Evidence map›Paper›PMID 25547110›Full record

ArticlePhysiological genomics2015

Temporal analysis of reciprocal miRNA-mRNA expression patterns predicts regulatory networks during differentiation in human skeletal muscle cells.

Rasmus J O Sjögren, Brendan Egan, Mutsumi Katayama, Juleen R Zierath, Anna Krook

Open access · greenAbstract read
In one paragraph

Article in Physiological genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
0.9field-weighted citation impact, top 27% 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

12 citing papers in PubMed, 27 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. The Role of miRNA in the Pathophysiology of Neuroendocrine Tumors.International journal of molecular sciences · 2021
    Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. MicroRNAs in metabolism.Acta physiologica (Oxford, England) · 2017
    Review
  12. 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

5 authors at 1 institution in 1 country.

Rasmus J O SjögrenSection of Integrative Physiology, Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden; and.
Brendan EganSection of Integrative Physiology, Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden; and.
Mutsumi KatayamaDepartment of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.
Juleen R ZierathSection of Integrative Physiology, Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden; and Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.
Anna KrookDepartment of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden Anna.Krook@ki.se.
Karolinska Institutet · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

microRNAs (miRNAs) are short noncoding RNAs that regulate gene expression through posttranscriptional repression of target genes. miRNAs exert a fundamental level of control over many developmental processes, but their role in the differentiation and development of skeletal muscle from myogenic progenitor cells in humans remains incompletely understood. Using primary cultures established from human skeletal muscle satellite cells, we performed microarray profiling of miRNA expression during differentiation of myoblasts (day 0) into myotubes at 48 h intervals (day 2, 4, 6, 8, and 10). Based on a time-course analysis, we identified 44 miRNAs with altered expression [false discovery rate (FDR) < 5%, fold change > ±1.2] during differentiation, including the marked upregulation of the canonical myogenic miRNAs miR-1, miR-133a, miR-133b, and miR-206. Microarray profiling of mRNA expression at day 0, 4, and 10 identified 842 and 949 genes differentially expressed (FDR < 10%) at day 4 and 10, respectively. At day 10, 42% of altered transcripts demonstrated reciprocal expression patterns in relation to the directional change of their in silico predicted regulatory miRNAs based on analysis using Ingenuity Pathway Analysis microRNA Target Filter. Bioinformatic analysis predicted networks of regulation during differentiation including myomiRs miR-1/206 and miR-133a/b, miRNAs previously established in differentiation including miR-26 and miR-30, and novel miRNAs regulated during differentiation of human skeletal muscle cells such as miR-138-5p and miR-20a. These reciprocal expression patterns may represent new regulatory nodes in human skeletal muscle cell differentiation. This analysis serves as a reference point for future studies of human skeletal muscle differentiation and development in healthy and disease states.

Indexed as

Gene Expression ProfilingGene Regulatory Networks3' Untranslated RegionsBiomarkersCell DifferentiationCells, CulturedGene Expression RegulationGene OntologyHumansMaleMicroRNAsMuscle CellsMuscle, SkeletalReal-Time Polymerase Chain ReactionReproducibility of ResultsRNA, Messenger3' Untranslated RegionsBiomarkersMicroRNAsRNA, MessengerdifferentiationmiRNAsatellite cellsskeletal muscletranscriptome

Identifiers

PMID25547110
PMCPMC4346736
OpenAlexW2145403287

What OpenQuestion holds

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Registered trials

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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.