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Comparative Meta-Analysis of Transcriptomics Data during Cellular Senescence and In Vivo Tissue Ageing.

Voutetakis K, Chatziioannou A, Gonos ES, Trougakos IP - Oxid Med Cell Longev (2015)

Bottom Line: Several studies have employed DNA microarrays to identify gene expression signatures that mark human ageing; yet the features underlying this complicated phenomenon remain elusive.We thus conducted a bioinformatics meta-analysis on transcriptomics data from human cell- and biopsy-based microarrays experiments studying cellular senescence or in vivo tissue ageing, respectively.Our reported meta-analysis has revealed novel age-related genes, setting thus the basis for more detailed future functional studies.

View Article: PubMed Central - PubMed

Affiliation: National Hellenic Research Foundation, Institute of Biology, Medicinal Chemistry and Biotechnology, 48 Vassileos Constantinou Avenue, 11635 Athens, Greece.

ABSTRACT
Several studies have employed DNA microarrays to identify gene expression signatures that mark human ageing; yet the features underlying this complicated phenomenon remain elusive. We thus conducted a bioinformatics meta-analysis on transcriptomics data from human cell- and biopsy-based microarrays experiments studying cellular senescence or in vivo tissue ageing, respectively. We report that coregulated genes in the postmitotic muscle and nervous tissues are classified into pathways involved in cancer, focal adhesion, actin cytoskeleton, MAPK signalling, and metabolism regulation. Genes that are differentially regulated during cellular senescence refer to pathways involved in neurodegeneration, focal adhesion, actin cytoskeleton, proteasome, cell cycle, DNA replication, and oxidative phosphorylation. Finally, we revealed genes and pathways (referring to cancer, Huntington's disease, MAPK signalling, focal adhesion, actin cytoskeleton, oxidative phosphorylation, and metabolic signalling) that are coregulated during cellular senescence and in vivo tissue ageing. The molecular commonalities between cellular senescence and tissue ageing are also highlighted by the fact that pathways that were overrepresented exclusively in the biopsy- or cell-based datasets are modules either of the same reference pathway (e.g., metabolism) or of closely interrelated pathways (e.g., thyroid cancer and melanoma). Our reported meta-analysis has revealed novel age-related genes, setting thus the basis for more detailed future functional studies.

No MeSH data available.


Related in: MedlinePlus

Identification of (gender-independent) age-related KEGG pathways that are coregulated in human postmitotic skeletal muscle and nervous tissues. (a) Venn diagram comparing differentially regulated KEGG pathways during ageing of human skeletal muscle and nervous tissues; data were assessed by the StRAnGER algorithm. The green circle represents the number of KEGG pathways as derived by the combinatorial analysis of six different experiments in skeletal muscle, whereas the blue circle represents the identified KEGG pathways as derived by the analysis of the GDS707 nervous tissue related dataset. The overlapping grey area represents those KEGG pathways that are commonly affected by ageing in the two tissues. (b) Distribution of the common overrepresented KEGG pathways in tissue datasets based on their (%) enrichment score. The (%) enrichment score represents the (%) ratio of the number of appearances of a KEGG ontology term in the list of DEGs versus the number which indicates the times that this KEGG ontology term exists in the annotation file of each microarray platform.
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fig3: Identification of (gender-independent) age-related KEGG pathways that are coregulated in human postmitotic skeletal muscle and nervous tissues. (a) Venn diagram comparing differentially regulated KEGG pathways during ageing of human skeletal muscle and nervous tissues; data were assessed by the StRAnGER algorithm. The green circle represents the number of KEGG pathways as derived by the combinatorial analysis of six different experiments in skeletal muscle, whereas the blue circle represents the identified KEGG pathways as derived by the analysis of the GDS707 nervous tissue related dataset. The overlapping grey area represents those KEGG pathways that are commonly affected by ageing in the two tissues. (b) Distribution of the common overrepresented KEGG pathways in tissue datasets based on their (%) enrichment score. The (%) enrichment score represents the (%) ratio of the number of appearances of a KEGG ontology term in the list of DEGs versus the number which indicates the times that this KEGG ontology term exists in the annotation file of each microarray platform.

Mentions: Finally, commonly regulated functions/pathways during ageing of the studied human postmitotic tissues (skeletal muscle and nervous tissues) included MAPK signalling, focal adhesion, regulation of actin cytoskeleton, metabolic pathways, calcium signalling, and pathways involved in cancer (Figure 3); the first three pathways were overrepresented in more than four of the datasets analysed (Figure 3(b); see also Supplemental Table, S4).


Comparative Meta-Analysis of Transcriptomics Data during Cellular Senescence and In Vivo Tissue Ageing.

Voutetakis K, Chatziioannou A, Gonos ES, Trougakos IP - Oxid Med Cell Longev (2015)

Identification of (gender-independent) age-related KEGG pathways that are coregulated in human postmitotic skeletal muscle and nervous tissues. (a) Venn diagram comparing differentially regulated KEGG pathways during ageing of human skeletal muscle and nervous tissues; data were assessed by the StRAnGER algorithm. The green circle represents the number of KEGG pathways as derived by the combinatorial analysis of six different experiments in skeletal muscle, whereas the blue circle represents the identified KEGG pathways as derived by the analysis of the GDS707 nervous tissue related dataset. The overlapping grey area represents those KEGG pathways that are commonly affected by ageing in the two tissues. (b) Distribution of the common overrepresented KEGG pathways in tissue datasets based on their (%) enrichment score. The (%) enrichment score represents the (%) ratio of the number of appearances of a KEGG ontology term in the list of DEGs versus the number which indicates the times that this KEGG ontology term exists in the annotation file of each microarray platform.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4419258&req=5

fig3: Identification of (gender-independent) age-related KEGG pathways that are coregulated in human postmitotic skeletal muscle and nervous tissues. (a) Venn diagram comparing differentially regulated KEGG pathways during ageing of human skeletal muscle and nervous tissues; data were assessed by the StRAnGER algorithm. The green circle represents the number of KEGG pathways as derived by the combinatorial analysis of six different experiments in skeletal muscle, whereas the blue circle represents the identified KEGG pathways as derived by the analysis of the GDS707 nervous tissue related dataset. The overlapping grey area represents those KEGG pathways that are commonly affected by ageing in the two tissues. (b) Distribution of the common overrepresented KEGG pathways in tissue datasets based on their (%) enrichment score. The (%) enrichment score represents the (%) ratio of the number of appearances of a KEGG ontology term in the list of DEGs versus the number which indicates the times that this KEGG ontology term exists in the annotation file of each microarray platform.
Mentions: Finally, commonly regulated functions/pathways during ageing of the studied human postmitotic tissues (skeletal muscle and nervous tissues) included MAPK signalling, focal adhesion, regulation of actin cytoskeleton, metabolic pathways, calcium signalling, and pathways involved in cancer (Figure 3); the first three pathways were overrepresented in more than four of the datasets analysed (Figure 3(b); see also Supplemental Table, S4).

Bottom Line: Several studies have employed DNA microarrays to identify gene expression signatures that mark human ageing; yet the features underlying this complicated phenomenon remain elusive.We thus conducted a bioinformatics meta-analysis on transcriptomics data from human cell- and biopsy-based microarrays experiments studying cellular senescence or in vivo tissue ageing, respectively.Our reported meta-analysis has revealed novel age-related genes, setting thus the basis for more detailed future functional studies.

View Article: PubMed Central - PubMed

Affiliation: National Hellenic Research Foundation, Institute of Biology, Medicinal Chemistry and Biotechnology, 48 Vassileos Constantinou Avenue, 11635 Athens, Greece.

ABSTRACT
Several studies have employed DNA microarrays to identify gene expression signatures that mark human ageing; yet the features underlying this complicated phenomenon remain elusive. We thus conducted a bioinformatics meta-analysis on transcriptomics data from human cell- and biopsy-based microarrays experiments studying cellular senescence or in vivo tissue ageing, respectively. We report that coregulated genes in the postmitotic muscle and nervous tissues are classified into pathways involved in cancer, focal adhesion, actin cytoskeleton, MAPK signalling, and metabolism regulation. Genes that are differentially regulated during cellular senescence refer to pathways involved in neurodegeneration, focal adhesion, actin cytoskeleton, proteasome, cell cycle, DNA replication, and oxidative phosphorylation. Finally, we revealed genes and pathways (referring to cancer, Huntington's disease, MAPK signalling, focal adhesion, actin cytoskeleton, oxidative phosphorylation, and metabolic signalling) that are coregulated during cellular senescence and in vivo tissue ageing. The molecular commonalities between cellular senescence and tissue ageing are also highlighted by the fact that pathways that were overrepresented exclusively in the biopsy- or cell-based datasets are modules either of the same reference pathway (e.g., metabolism) or of closely interrelated pathways (e.g., thyroid cancer and melanoma). Our reported meta-analysis has revealed novel age-related genes, setting thus the basis for more detailed future functional studies.

No MeSH data available.


Related in: MedlinePlus