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. 2013 Jun 1:14:371.
doi: 10.1186/1471-2164-14-371.

Microarray and deep sequencing cross-platform analysis of the mirRNome and isomiR variation in response to epidermal growth factor

Franc Llorens  1 , Manuela Hummel, Lorena Pantano, Xavier Pastor, Ana Vivancos, Ester Castillo, Heidi Mattlin, Anna Ferrer, Matthew Ingham, Marc Noguera, Robert Kofler, Juliane C Dohm, Raquel Pluvinet, Mònica Bayés, Heinz Himmelbauer, José Antonio del Rio, Eulàlia Martí, Lauro Sumoy
Affiliations

Microarray and deep sequencing cross-platform analysis of the mirRNome and isomiR variation in response to epidermal growth factor

Franc Llorens et al. BMC Genomics. .

Abstract

Background: Epidermal Growth Factor (EGF) plays an important function in the regulation of cell growth, proliferation, and differentiation by binding to its receptor (EGFR) and providing cancer cells with increased survival responsiveness. Signal transduction carried out by EGF has been extensively studied at both transcriptional and post-transcriptional levels. Little is known about the involvement of microRNAs (miRNAs) in the EGF signaling pathway. miRNAs have emerged as major players in the complex networks of gene regulation, and cancer miRNA expression studies have evidenced a direct involvement of miRNAs in cancer progression.

Results: In this study, we have used an integrative high content analysis approach to identify the specific miRNAs implicated in EGF signaling in HeLa cells as potential mediators of cancer mediated functions. We have used microarray and deep-sequencing technologies in order to obtain a global view of the EGF miRNA transcriptome with a robust experimental cross-validation. By applying a procedure based on Rankprod tests, we have delimited a solid set of EGF-regulated miRNAs. After validating regulated miRNAs by reverse transcription quantitative PCR, we have derived protein networks and biological functions from the predicted targets of the regulated miRNAs to gain insight into the potential role of miRNAs in EGF-treated cells. In addition, we have analyzed sequence heterogeneity due to editing relative to the reference sequence (isomiRs) among regulated miRNAs.

Conclusions: We propose that the use of global genomic miRNA cross-validation derived from high throughput technologies can be used to generate more reliable datasets inferring more robust networks of co-regulated predicted miRNA target genes.

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Figures

Figure 1
Figure 1
Time-course miRNA transcriptome profile of EGF-treated HeLa cells using Agilent miRNA arrays. Agilent miRNA microarrays were used to analyze miRNA expression in HeLa cells treated with EGF at the indicated time points. Average differential miRNA expression between EGF and control was calculated and the number differentially regulated probes (FC > 1.2) was plotted for each time-course point. Different lines show numbers of probes found: up-regulated (blue and purple), down-regulated (gray and green), regulated up or downwards (yellow and brown). Blue-gray-yellow: all regulated probes, without filtering for intensity; purple- green-brown: only regulated probes when at least half of the samples had log2intensity >7 (arbitrary detection limit to consider a miRNA as not absent).
Figure 2
Figure 2
miRNA transcriptome profiles of EGF-treated HeLa cells using Exiqon and Agilent miRNA arrays. A. Shared miRNA genes between Exiqon and Agilent microRNA arrays. Venn diagram showing the unique and named miRNAs genes shared between Agilent and Exiqon microarray platforms. The pool of 346 shared genes was used for all subsequent cross-platform analyses. Numbers outside diagrams indicate the total amount of miRNA genes contained in each platform. In brackets, percentage of miRNA genes contained in each platform over the total miRNA genes contained in all platforms. B. Regulated miRNAs after 6 hours EGF treatment using Exiqon and Agilent miRNA arrays. HeLa cells were serum-starved for 24 h and treated with EGF for 6 h. Total RNAs prepared from cells lysates were hybridized to Exiqon miRCURY LNA microRNA Array V9.2 and Agilent Human miRNA V2 Oligo Microarray. List shows regulated miRNAs identified by SAM analysis (FDR = 0.05) for miRNAs with a minimal expression change of 1.2 fold.
Figure 3
Figure 3
Correspondence between Microarrays and Illumina sequencing. In each platform, miRNAs were ranked based on EGF versus Control log ratios. The heatmap shows a color representation of those ranks, ranging from most up-regulated (dark red) to most down-regulated (light yellow) miRNAs. miRNAs are listed in decreasing Rankprod score, grouping genes that are most concordantly regulated across platforms at the top.
Figure 4
Figure 4
RT-qPCR validation of EGF-regulated miRNAs. Total RNA prepared from cells lysates were analyzed by quantitative real time PCR using the miRCURY LNA™ microRNA PCR System (Exiqon) for each of the miRNAs as indicated. A. Control treatment without inhibitors: HeLa cells were serum-starved for 24 hours and treated with EGF for 1 and 6 hours. B. Inhibitor treatment. HeLa cells were serum-starved for 24 hours and treated with EGF for 6 hours in the presence or absence of protein kinase inhibitors: AG1470 (EGFR inhibitor), U0126 (MEK inhibitor) and Wortmannin (PI3K inhibitor). In addition, HeLa cells were transfected with a constitutively active form of Ras (RasV12). Effective pathway inhibition was verified by western blotting in parallel samples from the same experiment (see Additional file 8).
Figure 5
Figure 5
Significant enrichment in mRNAs down-regulated by EGF among experimentally validated targets of EGF-dependent miRNAs. Plot comparing the percentage of observed versus randomly expected mRNAs regulated by EGF that are known experimentally validated targets of the nine miRNAs shown to be regulated by EGF in this study. Actual % (gray): experimentally observed; Simulated % (black); result of random permutations; DW 6 h: down regulated mRNAs at 6 h; UP 6 h, up-regulated mRNAs at 6 h. Two randomization tests were performed: choosing as many (n = 8) miRNAs as those found regulated (random miRNAs) and looking at their targets, or choosing as many target mRNAs (n = 168) among the experimentally validated targets of the entire miRTarBase.
Figure 6
Figure 6
miRNA heterogeneity in EGF-treated HeLa cells. Distribution of the different types of sequences mapping to the indicated miRNA loci. The percentage of sequences annotated as the reference-miRNA (Ref), and as the different types of isomiRs is shown in control cells (dark gray) and EGF treated cells (light gray). IsomiRs consist in trimming variants at the 3’ end of the reference miRNA (3’-trim), trimming variants at the 5’-end of the reference miRNA (5’-trim), variants showing changes in the nucleotide composition with respect the reference miRNA (subst) and variants consisting in nucleotide additions to the 3’ end of the reference miRNA (3’-add). Data are presented as the mean and the standard deviation of three independent experiments per condition.
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