Biomedical subjects

Ping Zhu

Publications and source records attributed to Ping Zhu.

4 recordsLinked to original sources

PubMed2026

hnRNPK condensates facilitate enhancer-promoter looping and RNA polymerase II recruitment.

Enhancer RNAs interact with promoter-derived RNAs to dictate enhancer-promoter looping, but the RNA-binding protein that mediates this process has remained unidentified. Here we identify hnRNPK as a general structural regulator that preferentially binds to nascent RNAs transcribed from enhancer and promoter regions, promoting enhancer-promoter looping and transcriptional activation. We further show that hnRNPK forms phase-separated, cavity-containing condensates that encapsulate RNA polymerase II (Pol II) via its RPB3 subunit, facilitating chromatin looping and potentially enabling recruitment of Pol II from enhancers to promoters through protein dimerization. Notably, a mutation associated with Au-Kline syndrome in hnRNPK (c.953+1dupG) alters its condensates from a liquid-like to a gel-like state, leading to developmental defects in knock-in mice. Fibroblasts derived from these mutants display reduced enhancer-promoter looping and decreased Pol II recruitment at promoters of key developmental genes. These findings suggest that hnRNPK is a structural regulator of enhancer-promoter communication and highlight the importance of RNA-RNA interactions mediated by RNA-binding proteins in transcriptional regulation.

RNA Polymerase II↗

PubMed2026

A genome-wide cross-trait analysis characterizes the shared genetic architecture between rheumatoid arthritis and psychiatric disorders.

OBJECTIVES: Patients with RA have a 2- to 3-fold elevated risk of psychiatric disorders, suggesting an underlying genetic link between these phenotypes. However, the shared genetic architectures and pathological mechanisms driving RA-psychiatric disorder comorbidity remain to be fully elucidated. Herein, we performed cross-trait analysis to investigate the shared genetic architecture between RA and psychiatric disorders. METHODS: Leveraging European-ancestry genome-wide association studies (GWASs) datasets of RA (n = 1 026 690) and 10 major psychiatric disorders (n = 14 307-1 222 882), we performed cross-trait pleiotropic analysis to identify the shared pleiotropic loci and genes between RA and psychiatric disorders, followed by functional annotation and Mendelian randomization analysis to explore the pathological mechanisms underlying RA-psychiatric disorder comorbidity. RESULTS: Our analysis revealed significant positive genetic correlations between RA and seven psychiatric disorders, such as major depressive disorder. From these correlations, we identified 61 pleiotropic loci jointly influencing RA and psychiatric disorder risk, along with 208 pleiotropic genes predominantly involved in immune and inflammatory response biological processes. Druggable target exploration identified 21 drug-gene interactions involving pleiotropic genes, with two genes (RHOA and TRAF3) classified in the clinically actionable category, representing potential therapeutic targets for both RA and psychiatric disorders. Mendelian randomization further demonstrated a bidirectional causal relationship between RA and schizophrenia, while supporting the causal roles of attention-deficit/hyperactivity disorder, major depressive disorder and post-traumatic stress disorder in increasing RA risk. CONCLUSION: Our findings elucidate the shared genetic architecture between RA and psychiatric disorders, providing novel insights into the pathological mechanisms underlying their comorbidity and laying the groundwork for improved comorbidity management.

Arthritis, Rheumatoid↗

PubMed2026

Integration of Genome-Wide Association Studies With Single-Cell and Bulk Expression Quantitative Trait Locus to Identify Stroke Susceptibility Genes.

BACKGROUND: Previous studies have integrated genome-wide association studies with expression quantitative trait locus (eQTL) data from bulk tissues to identify stroke susceptibility genes. However, eQTL data exhibit high cell-type specificity, and genetic variants may have distinct effects across stroke subtypes. METHODS: We applied the summary-data-based Mendelian randomization (MR) method to integrate eQTL data from 7 brain cell types with genome-wide association studies data for 5 stroke phenotypes (stroke, ischemic stroke, cardioembolic stroke, large artery stroke, and small vessel stroke). Results were compared with summary-data-based MR using eQTL data from 49 tissues in the Genotype-Tissue Expression project. Robustness of significant single-cell summary-data-based MR associations was assessed via MR and colocalization analyses. Further evaluations included single-cell RNA-seq differential expression, protein-protein interaction, druggability, and phenome-wide association studies. RESULTS: Single-cell summary-data-based MR identified many novel significant genes not detected using bulk tissue eQTL data. Validated associations revealed 2 stroke risk genes (LRCH1, ICA1L), 3 stroke protective genes (AHI1, LYRM9, CENPQ), 2 large artery stroke risk genes (LIPA, ELL), and 1 ischemic stroke protective gene (CENPQ). Single-cell RNA-seq showed significantly increased LIPA expression in mouse stroke samples compared with controls. Protein-protein interaction and druggability analyses, along with phenome-wide association studies, prioritized LIPA and LRCH1 as potential therapeutic targets for stroke while indicating possible adverse effects. CONCLUSIONS: Integrating single-cell eQTL with stroke-subtype genome-wide association studies uncovers novel cell-type-specific causal genes and highlights promising therapeutic targets, advancing understanding of stroke pathogenesis.

Genome-Wide Association Study↗

PubMed2025

Clinical and Multiorgan Proteomics Characteristics of the Diverse Fatal Phase in Super Elderly Patients With SARS-CoV-2 Infection: A Descriptive Study.

This study aims to identify the risk factors associated with clinical outcomes and the proteomic changes in organs related to fatal SARS-CoV-2 infection within the super-elderly population. This retrospective analysis included all elderly individuals with COVID-19 admitted to the Second Medical Center of PLA General Hospital from December 2022 to January 2023. The follow-up period ended on March 30, 2023. During this time, epidemiological, demographic, laboratory, and outcome data were analyzed descriptively. Proteomic sequencing was performed on super-elderly patients who died from COVID-19 at different stages of the disease. A total of 352 elderly COVID-19 patients, with a mean age of 89.84 ± 8.54 years, were included in this study. During a median follow-up period of 98 days, 79 patients died. Deceased patients were older and more likely to have cardiovascular and cerebrovascular diseases, with a lower prevalence of lipid-lowering therapy. The number of deaths in the acute and post-acute phases were 34 and 45, respectively. Proteomics data suggest that the immune systems of patients who died in the acute phase underwent a more rapid and severe onslaught. Patients in the post-acute phase showed higher levels of viral genome replication and a more robust immune response. However, the over-activation of the immune system led to systemic organ dysfunction. Effective management of comorbidities may improve the prognosis of COVID-19 in super-elderly patients. The continuous replication of the SARS-CoV-2 virus and its subsequent impact on the immune system are critical determinants of survival time in this demographic.

Humans↗