Evidence map›Paper›PMID 31784561›Full record

ArticleScientific reports2019

Construction of miRNA-target networks using microRNA profiles of CVB3-infected HeLa cells.

Hai Lan Yao, Mi Liu, Wen Jun Wang, Xin Ling Wang, Juan Song, Qin Qin Song, Jun Han

Abstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Molecular Biomarkers of Canine Reproductive Functions.Current issues in molecular biology · 2024
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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

7 authors.

Hai Lan YaoDepartment of Biochemistry & Immunology, Capital Institute of Pediatrics, 2 YaBao Rd, Beijing, 100020, China.
Mi LiuState Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, 155 Changbai Rd, Beijing, 102206, China.
Wen Jun WangState Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, 155 Changbai Rd, Beijing, 102206, China.
Xin Ling WangState Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, 155 Changbai Rd, Beijing, 102206, China.
Juan SongState Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, 155 Changbai Rd, Beijing, 102206, China.
Qin Qin SongState Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, 155 Changbai Rd, Beijing, 102206, China.
Jun HanState Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, 155 Changbai Rd, Beijing, 102206, China. hanjun_sci@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs (miRNAs) play an important role in regulating gene expression in multiple biological processes and diseases. Thus, to understand changes in miRNA during CVB3 infection, specific miRNA expression profiles were investigated at 3 h, 6 h, and 9 h postinfection in HeLa cells by small-RNA high-throughput sequencing. Biological implications of 68 differentially expressed miRNAs were analyzed through GO and KEGG pathways. Interaction networks between 34 known highly differentially expressed miRNAs and targets were constructed by mirDIP and Navigator. The predicted targets showed that FAM135A, IKZF2, PLAG1, ZNF148, PHC3, LCOR and DYRK1A, which are associated with cellular differentiation and transcriptional regulation, were recognized by 8 miRNAs or 9 miRNAs through interactional regulatory networks. Seven target genes were confirmed by RT-qPCR. The results showed that the expression of DYRK1A, FAM135A, PLAG1, ZNF148, and PHC3 were obviously inhibited at 3 h, 6 h, and 9 h postinfection. The expression of LCOR did not show a significant change, and the expression of IKZF2 increased gradually with prolonged infection time. Our findings improve the understanding of the pathogenic mechanism of CVB3 infection on cellular differentiation and development through miRNA regulation, which has implications for interventional approaches to CVB3-infection therapy. Our results also provide a new method for screening target genes of microRNA regulation in virus-infected cells.

Indexed as

Cell DifferentiationCluster AnalysisDNA-Binding ProteinsDyrk KinasesEnterovirus B, HumanGene OntologyGene Regulatory NetworksHeLa CellsHigh-Throughput Nucleotide SequencingHumansIntracellular Signaling Peptides and ProteinsMicroRNAsProtein Serine-Threonine KinasesProtein-Tyrosine KinasesSequence Analysis, RNADNA-Binding ProteinsDyrk KinasesFAM135B protein, humanIntracellular Signaling Peptides and ProteinsMicroRNAsPLAG1 protein, humanProtein Serine-Threonine KinasesProtein-Tyrosine Kinases

Identifiers

PMID31784561
PMCPMC6884461

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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.