Evidence map›Paper›PMID 42185992›Full record

ArticleBMC genomics2026

Evolutionary signals in metabolic networks of insect endosymbionts revealed through comparative topological modeling.

Mariana Reyes-Prieto, David José Martínez-Cano, Mercè Llabrés, Pere Palmer-Rodríguez, Carlos Vargas-Chávez, Luis Delaye, Rosario Gil, Andrés Moya

Abstract readComparative Study
In one paragraph

Article in BMC genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Mariana Reyes-Prieto *Institute for Integrative Systems Biology (I2SysBio), University of Valencia and CSIC, Valencia, 46980, Spain.
David José Martínez-Cano *Department of Genetic Engineering, Cinvestav Irapuato, Irapuato, 36824, México.
Mercè LlabrésDepartment of Mathematics and Computer Science, University of the Balearic Islands, Palma, 07120, Spain.
Pere Palmer-RodríguezDepartment of Mathematics and Computer Science, University of the Balearic Islands, Palma, 07120, Spain.
Carlos Vargas-ChávezInstitute for Integrative Systems Biology (I2SysBio), University of Valencia and CSIC, Valencia, 46980, Spain.
Luis DelayeDepartment of Genetic Engineering, Cinvestav Irapuato, Irapuato, 36824, México.
Rosario GilInstitute for Integrative Systems Biology (I2SysBio), University of Valencia and CSIC, Valencia, 46980, Spain. rosario.gil@uv.es.
Andrés MoyaInstitute for Integrative Systems Biology (I2SysBio), University of Valencia and CSIC, Valencia, 46980, Spain. andres.moya@uv.es.

Funding

Conselleria de Cultura, Educación y Ciencia, Generalitat Valenciana CIPROM/2021/042FEDER/MICINN/AEI PGC2018-096956-B-C43FEDER/MICINN/AEI PID2019-105969GB-I00MICIU/AEI/10.13039/501100011033 and by "ERDF/EU" PID2021-126114NB-C44
6 · The paper itself

Abstract

backgroundUnderstanding the organization and evolution of metabolic networks is essential for uncovering how organisms adapt to changing environments. Whereas free-living bacteria typically maintain robust and redundant metabolic systems, endosymbiotic bacteria undergo extreme genome reduction during their adaptation to intracellular life. This process results in highly streamlined and interconnected metabolic networks, in some cases smaller than the theoretical minimum required for sustaining independent cellular function.

resultsUsing a large-scale comparative framework, we analyzed 101 genomes of insect endosymbiotic bacteria by computing two metabolic network models: metabolite- and reaction-based. We found strong correlations between genome size and key topological properties, including clustering coefficient, network diameter, and number of nodes, indicating that genome reduction directly constrains metabolic network architecture. Despite extensive gene loss, endosymbiotic metabolic networks retain scale-free organization, suggesting the preservation of essential connectivity and robustness. Furthermore, clustering analyses revealed that network topology reflects phylogenetic relationships across bacterial taxa, demonstrating that metabolic organization retains evolutionary signals even in the most reduced genomes.

conclusionsOur findings show that the metabolic networks of insect endosymbiotic bacteria preserve clear evolutionary imprints, revealing a deep connection between genomic reduction, network structure, and phylogenetic history. The complementary use of metabolite- and reaction-based models provide a powerful framework for exploring how symbiotic evolution reshapes metabolic systems while maintaining essential biological organization.

Indexed as

BacteriaEvolution, MolecularInsectaMetabolic Networks and PathwaysSymbiosisAnimalsCluster AnalysisGenome, BacterialPhylogenyEndosymbiontsGenome evolutionGenome reductionMetabolic building blocksMetabolic networksMetaDAGSymbiosis

Identifiers

PMID42185992
PMCPMC13390223

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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.