Biology subjects

Bao, Y.

Publications and source records attributed to Bao, Y..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Stalling of elongating Pol II triggers Ser7 phosphorylation in trans to drive transcription recovery

DNA is scattered with obstacles that stall RNA polymerase II (Pol II) and block the production of full-length transcripts. Two mechanisms are known to resolve stalled Pol II: transcription-coupled nucleotide excision repair (TC-NER) and the "last resort" Pol II ubiquitylation-degradation pathway. Here, we uncover a third, distinct mechanism that alerts incoming Pol II molecules to roadblocks ahead and primes them for efficient elongation. We show that transcription stalling triggers GSK3-mediated phosphorylation of Ser7 residues (Ser7P) on the Pol II C-terminal domain. Unexpectedly, this phosphorylation occurs in trans: obstacles in gene bodies induce Ser7P on Pol II complexes at gene beginnings. This modification enables processive transcription while restraining excessive Pol II degradation by the "last resort" pathway. Our findings reveal an adaptive system that preserves transcriptional homeostasis by coordinating polymerase behavior across the gene in response to elongation stress.

molecular biology↗

bioRxiv2025

Alphaviral capsid proteins inhibit stress granule assembly via competitive RNA binding with G3BP1

Viral infection is one of the conditions that induces stress granule (SG) formation, a cellular defense mechanism that exerts antiviral effects. To counteract this host response, viruses have evolved a broad spectrum of strategies to inhibit SG formation. However, the molecular mechanisms underlying SG inhibition remain poorly understood. The nucleocapsid proteins play a critical role in virus replication and host interaction. Here, using Semliki Forest Virus (SFV) as a model, we uncover the function of the alphavirus nucleocapsid in SG inhibition. This inhibitory function depends on oligomerization mediated by an N-terminal -helix and with a positively charged intrinsically disordered region (IDR). We show that SFV capsid directly competes with G3BP1 for RNA binding, thereby disrupting G3BP1-RNA liquid-liquid phase separation (LLPS) in vitro and SG assembly in cells. This mechanism is conserved across the alphavirus family but is not shared by the nucleocapsid of SARS-CoV-2 or other endemic viruses examined. Notably, expression of a peptide from SFV capsid is sufficient to inhibit SG formation induced by Amyotrophic Lateral Sclerosis (ALS)-associated mutations, suggesting potential therapeutic applications. Our findings reveal mechanistic insight into SG modulation by the viral capsid protein and provide a possible bioengineering tool for probing SG dynamics in health and disease.

cell biology↗

bioRxiv2025

Brain-wide Genome Editing via STEP-RNPs for Treatment of Angelman Syndrome

Brain-wide genome-editing remains a major hurdle for the treatment of neurogenetic disorders. Here, we report a non-viral, non-nanoparticle, chemical modification-based method, called Stimuli-Responsive Traceless Engineering Platform (STEP) that achieves highly efficient and brain-wide genome-editing of neurons in mice. Using cholesterol-based STEP as a lead, we show that a single administration of STEP-ribonucleoproteins (RNPs) results in functional genetic rescue with significant improvements across a battery of neurobehavioral domains in the Angelman syndrome (AS) mouse model. No significant off-target events or general toxicity effects are observed. STEP-RNPs are also highly efficient at editing human neurons and cortical brain organoids differentiated from AS patient-derived iPSCs. scRNA-seq analysis confirms functional genetic rescue via reactivation of Ube3a/UBE3A expression in human and mouse STEP-RNP-treated neuronal cells. Genome editing via STEP-RNPs has broad applications and the potential to treat many other neurogenetic disorders.

genetics↗

bioRxiv2025

Multi-Dimensional Spatiotemporal Attention Neural Network for Next Generation Sequencing Basecalling

Next-generation sequencing (NGS) remains the most used sequencing technique in the field of genomics. Traditional basecall methods face significant challenges in decoding high density sequencing data due to inherent noise in biochemical reactions and limitations of instruments. Here, we present a multi-dimensional deep learning neural network based on spatiotemporal attention mechanism named AICall. The network skips computationally heavy but less effective steps of peak finding and brightness extraction/correction, and directly basecalls from the time sequence of multi-dimensional image stacks obtained in real time. By introducing attention mechanism, it effectively extracts spatial and time-related key information including spatial crosstalk, spectral crosstalk, phasing, base-quenching, and intensity decay, and significantly improves basecall accuracy. We demonstrate that AICall achieves an average error rate less than 0.01% and provides more reliable sequencing results for downstream analysis.

bioinformatics↗

bioRxiv2025

Joint analysis of expression and variation at single cell resolution by scVar

Tumor heterogeneity represents a critical determinant affecting both cancer diagnosis and therapeutic efficacy. Traditional bulk sequencing approaches are limited in their ability to resolve genomic alterations at the resolution of individual cells. In this study, we present scVar, an integrated analytical framework designed for mutation profiling using single-cell transcriptomic data. scVar enables sensitive and robust detection of single-nucleotide variants, particularly facilitating the identification of low-frequency variants. The framework incorporates customizable filtering parameters including variant allele frequency, and provides comprehensive functional and clinical annotations for identified mutations. Furthermore, scVar includes downstream analytical modules that facilitate the joint investigation of transcriptomic and mutational profiles within single cells. Benchmarking on simulated datasets and matched tumor samples demonstrates that scVar consistently outperforms existing methods, especially in detecting variants of low abundance, and shows strong concordance with whole-exome sequencing data. Application of scVar to non-small cell lung cancer samples effectively characterized ITH across spatially distinct tumor regions and diverse cellular populations. Collectively, scVar offers an integrated platform for concurrent analysis of somatic mutations and transcriptomic data derived from single-cell RNA sequencing, providing a powerful tool for elucidating tumor evolution and the complex interplay between genomic heterogeneity and the tumor microenvironment.

bioinformatics↗

bioRxiv2025

A correlative quantitative phase contrast and fluorescence super-resolution microscope for imaging molecules in their cellular context

Fluorescence microscopy has been widely used to reveal the spatial distribution of specifically labeled molecules, but it is blind to cellular context. Quantitative phase contrast microscopy (QPC) provides such complementary information. Here we have developed a platform that combines the QPC technique of correlative orientation-independent differential interference contrast (OI-DIC) microscopy with single-molecule super-resolution fluorescence microscopy. We demonstrate a detection sensitivity of 0.05 nm optical path difference, sufficient to detect single microtubules, and show its capability of 3D super-resolution fluorescence imaging in the cellular context. Additionally, we report deep-learning enabled digital staining, identifying nuclei, mitochondria and lipid droplets from OI-DIC data and demonstrate the potential of this approach for long-term live-cell imaging of organelles of interest without the need for fluorescence. OI-DIC can be easily integrated into most fluorescence microscopes and is readily adoptable by microscopy labs. ONE-SENTENCE TEASERA highly sensitive technique to visualize sub-cellular structures, dynamics, and molecules in their cellular context.

cell biology↗

bioRxiv2025

Agro-STARR-seq enables in planta genome-wide enhancer discovery and AI-empowerd enhancer design

Comprehensive enhancer screening is essential for dissecting gene regulatory networks. However, methods for such screening remain limited in plants. We present Agro-STARR-seq, an in planta enhancer screening platform using Agrobacterium-mediated delivery of STARR-seq libraries into Nicotiana benthamiana leaves. In the Arabidopsis genome, we identified 14,715 sequences with enhancer activity, of which 91.59% were located outside of open chromatin regions. This largest in planta enhancer dataset to date enabled AI-assisted design of highly active synthetic enhancers. For the large N. benthamiana genome, we pre-processed the genomic DNA with an Assay for Transposase Accessible Chromatin (ATAC) procedure before the Agro-STARR-seq protocol,and found that only one-third of open chromatin exhibited enhancer activity, 76.00% of which showed no canonical H3K27ac histone marks. Footprinting analysis further enables systematic linkage of transcription factor (TF) binding to enhancer function. Overall, Agro-STARR-seq provides a novel platform for dissecting and engineering plant enhancers.

genomics↗

bioRxiv2025

An Interactive AI Agent for Adaptive Modeling of RNA Region-Ligand Interactions via LLM-Generated Machine Learning Workflows

Precise modeling of RNA-ligand interactions is essential for understanding RNA functionality and designing RNA-targeted therapeutics. Current computational approaches largely focus on predicting discrete binding sites, limiting their applicability to complex RNA regions that may harbor multiple or diffuse ligand binding motifs. Here, we present RLAgent, an interactive agent framework designed to predict ligand interactions at the RNA region level, enabling higher-resolution and more flexible modeling than conventional site-centric approaches. RLAgent reframes the RNA-ligand prediction workflow as a dialogue-driven process. Through a natural language interface, users can interactively configure modeling preferences without writing code. A locally hosted large language model (LLM) acts as the core orchestration agent, automating all key components of the modeling pipeline, including data validation, feature encoding, model training, evaluation, and visualization. This agent-based design lowers technical barriers and enhances reproducibility, making RNA-ligand prediction more accessible for both computational and experimental researchers.

bioinformatics↗

bioRxiv2025

New Evidence of Altered Gut Microbiota in Autism Spectrum Disorders through Metagenomic Analysis

Cumulative evidence suggests alterations of gut microbiota in children with Autism Spectrum Disorder (ASD). However, due to highly diverse research approaches, the distinctive microbial patterns and underlying mechanisms driving ASD etiology remain elusive. Here, we constructed the metagenomic dataset of gut microbiota derived from Chinese children with ASD, incorporating a longitudinal study to explore the pivotal role of gut microbiota in the pathogenesis of ASD. At the bacterial species level, we identified a notable increase in the relative abundance of Clostridium scindens, which exhibited a positive correlation with the cholate degradation pathway and showing a decline concurrent with improved behavior in ASD children. Within the fungal community, we observed significant dysbiosis in twenty-six species, underlining their robust discriminatory potential between ASD and neurotypical subjects. Metabolic pathways related to mitochondrial dysfunction were enriched in ASD, alongside peptidoglycan, glutamate, and nucleotide pathways, suggesting potential interactions between the gut and brain through the innate immune system and vagus nerve in ASD. Our findings underscore the significance of the fungal community and mitochondrial dysfunction in ASD etiology, while also shedding light on how perturbed metabolic functions of gut microbiota collectively contribute to the pathogenesis of ASD.

microbiology↗

bioRxiv2025

Scalable automated segmentation quantifies mitochondrial proteins and morphology at the nanoscale

Ultrastructural changes of mitochondria are closely associated with metabolic dysfunction, leading to a variety of human disorders. These changes can be visualized by pan-Expansion Microscopy (pan-ExM), a 3D microscopy technique requiring only standard fluorescence microscopes, at a throughput exceeding that of the current gold standard, 3D electron microscopy, by orders of magnitude. However, a lack of tools that enable the characterization and quantification of the observed ultrastructural features in the acquired 3D datasets at a comparable throughput has hindered the widespread adoption of pan-ExM for quantitative imaging. Here, we present an automated deep-learning based segmentation approach that utilizes pan-ExMs power to acquire multi-channel images and uses specific labeling as the annotation for the training of the segmentation network. This molecular annotation reduces the required manual annotation effort for mitochondria substructures to just a few hours when setting up the experiment and thereby provides access to 3D suborganellar morphology of mitochondria at an unprecedented throughput. Our approach, which we term MAPS (Mitochondrial Automated Pan-ExM Segmentation), enables for the first time to quantify mitochondrial ultrastructural morphology at scale. We demonstrate this power by characterizing the 3D mitochondrial morphology at the organelle and sub-organelle level in tens of HeLa cells under different treatments and localizing mitochondrial proteins in the sub-organellar context. To demonstrate our technology in tissue, we compare the ultrastructural morphology of mitochondria in proximal tubules of kidneys of mice exhibiting acute kidney injury (AKI) with those of untreated mice, revealing striking differences in their cristae structure. MAPS can easily be adapted to different cell and tissue types, allowing the analysis of tens of samples per day, and therefore provides a versatile tool for a comprehensive understanding of mitochondrial ultrastructural changes in many disease contexts. Requiring only standard fluorescence microscopes and computer infrastructure, MAPS is readily adoptable by any lab. HighlightsO_LIMAPS utilizes specific labeling to train a segmentation model for 3D super-resolution pan-ExM images C_LIO_LISuborganellar mitochondrial features and their changes in disease models are quantified at high throughput C_LIO_LI3D protein distributions are correlated to ultrastructural features in mitochondria C_LIO_LIRequiring only standard lab infrastructure, MAPS is readily adoptable C_LI

cell biology↗

bioRxiv2025

Memo-Patho: Bridging Local-Global Transmembrane Protein Contexts with Contrastive Pretraining for Alignment-Free Pathogenicity Prediction

Understanding the pathogenic impact of protein mutations remains a fundamental challenge in genomic medicine, particularly for transmembrane proteins (TMPs), a functionally critical yet structurally under-annotated class that includes numerous drug targets. Current variant effect predictors often fall short in this domain due to computational burdens, reliance on multiple sequence alignments (MSAs), and limited ability to generalize to TMP-specific constraints. Here we present Memo-Patho, an alignment-free deep learning framework specifically optimized for TMP mutation pathogenicity prediction. Memo-Patho integrates global and local representations from protein language models (PLMs) and predicted structural features. Furthermore, it introduces a contrastive pre-training strategy that learns discriminative features by comparing benign and pathogenic mutations within the same protein backbone. This design enables the model to capture functional disruptions in challenging contexts without structural or evolutionary alignments. Memo-Patho outperforms state-of-the-art predictors on two types of TMP benchmark datasets (accuracy up to 0.93) and unseen-protein generalization tasks, showing strong agreement with evolutionary conservation profiles. Its predictions are further validated on a manually curated KCNQ1 dataset of 55 ion channel variants, achieving an accuracy of 0.84 and outperforming existing tools. The model also supports high-throughput scanning, efficiently analyzing over 15000 variants in under 80 minutes. Our results establish Memo-Patho as a fast, generalizable, and biologically grounded approach for variant effect prediction in TMPs. Its alignment-free design, contrastive learning paradigm, and clinical robustness mark a step forward towards scalable interpretation of TMP variation for both basic research and precision medicine applications.

bioinformatics↗

bioRxiv2025

Adaptive Evolution of Gene Regulatory Networks in Mammalian Neocortical Neurons

Mammals have evolved a plethora of adaptations that have enabled them to thrive in diverse environments. Among the most significant is the emergence of a more complex brain, exemplified by the dramatic transformation of the dorsal cortex from a single layer of excitatory projection neurons (ExNs) in ancestors to a multilayered cerebral neocortex enriched with diverse intratelencephalic (IT) and extratelencephalic (ET) ExN subtypes. These ExNs established specialized projection systems, such as the corticospinal tract and corpus callosum, enhancing brain connectivity and functionality. However, the evolutionary mechanisms underlying these mammalian-specific adaptations remain elusive. By comparing the landscape of gene expression and cis-regulatory elements (CREs) in mouse ExN subtypes and by cross-species examination of mammalian and non-mammalian CREs, we identified mammalian-specific CREs and expression patterns. The mammalian-specific CREs include a subset bound by ZBTB18 that are associated with genes defining IT and ET subtypes and connectivity. Both ZBTB18 and these target genes have previously been implicated in intellectual disability and autism. Deletion of Zbtb18 in mouse ExNs dysregulated target gene expression, reduced molecular diversity, diminished corticospinal and callosal projections, and increased intrahemispheric cortico-cortical association projections to the prefrontal cortex, resembling features of non-mammalian dorsal pallium. Interestingly, ZBTB18 binding motifs are highly enriched in callosally projecting IT-biased CREs, where they show higher conservation specifically in mammals. This study uncovers critical components and mammalian-specific evolutionary adaptations within a regulatory node essential for neocortical ExN identity and connectivity, with implications for neurodevelopmental and neuropsychiatric disorders.

neuroscience↗

bioRxiv2025

Influence of Macrocyclization Strategies on DNA-Encoded Cyclic Peptide Libraries

Discovery of cyclic peptide hits using DNA-encoded libraries (DELs) has recently been extensively researched, with significant efforts directed toward developing DEL-compatible macrocyclization methods. To investigate how different cyclic linkers influence DEL selection outcomes, we constructed eight distinct sub-libraries and screened them against two protein targets, MDM2 and GIT1, resulting in two representative yet contrasting scenarios. Validation studies for MDM2 revealed that structural similarity patterns observed across multiple sub-libraries could enhance confidence in the authenticity of identified hits. Notably, cmp-10 demonstrated potent inhibitory activity, exhibiting an inhibition constant of 11 nM. In contrast, selections against GIT1 produced discrete enrichment patterns. From these outcomes, two exemplary compounds were selected and validated through both on-DNA and off-DNA modes. cmp-17 was confirmed to bind specifically to the desired binding pocket, displaying a dissociation constant of 1.22 M. Furthermore, ITC experiments using mutant GIT1 proteins (GIT1L271A/L279A and GIT1L271L/L279L) provided additional insights into the mechanism by which cmp-17 disrupts the interaction between GIT1 and {beta}-Pix.

biochemistry↗

bioRxiv2025

The GSA Family in 2025: A Broadened Sharing Platform for Multi-Omics and Multimodal Data

The Genome Sequence Archive Family (GSA family) provides a suite of database resources for archiving, retrieving, and sharing multi-omics data for the global academic and industrial communities. It currently comprises four distinct database members: the Genome Sequence Archive (GSA, https://ngdc.cncb.ac.cn/gsa/), the Genome Sequence Archive for Human (GSA-Human, https://ngdc.cncb.ac.cn/gsa-human/), the open Archive for Miscellaneous Data (OMIX, https://ngdc.cncb.ac.cn/omix/), and the Open Biomedical Imaging Archive (OBIA, https://ngdc.cncb.ac.cn/obia). Compared to the 2021 version, the GSA family has significantly expanded by introducing a new database member, the OBIA, and through comprehensive upgrades to the existing ones. Notable enhancements to the existing members include broadening the range of accepted data types, enhancing the quality control systems, fine-tuning the data retrieval system, and refining the data-sharing management mechanisms.

genomics↗

bioRxiv2025

Loss of Kmt2c/d promotes gastric cancer initiation and confers vulnerability to mTORC1 inhibition and anti-PD1 immunotherapy

KMT2C and KMT2D (KMT2C/D) are frequently mutated in gastric adenocarcinoma, yet their function in cancer initiation remains poorly understood. In this study, based on the observation that loss-of-function mutations of KMT2C and KMT2D are enriched and co-occur in gastric adenocarcinoma, we developed genetically engineered mouse models to selectively knock out Kmt2c and Kmt2d in gastric epithelial cells with Tmprss2-CreERT2. Through histological staining and single-cell RNA sequencing, we observed that Kmt2c/d loss led to nuclear dysplasia and expansion of cells with mixed gastric lineage markers. When combined with Pten deletion, Kmt2c/d loss drove rapid development of muscle-invasive gastric adenocarcinoma as early as 3 weeks post Cre-mediated gene deletion. The adenocarcinoma exhibited decreased expression of gastric lineage markers and increased expression of intestinal differentiation markers, phenocopying human gastric adenocarcinoma. Kmt2c/d knockout reduced protein synthesis but upregulated transcription of ribosomal proteins, rendering sensitivity to mTORC1 inhibitors. Additionally, Kmt2c/d knockout increased MHC-I molecule expression and enhanced antigen presentation. Combination of mTROC1 inhibition and anti-PD1 immunotherapy significantly suppressed tumor growth in immune-competent mice. Together, these findings reveal the role of Kmt2c/d loss in gastric cancer initiation and suggest the potential therapeutic strategies for KMT2C/D-deficient gastric cancer.

cancer biology↗

bioRxiv2025

NeoAtlas and NeoBert: A Database and A Predictive Model for Canonical and Noncanonical Tumor Neoantigens

Neoantigens are classified into canonical and noncanonical types. Noncanonical neoantigens include those derived from noncoding regions, transposable elements (TE), and intron retention events, and they have recently gained considerable attention in cancer immunity. We curated 35,574 non-redundant neoantigen-HLA pairs from 14 immunopeptidomes studies, by analyzing unique features and differences across various sources of neoantigens. This knowledge enabled us to develop machine learning models for the prediction of different types of neoantigens. Our data and models are available at a public portal (https://ngdc.cncb.ac.cn/neoatlas) to facilitate broad access and future research. This resource offers advanced functionalities, including integration with epigenome browsers which allow easy navigation of epigenomic datasets to support and confirm the expression of neoantigens. We further demonstrate that combining our database with mass spectrometry analysis can identify noncanonical neoantigens. The resource we constructed holds significant value and promise for the development of neoantigen-based vaccines.

systems biology↗

bioRxiv2025

Transcriptomic splicing analysis reveals a gene-independent form of TE reactivation in tumors representing unrecognized source of oncogenes and neoantigens

Transposable elements (TEs) are reactivated in tumors and serve as significant contributors to tumor transcriptomic complexity and heterogeneity. However, their repetitive nature and diverse transcriptional forms have hindered efforts to characterize TE-derived transcripts independent of host gene contexts. Here, using our self-developed splicing-junction analysis tool ASJA, we systematically interrogated TE transcription across 32 cancer types, identifying transcripts autonomously initiated from and exclusively spliced among TEs with high TE content, termed TEtrans. Our analysis revealed 5,361 TEtrans exhibiting pan-cancer prevalence, tumor-specific expression and features of mature transcripts, including canonical splicing, 5 caps and polyA tails. Mostly unannotated and enriched in intergenic regions, TEtrans are predominantly derived from primate-specific classes with conserved splice pattern. TEtrans burden demonstrates heterogeneous associations with prognosis and immune activity across cancers, while their expression can be epigenetically dual-regulated by stemness- and inflammation-associated transcription factors. Functional studies uncovered that TEtrans could act as oncogenes, exemplified by tsTE1, a HERVH-derived transcript that promotes colorectal cancer proliferation by enhancing TOP1-mediated DNA supercoil relaxation. Remarkably, [~]7.2% of TEtrans are identified to encode tumor neoantigens, including viral proteins and unannotated peptides, which are shared among patients and validated by proteogenomic analysis. These TEtrans-derived neoantigens are immunogenic both in vitro and in vivo, and exceed neoepitopes from genomic alterations in abundance per tumor, particularly in cancers with low mutational burden. Collectively, as a gene-independent form of TE-derived transcripts, TEtrans represents a unique source of oncogenes and tumor neoantigens, expanding the functional repertoire of TEs in cancer biology and offering new avenues for therapeutic targeting. HighlightsO_LITEtrans are tumor-specific transcripts with conserved splicing patterns, predominantly derived from primate TEs in intergenic regions. C_LIO_LITEtrans expression is epigenetically activated and dually regulated by stemness/inflammation transcription factors. C_LIO_LITEtrans function as oncogenes, exemplified by tsTE1 enhancing TOP1-mediated DNA relaxation. C_LIO_LITEtrans encode tumor neoantigens that elicit CD8+ T cell responses, providing targets for immunotherapy, especially in low-mutation tumors. C_LI Abstract figure O_FIG O_LINKSMALLFIG WIDTH=198 HEIGHT=200 SRC="FIGDIR/small/640928v1_ufig1.gif" ALT="Figure 1"> View larger version (78K): org.highwire.dtl.DTLVardef@1d2d4cforg.highwire.dtl.DTLVardef@918f14org.highwire.dtl.DTLVardef@489465org.highwire.dtl.DTLVardef@1069d17_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Themis dominates T cell exhaustion by regulation of TCR and PD-1 signaling

T cell exhaustion is important to protect the host from immunopathology during chronic viral infection, but it also impairs T cell anti-tumor immunity1-5. A fundamental unresolved question is whether and how T cell exhaustion is determined at the onset of TCR signaling6-8. Here we report an unexpected role of Themis, a TCR-proximal signaling molecule9, in T cell exhaustion. Chronic viral infection in mice usually leads to T cell exhaustion and survival of the host. Surprisingly, Themis T-cell conditional knockout mice died from severe CD8+-dependent lung immunopathology in chronic viral infection, showing Themis importance in establishing T cell exhaustion. We found that Themis-deficient CD8+ T cells were hyperactivated at the single-cell level - producing more TNF and IFN{gamma} compared to wild-type counterparts - but defective in population-level expansion. Moreover, TCF-1 and TOX expression were inhibited in Themis-deficient CD8+ T cells, thereby impairing differentiation of exhausted T cell precursors (T-pex) and maintenance of terminally exhausted T cells (T-ex), respectively. Mechanistically, Themis initially promotes TCR signaling to induce PD-1 expression and subsequently mediates PD-1 signaling. In the latter, Themis binds to PD-1 and promotes PD-1 phosphorylation and its recruitment of SHP2, thereby acting as a negative regulator to inhibit T cell effector functions. Without Themis, the orderly regulation of TCR and PD-1 signaling, and therefore exhaustion, is disrupted. Thus, our results unequivocally demonstrate that Themis-mediated early TCR signaling plays a decisive role in T cell exhaustion and provide a novel mechanism of PD-1 signaling through Themis.

immunology↗