Unheralded high MHC class II polymorphism in the abundant Atlantic herring resolved by long-read sequencing

Minal Jamsandekar; Fahime Mohamadnejad Sangdehi; Florian Berg; Michael F Criscitiello; Brian W Davis; Mårten Larsson; Jingyi Li; Mats E Pettersson; Leif Andersson

doi:10.1126/sciadv.aea2862

. 2026 Jul 24;12(30):eaea2862. doi: 10.1126/sciadv.aea2862

Unheralded high MHC class II polymorphism in the abundant Atlantic herring resolved by long-read sequencing

Minal Jamsandekar ^1,^*,^†, Fahime Mohamadnejad Sangdehi ^2,^3,^†, Florian Berg ⁴, Michael F Criscitiello ⁵, Brian W Davis ⁵, Mårten Larsson ⁶, Jingyi Li ¹, Mats E Pettersson ², Leif Andersson ^1,^2,^3,^*

PMCID: PMC13398488 PMID: 42497266

Abstract

Although major histocompatibility complex (MHC) genes are the most polymorphic genes in vertebrates, we still lack a clear understanding about the extent of polymorphism and selection mechanisms dominantly driving it. Here, we use 29 haploid genome assemblies resolved by long-read sequencing to characterize MHC II genes in Atlantic herring, a pelagic fish and one of the most abundant vertebrates on Earth. We found nine MHC II loci in total distributed on four chromosomes, three of which were extremely polymorphic because of high sequence diversity, in particular at peptide binding sites, along with high copy number variation. This study reveals that the herring MHC class II genes are among the most, if not the most, polymorphic so far described in vertebrates.

MHC class II sequence diversity in Atlantic herring exceeds the records in other vertebrate species including humans.

INTRODUCTION

The major histocompatibility complex (MHC) is present in all jawed vertebrates (1). MHC genes are key components of the vertebrate immune system and encode MHC class I and class II molecules that present peptides to other cells in the immune system. MHC class I and class II genes show extremely high levels of genetic polymorphism in most vertebrates (2). Characteristic features of classical MHC polymorphisms are (i) a large number of alleles at multiple loci (3), (ii) alleles that differ by many nonsynonymous substitutions, and (iii) deep allele divergences that may predate speciation events, so called trans-species polymorphisms (2). This extreme diversity allows the immune system to recognize peptides from a wide range of pathogens and therefore enhance disease resistance (4–7). There is evidence that this extensive polymorphism is maintained by balancing selection mechanisms, which include heterozygote advantage, negative frequency-dependent selection, and fluctuating selection (8). The consensus view is that pathogen-mediated selection is the major mechanism for the maintenance of MHC polymorphisms as allelic variants differ in their capacity to present peptides to the immune system (1, 2, 8–10).

Since the first discovery of MHC genes in teleost fish in 1990 (11), there has been a sustained effort to understand their genomic organization and polymorphism (12–18). Teleosts, which represent approximately half of all extant vertebrate species (19), provide an exceptional model for studying the evolution of MHC genes across the broader jawed-vertebrate lineage. A major insight from early studies was that unlike in mammals, classical MHC class I and class II loci are not physically linked (20–22). The classical MHC class I genes are typically located within a conserved genomic region that also contains non-MHC genes involved in the antigen processing and peptide-loading pathway, whereas MHC class II genes show considerable variation in genomic location among species and lack conserved synteny even among closely related taxa (23). Another notable finding is that while MHC class I genes have been identified in all teleost species examined to date, MHC class II genes have been lost independently in certain lineages, such as gadiform fishes (24) and pipefish (25). These observations highlight the evolutionary plasticity of the MHC class II system and its diversification across vertebrates.

Similar to tetrapods, MHC class II genes in teleosts are divided into classical and nonclassical types on the basis of their degree of polymorphism, presence of a functional peptide-binding region, and expression pattern (23); only the classical genes are highly polymorphic. In humans, the classical MHC class II genes include HLA-DP, HLA-DQ, and HLA-DR, which play key roles in antigen presentation to CD4⁺ T cells (26), whereas the nonclassical molecules include HLA-DM and HLA-DO, which do not present peptides to T cell receptors (TCRs), are involved in regulating peptide loading of classical MHC class II molecules (27, 28). HLA-DO is a mammalian-specific lineage (29), while HLA-DM is an ancestral lineage conserved throughout tetrapods and Dipnoi fish (30). In teleosts, the classical MHC class II genes are usually denoted the DA lineage, which is present in all species having MHC class II and appears to perform functions analogous to mammalian classical MHC class II (31–33). Although nonclassical class II genes in teleosts show distinct clustering separate from the classical genes, the distinction between different nonclassical lineages is not clear because of lack of consistent clustering in phylogeny (31). The nonclassical genes have been denoted as DE and DB lineages (32, 33), where DE represents a true lineage but is present only in a subset of teleosts, while DB has been used as the designation for nonclassical class II genes in different teleost species, although orthologous relationships have not been established. Notably, teleosts appear to lack the DM-mediated peptide-loading system found in tetrapods, indicating that teleost nonclassical MHC class II genes (DB and DE) are not orthologous to tetrapod DM, and their precise functions remain unclear (23, 32).

The present study characterizes MHC class II genes in Atlantic herring (Clupea harengus) at the locus and haplotype level and tests the prediction that this species will show high MHC diversity if pathogen-mediated selection is the major mechanism causing allele diversity. Atlantic herring occur in the North Atlantic Ocean and the Baltic Sea. They are one of the most abundant vertebrates on Earth with a census population size of about 10¹² individuals and an estimated effective population size of 4.4 × 10⁵ (34); a single school may include a billion individuals. There are two major reasons for expecting unusually high levels of MHC polymorphism in herring. First, their large effective population size and high fecundity leave a large room for natural selection to operate. The loss of genetic diversity resulting from drift is minute (35). Second, the large schools of Atlantic herring, and their abundance, make them an attractive target for pathogens, promoting pathogen-driven selection.

Here, we have used PacBio long-read sequencing of 14 individuals to robustly characterize the genetic diversity at MHC class II loci of Atlantic herring. We report that this species has one of the most, if not the most, polymorphic MHC class II allele sets so far described. At the most, polymorphic MHC class II loci in essentially all individuals are heterozygous, and we detected up to 28 alleles per locus among 14 individuals. Results support the notion that pathogen-mediated selection is the primary driver for extreme polymorphism of MHC genes in vertebrates.

RESULTS

Genome organization and copy number variation of MHC II genes

PacBio long-read sequencing was carried out using six Atlantic herring sampled in the Celtic Sea, two Atlantic herring from the Norwegian Sea, and six Baltic herring, considered a distinct subspecies, from the Baltic Sea. Haplotype-phased genome assemblies were constructed using hifiasm for each individual (36), resulting in 28 high-quality PacBio haplotype assemblies from 14 individuals, all of which, together with the reference assembly, were used in the downstream analyses (table S1). Manual annotation of MHC class II genes in the current herring reference assembly (37), representing an Atlantic herring collected off the Norwegian coast, resulted in the detection of 9 α (including one pseudogene) and 10 β genes at six different loci, designated 1 to 6, distributed across three chromosomes [chromosome 5 (Chr5), chromosome 7, and chromosome 13]. Each of these loci contained one or more closely linked α-β gene pairs. Applying this to the 28 new haplotype assemblies (table S1) showed that almost all had a larger number of MHC class II genes than present in the reference assembly, suggesting that some MHC regions have been collapsed during the construction of the reference assembly but are more resolved in the new haplotype assemblies. In addition to chromosomes 5, 7, and 13, MHC class II genes were also present on chromosome 8 in some new assemblies. Thus, the total number of MHC class II loci increased from 6 in the reference assembly to 9, of which loci 1 to 6 were present in all assemblies, while loci 7 to 9 were present in only a few (Fig. 1A and fig. S1). All loci showed genes in α-β pairs, which are essential to form a functional MHC II molecule. The exception to this was DB genes at locus 1, which was present as one or two β chain genes but no α gene (fig. S2 and table S2).

Genetic distance trees for MHC class II α (Fig. 1B) and β (Fig. 1C) genes present in the reference assembly showed that both α and β genes formed two distinct clades that we denoted DA and DB, respectively. The genes in these clades showed a contrasting expression pattern in eight tissues examined, where DA genes were highly expressed, especially in the immune-related tissues such as the spleen and intestine, whereas DB genes showed low expression in all tissues (fig. S3). As sequences from the third teleost class II lineage, DE, form a separate clade (32), we did not find any sequence belonging to the DE lineage in Atlantic herring. Among the six common loci (1–6), only loci 3 and 6 exclusively contained DB genes, and locus 5 exclusively contained DA genes. Nonfunctional genes, indicated by null alleles or pseudogenes, were distributed across all loci (table S2). DAA (α) and DAB (β) genes as well as DBA (α) and DBB (β) genes were present as α-β tandem gene pairs (Fig. 2), which is one typical organization of MHC class II α-β gene pairs at a given locus in many species (38). Our proposed nomenclature of herring class II genes follows the recommended nomenclature for nonmodel organisms (39). For instance, DAA1.1 is a DA α gene belonging to locus 1 with the last digit (.1) used to handle the situation where multiple copies of α or β genes were present at a given locus (Fig. 1D). A maximum likelihood phylogeny using full-length amino acid sequences of MHC class II genes from other fish and tetrapods did not establish a clear orthologous relationship between herring genes and MHC class II genes outside the clupeid lineage (fig. S4), reflecting the deep splits in the teleost phylogeny and/or concerted evolution of class II genes in different lineages of fish. Clupeids share a common ancestor that lived more than 100 million years ago (timetree.org). The gene composition at the nine class II loci is summarized in Table 1 and showed in detail in table S2. An important component of the genetic diversity is the extensive copy number variation, particularly at loci 1, 2, and 4. As an example, the size of locus 1 varies from about 70 kb to more than 300 kb among haplotypes because of copy number variation of genes and variable length of noncoding sequences (Fig. 2 and fig. S2). It also has the most diverse set of gene copies where almost every copy in our dataset is a unique sequence (table S3) and all three populations studied show a similar number of genes and similar degree of diversity (fig. S5). For this reason, it is difficult to define MHC loci in Atlantic herring as harboring allelic series for single MHC class II genes. We found it more appropriate to define each locus as a cluster of closely linked MHC class II genes at a given genomic location, as illustrated in Fig. 2. Hence, we labeled genes according to their order of occurrence on the chromosome. For example, [DAA1.1, DAB1.1], [DAA1.2, DAB1.2], [DAA1.3, DAB1.3], and [DBA1.1, DBB1.1] denote three DA and one DB gene pairs for α and β chain heterodimers at locus 1, respectively.

Fig. 2. — Gene organization on the two haplotypes (hap1 and hap2) from five individual fish. The designation *DBB1.1Null* indicates that this gene encodes a mutation that disrupts the coding sequence.

Table 1. Number of MHC class II genes at each locus among haplotypes based on 28 PacBio-based haplotype assemblies from 14 individual Atlantic and Baltic herring.

The genomic intervals for loci 1 to 6 correspond to the reference assembly. As loci 7 to 9 are absent in the reference assembly, genomic intervals are from PacBio assemblies of BS4_hap2, CS5_hap1, and BS2_hap1, respectively. Chr, chromosome.

		Number of genes
Locus	Chr: interval (Mb)	DAA	DAB	DBA	DBB
1	7: 2.73–2.80	2-3	2-3	0	0-2
2	7: 3.52–3.58	1-3	1-3	1-2	1-2
3	7: 19.46–19.47	0	0	1	1
4	7: 28.81–28.84	1-2	1-2	0-2	0-2
5	13: 16.02–16.07	1	2	0	0
6	5: 26.53–26.54	0	0	1	1
7	7: 0.62–0.63	0-1	0-1	0	0
8	7: 30.30–30.37	0-2	0-2	0	0
9	8: 31.93–31.94	0-1	0-1	0	0

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A characteristic of MHC class II genes in Atlantic herring is that for highly polymorphic genes, it is not possible to assign a gene to a specific locus only on the basis of sequence identities because there is an overlap in intra- and interlocus sequence identities (Fig. 3 and figs. S5 and S6). Therefore, the gene designations used throughout this paper are a combination of sequence identity and genomic location. High interlocus identities only occurred for genes located on the same chromosomes, implying that intrachromosomal recombination may have contributed to sequence exchange between genes. In summary, our analyses using 28 assemblies identified nine MHC class II loci present on four chromosomes and two different clades of class II genes (DA and DB) and that copy number variation is an important component of the observed genetic diversity.

Fig. 3. — Heatmap and dendrogram showing hierarchical clustering of all DAB amino acid (AA) sequences in the upper panel and coding nucleotide sequences (CDS) in the lower panel, respectively, across the genome. Heatmap gradients correspond to pairwise percent differences. Source loci are color coded in the annotation bar.

Extensive sequence polymorphism of MHC class II genes

Analysis of nucleotide diversity (π) per exon of herring MHC class II genes revealed a clear pattern (Fig. 4, A and B). Most of DA genes are highly polymorphic, whereas none of the DB genes are highly polymorphic, implying that the members of these two lineages have different functions. Such differences are also supported by their opposing expression pattern (fig. S3), indicating that DA functionally corresponds to the classical class II genes, while DB may represent nonclassical genes. Such distinction is also observed in other species (23). We identified up to 29 different alleles among 29 assembled haplotypes at the most polymorphic DA loci (Fig. 4, A and B). The functional significance of the polymorphism is evident by the fact that exon 2, encoding the peptide binding site in MHC class II molecules (40), is by far the most polymorphic exon. The genome-wide average π in the herring genome is ∼0.3%, whereas π for exon 2 in the most polymorphic DA genes is in the range of 10 to 20%. However, not all DA genes are highly polymorphic; the DAA and DAB genes at loci 5 and 8 showed no or limited polymorphism in our sample of haplotypes (Fig. 4, A and B).

We next calculated the rate of nonsynonymous (dN) and synonymous (dS) substitutions for exon 2 and exon 3 sequences of DAA and DAB genes (Fig. 4, C and D, and table S4); a dN/dS ratio >1.0 provides evidence for positive or balancing selection, whereas a dN/dS ratio <1.0 is consistent with purifying selection. This analysis demonstrated that herring DAA and DAB genes are under strong balancing selection. This is indicated by dN/dS ratios higher than 1.0 for exon 2, while purifying selection (dN/dS << 1.0) dominates for exon 3 sequences. In the case of locus 5, only β genes showed high dN/dS, but α genes did not. This higher dN/dS is due to very low dS (table S4), indicating that locus 5 might be a young locus. Positively selected sites classified by PAML (phylogenetic analysis by maximum likelihood) analysis (41) are tabulated in data S1 and shown in the amino acid alignment in fig. S7.

We noted that dN/dS values for the most polymorphic genes in herring are much higher than those for the most polymorphic human MHC class II genes (dN/dS = ∼1.0) (42). To understand whether the higher dN/dS values are due to higher dN or lower dS, we compared the herring data with corresponding human dN and dS estimates (42). The most notable difference is that most herring genes have a much higher dN than their human counterparts (Fig. 4, E and F, and table S4). For class II β chain genes, there is also on average higher dS in herring. Thus, herring alleles tend to have a deeper ancestry (higher dS), and natural selection has had a more prominent effect on polymorphism in herring (higher dN/dS).

Sequence polymorphism is highly enriched at predicted peptide binding residues

To further characterize the DA polymorphism across exon 2 and how it relates to the peptide binding residues (PBRs), we first predicted the PBRs in herring MHC class II genes using known PBRs in humans (43) and chickens (Fig. 5, A and B, and fig. S8) (44). We then calculated normalized Shannon entropy values per individual amino acid residue, where higher values indicate a greater degree of polymorphism. Shannon entropy peaks were observed at approximately every three to four residues along the α helix, suggesting that these residues may be positioned on the same side of the helix and potentially correspond to the PBRs (Fig. 5, A and B). This pattern aligns with the structure of α helices, where every n + 3.6th residue is oriented toward the same side of the helix (45), in this case possibly facing the peptide-binding pocket. Notably, highly polymorphic residues in herring largely correspond to human and chicken PBR positions (Fig. 5, A and B, and fig. S8). Together, these results suggest that sequence polymorphism in herring MHC class II is concentrated at functionally important sites involved in peptide binding.

Fig. 5. — (A and B) Amino acid alignment for selected Atlantic herring, human (DRA01:01:01:01 and DRB101:01:01:01), and chicken (AY357253.1 and DQ008584.2) sequences and bar plot of normalized Shannon entropy values for each amino acid site in the α1 domain (A) and β1 domain (B) of all DA genes. Amino acid positions highlighted in green correspond to teleost-specific conserved cysteine residues in *DAA* genes and fully conserved cysteine residues in *DAB* genes that form disulfide bonds. Highly conserved residues involved in hydrogen bonding with the backbone of antigenic peptide are shaded in light red. Residues highlighted in yellow mark a four-amino-acid insertion (CTDC) in herring *DAA* sequences, which forms an additional disulfide bridge. Dashes represent gaps introduced for alignment. The black and red squares below the plots represent human (43) and chicken PBRs (44), respectively. Shannon entropy values are colored with red-white-blue gradient, indicating the highest to lowest diversity. The scale below the bar plot indicates amino acid positions beginning at the first residue of exon 2 in the full-length protein. The black and red squares above the bar plot represent human (43) and chicken PBRs (44), respectively. Arrows and helices below the bar plot correspond to the protein secondary structure β sheet and α helix, respectively. (C) Predicted quaternary structure of an MHC class II protein modeled with AlphaFold3 using protein sequences encoded by *CS4_hap1_DA2.2* as template. The color gradient from red-white-blue corresponds to the normalized Shannon entropy values for locus 2 genes.

We next built a protein quaternary structure model using locus 2 gene sequences (figs. S9 and S10). The normalized Shannon entropy values revealed a substantially strong association, where the most polymorphic residues are oriented toward the presumed peptide-binding pocket formed by the flanking α helices and β sheet floor of the MHC class II α and β chains (Fig. 5C and movie S1). The good agreement between this predicted structure and the corresponding structure for human class II molecules (43) is notable given the rather low sequence conservation (Fig. 5, A and B).

Nonrandom combinations of gene copies enhance diversity at the haplotype level

We performed inter- and intralocus comparisons of the amino acid sequences of MHC class II alleles to understand how the polymorphism is distributed across loci. In the interlocus comparison of DA lineage sequences, loci 5, 7, and 9 showed strict locus-specific clustering of alleles, as opposed to loci 1, 2, 4, and 8, indicating that the latter set of loci shared alleles with each other (Fig. 3 and figs. S5 and S6). We further made intralocus comparisons of polymorphic DA lineage sequences and found a distinct pattern for loci 2 and 4 with groups of highly similar sequences (Fig. 3 and fig. S11). To confirm these findings on the basis of amino acid differences, we constructed tanglegrams comparing the hierarchical clustering of α and β sequences (Fig. 6A and fig. S12A). These allelic groups were named as a combination of a number (locus) and a letter. In this way, 51 of 54 α and β sequences from locus 2 were clustered into five distinct groups, 2a to 2e (Fig. 6 and fig. S11B). Groups 2f to 2h contained only one sequence each, which could be due to the small sample size in this study. Similarly for locus 4, 32 sequences could be grouped into 4a and 4b, while 4c contained a single sequence (figs. S11C and S12). Similar groupings were also observed with maximum likelihood trees (figs. S13 and S14). The MHC class II DA genes at locus 1 did not show such clear groupings of DAA and DAB alleles (fig. S11A) despite being as polymorphic as locus 2 and 4 genes (Fig. 4, A and B). DAA2 sequences from allelic group 2b had a 12–bp (base pair) in-frame insertion in exon 2 inserted after residue 33, encoding a tetrapeptide CTDC (fig. S15). The protein model with this insertion was predicted to be more stable (ΔΔG = −2.84 kcal/mol) than alleles lacking the insertion, suggesting that the additional disulfide bond likely contributes to structural stabilization (figs. S16 to S18).

Fig. 6. — (A) Tanglegram illustrating the association between *DAA* and *DAB* alleles in locus 2 haplotypes. Each dendrogram represents hierarchical clustering on the basis of pairwise percent amino acid differences. Correspondence between paired α and β alleles is indicated by connecting lines. The entanglement measure, quantified using the dendextend R package, is presented below the plot. Sequences are colored according to their respective allelic group. α and β sequences are colored in lighter and darker shades of the same color, respectively. The color code for the allelic groups is provided at the bottom right. (B) Seventeen haplotypes inferred from the arrangement of α-β gene pairs on the basis of their allelic groupings. Numbers in parentheses reflect the number of observations per haplotype. Gene and intergenic region lengths are shown at a fixed scale. *DBA* and *DBB* genes are displayed in light and dark gray, respectively. Nonfunctional alleles are represented by open arrows. Gene names are indicated within arrows. N, null allele; Ψ, pseudogene.

To assess whether alleles in different groups are randomly distributed across haplotypes or follow a pattern, we examined the allele groups present on each of the assembled haplotypes. This led to three interesting observations. First, each closely linked α-β gene pair in the 28 haplotype-based assemblies belonged to matching α-β allelic groups, suggesting a strong nonrandom association and coevolution of α-β gene pairs at these loci (Fig. 6A and fig. S12A). For instance, DAA2.1 and DAB2.1 on CS2_hap1 belonged to the corresponding α and β allelic group 2a. Second, each haplotype carried α-β gene pairs from different allelic groups, thus maximizing the haplotype diversity (Fig. 6B and figs. S11, B and C, and S12). As an example, the three α-β gene pairs (DA2.1, DA2.2, and DA2.3) in CS2_hap1 correspond to sequence groups 2a, 2b, and 2e, respectively. This second observation was supported by the fact that intrahaplotype nonsynonymous substitutions (dN) were higher than expected for a random arrangement of gene sequences (fig. S19). Third, specific combinations of allelic groups dominated at loci 2 and 4. In the case of locus 2, 15 haplotypes harbored gene pairs from groups 2a, 2b, and 2c, while 10 haplotypes harbored gene pairs from 2e, 2d, and 2c (Fig. 6B). Most of the locus 4 haplotypes contained only a single gene pair, of which 15 belonged to group 4a and 8 belonged to group 4b (fig. S12B). This suggests that MHC haplotypes in herring often consist of a specific sequence of allelic groups, which is 2a-2b-2e and 2c-2d-2e in the case of locus 2, and 4a, 4b, and 4a-4c in the case of locus 4 (Fig. 6B; figs. S12, S20, and S21; and Supplementary Text).

One of the most unexpected findings was that several of the nonpolymorphic DB genes, as well as several of the highly polymorphic DA, contained mutations that are expected to disrupt the function, and these were classified as null alleles (table S5). Moreover, null alleles within the same allelic group in locus 2 carried the same inactivating mutation.

DISCUSSION

This study has revealed that MHC class II genes in Atlantic herring are extremely polymorphic. The most likely explanations for this extreme polymorphism are pathogen-driven selection and the enormous population size of the species that makes natural selection highly effective. This is a study exploring MHC polymorphism in natural populations using long-read sequencing, an approach that facilitated the accurate determination of the number of genes per locus and of complete gene sequences. A previous attempt to take advantage of the extensive amount of whole-genome, short-read sequencing available in this species (35, 37, 46) failed, because it was not possible to generate accurate locus-specific data even when using a dataset comprising a high-coverage sequence from a full-sib pedigree generated to estimate the mutation rate in the Atlantic herring (34). The extensive sequence diversity, copy number variation, and the overlap between intra- and interlocus sequence identities revealed in the present study explain why this previous attempt failed.

The extensive polymorphism and expression pattern strongly suggest that DA constitutes classical class II genes, while DB, which shows low expression and low polymorphism, constitutes nonclassical genes (Fig. 4 and fig. S3). DA and DB also form distinct clades in the phylogeny (Fig. 1, B and C). The low expression of DB genes does not imply that these are nonfunctional, as they may be expressed in tissues or developmental stages not included in the dataset used in this study. The presence of an open reading frame (ORF) and sequence conservation compared with other teleosts support this notion.

The human MHC class II region (HLA) is by far the most well studied in any vertebrate and includes some of the most polymorphic genes in the human genome (www.ebi.ac.uk/ipd/imgt/hla/). In this study, we explored how much diversity is present in a species that has population size and fecundity 1000-fold greater than that of humans and with a much larger long-term effective population size the last million years. As expected, on the basis of these differences, the class II diversity in Atlantic herring noticeably exceeds that of humans. In humans, there are four highly polymorphic class II genes—HLA-DPB, HLA-DQA, HLA-DQB, and HLA-DRB (47, 48)—whereas the number of highly polymorphic genes is clearly higher in herring (Fig. 4). Furthermore, copy number variation occurs in humans but is more prevalent in herring, adding an additional layer of diversity. Sequence polymorphisms in herring, like in humans and other species, occur primarily in exon 2, encoding the peptide binding domain (Fig. 4, A and B). Furthermore, footprints of natural selection intensity are evidently more prominent in herring, where the dN/dS ratios for the most polymorphic genes are ∼2.0, twice as high compared with the corresponding human genes (Fig. 4, E and F) (49).

More studies using long-read sequencing are required to enhance our understanding of MHC diversity in natural populations. Complex MHC architecture is also a feature of other species as evident in passerine birds as a result of extensive duplication and rearrangement (50). Previous reports based on short-read data have usually underestimated the number of polymorphic class II genes. This is clearly the case for a previous study in Atlantic herring, which found only one class II gene (51). Traditional approaches based on polymerase chain reaction, cloning, and targeted Illumina sequencing have been important in the past to shed light on MHC diversity, but those studies are error-prone and lead to misinterpretations (52, 53). In these studies, the number of distinct loci is often based on the maximum number of alleles observed in one individual and does not consider copy number variation and paralogs, leading to inaccurate and mostly inflated estimates of the number of loci. The report of the largest number of polymorphic loci in a teleost species is in Old world cichlids from five Oreochromis niloticus families (at least 17 MHC class IIB loci) (54). Given that this study lacks information on the gene copy number and the presence of pseudogenes, the estimates are uncertain. None of the previous studies on teleosts have an accurate estimate of the number of MHC class II loci (table S6) (28–39). We also note that the results for Atlantic herring are in stark contrast to the situation in the cod family (24) and pipefish (25), in which the MHC class II genes have been lost.

The extensive class II polymorphism in Atlantic herring provides the strongest support for the theory that MHC diversity is maintained by pathogen-driven natural selection (2, 8). The herring provides an ideal model for Hardy-Weinberg assumptions of random mating and infinite population size as is likely to be found for a vertebrate. A consequence of the large population size and high fecundity is that genetic drift is close to nonexistent and that the frequencies of alleles affecting fitness are determined by natural selection (55). The schooling behavior of herring and its abundance make it an attractive target for pathogens. It has been reported that MHC class I diversity in humans is positively correlated with pathogen richness among populations (56). The Atlantic herring pathogens have not been thoroughly characterized, but two pathogens, viral hemorrhagic septicemia virus (VHSV) and a fungal-like protist, Ichthyophonus hoferi, have been identified as major threats to Atlantic herring populations (57). VHSV genotype Ib is notably prevalent among Norwegian spring-spawning herring (58). Infection by I. hoferi has contributed to mass mortality of Atlantic herring from both sides of the North Atlantic Ocean (59, 60) and steep declines in the landings in the Gulf of St. Lawrence (61). A study comparing MHC genotypes among affected and nonaffected individuals as regard to these major pathogens would be of considerable interest, but the extremely high polymorphism implies that such studies need to be very large to give a reasonable power to document associations.

It has been hypothesized that the MHC diversity at the individual level is restricted to prevent TCR depletion, as too high diversity of MHC molecules may cause too many thymocytes to fail to be positively selected during T cell maturation in the thymus (62, 63). Although how this intricate balance between MHC and TCR loci occurs is still debated, a recent study in bank voles suggests that an intermediate level of MHC diversity is required to maintain an optimal number of TCRs (64, 65). It is possible that herring MHC diversity is at that balancing point, given that we found both considerable copy number variation (Fig. 2, Table 1, and fig. S3) and higher within-haplotype diversity than expected by random chance (fig. S19), but we also paradoxically found null alleles at many of the highly polymorphic genes (table S5), causing a reduction of the diversity of antigen-presenting molecules.

We were able to categorize almost all alleles on loci 2 and 4 into a handful of major groups using sequence similarity matrices (Fig. 6 and figs. S11, B and C, and S12). These could be referred to as supertypes, a concept developed for human MHC genes to group alleles sharing similar physiochemical properties at PBRs (66). We noted strong nonrandom association of MHC class II α-β gene pairs at these loci, which is a classical feature when both α and β chain molecules that form a dimer are both highly polymorphic; α and β chains together form the peptide binding site, and certain combinations are more fit than the others. Furthermore, the α-β gene pairs in each haploid assembly carried different sets of allelic groups (supertypes) at loci 2 and 4. Such an arrangement maximizes the diversity at the haplotype level, implying a broad peptide binding repertoire per haplotype (fig. S19). This supports the premise that evolution favors MHC haplotypes with different sets of supertypes and addresses the questions of why certain allelic variants are maintained together in haplotypes (67). It also supports the theoretical premise that supertypes are maintained as balanced selection under a Red Queen dynamic model, which allows fluctuation in frequencies of polymorphic alleles, while alleles within the supertypes are maintained by positive selection under a Red Queen arms race model of evolution, which promotes the maintenance of favorable alleles (68). Studies have shown that the presence of specific supertypes instead of alleles is associated with resistance to parasites (69–71).

Our previous short-read, whole-genome sequence analyses of a large number of Atlantic herring populations have resulted in the identification of hundreds of loci underlying ecological adaptation to differences in water salinity, spawning time, temperature (35, 46, 72), and light conditions (73). It is obvious that the MHC class II regions explored in this study have been blind spots in these analyses because of the difficulty in accurately scoring genetic diversity on the basis of short-read sequencing, an issue shared with other complex immune loci (74, 75). The current reference assembly is to some extent collapsed in MHC regions with duplicated gene copies, and the overlap between intra- and interlocus sequence identities makes the inference of genetic polymorphism challenging. This study is based on the combined analysis of 28 haplotypes from 14 individuals, which resulted in the identification of up to 28 different alleles at the most polymorphic genes. This implies that large-scale studies will be required to reveal how MHC diversity in Atlantic herring contributes to fitness. However, it is possible to interpret the extensive amount of short-read data in the context of the results of this study, for instance, as regard the segregation of major supertypes at loci 2 and 4. Furthermore, the cost for long-read sequencing has decreased steadily in recent years, making it possible to perform large-scale studies in the future and combine these with data on pathogen susceptibility in this extremely abundant marine fish.

MATERIALS AND METHODS

Samples and sequencing

The samples included a total of 14 Atlantic herring individuals, 6 from the Celtic Sea (collected on 11 November 11 2019 at latitude N51°59′ and longitude W6°48′), 6 from the Baltic Sea (collected on 18 May 2020 in the Baltic Sea, Hästskär, at latitude N60°35′ and longitude E17°48′), and 2 from the Norwegian Spring spawning stock (collected on 21 February 2021 in the Norwegian Sea at latitude N67°55′ and longitude E11°18′). High molecular weight DNA was prepared from tissue samples and subjected to PacBio HiFi long-read sequencing with average coverage ranging from 23× to 30×, as previously described (76). The consensus sequence of each HiFi read comes from at least three passes of a single template molecule, reaching an overall 99.8% accuracy (77). De novo genome assemblies were constructed using hifiasm (36). Hifiasm recommends an average coverage of >13× per haplotype (https://hifiasm.readthedocs.io) for high contiguity. The resulting high-quality 28 haploid PacBio genomes and the reference genome (37) were used for further analysis. All PacBio contig-level assemblies were scaffolded using the chromosome-level reference assembly and RagTag software version 2.0.1 (78).

Annotation and expression of MHC class II genes

Because of the complex structure of the MHC genomic regions, the ENSEMBL annotation of MHC class II genes on the reference assembly was incomplete with respect to ORFs, intron-exon structure, distinction between α and β genes, and unannotated genes. Thus, we manually annotated the genes first by BLAST (79, 80) using MHC class II genes from other species as queries (78 sequences from 16 species corresponding to all three groups, DA, DB, and DE; data S2) and visualized the BLAST hits on the genome using IGV (81) to obtain the approximate location of MHC class II genes in the herring genome. To obtain the accurate gene coordinates, we used previously generated RNA sequencing (RNA-seq) data from eight tissues (spleen, intestine, kidney, liver, heart, brain, eye, and muscle). (46). We aligned RNA-seq data against the reference genome using STAR (82) and visualized it using IGV (81) to locate ORFs, intron-exon junctions, and stop codons. Sequences that had the same gene structure as other MHC class II genes and that were not disrupted by inactivating mutations were annotated as functional genes. This annotation and the RNA-seq data were further used to obtain gene-level read counts from Subread (version 2.1.1) (83), where fragment-based counting was applied for paired-end reads. Multimapping RNA-seq reads were retained and assigned fractionally to genes, and only primary alignments were considered, given the presence of multiple MHC class II paralogs in the genome. Raw read counts were normalized to FPKM (fragments per kilobase of transcript per million mapped reads) to account for gene length and sequencing depth using a custom R script. Total mapped reads per library were obtained from the corresponding BAM files using SAMtools (version 1.22.1) (84). FPKM values were computed using a custom R script, and expression patterns were visualized using the ggplot2 package in R (version 4.4.3).

After annotating MHC class II genes on the reference assembly, we lifted over these annotations to the new haplotype assemblies using a genome annotation lift-over tool, LiftOff (85). However, because of the complexity of MHC genes, nearly 30% of the LiftOff-annotated genes were either incomplete or had a wrong intron-exon structure. LiftOff also failed to annotate all genes. Hence, to correct LiftOff annotations and obtain complete annotation, we used previously generated RNA-seq data from the spleen (46) and aligned these against all new assemblies using HiSat (86). We also used BLAST using the annotated genes as query. LiftOff annotations, RNA-seq reads, and BLAST hits for each PacBio genome were visualized using IGV, and all annotations were curated manually. If a gene was inactivated in all haplotypes, it was classified as a pseudogene. We found only one pseudogene, DBA2.1, carrying a 1-bp deletion (nt G) at the end of exon 2. Alleles with nonsense or indel mutations disrupting the reading frame or incomplete gene structure were referred to as “null” alleles. Table S5 contains a list of all null alleles and their inactivating mutations. Data S3 and S4 contain amino acid sequences from all 29 assemblies for α and β genes, respectively. Genomic organization of the annotated genes on each assembly was plotted using gggenomes (https://github.com/thackl/gggenomes). MHC regions and the flanking sequence between haplotypes were aligned using MUMmer (87).

From the genomic and gene organization (Figs. 1 and 2 and figs. S1 and S2), we observed assembly artifacts for samples CS7 and CS4. In the case of CS7, one haplotype assembly (haplotype 2) lacked genes from one locus (locus 1), but the alternative haplotype from the same individual carried two extra copies. This was most likely caused by incorrect phasing by hifiasm. We therefore decided to make the most conservative interpretation that both haplotypes contained locus 1 genes than to infer that this individual carried two unique haplotypes, one with zero and the other with five copies of locus 1 genes. In the case of CS4, one haplotype (hap2) lacked locus 4 genes. It is possible that this represents an assembly artifact, but in the absence of evidence for this, we did not make any manual correction of this haplotype.

Locus 1 contained two α-β gene pairs, named as DA1.1 and DA1.2. However, nine new assemblies had a third pair in between DA1.1 and DA1.2 where the α gene had an unusually long intron 1 ranging between 18 and 23 kb (table S7), indicating that these nine new assemblies share the same haplotype for locus 1 [which resulted in being true from the allele-clustering analysis (fig. S11A)]. To distinguish this different gene structure, we named this extra DA gene pair as DA1.3. However, BS2_hap1 DA1.3 did not have long intron 1, but we did not change its naming to keep the consistency with the rest of the naming.

Gene sequence analysis

Sequences were extracted from the genomes using gffread (88) for various analyses using custom scripts available on Zenodo repository. Phylogenetic trees of α and β MHC class II coding sequences from only Atlantic herring reference assembly were constructed using the neighbor-joining method in the R package ape (89). Phylogenetic analysis of representative MHC class II sequences from Atlantic herring, along with sequences from tetrapod and fish species retrieved from public databases and a previous study, was conducted separately for the α and β chains using full-length amino acid sequences. A list of MHC class II α and β chain sequences from other species that are used in the previous literature for comprehensive sequence analysis (32), along with their corresponding accession numbers, is provided in table S8. Phylogenetic trees were constructed using the maximum-likelihood method implemented in IQ-TREE (90, 91), with bootstrap support values (92) estimated from 2000 replicates. All trees were visualized using the R package ggtree (93). Nucleotide diversity (π) was calculated using the R package pegas (94), where sequences were aligned with ClustalO in the R package msa (95) for all four exons of all genes. Analysis for the ratio of nonsynonymous substitutions over synonymous substitutions (dN/dS) for exon 2 and exon 3 was carried out using the software MEGA 11 (96, 97) with the Nei-Gojobori method (Proportion) with 2000 bootstrap replicates. Substitution rates were modeled with gamma-distributed rate variation across sites, and gaps/missing data were treated under the pairwise deletion model. Amino acid sites under positive selection were estimated by Bayes estimation in the models for positive selection in PAML, M2 and M8 (41). PBRs in Atlantic herring MHC class II genes were predicted by comparison of Atlantic herring and tetrapod sequences from the previous literature (43, 44).

Protein structure modeling

Protein structure modeling was performed using AlphaFold3 (98) on two α-β gene pairs, CS4_hap1_DA2.2 and BS1_hap1_DA2.1, to explore the location of the most polymorphic amino acid residues and the functional consequences of the four-amino-acid insertion (CTDC) in the class II α chain, respectively. AlphaFold predicted five models for each protein, and the highest-ranking model of each protein was selected for further analysis. The transmembrane regions were predicted using DeepTMHMM 1.0 (figs. S10, A and B, and S17, A and B) (99). Signal peptides of α and β chains were predicted using SignalP 6.0 (100) with settings for Eukarya and the slow model mode (figs. S10, C and D, and S17, C and D). The signal peptide annotation for CS4_hap1_DAA2.2 was predicted with relatively low confidence (probability of 0.67), resulting in differing cleavage site predictions between DeepTMHMM and SignalP (fig. S10, A and C). Molecular graphics and figure rendering were performed with UCSF ChimeraX version 1.9 (101) and PyMOL version 2.3.0 (102).

We used Shannon entropy (103) to quantify the degree of polymorphism at each amino acid residue using all MHC class II DA α and β genes, as well as using only genes from locus 2. The Shannon entropy values were calculated using the R package bio3d (104). The values range from 0 to 4.32, where 0 represents a site with only one amino acid (highly conserved), while 4.32 represents a site with all 20 amino acids (highly polymorphic). To represent these values on a protein structure, we used normalized values by diving all values with the maximum.

To understand the effect of the CTDC insertion on the protein structure in the α chain, we substituted Cys³³ in the CTDC insert of the α chain to an alanine (C33A) to break the disulfide bond. The stability of the resulting protein model was analyzed using DDMut (105).

Haplotype sequence analysis

To assess amino acid sequence similarity in our dataset of 195 DAA, 220 DAB, 53 DBA, and 107 DBB sequences, we performed hierarchical clustering and heatmap visualization. Full-length amino acid sequences were aligned within each gene category using the MAFFT multiple sequence alignment program (106). Pairwise distance matrices were computed on the basis of percent amino acid mismatches, with insertions and deletions counted as mismatches. These distance matrices were used to construct hierarchical clustering using the UPGMA (unweighted pair group method with arithmetic mean) method, and the results were visualized as heatmaps with the ComplexHeatmap R package (107).

To evaluate intralocus diversity and investigate the haplotype structure, we performed locus-specific analyses for polymorphic genes at loci 1, 2, and 4. For these analyses, DAA and DAB sequences were clustered separately using the same alignment and distance matrix approach described above for the genome-wide analysis. The resulting dendrograms for each locus were then compared using tanglegrams, generated with the dendextend R package (108). Dendrograms were aligned to optimize tip correspondence, and entanglement scores were calculated to quantify the similarity between DAA and DAB clustering patterns at each locus.

We conducted further haplotype analysis for loci 2 and 4, as these loci showed clear clustering patterns among sequences. We assessed homology across intronic and intergenic regions. Pairwise comparisons of the entire locus sequences, including 20 kb of flanking sequence on either side, were performed across 28 phased haploid assemblies. Alignments were generated using NUCmer from the MUMmer package (87), and the resulting dot plots were constructed with mummerplot and visualized in R. Structural haplotypes for loci 2 and 4 were inferred on the basis of (i) amino acid sequence clustering, as visualized with tanglegrams, and (ii) pairwise alignment patterns across the entire locus, as visualized using dot plots. Null alleles, which were excluded from amino acid sequence clustering resulting from the absence of ORFs, were assigned to allelic groups solely on the basis of homology patterns in the dot plots. Haplotype schematics, depicting gene order and orientation with fixed gene lengths and intergenic intervals, were generated in R using the gggenes package (109).

Acknowledgments

The National Genomics Infrastructure (NGI)/Uppsala Genome Center provided service in massive parallel sequencing, and the computational infrastructure was provided by the Swedish National Infrastructure for Computing (SNIC) at UPPMAX partially funded by the Swedish Research Council through grant agreement no. 2018-05973.

Funding:

This work was supported by the following: Knut and Alice Wallenberg Foundation in Sweden, KAW 2023.0160 (to L.A.); Vetenskapsrådet, 2017-02907 (to L.A.); and Graduate Student Research Trainee Grant, Texas A&M University (to M.J.).

Author contributions:

Conceptualization: L.A., M.J., and F.M.S. Methodology: L.A., M.J., F.M.S., B.W.D., and M.L. Software: M.J. and F.M.S. Validation: M.J. and F.M.S. Formal analysis: M.J., F.M.S., and M.L. Investigation: L.A., M.J., F.M.S., and M.L. Resources: L.A. and F.B. Data curation: M.J. and F.M.S. Visualization: L.A., M.J., F.M.S., and M.L. Supervision: L.A., M.J., F.M.S., M.F.C., B.W.D., and J.L. Writing—original draft: L.A., M.J., and F.M.S. Writing—review and editing: L.A., M.J., F.M.S., F.B., M.F.C., B.W.D., M.L., J.L., and M.E.P. Project administration: L.A., M.J., and M.E.P. Funding acquisition: L.A. and M.J.

Competing interests:

The authors declare that they have no competing interests.

Data, code, and materials availability:

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. This study did not generate new materials. The sequence data generated in this study are available from BioProject PRJNA1023520 (www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1023520). The RNA-seq data used from the previous study are available from BioProject PRJEB31270 (www.ncbi.nlm.nih.gov/sra/?term=PRJEB31270). The analyses of data have been carried out with publicly available software, and all are cited in the Materials and Methods. Custom scripts used are available at https://doi.org/10.5281/zenodo.17675164.

Supplementary Materials

The PDF file includes:

Supplementary Text

Figs. S1 to S21

Tables S1 to S8

Legends for data S1 to S4

Legend for movie S1

References

sciadv.aea2862_sm.pdf^{(24.2MB, pdf)}

Other Supplementary Material for this manuscript includes the following:

Data S1 to S4

sciadv.aea2862_data_s1_to_s4.zip^{(41.9KB, zip)}

Movie S1

sciadv.aea2862_movie_s1.zip^{(17.2MB, zip)}

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Text

Figs. S1 to S21

Tables S1 to S8

Legends for data S1 to S4

Legend for movie S1

References

sciadv.aea2862_sm.pdf^{(24.2MB, pdf)}

Data S1 to S4

sciadv.aea2862_data_s1_to_s4.zip^{(41.9KB, zip)}

Movie S1

sciadv.aea2862_movie_s1.zip^{(17.2MB, zip)}

Data Availability Statement

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PERMALINK

Unheralded high MHC class II polymorphism in the abundant Atlantic herring resolved by long-read sequencing

Minal Jamsandekar

Fahime Mohamadnejad Sangdehi

Florian Berg

Michael F Criscitiello

Brian W Davis

Mårten Larsson

Jingyi Li

Mats E Pettersson

Leif Andersson

Roles

Abstract

INTRODUCTION

RESULTS

Genome organization and copy number variation of MHC II genes

Fig. 1. Overview of MHC class II genes in Atlantic herring.

Fig. 2. Overview of the polymorphic MHC class II locus 1 on chromosome 7 in Atlantic herring.

Table 1. Number of MHC class II genes at each locus among haplotypes based on 28 PacBio-based haplotype assemblies from 14 individual Atlantic and Baltic herring.

Fig. 3. Clustering of amino acid and coding nucleotide sequences from DA β genes.

Extensive sequence polymorphism of MHC class II genes

Fig. 4. Nucleotide diversity (π) and the ratio of nonsynonymous substitutions over synonymous substitutions (dN/dS) in MHC class II genes in Atlantic herring.

Sequence polymorphism is highly enriched at predicted peptide binding residues

Fig. 5. Amino acid and protein structure properties of MHC class II PBRs.

Nonrandom combinations of gene copies enhance diversity at the haplotype level

Fig. 6. Allelic grouping and inferred structural haplotypes for MHC class II locus 2.

DISCUSSION

MATERIALS AND METHODS

Samples and sequencing

Annotation and expression of MHC class II genes

Gene sequence analysis

Protein structure modeling

Haplotype sequence analysis

Acknowledgments

Funding:

Author contributions:

Competing interests:

Data, code, and materials availability:

Supplementary Materials

The PDF file includes:

Other Supplementary Material for this manuscript includes the following:

REFERENCES

Associated Data

Supplementary Materials

Data Availability Statement

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