Evidence map›Paper›PMID 31455266›Full record

ArticleBMC nephrology2019

Intrarenal microRNA signature related to the fibrosis process in chronic kidney disease: identification and functional validation of key miRNAs.

Jianwen Yu, Chaolun Yu, Boya Feng, Xiaojiang Zhan, Ning Luo, Xueqing Yu, Qin Zhou

Open access · goldAbstract read
In one paragraph

Article in BMC nephrology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
1.8field-weighted citation impact, top 15% 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

17 citing papers in PubMed, 37 citations in OpenAlex.

  1. Trial
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  5. Article
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  9. Article
  10. miRNA biomarkers in renal disease.International urology and nephrology · 2022
    Review
  11. Article
  12. Article
  13. MicroRNA-10 negatively regulates inflammation in diabetic kidney via targeting activation of the NLRP3 inflammasome.Molecular therapy : the journal of the American Society of Gene Therapy · 2021
    Article
  14. Article
  15. Fibrosis in Chronic Kidney Disease: Pathogenesis and Consequences.International journal of molecular sciences · 2021
    Review
  16. Article
  17. 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

7 authors at 4 institutions in 1 country.

Jianwen YuDepartment of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, 58th, Zhongshan Road II, Guangzhou, China.
Chaolun YuDepartment of Endocrinology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Boya FengTranslational Medical Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Xiaojiang ZhanDepartment of Nephrology, The First Affiliated Hospital, Nanchang University, Nanchang, China.
Ning LuoDepartment of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, 58th, Zhongshan Road II, Guangzhou, China.
Xueqing YuDepartment of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, 58th, Zhongshan Road II, Guangzhou, China.
Qin ZhouDepartment of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, 58th, Zhongshan Road II, Guangzhou, China. zhouqin3@mail.sysu.edu.cn.
Sun Yat-sen University · CNFirst Affiliated Hospital of Nanchang University · CNSun Yat-sen Memorial Hospital · CNThe First Affiliated Hospital, Sun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThough the roles of microRNAs (miRNAs) in renal diseases have been extensively investigated, a thorough screening and comparison of miRNAs among different types of chronic kidney disease (CKD) has never been performed.

methodsThe intrarenal miRNAs were profiled from fresh kidney tissues of patients with biopsy-proven minimal change disease (MCD), focal segmental glomerular sclerosis (FSGS) and diabetic nephropathy (DN) by using microarray. Commonly dysregulated miRNAs were validated by real-time PCR using paraffin-embedded renal tissues from all three types of CKD patients as well as mouse unilateral ureteral obstruction (UUO) model. Two novel miRNAs were selected and annotations of their target genes were performed using GO and KEGG pathway enrichment analysis. Biological functions of three two candidate miRNAs were explored in TGF-β1-induced cell model using human kidney proximal tubular cells (HK-2).

resultsThe kidney biopsy samples of three disease types represent different levels of damage and fibrosis, which were the mildest in MCD, moderate in FSGS, and the most severe in DN. 116 miRNAs were identified to be commonly dysregulated, including 40 up-regulated and 76 down-regulated in CKD tissues as compared with healthy donor kidney biopsy tissues. Two novel miRNAs, hsa-miR-3607-3p and hsa-miR-4709-3p, were verified as consistently differentially expressed among all three types of patient samples as well as in mouse model. In vitro, hsa-miR-3607-3p was repressed while hsa-miR-4709-3p was induced by TGF-β1 treatment. Inhibition of hsa-miR-3607-3p or overexpression of hsa-miR-4709-3p promoted TGF-β1-induced migration and F-actin assembling in HK-2 cells, which are characteristics of epithelial-mesenchymal transition (EMT). Further study identified that ITGB8 and CALM3 were the bona fide target genes of hsa-miR-3607-3p and hsa-miR-4709-3p respectively.

conclusionsThe present identify a unique miRNAs profile that probably relates to the common fibrosis process of CKD. Results of our study suggest that hsa-miR-3607-3p and hsa-miR-4709-3p may represent as promising therapeutic targets against kidney fibrosis.

Indexed as

AdultAnimalsCells, CulturedFemaleFibrosisHumansMaleMiceMice, Inbred C57BLMicroRNAsMiddle AgedRenal Insufficiency, ChronicMicroRNAsMIRN4709 microRNA, humanChronic kidney diseaseIntrarenal microRNAsRenal fibrosis

Identifiers

PMID31455266
PMCPMC6712721
OpenAlexW2970134059

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.