Evidence map›Paper›PMID 32143621›Full record

ArticleMicrobial cell factories2020

Metatranscriptomic analysis to define the Secrebiome, and 16S rRNA profiling of the gut microbiome in obesity and metabolic syndrome of Mexican children.

Luigui Gallardo-Becerra, Fernanda Cornejo-Granados, Rodrigo García-López, Alejandra Valdez-Lara, Shirley Bikel, Samuel Canizales-Quinteros, Blanca E López-Contreras, Alfredo Mendoza-Vargas, Henrik Nielsen, Adrián Ochoa-Leyva

Open access · goldAbstract read
In one paragraph

Article in Microbial cell factories, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
89citing papers in PubMed, 2 pooled it
7.4field-weighted citation impact, top 2% 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

89 citing papers in PubMed, 2 syntheses or guidelines pooled it, 146 citations in OpenAlex.

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  10. Depletion ofFrontiers in microbiology · 2026
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29 more citing papers are in PubMed but not listed here.

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

10 authors at 4 institutions in 2 countries.

Luigui Gallardo-BecerraDepartamento de Microbiología Molecular, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Avenida Universidad 2001, C.P. 62210, Cuernavaca, Morelos, Mexico.
Fernanda Cornejo-GranadosDepartamento de Microbiología Molecular, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Avenida Universidad 2001, C.P. 62210, Cuernavaca, Morelos, Mexico.
Rodrigo García-LópezDepartamento de Microbiología Molecular, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Avenida Universidad 2001, C.P. 62210, Cuernavaca, Morelos, Mexico.
Alejandra Valdez-LaraDepartamento de Microbiología Molecular, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Avenida Universidad 2001, C.P. 62210, Cuernavaca, Morelos, Mexico.
Shirley BikelDepartamento de Microbiología Molecular, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Avenida Universidad 2001, C.P. 62210, Cuernavaca, Morelos, Mexico.
Samuel Canizales-QuinterosUnidad de Genómica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM/Instituto Nacional de Medicina Genómica (INMEGEN), México City, Mexico.
Blanca E López-ContrerasUnidad de Genómica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM/Instituto Nacional de Medicina Genómica (INMEGEN), México City, Mexico.
Alfredo Mendoza-VargasInstituto Nacional de Medicina Genómica, Secretaría de Salud, México City, Mexico.
Henrik NielsenSection for Bioinformatics, Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark.
Adrián Ochoa-LeyvaDepartamento de Microbiología Molecular, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Avenida Universidad 2001, C.P. 62210, Cuernavaca, Morelos, Mexico. aochoa@ibt.unam.mx.ORCID http://orcid.org/0000-0002-4701-2303
Universidad Nacional Autónoma de México · MXNational Institute of Genomic Medicine · MXSecretaria de Salud · MXTechnical University of Denmark · DK

Funding

CONACyT SALUD-2014-C01-234188CONACYT CB-2013-223279PAPIIT IA203118PAPIIT IN215520
6 · The paper itself

Abstract

backgroundIn the last decade, increasing evidence has shown that changes in human gut microbiota are associated with diseases, such as obesity. The excreted/secreted proteins (secretome) of the gut microbiota affect the microbial composition, altering its colonization and persistence. Furthermore, it influences microbiota-host interactions by triggering inflammatory reactions and modulating the host's immune response. The metatranscriptome is essential to elucidate which genes are expressed under diseases. In this regard, little is known about the expressed secretome in the microbiome. Here, we use a metatranscriptomic approach to delineate the secretome of the gut microbiome of Mexican children with normal weight (NW) obesity (O) and obesity with metabolic syndrome (OMS). Additionally, we performed the 16S rRNA profiling of the gut microbiota.

resultsOut of the 115,712 metatranscriptome genes that codified for proteins, 30,024 (26%) were predicted to be secreted, constituting the Secrebiome of the gut microbiome. The 16S profiling confirmed an increased abundance in Firmicutes and decreased in Bacteroidetes in the obesity groups, and a significantly higher richness and diversity than the normal weight group. We found novel biomarkers for obesity with metabolic syndrome such as increased Coriobacteraceae, Collinsela, and Collinsella aerofaciens; Erysipelotrichaceae, Catenibacterium and Catenibacterium sp., and decreased Parabacteroides distasonis, which correlated with clinical and anthropometric parameters associated to obesity and metabolic syndrome. Related to the Secrebiome, 16 genes, homologous to F. prausniitzi, were overexpressed for the obese and 15 genes homologous to Bacteroides, were overexpressed in the obesity with metabolic syndrome. Furthermore, a significant enrichment of CAZy enzymes was found in the Secrebiome. Additionally, significant differences in the antigenic density of the Secrebiome were found between normal weight and obesity groups.

conclusionsThese findings show, for the first time, the role of the Secrebiome in the functional human-microbiota interaction. Our results highlight the importance of metatranscriptomics to provide novel information about the gut microbiome's functions that could help us understand the impact of the Secrebiome on the homeostasis of its human host. Furthermore, the metatranscriptome and 16S profiling confirmed the importance of treating obesity and obesity with metabolic syndrome as separate conditions to better understand the interplay between microbiome and disease.

Indexed as

Gene Expression ProfilingBacteriaChildCohort StudiesFecesFemaleGastrointestinal MicrobiomeGene ExpressionHost Microbial InteractionsHumansMaleMetabolic SyndromeMexicoPediatric ObesityRNA, Ribosomal, 16SSecretory PathwayRNA, Ribosomal, 16SAARCAZYMetabolic syndromeMetatranscriptomeMetatranscriptomicsMicrobiomeMicrobiotaObesitySecrebiomeSecretome

Identifiers

PMID32143621
PMCPMC7060530
OpenAlexW3012257370

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.