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. 2021 Jun;10(3):e1211.
doi: 10.1002/mbo3.1211.

Assessing a transmission network of Mycobacterium tuberculosis in an African city using single nucleotide polymorphism threshold analysis

Edriss Yassine  1 , Ronald Galiwango  2 , Willy Ssengooba  3   4 , Fred Ashaba  5 , Moses L Joloba  5 , Sarah Zalwango  5 , Christopher C Whalen  2 , Frederick Quinn  1
Affiliations

Assessing a transmission network of Mycobacterium tuberculosis in an African city using single nucleotide polymorphism threshold analysis

Edriss Yassine et al. Microbiologyopen. 2021 Jun.

Abstract

Tuberculosis (TB) is the leading cause of death in humans by a single infectious agent worldwide with approximately two billion humans latently infected with the bacterium Mycobacterium tuberculosis. Currently, the accepted method for controlling the disease is Tuberculosis Directly Observed Treatment Shortcourse (TB-DOTS). This program is not preventative and individuals may transmit disease before diagnosis, thus better understanding of disease transmission is essential. Using whole-genome sequencing and single nucleotide polymorphism analysis, we analyzed genomes of 145 M. tuberculosis clinical isolates from active TB cases from the Rubaga Division of Kampala, Uganda. We established that these isolates grouped into M. tuberculosis complex (MTBC) lineages 1, 2, 3, and 4, with the most isolates grouping into lineage 4. Possible transmission pairs containing ≤12 SNPs were identified in lineages 1, 3, and 4 with the prevailing transmission in lineages 3 and 4. Furthermore, investigating DNA codon changes as a result of specific SNPs in prominent virulence genes including plcA and plcB could indicate potentially important modifications in protein function. Incorporating this analysis with corresponding epidemiological data may provide a blueprint for the integration of public health interventions to decrease TB transmission in a region.

Keywords: Mycobacterium tuberculosis; single nucleotide polymorphism; social network; transmission; tuberculosis.

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Conflict of interest statement

None declared.

Figures

FIGURE 1
FIGURE 1
UPGMA rooted tree of the 113 isolates included in the analysis separated into color‐coded MTBC lineages using the Bionumerics SNP analysis pipeline. Branch numbers indicate the SNP distance between isolates. L1 includes 2 isolates; L2, one isolate; L3, 23 isolates; L4, 87 isolates. UPGMA, unweighted pair group method with arithmetic mean
FIGURE 2
FIGURE 2
UPGMA rooted tree of the 23 isolates grouped into MTBC lineage 3 separated into possible transmission clusters. Branch numbers indicate the SNP distance between isolates. Cluster 1 consists of 6 total isolates, and cluster 2 consists of 3 isolates. White circles are isolates that did not group into clusters. UPGMA, unweighted pair group method with arithmetic mean
FIGURE 3
FIGURE 3
MST of MTBC lineage 3, cluster 1. Numbers between branches indicate SNP distance. Two samples in one circle indicate identical isolates with 0 SNPs. An MST is a subnetwork that shows the strongest connections from a larger set of weighted connections (van Dellen et al., 2018). MSTs are used in epidemiology to delineate the most likely chain of transmission during events such as an outbreak. Here, it is used to infer possible transmission between our isolates. MST, minimum spanning tree
FIGURE 4
FIGURE 4
MST of MTBC lineage 3, cluster 2. Numbers between branches indicate SNP distance. Two samples in one circle indicate identical isolates with 0 SNPs. An MST is a subnetwork that shows the strongest connections from a larger set of weighted connections (van Dellen et al., 2018). MSTs are used in epidemiology to delineate the most likely chain of transmission during events such as an outbreak. Here, it is used to infer possible transmission between our isolates. MST, minimum spanning tree
FIGURE 5
FIGURE 5
UPGMA rooted tree of isolates grouped into MTBC lineage 4 and separated into possible transmission clusters. Branch numbers indicate the SNP distance between isolates. There were 19 clusters of interest identified, each represented by a different color. White circles are isolates that did not group into clusters. Clusters 2 and 9 contained pairs that had >12 SNPs and therefore no transmission pairs. UPGMA, unweighted pair group method with arithmetic mean
FIGURE 6
FIGURE 6
Pairwise SNP matrix visualized as a network colored by number of SNPs: 0–12 SNPs (red), 13–50 SNPs (blue), 51–100 SNPs (green) and >100 SNPs (black). Each node represents an individual isolate. Each node is connected to another if they are associated with each other within the network. Transmission networks were created using R statistical software and data visualization package qgraph. (a) Isolates grouping in MTBC lineage 3 forming a network. (b) Isolates grouping in MTBC lineage 4 forming a network
FIGURE A1
FIGURE A1
MST of MTBC lineage 4, cluster 1. Numbers between branches indicate SNP distance. MST, minimum spanning tree
FIGURE A2
FIGURE A2
MST of MTBC lineage 4, cluster 3. Numbers between branches indicate SNP distance. MST, minimum spanning tree
FIGURE A3
FIGURE A3
MST of MTBC lineage 4, cluster 4. Numbers between branches indicate SNP distance. MST, minimum spanning tree
FIGURE A4
FIGURE A4
MST of MTBC lineage 4, cluster 6. Numbers between branches indicate SNP distance. MST, minimum spanning tree
FIGURE A5
FIGURE A5
MST of MTBC lineage 4, cluster 13. Numbers between branches indicate SNP distance. Two samples in one circle indicate identical isolates with 0 SNPs. MST, minimum spanning tree
FIGURE A6
FIGURE A6
MST of MTBC lineage 4, cluster 15. Numbers between branches indicate SNP distance. MST, minimum spanning tree
FIGURE A7
FIGURE A7
NJT of MTBC lineage 4, cluster 17. Numbers between branches indicate SNP distance. MST, minimum spanning tree; NJT, Neighbor‐Joining Tree
FIGURE A8
FIGURE A8
MST of MTBC lineage 4, cluster 18. Numbers between branches indicate SNP distance. MST, minimum spanning tree
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