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A Deep Learning based Efficacy Prediction System for Drug Discovery [Trametinib]

ABSTRACT: Target protein-based drug and research compound discovery has been undeniably successful strategy in life science research, yet many diseases and biological processes lack obvious targets to enable these approaches. Here, to overcome this major challenge we have developed a deep-learning based efficacy prediction system (DLEPS) to identify potent agents to treat diverse diseases; DLEPS was trained using L1000 project chemical induced “changes of transcriptional profiles” (CTP) data as input. Strikingly, we found that the CTPs for previously unexamined molecules were precisely predicted (0.74 Pearson correlation coefficient). We used DLEPS to examine 4 disorders, and experimentally validated that perillen, chikusetsusaponin IV, trametinib, and liquiritin confer disease-relevant impacts against obesity, hyperuricemia, NASH, and COVID-19, respectively. Importantly, DLEPS also uncovered the biological insight that the MEK-ERK signaling pathway should be understood as a target for developing anti-NASH agents. Beyond illustrating that DLEPS is an effective tool for drug repurposing and development with diverse diseases (including those lacking targets), our study shows how diverse transcriptomics datasets can be harnessed to identify inhibitors and activator chemicals that can expand the scope of biological investigations well beyond mutant-bases analyses.

ORGANISM(S): Mus musculus

PROVIDER: GSE165174 | GEO | 2023/03/31

REPOSITORIES: GEO

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A Deep Learning based Efficacy Prediction System for Drug Discovery [Perillen]

Project description:Target protein-based drug and research compound discovery has been undeniably successful strategy in life science research, yet many diseases and biological processes lack obvious targets to enable these approaches. Here, to overcome this major challenge we have developed a deep-learning based efficacy prediction system (DLEPS) to identify potent agents to treat diverse diseases; DLEPS was trained using L1000 project chemical induced “changes of transcriptional profiles” (CTP) data as input. Strikingly, we found that the CTPs for previously unexamined molecules were precisely predicted (0.74 Pearson correlation coefficient). We used DLEPS to examine 4 disorders, and experimentally validated that perillen, chikusetsusaponin IV, trametinib, and liquiritin confer disease-relevant impacts against obesity, hyperuricemia, NASH, and COVID-19, respectively. Importantly, DLEPS also uncovered the biological insight that the MEK-ERK signaling pathway should be understood as a target for developing anti-NASH agents. Beyond illustrating that DLEPS is an effective tool for drug repurposing and development with diverse diseases (including those lacking targets), our study shows how diverse transcriptomics datasets can be harnessed to identify inhibitors and activator chemicals that can expand the scope of biological investigations well beyond mutant-bases analyses.

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A Deep Learning based Efficacy Prediction System for Drug Discovery [Chikusetsusaponin IV]

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Histidine-dependent protein methylation is required for compartmentalization of CTP synthase

Project description:CTP synthase (CTPS) forms compartmentalized filaments in response to substrate availability and environmental nutrient status. However, the physiological role of filaments and mechanisms for filament assembly are not well understood. Here, we provide evidence that CTPS forms filaments in response to histidine influx during glutamine starvation. CTPS protein levels remained stable in the presence of histidine during nutrient deprivation, followed by rapid cell growth following nutrient replenishment. Tetramer conformation-based filament formation restricted CTPS enzyme activity duringnutrient deprivation. Furthermore, we demonstrated that filament formation controlled at two levels. Starvation stress/glutamine deficiency activates the GCN2/ATF4/MTHFD2 axis to accelerate the folate cycle in mitochondria for energy homeostasis. In addition, histidine promotes re-methylation of homocysteine by donating one-carbon to the cytosolic folate cycle. CTPS filament formation induced by histidine-mediated methylation maybe a strategy usedby cancer cells to maintain homeostasis and ensure a growth advantage in adverse environments.

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Project description:Non-alcoholic steatohepatitis (NASH) is becoming one of the leading causes of hepatocellular carcinoma (HCC), replacing viral HBV-, HCV-, and alcohol-related chronic liver diseases as major etiological risk of factors for HCC. The goal of the present study was to investigate transcriptomic alterations in the livers of male C57BL/6J mice fed a choline- and folate-deficient (CFD) diet. Feeding mice a CFD diet for 12 weeks caused NASH-like liver injury exhibiting uniform morphological features of human NASH. High-throughput whole-genome transcriptomic analysis showed that 1750 genes were differentially expressed (344 down-regulated and 1396 up-regulated) in the livers of mice fed a CFD diet as compared to control mice. The functional pathway analysis revealed that these differentially-expressed genes were associated with diverse cellular processes, including cellular growth and differentiation, cell-to-cell signaling and interaction, lipid metabolism, inflammation, fibrosis, molecular transport, and drug metabolism. These findings demonstrate that deregulation of global gene expression is the prominent fundamental feature of NASH in mice induced by a CFD diet.

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