Back-to-Africa introductions of Mycobacterium tuberculosis as the main cause of tuberculosis in Dar es Salaam, Tanzania

Abstract

In settings with high tuberculosis (TB) endemicity, distinct genotypes of the Mycobacterium tuberculosis complex (MTBC) often differ in prevalence. However, the factors leading to these differences remain poorly understood. Here we studied the MTBC population in Dar es Salaam, Tanzania over a six-year period, using 1,082 unique patient-derived MTBC whole-genome sequences (WGS) and associated clinical data. We show that the TB epidemic in Dar es Salaam is dominated by multiple MTBC genotypes introduced to Tanzania from different parts of the world during the last 300 years. The most common MTBC genotypes deriving from these introductions exhibited differences in transmission rates and in the duration of the infectious period, but little differences in overall fitness, as measured by the effective reproductive number. Moreover, measures of disease severity and bacterial load indicated no differences in virulence between these genotypes during active TB. Instead, the combination of an early introduction and a high transmission rate accounted for the high prevalence of L3.1.1, the most dominant MTBC genotype in this setting. Yet, a longer co-existence with the host population did not always result in a higher transmission rate, suggesting that distinct life-history traits have evolved in the different MTBC genotypes. Taken together, our results point to bacterial factors as important determinants of the TB epidemic in Dar es Salaam.

Fig 1. Phylogeny of 1082 Mtb genomes sampled from 2013–2019 in Dar es Salaam.

The tree is rooted with a M. canettii strain (SAMN00102920) and the scale bar indicates substitutions per site. Tips are colored according to the MTBC lineage and the innermost heatmap indicates MTBC sublineages according to [47]. The second heatmap indicates whether a strain is considered as recently-introduced or early-introduced based on a threshold of 0.2 for the relative age (See methods). The outermost heatmap indicates the genotypic drug resistance profiles for most commonly used drugs in Dar es Salaam (See methods). Only mutations giving rise to first-line drugs are considered. The MTBC introductions into Tanzania leading to most cases in our cohort are labelled from 1–10.

Fig 2. The genotypes in Dar es Salaam resulting from introductions 1–10.

A—Geographic origin of the 10 introductions into Tanzania that led to most cases in our cohort. Introductions are labelled as in Fig 1 and are represented by colored arrows according to the lineage. Regions or countries identified as the origin of a successful introduction are colored (dark green: South America; brown: West Africa; dark blue: Malawi and Uganda; black: Tanzania; shades of blue: South, Central and East Asia; yellow: Europe). The age of the introductions were obtained from substitution rates inferred from our dataset except for L1 (See methods and S8 Table for details). The map was created with the R package rworldmap [109] and the shapefile for the map can be found under the following link: https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/110m/cultural/ne_110m_admin_0_countries.zip. B—Prevalence of the most prevalent genotypes within each lineage (inner circle) and across all lineages (outer circle).

Fig 3. Number of descendants resulting from the ten most successful introductions and the relative age of the latter.

The number above each introduction corresponds to the numbers in Figs 1 and 2. The pearson correlation coefficient (r) and p-value (p) were calculated.

Fig 4. Transmission analysis using three different approaches.

A and B compare the secondary case rate ratios between Introduction 10 of L3.1.1 and the other successful introductions identified determined based on clustering by using different age thresholds (in years) or different SNP thresholds for A or B, respectively. A secondary case rate ratio of 1 (indicated by a horizontal line) would mean that the secondary case rates of both introductions are the same. C Violin plots comparing the terminal branch lengths between the most successful introductions.

Fig 5. Transmission analysis of the four most successful introductions using phylodynamic modelling.

A, B, and C compare the posterior distribution of transmission rate, effective reproductive number, and infectious period respectively, for the most successful introductions, as estimated with a phylodynamic birth-death model.