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

ABSTRACT

ABSTRACT We aimed to investigate the genetic and demographic differences and interactions between areas where observed genomic variations in Mycobacterium tuberculosis (M. tb) were distributed uniformly in cold and hot spots.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 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.The percentage of the Beijing family strain in hot spots was significantly higher than that in cold spots (64.63% vs 50.69%, P = .022), among the elderly, people with a low BMI, and those having a history of contact with a TB patient (all P < .05). Individuals from cold spot areas had a higher frequency of out-of-town traveling (P < .05). The mutation of Rv1186c, Rv3900c, Rv1508c, Rv0210, and an Intergenic Region (SNP site 3847237) showed a significant difference between cold and hot spots. (P < .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%).Our data suggests a statistically 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 populations. The characteristic strains showed a local epidemic. Strengthening genotype monitoring of strains in various regions can be used as an early warning signal of epidemic spillover.