A protein interaction network links GIT1, an enhancer of huntingtin aggregation, to Huntington's disease

Exploring the human diseasome: the human disease network

Advances in genome-scale molecular biology and molecular genetics have greatly elevated our knowledge on the basic components of human biology and diseases. At the same time, the importance of cellular networks between those biological components is increasingly appreciated. Built upon these recent technological and conceptual advances, a new discipline called the network medicine, an approach to understand human diseases from a network point-of-view, is about to emerge. In this review article, we will survey some recent endeavours along this direction, centred on the concept and applications of the human diseasome and the human disease network. Questions, and partial answers thereof, such as how the connectivity between molecular parts translates into the relationships between the related disorders on a global scale and how central the disease-causing genetic components are in the cellular network, will be discussed. The use of the diseasome in combination with various interactome networks and other disease-related factors is also reviewed.

Diversity in genetic in vivo methods for protein-protein interaction studies: from the yeast two-hybrid system to the mammalian split-luciferase system

The yeast two-hybrid system pioneered the field of in vivo protein-protein interaction methods and undisputedly gave rise to a palette of ingenious techniques that are constantly pushing further the limits of the original method. Sensitivity and selectivity have improved because of various technical tricks and experimental designs. Here we present an exhaustive overview of the genetic approaches available to study in vivo binary protein interactions, based on two-hybrid and protein fragment complementation assays. These methods have been engineered and employed successfully in microorganisms such as Saccharomyces cerevisiae and Escherichia coli, but also in higher eukaryotes. From single binary pairwise interactions to whole-genome interactome mapping, the self-reassembly concept has been employed widely. Innovative studies report the use of proteins such as ubiquitin, dihydrofolate reductase, and adenylate cyclase as reconstituted reporters. Protein fragment complementation assays have extended the possibilities in protein-protein interaction studies, with technologies that enable spatial and temporal analyses of protein complexes. In addition, one-hybrid and three-hybrid systems have broadened the types of interactions that can be studied and the findings that can be obtained. Applications of these technologies are discussed, together with the advantages and limitations of the available assays.

[DNA repair and neurodegeneration: a common pathology shared by polyglutamine diseases]

Nuclear dysfunctions have been implicated in the pathology of polyglutamine diseases, while the details remain unclear. By employing various omics approaches, we have identified target molecules of mutant polyglutamine proteins in the nucleus. Proteome analysis of soluble nuclear proteins identified decreased HMGB1/2 in vulnerable neurons of Huntington's disease (HD) and spinocerebellar ataxia type 1 (SCA1). Interactome analysis unraveled Ku70 as a direct binding partner of mutant huntingtin. DNA damage repair is significantly impaired by interaction of mutant polyglutamine proteins with HMGB or Ku70, and the functional rescue of these molecules alleviated symptoms and pathologies of HD and SCA1. These results strongly suggest that impairment of DNA damage repair is a common pathology shared by multiple polyglutamine diseases.

Charting the landscape of tandem BRCT domain-mediated protein interactions

Eukaryotic cells have evolved an intricate system to resolve DNA damage to prevent its transmission to daughter cells. This system, collectively known as the DNA damage response (DDR) network, includes many proteins that detect DNA damage, promote repair, and coordinate progression through the cell cycle. Because defects in this network can lead to cancer, this network constitutes a barrier against tumorigenesis. The modular BRCA1 carboxyl-terminal (BRCT) domain is frequently present in proteins involved in the DDR, can exist either as an individual domain or as tandem domains (tBRCT), and can bind phosphorylated peptides. We performed a systematic analysis of protein-protein interactions involving tBRCT in the DDR by combining literature curation, yeast two-hybrid screens, and tandem affinity purification coupled to mass spectrometry. We identified 23 proteins containing conserved BRCT domains and generated a human protein-protein interaction network for seven proteins with tBRCT. This study also revealed previously unknown components in DNA damage signaling, such as COMMD1 and the target of rapamycin complex mTORC2. Additionally, integration of tBRCT domain interactions with DDR phosphoprotein studies and analysis of kinase-substrate interactions revealed signaling subnetworks that may aid in understanding the involvement of tBRCT in disease and DNA repair.

An in vitro perspective on the molecular mechanisms underlying mutant huntingtin protein toxicity

Huntington's disease (HD) is a devastating neurodegenerative disorder whose main hallmark is brain atrophy. However, several peripheral organs are considerably affected and their symptoms may, in fact, manifest before those resulting from brain pathology. HD is of genetic origin and caused by a mutation in the huntingtin gene. The mutated protein has detrimental effects on cell survival, but whether the mutation leads to a gain of toxic function or a loss of function of the altered protein is still highly controversial. Most currently used in vitro models have been designed, to a large extent, to investigate the effects of the aggregation process in neuronal-like cells. However, as the pathology involves several other organs, new in vitro models are critically needed to take into account the deleterious effects of mutant huntingtin in peripheral tissues, and thus to identify new targets that could lead to more effective clinical interventions in the early course of the disease. This review aims to present current in vitro models of HD pathology and to discuss the knowledge that has been gained from these studies as well as the new in vitro tools that have been developed, which should reflect the more global view that we now have of the disease.

Integrative proteomic profiling of protein activity and interactions using protein arrays

Proteomic studies based on abundance, activity, or interactions have been used to investigate protein functions in normal and pathological processes, but their combinatory approach has not been attempted. We present an integrative proteomic profiling method to measure protein activity and interaction using fluorescence-based protein arrays. We used an on-chip assay to simultaneously monitor the transamidating activity and binding affinity of transglutaminase 2 (TG2) for 16 TG2-related proteins. The results of this assay were compared with confidential scores provided by the STRING database to analyze the functional interactions of TG2 with these proteins. We further created a quantitative activity-interaction map of TG2 with these 16 proteins, categorizing them into seven groups based upon TG2 activity and interaction. This integrative proteomic profiling method can be applied to quantitative validation of previously known protein interactions, and in understanding the functions and regulation of target proteins in biological processes of interest.

Promoters are differentially sensitive to N-terminal mutant huntingtin-mediated transcriptional repression

Huntington’s disease (HD) is a neurodegenerative disorder caused by the inheritance of one mutant copy of the huntingtin gene. Mutant huntingtin protein (mHtt) contains an expanded polyglutamine repeat region near the N-terminus. Cleavage of mHtt releases an N-terminal fragment (N-mHtt) which accumulates in the nucleus. Nuclear accumulation of N-mHtt has been directly associated with cellular toxicity. Decreased transcription is among the earliest detected changes that occur in the brains of HD patients, animal and cellular models of HD. Transcriptional dysregulation may trigger many of the perturbations that occur later in disease progression. An understanding of the effects of mHtt may lead to strategies to slow the progression of HD. Current models of N-mHtt-mediated transcriptional dysregulation suggest that abnormal interactions between N-mHtt and transcription factors impair the ability of these transcription factors to associate at N-mHtt-affected promoters and properly regulate gene expression. We tested various aspects of the current models using two N-mHtt-affected promoters in two cell models of HD using overexpression of known N-mHtt-interacting transcription factors, promoter deletion and mutation analyses and in vitro promoter binding assays. Consequently, we proposed a new model of N-mHtt-mediated transcriptional dysregulation centered on the presence of N-mHtt at promoters. In this model, N-mHtt interacts with multiple partners whose presence and affinity for N-mHtt influence the severity of gene dysregulation. We concluded that simultaneous interaction of N-mHtt with multiple binding partners within the transcriptional machinery would explain the gene-specificity of N-mHtt-mediated transcriptional dysregulation, as well as why some genes are affected early in disease progression while others are affected later. Our model explains why alleviating N-mHtt-mediated transcriptional dysregulation through overexpression of N-mHtt-interacting proteins has proven to be difficult and suggests that the most realistic strategy for restoring gene expression across the spectrum of N-mHtt affected genes is by reducing the amount of soluble nuclear N-mHtt.

The adaptor protein and Arf GTPase-activating protein Cat-1/Git-1 is required for cellular transformation

Cat-1/Git-1 is a multifunctional protein that acts as a GTPase-activating protein (GAP) for Arf GTPases, as well as serves as a scaffold for a number of different signaling proteins. Cat-1 is best known for its role in regulating cell shape and promoting cell migration. However, whether Cat-1 might also contribute to cellular transformation is currently unknown. Here we show that ∼95% of cervical tumor samples examined overexpress Cat-1, suggesting that the up-regulation of Cat-1 expression is a frequent occurrence in this type of cancer. We demonstrate further that knocking down Cat-1 from NIH3T3 fibroblasts expressing an activated form of Cdc42 (Cdc42 F28L), or from the human cervical carcinoma (HeLa) cell line, inhibits the ability of these cells to form colonies in soft agar, an in vitro measure of tumorgenicity. The requirement for Cat-1 when assaying the anchorage-independent growth of transformed fibroblasts and HeLa cells is dependent on its ability to bind paxillin, while being negatively impacted by its Arf-GAP activity. Moreover, the co-expression of Cat-1 and an activated form of Arf6 in fibroblasts was sufficient to induce their transformation. These findings highlight novel roles for Cat-1 and its interactions with the Arf GTPases and paxillin in oncogenic transformation.

Network organization of the huntingtin proteomic interactome in mammalian brain

We used affinity-purification mass spectrometry to identify 747 candidate proteins that are complexed with Huntingtin (Htt) in distinct brain regions and ages in Huntington's disease (HD) and wild-type mouse brains. To gain a systems-level view of the Htt interactome, we applied Weighted Correlation Network Analysis to the entire proteomic data set to unveil a verifiable rank of Htt-correlated proteins and a network of Htt-interacting protein modules, with each module highlighting distinct aspects of Htt biology. Importantly, the Htt-containing module is highly enriched with proteins involved in 14-3-3 signaling, microtubule-based transport, and proteostasis. Top-ranked proteins in this module were validated as Htt interactors and genetic modifiers in an HD Drosophila model. Our study provides a compendium of spatiotemporal Htt-interacting proteins in the mammalian brain and presents an approach for analyzing proteomic interactome data sets to build in vivo protein networks in complex tissues, such as the brain.

Regulation of GPCR activity, trafficking and localization by GPCR-interacting proteins

GPCRs represent the largest family of integral membrane proteins and were first identified as receptor proteins that couple via heterotrimeric G-proteins to regulate a vast variety of effector proteins to modulate cellular function. It is now recognized that GPCRs interact with a myriad of proteins that not only function to attenuate their signalling but also function to couple these receptors to heterotrimeric G-protein-independent signalling pathways. In addition, intracellular and transmembrane proteins associate with GPCRs and regulate their processing in the endoplasmic reticulum, trafficking to the cell surface, compartmentalization to plasma membrane microdomains, endocytosis and trafficking between intracellular membrane compartments. The present review will overview the functional consequence of β-arrestin, receptor activity-modifying proteins (RAMPS), regulators of G-protein signalling (RGS), GPCR-associated sorting proteins (GASPs), Homer, small GTPases, PSD95/Disc Large/Zona Occludens (PDZ), spinophilin, protein phosphatases, calmodulin, optineurin and Src homology 3 (SH3) containing protein interactions with GPCRs.

Huntingtin protein interactions altered by polyglutamine expansion as determined by quantitative proteomic analysis

Huntington disease (HD) is a neurodegenerative disorder caused by an expansion of a polyglutamine repeat within the HD gene product, huntingtin. Huntingtin, a large (347 kDa) protein containing multiple HEAT repeats, acts as a scaffold for protein-protein interactions. Huntingtin-induced toxicity is believed to be mediated by a conformational change in expanded huntingtin, leading to protein misfolding and aggregation, aberrant protein interactions and neuronal cell death. While many non-systematic studies of huntingtin interactions have been reported, they were not designed to identify and quantify the changes in the huntingtin interactome induced by polyglutamine expansion. We used tandem affinity purification and quantitative proteomics to compare and quantify interactions of normal or expanded huntingtin isolated from a striatal cell line. We found that proteins preferentially interacting with expanded huntingtin are enriched for intrinsically disordered proteins, consistent with previously suggested roles of such proteins in neurodegenerative disorders. Our functional analysis indicates that proteins related to energy production, protein trafficking, RNA post-transcriptional modifications and cell death were significantly enriched among preferential interactors of expanded huntingtin. Expanded huntingtin interacted with many mitochondrial proteins, including AIFM1, consistent with a role for mitochondrial dysfunction in HD. Furthermore, expanded huntingtin interacted with the stress granule-associated proteins Caprin-1 and G3BP and redistributed to RNA stress granules under ER-stress conditions. These data demonstrate that a number of key cellular functions and networks may be disrupted by abnormal interactions of expanded huntingtin and highlight proteins and pathways that may be involved in HD cellular pathogenesis and that may serve as therapeutic targets.

Proteomic analysis of wild-type and mutant huntingtin-associated proteins in mouse brains identifies unique interactions and involvement in protein synthesis

Huntington disease is a neurodegenerative disorder caused by a CAG repeat amplification in the gene huntingtin (HTT) that is reflected by a polyglutamine expansion in the Htt protein. Nearly 20 years of research have uncovered roles for Htt in a wide range of cellular processes, and many of these discoveries stemmed from the identification of Htt-interacting proteins. However, no study has employed an impartial and comprehensive strategy to identify proteins that differentially associate with full-length wild-type and mutant Htt in brain tissue, the most relevant sample source to the disease condition. We analyzed Htt affinity-purified complexes from wild-type and HTT mutant juvenile mouse brain from two different biochemical fractions by tandem mass spectrometry. We compared variations in protein spectral counts relative to Htt to identify those proteins that are the most significantly contrasted between wild-type and mutant Htt purifications. Previously unreported Htt interactions with Myo5a, Prkra (PACT), Gnb2l1 (RACK1), Rps6, and Syt2 were confirmed by Western blot analysis. Gene Ontology analysis of these and other Htt-associated proteins revealed a statistically significant enrichment for proteins involved in translation among other categories. Furthermore, Htt co-sedimentation with polysomes in cytoplasmic mouse brain extracts is dependent upon the presence of intact ribosomes. Finally, wild-type or mutant Htt overexpression inhibits cap-dependent translation of a reporter mRNA in an in vitro system. Cumulatively, these data support a new role for Htt in translation and provide impetus for further study into the link between protein synthesis and Huntington disease pathogenesis.

Phosphorylation-regulated axonal dependent transport of syntaxin 1 is mediated by a Kinesin-1 adapter

Presynaptic nerve terminals are formed from preassembled vesicles that are delivered to the prospective synapse by kinesin-mediated axonal transport. However, precisely how the various cargoes are linked to the motor proteins remains unclear. Here, we report a transport complex linking syntaxin 1a (Stx) and Munc18, two proteins functioning in synaptic vesicle exocytosis at the presynaptic plasma membrane, to the motor protein Kinesin-1 via the kinesin adaptor FEZ1. Mutation of the FEZ1 ortholog UNC-76 in Caenorhabditis elegans causes defects in the axonal transport of Stx. We also show that binding of FEZ1 to Kinesin-1 and Munc18 is regulated by phosphorylation, with a conserved site (serine 58) being essential for binding. When expressed in C. elegans, wild-type but not phosphorylation-deficient FEZ1 (S58A) restored axonal transport of Stx. We conclude that FEZ1 operates as a kinesin adaptor for the transport of Stx, with cargo loading and unloading being regulated by protein kinases.

Large-scale functional RNAi screen in C. elegans identifies genes that regulate the dysfunction of mutant polyglutamine neurons

BACKGROUND

A central goal in Huntington's disease (HD) research is to identify and prioritize candidate targets for neuroprotective intervention, which requires genome-scale information on the modifiers of early-stage neuron injury in HD.

RESULTS

Here, we performed a large-scale RNA interference screen in C. elegans strains that express N-terminal huntingtin (htt) in touch receptor neurons. These neurons control the response to light touch. Their function is strongly impaired by expanded polyglutamines (128Q) as shown by the nearly complete loss of touch response in adult animals, providing an in vivo model in which to manipulate the early phases of expanded-polyQ neurotoxicity. In total, 6034 genes were examined, revealing 662 gene inactivations that either reduce or aggravate defective touch response in 128Q animals. Several genes were previously implicated in HD or neurodegenerative disease, suggesting that this screen has effectively identified candidate targets for HD. Network-based analysis emphasized a subset of high-confidence modifier genes in pathways of interest in HD including metabolic, neurodevelopmental and pro-survival pathways. Finally, 49 modifiers of 128Q-neuron dysfunction that are dysregulated in the striatum of either R/2 or CHL2 HD mice, or both, were identified.

CONCLUSIONS

Collectively, these results highlight the relevance to HD pathogenesis, providing novel information on the potential therapeutic targets for neuroprotection in HD.

Analyzing protein-protein interaction networks

The advent of the "omics" era in biology research has brought new challenges and requires the development of novel strategies to answer previously intractable questions. Molecular interaction networks provide a framework to visualize cellular processes, but their complexity often makes their interpretation an overwhelming task. The inherently artificial nature of interaction detection methods and the incompleteness of currently available interaction maps call for a careful and well-informed utilization of this valuable data. In this tutorial, we aim to give an overview of the key aspects that any researcher needs to consider when working with molecular interaction data sets and we outline an example for interactome analysis. Using the molecular interaction database IntAct, the software platform Cytoscape, and its plugins BiNGO and clusterMaker, and taking as a starting point a list of proteins identified in a mass spectrometry-based proteomics experiment, we show how to build, visualize, and analyze a protein-protein interaction network.

[Elucidation of molecular pathomechanisms of Huntington's disease]

In Huntington's disease, CAG repeat expansion of the Huntingtin gene produces mutant RNA and mutant protein containing elongated polyglutamine tract, which causes dysfunction and cell death of neurons. From our reseach of Huntington's disease and other polyglutamine diseases for nearly 20 years, we identified new disease-related genes including PQBP1, Ku70, HMGB, Maxer, and Omi. Through the analysis of these molecules, we unraveled new pathomechanisms deeply linked to nuclear functions such as transcription, splicing, DNA damage repair. These findings will become the basis to develop new molecule targeted therapeutics.

HIPPIE: Integrating protein interaction networks with experiment based quality scores

Protein function is often modulated by protein-protein interactions (PPIs) and therefore defining the partners of a protein helps to understand its activity. PPIs can be detected through different experimental approaches and are collected in several expert curated databases. These databases are used by researchers interested in examining detailed information on particular proteins. In many analyses the reliability of the characterization of the interactions becomes important and it might be necessary to select sets of PPIs of different confidence levels. To this goal, we generated HIPPIE (Human Integrated Protein-Protein Interaction rEference), a human PPI dataset with a normalized scoring scheme that integrates multiple experimental PPI datasets. HIPPIE's scoring scheme has been optimized by human experts and a computer algorithm to reflect the amount and quality of evidence for a given PPI and we show that these scores correlate to the quality of the experimental characterization. The HIPPIE web tool (available at http://cbdm.mdc-berlin.de/tools/hippie) allows researchers to do network analyses focused on likely true PPI sets by generating subnetworks around proteins of interest at a specified confidence level.

Role of neurofilament light polypeptide in head and neck cancer chemoresistance

Resistance to cisplatin-based chemotherapy is responsible for therapeutic failure of many common human cancers including cancer of head and neck (HNC). Mechanisms underlying cisplatin resistance remain unclear. In this study, we identified neurofilament light polypeptide (NEFL) as a novel hypermethylated gene associated with resistance to cisplatin-based chemotherapy in HNC. Analysis of 14 HNC cell lines revealed that downregulation of NEFL expression significantly correlated with increased resistance to cisplatin. Hypermethylation of NEFL promoter CpG islands was observed in cell lines as examined by bisulfite DNA sequencing and methylation-specific PCR (MSP) and tightly correlated with reduced NEFL mRNA and protein expression. Furthermore, in patient samples with HNC (n = 51) analyzed by quantitative MSP, NEFL promoter hypermethylation was associated with resistance to cisplatin-based chemotherapy [relative risk (RR), 3.045; 95% confidence interval (CI), 1.459-6.355; P = 0.007] and predicted diminished overall and disease-free survival for patients treated with cisplatin-based chemotherapy. Knockdown of NEFL by siRNA in the highly cisplatin-sensitive cell line PCI13 increased (P < 0.01) resistance to cisplatin. In cisplatin-resistant O11 and SCC25cp cells, restored expression of NEFL significantly increased sensitivity to the drug. Furthermore, NEFL physically associated with tuberous sclerosis complex 1 (TSC1), a known inhibitor of the mTOR pathway, and NEFL downregulation led to functional activation of mTOR pathway and consequentially conferred cisplatin resistance. This is the first study to show a role for NEFL in HNC chemoresistance. Our findings suggest that NEFL methylation is a novel mechanism for HNC chemoresistance and may represent a candidate biomarker predictive of chemotherapeutic response and survival in patients with HNC.

Characterization and evolutionary analysis of protein-protein interaction networks

While researchers have known the importance of the protein-protein interaction for decades, recent innovations in large-scale screening techniques have caused a shift in the paradigm of protein function analysis. Where the focus was once on the individual protein, attention is now directed to the surrounding network of protein associations. As protein interaction networks can provide useful insights into the potential function of and phenotypes associated with proteins, the increasing availability of large-scale protein interaction data suggests that molecular biologists can extract more meaningful hypotheses through examination of these large networks. Further, increasing availability of high-quality protein interaction data in multiple species has allowed interpretation of the properties of networks (i.e., the presence of hubs and modularity) from an evolutionary perspective. In this chapter, we discuss major previous findings derived from analyses of large-scale protein interaction data, focusing on approaches taken by landmark assays in evaluating the structure and evolution of these networks. We then outline basic techniques for protein interaction network analysis with the goal of pointing out the benefits and potential limitations of these approaches. As the majority of large-scale protein interaction data has been generated in budding yeast, literature described here focuses on this important model organism with references to other species included where possible.

A stringent yeast two-hybrid matrix screening approach for protein-protein interaction discovery

The yeast two-hybrid (Y2H) system is currently one of the most important techniques for protein-protein interaction (PPI) discovery. Here, we describe a stringent three-step Y2H matrix interaction approach that is suitable for systematic PPI screening on a proteome scale. We start with the identification and elimination of autoactivating strains that would lead to false-positive signals and prevent the identification of interactions. Nonautoactivating strains are used for the primary PPI screen that is carried out in quadruplicate with arrayed preys. Interacting pairs of baits and preys are identified in a pairwise retest step. Only PPI pairs that pass the retest step are regarded as potentially biologically relevant interactions and are considered for further analysis.

The potato sucrose transporter StSUT1 interacts with a DRM-associated protein disulfide isomerase

Organization of proteins into complexes is crucial for many cellular functions. Recently, the SUT1 protein was shown to form homodimeric complexes, to be associated with lipid raft-like microdomains in yeast as well as in plants and to undergo endocytosis in response to brefeldin A. We therefore aimed to identify SUT1-interacting proteins that might be involved in dimerization, endocytosis, or targeting of SUT1 to raft-like microdomains. Therefore, we identified potato membrane proteins, which are associated with the detergent-resistant membrane (DRM) fraction. Among the proteins identified, we clearly confirmed StSUT1 as part of DRM in potato source leaves. We used the yeast two-hybrid split ubiquitin system (SUS) to systematically screen for interaction between the sucrose transporter StSUT1 and other membrane-associated or soluble proteins in vivo. The SUS screen was followed by immunoprecipitation using affinity-purified StSUT1-specific peptide antibodies and mass spectrometric analysis of co-precipitated proteins. A large overlap was observed between the StSUT1-interacting proteins identified in the co-immunoprecipitation and the detergent-resistant membrane fraction. One of the SUT1-interacting proteins, a protein disulfide isomerase (PDI), interacts also with other sucrose transporter proteins. A potential role of the PDI as escort protein is discussed.

The integration and annotation of the human interactome in the UniHI Database

In recent years, remarkable progress has been made toward the systematic charting of human protein interactions. The utilization of the generated interaction data remained however challenging for biomedical researchers due to lack of integration of currently available resources. To facilitate the direct access and analysis of the human interactome, we have developed the Unified Human Interactome (UniHI) database. It provides researchers with a user-friendly Web-interface and integrates interaction data from 12 major resources in its latest version, establishing one of the largest catalogs for human PPIs worldwide. At present, UniHI houses over 250,000 distinct interactions between 22,300 unique proteins and is publically available at http://www.unihi.org.

A directed protein interaction network for investigating intracellular signal transduction

Cellular signal transduction is a complex process involving protein-protein interactions (PPIs) that transmit information. For example, signals from the plasma membrane may be transduced to transcription factors to regulate gene expression. To obtain a global view of cellular signaling and to predict potential signal modulators, we searched for protein interaction partners of more than 450 signaling-related proteins by means of automated yeast two-hybrid interaction mating. The resulting PPI network connected 1126 proteins through 2626 PPIs. After expansion of this interaction map with publicly available PPI data, we generated a directed network resembling the signal transduction flow between proteins with a naïve Bayesian classifier. We exploited information on the shortest PPI paths from membrane receptors to transcription factors to predict input and output relationships between interacting proteins. Integration of directed PPI with time-resolved protein phosphorylation data revealed network structures that dynamically conveyed information from the activated epidermal growth factor and extracellular signal-regulated kinase (EGF/ERK) signaling cascade to directly associated proteins and more distant proteins in the network. From the model network, we predicted 18 previously unknown modulators of EGF/ERK signaling, which we validated in mammalian cell-based assays. This generic experimental and computational approach provides a framework for elucidating causal connections between signaling proteins and facilitates the identification of proteins that modulate the flow of information in signaling networks.

Protein aggregates in Huntington's disease

Huntington's disease (HD) is an incurable neurodegenerative disease characterized by abnormal motor movements, personality changes, and early death. HD is caused by a mutation in the IT-15 gene that expands abnormally the number of CAG nucleotide repeats. As a result, the translated protein huntingtin contains disease-causing expansions of glutamines (polyQ) that make it prone to misfold and aggregate. While the gene and mutations that cause HD are known, the mechanisms underlying HD pathogenesis are not. Here we will review the state of knowledge of HD, focusing especially on a hallmark pathological feature-intracellular aggregates of mutant Htt called inclusion bodies (IBs). We will describe the role of IBs in the disease. We speculate that IB formation could be just one component of a broader coping response triggered by misfolded Htt whose efficacy may depend on the extent to which it clears toxic forms of mutant Htt. We will describe how IB formation might be regulated and which factors could determine different coping responses in different subsets of neurons. A differential regulation of IB formation as a function of the cellular context could, eventually, explain part of the neuronal vulnerability observed in HD.

An Arabidopsis GluTR binding protein mediates spatial separation of 5-aminolevulinic acid synthesis in chloroplasts

5-Aminolevulinic acid (ALA) is the universal precursor for tetrapyrrole biosynthesis and is synthesized in plants in three enzymatic steps: ligation of glutamate (Glu) to tRNA(Glu) by glutamyl-tRNA synthetase, reduction of activated Glu to Glu-1-semialdehyde by glutamyl-tRNA reductase (GluTR), and transamination to ALA by Glu 1-semialdehyde aminotransferase. ALA formation controls the metabolic flow into the tetrapyrrole biosynthetic pathway. GluTR is proposed to be the key regulatory enzyme that is tightly controlled at transcriptional and posttranslational levels. We identified a GluTR binding protein (GluTRBP; previously called PROTON GRADIENT REGULATION7) that is localized in chloroplasts and part of a 300-kD protein complex in the thylakoid membrane. Although the protein does not modulate activity of ALA synthesis, the knockout of GluTRBP is lethal in Arabidopsis thaliana, whereas mutants expressing reduced levels of GluTRBP contain less heme. GluTRBP expression correlates with a function in heme biosynthesis. It is postulated that GluTRBP contributes to subcompartmentalized ALA biosynthesis by maintaining a portion of GluTR at the plastid membrane that funnels ALA into the heme biosynthetic pathway. These results regarding GluTRBP support a model of plant ALA synthesis that is organized in two separate ALA pools in the chloroplast to provide appropriate substrate amounts for balanced synthesis of heme and chlorophyll.

ANAT: a tool for constructing and analyzing functional protein networks

Genome-scale screening studies are gradually accumulating a wealth of data on the putative involvement of hundreds of genes in various cellular responses or functions. A fundamental challenge is to chart the molecular pathways that underlie these systems. ANAT is an interactive software tool, implemented as a Cytoscape plug-in, for elucidating functional networks of proteins. It encompasses a number of network inference algorithms and provides access to networks of physical associations in several organisms. In contrast to existing software tools, ANAT can be used to infer subnetworks that connect hundreds of proteins to each other or to a given set of "anchor" proteins, a fundamental step in reconstructing cellular subnetworks. The interactive component of ANAT provides an array of tools for evaluating and exploring the resulting subnetwork models and for iteratively refining them. We demonstrate the utility of ANAT by studying the crosstalk between the autophagic and apoptotic cell death modules in humans, using a network of physical interactions. Relative to published software tools, ANAT is more accurate and provides more features for comprehensive network analysis. The latest version of the software is available at http://www.cs.tau.ac.il/~bnet/ANAT_SI.

Large-scale de novo prediction of physical protein-protein association

Information about the physical association of proteins is extensively used for studying cellular processes and disease mechanisms. However, complete experimental mapping of the human interactome will remain prohibitively difficult in the near future. Here we present a map of predicted human protein interactions that distinguishes functional association from physical binding. Our network classifies more than 5 million protein pairs predicting 94,009 new interactions with high confidence. We experimentally tested a subset of these predictions using yeast two-hybrid analysis and affinity purification followed by quantitative mass spectrometry. Thus we identified 462 new protein-protein interactions and confirmed the predictive power of the network. These independent experiments address potential issues of circular reasoning and are a distinctive feature of this work. Analysis of the physical interactome unravels subnetworks mediating between different functional and physical subunits of the cell. Finally, we demonstrate the utility of the network for the analysis of molecular mechanisms of complex diseases by applying it to genome-wide association studies of neurodegenerative diseases. This analysis provides new evidence implying TOMM40 as a factor involved in Alzheimer's disease. The network provides a high-quality resource for the analysis of genomic data sets and genetic association studies in particular. Our interactome is available via the hPRINT web server at: www.print-db.org.

Modularity-based credible prediction of disease genes and detection of disease subtypes on the phenotype-gene heterogeneous network

Background

Protein-protein interaction networks and phenotype similarity information have been synthesized together to discover novel disease-causing genes. Genetic or phenotypic similarities are manifested as certain modularity properties in a phenotype-gene heterogeneous network consisting of the phenotype-phenotype similarity network, protein-protein interaction network and gene-disease association network. However, the quantitative analysis of modularity in the heterogeneous network and its influence on disease-gene discovery are still unaddressed. Furthermore, the genetic correspondence of the disease subtypes can be identified by marking the genes and phenotypes in the phenotype-gene network. We present a novel network inference method to measure the network modularity, and in particular to suggest the subtypes of diseases based on the heterogeneous network.

Results

Based on a measure which is introduced to evaluate the closeness between two nodes in the phenotype-gene heterogeneous network, we developed a Hitting-Time-based method, CIPHER-HIT, for assessing the modularity of disease gene predictions and credibly prioritizing disease-causing genes, and then identifying the genetic modules corresponding to potential subtypes of the queried phenotype. The CIPHER-HIT is free to rely on any preset parameters. We found that when taking into account the modularity levels, the CIPHER-HIT method can significantly improve the performance of disease gene predictions, which demonstrates modularity is one of the key features for credible inference of disease genes on the phenotype-gene heterogeneous network. By applying the CIPHER-HIT to the subtype analysis of Breast cancer, we found that the prioritized genes can be divided into two sub-modules, one contains the members of the Fanconi anemia gene family, and the other contains a reported protein complex MRE11/RAD50/NBN.

Conclusions

The phenotype-gene heterogeneous network contains abundant information for not only disease genes discovery but also disease subtypes detection. The CIPHER-HIT method presented here is effective for network inference, particularly on credible prediction of disease genes and the subtype analysis of diseases, for example Breast cancer. This method provides a promising way to analyze heterogeneous biological networks, both globally and locally.

Building protein-protein interaction networks with proteomics and informatics tools

The systematic characterization of the whole interactomes of different model organisms has revealed that the eukaryotic proteome is highly interconnected. Therefore, biological research is progressively shifting away from classical approaches that focus only on a few proteins toward whole protein interaction networks to describe the relationship of proteins in biological processes. In this minireview, we survey the most common methods for the systematic identification of protein interactions and exemplify different strategies for the generation of protein interaction networks. In particular, we will focus on the recent development of protein interaction networks derived from quantitative proteomics data sets.

Huntington's disease, calcium, and mitochondria

Huntington's disease (HD) is caused by a mutation that increases the number of CAG repeats in the gene encoding for the protein Huntingtin (Htt). The mutation results in the pathological expansion of the polyQ stretch that is normally present within the N-terminal region of Htt. Even if Htt is ubiquitously expressed in tissues, the changes in the protein finally result in the clinical manifestation of motor and cognitive impairments observed in HD patients. The molecular ethiology of the disease is obscure: a number of cellular and animal models are used as essential tools in experimental approaches aimed at understanding it. Biochemical changes have been described that correlate with the malfunction of HD neurons (primarily in the striatum): consensus is gradually emerging that the dyshomeostasis of Ca(2+) and/or mitochondria stress are important factors in the linkage of the Htt mutation to the onset and progression of the disease. Here, we present a succint overview of the changes of Htt, of its possible effect on the transcription of critical genes and of its causative role in the disturbance of the neuronal Ca(2+) homeostasis. Particular emphasis will be placed on the role of mitochondria as key player in the molecular pathogenesis of the disease.

GIT1 is associated with ADHD in humans and ADHD-like behaviors in mice

Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder that affects ~5% of school-aged children; however, the mechanisms underlying ADHD remain largely unclear. Here we report a previously unidentified association between G protein-coupled receptor kinase-interacting protein-1 (GIT1) and ADHD in humans. An intronic single-nucleotide polymorphism in GIT1, the minor allele of which causes reduced GIT1 expression, shows a strong association with ADHD susceptibility in humans. Git1-deficient mice show ADHD-like phenotypes, with traits including hyperactivity, enhanced electroencephalogram theta rhythms and impaired learning and memory. Hyperactivity in Git1(-/-) mice is reversed by amphetamine and methylphenidate, psychostimulants commonly used to treat ADHD. In addition, amphetamine normalizes enhanced theta rhythms and impaired memory. GIT1 deficiency in mice leads to decreases in ras-related C3 botulinum toxin substrate-1 (RAC1) signaling and inhibitory presynaptic input; furthermore, it shifts the neuronal excitation-inhibition balance in postsynaptic neurons toward excitation. Our study identifies a previously unknown involvement of GIT1 in human ADHD and shows that GIT1 deficiency in mice causes psychostimulant-responsive ADHD-like phenotypes.

Meta-analysis of heterogeneous data sources for genome-scale identification of risk genes in complex phenotypes

Meta-analyses of large-scale association studies typically proceed solely within one data type and do not exploit the potential complementarities in other sources of molecular evidence. Here, we present an approach to combine heterogeneous data from genome-wide association (GWA) studies, protein-protein interaction screens, disease similarity, linkage studies, and gene expression experiments into a multi-layered evidence network which is used to prioritize the entire protein-coding part of the genome identifying a shortlist of candidate genes. We report specifically results on bipolar disorder, a genetically complex disease where GWA studies have only been moderately successful. We validate one such candidate experimentally, YWHAH, by genotyping five variations in 640 patients and 1,377 controls. We found a significant allelic association for the rs1049583 polymorphism in YWHAH (adjusted P = 5.6e-3) with an odds ratio of 1.28 [1.12-1.48], which replicates a previous case-control study. In addition, we demonstrate our approach's general applicability by use of type 2 diabetes data sets. The method presented augments moderately powered GWA data, and represents a validated, flexible, and publicly available framework for identifying risk genes in highly polygenic diseases. The method is made available as a web service at www.cbs.dtu.dk/services/metaranker.

Advances in analysis of transcriptional regulatory networks

A transcriptional regulatory network represents a molecular framework in which developmental or environmental cues are transformed into differential expression of genes. Transcriptional regulation is mediated by the combinatorial interplay between cis-regulatory DNA elements and trans-acting transcription factors, and is perhaps the most important mechanism for controlling gene expression. Recent innovations, most notably the method for detecting protein-DNA interactions genome-wide, can help provide a comprehensive catalog of cis-regulatory elements and their interaction with given trans-acting factors in a given condition. A transcriptional regulatory network that integrates such information can lead to a systems-level understanding of regulatory mechanisms. In this review, we will highlight the key aspects of current knowledge on eukaryotic transcriptional regulation, especially on known transcription factors and their interacting regulatory elements. Then we will review some recent technical advances for genome-wide mapping of DNA-protein interactions based on high-throughput sequencing. Finally, we will discuss the types of biological insights that can be obtained from a network-level understanding of transcription regulation as well as future challenges in the field.

Assembling a protein-protein interaction map of the SSU processome from existing datasets

Background

The small subunit (SSU) processome is a large ribonucleoprotein complex involved in small ribosomal subunit assembly. It consists of the U3 snoRNA and ∼72 proteins. While most of its components have been identified, the protein-protein interactions (PPIs) among them remain largely unknown, and thus the assembly, architecture and function of the SSU processome remains unclear.

Methodology

We queried PPI databases for SSU processome proteins to quantify the degree to which the three genome-wide high-throughput yeast two-hybrid (HT-Y2H) studies, the genome-wide protein fragment complementation assay (PCA) and the literature-curated (LC) datasets cover the SSU processome interactome.

Conclusions

We find that coverage of the SSU processome PPI network is remarkably sparse. Two of the three HT-Y2H studies each account for four and six PPIs between only six of the 72 proteins, while the third study accounts for as little as one PPI and two proteins. The PCA dataset has the highest coverage among the genome-wide studies with 27 PPIs between 25 proteins. The LC dataset was the most extensive, accounting for 34 proteins and 38 PPIs, many of which were validated by independent methods, thereby further increasing their reliability. When the collected data were merged, we found that at least 70% of the predicted PPIs have yet to be determined and 26 proteins (36%) have no known partners. Since the SSU processome is conserved in all Eukaryotes, we also queried HT-Y2H datasets from six additional model organisms, but only four orthologues and three previously known interologous interactions were found. This provides a starting point for further work on SSU processome assembly, and spotlights the need for a more complete genome-wide Y2H analysis.

Systems biology and the future of medicine

Contemporary views of human disease are based on simple correlation between clinical syndromes and pathological analysis dating from the late 19th century. Although this approach to disease diagnosis, prognosis, and treatment has served the medical establishment and society well for many years, it has serious shortcomings for the modern era of the genomic medicine that stem from its reliance on reductionist principles of experimentation and analysis. Quantitative, holistic systems biology applied to human disease offers a unique approach for diagnosing established disease, defining disease predilection, and developing individualized (personalized) treatment strategies that can take full advantage of modern molecular pathobiology and the comprehensive data sets that are rapidly becoming available for populations and individuals. In this way, systems pathobiology offers the promise of redefining our approach to disease and the field of medicine.

Large-scale protein interactome mapping: strategies and opportunities

Interactions between proteins are central to any cellular process, and mapping these into a protein network is informative both for the function of individual proteins and the functional organization of the cell as a whole. Many strategies have been developed that are up to this task, and the last 10 years have seen the high-throughput application of a number of those in large-scale, sometimes proteome-wide, interactome mapping efforts. Although initially the quality of the data produced in these screening campaigns has been questioned, quality standards and empirical validation schemes are now in place to ensure high-quality data generation. Through their integration with other 'omics' data, interactomics datasets have proven highly valuable towards applications in different areas of clinical importance.

Oligonucleotide therapeutic approaches for Huntington disease

Huntington disease is an autosomal dominant neurodegenerative disorder caused by a toxic expansion in the CAG repeat region of the huntingtin gene. Oligonucleotide approaches based on RNAi and antisense oligonucleotides provide promising new therapeutic strategies for direct intervention through reduced production of the causative mutant protein. Allele-specific and simultaneous mutant and wild-type allele-lowering strategies are being pursued with local delivery to the brain, each with relative merits. Delivery remains a key challenge for translational success, especially with chronic therapy. The potential of disease-modifying oligonucleotide approaches for Huntington disease will be revealed as they progress into clinical trials.

Functional and systems biology approaches to Huntington's disease

Huntington's disease (HD) is a hereditary, progressively degenerative and fatal brain disorder classified as a rare, or 'orphan', disease. HD is caused by the extension of trinucleotide repeats encoding a stretch of glutamine residues at the amino-terminal end of the large huntingtin (HTT) protein. Since the discovery of the mutated HTT gene in 1993, the mechanisms by which the mutant HTT protein induces neurodegeneration remain poorly understood and no disease-modifying therapy is currently available. Several functional approaches combining different experimental models and experimental technologies have been used to shed some light on the mechanisms underlying this disease. This review presents these functional approaches, highlights their potential and limitations.

Molecular biology of Huntington's disease

It has been more than 17 years since the causative mutation for Huntington's disease was discovered as the expansion of the triplet repeat in the N-terminal portion of the Huntingtin (HTT) gene. In the intervening time, researchers have discovered a great deal about Huntingtin's involvement in a number of cellular processes. However, the role of Huntingtin in the key pathogenic mechanism leading to neurodegeneration in the disease process has yet to be discovered. Here, we review the body of knowledge that has been uncovered since gene discovery and include discussions of the HTT gene, CAG triplet repeat expansion, HTT expression, protein features, posttranslational modifications, and many of its known protein functions and interactions. We also highlight potential pathogenic mechanisms that have come to light in recent years.

Network medicine: a network-based approach to human disease

Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.

Interactome mapping suggests new mechanistic details underlying Alzheimer's disease

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.

Allele-selective inhibition of mutant huntingtin expression with antisense oligonucleotides targeting the expanded CAG repeat

Huntington's disease (HD) is a currently incurable neurodegenerative disease caused by the expansion of a CAG trinucleotide repeat within the huntingtin (HTT) gene. Therapeutic approaches include selectively inhibiting the expression of the mutated HTT allele while conserving function of the normal allele. We have evaluated a series of antisense oligonucleotides (ASOs) targeted to the expanded CAG repeat within HTT mRNA for their ability to selectively inhibit expression of mutant HTT protein. Several ASOs incorporating a variety of modifications, including bridged nucleic acids and phosphorothioate internucleotide linkages, exhibited allele-selective silencing in patient-derived fibroblasts. Allele-selective ASOs did not affect the expression of other CAG repeat-containing genes and selectivity was observed in cell lines containing minimal CAG repeat lengths representative of most HD patients. Allele-selective ASOs left HTT mRNA intact and did not support ribonuclease H activity in vitro. We observed cooperative binding of multiple ASO molecules to CAG repeat-containing HTT mRNA transcripts in vitro. These results are consistent with a mechanism involving inhibition at the level of translation. ASOs targeted to the CAG repeat of HTT provide a starting point for the development of oligonucleotide-based therapeutics that can inhibit gene expression with allelic discrimination in patients with HD.

System-level comparison of protein-protein interactions between viruses and the human type I interferon system network

Innate immunity has evolved complex molecular pathways to protect organisms from viral infections. One pivotal line of cellular defense is the induction of the antiviral effect of interferon. To circumvent this primary response and achieve their own replication, viruses have developed complex molecular strategies. Here, we provide a systems-level study of the human type I interferon system subversion by the viral proteome, by reconstructing the underlying protein-protein interaction network. At this network level, viruses establish a massive and a gradual attack, from receptors to transcription factors, by interacting preferentially with highly connected and central proteins as well as interferon-induced proteins. We also demonstrate that viruses significantly target 22% of the proteins directly interacting with the type I interferon system network, suggesting the relevance of our network-based method to identify new candidates involved in the regulation of the antiviral response. Finally, based on the comparative analysis of interactome profiles across four viral families, we provide evidence of common and differential targeting strategies.

The KRAB-containing zinc-finger transcriptional regulator ZBRK1 activates SCA2 gene transcription through direct interaction with its gene product, ataxin-2

Gene transcription is controlled by transcriptional regulators acting with specific co-regulators to allow gene activation and repression. Here, we report the identification of the KRAB-containing zinc-finger transcriptional regulator, ZBRK1, as an interaction partner of the SCA2 gene product ataxin-2. Furthermore, we discovered that an elevated ZBRK1 level resulted in increased ataxin-2 levels, whereas interference on transcriptional and protein levels of ZBRK1 yielded reduced ataxin-2 levels, suggesting that a complex comprising ZBRK1 and ataxin-2 regulates SCA2 gene transcription. A bioinformatic analysis utilizing the known ZBRK1 consensus DNA-binding motif revealed ZBRK1-binding sites in the SCA2 promoter. These predicted sites were experimentally validated by chromatin-immunoprecipitation experiments along with luciferase-based promoter analyses corroborating that SCA2 gene transcription is controlled by a ZBRK1/ataxin-2 complex. Finally, we demonstrate that SCA2 gene transcription is significantly reduced in colon tumors possessing low ZBRK1 transcripts. Thus, our results provide first evidence that ataxin-2 acts as a co-regulator of ZBRK1 activating its own transcription, thereby representing the first identified ZBRK1 co-activator.