Phylogeographic Analysis of Mycobacterium kansasii Isolates from Patients with M. kansasii Lung Disease in Industrialized City, Taiwan.

Little is known about environmental transmission of Mycobacterium kansasii. We retrospectively investigated potential environmental acquisition, primarily water sources, of M. kansasii among 216 patients with pulmonary disease from an industrial city in Taiwan during 2015-2017. We analyzed sputum mycobacterial cultures using whole-genome sequencing and used hierarchical Bayesian spatial network methods to evaluate risk factors for genetic relatedness of M. kansasii strains. The mean age of participants was 67 years; 24.1% had previously had tuberculosis. We found that persons from districts served by 2 water purification plants were at higher risk of being infected with genetically related M. kansasii isolates. The adjusted odds ratios were 1.81 (1.25-2.60) for the Weng Park plant and 1.39 (1.12-1.71) for the Fongshan plant. Those findings unveiled the association between water purification plants and M. kansasii pulmonary disease, highlighting the need for further environmental investigations to evaluate the risk for M. kansasii transmission.

Spatial modeling of Mycobacterium tuberculosis transmission with dyadic genetic relatedness data.

Understanding factors that contribute to the increased likelihood of pathogen transmission between two individuals is important for infection control. However, analyzing measures of pathogen relatedness to estimate these associations is complicated due to correlation arising from the presence of the same individual across multiple dyadic outcomes, potential spatial correlation caused by unmeasured transmission dynamics, and the distinctive distributional characteristics of some of the outcomes. We develop two novel hierarchical Bayesian spatial methods for analyzing dyadic pathogen genetic relatedness data, in the form of patristic distances and transmission probabilities, that simultaneously address each of these complications. Using individual-level spatially correlated random effect parameters, we account for multiple sources of correlation between the outcomes as well as other important features of their distribution. Through simulation, we show the limitations of existing approaches in terms of estimating key associations of interest, and the ability of the new methodology to correct for these issues across datasets with different levels of correlation. All methods are applied to Mycobacterium tuberculosis data from the Republic of Moldova, where we identify previously unknown factors associated with disease transmission and, through analysis of the random effect parameters, key individuals, and areas with increased transmission activity. Model comparisons show the importance of the new methodology in this setting. The methods are implemented in the R package GenePair.

Comparing Mycobacterium tuberculosis transmission reconstruction models from whole genome sequence data.

Genomic epidemiology is routinely used worldwide to interrogate infectious disease dynamics. Multiple computational tools exist that reconstruct transmission networks by coupling genomic data with epidemiological models. Resulting inferences can improve our understanding of pathogen transmission dynamics, and yet the performance of these tools has not been evaluated for tuberculosis (TB), a disease process with complex epidemiology including variable latency and within-host heterogeneity. Here, we performed a systematic comparison of six publicly available transmission reconstruction models, evaluating their accuracy when predicting transmission events in simulated and real-world Mycobacterium tuberculosis outbreaks. We observed variability in the number of transmission links that were predicted with high probability (P ≥ 0.5) and low accuracy of these predictions against known transmission in simulated outbreaks. We also found a low proportion of epidemiologically supported case-contact pairs were identified in our real-world TB clusters. The specificity of all models was high, and a relatively high proportion of the total transmission events predicted by some models were true links, notably with TransPhylo, Outbreaker2, and Phybreak. Our findings may inform the choice of tools in TB transmission analyses and underscore the need for caution when interpreting transmission networks produced using probabilistic approaches.

Cov2clusters: genomic clustering of SARS-CoV-2 sequences.

BACKGROUND: The COVID-19 pandemic remains a global public health concern. Advances in sequencing technologies has allowed for high numbers of SARS-CoV-2 whole genome sequence (WGS) data and rapid sharing of sequences through global repositories to enable almost real-time genomic analysis of the pathogen. WGS data has been used previously to group genetically similar viral pathogens to reveal evidence of transmission, including methods that identify distinct clusters on a phylogenetic tree. Identifying clusters of linked cases can aid in the regional surveillance and management of the disease. In this study, we present a novel method for producing stable genomic clusters of SARS-CoV-2 cases, cov2clusters, and compare the accuracy and stability of our approach to previous methods used for phylogenetic clustering using real-world SARS-CoV-2 sequence data obtained from British Columbia, Canada. RESULTS: We found that cov2clusters produced more stable clusters than previously used phylogenetic clustering methods when adding sequence data through time, mimicking an increase in sequence data through the pandemic. Our method also showed high accuracy when predicting epidemiologically informed clusters from sequence data. CONCLUSIONS: Our new approach allows for the identification of stable clusters of SARS-CoV-2 from WGS data. Producing high-resolution SARS-CoV-2 clusters from sequence data alone can a challenge and, where possible, both genomic and epidemiological data should be used in combination.

Phylogeography and transmission of M. tuberculosis in Moldova: A prospective genomic analysis.

BACKGROUND: The incidence of multidrug-resistant tuberculosis (MDR-TB) remains critically high in countries of the former Soviet Union, where >20% of new cases and >50% of previously treated cases have resistance to rifampin and isoniazid. Transmission of resistant strains, as opposed to resistance selected through inadequate treatment of drug-susceptible tuberculosis (TB), is the main driver of incident MDR-TB in these countries. METHODS AND FINDINGS: We conducted a prospective, genomic analysis of all culture-positive TB cases diagnosed in 2018 and 2019 in the Republic of Moldova. We used phylogenetic methods to identify putative transmission clusters; spatial and demographic data were analyzed to further describe local transmission of Mycobacterium tuberculosis. Of 2,236 participants, 779 (36%) had MDR-TB, of whom 386 (50%) had never been treated previously for TB. Moreover, 92% of multidrug-resistant M. tuberculosis strains belonged to putative transmission clusters. Phylogenetic reconstruction identified 3 large clades that were comprised nearly uniformly of MDR-TB: 2 of these clades were of Beijing lineage, and 1 of Ural lineage, and each had additional distinct clade-specific second-line drug resistance mutations and geographic distributions. Spatial and temporal proximity between pairs of cases within a cluster was associated with greater genomic similarity. Our study lasted for only 2 years, a relatively short duration compared with the natural history of TB, and, thus, the ability to infer the full extent of transmission is limited. CONCLUSIONS: The MDR-TB epidemic in Moldova is associated with the local transmission of multiple M. tuberculosis strains, including distinct clades of highly drug-resistant M. tuberculosis with varying geographic distributions and drug resistance profiles. This study demonstrates the role of comprehensive genomic surveillance for understanding the transmission of M. tuberculosis and highlights the urgency of interventions to interrupt transmission of highly drug-resistant M. tuberculosis.

Using Whole-genome Sequencing to Determine the Timing of Secondary Tuberculosis in British Columbia, Canada.

Combined with epidemiological data, whole-genome sequencing (WGS) can help better resolve individual tuberculosis (TB) transmission events to a degree not possible with traditional genotyping. We combine WGS data with patient-level data to calculate the timing of secondary TB among contacts of people diagnosed with active TB in British Columbia, Canada.

Identifying mixed Mycobacterium tuberculosis infections from whole genome sequence data.

BACKGROUND: Mixed, polyclonal Mycobacterium tuberculosis infection occurs in natural populations. Developing an effective method for detecting such cases is important in measuring the success of treatment and reconstruction of transmission between patients. Using whole genome sequence (WGS) data, we assess two methods for detecting mixed infection: (i) a combination of the number of heterozygous sites and the proportion of heterozygous sites to total SNPs, and (ii) Bayesian model-based clustering of allele frequencies from sequencing reads at heterozygous sites. RESULTS: In silico and in vitro artificially mixed and known pure M. tuberculosis samples were analysed to determine the specificity and sensitivity of each method. We found that both approaches were effective in distinguishing between pure strains and mixed infection where there was relatively high (> 10%) proportion of a minor strain in the mixture. A large dataset of clinical isolates (n = 1963) from the Karonga Prevention Study in Northern Malawi was tested to examine correlations with patient characteristics and outcomes with mixed infection. The frequency of mixed infection in the population was found to be around 10%, with an association with year of diagnosis, but no association with age, sex, HIV status or previous tuberculosis. CONCLUSIONS: Mixed Mycobacterium tuberculosis infection was identified in silico using whole genome sequence data. The methods presented here can be applied to population-wide analyses of tuberculosis to estimate the frequency of mixed infection, and to identify individual cases of mixed infections. These cases are important when considering the evolution and transmission of the disease, and in patient treatment.

Improved calibration of the human mitochondrial clock using ancient genomes.

Reliable estimates of the rate at which DNA accumulates mutations (the substitution rate) are crucial for our understanding of the evolution and past demography of virtually any species. In humans, there are considerable uncertainties around these rates, with substantial variation among recent published estimates. Substitution rates have traditionally been estimated by associating dated events to the root (e.g., the divergence between humans and chimpanzees) or to internal nodes in a phylogenetic tree (e.g., first entry into the Americas). The recent availability of ancient mitochondrial DNA sequences allows for a more direct calibration by assigning the age of the sequenced samples to the tips within the human phylogenetic tree. But studies also vary greatly in the methodology employed and in the sequence panels analyzed, making it difficult to tease apart the causes for the differences between previous estimates. To clarify this issue, we compiled a comprehensive data set of 350 ancient and modern human complete mitochondrial DNA genomes, among which 146 were generated for the purpose of this study and estimated substitution rates using calibrations based both on dated nodes and tips. Our results demonstrate that, for the same data set, estimates based on individual dated tips are far more consistent with each other than those based on nodes and should thus be considered as more reliable.

The recent rapid expansion of multidrug resistant Ural lineage Mycobacterium tuberculosis in Moldova.

The projected trajectory of multidrug resistant tuberculosis (MDR-TB) epidemics depends on the reproductive fitness of circulating strains of MDR M. tuberculosis (Mtb). Previous efforts to characterize the fitness of MDR Mtb have found that Mtb strains of the Beijing sublineage (Lineage 2.2.1) may be more prone to develop resistance and retain fitness in the presence of resistance-conferring mutations than other lineages. Using Mtb genome sequences from all culture-positive cases collected over two years in Moldova, we estimate the fitness of Ural (Lineage 4.2) and Beijing strains, the two lineages in which MDR is concentrated in the country. We estimate that the fitness of MDR Ural strains substantially exceeds that of other susceptible and MDR strains, and we identify several mutations specific to these MDR Ural strains. Our findings suggest that MDR Ural Mtb has been transmitting efficiently in Moldova and poses a substantial risk of spreading further in the region.

Identifying local foci of tuberculosis transmission in Moldova using a spatial multinomial logistic regression model.

BACKGROUND: Multidrug resistant tuberculosis (MDR-TB) represents a major public health concern in the Republic of Moldova, with an estimated 31% of new and 56% of previously treated TB cases having MDR disease in 2022. A recent genomic epidemiology study of incident TB occurring in 2018 and 2019 found that 92% of MDR-TB was the result of transmission. The MDR phenotype was concentrated among two M. tuberculosis (Mtb) lineages: L2.2.1 (Beijing) and L4.2.1 (Ural). METHODS: We developed and applied a hierarchical Bayesian multinominal logistic regression model to Mtb genomic, spatial, and epidemiological data collected from all individuals with diagnosed TB in Moldova in 2018 and 2019 to identify locations in which specific Mtb strains are being transmitted. We then used a logistic regression model to estimate locality-level factors associated with local transmission. FINDINGS: We found differences in the spatial distribution and degree of local concentration of disease due to specific strains of Beijing and Ural lineage Mtb. Foci of transmission for four strains of Beijing lineage Mtb, predominantly of the MDR-TB phenotype, were located in several regions, but largely concentrated in Transnistria. In contrast, transmission of Ural lineage Mtb had less marked patterns of spatial aggregation, with a single strain (also of the MDR phenotype) spatially clustered in southern Transnistria. We found a 30% (95% credible interval 2%-80%) increase in odds of a locality being a transmission cluster for each increase of 100 persons per square kilometer, while higher local tuberculosis incidence and poverty were not associated with a locality being a transmission focus. INTERPRETATION: Our results identified localities where specific Mtb transmission networks were concentrated and quantified the association between locality-level factors and focal transmission. This analysis revealed Transnistria as the primary area where specific Mtb strains (predominantly of the MDR-TB phenotype) were locally transmitted and suggests that targeted intensified case finding in this region may be an attractive policy option. FUNDING: Funding for this work was provided by the National Institute of Allergy and Infectious Diseases at the US National Institutes of Health.

e3SIM: epidemiological-ecological-evolutionary simulation framework for genomic epidemiology.

Infectious disease dynamics are driven by the complex interplay of epidemiological, ecological, and evolutionary processes. Accurately modeling these interactions is crucial for understanding pathogen spread and informing public health strategies. However, existing simulators often fail to capture the dynamic interplay between these processes, resulting in oversimplified models that do not fully reflect real-world complexities in which the pathogen's genetic evolution dynamically influences disease transmission. We introduce the epidemiological-ecological-evolutionary simulator (e3SIM), an open-source framework that concurrently models the transmission dynamics and molecular evolution of pathogens within a host population while integrating environmental factors. Using an agent-based, discrete-generation, forward-in-time approach, e3SIM incorporates compartmental models, host-population contact networks, and quantitative-trait models for pathogens. This integration allows for realistic simulations of disease spread and pathogen evolution. Key features include a modular and scalable design, flexibility in modeling various epidemiological and population-genetic complexities, incorporation of time-varying environmental factors, and a user-friendly graphical interface. We demonstrate e3SIM's capabilities through simulations of realistic outbreak scenarios with SARS-CoV-2 and Mycobacterium tuberculosis, illustrating its flexibility for studying the genomic epidemiology of diverse pathogen types.

Phylogenetic identification of influenza virus candidates for seasonal vaccines.

The seasonal influenza (flu) vaccine is designed to protect against those influenza viruses predicted to circulate during the upcoming flu season, but identifying which viruses are likely to circulate is challenging. We use features from phylogenetic trees reconstructed from hemagglutinin (HA) and neuraminidase (NA) sequences, together with a support vector machine, to predict future circulation. We obtain accuracies of 0.75 to 0.89 (AUC 0.83 to 0.91) over 2016-2020. We explore ways to select potential candidates for a seasonal vaccine and find that the machine learning model has a moderate ability to select strains that are close to future populations. However, consensus sequences among the most recent 3 years also do well at this task. We identify similar candidate strains to those proposed by the World Health Organization, suggesting that this approach can help inform vaccine strain selection.

Genome-Wide Association with Uncertainty in the Genetic Similarity Matrix.

Genome-wide association studies (GWASs) are often confounded by population stratification and structure. Linear mixed models (LMMs) are a powerful class of methods for uncovering genetic effects, while controlling for such confounding. LMMs include random effects for a genetic similarity matrix, and they assume that a true genetic similarity matrix is known. However, uncertainty about the phylogenetic structure of a study population may degrade the quality of LMM results. This may happen in bacterial studies in which the number of samples or loci is small, or in studies with low-quality genotyping. In this study, we develop methods for linear mixed models in which the genetic similarity matrix is unknown and is derived from Markov chain Monte Carlo estimates of the phylogeny. We apply our model to a GWAS of multidrug resistance in tuberculosis, and illustrate our methods on simulated data.

The utility of SARS-CoV-2 genomic data for informative clustering under different epidemiological scenarios and sampling.

OBJECTIVES: Clustering pathogen sequence data is a common practice in epidemiology to gain insights into the genetic diversity and evolutionary relationships among pathogens. We can find groups of cases with a shared transmission history and common origin, as well as identifying transmission hotspots. Motivated by the experience of clustering SARS-CoV-2 cases using whole genome sequence data during the COVID-19 pandemic to aid with public health investigation, we investigated how differences in epidemiology and sampling can influence the composition of clusters that are identified. METHODS: We performed genomic clustering on simulated SARS-CoV-2 outbreaks produced with different transmission rates and levels of genomic diversity, along with varying the proportion of cases sampled. RESULTS: In single outbreaks with a low transmission rate, decreasing the sampling fraction resulted in multiple, separate clusters being identified where intermediate cases in transmission chains are missed. Outbreaks simulated with a high transmission rate were more robust to changes in the sampling fraction and largely resulted in a single cluster that included all sampled outbreak cases. When considering multiple outbreaks in a sampled jurisdiction seeded by different introductions, low genomic diversity between introduced cases caused outbreaks to be merged into large clusters. If the transmission and sampling fraction, and diversity between introductions was low, a combination of the spurious break-up of outbreaks and the linking of closely related cases in different outbreaks resulted in clusters that may appear informative, but these did not reflect the true underlying population structure. Conversely, genomic clusters matched the true population structure when there was relatively high diversity between introductions and a high transmission rate. CONCLUSION: Differences in epidemiology and sampling can impact our ability to identify genomic clusters that describe the underlying population structure. These findings can help to guide recommendations for the use of pathogen clustering in public health investigations.

Signatures of transmission in within-host M. tuberculosis variation.

Background: Because M. tuberculosis evolves slowly, transmission clusters often contain multiple individuals with identical consensus genomes, making it difficult to reconstruct transmission chains. Finding additional sources of shared M. tuberculosis variation could help overcome this problem. Previous studies have reported M. tuberculosis diversity within infected individuals; however, whether within-host variation improves transmission inferences remains unclear. Methods: To evaluate the transmission information present in within-host M. tuberculosis variation, we re-analyzed publicly available sequence data from three household transmission studies, using household membership as a proxy for transmission linkage between donor-recipient pairs. Findings: We found moderate levels of minority variation present in M. tuberculosis sequence data from cultured isolates that varied significantly across studies (mean: 6, 7, and 170 minority variants above a 1% minor allele frequency threshold, outside of PE/PPE genes). Isolates from household members shared more minority variants than did isolates from unlinked individuals in the three studies (mean 98 shared minority variants vs. 10; 0.8 vs. 0.2, and 0.7 vs. 0.2, respectively). Shared within-host variation was significantly associated with household membership (OR: 1.51 [1.30,1.71], for one standard deviation increase in shared minority variants). Models that included shared within-host variation improved the accuracy of predicting household membership in all three studies as compared to models without within-host variation (AUC: 0.95 versus 0.92, 0.99 versus 0.95, and 0.93 versus 0.91). Interpretation: Within-host M. tuberculosis variation persists through culture and could enhance the resolution of transmission inferences. The substantial differences in minority variation recovered across studies highlights the need to optimize approaches to recover and incorporate within-host variation into automated phylogenetic and transmission inference. Funding: NIAID: 5K01AI173385.

Genomic epidemiology offers high resolution estimates of serial intervals for COVID-19.

Serial intervals - the time between symptom onset in infector and infectee - are a fundamental quantity in infectious disease control. However, their estimation requires knowledge of individuals' exposures, typically obtained through resource-intensive contact tracing efforts. We introduce an alternate framework using virus sequences to inform who infected whom and thereby estimate serial intervals. We apply our technique to SARS-CoV-2 sequences from case clusters in the first two COVID-19 waves in Victoria, Australia. We find that our approach offers high resolution, cluster-specific serial interval estimates that are comparable with those obtained from contact data, despite requiring no knowledge of who infected whom and relying on incompletely-sampled data. Compared to a published serial interval, cluster-specific serial intervals can vary estimates of the effective reproduction number by a factor of 2-3. We find that serial interval estimates in settings such as schools and meat processing/packing plants are shorter than those in healthcare facilities.

Bayesian reconstruction of Mycobacterium tuberculosis transmission networks in a high incidence area over two decades in Malawi reveals associated risk factors and genomic variants.

Understanding host and pathogen factors that influence tuberculosis (TB) transmission can inform strategies to eliminate the spread of Mycobacterium tuberculosis (Mtb). Determining transmission links between cases of TB is complicated by a long and variable latency period and undiagnosed cases, although methods are improving through the application of probabilistic modelling and whole-genome sequence analysis. Using a large dataset of 1857 whole-genome sequences and comprehensive metadata from Karonga District, Malawi, over 19 years, we reconstructed Mtb transmission networks using a two-step Bayesian approach that identified likely infector and recipient cases, whilst robustly allowing for incomplete case sampling. We investigated demographic and pathogen genomic variation associated with transmission and clustering in our networks. We found that whilst there was a significant decrease in the proportion of infectors over time, we found higher transmissibility and large transmission clusters for lineage 2 (Beijing) strains. By performing evolutionary convergence testing (phyC) and genome-wide association analysis (GWAS) on transmitting versus non-transmitting cases, we identified six loci, PPE54, accD2, PE_PGRS62, rplI, Rv3751 and Rv2077c, that were associated with transmission. This study provides a framework for reconstructing large-scale Mtb transmission networks. We have highlighted potential host and pathogen characteristics that were linked to increased transmission in a high-burden setting and identified genomic variants that, with validation, could inform further studies into transmissibility and TB eradication.

Genetic Diversity of norA, Coding for a Main Efflux Pump of Staphylococcus aureus.

NorA is the best studied efflux system of Staphylococcus aureus and therefore frequently used as a model for investigating efflux-mediated resistance in this pathogen. NorA activity is associated with resistance to fluoroquinolones, several antiseptics and disinfectants and several reports have pointed out the role of efflux systems, including NorA, as a first-line response to antimicrobials in S. aureus. Genetic diversity studies of the gene norA have described three alleles; norAI, norAII and norAIII. However, the epidemiology of these alleles and their impact on NorA activity remains unclear. Additionally, increasing studies do not account for norA variability when establishing relations between resistance phenotypes and norA presence or reported absence, which actually corresponds, as we now demonstrate, to different norA alleles. In the present study we assessed the variability of the norA gene present in the genome of over 1,000 S. aureus isolates, corresponding to 112 S. aureus strains with whole genome sequences publicly available; 917 MRSA strains sourced from a London-based study and nine MRSA isolates collected in a major Hospital in Lisbon, Portugal. Our analyses show that norA is part of the core genome of S. aureus. It also suggests that occurrence of norA variants reflects the population structure of this major pathogen. Overall, this work highlights the ubiquitous nature of norA in S. aureus which must be taken into account when studying the role played by this important determinant on S. aureus resistance to antimicrobials.