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

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...

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Autores principales:	Abhijit Paul, Rajat Anand, Sonali Porey Karmakar, Surender Rawat, Nandadulal Bairagi, Samrat Chatterjee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5cc3d92f081c43a59c89d39d060694cf
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spelling	oai:doaj.org-article:5cc3d92f081c43a59c89d39d060694cf2021-12-02T15:13:10ZExploring gene knockout strategies to identify potential drug targets using genome-scale metabolic models10.1038/s41598-020-80561-12045-2322https://doaj.org/article/5cc3d92f081c43a59c89d39d060694cf2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80561-1https://doaj.org/toc/2045-2322Abstract 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.Abhijit PaulRajat AnandSonali Porey KarmakarSurender RawatNandadulal BairagiSamrat ChatterjeeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Abhijit Paul Rajat Anand Sonali Porey Karmakar Surender Rawat Nandadulal Bairagi Samrat Chatterjee Exploring gene knockout strategies to identify potential drug targets using genome-scale metabolic models
description	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.
format	article
author	Abhijit Paul Rajat Anand Sonali Porey Karmakar Surender Rawat Nandadulal Bairagi Samrat Chatterjee
author_facet	Abhijit Paul Rajat Anand Sonali Porey Karmakar Surender Rawat Nandadulal Bairagi Samrat Chatterjee
author_sort	Abhijit Paul
title	Exploring gene knockout strategies to identify potential drug targets using genome-scale metabolic models
title_short	Exploring gene knockout strategies to identify potential drug targets using genome-scale metabolic models
title_full	Exploring gene knockout strategies to identify potential drug targets using genome-scale metabolic models
title_fullStr	Exploring gene knockout strategies to identify potential drug targets using genome-scale metabolic models
title_full_unstemmed	Exploring gene knockout strategies to identify potential drug targets using genome-scale metabolic models
title_sort	exploring gene knockout strategies to identify potential drug targets using genome-scale metabolic models
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5cc3d92f081c43a59c89d39d060694cf
work_keys_str_mv	AT abhijitpaul exploringgeneknockoutstrategiestoidentifypotentialdrugtargetsusinggenomescalemetabolicmodels AT rajatanand exploringgeneknockoutstrategiestoidentifypotentialdrugtargetsusinggenomescalemetabolicmodels AT sonaliporeykarmakar exploringgeneknockoutstrategiestoidentifypotentialdrugtargetsusinggenomescalemetabolicmodels AT surenderrawat exploringgeneknockoutstrategiestoidentifypotentialdrugtargetsusinggenomescalemetabolicmodels AT nandadulalbairagi exploringgeneknockoutstrategiestoidentifypotentialdrugtargetsusinggenomescalemetabolicmodels AT samratchatterjee exploringgeneknockoutstrategiestoidentifypotentialdrugtargetsusinggenomescalemetabolicmodels
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Exploring gene knockout strategies to identify potential drug targets using genome-scale metabolic models

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