Spatiotemporal patterns and ecological factors of tuberculosis notification: A spatial panel data analysis in Guangxi, China

Virasakdi

et al. 2020

Preprint

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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.

Section: Introductionmentioning

confidence: 88%

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

Virasakdi

et al. 2020

Preprint

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“…Based on the spatial clustering analysis, three counties with significantly high TB notification were identified as hot spots, and three counties with significantly low notification rate were identified as cold spots [6]. In these six study sites, patients who were suspected to have pulmonary TB were confirmed at the designated hospitals for TB with chest radiography, sputum smear and culture.…”

Section: Study Settingmentioning

confidence: 99%

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

Virasakdi

et al. 2020

Preprint

Self Cite

Abstract Background At present, there are few studies on polymorphism of Mycobacterium tuberculosis (Mtb) gene and how it affects the TB epidemic. Objective 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.

“…Moran's I local spatial autocorrelation statistics, and space-time scan statistics were recruited to detect temporal and spatial clusters of tuberculosis reported incidence in Guangxi from 2010 to 2016 [2]. The spatiotemporal analysis identi ed 3 counties located in the central Guangxi with a signi cant high TBreported incidence cluster (hot spots), and 3 counties located in the eastern Guangxi with a signi cant low TB-reported incidence cluster (cold spots).…”

Section: Subjectsmentioning

confidence: 99%

“…Differences in reported morbidity rates across the province exceed 100/100,000 population. Regions where observed genomic variations in Mycobacterium tuberculosis (M. tb) are distributed uniformly (cold spots) or localized to some region (hot spots)have been previously reported in Guangxi [2]. Despite the reported correlation between TB incidence and environmental factors such as sunshine duration, GDP per capita and health insurance coverage, the correlation was relatively weak.…”

Section: Introductionmentioning

confidence: 98%

“…Bacille Calmette-Guérin (BCG), the only TB vaccine available currently, is derived from mycobacterium bovis (M. bovis) [4]. A comparative analysis indicted that 14 regions of difference (RD [1][2][3][4][5][6][7][8][9][10][11][12][13][14], having indels or frameshift deletions, were absent in the BCG vaccine, compared to M.tb H37Rv (INSDC accession AL123456.3) [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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Interaction analysis of Mycobacterium tuberculosis between host environment and highly mutated genes from population genetic structure comparison

Chang

et al. 2020

Preprint

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Objective We aimed to investigate the genetic and demographic differences and interactions between areas where observed genomic variations in Mycobacterium tuberculosis (M. Tb) are distributed uniformly in cold and hot spots. Methods The cold and hot spot areas were identified using the reported incidence of TB over the previous 5 years. Whole genome sequencing was performed on 291 M. tb isolates between January and June 2018. Analysis of molecular variance (AMOVA) and a multifactor dimensionality reduction (MDR) model was applied to test gene-gene-environment interactions. Adjusted odds ratios (OR) and 95% confidence intervals (CI) were computed to test the extent to which genetic mutation affects the TB epidemic using a multivariate logistic regression model. Results The percentage of the Beijing family strain in hot spots was significantly higher than that in cold spots (64.63% vs 50.69%, p = 0.022), among elderly, people with a low BMI and those having a history of contact with a TB patient (all p < 0.05). Individuals from cold spot areas had a higher frequency of out-of-town travelling (p < 0.05). The mutation of Rv1186c, Rv3900c, Rv1508c, Rv0210 and a Intergenic Region (SNP site: 3847237) showed a significant difference between cold and hot spots. (p < 0.001). The MDR model displayed a clear negative interaction effect of age groups with BMI (interaction entropy: -3.55%) and mutation of Rv0210 (interaction entropy: -2.39%). Through the mutations of Rv0210 and BMI had a low independent effect (interaction entropy: -1.46%). Conclusion Our data suggests a statistical significant role of age, BMI and the polymorphisms of Rv0210 genes in the transmission and development of M. tb. The results provide clues for the study of susceptibility genes of M. tb in different population.