Evidence map›Paper›PMID 38740909›Full record

ReviewNature protocols2024

High-throughput identification of gut microbiome-dependent metabolites.

Shuo Han, Emma R Guiberson, Yuxin Li, Justin L Sonnenburg

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature protocols, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Separative Techniques Coupled to Mass Spectrometry for Metabolomic Analysis.Advances in experimental medicine and biology · 2026
    Review
  7. Review
  8. Review
  9. Review
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

4 authors.

Shuo HanDepartment of Biochemistry, Duke University School of Medicine, Durham, NC, USA. shuo.han@duke.edu.ORCID 0000-0003-1181-351X
Emma R GuibersonDepartment of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0002-1579-7820
Yuxin LiBiochemistry Graduate Program, Duke University School of Medicine, Durham, NC, USA.ORCID 0000-0001-6593-1453
Justin L SonnenburgDepartment of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA. jsonnenburg@stanford.edu.ORCID 0000-0003-2299-6817

Funding

MOLECULAR BASIS OF HOST-PARASITE INTERACTIONT32AI007328 · NIAID · STANFORD UNIVERSITY · PI JUSTIN L SONNENBURG · 1987 to 2026
$11.9M
Dietary and Microbial Reprogramming of Intestinal Microbiota-Produced MetabolitesR01DK101674 · NIDDK · STANFORD UNIVERSITY · PI FISCHBACH, MICHAEL ANDREW, MEYER, TIMOTHY W · 2014 to 2024
$6.5M
Impact of Diet on Intestinal Microbiota-Host DynamicsR01DK085025 · NIDDK · STANFORD UNIVERSITY · PI JUSTIN L SONNENBURG · 2010 to 2026
$6.4M
Defining and Reconstructing the Human Ancestral MicrobiomeDP1AT009892 · NCCIH · STANFORD UNIVERSITY · PI SONNENBURG, JUSTIN L · 2017 to 2021
$5.5M
Impact of gut microbiota-derived molecules on mammalian host health and longevityF32AG062119 · NIA · STANFORD UNIVERSITY · PI HAN, SHUO · 2019 to 2021
$202k
NCCIH NIH HHS DP1 AT009892NIDDK NIH HHS R01 DK101674U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID) 5T32AI007328-35U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases) R01-DK085025U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases) R01-DK101674U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) F32AG062119Wellcome Trust 085025
6 · The paper itself

Abstract

A significant hurdle that has limited progress in microbiome science has been identifying and studying the diverse set of metabolites produced by gut microbes. Gut microbial metabolism produces thousands of difficult-to-identify metabolites, which present a challenge to study their roles in host biology. In recent years, mass spectrometry-based metabolomics has become one of the core technologies for identifying small metabolites. However, metabolomics expertise, ranging from sample preparation to instrument use and data analysis, is often lacking in academic labs. Most targeted metabolomics methods provide high levels of sensitivity and quantification, while they are limited to a panel of predefined molecules that may not be informative to microbiome-focused studies. Here we have developed a gut microbe-focused and wide-spectrum metabolomic protocol using liquid chromatography-mass spectrometry and bioinformatic analysis. This protocol enables users to carry out experiments from sample collection to data analysis, only requiring access to a liquid chromatography-mass spectrometry instrument, which is often available at local core facilities. By applying this protocol to samples containing human gut microbial metabolites, spanning from culture supernatant to human biospecimens, our approach enables high-confidence identification of >800 metabolites that can serve as candidate mediators of microbe-host interactions. We expect this protocol will lower the barrier to tracking gut bacterial metabolism in vitro and in mammalian hosts, propelling hypothesis-driven mechanistic studies and accelerating our understanding of the gut microbiome at the chemical level.

Indexed as

Gastrointestinal MicrobiomeMass SpectrometryMetabolomicsBacteriaChromatography, LiquidComputational BiologyHumansMetabolome

Identifiers

What OpenQuestion holds

Textmetadata
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.