Next Article in Journal
Expression Analysis and the Roles of the Sec1 Gene in Regulating the Composition of Mouse Gut Microbiota
Previous Article in Journal
Population Genetics of California Gray Foxes Clarify Origins of the Island Fox
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Genes
Volume 13
Issue 10
10.3390/genes13101856
genes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Genomic Signatures of Freshwater Adaptation in Pacific Herring (Clupea pallasii)

by
Artem Nedoluzhko
1,2,†,
Svetlana Yu. Orlova
3,4,*,†,
Denis S. Kurnosov
5,
Alexei M. Orlov
6,7,8,9,10,
Jorge Galindo-Villegas
11,*,† and
Sergey M. Rastorguev
2,12,*,†
1
Paleogenomics Laboratory, European University at Saint Petersburg, 191187 Saint Petersburg, Russia
2
Limited Liability Company ELGENE, 109029 Moscow, Russia
3
Laboratory of Molecular Genetics, Russian Federal Research Institute of Fisheries and Oceanography, 107140 Moscow, Russia
4
Laboratory of Genetic Basis of Identification, Vavilov Institute of General Genetics of the Russian Academy of Sciences, 119991 Moscow, Russia
5
Research Group of Intraspecific Differentiation, Russian Federal Research Institute of Fisheries and Oceanography, Pacific Branch (TINRO), 690091 Vladivostok, Russia
6
Laboratory of Oceanic Ichthyofauna, Shirshov Institute of Oceanology of the Russian Academy of Sciences, 117218 Moscow, Russia
7
Laboratory of Behavior of Lower Vertebrates, A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, 119071 Moscow, Russia
8
Department of Ichthyology, Dagestan State University, 367000 Makhachkala, Russia
9
Department of Ichthyology and Hydrobiology, Tomsk State University, 634050 Tomsk, Russia
10
Laboratory of Marine Biology, Caspian Institute of Biological Resources, Russian Academy of Sciences, 367000 Makhachkala, Russia
11
Genomics Division, Faculty of Biosciences and Aquaculture, Nord University, 8049 Bodø, Norway
12
Kurchatov Center for Genomic Research, National Research Centre “Kurchatov Institute”, 123182 Moscow, Russia
 
add Show full affiliation list
 
remove Hide full affiliation list
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Genes 2022, 13(10), 1856; https://doi.org/10.3390/genes13101856
Submission received: 5 September 2022 / Revised: 10 October 2022 / Accepted: 13 October 2022 / Published: 14 October 2022
(This article belongs to the Section Population and Evolutionary Genetics and Genomics)
Browse Figures
Versions Notes

Abstract

:
Pacific herring (Clupea pallasii) is an essential target of commercial fishing in the North Pacific Ocean. Previous studies have suggested the existence of marine and lake ecological forms of this species within its range. The lake ecological form of herring has a shortened life cycle, spending the winter and spawning in brackish waters near the shoreline without long migrations for feeding; it also has a relatively smaller body size than the marine form. Genetic-based studies have shown that brackish water Pacific herring not only can be distinguished as a separate lake ecological form but possibly has its genetic legacy. Here, as part of an ongoing study, using ddRAD-sequencing data for marine and lake ecological forms from a total of 54 individuals and methods of comparative bioinformatics, we describe genomic signatures of freshwater adaptivity in Pacific herring. In total, 253 genes containing discriminating SNPs were found, and part of those genes was organized into genome clusters, also known as “genomic islands of divergence”. Moreover, the Tajima’s D test showed that these loci are under directional selection in the lake populations of the Pacific herring. Yet, most discriminating loci between the lake and marine ecological forms of Pacific herring do not intersect (by gene name) with those in other known marine fish species with known freshwater/brackish populations. However, some are associated with the same physiological trait—osmoregulation.

1. Introduction

Freshwater adaptation among marine fish species opens them new ecological niches and evolutionary opportunities. In contrast, this environmental shift requires substantial changes in immunity, physiological and metabolic processes, osmoregulation, and behavior [1]. The genetic basis of adaptation to different salinity environments has been studied for the different teleost species—prickly sculpin (Cottus asper) [2], mummichog (Fundulus heteroclitus) [3], Atlantic cod (Gadus morhua) [4], rainwater killifish (Lucania parva) [5]. Genetic comparisons between anadromous and nonmigratory ecotypes of rainbow trout (Oncorhynchus mykiss) and Atlantic salmon (Salmo salar) were also carried out [1,6,7].
Significant advances have been achieved during in-depth studies of freshwater adaptation of the three-spined stickleback (Gasterosteus aculeatus), the well-established model for modern evolutionary biology [8]. Genetic comparisons in this species have been intensively studied in the migratory and nonmigratory ecotypes. Such studies are based on genomic data [9,10,11,12,13] and gene expression of protein-coding genes [14] or microRNAs [15]. These studies showed that the genes responsible for ion transmembrane transport play an essential role in adaptation to the freshwater environment. Moreover, these genes are under positive selection [16]. In many cases, genomic regions responsible for adaptation to the freshwater environments are grouped into compact genomic islands of divergence [4,10,11]. Atlantic herring (Clupea harengus) also has interpopulation genetic differentiation, which is related to different salinity levels and is associated with haplotype blocks, often spanning multiple genes and maintained by balancing selection [17].
The commercial importance of the Pacific herring (C. pallasii) and its trophic significance for other species in marine ecosystems prompt many studies aimed at its biology, reproduction, diversity, and genetic differentiation [18,19,20,21,22,23,24]. This species has marine and lake ecological forms. The marine ecological form of C. pallasii performs long feeding migrations and spends wintertime in the upper part of the deep sea. In addition, it uses to spawn in large sea bays. Conversely, the lake ecological form prefers brackish lagoon-type lakes and small bays to spawn and spend the winter [25]. Recent genetic-based studies showed the level of genetic differentiation between populations from the Russian part of the North Pacific Ocean and the Kara Sea; moreover, lake populations from the Kamchatka Peninsula and Sakhalin Island separated from the marine populations of Pacific herring [21,26]. At the same time, it has been suggested that the lake ecological form has its genetic component associated with freshwater adaptation [26].
In this study, we present the analysis of a double digestion restriction site-associated DNA (ddRAD) sequencing dataset of 54 individuals of Pacific herring, which was previously determined as lake (15 specimens) and marine (39 specimens) ecological forms. Genotype analysis of marine and lake ecological forms allows us to identify the genomic signatures of freshwater adaptation in Pacific herring and carry out the comparative analysis of freshwater adaptation loci with its congener—Atlantic herring.
The comparison of such discriminating loci in the genomes of marine fish species with known freshwater adaptation ability will determine the genetic mechanisms and directions of adaptation to the different osmotic conditions, particularly during the conquering of new freshwater ecological niches. It is assumed that other environmental factors related to the new habitat are also changing in addition to osmotic conditions. Another critical topic considered in this study is the representation of new data on how rigidly determined the evolutionary mechanisms of adaptation to an environmental condition and the habitats of different species. From a general point of view, it is assumed that such mechanisms may differ significantly in evolutionarily distant species in comparison with close evolutionary species [27]. Nevertheless, it involves common genes or gene categories that underlie adaptive traits. The comparative analysis of such adaptation-associated loci sheds light on the species-specific genomic mechanisms in higher vertebrates.

2. Materials and Methods

2.1. Pacific Herring Genomic Dataset Description

The sampling, DNA extraction, ddRAD library preparation and DNA sequencing were previously described [26]. Fifty-four individuals of two ecological forms of Pacific herring from the Northwest Pacific and the Kara Sea were involved in the present study. Specimens from twelve wild populations of Pacific herring were combined as two separate marine (39 specimens) and lake (15 specimens) ecological forms and used in a comparative analysis aimed at describing genomic signatures of freshwater adaptation (Table 1; Figure 1). The average salinity and temperature in the sampling regions in late spring—early summer are presented in Table 2.

2.2. Bioinformatics Analysis

Raw DNA reads were converted to FASTQ format and demultiplexed using bcl2fastq (v2.20). DNA reads quality was examined with the FastQC tool (v0.11.5) [38]. Library adapter trimming from the sequencing data and quality filtration (phred 30) were carried out using cutadapt (v2.10) [39].
Stacks package (v2.41) and its clone_filter module were used for PCR duplicate removal. In addition, the process_radtags module from the same package was used for dual index demultiplexing and additional quality filtration [40]. The output reads from the Stacks package were mapped against the Atlantic herring reference genome (Ch_v2.0.2, https://www.ncbi.nlm.nih.gov/assembly/GCF_900700415.2 (accessed on 4 July 2022)) using Bowtie2 under the “very-sensitive” parameter set [41]. The BAM files that were obtained using the Samtools (v1.7) [42] and uploaded to BCFtools (v1.9) [42] for SNP-calling. Only SNPs with coverage higher than 1000× for all 54 Pacific herring individuals were used in the subsequent analyses.
The filtered VCF file was loaded into the R statistical environment (v3.4.4) for discriminant analysis. The VCF file was then converted into genlight format and used for discriminant analysis of principal components (DAPC) in the adegenet R package (v2.1.3) [43].
The VCF file was also converted into internal plink format using PLINK (v1.9) package under the “make-bed” parameter [44]. To find the loci in which allele frequencies differ in marine and lake populations of Pacific herring, we used loci association analysis using the “assoc” command, as in the case–control association study, with Fisher’s p-value determination. Loci with p-value less than 1 × 10−6 were considered to discriminate between marine and lake Pacific herring ecological forms. Gene names with discriminating SNP loci were determined using coordinates of genes from the Atlantic herring reference genome (Ch_v2.0.2), SNPs coordinates, and bedtools software package (v2.27.1) under the “intersect” command [45].
The gene list, containing discriminating SNP loci was analyzed against the reference group of genes using the ShinyGO (v0.76) web service [46]. The genes with a single discriminating SNP in the filtered VCF dataset were also included in the comparison. A sliding window method from the ShinyGO tool was implicated in the genomic region prediction process with the SNPs that discriminate marine and lake ecological forms of Pacific herring. The window size was equal to two megabase pairs (Mbp). The hypergeometric test was used to determine if the genes were significantly overrepresented. The FDR cutoff for each window was equal to 0.001.
The neutrality test was performed by Tajima’s D method [47] using the Tajima command of the VCF-kit package [48]. The test was performed by evaluating the Tajima value in sliding windows with a length of 10,000 bp. The distribution of the Tajima D test values for frames containing discriminating genes was compared with those containing the remaining genes using the t.test function of the R environment. Analyses were carried out separately for the VCF file, which contained all Pacific herring specimen SNPs data, and for a VCF file which had only lake Pacific herring specimen SNPs data.

3. Results

3.1. Marine and Lake Ecological Form of Pacific Herring Dataset Description and Mapping Statistics

The total number of reads generated for 54 lake and marine specimens of Pacific herring was 216,433,546 (NCBI accession numbers are presented in Table 1). DNA reads were mapped to the reference genome of Atlantic herring (Ch_v2.0.2, https://www.ncbi.nlm.nih.gov/assembly/GCF_900700415.2 (accessed on 4 July 2022)) after adapter trimming and quality filtration. From 59.91 to 88.48% of reads per ddRAD library were mapped to the reference (Table S1). Only SNPs with coverage higher than 1000× (p < 0.05) were used after the SNPs calling.

3.2. Distribution of Discriminating Loci between Marine and Lake Ecological Forms of Pacific Herring

A total of 192,433 SNP loci from the 26 chromosomes of the Atlantic herring genome were involved in the PLINK analysis. The coverage of chromosomes by DNA reads was approximately uniform, and the number of SNP loci per chromosome was nearly proportional to the length of each chromosome. However, the distribution of discriminating loci between marine and lake ecological forms of Pacific herring was extraordinarily uneven. Most were found on the seventh, twelfth, and twentieth chromosomes (Figure 2).
We also conducted discriminant analyses of the principal component between marine and lake ecological forms of Pacific herring. To explore differences, we combined SNPs datasets from Ainskoe, Nerpiche, and Bolshoy Vilyuy lake populations in one lake group and the remaining in the marine group. As a result, the sample density along the discriminant function separates two ecological groups (Figure 3).

3.3. Genomic Divergence Islands between Marine and Lake Ecological Forms of Pacific Herring

SNPs localization in gene bodies was carried out by the intersection of their coordinates with the Atlantic herring reference genome GFF file (Ch_v2.0.2, https://www.ncbi.nlm.nih.gov/assembly/GCF_900700415.2 (accessed on 4 July 2022)). In total, we found SNPs in 10,649 genes of the reference genome of Atlantic herring. Interestingly, those SNP loci, which discriminate marine and lake ecological forms of Pacific herring, were observed only in 253 genes (Table S2). Moreover, 86 discriminating SNP loci are represented in 6 genes—dnai1, LOC105891580 ral guanine nucleotide dissociation stimulator, dlg2 channel-associated protein of synapse-110, dcc netrin 1 receptor, thoc2, and sptan1, which locate in chromosomes 7, 8, 12, and 20.
Remarkably, chromosomes 7, 8, and 20 contain many discriminating SNP loci (Table S2). The same results were shown by analyzing the list of genes using the ShinyGO web service [46]. Furthermore, a significant number of discriminating SNP loci are localized in chromosomes 6, 7, 12, and 20 and in certain chromosome regions, that were called “genomic islands of divergence” in the previous studies [11,49]. Figure 4 clearly shows how the discriminating SNP loci between the marine and lake ecological forms of Pacific herring are grouped into genomic divergence islands.
Gene ontology (GO) analysis was carried out for the list of 253 genes that contain discriminating SNP loci, using the ShinyGO (v0.76) web service. GO analysis combined 17 of them in neuron development category, which contains 221 gene (Fold Enrichment = 3.2; FDR = 0.033).
Not all discriminating SNP loci between ecological forms of Pacific herring were involved in the analysis since the ddRAD sequencing method does not cover the whole genome and reduces the complexity of the analysis by subsampling only at specific genomic sites defined by restriction enzymes [50].
In total, 133 from 253 genes, which contain discriminating SNP loci between ecological forms of Pacific herring, theare located in genomic divergence islands. Interestingly, these parts of chromosomes 6, 7, 12, and 20 contain 254 genes. Thus, the ddRAD sequencing method used in this study allowed us to determine at least half of them.
Recently, Velotta and colleagues summarized genomic mechanisms of adaptation to different salinity environment conditions in eight different fish species; they described the specific categories of genes that play a crucial role in such adaptation [16]. We found a significant number of these gene categories among our discriminating genes. Strikingly, we speculate that they were possibly involved in freshwater adaptation in Pacific herring. Notably, it has been shown that osmoregulation is influenced by insulin-like growth factor 1 (IGF-1), which receptor and associated binding proteins aid in its transport. This hormone is implicated in the proliferation and differentiation of ionocytes of fish gills [16]. The insulin-like growth factor 2 mRNA binding protein 1 (igf2bp1) and insulin receptor substrate 2b (irs2b) genes are described in the present study as discriminating between marine and lake ecological forms of Pacific herring. We suppose these genes can be involved in the proliferation of gill ionocytes, which are responsible for osmoregulation [51]. In addition, fibroblast growth factors (Fgf) are also represented among discriminating genes. Fibroblasts are implicated in forming connective tissue in animals, which is also necessary to protect body tissues from the changing environment. Moreover, it has been shown that Fgf underlies phenotypical adaptations of fish [52]. Transmembrane channel genes are important for freshwater adaptation. Previously, ATPase pump, passive ion cotransporter and transient receptor potential (TRP) channel genes were described as part of molecular pathways related to response to the changing of osmotic conditions [16,53]. In the case of the Pacific herring, a minimum of six genes (abcc8, trpm1a, trpc4b, cacna1bb, sclt1, and piezo1) from this category are revealed as discriminating between marine and lake ecological forms. Moreover, seven genes involved in transmembrane transporter activity (slc4a1b, slc39a10, slc15a4, slc20a1a, slc25a25b, slc45a2, and slc16a5a) and three genes involved in calcium homeostasis (cabp1, camk2a, and cacna1bb) were also described. Genome epigenetic modifications and non-coding RNAs play a significant role in the rapid adaptation to changes in the salinity of the environment in fish [15,54,55,56,57]. Table S2 represents several such genes, including piwil1 (piwi-like1 RNA-mediated gene).
The discriminating genes were tested for neutrality using Tajima′s D statistical test. As a result, we obtained weakly positive average Tajima′s D values for both groups of genes — discriminating (0.2044859) and the set of the other genes (0.3041617) with a high degree of significance (p-value = 9.042 × 10−5) for the 54 Pacific herring specimens. However, Tajima′s D values became sharply negative when we tested only specimens from the lake ecological form. The significantly negative values of Tajima′s D indicate the genes with an excess of low-frequency variation, which is consistent with the fact that the populations might have experienced an expansion after a recent bottleneck, or the genes targeted are under positive selective pressure. Moreover, the difference in the averages of Tajima′s D values for both groups of genes — discriminating (-40.30702) and the whole set of genes (-26.99115) in lake populations have a high degree of significance (p-value = 1.185 × 10−10). The most logical interpretation of this result is that in addition to the population demographic effect, which can be seen by the Tajima′s D for the whole gene set (-26.99), there is an additional effect of directional selection (-40.3), which is closely related to discriminating genes of the individuals from lake populations of pacific herring.

4. Discussion

Our previous studies allowed us to describe the genetic differentiation between the Pacific herring populations in the Russian part of its range using mitochondrial and nuclear genome markers [21,26]. Moreover, the comparative analysis of the genetic differentiation between the combined three lakes on one side and nine marine populations of Pacific herring on the other showed that lake form populations differ from the marine ones [26]. The origin of lake populations across the Russian Far East is still unclear. However, they most likely arose independently due to the significant geographical distance between Sakhalin Island and the Kamchatka Peninsula habitats. Nevertheless, Pacific herring individuals who inhabit fresh-/brackish water in different regions are clustered together based on genetic data [26]. Perhaps, such differences are explained by their ongoing contact with marine individuals, which can spread “freshwater” alleles between lake populations across Russian Far East.
In the present study, even though the relatively small number of gene categories most likely participating in the freshwater adaptation in Pacific herring, we succeeded in showing that the function of most of them are related to osmotic regulation. Moreover, in other teleost species, several of these genes have previously been correlated to the adaptation dynamic toward different salinity conditions [16]. In addition, among discriminative genes between marine and lake forms of Pacific herring, we have found a significant number of regulatory genes, which suggests that rearrangement of regulatory pathways with salinity-related environment changes is a way to quick adaption. Previously, it has been shown on the three-spined stickleback model where at least half of the genes from the genomic divergence islands have regulatory functions [10,11].
Non-coding RNAs are also implicated significantly in the freshwater adaptation of teleost fish by gene expression regulation. Moreover, the differentiation in the microRNA expression occurs in both cases when fish transfer from the marine to the freshwater environment and vice versa—physiological (quick) response or during the time-long period of adaptation (via generations)—evolutionary response. Interestingly, the different microRNAs are expressed in these two types of responses [15].
However, note that discriminative genes between marine and lake ecological forms of Pacific herring are not intersected directly with genes that evolved under positive selection in other fish species, like three-spined stickleback [11]. Indeed, that result is not unexpected. Such overlapping was not observed even between closely related three- and nine-spined (Pungitius pungitius) stickleback species [58], which have a possibility for intergeneric hybridization in natural conditions [59,60]. This observation perhaps could be expanded to other types of adaptation among species.
We also compared our discriminating gene list of marine and lake ecological forms of Pacific herring with the same one for Atlantic herring (the Atlantic Ocean and moderate-salted Baltic Sea populations) previously published [17]. Surprisingly, only microRNA silencing modulator—piwil1 overlapped among the two herring species. Thus, we hypothesize that microRNA-silencing is an essential factor in the process of ecological adaptation of fish species. However, it is not entirely accurate to compare our results with those of Barrio and colleagues since they only analyzed marine specimens which inhabit regions of different salinity [17]. Nevertheless, the gene families and categories associated with freshwater adaptation usually overlap between Pacific herring and other studied species [16]. However, the neuron development GO category has not been described previously as adaptive for changing salinity conditions.
Finally, we suggest that fish species go on their way to achieving their evolutionary goal—an adaptation to new environmental conditions. In addition to the necessary changes that adjust the physiological parameters, such as changes in ion channel activity, several unobvious genetic and phenotypical changes appear during the adaptation to the environment, which differ from species to species. Neuron development category innovations in the lake form of Pacific herring may allow adapting faster to the new ecological niches where this species meets with the new osmotic conditions, predators, changes in feeding, seasonal, sexual, migratory, and other forms of behavior.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/genes13101856/s1, Table S1: Illumina generated reads and ratio of mapped reads to Atlantic herring (Clupea harengus) reference genome sequence (Ch_v2.0.2); Table S2: Genes containing of discriminating SNP loci between marine and lack ecological forms of Pacific herring (Clupea pallasii).

Author Contributions

Conceptualization, S.Y.O., J.G.-V. and S.M.R.; formal analysis, S.M.R.; investigation, S.Y.O. and S.M.R.; resources, S.Y.O. and D.S.K.; data curation, S.M.R.; writing—original draft preparation, S.M.R. and A.N.; writing—review and editing, S.Y.O., S.M.R., A.M.O., J.G.-V. and A.N.; visualization, S.M.R. and A.N.; supervision, S.Y.O.; project administration, S.Y.O.; funding acquisition, S.Y.O. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Russian Scientific Foundation (RSF), grant number 22-24-01036. Nord University Open Access Fund covers the OA publication costs.

Institutional Review Board Statement

Ethical permissions are not applicable to this article. The genomic datasets analyzed in this study were retrieved from previously published databases [26].

Informed Consent Statement

Not applicable.

Data Availability Statement

Double digestion restriction site-associated DNA (ddRAD) sequencing dataset of 54 individuals of Pacific herring is available for download through National Center for Biotechnology Information (NCBI): http://www.ncbi.nlm.nih.gov/, PRJNA717668 (accessed on 13 October 2022).

Acknowledgments

This work has been carried out using computing resources of the federal collective usage center Complex for Simulation and Data Processing for Mega-science Facilities at NRC “Kurchatov Institute”, http://ckp.nrcki.ru/ (accessed on 4 July 2022).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Harder, A.M.; Christie, M.R. Genomic signatures of adaptation to novel environments: Hatchery and life history-associated loci in landlocked and anadromous Atlantic salmon (Salmo salar). Can. J. Fish. Aquat. Sci. 2022, 79, 761–770. [Google Scholar] [CrossRef]
  2. Dennenmoser, S.; Vamosi, S.M.; Nolte, A.W.; Rogers, S.M. Adaptive genomic divergence under high gene flow between freshwater and brackish-water ecotypes of prickly sculpin (Cottus asper) revealed by Pool-Seq. Mol. Ecol. 2017, 26, 25–42. [Google Scholar] [CrossRef]
  3. Brennan, R.S.; Healy, T.M.; Bryant, H.J.; La, M.V.; Schulte, P.M.; Whitehead, A. Integrative Population and Physiological Genomics Reveals Mechanisms of Adaptation in Killifish. Mol. Biol. Evol. 2018, 35, 2639–2653. [Google Scholar] [CrossRef] [Green Version]
  4. Berg, P.R.; Jentoft, S.; Star, B.; Ring, K.H.; Knutsen, H.; Lien, S.; Jakobsen, K.S.; Andre, C. Adaptation to Low Salinity Promotes Genomic Divergence in Atlantic Cod (Gadus morhua L.). Genome Biol. Evol. 2015, 7, 1644–1663. [Google Scholar] [CrossRef]
  5. Kozak, G.M.; Brennan, R.S.; Berdan, E.L.; Fuller, R.C.; Whitehead, A. Functional and population genomic divergence within and between two species of killifish adapted to different osmotic niches. Evolution 2014, 68, 63–80. [Google Scholar] [CrossRef]
  6. Hale, M.C.; Thrower, F.P.; Berntson, E.A.; Miller, M.R.; Nichols, K.M. Evaluating Adaptive Divergence Between Migratory and Nonmigratory Ecotypes of a Salmonid Fish, Oncorhynchus mykiss. G3 Genes Genomes Genet. 2013, 3, 1273–1285. [Google Scholar] [CrossRef] [Green Version]
  7. Kjaerner-Semb, E.; Edvardsen, R.B.; Ayllon, F.; Vogelsang, P.; Furmanek, T.; Rubin, C.J.; Veselov, A.E.; Nilsen, T.O.; McCormick, S.D.; Primmer, C.R.; et al. Comparison of anadromous and landlocked Atlantic salmon genomes reveals signatures of parallel and relaxed selection across the Northern Hemisphere. Evol. Appl. 2021, 14, 446–461. [Google Scholar] [CrossRef]
  8. Reid, K.; Bell, M.A.; Veeramah, K.R. Threespine Stickleback: A Model System For Evolutionary Genomics. Annu. Rev. Genom. Hum. Genet. 2021, 22, 357–383. [Google Scholar] [CrossRef] [PubMed]
  9. Hohenlohe, P.A.; Bassham, S.; Etter, P.D.; Stiffler, N.; Johnson, E.A.; Cresko, W.A. Population genomics of parallel adaptation in threespine stickleback using sequenced RAD tags. PLoS Genet. 2010, 6, e1000862. [Google Scholar] [CrossRef] [Green Version]
  10. Jones, F.C.; Grabherr, M.G.; Chan, Y.F.; Russell, P.; Mauceli, E.; Johnson, J.; Swofford, R.; Pirun, M.; Zody, M.C.; White, S.; et al. The genomic basis of adaptive evolution in threespine sticklebacks. Nature 2012, 484, 55–61. [Google Scholar] [CrossRef]
  11. Terekhanova, N.V.; Logacheva, M.D.; Penin, A.A.; Neretina, T.V.; Barmintseva, A.E.; Bazykin, G.A.; Kondrashov, A.S.; Mugue, N.S. Fast evolution from precast bricks: Genomics of young freshwater populations of threespine stickleback Gasterosteus aculeatus. PLoS Genet. 2014, 10, e1004696. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Ferchaud, A.L.; Hansen, M.M. The impact of selection, gene flow and demographic history on heterogeneous genomic divergence: Three-spine sticklebacks in divergent environments. Mol. Ecol. 2016, 25, 238–259. [Google Scholar] [CrossRef] [PubMed]
  13. Terekhanova, N.V.; Barmintseva, A.E.; Kondrashov, A.S.; Bazykin, G.A.; Mugue, N.S. Architecture of Parallel Adaptation in Ten Lacustrine Threespine Stickleback Populations from the White Sea Area. Genome Biol. Evol. 2019, 11, 2605–2618. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Rastorguev, S.M.; Nedoluzhko, A.V.; Gruzdeva, N.M.; Boulygina, E.S.; Tsygankova, S.V.; Oshchepkov, D.Y.; Mazur, A.M.; Prokhortchouk, E.B.; Skryabin, K.G. Gene Expression in the Three-Spined Stickleback (Gasterosteus aculeatus) of Marine and Freshwater Ecotypes. Acta Nat. 2018, 10, 66–74. [Google Scholar] [CrossRef]
  15. Rastorguev, S.M.; Nedoluzhko, A.V.; Gruzdeva, N.M.; Boulygina, E.S.; Sharko, F.S.; Ibragimova, A.S.; Tsygankova, S.V.; Artemov, A.V.; Skryabin, K.G.; Prokhortchouk, E.B. Differential miRNA expression in the three-spined stickleback, response to environmental changes. Sci. Rep. 2017, 7, 18089. [Google Scholar] [CrossRef] [Green Version]
  16. Velotta, J.P.; McCormick, S.D.; Whitehead, A.; Durso, C.S.; Schultz, E.T. Repeated Genetic Targets of Natural Selection Underlying Adaptation of Fishes to Changing Salinity. Integr. Comp. Biol. 2022, 62, 357–375. [Google Scholar] [CrossRef]
  17. Barrio, M.A.; Lamichhaney, S.; Fan, G.; Rafati, N.; Pettersson, M.; Zhang, H.; Dainat, J.; Ekman, D.; Hoppner, M.; Jern, P.; et al. The genetic basis for ecological adaptation of the Atlantic herring revealed by genome sequencing. Elife 2016, 5, e12081. [Google Scholar] [CrossRef]
  18. Andriyashev, A.P.; Chernova, N.V. Annotated list of fishlike vertebrates and fish of the Arctic seas and adjacent waters. J. Ichthyol. 1995, 35, 81–123. [Google Scholar]
  19. Gorbachev, V.V.; Solovenchuk, L.L.; Chernoivanova, L.A. Intraspecies structure of the pacific herring Clupea pallasii Valenciennes, 1847 (Clupeidae: Clupeiformes) in the Sea of Japan and the southern Sea of Okhotsk inferred from the variability of mitochondrial DNA control. Russ. J. Mar. Biol. 2011, 37, 489–493. [Google Scholar] [CrossRef]
  20. Hay, D.E. Reproductive Biology of Pacific Herring (Clupea harengus pallasi). Can. J. Fish. Aquat. Sci. 1985, 42, s111–s126. [Google Scholar] [CrossRef]
  21. Orlova, S.Y.; Kurnosov, D.S.; Chikurova, E.A.; Shchepetov, D.M. Genetic Relationship between Lake and Marine Forms of Pacific Herring Clupea pallasii. J. Ichthyol. 2019, 59, 843–852. [Google Scholar] [CrossRef]
  22. Petrou, E.L.; Fuentes-Pardo, A.P.; Rogers, L.A.; Orobko, M.; Tarpey, C.; Jimenez-Hidalgo, I.; Moss, M.L.; Yang, D.; Pitcher, T.J.; Sandell, T.; et al. Functional genetic diversity in an exploited marine species and its relevance to fisheries management. Proc. R. Soc. B: Biol. Sci. 2021, 288, 20202398. [Google Scholar] [CrossRef] [PubMed]
  23. Shimizu, Y.; Takahashi, H.; Takayanagi, S. Population structure of the Pacific herring, Clupea pallasii, Around Hokkaido Island inferred on the basis of mitochondrial DNA sequences. Bull. Hokkaido Natl. Fish. Res. Inst. 2018, 94, 1–40. [Google Scholar]
  24. Orlov, A.M.; Moiseev, S.I. Pacific Herring in the Western Bering Sea Predatory Fish Diet. In Herring: Expectations for a New Millenium. Proceedings of the 18th Lowell Wakefield Fisheries Symposium, Anchorage, Alaska, USA, 23–26 February 2000; University of Alaska: Fairbanks, AK, USA, 2000; pp. 81–89. [Google Scholar]
  25. Naumenko, N.I. Biologiya i promysel morskikh sel’dei Dal’nego Vostoka (Biology and Harvesting of Sea Herring in the Far East); Kamchatsky Pechatny Dvor: Petropavlovsk-Kamchatsky, Russia, 2001; p. 330. [Google Scholar]
  26. Orlova, S.Y.; Rastorguev, S.; Bagno, T.; Kurnosov, D.; Nedoluzhko, A. Genetic structure of marine and lake forms of Pacific herring Clupea pallasii. PeerJ 2021, 9, e12444. [Google Scholar] [CrossRef] [PubMed]
  27. Stern, D.L. The genetic causes of convergent evolution. Nat. Rev. Genet. 2013, 14, 751–764. [Google Scholar] [CrossRef] [PubMed]
  28. Shuntov, V.P. Biology of Far-Eastern Seas of Russia; TINRO-Center: Vladivostok, Russia, 2001; Volume 1, p. 580. [Google Scholar]
  29. Ivshina, E.R. Dinamika chislennosti pokoleniy sakhalino-hokkaidskoy seldi v svyazi s temperaturoy vody. In Proceedings of the Problemy Okhrany i Ratsionalnogo Ispolzovaniya Bioresursov Kamchatki, Petropavlovsk-Kamchatsky, Russia; 2000; pp. 57–58. [Google Scholar]
  30. Zuenko, Y.I.; Rachkov, V.I. Climatic changes of temperature, salinity and nutrients in the Amur Bay of the Japan Sea. Izvestiya TINRO 2015, 183, 186–199. [Google Scholar] [CrossRef]
  31. Kubryakov, A.; Stanichny, S.; Zatsepin, A. River plume dynamics in the Kara Sea from altimetry-based lagrangian model, satellite salinity and chlorophyll data. Remote Sens. Environ. 2016, 176, 177–187. [Google Scholar] [CrossRef]
  32. Pavlov, V.K.; Pfirman, S.L. Hydrographic structure and variability of the Kara Sea: Implications for pollutant distribution. Deep. Sea Res. Part II Top. Stud. Oceanogr. 1995, 42, 1369–1390. [Google Scholar] [CrossRef]
  33. Luchin, V.A.; Kruts, A.A. Properties of cores of the water masses in the Okhotsk Sea. Izvestya TINRO 2016, 184, 204–218. [Google Scholar] [CrossRef]
  34. Nikitin, V.D.; Metlenkov, A.V.; Prokhorov, A.P.; Safronenko, V.A.; Lukyanova, N.S.; Galenko, K.G. Water life biology, resources status and condition of inhabitation in Sakhalin-Kuril region and adjoining water areas. Trans. Sakhalin Res. Inst. Fish. Oceanogr. 2013, 14, 96–128. [Google Scholar]
  35. Gorin, S.L.; Chebanova, V.V. Salinization-related transformation of hydrological regime and benthos in the Nerpichye and Kultuchnoe Lakes at the Kamchatka River estuary. In Proceedings of the Vladimir Ya. Levanidov’s Biennial Memorial Meetings, Vladivostok, Russia; 2011; pp. 121–128. [Google Scholar]
  36. Trofimov, I.K. Nekotorie problemy issledovaniya seldi Clupea pallasii, obitaustchey u beregov Kamchatki. In Proceedings of the Materialy III Nauchnoy Konferentsii Sokhranenie Bioraznoobraziya Kamchatki i Prilegaustchikh Morey, Petropavlovsk-Kamchatsky, Russia; 2002; pp. 243–244. [Google Scholar]
  37. Gorin, S.L.; Popryadukhin, A.A. Izuchenie Gidrologicheskogo Rezhima Ozera Bolshoy Vilyuy i Ozera Presnoe v Period Vipuska Molodi Kety s Vilyuiskogo Lososevogo Ryborazvodnogo Zavoda; VNIRO: Moscow, Russia, 2002; p. 61. [Google Scholar]
  38. Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data. 2010. Available online: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (accessed on 4 July 2022).
  39. Marcel, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 2011, 17, 10–12. [Google Scholar] [CrossRef]
  40. Rochette, N.C.; Catchen, J.M. Deriving genotypes from RAD-seq short-read data using Stacks. Nat. Protoc. 2017, 12, 2640–2659. [Google Scholar] [CrossRef] [PubMed]
  41. Langmead, B.; Salzberg, S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 2012, 9, 357–359. [Google Scholar] [CrossRef] [PubMed]
  42. Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 2011, 27, 2987–2993. [Google Scholar] [CrossRef] [Green Version]
  43. Jombart, T.; Ahmed, I. Adegenet 1.3-1: New tools for the analysis of genome-wide SNP data. Bioinformatics 2011, 27, 3070–3071. [Google Scholar] [CrossRef] [Green Version]
  44. Chang, C.C.; Chow, C.C.; Tellier, L.C.; Vattikuti, S.; Purcell, S.M.; Lee, J.J. Second-generation PLINK: Rising to the challenge of larger and richer datasets. Gigascience 2015, 4, 7. [Google Scholar] [CrossRef]
  45. Quinlan, A.R.; Hall, I.M. BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics 2010, 26, 841–842. [Google Scholar] [CrossRef] [Green Version]
  46. Ge, S.X.; Jung, D.; Yao, R. ShinyGO: A graphical gene-set enrichment tool for animals and plants. Bioinformatics 2020, 36, 2628–2629. [Google Scholar] [CrossRef]
  47. Tajima, F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 1989, 123, 585–595. [Google Scholar] [CrossRef]
  48. Cook, D.E.; Andersen, E.C. VCF-kit: Assorted utilities for the variant call format. Bioinformatics 2017, 33, 1581–1582. [Google Scholar] [CrossRef] [Green Version]
  49. Sodeland, M.; Jorde, P.E.; Lien, S.; Jentoft, S.; Berg, P.R.; Grove, H.; Kent, M.P.; Arnyasi, M.; Olsen, E.M.; Knutsen, H. "Islands of Divergence" in the Atlantic Cod Genome Represent Polymorphic Chromosomal Rearrangements. Genome Biol. Evol. 2016, 8, 1012–1022. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  50. Davey, J.W.; Blaxter, M.L. RADSeq: Next-generation population genetics. Brief Funct. Genom. 2010, 9, 416–423. [Google Scholar] [CrossRef] [PubMed]
  51. Hwang, P.P.; Lee, T.H.; Lin, L.Y. Ion regulation in fish gills: Recent progress in the cellular and molecular mechanisms. Am. J. Physiol. Regul. Integr. Comp. Physiol. 2011, 301, R28–R47. [Google Scholar] [CrossRef]
  52. Nakamura, T.; Klomp, J.; Pieretti, J.; Schneider, I.; Gehrke, A.R.; Shubin, N.H. Molecular mechanisms underlying the exceptional adaptations of batoid fins. Proc. Natl. Acad. Sci. USA 2015, 112, 15940–15945. [Google Scholar] [CrossRef] [Green Version]
  53. Galindo-Villegas, J.; Montalban-Arques, A.; Liarte, S.; de Oliveira, S.; Pardo-Pastor, C.; Rubio-Moscardo, F.; Meseguer, J.; Valverde, M.A.; Mulero, V. TRPV4-Mediated Detection of Hyposmotic Stress by Skin Keratinocytes Activates Developmental Immunity. J. Immunol. 2016, 196, 738–749. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Artemov, A.V.; Mugue, N.S.; Rastorguev, S.M.; Zhenilo, S.; Mazur, A.M.; Tsygankova, S.V.; Boulygina, E.S.; Kaplun, D.; Nedoluzhko, A.V.; Medvedeva, Y.A.; et al. Genome-Wide DNA Methylation Profiling Reveals Epigenetic Adaptation of Stickleback to Marine and Freshwater Conditions. Mol. Biol. Evol. 2017, 34, 2203–2213. [Google Scholar] [CrossRef] [Green Version]
  55. Rastorguev, S.M.; Nedoluzhko, A.V.; Sharko, F.S.; Boulygina, E.S.; Sokolov, A.S.; Gruzdeva, N.M.; Skryabin, K.G.; Prokhortchouk, E.B. Identification of novel microRNA genes in freshwater and marine ecotypes of the three-spined stickleback (Gasterosteus aculeatus). Mol. Ecol. Resour. 2016, 16, 1491–1498. [Google Scholar] [CrossRef]
  56. Shwe, A.; Krasnov, A.; Visnovska, T.; Ramberg, S.; Ostbye, T.K.; Andreassen, R. Differential Expression of miRNAs and Their Predicted Target Genes Indicates That Gene Expression in Atlantic Salmon Gill Is Post-Transcriptionally Regulated by miRNAs in the Parr-Smolt Transformation and Adaptation to Sea Water. Int. J. Mol. Sci. 2022, 23, 8831. [Google Scholar] [CrossRef]
  57. Shwe, A.; Ostbye, T.K.; Krasnov, A.; Ramberg, S.; Andreassen, R. Characterization of Differentially Expressed miRNAs and Their Predicted Target Transcripts during Smoltification and Adaptation to Seawater in Head Kidney of Atlantic Salmon. Genes 2020, 11, 1059. [Google Scholar] [CrossRef]
  58. Fang, B.; Kemppainen, P.; Momigliano, P.; Merila, J. Population Structure Limits Parallel Evolution in Sticklebacks. Mol. Biol. Evol. 2021, 38, 4205–4221. [Google Scholar] [CrossRef]
  59. Nedoluzhko, A.; Sharko, F.; Tsygankova, S.; Boulygina, E.; Ibragimova, A.; Teslyuk, A.; Galindo-Villegas, J.; Rastorguev, S. Genomic evidence supports the introgression between two sympatric stickleback species inhabiting the White Sea basin. Heliyon 2021, 7, e06160. [Google Scholar] [CrossRef] [PubMed]
  60. Nedoluzhko, A.; Sharko, F.; Tsygankova, S.; Boulygina, E.; Slobodova, N.; Teslyuk, A.; Galindo-Villegas, J.; Rastorguev, S. Intergeneric hybridization of two stickleback species leads to introgression of membrane-associated genes and invasive TE expansion. Front. Genet. 2022, 13, 863547. [Google Scholar] [CrossRef] [PubMed]
Genes 13 01856 g001 550
Figure 1. Map showing the sampling sites of lake (marked by blue circles) and marine (marked by red circles) Pacific herring (Clupea pallasii) populations. The Pacific herring specimen description is presented in Table 1.
Figure 1. Map showing the sampling sites of lake (marked by blue circles) and marine (marked by red circles) Pacific herring (Clupea pallasii) populations. The Pacific herring specimen description is presented in Table 1.
Genes 13 01856 g001
Genes 13 01856 g002 550
Figure 2. Bar plots represent the number of SNP loci identified for Pacific herring’s marine and lake ecological forms. (a) Barplot shows the number of discriminating loci for Pacific herring’s marine and lake ecological forms. (b) The barplot shows the total number of SNP loci for each chromosome. The X-axis represents the number of SNP loci. The Y-axis represents chromosome id.
Figure 2. Bar plots represent the number of SNP loci identified for Pacific herring’s marine and lake ecological forms. (a) Barplot shows the number of discriminating loci for Pacific herring’s marine and lake ecological forms. (b) The barplot shows the total number of SNP loci for each chromosome. The X-axis represents the number of SNP loci. The Y-axis represents chromosome id.
Genes 13 01856 g002
Genes 13 01856 g003 550
Figure 3. Density plot of Pacific herring specimens along the first discriminant function from Discriminant Analysis of Principal Components (DAPC). Two ecological forms of Pacific herring are shown using different colors. The marine ecological form is in (red), and the lake ecological form (blue).
Figure 3. Density plot of Pacific herring specimens along the first discriminant function from Discriminant Analysis of Principal Components (DAPC). Two ecological forms of Pacific herring are shown using different colors. The marine ecological form is in (red), and the lake ecological form (blue).
Genes 13 01856 g003
Genes 13 01856 g004 550
Figure 4. Herring chromosome plot shows the location of discriminating SNP loci (red dots) between marine and lake ecological forms. Genomic divergence islands on chromosomes 6, 7, 12, and 20 are marked by purple lines. The X-axis shows the position on the chromosome in Mbp. The Y-axis represents chromosomes and their numbers.
Figure 4. Herring chromosome plot shows the location of discriminating SNP loci (red dots) between marine and lake ecological forms. Genomic divergence islands on chromosomes 6, 7, 12, and 20 are marked by purple lines. The X-axis shows the position on the chromosome in Mbp. The Y-axis represents chromosomes and their numbers.
Genes 13 01856 g004
Table
Table 1. The list of Pacific herring genomic datasets that were used in this study.
Table 1. The list of Pacific herring genomic datasets that were used in this study.
Ecological FormPopulation Name
(Abbreviation)		NCBI Accession Number
Marine
(n = 39)		Bering Sea, Gulf of Anadyr
(Scva)		SRR14076746; SRR14076757; SRR14076758
Sea of Japan, Aleksandrov Bay
(Salex)		SRR14076740; SRR14076741; SRR14076742; SRR14076743; SRR14076744
Sea of Japan, Amur Bay
(Samur)		SRR14076734; SRR14076736; SRR14076737; SRR14076738; SRR14076739
Kara Sea
(Scvk)		SRR14076713; SRR14076724; SRR14076735
Shelikhov Gulf, Sea of Okhotsk
(Seve)		SRR14076729; SRR14076730; SRR14076731; SRR14076732; SRR14076733
Bering Sea, seaward border of the continental shelf
(Sk12)		SRR14076723; SRR14076725; SRR14076726; SRR14076727; SRR14076728
Bering Sea, Karagin Bay
(Skrg)		SRR14076718; SRR14076719; SRR14076720; SRR14076721; SRR14076722
Kuril Islands, Pacific Ocean
(Sukur)		SRR14076712; SRR14076714; SRR14076715; SRR14076716; SRR14076717
Sea of Okhotsk, Tugur Bay
(Sclu7)		SRR14076709; SRR14076710; SRR14076711
Lake
(n = 15)		Ainskoe Lake, Sakhalin Island
(Fain)		SRR14076705; SRR14076706; SRR14076707; SRR14076708; SRR14076756
Nerpiche Lake, Kamchatka Peninsula (Fnerp)SRR14076751; SRR14076752; SRR14076753; SRR14076754; SRR14076755
Bolshoy Vilyuy Lake, Kamchatka Peninsula
(Fvil)		SRR14076745; SRR14076747; SRR14076748; SRR14076749; SRR14076750
Table
Table 2. Ecological information (average salinity and temperature) in the sampling places across the Pacific Ocean during late spring–early summer.
Table 2. Ecological information (average salinity and temperature) in the sampling places across the Pacific Ocean during late spring–early summer.
Ecological FormPopulation NameAbbreviationSalinity(‰)Temperature(°C)Reference
Marine(n = 39)Bering Sea, Gulf of AnadyrScva32.0–34.07.0–10.0[28]
Sea of Japan, Aleksandrov BaySalex≈29.513.0–17.0[29]
Sea of Japan, Amur BaySamur23.0–31.0 14.0–22.0[30]
Kara SeaScvk20.0–27.0>10.0–12.0[31,32]
Shelikhov Gulf, Sea of OkhotskSeve22.0–33.410.0–18.0[33]
Bering Sea, seaward border of the continental shelfSk1232.0–34.07.0–10.0[28]
Bering Sea, Karagin BaySkrg32.0–34.07.0–10.0[28]
Kuril Islands, Pacific OceanSukur33.2–33. 46.0–12.0[33]
Sea of Okhotsk, Tugur BaySclu722.0–33.410.0–18.0[33]
Lake (n = 15)Ainskoe Lake, Sakhalin IslandFain≈2.515.0–20.0[34]
Nerpiche Lake, Kamchatka PeninsulaFnerp≈4.0≈10.0[35,36]
Bolshoy Vilyuy Lake, Kamchatka PeninsulaFvil≈ 1.711.0–17.0[37]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Nedoluzhko, A.; Orlova, S.Y.; Kurnosov, D.S.; Orlov, A.M.; Galindo-Villegas, J.; Rastorguev, S.M. Genomic Signatures of Freshwater Adaptation in Pacific Herring (Clupea pallasii). Genes 2022, 13, 1856. https://doi.org/10.3390/genes13101856

AMA Style

Nedoluzhko A, Orlova SY, Kurnosov DS, Orlov AM, Galindo-Villegas J, Rastorguev SM. Genomic Signatures of Freshwater Adaptation in Pacific Herring (Clupea pallasii). Genes. 2022; 13(10):1856. https://doi.org/10.3390/genes13101856

Chicago/Turabian Style

Nedoluzhko, Artem, Svetlana Yu. Orlova, Denis S. Kurnosov, Alexei M. Orlov, Jorge Galindo-Villegas, and Sergey M. Rastorguev. 2022. "Genomic Signatures of Freshwater Adaptation in Pacific Herring (Clupea pallasii)" Genes 13, no. 10: 1856. https://doi.org/10.3390/genes13101856

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop