Evidence map›Paper›PMID 37306300›Full record

ArticleeLife2023

SIMMER employs similarity algorithms to accurately identify human gut microbiome species and enzymes capable of known chemical transformations.

Annamarie E Bustion, Renuka R Nayak, Ayushi Agrawal, Peter J Turnbaugh, Katherine S Pollard

Abstract read
In one paragraph

Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Article
  2. Intestinal microbiota and arthritis.EULAR rheumatology open · 2026
    Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Review
  14. Review
  15. Review
  16. Article
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

5 authors.

Annamarie E BustionPharmaceutical Sciences and Pharmacogenomics Graduate Program, University of California, San Francisco, San Francisco, United States.ORCID 0000-0002-7380-3619
Renuka R NayakRheumatology Division, Department of Medicine, University of California, San Francisco, San Francisco, United States.
Ayushi AgrawalInstitute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, United States.ORCID 0000-0003-2940-8926
Peter J TurnbaughDepartment of Microbiology & Immunology, University of California, San Francisco, San Francisco, United States.ORCID 0000-0002-0888-2875
Katherine S PollardInstitute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, United States.ORCID 0000-0002-9870-6196

Funding

PHARMACEUTICAL SCIENCES AND PHARMACOGENOMICST32GM007175 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI AHITUV, NADAV, KROETZ, DEANNA L · 1985 to 2021
$7.1M
Predicting and preventing drug metabolism by the human gut microbiomeR01HL122593 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Peter James Turnbaugh · 2016 to 2026
$5.7M
Employing the gut microbiome to accelerate effective initiation of rheumatoid arthritis therapyR01AR074500 · NIAMS · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Renuka Rajendra Nayak, Jose U. Scher · 2019 to 2026
$4.1M
From here to eternity: gut microbial response to drug therapy and inflammationR35GM151349 · NIGMS · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI Renuka Rajendra Nayak · 2023 to 2026
$1.5M
The impact of the gut microbiome on rheumatoid arthritis treatmentK08AR073930 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI NAYAK, RENUKA RAJENDRA · 2018 to 2022
$981k
Microbial metabolites impacting the response to methotrexate in rheumatoid arthritisR03AR082036 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI NAYAK, RENUKA RAJENDRA · 2023 to 2024
$159k
NHLBI NIH HHS R01 HL122593NHLBI NIH HHS R01HL122593NIAMS NIH HHS K08 AR073930NIAMS NIH HHS K08AR073930NIAMS NIH HHS R01 AR074500NIAMS NIH HHS R01AR074500NIAMS NIH HHS R03 AR082036NIGMS NIH HHS 2T32GM007175-41NIGMS NIH HHS 5T32GM007175-42NIGMS NIH HHS R35 GM151349NIGMS NIH HHS T32 GM007175
6 · The paper itself

Abstract

Bacteria within the gut microbiota possess the ability to metabolize a wide array of human drugs, foods, and toxins, but the responsible enzymes for these chemical events remain largely uncharacterized due to the time-consuming nature of current experimental approaches. Attempts have been made in the past to computationally predict which bacterial species and enzymes are responsible for chemical transformations in the gut environment, but with low accuracy due to minimal chemical representation and sequence similarity search schemes. Here, we present an in silico approach that employs chemical and protein

Indexed as

Gastrointestinal MicrobiomeMicrobiotaAlgorithmsBacteriaFoodHumansbiochemistrychemical biologyClostridium scindenscomputational biologyenzyme predictionmethotrexatemicrobiome metabolismsystems biology

Identifiers

PMID37306300
PMCPMC10289814

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.