An application based on bioinformatics and machine learning for risk prediction of sepsis at first clinical presentation using transcriptomic data.

Background: Linking genotypic changes to phenotypic traits based on machine learning methods has various challenges. In this study, we developed a workflow based on bioinformatics and machine learning methods using transcriptomic data for sepsis obtained at the first clinical presentation for predicting the risk of sepsis. By combining bioinformatics with machine learning methods, we have attempted to overcome current challenges in predicting disease risk using transcriptomic data. Methods: High-throughput sequencing transcriptomic data processing and gene annotation were performed using R software. Machine learning models were constructed, and model performance was evaluated by machine learning methods in Python. The models were visualized and interpreted using the Shapley Additive explanation (SHAP) method. Results: Based on the preset parameters and using recursive feature elimination implemented via machine learning, the top 10 optimal genes were screened for the establishment of the machine learning models. In a comparison of model performance, CatBoost was selected as the optimal model. We explored the significance of each gene in the model and the interaction between each gene through SHAP analysis. Conclusion: The combination of CatBoost and SHAP may serve as the best-performing machine learning model for predicting transcriptomic and sepsis risks. The workflow outlined may provide a new approach and direction in exploring the mechanisms associated with genes and sepsis risk.

Effect of melittin on iNOS and NF-κB expression induced by IL-1ßin C518 cells.

Melittin (Mel), a natural detergent, is a major component of bee venom. Mel exhibits favorable clinical effects on the treatment of rheumatoid osteoarthritis, myositis, lumbar muscle strain, and peripheral neurological disorders. Interleukin-1ß (IL-1ß) contributes to the progression of osteoarthritis and is one of the key proinflammatory cytokines. However, the effect of Mel on IL-1ß-induced osteoarthritis has not been reported. We examined the effects of Mel on the expressions of inducible NO synthase (iNOS), nuclear transcription factor κB (NF-κB), and I kappa B (I-κB) in the knee joint cells of C518 rats induced by IL-1ß. Western blot and qPCR results showed that Mel at 0.1µg/mL or higher significantly inhibited iNOS expression. Similarly, 1µg/mL of Mel prevented IL-ß-induced I-κB degradation in the cytoplasm and NF-κB migration from cytoplasm to nucleus. Mel exerts an inhibitory effect on IL-ß-induced NF-κB activation by inhibiting both I-κB degradation and NF-κB migration and can potentially be developed as a new anti-osteoarthritis drug. Further research is needed to clarify the detailed mechanism.

Promotive Role of CircATRNL1 on Chondrogenic Differentiation of BMSCs Mediated by miR-338-3p.

AIM: Bone marrow mesenchymal stem cells (BMSCs) are ideal seed cells for tissue engineering cartilage construction. However, the underlying mechanism of it has not been illuminate well. In this study, the effects of circATRNL1 (hsa_circ_0020093) on the differentiation of BMSCs into chondrocytes were investigated. METHODS: The degrees of chondrogenic differentiation of BMSCs on day 0, 14 and 21 mediums were detected by Alcian blue staining. Expressions of cartilage differentiation related factors SOX9, COL2 and Aggrecan, and circATRNL1 in BMSCs under differentiation were determined by western blot and quantitative real-time polymerase chain reaction (qRT-PCR) as needed. circATRNL1 knockdown or overexpression was performed in BMSCs. Then the viability of BMSCs and cartilage differentiation related factors were separately investigated through MTT assay, qRT-PCR, and western blot. Target gene of circATRNL1 and binding site were predicted using starbase and validated it by dual luciferase reporter. The effect of circATRNL1 and its target gene on chondrogenic differentiation of BMSCs was assessed using Alcian blue staining further. RESULTS: The degrees of chondrogenic differentiation of BMSCs were increased with time. Expressions of SOX9, COL2 and Aggrecan as well as circATRNL1 were enhanced during chondrogenic differentiation. Furthermore, overexpression of circATRNL1 enhanced BMSCs proliferation, SOX9, COL2 and Aggrecan expressions and the degree of chondrogenic differentiation of BMSCs. Further research showed that circATRNL1 targeted miR-338-3p. MiR-338-3p inhibited differentiation of BMSCs into cartilage but overexpression of circATRNL1 reversed it. CONCLUSION: CircATRNL1 is beneficial to BMSCs differentiation into cartilage by regulating miR-338-3p, which may be a new mechanism of action in the treatment of cartilage repair.