Evidence map›Paper›PMID 28837672›Full record

ArticlePloS one2017

Cardiac transcriptome profiling of diabetic Akita mice using microarray and next generation sequencing.

Varun Kesherwani, Hamid R Shahshahan, Paras K Mishra

Open access · goldAbstract readValidation Study
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 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
1.6field-weighted citation impact, top 18% 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

36 citing papers in PubMed, 48 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Molecular Basis of Cardiomyopathies in Type 2 Diabetes.International journal of molecular sciences · 2024
    Review
  6. Review
  7. The roles of long noncoding RNA NEAT1 in cardiovascular diseases.Hypertension research : official journal of the Japanese Society of Hypertension · 2024
    Review
  8. Article
  9. Review
  10. Genome Editing and Diabetic Cardiomyopathy.Advances in experimental medicine and biology · 2023
    Article
  11. Guidelines on models of diabetic heart disease.American journal of physiology. Heart and circulatory physiology · 2022
    Review
  12. Article
  13. Review
  14. Why the diabetic heart is energy inefficient: a ketogenesis and ketolysis perspective.American journal of physiology. Heart and circulatory physiology · 2021
    Article
  15. Review
  16. Diabetic fibrosis.Biochimica et biophysica acta. Molecular basis of disease · 2021
    Review
  17. Article
  18. Review
  19. Review
  20. Article
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

3 authors at 1 institution in 1 country.

Varun KesherwaniDepartment of Cellular and Integrative Physiology, University of Nebraska Medical Center, Omaha, NE, United States of America.
Hamid R ShahshahanDepartment of Cellular and Integrative Physiology, University of Nebraska Medical Center, Omaha, NE, United States of America.
Paras K MishraDepartment of Cellular and Integrative Physiology, University of Nebraska Medical Center, Omaha, NE, United States of America.ORCID http://orcid.org/0000-0002-7810-9239
University of Nebraska Medical Center · US

Funding

Inflammation, miRNA and autophagy in diabetesR01HL113281 · NHLBI · UNIVERSITY OF NEBRASKA MEDICAL CENTER · PI MISHRA, PARAS KUMAR · 2013 to 2017
$1.9M
Exercise and H2S mitigate homocysteine-mediated beta2-adrenergic receptor dysfuncR01HL116205 · NHLBI · UNIVERSITY OF NEBRASKA MEDICAL CENTER · PI MISHRA, PARAS KUMAR · 2013 to 2017
$1.9M
NHLBI NIH HHS R01 HL113281NHLBI NIH HHS R01 HL116205
6 · The paper itself

Abstract

Although diabetes mellitus (DM) causes cardiomyopathy and exacerbates heart failure, the underlying molecular mechanisms for diabetic cardiomyopathy/heart failure are poorly understood. Insulin2 mutant (Ins2+/-) Akita is a mouse model of T1DM, which manifests cardiac dysfunction. However, molecular changes at cardiac transcriptome level that lead to cardiomyopathy remain unclear. To understand the molecular changes in the heart of diabetic Akita mice, we profiled cardiac transcriptome of Ins2+/- Akita and Ins2+/+ control mice using next generation sequencing (NGS) and microarray, and determined the implications of differentially expressed genes on various heart failure signaling pathways using Ingenuity pathway (IPA) analysis. First, we validated hyperglycemia, increased cardiac fibrosis, and cardiac dysfunction in twelve-week male diabetic Akita. Then, we analyzed the transcriptome levels in the heart. NGS analyses on Akita heart revealed 137 differentially expressed transcripts, where Bone Morphogenic Protein-10 (BMP10) was the most upregulated and hairy and enhancer of split-related (HELT) was the most downregulated gene. Moreover, twelve long non-coding RNAs (lncRNAs) were upregulated. The microarray analyses on Akita heart showed 351 differentially expressed transcripts, where vomeronasal-1 receptor-180 (Vmn1r180) was the most upregulated and WD Repeat Domain 83 Opposite Strand (WDR83OS) was the most downregulated gene. Further, miR-101c and H19 lncRNA were upregulated but Neat1 lncRNA was downregulated in Akita heart. Eleven common genes were upregulated in Akita heart in both NGS and microarray analyses. IPA analyses revealed the role of these differentially expressed genes in key signaling pathways involved in diabetic cardiomyopathy. Our results provide a platform to initiate focused future studies by targeting these genes and/or non-coding RNAs, which are differentially expressed in Akita hearts and are involved in diabetic cardiomyopathy.

Indexed as

Gene Expression ProfilingOligonucleotide Array Sequence AnalysisTranscriptomeAnimalsDiabetes Mellitus, ExperimentalHigh-Throughput Nucleotide SequencingMaleMiceMyocardium

Identifiers

PMID28837672
PMCPMC5570368
OpenAlexW2745960060

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.