Deciphering the Tangible Spatio-Temporal Spread of a 25-Year Tuberculosis Outbreak Boosted by Social Determinants

Abstract

Outbreak strains of Mycobacterium tuberculosis are promising candidates as targets in the search for intrinsic determinants of transmissibility, as they are responsible for many cases with sustained transmission; however, the use of low-resolution typing methods and restricted geographical investigations represent flaws in assessing the success of long-lived outbreak strains. We can now address the nature of outbreak strains by combining large genomic data sets and phylodynamic approaches. We retrospectively sequenced the whole genome of representative samples assigned to an outbreak circulating in the Canary Islands (the GC strain) since 1993, which accounts for ~20% of local tuberculosis cases. We selected a panel of specific single nucleotide polymorphism (SNP) markers for an in-silico search for additional outbreak-related sequences within publicly available tuberculosis genomic data. Using this information, we inferred the origin, spread, and epidemiological parameters of the GC strain. Our approach allowed us to accurately trace the historical and more recent dispersion of the GC strain. We provide evidence of a highly successful nature within the Canarian archipelago but limited expansion abroad. Estimation of epidemiological parameters from genomic data disagree with a distinctive biology of the GC strain. With the increasing availability of genomic data allowing for the accurate inference of strain spread and critical epidemiological parameters, we can now revisit the link between Mycobacterium tuberculosis genotypes and transmission, as is routinely carried out for SARS-CoV-2 variants of concern. We demonstrate that social determinants rather than intrinsically higher bacterial transmissibility better explain the success of the GC strain. Importantly, our approach can be used to trace and characterize strains of interest worldwide. IMPORTANCE Infectious disease outbreaks represent a significant problem for public health. Tracing outbreak expansion and understanding the main factors behind emergence and persistence remain critical to effective disease control. Our study allows researchers and public health authorities to use Whole-Genome Sequencing-based methods to trace outbreaks, and shows how available epidemiological information helps to evaluate the factors underpinning outbreak persistence. Taking advantage of all the freely available information placed in public repositories, researchers can accurately establish the expansion of an outbreak beyond original boundaries, and determine the potential risk of a strain to inform health authorities which, in turn, can define target strategies to mitigate expansion and persistence. Finally, we show the need to evaluate strain transmissibility in different geographic contexts to unequivocally associate spread to local or pathogenic factors, an important lesson taken from genomic surveillance of SARS-CoV-2.

Keywords: genomic epidemiology; outbreak; phylodynamics; tuberculosis; whole-genome sequencing.

FIG 1

Workflow detailing the GC outbreak delineation procedure.

FIG 2

GC outbreak characterization and sequence distribution (A) ML tree highlights the outbreak’s circumscription and related strains identified with the partial SNP profile. (B and C) Density graphs of the pairwise number of SNPs between samples of different outbreaks Gran canaria (GC); Buenos Aires (BA) (5); Bern (6); Denmark (1); Thailand (4). (D) Distribution of sequences belonging to the outbreak (squares) and related to GC strain (circles). Colors indicate the source and shapes denote the meeting SNP profile of each sequence included in the study. The map was obtained from the R package rnaturalearth (https://docs.ropensci.org/rnaturalearth/articles/rnaturalearth.html).

FIG 3

Median-joining network analysis. Numbers in parentheses indicate the number of SNPs between nodes. Node size indicates the number of samples with the same genome; node color denotes sampling time; name color indicates different islands (blue: Lanzarote; red: Fuerteventura; green: Tenerife; black: Gran Canaria). Samples from the continent are indicated in italics and bold letters. GC1441 is an additional sample from the index case. Node A resolution with hSNP pos: 3910007 is detailed; reference (ref) and alternative (alt) allele distribution among samples is indicated. Names with asterisks indicate samples of the index case.

FIG 4

Phylodynamic analysis of the GC outbreak. (A) BDSKY results showing reproductive number (R_e) variation across outbreak time period, and recovery rate (δ). Rectangles in the upper section of the graph indicate periods using the same color scale as nodes in Fig. 3. (B) Histogram of the number of cases sequenced in the different time periods.

FIG 5

Dating, phylogeographic, and dispersal analysis. (A) ML global phylogeny of L2 highlighting sublineages. Time of the most recent common ancestor (tMRCA) of L2.2. is indicated. Colored branches correspond to GC strain (red), African-related clade (orange), and African-Vietnamese clade (yellow). (B) tMRCAs and origin of GC strain, closest nodes and oldest Chinese ancestor. Colors indicate the origin of either sample (labels) and the ancestor of different clades. (C and D). The phylogeographic analysis results indicate routes of dispersion since the GC strain’s oldest ancestor. (C) 3D view obtained with Google Earth (www.google.com/earth/). (D) Map obtained using the SPREAD3 software (https://rega.kuleuven.be/cev/ecv/software/SpreaD3_tutorial), lines are colored following the destination of the migration, and times from the oldest origin in China, to Vietnam and the Canary Islands are denoted.