Peptide-binding Residues Research Articles

DP-site: A dual deep learning-based method for protein-peptide interaction site prediction

BackgroundProtein-peptide interaction prediction is an important topic for several applications including various biological processes, understanding drug discovery, protein function abnormal cellular behaviors, and treating diseases. Over the years, studies have shown that experimental methods have improved the identification of this bio-molecular interaction. However, predicting protein-peptide interactions using these methods is laborious, time-consuming, dependent on third-party tools, and costly. MethodTo address these previous drawbacks, this study introduces a computational framework called DP-Site. The proposed framework concentrates on using a compound of a dual pipeline along with a combination predictor. A deep convolutional neural network for feature extraction and classification is embedded in pipeline 1. In addition, pipeline 2 includes a deep long-short-term memory-based and a random forest classifier for feature extraction and classification. In this investigation, the evolutionary, structure-based, sequence-based, and physicochemical information of proteins is utilized for identifying protein-peptide interaction at the residue level. ResultsThe proposed method is evaluated on both the ten-fold cross-validation and independent test sets. The robust and consistent results between cross-validation and independent test sets confirm the ability of the proposed method to predict peptide binding residues in proteins. Moreover, experimental findings demonstrate that DP-Site has significantly outperformed other state-of-the-art sequence-based and structure-based methods. The proposed method achieves a remarkable balance between a specificity of 0.799 and a sensitivity of 0.770, along with the best f-measure of 0.661 and the highest precision of 0.580 using an independent test set. ConclusionsThe outcome of various experiments confirms the proficiency of the proposed method and outperforms state-of-the-art sequence-based and structure-based methods in terms of the mentioned criteria. DP-Site can be accessed at https://github.com/shafiee 95/shima.shafiee.DP-Site.

PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features

Protein–peptide interactions play a crucial role in various cellular processes and are implicated in abnormal cellular behaviors leading to diseases such as cancer. Therefore, understanding these interactions is vital for both functional genomics and drug discovery efforts. Despite a significant increase in the availability of protein–peptide complexes, experimental methods for studying these interactions remain laborious, time-consuming, and expensive. Computational methods offer a complementary approach but often fall short in terms of prediction accuracy. To address these challenges, we introduce PepCNN, a deep learning-based prediction model that incorporates structural and sequence-based information from primary protein sequences. By utilizing a combination of half-sphere exposure, position specific scoring matrices from multiple-sequence alignment tool, and embedding from a pre-trained protein language model, PepCNN outperforms state-of-the-art methods in terms of specificity, precision, and AUC. The PepCNN software and datasets are publicly available at https://github.com/abelavit/PepCNN.git.

The antigen recognition portion of African buffalo class I MHC is highly polymorphic, consistent with a complex pathogen challenge environment, and the 3’ region suggests distinct haplotype configurations

African buffalo (Syncerus caffer) have been distinct from the Auroch lineage leading to domestic cattle for 5 million years, and are reservoirs of multiple pathogens, that affect introduced domestic cattle. To date, there has been no analysis of the class I MHC locus in African buffalo. We present the first data on African buffalo class I MHC, which demonstrates that gene and predicted protein coding sequences are approximately 86–87% similar to that of African domestic cattle in the peptide binding region. The study also shows concordance in the distribution of codons with elevated posterior probabilities of positive selection in the buffalo class I MHC and known antigen binding sites in cattle. Overall, the diversity in buffalo class I sequences appears greater than that in cattle, perhaps related to a more complex pathogen challenge environment in Africa. However, application of NetMHCpan suggested broad clustering of peptide binding specificities between buffalo and cattle. Furthermore, in the case of at least 20 alleles, critical peptide-binding residues appear to be conserved with those of cattle, including at secondary anchor residues. Alleles with six different length transmembrane regions were detected. This preliminary analysis suggests that like cattle, but unlike most other mammals, African buffalo appears to exhibit configuration (haplotype) variation in which the loci are expressed in distinct combinations.

Predicting protein-peptide binding residues via interpretable deep learning.

Identifying the protein-peptide binding residues is fundamentally important to understand the mechanisms of protein functions and explore drug discovery. Although several computational methods have been developed, most of them highly rely on third-party tools or complex data preprocessing for feature design, easily resulting in low computational efficacy and suffering from low predictive performance. To address the limitations, we propose PepBCL, a novel BERT (Bidirectional Encoder Representation from Transformers) -based contrastive learning framework to predict the protein-peptide binding residues based on protein sequences only. PepBCL is an end-to-end predictive model that is independent of feature engineering. Specifically, we introduce a well pre-trained protein language model that can automatically extract and learn high-latent representations of protein sequences relevant for protein structures and functions. Further, we design a novel contrastive learning module to optimize the feature representations of binding residues underlying the imbalanced dataset. We demonstrate that our proposed method significantly outperforms the state-of-the-art methods under benchmarking comparison, and achieves more robust performance. Moreover, we found that we further improve the performance via the integration of traditional features and our learnt features. Interestingly, the interpretable analysis of our model highlights the flexibility and adaptability of deep learning-based protein language model to capture both conserved and non-conserved sequential characteristics of peptide-binding residues. Finally, to facilitate the use of our method, we establish an online predictive platform as the implementation of the proposed PepBCL, which is now available at http://server.wei-group.net/PepBCL/. https://github.com/Ruheng-W/PepBCL. Supplementary data are available at Bioinformatics online.

Invasive Bullfrogs Maintain MHC Polymorphism Including Alleles Associated with Chytrid Fungal Infection.

Maintenance of genetic diversity at adaptive loci may facilitate invasions by non-native species by allowing populations to adapt to novel environments, despite the loss of diversity at neutral loci that typically occurs during founder events. To evaluate this prediction, we compared genetic diversity at major histocompatibility complex (MHC) and cytochrome b (cytb) loci from 20 populations of the American bullfrog (Rana catesbeiana) across theinvasive and native ranges in North America and quantified the presence of the pathogen Batrachochytrium dendrobatidis (Bd). Compared to native populations, invasive populations had significantly higher Bd prevalence and intensity, significantly higher pairwise MHC and cytb FST, and significantly lower cytb diversity, but maintained similar levels of MHC diversity. The two most common MHC alleles (LiCA_B and Rapi_33) were associated with a significant decreased risk of Bd infection, and we detected positive selection acting on four peptide binding residues. Phylogenetic analysis suggested invasive populations likely arose from a single founding population in the American Midwest with a possible subsequent invasion in the northwest. Overall, our study suggests that the maintenance of diversity at adaptive loci may contribute to invasion success and highlights the importance of quantifying diversity at functional loci to assess the evolutionary potential of invasive populations.

Differential patterns of diversity at neutral and adaptive loci in endangered Rhodeus pseudosericeus populations

Given the fact that threatened species are often composed of isolated small populations, spatial continuity or demography of the populations may be major factors that have shaped the species’ genetic diversity. Thus, neutral loci have been the most commonly-used markers in conservation genetics. However, the populations under the influence of different environmental factors may have evolved in response to different selective pressures, which cannot be fully reflected in neutral genetic variation. Rhodeus pseudosericeus, a bitterling species (Acheilognathidae; Cypriniformes) endemic to the Korean Peninsula, are only found in some limited areas of three rivers, Daecheon, Han and Muhan, that flow into the west coast. Here, we genotyped 24 microsatellite loci and two loci (DAB1 and DAB3) of MHC class II peptide-binding β1 domain for 222 individuals collected from seven populations. Our microsatellite analysis revealed distinctive differentiation between the populations of Daecheon and Muhan Rivers and the Han River populations, and populations were structured into two subgroups within the Han River. Apparent positive selection signatures were found in the peptide-binding residues (PBRs) of the MHC loci. The allelic distribution of MHC showed a degree of differentiation between the populations of Daecheon and Muhan Rivers and the Han River populations, partially similar to the results obtained for microsatellites, however showed rather complex patterns among populations in the Han River. Considering the apparent differences in the distribution of supertypes obtained based on the physicochemical differences induced by the polymorphisms of these PBRs, the differentiation in DAB1 between the two regional groups may result in the differences in immune function. No differentiation between these two regions was observed in the supertyping of DAB3, probably indicating that only DAB1 was associated with the response to locally specialized antigenic peptides.

Recapitulating the Binding Affinity of Nrf2 for KEAP1 in a Cyclic Heptapeptide, Guided by NMR, X-ray Crystallography, and Machine Learning.

Macrocycles, including macrocyclic peptides, have shown promise for targeting challenging protein-protein interactions (PPIs). One PPI of high interest is between Kelch-like ECH-Associated Protein-1 (KEAP1) and Nuclear Factor (Erythroid-derived 2)-like 2 (Nrf2). Guided by X-ray crystallography, NMR, modeling, and machine learning, we show that the full 20 nM binding affinity of Nrf2 for KEAP1 can be recapitulated in a cyclic 7-mer peptide, c[(D)-β-homoAla-DPETGE]. This compound was identified from the Nrf2-derived linear peptide GDEETGE (KD = 4.3 μM) solely by optimizing the conformation of the cyclic compound, without changing any KEAP1 interacting residue. X-ray crystal structures were determined for each linear and cyclic peptide variant bound to KEAP1. Despite large variations in affinity, no obvious differences in the conformation of the peptide binding residues or in the interactions they made with KEAP1 were observed. However, analysis of the X-ray structures by machine learning showed that locations of strain in the bound ligand could be identified through patterns of subangstrom distortions from the geometry observed for unstrained linear peptides. We show that optimizing the cyclic peptide affinity was driven partly through conformational preorganization associated with a proline substitution at position 78 and with the geometry of the noninteracting residue Asp77 and partly by decreasing strain in the ETGE motif itself. This approach may have utility in dissecting the trade-off between conformational preorganization and strain in other ligand-receptor systems. We also identify a pair of conserved hydrophobic residues flanking the core DxETGE motif which play a conformational role in facilitating the high-affinity binding of Nrf2 to KEAP1.

An Ancient, MHC-Linked, Nonclassical Class I Lineage in Cartilaginous Fish.

Cartilaginous fishes, or chondrichthyans, are the oldest jawed vertebrates that have an adaptive immune system based on the MHC and Ig superfamily-based AgR. In this basal group of jawed vertebrates, we identified a third nonclassical MHC class I lineage (UDA), which is present in all species analyzed within the two major cartilaginous subclasses, Holocephali (chimaeras) and Elasmobranchii (sharks, skates, and rays). The deduced amino acid sequences of UDA have eight out of nine typically invariant residues that bind to the N and C termini of bound peptide found in most vertebrae classical class I (UAA); additionally, the other predicted 28 peptide-binding residues are perfectly conserved in all elasmobranch UDA sequences. UDA is distinct from UAA in its differential tissue distribution and its lower expression levels and is mono- or oligomorphic unlike the highly polymorphic UAA UDA has a low copy number in elasmobranchs but is multicopy in the holocephalan spotted ratfish (Hydrolagus colliei). Using a nurse shark (Ginglymostoma cirratum) family, we found that UDA is MHC linked but separable by recombination from the tightly linked cluster of UAA, TAP, and LMP genes, the so-called class I region found in most nonmammalian vertebrates. UDA has predicted structural features that are similar to certain nonclassical class I genes in other vertebrates, and, unlike polymorpic classical class I, we anticipate that it may bind to a conserved set of specialized peptides.

Molecular Basis for Hormone Recognition and Activation of Corticotropin-Releasing Factor Receptors.

Corticotropin-releasing factor (CRF) and the three related peptides urocortins 1-3 (UCN1-UCN3) are endocrine hormones that control the stress responses by activating CRF1R and CRF2R, two members of class B G-protein-coupled receptors (GPCRs). Here, we present two cryoelectron microscopy (cryo-EM) structures of UCN1-bound CRF1R and CRF2R with the stimulatory G protein. In both structures, UCN1 adopts a single straight helix with its N terminus dipped into the receptor transmembrane bundle. Although the peptide-binding residues in CRF1R and CRF2R are different from other members of class B GPCRs, the residues involved in receptor activation and G protein coupling are conserved. In addition, both structures reveal bound cholesterol molecules to the receptor transmembrane helices. Our structures define the basis of ligand-binding specificity in the CRF receptor-hormone system, establish a common mechanism of class B GPCR activation and G protein coupling, and provide a paradigm for studying membrane protein-lipid interactions for class B GPCRs.

Improving Sequence-Based Prediction of Protein-Peptide Binding Residues by Introducing Intrinsic Disorder and a Consensus Method.

Protein-peptide interaction is crucial for many cellular processes. It is difficult to determine the interaction by experiments as peptides are often very flexible in structure. Accurate sequence-based prediction of peptide-binding residues can facilitate the study of this interaction. In this work, we developed two novel sequence-based methods SVMpep and PepBind to identify the peptide-binding residues. Recent studies demonstrate that the protein-peptide binding is closely associated with intrinsic disorder. We thus introduced intrinsic disorder in our feature design and developed the ab initio method SVMpep. Experiments show that intrinsic disorder contributes to 1.2-5.2% improvement in area under the receiver operating characteristic curve (AUC). Comparison to the recent sequence-based method SPRINT-Seq reveals that SVMpep improves the AUC and Matthews correlation coefficient (MCC) by at least 7.7% and 70%, respectively. In addition, by combining SVMpep with two template-based methods S-SITE and TM-SITE, we next proposed the consensus-based method PepBind. Remarkably, compared with the latest structure-based method SPRINT-Str, PepBind improves the AUC and MCC by 1.7% and 28.3%, respectively, on the same independent test set of SPRINT-Str. The success of PepBind is attributed to the improved prediction of the ab initio method SVMpep by introducing intrinsic disorder and the consensus prediction by combining three complementary methods. A web server that implements the proposed methods is freely available at http://yanglab.nankai.edu.cn/PepBind/ .

A global analysis of selection at the avian MHC.

Recent advancements in sequencing technology have resulted in rapid progress in the study of the major histocompatibility complex (MHC) in non-model avian species. Here, we analyze a global dataset of avian MHC class I and class II sequences (ca. 11,000 sequences from over 250 species) to gain insight into the processes that govern macroevolution of MHC genes in birds. Analysis of substitution rates revealed striking differences in the patterns of diversifying selection between passerine and non-passerine birds. Non-passerines showed stronger selection at MHC class II, which is primarily involved in recognition of extracellular pathogens, while passerines showed stronger selection at MHC class I, which is involved in recognition of intracellular pathogens. Positions of positively selected amino-acid residues showed marked discrepancies with peptide-binding residues (PBRs) of human MHC molecules, suggesting that using a human classification of PBRs to assess selection patterns at the avian MHC may be unjustified. Finally, our analysis provided evidence that indel mutations can make a substantial contribution to adaptive variation at the avian MHC.

PBRpredict-Suite: a suite of models to predict peptide-recognition domain residues from protein sequence.

Machine learning plays a substantial role in bioscience owing to the explosive growth in sequence data and the challenging application of computational methods. Peptide-recognition domains (PRDs) are critical as they promote coupled-binding with short peptide-motifs of functional importance through transient interactions. It is challenging to build a reliable predictor of peptide-binding residue in proteins with diverse types of PRDs from protein sequence alone. On the other hand, it is vital to cope up with the sequencing speed and to broaden the scope of study. In this paper, we propose a machine-learning-based tool, named PBRpredict, to predict residues in peptide-binding domains from protein sequence alone. To develop a generic predictor, we train the models on peptide-binding residues of diverse types of domains. As inputs to the models, we use a high-dimensional feature set of chemical, structural and evolutionary information extracted from protein sequence. We carefully investigate six different state-of-the-art classification algorithms for this application. Finally, we use the stacked generalization approach to non-linearly combine a set of complementary base-level learners using a meta-level learner which outperformed the winner-takes-all approach. The proposed predictor is found competitive based on statistical evaluation. PBRpredict-Suite software: http://cs.uno.edu/~tamjid/Software/PBRpredict/pbrpredict-suite.zip. Supplementary data are available at Bioinformatics online.

OR23 Whole gene analysis of HLA-A, -B and -C alleles observed in 100,000 individuals demonstrate distinct evolutionary and selection patterns in each Class I gene

Aim Classical HLA Class I genes (HLA-A, -B and -C) is thought to be evolved from ancestral gene(s) via duplication events and considered to be homologous. Exon 2 and 3 of those genes encodes Antigen Recognition Sites (ARS). These exons are highly polymorphic and it was believed due to positive selection. We have analyzed over 100,000 human samples that were whole gene sequenced for HLA-A, -B and -C and contained 1188, 1237 and 1185 unique patterns for each gene respectively. Methods We performed whole gene HLA Class I gene sequencing on PacificBio platform as described earlier (Cereb et al. Hum Immunol. 2015 Dec;76(12):963–74). Mean values of nonsynonymous and synonymous divergence (dN and dS, respectively) for all codons for all pairwise comparisons among alleles were determined using SNPGenie, an implementation of the Nei-Gojobori method (Nei and Gojobori, Mol. Biol. Evol. 3:418–426.) for use with next-generation sequencing data (Mol. Biol. Evol. 3:418–426.). Results In summary regarding positive selection, the following exons give signatures of positive selection: (1) HLA-A exons 2, 3, and 5 (weak); (2) HLA-B exons 2 and 3 (weak); and (3) HLA-C exons 1, 6, and 7. Regarding HLA-C, this seems quite revolutionary, and may help to explain previous studies that failed to identify evidence of positive selection specifically favoring amino acid charge differences in the peptide binding residues (PBR) of HLA-C only. HLA-C has the most extreme signatures of positive selection heretofore observed, but in non-PBR exons: dN/dS for exon 1 is 17.94, and dN/dS for exon 7 is 19.02 — both very high values. However, it is noteworthy that these huge ratios are not due to a great increase in dN, but rather to a great decrease in d. By contrast, PBR exons 2 and 3 have overall signatures of weak purifying selection at the HLA-C locus.Overall values for dN at HLA-A, -B, and -C are 0.0305, 0.0306, and 0.0240, respectively. Overall values for dS are 0.0395, 0.0344, and 0.0300, respectively. Therefore, in terms of selectively neutral diversity, diversity is greatest in HLA-A, less in -B, and least in -C. Conclusions Whole gene sequencing of HLA Class I will provide us the foundation to understand the evolutionary forces shaping the regions and the functions of various regions of those genes. N. Cereb: 6. Stock Shareholder; Company/Organization; PacBio and Hitogenetics. C.W. Nelson: 2. Consultant; Company/Organization; Histogenetics. S. Yang: 6. Stock Shareholder; Company/Organization; PacBio and Histogenetics.

Allorecognition of HLA-C Mismatches by CD8+ T Cells in Hematopoietic Stem Cell Transplantation Is a Complex Interplay between Mismatched Peptide-Binding Region Residues, HLA-C Expression, and HLA-DPB1 Disparities.

HLA-C locus mismatches (MMs) are the most frequent class I disparities in unrelated hematopoietic stem cell transplantation (HSCT) and have a detrimental impact on clinical outcome. Recently, a few retrospective clinical studies have reported some variability in the immunogenicity of HLA-C incompatibilities. To get better insight into presumably permissive HLA-C MMs, we have developed a one-way in vitro mixed lymphocyte reaction (MLR) assay allowing to quantify activated CD56−CD137+CD8+ lymphocytes in HLA-C incompatible combinations. T cell-mediated alloresponses were correlated with genetic markers such as HLA-C mRNA expression and the number of amino acid (aa) MMs in the α1/α2 domains (peptide-binding region). Because of the high rate of HLA-DPB1 incompatibilities in HLA-A-, B-, C-, DRB1-, and DQB1-matched unrelated HSCT patient/donor pairs, the impact of HLA-DPB1 mismatching, a potential bystander of CD4+ T cell activation, was also considered. Heterogeneous alloresponses were measured in 63 HLA-C-mismatched pairs with a positive assay in 52% of the combinations (2.3–18.6% activated CTLs), representing 24 different HLA-A~B~DRB1~DQB1 haplotypes. There was no correlation between measured alloresponses and mRNA expression of the mismatched HLA-C alleles. The HLA-C*03:03/03:04 MM did not induce any positive alloresponse in five MLRs. We also identified HLA-C*02:02 and HLA-C*06:02 as mismatched alleles with lower immunogenicity, and HLA-C*14:02 as a more immunogenic MM. A difference of at least 10 aa residues known to impact peptide/T cell receptor (TCR) binding and a bystander HLA-DPB1 incompatibility had a significant impact on CTL alloreactivity (p = 0.021). The same HLA-C MM, when recognized by two different responders with the same HLA haplotypes, was recognized differently, emphasizing the role of the T-cell repertoire of responding cells. In conclusion, mismatched HLA-C alleles differing by 10 or more aas in the peptide/TCR-binding region, when occurring together with HLA-DPB1 incompatibilities, should be considered as high-risk MMs in unrelated HSCT.

The Silencing of a 14-3-3ɛ Homolog in Tenebrio molitor Leads to Increased Antimicrobial Activity in Hemocyte and Reduces Larval Survivability.

The 14-3-3 family of phosphorylated serine-binding proteins acts as signaling molecules in biological processes such as metabolism, division, differentiation, autophagy, and apoptosis. Herein, we report the requirement of 14-3-3ɛ isoform from Tenebrio molitor (Tm14-3-3ɛ) in the hemocyte antimicrobial activity. The Tm14-3-3ɛ transcript is 771 nucleotides in length and encodes a polypeptide of 256 amino acid residues. The protein has the typical 14-3-3 domain, the nuclear export signal (NES) sequence, and the peptide binding residues. The Tm14-3-3ɛ transcript shows a significant three-fold expression in the hemocyte of T. molitor larvae when infected with Escherichia coli Tm14-3-3ɛ silenced larvae show significantly lower survival rates when infected with E. coli. Under Tm14-3-3ɛ silenced condition, a strong antimicrobial activity is elicited in the hemocyte of the host inoculated with E. coli. This suggests impaired secretion of antimicrobial peptides (AMP) into the hemolymph. Furthermore, a reduction in AMP secretion under Tm14-3-3ɛ silenced condition would be responsible for loss in the capacity to kill bacteria and might explain the reduced survivability of the larvae upon E. coli challenge. This shows that Tm14-3-3ɛ is required to maintain innate immunity in T. molitor by enabling antimicrobial secretion into the hemolymph and explains the functional specialization of the isoform.

OR13 Human leukocyte antigen analysis using high resolution SNP data: Imputation, association and amino acid binding pocket residues investigation in IgAD patients

Background Genetic variation at human leukocyte antigen (HLA) alleles influences many phenotypes and mediate susceptibility to a wide range of human diseases include Immunoglobulin A Deficiency (IgAD). The high resolution SNP genotyping has enabled large cohort studies in this complex region. Aim To Investigate the HLA association of IgAD using high resolution SNP data and to identify the combinations of biologically relevant amino acid (AA) residues directly involved in disease. Methods HLA imputation was performed in 1046 samples including 758 IgAD individual and 288 ethnically matched controls. The imputation results were further confirmed by high resolution typing. Subsequently, multiple tools include SKDM HLA tools, SNP2HLA and Plink were used to investigate the amino acid binding pocket association in IgAD cohorts. Finally, 3D visualization of the predicted HLA amino acid sequence was performed using RaptorX. Results Strong IgAD association were observed in HLA-B*0801 ( p = 5.364 ∗ 10 −14 ), HLA-B*1402 ( p = 0.0016), HLA-DRB1*0301 ( p = 3.276 ∗ 10 −12 ), HLA-DRB1*0102 ( p = 0.001 ∗ 10 −12 ), HLA-DRB1*1201 ( p = 1.684 ∗ 10 −4 ), HLA-DQB1*0201 ( p = 1.835 ∗ 10 −12 ). Conversely, HLA-B*1501 ( p = 1.358 ∗ 10 −5 ), HLA-B*0702 ( p = 5.662 ∗ 10 −7 ), HLA-DRB1*1501 ( p = 6.34 ∗ 10 −23 ), HLA-DQB1*0402 ( p = 0.004) and HLA-DQB1*0602 ( p = 9.396 ∗ 10 −23 ) showed strong protection against IgAD. In addition, most significant AA residues in different HLA region associated with IgAD are: HLA-B ((AA 9, Asp, pockets 2 and 3, p = 8.047 ∗ 10 −14 ); (AA 156, Asp, pockets 4 and 5, p = 4.396 ∗ 10 −13 )); HLA-DRB1 ((AA 26, Tyr, pocket 4, p = 8.249 ∗ 10 −10 ); (AA 67, Leu, pocket 7, p = 5.236 ∗ 10 −8 )); HLA-DQB1 ((AA 38, Val, pocket 9, p = 4.894 ∗ 10 −10 ); (AA 37, Ile, pocket 9, p = 8.216 ∗ 10 −9 ), (AA 28 Ser and AA 47 Phe, pocket 7, p = 8.216 ∗ 10 −9 ); (AA 30, Ser, pocket 6, p = 8.216 ∗ 10 −9 ); (AA 71, Lys, pockets 4 and 7, p = 8.216 ∗ 10 −9 )). Conclusion The results suggested that certain peptide-binding residues of the HLA class I and class II molecule is associated with susceptibility to IgAD.

A nonclassical MHC class I U lineage locus in zebrafish with a null haplotypic variant.

Three sequence lineages of MHC class I genes have been described in zebrafish (Danio rerio): U, Z, and L. The U lineage genes encoded on zebrafish chromosome 19 are predicted to provide the classical function of antigen presentation. This MHC class I locus displays significant haplotypic variation and is the only MHC class I locus in zebrafish that shares conserved synteny with the core mammalian MHC. Here, we describe two MHC class I U lineage genes, mhc1ula and mhc1uma, that map to chromosome 22. Unlike the U lineage proteins encoded on chromosome 19, Ula and Uma likely play a nonclassical role as they lack conservation of key peptide binding residues, display limited polymorphic variation, and exhibit tissue-specific expression. We also describe a null haplotype at this chromosome 22 locus in which the mhc1ula and mhc1uma genes are absent due to a ~30kb deletion with no other MHC class I sequences present. Functional and non-functional transcripts of mhc1ula and mhc1uma were identified; however, mhc1uma transcripts were often not amplified or amplified at low levels from individuals possessing an apparently bona fide gene. These distinct U lineage genes may be restricted to the superorder Ostariophysi as similar sequences only could be identified from the blind cavefish (Astyanax mexicanus), fathead minnow (Pimephales promelas), goldfish (Carassius auratus), and grass carp (Ctenopharyngodon idella).

Selection on MHC class II supertypes in the New Zealand endemic Hochstetter's frog.

BackgroundThe New Zealand native frogs, family Leiopelmatidae, are among the most archaic in the world. Leiopelma hochstetteri (Hochstetter’s frog) is a small, semi-aquatic frog with numerous, fragmented populations scattered across New Zealand’s North Island. We characterized a major histocompatibility complex (MHC) class II B gene (DAB) in L. hochstetteri from a spleen transcriptome, and then compared its diversity to neutral microsatellite markers to assess the adaptive genetic diversity of five populations (“evolutionarily significant units”, ESUs).ResultsL. hochstetteri possessed very high MHC diversity, with 74 DAB alleles characterized. Extremely high differentiation was observed at the DAB locus, with only two alleles shared between populations, a pattern that was not reflected in the microsatellites. Clustering analysis on putative peptide binding residues of the DAB alleles indicated four functional supertypes, all of which were represented in 4 of 5 populations, albeit at different frequencies. Otawa was an exception to these observations, with only two DAB alleles present.ConclusionsThis study of MHC diversity highlights extreme population differentiation at this functional locus. Supertype differentiation was high among populations, suggesting spatial and/or temporal variation in selection pressures. Low DAB diversity in Otawa may limit this population’s adaptive potential to future pathogenic challenges.Electronic supplementary materialThe online version of this article (doi:10.1186/s12862-015-0342-0) contains supplementary material, which is available to authorized users.

Positive selection drives the evolution of a major histocompatibility complex gene in an endangered Mexican salamander species complex.

Immune gene evolution can be critical to species survival in the face of infectious disease. In particular, polymorphism in the genes of the major histocompatibility complex (MHC) helps vertebrates combat novel and diverse pathogens by increasing the number of pathogen-derived proteins that can initiate the host's acquired immune response. In this study, we used a combination of presumably adaptive and neutral markers to investigate MHC evolution in populations of five salamander species within the Ambystoma velasci complex, a group consisting of 15 recently diverged species, several of which are endangered. We isolated 31 unique MHC class II β alleles from 75 total individuals from five species in this complex. MHC heterozygosity was significantly lower than expected for all five species, and we found no clear relationship between number of MHC alleles and species range, life history, or level of heterozygosity. We inferred a phylogeny representing the evolutionary history of Ambystoma MHC, with which we found signatures of positive selection on the overall gene, putative peptide-binding residues, and allelic lineages. We identified several instances of trans-species polymorphism, a hallmark of balancing selection observed in other groups of closely related species. In contrast, we did not detect comparable allelic diversity or signatures of selection on neutral loci. Additionally, we identified 17 supertypes among the 44 unique Ambystoma alleles, indicating that these sequences may encode functionally distinct MHC variants. We therefore have strong evidence that positive selection is a major evolutionary force driving patterns of MHC polymorphism in this recently radiated species complex.

Amino acid substitution at peptide-binding pockets of HLA class I molecules increases risk of severe acute GVHD and mortality

AbstractHLA disparity has a negative impact on the outcomes of hematopoietic cell transplantation (HCT). We studied the independent impact of amino acid substitution (AAS) at peptide-binding positions 9, 99, 116, and 156, and killer immunoglobulin-like receptor binding position 77 of HLA-A, B, or C, on the risks for grade 3-4 acute graft-versus-host disease (GVHD), chronic GVHD, treatment-related mortality (TRM), relapse, and overall survival. In multivariate analysis, a mismatch at HLA-C position 116 was associated with increased risk for severe acute GVHD (hazard ratio [HR] = 1.45, 95% confidence interval [CI] = 1.15-1.82, P = .0016). Mismatch at HLA-C position 99 was associated with increased transplant-related mortality (HR = 1.37, 95% CI = 1.1-1.69, P = .0038). Mismatch at HLA-B position 9 was associated with increased chronic GVHD (HR = 2.28, 95% CI = 1.36-3.82, P = .0018). No AAS were significantly associated with outcome at HLA-A. Specific AAS pair combinations with a frequency >30 were tested for association with HCT outcomes. Cysteine to tyrosine substitution at position 99 of HLA-C was associated with increased TRM (HR = 1.78, 95% = CI 1.27-2.51, P = .0009). These results demonstrate that donor-recipient mismatch for certain peptide-binding residues of the HLA class I molecule is associated with increased risk for acute and chronic GVHD and death.Key Points