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Integrated analysis of mRNA, microRNA and protein in systemic lupus erythematosus-specific induced pluripotent stem cells from urine

Overview of attention for article published in BMC Genomics, July 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

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1 blog
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Citations

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16 Dimensions

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36 Mendeley
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Title
Integrated analysis of mRNA, microRNA and protein in systemic lupus erythematosus-specific induced pluripotent stem cells from urine
Published in
BMC Genomics, July 2016
DOI 10.1186/s12864-016-2809-9
Pubmed ID
Authors

Donge Tang, Yuyu Chen, Huiyan He, Jianrong Huang, Wenbiao Chen, Wujian Peng, Qianjin Lu, Yong Dai

Abstract

In clinical practice, it is difficult to monitor the repeating relapse in patients who have been suffering from systemic lupus erythematosus (SLE). The underlying etiology remains largely unknown. Aiming to understand the pathogenesis of SLE, a detailed study was conducted. Renal tubular cells-derived iPSCs were successfully obtained from the urine of SLE patients and healthy controls. With the purpose to identify simultaneous expression profiling of microRNA, mRNA and protein, Illumina HiSeq™ 2000 System and iTRAQ-coupled 2D LC-MS/MS analysis were utilized in systemic lupus erythematosus-specific induced pluripotent stem cells (SLE-iPSCs) and normal control-iPSCs (NC-iPSCs). The integration of multiple profiling datasets was realized since it could facilitate the identification of non-seed miRNA targets, as well as differentially expressed mRNAs and proteins. For this study, profiling datasets of 1099 differentially expressed mRNAs, 223 differentially expressed microRNAs and 94 differentially expressed proteins were integrated. In order to investigate the influence of miRNA on the processes of regulating mRNAs and proteins' levels, potential targets of differentially expressed mRNAs and proteins were predicted using miRanda, TargetScan and Pictar. Multiple profiling datasets were integrated to facilitate the identification of miRNA targets, as well as differentially expressed mRNAs and proteins. Through gene ontology (GO) analysis of differentially expressed mRNAs and proteins, biological processes that drive proliferation were identified, such as mRNA processing and translation. Western blot and Q-PCR confirmed AK4 protein and mRNA up-regulation. The findings also showed that TAGLN's protein and mRNA level were down-regulated in SLE-iPSCs, both miR-371a-5p and let-7a-5p in SLE-iPSC were down-regulated and verified using Q-PCR. The up-regulation of AK4 involved in nucleotide biosynthesis suggested a general acceleration of anabolic metabolism induced by down-regulated miR-371a-5p, which might contribute to SLE. Based on high throughput analysis, integrated miRNA, mRNA, and protein expression data were generated. Differentially expressed dates were also adopted in conjunction with in-silico tools to identify potential candidates for SLE-iPSCs. Representative miRNA, mRNA and proteins were verified. It was also expected that the knowledge gained from this study can be applied to assess the usefulness of pathogenesis and novel biomarker candidates of SLE, which may develop a new way for SLE diagnosis.

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X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 1 3%
Unknown 35 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 19%
Student > Master 6 17%
Professor 3 8%
Student > Ph. D. Student 3 8%
Student > Doctoral Student 2 6%
Other 5 14%
Unknown 10 28%
Readers by discipline Count As %
Medicine and Dentistry 8 22%
Biochemistry, Genetics and Molecular Biology 5 14%
Immunology and Microbiology 4 11%
Agricultural and Biological Sciences 2 6%
Nursing and Health Professions 2 6%
Other 1 3%
Unknown 14 39%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 July 2016.
All research outputs
#3,582,361
of 22,880,691 outputs
Outputs from BMC Genomics
#1,358
of 10,666 outputs
Outputs of similar age
#64,483
of 354,317 outputs
Outputs of similar age from BMC Genomics
#38
of 252 outputs
Altmetric has tracked 22,880,691 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,666 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 87% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 354,317 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 252 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.