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The apoptotic machinery as a biological complex system: analysis of its omics and evolution, identification of candidate genes for fourteen major types of cancer, and experimental validation in CML and neuroblastoma.

Di Pietro C, Ragusa M, Barbagallo D, Duro LR, Guglielmino MR, Majorana A, Angelica R, Scalia M, Statello L, Salito L, Tomasello L, Pernagallo S, Valenti S, D'Agostino V, Triberio P, Tandurella I, Palumbo GA, La Cava P, Cafiso V, Bertuccio T, Santagati M, Li Destri G, Lanzafame S, Di Raimondo F, Stefani S, Mishra B, Purrello M - BMC Med Genomics (2009)

Bottom Line: This project exploited the methodology commonly used in Computational Biology (i.e., mining of many omics databases of the web) as well as the High Throughput biomolecular analytical techniques.The comparison of the fourteen mutated AM networks (both protein- as MIR-based) has allowed us to pinpoint the hubs with a general and critical role in tumour development and, conversely, in cell physiology: in particular, we found that some of these had already been used as targets for pharmacological anticancer therapy.This approach could pave the way for future studies and applications in molecular and clinical Medicine with important perspectives both for Oncology as for Regenerative Medicine.

View Article: PubMed Central - HTML - PubMed

Affiliation: Dipartimento di Scienze BioMediche, Sezione di Biologia Generale, Biologia Cellulare, Genetica Molecolare G Sichel, Unità di Biologia Genomica e dei Sistemi Complessi, Genetica, Bioinformatica, Università di Catania, 95123 Catania, Italy. dipietro@unict.it

ABSTRACT

Background: Apoptosis is a critical biological phenomenon, executed under the guidance of the Apoptotic Machinery (AM), which allows the physiologic elimination of terminally differentiated, senescent or diseased cells. Because of its relevance to BioMedicine, we have sought to obtain a detailed characterization of AM Omics in Homo sapiens, namely its Genomics and Evolution, Transcriptomics, Proteomics, Interactomics, Oncogenomics, and Pharmacogenomics.

Methods: This project exploited the methodology commonly used in Computational Biology (i.e., mining of many omics databases of the web) as well as the High Throughput biomolecular analytical techniques.

Results: In Homo sapiens AM is comprised of 342 protein-encoding genes (possessing either anti- or pro-apoptotic activity, or a regulatory function) and 110 MIR-encoding genes targeting them: some have a critical role within the system (core AM nodes), others perform tissue-, pathway-, or disease-specific functions (peripheral AM nodes). By overlapping the cancer type-specific AM mutation map in the fourteen most frequent cancers in western societies (breast, colon, kidney, leukaemia, liver, lung, neuroblastoma, ovary, pancreas, prostate, skin, stomach, thyroid, and uterus) to their transcriptome, proteome and interactome in the same tumour type, we have identified the most prominent AM molecular alterations within each class. The comparison of the fourteen mutated AM networks (both protein- as MIR-based) has allowed us to pinpoint the hubs with a general and critical role in tumour development and, conversely, in cell physiology: in particular, we found that some of these had already been used as targets for pharmacological anticancer therapy. For a better understanding of the relationship between AM molecular alterations and pharmacological induction of apoptosis in cancer, we examined the expression of AM genes in K562 and SH-SY5Y after anticancer treatment.

Conclusion: We believe that our data on the Apoptotic Machinery will lead to the identification of new cancer genes and to the discovery of new biomarkers, which could then be used to profile cancers for diagnostic purposes and to pinpoint new targets for pharmacological therapy. This approach could pave the way for future studies and applications in molecular and clinical Medicine with important perspectives both for Oncology as for Regenerative Medicine.

No MeSH data available.


Related in: MedlinePlus

Interactomics of AM. Panel A. AM Network: red circles represent the proteins; green triangles represent experimentally verified microRNAs; blue triangles represent predicted microRNAs; blue lines represent the interactions (protein-protein, protein-DNA, microRNA-mRNA interactions). Panel B. Matrix of interactions of AM. Each axis represents all AM proteins and the red dots indicate the presence of an interaction of a given pair of proteins. The blue lines point out the BCL2, CASPASE and STAT family members. Panel C. Heat map of centrality values of AM proteins: the X-axis represents the AM proteins with the highest centrality, while the Y-axis represents different centrality parameters (betweenness, centroid, closeness, degree, eccentricity). The colours indicate the centrality levels, according to the colour bar shown on the right of the matrix.
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Figure 3: Interactomics of AM. Panel A. AM Network: red circles represent the proteins; green triangles represent experimentally verified microRNAs; blue triangles represent predicted microRNAs; blue lines represent the interactions (protein-protein, protein-DNA, microRNA-mRNA interactions). Panel B. Matrix of interactions of AM. Each axis represents all AM proteins and the red dots indicate the presence of an interaction of a given pair of proteins. The blue lines point out the BCL2, CASPASE and STAT family members. Panel C. Heat map of centrality values of AM proteins: the X-axis represents the AM proteins with the highest centrality, while the Y-axis represents different centrality parameters (betweenness, centroid, closeness, degree, eccentricity). The colours indicate the centrality levels, according to the colour bar shown on the right of the matrix.

Mentions: According to BIND and HPRD data, protein-protein and protein-nucleic acids interactions are known for 86% of AM genes for which we found 1012 interactions (Figure 3, Panel A). The AM network is structurally and functionally based on the interactions among members of the BCL2, Caspase, and STAT families, which comprise about 42% of all links (Figure 3, Panel B). Its hubs, interacting with more than 10% of their neighbours, are CASP3 (13.5%), CASP8 (12.4%), TRAF2 (12%), and BCL2 (11%). By considering different centrality measures (betweenness, centroid, closeness, degree, eccentricity), the most central AM nodes are AKT1, CASP3, CASP8, MAPK1 (Figure 3, Panel C). These nodes, as many others with high centrality such as BCL2 or TRAF2, represent lethal embryonic perinatal or lethal postnatal genes in the mouse (MGI phenome data). Inside the AM network we identified two clusters of highly interconnected nodes: (a) the BCL2 family interaction cluster (BAX, BAK1, BCL2, BCL2L1, and BCL2L10); (b) the STAT family interaction cluster (CSF2RB, EPOR, NMI, STAT1, STAT3, STAT5A, and STAT5B). This could be potentially important for understanding the functional relationships among these gene in physiology and pathology. By comparing the human AM network with that of M. Musculus, C. elegans, D. Melanogaster, we found that the interactions between BCL2/APAF1, BCL2/BAX, BIRCs/CASPs, CASP9/APAF1, and CASPs/CASPs were conserved in these organisms (Figure 4). Interestingly, we failed to find any significant correlation between network centrality and specific phenotypic features.


The apoptotic machinery as a biological complex system: analysis of its omics and evolution, identification of candidate genes for fourteen major types of cancer, and experimental validation in CML and neuroblastoma.

Di Pietro C, Ragusa M, Barbagallo D, Duro LR, Guglielmino MR, Majorana A, Angelica R, Scalia M, Statello L, Salito L, Tomasello L, Pernagallo S, Valenti S, D'Agostino V, Triberio P, Tandurella I, Palumbo GA, La Cava P, Cafiso V, Bertuccio T, Santagati M, Li Destri G, Lanzafame S, Di Raimondo F, Stefani S, Mishra B, Purrello M - BMC Med Genomics (2009)

Interactomics of AM. Panel A. AM Network: red circles represent the proteins; green triangles represent experimentally verified microRNAs; blue triangles represent predicted microRNAs; blue lines represent the interactions (protein-protein, protein-DNA, microRNA-mRNA interactions). Panel B. Matrix of interactions of AM. Each axis represents all AM proteins and the red dots indicate the presence of an interaction of a given pair of proteins. The blue lines point out the BCL2, CASPASE and STAT family members. Panel C. Heat map of centrality values of AM proteins: the X-axis represents the AM proteins with the highest centrality, while the Y-axis represents different centrality parameters (betweenness, centroid, closeness, degree, eccentricity). The colours indicate the centrality levels, according to the colour bar shown on the right of the matrix.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2683874&req=5

Figure 3: Interactomics of AM. Panel A. AM Network: red circles represent the proteins; green triangles represent experimentally verified microRNAs; blue triangles represent predicted microRNAs; blue lines represent the interactions (protein-protein, protein-DNA, microRNA-mRNA interactions). Panel B. Matrix of interactions of AM. Each axis represents all AM proteins and the red dots indicate the presence of an interaction of a given pair of proteins. The blue lines point out the BCL2, CASPASE and STAT family members. Panel C. Heat map of centrality values of AM proteins: the X-axis represents the AM proteins with the highest centrality, while the Y-axis represents different centrality parameters (betweenness, centroid, closeness, degree, eccentricity). The colours indicate the centrality levels, according to the colour bar shown on the right of the matrix.
Mentions: According to BIND and HPRD data, protein-protein and protein-nucleic acids interactions are known for 86% of AM genes for which we found 1012 interactions (Figure 3, Panel A). The AM network is structurally and functionally based on the interactions among members of the BCL2, Caspase, and STAT families, which comprise about 42% of all links (Figure 3, Panel B). Its hubs, interacting with more than 10% of their neighbours, are CASP3 (13.5%), CASP8 (12.4%), TRAF2 (12%), and BCL2 (11%). By considering different centrality measures (betweenness, centroid, closeness, degree, eccentricity), the most central AM nodes are AKT1, CASP3, CASP8, MAPK1 (Figure 3, Panel C). These nodes, as many others with high centrality such as BCL2 or TRAF2, represent lethal embryonic perinatal or lethal postnatal genes in the mouse (MGI phenome data). Inside the AM network we identified two clusters of highly interconnected nodes: (a) the BCL2 family interaction cluster (BAX, BAK1, BCL2, BCL2L1, and BCL2L10); (b) the STAT family interaction cluster (CSF2RB, EPOR, NMI, STAT1, STAT3, STAT5A, and STAT5B). This could be potentially important for understanding the functional relationships among these gene in physiology and pathology. By comparing the human AM network with that of M. Musculus, C. elegans, D. Melanogaster, we found that the interactions between BCL2/APAF1, BCL2/BAX, BIRCs/CASPs, CASP9/APAF1, and CASPs/CASPs were conserved in these organisms (Figure 4). Interestingly, we failed to find any significant correlation between network centrality and specific phenotypic features.

Bottom Line: This project exploited the methodology commonly used in Computational Biology (i.e., mining of many omics databases of the web) as well as the High Throughput biomolecular analytical techniques.The comparison of the fourteen mutated AM networks (both protein- as MIR-based) has allowed us to pinpoint the hubs with a general and critical role in tumour development and, conversely, in cell physiology: in particular, we found that some of these had already been used as targets for pharmacological anticancer therapy.This approach could pave the way for future studies and applications in molecular and clinical Medicine with important perspectives both for Oncology as for Regenerative Medicine.

View Article: PubMed Central - HTML - PubMed

Affiliation: Dipartimento di Scienze BioMediche, Sezione di Biologia Generale, Biologia Cellulare, Genetica Molecolare G Sichel, Unità di Biologia Genomica e dei Sistemi Complessi, Genetica, Bioinformatica, Università di Catania, 95123 Catania, Italy. dipietro@unict.it

ABSTRACT

Background: Apoptosis is a critical biological phenomenon, executed under the guidance of the Apoptotic Machinery (AM), which allows the physiologic elimination of terminally differentiated, senescent or diseased cells. Because of its relevance to BioMedicine, we have sought to obtain a detailed characterization of AM Omics in Homo sapiens, namely its Genomics and Evolution, Transcriptomics, Proteomics, Interactomics, Oncogenomics, and Pharmacogenomics.

Methods: This project exploited the methodology commonly used in Computational Biology (i.e., mining of many omics databases of the web) as well as the High Throughput biomolecular analytical techniques.

Results: In Homo sapiens AM is comprised of 342 protein-encoding genes (possessing either anti- or pro-apoptotic activity, or a regulatory function) and 110 MIR-encoding genes targeting them: some have a critical role within the system (core AM nodes), others perform tissue-, pathway-, or disease-specific functions (peripheral AM nodes). By overlapping the cancer type-specific AM mutation map in the fourteen most frequent cancers in western societies (breast, colon, kidney, leukaemia, liver, lung, neuroblastoma, ovary, pancreas, prostate, skin, stomach, thyroid, and uterus) to their transcriptome, proteome and interactome in the same tumour type, we have identified the most prominent AM molecular alterations within each class. The comparison of the fourteen mutated AM networks (both protein- as MIR-based) has allowed us to pinpoint the hubs with a general and critical role in tumour development and, conversely, in cell physiology: in particular, we found that some of these had already been used as targets for pharmacological anticancer therapy. For a better understanding of the relationship between AM molecular alterations and pharmacological induction of apoptosis in cancer, we examined the expression of AM genes in K562 and SH-SY5Y after anticancer treatment.

Conclusion: We believe that our data on the Apoptotic Machinery will lead to the identification of new cancer genes and to the discovery of new biomarkers, which could then be used to profile cancers for diagnostic purposes and to pinpoint new targets for pharmacological therapy. This approach could pave the way for future studies and applications in molecular and clinical Medicine with important perspectives both for Oncology as for Regenerative Medicine.

No MeSH data available.


Related in: MedlinePlus