Biology subjects

Kong, S. W.

Publications and source records attributed to Kong, S. W..

5 recordsLinked to original sources

bioRxiv2025

Neuropilin 2 functions as a co-inhibitory receptor to regulate antigen-induced inflammation and allograft rejection

Co-inhibitory receptors function as central modulators of the immune response to resolve T effector activation and/or to sustain immune homeostasis. Here, using humanized SCID mice, we found that NRP2 is inducible on late effector and exhausted subsets of human CD4+ T cells and that it is co-expressed with established co-inhibitory molecules including PD-1, CTLA4, TIGIT, LAG3 and TIM3. In murine models, we also found that NRP2 is expressed on effector memory CD4+ T cells with an exhausted phenotype and that it functions as a key co-inhibitory molecule. Knockout of NRP2 resulted in hyperactive CD4+ T cell responses, and enhanced inflammation in delayed type hypersensitivity and transplantation models. Following cardiac transplantation, allograft rejection and graft failure was accelerated in global as well as CD4+ T cell-specific knockout recipients, and enhanced alloimmunity was dependent on NRP2 expression on CD4+ T effectors, and not on CD4+Foxp3+ T regulatory cells. Also, knockout T regulatory cells were found to be as efficient as wild type cells in the suppression of effector responses in vitro and in vivo. These collective findings identify NRP2 as a novel coinhibitory receptor and demonstrate that its expression on CD4+ T effector cells is of great functional importance in immunity.

immunology↗

bioRxiv2024

AI-readiness for Biomedical Data: Bridge2AI Recommendations

Biomedical research is rapidly adopting artificial intelligence (AI). Yet the inherent complexity of biomedical data preparation requires implementing actionable, robust criteria for ethical and explainable AI (XAI) at the "pre-model" stage, encompassing data acquisition, detailed transformations, and ethical governance. Simple conformance to FAIR (Findable, Accessible, Interoperable, Reusable) Principles is insufficient. Here, we define criteria and practices for reliable AI-readiness of biomedical data, developed by the NIH Bridge to Artificial Intelligence (Bridge2AI) Standards Working Group across seven core dimensions of dataset AI-readiness: FAIRness, Provenance, Characterization, Ethics, Pre-model Explainability, Sustainability, and Computability. Conformance to these criteria provides a basis for pre-model scientific rigor and ethical integrity, mitigating downstream risks of bias and error prior to AI modeling. We apply and evaluate these standards across all four Bridge2AI flagship datasets, spanning functional genomics to clinical medicine, and encode them in machine-actionable metadata bound to the datasets. This framework sets a benchmark for preparing ethical, reusable datasets in biomedical AI and provides standardized methods for reliable pre-model data evaluation.

bioinformatics↗

bioRxiv2020

BrainGENIE: The Brain Gene Expression and Network Imputation Engine

In vivo experimental analysis of human brain tissue poses substantial challenges and ethical concerns. We developed a novel method called the Brain Gene Expression and Network Imputation Engine (BrainGENIE) that uses peripheral-blood transcriptomes to predict brain-tissue-specific gene-expression levels. BrainGENIE reliably predicted brain-tissue-specific expression levels for 1,733 - 11,569 genes (false-discovery rate-adjusted p<0.05), including many transcripts that cannot be predicted reliably by a transcriptome imputation method such as PrediXcan. We tested the generalizability of BrainGENIE in external within-individual data from ex vivo peripheral blood and postmortem brain samples from the Religious Orders Study and Memory and Aging Project, wherein we validated 39% of predicted gene expression levels as concordant with observed expression levels in dorsolateral prefrontal cortex and 23% in caudate. BrainGENIE recapitulated diagnosis-related gene expression changes in brain better than direct correlations from blood and predictions from PrediXcan. BrainGENIE complements and, in some ways, outperforms existing transcriptome-imputation tools, providing biologically meaningful predictions and opening new research avenues.

bioinformatics↗

bioRxiv2018

WEScover: whole exome sequencing vs. gene panel testing

MotivationWhole exome sequencing (WES) is widely adopted in clinical and research settings. However, there is potential for false negatives due to incomplete breadth and depth of coverage for several exons in clinically implicated genes. In some cases, a targeted gene panel testing may be a dependable option to ascertain true negatives for genomic variants in known phenotype associated genes. We developed a tool for quickly gauging whether all genes of interest would be reliably covered by WES or whether targeted gene panel testing should instead be considered to minimize false negatives in candidate genes. ResultsWEScover is a novel web application that provides an interface for discovering breadth and depth of coverage across population scale WES datasets, searching either by phenotype, by targeted gene panels and by gene(s). Moreover, the application shows metrics from the Genome Aggregation Database to provide gene-centric view on breadth of coverage. ConclusionWEScover allows users to efficiently query genes and phenotype for exome coverage of associated exons, and recommends use of panel tests for genes that are potentially not well covered by WES.

bioinformatics↗

bioRxiv2018

Solving for X: evidence for sex-specific autism biomarkers across multiple transcriptomic studies

Autism spectrum disorder (ASD) is a markedly heterogeneous condition with a varied phenotypic presentation. Its high concordance among siblings, as well as its clear association with specific genetic disorders, both point to a strong genetic etiology. However, the molecular basis of ASD is still poorly understood, although recent studies point to the existence of sex-specific ASD pathophysiologies and biomarkers. Despite this, little is known about how exactly sex influences the gene expression signatures of ASD probands. In an effort to identify sex-dependent biomarkers (and characterise their function), we present an analysis of a single paired-end post-mortem brain RNA-Seq data set and a meta-analysis of six blood-based microarray data sets. Here, we identify several genes with sex-dependent dysregulation, and many more with sex-independent dysregulation. Moreover, through pathway analysis, we find that these sex-independent biomarkers have substantially different biological roles than the sex-dependent biomarkers, and that some of these pathways are ubiquitously dysregulated in both post-mortem brain and blood. We conclude by synthesizing the discovered biomarker profiles with the extant literature, by highlighting the advantage of studying sex-specific dysregulation directly, and by making a call for new transcriptomic data that comprise large female cohorts.

neuroscience↗