MiR-148a-3p within HucMSC-Derived Extracellular Vesicles Suppresses Hsp90b1 to Prevent Fibroblast Collagen Synthesis and Secretion in Silica-Induced Pulmonary Fibrosis.

Silicosis is a fatal occupational respiratory disease caused by the prolonged inhalation of respirable silica. The core event of silicosis is the heightened activity of fibroblasts, which excessively synthesize extracellular matrix (ECM) proteins. Our previous studies have highlighted that human umbilical cord mesenchymal stem cell-derived extracellular vesicles (hucMSC-EVs) hold promise in mitigating silicosis and the significant role played by microRNAs (miRNAs) in this process. Delving deeper into this mechanism, we found that miR-148a-3p was the most abundant miRNA of the differential miRNAs in hucMSC-EVs, with the gene heat shock protein 90 beta family member 1 (Hsp90b1) as a potential target. Notably, miR-148a-3p's expression was downregulated during the progression of silica-induced pulmonary fibrosis both in vitro and in vivo, but was restored after hucMSC-EVs treatment (p < 0.05). Introducing miR-148a-3p mimics effectively hindered the collagen synthesis and secretion of fibroblasts induced by transforming growth factor-ß1 (TGF-ß1) (p < 0.05). Confirming our hypothesis, Hsp90b1 was indeed targeted by miR-148a-3p, with significantly reduced collagen activity in TGF-ß1-treated fibroblasts upon Hsp90b1 inhibition (p < 0.05). Collectively, our findings provide compelling evidence that links miR-148a-3p present in hucMSC-EVs with the amelioration of silicosis, suggesting its therapeutic potential by specifically targeting Hsp90b1, thereby inhibiting fibroblast collagen activities. This study sheds light on the role of miR-148a-3p in hucMSC-EVs, opening avenues for innovative therapeutic interventions targeting molecular pathways in pulmonary fibrosis.

Investigation of the mechanism of silica-induced pulmonary fibrosis: The role of lung microbiota dysbiosis and the LPS/TLR4 signaling pathway.

The widespread manufacture of silica and its extensive use, and potential release of silica into the environment pose a serious human health hazard. Silicosis, a severe global public health issue, is caused by exposure to silica, leading to persistent inflammation and fibrosis of the lungs. The underlying pathogenic mechanisms of silicosis remain elusive. Lung microbiota dysbiosis is associated with the development of inflammation and fibrosis. However, limited information is currently available regarding the role of lung microbiota in silicosis. The study therefore is designed to conduct a comprehensive analysis of the role of lung microbiota dysbiosis and establish a basis for future investigations into the potential mechanisms underlying silicosis. Here, the pathological and biochemical parameters were used to systematically assessed the degree of inflammation and fibrosis following silica exposure and treatment with combined antibiotics. The underlying mechanisms were studied via integrative multi-omics analyses of the transcriptome and microbiome. Analysis of 16S ribosomal DNA revealed dysbiosis of the microbial community in silicosis, characterized by a predominance of gram-negative bacteria. Exposure to silica has been shown to trigger lung inflammation and fibrosis, leading to an increased concentration of lipopolysaccharides in the bronchoalveolar lavage fluid. Furthermore, Toll-like receptor 4 was identified as a key molecule in the lung microbiota dysbiosis associated with silica-induced lung fibrosis. All of these outcomes can be partially controlled through combined antibiotic administration. The study findings demonstrate that the dysbiosis of lung microbiota enhances silica-induced fibrosis associated with the lipopolysaccharides/Toll-like receptor 4 pathway and provided a promising target for therapeutic intervention of silicosis.

Silica aggravates pulmonary fibrosis through disrupting lung microbiota and amino acid metabolites.

Silicosis, recognized as a severe global public health issue, is an irreversible pulmonary fibrosis caused by the long-term inhalation of silica particles. Given the intricate pathogenesis of silicosis, there is no effective intervention measure, which poses a severe threat to public health. Our previous study reported that dysbiosis of lung microbiota is associated with the development of pulmonary fibrosis, potentially involving the lipopolysaccharides/toll-like receptor 4 pathway. Similarly, the process of pulmonary fibrosis is accompanied by alterations in metabolic pathways. This study employed a combined approach of 16S rDNA sequencing and metabolomic analysis to investigate further the role of lung microbiota in silicosis delving deeper into the potential pathogenesis of silicosis. Silica exposure can lead to dysbiosis of the lung microbiota and the occurrence of pulmonary fibrosis, which was alleviated by a combination antibiotic intervention. Additionally, significant metabolic disturbances were found in silicosis, involving 85 differential metabolites among the three groups, which are mainly focused on amino acid metabolic pathways. The changed lung metabolites showed a substantial correlation with lung microbiota. The relative abundance of Pseudomonas negatively correlated with L-Aspartic acid, L-Glutamic acid, and L-Threonine levels. These results indicate that dysbiosis in pulmonary microbiota exacerbates silica-induced fibrosis through impacts on amino acid metabolism, providing new insights into the potential mechanisms and interventions of silicosis.

Stacking learning based on micro-CT radiomics for outcome prediction in the early-stage of silica-induced pulmonary fibrosis model.

Silicosis is a progressive pulmonary fibrosis disease caused by long-term inhalation of silica. The early diagnosis and timely implementation of intervention measures are crucial in preventing silicosis deterioration further. However, the lack of screening and diagnostic measures for early-stage silicosis remains a significant challenge. In this study, silicosis models of varying severity were established through a single exposure to silica with different doses (2.5mg/mice or 5mg/mice) and durations (4 weeks or 12 weeks). The diagnostic performance of computed tomography (CT) quantitative analysis was assessed using lung density biomarkers and the lung density distribution histogram, with a particular focus on non-aerated lung volume. Subsequently, we developed and evaluated a stacking learning model for early diagnosis of silicosis after extracting and selecting features from CT images. The CT quantitative analysis reveals that while the lung densitometric biomarkers and lung density distribution histogram, as traditional indicators, effectively differentiate severe fibrosis models, they are unable to distinguish early-stage silicosis. Furthermore, these findings remained consistent even when employing non-aerated areas, which is a more sensitive indicator. By establishing a radiomics stacking learning model based on non-aerated areas, we can achieve remarkable diagnostic performance to distinguish early-stage silicosis, which can provide a valuable tool for clinical assistant diagnosis. This study reveals the potential of using non-aerated lung areas as a region of interest in stacking learning for early diagnosis of silicosis, providing new insights into early detection of this disease.