Unraveling tuberculosis patient cluster transmission chains: integrating WGS-based network with clinical and epidemiological insights

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Abstract

Background

Tuberculosis remains a global health threat, and the World Health Organization reports a limited reduction in disease incidence rates, including both new and relapse cases. Therefore, studies targeting tuberculosis transmission chains and recurrent episodes are crucial for developing the most effective control measures. Herein, multiple tuberculosis clusters were retrospectively investigated by integrating patients’ epidemiological and clinical information with median-joining networks recreated based on whole genome sequencing (WGS) data of Mycobacterium tuberculosis isolates.

Methods

Epidemiologically linked tuberculosis patient clusters were identified during the source case investigation for pediatric tuberculosis patients. Only M. tuberculosis isolate DNA samples with previously determined spoligotypes identical within clusters were subjected to WGS and further median-joining network recreation. Relevant clinical and epidemiological data were obtained from patient medical records.

Results

We investigated 18 clusters comprising 100 active tuberculosis patients 29 of whom were children at the time of diagnosis; nine patients experienced recurrent episodes. M. tuberculosis isolates of studied clusters belonged to Lineages 2 (sub-lineage 2.2.1) and 4 (sub-lineages 4.3.3, 4.1.2.1, 4.8, and 4.2.1), while sub-lineage 4.3.3 (LAM) was the most abundant. Isolates of six clusters were drug-resistant. Within clusters, the maximum genetic distance between closely related isolates was only 5–11 single nucleotide variants (SNVs). Recreated median-joining networks, integrated with patients’ diagnoses, specimen collection dates, sputum smear microscopy, and epidemiological investigation results indicated transmission directions within clusters and long periods of latent infection. It also facilitated the identification of potential infection sources for pediatric patients and recurrent active tuberculosis episodes refuting the reactivation possibility despite the small genetic distance of ≤5 SNVs between isolates. However, unidentified active tuberculosis cases within the cluster, the variable mycobacterial mutation rate in dormant and active states, and low M. tuberculosis genetic variability inferred precise transmission chain delineation. In some cases, heterozygous SNVs with an allelic frequency of 10–73% proved valuable in identifying direct transmission events.

Conclusion

The complex approach of integrating tuberculosis cluster WGS-data-based median-joining networks with relevant epidemiological and clinical data proved valuable in delineating epidemiologically linked patient transmission chains and deciphering causes of recurrent tuberculosis episodes within clusters.

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Most cited references 84

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Subha Kalyaanamoorthy, Bui Minh, Thomas Wong … (2017)

Model-based molecular phylogenetics plays an important role in comparisons of genomic data, and model selection is a key step in all such analyses. We present ModelFinder, a fast model-selection method that greatly improves the accuracy of phylogenetic estimates. The improvement is achieved by incorporating a model of rate-heterogeneity across sites not previously considered in this context, and by allowing concurrent searches of model-space and tree-space.

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IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era

Bui Minh, Heiko Schmidt, Olga Chernomor … (2020)

Abstract IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.

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UFBoot2: Improving the Ultrafast Bootstrap Approximation

Diep Thi Hoang, Olga Chernomor, Arndt von Haeseler … (2017)

Abstract The standard bootstrap (SBS), despite being computationally intensive, is widely used in maximum likelihood phylogenetic analyses. We recently proposed the ultrafast bootstrap approximation (UFBoot) to reduce computing time while achieving more unbiased branch supports than SBS under mild model violations. UFBoot has been steadily adopted as an efficient alternative to SBS and other bootstrap approaches. Here, we present UFBoot2, which substantially accelerates UFBoot and reduces the risk of overestimating branch supports due to polytomies or severe model violations. Additionally, UFBoot2 provides suitable bootstrap resampling strategies for phylogenomic data. UFBoot2 is 778 times (median) faster than SBS and 8.4 times (median) faster than RAxML rapid bootstrap on tested data sets. UFBoot2 is implemented in the IQ-TREE software package version 1.6 and freely available at http://www.iqtree.org.

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Author and article information

Contributors

Darja Sadovska: URI : https://loop.frontiersin.org/people/2641062/overviewRole: Role: Role: Role: Role: Role: Role: Role: Role:

Iveta Ozere: URI : https://loop.frontiersin.org/people/487238/overviewRole: Role: Role: Role: Role: Role: Role:

Ilva Pole: Role: Role: Role: Role: Role:

Jānis Ķimsis: Role: Role:

Annija Vaivode: Role: Role:

Anda Vīksna: Role: Role:

Inga Norvaiša: Role: Role:

Ineta Bogdanova: Role: Role:

Viktorija Ulanova: Role: Role:

Valentīna Čapligina: Role: Role:

Dace Bandere: URI : https://loop.frontiersin.org/people/924310/overviewRole: Role:

Renāte Ranka: URI : https://loop.frontiersin.org/people/2565488/overviewRole: Role: Role: Role: Role: Role: Role: Role:

Journal

Journal ID (nlm-ta): Front Public Health

Journal ID (iso-abbrev): Front Public Health

Journal ID (publisher-id): Front. Public Health

Title: Frontiers in Public Health

Publisher: Frontiers Media S.A.

ISSN (Electronic): 2296-2565

Publication date (Electronic): 20 May 2024

Publication date Collection: 2024

Volume: 12

Electronic Location Identifier: 1378426

Affiliations

[1] ¹Laboratory of Molecular Microbiology, Latvian Biomedical Research and Study Centre , Riga, Latvia

[2] ²Centre of Tuberculosis and Lung Diseases, Riga East University Hospital , Upeslejas, Latvia

[3] ³Department of Infectology, Riga Stradiņš University , Riga, Latvia

[4] ⁴Department of Pharmaceutical Chemistry, Riga Stradiņš University , Riga, Latvia

Author notes

Edited by: Christophe Sola, Université Paris-Saclay, France

Reviewed by: Emilyn Costa Conceição, Stellenbosch University, South Africa

Hind Yahyaoui Azami, University of Georgia, United States

*Correspondence: Darja Sadovska, darja.aleinikova@ 123456biomed.lu.lv

Article

DOI: 10.3389/fpubh.2024.1378426

PMC ID: 11144917

PubMed ID: 38832230

SO-VID: 68e21ebc-fa81-46e8-95de-4266370f9db6

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

History

Date received : 29 January 2024

Date accepted : 07 May 2024

Page count

Figures: 4, Tables: 1, Equations: 0, References: 85, Pages: 18, Words: 15089

Funding

Funded by: European Regional Development Fund, doi 10.13039/501100008530;

Award ID: 1.1.1.1/20/A/046

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was funded by the European Regional Development Fund grant No. 1.1.1.1/20/A/046 and the academic career doctoral grant “The role of Mycobacterium tuberculosis drug resistance, multi-strain co-infection and the effectiveness of antimycobacterial therapy in the reactivation of pulmonary tuberculosis” of the European Union Recovery and Resilience Mechanism Plan 5.2 reform and investment direction “Ensuring a change in the management model of higher education institutions” (5.2.1.r reform “Excellence and management reform of higher education and science,” 5.2.1.1.i. investment “Research, development, and consolidation grants,” second round “Consolidation and management change implementation grants”).

Custom metadata

section-at-acceptance Infectious Diseases: Epidemiology and Prevention

Keywords: tuberculosis,wgs,transmission,network,recurrence,reactivation,reinfection

Data availability:

Keywords: tuberculosis, wgs, transmission, network, recurrence, reactivation, reinfection

Unraveling tuberculosis patient cluster transmission chains: integrating WGS-based network with clinical and epidemiological insights

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Abstract

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Methods

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Conclusion

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Point-Of-Care Testing for Tuberculosis

Most cited references 84

ModelFinder: Fast Model Selection for Accurate Phylogenetic Estimates

IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era

UFBoot2: Improving the Ultrafast Bootstrap Approximation

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