Exploring candidate genes for human brain diseases from a brain-specific gene network

Liu, Bing; Jiang, Tianzi; Ma, Songde; Zhao, Hengyu; Li, Jun; Jiang, Xingpeng; Zhang, Jing

doi:10.1016/j.bbrc.2006.08.168

Cited by 23 publications

(10 citation statements)

References 25 publications

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“…This observation was shown to be true for the four major causative genes identified for AD (Chen et al 2006;Liu et al 2006). Based on this observation, we included in our set all 12 wellestablished AD causative/susceptibility genes (Table 1), that we name ''seeds,'' and checked the interconnectivity between them to see whether it was still significantly higher than expected.…”

Section: Resultsmentioning

confidence: 86%

“…Indeed, the number of interactions observed between disease-causing genes in several pathologies is often much higher than what would be expected by chance, and the discovery of unexpected relationships between apparently unrelated genes has emerged as a powerful tool for the identification of novel genes involved in complex diseases such as breast cancer (Pujana et al 2007), Huntington (Goehler et al 2004), schizophrenia (Camargo et al 2007), or cerebral ataxias (Lim et al 2006). In the particular case of AD, computational analyses showed that the integration of genetic information with physical and functional interaction data can be useful for prioritizing candidate genes (Krauthammer et al 2004;Chen et al 2006;Liu et al 2006).…”

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confidence: 99%

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Interactome mapping suggests new mechanistic details underlying Alzheimer's disease

Soler-López¹,

Zanzoni²,

Lluís³

et al. 2010

Genome Res.

109

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Recent advances toward the characterization of Alzheimer's disease (AD) have permitted the identification of a dozen of genetic risk factors, although many more remain undiscovered. In parallel, works in the field of network biology have shown a strong link between protein connectivity and disease. In this manuscript, we demonstrate that AD-related genes are indeed highly interconnected and, based on this observation, we set up an interaction discovery strategy to unveil novel AD causative and susceptibility genes. In total, we report 200 high-confidence protein-protein interactions between eight confirmed AD-related genes and 66 candidates. Of these, 31 are located in chromosomal regions containing susceptibility loci related to the etiology of late-onset AD, and 17 show dysregulated expression patterns in AD patients, which makes them very good candidates for further functional studies. Interestingly, we also identified four novel direct interactions among well-characterized AD causative/susceptibility genes (i.e., APP, A2M, APOE, PSEN1, and PSEN2), which support the suggested link between plaque formation and inflammatory processes and provide insights into the intracellular regulation of APP cleavage. Finally, we contextualize the discovered relationships, integrating them with all the interaction data reported in the literature, building the most complete interactome associated to AD. This general view facilitates the analyses of global properties of the network, such as its functional modularity, and triggers many hypotheses on the molecular mechanisms implicated in AD. For instance, our analyses suggest a putative role for PDCD4 as a neuronal death regulator and ECSIT as a molecular link between oxidative stress, inflammation, and mitochondrial dysfunction in AD.

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Section: Resultsmentioning

confidence: 86%

mentioning

confidence: 99%

Interactome mapping suggests new mechanistic details underlying Alzheimer's disease

Soler-López¹,

Zanzoni²,

Lluís³

et al. 2010

Genome Res.

109

View full text Add to dashboard Cite

show abstract

“…However, to determine whether protein interactions can physically occur, it is possible to incorporate direct protein-protein interaction data from yeast 2-hybrid (Y2H) 206 screens. For example, Liu et al , 207 combined gene co-expression, co-citation data mining, Y2H data and other data sources to build a Bayesian network. The authors used the network to predict genes associated with APOE, APP, PSEN1 and PSEN2, identifying multiple previously known AD-associated genes, and some previously unknown genes.…”

Section: A Review Of Systems Studies Of Neurodegenerative Diseasesmentioning

confidence: 99%

Systems biology of neurodegenerative diseases

2015

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Neurodegenerative diseases (NDs) collectively afflict more than 40 million people worldwide. The majority of these diseases lack therapies to slow or stop progression due in large part to the challenge of disentangling the simultaneous presentation of broad, multifaceted pathophysiologic changes. Present technologies and computational capabilities suggest an optimistic future for deconvolving these changes to identify novel mechanisms driving ND onset and progression. In particular, integration of highly multi-dimensional omic analytical techniques (e.g., microarray, mass spectrometry) with computational systems biology approaches provides a systematic methodology to elucidate new mechanisms driving NDs. In this review, we begin by summarizing the complex pathophysiology of NDs associated with protein aggregation, emphasizing the shared complex dysregulation found in all of these diseases, and discuss available experimental ND models. Next, we provide an overview of technological and computational techniques used in systems biology that are applicable to studying NDs. We conclude by reviewing prior studies that have applied these approaches to NDs and comment on the necessity of combining analysis from both human tissues and model systems to identify driving mechanisms. We envision that the integration of computational approaches with multiple omic analyses of human tissues, and mouse and in vitro models, will enable the discovery of new therapeutic strategies for these devastating diseases.

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“…For age-related dementias, the discovery and characterization of shared pathways could pave the way towards common therapeutic interventions. To uncover the networks and complexes in which disease-linked proteins are embedded, interaction studies of many disease linked proteins, such as AD-associated (Chen et al, 2006, Krauthammer et al, 2004, Liu et al, 2006, Norstrom et al, 2010, Perreau et al, 2010, Soler-Lopez et a., 2010, Tamayev et al, 2009), PD-associated (Engelender et al, 1999, Meixner et al, 2011, Schnack et al, 2008, Suzuki, 2006, Woods et al, 2007, Zheng et al, 2008, HD-associated (Goehler et al, 2004, Kaltenbach et al, 2007 and ataxia-associated proteins (Kahle et al, 2011, Lim et al, 2006 as well as of prion protein (PrP) (Nieznanski, 2010) have been conducted. In addition, an interactome study that utilizes artificially designed amyloid-like fibrils has been performed and focused more on the general mechanisms underlying the toxic gain-of-function of β-sheet rich proteins (Olzscha et al, 2011).…”

Section: Interactome Mapping Of Disease-linked Proteins In Neurodegenmentioning

confidence: 99%

Defining the Cellular Interactome of Disease-Linked Proteins in Neurodegeneration

Arndt¹,

Vorberg²

2012

Protein Interactions

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Exploring candidate genes for human brain diseases from a brain-specific gene network

Cited by 23 publications

References 25 publications

Interactome mapping suggests new mechanistic details underlying Alzheimer's disease

Interactome mapping suggests new mechanistic details underlying Alzheimer's disease

Systems biology of neurodegenerative diseases

Defining the Cellular Interactome of Disease-Linked Proteins in Neurodegeneration

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