Biology subjects

Yang, J.-M.

Publications and source records attributed to Yang, J.-M..

15 recordsLinked to original sources

bioRxiv2026

TIGIT-NECTIN2/3 signaling preserves ignorant CD8⁺ T cells for favorable immune checkpoint outcomes in HBV-related hepatocellular carcinoma

BackgroundImmune checkpoint inhibitors (ICIs) have revolutionized cancer therapy by restoring anti-tumor immunity. However, persistent antigen exposure drives T cell exhaustion, limiting the effectiveness of ICIs. Ignorant T cells are antigen-specific T cells that maintain a naive state by regaining stem-like properties, allowing them to remain fully responsive to subsequent immunization. Virus-related hepatocellular carcinoma (HCC) demonstrates superior responses to ICIs compared to non-viral HCC, prompting us to investigate whether immunologically ignorant T cells exist in HBV-associated HCC and represent a promising target for improving immunotherapy outcomes. MethodsSingle-cell RNA sequencing (scRNA-seq) was performed on tumor tissues from patients with HBV-associated HCC. For validation, immunostaining was conducted on the discovery cohort and an independent cohort of 16 non-B non-C HCC and 22 HBV HCC. The enrichment of TIGIT and NECTIN3 in the proposed ignorant T cell was further validated using the TCGA database. ResultsscRNA-seq identified distinct HBV-infected HCC populations and revealed NECTIN3 upregulation in HBV-enriched subsets. CellChat analysis uncovered a novel NECTIN3-TIGIT tumor-immune interaction in HBV-enriched subsets, which shifted toward TIGIT-NECTIN2 as viral transcription declines. Trajectory analysis revealed the emergence of ignorant CD8 T cells following T cell exhaustion. TIGIT-NECTIN2/3 interactions deliver a weak exhaustion signal. This allows T cells to survive and regain naive-like properties as ignorant cells. Integration of bulk RNA-seq data identified CD24, STMN1, and EZH2 as potential biomarkers of ignorant CD8 T cells. ConclusionsTIGIT-NECTIN2/3 interactions present a promising axis for preserving immunologically ignorant T cells and sustaining ICI responsiveness in HBV-associated HCC.

immunology↗

bioRxiv2026

The targeted cytosolic degradation of class I histone deacetylases is essential for efficient alphaherpesvirus replication

Viral infection triggers a robust DNA damage response (DDR), reshaping the host chromatin landscape to facilitate viral replication. Here, we uncover a novel mechanism by which alphaherpesviruses exploit the DDR pathway. We demonstrated that herpes simplex virus 1 (HSV-1) and pseudorabies virus (PRV) induced selective degradation of class I histone deacetylases (HDAC1/2), leading to histone hyperacetylation and subsequent DDR activation. Strikingly, viral infection promoted nuclear export of HDAC1/2, followed by MDM2-mediated K63-linked polyubiquitination and proteasomal degradation in the cytoplasm. Pharmacological inhibition of either DDR signaling or HDAC1/2 nuclear export significantly affected viral replication in vitro and in vivo. Our findings reveal a unique viral strategy to hijack host epigenetic regulation for efficient replication and identify potential therapeutic targets for alphaherpesvirus infections.

cell biology↗

bioRxiv2025

Cell barcoding with tandem fluorescent proteins enables high-throughput signaling dynamics analysis

Cell barcodes are essential for a wide array of experimental applications, including lineage tracing, genetic screening, and single-cell analysis. An optimal barcode library would provide high diversity, live-cell compatible identification, and simple readout. In this work, we introduce single chain tandem fluorescent protein (sctFP) barcodes, constructed by linking different fluorescent proteins (FPs) into a single polypeptide chain with varied copy numbers. We found that the fluorescence signal intensity ratio at different wavelengths can reliably differentiate sctFPs generated using cnidarian FPs, but not prokaryotic FPs that require exogenous cofactors. The sctFPs enable the multiplexing of genetically encoded fluorescent biosensors, enhancing current biosensor multiplexing methods through a simplified imaging and analysis pipeline that support high-throughput applications. Their robust spectral profiles are compatible with a broad range of biosensor types. Using sctFPs, we demonstrate simultaneous tracking of various signaling activities with biosensors of different spectral properties. Together, this strategy provides a robust and scalable method for barcoding cells across diverse experimental contexts.

cell biology↗

bioRxiv2025

Integrating Drug-like Moieties and Binding Site Evolution for Kinase Inhibitor Prediction Using Ensemble Learning Models

Protein kinases play a pivotal role in regulating cellular signaling pathways, and their dysregulation is closely associated with numerous diseases, including cancer, autoimmune disorders, and inflammation. Although over 100,000 kinase inhibitors have been developed, only a small fraction has achieved FDA approval, primarily due to off-target effects stemming from the high conservation of kinase binding sites. To address this challenge, we present an ensemble learning framework that integrates both chemical and protein-level information to improve the prediction of selective kinase inhibitors. On the compound side, we construct a 1,048-dimensional feature representation encompassing topological fingerprints, drug-like moieties, atomic composition, and stereochemical descriptors. On the protein side, we develop a 1,700- dimensional representation of kinase binding site environments using multiple sequence alignment and evolutionary conservation information. Comprehensive evaluations across 131 human kinases show that the integration of these features significantly improves model performance, achieving 93.6% accuracy on an independent test set. Furthermore, SHAP-based model interpretation reveals that high-impact features correspond to known binding motifs, such as the P-loop, Hinge region, and DFG motif, as confirmed by crystal structure validation. Lastly, we apply the model to a curated dataset of flavonoid-like compounds, identifying potential natural product-derived kinase inhibitors. This study demonstrates that the proposed integrative approach not only enhances predictive accuracy but also provides interpretable insights into kinase-ligand interactions, offering a promising direction for rational kinase inhibitor design.

bioinformatics↗

bioRxiv2025

Encoding Drug-Target-Pathway-Disease profiles for Drug-Cancer Association Prediction using Graph Transformer-Convolution Networks

New drug development is costly, time-consuming, and has a low success rate, leading to a decline in drug discovery efficiency over time. Drug repurposing has emerged as an effective alternative, applying existing, safe drugs to new diseases, thereby reducing development time and costs by bypassing preclinical toxicology testing. With the increasing availability of large-scale interaction data (e.g., drug-protein, protein-protein, and drug-disease networks) and advancements in generative AI, new opportunities have arisen for drug discovery. However, AI-based methods still face challenges: (1) ineffective integration of heterogeneous biological data across drugs, proteins, pathways, and diseases, and (2) lack of interpretability, limiting insights into drug mechanisms of action. To address these challenges, we propose a Graph Transformer-Convolution Network (GTCN) that integrates Graph Transformer Networks (GTNs) and Graph Convolution Networks (GCNs). By leveraging dynamic heterogeneous graph learning and attention mechanisms, our model optimizes relational structures within biological networks (drug-target-pathway-disease) and extracts more discriminative node features. Unlike traditional models that only encode direct drug-disease relationships, our approach captures how drugs act on proteins and regulate pathways to treat diseases. Furthermore, we design an interpretability framework that identifies critical elements for drug-cancer predictions, offering insights into disease mechanisms and drug mechanisms of action (MoA). This facilitates the discovery of new therapeutic strategies with biologically interpretable visualizations. The proposed dataset and code are available at https://github.com/zhengyutong99/GTCN

bioinformatics↗

bioRxiv2025

Expanding Chemical Space: Developing a Compound Generative Pre-trained Transformer for De Novo Drug Design

The drug development process is time-consuming and costly. With the success of attention mechanism models across various domains, including drug development, their adoption has significantly increased. pre-trained a model on large datasets enhances its ability to understand the vast chemical space, enabling effective drug design. However, despite numerous generative models for drug discovery, challenges remain: (i) Most current models utilize less than 2 million compounds, which are insufficient to cover the vast chemical space and often results in limited interpretability. (ii) Existing pre-trained models for compound generation struggle to represent the full diversity of potential molecular structures due to restricted training data. Here, we developed a generic compound generator leveraging extensive chemical spaces. Training data derived from 200 million compounds in the ZINC20 database enabled the model to capture and represent the SMILES syntax and the key features of compounds. Using the attention mechanisms of the generative model, we achieved an interpretable understanding of chemical structures, ensuring robust performance in diverse chemical spaces. Our approach demonstrated 99% novelty, surpassing state-of-the-art methods to generating chemically valid and unique compounds.

bioinformatics↗

bioRxiv2025

Off-target prediction of SGLT2 inhibitors: an integrative bioinformatics approach to uncover structural mechanisms

Gliflozin is known to inhibit sodium glucose transporter 2 (SGLT2) inhibitors (SGLT2i). They have been approved for the treatments of diabetes mellitus, cardiovascular diseases, and chronic kidney disease in recent years. However, the mechanisms for the multifunction remain unclear. We here propose a hypothesis of other protein targets for gliflozins. Additionally, the multistage of the protein, the specificity of the drugs, along with the structural data shortage have posed a hindrance to disclose gliflozin and its binding environment (BE). Considering the difficulties on the issue, this thesis provides an approach to uncover protein off-targets and to find the underlying pathways. We predicted pyranose was a critical substructure from > 1,500 SGLT2i. Hierarchical clustering on atom-based interaction, combined with propensity, revealed key interaction among 1,572 pyranose BEs. The other criterium presented was the concept of compound spanning space via protein pocket size. Binding sites (BSs) from proteins like SGLT2 were able to provide similar pockets, approximating the occupied space of the ligands. Finally, gliflozin binding pockets prefer (1) aromatic residues for van der Waals force, (2) aspartate or asparagine as hydrogen bond providers, and (3) protein pocket size [greater double equals]442 [A] 2. These criteria were integrated into a scoring function ST and predicted several possible proteins. This research presents a pipeline for target identification of emerging drug agents and proteins with crystallographic difficulties. The study provides an innovative computational methodology on extraction of binding features on transmembrane proteins and further pharmaceutical development.

bioinformatics↗

bioRxiv2024

A single-gene-based AI model to identify core and context-specific essential genes by biological interpretation from pooled genome-wide CRISPR and omics data

Genome-wide Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) is a powerful tool for screening essential genes (EGs) and studying gene function by systematically knocking out individual genes. EGs are essential for the survival of organisms and can be divided into core EGs (CEGs) and context-specific EGs, which are crucial for the development of drugs. Although a variety of CEGs have been identified using knockout technology, the concordance among these CEG sets is extremely low; there is a lack of studies on the EG mechanisms. Therefore, developing systematic methods to identify CEGs and reveal corresponding mechanisms are important biological issues. To address these issues, we propose a comprehensive ensembled-based model utilizing gene community-regulated pathways to decipher pan-cancer CEGs and context-specific EGs across 29 cancer types and provide insights into their regulatory abilities for each pathway. The project aims include developing a model with Systematic Identification of Essential Gene (SIEG) scores for CEGs and Context-Specific Enrichment (CSE) scores for context-specific EGs. Subsequently, we assess the regulated pathways and mechanisms of these identified EGs by integrating diverse data sources such as genome-wide CRISPR/Cas9 knockout screens, multiple omics data, KEGG pathways, and Gene Ontology. Ultimately, we aim to establish a user-friendly web service. In our preliminary results, we gathered 1,845 genome-wide CRISPR datasets and various omics datasets, including 8,941 clinical samples and 1,346 cell lines, leading to the identification of 3,213 CEGs. By employing the SIEG score, we found that 1,178 of these pan-cancer CEGs overlapped with previously defined CEGs, reflecting a 60% similarity rate. These 3,213 CEGs play crucial roles in regulating pathways associated with cell viability, expansion, and proliferation. Additionally, they exhibit characteristics typical of CEGs, showing less favorable as therapeutic targets and centralizing within protein-protein interaction networks. Moreover, we delineated six pathway signatures of pan-cancer CEGs, encompassing transcription, translation, protein folding, replication and repair, cell growth and death, as well as energy. We anticipate that these signatures will contribute to the future redefinition of CEGs.

bioinformatics↗

bioRxiv2024

Calibration of FRET-based biosensors using multiplexed biosensor barcoding

Forster resonance energy transfer (FRET) between fluorescent proteins (FPs) is widely used in the design of genetically encoded fluorescent biosensors, which are powerful tools for monitoring the dynamics of biochemical activities in live cells. FRET ratio, defined as the ratio between acceptor and donor signals, is often used as a proxy for the actual FRET efficiency, which must be corrected for signal crosstalk using donor-only and acceptor-only samples. However, the FRET ratio is highly sensitive to imaging conditions, making direct comparisons across different experiments and over time challenging. Inspired by a method for multiplexed biosensor imaging using barcoded cells, we reasoned that calibration standards with fixed FRET efficiency can be introduced into a subset of cells for normalization of biosensor signals. Our theoretical analysis indicated that the FRET ratio of high-FRET species relative to non-FRET species slightly decreases at high excitation intensity, suggesting the need for calibration using both high and low FRET standards. To test these predictions, we created FRET donor-acceptor pairs locked in "FRET-ON" and "FRET-OFF" conformations and introduced them into a subset of barcoded cells. Our results confirmed the theoretical predictions and showed that the calibrated FRET ratio is independent of imaging settings. We also provided a strategy for calculating the FRET efficiency. Together, our study presents a simple strategy for calibrated and highly multiplexed imaging of FRET biosensors, facilitating reliable comparisons across experiments and supporting long-term imaging applications.

biophysics↗

bioRxiv2023

Targeting Ras signaling excitability in cancer cells through combined inhibition of FAK and PI3K

The Ras/PI3K/ERK signaling network is frequently mutated in various human cancers including cervical cancer and pancreatic cancer. Previous studies showed that the Ras/PI3K/ERK signaling network displays features of excitable systems including propagation of activity waves, all-or-none responses, and refractoriness. Oncogenic mutations lead to enhanced excitability of the network. A positive feedback loop between Ras, PI3K, the cytoskeleton, and FAK was identified as a driver of excitability. In this study, we investigated the effectiveness of targeting signaling excitability by inhibiting both FAK and PI3K in cervical and pancreatic cancer cells. We found that the combination of FAK and PI3K inhibitors synergistically suppressed the growth of select cervical and pancreatic cancer cell lines through increased apoptosis and decreased mitosis. In particular, FAK inhibition caused downregulation of PI3K and ERK signaling in cervical cancer but not pancreatic cancer cells. Interestingly, PI3K inhibitors activated multiple receptor tyrosine kinases (RTKs), including insulin receptor and IGF-1R in cervical cancer cells, as well as EGFR, Her2, Her3, Axl, and EphA2 in pancreatic cancer cells. Our results highlight the potential of combining FAK and PI3K inhibition for treating cervical and pancreatic cancer, although appropriate biomarkers for drug sensitivity are needed, and concurrent targeting of RTKs may be required for resistant cells.

cancer biology↗

bioRxiv2022

NAC1 Modulates Autoimmunity by Suppressing Regulatory T Cell- Mediated Tolerance

FoxP3+ regulatory T cells (Tregs) are a distinct subset of CD4+ T cells integral to the maintenance of the balance of the immune system, and their dysregulation is a trigger of autoimmunity. We report here that nucleus accumbens-associated protein-1 (NAC1), a nuclear factor of the broad complex, tramtrack, bric-a-brac / poxvirus and zinc finger (BTB/POZ) gene family, is a negative regulator of FoxP3 in Tregs and a critical determinant of immune tolerance. Phenotypically, NAC1-/- mice show substantial tolerance to the induction of autoimmunity, as evidenced by the significantly decreased occurrences of autoimmune arthritis and colitis. Analysis of T cells from the wild-type (WT) or NAC1 knockout (-/-) mice found that NAC1 is crucially involved in the early stage of T cell development. NAC1 positively affects CD8+ T cell differentiation, but negatively regulates Treg development. Compared with WT animals, NAC1-/- mice displayed defects in CD8+ T cell development but generated a larger amount of CD4+ regulatory Tregs that exhibit a higher metabolic profile and immune suppressive activity, increased acetylation, and expression of FoxP3, and slower turnover of this transcriptional factor. Furthermore, treatment of Tregs with the pro-inflammatory cytokines IL-1{beta} or TNF- induced a robust upregulation of NAC1 but an evident downregulation of FoxP3 as well as the acetylated FoxP3, suggesting that the reduction of FoxP3 by the NAC1-mediated deacetylation and destabilization of this lineage-specific transcriptional factor contributes considerably to break of immune tolerance. These findings imply that the pro-inflammatory cytokines-stimulated upregulation of NAC1 acts as a trigger of the immune response through destabilization of Tregs and suppression of tolerance induction, and that therapeutic targeting of NAC1 warrants further exploration as a potential tolerogenic strategy for treatment of autoimmune disorders.

immunology↗

bioRxiv2020

Highly multiplexed imaging of biosensors in live cells

Fluorescent biosensors allow for real-time monitoring of biochemical activities in cells, but their multiplexing capacity is severely limited by the availability of spectral space. We overcome this problem by developing a set of barcoding proteins that are spectrally separable from commonly used FRET (fluorescence resonance energy transfer)-based and single-fluorophore biosensors. Mixed populations of barcoded cells expressing different biosensors can be concurrently imaged and computationally unmixed to achieve highly multiplexed tracking of biochemical activities in live cells.

cell biology↗

bioRxiv2020

miR6236, a microRNA suppressed by the anisotropic surface topography, regulates neuronal development and regeneration.

It has been well studied that the surface topography affects the growth and development of neurons. However, the precise mechanism that the surface topography leads to cellular changes remains unknown. In this study, we created an anisotropic surface using nanodiamonds and discovered this surface topography accelerates the development of primary neurons from both the central and peripheral nervous systems. Using RNA sequencing technology, a previously uncharacterized microRNA (miR6236) was found to exhibit significant and the most substantial decrease when neurons are cultured on this nanodiamond surface. Gain- and loss-of-function assays confirm that miR6236 is the predominant molecule responsible for converting the surface topography into biological responses. We further demonstrate that the depletion of miR6236 can enhance neuroregeneration on inhibitory substrate, uncovering its therapeutic potential for promoting central nervous system regeneration.

neuroscience↗

bioRxiv2019

EphB6 regulates social behavior through gut microbiota-mediated vitamin B6 metabolism and excitation/inhibition balance of medial prefrontal cortex in mice

Autism spectrum disorder (ASD) is a developmental disorder with no effective pharmacological treatments so far. Gut microbiota has been suggested to contribute to autistic symptoms. However, the key genes and the mechanisms linking gut microbiota and brain dysfunctions in ASD are still unclear. Here, we found deletion of EphB6, an ASD-associated candidate gene, induced dysregulated gut microbiota and autism-like behavior in mice. More importantly, transplanting fecal microbiota from EphB6-deficient mice resulted in disturbed gut microbiota and autism-like behavior in antibiotics-treated C57BL/6J mice. Meanwhile, transplanting fecal microbiota from wild-type mice ameliorated disturbed gut microbiota and autism-like behavior in mice with deletion of EphB6. At the metabolic levels, dysregulated gut microbiota led to vitamin B6 and dopamine defects in EphB6-deficient mice. At the cellular levels, excitation/inhibition (E/I) imbalance in medial prefrontal cortex was induced by gut microbiota-mediated defects of vitamin B6 metabolism in EphB6-deficient mice. Our study uncovers a key role for EphB6 in regulation of social behavior by gut microbiota-mediated vitamin B6 metabolism, dopamine synthesis and E/I balance, suggesting a new strategy for treatment of ASD patients.

neuroscience↗

bioRxiv2018

ErbB4 deletion in noradrenergic neurons in the locus coeruleus induces mania-like behavior via elevated catecholamines

Dysfunction of the noradrenergic (NE) neurons is implicated in the pathogenesis of manic-depressive psychosis (MDP). ErbB4 is highly expressed in NE neurons, and its genetic variation has been linked to MDP; however, how ErbB4 regulates NE neuronal function and contributes to MDP pathogenesis is unclear. Here we find that conditional deletion of ErbB4 in locus coeruleus (LC) NE neurons increases neuronal spontaneous firing through NMDA receptor hyperfunction, and elevates catecholamines in the cerebrospinal fluid (CSF). Furthermore, ErbB4-deficient mice present mania-like behaviors, including hyperactivity, reduced anxiety and depression, and increased sucrose preference. These behaviors are completely rescued by the anti-manic drug lithium or antagonists of catecholaminergic receptors. Our study demonstrates the critical role of ErbB4 signaling in regulating LC-NE neuronal function, reinforcing the view that dysfunction of the NE system may contribute to the pathogenesis of mania-associated disorder.

neuroscience↗