Bacterial and host determinants of cough aerosol culture positivity in patients with drug-resistant versus drug-susceptible tuberculosis

Abstract

A burgeoning epidemic of drug-resistant tuberculosis (TB) threatens to derail global control efforts. Although the mechanisms remain poorly clarified, drug-resistant strains are widely believed to be less infectious than drug-susceptible strains. Consequently, we hypothesized that lower proportions of patients with drug-resistant TB would have culturable Mycobacterium tuberculosis from respirable, cough-generated aerosols compared to patients with drug-susceptible TB, and that multiple factors, including mycobacterial genomic variation, would predict culturable cough aerosol production. We enumerated the colony forming units in aerosols (≤10 µm) from 452 patients with TB (227 with drug resistance), compared clinical characteristics, and performed mycobacterial whole-genome sequencing, dormancy phenotyping and drug-susceptibility analyses on M. tuberculosis from sputum. After considering treatment duration, we found that almost half of the patients with drug-resistant TB were cough aerosol culture-positive. Surprisingly, neither mycobacterial genomic variants, lineage, nor dormancy status predicted cough aerosol culture positivity. However, mycobacterial sputum bacillary load and clinical characteristics, including a lower symptom score and stronger cough, were strongly predictive, thereby supporting targeted transmission-limiting interventions. Effective treatment largely abrogated cough aerosol culture positivity; however, this was not always rapid. These data question current paradigms, inform public health strategies and suggest the need to redirect TB transmission-associated research efforts toward host–pathogen interactions.

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Fig. 1: CASS used to measure culturable M. tuberculosis in cough aerosol droplets.
Fig. 2: Comparison of mycobacterial factors in CASS-positive and CASS-negative patients.
Fig. 3: Repeat cough aerosol sampling results in patients initially CASS-positive who were resampled until CASS-negative.

Data availability

The deidentified datasets generated and/or analyzed during the study are available from the corresponding author without restriction. The sequencing data are available at the Sequence Read Archive under accession no. PRJNA600338). Source data for Figs. 13 and Extended Data Figs. 25 are included with this paper.

References

  1. 1.

    Dheda, K. et al. The epidemiology, pathogenesis, transmission, diagnosis, and management of multidrug-resistant, extensively drug-resistant, and incurable tuberculosis. Lancet Respir. Med. 5, 291–360 (2017).

    Google Scholar 

  2. 2.

    Riley, R. et al. Infectiousness of air from a tuberculosis ward. Ultraviolet irradiation of infected air: comparative infectiousness of different patients. Am. Rev. Respir. Dis. 85, 511–525 (1962).

    CAS  PubMed  Google Scholar 

  3. 3.

    Sultan, L. et al. Tuberculosis disseminators. A study of the variability of aerial infectivity of tuberculous patients. Am. Rev. Respir. Dis. 82, 358–369 (1960).

    CAS  PubMed  Google Scholar 

  4. 4.

    Tostmann, A. et al. Tuberculosis transmission by patients with smear-negative pulmonary tuberculosis in a large cohort in the Netherlands. Clin. Infect. Dis. 47, 1135–1142 (2008).

    PubMed  Google Scholar 

  5. 5.

    Walker, T. M. et al. Whole-genome sequencing to delineate Mycobacterium tuberculosis outbreaks: a retrospective observational study. Lancet Infect. Dis. 13, 137–146 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    van Geuns, H., Meijer, J. & Styblo, K. Results of contact examination in Rotterdam, 1967–1969. Bull. Int. Union Tuberc. 50, 107–121 (1975).

    CAS  PubMed  Google Scholar 

  7. 7.

    Melsew, Y. A. et al. The role of super-spreading events in Mycobacterium tuberculosis transmission: evidence from contact tracing. BMC Infect. Dis. 19, 244 (2019).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Ypma, R. J., Altes, H. K., van Soolingen, D., Wallinga, J. & van Ballegooijen, W. M. A sign of superspreading in tuberculosis: highly skewed distribution of genotypic cluster sizes. Epidemiology 24, 395–400 (2013).

    PubMed  Google Scholar 

  9. 9.

    Lloyd-Smith, J. O., Schreiber, S. J., Kopp, P. E. & Getz, W. Superspreading and the effect of individual variation on disease emergence. Nature 438, 355–359 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    McCreesh, N. & White, R. G. An explanation for the low proportion of tuberculosis that results from transmission between household and known social contacts. Sci. Rep. 8, 5382 (2018).

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Woolhouse, M. E. et al. Heterogeneities in the transmission of infectious agents: implications for the design of control programs. Proc. Natl Acad. Sci. USA 94, 338–342 (1997).

    CAS  PubMed  Google Scholar 

  12. 12.

    Kodama, C. et al. Mycobacterium tuberculosis transmission from patients with drug-resistant compared to drug-susceptible TB: a systematic review and meta-analysis. Eur Respir. J. 50, 1701044 (2017).

    PubMed  Google Scholar 

  13. 13.

    Fennelly, K. P. et al. Cough-generated aerosols of Mycobacterium tuberculosis: a new method to study infectiousness. Am. J. Respir. Crit. Care Med. 169, 604–609 (2004).

    PubMed  Google Scholar 

  14. 14.

    Gagneux, S. et al. Impact of bacterial genetics on the transmission of isoniazid-resistant Mycobacterium tuberculosis. PLoS Pathog. 2, e61 (2006).

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Fennelly, K. P. et al. Variability of infectious aerosols produced during coughing by patients with pulmonary tuberculosis. Am. J. Respir. Crit. Care Med. 186, 450–457 (2012).

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    Jones-López, E. C. et al. Cough aerosols of Mycobacterium tuberculosis predict new infection: a household contact study. Am. J. Respir. Crit. Care Med. 187, 1007–1015 (2013).

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Wejse, C. et al. TBscore: signs and symptoms from tuberculosis patients in a low-resource setting have predictive value and may be used to assess clinical course. Scand. J. Infect. Dis. 40, 111–120 (2008).

    PubMed  Google Scholar 

  18. 18.

    Garton, N. J., Christensen, H., Minnikin, D. E., Adegbola, R. A. & Barer, M. R. Intracellular lipophilic inclusions of mycobacteria in vitro and in sputum. Microbiology 148, 2951–2958 (2002).

    CAS  PubMed  Google Scholar 

  19. 19.

    South African Department of Health Management of Drug-Resistant Tuberculosis (2011); https://health-e.org.za/wp-content/uploads/2014/06/MDR-TB-Clinical-Guidelines-Updated-Jan-2013.pdf

  20. 20.

    Six and Two Stage Viable Samplers. Instruction Manual (Thermo Fisher Scientific, 2009); http://tools.thermofisher.com/content/sfs/manuals/EPM-manual-SixStageAnd.pdf

  21. 21.

    Landis, J. R. & Koch, G. G. The measurement of observer agreement for categorical data. Biometrics 33, 159–174 (1977).

    CAS  Google Scholar 

  22. 22.

    Koo, T. K. & Li, M. Y. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J. Chiropr. Med. 15, 155–163 (2016).

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Stoltz, A. C. et al. Multi-drug resistant TB treatment regimen, including bedaquiline and linezolid, failed to reduce transmission over 14 days. In American Thoracic Society 2017 International Conf. (2020).

  24. 24.

    Ashley, K. & Fey O’Connor, P. NIOSH Manual of Analytical Methods (NMAM) 5th edn (NIOSH CDC, 2017).

  25. 25.

    Acuña-Villaorduña, C. et al. Host determinants of infectiousness in smear-positive patients with pulmonary tuberculosis. Open Forum Infect. Dis. 6, ofz184 (2019).

  26. 26.

    Calligaro, G. L. et al. Effect of new tuberculosis diagnostic technologies on community-based intensified case finding: a multicentre randomised controlled trial. Lancet Infect. Dis. 17, 441–450 (2017).

    PubMed  Google Scholar 

  27. 27.

    Rouillon, A., Perdrizet, S. & Parrot, R. Transmission of tubercle bacilli: the effects of chemotherapy. Tubercle 57, 275–299 (1976).

    CAS  PubMed  Google Scholar 

  28. 28.

    Noble, R. C. Infectiousness of pulmonary tuberculosis after starting chemotherapy: review of the available data on an unresolved question. Am. J. Infect. Control 9, 6–10 (1981).

    Google Scholar 

  29. 29.

    Dharmadhikari, A. S. et al. Rapid impact of effective treatment on transmission of multidrug-resistant tuberculosis. Int. J. Tuberc. Lung Dis. 18, 1019–1025 (2014).

  30. 30.

    Dheda, K. et al. Drug-penetration gradients associated with acquired drug resistance in patients with tuberculosis. Am. J. Respir. Crit. Care Med. 198, 1208–1219 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Fitzwater, S. P. et al. Prolonged infectiousness of tuberculosis patients in a directly observed therapy short-course program with standardized therapy. Clin. Infect. Dis. 51, 371–378 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Pietersen, E. et al. Long-term outcomes of patients with extensively drug-resistant tuberculosis in South Africa: a cohort study. Lancet 383, 1230–1239 (2014).

    PubMed  Google Scholar 

  33. 33.

    Yates, T. A. et al. The transmission of Mycobacterium tuberculosis in high burden settings. Lancet Infect. Dis. 16, 227–238 (2016).

    PubMed  Google Scholar 

  34. 34.

    Escombe, A. R. et al. The detection of airborne transmission of tuberculosis from HIV-infected patients, using an in vivo air sampling model. Clin. Infect. Dis. 44, 1349–1357 (2007).

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Dharmadhikari, A. S. et al. Natural infection of guinea pigs exposed to patients with highly drug-resistant tuberculosis. Tuberculosis 91, 329–338 (2011).

    PubMed  Google Scholar 

  36. 36.

    Lin, P. L. et al. Early events in Mycobacterium tuberculosis infection in cynomolgus macaques. Infect. Immun. 74, 3790–3803 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Jones-López, E. C. et al. Cough aerosols of Mycobacterium tuberculosis predict incident tuberculosis disease in household contacts. Clin. Infect. Dis. 63, 10–20 (2016).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Chengalroyen, M. D. et al. Detection and quantification of differentially culturable tubercle bacteria in sputum from patients with tuberculosis. Am. J. Respir. Crit. Care Med. 194, 1532–1540 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Johnson, R. et al. Drug-resistant tuberculosis epidemic in the Western Cape driven by a virulent Beijing genotype strain. Int. J. Tuberc. Lung Dis. 14, 119–121 (2010).

    CAS  PubMed  Google Scholar 

  40. 40.

    Yu, X. et al. Sensititre® MYCOTB MIC plate for drug susceptibility testing of Mycobacterium tuberculosis complex isolates. Int. J. Tuberc. Lung Dis. 20, 329–334 (2016).

    CAS  PubMed  Google Scholar 

  41. 41.

    Dheda, K. et al. Outcomes, infectiousness, and transmission dynamics of patients with extensively drug-resistant tuberculosis and home-discharged patients with programmatically incurable tuberculosis: a prospective cohort study. Lancet Respir. Med. 5, 269–281 (2017).

    PubMed  Google Scholar 

  42. 42.

    Te Riele, J. B. et al. Relationship between chest radiographic characteristics, sputum bacterial load, and treatment outcomes in patients with extensively drug-resistant tuberculosis. Int. J. Infect. Dis. 79, 65–71 (2019).

    CAS  PubMed  Google Scholar 

  43. 43.

    World Health Organization Mycobacteriology Laboratory Manual 1st edn (Global Laboratory Initiative, 2014); https://www.who.int/tb/laboratory/mycobacteriology-laboratory-manual.pdf

  44. 44.

    Rieder, H. L. et al. The Public Health Service National Tuberculosis Reference Laboratory and the National Laboratory Network. Minimum Requirements, Role and Operation in a Low-Income Country (International Union Against Tuberculosis and Lung Disease, 1998); https://www.ghdonline.org/uploads/The_Public_Health_Service_National_Tuberculosis_Reference_La.pdf

  45. 45.

    Lee, J. et al. Sensititre MYCOTB MIC plate for testing Mycobacterium tuberculosis susceptibility to first- and second-line drugs. Antimicrob. Agents Chemother. 58, 11–18 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Sloan, D. J. et al. Pharmacodynamic modeling of bacillary elimination rates and detection of bacterial lipid bodies in sputum to predict and understand outcomes in treatment of pulmonary tuberculosis. Clin. Infect. Dis. 61, 1–8 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Warren, R. et al. Safe Mycobacterium tuberculosis DNA extraction method that does not compromise integrity. J. Clin. Microbiol. 44, 254–256 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2016).

  49. 49.

    Casali, N. et al. Evolution and transmission of drug-resistant tuberculosis in a Russian population. Nat. Genet. 46, 279–286 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Trauner, A., Borrell, S., Reither, K. & Gagneux, S. Evolution of drug resistance in tuberculosis: recent progress and implications for diagnosis and therapy. Drugs 74, 1063–1072 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Song, T. et al. Fitness costs of rifampicin resistance in Mycobacterium tuberculosis are amplified under conditions of nutrient starvation and compensated by mutation in the β' subunit of RNA polymerase. Mol. Microbiol. 91, 1106–1119 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    de Vos, M. et al. Putative compensatory mutations in the rpoC gene of rifampin-resistant Mycobacterium tuberculosis are associated with ongoing transmission. Antimicrob. Agents Chemother. 57, 827–832 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Farhat, M. R. et al. Genomic analysis identifies targets of convergent positive selection in drug-resistant Mycobacterium tuberculosis. Nat. Genet. 45, 1183–1189 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Comas, I. et al. Whole-genome sequencing of rifampicin-resistant Mycobacterium tuberculosis strains identifies compensatory mutations in RNA polymerase genes. Nat. Genet. 44, 106–110 (2012).

    CAS  Google Scholar 

  55. 55.

    Walker, T. M. et al. Whole-genome sequencing for prediction of Mycobacterium tuberculosis drug susceptibility and resistance: a retrospective cohort study. Lancet Infect. Dis. 15, 1193–1202 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Coll, F. et al. Rapid determination of anti-tuberculosis drug resistance from whole-genome sequences. Genome Med. 7, 51 (2015).

    PubMed  PubMed Central  Google Scholar 

  57. 57.

    Sirgel, F. A. The rationale for using rifabutin in the treatment of MDR and XDR tuberculosis outbreaks. PLoS ONE 8, e59414 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Whitfield, M. G. et al. Mycobacterium tuberculosis pncA polymorphisms that do not confer pyrazinamide resistance at a breakpoint concentration of 100 micrograms per milliliter in MGIT. J. Clin. Microbiol. 53, 3633–3635 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Definitions and Reporting Framework for Tuberculosis, 2013 Revision (World Health Organization, 2013); https://www.who.int/tb/publications/definitions/en/

  60. 60.

    De Stavola, B. L. & Cox, D. R. On the consequences of overstratification. Biometrika 95, 992–996 (2008).

    Google Scholar 

  61. 61.

    WHO Treatment Guidelines for Drug-Resistant Tuberculosis (2016 Update) (World Health Organization, 2016); http://apps.who.int/iris/bitstream/10665/250125/5/9789241549639-webannexes-eng.pdf

  62. 62.

    Automated Ral-Time Nucleic Acid Amplification Technology for Rapid and Simultaneous Detection of Tuberculosis and Rifampicin Resistance: Xpert MTB/RIF System. Policy Statement (World Health Organization, 2011); https://www.who.int/tb/publications/tb-amplificationtechnology-statement/en/

  63. 63.

    Demay, C. et al. SITVITWEB: a publicly available international multimarker database for studying Mycobacterium tuberculosis genetic diversity and molecular epidemiology. Infect. Genet. Evol. 12, 755–766 (2012).

  64. 64.

    Coll, F. et al. SpolPred: rapid and accurate prediction of Mycobacterium tuberculosis spoligotypes from short genomic sequences. Bioinformatics 28, 2991–2993 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Global Tuberculosis Report 2016 (World Health Organization, 2016); https://apps.who.int/iris/handle/10665/250441

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Acknowledgements

We are indebted to the patients who participated. The study would not have been possible without the remarkable efforts of R. Wilson and G. Pretorius. We also thank for their invaluable assistance M. Pienaar and M. Pretorius, P. Spiller, M. Barnard, J. Simpson, T. Dolby, R. van Aarde, C. Clarke and B. Derendinger. The WGS computations were done using facilities provided by the University of Cape Town’s ICTS High Performance Computing team. The project was funded by the National Institutes of Health (grant no. 1R01AI104817-01), Wellcome Trust (grant no. 099854/Z/12/Z), South African Medical Research Council (Career Development Award, Self-initiated Research Award) and the National Research Foundation of South Africa. The content is solely the responsibility of the authors and does not necessarily represent the official views of the South African Medical Research Council. G.T. acknowledges funding from the South African Medical Research Council (grant no. RFA-IFSP-01-2013), the EDCTP2 programme supported by the European Union (grant no. SF1401, OPTIMAL DIAGNOSIS) and the Faculty of Medicine and Health Sciences, Stellenbosch University. K.D. acknowledges funding from the South African Medical Research Council (grant no. RFA-EMU-02-2017), EDCTP (grant nos. TMA-2015SF-1043, TMA-1051-TESAII and TMA-CDF2015), UK Medical Research Council (grant no. MR/S03563X/1) and the Wellcome Trust (grant no. MR/S027777/1). K.F. was supported by the Intramural Research Program of the National Institutes of Health, National Heart, Lung, and Blood Institute. We acknowledge the financial assistance of the National Research Foundation toward this research. The opinions expressed and conclusions arrived at are those of the author(s) and should not necessarily to be attributed to the National Research Foundation.

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K.D., K.F., R.W. and G.T. conceived the study and acquired the funding. G.T., J.L., R.V., L.S., E.P., A.E., G.C., J.t.R. and M.d.K. acquired the data. K.D., P.v.H., T.G., T.G.C. and R.W. provided important research infrastructure. G.T., K.D., J.L., R.V. and L.S. carried out the analyses. G.T. prepared the first draft of the manuscript. All authors provided critical feedback.

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Correspondence to Keertan Dheda.

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Peer review information Alison Farrell is the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Study profile and participant overview.

Abbreviations: CASS, cough aerosol sampling system; DS, drug-susceptible; INH, isoniazid; MDR, multidrug-resistant; MGIT, mycobacterial growth indicator tube; RIF, rifampicin; TB, tuberculosis; XDR, extensively drug-resistant; VL, viral load; WGS, whole genome sequencing. *Done using Sensititre MYCOTB plates for isoniazid, rifampicin, ethambutol, ethionamide, kanamycin, ofloxacin, p-aminosalicylic acid, rifabutin, streptomycin, cycloserine, amikacin, and moxifloxacin.

Extended Data Fig. 2 M. tuberculosis CFU from cough aerosol particles at recruitment as a function of days on treatment before CASS stratified by regimen type.

Beyond eight days, no patients receiving the first-line regimen were CASS-positive, whereas patients receiving second-line regimens had CFU in their aerosol for months. The y-axis is logarithmic and one was added to CFU counts. Abbreviations: ACI, Andersen Cascade Impactor; CLF, clofazimine; FQ, fluoroquinolone; IQR, interquartile range; SLID, second-line injectable drug. Source data

Extended Data Fig. 3

Relationships between sputum bacillary load (liquid culture time-to-positivity) and disease extent (a and b) and cavitation score (c), and between CASS-status and cavitation score (d) assessed by chest radiography using the standardised reporting system in Supplementary Table 2. No legend required in addition to title. Source data

Extended Data Fig. 4 Maximum likelihood phylogenetic tree of baseline CASS isolates (n=318) rooted to Mycobacterium bovis BCG.

Heatmaps to the right of the tree denote from left to right CASS status (also denoted on the branch nodes), treatment duration, drug resistance group and M. tuberculosis lineage. No legend required in addition to title. Source data

Extended Data Fig. 5 A Manhattan plot of multivariate genome wide association study (GWAS) using nsSNPs per gene for CASS-positivity including two principal components, patient age, TB symptom score, HIV-status, duration of treatment less than 48h, PCF, culture time to positivity, and drug-resistance category did not detect significant associations.

The horizontal dashed line represents the threshold of statistical significance. Source data

Supplementary information

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Supplementary Tables 1–22, results text and references.

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Theron, G., Limberis, J., Venter, R. et al. Bacterial and host determinants of cough aerosol culture positivity in patients with drug-resistant versus drug-susceptible tuberculosis. Nat Med 26, 1435–1443 (2020). https://doi.org/10.1038/s41591-020-0940-2

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