AUTHOR=Li Peng , Bai Chujie , Zhan Lingmin , Zhang Haoran , Zhang Yuanyuan , Zhang Wuxia , Wang Yingdong , Zhao Jinzhong 

TITLE=Specific gene module pair-based target identification and drug discovery

JOURNAL=Frontiers in Pharmacology

VOLUME=Volume 13 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2022.1089217

DOI=10.3389/fphar.2022.1089217

ISSN=1663-9812

ABSTRACT=Identification of the biological targets of a compound is of paramount importance for the exploration of mechanism of action of drugs and for the development of novel drugs. A concept of the Connectivity Map (CMap) was previously proposed to connect genes, drugs and disease states based on the common gene-expression signatures. For a new query compound, CMap-based method can infer its potential targets by searching similar drugs with known targets (reference drugs) by measuring the similarities into their specific transcriptional responses between the query compound and those reference drugs. However, the available methods are often inefficient as the requirement of the reference drugs as medium to link the query agent and targets. We here developed a general procedure to extract target induced consensus gene modules from the transcriptional profiles induced by the treatment of perturbagens of a target. A specific transcriptional gene module pair (GMP) were automatically identified for each target and can be used as a direct target signature. Based on the GMPs, we built the target network and identify some target gene cluster with similar biological mechanisms. Moreover, a Gene Module Pair based Target Identification (GMPTI) approach was proposed to predict novel compound-target interactions. Using the method, we have discovered novel inhibitors for three PI3K pathway proteins PI3Kα/β/δ including PU-H71, alvespimycin, reversine, astemizole, raloxifene HCl and tamoxifen.