Evidence map›Paper›PMID 40827925›Full record

ArticlemSystems2025

MiFoDB, a workflow for microbial food metagenomic characterization, enables high-resolution analysis of fermented food microbial dynamics.

Elisa B Caffrey, Matthew R Olm, Caroline I Kothe, Hannah C Wastyk, Joshua D Evans, Justin L Sonnenburg

Abstract read
In one paragraph

Article in mSystems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–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

6 citing papers in PubMed.

  1. Review
  2. Review
  3. ZipStrain Enables Rapid and Precise Strain-Resolved Metagenomics.bioRxiv : the preprint server for biology · 2026
    Article
  4. Article
  5. Article
  6. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Elisa B CaffreyDepartment of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California, USA.ORCID 0000-0001-6223-5137
Matthew R OlmDepartment of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California, USA.
Caroline I KotheSustainable Food Innovation Group, The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Capital Region of Denmark, Denmark.
Hannah C WastykDepartment of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California, USA.
Joshua D EvansSustainable Food Innovation Group, The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Capital Region of Denmark, Denmark.
Justin L SonnenburgDepartment of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California, USA.ORCID 0000-0003-2299-6817

Funding

Impact of Diet on Intestinal Microbiota-Host DynamicsR01DK085025 · NIDDK · STANFORD UNIVERSITY · PI JUSTIN L SONNENBURG · 2010 to 2026
$6.4M
Understanding mechanisms by which microbial strains and metabolites in fermented foods decrease systemic inflammationF32DK128865 · NIDDK · STANFORD UNIVERSITY · PI OLM, MATTHEW RAYMOND · 2021 to 2023
$186k
Bill and Melinda Gates Foundation R01-DK085025NIDDK NIH HHS F32 DK128865NIDDK NIH HHS R01 DK085025Novo Nordisk NNF20CC0035580U.S. Department of Health and Human Services F32-DK128865
6 · The paper itself

Abstract

Fermented foods, which contain a diversity of microbes and microbial metabolites, have been used for millennia to increase food security, flavor, and nutritional content; more recently, they have been recognized as potential mediators of human health. Metagenomics is a powerful approach to characterize microbes in fermented foods, providing high taxonomic resolution and functional insights. Here, we introduce the Microbial Food DataBase, a metagenomics-based approach designed for the identification of fermentation-associated microbes. Using this primary database of metagenome-assembled genomes and relevant deposited genomes of prokaryotes, eukaryotes, and common food-relevant substrates, we investigated 89 fermented food samples. We present a streamlined high-confidence characterization of microbial diversity in fermented food, identifying previously undiscovered genomes and facilitating strain-level tracking across food environments. The easy and robust functionality of the workflow has significant implications for advancing food safety, promoting desired microbial communities, and increasing sustainability in food production.IMPORTANCEFermented foods have microbial communities that influence food safety, flavor, and human health. Microbial Food DataBase (MiFoDB), an alignment-based sequencing workflow and database, addresses the limitations of existing tools by enabling strain-level resolution, identifying novel genomes, and providing functional insights into microbial communities. Applying MiFoDB to fermented food samples, we demonstrate its ability to uncover novel species, track microbial strains across substrates, and integrate functional annotations. Additionally, the outlined workflow is highly customizable and can be used to generate alignment-based databases for other microbial ecosystems. This work highlights the importance of fermentation-specific workflows for studying microbial food ecosystems, advancing food safety, sustainability, and innovation in fermented food research.

Indexed as

Fermented FoodsFood MicrobiologyMetagenomicsBacteriaFermentationHumansMetagenomeMicrobiotaWorkflowfermentationmetagenomicsmicrobial communitiesnonhuman microbiome

Identifiers

PMID40827925
PMCPMC12456020

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.