Trigonelline hydrochloride attenuates silica-induced pulmonary fibrosis by orchestrating fibroblast to myofibroblast differentiation.

BACKGROUND: Silicosis represents a paramount occupational health hazard globally, with its incidence, morbidity, and mortality on an upward trajectory, posing substantial clinical dilemmas due to limited effective treatment options available. Trigonelline (Trig), a plant alkaloid extracted mainly from coffee and fenugreek, have diverse biological properties such as protecting dermal fibroblasts against ultraviolet radiation and has the potential to inhibit collagen synthesis. However, it's unclear whether Trig inhibits fibroblast activation to attenuate silicosis-induced pulmonary fibrosis is unclear. METHODS: To evaluate the therapeutic efficacy of Trig in the context of silicosis-related pulmonary fibrosis, a mouse model of silicosis was utilized. The investigation seeks to elucidated Trig's impact on the progression of silica-induced pulmonary fibrosis by evaluating protein expression, mRNA levels and employing Hematoxylin and Eosin (H&E), Masson's trichrome, and Sirius Red staining. Subsequently, we explored the mechanism underlying of its functions. RESULTS: In vivo experiment, Trig has been demonstrated the significant efficacy in mitigating SiO2-induced silicosis and BLM-induced pulmonary fibrosis, as evidenced by improved histochemical staining and reduced fibrotic marker expressions. Additionally, we showed that the differentiation of fibroblast to myofibroblast was imped in Trig + SiO2 group. In terms of mechanism, we obtained in vitro evidence that Trig inhibited fibroblast-to-myofibroblast differentiation by repressing TGF-ß/Smad signaling according to the in vitro evidence. Notably, our finding indicated that Trig seemed to be safe in mice and fibroblasts. CONCLUSION: In summary, Trig attenuated the severity of silicosis-related pulmonary fibrosis by alleviating the differentiation of myofibroblasts, indicating the development of novel therapeutic approaches for silicosis fibrosis.

Genetic variation and marker-trait association affect the genomic selection prediction accuracy of soybean protein and oil content.

Introduction: Genomic selection (GS) is a potential breeding approach for soybean improvement. Methods: In this study, GS was performed on soybean protein and oil content using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) based on 1,007 soybean accessions. The SoySNP50K SNP dataset of the accessions was obtained from the USDA-ARS, Beltsville, MD lab, and the protein and oil content of the accessions were obtained from GRIN. Results: Our results showed that the prediction accuracy of oil content was higher than that of protein content. When the training population size was 100, the prediction accuracies for protein content and oil content were 0.60 and 0.79, respectively. The prediction accuracy increased with the size of the training population. Training populations with similar phenotype or with close genetic relationships to the prediction population exhibited better prediction accuracy. A greatest prediction accuracy for both protein and oil content was observed when approximately 3,000 markers with -log10(P) greater than 1 were included. Discussion: This information will help improve GS efficiency and facilitate the application of GS.