Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis

ABSTRACT The rapid spread of multidrug-resistant strains has created a pressing need for new drug regimens to treat tuberculosis (TB), which kills 1.8 million people each year. Identifying new regimens has been challenging due to the slow growth of the pathogen Mycobacterium tuberculosis (MTB), coup...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Shuyi Ma, Suraj Jaipalli, Jonah Larkins-Ford, Jenny Lohmiller, Bree B. Aldridge, David R. Sherman, Sriram Chandrasekaran
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2019
Materias:	tuberculosis drug combinations transcription factors drug synergy transcriptomics Mycobacterium tuberculosis Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/2e46663689eb483f82794186d5d7f4bf
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:2e46663689eb483f82794186d5d7f4bf
record_format	dspace
spelling	oai:doaj.org-article:2e46663689eb483f82794186d5d7f4bf2021-11-15T15:54:46ZTranscriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis10.1128/mBio.02627-192150-7511https://doaj.org/article/2e46663689eb483f82794186d5d7f4bf2019-12-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mBio.02627-19https://doaj.org/toc/2150-7511ABSTRACT The rapid spread of multidrug-resistant strains has created a pressing need for new drug regimens to treat tuberculosis (TB), which kills 1.8 million people each year. Identifying new regimens has been challenging due to the slow growth of the pathogen Mycobacterium tuberculosis (MTB), coupled with the large number of possible drug combinations. Here we present a computational model (INDIGO-MTB) that identified synergistic regimens featuring existing and emerging anti-TB drugs after screening in silico more than 1 million potential drug combinations using MTB drug transcriptomic profiles. INDIGO-MTB further predicted the gene Rv1353c as a key transcriptional regulator of multiple drug interactions, and we confirmed experimentally that Rv1353c upregulation reduces the antagonism of the bedaquiline-streptomycin combination. A retrospective analysis of 57 clinical trials of TB regimens using INDIGO-MTB revealed that synergistic combinations were significantly more efficacious than antagonistic combinations (P value = 1 × 10−4) based on the percentage of patients with negative sputum cultures after 8 weeks of treatment. Our study establishes a framework for rapid assessment of TB drug combinations and is also applicable to other bacterial pathogens. IMPORTANCE Multidrug combination therapy is an important strategy for treating tuberculosis, the world’s deadliest bacterial infection. Long treatment durations and growing rates of drug resistance have created an urgent need for new approaches to prioritize effective drug regimens. Hence, we developed a computational model called INDIGO-MTB that identifies synergistic drug regimens from an immense set of possible drug combinations using the pathogen response transcriptome elicited by individual drugs. Although the underlying input data for INDIGO-MTB was generated under in vitro broth culture conditions, the predictions from INDIGO-MTB correlated significantly with in vivo drug regimen efficacy from clinical trials. INDIGO-MTB also identified the transcription factor Rv1353c as a regulator of multiple drug interaction outcomes, which could be targeted for rationally enhancing drug synergy.Shuyi MaSuraj JaipalliJonah Larkins-FordJenny LohmillerBree B. AldridgeDavid R. ShermanSriram ChandrasekaranAmerican Society for Microbiologyarticletuberculosisdrug combinationstranscription factorsdrug synergytranscriptomicsMycobacterium tuberculosisMicrobiologyQR1-502ENmBio, Vol 10, Iss 6 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	tuberculosis drug combinations transcription factors drug synergy transcriptomics Mycobacterium tuberculosis Microbiology QR1-502
spellingShingle	tuberculosis drug combinations transcription factors drug synergy transcriptomics Mycobacterium tuberculosis Microbiology QR1-502 Shuyi Ma Suraj Jaipalli Jonah Larkins-Ford Jenny Lohmiller Bree B. Aldridge David R. Sherman Sriram Chandrasekaran Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis
description	ABSTRACT The rapid spread of multidrug-resistant strains has created a pressing need for new drug regimens to treat tuberculosis (TB), which kills 1.8 million people each year. Identifying new regimens has been challenging due to the slow growth of the pathogen Mycobacterium tuberculosis (MTB), coupled with the large number of possible drug combinations. Here we present a computational model (INDIGO-MTB) that identified synergistic regimens featuring existing and emerging anti-TB drugs after screening in silico more than 1 million potential drug combinations using MTB drug transcriptomic profiles. INDIGO-MTB further predicted the gene Rv1353c as a key transcriptional regulator of multiple drug interactions, and we confirmed experimentally that Rv1353c upregulation reduces the antagonism of the bedaquiline-streptomycin combination. A retrospective analysis of 57 clinical trials of TB regimens using INDIGO-MTB revealed that synergistic combinations were significantly more efficacious than antagonistic combinations (P value = 1 × 10−4) based on the percentage of patients with negative sputum cultures after 8 weeks of treatment. Our study establishes a framework for rapid assessment of TB drug combinations and is also applicable to other bacterial pathogens. IMPORTANCE Multidrug combination therapy is an important strategy for treating tuberculosis, the world’s deadliest bacterial infection. Long treatment durations and growing rates of drug resistance have created an urgent need for new approaches to prioritize effective drug regimens. Hence, we developed a computational model called INDIGO-MTB that identifies synergistic drug regimens from an immense set of possible drug combinations using the pathogen response transcriptome elicited by individual drugs. Although the underlying input data for INDIGO-MTB was generated under in vitro broth culture conditions, the predictions from INDIGO-MTB correlated significantly with in vivo drug regimen efficacy from clinical trials. INDIGO-MTB also identified the transcription factor Rv1353c as a regulator of multiple drug interaction outcomes, which could be targeted for rationally enhancing drug synergy.
format	article
author	Shuyi Ma Suraj Jaipalli Jonah Larkins-Ford Jenny Lohmiller Bree B. Aldridge David R. Sherman Sriram Chandrasekaran
author_facet	Shuyi Ma Suraj Jaipalli Jonah Larkins-Ford Jenny Lohmiller Bree B. Aldridge David R. Sherman Sriram Chandrasekaran
author_sort	Shuyi Ma
title	Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis
title_short	Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis
title_full	Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis
title_fullStr	Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis
title_full_unstemmed	Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis
title_sort	transcriptomic signatures predict regulators of drug synergy and clinical regimen efficacy against tuberculosis
publisher	American Society for Microbiology
publishDate	2019
url	https://doaj.org/article/2e46663689eb483f82794186d5d7f4bf
work_keys_str_mv	AT shuyima transcriptomicsignaturespredictregulatorsofdrugsynergyandclinicalregimenefficacyagainsttuberculosis AT surajjaipalli transcriptomicsignaturespredictregulatorsofdrugsynergyandclinicalregimenefficacyagainsttuberculosis AT jonahlarkinsford transcriptomicsignaturespredictregulatorsofdrugsynergyandclinicalregimenefficacyagainsttuberculosis AT jennylohmiller transcriptomicsignaturespredictregulatorsofdrugsynergyandclinicalregimenefficacyagainsttuberculosis AT breebaldridge transcriptomicsignaturespredictregulatorsofdrugsynergyandclinicalregimenefficacyagainsttuberculosis AT davidrsherman transcriptomicsignaturespredictregulatorsofdrugsynergyandclinicalregimenefficacyagainsttuberculosis AT sriramchandrasekaran transcriptomicsignaturespredictregulatorsofdrugsynergyandclinicalregimenefficacyagainsttuberculosis
_version_	1718427205573279744

Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis

Ejemplares similares