Evidence map›Paper›PMID 28968432›Full record

ArticlePloS one2017

Gene expression signature predicts human islet integrity and transplant functionality in diabetic mice.

Sunil M Kurian, Kevin Ferreri, Chia-Hao Wang, Ivan Todorov, Ismail H Al-Abdullah, Jeffrey Rawson, Yoko Mullen, Daniel R Salomon, Fouad Kandeel

Abstract read
In one paragraph

Article in PloS one, 2017. 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
–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

5 citing papers in PubMed.

  1. Article
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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

9 authors.

Sunil M KurianDepartment of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, California, United States of America.
Kevin FerreriDepartment of Translational Research and Cellular Therapeutics, Diabetes, and Metabolism Research Institute, City of Hope National Medical Center, Duarte, California, United States of America.
Chia-Hao WangDepartment of Translational Research and Cellular Therapeutics, Diabetes, and Metabolism Research Institute, City of Hope National Medical Center, Duarte, California, United States of America.
Ivan TodorovDepartment of Translational Research and Cellular Therapeutics, Diabetes, and Metabolism Research Institute, City of Hope National Medical Center, Duarte, California, United States of America.
Ismail H Al-AbdullahDepartment of Translational Research and Cellular Therapeutics, Diabetes, and Metabolism Research Institute, City of Hope National Medical Center, Duarte, California, United States of America.
Jeffrey RawsonDepartment of Translational Research and Cellular Therapeutics, Diabetes, and Metabolism Research Institute, City of Hope National Medical Center, Duarte, California, United States of America.
Yoko MullenDepartment of Translational Research and Cellular Therapeutics, Diabetes, and Metabolism Research Institute, City of Hope National Medical Center, Duarte, California, United States of America.
Daniel R SalomonDepartment of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, California, United States of America.
Fouad KandeelDepartment of Translational Research and Cellular Therapeutics, Diabetes, and Metabolism Research Institute, City of Hope National Medical Center, Duarte, California, United States of America.ORCID http://orcid.org/0000-0003-3746-5015

Funding

SOUTHERN CALIFORNIA ISLET CELL RESOURCES (SCI-ICR) CENTERU42RR016607 · NCRR · CITY OF HOPE/BECKMAN RESEARCH INSTITUTE · PI RIGGS, ARTHUR D · 2001 to 2009
$6.6M
NCRR NIH HHS U42 RR016607
6 · The paper itself

Abstract

There is growing evidence that transplantation of cadaveric human islets is an effective therapy for type 1 diabetes. However, gauging the suitability of islet samples for clinical use remains a challenge. We hypothesized that islet quality is reflected in the expression of specific genes. Therefore, gene expression in 59 human islet preparations was analyzed and correlated with diabetes reversal after transplantation in diabetic mice. Analysis yielded 262 differentially expressed probesets, which together predict islet quality with 83% accuracy. Pathway analysis revealed that failing islet preparations activated inflammatory pathways, while functional islets showed increased regeneration pathway gene expression. Gene expression associated with apoptosis and oxygen consumption showed little overlap with each other or with the 262 probeset classifier, indicating that the three tests are measuring different aspects of islet cell biology. A subset of 36 probesets surpassed the predictive accuracy of the entire set for reversal of diabetes, and was further reduced by logistic regression to sets of 14 and 5 without losing accuracy. These genes were further validated with an independent cohort of 16 samples. We believe this limited number of gene classifiers in combination with other tests may provide complementary verification of islet quality prior to their clinical use.

Indexed as

Gene Expression ProfilingIslets of Langerhans TransplantationAnimalsDiabetes Mellitus, Type 1HumansIslets of LangerhansLogistic ModelsMice

Identifiers

PMID28968432
PMCPMC5624587

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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.