- Open Access
Genetic diversity and connectivity of chemosynthetic cold seep mussels from the U.S. Atlantic margin
BMC Ecology and Evolution volume 22, Article number: 76 (2022)
Deep-sea mussels in the subfamily Bathymodiolinae have unique adaptations to colonize hydrothermal-vent and cold-seep environments throughout the world ocean. These invertebrates function as important ecosystem engineers, creating heterogeneous habitat and promoting biodiversity in the deep sea. Despite their ecological significance, efforts to assess the diversity and connectivity of this group are extremely limited. Here, we present the first genomic-scale diversity assessments of the recently discovered bathymodioline cold-seep communities along the U.S. Atlantic margin, dominated by Gigantidas childressi and Bathymodiolus heckerae.
A Restriction-site Associated DNA Sequencing (RADSeq) approach was used on 177 bathymodiolines to examine genetic diversity and population structure within and between seep sites. Assessments of genetic differentiation using single-nucleotide polymorphism (SNP) data revealed high gene flow among sites, with the shallower and more northern sites serving as source populations for deeper occurring G. childressi. No evidence was found for genetic diversification across depth in G. childressi, likely due to their high dispersal capabilities. Kinship analyses indicated a high degree of relatedness among individuals, and at least 10–20% of local recruits within a particular site. We also discovered candidate adaptive loci in G. childressi and B. heckerae that suggest differences in developmental processes and depth-related and metabolic adaptations to chemosynthetic environments.
These results highlight putative source communities for an important ecosystem engineer in the deep sea that may be considered in future conservation efforts. Our results also provide clues into species-specific adaptations that enable survival and potential speciation within chemosynthetic ecosystems.
Bathymodioline mussels that dominate many cold-seep communities serve as characteristic indicator fauna for methane and sulfide rich environments (reviewed in ). They thrive in both cold-seep and hydrothermal-vent environments , largely due to a crucial symbiosis with chemoautotrophic and/or methanotrophic bacteria that oxidize sulfur and/or methane for nutrition [1, 3, 4]. Bathymodiolines function as ecosystem engineers, forming large mussel bed aggregations on the benthos that serve as important foundation habitat for other seep-adapted fauna , including a variety of endemic species . Further characterization of the population structure, dynamics and connectivity for this ecologically important group of deep-sea species is timely as resource (e.g., mineral, oil and gas) extraction efforts continue in deep waters and may impact these habitats.
Chemosynthetic cold seeps are numerous along the U.S. Atlantic margin , though they remain poorly characterized. Widespread methane leakage with > 500 gas plumes have been noted from Cape Hatteras to Georges Bank , however, there have been few ground-truthing surveys and only a handful of bathymodioline mussel beds have been documented [7,8,9]. Observations of mussel beds along the margin include one of the first seeps discovered in the region in deep waters (~ 2100 m) on the Blake Ridge diaper off South Carolina [10, 11] and among submarine canyons off the coast of Baltimore, Maryland and Norfolk, Virginia [7,8,9] (Fig. 1). Understanding the dynamics of these foundation species, their influence on nearby and/or distant communities and the factors that impact larval dispersal, settlement and survival is crucial for informing future exploration and management efforts, given seep habitats are still being discovered.
Previous investigations of cold-seep communities along the Atlantic Equatorial Belt, from the Gulf of Mexico to the Gulf of Guinea, found that depth, rather than geographic distance, was the primary driver structuring communities . Evidence for segregation with depth also exists among bathymodioline species commonly found in the U.S. Atlantic. For example, Gigantidas childressi  (= Bathymodiolus childresssi), which dominates the known seep sites within the aforementioned submarine canyons , is typically found between 400 and 2200 m, while Bathymodiolus heckerae  is the dominant species found among deeper cold seeps between 2200 and 3300 m , including those found at Blake Ridge [11, 14]. As several bathymodioline mussel species are believed to be panmictic [15,16,17] with long-range dispersal capabilities , underlying genetic differences may be influencing the fitness of one or both species and their ability to colonize and thrive at different depths. Of particular note, bathymodiolines are thought to host different chemosynthetic endosymbiotic bacteria (e.g. ) that may enable optimal utilization and turnover of chemical nutrients for energy. In addition to nutrient type and availability, depth segregation  suggests adaptations among species inhabiting specific depth niches. Therefore, genetic differences that are influenced by environmental changes associated with depth (e.g., temperature and pressure) and/or the chemical composition of seep fluids likely exist among bathymodiolines. With the continual optimization of high-throughput sequencing approaches for non-model organisms, genome-wide diversity assessments could clarify how selective pressures have influenced diversification within this group.
Most previous studies focusing on population connectivity and genetic diversity in bathymodiolines have used markers that were perhaps not informative enough (see ) or not neutrally-evolving (e.g., strong or relaxed purifying selection in several mitochondrial genes, ), thereby hindering accurate inference of gene flow, population differentiation, and genomic diversity [20, 21]. Although prior studies have provided important insights on the evolution within this genus, these studies along with others that used a handful of more variable markers (i.e., microsatellites) often concluded regional panmixia within a species across 10 to 100s of km and 100s of m of depth (e.g., [16, 17, 22]). These findings raise the question of whether the markers used were informative enough to detect fine-scale population structure, if it exists.
While there are many methods to ascertain genome-wide informative markers, Restriction-Site Associated DNA Sequencing (RADSeq) has emerged as a popular method for marker discovery in population genomics [23,24,25]. RADSeq relies on restriction enzymes and high-throughput sequencing to produce 1000 s of genomic markers, from which SNPs can be attained and subsequently used in downstream analyses. SNPs can be used to determine levels of genetic diversity and rates of gene flow and to discover genes that are potentially indicative of adaptation to environmental conditions.
Here, we present a genome-wide diversity and connectivity assessment of the recently discovered bathymodioline cold-seep communities along the U.S. Atlantic margin, dominated by G. childressi along the upper to middle continental slope and B. heckerae in lower-slope depths at Blake Ridge. Due to the potentially large dispersal range of these mussels, we hypothesized that the discrete communities of G. childressi would show minimal levels of differentiation that would likely increase across depth, with the highest levels of divergence between the shallowest (Baltimore Canyon) and deepest (Norfolk Canyon) seeps. We also examined whether genes potentially under selection in bathymodiolines would be related to the different depths that they inhabit, thus providing further insight into molecular underpinnings of niche ranges in cold-seep mussels.
Sample collections and extraction
G. childressi were collected from Norfolk Canyon Seep (NCS; 1485–1600 m depth), Baltimore Canyon Seep (BCS; 360–430 m depth) and Chincoteague Seep (CTS; 1040 m) (Fig. 1) using a scooping method from a remotely operated vehicle, with scoops of mussels placed in a lidded-collection container on the working basket of the vehicle (see [12, 26] for further information). The vast majority of B. heckerae samples were collected from Blake Ridge Seep (BRS) using mussel ‘pots’ (lined pots placed in quivers- i.e., plastic buckets) from either the human-operated vehicle (HOV) Alvin or the remotely-operated vehicle (ROV) Jason II (dives A4967 and J2-1136) with the exception of two individuals. These two samples were collected via (1) slurp, a vacuum-like device with an enclosed system where the sample is housed for the duration of the dive (1: dive J2-1136, BRS) and (2) by a ROV arm grab (2: dive HRS1704-GEX03-023, from a shallower region near NCS). Tissues were preserved in 100% ethanol and kept at − 20 °C until they were processed. Mantle or foot tissues were ultimately used for total genomic DNA extractions to limit DNA contamination by symbionts hosted in the gills using either an AutoGenPrep 965 Kit (Autogen, Holliston, MA, USA), the E.Z.N.A.® Mollusc DNA Kit (Omega Bio-Tek, Norcross, GA, USA) for smaller tissue-limited samples, or MagAttract HMW DNA Kit (Qiagen, Hilden, Germany) for low yield samples.
PCR and sanger sequencings
Species identifications were confirmed using morphology (as in ) and mitochondrial COI barcodes. G. childressi individuals were confirmed in a previous study , and representative COI sequences (NCBI PopSet: 1485777102) from that study were compared to examine overall sequence divergence. To generate B. heckerae cytochrome c oxidase subunit 1 (COI) barcodes, PCR was conducted in 10 µl volumes (3.2 µl nuclease-free water, 0.1 µl BSA, 0.1 µl DMSO, 0.3 µl forward primer, 0.3 µl reverse primer and 5.0 µl GoTaq® Hot Start Master Mix (Promega, Madison, WI, USA) using the following primers: jgLCOI (TITCIACIAAYCAYAARGAYATTGG) and jgHCOI (TAIACYTCIGGRTGICCRAARAAYCA) . The targeted COI region was amplified with the following protocol: initial denaturation at 95 °C for 7 min, followed by 40 cycles of (denaturation at 95 °C for 45 s, annealing at 42 °C for 45 s, extension at 72 °C for 60 s), and a final extension of 72 °C for 5 min. Bidirectional Sanger sequencing was performed at the National Museum of Natural History’s Laboratories of Analytical Biology using ABI 3730xl Genetic Analyzer (Thermo Fisher, Waltham, MA, USA). Raw chromatograms were assembled into bidirectional consensus sequences and edited using Geneious Prime 2020.1.2  (https://www.geneious.com). For both species, COI sequence divergence (i.e., 100% identity) was estimated in Geneious following sequence alignment with MUSCLE using default parameters.
Bathymodiolus heckerae and G. childressi genomic (g)DNA were quantified with the Qubit dsDNA BR Assay Kit (Thermo Fisher), and the presence of high molecular weight DNA was confirmed with 0.8% agarose gel electrophoresis. The gDNA was normalized to 20 ng/µl in 50 µl volumes and sent to Floragenex (Beaverton, OR). DNA libraries were constructed using the 6-cutter PstI enzyme, followed by sequencing 100 bp single-end reads across four lanes of an Illumina HiSeq2500 (University of Oregon’s Genomics and Cell Characterization Core Facility lab). In total 183 bathymodiolines were sequenced, 86 G. childressi and 97 B. heckerae samples (Additional file 1: Table S1).
Raw data were quality checked using the program FASTQC  and de-muliplexed using the program STACKS (process_radtags, -e pstI --inline_null) with default parameters to clean the data (-c: removing any read with an uncalled base and -q: discarding reads with low quality scores) . RADseq data for each species was assembled discretely with iPYRAD v 0.9.12  using the genome of Gigantidas platifrons  as reference  (NCBI accession GCA_002080005.1). As there is no available genome for either species, the only available genome of a bathymodioline relative was used to guide the assembly of the RADseq data based on the recommendations of . Reference-based assembly methods assist the assembly process by removing/masking repetitive regions and paralogous sequences and leads to a higher number of SNPs, reduces FIS and transition to transversion ratios, and produces more accurate summary statistics as compared to de novo approaches . The referenced based assemblies were done using default parameters, with a sequence similarity (clust_threshold) of 0.95, maximum missing data of 20% (min_samples_locus = 61 and 72 for G. childressi and B. heckerae respectively), and a maximum number of heterozygous sites (max_shared_Hs_locus) of 0.5. To maximize the loci (gene or allele position) recovered, individuals with < 1000 k assembled contigs (G. childressi n = 6 and B. heckerae n = 9) were excluded from the analyses. Following assembly, loci and corresponding SNP information were recovered for each species (Table 1).
Additional downstream analyses were run in iPYRAD using jupyter notebook according to the API: iPYRAD assembly workflow . To examine genetic population structure between the seep sites for G. childressi, unlinked SNPs (n = 21,220) were used in the program STRUCTURE v.2.3.4 via the iPYRAD-analysis toolkit, as described in the workflow. STRUCTURE  estimates admixture (i.e., hybridization) and population structure between populations (sites) using multi-locus genotypes (e.g., SNPs). Population information was assigned based on seep locality (group = Norfolk, Baltimore or Chincoteague) for G. childressi, requiring 50% SNP coverage in each group defined above (minmap = 0.5). SNPs were further filtered by removing those not shared across 90% of all samples (mincov = 0.9). STRUCTURE was run in replicate (n = 10, burnin = 20,000, numreps = 100,000) using several population (K) values (K = 1 – 4). The rate of change in log probabilities (deltaK) and mean log probabilities were plotted in the package toyplot to determine the most likely number of (K) populations . Probability of membership of each individual to K clusters were averaged across 10 iterations and also plotted in toyplot.
Further assessment of genetic differentiation among G. childressi and B. heckerae was done in R (v.3.5.0) using the package adegenet . For G. childressi, site information was added as a hypothetical population factor (genind@pop) to examine genetic diversity among the northernmost seep sites (NCS, BCS, CTS; Fig. 1). As almost all B. heckerae samples (except one individual) were sampled from the same locality (BRS), sampling methods were added to the analysis to examine the influence of sampling methodology (ROV, slurp or Mussel pot) on genetic diversity estimates. For G. childressi, loci were further filtered for missing data in R using poppr v2.8.5  to remove loci with any missing data across individuals (missingno, type = “loci”, cutoff = 0). This subset (n = 16,720) was further filtered to remove non-neutral loci (p < 0.05), potentially under selection, based on Hardy-Weinberg Equilibrium (HWE) tests (hw.test) conducted in R using pegas  resulting in 11,995 loci. Due to a lower yield of assembled loci for B. heckerae (~ 0.2X), no further filtering was conducted on the loci to maximize data available for downstream analyses. Genetic diversity statistics were computed using the basic.stats function from the package hierfstat , including the population differentiation fixation index (F, inbreeding coefficient (FIS), mean observed heterozygosity (HO), mean gene diversities within populations (HS), mean gene diversity overall (HT), all calculated according to Nei . Additionally, a measure of population differentiation (DEST) as in Jost  was calculated. Pairwise FST comparisons were also computed between the cold-seep localities for G. childressi. A Principal Components Analysis (PCA) was performed in R on both the putatively neutral G. childressi subset (n = 11,995 loci) and B. heckerae assembly using ade4 . Missing values were replaced by the mean allele frequencies (NA.method = "mean”).
Connectivity and gene flow
To assess relatedness among (G. childressi) and within (G. childressi and B. heckerae) seep communities, kinship analyses were conducted in SEQUOIA  which performs pedigree reconstruction based on SNP data. The vcf file from iPYRAD was converted into a raw readable format using PLINK (v1.90b6.21,http://pngu.mgh.harvard.edu/purcell/plink/ ). The SNPs were then randomly subset with PLINK following recommendations in , with parameter thresholds for missingness (--geno 0.2), minor allele frequency (--maf 0.3) and the sliding window (--indep 50 5 1). The SNP subsets (G. childressi n = 669 and B. heckerae n = 628) were then extracted using PLINK (--extract --recodeA) and converted into genotype data in SEQUOIA (GenoConvert) to use as input for the pedigree reconstruction (function sequoia, MaxSibIter = 40, Err = 0.001, FindMaybeRel = TRUE). For each pair of individuals, likelihoods were calculated to determine parent-offspring (PO), full siblings (FS), half siblings (HS), grandparents (GP), full avuncular (niece/nephew - aunt/uncle; FA), half avuncular/ great-grandparental/cousins (HA), or unrelated (U) relationships. Kinship assignments were made if the log likelihood ratio (LLR) between a given relationship and the most likely alternative exceeded the default threshold.
To further assess contemporary connectivity and the directionality of gene flow among G. childressi communities, recent migration rates were calculated between the three sampling localities (NCS, CTS, BCS) in BayesAss v3.04 (BA3) . BA3 assumes that first generation immigrants can be detected and mean immigration rates for each population can be estimated. Migration rates were calculated using only neutral loci and unlinked SNPs. First, the locus subset filtered for missing data (n = 16,720 loci) was used to calculate site-specific inbreeding coefficients with BA3 using 10,000,000 iterations, with a burnin of 250,000 and sampling frequency every 100 generations. Parameters were set to ensure acceptance rates between 20 and 60% for all adjustable parameters (according to ), with allele (-a) frequencies at 0.3, inbreeding coefficients (-f) at 0.02, and migration rates (-m) at 0.1 (default). Then to calculate migration rates, the neutral locus dataset (n = 11,995 loci) was then re-run in BA3 using the same run settings, but with parameters slightly adjusted to ensure acceptance rates remained within the appropriate range (a = 0.35, f = 0.02 and m = 0.15). In both scenarios, convergence was examined and confirmed using Tracer v1.7.1 .
Signature of selection by depth
To examine loci under potential selection in each species that inhabit distinct depth ranges (G. childressi - shallower seeps, 400–2200 m vs. B. heckerae - deeper seeps, 2200 to 3300 m; ), a subset of sequence data that maximized the quality and size of locus yield were chosen for analyses (Table 1). Twenty-eight individuals were chosen based on the number of loci assembled in each species-specific assembly (G. childressi > 28 K loci with spread across all three sampling localities (NCS = 14, BTC = 10, CTS = 4); B. heckerae > 3.8 K loci). The 54 individuals were re-run in iPYRAD  with the reference genome of G. platifrons, with default parameters described above (clust_threshold = 0.85, min_samples_locus = 45, max_shared_Hs_locus = 0.25). Genotype data were extracted from the vcf file and converted to bed format using PLINK. These data were then analyzed in R using pcadapt  following the pcadapt vignette (https://github.com/bcm-uga/pcadapt/blob/master/vignettes/pcadapt.Rmd) to detect genetic markers potentially under selection (outlier SNPs) using Principal Components Analysis (PCA). Following , the PCA was first performed with a large number of principal components (K = 20) and examined to assess the percentage of variance explained by each PC. This was visualized to assess the PC/eigenvalues that correspond to population structure (steep curve, left of straight line) vs. random variation (straight line). In addition, a score plot was generated to visualize population structure (or in this case species structure) of the samples according to the principal components (PC). The optimal number of PCs was determined from these plots (K = 2) and pcadapt was re-run accordingly with default parameters. Test statistics for each SNP based on the PCA and outliers based on Mahalanobis distance, a multidimensional approach to measure distance from the mean , were estimated using pcadapt. A Bonferroni correction was applied and SNPs were considered outliers based on the adjusted p-values (≤ 0.05).
Corresponding loci and genomic annotations from the published G. platifrons genome were identified for the outlier SNPs via custom scripts (see https://github.com/deleod) using biopython . Outlier SNPs were linked back to the G. platifrons scaffolds with annotation information from the G. platifrons genome  downloaded from NCBI (BioProject PRJNA328542, accession MJUT00000000). BEDTools  was then used to extract gene information from the corresponding genome GFF annotation file, which has gene ontology (GO) and KEGG orthology (KO) information. REVIGO (http://revigo.irb.hr/, ) was then used to produce a reduced visualization of the biological processes and molecular functions associated with these outlier SNPs, indicative of select loci under putative selection. REVIGO summarizes GO information by reducing functional redundancies based on semantic similarity (most informative common term). KEGG (Kyoto Encyclopedia of Genes and Genomes) reconstruction pathway mapper  was further used to elucidate corresponding pathways associated with the outlier SNPs. KEGG is a collection of databases corresponding to biological pathways in addition to genomic, disease and chemical substance information. The KEGG Reconstruct tool can be used to “reconstruct” biological pathway maps from a set of K numbers (KO identifiers or annotation IDs) obtained from the annotated genome of G. platifrons.
An average of 96.2% of reads were retained after demultiplexing and filtering with STACKS. This includes 4.27 M (± 2.18 M) reads for G. childressi and 4.18 M (± 3.63 M) for B. heckerae. DNA barcoding of the COI gene indicated pairwise sequence divergences ranging from 0 to 2.12% for G. childressi and 0 to 1.22% for B. heckerae.
Genetic connectivity and diversity
On average ~ 99.9% of reads for G. childressi passed the additional filtering step in iPYRAD, which generated an average of ~ 271.4 K total clusters per individual with an average heterozygosity estimate of 0.01 and error estimate of 0.002. A total of 417,015 loci were assembled using the reference genome of G. platifrons. Loci retained following filtering for a maximum of 20% missing individuals per locus, resulted in 21,292 loci and 283,754 total SNPs (39.0% missing sites and 21,220 unlinked SNPs).
Assessments of genetic differentiation among G. childressi yielded an overall FST of 0.002 with a low overall mean observed heterozygosity (HO = 0.09), within population gene diversity (HS = 0.13) and overall gene diversity (HT = 0.13) (Table 2). These results indicated low heterozygosity and relatively equal allele frequencies among G. childressi individuals collected from the different seep sites along the Mid-Atlantic margin. Further, G. childressi had an overall inbreeding coefficient (FIS) of approximately 0.32, indicating a high level of inbreeding among the total population of sampled individuals in this region. However, inbreeding coefficients were generally lower at each site (FIS= 0.04–0.10) (Table 3).
Pairwise FST values between the collection localities were low and ranged from 0.003 to 0.006 (Table 4). The highest pairwise FST value was between Baltimore Canyon Seep (BCS) and Chincoteague Seep (CTS), followed by Norfolk Canyon Seep (NCS) and CTS, indicating relatively larger genetic differences between the canyon seeps and CTS. This is despite Chincoteague’s relatively intermediate location and depth, though this may be due to the low number of samples available for this locality (n = 6).
Principal Components Analysis (PCA) used to visualize genetic clustering among G. childressi individuals and across collection localities also showed a considerable amount of overlap among samples (1: NCS [n = 53], 2: CTS [n = 6], 3: BCS [n = 22], Fig. 2). The first PCA axis explained 7.3% of variation while the second axis explained 1.6%. Genetic similarity among sites was also apparent by the lack of clustering among individuals collected from the same seep community. Similarly, STRUCTURE analyses indicated no population structure among G. childressi from the three seeps. Plots based on both K = 2 and K = 3, which had similar deltaK values, indicated panmixia (Fig. 3; Additional file 4: Fig. S1).
Kinship analyses (SEQUOIA) revealed approximately 45% of individuals were related (36 probable relationships among the 81 samples), including parent-offspring (PO = 2), full siblings (FS = 3), grandparent (GP = 29) and half avuncular (HA = 2) - great-grandparents/cousins (Additional file 2: Table S2), among BCS, NCS, and CTS. The relationships predicted are indicative of gene flow among the sites and suggest BCS (shallowest and northernmost seep) serves as a source population, supplying gametes/larvae to NCS (deepest and southernmost seep). These analyses also indicate that at least 14 out of 81 samples (17%) at NCS are sourced from NCS, suggesting high local recruitment.
Further assessments of gene flow (BA3) between the three seep sites (NCS, CTS, BCS) dominated by G. childressi indicated some degree of gene flow in this system (Table 5). At least 25% of individuals at NCS were migrants from BCS. Similarly, approximately 20% of migrants at CTS were from BCS. However, BayesAss also indicated a high fraction of non-migrants at each seep site (75–92%) (Table 5).
On average ~ 99.9% of reads for B. heckerae passed the additional quality filtering step in iPYRAD, which generated an average of ~ 98.4 K total clusters per individual with a heterozygosity estimate of 0.01 and an error estimate of 0.003. Following assembly, there were 4142 filtered (max. 20% missing data) loci remaining of the 259,443 assembled in total using the G. platifrons genome, yielding 52,904 total SNPs (37.31% missing sites and 4114 unlinked SNPs).
Assessments of site-wide genetic differentiation among B. heckerae individuals collected at BRS yielded an overall FST of 0.00 and an inbreeding coefficient (FIS) of approximately 0.30 (Table 2), similar to the high-level of inbreeding found among G. childressi. The low overall mean observed heterozygosity (HO = 0.07), within population gene diversity (HS = 0.10) and overall gene diversity (HT = 0.10) also indicate low heterozygosity and relatively equal allele frequencies among B. heckerae individuals collected in the Blake Ridge region.
To identify whether there was cohort-level population structure among B. heckerae a PCA was performed to visualize genetic differentiation among B. heckerae individuals—with samples grouped by collection (1: mussel pot B6 [n = 33], 2: ROV_G [n = 1], 3: SBlue_02 [n = 1], 4: mussel pot B1 [n = 17], 5: mussel pot B2 [n = 10], 6: mussel pot B4 [n = 25]). This revealed broad genetic similarity among the discrete collections via a considerable amount of overlap and a lack of distinct clustering by collection group (Fig. 4).
The kinship analyses (SEQUOIA) revealed approximately 10% of the sampled B. heckerae individuals were related (Eight probable relationships among the 87 samples), including grandparent (GP = 6) and half avuncular (HA = 2)—great-grandparental/ cousins (Additional file 3: Table S3). This suggests relatively high levels of local recruitment despite large dispersal capabilities, considering the subsample of individuals examined here from Blake Ridge seep. These kinship results also included a probable grandparental relationship between sample HRS-1704-CM-35 collected in a region near Norfolk Canyon, and individual CM-00151 collected with mussel pot B6 at BRS, indicating relatively recent dispersal and highlighting gene flow capabilities similar to G. childressi.
Signatures of selection
Using conservative methods for outlier detection between the two bathymodioline species, 3,429 outlier SNPs (both linked and unlinked) were identified using an adjusted p-value cutoff of 0.05 (Fig. 5A). The score plot from pcadapt (Fig. 5B), which displays the projection of each sample onto the principal components of the PCA, revealed species structure among the SNP outliers; B. heckerae specimen HRS-1704-CM-35 from the NCS region was distinct from the B. heckerae from BRS. PCA analysis also indicated no clustering of G. childressi sampled at the different canyon sites. These data suggest that there is no evidence for adaptation with gene flow between BCS, NCS and CTS. Analysis of SNP outliers (n = 3,429) specific to G. childressi also confirmed a lack of population structure among the sampled SNPs (Additional file 5: Fig. S2).
Based on the annotations for the G. platifrons genome, we obtained annotation information for 1,259 of the outlier SNPs, which corresponded to 427 unique G. platifrons genes (Additional file 7: Table S6). Among those annotated genes are a variety of known environmental response genes including- a probable cytochrome p450, ABC transporters, collagens, various zinc finger proteins, solute carriers, serine-threonine kinases and ion receptors, glutathione S-transferase (GST), heat shock protein 70, carbonic anhydrases and the stress response protein nhaX, and a diagnostic cancer biomarker protein Cubillin. The gene list also included development-related genes, such as the developmental homeobox gene Hox-A9, and the transcription factor SOX-30 involved in Wnt signaling and spermatid development. Additionally, sulfite oxidase was an outlier, which is a gene involved in sulfur metabolism. Complementary Gene Ontology (GO) analyses also indicated that the loci under putative selection in bathymodiolines are associated with a variety of biological processes including ATP and carbohydrate metabolism, response to ionizing radiation (i.e., reactive oxygen species (ROS) and DNA damage), inhibition of coagulation, toxin transport, microtubule-based movement, protein folding, epigenetic modifications (such as methylation, phosphorylation and ubiquitination), cellular response to starvation, nitrate assimilation, Wnt signaling, establishment of an endothelial barrier, and organelle and cytoskeletal organization (Fig. 6). Grouping these GO terms based on overlapping ontology terms revealed various putative adaptive loci function in responding to environmental stress (e.g. response to ionizing radiation, toxin transport), cellular regulation and organization, DNA and protein modifications and metabolism (Fig. 6, Additional file 7: Fig. S3).
BRITE functional hierarchy of the KEGG annotations retrieved from the G. platifrons genome indicated that the SNPs correspond to loci associated with three primary protein families- (1) metabolism, (2) genetic information processing, and (3) signaling and cellular processes (Additional file 6: Table S6). The most highly represented functional categories within those families include: (1: metabolism) enzymes (n = 67), protein kinases, phosphatases and associated proteins (n = 16), (2: genetic information processing) chromosome and associated proteins (n = 30), membrane trafficking (n = 16), ubiquitin system (n = 11) and DNA repair and recombination proteins (n = 9), and (3: signaling/cellular processes) exosome (n = 14), transporters (n = 8) and cytoskeletal proteins (n = 8). KEGG pathway reconstruction further revealed functional associations with, but not limited to, microbial metabolism in diverse environments, sulfur and nitrogen energy metabolism, regulation of actin cytoskeleton, circadian rhythm and thermogenesis (Additional file 6: Table S6).
Genetic connectivity and diversity
Gigantidas childressi is a chemosynthetic-ecosystem engineer distributed widely throughout the North Atlantic Ocean. To date, this species has been recorded from cold seeps and brine pools in the Gulf of Mexico , along the Mid-Atlantic U.S. margin [13, 50], and off Trinidad and Tobago . This widespread broadcast spawning species, with a long (up to 16.5 months) planktotrophic dispersal phase and vertically migrating larvae [58,59,60,61], has the ability to disperse long distances across a range of environmental conditions and thus colonize a variety of chemosynthetic habitats in the deep sea . Similarly, Bathymodiolus heckerae is distributed widely throughout the North Atlantic, with records in the Gulf of Mexico [56, 63], Mid-Atlantic margin , and Blake Ridge off South Carolina . Based on distribution patterns and life history characteristics, it is not too surprising that population structure was not detected among G. childressi sites separated by 135 km and > 1000 m difference in depth or among collections of B. heckerae at Blake Ridge. Both STRUCTURE and PCA plots combined with very low to negligible FST values indicated a high degree of shared genetic variation among G. childressi from the different sites, indicating that there is likely one large population of G. childressi present across the Mid-Atlantic region; additional sampling efforts would confirm the extent of this connectivity. Our results are similar to findings for G. childressi populations in the Gulf of Mexico, which showed minimal genetic differentiation over 500 km of geographical distance and 1500 m vertical distance based on restriction fragment length polymorphism data [16, 64].
We were able to estimate contemporary gene flow rates and directionality among sites with G. childressi sub-populations. BayesAss analysis, which can infer gene flow rates and directionality over the last few generations , indicated that ~ 25% of migrants moved from the shallower BCS site to the deeper NCS site and ~ 20% moved from BCS to the intermediate CTS site. A relatively negligible fraction (8%) of migrants from NCS and CTS were found at BCS. In addition, the kinship analyses suggested that many of the kin relationships observed were sourced from BCS. This pattern is consistent with an onshore to offshore or down slope pattern of dispersal, which has been hypothesized to be a predominant mode of genetic diversification in deep-sea taxa (e.g., [65,66,67]). However, this directionality in gene flow, from BCS to CTS and NCS, is also oriented in a north to south direction, which corresponds with the prevailing current structure in the region. The Labrador Current (and derived Labrador Slope water) is the predominant current along the shelf and slope off the northeastern U.S., bringing cold waters from the northern Labrador Sea southward to an area off Cape Hatteras, North Carolina, where the Labrador Current converges with the Gulf Stream [68, 69]. The high level of non-migrants (92%) calculated at BCS is also noteworthy, but we expect this estimate is because source populations north of BCS were not included in this study. Nevertheless, our results indicate that BCS is an important source of genetic material to downstream and deeper sites in the region.
Unexpectedly, kinship analyses suggested a high degree of relatedness among individuals, not only among sites but also within a particular site. Of the G. childressi individuals collected from the three seep sites, 45% were kin (and total inbreeding coefficient among the sites was high). Notably, 17% of G. childressi were related at the NCS. Similarly, 10% of B. heckerae were related to one another at Blake Ridge, while a high inbreeding coefficient was estimated at this site as well. This was quite surprising as life history strategies of this species (i.e., long pelagic larval duration, vertical migration of larvae) suggest long-range dispersal. So, how do bathymodioline larvae disperse and then recruit back to natal or nearby natal sites?
We hypothesize that local current patterns serve as conduits for entrainment and dispersal of vertically-migrating  bathymodioline larvae. Just south of our study site off Cape Hatteras, North Carolina, the Gulf Stream separates from the continental margin and turns eastward across the North Atlantic Ocean. Downstream of this separation point, the Gulf Stream meanders off its mean path and sporadically impinges onto the study area, particularly over the NCS site . As the Gulf Stream moves seaward, meanders grow in amplitude, leading to the formation of warm-core eddies that entrain shelf waters . The eddies circle clockwise, last for 30–90 days  and propagate across the Mid-Atlantic U.S. margin. We hypothesize that Gulf Stream meanders and eddies could entrain bathymodioline larvae and advect them back to the Mid-Atlantic margin, enabling periodic local recruitment. Similarly, larval dispersal models for the glass sponge Vasella pourtalesii indicated low dispersal distances and high retention for a population simulated in this region . Chemical cues from the chemosynthetic environment could subsequently trigger bathymodioline larvae to settle onto the benthos (see ). Larval entrainment and advection via Gulf Stream eddies are a well-known mechanism of larval fish transport in this region .
An alternative mechanism of retention could be associated with nearby submarine canyons. Submarine canyons have highly dynamic hydrography (e.g., [75, 76]). Benthic lander observations combined with conductivity-temperature-depth casts have indicated complex water mass structure, current speeds and directions, tidal forcing, and internal waves within the canyons . Although the exact mechanisms are unclear, it is possible that larvae are entrained within canyon environments within particular months or seasons . G. childressi larvae are known to ontogenetically migrate to the surface, but they have also been collected near the seabed [58, 61, 73]. Therefore, it is possible that larvae can survive (weeks to months) in the canyon-rich environments and remain relatively close to their natal seep sites.
Notably, our kinship results also suggested that at least 10 and 17% of individuals were related at both NCS and BRS, respectively. These percentages seem quite high for a random sampling of a relatively small portion of the sub-population at each site. One hypothesis that has been proposed to explain high occurrences of kin in settlement patches is the ‘sweepstakes reproductive success’, whereby relatively few individuals produce a majority of successful recruits due to stochastic processes [79,80,81]. Variance in reproductive success could limit the effective population sizes, further reducing genetic diversity through genetic drift . Alternatively, the observed patterns of relatedness could be driven by successful dispersal events of kin that remain in cohesive or collective dispersal kernels . Sweepstakes reproductive success and/or a founder event of a cohesive larval cohort(s) of bathymodiolines could perhaps contribute to the high inbreeding coefficients and low observed heterozygosity within both bathymodioline species. We suggest that future research explore these hypotheses through further sampling and estimation of kin relationships across the spatial structure of bathymodioline mussel beds.
Signatures of selection
Results of our study indicated that 427 genes are potentially under selection between the bathymodioline species surveyed. Based on the KEGG annotations, many of these genes can be grouped under three primary protein functional families: (1) metabolism, (2) genetic information processing, and (3) signaling and cellular processes. These genomic differences are likely related to environmental adaptations of the two species combined with the endosymbiont repertoire that each species harbors. In addition, some genes associated with development were found as outliers, suggesting potential developmental differences between these bathymodioline species. Below we highlight some of these genes under the three protein families, particularly as they relate to environmental or chemosynthetic adaptations, but we provide the full list of 427 genes in Additional file 6: Table S6. Our hope for the below discussion is that it will foster further investigation into gene function and evolution of bathymodiolines to advance our understanding of the biology and diversification of this successful group of chemosynthetic inhabitants.
Genes associated with metabolism were found to be under putative selection. Of particular interest here is the enzyme sulfite oxidase, which catalyzes sulfite to sulfate, and is known to play an important role in metabolism of sulfur-containing amino acids and in detoxifying exogenous sulfite and sulfur dioxide . In cold seeps, reduced sulfur (i.e., hydrogen sulfide) can seep from the seafloor along with methane . Reduced sulfur can be oxidized by free-living bacteria and via thiotrophic symbionts that live within the gill tissues of some bathymodioline mussel species (to help obtain nutrition, [12, 19, 84, 85]). Thus, sulfite oxidase is potentially under selection in bathymodiolines to fully utilize available sulfide sources, allowing them to thrive in sulfide-rich chemosynthetic habitats. A recent gene expression study on the bathymodioline G. platifrons indicated that several genes in the sulfide oxidation pathway, including sulfite oxidase, are upregulated in response to increased sulfide concentrations . Although recent studies indicated that some individuals of G. childressi from BCS and NCS can harbor thiotrophs  and 14–16% of their nutrition can be derived from hydrogen sulfide , B. heckerae is known to harbor multiple thiotroph phylotypes  and potentially derive up to 40% of their carbon (at BRS) from thiotrophic symbionts . Thus, selection of sulfite oxidase may be driven by endosymbiont repertoire. If both mussel species are capable of benefiting from thiotrophy, the lack of (large-scale) co-occurrence among B. heckerae and G. childressi may be associated with competition for limited resources, in which B. heckerae’s diverse repertoire of endosymbionts may enable them to outcompete other seep bivalves. It is also possible that selection of sulfite oxidase is driven by high concentrations of reduced sulfur in the environment. Further investigation is needed to elucidate the differences in seepage conditions between the ridge (deeper) and canyon (shallower) seeps.
Our candidate SNPs associated with gene sets enriched for functions related to genetic information processing were found to be outliers and potentially under selection. One functional gene category, the ubiquitin system (here including ubiquitin activating enzymes, regulators and ubiquitin-protein ligases), is known to be important in response to environmental stress [87, 88]. This system is involved in rapid degradation of short-lived proteins  and plays a critical role in regulation of physiological processes in both animals and plants . In plants, for example, the ubiquitin system has been shown to be important for tolerance to drought, salinity, cold, nutrient deprivation and pathogens, enabling them to efficiently respond to environmental stress . Marine invertebrates up-regulate ubiquitin in response to temperature extremes (see ). Spatial and temporal variation in protein ubiquitination has been observed for the mussel Mytilus galloprovincialis and associated with cold stress and increased protein turnover. This is in contrast to congeners that are less sensitive to cold temperatures . We hypothesize that this system is under selection for temperature-related adaptation, as B. heckerae is generally found in much deeper depths than G. childressi and thus subjected to colder temperatures. Ubiquitin proteins have been found to be under positive selection in deep-sea hydrothermal vent crustaceans  and differentially expressed in marine bivalves in response to other environmental stressors (e.g., trace metals ). Alternatively, putative selection in the bathymodioline ubiquitin system could be related to regulating symbioses, as it has been shown that the ubiquitin system can be up-regulated in response to experimental symbiont loss in Bathymodiolus .
Signaling and cellular processing genes associated with candidate SNPs, including eight cytoskeletal proteins, were also overrepresented in our SNP outlier analyses. Similarly, in a study of the deep-sea fish, Aldrovandia affinis, a set of proteins involved in cytoskeleton organization (particularly proteins stabilizing actin and microtubules and nucleic-binding proteins involved in genetic information processing) had clear signatures of positive selection . We hypothesize that cytoskeletal proteins are under positive selection in B. heckerae to tolerate extreme environmental conditions, such as sulfide-rich environments, cold temperatures and high hydrostatic pressure. Cytoskeletal proteins are known to be pressure sensitive, with actin and tubulin dissociating under pressure (see ), and these proteins are often upregulated in gene-expression studies investigating environmental pollution, temperature and salinity stress in bivalves [96,97,98].
With more than 20K SNPs recovered from each bathymodioline species, we provided the first estimates of genome-wide genetic diversity of these deep-sea ecosystem engineers along the U.S. Atlantic margin. Our analyses indicated lack of population differentiation within G. childressi among three sites spanning 135 km and > 1000 m depth. Further, our analyses indicated that contemporary gene flow within G. childressi occurs from shallow to deeper depths in a southerly direction, indicative of an onshore to offshore pattern of connectivity that is also consistent with the directionality of the southward-flowing Labrador Current. Our unique kinship analyses further supported these conclusions and highlighted that 10–20% of individuals at any one site are kin; raising the question of how these highly interconnected populations, with high dispersal rates, are also able to locally recruit to a site or a nearby site from which they were spawned. Finally, signatures of putative selection associated with 427 genes provided clues into species-specific adaptations that could enable survival, persistence, and potential speciation within chemosynthetic ecosystems in the deep sea. We urge further research on these functional categories of genes, including, metabolism, genetic information processing, and signaling and cellular processes, which could significantly advance our understanding of the biology and evolution of this successful group of chemosynthetic inhabitants.
Availability of data and materials
The datasets supporting the conclusions of this article are available in the Sequence Read Archive (SRA) database under BioProject PRJNA769076. The COI barcode dataset generated for B. heckerae are available on Genbank (TBD). The COI barcode dataset used for G. childressi are publicly available on NCBI (PopSet: 1,485,777,102). Code to accompany the RADseq data processing, assembly and analyses can be found on GitHub (https://github.com/deleod).
- RADSeq :
Restriction-Site Associated DNA Sequencing
- SNPs :
Single nucleotide polymorphisms
- NCS :
Norfolk Canyon Seep
- BCS :
Baltimore Canyon Seep
- CTS :
- BRS :
Blake Ridge Seep
- HOV :
- ROV :
- HWE :
- PCA :
Principal Component Analysis
- PC :
- PO :
- FS :
- HS :
- GP :
- FA :
Full avuncular (niece/nephew or aunt/uncle)
- HA :
Half avuncular (great-grandparental/cousins)
- U :
- LLR :
(log10) likelihood ratio
- BA3 :
BayesAss v3.04 program
- KO :
Sibuet M, Olu K. Biogeography, biodiversity and fluid dependence of deep-sea cold-seep communities at active and passive margins. Deep Sea Res Part II. 1998;45(1–3):517–67.
Jones WJ, Won YJ, Maas PAY, Smith PJ, Lutz RA, Vrijenhoek RC. Evolution of habitat use by deep-sea mussels. Mar Biol. 2006;148(4):841–51.
Van Dover CL, Aharon P, Bernhard JM, Caylor E, Doerries M, Flickinger W, et al. Blake Ridge methane seeps: characterization of a soft-sediment, chemosynthetically based ecosystem. Deep Sea Res Part I. 2003;50(2):281–300.
Duperron S, Guezi H, Gaudron SM, Pop Ristova P, Wenzhöfer F, Boetius A. Relative abundances of methane-and sulphur-oxidising symbionts in the gills of a cold seep mussel and link to their potential energy sources. Geobiology. 2011;9(6):481–91.
Cordes EE, Becker EL, Hourdez S, Fisher CR. Influence of foundation species, depth, and location on diversity and community composition at Gulf of Mexico lower-slope cold seeps. Deep Sea Res Part II. 2010;57(21–23):1870–81.
Bergquist DC, Fleckenstein C, Knisel J, Begley B, MacDonald IR, Fisher CR. Variations in seep mussel bed communities along physical and chemical environmental gradients. Mar Ecol Prog Ser. 2005;293:99–108.
Skarke A, Ruppel C, Kodis M, Brothers D, Lobecker E. Widespread methane leakage from the sea floor on the northern US Atlantic margin. Nat Geosci. 2014;7(9):657–61.
Bourque JR, Robertson CM, Brooke S, Demopoulos AW. Macrofaunal communities associated with chemosynthetic habitats from the US Atlantic margin: a comparison among depth and habitat types. Deep Sea Res Part II. 2017;137:42–55.
Demopoulos AW, McClain-Counts J, Ross SW, Brooke S, Mienis F. Food-web dynamics and isotopic niches in deep-sea communities residing in a submarine canyon and on the adjacent open slopes. Mar Ecol Prog Ser. 2017;578:19–33.
Paull CK, Ussler W III, Borowski WS, Spiess FN. Methane-rich plumes on the Carolina continental rise: associations with gas hydrates. Geology. 1995;23(1):89–92.
Olu K, Cordes EE, Fisher CR, Brooks JM, Sibuet M, Desbruyères D. Biogeography and potential exchanges among the Atlantic equatorial belt cold-seep faunas. PLoS ONE. 2010;5(8): e11967.
Gustafson RG, Turner RD, Lutz RA, Vrijenhoek RC. A new genus and five new species of mussels (Bivalvia, Mytilidae) from deep-sea sulfide/hydrocarbon seeps in the Gulf of Mexico. Malacologia. 1998;40(1–2):63–112.
Coykendall DK, Cornman RS, Prouty NG, Brooke S, Demopoulos AW, Morrison CL. Molecular characterization of Bathymodiolus mussels and gill symbionts associated with chemosynthetic habitats from the US Atlantic margin. PLoS ONE. 2019;14(3): e0211616.
Cordes EE, Bergquist DC, Fisher CR. Macro-ecology of Gulf of Mexico cold seeps. Ann Rev Mar Sci. 2009;1:143–68.
Won Y-J, AY P, Van Dover CL, Vrijenhoek RC. Habitat reversal in vent and seep mussels: seep species, Bathymodiolus heckerae, derived from vent ancestors. Cah Biol Mar 20002;43:387–390.
Craddock C, Hoeh WR, Lutz RA, Vrijenhoek RC. Extensive gene flow among mytilid (Bathymodiolus thermophilus) populations from hydrothermal vents of the eastern Pacific. Mar Biol. 1995;124(1):137–46.
Carney SL, Formica MI, Divatia H, Nelson K, Fisher CR, Schaeffer SW. Population structure of the mussel “Bathymodiolus” childressi from Gulf of Mexico hydrocarbon seeps. Deep Sea Res Part I. 2006;53(6):1061–72.
Breusing C, Johnson SB, Tunnicliffe V, Vrijenhoek RC. Population structure and connectivity in Indo-Pacific deep-sea mussels of the Bathymodiolus septemdierum complex. Conserv Genet. 2015;16(6):1415–30.
Arellano SM, Young CM. Spawning, development, and the duration of larval life in a deep-sea cold-seep mussel. Biol Bull. 2009;216(2):149–62.
Duperron S, Sibuet M, MacGregor BJ, Kuypers MM, Fisher CR, Dubilier N. Diversity, relative abundance and metabolic potential of bacterial endosymbionts in three Bathymodiolus mussel species from cold seeps in the Gulf of Mexico. Environ Microbiol. 2007;9(6):1423–38.
Zhang K, Sun J, Xu T, Qiu JW, Qian PY. Phylogenetic relationships and adaptation in deep-sea mussels: insights from mitochondrial genomes. Int J Mol Sci. 2021;22(4):1900.
Bossart JL, Prowell DP. Genetic estimates of population structure and gene flow: limitations, lessons and new directions. Trends Ecol Evol. 1998;13(5):202–6.
Whitlock MC, McCauley DE. Indirect measures of gene flow and migration: FST≠ 1/(4Nm+ 1). Heredity. 1999;82(2):117–25.
Thaler AD, Saleu W, Carlsson J, Schultz TF, Van Dover CL. Population structure of Bathymodiolus manusensis, a deep-sea hydrothermal vent-dependent mussel from Manus Basin. Papua New Guinea PeerJ. 2017;5: e3655.
Miller MR, Dunham JP, Amores A, Cresko WA, Johnson EA. Rapid and cost-effective polymorphism identification and genotyping using restriction site associated DNA (RAD) markers. Genome Res. 2007;17(2):240–8.
Davey JW, Blaxter ML. RADSeq: next-generation population genetics. Brief Funct Genomics. 2010;9(5–6):416–23.
Andrews KR, Good JM, Miller MR, Luikart G, Hohenlohe PA. Harnessing the power of RADseq for ecological and evolutionary genomics. Nat Rev Genet. 2016;17(2):81–92.
Demopoulos AW, McClain-Counts JP, Bourque JR, Prouty NG, Smith BJ, Brooke S, et al. Examination of Bathymodiolus childressi nutritional sources, isotopic niches, and food-web linkages at two seeps in the US Atlantic margin using stable isotope analysis and mixing models. Deep Sea Res Part I. 2019;148:53–66.
Xu FQ, Xue HW. The ubiquitin-proteasome system in plant responses to environments. Plant Cell Environ. 2019;42(10):2931–44.
Geller J, Meyer C, Parker M, Hawk H. Redesign of PCR Primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Mol Ecol Resour. 2013;13:851–61.
Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics. 2012;28(12):1647–9.
Andrews S. FastQC: a quality control tool for high throughput sequence data. 2010.
Catchen J, Hohenlohe PA, Bassham S, Amores A, Cresko WA. Stacks: an analysis tool set for population genomics. Mol Ecol. 2013;22(11):3124–40.
Eaton DA, Overcast I. ipyrad: Interactive assembly and analysis of RADseq datasets. Bioinformatics. 2020;36(8):2592–4.
Hashimoto J, Okutani T. Four new mytilid mussels associated with deepsea chemosynthetic communities around Japan. Venus (Japanese J Malacol). 1994;53(2):61–83.
Sun J, Zhang Y, Xu T, Zhang Y, Mu H, Zhang Y, et al. Adaptation to deep-sea chemosynthetic environments as revealed by mussel genomes. Nat Ecol Evol. 2017;1(5):1–7.
Shafer ABA, Peart CR, Tusso S, Maayan I, Brelsford A, Wheat CW, Wolf JBW. Bioinformatic processing of RAD-seq data dramatically impacts downstream population genetic inference. Methods Ecol Evol. 2017;8:907–17. https://doi.org/10.1111/2041-210X.12700.
Eaton DA, Ree RH. Inferring phylogeny and introgression using RADseq data: an example from flowering plants (Pedicularis: Orobanchaceae). Syst Biol. 2013;62(5):689–706.
Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155(2):945–59.
Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005;14(8):2611–20.
Jombart T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics. 2008;24(11):1403–5.
Kamvar ZN, Tabima JF, Grünwald NJ. Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ. 2014;2: e281.
Guo SW, Thompson EA. Performing the exact test of Hardy-Weinberg proportion for multiple alleles. Biometrics. 1992;361–372.
Goudet J. Hierfstat, a package for R to compute and test hierarchical F-statistics. Mol Ecol Notes. 2005;5(1):184–6.
Nei M. Molecular evolutionary genetics. Columbia University Press; 1987.
Jost L. GST and its relatives do not measure differentiation. Mol Ecol. 2008;17:4015–26.
Dray S, Dufour AB. The ade4 package: implementing the duality diagram for ecologists. J Stat Softw. 2007;22:1–20.
Huisman J. Pedigree reconstruction from SNP data: parentage assignment, sibship clustering and beyond. Mol Ecol Resour. 2017;17(5):1009–24.
Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4(1):s13742–4015.
Wilson GA, Rannala B. Bayesian inference of recent migration rates using multilocus genotypes. Genetics. 2003;163(3):1177–91.
Rambaut A, Drummond AJ. Tracer: MCMC trace analysis tool, version 1.5. Oxford: University of Oxford; 2009.
Turner PJ, Ball B, Diana Z, Fariñas-Bermejo A, Grace I, McVeigh D, Powers MM, Van Audenhaege L, Maslakova S, Young CM, Van Dover CL. Methane Seeps on the US Atlantic Margin and their potential importance to populations of the commercially valuable deep-sea red crab, Chaceon quinquedens. Front Mar Sci. 2020;7:75. https://doi.org/10.3389/fmars.2020.00075.
Luu K, Bazin E, Blum MG. pcadapt: an R package to perform genome scans for selection based on principal component analysis. Mol Ecol Resour. 2017;17(1):67–77.
Cock PJ, Antao T, Chang JT, Chapman BA, Cox CJ, Dalke A, et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics. 2009;25(11):1422–3.
Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26(6):841–2.
Supek F, Bošnjak M, Škunca N, Šmuc T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS ONE. 2011;6(7): e21800.
Kanehisa M, Sato Y. KEGG Mapper for inferring cellular functions from protein sequences. Protein Sci. 2020;29(1):28–35.
Amon DJ, Gobin J, Van Dover CL, Levin LA, Marsh L, Raineault NA. Characterization of methane-seep communities in a deep-sea area designated for oil and natural gas exploitation off Trinidad and Tobago. Front Mar Sci. 2017;4:342.
Eckelbarger KJ, Young CM. Ultrastructure of gametogenesis in a chemosynthetic mytilid bivalve (Bathymodiolus childressi) from a bathyal, methane seep environment (northern Gulf of Mexico). Mar Biol. 1999;135(4):635–46.
Tyler PA, Young CM. Reproduction and dispersal at vents and cold seeps. J Mar Biol Assoc UK. 1999;79(2):193–208.
Arellano SM, Van Gaest AL, Johnson SB, Vrijenhoek RC, Young CM. Larvae from deep-sea methane seeps disperse in surface waters. Proc R Soc B Biol Sci. 2014;281(1786):20133276.
McVeigh DM, Eggleston DB, Todd AC, Young CM, He R. The influence of larval migration and dispersal depth on potential larval trajectories of a deep-sea bivalve. Deep Sea Res Part I Oceanogr Res Pap. 2017;127:57–64. https://doi.org/10.1016/j.dsr.2017.08.002.
Hecker B. Fauna from a cold sulfur-seep in the Gulf of Mexico: comparison with hydrothermal vent communities and evolutionary implications. Bull Biol Soc Wash. 1985;6:465–73.
Faure B, Schaeffer SW, Fisher CR. Species distribution and population connectivity of deep-sea mussels at hydrocarbon seeps in the Gulf of Mexico. PLoS ONE. 2015;10(4): e0118460.
Jablonski D, Sepkoski JJ Jr, Bottjer DJ, Sheehan PM. Onshore-offshore patterns in the evolution of Phanerozoic shelf communities. Science. 1983;222(4628):1123–5.
Hessler RR, Wilson GDF. The origin and biogeography of malacostracan crustaceans in the deep sea. Evol Time Space Emerg Biosphere. 1983;227–54.
Little CT, Vrijenhoek RC. Are hydrothermal vent animals living fossils? Trends Ecol Evol. 2003;18(11):582–8.
Fratantoni PS, Pickart RS. The western North Atlantic shelfbreak current system in summer. J Phys Oceanogr. 2007;37(10):2509–33.
New AL, Smeed DA, Czaja A, Blaker AT, Mecking JV, Mathews JP, Sanchez-Franks A. Labrador Slope Water connects the subarctic with the Gulf Stream. Environ Res Lett. 2021;16(8): 084019.
Halliwell GR Jr, Mooers CN. Meanders of the Gulf Stream downstream from Cape Hatteras 1975–1978. J Phys Oceanogr. 1983;13(7):1275–92.
Silva ENS, Gangopadhyay A, Fay G, Welandawe MK, Gawarkiewicz G, Silver AM, et al. A survival analysis of the Gulf Stream warm core rings. J Geophys Res Oceans. 2020;125(10):e2020JC016507.
Wang S, Kenchington E, Wang Z, Davies AJ. Life in the fast lane: modeling the fate of glass sponge larvae in the Gulf Stream. Front Mar Sci. 2021;8.
Laming SR., Gaudron SM, Duperron S. Lifecycle ecology of deep-sea chemosymbiotic mussels: a review. Front Mar Sci. 2018;282.
Hare JA, Churchill JH, Cowen RK, Berger TJ, Cornillon PC, Dragos P, et al. Routes and rates of larval fish transport from the southeast to the northeast United States continental shelf. Limnol Oceanogr. 2002;47(6):1774–89.
Shepard FP, Marshall NF, McLoughlin PA, Sullivan GG. Currents in submarine canyons and other seavalleys. 1979.
Puig P, Palanques A, Martín J. Contemporary sediment-transport processes in submarine canyons. Ann Rev Mar Sci. 2014;6:53–77.
Robertson CM. Macrofaunal diversity and functioning within submarine canyons of the Mid-Atlantic Bight. Western North Atlantic: Bangor University (United Kingdom); 2018.
Metaxas A, Lacharite M, De Mendonca SN. Hydrodynamic connectivity of habitats of deep-water corals in Corsair Canyon, Northwest Atlantic: a case for cross-boundary conservation. Front Mar Sci. 2019;6:159.
Hedgecock D. Does variance in reproductive success limit effective population sizes of marine organisms? In: Beaumont A, editor. Genetics and evolution of aquatic organisms. London: Chapman and Hall; 1994. p. 122–34.
Hedgecock D. Temporal and spatial genetic structure of marine animal populations in the California current. CalCOFI Reports. 1994;35:73–81.
Hedgecock D, Pudovkin AI. Sweepstakes reproductive success in highly fecund marine fish and shellfish: a review and commentary. Bull Mar Sci. 2011;87(4):971–1002.
D’Aloia CC, Neubert MG. The formation of marine kin structure: effects of dispersal, larval cohesion, and variable reproductive success. Ecology. 2018;99(10):2374–84.
Feng C, Tollin G, Enemark JH. Sulfite oxidizing enzymes. Biochim Et Biophys (BBA) Acta Proteins Proteomics. 2007;1774(5):527–39.
Levin LA. Ecology of cold seep sediments: interactions of fauna with flow, chemistry and microbes. In Oceanography and marine biology. CRC Press; 2005. pp. 11–56.
Distel DL, Lane DJ, Olsen GJ, Giovannoni SJ, Pace B, Pace NR, Felbeck H. Sulfur-oxidizing bacterial endosymbionts: analysis of phylogeny and specificity by 16S rRNA sequences. J Bacteriol. 1988;170(6):2506–10.
Prouty NG, Sahy D, Ruppel CD, Roark EB, Condon D, Brooke S, Demopoulos AW. Insights into methane dynamics from analysis of authigenic carbonates and chemosynthetic mussels at newly-discovered Atlantic Margin seeps. Earth Planet Sci Lett. 2016;449:332–44.
Hershko A, Ciechanover A. The ubiquitin system. Annu Rev Biochem. 1998;67(1):425–79.
Xu T, Feng D, Tao J, Qiu JW. A new species of deep-sea mussel (Bivalvia: Mytilidae: Gigantidas) from the South China Sea: morphology, phylogenetic position, and gill-associated microbes. Deep Sea Res Part I. 2019;146:79–90.
Dahlhoff EP. Biochemical indicators of stress and metabolism: applications for marine ecological studies. Annu Rev Physiol. 2004;66:183–207.
Dutton JM, Hofmann GE. Spatial and temporal variation in distribution and protein ubiquitination for Mytilus congeners in the California hybrid zone. Mar Biol. 2008;154(6):1067–75.
Yuan J, Zhang X, Gao Y, Zhang X, Liu C, Xiang J, Li F. Adaptation and molecular evidence for convergence in decapod crustaceans from deep-sea hydrothermal vent environments. Mol Ecol. 2020;29(20):3954–69.
Götze S, Matoo OB, Beniash E, Saborowski R, Sokolova IM. Interactive effects of CO2 and trace metals on the proteasome activity and cellular stress response of marine bivalves Crassostrea virginica and Mercenaria mercenaria. Aquat Toxicol. 2014;149:65–82.
Wang H, Zhang H, Wang M, Chen H, Lian C, Li C. Comparative transcriptomic analysis illuminates the host-symbiont interactions in the deep-sea mussel Bathymodiolus platifrons. Deep Sea Res Part I. 2019;151: 103082.
Lan Y, Sun J, Xu T, Chen C, Tian R, Qiu JW, Qian PY. De novo transcriptome assembly and positive selection analysis of an individual deep-sea fish. BMC Genomics. 2018;19(1):1–9.
Pradillon F, Gaill F. Adaptation to deep-sea hydrothermal vents: some molecular and developmental aspects. J Mar Sci Technol. 2007;15(5):5.
Zhao X, Yu H, Kong L, Li Q. Transcriptomic responses to salinity stress in the Pacific oyster Crassostrea gigas. PLoS ONE. 2012;7:e46244.
Artigaud S, Richard J, Thorne MA, Lavaud R, Flye-Sainte-Marie J, F. Deciphering the molecular adaptation of the king scallop (Pecten maximus) to heat stress using transcriptomics and proteomics. BMC Genomics. 2015;16(1):1–14.
Cao R, Zhang Y, Ju Y, Wang W, Xi C, Liu W, Liu K. Exacerbation of copper pollution toxicity from ocean acidification: a comparative analysis of two bivalve species with distinct sensitivities. Environ Pollut. 2022;293: 118525.
Sun Y, Wang M, Zhong Z, Chen H, Wang H, Zhou L, Li C. Adaption to hydrogen sulfide-rich environments: Strategies for active detoxification in deep-sea symbiotic mussels. Gigantidas platifrons. Sci Tot Environ. 2022;804:150054.
We thank E. Cordes, Chief Scientist of the R/V Atlantis AT41 Cruise and R/V Ron Brown RB-19 Cruise. We also thank S. Ross and S. Brooke, Chief Scientists, and crews of the NOAA Ships Nancy Foster and Ron Brown, the University-National Oceanographic Laboratory System R/V Hugh R. Sharp (HRS) and the ROV Global Explorer, ROV Jason II Group (WHOI), ROV Kraken group (UConn). Carolyn Ruppel (USGS) and Jennifer McClain-Counts assisted with mussel collections from the HRS expedition. We thank D. Katharine Coykendall for assistance with sampling and initial data collection. We also thank Bernie Bernard and the TDI Brooks team for support. Vanessa Gonzalez helped with outlier assignment to the reference genome. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
Study concept, oversight, and funding were provided by the U.S. Department of the Interior, Bureau of Ocean Energy Management, Environmental Studies Program, Washington, DC under. Contract Number M17PC00009.
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Sample information for Gigantidas childressi and Bathymodiolus heckerae individuals collected from the study area from either Norfolk Canyon Seep (NCS), Baltimore Canyons Seep (BCS), Chincoteague Seep (CTS) or Blake Ridge Seep (BRS). The asterisk (*) denotes samples used in selection analyses, while the (x) denotes samples excluded from analyses based on sequencing results.
. Kinship associations and associated log-likelihood ratios (LLR) among G. childressi individuals (ID) collected from the three different seep sites as predicted by SEQUOIA. TopRel = second column ID relative to first column ID and includes parent-offspring (PO), full siblings (FS), grandparent (GP) and half avuncular (HA) - great-grandparents /cousins.
. Kinship associations and associated log-likelihood ratios (LLR) among B. heckerae individuals (ID) collected from Blake Ridge seep, with one individual collected near Norfolk canyon, as predicted by SEQUOIA. TopRel = second column ID relative to first column ID and includes grandparent (GP) and half avuncular (HA) - great-grandparents/cousins.
. Average probability of membership graph for G. childressi (n = 81) collected from seeps at Norfolk canyon (NCS), Chincoteague (CTS) and Baltimore Canyon (BCS). K = 3 clusters (ancestral populations), as identified by STRUCTURE, and also reported here as it had similar deltaK values as K=2, both indicating panmixia.
A score plot displaying the projection of each G. childressi sample, used in the selection analyses, onto the principal components of the PCA conducted in pcadapt. Samples are color coded by seep site: Baltimore Canyon Seep (BCS), pink; Norfolk Canyon Seep (NCS), blue; and Chincoteague Seep (CTS), green.
Reduced representation of the Gene Ontology (GO) Biological Process categories shown in Figure 6, and corresponding putative number of genes linked to each process, that are associated with the outlier SNPs identified with pcadapt.
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DeLeo, D.M., Morrison, C.L., Sei, M. et al. Genetic diversity and connectivity of chemosynthetic cold seep mussels from the U.S. Atlantic margin. BMC Ecol Evo 22, 76 (2022). https://doi.org/10.1186/s12862-022-02027-4
- Gigantidas childressi
- Bathymodiolus heckerae