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How microRNA and transcription factor co-regulatory networks affect osteosarcoma cell proliferation.

Poos K, Smida J, Nathrath M, Maugg D, Baumhoer D, Korsching E - PLoS Comput. Biol. (2013)

Bottom Line: These alterations affect the expression and function of several genes due to drastic changes in the underlying gene regulatory network.We identified key co-regulators comprising the microRNAs miR-9-5p, miR-138, and miR-214 and the TFs SP1 and MYC in the derived networks.This study illustrates the benefit of systems biological approaches in the analysis of complex diseases.

View Article: PubMed Central - PubMed

Affiliation: Institute of Bioinformatics, University of Münster, Münster, Germany.

ABSTRACT
Osteosarcomas (OS) are complex bone tumors with various genomic alterations. These alterations affect the expression and function of several genes due to drastic changes in the underlying gene regulatory network. However, we know little about critical gene regulators and their functional consequences on the pathogenesis of OS. Therefore, we aimed to determine microRNA and transcription factor (TF) co-regulatory networks in OS cell proliferation. Cell proliferation is an essential part in the pathogenesis of OS and deeper understanding of its regulation might help to identify potential therapeutic targets. Based on expression data of OS cell lines divided according to their proliferative activity, we obtained 12 proliferation-related microRNAs and corresponding target genes. Therewith, microRNA and TF co-regulatory networks were generated and analyzed regarding their structure and functional influence. We identified key co-regulators comprising the microRNAs miR-9-5p, miR-138, and miR-214 and the TFs SP1 and MYC in the derived networks. These regulators are implicated in NFKB- and RB1-signaling and focal adhesion processes based on their common or interacting target genes (e.g., CDK6, CTNNB1, E2F4, HES1, ITGA6, NFKB1, NOTCH1, and SIN3A). Thus, we proposed a model of OS cell proliferation which is primarily co-regulated through the interactions of the mentioned microRNA and TF combinations. This study illustrates the benefit of systems biological approaches in the analysis of complex diseases. We integrated experimental data with publicly available information to unravel the coordinated (post)-transcriptional control of microRNAs and TFs to identify potential therapeutic targets in OS. The resulting microRNA and TF co-regulatory networks are publicly available for further exploration to generate or evaluate own hypotheses of the pathogenesis of OS (http://www.complex-systems.uni-muenster.de/co_networks.html).

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Clustering of differentially expressed microRNAs.The heatmap illustrates the expression profiles of DE microRNAs (log2 FC≥/1/ & FDR<0.05, y-axis) among all OS cell samples (x-axis). High (dark blue) and low (light blue) proliferative OS samples are clearly separated. The red/green color code corresponds to the expression deviation from the average expression among all samples. Complete-linkage clustering was performed with the Pearson correlation as distance metric.
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pcbi-1003210-g002: Clustering of differentially expressed microRNAs.The heatmap illustrates the expression profiles of DE microRNAs (log2 FC≥/1/ & FDR<0.05, y-axis) among all OS cell samples (x-axis). High (dark blue) and low (light blue) proliferative OS samples are clearly separated. The red/green color code corresponds to the expression deviation from the average expression among all samples. Complete-linkage clustering was performed with the Pearson correlation as distance metric.

Mentions: In order to investigate the deregulated microRNA and TF co-regulatory networks of proliferative OS cells, we used seven authenticated OS cell lines. The cell lines were divided according to their proliferative activity by performing a proliferation assay. Four OS cell lines exhibited a high proliferative activity with a doubling time <10 hours while three showed less proliferation (Table 1). MNNG/HOS, U2-OS, and SJSA-1 showed additional extensive migratory capabilities. The expression analysis of the microRNAs was based on these two proliferation groups. The analysis yielded nine down-regulated and eight up-regulated microRNAs that passed the differential expression criteria (False discovery rate (FDR) <0.05 & log2 Fold change (FC)≥/1/, Table 2). The derived DE microRNAs have been reported in association with neoplastic disease either due to oncogenic or tumor suppressor properties. Hierarchical clustering of them clearly separated the OS cell samples according to their proliferative activity (Figure 2). Hence, we selected the DE microRNAs as candidates that might affect OS cell proliferation for further analysis.


How microRNA and transcription factor co-regulatory networks affect osteosarcoma cell proliferation.

Poos K, Smida J, Nathrath M, Maugg D, Baumhoer D, Korsching E - PLoS Comput. Biol. (2013)

Clustering of differentially expressed microRNAs.The heatmap illustrates the expression profiles of DE microRNAs (log2 FC≥/1/ & FDR<0.05, y-axis) among all OS cell samples (x-axis). High (dark blue) and low (light blue) proliferative OS samples are clearly separated. The red/green color code corresponds to the expression deviation from the average expression among all samples. Complete-linkage clustering was performed with the Pearson correlation as distance metric.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3757060&req=5

pcbi-1003210-g002: Clustering of differentially expressed microRNAs.The heatmap illustrates the expression profiles of DE microRNAs (log2 FC≥/1/ & FDR<0.05, y-axis) among all OS cell samples (x-axis). High (dark blue) and low (light blue) proliferative OS samples are clearly separated. The red/green color code corresponds to the expression deviation from the average expression among all samples. Complete-linkage clustering was performed with the Pearson correlation as distance metric.
Mentions: In order to investigate the deregulated microRNA and TF co-regulatory networks of proliferative OS cells, we used seven authenticated OS cell lines. The cell lines were divided according to their proliferative activity by performing a proliferation assay. Four OS cell lines exhibited a high proliferative activity with a doubling time <10 hours while three showed less proliferation (Table 1). MNNG/HOS, U2-OS, and SJSA-1 showed additional extensive migratory capabilities. The expression analysis of the microRNAs was based on these two proliferation groups. The analysis yielded nine down-regulated and eight up-regulated microRNAs that passed the differential expression criteria (False discovery rate (FDR) <0.05 & log2 Fold change (FC)≥/1/, Table 2). The derived DE microRNAs have been reported in association with neoplastic disease either due to oncogenic or tumor suppressor properties. Hierarchical clustering of them clearly separated the OS cell samples according to their proliferative activity (Figure 2). Hence, we selected the DE microRNAs as candidates that might affect OS cell proliferation for further analysis.

Bottom Line: These alterations affect the expression and function of several genes due to drastic changes in the underlying gene regulatory network.We identified key co-regulators comprising the microRNAs miR-9-5p, miR-138, and miR-214 and the TFs SP1 and MYC in the derived networks.This study illustrates the benefit of systems biological approaches in the analysis of complex diseases.

View Article: PubMed Central - PubMed

Affiliation: Institute of Bioinformatics, University of Münster, Münster, Germany.

ABSTRACT
Osteosarcomas (OS) are complex bone tumors with various genomic alterations. These alterations affect the expression and function of several genes due to drastic changes in the underlying gene regulatory network. However, we know little about critical gene regulators and their functional consequences on the pathogenesis of OS. Therefore, we aimed to determine microRNA and transcription factor (TF) co-regulatory networks in OS cell proliferation. Cell proliferation is an essential part in the pathogenesis of OS and deeper understanding of its regulation might help to identify potential therapeutic targets. Based on expression data of OS cell lines divided according to their proliferative activity, we obtained 12 proliferation-related microRNAs and corresponding target genes. Therewith, microRNA and TF co-regulatory networks were generated and analyzed regarding their structure and functional influence. We identified key co-regulators comprising the microRNAs miR-9-5p, miR-138, and miR-214 and the TFs SP1 and MYC in the derived networks. These regulators are implicated in NFKB- and RB1-signaling and focal adhesion processes based on their common or interacting target genes (e.g., CDK6, CTNNB1, E2F4, HES1, ITGA6, NFKB1, NOTCH1, and SIN3A). Thus, we proposed a model of OS cell proliferation which is primarily co-regulated through the interactions of the mentioned microRNA and TF combinations. This study illustrates the benefit of systems biological approaches in the analysis of complex diseases. We integrated experimental data with publicly available information to unravel the coordinated (post)-transcriptional control of microRNAs and TFs to identify potential therapeutic targets in OS. The resulting microRNA and TF co-regulatory networks are publicly available for further exploration to generate or evaluate own hypotheses of the pathogenesis of OS (http://www.complex-systems.uni-muenster.de/co_networks.html).

Show MeSH
Related in: MedlinePlus