We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

LabMedica

Download Mobile App
Recent News Expo Clinical Chem. Molecular Diagnostics Hematology Immunology Microbiology Pathology Technology Industry Focus

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).
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

Platinum Member
Automated Coagulation Analyzer
Hemolumi H6
Gold Member
Electrolyte Analyzer
CBS-4000 (CBS-400)
Food Allergy Screening ELISA Kit
Allerquant 14G B ELISA
Clinical Informatics Platform
CLARION™

Channels

Molecular Diagnostics

view channel
Image: The researchers Manel Pérez Pons y Carlos Rodriguez Muñoz at the IRBLleida laboratory (Photo courtesy of IRBLleida)

New Blood RNA Markers Help Advance Precision Medicine for Respiratory Patients

Risk stratification in hospitalized respiratory disease, particularly among older adults with COVID-19, remains challenging despite rich clinical datasets. Blood-based non-coding RNA biomarkers are promising,... Read more

Pathology

view channel
Image: Researchers evaluated AI models that quantify tumor-infiltrating lymphocytes (TIL) on routine breast tissue slides, where higher TIL levels reflect stronger antitumor response and improved breast cancer outcomes (Image Credit: Shutterstock)

AI Matches Pathologists in Predicting Breast Cancer Prognosis from Immune Cells

Breast cancer is the most common cancer in Australian women, with more than 20,000 cases each year. Prognosis can be informed by counting tumor-infiltrating lymphocytes (TILs) on routine pathology slides,... Read more

Industry

view channel
Image: RaDaR ST uses a tumor-informed approach that identifies up to 48 patient-specific variants through whole-exome sequencing and tracks those variants in plasma to detect circulating tumor DNA (ctDNA) at very low variant allele fractions (VAFs) (Photo courtesy of Neogenomics)

Tumor-Informed MRD Assay Gains Medicare Coverage for Immunotherapy Monitoring

NeoGenomics’ RaDaR ST molecular residual disease (MRD) assay has received expanded coverage from the Centers for Medicare & Medicaid Services’ Molecular Diagnostic Services Program (MolDX) for monitoring... Read more