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

Native top-down mass spectrometry for the structural characterization of human hemoglobin

Native mass spectrometry (MS) has become an invaluable tool for the characterization of proteins and noncovalent protein complexes under near physiological solution conditions. Here we report the structural characterization of human hemoglobin (Hb), a 64 kDa oxygen-transporting protein complex, by high resolution native top-down MS using electrospray ionization and a 15-Tesla Fourier transform ion cyclotron resonance mass spectrometer. Native MS preserves the noncovalent interactions between the globin subunits, and electron capture dissociation (ECD) produces fragments directly from the intact Hb complex without dissociating the subunits. Using activated ion ECD, we observe the gradual unfolding process of the Hb complex in the gas phase. Without protein ion activation, the native Hb shows very limited ECD fragmentation from the N-termini, suggesting a tightly packed structure of the native complex and therefore a low fragmentation efficiency. Precursor ion activation allows a steady increase in N-terminal fragment ions, while the C-terminal fragments remain limited (38 c ions and four z ions on the α chain; 36 c ions and two z ions on the β chain). This ECD fragmentation pattern suggests that upon activation, the Hb complex starts to unfold from the N-termini of both subunits, whereas the C-terminal regions and therefore the potential regions involved in the subunit binding interactions remain intact. ECD-MS of the Hb dimer shows similar fragmentation patterns as the Hb tetramer, providing further evidence for the hypothesized unfolding process of the Hb complex in the gas phase. Native top-down ECD-MS allows efficient probing of the Hb complex structure and the subunit binding interactions in the gas phase. It may provide a fast and effective means to probe the structure of novel protein complexes that are intractable to traditional structural characterization tools.

Update on Huntington's disease: advances in care and emerging therapeutic options

INTRODUCTION

Huntington's disease (HD) is the most common hereditary neurodegenerative disorder. Despite the fact that both the gene and the mutation causing this monogenetic disorder were identified more than 20 years ago, disease-modifying therapies for HD have not yet been established.

REVIEW

While intense preclinical research and large cohort studies in HD have laid foundations for tangible improvements in understanding HD and caring for HD patients, identifying targets for therapeutic interventions and developing novel therapeutic modalities (new chemical entities and advanced therapies using DNA and RNA molecules as therapeutic agents) continues to be an ongoing process. The authors review recent achievements in HD research and focus on approaches towards disease-modifying therapies, ranging from huntingtin-lowering strategies to improving huntingtin clearance that may be promoted by posttranslational HTT modifications.

CONCLUSION

The nature and number of upcoming clinical studies/trials in HD is a reason for hope for HD patients and their families.

Network-based identification of biomarkers coexpressed with multiple pathways

Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication. Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson’s correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic and prognostic biomarkers. The results show that implication networks, implemented in Genet package, identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases; meanwhile, implication networks revealed more biologically relevant molecular interactions than Boolean networks, Bayesian networks, and Pearson’s correlation networks when evaluated with MSigDB database.

Hope for GWAS: relevant risk genes uncovered from GWAS statistical noise

Hundreds of genetic variants have been associated to common diseases through genome-wide association studies (GWAS), yet there are limits to current approaches in detecting true small effect risk variants against a background of false positive findings. Here we addressed the missing heritability problem, aiming to test whether there are indeed risk variants within GWAS statistical noise and to develop a systematic strategy to retrieve these hidden variants. Employing an integrative approach, which combines protein-protein interactions with association data from GWAS for 6 common diseases, we found that associated-genes at less stringent significance levels (p < 0.1) with any of these diseases are functionally connected beyond noise expectation. This functional coherence was used to identify disease-relevant subnetworks, which were shown to be enriched in known genes, outperforming the selection of top GWAS genes. As a proof of principle, we applied this approach to breast cancer, supporting well-known breast cancer genes, while pinpointing novel susceptibility genes for experimental validation. This study reinforces the idea that GWAS are under-analyzed and that missing heritability is rather hidden. It extends the use of protein networks to reveal this missing heritability, thus leveraging the large investment in GWAS that produced so far little tangible gain.

Roles of Rac1 and Rac3 GTPases during the development of cortical and hippocampal GABAergic interneurons

Rac GTPases are regulators of the cytoskeleton that play an important role in several aspects of neuronal and brain development. Two distinct Rac GTPases are expressed in the developing nervous system, the widely expressed Rac1 and the neural-specific Rac3 proteins. Recent experimental evidence supports a central role of these two Rac proteins in the development of inhibitory GABAergic interneurons, important modulatory elements of the brain circuitry. The combined inactivation of the genes for the two Rac proteins has profound effects on distinct aspects of interneuron development, and has highlighted a synergistic contribution of the two proteins to the postmitotic maturation of specific populations of cortical and hippocampal interneurons. Rac function is modulated by different types of regulators, and can influence the activity of specific effectors. Some of these proteins have been associated to the development and maturation of interneurons. Cortical interneuron dysfunction is implicated in several neurological and psychiatric diseases characterized by cognitive impairment. Therefore the description of the cellular processes regulated by the Rac GTPases, and the identification of the molecular networks underlying these processes during interneuron development is relevant to the understanding of the role of GABAergic interneurons in cognitive functions.

Integration strategy is a key step in network-based analysis and dramatically affects network topological properties and inferring outcomes

An increasing number of experiments have been designed to detect intracellular and intercellular molecular interactions. Based on these molecular interactions (especially protein interactions), molecular networks have been built for using in several typical applications, such as the discovery of new disease genes and the identification of drug targets and molecular complexes. Because the data are incomplete and a considerable number of false-positive interactions exist, protein interactions from different sources are commonly integrated in network analyses to build a stable molecular network. Although various types of integration strategies are being applied in current studies, the topological properties of the networks from these different integration strategies, especially typical applications based on these network integration strategies, have not been rigorously evaluated. In this paper, systematic analyses were performed to evaluate 11 frequently used methods using two types of integration strategies: empirical and machine learning methods. The topological properties of the networks of these different integration strategies were found to significantly differ. Moreover, these networks were found to dramatically affect the outcomes of typical applications, such as disease gene predictions, drug target detections, and molecular complex identifications. The analysis presented in this paper could provide an important basis for future network-based biological researches.

Rectifying rectifier channels in Huntington disease

Neuronal abnormalities in neurodegenerative disorders such as Huntington disease, Alzheimer disease or Parkinson disease have been the primary focus of decades of research. However, increasing evidences indicate that glial cells and more specifically astrocytes could be as important players as their big brother. It is now particularly evident in Huntington disease where astrocytal potassium channels have emerged as a likely key factor in the pathogenesis of the disease.

Identification of inflammatory bowel disease-related proteins using a reverse k-nearest neighbor search

Inflammatory bowel disease (IBD) is a chronic disease whose incidence and prevalence increase every year; however, the pathogenesis of IBD is still unclear. Thus, identifying IBD-related proteins is important for understanding its complex disease mechanism. Here, we propose a new and simple network-based approach using a reverse k-nearest neighbor ( R k NN ) search to identify novel IBD-related proteins. Protein–protein interactions (PPI) and Genome-Wide Association Studies (GWAS) were used in this study. After constructing the PPI network, the R k NN search was applied to all of the proteins to identify sets of influenced proteins among their k-nearest neighbors ( R k NNs ). An observed protein whose influenced proteins were mostly known IBD-related proteins was statistically identified as a novel IBD-related protein. Our method outperformed a random aspect, k NN search, and centrality measures based on the network topology. A total of 39 proteins were identified as IBD-related proteins. Of these proteins, 71% were reported at least once in the literature as related to IBD. Additionally, these proteins were found over-represented in the IBD pathway and enriched in importantly functional pathways in IBD. In conclusion, the R k NN search with the statistical enrichment test is a great tool to identify IBD-related proteins to better understand its complex disease mechanism.

Prion-like proteins sequester and suppress the toxicity of huntingtin exon 1

Expansions of preexisting polyglutamine (polyQ) tracts in at least nine different proteins cause devastating neurodegenerative diseases. There are many unique features to these pathologies, but there must also be unifying mechanisms underlying polyQ toxicity. Using a polyQ-expanded fragment of huntingtin exon-1 (Htt103Q), the causal protein in Huntington disease, we and others have created tractable models for investigating polyQ toxicity in yeast cells. These models recapitulate key pathological features of human diseases and provide access to an unrivalled genetic toolbox. To identify toxicity modifiers, we performed an unbiased overexpression screen of virtually every protein encoded by the yeast genome. Surprisingly, there was no overlap between our modifiers and those from a conceptually identical screen reported recently, a discrepancy we attribute to an artifact of their overexpression plasmid. The suppressors of Htt103Q toxicity recovered in our screen were strongly enriched for glutamine- and asparagine-rich prion-like proteins. Separated from the rest of the protein, the prion-like sequences of these proteins were themselves potent suppressors of polyQ-expanded huntingtin exon-1 toxicity, in both yeast and human cells. Replacing the glutamines in these sequences with asparagines abolished suppression and converted them to enhancers of toxicity. Replacing asparagines with glutamines created stronger suppressors. The suppressors (but not the enhancers) coaggregated with Htt103Q, forming large foci at the insoluble protein deposit in which proteins were highly immobile. Cells possessing foci had fewer (if any) small diffusible oligomers of Htt103Q. Until such foci were lost, cells were protected from death. We discuss the therapeutic implications of these findings.

The sucrose transporter SlSUT2 from tomato interacts with brassinosteroid functioning and affects arbuscular mycorrhiza formation

Mycorrhizal plants benefit from the fungal partners by getting better access to soil nutrients. In exchange, the plant supplies carbohydrates to the fungus. The additional carbohydrate demand in mycorrhizal plants was shown to be balanced partially by higher CO2 assimilation and increased C metabolism in shoots and roots. In order to test the role of sucrose transport for fungal development in arbuscular mycorrhizal (AM) tomato, transgenic plants with down-regulated expression of three sucrose transporter genes were analysed. Plants that carried an antisense construct of SlSUT2 (SlSUT2as) repeatedly exhibited increased mycorrhizal colonization and the positive effect of plants to mycorrhiza was abolished. Grafting experiments between transgenic and wild-type rootstocks and scions indicated that mainly the root-specific function of SlSUT2 has an impact on colonization of tomato roots with the AM fungus. Localization of SISUT2 to the periarbuscular membrane indicates a role in back transport of sucrose from the periarbuscular matrix into the plant cell thereby affecting hyphal development. Screening of an expression library for SlSUT2-interacting proteins revealed interactions with candidates involved in brassinosteroid (BR) signaling or biosynthesis. Interaction of these candidates with SlSUT2 was confirmed by bimolecular fluorescence complementation. Tomato mutants defective in BR biosynthesis were analysed with respect to mycorrhizal symbiosis and showed indeed decreased mycorrhization. This finding suggests that BRs affect mycorrhizal infection and colonization. If the inhibitory effect of SlSUT2 on mycorrhizal growth involves components of BR synthesis and of the BR signaling pathway is discussed.

Iron dysregulation in Huntington's disease

Huntington's disease (HD) is one of many neurodegenerative diseases with reported alterations in brain iron homeostasis that may contribute to neuropathogenesis. Iron accumulation in the specific brain areas of neurodegeneration in HD has been proposed based on observations in post-mortem tissue and magnetic resonance imaging studies. Altered magnetic resonance imaging signal within specific brain regions undergoing neurodegeneration has been consistently reported and interpreted as altered levels of brain iron. Biochemical studies using various techniques to measure iron species in human samples, mouse tissue, or in vitro has generated equivocal data to support such an association. Whether elevated brain iron occurs in HD, plays a significant contributing role in HD pathogenesis, or is a secondary effect remains currently unclear.

Polyglutamine-rich suppressors of huntingtin toxicity act upstream of Hsp70 and Sti1 in spatial quality control of amyloid-like proteins

Protein conformational maladies such as Huntington Disease are characterized by accumulation of intracellular and extracellular protein inclusions containing amyloid-like proteins. There is an inverse correlation between proteotoxicity and aggregation, so facilitated protein aggregation appears cytoprotective. To define mechanisms for protective protein aggregation, a screen for suppressors of nuclear huntingtin (Htt103Q) toxicity was conducted. Nuclear Htt103Q is highly toxic and less aggregation prone than its cytosolic form, so we identified suppressors of cytotoxicity caused by Htt103Q tagged with a nuclear localization signal (NLS). High copy suppressors of Htt103Q-NLS toxicity include the polyQ-domain containing proteins Nab3, Pop2, and Cbk1, and each suppresses Htt toxicity via a different mechanism. Htt103Q-NLS appears to inactivate the essential functions of Nab3 in RNA processing in the nucleus. Function of Pop2 and Cbk1 is not impaired by nuclear Htt103Q, as their respective polyQ-rich domains are sufficient to suppress Htt103Q toxicity. Pop2 is a subunit of an RNA processing complex and is localized throughout the cytoplasm. Expression of just the Pop2 polyQ domain and an adjacent proline-rich stretch is sufficient to suppress Htt103Q toxicity. The proline-rich domain in Pop2 resembles an aggresome targeting signal, so Pop2 may act in trans to positively impact spatial quality control of Htt103Q. Cbk1 accumulates in discrete perinuclear foci and overexpression of the Cbk1 polyQ domain concentrates diffuse Htt103Q into these foci, which correlates with suppression of Htt toxicity. Protective action of Pop2 and Cbk1 in spatial quality control is dependent upon the Hsp70 co-chaperone Sti1, which packages amyloid-like proteins into benign foci. Protein:protein interactions between Htt103Q and its intracellular neighbors lead to toxic and protective outcomes. A subset of polyQ-rich proteins buffer amyloid toxicity by funneling toxic aggregation intermediates to the Hsp70/Sti1 system for spatial organization into benign species.

Computational approaches for human disease gene prediction and ranking

While candidate gene association studies continue to be the most practical and frequently employed approach in disease gene investigation for complex disorders, selecting suitable genes to test is a challenge. There are several computational approaches available for selecting and prioritizing disease candidate genes. A majority of these tools are based on guilt-by-association principle where novel disease candidate genes are identified and prioritized based on either functional or topological similarity to known disease genes. In this chapter we review the prioritization criteria and the algorithms along with some use cases that demonstrate how these tools can be used for identifying and ranking human disease candidate genes.

Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism

Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases.

Evidence Implicating CCNB1IP1, a RING Domain-Containing Protein Required for Meiotic Crossing Over in Mice, as an E3 SUMO Ligase

The RING domain-containing protein CCNB1IP1 (Cyclin B1 Interacting Protein 1) is a putative ubiquitin E3 ligase that is essential for chiasmata formation, and hence fertility, in mice. Previous studies in cultured cells indicated that CCNB1IP1 targets Cyclin B for degradation, thus playing a role in cell cycle regulation. Mice homozygous for a mutant allele (mei4) of Ccnb1ip1 display no detectable phenotype other than meiotic failure from an absence of chiasmata. CCNB1IP1 is not conserved in key model organisms such as yeast and Drosophila, and there are no features of the protein that implicate clear mechanisms for a role in recombination. To gain insight into CCNB1IP1’s function in meiotic cells, we raised a specific antibody and determined that the protein appears in pachynema. This indicates that CCNB1IP1 is involved with crossover intermediate maturation, rather than early (leptotene) specification of a subset of SPO11-induced double strand breaks towards the crossover pathway. Additionally, a yeast 2-hybrid (Y2H) screen revealed that CCNB1IP1 interacts with SUMO2 and a set of proteins enriched for consensus sumoylation sites. The Y2H studies, combined with scrutiny of CCNB1IP1 domains, implicate this protein as an E3 ligase of the sumoylation cascade. We hypothesize CCNB1IP1 represents a novel meiosis-specific SUMO E3 ligase critical to resolution of recombination intermediates into mature chiasmata.

Large-scale RNA interference screening in mammalian cells identifies novel regulators of mutant huntingtin aggregation

In polyglutamine (polyQ) diseases including Huntington's disease (HD), mutant proteins containing expanded polyQ stretch form aggregates in neurons. Genetic or RNAi screenings in yeast, C. elegans or Drosophila have identified multiple genes modifying polyQ aggregation, a few of which are confirmed effective in mammals. However, the overall molecular mechanism underlying polyQ protein aggregation in mammalian cells still remains obscure. We here perform RNAi screening in mouse neuro2a cells to identify mammalian modifiers for aggregation of mutant huntingtin, a causative protein of HD. By systematic cell transfection and automated cell image analysis, we screen ∼12000 shRNA clones and identify 111 shRNAs that either suppress or enhance mutant huntingtin aggregation, without altering its gene expression. Classification of the shRNA-targets suggests that genes with various cellular functions such as gene transcription and protein phosphorylation are involved in modifying the aggregation. Subsequent analysis suggests that, in addition to the aggregation-modifiers sensitive to proteasome inhibition, some of them, such as a transcription factor Tcf20, and kinases Csnk1d and Pik3c2a, are insensitive to it. As for Tcf20, which contains polyQ stretches at N-terminus, its binding to mutant huntingtin aggregates is observed in neuro2a cells and in HD model mouse neurons. Notably, except Pik3c2a, the rest of the modifiers identified here are novel. Thus, our first large-scale RNAi screening in mammalian system identifies previously undescribed genetic players that regulate mutant huntingtin aggregation by several, possibly mammalian-specific mechanisms.

The palmitoyl acyltransferase HIP14 shares a high proportion of interactors with huntingtin: implications for a role in the pathogenesis of Huntington's disease

HIP14 is the most highly conserved of 23 human palmitoyl acyltransferases (PATs) that catalyze the post-translational addition of palmitate to proteins, including huntingtin (HTT). HIP14 is dysfunctional in the presence of mutant HTT (mHTT), the causative gene for Huntington disease (HD), and we hypothesize that reduced palmitoylation of HTT and other HIP14 substrates contributes to the pathogenesis of the disease. Here we describe the yeast two-hybrid (Y2H) interactors of HIP14 in the first comprehensive study of interactors of a mammalian PAT. Unexpectedly, we discovered a highly significant overlap between HIP14 interactors and 370 published interactors of HTT, 4-fold greater than for control proteins (P = 8 × 10(-5)). Nearly half of the 36 shared interactors are already implicated in HD, supporting a direct link between HIP14 and the disease. The HIP14 Y2H interaction set is significantly enriched for palmitoylated proteins that are candidate substrates. We confirmed that three of them, GPM6A, and the Sprouty domain-containing proteins SPRED1 and SPRED3, are indeed palmitoylated by HIP14; the first enzyme known to palmitoylate these proteins. These novel substrates functions might be affected by reduced palmitoylation in HD. We also show that the vesicular cargo adapter optineurin, an established HTT-binding protein, co-immunoprecipitates with HIP14 but is not palmitoylated. mHTT leads to mislocalization of optineurin and aberrant cargo trafficking. Therefore, it is possible that optineurin regulates trafficking of HIP14 to its substrates. Taken together, our data raise the possibility that defective palmitoylation by HIP14 might be an important mechanism that contributes to the pathogenesis of HD.

Identification of two tyrosine residues required for the intramolecular mechanism implicated in GIT1 activation

GIT1 is an ArfGAP and scaffolding protein regulating cell adhesion and migration. The multidomain structure of GIT1 allows the interaction with several partners. Binding of GIT1 to some of its partners requires activation of the GIT1 polypeptide. Our previous studies indicated that binding of paxillin to GIT1 is enhanced by release of an intramolecular interaction between the amino-terminal and carboxy-terminal portions that keeps the protein in a binding-incompetent state. Here we have addressed the mechanism mediating this intramolecular inhibitory mechanism by testing the effects of the mutation of several formerly identified GIT1 phosphorylation sites on the binding to paxillin. We have identified two tyrosines at positions 246 and 293 of the human GIT1 polypeptide that are needed to keep the protein in the inactive conformation. Interestingly, mutation of these residues to phenylalanine did not affect binding to paxillin, while mutation to either alanine or glutamic acid enhanced binding to paxillin, without affecting the constitutive binding to the Rac/Cdc42 exchange factor βPIX. The involvement of the two tyrosine residues in the intramolecular interaction was supported by reconstitution experiments showing that these residues are important for the binding between the amino-terminal fragment and carboxy-terminal portions of GIT1. Either GIT1 or GIT1-N tyrosine phosphorylation by Src and pervanadate treatment to inhibit protein tyrosine phosphatases did not affect the intramolecular binding between the amino- and carboxy-terminal fragments, nor the binding of GIT1 to paxillin. Mutations increasing the binding of GIT1 to paxillin positively affected cell motility, measured both by transwell migration and wound healing assays. Altogether these results show that tyrosines 246 and 293 of GIT1 are required for the intramolecular inhibitory mechanism that prevents the binding of GIT1 to paxillin. The data also suggest that tyrosine phosphorylation may not be sufficient to release the intramolecular interaction that keeps GIT1 in the inactive conformation.

Tomosyn interacts with the SUMO E3 ligase PIASγ

Protein modification by Small Ubiquitin-like MOdifier (SUMO) entities is involved in a number of neuronal functions, including synaptogenesis and synaptic plasticity. Tomosyn-1 (syntaxin-binding protein 5; STXPB5) binds to t-SNARE (Soluble NSF Attachment Protein Receptor) proteins to regulate neurotransmission and is one of the few neuronal SUMO substrate proteins identified. Here we used yeast two-hybrid screening to show that tomosyn-1 interacts with the SUMO E3 ligase PIASγ (Protein Inhibitor of Activated STAT; PIAS4 or ZMIZ6). This novel interaction involved the C-terminus of tomosyn-1 and the N-terminus of PIASγ. It was confirmed by two-way immunoprecipitation experiments using the full-length proteins expressed in HEK293T cells. Tomosyn-1 was preferentially modified by the SUMO-2/3 isoform. PIASγ-dependent modification of tomosyn-1 with SUMO-2/3 presents a novel mechanism to adapt secretory strength to the dynamic synaptic environment.

Evolutionarily conserved heterogeneous nuclear ribonucleoprotein (hnRNP) A/B proteins functionally interact with human and Drosophila TAR DNA-binding protein 43 (TDP-43)

Human TDP-43 represents the main component of neuronal inclusions found in patients with neurodegenerative diseases, especially frontotemporal lobar degeneration and amyotrophic lateral sclerosis. In vitro and in vivo studies have shown that the TAR DNA-binding protein 43 (TDP-43) Drosophila ortholog (TBPH) can biochemically and functionally overlap the properties of the human factor. The recent direct implication of the human heterogeneous nuclear ribonucleoproteins (hnRNPs) A2B1 and A1, known TDP-43 partners, in the pathogenesis of multisystem proteinopathy and amyotrophic lateral sclerosis supports the hypothesis that the physical and functional interplay between TDP-43 and hnRNP A/B orthologs might play a crucial role in the pathogenesis of neurodegenerative diseases. To test this hypothesis and further validate the fly system as a useful model to study this type of diseases, we have now characterized human TDP-43 and Drosophila TBPH similarity in terms of protein-protein interaction pathways. In this work we show that TDP-43 and TBPH share the ability to associate in vitro with Hrp38/Hrb98DE/CG9983, the fruit fly ortholog of the human hnRNP A1/A2 factors. Interestingly, the protein regions of TDP-43 and Hrp38 responsible for reciprocal interactions are conserved through evolution. Functionally, experiments in HeLa cells demonstrate that TDP-43 is necessary for the inhibitory activity of Hrp38 on splicing. Finally, Drosophila in vivo studies show that Hrp38 deficiency produces locomotive defects and life span shortening in TDP-43 with and without animals. These results suggest that hnRNP protein levels can play a modulatory role on TDP-43 functions.

A large scale Huntingtin protein interaction network implicates Rho GTPase signaling pathways in Huntington disease

Huntington disease (HD) is an inherited neurodegenerative disease caused by a CAG expansion in the HTT gene. Using yeast two-hybrid methods, we identified a large set of proteins that interact with huntingtin (HTT)-interacting proteins. This network, composed of HTT-interacting proteins (HIPs) and proteins interacting with these primary nodes, contains 3235 interactions among 2141 highly interconnected proteins. Analysis of functional annotations of these proteins indicates that primary and secondary HIPs are enriched in pathways implicated in HD, including mammalian target of rapamycin, Rho GTPase signaling, and oxidative stress response. To validate roles for HIPs in mutant HTT toxicity, we show that the Rho GTPase signaling components, BAIAP2, EZR, PIK3R1, PAK2, and RAC1, are modifiers of mutant HTT toxicity. We also demonstrate that Htt co-localizes with BAIAP2 in filopodia and that mutant HTT interferes with filopodial dynamics. These data indicate that HTT is involved directly in membrane dynamics, cell attachment, and motility. Furthermore, they implicate dysregulation in these pathways as pathological mechanisms in HD.

Autoinhibitory structure of the WW domain of HYPB/SETD2 regulates its interaction with the proline-rich region of huntingtin

Huntington's disease (HD) is an autosomally dominant neurodegenerative disorder caused by expansion of polyglutamine (polyQ) in the huntingtin (Htt) protein. Htt yeast two-hybrid protein B (HYPB/SETD2), a histone methyltransferase, directly interacts with Htt and is involved in HD pathology. Using NMR techniques, we characterized a polyproline (polyP) stretch at the C terminus of HYPB, which directly interacts with the following WW domain and leads this domain predominantly to be in a closed conformational state. The solution structure shows that the polyP stretch extends from the back and binds to the WW core domain in a typical binding mode. This autoinhibitory structure regulates interaction between the WW domain of HYPB and the proline-rich region (PRR) of Htt, as evidenced by NMR and immunofluorescence techniques. This work provides structural and mechanistic insights into the intramolecular regulation of the WW domain in Htt-interacting partners and will be helpful for understanding the pathology of HD.

The LC3 interactome at a glance

Continuous synthesis of all cellular components requires their constant turnover in order for a cell to achieve homeostasis. To this end, eukaryotic cells are endowed with two degradation pathways - the ubiquitin-proteasome system and the lysosomal pathway. The latter pathway is partly fed by autophagy, which targets intracellular material in distinct vesicles, termed autophagosomes, to the lysosome. Central to this pathway is a set of key autophagy proteins, including the ubiquitin-like modifier Atg8, that orchestrate autophagosome initiation and biogenesis. In higher eukaryotes, the Atg8 family comprises six members known as the light chain 3 (LC3) or γ-aminobutyric acid (GABA)-receptor-associated protein (GABARAP) proteins. Considerable effort during the last 15 years to decipher the molecular mechanisms that govern autophagy has significantly advanced our understanding of the functioning of this protein family. In this Cell Science at a Glance article and the accompanying poster, we present the current LC3 protein interaction network, which has been and continues to be vital for gaining insight into the regulation of autophagy.

Pathways systematically associated to Hirschsprung's disease

Despite it has been reported that several loci are involved in Hirschsprung's disease, the molecular basis of the disease remains yet essentially unknown. The study of collective properties of modules of functionally-related genes provides an efficient and sensitive statistical framework that can overcome sample size limitations in the study of rare diseases. Here, we present the extension of a previous study of a Spanish series of HSCR trios to an international cohort of 162 HSCR trios to validate the generality of the underlying functional basis of the Hirschsprung's disease mechanisms previously found. The Pathway-Based Analysis (PBA) confirms a strong association of gene ontology (GO) modules related to signal transduction and its regulation, enteric nervous system (ENS) formation and other processes related to the disease. In addition, network analysis recovers sub-networks significantly associated to the disease, which contain genes related to the same functionalities, thus providing an independent validation of these findings. The functional profiles of association obtained for patients populations from different countries were compared to each other. While gene associations were different at each series, the main functional associations were identical in all the five populations. These observations would also explain the reported low reproducibility of associations of individual disease genes across populations.

Edgotype: a fundamental link between genotype and phenotype

Classical 'one-gene/one-disease' models cannot fully reconcile with the increasingly appreciated prevalence of complicated genotype-to-phenotype associations in human disease. Genes and gene products function not in isolation but as components of intricate networks of macromolecules (DNA, RNA, or proteins) and metabolites linked through biochemical or physical interactions, represented in 'interactome' network models as 'nodes' and 'edges', respectively. Accordingly, mechanistic understanding of human disease will require understanding of how disease-causing mutations affect systems or interactome properties. The study of 'edgetics' uncovers specific loss or gain of interactions (edges) to interpret genotype-to-phenotype relationships. We review how distinct genetic variants, the genotype, lead to distinct phenotypic outcomes, the phenotype, through edgetic perturbations in interactome networks altogether representing the 'edgotype'.

Exploring mitochondrial system properties of neurodegenerative diseases through interactome mapping

Mitochondria are double membraned, dynamic organelles that are required for a large number of cellular processes, and defects in their function have emerged as causative factors for a growing number of human disorders and are highly associated with cancer, metabolic, and neurodegenerative (ND) diseases. Biochemical and genetic investigations have uncovered small numbers of candidate mitochondrial proteins (MPs) involved in ND disease, but given the diversity of processes affected by MP function and the difficulty of detecting interactions involving these proteins, many more likely remain unknown. However, high-throughput proteomic and genomic approaches developed in genetically tractable model prokaryotes and lower eukaryotes have proven to be effective tools for querying the physical (protein-protein) and functional (gene-gene) relationships between diverse types of proteins, including cytosolic and membrane proteins. In this review, we highlight how experimental and computational approaches developed recently by our group and others can be effectively used towards elucidating the mitochondrial interactome in an unbiased and systematic manner to uncover network-based connections. We discuss how the knowledge from the resulting interaction networks can effectively contribute towards the identification of new mitochondrial disease gene candidates, and thus further clarify the role of mitochondrial biology and the complex etiologies of ND disease.

BIOLOGICAL SIGNIFICANCE

Biochemical and genetic investigations have uncovered small numbers of candidate mitochondrial proteins (MPs) involved in neurodegenerative (ND) diseases, but given the diversity of processes affected by MP function and the difficulty of detecting interactions involving these proteins, many more likely remain unknown. Large-scale proteomic and genomic approaches developed in model prokaryotes and lower eukaryotes have proven to be effective tools for querying the physical (protein-protein) and functional (gene-gene) relationships between diverse types of proteins. Extension of this new framework to the mitochondrial sub-system in human will likewise provide a universally informative systems-level view of the physical and functional landscape for exploring the evolutionary principles underlying mitochondrial function. In this review, we highlight how experimental and computational approaches developed recently by our group and others can be effectively used towards elucidating the mitochondrial interactome in an unbiased and systematic manner to uncover network-based connections. We anticipate that the knowledge from these resulting interaction networks can effectively contribute towards the identification of new mitochondrial disease gene candidates, and thus foster a deeper molecular understanding of mitochondrial biology as well as the etiology of mitochondrial diseases. This article is part of a Special Issue: Can Proteomics Fill the Gap Between Genomics and Phenotypes?

High-resolution network biology: connecting sequence with function

Proteins are not monolithic entities; rather, they can contain multiple domains that mediate distinct interactions, and their functionality can be regulated through post-translational modifications at multiple distinct sites. Traditionally, network biology has ignored such properties of proteins and has instead examined either the physical interactions of whole proteins or the consequences of removing entire genes. In this Review, we discuss experimental and computational methods to increase the resolution of protein-protein, genetic and drug-gene interaction studies to the domain and residue levels. Such work will be crucial for using interaction networks to connect sequence and structural information, and to understand the biological consequences of disease-associated mutations, which will hopefully lead to more effective therapeutic strategies.

Specific and non-specific protein association in solution: computation of solvent effects and prediction of first-encounter modes for efficient configurational bias Monte Carlo simulations

Weak and ultraweak protein-protein association play a role in molecular recognition and can drive spontaneous self-assembly and aggregation. Such interactions are difficult to detect experimentally, and are a challenge to the force field and sampling technique. A method is proposed to identify low-population protein-protein binding modes in aqueous solution. The method is designed to identify preferential first-encounter complexes from which the final complex(es) at equilibrium evolve. A continuum model is used to represent the effects of the solvent, which accounts for short- and long-range effects of water exclusion and for liquid-structure forces at protein/liquid interfaces. These effects control the behavior of proteins in close proximity and are optimized on the basis of binding enthalpy data and simulations. An algorithm is described to construct a biasing function for self-adaptive configurational-bias Monte Carlo of a set of interacting proteins. The function allows mixing large and local changes in the spatial distribution of proteins, thereby enhancing sampling of relevant microstates. The method is applied to three binary systems. Generalization to multiprotein complexes is discussed.

Managing intracellular transport

Formation and normal function of neuronal synapses are intimately dependent on the delivery to and removal of biological materials from synapses by the intracellular transport machinery. Indeed, defects in intracellular transport contribute to the development and aggravation of neurodegenerative disorders. Despite its importance, regulatory mechanisms underlying this machinery remain poorly defined. We recently uncovered a phosphorylation-regulated mechanism that controls FEZ1-mediated Kinesin-1-based delivery of Stx1 into neuronal axons. Using C. elegans as a model organism to investigate transport defects, we show that FEZ1 mutations resulted in abnormal Stx1 aggregation in neuronal cell bodies and axons. This phenomenon closely resembles transport defects observed in neurodegenerative disorders. Importantly, diminished transport due to mutations of FEZ1 and Kinesin-1 were concomitant with increased accumulation of autophagosomes. Here, we discuss the significance of our findings in a broader context in relation to regulation of Kinesin-mediated transport and neurodegenerative disorders.

SUMO-2 and PIAS1 modulate insoluble mutant huntingtin protein accumulation

A key feature in Huntington disease (HD) is the accumulation of mutant Huntingtin (HTT) protein, which may be regulated by posttranslational modifications. Here, we define the primary sites of SUMO modification in the amino-terminal domain of HTT, show modification downstream of this domain, and demonstrate that HTT is modified by the stress-inducible SUMO-2. A systematic study of E3 SUMO ligases demonstrates that PIAS1 is an E3 SUMO ligase for both HTT SUMO-1 and SUMO-2 modification and that reduction of dPIAS in a mutant HTT Drosophila model is protective. SUMO-2 modification regulates accumulation of insoluble HTT in HeLa cells in a manner that mimics proteasome inhibition and can be modulated by overexpression and acute knockdown of PIAS1. Finally, the accumulation of SUMO-2-modified proteins in the insoluble fraction of HD postmortem striata implicates SUMO-2 modification in the age-related pathogenic accumulation of mutant HTT and other cellular proteins that occurs during HD progression.

Suppressing aberrant GluN3A expression rescues synaptic and behavioral impairments in Huntington's disease models

Huntington's disease is caused by an expanded polyglutamine repeat in the huntingtin protein (HTT), but the pathophysiological sequence of events that trigger synaptic failure and neuronal loss are not fully understood. Alterations in N-methyl-D-aspartate (NMDA)-type glutamate receptors (NMDARs) have been implicated. Yet, it remains unclear how the HTT mutation affects NMDAR function, and direct evidence for a causative role is missing. Here we show that mutant HTT redirects an intracellular store of juvenile NMDARs containing GluN3A subunits to the surface of striatal neurons by sequestering and disrupting the subcellular localization of the endocytic adaptor PACSIN1, which is specific for GluN3A. Overexpressing GluN3A in wild-type mouse striatum mimicked the synapse loss observed in Huntington's disease mouse models, whereas genetic deletion of GluN3A prevented synapse degeneration, ameliorated motor and cognitive decline and reduced striatal atrophy and neuronal loss in the YAC128 Huntington's disease mouse model. Furthermore, GluN3A deletion corrected the abnormally enhanced NMDAR currents, which have been linked to cell death in Huntington's disease and other neurodegenerative conditions. Our findings reveal an early pathogenic role of GluN3A dysregulation in Huntington's disease and suggest that therapies targeting GluN3A or pathogenic HTT-PACSIN1 interactions might prevent or delay disease progression.

Fkh1 and Fkh2 associate with Sir2 to control CLB2 transcription under normal and oxidative stress conditions

The Forkhead (Fkh) box family of transcription factors is evolutionary conserved from yeast to higher eukaryotes and its members are involved in many physiological processes including metabolism, DNA repair, cell cycle, stress resistance, apoptosis, and aging. In budding yeast, four Fkh transcription factors were identified, namely Fkh1, Fkh2, Fhl1, and Hcm1, which are implicated in chromatin silencing, cell cycle regulation, and stress response. These factors impinge transcriptional regulation during cell cycle progression, and histone deacetylases (HDACs) play an essential role in this process, e.g., the nuclear localization of Hcm1 depends on Sir2 activity, whereas Sin3/Rpd3 silence cell cycle specific gene transcription in G2/M phase. However, a direct involvement of Sir2 in Fkh1/Fkh2-dependent regulation of target genes is at present unknown. Here, we show that Fkh1 and Fkh2 associate with Sir2 in G1 and M phase, and that Fkh1/Fkh2-mediated activation of reporter genes is antagonized by Sir2. We further report that Sir2 overexpression strongly affects cell growth in an Fkh1/Fkh2-dependent manner. In addition, Sir2 regulates the expression of the mitotic cyclin Clb2 through Fkh1/Fkh2-mediated binding to the CLB2 promoter in G1 and M phase. We finally demonstrate that Sir2 is also enriched at the CLB2 promoter under stress conditions, and that the nuclear localization of Sir2 is dependent on Fkh1 and Fkh2. Taken together, our results show a functional interplay between Fkh1/Fkh2 and Sir2 suggesting a novel mechanism of cell cycle repression. Thus, in budding yeast, not only the regulation of G2/M gene expression but also the protective response against stress could be directly coordinated by Fkh1 and Fkh2.

Experimental models for identifying modifiers of polyglutamine-induced aggregation and neurodegeneration

Huntington's disease (HD) typifies a class of inherited neurodegenerative disorders in which a CAG expansion in a single gene leads to an extended polyglutamine tract and misfolding of the expressed protein, driving cumulative neural dysfunction and degeneration. HD is invariably fatal with symptoms that include progressive neuropsychiatric and cognitive impairments, and eventual motor disability. No curative therapies yet exist for HD and related polyglutamine diseases; therefore, substantial efforts have been made in the drug discovery field to identify potential drug and drug target candidates for disease-modifying treatment. In this context, we review here a range of early-stage screening approaches based in in vitro, cellular, and invertebrate models to identify pharmacological and genetic modifiers of polyglutamine aggregation and induced neurodegeneration. In addition, emerging technologies, including high-content analysis, three-dimensional culture models, and induced pluripotent stem cells are increasingly being incorporated into drug discovery screening pipelines for protein misfolding disorders. Together, these diverse screening strategies are generating novel and exciting new probes for understanding the disease process and for furthering development of therapeutic candidates for eventual testing in the clinical setting.

Prediction of novel genes associated with negative regulators of toll-like receptors-induced inflammation based on endotoxin tolerance

Prior exposure of innate immune cells to lipopolysaccharide (LPS) has caused them to be refractory to further endotoxin stimulation, also termed endotoxin tolerance (ET). Bacterial LPS signals through Toll-like receptor (TLR) 4, which was thought to enable the innate immune system to deal with invasive pathogens and to restrain systemic inflammation efficiently. We established a robust model of ET and determined the level of TNF-α and IL-6 in cultured human monocytes. Then, microarray assay was applied to assess gene expression in this model. The results showed that 356 non-tolerizable genes were differentially expressed at a high level in tolerant monocytes. The genes selected were classified into several categories based on gene ontology (GO) and KEGG pathway database. And then literature annotations, protein-protein interaction (PPI) network, and functional consistency were applied to analyze the non-tolerizable genes. Finally, the microarray results were verified by quantitative real-time PCR of seven representative genes, including the two candidate genes, Spry2 and Smurf2, which were supposed to play a critical role in TLRs-induced inflammation based on literature retrieval. Our results would provide useful information for further analysis of regulating TLRs-induced inflammation, and would facilitate the study of associated mechanisms.

Trinucleotide repeats: a structural perspective

Trinucleotide repeat (TNR) expansions are present in a wide range of genes involved in several neurological disorders, being directly involved in the molecular mechanisms underlying pathogenesis through modulation of gene expression and/or the function of the RNA or protein it encodes. Structural and functional information on the role of TNR sequences in RNA and protein is crucial to understand the effect of TNR expansions in neurodegeneration. Therefore, this review intends to provide to the reader a structural and functional view of TNR and encoded homopeptide expansions, with a particular emphasis on polyQ expansions and its role at inducing the self-assembly, aggregation and functional alterations of the carrier protein, which culminates in neuronal toxicity and cell death. Detail will be given to the Machado-Joseph Disease-causative and polyQ-containing protein, ataxin-3, providing clues for the impact of polyQ expansion and its flanking regions in the modulation of ataxin-3 molecular interactions, function, and aggregation.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

Vesicular glycolysis provides on-board energy for fast axonal transport

Fast axonal transport (FAT) requires consistent energy over long distances to fuel the molecular motors that transport vesicles. We demonstrate that glycolysis provides ATP for the FAT of vesicles. Although inhibiting ATP production from mitochondria did not affect vesicles motility, pharmacological or genetic inhibition of the glycolytic enzyme GAPDH reduced transport in cultured neurons and in Drosophila larvae. GAPDH localizes on vesicles via a huntingtin-dependent mechanism and is transported on fast-moving vesicles within axons. Purified motile vesicles showed GAPDH enzymatic activity and produced ATP. Finally, we show that vesicular GAPDH is necessary and sufficient to provide on-board energy for fast vesicular transport. Although detaching GAPDH from vesicles reduced transport, targeting GAPDH to vesicles was sufficient to promote FAT in GAPDH deficient neurons. This specifically localized glycolytic machinery may supply constant energy, independent of mitochondria, for the processive movement of vesicles over long distances in axons.

Huntingtin's function in axonal transport is conserved in Drosophila melanogaster

Huntington’s disease (HD) is a devastating dominantly inherited neurodegenerative disorder caused by an abnormal polyglutamine expansion in the N-terminal part of the huntingtin (HTT) protein. HTT is a large scaffold protein that interacts with more than a hundred proteins and is probably involved in several cellular functions. The mutation is dominant, and is thought to confer new and toxic functions to the protein. However, there is emerging evidence that the mutation also alters HTT’s normal functions. Therefore, HD models need to recapitulate this duality if they are to be relevant. Drosophila melanogaster is a useful in vivo model, widely used to study HD through the overexpression of full-length or N-terminal fragments of mutant human HTT. However, it is unclear whether Drosophila huntingtin (DmHTT) shares functions similar to the mammalian HTT. Here, we used various complementary approaches to analyze the function of DmHTT in fast axonal transport. We show that DmHTT interacts with the molecular motor dynein, associates with vesicles and co-sediments with microtubules. DmHTT co-localizes with Brain-derived neurotrophic factor (BDNF)-containing vesicles in rat cortical neurons and partially replaces mammalian HTT in a fast axonal transport assay. DmHTT-KO flies show a reduced fast axonal transport of synaptotagmin vesicles in motoneurons in vivo. These results suggest that the function of HTT in axonal transport is conserved between flies and mammals. Our study therefore validates Drosophila melanogaster as a model to study HTT function, and its dysfunction associated with HD.

A Y2H-seq approach defines the human protein methyltransferase interactome

To accelerate high-density interactome mapping, we developed a yeast two-hybrid interaction screening approach involving short-read second-generation sequencing (Y2H-seq) with improved sensitivity and a quantitative scoring readout allowing rapid interaction validation. We applied Y2H-seq to investigate enzymes involved in protein methylation, a largely unexplored post-translational modification. The reported network of 523 interactions involving 22 methyltransferases or demethylases is comprehensively annotated and validated through coimmunoprecipitation experiments and defines previously undiscovered cellular roles of nonhistone protein methylation.

Dynamic circadian protein-protein interaction networks predict temporal organization of cellular functions

Essentially all biological processes depend on protein-protein interactions (PPIs). Timing of such interactions is crucial for regulatory function. Although circadian (~24-hour) clocks constitute fundamental cellular timing mechanisms regulating important physiological processes, PPI dynamics on this timescale are largely unknown. Here, we identified 109 novel PPIs among circadian clock proteins via a yeast-two-hybrid approach. Among them, the interaction of protein phosphatase 1 and CLOCK/BMAL1 was found to result in BMAL1 destabilization. We constructed a dynamic circadian PPI network predicting the PPI timing using circadian expression data. Systematic circadian phenotyping (RNAi and overexpression) suggests a crucial role for components involved in dynamic interactions. Systems analysis of a global dynamic network in liver revealed that interacting proteins are expressed at similar times likely to restrict regulatory interactions to specific phases. Moreover, we predict that circadian PPIs dynamically connect many important cellular processes (signal transduction, cell cycle, etc.) contributing to temporal organization of cellular physiology in an unprecedented manner.

A human skeletal muscle interactome centered on proteins involved in muscular dystrophies: LGMD interactome

Background

The complexity of the skeletal muscle and the identification of numerous human disease-causing mutations in its constitutive proteins make it an interesting tissue for proteomic studies aimed at understanding functional relationships of interacting proteins in both health and diseases.

Method

We undertook a large-scale study using two-hybrid screens and a human skeletal-muscle cDNA library to establish a proteome-scale map of protein-protein interactions centered on proteins involved in limb-girdle muscular dystrophies (LGMD). LGMD is a group of more than 20 different neuromuscular disorders that principally affect the proximal pelvic and shoulder girdle muscles.

Results and conclusion

The interaction network we unraveled incorporates 1018 proteins connected by 1492 direct binary interactions and includes 1420 novel protein-protein interactions. Computational, experimental and literature-based analyses were performed to assess the overall quality of this network. Interestingly, LGMD proteins were shown to be highly interconnected, in particular indirectly through sarcomeric proteins. In-depth mining of the LGMD-centered interactome identified new candidate genes for orphan LGMDs and other neuromuscular disorders. The data also suggest the existence of functional links between LGMD2B/dysferlin and gene regulation, between LGMD2C/γ-sarcoglycan and energy control and between LGMD2G/telethonin and maintenance of genome integrity. This dataset represents a valuable resource for future functional investigations.

NeuroDNet - an open source platform for constructing and analyzing neurodegenerative disease networks

Background

Genetic networks control cellular functions. Aberrations in normal cellular function are caused by mutations in genes that disrupt the fine tuning of genetic networks and cause disease or disorder. However, the large number of signalling molecules, genes and proteins that constitute such networks, and the consequent complexity of interactions, has restrained progress in research elucidating disease mechanisms. Hence, carrying out a systematic analysis of how diseases alter the character of these networks is important. We illustrate this through our work on neurodegenerative disease networks. We created a database, NeuroDNet, which brings together relevant information about signalling molecules, genes and proteins, and their interactions, for constructing neurodegenerative disease networks.

Description

NeuroDNet is a database with interactive tools that enables the creation of interaction networks for twelve neurodegenerative diseases under one portal for interrogation and analyses. It is the first of its kind, which enables the construction and analysis of neurodegenerative diseases through protein interaction networks, regulatory networks and Boolean networks. The database has a three-tier architecture - foundation, function and interface. The foundation tier contains the human genome data with 23857 protein-coding genes linked to more than 300 genes reported in clinical studies of neurodegenerative diseases. The database architecture was designed to retrieve neurodegenerative disease information seamlessly through the interface tier using specific functional information. Features of this database enable users to extract, analyze and display information related to a disease in many different ways.

Conclusions

The application of NeuroDNet was illustrated using three case studies. Through these case studies, the construction and analyses of a PPI network for angiogenin protein in amyotrophic lateral sclerosis, a signal-gene-protein interaction network for presenilin protein in Alzheimer's disease and a Boolean network for a mammalian cell cycle was demonstrated. NeuroDNet is accessible at http://bioschool.iitd.ac.in/NeuroDNet/.

Development and application of a DNA microarray-based yeast two-hybrid system

The yeast two-hybrid (Y2H) system is the most widely applied methodology for systematic protein–protein interaction (PPI) screening and the generation of comprehensive interaction networks. We developed a novel Y2H interaction screening procedure using DNA microarrays for high-throughput quantitative PPI detection. Applying a global pooling and selection scheme to a large collection of human open reading frames, proof-of-principle Y2H interaction screens were performed for the human neurodegenerative disease proteins huntingtin and ataxin-1. Using systematic controls for unspecific Y2H results and quantitative benchmarking, we identified and scored a large number of known and novel partner proteins for both huntingtin and ataxin-1. Moreover, we show that this parallelized screening procedure and the global inspection of Y2H interaction data are uniquely suited to define specific PPI patterns and their alteration by disease-causing mutations in huntingtin and ataxin-1. This approach takes advantage of the specificity and flexibility of DNA microarrays and of the existence of solid-related statistical methods for the analysis of DNA microarray data, and allows a quantitative approach toward interaction screens in human and in model organisms.

Four new loci associations discovered by pathway-based and network analyses of the genome-wide variability profile of Hirschsprung's disease

Finding gene associations in rare diseases is frequently hampered by the reduced numbers of patients accessible. Conventional gene-based association tests rely on the availability of large cohorts, which constitutes a serious limitation for its application in this scenario. To overcome this problem we have used here a combined strategy in which a pathway-based analysis (PBA) has been initially conducted to prioritize candidate genes in a Spanish cohort of 53 trios of short-segment Hirschsprung’s disease. Candidate genes have been further validated in an independent population of 106 trios. The study revealed a strong association of 11 gene ontology (GO) modules related to signal transduction and its regulation, enteric nervous system (ENS) formation and other HSCR-related processes. Among the preselected candidates, a total of 4 loci, RASGEF1A, IQGAP2, DLC1 and CHRNA7, related to signal transduction and migration processes, were found to be significantly associated to HSCR. Network analysis also confirms their involvement in the network of already known disease genes. This approach, based on the study of functionally-related gene sets, requires of lower sample sizes and opens new opportunities for the study of rare diseases.

Chapter 4: Protein interactions and disease

Proteins do not function in isolation; it is their interactions with one another and also with other molecules (e.g. DNA, RNA) that mediate metabolic and signaling pathways, cellular processes, and organismal systems. Due to their central role in biological function, protein interactions also control the mechanisms leading to healthy and diseased states in organisms. Diseases are often caused by mutations affecting the binding interface or leading to biochemically dysfunctional allosteric changes in proteins. Therefore, protein interaction networks can elucidate the molecular basis of disease, which in turn can inform methods for prevention, diagnosis, and treatment. In this chapter, we will describe the computational approaches to predict and map networks of protein interactions and briefly review the experimental methods to detect protein interactions. We will describe the application of protein interaction networks as a translational approach to the study of human disease and evaluate the challenges faced by these approaches.

A vertex similarity-based framework to discover and rank orphan disease-related genes

Background

A rare or orphan disease (OD) is any disease that affects a small percentage of the population. While opportunities now exist to accelerate progress toward understanding the basis for many more ODs, the prioritization of candidate genes is still a critical step for disease-gene identification. Several network-based frameworks have been developed to address this problem with varied results.

Result

We have developed a novel vertex similarity (VS) based parameter-free prioritizing framework to identify and rank orphan disease candidate genes. We validate our approach by using 1598 known orphan disease-causing genes (ODGs) representing 172 orphan diseases (ODs). We compare our approach with a state-of-art parameter-based approach (PageRank with Priors or PRP) and with another parameter-free method (Interconnectedness or ICN). Our results show that VS-based approach outperforms ICN and is comparable to PRP. We further apply VS-based ranking to identify and rank potential novel candidate genes for several ODs.

Conclusion

We demonstrate that VS-based parameter-free ranking approach can be successfully used for disease candidate gene prioritization and can complement other network-based methods for candidate disease gene ranking. Importantly, our VS-ranked top candidate genes for the ODs match the known literature, suggesting several novel causal relationships for further investigation.

Interactome mapping for analysis of complex phenotypes: insights from benchmarking binary interaction assays

Protein interactions mediate essentially all biological processes and analysis of protein-protein interactions using both large-scale and small-scale approaches has contributed fundamental insights to the understanding of biological systems. In recent years, interactome network maps have emerged as an important tool for analyzing and interpreting genetic data of complex phenotypes. Complementary experimental approaches to test for binary, direct interactions, and for membership in protein complexes are used to explore the interactome. The two approaches are not redundant but yield orthogonal perspectives onto the complex network of physical interactions by which proteins mediate biological processes. In recent years, several publications have demonstrated that interactions from high-throughput experiments can be equally reliable as the high quality subset of interactions identified in small-scale studies. Critical for this insight was the introduction of standardized experimental benchmarking of interaction and validation assays using reference sets. The data obtained in these benchmarking experiments have resulted in greater appreciation of the limitations and the complementary strengths of different assays. Moreover, benchmarking is a central element of a conceptual framework to estimate interactome sizes and thereby measure progress toward near complete network maps. These estimates have revealed that current large-scale data sets, although often of high quality, cover only a small fraction of a given interactome. Here, I review the findings of assay benchmarking and discuss implications for quality control, and for strategies toward obtaining a near-complete map of the interactome of an organism.