PHA4GE quality control contextual data tags: standardized annotations for sharing public health sequence datasets with known quality issues to facilitate testing and training.

As public health laboratories expand their genomic sequencing and bioinformatics capacity for the surveillance of different pathogens, labs must carry out robust validation, training, and optimization of wet- and dry-lab procedures. Achieving these goals for algorithms, pipelines and instruments often requires that lower quality datasets be made available for analysis and comparison alongside those of higher quality. This range of data quality in reference sets can complicate the sharing of sub-optimal datasets that are vital for the community and for the reproducibility of assays. Sharing of useful, but sub-optimal datasets requires careful annotation and documentation of known issues to enable appropriate interpretation, avoid being mistaken for better quality information, and for these data (and their derivatives) to be easily identifiable in repositories. Unfortunately, there are currently no standardized attributes or mechanisms for tagging poor-quality datasets, or datasets generated for a specific purpose, to maximize their utility, searchability, accessibility and reuse. The Public Health Alliance for Genomic Epidemiology (PHA4GE) is an international community of scientists from public health, industry and academia focused on improving the reproducibility, interoperability, portability, and openness of public health bioinformatic software, skills, tools and data. To address the challenges of sharing lower quality datasets, PHA4GE has developed a set of standardized contextual data tags, namely fields and terms, that can be included in public repository submissions as a means of flagging pathogen sequence data with known quality issues, increasing their discoverability. The contextual data tags were developed through consultations with the community including input from the International Nucleotide Sequence Data Collaboration (INSDC), and have been standardized using ontologies - community-based resources for defining the tag properties and the relationships between them. The standardized tags are agnostic to the organism and the sequencing technique used and thus can be applied to data generated from any pathogen using an array of sequencing techniques. The tags can also be applied to synthetic (lab created) data. The list of standardized tags is maintained by PHA4GE and can be found at https://github.com/pha4ge/contextual_data_QC_tags. Definitions, ontology IDs, examples of use, as well as a JSON representation, are provided. The PHA4GE QC tags were tested, and are now implemented, by the FDA's GenomeTrakr laboratory network as part of its routine submission process for SARS-CoV-2 wastewater surveillance. We hope that these simple, standardized tags will help improve communication regarding quality control in public repositories, in addition to making datasets of variable quality more easily identifiable. Suggestions for additional tags can be submitted to PHA4GE via the New Term Request Form in the GitHub repository. By providing a mechanism for feedback and suggestions, we also expect that the tags will evolve with the needs of the community.

SARS-CoV-2 wastewater variant surveillance: pandemic response leveraging FDA's GenomeTrakr network.

Wastewater surveillance has emerged as a crucial public health tool for population-level pathogen surveillance. Supported by funding from the American Rescue Plan Act of 2021, the FDA's genomic epidemiology program, GenomeTrakr, was leveraged to sequence SARS-CoV-2 from wastewater sites across the United States. This initiative required the evaluation, optimization, development, and publication of new methods and analytical tools spanning sample collection through variant analyses. Version-controlled protocols for each step of the process were developed and published on protocols.io. A custom data analysis tool and a publicly accessible dashboard were built to facilitate real-time visualization of the collected data, focusing on the relative abundance of SARS-CoV-2 variants and sub-lineages across different samples and sites throughout the project. From September 2021 through June 2023, a total of 3,389 wastewater samples were collected, with 2,517 undergoing sequencing and submission to NCBI under the umbrella BioProject, PRJNA757291. Sequence data were released with explicit quality control (QC) tags on all sequence records, communicating our confidence in the quality of data. Variant analysis revealed wide circulation of Delta in the fall of 2021 and captured the sweep of Omicron and subsequent diversification of this lineage through the end of the sampling period. This project successfully achieved two important goals for the FDA's GenomeTrakr program: first, contributing timely genomic data for the SARS-CoV-2 pandemic response, and second, establishing both capacity and best practices for culture-independent, population-level environmental surveillance for other pathogens of interest to the FDA. IMPORTANCE: This paper serves two primary objectives. First, it summarizes the genomic and contextual data collected during a Covid-19 pandemic response project, which utilized the FDA's laboratory network, traditionally employed for sequencing foodborne pathogens, for sequencing SARS-CoV-2 from wastewater samples. Second, it outlines best practices for gathering and organizing population-level next generation sequencing (NGS) data collected for culture-free, surveillance of pathogens sourced from environmental samples.

Putting everything in its place: using the INSDC compliant Pathogen Data Object Model to better structure genomic data submitted for public health applications.

Fast, efficient public health actions require well-organized and coordinated systems that can supply timely and accurate knowledge. Public databases of pathogen genomic data, such as the International Nucleotide Sequence Database Collaboration (INSDC), have become essential tools for efficient public health decisions. However, these international resources began primarily for academic purposes, rather than for surveillance or interventions. Now, queries need to access not only the whole genomes of multiple pathogens but also make connections using robust contextual metadata to identify issues of public health relevance. Databases that over time developed a patchwork of submission formats and requirements need to be consistently organized and coordinated internationally to allow effective searches.To help resolve these issues, we propose a common pathogen data structure called the Pathogen Data Object Model (DOM) that will formalize the minimum pieces of sequence data and contextual data necessary for general public health uses, while recognizing that submitters will likely withhold a wide range of non-public contextual data. Further, we propose contributors use the Pathogen DOM for all pathogen submissions (bacterial, viral, fungal, and parasites), which will simplify data submissions and provide a consistent and transparent data structure for downstream data analyses. We also highlight how improved submission tools can support the Pathogen DOM, offering users additional easy-to-use methods to ensure this structure is followed.

Enterobacterales draft genome sequences: 15 historical (1998-2004) and 30 contemporary (2015-2016) clinical isolates from Pakistan.

The continued emergence and spread of antimicrobial resistance among pathogenic bacteria are ever-growing threats to health and economy. Here, we report the draft genomes for 45 Enterobacterales clinical isolates, including historical and contemporary drug-resistant organisms, obtained in Pakistan between 1998 and 2016: 5 Serratia, 3 Salmonella, 3 Enterobacter, and 34 Klebsiella.

Performance of methods for SARS-CoV-2 variant detection and abundance estimation within mixed population samples.

Background: The accurate identification of SARS-CoV-2 (SC2) variants and estimation of their abundance in mixed population samples (e.g., air or wastewater) is imperative for successful surveillance of community level trends. Assessing the performance of SC2 variant composition estimators (VCEs) should improve our confidence in public health decision making. Here, we introduce a linear regression based VCE and compare its performance to four other VCEs: two re-purposed DNA sequence read classifiers (Kallisto and Kraken2), a maximum-likelihood based method (Lineage deComposition for Sars-Cov-2 pooled samples (LCS)), and a regression based method (Freyja). Methods: We simulated DNA sequence datasets of known variant composition from both Illumina and Oxford Nanopore Technologies (ONT) platforms and assessed the performance of each VCE. We also evaluated VCEs performance using publicly available empirical wastewater samples collected for SC2 surveillance efforts. Bioinformatic analyses were performed with a custom NextFlow workflow (C-WAP, CFSAN Wastewater Analysis Pipeline). Relative root mean squared error (RRMSE) was used as a measure of performance with respect to the known abundance and concordance correlation coefficient (CCC) was used to measure agreement between pairs of estimators. Results: Based on our results from simulated data, Kallisto was the most accurate estimator as it had the lowest RRMSE, followed by Freyja. Kallisto and Freyja had the most similar predictions, reflected by the highest CCC metrics. We also found that accuracy was platform and amplicon panel dependent. For example, the accuracy of Freyja was significantly higher with Illumina data compared to ONT data; performance of Kallisto was best with ARTICv4. However, when analyzing empirical data there was poor agreement among methods and variations in the number of variants detected (e.g., Freyja ARTICv4 had a mean of 2.2 variants while Kallisto ARTICv4 had a mean of 10.1 variants). Conclusion: This work provides an understanding of the differences in performance of a number of VCEs and how accurate they are in capturing the relative abundance of SC2 variants within a mixed sample (e.g., wastewater). Such information should help officials gauge the confidence they can have in such data for informing public health decisions.

Use of Whole Genome Sequencing by the Federal Interagency Collaboration for Genomics for Food and Feed Safety in the United States.

ABSTRACT: This multiagency report developed by the Interagency Collaboration for Genomics for Food and Feed Safety provides an overview of the use of and transition to whole genome sequencing (WGS) technology for detection and characterization of pathogens transmitted commonly by food and for identification of their sources. We describe foodborne pathogen analysis, investigation, and harmonization efforts among the following federal agencies: National Institutes of Health; Department of Health and Human Services, Centers for Disease Control and Prevention (CDC) and U.S. Food and Drug Administration (FDA); and the U.S. Department of Agriculture, Food Safety and Inspection Service, Agricultural Research Service, and Animal and Plant Health Inspection Service. We describe single nucleotide polymorphism, core-genome, and whole genome multilocus sequence typing data analysis methods as used in the PulseNet (CDC) and GenomeTrakr (FDA) networks, underscoring the complementary nature of the results for linking genetically related foodborne pathogens during outbreak investigations while allowing flexibility to meet the specific needs of Interagency Collaboration partners. We highlight how we apply WGS to pathogen characterization (virulence and antimicrobial resistance profiles) and source attribution efforts and increase transparency by making the sequences and other data publicly available through the National Center for Biotechnology Information. We also highlight the impact of current trends in the use of culture-independent diagnostic tests for human diagnostic testing on analytical approaches related to food safety and what is next for the use of WGS in the area of food safety.

Future-proofing and maximizing the utility of metadata: The PHA4GE SARS-CoV-2 contextual data specification package.

BACKGROUND: The Public Health Alliance for Genomic Epidemiology (PHA4GE) (https://pha4ge.org) is a global coalition that is actively working to establish consensus standards, document and share best practices, improve the availability of critical bioinformatics tools and resources, and advocate for greater openness, interoperability, accessibility, and reproducibility in public health microbial bioinformatics. In the face of the current pandemic, PHA4GE has identified a need for a fit-for-purpose, open-source SARS-CoV-2 contextual data standard. RESULTS: As such, we have developed a SARS-CoV-2 contextual data specification package based on harmonizable, publicly available community standards. The specification can be implemented via a collection template, as well as an array of protocols and tools to support both the harmonization and submission of sequence data and contextual information to public biorepositories. CONCLUSIONS: Well-structured, rich contextual data add value, promote reuse, and enable aggregation and integration of disparate datasets. Adoption of the proposed standard and practices will better enable interoperability between datasets and systems, improve the consistency and utility of generated data, and ultimately facilitate novel insights and discoveries in SARS-CoV-2 and COVID-19. The package is now supported by the NCBI's BioSample database.

Microbiome Metadata Standards: Report of the National Microbiome Data Collaborative's Workshop and Follow-On Activities.

Microbiome samples are inherently defined by the environment in which they are found. Therefore, data that provide context and enable interpretation of measurements produced from biological samples, often referred to as metadata, are critical. Important contributions have been made in the development of community-driven metadata standards; however, these standards have not been uniformly embraced by the microbiome research community. To understand how these standards are being adopted, or the barriers to adoption, across research domains, institutions, and funding agencies, the National Microbiome Data Collaborative (NMDC) hosted a workshop in October 2019. This report provides a summary of discussions that took place throughout the workshop, as well as outcomes of the working groups initiated at the workshop.

Phylogeny of Salmonella enterica subspecies arizonae by whole-genome sequencing reveals high incidence of polyphyly and low phase 1 H antigen variability.

Salmonella enterica subspecies arizonae is frequently associated with animal reservoirs, particularly reptiles, and can cause illness in some mammals, including humans. Using whole-genome sequencing data, core genome phylogenetic analyses were performed using 112 S. enterica subsp. arizonae isolates, representing 46 of 102 described serovars. Nearly one-third of these are polyphyletic, including two serovars that appear in four and five distinct evolutionary lineages. Subspecies arizonae has a monophasic H antigen. Among the 46 serovars investigated, only 8 phase 1 H antigens were identified, demonstrating high conservation for this antigen. Prophages and plasmids were found throughout this subspecies, including five novel prophages. Polyphyly was also reflected in prophage content, although some clade-specific enrichment for some phages was observed. IncFII(S) was the most frequent plasmid replicon identified and was found in a quarter of S. enterica subsp. arizonae genomes. Salmonella pathogenicity islands (SPIs) 1 and 2 are present across all Salmonella, including this subspecies, although effectors sipA, sptP and arvA in SPI-1 and sseG and ssaI in SPI-2 appear to be lost in this lineage. SPI-20, encoding a type VI secretion system, is exclusive to this subspecies and is well maintained in all genomes sampled. A number of fimbral operons were identified, including the sas operon that appears to be a synapomorphy for this subspecies, while others exhibited more clade-specific patterns. This work reveals evolutionary patterns in S. enterica subsp. arizonae that make this subspecies a unique lineage within this very diverse species.

Antimicrobial and Genomic Characterization of Salmonella Nigeria from Pigs and Poultry in Ilorin, North-central, Nigeria.

INTRODUCTION: Non-typhoidal Salmonella are major foodborne pathogens causing serious challenges to public health and food safety worldwide. This study aimed to determine the resistance, virulence genes, sequence type, using multi-locus sequence typing, plasmids and Single Nucleotide Polymorphisms (SNPs) of Salmonella enterica subsp. enterica serovar Nigeria (S. Nigeria) from livestock in Ilorin, North central Nigeria. METHODOLOGY: A total of 1,500 samples from pig (feces; n = 600) and poultry (feces, postmortem samples; n = 900) were collected and analyzed between 2014 to 2017. Presumptive Salmonella isolates were characterized by Whole Genome Sequencing (WGS). RESULTS: We recovered nine S. Nigeria serovars. All the isolates harbored a single point mutation parC(T57S) in addition to qnrB19 and the tetA gene. Furthermore, two plasmids, Col(pHAD28) and IncQ1 predicted to encode qnrB19 and tetA genes, respectively, were detected in all the strains. All the isolates belonged to a single sequence type (ST) 4911, the SNP-based phylogeny showed all the isolates to be highly related, in addition two clinical isolates from the United Kingdom (UK) and Canada, collected outside of this study, also fell into this cluster. Twenty virulence genes were identified from Salmonella Pathogenicity Islands (SPI), chromosomal and fimbriae loci. CONCLUSIONS: This study highlights the roles of pig and poultry in the emergence and spread of S. Nigeria serovar in Nigeria, sub-Sahara Africa. It also highlighted the importance of WGS in clinical and epidemiological surveillance. There is the need for collaborative research studies to investigate the public health importance of Salmonella enterica serovar Nigeria.

Gen-FS coordinated proficiency test data for genomic foodborne pathogen surveillance, 2017 and 2018 exercises.

The US PulseNet and GenomeTrakr laboratory networks work together within the Genomics for Food Safety (Gen-FS) consortium to collect and analyze genomic data for foodborne pathogen surveillance (species include Salmonella enterica, Listeria monocytogenes, Escherichia coli (STECs), and Campylobactor). In 2017 these two laboratory networks started harmonizing their respective proficiency test exercises, agreeing on distributing a single strain-set and following the same standard operating procedure (SOP) for genomic data collection, running a jointly coordinated annual proficiency test exercise. In this data release we are publishing the reference genomes and raw data submissions for the 2017 and 2018 proficiency test exercises.

Optimizing open data to support one health: best practices to ensure interoperability of genomic data from bacterial pathogens.

The holistic approach of One Health, which sees human, animal, plant, and environmental health as a unit, rather than discrete parts, requires not only interdisciplinary cooperation, but standardized methods for communicating and archiving data, enabling participants to easily share what they have learned and allow others to build upon their findings. Ongoing work by NCBI and the GenomeTrakr project illustrates how open data platforms can help meet the needs of federal and state regulators, public health laboratories, departments of agriculture, and universities. Here we describe how microbial pathogen surveillance can be transformed by having an open access database along with Best Practices for contributors to follow. First, we describe the open pathogen surveillance framework, hosted on the NCBI platform. We cover the current community standards for WGS quality, provide an SOP for assessing your own sequence quality and recommend QC thresholds for all submitters to follow. We then provide an overview of NCBI data submission along with step by step details. And finally, we provide curation guidance and an SOP for keeping your public data current within the database. These Best Practices can be models for other open data projects, thereby advancing the One Health goals of Findable, Accessible, Interoperable and Re-usable (FAIR) data.

Phylogenomic Pipeline Validation for Foodborne Pathogen Disease Surveillance.

Foodborne pathogen surveillance in the United States is transitioning from strain identification using restriction digest technology (pulsed-field gel electrophoresis [PFGE]) to shotgun sequencing of the entire genome (whole-genome sequencing [WGS]). WGS requires a new suite of analysis tools, some of which have long histories in academia but are new to the field of public health and regulatory decision making. Although the general workflow is fairly standard for collecting and analyzing WGS data for disease surveillance, there are a number of differences in how the data are collected and analyzed across public health agencies, both nationally and internationally. This impedes collaborative public health efforts, so national and international efforts are underway to enable direct comparison of these different analysis methods. Ultimately, the harmonization efforts will allow the (mutually trusted and understood) production and analysis of WGS data by labs and agencies worldwide, thus improving outbreak response capabilities globally. This review provides a historical perspective on the use of WGS for pathogen tracking and summarizes the efforts underway to ensure the major steps in phylogenomic pipelines used for pathogen disease surveillance can be readily validated. The tools for doing this will ensure that the results produced are sound, reproducible, and comparable across different analytic approaches.

Utilizing the Public GenomeTrakr Database for Foodborne Pathogen Traceback.

This protocol outlines the all the steps necessary to become a GenomeTrakr data contributor. GenomeTrakr is an international genomic reference database of mostly food and environmental isolates from foodborne pathogens. The data and analyses are housed at the National Center for Biotechnology Information (NCBI), which is a database freely available to anyone in the world. The Pathogen Detection browser at NCBI computes daily cluster results adding the newly submitted data to the existing phylogenetic clusters of closely related genomes. Contributors to this database can see how their new isolates are related to the real-time foodborne pathogen surveillance program established in the USA and a few other countries, and at the same time adding valuable new data to the reference database.

Correction to: Utilizing the Public GenomeTrakr Database for Foodborne Pathogen Traceback.

The chapter "Utilizing the Public GenomeTrakr Database for Foodborne Pathogen Traceback" is changed to open access, per the author's request in this revised version of the book.

Salmonella enterica Phylogeny Based on Whole-Genome Sequencing Reveals Two New Clades and Novel Patterns of Horizontally Acquired Genetic Elements.

Using whole-genome sequence (WGS) data from the GenomeTrakr network, a globally distributed network of laboratories sequencing foodborne pathogens, we present a new phylogeny of Salmonella enterica comprising 445 isolates from 266 distinct serovars and originating from 52 countries. This phylogeny includes two previously unidentified S. enterica subsp. enterica clades. Serovar Typhi is shown to be nested within clade A. Our findings are supported by both phylogenetic support, based on a core genome alignment, and Bayesian approaches, based on single-nucleotide polymorphisms. Serovar assignments were refined by in silico analysis using SeqSero. More than 10% of serovars were either polyphyletic or paraphyletic. We found variable genetic content in these isolates relating to gene mobilization and virulence factors which have different distributions within clades. Gifsy-1- and Gifsy-2-like phages appear more prevalent in clade A; other viruses are more evenly distributed. Our analyses reveal IncFII is the predominant plasmid replicon in S. enterica Few core or clade-defining virulence genes are observed, and their distributions appear probabilistic in nature. Together, these patterns demonstrate that genetic exchange within S. enterica is more extensive and frequent than previously realized, which significantly alters how we view the genetic structure of the bacterial species.IMPORTANCE Rapid improvements in nucleotide sequencing access and affordability have led to a drastic increase in availability of genetic information. This information will improve the accuracy of molecular descriptions, including serovars, within S. enterica Although the concept of serovars continues to be useful, it may have more significant limitations than previously understood. Furthermore, the discrete absence or presence of specific genes can be an unstable indicator of phylogenetic identity. Whole-genome sequencing provides more rigorous tools for assessing the distributions of these genes. Our phylogenetic and genetic content analyses reveal how active genetic elements are dynamically distributed within a species, allowing us to better understand genetic reservoirs and underlying bacterial evolution.

GenomeTrakr proficiency testing for foodborne pathogen surveillance: an exercise from 2015.

Pathogen monitoring is becoming more precise as sequencing technologies become more affordable and accessible worldwide. This transition is especially apparent in the field of food safety, which has demonstrated how whole-genome sequencing (WGS) can be used on a global scale to protect public health. GenomeTrakr coordinates the WGS performed by public-health agencies and other partners by providing a public database with real-time cluster analysis for foodborne pathogen surveillance. Because WGS is being used to support enforcement decisions, it is essential to have confidence in the quality of the data being used and the downstream data analyses that guide these decisions. Routine proficiency tests, such as the one described here, have an important role in ensuring the validity of both data and procedures. In 2015, the GenomeTrakr proficiency test distributed eight isolates of common foodborne pathogens to participating laboratories, who were required to follow a specific protocol for performing WGS. Resulting sequence data were evaluated for several metrics, including proper labelling, sequence quality and new single nucleotide polymorphisms (SNPs). Illumina MiSeq sequence data collected for the same set of strains across 21 different laboratories exhibited high reproducibility, while revealing a narrow range of technical and biological variance. The numbers of SNPs reported for sequencing runs of the same isolates across multiple laboratories support the robustness of our cluster analysis pipeline in that each individual isolate cultured and resequenced multiple times in multiple places are all easily identifiable as originating from the same source.

Resistome of carbapenem- and colistin-resistant Klebsiella pneumoniae clinical isolates.

The emergence and dissemination of carbapenemases, bacterial enzymes able to inactivate most ß-lactam antibiotics, in Enterobacteriaceae is of increasing concern. The concurrent spread of resistance against colistin, an antibiotic of last resort, further compounds this challenge further. Whole-genome sequencing (WGS) can play a significant role in the rapid and accurate detection/characterization of existing and emergent resistance determinants, an essential aspect of public health surveillance and response activities to combat the spread of antimicrobial resistant bacteria. In the current study, WGS data was used to characterize the genomic content of antimicrobial resistance genes, including those encoding carbapenemases, in 10 multidrug-resistant Klebsiella pneumoniae isolates from Pakistan. These clinical isolates represented five sequence types: ST11 (n = 3 isolates), ST14 (n = 3), ST15 (n = 1), ST101 (n = 2), and ST307 (n = 1). Resistance profiles against 25 clinically-relevant antimicrobials were determined by broth microdilution; resistant phenotypes were observed for at least 15 of the 25 antibiotics tested in all isolates except one. Specifically, 8/10 isolates were carbapenem-resistant and 7/10 isolates were colistin-resistant. The blaNDM-1 and blaOXA-48 carbapenemase genes were present in 7/10 and 5/10 isolates, respectively; including 2 isolates carrying both genes. No plasmid-mediated determinants for colistin resistance (e.g. mcr) were detected, but disruptions and mutations in chromosomal loci (i.e. mgrB and pmrB) previously reported to confer colistin resistance were observed. A blaOXA-48-carrying IncL/M-type plasmid was found in all blaOXA-48-positive isolates. The application of WGS to molecular epidemiology and surveillance studies, as exemplified here, will provide both a more complete understanding of the global distribution of MDR isolates and a robust surveillance tool useful for detecting emerging threats to public health.