A computational analysis of protein-protein interaction networks in neurodegenerative diseases

Goñi, Joaquín; Esteban, Francisco J.; Mendizábal, Nieves Vélez de; Sepulcre, Jorge; Ardanza-Trevijano, Sergio; Agirrezabal, Ion; Villoslada, Pablo

doi:10.1186/1752-0509-2-52

Cited by 104 publications

(70 citation statements)

References 37 publications

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“…Recently, a number of approaches analyzed bottlenecks in human protein-protein interaction networks. These studies have identified genes involved in neurodegenerative diseases (Goni et al, 2008), and characterized properties of successful drug targets (Yao and Rzhetsky, 2008) and targets of different pathogens (Dyer et al, 2008). Bottleneck nodes in other types of networks, for example those derived from mining literature, have also been used to identify disease-associated genes (Ozgur et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Bottlenecks and Hubs in Inferred Networks Are Important for Virulence in Salmonella typhimurium

McDermott

Taylor

Yoon

et al. 2009

Journal of Computational Biology

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Recent advances in experimental methods have provided sufficient data to consider systems as large networks of interconnected components. High-throughput determination of protein-protein interaction networks has led to the observation that topological bottlenecks, proteins defined by high centrality in the network, are enriched in proteins with systems-level phenotypes such as essentiality. Global transcriptional profiling by microarray analysis has been used extensively to characterize systems, for example, examining cellular response to environmental conditions and effects of genetic mutations. These transcriptomic datasets have been used to infer regulatory and functional relationship networks based on co-regulation. We use the context likelihood of relatedness (CLR) method to infer networks from two datasets gathered from the pathogen Salmonella typhimurium: one under a range of environmental culture conditions and the other from deletions of 15 regulators found to be essential in virulence. Bottleneck and hub genes were identified from these inferred networks, and we show for the first time that these genes are significantly more likely to be essential for virulence than their non-bottleneck or non-hub counterparts. Networks generated using simple similarity metrics (correlation and mutual information) did not display this behavior. Overall, this study demonstrates that topology of networks inferred from global transcriptional profiles provides information about the systems-level roles of bottleneck genes. Analysis of the differences between the two CLR-derived networks suggests that the bottleneck nodes are either mediators of transitions between system states or sentinels that reflect the dynamics of these transitions.

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Section: Introductionmentioning

confidence: 99%

Bottlenecks and Hubs in Inferred Networks Are Important for Virulence in Salmonella typhimurium

McDermott

Taylor

Yoon

et al. 2009

Journal of Computational Biology

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“…Analysis of the constructed DEN revealed that six genes (APP, UBC, CAND1, RPA, YWHAG, and NEDD8) were hub nodes and none was a DEG. DEGs, showing significantly different expression profiles in the case and control groups, are usually found using traditional statistical techniques, such as the t-test or fold change (Goñi et al, 2008;Ray and Zhang, 2010). Although DEGs are regarded as candidates for a role in pathogenesis, they are generally selected out separately, while co-expression of genes is ignored (Kostka and Spang, 2004).…”

Section: Discussionmentioning

confidence: 99%

Selecting key genes associated with osteosarcoma based on a differential expression network

Wang¹,

Jia²,

Xu³

et al. 2015

Genet. Mol. Res.

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ABSTRACT. Despite recent advances in osteosarcoma diagnosis and therapy, much remains unclear about the molecular mechanisms involved in the disorder, and the discovery of novel drug-targeted genes is essential. We explored the potential molecular mechanisms and target genes involved in the development and progression of osteosarcoma. First, we identified the differentially expressed genes in osteosarcoma patients and matching normal controls. We then constructed a differential expression network based on differential and non-differential interactions. Pathwayenrichment analysis was performed based on the nodes contained in the main differential expression network. Centrality analysis was used to select hub genes that may play vital roles in the progression of human osteosarcoma. Our research revealed a total of 176 differentially expressed genes including 82 upregulated and 94 downregulated genes. A differential expression network was constructed that included 992 gene pairs (1043 nodes). Pathway-enrichment analysis indicated that the nodes in the 17709 Key genes associated with osteosarcoma ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 14 (4): 17708-17717 (2015) differential expression network were mainly enriched in several pathways such as those involved in cancer, cell cycle, ubiquitin-mediated proteolysis, DNA replication, ribosomes, T-cell receptor signaling, spliceosomes, neurotrophin signaling, oxidative phosphorylation, and tight junctions. Six hub genes (APP, UBC, CAND1, RPA, YWHAG, and NEDD8) were discovered; of these, two genes (UBC and RPA) were also found to be disease genes. Our study predicted that UBC and RPA had potential as target genes for the diagnosis and treatment of osteosarcoma.

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“…Systems biology has applications in various areas related to medicine, including biomarker discovery, improvement of drug target identification and prognosis assessment [101,102]. For example, using network analysis, one of the most pursued approaches in systems biology, we were able to identify Jagged-1/Notch as a candidate therapeutic target for MS [103].…”

Section: Systems Biology Approach For the Development Of Personalizedmentioning

confidence: 98%

Pharmacogenomics of multiple sclerosis: in search for a personalized therapy

Martínez-Forero

Peláez

Villoslada

2008

Expert Opinion on Pharmacotherapy

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Background: Multiple sclerosis (MS) is an inflammatory disease of the central nervous system that affects young adults and provokes severe disability, imposing a high health and social burden. Current therapies for MS include interferon-β, glatiramer acetate, natalizumab and chemotherapy. These therapies decrease the number of relapses and partially prevent disability accumulation. However, their efficacy is only moderate, they have common adverse effects and impose a high cost to health systems. The identification of biomarkers will allow responders and non-responders to therapy to be identified, increasing the efficacy and adherence to therapy, and the pharmaco-economic profile of theses drugs. Objectives and Conclusion: In this review we examine the pharmacogenetic studies that have evaluated the clinical response to interferon-β, and to a lesser extent, glatiramer acetate and natalizumab. Finally, we discuss how systems biology can be used to integrate biological and clinical data in order to develop personalized medicine for MS.

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A computational analysis of protein-protein interaction networks in neurodegenerative diseases

Cited by 104 publications

References 37 publications

Bottlenecks and Hubs in Inferred Networks Are Important for Virulence in Salmonella typhimurium

Bottlenecks and Hubs in Inferred Networks Are Important for Virulence in Salmonella typhimurium

Selecting key genes associated with osteosarcoma based on a differential expression network

Pharmacogenomics of multiple sclerosis: in search for a personalized therapy

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