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Biomedical subjects

Bing Liu

Publications and source records attributed to Bing Liu.

3 recordsLinked to original sources

Phylogenomics and evolution of the Lauraceae based on targeted capture data.

The family Lauraceae, a hyper-diverse magnoliid family comprising approximately 63 genera and over 3,000 species, plays a key ecological role in tropical and subtropical forests. Yet deep relationships among its nine tribes remain unresolved, likely due to limited sampling and complex evolutionary processes such as incomplete lineage sorting (ILS) and gene flow. To address these challenges, we generated datasets of 255 single-copy nuclear genes and chloroplast genomes using a newly designed Lauraceae-specific probe set, achieving the most comprehensive genus-level sampling (84%) to date. Phylogenomic analyses reconstructed a robust nuclear tree, which resolved the Neocinnamomeae as sister to the Caryodaphnopsideae and revealed pronounced gene tree conflict and pervasive cytonuclear discordance. To investigate the evolutionary processes underlying these patterns, comprehensive analyses were conducted. The results indicate that conflicting nuclear gene trees reflect the combined effects of ILS, gene tree estimation error, and gene flow, with ILS dominating across the core Lauraceae, whereas cytonuclear discordance is primarily driven by extensive gene flow. Diversification analyses further indicate that episodes of rapid lineage accumulation coincide with major gene flow events, suggesting a potential role of gene flow in the diversification of Lauraceae. Overall, this study provides a robust nuclear phylogenomic framework for Lauraceae and demonstrates that gene flow had profound effects on its evolutionary history, shedding light on the contribution of gene flow to the diversification of hyper-diverse tropical plant lineages.

Cytonuclear discordance

Multi-omics uncovers the pleiotropic genetic mechanisms linking MASLD and cardiometabolic syndromes.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) and cardiovascular-kidney-metabolic (CKM) syndrome are interrelated conditions with shared pathophysiological features; however, the genetic architecture underlying their relationship has not been fully elucidated. Deciphering this shared genetic basis holds promise for advancing mechanistic insights and therapeutic discovery. METHODS: We performed an integrated genome-wide cross-trait analysis using GWAS summary statistics for MASLD and 38 CKM traits. Our analysis estimated genetic correlations, inferred causal relationships, and identified pleiotropic variants. Candidate causal genes and druggable targets were subsequently prioritized through integrating multi-omics data. RESULTS: MASLD exhibited significant genetic correlations with 16 CKM traits, especially metabolic and cardiovascular conditions. Bidirectional causal relationships were observed between MASLD and T2D, adiposity, and lipid traits. We discovered 116 pleiotropic loci, including 65 shared causal variants such as rs429358 near APOE, which exerted influence across multiple traits. Gene-based analyses prioritized 152 unique candidate pleiotropic genes, enriched in lipid and cholesterol metabolism, and highly expressed in the liver, adipose, and immune-related cell types, such as macrophages and endothelial cells. Multi-omics integration validated 131 genes using eQTL and pQTL data from multiple tissues and cohorts. Notably, FTO and APOE emerged as central pleiotropic hubs, and druggability evaluation highlighted APOE, LPL, PPARG, and GPBAR1 as established therapeutic targets for metabolic diseases. CONCLUSION: This study provides a comprehensive map of the shared genetic architecture between MASLD and CKM syndrome, reveals novel causal genes and repurposable drug targets, and offers insights into precision medicine approaches for cardiometabolic and liver diseases.

Humans

An integrated multiscale air quality modelling framework for industrial park pollution: Linking local emissions to regional transport.

Capturing the spatiotemporal distribution of pollutants in industrial parks remains challenging for regional air quality models because of their coarse resolution (3 km), resulting in uncertainties in local emission quantification. To address this, we developed the Integrated Multiscale Air Quality Modelling System for Industry (IAQMS-Industry), coupling the regional Nested Air Quality Prediction Modelling System (NAQPMS) with a city-scale chemical transport model. This framework integrates point-source locations and Gaussian plume dispersion to simulate particulate matter with a diameter smaller than 2.5 micrometres (PM2.5) at 100 m resolution. Applied to the Beijing Yi Zhuang and Tangshan industrial parks and evaluated against observations. The coupled model achieved a normalized mean bias (NMB) ranging from 3.1 % to 6.2 %, improving upon NAQPMS (-16.9 % to -7.7 %). Spatial analysis revealed that coarse regional grids underestimated the PM2.5​ concentrations at industrial sites by smoothing gradients, whereas IAQMS-Industry successfully resolved spatial patterns. Industrial point emissions accounted for 22.9 %-26.4 % of PM2.5 in the coupled model, which was significantly greater than the regional model estimates of 1.6 %-13.7 %. These findings indicate that regional models overestimate pollutant dispersion processes in industrial parks while underestimating local industrial impacts. By explicitly resolving point-source dynamics and linking them to regional transport, IAQMS-Industry provides a robust tool for designing targeted emission controls in industrial cities and balancing local air quality improvements with minimized regional pollution outflow. This study underscores the necessity of multiscale modelling for accurate source apportionment and informed environmental governance in industrial zones.

Air Pollution