MHC class I molecules exploit the low G+C content of pathogen genomes for enhanced presentation

Analysis of Somatic Mutations in the TCGA-LIHC Whole Exome Sequence to Identify the Neoantigen for Immunotherapy in Hepatocellular Carcinoma

There are numerous clinically proven methods for treating cancer worldwide. Immunotherapy has been used to treat cancer with significant success in the current studies. The purpose of this work is to identify somatically altered target gene neoantigens and investigate liver cancer-related immune cell interaction and functional changes for potential immunotherapy in future clinical trials. Clinical patient data from the Cancer Genome Atlas (TCGA) database were used in this investigation. The R maf utility package was used to perform somatic analysis. The 17-mer peptide neoantigens were extracted using an in-house Python software called Peptide.py. Additionally, the epitope analysis was conducted using NetMHCpan4.1 program. Neopeptide immunogenicity was assessed using DeepCNN-Ineo, and tumor immune interaction, association with immune cells, correlation, and survival analysis were assessed using the TIMER web server. Based on somatic mutation analysis, we have identified the top 10 driver genes (TP53, TNN, CTNNB1, MUC16, ALB, PCLO, MUC4, ABCA13, APOB, and RYR2). From the superfamily of 20 HLA (Human leukocyte antigens) allele epitopes, we discovered 5653 neopeptides. Based on T cell receptor face hydrophobic analysis, these neopeptides were subjected to immunogenicity investigation. A mutation linked to tumor growth may have an impact on immune cells. According to this study's correlation and survival analysis, all driver genes may function as immune targets for liver cancer. These genes are recognized to be immune targets. In the future, immune checkpoint inhibitors may be developed to prolong patient survival times and prevent hepatocellular carcinoma (HCC) through immunotherapy.

Unique pathogen peptidomes facilitate pathogen-specific selection and specialization of MHC alleles

A key component of pathogen-specific adaptive immunity in vertebrates is the presentation of pathogen-derived antigenic peptides by major histocompatibility complex (MHC) molecules. The excessive polymorphism observed at MHC genes is widely presumed to result from the need to recognize diverse pathogens, a process called pathogen-driven balancing selection. This process assumes that pathogens differ in their peptidomes—the pool of short peptides derived from the pathogen’s proteome—so that different pathogens select for different MHC variants with distinct peptide-binding properties. Here, we tested this assumption in a comprehensive data set of 51.9 Mio peptides, derived from the peptidomes of 36 representative human pathogens. Strikingly, we found that 39.7% of the 630 pairwise comparisons among pathogens yielded not a single shared peptide and only 1.8% of pathogen pairs shared more than 1% of their peptides. Indeed, 98.8% of all peptides were unique to a single pathogen species. Using computational binding prediction to characterize the binding specificities of 321 common human MHC class-I variants, we investigated quantitative differences among MHC variants with regard to binding peptides from distinct pathogens. Our analysis showed signatures of specialization toward specific pathogens especially by MHC variants with narrow peptide-binding repertoires. This supports the hypothesis that such fastidious MHC variants might be maintained in the population because they provide an advantage against particular pathogens. Overall, our results establish a key selection factor for the excessive allelic diversity at MHC genes observed in natural populations and illuminate the evolution of variable peptide-binding repertoires among MHC variants.

Broad cross-reactivity of the T-cell repertoire achieves specific and sufficiently rapid target searching

The molecular recognition of T-cell receptors is the hallmark of the adaptive immunity. Given the finiteness of the T-cell repertoire, individual T-cell receptors are necessary to be cross-reactive to multiple antigenic peptides. In this study, we quantify the variability of the cross-reactivity by using a string model that estimates the binding affinity between two sequences of amino acids. We examine sequences of 10,000 human T-cell receptors and 10,000 antigenic peptides, and obtain a full spectrum of cross-reactivity of the receptor-peptide binding. Then, we find that the cross-reactivity spectrum is broad. Some T-cells are reactive to 1000 peptides, but some T-cells are reactive to only one or two peptides. Since the degree of cross-reactivity has a correlation with the (un)binding affinity of receptors, we further investigate how the broad cross-reactivity affects the target searching of T-cells. High cross-reactive T-cells may not require many trials for searching correct targets, but they may spend long time to unbind from incorrect targets. In contrast, low cross-reactive T-cells may not spend long time to ignore incorrect targets, but they require many trials for screening correct targets. We evaluate this hypothesis, and show that the broad cross-reactivity of the natural T-cell repertoire can balance the trade-off between the rapid screening and unbinding penalty.

TCR contact residue hydrophobicity is a hallmark of immunogenic CD8+ T cell epitopes

Despite the availability of major histocompatibility complex (MHC)-binding peptide prediction algorithms, the development of T-cell vaccines against pathogen and tumor antigens remains challenged by inefficient identification of immunogenic epitopes. CD8(+) T cells must distinguish immunogenic epitopes from nonimmunogenic self peptides to respond effectively against an antigen without endangering the viability of the host. Because this discrimination is fundamental to our understanding of immune recognition and critical for rational vaccine design, we interrogated the biochemical properties of 9,888 MHC class I peptides. We identified a strong bias toward hydrophobic amino acids at T-cell receptor contact residues within immunogenic epitopes of MHC allomorphs, which permitted us to develop and train a hydrophobicity-based artificial neural network (ANN-Hydro) to predict immunogenic epitopes. The immunogenicity model was validated in a blinded in vivo overlapping epitope discovery study of 364 peptides from three HIV-1 Gag protein variants. Applying the ANN-Hydro model on existing peptide-MHC algorithms consistently reduced the number of candidate peptides across multiple antigens and may provide a correlate with immunodominance. Hydrophobicity of TCR contact residues is a hallmark of immunogenic epitopes and marks a step toward eliminating the need for empirical epitope testing for vaccine development.

Properties of MHC class I presented peptides that enhance immunogenicity

T-cells have to recognize peptides presented on MHC molecules to be activated and elicit their effector functions. Several studies demonstrate that some peptides are more immunogenic than others and therefore more likely to be T-cell epitopes. We set out to determine which properties cause such differences in immunogenicity. To this end, we collected and analyzed a large set of data describing the immunogenicity of peptides presented on various MHC-I molecules. Two main conclusions could be drawn from this analysis: First, in line with previous observations, we showed that positions P4–6 of a presented peptide are more important for immunogenicity. Second, some amino acids, especially those with large and aromatic side chains, are associated with immunogenicity. This information was combined into a simple model that was used to demonstrate that immunogenicity is, to a certain extent, predictable. This model (made available at http://tools.iedb.org/immunogenicity/) was validated with data from two independent epitope discovery studies. Interestingly, with this model we could show that T-cells are equipped to better recognize viral than human (self) peptides. After the past successful elucidation of different steps in the MHC-I presentation pathway, the identification of variables that influence immunogenicity will be an important next step in the investigation of T-cell epitopes and our understanding of cellular immune responses.

T-cells have to recognize peptides presented on MHC molecules to be activated and elicit their effector functions. Some peptide-MHC-I complexes (pMHCs) are better recognized by T-cells; we call such pMHCs more immunogenic. For other pMHCs, no recognizing T-cells seem to exist; we call such pMHCs non-immunogenic. We set out to determine which properties of pMHCs cause such differences in immunogenicity, by carefully collecting a large set of immunogenic and non-immunogenic pMHCs, and analysing the difference between these sets. Two important observations were made: First, in line with previous observations, we showed that positions P4–6 of a presented peptide are more important for immunogenicity. Second, some amino acids, especially those with large and aromatic side chains, seem to be better recognized by T-cells as they associate with immunogenicity. Next, this information was combined into a simple model to predict the immunogenicity of new pMHCs (this model is made available at http://tools.iedb.org/immunogenicity/). Interestingly, with this model we could show that T-cells are equipped to strongly recognize viral peptides. After the past successful elucidation of different steps in the MHC-I presentation pathway, the identification of variables that influence immunogenicity will be an important next step in the investigation of T-cell epitopes and our understanding of cellular immune responses.

Positional bias of MHC class I restricted T-cell epitopes in viral antigens is likely due to a bias in conservation

The immune system rapidly responds to intracellular infections by detecting MHC class I restricted T-cell epitopes presented on infected cells. It was originally thought that viral peptides are liberated during constitutive protein turnover, but this conflicts with the observation that viral epitopes are detected within minutes of their synthesis even when their source proteins exhibit half-lives of days. The DRiPs hypothesis proposes that epitopes derive from Defective Ribosomal Products (DRiPs), rather than degradation of mature protein products. One potential source of DRiPs is premature translation termination. If this is a major source of DRiPs, this should be reflected in positional bias towards the N-terminus. By contrast, if downstream initiation is a major source of DRiPs, there should be positional bias towards the C-terminus. Here, we systematically assessed positional bias of epitopes in viral antigens, exploiting the large set of data available in the Immune Epitope Database and Analysis Resource. We show a statistically significant degree of positional skewing among epitopes; epitopes from both ends of antigens tend to be under-represented. Centric-skewing correlates with a bias towards class I binding peptides being over-represented in the middle, in parallel with a higher degree of evolutionary conservation.

Character of chicken polymorphic major histocompatibility complex class II alleles of 3 Chinese local breeds

To better understand the major histocompatibility complex (MHC) genetic character of domestic birds, we sequenced and analyzed chicken MHC II (B-L) genes of 3 local chicken breeds, derived from 3 separate areas in China. We amplified cDNA sequences from 105 individuals, accounting for 35 alleles. Some of the same B-LB alleles with a high frequency were found in all samples. The putative B-L α-chain had few polymorphic sites, whereas the B-L β-chain had several polymorphic sites. Most of the mutation positions were located in the B-LB β1 domain encoded by exon 2, especially in the peptide-binding region. This indicated that the highly polymorphic peptide-binding region could potentiate binding diverse antigen epitopes. The comparison of 3-D molecule structures of chicken B-L and human HLA-DR1 revealed a distinctly structural similarity, but the chicken B-L molecule had more polymorphic sites than the human HLA-DR1 molecule, which presumably might be a mechanism to compensate for responding to a wider array of pathogens due to fewer loci for chicken. Moreover, some conserved sites in human and chicken MHC class II molecules reflected their common ancestry and similar functions. These results suggest that the chicken B-L gene showed more polymorphic sites and distinctly dominant trans-breed alleles, potentially to adapt to pathogens.

Estimating the fitness cost of escape from HLA presentation in HIV-1 protease and reverse transcriptase

Human immunodeficiency virus (HIV-1) is, like most pathogens, under selective pressure to escape the immune system of its host. In particular, HIV-1 can avoid recognition by cytotoxic T lymphocytes (CTLs) by altering the binding affinity of viral peptides to human leukocyte antigen (HLA) molecules, the role of which is to present those peptides to the immune system. It is generally assumed that HLA escape mutations carry a replicative fitness cost, but these costs have not been quantified. In this study, we assess the replicative cost of mutations which are likely to escape presentation by HLA molecules in the region of HIV-1 protease and reverse transcriptase. Specifically, we combine computational approaches for prediction of in vitro replicative fitness and peptide binding affinity to HLA molecules. We find that mutations which impair binding to HLA-A molecules tend to have lower in vitro replicative fitness than mutations which do not impair binding to HLA-A molecules, suggesting that HLA-A escape mutations carry higher fitness costs than non-escape mutations. We argue that the association between fitness and HLA-A binding impairment is probably due to an intrinsic cost of escape from HLA-A molecules, and these costs are particularly strong for HLA-A alleles associated with efficient virus control. Counter-intuitively, we do not observe a significant effect in the case of HLA-B, but, as discussed, this does not argue against the relevance of HLA-B in virus control. Overall, this article points to the intriguing possibility that HLA-A molecules preferentially target more conserved regions of HIV-1, emphasizing the importance of HLA-A genes in the evolution of HIV-1 and RNA viruses in general.

Our immune system can recognize and kill virus-infected cells by distinguishing between self and virus-derived protein fragments, called peptides, displayed on the surface of each cell. One requirement for a successful recognition is that those peptides bind to the human leukocyte antigen (HLA) class I molecules, which present them to the immune system. As a counter-strategy, human immunodeficiency virus type 1 (HIV-1) can acquire mutations that prevent this binding, thereby helping the virus to escape the surveillance of T-lymphocytes. It is likely that the virus pays a replicative cost for such escape mutations, but the magnitude of this cost has remained elusive. Here, we quantified this fitness cost in HIV-1 protease and reverse transcriptase by combining two computational systems biology approaches: one for prediction of in vitro replicative fitness, and one for the prediction of the efficiency of peptide binding to HLA. We found that in viral proteins targeted by HLA-A molecules, mutations which disrupt binding to those molecules carry a lower replicative fitness than mutations which do not have such an effect. We argue that these results are consistent with the hypothesis that our immune systems might have evolved to target genetic regions of RNA viruses which are costly for the pathogen to alter.

Proteome sampling by the HLA class I antigen processing pathway

The peptide repertoire that is presented by the set of HLA class I molecules of an individual is formed by the different players of the antigen processing pathway and the stringent binding environment of the HLA class I molecules. Peptide elution studies have shown that only a subset of the human proteome is sampled by the antigen processing machinery and represented on the cell surface. In our study, we quantified the role of each factor relevant in shaping the HLA class I peptide repertoire by combining peptide elution data, in silico predictions of antigen processing and presentation, and data on gene expression and protein abundance. Our results indicate that gene expression level, protein abundance, and rate of potential binding peptides per protein have a clear impact on sampling probability. Furthermore, once a protein is available for the antigen processing machinery in sufficient amounts, C-terminal processing efficiency and binding affinity to the HLA class I molecule determine the identity of the presented peptides. Having studied the impact of each of these factors separately, we subsequently combined all factors in a logistic regression model in order to quantify their relative impact. This model demonstrated the superiority of protein abundance over gene expression level in predicting sampling probability. Being able to discriminate between sampled and non-sampled proteins to a significant degree, our approach can potentially be used to predict the sampling probability of self proteins and of pathogen-derived proteins, which is of importance for the identification of autoimmune antigens and vaccination targets.

HLA class I molecules are expressed on the cell surface of almost all cells of the human body in complex with short fragments (peptides) of cytosolic proteins, thereby providing a snapshot of the intracellular state of a cell to circulating CD8+ T cells. Several processes are involved in shaping the peptide ligand repertoire of an HLA class I molecule, which generally represents only a small fraction of the proteins available in the cytosol. In our work we addressed protein sampling by HLA class I molecules to answer two questions: 1) Which proteins are sampled by the antigen processing pathway and why, and 2) which peptides of a given protein are picked to represent the source protein on the cell surface? To this end we quantified the contribution of each process involved in peptide processing and presentation individually and combined them into a logistic regression model. This simple model enabled us to predict the sampling probability of self proteins and may aid in the identification of autoimmune antigens.

Degenerate T-cell recognition of peptides on MHC molecules creates large holes in the T-cell repertoire

The cellular immune system screens peptides presented by host cells on MHC molecules to assess if the cells are infected. In this study we examined whether the presented peptides contain enough information for a proper self/nonself assessment by comparing the presented human (self) and bacterial or viral (nonself) peptides on a large number of MHC molecules. For all MHC molecules tested, only a small fraction of the presented nonself peptides from 174 species of bacteria and 1000 viral proteomes ([Formula: see text]0.2%) is shown to be identical to a presented self peptide. Next, we use available data on T-cell receptor-peptide-MHC interactions to estimate how well T-cells distinguish between similar peptides. The recognition of a peptide-MHC by the T-cell receptor is flexible, and as a result, about one-third of the presented nonself peptides is expected to be indistinguishable (by T-cells) from presented self peptides. This suggests that T-cells are expected to remain tolerant for a large fraction of the presented nonself peptides, which provides an explanation for the "holes in the T-cell repertoire" that are found for a large fraction of foreign epitopes. Additionally, this overlap with self increases the need for efficient self tolerance, as many self-similar nonself peptides could initiate an autoimmune response. Degenerate recognition of peptide-MHC-I complexes by T-cells thus creates large and potentially dangerous overlaps between self and nonself.

Percent of highly immunogenic amino acid residues forming B-cell epitopes is higher in homologous proteins encoded by GC-rich genes

We analyzed the dependence of the percent of highly immunogenic amino acid residues included in B-cell epitopes of homologous proteins on the GC-content (G+C) of genes coding for them in twenty-seven lineages of proteins (and subsequent genes), which belong to seven Varicello and five Simplex viruses. We found out that proteins encoded by genes of a high GC-content usually contain more targets for humoral immune response than their homologs encoded by GC-poor genes. This tendency is characteristic not only to the lineages of glycoproteins, which are the main targets for humoral immune response against Simplex and Varicello viruses, but also to the lineages of capsid proteins and even "housekeeping" enzymes. The percent of amino acids included in linear B-cell epitopes has been predicted for 324 proteins by BepiPred algorithm (www.cbs.dtu.dk/services/BepiPred), the percent of highly immunogenic amino acids included in discontinuous B-cell epitopes and the percent of exposed amino acid residues have been predicted by Epitopia algorithm (http://epitopia.tau.ac.il/). Immunological consequences of the directional mutational GC-pressure are mostly due to the decrease in the total usage of highly hydrophobic amino acids and due to the increase in proline and glycine levels of usage in proteins. The weaker the negative selection on amino acid substitutions caused by symmetric mutational pressure, the higher the slope of direct dependence of the percent of highly immunogenic amino acids included in B-cell epitopes on G+C.

Was the ancestral MHC involved in innate immunity?

Understanding the nature of MHC class I presentation preferences is a challenging prospect. Large sets of peptide-MHC-class I complexes have been screened for their binding affinities and recent studies have shown that HLA-A share a preference for binding peptides derived from pathogens; however, no mechanism explaining the observed preferences has been demonstrated so far. In this issue of the European Journal of Immunology, a study demonstrates that HLA-A, but not HLA-B, preferentially recognises peptides enriched in amino acids encoded by sequences with low G+C content, and therefore recognises peptides associated with pathogens - low G+C content being a general feature of lower organisms. The authors of the study provide exciting results contributing to the understanding of the nature of MHC-I presentation preferences and MHC-I evolution. Although significant results are presented by the authors, here, we challenge the interpretation whereby HLA-A has been evolutionarily selected for such a function and appeal for the use of comparative phylogenetic methods to substantiate it. We propose a method to ascertain whether ancestral MHC recognised peptides from pathogens and hence was involved in the non-specific recognition of such organisms. Moreover, we suggest that ancestral MHC may have been involved in innate immune responses before being recruited for adaptive immunity.