Using genetic data to identify transmission risk factors: Statistical assessment and application to tuberculosis transmission

Phylogenetic-informed graph deep learning to classify dynamic transmission clusters in infectious disease epidemics

Motivation

In the midst of an outbreak, identification of groups of individuals that represent risk for transmission of the pathogen under investigation is critical to public health efforts. Dynamic transmission patterns within these clusters, whether it be the result of changes at the level of the virus (e.g. infectivity) or host (e.g. vaccination), are critical in strategizing public health interventions, particularly when resources are limited. Phylogenetic trees are widely used not only in the detection of transmission clusters, but the topological shape of the branches within can be useful sources of information regarding the dynamics of the represented population.

Results

We evaluated the limitation of existing tree shape metrics when dealing with dynamic transmission clusters and propose instead a phylogeny-based deep learning system -DeepDynaTree- for dynamic classification. Comprehensive experiments carried out on a variety of simulated epidemic growth models and HIV epidemic data indicate that this graph deep learning approach is effective, robust, and informative for cluster dynamic prediction. Our results confirm that DeepDynaTree is a promising tool for transmission cluster characterization that can be modified to address the existing limitations and deficiencies in knowledge regarding the dynamics of transmission trajectories for groups at risk of pathogen infection.

Availability and implementation

DeepDynaTree is available under an MIT Licence in https://github.com/salemilab/DeepDynaTree.

Novel insights on unraveling dynamics of transmission clusters in outbreaks using phylogeny-based methods

Molecular data analysis is invaluable in understanding the overall behavior of a rapidly spreading virus population when epidemiological surveillance is problematic. It is also particularly beneficial in describing subgroups within the population, often identified as clades within a phylogenetic tree that represent individuals connected via direct transmission or transmission via differing risk factors in viral spread. However, transmission patterns or viral dynamics within these smaller groups should not be expected to exhibit homogeneous behavior over time. As such, standard phylogenetic approaches that identify clusters based on summary statistics would not be expected to capture dynamic clusters of transmission. We, therefore, sought to evaluate the performance of existing and adapted phylogeny-based cluster identification tools on simulated transmission clusters exhibiting dynamic transmission behavior over time. Despite the complementarity of the tools, we provide strong evidence that novel cluster identification methods are needed for reliable detection of epidemiologically linked individuals, particularly those exhibiting changing transmission dynamics during dynamic outbreak scenarios.

An overview of next generation sequencing strategies and genomics tools used for tuberculosis research

Tuberculosis (TB) is a grave public health concern and is considered the foremost contributor to human mortality resulting from infectious disease. Due to the stringent clonality and extremely restricted genomic diversity, conventional methods prove inefficient for in-depth exploration of minor genomic variations and the evolutionary dynamics operating in Mycobacterium tuberculosis (M.tb) populations. Until now, the majority of reviews have primarily focused on delineating the application of whole-genome sequencing (WGS) in predicting antibiotic resistant genes, surveillance of drug resistance strains, and M.tb lineage classifications. Despite the growing use of next generation sequencing (NGS) and WGS analysis in TB research, there are limited studies that provide a comprehensive summary of there role in studying macroevolution, minor genetic variations, assessing mixed TB infections, and tracking transmission networks at an individual level. This highlights the need for systematic effort to fully explore the potential of WGS and its associated tools in advancing our understanding of TB epidemiology and disease transmission. We delve into the recent bioinformatics pipelines and NGS strategies that leverage various genetic features and simultaneous exploration of host-pathogen protein expression profile to decipher the genetic heterogeneity and host-pathogen interaction dynamics of the M.tb infections. This review highlights the potential benefits and limitations of NGS and bioinformatics tools and discusses their role in TB detection and epidemiology. Overall, this review could be a valuable resource for researchers and clinicians interested in NGS-based approaches in TB research.

HIV co-infection is associated with reduced Mycobacterium tuberculosis transmissibility in sub-Saharan Africa

Persons living with HIV are known to be at increased risk of developing tuberculosis (TB) disease upon infection with Mycobacterium tuberculosis (Mtb). However, it has remained unclear how HIV co-infection affects subsequent Mtb transmission from these patients. Here, we customized a Bayesian phylodynamic framework to estimate the effects of HIV co-infection on the Mtb transmission dynamics from sequence data. We applied our model to four Mtb genomic datasets collected in sub-Saharan African countries with a generalized HIV epidemic. Our results confirm that HIV co-infection is a strong risk factor for developing active TB. Additionally, we demonstrate that HIV co-infection is associated with a reduced effective reproductive number for TB. Stratifying the population by CD4+ T-cell count yielded similar results, suggesting that, in this context, CD4+ T-cell count is not a better predictor of Mtb transmissibility than HIV infection status alone. Together, our genome-based analyses complement observational household contact studies, and more firmly establish the negative association between HIV co-infection and Mtb transmissibility.

DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction

In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and patterns of spread is critical to inform public health programs. Projections of transmission growth or decline among specific risk groups can aid in optimizing interventions, particularly when resources are limited. Phylogenetic trees have been widely used in the detection of transmission chains and high-risk populations. Moreover, tree topology and the incorporation of population parameters (phylodynamics) can be useful in reconstructing the evolutionary dynamics of an epidemic across space and time among individuals. We now demonstrate the utility of phylodynamic trees for transmission modeling and forecasting, developing a phylogeny-based deep learning system, referred to as DeepDynaForecast. Our approach leverages a primal-dual graph learning structure with shortcut multi-layer aggregation, which is suited for the early identification and prediction of transmission dynamics in emerging high-risk groups. We demonstrate the accuracy of DeepDynaForecast using simulated outbreak data and the utility of the learned model using empirical, large-scale data from the human immunodeficiency virus epidemic in Florida between 2012 and 2020. Our framework is available as open-source software (MIT license) at github.com/lab-smile/DeepDynaForcast.

Effect of compensatory evolution in the emergence and transmission of rifampicin-resistant Mycobacterium tuberculosis in Cape Town, South Africa: a genomic epidemiology study

BACKGROUND

Experimental data show that drug-resistance-conferring mutations are often associated with a decrease in the replicative fitness of bacteria in vitro, and that this fitness cost can be mitigated by compensatory mutations; however, the role of compensatory evolution in clinical settings is less clear. We assessed whether compensatory evolution was associated with increased transmission of rifampicin-resistant tuberculosis in Khayelitsha, Cape Town, South Africa.

METHODS

We did a genomic epidemiological study by analysing available M tuberculosis isolates and their associated clinical data from individuals routinely diagnosed with rifampicin-resistant tuberculosis in primary care and hospitals in Khayelitsha, Cape Town, South Africa. Isolates were collected as part of a previous study. All individuals diagnosed with rifampicin-resistant tuberculosis and with linked biobanked specimens were included in this study. We applied whole-genome sequencing, Bayesian reconstruction of transmission trees, and phylogenetic multivariable regression analysis to identify individual and bacterial factors associated with the transmission of rifampicin-resistant M tuberculosis strains.

FINDINGS

Between Jan 1, 2008, and Dec 31, 2017, 2161 individuals were diagnosed with multidrug-resistant or rifampicin-resistant tuberculosis in Khayelitsha, Cape Town, South Africa. Whole-genome sequences were available for 1168 (54%) unique individual M tuberculosis isolates. Compensatory evolution was associated with smear-positive pulmonary disease (adjusted odds ratio 1·49, 95% CI 1·08-2·06) and a higher number of drug-resistance-conferring mutations (incidence rate ratio 1·38, 95% CI 1·28-1·48). Compensatory evolution was also associated with increased transmission of rifampicin-resistant disease between individuals (adjusted odds ratio 1·55; 95% CI 1·13-2·12), independent of other patient and bacterial factors.

INTERPRETATION

Our findings suggest that compensatory evolution enhances the in vivo fitness of drug-resistant M tuberculosis genotypes, both within and between patients, and that the in vitro replicative fitness of rifampicin-resistant M tuberculosis measured in the laboratory correlates with the bacterial fitness measured in clinical settings. These results emphasise the importance of enhancing surveillance and monitoring efforts to prevent the emergence of highly transmissible clones capable of rapidly accumulating new drug resistance mutations. This concern becomes especially crucial at present, because treatment regimens incorporating novel drugs are being implemented.

FUNDING

Funding for this study was provided by a Swiss and South Africa joint research award (grant numbers 310030_188888, CRSII5_177163, and IZLSZ3_170834), the European Research Council (grant number 883582), and a Wellcome Trust fellowship (to HC; reference number 099818/Z/12/Z). ZS-D was funded through a PhD scholarship from the South African National Research Foundation and RMW was funded through the South African Medical Research Council.