Understanding drivers of phylogenetic clustering and terminal branch lengths distribution in epidemics of Mycobacterium tuberculosis

Abstract

Detecting factors associated with transmission is important to understand disease epidemics, and to design effective public health measures. Clustering and terminal branch lengths (TBL) analyses are commonly applied to genomic data sets of Mycobacterium tuberculosis (MTB) to identify sub-populations with increased transmission. Here, I used a simulation-based approach to investigate what epidemiological processes influence the results of clustering and TBL analyses, and whether differences in transmission can be detected with these methods. I simulated MTB epidemics with different dynamics (latency, infectious period, transmission rate, basic reproductive number R0, sampling proportion, sampling period, and molecular clock), and found that all considered factors, except for the length of the infectious period, affect the results of clustering and TBL distributions. I show that standard interpretations of this type of analyses ignore two main caveats: (1) clustering results and TBL depend on many factors that have nothing to do with transmission, (2) clustering results and TBL do not tell anything about whether the epidemic is stable, growing, or shrinking, unless all the additional parameters that influence these metrics are known, or assumed identical between sub-populations. An important consequence is that the optimal SNP threshold for clustering depends on the epidemiological conditions, and that sub-populations with different epidemiological characteristics should not be analyzed with the same threshold. Finally, these results suggest that different clustering rates and TBL distributions, that are found consistently between different MTB lineages, are probably due to intrinsic bacterial factors, and do not indicate necessarily differences in transmission or evolutionary success.

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

“…Finally, to account for potential confounder effects based on genetic distances which result from processes unrelated to transmission and which are extensively discussed in [43], we used phylodynamic modelling to quantify transmission rates (λ), the effective reproductive number (Re) and the duration of the infectious period for the L3.1.1, L4.3.4, L1.1.2, and L2.2.1 representatives descending from introductions 10, 5, 9, and 1, respectively (Fig 5A -C). While transmission rates represent the average rate at which infected individuals produce new infections (number of transmission events per unit of time), Re represents the total number of transmission events per infected individual during the period the individual remains infectious.…”

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

“…Finally, to account for potential confounder effects based on genetic distances which result from processes unrelated to transmission (35), we used phylodynamic modelling to quantify transmission rates (λ), the effective reproductive number (Re) and the duration of the infectious period for the L3.1.1, L4.…”

“…Finally, to account for potential confounder effects based on genetic distances which result from processes unrelated to transmission (35), we used phylodynamic modelling to quantify transmission rates (λ), the effective reproductive number (Re) and the duration of the infectious period for the L3.1.1, L4.3.4, L1.1.2, and L2.2.1 20 representatives descending from introductions 10, 5, 9, and 1, respectively (Figure 5 A-C). While transmission rates represent the average rate at which infected individuals produce new infections (number of transmission events per unit of time), Re represents the total number of transmission events per infected individual during the period the individual remains infectious.…”

The Global Success of Mycobacterium tuberculosis Modern Beijing Family Is Driven by a Few Recently Emerged Strains

“…It was recently proposed that factors such as sampling bias and molecular clock rate may bias estimations of recent transmission based on clustering rate or terminal branch length ( 39 ). However, our clustering rates are based on population-based studies with an unbiased sampling of the different L2.3 clades, and we used different clustering thresholds to assess recent transmission rates.…”