Genomics Technique Accelerate Detection of Foodborne Bacterial Outbreaks
|
By LabMedica International staff writers Posted on 15 Dec 2016 |

Image: Bacterial colonies of Staphylococcus aureus growing on horse blood agar (Photo courtesy of OMICS International).
Diagnostic testing for foodborne pathogens relies on culture-based techniques that are not rapid enough for real-time disease surveillance and do not give a quantitative picture of pathogen abundance or the response of the natural microbiome.
Metagenomics identifies the microbes present by sequencing the entire DNA present in a sample and comparing the genomic data to a database of known microbes. In addition to identifying the bacteria present in the samples, the methodology can also measure the relative abundance of each microbial species and their virulence potential, among other things.
A collaboration of scientists from the Centers for Disease Control and Prevention (Atlanta, GA USA) and the Georgia Institute of Technology (Atlanta, GA, USA) applied shotgun metagenomics to stool samples collected from two geographically isolated foodborne outbreaks in Alabama and Colorado, where the etiologic agents were identified as distinct strains of Salmonella enterica serovar Heidelberg by culture-dependent methods. The metagenomics data provided specific information about the bacterial phenotype involved and identified a secondary Staphylococcus aureus pathogen present in two of the samples tested. Knowing the specific phenotype can help in pinpointing the origins of an outbreak, while information about the secondary infection may help explain related factors such as the severity of the infection.
The scientists were also able to rule out one species, Escherichia coli (or E. coli), because the variant present was not of a virulent type. Variants of these bacteria are present naturally in the gut microbiome (called "commensal E. coli") while other variants are notorious enteric pathogens. Metagenomics showed the abundant E. coli population in the outbreak samples was probably commensal, and its growth may have been accelerated when conditions became more favorable during the Salmonella infection. In the two cases evaluated, scientists were able to determine that although the symptoms were similar, the outbreaks were caused by different variants of Salmonella and therefore were probably not connected.
Andrew D. Huang, PhD, a microbiologist/ bioinformatician and lead author of the study said, “Currently, the most advanced DNA fingerprinting method, whole genome sequencing, requires first pulling out, or isolating in a pure culture, the bacteria that made a person sick to generate a fingerprint. Metagenomics differs from whole genome sequencing because it could allow us to sequence the entire DNA in a patient's sample. It could allow us to skip the isolation steps and go directly from a stool sample to a highly detailed DNA fingerprint of the bacteria that made you sick. This method saves time and provides more detail that could be helpful for diagnosing a patient and identifying an outbreak.” The study was published on November 23, 2016, in the journal Applied and Environmental Microbiology.
Related Links:
Centers for Disease Control and Prevention
Georgia Institute of Technology
Metagenomics identifies the microbes present by sequencing the entire DNA present in a sample and comparing the genomic data to a database of known microbes. In addition to identifying the bacteria present in the samples, the methodology can also measure the relative abundance of each microbial species and their virulence potential, among other things.
A collaboration of scientists from the Centers for Disease Control and Prevention (Atlanta, GA USA) and the Georgia Institute of Technology (Atlanta, GA, USA) applied shotgun metagenomics to stool samples collected from two geographically isolated foodborne outbreaks in Alabama and Colorado, where the etiologic agents were identified as distinct strains of Salmonella enterica serovar Heidelberg by culture-dependent methods. The metagenomics data provided specific information about the bacterial phenotype involved and identified a secondary Staphylococcus aureus pathogen present in two of the samples tested. Knowing the specific phenotype can help in pinpointing the origins of an outbreak, while information about the secondary infection may help explain related factors such as the severity of the infection.
The scientists were also able to rule out one species, Escherichia coli (or E. coli), because the variant present was not of a virulent type. Variants of these bacteria are present naturally in the gut microbiome (called "commensal E. coli") while other variants are notorious enteric pathogens. Metagenomics showed the abundant E. coli population in the outbreak samples was probably commensal, and its growth may have been accelerated when conditions became more favorable during the Salmonella infection. In the two cases evaluated, scientists were able to determine that although the symptoms were similar, the outbreaks were caused by different variants of Salmonella and therefore were probably not connected.
Andrew D. Huang, PhD, a microbiologist/ bioinformatician and lead author of the study said, “Currently, the most advanced DNA fingerprinting method, whole genome sequencing, requires first pulling out, or isolating in a pure culture, the bacteria that made a person sick to generate a fingerprint. Metagenomics differs from whole genome sequencing because it could allow us to sequence the entire DNA in a patient's sample. It could allow us to skip the isolation steps and go directly from a stool sample to a highly detailed DNA fingerprint of the bacteria that made you sick. This method saves time and provides more detail that could be helpful for diagnosing a patient and identifying an outbreak.” The study was published on November 23, 2016, in the journal Applied and Environmental Microbiology.
Related Links:
Centers for Disease Control and Prevention
Georgia Institute of Technology
Latest Microbiology News
- Rapid Gastrointestinal PCR Panels Deliver One-Hour Results
- H. pylori Screening Within Colorectal Program Aids Gastric Cancer Prevention
- Machine Learning Reveals Consistent Gut Microbiome Patterns in Colorectal Cancer
- Study Reveals Widespread Community Spread of Drug-Resistant Klebsiella
- Stronger Laboratory Services Support Timely Melioidosis Diagnosis Amid Global Spread
- Extracellular Vesicle Biomarker May Enable Noninvasive Monitoring of H. pylori
- Rapid Molecular Screening Aims to Accelerate Hospital Infection Control for CPE
- New Protein Targets Support Diagnostics for Louse-Borne Relapsing Fever
- TORCH Infection Trends Point to Need for Tailored Screening in Pregnancy
- Automated Blood Culture System Speeds Detection of Bloodstream Infections
- New Culture Medium Speeds C. difficile Resistance Detection and Reduces Costs
- Gut Microbiome Signatures Help Identify Risk of IBD Progression
- FDA-Cleared Gastrointestinal Panel Detects 24 Pathogen Targets
- New AMR Assay Supports Rapid Infection Control Screening in Hospitals
- Diagnostic Gaps Complicate Bundibugyo Ebola Outbreak Response in Congo
- Study Finds Hidden Mpox Infections May Drive Ongoing Spread
Channels
Clinical Chemistry
view channel
FDA-Approved Test Identifies Low Risk of Large Esophageal Varices in Cirrhosis
Chronic liver disease contributes substantially to mortality, and clinicians routinely screen adults with compensated cirrhosis for varices to prevent bleeding. However, endoscopy is invasive and reso... Read more
Blood Protein Signature Diagnoses Pediatric IBD and Distinguishes Subtypes
Confirming pediatric inflammatory bowel disease (IBD) often requires imaging, endoscopy, and histopathology, prolonging time to diagnosis. Reliable, noninvasive blood tests remain an unmet need in routine... Read moreMolecular Diagnostics
view channel
New Molecular Marker Helps Predict Multiple Myeloma Prognosis
Multiple myeloma is a bone marrow cancer marked by resistance to therapy and frequent relapse, complicating long-term disease control. Better molecular markers are needed to refine risk assessment and... Read more
Blood-Based RNA Biomarker Improves Prediction of Alzheimer’s Onset
Timely identification of patients approaching symptomatic Alzheimer’s disease (AD) remains a major clinical challenge, even as blood-based biomarkers continue to advance. Current assays are highly effective... Read moreHematology
view channel
Next-Generation Hematology Platform Streamlines High-Complexity Lab Workflows
Sysmex America (Chicago, IL, USA) has introduced the next generation XR-Series, centered on the XR-10 Automated Hematology Module for high-complexity laboratories. The platform builds on the widely used... Read more
Blood Eosinophil Count May Predict Cancer Immunotherapy Response and Toxicity
Immune checkpoint inhibitors have improved outcomes across many cancers, yet only a subset of patients derive durable benefit and biomarkers to guide treatment remain limited. Eosinophils, best known for... Read moreImmunology
view channel
Anti-Lipid Antibody Biomarkers May Identify Early Lyme Disease and Persistent Symptoms
Lyme disease is often missed during its earliest and most treatable stage, while current serologic assays cannot distinguish active infection from prior exposure. Nearly half a million Americans are diagnosed... Read more
Emergency Department Opt-Out Testing Program Identifies Undiagnosed HIV
Undiagnosed HIV continues to drive avoidable morbidity and transmission, with many people identified only after substantial immune damage has occurred. In England, about one in 20 people living with HIV... Read more
Immune Biomarkers Could Identify Risk of Chronic Critical Illness on ICU Admission
Severe traumatic injury can trigger immune and organ dysfunction that complicates recovery in the intensive care unit. A subset of patients develop chronic critical illness, defined as dependence on intensive... Read morePathology
view channel
AI Pathology Tool Predicts Immunotherapy Response in Rare Cancers
Immunotherapy has transformed care for select malignancies, yet predicting which patients with rare cancers are most likely to benefit remains challenging. Clinicians often have only limited biomarkers... Read more
Uncertainty-Aware AI Tool Improves Digital Pathology for Cancer Subtyping
Reliable histologic subtyping guides therapy selection in oncology, yet diagnostic workflows grow more complex as whole-slide imaging and artificial intelligence (AI) expand. A persistent obstacle to clinical... Read moreTechnology
view channel
AI Platform Links Biomarker Results to Cancer Clinical Trials and Guidelines
Oncology teams must manage growing volumes of genomic data, rapidly evolving clinical trial options, and frequently updated care guidelines, all within tight clinic schedules. Translating complex tumor... Read more
Agentic AI Platform Supports Genomic Decision-Making in Oncology
Oncology care teams increasingly face the challenge of managing complex molecular diagnostics, evolving treatment options, and extensive electronic health record documentation. Translating multimodal data... Read moreIndustry
view channel
Partnership Integrates Automated DNA Extraction with Single-Molecule Digital PCR
Countable Labs (Palo Alto, CA, USA) and Promega (Madison, WI, USA) have entered a co-marketing agreement that integrates the Promega Maxwell System for nucleic acid extraction with Countable Labs’ Countable... Read more








