Evidence map›Paper›PMID 26873315›Full record

ArticleApplied and environmental microbiology2016

Use of Metagenomic Shotgun Sequencing Technology To Detect Foodborne Pathogens within the Microbiome of the Beef Production Chain.

Xiang Yang, Noelle R Noyes, Enrique Doster, Jennifer N Martin, Lyndsey M Linke, Roberta J Magnuson, Hua Yang, Ifigenia Geornaras, Dale R Woerner, Kenneth L Jones and 4 more

Abstract read
In one paragraph

Article in Applied and environmental microbiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
60citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

60 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  6. Foods (Basel, Switzerland) · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Xiang YangDepartment of Animal Sciences, Colorado State University, Fort Collins, Colorado, USA.
Noelle R NoyesDepartment of Clinical Sciences, Colorado State University, Fort Collins, Colorado, USA.
Enrique DosterDepartment of Clinical Sciences, Colorado State University, Fort Collins, Colorado, USA.
Jennifer N MartinDepartment of Animal Sciences, Colorado State University, Fort Collins, Colorado, USA.
Lyndsey M LinkeDepartment of Clinical Sciences, Colorado State University, Fort Collins, Colorado, USA.
Roberta J MagnusonDepartment of Clinical Sciences, Colorado State University, Fort Collins, Colorado, USA.
Hua YangDepartment of Animal Sciences, Colorado State University, Fort Collins, Colorado, USA.
Ifigenia GeornarasDepartment of Animal Sciences, Colorado State University, Fort Collins, Colorado, USA.
Dale R WoernerDepartment of Animal Sciences, Colorado State University, Fort Collins, Colorado, USA.
Kenneth L JonesDepartment of Biochemistry and Molecular Genetics, University of Colorado Denver School of Medicine, Aurora, Colorado, USA.
Jaime RuizDepartment of Computer Science, Colorado State University, Fort Collins, Colorado, USA.
Christina BoucherDepartment of Computer Science, Colorado State University, Fort Collins, Colorado, USA.
Paul S MorleyDepartment of Clinical Sciences, Colorado State University, Fort Collins, Colorado, USA.
Keith E BelkDepartment of Animal Sciences, Colorado State University, Fort Collins, Colorado, USA keith.belk@colostate.edu.

Funding

Colorado Clinical and Translational Sciences InstituteUL1TR001082 · NCATS · UNIVERSITY OF COLORADO DENVER · PI SOKOL, RONALD J. · 2013 to 2017
$48.0M
Veterinary Pre-Doctoral Research Scholars ProgramT32OD012201 · OD · COLORADO STATE UNIVERSITY · PI VANDEWOUDE, SUE · 2013 to 2019
$795k
NCATS NIH HHS UL1 TR001082NIH HHS T32 OD012201
6 · The paper itself

Abstract

Foodborne illnesses associated with pathogenic bacteria are a global public health and economic challenge. The diversity of microorganisms (pathogenic and nonpathogenic) that exists within the food and meat industries complicates efforts to understand pathogen ecology. Further, little is known about the interaction of pathogens within the microbiome throughout the meat production chain. Here, a metagenomic approach and shotgun sequencing technology were used as tools to detect pathogenic bacteria in environmental samples collected from the same groups of cattle at different longitudinal processing steps of the beef production chain: cattle entry to feedlot, exit from feedlot, cattle transport trucks, abattoir holding pens, and the end of the fabrication system. The log read counts classified as pathogens per million reads for Salmonella enterica,Listeria monocytogenes,Escherichia coli,Staphylococcus aureus, Clostridium spp. (C. botulinum and C. perfringens), and Campylobacter spp. (C. jejuni,C. coli, and C. fetus) decreased over subsequential processing steps. Furthermore, the normalized read counts for S. enterica,E. coli, and C. botulinumwere greater in the final product than at the feedlots, indicating that the proportion of these bacteria increased (the effect on absolute numbers was unknown) within the remaining microbiome. From an ecological perspective, data indicated that shotgun metagenomics can be used to evaluate not only the microbiome but also shifts in pathogen populations during beef production. Nonetheless, there were several challenges in this analysis approach, one of the main ones being the identification of the specific pathogen from which the sequence reads originated, which makes this approach impractical for use in pathogen identification for regulatory and confirmation purposes.

Indexed as

Environmental MicrobiologyFood HandlingMicrobiotaAnimalsBacteriaCattleMetagenomicsRed MeatSequence Analysis, DNA

Identifiers

PMID26873315
PMCPMC4959480

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.