Evidence map›Paper›PMID 26589719›Full record

ArticleBMC evolutionary biology2015

A comparison of human and mouse gene co-expression networks reveals conservation and divergence at the tissue, pathway and disease levels.

Gianni Monaco, Sipko van Dam, João Luis Casal Novo Ribeiro, Anis Larbi, João Pedro de Magalhães

Open access · goldAbstract readComparative Study
In one paragraph

Article in BMC evolutionary biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
72citing papers in PubMed, 1 pooled it
4.1field-weighted citation impact, top 5% 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

72 citing papers in PubMed, 1 synthesis or guideline pooled it, 123 citations in OpenAlex.

  1. Pooled it
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  9. Deciphering Depigmentation: Mouse Models for Vitiligo Research.The Journal of investigative dermatology · 2025
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  14. Spinal cord morphology and PKD2L1Frontiers in neuroanatomy · 2025
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12 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

5 authors at 3 institutions in 2 countries.

Gianni MonacoIntegrative Genomics of Ageing Group, Institute of Integrative Biology, University of Liverpool, Biosciences Building, Room 245, Crown Street, Liverpool, L69 7ZB, UK. mongianni1@gmail.com.
Sipko van DamIntegrative Genomics of Ageing Group, Institute of Integrative Biology, University of Liverpool, Biosciences Building, Room 245, Crown Street, Liverpool, L69 7ZB, UK. sipkovandam@gmail.com.
João Luis Casal Novo RibeiroIntegrative Genomics of Ageing Group, Institute of Integrative Biology, University of Liverpool, Biosciences Building, Room 245, Crown Street, Liverpool, L69 7ZB, UK. joaowuis@gmail.com.
Anis LarbiSingapore Immunology Network (SIgN), Agency for Science Technology and Research, Biopolis, 8A Biomedical Grove, Singapore, 138648, Singapore. Anis_Larbi@immunol.a-star.edu.sg.
João Pedro de MagalhãesIntegrative Genomics of Ageing Group, Institute of Integrative Biology, University of Liverpool, Biosciences Building, Room 245, Crown Street, Liverpool, L69 7ZB, UK. jp@senescence.info.
Genomics (United Kingdom) · GBUniversity of Liverpool · GBAgency for Science, Technology and Research · SG

Funding

Biotechnology and Biological Sciences Research Council BB/K016741/1
6 · The paper itself

Abstract

backgroundA deeper understanding of differences and similarities in transcriptional regulation between species can uncover important information about gene functions and the role of genes in disease. Deciphering such patterns between mice and humans is especially important since mice play an essential role in biomedical research.

resultsHere, in order to characterize evolutionary changes between humans and mice, we compared gene co-expression maps to evaluate the conservation of co-expression. We show that the conservation of co-expression connectivity of homologous genes is negatively correlated with molecular evolution rates, as expected. Then we investigated evolutionary aspects of gene sets related to functions, tissues, pathways and diseases. Genes expressed in the testis, eye and skin, and those associated with regulation of transcription, olfaction, PI3K signalling, response to virus and bacteria were more divergent between mice and humans in terms of co-expression connectivity. Surprisingly, a deeper investigation of the PI3K signalling cascade revealed that its divergence is caused by the most crucial genes of this pathway, such as mTOR and AKT2. On the other hand, our analysis revealed that genes expressed in the brain and in the bone, and those associated with cell adhesion, cell cycle, DNA replication and DNA repair are most strongly conserved in terms of co-expression network connectivity as well as having a lower rate of duplication events. Genes involved in lipid metabolism and genes specific to blood showed a signature of increased co-expression connectivity in the mouse. In terms of diseases, co-expression connectivity of genes related to metabolic disorders is the most strongly conserved between mice and humans and tumor-related genes the most divergent.

conclusionsThis work contributes to discerning evolutionary patterns between mice and humans in terms of gene interactions. Conservation of co-expression is a powerful approach to identify gene targets and processes with potential similarity and divergence between mice and humans, which has implications for drug testing and other studies employing the mouse as a model organism.

Indexed as

Evolution, MolecularGene Expression ProfilingAnimalsEpistasis, GeneticGene ExpressionGene Expression RegulationHumansMetabolic DiseasesMiceNeoplasmsOligonucleotide Array Sequence AnalysisOrgan Specificity

Identifiers

PMID26589719
PMCPMC4654840
OpenAlexW2175297783

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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.