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Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis.

Llorens F, Hummel M, Pastor X, Ferrer A, Pluvinet R, Vivancos A, Castillo E, Iraola S, Mosquera AM, González E, Lozano J, Ingham M, Dohm JC, Noguera M, Kofler R, del Río JA, Bayés M, Himmelbauer H, Sumoy L - BMC Genomics (2011)

Bottom Line: In addition, we find an entirely new set of genes previously unrelated to the currently accepted EGF associated cellular functions.We propose that the use of global genomic cross-validation derived from high content technologies (microarrays or deep sequencing) can be used to generate more reliable datasets.This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Genomic Regulation (CRG)-Universitat Pompeu Fabra (UPF), Barcelona, Spain.

ABSTRACT

Background: Epidermal Growth Factor (EGF) is a key regulatory growth factor activating many processes relevant to normal development and disease, affecting cell proliferation and survival. Here we use a combined approach to study the EGF dependent transcriptome of HeLa cells by using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer.

Results: By applying a procedure for cross-platform data meta-analysis based on RankProd and GlobalAncova tests, we establish a well validated gene set with transcript levels altered after EGF treatment. We use this robust gene list to build higher order networks of gene interaction by interconnecting associated networks, supporting and extending the important role of the EGF signaling pathway in cancer. In addition, we find an entirely new set of genes previously unrelated to the currently accepted EGF associated cellular functions.

Conclusions: We propose that the use of global genomic cross-validation derived from high content technologies (microarrays or deep sequencing) can be used to generate more reliable datasets. This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data.

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Higher order network of interactions among EGF-regulated genes. Network of genomic interactions among EGF-regulated pathways (Holm-adjusted p-value < 0.01) as defined by GlobalAncova using KEGG database functional annotation. Nodes (pathways) that have at least two regulated genes (as defined by RankProd analysis) in common with other pathways are connected by continuous lines to these other pathways. The strength of each pathway interconnection (i.e. the number of shared regulated genes) is expressed by the width of the continuous lines connecting the two nodes. The node color indicates the interconnectivity of the nodes ranging from no connection to any other pathway (white) to many connections with other pathways (red). Numbers define KEGG categories as listed in Table 3.
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Figure 7: Higher order network of interactions among EGF-regulated genes. Network of genomic interactions among EGF-regulated pathways (Holm-adjusted p-value < 0.01) as defined by GlobalAncova using KEGG database functional annotation. Nodes (pathways) that have at least two regulated genes (as defined by RankProd analysis) in common with other pathways are connected by continuous lines to these other pathways. The strength of each pathway interconnection (i.e. the number of shared regulated genes) is expressed by the width of the continuous lines connecting the two nodes. The node color indicates the interconnectivity of the nodes ranging from no connection to any other pathway (white) to many connections with other pathways (red). Numbers define KEGG categories as listed in Table 3.

Mentions: We asked ourselves if this interconnectivity between networks would allow us to model a higher order network in these interactions. In order to measure pathway interconnectivity, the GlobalAncova method was applied on the classical pathways (as defined in the KEGG database). In this approach a global regulation score is computed for each pathway taking into account the expression values of all the genes belonging to it. Again, this analysis indicated that many of the regulated pathways are not independent since they share a large number of genes (Figure 7). As expected, many pathways related to cell growth and proliferation, cell death and cell cycle are represented. Many of the most significant pathways belonged to the signal transduction class and contained the hub proteins central to the networks found significant by IPA analysis. In addition, among disease related pathways, the top regulated ones were mostly related to cancer, being "Pathways in cancer" the top one with a total of 31 genes and 131 connections (Table 3; Additional file 13, Table S7 for full GlobalAncova analysis).


Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis.

Llorens F, Hummel M, Pastor X, Ferrer A, Pluvinet R, Vivancos A, Castillo E, Iraola S, Mosquera AM, González E, Lozano J, Ingham M, Dohm JC, Noguera M, Kofler R, del Río JA, Bayés M, Himmelbauer H, Sumoy L - BMC Genomics (2011)

Higher order network of interactions among EGF-regulated genes. Network of genomic interactions among EGF-regulated pathways (Holm-adjusted p-value < 0.01) as defined by GlobalAncova using KEGG database functional annotation. Nodes (pathways) that have at least two regulated genes (as defined by RankProd analysis) in common with other pathways are connected by continuous lines to these other pathways. The strength of each pathway interconnection (i.e. the number of shared regulated genes) is expressed by the width of the continuous lines connecting the two nodes. The node color indicates the interconnectivity of the nodes ranging from no connection to any other pathway (white) to many connections with other pathways (red). Numbers define KEGG categories as listed in Table 3.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3141672&req=5

Figure 7: Higher order network of interactions among EGF-regulated genes. Network of genomic interactions among EGF-regulated pathways (Holm-adjusted p-value < 0.01) as defined by GlobalAncova using KEGG database functional annotation. Nodes (pathways) that have at least two regulated genes (as defined by RankProd analysis) in common with other pathways are connected by continuous lines to these other pathways. The strength of each pathway interconnection (i.e. the number of shared regulated genes) is expressed by the width of the continuous lines connecting the two nodes. The node color indicates the interconnectivity of the nodes ranging from no connection to any other pathway (white) to many connections with other pathways (red). Numbers define KEGG categories as listed in Table 3.
Mentions: We asked ourselves if this interconnectivity between networks would allow us to model a higher order network in these interactions. In order to measure pathway interconnectivity, the GlobalAncova method was applied on the classical pathways (as defined in the KEGG database). In this approach a global regulation score is computed for each pathway taking into account the expression values of all the genes belonging to it. Again, this analysis indicated that many of the regulated pathways are not independent since they share a large number of genes (Figure 7). As expected, many pathways related to cell growth and proliferation, cell death and cell cycle are represented. Many of the most significant pathways belonged to the signal transduction class and contained the hub proteins central to the networks found significant by IPA analysis. In addition, among disease related pathways, the top regulated ones were mostly related to cancer, being "Pathways in cancer" the top one with a total of 31 genes and 131 connections (Table 3; Additional file 13, Table S7 for full GlobalAncova analysis).

Bottom Line: In addition, we find an entirely new set of genes previously unrelated to the currently accepted EGF associated cellular functions.We propose that the use of global genomic cross-validation derived from high content technologies (microarrays or deep sequencing) can be used to generate more reliable datasets.This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Genomic Regulation (CRG)-Universitat Pompeu Fabra (UPF), Barcelona, Spain.

ABSTRACT

Background: Epidermal Growth Factor (EGF) is a key regulatory growth factor activating many processes relevant to normal development and disease, affecting cell proliferation and survival. Here we use a combined approach to study the EGF dependent transcriptome of HeLa cells by using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer.

Results: By applying a procedure for cross-platform data meta-analysis based on RankProd and GlobalAncova tests, we establish a well validated gene set with transcript levels altered after EGF treatment. We use this robust gene list to build higher order networks of gene interaction by interconnecting associated networks, supporting and extending the important role of the EGF signaling pathway in cancer. In addition, we find an entirely new set of genes previously unrelated to the currently accepted EGF associated cellular functions.

Conclusions: We propose that the use of global genomic cross-validation derived from high content technologies (microarrays or deep sequencing) can be used to generate more reliable datasets. This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data.

Show MeSH
Related in: MedlinePlus