Evidence map›Paper›PMID 34573434›Full record

ArticleGenes2021

Novel Approach Combining Transcriptional and Evolutionary Signatures to Identify New Multiciliation Genes.

Audrey Defosset, Dorine Merlat, Laetitia Poidevin, Yannis Nevers, Arnaud Kress, Olivier Poch, Odile Lecompte

Abstract read
In one paragraph

Article in Genes, 2021. 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

7 authors.

Audrey DefossetDepartment of Computer Science, ICube, UMR 7357, Centre de Recherche en Biomédecine de Strasbourg, University of Strasbourg, CNRS, 67000 Strasbourg, France.ORCID 0000-0002-3837-6519
Dorine MerlatDepartment of Computer Science, ICube, UMR 7357, Centre de Recherche en Biomédecine de Strasbourg, University of Strasbourg, CNRS, 67000 Strasbourg, France.ORCID 0000-0001-8249-3045
Laetitia PoidevinDepartment of Computer Science, ICube, UMR 7357, Centre de Recherche en Biomédecine de Strasbourg, University of Strasbourg, CNRS, 67000 Strasbourg, France.ORCID 0000-0002-6750-3732
Yannis NeversDepartment of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland.
Arnaud KressDepartment of Computer Science, ICube, UMR 7357, Centre de Recherche en Biomédecine de Strasbourg, University of Strasbourg, CNRS, 67000 Strasbourg, France.ORCID 0000-0002-7616-8876
Olivier PochDepartment of Computer Science, ICube, UMR 7357, Centre de Recherche en Biomédecine de Strasbourg, University of Strasbourg, CNRS, 67000 Strasbourg, France.
Odile LecompteDepartment of Computer Science, ICube, UMR 7357, Centre de Recherche en Biomédecine de Strasbourg, University of Strasbourg, CNRS, 67000 Strasbourg, France.ORCID 0000-0002-2005-460X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multiciliogenesis is a complex process that allows the generation of hundreds of motile cilia on the surface of specialized cells, to create fluid flow across epithelial surfaces. Dysfunction of human multiciliated cells is associated with diseases of the brain, airway and reproductive tracts. Despite recent efforts to characterize the transcriptional events responsible for the differentiation of multiciliated cells, a lot of actors remain to be identified. In this work, we capitalize on the ever-growing quantity of high-throughput data to search for new candidate genes involved in multiciliation. After performing a large-scale screening using 10 transcriptomics datasets dedicated to multiciliation, we established a specific evolutionary signature involving Otomorpha fish to use as a criterion to select the most likely targets. Combining both approaches highlighted a list of 114 potential multiciliated candidates. We characterized these genes first by generating protein interaction networks, which showed various clusters of ciliated and multiciliated genes, and then by computing phylogenetic profiles. In the end, we selected 11 poorly characterized genes that seem like particularly promising multiciliated candidates. By combining functional and comparative genomics methods, we developed a novel type of approach to study biological processes and identify new promising candidates linked to that process.

Indexed as

FishesAnimalsBiological EvolutionCell DifferentiationCiliaDatabases, GeneticFish ProteinsGene ExpressionGenomicsHumansPhylogenyTranscriptomeFish Proteinscomparative genomicsevolutionfunctional genomicsmulticiliationmulti-omics data integrationrare diseases

Identifiers

PMID34573434
PMCPMC8470418

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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.