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Integrated analysis of microRNA and mRNA expression and association with HIF binding reveals the complexity of microRNA expression regulation under hypoxia.

Camps C, Saini HK, Mole DR, Choudhry H, Reczko M, Guerra-Assunção JA, Tian YM, Buffa FM, Harris AL, Hatzigeorgiou AG, Enright AJ, Ragoussis J - Mol. Cancer (2014)

Bottom Line: SiRNA against HIF-1α and HIF-2α were performed as previously published.Moreover the expression of hsa-miR-27a-3p and hsa-miR-24-3p was found positively associated to a hypoxia gene signature in breast cancer.Gene expression analysis showed no full coordination between pri-miRNA and microRNA expression, pointing towards additional levels of regulation.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford, United Kingdom. ccamps@well.ox.ac.uk.

ABSTRACT

Background: In mammalians, HIF is a master regulator of hypoxia gene expression through direct binding to DNA, while its role in microRNA expression regulation, critical in the hypoxia response, is not elucidated genome wide. Our aim is to investigate in depth the regulation of microRNA expression by hypoxia in the breast cancer cell line MCF-7, establish the relationship between microRNA expression and HIF binding sites, pri-miRNA transcription and microRNA processing gene expression.

Methods: MCF-7 cells were incubated at 1% Oxygen for 16, 32 and 48 h. SiRNA against HIF-1α and HIF-2α were performed as previously published. MicroRNA and mRNA expression were assessed using microRNA microarrays, small RNA sequencing, gene expression microarrays and Real time PCR. The Kraken pipeline was applied for microRNA-seq analysis along with Bioconductor packages. Microarray data was analysed using Limma (Bioconductor), ChIP-seq data were analysed using Gene Set Enrichment Analysis and multiple testing correction applied in all analyses.

Results: Hypoxia time course microRNA sequencing data analysis identified 41 microRNAs significantly up- and 28 down-regulated, including hsa-miR-4521, hsa-miR-145-3p and hsa-miR-222-5p reported in conjunction with hypoxia for the first time. Integration of HIF-1α and HIF-2α ChIP-seq data with expression data showed overall association between binding sites and microRNA up-regulation, with hsa-miR-210-3p and microRNAs of miR-27a/23a/24-2 and miR-30b/30d clusters as predominant examples. Moreover the expression of hsa-miR-27a-3p and hsa-miR-24-3p was found positively associated to a hypoxia gene signature in breast cancer. Gene expression analysis showed no full coordination between pri-miRNA and microRNA expression, pointing towards additional levels of regulation. Several transcripts involved in microRNA processing were found regulated by hypoxia, of which DICER (down-regulated) and AGO4 (up-regulated) were HIF dependent. DICER expression was found inversely correlated to hypoxia in breast cancer.

Conclusions: Integrated analysis of microRNA, mRNA and ChIP-seq data in a model cell line supports the hypothesis that microRNA expression under hypoxia is regulated at transcriptional and post-transcriptional level, with the presence of HIF binding sites at microRNA genomic loci associated with up-regulation. The identification of hypoxia and HIF regulated microRNAs relevant for breast cancer is important for our understanding of disease development and design of therapeutic interventions.

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Low correlation between microRNA and corresponding host gene expression. Expression of genes hosting microRNAs was obtained from microarray data and expression of corresponding microRNAs was obtained from microRNA-seq data. The hypoxic regulation at 32 h and 48 h for both groups was then compared from two different perspectives. First, hosting genes were sorted in 3 groups at each time point: significantly down-regulated (down), not significantly regulated (unv) and significantly up-regulated (up). The fold-change distribution for each group of genes at 32 h and 48 h of hypoxia compared to normoxia is shown in boxplots (A). For each group of genes, the fold-change distribution of corresponding microRNAs at 32 h and 48 h compared to normoxia is also shown in boxplots for comparison (B). Second, microRNAs hosted within genes were sorted in 3 groups according to their hypoxic regulation at 32 h and 48 h: significantly down-regulated (down), not significantly regulated (unv) and significantly up-regulated (up). The fold-change distribution for each group of microRNAs at 32 h and 48 h of hypoxia compared to normoxia is shown in boxplots (C). For each group of microRNAs, the fold-change distribution of corresponding host genes at 32 h and 48 h compared to normoxia is also shown in boxplots for comparison (D). All fold-change distributions are shown in linear scale.
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Figure 4: Low correlation between microRNA and corresponding host gene expression. Expression of genes hosting microRNAs was obtained from microarray data and expression of corresponding microRNAs was obtained from microRNA-seq data. The hypoxic regulation at 32 h and 48 h for both groups was then compared from two different perspectives. First, hosting genes were sorted in 3 groups at each time point: significantly down-regulated (down), not significantly regulated (unv) and significantly up-regulated (up). The fold-change distribution for each group of genes at 32 h and 48 h of hypoxia compared to normoxia is shown in boxplots (A). For each group of genes, the fold-change distribution of corresponding microRNAs at 32 h and 48 h compared to normoxia is also shown in boxplots for comparison (B). Second, microRNAs hosted within genes were sorted in 3 groups according to their hypoxic regulation at 32 h and 48 h: significantly down-regulated (down), not significantly regulated (unv) and significantly up-regulated (up). The fold-change distribution for each group of microRNAs at 32 h and 48 h of hypoxia compared to normoxia is shown in boxplots (C). For each group of microRNAs, the fold-change distribution of corresponding host genes at 32 h and 48 h compared to normoxia is also shown in boxplots for comparison (D). All fold-change distributions are shown in linear scale.

Mentions: Some of the microRNAs found up- or down-regulated under hypoxia are located within intronic regions of protein coding genes. Therefore, we generated mRNA expression profiles of the same time course samples using Illumina arrays. If only considering microRNAs with a unique genomic position, we found 185 microRNAs encoded within 120 host genes that were detected in our microarray experiment (Additional file 1: Table S5). No correlation was found between microRNA expression and host gene expression at any hypoxia time point (Spearman rank correlation: 0.137 (P.val = 0.0613) at 16 h; -0.0949 (P.val = 0.1961) at 32 h; -0.0127 (P.val = 0.8621) at 48 h). Looking at the expression of genes containing microRNAs, we observed that they do not show a lot of variation. When dividing them in 3 groups according to whether they were significantly up-regulated, significantly down-regulated or without changes in hypoxia, the average fold-change of the up- or down-regulated groups is not very different to the group without changes (Figure 4A), suggesting that these genes are very lightly regulated by hypoxia at transcriptional level. For each of the group of genes, we represented boxplots with the expression of the microRNAs they contained (Figure 4B), showing the lack of correlation between the average fold-change of the microRNAs and the average fold-change of the corresponding host genes. Similarly from the microRNA point of view, we divided the microRNAs located within a host gene in three groups depending on their fold-change expression at 32 h and 48 h of hypoxia (up-regulated, down-regulated or no change, Figure 4C). Then for each group of microRNAs, we represented the fold-changes of the corresponding host genes (Figure 4D). While up-regulated and down-regulated microRNAs showed an average fold-change of 2 and 0.5 respectively (Figure 4C), the average fold-change of the corresponding host genes was very similar and close to 1 for both groups (Figure 4D). The picture is the same when Agilent microarray data are used to determine microRNA expression fold changes instead of small RNA-seq (Additional file 3: Figure S2). Detailed examples of these relationships can be seen in Additional file 1: Table S6. In general we observed around a 50% agreement between microRNA and host gene expression. In order to gain more information, we focused on cases where there is no correlation between microRNA and host gene expression and performed additional validations. The first example is hsa-miR-942-5p, found significantly up-regulated at 32 and 48 h, whereas its hosting gene TTF2 was significantly down-regulated in array data (Additional file 1: Tables S5 and S6). Indeed TTF2 was found significantly down-regulated in hypoxia by qPCR (adj.p-val ≤ 0.05, data not shown). Also hsa-miR-3140-3p is up-regulated in sequencing data whereas the host gene FBXW7 is down-regulated in array data (Additional file 1: Tables S5 and S6). FBXW7 has four isoforms and only two would contain MIR3140 within its sequence (Figure 5A). We designed a pair of primers (Figure 5A, F1 and R1) that would amplify three isoforms, including two that do not include the MIR3140 locus and a pair of primers specific for the two isoforms containing MIR3140 (Figure 5A, F2 and R2). All products were found significantly down-regulated in hypoxia by qPCR (Figure 5A, adj.p-val ≤ 0.05).


Integrated analysis of microRNA and mRNA expression and association with HIF binding reveals the complexity of microRNA expression regulation under hypoxia.

Camps C, Saini HK, Mole DR, Choudhry H, Reczko M, Guerra-Assunção JA, Tian YM, Buffa FM, Harris AL, Hatzigeorgiou AG, Enright AJ, Ragoussis J - Mol. Cancer (2014)

Low correlation between microRNA and corresponding host gene expression. Expression of genes hosting microRNAs was obtained from microarray data and expression of corresponding microRNAs was obtained from microRNA-seq data. The hypoxic regulation at 32 h and 48 h for both groups was then compared from two different perspectives. First, hosting genes were sorted in 3 groups at each time point: significantly down-regulated (down), not significantly regulated (unv) and significantly up-regulated (up). The fold-change distribution for each group of genes at 32 h and 48 h of hypoxia compared to normoxia is shown in boxplots (A). For each group of genes, the fold-change distribution of corresponding microRNAs at 32 h and 48 h compared to normoxia is also shown in boxplots for comparison (B). Second, microRNAs hosted within genes were sorted in 3 groups according to their hypoxic regulation at 32 h and 48 h: significantly down-regulated (down), not significantly regulated (unv) and significantly up-regulated (up). The fold-change distribution for each group of microRNAs at 32 h and 48 h of hypoxia compared to normoxia is shown in boxplots (C). For each group of microRNAs, the fold-change distribution of corresponding host genes at 32 h and 48 h compared to normoxia is also shown in boxplots for comparison (D). All fold-change distributions are shown in linear scale.
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Figure 4: Low correlation between microRNA and corresponding host gene expression. Expression of genes hosting microRNAs was obtained from microarray data and expression of corresponding microRNAs was obtained from microRNA-seq data. The hypoxic regulation at 32 h and 48 h for both groups was then compared from two different perspectives. First, hosting genes were sorted in 3 groups at each time point: significantly down-regulated (down), not significantly regulated (unv) and significantly up-regulated (up). The fold-change distribution for each group of genes at 32 h and 48 h of hypoxia compared to normoxia is shown in boxplots (A). For each group of genes, the fold-change distribution of corresponding microRNAs at 32 h and 48 h compared to normoxia is also shown in boxplots for comparison (B). Second, microRNAs hosted within genes were sorted in 3 groups according to their hypoxic regulation at 32 h and 48 h: significantly down-regulated (down), not significantly regulated (unv) and significantly up-regulated (up). The fold-change distribution for each group of microRNAs at 32 h and 48 h of hypoxia compared to normoxia is shown in boxplots (C). For each group of microRNAs, the fold-change distribution of corresponding host genes at 32 h and 48 h compared to normoxia is also shown in boxplots for comparison (D). All fold-change distributions are shown in linear scale.
Mentions: Some of the microRNAs found up- or down-regulated under hypoxia are located within intronic regions of protein coding genes. Therefore, we generated mRNA expression profiles of the same time course samples using Illumina arrays. If only considering microRNAs with a unique genomic position, we found 185 microRNAs encoded within 120 host genes that were detected in our microarray experiment (Additional file 1: Table S5). No correlation was found between microRNA expression and host gene expression at any hypoxia time point (Spearman rank correlation: 0.137 (P.val = 0.0613) at 16 h; -0.0949 (P.val = 0.1961) at 32 h; -0.0127 (P.val = 0.8621) at 48 h). Looking at the expression of genes containing microRNAs, we observed that they do not show a lot of variation. When dividing them in 3 groups according to whether they were significantly up-regulated, significantly down-regulated or without changes in hypoxia, the average fold-change of the up- or down-regulated groups is not very different to the group without changes (Figure 4A), suggesting that these genes are very lightly regulated by hypoxia at transcriptional level. For each of the group of genes, we represented boxplots with the expression of the microRNAs they contained (Figure 4B), showing the lack of correlation between the average fold-change of the microRNAs and the average fold-change of the corresponding host genes. Similarly from the microRNA point of view, we divided the microRNAs located within a host gene in three groups depending on their fold-change expression at 32 h and 48 h of hypoxia (up-regulated, down-regulated or no change, Figure 4C). Then for each group of microRNAs, we represented the fold-changes of the corresponding host genes (Figure 4D). While up-regulated and down-regulated microRNAs showed an average fold-change of 2 and 0.5 respectively (Figure 4C), the average fold-change of the corresponding host genes was very similar and close to 1 for both groups (Figure 4D). The picture is the same when Agilent microarray data are used to determine microRNA expression fold changes instead of small RNA-seq (Additional file 3: Figure S2). Detailed examples of these relationships can be seen in Additional file 1: Table S6. In general we observed around a 50% agreement between microRNA and host gene expression. In order to gain more information, we focused on cases where there is no correlation between microRNA and host gene expression and performed additional validations. The first example is hsa-miR-942-5p, found significantly up-regulated at 32 and 48 h, whereas its hosting gene TTF2 was significantly down-regulated in array data (Additional file 1: Tables S5 and S6). Indeed TTF2 was found significantly down-regulated in hypoxia by qPCR (adj.p-val ≤ 0.05, data not shown). Also hsa-miR-3140-3p is up-regulated in sequencing data whereas the host gene FBXW7 is down-regulated in array data (Additional file 1: Tables S5 and S6). FBXW7 has four isoforms and only two would contain MIR3140 within its sequence (Figure 5A). We designed a pair of primers (Figure 5A, F1 and R1) that would amplify three isoforms, including two that do not include the MIR3140 locus and a pair of primers specific for the two isoforms containing MIR3140 (Figure 5A, F2 and R2). All products were found significantly down-regulated in hypoxia by qPCR (Figure 5A, adj.p-val ≤ 0.05).

Bottom Line: SiRNA against HIF-1α and HIF-2α were performed as previously published.Moreover the expression of hsa-miR-27a-3p and hsa-miR-24-3p was found positively associated to a hypoxia gene signature in breast cancer.Gene expression analysis showed no full coordination between pri-miRNA and microRNA expression, pointing towards additional levels of regulation.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford, United Kingdom. ccamps@well.ox.ac.uk.

ABSTRACT

Background: In mammalians, HIF is a master regulator of hypoxia gene expression through direct binding to DNA, while its role in microRNA expression regulation, critical in the hypoxia response, is not elucidated genome wide. Our aim is to investigate in depth the regulation of microRNA expression by hypoxia in the breast cancer cell line MCF-7, establish the relationship between microRNA expression and HIF binding sites, pri-miRNA transcription and microRNA processing gene expression.

Methods: MCF-7 cells were incubated at 1% Oxygen for 16, 32 and 48 h. SiRNA against HIF-1α and HIF-2α were performed as previously published. MicroRNA and mRNA expression were assessed using microRNA microarrays, small RNA sequencing, gene expression microarrays and Real time PCR. The Kraken pipeline was applied for microRNA-seq analysis along with Bioconductor packages. Microarray data was analysed using Limma (Bioconductor), ChIP-seq data were analysed using Gene Set Enrichment Analysis and multiple testing correction applied in all analyses.

Results: Hypoxia time course microRNA sequencing data analysis identified 41 microRNAs significantly up- and 28 down-regulated, including hsa-miR-4521, hsa-miR-145-3p and hsa-miR-222-5p reported in conjunction with hypoxia for the first time. Integration of HIF-1α and HIF-2α ChIP-seq data with expression data showed overall association between binding sites and microRNA up-regulation, with hsa-miR-210-3p and microRNAs of miR-27a/23a/24-2 and miR-30b/30d clusters as predominant examples. Moreover the expression of hsa-miR-27a-3p and hsa-miR-24-3p was found positively associated to a hypoxia gene signature in breast cancer. Gene expression analysis showed no full coordination between pri-miRNA and microRNA expression, pointing towards additional levels of regulation. Several transcripts involved in microRNA processing were found regulated by hypoxia, of which DICER (down-regulated) and AGO4 (up-regulated) were HIF dependent. DICER expression was found inversely correlated to hypoxia in breast cancer.

Conclusions: Integrated analysis of microRNA, mRNA and ChIP-seq data in a model cell line supports the hypothesis that microRNA expression under hypoxia is regulated at transcriptional and post-transcriptional level, with the presence of HIF binding sites at microRNA genomic loci associated with up-regulation. The identification of hypoxia and HIF regulated microRNAs relevant for breast cancer is important for our understanding of disease development and design of therapeutic interventions.

Show MeSH
Related in: MedlinePlus