ResponseNet: revealing signaling and regulatory networks linking genetic transcriptomic screening data

Cellular response to stimuli is typically complex and involves both regulatory and metabolic processes. Large-scale experimental efforts to identify components of these processes often comprise of genetic screening and transcriptomic profiling assays. We previously established that in yeast genetic screens tend to identify response regulators, while transcriptomic profiling assays tend to identify components of metabolic processes. ResponseNet is a network-optimization approach that integrates the results from these assays with data of known molecular interactions. Specifically, ResponseNet identifies a high-probability sub-network, composed of signaling and regulatory molecular interaction paths, through which putative response regulators may lead to the measured transcriptomic changes. Computationally, this is achieved by formulating a minimum-cost flow optimization problem and solving it efficiently using linear programming tools. The ResponseNet web server offers a simple interface for applying ResponseNet. Users can upload weighted lists of proteins and genes and obtain a sparse, weighted, molecular interaction sub-network connecting their data. The predicted sub-network and its gene ontology enrichment analysis are presented graphically or as text. Consequently, the ResponseNet web server enables researchers that were previously limited to separate analysis of their distinct, large-scale experiments, to meaningfully integrate their data and substantially expand their understanding of the underlying cellular response. ResponseNet is available at http://bioinfo.bgu.ac.il/respnet.Seventh Framework Programme (European Commission) (FP7-PEOPLE-MCA-IRG)United States-Israel Binational Science Foundation (Grant 2009323

The landscape of molecular chaperones across human tissues reveals a layered architecture of core and variable chaperones

The sensitivity of the protein-folding environment to chaperone disruption can be highly tissue-specific. Yet, the organization of the chaperone system across physiological human tissues has received little attention. Through computational analyses of large-scale tissue transcriptomes, we unveil that the chaperone system is composed of core elements that are uniformly expressed across tissues, and variable elements that are differentially expressed to fit with tissue-specific requirements. We demonstrate via a proteomic analysis that the muscle-specific signature is functional and conserved. Core chaperones are significantly more abundant across tissues and more important for cell survival than variable chaperones. Together with variable chaperones, they form tissue-specific functional networks. Analysis of human organ development and aging brain transcriptomes reveals that these functional networks are established in development and decline with age. In this work, we expand the known functional organization of de novo versus stress-inducible eukaryotic chaperones into a layered core-variable architecture in multi-cellular organisms

Cycle-centrality in complex networks

Networks are versatile representations of the interactions between entities in complex systems. Cycles on such networks represent feedback processes which play a central role in system dynamics. In this work, we introduce a measure of the importance of any individual cycle, as the fraction of the total information flow of the network passing through the cycle. This measure is computationally cheap, numerically well-conditioned, induces a centrality measure on arbitrary subgraphs and reduces to the eigenvector centrality on vertices. We demonstrate that this measure accurately reflects the impact of events on strategic ensembles of economic sectors, notably in the US economy. As a second example, we show that in the protein-interaction network of the plant Arabidopsis thaliana, a model based on cycle-centrality better accounts for pathogen activity than the state-of-art one. This translates into pathogen-targeted-proteins being concentrated in a small number of triads with high cycle-centrality. Algorithms for computing the centrality of cycles and subgraphs are available for download

A centrality measure for cycles and subgraphs II

In a recent work we introduced a measure of importance for groups of vertices in a complex network. This centrality for groups is always between 0 and 1 and induces the eigenvector centrality over vertices. Furthermore, its value over any group is the fraction of all network flows intercepted by this group. Here we provide the rigorous mathematical constructions underpinning these results via a semi-commutative extension of a number theoretic sieve. We then established further relations between the eigenvector centrality and the centrality proposed here, showing that the latter is a proper extension of the former to groups of nodes. We finish by comparing the centrality proposed here with the notion of group-centrality introduced by Everett and Borgatti on two real-world networks: the Wolfe’s dataset and the protein-protein interaction network of the yeast Saccharomyces cerevisiae. In this latter case, we demonstrate that the centrality is able to distinguish protein complexe

Compounds from an Unbiased Chemical Screen Reverse Both Er-to-Golgi Trafficking Defects and Mitochondrial Dysfunction in Parkinson's Disease Models

α-Synuclein (α-syn) is a small lipid-binding protein involved in vesicle trafficking whose function is poorly characterized. It is of great interest to human biology and medicine because α-syn dysfunction is associated with several neurodegenerative disorders, including Parkinson’s disease (PD). We previously created a yeast model of α-syn pathobiology, which established vesicle trafficking as a process that is particularly sensitive to α-syn expression. We also uncovered a core group of proteins with diverse activities related to α-syn toxicity that is conserved from yeast to mammalian neurons. Here, we report that a yeast strain expressing a somewhat higher level of α-syn also exhibits strong defects in mitochondrial function. Unlike our previous strain, genetic suppression of endoplasmic reticulum (ER)-to-Golgi trafficking alone does not suppress α-syn toxicity in this strain. In an effort to identify individual compounds that could simultaneously rescue these apparently disparate pathological effects of α-syn, we screened a library of 115,000 compounds. We identified a class of small molecules that reduced α-syn toxicity at micromolar concentrations in this higher toxicity strain. These compounds reduced the formation of α-syn foci, re-established ER-to-Golgi trafficking and ameliorated α-syn-mediated damage to mitochondria. They also corrected the toxicity of α-syn in nematode neurons and in primary rat neuronal midbrain cultures. Remarkably, the compounds also protected neurons against rotenone-induced toxicity, which has been used to model the mitochondrial defects associated with PD in humans. That single compounds are capable of rescuing the diverse toxicities of α-syn in yeast and neurons suggests that they are acting on deeply rooted biological processes that connect these toxicities and have been conserved for a billion years of eukaryotic evolution. Thus, it seems possible to develop novel therapeutic strategies to simultaneously target the multiple pathological features of PD.MGH/MIT Morris Udall Center of Excellence in Parkinson Disease Research (NS038372)Michael J. Fox Foundation for Parkinson's ResearchHoward Hughes Medical InstituteUnited States. National Institutes of Health (NS049221)American Parkinson Disease Association, Inc

Small heat-shock protein HSPB3 promotes myogenesis by regulating the lamin B receptor

One of the critical events that regulates muscle cell differentiation is the replacement of the lamin B receptor (LBR)-tether with the lamin A/C (LMNA)-tether to remodel transcription and induce differentiation-specific genes. Here, we report that localization and activity of the LBR-tether are crucially dependent on the muscle-specific chaperone HSPB3 and that depletion of HSPB3 prevents muscle cell differentiation. We further show that HSPB3 binds to LBR in the nucleoplasm and maintains it in a dynamic state, thus promoting the transcription of myogenic genes, including the genes to remodel the extracellular matrix. Remarkably, HSPB3 overexpression alone is sufficient to induce the differentiation of two human muscle cell lines, LHCNM2 cells, and rhabdomyosarcoma cells. We also show that mutant R116P-HSPB3 from a myopathy patient with chromatin alterations and muscle fiber disorganization, forms nuclear aggregates that immobilize LBR. We find that R116P-HSPB3 is unable to induce myoblast differentiation and instead activates the unfolded protein response. We propose that HSPB3 is a specialized chaperone engaged in muscle cell differentiation and that dysfunctional HSPB3 causes neuromuscular disease by deregulating LBR

Bridging topological and functional information in protein interaction networks by short loops profiling

Protein-protein interaction networks (PPINs) have been employed to identify potential novel interconnections between proteins as well as crucial cellular functions. In this study we identify fundamental principles of PPIN topologies by analysing network motifs of short loops, which are small cyclic interactions of between 3 and 6 proteins. We compared 30 PPINs with corresponding randomised null models and examined the occurrence of common biological functions in loops extracted from a cross-validated high-confidence dataset of 622 human protein complexes. We demonstrate that loops are an intrinsic feature of PPINs and that specific cell functions are predominantly performed by loops of different lengths. Topologically, we find that loops are strongly related to the accuracy of PPINs and define a core of interactions with high resilience. The identification of this core and the analysis of loop composition are promising tools to assess PPIN quality and to uncover possible biases from experimental detection methods. More than 96% of loops share at least one biological function, with enrichment of cellular functions related to mRNA metabolic processing and the cell cycle. Our analyses suggest that these motifs can be used in the design of targeted experiments for functional phenotype detection.This research was supported by the Biotechnology and Biological Sciences Research Council (BB/H018409/1 to AP, ACCC and FF, and BB/J016284/1 to NSBT) and by the Leukaemia & Lymphoma Research (to NSBT and FF). SSC is funded by a Leukaemia & Lymphoma Research Gordon Piller PhD Studentship

Exploring and challenging the network of angiogenesis

Angiogenesis is one of the hallmarks of cancer and, as such, one of the alternative general targets for anticancer therapy. Since angiogenesis is a complex process involving a high number of interconnected components, a network approach would be a convenient systemic way to analyse responses to directed drug attacks. Herein we show that, although the angiogenic network is easily broken by short combinations of directed attacks, it still remains essentially functional by keeping the global patterns and local efficiency essentially unaltered after these attacks. This is a clear sign of its high robustness and resilience and stresses the need of directed, combined attacks for an effective blockade of the process. The results of this theoretical study could be relevant for the design of new antiangiogenic therapies and the selection of their targets

The Index-Based Subgraph Matching Algorithm (ISMA): Fast Subgraph Enumeration in Large Networks Using Optimized Search Trees

Subgraph matching algorithms are designed to find all instances of predefined subgraphs in a large graph or network and play an important role in the discovery and analysis of so-called network motifs, subgraph patterns which occur more often than expected by chance. We present the index-based subgraph matching algorithm (ISMA), a novel tree-based algorithm. ISMA realizes a speedup compared to existing algorithms by carefully selecting the order in which the nodes of a query subgraph are investigated. In order to achieve this, we developed a number of data structures and maximally exploited symmetry characteristics of the subgraph. We compared ISMA to a naive recursive tree-based algorithm and to a number of well-known subgraph matching algorithms. Our algorithm outperforms the other algorithms, especially on large networks and with large query subgraphs. An implementation of ISMA in Java is freely available at http://sourceforge.net/projects/isma

CRISPR-Cas9 screens in human cells and primary neurons identify modifiers of C9ORF72 dipeptide-repeat-protein toxicity.

Hexanucleotide-repeat expansions in the C9ORF72 gene are the most common cause of amyotrophic lateral sclerosis and frontotemporal dementia (c9ALS/FTD). The nucleotide-repeat expansions are translated into dipeptide-repeat (DPR) proteins, which are aggregation prone and may contribute to neurodegeneration. We used the CRISPR-Cas9 system to perform genome-wide gene-knockout screens for suppressors and enhancers of C9ORF72 DPR toxicity in human cells. We validated hits by performing secondary CRISPR-Cas9 screens in primary mouse neurons. We uncovered potent modifiers of DPR toxicity whose gene products function in nucleocytoplasmic transport, the endoplasmic reticulum (ER), proteasome, RNA-processing pathways, and chromatin modification. One modifier, TMX2, modulated the ER-stress signature elicited by C9ORF72 DPRs in neurons and improved survival of human induced motor neurons from patients with C9ORF72 ALS. Together, our results demonstrate the promise of CRISPR-Cas9 screens in defining mechanisms of neurodegenerative diseases