naturalized
Magallana gigas to ocean acidification treatments with varying set points and testing amplitudes.
Chapter two first aimed to build and implement a small-scale experimental ocean acidification
tank system, then exposed two local mussel species: the native California mussel Mytilus
californianus and the naturalized Mytilus galloprovincialis to mild OA conditions and
investigated growth and survival of the two species throughout exposure, as well as following a
ten month grow-out period on a local aquafarm. Larval M. galloprovincialis samples from the
initial and end time points of the experiment in chapter two were then pooled and whole genome
sequenced (WGS) in order to identify shifts in allele frequencies indicative of genomic selection
to OA. Finally, unmapped WGS reads from chapter three were used to assemble metagenome
assembled genomes (MAGs) and mapped to the 16S rRNA subunit to characterize changes in
microbial membership in response to OA in chapter 4. In addition to MAG assembly of WGS

9
reads, samples from both M. galloprovincialis and M. californianus were used for 16S rDNA
amplicon sequencing of the V3-V4 region from six different time points throughout the OA
exposure experiment in chapter two to characterize microbial community membership of both
control and OA-treated mussels of each species throughout time. The findings of these studies
provide a comprehensive look at the effects of OA on the physiology, microbiome,
transcriptome, and adaptive potential of local shellfish species in the Southern California Current
System and contribute to this growing body of research. These results have the potential to
directly benefit aquaculture and environmental stakeholders by providing invaluable insights into
long-term survival and production of local shellfish species for both commercial and ecological
applications.

10
Chapter 1. Differential gene expression reveals different molecular
mechanisms for physiological resilience to ocean acidification stress
in native and introduced oyster species
Jordan L. Chancellor1
, Maxim Kovalev2
, Andrew Gracey1
, Nina Bednaršek3
1 Department of Marine and Environmental Biology, University of Southern California, Los Angeles, California, United States
2 Department of Molecular and Computation Biology, University of Southern California, Los Angeles, California, United States
3 Southern California Coastal Water Research Project, Costa Mesa, California, United States
1.1 Abstract
Coastal-estuarine habitats are under immediate threats from climate change and are rapidly
changing with respect to ocean acidification (OA). Coastal habitats are high in productivity and
rich in nutrients, making them key areas for aquaculture production. Along the West Coast of the
United States, the native Olympia oyster Ostrea lurida has experienced population collapse due
to the rise of commercial fishing alongside the introduction of the non-native Pacific oyster
Crassostrea gigas, a now-globally farmed aquaculture species. Despite intense focus on the
shell biomineralization and dissolution in two species under OA scenarios, molecular
underpinnings of these physiological responses have yet to be fully described. Juvenile oysters of
both species were exposed to a range of diel fluctuating pH manipulations over the course of six
weeks to investigate gene expression responses under various current conditions found across the
US west coast estuaries. Our results demonstrate that Pacific oysters experience increased
differential gene expression compared to Olympia oysters. OA demonstrated a significant effect
on shell dissolution for both Pacific and Olympia oysters, however, no significant difference
between the two species regardless of treatment was observed. Differential gene expression and
shell dissolution analysis revealed distinct differences in responses of the Pacific and Olympia
oysters to OA, and suggest that differences in demographic history may play a key role in future
OA resilience and restoration of native species.

11
1.2 Introduction
Coastal regions and estuarine habitats are highly productive ecosystems which provide valuable
ecosystem services, harbor high levels of biodiversity, and support aquaculture industries
(Alleway et al., 2019; Doney et al., 2020; Grabowski et al., 2012). Coastal estuarine systems are
defined by intense diel fluctuations of temperature, pH, dissolved oxygen, and physical-chemical
processes such as photosynthesis, respiration, and tides, amongst numerous other environmental
dynamics, and organisms which inhabit these zones and cope with these daily variations
(Baumann et al., 2014; Cai, Feely, et al., 2020). The effects of global climate change on physical,
chemical, and biological processes in intertidal zones are complex and interlaced, but their
environmental impacts are evident through increased frequency, magnitude, and severity of
extreme events and their subsequent economic ramifications (Richard A Feely et al., 2010;
García-Reyes & Largier, 2010). Of these, climate change-driven ocean acidification (OA) poses
an immediate threat to organisms and the industries upon which they rely. OA is the decreased
pH of seawater resulting from oceanic uptake of anthropogenic carbon dioxide from the
atmosphere. This process increases the hydrogen ion concentration in seawater, reducing
carbonate ion concentrations, and thereby results in a decreased pH (Caldeira & Wickett, 2003,
2005; Doney et al., 2009; Richard A. Feely et al., 2004, 2009; Orr et al., 2005; Sabine, 2004).
Additionally, the reaction of CO2 with seawater decreases the amount of carbonate ions available
to marine calcifiers which produce calcium carbonate (CaCO3)-based shells and skeletons
(Frédéric Gazeau et al., 2013; Tresguerres & Hamilton, 2017). It has been demonstrated that OA
adversely affects calcifying organisms including corals (Allemand & Osborn, 2019; Andersson
& Gledhill, 2013; Hoegh-Guldberg et al., 2007; Pandolfi et al., 2011), molluscs (Bednaršek et al.,
2021; Bressan et al., 2014; Gazeau et al., 2010; Lischka et al., 2011; Orr et al., 2005),
crustaceans (Kroeker et al., 2010; Rato et al., 2017; Siegel et al., 2022; Whiteley, 2011), and fish

12
(Cattano et al., 2018; Esbaugh, 2018; Espinel-Velasco et al., 2018; Heuer & Grosell, 2014).
Identifying and understanding environmental thresholds and the extent to which OA affects these
habitats and the species within is imperative to conservation and continued production of coastal
zones (Cooley et al., 2012; Cooley & Doney, 2009; Lemasson et al., 2017; Lutier et al., 2022).
When calcium carbonate drops below a critical saturation threshold, marine calcifying
organisms, including many commercial shellfish species, experience internal acidosis and
struggle to secrete and maintain their external calcium carbonate shells (Claudine Hauri et al.,
2009; Orr et al., 2005). Bivalve shells are composed of calcium carbonate composites embedded
in complex organic matrices, and shell biomineralization during the larval stage is a highly
complex and energetic process (Frieder et al., 2017; Furuhashi et al., 2009; Yarra et al., n.d.; G.
Zhang et al., 2012b). In addition, OA has been shown to result not only in shell dissolution, but
also immune stress, increased susceptibility to pathogens and predators, and overall lower
organismal fitness (Bednaršek et al., 2022; Bressan et al., 2014; Cao et al., 2018; Clark et al.,
2013; Espinel-Velasco et al., 2018; Frieder et al., 2017; F Gazeau et al., 2010b; Q. Wang et al.,
2016). The physiological effects of OA on shellfish have been well documented, particularly in
the economically important Pacific oyster Magallana (formerly Crassostrea) gigas, however, the
molecular responses of these processes and resilience to OA stress has only recently been the
target of study (M. C. Bitter et al., 2019; Chandra Rajan et al., 2021; De Wit et al., 2018; Fang et
al., 2011; Goncalves et al., 2017; Hüning et al., 2012; Lydia Kapsenberg et al., 2022; W. Liu et
al., 2017). Oysters are one of the most important aquaculture species globally, and the oyster
industry in the United States alone is valued at over $180 million annually (NOAA, 2018). The
continued survival of local oysters stocks in the face of global climate change is imperative to the
reliable production of oysters for the seafood industry.

13
One component of resilience to environmental stress is linked to the predictability of
heterogenous OA patters, such as diel cycling and temporal variation, which allows organisms
the ability to predict future conditions and adjust their responses accordingly (Bernhardt et al.,
2020; M. Bitter et al., 2020; M. C. Bitter et al., 2021; Botero et al., 2014; Reed et al., 2010).
Phenotypic plasticity, the ability of a genotype to alter phenotype in response to changes in
environmental conditions, is a favored mechanism for shellfish resilience to diel OA changes,
and recent research has attempted to determine reaction norms and tipping points of oysters
under OA exposure (Cornwall et al., 2013; L Kapsenberg et al., 2018; Lutier et al., 2022;
Mangan et al., 2017; Onitsuka et al., 2018). Extreme departures from natural variations in pH
inhibit marine calcifiers from correctly predicting and adjusting to their environment, resulting in
increased physiological and fitness challenge. Failing to properly predict, interpret and acclimate
to the environment both on the molecular and physiological scale can result in increased
organismal stress, decreased fitness Environmental changes that are unpredictably to the extent
to be outside the range of natural conditions can result in mass shellfish mortality events,
consequences of which can be complete stock loss, degradation of ecosystem services, and
ecosystem collapse (Atkinson et al., 2003; Jones et al., 2017). Although recent studies have
characterized physiological and transcriptomic responses of oysters to OA, the extent to which
organisms can cope with this stress not fully understood. Previous studies have focused on broad
transcriptomic response to OA in oysters and identified physiological tipping points, but how
transcriptomic responses of individuals change across fluctuating OA intensities and duration of
exposure has not been disentangled (Ducker & Falkenberg, 2020; Strader et al., 2020).
The Olympia oyster Ostrea lurida was formerly widely abundant along the Pacific coast
of the United States and Canada, with a distribution from Stika, AK, to Baja, Mexico (Baker,

14
1995; Coan et al., 2000; Gillespie, 2009; Groth & Rumrill, 2009; Pritchard et al., 2015). Olympia
oysters were historically harvested at low intensities until the populations collapsed after the rise
of commercial fishing in the mid to late 1800s (Baker, 1995; E. M. Barrett, 1963; White et al.,
2009). In search of an improved oyster stock, Pacific oysters Magallana gigas were introduced
from Japan to the West Coast of the United States in the 1930s (Baker, 1995; E. M. Barrett,
1963; White et al., 2009). The Pacific oyster is a globalized, introduced species and is one of
highest produced aquaculture species worldwide, often times favored for commercial production
over local species that may be smaller, take longer to reach market size, and inhabit local niches.
(FAO, 2022; Herbert et al., 2016; Ruesink et al., 2005; Schmidt et al., 2008). M. gigas, in
comparison to O. lurida, is a large, fast-growing oyster species, and successful propagation of
the faster-growing Pacific oyster following its introduction to the United States resulted in the
continuous depletion of native Olympia oyster populations, and although Olympia oysters persist
in a number of locations along their historical distribution, they are generally rare, and
populations are represented by scattered individuals (Baker, 1995; Polson & Zacherl, 2009; L. L.
Price, 2018; Pritchard et al., 2015; Wasson, 2010). Recently, the Olympia oyster has been the
target of ecosystem restoration efforts, as well as a growing interest in cultivating the local
species for commercial-scale production (Kornbluth et al., 2022; Pritchard et al., 2015; Ridlon et
al., 2021; Ruesink et al., 2005; Silliman, 2019; Trimble et al., 2009; Wasson, 2010; White et al.,
2009).
In the present study, we exposed Pacific Magallana gigas and Olympia Ostrea lurida
oysters, two economically and ecologically relevant species on the West Coast of the United
States, to OA treatments of various set points and amplification intensities and characterized
transcriptomic and physiological responses in order to better understand nuanced shifts in

15
molecular responses to OA across different oyster species. Along the West Coast of the United
States, the prevailing eastern boundary current produces an upwelling system in which deep,
cool, nutrient-rich water with a generally lower pH is regularly brought to the surface waters
(Richard A Feely et al., 2008; Claudine Hauri et al., 2009; Wolfe et al., 2023). Organisms native
to these upwelling systems, such as O. lurida, commonly experience OA conditions and
aragonite saturation states that are well-below calcium carbonate shell building thresholds, and
therefore may be better adapted to managing these variable environmental fluctuations in
comparison to introduced species, such as M. gigas, whose historic environment of the Miyagi
region of Japan tend to be less dynamic (Ishii et al., 2011; Yamamoto-Kawai et al., 2015).
Although both species are present in bays and estuaries along the coast, they tend to occupy
different zones within the intertidal. M. gigas typically occurs in the lower intertidal to subtidal
zone, whereas O. lurida tends to occupy the mid-intertidal zone (Kornbluth et al., 2022; Tronske
et al., 2018a).
We hypothesized to observe the greatest deviations in oyster response to occur in OA
treatments with the lowest pH and greatest diel pH amplitude. In addition, we expected that O.
lurida, which is highly adapted to variations in pH, would display a higher tolerance of OA,
demonstrated by a muted response in differential gene expression, in comparison to M. gigas,
which may not be as well adapted to the historic conditions in the California Current sytem.
Additionally, OA tolerance between species may be observed in the functional enrichment of
different molecular pathways, such as those involved in energy allocation, immune response, and
biomineralization, as well as phenotypic differences in severity of dissolution and other measures
of environmental fitness, such as respiration and calcification.

16
1.3 Methods
1.3.1 Experimental ocean acidification exposures design
Experimental treatments and exposures are described in detail in Bednaršek et al. (2022). In
short, five different pH treatments mimicking ocean acidification conditions of varying testing
amplitudes were applied in a flow-through Dynamic Stressor Exposure Research Facility for six
weeks of juvenile development of both Pacific Magallana gigas and Olympia Ostrea lurida
oysters (Figure 1.1). Treatments were chosen to simulate both current and future conditions, as
well as to mimic diel fluctuations that organisms experience within intertidal zones.
Experimental conditions spanned the range of current mean and extreme conditions found along
Pacific coast estuaries based on long-term buoy data collected by Bednaršek et al., 2022 as well
as future climate change projections for the year 2100 from Pacella et al., 2018. Treatments were,
in order of increasing intensity: a constant pH of 8.0 (8.0C), a constant pH of 7.7 (7.7C), a pH of
8.0 with a testing amplitude of 0.2 (8.0A0.2), a pH of 7.7 with a testing amplitude of 0.2
(7.7A0.2), and a pH of 7.7 with a testing amplitude of 0.5 (7.7A0.5). Fluctuating conditions were
controlled through a series of solenoids and mass flow controllers (National Instruments
hardware) using a pH feedback mechanisms setpoint detection system (Labview software). pH
changes were programmed to occur in evenly distributed incremental adjustments to achieve the
desired pattern for each treatment. Juvenile Pacific and Olympia oysters were placed in
individual exposure jars into which pH-adjusted seawater flowed in by gravity. Oysters were fed
twice daily with 400 uL of Shellfish Diet 1800 (Reed Mariculture) diluted 1:1 with seawater.
Whole juvenile oysters were collected every two weeks, shucked, flash frozen using liquid
nitrogen, and stored at -80 C for later analysis.

17
Figure 1.1. One-week schematic of ocean acidification experimental conditions across the four treatments.
Experimental treatments included a control, static treatment (8.0C), a static, ocean acidification treatment (7.7C),
and two dynamic ocean acidification treatments of varying testing amplitudes (7.7A0.2, 7.7A0.5). Figure adapted
from Bednarsek et al. (2022).
1.3.2 RNAseq library construction and sequencing
Six individual oysters from weeks two and six (W2, W6) of four out of the five experimental
treatments (8.0C (control), 7.7C, 7.7A0.2, 7.7A0.5) from each of the two species were processed
for sequencing and downstream differential gene expression analysis (96 total). Total RNA was
isolated following a standard TRIzol protocol and the RNA was further purified on silicacolumns (RNeasy minikit, Qiagen). RNA purity and quantification was determined using a
NanoDrop Spectrophotometer (Thermo Fisher Scientific) and stored at -80C prior to library
construction. Individual RNA samples were converted to cDNA libraries using a modified 3' tag
library construction protocol (Hall & Gracey, 2021). Briefly, total RNA was fragmented using
uniquely indexed anchored-oligo(dT)30 primers containing the Illumina p7 sequence. RNA was
then reverse transcribed by the addition of MMLV-HP reverse transcriptase (Lucigen) and a
template switching oligonucleotide that contained the Illumina p5 sequence. Following reversetranscription, cDNA products were pooled, bead-cleaned (AMPure, Beckman-Coulter), and
amplified. Libraries were quantified and integrity was determined using Qubit 2.0 Fluorometer
(Thermo Fisher Scientific) and Agilent 2100 Bioanlyzer (Agilent Technologies). RNAseq

18
libraries were Illumina Sequenced on a HiSeq System and 150 bp paired-end reads were
generated and demultiplexed by the sequencing facility (Novogene Corporation Inc.,
Sacramento, CA).
1.3.3 Sequencing and read processing
Quality control of reads was performed for both raw and trimmed reads using FastQC version
0.12.2 (Andrews, 2010) and aggregated by MultiQC version 1.6 (Ewels et al., 2016). For
trimming the reads, we utilized Trimmomatic (Bolger et al., 2014) with parameters
(LEADING=3, TRAILING=3, SLIDINGWINDOW=4:15, MINLEN=70) and a custom adapters
file, which removed poly-A and poly-T tails, as well as NEBNext Illumina index primers,
resulting in three separate datasets for each replicate: survived pair-ended reads (PE), survived
only forward reads (SF), and survived only reverse reads (SR). Given the Trimmomatic
summary, three replicates per time and treatment having the highest combined number of
pairs/reads were selected to proceed with the downstream analysis, reducing the number of
samples from 48 to 24 for each species.
We used all three datasets per replicate (PE, SF, SR) to obtain count matrices. For
Magallana gigas, the reads were mapped to a reference genome obtained by Wellcome Sanger
Institute (NCBI accession GCF_963853765.1) using HISAT2 version 2.2.1 (Kim et al., 2019).
The mapped reads were converted to bam files with SAMtools version 1.17 (Heng Li et al.,
2009) and counted by featureCounts version 2.0.6 (Y. Liao et al., 2014), with parameters (-s 1)
for SF, (-s 2) for SR, and (-p --countReadPairs -C) for PE. In the case of Ostrea lurida, we used
the de novo assembled transcriptome published in (Maynard et al., 2018, NCBI accession
GSE98355), given the unavailability of robust reference genomes. We thinned this transcriptome
with CD-HIT version 4.8.1 (L. Fu et al., 2012) to reduce redundancy due to transcriptional
artifacts and alternative splicing. Reads from selected samples were mapped to this thinned

19
transcriptome with Bowtie 2 version 2.5.1 (Langmead & Salzberg, 2012). Resulting bam files
were input into salmon version 1.10.3 (Patro et al., 2017) using the quant command in order to
obtain count matrices for each set of reads. For each species, the count matrices for PE, SF, and
SR reads were combined into a single count matrix for downstream differential expression
analysis. Additionally, we assessed the quality of the reference genome, the original
transcriptome, and the CD-HIT-thinned transcriptome via BUSCO (Manni et al., 2021) with
three different lineage datasets (eukaryota_odb10, metazoa_obd10, mollusca_odb10).
1.3.4 Differential Gene Expression Analysis
Before running the DESeq2 function (Michael I Love et al., 2014), low-count genes were filtered
out according to suggestions from the DESeq2 tutorial (M.I. Love et al., Analyzing RNA-seq data
with DESeq2). The smallest group size was defined as 3 (3 replicates per treatment per time), and
we used a minimum threshold of 10 counts (as stated in the tutorial) for M. gigas and of five
counts for O. lurida to encompass more data. Following count filtering, the DESeq2 function
was ran for each species and results were contrasted using treatment 8.0C as the control.
Principal component analysis (PCA) was performed on DESEq2 results to examine
patterns of variation between different treatments and timepoints. For PCA, we obtained
normalized read counts by performing the variance stabilizing transformation (VST, default nsub
for M. gigas and nsub = 600 for O. lurida) to be used as input for the PCA function from
FactoMineR R package (Lê et al., 2008). We assessed three types of grouping: treatment (4
groups), type of pH (2 groups, constant (C) and variable amplitude (A)), and time (2 groups). To
find the principle component (PC) that best described each grouping, we applied Kruskall-Wallis
test followed by the Dunn test for the treatment grouping and Wilcoxon rank sum test for the
type of pH and time grouping implemented in rstatix R package (Kassambara, 2023). The tests

20
were applied to PCs explaining 95% of the data variation, and the best ones were selected based
on the lowest p-value.
Differentially expressed genes (DEGs) were determined from DESeq2 results as genes
with |log2FoldChange| >= 1 and not-adjusted p-value <= 0.01. The choice of using p-value over
adjusted p-value one was dictated by the low statistical power of the datasets, which contained
only 3 replicates per treatment and time point and low numbers of reads. DEGs were analyzed by
assessing different intersections between treatments, times, and their combinations for each
species separately. Gene set enrichment analysis (GSEA) was performed using the clusterProfiler
R package (S. Xu et al., 2024) for each species, condition, and GO type (cellular component
(CC), biological process (BP), molecular function (MF)), resulting in 36 datasets (2 species * 6
conditions * 3 GO types). For the downstream analysis, we focused on NES (normalized
enrichment score). More positive values of NES indicate that a GO term is represented mainly by
upregulated genes, while more negative values show representation by downregulated genes. We
consider a GO term to be enriched statistically significant if p-value is less or equal to 0.01.
1.3.5 Biomineralization expression patterns
We utilized the curated list of genes involved in shell biomineralization (30 genes total, 27 gene
product names) by Chandra Rajan et al. (2021) (Supplementary Table 1) to further investigate the
expression patterns of these genes of interest in our dataset. Using this gene list and the available
genome of the Hong Kong oyster Magallana hongkongenisis (NCBI accession
GCA_016163765.1) used in the Chandra Rajan et al. (2021) study, we narrowed down our DEGs
to those which matched these gene descriptions. We visualized the expression patterns of
biomineralization genes in our dataset using heatmaps and clustered genes with hierarchical
clustering.

21
1.3.6 Reference re-annotation
To maintain consistency while comparing genes from different organisms, we re-annotated the
genomes of M. gigas and M. hongkongensis and the thinned transcriptome of O. lurida. For that,
we applied the approach described in (Maia et al., 2022). First, proteins were aligned to
SwissProt/UniProtKB database (The UniProt Consortium, 2023) by the BLASTp algorithm
implemented in Diamond [11] (with parameters -k 1 and --very-sensitive). After that, sequences
not matched to any from the database were aligned to TrEMBL/UniProtKB database. Gene
Ontology (GO) (Ashburner et al., 2000; Harris et al., 2004) annotation was done based on
relative records in the aforementioned databases.
1.3.7 Oyster Shell Dissolution
Oyster shells were examined under scanning electron microscopy (SEM) following the protocol
outlined in Bednaršek et al., 2022 for shell cleaning, dissolution characterization and
quantification. For shell cleaning, we used sodium hypochlorite (NaOCl, commercial bleach
diluted to obtain 5% v/v NaOCl) for approximately1− 1.5h. SEM was then used to measure and
characterize mean shell dissolution from 190 Pacific and 185 Olympia oysters from all
experimental ocean acidification treatments. Mean dissolution was scored on a scale of 1-3 and
was categorized based on the type of dissolution observed: type I dissolution indicated an intact
shell in which the prism surfaces were mostly intact with a smooth appearance; type II
dissolution involves moderate dissolution with partially eroded upper prismatic layers, giving a
concentric, spherulitic appearance, and type III dissolution indicated severe dissolution, which
was the observation of a completely removed prismatic layer and partially exposed lower, crosslamellar layer. Dissolution data and analyses was previously published in Bednaršek et al., 2022,
here we have focused on determining the mean shell dissolution across treatments and species
using a linear model on mean values and a two-way analysis of variance (ANOVA) was used to

22
calculate significant effects of treatment, time, and species on dissolution mean. All statistics
were calculated within R.
1.4 Results
1.4.1 Reference genome and transcriptome mapping
Sequencing quality of libraries was poor based on FastQC and MultiQC results, and total
read numbers, reads surviving trimming and quality filtration, and mapping rates varied between
read sets (PE, SF, SR) for each species, but were lower than what is generally recommended for
differential expression analysis (Supplementary Table 2). In order to maintain library and read
integrity, we performed stringent filtering based on read quality and length, which resulted in
less than 100,000 read pairs surviving post-filtering, primarily due to the low quality of reverse
reads. In order to retain as many quality reads as possible for downstream analysis, we
implemented a tripartite strategy that separately mapped each set of reads (PE, SF, SR) from the
top three samples with the highest combined read totals to the respective their respective genome
(M. gigas) or transcriptome (O. lurida) to generate abundance tables (Figure 1.2). In addition,
prior to mapping, we thinned the available O. lurida reference transcriptome and decreased the
number of duplicate transcripts in order to avoid mapping reads to redundant isoforms
(Supplemental Figure 2). M. gigas libraries overall had higher numbers of retained reads across
all filtering steps as compared to O. lurida, reflective of differences in library preparation and
sequencing quality (Figure 1.2). Mapping rates on average were higher for paired reads in each
species as compared to forward and reverse single reads, and while the total number of reads and
read pairs mapped was higher for M. gigas, the average percentage of reads mapped to the
reference was higher for single forward and single reverse reads in O. lurida (Table 1.1).

23
Figure 1.2. Number of read pairs (second from top), forward reads (second from bottom), and reverse reads (bottom)
surviving each step of pre-processing. Total reads (top, “Total”) were first trimmed and quality filtered with
trimmomatic, and the three libraries with the highest combined number of reads were retained (“Selection”) and
mapped against their respective reference (“Mapping”).
Table 1.1. Average total number and percent reads mapped per read type (PE, SF, SR) for each species.
M. gigas O. lurida
Read Type Total Reads Mapped % Mapped Total Reads Mapped % Mapped
Paired 91,463 54.8 43,343 34.5
Single Forward 3,715,616 16.2 1,584,046 19.6
Single Reverse 28,995 15.9 14,361 17.6
1.4.2 PCA
Principal component analysis (PCA) revealed the 95% of the variation in the data was explained
by the first 18 and 17 PCs for M. gigas and O. lurida, respectively. Results of the Kuskall-Wallis
test for identifying PCs which best described treatment determined that for M. gigas PC4 best

24
described the data (p-value < 0.01), while for O. lurida PC9 was the most significant in
describing treatment (p-value < 0.05) (Figure 1.3., Supplementary Table 3, Supplementary Table
6). The Dunn test was then applied to these results in order to identify significant differences in
treatment comparisons within species. In M. gigas, all treatments were significantly different
from each other (p-value < 0.05) except for comparisons between treatments 8.0C-7.7A0.2 and
7.7C-7.7A0.5 (Supplementary Table 4). Interestingly, in O. lurida the only treatment comparison
which was significantly different (p-value < 0.05) according to the Dunn test was 7.7C-7.7A0.2
(Supplementary Table 7).
Utilizing the Wilcoxon rank sum test to determine which PC best described pH type of
either constant (7.7C and 8.0C) or amplitudinal (7.7A0.2, 7.7A0.5), we found that for M. gigas
PC2 best described this difference (p-value < 0.01), and for O. lurida PC5 was the most
significant (p-value < 0.01) (Figure 1.3, Supplementary Table 5, Supplementary Table 8). Using
the same test to determine best fit PCs for time (week 2 or week 6), we found that for both
species PC1 best described the data (p-value < 0.01) (Supplementary Table 5, Supplementary
Table 8).

25
Figure 1.3. PCA results for treatment (left) and pH type (constant or fluctuating, right) grouping for M. gigas (top)
and O. lurida (bottom). The x-axis is determined by PC1, while the y-axis reflects the most significant (lowest pvalue) PC and best describe the respective data type. Colors represent different treatments and shapes represent
week.
1.4.3 Differential gene expression
In order to determine an appropriate filtration threshold for low-count genes, we built a
dependency between minimum count threshold and percent genes remaining after filtration
(Supplemental Figure 1). Using this data, we determined minimum count thresholds of 10 and 5
for M. gigas and O. lurida, respectively. While 10 is the recommended default threshold for
filtering prior to DESeq2 analysis, this value resulted in a high proportion of genes lost in O.
lurida, therefore we lowered this threshold in order to capture enough genes for downstream
analysis. There were 33,068 genes prior to filtration M. gigas, and 6,119 genes (18-19%)

26
following filtration. For O. lurida, these values were 38,999 genes and 1,394 genes (3-4%) prior
to and post-filtration.
After performing the DESeq2 function, we determined differentially expressed genes
(DEGs) to be genes with had a log fold change (LFC) absolute value of greater than or equal to
one and a p-value of less than or equal to 0.01 (Figure 1.4, Figure 1.5). Overall, M. gigas had
higher numbers of differentially expressed genes as compared to O. lurida across all treatments
and time points (Table 1.2). For each species at week two, treatment 7.7A0.2 had the highest
number of DEGs, followed by 7.7A0.5 and 7.7C for M. gigas and the reverse for O. lurida
(Figure 1.6). At week six, this pattern shifted, with treatment 7.7A0.5 resulting in the most DEGs
for each species, followed by 7.7C and 7.7A0.2 Figure 1.6. For each species, the total number of
DEGs increased from week two to week six for treatments 7.7C and 7.7A0.5, however, in M.
gigas total DEGs decreased from week two to week six in treatment 7.7A0.2 while they
increased in O. lurida (Table 1.2, Figure 1.6). Within each timepoint, relatively few DEGs were
shared across treatments (Figure 1.6).
We present the top 20 differentially expressed genes (or all DEGs if total number is < 20)
for each treatment, timepoint, and species in Supplementary Table 9 and Supplementary Table
10. Across all treatments and timepoints for M. gigas, only 10 DEGs out of these were shared
between certain conditions (Supplementary Table 11), and for O. lurida the number of shared
DEGs out of total was only 13 (Supplementary Table 15). For M. gigas, these included genes
involved in immune response, such as C-type lectin domain family 17, member A, Beta-1,4-
galactosyltransferase 2, Neutral ceramidase B, Leucine-rich repeats and immunoglobulin-like
domains protein 3, and CUB domain-containing protein (J. Li et al., 2015; Sun et al., 2021), as
well as those involved in transcription regulation and gene expression, such as MYND-type

27
domain-containing protein, RNA binding proteins, and N-lysine methyltransferase KMT5A
(Spellmon et al., 2015). In O. lurida, DEGs shared across treatments included Copine-3, which is
involved in immune response of oysters (Corporeau et al., 2022), Calicipression-1, which
regulates the activity of calcineurin, a protein highly involved in intracellular calcium signaling
(C. Li et al., 2009), as well as Transposable element Tcb1 transposase and Retrovirus-related Pol
polyprotein from transposon 17.6.
Figure 1.4. Volcano plot of genes identified by DESeq2 for M. gigas. Red circles represent differentially expressed
genes defined as |log2FoldChange| >= 1 and p-value <= 0.01. All treatments are compared with treatment 8.0C as
the control.

28
Figure 1.5. Volcano plot of genes identified by DESeq2 for O. lurida. Red circles represent differentially expressed
genes defined as |log2FoldChange| >= 1 and p-value <= 0.01. All treatments are compared with treatment 8.0C as
the control.
Table 1.2. Number of differentially expressed genes for each treatment and time point for both species.
Species Time 7.7C 7.7A0.2 7.7A0.5
M. gigas W2 154 671 339
M. gigas W6 475 264 606
O. lurida W2 17 54 13
O. lurida W6 94 66 214

29
Figure 1.6. Venn diagram of differentially expressed genes from M. gigas (left) and O. lurida (right) shared between
each treatment at weeks two (top) and six (bottom). Differential expression for a treatment was defined as a gene with
|log2FoldChange| >= 1 and p-value <= 0.01 as compared to the control treatment (8.0C).
To further investigate gene expression patterns across timepoints, we identified genes
which were shared between weeks two and six within the same treatment (Supplementary Table
12, Supplementary Table 13, Supplementary Table 14, Supplementary Table 16, Supplementary
Table 17, Supplementary Table 18). Shared genes between treatments within each time point, as
well as shared genes within treatments across timepoints, were relatively small. No genes were
shared between all three treatments between time points for either species, nor with amplitude
pH treatments (7.7A0.2, 7.7A0.5) between weeks two and six.

30
For M. gigas, 15 DEGs were shared between weeks for treatment 7.7C, which included
Neutral ceramidase B, which is involved in immune response (Timmins-Schiffman & Roberts,
2012), EF-hand calcium-binding domain-containing protein 5, which is involved in intracellular
calcium binding (Huang et al., 2007), LMKI67 FHA domain-interacting nucleolar
phosphoprotein-like, Growth hormone secretagogue receptor type, which could have
implications in cell growth. In treatment 7.7A0.2, there were 16 shared DEGs across weeks,
some of which were components of invertebrate muscles, such as paramyosin (Huijuan Li et al.,
2021) and Troponin-C, a regulator of muscle contraction that is influenced by calcium binding
(Funabara et al., 2018), shell formation, including a Tyrosinase-like protein 2 (Y. Zhu et al.,
2022), as well as genes involved in translation and transcription. Treatment 7.7A0.5 had 58
shared DEGs across weeks two and six, which included genes involved in translation,
cytoskeleton structure, ion channel permeability, and notably, protcadherin, which is highly
involved in the calcium binding in oysters (X. Wang et al., 2020; Zhao et al., 2012)
In O. lurida, only 2 DEGs were shared at treatment 7.7C, Meiosis regulator and mRNA
stability factor 1 and Vacuolar protein sorting-associated protein 33A. 3 genes were shared at
treatment 7.7A0.2, but only one gene of these, Collagen alpha-1(IV) chain, was annotated.
Finally, treatment 7.7A0.5 had 4 shared DEGs across weeks, Collagen alpha-2(I) chain
(Fragment), Calcipressin-1, and Transposable element Tcb1 transposase. Of these, Collagen
alpha-2(I) chain is known to be involved in bone formation in humans, and Calicipression-1 is
involved in the same pathways as calcineurin and calmodulin, each of which are involved in
shell biosynthesis in oysters and affected by ocean acidification (C. Li et al., 2009; Xin et al.,
2022). Overall, across treatments and time points for each species, we see DEGs predominantly

31
involved in cytoskeletal structure and organization, immune and stress response, shell
biosynthesis, and transcription.
1.4.4 Gene set enrichment analysis
Gene set enrichment analysis (GSEA) showed markedly different patterns in M. gigas versus O.
lurida, as well as across time. Across all treatments and timepoints, M. gigas had dramatically
more enriched GO terms than O. lurida (Table 1.3, Figure 1.7, Supplemental Figure 3,
Supplemental Figure 4, Supplemental Figure 5), however, patterns between weeks were different
between the two species. There were GO terms which were significantly enriched in M. gigas
which never appeared in O. lurida, such as signal transduction, calcium ion binding, cell
adhesion, nervous system development, innate immune response, and DNA repair (Figure 1.7,
Supplemental Figure 4, Supplemental Figure 5). Additionally, there were those which were
consistently enriched in M. gigas but were only enriched in O. lurida after long-term exposure at
the last time point (week six), such as protein binding, DNA binding, RNA binding, chromatin
remodeling, metal ion binding, and zinc ion binding (Figure 1.7, Supplemental Figure 4,
Supplemental Figure 5).
Both species showed an increase in enriched GO terms from week two to week six in
treatments 7.7C and 7.7A0.5, however, while O. lurida also increased the number of enriched
gene sets over time in treatment 7.7A0.2, M. gigas had a large decrease over time (Table 1.3,
Figure 1.7). Our GSEA results show that treatment 7.7C is mildly stressful for M. gigas already
at week two, and becomes increasingly stressful by week six, which is demonstrated by the
increase in enriched go terms. However, at this treatment, we see little to no response in O.
lurida until week six, when only a handful of gene sets are enriched. This pattern is also
observed in treatment 7.7A0.5, where M. gigas is both initially stressed by the treatment at week
two and this stress response is amplified by week six, whereas in O. lurida a transcriptional

32
response is only observed at week 6 and at a much lower magnitude than M. gigas. Treatment
7.7A0.2 presents an interesting pattern opposite of that observed within other treatments. In
treatment 7.7A0.2 at week two M. gigas has the highest number of enriched GO terms of all
treatments and timepoints, suggesting they are highly stressed by these conditions, however, they
appear to acclimate to this treatment as is demonstrated by the decrease in enriched GO terms at
week six. O. lurida, however, shows an increase in enriched GO terms over time at this
treatment, though the magnitude of the response remains relatively small.
Table 1.3. Number of unique enriched GO terms for each treatment and time point for both species.
Species 7.7C_W2 7.7C_W6 7.7A0.2_W2 7.7A0.2_W6 7.7A0.5_W2 7.7A0.5_W6
M. gigas 13 47 170 29 49 111
O. lurida 0 6 1 6 0 18
Figure 1.7. GSEA results of GO terms which were statistically significant in at least one treatment, time point, or
species. Color is representative of degree and direction of expression (LFC), and triangles represent statistical
significance (p-value  0.01).
1.4.5 Biomineralization gene expression patterns
Following genome and transcriptome re-annotation, we compared gene names between our
dataset and those described by Chandra Rajan et al. (2021) to identify biomineralization genes.

33
Between the curated biomineralization gene list and our data, we found 24 genes with exact
matches, as well as six genes which had slightly different names, but were close matches
belonging to the same general gene description (for example: Probable serine/threonine-protein
kinase DDB_G0267514 versus Serine/threonine-protein kinase CTR1). The two remaining genes
from the biomineralization gene list (Mho_013910 and Mho_026820) had ambiguous matches
with our dataset, and therefore we not used for further analysis. Out of these matches, 17 gene
description matches were found in our M. gigas data set, which resulted in 53 total
biomineralization genes for this species. In O. lurida, we found five name matches for a total of
eight biomineralization genes. Only four genes were found in both M. gigas and O. lurida
datasets: proton channel OtopLc, organic cation transporter protein, von Willebrand factor D and
EGF domain-containing protein, and protocadherin Fat 4.
Expression patterns of biomineralization genes was different between M. gigas and
O.lurida (Figure 1.8). In M. gigas there was significant suppression of perlucin-like proteins,
protocadherin, and tyrosinase-like proteins, particularly at later time points, which is a common
response to OA stress in this species (Ramadoss Dineshram et al., 2021), however, this pattern
was not observed in O. lurida (Figure 1.8, Figure 1.9). Additionally, O. lurida had no
biomineralization genes which were significantly differentially expressed within any treatments
or timepoints, suggesting these pathways are not targets of differential expression in response to
these conditions as they are in M. gigas.

34
Figure 1.8. Heatmap of biomineralization genes for M. gigas (top) and O. lurida (bottom) within our dataset. Color
gradient describes degree and direction of expression and asterisk denote statistically significant DEGs. Rows were
hierarchically clustered separately for each species.

35
Figure 1.9. Heatmap of biomineralization genes found in both M. gigas (top) and O. lurida (bottom) datasets. Color
gradient describes degree and direction of expression and asterisk denote statistically significant DEGs.
1.4.6 Shell dissolution
Mean dissolution was evaluated using an analysis of variance model including species and
treatment. Treatments were shown to have a highly statistically significant effect on the mean
dissolution (P < 0.001) in both species, demonstrating high sensitivity to dissolution under lower
pH in combination with higher pH amplitude. There was no statistically significant difference in
mean shell dissolution between species across the treatments at any time point (Figure 5).
Duration of the exposure did not have any significance on variance in the model.

36
Figure 1.10. Mean dissolution of both M. gigas and O. lurida throughout experimental ocean acidification exposure
across various static and fluctuating pH treatments. Mean dissolution was scored on a scale of 1-3 based on the type
of dissolution observed from SEM of oyster shells (see Bednaršek et al., 2022). A linear model fit of mean dissolution
revealed a statistically significant effect of treatment on mean dissolution, however, a two-way ANOVA revealed no
significant difference of species on shell of dissolution.
1.5 Discussion
1.5.1 Effects of experimental ocean acidification vary between oyster species
Ocean acidification has been shown to have detrimental effects on oyster shell biomineralization
and dissolution, physiological immune and stress responses, larval development and survival,
and differential gene expression, all of which have been well-documented in the Pacific oyster
(Barros et al., 2013; Barton et al., 2012; Chandra Rajan et al., 2021; R Dineshram et al., 2015;
Frieder et al., 2017; Frédéric Gazeau et al., 2011, 2013; Gibson et al., 2011; Kurihara, 2008; Pan
et al., 2015; Schwaner et al., 2023; Timmins-Schiffman et al., 2012). Although there has been
less attention focused on non-commercial, local niche oyster species, recent research has
suggested that native oysters of the Ostrea genus, including the Olympia oyster Ostrea lurida,
may be more tolerant than non-native oysters to OA stress, where they have shown to maintain
normal calcification, respiration, clearance, and survival rates when challenged with low pH (AJ,
2021; Cole et al., 2016; Hettinger et al., 2013; Lawlor & Arellano, 2020; Lemasson et al., 2018;

37
Navarro et al., 2020; Spencer et al., 2020; G. Waldbusser et al., 2016), especially in the presence
of factors that could offset negative OA effects, such as the presence of high food availability.
Waldbusser et al. (2016) also examined the responses to acidification of the brooding Olympia
oyster in comparison to the broadcast spawning Pacific oyster during the larval stage, and found
that O. lurida larvae showed no acute negative responses to acidification stress in comparison to
M. gigas, which showed a much higher energetic burden of calcification and energy lipid
consumption in the same conditions. They posit that gamete brooding, slow shell building and
lower growth rates in Ostrea oysters during the developmental stage may be a mechanism to
mitigate OA stress by lessening the energetic burden of calcification. Additionally, Lawlor and
Arellano (2020) reared Olympia oyster larvae under interacting gradients of temperature,
salinity, and pH and found that while temperature and salinity greatly affected O. lurida larvae,
no negative responses to acidification treatments were observed. While there are a number of
hypotheses as to why OA tolerance is higher in Olympia oysters, it is generally theorized that
organisms native to the coastal California Current upwelling system and estuarine processes have
adapted to the historically wide-ranging fluctuations of pH of the region.
1.5.2 Differential gene expression and enriched GO term patterns between species reflect
differences in demographic history and adaptation to low pH environments
The experimental ocean acidification treatments oysters were challenged within the present study
were modeled on buoy data collected along the West Coast of the United States, and were
designed to increase in their level of OA challenge, with the lowest, control treatment set at a
constant pH of 8.0, and increasing in challenge to pH 7.7 at a constant set point, and with various
diel amplitude fluctuations of either 0.2 or 0.5 (Bednaršek et al., 2022). The observed low
numbers of differential gene expression overall in O. lurida could be explained by the fact that
our treatments were modeled on the natural, historic environment of this species, and the high

38
numbers of DEGs observed in the introduced M. gigas species can be attributed to the fact that
they have not yet sufficiently adapted to the low-pH conditions of this region. The historic
habitat of introduced M. gigas tends to have higher, and less fluctuating pH conditions, and
although M. gigas has been established along the West Coast of the United States for nearly one
hundred years since first being introduced, the species generally occurs lower in the intertidal
zone as compared to O. lurida, where the environment is relatively stable compared to the
extremes experienced by organisms in the mid-upper intertidal zone (Kornbluth et al., 2022;
Leeuwis & Gamperl, 2022; Somero, 2002; Tomanek & Helmuth, 2002; Tronske et al., 2018b).
Both oyster species at treatment 7.7C saw a large increase in DEGs from week two to
week six, though these values were much larger in M. gigas than O. lurida. pH 7.7 is known to
result in both cellular and physiological stress, and our results show that the effects of this OA
treatment are additive throughout time, and oysters struggle to maintain normal functioning and
morphological structures at persistently mid-to-low pH levels. Given our results, O. lurida
appears to be less affected by and more acclimated to this treatment as compared to M. gigas,
however given long exposure periods may also experience extreme responses. At treatment
7.7A0.2 M. gigas exhibited the highest number of DEGs out of all treatments, however, this
number decreased by more than half by week six. So while the first few weeks the organism is
initially shocked, M. gigas is able to acclimate to these conditions. We speculate that this is
because animals under the fluctuating conditions spend some portion of the day in benign
conditions of higher pH ( > 8.0). Given the highly fluctuating environment of the Southern
California Current ecosystem, this OA treatments likely mimics conditions historically
experienced by the native O. lurida species, which is why we observe only a moderate response.
Although oysters in treatment 7.7A0.5 also experience part of the day at a pH > 8.0, this

39
treatment also exposed them to much lower pH conditions (pH 7.2) than would generally be
experienced in the wild. Therefore, at these conditions organisms cannot acclimate to the effects
of severe OA, as is demonstrated by high levels of mean shell dissolution as well as increased
DEGs and GO term enrichment. The similar pattern of increased differential expression in
treatment 7.7A0.5 from week two to week six in both species suggests that persistent exposure to
these conditions may represent an OA threshold for both native and introduced oyster species.
We expect that long-term exposure to pH below 7.7 would result in serious and likely lethal
molecular and physiological effects on both species.
Gene set enrichment results reflected observations in differential gene expression
between the two species. The muted response of O. lurida under all conditions as compared to
the high number of pathways enriched in M. gigas across treatments and timepoints suggest that
Pacific oysters differentially regulate ion, RNA, DNA, and protein binding, translation,
cytoskeletal rearrangement, immune response, and signal transduction pathways in an attempt to
manage OA stress at these conditions, while O. lurida appear to have the capacity to maintain
normal cellular functioning. Our GSEA results also support that observation that M. gigas has the
ability to acclimate to mild OA stress (7.7A0.2), as enriched GO terms decreased over time at
this treatment. However, our results suggest that long-term OA stress, even at mild levels, cause
both species to differentially regulate pathways in order to maintain normal functioning. These
results may inform future selective breeding for Pacific oysters by identifying genomic regions
and pathways which may underlie enhanced resilience to OA and which could be targets for
genetic manipulation. This also suggests that farmers should focus more farming efforts towards
cultivating and restoring native species, which may be better adapted to present and future
environmental conditions.

40
1.5.3 Physiological responses to ocean acidification do not vary between species despite
differences in molecular expression
Previous work using the same experimental treatments (Bednaršek et al., 2022) also found no
significant difference between the treatments or time periods across a number of physiological
responses to experimental ocean acidification conditions. Both M. gigas and O. lurida species
retained similar calcification, clearance and respiration rates across treatments and time points.
The same study also explored shell dissolution response, which showed an increasing dissolution
pattern from pH 8 (control), to 7.7, with significant increase for the two variable treatments,
7.7A0.2 and 7.7A0.5. The same pattern was found for both species. This indicates that while
dissolution was increasing under more variable treatments, no commensurate increase was noted
in calcification, indicating that eventually shell dissolution prevails in the more variable
treatments and will represent the most important problem for oyster shell growth, fragility and
integrity. Similar results have been observed in other studies of oyster shell parameters under
OA, where shell dissolution has been identified as the primary problem associated with OA, as
opposed to biomineralization, which is maintained throughout OA exposure by increased
expression of associated pathways (Chandra Rajan et al., 2021).
Greatest similarities between both species in their ocean acidification responses are
related to shell dissolution. An increase at both lower pH and higher amplitude shows that neither
oyster species currently has the adaptation potential to offset these effects. Under the conditions
in this study, organisms would not be able to maintain shell structures in the long-term. Although
they might be able to acclimate to these conditions at the molecular level, shell dissolution would
ultimately be a bottleneck in organismal survival. No statistically significant difference between
species in shell dissolution at any treatment suggests that M. gigas may be able to compensate
for ocean acidification stress through elevated expression of biomineralization pathways,

41
however, O. lurida can manage OA stress without elevated expression or stress, as demonstrated
by the maintenance of comparative physiological measures between the two species. Our results
demonstrate that oysters can maintain shell by increased differential gene expression up to a
threshold, but prolonged exposure to such conditions would likely be highly detrimental.
1.5.4 Conclusion
Local adaptation of populations of the same species is also widely thought to play a role in
resilience of marine invertebrates to environmental stress, including ocean acidification (Durland
et al., 2021; Kelly & Hofmann, 2013; Spencer et al., 2023; Swezey et al., 2020; Vargas et al.,
2017). Distinct populations of oysters along the West Coast have been documented, and
geographic populations of Olympia oysters possess unique outlier loci that suggest a genetic
basis for the local adaptation observed within the species (Silliman, 2019). Additionally, Spencer
et al. (2023) challenged three geographic populations of Olympia oysters to OA and found
distinctly different transcriptional responses between groups, which they attributed to populationspecific physiotypes. Recent studies of historical and local adaptation of Olympia oyster
populations to OA events provides strong support for the observed differences in transcriptional
response between the two species in this study.
It is worth noting that tipping points that result in irreversible physiological effects and
organism mortality may be much lower than suggested by the results of this study, which only
tested OA up to a 0.5 diel amplitude from a pH 7.7 median (pH 7.2 – 8.2). Although this
treatment results in oysters spending a significant portion of the day below critical saturation
states, they also experience a lapse in OA conditions every 24 hours. This could provide the
organism with enough time and resources to maintain normal processes. While this adequately
mimics natural, average diel variations, extreme OA events many times last up to several weeks,
during which intertidal organisms experience sustained low pH conditions (Richard A Feely et

42
al., 2008). Lutier et al. (2022) identified tipping points for physiological traits at a pH as low as
6.9-7.3, significantly lower than conditions investigated in this study. They found that at these
tipping points, oysters experience a major reshuffling in membrane lipids and transcriptomes, but
even at a slightly higher pH organisms experience a decrease in fitness. Although it is likely that
OA tipping points and threshold are lower than suggested within this study, the treatments
investigated here based on current and projected estuarine pH data identify realistic conditions
that organisms will likely face within the next decade, and thus provide insight into how these
species will respond and acclimate in the short-term. The results from studies utilizing real buoy
data provide guidelines to aquaculture stakeholders that can be immediately utilized and
implemented into farming and restoration practices.
The present study aimed to characterize the transcriptomic response of both native,
Olympia oysters and recently-introduced Pacific oysters, which share similar distributions and
habitats along the West Coast of the United States. We observed a distinct difference in
differential gene expression patterns and enriched gene ontology pathways in the Pacific oyster
as compared to the Olympia oyster across treatments, however, both species exhibited elevated
molecular stress and high levels of shell dissolution when faced with a low pH paired with high
diel amplitude oscillations. These findings have implications in directing future breeding,
farming, and restoration efforts. While a strong effect of OA on shell dissolution was observed,
no significant difference between dissolution in the two species despite marked differences in
gene expression demonstrates organismal resilience and robustness to immediate OA conditions
by the use of elevated molecular pathways. Further studies are need in order to identify fine-scale
molecular and physiological thresholds, as well as the effects of decreased availability of energy

43
for other processes such as growth, reproduction, susceptibility to pathogens, and multi-stressor
environmental events.
1.6 Author Contributions
NB performed experiments. JC prepared samples and writing. JC and MK performed
bioinformatics and downstream data analysis. AG assisted in sample preparation and data
analysis. MK performed all statistical analysis. NB and AG assisted in project conceptualization,
experimental design, and acquired funding.

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Chapter 2. Assessing effects of ocean acidification on Mediterranean
(Mytilus galloprovincialis) and California (Mytilus californianus)
mussel larval rearing in a small-scale experimental hatchery system
Jordan L. Chancellor1
, Nathan Churches2
, Diane Kim2
, Ian Jacobson2
, Andrew Y. Gracey1
, and
Sergey V. Nuzhdin3
1 Department of Marine and Environmental Biology, University of Southern California, Los Angeles, California, United States
2 Holdfast Aquaculture, San Pedro, California, United States
3 Department of Molecular and Computation Biology, University of Southern California, Los Angeles, California, United States
2.1 Abstract
Ocean acidification affects marine calcifying organisms, which utilize calcium carbonate species
to secrete their external shells. With oceanic pH project to drop to 7.7 or lower by the year 2100,
it is imperative to the aquaculture industry to characterize the affects these environmental
conditions will have on species of economic and ecological importance. This study aimed to
describe growth and survival of two local Southern California mussel species, the Mediterranean
mussel Mytilus galloprovincialis and the California mussel Mytilus californianus in response to
experimental ocean acidification exposure throughout the duration of the larval stage (2 to 21
days post-fertilization). In order to accomplish this, we first developed a small-scale, low-budget
experimental system in which we could accurately and reproducibly control tank parameters.
Then, we spawned larval cohorts of each species and subjected a cohort to pH 7.7 conditions up
until settlement. We found that experimental ocean acidification resulted in significant decreases
in growth rate, but did not highly affect survival probability in both species. After larval
exposure, we out-planted cohorts onto farm longlines and harvested them ten months later for
measurements of commercial interest. We found that larval ocean acidification exposure resulted
in carry-over effects into the adult stage, as adult mussels were significant smaller and had less
wet weight than control cohorts.

45
2.2 Introduction
Ocean acidification (OA), the uptake of excess carbon dioxide from the atmosphere
resulting in decreased oceanic pH, is disproportionately affecting organisms which utilize
calcium carbonate from the marine environment to form shell and skeletal structures (Frédéric
Gazeau et al., 2013; Orr et al., 2005). The effects of OA alongside other environmental stressors
are projected to cause declines in aquaculture productivity and efficiency globally (Froehlich et
al., 2018). Upwelling regions, as well as other regions with naturally occurring drops in pH and
aragonite saturation states, which includes Southern California, are already and will likely
continue to experience rapid OA and subsequent ecological effects within only a few decades
(L.-Q. Jiang et al., 2024; Li-Qing Jiang et al., 2015; Sharp et al., 2024). For this reason, it is
imperative that the effects of OA on marine species are characterized and adaptation strategies
are implemented quickly to address these concerns and ensure long-term productivity of the local
aquaculture industry.
It is common in shellfish aquaculture to purchase “seed” (small, juvenile individuals)
from hatcheries for out-planting onto longline farms, however, seed survival is highly variable
due to a wide-range of factors including seed health, settlement strategies, farm environment, and
genetics (South et al., 2022; Stirling & Okumuş, 1994). Currently, Southern California
aquafarms source mussel seed from wild sets or other hatcheries, which are generally located in
different regions, such as the Pacific Northwest or even internationally, such as Japan. Each
source presents a unique set of problems: wild set seed is unpredictable and highly variable from
year to year, particularly in the context of climate change, and imported seed generally
underperforms in terms of growth and survival when transplanted due to high local adaptation of
stocks (Dickie et al., 1984; Johannesson et al., 1990; Kautsky et al., 1990; Sanford & Kelly,
2011; Yanick et al., 2003). Locally-produced and domesticated mussel seed would theoretically

46
provide Southern California farmers with consistent and reliable access to individuals better
adapted to local conditions, particular to the unique carbonate environment and OA events
experienced in the region (X. Guo, 2021).
Efforts to understand domestication potential in shellfish with regards to OA have been
the subject of recent research (Durland et al., 2021; Kapsenberg et al., 2018, 2022). This chapter,
in collaboration with Holdfast Aquaculture, aimed to build upon this work by challenging larval
naturalized Mediterranean Mytilus galloprovincialis and native California Mytilus californianus
mussels with IPCC-projected ocean acidification conditions for 2100 (IPCC, 2014; Li-Qing
Jiang et al., 2019, 2023; Orr et al., 2005) in an attempt to breed locally-adapted Pacific
Southwest mussels with an increased tolerance to low pH conditions. First, a small-scale, lowbudget experimental system was designed in order to maintain replicate mussel cohorts from
various treatments throughout the entirety of the larval phase (~21 days). Second, mussel cohorts
from each species were spawned and exposed to OA conditions from day two up to settlement
stage. Third, the effects of OA on organismal survival and growth were assessed. Fourth, spat
were out-planted onto a longline system at Santa Barbara Mariculture aquafarm and harvested
ten months later for analysis of long-term effects of OA.
2.3 Methods
2.3.1 Broodstock collection and spawning
Local, wild-set M. galloprovincialis and M. californianus mussels were collected from
the commercial shellfish farm Santa Barbara Mariculture, kept on ice for 24 hours, and bulkspawned following a standard thermal cycling protocol. This spawning protocol, in brief,
consisted of exposing mussels to oscillating water baths of filtered ambient sea water (20C) and
warm filtered sea water (25C) in order to induce spawning via heat-shock (Allan & Burnell,

47
2013; Helm & Bourne, 2004). Immediately upon spawning, male and female adult mussels were
separated from each other, and eggs and sperm were collected, isolated, and counted. Egg and
sperm health and maturity were evaluated visually by size, shape, and sperm motility by
microscopy. For fertilization, sperm was added incrementally until a ratio of approximately ten
sperm to one egg was observed, and fertilization continued until first polar body formation was
observed on roughly half of the eggs (~15 minutes). Following fertilization, eggs were
transferred into a single, 200 L conical tank containing filtered seawater of ambient temperature,
pH, and salinity until development to the D-hinge stage at 2 days post-fertilization (dpf).
2.3.2 Experimental tank design and monitoring
Beginning at the D-hinge stage (2 dpf), larval mussels were equally split and transferred to an
experimental tank system (Figure 2.2). This system was designed and built specifically to house
relatively-large replicate shellfish cohorts at constant conditions throughout the entirety of the
larval period, while keeping in mind the high labor demands that shellfish husbandry at this life
stage requires. The design implemented the use of a recirculating aquarium system in order to
decrease filtered seawater demands and remain in compliance with lease agreements of our space
at Altasea at the Port of Los Angeles, where experiments were constructed, housed, and
conducted. For the requirements of this experiment, two identical, recirculating tank systems
were constructed, one of which would hold the experimental ocean acidification cohorts in
manipulated environmental conditions, and the other of which would serve as a control tank,
where control mussel cohorts would be exposed to ambient, controlled environmental conditions.
Each tank system had its own individual particulate filters (20, 10, and 1 micron; ), UV filter,
chiller, and pump, ensuring that the two tank systems were replicates, whilst remaining isolated
to ensure no cross-contamination would take place during the experiment (Figure 2.1). Although
water was pre-filtered prior to being used in this study, additional water filters are implemented

48
in recirculating aquaculture systems to decrease instances of contamination and ensure animal
health between full-system water changes. Each tank system was composed of four replicate
buckets (eight buckets total across the two systems), each with its own internal filters to ensure
that each replicate was receiving the same water conditions, while remaining isolated from each
other, and to prevent larval escape. Only three replicate buckets (six buckets total across the two
systems) were used in the M. galloprovincialis exposure. High losses of larvae at early
developmental stages is common, particularly when cohorts are kept in small containers, where
water quality and pathogens can escalate rapidly; maintaining replicate cohorts helps ensure that
if one bucket does not survive, the experiment can continue with the remaining cohorts. This
unique tank construction allowed for replication in the experiment and isolation of cohorts both
within and between treatments, while streamlining tank cleaning and water changes which occur
on alternate days and require huge amounts of physical labor.
Throughout the duration of the experiment, a range of tank parameters were continuously
monitored and measured in order to ensure consistent and controlled environmental conditions
between the treatment and control tanks, as well as to monitor water quality and animal health.
These included: temperature, pH, salinity, dissolved inorganic carbon (DIC), total alkalinity
(TA), the partial pressure of aqueous carbon dioxide (pCO2), and aragonite saturation state (Ωar).
In order to assess larval mussel growth and survival under ocean acidification conditions, control
cohorts were kept at ambient conditions (pH 8.1), while treatment cohorts were exposed to low
pH conditions by addition of CO2 gas directly via a solenoid (pH 7.7). Aside from pH, all other
tank conditions remained the same between treatment and control tanks. CO2 bubbling rates were
equilibrated and set to remain within the IPCC 2100 expected pH values (pH 7.6-8.0) and were
monitored hourly, alongside temperature, throughout the duration of the experiment using an

49
Aquacontroller Jr. (Neptune Systems) (Figure 2.3). Salinity was measured using a handheld
salinity probe (Hanna Instruments). Water samples for DIC and TA analysis were taken by
collecting a 25 mL water sample in borosilicate glass scintillation vial and killed with 0.05%
HgCl2 and stored in a cool, dark place for later analysis. DIC and TA were later measured using
an open cell titration analyzer and coulometer, respectively, calibrated using certified reference
material (A. G. Dickson, Scripps Institution of Oceanography, University of California San
Diego). pCO2 and Ωar were calculated with the seacarb R package using measured experimental
data as inputs and default constants for seawater (A. Dickson et al., 2007; A. G. Dickson &
Goyet, 1994; Frankignoulle, 1994; Gattuso et al., 2020; Zeebe & Wolf-Gladrow, 2001).
Conditions between treatment and control tanks, as well as between the two experimental runs
for each species, were analyzed by use of two-way ANOVA and post-hoc Tukey test in order to
determine statistical differences and environmental control within the experimental system. All
statistical analysis was done using R.

50
Figure 2.1. Final constructed tank system utilized in this study. Identical control and treatment tanks with triplicate
buckets were built and placed side-by-side and elevated off the ground in order to streamline husbandry and minimize
tank footprint. Each tank system was composed of particulate filters, UV filter, chiller, and pump.
2.3.3 Larval husbandry and ocean acidification experimental exposure
Larvae were maintained using general mussel hatchery husbandry as outlined in Allan and
Burnell (2013) and Helm and Bourne (2004) practical aquaculture manuals, as well as under the
guidance of hatchery professionals at Holdfast Aquaculture. Full-system water changes were
carried out on alternate days for all tanks and buckets during the larval stage until settlement at
the spat stage (approximately 21 dpf). Larvae were fed a mixed-algal diet composed of
Isochrysis galbana, Chaetoceros calcitrans, and Tetraselmis spp. based on established hatchery
feeding regimes which account for larval size, feeding rate, and stocking density and were

51
modified to best fit our experiment (Allan & Burnell, 2013; Helm & Bourne, 2004). On each
water change day, larvae were collected, rinsed, measured for growth rate, counted for survival
estimates, and samples were taken for later molecular analysis. Larval health and morphological
development was assessed via microscopy during daily checks.
At settlement (~21 dpf) juvenile mussel spat from each treatment were settled onto fuzzy
ropes, during which ropes were placed into the bottom of the buckets, and water changes were
continued until all larvae had settled (no larvae remained in the water column). Once spat-stage
mussels were visible on the ropes with the naked eye and had reached a size of approximately
3mm, the lines were transferred to Santa Barbara Mariculture for a ten month grow-out period on
a longline offshore farm. M. galloprovincialis and M. californianus experimental runs were done
in succession. Prior to starting the next experimental exposure, the experimental mussel tank
system was drained, cleaned, rinsed with fresh water, bleached, and allowed to dry completely in
order to eliminate any contamination from the previous experiment on the alternate species.
To asses larval survival throughout the experimental exposure, a Kaplan-Meier curve was
fit to the count data at each water change day and a log-rank test was used to analyze larval
survival probability. To calculate larval growth rates, images were taken of a subsample of larvae
(n=100) using a dissecting microscope on each water change day and measured at a later date
using ImageJ software (Schneider et al., 2012). Larval size was determined by measuring the
maximum shell length parallel to the hinge. Following a similar analysis by Bitter et al., 2019,
larval size data did not pass the Shapiero-Wilk test for normality, therefore was log-transformed
and fit with a generalized linear model with species, treatment, and day as interaction terms in
order to determine growth rate (M. C. Bitter et al., 2019).

52
2.3.4 Adult mussel phenotyping
Ten months after out-planting of spat-settled lines onto the farm, experimental cohorts were
retrieved and returned to the laboratory space at Altasea at the Port of Los Angeles for
phenotyping. Unfortunately, adult M. californianus were lost in transit to Los Angeles, so only
phenotyping data for M. galloprovinciales is included. Adult mussels were measured for wet
weight (g) and length (cm) before being returned to a broodstock holding space at the Port of Los
Angeles. Phenotypic differences between control and treatment group means was analyzed by
two-sample t-test.
Figure 2.2. Schematic of experimental ocean acidification exposure. The experiment was carried out separately, but
identically, for each species, Mytilus galloprovincialis and Mytilus californianus. All environmental parameters, target
stocking densities, feeding regimens, sample collection, and animal husbandry remained the same, except for the pH
of the control (8.1) and treatment (7.7) tanks. Figure created in BioRender (biorender.com).

53
2.4 Results
2.4.1 Tank pH, temperature, and salinity conditions across treatments and species
Aside from the manipulated pH variable, conditions between experimental and control tanks
were consistent throughout the entirety of the experiment for both species (Figure 2.3, Table 2.1).
pH in OA exposure tanks was held at an average level of 7.7 for both species, while control tank
pH on average was 8.2 for M. galloprovincialis and 8.1 for M. californianus, reflective of pH
levels in local waters. More variable pH observed in control tanks can be attributed to natural
variations in the local marine environment, as seawater in control tanks was unchanged aside
from filtering for potentially harmful pathogens and temperature controlled by the use of a water
chiller during recirculation (Supplementary Table 19).
Temperature throughout the course of the experiment was less constant than pH for both
species and treatments. For both M. galloprovincialis and M. californianus average temperature
between control and OA tanks was statistically significant, and within control tanks M.
galloprovincialis had statistically higher temperatures (20.3C) as compared to M. californianus
(16.38C). Temperature in OA tanks were only slightly different between species (19.75C in M.
galloprovincialis versus 19.82C in M. californianus). Mean salinity for all treatments and
species was between 32.11-32.81 ppm, and only statistically varied between the two replicate
experiments (Supplementary Tables 20-23).

54
Figure 2.3. pH (top), salinity (ppm, middle), and temperature (C , bottom) data of control and treatment tanks
throughout the course of ocean acidification exposure experiments for M. galloprovincialis (left) and M. californianus
(right) species.
Along with pH, temperature, and salinity, the full carbonate chemistry suite was
calculated in order to confirm simulation of OA exposure within treatment tanks, and establish
baselines within control tanks. These parameters included dissolved inorganic carbon (DIC), TA
(total alkalinity), pCO2 (partial pressure of aqueous carbon dioxide), and aragonite saturation
state (Ωar), which alongside pH, are interconnected within the carbon cycle, and the relative
abundance of contribute to the aquatic chemical environment. There was no significant
difference between treatments or species for DIC and total alkalinity. pCO2 and Ωar were
significantly different between control and OA treatments (p < 0.01), but not between species
(Figure 2.4, Table 2.1, Supplementary Table 24-29). Mean values and standard deviations for
each parameter are listed in Table 2.1.

55
Figure 2.4. DIC (uM/kg), TA (ueq/kg, second from top), pCO2 (µatm, second from bottom), and aragonite saturation
(Ωar, bottom) data of control and treatment tanks throughout the course of ocean acidification exposure experiments
for M. galloprovincialis (left) and M. californianus (right) species.
Table 2.1. Mean and standard deviation for all measured tank parameters.
M. galloprovincialis M. californianus
Control OA Control OA
Temperature (°C)
Mean 16.38 19.82 20.31 19.75
Temperature (°C) SD 1.41 0.06 0.46 0.20
pH Mean 8.18 7.77 8.22 7.76
pH SD 0.07 0.14 0.07 0.05
Salinity (ppm) Mean 32.81 32.53 32.14 32.11
Salinity (ppm) SD 0.84 0.74 0.14 0.19
DIC (µM/kg) Mean 2617.18 3174.40 2604.33 2829.67
DIC (µM/kg) SD 443.69 157.00 430.78 551.09
TA (µeq/kg) Mean 2982.31 3299.19 3030.89 3070.56
TA (µeq/kg) SD 489.89 130.62 548.07 548.98
pCO2 (µatm) Mean 349.06 1470.83 343.52 1205.45
pCO2 (µatm) SD 84.95 389.83 43.38 357.03
𝛀ar Mean 4.24 2.01 5.01 1.91
𝛀ar SD 0.79 0.29 1.61 0.17

56
2.4.2 Larval survival and growth throughout high PCO2 exposure
We hypothesized that OA would have a strong effect on survival probabilities of larval mussels,
however, our results suggest that OA does not have an overarching negative effect on larval
survival to settlement in either species. M. galloprovincialis OA mussels exhibited statistically
significant lower numbers of deaths than expected as compared to the control cohort (p < 0.01),
whereas M. californianus showed no strong evidence to suggest a difference in survival between
the OA and control groups (Figure 2.5). Regarding the two species, M. galloprovincialis had a
statistically lower probability of survival to settlement than M. californianus (p < 0.01). Mean
survival for M. galloprovincialis was 5.79 days for control and 5.87 days for OA groups, and for
M. californianus survival was 5.68 days and 5.82 days for control and OA groups, respectively.
The chance of surviving to approximately settlement age (~day 20) for M. galloprovincialis was
0.0162% for control and 0.0221% for OA groups, and for M. californianus it was 0.0356% and
0.0628% for control and OA groups, respectively.

57
Figure 2.5. Survival probability curves of treatment and control larval mussels throughout the ocean acidification
exposure experiments for M. galloprovincialis (left) and M. californianus (right) species. Dashed lines represent mean
survival time for control (black) and treatment (grey) cohorts.
As opposed to survival rates, larval growth between OA and control cohorts was
statistically significant in both species, with OA-exposed cohorts exhibiting slower growth rates
and a smaller size at settlement (Figure 2.6, Supplementary Table 30). The growth rate of M.
californianus was also significantly slower than that of M. galloprovincialis for both treatments.
The control growth rates from this study can be considered the baseline growth rate of these two
species within a hatchery under standard conditions, which for M. californianus was 7.21
um/day, and 8.28 um/day for M. galloprovincialis. Growth rates for the OA-treatment cohorts
were 5.98 um/day for M. californianus and 6.24 um/day for M. galloprovincialis. In addition to a
slower overall growth rate, the OA cohorts took on average one day longer to reach settlement
stage and exhibited more visual morphological abnormalities than control cohorts, further
demonstrating the strong effect of OA conditions on larval development.

58
Figure 2.6. Growth of treatment and control larval mussels throughout the ocean acidification exposure experiments
for M. galloprovincialis (left) and M. californianus (right) species.
2.4.3 Adult mussel phenotype at harvest
After a ten month grow-out period, Mytilus galloprovincialis mussels were phenotyped for
commercially-relevant traits length and wet weight. In both of these traits, carryover effects of
larval ocean acidification exposure were observed in adult mussels that were subjected to lowpH conditions during development, as these mussels showed significantly smaller shell lengths
and wet weights (Figure 2.7, Supplementary Table 31, p < 0.01). Control mussels had an average
length and wet weight of 5.89 cm and 20.59 g, respectively, while treatment adult mussels had an
average length of 5.06 cm and wet weight of 15.32 grams.
Figure 2.7. Length (left) and wet weight (right) of control and treatment adult M. galloprovincialis mussels after ten
months of grow-out on longlines at Santa Barbara Mariculture shellfish farm.

59
2.5 Discussion
2.5.1 Tank parameters and experimental repeatability can be maintained in small-scale,
inexpensive experimental systems
The tank system designed for long-term growth and development of experimental mussel cohorts
was successfully used for two replicate ocean acidification exposure experiments for two local
mussel species, Mytilus galloprovincialis and Mytilus californianus. Throughout the course of
both experiments, environmental tank parameters were assessed, which included: temperature,
salinity, pH, dissolved inorganic carbon (DIC), TA (total alkalinity), pCO2 (partial pressure of
aqueous carbon dioxide) and aragonite saturation state (Ωar). Dissolved oxygen (DO) was not
directly measured in this experiment, however, each individual cohort bucket was bubbled with
O2 to ensure sufficient oxygen levels and aeration for larval development. Maintaining control
setpoints is experimental systems is imperative for decoupling organismal responses to specific
environmental stressors, particularly regarding effects of OA, as temperature and TA constrain
carbon uptake and speciation in seawater, directly influencing pH, and changes in these
parameters directly affect mussel calcification and dissolution rates (Doney, 2010; Gazeau et al.,
2010,2014; Kroeker et al., 2014; Kurihara et al., 2008).
The salinity of the local Southern California region where these experiments were
conducted is on average 33.4-33.7 ppm, and slight difference in salinity observed between
species could be attributed annual changes in the regional environment due to the experiments
taking place at different times during the year (Byrne et al., 2023). Our slightly lower
experimental salinity measurements could be representative of higher levels of freshwater inputs,
as seawater for this experiment was collected near-shore. Temperature within our two treatment
tanks was significantly different for both experiments, but remained within the normal range for
Mytilus mussel development and local Santa Monica Bay conditions (Coe & Fox, 2005;

60
Lockwood & Somero, 2011; Rasmussen et al., 2020). The patterns observed in temperature
could be a result of differences in recirculation, flow rates, chiller efficiency, among a wide range
of other factors that vary between aquarium systems. Because of the large role temperature plays
in oceanic uptake of CO2 and other gasses, which influence major shifts in biogeochemistry as
well as physiological effects on marine calcifiers, it is imperative to control this parameter to
maintain desired OA conditions and avoid multi-stressor conditions.
pH along the Southern California region is about 8.1 on average, although this value is
subject to seasonal and diurnal changes, as well as long-term climate events such as El NiñoSouthern Oscillation (ENSO) (Cai, Xu, et al., 2020; Turi et al., 2018). We were able to
successfully mimic both ambient (pH ~8.1) and mild-OA (pH ~7.7) conditions in our control and
treatment tanks. Our OA condition was chosen based on projected sea surface pH levels for the
year 2100, which are hypothesized to have detrimental effects on marine calcifying organisms
(Richard A Feely et al., 2023; Li-Qing Jiang et al., 2019, 2023). However, because pH is not the
only component of the marine carbonate system, we measured all carbonate parameters within
our experiment in an effort to characterize the entire carbonate environment of these organisms.
pCO2, the partial pressure of dissolved carbon dioxide, describes how much carbon dioxide is in
seawater and is directly determined by how much atmospheric CO2 is absorbed in marine waters
from the atmosphere. Ambient pCO2 in the Southern California region is generally around 400-
450 µatm (Cai, Xu, et al., 2020; Gallego et al., 2018), which is slightly higher than our control
means of 343 and 349 µatm for M. galloprovincialis and M. californianus, respectively.
However, mean pCO2 in OA tanks was significantly higher (1205 µatm M. galloprovincialis;
1470 µatm M. californianus) than projected values of ~850-880 µatm for the year 2100 (Gallego
et al., 2018). The higher observed carbon dioxide levels in our treatment tank could be attributed

61
to the direct bubbling of CO2 gas into the aquarium, which allows more CO2 to enter the water as
opposed to natural oceanic carbon dioxide absorption which is dependent on atmospheric pCO2
levels, temperature, biological activity, and physical mixing (Feely et al., 2004, 2006). The high
pCO2 values measured within our system could also be the result of high levels of alkalinity.
Total alkalinity, the measure of the concentration of molecules that can neutralize acid and buffer
seawater pH, was also high as compared to normal values for the region ( ~ 2250 µeq/kg, Cai et
al., 2020), and in higher alkalinity waters more CO2 is required to generate a change in pH level
and results in smaller changes in Ωar. In regions of low natural alkalinity treatment of filtered
seawater with substances such as soda ash have the potential to mitigate low-pH effects on
organisms in hatcheries by increasing the alkalinity of the water and is a common practice on
many commercial farms.
Mild variations were observed in tank conditions throughout both experiments, which is
to be expected with small-scale aquarium systems filled with filtered seawater collected locally,
and therefore subject to changes in the larger regional marine environment. Precise control of
aquarium environments requires costly monitoring and control technology, which were outside
of the resources available for this project. Frequent water changes aid in minimizing large
perturbations in tank environments and maintaining system health in experimental-scale systems,
however, these are highly time and labor intensive, and conducting experiments such as this at a
larger scale would require an investment in advanced systems to control tank environments more
precisely. Further efforts can be made to fine-tune the experimental system, but long-term
consistent parameter control would require higher-level aquarium technology. More advanced
systems would also allow for multi-stressor investigations, which would provide valuable insight
and better encapsulate larger climate change-driven effects on marine organisms.

62
2.5.2 Larval growth is hindered while survival is stable under experimental ocean acidification
conditions
Larval growth rates and morphological development were substantially affected by OA exposure.
This observation has been documented in a wide range of mollusc species, and further efforts to
understand molecular and physiological components of these effects have been undertaken
(Barros et al., 2013; Barton et al., 2012; Bressan et al., 2014; Chandra Rajan et al., 2021; Frieder
et al., 2017; F Gazeau et al., 2010a; Frédéric Gazeau et al., 2014; Lydia Kapsenberg et al., 2021;
Kurihara et al., 2008; Z. Liao et al., 2023; Lutier et al., 2022; Schwaner et al., 2024; Shang et al.,
2023; Strader et al., 2020; Timmins-Schiffman et al., 2012; G. G. Waldbusser et al., 2015).
Despite lower growth rates, the probability of survival to settlement was not influenced by OA
treatment in M. californianus mussels, and was higher for OA as compared to control cohorts in
M. galloprovincialis. These findings suggest that although mussel larvae growth and
development under OA conditions are highly affected, they do not suffer from high mortality
rates as a result of solely low-pH. Similar results have been observed in M. galloprovincialis in
mild OA conditions (pH decrease ~ 0.3), where acidification treatments alone do not result in
higher mortalities (Gazeau et al., 2014). The high TA levels within this study could explain why
small differences in mortality between cohorts was observed despite high pCO2 and low pH. As a
result of high alkalinity, the saturation state of aragonite, the key mineral comprising mussel
shells, was on average Ωar > 1 throughout all experimental treatments, which thermodynamically
favors the preservation and precipitation of carbonate minerals (Mucci et al., 1989). So, although
mussels were challenged with low-pH and likely experienced hypercapnia and other cellular
stressors which contributed to slower growth rates, acidification conditions were not extreme
enough to elicit highly lethal effects.

63
There has also been recent research which suggests that native shellfish species may
more resilient to acidification conditions due to local adaptation and greater physiological
tolerance and fitness than introduced species (Lemasson et al., 2018; Waldbusser et al.,
2015,2016). In this case, we would expect the local California mussel Mytilus californianus
species to exhibit increased fitness as compared to the introduced Mediterranean mussel Mytilus
galloprovincialis. In our study, we observed a significantly slower control growth rate for M.
californianus as compared to M. galloprovincialis over the course of the entire larval period (~21
days). Waldbusser et al., 2016 posits that slow-shell building in the native Olympia oyster Ostrea
lurida species lessens the energetic burden of acidification as compared to the introduced Pacific
oyster Crassostrea gigas, however, the same group in 2015 compared development of M.
galloprovincialis and M. californianus mussels and found no differences between the two
species, however, they only surveyed the two species for the first 48 hours of development. Our
long-term growth differences agree with the hypothesis that slower growth may be a selected
trait which improves fitness for species which experience historically low Ωar conditions.
However, growth rates cannot directly be extrapolated to calcification rates, as ocean
acidification stunts net calcification, alters the internal calcium carbonate matrix structure, and
results in shell thinning and weakness (Fitzer et al., 2015). Calcification rates and scanning
electron microscopy of developing shells would provide valuable insight into morphological
development of these two species throughout the larval period as opposed to growth rate alone.
2.5.3 Larval ocean acidification exposure during development carries over into adult stage
We observed significant long-term effects of larval ocean acidification treatment on adult
mussels harvested approximately ten months after deployment onto a standard commercial
aquafarm. The OA-treated cohort exhibited lower wet weights and smaller shell lengths, two
commercially important traits. Similar results have been reported in oysters and other marine

64
invertebrates where stress at the “weakest” developmental stage, in this case the larval period,
had a strong carry-over effect into adulthood (Dupont et al., 2013; Gobler & Talmage, 2013;
Hettinger et al., 2012, 2013). Our findings support that a single OA event can have long-term
effects on organisms, even after acclimation to ambient conditions. Dupont et al., 2013 found
that adult green sea urchins which were exposed to a four month OA treatment had lowered
offspring larvae settlement success, suggesting that short-term stressors can have multigenerational effects on population dynamics. A future survey of subsequent generations from the
adults produced in this study could elucidate these effects. It is also important to note that
although the impacts of OA alone on organismal health are extensive, it has been suggested that
these effects combined multiple climate-stressors are additive in bivalves (Bednaršek et al.,
2022; Greenhough et al., n.d.; NH, 2023; Vasquez et al., 2022). Multi-stressor experiments with
highly controlled environmental parameters must be employed in order to fully characterize the
effects of climate change on mussel physiology, survivability, and suitability as a future
commercial food product. This study characterized acute and long-term effects of ocean
acidification exposure on two ecologically important species of mussels on the West Coast of the
US within an inexpensive experimental tank system, which contributes to OA literature of the
two species and demonstrates the capacity to conduct long-term and rigorous scientific studies
with limited resources.
2.6 Author Contributions
JC performed experiments, prepared samples, conducted data analysis and writing. IJ assisted
with conducting experiments. NC, DK, SN and AG contributed to project conceptualization and
experimental design. NC, DK and SN acquired project funding.

65
Chapter 3: Experimental ocean acidification results in allele
frequency divergence in larval Mediterranean mussels Mytilus
galloprovincialis
Jordan L. Chancellor1
, Maxim Kovalev2
, Andrew Y. Gracey1
, and Sergey V. Nuzhdin2
1 Department of Marine and Environmental Biology, University of Southern California, Los Angeles, California, United States
2 Department of Molecular and Computation Biology, University of Southern California, Los Angeles, California, United States
3.1 Abstract
Ocean acidification has been shown to result in changes in allele frequency and population
differentiation in marine invertebrates. In the present study, we employed a pooled-sequencing
approach to determine genome-wide allele frequency differences between OA exposed and
control cohorts of larval Mediterranean Mytilus galloprovincialis mussels from before and after
the exposure period. We found significant differences in allele frequencies between the two
cohorts, and found that GO terms and genes enriched in the OA group included those involved in
immune response, biomineralization, protein processing, and cellular signaling. We also found a
significant number of moderate effect mutations within these genes, suggesting that standing
variation in mussel species is under selective pressure of OA, and soft sweeps on these alleles
will likely lead to environmental adaptation.
3.2 Introduction
Oceanic CO2 concentrations are increasing at a rapid rate globally, resulting in increased
instances of ocean acidification (OA) and aragonite undersaturation (Doney et al., 2009; et al.
Sabine, 2004; Feely et al., 2009; Li-Qing Jiang et al., 2015). OA is known to have negative
effects on marine organisms, including: hypercapnia, increased mortality and cellular stress,
decreased growth and metabolism, and morphological abnormalities (Fabry et al., 2008). OA is
disproportionately affecting upwelling regions, such as the California Current System, and are
projected to amplify substantially within the century, greatly affecting marine resources and

66
populations within this region (Richard A Feely et al., 2008; C Hauri et al., 2013; Sharp et al.,
2024; Wolfe et al., 2023). Many marine calcifiers are further challenged by OA due to the
resulting decrease in available carbonate ions which are necessary to mineralize their calcium
carbonate structures (Frédéric Gazeau et al., 2013; Orr et al., 2005; J. Ries et al., 2009).
Additionally, these organisms have an early larval life stage before they fully-develop calcified
structures, during which they are highly susceptible to the effects of OA (Gibson et al., 2011;
Kurihara et al., 2008). These effects have direct ecological and economic consequences as
marine calcifiers such as oysters, mussels, clams, urchins, and scallops contribute to both
ecosystem stability and commercial industries. In the face of climate change, the importance of
characterizing species ability to adapt will be invaluable in conserving natural populations and
sustainable management of commercial fisheries (Harley et al., 2006; Yadav et al., 2024).
Adaptation to rapid environmental changes will likely be driven by standing genetic
variation, as opposed to new high impact mutations (R. D. H. Barrett & Schluter, 2008;
Hermisson & Pennings, 2005; Lande & Shannon, 1996; Messer et al., 2016). In this case, we
expect soft sweeps across many loci under selective pressure to confer adaptation to a particular
environment. Further, this suggests that organisms with high levels of genomic diversity will
adapt more quickly and retain higher survivorship when faced with novel environments (R. D. H.
Barrett & Schluter, 2008). For this reason, understanding the full genetic landscape, as opposed
to a few loci of high impact, is integral for predicting how populations will respond to a changing
environment. Whole-genome sequencing (WGS) a pool of individuals, known as Pool-seq, is a
cost effective approach to characterizing genome-wide allele frequencies from a larger number of
individuals than traditional WGS of individuals with the same accuracy of sequencing (Gautier et
al., 2013; Rellstab et al., 2013; Schlötterer et al., 2014). Pool-seq is therefore a powerful tool to

67
study adaptation, selection, and domestication, and has already successfully been applied for
gene identification in crop species (Beissinger et al., 2014; D. Ries et al., 2016), human disease,
and environmental adaptation (Kozak et al., 2014).
Adaptation to ocean acidification in marine invertebrates has been the focus of recent
research, and results demonstrate the adaptive potential of these organisms by changes in
genome-wide allele frequencies (M. C. Bitter et al., 2019; Brennan et al., 2019; Durland et al.,
2021; Pespeni et al., 2013; Schiebelhut et al., 2018). While these studies utilize allele frequency
changes to demonstrate standing variation’s role in organismal adaptation to OA, only a handful
of studies have utilized pooled-sequences, of which focus on characterizing population
differentiation between different morphological phenotypes using measures such as Fst and
significant allele frequency divergence based on p-values from statistical tests such as Fisher’s
Exact Test, but do not identify species genes or pathways under selection. In the present study,
we employ a pooled-sequencing approach to characterize population divergence of OA-exposed
and control larval cohorts of the commercially-important Mediterranean mussel species Mytilus
galloprovincialis and identify genes and pathways under selection within a single generation
early life stage. We hypothesized that long-term OA exposure throughout the larval period of M.
galloprovincialis mussels would result in measurable changes in allele frequencies across the
genome, mimicking an evolutionary bottleneck event from one development stage to the next.
Our findings suggest that genes involved in immune defense, biomineralization, and cellular
signaling are undergoing active adaptive selection pressure from early larval (2 days postfertilization, dpf) to settlement stage (23-25 dpf) in response to OA challenge, resulting in
significant population divergence within a short timeframe.

68
3.3 Methods
3.3.1 Larval ocean acidification exposure
Experimental methods are described in detail in section 2.3. In short, larval Mytilus
galloprovincialis mussels were bulk-spawned following a thermal-shock protocol, and at day
two of development were split evenly into either a control or treatment tank. Control tanks were
maintained at ambient pH conditions (pH ~8.1), and treatment tanks were set to a moderate
ocean acidification treatment (pH ~7.7). For molecular analysis, larvae from each tank (~ 100
individuals) were collected, rinsed, pooled into a single sample, and flash-frozen for downstream
processing.
3.3.2 DNA extraction and shotgun pool-sequencing
DNA was extracted from pooled larval samples using a Proteinase K protocol. In brief, a 1:15
solution of Proteinase K (ThermoFisher Scientific) and Tissue and Cell Lysis solution (Fisher
Scientific) was prepared and added to pooled samples and vortexed to lyse cells. Samples were
incubated at 55C for 10 minutes before being placed on ice for five minutes. 150 L of MPC
Protein Precipitation Solution (Astral Scientific) was added to each sample and vortexed before
centrifuging at 4C for 10 minutes at maximum speed. The resulting supernatant was collected
and purified using a genomic Quick-DNA kit (Zymo Research Corporation). DNA quality was
assessed using a NanoDrop Spectrophotometer (Thermo Fisher Scientific) and quantified with a
Qubit 2.0 Fluorometer (Thermo Fisher Scientific). Three replicate samples from each of three
treatments (initial pre-exposure, post-exposure control, post-exposure OA, 9 samples total) were
extracted and sent for whole-genome library preparation and 150bp paired end 10X shotgun
sequencing on an Illumina NovaSeq sequencing platform (Novogene Corporation Inc.,
Sacramento, CA).

69
Quality control of reads was performed for both raw and trimmed reads using FastQC
version 0.12.2 (Andrews, 2010) and aggregated by MultiQC version 1.6 (Ewels et al., 2016).
Trim Galore! version 0.6.10 was used for read trimming in paired end mode with the parameters
-j 1 -e 0.1 -q 20 -O 1 -a (Illumina TruSeq adaptor autodetect) and a minimum length of 36bp
(Krueger et al., 2023). Replicate trimmed and quality filtered reads from each sample were
concatenated into a single sample fastq file and reads were mapped to the Mytilus
galloprovincialis reference genome (NCBI accession JAWDJN000000000.1, Han et al., 2024)
with BWA mem version 0.7.17 (Heng Li & Durbin, 2009). Output SAM files from BWA were
converted to BAM format with SAMtools (Heng Li et al., 2009), sorted, and duplicates were
removed with Picard version 2.27.1 (Broad Institute, 2019). Low quality alignments were
removed using SAMtools and final BAM files were validated with GATK version 4.2.6.1 (Van
der Auwera & O’Connor, 2020) and indexed with SAMtools.
3.3.3 SNP calling and filtration
Final BAM files for each sample were merged and used to generate mpileup files for each
chromosome with SAMtools. Using the popoolation2 software version 1.014 (Kofler et al.,
2011), mpileup files were converted to mpileup sync files for downstream analysis within
popoolation2. Using these mpileup sync files, we determined mean depth statistics to define
filtering cutoffs of SNPs based on genome-wide depth. Genome-wide mean depth of our data
was 16.5, and SNPs which had a minimum depth of 8 and a maximum depth of 300 were filtered
from the dataset. Chromosomal mpileup sync files were merged, allele frequencies were
calculated using popoolation2 snp-frequency-diff.pl with the parameters --min-count 3 --mincoverage 8 --max-coverage 300.
From these results, we kept only bialleleic SNPs (allele_count == 2), and ensured
accurate allele frequencies by filtering out SNPs in which the frequency of the major allele and

70
the frequency of the minor allele did not sum up to exactly one. We then selected only positions
which were 100% SNPs (containing two alleles) at our first time point, day two (pre-exposure)
and in which at least one allele matched the reference allele at this position.
3.3.4 SNP effect annotation
In order to fully characterize the effects of SNPs across the M. galloprovincialis genome, we
annotated these positions using the snpEff version 5.2c (Cingolani et al., 2012). In short, we first
ran a custom script to define whether a SNP was located within gene or intergenic region, built a
database from the available reference genome fasta and annotation (gff3) files within snpEff, and
ran snpEff to characterize SNP effects. We then combined the results of snpEff for the M.
galloprovincialis reference genome with our allele frequency data from popoolation2, and
filtered these results by removing records with warnings and selecting only records for which
SNPs were located within a gene, leaving only SNPs remaining which were quality filtered and
annotated.
3.3.5 Identification of genes under selective pressure
In order to filter our SNP dataset for genes which contained SNPs with large allele frequency
differences, we first counted the number of SNPs within each gene and plotted their density.
Using this, we set a minimum SNP cutoff filter (filter #1) of 30 SNPs per gene, ensuring that we
are investigating highly variable genes with high standing variation (Supplemental Figure 6).
Next, we defined difference in allele frequencies of different treatments and timepoints as
follows:
∆𝑇𝐶= |𝐴𝐹𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 − 𝐴𝐹𝐶𝑜𝑛𝑡𝑟𝑜𝑙|
∆𝑇𝐼= |𝐴𝐹𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 − 𝐴𝐹𝐼𝑛𝑖𝑡𝑖𝑎𝑙|
∆𝐶𝐼= |𝐴𝐹𝐶𝑜𝑛𝑡𝑟𝑜𝑙 − 𝐴𝐹𝐼𝑛𝑖𝑡𝑖𝑎𝑙|

71
where AF stands for allele frequency, and treatment was either OA or control. We then selected
only genes with at least one SNP with ∆𝑇𝐶 above or equal to the 99.99th percentile (|∆𝑇𝐶| 
0.653, filter #2), resulting in 930 genes under strong selection pressure. Finally, we calculated
∆𝑇𝐼 and ∆𝐶𝐼 for all SNPs within these 930 genes and plotted their distributions. We applied the
paired Wilcoxon signed rank test to identify statistically significant differences between the two
distributions.
3.3.6 Biomineralization gene set orthologs and annotations
We employed the same protocol for identifying orthologous biomineralization genes identified in
Chandra Rajan et al. (2021) as described in section 1.3.5. Briefly, sequences from the reference
M. galloprovincialis genome as well as M. hongkongenisis were aligned to SwissProt/UniProtKB
database (The UniProt Consortium, 2023) by the BLASTp algorithm implemented in Diamond
[11] (with parameters -k 1 and --very-sensitive) to identify orthologous sequences. After that,
sequences not matched to any from the database were aligned to TrEMBL/UniProtKB database.
Gene Ontology (GO) annotation (Ashburner et al., 2000; Harris et al., 2004) was done based on
relative records in the aforementioned databases. We utilized these gene annotations to identify
biomineralization genes within our dataset under selection pressure.
3.3.7 Gene set enrichment analysis
We assigned a customized weighted scoring metric to each of the genes which passed filters #1
and #2 in order to run gene set enrichment analysis (GSEA) on our genes under selection. This
scoring metric took into account the full distribution of SNP ∆𝑇𝐶 values, including the maximum
(max), mode (kmod), and interquartile range (iqr) of this distribution. These parameters were
determined as follows: for each gene with multiple SNPs and each SNP with a unique ∆𝑇𝐶 value,
we first estimated kmod by estimating kernel density of the ∆𝑇𝐶 using the density function within

72
R with the parameter adjust =1.5 for smoother distributions and determined which ∆𝑇𝐶 density
had the maximum value. Then, we estimated iqr using the standard R function IQR, and finally,
we estimated the max value with the R standard function max. We then scaled the max, kmod,
and iqr between 0 and 1 for all genes and computed the score as:
𝑆𝑐𝑜𝑟𝑒 = 1 − (0.5 ∗ ( 1 − 𝑚𝑎𝑥)
2 + 0.3 ∗ (1 − 𝑘𝑚𝑜𝑑)
2 + 0.2 ∗ (1 − 𝑖𝑞𝑟)
2
)
After assigning ranks to each gene (see example in Supplemental Figure 8), we ran
GSEA using the clusterProfiler R package (S. Xu et al., 2024) with a minimum gene set value of
five genes and maximum of 500. To better encapsulate GSEA results, we computed a custom
score to describe the results based on both the normalized enrichment score (NES) and -log10(pvalue) (mlp). To do this, we first scaled both NES and mlp to range between 0 and 1, and
calculated the final GO term score as:
𝑆𝑐𝑜𝑟𝑒 = 1 − (0.5 ∗ ( 1 − 𝑁𝐸𝑆)
2 + 0.5 ∗ (1 − mlp)
2
We selected enriched GO terms which were at or above the 90th percentile of the result scores,
and investigated each of these GO terms to determine their relevance (pass/fail) to molluscs
species. We retained only GO terms which were deemed appropriate (pass) to our study species.
We chose to filter GO terms due to the observed artifacts from our annotations using global
protein databases which include all species regardless of their phylogenetic relationship to the
species of interest (see section 3.3.6). We then extracted genes which were enriched within these
top enriched GO terms by selecting the genes which appeared more than once.
3.3.8 Analyzing SNP effects of enriched genes
We determined the direction of change (increase, decrease, or no change) of allele frequency
from the initial pre-exposure timepoint and final timepoint for both OA and control groups for all
56 genes of interest from enriched GO terms and ran the Chi-squared test for type of SNP effect

73
(high, moderate, low, modifier) for each direction of allele frequency change. We then ran a
logistic regression model which included all effects (gene length, high impact, moderate impact,
low impact, and modifier impact), as well as separately for each SNP type and gene length. In
addition, we ran these same logistic regression models with 10,000 permutations for our 56
selected genes and 56 random genes.
3.4 Results
3.4.1 Reference genome mapping, SNP calling, and SNP effect annotation
Sequenced larval pools were of high quality, and large numbers of read pairs were retained postfiltering and quality control (Supplementary Table 32). Filtered reads therefore had high mapping
rates to the available and high-quality M. galloprovincialis reference genome (Supplementary
Table 33). After read mapping, SAM files were further processed and SNPs were called using the
popoolation2 software, which calculates population allele frequencies of pools. For poppolation2
calculations, we determined genome-wide depth of sequencing and set the parameters --mincoverage 8 --max-coverage 300 based on coverage densities (Figure 3.1). After SNPs were
identified and allele frequencies calculated, we filtered this dataset for bialleleic SNPs and ensure
accurate frequencies, which resulted in 43,795,769 SNPs after pre-filtering.

74
Figure 3.1. Genome-wide depth histograms of mpileup files generated from BAM files outputted from BWA mem
mapping of pooled larval M. galloprovincialis samples to the reference genome.
After annotation SNP effects within the reference genome, we merged this data with our
allele frequency data from popoolation2, which resulted in a total of 62,852,695 records. After
further quality filtering, we had a total of 14,061,932 SNPs for allele frequency analysis.
3.4.2 Genes with significant allele frequency differences between control and ocean
acidification cohorts
Applying filter #1 (selecting only genes with a minimum of 30 SNPs per gene) to our SNP
dataset, resulted in 35,524 genes, all of which are assumed to harbor high levels of standing
variation. To narrow-down our dataset, we applied filter #2 (genes with |∆𝑇𝐶|  0.653), which
resulted in 930 genes with high absolute differences in allele frequency between the exposure
end date (either OA treatment or control) and the initial time point. After calculating ∆𝑇𝐼 and ∆𝐶𝐼
of genes passing both filters for OA and control groups, respectively, we found that these genes

75
were found to have significantly different distributions depending on which treatment was
applied (p-value < 0.001, Figure 3.2).
Figure 3.2. Absolute allele frequency difference distribution of genes passing filter #1 (left) as well as filter #2 in
(right) control and OA treatments.
3.4.3 Identification of genes highly involved in biomineralization under selective pressure
Using our annotated and curated biomineralization gene list, we were able to identify 28 genes
belonging to 9 unique gene descriptions in our filtered SNP dataset (930 genes passing both
filters #1 and #2) with high differences in allele frequency between OA and control treatments,
suggesting that these genes are responding to selective pressure under experimental OA
conditions. These genes were Cadherin-23, Complement C1q-like protein 2, Organic cation
transporter protein, Perlucin-like protein, Protocadherin Fat 4, Serine/threonine-protein kinase
mTOR, VWFA and cache domain-containing protein 1, von Willebrand factor A domain-

76
containing protein 7, von Willebrand factor D and EGF domain-containing protein. We plotted
the distribution of absolute allele frequencies within each treatment for this subset of
biomineralization genes as above (see Figure 3.2), but these distributions were not found to be
statistically different from each other (Supplemental Figure 7).
3.4.4 Gene set enrichment analysis
Using our 930 genes with high absolute allele frequency differences (passing filters #1 and #2) to
run GSEA and selecting enriched GO terms at or above 90th percentile of our custom result
scores, we had 41 preliminary enriched GO terms. After filtering, 32 GO terms were identified as
being highly enriched within our dataset (Figure 3.2, Supplementary Table 34). Notable GO
terms enriched in our dataset included collagen-containing extracellular matrix, defense response
to bacterium, modulation of chemical synaptic transmission, protein ubiquitination, innate
immune response, calcium ion transmembrane transport, zinc ion binding, calmodulin binding,
ubiquitin-protein transferase activity, and methylation.

77
Figure 3.3. GSEA results of the 32 top enriched gene sets within our filtered dataset.
3.4.5 Investigation of genes highly involved in enriched GO terms with high allele frequency
differences
In the interest of identifying genes within our enriched GO terms that are widely involved in
many biological pathways under selective pressure, we extracted genes which appeared more
than once across our 32 top enriched GO terms, resulting in 56 genes of high importance in
selective response (Figure 3.4, Supplementary Table 35). None of these genes were
biomineralization genes of interest.

78
Figure 3.4. Genes (56) which appear in more than one enriched GO term and which terms they appear in
Chi-squared test results of the direction of allele frequency change (increase in frequency,
decrease in frequency, or no change in frequency) from the initial to final timepoint for each SNP
effect type showed that only modifier SNPs were statistically significant (Table 3.1).
Table 3.1. Chi-squared test results of each SNP effect type and direction of allele frequency change from the intial to
final timepoint.
Allele impact Type of
AF
change
Number of
changes
Df ChiSq
statistics
p-value
Control OA
HIGH Negative 23 25 2 0.301515152 0.86
No
change
3 2
Positive 28 27
MODERATE Negative 1395 1459 2 2.862197062 0.239
No
change
51 52
Positive 1523 1458
LOW Negative 1518 1559 2 1.559903781 0.458
No
change
52 44
Positive 1586 1553
MODIFIER Negative 19824 20271 2 10.17635815 0.00617

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No
change
769 786
Positive 21731 21267
We found that gene length in our 56 selected genes was higher on average than all other
genes in the dataset (~44,000) (Supplemental Figure 9), and therefore included this parameter as
a fixed effect in our logistic regression models. In all of our regression models, gene length is
found to be significantly in our selected genes versus all other genes (p-value < 0.001,Table 3.2).
Our logistic regression model which included all effects (gene length, high impact, moderate
impact, low impact, and modifier impact) found that in addition to gene length, moderate impact
mutations were significantly different in our selected genes (p-value < 0.05, Table 3.2). In
addition, we reduced the model to include only gene length and a single modifier type, and in
these reduced models also only showed a significant difference in moderate effects (p-value <
0.001,Table 3.2).
Table 3.2. Logistic regression results of the full model and reduced models for our selected genes (56) against all other
genes (~44,000).
Logistic regression
model
Intercept Gene
Length
HIGH MODERATE LOW MODIFIER
Full 2.50E300
6.69E05
0.314189245 0.011704611 0.671717448 0.651192021
Reduced.HIGH 1.40E302
3.14E19
0.637133354
Reduced.MODERATE 0 1.26E16
0.000734393
Reduced.LOW 0 1.52E12
0.063913685
Reduced.MODIFIER 0 4.11E05
0.593229878
We then ran the same logistic regression model with 10,000 permutations for the 56
selected genes against 56 randomly selected genes, and results from this model showed that in

80
the reduced model for moderate effect types, 67% of the time moderate mutations result in
statistical significance (p-value < 0.05), and low effect types produce statistically significant
results (p-value < 0.05), 55% of the time for the reduced low effect model (Table 3.3).
Table 3.3. Logistic regression results of the frequency (%) of significant results of each SNP effect type for the full
model and reduced models for our selected genes against and equal number of random genes for 10,000 permutations.
Logistic regression model Intercept Gene
Length
HIGH MODERATE LOW MODIFIER
Full 99.82 78.05 5.22 12.94 11.79 11.52
Reduced.HIGH 98.79 100 3.89
Reduced.MODERATE 99.99 100 66.38
Reduced.LOW 99.97 99.54 55.08
Reduced.MODIFIER 99.84 68.12 3.59
3.5 Discussion
3.5.1 Mussels show a genetic signature of selection in response to ocean acidification within a
single generation
Genetic differentiation at individual loci due to environmental clines has been documented in
mussels as early as the 1990’s, when blue mussels Mytilus edulis within a single bay with gene
flow were determined to have diverging allele frequencies as a result of salinity (E. M. Gosling
& McGrath, 1990). In recent years, similar differences in genome-wide allele frequencies have
been observed in a variety of marine species in response environmental and disease conditions
(M. C. Bitter et al., 2019; Brennan et al., 2019; Durland et al., 2021; Pespeni et al., 2013;
Schiebelhut et al., 2018). For example, a 50 day ocean acidification exposure of larval purple
urchins Strongylocentrotus purpuratus detected widespread allele frequency changes to tolerate
OA (Pespeni et al., 2013), and a similar study in the same species identified consistent changes in

81
allele frequencies and increased linkage disequilibrium around selected loci, demonstrating
selection on standing variation in response to OA exposure (Brennan et al., 2019). Similar
patterns have also been shown in M. galloprovincialis, where allele frequency patterns showed
genomic differentiation between control and treatment populations in response to OA exposure
(M. C. Bitter et al., 2019). In the present study, we utilized pooled-whole genome sequencing in
order to characterize genome-wide allele frequency differences between control and treatment
populations in response to OA. Our results demonstrate a significant difference in allele
frequencies between these two cohorts, demonstrating a population-wide genomic selection
response within a single generation as a result of OA exposure (Figure 3.2).
Although we observed decreased growth over the course of larval development at the
phenotypic level, in Chapter 2. Assessing effects of ocean acidification on Mediterranean
(Mytilus galloprovincialis) and California (Mytilus californianus) mussel larval rearing in a
small-scale experimental hatchery system, we observed that OA-exposed M. galloprovincialis
actually had a statistically higher likelihood of survival to settlement, although mortality and
survival rates between the two cohorts were relatively similar (Section 2.5.2). These observations
demonstrate that environmental stressors such as OA present a trade-off scenario for organisms
(Lemasson et al., 2017), and do not always result in decreased fitness.
3.5.2 Ocean acidification results in selection of biological pathways involved in immune
defense, biomineralization, and protein regulation
Biological trade-offs of ocean acidification exposure, such as prioritizing immune defense,
calcification, transcription, and protein regulation are observed in the enriched GO terms and
genes identified within this study. GO terms involved in biomineralization and growth included
collagen-containing extracellular matrix, calcium ion transmembrane transport, calmodulin
binding. For immune response, we see significant enrichment in the GO terms early endosome,

82
extracellular exosome, endosome membrane, defense response to bacterium, innate immune
response, and protease binding. Finally, we observe selection response to OA in protein
regulation, transcription, and cell signaling pathways in enriched GO terms belonging to
methylation, ribosome biogenesis, positive regulation of gene expression, protein ubiquitination,
intracellular protein transport, protein folding, zinc ion binding, methyltransferase activity,
mRNA binding, receptor binding, protein tyrosine kinase activity, receptor binding, and
ubiquitin-protein transferase activity (Figure 3.3).
These selected pathways are reflected in our 56 genes highly involved in selection
response (Figure 3.4), which included genes such as Protocadherin Fat 2 (Chandra Rajan et al.,
2021), Receptor-type tyrosine-protein phosphatase alpha (C. Wang et al., 2023), E3 ubiquitinprotein ligases (F. Guo et al., 2024), Sorting nexin-27 (Yu et al., 2024), Furin (T. et al., 2015),
Ficolin-1 (Araya et al., 2010; T. Li et al., 2017), A disintegrin and metalloproteinase with
thrombospondin motifs 20 (Y. Liu et al., 2022), Histone H4 transcription factor (Navarro‐Martín
et al., 2023), Titin (Artigaud et al., 2015), Transient receptor potential cation channel subfamily
M member 3 (H. Fu et al., 2021), and L-amino-acid oxidase (Bishop et al., 1981), all of which
are known to be highly involved in in response to environmental and immune stress. Our results
show that these genes are under selection in response to OA in M. galloprovincialis mussels and
likely involved in long-term resilience and adaptation to OA.
3.5.3 Standing variation and moderate impact mutations provide the basis for adaptation to
ocean acidification in mussels
It has been suggested that soft sweeps on standing genetic variation will most likely be the basis
for environmental adaptation as opposed to hard sweeps on high impact mutations (R. D. H.
Barrett & Schluter, 2008; Hermisson & Pennings, 2005; Lande & Shannon, 1996; Messer et al.,
2016). Our finding that moderate impact mutations have an outsized effect in OA-exposed

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cohorts as compared with random genes corroborates this hypothesis (Table 3.2, Table 3.3).
Shellfish are known to have high genetic diversity and rates of mutation (Launey & Hedgecock,
2001; Plough et al., 2016; G. Zhang et al., 2012a), which is theorized to allow for high rates of
adaptation over a smaller number of generations to environmental selection pressures. Our
findings reveal the basis of these mechanisms within a single generation life stage and suggest
long-term adaptability to OA over multiple generations. Long-term adaptation potential has been
observed in the closely related blue mussel Mytilus edulis, where individuals exposed to OA
conditions over three years demonstrated higher fitness than controlled cohorts, specifically in
calcification and settlement rates (Thomsen et al., 2024). Based on previous research and the
enrichment of GO terms involved in growth and biomineralization in the present study, we
would expect to observe similar responses to long-term OA exposure in M. galloprovincialis
mussels.
Although allele frequency differences in response to ocean acidification have been shown
previously, this study is the first to leverage pooled sequencing methods to capture genome-wide
trends in entire experimental populations and to identify specific genes and biological pathways
under selection to OA. Our findings provide the framework required to conduct large-scale
genome wide association studies and guide genomic selection breeding regimes for adaptation of
mussels to future OA conditions, as well as confirm the ability of these organisms to cope and
adapt to environmental stressors.
3.6 Author contributions
JC performed experiments and prepared samples. JC and MK performed bioinformatics and
writing. MK performed all statistical analysis. SN and AG contributed to project
conceptualization and experimental design. SN acquired project funding.

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Chapter 4: Ocean acidification shapes larval mussel microbiome
community composition
Jordan L. Chancellor1
, Emily Aguirre2
, Andrew Y. Gracey1
, and Sergey V. Nuzhdin2,3
1 Department of Marine and Environmental Biology, University of Southern California, Los Angeles, California, United States
2 Kelp Ark, San Pedro, California, United States
3 Department of Molecular and Computation Biology, University of Southern California, Los Angeles, California, United States
4.1 Abstract
The microbial community can have a strong impact on organismal health, however, the
microbiome of many commercial aquaculture species are only beginning to be fully
characterized. Ocean acidification and other environmental stressors will likely reshape
microbial communities, therefore affecting organism growth, survival, and other traits of
commercial and ecological interest. In this study, we characterized the microbiome community
over time in larval Mediterranean Mytilus galloprovincialis and California Mytilus californianus
mussels in response to experimental ocean acidification exposure. We used two approaches to do
this: 1) we assembled metagenome assembled genomes from whole genomes sequencing reads
prior to and following exposure and predicted metabolic function, and 2) we amplicon sequenced
the V3-V4 region of the 16S ribosomal subunit across time points throughout the duration of the
experiment. We found that microbial membership and relative abundance between the two
species was similar, however, Mytilus californianus showed signs of immune suppression. Beta
and alpha diversity measures were significantly different between the two species, and within
species, OA treatment resulted in a significant effect on diversity and dispersion in Mytilus
galloprovincialis.
4.2 Introduction
An organism’s microbiome is comprised of the complete community of microorganisms, (fungi,
bacteria, and viruses) which exist both on and within the organism. Microbiome research has

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exploded in recent decades, and extensive research linking microbiome communities to human
(Aggarwal et al., 2023; Proctor, 2019), environmental (Blaser et al., 2016), and crop (Busby et
al., 2017; Singh & Trivedi, 2017) health has highlighted the importance of microorganisms
across interdisciplinary fields. In agriculture, the microbiome has been harnessed for improving
animal and crop health and productivity (Lindsey et al., 2020; Santos et al., 2019; Vishwakarma
et al., 2020). However, microbial research has not been as widely applied to aquaculture, and the
vast majority of aquaculture microbiology research to date has been focused on disease control
(Lorgen-Ritchie et al., 2023), probiotic development (Kesarcodi-Watson et al., 2008; Leong et
al., 2023; Ringø, 2020; Stevick et al., 2019), and water quality management (Attramadal et al.,
2012; Dahle et al., 2022).
The microbiome is associated with a variety of functions in shellfish including nutrient
uptake, immune defense, and regulation of other physiological functions (A D Diwan et al.,
2023; Arvind D Diwan et al., 2022). In bivalve molluscs, such as oysters and mussels,
microorganisms can occupy gut, gill, hemolymph, and external shell environments, supporting
the functions of each structure to host health. Mollusc microbiomes are uniquely shaped by their
aquatic environment due to their filter feeding nature. Because these species are in a constant
exchange with surrounding seawater, their microbial composition can change dramatically in
accordance with changes in water quality, temperature, season, and location, among other factors
(Burge et al., 2014; Green et al., 2019; Sunagawa et al., 2015). The use of on-shore hatcheries for
rearing of early life stages allows growers to control water quality, but there still remains a
constant flow of bacteria through systems, many of which are essential in breaking down waste
products and maintaining a stable, healthy microbial community, but also potentially introducing
pathogenic bacteria (Arfken et al., 2021; Attramadal et al., 2012; Lorgen-Ritchie et al., 2023).

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In addition to water quality management, microbial communities have been the target of
research into disease management and improved survival in shellfish hatcheries by the use of
pro- and pre-biotics in place of antibiotic or antimicrobial drug treatments (Prado et al., 2010;
Ringø, 2020). For example, Stevick et al. (2019) found that daily treatments of the probiotic
Bacilus pumilus R106-95 to water in Eastern oyster Crassostrea virginica rearing tanks was
effective in decreasing potentially pathogenic bacterial species. Additionally, Kesarcodi-Watson
et al. (2010) identified Alteromonas macleodii 044 and Neptunomonas sp. 0536 strains as
effective probiotics that improved survival of Greenshell mussel Perna cancaliculus larvae
when challenged with pathogenic Vibrio spp. (Kesarcodi-Watson et al., 2010). The use of
probiotic microbial strains for improving mollusc growth have been explored in commercial
oyster species (Aguilar‐Macías et al., 2010; Douillet & Langdon, 1994) and the Greenshell
mussel (Kesarcodi-Watson et al., 2012), but very little research to date has focused on Mytilus
mussel species, which are the most cultivated mussel genus worldwide (FAO, 2022). The
microbiome of adult Mytilus galloprovincialis mussels has been described (Musella et al., 2020;
Venier et al., 2011), but insights into microbial recruitment, environmental effects on microbial
community membership, and potential probiotics have not been investigated. Additionally,
microbial communities for non-commercial mussels mussel species have not been welldocumented despite the fact that these species and their associated microbes play large ecological
roles.
The effects of warming and multi-stressors have been shown to cause shifts in Mytilus
mussel microbial communities (Y.-F. Li et al., 2019; Ullah Khan et al., 2021; Y.-T. Zhu et al.,
2024). However, the majority of these studies focus on the gut microbiota of adult mussels, and
no studies to date have characterized the direct effect of ocean acidification (OA) challenge on

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larval Mytilus mussel community composition and diversity and how the larval microbiome
changes throughout development within a recirculating aquaculture system. We aim to address
these knowledge gaps in the present study by the combined use of metagenomics and 16Stargeted amplicon sequencing. In short, we challenged Mediterranean Mytilus galloprovincialis
and California Mytilus californianus mussels with OA conditions (pH 7.7) for the entirety of the
larval period (~21 days). We assembled metagenome assembled genomes (MAGs) from shotgun
sequencing reads of Mytilus galloprovincialis mussels prior to (pre-exposure) and following
either OA or ambient (control) rearing conditions (see chapter 2 for experimental design; chapter
3 for sequencing methods) in order to elucidate putative bacterial metabolic capacities.
Metagenomics is a powerful tool for understanding both microbial diversity and gene function,
and the application of metagenomics in aquaculture is only beginning to be realized (A D Diwan
et al., 2023; Arvind D Diwan et al., 2022; Y. Zhou et al., 2022). In addition, we sequenced the
V3/V4 region of the 16S ribosomal RNA gene in both Mytilus galloprovincialis and Mytilus
californianus larvae at six timepoints throughout the larval stage in order to characterize
microbial community membership and diversity throughout time.
4.3 Methods
4.3.1 MAG assembly, classification, and functional annotation of Mytilus galloprovincialisassociated microbes
Quality-filtered and trimmed shotgun sequenced reads from Chapter 2 were further utilized to
assemble metagenome assembled genomes (MAG) in order to characterize microbial diversity
and function in our experimental cohort prior to and following OA exposure. After mapping
prepossessed reads to the Mytilus galloprovincialis reference genome using the Burrow-Wheeler
Alignment tool (Heng Li & Durbin, 2009), unmapped reads from each treatment (pre-exposure,
control, and OA) were extracted and reverted to fastq format with SAMtools (Heng Li et al.,

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2009), and error corrected using SPAdes BayesHammer correction with the option --only-errorcorrection (Nikolenko et al., 2013; Prjibelski et al., 2020).
Metagenomes were assembled using two separate assemblers: MEGAHIT and
metaSPAdes (D. Li et al., 2015; Nurk et al., 2017). Assembly quality was assessed using
metaQUAST from the QUAST package (Mikheenko et al., 2016). Based on the results from
metaQUAST, the MEGAHIT assembly was used for downstream analysis. Assembled
metagenomes were then binned using three binning tools: metaBAT 2, CONCOCT, and MaxBin,
and highest quality bins were determined using DAS Tool (Alneberg et al., 2014; Kang et al.,
2019; Sieber et al., 2018; Wu et al., 2014). The quality of selected bins was then assessed using
CheckM software, and bins were filtered and retained if they had greater than 80% completion
and less than 10% contamination (Parks et al., 2015). Remaining bins were then dereplicated
using dRep (Olm et al., 2017), and resulting bins were considered to be complete MAGs.
Taxonomic classification of MAGs was done using the GTDB-Tk classify workflow,
which classifies assembled MAGs based on the Genome Database Taxonomy (Chaumeil et al.,
2022). MAG sequences were then used to infer approximately-maximum-likelihood
phylogenetic trees from alignments with FastTree 2 (M. N. Price et al., 2010). Functional
annotation of sequences was done by first using Prodigal (Hyatt et al., 2010) to predict proteincoding genes, which were then supplied to METABOLIC (Z. Zhou et al., 2022) in order classify
metabolic capabilities of the input genomes. To determine MAG relative abundance the read
coverage calculator for metagenomics, CoverM, was used in genome mode to obtain abundance
tables for fastq reads mapped to assembled using the Burrows-Wheeler Alignment tool (Aroney
et al., 2024).

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4.3.2 16S classification of Mytilus galloprovincialis shotgun sequenced reads
Error-corrected shotgun sequencing reads used to assemble MAGs were additionally mapped and
classified to the 16S ribosomal subunit region using the metaxa2 version 2.2 software
(Bengtsson-Palme et al., 2015), which utilized the BWA mem mapping algorithm with
parameters -min-read-percent-identity 97% and -min-read-aligned-percent 75% . The blast
search option and SILVA reference database were used within metaxa2 for read classification,
and the metaxa2 transversal tool and data collector were used to organize identified rRNA gene
sequences into taxonomy and abundance tables. These tables were then analyzed with the
phyloseq package in R (McMurdie & Holmes, 2013).
4.3.3 16S rDNA sequencing of Mytilus galloprovincialis and Mytilus californianus larvae
across developmental timepoints
In order to elucidate microbial diversity and community composition changes throughout the
larval developmental stage, we performed amplicon sequencing of the 16S ribosomal subunit in
addition to shotgun sequencing. We extracted DNA using a Proteinase K in Tissue and Cell Lysis
buffer (ThermoFisher Scientific) extraction protocol, as described in chapter 3 from four
individuals from each treatment (control and OA) at six timepoints for two species (Mytilus
galloprovincialis and Mytilus californianus) for a total of 96 sequenced individuals.
Amplification of the V3/V4 region of the 16S rDNA gene was done using the 338F (5′-
ACTCCTACGGGAGGCAGCA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) primer
set with the addition of Illumina-specific adaptors, which has been previously used to describe
the microbial community in Mytilus mussels (Y.-F. Li et al., 2019; Y.-T. Zhu et al., 2024). 50 ng
of extracted DNA was amplified in 25 L reactions using 1.25 L of 10 M forward and reverse
primers each, 12.5 L Q5 High-Fidelity 2X Master Mix (New England BioLabs, Inc.) and
nuclease-free water. PCR conditions were modified from Aguirre et al. (2023), with the

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following modifications: 30 cycles of denaturation at 98C for 10 seconds followed by annealing
at 65C for 30 seconds and extension at 72C for 30 seconds. Five L of PCR-amplified sample
was added to an additional three-step PCR to incorporate sample-specific Illumina barcodes
using an amplification protocol of 98C for 10 seconds, 59C for 30 seconds, and 72C for 30
seconds for five cycles. Barcoded samples were pooled in equimolar amounts and 150bp paired
end sequencing on an Illumina NovaSeq X Plus platform.
Paired ends reads were demultiplexed by the sequencing facility (Novogene Corporation
Inc., Sacramento, CA) and quality checked with FastQC (Andrews, 2010). Quality trimming and
adaptor content removal was done using Trimmomatic (Bolger et al., 2014) with the following
parameters: ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3
SLIDINGWINDOW:4:20 MINLEN:36. Quality filtered and trimmed reads from each species
were imported into the microbiology analysis software qiime2 (Bolyen et al., 2019), and further
quality filtered and denoised using Deblur (Amnon et al., 2017) within qiime2. Taxonomy was
assigned to the resulting operational taxonomic units (OTUs) with the qiime feature-classifier
classify-sklearn command using qiime2-compatible SILVA database release 132 trained on our
specific 16S primers with 97% similarity with qiime command feature-classifier fit-classifiernaïve-bayes (Quast et al., 2013; Robeson et al., 2020). Phylogenetic relationships of denoised
sequences were inferred with the qiime fragment-insertion sepp command using SILVA database
release 128. Resulting taxonomic assignments, phylogenetic trees, and OTU tables were
exported for downstream analysis with the R package phyloseq (McMurdie & Holmes, 2013).
4.3.4 Relative abundance, ordination, and diversity analysis of microbial community
composition
For MAGs, 16S-mapped WGS reads, and 16S V3-V4 amplicon sequenced reads, relative
abundance was calculated based on OTU count tables and plotted using the ggplot2 package in R

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(H., 2016) across taxonomic ranks, treatments, and time points in order to determine the
composition and contribution of microbial community members. Alpha diversity measures were
calculated using the estimate_richness function within the phyloseq package, and significance
between groups was tested using the Kruskal-Wallis test for significance between two groups,
followed by the post-hoc Dunn’s test, and one-way analysis of variance (ANOVA) followed by
Tukey post-hoc test for multiple groups. Due to the compositional structure of microbial data
(Gloor et al., 2017), counts were log-ratio transformed using the microbiome package in R (Lahti
et al., 2017). Aitchison distance was then calculated for the transformed counts and visualized
with PCA ordination (Aitchison, 1986) using phyloseq plot_ordination. Significant differences in
ordination group centroids was determined with PERMANOVA using the Adonis2 function from
the vegan package in R (Oksanen, 2010), and significant differences in beta-dispersion were
tested with PERMDISP2 using the vegan beadisper function. Significant results from were
further investigated with ANOSIM using the vegan anosim function.
4.4 Results
4.4.1 MAG, classification, relative abundance, and predicted metabolic function
Our metagenome assembled genome (MAG) assembly and filtration pipeline resulted in 38 total
MAGs, which was comprised of one MAG from pre-exposure, 17 from control post-exposure,
and 20 from OA post-exposure. All MAGs had a completion of greater than 82% and
contamination levels less than 10%. All except three MAGs could be classified up to the family
level, and 25 MAGs (65%) could be classified to the genus level. The vast majority of MAGs
belonged to the proteobacteria phylum, predominantly from the gammaproteobacterial and
alphaproteobacterial classes (Table 1).
Only one MAG was assembled from day two pre-exposure larval cohorts belonging to
the genus DT-34, which is a Flavobacteriaceae bacterium assembled from deep trap sequencing

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at station ALOHA in the North Pacific Gyre (Simons Collaboration on Ocean Processes and
Ecology, GenBank assembly GCA_013215095.1, Boeuf et al., 2019). Post-exposure microbial
communities from both control and OA cohorts showed higher levels of taxonomic diversity,
with the majority of bacteria belonged to the classes of Alphaproteobacterial and
Gammaproteobacteria (Figure 4.1). Shared between both control and treatment MAGs at the
order level were Rhodobacteraceae, Chlamydiales, Pseudomonadales, and Xanthomonadales.
Predicted metabolic function and gene presence varied widely across MAGs, and musselassociated bacteria appear to be widely diverse in metabolic function. Genes involved in
fermentation, sulfur cycling, nitrogen cycling, complex carbon degradation, C1 metabolism, iron
cycling, and oxidative phosphorylation were observed across all treatments and timepoints, but
unique taxa and metabolic function in each treatment emerged (see 4.5.1).
Table 4.1. MAG taxonomic classifications as determined by GTDB-TK. MAG Treatment Domain Phylum Class Order Family Genus Species
MG22_D2.1 pre-exposure Bacteria Bacteroidota Bacteroidia Flavobacteriales Flavobacteriaceae DT-34 Unclassified DT-34 MG22_D23_C.011 Control Bacteria Proteobacteria Alphaproteobacteria Sphingomonadales Unclassified Sphingomonadales Unclassified Sphingomonadales Unclassified Sphingomonadales MG22_D23_C.21 Control Bacteria Verrucomicrobiota Verrucomicrobiae Verrucomicrobiales DEV007 JABDJT01 Unclassified JABDJT01 MG22_D23_C.22 Control Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Porticoccaceae 50-400-T64 Unclassified 50-400-T64 MG22_D23_C.23_sub Control Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Halieaceae Halioglobus Unclassified Halioglobus MG22_D23_C.2_sub Control Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Roseovarius Unclassified Roseovarius MG22_D23_C.34 Control Bacteria Proteobacteria Gammaproteobacteria UBA4486 UBA4486 UBA7359 Unclassified UBA7359 MG22_D23_C.45 Control Bacteria Proteobacteria Gammaproteobacteria Burkholderiales Methylophilaceae GCA-2401735 Unclassified GCA-2401735 MG22_D23_C.5 Control Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae WLWX01 WLWX01 WLWX01 sp012103375 MG22_D23_C.55 Control Bacteria Proteobacteria Gammaproteobacteria Ga0077536 Ga0077536 UBA11873 Unclassified UBA11873 MG22_D23_C.57 Control Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Amylibacter Unclassified Amylibacter MG22_D23_C.6 Control Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Ruegeria Ruegeria Ruegeria sp003443535 MG22_D23_C_102 Control Bacteria Chlamydiota Chlamydiia Chlamydiales Simkaniaceae Neptunochlamydia Unclassified Neptunochlamydia MG22_D23_C_112 Control Bacteria Proteobacteria Gammaproteobacteria Xanthomonadales Wenzhouxiangellaceae Unclassified Wenzhouxiangellaceae Unclassified Wenzhouxiangellaceae MG22_D23_C_12 Control Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Hyphomicrobiaceae Unclassified Hyphomicrobiaceae Unclassified Hyphomicrobiaceae MG22_D23_C_21 Control Bacteria Proteobacteria Gammaproteobacteria Xanthomonadales SZUA-36 W260 Unclassified W260 MG22_D23_C_5 Control Bacteria Myxococcota Polyangia Nannocystales Nannocystaceae Unclassified Nannocystaceae Unclassified Nannocystaceae MG22_D23_C_69_sub Control Bacteria Proteobacteria Alphaproteobacteria Sphingomonadales Unclassified Sphingomonadales Unclassified Sphingomonadales Unclassified Sphingomonadales MG22_D25_T.003_sub OA Bacteria Proteobacteria Gammaproteobacteria Xanthomonadales Wenzhouxiangellaceae Unclassified Wenzhouxiangellaceae Unclassified Wenzhouxiangellaceae MG22_D25_T.011 OA Bacteria Proteobacteria Alphaproteobacteria Bin95 Bin95 Unclassified Bin95 Unclassified Bin95 MG22_D25_T.014 OA Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudohongiellaceae UBA9145 Unclassified UBA9145 MG22_D25_T.17 OA Bacteria Planctomycetota Phycisphaerae Phycisphaerales UBA1924 Unclassified UBA1924 Unclassified UBA1924 MG22_D25_T.30 OA Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Porticoccaceae 50-400-T64 Unclassified 50-400-T64 MG22_D25_T.39 OA Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae JL08 Unclassified JL08 MG22_D25_T.44 OA Bacteria Proteobacteria Gammaproteobacteria Enterobacterales Alteromonadaceae Colwellia Unclassified Colwellia MG22_D25_T.4_sub OA Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Pelagimonas Unclassified Pelagimonas MG22_D25_T.6 OA Bacteria Proteobacteria Alphaproteobacteria Minwuiales Minwuiaceae Minwuia Unclassified Minwuia MG22_D25_T.61 OA Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Pseudoprimorskyibacter Unclassified Pseudoprimorskyibacter MG22_D25_T.65 OA Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Nitrincolaceae Rs1 Unclassified Rs1 MG22_D25_T_0 OA Bacteria Proteobacteria Alphaproteobacteria Sphingomonadales Kordiimonadaceae Kordiimonas Unclassified Kordiimonas MG22_D25_T_123_sub OA Bacteria Planctomycetota UBA1135 UBA1135 UBA1135 GCA-2705055 Unclassified GCA-2705055 MG22_D25_T_127 OA Bacteria Acidobacteriota Holophagae Acanthopleuribacterales Acanthopleuribacteraceae Unclassified Acanthopleuribacteraceae Unclassified Acanthopleuribacteraceae MG22_D25_T_132 OA Bacteria Bdellovibrionota Bacteriovoracia Bacteriovoracales Bacteriovoracaceae Unclassified Bacteriovoracaceae Unclassified Bacteriovoracaceae MG22_D25_T_133 OA Bacteria Proteobacteria Alphaproteobacteria Sphingomonadales Unclassified Sphingomonadales Unclassified Sphingomonadales Unclassified Sphingomonadales MG22_D25_T_145 OA Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Oleiphilaceae Marinobacter Marinobacter Marinobacter adhaerens MG22_D25_T_15 OA Bacteria Bacteroidota Bacteroidia Flavobacteriales Flavobacteriaceae Gilvibacter Gilvibacter Gilvibacter sp002163875 MG22_D25_T_165_sub OA Bacteria Myxococcota Polyangia Haliangiales Haliangiaceae Unclassified Haliangiaceae Unclassified Haliangiaceae MG22_D25_T_72_sub OA Bacteria Chlamydiota Chlamydiia Chlamydiales Criblamydiaceae Unclassified Criblamydiaceae Unclassified Criblamydiaceae

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Figure 4.1. Relative abundance of reads mapped to assembled MAGs across taxa levels pre-exposure (left) and postexposure for control (middle) and OA (right) M. galloprovincialis cohorts.
4.4.2 16S ribosomal classification and abundance of shotgun sequenced reads
Aligning WGS reads to the 16S ribosomal RNA subunit allows a broader snapshot of taxonomic
diversity as opposed to MAG assembly methods, however, MAG assembly provides higher
taxonomic resolution and predictive functional annotation of assembled whole-genomes. We
observed low overall mapping rates of WGS reads to the 16S rRNA gene (Supplementary Table
39), however, this was expected as DNA extraction protocols were optimized for bivalve tissues
and library preparation was specific to shotgun sequencing. As is also expected, more taxa were

94
identified when reads were mapped to the 16S rRNA gene, however, taxonomic classification
was generally limited to Family-level (Figure 4.2).
We captured more taxa at the initial, day two time point as compared to MAG assembly
of a single whole-genome. This confirmed the presence of common marine bacteria including
Flavobacteriales and Rhodobacterales, which were present in MAG classification at later
timepoints (Figure 4.1), but also revealed Orders of Vibrionales, Alteromondales, and
Oceanospirillales at high relative abundance at day two. Oceanospirillales were also present
across all treatments.
We assembled MAGs from unique bacterial orders between control and OA conditions,
many of which did not have any reads from their respective treatments mapping to the MAG
assembled from the alternate treatment. In control MAGs, we observed the unique orders
Burkholderiales, Ga0077536, Nannocystales, Rhizobiales, UBA4486, and Verrucomicrobiales. In
the OA condition, unique orders included Acanthopleuribacterales, Bacteriovoracales,
Enterobacterales, Haliangiales, Phycisphaerales, Minwuiales, and UBA1135. The most dramatic
shift in microbial communities between control and treatment conditions at the end of OA
exposure observed in both MAG and 16S rRNA mapping was the increase in Colwellia spp. in
OA-treated mussels (Figure 4.1, Figure 4.2).

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Figure 4.2. Relative abundance of 16S-classified shotgun sequencing reads across taxa levels pre-exposure (left) and
post-exposure for control (middle) and OA (right) M. galloprovincialis cohorts.
4.4.3 16S region V3-V4 amplicon sequencing throughout larval development
16S targeted amplicon sequencing of the V3-V4 regions of the ribosomal subunit confirmed
observations from both MAG assembly and 16S-mapping of WGS reads, but resulted in more
taxonomic diversity and allowed for estimations of population-specific alpha and beta diversity
statistics for both M. galloprovincialis and M. californianus species. Dominant bacterial Classes
across all timepoints for both species were Gammaproteobacteria, Bacteroidia, and
Alphaproteobacteria, as was expected based on previous results (Supplemental Figure 11). At the

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Order level in both species, we observed dominance by Alteromonadales, Rhodobacterales, and
Flavobacteriales, as was seen in the 16S-mapped reads. However, we observed for the first time
the Order Micrococcales, specifically the genus Sinomonas, which appears across nearly all time
points in both species. M. californianus had a large relative abundance of the Order
Chitinophagales as compared to M. galloprovincialis, specifically from the Family
Saprospiraceae.
At the genus level, we observed high relative abundance of a handful of dominant genera
alongside many low-abundance groups (Figure 4.5). The dominant Genera slightly varied
between M. californianus and M. galloprovincialis. For both species, Sinomonas appears to be a
core member of the microbiome, although relative abundance of this genus is much higher in M.
galloprocincialis. In alignment with results of 16S-mapped WGS reads, M. galloprovincialis
membership at this level was also dominated by Colwellia, and relative abundance of this genus
was much higher in OA treatments. In M. californianus, we observed high relative abundance of
Aureispira as compared to M. galloprovincialis.

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Figure 4.3. Relative abundance of 16S rDNA V3-V4 amplicon sequenced reads agglomerated at the Order level for
M. californianus (left) and M. galloprovincialis (right).

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Figure 4.4. Relative abundance of 16S rDNA V3-V4 amplicon sequenced reads agglomerated at the Family level for
M. californianus (left) and M. galloprovincialis (right).

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Figure 4.5. Relative abundance of 16S rDNA V3-V4 amplicon sequenced reads agglomerated at the Genus level for
M. californianus (left) and M. galloprovincialis (right).
Variance-based compositional principal component analysis (PCA) was performed to
visualize differences between microbial communities (beta diversity) both within and between
species. Visual ordination of sample distances showed distinct differences in clustering patterns
between the two species (Figure 4.6). PERMANOVA revealed that M. californianus microbial
community composition was highly significantly different between time points (p <0.001) and
moderately significant between treatments (p < 0.05). Within-group dispersion was statistically
significant for time (PERMDISP2, p < 0.05, Supplemental Figure 13), with day 21 having the
greatest distance to the centroid, but not for treatment (Supplemental Figure 12). For time, the
distance between groups was statistically significantly greater than within groups (ANOSIM, p <
0.001, Supplemental Figure 14). In M. galloprovincialis, microbial community composition was
highly significantly different between both time and treatment (PERMANOVA, p < 0.001).
However, within-group dispersion patterns for this species were the opposite of those observed
in M. californianus, treatment was highly significant (PERMDISP2, p < 0.001, Supplemental

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Figure 15), however, time was not (Supplemental Figure 16). Distance between groups was
significantly greater than within groups for both time and treatment (ANOSIM, p < 0.001,
Supplemental Figure 17). All measures of beta diversity were statistically significant between
species (p < 0.001, Supplemental Figure 18).

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Figure 4.6. PCA of clr-normalized 16S rDNA V3-V4 amplicon sequenced reads for M. californianus and M.
galloprovincialis.
A variety of alpha diversity measures were used to examines microbial complexity within
a community (Figure 4.7). These included observed diversity, or richness, which is the total
number of observed species within a sample, Chao1, an estimate of total richness including
unobserved species, Pielou’s evenness, which provides information about equity in species
abundance, and the Shannon index, which takes into account both species richness and evenness.
In the full model (day + treatment + species) day and species had a significant effect on observed
richness (p < 0.05), Chao1 (p < 0.05), but for the Shannon index and Pielou’s evenness day and
treatment had a significant effect (p < 0.01) (Figure 4.7).
When these statistics were computed within each species, different patterns emerged
between the two (Figure 4.7). For M. californianus, day had a significant effect on all alpha
diversity metric, while treatment was not significant. This was not the case for M.
galloprovincialis, where treatment had a significant effect on all alpha diversity metrics (p <
0.001), while day was only statistically significant for Shannon diversity and Pielou’s evenness
(p < 0.001).

103

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Figure 4.7. Alpha diversity metrics of 16S rDNA V3-V4 amplicon sequenced reads for M. californianus (left) and
M. galloprovincialis (right).
4.5 Discussion
4.5.1 MAG diversity and predictive function
At day two, the initial, pre-exposure timepoint, only one MAG was assembled, which was
classified down to the Genus level as Flavobacteriaceae DT-34. Flavobacteriaceae are common
marine microorganisms which have been documented to be abundant in Mytilus
galloprovincialis, and assumed to be recruited from surrounding seawater via filtration (Musella
et al., 2020). Flavobacteriaceae are highly involved in nutrient cycling and often associated with
algal blooms (Buchan et al., 2014), and the assembled MAG DT-34 contains genes involved with
nitrogen cycling (nitrate reduction, nitrite reduction) and sulfur cycling (metabolism of organic
sulfur).
In the post-treatment groups, 17 and 20 MAGs were assembled from the OA and control
treatment, respectively. Shared members between the two treatments included
Rhodobacteraceae, which are common marine surface water heterotrophic bacteria that have
been shown to play a key role in the transformation of dissolved organic matter (O’Brien et al.,
2022), as well as Xanthomonadales, including the family Wenzhouxiangellaceae, which are
bacteria originating from sea water that thrive in near-neutral pH and moderately saline
conditions (Sorokin et al., 2020). Some species of Wenzhouxiangellaceae are known to be
capable of complete denitrification (Sorokin et al., 2020), and metabolic functional annotation of
the strain found in our mussels also contained genes for these processes NirS, NorB, and NorC,
all of which are involved in nitrite and nitrous oxide reduction. Mussels are known to play a key
role in seawater denitrification by ingesting particulate nitrogen and depositing it onto sediments
where denitrification then occurs (Newell, 2004). The other MAG assembled from the

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Xanthomonadales order in the family SZUA-36 in control samples was found to contain genes
involved in nitrite reduction. The presence of denitrification genes in MAGs assembled from
both control and OA cohorts suggests that mussels’ involvement in denitrification may be twofold, both by biodeposition and by associated microbial communities.
In the OA group, we assembled a MAG belonging to the Family Bacteriovoracaceae,
which is predatory bacteria that commonly feeds on Vibrio species in marine environments
(Chen et al., 2011; Pineiro et al., 2007). No reads from control WGS MAGs mapped to this
family from day two or treatment groups. However, in the 16S-mapped data, this Family was
observed only when Vibrio was also present, which was at the pre-exposure, day two timepoint.
Within control samples, we assembled a MAG belonging to the Methylophilaceae family
and genus GCA-2401735. Interestingly, this MAG had no reads from treatment or pre-exposure
cohorts map, and it appeared again in our 16S amplicon data only in control samples from M.
galloprovincialis. This Genus appears to have evolved from a marine sediment lineage (Arthi et
al., 2021) that has adapted to the pelagic marine environment. However, it has been suggested
that this genus is not as genomically-streamlined as other phylogenetically related species
(Chiriac et al., 2023). This clade is known to use urea as its nitrogen source, which is
corroborated by our functional annotations, which reveal genes involved in urea utilization in
this MAG. However, members of the Methylophilaceae Family utilize methylamine or methanol
as carbon or energy sources, but no genes for methylamine dehydrogenase nor methanol
dehydrogenase were present. While this is surprising, Chiriac et al. (2023) describes the
transition from sediment to pelagic environments of these organisms and accompanying loss of
redundant C1 utilization pathways, as well as extensive genome remodeling. At present, this
Genus has not been observed in Southern California coastal waters, and our results suggest that it

106
may have undergone strong genomic adaptations to this environment, which were further
challenged by OA exposure. The Methylophilaceae family has become a model for studying
evolutionary adaptation to diverse aquatic environment, and this Genus could be of research
interest in this field moving forward.
4.5.2 Core microbiome membership varies between M. californianus and M. galloprovincialis
larvae
Oceanospirillales, which are heterotrophic bacteria involved in symbiotic interactions with
marine invertebrates, including mussels, were present across all treatments and timepoints in
both species, however, their functional role in mussel microbiomes remains unknown (Jensen et
al., 2010; Yi et al., 2014). Alteromondales and Vibrionales, which were also observed in both
species, though at different relative abundances, are common marine bacteria and are known to
be widely distributed within seawater (Tada et al., 2016; Vezzulli et al., 2013). These two groups
have also been documented as core microbiome members in early development (24-48 hours
post-fertilization) of M. galloprovincialis larvae (Balbi et al., 2020). Some species of Vibrio are
known marine pathogens and are commonly associated with disease outbreaks in shellfish
hatcheries (“Vibrio Spp. Infections,” 2018). We observe a decrease in Vibrio relative abundance
over time, suggesting that they do not remain members of the mussel core microbiome beyond
early stages, and that mussels may develop increased resistance to these bacteria and potential
infection throughout the course of development.
The Genus Sinomonas, of the Order Micrococcales was observed across all treatments
and time points in both species. Micrococcales has been observed and isolated from seawater
samples (Rodrigues & de Carvalho, 2022), however, in a study that identified gut microbiome
changes in response to ocean acidification in oysters observed Micrococcales only rarely (Dang
et al., 2023), and Micrococcales has not been identified as a core microbiome member of Mytilus

107
mussels, although our results suggest this order could be considered as a core member.
Interestingly, M. californianus also had a large relative abundance of the Order Chitinophagales
as compared to M. galloprovincialis, specifically from the Family Saprospiraceae.
Saprospiraceae is not considered a core member of the M. galloprovincialis microbiome, likely
due to its low abundance, but has been well-documented as a core bacteria in the Pacific oyster
Magallana gigas (Anna et al., 2024). In M. gigas, Saprospiraceae relative abundance was not
altered by ocean acidification conditions (Z. K. Xu et al., 2024), which is in alignment with our
observations (Figure 4.4). Our results suggest that the order Chitinophagales could be a core
member of the M. californianus microbiome and that these bacteria are resilient to changes in
pH.
4.5.3 Ocean acidification of seawater is reflected in M. galloprovincialis microbiome
community membership
Colwellia was present in both species, but had a large increase in relative abundance in M.
galloprovincialis OA treatments. Colwellia are heterotrophic and facultatively anaerobic
extremophiles widely distributed across global oceans with a unique ability to degrade
hydrocarbons (Bowman, 2014; Mason et al., 2014). Colwellia spp. are commonly found in M.
galloprovincialis microbiota (Musella et al., 2020), however, this shift in abundance of Colwellia
in the OA treatment microbiota is notable because this Genus has recently been identified as a
potential indicator species present in corrosive waters (Rhodes et al., 2024). The increased
relative abundance of Colwellia in M. galloprovincialis tissues could be reflective of a shift in
their abundance in surrounding acidified seawater, and thus the observed increase in this genus in
the mussel microbiome is a result of filtration. This pattern was not observed in M. californianus,
which could be due to different naturally occurring bacterial community compositions between
the two species, or a shift in abundance of other taxa. While the experiments were replicates and

108
environmental parameters were kept consistent between the two, the experiments were run at
different points in the year (M. galloprovincialis in June, M. californianus in October), and
differences in relative abundance of specific taxa could be reflective of seasonal changes in
bacterial communities in coastal waters, as water used in these experiments was collected,
filtered natural seawater. Seasonal changes in the microbiome composition of genetically-similar
bivalve species have been reported (Pierce et al., 2016), therefore we cannot completely
disentangle the effects of season and species on observed differences in microbiomes between
the two species in this study.
4.5.4 Ocean acidification diminishes microbial diversity in M. galloprovincialis and may
compromise immunity in M. californianus
Aureispira and Vibrio were present in both species at various treatments and timepoints,
however, relative abundance of these species were higher in M. californianus. Aureispira is a
known predator of Vibrio species (Yeoh et al., 2021), and its presence coincided with the
presence of Vibrio in M. californianus samples. Vibrio relative abundance increases throughout
time in the OA condition in M. californianus, while the inverse is true for Aureispira. These
results suggest that in control treatments Aureispira is controlling opportunistic Vibrio
abundances, but in OA treatments Vibrio dominates. The presence of these genera could be
indicative of immune-suppression and colonization of pathogens which could eventually lead to
death (Dugeny et al., 2022). The lower relative abundance of Vibrio in M. galloprovincialis
samples suggests that this species is not immunocompromised at either control or OA treatments,
and may be more resilient to both infection by these bacteria and OA exposure.
4.5.5 OA effects on diversity vary between mussel species
Clear clustering between species, treatments, and timepoints was observed in PCA ordination,
and all measures of beta diversity were statistically significantly different between the two

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species. PCA and PERMANOVA results show that treatment had a more statistically significant
effect on M. galloprovincialis than M. californianus species composition and dispersion.
Treatment was also shown to have a significant decrease on diversity and evenness in M.
galloprovincialis, however, this was not the case in M. californianus. These results suggest that
in the case of alpha and beta diversity, M. californianus microbiota may be more resilient to OA
exposure.
Time played a significant role in both beta and alpha diversity, which is most likely due
to changes in microbiome composition throughout the course of larval development. Mussels,
along with other filter feeders, recruit bacteria from the surrounding seawater through filtration,
but also via ingestion of phytoplankton. As larvae grow and develop, phytoplankton consumption
changes in relation to this increase in size, and thus microbiome composition also changes
(Møhlenberg & Riisgård, 1978). Diet has been shown to have a strong impact on the gut
microbiome of bivalves (Simons et al., 2018), so we would expect the observed changes in
response to diet and development. To account for small larval size, we pooled whole-animal
samples for DNA extraction and sequencing, therefore we are unable to determine which tissues
microbiome members in this study originated, or whether they were recruited via ingestion.
4.6 Author Contributions
JC performed all experiments, sample preparation, bioinformatics, data analysis, and writing.
EA, AG, and SN contributed to project conceptualization and experimental design. SN acquired
project funding.

110
Conclusion
This thesis aimed to characterize effects of ocean acidification on ecologically and economically
important shellfish species of the Southern California Current System using a variety of
genomics approaches and methods. The results of the combined research can be utilized to guide
future farming, restoration, and conservation efforts. Across all chapters, we observe that OA
results in a range of transcriptomic, genomic, metagenomic, and phenotypic effects on shellfish
species. Specifically, we observe biomineralization and immune stress as an overarching result of
OA across phylogenic clades and species. Many biomineralization and immune pathways appear
to be conserved across bivalve species, which allows the results of these studies to be
extrapolated to related species. For example, we see shared key biomineralization genes
undergoing differential expression in oyster species in chapter one that are under selective
pressure in mussel species in chapter three.
OA was shown in all chapters to negatively affect bivalve phenotypes within the first
generation, for instance, oysters underwent high dissolution in response to OA in chapter one,
and mussels grew significantly slower and had carry-over affects into adulthood in chapter three.
However, alongside these negative effects are hopeful observations of acclimation to OA
exposure across all experiments and species: in chapter one, oysters were able to mitigate OA
stress by the use of increased gene expression, chapter two mussels, although growing more
slowly, did not have lower survival probability in OA treatments, chapter three mussels showed
significant selection response and adaptive potential to OA exposure within a single generation,
and chapter four mussels’ microbial communities were altered, but not dismantled by OA. The
results of the research of this thesis provides farmers, researchers, and other aquaculture industry
stakeholders with a comprehensive description of future OA effects on bivalve species in

111
Southern California. The results also provide specific genes and biological pathways to target for
domestication to OA and form a foundation for mitigation strategy development to ensure
continued production and efficiency of the aquaculture industry over the next century.

112
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146
Appendix A: Supplementary Tables
Supplementary Table 1. List of genes involved in biomineralization from Chandra Rajan et al. (2021) identified in
Magallana hongkongensis used to identify biomineralization genes within this study following genome/transcriptome
re-annotation.
Gene_ID log2FC pval pad
j
description Gene
symbol
Mho_000005 2.42 0.04 1.00 Serine/threonine-protein kinase
tousled-like 1
Tlk1
Mho_000146 5.45 0.03 1.00 Transmembrane protein 120A tmem120a
Mho_000188 1.05 0.01 1.00 Androglobin ADGB
Mho_000630 -1.96 0.00 0.19 von Willebrand factor D and
EGF domain-containing protein
VWDE
Mho_000985 2.97 0.00 0.38 Nacrein-like protein (Fragment)
Mho_003329 4.17 0.03 1.00 Probable
sodium/potassium/calcium
exchanger CG1090
CG1090
Mho_004271 2.32 0.03 1.00 Serine/threonine-protein kinase
mTOR
Mtor
Mho_004541 1.58 0.02 1.00 Proton channel OtopLc CG42265
Mho_012125 -1.93 0.04 1.00 von Willebrand factor D and
EGF domain-containing protein
VWDE
Mho_013910 -1.40 0.03 1.00 Collagen alpha-1(X) chain COL10A1
Mho_018308 -1.55 0.04 1.00 von Willebrand factor A
domain-containing protein 7
Vwa7
Mho_019660 -4.08 0.02 1.00 Perlucin-like protein
Mho_020126 5.23 0.03 1.00 Organic cation transporter
protein
Orct
Mho_022204 3.08 0.01 1.00 Cadherin-23 Cdh23
Mho_024870 -1.32 0.00 0.68 Perlucin-like protein
Mho_028153 1.42 0.01 1.00 Vacuolar protein sortingassociated protein 13C
VPS13C
Mho_031208 #NAME
?
0.01 1.00 Perlucin-like protein
Mho_031816 -2.51 0.05 1.00 Matrilin-2 MATN2
Mho_033417 -2.04 0.04 1.00 Collagen alpha-2(VIII) chain COL8A2
Mho_034930 -1.17 0.04 1.00 Caveolin-1 CAV1
Mho_040039 2.04 0.00 0.49 Fibroblast growth factor
receptor 1
FGFR1
Mho_044653 1.37 0.01 1.00 Protocadherin Fat 4 Fat4
Mho_046494 3.25 0.02 1.00 H(+)/Cl(-) exchange transporter
7
CLCN7
Mho_046870 #NAME
?
0.02 1.00 VWFA and cache domaincontaining protein 1
CACHD1
Mho_047040 4.04 0.01 1.00 Laminin subunit beta-3 LAMB3

147
Mho_015709 1.60 0.03 1.00 Complement C1q-like protein 2 C1QL2
Mho_021568 1.33 0.00 0.43 Putative tyrosinase-like protein
tyr-3
tyr-3
Mho_026820 -1.57 0.01 1.00 EGF-like repeat and discoidin Ilike domain-containing protein 3
Edil3
Mho_016202 1.10 0.00 0.63 Cell surface hyaluronidase cemip2
Mho_043568 -4.99 0.02 1.00 Probable serine/threonineprotein kinase DDB_G0267514
DDB_G026751
4
Supplementary Table 2. Fastq read filtering and mapping statistics for Pacific and Olympia oysters..
Specie
s
Treat
ment
Ti
m
e
Sample N.To
tal
N.Su
rv
N.Su
rv.Pa
N.Su
rv.SF
N.Su
rv.SR
N.S
el.P
a
N.Sel
.SF
N.S
el.S
R
N.M
ap.P
a
N.M
ap.S
F
N.M
ap.S
R
%.Map
ped.Pa
%.Map
ped.SF
%.Map
ped.SR
M.
gigas
8.0C W
2
P_T1_W2
_54_3_Bl
4130
273
1324
985
7529
3
1214
154
3553
8
752
93
1214
154
355
38
5641
7
1575
97
8632 74.93% 12.98
%
24.29%
M.
gigas
8.0C W
2
P_T1_W2
_2_2_Pi
4322
227
1192
835
7945
3
1082
378
3100
4
794
53
1082
378
310
04
4704
4
1943
95
6133 59.21% 17.96
%
19.78%
M.
gigas
8.0C W
2
P_T1_W2
_2_1_Pi
4874
906
1093
648
1191
24
9223
94
5213
0
119
124
9223
94
521
30
7375
0
1593
90
1064
5
61.91% 17.28
%
20.42%
M.
gigas
8.0C W
2
P_T1_W2
_54_3_R
2560
129
6209
76
1104
1
6050
41
4894 NA NA NA NA NA NA NA NA NA
M.
gigas
8.0C W
2
P_T1_W2
_22_2_Pu
1838
243
3613
25
5214
7
2799
00
2927
8
NA NA NA NA NA NA NA NA NA
M.
gigas
8.0C W
2
P_T1_W2
_54_3_Gr
4650
76
8199
8
1768
6
5302
7
1128
5
NA NA NA NA NA NA NA NA NA
M.
gigas
8.0C W
6
P_T1_W6
_67_3_O
6275
548
2835
585
7382
5
2730
178
3158
2
738
25
2730
178
315
82
5014
9
5340
23
5988 67.93% 19.56
%
18.96%
M.
gigas
8.0C W
6
P_T1_W6
_36_1_Pu
5964
304
2353
460
3711
8
2298
682
1766
0
371
18
2298
682
176
60
1602
8
3903
16
3075 43.18% 16.98
%
17.41%
M.
gigas
8.0C W
6
P_T1_W6
_36_1_Bl
8518
320
2083
221
6114
0
1989
702
3237
9
611
40
1989
702
323
79
2470
1
3296
94
3656 40.40% 16.57
%
11.29%
M.
gigas
8.0C W
6
P_T1_W6
_50_1_Pu
4558
476
1915
484
2133
01
1629
457
7272
6
NA NA NA NA NA NA NA NA NA
M.
gigas
8.0C W
6
P_T1_W6
_36_1_O
2101
889
7386
19
1077
44
6005
05
3037
0
NA NA NA NA NA NA NA NA NA
M.
gigas
8.0C W
6
P_T1_W6
_50_2_W
3514
51
1055
60
2342
1
6596
5
1617
4
NA NA NA NA NA NA NA NA NA
M.
gigas
7.7C W
2
P_T2_W2
_6_1_Pu
2453
1477
1320
4879
3142
39
1282
2443
6819
7
314
239
1282
2443
681
97
1837
98
2345
225
1455
3
58.49% 18.29
%
21.34%
M.
gigas
7.7C W
2
P_T2_W2
_20_1_Bc
1401
9297
8653
910
2397
90
8356
871
5724
9
239
790
8356
871
572
49
1794
83
8415
37
1095
7
74.85% 10.07
%
19.14%
M.
gigas
7.7C W
2
P_T2_W2
_6_2_W
6946
883
2262
144
1437
05
2055
589
6285
0
143
705
2055
589
628
50
1065
86
2345
43
1133
8
74.17% 11.41
%
18.04%
M.
gigas
7.7C W
2
P_T2_W2
_6_2_Bc
1942
096
5845
00
2172
07
2423
15
1249
78
NA NA NA NA NA NA NA NA NA
M.
gigas
7.7C W
2
P_T2_W2
_6_2_O
3915
84
9354
2
1782
4
6454
0
1117
8
NA NA NA NA NA NA NA NA NA
M.
gigas
7.7C W
2
P_T2_W2
_6_1_O
3551
26
5665
3
9617 4053
1
6505 NA NA NA NA NA NA NA NA NA
M.
gigas
7.7C W
6
P_T2_W6
_30_3_Gr
1160
0780
6569
479
1633
78
6375
634
3046
7
163
378
6375
634
304
67
1007
06
1069
831
6587 61.64% 16.78
%
21.62%
M.
gigas
7.7C W
6
P_T2_W6
_27_2_W
1158
9622
6146
618
1609
47
5950
013
3565
8
160
947
5950
013
356
58
1003
67
1031
137
7228 62.36% 17.33
%
20.27%
M.
gigas
7.7C W
6
P_T2_W6
_30_1_Gr
1032
8598
4992
540
1062
17
4861
273
2505
0
106
217
4861
273
250
50
6118
1
8332
22
3933 57.60% 17.14
%
15.70%
M.
gigas
7.7C W
6
P_T2_W6
_27_1_O
4301
807
8660
17
4178
2
7947
80
2945
5
NA NA NA NA NA NA NA NA NA
M.
gigas
7.7C W
6
P_T2_W6
_27_3_Gr
1234
45
5713
3
1790
9
1643
8
2278
6
NA NA NA NA NA NA NA NA NA

148
M.
gigas
7.7C
W6
P_T2_W6
_30_3_Bl
2256
48
46510
7666 29627
9217 NA NA NA NA NA NA NA NA NA
M.
gigas
7.7A
0.2
W2
P_T3_W2
_55_1_Pu
8621
59
1643
57
14098
1368
51
13408
140
98
1368
51
134
08
6685 27439
2342 47.42% 20.05%
17.47%
M.
gigas
7.7A
0.2
W2
P_T3_W2
_52_3_Bl
1310
44
54339
24563
12551
17225
245
63
12551
172
25
11599
2104 3245 47.22% 16.76%
18.84%
M.
gigas
7.7A
0.2
W2
P_T3_W2
_52_2_R
50870
14368
291 13797
280 291 13797
280 124 2047 30 42.61% 14.84%
10.71%
M.
gigas
7.7A
0.2
W2
P_T3_W2
_42_1_R
41216
13939
1631 9509 2799 NA NA NA NA NA NA NA NA NA
M.
gigas
7.7A
0.2
W2
P_T3_W2
_55_3_O
38818
11307
240 10953
114 NA NA NA NA NA NA NA NA NA
M.
gigas
7.7A
0.2
W2
P_T3_W2
_52_2_Gr
35429
10061
346 9466 249 NA NA NA NA NA NA NA NA NA
M.
gigas
7.7A
0.2
W6
P_T3_W6
_35_2_Pu
4549
123
1690
256
46394
1623
313
20549
463
94
1623
313
205
49
17115
3014
49
2741 36.89% 18.57%
13.34%
M.
gigas
7.7A
0.2
W6
P_T3_W6
_64_1_O
2973
295
1314
861
1712
26
1083
852
59783
171
226
1083
852
597
83
1303
54
1978
03
8208 76.13% 18.25%
13.73%
M.
gigas
7.7A
0.2
W6
P_T3_W6
_64_2_O
2110
24
69187
1521 66228
1438 1521
66228
1438
254 8563 85 16.70% 12.93%
5.91%
M.
gigas
7.7A
0.2
W6
P_T3_W6
_35_1_R
1006
63
40737
7121 28119
5497 NA NA NA NA NA NA NA NA NA
M.
gigas
7.7A
0.2
W6
P_T3_W6
_35_2_Pi
59360
17418
863 15496
1059 NA NA NA NA NA NA NA NA NA
M.
gigas
7.7A
0.2
W6
P_T3_W6
_84_2_Gr
21540
6382 188 6092 102 NA NA NA NA NA NA NA NA NA
M.
gigas
7.7A
0.5
W2
P_T5_W2
_3_1_O
1827
5908
6747
171
88056
6630
470
28645
880
56
6630
470
286
45
61005
8334
50
4469 69.28% 12.57%
15.60%
M.
gigas
7.7A
0.5
W2
P_T5_W2
_3_1_Gr
1633
2057
4738
852
38009
4685
468
15375
380
09
4685
468
153
75
23695
6002
08
1745 62.34% 12.81%
11.35%
M.
gigas
7.7A
0.5
W2
P_T5_W2
_3_2_O
1248
0835
3841
984
20389
3811
779
9816 203
89
3811
779
9816
10313
3857
52
1051 50.58% 10.12%
10.71%
M.
gigas
7.7A
0.5
W2
P_T5_W2
_3_2_Pi
1031
60
25569
1213 20444
3912 NA NA NA NA NA NA NA NA NA
M.
gigas
7.7A
0.5
W2
P_T5_W2
_25_2_O
17333
4374 258 3781 335 NA NA NA NA NA NA NA NA NA
M.
gigas
7.7A
0.5
W2
P_T5_W2
_3_3_Gr
14389
3479 106 3276 97 NA NA NA NA NA NA NA NA NA
M.
gigas
7.7A
0.5
W6
P_T5_W6
_74_3_Bl
1322
1400
1008
5639
80941
9987
036
17662
809
41
9987
036
176
62
42866
1967
446
2467 52.96% 19.70%
13.97%
M.
gigas
7.7A
0.5
W6
P_T5_W6
_74_1_Gr
1401
6315
9179
295
1022
72
9053
746
23277
102
272
9053
746
232
77
45654
1733
792
3701 44.64% 19.15%
15.90%
M.
gigas
7.7A
0.5
W6
P_T5_W6
_34_3_Pi
2639
995
1452
169
33130
1410
379
8660 331
30
1410
379
8660
11142
2967
44
640 33.63% 21.04%
7.39%
M.
gigas
7.7A
0.5
W6
P_T5_W6
_37_3_Pu
3669
59
2289
08
2628 2256
10
670 NA NA NA NA NA NA NA NA NA
M.
gigas
7.7A
0.5
W6
P_T5_W6
_34_3_Pu
14838
6167 134 5802 231 NA NA NA NA NA NA NA NA NA
M.
gigas
7.7A
0.5
W6
P_T5_W6
_29_2_R
16475
4099 88 3921 90 NA NA NA NA NA NA NA NA NA
O.
lurida
8.0C
W2
O_T1_W2
_51_1_Gr
2182
235
8941
12
16138
8731
44
4830 161
38
8731
44
4830
4483 1934
01
759 27.78% 22.15%
15.71%
O.
lurida
8.0C
W2
O_T1_W2
_22_1_Bl
3570
84
92363
2795 88468
1100 2795
88468
1100
657 16968
418 23.49% 19.18%
38.00%
O.
lurida
8.0C
W2
O_T1_W2
_22_1_Pi
73238
24231
1558 20892
1781 1558
20892
1781
342 4561 188 21.92% 21.83%
10.56%
O.
lurida
8.0C
W2
O_T1_W2
_2_1_O
73484
18679
972 16555
1152 NA NA NA NA NA NA NA NA NA
O.
lurida
8.0C
W2
O_T1_W2
_22_2_O
31409
11281
406 10495
380 NA NA NA NA NA NA NA NA NA
O.
lurida
8.0C
W2
O_T1_W2
_22_3_Pi
30271
8904 475 7688 741 NA NA NA NA NA NA NA NA NA
O.
lurida
8.0C
W6
O_T1_W6
_67_1_Pu
1151
7653
5981
484
86672
5870
749
24063
866
72
5870
749
240
63
38006
1499
389
6045 43.85% 25.54%
25.12%

149
O.
lurida
8.0C
W6
O_T1_W6
_67_3_Gr
6701
056
3936
808
56546
3869
451
10811
565
46
3869
451
108
11
25417
9499
50
1596 44.95% 24.55%
14.76%
O.
lurida
8.0C
W6
O_T1_W6
_67_1_W
3302
69
95380
4580 87841
2959 4580
87841
2959
1757 16497
497 38.36% 18.78%
16.80%
O.
lurida
8.0C
W6
O_T1_W6
_36_3_Gr
1752
98
52141
6283 40598
5260 NA NA NA NA NA NA NA NA NA
O.
lurida
8.0C
W6
O_T1_W6
_36_1_Gr
1392
00
32833
3556 25397
3880 NA NA NA NA NA NA NA NA NA
O.
lurida
8.0C
W6
O_T1_W6
_67_2_W
28105
10683
762 9600 321 NA NA NA NA NA NA NA NA NA
O.
lurida
7.7C
W2
O_T2_W2
_76_1_W
1381
648
3413
54
40773
2742
10
26371
407
73
2742
10
263
71
14968
57337
8025 36.71% 20.91%
30.43%
O.
lurida
7.7C
W2
O_T2_W2
_20_3_Pu
1852
55
75361
2458 70869
2034 2458
70869
2034
282 7264 201 11.47% 10.25%
9.88%
O.
lurida
7.7C
W2
O_T2_W2
_20_1_Bl
1550
02
68058
5238 58669
4151 5238
58669
4151
219 7627 183 4.18% 13.00%
4.41%
O.
lurida
7.7C
W2
O_T2_W2
_76_1_Bl
2371
36
60202
22051
26063
12088
NA NA NA NA NA NA NA NA NA
O.
lurida
7.7C
W2
O_T2_W2
_6_1_Gr
75302
22090
4448 12450
5192 NA NA NA NA NA NA NA NA NA
O.
lurida
7.7C
W2
O_T2_W2
_6_3_Pu
47853
11517
1463 7737 2317 NA NA NA NA NA NA NA NA NA
O.
lurida
7.7C
W6
O_T2_W6
_30_2_Gr
1797
903
8163
07
29676
7781
00
8531 296
76
7781
00
8531
10615
1630
12
942 35.77% 20.95%
11.04%
O.
lurida
7.7C
W6
O_T2_W6
_60_2_Bl
2525
569
6664
78
27701
6307
82
7995 277
01
6307
82
7995
6006 1125
95
1031 21.68% 17.85%
12.90%
O.
lurida
7.7C
W6
O_T2_W6
_27_1_Bl
4595
86
1890
44
14964
1689
19
5161 149
64
1689
19
5161
5064 26182
392 33.84% 15.50%
7.60%
O.
lurida
7.7C
W6
O_T2_W6
_30_3_Gr
49165
14073
1530 9919 2624 NA NA NA NA NA NA NA NA NA
O.
lurida
7.7C
W6
O_T2_W6
_30_2_Bl
40766
12969
4269 5329 3371 NA NA NA NA NA NA NA NA NA
O.
lurida
7.7C
W6
O_T2_W6
_27_1_W
35026
8454 962 5688 1804 NA NA NA NA NA NA NA NA NA
O.
lurida
7.7A
0.2
W2
O_T3_W2
_55_3_Gr
2100
226
8218
81
41305
7642
90
16286
413
05
7642
90
162
86
17517
1861
05
4799 42.41% 24.35%
29.47%
O.
lurida
7.7A
0.2
W2
O_T3_W2
_55_2_Pi
1536
661
7493
27
12910
7332
57
3160 129
10
7332
57
3160
3030 1337
46
684 23.47% 18.24%
21.65%
O.
lurida
7.7A
0.2
W2
O_T3_W2
_55_2_Gr
1032
017
4050
18
15178
3846
90
5150 151
78
3846
90
5150
4685 59204
858 30.87% 15.39%
16.66%
O.
lurida
7.7A
0.2
W2
O_T3_W2
_55_2_O
5931
45
2491
58
19498
2242
29
5431 NA NA NA NA NA NA NA NA NA
O.
lurida
7.7A
0.2
W2
O_T3_W2
_55_3_Pi
3381
91
1394
43
5579 1306
90
3174 NA NA NA NA NA NA NA NA NA
O.
lurida
7.7A
0.2
W2
O_T3_W2
_55_1_Pu
1796
64
71230
20308
30020
20902
NA NA NA NA NA NA NA NA NA
O.
lurida
7.7A
0.2
W6
O_T3_W6
_35_1_Bl
4672
402
2066
954
43386
2010
235
13333
433
86
2010
235
133
33
21376
4615
50
3091 49.27% 22.96%
23.18%
O.
lurida
7.7A
0.2
W6
O_T3_W6
_64_2_Pu
1649
203
6957
14
16920
6717
55
7039 169
20
6717
55
7039
8753 1520
85
867 51.73% 22.64%
12.32%
O.
lurida
7.7A
0.2
W6
O_T3_W6
_84_1_Bl
9878
06
2745
47
21104
2426
56
10787
211
04
2426
56
107
87
9455 50084
2968 44.80% 20.64%
27.51%
O.
lurida
7.7A
0.2
W6
O_T3_W6
_84_1_Pu
3614
50
1153
54
5456 1070
84
2814 NA NA NA NA NA NA NA NA NA
O.
lurida
7.7A
0.2
W6
O_T3_W6
_35_1_Pi
2274
59
72233
7142 60326
4765 NA NA NA NA NA NA NA NA NA
O.
lurida
7.7A
0.2
W6
O_T3_W6
_84_1_W
2266
80
59819
3931 53236
2652 NA NA NA NA NA NA NA NA NA
O.
lurida
7.7A
0.5
W2
O_T5_W2
_25_3_Bl
8951
90
2621
21
17469
2369
82
7670 174
69
2369
82
7670
5005 37396
1175 28.65% 15.78%
15.32%
O.
lurida
7.7A
0.5
W2
O_T5_W2
_25_1_Bl
6257
15
2133
85
14603
1912
26
7556 146
03
1912
26
7556
6316 30902
1635 43.25% 16.16%
21.64%
O.
lurida
7.7A
0.5
W2
O_T5_W2
_25_1_R
3452
99
1214
28
3609 1150
04
2815 3609
1150
04
2815
1089 17734
576 30.17% 15.42%
20.46%

150
O.
lurida
7.7A
0.5
W
2
O_T5_W2
_25_2_Gr
1868
91
4877
5
7043 3659
2
5140 NA NA NA NA NA NA NA NA NA
O.
lurida
7.7A
0.5
W
2
O_T5_W2
_25_2_Bc
1567
19
4221
4
7413 2939
2
5409 NA NA NA NA NA NA NA NA NA
O.
lurida
7.7A
0.5
W
2
O_T5_W2
_25_1_O
4755
1
1402
5
3136 7545 3344 NA NA NA NA NA NA NA NA NA
O.
lurida
7.7A
0.5
W
6
O_T5_W6
_37_3_Gr
3075
1527
1547
7693
4184
17
1493
3789
1254
87
418
417
1493
3789
125
487
2103
38
3849
931
1362
8
50.27% 25.78
%
10.86%
O.
lurida
7.7A
0.5
W
6
O_T5_W6
_74_1_W
9714
629
4287
086
1166
13
4135
223
3525
0
116
613
4135
223
352
50
5083
2
9440
71
4491 43.59% 22.83
%
12.74%
O.
lurida
7.7A
0.5
W
6
O_T5_W6
_34_3_Gr
2180
941
8459
01
2964
1
8059
08
1035
2
296
41
8059
08
103
52
1368
5
1672
26
1305 46.17% 20.75
%
12.61%
O.
lurida
7.7A
0.5
W
6
O_T5_W6
_37_3_Pi
2703
83
1076
53
6046 9643
8
5169 NA NA NA NA NA NA NA NA NA
O.
lurida
7.7A
0.5
W
6
O_T5_W6
_29_3_Pu
5402
2
1332
2
1121 1021
3
1988 NA NA NA NA NA NA NA NA NA
O.
lurida
7.7A
0.5
W
6
O_T5_W6
_34_3_Bl
3328
0
1085
7
1844 6507 2506 NA NA NA NA NA NA NA NA NA

151
Supplementary Table 3. Kruskall-Wallis test results for determining best PC describing treatment grouping for M.
gigas
PC.Num .y. n statistic df p p.adj
4 PC.Val 24 12.82 3 0.00504 0.00504
9 PC.Val 24 8.006666667 3 0.0459 0.0459
2 PC.Val 24 6.793333333 3 0.0788 0.0788
8 PC.Val 24 6.546666667 3 0.0878 0.0878
14 PC.Val 24 6.306666667 3 0.0976 0.0976
1 PC.Val 24 5.726666667 3 0.126 0.126
11 PC.Val 24 5.446666667 3 0.142 0.142
6 PC.Val 24 4.833333333 3 0.184 0.184
16 PC.Val 24 4.073333333 3 0.254 0.254
5 PC.Val 24 3.8 3 0.284 0.284
15 PC.Val 24 3.32 3 0.345 0.345
10 PC.Val 24 2.553333333 3 0.466 0.466
13 PC.Val 24 2.513333333 3 0.473 0.473
7 PC.Val 24 2 3 0.572 0.572
18 PC.Val 24 1.366666667 3 0.713 0.713
17 PC.Val 24 1.133333333 3 0.769 0.769
12 PC.Val 24 0.82 3 0.845 0.845
3 PC.Val 24 0.606666667 3 0.895 0.895
Supplementary Table 4. Dunn test results for significant differences between treatment groups for M. gigas
group1 group2 n1 n2 statistic p p.adj p.adj.signif
8.0C 7.7C 6 6 -2.9393877 0.00328861 0.00328861 **
7.7C 7.7A0.2 6 6 2.57196423 0.01011233 0.01011233 *
8.0C 7.7A0.5 6 6 -2.4086649 0.01601099 0.01601099 *
7.7A0.2 7.7A0.5 6 6 -2.0412415 0.04122683 0.04122683 *
7.7C 7.7A0.5 6 6 0.53072278 0.5956109 0.5956109 ns
8.0C 7.7A0.2 6 6 -0.3674235 0.71330317 0.71330317 ns
Supplementary Table 5. Wilcoxon rank sum test results for determining best PC describing pH type and time for M.
gigas
Factor PC.Nu
m
.y. group1 group2 n
1
n
2
statistic p p.adj
Time 1 PC.Val W2 W6 1
2
1
2
16 0.000656 0.00065
6
Time 2 PC.Val W2 W6 1
2
1
2
116 0.01 0.01
Time 3 PC.Val W2 W6 1
2
1
2
36 0.039 0.039

152
Time 13 PC.Val W2 W6
12
12
44 0.114 0.114
Time 14 PC.Val W2 W6
12
12
93 0.242 0.242
Time
4 PC.Val W2 W6
12
12
53 0.291 0.291
Time
9 PC.Val W2 W6
12
12
89 0.347 0.347
Time
5 PC.Val W2 W6
12
12
58 0.443 0.443
Time 18 PC.Val W2 W6
12
12
59 0.478 0.478
Time
8 PC.Val W2 W6
12
12
60 0.514 0.514
Time 10 PC.Val W2 W6
12
12
61 0.551 0.551
Time 16 PC.Val W2 W6
12
12
83 0.551 0.551
Time 11 PC.Val W2 W6
12
12
65 0.713 0.713
Time 17 PC.Val W2 W6
12
12
66 0.755 0.755
Time 12 PC.Val W2 W6
12
12
67 0.799 0.799
Time
6 PC.Val W2 W6
12
12
68 0.843 0.843
Time
7 PC.Val W2 W6
12
12
74 0.932 0.932
Time 15 PC.Val W2 W6
12
12
72
1
1
pH
2 PC.Val Constan t
Variabl e
12
12
114 0.015 0.015
pH 11 PC.Val Constan t
Variabl e
12
12
37 0.045 0.045
pH 10 PC.Val Constan t
Variabl e
12
12
98 0.143 0.143
pH 13 PC.Val Constan t
Variabl e
12
12
96 0.178 0.178
pH
9 PC.Val Constan t
Variabl e
12
12
49 0.198 0.198
pH
8 PC.Val Constan t
Variabl e
12
12
94 0.219 0.219
pH
7 PC.Val Constan t
Variabl e
12
12
92 0.266 0.266
pH 15 PC.Val Constan t
Variabl e
12
12
92 0.266 0.266

153
pH 18 PC.Val Constan
t
Variabl
e
1
2
1
2
89 0.347 0.347
pH 14 PC.Val Constan
t
Variabl
e
1
2
1
2
56 0.378 0.378
pH 12 PC.Val Constan
t
Variabl
e
1
2
1
2
58 0.443 0.443
pH 6 PC.Val Constan
t
Variabl
e
1
2
1
2
84 0.514 0.514
pH 17 PC.Val Constan
t
Variabl
e
1
2
1
2
84 0.514 0.514
pH 1 PC.Val Constan
t
Variabl
e
1
2
1
2
82 0.59 0.59
pH 3 PC.Val Constan
t
Variabl
e
1
2
1
2
77 0.799 0.799
pH 5 PC.Val Constan
t
Variabl
e
1
2
1
2
68 0.843 0.843
pH 16 PC.Val Constan
t
Variabl
e
1
2
1
2
69 0.887 0.887
pH 4 PC.Val Constan
t
Variabl
e
1
2
1
2
70 0.932 0.932
Supplementary Table 6. Kruskall-Wallis test results for determining best PC describing treatment grouping for O.
lurida
PC.Num .y. n statistic df p p.adj
9 PC.Val 24 7.58 3 0.0555 0.0555
14 PC.Val 24 7.326666667 3 0.0622 0.0622
5 PC.Val 24 6.966666667 3 0.073 0.073
4 PC.Val 24 6.086666667 3 0.107 0.107
1 PC.Val 24 5.68 3 0.128 0.128
16 PC.Val 24 5.313333333 3 0.15 0.15
12 PC.Val 24 4.846666667 3 0.183 0.183
10 PC.Val 24 3.866666667 3 0.276 0.276
17 PC.Val 24 3.606666667 3 0.307 0.307
2 PC.Val 24 2.86 3 0.414 0.414
6 PC.Val 24 2.793333333 3 0.425 0.425
15 PC.Val 24 2.186666667 3 0.535 0.535
13 PC.Val 24 2 3 0.572 0.572
11 PC.Val 24 1.686666667 3 0.64 0.64
8 PC.Val 24 1.646666667 3 0.649 0.649
7 PC.Val 24 1.233333333 3 0.745 0.745
3 PC.Val 24 1.166666667 3 0.761 0.761

154
Supplementary Table 7. Dunn test results for significant differences between treatment groups for O. lurida
group1 group2 n1 n2 statistic p p.adj p.adj.signif
7.7C 7.7A0.2 6 6 2.65361389 0.00796349 0.00796349 **
7.7A0.2 7.7A0.5 6 6 -1.9595918 0.05004352 0.05004352 ns
8.0C 7.7A0.2 6 6 1.59216833 0.11134689 0.11134689 ns
8.0C 7.7C 6 6 -1.0614456 0.28848746 0.28848746 ns
7.7C 7.7A0.5 6 6 0.69402209 0.48766835 0.48766835 ns
8.0C 7.7A0.5 6 6 -0.3674235 0.71330317 0.71330317 ns
Supplementary Table 8. Wilcoxon rank sum test results for determining best PC describing pH type and time for O.
lurida
Factor PC.Num .y. group1 group2 n1 n2 statistic p p.adj
Time 1 PC.Val W2 W6 12 12 12 0.000201 0.000201
Time 8 PC.Val W2 W6 12 12 33 0.024 0.024
Time 14 PC.Val W2 W6 12 12 38 0.052 0.052
Time 15 PC.Val W2 W6 12 12 95 0.198 0.198
Time 17 PC.Val W2 W6 12 12 51 0.242 0.242
Time 5 PC.Val W2 W6 12 12 54 0.319 0.319
Time 3 PC.Val W2 W6 12 12 55 0.347 0.347
Time 7 PC.Val W2 W6 12 12 56 0.378 0.378
Time 12 PC.Val W2 W6 12 12 60 0.514 0.514
Time 11 PC.Val W2 W6 12 12 61 0.551 0.551
Time 2 PC.Val W2 W6 12 12 63 0.63 0.63
Time 6 PC.Val W2 W6 12 12 64 0.671 0.671
Time 13 PC.Val W2 W6 12 12 65 0.713 0.713
Time 4 PC.Val W2 W6 12 12 75 0.887 0.887
Time 16 PC.Val W2 W6 12 12 70 0.932 0.932
Time 9 PC.Val W2 W6 12 12 71 0.977 0.977
Time 10 PC.Val W2 W6 12 12 72 1 1
pH 5 PC.Val Constant Variable 12 12 27 0.008 0.008
pH 16 PC.Val Constant Variable 12 12 35 0.033 0.033
pH 17 PC.Val Constant Variable 12 12 40 0.068 0.068
pH 4 PC.Val Constant Variable 12 12 41 0.078 0.078
pH 9 PC.Val Constant Variable 12 12 44 0.114 0.114
pH 10 PC.Val Constant Variable 12 12 48 0.178 0.178
pH 12 PC.Val Constant Variable 12 12 50 0.219 0.219
pH 2 PC.Val Constant Variable 12 12 52 0.266 0.266
pH 13 PC.Val Constant Variable 12 12 52 0.266 0.266

155
pH 3 PC.Val Constant Variable 12 12 54 0.319 0.319
pH 6 PC.Val Constant Variable 12 12 87 0.41 0.41
pH 14 PC.Val Constant Variable 12 12 87 0.41 0.41
pH 11 PC.Val Constant Variable 12 12 59 0.478 0.478
pH 8 PC.Val Constant Variable 12 12 75 0.887 0.887
pH 1 PC.Val Constant Variable 12 12 74 0.932 0.932
pH 7 PC.Val Constant Variable 12 12 71 0.977 0.977
pH 15 PC.Val Constant Variable 12 12 72 1 1
Supplementary Table 9. Top 20 differentially expressed genes and their products for each treatment and timepoint for
M. gigas
Condition Rank Gene Product
7.7C_W2 1 LOC136271011 ATP-dependent DNA helicase RRM3
7.7C_W2 2 LOC105344500 Stimulator of interferon genes protein
7.7C_W2 3 LOC105339644 Protein SET
7.7C_W2 4 LOC105321511 Protein retinal degeneration B
7.7C_W2 5 LOC105349145 Leucine-rich repeats and immunoglobulin-like domains protein 3
7.7C_W2 6 LOC105348685 N-lysine methyltransferase KMT5A
7.7C_W2 7 LOC117686279 Metalloproteinase inhibitor 3
7.7C_W2 8 LOC105346472
7.7C_W2 9 LOC136274041
7.7C_W2 10 LOC105327516 Soma ferritin
7.7C_W2 11 LOC105321429
7.7C_W2 12 LOC136269478 Ubiquitin carboxyl-terminal hydrolase 18
7.7C_W2 13 LOC105346721 Lon protease homolog 2, peroxisomal
7.7C_W2 14 LOC105338699
7.7C_W2 15 LOC136269815
7.7C_W2 16 LOC105317581 Kinesin-like protein KIF14
7.7C_W2 17 LOC105331958 RNA-binding protein 5-A
7.7C_W2 18 LOC105323152 Transient receptor potential cation channel subfamily A member 1
7.7C_W2 19 LOC105337439 MYND-type domain-containing protein
7.7C_W2 20 LOC105344231 Peripheral plasma membrane protein CASK
7.7C_W6 1 LOC105317513 C-type lectin domain family 17, member A
7.7C_W6 2 LOC105319220 Beta-1,4-galactosyltransferase 2
7.7C_W6 3 LOC117681168 Complement C1q-like protein 4
7.7C_W6 4 LOC105347438 Neutral ceramidase B
7.7C_W6 5 LOC105343176 EGF-like domain-containing protein
7.7C_W6 6 LOC117693178 CUB domain-containing protein
7.7C_W6 7 LOC105320528 Lysophospholipid acyltransferase 2

156
7.7C_W6 8 LOC105336002 Fanconi anemia group I protein
7.7C_W6 9 LOC105317891 EF-hand domain-containing protein
7.7C_W6 10 LOC105332336
7.7C_W6 11 LOC105339306 Sodium leak channel NALCN
7.7C_W6 12 LOC117681844
7.7C_W6 13 LOC136276206
7.7C_W6 14 LOC136269743
7.7C_W6 15 LOC117689161
7.7C_W6 16 LOC105333626
7.7C_W6 17 LOC105334035 Zinc finger protein 711
7.7C_W6 18 LOC105338111
7.7C_W6 19 LOC105322577 Carbohydrate sulfotransferase 15
7.7C_W6 20 LOC105324941
7.7A0.2_W2 1 LOC109620592
7.7A0.2_W2 2 LOC105331314 Non-neuronal cytoplasmic intermediate filament protein
7.7A0.2_W2 3 LOC105321541 E3 ubiquitin-protein ligase arih1l
7.7A0.2_W2 4 LOC105317566 Chitin synthase chs-2
7.7A0.2_W2 5 LOC105322305 Laminin subunit gamma-1
7.7A0.2_W2 6 LOC105340132 RNA-binding protein PNO1
7.7A0.2_W2 7 LOC105318129 RNA-binding protein 25
7.7A0.2_W2 8 LOC105335527 Nuclear factor 1 A-type
7.7A0.2_W2 9 LOC105330432 Metabotropic glutamate receptor 3
7.7A0.2_W2 10 LOC117680537 Tyrosinase-like protein 2
7.7A0.2_W2 11 LOC105345989 Heat shock protein HSP 90-beta
7.7A0.2_W2 12 LOC105343585 Beta-hexosaminidase subunit beta
7.7A0.2_W2 13 LOC117689403 Serine/threonine-protein phosphatase 2A 56 kDa regulatory subunit
delta isoform
7.7A0.2_W2 14 LOC105344999
7.7A0.2_W2 15 LOC105332140 Nucleolar protein 58
7.7A0.2_W2 16 LOC105347242 Mitochondrial glycine transporter
7.7A0.2_W2 17 LOC105318789 Small ribosomal subunit protein eS26
7.7A0.2_W2 18 LOC105349012 E3 ubiquitin-protein ligase rnf213-alpha
7.7A0.2_W2 19 LOC136271451 Reverse transcriptase
7.7A0.2_W2 20 LOC105321535
7.7A0.2_W6 1 LOC105323905 Small ribosomal subunit protein uS11
7.7A0.2_W6 2 LOC105348685 N-lysine methyltransferase KMT5A
7.7A0.2_W6 3 LOC105347217 Cytochrome c oxidase subunit 4 isoform 1, mitochondrial
(Fragment)
7.7A0.2_W6 4 LOC105346848 piRNA biogenesis protein EXD1

157
7.7A0.2_W6 5 LOC105346536
7.7A0.2_W6 6 LOC105334676 Complement C1q-like protein 4
7.7A0.2_W6 7 LOC105317513 C-type lectin domain family 17, member A
7.7A0.2_W6 8 LOC105341065 Histone-lysine N-methyltransferase Set8
7.7A0.2_W6 9 LOC105341089 Agrin
7.7A0.2_W6 10 LOC105343662 Short-chain collagen C4
7.7A0.2_W6 11 LOC105330730 Coiled-coil domain-containing protein 180
7.7A0.2_W6 12 LOC105322191
7.7A0.2_W6 13 LOC105320232 Macrophage-expressed gene 1 protein
7.7A0.2_W6 14 LOC105332694 Transient receptor potential cation channel subfamily M memberlike 2
7.7A0.2_W6 15 LOC117688966 BPTI/Kunitz domain-containing protein 5
7.7A0.2_W6 16 LOC105337660 Protein madd-4
7.7A0.2_W6 17 LOC105335627 RIMS-binding protein 2
7.7A0.2_W6 18 LOC105327330 Innexin unc-9
7.7A0.2_W6 19 LOC105337341 Piwi-like protein 2
7.7A0.2_W6 20 LOC105319688 Meprin A subunit beta
7.7A0.5_W2 1 LOC105331958 RNA-binding protein 5-A
7.7A0.5_W2 2 LOC105318610 Solute carrier organic anion transporter family member 4A1
7.7A0.5_W2 3 LOC105349145 Leucine-rich repeats and immunoglobulin-like domains protein 3
7.7A0.5_W2 4 LOC105320898 Low-density lipoprotein receptor-related protein 2
7.7A0.5_W2 5 LOC105337439 MYND-type domain-containing protein
7.7A0.5_W2 6 LOC105333296
7.7A0.5_W2 7 LOC105326685 WAP domain-containing protein
7.7A0.5_W2 8 LOC105340928 Enteropeptidase
7.7A0.5_W2 9 LOC105347589 AT-rich interactive domain-containing protein 2
7.7A0.5_W2 10 LOC105319325 Cytochrome b-245 heavy chain
7.7A0.5_W2 11 LOC105333580 Ethanolamine-phosphate phospho-lyase
7.7A0.5_W2 12 LOC105337089 Succinate dehydrogenase [ubiquinone] flavoprotein subunit A,
mitochondrial
7.7A0.5_W2 13 LOC105343324 Dynein gamma chain, flagellar outer arm
7.7A0.5_W2 14 LOC105321818 Kalirin
7.7A0.5_W2 15 LOC105323093 E3 SUMO-protein ligase RanBP2
7.7A0.5_W2 16 LOC105325492
7.7A0.5_W2 17 LOC105345158 Protein CASC3
7.7A0.5_W2 18 LOC105341497 Inactive phospholipase C-like protein 2
7.7A0.5_W2 19 LOC105339951 Eukaryotic translation initiation factor 3 subunit A
7.7A0.5_W2 20 LOC105337592 Septin-7 (Fragment)
7.7A0.5_W6 1 LOC105333446

158
7.7A0.5_W6 2 LOC105328796 Maltase-glucoamylase
7.7A0.5_W6 3 LOC105319220 Beta-1,4-galactosyltransferase 2
7.7A0.5_W6 4 LOC136273022
7.7A0.5_W6 5 LOC105347438 Neutral ceramidase B
7.7A0.5_W6 6 LOC117689834 Heat shock 70 kDa protein 12A
7.7A0.5_W6 7 LOC105325989 Cysteine and tyrosine-rich protein 1
7.7A0.5_W6 8 LOC117693178 CUB domain-containing protein
7.7A0.5_W6 9 LOC136269859
7.7A0.5_W6 10 LOC136269815
7.7A0.5_W6 11 LOC105338999 Delta-like protein 4
7.7A0.5_W6 12 LOC136276305
7.7A0.5_W6 13 LOC136270292
7.7A0.5_W6 14 LOC105319337 N-acetylated-alpha-linked acidic dipeptidase 2
7.7A0.5_W6 15 LOC117690623
7.7A0.5_W6 16 LOC105344773 Calcium-activated chloride channel regulator 1
7.7A0.5_W6 17 LOC117686549
7.7A0.5_W6 18 LOC105330064 Sphingomyelin synthase-related protein 1
7.7A0.5_W6 19 LOC105339281
7.7A0.5_W6 20 LOC136276206
Supplementary Table 10. Top 20 differentially expressed genes and their products for each treatment and timepoint
for O. lurida
Condition Rank Gene Product
7.7C_W2 1 oystercontig_51382 Retrovirus-related Pol polyprotein from transposon 17.6
7.7C_W2 2 oystercontig_1896
7.7C_W2 3 oystercontig_5151 Copine-3
7.7C_W2 4 oystercontig_18474 Meiosis regulator and mRNA stability factor 1
7.7C_W2 5 oystercontig_42731 Transposable element Tcb1 transposase
7.7C_W2 6 oystercontig_15860 Uncharacterized protein
7.7C_W2 7 oystercontig_8658 Afadin
7.7C_W2 8 oystercontig_43301 Centrosome-associated protein ALMS1
7.7C_W2 9 oystercontig_22895 Importin subunit alpha-4
7.7C_W2 10 oystercontig_5940 Uncharacterized protein LOC111106351
7.7C_W2 11 oystercontig_522 CBY1-interacting BAR domain-containing protein 1
7.7C_W2 12 oystercontig_6991 Calcium homeostasis endoplasmic reticulum protein
7.7C_W2 13 oystercontig_22342 Vacuolar protein sorting-associated protein 33A
7.7C_W2 14 oystercontig_20925 Pre-mRNA-processing-splicing factor 8
7.7C_W2 15 oystercontig_40478 Bromodomain-containing protein 2
7.7C_W2 16 oystercontig_10264 non-specific serine/threonine protein kinase

159
7.7C_W2 17 oystercontig_20532
7.7C_W6 1 oystercontig_41032 RNA ligase 1
7.7C_W6 2 oystercontig_45625 Integrase/recombinase xerD homolog
7.7C_W6 3 oystercontig_15112 RING-type domain-containing protein
7.7C_W6 4 oystercontig_18665 Cilia- and flagella-associated protein 43
7.7C_W6 5 oystercontig_7956 Serine/threonine-protein kinase TBK1
7.7C_W6 6 oystercontig_32073
7.7C_W6 7 oystercontig_16989
7.7C_W6 8 oystercontig_26803
7.7C_W6 9 oystercontig_11311 Uncharacterized protein F54H12.2
7.7C_W6 10 oystercontig_686 Next to BRCA1 gene 1 protein
7.7C_W6 11 oystercontig_5905
7.7C_W6 12 oystercontig_29191 Golgin subfamily A member 4
7.7C_W6 13 oystercontig_45987
7.7C_W6 14 oystercontig_41222
7.7C_W6 15 oystercontig_46796
7.7C_W6 16 oystercontig_33737 CUB domain-containing protein
7.7C_W6 17 oystercontig_19226 Zinc finger CCCH domain-containing protein 10
7.7C_W6 18 oystercontig_20171
7.7C_W6 19 oystercontig_49688
7.7C_W6 20 oystercontig_21045 Signal transducer and activator of transcription 5A
7.7A0.2_W2 1 oystercontig_39836
7.7A0.2_W2 2 oystercontig_12379
7.7A0.2_W2 3 oystercontig_27145
7.7A0.2_W2 4 oystercontig_8286
7.7A0.2_W2 5 oystercontig_5151 Copine-3
7.7A0.2_W2 6 oystercontig_10241 WD repeat-containing protein 11
7.7A0.2_W2 7 oystercontig_39791
7.7A0.2_W2 8 oystercontig_14567 Neural cell adhesion molecule L1
7.7A0.2_W2 9 oystercontig_13522 Tripartite motif-containing protein 66
7.7A0.2_W2 10 oystercontig_10487 Sarcoplasmic calcium-binding protein
7.7A0.2_W2 11 oystercontig_51382 Retrovirus-related Pol polyprotein from transposon 17.6
7.7A0.2_W2 12 oystercontig_1963 Neuronal acetylcholine receptor subunit alpha-6
7.7A0.2_W2 13 oystercontig_9671 Transcription factor 4
7.7A0.2_W2 14 oystercontig_17984 Genome polyprotein
7.7A0.2_W2 15 oystercontig_6551 Dynein beta chain, ciliary
7.7A0.2_W2 16 oystercontig_8833 Sushi, von Willebrand factor type A, EGF and pentraxin
domain-containing protein 1
7.7A0.2_W2 17 oystercontig_30318 ATP-dependent translocase ABCB1

160
7.7A0.2_W2 18 oystercontig_36818 Small ribosomal subunit protein bS18m
7.7A0.2_W2 19 oystercontig_524 Protein ALP1-like
7.7A0.2_W2 20 oystercontig_8265 Retrovirus-related Pol polyprotein from transposon 412
7.7A0.2_W6 1 oystercontig_22269 c-SKI SMAD4-binding domain-containing protein
7.7A0.2_W6 2 oystercontig_9576 DNA-directed RNA polymerase II subunit RPB2
7.7A0.2_W6 3 oystercontig_39836
7.7A0.2_W6 4 oystercontig_18665 Cilia- and flagella-associated protein 43
7.7A0.2_W6 5 oystercontig_9314
7.7A0.2_W6 6 oystercontig_11 Probable DNA polymerase
7.7A0.2_W6 7 oystercontig_46549
7.7A0.2_W6 8 oystercontig_26983 Uncharacterized protein LOC111108191
7.7A0.2_W6 9 oystercontig_35366
7.7A0.2_W6 10 oystercontig_5959 Constitutive coactivator of PPAR-gamma-like protein 1
homolog
7.7A0.2_W6 11 oystercontig_34223 Zinc finger protein 827
7.7A0.2_W6 12 oystercontig_12823 Bifunctional glutamate/proline--tRNA ligase
7.7A0.2_W6 13 oystercontig_8043
7.7A0.2_W6 14 oystercontig_24755 Protein phosphatase Slingshot homolog 2
7.7A0.2_W6 15 oystercontig_21673
7.7A0.2_W6 16 oystercontig_2562 ATP-binding cassette sub-family F member 2
7.7A0.2_W6 17 oystercontig_21420 Na(+)/H(+) exchange regulatory cofactor NHE-RF1
7.7A0.2_W6 18 oystercontig_9118 Ubiquitin-associated protein 2-like
7.7A0.2_W6 19 oystercontig_9411 ATPase family AAA domain-containing protein 5
7.7A0.2_W6 20 oystercontig_8286
7.7A0.5_W2 1 oystercontig_5151 Copine-3
7.7A0.5_W2 2 oystercontig_37832 Calcipressin-1
7.7A0.5_W2 3 oystercontig_31661 Collagen alpha-5(VI) chain
7.7A0.5_W2 4 oystercontig_26972 Collagen alpha-2(I) chain (Fragment)
7.7A0.5_W2 5 oystercontig_11927
7.7A0.5_W2 6 oystercontig_14013 Regulator of nonsense transcripts 1
7.7A0.5_W2 7 oystercontig_42731 Transposable element Tcb1 transposase
7.7A0.5_W2 8 oystercontig_41743
7.7A0.5_W2 9 oystercontig_25030 Sterol regulatory element-binding protein 1
7.7A0.5_W2 10 oystercontig_32326 Mediator of RNA polymerase II transcription subunit 1
7.7A0.5_W2 11 oystercontig_11827 Collagen alpha-1(IV) chain
7.7A0.5_W2 12 oystercontig_19476 Uncharacterized protein LOC111114716
7.7A0.5_W2 13 oystercontig_47327 Uncharacterized protein K02A2.6
7.7A0.5_W6 1 oystercontig_7793 Threonine synthase-like 1
7.7A0.5_W6 2 oystercontig_3618 Neural-cadherin

161
7.7A0.5_W6 3 oystercontig_37450
7.7A0.5_W6 4 oystercontig_3109
7.7A0.5_W6 5 oystercontig_18474 Meiosis regulator and mRNA stability factor 1
7.7A0.5_W6 6 oystercontig_26983 Uncharacterized protein LOC111108191
7.7A0.5_W6 7 oystercontig_19145 RNA-directed DNA polymerase from mobile element jockey
7.7A0.5_W6 8 oystercontig_37832 Calcipressin-1
7.7A0.5_W6 9 oystercontig_13471 1-phosphatidylinositol 3-phosphate 5-kinase
7.7A0.5_W6 10 oystercontig_45987
7.7A0.5_W6 11 oystercontig_27082 Myosin light chain kinase, smooth muscle
7.7A0.5_W6 12 oystercontig_5940 Uncharacterized protein LOC111106351
7.7A0.5_W6 13 oystercontig_29120 Multiple PDZ domain protein
7.7A0.5_W6 14 oystercontig_21691
7.7A0.5_W6 15 oystercontig_1243 Zinc finger MYND domain-containing protein 10
7.7A0.5_W6 16 oystercontig_19476 Uncharacterized protein LOC111114716
7.7A0.5_W6 17 oystercontig_16719 Tropomyosin
7.7A0.5_W6 18 oystercontig_8011 E3 ubiquitin-protein ligase TRIM45
7.7A0.5_W6 19 oystercontig_16989
7.7A0.5_W6 20 oystercontig_2316 Rho guanine nucleotide exchange factor 33
Supplementary Table 11. Genes shared between conditions out of top 20 differentially expressed genes for each
treatment for M. gigas
Gene Product Conditions
LOC105317513 C-type lectin domain family 17, member A 7.7C_W6, 7.7A0.2_W6
LOC105319220 Beta-1,4-galactosyltransferase 2 7.7C_W6, 7.7A0.5_W6
LOC105331958 RNA-binding protein 5-A 7.7C_W2, 7.7A0.5_W2
LOC105337439 MYND-type domain-containing protein 7.7C_W2, 7.7A0.5_W2
LOC105347438 Neutral ceramidase B 7.7C_W6, 7.7A0.5_W6
LOC105348685 N-lysine methyltransferase KMT5A 7.7C_W2, 7.7A0.2_W6
LOC105349145 Leucine-rich repeats and immunoglobulin-like domains protein 3 7.7C_W2, 7.7A0.5_W2
LOC117693178 CUB domain-containing protein 7.7C_W6, 7.7A0.5_W6
LOC136269815 7.7C_W2, 7.7A0.5_W6
LOC136276206 7.7C_W6, 7.7A0.5_W6
Supplementary Table 12. Genes shared between treatment 7.7C week 2 and week 6 for M. gigas
Gene Product
LOC117681553 BMP-binding endothelial regulator protein
LOC105339644 Protein SET
LOC105344974

162
LOC105344119 EF-hand calcium-binding domain-containing protein 5
LOC105333146
LOC105342563 Proteasome subunit alpha type-5
LOC105347692 Cytochrome P450 7A1
LOC117686279 Metalloproteinase inhibitor 3
LOC117686276
LOC105330223 MKI67 FHA domain-interacting nucleolar phosphoproteinlike
LOC105341458 Growth hormone secretagogue receptor type 1
LOC105347438 Neutral ceramidase B
LOC105332572 FRAS1-related extracellular matrix protein 1
LOC105338699
LOC105333791 Otogelin
Supplementary Table 13. Genes shared between treatment 7.7A0.2 week 2 and week 6 for M. gigas
Gene Product
LOC105337984 Mothers against decapentaplegic homolog 3
LOC105331314 Non-neuronal cytoplasmic intermediate filament
protein
LOC105329634 Paramyosin
LOC105325691 NEDD8 ultimate buster 1
LOC105321001 Villin-1
LOC105337439 MYND-type domain-containing protein
LOC117680537 Tyrosinase-like protein 2
LOC105327861 Eukaryotic translation initiation factor 4H
LOC105341623
LOC105325809 E3 ubiquitin-protein ligase RNF19A
LOC105335098 Probable splicing factor, arginine/serine-rich 7
LOC105329481 Troponin C
LOC105339082 Interleukin-6 receptor subunit beta
LOC105335284 Actin, cytoplasmic
LOC105340237 Protein phosphatase 1 regulatory subunit 3B
LOC105326194 Formin-like protein
Supplementary Table 14. Genes shared between treatment 7.7A0.5 week 2 and week 6 for M. gigas
Gene Product
LOC105346763 Eppin
LOC136274619 TRPM SLOG domain-containing protein
LOC105321680 Supervillin

163
LOC105338008 Sodium/potassium-transporting ATPase subunit alpha
LOC105345685
LOC105326132 Glutamate receptor
LOC105335866 Kelch-like protein 2
LOC117681015 Neogenin
LOC105339644 Protein SET
LOC105325906 Eukaryotic translation initiation factor 4E transporter
LOC105329071 L-rhamnose-binding lectin CSL3
LOC117693178 CUB domain-containing protein
LOC105334519 Neuronal acetylcholine receptor subunit alpha-7
LOC105329630
LOC105345794 GDP-D-glycero-alpha-D-manno-heptose dehydrogenase
LOC105323542 NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 11,
mitochondrial
LOC105342563 Proteasome subunit alpha type-5
LOC105334889 Ectopic P granules protein 5 homolog
LOC105320528 Lysophospholipid acyltransferase 2
LOC105323503 Protein sleepless
LOC105341780 Protocadherin Fat 4
LOC105324981 Myelin regulatory factor
LOC105338957 Elongation factor 1-alpha
LOC105337089 Succinate dehydrogenase [ubiquinone] flavoprotein subunit A,
mitochondrial
LOC105338262 Dual specificity protein kinase CLK2
LOC105330349
LOC105333380 TAF5-like RNA polymerase II p300/CBP-associated factorassociated factor 65 kDa subunit 5L
LOC136276305
LOC105328103 Neurexin 1
LOC105327226 Dynein axonemal heavy chain 6
LOC105333338 Ryanodine receptor
LOC105319337 N-acetylated-alpha-linked acidic dipeptidase 2
LOC105329461 Adhesion G-protein coupled receptor V1
LOC136276392
LOC105335663 Lysosomal-trafficking regulator
LOC105346955 Microtubule-associated protein futsch
LOC105318191 Uncharacterized protein
LOC136269795
LOC105323136 Dystonin
LOC105344514 Protocadherin Fat 4

164
LOC105347347 Tubulin alpha-1A chain
LOC105328796 Maltase-glucoamylase
LOC105329692 Peptidyl-prolyl cis-trans isomerase FKBP1A
LOC105326685 WAP domain-containing protein
LOC105347438 Neutral ceramidase B
LOC136269941
LOC105330502 Protocadherin Fat 4
LOC105340469 Actin
LOC105332572 FRAS1-related extracellular matrix protein 1
LOC105341240 Lipoxygenase homology domain-containing protein 1
LOC105339306 Sodium leak channel NALCN
LOC105344231 Peripheral plasma membrane protein CASK
LOC105339281
LOC105334406 Ubiquinone biosynthesis protein
LOC105320695 DENN domain-containing protein 5A
LOC117684133
LOC105345742 Unconventional myosin-Id
LOC105318373 ATP-dependent translocase ABCB1
Supplementary Table 15. Genes shared between conditions out of top 20 differentially expressed genes for each
treatment for O. lurida
Gene Product Conditions
oystercontig_16
989
7.7C_W6, 7.7A0.5_W6
oystercontig_18
474
Meiosis regulator and mRNA stability factor 1 7.7C_W2, 7.7A0.5_W6
oystercontig_18
665
Cilia- and flagella-associated protein 43 7.7C_W6, 7.7A0.2_W6
oystercontig_19
476
Uncharacterized protein LOC111114716 7.7A0.5_W2, 7.7A0.5_W6
oystercontig_26
983
Uncharacterized protein LOC111108191 7.7A0.2_W6, 7.7A0.5_W6
oystercontig_37
832
Calcipressin-1 7.7A0.5_W2, 7.7A0.5_W6
oystercontig_39
836
7.7A0.2_W2, 7.7A0.2_W6
oystercontig_42
731
Transposable element Tcb1 transposase 7.7C_W2, 7.7A0.5_W2
oystercontig_45
987
7.7C_W6, 7.7A0.5_W6
oystercontig_51
382
Retrovirus-related Pol polyprotein from
transposon 17.6
7.7C_W2, 7.7A0.2_W2

165
oystercontig_51
51
Copine-3 7.7C_W2, 7.7A0.2_W2,
7.7A0.5_W2
oystercontig_59
40
Uncharacterized protein LOC111106351 7.7C_W2, 7.7A0.5_W6
oystercontig_82
86
7.7A0.2_W2, 7.7A0.2_W6
Supplementary Table 16. Genes shared between treatment 7.7C week 2 and week 6 for O. lurida
Gene Product
oystercontig_18474 Meiosis regulator and mRNA stability factor 1
oystercontig_22342 Vacuolar protein sorting-associated protein 33A
Supplementary Table 17. Genes shared between treatment 7.7A0.2 week 2 and week 6 for O. lurida
Gene Product
oystercontig_8286
oystercontig_11827 Collagen alpha-1(IV) chain
oystercontig_39836
Supplementary Table 18. Genes shared between treatment 7.7A0.5 week 2 and week 6 for O. lurida
Gene Product
oystercontig_19476 Uncharacterized protein LOC111114716
oystercontig_26972 Collagen alpha-2(I) chain (Fragment)
oystercontig_37832 Calcipressin-1
oystercontig_42731 Transposable element Tcb1 transposase
Supplementary Table 19. Two-way ANOVA results for pH for species and treatment group
term df sumsq means
q
statisti
c
p.value logLik AIC BIC devian
ce
no
bs
r.squar
ed
Group 1 4.0151
3047
4.0151
3047
744.04
7005
7.15E121
NA NA NA NA NA NA
Species 1 3.82E06
3.82E06
0.0007
0841
0.9787
7178
NA NA NA NA NA NA
Group:S
pecies
1 0.0098
342
0.0098
342
1.8223
8261
0.1773
5804
NA NA NA NA NA NA
Residual
s
9
3
1
5.0239
9236
0.0053
9634
NA NA NA NA NA NA NA NA
NA N
A
NA NA NA NA 1116.5
9783
-
2223.1
957
-
2198.9
929
5.0239
9236
93
5
0.4447
9897

166
Supplementary Table 20. Two-way ANOVA results for temperature for species and treatment group
term df sumsq means
q
statisti
c
p.value logLik AIC BIC devian
ce
no
bs
r.squar
ed
Group 1 40.112
7481
40.112
7481
1057.8
757
1.25E155
NA NA NA NA NA NA
Species 1 0.1719
4893
0.1719
4893
4.5347
3286
0.0334
7545
NA NA NA NA NA NA
Group:S
pecies
1 77.517
3074
77.517
3074
2044.3
2955
4.11E237
NA NA NA NA NA NA
Residual
s
9
3
1
35.301
8491
0.0379
1821
NA NA NA NA NA NA NA NA
NA N
A
NA NA NA NA 205.1
082
-
400.2
164
-
376.01
367
35.301
8491
93
5
0.7694
2547
Supplementary Table 21. Tukey post-hoc test results for temperature
term contrast null.val
ue
estimate conf.low conf.hig
h
adj.p.val
ue
Group OA-Control 0 1.43159
927
1.34521
847
1.51798
007
0
Species M. galloprovincialis-M. californianus 0 0.02712
397
0.00212
678
0.05212
116
0.03347
572
Group:Spe
cies
OA:M. californianus-Control:M.
californianus
0 3.42137
509
3.26119
15
3.58155
868
0
Group:Spe
cies
Control:M. galloprovincialis-Control:M.
californianus
0 3.92222
222
3.69809
586
4.14634
859
0
Group:Spe
cies
OA:M. galloprovincialis-Control:M.
californianus
0 3.36334
808
3.20312
329
3.52357
288
0
Group:Spe
cies
Control:M. galloprovincialis-OA:M.
californianus
0 0.50084
713
0.34066
354
0.66103
072
0
Group:Spe
cies
OA:M. galloprovincialis-OA:M.
californianus
0 -
0.05802
7
-
0.09116
52
-
0.02488
88
4.39E-05
Group:Spe
cies
OA:M. galloprovincialis-Control:M.
galloprovincialis
0 -
0.55887
41
-
0.71909
89
-
0.39864
93
0
Supplementary Table 22. Two-way ANOVA results for salinity for species and treatment group
term d
f
sums
q
means
q
statistic p.value logLik AIC BIC devia
nce
no
bs
r.squar
ed
Group 1 0.24
025
0.2402
5
0.7303
0482
0.3984
3585
NA NA NA NA NA NA

167
Species 1 2.97
025
2.9702
5
9.0288
7782
0.0048
1607
NA NA NA NA NA NA
Group:Sp
ecies
1 0.15
625
0.1562
5
0.4749
6411
0.4951
3054
NA NA NA NA NA NA
Residuals 3
6
11.8
43
0.3289
7222
NA NA NA NA NA NA NA NA
NA N
A
NA NA NA NA -
32.414
692
74.829
3835
83.273
7808
11.84
3
40 0.2213
5472
Supplementary Table 23. Tukey post-hoc test results for salinity
term contrast null.val
ue
estim
ate
conf.lo
w
conf.hig
h
adj.p.val
ue
Group OA-Control 0 -0.155 -
0.52284
74
0.21284
74
0.39843
585
Species M. galloprovincialis-M. californianus 0 -0.545 -
0.91284
74
-
0.17715
26
0.00481
607
Group:Spe
cies
OA:M. californianus-Control:M.
californianus
0 -0.28 -
0.97082
43
0.41082
429
0.69684
612
Group:Spe
cies
Control:M. galloprovincialis-Control:M.
californianus
0 -0.67 -
1.36082
43
0.02082
429
0.06010
155
Group:Spe
cies
OA:M. galloprovincialis-Control:M.
californianus
0 -0.7 -
1.39082
43
-
0.00917
57
0.04604
547
Group:Spe
cies
Control:M. galloprovincialis-OA:M.
californianus
0 -0.39 -
1.08082
43
0.30082
429
0.43619
429
Group:Spe
cies
OA:M. galloprovincialis-OA:M.
californianus
0 -0.42 -
1.11082
43
0.27082
429
0.37117
324
Group:Spe
cies
OA:M. galloprovincialis-Control:M.
galloprovincialis
0 -0.03 -
0.72082
43
0.66082
429
0.99941
675
Supplementary Table 24. Two-way ANOVA results for DIC for species and treatment group
term d
f
sumsq means
q
statisti
c
p.valu
e
logLik AIC BIC devian
ce
no
bs
r.squar
ed
Treatment 1 60274
6.001
60274
6.001
3.6683
9339
0.0844
6165
NA NA NA NA N
A
NA

168
Species 1 10959
4.183
10959
4.183
0.6670
0497
0.4331
2301
NA NA NA NA N
A
NA
Treatment:
Species
1 94416.
0672
94416.
0672
0.5746
2891
0.4659
1431
NA NA NA NA N
A
NA
Residuals 1
0
16430
78.96
16430
7.896
NA NA NA NA NA NA N
A
NA
NA N
A
NA NA NA NA -
101.57
632
213.15
2631
216.34
7917
16430
78.96
14 0.3293
1042
Supplementary Table 25. Two-way ANOVA results for TA for species and treatment group
term d
f
sumsq means
q
statisti
c
p.value logLik AIC BIC devia
nce
no
bs
r.squar
ed
Treatment 1 13731
8.96
13731
8.96
0.6953
9728
0.4238
0631
NA NA NA NA NA NA
Species 1 27785.
5732
27785.
5732
0.1407
0899
0.7154
1054
NA NA NA NA NA NA
Treatment:
Species
1 65864.
7
65864.
7
0.3335
4559
0.5763
503
NA NA NA NA NA NA
Residuals 1
0
19746
83.6
19746
8.36
NA NA NA NA NA NA NA NA
NA N
A
NA NA NA NA -
102.86
317
215.72
6339
218.92
1625
19746
83.6
14 0.1047
1695
Supplementary Table 26. Two-way ANOVA results for pCO2for species and treatment group
term d
f
sumsq means
q
statisti
c
p.value logLik AIC BIC devian
ce
no
bs
r.squar
ed
Treatment 1 35732
51.34
35732
51.34
48.532
0828
3.87E05
NA NA NA NA N
A
NA
Species 1 62910.
9878
62910.
9878
0.8544
6026
0.3770
6003
NA NA NA NA N
A
NA
Treatment:
Species
1 57875.
7986
57875.
7986
0.7860
7206
0.3961
1193
NA NA NA NA N
A
NA
Residuals 1
0
73626
5.814
73626.
5814
NA NA NA NA NA NA N
A
NA
NA N
A
NA NA NA NA -
95.957
164
201.91
4327
205.10
9614
73626
5.814
14 0.8338
1144
Supplementary Table 27. Tukey post-hoc test results for pCO2
term contrast null.va
lue
estimat
e
conf.lo
w
conf.hig
h
adj.p.val
ue

169
Treatment OA-Control 0 -
2.60204
24
-3.6229 -
1.58118
49
0.00020
405
Species M. galloprovincialis-M. californianus 0 0.33589
321
-
0.69554
35
1.36732
988
0.48471
502
Treatment:S
pecies
OA:M. californianus-Control:M.
californianus
0 -
2.22592
74
-
4.08019
27
-
0.37166
21
0.01887
706
Treatment:S
pecies
Control:M. galloprovincialisControl:M. californianus
0 0.77469
403
-
1.22814
14
2.77752
947
0.65003
937
Treatment:S
pecies
OA:M. galloprovincialis-Control:M.
californianus
0 -
2.32883
5
-
4.33167
05
-
0.32599
96
0.02265
019
Treatment:S
pecies
Control:M. galloprovincialis-OA:M.
californianus
0 3.00062
145
0.99778
602
5.00345
689
0.00464
49
Treatment:S
pecies
OA:M. galloprovincialis-OA:M.
californianus
0 -
0.10290
76
-
2.10574
3
1.89992
782
0.99852
111
Treatment:S
pecies
OA:M. galloprovincialis-Control:M.
galloprovincialis
0 -
3.10352
91
-
5.24465
02
-
0.96240
79
0.00580
866
Supplementary Table 28. Two-way ANOVA results for Ωarsaturation for species and treatment group
term d
f
sumsq means
q
statisti
c
p.value logLik AIC BIC devian
ce
no
bs
r.squar
ed
Treatment 1 23.697
1865
23.697
1865
32.253
9534
0.0002
0405
NA NA NA NA N
A
NA
Species 1 0.3868
26
0.3868
26
0.5265
0418
0.4847
1502
NA NA NA NA N
A
NA
Treatment:
Species
1 0.6601
5827
0.6601
5827
0.8985
3342
0.3655
1898
NA NA NA NA N
A
NA
Residuals 1
0
7.3470
6416
0.7347
0642
NA NA NA NA NA NA N
A
NA
NA N
A
NA NA NA NA -
15.351
844
40.703
6875
43.898
9741
7.3470
6416
14 0.7710
5698
Supplementary Table 29. Tukey post-hoc test results for Ωarsaturation
term contrast null.va
lue
estimat
e
conf.lo
w
conf.hig
h
adj.p.val
ue

170
Treatment OA-Control 0 -
2.60204
24
-3.6229 -
1.58118
49
0.00020
405
Species M. galloprovincialis-M. californianus 0 0.33589
321
-
0.69554
35
1.36732
988
0.48471
502
Treatment:S
pecies
OA:M. californianus-Control:M.
californianus
0 -
2.22592
74
-
4.08019
27
-
0.37166
21
0.01887
706
Treatment:S
pecies
Control:M. galloprovincialisControl:M. californianus
0 0.77469
403
-
1.22814
14
2.77752
947
0.65003
937
Treatment:S
pecies
OA:M. galloprovincialis-Control:M.
californianus
0 -
2.32883
5
-
4.33167
05
-
0.32599
96
0.02265
019
Treatment:S
pecies
Control:M. galloprovincialis-OA:M.
californianus
0 3.00062
145
0.99778
602
5.00345
689
0.00464
49
Treatment:S
pecies
OA:M. galloprovincialis-OA:M.
californianus
0 -
0.10290
76
-
2.10574
3
1.89992
782
0.99852
111
Treatment:S
pecies
OA:M. galloprovincialis-Control:M.
galloprovincialis
0 -
3.10352
91
-
5.24465
02
-
0.96240
79
0.00580
866
Supplementary Table 30. Generalized linear model fit results for log-transformed larval size
term estimate std.error statistic p.value
(Intercept) 4.71398042 0.01537233 306.653635 0
SpeciesM. galloprovincialis -0.2804914 0.02101173 -13.349277 2.01E-38
TreatmentOA -0.0216292 0.0208533 -1.0372079 0.29981191
Day 0.03975219 0.00114475 34.7256474 8.59E-193
SpeciesM. galloprovincialis:TreatmentOA 0.00650806 0.0296261 0.21967338 0.82615646
SpeciesM. galloprovincialis:Day 0.01123375 0.0015185 7.3979436 2.33E-13
TreatmentOA:Day -0.0055381 0.00159913 -3.4631952 0.00054933
SpeciesM. galloprovincialis:TreatmentOA:Day -0.0054146 0.00211152 -2.5643021 0.01043832
Supplementary Table 31. Two-sample t-test results for M. galloprovincialis phenotypes
phenot
ype
estimat
e
estimat
e1
estimat
e2
statisti
c
p.val
ue
param
eter
conf.lo
w
conf.hi
gh
method altern
ative
length 0.8338
5406
5.8942
8571
5.0604
3165
4.4069
9543
1.68E
-05
207 0.4608
2548
1.2068
8264
Two
Sample ttest
two.si
ded

171
wet_w
eight
5.2712
8469
20.591
4286
15.320
1439
4.0127
4377
8.39E
-05
207 2.6814
6393
7.8611
0545
Two
Sample ttest
two.si
ded
Supplementary Table 32. Trimgalore! read filtering statistics for M. galloprovincialis WGS reads
sample_id total_reads_processed reads_with_adapters reads_written
MG22_D2_1_CKDN230011060-
1A_H5NHYDSX7_L1_1
29465009 11749480 29465009
MG22_D2_1_CKDN230011060-
1A_H5NHYDSX7_L1_2
29465009 11743661 29465009
MG22_D2_1_CKDN230011060-
1A_H5T5GDSX7_L3_1
38774197 15477119 38774197
MG22_D2_1_CKDN230011060-
1A_H5T5GDSX7_L3_2
38774197 15522109 38774197
MG22_D2_2_CKDN230011061-
1A_H5NJJDSX7_L2_1
2560459 1009949 2560459
MG22_D2_2_CKDN230011061-
1A_H5NJJDSX7_L2_2
2560459 1011973 2560459
MG22_D2_2_CKDN230011061-
1A_H5NKYDSX7_L1_1
51497330 20312296 51497330
MG22_D2_2_CKDN230011061-
1A_H5NKYDSX7_L1_2
51497330 20361430 51497330
MG22_D23_C_1_CKDN230011063-
1A_H5T5GDSX7_L3_1
55260561 23134086 55260561
MG22_D23_C_1_CKDN230011063-
1A_H5T5GDSX7_L3_2
55260561 23219450 55260561
MG22_D23_C_2_CKDN230011064-
1A_H5NHYDSX7_L2_1
29464987 11665853 29464987
MG22_D23_C_2_CKDN230011064-
1A_H5NHYDSX7_L2_2
29464987 11652071 29464987
MG22_D23_C_2_CKDN230011064-
1A_H5T5GDSX7_L3_1
38124170 14985780 38124170
MG22_D23_C_2_CKDN230011064-
1A_H5T5GDSX7_L3_2
38124170 15046017 38124170
MG22_D23_C_3_CKDN230011065-
1A_H5NHYDSX7_L1_1
28393923 11446102 28393923
MG22_D23_C_3_CKDN230011065-
1A_H5NHYDSX7_L1_2
28393923 11440792 28393923
MG22_D23_C_3_CKDN230011065-
1A_H5T5GDSX7_L3_1
40127490 16190751 40127490
MG22_D23_C_3_CKDN230011065-
1A_H5T5GDSX7_L3_2
40127490 16233149 40127490

172
MG22_D2_3_CKDN230011062-
1A_H5NJJDSX7_L2_1
2438605 967297 2438605
MG22_D2_3_CKDN230011062-
1A_H5NJJDSX7_L2_2
2438605 966607 2438605
MG22_D2_3_CKDN230011062-
1A_H5NKYDSX7_L1_1
51366617 20295526 51366617
MG22_D2_3_CKDN230011062-
1A_H5NKYDSX7_L1_2
51366617 20345983 51366617
MG22_D25_T_1_CKDN230011066-
1A_H5NHYDSX7_L1_1
24406488 10299521 24406488
MG22_D25_T_1_CKDN230011066-
1A_H5NHYDSX7_L1_2
24406488 10261427 24406488
MG22_D25_T_1_CKDN230011066-
1A_H5T5GDSX7_L3_1
44390796 18771457 44390796
MG22_D25_T_1_CKDN230011066-
1A_H5T5GDSX7_L3_2
44390796 18728672 44390796
MG22_D25_T_2_CKDN230011067-
1A_H5NHYDSX7_L1_1
25614687 10646217 25614687
MG22_D25_T_2_CKDN230011067-
1A_H5NHYDSX7_L1_2
25614687 10619142 25614687
MG22_D25_T_2_CKDN230011067-
1A_H5T5GDSX7_L3_1
44119713 18354734 44119713
MG22_D25_T_2_CKDN230011067-
1A_H5T5GDSX7_L3_2
44119713 18341083 44119713
MG22_D25_T_3_CKDN230011068-
1A_H5T5GDSX7_L3_1
54329766 22131145 54329766
MG22_D25_T_3_CKDN230011068-
1A_H5T5GDSX7_L3_2
54329766 22116384 54329766
Supplementary Table 33. BWA mem mapping statistics of quality filtered and trimmed reads to the M.
galloprovincialis reference genome
sampl
e_id
treat
ment
total
_read
s
primar
y_read
s
seconda
ry_read
s
suppleme
ntary_rea
ds
reads_
mappe
d
%_m
appe
d
primary
_mappe
d
%_primar
y_mappe
d
properl
y_paire
d
%_prope
rly_paire
d
singl
eton
s
%_sin
gleton
s
MG22
_D2
preexpos
ure
1710
2721
4
16901
6549
0 2010665 171027
214
100 169016
549
100 169016
549
100 0 0
MG22
_D23_
C
contro
l
1709
2360
7
16905
9070
0 1864537 170923
607
100 169059
070
100 169059
070
100 0 0
MG22
_D25_
T
OA 4009
4887
7
38546
2552
0 15486325 323149
529
80.6 307663
204
79.82 267338
372
69.36 4394
348
1.14

173
Supplementary Table 34. GSEA results of top enriched GO terms.
GO.
Ter
m
G
O.
Ty
pe
Description se
tS
iz
e
enric
hme
ntSc
ore
NE
S
pva
lue
p.a
dju
st
mlp sc.
NE
S
sc.
mlp
GS
EA
Sco
re
qva
lue
r
a
n
k
GO:
000
576
9
C
C
early endosome 1
1
0.61
5145
81
2.0
887
198
6
0.0
021
3
0.0
021
3
2.6
716
208
3
1 1 1 0.2
354
208
2
1
2
9
GO:
007
006
2
C
C
extracellular exosome 4
3
0.35
9412
4
1.5
878
549
1
0.0
211
929
8
0.0
211
929
8
1.6
738
08
0.7
562
047
4
0.6
263
993
4
0.9
004
932
1
0.5
860
983
6
2
0
0
GO:
000
079
4
C
C
condensed nuclear
chromosome
7 0.53
5561
1
1.6
365
955
7
0.0
661
099
3
0.0
661
099
3
1.1
797
332
8
0.7
799
291
8
0.4
414
081
0.8
197
719
6
0.6
356
245
7
1
5
8
GO:
006
202
3
C
C
collagen-containing
extracellular matrix
4
0
0.33
7355
05
1.4
737
066
2
0.0
542
229
5
0.0
542
229
5
1.2
658
168
9
0.7
006
432
3
0.4
736
394
9
0.8
166
650
7
0.6
356
245
7
2
4
1
GO:
003
190
1
C
C
early endosome
membrane
6 0.54
8496
81
1.6
155
763
0.0
780
039
2
0.0
780
039
2
1.1
078
835
6
0.7
696
980
8
0.4
145
061
6
0.8
020
789
9
0.6
356
245
7
1
4
3
GO:
001
000
8
C
C
endosome membrane 1
0
0.47
1800
48
1.5
676
260
5
0.0
777
759
2
0.0
777
759
2
1.1
091
548
3
0.7
463
583
8
0.4
149
821
5
0.7
967
100
2
0.6
356
245
7
1
2
9
GO:
003
225
9
B
P
methylation 6 0.68
5837
42
2.0
227
975
8
0.0
069
059
9
0.0
069
059
9
2.1
607
738
6
0.9
679
124
3
0.8
087
289
0.9
811
928
8
0.8
221
743
8
2
2
4
GO:
004
274
2
B
P
defense response to
bacterium
6 0.62
4458
87
1.8
417
687
2
0.0
238
649
8
0.0
238
649
8
1.6
222
39
0.8
797
969
1
0.6
070
909
0.9
155
868
3
0.8
221
743
8
3
5
3
GO:
001
619
2
B
P
vesicle-mediated
transport
1
3
0.49
7718
02
1.7
567
863
4
0.0
231
699
8
0.0
231
699
8
1.6
350
744
0.8
384
318
6
0.6
118
967
3
0.9
116
357
9
0.8
221
743
8
1
1
2
GO:
004
225
4
B
P
ribosome biogenesis 8 0.51
9049
72
1.6
400
751
9
0.0
613
679
4
0.0
613
679
4
1.2
120
584
6
0.7
816
228
9
0.4
535
112
8
0.8
268
307
6
0.8
221
743
8
2
3
8

174
GO:
000
715
6
B
P
homophilic cell
adhesion via plasma
membrane adhesion
molecules
2
8
0.36
5975
01
1.5
059
866
1
0.0
587
569
4
0.0
587
569
4
1.2
309
408
2
0.7
163
554
7
0.4
605
812
1
0.8
142
865
7
0.8
221
743
8
4
5
6
GO:
000
716
5
B
P
signal transduction 3
3
0.35
1119
31
1.4
876
547
4
0.0
580
809
4
0.0
580
809
4
1.2
359
663
5
0.7
074
324
6
0.4
624
628
6
0.8
127
290
3
0.8
221
743
8
2
5
6
GO:
001
062
8
B
P
positive regulation of
gene expression
1
5
0.40
5299
43
1.4
752
284
0.1
002
039
0.1
002
039
0.9
991
153
8
0.7
013
839
6
0.3
737
812
2
0.7
593
392
5
0.8
221
743
8
1
5
8
GO:
001
656
7
B
P
protein ubiquitination 2
1
0.36
9166
49
1.4
395
981
9
0.1
015
529
0.1
015
529
0.9
933
076
8
0.6
840
410
1
0.3
716
067
0.7
526
458
9
0.8
221
743
8
1
6
5
GO:
003
582
1
B
P
modulation of process
of another organism
5 0.54
0540
54
1.5
249
676
9
0.1
227
908
8
0.1
227
908
8
0.9
108
339
0.7
255
944
8
0.3
407
269
1
0.7
450
303
0.8
221
743
8
4
3
0
GO:
007
058
8
B
P
calcium ion
transmembrane
transport
9 0.45
6281
96
1.4
818
650
8
0.1
192
768
8
0.1
192
768
8
0.9
234
437
3
0.7
046
143
5
0.3
454
482
7
0.7
421
546
8
0.8
221
743
8
4
4
0
GO:
000
688
6
B
P
intracellular protein
transport
7 0.48
8409
02
1.4
948
863
0.1
230
228
8
0.1
230
228
8
0.9
100
141
2
0.7
109
524
1
0.3
404
199
7
0.7
407
028
3
0.8
221
743
8
1
1
2
GO:
004
508
7
B
P
innate immune
response
2
6
0.34
4815
14
1.4
001
870
9
0.1
114
968
9
0.1
114
968
9
0.9
527
372
5
0.6
648
577
1
0.3
564
163
4
0.7
367
398
6
0.8
221
743
8
3
5
2
GO:
000
645
7
B
P
protein folding 5 0.53
1225
11
1.4
986
870
9
0.1
355
418
6
0.1
355
418
6
0.8
679
265
4
0.7
128
024
4
0.3
246
615
5
0.7
307
177
7
0.8
221
743
8
1
9
2
GO:
000
663
1
B
P
fatty acid metabolic
process
7 0.47
6773
09
1.4
592
718
9
0.1
413
958
6
0.1
413
958
6
0.8
495
633
1
0.6
936
171
5
0.3
177
86
0.7
203
568
0.8
221
743
8
4
0
9
GO:
000
816
8
M
F
methyltransferase
activity
6 0.68
5837
42
2.0
219
958
0.0
069
969
9
0.0
069
969
9
2.1
550
885
6
0.9
675
221
6
0.8
066
002
1
0.9
807
708
6
0.5
415
619
6
2
2
4

175
GO:
000
827
0
M
F
zinc ion binding 3
6
0.40
0381
54
1.7
203
830
5
0.0
086
649
9
0.0
086
649
9
2.0
622
318
7
0.8
207
126
1
0.7
718
328
4
0.9
578
978
9
0.5
415
619
6
3
3
6
GO:
000
202
0
M
F
protease binding 8 0.56
8787
01
1.7
963
126
9
0.0
260
539
7
0.0
260
539
7
1.5
841
260
3
0.8
576
712
5
0.5
928
206
6
0.9
069
737
6
0.6
387
433
1
1
5
8
GO:
000
823
3
M
F
peptidase activity 5 0.64
5721
7
1.8
216
279
0.0
299
449
7
0.0
299
449
7
1.5
236
761
2
0.8
699
933
9
0.5
701
870
3
0.8
991
795
5
0.6
387
433
1
8
4
GO:
000
372
9
M
F
mRNA binding 6 0.60
0649
35
1.7
708
431
1
0.0
361
199
6
0.0
361
199
6
1.4
422
526
9
0.8
452
739
7
0.5
397
005
1
0.8
820
921
2
0.6
387
433
1
3
7
5
GO:
006
163
0
M
F
ubiquitin protein
ligase activity
1
9
0.42
0083
91
1.6
060
632
2
0.0
425
479
6
0.0
425
479
6
1.3
711
212
8
0.7
650
676
1
0.5
130
675
1
0.8
538
517
6
0.6
387
433
1
3
3
6
GO:
001
989
9
M
F
enzyme binding 1
3
0.45
7533
02
1.6
144
405
3
0.0
530
319
5
0.0
530
319
5
1.2
754
624
3
0.7
691
452
5
0.4
772
509
7
0.8
367
197
7
0.6
387
433
1
2
1
4
GO:
001
687
4
M
F
ligase activity 5 0.58
4516
56
1.6
489
637
5
0.0
726
069
3
0.0
726
069
3
1.1
390
219
4
0.7
859
493
7
0.4
261
649
8
0.8
124
478
5
0.6
387
433
1
2
5
4
GO:
000
551
6
M
F
calmodulin binding 9 0.48
7058
46
1.5
814
968
1
0.0
763
569
2
0.0
763
569
2
1.1
171
515
8
0.7
531
099
4
0.4
179
762
9
0.8
001
468
5
0.6
387
433
1
3
2
8
GO:
000
471
3
M
F
protein tyrosine
kinase activity
1
0
0.47
2906
46
1.5
733
614
3
0.0
756
379
2
0.0
756
379
2
1.1
212
604
0.7
491
500
6
0.4
195
147
1
0.8
000
555
7
0.6
387
433
1
2
1
0
GO:
000
510
2
M
F
signaling receptor
binding
2
6
0.36
2960
16
1.4
736
211
6
0.0
746
909
3
0.0
746
909
3
1.1
267
321
6
0.7
006
016
3
0.4
215
634
4
0.7
878
858
8
0.6
387
433
1
1
0
2
GO:
000
484
2
M
F
ubiquitin-protein
transferase activity
1
3
0.42
7933
05
1.5
099
947
3
0.0
902
909
1
0.0
902
909
1
1.0
443
559
7
0.7
183
064
2
0.3
907
201
9
0.7
747
134
2
0.6
387
433
1
1
9
4

176
Supplementary Table 35. Descriptions of 56 selected genes which appear > 1 across selected enriched GO terms.
Gene Description
MG449890 Titin
MG449900 Titin
MG091800 E3 ubiquitin-protein ligase MIB2
MG099830 Sorting nexin-27
MG267780 Furin
MG461020 Sortilin-related receptor
MG578030 Angiopoietin-1
MG018160 E3 ubiquitin-protein ligase MIB2
MG381560 Transcription intermediary factor 1-beta
MG564130 Low-density lipoprotein receptor
MG432630 Hemicentin-1
MG485060 Hemicentin-1
MG261490 Ficolin-1
MG481050 A disintegrin and metalloproteinase with thrombospondin motifs 20
MG027330 NFX1-type zinc finger-containing protein 1
MG105950 B box-type domain-containing protein
MG202190 E3 ubiquitin-protein ligase XIAP
MG301590 F-box only protein 11
MG301800 E3 ubiquitin-protein ligase RNF13
MG524780 Zinc finger protein ZPR1
MG286880 Proto-oncogene tyrosine-protein kinase Yrk
MG552660 Ras-related protein Rab-32
MG083800 Protocadherin Fat 2
MG201750 Nidogen-1
MG233130 Receptor-type tyrosine-protein phosphatase alpha
MG315500 EGF-like repeat and discoidin I-like domain-containing protein 3
MG372310 Receptor-type tyrosine-protein phosphatase alpha
MG152270 Ryncolin-2
MG172920 Fibrinogen-like protein 1
MG239090 Plexin-B2
MG489890 Hemicentin-2
MG009450 Caspase recruitment domain-containing protein
MG018120 E3 ubiquitin-protein ligase MIB2
MG268290 E3 ubiquitin-protein ligase TRIM45
MG473070 WD repeat and SOCS box-containing protein 1
MG076520 CD320 antigen

177
MG082620 Pulmonary surfactant-associated protein D
MG217740 Receptor-type tyrosine-protein phosphatase T
MG323840 Solute carrier family 15 member 4
MG382160 Protein tirA
MG496250 Dual specificity calcium/calmodulin-dependent 3',5'-cyclic nucleotide phosphodiesterase 1C
MG124570 Nucleolar protein 14
MG285010 Nesprin-1
MG289280 Propionyl-CoA carboxylase alpha chain, mitochondrial
MG451120 Transient receptor potential cation channel subfamily M member 3
MG457930 Transient receptor potential cation channel subfamily M member 3
MG493240 Titin
MG541470 Histone H4 transcription factor
MG007710 tRNA (cytidine(32)/guanosine(34)-2'-O)-methyltransferase
MG119770 L-amino-acid oxidase
MG161820 tRNA (cytosine(72)-C(5))-methyltransferase NSUN6
MG358290 Uncharacterized protein
MG391500 Ribosomal RNA-processing protein 8
MG423760 Histone-lysine N-methyltransferase MLL3
MG317700 Ribosome biogenesis regulatory protein homolog
MG427140 Beta-alanine-activating enzyme

178
Supplementary Table 36. DASTool statistics of highest quality bins from Metabat, CONCOCT, and MaxBin from
Megahit metagenome assembly.
bin bin_set unique_SCGs redundant_SCGs SCG_set size contigs N50 bin_score SCG_completeness SCG_redundancy
MG22_D2.1 metabat 50 0 bacteria 3110886 310 14315 0.980392157 98 0
MG22_D23_C_112 concoct 51 0 bacteria 4942920 78 168138 1 100 0
MG22_D23_C.22 metabat 51 0 bacteria 3051545 63 77520 1 100 0
MG22_D23_C.011 maxbin 50 0 bacteria 3193247 120 109179 0.980392157 98 0
MG22_D23_C.34 metabat 50 1 bacteria 2718361 169 26689 0.958588235 98 2
MG22_D23_C.5 metabat 48 0 bacteria 3960418 243 29703 0.941176471 94 0
MG22_D23_C.57 metabat 47 0 bacteria 2932665 267 14175 0.921568627 92 0
MG22_D23_C_102 concoct 46 0 bacteria 2065380 149 22563 0.901960784 90 0
MG22_D23_C_21 concoct 46 0 bacteria 4215785 556 9606 0.901960784 90 0
MG22_D23_C.23_sub metabat 45 0 bacteria 3376886 598 6234 0.882352941 88 0
MG22_D23_C.55 metabat 45 0 bacteria 4236561 776 5915 0.882352941 88 0
MG22_D23_C_5 concoct 48 4 bacteria 11973525 1565 9456 0.851960784 94 8
MG22_D23_C.21 metabat 44 1 bacteria 5250229 342 22097 0.839304813 86 2
MG22_D23_C_12 concoct 41 0 bacteria 2697195 564 5566 0.803921569 80 0
MG22_D23_C.58 metabat 38 0 bacteria 4152853 767 5844 0.745098039 75 0
MG22_D23_C_23 concoct 38 0 bacteria 6139372 3473 1815 0.745098039 75 0
MG22_D23_C.45 metabat 36 0 bacteria 1830821 185 13165 0.705882353 71 0
MG22_D23_C.013_sub maxbin 35 0 bacteria 8075129 4106 2075 0.68627451 69 0
MG22_D23_C.2_sub metabat 37 3 bacteria 3784991 502 9108 0.647429783 73 6
MG22_D23_C_69_sub concoct 33 0 bacteria 2900341 90 41962 0.647058824 65 0
MG22_D23_C_107_sub concoct 42 8 bacteria 2994149 924 3531 0.630812325 82 16
MG22_D23_C.6 metabat 44 10 bacteria 3589921 131 45251 0.628342246 86 20
MG22_D23_C.14 metabat 38 6 bacteria 3023970 541 6186 0.591537668 75 12
MG22_D23_C_34_sub concoct 27 0 bacteria 2746331 746 4152 0.529411765 53 0
MG22_D25_T_133 concoct 51 0 bacteria 3132595 53 144266 1 100 0
MG22_D25_T.014 maxbin 51 0 bacteria 4394415 449 26106 1 100 0
MG22_D25_T_127 concoct 48 0 bacteria 8441730 1237 9178 0.941176471 94 0
MG22_D25_T.011 maxbin 50 2 bacteria 5367387 190 64123 0.936784314 98 4
MG22_D25_T_123_sub concoct 47 0 bacteria 4000571 15 672640 0.921568627 92 0
MG22_D25_T.17 metabat 47 0 bacteria 3365451 96 60796 0.921568627 92 0
MG22_D25_T_0 concoct 47 0 bacteria 3507906 191 28391 0.921568627 92 0
MG22_D25_T_72_sub concoct 48 1 bacteria 2775640 228 16873 0.918872549 94 2
MG22_D25_T_145 concoct 51 3 bacteria 3812781 436 10682 0.905882353 100 6
MG22_D25_T.65 metabat 46 0 bacteria 5581729 170 71981 0.901960784 90 0
MG22_D25_T.30 metabat 49 3 bacteria 2595005 312 10338 0.894637855 96 6
MG22_D25_T.003_sub maxbin 51 5 bacteria 5229548 140 144501 0.892156863 100 10
MG22_D25_T.4_sub metabat 45 0 bacteria 4609781 99 103934 0.882352941 88 0
MG22_D25_T.6 metabat 45 0 bacteria 3792526 217 26400 0.882352941 88 0
MG22_D25_T_132 concoct 43 0 bacteria 3070794 871 4337 0.843137255 84 0
MG22_D25_T_74 concoct 48 5 bacteria 4835306 2118 2516 0.829656863 94 10
MG22_D25_T_81 concoct 44 2 bacteria 3564367 1333 3189 0.815864528 86 4
MG22_D25_T.009_sub maxbin 49 7 bacteria 4453595 1640 3288 0.806442577 96 14
MG22_D25_T.61 metabat 41 0 bacteria 3133441 131 38658 0.803921569 80 0
MG22_D25_T.13 metabat 40 0 bacteria 1836725 336 6061 0.784313725 78 0
MG22_D25_T_53_sub concoct 43 3 bacteria 3254690 694 4975 0.771865025 84 6
MG22_D25_T_15 concoct 46 6 bacteria 2520949 364 9262 0.764876385 90 12
MG22_D25_T_165_sub concoct 41 2 bacteria 7832811 1530 5820 0.755045433 80 4
MG22_D25_T_161 concoct 38 0 bacteria 4911646 2316 2297 0.745098039 75 0
MG22_D25_T_163_sub concoct 43 6 bacteria 3720664 970 4436 0.700592795 84 12
MG22_D25_T.062 maxbin 35 1 bacteria 2530100 1258 2174 0.659327731 69 2
MG22_D25_T.063_sub maxbin 43 8 bacteria 7834484 2264 4938 0.653077975 84 16
MG22_D25_T.005_sub maxbin 35 2 bacteria 558369 213 3109 0.632380952 69 4
MG22_D25_T.36 metabat 32 0 bacteria 816484 212 3762 0.62745098 63 0
MG22_D25_T.007 maxbin 30 0 bacteria 2441064 43 131926 0.588235294 59 0
MG22_D25_T_148_sub concoct 31 1 bacteria 1111777 423 2647 0.578684377 61 2
MG22_D25_T_90 concoct 31 1 bacteria 5878821 2971 2089 0.578684377 61 2
MG22_D25_T.44 metabat 32 2 bacteria 4790117 47 162138 0.570343137 63 4
MG22_D25_T.021_sub maxbin 51 20 bacteria 5520043 965 8820 0.549019608 100 39
MG22_D25_T_88_sub concoct 43 12 bacteria 6676150 1586 4738 0.548244414 84 24
MG22_D25_T.39 metabat 27 0 bacteria 3492337 259 18125 0.529411765 53 0
MG22_D25_T.081_sub maxbin 37 8 bacteria 5847788 2878 2182 0.517329094 73 16
MG22_D25_T_28_sub concoct 29 2 bacteria 1593594 780 2103 0.507640297 57 4
MG22_D25_T.7_sub metabat 34 6 bacteria 3363812 725 4767 0.501960784 67 12

179
Supplementary Table 37. CheckM statistics of bins selected by DASTool.
Name Completeness Contamination Completeness_Model_Used Translation_Table_Used Coding_Density Contig_N50 Average_Gene_Length Genome_Size GC_Content Total_Coding_Sequences Total_Contigs Max_Contig_Length MG22_D2.1 82.07 1.78 Neural Network (Specific Model) 11 0.926 14315 306.4156838 3110886 0.31 3137 310 63678 MG22_D23_C.011 99.72 0.03 Gradient Boost (General Model) 11 0.905 109179 315.6247956 3193247 0.49 3057 120 459871 MG22_D23_C.013_sub 60 6.92 Gradient Boost (General Model) 11 0.317 2075 89.15393387 8075129 0.31 9647 4106 62173 MG22_D23_C.14 70.76 5.36 Neural Network (Specific Model) 11 0.919 6186 272.337929 3023970 0.58 3409 541 33514 MG22_D23_C.21 96.54 1.31 Gradient Boost (General Model) 11 0.925 22097 354.7593524 5250229 0.53 4571 342 91358 MG22_D23_C.22 100 0.81 Gradient Boost (General Model) 11 0.885 77520 320.6116643 3051545 0.49 2812 63 213201 MG22_D23_C.23_sub 82.06 3.27 Gradient Boost (General Model) 11 0.924 6234 293.3639178 3376886 0.56 3553 598 21710 MG22_D23_C.2_sub 87.35 4.46 Neural Network (Specific Model) 11 0.894 9108 272.807098 3784991 0.53 4142 502 46525 MG22_D23_C.34 99.81 1.63 Gradient Boost (General Model) 11 0.91 26689 302.7004034 2718361 0.39 2727 169 138536 MG22_D23_C.45 82.78 1.12 Neural Network (Specific Model) 11 0.927 13165 291.1953728 1830821 0.42 1945 185 49406 MG22_D23_C.5 95.05 6.95 Gradient Boost (General Model) 11 0.915 29703 302.781336 3960418 0.62 3997 243 116966 MG22_D23_C.55 91.38 6.42 Gradient Boost (General Model) 11 0.904 5915 286.3869234 4236561 0.61 4466 776 26462 MG22_D23_C.57 93.69 1.61 Gradient Boost (General Model) 11 0.917 14175 294.3352999 2932665 0.47 3051 267 55274 MG22_D23_C.58 73.6 3.33 Gradient Boost (General Model) 11 0.896 5844 276.6301492 4152853 0.62 4491 767 39431 MG22_D23_C.6 83.79 4.29 Neural Network (Specific Model) 11 0.913 45251 296.2443182 3589921 0.58 3696 131 158154 MG22_D23_C_102 99.42 0.6 Neural Network (Specific Model) 11 0.807 22563 300.5969828 2065380 0.37 1856 149 55717 MG22_D23_C_107_sub 74.32 9.87 Gradient Boost (General Model) 11 0.94 3531 260.3954583 2994149 0.54 3611 924 18214 MG22_D23_C_112 99.99 0.45 Neural Network (Specific Model) 11 0.863 168138 341.5798944 4942920 0.47 4168 78 434655 MG22_D23_C_12 85.08 0.8 Gradient Boost (General Model) 11 0.858 5566 273.9259391 2697195 0.51 2822 564 26405 MG22_D23_C_21 85.78 1.86 Gradient Boost (General Model) 11 0.912 9606 324.5647118 4215785 0.51 3956 556 45207 MG22_D23_C_23 60.52 2.95 Gradient Boost (General Model) 11 0.877 1815 245.047736 6139372 0.43 7332 3473 16329 MG22_D23_C_34_sub 68.88 1.85 Gradient Boost (General Model) 11 0.941 4152 259.1692677 2746331 0.53 3332 746 30694 MG22_D23_C_5 94.19 2.7 Gradient Boost (General Model) 11 0.832 9456 335.2853398 11973525 0.69 9918 1565 47294 MG22_D23_C_69_sub 92.74 0.34 Gradient Boost (General Model) 11 0.921 41962 318.1650624 2900341 0.5 2805 90 161645 MG22_D25_T.003_sub 99.33 3 Gradient Boost (General Model) 11 0.854 144501 334.8267116 5229548 0.47 4455 140 358520 MG22_D25_T.005_sub 42.81 0.15 Gradient Boost (General Model) 11 0.466 3109 145.2933333 558369 0.32 600 213 62173 MG22_D25_T.007 64.64 0.48 Neural Network (Specific Model) 11 0.889 131926 325.4892183 2441064 0.39 2226 43 235717 MG22_D25_T.009_sub 95.69 36.07 Gradient Boost (General Model) 11 0.91 3288 244.9104721 4453595 0.54 5529 1640 46607 MG22_D25_T.011 100 4.54 Gradient Boost (General Model) 11 0.897 64123 322.4467402 5367387 0.62 4985 190 211042 MG22_D25_T.014 97.76 5.78 Gradient Boost (General Model) 11 0.863 26106 303.8931059 4394415 0.54 4163 449 80897 MG22_D25_T.021_sub 100 47.45 Neural Network (Specific Model) 11 0.923 8820 275.0499192 5520043 0.56 6190 965 79134 MG22_D25_T.062 63.06 7.05 Gradient Boost (General Model) 11 0.912 2174 218.3904789 2530100 0.56 3529 1258 14800 MG22_D25_T.063_sub 86.43 19.47 Gradient Boost (General Model) 11 0.872 4938 286.0353206 7834484 0.55 7984 2264 32652 MG22_D25_T.081_sub 80.15 14.39 Gradient Boost (General Model) 11 0.875 2182 250.7069952 5847788 0.38 6819 2878 19021 MG22_D25_T.13 75.53 1.92 Gradient Boost (General Model) 11 0.913 6061 277.5701537 1836725 0.39 2017 336 29795 MG22_D25_T.17 94.86 0.36 Gradient Boost (General Model) 11 0.909 60796 345.3129229 3365451 0.65 2956 96 233004 MG22_D25_T.30 89.28 6.61 Gradient Boost (General Model) 11 0.887 10338 293.2768232 2595005 0.49 2619 312 32647 MG22_D25_T.36 53.49 1.52 Gradient Boost (General Model) 11 0.854 3762 280.1442308 816484 0.42 832 212 15682 MG22_D25_T.39 90.95 8.37 Neural Network (Specific Model) 11 0.924 18125 296.0310269 3492337 0.58 3642 259 151854 MG22_D25_T.44 94.4 5.44 Neural Network (Specific Model) 11 0.882 162138 334.7236467 4790117 0.37 4212 47 347582 MG22_D25_T.4_sub 94.11 2.2 Neural Network (Specific Model) 11 0.9 103934 306.0318162 4609781 0.61 4526 99 309403 MG22_D25_T.6 87.58 4.09 Neural Network (Specific Model) 11 0.924 26400 318.6996457 3792526 0.64 3669 217 90455 MG22_D25_T.61 80.53 0.23 Neural Network (Specific Model) 11 0.912 38658 306.6609897 3133441 0.56 3112 131 154797 MG22_D25_T.65 93.18 3.74 Gradient Boost (General Model) 11 0.836 71981 315.9026567 5581729 0.42 4931 170 254674 MG22_D25_T.7_sub 67.36 4.43 Gradient Boost (General Model) 11 0.926 4767 271.966022 3363812 0.53 3826 725 22731 MG22_D25_T_0 100 0.74 Gradient Boost (General Model) 11 0.905 28391 307.9363742 3507906 0.43 3442 191 113603 MG22_D25_T_123_sub 86.92 0.55 Gradient Boost (General Model) 11 0.948 672640 381.3960277 4000571 0.61 3323 15 1086421 MG22_D25_T_127 87.08 1.95 Gradient Boost (General Model) 11 0.881 9178 360.3429152 8441730 0.5 6888 1237 36327 MG22_D25_T_132 85.08 2.87 Gradient Boost (General Model) 11 0.918 4337 280.880775 3070794 0.35 3355 871 14057 MG22_D25_T_133 99.96 0.01 Gradient Boost (General Model) 11 0.915 144266 310.3520259 3132595 0.49 3085 53 780151 MG22_D25_T_145 84.56 4.05 Neural Network (Specific Model) 11 0.919 10682 301.4278722 3812781 0.58 3882 436 53208 MG22_D25_T_148_sub 45.85 2.98 Gradient Boost (General Model) 11 0.917 2647 233.985567 1111777 0.39 1455 423 12255 MG22_D25_T_15 88.78 2.67 Neural Network (Specific Model) 11 0.933 9262 301.1027213 2520949 0.44 2609 364 46413 MG22_D25_T_161 77.14 3.41 Gradient Boost (General Model) 11 0.856 2297 251.7960538 4911646 0.56 5575 2316 18734 MG22_D25_T_163_sub 78.28 2.81 Neural Network (Specific Model) 11 0.901 4436 273.0505001 3720664 0.49 4099 970 21869 MG22_D25_T_165_sub 81.69 3.88 Gradient Boost (General Model) 11 0.901 5820 324.1686846 7832811 0.65 7268 1530 34916 MG22_D25_T_28_sub 53.62 11.54 Gradient Boost (General Model) 11 0.873 2103 237.27231 1593594 0.42 1961 780 18305 MG22_D25_T_53_sub 64.2 4.7 Gradient Boost (General Model) 11 0.894 4975 268.0212355 3254690 0.62 3626 694 50691 MG22_D25_T_72_sub 94.57 2.24 Neural Network (Specific Model) 11 0.871 16873 336.9040467 2775640 0.4 2397 228 48189 MG22_D25_T_74 85.19 10.46 Gradient Boost (General Model) 11 0.899 2516 242.0305102 4835306 0.61 5998 2118 15659 MG22_D25_T_81 82.56 10.19 Neural Network (Specific Model) 11 0.923 3189 256.1186204 3564367 0.4 4291 1333 16812 MG22_D25_T_88_sub 82.52 18.06 Gradient Boost (General Model) 11 0.905 4738 290.1003883 6676150 0.38 6953 1586 39285 MG22_D25_T_90 65.6 4.34 Gradient Boost (General Model) 11 0.883 2089 267.5354221 5878821 0.47 6479 2971 9443

180
Supplementary Table 38. CheckM statistics of filtered and dereplicated bins used in final metagenome analysis in
Chapter 4: Ocean acidification shapes larval mussel microbiome community composition.
Bin Id Marker lineage # genomes # markers # marker sets 0 1 2 3 4 5+ Completeness Contamination Strain heterogeneity
MG22_D2.1 s__algicola (UID2846) 47 571 303 61 495 15 0 0 0 88.14 2.61 66.67
MG22_D23_C.011 c__Alphaproteobacteria (UID3305) 564 349 230 1 343 5 0 0 0 99.99 1.54 60
MG22_D23_C.21 k__Bacteria (UID2982) 88 229 147 5 214 10 0 0 0 97.28 3.37 30
MG22_D23_C.22 c__Gammaproteobacteria (UID4443) 356 451 270 3 444 4 0 0 0 99.32 0.99 25
MG22_D23_C.23_sub c__Gammaproteobacteria (UID4444) 263 503 230 121 368 13 1 0 0 72.93 3.02 12.5
MG22_D23_C.2_sub f__Rhodobacteraceae (UID3361) 46 654 332 86 536 29 3 0 0 86.31 4 10.53
MG22_D23_C.34 c__Gammaproteobacteria (UID4267) 119 544 284 15 520 9 0 0 0 96.13 1.91 0
MG22_D23_C.45 c__Betaproteobacteria (UID3888) 323 387 234 66 316 5 0 0 0 85.64 1.21 40
MG22_D23_C.5 f__Rhodobacteraceae (UID3356) 67 617 330 23 555 39 0 0 0 96.13 8.21 10.26
MG22_D23_C.55 c__Gammaproteobacteria (UID4267) 119 544 284 82 427 34 1 0 0 83.9 7.77 5.41
MG22_D23_C.57 f__Rhodobacteraceae (UID3356) 67 615 329 34 571 10 0 0 0 93.9 1.58 40
MG22_D23_C.6 f__Rhodobacteraceae (UID3360) 56 582 313 93 469 20 0 0 0 83.05 1.97 85
MG22_D23_C_102 k__Bacteria (UID2982) 88 230 148 6 223 1 0 0 0 96.28 0.68 0
MG22_D23_C_112 c__Gammaproteobacteria (UID4202) 67 481 276 5 463 12 1 0 0 99.28 4.53 0
MG22_D23_C_12 o__Rhizobiales (UID3447) 356 413 248 63 344 6 0 0 0 85.38 1.2 0
MG22_D23_C_21 c__Gammaproteobacteria (UID4202) 67 481 276 73 392 16 0 0 0 82.44 4.04 12.5
MG22_D23_C_5 c__Deltaproteobacteria (UID3216) 83 247 155 22 212 10 3 0 0 87.58 4.67 0
MG22_D23_C_69_sub c__Alphaproteobacteria (UID3305) 564 349 230 20 326 3 0 0 0 98.06 1.3 33.33
MG22_D25_T.003_sub k__Bacteria (UID203) 5449 104 58 0 83 21 0 0 0 100 12.07 4.76
MG22_D25_T.011 c__Alphaproteobacteria (UID3305) 564 349 230 13 326 10 0 0 0 95.09 2.7 10
MG22_D25_T.014 c__Gammaproteobacteria (UID4443) 356 451 270 16 407 27 1 0 0 94.44 6.9 20
MG22_D25_T.17 k__Bacteria (UID2565) 2921 143 88 12 131 0 0 0 0 96.69 0 0
MG22_D25_T.30 c__Gammaproteobacteria (UID4443) 356 451 270 57 378 15 1 0 0 86.2 3.68 11.11
MG22_D25_T.39 f__Rhodobacteraceae (UID3360) 56 582 313 76 467 38 1 0 0 90.97 7.09 9.76
MG22_D25_T.44 c__Gammaproteobacteria (UID4761) 52 693 297 36 609 48 0 0 0 97.25 5.95 33.33
MG22_D25_T.4_sub f__Rhodobacteraceae (UID3361) 46 654 332 39 601 14 0 0 0 96.01 2.11 14.29
MG22_D25_T.6 c__Alphaproteobacteria (UID3305) 564 349 230 44 299 6 0 0 0 88.48 2.1 0
MG22_D25_T.61 f__Rhodobacteraceae (UID3361) 46 654 332 121 525 8 0 0 0 82.03 1.06 0
MG22_D25_T.65 o__Oceanospirillales (UID4446) 43 529 233 30 482 17 0 0 0 96.47 3.53 0
MG22_D25_T_0 c__Alphaproteobacteria (UID3305) 564 349 230 5 338 6 0 0 0 99.09 1.46 33.33
MG22_D25_T_123_sub k__Bacteria (UID2565) 2921 152 93 5 146 1 0 0 0 95.88 1.08 0
MG22_D25_T_127 k__Bacteria (UID3187) 2258 188 117 24 161 3 0 0 0 90.76 2.14 0
MG22_D25_T_132 k__Bacteria (UID3187) 2258 182 112 30 147 5 0 0 0 77.83 2.78 20
MG22_D25_T_133 c__Alphaproteobacteria (UID3305) 564 349 230 7 341 1 0 0 0 97.83 0.22 100
MG22_D25_T_145 k__Bacteria (UID203) 5449 104 58 3 82 10 6 3 0 94.83 18.73 21.74
MG22_D25_T_15 s__algicola (UID2847) 33 496 263 46 434 16 0 0 0 88.8 2.66 0
MG22_D25_T_165_sub c__Deltaproteobacteria (UID3216) 83 247 155 63 177 6 1 0 0 72.54 3.23 11.11
MG22_D25_T_72_sub k__Bacteria (UID2982) 88 230 148 2 224 4 0 0 0 98.99 2.03 0
Supplementary Table 39. coverm mapping statistics of WGS M. galloprovincialis reads to the 16S rRNA subunit.
sample_id total_reads reads_mapped mapped_percentage
MG22_D2 17148522 453369 2.64
MG22_D23_C 69213690 7424292 10.73
MG22_D25_T 72864678 19130770 26.26

181
Appendix B: Supplementary Figures
Supplemental Figure 1. Percent of genes remaining after filtering low-count genes prior to running the DESeq2
function. x-axis values correspond to minimum count requirement per gene.

182

183
Supplemental Figure 2. BUSCO results of the M. gigas reference genome, the O. lurida transcriptome, and the CDHIT thinned O. lurida transcriptome using in this study using the eukaryota (top), metazoa (middle), and mollusca
(bottom) datasets.

184
Supplemental Figure 3. Cellular component gene set enrichment analysis results for all treatments and timepoints in
both M. gigas and O. lurida. Color is representative of the degree and direction of expression (LFC), and triangles
denote statistical significance (p-value  0.01).

185
Supplemental Figure 4. Biological process gene set enrichment analysis results for all treatments and timepoints in
both M. gigas and O. lurida. Color is representative of the degree of normalized enrichment score, and significant
gene sets (p-value  0.01) are denoted by a triangle shape.

186
Supplemental Figure 5. Molecular function gene set enrichment analysis results for all treatments and timepoints in
both M. gigas and O. lurida. Color is representative of the degree of normalized enrichment score, and significant
gene sets (p-value  0.01) are denoted by a triangle shape.

187
Supplemental Figure 6. Density histogram of the number of SNPs and number of genes used to select filter #1
values.

188
Supplemental Figure 7. Absolute allele frequency difference distribution of 28 biomineralization genes passing both
filter #1 as well as filter #2 in control and OA treatments

189
Supplemental Figure 8. Example of custom scoring metric used to rank each of the 930 genes found to have high
absolute differences in allele frequencies for input into GSEA. The panels show genes with high (top), medium
(middle), and low (bottom) scores based on maximum, mode, and interquartile range of the SNP ∆𝑇𝐶 distribution
within each gene.

190
Supplemental Figure 9. Gene length (Kbp) of selected genes from enriched GO terms (56) versus all other genes in
the dataset (43779).

191
Supplemental Figure 10. Relative abundance of 16S rDNA V3-V4 amplicon sequenced reads agglomerated at the
Phylum level for M. californianus (left) and M. galloprovincialis (right).

192
Supplemental Figure 11. Relative abundance of 16S rDNA V3-V4 amplicon sequenced reads agglomerated at the
Class level for M. californianus (left) and M. galloprovincialis (right).

193
Supplemental Figure 12. PERMDISP2 within-group dispersion and group distance from centroid beta diversity
results for treatment for M. californianus

194
Supplemental Figure 13. PERMDISP2 within-group dispersion and group distance from centroid beta diversity
results for time for M. californianus

195
Supplemental Figure 14. ANOSIM group dissimilarity beta diversity results for treatment (top) and time (bottom)
for M. californianus

196
Supplemental Figure 15. PERMDISP2 within-group dispersion and group distance from centroid beta diversity
results for treatment for M. galloprovincialis

197
Supplemental Figure 16. PERMDISP2 within-group dispersion and group distance from centroid beta diversity
results for time for M. galloprovincialis

198
Supplemental Figure 17. ANOSIM group dissimilarity beta diversity results for treatment (top) and time (bottom)
for M. galloprovincialis

199
Supplemental Figure 18. PERMDISP2 within-group dispersion (top), group distance from centroid (middle), and
ANOSIM beta diversity results for species.

Abstract (if available)

Abstract Aquaculture, the cultivation of aquatic organisms, is a sector of growing global demand and commercial interest, particularly within the context of climate change and sustainability. Aquaculture products have been developed and utilized across various industries, though most notably for consumption. Of aquaculture species farmed for food, shellfish are a multifaceted product, providing both ecosystem services and supporting the food industry. Shellfish farming has been highlighted as an avenue for sustainable food production due to its small environmental footprint. The West Coast of the United States has many commercial shellfish farms, however, the California Current, which drives the upwelling ecosystem along this coast, has experienced rapid ocean acidification (OA) over recent decades as a result of increased atmospheric carbon dioxide (CO2). The absorption of CO2 by seawater decreases pH levels, resulting in a myriad of physiological effects on marine species. Shellfish are particularly susceptible to these effects due to the nature of their calcium carbonate shells and early larval life stages, which struggle under low pH. It is therefore imperative to understand the effects of OA to ensure continued production. Molecular biology has been utilized in crop development to improve yields, disease resistance, and desirable traits, however, these methods have only recently been applied to aquaculture. The present study employed multi-omics approaches to investigate OA effects on local oyster and mussel species in order to characterize and disentangle effects of OA and inform future farming and restoration. The results provide valuable insights into selectively breeding local shellfish species for OA resilience.

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Asset Metadata

Creator Chancellor, Jordan Lynn (author)

Core Title A multi-omics investigation into breeding shellfish for ocean acidification resilience in the California current system

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Biology (Marine Biology and Biological Oceanography)

Degree Conferral Date 2024-12

Publication Date 12/18/2024

Defense Date 12/06/2024

Publisher Los Angeles, California (original), University of Southern California (original), University of Southern California. Libraries (digital)

Tag aquaculture,genomics,OAI-PMH Harvest,ocean acidification,shellfish

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Gracey, Andrew (committee chair), Nuzhdin, Sergey (committee chair), Applebaum, Scott (committee member), Hutchins, David (committee member), Kiefer, Dale (committee member)

Creator Email jchancel@usc.edu,jordan.chancellor.15@gmail.com

Unique identifier UC11399EZL7

Identifier etd-Chancellor-13703.pdf (filename)

Legacy Identifier etd-Chancellor-13703

Document Type Dissertation

Format theses (aat)

Rights Chancellor, Jordan Lynn

Internet Media Type application/pdf

Type texts

Source 20241223-usctheses-batch-1230 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright.

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu