Biomedical subjects

Xiaobo Sun

Publications and source records attributed to Xiaobo Sun.

3 recordsLinked to original sources

PubMed2026

Protein arginine methyltransferases as metabolic regulators: many roles beyond cancer.

Metabolic syndrome (MetS) comprises a cluster of interconnected metabolic abnormalities that collectively elevate the risk of cardiovascular disease and mortality. With its global prevalence escalating, understanding the molecular underpinnings of MetS has become increasingly imperative. Protein arginine methyltransferases (PRMTs), classically studied for their epigenetic functions and oncogenic properties, are now recognized as pivotal regulators of metabolic homeostasis. Emerging research reveals that these enzymes coordinate crucial aspects of cellular metabolism through multiple mechanisms, including methylation of metabolic transcription factors, modulation of nutrient-sensing pathways, and direct regulation of enzymatic activities in glucose and lipid metabolism. This review summarizes current knowledge on the metabolic roles of PRMTs, specifying their roles in the development and function of major metabolic tissues and their associations with various metabolic disorders. We further review how PRMTs influence metabolic processes by modifying key transcriptional networks and signaling cascades through methylation of different substrates. By integrating these insights, we establish PRMTs as central players in metabolic regulation and assess their potential as therapeutic targets for metabolic diseases beyond their established roles in cancer biology, thereby providing a framework for future research and clinical development.

glucose metabolism↗

PubMed2026

CoxFormer enables spatial omics inference with multimodal generative modeling.

Gene co-expression maps transcriptome-wide gene-gene relationships, yet high-quality estimates cover less than half the genome. Meanwhile, spatial omics either profiles restricted in situ panels or lacks cellular resolution. Extending co-expression transcriptome-wide could overcome these limitations by inferring unassayed gene expression at subcellular resolution. Here we show that CoxFormer integrates literature-derived gene knowledge with co-expression networks from bulk tissues and large-scale single-cell atlases to learn 512-dimensional representations for 32,016 human genes. These embeddings capture functional gene relationships and serve as a generative prior for spatial inference across platforms and modalities. Without requiring a matched single-cell RNA-sequencing reference, CoxFormer supports four applications beyond measured genes: histology-based expression imputation, gene activity prediction from chromatin accessibility, subcellular super-resolution inference, and pathological region detection. Together, CoxFormer extends gene embedding from gene- and cell-level tasks to whole-transcriptome spatial inference, providing a unified framework for biological analysis beyond the limited gene coverage of current spatial omics technologies.

Humans↗

PubMed2025

SIGEL: a context-aware genomic representation learning framework for spatial genomics analysis.

Spatial transcriptomics (ST) integrates spatial information into genomics, yet methods for generating spatially-informed gene representations are limited and computationally intensive. We present SIGEL, a cost-effective framework that derives gene manifolds from ST data by exploiting spatial genomic context. The resulting SIGEL-generated gene representations (SGRs) are context-aware, biologically meaningful, and robust across samples, making them highly effective for key downstream tasks, including imputing missing genes, detecting spatial expression patterns, identifying disease-related genes and interactions, and improving spatial clustering. Extensive experiments across diverse ST datasets validate SIGEL's effectiveness and highlight its potential in advancing spatial genomics research.

Genomics↗