Identification of Causal Genetic Drivers of Human Disease through Systems-Level Analysis of Regulatory Networks

Chen, James C.; Alvarez, Mariano J.; Talos, Flaminia; Dhruv, Harshil D.; Rieckhof, Gabrielle E.; Iyer, Archana; Diefes, Kristin; Aldape, Kenneth; Berens, Michael E.; Shen, Michael M.; Califano, Andrea

doi:10.1016/j.cell.2016.07.036

Cited by 26 publications

(31 citation statements)

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“…These alterations were not statistically significant by genome wide association studies. In addition, novel alterations predicted by the methodology for the mesenchymal subtype of high-grade glioma were experimentally validated as the most frequent causal determinants of the disease subtype [40]. Critically, this analysis could be performed on an individual sample basis, thus completely avoiding mechanism heterogeneity as a confounding factor that would negatively affect discovery when averaging on patients representing distinct mechanisms (e.g., all AD-affected individuals).…”

Section: Creating the Assembly Manual Of The Alzheimer's Cellmentioning

confidence: 99%

“…More recently, we have shown that MARINa-inferred MRs represent tight regulatory bottlenecks that integrate a large spectrum of germline variants and somatic alterations in upstream pathways [40]. These bottlenecks allow genetic variants and alterations patterns that are substantially distinct in different patients to converge on activating or inactivating key genetic programs that represent the hallmarks of the disease.…”

Section: Creating the Assembly Manual Of The Alzheimer's Cellmentioning

confidence: 99%

“…The MARINa algorithm was developed to infer the regulators that are causally responsible for implementing a phenotype-specific gene expression signature initially from multiple gene expression profiles [12,14], and, more recently, even from a single gene expression profile [7,40]. This is accomplished by measuring the enrichment of over-and underexpressed genes in positively regulated and repressed targets in the regulon of every possible regulator protein represented in the interactome, thus providing an accurate and extremely robust predictor of the differential activity of a regulator.…”

Section: Creating the Assembly Manual Of The Alzheimer's Cellmentioning

confidence: 99%

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A Systems Approach to Drug Discovery in Alzheimer's Disease

et al. 2015

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In the articles included in this volume, one feels a strong frustration among the writers with the slow course of therapeutics development for Alzheimer's disease and with the clinical failure of targeted therapeutic agents despite substantial progress in our understanding of the biology and biochemistry of the disease. Keywords Master regulator . Interactome . Human neuronsTo date, approaches to Alzheimer disease (AD) therapeutics have followed 2 main avenues. The first addresses neurotransmitter deficits in the early phases of the disease. This approach has led to the handful of AD drugs currently on the market that provide short-term symptomatic improvement but do not alter the course of the disease. The second approach has been directed at decreasing the burden of beta-amyloid (Aβ) in the brain by active or passive immunization or by inhibiting activity of β-or γ-secretases. To date, none of the approaches in this second class has been successful, although trials on β-or γ-secretase (BACE) inhibitors are ongoing.The lack of overt success has been even more frustrating because, in transgenic (Aβ-overproducing) mouse models of AD, Aβ-lowering approaches, as well as treatments with small molecules and biologics, have been successful in blocking memory loss, restoring memory function, reversing dendritic spine alterations, and reversing inhibition of longterm potentiation [1][2][3]. Indeed, as animal models only partially recapitulate the human AD phenotype and because human brain tissue is available only postmortem, translation of preclinical findings to the clinics has been fraught with difficulties.For instance, the characteristic intraneuronal neurofibrillary tangles and extensive neuronal loss observed in human AD are absent in mouse models of the disease. This dichotomy suggests that mouse models replicate only presymptomatic AD stages, while clinical manifestations appear only after neuronal loss becomes irreversible. This hypothesis is directly addressed by ongoing trials, where individuals with genetic mutations that predispose to early AD onset are being treated with Aβ-targeted therapies to assess whether presymptomatic treatment may block an otherwise certain disease progression. Beyond Aβ-targeted therapies, novel approaches are being developed based on inhibition of tau phosphorylation and aggregation, and on blockade of synaptic loss, leveraging "candidate" target approaches derived from human and animal data. Unfortunately, it has so far been impossible to discern whether "candidate genes" represent causal determinants of the disease process or are the downstream result of them.In this review, we will discuss the potential of adapting integrative systems biology approaches that have already proven extremely valuable in understanding multiple cancer related phenotypes to human neurodegenerative diseases.

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Section: Creating the Assembly Manual Of The Alzheimer's Cellmentioning

confidence: 99%

Section: Creating the Assembly Manual Of The Alzheimer's Cellmentioning

confidence: 99%

Section: Creating the Assembly Manual Of The Alzheimer's Cellmentioning

confidence: 99%

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A Systems Approach to Drug Discovery in Alzheimer's Disease

et al. 2015

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“…The methods have shifted from intuitive inference of local connectivities to comprehensive analysis of large networks, involving heterogeneous data sets from high-throughput experiments and complex theoretical tools (6)(7)(8)(9)(10). Despite significant advances, a fundamental reverse engineering bottleneck is the ability to discriminate between direct and indirect connections.…”

mentioning

confidence: 99%

Discriminating direct and indirect connectivities in biological networks

Kang

Moore

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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Reverse engineering of biological pathways involves an iterative process between experiments, data processing, and theoretical analysis. Despite concurrent advances in quality and quantity of data as well as computing resources and algorithms, difficulties in deciphering direct and indirect network connections are prevalent. Here, we adopt the notions of abstraction, emulation, benchmarking, and validation in the context of discovering features specific to this family of connectivities. After subjecting benchmark synthetic circuits to perturbations, we inferred the network connections using a combination of nonparametric single-cell data resampling and modular response analysis. Intriguingly, we discovered that recovered weights of specific network edges undergo divergent shifts under differential perturbations, and that the particular behavior is markedly different between topologies. Our results point to a conceptual advance for reverse engineering beyond weight inference. Investigating topological changes under differential perturbations may address the longstanding problem of discriminating direct and indirect connectivities in biological networks. A focal point of systems biology is the reverse engineering of gene regulatory networks (1-5). The methods have shifted from intuitive inference of local connectivities to comprehensive analysis of large networks, involving heterogeneous data sets from high-throughput experiments and complex theoretical tools (6-10). Despite significant advances, a fundamental reverse engineering bottleneck is the ability to discriminate between direct and indirect connections. In a simple case, assuming three nodes in a cascade formulation, where an input node is activating an intermediary node which in turn is activating an output node, a reverse engineering algorithm may infer an activating edge from the input node to the output, even though there is no direct biological interaction.Unfortunately, the limitation in correctly distinguishing the effects stemming from indirect connectivities is pervasive (11-13) and justifies the urgent need for new and reliable methods to eliminate spurious edges. Importantly, remedies to address this problem should not further muddle the interpretation by removing true network edges (14). A number of theoretical approaches have been proposed to overcome this hurdle (4, 15-18), but the ability to experimentally verify the conclusions drawn by reverse engineering tools remains paramount.The majority of efforts to address the verification issue adopt in silico benchmark suites that are based on biological pathway approximations (19). Although these models do include a number of commonly observed topologies and have provided significant insights, they do not fully capture the complexity of the biological realm and the associated heterogeneity and intrinsic variability. On the other hand, engineered synthetic gene circuits are orthogonal to the endogenous pathways yet operate within the natural cellular context using the available resources. Thus, sy...

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“…Recurrent epigenetic alterations, especially DNA methylation [52], and large-scale functional screens [53][54][55][56] can also be used to identified novel candidate drivers. Then, these alterations or perturbations are integrative analyzed with gene expression variations (either cis-or trans-acting effects), the functional indications of genetic alterations [57][58][59][60][61]. TCGA researchers have used this method to identify molecular subtypes of various cancers, such as the BRAF V600E -like and RAS-like subtypes of papillary thyroid carcinoma [62].…”

Section: Regulatory Integrative Clusteringmentioning

confidence: 99%

Integrative clustering methods of multi‐omics data for molecule‐based cancer classifications

Wang

2016

Quant. Biol.

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One goal of precise oncology is to re-classify cancer based on molecular features rather than its tissue origin. Integrative clustering of large-scale multi-omics data is an important way for molecule-based cancer classification. The data heterogeneity and the complexity of inter-omics variations are two major challenges for the integrative clustering analysis. According to the different strategies to deal with these difficulties, we summarized the clustering methods as three major categories: direct integrative clustering, clustering of clusters and regulatory integrative clustering. A few practical considerations on data pre-processing, post-clustering analysis and pathway-based analysis are also discussed.

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Identification of Causal Genetic Drivers of Human Disease through Systems-Level Analysis of Regulatory Networks

Cited by 26 publications

References 0 publications

A Systems Approach to Drug Discovery in Alzheimer's Disease

A Systems Approach to Drug Discovery in Alzheimer's Disease

Discriminating direct and indirect connectivities in biological networks

Integrative clustering methods of multi‐omics data for molecule‐based cancer classifications

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