Wan, Y.-J. Y. | bioRxiv Science

bioRxiv2026

Galectin-3 binds to the RGD-binding site in a glycan-independent manner and to the allosteric site and activates integrins αvβ3, αIIbβ3, and α5β1

Galectin-3 (Gal3) is one of the most pro-inflammatory proteins and a biomarker of inflammatory diseases and cancer. Previous studies showed that Gal3 binds to v and {beta}1 integrins but it is unclear how Gal3 binds to integrins. Here, we show that Gal3 bound to soluble v{beta}3 and IIb{beta}3 integrins in 1 mM Mn2+ in cell-free conditions in a glycan-independent manner. Docking simulation predicts that Gal3 binds to the classical RGD-binding site (site 1) of v{beta}3, but the predicted Gal3-binding site does not include galactose-binding site. RGDfV or eptifibatide inhibited Gal3 binding to v{beta}3 and IIb{beta}3, respectively, but lactose, pan-galectin inhibitor, did not inhibit Gal3 binding to integrins. Point mutations of the predicted site 1 binding interface of Gal3 effectively inhibited Gal3 binding to site 1. Site 2 is involved in pro-inflammatory signaling (e.g., TNF and IL-6 secretion) and we previously showed that pro-inflammatory cytokines (e.g., CCL5 and TNF) bind to site 2 and allosteric integrin activation. Docking simulation predicts that Gal3 binds to site 2 of v{beta}3 and 5{beta}1. We found that Gal3 induced allosteric activation of soluble integrins v{beta}3, IIb{beta}3, and 5{beta}1 in 1 mM Ca2+ in cell-free conditions. Point mutations in the predicted site 2-binding interface inhibited Gal3-induced integrin activation, suggesting that Gal3 binding to site 2 is required for Gal3-induced integrin activation. Known anti-inflammatory agents, Ivermectin, NRG1, and FGF1 inhibited integrin activation induced by Gal3 in v{beta}3 and IIb{beta}3. These findings suggest that Gal3 binding to site 2 may be a potential mechanism of pro-inflammatory and pro-thrombotic action of Gal3.

biochemistry↗

bioRxiv2023

Machine Learning to Identify Molecular Markers for Metabolic Disease Development Using Mouse Models

BackgroundAging, Western diet (WD) intake, and bile acid (BA) receptor farnesoid X receptor (FXR) inactivation are risk factors for metabolic disease development including nonalcoholic fatty liver disease (NAFLD) and chronic inflammation-related health issues such as dementia. The progression of the metabolic disease can be escalated when those risks are combined. Inactivation of FXR is cancer prone in both humans and mice. The current study used omics data generated within the gut-liver axis to classify those risks using bioinformatics and machine learning approaches. MethodsDifferent ages (5, 10, and 15 months) of wild-type (WT) and FXR knockout (KO) male mice were fed with either a healthy control diet (CD) or a WD since weaning. Hepatic transcripts, liver, serum, and urine metabolites, hepatic bile acids (BAs), as well as gut microbiota were used for risk prediction. A linear support vector machine with K-fold cross-validation was used for classification and feature selection. ResultsIncreased urine sucrose alone achieved 91% accuracy in predicting WD intake. Hepatic lithocholic acid (LCA) and serum pyruvate had 100% and 95% accuracy, respectively to classify age. Association analyses showed hepatic LCA was positively associated with serum concentrations of acetone, a ketone body, and 1,3-dihydroxyacetone (DHA), but negatively correlated with serum pyruvate. Urine metabolites (decreased creatinine and taurine as well as increased succinate) or gut microbiota (increased Dorea, Dehalobacterium, and Oscillospira) could predict FXR functional status with greater than 90% accuracy. Integrated pathway analyses revealed that the predictors for diet and FXR expression were implicated in the central carbon metabolism in cancer. To assess the translational relevance, mouse hepatic transcripts were crosschecked with human NAFLD and hepatocellular carcinoma (HCC) datasets. WD-affected hepatic Cyp39a1 and Gramd1b expression were associated with human HCC and NAFLD, respectively. The metabolites and diseases interaction analyses uncovered that the identified features are implicated in human metabolic diseases, mental disorders, and cancer. ConclusionThe risk prediction using mouse models contributes to the identification of noninvasive biomarkers for early diagnosis of metabolic disease development.

bioinformatics↗