Conditioned medium of human mesenchymal stem cells affects stem cell senescence in osteoporosis.

Systemic transplantation of mesenchymal stem cells (MSCs) and conditioned medium derived from MSCs have been reported to recover bone loss in animal models of osteoporosis; however, the underlying mechanisms remain unclear. We recently reported that extracellular vesicles released from human mesenchymal stem cells (hMSCs) prevent senescence of stem cells in bisphosphonate-related osteonecrosis of the jaw model. In this study, we aimed to compare the effects of conditioned medium (hMSCs-CM) from early and late passage hMSCs on cellular senescence and to verify the benefits of CM from early passage hMSCs in mitigating the progression of osteoporosis through the prevention of cellular senescence. We investigated the distinct endocrine effects of early (P5) and late (P17) passage hMSCs in vitro, as well as the preventive benefits of early passage hMSCs-CM in osteoporosis model triggered by ovariectomy. Our results indicate that long-term cultured hMSCs contributed to the progression of inflammatory transcriptional programs in P5 hMSCs, ultimately impairing their functionality and enhancing senescence-related characteristics. Conversely, early passage hMSCs reversed these alterations. Moreover, early passage hMSCs-CM infused intravenously in a postmenopausal osteoporosis mouse model suppressed bone degeneration and prevented osteoporosis by reducing ovariectomy-induced senescence in bone marrow MSCs and reducing the expression of senescence-associated secretory phenotype-related cytokines. Our findings highlight the high translational value of early passage hMSCs-CM in antiaging intervention and osteoporosis prevention.

Unveiling Small-Sized Plastic Particles Hidden behind Large-Sized Ones in Human Excretion and Their Potential Sources.

Health risks of microplastic exposure have drawn growing global concerns due to the widespread distribution of microplastics in the environment. However, more evidence is needed to understand the exposure characteristics of microplastics owing to the limitation of current spectrum technologies, especially the missing information on small-sized particles. In the present study, laser direct infrared spectroscopy and thermal desorption-gas chromatography-mass spectrometry combined pyrolysis using a tubular furnace (TD-GC/MS) were employed to comprehensively detect the presence of plastic particles down to 0.22 µm in human excreted samples. The results showed that polyethylene (PE), polyvinyl chloride, PE terephthalate (PET), and polypropylene dominated large-sized (>20 µm) and small-sized plastic plastics (0.22-20 µm) in feces and urine. Moreover, fragments accounted for 60.71 and 60.37% in feces and urine, respectively, representing the most pervasive shape in excretion. Surprisingly, the concentration of small-sized particles was significantly higher than that of large-sized microplastics, accounting for 56.54 and 50.07% in feces (345.58 µg/g) and urine (6.49 µg/mL). Significant positive correlations were observed between the level of plastic particles in feces and the use of plastic containers and the consumption of aquatic products (Spearman correlation analysis, p < 0.01), suggesting the potential sources for plastic particles in humans. Furthermore, it is estimated that feces was the primary excretory pathway, consisting of 94.0% of total excreted microplastics daily. This study provides novel evidence regarding small-sized plastic particles, which are predominant fractions in human excretion, increasing the knowledge of the potential hazards of omnipresent microplastics to human exposure.

Metabolomics and Transcriptomics Analyses of Curcumin Alleviation of Ochratoxin A-Induced Hepatotoxicity.

Ochratoxin A (OTA) is one of the mycotoxins that poses a serious threat to human and animal health. Curcumin (CUR) is a major bioactive component of turmeric that provides multiple health benefits. CUR can reduce the toxicities induced by mycotoxins, but the underlying molecular mechanisms remain largely unknown. To explore the effects of CUR on OTA toxicity and identify the key regulators and metabolites involved in the biological processes, we performed metabolomic and transcriptomic analyses of livers from OTA-exposed mice. We found that CUR can alleviate the toxic effects of OTA on body growth and liver functions. In addition, CUR supplementation significantly affects the expressions of 1584 genes and 97 metabolites. Integrated analyses of transcriptomic and metabolomic data showed that the pathways including Arachidonic acid metabolism, Purine metabolism, and Cholesterol metabolism were significantly enriched. Pantothenic acid (PA) was identified as a key metabolite, the exogenous supplementation of which was observed to significantly alleviate the OTA-induced accumulation of reactive oxygen species and cell apoptosis. Further mechanistical analyses revealed that PA can downregulate the expression level of proapoptotic protein BAX, enhance the expression level of apoptosis inhibitory protein BCL2, and decrease the level of phosphorylated extracellular signal-regulated kinase 1/2 (pERK1/2). This study demonstrated that CUR can alleviate the adverse effects of OTA by influencing the transcriptomic and metabolomic profiles of livers, which may contribute to the application of CUR in food and feed products for the prevention of OTA toxicity.

WGCNA combined with machine learning to find potential biomarkers of liver cancer.

The incidence of hepatocellular carcinoma (HCC) has been increasing in recent years. With the development of various detection technologies, machine learning is an effective method to screen disease characteristic genes. In this study, weighted gene co-expression network analysis (WGCNA) and machine learning are combined to find potential biomarkers of liver cancer, which provides a new idea for future prediction, prevention, and personalized treatment. In this study, the "limma" software package was used. P < .05 and log2 |fold-change| > 1 is the standard screening differential genes, and then the module genes obtained by WGCNA analysis are crossed to obtain the key module genes. Gene Ontology and Kyoto Gene and Genome Encyclopedia analysis was performed on key module genes, and 3 machine learning methods including lasso, support vector machine-recursive feature elimination, and RandomForest were used to screen feature genes. Finally, the validation set was used to verify the feature genes, the GeneMANIA (http://www.genemania.org) database was used to perform protein-protein interaction networks analysis on the feature genes, and the SPIED3 database was used to find potential small molecule drugs. In this study, 187 genes associated with HCC were screened by using the "limma" software package and WGCNA. After that, 6 feature genes (AADAT, APOF, GPC3, LPA, MASP1, and NAT2) were selected by RandomForest, Absolute Shrinkage and Selection Operator, and support vector machine-recursive feature elimination machine learning algorithms. These genes are also significantly different on the external dataset and follow the same trend as the training set. Finally, our findings may provide new insights into targets for diagnosis, prevention, and treatment of HCC. AADAT, APOF, GPC3, LPA, MASP1, and NAT2 may be potential genes for the prediction, prevention, and treatment of liver cancer in the future.