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Potrony M, Haddad TS, Tell-Martí G, Gimenez-Xavier P, Leon C, Pevida M, Mateu J, Badenas C, Carrera C, Malvehy J, Aguilera P, Llames S, Escámez MJ, Puig-Butillé JA, Del Río M, Puig S. DNA Repair and Immune Response Pathways Are Deregulated in Melanocyte-Keratinocyte Co-cultures Derived From the Healthy Skin of Familial Melanoma Patients. Front Med (Lausanne) 2021; 8:692341. [PMID: 34660619 PMCID: PMC8517393 DOI: 10.3389/fmed.2021.692341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 09/07/2021] [Indexed: 11/17/2022] Open
Abstract
Familial melanoma accounts for 10% of cases, being CDKN2A the main high-risk gene. However, the mechanisms underlying melanomagenesis in these cases remain poorly understood. Our aim was to analyze the transcriptome of melanocyte-keratinocyte co-cultures derived from healthy skin from familial melanoma patients vs. controls, to unveil pathways involved in melanoma development in at-risk individuals. Accordingly, primary melanocyte-keratinocyte co-cultures were established from the healthy skin biopsies of 16 unrelated familial melanoma patients (8 CDKN2A mutant, 8 CDKN2A wild-type) and 7 healthy controls. Whole transcriptome was captured using the SurePrint G3 Human Microarray. Transcriptome analyses included: differential gene expression, functional enrichment, and protein-protein interaction (PPI) networks. We identified a gene profile associated with familial melanoma independently of CDKN2A germline status. Functional enrichment analysis of this profile showed a downregulation of pathways related to DNA repair and immune response in familial melanoma (P < 0.05). In addition, the PPI network analysis revealed a network that consisted of double-stranded DNA repair genes (including BRCA1, BRCA2, BRIP1, and FANCA), immune response genes, and regulation of chromosome segregation. The hub gene was BRCA1. In conclusion, the constitutive deregulation of BRCA1 pathway genes and the immune response in healthy skin could be a mechanism related to melanoma risk.
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Affiliation(s)
- Miriam Potrony
- Biochemistry and Molecular Genetics Department, Melanoma Unit, Hospital Clínic de Barcelona, Institut d'Investigacions Biomédiques August Pi i Sunyer, Universitat de Barcelona, Barcelona, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Raras, Instituto de Salud Carlos III, Madrid, Spain
| | - Tariq Sami Haddad
- Dermatology Department, Melanoma Unit, Hospital Clínic de Barcelona, Institut d'Investigacions Biomédiques August Pi i Sunyer, Universitat de Barcelona, Barcelona, Spain
| | - Gemma Tell-Martí
- Centro de Investigación Biomédica en Red de Enfermedades Raras, Instituto de Salud Carlos III, Madrid, Spain.,Dermatology Department, Melanoma Unit, Hospital Clínic de Barcelona, Institut d'Investigacions Biomédiques August Pi i Sunyer, Universitat de Barcelona, Barcelona, Spain
| | - Pol Gimenez-Xavier
- Dermatology Department, Melanoma Unit, Hospital Clínic de Barcelona, Institut d'Investigacions Biomédiques August Pi i Sunyer, Universitat de Barcelona, Barcelona, Spain
| | - Carlos Leon
- Centro de Investigación Biomédica en Red de Enfermedades Raras, Instituto de Salud Carlos III, Madrid, Spain.,Departamento de Bioingeniería, Universidad Carlos III de Madrid, Leganés, Spain.,Cátedra de Medicina Regenerativa y Bioingeniería de Tejidos, Instituto de Investigación Sanitaria de la Fundación Jiménez Díaz, Madrid, Spain
| | - Marta Pevida
- Centro de Investigación Biomédica en Red de Enfermedades Raras, Instituto de Salud Carlos III, Madrid, Spain.,Tissue Engineering Unit, Centro Comunitario de Sangre y Tejidos de Asturias, Oviedo, Spain.,Instituto Universitario Fdez-Vega, Fundación de Investigación Oftalmológica, Universidad de Oviedo, Oviedo, Spain
| | - Judit Mateu
- Dermatology Department, Melanoma Unit, Hospital Clínic de Barcelona, Institut d'Investigacions Biomédiques August Pi i Sunyer, Universitat de Barcelona, Barcelona, Spain
| | - Celia Badenas
- Biochemistry and Molecular Genetics Department, Melanoma Unit, Hospital Clínic de Barcelona, Institut d'Investigacions Biomédiques August Pi i Sunyer, Universitat de Barcelona, Barcelona, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Raras, Instituto de Salud Carlos III, Madrid, Spain
| | - Cristina Carrera
- Centro de Investigación Biomédica en Red de Enfermedades Raras, Instituto de Salud Carlos III, Madrid, Spain.,Dermatology Department, Melanoma Unit, Hospital Clínic de Barcelona, Institut d'Investigacions Biomédiques August Pi i Sunyer, Universitat de Barcelona, Barcelona, Spain
| | - Josep Malvehy
- Centro de Investigación Biomédica en Red de Enfermedades Raras, Instituto de Salud Carlos III, Madrid, Spain.,Dermatology Department, Melanoma Unit, Hospital Clínic de Barcelona, Institut d'Investigacions Biomédiques August Pi i Sunyer, Universitat de Barcelona, Barcelona, Spain
| | - Paula Aguilera
- Centro de Investigación Biomédica en Red de Enfermedades Raras, Instituto de Salud Carlos III, Madrid, Spain.,Dermatology Department, Melanoma Unit, Hospital Clínic de Barcelona, Institut d'Investigacions Biomédiques August Pi i Sunyer, Universitat de Barcelona, Barcelona, Spain
| | - Sara Llames
- Centro de Investigación Biomédica en Red de Enfermedades Raras, Instituto de Salud Carlos III, Madrid, Spain.,Cátedra de Medicina Regenerativa y Bioingeniería de Tejidos, Instituto de Investigación Sanitaria de la Fundación Jiménez Díaz, Madrid, Spain.,Tissue Engineering Unit, Centro Comunitario de Sangre y Tejidos de Asturias, Oviedo, Spain.,Instituto Universitario Fdez-Vega, Fundación de Investigación Oftalmológica, Universidad de Oviedo, Oviedo, Spain
| | - Maria José Escámez
- Centro de Investigación Biomédica en Red de Enfermedades Raras, Instituto de Salud Carlos III, Madrid, Spain.,Departamento de Bioingeniería, Universidad Carlos III de Madrid, Leganés, Spain.,Cátedra de Medicina Regenerativa y Bioingeniería de Tejidos, Instituto de Investigación Sanitaria de la Fundación Jiménez Díaz, Madrid, Spain.,Centro de Investigaciones Energéticas Mediambientales y Tecnonlógicas, Madrid, Spain
| | - Joan A Puig-Butillé
- Centro de Investigación Biomédica en Red de Enfermedades Raras, Instituto de Salud Carlos III, Madrid, Spain.,Molecular Biology Core, Biomedical Diagnostic Center, Melanoma Unit, Hospital Clínic de Barcelona, Institut d'Investigacions Biomédiques August Pi i Sunyer, University of Barcelona, Barcelona, Spain
| | - Marcela Del Río
- Centro de Investigación Biomédica en Red de Enfermedades Raras, Instituto de Salud Carlos III, Madrid, Spain.,Departamento de Bioingeniería, Universidad Carlos III de Madrid, Leganés, Spain.,Cátedra de Medicina Regenerativa y Bioingeniería de Tejidos, Instituto de Investigación Sanitaria de la Fundación Jiménez Díaz, Madrid, Spain.,Centro de Investigaciones Energéticas Mediambientales y Tecnonlógicas, Madrid, Spain
| | - Susana Puig
- Centro de Investigación Biomédica en Red de Enfermedades Raras, Instituto de Salud Carlos III, Madrid, Spain.,Dermatology Department, Melanoma Unit, Hospital Clínic de Barcelona, Institut d'Investigacions Biomédiques August Pi i Sunyer, Universitat de Barcelona, Barcelona, Spain
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2
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Gonzalez-Dominguez J, Martin MJ. MPIGeneNet: Parallel Calculation of Gene Co-Expression Networks on Multicore Clusters. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:1732-1737. [PMID: 29028205 DOI: 10.1109/tcbb.2017.2761340] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In this work, we present MPIGeneNet, a parallel tool that applies Pearson's correlation and Random Matrix Theory to construct gene co-expression networks. It is based on the state-of-the-art sequential tool RMTGeneNet, which provides networks with high robustness and sensitivity at the expenses of relatively long runtimes for large scale input datasets. MPIGeneNet returns the same results as RMTGeneNet but improves the memory management, reduces the I/O cost, and accelerates the two most computationally demanding steps of co-expression network construction by exploiting the compute capabilities of common multicore CPU clusters. Our performance evaluation on two different systems using three typical input datasets shows that MPIGeneNet is significantly faster than RMTGeneNet. As an example, our tool is up to 175.41 times faster on a cluster with eight nodes, each one containing two 12-core Intel Haswell processors. The source code of MPIGeneNet, as well as a reference manual, are available at https://sourceforge.net/projects/mpigenenet/.
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Gassó P, Mas S, Rodríguez N, Boloc D, García-Cerro S, Bernardo M, Lafuente A, Parellada E. Microarray gene-expression study in fibroblast and lymphoblastoid cell lines from antipsychotic-naïve first-episode schizophrenia patients. J Psychiatr Res 2017; 95:91-101. [PMID: 28822801 DOI: 10.1016/j.jpsychires.2017.08.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Revised: 07/25/2017] [Accepted: 08/04/2017] [Indexed: 12/16/2022]
Abstract
Schizophrenia (SZ) is a chronic psychiatric disorder whose onset of symptoms occurs in late adolescence and early adulthood. The etiology is complex and involves important gene-environment interactions. Microarray gene-expression studies on SZ have identified alterations in several biological processes. The heterogeneity in the results can be attributed to the use of different sample types and other important confounding factors including age, illness chronicity and antipsychotic exposure. The aim of the present microarray study was to analyze, for the first time to our knowledge, differences in gene expression profiles in 18 fibroblast (FCLs) and 14 lymphoblastoid cell lines (LCLs) from antipsychotic-naïve first-episode schizophrenia (FES) patients and healthy controls. We used an analytical approach based on protein-protein interaction network construction and functional annotation analysis to identify the biological processes that are altered in SZ. Significant differences in the expression of 32 genes were found when LCLs were assessed. The network and gene set enrichment approach revealed the involvement of similar biological processes in FCLs and LCLs, including apoptosis and related biological terms such as cell cycle, autophagy, cytoskeleton organization and response to stress and stimulus. Metabolism and other processes, including signal transduction, kinase activity and phosphorylation, were also identified. These results were replicated in two independent cohorts using the same analytical approach. This provides more evidence for altered apoptotic processes in antipsychotic-naïve FES patients and other important biological functions such as cytoskeleton organization and metabolism. The convergent results obtained in both peripheral cell models support their usefulness for transcriptome studies on SZ.
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Affiliation(s)
- Patricia Gassó
- Dept. of Basic Clinical Practice, University of Barcelona, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
| | - Sergi Mas
- Dept. of Basic Clinical Practice, University of Barcelona, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain
| | | | - Daniel Boloc
- Dept. of Basic Clinical Practice, University of Barcelona, Spain
| | | | - Miquel Bernardo
- Barcelona Clínic Schizophrenia Unit (BCSU), Neuroscience Institute, Hospital Clínic de Barcelona, Spain; Dept. of Medicine, University of Barcelona, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain
| | - Amalia Lafuente
- Dept. of Basic Clinical Practice, University of Barcelona, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain
| | - Eduard Parellada
- Dept. of Basic Clinical Practice, University of Barcelona, Spain; Barcelona Clínic Schizophrenia Unit (BCSU), Neuroscience Institute, Hospital Clínic de Barcelona, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain
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4
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OncoBinder facilitates interpretation of proteomic interaction data by capturing coactivation pairs in cancer. Oncotarget 2017; 7:17608-15. [PMID: 26872056 PMCID: PMC4951236 DOI: 10.18632/oncotarget.7305] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2015] [Accepted: 01/29/2016] [Indexed: 11/25/2022] Open
Abstract
High-throughput methods such as co-immunoprecipitationmass spectrometry (coIP-MS) and yeast 2 hybridization (Y2H) have suggested a broad range of unannotated protein-protein interactions (PPIs), and interpretation of these PPIs remains a challenging task. The advancements in cancer genomic researches allow for the inference of "coactivation pairs" in cancer, which may facilitate the identification of PPIs involved in cancer. Here we present OncoBinder as a tool for the assessment of proteomic interaction data based on the functional synergy of oncoproteins in cancer. This decision tree-based method combines gene mutation, copy number and mRNA expression information to infer the functional status of protein-coding genes. We applied OncoBinder to evaluate the potential binders of EGFR and ERK2 proteins based on the gastric cancer dataset of The Cancer Genome Atlas (TCGA). As a result, OncoBinder identified high confidence interactions (annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) or validated by low-throughput assays) more efficiently than co-expression based method. Taken together, our results suggest that evaluation of gene functional synergy in cancer may facilitate the interpretation of proteomic interaction data. The OncoBinder toolbox for Matlab is freely accessible online.
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5
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Wang L, Ma H, Zhu L, Ma L, Cao L, Wei H, Xu J. Screening for the optimal gene and functional gene sets related to breast cancer using differential co-expression and differential expression analysis. Cancer Biomark 2016; 17:463-471. [PMID: 27802197 DOI: 10.3233/cbm-160663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
OBJECTIVE To investigate novel gene sets related to breast cancer (BC) using differential co-expression and differential expression (DECODE). METHODS T statistics was used to quantify the degree of DE of each gene, and then Z was adopted to quantify the correlation difference between expression levels of two genes. Two optimal thresholds for defining substantial change in DE and DC were selected for each gene using chi-square maximization, and the corresponding gene was defined as the optimal gene. Based on the optimal thresholds, genes were categorized into four partitions with either high or low DC and DE characteristics. Finally, we evaluated the functional relevance of a gene partition with high DE and high DC, and the gene set with best association was considered as the optimal functional gene set. RESULTS The optimal thresholds for DC and DE were respective 2.254 and 1.616, and the optimal gene was UBE2Q2L. Based on the optimal thresholds, genes were divided into four partitions including HDE-HDC (875 genes), HED-LDC (8038 genes), LDE-HDC (678 genes), and LDE-LDC (10516 genes). The best associated gene set was ``fatty acid catabolic process'' with 34 HDC and HDE partitions. Among these partitions, UBE2Q2L attained the highest minimum FI gain of 18.973. CONCLUSION UBE2Q2L and fatty acid catabolic process might be potentially useful signatures in diagnostic purposes for BC.
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Affiliation(s)
- Lei Wang
- Department of Science and Education, The People's Hospital of Zhangqiu, Zhangqiu, Shandong, China
| | - Hong Ma
- Pharmacy Intravenous Admixture Service, The People's Hospital of Zhangqiu, Zhangqiu, Shandong, China
| | - Lixia Zhu
- Department of Neurosurgery, The People's Hospital of Zhangqiu, Zhangqiu, Shandong, China
| | - Liping Ma
- Department of Science and Education, The People's Hospital of Zhangqiu, Zhangqiu, Shandong, China
| | - Lanting Cao
- Department of Cardiology, The People's Hospital of Zhangqiu, Zhangqiu, Shandong, China
| | - Hui Wei
- Department of General Surgery, The People's Hospital of Zhangqiu, Zhangqiu, Shandong, China
| | - Jumei Xu
- Department of General Surgery, The People's Hospital of Zhangqiu, Zhangqiu, Shandong, China
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6
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Mas S, Gassó P, Lafuente A. Applicability of gene expression and systems biology to develop pharmacogenetic predictors; antipsychotic-induced extrapyramidal symptoms as an example. Pharmacogenomics 2015; 16:1975-88. [PMID: 26556470 DOI: 10.2217/pgs.15.134] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Pharmacogenetics has been driven by a candidate gene approach. The disadvantage of this approach is that is limited by our current understanding of the mechanisms by which drugs act. Gene expression could help to elucidate the molecular signatures of antipsychotic treatments searching for dysregulated molecular pathways and the relationships between gene products, especially protein-protein interactions. To embrace the complexity of drug response, machine learning methods could help to identify gene-gene interactions and develop pharmacogenetic predictors of drug response. The present review summarizes the applicability of the topics presented here (gene expression, network analysis and gene-gene interactions) in pharmacogenetics. In order to achieve this, we present an example of identifying genetic predictors of extrapyramidal symptoms induced by antipsychotic.
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Affiliation(s)
- Sergi Mas
- Department of Pathological Anatomy, Pharmacology & Microbiology, University of Barcelona, Spain.,Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain
| | - Patricia Gassó
- Department of Pathological Anatomy, Pharmacology & Microbiology, University of Barcelona, Spain.,Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Amelia Lafuente
- Department of Pathological Anatomy, Pharmacology & Microbiology, University of Barcelona, Spain.,Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain
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7
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Quantitative assessment of gene expression network module-validation methods. Sci Rep 2015; 5:15258. [PMID: 26470848 PMCID: PMC4607977 DOI: 10.1038/srep15258] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2015] [Accepted: 09/21/2015] [Indexed: 02/01/2023] Open
Abstract
Validation of pluripotent modules in diverse networks holds enormous potential for systems biology and network pharmacology. An arising challenge is how to assess the accuracy of discovering all potential modules from multi-omic networks and validating their architectural characteristics based on innovative computational methods beyond function enrichment and biological validation. To display the framework progress in this domain, we systematically divided the existing Computational Validation Approaches based on Modular Architecture (CVAMA) into topology-based approaches (TBA) and statistics-based approaches (SBA). We compared the available module validation methods based on 11 gene expression datasets, and partially consistent results in the form of homogeneous models were obtained with each individual approach, whereas discrepant contradictory results were found between TBA and SBA. The TBA of the Zsummary value had a higher Validation Success Ratio (VSR) (51%) and a higher Fluctuation Ratio (FR) (80.92%), whereas the SBA of the approximately unbiased (AU) p-value had a lower VSR (12.3%) and a lower FR (45.84%). The Gray area simulated study revealed a consistent result for these two models and indicated a lower Variation Ratio (VR) (8.10%) of TBA at 6 simulated levels. Despite facing many novel challenges and evidence limitations, CVAMA may offer novel insights into modular networks.
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8
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Lui TWH, Tsui NBY, Chan LWC, Wong CSC, Siu PMF, Yung BYM. DECODE: an integrated differential co-expression and differential expression analysis of gene expression data. BMC Bioinformatics 2015; 16:182. [PMID: 26026612 PMCID: PMC4449974 DOI: 10.1186/s12859-015-0582-4] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2014] [Accepted: 04/22/2015] [Indexed: 01/30/2023] Open
Abstract
BACKGROUND Both differential expression (DE) and differential co-expression (DC) analyses are appreciated as useful tools in understanding gene regulation related to complex diseases. The performance of integrating DE and DC, however, remains unexplored. RESULTS In this study, we proposed a novel analytical approach called DECODE (Differential Co-expression and Differential Expression) to integrate DC and DE analyses of gene expression data. DECODE allows one to study the combined features of DC and DE of each transcript between two conditions. By incorporating information of the dependency between DC and DE variables, two optimal thresholds for defining substantial change in expression and co-expression are systematically defined for each gene based on chi-square maximization. By using these thresholds, genes can be categorized into four groups with either high or low DC and DE characteristics. In this study, DECODE was applied to a large breast cancer microarray data set consisted of two thousand tumor samples. By identifying genes with high DE and high DC, we demonstrated that DECODE could improve the detection of some functional gene sets such as those related to immune system, metastasis, lipid and glucose metabolism. Further investigation on the identified genes and the associated functional pathways would provide an additional level of understanding of complex disease mechanism. CONCLUSIONS By complementing the recent DC and the traditional DE analyses, DECODE is a valuable methodology for investigating biological functions of genes exhibiting disease-associated DE and DC combined characteristics, which may not be easily revealed through DC or DE approach alone. DECODE is available at the Comprehensive R Archive Network (CRAN): http://cran.r-project.org/web/packages/decode/index.html .
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Affiliation(s)
- Thomas W H Lui
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.
| | - Nancy B Y Tsui
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.
| | - Lawrence W C Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.
| | - Cesar S C Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.
| | - Parco M F Siu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.
| | - Benjamin Y M Yung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.
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9
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Mas S, Gassó P, Parellada E, Bernardo M, Lafuente A. Network analysis of gene expression in peripheral blood identifies mTOR and NF-κB pathways involved in antipsychotic-induced extrapyramidal symptoms. THE PHARMACOGENOMICS JOURNAL 2015; 15:452-60. [PMID: 25623440 DOI: 10.1038/tpj.2014.84] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2014] [Revised: 09/22/2014] [Accepted: 11/05/2014] [Indexed: 02/06/2023]
Abstract
To identify the candidate genes for pharmacogenetic studies of antipsychotic (AP)-induced extrapyramidal symptoms (EPS), we propose a systems biology analytical approach, based on protein-protein interaction network construction and functional annotation analysis, of changes in gene expression (Human Genome U219 Array Plate) induced by treatment with risperidone or paliperidone in peripheral blood. 12 AP-naïve patients with first-episode psychosis participated in the present study. Our analysis revealed that, in response to AP treatment, constructed networks were enriched for different biological processes in patients without EPS (ubiquitination, protein folding and adenosine triphosphate (ATP) metabolism) compared with those presenting EPS (insulin receptor signaling, lipid modification, regulation of autophagy and immune response). Moreover, the observed differences also involved specific pathways, such as anaphase promoting complex /cdc20, prefoldin/CCT/triC and ATP synthesis in no-EPS patients, and mammalian target of rapamycin and NF-κB kinases in patients with EPS. Our results showing different patterns of gene expression in EPS patients, offer new and valuable markers for pharmacogenetic studies.
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Affiliation(s)
- S Mas
- Department Pathological Anatomy, Pharmacology and Microbiology, University of Barcelona, Barcelona, Spain.,Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Spain
| | - P Gassó
- Department Pathological Anatomy, Pharmacology and Microbiology, University of Barcelona, Barcelona, Spain.,Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - E Parellada
- Department Pathological Anatomy, Pharmacology and Microbiology, University of Barcelona, Barcelona, Spain.,Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Spain.,Clinic Schizophrenia program, Psychiatry service, Hospital Clínic de Barcelona, Barcelona, Spain
| | - M Bernardo
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Spain.,Clinic Schizophrenia program, Psychiatry service, Hospital Clínic de Barcelona, Barcelona, Spain.,Department Psychiatry and Clinical Psychobiology, University of Barcelona, Barcelona, Spain
| | - A Lafuente
- Department Pathological Anatomy, Pharmacology and Microbiology, University of Barcelona, Barcelona, Spain.,Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Spain
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10
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Bruhn S, Fang Y, Barrenäs F, Gustafsson M, Zhang H, Konstantinell A, Krönke A, Sönnichsen B, Bresnick A, Dulyaninova N, Wang H, Zhao Y, Klingelhöfer J, Ambartsumian N, Beck MK, Nestor C, Bona E, Xiang Z, Benson M. A generally applicable translational strategy identifies S100A4 as a candidate gene in allergy. Sci Transl Med 2014; 6:218ra4. [PMID: 24401939 DOI: 10.1126/scitranslmed.3007410] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The identification of diagnostic markers and therapeutic candidate genes in common diseases is complicated by the involvement of thousands of genes. We hypothesized that genes co-regulated with a key gene in allergy, IL13, would form a module that could help to identify candidate genes. We identified a T helper 2 (TH2) cell module by small interfering RNA-mediated knockdown of 25 putative IL13-regulating transcription factors followed by expression profiling. The module contained candidate genes whose diagnostic potential was supported by clinical studies. Functional studies of human TH2 cells as well as mouse models of allergy showed that deletion of one of the genes, S100A4, resulted in decreased signs of allergy including TH2 cell activation, humoral immunity, and infiltration of effector cells. Specifically, dendritic cells required S100A4 for activating T cells. Treatment with an anti-S100A4 antibody resulted in decreased signs of allergy in the mouse model as well as in allergen-challenged T cells from allergic patients. This strategy, which may be generally applicable to complex diseases, identified and validated an important diagnostic and therapeutic candidate gene in allergy.
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Affiliation(s)
- Sören Bruhn
- The Center for Individualized Medication, Department of Clinical and Experimental Medicine, Linköping University, 581 85 Linköping, Sweden
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Garcia-Alonso L, Jiménez-Almazán J, Carbonell-Caballero J, Vela-Boza A, Santoyo-López J, Antiñolo G, Dopazo J. The role of the interactome in the maintenance of deleterious variability in human populations. Mol Syst Biol 2014; 10:752. [PMID: 25261458 PMCID: PMC4299661 DOI: 10.15252/msb.20145222] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2014] [Revised: 08/23/2014] [Accepted: 08/28/2014] [Indexed: 12/25/2022] Open
Abstract
Recent genomic projects have revealed the existence of an unexpectedly large amount of deleterious variability in the human genome. Several hypotheses have been proposed to explain such an apparently high mutational load. However, the mechanisms by which deleterious mutations in some genes cause a pathological effect but are apparently innocuous in other genes remain largely unknown. This study searched for deleterious variants in the 1,000 genomes populations, as well as in a newly sequenced population of 252 healthy Spanish individuals. In addition, variants causative of monogenic diseases and somatic variants from 41 chronic lymphocytic leukaemia patients were analysed. The deleterious variants found were analysed in the context of the interactome to understand the role of network topology in the maintenance of the observed mutational load. Our results suggest that one of the mechanisms whereby the effect of these deleterious variants on the phenotype is suppressed could be related to the configuration of the protein interaction network. Most of the deleterious variants observed in healthy individuals are concentrated in peripheral regions of the interactome, in combinations that preserve their connectivity, and have a marginal effect on interactome integrity. On the contrary, likely pathogenic cancer somatic deleterious variants tend to occur in internal regions of the interactome, often with associated structural consequences. Finally, variants causative of monogenic diseases seem to occupy an intermediate position. Our observations suggest that the real pathological potential of a variant might be more a systems property rather than an intrinsic property of individual proteins.
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Affiliation(s)
- Luz Garcia-Alonso
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, Spain
| | - Jorge Jiménez-Almazán
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, Spain Bioinformatics of Rare Diseases (BIER), CIBER de Enfermedades Raras (CIBERER), Valencia, Spain
| | - Jose Carbonell-Caballero
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, Spain
| | - Alicia Vela-Boza
- Medical Genome Project, Genomics and Bioinformatics Platform of Andalusia (GBPA), Seville, Spain
| | - Javier Santoyo-López
- Medical Genome Project, Genomics and Bioinformatics Platform of Andalusia (GBPA), Seville, Spain
| | - Guillermo Antiñolo
- Medical Genome Project, Genomics and Bioinformatics Platform of Andalusia (GBPA), Seville, Spain Department of Genetics, Reproduction and Fetal Medicine, Institute of Biomedicine of Seville, University Hospital Virgen del Rocio/Consejo Superior de Investigaciones Científicas/University of Seville, Seville, Spain Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Seville, Spain
| | - Joaquin Dopazo
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, Spain Bioinformatics of Rare Diseases (BIER), CIBER de Enfermedades Raras (CIBERER), Valencia, Spain Medical Genome Project, Genomics and Bioinformatics Platform of Andalusia (GBPA), Seville, Spain Functional Genomics Node, (INB) at CIPF, Valencia, Spain
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12
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Ponzoni I, Nueda M, Tarazona S, Götz S, Montaner D, Dussaut J, Dopazo J, Conesa A. Pathway network inference from gene expression data. BMC SYSTEMS BIOLOGY 2014; 8 Suppl 2:S7. [PMID: 25032889 PMCID: PMC4101702 DOI: 10.1186/1752-0509-8-s2-s7] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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13
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Fernández RM, Bleda M, Luzón-Toro B, García-Alonso L, Arnold S, Sribudiani Y, Besmond C, Lantieri F, Doan B, Ceccherini I, Lyonnet S, Hofstra RMW, Chakravarti A, Antiñolo G, Dopazo J, Borrego S. Pathways systematically associated to Hirschsprung's disease. Orphanet J Rare Dis 2013; 8:187. [PMID: 24289864 PMCID: PMC3879038 DOI: 10.1186/1750-1172-8-187] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2013] [Accepted: 11/19/2013] [Indexed: 02/08/2023] Open
Abstract
Despite it has been reported that several loci are involved in Hirschsprung's disease, the molecular basis of the disease remains yet essentially unknown. The study of collective properties of modules of functionally-related genes provides an efficient and sensitive statistical framework that can overcome sample size limitations in the study of rare diseases. Here, we present the extension of a previous study of a Spanish series of HSCR trios to an international cohort of 162 HSCR trios to validate the generality of the underlying functional basis of the Hirschsprung's disease mechanisms previously found. The Pathway-Based Analysis (PBA) confirms a strong association of gene ontology (GO) modules related to signal transduction and its regulation, enteric nervous system (ENS) formation and other processes related to the disease. In addition, network analysis recovers sub-networks significantly associated to the disease, which contain genes related to the same functionalities, thus providing an independent validation of these findings. The functional profiles of association obtained for patients populations from different countries were compared to each other. While gene associations were different at each series, the main functional associations were identical in all the five populations. These observations would also explain the reported low reproducibility of associations of individual disease genes across populations.
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Affiliation(s)
- Raquel M Fernández
- Department of Genetics, Reproduction and Fetal Medicine, Institute of Biomedicine of Seville (IBIS), University Hospital Virgen del Rocío/CSIC/University of Seville, Av. Manuel Siurot s/n, Seville, 41013, Spain
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Valencia, Spain
| | - Marta Bleda
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Valencia, Spain
- Department of Computational Genomics, Centro de Investigación Príncipe Felipe (CIPF), c/Eduardo Primo Yufera, 3, Valencia, 46012, Spain
| | - Berta Luzón-Toro
- Department of Genetics, Reproduction and Fetal Medicine, Institute of Biomedicine of Seville (IBIS), University Hospital Virgen del Rocío/CSIC/University of Seville, Av. Manuel Siurot s/n, Seville, 41013, Spain
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Valencia, Spain
| | - Luz García-Alonso
- Department of Computational Genomics, Centro de Investigación Príncipe Felipe (CIPF), c/Eduardo Primo Yufera, 3, Valencia, 46012, Spain
| | - Stacey Arnold
- Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Yunia Sribudiani
- Department of Medical Genetics, University of Groningen, Groningen, The Netherlands
| | - Claude Besmond
- INSERM U-781, AP-HP Hôpital Necker-Enfants Malades, Paris, France
| | | | - Betty Doan
- Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | | | - Robert MW Hofstra
- Department of Medical Genetics, University of Groningen, Groningen, The Netherlands
| | - Aravinda Chakravarti
- Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Guillermo Antiñolo
- Department of Genetics, Reproduction and Fetal Medicine, Institute of Biomedicine of Seville (IBIS), University Hospital Virgen del Rocío/CSIC/University of Seville, Av. Manuel Siurot s/n, Seville, 41013, Spain
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Valencia, Spain
| | - Joaquín Dopazo
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Valencia, Spain
- Department of Computational Genomics, Centro de Investigación Príncipe Felipe (CIPF), c/Eduardo Primo Yufera, 3, Valencia, 46012, Spain
- Functional Genomics Node (INB), CIPF, Valencia, Spain
| | - Salud Borrego
- Department of Genetics, Reproduction and Fetal Medicine, Institute of Biomedicine of Seville (IBIS), University Hospital Virgen del Rocío/CSIC/University of Seville, Av. Manuel Siurot s/n, Seville, 41013, Spain
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Valencia, Spain
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14
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Jacunski A, Tatonetti NP. Connecting the dots: applications of network medicine in pharmacology and disease. Clin Pharmacol Ther 2013; 94:659-69. [PMID: 23995266 DOI: 10.1038/clpt.2013.168] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2013] [Accepted: 08/16/2013] [Indexed: 11/09/2022]
Abstract
In 2011, >2.5 million people died from only 15 causes in the United States. Ten of these involved complex or infectious diseases for which there is insufficient knowledge or treatment, such as heart disease, influenza, and Alzheimer's disease.(1) Complex diseases have been difficult to understand due to their multifarious genetic and molecular fingerprints, while certain infectious agents have evolved to elude treatment and prophylaxis. Network medicine provides a macroscopic approach to understanding and treating such illnesses. It integrates experimental data on gene, protein, and metabolic interactions with clinical knowledge of disease and pharmacology in order to extend the understanding of diseases and their treatments. The resulting "big picture" allows for the development of computational and mathematical methods to identify novel disease pathways and predict patient drug response, among others. In this review, we discuss recent advances in network medicine.
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Affiliation(s)
- A Jacunski
- 1] Integrated Program in Cellular, Molecular and Biomedical Studies, Columbia University Medical Center, New York, New York, USA [2] Department of Biomedical Informatics, Columbia University Medical Center, New York, New York, USA [3] Department of Systems Biology, Columbia University Medical Center, New York, New York, USA [4] Department of Medicine, Columbia University Medical Center, New York, New York, USA
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15
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Filteau M, Pavey SA, St-Cyr J, Bernatchez L. Gene coexpression networks reveal key drivers of phenotypic divergence in lake whitefish. Mol Biol Evol 2013; 30:1384-96. [PMID: 23519315 DOI: 10.1093/molbev/mst053] [Citation(s) in RCA: 95] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
A functional understanding of processes involved in adaptive divergence is one of the awaiting opportunities afforded by high-throughput transcriptomic technologies. Functional analysis of coexpressed genes has succeeded in the biomedical field in identifying key drivers of disease pathways. However, in ecology and evolutionary biology, functional interpretation of transcriptomic data is still limited. Here, we used Weighted Gene Co-Expression Network Analysis (WGCNA) to identify modules of coexpressed genes in muscle and brain tissue of a lake whitefish backcross progeny. Modules were connected to gradients of known adaptive traits involved in the ecological speciation process between benthic and limnetic ecotypes. Key drivers, that is, hub genes of functional modules related to reproduction, growth, and behavior were identified, and module preservation was assessed in natural populations. Using this approach, we identified modules of coexpressed genes involved in phenotypic divergence and their key drivers, and further identified a module part specifically rewired in the backcross progeny. Functional analysis of transcriptomic data can significantly contribute to the understanding of the mechanisms underlying ecological speciation. Our findings point to bone morphogenetic protein and calcium signaling as common pathways involved in coordinated evolution of trophic behavior, trophic morphology (gill rakers), and reproduction. Results also point to pathways implicating hemoglobins and constitutive stress response (HSP70) governing growth in lake whitefish.
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Affiliation(s)
- Marie Filteau
- Département de Biologie, Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, Canada
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16
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Fernández RM, Bleda M, Núñez-Torres R, Medina I, Luzón-Toro B, García-Alonso L, Torroglosa A, Marbà M, Enguix-Riego MV, Montaner D, Antiñolo G, Dopazo J, Borrego S. Four new loci associations discovered by pathway-based and network analyses of the genome-wide variability profile of Hirschsprung's disease. Orphanet J Rare Dis 2012; 7:103. [PMID: 23270508 PMCID: PMC3575329 DOI: 10.1186/1750-1172-7-103] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2012] [Accepted: 12/19/2012] [Indexed: 12/23/2022] Open
Abstract
Finding gene associations in rare diseases is frequently hampered by the reduced numbers of patients accessible. Conventional gene-based association tests rely on the availability of large cohorts, which constitutes a serious limitation for its application in this scenario. To overcome this problem we have used here a combined strategy in which a pathway-based analysis (PBA) has been initially conducted to prioritize candidate genes in a Spanish cohort of 53 trios of short-segment Hirschsprung’s disease. Candidate genes have been further validated in an independent population of 106 trios. The study revealed a strong association of 11 gene ontology (GO) modules related to signal transduction and its regulation, enteric nervous system (ENS) formation and other HSCR-related processes. Among the preselected candidates, a total of 4 loci, RASGEF1A, IQGAP2, DLC1 and CHRNA7, related to signal transduction and migration processes, were found to be significantly associated to HSCR. Network analysis also confirms their involvement in the network of already known disease genes. This approach, based on the study of functionally-related gene sets, requires of lower sample sizes and opens new opportunities for the study of rare diseases.
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Affiliation(s)
- Raquel Ma Fernández
- Department of Genetics, Reproduction and Fetal Medicine, Institute of Biomedicine of Seville (IBIS, University Hospital Virgen del Rocío/CSIC/University of Seville, Seville, Spain
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17
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Pradhan MP, Nagulapalli K, Palakal MJ. Cliques for the identification of gene signatures for colorectal cancer across population. BMC SYSTEMS BIOLOGY 2012; 6 Suppl 3:S17. [PMID: 23282040 PMCID: PMC3524317 DOI: 10.1186/1752-0509-6-s3-s17] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Background Colorectal cancer (CRC) is one of the most commonly diagnosed cancers worldwide. Studies have correlated risk of CRC development with dietary habits and environmental conditions. Gene signatures for any disease can identify the key biological processes, which is especially useful in studying cancer development. Such processes can be used to evaluate potential drug targets. Though recognition of CRC gene-signatures across populations is crucial to better understanding potential novel treatment options for CRC, it remains a challenging task. Results We developed a topological and biological feature-based network approach for identifying the gene signatures across populations. In this work, we propose a novel approach of using cliques to understand the variability within population. Cliques are more conserved and co-expressed, therefore allowing identification and comparison of cliques across a population which can help researchers study gene variations. Our study was based on four publicly available expression datasets belonging to four different populations across the world. We identified cliques of various sizes (0 to 7) across the four population networks. Cliques of size seven were further analyzed across populations for their commonality and uniqueness. Forty-nine common cliques of size seven were identified. These cliques were further analyzed based on their connectivity profiles. We found associations between the cliques and their connectivity profiles across networks. With these clique connectivity profiles (CCPs), we were able to identify the divergence among the populations, important biological processes (cell cycle, signal transduction, and cell differentiation), and related gene pathways. Therefore the genes identified in these cliques and their connectivity profiles can be defined as the gene-signatures across populations. In this work we demonstrate the power and effectiveness of cliques to study CRC across populations. Conclusions We developed a new approach where cliques and their connectivity profiles helped elucidate the variation and similarity in CRC gene profiles across four populations with unique dietary habits.
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Affiliation(s)
- Meeta P Pradhan
- School of Informatics, Indiana University Purdue University Indianapolis, IN, USA
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18
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Kumari S, Nie J, Chen HS, Ma H, Stewart R, Li X, Lu MZ, Taylor WM, Wei H. Evaluation of gene association methods for coexpression network construction and biological knowledge discovery. PLoS One 2012; 7:e50411. [PMID: 23226279 PMCID: PMC3511551 DOI: 10.1371/journal.pone.0050411] [Citation(s) in RCA: 81] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2012] [Accepted: 10/18/2012] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Constructing coexpression networks and performing network analysis using large-scale gene expression data sets is an effective way to uncover new biological knowledge; however, the methods used for gene association in constructing these coexpression networks have not been thoroughly evaluated. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical. METHODS AND RESULTS In this study, we compared eight gene association methods - Spearman rank correlation, Weighted Rank Correlation, Kendall, Hoeffding's D measure, Theil-Sen, Rank Theil-Sen, Distance Covariance, and Pearson - and focused on their true knowledge discovery rates in associating pathway genes and construction coordination networks of regulatory genes. We also examined the behaviors of different methods to microarray data with different properties, and whether the biological processes affect the efficiency of different methods. CONCLUSIONS We found that the Spearman, Hoeffding and Kendall methods are effective in identifying coexpressed pathway genes, whereas the Theil-sen, Rank Theil-Sen, Spearman, and Weighted Rank methods perform well in identifying coordinated transcription factors that control the same biological processes and traits. Surprisingly, the widely used Pearson method is generally less efficient, and so is the Distance Covariance method that can find gene pairs of multiple relationships. Some analyses we did clearly show Pearson and Distance Covariance methods have distinct behaviors as compared to all other six methods. The efficiencies of different methods vary with the data properties to some degree and are largely contingent upon the biological processes, which necessitates the pre-analysis to identify the best performing method for gene association and coexpression network construction.
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Affiliation(s)
- Sapna Kumari
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
| | - Jeff Nie
- Morgridge Institute for Research, Madison, Wisconsin, United States of America
| | - Huann-Sheng Chen
- Statistical Methodology and Applications Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Hao Ma
- Division of Animal and Nutritional Sciences, West Virginia University, Morgantown, West Virginia, United States of America
| | - Ron Stewart
- Morgridge Institute for Research, Madison, Wisconsin, United States of America
| | - Xiang Li
- Department of Computer Science, Michigan Technological University, Houghton, Michigan, United States of America
| | - Meng-Zhu Lu
- State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, P.R. China
| | - William M. Taylor
- Department of Computer Science, University of Wisconsin, Madison, Wisconsin, United States of America
| | - Hairong Wei
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
- Department of Computer Science, Michigan Technological University, Houghton, Michigan, United States of America
- Biotechnology Research Center, Michigan Technological University, Houghton, Michigan, United States of America
- School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, Michigan, United States of America
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Zhang J, Lu K, Xiang Y, Islam M, Kotian S, Kais Z, Lee C, Arora M, Liu HW, Parvin JD, Huang K. Weighted frequent gene co-expression network mining to identify genes involved in genome stability. PLoS Comput Biol 2012; 8:e1002656. [PMID: 22956898 PMCID: PMC3431293 DOI: 10.1371/journal.pcbi.1002656] [Citation(s) in RCA: 68] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2012] [Accepted: 07/09/2012] [Indexed: 12/20/2022] Open
Abstract
Gene co-expression network analysis is an effective method for predicting gene functions and disease biomarkers. However, few studies have systematically identified co-expressed genes involved in the molecular origin and development of various types of tumors. In this study, we used a network mining algorithm to identify tightly connected gene co-expression networks that are frequently present in microarray datasets from 33 types of cancer which were derived from 16 organs/tissues. We compared the results with networks found in multiple normal tissue types and discovered 18 tightly connected frequent networks in cancers, with highly enriched functions on cancer-related activities. Most networks identified also formed physically interacting networks. In contrast, only 6 networks were found in normal tissues, which were highly enriched for housekeeping functions. The largest cancer network contained many genes with genome stability maintenance functions. We tested 13 selected genes from this network for their involvement in genome maintenance using two cell-based assays. Among them, 10 were shown to be involved in either homology-directed DNA repair or centrosome duplication control including the well-known cancer marker MKI67. Our results suggest that the commonly recognized characteristics of cancers are supported by highly coordinated transcriptomic activities. This study also demonstrated that the co-expression network directed approach provides a powerful tool for understanding cancer physiology, predicting new gene functions, as well as providing new target candidates for cancer therapeutics.
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Affiliation(s)
- Jie Zhang
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA
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20
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García-Alonso L, Alonso R, Vidal E, Amadoz A, de María A, Minguez P, Medina I, Dopazo J. Discovering the hidden sub-network component in a ranked list of genes or proteins derived from genomic experiments. Nucleic Acids Res 2012; 40:e158. [PMID: 22844098 PMCID: PMC3488210 DOI: 10.1093/nar/gks699] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Genomic experiments (e.g. differential gene expression, single-nucleotide polymorphism association) typically produce ranked list of genes. We present a simple but powerful approach which uses protein–protein interaction data to detect sub-networks within such ranked lists of genes or proteins. We performed an exhaustive study of network parameters that allowed us concluding that the average number of components and the average number of nodes per component are the parameters that best discriminate between real and random networks. A novel aspect that increases the efficiency of this strategy in finding sub-networks is that, in addition to direct connections, also connections mediated by intermediate nodes are considered to build up the sub-networks. The possibility of using of such intermediate nodes makes this approach more robust to noise. It also overcomes some limitations intrinsic to experimental designs based on differential expression, in which some nodes are invariant across conditions. The proposed approach can also be used for candidate disease-gene prioritization. Here, we demonstrate the usefulness of the approach by means of several case examples that include a differential expression analysis in Fanconi Anemia, a genome-wide association study of bipolar disorder and a genome-scale study of essentiality in cancer genes. An efficient and easy-to-use web interface (available at http://www.babelomics.org) based on HTML5 technologies is also provided to run the algorithm and represent the network.
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Affiliation(s)
- Luz García-Alonso
- Department of Bioinformatics, Centro de Investigación Príncipe Felipe (CIPF), Valencia, Spain
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Barrenäs F, Chavali S, Alves AC, Coin L, Jarvelin MR, Jörnsten R, Langston MA, Ramasamy A, Rogers G, Wang H, Benson M. Highly interconnected genes in disease-specific networks are enriched for disease-associated polymorphisms. Genome Biol 2012; 13:R46. [PMID: 22703998 PMCID: PMC3446318 DOI: 10.1186/gb-2012-13-6-r46] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2012] [Revised: 05/25/2012] [Accepted: 06/15/2012] [Indexed: 02/07/2023] Open
Abstract
Background Complex diseases are associated with altered interactions between thousands of genes. We developed a novel method to identify and prioritize disease genes, which was generally applicable to complex diseases. Results We identified modules of highly interconnected genes in disease-specific networks derived from integrating gene-expression and protein interaction data. We examined if those modules were enriched for disease-associated SNPs, and could be used to find novel genes for functional studies. First, we analyzed publicly available gene expression microarray and genome-wide association study (GWAS) data from 13, highly diverse, complex diseases. In each disease, highly interconnected genes formed modules, which were significantly enriched for genes harboring disease-associated SNPs. To test if such modules could be used to find novel genes for functional studies, we repeated the analyses using our own gene expression microarray and GWAS data from seasonal allergic rhinitis. We identified a novel gene, FGF2, whose relevance was supported by functional studies using combined small interfering RNA-mediated knock-down and gene expression microarrays. The modules in the 13 complex diseases analyzed here tended to overlap and were enriched for pathways related to oncological, metabolic and inflammatory diseases. This suggested that this union of the modules would be associated with a general increase in susceptibility for complex diseases. Indeed, we found that this union was enriched with GWAS genes for 145 other complex diseases. Conclusions Modules of highly interconnected complex disease genes were enriched for disease-associated SNPs, and could be used to find novel genes for functional studies.
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Affiliation(s)
- Fredrik Barrenäs
- The Centre for Individualized Medication, Linköping University Hospital, Linköping University, Linköping, Sweden
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Sanz-Pamplona R, Berenguer A, Sole X, Cordero D, Crous-Bou M, Serra-Musach J, Guinó E, Pujana MÁ, Moreno V. Tools for protein-protein interaction network analysis in cancer research. Clin Transl Oncol 2012; 14:3-14. [DOI: 10.1007/s12094-012-0755-9] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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