The solute carrier family is an important protein class governing compound transport across membranes. However, some of its members remain functionally unidentified. We analyzed ChIP‐seq data for the NF‐κB family transcription factor RelA and identified GLUT6 as a functionally uncharacterized transporter that putatively works in inflammatory responses. Inflammatory stimuli increase GLUT6 expression level, although GLUT6‐knockout mice exhibit a subtle phenotype to lipopolysaccharide administration. Metabolomics and in vitro analyses show that GLUT6 functions as a glycolysis modulator in inflammatory macrophages. GLUT6 does not mediate glucose uptake and is localized on lysosomal membranes. We conclude that GLUT6 is a lysosomal transporter that is regulated by inflammatory stimuli and modulates inflammatory responses by affecting the metabolic shift in macrophages.
Drugs have multiple, not single, effects. Decomposition of drug effects into basic components helps us to understand the pharmacological properties of a drug and contributes to drug discovery. We have extended factor analysis and developed a novel profile data analysis method: orthogonal linear separation analysis (OLSA). OLSA contracted 11,911 genes to 118 factors from transcriptome data of MCF7 cells treated with 318 compounds in a Connectivity Map. Ontology of the main genes constituting the factors detected significant enrichment of the ontology in 65 of 118 factors and similar results were obtained in two other data sets. In further analysis of the Connectivity Map data set, one factor discriminated two Hsp90 inhibitors, geldanamycin and radicicol, while clustering analysis could not. Doxorubicin and other topoisomerase inhibitors were estimated to inhibit Na+/K+ ATPase, one of the suggested mechanisms of doxorubicin-induced cardiotoxicity. Based on the factor including PI3K/AKT/mTORC1 inhibition activity, 5 compounds were predicted to be novel inducers of autophagy, and other analyses including western blotting revealed that 4 of the 5 actually induced autophagy. These findings indicate the potential of OLSA to decompose the effects of a drug and identify its basic components.
Supplemental table 1. NDC codes for SGLT-2is, GLP-1RAs, and DPP-4is Supplemental table 2. Therapeutic Class Supplemental table 3. Dummy variables of drug classes for identification for confounding factors Supplemental table 1. NDC codes for SGLT-2is, GLP1-RAs, and DPP-4is Data source: Kyoto Encyclopedia of Genes and Genomes
Toxicopathological images acquired during safety assessment elucidate an individual's biological responses to a given compound, and their numerization can yield valuable insights contributing to the assessment of compound properties. Currently, toxicopathological images are mainly encoded as pathological findings, evaluated by pathologists, which introduces challenges when used as input for modeling, specifically in terms of representation capability and comparability. In this study, we assessed the usefulness of latent representations extracted from toxicopathological images using Convolutional Neural Network (CNN) in estimating compound properties in vivo. Special emphasis was placed on examining the impact of learning pathological findings, the depth of frozen layers during learning, and the selection of the layer for latent representation. Our findings demonstrate that a machine learning model fed with the latent representation as input surpassed the performance of a model directly employing pathological findings as input, particularly in the classification of a compound's Mechanism of Action and in predicting late-phase findings from early-phase images in repeated-dose tests. While learning pathological findings did improve accuracy, the magnitude of improvement was relatively modest. Similarly, the effect of freezing layers during learning was also limited. Notably, the selection of the layer for latent representation had a substantial impact on the accurate estimation of compound properties in vivo.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.