Evidence map›Paper›PMID 40182618›Full record

ArticleFrontiers in molecular biosciences2025

A multi-omics reciprocal analysis for characterization of bacterial metabolism.

Gabriel Santos Arini, Tiago Cabral Borelli, Elthon Góis Ferreira, Rafael de Felício, Paula Rezende-Teixeira, Matheus Pedrino, Franciene Rabiço, Guilherme Marcelino Viana de Siqueira, Luiz Gabriel Mencucini, Henrique Tsuji and 10 more

Abstract read
In one paragraph

Article in Frontiers in molecular biosciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

20 authors.

Gabriel Santos Arini *Computational Chemical Biology Laboratory, Department of BioMolecular Sciences, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, São Paulo, Brazil.
Tiago Cabral Borelli *Computational Chemical Biology Laboratory, Department of BioMolecular Sciences, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, São Paulo, Brazil.
Elthon Góis FerreiraMarine Pharmacology Laboratory, Department of Pharmacology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil.
Rafael de FelícioBrazilian Biosciences National Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, Brazil.
Paula Rezende-TeixeiraMarine Pharmacology Laboratory, Department of Pharmacology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil.
Matheus PedrinoCellular and Molecular Biology Program, Department of Cellular and Molecular Biology of Ribeirão Preto, School of Medicine, University of São Paulo, São Paulo, Brazil.
Franciene RabiçoCellular and Molecular Biology Program, Department of Cellular and Molecular Biology of Ribeirão Preto, School of Medicine, University of São Paulo, São Paulo, Brazil.
Guilherme Marcelino Viana de SiqueiraCellular and Molecular Biology Program, Department of Cellular and Molecular Biology of Ribeirão Preto, School of Medicine, University of São Paulo, São Paulo, Brazil.
Luiz Gabriel MencuciniComputational Chemical Biology Laboratory, Department of BioMolecular Sciences, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, São Paulo, Brazil.
Henrique TsujiComputational Chemical Biology Laboratory, Department of BioMolecular Sciences, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, São Paulo, Brazil.
Lucas Sousa Neves AndradeLaboratory of Bioproducts, Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil.
Leandro Maza GarridoLaboratory of Bioproducts, Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil.
Gabriel PadillaLaboratory of Bioproducts, Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil.
Alberto Gil-de-la-FuenteCentro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, Boadilla del Monte, Spain.
Mingxun WangDepartment of Computer Science and Engineering, University of California Riverside, Riverside, CA, United States.
Norberto Peporine LopesNPPNS, Department of BioMolecular Sciences, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, São Paulo, Brazil.
Daniela Barretto Barbosa TrivellaBrazilian Biosciences National Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, Brazil.
Letícia Veras Costa-LotufoMarine Pharmacology Laboratory, Department of Pharmacology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil.
María-Eugenia GuazzaroniMetaGenLab Laboratory, Department of Biology, FFCLRP, University of São Paulo of Ribeirão Preto, School of Medicine, University of São Paulo, São Paulo, Brazil.
Ricardo Roberto da SilvaComputational Chemical Biology Laboratory, Department of BioMolecular Sciences, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, São Paulo, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Exploiting microbial natural products is a key pursuit of the bioactive compound discovery field. Recent advances in modern analytical techniques have increased the volume of microbial genomes and their encoded biosynthetic products measured by mass spectrometry-based metabolomics. However, connecting multi-omics data to uncover metabolic processes of interest is still challenging. This results in a large portion of genes and metabolites remaining unannotated. Further exacerbating the annotation challenge, databases and tools for annotation and omics integration are scattered, requiring complex computations to annotate and integrate omics datasets. Methods: Here we performed a two-way integrative analysis combining genomics and metabolomics data to describe a new approach to characterize the marine bacterial isolate BRA006 and to explore its biosynthetic gene cluster (BGC) content as well as the bioactive compounds detected by metabolomics. Results and Discussion: We described BRA006 genomic content and structure by comparing Illumina and Oxford Nanopore MinION sequencing approaches. Digital DNA:DNA hybridization (dDDH) taxonomically assigned BRA006 as a potential new species of the Micromonospora genus. Starting from LC-ESI(+)-HRMS/MS data, and mapping the annotated enzymes and metabolites belonging to the same pathways, our integrative analysis allowed us to correlate the compound Brevianamide F to a new BGC, previously assigned to other function.

Indexed as

bioinformacticsgenomicsmicrobiologymulti-omics analysisuntargeted metabolomics

Identifiers

PMID40182618
PMCPMC11965639

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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.