Glutathione disulfide

Deciphering cysteamine's impact on cashmere-producing traits of cashmere goats: a transcriptomic and metabolomic perspective.[Pubmed:42824821]

Front Vet Sci. 2026 Sep 17;13:1925080.

INTRODUCTION: This study addresses the practical production problem of inferior cashmere quality in cashmere goats. We supplemented cysteamine (CS) in diets to regulate cashmere‑production‑related traits and explore its underlying nutritional regulatory pathways, so as to support the scientific and rational application of cysteamine in cashmere goat production. METHODS: Sixty healthy 18‑month‑old female Inner Mongolia cashmere goats were selected for this single‑factor randomized trial and randomly allocated into three groups (n = 20 per group). The control group (CON) received basal diet; treatment groups were orally administrated cysteamine at 60 mg/kg body weight (BW)(LCS group) and 120 mg/kg BW (HCS group). Cashmere samples were collected from the designated trunk region. Fiber traits including diameter, staple length, breaking strength and elongation at break were measured, and fiber surface microstructure was observed by scanning electron microscopy (SEM). Skin tissues (n = 6 per group) were harvested for transcriptome and untargeted metabolomic sequencing to identify differentially expressed genes (DEGs) and differential metabolites (DEMs). KEGG enrichment and multi‑omics integrative analysis were further performed. RESULTS: Dietary CS numerically improved cashmere yield, weight and scale height without statistical significance. The LCS treatment significantly increased straightened length, while staple length slightly decreased in the HCS group but remained higher than in CON. Initial cashmere diameter, strength and elongation at break showed no inter‑group differences; these three indicators increased after CS supplementation compared with baseline values. In total, 426 DEGs and 72 DEMs were detected, indicating obvious transcriptional and metabolic changes induced by CS. KEGG analysis highlighted divergence in the cAMP signaling pathway and thyroid hormone synthesis. DEMs were mainly enriched in lipid metabolism, organic acid metabolism and heterocyclic compound metabolism; L‑cysteine‑Glutathione disulfide and alpha‑lipoic acid were key sulfur‑containing differential metabolites. Multi‑omics integration confirmed differences in lipid metabolism and cyclic nucleotide signaling pathways, with lipid‑related genes (PLA2G2C, VDAC3, CHRM5, SYT4) and cyclic‑nucleotide‑related gene IL24 significantly up‑regulated in CS groups. DISCUSSION: Multi‑omics results indicate that cysteamine may improve cashmere‑goat productive traits by providing endogenous organic sulfur sources, accumulating sulfur‑containing metabolic intermediates and coordinating hair‑follicle‑development‑related signaling pathways. This work supplies experimental evidence for cashmere fiber quality improvement.

Gene-Environment Interaction Mendelian Randomization Reveals Statin-Dependent Associations Between Metabolites and Lipid Traits.[Pubmed:42797520]

Pharmaceuticals (Basel). 2026 Sep 17;19(9):1475.

Introduction/Background: Observational links between circulating metabolites and lipid traits are often confounded by environmental factors. This study applied Mendelian randomization (MR) and gene-environment interaction MR (MR-GxE) to assess the average causal effects of genetically predicted metabolites on lipid traits and their modification by statin use. Methods: Participants (n = 2810) from Qatar Biobank were analyzed including genetics, metabolomics and lipid profiles. The cohort was divided into discovery (n = 1968) and validation (n = 842). One-sample MR using two-stage least squares regression was applied to estimate the average causal effect of metabolites on LDL, HDL, and Triglycerides, while MR-GxE analysis was also applied to investigate whether genetically predicted metabolite-LDL/HDL associations differ by statin use. Genetic variants associated with metabolites were used to generate a polygenic risk score (PRS). The interaction term between the PRS and statin use was incorporated in MR-GxE, while adjusting for confounders. Results: A total of 167 metabolites were commonly associated with LDL, HDL, and Triglycerides. One-sample MR showed no significant average causal effect of genetically predicted metabolites on lipid traits in the overall population. However, MR-GxE analysis revealed significant statin-dependent effects. Genetically predicted 2-amino-octanoate and cysteine-Glutathione disulfide demonstrated strong and reproducible positive interaction effects with LDL levels in statin users in both discovery and validation cohorts. While no overall average causal effects were observed, MR-GxE revealed that genetically predicted metabolites can influence LDL levels in the context of statin use. Conclusions: These findings highlight the importance of incorporating pharmacological exposures in MR analyses and support the role of drug-metabolite interactions in precision lipid management.

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