Evidence map›Paper›PMID 26367535›Full record

ArticlePloS one2015

Identification of Genes Whose Expression Profile Is Associated with Non-Progression towards AIDS Using eQTLs.

Jean-Louis Spadoni, Pierre Rucart, Sigrid Le Clerc, Daniëlle van Manen, Cédric Coulonges, Damien Ulveling, Vincent Laville, Taoufik Labib, Lieng Taing, Olivier Delaneau and 4 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.5field-weighted citation impact, top 35% 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

7 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Polymorphism rs1385129 Within Glut1 GeneFrontiers in immunology · 2018
    Article
  7. 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

14 authors at 4 institutions in 3 countries.

Jean-Louis SpadoniChaire de Bioinformatique; Laboratoire Génomique, Bioinformatique, et Applications (EA 4627), Conservatoire National des Arts et Métiers, Paris, France.
Pierre RucartChaire de Bioinformatique; Laboratoire Génomique, Bioinformatique, et Applications (EA 4627), Conservatoire National des Arts et Métiers, Paris, France.
Sigrid Le ClercChaire de Bioinformatique; Laboratoire Génomique, Bioinformatique, et Applications (EA 4627), Conservatoire National des Arts et Métiers, Paris, France.
Daniëlle van ManenDepartment of Experimental Immunology, Sanquin Research, Landsteiner Laboratory, and Center for Infectious Diseases and Immunity Amsterdam (CINIMA), Academic Medical Center, University of Amsterdam, Meibergdreef 15, 1105 AZ, Amsterdam, The Netherlands; Crucell Holland B.V., Archimedesweg 4-6, 2333 CN, Leiden, The Netherlands.
Cédric CoulongesChaire de Bioinformatique; Laboratoire Génomique, Bioinformatique, et Applications (EA 4627), Conservatoire National des Arts et Métiers, Paris, France.
Damien UlvelingChaire de Bioinformatique; Laboratoire Génomique, Bioinformatique, et Applications (EA 4627), Conservatoire National des Arts et Métiers, Paris, France.
Vincent LavilleChaire de Bioinformatique; Laboratoire Génomique, Bioinformatique, et Applications (EA 4627), Conservatoire National des Arts et Métiers, Paris, France.
Taoufik LabibChaire de Bioinformatique; Laboratoire Génomique, Bioinformatique, et Applications (EA 4627), Conservatoire National des Arts et Métiers, Paris, France.
Lieng TaingChaire de Bioinformatique; Laboratoire Génomique, Bioinformatique, et Applications (EA 4627), Conservatoire National des Arts et Métiers, Paris, France.
Olivier DelaneauDépartement de Génétique et Développement, Faculté de Médecine, Université de Genève, Switzerland.
Matthieu MontesChaire de Bioinformatique; Laboratoire Génomique, Bioinformatique, et Applications (EA 4627), Conservatoire National des Arts et Métiers, Paris, France.
Hanneke SchuitemakerDepartment of Experimental Immunology, Sanquin Research, Landsteiner Laboratory, and Center for Infectious Diseases and Immunity Amsterdam (CINIMA), Academic Medical Center, University of Amsterdam, Meibergdreef 15, 1105 AZ, Amsterdam, The Netherlands; Crucell Holland B.V., Archimedesweg 4-6, 2333 CN, Leiden, The Netherlands.
Josselin NoirelChaire de Bioinformatique; Laboratoire Génomique, Bioinformatique, et Applications (EA 4627), Conservatoire National des Arts et Métiers, Paris, France.
Jean-François ZaguryChaire de Bioinformatique; Laboratoire Génomique, Bioinformatique, et Applications (EA 4627), Conservatoire National des Arts et Métiers, Paris, France.
Conservatoire National des Arts et Métiers · FRGénomique Bioinformatique et Applications · FRJohnson & Johnson (Netherlands) · NLUniversity of Geneva · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMany genome-wide association studies have been performed on progression towards the acquired immune deficiency syndrome (AIDS) and they mainly identified associations within the HLA loci. In this study, we demonstrate that the integration of biological information, namely gene expression data, can enhance the sensitivity of genetic studies to unravel new genetic associations relevant to AIDS.

methodsWe collated the biological information compiled from three databases of expression quantitative trait loci (eQTLs) involved in cells of the immune system. We derived a list of single nucleotide polymorphisms (SNPs) that are functional in that they correlate with differential expression of genes in at least two of the databases. We tested the association of those SNPs with AIDS progression in two cohorts, GRIV and ACS. Tests on permuted phenotypes of the GRIV and ACS cohorts or on randomised sets of equivalent SNPs allowed us to assess the statistical robustness of this method and to estimate the true positive rate.

resultsEight genes were identified with high confidence (p = 0.001, rate of true positives 75%). Some of those genes had previously been linked with HIV infection. Notably, ENTPD4 belongs to the same family as CD39, whose expression has already been associated with AIDS progression; while DNAJB12 is part of the HSP90 pathway, which is involved in the control of HIV latency. Our study also drew our attention to lesser-known functions such as mitochondrial ribosomal proteins and a zinc finger protein, ZFP57, which could be central to the effectiveness of HIV infection. Interestingly, for six out of those eight genes, down-regulation is associated with non-progression, which makes them appealing targets to develop drugs against HIV.

Indexed as

Polymorphism, Single NucleotideQuantitative Trait LociTranscriptomeAcquired Immunodeficiency SyndromeCohort StudiesDatabases, GeneticDisease ProgressionDNA-Binding ProteinsGene Expression ProfilingGene Expression RegulationGenetic Association StudiesGenetic Predisposition to DiseaseHSP40 Heat-Shock ProteinsHumansPyrophosphatasesRandom AllocationDNA-Binding ProteinsDNAJB12 protein, humanENTPD4 protein, humanHSP40 Heat-Shock ProteinsPyrophosphatasesRepressor ProteinsTranscription FactorsZFP57 protein, human

Identifiers

PMID26367535
PMCPMC4569262
OpenAlexW2210167287

What OpenQuestion holds

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LicenceCC BY
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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.