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Proteins interaction network and modeling of IGVH mutational status in chronic lymphocytic leukemia

BACKGROUND: Chronic lymphocytic leukemia (CLL) is an incurable malignancy of mature B-lymphocytes, characterized as being a heterogeneous disease with variable clinical manifestation and survival. Mutational statuses of rearranged immunoglobulin heavy chain variable (IGVH) genes has been consider on...

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Autores principales: Álvarez-Silva, María Camila, Yepes, Sally, Torres, Maria Mercedes, González Barrios, Andrés Fernando
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4479082/
https://www.ncbi.nlm.nih.gov/pubmed/26088082
http://dx.doi.org/10.1186/s12976-015-0008-z
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author Álvarez-Silva, María Camila
Yepes, Sally
Torres, Maria Mercedes
González Barrios, Andrés Fernando
author_facet Álvarez-Silva, María Camila
Yepes, Sally
Torres, Maria Mercedes
González Barrios, Andrés Fernando
author_sort Álvarez-Silva, María Camila
collection PubMed
description BACKGROUND: Chronic lymphocytic leukemia (CLL) is an incurable malignancy of mature B-lymphocytes, characterized as being a heterogeneous disease with variable clinical manifestation and survival. Mutational statuses of rearranged immunoglobulin heavy chain variable (IGVH) genes has been consider one of the most important prognostic factors in CLL, but despite of its proven value to predict the course of the disease, the regulatory programs and biological mechanisms responsible for the differences in clinical behavior are poorly understood. METHODS: In this study, (i) we performed differential gene expression analysis between the IGVH statuses using multiple and independent CLL cohorts in microarrays platforms, based on this information, (ii) we constructed a simplified protein-protein interaction (PPI) network and (iii) investigated its structure and critical genes. This provided the basis to (iv) develop a Boolean model, (v) infer biological regulatory mechanism and (vi) performed perturbation simulations in order to analyze the network in dynamic state. RESULTS: The result of topological analysis and the Boolean model showed that the transcriptional relationships of IGVH mutational status were determined by specific regulatory proteins (PTEN, FOS, EGR1, TNF, TGFBR3, IFGR2 and LPL). The dynamics of the network was controlled by attractors whose genes were involved in multiple and diverse signaling pathways, which may suggest a variety of mechanisms related with progression occurring over time in the disease. The overexpression of FOS and TNF fixed the fate of the system as they can activate important genes implicated in the regulation of process of adhesion, apoptosis, immune response, cell proliferation and other signaling pathways related with cancer. CONCLUSION: The differences in prognosis prediction of the IGVH mutational status are related with several regulatory hubs that determine the dynamic of the system. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12976-015-0008-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-44790822015-06-25 Proteins interaction network and modeling of IGVH mutational status in chronic lymphocytic leukemia Álvarez-Silva, María Camila Yepes, Sally Torres, Maria Mercedes González Barrios, Andrés Fernando Theor Biol Med Model Research BACKGROUND: Chronic lymphocytic leukemia (CLL) is an incurable malignancy of mature B-lymphocytes, characterized as being a heterogeneous disease with variable clinical manifestation and survival. Mutational statuses of rearranged immunoglobulin heavy chain variable (IGVH) genes has been consider one of the most important prognostic factors in CLL, but despite of its proven value to predict the course of the disease, the regulatory programs and biological mechanisms responsible for the differences in clinical behavior are poorly understood. METHODS: In this study, (i) we performed differential gene expression analysis between the IGVH statuses using multiple and independent CLL cohorts in microarrays platforms, based on this information, (ii) we constructed a simplified protein-protein interaction (PPI) network and (iii) investigated its structure and critical genes. This provided the basis to (iv) develop a Boolean model, (v) infer biological regulatory mechanism and (vi) performed perturbation simulations in order to analyze the network in dynamic state. RESULTS: The result of topological analysis and the Boolean model showed that the transcriptional relationships of IGVH mutational status were determined by specific regulatory proteins (PTEN, FOS, EGR1, TNF, TGFBR3, IFGR2 and LPL). The dynamics of the network was controlled by attractors whose genes were involved in multiple and diverse signaling pathways, which may suggest a variety of mechanisms related with progression occurring over time in the disease. The overexpression of FOS and TNF fixed the fate of the system as they can activate important genes implicated in the regulation of process of adhesion, apoptosis, immune response, cell proliferation and other signaling pathways related with cancer. CONCLUSION: The differences in prognosis prediction of the IGVH mutational status are related with several regulatory hubs that determine the dynamic of the system. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12976-015-0008-z) contains supplementary material, which is available to authorized users. BioMed Central 2015-06-20 /pmc/articles/PMC4479082/ /pubmed/26088082 http://dx.doi.org/10.1186/s12976-015-0008-z Text en © Alvarez-Silva et al. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Álvarez-Silva, María Camila
Yepes, Sally
Torres, Maria Mercedes
González Barrios, Andrés Fernando
Proteins interaction network and modeling of IGVH mutational status in chronic lymphocytic leukemia
title Proteins interaction network and modeling of IGVH mutational status in chronic lymphocytic leukemia
title_full Proteins interaction network and modeling of IGVH mutational status in chronic lymphocytic leukemia
title_fullStr Proteins interaction network and modeling of IGVH mutational status in chronic lymphocytic leukemia
title_full_unstemmed Proteins interaction network and modeling of IGVH mutational status in chronic lymphocytic leukemia
title_short Proteins interaction network and modeling of IGVH mutational status in chronic lymphocytic leukemia
title_sort proteins interaction network and modeling of igvh mutational status in chronic lymphocytic leukemia
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4479082/
https://www.ncbi.nlm.nih.gov/pubmed/26088082
http://dx.doi.org/10.1186/s12976-015-0008-z
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