Biology subjects

Li, x.

Publications and source records attributed to Li, x..

9 recordsLinked to original sources

bioRxiv2026

Preferential IsomiR Enrichment in Extracellular Vesicles Improves Identification of Their Cellular Origins

Extracellular vesicles (EVs) carry microRNAs (miRNAs) that mediate intercellular communication and have strong potential as disease biomarkers, yet the roles of miRNA isoforms (isomiRs) in EVs remain poorly understood. Here, we analyzed 96 human EV and corresponding source samples from nine public datasets. We found that EV samples consistently contained substantially higher proportions of isomiR reads than their corresponding source samples, indicating widespread isomiR enrichment in EVs. Although individual isomiRs showed limited reproducibility across biological replicates and limited sharing between EVs and their corresponding source samples, the parent miRNAs that generated these isomiRs remained highly reproducible across replicates and strongly shared between EV-source pairs. Despite extensive isomiR diversification, EV-source pairs retained highly correlated miRNA expression profiles. Using integrated miRNA- and isomiR-related features, we further developed a random forest model that successfully associated EV samples with their corresponding source samples, with improved performance when isomiR information was included. Together, our results demonstrate that EVs are enriched for biologically meaningful isomiRs while preserving source-associated miRNA landscapes, highlighting the importance of incorporating isomiRs into future EV studies.

bioinformatics↗

bioRxiv2026

misoTar: A novel approach for predicting miRNA and isomiR targets

Understanding the interactions between microRNAs/isomiRs and mRNAs has long been a major challenge in RNA biology. Although numerous computational approaches have been developed to predict these interactions, most fail to account for isomiR mediated targeting. To address this limitation, we developed misoTar, a deep learning framework trained on more than 6.662 million positive and negative interaction pairs derived from 67 publicly available human samples across six independent studies. In five-fold cross-validation, misoTar achieved an average precision of 0.930 and a recall of 0.898. Evaluation on independent test datasets demonstrated consistently superior or comparable performance relative to existing tools, including TargetScan, Mimosa, DMISO, and TEC-miTarget. In addition, single-nucleotide mutation analyses of true positive interactions revealed the critical functional contributions of non-seed regions in microRNA/isomiR targeting. Overall, misoTar provides a robust and accurate framework for predicting microRNA/isomiR interactions while offering new biological insights into microRNA targeting mechanisms. The misoTar tool is publicly available at https://figshare.com/projects/misoTar/262723.

bioinformatics↗

bioRxiv2025

Deep learning inference of miRNA expression from bulk and single-cell mRNA expression

Understanding the activity of miRNA in individual cells presents a challenge due to the limitations of single-cell technologies in capturing miRNAs. To tackle this obstacle, we introduce two deep learning models: Cross-Modality (CM) and Single-Modality (SM). These models utilize encoder-decoder architectures to predict miRNA expression at the bulk and single-cell levels from mRNA data. We compared CM and SM with a state-of-the-art approach, miRSCAPE, using both bulk and single-cell datasets. We found that both CM and SM outperformed miRSCAPE in terms of accuracy. We also observed that integrating miRNA target information led to a significant enhancement in performance compared to using all genes. These models offer valuable tools for predicting miRNA expression from single-cell mRNA data.

bioinformatics↗

bioRxiv2024

A Germinal Center Checkpoint of AIRE in B Cells LimitsAntibody Diversification

In response to antigens, B cells undergo antibody diversification, including affinity maturation and class switching, mediated by activation-induced cytidine deaminase (AID) in secondary lymphoid organs, but uncontrolled AID activity can precipitate autoimmunity and cancer. The regulation of antibody diversification is of fundamental importance, though the mechanisms underlying these pathways are not well understood. We found that autoimmune regulator (AIRE), the molecule essential for T cell tolerance, is expressed in germinal center (GC) B cells in a CD40-dependent manner, interacts with AID and negatively regulates antibody affinity maturation and class switching by inhibiting AID function. AIRE deficiency in B cells caused altered antibody repertoire, increased somatic hypermutations, elevated autoantibodies to T helper 17 effector cytokines and defective control of skin Candida albicans. These results define a GC B cell checkpoint of humoral immunity and illuminate new approaches of generating high-affinity neutralizing antibodies for immunotherapy.

immunology↗

bioRxiv2023

Selected humanization of yeast U1 snRNP leads to global suppression of pre-mRNA splicing and mitochondrial dysfunction in the budding yeast

The recognition of 5 splice site (5 ss) is one of the earliest steps of pre-mRNA splicing. To better understand the mechanism and regulation of 5 ss recognition, we selectively humanized components of the yeast U1 snRNP to reveal the function of these components in 5 ss recognition and splicing. We targeted U1C and Luc7, two proteins that interact with and stabilize the yeast U1 (yU1) snRNA and the 5 ss RNA duplex. We replaced the Zinc-Finger (ZnF) domain of yU1C with its human counterpart, which resulted in cold-sensitive growth phenotype and moderate splicing defects. Next, we added an auxin-inducible degron to yLuc7 protein and found that Luc7-depleted yU1 snRNP resulted in the concomitant loss of PRP40 and Snu71 (two other essential yeast U1 snRNP proteins), and further biochemical analyses suggest a model of how these three proteins interact with each other in the U1 snRNP. The loss of these proteins resulted in a significant growth retardation accompanied by a global suppression of pre-mRNA splicing. The splicing suppression led to mitochondrial dysfunction as revealed by a release of Fe2+ into the growth medium and an induction of mitochondrial reactive oxygen species. Together, these observations indicate that the human U1C ZnF can substitute that of yeast, Luc7 is essential for the incorporation of the Luc7-Prp40-Snu71 trimer into yeast U1 snRNP, and splicing plays a major role in the regulation of mitochondria function in yeast.

biochemistry↗

bioRxiv2022

A systematic study of HIF1A cofactors in hypoxic cancer cells

Hypoxia inducible factor 1 alpha (HIF1A) is a transcription factor (TF) that forms highly structural and functional protein-protein interactions with other TFs to promote gene expression in hypoxic cancer cells. Despite the importance of these TF-TF interactions, we still lack a comprehensive view of many of the TF cofactors involved and how they cooperate in hypoxic cancer cells. In this study, we systematically studied HIF1A cofactors in eight cancer cell lines and discovered 201 potential HIF1A cofactors. These cofactors were statistically and biologically significant, with 30 of the top 37 cofactors validated or supported in the literature. Moreover, these predicted cofactors include 21 of the 29 known HIF1A TF cofactors in public databases. These discovered cofactors can be essential to HIF1As regulatory functions and may lead to the discovery of new therapeutic targets in cancer treatment.

bioinformatics↗

bioRxiv2022

INSISTC: Incorporating Network Structure Information for Single-Cell Type Classification

MotivationUncovering gene regulatory mechanisms in individual cells can provide insight into cell heterogeneity and function. Recent accumulated single-cell RNA sequencing data have made it possible to analyze gene regulation at single-cell resolution. On the other hand, understanding cell-type-specific gene regulation can also assist in more accurate cell type and state identification. Computational approaches utilizing gene regulatory relationships for single-cell type classification are under development. Methods pioneering in integrating gene regulatory mechanism discovery with cell-type classification encounter challenges such as how to accurately determine gene regulatory relation-ships and how to incorporate gene regulatory network structure into consideration. ResultsWe developed a computational method to incorporate gene regulatory network structure information for single-cell type classification (INSISTC). INSISTC is capable of identifying cell-type-specific gene regulatory mechanisms while performing single cell type classification. Tested on three mouse scRNA-Seq datasets, including thousands of single-cell samples, INSISTC demonstrated its accuracy in cell type classification and its potential for providing insight into molecular mechanisms specific to individual cells. In comparison with the alternative methods, INSISTC demonstrated its complementary performance for gene regulation interpretation. Availabilityhttps://hulab.ucf.edu/research/projects/INSISTC/ Contacthaihu@cs.ucf.edu or xiaoman@mail.ucf.edu Supplementary informationSupplementary data are available at xxxxxx online.

bioinformatics↗

bioRxiv2022

A revisit to universal single-copy genes in bacterial genomes

Universal single-copy genes (USCGs) are widely used for species classification and taxonomic profiling. Despite many studies on USCGs, our understanding of USCGs in bacterial genomes might be out of date, especially how different the USCGs are in different studies, how well a set of USCGs can distinguish two bacterial species, whether USCGs can separate different strains of a bacterial species, to name a few. To fill the void, we studied USCGs in the most updated bacterial RefSeq genomes. We showed that different USCG sets are quite different while coming from highly similar functional categories. We also found that although USCGs occur once in almost all bacterial genomes, each USCG does occur multiple times in certain genomes. We demonstrated that USCGs are reliable markers to distinguish different species while they cannot distinguish different strains of most bacterial species. Our study shed new light on the usage and limitations of USCGs, which will facilitate their applications in evolutionary, phylogenomic, and metagenomic studies.

bioinformatics↗

bioRxiv2020

RBM39 alters phosphorylation of c-Jun and binds to viral RNA to promote PRRSV proliferation

ABTRASTAs transcriptional co-activator of AP-1/Jun, estrogen receptors and NF-{kappa}B, nuclear protein RBM39 also involves in precursor mRNA (pre-mRNA) splicing. Porcine reproductive and respiratory syndrome virus (PRRSV) causes sow reproductive disorders and piglet respiratory diseases, which resulted in serious economic losses worldwide. In this study, the up-regulated expression of RBM39 and down-regulated of inflammatory cytokines (TNF, IL-1{beta}) were determined in PRRSV-infected 3D4/21 cells, and accompanied with the PRRSV proliferation. The roles of RBM39 altering phosphorylation of c-Jun to inhibit the AP-1 pathway to promote PRRSV proliferation were further verified. In addition, the nucleocytoplasmic translocation of RBM39 and c-Jun from nucleus to cytoplasm were enhanced in PRRSV-infected cells. The three RRM domain of RBM39 are crucial to support the proliferation of PRRSV. several PRRSV RNA (nsp4, nsp5, nsp11 and N) binding with RBM39 were determined, which may also contribute to the PRRSV proliferation. Our results revealed a complex mechanism of RBM39 by altering c-Jun phosphorylation and nucleocytoplasmic translocation, and regulating binding of RBM39 with viral RNA to prompt PRRSV proliferation. The results provide new viewpoints to understand the immune escape mechanism of PRRSV infection.

immunology↗