Comparison of routine field epidemiology and whole genome sequencing to identify tuberculosis transmission in a remote setting

Successful detection of an unrecognized outbreak of Mycobacterium tuberculosis in the modern Beijing subfamily through combined molecular epidemiological and population genetic analyses

INTRODUCTION

While multi-locus variable number tandem repeat analysis (MLVA) as molecular epidemiological analysis has been used to detect Mycobacterium tuberculosis outbreaks, its discriminatory power for identifying strains is limited. Whole-genome sequencing (WGS) offers high discriminatory power but is expensive. This study was established to develop a strategy to overcome these limitations of molecular epidemiological analysis by combining it with population genetic analysis.

METHODS

MLVA data from 2732 M.tuberculosis strains isolated in Chiba Prefecture, Japan, in 2008-2016 were subjected to Bayesian population genetic analysis to subdivide the strains into subfamilies and estimate subpopulations within each subfamily. Annual changes in the number of strains within subpopulations exhibiting linkage disequilibrium (LD) in MLVA data were examined. Only strains from subpopulations displaying significant increases were analyzed by WGS.

RESULTS

Significant LD was observed in one subpopulation using Bayesian analysis (designated P3) within the modern Beijing subfamily, which exhibited a significant increase in strain number in 2016. WGS analysis of strains belonging to P3 from 2016 revealed that 17 out of 21 of them differed by three or fewer single-nucleotide polymorphisms from their most similar strain, indicating that they had a common origin (i.e., an outbreak). Among these common-origin strains, one exhibited a four-locus variant in the MLVA, which would not be suspected of being an outbreak-related strain based on MLVA alone without Bayesian analysis.

CONCLUSION

The combination of Bayesian population genetic analysis with MLVA successfully detected M. tuberculosis strains from an unrecognized outbreak by performing WGS on only a subset of the strains.

Mycobacterium tuberculosis epidemiology in Oman: whole-genome sequencing uncovers transmission pathways

Tuberculosis (TB) originating from expatriates that hail from high TB-burden countries is hypothesized to play a role in continued TB transmission in Oman. Here, we used whole-genome sequencing (WGS) to assess national TB transmission dynamics. The annual incidence per 100,000 population per year was calculated for nationals and expatriates. A convenience sample of Mycobacterium tuberculosis (MTB) isolates from 2018 to 2019 was sequenced and analyzed with publicly available TB sequences from Bangladesh, Tanzania, the Philippines, India, and Pakistan. Relatedness was assessed by generating core-genome single nucleotide polymorphism (SNP) distances. The incidence of TB was five cases per 100,000 persons in 2018 and seven cases per 100,000 persons in 2020 (R2 = 0.34, P = 0.60). Incidence among nationals was 3.9 per 100,000 persons in 2018 and 3.5 per 100,000 persons in 2020 (R2 = 0.20, P = 0.70), and incidence among expatriates was 7.2 per 100,000 persons in 2018 and 12.7 per 100,000 persons in 2020 (R2 = 0.74, P = 0.34). Sixty-eight local MTB isolates were sequenced and analyzed with 393 global isolates. Isolates belonged to nine distinct spoligotypes. Two isolates, originating from an expatriate and an Omani national, were grouped into a WGS-based cluster (SNP distance < 12), which was corroborated by an epidemiological investigation. Relatedness of local and global isolates (SNP distance < 100) was also seen. The relatedness between MTB strains in Oman and those in expatriate countries of origin can aid inform TB control policy. Our results provide evidence that WGS can complement epidemiological analysis to achieve the End TB strategy goal in Oman. IMPORTANCE Tuberculosis (TB) incidence in Oman remains above national program control targets. TB transmission originating from expatriates from high TB-burden countries has been hypothesized to play a role. We used whole-genome sequencing (WGS) to assess TB transmission dynamics between expatriates and Omani nationals to inform TB control efforts. Available Mycobacterium tuberculosis isolates from 2018 to 2019 underwent WGS and analysis with publicly available TB sequences from Bangladesh, the Philippines, India, and Pakistan to assess for genetic relatedness. Our analysis revealed evidence of previously unrecognized transmission between an expatriate and an Omani national, which was corroborated by epidemiological investigation. Analysis of local and global isolates revealed evidence of distant relatedness between local and global isolates. Our results provide evidence that WGS can complement classic public health surveillance to inform targeted interventions to achieve the End TB strategy goal in Oman.

Transmission of Mycobacterium tuberculosis in schools: a molecular epidemiological study using whole-genome sequencing in Guangzhou, China

Background

China is a country with a high burden of tuberculosis (TB). TB outbreaks are frequent in schools. Thus, understanding the transmission patterns is crucial for controlling TB.

Method

In this genomic epidemiological study, the conventional epidemiological survey data combined with whole-genome sequencing was used to assess the genotypic distribution and transmission characteristics of Mycobacterium tuberculosis strains isolated from patients with TB attending schools during 2015 to 2019 in Guangzhou, China.

Result

The TB incidence was mainly concentrated in regular secondary schools and technical and vocational schools. The incidence of drug resistance among the students was 16.30% (22/135). The phylogenetic tree showed that 79.26% (107/135) and 20.74% (28/135) of the strains belonged to lineage 2 (Beijing genotype) and lineage 4 (Euro-American genotype), respectively. Among the 135 isolates, five clusters with genomic distance within 12 single nucleotide polymorphisms were identified; these clusters included 10 strains, accounting for an overall clustering rate of 7.4% (10/135), which showed a much lower transmission index. The distance between the home or school address and the interval time of symptom onset or diagnosis indicated that campus dissemination and community dissemination may be existed both, and community dissemination is the main.

Conclusion and recommendation

TB cases in Guangzhou schools were mainly disseminated and predominantly originated from community transmission. Accordingly, surveillance needs to be strengthened to stop the spread of TB in schools.

Model-based Analysis of Tuberculosis Genotype Clusters in the United States Reveals High Degree of Heterogeneity in Transmission and State-level Differences Across California, Florida, New York, and Texas

BACKGROUND

Reductions in tuberculosis (TB) transmission have been instrumental in lowering TB incidence in the United States. Sustaining and augmenting these reductions are key public health priorities.

METHODS

We fit mechanistic transmission models to distributions of genotype clusters of TB cases reported to the Centers for Disease Control and Prevention during 2012-2016 in the United States and separately in California, Florida, New York, and Texas. We estimated the mean number of secondary cases generated per infectious case (R0) and individual-level heterogeneity in R0 at state and national levels and assessed how different definitions of clustering affected these estimates.

RESULTS

In clusters of genotypically linked TB cases that occurred within a state over a 5-year period (reference scenario), the estimated R0 was 0.29 (95% confidence interval [CI], .28-.31) in the United States. Transmission was highly heterogeneous; 0.24% of simulated cases with individual R0 >10 generated 19% of all recent secondary transmissions. R0 estimate was 0.16 (95% CI, .15-.17) when a cluster was defined as cases occurring within the same county over a 3-year period. Transmission varied across states: estimated R0s were 0.34 (95% CI, .3-.4) in California, 0.28 (95% CI, .24-.36) in Florida, 0.19 (95% CI, .15-.27) in New York, and 0.38 (95% CI, .33-.46) in Texas.

CONCLUSIONS

TB transmission in the United States is characterized by pronounced heterogeneity at the individual and state levels. Improving detection of transmission clusters through incorporation of whole-genome sequencing and identifying the drivers of this heterogeneity will be essential to reducing TB transmission.

An implementation science approach to evaluating pathogen whole genome sequencing in public health

BACKGROUND

Pathogen whole genome sequencing (WGS) is being incorporated into public health surveillance and disease control systems worldwide and has the potential to make significant contributions to infectious disease surveillance, outbreak investigation and infection prevention and control. However, to date, there are limited data regarding (i) the optimal models for integration of genomic data into epidemiological investigations and (ii) how to quantify and evaluate public health impacts resulting from genomic epidemiological investigations.

METHODS

We developed the Pathogen Genomics in Public HeAlth Surveillance Evaluation (PG-PHASE) Framework to guide examination of the use of WGS in public health surveillance and disease control. We illustrate the use of this framework with three pathogens as case studies: Listeria monocytogenes, Mycobacterium tuberculosis and SARS-CoV-2.

RESULTS

The framework utilises an adaptable whole-of-system approach towards understanding how interconnected elements in the public health application of pathogen genomics contribute to public health processes and outcomes. The three phases of the PG-PHASE Framework are designed to support understanding of WGS laboratory processes, analysis, reporting and data sharing, and how genomic data are utilised in public health practice across all stages, from the decision to send an isolate or sample for sequencing to the use of sequence data in public health surveillance, investigation and decision-making. Importantly, the phases can be used separately or in conjunction, depending on the need of the evaluator. Subsequent to conducting evaluation underpinned by the framework, avenues may be developed for strategic investment or interventions to improve utilisation of whole genome sequencing.

CONCLUSIONS

Comprehensive evaluation is critical to support health departments, public health laboratories and other stakeholders to successfully incorporate microbial genomics into public health practice. The PG-PHASE Framework aims to assist public health laboratories, health departments and authorities who are either considering transitioning to whole genome sequencing or intending to assess the integration of WGS in public health practice, including the capacity to detect and respond to outbreaks and associated costs, challenges and facilitators in the utilisation of microbial genomics and public health impacts.

Logically Inferred Tuberculosis Transmission (LITT): A Data Integration Algorithm to Rank Potential Source Cases

Understanding tuberculosis (TB) transmission chains can help public health staff target their resources to prevent further transmission, but currently there are few tools to automate this process. We have developed the Logically Inferred Tuberculosis Transmission (LITT) algorithm to systematize the integration and analysis of whole-genome sequencing, clinical, and epidemiological data. Based on the work typically performed by hand during a cluster investigation, LITT identifies and ranks potential source cases for each case in a TB cluster. We evaluated LITT using a diverse dataset of 534 cases in 56 clusters (size range: 2–69 cases), which were investigated locally in three different U.S. jurisdictions. Investigators and LITT agreed on the most likely source case for 145 (80%) of 181 cases. By reviewing discrepancies, we found that many of the remaining differences resulted from errors in the dataset used for the LITT algorithm. In addition, we developed a graphical user interface, user's manual, and training resources to improve LITT accessibility for frontline staff. While LITT cannot replace thorough field investigation, the algorithm can help investigators systematically analyze and interpret complex data over the course of a TB cluster investigation.

Code available at:https://github.com/CDCgov/TB_molecular_epidemiology/tree/1.0; https://zenodo.org/badge/latestdoi/166261171.

The intersection of genomics and big data with public health: Opportunities for precision public health

Muin Khoury and co-authors discuss anticipated contributions of genomics and other forms of large-scale data in public health.