Ranking Candidate Disease Genes from Gene Expression and Protein Interaction: A Katz-Centrality Based Approach

Zhao, Jing; Yang, Tinghong; Huang, Yongxu; Holme, Petter

doi:10.1371/journal.pone.0024306

Cited by 79 publications

(50 citation statements)

References 43 publications

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“…This has been the computational approach most commonly used to create disease-relevant interaction networks (Chuang et al 2007;Zhao et al 2011). This procedure yielded the interaction network PPI D (Supplemental Fig.…”

Section: Resultsmentioning

confidence: 99%

Systematic interaction network filtering identifies CRMP1 as a novel suppressor of huntingtin misfolding and neurotoxicity

et al. 2015

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Assemblies of huntingtin (HTT) fragments with expanded polyglutamine (polyQ) tracts are a pathological hallmark of Huntington's disease (HD). The molecular mechanisms by which these structures are formed and cause neuronal dysfunction and toxicity are poorly understood. Here, we utilized available gene expression data sets of selected brain regions of HD patients and controls for systematic interaction network filtering in order to predict disease-relevant, brain region-specific HTT interaction partners. Starting from a large protein-protein interaction (PPI) data set, a step-by-step computational filtering strategy facilitated the generation of a focused PPI network that directly or indirectly connects 13 proteins potentially dysregulated in HD with the disease protein HTT. This network enabled the discovery of the neuron-specific protein CRMP1 that targets aggregation-prone, N-terminal HTT fragments and suppresses their spontaneous self-assembly into proteotoxic structures in various models of HD. Experimental validation indicates that our network filtering procedure provides a simple but powerful strategy to identify disease-relevant proteins that influence misfolding and aggregation of polyQ disease proteins.

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Section: Resultsmentioning

confidence: 99%

Systematic interaction network filtering identifies CRMP1 as a novel suppressor of huntingtin misfolding and neurotoxicity

et al. 2015

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“…It is well confirmed that the propensity of many diseases can be reflected in a difference of gene expression levels in particular cell types40. For this reason, genes showing a different expression levels in control crowds (i.e.…”

Section: Methodsmentioning

confidence: 88%

Spatio-temporal analysis of type 2 diabetes mellitus based on differential expression networks

Sun

Liu

Zeng

et al. 2013

Sci Rep

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T2DM is complex in its dynamical dependence on multiple tissues, disease states, and factors' interactions. However, most existing work devoted to characterizing its pathophysiology from one static tissue, individual factors, or single state. Here we perform a spatio-temporal analysis on T2DM by developing a new form of molecular network, i.e. ‘differential expression network’ (DEN), which can reflect phenotype differences at network level. Static DENs show that three tissues (white adipose, skeletal muscle, and liver) all suffer from severe inflammation and perturbed metabolism, among which metabolic functions are seriously affected in liver. Dynamical analysis on DENs reveals metabolic function changes in adipose and liver are consistent with insulin resistance (IR) deterioration. Close investigation on IR pathway identifies ‘disease interactions’, revealing that IR deterioration is earlier than that on SlC2A4 in adipose and muscle. Our analysis also provides evidence that rising of insulin secretion is the root cause of IR in diabetes.

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“…Starting with a group of SpA-active genes as seeds, we applied a Katz' centrality based index [33] to prioritize candidate genes in the PPI network [15]. Given a weighted human interactome represented as a matrix W corresponding to the interaction strength between genes, and a set D of k known disease-active genes as seeds, we define vector x = ( x 1 , x 2 ,..., x n ) T as initially known activity of genes in the disease, with x i = 1 if gene i is in the set D, x i = 0 otherwise.…”

Section: Resultsmentioning

confidence: 99%

Insights into the pathogenesis of axial spondyloarthropathy from network and pathway analysis

Zhao

Chen

Yang

et al. 2012

BMC Systems Biology

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BackgroundComplex chronic diseases are usually not caused by changes in a single causal gene but by an unbalanced regulating network resulting from the dysfunctions of multiple genes or their products. Therefore, network based systems approach can be helpful for the identification of candidate genes related to complex diseases and their relationships. Axial spondyloarthropathy (SpA) is a group of chronic inflammatory joint diseases that mainly affect the spine and the sacroiliac joints. The pathogenesis of SpA remains largely unknown.ResultsIn this paper, we conducted a network study of the pathogenesis of SpA. We integrated data related to SpA, from the OMIM database, proteomics and microarray experiments of SpA, to prioritize SpA candidate disease genes in the context of human protein interactome. Based on the top ranked SpA related genes, we constructed a SpA specific PPI network, identified potential pathways associated with SpA, and finally sketched an overview of biological processes involved in the development of SpA.ConclusionsThe protein-protein interaction (PPI) network and pathways reflect the link between the two pathological processes of SpA, i.e., immune mediated inflammation, as well as imbalanced bone modelling caused new boneformation and bone loss. We found that some known disease causative genes, such as TNFand ILs, play pivotal roles in this interaction.

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Ranking Candidate Disease Genes from Gene Expression and Protein Interaction: A Katz-Centrality Based Approach

Cited by 79 publications

References 43 publications

Systematic interaction network filtering identifies CRMP1 as a novel suppressor of huntingtin misfolding and neurotoxicity

Systematic interaction network filtering identifies CRMP1 as a novel suppressor of huntingtin misfolding and neurotoxicity

Spatio-temporal analysis of type 2 diabetes mellitus based on differential expression networks

Insights into the pathogenesis of axial spondyloarthropathy from network and pathway analysis

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