Title Application of whole-genome sequencing for distinguishing relapse from reinfection in tuberculosis patients from Lithuania
Authors Vasiliauskaitė, Laima ; Zinola, Alma ; Marco, Federico Di ; Davidavičienė, Valerija Edita ; Nakčerienė, Birutė ; Vaitulionytė, Agnė ; Cirillo, Daniela Maria ; Kačergius, Tomas
DOI 10.1016/j.ijid.2025.108203
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Is Part of International journal of infectious diseases.. Amsterdam : Elsevier BV. 2026, vol. 162, art. no. 108203, p. [1-7].. ISSN 1201-9712. eISSN 1878-3511
Keywords [eng] tuberculosis ; recurrence ; relapse ; reinfection ; whole-genome sequencing
Abstract [eng] Objectives Tuberculosis (TB) can reoccur even after successful treatment due to endogenous reactivation or exogenous reinfection. Understanding the aetiology of TB recurrence might prevent further transmission and development of resistance. Therefore, this study aimed to assess the rate of true TB relapses versus reinfection among patients with TB recurrence in Lithuania using whole-genome sequencing (WGS). Methods This study included 62 Mycobacterium tuberculosis complex (MTBC) strains recovered from 29 pulmonary TB patients who had at least one reported TB recurrence or treatment failure episode between 2016 and 2023. To investigate potential sources of transmission in reinfected patients, 4 additional MTBC sequences were included in the analysis. The analysis of WGS results was performed using an in-house bioinformatic pipeline. A cut-off of 5 single nucleotide polymorphisms was used to differentiate between relapse and reinfection. Results Majority (60%) of all recurrent TB cases were caused by true relapse, while reinfections with a different strain accounted for 40%. Moreover, half of the treatment failures were also found to be reinfections. Conclusions The risk of reinfection is underestimated in Lithuania, highlighting the need for rapid changes in diagnostics and infection control strategies to contain the transmission of extensively drug-resistant TB (XDR-TB) strains in Lithuania.
Published Amsterdam : Elsevier BV
Type Journal article
Language English
Publication date 2026
CC license CC license description