HeteroTCR: A heterogeneous graph neural network-based method for predicting peptide-TCR interaction.

Identifying interactions between T-cell receptors (TCRs) and immunogenic peptides holds profound implications across diverse research domains and clinical scenarios. Unsupervised clustering models (UCMs) cannot predict peptide-TCR binding directly, while supervised predictive models (SPMs) often face challenges in identifying antigens previously unencountered by the immune system or possessing limited TCR binding repertoires. Therefore, we propose HeteroTCR, an SPM based on Heterogeneous Graph Neural Network (GNN), to accurately predict peptide-TCR binding probabilities. HeteroTCR captures within-type (TCR-TCR or peptide-peptide) similarity information and between-type (peptide-TCR) interaction insights for predictions on unseen peptides and TCRs, surpassing limitations of existing SPMs. Our evaluation shows HeteroTCR outperforms state-of-the-art models on independent datasets. Ablation studies and visual interpretation underscore the Heterogeneous GNN module's critical role in enhancing HeteroTCR's performance by capturing pivotal binding process features. We further demonstrate the robustness and reliability of HeteroTCR through validation using single-cell datasets, aligning with the expectation that pMHC-TCR complexes with higher predicted binding probabilities correspond to increased binding fractions.

Accurate Early Detection and EGFR Mutation Status Prediction of Lung Cancer Using Plasma cfDNA Coverage Patterns: A Proof-of-Concept Study.

Lung cancer is a major global health concern with a low survival rate, often due to late-stage diagnosis. Liquid biopsy offers a non-invasive approach to cancer detection and monitoring, utilizing various features of circulating cell-free DNA (cfDNA). In this study, we established two models based on cfDNA coverage patterns at the transcription start sites (TSSs) from 6X whole-genome sequencing: an Early Cancer Screening Model and an EGFR mutation status prediction model. The Early Cancer Screening Model showed encouraging prediction ability, especially for early-stage lung cancer. The EGFR mutation status prediction model exhibited high accuracy in distinguishing between EGFR-positive and wild-type cases. Additionally, cfDNA coverage patterns at TSSs also reflect gene expression patterns at the pathway level in lung cancer patients. These findings demonstrate the potential applications of cfDNA coverage patterns at TSSs in early cancer screening and in cancer subtyping.

VitTCR: A deep learning method for peptide recognition prediction.

This study introduces VitTCR, a predictive model based on the vision transformer (ViT) architecture, aimed at identifying interactions between T cell receptors (TCRs) and peptides, crucial for developing cancer immunotherapies and vaccines. VitTCR converts TCR-peptide interactions into numerical AtchleyMaps using Atchley factors for prediction, achieving AUROC (0.6485) and AUPR (0.6295) values. Benchmark analysis indicates VitTCR's performance is comparable to other models, with further comparative studies suggested to understand its effectiveness in varied contexts. Additionally, integrating a positional bias weight matrix (PBWM), derived from amino acid contact probabilities in structurally resolved pMHC-TCR complexes, slightly improves VitTCR's accuracy. The model's predictions show weak yet statistically significant correlations with immunological factors like T cell clonal expansion and activation percentages, underscoring the biological relevance of VitTCR's predictive capabilities. VitTCR emerges as a valuable computational tool for predicting TCR-peptide interactions, offering insights for immunotherapy and vaccine development.

Dysregulation of Wnt/ß-catenin signaling contributes to intestinal inflammation through regulation of group 3 innate lymphoid cells.

RORγt+ group 3 innate lymphoid cells (ILC3s) are essential for intestinal homeostasis. Dysregulation of ILC3s has been found in the gut of patients with inflammatory bowel disease and colorectal cancer, yet the specific mechanisms still require more investigation. Here we observe increased ß-catenin in intestinal ILC3s from inflammatory bowel disease and colon cancer patients compared with healthy donors. In contrast to promoting RORγt expression in T cells, activation of Wnt/ß-catenin signaling in ILC3s suppresses RORγt expression, inhibits its proliferation and function, and leads to a deficiency of ILC3s and subsequent intestinal inflammation in mice. Activated ß-catenin and its interacting transcription factor, TCF-1, cannot directly suppress RORγt expression, but rather alters global chromatin accessibility and inhibits JunB expression, which is essential for RORγt expression in ILC3s. Together, our findings suggest that dysregulated Wnt/ß-catenin signaling impairs intestinal ILC3s through TCF-1/JunB/RORγt regulation, further disrupting intestinal homeostasis, and promoting inflammation and cancer.

Multiomics-integrated deep language model enables in silico genome-wide detection of transcription factor binding site in unexplored biosamples.

MOTIVATION: Transcription factor binding sites (TFBS) are regulatory elements that have significant impact on transcription regulation and cell fate determination. Canonical motifs, biological experiments, and computational methods have made it possible to discover TFBS. However, most existing in silico TFBS prediction models are solely DNA-based, and are trained and utilized within the same biosample, which fail to infer TFBS in experimentally unexplored biosamples. RESULTS: Here, we propose TFBS prediction by modified TransFormer (TFTF), a multimodal deep language architecture which integrates multiomics information in epigenetic studies. In comparison to existing computational techniques, TFTF has state-of-the-art accuracy, and is also the first approach to accurately perform genome-wide detection for cell-type and species-specific TFBS in experimentally unexplored biosamples. Compared to peak calling methods, TFTF consistently discovers true TFBS in threshold tuning-free way, with higher recalled rates. The underlying mechanism of TFTF reveals greater attention to the targeted TF's motif region in TFBS, and general attention to the entire peak region in non-TFBS. TFTF can benefit from the integration of broader and more diverse data for improvement and can be applied to multiple epigenetic scenarios. AVAILABILITY AND IMPLEMENTATION: We provide a web server (https://tftf.ibreed.cn/) for users to utilize TFTF model. Users can train TFTF model and discover TFBS with their own data.

Glioma-derived ANXA1 suppresses the immune response to TLR3 ligands by promoting an anti-inflammatory tumor microenvironment.

A highly immunosuppressive tumor microenvironment (TME) and the presence of the blood‒brain barrier are the two major obstacles to eliciting an effective immune response in patients with high-grade glioma (HGG). Here, we tried to enhance the local innate immune response in relapsed HGG by intracranially injecting poly(I:C) to establish a robust antitumor immune response in this registered clinical trial (NCT03392545). During the follow-up, 12/27 (44.4%) patients who achieved tumor control concomitant with survival benefit were regarded as responders in our study. We found that the T-cell receptor (TCR) repertoire in the TME was reshaped after poly(I:C) treatment. Based on the RNA-seq analysis of tumor samples, the expression of annexin A1 (ANXA1) was significantly upregulated in the tumor cells of nonresponders, which was further validated at the protein level. In vitro and in vivo experiments showed that ANXA1 could induce the production of M2-like macrophages and microglia via its surface receptor formyl peptide receptor 1 (FPR1) to establish a Treg cell-driven immunosuppressive TME and suppress the antitumor immune response facilitated by poly(I:C). The ANXA1/FPR1 signaling axis can inhibit the innate immune response of glioma patients by promoting an anti-inflammatory and Treg-driven TME. Moreover, ANXA1 could serve as a reliable predictor of response to poly(I:C), with a notable predictive accuracy rate of 92.3%. In light of these notable findings, this study unveils a new perspective of immunotherapy for gliomas.

Aberrant accumulation of Kras-dependent pervasive transcripts during tumor progression renders cancer cells dependent on PAF1 expression.

KRAS is the most commonly mutated oncogene in human cancer, and mutant KRAS is responsible for over 90% of pancreatic ductal adenocarcinoma (PDAC), the most lethal cancer. Here, we show that RNA polymerase II-associated factor 1 complex (PAF1C) is specifically required for survival of PDAC but not normal adult pancreatic cells. We show that PAF1C maintains cancer cell genomic stability by restraining overaccumulation of enhancer RNAs (eRNAs) and promoter upstream transcripts (PROMPTs) driven by mutant Kras. Loss of PAF1C leads to cancer-specific lengthening and accumulation of pervasive transcripts on chromatin and concomitant aberrant R-loop formation and DNA damage, which, in turn, trigger cell death. We go on to demonstrate that the global transcriptional hyperactivation driven by Kras signaling during tumorigenesis underlies the specific demand for PAF1C by cancer cells. Our work provides insights into how enhancer transcription hyperactivation causes general transcription factor addiction during tumorigenesis.

scRNA-seq of gastric tumor shows complex intercellular interaction with an alternative T cell exhaustion trajectory.

The tumor microenvironment (TME) in gastric cancer (GC) has been shown to be important for tumor control but the specific characteristics for GC are not fully appreciated. We generated an atlas of 166,533 cells from 10 GC patients with matched paratumor tissues and blood. Our results show tumor-associated stromal cells (TASCs) have upregulated activity of Wnt signaling and angiogenesis, and are negatively correlated with survival. Tumor-associated macrophages and LAMP3+ DCs are involved in mediating T cell activity and form intercellular interaction hubs with TASCs. Clonotype and trajectory analysis demonstrates that Tc17 (IL-17+CD8+ T cells) originate from tissue-resident memory T cells and can subsequently differentiate into exhausted T cells, suggesting an alternative pathway for T cell exhaustion. Our results indicate that IL17+ cells may promote tumor progression through IL17, IL22, and IL26 signaling, highlighting the possibility of targeting IL17+ cells and associated signaling pathways as a therapeutic strategy to treat GC.

Rapid screening of TCR-pMHC interactions by the YAMTAD system.

Personalized immunotherapy, such as cancer vaccine and TCR-T methods, demands rapid screening of TCR-pMHC interactions. While several screening approaches have been developed, their throughput is limited. Here, the Yeast Agglutination Mediated TCR antigen Discovery system (YAMTAD) was designed and demonstrated to allow fast and unbiased library-on-library screening of TCR-pMHC interactions. Our proof-of-principle study achieved high sensitivity and specificity in identifying antigens for a given TCR and identifying TCRs recognizing a given pMHC for modest library sizes. Finally, the enrichment of high-affinity TCR-pMHC interactions by YAMTAD in library-on-library screening was demonstrated. Given the high throughput (106-108 × 106-108 in theory) and simplicity (identifying TCR-pMHC interactions without purification of TCR and pMHC) of YAMTAD, this study provides a rapid but effective platform for TCR-pMHC interaction screening, with valuable applications in future personalized immunotherapy.

Tumor-derived Jagged1 promotes cancer progression through immune evasion.

Immune checkpoint inhibitor (ICI) therapy is generating remarkable responses in individuals with cancer, but only a small portion of individuals with breast cancer respond well. Here we report that tumor-derived Jagged1 is a key regulator of the tumor immune microenvironment. Jagged1 promotes tumorigenesis in multiple spontaneous mammary tumor models. Through Jagged1-induced Notch activation, tumor cells increase expression and secretion of multiple cytokines to help recruit macrophages into the tumor microenvironment. Educated macrophages crosstalk with tumor-infiltrating T cells to inhibit T cell proliferation and tumoricidal activity. In individuals with triple-negative breast cancer, a high expression level of Jagged1 correlates with increased macrophage infiltration and decreased T cell activity. Co-administration of an ICI PD-1 antibody with a Notch inhibitor significantly inhibits tumor growth in breast cancer models. Our findings establish a distinct signaling cascade by which Jagged1 promotes adaptive immune evasion of tumor cells and provide several possible therapeutic targets.

SIGNET: single-cell RNA-seq-based gene regulatory network prediction using multiple-layer perceptron bagging.

High-throughput single-cell RNA-seq data have provided unprecedented opportunities for deciphering the regulatory interactions among genes. However, such interactions are complex and often nonlinear or nonmonotonic, which makes their inference using linear models challenging. We present SIGNET, a deep learning-based framework for capturing complex regulatory relationships between genes under the assumption that the expression levels of transcription factors participating in gene regulation are strong predictors of the expression of their target genes. Evaluations based on a variety of real and simulated scRNA-seq datasets showed that SIGNET is more sensitive to ChIP-seq validated regulatory interactions in different types of cells, particularly rare cells. Therefore, this process is more effective for various downstream analyses, such as cell clustering and gene regulatory network inference. We demonstrated that SIGNET is a useful tool for identifying important regulatory modules driving various biological processes.

Aberrant NAD+ metabolism underlies Zika virus-induced microcephaly.

Zika virus (ZIKV) infection during pregnancy can cause microcephaly in newborns, yet the underlying mechanisms remain largely unexplored. Here, we reveal extensive and large-scale metabolic reprogramming events in ZIKV-infected mouse brains by performing a multi-omics study comprising transcriptomics, proteomics, phosphoproteomics and metabolomics approaches. Our proteomics and metabolomics analyses uncover dramatic alteration of nicotinamide adenine dinucleotide (NAD+)-related metabolic pathways, including oxidative phosphorylation, TCA cycle and tryptophan metabolism. Phosphoproteomics analysis indicates that MAPK and cyclic GMP-protein kinase G signaling may be associated with ZIKV-induced microcephaly. Notably, we demonstrate the utility of our rich multi-omics datasets with follow-up in vivo experiments, which confirm that boosting NAD+ by NAD+ or nicotinamide riboside supplementation alleviates cell death and increases cortex thickness in ZIKV-infected mouse brains. Nicotinamide riboside supplementation increases the brain and body weight as well as improves the survival in ZIKV-infected mice. Our study provides a comprehensive resource of biological data to support future investigations of ZIKV-induced microcephaly and demonstrates that metabolic alterations can be potentially exploited for developing therapeutic strategies.

The coSIR model predicts effective strategies to limit the spread of SARS-CoV-2 variants with low severity and high transmissibility.

Multiple new variants of SARS-CoV-2 have been identified as the COVID-19 pandemic spreads across the globe. However, most epidemic models view the virus as static and unchanging and thus fail to address the consequences of the potential evolution of the virus. Here, we built a competitive susceptible-infected-removed (coSIR) model to simulate the competition between virus strains of differing severities or transmissibility under various virus control policies. The coSIR model predicts that although the virus is extremely unlikely to evolve into a "super virus" that causes an increased fatality rate, virus variants with less severe symptoms can lead to potential new outbreaks and can cost more lives over time. The present model also demonstrates that the protocols restricting the transmission of the virus, such as wearing masks and social distancing, are the most effective strategy in reducing total mortality. A combination of adequate testing and strict quarantine is a powerful alternative to policies such as mandatory stay-at-home orders, which may have an enormous negative impact on the economy. In addition, building Mobile Cabin Hospitals can be effective and efficient in reducing the mortality rate of highly infectious virus strains. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11071-021-06705-8.

Chrom-Lasso: a lasso regression-based model to detect functional interactions using Hi-C data.

Hi-C is a genome-wide assay based on Chromosome Conformation Capture and high-throughput sequencing to decipher 3D chromatin organization in the nucleus. However, computational methods to detect functional interactions utilizing Hi-C data face challenges including the correction for various sources of biases and the identification of functional interactions with low counts of interacting fragments. We present Chrom-Lasso, a lasso linear regression model that removes complex biases assumption-free and identifies functional interacting loci with increased power by combining information of local reads distribution surrounding the area of interest. We showed that interacting regions identified by Chrom-Lasso are more enriched for 5C validated interactions and functional GWAS hits than that of GOTHiC and Fit-Hi-C. To further demonstrate the ability of Chrom-Lasso to detect interactions of functional importance, we performed time-series Hi-C and RNA-seq during T cell activation and exhaustion. We showed that the dynamic changes in gene expression and chromatin interactions identified by Chrom-Lasso were largely concordant with each other. Finally, we experimentally confirmed Chrom-Lasso's finding that Erbb3 was co-regulated with distinct neighboring genes at different states during T cell activation. Our results highlight Chrom-Lasso's utility in detecting weak functional interaction between cis-regulatory elements, such as promoters and enhancers.

Quasispecies of SARS-CoV-2 revealed by single nucleotide polymorphisms (SNPs) analysis.

New SARS-CoV-2 mutants have been continuously indentified with enhanced transmission ever since its outbreak in early 2020. As an RNA virus, SARS-CoV-2 has a high mutation rate due to the low fidelity of RNA polymerase. To study the single nucleotide polymorphisms (SNPs) dynamics of SARS-CoV-2, 158 SNPs with high confidence were identified by deep meta-transcriptomic sequencing, and the most common SNP type was C > T. Analyses of intra-host population diversity revealed that intra-host quasispecies' composition varies with time during the early onset of symptoms, which implicates viral evolution during infection. Network analysis of co-occurring SNPs revealed the most abundant non-synonymous SNP 22,638 in the S glycoprotein RBD region and 28,144 in the ORF8 region. Furthermore, SARS-CoV-2 variations differ in an individual's respiratory tissue (nose, throat, BALF, or sputum), suggesting independent compartmentalization of SARS-CoV-2 populations in patients. The positive selection analysis of the SARS-CoV-2 genome uncovered the positive selected amino acid G251V on ORF3a. Alternative allele frequency spectrum (AAFS) of all variants revealed that ORF8 could bear alternate alleles with high frequency. Overall, the results show the quasispecies' profile of SARS-CoV-2 in the respiratory tract in the first two months after the outbreak.

Knockout of immunotherapy prognostic marker genes eliminates the effect of the anti-PD-1 treatment.

The efficacy of immunotherapy is largely patient-specific due to heterogeneity in tumors. Combining statistic power from a variety of immunotherapies across cancer types, we found four biological pathways significantly correlated with patient survival following immunotherapy. The expression of immunotherapy prognostic marker genes (IPMGs) in these pathways can predict the patient survival with high accuracy not only in the TCGA cohort (89.36%) but also in two other independent cohorts (80.91%), highlighting that the activity of the IPMGs can reflect the sensitivity of the tumor immune microenvironment (TIME) to immunotherapies. Using mouse models, we show that knockout of one of the IPMGs, MALT1, which is critical for the T-cell receptor signaling, can eliminate the antitumor effect of anti-PD-1 treatment completely by impairing the activation of CD8+ T cells. Notably, knockout of another IPMG, CLEC4D, a C-type lectin receptor that expressed on myeloid cells, also reduced the effect of anti-PD-1 treatment potentially through maintaining the immunosuppressive effects of myeloid cells. Our results suggest that priming TIME via activating the IPMGs may increase the response rate and the effect of immune checkpoint blockers.