title: Profiling persistent tubercule bacilli from patient sputa during therapy predicts early drug efficacy.
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.
  
  
  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.
authors.affiliation: Centre for Clinical Microbiology, University College London, London, NW3 2PF, UK.
authors.forename: Isobella
authors.initials: I
authors.lastname: Honeyborne
authors.affiliation: Centre for Clinical Microbiology, University College London, London, NW3 2PF, UK.
authors.forename: Timothy D
authors.initials: TD
authors.lastname: McHugh
authors.affiliation: Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Espoo, Finland.
authors.forename: Iitu
authors.initials: I
authors.lastname: Kuittinen
authors.affiliation: Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Espoo, Finland.|Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland.
authors.forename: Anna
authors.initials: A
authors.lastname: Cichonska
authors.affiliation: Centre for Clinical Microbiology, University College London, London, NW3 2PF, UK.
authors.forename: Dimitrios
authors.initials: D
authors.lastname: Evangelopoulos
authors.affiliation: Department of Science and Technology/National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research and Medical Research Council Centre for TB Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Western Cape, South Africa.
authors.forename: Katharina
authors.initials: K
authors.lastname: Ronacher
authors.affiliation: Department of Science and Technology/National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research and Medical Research Council Centre for TB Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Western Cape, South Africa.
authors.forename: Paul D
authors.initials: PD
authors.lastname: van Helden
authors.affiliation: Medical and Biological Sciences Building, University of St Andrews, North Haugh, St Andrews, Fife, KY16 9TF, UK.
authors.forename: Stephen H
authors.initials: SH
authors.lastname: Gillespie
authors.affiliation: Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK.|Department of Paediatrics, University College Hospital, College of Medicine of the University of Ibadan, Ibadan, Nigeria.
authors.forename: Delmiro
authors.initials: D
authors.lastname: Fernandez-Reyes
authors.affiliation: Department of Science and Technology/National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research and Medical Research Council Centre for TB Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Western Cape, South Africa.
authors.forename: Gerhard
authors.initials: G
authors.lastname: Walzl
authors.affiliation: Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Espoo, Finland.
authors.forename: Juho
authors.initials: J
authors.lastname: Rousu
authors.affiliation: Institute for Infection and Immunity, St George's University of London, London, SW17 0RE, UK.
authors.forename: Philip D
authors.initials: PD
authors.lastname: Butcher
authors.affiliation: Brighton and Sussex Medical School, University of Sussex, Brighton, BN1 9PX, UK. s.waddell@bsms.ac.uk.
authors.forename: Simon J
authors.initials: SJ
authors.lastname: Waddell
mesh_terms: D000995:Antitubercular Agents / Q000008:administration & dosage*; D001407:Bacillus; D002874:Chromosome Mapping / Q000379:methods; D016903:Drug Monitoring / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009169:Mycobacterium tuberculosis / Q000187:drug effects / Q000235:genetics / Q000302:isolation & purification; D011237:Predictive Value of Tests; D012333:RNA, Messenger / Q000032:analysis*; D013183:Sputum / Q000382:microbiology; D014397:Tuberculosis, Pulmonary / Q000175:diagnosis / Q000188:drug therapy / Q000382:microbiology
chemical_list: D000995:Antitubercular Agents; D012333:RNA, Messenger
keywords: Mycobacterium tuberculosis; Persistent infection; Predictive biomarker; Sputum; Transcriptional profiling