Genetic polymorphism in TCF7L2 and risk of hypertension in Thai elderly subjects

Prediction of Drug Targets for Specific Diseases Leveraging Gene Perturbation Data: A Machine Learning Approach

Identification of the correct targets is a key element for successful drug development. However, there are limited approaches for predicting drug targets for specific diseases using omics data, and few have leveraged expression profiles from gene perturbations. We present a novel computational approach for drug target discovery based on machine learning (ML) models. ML models are first trained on drug-induced expression profiles with outcomes defined as whether the drug treats the studied disease. The goal is to “learn” the expression patterns associated with treatment. Then, the fitted ML models were applied to expression profiles from gene perturbations (overexpression (OE)/knockdown (KD)). We prioritized targets based on predicted probabilities from the ML model, which reflects treatment potential. The methodology was applied to predict targets for hypertension, diabetes mellitus (DM), rheumatoid arthritis (RA), and schizophrenia (SCZ). We validated our approach by evaluating whether the identified targets may ‘re-discover’ known drug targets from an external database (OpenTargets). Indeed, we found evidence of significant enrichment across all diseases under study. A further literature search revealed that many candidates were supported by previous studies. For example, we predicted PSMB8 inhibition to be associated with the treatment of RA, which was supported by a study showing that PSMB8 inhibitors (PR-957) ameliorated experimental RA in mice. In conclusion, we propose a new ML approach to integrate the expression profiles from drugs and gene perturbations and validated the framework. Our approach is flexible and may provide an independent source of information when prioritizing drug targets.

The broad pathogenetic role of TCF7L2 in human diseases beyond type 2 diabetes

The TCF7L2 protein is a key transcriptional effector of the Wnt/β‐catenin signaling pathway, regulating gene expression. It was initially identified in cancer research and embryologic developmental studies. Later, the TCF7L2 gene was linked to type 2 diabetes (T2D), implicating TCF7L2 and Wnt‐signaling in metabolic disorders and homeostasis. In fact, TCF7L2‐T2D variants confer the greatest relative risk for T2D, unquestionably predicting conversion to T2D in individuals with impaired glucose tolerance. We aim to describe the relevance of TCF7L2 in other human disorders. The TCF7L2‐single nucleotide polymorphisms (SNPs) and T2D‐risk association have been replicated in numerous follow‐up studies, and research has now been performed in several other diseases. In this article, we discuss common TCF7L2‐T2D variants within the framework of their association with human diseases. The TCF7L2 functional regions need to be further investigated because the molecular and cellular mechanisms through which TCF7L2 contributes to risk associations with different diseases are still not fully elucidated. In this review, we show the association of common TCF7L2‐T2D variants with many types of diseases. However, the role of rare genetic variations in the TCF7L2 gene in distinct diseases and ethnic groups has not been explored, and understanding their impact on specific phenotypes will be of clinical relevance. This offers an excellent opportunity to gain a clearer picture of the role that the TCF7L2 gene plays in the pathophysiology of human diseases. The potential pleiotropic role of TCF7L2 may underlie a possible pathway for comorbidity in human disorders.