Biomedical subjects

Boxiang Liu

Publications and source records attributed to Boxiang Liu.

2 recordsLinked to original sources

PubMed2025

Impact of polymorphisms on gene expression and splicing in response to exercise and diet-induced weight loss in human skeletal muscle tissues.

Weight loss through exercise and diet reduces the risk of type 2 diabetes, but the genetic regulation of gene expression and splicing in response to weight loss remains unclear in humans. We collected clinical data and skeletal muscle biopsies from 54 overweight/obese Asian individuals before and after a 16-week lifestyle intervention, which resulted in an average of ∼10% weight loss, accompanied by an ∼30% increase in insulin-stimulated glucose uptake. Improvements were observed in 118 of 252 clinical traits and six blood lipids. Transcriptomic analysis of paired skeletal muscle biopsies identified 505 differentially expressed genes enriched in mitochondrial function and insulin sensitivity. Thousands of muscle-specific expression/splicing quantitative trait loci (e/sQTLs) were detected pre- and post-intervention, including hundreds of lifestyle-responsive e/sQTLs. Notably, approximately 4.2% of eQTLs and 7.3% of sQTLs showed Asian specificity. Joint analysis with genome-wide association study (GWAS) identified 16 putative metabolic risk genes. Our study reveals gene-by-lifestyle interactions and how lifestyle modulates gene regulation in skeletal muscle.

Humans↗

PubMed2024

Quantification of escape from X chromosome inactivation with single-cell omics data reveals heterogeneity across cell types and tissues.

Several X-linked genes escape from X chromosome inactivation (XCI), while differences in escape across cell types and tissues are still poorly characterized. Here, we developed scLinaX for directly quantifying relative gene expression from the inactivated X chromosome with droplet-based single-cell RNA sequencing (scRNA-seq) data. The scLinaX and differentially expressed gene analyses with large-scale blood scRNA-seq datasets consistently identified the stronger escape in lymphocytes than in myeloid cells. An extension of scLinaX to a 10x multiome dataset (scLinaX-multi) suggested a stronger escape in lymphocytes than in myeloid cells at the chromatin-accessibility level. The scLinaX analysis of human multiple-organ scRNA-seq datasets also identified the relatively strong degree of escape from XCI in lymphoid tissues and lymphocytes. Finally, effect size comparisons of genome-wide association studies between sexes suggested the underlying impact of escape on the genotype-phenotype association. Overall, scLinaX and the quantified escape catalog identified the heterogeneity of escape across cell types and tissues.

X Chromosome Inactivation↗