Genomic analyses of Mycobacterium tuberculosis from human lung resections reveal a high frequency of polyclonal infections

Abstract

Polyclonal infections occur when at least two unrelated strains of the same pathogen are detected in an individual. This has been linked to worse clinical outcomes in tuberculosis, as undetected strains with different antibiotic resistance profiles can lead to treatment failure. Here, we examine the amount of polyclonal infections in sputum and surgical resections from patients with tuberculosis in the country of Georgia. For this purpose, we sequence and analyse the genomes of Mycobacterium tuberculosis isolated from the samples, acquired through an observational clinical study (NCT02715271). Access to the lung enhanced the detection of multiple strains (40% of surgery cases) as opposed to just using a sputum sample (0-5% in the general population). We show that polyclonal infections often involve genetically distant strains and can be associated with reversion of the patient's drug susceptibility profile over time. In addition, we find different patterns of genetic diversity within lesions and across patients, including mutational signatures known to be associated with oxidative damage; this suggests that reactive oxygen species may be acting as a selective pressure in the granuloma environment. Our results support the idea that the magnitude of polyclonal infections in high-burden tuberculosis settings is underestimated when only testing sputum samples.

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1. Theoretical scenarios for Mycobacterium tuberculosis polyclonal infections.

Blue represents healthy patients and red represents infected patients. a A transmission event of two strains from an infected individual to another result in two different genotypes being present in the same space or sample. b An infected patient on treatment clears infection and gets infected again resulting in two different genotypes present over time. c An already infected patient get superinfected with a different genotype. The second genotype will either coexist with the first one or replace it.

Fig. 2. Phylogenetic diversity in Georgia and identification of polyclonal infections.

a Phylogeny of all Georgian M. tuberculosis isolates included in this study (L2 in blue, L4 in red). Surgery patients are highlighted in different colors. Drug resistance profiles are illustrated in the inner band (see legend) and transmission clusters represented on the outer band were estimated using a 10 SNP phylogenetic distance threshold. Branch lengths and bootstrap values not represented. b Curves connecting patient samples located in distant places of the phylogeny, suggesting polyclonal infections. The phylogeny and associated data can be browsed at the ITOL website (https://itol.embl.de/tree/161111218247381131580915038).

Fig. 3. Dynamics of polyclonal infection with two different strains across the granuloma.

a Descriptive summary of patient G039 genotypes frequencies and distribution across the lesion. The PCA graph illustrates their separation: while the X-axis explains ~90% of the variance, leaving N and S apart from the rest, the Y-axis only explains ~5%, mainly defined by low-frequency variants not shared by N and S (due to reading depth differences). b Antibiotic resistance-associated mutations across surgical and sputum samples for the two co-existing genotypes. c Deconvolution of the two genotypes by frequency patterns clustering. Resistance-associated variants from (b) are represented in purple. Unassigned subpopulations (in orange) are low-frequency variants that we cannot assign to any of the two genotypes and shared fixed variants (in green) are common to both genotypes. C caseum, I inner wall, E external wall, H healthy tissue, N nodule, S sputum. Source data are provided as a Source Data file.

Fig. 4. Impact of the granuloma microenvironment on within-host diversity.

a Average diversity of each patient’s surgical samples (measured by a number of vSNPs and excluding polyclonal infections, n = number of independent surgical samples available for each patient, range 1–3). Data are presented as mean values ± SD. b Pooled comparisons of ROS signature in the different datasets. Categories include surgery specimens, sputum samples from surgery patients, and serial sputum samples from the same patient. c Individual values of caseum and sputum from surgery patients. Purple lines connect those patients whose differences are statistically significant by the two-tailed X2 test. Source data are provided as a Source Data file.

Fig. 5. Details on the surgical cohort.

a Sampling sites for the surgical cohort. b Timeline of key events for the surgical cohort. Treatment periods are depicted in red, sputum samples date in blue points, and surgical samples date in green points. Starting case definition and treatment outcome are also represented. Source data are provided as a Source Data file.