Literature DB >> 26217794

Integrated analysis of mRNA and miRNA expression in response to interleukin-6 in hepatocytes.

Samuel W Lukowski1, Richard J Fish1, Juliette Martin-Levilain2, Carmen Gonelle-Gispert3, Leo H Bühler3, Pierre Maechler2, Emmanouil T Dermitzakis4, Marguerite Neerman-Arbez1.   

Abstract

Understanding the interactions between miRNAs and genes they regulate during the acute phase response is crucial to our understanding of inflammatory diseases and processes. Inducing the acute phase response in hepatocytes by stimulating them with interleukin-6 [1] and then examining global changes in mRNA and miRNA expression can provide insight into the timing and dynamics of these interactions. Here we provide additional data for our study, Ref. [2]. In this data, we identify and validate IL-6-induced changes in gene expression [3-6] and their functional relationships over time and between cell types by gene ontology [7,8]. We also provide data showing the enrichment of miRNA binding motifs in the 3׳UTRs of differentially expressed genes [9], and their predicted gene targets derived from our RNA-seq data [10].

Entities:  

Year:  2015        PMID: 26217794      PMCID: PMC4510544          DOI: 10.1016/j.dib.2015.05.023

Source DB:  PubMed          Journal:  Data Brief        ISSN: 2352-3409


Specifications table

Value of the data

This data provides an integrated analysis of miRNA and mRNA expression in a model of the acute phase response in human and mouse hepatocytes. We investigated mRNA and miRNA expression between cell types after IL-6 stimulation. We observed a delayed response in gene expression changes in mouse hepatocytes compared to HepG2 cells and human hepatocytes. This is also reflected in the gene ontology and pathways analyses. We identified a subset of differentially expressed miRNAs that regulate the expression of important acute phase response genes at specific time points, and in different hepatocyte models, following induction of the IL-6-mediated acute phase response.

Data, experimental design, materials and methods

Figure 1: Top 20 up- and down-regulated genes in HepG2 (A–E), human primary hepatocytes (F–J) and mouse primary hepatocytes (K–O) between 0–6 h, 0–24 h, 6–24 h, 6 h±IL-6 and 24 h±IL-6. Time zero=untreated cells. Plotted values are the log2 fold-change of gene expression. Figure 2: ConsensusPathDB (CPdB) analyses of GO terms and enriched pathways in HepG2 cells. The p-value cutoff was 0.01 and the minimum input overlap was 2. Figure 3: CPdB analyses of GO terms and enriched pathways in human primary hepatocytes. The p-value cutoff was 0.01 and the minimum input overlap was 2. Figure 4: CPdB analyses of GO terms and enriched pathways in mouse primary hepatocytes. The p-value cutoff was 0.01 and the minimum input overlap was 2. Figure 5: Heatmap of mean expression (log2 RPKM) of the 0–24 h intersection genes in all three cell types (23 genes), with expression data for untreated (UT), 6 h and 24 h post-IL-6 induction – see Figure 1 in Ref. [2]. Figure 6: Validation of RNA-seq data (RPKM) by qPCR and western blot. Fibrinogen mRNA expression (FGA, FGB, FGG) was validated using qPCR in human primary hepatocytes (A and B) and mouse primary hepatocytes (C and D). Gene expression is expressed as a percentage of the IL-6 untreated control (black bars). The time zero data in the IL-6-positive samples is also untreated. Panel E shows a western blot of secreted fibrinogen protein, reduced to the individual chains (Aα, Bβ, and Ɣ), in conditioned media from human primary hepatocyte cultures that were either untreated or treated with IL-6 for 6 h or 24 h. The protein control is purified fibrinogen preparation, and the loading control is secreted albumin in the conditioned media. Table 1: Differentially expressed miRNAs in IL-6-stimulated hepatocytes (heatmap data, untransformed cpm values – see Figure 4 in Ref. [2]). Table 2: Significant miRNAs binding to over-represented 8nt motif in up- or down-regulated DE mRNA targets. P-value is the significance of the complementarity between the Weeder-calculated sequence motif and the miRNA seed. Asterisk represents the star-arm of miRNA precursor. Table 3: Hypergeometric analysis of DE miRNAs and their up- or down-regulated, differentially expressed TargetScan-predicted targets. Entries in bold text are statistically significant (P<0.05). Table 4: Up-regulated DE mRNA targets of down-regulated DE miRNAs in HepG2 cells. Table 5: Up-regulated DE mRNA targets of down-regulated DE miRNAs in human primary hepatocytes. Table 6: Up-regulated DE mRNA targets of down-regulated DE miRNAs in mouse primary hepatocytes.
Subject areaBiology
More specific subject areaHepG2, human and mouse hepatocyte mRNA and miRNA transcriptome
Type of dataTables, graphs, image
How data was acquiredRNA-seq using Illumina Hi-Seq 2000, qPCR, western blot
Data formatAnalyzed
Experimental factorsHepatocytes were either untreated or stimulated with interleukin-6, and mRNA and small RNA-seq libraries were generated for untreated, 6 h and 24 h post-IL-6 timepoints.
Experimental featuresSamples were HepG2 cells, human primary hepatocytes derived from healthy liver tissue, and mouse hepatocytes were derived from healthy mice with a mixed 129 Sv/C57Bl6J genetic background.
Data source locationGeneva, Switzerland
Data accessibilityData is available with the article
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1.  Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets.

Authors:  Benjamin P Lewis; Christopher B Burge; David P Bartel
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Review 2.  Stimulation of hepatic acute phase response by cytokines and glucocorticoids.

Authors:  H Baumann; K R Prowse; S Marinković; K A Won; G P Jahreis
Journal:  Ann N Y Acad Sci       Date:  1989       Impact factor: 5.691

3.  GENCODE: the reference human genome annotation for The ENCODE Project.

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Journal:  Genome Res       Date:  2012-09       Impact factor: 9.043

4.  The ConsensusPathDB interaction database: 2013 update.

Authors:  Atanas Kamburov; Ulrich Stelzl; Hans Lehrach; Ralf Herwig
Journal:  Nucleic Acids Res       Date:  2012-11-11       Impact factor: 16.971

5.  miRvestigator: web application to identify miRNAs responsible for co-regulated gene expression patterns discovered through transcriptome profiling.

Authors:  Christopher L Plaisier; J Christopher Bare; Nitin S Baliga
Journal:  Nucleic Acids Res       Date:  2011-05-20       Impact factor: 16.971

6.  HTSeq--a Python framework to work with high-throughput sequencing data.

Authors:  Simon Anders; Paul Theodor Pyl; Wolfgang Huber
Journal:  Bioinformatics       Date:  2014-09-25       Impact factor: 6.937

7.  ConsensusPathDB--a database for integrating human functional interaction networks.

Authors:  Atanas Kamburov; Christoph Wierling; Hans Lehrach; Ralf Herwig
Journal:  Nucleic Acids Res       Date:  2008-10-21       Impact factor: 16.971

8.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.

Authors:  Mark D Robinson; Davis J McCarthy; Gordon K Smyth
Journal:  Bioinformatics       Date:  2009-11-11       Impact factor: 6.937

9.  Integrated analysis of mRNA and miRNA expression in response to interleukin-6 in hepatocytes.

Authors:  Samuel W Lukowski; Richard J Fish; Juliette Martin-Levilain; Carmen Gonelle-Gispert; Leo H Bühler; Pierre Maechler; Emmanouil T Dermitzakis; Marguerite Neerman-Arbez
Journal:  Data Brief       Date:  2015-06-10

10.  TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions.

Authors:  Daehwan Kim; Geo Pertea; Cole Trapnell; Harold Pimentel; Ryan Kelley; Steven L Salzberg
Journal:  Genome Biol       Date:  2013-04-25       Impact factor: 13.583

  10 in total
  2 in total

Review 1.  Hepatocytes as Immunological Agents.

Authors:  Ian N Crispe
Journal:  J Immunol       Date:  2016-01-01       Impact factor: 5.422

2.  Integrated analysis of mRNA and miRNA expression in response to interleukin-6 in hepatocytes.

Authors:  Samuel W Lukowski; Richard J Fish; Juliette Martin-Levilain; Carmen Gonelle-Gispert; Leo H Bühler; Pierre Maechler; Emmanouil T Dermitzakis; Marguerite Neerman-Arbez
Journal:  Data Brief       Date:  2015-06-10
  2 in total

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