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Network based meta-analysis prediction of microenvironmental relays involved in stemness of human embryonic stem cells.

Mournetas V, Nunes QM, Murray PA, Sanderson CM, Fernig DG - PeerJ (2014)

Bottom Line: The STRING database was utilised to construct a protein-protein interaction network focused on extracellular and transcription factor components contained within the assembled transcriptome.Conclusion.We hypothesise that this list of proteins, either connecting extracellular components with transcriptional networks, or with hub or bottleneck properties, may contain proteins likely to be involved in determining stemness.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Cellular and Molecular Physiology, Institute of Translational Medicine, University of Liverpool , Liverpool , United Kingdom ; Department of Biochemistry, Institute of Integrative Biology, University of Liverpool , Liverpool , United Kingdom.

ABSTRACT
Background. Human embryonic stem cells (hESCs) are pluripotent cells derived from the inner cell mass of in vitro fertilised blastocysts, which can either be maintained in an undifferentiated state or committed into lineages under determined culture conditions. These cells offer great potential for regenerative medicine, but at present, little is known about the mechanisms that regulate hESC stemness; in particular, the role of cell-cell and cell-extracellular matrix interactions remain relatively unexplored. Methods and Results. In this study we have performed an in silico analysis of cell-microenvironment interactions to identify novel proteins that may be responsible for the maintenance of hESC stemness. A hESC transcriptome of 8,934 mRNAs was assembled using a meta-analysis approach combining the analysis of microarrays and the use of databases for annotation. The STRING database was utilised to construct a protein-protein interaction network focused on extracellular and transcription factor components contained within the assembled transcriptome. This interactome was structurally studied and filtered to identify a short list of 92 candidate proteins, which may regulate hESC stemness. Conclusion. We hypothesise that this list of proteins, either connecting extracellular components with transcriptional networks, or with hub or bottleneck properties, may contain proteins likely to be involved in determining stemness.

No MeSH data available.


Related in: MedlinePlus

Flow chart of the microarray dataset analysis.This flow chart describes the microarray meta-analysis process ending by the transcriptome establishment of hESC, endothelial cells, fibroblasts and mixed hESC-derived cells.
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fig-1: Flow chart of the microarray dataset analysis.This flow chart describes the microarray meta-analysis process ending by the transcriptome establishment of hESC, endothelial cells, fibroblasts and mixed hESC-derived cells.

Mentions: The microarray datasets used to establish a high coverage hESC transcriptome were raw data (.CEL image files) of single channel Human Genome U133 Plus 2.0 Affymetrix microarrays downloaded from the ArrayExpress public database (Parkinson et al., 2007). Probe intensity extraction and normalisation procedures were performed with BRB-ArrayTools 4.3.0 beta 1 (Simon et al., 2007) using default median array values (selected by BRB-ArrayTools 4.3.0 beta 1) as reference. The minimum required fold change was 1.5. If less than 20% of the expression values met this value, the gene was excluded. Each individual dataset was first analysed using the three available algorithms: Robust Multi-array Analysis (RMA) (Irizarry et al., 2003), GC-RMA (Wu et al., in press) and Micro Array Suite 5.0 (MAS5.0) (Hubbell, Liu & Mei, 2002). The three lists of expressed genes were either combined to create a total list containing all expressed genes, or compared to create an intersection list containing only overlapping genes. For the hESC datasets, when the intersection list contained at least 50% of the genes of the total list, the dataset was used to perform a meta-analysis to establish the hESC transcriptome. Thus, all hESC datasets matching this criterion were grouped to be analysed together and generate the final intersection list used as the hESC transcriptome for further analysis (Fig. 1). For the hESC-derived cell datasets, if the intersection list contained at least 50% of the genes of the total list, the full transcriptome (fibroblasts and endothelial cells) was used for transcriptomic comparisons; otherwise the datasets were combined to build the final intersection list and form the hESC-derived cell transcriptome, which was used for transcriptomic comparisons (Fig. 1). The identifiers were EntrezGene IDs and Official Gene Symbol identifiers. The identifier conversion was done with the database for annotation, visualization and integrated discovery (DAVID) 6.7 (Huang, Sherman & Lempicki, 2009a; Huang, Sherman & Lempicki, 2009b).


Network based meta-analysis prediction of microenvironmental relays involved in stemness of human embryonic stem cells.

Mournetas V, Nunes QM, Murray PA, Sanderson CM, Fernig DG - PeerJ (2014)

Flow chart of the microarray dataset analysis.This flow chart describes the microarray meta-analysis process ending by the transcriptome establishment of hESC, endothelial cells, fibroblasts and mixed hESC-derived cells.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4217173&req=5

fig-1: Flow chart of the microarray dataset analysis.This flow chart describes the microarray meta-analysis process ending by the transcriptome establishment of hESC, endothelial cells, fibroblasts and mixed hESC-derived cells.
Mentions: The microarray datasets used to establish a high coverage hESC transcriptome were raw data (.CEL image files) of single channel Human Genome U133 Plus 2.0 Affymetrix microarrays downloaded from the ArrayExpress public database (Parkinson et al., 2007). Probe intensity extraction and normalisation procedures were performed with BRB-ArrayTools 4.3.0 beta 1 (Simon et al., 2007) using default median array values (selected by BRB-ArrayTools 4.3.0 beta 1) as reference. The minimum required fold change was 1.5. If less than 20% of the expression values met this value, the gene was excluded. Each individual dataset was first analysed using the three available algorithms: Robust Multi-array Analysis (RMA) (Irizarry et al., 2003), GC-RMA (Wu et al., in press) and Micro Array Suite 5.0 (MAS5.0) (Hubbell, Liu & Mei, 2002). The three lists of expressed genes were either combined to create a total list containing all expressed genes, or compared to create an intersection list containing only overlapping genes. For the hESC datasets, when the intersection list contained at least 50% of the genes of the total list, the dataset was used to perform a meta-analysis to establish the hESC transcriptome. Thus, all hESC datasets matching this criterion were grouped to be analysed together and generate the final intersection list used as the hESC transcriptome for further analysis (Fig. 1). For the hESC-derived cell datasets, if the intersection list contained at least 50% of the genes of the total list, the full transcriptome (fibroblasts and endothelial cells) was used for transcriptomic comparisons; otherwise the datasets were combined to build the final intersection list and form the hESC-derived cell transcriptome, which was used for transcriptomic comparisons (Fig. 1). The identifiers were EntrezGene IDs and Official Gene Symbol identifiers. The identifier conversion was done with the database for annotation, visualization and integrated discovery (DAVID) 6.7 (Huang, Sherman & Lempicki, 2009a; Huang, Sherman & Lempicki, 2009b).

Bottom Line: The STRING database was utilised to construct a protein-protein interaction network focused on extracellular and transcription factor components contained within the assembled transcriptome.Conclusion.We hypothesise that this list of proteins, either connecting extracellular components with transcriptional networks, or with hub or bottleneck properties, may contain proteins likely to be involved in determining stemness.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Cellular and Molecular Physiology, Institute of Translational Medicine, University of Liverpool , Liverpool , United Kingdom ; Department of Biochemistry, Institute of Integrative Biology, University of Liverpool , Liverpool , United Kingdom.

ABSTRACT
Background. Human embryonic stem cells (hESCs) are pluripotent cells derived from the inner cell mass of in vitro fertilised blastocysts, which can either be maintained in an undifferentiated state or committed into lineages under determined culture conditions. These cells offer great potential for regenerative medicine, but at present, little is known about the mechanisms that regulate hESC stemness; in particular, the role of cell-cell and cell-extracellular matrix interactions remain relatively unexplored. Methods and Results. In this study we have performed an in silico analysis of cell-microenvironment interactions to identify novel proteins that may be responsible for the maintenance of hESC stemness. A hESC transcriptome of 8,934 mRNAs was assembled using a meta-analysis approach combining the analysis of microarrays and the use of databases for annotation. The STRING database was utilised to construct a protein-protein interaction network focused on extracellular and transcription factor components contained within the assembled transcriptome. This interactome was structurally studied and filtered to identify a short list of 92 candidate proteins, which may regulate hESC stemness. Conclusion. We hypothesise that this list of proteins, either connecting extracellular components with transcriptional networks, or with hub or bottleneck properties, may contain proteins likely to be involved in determining stemness.

No MeSH data available.


Related in: MedlinePlus