Comparison of Whole Genome Sequencing and PCR-based Methods in Identifying Drug-Resistant Tuberculosis Outbreak Clusters
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Abstract
Identifying outbreak clusters of Multidrug-Resistant Tuberculosis (MDR-TB) is a significant challenge in disease control. Widely used PCR-based methods have limitations in strain resolution, which makes it difficult to confirm clonal spread and accurately identify disease hotspots. This study aimed to apply Whole Genome Sequencing (WGS) and compare it with the PCR-based method for analyzing Mycobacterium tuberculosis isolates from 188 MDR-TB patients in Tha Maka District, a high-risk area, to understand transmission dynamics at both the genetic and epidemiological levels.
Results from the PCR-based method categorized patients according to WHO definitions as MDR-TB (167 cases, 88.8%), pre-XDR-TB (8 cases, 4.3%), and XDR-TB (13 cases, 6.9%). In contrast, WGS analysis achieved a much finer differentiation, distinguishing the isolates into 10 distinct sub-lineage clusters, clearly demonstrating its superior accuracy and genetic resolution over conventional methods. The L2.2.M3 strain (Beijing lineage) was the most prevalent (77.7%) and was strongly associated with high levels of drug resistance, being detected in 100% of pre-XDR-TB samples. This highlights the critical role of this lineage in the severity and spread of drug-resistant tuberculosis. Furthermore, geospatial analysis revealed a clear clustering of the L2.2.M3 strain in the Tha Maka districts, strongly indicating clonal spread. Integrating data from WGS and geospatial analysis precisely identified hotspots and high-risk strains, enabling an accurate and reliable explanation of local MDR-TB transmission patterns.
In conclusion, WGS combined with geospatial analysis is a vital tool for monitoring, controlling, and implementing proactive measures in areas highly susceptible to drug-resistant tuberculosis.
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References
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