Interpreting whole genome sequencing for investigating tuberculosis transmission: a systematic review

Abstract

Background: Whole genome sequencing (WGS) is becoming an important part of epidemiological investigations of infectious diseases due to greater resolution and cost reductions compared to traditional typing approaches. Many public health and clinical teams will increasingly use WGS to investigate clusters of potential pathogen transmission, making it crucial to understand the benefits and assumptions of the analytical methods for investigating the data. We aimed to understand how different approaches affect inferences of transmission dynamics and outline limitations of the methods.

Methods: We comprehensively searched electronic databases for studies that presented methods used to interpret WGS data for investigating tuberculosis (TB) transmission. Two authors independently selected studies for inclusion and extracted data. Due to considerable methodological heterogeneity between studies, we present summary data with accompanying narrative synthesis rather than pooled analyses.

Results: Twenty-five studies met our inclusion criteria. Despite the range of interpretation tools, the usefulness of WGS data in understanding TB transmission often depends on the amount of genetic diversity in the setting. Where diversity is small, distinguishing re-infections from relapses may be impossible; interpretation may be aided by the use of epidemiological data, examining minor variants and deep sequencing. Conversely, when within-host diversity is large, due to genetic hitchhiking or co-infection of two dissimilar strains, it is critical to understand how it arose. Greater understanding of microevolution and mixed infection will enhance interpretation of WGS data.

Conclusions: As sequencing studies have sampled more intensely and integrated multiple sources of information, the understanding of TB transmission and diversity has grown, but there is still much to be learnt about the origins of diversity that will affect inferences from these data. Public health teams and researchers should combine epidemiological, clinical and WGS data to strengthen investigations of transmission.

Keywords: Systematic review; Transmission; Tuberculosis; Whole genome sequencing.

Fig. 1

Visual representation of the four topics of the review, with colours representing different strains of TB. a Direction of transmission: permissible either way for individuals with the same strain (same colour); excluded for cases with different strains. b Within-host diversity, in the first instance as microevolution of an infecting strain and in the second due to mixed infection. A source case with a diverse burden can transmit different combinations of strains. c Strain diversity over time. d Drug resistance patterns in the form of acquired drug resistance mutations (red line) followed by transmission

Fig. 2

PRISMA flowchart. *Includes one additional study that was found through reference list screening. M.tb, Mycobacterium tuberculosis; TB, tuberculosis; WGS, whole genome sequencing

Fig. 3

Effect of sampling on the phylogenetic tree. a Representation of a transmission tree, where nodes represent individuals, numbers represent the order of infection chronologically and the arrows show the direction of transmission. b Phylogenetic tree when all individuals in the outbreak are sampled. Transmission pairs are not necessarily paired on the tree as they may not be the most similar within the context of the outbreak. For example, if we assume that 1 had a long, chronic TB infection then because of the amount of diversity that can accumulate over time it is possible for the genomes from 2 and 3 to be more closely related to each other than to the genome from 1, even though 1 infected them both. This is because the strain that was sampled from 1 has evolved since 1 infected 2 and 3. While rejecting pairs not adjacent on the phylogenetic tree seems sound when sampling is sparse (as transmission pairs would then be relatively rare in the dataset and closer in phylogenetic distance than typical pairs of tips), when sampling is dense (as is desirable in epidemiological investigations). c Individuals 2, 3, 4 and 8 have not been sampled for the reconstruction of this tree. This makes the distances between the average pair of tips in the tree larger, highlights the close phylogenetic distance between 6 and 7 and (correctly) suggests transmission occurred between these individuals