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

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.

Exploring Variability in Rifampicin Plasma Exposure and Development of Anti-Tuberculosis Drug-Induced Liver Injury among Patients with Pulmonary Tuberculosis from the Pharmacogenetic Perspective.

Genetic polymorphisms can exert a considerable impact on drug pharmacokinetics (PK) and the development of adverse drug reactions (ADR). However, the effect of genetic polymorphisms on the anti-tuberculosis (anti-TB) drug, and particularly rifampicin (RIF), exposure or anti-TB drug-induced liver injury (DILI) remains uncertain. Here, we evaluated the relationship between single nucleotide polymorphisms (SNPs) detected in the RIF pharmacogenes (AADAC, SLCO1B1, SLCO1B3, ABCB1, and NR1I2) and RIF PK parameters, as well as anti-TB treatment-associated DILI. In total, the study enrolled 46 patients with drug-susceptible pulmonary TB. The RIF plasma concentration was measured using the LC-MS/MS method in the blood samples collected pre-dose and 2 and 6 h post-dose, whilst the DILI status was established using the results from blood biochemical analysis performed before and 10-12 days after treatment onset. The genotyping was conducted using a targeted NGS approach. After adjustment for confounders, the patients carrying the rs3732357 GA/AA genotype of the NR1I2 gene were found to have significantly lower RIF plasma AUC0-6 h in comparison to those with GG genotype, while the difference in RIF plasma Cmax was insignificant. None of the analyzed SNPs was related to DILI. Hence, we are the first to report NR1I2 intronic SNP rs3732357 as the genetic component of variability in RIF exposure. Regarding anti-TB treatment-associated DILI, the other preexisting factors promoting this ADR should be considered.

Genotypic and phenotypic comparison of drug resistance profiles of clinical multidrug-resistant Mycobacterium tuberculosis isolates using whole genome sequencing in Latvia.

BACKGROUND: Multidrug-resistant tuberculosis (MDR-TB) remains a major public health problem in many high tuberculosis (TB) burden countries. Phenotypic drug susceptibility testing (DST) take several weeks or months to result, but line probe assays and Xpert/Rif Ultra assay detect a limited number of resistance conferring gene mutations. Whole genome sequencing (WGS) is an advanced molecular testing method which theoretically can predict the resistance of M. tuberculosis (Mtb) isolates to all anti-TB agents through a single analysis. METHODS: Here, we aimed to identify the level of concordance between the phenotypic and WGS-based genotypic drug susceptibility (DS) patterns of MDR-TB isolates. Overall, data for 12 anti-TB medications were analyzed. RESULTS: In total, 63 MDR-TB Mtb isolates were included in the analysis, representing 27.4% of the total number of MDR-TB cases in Latvia in 2012-2014. Among them, five different sublineages were detected, and 2.2.1 (Beijing group) and 4.3.3 (Latin American-Mediterranean group) were the most abundant. There were 100% agreement between phenotypic and genotypic DS pattern for isoniazid, rifampicin, and linezolid. High concordance rate (> 90%) between phenotypic and genotypic DST results was detected for ofloxacin (93.7%), pyrazinamide (93.7%) and streptomycin (95.4%). Phenotypic and genotypic DS patterns were poorly correlated for ethionamide (agreement 56.4%), ethambutol (85.7%), amikacin (82.5%), capreomycin (81.0%), kanamycin (85.4%), and moxifloxacin (77.8%). For capreomycin, resistance conferring mutations were not identified in several phenotypically resistant isolates, and, in contrary, for ethionamide, ethambutol, amikacin, kanamycin, and moxifloxacin the resistance-related mutations were identified in several phenotypically sensitive isolates. CONCLUSIONS: WGS is a valuable tool for rapid genotypic DST for all anti-TB agents. For isoniazid and rifampicin phenotypic DST potentially can be replaced by genotypic DST based on 100% agreement between the tests. However, discrepant results for other anti-TB agents limit their prescription based solely on WGS data. For clinical decision, at the current level of knowledge, there is a need for combination of genotypic DST with modern, validated phenotypic DST methodologies for those medications which did not showed 100% agreement between the methods.