Profiling persistent tubercule bacilli from patient sputa during therapy predicts early drug efficacy

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en Honeyborne, Isobella McHugh, Timothy D. Kuittinen, Iitu Cichonska, Anna Evangelopoulos, Dimitrios Ronacher, Katharina van Helden, Paul D. Gillespie, Stephen H. Fernandez-Reyes, Delmiro Walzl, Gerhard Rousu, Juho Butcher, Philip D. Waddell, Simon J. 2017-05-11T09:06:55Z 2017-05-11T09:06:55Z 2016-04-07
dc.identifier.citation Honeyborne , I , McHugh , T D , Kuittinen , I , Cichonska , A , Evangelopoulos , D , Ronacher , K , van Helden , P D , Gillespie , S H , Fernandez-Reyes , D , Walzl , G , Rousu , J , Butcher , P D & Waddell , S J 2016 , ' Profiling persistent tubercule bacilli from patient sputa during therapy predicts early drug efficacy ' BMC MEDICINE , vol 14 , no. 1 , 68 , pp. 1-13 . DOI: 10.1186/s12916-016-0609-3 en
dc.identifier.issn 1741-7015
dc.identifier.other PURE UUID: c6246ad1-8035-45c8-9fe8-dffd65aac360
dc.identifier.other PURE ITEMURL:
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dc.description.abstract Background: New treatment options are needed to maintain and improve therapy for tuberculosis, which caused the death of 1.5 million people in 2013 despite potential for an 86 % treatment success rate. A greater understanding of Mycobacterium tuberculosis (M.tb) bacilli that persist through drug therapy will aid drug development programs. Predictive biomarkers for treatment efficacy are also a research priority. Methods and Results: Genome-wide transcriptional profiling was used to map the mRNA signatures of M.tb from the sputa of 15 patients before and 3, 7 and 14 days after the start of standard regimen drug treatment. The mRNA profiles of bacilli through the first 2 weeks of therapy reflected drug activity at 3 days with transcriptional signatures at days 7 and 14 consistent with reduced M.tb metabolic activity similar to the profile of pre-chemotherapy bacilli. These results suggest that a pre-existing drug-tolerant M.tb population dominates sputum before and after early drug treatment, and that the mRNA signature at day 3 marks the killing of a drug-sensitive sub-population of bacilli. Modelling patient indices of disease severity with bacterial gene expression patterns demonstrated that both microbiological and clinical parameters were reflected in the divergent M.tb responses and provided evidence that factors such as bacterial load and disease pathology influence the host-pathogen interplay and the phenotypic state of bacilli. Transcriptional signatures were also defined that predicted measures of early treatment success (rate of decline in bacterial load over 3 days, TB test positivity at 2 months, and bacterial load at 2 months). Conclusions: This study defines the transcriptional signature of M.tb bacilli that have been expectorated in sputum after two weeks of drug therapy, characterizing the phenotypic state of bacilli that persist through treatment. We demonstrate that variability in clinical manifestations of disease are detectable in bacterial sputa signatures, and that the changing M.tb mRNA profiles 0-2 weeks into chemotherapy predict the efficacy of treatment 6 weeks later. These observations advocate assaying dynamic bacterial phenotypes through drug therapy as biomarkers for treatment success. en
dc.format.extent 1-13
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries BMC MEDICINE en
dc.relation.ispartofseries Volume 14, issue 1 en
dc.rights openAccess en
dc.subject.other Medicine(all) en
dc.subject.other 113 Computer and information sciences en
dc.title Profiling persistent tubercule bacilli from patient sputa during therapy predicts early drug efficacy en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department University College London
dc.contributor.department Department of Computer Science
dc.contributor.department University of Stellenbosch
dc.contributor.department University of St Andrews
dc.contributor.department St. George's University of London
dc.contributor.department University of Sussex
dc.subject.keyword Mycobacterium tuberculosis
dc.subject.keyword Persistent infection
dc.subject.keyword Predictive biomarker
dc.subject.keyword Sputum
dc.subject.keyword Transcriptional profiling
dc.subject.keyword Medicine(all)
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201705114219
dc.identifier.doi 10.1186/s12916-016-0609-3
dc.type.version publishedVersion

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