Evidence map›Paper›PMID 28725461›Full record

ArticleNPJ systems biology and applications2015

Cross-species gene expression analysis identifies a novel set of genes implicated in human insulin sensitivity.

Rima Chaudhuri, Poh Sim Khoo, Katherine Tonks, Jagath R Junutula, Ganesh Kolumam, Zora Modrusan, Dorit Samocha-Bonet, Christopher C Meoli, Samantha Hocking, Daniel J Fazakerley and 5 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ systems biology and applications, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.6field-weighted citation impact, top 29% 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

5 citing papers in PubMed, 9 citations in OpenAlex.

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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

15 authors at 4 institutions in 1 country.

Rima ChaudhuriCharles Perkins Centre, School of Molecular Bioscience, The University of Sydney, Sydney, NSW, Australia.
Poh Sim KhooDiabetes and Obesity Program, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.
Katherine TonksDiabetes and Obesity Program, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.
Jagath R JunutulaGenentech Incorporated, South San Francisco, CA, USA.
Ganesh KolumamGenentech Incorporated, South San Francisco, CA, USA.
Zora ModrusanGenentech Incorporated, South San Francisco, CA, USA.
Dorit Samocha-BonetDiabetes and Obesity Program, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.
Christopher C MeoliDiabetes and Obesity Program, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.
Samantha HockingDepartment of Endocrinology, Royal North Shore Hospital, Sydney, NSW, Australia.
Daniel J FazakerleyCharles Perkins Centre, School of Molecular Bioscience, The University of Sydney, Sydney, NSW, Australia.
Jacqueline StöckliCharles Perkins Centre, School of Molecular Bioscience, The University of Sydney, Sydney, NSW, Australia.
Kyle L HoehnSchool of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia.
Jerry R GreenfieldDiabetes and Obesity Program, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.
Jean Yee Hwa YangSchool of Mathematics and Statistics, University of Sydney, Sydney, NSW, Australia.
David E JamesCharles Perkins Centre, School of Molecular Bioscience, The University of Sydney, Sydney, NSW, Australia.
Garvan Institute of Medical Research · AUUniversity of Sydney · AUSt Vincent's Hospital Sydney · AUUNSW Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveInsulin resistance (IR) is one of the earliest predictors of type 2 diabetes. However, diagnosis of IR is limited. High fat fed mouse models provide key insights into IR. We hypothesized that early features of IR are associated with persistent changes in gene expression (GE) and endeavored to (a) develop novel methods for improving signal:noise in analysis of human GE using mouse models; (b) identify a GE motif that accurately diagnoses IR in humans; and (c) identify novel biology associated with IR in humans.

methodsWe integrated human muscle GE data with longitudinal mouse GE data and developed an unbiased three-level cross-species analysis platform (single gene, gene set, and networks) to generate a gene expression motif (GEM) indicative of IR. A logistic regression classification model validated GEM in three independent human data sets (

resultsThis GEM of 93 genes substantially improved diagnosis of IR compared with routine clinical measures across multiple independent data sets. Individuals misclassified by GEM possessed other metabolic features raising the possibility that they represent a separate metabolic subclass. The GEM was enriched in pathways previously implicated in insulin action and revealed novel associations between

conclusionsThis study shows that systems approaches for identifying molecular signatures provides a powerful way to stratify individuals into discrete metabolic groups. Moreover, we speculate that the β-catenin pathway may represent a novel biomarker for IR in humans that warrant future investigation.

Identifiers

PMID28725461
PMCPMC5516867
OpenAlexW2591582945

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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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.