Exploring gene knockout strategies to identify potential drug targets using genome-scale metabolic models

Abhijit Paul,Nandadulal Bairagi,Rajat Anand,Surender Rawat,Sonali Porey Karmakar,Samrat Chatterjee

doi:10.1038/s41598-020-80561-1

Abstract

Research on new cancer drugs is performed either through gene knockout studies or phenotypic screening of drugs in cancer cell-lines. Both of these approaches are costly and time-consuming. Computational framework, e.g., genome-scale metabolic models (GSMMs), could be a good alternative to find potential drug targets. The present study aims to investigate the applicability of gene knockout strategies to be used as the finding of drug targets using GSMMs. We performed single-gene knockout studies on existing GSMMs of the NCI-60 cell-lines obtained from 9 tissue types. The metabolic genes responsible for the growth of cancerous cells were identified and then ranked based on their cellular growth reduction. The possible growth reduction mechanisms, which matches with the gene knockout results, were described. Gene ranking was used to identify potential drug targets, which reduce the growth rate of cancer cells but not of the normal cells. The gene ranking results were also compared with existing shRNA screening data. The rank-correlation results for most of the cell-lines were not satisfactory for a single-gene knockout, but it played a significant role in deciding the activity of drug against cell proliferation, whereas multiple gene knockout analysis gave better correlation results. We validated our theoretical results experimentally and showed that the drugs mitotane and myxothiazol can inhibit the growth of at least four cell-lines of NCI-60 database.

Highlights

Research on new cancer drugs is performed either through gene knockout studies or phenotypic screening of drugs in cancer cell-lines
We considered the genome-scale metabolic models (GSMMs) of the NCI-60 cell-lines built by Yizhak et al.[29] using Personalized Reconstruction of Metabolic models (PRIME) approach
GSMM gained a lot of attention in drug discovery

Summary

Introduction

Research on new cancer drugs is performed either through gene knockout studies or phenotypic screening of drugs in cancer cell-lines. Another problem with the existing cancer drugs is that a particular drug shows different responses when applied to different individuals This is because the effects of a drug on a patient depend on the interaction with its targets and on the activities of many other enzymes which form a complex network of metabolic reactions in which the products of a reaction become the substrates of other r eactions[6]. We considered the GSMMs of the NCI-60 cell-lines built by Yizhak et al.[29] using Personalized Reconstruction of Metabolic models (PRIME) approach They established their cell-specific model based on molecular and phenotypic data. We applied their model for a more comprehensive study on the single and multiple gene knockout effects on the growth rate of the cancer cell-line and compared the results with the online experimental database. It is observed that multiple knockout tests give a better correlation with experimental observation than single-gene knockout results

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Conclusion