Use of High-Resolution Geospatial and Genomic Data to Characterize Recent Tuberculosis Transmission, Botswana

Abstract

Combining genomic and geospatial data can be useful for understanding Mycobacterium tuberculosis transmission in high-burden tuberculosis (TB) settings. We performed whole-genome sequencing on M. tuberculosis DNA extracted from sputum cultures from a population-based TB study conducted in Gaborone, Botswana, during 2012-2016. We determined spatial distribution of cases on the basis of shared genotypes among isolates. We considered clusters of isolates with ≤5 single-nucleotide polymorphisms identified by whole-genome sequencing to indicate recent transmission and clusters of ≥10 persons to be outbreaks. We obtained both molecular and geospatial data for 946/1,449 (65%) participants with culture-confirmed TB; 62 persons belonged to 5 outbreaks of 10-19 persons each. We detected geospatial clustering in just 2 of those 5 outbreaks, suggesting heterogeneous spatial patterns. Our findings indicate that targeted interventions applied in smaller geographic areas of high-burden TB identified using integrated genomic and geospatial data might help interrupt TB transmission during outbreaks.

Keywords: Botswana; bacteria; geographic heterogeneity; infectious disease control; outbreaks; respiratory infections; spatial analysis; tuberculosis and other mycobacteria; whole-genome sequencing.

Figure 1

Phylogenetic tree representation for Mycobacterium tuberculosis lineage 4 for selected genotypic cluster groups (≤5 single-nucleotide polymorphisms) in study of high-resolution geospatial and genomic data to characterize recent tuberculosis transmission, Gaborone, Botswana, 2012–2016. Colors indicate the location of isolates in each genotypic cluster group. Branches within each of the groups are expanded for visualization.

Figure 2

Kernel density map, median center point, and directional distribution for genotypic groups A–E (≤5 single-nucleotide polymorphisms) (panels A–E) and genotypically ungrouped Mycobacterium tuberculosis strains (F) in study of high-resolution geospatial and genomic data to characterize recent tuberculosis transmission, Gaborone, Botswana, 2012–2016. The blue ovals encompass the area within the SD ellipse, representing the geographic distance and directional orientation of participant locations within each group. Density is shown on a different scale (up to 35 cases/km²) for ungrouped participants than for participants in the genotypic cluster groups (up to 5 cases/km²) because of differences in size of the datasets.

Figure 3

Median center points for Mycobacterium tuberculosis genotypic groups A–E (≤5 single-nucleotide polymorphisms) and genotypically ungrouped strains in study of high-resolution geospatial and genomic data to characterize recent tuberculosis transmission, Gaborone, Botswana, 2012–2016. The median center represents a centralized geographic location that is estimated by minimizing the distance to all other participant locations being analyzed.

Figure 4

K-function differences for Mycobacterium tuberculosis genotypic groups A–E (≤5 single-nucleotide polymorphisms) compared with ungrouped strains in study of high-resolution geospatial and genomic data to characterize recent tuberculosis transmission, Gaborone, Botswana, 2012–2016. Differences in K-functions were used to assess geospatial clustering among participants in each group relative to participants with ungrouped strains. Observations falling above the upper 95% envelope indicate significant spatial clustering.

Figure 5

Incident tuberculosis by geographic distance from first study participant by genotypic cluster group (≤5 single-nucleotide polymorphisms) in study of high-resolution geospatial and genomic data to characterize recent tuberculosis transmission, Gaborone, Botswana, 2012–2016. Plots represent each participant by date of tuberculosis diagnosis and by geographic distance (based on participant’s primary residence) from the first participant (shown in each plot at a distance of 0 km) in each genotypic cluster group.

Figure 6

Pairwise SNP distances by ≤5 single-nucleotide polymorphism (SNP) genotypic cluster group in study of high-resolution geospatial and genomic data to characterize recent tuberculosis transmission, Gaborone, Botswana, 2012–2016. Box plots with individual data points superimposed display SNP distance summaries by group. Median within-group SNP distance was <5 SNPs for all groups except group A, which had a median of 7 SNPs. Horizontal lines within boxes indicate medians; box tops and bottoms indicate interquartile ranges; error bars indicate 95% CIs.

Figure 7

Correlation between pairwise single-nucleotide polymorphisms (SNP) distance and pairwise geographic distance for genotypic cluster groups ≤5 SNP (A–E) and ungrouped cases (F) in study of high-resolution geospatial and genomic data to characterize recent tuberculosis transmission, Gaborone, Botswana, 2012–2016. Points represent measurements for within-group pairs. There was low positive correlation between pairwise geographic and SNP distances overall (Spearman ρ = 0.1; p = 0.06). SNP, single-nucleotide polymorphism.

Figure 8

Representation of phylogenetic trees for Mycobacterium tuberculosis genotypic cluster groups A–E (≤5 single-nucleotide polymorphisms) projected onto geographic maps in study of high-resolution geospatial and genomic data to characterize recent tuberculosis transmission, Gaborone, Botswana, 2012–2016. The location of each M. tuberculosis isolate in the tree is displayed with a link drawn to its corresponding geographic location. Tree tips on the same bifurcating branches represent the most closely related isolates.