Evidence map›Paper›PMID 26629415›Full record

ArticleMeta gene2015

Genetic architecture of early pre-inflammatory stage transcription signatures of autoimmune diabetes in the pancreatic lymph nodes of the NOD mouse reveals significant gene enrichment on chromosomes 6 and 7.

Beatrice Regnault, Evie Melanitou

Open access · hybridAbstract read
In one paragraph

Article in Meta gene, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 97% 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

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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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

2 authors at 1 institution in 1 country.

Beatrice RegnaultDepartment of Genomes and Genetics, Eukaryote Genotyping Platform, Institut Pasteur, 25-28 rue du Dr Roux, 75725 Paris, Cedex 15, France.
Evie MelanitouDepartment of Parasites and Insect-Vectors, Institut Pasteur, 25-28 rue du Dr Roux, 75725 Paris, Cedex 15, France.
Institut Pasteur · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Autoimmune diseases are characterized by the stimulation of an excessive immune response to self-tissues by inner and/or outer organism factors. Common characteristics in their etiology include a complex genetic predisposition and environmental triggers as well as the implication of the major histocompatibility (MHC) locus on human chromosome 6p21. A restraint number of non-MHC susceptibility genes, part of the genetic component of type 1 diabetes have been identified in human and in animal models, while the complete spectrum of genes involved remains unknown. We elaborate herein patterns of chromosomal organization of 162 genes differentially expressed in the pancreatic lymph nodes of Non-Obese Diabetic mice, carefully selected by early sub-phenotypic evaluation (presence or absence of insulin autoantibodies). Chromosomal assignment of these genes revealed a non-random distribution on five chromosomes (47%). Significant gene enrichment was observed in particular for two chromosomes, 6 and 7. While a subset of these genes coding for secreted proteins showed significant enrichment on both chromosomes, the overall pool of genes was significantly enriched on chromosome 7. The significance of this unexpected gene distribution on the mouse genome is discussed in the light of novel findings indicating that genes affecting common diseases map to recombination "hotspot" regions of mammalian genomes. The genetic architecture of transcripts differentially expressed in specific stages of autoimmune diabetes offers novel venues towards our understanding of patterns of inheritance potentially affecting the pathological disease mechanisms.

Indexed as

GenomicsPancreatic lymph nodesPolymorphismsTranscriptomeType 1 diabetes

Identifiers

PMID26629415
PMCPMC4634356
OpenAlexW1795960400

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LicenceCC BY-NC-ND
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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.