Survey on the Use of Whole-Genome Sequencing for Infectious Diseases Surveillance: Rapid Expansion of European National Capacities, 2015-2016

Applying whole genome sequencing to predict phenotypic drug resistance in Mycobacterium tuberculosis: leveraging 20 years of nationwide data from Denmark

Infection with Mycobacterium tuberculosis remains one of the biggest causes of death from a single microorganism worldwide, and the continuous emergence of drug resistance aggravates our ability to cure the disease. New improved resistance detection methods are needed to provide adequate treatment, such as whole genome sequencing (WGS), which has been used increasingly to identify resistance-conferring mutations over the last decade. The steadily increasing knowledge of resistance-conferring mutations increases our ability to predict resistance based on genomic data alone. This study evaluates the performance of WGS to predict M. tuberculosis complex resistance. It compares WGS predictions with the phenotypic (culture-based) drug susceptibility results based on 20 years of nationwide Danish data. Analyzing 6,230 WGS-sequenced samples, the sensitivities for isoniazid, rifampicin, ethambutol, and pyrazinamide were 82.5% [78.0%-86.5%, 95% confidence interval (CI)], 97.3% (90.6%-99.7%, 95% CI), 58.0% (43.2%-71.8%, 95% CI), and 60.5% (49.0%-71.2%, 95% CI), respectively, and specificities were 99.8% (99.7%-99.9%, 95% CI), 99.8% (99.7%-99.9%, 95% CI), 99.4% (99.2%-99.6%, 95% CI), and 99.9% (99.7%-99.9%, 95% CI), respectively. A broader range of both sensitivities and specificities was observed for second-line drugs. The results conform with previously reported values and indicate that WGS is reliable for routine resistance detection in resource-rich tuberculosis low-incidence and low-resistance settings such as Denmark.

Assessment of the pathogen genomics landscape highlights disparities and challenges for effective AMR Surveillance and outbreak response in the East African community

The East African Community (EAC) grapples with many challenges in tackling infectious disease threats and antimicrobial resistance (AMR), underscoring the importance of regional and robust pathogen genomics capacities. However, a significant disparity exists among EAC Partner States in harnessing bacterial pathogen sequencing and data analysis capabilities for effective AMR surveillance and outbreak response. This study assesses the current landscape and challenges associated with pathogen next-generation sequencing (NGS) within EAC, explicitly focusing on World Health Organization (WHO) AMR-priority pathogens. The assessment adopts a comprehensive approach, integrating a questionnaire-based survey amongst National Public Health Laboratories (NPHLs) with an analysis of publicly available metadata on bacterial pathogens isolated in the EAC countries. In addition to the heavy reliance on third-party organizations for bacterial NGS, the findings reveal a significant disparity among EAC member States in leveraging bacterial pathogen sequencing and data analysis. Approximately 97% (n = 4,462) of publicly available high-quality bacterial genome assemblies of samples collected in the EAC were processed and analyzed by external organizations, mainly in Europe and North America. Tanzania led in-country sequencing efforts, followed by Kenya and Uganda. The other EAC countries had no publicly available samples or had all their samples sequenced and analyzed outside the region. Insufficient local NGS sequencing facilities, limited bioinformatics expertise, lack of adequate computing resources, and inadequate data-sharing mechanisms are among the most pressing challenges that hinder the EAC's NPHLs from effectively leveraging pathogen genomics data. These insights emphasized the need to strengthen microbial pathogen sequencing and data analysis capabilities within the EAC to empower these laboratories to conduct pathogen sequencing and data analysis independently. Substantial investments in equipment, technology, and capacity-building initiatives are crucial for supporting regional preparedness against infectious disease outbreaks and mitigating the impact of AMR burden. In addition, collaborative efforts should be developed to narrow the gap, remedy regional imbalances, and harmonize NGS data standards. Supporting regional collaboration, strengthening in-country genomics capabilities, and investing in long-term training programs will ultimately improve pathogen data generation and foster a robust NGS-driven AMR surveillance and outbreak response in the EAC, thereby supporting global health initiatives.

Epidemiological and Whole Genome Sequencing Analysis of Restaurant Salmonella Enteritidis Outbreak Associated with an Infected Food Handler in Jiangxi Province, China, 2023

In China, Salmonella is one of the most frequent causes of bacterial gastroenteritis, and food handlers in restaurants as an important contaminated source were rarely reported. In May 2023, an outbreak of Salmonella enterica serovar Enteritidis infection in a restaurant in Jiangxi Province, China, was investigated. Cases were interviewed. Stool samples from cases, anal swabs from restaurant employees, suspicious raw food materials, and semifinished food were collected and examined. Pulsed-field gel electrophoresis (PFGE) and whole genome sequencing (WGS) were performed to determine the relatedness of the pathogen isolates. Antimicrobial resistance genes and virulence genes of isolates were analyzed by WGS. The antimicrobial profile of the isolates was detected by broth microdilution, which involved 20 different antibiotics. Among the 31 patrons, 26 showed gastrointestinal symptoms. Five Salmonella Enteritidis strains were isolated from patients (2), semifinished food (2), and food handler (1). The results of PFGE and single-nucleotide polymorphism showed that these five isolates were identical clones. These findings demonstrated that this outbreak was a restaurant Salmonella Enteritidis outbreak associated with an infected food handler. The rates of resistance to nalidixic acid and colistin and intermediate resistance to ciprofloxacin were 100%, 80%, and 100%, respectively. These outbreak isolates harbored point mutation gyrA p.D87G. The cause of inconsistency between the genotype and phenotype of resistance was deeply discussed. A total of 107 virulence genes were found in each isolate, with many being associated with Salmonella pathogenicity island (SPI)-1 and SPI-2. As an overlooked contamination source, infected food handlers can easily cause large-scale outbreaks. This outbreak highlighted that the government should enhance the training and supervision of food hygiene and safety for food handlers to prevent foodborne outbreaks.

Evolutionary analysis of seasonal influenza A viruses in Pakistan 2020-2023

INTRODUCTION

Influenza A viruses cause global health concerns due to their high amino acid substitution rates. They are linked to yearly seasonal epidemics and occasional pandemics. This study focused on sequencing influenza A virus strains in Pakistan.

MATERIALS AND METHODS

We analyzed the genetic characteristics of influenza A(H1N1)pdm09 and A(H3N2) viruses circulating in Pakistan from January 2020 to January 2023. Whole genome sequences from influenza A (n = 126) virus isolates were amplified and sequenced by the Oxford Nanopore (MinION) platform.

RESULTS

The HA genes of influenza A(H1N1)pdm09 underwent amino acid substitutions at positions K54Q, A186T, Q189E, E224A, R259K, and K308R in sequenced samples. The HA genes of influenza A(H3N2) had amino acid substitutions at G53D, E83K, D104G, I140M, S205F, A212T, and K276R in the sequenced samples. Furthermore, the HA gene sequences of influenza A(H1N1)pdm09 in this study belonged to subclade 6B.1A.5a.2a. Similarly, the HA gene sequences of influenza A(H3N2) were classified under six subclades (3C.3a.1 and 3C.2a1b.2a [2, 2a.1, 2b, 2c, and 2a.3b]). Notably, amino acid substitutions in other gene segments of influenza A(H1N1)pdm09 and A(H3N2) were also found.

CONCLUSION

These findings indicate influenza A(H1N1)pdm09 and A(H3N2) viruses co-circulated during the 2020-2023 influenza season in Pakistan. Continued surveillance is crucial for real-time monitoring of possible high-virulence variation and their relevance to existing vaccine strains.

Contamination of Plant Foods with Bacillus cereus in a Province and Analysis of Its Traceability

Bacillus cereus is an important zoonotic foodborne conditional pathogen. It is found in vegetables, dairy products, rice, and other foods, thereby greatly endangering human health. Investigations on B. cereus contamination in China primarily focus on raw milk, dairy products, meat, and others, and limited research has been conducted on plant-based foodstuffs. The rapid development of sequencing technology and the application of bioinformatics-related techniques means that analysis based on whole-genome sequencing has become an important tool for the molecular-epidemiology investigation of B. cereus. In this study, we investigated the contamination of B. cereus in six types of commercially available plant foods from eight regions of a province. The molecular epidemiology of the isolated B. cereus was analyzed by whole-genome sequencing. We aimed to provide fundamental data for the surveillance and epidemiology analysis of B. cereus in food products in China. The rapid traceability system of B. cereus established in this study can provide a basis for rapid molecular epidemiology analysis of B. cereus, as well as for the prevention and surveillance of B. cereus. Moreover, it can also be expanded to monitoring and rapid tracing of more foodborne pathogens.

Economic evaluations of whole-genome sequencing for pathogen identification in public health surveillance and health-care-associated infections: a systematic review

Whole-genome sequencing (WGS) has resulted in improvements to pathogen characterisation for the rapid investigation and management of disease outbreaks and surveillance. We conducted a systematic review to synthesise the economic evidence of WGS implementation for pathogen identification and surveillance. Of the 2285 unique publications identified through online database searches, 19 studies met the inclusion criteria. The economic evidence to support the broader application of WGS as a front-line pathogen characterisation and surveillance tool is insufficient and of low quality. WGS has been evaluated in various clinical settings, but these evaluations are predominantly investigations of a single pathogen. There are also considerable variations in the evaluation approach. Economic evaluations of costs, effectiveness, and cost-effectiveness are needed to support the implementation of WGS in public health settings.

Epidemic Trends and Biofilm Formation Mechanisms of Haemophilus influenzae: Insights into Clinical Implications and Prevention Strategies

Haemophilus influenzae (H. influenzae) is a significant pathogen responsible for causing respiratory tract infections and invasive diseases, leading to a considerable disease burden. The Haemophilus influenzae type b (Hib) conjugate vaccine has notably decreased the incidence of severe infections caused by Hib strains, and other non-typable H. influenzae (NTHi) serotypes have emerged as epidemic strains worldwide. As a result, the global epidemic trends and antibiotic resistance characteristics of H. influenzae have been altered. Researches on the virulence factors of H. influenzae, particularly the mechanisms underlying biofilm formation, and the development of anti-biofilm strategies hold significant clinical value. This article provides a summary of the epidemic trends, typing methods, virulence factors, biofilm formation mechanisms, and prevention strategies of H. influenzae. The increasing prevalence of NTHi strains and antibiotic resistance among H. influenzae, especially the high β-lactamase positivity and the emergence of BLNAR strains have increased clinical difficulties. Understanding its virulence factors, especially the formation mechanism of biofilm, and formulating effective anti-biofilm strategies may help to reduce the clinical impact. Therefore, future research efforts should focus on developing new approaches to prevent and control H. influenzae infections.

Transforming Shiga toxin-producing Escherichia coli surveillance through whole genome sequencing in food safety practices

Introduction

Shiga toxin-producing Escherichia coli (STEC) is a gastrointestinal pathogen causing foodborne outbreaks. Whole Genome Sequencing (WGS) in STEC surveillance holds promise in outbreak prevention and confinement, in broadening STEC epidemiology and in contributing to risk assessment and source attribution. However, despite international recommendations, WGS is often restricted to assist outbreak investigation and is not yet fully implemented in food safety surveillance across all European countries, in contrast to for example in the United States.

Methods

In this study, WGS was retrospectively applied to isolates collected within the context of Belgian food safety surveillance and combined with data from clinical isolates to evaluate its benefits. A cross-sector WGS-based collection of 754 strains from 1998 to 2020 was analyzed.

Results

We confirmed that WGS in food safety surveillance allows accurate detection of genomic relationships between human cases and strains isolated from food samples, including those dispersed over time and geographical locations. Identifying these links can reveal new insights into outbreaks and direct epidemiological investigations to facilitate outbreak management. Complete WGS-based isolate characterization enabled expanding epidemiological insights related to circulating serotypes, virulence genes and antimicrobial resistance across different reservoirs. Moreover, associations between virulence genes and severe disease were determined by incorporating human metadata into the data analysis. Gaps in the surveillance system were identified and suggestions for optimization related to sample centralization, harmonizing isolation methods, and expanding sampling strategies were formulated.

Discussion

This study contributes to developing a representative WGS-based collection of circulating STEC strains and by illustrating its benefits, it aims to incite policymakers to support WGS uptake in food safety surveillance.

Brevundimonas brasiliensis sp. nov.: a New Multidrug-Resistant Species Isolated from a Patient in Brazil

To increase knowledge on Brevundimonas pathogens, we conducted in-depth genomic and phenotypic characterization of a Brevundimonas strain isolated from the cerebrospinal fluid of a patient admitted in a neonatal intensive care unit. The strain was identified as a member of the genus Brevundimonas based on Vitek 2 system results and 16S rRNA gene sequencing and presented a multidrug resistance profile (MDR). Several molecular and biochemical tests were used to characterize and identify the species for in-depth results. The draft genome assembly of the isolate has a total length of 3,261,074 bp and a G+C of 66.86%, similar to other species of the genus. Multilocus sequence analysis, Type (Strain) Genome Server, digital DNA-DNA hybridization, and average nucleotide identity confirmed that the Brevundimonas sp. studied represents a distinct species, for which we propose the name Brevundimonas brasiliensis sp. nov. In silico analysis detected antimicrobial resistance genes (AMRGs) mediating resistance to β-lactams (penP, blaTEM-16, and blaBKC-1) and aminoglycosides [strA, strB, aac(6')-Ib, and aac(6')-Il]. We also found AMRGs encoding the AcrAB efflux pump that confers resistance to a broad spectrum of antibiotics. Colistin and quinolone resistance can be attributed to mutation in qseC and/or phoP and GyrA/GyrB, respectively. The Brevundimonas brasiliensis sp. nov. genome contained copies of type IV secretion system (T4SS)-type integrative and conjugative elements (ICEs); integrative mobilizable elements (IME); and Tn3-type and IS3, IS6, IS5, and IS1380 families, suggesting an important role in the development and dissemination of antibiotic resistance. The isolate presented a range of virulence-associated genes related to biofilm formation, adhesion, and invasion that can be relevant for its pathogenicity. Our findings provide a wealth of data to hinder the transmission of MDR Brevundimonas and highlight the need for monitoring and identifying new bacterial species in hospital environments. IMPORTANCE Brevundimonas species is considered an opportunistic human pathogen that can cause multiple types of invasive and severe infections in patients with underlying pathologies. Treatment of these pathogens has become a major challenge because many isolates are resistant to most antibiotics used in clinical practice. Furthermore, there are no consistent therapeutic results demonstrating the efficacy of antibacterial agents. Although considered a rare pathogen, recent studies have provided evidence of the emergence of Brevundimonas in clinical settings. Hence, we identified a novel pathogenic bacterium, Brevundimonas brasiliensis sp. nov., that presented a multidrug resistance (MDR) profile and carried diverse genes related to drug resistance, virulence, and mobile genetic elements. Such data can serve as a baseline for understanding the genomic diversity, adaptation, evolution, and pathogenicity of MDR Brevundimonas.

Whole-genome sequencing for genetic diversity analysis of Iranian Brucella spp. isolated from humans and livestock

Brucellosis is one of the most common zoonoses in the Middle East. It is causing economic losses to the livestock industry and has a great public health concern. Little is known about the genetic diversity and distribution of brucellae in Iran. Therefore, forty Brucella spp. strains (B. abortus and B. melitensis) isolated from animals and humans were analyzed by whole genome sequencing (WGS) technology using single nucleotide polymorphism (SNP) analysis and core genome multilocus sequence typing (cgMLST). Brucella isolates were obtained from lymph nodes (cows and camels), milk (cows, camels and sheep), and aborted foetus samples (sheep and goats), as well as cerebrospinal fluid and blood of humans. The isolates were originating from thirteen provinces of Iran and isolated between 2015 and 2020. According to in-silico MLST, ST8 and ST2 were the most frequent sequence types in B. melitensis and B. abortus, respectively. Based on phylogeographic reconstruction using cgSNP analysis, the investigated Iranian B. melitensis strains belonged to the American and Mediterranean lineages of the B. melitensis phylogeny. Furthermore, cgSNP analysis revealed a similarity between Iranian B. abortus isolates and strains from Iraq and Egypt. Therefore, the origin of the Iranian strains can be suggested to be strains from neighboring and Middle East countries. Moreover, cgMLST analysis showed that the Iranian B. melitensis strains were closely relative to strains recovered from sheep and humans in Iraq, Afghanistan, Syria, Turkmenistan, and Pakistan. In the current panel of strains, cgMLST and cgSNP analysis provided an appropriate and accurate tool for effective traceback analyses for Brucella spp. from Iran. The results of cgSNP and cgMLST helped to understand the geographic distribution and interspecies transmission of Iranian strains and highlight the importance of specific brucellosis control measures in Iran with regard to the One-Health approach.

Advantages of Whole Genome Sequencing in Mitigating the Helicobacter pylori Antimicrobial Resistance Problem

Helicobacter pylori antimicrobial resistance is a critical public health issue. Typically, antimicrobial resistance epidemiology reports include only the antimicrobial susceptibility test results for H. pylori. However, this phenotypic approach is less capable of answering queries related to resistance mechanisms and specific mutations found in particular global regions. Whole genome sequencing can help address these two questions while still offering quality control and is routinely validated against AST standards. A comprehensive understanding of the mechanisms of resistance should improve H. pylori eradication efforts and prevent gastric cancer.

Multicenter Preparedness Exercise Enables Rapid Development of Cluster-Specific PCR-Based Screening Assays from Bacterial Genomic Data

PCR-based screening assays targeting strain-specific genetic markers allow the timely detection and specific differentiation of bacterial strains. Especially in situations where an infection cluster occurs, fast assay development is crucial for supporting targeted control measures. However, the turnaround times (TATs) for assay setup may be high due to insufficient knowledge about screening assay methods, workflows, and software tools. Here, two blind-coded and quality-controlled ring trials were performed in which five German laboratories established PCR-based screening assays from genomic data that specifically target selected bacterial clusters within two bacterial monospecies sample panels. While the first ring trial was conducted without a time limit to train the participants and assess assay feasibility, in the second ring trial, a challenging time limit of 2 weeks was set to force fast assay development as soon as genomic data were available. During both ring trials, we detected high interlaboratory variability regarding the screening assay methods and targets, the TATs for assay setup, and the number of screening assays. The participants designed between one and four assays per cluster that targeted cluster-specific unique genetic sequences, genes, or single nucleotide variants using conventional PCRs, high-resolution melting assays, or TaqMan PCRs. Assays were established within the 2-week time limit, with TATs ranging from 4 to 13 days. TaqMan probe delivery times strongly influenced TATs. In summary, we demonstrate that a specific exercise improved the preparedness to develop functional cluster-specific PCR-based screening assays from bacterial genomic data. Furthermore, the parallel development of several assays enhances assay availability.

Machine-Learning Model for Prediction of Cefepime Susceptibility in Escherichia coli from Whole-Genome Sequencing Data

The declining cost of performing bacterial whole-genome sequencing (WGS) coupled with the availability of large libraries of sequence data for well-characterized isolates have enabled the application of machine-learning (ML) methods to the development of nonlinear sequence-based predictive models. We tested the ML-based model developed by Next Gen Diagnostics for prediction of cefepime phenotypic susceptibility results in Escherichia coli. A cohort of 100 isolates of E. coli recovered from urine (n = 77) and blood (n = 23) cultures were used. The cefepime MIC was determined in triplicate by reference broth microdilution and classified as susceptible (MIC of ≤2 μg/mL) or not susceptible (MIC of ≥4 μg/mL) using the 2022 Clinical and Laboratory Standards Institute breakpoints. Five isolates generated both susceptible and not susceptible MIC results, yielding categorical agreement of 95% for the reference method to itself. Categorical agreement of ML to MIC interpretations was 97%, with 2 very major (false, susceptible) and 1 major (false, not susceptible) errors. One very major error occurred for an isolate with blaCTX-M-27 (MIC mode, ≥32 μg/mL) and one for an isolate with blaTEM-34 for which the MIC cefepime mode was 4 μg/mL. One major error was for an isolate with blaCTX-M-27 but with a MIC mode of 2 μg/mL. These preliminary data demonstrated performance of ML for a clinically important antimicrobial-species pair at a caliber similar to phenotypic methods, encouraging wider development of sequence-based susceptibility prediction and its validation and use in clinical practice.

Experiences from 4 Years of Organization of an External Quality Assessment for Mycobacterium tuberculosis Whole-Genome Sequencing in the European Union/European Economic Area

Here, we report the development and key features of the first external quality assessment (EQA) scheme for Mycobacterium tuberculosis whole-genome sequencing (WGS). The results of four rounds (2017 to 2020) of implementation within the European tuberculosis reference laboratories network (ERLTB-Net-2) are presented and discussed. EQA panels comprising 10 genomic DNAs were distributed to ERLTB-Net 2 laboratories volunteering to participate in this exercise. Since 2018, five FASTQ files were added to better assess the dry WGS processes, and in 2020, three of the five files were replaced by synthetic files (providing additional flexibility for the mutations included in the panels). Ten National tuberculosis reference laboratories participated in all four EQA rounds, and seven participated in at least one. High-confidence resistance mutations were correctly identified by all laboratories, but challenges remained with respect to the identification of mixed loci and interpretation of rare mutations. M. tuberculosis genotyping and clustering analysis was >90% accurate for pure samples with the main challenges being related to the analysis of mixed genotypes and DNA FASTQ files. The development and implementation of this WGS EQA scheme has contributed to the continuous improvement in performance of participating laboratories in M. tuberculosis WGS and data analysis. This scheme can serve as a model of comprehensive quality assessment for M. tuberculosis WGS that can be replicated in different settings worldwide. IMPORTANCE The wider availability of whole-genome sequencing (WGS) coupled to new developments in bioinformatic tools and databases to interpret Mycobacterium tuberculosis complex WGS data has accelerated the adoption of this method for the routine prediction of antimycobacterial drug resistance and genotyping, thus necessitating the establishment of a comprehensive external quality control system. Here, we report 4 years of development and results from such a panel.

A systematic review of economic evaluations of whole-genome sequencing for the surveillance of bacterial pathogens

Whole-genome sequencing (WGS) has unparalleled ability to distinguish between bacteria, with many public health applications. The generation and analysis of WGS data require significant financial investment. We describe a systematic review summarizing economic analyses of genomic surveillance of bacterial pathogens, reviewing the evidence for economic viability. The protocol was registered on PROSPERO (CRD42021289030). Six databases were searched on 8 November 2021 using terms related to 'WGS', 'population surveillance' and 'economic analysis'. Quality was assessed with the Drummond-Jefferson checklist. Following data extraction, a narrative synthesis approach was taken. Six hundred and eighty-one articles were identified, of which 49 proceeded to full-text screening, with 9 selected for inclusion. All had been published since 2019. Heterogeneity was high. Five studies assessed WGS for hospital surveillance and four analysed foodborne pathogens. Four were cost-benefit analyses, one was a cost-utility analysis, one was a cost-effectiveness analysis, one was a combined cost-effectiveness and cost-utility analysis, one combined cost-effectiveness and cost-benefit analyses and one was a partial analysis. All studies supported the use of WGS as a surveillance tool on economic grounds. The available evidence supports the use of WGS for pathogen surveillance but is limited by marked heterogeneity. Further work should include analysis relevant to low- and middle-income countries and should use real-world effectiveness data.

Whole-genome sequencing for surveillance of antimicrobial resistance in Ecuador: present and future implications

Whole-genome sequencing is becoming the gold standard for pathogen characterization and offers considerable advantages for understanding the evolution and dissemination of new determinants of antimicrobial resistance. Despite the benefits of whole-genome sequencing for pathogen characterization, implementation costs and lack of expertise may limit its use by public health laboratories. This article reviews the advantages of whole-genome sequencing for pathogen characterization and the current status of the use of whole-genome sequencing for antimicrobial resistance surveillance in Ecuador. A roadmap is suggested for including whole-genome sequencing for pathogen characterization based on the needs of the health reference institutions through alliances with Ecuadorian universities. Establishing a partnership between public health institutions and academia would be valuable for clinicians, policy-makers, and epidemiologists who could then take reasonable measures in those areas and establish a basis for adapting One Health strategies to tackle antimicrobial resistance in Ecuador.

[Assessment of available and currently applied typing methods including genome-based methods for zoonotic pathogens with a focus on Salmonella enterica]

BACKGROUND

In recent years, whole genome sequencing (WGS) in combination with bioinformatic analyses has become state of the art in evaluating the pathogenicity/resistance potential and relatedness of bacteria. WGS analysis thus represents a central tool in the investigation of the resistance and virulence potential of pathogens, as well as their dissemination via outbreak clusters and transmission chains within the framework of molecular epidemiology. In order to gain an overview of the available genotypic and phenotypic methods used for pathogen typing of Salmonella and Shiga toxin-producing and enterohemorrhagic Escherichia coli (STEC/EHEC) in Germany at state and federal level, along with the availability of WGS-based typing and corresponding analytical methods, a survey of laboratories was conducted.

METHODS

An electronic survey of laboratories working for public health protection and consumer health protection was conducted from February to June 2020.

RESULTS AND CONCLUSION

The results of the survey showed that many of the participating laboratories provide a wide range of phenotypic and molecular methods. Molecular typing is most commonly used for species identification of Salmonella. In many cases, WGS-based methods have already been established at federal and state institutions or are in the process of being established. The Illumina sequencing technology is the most widely used technology. The survey confirms the importance of molecular biology and whole genome typing technologies for laboratories in the diagnosis of bacterial zoonotic pathogens.

Single Nucleotide Polymorphism-Based Real-Time PCR Screening Assay for Rapid Tracking of Bacterial Infection Clusters To Complement Whole-Genome Sequencing Efforts during Outbreak Investigations

Infection clusters of multidrug-resistant bacteria increase mortality and entail expensive infection control measures. Whereas whole-genome sequencing (WGS) is the current gold standard to confirm infection clusters, PCR-based assays targeting cluster-specific signatures, such as single nucleotide polymorphisms (SNPs) derived from WGS data, are more suitable to initially screen for cluster isolates within large sample sizes. Here, we evaluated four software tools (SeqSphere+, RUCS, Gegenees, and Find Differential Primers) regarding their efficiency to find SNPs within WGS data sets that were specific for two bacterial monospecies infection clusters but were absent from a WGS reference data set comprising several hundred diverse genotypes of the same bacterial species. Cluster-specific SNPs were subsequently used to establish a probe-based real-time PCR screening assay for in vitro differentiation between cluster and noncluster isolates. SeqSphere+ and RUCS found 2 and 24 SNPs for clusters 1 and 14 and 24 SNPs for cluster 2, respectively. However, some signatures detected by RUCS were not cluster specific. Interestingly, all SNPs identified by SeqSphere+ were also detected by RUCS. In contrast, analyses with the remaining tools either resulted in no SNPs (with Find Differential Primers) or failed (Gegenees). Design of six cluster-specific real-time PCR assays enabled reliable cluster screening in vitro. Our evaluation revealed that SeqSphere+ and RUCS identified cluster-specific SNPs that could be used for large-scale screening in surveillance samples via real-time PCR, thereby complementing WGS efforts. This faster and simplified approach for the surveillance of bacterial clusters will improve infection control measures and will enhance protection of patients and physicians. IMPORTANCE Infection clusters of multidrug-resistant bacteria threaten medical facilities worldwide and cause immense health care costs. In recent years, whole-genome sequencing (WGS) has been increasingly applied to detect and to further control bacterial clusters. However, as WGS is still expensive and time-consuming, its exclusive application for screening and confirmation of bacterial infection clusters contributes to high costs and enhanced turnaround times, which many hospitals cannot afford. Therefore, there is need for alternative methods that can enable further surveillance of bacterial clusters that are initially detected by WGS in a faster and more cost-efficient way. Here, we established a system based on real-time PCR that enables rapid large-scale sample screening for bacterial cluster isolates within 7 days after the initial detection of an infection cluster, thereby complementing WGS efforts. This faster and simplified surveillance of bacterial clusters will improve infection control measures and will enhance protection of patients and physicians.

MixviR: an R Package for Exploring Variation Associated with Genomic Sequence Data from Environmental SARS-CoV-2 and Other Mixed Microbial Samples

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)/coronavirus disease 2019 (COVID-19) pandemic has highlighted an important role for efficient surveillance of microbial pathogens. High-throughput sequencing technologies provide valuable surveillance tools, offering opportunities to conduct high-resolution monitoring from diverse sample types, including from environmental sources. However, given their large size and potential to contain mixtures of lineages within samples, such genomic data sets can present challenges for analyzing the data and communicating results with diverse stakeholders. Here, we report MixviR, an R package for exploring, analyzing, and visualizing genomic data from potentially mixed samples of a target microbial group. MixviR characterizes variation at both the nucleotide and amino acid levels and offers the RShiny interactive dashboard for exploring data. We demonstrate MixviR's utility with validation studies using mixtures of known lineages from both SARS-CoV-2 and Mycobacterium tuberculosis and with a case study analyzing lineages of SARS-CoV-2 in wastewater samples over time at a sampling location in Ohio, USA. IMPORTANCE High-throughput sequencing technologies hold great potential for contributing to genomic-based surveillance of microbial diversity from environmental samples. However, the size of the data sets, along with the potential for environmental samples to contain multiple evolutionary lineages of interest, present challenges around analyzing and effectively communicating inferences from these data sets. The software described here provides a novel and valuable tool for exploring such data. Though originally designed and used for monitoring SARS-CoV-2 lineages in wastewater, it can also be applied to analyses of genomic diversity in other microbial groups.

Sequence-based detection and typing procedures for Burkholderia mallei: Assessment and prospects

Although glanders has been eradicated in most of the developed world, the disease still persists in various countries such as Brazil, India, Pakistan, Bangladesh, Nepal, Iran, Bahrain, UAE and Turkey. It is one of the notifiable diseases listed by the World Organization for Animal Health. Occurrence of glanders imposes restriction on equestrian events and restricts equine movement, thus causing economic losses to equine industry. The genetic diversity and global distribution of the causing agent, Burkholderia (B.) mallei, have not been assessed in detail and are complicated by the high clonality of this organism. Among the identification and typing methods, PCR-based methods for distinguishing B. mallei from its close relative B. pseudomallei as well as genotyping using tandem repeat regions (MLVA) are established. The advent and continuous advancement of the sequencing techniques and the reconstruction of closed genomes enable the development of genome guided epidemiological tools. For achieving a higher genomic resolution, genotyping methods based on whole genome sequencing data can be employed, like genome-wide single nucleotide polymorphisms. One of the limitations in obtaining complete genomic sequences for further molecular characterization of B. mallei is its high GC content. In this review, we aim to provide an overview of the widely used detection and typing methods for B. mallei and illustrate gaps that still require development. The genomic features of Burkholderia, their high homology and clonality will be first described from a comparative genomics perspective. Then, the commonly used molecular detection (PCR systems) and typing systems (e.g., multilocus sequence typing, variable number of tandem repeat analysis) will be presented and put in perspective with recently developed genomic methods. Also, the increasing availability of B. mallei genomic sequences and evolution of the sequencing methods offers exciting prospects for further refinement of B. mallei typing, that could overcome the difficulties presently encountered with this particular bacterium.

Next Generation and Other Sequencing Technologies in Diagnostic Microbiology and Infectious Diseases

Next-generation sequencing (NGS) technologies have become increasingly available for use in the clinical microbiology diagnostic environment. There are three main applications of these technologies in the clinical microbiology laboratory: whole genome sequencing (WGS), targeted metagenomics sequencing and shotgun metagenomics sequencing. These applications are being utilized for initial identification of pathogenic organisms, the detection of antimicrobial resistance mechanisms and for epidemiologic tracking of organisms within and outside hospital systems. In this review, we analyze these three applications and provide a comprehensive summary of how these applications are currently being used in public health, basic research, and clinical microbiology laboratory environments. In the public health arena, WGS is being used to identify and epidemiologically track food borne outbreaks and disease surveillance. In clinical hospital systems, WGS is used to identify multi-drug-resistant nosocomial infections and track the transmission of these organisms. In addition, we examine how metagenomics sequencing approaches (targeted and shotgun) are being used to circumvent the traditional and biased microbiology culture methods to identify potential pathogens directly from specimens. We also expand on the important factors to consider when implementing these technologies, and what is possible for these technologies in infectious disease diagnosis in the next 5 years.

Whole genome sequencing of the multidrug-resistant Chryseobacterium indologenes isolated from a patient in Brazil

Chryseobacterium indologenes is a non-glucose-fermenting Gram-negative bacillus. This emerging multidrug resistant opportunistic nosocomial pathogen can cause severe infections in neonates and immunocompromised patients. This study aimed to present the first detailed draft genome sequence of a multidrug-resistant C. indologenes strain isolated from the cerebrospinal fluid of an infant hospitalized at the Neonatal Intensive Care Unit of Brazilian Tertiary Hospital. We first analyzed the susceptibility of C. indologenes strain to different antibiotics using the VITEK 2 system. The strain demonstrated an outstanding resistance to all the antibiotic classes tested, including β-lactams, aminoglycosides, glycylcycline, and polymyxin. Next, C. indologenes was whole-genome-sequenced, annotated using Prokka and Rapid Annotation using Subsystems Technology (RAST), and screened for orthologous groups (EggNOG), gene ontology (GO), resistance genes, virulence genes, and mobile genetic elements using different software tools. The draft genome contained one circular chromosome of 4,836,765 bp with 37.32% GC content. The genomic features of the chromosome present numerous genes related to cellular processes that are essential to bacteria. The MDR C. indologenes revealed the presence of genes that corresponded to the resistance phenotypes, including genes to β-lactamases (bla_IND–13, bla_CIA–3, bla_TEM–116, bla_OXA–209, bla_VEB–15), quinolone (mcbG), tigecycline (tet(X6)), and genes encoding efflux pumps which confer resistance to aminoglycosides (RanA/RanB), and colistin (HlyD/TolC). Amino acid substitutions related to quinolone resistance were observed in GyrA (S83Y) and GyrB (L425I and K473R). A mutation that may play a role in the development of colistin resistance was detected in lpxA (G68D). Chryseobacterium indologenes isolate harbored 19 virulence factors, most of which were involved in infection pathways. We identified 13 Genomic Islands (GIs) and some elements associated with one integrative and conjugative element (ICEs). Other elements linked to mobile genetic elements (MGEs), such as insertion sequence (ISEIsp1), transposon (Tn5393), and integron (In31), were also present in the C. indologenes genome. Although plasmids were not detected, a ColRNAI replicon type and the most resistance genes detected in singletons were identified in unaligned scaffolds. We provided a wide range of information toward the understanding of the genomic diversity of C. indologenes, which can contribute to controlling the evolution and dissemination of this pathogen in healthcare settings.

Whole Genome Sequencing Contributions and Challenges in Disease Reduction Focused on Malaria

Malaria elimination remains an important goal that requires the adoption of sophisticated science and management strategies in the era of the COVID-19 pandemic. The advent of next generation sequencing (NGS) is making whole genome sequencing (WGS) a standard today in the field of life sciences, as PCR genotyping and targeted sequencing provide insufficient information compared to the whole genome. Thus, adapting WGS approaches to malaria parasites is pertinent to studying the epidemiology of the disease, as different regions are at different phases in their malaria elimination agenda. Therefore, this review highlights the applications of WGS in disease management, challenges of WGS in controlling malaria parasites, and in furtherance, provides the roles of WGS in pursuit of malaria reduction and elimination. WGS has invaluable impacts in malaria research and has helped countries to reach elimination phase rapidly by providing required information needed to thwart transmission, pathology, and drug resistance. However, to eliminate malaria in sub-Saharan Africa (SSA), with high malaria transmission, we recommend that WGS machines should be readily available and affordable in the region.

A roadmap for the generation of benchmarking resources for antimicrobial resistance detection using next generation sequencing

Next Generation Sequencing technologies significantly impact the field of Antimicrobial Resistance (AMR) detection and monitoring, with immediate uses in diagnosis and risk assessment. For this application and in general, considerable challenges remain in demonstrating sufficient trust to act upon the meaningful information produced from raw data, partly because of the reliance on bioinformatics pipelines, which can produce different results and therefore lead to different interpretations. With the constant evolution of the field, it is difficult to identify, harmonise and recommend specific methods for large-scale implementations over time. In this article, we propose to address this challenge through establishing a transparent, performance-based, evaluation approach to provide flexibility in the bioinformatics tools of choice, while demonstrating proficiency in meeting common performance standards. The approach is two-fold: first, a community-driven effort to establish and maintain “live” (dynamic) benchmarking platforms to provide relevant performance metrics, based on different use-cases, that would evolve together with the AMR field; second, agreed and defined datasets to allow the pipelines’ implementation, validation, and quality-control over time. Following previous discussions on the main challenges linked to this approach, we provide concrete recommendations and future steps, related to different aspects of the design of benchmarks, such as the selection and the characteristics of the datasets (quality, choice of pathogens and resistances, etc.), the evaluation criteria of the pipelines, and the way these resources should be deployed in the community.

The Easy-to-Use SARS-CoV-2 Assembler for Genome Sequencing: Development Study

Background

Early sequencing and quick analysis of the SARS-CoV-2 genome have contributed to the understanding of the dynamics of COVID-19 epidemics and in designing countermeasures at a global level.

Objective

Amplicon-based next-generation sequencing (NGS) methods are widely used to sequence the SARS-CoV-2 genome and to identify novel variants that are emerging in rapid succession as well as harboring multiple deletions and amino acid–changing mutations.

Methods

To facilitate the analysis of NGS sequencing data obtained from amplicon-based sequencing methods, here, we propose an easy-to-use SARS-CoV-2 genome assembler: the Easy-to-use SARS-CoV-2 Assembler (ESCA) pipeline.

Results

Our results have shown that ESCA could perform high-quality genome assembly from Ion Torrent and Illumina raw data and help the user in easily correct low-coverage regions. Moreover, ESCA includes the possibility of comparing assembled genomes of multisample runs through an easy table format.

Conclusions

In conclusion, ESCA automatically furnished a variant table output file, fundamental to rapidly recognizing variants of interest. Our pipeline could be a useful method for obtaining a complete, rapid, and accurate analysis even with minimal knowledge in bioinformatics.

Search and Contain: Impact of an Integrated Genomic and Epidemiological Surveillance and Response Program for Control of Carbapenemase-producing Enterobacterales

BACKGROUND

Multiresistant organisms (MROs) pose a critical threat to public health. Population-based programs for control of MROs such as carbapenemase-producing Enterobacterales (CPE) have emerged and evaluation is needed. We assessed the feasibility and impact of a statewide CPE surveillance and response program deployed across Victoria, Australia (population 6.5 million).

METHODS

A prospective multimodal intervention including active screening, carrier isolation, centralized case investigation, and comparative pathogen genomics was implemented. We analyzed trends in CPE incidence and clinical presentation, risk factors, and local transmission over the program's first 3 years (2016-2018).

RESULTS

CPE case ascertainment increased over the study period to 1.42 cases/100 000 population, linked to increased screening without a concomitant rise in active clinical infections (0.45-0.60 infections/100 000 population, P = .640). KPC-2 infection decreased from 0.29 infections/100 000 population prior to intervention to 0.03 infections/100 000 population in 2018 (P = .003). Comprehensive case investigation identified instances of overseas community acquisition. Median time between isolate referral and genomic and epidemiological assessment for local transmission was 11 days (IQR, 9-14). Prospective surveillance identified numerous small transmission networks (median, 2; range, 1-19 cases), predominantly IMP and KPC, with median pairwise distance of 8 (IQR, 4-13) single nucleotide polymorphisms; low diversity between clusters of the same sequence type suggested genomic cluster definitions alone are insufficient for targeted response.

CONCLUSIONS

We demonstrate the value of centralized CPE control programs to increase case ascertainment, resolve risk factors, and identify local transmission through prospective genomic and epidemiological surveillance; methodologies are transferable to low-prevalence settings and MROs globally.

Train-the-Trainer as an Effective Approach to Building Global Networks of Experts in Genomic Surveillance of Antimicrobial Resistance (AMR)

Advanced genomics and sequencing technologies are increasingly becoming critical for global health applications such as pathogen and antimicrobial resistance (AMR) surveillance. Limited resources challenge capacity development in low- and middle-income countries (LMICs), with few countries having genomics facilities and adequately trained staff. Training research and public health experts who are directly involved in the establishment of such facilities offers an effective, but limited, solution to a growing need. Instead, training them to impart their knowledge and skills to others provides a sustainable model for scaling up the much needed capacity and capability for genomic sequencing and analysis locally with global impact. We designed and developed a Train-the-Trainer course integrating pedagogical aspects with genomic and bioinformatics activities. The course was delivered to 18 participants from 12 countries in Africa, Asia, and Latin America. A combination of teaching strategies culminating in a group project created a foundation for continued development at home institutions. Upon follow-up after 6 months, at least 40% of trainees had initiated training programs and collaborations to build capacity at local, national, and regional level. This work provides a framework for implementing a training and capacity building program for the application of genomics tools and resources in AMR surveillance.

Identification of bacterial antibiotic resistance genes in next-generation sequencing data (review of literature)

The spread of antibiotic-resistant human bacterial pathogens is a serious threat to modern medicine. Antibiotic susceptibility testing is essential for treatment regimens optimization and preventing dissemination of antibiotic resistance. Therefore, development of antibiotic susceptibility testing methods is a priority challenge of laboratory medicine. The aim of this review is to analyze the capabilities of the bioinformatics tools for bacterial whole genome sequence data processing. The PubMed database, Russian scientific electronic library eLIBRARY, information networks of World health organization and European Society of Clinical Microbiology and Infectious Diseases (ESCMID) were used during the analysis. In this review, the platforms for whole genome sequencing, which are suitable for detection of bacterial genetic resistance determinants, are described. The classic step of genetic resistance determinants searching is an alignment between the query nucleotide/protein sequence and the subject (database) nucleotide/protein sequence, which is performed using the nucleotide and protein sequence databases. The most commonly used databases are Resfinder, CARD, Bacterial Antimicrobial Resistance Reference Gene Database. The results of the resistance determinants searching in genome assemblies is more correct in comparison to results of the searching in contigs. The new resistance genes searching bioinformatics tools, such as neural networks and machine learning, are discussed in the review. After critical appraisal of the current antibiotic resistance databases we designed a protocol for predicting antibiotic resistance using whole genome sequence data. The designed protocol can be used as a basis of the algorithm for qualitative and quantitative antimicrobial susceptibility testing based on whole genome sequence data.

Evaluation of WGS performance for bacterial pathogen characterization with the Illumina technology optimized for time-critical situations

Whole genome sequencing (WGS) has become the reference standard for bacterial outbreak investigation and pathogen typing, providing a resolution unattainable with conventional molecular methods. Data generated with Illumina sequencers can however only be analysed after the sequencing run has finished, thereby losing valuable time during emergency situations. We evaluated both the effect of decreasing overall run time, and also a protocol to transfer and convert intermediary files generated by Illumina sequencers enabling real-time data analysis for multiple samples part of the same ongoing sequencing run, as soon as the forward reads have been sequenced. To facilitate implementation for laboratories operating under strict quality systems, extensive validation of several bioinformatics assays (16S rRNA species confirmation, gene detection against virulence factor and antimicrobial resistance databases, SNP-based antimicrobial resistance detection, serotype determination, and core genome multilocus sequence typing) for three bacterial pathogens (Mycobacterium tuberculosis , Neisseria meningitidis , and Shiga-toxin producing Escherichia coli ) was performed by evaluating performance in function of the two most critical sequencing parameters, i.e. read length and coverage. For the majority of evaluated bioinformatics assays, actionable results could be obtained between 14 and 22 h of sequencing, decreasing the overall sequencing-to-results time by more than half. This study aids in reducing the turn-around time of WGS analysis by facilitating a faster response in time-critical scenarios and provides recommendations for time-optimized WGS with respect to required read length and coverage to achieve a minimum level of performance for the considered bioinformatics assay(s), which can also be used to maximize the cost-effectiveness of routine surveillance sequencing when response time is not essential.

Evaluation of the added value of viral genomic information for predicting severity of influenza infection

BACKGROUND

The severity of an influenza infection is influenced by both host and viral characteristics. This study aims to assess the relevance of viral genomic data for the prediction of severe influenza A(H3N2) infections among patients hospitalized for severe acute respiratory infection (SARI), in view of risk assessment and patient management.

METHODS

160 A(H3N2) influenza positive samples from the 2016-2017 season originating from the Belgian SARI surveillance were selected for whole genome sequencing. Predictor variables for severity were selected using a penalized elastic net logistic regression model from a combined host and genomic dataset, including patient information and nucleotide mutations identified in the viral genome. The goodness-of-fit of the model combining host and genomic data was compared using a likelihood-ratio test with the model including host data only. Internal validation of model discrimination was conducted by calculating the optimism-adjusted area under the Receiver Operating Characteristic curve (AUC) for both models.

RESULTS

The model including viral mutations in addition to the host characteristics had an improved fit ([Formula: see text]=12.03, df = 3, p = 0.007). The optimism-adjusted AUC increased from 0.671 to 0.732.

CONCLUSIONS

Adding genomic data (selected season-specific mutations in the viral genome) to the model containing host characteristics improved the prediction of severe influenza infection among hospitalized SARI patients, thereby offering the potential for translation into a prospective strategy to perform early season risk assessment or to guide individual patient management.

An implementation science approach to evaluating pathogen whole genome sequencing in public health

BACKGROUND

Pathogen whole genome sequencing (WGS) is being incorporated into public health surveillance and disease control systems worldwide and has the potential to make significant contributions to infectious disease surveillance, outbreak investigation and infection prevention and control. However, to date, there are limited data regarding (i) the optimal models for integration of genomic data into epidemiological investigations and (ii) how to quantify and evaluate public health impacts resulting from genomic epidemiological investigations.

METHODS

We developed the Pathogen Genomics in Public HeAlth Surveillance Evaluation (PG-PHASE) Framework to guide examination of the use of WGS in public health surveillance and disease control. We illustrate the use of this framework with three pathogens as case studies: Listeria monocytogenes, Mycobacterium tuberculosis and SARS-CoV-2.

RESULTS

The framework utilises an adaptable whole-of-system approach towards understanding how interconnected elements in the public health application of pathogen genomics contribute to public health processes and outcomes. The three phases of the PG-PHASE Framework are designed to support understanding of WGS laboratory processes, analysis, reporting and data sharing, and how genomic data are utilised in public health practice across all stages, from the decision to send an isolate or sample for sequencing to the use of sequence data in public health surveillance, investigation and decision-making. Importantly, the phases can be used separately or in conjunction, depending on the need of the evaluator. Subsequent to conducting evaluation underpinned by the framework, avenues may be developed for strategic investment or interventions to improve utilisation of whole genome sequencing.

CONCLUSIONS

Comprehensive evaluation is critical to support health departments, public health laboratories and other stakeholders to successfully incorporate microbial genomics into public health practice. The PG-PHASE Framework aims to assist public health laboratories, health departments and authorities who are either considering transitioning to whole genome sequencing or intending to assess the integration of WGS in public health practice, including the capacity to detect and respond to outbreaks and associated costs, challenges and facilitators in the utilisation of microbial genomics and public health impacts.

Characterisation of Salmonella enterica clones carrying mcr-1 plasmids in meat products and patients in Northern Thailand using long read sequencing

Salmonella spp. is an important foodborne pathogen associated with consumption of contaminated food, especially food of livestock origin. Antimicrobial resistance (AMR) in Salmonella has been reported globally and increasing AMR in food production is a major public health issue worldwide. The objective of this study was to describe the genetic relatedness among Salmonella enterica isolates, which displayed identical DNA fingerprint profiles. Ten S. enterica isolates were selected from meat and human cases with an identical rep-PCR profile of serovars Rissen (n = 4), Weltevreden (n = 4), and Stanley (n = 2). We used long-read whole genome sequencing (WGS) on the MinION sequencing platform to type isolates and investigate in silico the presence of specific AMR genes. Antimicrobial susceptibility testing was tested by disk diffusion and gradient diffusion method to corroborate the AMR phenotype. Multidrug resistance and resistance to more than one antimicrobial agent were observed in eight and nine isolates, respectively. Resistance to colistin with an accompanying mcr-1 gene was observed among the Salmonella isolates. The analysis of core genome and whole genome MLST revealed that the Salmonella from meat and human salmonellosis were genetically related. Hence, it could be concluded that meat is one of the important sources for Salmonella infection in human.

A roadmap for the generation of benchmarking resources for antimicrobial resistance detection using next generation sequencing

Next Generation Sequencing technologies significantly impact the field of Antimicrobial Resistance (AMR) detection and monitoring, with immediate uses in diagnosis and risk assessment. For this application and in general, considerable challenges remain in demonstrating sufficient trust to act upon the meaningful information produced from raw data, partly because of the reliance on bioinformatics pipelines, which can produce different results and therefore lead to different interpretations. With the constant evolution of the field, it is difficult to identify, harmonise and recommend specific methods for large-scale implementations over time. In this article, we propose to address this challenge through establishing a transparent, performance-based, evaluation approach to provide flexibility in the bioinformatics tools of choice, while demonstrating proficiency in meeting common performance standards. The approach is two-fold: first, a community-driven effort to establish and maintain “live” (dynamic) benchmarking platforms to provide relevant performance metrics, based on different use-cases, that would evolve together with the AMR field; second, agreed and defined datasets to allow the pipelines’ implementation, validation, and quality-control over time. Following previous discussions on the main challenges linked to this approach, we provide concrete recommendations and future steps, related to different aspects of the design of benchmarks, such as the selection and the characteristics of the datasets (quality, choice of pathogens and resistances, etc.), the evaluation criteria of the pipelines, and the way these resources should be deployed in the community.

Cross-border spread of bla NDM-1- and bla OXA-48-positive Klebsiella pneumoniae: a European collaborative analysis of whole genome sequencing and epidemiological data, 2014 to 2019

Analysis of sequencing data for 143 bla_NDM-1- and bla_OXA-48-positive Klebsiella pneumoniae isolates from 13 European national collections and the public domain resulted in the identification of 15 previously undetected multi-country transmission clusters. For 10 clusters, cases had prior travel/hospitalisation history in countries outside of the European Union including Egypt, Iran, Morocco, Russia, Serbia, Tunisia and Turkey. These findings highlight the benefit of European whole genome sequencing-based surveillance and data sharing for control of antimicrobial resistance.

Outbreak of gastroenteritis highlighting the diagnostic and epidemiological challenges of enteroinvasive Escherichia coli, County of Halland, Sweden, November 2017

An outbreak of gastroenteritis with 83 cases occurred at a conference venue in November 2017 in Halland County, Sweden. Stool samples from two venue visitors and a symptomatic secondary case attributed to household transmission were PCR-positive for the ipaH gene, a target found in both Shigella spp. and enteroinvasive Escherichia coli (EIEC). EIEC was isolated from stool samples and whole genome sequencing analysis confirmed EIEC O96:H19 to be the aetiological agent. A cohort study was conducted among venue attendees and employees and the findings implicated contaminated leafy greens as the vehicle of infection, however, no microbiological evidence could support the study results. Here, we report the investigation into the first recorded EIEC outbreak in Sweden and illustrate the challenges associated with the differential laboratory diagnostics of Shigella/EIEC in an outbreak setting.

Impact of DNA extraction on whole genome sequencing analysis for characterization and relatedness of Shiga toxin-producing Escherichia coli isolates

Whole genome sequencing (WGS) has proven to be the ultimate tool for bacterial isolate characterization and relatedness determination. However, standardized and harmonized workflows, e.g. for DNA extraction, are required to ensure robust and exchangeable WGS data. Data sharing between (inter)national laboratories is essential to support foodborne pathogen control, including outbreak investigation. This study evaluated eight commercial DNA preparation kits for their potential influence on: (i) DNA quality for Nextera XT library preparation; (ii) MiSeq sequencing (data quality, read mapping against plasmid and chromosome references); and (iii) WGS data analysis, i.e. isolate characterization (serotyping, virulence and antimicrobial resistance genotyping) and phylogenetic relatedness (core genome multilocus sequence typing and single nucleotide polymorphism analysis). Shiga toxin-producing Escherichia coli (STEC) was selected as a case study. Overall, data quality and inferred phylogenetic relationships between isolates were not affected by the DNA extraction kit choice, irrespective of the presence of confounding factors such as EDTA in DNA solution buffers. Nevertheless, completeness of STEC characterization was, although not substantially, influenced by the plasmid extraction performance of the kits, especially when using Nextera XT library preparation. This study contributes to addressing the WGS challenges of standardizing protocols to support data portability and to enable full exploitation of its potential.

Perceived utility and feasibility of pathogen genomics for public health practice: a survey among public health professionals working in the field of infectious diseases, Belgium, 2019

BACKGROUND

Pathogen genomics is increasingly being translated from the research setting into the activities of public health professionals operating at different levels. This survey aims to appraise the literacy level and gather the opinions of public health experts and allied professionals working in the field of infectious diseases in Belgium concerning the implementation of next-generation sequencing (NGS) in public health practice.

METHODS

In May 2019, Belgian public health and healthcare professionals were invited to complete an online survey containing eight main topics including background questions, general attitude towards pathogen genomics for public health practice and main concerns, genomic literacy, current and planned NGS activities, place of NGS in diagnostic microbiology pathways, data sharing obstacles, end-user requirements, and key drivers for the implementation of NGS. Descriptive statistics were used to report on the frequency distribution of multiple choice responses whereas thematic analysis was used to analyze free text responses. A multivariable logistic regression model was constructed to identify important predictors for a positive attitude towards the implementation of pathogen genomics in public health practice.

RESULTS

146 out of the 753 invited public health professionals completed the survey. 63% of respondents indicated that public health agencies should be using genomics to understand and control infectious diseases. Having a high level of expertise in the field of pathogen genomics was the strongest predictor of a positive attitude (OR = 4.04, 95% CI = 1.11 - 17.23). A significantly higher proportion of data providers indicated to have followed training in the field of pathogen genomics compared to data end-users (p < 0.001). Overall, 79% of participants expressed interest in receiving further training. Main concerns were related to the cost of sequencing technologies, data sharing, data integration, interdisciplinary working, and bioinformatics expertise.

CONCLUSIONS

Belgian health professionals expressed favorable views about implementation of pathogen genomics in their work activities related to infectious disease surveillance and control. They expressed the need for suitable training initiatives to strengthen their competences in the field. Their perception of the utility and feasibility of pathogen genomics for public health purposes will be a key driver for its further implementation.

WGS-Based Prediction and Analysis of Antimicrobial Resistance in Campylobacter jejuni Isolates From Israel

Rapid developments in the field of whole genome sequencing (WGS) make in silico antimicrobial resistance (AMR) a target within reach. Campylobacter jejuni is a leading cause of foodborne infections in Israel with increasing rates of resistance. We applied WGS analysis to study the prevalence and genetic basis of AMR in 263 C. jejuni human and veterinary representative isolates retrieved from a national collection during 2003–2012. We evaluated the prediction of phenotypic AMR from genomic data. Genomes were screened by the NCBI AMRFinderPlus and the BioNumerics tools for acquired AMR genes and point mutations. The results were compared to phenotypic resistance determined by broth microdilution. The most prevalent resistant determinants were the multi-drug efflux transporter gene cmeABC (100%), the tetracycline resistance tet(O) gene (82.1%), the quinolone resistance gyrA T861 point mutation (75.7%), and the aadE streptomycin resistance gene. A variety of 12 known β lactam resistance genes (bla_OXA variants) were detected in 241 (92%) isolates, the most prevalent being bla_OXA−193, bla_OXA−461, and bla_OXA−580 (56, 16, and 7%, respectively). Other aminoglycoside resistance genes and the macrolide resistance point mutation were rare (<1%). The overall correlation rate between WGS-based genotypic prediction and phenotypic resistance was 98.8%, sensitivity, specificity, positive, and negative predictive values being 98.0, 99.3, 99.1, and 98.5%, respectively. wgMLST-based phylogeny indicated a high level of clonality and clustering among the studied isolates. Closely related isolates that were part of a genetic cluster (single linkage distance ≤ 15 alleles) based on wgMLST phylogeny mostly shared a homogenous AMR determinant profile. This was observed in 18 of 20 (90.0%) clusters within clonal complex-21, suggesting clonal expansion of resistant isolates. Strong association to lineage was noted for the aadE gene and the various bla_OXA genes. High resistance rates to tetracycline and quinolones and a low resistance rate to macrolides were detected among the Israeli C. jejuni isolates. While a high genotypic-phenotypic correlation was found, some resistance phenotypes could not be predicted by the presence of AMR determinants, and particularly not the level of resistance. WGS-based prediction of antimicrobial resistance in C. jejuni requires further optimization in order to integrate this approach in the routine workflow of public health laboratories for foodborne surveillance.

A Practical Method to Implement Strain-Level Metagenomics-Based Foodborne Outbreak Investigation and Source Tracking in Routine

The management of a foodborne outbreak depends on the rapid and accurate identification of the responsible food source. Conventional methods based on isolation of the pathogen from the food matrix and target-specific real-time polymerase chain reactions (qPCRs) are used in routine. In recent years, the use of whole genome sequencing (WGS) of bacterial isolates has proven its value to collect relevant information for strain characterization as well as tracing the origin of the contamination by linking the food isolate with the patient's isolate with high resolution. However, the isolation of a bacterial pathogen from food matrices is often time-consuming and not always successful. Therefore, we aimed to improve outbreak investigation by developing a method that can be implemented in reference laboratories to characterize the pathogen in the food vehicle without its prior isolation and link it back to human cases. We tested and validated a shotgun metagenomics approach by spiking food pathogens in specific food matrices using the Shiga toxin-producing Escherichia coli (STEC) as a case study. Different DNA extraction kits and enrichment procedures were investigated to obtain the most practical workflow. We demonstrated the feasibility of shotgun metagenomics to obtain the same information as in ISO/TS 13136:2012 and WGS of the isolate in parallel by inferring the genome of the contaminant and characterizing it in a shorter timeframe. This was achieved in food samples containing different E. coli strains, including a combination of different STEC strains. For the first time, we also managed to link individual strains from a food product to isolates from human cases, demonstrating the power of shotgun metagenomics for rapid outbreak investigation and source tracking.

The Benefits of Whole Genome Sequencing for Foodborne Outbreak Investigation from the Perspective of a National Reference Laboratory in a Smaller Country

Gradually, conventional methods for foodborne pathogen typing are replaced by whole genome sequencing (WGS). Despite studies describing the overall benefits, National Reference Laboratories of smaller countries often show slower uptake of WGS, mainly because of significant investments required to generate and analyze data of a limited amount of samples. To facilitate this process and incite policy makers to support its implementation, a Shiga toxin-producing Escherichia coli (STEC) O157:H7 (stx1+, stx2+, eae+) outbreak (2012) and a STEC O157:H7 (stx2+, eae+) outbreak (2013) were retrospectively analyzed using WGS and compared with their conventional investigations. The corresponding results were obtained, with WGS delivering even more information, e.g., on virulence and antimicrobial resistance genotypes. Besides a universal, all-in-one workflow with less hands-on-time (five versus seven actual working days for WGS versus conventional), WGS-based cgMLST-typing demonstrated increased resolution. This enabled an accurate cluster definition, which remained unsolved for the 2013 outbreak, partly due to scarce epidemiological linking with the suspect source. Moreover, it allowed detecting two and one earlier circulating STEC O157:H7 (stx1+, stx2+, eae+) and STEC O157:H7 (stx2+, eae+) strains as closely related to the 2012 and 2013 outbreaks, respectively, which might have further directed epidemiological investigation initially. Although some bottlenecks concerning centralized data-sharing, sampling strategies, and perceived costs should be considered, we delivered a proof-of-concept that even in smaller countries, WGS offers benefits for outbreak investigation, if a sufficient budget is available to ensure its implementation in surveillance. Indeed, applying a database with background isolates is critical in interpreting isolate relationships to outbreaks, and leveraging the true benefit of WGS in outbreak investigation and/or prevention.

Next-generation sequencing capacity and capabilities within the National Animal Health Laboratory Network

With the cost of next-generation sequencing (NGS) decreasing, this technology is rapidly being integrated into the workflows of veterinary clinical and diagnostic laboratories nationwide. The mission of the U.S. Department of Agriculture-National Animal Health Laboratory Network (NAHLN) is in part to evaluate new technologies and develop standardized processes for deploying these technologies to network laboratories for improving detection and response to emerging and foreign animal diseases. Thus, in 2018, the NAHLN identified the integration of NGS into the network as a top priority. In order to assess the current state of preparedness across NAHLN laboratories and to identify which have the capability for performing NGS, a questionnaire was developed by the NAHLN Methods Technical Working Group and submitted to all NAHLN laboratories in December 2018. Thirty of 59 laboratories completed the questionnaire, of which 18 (60%) reported having some sequencing capability. Multiple sequencing platforms and reagents were identified, and limited standardized quality control parameters were reported. Our results confirm that NGS capacity is available within the NAHLN, but several gaps remain. Gaps include not having sufficient personnel trained in bioinformatics and data interpretation, lack of standardized methods and equipment, and maintenance of sufficient computing capacity to meet the growing demand for this technology.

A Core Genome Multilocus Sequence Typing Scheme for Pseudomonas aeruginosa

Pseudomonas aeruginosa is a ubiquitous microorganism and an important opportunistic pathogen responsible for a broad spectrum of infections mainly in immunosuppressed and critically ill patients. Molecular investigations traditionally rely on pulsed field gel electrophoresis (PFGE) and multilocus sequence typing (MLST). In this work we propose a core genome multilocus sequence typing (cgMLST) scheme for P. aeruginosa, a methodology that combines traditional MLST principles with whole genome sequencing data. All publicly available complete P. aeruginosa genomes, representing the diversity of this species, were used to establish a cgMLST scheme targeting 2,653 genes. The scheme was then tested using genomes available at contig, chromosome and scaffold levels. The proposed cgMLST scheme for P. aeruginosa typed over 99% (2,314/2,325) of the genomes available for this study considering at least 95% of the cgMLST target genes present. The absence of a certain number gene targets at the threshold considered for both the creation and validation steps due to low genome sequence quality is possibly the main reason for this result. The cgMLST scheme was compared with previously published whole genome single nucleotide polymorphism analysis for the characterization of the population structure of the epidemic clone ST235 and results were highly similar. In order to evaluate the typing resolution of the proposed scheme, collections of isolates belonging to two important STs associated with cystic fibrosis, ST146 and ST274, were typed using this scheme, and ST235 isolates associated with an outbreak were evaluated. Besides confirming the relatedness of all the isolates, earlier determined by MLST, the higher resolution of cgMLST denotes that it may be suitable for surveillance programs, overcoming possible shortcomings of classical MLST. The proposed scheme is publicly available at: https://github.com/BioinformaticsHIAEMolecularMicrobiology/cgMLST-Pseudomonas-aeruginosa.

Consolidation of Clinical Microbiology Laboratories and Introduction of Transformative Technologies

Clinical microbiology is experiencing revolutionary advances in the deployment of molecular, genome sequencing-based, and mass spectrometry-driven detection, identification, and characterization assays. Laboratory automation and the linkage of information systems for big(ger) data management, including artificial intelligence (AI) approaches, also are being introduced. The initial optimism associated with these developments has now entered a more reality-driven phase of reflection on the significant challenges, complexities, and health care benefits posed by these innovations. With this in mind, the ongoing process of clinical laboratory consolidation, covering large geographical regions, represents an opportunity for the efficient and cost-effective introduction of new laboratory technologies and improvements in translational research and development. This will further define and generate the mandatory infrastructure used in validation and implementation of newer high-throughput diagnostic approaches. Effective, structured access to large numbers of well-documented biobanked biological materials from networked laboratories will release countless opportunities for clinical and scientific infectious disease research and will generate positive health care impacts. We describe why consolidation of clinical microbiology laboratories will generate quality benefits for many, if not most, aspects of the services separate institutions already provided individually. We also define the important role of innovative and large-scale diagnostic platforms. Such platforms lend themselves particularly well to computational (AI)-driven genomics and bioinformatics applications. These and other diagnostic innovations will allow for better infectious disease detection, surveillance, and prevention with novel translational research and optimized (diagnostic) product and service development opportunities as key results.

A megaplasmid family driving dissemination of multidrug resistance in Pseudomonas

Multidrug resistance (MDR) represents a global threat to health. Here, we used whole genome sequencing to characterise Pseudomonas aeruginosa MDR clinical isolates from a hospital in Thailand. Using long-read sequence data we obtained complete sequences of two closely related megaplasmids (>420 kb) carrying large arrays of antibiotic resistance genes located in discrete, complex and dynamic resistance regions, and revealing evidence of extensive duplication and recombination events. A comprehensive pangenomic and phylogenomic analysis indicates that: 1) these large plasmids comprise an emerging family present in different members of the Pseudomonas genus, and associated with multiple sources (geographical, clinical or environmental); 2) the megaplasmids encode diverse niche-adaptive accessory traits, including multidrug resistance; 3) the accessory genome of the megaplasmid family is highly flexible and diverse. The history of the megaplasmid family, inferred from our analysis of the available database, suggests that members carrying multiple resistance genes date back to at least the 1970s.

ASA3P: An automatic and scalable pipeline for the assembly, annotation and higher-level analysis of closely related bacterial isolates

Whole genome sequencing of bacteria has become daily routine in many fields. Advances in DNA sequencing technologies and continuously dropping costs have resulted in a tremendous increase in the amounts of available sequence data. However, comprehensive in-depth analysis of the resulting data remains an arduous and time-consuming task. In order to keep pace with these promising but challenging developments and to transform raw data into valuable information, standardized analyses and scalable software tools are needed. Here, we introduce ASA3P, a fully automatic, locally executable and scalable assembly, annotation and analysis pipeline for bacterial genomes. The pipeline automatically executes necessary data processing steps, i.e. quality clipping and assembly of raw sequencing reads, scaffolding of contigs and annotation of the resulting genome sequences. Furthermore, ASA3P conducts comprehensive genome characterizations and analyses, e.g. taxonomic classification, detection of antibiotic resistance genes and identification of virulence factors. All results are presented via an HTML5 user interface providing aggregated information, interactive visualizations and access to intermediate results in standard bioinformatics file formats. We distribute ASA3P in two versions: a locally executable Docker container for small-to-medium-scale projects and an OpenStack based cloud computing version able to automatically create and manage self-scaling compute clusters. Thus, automatic and standardized analysis of hundreds of bacterial genomes becomes feasible within hours. The software and further information is available at: asap.computational.bio.

Concordance of SNP- and allele-based typing workflows in the context of a large-scale international Salmonella Enteritidis outbreak investigation

A large European multi-country Salmonella enterica serovar Enteritidis outbreak associated with Polish eggs was characterized by whole-genome sequencing (WGS)-based analysis, with various European institutes using different analysis workflows to identify isolates potentially related to the outbreak. The objective of our study was to compare the output of six of these different typing workflows (distance matrices of either SNP-based or allele-based workflows) in terms of cluster detection and concordance. To this end, we analysed a set of 180 isolates coming from confirmed and probable outbreak cases, which were representative of the genetic variation within the outbreak, supplemented with 22 unrelated contemporaneous S. enterica serovar Enteritidis isolates. Since the definition of a cluster cut-off based on genetic distance requires prior knowledge on the evolutionary processes that govern the bacterial populations in question, we used a variety of hierarchical clustering methods (single, average and complete) and selected the optimal number of clusters based on the consensus of the silhouette, Dunn2, and McClain-Rao internal validation indices. External validation was done by calculating the concordance with the WGS-based case definition (SNP-address) for this outbreak using the Fowlkes-Mallows index. Our analysis indicates that with complete-linkage hierarchical clustering combined with the optimal number of clusters, as defined by three internal validity indices, the six different allele- and SNP-based typing workflows generate clusters with similar compositions. Furthermore, we show that even in the absence of coordinated typing procedures, but by using an unsupervised machine learning methodology for cluster delineation, the various workflows that are currently in use by six European public-health authorities can identify concordant clusters of genetically related S. enterica serovar Enteritidis isolates; thus, providing public-health researchers with comparable tools for detection of infectious-disease outbreaks.