Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape

We describe protein interaction quantitation (PIQ), a computational method for modeling the magnitude and shape of genome-wide DNase I hypersensitivity profiles to identify transcription factor (TF) binding sites. Through the use of machine-learning techniques, PIQ identified binding sites for >700 TFs from one DNase I hypersensitivity analysis followed by sequencing (DNase-seq) experiment with accuracy comparable to that of chromatin immunoprecipitation followed by sequencing (ChIP-seq). We applied PIQ to analyze DNase-seq data from mouse embryonic stem cells differentiating into prepancreatic and intestinal endoderm. We identified 120 and experimentally validated eight 'pioneer' TF families that dynamically open chromatin. Four pioneer TF families only opened chromatin in one direction from their motifs. Furthermore, we identified 'settler' TFs whose genomic binding is principally governed by proximity to open chromatin. Our results support a model of hierarchical TF binding in which directional and nondirectional pioneer activity shapes the chromatin landscape for population by settler TFs.National Institutes of Health (U.S.) (Common Fund 5UL1DE019581)National Institutes of Health (U.S.) (Common Fund RL1DE019021)National Institutes of Health (U.S.) (Common Fund 5TL1EB008540)National Institutes of Health (U.S.) (Grant 1U01HG007037)National Institutes of Health (U.S.) (Grant 5P01NS055923

Genome organization and chromatin analysis identify transcriptional downregulation of insulin-like growth factor signaling as a hallmark of aging in developing B cells.

BACKGROUND: Aging is characterized by loss of function of the adaptive immune system, but the underlying causes are poorly understood. To assess the molecular effects of aging on B cell development, we profiled gene expression and chromatin features genome-wide, including histone modifications and chromosome conformation, in bone marrow pro-B and pre-B cells from young and aged mice. RESULTS: Our analysis reveals that the expression levels of most genes are generally preserved in B cell precursors isolated from aged compared with young mice. Nonetheless, age-specific expression changes are observed at numerous genes, including microRNA encoding genes. Importantly, these changes are underpinned by multi-layered alterations in chromatin structure, including chromatin accessibility, histone modifications, long-range promoter interactions, and nuclear compartmentalization. Previous work has shown that differentiation is linked to changes in promoter-regulatory element interactions. We find that aging in B cell precursors is accompanied by rewiring of such interactions. We identify transcriptional downregulation of components of the insulin-like growth factor signaling pathway, in particular downregulation of Irs1 and upregulation of Let-7 microRNA expression, as a signature of the aged phenotype. These changes in expression are associated with specific alterations in H3K27me3 occupancy, suggesting that Polycomb-mediated repression plays a role in precursor B cell aging. CONCLUSIONS: Changes in chromatin and 3D genome organization play an important role in shaping the altered gene expression profile of aged precursor B cells. Components of the insulin-like growth factor signaling pathways are key targets of epigenetic regulation in aging in bone marrow B cell precursors

In silico identification of vaccine targets for 2019-nCoV

BACKGROUND: The newly identified coronavirus known as 2019-nCoV has posed a serious global health threat. According to the latest report (18-February-2020), it has infected more than 72,000 people globally and led to deaths of more than 1,016 people in China. METHODS: The 2019 novel coronavirus proteome was aligned to a curated database of viral immunogenic peptides. The immunogenicity of detected peptides and their binding potential to HLA alleles was predicted by immunogenicity predictive models and NetMHCpan 4.0. RESULTS: We report in silico identification of a comprehensive list of immunogenic peptides that can be used as potential targets for 2019 novel coronavirus (2019-nCoV) vaccine development. First, we found 28 nCoV peptides identical to Severe acute respiratory syndrome-related coronavirus (SARS CoV) that have previously been characterized immunogenic by T cell assays. Second, we identified 48 nCoV peptides having a high degree of similarity with immunogenic peptides deposited in The Immune Epitope Database (IEDB). Lastly, we conducted a de novo search of 2019-nCoV 9-mer peptides that i) bind to common HLA alleles in Chinese and European population and ii) have T Cell Receptor (TCR) recognition potential by positional weight matrices and a recently developed immunogenicity algorithm, iPred, and identified in total 63 peptides with a high immunogenicity potential. CONCLUSIONS: Given the limited time and resources to develop vaccine and treatments for 2019-nCoV, our work provides a shortlist of candidates for experimental validation and thus can accelerate development pipeline

Predicting cross-reactivity and antigen specificity of T cell receptors

Adaptive immune recognition is mediated by specific interactions between heterodimeric T cell receptors (TCRs) and their cognate peptide-MHC (pMHC) ligands, and the methods to accurately predict TCR:pMHC interaction would have profound clinical, therapeutic and pharmaceutical applications. Herein, we review recent developments in predicting cross-reactivity and antigen specificity of TCR recognition. We discuss current experimental and computational approaches to investigate cross-reactivity and antigen-specificity of TCRs and highlight how integrating kinetic, biophysical and structural features may offer valuable insights in modeling immunogenicity. We further underscore the close inter-relationship of these two interconnected notions and the need to investigate each in the light of the other for a better understanding of T cell responsiveness for the effective clinical applications

Potential CD8+ T cell cross-reactivity against SARS-CoV-2 conferred by other coronavirus strains

While individuals infected with coronavirus disease 2019 (COVID-19) manifested a broad range in susceptibility and severity to the disease, the pre-existing immune memory to related pathogens cross-reactive against SARS-CoV-2 can influence the disease outcome in COVID-19. Here, we investigated the potential extent of T cell cross-reactivity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that can be conferred by other coronaviruses and influenza virus, and generated an in silico map of public and private CD8+ T cell epitopes between coronaviruses. We observed 794 predicted SARS-CoV-2 epitopes of which 52% were private and 48% were public. Ninety-nine percent of the public epitopes were shared with SARS-CoV and 5.4% were shared with either one of four common coronaviruses, 229E, HKU1, NL63, and OC43. Moreover, to assess the potential risk of self-reactivity and/or diminished T cell response for peptides identical or highly similar to the host, we identified predicted epitopes with high sequence similarity with human proteome. Lastly, we compared predicted epitopes from coronaviruses with epitopes from influenza virus deposited in IEDB, and found only a small number of peptides with limited potential for cross-reactivity between the two virus families. We believe our comprehensive in silico profile of private and public epitopes across coronaviruses would facilitate design of vaccines, and provide insights into the presence of pre-existing coronavirus-specific memory CD8+ T cells that may influence immune responses against SARS-CoV-2

A robust deep learning workflow to predict CD8 + T-cell epitopes

Abstract Background T-cells play a crucial role in the adaptive immune system by triggering responses against cancer cells and pathogens, while maintaining tolerance against self-antigens, which has sparked interest in the development of various T-cell-focused immunotherapies. However, the identification of antigens recognised by T-cells is low-throughput and laborious. To overcome some of these limitations, computational methods for predicting CD8 + T-cell epitopes have emerged. Despite recent developments, most immunogenicity algorithms struggle to learn features of peptide immunogenicity from small datasets, suffer from HLA bias and are unable to reliably predict pathology-specific CD8 + T-cell epitopes. Methods We developed TRAP (T-cell recognition potential of HLA-I presented peptides), a robust deep learning workflow for predicting CD8 + T-cell epitopes from MHC-I presented pathogenic and self-peptides. TRAP uses transfer learning, deep learning architecture and MHC binding information to make context-specific predictions of CD8 + T-cell epitopes. TRAP also detects low-confidence predictions for peptides that differ significantly from those in the training datasets to abstain from making incorrect predictions. To estimate the immunogenicity of pathogenic peptides with low-confidence predictions, we further developed a novel metric, RSAT (relative similarity to autoantigens and tumour-associated antigens), as a complementary to ‘dissimilarity to self’ from cancer studies. Results TRAP was used to identify epitopes from glioblastoma patients as well as SARS-CoV-2 peptides, and it outperformed other algorithms in both cancer and pathogenic settings. TRAP was especially effective at extracting immunogenicity-associated properties from restricted data of emerging pathogens and translating them onto related species, as well as minimising the loss of likely epitopes in imbalanced datasets. We also demonstrated that the novel metric termed RSAT was able to estimate immunogenic of pathogenic peptides of various lengths and species. TRAP implementation is available at: https://github.com/ChloeHJ/TRAP . Conclusions This study presents a novel computational workflow for accurately predicting CD8 + T-cell epitopes to foster a better understanding of antigen-specific T-cell response and the development of effective clinical therapeutics

Single-cell atlas of colonic CD8+ T cells in ulcerative colitis

Colonic antigen-experienced lymphocytes such as tissue-resident memory CD8+ T cells can respond rapidly to repeated antigen exposure. However, their cellular phenotypes and the mechanisms by which they drive immune regulation and inflammation remain unclear. Here we compiled an unbiased atlas of human colonic CD8+ T cells in health and ulcerative colitis (UC) using single-cell transcriptomics with T-cell receptor repertoire analysis and mass cytometry. We reveal extensive heterogeneity in CD8+ T-cell composition, including expanded effector and post-effector terminally differentiated CD8+ T cells. While UC-associated CD8+ effector T cells can trigger tissue destruction and produce tumor necrosis factor (TNF)-α, post-effector cells acquire innate signatures to adopt regulatory functions that may mitigate excessive inflammation. Thus, we identify colonic CD8+ T-cell phenotypes in health and UC, define their clonal relationships and characterize terminally differentiated dysfunctional UC CD8+ T cells expressing IL-26, which attenuate acute colitis in a humanized IL-26 transgenic mouse model

Dual RNA sequencing reveals dendritic cell reprogramming in response to typhoidal Salmonella invasion

Salmonella enterica represent a major disease burden worldwide. S. enterica serovar Typhi (S. Typhi) is responsible for potentially life-threatening Typhoid fever affecting 10.9 million people annually. While non-typhoidal Salmonella (NTS) serovars usually trigger self-limiting diarrhoea, invasive NTS bacteraemia is a growing public health challenge. Dendritic cells (DCs) are key professional antigen presenting cells of the human immune system. The ability of pathogenic bacteria to subvert DC functions and prevent T cell recognition contributes to their survival and dissemination within the host. Here, we adapted dual RNA-sequencing to define how different Salmonella pathovariants remodel their gene expression in tandem with that of infected DCs. We find DCs harness iron handling pathways to defend against invading Salmonellas, which S. Typhi is able to circumvent by mounting a robust response to nitrosative stress. In parallel, we uncover the alternative strategies invasive NTS employ to impair DC functions