Limits...
Correlation of microRNA levels during hypoxia with predicted target mRNAs through genome-wide microarray analysis.

Guimbellot JS, Erickson SW, Mehta T, Wen H, Page GP, Sorscher EJ, Hong JS - BMC Med Genomics (2009)

Bottom Line: To date, few studies have investigated an environmental perturbation for effects on genome-wide miRNA levels, or their consequent influence on mRNA output.Target prediction programs and expression profiling techniques do not yet adequately represent the complexity of miRNA-mediated gene repression, and new methods may be required to better elucidate these pathways.Our data suggest the physiologic impact of miRNAs on cellular transcription results from a multifaceted network of miRNA and mRNA relationships, working together in an interconnected system and in context of hundreds of RNA species.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Genetics, University of Alabama at Birmingham, Birmingham, AL 35294, USA. jsguimbellot@uasom.uab.edu

ABSTRACT

Background: Low levels of oxygen in tissues, seen in situations such as chronic lung disease, necrotic tumors, and high altitude exposures, initiate a signaling pathway that results in active transcription of genes possessing a hypoxia response element (HRE). The aim of this study was to investigate whether a change in miRNA expression following hypoxia could account for changes in the cellular transcriptome based on currently available miRNA target prediction tools.

Methods: To identify changes induced by hypoxia, we conducted mRNA- and miRNA-array-based experiments in HT29 cells, and performed comparative analysis of the resulting data sets based on multiple target prediction algorithms. To date, few studies have investigated an environmental perturbation for effects on genome-wide miRNA levels, or their consequent influence on mRNA output.

Results: Comparison of miRNAs with predicted mRNA targets indicated a lower level of concordance than expected. We did, however, find preliminary evidence of combinatorial regulation of mRNA expression by miRNA.

Conclusion: Target prediction programs and expression profiling techniques do not yet adequately represent the complexity of miRNA-mediated gene repression, and new methods may be required to better elucidate these pathways. Our data suggest the physiologic impact of miRNAs on cellular transcription results from a multifaceted network of miRNA and mRNA relationships, working together in an interconnected system and in context of hundreds of RNA species. The methods described here for comparative analysis of cellular miRNA and mRNA will be useful for understanding genome wide regulatory responsiveness and refining miRNA predictive algorithms.

No MeSH data available.


Related in: MedlinePlus

Correlation of specific genes with predicted groups of miRNAs (miRanda/miRBase). Panel a shows histogram of the p-values for miRNA groups of all coding genes represented in the study for miRanda/miRBase. Frequency on y-axis refers to the number of genes involved. Panels b-d depict CFTR, NRBF2, and LARP1 as examples of mRNA regulation by gene-specific miRNA groups. The red dots indicate gene specific miRNAs for each given gene.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2667434&req=5

Figure 11: Correlation of specific genes with predicted groups of miRNAs (miRanda/miRBase). Panel a shows histogram of the p-values for miRNA groups of all coding genes represented in the study for miRanda/miRBase. Frequency on y-axis refers to the number of genes involved. Panels b-d depict CFTR, NRBF2, and LARP1 as examples of mRNA regulation by gene-specific miRNA groups. The red dots indicate gene specific miRNAs for each given gene.

Mentions: A representative sample of statistically significant gene-specific miRNA groups is shown in Figures 8b–d. We chose to analyze three genes; CFTR, KIAA2026, and C16orf73. Selection of CFTR was based on our laboratory interest in regulation of that particular gene product. The other two genes shown in the figure (whose functions are not known) were selected because of highly significant p-values, indicating very strong co-regulation by gene-specific miRNA groups. Scatterplots were identical by this method (black dots representing all miRNAs), indicating the group of miRNAs predicted to regulate expression of a particular gene of interest. Significant deviation from the line of equality (dotted line) indicates co-regulation of a cohort of miRNAs. The results presented here therefore provide some of the first evidence to suggest that miRNAs may be coordinately regulated in groups relevant to specific 3' UTRs. For example, Figure 8b shows predicted regulation of CFTR mRNA expression by CFTR specific miRNAs (red dots, 9 predicted by miRanda(microrna.org)). The regression line indicates considerable deviation, suggesting that coordinated regulation of CFTR mRNA may exist by these specific miRNAs. CFTR mRNA levels were among the most significantly decreased under hypoxia based on the mRNA array (Table 1). Figure 8 shows results for miRanda-predicted targets. Results for PicTar, TargetScanS, and miRanda(miRBase) are provided in the Figures 9, 10, 11. All four algorithms indicate gene-specific groups of miRNAs reacted coordinately to hypoxia, as shown by the histograms of p-values. However, different algorithms identified somewhat different gene-specific groups as being most strongly affected by hypoxia, as shown by the scatterplots (Figures 8b–d; and Figures 9, 10, 11).


Correlation of microRNA levels during hypoxia with predicted target mRNAs through genome-wide microarray analysis.

Guimbellot JS, Erickson SW, Mehta T, Wen H, Page GP, Sorscher EJ, Hong JS - BMC Med Genomics (2009)

Correlation of specific genes with predicted groups of miRNAs (miRanda/miRBase). Panel a shows histogram of the p-values for miRNA groups of all coding genes represented in the study for miRanda/miRBase. Frequency on y-axis refers to the number of genes involved. Panels b-d depict CFTR, NRBF2, and LARP1 as examples of mRNA regulation by gene-specific miRNA groups. The red dots indicate gene specific miRNAs for each given gene.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2667434&req=5

Figure 11: Correlation of specific genes with predicted groups of miRNAs (miRanda/miRBase). Panel a shows histogram of the p-values for miRNA groups of all coding genes represented in the study for miRanda/miRBase. Frequency on y-axis refers to the number of genes involved. Panels b-d depict CFTR, NRBF2, and LARP1 as examples of mRNA regulation by gene-specific miRNA groups. The red dots indicate gene specific miRNAs for each given gene.
Mentions: A representative sample of statistically significant gene-specific miRNA groups is shown in Figures 8b–d. We chose to analyze three genes; CFTR, KIAA2026, and C16orf73. Selection of CFTR was based on our laboratory interest in regulation of that particular gene product. The other two genes shown in the figure (whose functions are not known) were selected because of highly significant p-values, indicating very strong co-regulation by gene-specific miRNA groups. Scatterplots were identical by this method (black dots representing all miRNAs), indicating the group of miRNAs predicted to regulate expression of a particular gene of interest. Significant deviation from the line of equality (dotted line) indicates co-regulation of a cohort of miRNAs. The results presented here therefore provide some of the first evidence to suggest that miRNAs may be coordinately regulated in groups relevant to specific 3' UTRs. For example, Figure 8b shows predicted regulation of CFTR mRNA expression by CFTR specific miRNAs (red dots, 9 predicted by miRanda(microrna.org)). The regression line indicates considerable deviation, suggesting that coordinated regulation of CFTR mRNA may exist by these specific miRNAs. CFTR mRNA levels were among the most significantly decreased under hypoxia based on the mRNA array (Table 1). Figure 8 shows results for miRanda-predicted targets. Results for PicTar, TargetScanS, and miRanda(miRBase) are provided in the Figures 9, 10, 11. All four algorithms indicate gene-specific groups of miRNAs reacted coordinately to hypoxia, as shown by the histograms of p-values. However, different algorithms identified somewhat different gene-specific groups as being most strongly affected by hypoxia, as shown by the scatterplots (Figures 8b–d; and Figures 9, 10, 11).

Bottom Line: To date, few studies have investigated an environmental perturbation for effects on genome-wide miRNA levels, or their consequent influence on mRNA output.Target prediction programs and expression profiling techniques do not yet adequately represent the complexity of miRNA-mediated gene repression, and new methods may be required to better elucidate these pathways.Our data suggest the physiologic impact of miRNAs on cellular transcription results from a multifaceted network of miRNA and mRNA relationships, working together in an interconnected system and in context of hundreds of RNA species.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Genetics, University of Alabama at Birmingham, Birmingham, AL 35294, USA. jsguimbellot@uasom.uab.edu

ABSTRACT

Background: Low levels of oxygen in tissues, seen in situations such as chronic lung disease, necrotic tumors, and high altitude exposures, initiate a signaling pathway that results in active transcription of genes possessing a hypoxia response element (HRE). The aim of this study was to investigate whether a change in miRNA expression following hypoxia could account for changes in the cellular transcriptome based on currently available miRNA target prediction tools.

Methods: To identify changes induced by hypoxia, we conducted mRNA- and miRNA-array-based experiments in HT29 cells, and performed comparative analysis of the resulting data sets based on multiple target prediction algorithms. To date, few studies have investigated an environmental perturbation for effects on genome-wide miRNA levels, or their consequent influence on mRNA output.

Results: Comparison of miRNAs with predicted mRNA targets indicated a lower level of concordance than expected. We did, however, find preliminary evidence of combinatorial regulation of mRNA expression by miRNA.

Conclusion: Target prediction programs and expression profiling techniques do not yet adequately represent the complexity of miRNA-mediated gene repression, and new methods may be required to better elucidate these pathways. Our data suggest the physiologic impact of miRNAs on cellular transcription results from a multifaceted network of miRNA and mRNA relationships, working together in an interconnected system and in context of hundreds of RNA species. The methods described here for comparative analysis of cellular miRNA and mRNA will be useful for understanding genome wide regulatory responsiveness and refining miRNA predictive algorithms.

No MeSH data available.


Related in: MedlinePlus