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ABSTRACT 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.IMPORTANCEThis 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.

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.

Abstract 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.

ABSTRACT 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.

Abstract Echinacea is a common botanical used in dietary supplements, primarily to treat upper respiratory tract infections and to support immune function. There are currently thought to be nine species in the genus Echinacea. Due to very low molecular divergence among sister species, traditional DNA barcoding has not been successful for differentiation of Echinacea species. Here, we present the use of full chloroplast genomes to distinguish between all 9 reported species. Total DNA was extracted from specimens stored at the National Museum of Natural History, Smithsonian Institution, which had been collected from the wild with species identification documented by experts in the field. We used Next Generation Sequencing (NGS) and CLC Genomics Workbench to assemble complete chloroplast genomes for all nine species. Full chloroplasts unambiguously differentiated all nine species, compared with the very few single nucleotide polymorphisms (SNPs) available with core DNA barcoding markers. SNPs for any two Echinacea chloroplast genomes ranged from 181 to 910, and provided robust data for unambiguous species delimitation. Implications for DNA-based species identification assays derived from chloroplast genome sequences are discussed in light of product safety, adulteration and quality issues.

ABSTRACT 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.

Background As next generation sequence technology has advanced, there have been parallel advances in genome-scale analysis programs for determining evolutionary relationships as proxies for epidemiological relationship in public health. Most new programs skip traditional steps of ortholog determination and multi-gene alignment, instead identifying variants across a set of genomes, then summarizing results in a matrix of single-nucleotide polymorphisms or alleles for standard phylogenetic analysis. However, public health authorities need to document the performance of these methods with appropriate and comprehensive datasets so they can be validated for specific purposes, e.g., outbreak surveillance. Here we propose a set of benchmark datasets to be used for comparison and validation of phylogenomic pipelines. Methods We identified four well-documented foodborne pathogen events in which the epidemiology was concordant with routine phylogenomic analyses (reference-based SNP and wgMLST approaches). These are ideal benchmark datasets, as the trees, WGS data, and epidemiological data for each are all in agreement. We have placed these sequence data, sample metadata, and “known” phylogenetic trees in publicly-accessible databases and developed a standard descriptive spreadsheet format describing each dataset. To facilitate easy downloading of these benchmarks, we developed an automated script that uses the standard descriptive spreadsheet format. Results Our “outbreak” benchmark datasets represent the four major foodborne bacterial pathogens (Listeria monocytogenes, Salmonella enterica, Escherichia coli, and Campylobacter jejuni) and one simulated dataset where the “known tree” can be accurately called the “true tree”. The downloading script and associated table files are available on GitHub: https://github.com/WGS-standards-and-analysis/datasets. Discussion These five benchmark datasets will help standardize comparison of current and future phylogenomic pipelines, and facilitate important cross-institutional collaborations. Our work is part of a global effort to provide collaborative infrastructure for sequence data and analytic tools—we welcome additional benchmark datasets in our recommended format, and, if relevant, we will add these on our GitHub site. Together, these datasets, dataset format, and the underlying GitHub infrastructure present a recommended path for worldwide standardization of phylogenomic pipelines.

Evolutionary studies of clustered regularly interspaced short palindromic repeats (CRISPRs) and their associated (cas) genes can provide insights into host-pathogen co-evolutionary dynamics and the frequency at which different genomic events (e.g., horizontal vs. vertical transmission) occur. Within this study, we used whole genome sequence (WGS) data to determine the evolutionary history and genetic diversity of CRISPR loci and cas genes among a diverse set of 427 Salmonella enterica ssp. enterica isolates representing 64 different serovars. We also evaluated the performance of CRISPR loci for typing when compared to whole genome and multilocus sequence typing (MLST) approaches. We found that there was high diversity in array length within both CRISPR1 (median = 22; min = 3; max = 79) and CRISPR2 (median = 27; min = 2; max = 221). There was also much diversity within serovars (e.g., arrays differed by as many as 50 repeat-spacer units among Salmonella ser. Senftenberg isolates). Interestingly, we found that there are two general cas gene profiles that do not track phylogenetic relationships, which suggests that non-vertical transmission events have occurred frequently throughout the evolutionary history of the sampled isolates. There is also considerable variation among the ranges of pairwise distances estimated within each cas gene, which may be indicative of the strength of natural selection acting on those genes. We developed a novel clustering approach based on CRISPR spacer content, but found that typing based on CRISPRs was less accurate than the MLST-based alternative; typing based on WGS data was the most accurate. Notwithstanding cost and accessibility, we anticipate that draft genome sequencing, due to its greater discriminatory power, will eventually become routine for traceback investigations.

The emergence and dissemination of carbapenemases, bacterial enzymes able to inactivate most β-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.

The methylation of DNA bases plays an important role in numerous biological processes including development, gene expression, and DNA replication. Salmonella is an important foodborne pathogen, and methylation in Salmonella is implicated in virulence. Using single molecule real-time (SMRT) DNA-sequencing, we sequenced and assembled the complete genomes of eleven Salmonella enterica isolates from nine different serovars, and analysed the whole-genome methylation patterns of each genome. We describe 16 distinct N6-methyladenine (m6A) methylated motifs, one N4-methylcytosine (m4C) motif, and one combined m6A-m4C motif. Eight of these motifs are novel, i.e., they have not been previously described. We also identified the methyltransferases (MTases) associated with 13 of the motifs. Some motifs are conserved across all Salmonella serovars tested, while others were found only in a subset of serovars. Eight of the nine serovars contained a unique methylated motif that was not found in any other serovar (most of these motifs were part of Type I restriction modification systems), indicating the high diversity of methylation patterns present in Salmonella.

The tremendous diversity of land plants all descended from a single charophyte green alga that colonized the land somewhere between 430 and 470 million years ago. Six orders of charophyte green algae, in addition to embryophytes, comprise the Streptophyta s.l. Previous studies have focused on reconstructing the phylogeny of organisms tied to this key colonization event, but wildly conflicting results have sparked a contentious debate over which lineage gave rise to land plants. The dominant view has been that 'stoneworts,' or Charales, are the sister lineage, but an alternative hypothesis supports the Zygnematales (often referred to as "pond scum") as the sister lineage. In this paper, we provide a well-supported, 160-nuclear-gene phylogenomic analysis supporting the Zygnematales as the closest living relative to land plants. Our study makes two key contributions to the field: 1) the use of an unbiased method to collect a large set of orthologs from deeply diverging species and 2) the use of these data in determining the sister lineage to land plants. We anticipate this updated phylogeny not only will hugely impact lesson plans in introductory biology courses, but also will provide a solid phylogenetic tree for future green-lineage research, whether it be related to plants or green algae.