Epidemiologic data and pathogen genome sequences: a powerful synergy for public health

ViralFlow v1.0-a computational workflow for streamlining viral genomic surveillance

ViralFlow v1.0 is a computational workflow developed for viral genomic surveillance. Several key changes turned ViralFlow into a general-purpose reference-based genome assembler for all viruses with an available reference genome. New virus-agnostic modules were implemented to further study nucleotide and amino acid mutations. ViralFlow v1.0 runs on a broad range of computational infrastructures, from laptop computers to high-performance computing (HPC) environments, and generates standard and well-formatted outputs suited for both public health reporting and scientific problem-solving. ViralFlow v1.0 is available at: https://viralflow.github.io/index-en.html.

Demographic fluctuations in bloodstream Staphylococcus aureus lineages configure the mobile gene pool and antimicrobial resistance

Staphylococcus aureus in the bloodstream causes high morbidity and mortality, exacerbated by the spread of multidrug-resistant and methicillin-resistant S. aureus (MRSA). We aimed to characterize the circulating lineages of S. aureus from bloodstream infections and the contribution of individual lineages to resistance over time. Here, we generated 852 high-quality short-read draft genome sequences of S. aureus isolates from patient blood cultures in a single hospital from 2010 to 2022. A total of 80 previously recognized sequence types (ST) and five major clonal complexes are present in the population. Two frequently detected lineages, ST5 and ST8 exhibited fluctuating demographic structures throughout their histories. The rise and fall in their population growth coincided with the acquisition of antimicrobial resistance, mobile genetic elements, and superantigen genes, thus shaping the accessory genome structure across the entire population. These results reflect undetected selective events and changing ecology of multidrug-resistant S. aureus in the bloodstream.

Patient-Related Metadata Reported in Sequencing Studies of SARS-CoV-2: Protocol for a Scoping Review and Bibliometric Analysis

Background

There has been an unprecedented effort to sequence the SARS-CoV-2 virus and examine its molecular evolution. This has been facilitated by the availability of publicly accessible databases, the Global Initiative on Sharing All Influenza Data (GISAID) and GenBank, which collectively hold millions of SARS-CoV-2 sequence records. Genomic epidemiology, however, seeks to go beyond phylogenetic analysis by linking genetic information to patient characteristics and disease outcomes, enabling a comprehensive understanding of transmission dynamics and disease impact.While these repositories include fields reflecting patient-related metadata for a given sequence, inclusion of these demographic and clinical details is scarce. The extent to which patient-related metadata is reported in published sequencing studies and its quality remains largely unexplored.

Methods

The NIH's LitCovid collection will be used for automated classification of articles reporting having deposited SARS-CoV-2 sequences in public repositories, while an independent search will be conducted in PubMed for validation. Data extraction will be conducted using Covidence. The extracted data will be synthesized and summarized to quantify the availability of patient metadata in the published literature of SARS-CoV-2 sequencing studies. For the bibliometric analysis, relevant data points, such as author affiliations and citation metrics will be extracted.

Discussion

This scoping review will report on the extent and types of patient-related metadata reported in genomic viral sequencing studies of SARS-CoV-2, identify gaps in this reporting, and make recommendations for improving the quality and consistency of reporting in this area. The bibliometric analysis will uncover trends and patterns in the reporting of patient-related metadata, including differences in reporting based on study types or geographic regions. Co-occurrence networks of author keywords will also be presented. The insights gained from this study may help improve the quality and consistency of reporting patient metadata, enhancing the utility of sequence metadata and facilitating future research on infectious diseases.

Estimates of early outbreak-specific SARS-CoV-2 epidemiological parameters from genomic data

We estimate the basic reproductive number and case counts for 15 distinct Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreaks, distributed across 11 populations (10 countries and one cruise ship), based solely on phylodynamic analyses of genomic data. Our results indicate that, prior to significant public health interventions, the reproductive numbers for 10 (out of 15) of these outbreaks are similar, with median posterior estimates ranging between 1.4 and 2.8. These estimates provide a view which is complementary to that provided by those based on traditional line listing data. The genomic-based view is arguably less susceptible to biases resulting from differences in testing protocols, testing intensity, and import of cases into the community of interest. In the analyses reported here, the genomic data primarily provide information regarding which samples belong to a particular outbreak. We observe that once these outbreaks are identified, the sampling dates carry the majority of the information regarding the reproductive number. Finally, we provide genome-based estimates of the cumulative number of infections for each outbreak. For 7 out of 11 of the populations studied, the number of confirmed cases is much bigger than the cumulative number of infections estimated from the sequence data, a possible explanation being the presence of unsequenced outbreaks in these populations.

The emergence and successful elimination of SARS-CoV-2 dominant strains with increasing epidemic potential in Taiwan's 2021 outbreak

Taiwan's experience with severe acute respiratory syndrome coronavirus (SARS-CoV) in 2003 guided its development of strategies to defend against SARS-CoV-2 in 2020, which enabled the successful control of Coronavirus disease 2019 (COVID-19) cases from 2020 through March 2021. However, in late-April 2021, the imported Alpha variant began to cause COVID-19 outbreaks at an exceptional rate in Taiwan. In this study, we aimed to determine what epidemiological conditions enabled the SARS-CoV-2 Alpha variant strains to become dominant and decline later during a surge in the outbreak. In conjunction with contact-tracing investigations, we used our bioinformatics software, CoVConvert and IniCoV, to analyze whole-genome sequences of 101 Taiwan Alpha strains. Univariate and multivariable regression analyses revealed the epidemiological factors associated with viral dominance. Univariate analysis showed the dominant Alpha strains were preferentially selected in the surge's epicenter (p = 0.0024) through intensive human-to-human contact and maintained their dominance for 1.5 months until the Zero-COVID Policy was implemented. Multivariable regression found that the epidemic periods (p = 0.007) and epicenter (p = 0.001) were two significant factors associated with the dominant virus strains spread in the community. These dominant virus strains emerged at the outbreak's epicenter with frequent human-to-human contact and low vaccination coverage. The Level 3 Restrictions and Zero-COVID policy successfully controlled the outbreak in the community without city lockdowns. Our integrated method can identify the epidemiological conditions for emerging dominant virus with increasing epidemiological potential and support decision makers in rapidly containing outbreaks using public health measures that target fast-spreading virus strains.

Threat Net: A Metagenomic Surveillance Network for Biothreat Detection and Early Warning

Early detection of novel pathogens can prevent or substantially mitigate biological incidents, including pandemics. Metagenomic next-generation sequencing (mNGS) of symptomatic clinical samples may enable detection early enough to contain outbreaks, limit international spread, and expedite countermeasure development. In this article, we propose a clinical mNGS architecture we call "Threat Net," which focuses on the hospital emergency department as a high-yield surveillance location. We develop a susceptible-exposed-infected-removed (SEIR) simulation model to estimate the effectiveness of Threat Net in detecting novel respiratory pathogen outbreaks. Our analysis serves to quantify the value of routine clinical mNGS for respiratory pandemic detection by estimating the cost and epidemiological effectiveness at differing degrees of hospital coverage across the United States. We estimate that a biological threat detection network such as Threat Net could be deployed across hospitals covering 30% of the population in the United States. Threat Net would cost between $400 million and $800 million annually and have a 95% chance of detecting a novel respiratory pathogen with traits of SARS-CoV-2 after 10 emergency department presentations and 79 infections across the United States. Our analyses suggest that implementing Threat Net could help prevent or substantially mitigate the spread of a respiratory pandemic pathogen in the United States.

Machine learning to predict foodborne salmonellosis outbreaks based on genome characteristics and meteorological trends

Several studies have shown a correlation between outbreaks of Salmonella enterica and meteorological trends, especially related to temperature and precipitation. Additionally, current studies based on outbreaks are performed on data for the species Salmonella enterica, without considering its intra-species and genetic heterogeneity. In this study, we analyzed the effect of differential gene expression and a suite of meteorological factors on salmonellosis outbreak scale (typified by case numbers) using a combination of machine learning and count-based modeling methods. Elastic Net regularization model was used to identify significant genes from a Salmonella pan-genome, and a multi-variable Poisson regression developed to fit the individual and mixed effects data. The best-fit Elastic Net model (α = 0.50; λ = 2.18) identified 53 significant gene features. The final multi-variable Poisson regression model (χ² = 5748.22; pseudo R² = 0.669; probability > χ² = 0) identified 127 significant predictor terms (p < 0.10), comprising 45 gene-only predictors, average temperature, average precipitation, and average snowfall, and 79 gene-meteorological interaction terms. The significant genes ranged in functionality from cellular signaling and transport, virulence, metabolism, and stress response, and included gene variables not considered as significant by the baseline model. This study presents a holistic approach towards evaluating multiple data sources (such as genomic and environmental data) to predict outbreak scale, which could help in revising the estimates for human health risk.

Development of a novel machine learning-based weighted modeling approach to incorporate Salmonella enterica heterogeneity on a genetic scale in a dose-response modeling framework

Estimating microbial dose-response is an important aspect of a food safety risk assessment. In recent years, there has been considerable interest to advance these models with potential incorporation of gene expression data. The aim of this study was to develop a novel machine learning model that considers the weights of expression of Salmonella genes that could be associated with illness, given exposure, in hosts. Here, an elastic net-based weighted Poisson regression method was proposed to identify Salmonella enterica genes that could be significantly associated with the illness response, irrespective of serovar. The best-fit elastic net model was obtained by 10-fold cross-validation. The best-fit elastic net model identified 33 gene expression-dose interaction terms that added to the predictability of the model. Of these, nine genes associated with Salmonella metabolism and virulence were found to be significant by the best-fit Poisson regression model (p < 0.05). This method could improve or redefine dose-response relationships for illness from relative proportions of significant genes from a microbial genetic dataset, which would help in refining endpoint and risk estimations.

Large-scale investigation of zoonotic viruses in the era of high-throughput sequencing

Zoonotic diseases considerably impact public health and socioeconomics. RNA viruses reportedly caused approximately 94% of zoonotic diseases documented from 1990 to 2010, emphasizing the importance of investigating RNA viruses in animals. Furthermore, it has been estimated that hundreds of thousands of animal viruses capable of infecting humans are yet to be discovered, warning against the inadequacy of our understanding of viral diversity. High-throughput sequencing (HTS) has enabled the identification of viral infections with relatively little bias. Viral searches using both symptomatic and asymptomatic animal samples by HTS have revealed hidden viral infections. This review introduces the history of viral searches using HTS, current analytical limitations, and future potentials. We primarily summarize recent research on large-scale investigations on viral infections reusing HTS data from public databases. Furthermore, considering the accumulation of uncultivated viruses, we discuss current studies and challenges for connecting viral sequences to their phenotypes using various approaches: performing data analysis, developing predictive modeling, or implementing high-throughput platforms of virological experiments. We believe that this article provides a future direction in large-scale investigations of potential zoonotic viruses using the HTS technology.

Canary in the Coal Mine: How Resistance Surveillance in Commensals Could Help Curb the Spread of AMR in Pathogenic Neisseria

Antimicrobial resistance (AMR) is widespread within Neisseria gonorrhoeae populations. Recent work has highlighted the importance of commensal Neisseria (cN) as a source of AMR for their pathogenic relatives through horizontal gene transfer (HGT) of AMR alleles, such as mosaic penicillin binding protein 2 (penA), multiple transferable efflux pump (mtr), and DNA gyrase subunit A (gyrA) which impact beta-lactam, azithromycin, and ciprofloxacin susceptibility, respectively. However, nonpathogenic commensal species are rarely characterized. Here, we propose that surveillance of the universally carried commensal Neisseria may play the role of the "canary in the coal mine," and reveal circulating known and novel antimicrobial resistance determinants transferable to pathogenic Neisseria. We summarize the current understanding of commensal Neisseria as an AMR reservoir, and call to increase research on commensal Neisseria species, through expanding established gonococcal surveillance programs to include the collection, isolation, antimicrobial resistance phenotyping, and whole-genome sequencing (WGS) of commensal isolates. This will help combat AMR in the pathogenic Neisseria by: (i) determining the contemporary AMR profile of commensal Neisseria, (ii) correlating AMR phenotypes with known and novel genetic determinants, (iii) qualifying and quantifying horizontal gene transfer (HGT) for AMR determinants, and (iv) expanding commensal Neisseria genomic databases, perhaps leading to the identification of new drug and vaccine targets. The proposed modification to established Neisseria collection protocols could transform our ability to address AMR N. gonorrhoeae, while requiring minor modifications to current surveillance practices. IMPORTANCE Contemporary increases in the prevalence of antimicrobial resistance (AMR) in Neisseria gonorrhoeae populations is a direct threat to global public health and the effective treatment of gonorrhea. Substantial effort and financial support are being spent on identifying resistance mechanisms circulating within the gonococcal population. However, these surveys often overlook a known source of resistance for gonococci-the commensal Neisseria. Commensal Neisseria and pathogenic Neisseria frequently share DNA through horizontal gene transfer, which has played a large role in rendering antibiotic therapies ineffective in pathogenic Neisseria populations. Here, we propose the expansion of established gonococcal surveillance programs to integrate a collection, AMR profiling, and genomic sequencing pipeline for commensal species. This proposed expansion will enhance the field's ability to identify resistance in and from nonpathogenic reservoirs and anticipate AMR trends in pathogenic Neisseria.

ProbeTools: designing hybridization probes for targeted genomic sequencing of diverse and hypervariable viral taxa

Background

Sequencing viruses in many specimens is hindered by excessive background material from hosts, microbiota, and environmental organisms. Consequently, enrichment of target genomic material is necessary for practical high-throughput viral genome sequencing. Hybridization probes are widely used for enrichment in many fields, but their application to viral sequencing faces a major obstacle: it is difficult to design panels of probe oligo sequences that broadly target many viral taxa due to their rapid evolution, extensive diversity, and genetic hypervariability. To address this challenge, we created ProbeTools, a package of bioinformatic tools for generating effective viral capture panels, and for assessing coverage of target sequences by probe panel designs in silico. In this study, we validated ProbeTools by designing a panel of 3600 probes for subtyping the hypervariable haemagglutinin (HA) and neuraminidase (NA) genome segments of avian-origin influenza A viruses (AIVs). Using in silico assessment of AIV reference sequences and in vitro capture on egg-cultured viral isolates, we demonstrated effective performance by our custom AIV panel and ProbeTools’ suitability for challenging viral probe design applications.

Results

Based on ProbeTool’s in silico analysis, our panel provided broadly inclusive coverage of 14,772 HA and 11,967 NA reference sequences. For each reference sequence, we calculated the percentage of nucleotide positions covered by our panel in silico; 90% of HA and NA references sequences had at least 90.8 and 95.1% of their nucleotide positions covered respectively. We also observed effective in vitro capture on a representative collection of 23 egg-cultured AIVs that included isolates from wild birds, poultry, and humans and representatives from all HA and NA subtypes. Forty-two of forty-six HA and NA segments had over 98.3% of their nucleotide positions significantly enriched by our custom panel. These in vitro results were further used to validate ProbeTools’ in silico coverage assessment algorithm; 89.2% of in silico predictions were concordant with in vitro results.

Conclusions

ProbeTools generated an effective panel for subtyping AIVs that can be deployed for genomic surveillance, outbreak prevention, and pandemic preparedness. Effective probe design against hypervariable AIV targets also validated ProbeTools’ design and coverage assessment algorithms, demonstrating their suitability for other challenging viral capture applications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08790-4.

Sequence to structure analysis of the ORF4 protein from Hepatitis E virus

Hepatitis E virus (HEV) is the main cause of acute hepatitis worldwide. HEV accounts for up to 30% mortality rate in pregnant women, with highest incidences reported for genotype 1 (G1) HEV. The contributing factors in adverse cases during pregnancy in women due to HEV infection is still debated. The mechanism underlying the pathogenesis of viral infection is attributed to different genomic component of HEV, i.e., open reading frames (ORFs): ORF1, ORF2, ORF3 and ORF4. Recently, ORF4 has been discovered in enhancing the replication of GI isolates of HEV through regulation of an IRES-like RNA element. However, its characterization through computational methodologies remains unexplored. In this novel study, we provide comprehensive overview of ORF4 protein's genetic and molecular characteristics through analyzing its sequence and different structural levels. A total of three different datasets (Human, Rat and Ferret) of ORF4 genomes were built and comparatively analyzed. Several non-synonymous mutations in conjunction with higher entropy values were observed in rat and ferret datasets, however, limited variation was observed in human ORF4 genomes. Higher transition to tranversion ratio was observed in the ORF4 genomes. Studies have reported the association of intrinsic disordered proteins (IDP) with drug discovery due to its role in several signaling and regulatory processes through protein-protein interactions (PPIs). As PPIs are potent drug target sources, thus the ORF4 protein was explored by analyzing its polypeptide structure in order to shed light on its intrinsic disorder. Pressures that lead towards preponderance of disordered-promoting amino acid residues shaped the evolution of ORF4. The intrinsic disorder propensity analysis revealed ORF4 protein (Human) as a highly disordered protein (IDP). Predominance of coils and lack of secondary structure further substantiated our findings suggesting its involvement in binding to ligand molecules. Thus, ORF4 contributes to cellular signaling processes through protein-protein interactions, as IDPs are targets for regulation to accelerate the process of drug designing strategies against HEV infections.

Newer variants of COVID-19, newer challenges of whole-genome strategy in India: A public health perspective

The sudden upsurge in the newly emerging COVID-19 variants acted as a catalyst for India to scale up the viral Genomic surveillance in order to understand the nature and trends of the newer variants of concern and strengthen public health interventions across the country. The Government of India has proposed the Indian SARS-CoV-2 Genomics Consortium to expand the whole-genome sequencing (WGS) of this virus. However, in a vast country like India introduction and implementation of any new strategies amidst the already existing barriers due to COVID-19 will be a herculean task. This paper talks about how the primary care physicians can play a vital role in successful implementation of the above strategy in addition to the surveillance systems in India.

Comparison of CRISPR-Cas Immune Systems in Healthcare-Related Pathogens

The ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) and Clostridium difficile have been identified as the leading global cause of multidrug-resistant bacterial infections in hospitals. CRISPR-Cas systems are bacterial immune systems, empowering the bacteria with defense against invasive mobile genetic elements that may carry the antimicrobial resistance (AMR) genes, among others. On the other hand, the CRISPR-Cas systems are themselves mobile. In this study, we annotated and compared the CRISPR-Cas systems in these pathogens, utilizing their publicly available large numbers of sequenced genomes (e.g., there are more than 12 thousands of S. aureus genomes). The presence of CRISPR-Cas systems showed a very broad spectrum in these pathogens: S. aureus has the least tendency of obtaining the CRISPR-Cas systems with only 0.55% of its isolates containing CRISPR-Cas systems, whereas isolates of C. difficile we analyzed have CRISPR-Cas systems each having multiple CRISPRs. Statistical tests show that CRISPR-Cas containing isolates tend to have more AMRs for four of the pathogens (A. baumannii, E. faecium, P. aeruginosa, and S. aureus). We made available all the annotated CRISPR-Cas systems in these pathogens with visualization at a website (https://omics.informatics.indiana.edu/CRISPRone/pathogen), which we believe will be an important resource for studying the pathogens and their arms-race with invaders mediated through the CRISPR-Cas systems, and for developing potential clinical applications of the CRISPR-Cas systems for battles against the antibiotic resistant pathogens.

Bacterial genomic epidemiology with mixed samples

Genomic epidemiology is a tool for tracing transmission of pathogens based on whole-genome sequencing. We introduce the mGEMS pipeline for genomic epidemiology with plate sweeps representing mixed samples of a target pathogen, opening the possibility to sequence all colonies on selective plates with a single DNA extraction and sequencing step. The pipeline includes the novel mGEMS read binner for probabilistic assignments of sequencing reads, and the scalable pseudoaligner Themisto. We demonstrate the effectiveness of our approach using closely related samples in a nosocomial setting, obtaining results that are comparable to those based on single-colony picks. Our results lend firm support to more widespread consideration of genomic epidemiology with mixed infection samples.

The impact of testing and infection prevention and control strategies on within-hospital transmission dynamics of COVID-19 in English hospitals

Nosocomial transmission of SARS-CoV-2 is a key concern, and evaluating the effect of testing and infection prevention and control strategies is essential for guiding policy in this area. Using a within-hospital SEIR transition model of SARS-CoV-2 in a typical English hospital, we estimate that between 9 March 2020 and 17 July 2020 approximately 20% of infections in inpatients, and 73% of infections in healthcare workers (HCWs) were due to nosocomial transmission. Model results suggest that placing suspected COVID-19 patients in single rooms or bays has the potential to reduce hospital-acquired infections in patients by up to 35%. Periodic testing of HCWs has a smaller effect on the number of hospital-acquired COVID-19 cases in patients, but reduces infection in HCWs by as much as 37% and results in only a small proportion of staff absences (approx. 0.3% per day). This is considerably less than the 20-25% of staff that have been reported to be absent from work owing to suspected COVID-19 and self-isolation. Model-based evaluations of interventions, informed by data collected so far, can help to inform policy as the pandemic progresses and help prevent transmission in the vulnerable hospital population. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.

Nosocomial transmission and outbreaks of coronavirus disease 2019: the need to protect both patients and healthcare workers

OBJECTIVES

To compile current published reports on nosocomial outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), evaluate the role of healthcare workers (HCWs) in transmission, and evaluate outbreak management practices.

METHODS

Narrative literature review.

SHORT CONCLUSION

The coronavirus disease 2019 (COVID-19) pandemic has placed a large burden on hospitals and healthcare providers worldwide, which increases the risk of nosocomial transmission and outbreaks to "non-COVID" patients or residents, who represent the highest-risk population in terms of mortality, as well as HCWs. To date, there are several reports on nosocomial outbreaks of SARS-CoV-2, and although the attack rate is variable, it can be as high as 60%, with high mortality. There is currently little evidence on transmission dynamics, particularly using genomic sequencing, and the role of HCWs in initiating or amplifying nosocomial outbreaks is not elucidated. There has been a paradigm shift in management practices of viral respiratory outbreaks, that includes widespread testing of patients (or residents) and HCWs, including asymptomatic individuals. These expanded testing criteria appear to be crucial in identifying and controlling outbreaks.

Molecular epidemiology in the HIV and SARS-CoV-2 pandemics

PURPOSE OF REVIEW

The aim of this review was to compare and contrast the application of molecular epidemiology approaches for the improved management and understanding of the HIV versus SARS-CoV-2 epidemics.

RECENT FINDINGS

Molecular biology approaches, including PCR and whole genome sequencing (WGS), have become powerful tools for epidemiological investigation. PCR approaches form the basis for many high-sensitivity diagnostic tests and can supplement traditional contact tracing and surveillance strategies to define risk networks and transmission patterns. WGS approaches can further define the causative agents of disease, trace the origins of the pathogen, and clarify routes of transmission. When coupled with clinical datasets, such as electronic medical record data, these approaches can investigate co-correlates of disease and pathogenesis. In the ongoing HIV epidemic, these approaches have been effectively deployed to identify treatment gaps, transmission clusters and risk factors, though significant barriers to rapid or real-time implementation remain critical to overcome. Likewise, these approaches have been successful in addressing some questions of SARS-CoV-2 transmission and pathogenesis, but the nature and rapid spread of the virus have posed additional challenges.

SUMMARY

Overall, molecular epidemiology approaches offer unique advantages and challenges that complement traditional epidemiological tools for the improved understanding and management of epidemics.

Molecular Characterization of Cronobacter sakazakii Strains Isolated from Powdered Milk

Cronobacter spp. are opportunistic pathogens of the Enterobacteriaceae family. The organism causes infections in all age groups, but the most serious cases occur in outbreaks related to neonates with meningitis and necrotizing enterocolitis. The objective was to determine the in silico and in vitro putative virulence factors of six Cronobacter sakazakii strains isolated from powdered milk (PM) in the Czech Republic. Strains were identified by MALDI-TOF MS and whole-genome sequencing (WGS). Virulence and resistance genes were detected with the Ridom SeqSphere+ software task template and the Comprehensive Antibiotic Resistance Database (CARD) platform. Adherence and invasion ability were performed using the mouse neuroblastoma (N1E-115 ATCCCRL-2263) cell line. The CRISPR-Cas system was searched with CRISPRCasFinder. Core genome MLST identified four different sequence types (ST1, ST145, ST245, and ST297) in six isolates. Strains 13755-1B and 1847 were able to adhere in 2.2 and 3.2 × 10⁶ CFU/mL, while 0.00073% invasion frequency was detected only in strain 1847. Both strains 13755-1B and 1847 were positive for three (50.0%) and four virulence genes, respectively. The cpa gene was not detected. Twenty-eight genes were detected by WGS and grouped as flagellar or outer membrane proteins, chemotaxis, hemolysins, and invasion, plasminogen activator, colonization, transcriptional regulator, and survival in macrophages. The colistin-resistance-encoding mcr-9.1 and cephalothin-resis-encoding bla_CSA genes and IncFII(pECLA) and IncFIB(pCTU3) plasmids were detected. All strains exhibited CRISPR matrices and four of them two type I-E and I-F matrices. Combined molecular methodologies improve Cronobacter spp. decision-making for health authorities to protect the population.

Monitoring Plasmodium falciparum and Plasmodium vivax using microsatellite markers indicates limited changes in population structure after substantial transmission decline in Papua New Guinea

Monitoring the genetic structure of pathogen populations may be an economical and sensitive approach to quantify the impact of control on transmission dynamics, highlighting the need for a better understanding of changes in population genetic parameters as transmission declines. Here we describe the first population genetic analysis of two major human malaria parasites, Plasmodium falciparum (Pf) and Plasmodium vivax (Pv), following nationwide distribution of long-lasting insecticide-treated nets (LLINs) in Papua New Guinea (PNG). Parasite isolates from pre- (2005-2006) and post-LLIN (2010-2014) were genotyped using microsatellite markers. Despite parasite prevalence declining substantially (East Sepik Province: Pf = 54.9%-8.5%, Pv = 35.7%-5.6%, Madang Province: Pf = 38.0%-9.0%, Pv: 31.8%-19.7%), genetically diverse and intermixing parasite populations remained. Pf diversity declined modestly post-LLIN relative to pre-LLIN (East Sepik: R_s  = 7.1-6.4, H_E  = 0.77-0.71; Madang: R_s  = 8.2-6.1, H_E  = 0.79-0.71). Unexpectedly, population structure present in pre-LLIN populations was lost post-LLIN, suggesting that more frequent human movement between provinces may have contributed to higher gene flow. Pv prevalence initially declined but increased again in one province, yet diversity remained high throughout the study period (East Sepik: R_s  = 11.4-9.3, H_E  = 0.83-0.80; Madang: R_s  = 12.2-14.5, H_E  = 0.85-0.88). Although genetic differentiation values increased between provinces over time, no significant population structure was observed at any time point. For both species, a decline in multiple infections and increasing clonal transmission and significant multilocus linkage disequilibrium post-LLIN were positive indicators of impact on the parasite population using microsatellite markers. These parameters may be useful adjuncts to traditional epidemiological tools in the early stages of transmission reduction.

Whole-genome sequencing as part of national and international surveillance programmes for antimicrobial resistance: a roadmap

The global spread of antimicrobial resistance (AMR) and lack of novel alternative treatments have been declared a global public health emergency by WHO. The greatest impact of AMR is experienced in resource-poor settings, because of lack of access to alternative antibiotics and because the prevalence of multidrug-resistant bacterial strains may be higher in low-income and middle-income countries (LMICs). Intelligent surveillance of AMR infections is key to informed policy decisions and public health interventions to counter AMR. Molecular surveillance using whole-genome sequencing (WGS) can be a valuable addition to phenotypic surveillance of AMR. WGS provides insights into the genetic basis of resistance mechanisms, as well as pathogen evolution and population dynamics at different spatial and temporal scales. Due to its high cost and complexity, WGS is currently mainly carried out in high-income countries. However, given its potential to inform national and international action plans against AMR, establishing WGS as a surveillance tool in LMICs will be important in order to produce a truly global picture. Here, we describe a roadmap for incorporating WGS into existing AMR surveillance frameworks, including WHO Global Antimicrobial Resistance Surveillance System, informed by our ongoing, practical experiences developing WGS surveillance systems in national reference laboratories in Colombia, India, Nigeria and the Philippines. Challenges and barriers to WGS in LMICs will be discussed together with a roadmap to possible solutions.

Real-time tracking of Tomato brown rugose fruit virus (ToBRFV) outbreaks in the Netherlands using Nextstrain

Tomato brown rugose fruit virus (ToBRFV) is a Tobamovirus that was first observed in 2014 and 2015 on tomato plants in Israel and Jordan respectively. Since the first description, the virus has been reported from all continents except Oceania and Antarctica, and has been found infecting both tomato and pepper crops. In October 2019, the Dutch National Plant Protection Organization received a ToBRFV infected tomato sample as part of a generic survey targeting tomato pests. Presence of the virus was verified using Illumina sequencing. A follow-up survey was initiated to determine the extent of ToBRFV presence in the Dutch tomato horticulture and identify possible linkages between ToBRFV genotypes, companies and epidemiological traits. Nextstrain was used to visualize these potential connections. By November 2019, 68 companies had been visited of which 17 companies were found to be infected. The 50 ToBRFV genomes from these outbreak locations group in three main clusters, which are hypothesized to represent three original sources. No correlation was found between genotypes, companies and epidemiological traits, and the source(s) of the Dutch ToBRFV outbreak remain unknown. This paper describes a Nextstrain build containing ToBRFV genomes up to and including November 2019. Sharing data with this interactive online tool will enable the plant virology field to better understand and communicate the diversity and spread of this new virus. Organizations are invited to share data or materials for inclusion in the Nextstrain build, which can be accessed at https://nextstrain.nrcnvwa.nl/ToBRFV/20191231.

The intersection of genomics and big data with public health: Opportunities for precision public health

Muin Khoury and co-authors discuss anticipated contributions of genomics and other forms of large-scale data in public health.

Genomic Epidemiology and Evolution of Diverse Lineages of Clinical Campylobacter jejuni Cocirculating in New Hampshire, USA, 2017

Campylobacter jejuni is one of the leading causes of bacterial gastroenteritis worldwide. In the United States, New Hampshire was one of the 18 states that reported cases in the 2016 to 2018 multistate outbreak of multidrug-resistant C. jejuni. Here, we aimed to elucidate the baseline diversity of the wider New Hampshire C. jejuni population during the outbreak. We used genome sequences of 52 clinical isolates sampled in New Hampshire in 2017, including 1 of the 2 isolates from the outbreak.

Campylobacter jejuni is one of the leading causes of bacterial gastroenteritis worldwide. In the United States, New Hampshire was one of the 18 states that reported cases in the 2016 to 2018 multistate outbreak of multidrug-resistant C. jejuni. Here, we aimed to elucidate the baseline diversity of the wider New Hampshire C. jejuni population during the outbreak. We used genome sequences of 52 clinical isolates sampled in New Hampshire in 2017, including 1 of the 2 isolates from the outbreak. Results revealed a remarkably diverse population composed of at least 28 sequence types, which are mostly represented by 1 or a few strains. A comparison of our isolates with 249 clinical C. jejuni from other states showed frequent phylogenetic intermingling, suggesting a lack of geographical structure and minimal local diversification within the state. Multiple independent acquisitions of resistance genes from 5 classes of antibiotics characterize the population, with 47/52 (90.4%) of the genomes carrying at least 1 horizontally acquired resistance gene. Frequently recombining genes include those associated with heptose biosynthesis, colonization, and stress resistance. We conclude that the diversity of clinical C. jejuni in New Hampshire in 2017 was driven mainly by the coexistence of phylogenetically diverse antibiotic-resistant lineages, widespread geographical mixing, and frequent recombination. This study provides an important baseline census of the standing pangenomic variation and drug resistance to aid the development of a statewide database for epidemiological studies and clinical decision making. Continued genomic surveillance will be necessary to accurately assess how the population of C. jejuni changes over the long term.

Assessing genetic evolution and detecting human papillomavirus by matching two complementary highly sensitive approaches, nested-qPCR and sequencing

Becoming armed with an appropriate strategy to isolate the minimum number of human papillomaviruses (HPV), regardless of DNA extraction method, can be a huge step in preventing false negative; it has a significant effect on the management and control of HPV infection among women's population. This study was conducted in Qom province, considering the risk factors associated with HPV. It was able to analyze genetic evolution in its genotypes and evaluated the limit of detection by a new diagnostic approach. Totally, 486 Pap smear samples were tested; then, the HPV DNA was developed by a semi-nested quantification PCR. Positive samples were sequenced and submitted to the GenBank (MG825048-MG825061). After alignment, phylogenetic and polymorphism analyses were performed on the sequenced samples with a number of GenBank sequences. The overall HPV prevalence among all women in Qom was 11.7%. HPV6 (43.24%) and HPV16 (6.75%) were the most frequent LR and HR genotypes, respectively. Although the Tajima's D of all genotypes was positive, it was negative individually. The position of genotypes 6, 11, and 73 was controversial on phylogenetic trees. Limit of detection (LOD) was obtained as about 10-100 copies per reaction in various genotypes of HPV by semi-nested qPCR. The nature of HPV could be preserved during natural selection. This research, through innovative usage of the primers, could detect different genotypes of the HPV, and inform the women society of the probable risk through its prevalence determination.

The Contribution of Genetic Variation of Streptococcus pneumoniae to the Clinical Manifestation of Invasive Pneumococcal Disease

Background

Different clinical manifestations of invasive pneumococcal disease (IPD) have thus far mainly been explained by patient characteristics. Here we studied the contribution of pneumococcal genetic variation to IPD phenotype.

Methods

The index cohort consisted of 349 patients admitted to 2 Dutch hospitals between 2000-2011 with pneumococcal bacteremia. We performed genome-wide association studies to identify pneumococcal lineages, genes, and allelic variants associated with 23 clinical IPD phenotypes. The identified associations were validated in a nationwide (n = 482) and a post-pneumococcal vaccination cohort (n = 121). The contribution of confirmed pneumococcal genotypes to the clinical IPD phenotype, relative to known clinical predictors, was tested by regression analysis.

Results

Among IPD patients, the presence of pneumococcal gene slaA was a nationwide confirmed independent predictor of meningitis (odds ratio [OR], 10.5; P = .001), as was sequence cluster 9 (serotype 7F: OR, 3.68; P = .057). A set of 4 pneumococcal genes co-located on a prophage was a confirmed independent predictor of 30-day mortality (OR, 3.4; P = .003). We could detect the pneumococcal variants of concern in these patients' blood samples.

Conclusions

In this study, knowledge of pneumococcal genotypic variants improved the clinical risk assessment for detrimental manifestations of IPD. This provides us with novel opportunities to target, anticipate, or avert the pathogenic effects related to particular pneumococcal variants, and indicates that information on pneumococcal genotype is important for the diagnostic and treatment strategy in IPD. Ongoing surveillance is warranted to monitor the clinical value of information on pneumococcal variants in dynamic microbial and susceptible host populations.

Pathogen Genomics in Public Health

Rapid advances in DNA sequencing technology ("next-generation sequencing") have inspired optimism about the potential of human genomics for "precision medicine." Meanwhile, pathogen genomics is already delivering "precision public health" through more effective investigations of outbreaks of foodborne illnesses, better-targeted tuberculosis control, and more timely and granular influenza surveillance to inform the selection of vaccine strains. In this article, we describe how public health agencies have been adopting pathogen genomics to improve their effectiveness in almost all domains of infectious disease. This momentum is likely to continue, given the ongoing development in sequencing and sequencing-related technologies.

Whole-Genome Sequencing To Identify Drivers of Carbapenem-Resistant Klebsiella pneumoniae Transmission within and between Regional Long-Term Acute-Care Hospitals

Carbapenem-resistant Klebsiella pneumoniae (CRKP) is an antibiotic resistance threat of the highest priority. Given the limited treatment options for this multidrug-resistant organism (MDRO), there is an urgent need for targeted strategies to prevent transmission. Here, we applied whole-genome sequencing to a comprehensive collection of clinical isolates to reconstruct regional transmission pathways and analyzed this transmission network in the context of statewide patient transfer data and patient-level clinical data to identify drivers of regional transmission. We found that high regional CRKP burdens were due to a small number of regional introductions, with subsequent regional proliferation occurring via patient transfers among health care facilities. While CRKP was predicted to have been imported into each facility multiple times, there was substantial variation in the ratio of intrafacility transmission events per importation, indicating that amplification occurs unevenly across regional facilities. While myriad factors likely influence intrafacility transmission rates, an understudied one is the potential for clinical characteristics of colonized and infected patients to influence their propensity for transmission. Supporting the contribution of high-risk patients to elevated transmission rates, we observed that patients colonized and infected with CRKP in high-transmission facilities had higher rates of carbapenem use, malnutrition, and dialysis and were older. This report highlights the potential for regional infection prevention efforts that are grounded in genomic epidemiology to identify the patients and facilities that make the greatest contribution to regional MDRO prevalence, thereby facilitating the design of precision interventions of maximal impact.

Molecular surveillance of hepatitis C virus genotypes identifies the emergence of a genotype 4d lineage among men in Quebec, 2001-2017

Background

Molecular phylogenetics are generally used to confirm hepatitis C virus (HCV) transmission events. In addition, the Laboratoire de santé publique du Québec (LSPQ) has been using molecular phylogenetics for surveillance of HCV genotyping since November 2001.

Objectives

To describe the emergence of a specific lineage of HCV genotype 4d (G4d) and its characteristics using molecular phylogenetics as a surveillance tool for identifying HCV strain clustering.

Methods

The LSPQ prospectively applied Sanger sequencing and phylogenetic analysis to determine the HCV genotype on samples collected from November 2001 to December 2017. When a major G4d cluster was identified, demographic information, HIV-infection status and syphilis test results were analyzed.

Results

Phylogenetic analyses performed on approximately 22,000 cases identified 122 G4d cases. One major G4d cluster composed of 37 cases was singled out. Two cases were identified in 2010, 10 from 2011-2014 and 25 from 2015-2017. Cases in the cluster were concentrated in two urban health regions. Compared to the other G4d cases, cluster cases were all male (p<0.001) and more likely to be HIV-positive (adjusted risk ratio: 4.4; 95% confidence interval: 2.5-7.9). A positive syphilis test result was observed for 27 (73%) of the cluster cases. The sequences in this cluster and of four outlier cases were located on the same monophyletic lineage as G4d sequences reported in HIV-positive men who have sex with men (MSM) in Europe.

Conclusion

Molecular phylogenetics enabled the identification and surveillance of ongoing transmission of a specific HCV G4d lineage in HIV-positive and HIV-negative men in Quebec and its cross-continental spread. This information can orient intervention strategies to avoid transmission of HCV in MSM.

Status and potential of bacterial genomics for public health practice: a scoping review

Background

Next-generation sequencing (NGS) is increasingly being translated into routine public health practice, affecting the surveillance and control of many pathogens. The purpose of this scoping review is to identify and characterize the recent literature concerning the application of bacterial pathogen genomics for public health practice and to assess the added value, challenges, and needs related to its implementation from an epidemiologist’s perspective.

Methods

In this scoping review, a systematic PubMed search with forward and backward snowballing was performed to identify manuscripts in English published between January 2015 and September 2018. Included studies had to describe the application of NGS on bacterial isolates within a public health setting. The studied pathogen, year of publication, country, number of isolates, sampling fraction, setting, public health application, study aim, level of implementation, time orientation of the NGS analyses, and key findings were extracted from each study. Due to a large heterogeneity of settings, applications, pathogens, and study measurements, a descriptive narrative synthesis of the eligible studies was performed.

Results

Out of the 275 included articles, 164 were outbreak investigations, 70 focused on strategy-oriented surveillance, and 41 on control-oriented surveillance. Main applications included the use of whole-genome sequencing (WGS) data for (1) source tracing, (2) early outbreak detection, (3) unraveling transmission dynamics, (4) monitoring drug resistance, (5) detecting cross-border transmission events, (6) identifying the emergence of strains with enhanced virulence or zoonotic potential, and (7) assessing the impact of prevention and control programs. The superior resolution over conventional typing methods to infer transmission routes was reported as an added value, as well as the ability to simultaneously characterize the resistome and virulome of the studied pathogen. However, the full potential of pathogen genomics can only be reached through its integration with high-quality contextual data.

Conclusions

For several pathogens, it is time for a shift from proof-of-concept studies to routine use of WGS during outbreak investigations and surveillance activities. However, some implementation challenges from the epidemiologist’s perspective remain, such as data integration, quality of contextual data, sampling strategies, and meaningful interpretations. Interdisciplinary, inter-sectoral, and international collaborations are key for an appropriate genomics-informed surveillance.

Electronic supplementary material

The online version of this article (10.1186/s13012-019-0930-2) contains supplementary material, which is available to authorized users.

Personal clinical history predicts antibiotic resistance of urinary tract infections

Antibiotic resistance is prevalent among the bacterial pathogens causing urinary tract infections. However, antimicrobial treatment is often prescribed “empirically”, in the absence of antibiotic susceptibility testing, risking mismatched and therefore ineffective treatment. Here, linking a 10-year longitudinal dataset of over 700,000 community-acquired UTIs with over 5,000,000 individually-resolved records of antibiotic purchases, we identify strong associations of antibiotic resistance with the demographics, records of past urine cultures and history of drug purchases of the patients. When combined together, these associations allow for machine learning-based personalized drug-specific predictions of antibiotic resistance, thereby enabling drug-prescribing algorithms that match antibiotic treatment recommendation to the expected resistance of each sample. Applying these algorithms retrospectively, over a one-year test period, we find that they much reduce the risk of mismatched treatment compared to the current standard-of-care. The clinical application of such algorithms may help improve the effectiveness of antimicrobial treatments.

hicap: In Silico Serotyping of the Haemophilus influenzae Capsule Locus

Haemophilus influenzae exclusively colonizes the human nasopharynx and can cause a variety of respiratory infections as well as invasive diseases, including meningitis and sepsis. A key virulence determinant of H. influenzae is the polysaccharide capsule, of which six serotypes are known, each encoded by a distinct variation of the capsule biosynthesis locus (cap-a to cap-f).

Haemophilus influenzae exclusively colonizes the human nasopharynx and can cause a variety of respiratory infections as well as invasive diseases, including meningitis and sepsis. A key virulence determinant of H. influenzae is the polysaccharide capsule, of which six serotypes are known, each encoded by a distinct variation of the capsule biosynthesis locus (cap-a to cap-f). H. influenzae type b (Hib) was historically responsible for the majority of invasive H. influenzae disease, and its prevalence has been markedly reduced in countries that have implemented vaccination programs targeting this serotype. In the postvaccine era, nontypeable H. influenzae emerged as the most dominant group causing disease, but in recent years a resurgence of encapsulated H. influenzae strains has also been observed, most notably serotype a. Given the increasing incidence of encapsulated strains and the high frequency of Hib in countries without vaccination programs, there is growing interest in genomic epidemiology of H. influenzae. Here we present hicap, a software tool for rapid in silico serotype prediction from H. influenzae genome sequences. hicap is written using Python3 and is freely available at https://github.com/scwatts/hicap under the GNU General Public License v3 (GPL3). To demonstrate the utility of hicap, we used it to investigate the cap locus diversity and distribution in 691 high-quality H. influenzae genomes from GenBank. These analyses identified cap loci in 95 genomes and confirmed the general association of each serotype with a unique clonal lineage, and they also identified occasional recombination between lineages that gave rise to hybrid cap loci (2% of encapsulated strains).

Operational models and criteria for incorporating microbial whole genome sequencing in hospital microbiology - A systematic literature review

BACKGROUND

Microbial whole genome sequencing (WGS) has many advantages over standard microbiological methods. However, it is not yet widely implemented in routine hospital diagnostics due to notable challenges.

OBJECTIVES

The aim was to extract managerial, financial and clinical criteria supporting the decision to implement WGS in routine diagnostic microbiology, across different operational models of implementation in the hospital setting.

METHODS

This was a systematic review of literature identified through PubMed and Web of Science. English literature studies discussing the applications of microbial WGS without limitation on publication date were eligible. A narrative approach for categorization and synthesis of the sources identified was adopted.

RESULTS

A total of 98 sources were included. Four main alternative operational models for incorporating WGS in clinical microbiology laboratories were identified: full in-house sequencing and analysis, full outsourcing of sequencing and analysis and two hybrid models combining in-house/outsourcing of the sequencing and analysis components. Six main criteria (and multiple related sub-criteria) for WGS implementation emerged from our review and included cost (e.g. the availability of resources for capital and operational investment); manpower (e.g. the ability to provide training programmes or recruit trained personnel), laboratory infrastructure (e.g. the availability of supplies and consumables or sequencing platforms), bioinformatics requirements (e.g. the availability of valid analysis tools); computational infrastructure (e.g. the availability of storage space or data safety arrangements); and quality control (e.g. the existence of standardized procedures).

CONCLUSIONS

The decision to incorporate WGS in routine diagnostics involves multiple, sometimes competing, criteria and sub-criteria. Mapping these criteria systematically is an essential stage in developing policies for adoption of this technology, e.g. using a multicriteria decision tool. Future research that will prioritize criteria and sub-criteria that were identified in our review in the context of operational models will inform decision-making at clinical and managerial levels with respect to effective implementation of WGS for routine use. Beyond WGS, similar decision-making challenges are expected with respect to future integration of clinical metagenomics.

Statistical outbreak detection by joining medical records and pathogen similarity

We present a statistical inference model for the detection and characterization of outbreaks of hospital associated infection. The approach combines patient exposures, determined from electronic medical records, and pathogen similarity, determined by whole-genome sequencing, to simultaneously identify probable outbreaks and their root-causes. We show how our model can be used to target isolates for whole-genome sequencing, improving outbreak detection and characterization even without comprehensive sequencing. Additionally, we demonstrate how to learn model parameters from reference data of known outbreaks. We demonstrate model performance using semi-synthetic experiments.

Shared genome analyses of notable listeriosis outbreaks, highlighting the critical importance of epidemiological evidence, input datasets and interpretation criteria

The persuasiveness of genomic evidence has pressured scientific agencies to supplement or replace well-established methodologies to inform public health and food safety decision-making. This study of 52 epidemiologically defined Listeria monocytogenes isolates, collected between 1981 and 2011, including nine outbreaks, was undertaken (1) to characterize their phylogenetic relationship at finished genome-level resolution, (2) to elucidate the underlying genetic diversity within an endemic subtype, CC8, and (3) to re-evaluate the genetic relationship and epidemiology of a CC8-delimited outbreak in Canada in 2008. Genomes representing Canadian Listeria outbreaks between 1981 and 2010 were closed and manually annotated. Single nucleotide variants (SNVs) and horizontally acquired traits were used to generate phylogenomic models. Phylogenomic relationships were congruent with classical subtyping and epidemiology, except for CC8 outbreaks, wherein the distribution of SNV and prophages revealed multiple co-evolving lineages. Chronophyletic reconstruction of CC8 evolution indicates that prophage-related genetic changes among CC8 strains manifest as PFGE subtype reversions, obscuring the relationship between CC8 isolates, and complicating the public health interpretation of subtyping data, even at maximum genome resolution. The size of the shared genome interrogated did not change the genetic relationship measured between highly related isolates near the tips of the phylogenetic tree, illustrating the robustness of these approaches for routine public health applications where the focus is recent ancestry. The possibility exists for temporally and epidemiologically distinct events to appear related even at maximum genome resolution, highlighting the continued importance of epidemiological evidence.

Occurrence of virulence factors in Cronobacter sakazakii and Cronobacter malonaticus originated from clinical samples

BACKGROUND

Cronobacter spp. are Gram-negative, facultative-anaerobic, non-spore forming, enteric coliform bacteria, which belongs to the Enterobacteriaceae family. Cronobacter spp. are opportunistic pathogens that have brought rare but life-threatening infections such as meningitis, necrotizing enterocolitis and bloodstream infections in neonates and infants. Information on the diversity, pathogenicity and virulence of Cronobacter species obtained from various sources is still relatively scarce and fragmentary. The aim of this study was to examine and analyse different pathogenicity and virulence factors among C. sakazakii and C. malonaticus strains isolated from clinical samples.

METHODS

The thirty-six clinical Cronobacter strains have been used in this study. This bacterial collection consists of 25 strains of C. sakazakii and 11 strains of C. malonaticus, isolated from different clinical materials. Seven genes (ompA, inv, sip, aut, hly, fliC, cpa) were amplified by PCR. Moreover, the motility and the ability of these strains to adhere and invade human colorectal adenocarcinoma (HT-29) and mouse neuroblastoma (N1E-115) cell lines were investigated.

RESULTS

Our results showed that all tested strains were able to adhere to both used cell lines, HT-29 and N1E-115 cells. The invasion assay showed that 66.7% (24/36) of isolates were able to invade N1-E115 cells while 83% (30/36) of isolates were able to invade HT-29 cells. On the average, 68% of the C. sakazakii strains exhibited seven virulence factors and only 18% in C. malonaticus. All strains amplified ompA and fliC genes. The other genes were detected as follow: sip 97% (35/36), hlyA 92% (33/36), aut 94% (34/36), cpa 67% (24/36), and inv 69% (25/36).

CONCLUSIONS

C. sakazakii and C malonaticus strains demonstrate the diversity of the virulence factors present among these pathogens. It is necessary to permanently monitor the hospital environment to appropriately treat and resolve cases associated with disease. Furthermore, in-depth knowledge is needed about the source and transmission vehicles of pathogens in hospitals to adopt pertinent prevention measures.

Geographic transmission hubs of the 2009 influenza pandemic in the United States

A key issue in infectious disease epidemiology is to identify and predict geographic sites of epidemic establishment that contribute to onward spread, especially in the context of invasion waves of emerging pathogens. Conventional wisdom suggests that these sites are likely to be in densely-populated, well-connected areas. For pandemic influenza, however, epidemiological data have not been available at a fine enough geographic resolution to test this assumption. Here, we make use of fine-scale influenza-like illness incidence data derived from electronic medical claims records gathered from 834 3-digit ZIP (postal) codes across the US to identify the key geographic establishment sites, or "hubs", of the autumn wave of the 2009 A/H1N1pdm influenza pandemic in the United States. A mechanistic spatial transmission model is fit to epidemic onset times inferred from the data. Hubs are identified by tracing the most probable transmission routes back to a likely first establishment site. Four hubs are identified: two in the southeastern US, one in the central valley of California, and one in the midwestern US. According to the model, 75% of the 834 observed ZIP-level outbreaks in the US were seeded by these four hubs or their epidemiological descendants. Counter-intuitively, the pandemic hubs do not coincide with large and well-connected cities, indicating that factors beyond population density and travel volume are necessary to explain the establishment sites of the major autumn wave of the pandemic. Geographic regions are identified where infection can be statistically traced back to a hub, providing a testable prediction of the outbreak's phylogeography. Our method therefore provides an important way forward to reconcile spatial diffusion patterns inferred from epidemiological surveillance data and pathogen sequence data.

Are Food Animals Responsible for Transfer of Antimicrobial-Resistant Escherichia coli or Their Resistance Determinants to Human Populations? A Systematic Review

The role of farm animals in the emergence and dissemination of both AMR bacteria and their resistance determinants to humans is poorly understood and controversial. Here, we systematically reviewed the current evidence that food animals are responsible for transfer of AMR to humans. We searched PubMed, Web of Science, and EMBASE for literature published between 1940 and 2016. Our results show that eight studies (18%) suggested evidence of transmission of AMR from food animals to humans, 25 studies (56%) suggested transmission between animals and humans with no direction specified and 12 studies (26%) did not support transmission. Quality of evidence was variable among the included studies; one study (2%) used high resolution typing tools, 36 (80%) used intermediate resolution typing tools, six (13%) relied on low resolution typing tools, and two (5%) based conclusions on co-occurrence of resistance. While some studies suggested to provide evidence that transmission of AMR from food animals to humans may occur, robust conclusions on the directionality of transmission cannot be drawn due to limitations in study methodologies. Our findings highlight the need to combine high resolution genomic data analysis with systematically collected epidemiological evidence to reconstruct patterns of AMR transmission between food animals and humans.

Phylogeography of H5N1 avian influenza virus in Indonesia

Highly pathogenic avian influenza (HPAI) viruses of the H5N1 subtype are a major concern to human and animal health in Indonesia. This study aimed to characterize transmission dynamics of H5N1 over time using novel Bayesian phylogeography methods to identify factors which have influenced the spread of H5N1 in Indonesia. We used publicly available hemagglutinin sequence data sampled between 2003 and 2016 to model ancestral state reconstruction of HPAI H5N1 evolution. We found strong support for H5N1 transmission routes between provinces in Java Island and inter-island transmissions, such as between Nusa Tenggara and Kalimantan Islands, not previously described. The spread is consistent with wild bird flyways and poultry trading routes. H5N1 migration was associated with the regions of high chicken densities and low human development indices. These results can be used to inform more targeted planning of H5N1 control and prevention activities in Indonesia.

Retrospective genome-wide comparisons of Salmonella enterica serovar Enteritidis from suspected outbreaks in Singapore

The number of salmonellosis cases in Singapore has increased over the years. Salmonella enterica serovar Enteritidis has always been the most predominant serovar in the last five years. The National Public Health Laboratory assisted outbreak investigations by performing multilocus variable number tandem repeat analysis (MLVA) on isolates that were collected at the time of the investigations. Isolates were defined as belonging to a particular cluster if they had identical MLVA patterns. Whilst MLVA has been instrumental in outbreak investigations, it may not be useful when outbreaks are caused by an endemic MLVA type. In this study, we analysed 67 isolates from 12 suspected outbreaks with known epidemiological links to explore the use of next-generation sequencing (NGS) for defining outbreaks. We found that NGS can confidently group isolates into their respective outbreaks. The isolates from each suspected outbreak were closely related and differed by a maximum of 3 single nucleotide polymorphisms (SNPs). They were also clearly separated from isolates that belonged to different suspected outbreaks. This study provides an important insight and further evidence on the value of NGS for routine surveillance and outbreak detection of S. Enteritidis.

Routine Whole-Genome Sequencing for Outbreak Investigations of Staphylococcus aureus in a National Reference Center

The French National Reference Center for Staphylococci currently uses DNA arrays and spa typing for the initial epidemiological characterization of Staphylococcus aureus strains. We here describe the use of whole-genome sequencing (WGS) to investigate retrospectively four distinct and virulent S. aureus lineages [clonal complexes (CCs): CC1, CC5, CC8, CC30] involved in hospital and community outbreaks or sporadic infections in France. We used a WGS bioinformatics pipeline based on de novo assembly (reference-free approach), single nucleotide polymorphism analysis, and on the inclusion of epidemiological markers. We examined the phylogeographic diversity of the French dominant hospital-acquired CC8-MRSA (methicillin-resistant S. aureus) Lyon clone through WGS analysis which did not demonstrate evidence of large-scale geographic clustering. We analyzed sporadic cases along with two outbreaks of a CC1-MSSA (methicillin-susceptible S. aureus) clone containing the Panton–Valentine leukocidin (PVL) and results showed that two sporadic cases were closely related. We investigated an outbreak of PVL-positive CC30-MSSA in a school environment and were able to reconstruct the transmission history between eight families. We explored different outbreaks among newborns due to the CC5-MRSA Geraldine clone and we found evidence of an unsuspected link between two otherwise distinct outbreaks. Here, WGS provides the resolving power to disprove transmission events indicated by conventional methods (same sequence type, spa type, toxin profile, and antibiotic resistance profile) and, most importantly, WGS can reveal unsuspected transmission events. Therefore, WGS allows to better describe and understand outbreaks and (inter-)national dissemination of S. aureus lineages. Our findings underscore the importance of adding WGS for (inter-)national surveillance of infections caused by virulent clones of S. aureus but also substantiate the fact that technological optimization at the bioinformatics level is still urgently needed for routine use. However, the greatest limitation of WGS analysis is the completeness and the correctness of the reference database being used and the conversion of floods of data into actionable results. The WGS bioinformatics pipeline (EpiSeq^TM) we used here can easily generate a uniform database and associated metadata for epidemiological applications.

MentaLiST - A fast MLST caller for large MLST schemes

MLST (multi-locus sequence typing) is a classic technique for genotyping bacteria, widely applied for pathogen outbreak surveillance. Traditionally, MLST is based on identifying sequence types from a small number of housekeeping genes. With the increasing availability of whole-genome sequencing data, MLST methods have evolved towards larger typing schemes, based on a few hundred genes [core genome MLST (cgMLST)] to a few thousand genes [whole genome MLST (wgMLST)]. Such large-scale MLST schemes have been shown to provide a finer resolution and are increasingly used in various contexts such as hospital outbreaks or foodborne pathogen outbreaks. This methodological shift raises new computational challenges, especially given the large size of the schemes involved. Very few available MLST callers are currently capable of dealing with large MLST schemes. We introduce MentaLiST, a new MLST caller, based on a k-mer voting algorithm and written in the Julia language, specifically designed and implemented to handle large typing schemes. We test it on real and simulated data to show that MentaLiST is faster than any other available MLST caller while providing the same or better accuracy, and is capable of dealing with MLST schemes with up to thousands of genes while requiring limited computational resources. MentaLiST source code and easy installation instructions using a Conda package are available at https://github.com/WGS-TB/MentaLiST.

Shared Genomic Variants: Identification of Transmission Routes Using Pathogen Deep-Sequence Data

Sequencing pathogen samples during a communicable disease outbreak is becoming an increasingly common procedure in epidemiologic investigations. Identifying who infected whom sheds considerable light on transmission patterns, high-risk settings and subpopulations, and the effectiveness of infection control. Genomic data shed new light on transmission dynamics and can be used to identify clusters of individuals likely to be linked by direct transmission. However, identification of individual routes of infection via single genome samples typically remains uncertain. We investigated the potential of deep sequence data to provide greater resolution on transmission routes, via the identification of shared genomic variants. We assessed several easily implemented methods to identify transmission routes using both shared variants and genetic distance, demonstrating that shared variants can provide considerable additional information in most scenarios. While shared-variant approaches identify relatively few links in the presence of a small transmission bottleneck, these links are highly accurate. Furthermore, we propose a hybrid approach that also incorporates phylogenetic distance to provide greater resolution. We applied our methods to data collected during the 2014 Ebola outbreak, identifying several likely routes of transmission. Our study highlights the power of data from deep sequencing of pathogens as a component of outbreak investigation and epidemiologic analyses.

Large-scale genomic analyses reveal the population structure and evolutionary trends of Streptococcus agalactiae strains in Brazilian fish farms

Streptococcus agalactiae is a major pathogen and a hindrance on tilapia farming worldwide. The aims of this work were to analyze the genomic evolution of Brazilian strains of S. agalactiae and to establish spatial and temporal relations between strains isolated from different outbreaks of streptococcosis. A total of 39 strains were obtained from outbreaks and their whole genomes were sequenced and annotated for comparative analysis of multilocus sequence typing, genomic similarity and whole genome multilocus sequence typing (wgMLST). The Brazilian strains presented two sequence types, including a newly described ST, and a non-typeable lineage. The use of wgMLST could differentiate each strain in a single clone and was used to establish temporal and geographical correlations among strains. Bayesian phylogenomic analysis suggests that the studied Brazilian population was co-introduced in the country with their host, approximately 60 years ago. Brazilian strains of S. agalactiae were shown to be heterogeneous in their genome sequences and were distributed in different regions of the country according to their genotype, which allowed the use of wgMLST analysis to track each outbreak event individually.

A novel cause of chronic viral meningoencephalitis: Cache Valley virus

Objective

Immunodeficient patients are particularly vulnerable to neuroinvasive infections that can be challenging to diagnose. Metagenomic next generation sequencing can identify unusual or novel microbes and is therefore well suited for investigating the etiology of chronic meningoencephalitis in immunodeficient patients.

Methods

We present the case of a 34‐year‐old man with X‐linked agammaglobulinemia from Australia suffering from 3 years of meningoencephalitis that defied an etiologic diagnosis despite extensive conventional testing, including a brain biopsy. Metagenomic next generation sequencing of his cerebrospinal fluid and brain biopsy tissue was performed to identify a causative pathogen.

Results

Sequences aligning to multiple Cache Valley virus genes were identified via metagenomic next generation sequencing. Reverse transcription polymerase chain reaction and immunohistochemistry subsequently confirmed the presence of Cache Valley virus in the brain biopsy tissue.

Interpretation

Cache Valley virus, a mosquito‐borne orthobunyavirus, has only been identified in 3 immunocompetent North American patients with acute neuroinvasive disease. The reported severity ranges from a self‐limiting meningitis to a rapidly fatal meningoencephalitis with multiorgan failure. The virus has never been known to cause a chronic systemic or neurologic infection in humans. Cache Valley virus has also never previously been detected on the Australian continent. Our research subject traveled to North and South Carolina and Michigan in the weeks prior to the onset of his illness. This report demonstrates that metagenomic next generation sequencing allows for unbiased pathogen identification, the early detection of emerging viruses as they spread to new locales, and the discovery of novel disease phenotypes. Ann Neurol 2017;82:105–114

Context Is Everything: Harmonization of Critical Food Microbiology Descriptors and Metadata for Improved Food Safety and Surveillance

Globalization of food networks increases opportunities for the spread of foodborne pathogens beyond borders and jurisdictions. High resolution whole-genome sequencing (WGS) subtyping of pathogens promises to vastly improve our ability to track and control foodborne disease, but to do so it must be combined with epidemiological, clinical, laboratory and other health care data (called “contextual data”) to be meaningfully interpreted for regulatory and health interventions, outbreak investigation, and risk assessment. However, current multi-jurisdictional pathogen surveillance and investigation efforts are complicated by time-consuming data re-entry, curation and integration of contextual information owing to a lack of interoperable standards and inconsistent reporting. A solution to these challenges is the use of ‘ontologies’ - hierarchies of well-defined and standardized vocabularies interconnected by logical relationships. Terms are specified by universal IDs enabling integration into highly regulated areas and multi-sector sharing (e.g., food and water microbiology with the veterinary sector). Institution-specific terms can be mapped to a given standard at different levels of granularity, maximizing comparability of contextual information according to jurisdictional policies. Fit-for-purpose ontologies provide contextual information with the auditability required for food safety laboratory accreditation. Our research efforts include the development of a Genomic Epidemiology Ontology (GenEpiO), and Food Ontology (FoodOn) that harmonize important laboratory, clinical and epidemiological data fields, as well as existing food resources. These efforts are supported by a global consortium of researchers and stakeholders worldwide. Since foodborne diseases do not respect international borders, uptake of such vocabularies will be crucial for multi-jurisdictional interpretation of WGS results and data sharing.

Origin and Evolution of the Kiwifruit Canker Pandemic

Recurring epidemics of kiwifruit (Actinidia spp.) bleeding canker disease are caused by Pseudomonas syringae pv. actinidiae (Psa). In order to strengthen understanding of population structure, phylogeography, and evolutionary dynamics, we isolated Pseudomonas from cultivated and wild kiwifruit across six provinces in China. Based on the analysis of 80 sequenced Psa genomes, we show that China is the origin of the pandemic lineage but that strain diversity in China is confined to just a single clade. In contrast, Korea and Japan harbor strains from multiple clades. Distinct independent transmission events marked introduction of the pandemic lineage into New Zealand, Chile, Europe, Korea, and Japan. Despite high similarity within the core genome and minimal impact of within-clade recombination, we observed extensive variation even within the single clade from which the global pandemic arose.

Towards an integrated food safety surveillance system: a simulation study to explore the potential of combining genomic and epidemiological metadata

Foodborne infection is a result of exposure to complex, dynamic food systems. The efficiency of foodborne infection is driven by ongoing shifts in genetic machinery. Next-generation sequencing technologies can provide high-fidelity data about the genetics of a pathogen. However, food safety surveillance systems do not currently provide similar high-fidelity epidemiological metadata to associate with genetic data. As a consequence, it is rarely possible to transform genetic data into actionable knowledge that can be used to genuinely inform risk assessment or prevent outbreaks. Big data approaches are touted as a revolution in decision support, and pose a potentially attractive method for closing the gap between the fidelity of genetic and epidemiological metadata for food safety surveillance. We therefore developed a simple food chain model to investigate the potential benefits of combining 'big' data sources, including both genetic and high-fidelity epidemiological metadata. Our results suggest that, as for any surveillance system, the collected data must be relevant and characterize the important dynamics of a system if we are to properly understand risk: this suggests the need to carefully consider data curation, rather than the more ambitious claims of big data proponents that unstructured and unrelated data sources can be combined to generate consistent insight. Of interest is that the biggest influencers of foodborne infection risk were contamination load and processing temperature, not genotype. This suggests that understanding food chain dynamics would probably more effectively generate insight into foodborne risk than prescribing the hazard in ever more detail in terms of genotype.

Genome sequencing of an Indian peste des petits ruminants virus isolate, Izatnagar/94, and its implications for virus diversity, divergence and phylogeography

Peste des petits ruminants is an important transboundary disease infecting small ruminants. Genome or gene sequence analysis enriches our knowledge about the evolution and transboundary nature of the causative agent of this disease, peste des petits ruminants virus (PPRV). Although analysis using whole genome sequences of pathogens leads to more precise phylogenetic relationships, when compared to individual genes or partial sequences, there is still a need to identify specific genes/genomic regions that can provide evolutionary assessments consistent with those predicted with full-length genome sequences. Here the virulent Izatnagar/94 PPRV isolate was assembled and compared to all available complete genome sequences (currently in the NCBI database) to estimate nucleotide diversity and to deduce evolutionary relationships between genes/genomic regions and the full length genomes. Our aim was to identify the preferred candidate gene for use as a phylogenetic marker, as well as to predict divergence time and explore PPRV phylogeography. Among all the PPRV genes, the H gene was identified to be the most diverse with the highest evolutionary relationship with the full genome sequences. Hence it is considered as the most preferred candidate gene for phylogenetic study with 93% identity set as a nucleotide cutoff. A whole genome nucleotide sequence cutoff value of 94% permitted specific differentiation of PPRV lineages. All the isolates examined in the study were found to have a most recent common ancestor in the late 19th or in the early 20th century with high posterior probability values. The Bayesian skyline plot revealed a decrease in genetic diversity among lineage IV isolates since the start of the vaccination program and the network analysis localized the ancestry of PPRV to Africa.