Evidence map›Paper›PMID 36814842›Full record

ArticleCell reports methods2023

Metatranscriptomics-guided genome-scale metabolic modeling of microbial communities.

Guido Zampieri, Stefano Campanaro, Claudio Angione, Laura Treu

Open access · goldAbstract read
In one paragraph

Article in Cell reports methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 1 pooled it
11.1field-weighted citation impact, top 1% of its field
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

38 citing papers in PubMed, 1 synthesis or guideline pooled it, 72 citations in OpenAlex.

  1. Pooled it
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  12. Systematic evaluation of metatranscriptomic differential gene expressionbioRxiv : the preprint server for biology · 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

4 authors at 2 institutions in 2 countries.

Guido ZampieriDepartment of Biology, University of Padova, Padova 35121, Italy.
Stefano CampanaroDepartment of Biology, University of Padova, Padova 35121, Italy.
Claudio AngioneSchool of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK.
Laura TreuDepartment of Biology, University of Padova, Padova 35121, Italy.
University of Padua · ITTeesside University · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multi-omics data integration via mechanistic models of metabolism is a scalable and flexible framework for exploring biological hypotheses in microbial systems. However, although most microorganisms are unculturable, such multi-omics modeling is limited to isolate microbes or simple synthetic communities. Here, we developed an approach for modeling microbial activity and interactions that leverages the reconstruction of metagenome-assembled genomes and associated genome-centric metatranscriptomes. At its core, we designed a method for condition-specific metabolic modeling of microbial communities through the integration of metatranscriptomic data. Using this approach, we explored the behavior of anaerobic digestion consortia driven by hydrogen availability and human gut microbiota dysbiosis associated with Crohn's disease, identifying condition-dependent amino acid requirements in archaeal species and a reduced short-chain fatty acid exchange network associated with disease, respectively. Our approach can be applied to complex microbial communities, allowing a mechanistic contextualization of multi-omics data on a metagenome scale.

Indexed as

Gastrointestinal MicrobiomeMicrobiotaArchaeaHumansMetagenomeanaerobic digestiongut microbiotametabolic modelingmetagenomicsmetatranscriptomicsmicrobial community

Identifiers

PMID36814842
PMCPMC9939383
OpenAlexW4313645415

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.