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Global microRNA level regulation of EGFR-driven cell-cycle protein network in breast cancer.

Uhlmann S, Mannsperger H, Zhang JD, Horvat EÁ, Schmidt C, Küblbeck M, Henjes F, Ward A, Tschulena U, Zweig K, Korf U, Wiemann S, Sahin O - Mol. Syst. Biol. (2012)

Bottom Line: Here, we combined a large-scale miRNA screening approach with a high-throughput proteomic readout and network-based data analysis to identify which miRNAs are involved, and to uncover potential regulatory patterns.Our results indicated that the regulation of proteins by miRNAs is dominated by the nucleotide matching mechanism between seed sequences of the miRNAs and 3'-UTR of target genes.Furthermore, the novel network-analysis methodology we developed implied the existence of consistent intrinsic regulatory patterns where miRNAs simultaneously co-regulate several proteins acting in the same functional module.

View Article: PubMed Central - PubMed

Affiliation: Division of Molecular Genome Analysis, German Cancer Research Center, Heidelberg, Germany.

ABSTRACT
The EGFR-driven cell-cycle pathway has been extensively studied due to its pivotal role in breast cancer proliferation and pathogenesis. Although several studies reported regulation of individual pathway components by microRNAs (miRNAs), little is known about how miRNAs coordinate the EGFR protein network on a global miRNA (miRNome) level. Here, we combined a large-scale miRNA screening approach with a high-throughput proteomic readout and network-based data analysis to identify which miRNAs are involved, and to uncover potential regulatory patterns. Our results indicated that the regulation of proteins by miRNAs is dominated by the nucleotide matching mechanism between seed sequences of the miRNAs and 3'-UTR of target genes. Furthermore, the novel network-analysis methodology we developed implied the existence of consistent intrinsic regulatory patterns where miRNAs simultaneously co-regulate several proteins acting in the same functional module. Finally, our approach led us to identify and validate three miRNAs (miR-124, miR-147 and miR-193a-3p) as novel tumor suppressors that co-target EGFR-driven cell-cycle network proteins and inhibit cell-cycle progression and proliferation in breast cancer.

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Whole-genome miRNA regulation of the EGFR/cell-cycle protein network. (A) Histogram of the effects of the whole-genome set of miRNAs on the given protein PLCG1 (for the histogram of the other proteins measured in this study, see Supplementary Figure S4). While the x axis demonstrates normalized z-scores of the expression change of PLCG1 upon miRNA transfections, y axis shows the frequency (count) of miRNAs. (B) Edge numbers for different z-score thresholds. With increasing stringencies of z-score, number of edges decreases rapidly. Two commonly used significance thresholds, P<0.05 and P<0.001 (equivalent to ∣z∣>1.96 and ∣z∣>3.29, respectively), were shown with red dots on the curve. (C) Dense miRNA–protein network at the absolute z-score threshold of 1.96. Blue circles represent the proteins and black circles indicate the miRNAs. While green edges between an miRNA and a protein show downregulation of that protein by miRNA, red edges show the upregulation of the protein by the given miRNA. miRNAs that regulate more than one protein are located on the circle and those miRNAs that regulate only one network protein are located outside of the circle. (D) miRNA regulation of the EGFR network at a more stringent threshold (∣z∣>3.29). The resulting network is much less dense compared with the graph shown in (C).
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f2: Whole-genome miRNA regulation of the EGFR/cell-cycle protein network. (A) Histogram of the effects of the whole-genome set of miRNAs on the given protein PLCG1 (for the histogram of the other proteins measured in this study, see Supplementary Figure S4). While the x axis demonstrates normalized z-scores of the expression change of PLCG1 upon miRNA transfections, y axis shows the frequency (count) of miRNAs. (B) Edge numbers for different z-score thresholds. With increasing stringencies of z-score, number of edges decreases rapidly. Two commonly used significance thresholds, P<0.05 and P<0.001 (equivalent to ∣z∣>1.96 and ∣z∣>3.29, respectively), were shown with red dots on the curve. (C) Dense miRNA–protein network at the absolute z-score threshold of 1.96. Blue circles represent the proteins and black circles indicate the miRNAs. While green edges between an miRNA and a protein show downregulation of that protein by miRNA, red edges show the upregulation of the protein by the given miRNA. miRNAs that regulate more than one protein are located on the circle and those miRNAs that regulate only one network protein are located outside of the circle. (D) miRNA regulation of the EGFR network at a more stringent threshold (∣z∣>3.29). The resulting network is much less dense compared with the graph shown in (C).

Mentions: To build an miRNome–protein interaction network, we first identified miRNA–protein pairs where the regulation was statistically significant. For each protein, we used the z-score method to quantify the effects of all miRNAs on its expression (see Materials and methods). While a positive z-score suggests upregulation of protein expression, a negative value indicated a downregulation upon miRNA expression. For most proteins analyzed, the global effect of miRNAs followed a normal distribution with relatively short tails. This pattern suggests that the effects of miRNAs are rather mild, thereby fine-tuning the protein expression (in Figure 2A, PLCG1 is shown as an example. Histograms for all other proteins are given in Supplementary Figure S4).


Global microRNA level regulation of EGFR-driven cell-cycle protein network in breast cancer.

Uhlmann S, Mannsperger H, Zhang JD, Horvat EÁ, Schmidt C, Küblbeck M, Henjes F, Ward A, Tschulena U, Zweig K, Korf U, Wiemann S, Sahin O - Mol. Syst. Biol. (2012)

Whole-genome miRNA regulation of the EGFR/cell-cycle protein network. (A) Histogram of the effects of the whole-genome set of miRNAs on the given protein PLCG1 (for the histogram of the other proteins measured in this study, see Supplementary Figure S4). While the x axis demonstrates normalized z-scores of the expression change of PLCG1 upon miRNA transfections, y axis shows the frequency (count) of miRNAs. (B) Edge numbers for different z-score thresholds. With increasing stringencies of z-score, number of edges decreases rapidly. Two commonly used significance thresholds, P<0.05 and P<0.001 (equivalent to ∣z∣>1.96 and ∣z∣>3.29, respectively), were shown with red dots on the curve. (C) Dense miRNA–protein network at the absolute z-score threshold of 1.96. Blue circles represent the proteins and black circles indicate the miRNAs. While green edges between an miRNA and a protein show downregulation of that protein by miRNA, red edges show the upregulation of the protein by the given miRNA. miRNAs that regulate more than one protein are located on the circle and those miRNAs that regulate only one network protein are located outside of the circle. (D) miRNA regulation of the EGFR network at a more stringent threshold (∣z∣>3.29). The resulting network is much less dense compared with the graph shown in (C).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3293631&req=5

f2: Whole-genome miRNA regulation of the EGFR/cell-cycle protein network. (A) Histogram of the effects of the whole-genome set of miRNAs on the given protein PLCG1 (for the histogram of the other proteins measured in this study, see Supplementary Figure S4). While the x axis demonstrates normalized z-scores of the expression change of PLCG1 upon miRNA transfections, y axis shows the frequency (count) of miRNAs. (B) Edge numbers for different z-score thresholds. With increasing stringencies of z-score, number of edges decreases rapidly. Two commonly used significance thresholds, P<0.05 and P<0.001 (equivalent to ∣z∣>1.96 and ∣z∣>3.29, respectively), were shown with red dots on the curve. (C) Dense miRNA–protein network at the absolute z-score threshold of 1.96. Blue circles represent the proteins and black circles indicate the miRNAs. While green edges between an miRNA and a protein show downregulation of that protein by miRNA, red edges show the upregulation of the protein by the given miRNA. miRNAs that regulate more than one protein are located on the circle and those miRNAs that regulate only one network protein are located outside of the circle. (D) miRNA regulation of the EGFR network at a more stringent threshold (∣z∣>3.29). The resulting network is much less dense compared with the graph shown in (C).
Mentions: To build an miRNome–protein interaction network, we first identified miRNA–protein pairs where the regulation was statistically significant. For each protein, we used the z-score method to quantify the effects of all miRNAs on its expression (see Materials and methods). While a positive z-score suggests upregulation of protein expression, a negative value indicated a downregulation upon miRNA expression. For most proteins analyzed, the global effect of miRNAs followed a normal distribution with relatively short tails. This pattern suggests that the effects of miRNAs are rather mild, thereby fine-tuning the protein expression (in Figure 2A, PLCG1 is shown as an example. Histograms for all other proteins are given in Supplementary Figure S4).

Bottom Line: Here, we combined a large-scale miRNA screening approach with a high-throughput proteomic readout and network-based data analysis to identify which miRNAs are involved, and to uncover potential regulatory patterns.Our results indicated that the regulation of proteins by miRNAs is dominated by the nucleotide matching mechanism between seed sequences of the miRNAs and 3'-UTR of target genes.Furthermore, the novel network-analysis methodology we developed implied the existence of consistent intrinsic regulatory patterns where miRNAs simultaneously co-regulate several proteins acting in the same functional module.

View Article: PubMed Central - PubMed

Affiliation: Division of Molecular Genome Analysis, German Cancer Research Center, Heidelberg, Germany.

ABSTRACT
The EGFR-driven cell-cycle pathway has been extensively studied due to its pivotal role in breast cancer proliferation and pathogenesis. Although several studies reported regulation of individual pathway components by microRNAs (miRNAs), little is known about how miRNAs coordinate the EGFR protein network on a global miRNA (miRNome) level. Here, we combined a large-scale miRNA screening approach with a high-throughput proteomic readout and network-based data analysis to identify which miRNAs are involved, and to uncover potential regulatory patterns. Our results indicated that the regulation of proteins by miRNAs is dominated by the nucleotide matching mechanism between seed sequences of the miRNAs and 3'-UTR of target genes. Furthermore, the novel network-analysis methodology we developed implied the existence of consistent intrinsic regulatory patterns where miRNAs simultaneously co-regulate several proteins acting in the same functional module. Finally, our approach led us to identify and validate three miRNAs (miR-124, miR-147 and miR-193a-3p) as novel tumor suppressors that co-target EGFR-driven cell-cycle network proteins and inhibit cell-cycle progression and proliferation in breast cancer.

Show MeSH
Related in: MedlinePlus