Subtopic Deep Dive
Salmonella and Campylobacter Whole Genome Sequencing Surveillance
Research Guide
What is Salmonella and Campylobacter Whole Genome Sequencing Surveillance?
Salmonella and Campylobacter Whole Genome Sequencing Surveillance uses WGS platforms for real-time outbreak detection, strain tracking, and metadata-integrated analysis via core genome MLST.
National WGS surveillance replaces serotyping with genomic subtyping for Salmonella enterica and Campylobacter jejuni. Tools like SISTR enable rapid in silico typing of draft assemblies (Yoshida et al., 2016, 568 citations). Over 50 papers document WGS implementation for foodborne pathogen monitoring.
Why It Matters
WGS surveillance accelerates outbreak detection from days to hours, as shown in hospital Salmonella outbreaks using nanopore sequencing (Quick et al., 2015). It enables international strain comparison, replacing MLST with cgMLST for higher resolution (Achtman et al., 2012; Alikhan et al., 2018). Public health agencies use WGS to trace produce-linked multistate outbreaks (Carstens et al., 2019), reducing illnesses from Salmonella and Campylobacter.
Key Research Challenges
Real-time WGS Analysis
Processing draft genomes for immediate outbreak linkage requires rapid pipelines. Leekitcharoenphon et al. (2014) evaluated WGS for Salmonella detection, noting computational demands. Nanopore sequencing aids speed but needs validation (Quick et al., 2015).
Metadata Integration
Linking genomic data to epidemiological metadata remains inconsistent across platforms. SISTR tool incorporates serotype prediction with metadata (Yoshida et al., 2016). Alikhan et al. (2018) highlight population structure analysis needing unified databases.
Campylobacter Standardization
Campylobacter WGS lacks Salmonella-level serovar frameworks. Whiley et al. (2013) discuss environmental reservoirs, but genomic surveillance lags. MLST replacement needs Campylobacter-specific cgMLST schemes (Achtman et al., 2012).
Essential Papers
Foodborne pathogens
Thomas Bintsis · 2017 · AIMS Microbiology · 817 citations
Foodborne pathogens are causing a great number of diseases with significant effects on human health and economy. The characteristics of the most common pathogenic bacteria (<i>Bacillus cereus</i>, ...
Multilocus Sequence Typing as a Replacement for Serotyping in Salmonella enterica
Mark Achtman, John Wain, François‐Xavier Weill et al. · 2012 · PLoS Pathogens · 692 citations
Salmonella enterica subspecies enterica is traditionally subdivided into serovars by serological and nutritional characteristics. We used Multilocus Sequence Typing (MLST) to assign 4,257 isolates ...
Multiplex PCR for detection of plasmid-mediated colistin resistance determinants, mcr-1, mcr-2, mcr-3, mcr-4 and mcr-5 for surveillance purposes
Ana Rita Rebelo, Valeria Bortolaia, Jette Sejer Kjeldgaard et al. · 2018 · Eurosurveillance · 654 citations
Background and aim Plasmid-mediated colistin resistance mechanisms have been identified worldwide in the past years. A multiplex polymerase chain reaction (PCR) protocol for detection of all curren...
The Salmonella In Silico Typing Resource (SISTR): An Open Web-Accessible Tool for Rapidly Typing and Subtyping Draft Salmonella Genome Assemblies
Catherine E. Yoshida, Peter Kruczkiewicz, Chad Laing et al. · 2016 · PLoS ONE · 568 citations
For nearly 100 years serotyping has been the gold standard for the identification of Salmonella serovars. Despite the increasing adoption of DNA-based subtyping approaches, serotype information rem...
A genomic overview of the population structure of Salmonella
Nabil-Fareed Alikhan, Zhemin Zhou, Martin J. Sergeant et al. · 2018 · PLoS Genetics · 555 citations
For many decades, Salmonella enterica has been subdivided by serological properties into serovars or further subdivided for epidemiological tracing by a variety of diagnostic tests with higher reso...
Multistate Outbreaks of Foodborne Illness in the United States Associated With Fresh Produce From 2010 to 2017
Christina K. Carstens, Joelle K. Salazar, Charles Darkoh · 2019 · Frontiers in Microbiology · 405 citations
In the United States, the consumption of fresh fruits and vegetables has increased during recent years as consumers seek to make healthier lifestyle choices. However, the number of outbreaks associ...
Implementation of Nationwide Real-time Whole-genome Sequencing to Enhance Listeriosis Outbreak Detection and Investigation
Brendan R. Jackson, Cheryl L. Tarr, Errol Strain et al. · 2016 · Clinical Infectious Diseases · 362 citations
Listeria monocytogenes (Lm) causes severe foodborne illness (listeriosis). Previous molecular subtyping methods, such as pulsed-field gel electrophoresis (PFGE), were critical in detecting outbreak...
Reading Guide
Foundational Papers
Start with Achtman et al. (2012, 692 citations) for MLST-to-WGS transition and Leekitcharoenphon et al. (2014) for outbreak detection validation; these establish subtyping baselines.
Recent Advances
Study Yoshida et al. (2016, SISTR tool) for practical platforms and Quick et al. (2015) for nanopore in outbreaks; Alikhan et al. (2018) for genomic population structures.
Core Methods
Core techniques: cgMLST (Achtman et al., 2012), SNP-based clustering (Leekitcharoenphon et al., 2014), in silico typing (Yoshida et al., 2016), nanopore sequencing (Quick et al., 2015).
How PapersFlow Helps You Research Salmonella and Campylobacter Whole Genome Sequencing Surveillance
Discover & Search
Research Agent uses searchPapers and citationGraph to map WGS surveillance evolution from Achtman et al. (2012, 692 citations) to Yoshida et al. (2016). exaSearch finds real-time implementations; findSimilarPapers clusters Salmonella outbreak papers like Quick et al. (2015).
Analyze & Verify
Analysis Agent applies readPaperContent to extract cgMLST methods from Leekitcharoenphon et al. (2014), then verifyResponse with CoVe checks outbreak detection claims. runPythonAnalysis computes SNP distances on Salmonella genomes via pandas/NumPy; GRADE grades evidence for WGS vs. serotyping.
Synthesize & Write
Synthesis Agent detects gaps in Campylobacter WGS standardization, flagging contradictions between MLST papers. Writing Agent uses latexEditText for surveillance workflow diagrams, latexSyncCitations for 50+ papers, and latexCompile for reports; exportMermaid visualizes strain phylogenies.
Use Cases
"Compare SNP distances in Salmonella outbreaks from Leekitcharoenphon 2014 and Quick 2015"
Research Agent → searchPapers → Analysis Agent → runPythonAnalysis (pandas SNP matrix) → matplotlib phylogeny plot → GRADE verification.
"Draft LaTeX review of WGS replacing MLST in Salmonella surveillance"
Synthesis Agent → gap detection → Writing Agent → latexEditText (core genome MLST section) → latexSyncCitations (Achtman 2012 et al.) → latexCompile → PDF export.
"Find GitHub repos for SISTR Salmonella typing tool"
Research Agent → paperExtractUrls (Yoshida 2016) → Code Discovery → paperFindGithubRepo → githubRepoInspect → runPythonAnalysis on repo scripts.
Automated Workflows
Deep Research workflow scans 50+ papers for systematic WGS surveillance review: searchPapers → citationGraph → DeepScan checkpoints → structured report with GRADE scores. DeepScan verifies real-time nanopore claims (Quick et al., 2015) via CoVe chain. Theorizer generates hypotheses on Campylobacter cgMLST from MLST baselines (Achtman et al., 2012).
Frequently Asked Questions
What defines WGS surveillance for Salmonella and Campylobacter?
WGS surveillance sequences full pathogen genomes for real-time subtyping, outbreak clustering via SNPs/cgMLST, and metadata linkage, replacing serology (Yoshida et al., 2016).
What are core methods in this subtopic?
Core methods include in silico serotyping (SISTR; Yoshida et al., 2016), cgMLST (Achtman et al., 2012), and nanopore real-time sequencing (Quick et al., 2015).
What are key papers?
Foundational: Achtman et al. (2012, MLST, 692 citations); Leekitcharoenphon et al. (2014, WGS evaluation, 255 citations). Recent: Yoshida et al. (2016, SISTR, 568 citations); Alikhan et al. (2018, population structure).
What open problems exist?
Challenges include Campylobacter genomic standardization, scalable metadata integration, and real-time analysis for non-model pathogens beyond Salmonella (Whiley et al., 2013).
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