Inference of Infectious Disease Transmission through a Relaxed Bottleneck Using Multiple Genomes Per Host.

In recent times, pathogen genome sequencing has become increasingly used to investigate infectious disease outbreaks. When genomic data is sampled densely enough amongst infected individuals, it can help resolve who infected whom. However, transmission analysis cannot rely solely on a phylogeny of the genomes but must account for the within-host evolution of the pathogen, which blurs the relationship between phylogenetic and transmission trees. When only a single genome is sampled for each host, the uncertainty about who infected whom can be quite high. Consequently, transmission analysis based on multiple genomes of the same pathogen per host has a clear potential for delivering more precise results, even though it is more laborious to achieve. Here, we present a new methodology that can use any number of genomes sampled from a set of individuals to reconstruct their transmission network. Furthermore, we remove the need for the assumption of a complete transmission bottleneck. We use simulated data to show that our method becomes more accurate as more genomes per host are provided, and that it can infer key infectious disease parameters such as the size of the transmission bottleneck, within-host growth rate, basic reproduction number, and sampling fraction. We demonstrate the usefulness of our method in applications to real datasets from an outbreak of Pseudomonas aeruginosa amongst cystic fibrosis patients and a nosocomial outbreak of Klebsiella pneumoniae.

Within-host diversity improves phylogenetic and transmission reconstruction of SARS-CoV-2 outbreaks.

Accurate inference of who infected whom in an infectious disease outbreak is critical for the delivery of effective infection prevention and control. The increased resolution of pathogen whole-genome sequencing has significantly improved our ability to infer transmission events. Despite this, transmission inference often remains limited by the lack of genomic variation between the source case and infected contacts. Although within-host genetic diversity is common among a wide variety of pathogens, conventional whole-genome sequencing phylogenetic approaches exclusively use consensus sequences, which consider only the most prevalent nucleotide at each position and therefore fail to capture low-frequency variation within samples. We hypothesized that including within-sample variation in a phylogenetic model would help to identify who infected whom in instances in which this was previously impossible. Using whole-genome sequences from SARS-CoV-2 multi-institutional outbreaks as an example, we show how within-sample diversity is partially maintained among repeated serial samples from the same host, it can transmitted between those cases with known epidemiological links, and how this improves phylogenetic inference and our understanding of who infected whom. Our technique is applicable to other infectious diseases and has immediate clinical utility in infection prevention and control.

During an infectious disease outbreak, tracing who infected whom allows public health scientists to see how a pathogen is spreading and to establish effective control measures. Traditionally, this involves identifying the individuals an infected person comes into contact with and monitoring whether they also become unwell. However, this information is not always available and can be inaccurate. One alternative is to track the genetic data of pathogens as they spread. Over time, pathogens accumulate mutations in their genes that can be used to distinguish them from one another. Genetically similar pathogens are more likely to have spread during the same outbreak, while genetically dissimilar pathogens may have come from different outbreaks. However, there are limitations to this approach. For example, some pathogens accumulate genetic mutations very slowly and may not change enough during an outbreak to be distinguishable from one another. Additionally, some pathogens can spread rapidly, leaving less time for mutations to occur between transmission events. To overcome these challenges, Torres Ortiz et al. developed a more sensitive approach to pathogen genetic testing that took advantage of the multiple pathogen populations that often coexist in an infected patient. Rather than tracking only the most dominant genetic version of the pathogen, this method also looked at the less dominant ones. Torres Ortiz et al. performed genome sequencing of SARS-CoV-2 (the virus that causes COVID-19) samples from 451 healthcare workers, patients, and patient contacts at participating London hospitals. Analysis showed that it was possible to detect multiple genetic populations of the virus within individual patients. These subpopulations were often more similar in patients that had been in contact with one another than in those that had not. Tracking the genetic data of all viral populations enabled Torres Ortiz et al. to trace transmission more accurately than if only the dominant population was used. More accurate genetic tracing could help public health scientists better track pathogen transmission and control outbreaks. This may be especially beneficial in hospital settings where outbreaks can be smaller, and it is important to understand if transmission is occurring within the hospital or if the pathogen is imported from the community. Further research will help scientists understand how pathogen population genetics evolve during outbreaks and may improve the detection of subpopulations present at very low frequencies.

Investigation of the individual genetic evolution of SARS-CoV-2 in a small cluster during the rapid spread of the BF.5 lineage in Tokyo, Japan.

There has been a decreasing trend in new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases and fatalities worldwide. The virus has been evolving, indicating the potential emergence of new variants and uncertainties. These challenges necessitate continued efforts in disease control and mitigation strategies. We investigated a small cluster of SARS-CoV-2 Omicron variant infections containing a common set of genomic mutations, which provided a valuable model for investigating the transmission mechanism of genetic alterations. We conducted a study at a medical center in Japan during the Omicron surge (sub-lineage BA.5), sequencing the entire SARS-CoV-2 genomes from infected individuals and evaluating the phylogenetic tree and haplotype network among the variants. We compared the mutations present in each strain within the BA.5 strain, TKYnat2317, which was first identified in Tokyo, Japan. From June 29th to July 4th 2022, nine healthcare workers (HCWs) tested positive for SARS-CoV-2 by real-time PCR. During the same period, five patients also tested positive by real-time PCR. Whole genome sequencing revealed that the infected patients belonged to either the isolated BA.2 or BA.5 sub-lineage, while the healthcare worker infections were classified as BF.5. The phylogenetic tree and haplotype network clearly showed the specificity and similarity of the HCW cluster. We identified 12 common mutations in the cluster, including I110V in nonstructural protein 4 (nsp4), A1020S in the Spike protein, and H47Y in ORF7a, compared to the BA.5 reference. Additionally, one case had the extra nucleotide-deletion mutation I27* in ORF10, and low frequencies of genetic alterations were also found in certain instances. The results of genome sequencing showed that the nine HCWs shared a set of genetic mutations, indicating transmission within the cluster. Minor mutations observed in five HCW individuals suggested the emergence of new virus variants. Five amino acid substitutions occurred in nsp3, which could potentially affect virus replication or immune escape. Intra-host evolution also generated additional mutations. The cluster exhibited a mild disease course, with individuals in this case, recovering without requiring any medical treatments. Further investigation is needed to understand the relationship between the genetic evolution of the virus and the symptoms.

SARS-CoV-2 within-host diversity of human hosts and its implications for viral immune evasion.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is continuously evolving, bringing great challenges to the control of the virus. In the present study, we investigated the characteristics of SARS-CoV-2 within-host diversity of human hosts and its implications for immune evasion using about 2,00,000 high-depth next-generation genome sequencing data of SARS-CoV-2. A total of 44% of the samples showed within-host variations (iSNVs), and the average number of iSNVs in the samples with iSNV was 1.90. C-to-U is the dominant substitution pattern for iSNVs. C-to-U/G-to-A and A-to-G/U-to-C preferentially occur in 5'-CG-3' and 5'-AU-3' motifs, respectively. In addition, we found that SARS-CoV-2 within-host variations are under negative selection. About 15.6% iSNVs had an impact on the content of the CpG dinucleotide (CpG) in SARS-CoV-2 genomes. We detected signatures of faster loss of CpG-gaining iSNVs, possibly resulting from zinc-finger antiviral protein-mediated antiviral activities targeting CpG, which could be the major reason for CpG depletion in SARS-CoV-2 consensus genomes. The non-synonymous iSNVs in the S gene can largely alter the S protein's antigenic features, and many of these iSNVs are distributed in the amino-terminal domain (NTD) and receptor-binding domain (RBD). These results suggest that SARS-CoV-2 interacts actively with human hosts and attempts to take different evolutionary strategies to escape human innate and adaptive immunity. These new findings further deepen and widen our understanding of the within-host evolutionary features of SARS-CoV-2. IMPORTANCE Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative pathogen of the coronavirus disease 2019, has evolved rapidly since it was discovered. Recent studies have pointed out that some mutations in the SARS-CoV-2 S protein could confer SARS-CoV-2 the ability to evade the human adaptive immune system. In addition, it is observed that the content of the CpG dinucleotide in SARS-CoV-2 genome sequences has decreased over time, reflecting the adaptation to the human host. The significance of our research is revealing the characteristics of SARS-CoV-2 within-host diversity of human hosts, identifying the causes of CpG depletion in SARS-CoV-2 consensus genomes, and exploring the potential impacts of non-synonymous within-host variations in the S gene on immune escape, which could further deepen and widen our understanding of the evolutionary features of SARS-CoV-2.

Intra-Host Co-Existing Strains of SARS-CoV-2 Reference Genome Uncovered by Exhaustive Computational Search.

The COVID-19 pandemic caused by SARS-CoV-2 has had a severe impact on people worldwide. The reference genome of the virus has been widely used as a template for designing mRNA vaccines to combat the disease. In this study, we present a computational method aimed at identifying co-existing intra-host strains of the virus from RNA-sequencing data of short reads that were used to assemble the original reference genome. Our method consisted of five key steps: extraction of relevant reads, error correction for the reads, identification of within-host diversity, phylogenetic study, and protein binding affinity analysis. Our study revealed that multiple strains of SARS-CoV-2 can coexist in both the viral sample used to produce the reference sequence and a wastewater sample from California. Additionally, our workflow demonstrated its capability to identify within-host diversity in foot-and-mouth disease virus (FMDV). Through our research, we were able to shed light on the binding affinity and phylogenetic relationships of these strains with the published SARS-CoV-2 reference genome, SARS-CoV, variants of concern (VOC) of SARS-CoV-2, and some closely related coronaviruses. These insights have important implications for future research efforts aimed at identifying within-host diversity, understanding the evolution and spread of these viruses, as well as the development of effective treatments and vaccines against them.

Single nucleotide variants in Pseudomonas aeruginosa populations from sputum correlate with baseline lung function and predict disease progression in individuals with cystic fibrosis.

The severity and progression of lung disease are highly variable across individuals with cystic fibrosis (CF) and are imperfectly predicted by mutations in the human gene CFTR, lung microbiome variation or other clinical factors. The opportunistic pathogen Pseudomonas aeruginosa (Pa) dominates airway infections in most CF adults. Here we hypothesized that within-host genetic variation of Pa populations would be associated with lung disease severity. To quantify Pa genetic variation within CF sputum samples, we used deep amplicon sequencing (AmpliSeq) of 209 Pa genes previously associated with pathogenesis or adaptation to the CF lung. We trained machine learning models using Pa single nucleotide variants (SNVs), microbiome diversity data and clinical factors to classify lung disease severity at the time of sputum sampling, and to predict lung function decline after 5 years in a cohort of 54 adult CF patients with chronic Pa infection. Models using Pa SNVs alone classified lung disease severity with good sensitivity and specificity (area under the receiver operating characteristic curve: AUROC=0.87). Models were less predictive of lung function decline after 5 years (AUROC=0.74) but still significantly better than random. The addition of clinical data, but not sputum microbiome diversity data, yielded only modest improvements in classifying baseline lung function (AUROC=0.92) and predicting lung function decline (AUROC=0.79), suggesting that Pa AmpliSeq data account for most of the predictive value. Our work provides a proof of principle that Pa genetic variation in sputum tracks lung disease severity, moderately predicts lung function decline and could serve as a disease biomarker among CF patients with chronic Pa infections.

Challenges in Harnessing Shared Within-Host Severe Acute Respiratory Syndrome Coronavirus 2 Variation for Transmission Inference.

Background: The limited variation observed among severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) consensus sequences makes it difficult to reconstruct transmission linkages in outbreak settings. Previous studies have recovered variation within individual SARS-CoV-2 infections but have not yet measured the informativeness of within-host variation for transmission inference. Methods: We performed tiled amplicon sequencing on 307 SARS-CoV-2 samples, including 130 samples from 32 individuals in 14 households and 47 longitudinally sampled individuals, from 4 prospective studies with household membership data, a proxy for transmission linkage. Results: Consensus sequences from households had limited diversity (mean pairwise distance, 3.06 single-nucleotide polymorphisms [SNPs]; range, 0-40). Most (83.1%, 255 of 307) samples harbored at least 1 intrahost single-nucleotide variant ([iSNV] median, 117; interquartile range [IQR], 17-208), above a minor allele frequency threshold of 0.2%. Pairs in the same household shared significantly more iSNVs (mean, 1.20 iSNVs; 95% confidence interval [CI], 1.02-1.39) than did pairs in different households infected with the same viral clade (mean, 0.31 iSNVs; 95% CI, .28-.34), a signal that decreases with increasingly stringent minor allele frequency thresholds. The number of shared iSNVs was significantly associated with an increased odds of household membership (adjusted odds ratio, 1.35; 95% CI, 1.23-1.49). However, the poor concordance of iSNVs detected across sequencing replicates (24.8% and 35.0% above a 0.2% and 1% threshold) confirms technical concerns that current sequencing and bioinformatic workflows do not consistently recover low-frequency within-host variants. Conclusions: Shared within-host variation may augment the information in consensus sequences for predicting transmission linkages. Improving sensitivity and specificity of within-host variant identification will improve the informativeness of within-host variation.

Novel Multilocus Sequence Typing and Global Sequence Clustering Schemes for Characterizing the Population Diversity of Streptococcus mitis.

Streptococcus mitis is a common oral commensal and an opportunistic pathogen that causes bacteremia and infective endocarditis; however, the species has received little attention compared to other pathogenic streptococcal species. Effective and easy-to-use molecular typing tools are essential for understanding bacterial population diversity and biology, but schemes specific for S. mitis are not currently available. We therefore developed a multilocus sequence typing (MLST) scheme and defined sequence clusters or lineages of S. mitis using a comprehensive global data set of 322 genomes (148 publicly available and 174 newly sequenced). We used internal 450-bp sequence fragments of seven housekeeping genes (accA, gki, hom, oppC, patB, rlmN, and tsf) to define the MLST scheme and derived the global S. mitis sequence clusters using the PopPUNK clustering algorithm. We identified an initial set of 259 sequence types (STs) and 258 global sequence clusters. The schemes showed high concordance (100%), capturing extensive S. mitis diversity with strains assigned to multiple unique STs and global sequence clusters. The tools also identified extensive within- and between-host S. mitis genetic diversity among isolates sampled from a cohort of healthy individuals, together with potential transmission events, supported by both phylogeny and pairwise single nucleotide polymorphism (SNP) distances. Our novel molecular typing and strain clustering schemes for S. mitis allow for the integration of new strain data, are electronically portable at the PubMLST database (https://pubmlst.org/smitis), and offer a standardized approach to understanding the population structure of S. mitis. These robust tools will enable new insights into the epidemiology of S. mitis colonization, disease and transmission.

Phylogenetic inference of pneumococcal transmission from cross-sectional data, a pilot study.

Background: Inference on pneumococcal transmission has mostly relied on longitudinal studies which are costly and resource intensive. Therefore, we conducted a pilot study to test the ability to infer who infected whom from cross-sectional pneumococcal sequences using phylogenetic inference. Methods: Five suspected transmission pairs, for which there was epidemiological evidence of who infected whom, were selected from a household study. For each pair, Streptococcus pneumoniae full genomes were sequenced from nasopharyngeal swabs collected on the same day. The within-host genetic diversity of the pneumococcal population was used to infer the transmission direction and then cross-validated with the direction suggested by the epidemiological records. Results: The pneumococcal genomes clustered into the five households from which the samples were taken. The proportion of concordantly inferred transmission direction generally increased with increasing minimum genome fragment size and single nucleotide polymorphisms. We observed a larger proportion of unique polymorphic sites in the source bacterial population compared to that of the recipient in four of the five pairs, as expected in the case of a transmission bottleneck. The only pair that did not exhibit this effect was also the pair that had consistent discordant transmission direction compared to the epidemiological records suggesting potential misdirection as a result of false-negative sampling. Conclusions: This pilot provided support for further studies to test if the direction of pneumococcal transmission can be reliably inferred from cross-sectional samples if sequenced with sufficient depth and fragment length.

Within-Host Genotypic and Phenotypic Diversity of Contemporaneous Carbapenem-Resistant Klebsiella pneumoniae from Blood Cultures of Patients with Bacteremia.

It is unknown whether bacterial bloodstream infections (BSIs) are commonly caused by single organisms or mixed microbial populations. We hypothesized that contemporaneous carbapenem-resistant Klebsiella pneumoniae (CRKP) strains from blood cultures of individual patients are genetically and phenotypically distinct. We determined short-read whole-genome sequences of 10 sequence type 258 (ST258) CRKP strains from blood cultures in each of 6 patients (Illumina HiSeq). Strains clustered by patient by core genome and pan-genome phylogeny. In 5 patients, there was within-host strain diversity by gene mutations, presence/absence of antibiotic resistance or virulence genes, and/or plasmid content. Accessory gene phylogeny revealed strain diversity in all 6 patients. Strains from 3 patients underwent long-read sequencing for genome completion (Oxford Nanopore) and phenotypic testing. Genetically distinct strains within individuals exhibited significant differences in carbapenem and other antibiotic responses, capsular polysaccharide (CPS) production, mucoviscosity, and/or serum killing. In 2 patients, strains differed significantly in virulence during mouse BSIs. Genetic or phenotypic diversity was not observed among strains recovered from blood culture bottles seeded with index strains from the 3 patients and incubated in vitro at 37°C. In conclusion, we identified genotypic and phenotypic variant ST258 CRKP strains from blood cultures of individual patients with BSIs, which were not detected by the clinical laboratory or in seeded blood cultures. The data suggest a new paradigm of CRKP population diversity during BSIs, at least in some patients. If validated for BSIs caused by other bacteria, within-host microbial diversity may have implications for medical, microbiology, and infection prevention practices and for understanding antibiotic resistance and pathogenesis. IMPORTANCE The long-standing paradigm for pathogenesis of bacteremia is that, in most cases, a single organism passes through a bottleneck and establishes itself in the bloodstream (single-organism hypothesis). In keeping with this paradigm, standard practice in processing positive microbiologic cultures is to test single bacterial strains from morphologically distinct colonies. This study is the first genome-wide analysis of within-host diversity of Klebsiella pneumoniae strains recovered from individual patients with bloodstream infections (BSIs). Our finding that positive blood cultures comprised genetically and phenotypically heterogeneous carbapenem-resistant K. pneumoniae strains challenges the single-organism hypothesis and suggests that at least some BSIs are caused by mixed bacterial populations that are unrecognized by the clinical laboratory. The data support a model of pathogenesis in which pressures in vivo select for strain variants with particular antibiotic resistance or virulence attributes and raise questions about laboratory protocols and treatment decisions directed against single strains.

SARS-CoV-2 Within-Host and in vitro Genomic Variability and Sub-Genomic RNA Levels Indicate Differences in Viral Expression Between Clinical Cohorts and in vitro Culture.

Background: Low frequency intrahost single nucleotide variants (iSNVs) of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) have been increasingly recognised as predictive indicators of positive selection. Particularly as growing numbers of SARS-CoV-2 variants of interest (VOI) and concern (VOC) emerge. However, the dynamics of subgenomic RNA (sgRNA) expression and its impact on genomic diversity and infection outcome remain poorly understood. This study aims to investigate and quantify iSNVs and sgRNA expression in single and longitudinally sampled cohorts over the course of mild and severe SARS-CoV-2 infection, benchmarked against an in vitro infection model. Methods: Two clinical cohorts of SARS-CoV-2 positive cases in New South Wales, Australia collected between March 2020 and August 2021 were sequenced. Longitudinal samples from cases hospitalised due to SARS-CoV-2 infection (severe) (n = 16) were analysed and compared with cases that presented with SARS-CoV-2 symptoms but were not hospitalised (mild) (n = 23). SARS-CoV-2 genomic diversity profiles were also examined from daily sampling of culture experiments for three SARS-CoV-2 variants (Lineage A, B.1.351, and B.1.617.2) cultured in VeroE6 C1008 cells (n = 33). Results: Intrahost single nucleotide variants were detected in 83% (19/23) of the mild cohort cases and 100% (16/16) of the severe cohort cases. SNP profiles remained relatively fixed over time, with an average of 1.66 SNPs gained or lost, and an average of 4.2 and 5.9 low frequency variants per patient were detected in severe and mild infection, respectively. sgRNA was detected in 100% (25/25) of the mild genomes and 92% (24/26) of the severe genomes. Total sgRNA expressed across all genes in the mild cohort was significantly higher than that of the severe cohort. Significantly higher expression levels were detected in the spike and the nucleocapsid genes. There was significantly less sgRNA detected in the culture dilutions than the clinical cohorts. Discussion and Conclusion: The positions and frequencies of iSNVs in the severe and mild infection cohorts were dynamic overtime, highlighting the importance of continual monitoring, particularly during community outbreaks where multiple SARS-CoV-2 variants may co-circulate. sgRNA levels can vary across patients and the overall level of sgRNA reads compared to genomic RNA can be less than 1%. The relative contribution of sgRNA to the severity of illness warrants further investigation given the level of variation between genomes. Further monitoring of sgRNAs will improve the understanding of SARS-CoV-2 evolution and the effectiveness of therapeutic and public health containment measures during the pandemic.

Low Pathogenicity H7N3 Avian Influenza Viruses Have Higher Within-Host Genetic Diversity Than a Closely Related High Pathogenicity H7N3 Virus in Infected Turkeys and Chickens.

Within-host viral diversity offers a view into the early stages of viral evolution occurring after a virus infects a host. In recent years, advances in deep sequencing have allowed for routine identification of low-frequency variants, which are important sources of viral genetic diversity and can potentially emerge as a major virus population under certain conditions. We examined within-host viral diversity in turkeys and chickens experimentally infected with closely related H7N3 avian influenza viruses (AIVs), specifically one high pathogenicity AIV (HPAIV) and two low pathogenicity AIV (LPAIVs) with different neuraminidase protein stalk lengths. Consistent with the high mutation rates of AIVs, an abundance of intra-host single nucleotide variants (iSNVs) at low frequencies of 2-10% was observed in all samples collected. Furthermore, a small number of common iSNVs were observed between turkeys and chickens, and between directly inoculated and contact-exposed birds. Notably, the LPAIVs have significantly higher iSNV diversities and frequencies of nonsynonymous changes than the HPAIV in both turkeys and chickens. These findings highlight the dynamics of AIV populations within hosts and the potential impact of genetic changes, including mutations in the hemagglutinin gene that confers the high pathogenicity pathotype, on AIV virus populations and evolution.

The microbial ecology of Escherichia coli in the vertebrate gut.

Escherichia coli has a rich history as biology's 'rock star', driving advances across many fields. In the wild, E. coli resides innocuously in the gut of humans and animals but is also a versatile pathogen commonly associated with intestinal and extraintestinal infections and antimicrobial resistance-including large foodborne outbreaks such as the one that swept across Europe in 2011, killing 54 individuals and causing approximately 4000 infections and 900 cases of haemolytic uraemic syndrome. Given that most E. coli are harmless gut colonizers, an important ecological question plaguing microbiologists is what makes E. coli an occasionally devastating pathogen? To address this question requires an enhanced understanding of the ecology of the organism as a commensal. Here, we review how our knowledge of the ecology and within-host diversity of this organism in the vertebrate gut has progressed in the 137 years since E. coli was first described. We also review current approaches to the study of within-host bacterial diversity. In closing, we discuss some of the outstanding questions yet to be addressed and prospects for future research.

Population genomics of Bacillus anthracis from an anthrax hyperendemic area reveals transmission processes across spatial scales and unexpected within-host diversity.

Genomic sequencing has revolutionized our understanding of bacterial disease epidemiology, but remains underutilized for zoonotic pathogens in remote endemic settings. Anthrax, caused by the spore-forming bacterium Bacillus anthracis, remains a threat to human and animal health and rural livelihoods in low- and middle-income countries. While the global genomic diversity of B. anthracis has been well-characterized, there is limited information on how its populations are genetically structured at the scale at which transmission occurs, critical for understanding the pathogen's evolution and transmission dynamics. Using a uniquely rich dataset, we quantified genome-wide SNPs among 73 B. anthracis isolates derived from 33 livestock carcasses sampled over 1 year throughout the Ngorongoro Conservation Area, Tanzania, a region hyperendemic for anthrax. Genome-wide SNPs distinguished 22 unique B. anthracis genotypes (i.e. SNP profiles) within the study area. However, phylogeographical structure was lacking, as identical SNP profiles were found throughout the study area, likely the result of the long and variable periods of spore dormancy and long-distance livestock movements. Significantly, divergent genotypes were obtained from spatio-temporally linked cases and even individual carcasses. The high number of SNPs distinguishing isolates from the same host is unlikely to have arisen during infection, as supported by our simulation models. This points to an unexpectedly wide transmission bottleneck for B. anthracis, with an inoculum comprising multiple variants being the norm. Our work highlights that inferring transmission patterns of B. anthracis from genomic data will require analytical approaches that account for extended and variable environmental persistence, as well as co-infection.

Insights into the Complexity of a Dormant Mycobacterium tuberculosis Cluster Once Transmission Is Resumed.

Genotyping tools help identify the complexity in Mycobacterium tuberculosis transmission clusters. We carried out a thorough analysis of the epidemiological and bacteriological complexity of a cluster in Almería, Spain. The cluster, initially associated with Moroccan migrants and with no secondary cases identified in 4 years, then reappeared in Spanish-born individuals. In one case, two Mycobacterium tuberculosis clonal variants were identified. We reanalyzed the cluster, supported by the characterization of multiple cultured isolates and respiratory specimens, whole-genome sequencing, and epidemiological case interviews. Our findings showed that the cluster, which was initially thought to have restarted activity with just a single case harboring a small degree of within-host diversity, was in fact currently growing due to coincidental reactivation of past exposures, with clonal diversity transmitted throughout the cluster. In one case, within-host diversity was amplified, probably due to prolonged diagnostic delay. IMPORTANCE The precise study of the dynamics of tuberculosis transmission in socio-epidemiologically complex scenarios may require more thorough analysis than the standard molecular epidemiology strategies. Our study illustrates the epidemiological and bacteriological complexity present in a transmission cluster in a challenging epidemiological setting with a high proportion of migrant cases. The combination of whole-genome sequencing, refined and refocused epidemiological interviews, and in-depth analysis of the bacterial composition of sputa and cultured isolates was crucial in order to correctly reinterpret the true nature of this cluster. Our global approach allowed us to reinterpret correctly the unnoticed epidemiological and bacteriological complexity involved in the Mycobacterium tuberculosis transmission event under study, which had been overlooked by the usual molecular epidemiology approaches.

Population genetic analysis of the Plasmodium falciparum circumsporozoite protein in two distinct ecological regions in Ghana.

BACKGROUND: Extensive genetic diversity in the Plasmodium falciparum circumsporozoite protein (PfCSP) is a major contributing factor to the moderate efficacy of the RTS,S/AS01 vaccine. The transmission intensity and rates of recombination within and between populations influence the extent of its genetic diversity. Understanding the extent and dynamics of PfCSP genetic diversity in different transmission settings will help to interpret the results of current RTS,S efficacy and Phase IV implementation trials conducted within and between populations in malaria-endemic areas such as Ghana. METHODS: Pfcsp sequences were retrieved from the Illumina-generated paired-end short-read sequences of 101 and 131 malaria samples from children aged 6-59 months presenting with clinical malaria at health facilities in Cape Coast (in the coastal belt) and Navrongo (Guinea savannah region), respectively, in Ghana. The sequences were mapped onto the 3D7 reference strain genome to yield high-quality genome-wide coding sequence data. Following data filtering and quality checks to remove missing data, 220 sequences were retained and analysed for the allele frequency spectrum, genetic diversity both within the host and between populations and signatures of selection. Population genetics tools were used to determine the extent and dynamics of Pfcsp diversity in P. falciparum from the two geographically distinct locations in Ghana. RESULTS: Pfcsp showed extensive diversity at the two sites, with the higher transmission site, Navrongo, exhibiting higher within-host and population-level diversity. The vaccine strain C-terminal epitope of Pfcsp was found in only 5.9% and 45.7% of the Navrongo and Cape Coast sequences, respectively. Between 1 and 6 amino acid variations were observed in the TH2R and TH3R epitope regions of PfCSP. Tajima's D was negatively skewed, especially for the population from Cape Coast, given the expected historical population expansion. In contrast, a positive Tajima's D was observed for the Navrongo P. falciparum population, consistent with balancing selection acting on the immuno-dominant TH2R and TH3R vaccine epitopes. CONCLUSION: The low frequencies of the Pfcsp vaccine haplotype in the analysed populations indicate a need for additional molecular and immuno-epidemiological studies with broader temporal and geographic sampling in endemic populations targeted for RTS,S application. These results have implications for the efficacy of the vaccine in Ghana and will inform the choice of alleles to be included in future multivalent or chimeric vaccines.

Within-Host Diversity of SARS-CoV-2 in COVID-19 Patients With Variable Disease Severities.

Background: The ongoing pandemic of SARS-COV-2 has already infected more than eight million people worldwide. The majority of COVID-19 patients either are asymptomatic or have mild symptoms. Yet, about 15% of the cases experience severe complications and require intensive care. Factors determining disease severity are not yet fully characterized. Aim: Here, we investigated the within-host virus diversity in COVID-19 patients with different clinical manifestations. Methods: We compared SARS-COV-2 genetic diversity in 19 mild and 27 severe cases. Viral RNA was extracted from nasopharyngeal samples and sequenced using the Illumina MiSeq platform. This was followed by deep-sequencing analyses of SARS-CoV-2 genomes at both consensus and sub-consensus sequence levels. Results: Consensus sequences of all viruses were very similar, showing more than 99.8% sequence identity regardless of the disease severity. However, the sub-consensus analysis revealed significant differences in within-host diversity between mild and severe cases. Patients with severe symptoms exhibited a significantly (p-value 0.001) higher number of variants in coding and non-coding regions compared to mild cases. Analysis also revealed higher prevalence of some variants among severe cases. Most importantly, severe cases exhibited significantly higher within-host diversity (mean = 13) compared to mild cases (mean = 6). Further, higher within-host diversity was observed in patients above the age of 60 compared to the younger age group. Conclusion: These observations provided evidence that within-host diversity might play a role in the development of severe disease outcomes in COVID-19 patients; however, further investigations are required to elucidate this association.

Previously undetected super-spreading of Mycobacterium tuberculosis revealed by deep sequencing.

Tuberculosis disproportionately affects the Canadian Inuit. To address this, it is imperative we understand transmission dynamics in this population. We investigate whether 'deep' sequencing can provide additional resolution compared to standard sequencing, using a well-characterized outbreak from the Arctic (2011-2012, 50 cases). Samples were sequenced to ~500-1000x and reads were aligned to a novel local reference genome generated with PacBio SMRT sequencing. Consensus and heterogeneous variants were identified and compared across genomes. In contrast with previous genomic analyses using ~50x depth, deep sequencing allowed us to identify a novel super-spreader who likely transmitted to up to 17 other cases during the outbreak (35% of the remaining cases that year). It is increasingly evident that within-host diversity should be incorporated into transmission analyses; deep sequencing may facilitate more accurate detection of super-spreaders and transmission clusters. This has implications not only for TB, but all genomic studies of transmission - regardless of pathogen.

In Canada, tuberculosis disproportionately affects the Inuit, a group of indigenous people inhabiting the Arctic regions. Canada is aiming to eliminate tuberculosis among the Inuit by 2030. One way to help stop transmission and prevent future outbreaks is to trace how and where the disease spreads using DNA sequencing. This information can then be used by public health organizations to identify possible interventions. Typically, the DNA of the bacterium that causes tuberculosis ­ Mycobacterium tuberculosis, or Mtb for short ­ is sequenced 50­100 times and a consensus DNA sequence is then generated for each patient from this data. These consensus DNA sequences are then compared to help piece together who infected whom. Recently, scientists have realized that the bacteria a person is infected with may have different DNA sequences due to people being infected with more than one bacterium or the bacterium developing variations in its genome after the infection. However, current DNA sequencing practices may miss these differences, making it harder to trace how the disease spreads. Now, Lee et al. show that sequencing the DNA of Mtb from an infected person 500­1000 times (i.e. ∼10-20 times more than usual) makes it easier to detect genetic differences and determine how tuberculosis spreads. This approach, also known as 'deep sequencing', was used to analyze DNA samples of Mtb collected from about 50 people during an outbreak of tuberculosis in 2011-2012, which had previously undergone standard DNA sequencing. This deep sequencing approach identified a 'super-spreading event' where one person had likely transmitted tuberculosis to up to 17 others during the outbreak. Lee et al. found that most of these people had visited the same 'gathering houses' which are social venues in the community. Implementing targeted public health interventions at these sites may help stop future outbreaks. To fully understand how useful this method will be for tracking the spread of tuberculosis, deep and routine sequencing will need to be compared against each other in different settings and outbreaks. Furthermore, the approach used in this study may be useful for tracking the transmission of other infectious diseases.

Within-host heterogeneity and flexibility of mcr-1 transmission in chicken gut.

OBJECTIVES: To characterize the colistin-resistant bacterial population in the gut and assess diversity of mcr-1 transmission within a single individual. METHODS: Large numbers of isolates (>100 colonies/chicken cecum sample) were collected from nine randomly selected mcr-1-positive chickens in China and used for comprehensive microbiological, molecular and comparative genomics analyses. RESULTS: Of 1273 colonies, 968 were mcr-1 positive (962 Escherichia coli, two Escherichia fergusonii, two Klebsiella pneumoniae and two Klebsiella quasipneumoniae). One to six colistin-resistant species and three to 10 E. coli pulsed-field gel electrophoresis (PFGE) clusters could be identified from each sample. Whole-genome sequencing (WGS) analysis of the representative E. coli strains revealed three to nine sequence types observed in a single chicken host. The mcr-1 genes are located in either chromosomes or plasmids of different types, including IncI2 (n=30), IncHI2 (n=14), IncX4 (n=4), p0111(n=2) and IncHI1(n=1). Strikingly, in single cecum samples, one to five Inc type plasmids harbouring mcr-1 could be identified. Great diversity was also observed for the same IncI2 plasmid within a single chicken host. In addition, up to eight genetic contexts of the mcr-1 gene occurred within a single chicken. CONCLUSIONS: There is extensive heterogeneity and flexibility of mcr-1 transmission in chicken gut due to bacterial species differences, distant clonal relatedness of isolates, many types and variations of mcr-positive plasmids, and the flexible genetic context of the mcr-1 gene. These compelling findings indicate that the gut is a 'melting pot' for active horizontal transfer of the mcr-1 gene.

Detection of low-density Plasmodium falciparum infections using amplicon deep sequencing.

BACKGROUND: Deep sequencing of targeted genomic regions is becoming a common tool for understanding the dynamics and complexity of Plasmodium infections, but its lower limit of detection is currently unknown. Here, a new amplicon analysis tool, the Parallel Amplicon Sequencing Error Correction (PASEC) pipeline, is used to evaluate the performance of amplicon sequencing on low-density Plasmodium DNA samples. Illumina-based sequencing of two Plasmodium falciparum genomic regions (CSP and SERA2) was performed on two types of samples: in vitro DNA mixtures mimicking low-density infections (1-200 genomes/µl) and extracted blood spots from a combination of symptomatic and asymptomatic individuals (44-653,080 parasites/µl). Three additional analysis tools-DADA2, HaplotypR, and SeekDeep-were applied to both datasets and the precision and sensitivity of each tool were evaluated. RESULTS: Amplicon sequencing can contend with low-density samples, showing reasonable detection accuracy down to a concentration of 5 Plasmodium genomes/µl. Due to increased stochasticity and background noise, however, all four tools showed reduced sensitivity and precision on samples with very low parasitaemia (< 5 copies/µl) or low read count (< 100 reads per amplicon). PASEC could distinguish major from minor haplotypes with an accuracy of 90% in samples with at least 30 Plasmodium genomes/µl, but only 61% at low Plasmodium concentrations (< 5 genomes/µl) and 46% at very low read counts (< 25 reads per amplicon). The four tools were additionally used on a panel of extracted parasite-positive blood spots from natural malaria infections. While all four identified concordant patterns of complexity of infection (COI) across four sub-Saharan African countries, COI values obtained for individual samples differed in some cases. CONCLUSIONS: Amplicon deep sequencing can be used to determine the complexity and diversity of low-density Plasmodium infections. Despite differences in their approach, four state-of-the-art tools resolved known haplotype mixtures with similar sensitivity and precision. Researchers can therefore choose from multiple robust approaches for analysing amplicon data, however, error filtration approaches should not be uniformly applied across samples of varying parasitaemia. Samples with very low parasitaemia and very low read count have higher false positive rates and call for read count thresholds that are higher than current default recommendations.