Application of quasimetagenomics methods to define microbial diversity and subtype Listeria monocytogenes in dairy and seafood production facilities

IMPORTANCE

In developed countries, the human diet is predominated by food commodities, which have been manufactured, processed, and stored in a food production facility. Little is known about the application of metagenomic sequencing approaches for detecting foodborne pathogens, such as L. monocytogenes, and characterizing microbial diversity in food production ecosystems. In this work, we investigated the utility of 16S rRNA amplicon and quasimetagenomic sequencing for the taxonomic and phylogenetic classification of Listeria culture enrichments of environmental swabs collected from dairy and seafood production facilities. We demonstrated that single-nucleotide polymorphism (SNP) analyses of L. monocytogenes metagenome-assembled genomes (MAGs) from quasimetagenomic data sets can achieve similar resolution as culture isolate whole-genome sequencing. To further understand the impact of genome coverage on MAG SNP cluster resolution, an in silico downsampling approach was employed to reduce the percentage of target pathogen sequence reads, providing an initial estimate of required MAG coverage for subtyping resolution of L. monocytogenes.

The worm Adult Activity Test (wAAT): A de novo mathematical model for detecting acute chemical effects in Caenorhabditis elegans

We have adapted a semiautomated method for tracking Caenorhabditis elegans spontaneous locomotor activity into a quantifiable assay by developing a sophisticated method for analyzing the time course of measured activity. The 16-h worm Adult Activity Test (wAAT) can be used to measure C. elegans activity levels for efficient screening for pharmacological and toxicity-induced effects. As with any apical endpoint assay, the wAAT is mode of action agnostic, allowing for detection of effects from a broad spectrum of response pathways. With caffeine as a model mild stimulant, the wAAT showed transient hyperactivity followed by reversion to baseline. Mercury chloride (HgCl2 ) produced an early dose-response hyperactivity phase followed by pronounced hypoactivity, a behavior pattern we have termed a toxicant "escape response." Methylmercury chloride (meHgCl) produced a similar pattern to HgCl2 , but at much lower concentrations, a weaker hyperactivity response, and more pronounced hypoactivity. Sodium arsenite (NaAsO2 ) and dimethylarsinic acid (DMA) induced hypoactivity at high concentrations. Acute toxicity, as measured by hypoactivity in C. elegans adults, was ranked: meHgCl > HgCl2  > NaAsO2  = DMA. Caffeine was not toxic with the wAAT at tested concentrations. Methods for conducting the wAAT are described, along with instructions for preparing C. elegans Habitation Medium, a liquid nutrient medium that allows for developmental timing equivalent to that found with C. elegans grown on agar with OP50 Escherichia coli feeder cultures. A de novo mathematical parametric model for adult C. elegans activity and the application of this model in ranking exposure toxicity are presented.

Assessment of plasmids for relating the 2020 Salmonella enterica serovar Newport onion outbreak to farms implicated by the outbreak investigation

BACKGROUND

The Salmonella enterica serovar Newport red onion outbreak of 2020 was the largest foodborne outbreak of Salmonella in over a decade. The epidemiological investigation suggested two farms as the likely source of contamination. However, single nucleotide polymorphism (SNP) analysis of the whole genome sequencing data showed that none of the Salmonella isolates collected from the farm regions were linked to the clinical isolates-preventing the use of phylogenetics in source identification. Here, we explored an alternative method for analyzing the whole genome sequencing data driven by the hypothesis that if the outbreak strain had come from the farm regions, then the clinical isolates would disproportionately contain plasmids found in isolates from the farm regions due to horizontal transfer.

RESULTS

SNP analysis confirmed that the clinical isolates formed a single, nearly-clonal clade with evidence for ancestry in California going back a decade. The clinical clade had a large core genome (4,399 genes) and a large and sparsely distributed accessory genome (2,577 genes, at least 64% on plasmids). At least 20 plasmid types occurred in the clinical clade, more than were found in the literature for Salmonella Newport. A small number of plasmids, 14 from 13 clinical isolates and 17 from 8 farm isolates, were found to be highly similar (> 95% identical)-indicating they might be related by horizontal transfer. Phylogenetic analysis was unable to determine the geographic origin, isolation source, or time of transfer of the plasmids, likely due to their promiscuous and transient nature. However, our resampling analysis suggested that observing a similar number and combination of highly similar plasmids in random samples of environmental Salmonella enterica within the NCBI Pathogen Detection database was unlikely, supporting a connection between the outbreak strain and the farms implicated by the epidemiological investigation.

CONCLUSION

Horizontally transferred plasmids provided evidence for a connection between clinical isolates and the farms implicated as the source of the outbreak. Our case study suggests that such analyses might add a new dimension to source tracking investigations, but highlights the need for detailed and accurate metadata, more extensive environmental sampling, and a better understanding of plasmid molecular evolution.

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.

PRAWNS: compact pan-genomic features for whole-genome population genomics

MOTIVATION

Scientists seeking to understand the genomic basis of bacterial phenotypes, such as antibiotic resistance, today have access to an unprecedented number of complete and nearly complete genomes. Making sense of these data requires computational tools able to perform multiple-genome comparisons efficiently, yet currently available tools cannot scale beyond several tens of genomes.

RESULTS

We describe PRAWNS, an efficient and scalable tool for multiple-genome analysis. PRAWNS defines a concise set of genomic features (metablocks), as well as pairwise relationships between them, which can be used as a basis for large-scale genotype-phenotype association studies. We demonstrate the effectiveness of PRAWNS by identifying genomic regions associated with antibiotic resistance in Acinetobacter baumannii.

AVAILABILITY AND IMPLEMENTATION

PRAWNS is implemented in C++ and Python3, licensed under the GPLv3 license, and freely downloadable from GitHub (https://github.com/KiranJavkar/PRAWNS.git).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Transient and resident pathogens: Intra-facility genetic diversity of Listeria monocytogenes and Salmonella from food production environments

Food production facilities are often routinely tested over time for the presence of foodborne pathogens (e.g., Listeria monocytogenes or Salmonella enterica subsp. enterica). Strains detected in a single sampling event can be classified as transient; positive findings of the same strain across multiple sampling events can be classified as resident pathogens. We analyzed whole-genome sequence (WGS) data from 4,758 isolates (L. monocytogenes = 3,685; Salmonella = 1,073) from environmental samples taken by FDA from 536 U.S. facilities. Our primary objective was to determine the frequency of transient or resident pathogens within food production facilities. Strains were defined as isolates from the same facility that are less than 50 SNP (single-nucleotide polymorphisms) different from one another. Resident pathogens were defined as strains that had more than one isolate collected >59 days apart and from the same facility. We found 1,076 strains (median = 1 and maximum = 21 strains per facility); 180 were resident pathogens, 659 were transient, and 237 came from facilities that had only been sampled once. As a result, 21% of strains (180/ 839) from facilities with positive findings and that were sampled multiple times were found to be resident pathogens; nearly 1 in 4 (23%) of L. monocytogenes strains were found to be resident pathogens compared to 1 in 6 (16%) of Salmonella strains. Our results emphasize the critical importance of preventing the colonization of food production environments by foodborne pathogens, since when colonization does occur, there is an appreciable chance it will become a resident pathogen that presents an ongoing potential to contaminate product.

Using Evolutionary Analyses to Refine Whole-Genome Sequence Match Criteria

Whole-genome sequence databases continue to grow. Collection times between samples are also growing, providing both a challenge for comparing recently collected sequence data to historical samples and an opportunity for evolutionary analyses that can be used to refine match criteria. We measured evolutionary rates for 22 Salmonella enterica serotypes. Based upon these measurements, we propose using an evolutionary rate of 1.97 single-nucleotide polymorphisms (SNPs) per year when determining whether genome sequences match.

Whole-Genome Assessment of Clinical Acinetobacter baumannii Isolates Uncovers Potentially Novel Factors Influencing Carbapenem Resistance

Carbapenems-one of the important last-line antibiotics for the treatment of gram-negative infections-are becoming ineffective for treating Acinetobacter baumannii infections. Studies have identified multiple genes (and mechanisms) responsible for carbapenem resistance. In some A. baumannii strains, the presence/absence of putative resistance genes is not consistent with their resistance phenotype-indicating the genomic factors underlying carbapenem resistance in A. baumannii are not fully understood. Here, we describe a large-scale whole-genome genotype-phenotype association study with 349 A. baumannii isolates that extends beyond the presence/absence of individual antimicrobial resistance genes and includes the genomic positions and pairwise interactions of genes. Ten known resistance genes exhibited statistically significant associations with resistance to imipenem, a type of carbapenem: blaOXA-23, qacEdelta1, sul1, mphE, msrE, ant(3")-II, aacC1, yafP, aphA6, and xerD. A review of the strains without any of these 10 genes uncovered a clade of isolates with diverse imipenem resistance phenotypes. Finer resolution evaluation of this clade revealed the presence of a 38.6 kbp conserved chromosomal region found exclusively in imipenem-susceptible isolates. This region appears to host several HTH-type DNA binding transcriptional regulators and transporter genes. Imipenem-susceptible isolates from this clade also carried two mutually exclusive plasmids that contain genes previously known to be specific to imipenem-susceptible isolates. Our analysis demonstrates the utility of using whole genomes for genotype-phenotype correlations in the context of antibiotic resistance and provides several new hypotheses for future research.

Interpretative labor and the bane of non-standardized metadata in public health surveillance and food safety

Open source DNA sequence databases have long been touted as beneficial to public health, including the facilitation of earlier detection and response to infectious disease outbreaks. Of critical importance to harnessing these benefits is the metadata which describes general and other domain specific attributes (e.g., collection location, isolate type, etc.) of a sample. Unlike the sequence data, the metadata is often incomplete and lacks adherence to an international standard. We describe the problem posed by such variable and incomplete metadata in terms of interpretative labor costs (the time and energy necessary to make sense of the signal in the genetic data), and the impact such metadata has on foodborne outbreak detection and response. Improving the quality of sequence-associated metadata would allow for earlier detection of emerging food safety hazards and allow faster response to foodborne outbreaks.

Evaluating the accuracy of Listeria monocytogenes assemblies from quasimetagenomic samples using long and short reads

BACKGROUND

Whole genome sequencing of cultured pathogens is the state of the art public health response for the bioinformatic source tracking of illness outbreaks. Quasimetagenomics can substantially reduce the amount of culturing needed before a high quality genome can be recovered. Highly accurate short read data is analyzed for single nucleotide polymorphisms and multi-locus sequence types to differentiate strains but cannot span many genomic repeats, resulting in highly fragmented assemblies. Long reads can span repeats, resulting in much more contiguous assemblies, but have lower accuracy than short reads.

RESULTS

We evaluated the accuracy of Listeria monocytogenes assemblies from enrichments (quasimetagenomes) of naturally-contaminated ice cream using long read (Oxford Nanopore) and short read (Illumina) sequencing data. Accuracy of ten assembly approaches, over a range of sequencing depths, was evaluated by comparing sequence similarity of genes in assemblies to a complete reference genome. Long read assemblies reconstructed a circularized genome as well as a 71 kbp plasmid after 24 h of enrichment; however, high error rates prevented high fidelity gene assembly, even at 150X depth of coverage. Short read assemblies accurately reconstructed the core genes after 28 h of enrichment but produced highly fragmented genomes. Hybrid approaches demonstrated promising results but had biases based upon the initial assembly strategy. Short read assemblies scaffolded with long reads accurately assembled the core genes after just 24 h of enrichment, but were highly fragmented. Long read assemblies polished with short reads reconstructed a circularized genome and plasmid and assembled all the genes after 24 h enrichment but with less fidelity for the core genes than the short read assemblies.

CONCLUSION

The integration of long and short read sequencing of quasimetagenomes expedited the reconstruction of a high quality pathogen genome compared to either platform alone. A new and more complete level of information about genome structure, gene order and mobile elements can be added to the public health response by incorporating long read analyses with the standard short read WGS outbreak response.

Genomic diversity of Salmonella enterica isolated from papaya samples collected during multiple outbreaks in 2017

In 2017, the US Food and Drug Administration investigated the sources of multiple outbreaks of salmonellosis. Epidemiologic and traceback investigations identified Maradol papayas as the suspect vehicles. During the investigations, the genomes of 55 Salmonella enterica that were isolated from papaya samples were sequenced. Serovar assignments and phylogenetic analysis placed the 55 isolates into ten distinct groups, each representing a different serovar. Within-serovar SNP differences are generally between 0 and 20 SNPs, while the median between-serovar distance is 51 812 SNPs. We observed two groups with SNP distances between 21 and 100 SNPs. These relatively large within-serovar SNP distances may indicate that the isolates represent either diverse populations or multiple, genetically distinct subpopulations. Further inspection of these cases with traceback evidence allowed us to identify an 11th population. We observed that high levels of genomic diversity from individual firms is possible, with one firm yielding five of the ten serovars. Also, high levels of diversity are possible within small geographic regions, as five of the serovars were isolated from papayas that originated from farms located in Armería and Tecomán, Colima. In addition, we identified AMR genes that are present in three of the serovars studied here (aph(3')-lb, aph(6)-ld, tet(C), fosA7, and qnrB19) and we detected the presence of the plasmid IncHI2A among S. Urbana isolates.

GalaxyTrakr: a distributed analysis tool for public health whole genome sequence data accessible to non-bioinformaticians

Background

Processing and analyzing whole genome sequencing (WGS) is computationally intense: a single Illumina MiSeq WGS run produces ~ 1 million 250-base-pair reads for each of 24 samples. This poses significant obstacles for smaller laboratories, or laboratories not affiliated with larger projects, which may not have dedicated bioinformatics staff or computing power to effectively use genomic data to protect public health. Building on the success of the cloud-based Galaxy bioinformatics platform (http://galaxyproject.org), already known for its user-friendliness and powerful WGS analytical tools, the Center for Food Safety and Applied Nutrition (CFSAN) at the U.S. Food and Drug Administration (FDA) created a customized ‘instance’ of the Galaxy environment, called GalaxyTrakr (https://www.galaxytrakr.org), for use by laboratory scientists performing food-safety regulatory research. The goal was to enable laboratories outside of the FDA internal network to (1) perform quality assessments of sequence data, (2) identify links between clinical isolates and positive food/environmental samples, including those at the National Center for Biotechnology Information sequence read archive (https://www.ncbi.nlm.nih.gov/sra/), and (3) explore new methodologies such as metagenomics. GalaxyTrakr hosts a variety of free and adaptable tools and provides the data storage and computing power to run the tools. These tools support coordinated analytic methods and consistent interpretation of results across laboratories. Users can create and share tools for their specific needs and use sequence data generated locally and elsewhere.

Results

In its first full year (2018), GalaxyTrakr processed over 85,000 jobs and went from 25 to 250 users, representing 53 different public and state health laboratories, academic institutions, international health laboratories, and federal organizations. By mid-2020, it has grown to 600 registered users and processed over 450,000 analytical jobs. To illustrate how laboratories are making use of this resource, we describe how six institutions use GalaxyTrakr to quickly analyze and review their data. Instructions for participating in GalaxyTrakr are provided.

Conclusions

GalaxyTrakr advances food safety by providing reliable and harmonized WGS analyses for public health laboratories and promoting collaboration across laboratories with differing resources. Anticipated enhancements to this resource will include workflows for additional foodborne pathogens, viruses, and parasites, as well as new tools and services.

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.

Measurement(s)	DNA • genome • sequence_assembly
Technology Type(s)	DNA sequencing • sequence assembly process
Factor Type(s)	species of foodborne pathogen
Sample Characteristic - Organism	Salmonella enterica • Escherichia coli • Listeria monocytogenes

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13135046

Within-species contamination of bacterial whole-genome sequence data has a greater influence on clustering analyses than between-species contamination

Although it is assumed that contamination in bacterial whole-genome sequencing causes errors, the influences of contamination on clustering analyses, such as single-nucleotide polymorphism discovery, phylogenetics, and multi-locus sequencing typing, have not been quantified. By developing and analyzing 720 Listeria monocytogenes, Salmonella enterica, and Escherichia coli short-read datasets, we demonstrate that within-species contamination causes errors that confound clustering analyses, while between-species contamination generally does not. Contaminant reads mapping to references or becoming incorporated into chimeric sequences during assembly are the sources of those errors. Contamination sufficient to influence clustering analyses is present in public sequence databases.

Genetic Diversity of Salmonella and Listeria Isolates from Food Facilities

Food production-related facilities (farms, packing houses, etc.) are monitored for foodborne pathogens, and data from these facilities can provide a rich source of information about the population structure and genetic diversity of Salmonella and Listeria. This information is of both academic interest for understanding the evolutionary forces acting on these organisms and of practical interest to those responsible for controlling pathogens in facilities and to those analyzing data from facilities in the context of public health decision making. We have collected information about all positive isolates from facility inspections performed by the U.S. Food and Drug Administration for which whole genome sequencing data are available. The within- and between-facilities observed genetic diversity of isolates was computed and related to the common origin of isolates (as the common collected facility). This relationship provides quantification for assessing the relationship between isolates based on their genetic similarity quantified by single-nucleotide polymorphisms (SNPs). Our results show that if the genetic distance ( D) between two isolates is low, then more likely than not they are from the same facility or have some overlap in their supply chain. For example, if the genetic distance is no more than 20 SNPs, the probability ( P) that two isolates come from the same facility = 0.66 for Salmonella and 0.70 for Listeria. However, if two isolates come from different facilities, their genetic distance is likely large (for Salmonella, P( D > 20 SNPs) = 0.99982; for Listeria, P( D > 20 SNPs) = 0.99949); even if two isolates come from the same facility, their genetic distance is also very likely large (for Salmonella, P( D > 20 SNPs) = 0.794; for Listeria, P( D > 20 SNPs) = 0.692). These results provide insight into what SNP thresholds might be appropriate when determining whether two isolates are from the same facility and thus would be of interest to those investigating foodborne outbreaks and conducting traceback investigations.

Interpreting Whole-Genome Sequence Analyses of Foodborne Bacteria for Regulatory Applications and Outbreak Investigations

Whole-genome sequence (WGS) analysis has revolutionized the food safety industry by enabling high-resolution typing of foodborne bacteria. Higher resolving power allows investigators to identify origins of contamination during illness outbreaks and regulatory activities quickly and accurately. Government agencies and industry stakeholders worldwide are now analyzing WGS data routinely. Although researchers have published many studies that assess the efficacy of WGS data analysis for source attribution, guidance for interpreting WGS analyses is lacking. Here, we provide the framework for interpreting WGS analyses used by the Food and Drug Administration's Center for Food Safety and Applied Nutrition (CFSAN). We based this framework on the experiences of CFSAN investigators, collaborations and interactions with government and industry partners, and evaluation of the published literature. A fundamental question for investigators is whether two or more bacteria arose from the same source of contamination. Analysts often count the numbers of nucleotide differences [single-nucleotide polymorphisms (SNPs)] between two or more genome sequences to measure genetic distances. However, using SNP thresholds alone to assess whether bacteria originated from the same source can be misleading. Bacteria that are isolated from food, environmental, or clinical samples are representatives of bacterial populations. These populations are subject to evolutionary forces that can change genome sequences. Therefore, interpreting WGS analyses of foodborne bacteria requires a more sophisticated approach. Here, we present a framework for interpreting WGS analyses that combines SNP counts with phylogenetic tree topologies and bootstrap support. We also clarify the roles of WGS, epidemiological, traceback, and other evidence in forming the conclusions of investigations. Finally, we present examples that illustrate the application of this framework to real-world situations.

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.

Benchmark datasets for phylogenomic pipeline validation, applications for foodborne pathogen surveillance

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.

Segal's Law, 16S rRNA gene sequencing, and the perils of foodborne pathogen detection within the American Gut Project

Obtaining human population level estimates of the prevalence of foodborne pathogens is critical for understanding outbreaks and ameliorating such threats to public health. Estimates are difficult to obtain due to logistic and financial constraints, but citizen science initiatives like that of the American Gut Project (AGP) represent a potential source of information concerning enteric pathogens. With an emphasis on genera Listeria and Salmonella, we sought to document the prevalence of those two taxa within the AGP samples. The results provided by AGP suggest a surprising 14% and 2% of samples contained Salmonella and Listeria, respectively. However, a reanalysis of those AGP sequences described here indicated that results depend greatly on the algorithm for assigning taxonomy and differences persisted across both a range of parameter settings and different reference databases (i.e., Greengenes and HITdb). These results are perhaps to be expected given that AGP sequenced the V4 region of 16S rRNA gene, which may not provide good resolution at the lower taxonomic levels (e.g., species), but it was surprising how often methods differ in classifying reads-even at higher taxonomic ranks (e.g., family). This highlights the misleading conclusions that can be reached when relying on a single method that is not a gold standard; this is the essence of Segal's Law: an individual with one watch knows what time it is but an individual with two is never sure. Our results point to the need for an appropriate molecular marker for the taxonomic resolution of interest, and calls for the development of more conservative classification methods that are fit for purpose. Thus, with 16S rRNA gene datasets, one must be cautious regarding the detection of taxonomic groups of public health interest (e.g., culture independent identification of foodborne pathogens or taxa associated with a given phenotype).

Evaluation of Three Real-Time PCR Methods for Detection of Salmonella from Cloves

The purpose of the study was to evaluate three real-time PCR platforms for rapid detection of Salmonella from cloves and to compare three different DNA extraction methods. Six trials were conducted with two clove cultivars, Ceylon and Madagascar, and three Salmonella serotypes, Montevideo, Typhimurium, and Weltevreden. Each trial consisted of 20 test portions. The preenrichment cultures were used to perform PCR for comparison of the effectiveness of U.S. Food and Drug Administration, Pacific Regional Laboratory Southwest (FDA-PRLSW), Applied Biosystems Inc. (ABI) MicroSEQ, and GeneDisc platforms for detection of Salmonella. Three DNA extraction methods were used: standard extraction method for each PCR platform, boil preparation, and LyseNow food pathogen DNA extraction cards. The results from real-time PCR correlated well with FDA Bacteriological Analytical Manual culture assay results, with a wide range of cycle threshold (C_T) values among the three PCR platforms for intended positive samples. The mean C_T values for MicroSEQ (16.36 ± 2.78) were significantly lower than for PRLSW (20.37 ± 3.45) and GeneDisc (23.88 ± 2.90) (P < 0.0001). Pairwise comparisons between PCR platforms using different DNA extraction methods indicate that the C_T values are inversely proportional to the relative DNA quantity (RDQ) yields by different platform-extraction combinations. The pairing of MicroSEQ and boil preparation generated the highest RDQ of 120 and the lowest average C_T value of 14.48, whereas the pairing of GeneDisc and LyseNow generated the lowest RDQ of 0.18 and the highest average C_T of 25.97. Boil preparation yielded higher RDQ than the other extraction methods for all three PCR platforms. Although the MicroSEQ platform generated the lowest C_T values, its sensitivity was compromised by narrow separations between the positive and negative samples. The PRLSW platform generated the best segregation between positive and negative groups and is less likely to produce false results. In conclusion, FDA-PRLSW was the most efficient PCR assay for Salmonella detection, and boil preparation was the best method for DNA extraction.

An evaluation of alternative methods for constructing phylogenies from whole genome sequence data: a case study with Salmonella

Comparative genomics based on whole genome sequencing (WGS) is increasingly being applied to investigate questions within evolutionary and molecular biology, as well as questions concerning public health (e.g., pathogen outbreaks). Given the impact that conclusions derived from such analyses may have, we have evaluated the robustness of clustering individuals based on WGS data to three key factors: (1) next-generation sequencing (NGS) platform (HiSeq, MiSeq, IonTorrent, 454, and SOLiD), (2) algorithms used to construct a SNP (single nucleotide polymorphism) matrix (reference-based and reference-free), and (3) phylogenetic inference method (FastTreeMP, GARLI, and RAxML). We carried out these analyses on 194 whole genome sequences representing 107 unique Salmonella enterica subsp. enterica ser. Montevideo strains. Reference-based approaches for identifying SNPs produced trees that were significantly more similar to one another than those produced under the reference-free approach. Topologies inferred using a core matrix (i.e., no missing data) were significantly more discordant than those inferred using a non-core matrix that allows for some missing data. However, allowing for too much missing data likely results in a high false discovery rate of SNPs. When analyzing the same SNP matrix, we observed that the more thorough inference methods implemented in GARLI and RAxML produced more similar topologies than FastTreeMP. Our results also confirm that reproducibility varies among NGS platforms where the MiSeq had the lowest number of pairwise differences among replicate runs. Our investigation into the robustness of clustering patterns illustrates the importance of carefully considering how data from different platforms are combined and analyzed. We found clear differences in the topologies inferred, and certain methods performed significantly better than others for discriminating between the highly clonal organisms investigated here. The methods supported by our results represent a preliminary set of guidelines and a step towards developing validated standards for clustering based on whole genome sequence data.

Evaluation of c-FLIP(long), c-FLIP(short), and caspase 8 expression in primary tumors using a semiquantitative multiplex LSC-based assay

Recent studies have demonstrated the value in quantification of tumor expression and mutational status of growth factor signaling components as potential predictors of clinical response. Quantification of elements in the apoptosis pathways may provide a similar value in predicting clinical response to new therapies based on induction of apoptosis through death-receptor-mediated signaling. To that end, we have qualified a 5-color multiplexed immune histofluorescent tissue-based assay to provide semiquantitative measurement of expression of death-inducing signaling complex (DISC) components. The DISC components we measured in FFPE archival samples from human solid tumors are caspase 8, c-FLIP(long) and c-FLIP(short). Additional components of the multiplex were a pan-keratin reagent for identification of cytokeratin expressing epithelial tumor cells, and a histone H3 reagent for staining quality control. Image collection was accomplished via laser scanning cytometry (LSC) with a data pipeline which generates FCS3.0 (flow cytometry standard 3.0) files. This allowed intensity analysis of gated tumor enriched regions via standard flow cytometry analysis software. Antibodies were identified via in vitro screens using high/low target expressing cell lines, and further tested using xenografts of the cell lines of interest. The assay was qualified by examining day-to-day, slide-to-slide, and inter-operator variability using xenograft TMA (tissue micro-array) controls. The intensity of stained samples over 24 hours was stable. Total experimental %CV was demonstrated to be less than 25%. We analyzed 6 commercially available cancer TMA slides containing 387 tumor cores representing colon, lung, pancreas, breast, sarcoma, and non-Hodgkin's lymphoma, along with 43 corresponding normal cores. We found a wide range of expression of caspase 8, c-FLIP(l), and c-FLIP(s) within each tumor type, as well as clear differences between normal and tumor tissues. These findings indicate a potential for stratification of clinical response by DISC protein expression in therapeutics that act via death receptor mediated apoptosis.

Scan-Rate-Dependent Melting Transitions of Interleukin-1 Receptor (Type II): Elucidation of Meaningful Thermodynamic and Kinetic Parameters of Aggregation Acquired from DSC Simulations

The role of thermal unfolding as it pertains to thermodynamic properties of proteins and their stability has been the subject of study for more than 50 years. Moreover, exactly how the unfolding properties of a given protein system may influence the kinetics of aggregation has not been fully characterized. In the study of recombinant human Interleukin-1 receptor type II (rhuIL-1R(II)) aggregation, data obtained from size exclusion chromatography and differential scanning calorimetry (DSC) were used to model the thermodynamic and kinetic properties of irreversible denaturation. A break from linearity in the initial aggregation rates as a function of 1/T was observed in the vicinity of the melting transition temperature (T(m) approximately 53.5 degrees C), suggesting significant involvement of protein unfolding in the reaction pathway. A scan-rate dependence in the DSC experiment testifies to the nonequilibrium influences of the aggregation process. A mechanistic model was developed to extract meaningful thermodynamic and kinetic parameters from an irreversibly denatured process. The model was used to simulate how unfolding properties could be used to predict aggregation rates at different temperatures above and below the T(m) and to account for concentration dependence of reaction rates. The model was shown to uniquely identify the thermodynamic parameters DeltaC(P) (1.3 +/- 0.7 kcal/mol-K), DeltaH(m) (74.3 +/- 6.8 kcal/mol), and T(m) with reasonable variances.

Who was Rumpelstiltskin?

The author describes the way in which Rumpelstiltskin has perplexed and enthralled readers since the brothers Grimm recovered the tale from the realm of folklore. In standard translation, the story now lives in a fixed literary form and, consequently, in the imagination of every child who has ever heard or read the story--virtually every person in the English-speaking world. Therefore, deeply rooted in childhood experiences, Rumpelstiltskin can be expected to appear in analysis, and he does. The compelling central character is the title figure, Rumpelstiltskin, whose name and actions tell us who he is and what he was intended to 'mean'--especially to his contemplated audience. The original narrators of and listeners to this tale were female visitors to the evening spinning chamber (Spinnstube), where women gathered and told tales to amuse themselves to ward off sleep while they spun. The butt of this story is male impotence and bluster, and the key to the story's meaning arises from matching the etymological roots of the central character's name with his actions as they appear philologically and psychoanalytically.