Connecting MHC-I-binding motifs with HLA alleles via deep learning

The selection of peptides presented by MHC molecules is crucial for antigen discovery. Previously, several predictors have shown impressive performance on binding affinity. However, the decisive MHC residues and their relation to the selection of binding peptides are still unrevealed. Here, we connected HLA alleles with binding motifs via our deep learning-based framework, MHCfovea. MHCfovea expanded the knowledge of MHC-I-binding motifs from 150 to 13,008 alleles. After clustering N-terminal and C-terminal sub-motifs on both observed and unobserved alleles, MHCfovea calculated the hyper-motifs and the corresponding allele signatures on the important positions to disclose the relation between binding motifs and MHC-I sequences. MHCfovea delivered 32 pairs of hyper-motifs and allele signatures (HLA-A: 13, HLA-B: 12, and HLA-C: 7). The paired hyper-motifs and allele signatures disclosed the critical polymorphic residues that determine the binding preference, which are believed to be valuable for antigen discovery and vaccine design when allele specificity is concerned.

Exchange catalysis by tapasin exploits conserved and allele-specific features of MHC-I molecules

The repertoire of peptides presented by major histocompatibility complex class I (MHC-I) molecules on the cell surface is tailored by the ER-resident peptide loading complex (PLC), which contains the exchange catalyst tapasin. Tapasin stabilizes MHC-I molecules and promotes the formation of stable peptide-MHC-I (pMHC-I) complexes that serve as T cell antigens. Exchange of suboptimal by high-affinity ligands is catalyzed by tapasin, but the underlying mechanism is still elusive. Here we analyze the tapasin-induced changes in MHC-I dynamics, and find the catalyst to exploit two essential features of MHC-I. First, tapasin recognizes a conserved allosteric site underneath the α2-1-helix of MHC-I, ‘loosening’ the MHC-I F-pocket region that accomodates the C-terminus of the peptide. Second, the scoop loop11–20 of tapasin relies on residue L18 to target the MHC-I F-pocket, enabling peptide exchange. Meanwhile, tapasin residue K16 plays an accessory role in catalysis of MHC-I allotypes bearing an acidic F-pocket. Thus, our results provide an explanation for the observed allele-specificity of catalyzed peptide exchange.

Allele-specific epigenetic activity in prostate cancer and normal prostate tissue implicates prostate cancer risk mechanisms

Background: Genome-wide association studies of prostate cancer have identified >250 significant risk loci, but the causal variants and mechanisms for these loci remain largely unknown. Here, we sought to identify and characterize risk harboring regulatory elements by integrating epigenomes from primary prostate tumor and normal tissues of 27 patients across the H3K27ac, H3K4me3, and H3K4me2 histone marks and FOXA1 and HOXB13 transcription factors. Results: We identified 7,371 peaks with significant allele-specificity (asQTL peaks). Showcasing their relevance to prostate cancer risk, H3K27ac T-asQTL peaks were the single annotation most enriched for prostate cancer GWAS heritability (40x), significantly higher than corresponding non-asQTL H3K27ac peaks (14x) or coding regions (14x). Surprisingly, fine-mapped GWAS risk variants were most significantly enriched for asQTL peaks observed in tumors, including asQTL peaks that were differentially imbalanced with respect to tumor-normal states. These data pinpointed putative causal regulatory elements at 20 GWAS loci, of which 11 were detected only in the tumor samples. More broadly, tumor-specific asQTLs were enriched for expression QTLs in benign tissues as well as accessible regions found in stem cells, supporting a hypothesis where some germline variants become reactivated during/after transformation and can be captured by epigenomic profiling of the tumor. Conclusion: Our study demonstrates the power of allele-specificity in chromatin signals to uncover GWAS mechanisms, highlights the relevance of tumor-specific regulation in the context of cancer risk, and prioritizes multiple loci for experimental follow-up.

Exonic variants undergoing allele-specific selection in cancers

Background Allelic imbalance (AI) in tumors is caused by chromosomal and sub-chromosomal gains and losses. Results We evaluated AI at 109,086 germline exonic SNP loci in four cancer types, and identified a set of SNPs that demonstrate strong tumor allele specificity in AI events. Further analyses demonstrated that these alleles show consistently different frequencies in the cancer population compared to the healthy population and are significantly enriched for predicted protein-damaging variants. Moreover, genes harboring SNPs that demonstrate allele specificity are enriched for cancer-related biological processes and are more likely to be essential in cancer cells. Conclusions In summary, our study provides a unique and complementary method to identify genes and variants that are relevant to carcinogenesis.

Connecting MHC-I-binding motifs with HLA alleles via deep learning

The selection of peptides presented by MHC molecules is crucial for antigen discovery. Previously, several predictors have shown impressive performance on binding affinity. However, the decisive MHC residues and their relation to the selection of binding peptides are still unrevealed. Here, we connected HLA alleles with binding motifs via our deep learning-based framework, MHCfovea. MHCfovea expanded the knowledge of MHC-I-binding motifs from 150 to 13,008 alleles. After clustering N-terminal and C-terminal sub-motifs on both observed and unobserved alleles, MHCfovea calculated the hyper-motifs and the corresponding allele signatures on the important positions to disclose the relation between binding motifs and MHC-I sequences. MHCfovea delivered 32 pairs of hyper-motifs and allele signatures (HLA-A: 13, HLA-B: 12, and HLA-C: 7). The paired hyper-motifs and allele signatures disclosed the critical polymorphic residues that determine the binding preference, which are believed to be valuable for antigen discovery and vaccine design when allele specificity is concerned.

The sugarcane and sorghum kinomes: insights into evolutionary expansion and diversification

The protein kinase (PK) superfamily is one of the largest superfamilies in plants and is the core regulator of cellular signaling. Even considering this substantial importance, the kinomes of sugarcane and sorghum have not been profiled. Here we identified and profiled the complete kinomes of the polyploid Saccharum spontaneum (Ssp) and Sorghum bicolor (Sbi), a close diploid relative. The Sbi kinome was composed of 1,210 PKs; for Ssp, we identified 2,919 PKs when disregarding duplications and allelic copies, which were related to 1,345 representative gene models. The Ssp and Sbi PKs were grouped into 20 groups and 120 subfamilies and exhibited high compositional similarities and evolutionary divergences. By utilizing the collinearity between these species, this study offers insights about Sbi and Ssp speciation, PK differentiation and selection. We assessed the PK subfamily expression profiles via RNA-Seq, identifying significant similarities between Sbi and Ssp. Moreover, through coexpression networks, we inferred a core structure of kinase interactions with specific key elements. This study is the first to categorize the allele specificity of a kinome and provides a wide reservoir of molecular and genetic information, enhancing the understanding of the evolutionary history of Sbi and Ssp PKs.HighlightThis study describes the catalog of kinase gene family in Saccharum spontaneum and Sorghum bicolor, providing a reservoir of molecular features and expression patterns based on RNA-Seq and co-expression networks.

Quantification of DNA methylation using methylation-sensitive restriction enzymes and multiplex digital PCR

ABSTRACTEpigenetic regulation is important in human health and disease, but the exact mechanisms remain largely enigmatic. DNA methylation represents one well-studied aspect of epigenetic regulation, but is challenging to quantify accurately. In this study, we introduce a digital approach for the absolute quantification of the amount, density and allele-specificity of DNA methylation. Combining the efficiency of methylation-sensitive restriction enzymes with the quantitative power of digital PCR, DNA methylation is measured accurately without the need to treat the DNA samples with sodium bisulphite. Moreover, as the combination of PCR amplicon and restriction enzyme is flexible, the context and density of DNA methylation can be taken into account. Additionally, by extending the experimental setup to a multiplex digital PCR, methylation markers may be analysed together with physically linked genetic markers to determine the allele-specificity of the methylation. In-silico simulations demonstrated the mathematical validity of the experimental setup. Next the approach was validated in a variety of healthy and malignant reference samples in the context of RASSF1A promotor methylation. RASSF1A is an established tumour suppressor gene, that is aberrantly methylated in many human cancers. A dilution series of well-characterized reference samples cross-validated the sensitivity and dynamic range of the approach. Compared to conventional PCR based methods, digital PCR provides a more accurate and more sensitive approach to quantify DNA methylation. As no sodium bisulphite conversion is needed, also analysis of minute amounts of DNA could be carried out efficiently.