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. 2016 Oct 24:6:35671.
doi: 10.1038/srep35671.

RNA sequencing analysis of human podocytes reveals glucocorticoid regulated gene networks targeting non-immune pathways

Lulu Jiang  1 , Charles C T Hindmarch  2   3 , Mark Rogers  4 , Colin Campbell  4 , Christy Waterfall  5 , Jane Coghill  5 , Peter W Mathieson  6 , Gavin I Welsh  1
Affiliations

RNA sequencing analysis of human podocytes reveals glucocorticoid regulated gene networks targeting non-immune pathways

Lulu Jiang et al. Sci Rep. .

Abstract

Glucocorticoids are steroids that reduce inflammation and are used as immunosuppressive drugs for many diseases. They are also the mainstay for the treatment of minimal change nephropathy (MCN), which is characterised by an absence of inflammation. Their mechanisms of action remain elusive. Evidence suggests that immunomodulatory drugs can directly act on glomerular epithelial cells or 'podocytes', the cell type which is the main target of injury in MCN. To understand the nature of glucocorticoid effects on non-immune cell functions, we generated RNA sequencing data from human podocyte cell lines and identified the genes that are significantly regulated in dexamethasone-treated podocytes compared to vehicle-treated cells. The upregulated genes are of functional relevance to cytoskeleton-related processes, whereas the downregulated genes mostly encode pro-inflammatory cytokines and growth factors. We observed a tendency for dexamethasone-upregulated genes to be downregulated in MCN patients. Integrative analysis revealed gene networks composed of critical signaling pathways that are likely targeted by dexamethasone in podocytes.

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Figures

Figure 1
Figure 1. RNA sequencing analysis of 3 podocyte cell lines derived from independent healthy donors.
(A) Experimental design. Conditionally immortalized human podocytes were developed from 3 kidney transplant donors. Differentiated cells were treated with PBS or 0.1 μM dexamethasone for 24 hours, which were then subjected to RNA extraction and sequencing. On the top right illustrates the structural composition of glomerular filtration barrier and the occurrence of protein loss into urine in disease. Notation: Con, control; Dex, dexamethasone; GBM, glomerular basement membrane; CL1-3, cell lines 1–3. (B) Cell line-specific transcriptome changes in response to dexamethasone. Clustering and visualization of differentially expressed genes were analysed using a supra-hexagonal map. Genes with similar patterns across cell lines are mapped onto the same or nearby regions in the map. (C) A map illustrating 7 gene meta-clusters.
Figure 2
Figure 2. Enrichment analysis of the clustered genes.
Functional enrichment analysis for genes in each of the 7 clusters is based on Gene Ontology molecular function (GOMF) and biological process (GOBP). KEGG, BioCarta and Reactome pathways are used for pathway enrichment analysis, and transcription factor binding sites (TFBSs) for TF enrichment analysis. Notation: CL1–3, cell lines 1–3.
Figure 3
Figure 3. Comparative analysis of RNA-seq data with the transcriptome data of MCN patients.
(A,B) GSEA shows that genes in the MCN-downregulated signature had a tendency of being significantly upregulated by dexamethasone, whereas no such tendency was observed for genes in the MCN-upregulated signature. (C) The list of 9 therapeutic candidate target genes identified as being repressed in MCN but upregulated by dexamethasone. (D) Real-time RT-PCR validation of the identified potential therapeutic targets. Cells were treated with vehicle or dexamethasone (0.1 μM) for 24 hours and then RNA was extracted and analyzed by real-time RT-PCR. Values were normalized to GAPDH and fold changes compared with vehicles were plotted as means ± SE from triplicates of four independent experiments. Dotted line indicates the basal expression. Notation: dexamethasone treatment versus vehicle (*); Significance: **P < 0.01, ***P < 0.001.
Figure 4
Figure 4. Gene network upregulated by dexamethasone in podocytes.
The network is identified through integrative analysis of our RNA-seq expression data with known protein interaction data. Four communities were identified (C1–C4). Enrichment analysis shows the crosstalk of RAS/MAPK signalling, regulation of actin cytoskeleton and focal adhesion, and calcium signalling in this gene network.
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