Evidence map›Paper›PMID 25323955›Full record

ArticleGenome biology and evolution2014

New tricks for "old" domains: how novel architectures and promiscuous hubs contributed to the organization and evolution of the ECM.

Graham Cromar, Ka-Chun Wong, Noeleen Loughran, Tuan On, Hongyan Song, Xuejian Xiong, Zhaolei Zhang, John Parkinson

Open access · goldAbstract read
In one paragraph

Article in Genome biology and evolution, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.1field-weighted citation impact, top 25% 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

14 citing papers in PubMed, 29 citations in OpenAlex.

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  6. Simulating domain architecture evolution.Bioinformatics (Oxford, England) · 2022
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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

8 authors at 2 institutions in 1 country.

Graham CromarProgram in Molecular Structure and Function, Hospital for Sick Children, Toronto, Ontario, Canada Department of Molecular Genetics, University of Toronto, Ontario, Canada.
Ka-Chun WongDepartment of Computer Science, University of Toronto, Ontario, Canada Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Ontario, Canada.
Noeleen LoughranProgram in Molecular Structure and Function, Hospital for Sick Children, Toronto, Ontario, Canada.
Tuan OnProgram in Molecular Structure and Function, Hospital for Sick Children, Toronto, Ontario, Canada Department of Molecular Genetics, University of Toronto, Ontario, Canada.
Hongyan SongProgram in Molecular Structure and Function, Hospital for Sick Children, Toronto, Ontario, Canada.
Xuejian XiongProgram in Molecular Structure and Function, Hospital for Sick Children, Toronto, Ontario, Canada.
Zhaolei ZhangDepartment of Molecular Genetics, University of Toronto, Ontario, Canada Department of Computer Science, University of Toronto, Ontario, Canada Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Ontario, Canada Banting and Best Department of Medical Research, University of Toronto, Ontario, Canada.
John ParkinsonProgram in Molecular Structure and Function, Hospital for Sick Children, Toronto, Ontario, Canada Department of Molecular Genetics, University of Toronto, Ontario, Canada Department of Biochemistry, University of Toronto, Ontario, Canada john.parkinson@utoronto.ca.
Hospital for Sick Children · CAUniversity of Toronto · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The extracellular matrix (ECM) is a defining characteristic of metazoans and consists of a meshwork of self-assembling, fibrous proteins, and their functionally related neighbours. Previous studies, focusing on a limited number of gene families, suggest that vertebrate complexity predominantly arose through the duplication and subsequent modification of retained, preexisting ECM genes. These genes provided the structural underpinnings to support a variety of specialized tissues, as well as a platform for the organization of spatio-temporal signaling and cell migration. However, the relative contributions of ancient versus novel domains to ECM evolution have not been quantified across the full range of ECM proteins. Here, utilizing a high quality list comprising 324 ECM genes, we reveal general and clade-specific domain combinations, identifying domains of eukaryotic and metazoan origin recruited into new roles in approximately two-third of the ECM proteins in humans representing novel vertebrate proteins. We show that, rather than acquiring new domains, sampling of new domain combinations has been key to the innovation of paralogous ECM genes during vertebrate evolution. Applying a novel framework for identifying potentially important, noncontiguous, conserved arrangements of domains, we find that the distinct biological characteristics of the ECM have arisen through unique evolutionary processes. These include the preferential recruitment of novel domains to existing architectures and the utilization of high promiscuity domains in organizing the ECM network around a connected array of structural hubs. Our focus on ECM proteins reveals that distinct types of proteins and/or the biological systems in which they operate have influenced the types of evolutionary forces that drive protein innovation. This emphasizes the need for rigorously defined systems to address questions of evolution that focus on specific systems of interacting proteins.

Indexed as

Evolution, MolecularAnimalsExtracellular MatrixExtracellular Matrix ProteinsHumansTandem Repeat SequencesExtracellular Matrix Proteinsdomain architecturedomain networksevolutionextracellular matrixprotein domains

Identifiers

PMID25323955
PMCPMC4224354
OpenAlexW2153749186

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.