Evidence map›Paper›PMID 29322359›Full record

ArticleIn vitro cellular & developmental biology. Animal2018

Expression profiles of microRNAs in oxidized low-density lipoprotein-stimulated RAW 264.7 cells.

Xiaokai Li, Siyuan Feng, Yi Luo, Keren Long, Zhenghao Lin, Jideng Ma, Anan Jiang, Long Jin, Qianzi Tang, Mingzhou Li and 1 more

Abstract read
PubMed Publisher
In one paragraph

Article in In vitro cellular & developmental biology. Animal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.7field-weighted citation impact, top 33% 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

7 citing papers in PubMed, 15 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

11 authors at 1 institution in 1 country.

Xiaokai LiInstitute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China.
Siyuan FengInstitute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China.
Yi LuoInstitute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China.
Keren LongInstitute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China.
Zhenghao LinInstitute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China.
Jideng MaInstitute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China.
Anan JiangInstitute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China.
Long JinInstitute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China.
Qianzi TangInstitute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China.
Mingzhou LiInstitute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China. mingzhou.li@sicau.edu.cn.
Xun WangInstitute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China. xunwang@sicau.edu.cn.
Sichuan Agricultural University · CN

Funding

the Application Basic Research Plan Project of Sichuan Province 2016JY0167The National Natural Science Foundation of China 31522055, 31530073 and 31472081the Program for Innovative Research Team of Sichuan Province 2015TD0012the Project of Sichuan Education Department 15ZA0008the Science & Technology Support Program of Sichuan 2016NYZ0042
6 · The paper itself

Abstract

Macrophage-derived foam cells were one of the hallmarks of atherosclerosis, and microRNAs played an important role in the formation of foam cells. In order to explore the roles of miRNA in the formation of foam cells, we investigated miRNA expression profiles in foam cells through high-throughput sequencing technology. A total of 84 miRNAs were differentially expressed between RAW 264.7 macrophages and foam cells induced by ox-LDL. Thirty miRNAs were upregulated and 54 miRNAs were downregulated. GO terms and KEGG pathways analysis revealed that the target genes of most of DE miRNAs were mainly enriched in "cell differentiation," "endocytosis," "MAPK signaling pathway," and "FoxO signaling pathway." The target genes of some DE miRNAs were enriched in "Insulin signaling pathway," "Hippo signaling pathway," "TNF signaling pathway," "NF-kappa B signaling pathway," and "cell death." Using bioinformatics analyses and dual-luciferase reporter assays, we found that miR-28a-5p and miR-30c-1-3p directly inhibited LRAD3 and LOX-1 mRNA expression through targeting the 3'UTR of LRAD3 and LOX-1 mRNA, respectively. Our study indicates that miRNAs are extensively involved in the formation of foam cells, and provides a valuable resource for further study the role of miRNAs in atherosclerosis.

Indexed as

Transcriptome3' Untranslated RegionsAnimalsAtherosclerosisCell DifferentiationFoam CellsHeLa CellsHigh-Throughput Nucleotide SequencingHumansInsulinLipoproteins, LDLMiceMicroRNAsRAW 264.7 CellsReceptors, LDLReproducibility of Results3' Untranslated RegionsInsulinLipoproteins, LDLMicroRNAsOlr1 protein, mouseoxidized low density lipoproteinReceptors, LDLScavenger Receptors, Class EAtherosclerosisExpression profileFoam cellsHigh-throughput sequencingmiRNAs

Identifiers

PMID29322359
OpenAlexW2783621033

What OpenQuestion holds

Textmetadata
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.