Iida, N. | Search bioRxiv

bioRxiv2023

Impact of High Fat Diet and Sex in a Rabbit Model of Carpal Tunnel Syndrome

Carpal tunnel syndrome (CTS) is a common musculoskeletal disorder, characterized by thickening and fibrosis of the subsynovial connective tissue (SSCT). Risk factors for CTS includes sex, metabolic dysfunction and age. In this study we hypothesized that a high-fat diet (HFD), a common driver of metabolic dysfunction, would promote SSCT thickening in CTS and that this response would be sex dependent. To test this, we examined the effects of HFD and sex on SSCT thickening and markers of fibrosis using our established CTS rabbit model of SSCT thickening. Forty-eight (24 male, 24 female) adult rabbits were split into four groups including HFD or standard diet with and without CTS induction. SSCT was collected for histological and gene expression analysis. HFD promoted SSCT thickening and upregulated profibrotic genes, including TGF-{beta}. Fibrotic genes were differentially expressed in males and females. Interestingly while the overall prevalence of CTS is greater in women than in men, under conditions of metabolic dysfunction men have a higher incidence. This suggests a focus on metabolic and sex specific therapeutic strategies for the treatment of patients with CTS.

physiology↗

bioRxiv2021

Systematic identification of intron retention associated variants from massive publicly available transcriptome sequencing data

Many disease-associated genomic variants disrupt gene function through abnormal splicing. With the advancement of genomic medicine, identifying disease-associated splicing associated variants has become more important than ever. Most bioinformatics approaches to detect splicing associated variants require both genome and transcriptomic data. However, there are not many datasets where both of them are available. In this study, we developed a methodology to detect genomic variants that cause splicing changes (more specifically, intron retention), using transcriptome sequencing data alone. After demonstrating its high sensitivity and precision, we have applied it to 230,988 transcriptome sequencing data from the publicly available repository and identified 27,937 intron retention associated variants (IRAVs). In addition, by exploring positional relationships with variants registered in existing disease databases, we extracted 3,077 putative disease-associated IRAVs, which range from cancer drivers to variants linked with autosomal recessive disorders. The new in-silico screening framework proposed here provides a foundation for a platform that can automatically acquire medical knowledge making the most of massively accumulated publicly available sequencing data. Collections of IRAVs identified in this study are available through IRAVDB (https://iravdb.io/).

bioinformatics↗