The geno-spatio analysis of Mycobacterium tuberculosis complex in hot and cold spots of Guangxi, China

Abstract

Background: At present, there are few studies on polymorphism of Mycobacterium tuberculosis (Mtb) gene and how it affects the TB epidemic. This study aimed to document the differences of polymorphisms between tuberculosis hot and cold spot areas of Guangxi Zhuang Autonomous Region, China. Methods: The cold and hot spot areas, each with 3 counties, had been pre-identified by TB incidence for 5 years from the surveillance database. Whole genome sequencing analysis was performed on all sputum Mtb isolates from the detected cases during January and June 2018. Single nucleotide polymorphism (SNP) of each isolate compared to the H37Rv strain were called and used for lineage and sub-lineage identification. Pairwise SNP differences between every pair of isolates were computed. Analyses of Molecular Variance (AMOVA) across counties of the same hot or cold spot area and between the two areas were performed. Results: As a whole, 59.8% (57.7% sub-lineage 2.2 and 2.1% sub-lineage 2.1) and 39.8% (17.8% sub-lineage 4.4, 6.5% sub-lineage 4.2 and 15.5% sub-lineage 4.5) of the Mtb strains were Lineage 2 and Lineage 4 respectively. The percentages of sub-lineage 2.2 (Beijing family strains) are significantly higher in hot spots. Through the MDS dimension reduction, the genomic population structure in the three hot spot counties is significantly different from those three cold spot counties (T-test p = 0.05). The median of SNPs distances among Mtb isolates in cold spots was greater than that in hot spots (897 vs 746, Rank-sum test p < 0.001). Three genomic clusters, each with genomic distance ≤12 SNPs, were identified with 2, 3 and 4 consanguineous strains. Two clusters were from hot spots and one was from cold spots. Conclusion: Narrower genotype diversity in the hot area may indicate higher transmissibility of the Mtb strains in the area compared to those in the cold spot area.

Spatial analysis of COVID-19 spread in Iran: Insights into geographical and structural transmission determinants at a province level

“…Given the role of climate and socio-economic factors in determining the distribution of cases and its impact world-wide, we also aimed to incorporate said factors as predictors of SARS-CoV-2 spread [9,10]. This could aid to understand the burden of COVID-19, its distribution in the country, and its implication on public health within Iran and similar countries [11] and could contribute to public health measures by providing insight to inform the implementation of interventions or to understand socio-demographic factors associated with the SARS-CoV-2 spread and COVID-19 heterogeneity as it has been applied to previous infectious diseases [12][13][14][15][16].…”

Genomic characteristics of Mycobacterium tuberculosis isolates of cutaneous tuberculosis

“…Therefore, we consider that the clinical diversity of CTB has little relationship with the genetic changes in MTB. The lower consistency of CTB isolates is probably associated with the sporadic incidence of CTB (Lin et al, 2020 ).…”

Whole Genome Sequencing Links Mycobacterium bovis From Cattle, Cheese and Humans in Baja California, Mexico

“…A cut-off value for pairwise SNP distances between isolates of 12 SNP has been widely used for M. tuberculosis transmission studies (39)(40)(41). Here, a cut-off value of 10 SNP was used based on patterns observed in the data by the careful visual inspection of the SNP matrices (Excel tables) obtained from the vSNP pipeline.…”