Beyond the SNP Threshold: Identifying Outbreak Clusters Using Inferred Transmissions

Genomic epidemiology of tuberculosis in eastern Malaysia: insights for strengthening public health responses

Tuberculosis is a leading public health priority in eastern Malaysia. Knowledge of the genomic epidemiology of tuberculosis can help tailor public health interventions. Our aims were to determine tuberculosis genomic epidemiology and characterize resistance mutations in the ethnically diverse city of Kota Kinabalu, Sabah, located at the nexus of Malaysia, Indonesia, Philippines and Brunei. We used an archive of prospectively collected Mycobacterium tuberculosis samples paired with epidemiological data. We collected sputum and demographic data from consecutive consenting outpatients with pulmonary tuberculosis at the largest tuberculosis clinic from 2012 to 2014, and selected samples from tuberculosis inpatients from the tertiary referral centre during 2012-2014 and 2016-2017. Two hundred and eight M. tuberculosis sequences were available for analysis, representing 8 % of cases notified during the study periods. Whole-genome phylogenetic analysis demonstrated that most strains were lineage 1 (195/208, 93.8 %), with the remainder being lineages 2 (8/208, 3.8 %) or 4 (5/208, 2.4 %). Lineages or sub-lineages were not associated with patient ethnicity. The lineage 1 strains were diverse, with sub-lineage 1.2.1 being dominant (192, 98 %). Lineage 1.2.1.3 isolates were geographically most widely distributed. The greatest diversity occurred in a border town sub-district. The time to the most recent common ancestor for the three major lineage 1.2.1 clades was estimated to be the year 1966 (95 % HPD 1948-1976). An association was found between failure of culture conversion by week 8 of treatment and infection with lineage 2 (4/6, 67 %) compared with lineage 1 strains (4/83, 5 %) (P<0.001), supporting evidence of greater virulence of lineage 2 strains. Eleven potential transmission clusters (SNP difference ≤12) were identified; at least five included people living in different sub-districts. Some linked cases spanned the whole 4-year study period. One cluster involved a multidrug-resistant tuberculosis strain matching a drug-susceptible strain from 3 years earlier. Drug resistance mutations were uncommon, but revealed one phenotype-genotype mismatch in a genotypically multidrug-resistant isolate, and rare nonsense mutations within the katG gene in two isolates. Consistent with the regionally mobile population, M. tuberculosis strains in Kota Kinabalu were diverse, although several lineage 1 strains dominated and were locally well established. Transmission clusters - uncommonly identified, likely attributable to incomplete sampling - showed clustering occurring across the community, not confined to households or sub-districts. The findings indicate that public health priorities should include active case finding and early institution of tuberculosis management in mobile populations, while there is a need to upscale effective contact investigation beyond households to include other contacts within social networks.

Accessory Genome Dynamics and Structural Variation of Shigella from Persistent Infections

Shigellosis is a diarrheal disease caused mainly by Shigella flexneri and Shigella sonnei Infection is thought to be largely self-limiting, with short- to medium-term and serotype-specific immunity provided following clearance. However, cases of men who have sex with men (MSM)-associated shigellosis have been reported where Shigella of the same serotype were serially sampled from individuals between 1 and 1,862 days apart, possibly due to persistent carriage or reinfection with the same serotype. Here, we investigate the accessory genome dynamics of MSM-associated S. flexneri and S. sonnei isolates serially sampled from individual patients at various days apart to shed light on the adaptation of these important pathogens during infection. We find that pairs likely associated with persistent infection/carriage and with a smaller single nucleotide polymorphism (SNP) distance, demonstrated significantly less variation in accessory genome content than pairs likely associated with reinfection, and with a greater SNP distance. We observed antimicrobial resistance acquisition during Shigella carriage, including the gain of an extended-spectrum beta-lactamase gene during carriage. Finally, we explored large chromosomal structural variations and rearrangements in seven (five chronic and two reinfection associated) pairs of S. flexneri 3a isolates from an MSM-associated epidemic sublineage, which revealed variations at several common regions across isolate pairs, mediated by insertion sequence elements and comprising a distinct predicted functional profile. This study provides insight on the variation of accessory genome dynamics and large structural genomic changes in Shigella during persistent infection/carriage. In addition, we have also created a complete reference genome and biobanked isolate of the globally important pathogen, S. flexneri 3a.IMPORTANCEShigella spp. are Gram-negative bacteria that are the etiological agent of shigellosis, the second most common cause of diarrheal illness among children under the age of five in low-income countries. In high-income countries, shigellosis is also a sexually transmissible disease among men who have sex with men. Within the latter setting, we have captured prolonged and/or recurrent infection with shigellae of the same serotype, challenging the belief that Shigella infection is short lived and providing an early opportunity to study the evolution of the pathogen over the course of infection. Using this recently emerged transmission scenario, we comprehensively characterize the genomic changes that occur over the course of individual infection with Shigella and uncover a distinct functional profile of variable genomic regions, findings that have relevance for other Enterobacteriaceae.

Estimating the relative probability of direct transmission between infectious disease patients

BACKGROUND

Estimating infectious disease parameters such as the serial interval (time between symptom onset in primary and secondary cases) and reproductive number (average number of secondary cases produced by a primary case) are important in understanding infectious disease dynamics. Many estimation methods require linking cases by direct transmission, a difficult task for most diseases.

METHODS

Using a subset of cases with detailed genetic and/or contact investigation data to develop a training set of probable transmission events, we build a model to estimate the relative transmission probability for all case-pairs from demographic, spatial and clinical data. Our method is based on naive Bayes, a machine learning classification algorithm which uses the observed frequencies in the training dataset to estimate the probability that a pair is linked given a set of covariates.

RESULTS

In simulations, we find that the probabilities estimated using genetic distance between cases to define training transmission events are able to distinguish between truly linked and unlinked pairs with high accuracy (area under the receiver operating curve value of 95%). Additionally, only a subset of the cases, 10-50% depending on sample size, need to have detailed genetic data for our method to perform well. We show how these probabilities can be used to estimate the average effective reproductive number and apply our method to a tuberculosis outbreak in Hamburg, Germany.

CONCLUSIONS

Our method is a novel way to infer transmission dynamics in any dataset when only a subset of cases has rich contact investigation and/or genetic data.

Whole genome sequencing based differentiation between re-infection and relapse in Indian patients with tuberculosis recurrence, with and without HIV co-infection

INTRODUCTION

Differentiation between relapse and reinfection in cases with tuberculosis (TB) recurrence has important implications for public health, especially in patients with human immunodeficiency virus (HIV) co-infection. We compared Mycobacterial Interspersed Repeat Unit (MIRU) typing and spoligotyping with whole genome sequencing (WGS) to differentiate between relapse and reinfection in patients (HIV-positive and HIV-negative) with TB recurrence. We also assessed the value of WGS to track acquired drug resistance in those with relapse after successful treatment.

METHOD

Forty-one paired M. tuberculosis isolates collected from 20 HIV-positive and 21 HIV-negative patients were subjected to WGS in addition to spoligotyping and MIRU typing. Phylogenetic and Single Nucleotide Substitution (SNP) clustering analyses were performed to determine whether recurrences were due to relapse or re-infection.

RESULTS

Comparison of M. tuberculosis genomes indicated that 95% of TB recurrences in the HIV-negative cohort were due to relapse, while the majority of TB recurrences (75%) in the HIV-positive cohort was due to reinfection (P = 0.0001). New drug resistance mutations were acquired in 5/24 cases (20.8%) that experienced relapse.

CONCLUSIONS

WGS provided increased resolution, but differentiation between relapse and reinfection was broadly consistent with MIRU and spoligotyping. The high contribution of reinfection among HIV infected patients experiencing TB recurrence warrants further study to explore risk factors for TB exposure.

Genomic epidemiology of COVID-19 in care homes in the east of England

COVID-19 poses a major challenge to care homes, as SARS-CoV-2 is readily transmitted and causes disproportionately severe disease in older people. Here, 1167 residents from 337 care homes were identified from a dataset of 6600 COVID-19 cases from the East of England. Older age and being a care home resident were associated with increased mortality. SARS-CoV-2 genomes were available for 700 residents from 292 care homes. By integrating genomic and temporal data, 409 viral clusters within the 292 homes were identified, indicating two different patterns - outbreaks among care home residents and independent introductions with limited onward transmission. Approximately 70% of residents in the genomic analysis were admitted to hospital during the study, providing extensive opportunities for transmission between care homes and hospitals. Limiting viral transmission within care homes should be a key target for infection control to reduce COVID-19 mortality in this population.

Genomic surveillance, characterization and intervention of a polymicrobial multidrug-resistant outbreak in critical care

Background. Infections caused by carbapenem-resistant Acinetobacter baumannii (CR-Ab) have become increasingly prevalent in clinical settings and often result in significant morbidity and mortality due to their multidrug resistance (MDR). Here we present an integrated whole-genome sequencing (WGS) response to a persistent CR-Ab outbreak in a Brisbane hospital between 2016-2018.Methods. A. baumannii, Klebsiella pneumoniae, Serratia marcescens and Pseudomonas aeruginosa isolates were sequenced using the Illumina platform primarily to establish isolate relationships based on core-genome SNPs, MLST and antimicrobial resistance gene profiles. Representative isolates were selected for PacBio sequencing. Environmental metagenomic sequencing with Illumina was used to detect persistence of the outbreak strain in the hospital.Results. In response to a suspected polymicrobial outbreak between May to August of 2016, 28 CR-Ab (and 21 other MDR Gram-negative bacilli) were collected from Intensive Care Unit and Burns Unit patients and sent for WGS with a 7 day turn-around time in clinical reporting. All CR-Ab were sequence type (ST)1050 (Pasteur ST2) and within 10 SNPs apart, indicative of an ongoing outbreak, and distinct from historical CR-Ab isolates from the same hospital. Possible transmission routes between patients were identified on the basis of CR-Ab and K. pneumoniae SNP profiles. Continued WGS surveillance between 2016 to 2018 enabled suspected outbreak cases to be refuted, but a resurgence of the outbreak CR-Ab mid-2018 in the Burns Unit prompted additional screening. Environmental metagenomic sequencing identified the hospital plumbing as a potential source. Replacement of the plumbing and routine drain maintenance resulted in rapid resolution of the secondary outbreak and significant risk reduction with no discernable transmission in the Burns Unit since.Conclusion. We implemented a comprehensive WGS and metagenomics investigation that resolved a persistent CR-Ab outbreak in a critical care setting.

Approaches for characterizing and tracking hospital-associated multidrug-resistant bacteria

Hospital-associated infections are a major concern for global public health. Infections with antibiotic-resistant pathogens can cause empiric treatment failure, and for infections with multidrug-resistant bacteria which can overcome antibiotics of "last resort" there exists no alternative treatments. Despite extensive sanitization protocols, the hospital environment is a potent reservoir and vector of antibiotic-resistant organisms. Pathogens can persist on hospital surfaces and plumbing for months to years, acquire new antibiotic resistance genes by horizontal gene transfer, and initiate outbreaks of hospital-associated infections by spreading to patients via healthcare workers and visitors. Advancements in next-generation sequencing of bacterial genomes and metagenomes have expanded our ability to (1) identify species and track distinct strains, (2) comprehensively profile antibiotic resistance genes, and (3) resolve the mobile elements that facilitate intra- and intercellular gene transfer. This information can, in turn, be used to characterize the population dynamics of hospital-associated microbiota, track outbreaks to their environmental reservoirs, and inform future interventions. This review provides a detailed overview of the approaches and bioinformatic tools available to study isolates and metagenomes of hospital-associated bacteria, and their multi-layered networks of transmission.

Genomic Epidemiology Analysis of Infectious Disease Outbreaks Using TransPhylo

Comparing the pathogen genomes from several cases of an infectious disease has the potential to help us understand and control outbreaks. Many methods exist to reconstruct a phylogeny from such genomes, which represents how the genomes are related to one another. However, such a phylogeny is not directly informative about transmission events between individuals. TransPhylo is a software tool implemented as an R package designed to bridge the gap between pathogen phylogenies and transmission trees. TransPhylo is based on a combined model of transmission between hosts and pathogen evolution within each host. It can simulate both phylogenies and transmission trees jointly under this combined model. TransPhylo can also reconstruct a transmission tree based on a dated phylogeny, by exploring the space of transmission trees compatible with the phylogeny. A transmission tree can be represented as a coloring of a phylogeny where each color represents a different host of the pathogen, and TransPhylo provides convenient ways to plot these colorings and explore the results. This article presents the basic protocols that can be used to make the most of TransPhylo. © 2021 The Authors. Basic Protocol 1: First steps with TransPhylo Basic Protocol 2: Simulation of outbreak data Basic Protocol 3: Inference of transmission Basic Protocol 4: Exploring the results of inference.

Definition of a genetic relatedness cutoff to exclude recent transmission of meticillin-resistant Staphylococcus aureus: a genomic epidemiology analysis

Background

Whole-genome sequencing (WGS) can be used in genomic epidemiology investigations to confirm or refute outbreaks of bacterial pathogens, and to support targeted and efficient infection control interventions. We aimed to define a genetic relatedness cutoff, quantified as a number of single-nucleotide polymorphisms (SNP), for meticillin-resistant Staphylococcus aureus (MRSA), above which recent (ie, within 6 months) patient-to-patient transmission could be ruled out.

Methods

We did a retrospective genomic and epidemiological analysis of MRSA data from two prospective observational cohort studies in the UK to establish SNP cutoffs for genetic relatedness, above which recent transmission was unlikely. We used three separate approaches to calculate these thresholds. First, we applied a linear mixed model to estimate the S aureus substitution rate and 95th percentile within-host diversity in a cohort in which multiple isolates were sequenced per individual. Second, we applied a simulated transmission model to this same genomic dataset. Finally, in a second cohort, we determined the genetic distance (ie, the number of SNPs) that would capture 95% of epidemiologically linked cases. We applied the three approaches to both whole-genome and core-genome sequences.

Findings

In the linear mixed model, the estimated substitution rate was roughly 5 whole-genome SNPs (wgSNPs) or 3 core-genome SNPs (cgSNPs) per genome per year, and the 95th percentile within-host diversity was 19 wgSNPs or 10 cgSNPs. The combined SNP cutoffs for detection of MRSA transmission within 6 months per this model were thus 24 wgSNPs or 13 cgSNPs. The simulated transmission model suggested that cutoffs of 17 wgSNPs or 12 cgSNPs would detect 95% of MRSA transmission events within the same timeframe. Finally, in the second cohort, cutoffs of 22 wgSNPs or 11 cgSNPs captured 95% of epidemiologically linked cases within 6 months.

Interpretation

On the basis of our results, we propose conservative cutoffs of 25 wgSNPs or 15 cgSNPS above which transmission of MRSA within the previous 6 months can be ruled out. These cutoffs could potentially be used as part of a genomic sequencing approach to the management of outbreaks of MRSA in conjunction with traditional epidemiological techniques.

Funding

UK Department of Health, Wellcome Trust, UK National Institute for Health Research.

The Bacteria Genome Pipeline (BAGEP): an automated, scalable workflow for bacteria genomes with Snakemake

Next generation sequencing technologies are becoming more accessible and affordable over the years, with entire genome sequences of several pathogens being deciphered in few hours. However, there is the need to analyze multiple genomes within a short time, in order to provide critical information about a pathogen of interest such as drug resistance, mutations and genetic relationship of isolates in an outbreak setting. Many pipelines that currently do this are stand-alone workflows and require huge computational requirements to analyze multiple genomes. We present an automated and scalable pipeline called BAGEP for monomorphic bacteria that performs quality control on FASTQ paired end files, scan reads for contaminants using a taxonomic classifier, maps reads to a reference genome of choice for variant detection, detects antimicrobial resistant (AMR) genes, constructs a phylogenetic tree from core genome alignments and provide interactive short nucleotide polymorphism (SNP) visualization across core genomes in the data set. The objective of our research was to create an easy-to-use pipeline from existing bioinformatics tools that can be deployed on a personal computer. The pipeline was built on the Snakemake framework and utilizes existing tools for each processing step: fastp for quality trimming, snippy for variant calling, Centrifuge for taxonomic classification, Abricate for AMR gene detection, snippy-core for generating whole and core genome alignments, IQ-TREE for phylogenetic tree construction and vcfR for an interactive heatmap visualization which shows SNPs at specific locations across the genomes. BAGEP was successfully tested and validated with Mycobacterium tuberculosis (n = 20) and Salmonella enterica serovar Typhi (n = 20) genomes which are about 4.4 million and 4.8 million base pairs, respectively. Running these test data on a 8 GB RAM, 2.5 GHz quad core laptop took 122 and 61 minutes on respective data sets to complete the analysis. BAGEP is a fast, calls accurate SNPs and an easy to run pipeline that can be executed on a mid-range laptop; it is freely available on: https://github.com/idolawoye/BAGEP.

Microbe hunting in the modern era: reflecting on a decade of microbial genomic epidemiology

Since the first recognition that infectious microbes serve as the causes of many human diseases, physicians and scientists have sought to understand and control their spread. For the past 150+ years, these ‘microbe hunters’ have learned to combine epidemiological information with knowledge of the infectious agent(s). In this essay, I reflect on the evolution of microbe hunting, beginning with the history of pre-germ theory epidemiological studies, through the microbiological and molecular eras. Now in the genomic age, modern-day microbe hunters are combining pathogen whole-genome sequencing with epidemiological data to enhance epidemiological investigations, advance our understanding of the natural history of pathogens and drivers of disease, and ultimately reshape our plans and priorities for global disease control and eradication. Indeed, as we have seen during the ongoing Covid-19 pandemic, the role of microbe hunters is now more important than ever. Despite the advances already made by microbial genomic epidemiology, the field is still maturing, with many more exciting developments on the horizon.

Quantifying the transmission of antimicrobial resistance at the human and livestock interface with genomics

BACKGROUND

Livestock have been implicated as a reservoir for antimicrobial resistance (AMR) that can spread to humans. Close proximity and ecological interfaces involving livestock have been posited as risk factors for the transmission of AMR. In spite of this, there are sparse data and limited agreement on the transmission dynamics that occur.

OBJECTIVES

To identify how genome sequencing approaches can be used to quantify the dynamics of AMR transmission at the human-livestock interface, and where current knowledge can be improved to better understand the impact of transmission on the spread of AMR.

SOURCES

Key articles investigating various aspects of AMR transmission at the human-livestock interface are discussed, with a focus on Escherichia coli.

CONTENT

We recapitulate the current understanding of the transmission of AMR between humans and livestock based on current genomic and epidemiological approaches. We discuss how the use of well-designed, high-resolution genome sequencing studies can improve our understanding of the human-livestock interface.

IMPLICATIONS

A better understanding of the human-livestock interface will aid in the development of evidence-based and effective One Health interventions that can ultimately reduce the burden of AMR in humans.

Towards standardisation: comparison of five whole genome sequencing (WGS) analysis pipelines for detection of epidemiologically linked tuberculosis cases

Background

Whole genome sequencing (WGS) is a reliable tool for studying tuberculosis (TB) transmission. WGS data are usually processed by custom-built analysis pipelines with little standardisation between them.

Aim

To compare the impact of variability of several WGS analysis pipelines used internationally to detect epidemiologically linked TB cases.

Methods

From the Netherlands, 535 Mycobacterium tuberculosis complex (MTBC) strains from 2016 were included. Epidemiological information obtained from municipal health services was available for all mycobacterial interspersed repeat unit-variable number of tandem repeat (MIRU-VNTR) clustered cases. WGS data was analysed using five different pipelines: one core genome multilocus sequence typing (cgMLST) approach and four single nucleotide polymorphism (SNP)-based pipelines developed in Oxford, United Kingdom; Borstel, Germany; Bilthoven, the Netherlands and Copenhagen, Denmark. WGS clusters were defined using a maximum pairwise distance of 12 SNPs/alleles.

Results

The cgMLST approach and Oxford pipeline clustered all epidemiologically linked cases, however, in the other three SNP-based pipelines one epidemiological link was missed due to insufficient coverage. In general, the genetic distances varied between pipelines, reflecting different clustering rates: the cgMLST approach clustered 92 cases, followed by 84, 83, 83 and 82 cases in the SNP-based pipelines from Copenhagen, Oxford, Borstel and Bilthoven respectively.

Conclusion

Concordance in ruling out epidemiological links was high between pipelines, which is an important step in the international validation of WGS data analysis. To increase accuracy in identifying TB transmission clusters, standardisation of crucial WGS criteria and creation of a reference database of representative MTBC sequences would be advisable.

Genomic variant-identification methods may alter Mycobacterium tuberculosis transmission inferences

Pathogen genomic data are increasingly used to characterize global and local transmission patterns of important human pathogens and to inform public health interventions. Yet, there is no current consensus on how to measure genomic variation. To test the effect of the variant-identification approach on transmission inferences for Mycobacterium tuberculosis, we conducted an experiment in which five genomic epidemiology groups applied variant-identification pipelines to the same outbreak sequence data. We compared the variants identified by each group in addition to transmission and phylogenetic inferences made with each variant set. To measure the performance of commonly used variant-identification tools, we simulated an outbreak. We compared the performance of three mapping algorithms, five variant callers and two variant filters in recovering true outbreak variants. Finally, we investigated the effect of applying increasingly stringent filters on transmission inferences and phylogenies. We found that variant-calling approaches used by different groups do not recover consistent sets of variants, which can lead to conflicting transmission inferences. Further, performance in recovering true variation varied widely across approaches. While no single variant-identification approach outperforms others in both recovering true genome-wide and outbreak-level variation, variant-identification algorithms calibrated upon real sequence data or that incorporate local reassembly outperform others in recovering true pairwise differences between isolates. The choice of variant filters contributed to extensive differences across pipelines, and applying increasingly stringent filters rapidly eroded the accuracy of transmission inferences and quality of phylogenies reconstructed from outbreak variation. Commonly used approaches to identify M. tuberculosis genomic variation have variable performance, particularly when predicting potential transmission links from pairwise genetic distances. Phylogenetic reconstruction may be improved by less stringent variant filtering. Approaches that improve variant identification in repetitive, hypervariable regions, such as long-read assemblies, may improve transmission inference.

Modeling Missing Cases and Transmission Links in Networks of Extensively Drug-Resistant Tuberculosis in KwaZulu-Natal, South Africa

Patterns of transmission of drug-resistant tuberculosis (TB) remain poorly understood, despite over half a million incident cases worldwide in 2017. Modeling TB transmission networks can provide insight into drivers of transmission, but incomplete sampling of TB cases can pose challenges for inference from individual epidemiologic and molecular data. We assessed the effect of missing cases on a transmission network inferred from Mycobacterium tuberculosis sequencing data on extensively drug-resistant TB cases in KwaZulu-Natal, South Africa, diagnosed in 2011-2014. We tested scenarios in which cases were missing at random, missing differentially by clinical characteristics, or missing differentially by transmission (i.e., cases with many links were under- or oversampled). Under the assumption that cases were missing randomly, the mean number of transmissions per case in the complete network needed to be larger than 20, far higher than expected, to reproduce the observed network. Instead, the most likely scenario involved undersampling of high-transmitting cases, and models provided evidence for super-spreading. To our knowledge, this is the first analysis to have assessed support for different mechanisms of missingness in a TB transmission study, but our results are subject to the distributional assumptions of the network models we used. Transmission studies should consider the potential biases introduced by incomplete sampling and identify host, pathogen, or environmental factors driving super-spreading.

Citywide Transmission of Multidrug-resistant Tuberculosis Under China's Rapid Urbanization: A Retrospective Population-based Genomic Spatial Epidemiological Study

BACKGROUND

Population movement could extend multidrug-resistant tuberculosis (MDR-TB) transmission and complicate its global prevalence. We sought to identify the high-risk populations and geographic sites of MDR-TB transmission in Shenzhen, the most common destination for internal migrants in China.

METHODS

We performed a population-based, retrospective study in patients diagnosed with MDR-TB in Shenzhen during 2013-2017. By defining genomic clusters with a threshold of 12-single-nucleotide polymorphism distance based on whole-genome sequencing of their clinical strains, the clustering rate was calculated to evaluate the level of recent transmission. Risk factors were identified by multivariable logistic regression. To further delineate the epidemiological links, we invited the genomic-clustered patients to an in-depth social network investigation.

RESULTS

In total, 105 (25.2%) of the 417 enrolled patients with MDR-TB were grouped into 40 genome clusters, suggesting recent transmission of MDR strains. The adjusted risk for student to have a clustered strain was 4.05 (95% confidence interval, 1.06-17.0) times greater than other patients. The majority (70%, 28/40) of the genomic clusters involved patients who lived in different districts, with residences separated by an average of 8.76 kilometers. Other than household members, confirmed epidemiological links were also identified among classmates and workplace colleagues.

CONCLUSIONS

These findings demonstrate that local transmission of MDR-TB is a serious problem in Shenzhen. While most transmission occurred between people who lived distant from each other, there was clear evidence that transmission occurred in schools and workplaces, which should be included as targeted sites for active case finding.The average residential distance between genomic-clustered cases was more than 8 kilometers, while schools and workplaces, identified as sites of transmission in this study, deserve increased vigilance for targeted case finding of multidrug-resistant tuberculosis.

Whole Genome Sequencing and Spatial Analysis Identifies Recent Tuberculosis Transmission Hotspots in Ghana

Whole genome sequencing (WGS) is progressively being used to investigate the transmission dynamics of Mycobacterium tuberculosis complex (MTBC). We used WGS analysis to resolve traditional genotype clusters and explored the spatial distribution of confirmed recent transmission clusters. Bacterial genomes from a total of 452 MTBC isolates belonging to large traditional clusters from a population-based study spanning July 2012 and December 2015 were obtained through short read next-generation sequencing using the illumina HiSeq2500 platform. We performed clustering and spatial analysis using specified R packages and ArcGIS. Of the 452 traditional genotype clustered genomes, 314 (69.5%) were confirmed clusters with a median cluster size of 7.5 genomes and an interquartile range of 4–12. Recent tuberculosis (TB) transmission was estimated as 24.7%. We confirmed the wide spread of a Cameroon sub-lineage clone with a cluster size of 78 genomes predominantly from the Ablekuma sub-district of Accra metropolis. More importantly, we identified a recent transmission cluster associated with isoniazid resistance belonging to the Ghana sub-lineage of lineage 4. WGS was useful in detecting unsuspected outbreaks; hence, we recommend its use not only as a research tool but as a surveillance tool to aid in providing the necessary guided steps to track, monitor, and control TB.

Whole Genome Sequencing Detects Minimal Clustering Among Escherichia coli Sequence Type 131-H30 Isolates Collected From United States Children's Hospitals

We applied whole genome sequencing to identify putative transmission clusters among clinical multidrug-resistant Escherichia coli sequence type 131-H30 isolates from 4 United States children's hospitals. Of 126 isolates, 17 were involved in 8 putative transmission clusters; 4 clusters showed evidence of healthcare-associated epidemiologic linkages. Geographic clustering analyses showed weak geographic clustering.

A retrospective genomic analysis of drug-resistant strains of M. tuberculosis in a high-burden setting, with an emphasis on comparative diagnostics and reactivation and reinfection status

Background

Recurrence of drug-resistant tuberculosis (DR-TB) after treatment occurs through relapse of the initial infection or reinfection by a new drug-resistant strain. Outbreaks of DR-TB in high burden regions present unique challenges in determining recurrence status for effective disease management and treatment. In the Republic of Moldova the burden of DR-TB is exceptionally high, with many cases presenting as recurrent.

Methods

We performed a retrospective analysis of Mycobacterium tuberculosis from Moldova to better understand the genomic basis of drug resistance and its effect on the determination of recurrence status in a high DR-burden environment. To do this we analyzed genomes from 278 isolates collected from 189 patients, including 87 patients with longitudinal samples. These pathogen genomes were sequenced using Illumina technology, and SNP panels were generated for each sample for use in phylogenetic and network analysis. Discordance between genomic resistance profiles and clinical drug-resistance test results was examined in detail to assess the possibility of mixed infection.

Results

There were clusters of multiple patients with 10 or fewer differences among DR-TB samples, which is evidence of person-to-person transmission of DR-TB. Analysis of longitudinally collected isolates revealed that many infections exhibited little change over time, though 35 patients demonstrated reinfection by divergent (number of differences > 10) lineages. Additionally, several same-lineage sample pairs were found to be more divergent than expected for a relapsed infection. Network analysis of the H3/4.2.1 clade found very close relationships among 61 of these samples, making differentiation of reactivation and reinfection difficult. There was discordance between genomic profile and clinical drug sensitivity test results in twelve samples, and four of these had low level (but not statistically significant) variation at DR SNPs suggesting low-level mixed infections.

Conclusions

Whole-genome sequencing provided a detailed view of the genealogical structure of the DR-TB epidemic in Moldova, showing that reinfection may be more prevalent than currently recognized. We also found increased evidence of mixed infection, which could be more robustly characterized with deeper levels of genomic sequencing.

A molecular assessment of infectious agents carried by Atlantic salmon at sea and in three eastern Canadian rivers, including aquaculture escapees and North American and European origin wild stocks

Infectious agents are key components of animal ecology and drivers of host population dynamics. Knowledge of their diversity and transmission in the wild is necessary for the management and conservation of host species like Atlantic salmon ( Salmo salar). Although pathogen exchange can occur throughout the salmon life cycle, evidence is lacking to support transmission during population mixing at sea or between farmed and wild salmon due to aquaculture exposure. We tested these hypotheses using a molecular approach that identified infectious agents and transmission potential among sub-adult Atlantic salmon at marine feeding areas and adults in three eastern Canadian rivers with varying aquaculture influence. We used high-throughput qPCR to quantify infection profiles and next generation sequencing to measure genomic variation among viral isolates. We identified 14 agents, including five not yet described as occurring in Eastern Canada. Phylogenetic analysis of piscine orthoreovirus showed homology between isolates from European and North American origin fish at sea, supporting the hypothesis of intercontinental transmission. We found no evidence to support aquaculture influence on wild adult infections, which varied relative to environmental conditions, life stage, and host origin. Our findings identify research opportunities regarding pathogen transmission and biological significance for wild Atlantic salmon populations.

Genomic analysis of carbapenemase-producing Enterobacteriaceae in Queensland reveals widespread transmission of blaIMP-4 on an IncHI2 plasmid

Carbapenemase-producing Enterobacteriaceae (CPE) are an increasingly common cause of healthcare-associated infections and may occasionally be identified in patients without extensive healthcare exposure. blaIMP-4 is the most frequently detected carbapenemase gene in Enterobacteriaceae within Australia, but little is known about the mechanisms behind its persistence. Here we used whole genome sequencing (WGS) to investigate the molecular epidemiology of blaIMP-4 in Queensland, Australia. In total, 107 CPE were collected between 2014 and 2017 and sent for WGS on an Illumina NextSeq500. Resistance genes and plasmid types were detected using a combination of read mapping and nucleotide comparison of de novo assemblies. Six isolates were additionally sequenced using Oxford Nanopore MinION to generate long-reads and fully characterize the context of the blaIMP-4 gene. Of 107 CPE, 93 carried the blaIMP-4 gene; 74/107 also carried an IncHI2 plasmid, suggesting carriage of the blaIMP-4 gene on an IncHI2 plasmid. Comparison of these isolates to a previously characterized IncHI2 plasmid pMS7884A (isolated from an Enterobacter hormaechei strain in Brisbane) suggested that all isolates carried a similar plasmid. Five of six representative isolates sequenced using Nanopore long-read technology carried IncHI2 plasmids harbouring the blaIMP-4 gene. While the vast majority of isolates represented E. hormaechei, several other species were also found to carry the IncHI2 plasmid, including Klebsiella species, Escherichia coli and Citrobacter species. Several clonal groups of E. hormaechei were also identified, suggesting that persistence of blaIMP-4 is driven by both presence on a common plasmid and clonal spread of certain E. hormaechei lineages.

Computational pan-genome mapping and pairwise SNP-distance improve detection of Mycobacterium tuberculosis transmission clusters

Next-generation sequencing based base-by-base distance measures have become an integral complement to epidemiological investigation of infectious disease outbreaks. This study introduces PANPASCO, a computational pan-genome mapping based, pairwise distance method that is highly sensitive to differences between cases, even when located in regions of lineage specific reference genomes. We show that our approach is superior to previously published methods in several datasets and across different Mycobacterium tuberculosis lineages, as its characteristics allow the comparison of a high number of diverse samples in one analysis—a scenario that becomes more and more likely with the increased usage of whole-genome sequencing in transmission surveillance.

Tuberculosis still is a threat to global health. It is essential to detect and interrupt transmissions to stop the spread of this infectious disease. With the rising use of next-generation sequencing methods, its application in the surveillance of Mycobacterium tuberculosis has become increasingly important in the last years. The main goal of molecular surveillance is the identification of patient-patient transmission and cluster detection. The mutation rate of M. tuberculosis is very low and stable. Therefore, many existing methods for comparative analysis of isolates provide inadequate results since their resolution is too limited. There is a need for a method that takes every detectable difference into account. We developed PANPASCO, a novel approach for comparing pairs of isolates using all genomic information available for each pair. We combine improved SNP-distance calculation with the use of a pan-genome incorporating more than 100 M. tuberculosis reference genomes representing lineages 1-4 for read mapping prior to variant detection. We thereby enable the collective analysis and comparison of similar and diverse isolates associated with different M. tuberculosis strains.

Genotyping of Mycobacterium tuberculosis spreading in Hanoi, Vietnam using conventional and whole genome sequencing methods

Hanoi is the capital of Vietnam, one of the 30 countries with a high tuberculosis (TB) burden. Fundamental data on the molecular epidemiology of the disease is required for future TB management. To identify lineages and genotypes of Mycobacterium tuberculosis (Mtb), conventional genotyping data from clinical isolates of the Hanoi area was compared with whole genome sequencing (WGS) analysis from 332 of 470 samples. It was obtained from lineage-specific single nucleotide variants (SNVs), large sequence polymorphisms, spoligotyping, and variable number of tandem repeats (VNTR) analysis using mycobacterial interspersed repetitive unit (MIRU) and Japan anti-tuberculosis association (JATA) locus sets. This information was directly compared with results obtained from WGS. Mini-satellite repeat unit variants were identified using BLAST search against concatenated short read sequences, the RepUnitTyping tool. WGS analysis revealed that the Mtb strains tested are diverse and classified into lineage (L) 1, 2 and 4 (24.7, 57.2 and 18.1% respectively). The majority of the L2 strains were further divided into ancient and modern Beijing genotypes, and most of the L1 group were EAI4_VNM strains. Although conventional PCR-based genotyping results were mostly consistent with information obtained through WGS analysis, in-depth analysis identified aberrant deletions and spacers that may cause discordance. JATA-VNTR sets, including hypervariable loci, separated large Beijing genotypic clusters generated by MIRU15 into smaller groups. The distribution of repeat unit variants observed within 33 VNTR loci showed clear variation depending on the three lineages. WGS-based pairwise-SNV differences within VNTR-defined genotypic clusters were greater in L1 than in L2 and L4 (P = .001). Direct comparisons between results of PCR-based genotyping and in silico analysis of WGS data would bridge a gap between classical and modern technologies during this transition period, and provide further information on Mtb genotypes in specific geographical areas.

A Simple and Robust Statistical Method to Define Genetic Relatedness of Samples Related to Outbreaks at the Genomic Scale - Application to Retrospective Salmonella Foodborne Outbreak Investigations

The investigation of foodborne outbreaks (FBOs) from genomic data typically relies on inspecting the relatedness of samples through a phylogenomic tree computed on either SNPs, genes, kmers, or alleles (i.e., cgMLST and wgMLST). The phylogenomic reconstruction is often time-consuming, computation-intensive and depends on hidden assumptions, pipelines implementation and their parameterization. In the context of FBO investigations, robust links between isolates are required in a timely manner to trigger appropriate management actions. Here, we propose a non-parametric statistical method to assert the relatedness of samples (i.e., outbreak cases) or whether to reject them (i.e., non-outbreak cases). With typical computation running within minutes on a desktop computer, we benchmarked the ability of three non-parametric statistical tests (i.e., Wilcoxon rank-sum, Kolmogorov–Smirnov and Kruskal–Wallis) on six different genomic features (i.e., SNPs, SNPs excluding recombination events, genes, kmers, cgMLST alleles, and wgMLST alleles) to discriminate outbreak cases (i.e., positive control: C+) from non-outbreak cases (i.e., negative control: C−). We leveraged four well-characterized and retrospectively investigated FBOs of Salmonella Typhimurium and its monophasic variant S. 1,4,[5],12:i:- from France, setting positive and negative controls in all the assays. We show that the approaches relying on pairwise SNP differences distinguished all four considered outbreaks in contrast to the other tested genomic features (i.e., genes, kmers, cgMLST alleles, and wgMLST alleles). The freely available non-parametric method written in R has been designed to be independent of both the phylogenomic reconstruction and the detection methods of genomic features (i.e., SNPs, genes, kmers, or alleles), making it widely and easily usable to anybody working on genomic data from suspected samples.

High-resolution mapping of tuberculosis transmission: Whole genome sequencing and phylogenetic modelling of a cohort from Valencia Region, Spain

BACKGROUND

Whole genome sequencing provides better delineation of transmission clusters in Mycobacterium tuberculosis than traditional methods. However, its ability to reveal individual transmission links within clusters is limited. Here, we used a 2-step approach based on Bayesian transmission reconstruction to (1) identify likely index and missing cases, (2) determine risk factors associated with transmitters, and (3) estimate when transmission happened.

METHODS AND FINDINGS

We developed our transmission reconstruction method using genomic and epidemiological data from a population-based study from Valencia Region, Spain. Tuberculosis (TB) incidence during the study period was 8.4 cases per 100,000 people. While the study is ongoing, the sampling frame for this work includes notified TB cases between 1 January 2014 and 31 December 2016. We identified a total of 21 transmission clusters that fulfilled the criteria for analysis. These contained a total of 117 individuals diagnosed with active TB (109 with epidemiological data). Demographic characteristics of the study population were as follows: 80/109 (73%) individuals were Spanish-born, 76/109 (70%) individuals were men, and the mean age was 42.51 years (SD 18.46). We found that 66/109 (61%) TB patients were sputum positive at diagnosis, and 10/109 (9%) were HIV positive. We used the data to reveal individual transmission links, and to identify index cases, missing cases, likely transmitters, and associated transmission risk factors. Our Bayesian inference approach suggests that at least 60% of index cases are likely misidentified by local public health. Our data also suggest that factors associated with likely transmitters are different to those of simply being in a transmission cluster, highlighting the importance of differentiating between these 2 phenomena. Our data suggest that type 2 diabetes mellitus is a risk factor associated with being a transmitter (odds ratio 0.19 [95% CI 0.02-1.10], p < 0.003). Finally, we used the most likely timing for transmission events to study when TB transmission occurred; we identified that 5/14 (35.7%) cases likely transmitted TB well before symptom onset, and these were largely sputum negative at diagnosis. Limited within-cluster diversity does not allow us to extrapolate our findings to the whole TB population in Valencia Region.

CONCLUSIONS

In this study, we found that index cases are often misidentified, with downstream consequences for epidemiological investigations because likely transmitters can be missed. Our findings regarding inferred transmission timing suggest that TB transmission can occur before patient symptom onset, suggesting also that TB transmits during sub-clinical disease. This result has direct implications for diagnosing TB and reducing transmission. Overall, we show that a transition to individual-based genomic epidemiology will likely close some of the knowledge gaps in TB transmission and may redirect efforts towards cost-effective contact investigations for improved TB control.

The molecular clock of Mycobacterium tuberculosis

The molecular clock and its phylogenetic applications to genomic data have changed how we study and understand one of the major human pathogens, Mycobacterium tuberculosis (MTB), the etiologic agent of tuberculosis. Genome sequences of MTB strains sampled at different times are increasingly used to infer when a particular outbreak begun, when a drug-resistant clone appeared and expanded, or when a strain was introduced into a specific region. Despite the growing importance of the molecular clock in tuberculosis research, there is a lack of consensus as to whether MTB displays a clocklike behavior and about its rate of evolution. Here we performed a systematic study of the molecular clock of MTB on a large genomic data set (6,285 strains), covering different epidemiological settings and most of the known global diversity. We found that sampling times below 15-20 years were often insufficient to calibrate the clock of MTB. For data sets where such calibration was possible, we obtained a clock rate between 1x10-8 and 5x10-7 nucleotide changes per-site-per-year (0.04-2.2 SNPs per-genome-per-year), with substantial differences between clades. These estimates were not strongly dependent on the time of the calibration points as they changed only marginally when we used epidemiological isolates (sampled in the last 40 years) or three ancient DNA samples (about 1,000 years old) to calibrate the tree. Additionally, the uncertainty and the discrepancies in the results of different methods were sometimes large, highlighting the importance of using different methods, and of considering carefully their assumptions and limitations.

Evaluating the use of whole genome sequencing for the investigation of a large mumps outbreak in Ontario, Canada

In 2017 Ontario experienced the largest mumps outbreak in the province in 8 years, at a time when multiple outbreaks were occurring across North America. Of 259 reported cases, 143 occurred in Toronto, primarily among young adults. Routine genotyping of the small hydrophobic gene indicated that the outbreak was due to mumps virus genotype G. We performed a retrospective study of whole genome sequencing of 26 mumps virus isolates from early in the outbreak, using a tiling amplicon method. Results indicated that two of the cases were genetically divergent, with the remaining 24 cases belonging to two major clades and one minor clade. Phylogeographic analysis confirmed circulation of virus from each clade between Toronto and other regions in Ontario. Comparison with other genotype G strains from North America suggested that the presence of co-circulating major clades may have been due to separate importation events from outbreaks in the United States. A transmission network analysis performed with the software program TransPhylo was compared with previously collected epidemiological data. The transmission tree correlated with known epidemiological links between nine patients and identified new potential clusters with no known epidemiological links.

Genomics for Molecular Epidemiology and Detecting Transmission of Carbapenemase-Producing Enterobacterales in Victoria, Australia, 2012 to 2016

Carbapenemase-producing Enterobacterales (CPE) are being increasingly reported in Australia, and integrated clinical and genomic surveillance is critical to effectively manage this threat. We sought to systematically characterize CPE in Victoria, Australia, from 2012 to 2016.

Carbapenemase-producing Enterobacterales (CPE) are being increasingly reported in Australia, and integrated clinical and genomic surveillance is critical to effectively manage this threat. We sought to systematically characterize CPE in Victoria, Australia, from 2012 to 2016. Suspected CPE were referred to the state public health laboratory in Victoria, Australia, from 2012 to 2016 and examined using phenotypic, multiplex PCR and whole-genome sequencing (WGS) methods and compared with epidemiological metadata. Carbapenemase genes were detected in 361 isolates from 291 patients (30.8% of suspected CPE isolates), mostly from urine (42.1%) or screening samples (34.8%). IMP-4 (28.0% of patients), KPC-2 (25.3%), NDM (24.1%), and OXA carbapenemases (22.0%) were most common. Klebsiella pneumoniae (48.8% of patients) and Escherichia coli (26.1%) were the dominant species. Carbapenemase-inactivation method (CIM) testing reliably detected carbapenemase-positive isolates (100% sensitivity, 96.9% specificity), identifying an additional five CPE among 159 PCR-negative isolates (IMI and SME carbapenemases). When epidemiologic investigations were performed, all pairs of patients designated “highly likely” or “possible” local transmission had ≤23 pairwise single-nucleotide polymorphisms (SNPs) by genomic transmission analysis; conversely, all patient pairs designated “highly unlikely” local transmission had ≥26 pairwise SNPs. Using this proposed threshold, possible local transmission was identified involving a further 16 patients for whom epidemiologic data were unavailable. Systematic application of genomics has uncovered the emergence of polyclonal CPE as a significant threat in Australia, providing important insights to inform local public health guidelines and interventions. Using our workflow, pairwise SNP distances between CPE isolates of ≤23 SNPs suggest local transmission.

Effects of Host, Sample, and in vitro Culture on Genomic Diversity of Pathogenic Mycobacteria

Mycobacterium tuberculosis (M. tb), an obligate human pathogen and the etiological agent of tuberculosis (TB), remains a major threat to global public health. Comparative genomics has been invaluable for monitoring the emergence and spread of TB and for gaining insight into adaptation of M. tb. Most genomic studies of M. tb are based on single bacterial isolates that have been cultured for several weeks in vitro. However, in its natural human host, M. tb comprises complex, in some cases massive bacterial populations that diversify over the course of infection and cannot be wholly represented by a single genome. Recently, enrichment via hybridization capture has been used as a rapid diagnostic tool for TB, circumventing culturing protocols and enabling the recovery of M. tb genomes directly from sputum. This method has further applicability to the study of M. tb adaptation, as it enables a higher resolution and more direct analysis of M. tb genetic diversity within hosts with TB. Here we analyzed genomic material from M. tb and Mycobacterium bovis populations captured directly from sputum and from cultured samples using metagenomic and Pool-Seq approaches. We identified effects of sampling, patient, and sample type on bacterial genetic diversity. Bacterial genetic diversity was more variable and on average higher in sputum than in culture samples, suggesting that manipulation in the laboratory reshapes the bacterial population. Using outlier analyses, we identified candidate bacterial genetic loci mediating adaptation to these distinct environments. The study of M. tb in its natural human host is a powerful tool for illuminating host pathogen interactions and understanding the bacterial genetic underpinnings of virulence.

A Bayesian model of acquisition and clearance of bacterial colonization incorporating within-host variation

Bacterial populations that colonize a host can play important roles in host health, including serving as a reservoir that transmits to other hosts and from which invasive strains emerge, thus emphasizing the importance of understanding rates of acquisition and clearance of colonizing populations. Studies of colonization dynamics have been based on assessment of whether serial samples represent a single population or distinct colonization events. With the use of whole genome sequencing to determine genetic distance between isolates, a common solution to estimate acquisition and clearance rates has been to assume a fixed genetic distance threshold below which isolates are considered to represent the same strain. However, this approach is often inadequate to account for the diversity of the underlying within-host evolving population, the time intervals between consecutive measurements, and the uncertainty in the estimated acquisition and clearance rates. Here, we present a fully Bayesian model that provides probabilities of whether two strains should be considered the same, allowing us to determine bacterial clearance and acquisition from genomes sampled over time. Our method explicitly models the within-host variation using population genetic simulation, and the inference is done using a combination of Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC). We validate the method with multiple carefully conducted simulations and demonstrate its use in practice by analyzing a collection of methicillin resistant Staphylococcus aureus (MRSA) isolates from a large recently completed longitudinal clinical study. An R-code implementation of the method is freely available at: https://github.com/mjarvenpaa/bacterial-colonization-model.