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Nature Communications logoLink to Nature Communications
. 2023 Jan 19;14:303. doi: 10.1038/s41467-023-35813-9

Phylogeography and transmission of Mycobacterium tuberculosis spanning prisons and surrounding communities in Paraguay

Gladys Estigarribia Sanabria 1,✉,#, Guillermo Sequera 2,3,#, Sarita Aguirre 3, Julieta Méndez 1, Paulo César Pereira dos Santos 4, Natalie Weiler Gustafson 5, Margarita Godoy 5, Analía Ortiz 1, Cynthia Cespedes 3, Gloria Martínez 1, Alberto L García-Basteiro 2,6,7, Jason R Andrews 8, Julio Croda 9,10,11, Katharine S Walter 12,✉
PMCID: PMC9849832  PMID: 36658111

Abstract

Recent rises in incident tuberculosis (TB) cases in Paraguay and the increasing concentration of TB within prisons highlight the urgency of targeting strategies to interrupt transmission and prevent new infections. However, whether specific cities or carceral institutions play a disproportionate role in transmission remains unknown. We conducted prospective genomic surveillance, sequencing 471 Mycobacterium tuberculosis complex genomes, from inside and outside prisons in Paraguay’s two largest urban areas, Asunción and Ciudad del Este, from 2016 to 2021. We found genomic evidence of frequent recent transmission within prisons and transmission linkages spanning prisons and surrounding populations. We identified a signal of frequent M. tuberculosis spread between urban areas and marked recent population size expansion of the three largest genomic transmission clusters. Together, our findings highlight the urgency of strengthening TB control programs to reduce transmission risk within prisons in Paraguay, where incidence was 70 times that outside prisons in 2021.

Subject terms: Tuberculosis, Molecular evolution, Phylogenetics

To role that carceral institutions play in Mycobacterium tuberculosis transmission remains somewhat unknown. Authors perform a prospective genomic surveillance study, to assess transmission dynamics in prisons and surrounding communities in Paraguay.

Introduction

Despite significant tuberculosis (TB) control efforts, the incidence rate of TB has declined only slowly in the World Health Organization Region of the Americas, and, alarmingly, has stagnated since 20141. The COVID-19 pandemic disrupted access to healthcare—including critical TB diagnostic and treatment programs—compounding the burden of TB and reversing decades of progress in TB control1.

New approaches to limit transmission are urgently needed in Paraguay, where TB control is chronically underfunded1 and where TB incidence was 48 (41–56) per 100,000 people in 2020, higher than the mean incidence rate across the region1. More than a quarter of the country’s population lives below the national poverty line2 and are at heightened risk of TB infection and mortality. Further, recent dramatic increases in incarceration3,4 put a rapidly growing population at high risk of infection and disease5–7. To guide interventions in Paraguay, there is a critical need to identify the populations at greatest risk of infection and locations and institutions where transmission most frequently occurs8.

Whole genome sequencing of the Mycobacterium tuberculosis complex has been powerfully applied to characterize recent transmission dynamics. Genomic approaches have dated introductions of M. tuberculosis and reconstructed patterns of historic spread across Central and South America9,10, estimated the contribution of recent transmission to incident TB cases11, reconstructed the emergence of resistance-associated mutations12, and inferred likely individual-level transmission events13. In Brazil14 and Georgia13, for example, genomic approaches identified frequent transmission within prisons as well as evidence of spillover from prisons to surrounding communities. A single M. tuberculosis molecular study from Paraguay15 on strains collected in 2003 reported that M. tuberculosis families found across South America, including the Latin-American (LAM; sub-lineage 4.3) and Haarlem (4.1.2.1) were also common in Paraguay15,16.

Genomic approaches have not been applied to address major gaps in our understanding of TB transmission in Paraguay. First, the conditions of incarceration put people at high risk of many infectious diseases, and globally, over the past twenty years, the incarcerated population in Central and South America has grown by 206%, the greatest increase in the world4. Escalating incarceration rates have been paralleled by an increasing concentration of notified TB among incarcerated individuals6. Yet the role of prison environments on TB transmission both inside and beyond prisons, as sources of broader infection, has not yet been described in Paraguay. Second, while incidence of TB is heterogeneous across the country15, it remains unknown whether specific cities or regions function as hotspots, fueling transmission elsewhere. Finally, due to limited surveillance infrastructure, the prevalence of drug-resistance and multi-drug-resistance has not yet been systematically measured17–19. Only 56% of bacteriologically confirmed new cases of pulmonary TB were tested for rifampicin resistance in 20201.

To characterize transmission dynamics and circulating diversity of M. tuberculosis complex strains in Paraguay, we conducted prospective genomic surveillance across the country from 2016 to 2021, including surveillance within and outside prisons, generating a genomic resource for continued surveillance in Paraguay. We estimated the role of likely recent M. tuberculosis transmission within prisons, the relatedness of prison and community transmission, and the frequent movement of M. tuberculosis between Paraguay’s urban centers.

Results

Population-based genomic surveillance

From 2016 to 2021, 16,734 TB cases were notified in Paraguay, with the majority of cases (60%; 10,095/16,734) occurring in the urban departments Central and Distrito Capital (which together comprise Asunción) and Alto Paraná (Ciudad del Este), where we conducted prospective genomic surveillance (Fig. 1a, Fig. S1). In 2021, the TB notification rate was 70 times higher in prisons than outside (3378 cases per 100,000 in prisons/49 cases per 100,000 in the general population) (Fig. 1b). Therefore, we focused genomic surveillance in the two largest prisons in the country, Tacumbú Prison and the Prison of Ciudad del Este, which together hold 36% (4950/13,821) of Paraguay’s incarcerated population, notification rates are 2000 and 3500 per 100,000 people, respectively.

Fig. 1. Genomic surveillance within and outside prisons in Paraguay’s urban centers.

Fig. 1

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a Map of South America, with Paraguay highlighted, and as an inset. Paraguay’s departments are colored by population size density and points indicate the two largest urban centers in Paraguay, where we conducted focused genomic surveillance. b Notification rate of TB per 100,000 people in prisons (red) and in the general population (blue) from 2009 to 2020. Source data are provided as a Source Data file.

Of the 7780 TB cases notified in Asunción during the study period, 781 (10%) occurred among incarcerated individuals (Fig. S1). 64% (503/781) of these were culture-positive and, of these, we sequenced 21% (107/503). 33% (2306/6999) of non-incarcerated individuals with TB in Asunción were culture-positive and of these, we sequenced 7% (172/2,306). Of the 2,315 TB cases notified in Ciudad del Este during the study period, 422 (18%) occurred among incarcerated individuals (Fig. S1). 64% (269/422) of these were culture-positive and, of these, we sequenced 20% (55/269). 31% (578/1893) of non-incarcerated individuals with TB in Ciudad del Este were culture-positive and of these, we sequenced 27% (158/578) (Fig. S1).

Whole genome sequences (WGS) for a total of 532 isolates met our coverage and quality criteria (Methods), including 488 from unique TB notifications. Of the samples passing filters, 158 were from individuals diagnosed with TB while in prison and 330 were from people diagnosed in the community. TB isolates were collected in Asunción (274/488) and in Ciudad del Este (214/488). We excluded 17 isolates with evidence of mixed infection with more than one sub-lineage detected, resulting in 471 M. tuberculosis isolates for following analyses.

Genotypic resistance

The majority, 96% (454/471) of sampled M. tuberculosis, were drug-sensitive; 3% (15/471) were resistant to at least one drug; and 0.42% (2/471) were multi-drug resistant, resistant to both isoniazid and rifampin. Resistance was not associated with sub-lineage (X2 (11) = 7.7, p = 0.74). We identified three unique isoniazid resistance-conferring mutations on the genes fabG1, katG, or both among the 10 isolates with any isoniazid resistance; the three rifampicin-conferring mutations in rpoB (two on multi-drug resistant isolates) were unique (Fig. 2).

Fig. 2. M. tuberculosis isolates from incarcerated and non-incarcerated people are closely related across Paraguay.

Fig. 2

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A maximum likelihood phylogeny of 471 tuberculosis isolates from Lineage 4 inferred from a multiple sequence alignment of 9966 SNPs and rooted on three sub-lineage 4.8 samples from this study. Branch lengths are in units of substitutions per site. Branches are colored by sub-lineage. From the inside, rings are colored by antimicrobial resistance; city of sampling; and incarceration status at the time of TB notification. Other isoniazid includes isoniazid mono-resistant isolates without an ahpC promoter mutation. Rifampicin indicates rifampicin mono-resistant isolates. Other resistance includes isolates with mutations associated with resistance to pyrazinamide, streptomycin, or fluoroquinolones and not isoniazid or rifampicin. Source data are provided as a Source Data file.

Stable genomic diversity of M. tuberculosis in Paraguay

After excluding mixed infections, all M. tuberculosis isolates were strains from M. tuberculosis lineage 4. A single mixed lineage infection was co-infected with strains from both lineages 1 and 4. Samples predominantly fell into four sub-lineages: 4.3.3/LAM (42.5%; 200/471), 4.1.2 /Haarlem (38.2%; 180/471), 4.4.1/S (12.3%; 58/471), and 4.3.4/LAM (3.2%; 15/471) (Fig. 2). The distribution of strains representing these sublineages was stable and did not change significantly from a collection of 173 M. tuberculosis isolates collected in 200315 (Fig. S1).

Recent expansion of M. tuberculosis transmission clusters

We next explored evidence of recent M. tuberculosis transmission in Paraguay. As seen in a maximum likelihood phylogeny (Fig. 2), sampled M. tuberculosis diversity was dominated by several highly related clones. Seventy-eight percent (369/471) of all isolates fell within 26 genomic clusters (each including 2 to 159 isolates) defined by a 12-SNP threshold20, suggesting TB notifications were often attributable to recent transmission.

We reconstructed population size dynamics of the three largest genomic clusters—which comprised 56% (264/471) of our sample—with a Bayesian coalescent population size model. The three largest genomic clusters (including 159, 91, and 15 samples) increased in effective population size by 200, 90, and 40-fold, respectively. Cluster growth was relatively recent, with cluster most recent common ancestors (MRCA) occurring in 1998 (95% HDI: 1994–2001), 1996 (95% HDI: 1991–2000), and 1998 (95% HDI: 1992–2003) respectively, to 2021, when the most recent samples were collected (Fig. 3). All three clusters included isolates from individuals notified with TB during incarceration and individuals with no incarceration history.

Fig. 3. Genomic transmission clusters spanning prisons and neighboring communities have recently expanded.

Fig. 3

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a Effective population size (Ne) estimates for the three largest genomic clusters in our sample over time. Black lines indicate Ne inferred in a Bayesian Skyline Coalescent model and grey shading indicates 95% high posterior density estimates. b Median clade credibility trees inferred from the Bayesian Skyline Coalescent model. Branch lengths are in years and grey bars indicate 95% high posterior density estimates of node date. The heatmap to the right of the phylogeny indicates patient incarceration status at the time of TB notification. Source data are provided as a Source Data file.

We found no evidence that genomic loci were associated with successful genomic clusters, which we defined as the clusters containing more than 15 M. tuberculosis isolates, using a bacterial GWAS approach which controls for clonality and strong population structure21. We similarly found no association between genomic loci and clustered phenotype when we examined membership in a cluster with 10 or more isolates or 2 or more isolates.

M. tuberculosis genomic clusters span prisons and the general population

In a maximum likelihood phylogeny (Fig. 2), M. tuberculosis isolates sampled from incarcerated and non-incarcerated people are distributed across the tree and did not form distinct clades, indicating a recent shared evolutionary history of isolates sampled from prisons and the community. However, sub-lineage was associated with incarceration status (χ2(22) = 52.3, ρ < 0.001), with strains from sub-lineage 4.1.2.1 more frequently infecting people with a history of incarceration (46.1%; 83/180) compared to individuals with no incarceration history (33.0%; 96/291; p = 0.006).

Isolates from incarcerated people were more frequently clustered (92.6%, 138/149), than those from formerly incarcerated (71.0%, 22/31, χ2(1) = 10.1, ρ = 0.001) or never incarcerated people (71.8%, 209/291; χ2(1) = 24.3, p < 0.001), likely reflecting more recent transmission within prisons. With a stricter threshold of 5 SNPs, 45.4% (214/471) of all isolates in genomic transmission clusters. With this threshold, isolates from incarcerated individuals were again more frequently clustered (58.3%; 87/149) than those from those formerly incarcerated (45.2%; 14/31), though not significantly so (χ2(1) = 1.3, ρ = 0.25), and isolates from incarcerated individuals were more frequently clustered than those from never incarcerated individuals (38.8%; 113/291; χ2(1) = 24.3, ρ < 0.001).

We predicted that if prison and community-associated epidemics were distinct, isolates from the community would be most closely related to and cluster with other isolates from the community and vice versa. Approximately half (48.0%; 12/25) of genomic clusters, including people with no incarceration history also included individuals with a recent history of incarceration. The consequence is that 85.2% (178/209) of individuals with evidence of recent transmission and no recent incarceration were within transmission clusters, including individuals with prior incarceration.

We additionally quantified M. tuberculosis recent transmission with time-scaled haplotype diversity, a measure of the centrality of a single tip isolate to all other isolates on the tree22. Individuals who were incarcerated at the time of TB notification had a higher time-scaled haplotype index for a short epidemic timescale (median: 0.59, IQR: 0.24–0.72) than did formerly (median: 0.18, IQR: −0.37–0.71; t(36) = 1.7, p = 0.03) or never incarcerated individuals (median: 0.20, IQR: −0.71–0.66; t(360) = 5.9, p < 0.001) (Fig. S3). This finding was consistent across epidemic timescales considered (Fig. S3). After adjusting for population structure, we found that incarceration status was significantly associated with time-scaled haplotype diversity (one-way ANOVA: F(285) = 85, p < 0.001), evidence that the association was independent of TB lineage.

Geographic structure despite frequent migration across M. tuberculosis sub-lineages

We found pattern of moderate geographic structure in sampled M. tuberculosis (Fig. 4), with strains from sub-lineage 4.1.2.1 dominant in Asunción (54.1%, 142/262 samples) and strains from sub-lineage 4.3.3 dominant in Ciudad del Este (60.8%, 127/209) (χ2(2) = 72, ρ < 0.001) (Fig. 4). While we observed geographically distinct patterns of M. tuberculosis diversity in Asunción and Ciudad del Este, reconstruction of the ancestral locations for the three most prevalent sub-lineages revealed frequent movement of M. tuberculosis (Fig. 4).

Fig. 4. Frequent gene flow of M. tuberculosis connects Paraguay’s major urban centers.

Fig. 4

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a–c We used discrete ancestral state reconstruction to reconstruct migration between the two cities for the three dominant sub-lineages in our sample; lineages 4.1.2.1, 4.3.3, and 4.4.1.1. Bayesian maximum clade credibility trees of samples in the three dominant sub-lineages with tip points colored by city of sampling and pie charts at nodes indicating the inferred ancestral location. Branch lengths are in years and grey bars indicate 95% high posterior density estimates of node date. A model of asymmetric rates of movement between the two cities was supported for sub-lineages 4.1.2.1 a model of symmetric rates of movement was supported for 4.3.3 and 4.4.1.1. d Map of Paraguay with pie charts indicating the genomic diversity sampled in Asunción (at Paraguay’s western border) and Ciudad del Este (eastern border). Arrows are colored by sub-lineage and are weighted by the relative rate of migration between cities. Bi-directional arrows indicate equal rates of migration in each direction. Source data are provided as a Source Data file.

To test whether Asunción and Ciudad del Este served as sources for M. tuberculosis, exporting infection elsewhere, we compared rates of arrival and export of each sub-lineage. Sub-lineage 4.1.2.1 moved more frequently Asunción to Ciudad del Este (mean: 75 transitions) compared to vice versa (mean: 70 transitions), and a model for asymmetric rates was supported (χ2(2) = 4.1, ρ = 0.04) (Fig. 4). Both sub-lineages 4.4.1.1 (with the prevalent ahpC mutation) (χ2(2) = 0.16, ρ = 0.69) and 4.3.3 (χ2(2) = 0.56, ρ = 0.46) had similar rates of migration to and from Ciudad del Este to Asunción. Despite the geographic structure observed, there was not a sufficient signal to infer a likely geographic source for any of the dominant sub-lineages.

Emergence of a putative resistance-associated ahpC promoter mutation

Eleven percent of samples (50/471) shared a mutation in ahpC promoter (G > A, 74 bases upstream of the 5’ start codon), previously considered a location for compensatory mutations co-occurring with katG mutations in isoniazid-resistant isolates23,24. While ahpC promoter mutations are not included as an independent resistance-conferring mutation in the WHO resistance catalogue23, in our collection, ahpC mutations occurred on otherwise susceptible genomic background within sub-lineage 4.4.1.1. The ahpC mutation occurred in a monophyletic clade of 49 samples in sublineage 4.4.1.1 (Fig. 5), which shared a most recent common ancestor in 1903 (95% HDI: 1888–1916), likely reflecting a single emergence event. Among the basal group of nine samples without a fixed ahpC promoter locus (ahpC−74) mutation, one sample was polymorphic, with 16% (13/79) of reads representing the ahpC mutation. Among the samples sharing the ahpC mutation, a single isolate had a co-occurring rifampicin resistance-conferring mutation in rpoB (His445Leu) (Fig. 5).

Fig. 5. Emergence of a putative resistance-associated ahpC promoter mutation.

Fig. 5

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Time-scaled Bayesian maximum clade credibility tree for 58 samples in sublineage 4.4.1.1. Branch lengths are in years and grey bars indicate 95% high posterior density estimates of node date. The heatmap to the right of the phylogeny indicates patient incarceration status at the time of TB notification, ahpC mutation frequency within an individual’s infection, and the occurrence of a rifampicin resistance-conferring mutation (rpoB His445Leu). Source data are provided as a Source Data file.

We tested whether the success of the ahpC mutation in the absence of a katG mutation (i.e. outside of a compensatory context) we observed in Paraguay could be explained by an increase in associated transmissibility. The ahpC mutation was not associated with an increased time-scaled haplotype density (ahpC mutants, median 0.19, IQR: 0.09–0.22; ahpC non-mutants, median: 0.56, IQR: −0.44–0.71, p = 0.93). Further, individuals with an incarceration history (currently or formerly incarcerated) were no more likely to be infected with a M. tuberculosis isolate with the ahpC mutation than were individuals with no incarceration history (χ2(1) = 0.25, ρ = 0.62).

Discussion

We generated, to our knowledge, the first genomic portrait of circulating M. tuberculosis diversity and transmission dynamics to directly inform Paraguay’s national TB control program priorities. We found the majority of TB cases included in our study were likely attributable to recent transmission and identified three dominant clones, which dramatically expanded over the past twenty years and spanned prisons and surrounding communities. Overall, we found a pattern of close genomic relatedness between M. tuberculosis sampled within and outside prisons. While M. tuberculosis is geographically structured in Paraguay, we identified a signal of continuous movement of M. tuberculosis between Paraguay’s major urban centers.

We found that most sampled infections were likely attributable to recent transmission rather than long-distance migration or activation of latent disease, similar to what has been reported in other medium-incidence countries14. Consistent with expectations that clustering rates may correlate with incidence, when applying a 5-SNP threshold, we found that isolates from Paraguay were more frequently clustered (45%) than those from a low-incidence setting in Spain (23%) and less frequently clustered than in a high-incidence setting in Mozambique (58%). Interestingly, we found a higher rates of clustering compared to what was reported in Malawi (36%), a high-incidence setting11. This could reflect the shorter, one year sampling timeframe of the Malawi study11, resulting in different genomic sampling rates, the use of different genomic sequencing pipelines, or true differences in transmission in the sampled population.

Paraguay’s incarceration rate has dramatically increased, from 60 per 100,000 people in 2000 to 194 per 100,000 in 20203,4,6. More than seventy percent of the incarcerated population are pre-trial detainees, the highest proportion in South America3. The unhealthy conditions of prison environments put people at heightened risk of disease and mortality; this risk translates into an increasing concentration of TB within prisons, with 18% (537/2593) of notified TB cases in Paraguay occurring among incarcerated individuals in 20206. Paraguay’s TB Control Program has worked in prisons since 2004 to provide trainings for healthcare providers and all diagnostic and treatment supplies, including laboratory capacity for microbiological testing in four prisons.

Our findings highlight the critical need to expand and strengthen existing programs to detect and treat TB early and to expand awareness and knowledge of the risks associated with prison environments. Isolates sampled from prisons were more frequently found in genomic transmission clusters and had a higher time-scaled haplotype density than did isolates from outside prisons, phylogenetic evidence that recent transmission was more frequent in prisons than in communities outside prisons. Further, M. tuberculosis sampled from prisons and the community were closely evolutionarily related and the majority of putative transmission clusters, including individuals who were never incarcerated also included people who had a recent incarceration history, indicating that reducing transmission risk within prisons is an urgent public health priority with consequences both within and outside prisons.

While rates of drug resistance were relatively low, we found several phylogenetically unique mutations associated with both isoniazid and rifampicin resistance. These unique mutations could reflect either the de novo acquisition of a resistance mutation or the importation of a resistance mutation from outside Paraguay. Regardless, there is a critical need for expanding drug-susceptibility testing, including both rapid testing for rifampicin resistance in addition to isoniazid monoresistance are critical to ensure patients are put on correct treatment courses and to reduce the risk of further resistance acquisition18,19.

The emergence of an independent ahpC mutation within a single sublineage opens questions about its phenotypic consequences. Previous studies in laboratory strains have reported that ahpC mutations are compensatory in the context of katG isoniazid resistance-conferring mutations, by recovering the bacterium’s ability to detoxify organic peroxides, but did not find measurable isoniazid resistance conferred by independent ahpC mutations24. Genome-wide association studies of clinical M. tuberculosis isolates confirmed the compensatory role of ahpC mutations25. ahpC mutations did not meet the criteria for being included in the 2021 WHO catalogue of resistance-conferring mutations because they were either too rare or had a low positive predictive value for isoniazid resistance as an independent mutation23. However, a study of isoniazid-resistant isolates from Brazil reported that while ahpC mutations often co-occurred with katG mutations, they were also found in the absence of known resistance mutations in katG or inhA26.

A previous genome-wide survival analysis identified lineages and specific mutations associated with pre-resistance, genomic backgrounds that had a heightened likelihood of acquiring resistance-conferring mutations27. Whether ahpC acts as in a similar way, generating a “pre-compensated” genomic background, increasing the likelihood of future katG mutations, remains unknown.

Our study has several limitations. First, while we sequenced all available M. tuberculosis cultures, our final sample size of M. tuberculosis genomes was small relative to the number of notified TB cases in our study departments over the study period. Some locally circulating genotypes may, therefore, not be included in our sample and may lead to an underestimate of the contribution of recent transmission to incident TB. However, we sampled over a moderately long timeframe (five years) and included samples from high-incidence prisons and neighboring communities, providing greater opportunity to recover transmission events. Second, surveillance focused on Paraguay’s urban centers, where the majority of TB notifications occur. Future M. tuberculosis genomic surveillance in the Chaco, western Paraguay, where incidence is three times higher than in eastern Paraguay15, is needed. Additionally, further analysis at the regional-level will be critical for understanding transmission between Paraguay and neighboring countries. Third, we sampled TB infections from prisons at a higher rate than infections outside prisons, potentially biasing upwards estimates of the rate of genomic clustering within prisons compared to outside prisons. Further, we did not have access to more detailed epidemiological data, such as contact information. Future studies integrating genomic data with additional epidemiological data could be used to identify other locations potentially contributing disproportionately to M. tuberculosis transmission in Paraguay.

Finally, we sequenced isolates from cultured sputum, as is routinely done for M. tuberculosis genomic epidemiology, but which limits the within-host diversity recovered from an individual’s infection. Future research is needed to develop sequencing approaches to recover within-host M. tuberculosis variation and incorporate this level of variation into the transmission and ancestral state reconstruction.

Together, our results underscore an urgent need for TB control measures to interrupt ongoing transmission in Paraguay, particularly in high-incidence prison settings, which have an outsized role in broader transmission. Further, the connectivity of Paraguay’s urban centers indicates that TB control needs to be coordinated country-wide.

Methods

Study protocol

This research complies with all ethical regulations. The study was approved by the ethics committee of the Central Laboratory of Public Health of the Ministry of Health and Social Welfare of Paraguay (International Certification FWA N° FWAOOO20088) with code CEI-LCSP 91/010217. Informed consent was obtained (Prospective population surveillance).

Inclusion and ethics

This study was designed and led by a team of researchers in Paraguay (GES, GS, SA) and Brazil (JC). The research seeks to characterize M. tuberculosis transmission in order to directly inform priorities of the National Program for Tuberculosis Control (NPTC) of Paraguay (SA, GS) and, more broadly, the Paraguayan Ministry of Health (GS). This work comprises part of the dissertation research of GES and GS. The research was approved by a local ethics review committee and involved minimal risk to study participants.

Prospective population surveillance

We conducted population-based genomic surveillance in three of Paraguay’s departments: Central, Distrito Capital, and Alto Paraná, which together comprise approximately half (3,392,429 people) of the country’s 2021 population of 7.4 million. Sputum samples are routinely collected from all individuals presenting with symptoms of TB at primary health clinics and sent to the National Program for Tuberculosis Control (NPTC) of Paraguay reference laboratory for microbial diagnostics including culture and smear microscopy. We sequenced all available culture-positive isolates from these three departments.

Study recruitment was done by study staff who visited patients at home and in prisons at the time patients began treatment (Directly Observed Therapy, DOT). At this time, the standard National TB Control Program questionnaire was conducted and patients who chose to enroll provided written consent for sequencing residual mycobacterial cultures for culture-positive samples. Study staff also collected additional demographic, clinical, residential, and epidemiolocal data, including information on history of prior or current incarceration with a structured questionnaire.

Sex was not considered in study design; it was determined through self-report. Of the 488 individual participants, 394 were men and 94 were women, reflecting that recruitment focused on prisons. In 2022, the incarcerated population in Paraguay was 95% male3. Median age of study participants was 31 (IQR: 24–44).

Laboratory and sequencing methods

Sputum samples were cultured in the Ogawa-Kudoh Method28,29. Cultures were incubated at 37 °C and observed for growth twice a week for 60 days. M. tuberculosis DNA was extracted using Cetyltrimethyl ammonium bromide (CTAB) method30.

Sequencing was conducted at the Laboratorio Central de Salud Pública (LCSP), Paraguay Ministry of Health; Centro para el Desarrollo a la investigación Científica (CEDIC), Paraguay; and the Translational Genomics Research Institute (TGen), Arizona, US. DNA sequencing libraries were prepared with the Illumina DNA Prep library kit and sequenced on an Illumina MiSeq in Paraguay and an Illumina NextSeq (2 × 151-bp), at TGen. Raw sequence reads for samples passing filters are available at the Sequence Read Archive (PRJNA870648).

Variant identification

We identified M. tuberculosis genomic variation from whole genome sequence data with a pipeline available at https://github.com/ksw9/mtb_pipeline (v1)31. We previously conducted a variant identification experiment to compare commonly used mapping and variant calling algorithms in M. tuberculosis genomic epidemiology32. Briefly, we generated 20 independent Illumina readsets (2 × 151 bp) from the M. tuberculosis strain CDC1551 genome in silico, with the next-generation sequence-read simulator ART v. 2.5.833. Measuring performance requires a truth VCF of true variant sites in the query genome with respect to a given reference genome. We generated a truth VCF for the strain CDC1551 query genome with respect to the H37Rv reference genome by pairwise aligning the query genome (strain CDC1551) to H37Rv with MUMmer 3.2.034 (nucmer maxmatch -c 1500). We identified SNP variants from the pairwise alignments using MUMmer show-snps, excluding SNPs with ambiguous mapping and indels (show-snps -CIr). Simulated M. tuberculosis genomic data and truth VCF files indicating variants with respect to the reference genome are available here: https://purl.stanford.edu/mr554nj9219. We compared variants identified with our pipeline with the “truth VCF” to determine sensitivity and precision of our pipeline (Table S1).

We previously found that the combination of the bwa35 mapping algorithm and GATK36,37 variant caller routinely minimizes false positive variant calls with minimal cost to sensitivity as compared to other tool combinations32, in particular, when the PE/PPE genes are excluded. We, therefore, used this combination of tools in our pipeline. We report the performance of our computational pipeline in recovering true variants between the CDC1551 query genome and H37Rv reference genome in Table S1.

Briefly, we trimmed low-quality bases (Phred-scaled base quality <20) and removed adapters with Trim Galore v. 0.6.5 (stringency=3)38. We used CutAdapt v.4.2 to further filter reads (–nextseq-trim=20–minimum-length=20–pair-filter=any)39.To exclude potential contamination, we used Kraken2 to taxonomically classify reads and removed reads that were not assigned to the Mycobacterium genus or that were assigned to a Mycobacterium species other than M. tuberculosis40. We mapped reads with bwa v. 0.7.15 (bwa mem)35 to the H37Rv reference genome (NCBI Accession: NC_000962.3) and removed duplicates with sambamba41. We called variants with GATK 4.1 HaplotypeCaller36, setting sample ploidy to 1, and GenotypeGVCFs, including non-variant sites in output VCF files. We included variant sites with a minimum depth of 10X and a minimum variant quality score 40 and constructed consensus sequences with bcftools consensus42, excluding indels. We excluded SNPs in previously defined repetitive regions (PPE and PE-PGRS genes, phages, insertion sequences and repeats longer than 50 bp)43. We identified sub-lineage and evidence of mixed infection with TBProfiler v.4.2.044,45, which is based on the identification of >1000 lineage-specific SNPs. We additionally used TBProfiler with the TBDB repository (https://github.com/jodyphelan/tbdb) which includes >2,000 resistance-associated mutations44,45 compiled from several sources, including but not limited to the World Health Organization catalogue25,44,45

We do not categorize isolates harboring an independent (ahpC) mutation as drug-resistant in phylogenies because it is not considered independently associated with resistance in the World Health Organization Catalogue23 or other references.

Phylogenetic and Bayesian evolutionary analysis

We constructed full-length consensus FASTA sequences from VCF files, setting missing genotypes to missing, and used SNP-sites to extract a multiple alignment of internal variant sites only46. We used the R package ape to measure pairwise differences between samples (pairwise.deletion=TRUE)47. We selected a best fit substitution model with ModelFinder48, implemented in IQ-TREE multicore version 2.2.049, evaluating all models that included an ascertainment bias correction for the use of an alignment of SNPs only. The best fit model according to Bayesian Information Criterion was K3Pu + F + ASC + R5, a three substitution types model with unequal base frequencies, an ascertainment bias correction, and a FreeRate model of rate heterogeneity across sites, including four categories. We then fit a maximum likelihood tree with IQ-TREE, with 1000 ultrafast bootstrap replicates49,50.

Genomic clustering is often used as a proxy measure of recent M. tuberculosis transmission; isolates that are more closely genetically related are hypothesized to be more likely linked through recent transmission rather than travel-associated importation or re-activation of genetically distinct latent infections.20,51 We applied a commonly used genetic distance thresholds of 12- and 5- or fewer SNPs to identify genomic clusters51–53.

To investigate transmission patterns in the three largest genomic clusters more closely, we fit timed Bayesian trees to multiple sequence alignments with BEAST 2.6.254, using TB notification dates to calibrate tips. Because of the short sampling timeframe of our data, we fixed the substitution rate to 1 × 10−7 mutations/site/year, as previously described55, and consistent with previous estimates for the M. tuberculosis lineage 4 substitution rate56. To examine population dynamics in the three largest clusters, we used a Coalescent Bayesian Skyline model57 with 5 dimensions, allowing the effective population size to change 4 times over the tree. We additionally fit a Bayesian tree to sublineage 4.2.1.1 samples using a constant population size, fixed substitution rate model. Markov chain Monte Carlo chains were run for 200 million iterations, or longer, if required for convergence, excluding 10% of samples as burn-in. We used treeannotator to produce maximum clade credibility trees. We used the R package beautier to construct XML files58 and corrected XML files for the number of constant positions in SNP alignments. We visualized phylogenetic trees with the R package ggtree59,60.

We calculated time-scaled haplotype density from a matrix of pairwise SNP distances with the R package thd as previously described22 and compared time-scaled haplotype density between individuals who were never, formerly, or currently incarcerated with t-tests. We set the M. tuberculosis substitution rate to 1 × 10−7 substitutions per site per year and included an effective genome length of 3,916,441 basepairs (the length of the reference genome minus the PE/PPE regions excluded from variant calling) and used a short (20 year) and long (50 year) epidemic timescale. We compared time-scaled haplotype density by incarceration status with t-tests and used analysis of variance to test for the independent effect of incarceration status after controlling for M. tuberculosis population structure22.

We tested for the association between sub-lineage and city with Chi-square tests. We included isolates within the three largest sub-lineages identified (4.1.2.1, 4.3.3, and 4.4.1.1) to avoid comparison of small sample sizes. We conducted discrete ancestral state reconstruction for sampling location with the R package ape for the three largest sub-lineages in our collection47. We restricted samples to those from Asunción and Ciudad del Este because of the small sample size outside those cities. We compared symmetric and asymmetric rates models fit with the R package diversitree (make.mk2) and compared model fits with analysis of variance61. We used stochastic character mapping62 in the R package diversitree61 to sample 500 location histories for each sublineage tree; we summarized these as the number of average movements between cities over the tree.

To test for genomic loci associated with transmissibility, we conducted a bacterial Genome-Wide Association Study implemented in the R package treewas21. This approach controls for bacterial clonality and population structure by simulating null genomic datasets, in which there is no genotype-phenotype association, to compare with the empirical dataset21. We tested if the binary phenotype of membership in a genomic cluster of size 15 or greater (including the three dominant clusters identified in our study) was associated with genotype. We additionally tested for a genotypic association with membership in a genomic cluster of size 10 or more, or any clustering (membership in a genomic cluster of size 2 or more).

Statistics & reproducibility

We included all M. tuberculosis genomes passing coverage and quality thresholds. We excluded isolates with evidence of mixed lineage infection from the analysis. No statistical method was used to predetermine the sample size.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Supplementary information

Supplementary Information (99.3KB, docx)
Reporting Summary (7.2MB, pdf)

Acknowledgements

The authors thank the following health staff who facilitated the field work laboratory analyses and clinical follow up: Ruth Martinez, Nestor Moreno, Natalia Sosa. We also would like to express our gratitude to Pilar Muñoz, Johana Monteserin, CEDIC, LCSP and the Penitentiary Health Department from the Ministry of Justice for its support and advice for this project. The study was supported by the grant PIN15-705 from National Commission of Science and Technology (CONACYT) of Paraguay (GES and GS) and National Institutes of Health grants R01 AI130058 (JRA) and R01 AI149620 (JRA and JC) and K01AI173385 (KSW).

Source data

Source Data (256.5KB, zip)

Author contributions

All authors contributed extensively to work presented in this paper. G.E.S., G.S., S.A., and J.C. designed the study and sampling protocol and obtained ethical approval. J.M., P.C.P.S., N.W.G., M.G., A.O., C.C., and G.M. enrolled participants, and conducted laboratory work, and database management. A.L.G., J.R.A., and J.C. supervised analysis and study design. G.E.S., G.S., J.R.A., J.C., and K.S.W. analyzed the data and wrote the manuscript. All authors discussed the results and implications and commented on the manuscript at all stages. J.C. and K.S.W. contributed equally to the manuscript.

Peer review

Peer review information

Nature Communications thanks Stefan Niemann and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Data availability

Raw sequence data generated in this study have been deposited in the Sequence Read Archive under accession PRJNA870648. The H37Rv reference genome is available on NCBI under accession NC_000962.3) The phylogenetic trees and results of ancestral state reconstruction and all other information displayed in figures are provided in the Source Data files. Source data are provided with this paper.

Code availability

Our M. tuberculosis variant identification pipeline is available at https://github.com/ksw9/mtb_pipeline31.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Gladys Estigarribia Sanabria, Guillermo Sequera.

Contributor Information

Gladys Estigarribia Sanabria, Email: lalyestigarr@gmail.com.

Katharine S. Walter, Email: katharine.walter@hsc.utah.edu

Supplementary information

The online version contains supplementary material available at 10.1038/s41467-023-35813-9.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Information (99.3KB, docx)
Reporting Summary (7.2MB, pdf)

Data Availability Statement

Raw sequence data generated in this study have been deposited in the Sequence Read Archive under accession PRJNA870648. The H37Rv reference genome is available on NCBI under accession NC_000962.3) The phylogenetic trees and results of ancestral state reconstruction and all other information displayed in figures are provided in the Source Data files. Source data are provided with this paper.

Our M. tuberculosis variant identification pipeline is available at https://github.com/ksw9/mtb_pipeline31.

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