Stearic acid promotes lipid synthesis through CD36/Fyn/FAK/mTORC1 axis in bovine mammary epithelial cells.

Stearic acid (C18:0, SA) is a saturated long-chain fatty acid (LCFA) that has a prominent function in lactating dairy cows. It is obtained primarily from the diet and is stored in the form of triacylglycerol (TAG) molecules. The transmembrane glycoprotein cluster of differentiation 36 (CD36) is also known as fatty acid translocase, but whether SA promotes lipid synthesis through CD36 and FAK/mTORC1 signaling is unknown. In this study, we examined the function and mechanism of CD36-mediated SA-induced lipid synthesis in bovine mammary epithelial cells (BMECs). SA-enriched supplements enhanced lipid synthesis and the FAK/mTORC1 pathway in BMECs. SA-induced lipid synthesis, FAK/mTORC1 signaling, and the expression of lipogenic genes were impaired by anti-CD36 and the CD36-specific inhibitor SSO, whereas overexpression of CD36 effected the opposite results. Inhibition of FAK/mTORC1 by TAE226/Rapamycin attenuated SA-induced TAG synthesis, inactivated FAK/mTORC1 signaling, and downregulated the lipogenic genes PPARG, CD36, ACSL1, SCD, GPAT4, LIPIN1, and DGAT1 at the mRNA and protein levels in BMECs. By coimmunoprecipitation and yeast two-hybrid screen, CD36 interacted directly with Fyn but not Lyn, and Fyn bound directly to FAK; FAK also interacted directly with TSC2. CD36 linked FAK through Fyn, and FAK coupled mTORC1 through TSC2 to form the CD36/Fyn/FAK/mTORC1 signaling axis. Thus, stearic acid promotes lipogenesis through CD36 and Fyn/FAK/mTORC1 signaling in BMECs. Our findings provide novel insights into the underlying molecular mechanisms by which LCFA supplements promote lipid synthesis in BMECs.

Therapeutic drug combinations against COVID-19 obtained by employing a collaborative filtering method.

The outbreak of coronavirus disease 2019 (COVID-19) has severely harmed human society and health. Because there is currently no specific drug for the treatment and prevention of COVID-19, we used a collaborative filtering algorithm to predict which traditional Chinese medicines (TCMs) would be effective in combination for the prevention and treatment of COVID-19. First, we performed drug screening based on the receptor structure prediction method, molecular docking using q-vina to measure the binding ability of TCMs, TCM formulas, and neo-coronavirus proteins, and then performed synergistic filtering based on Laplace matrix calculations to predict potentially effective TCM formulas. Combining the results of molecular docking and synergistic filtering, the new recommended formulas were analyzed by reviewing data platforms or tools such as PubMed, Herbnet, the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, the Guide to the Dispensing of Medicines for Clinical Evidence, and the Dictionary of Chinese Medicine Formulas, as well as medical experts' treatment consensus in terms of herbal efficacy, modern pharmacological studies, and clinical identification and typing of COVID-19 pneumonia, to determine the recommended solutions. We found that the therapeutic effect of a combination of six TCM formulas on the COVID-19 virus is the result of the overall effect of the formula rather than that of specific components of the formula. Based on this, we recommend a formula similar to that of Jinhua Qinggan Granules for the treatment of COVID-19 pneumonia. This study may provide new ideas and new methods for future clinical research. Classification: Biological Science.

Signaling repurposable drug combinations against COVID-19 by developing the heterogeneous deep herb-graph method.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has spurred a boom in uncovering repurposable existing drugs. Drug repurposing is a strategy for identifying new uses for approved or investigational drugs that are outside the scope of the original medical indication. MOTIVATION: Current works of drug repurposing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are mostly limited to only focusing on chemical medicines, analysis of single drug targeting single SARS-CoV-2 protein, one-size-fits-all strategy using the same treatment (same drug) for different infected stages of SARS-CoV-2. To dilute these issues, we initially set the research focusing on herbal medicines. We then proposed a heterogeneous graph embedding method to signaled candidate repurposing herbs for each SARS-CoV-2 protein, and employed the variational graph convolutional network approach to recommend the precision herb combinations as the potential candidate treatments against the specific infected stage. METHOD: We initially employed the virtual screening method to construct the 'Herb-Compound' and 'Compound-Protein' docking graph based on 480 herbal medicines, 12,735 associated chemical compounds and 24 SARS-CoV-2 proteins. Sequentially, the 'Herb-Compound-Protein' heterogeneous network was constructed by means of the metapath-based embedding approach. We then proposed the heterogeneous-information-network-based graph embedding method to generate the candidate ranking lists of herbs that target structural, nonstructural and accessory SARS-CoV-2 proteins, individually. To obtain precision synthetic effective treatments forvarious COVID-19 infected stages, we employed the variational graph convolutional network method to generate candidate herb combinations as the recommended therapeutic therapies. RESULTS: There were 24 ranking lists, each containing top-10 herbs, targeting 24 SARS-CoV-2 proteins correspondingly, and 20 herb combinations were generated as the candidate-specific treatment to target the four infected stages. The code and supplementary materials are freely available at https://github.com/fanyang-AI/TCM-COVID19.