BACKGROUND:A hub protein is one that interacts with many functional partners. The annotation of hub proteins, or more generally the protein-protein interaction "degree" of each gene, requires quality genome-wide data. Data obtained using yeast two-hybrid methods contain many false positive interactions between proteins that rarely encounter each other in living cells, and such data have fallen out of favor.RESULTS:We find that protein "stickiness", measured as network degree in ostensibly low quality yeast two-hybrid data, is a more predictive genomic metric than the number of functional protein-protein interactions, as assessed by supposedly higher quality high throughput affinity capture mass spectrometry data. In the yeast Saccharomyces cerevisiae, a protein's high stickiness, but not its high number of functional interactions, predicts low stochastic noise in gene expression, low plasticity of gene expression across different environments, and high probability of forming a homo-oligomer. Our results are robust to a multiple regression analysis correcting for other known predictors including protein abundance, presence of a TATA box and whether a gene is essential. Once the higher stickiness of homo-oligomers is controlled for, we find that homo-oligomers have noisier and more plastic gene expression than other proteins, consistent with a role for homo-oligomerization in mediating robustness.CONCLUSIONS:Our work validates use of the number of yeast two-hybrid interactions as a metric for protein stickiness. Sticky proteins exhibit low stochastic noise in gene expression, and low plasticity in expression across different environments.

Proteins participate in the majority of cellular processes. To determine the function of a protein it is not sufficient to solely know its sequence, its structure in isolation, or how it works individually. Additionally, we need to know how the protein interacts with other proteins in biological networks. This is because most of the proteins perform their main function through interactions. This thesis sets out to improve the understanding of protein-protein interaction networks (PPINs). For this, we propose three approaches: (1) Studying measures and methods used in social and complex networks. The methods, measures, and properties of social networks allow us to gain an understanding of PPINs via the comparison of different types of network families. We studied models that describe social networks to see which models are useful in describing biological networks. We investigate the similarities and differences in terms of the network community profile and centrality measures. (2) Studying PPINs and their role in evolution. We are interested in the relationship of PPINs and the evolutionary changes between species. We investigate whether the centrality measures are correlated with the variability and similarity in orthologous proteins. (3) Studying protein features that are important to evaluate, classify, and predict interactions. Interactions can be classified according to different characteristics. One characteristic is the energy (that is the attraction or repulsion of the molecules) that occurs in interacting proteins. We identify which type of energy values contributes better to predicting PPIs. We argue that the number of energetic features and their contribution to the interactions can be a key factor in predicting transient and permanent interactions. Contributions of this thesis include: (1) We identified the best community sizes in PPINs. This finding will help to identify important groups of interacting proteins in order to better understand their particular interactions. We furthermore find that the generative model describing biological networks is very different from the model describing social networks A generative model is a model for randomly generating observable data. We showed that the best community size for PPINs is around ten, different from the best community size for social and complex network (around 100). We revealed differences in terms of the network community profile and correlations of centrality measures; (2) We outline a method to test correlation of centrality measures with the percentage of sequence similarity and evolutionary rate for orthologous proteins. We conjecture that a strong correlation exists. While not obtaining positive results for our data. Therefore, (3) we investigate a method to discriminate energetic features of protein interactions that in turn will improve the PPIN data. The use of multiple data sets makes possible to identify the energy values that are useful to classify interactions. For each data set, we performed Random Forest and Support Vector Machine with linear, polynomial, radial, and sigmoid kernels. The accuracy obtained in this analysis reinforces the idea that energetic features in the protein interface help to discriminate between transient and permanent interactions. / Graduate / 0984

Genome-wide studies of diseases and chronic conditions frequently fail to uncover marked or consistent differences in RNA or protein concentrations. However, the developing field of kinetic proteomics has made promising discoveries in differences in the turnover rate of these same proteins, even when concentrations were not necessarily different. The situation can theoretically be modeled mathematically using bifurcation equations, but uncovering the proper form of these is difficult. To this end, we developed TWIG, a method for characterizing bifurcations that leverages information geometry to identify drivers of complex systems. Using this, we characterized the bifurcation and stability properties of all 132 possible 3- and 22,662 possible 4-node subgraphs (motifs) of protein-protein interaction networks. Analyzing millions of real world protein networks indicates that natural selection has little preference for motifs that are stable per se, but a great preference for motifs who have parameter regions that are exclusively stable, rather than poorly constrained mixtures of stability and instability. We apply this knowledge to mice on calorie restricted (CR) diets, demonstrating that changes in their protein turnover rates do indeed make their protein networks more stable, explaining why CR is the most robust way known to extend lifespan.

Parkinson’s Disease (PD) is the second most common neurodegenerative disease in the Western world. It shows a high degree of genetic and phenotypic complexity with many implicated factors, various disease manifestations but few clear causal links. Ongoing research has identified a growing number of molecular alterations linked to the disease. Dopaminergic neurons in the substantia nigra, specifically their synapses, are the key-affected region in PD. Therefore, this work focuses on understanding the disease effects on the synapse, aiming to identify potential genetic triggers and synaptic PD associated mechanisms. Currently, one of the main challenges in this area is data quality and accessibility. In order to study PD, publicly available data were systematically retrieved and analysed. 418 PD associated genes could be identified, based on mutations and curated annotations. I curated an up-to-date and complete synaptic proteome map containing a total of 6,706 proteins. Region specific datasets describing the presynapse, postsynapse and synaptosome were also delimited. These datasets were analysed, investigating similarities and differences, including reproducibility and functional interpretations. The use of Protein-Protein-Interaction Network (PPIN) analysis was chosen to gain deeper knowledge regarding specific effects of PD on the synapse. Thus I generated a customised, filtered, human specific Protein-Protein Interaction (PPI) dataset, containing 211,824 direct interactions, from four public databases. Proteomics data and PPI information allowed the construction of PPINs. These were analysed and a set of low level statistics, including modularity, clustering coefficient and node degree, explaining the network’s topology from a mathematical point of view were obtained. Apart from low-level network statistics, high-level topology of the PPINs was studied. To identify functional network subgroups, different clustering algorithms were investigated. In the context of biological networks, the underlying hypothesis is that proteins in a structural community are more likely to share common functions. Therefore I attempted to identify PD enriched communities of synaptic proteins. Once identified, they were compared amongst each other. Three community clusters could be identified as containing largely overlapping gene sets. These contain 24 PD associated genes. Apart from the known disease associated genes in these communities, a total of 322 genes was identified. Each of the three clusters is specifically enriched for specific biological processes and cellular components, which include neurotransmitter secretion, positive regulation of synapse assembly, pre- and post-synaptic membrane, scaffolding proteins, neuromuscular junction development and complement activation (classical pathway) amongst others. The presented approach combined a curated set of PD associated genes, filtered PPI information and synaptic proteomes. Various small- and large-scale analytical approaches, including PPIN topology analysis, clustering algorithms and enrichment studies identified highly PD affected synaptic proteins and subregions. Specific disease associated functions confirmed known research insights and allowed me to propose a new list of so far unknown potential disease associated genes. Due to the open design, this approach can be used to answer similar research questions regarding other complex diseases amongst others.

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No description available.

Naïvement perçu, le processus d’évolution est une succession d’événements de duplication et de mutations graduelles dans le génome qui mènent à des changements dans les fonctions et les interactions du protéome. La famille des hydrolases de guanosine triphosphate (GTPases) similaire à Ras constitue un bon modèle de travail afin de comprendre ce phénomène fondamental, car cette famille de protéines contient un nombre limité d’éléments qui diffèrent en fonctionnalité et en interactions. Globalement, nous désirons comprendre comment les mutations singulières au niveau des GTPases affectent la morphologie des cellules ainsi que leur degré d’impact sur les populations asynchrones. Mon travail de maîtrise vise à classifier de manière significative différents phénotypes de la levure Saccaromyces cerevisiae via l’analyse de plusieurs critères morphologiques de souches exprimant des GTPases mutées et natives. Notre approche à base de microscopie et d’analyses bioinformatique des images DIC (microscopie d’interférence différentielle de contraste) permet de distinguer les phénotypes propres aux cellules natives et aux mutants. L’emploi de cette méthode a permis une détection automatisée et une caractérisation des phénotypes mutants associés à la sur-expression de GTPases constitutivement actives. Les mutants de GTPases constitutivement actifs Cdc42 Q61L, Rho5 Q91H, Ras1 Q68L et Rsr1 G12V ont été analysés avec succès. En effet, l’implémentation de différents algorithmes de partitionnement, permet d’analyser des données qui combinent les mesures morphologiques de population native et mutantes. Nos résultats démontrent que l’algorithme Fuzzy C-Means performe un partitionnement efficace des cellules natives ou mutantes, où les différents types de cellules sont classifiés en fonction de plusieurs facteurs de formes cellulaires obtenus à partir des images DIC. Cette analyse démontre que les mutations Cdc42 Q61L, Rho5 Q91H, Ras1 Q68L et Rsr1 G12V induisent respectivement des phénotypes amorphe, allongé, rond et large qui sont représentés par des vecteurs de facteurs de forme distincts. Ces distinctions sont observées avec différentes proportions (morphologie mutante / morphologie native) dans les populations de mutants. Le développement de nouvelles méthodes automatisées d’analyse morphologique des cellules natives et mutantes s’avère extrêmement utile pour l’étude de la famille des GTPases ainsi que des résidus spécifiques qui dictent leurs fonctions et réseau d’interaction. Nous pouvons maintenant envisager de produire des mutants de GTPases qui inversent leur fonction en ciblant des résidus divergents. La substitution fonctionnelle est ensuite détectée au niveau morphologique grâce à notre nouvelle stratégie quantitative. Ce type d’analyse peut également être transposé à d’autres familles de protéines et contribuer de manière significative au domaine de la biologie évolutive. / Evolution is a gradual process that gives rise to changes in the form of mutations that are reflected at the protein level. We propose that evolution of new pathways occurs by switching binding partners, hence creating new functions. The different functions encountered in a given family of related proteins have emerged from a common ancestor that has been duplicated and mutated to become implicated in new interactions and to gain new functions. In this study, we will use native and constitutive active mutant variants of the Ras-like family of small GTPases as working model, to explore such gene duplications, followed by neo / sub-functionalization. The reason for choosing this family resides in the fact that it is a defined set of proteins with well known functions that are mediated through multiple protein-protein interactions. The aim of this master is to perform a classification of budding yeast phenotypes using different approaches in order to statistically determine at which level of the population these constitutively active mutations are capable to affect cell morphology. Working with a subset of the Ras-like small GTPases family, we recently developed an approach to catalogue and classify these proteins based on multiple physical and chemical criteria. Using microscopic and bioinformatics methods, we characterized phenotypes associated with over-expression of the native small GTPases of the budding yeast Saccharomyces cerevisiae, showing that an established classification is not very clear. We are interested to investigate how point mutations in small GTPases can affect the cell morphology and their level of impact on asynchronous population. We want to establish a method to determine and quantify mutant and wild type-like phenotypes on these populations using Differential interference contrast microscopy (DIC) images only. As for the first aim of this study, we hypothesize that clustering algorithms can partition mutant cells from wild type cells based on cell shape factor measurements. To prove this hypothesis, we proposed to implement different clustering algorithms to analyze datasets which combines measurements from wild type and respective mutant populations. We created constitutively active forms of these small GTPases and used Cdc42, Rho5, Ras1 and Rsr1 to validate our results. We observed that Cdc42 Q61L, Rho5 Q91H, Ras1 Q68L and Rsr1 G12V mutations induced characteristic amorphous, clumped/elongated, rounded and discrete large phenotypes respectively. This classification allowed us to define a phenotypical classification related to functions. Phenotype classification of the small GTPases has been confirmed using shape factor formulas accompanied with bioinformatics approaches. These approaches which involved different clustering methods allowed an automated quantitative characterization of the phenotypes of up to 7293 mutant cells. Sequence alignment of Cdc42 and Rho5 showed 46.1% identity as well as 62.6% for Ras1 and Rsr1 allowing the identification of diverged residues potentially involved in specific functions and protein-protein interactions. Directed mutagenesis and substitution of these sites from one gene to another have been performed in some positions to test for specificity and involvement in morphology changes. In parallel, interactions observed for native and constitutively active mutants Cdc42 and Rho5 will be assayed with protein-fragment complementation assay (PCA). This will enable us to determine whether a high correlation exists between functions switches and binding partner’s switches. We propose to expand this approach to the whole Ras-like small GTPases family and monitor protein-protein interactions and functions at a network scale. This research will confirm whether enrichment or depletion of residues in specific sites induces a switch of function due to switching binding partners. Understanding the mechanism underlying such correlation is important to gain insight in the biological mechanisms underlying the Ras-like small GTPases and other proteins evolution. Such knowledge is of fundamental importance in biomedical and pharmaceutical fields, since Ras-like small GTPases represent important targets for therapeutic interventions and for the evolutionary biology field.

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Chorea Huntington (HD) ist eine neurodegenerative Erkrankung, die durch Ablagerungen von N-terminal Polyglutamin-reichen Huntingtin (Htt) -Fragmenten in den betroffenen Neuronen charakterisiert ist. Das mutierte Htt (mHtt) Protein wird ubiquitär exprimiert. Das zellspezifische Absterben von „medium-sized spiny neurons“ (MSN) wird jedoch im Striatum von HD Patienten verursacht (Albin, 1995). Es wird angenommen, dass Striatum-spezifische Proteine, die mit Htt interagieren, eine wichtige Rolle in der Pathogenese von HD spielen (Ross, 1995). Protein-Protein-Interaktionsstudien haben gezeigt, dass einige der Htt-Interaktionspartner mit unlöslichen Htt-Ablagerungen in den Gehirnen von HD-Patienten kolokalisieren und die Bildung von Protein-Aggregaten beeinflussen (Goehler, 2004). Kürzlich wurde durch die Integration von Genexpressions- und Interaktionsdaten ein Striatum-spezifisches Protein-Interaktionsnetzwerk erstellt (Chaurasia, unveröffentlichte Daten). Eines der identifizierten Proteine ist CRMP1 (collapsin response mediator protein 1), das spezifisch in Neuronen exprimiert wird und möglicherweise eine wichtige Rolle bei der Pathogenese von HD spielt. Experimentelle Untersuchungen mithilfe eines Filter-Retardationsassays zeigten, dass CRMP1 die Anordnung von Htt zu fibrillären, SDS-unlöslichen Aggregaten verringert. Durch Rasterkraftmikroskopie wurde der direkte Effekt von CRMP1 auf den Aggregationsprozess von Htt bestätigt. Ko-Immunopräzipitationsstudien zeigten, dass CRMP1 und Htt in Säugerzellen unter physiologischen Bedingungen miteinander interagieren. Es wurde nachgewiesen, dass CRMP1 die Polyglutamin-abhängige Aggregation und Toxizität von Htt in Zell- und Drosophila-Modellen von HD moduliert. Außerdem konnte CRMP1 in neuronalen Ablagerungen in R6/2 Mäusegehirnen und dessen selektive Spaltung durch Calpaine gezeigt werden. Diese Ergebnisse deuten darauf hin, dass die Lokalisation und Funktion von CRMP1 bei der Krankheitsentstehung verändert werden. / Huntington’s disease (HD) is a neurodegenerative disorder characterized by the accumulation of N-terminal polyglutamine (polyQ)-containing huntingtin (Htt) fragments in affected neurons. The mutant Htt (mHtt) protein is ubiquitously expressed but causes specific dysfunction and death of striatal medium-sized spiny neurons (MSNs) (Albin, 1995). It is assumed that striatum specific proteins interacting with Htt might play an important role in HD pathogenesis (Ross, 1995). Previous protein-protein interaction (PPI) studies demonstrated that many Htt-interacting proteins colocalize with insoluble Htt inclusions in HD brains and modulate the mHtt phenotype (Goehler 2004). A striatum-specific, dysregulated PPI network has been created recently by integrating PPI networks with information from gene expression profiling data (Chaurasia, unpublished data). One of the identified dysregulated proteins potentially involved in HD pathogenesis was the neuron-specific collapsin response-mediator protein 1 (CRMP1). Here, I show that CRMP1 reduces the self-assembly of SDS-insoluble mHtt protein aggregates in vitro, indicating a direct role of CRMP1 on the mHtt aggregation process. Coimmunoprecipitation studies showed that CRMP1 and Htt associate in mammalian cells under physiological conditions. In addition, CRMP1 localizes to abnormal neuronal inclusions and efficiently modulates polyQ-mediated Htt aggregation and toxicity in cell and Drosophila models of HD. This suggests that dysfunction of the protein is crucial for disease pathogenesis. Finally, I observed that CRMP1 localizes to neuronal inclusions and is selectively cleaved by calpains in R6/2 mouse brains, indicating that its distribution and function are altered in pathogenesis. In conclusion, this study presents new findings on the function of CRMP1 and its role in the pathogenesis of HD. The protein interacts with Htt and modulates its aggregation and toxicity, in this way influencing the molecular course of the disease.

Proteins are indispensable players in virtually all biological events. The functions of proteins are determined by their three dimensional (3D) structure and coordinated through intricate networks of protein-protein interactions (PPIs). Hence, a deep comprehension of such networks turns out to be crucial for understanding the cellular biology. Computational approaches have become critical tools for analysing PPI networks. In silico methods take advantage of the existing PPI knowledge to both predict new interactions and predict the function of proteins. Regarding the task of predicting PPIs, several methods have been already developed. However, recent findings demonstrate that such methods could take advantage of the knowledge on non-interacting protein pairs (NIPs). On the task of predicting the function of proteins,the Guilt-by-Association (GBA) principle can be exploited to extend the functional annotation of proteins over PPI networks. In this thesis, a new algorithm for PPI prediction and a protocol to complete cell signalling networks are presented. iLoops is a method that uses NIP data and structural information of proteins to predict the binding fate of protein pairs. A novel protocol for completing signalling networks –a task related to predicting the function of a protein, has also been developed. The protocol is based on the application of GBA principle in PPI networks. / Les proteïnes tenen un paper indispensable en virtualment qualsevol procés biològic. Les funcions de les proteïnes estan determinades per la seva estructura tridimensional (3D) i són coordinades per mitjà d’una complexa xarxa d’interaccions protiques (en anglès, protein-protein interactions, PPIs). Axí doncs, una comprensió en profunditat d’aquestes xarxes és fonamental per entendre la biologia cel•lular. Per a l’anàlisi de les xarxes d’interacció de proteïnes, l’ús de tècniques computacionals ha esdevingut fonamental als darrers temps. Els mètodes in silico aprofiten el coneixement actual sobre les interaccions proteiques per fer prediccions de noves interaccions o de les funcions de les proteïnes. Actualment existeixen diferents mètodes per a la predicció de noves interaccions de proteines. De tota manera, resultats recents demostren que aquests mètodes poden beneficiar-se del coneixement sobre parelles de proteïnes no interaccionants (en anglès, non-interacting pairs, NIPs). Per a la tasca de predir la funció de les proteïnes, el principi de “culpable per associació” (en anglès, guilt by association, GBA) és usat per extendre l’anotació de proteïnes de funció coneguda a través de xarxes d’interacció de proteïnes. En aquesta tesi es presenta un nou mètode pre a la predicció d’interaccions proteiques i un nou protocol basat per a completar xarxes de senyalització cel•lular. iLoops és un mètode que utilitza dades de parells no interaccionants i coneixement de l’estructura 3D de les proteïnes per a predir interaccions de proteïnes. També s’ha desenvolupat un nou protocol per a completar xarxes de senyalització cel•lular, una tasca relacionada amb la predicció de les funcions de les proteïnes. Aquest protocol es basa en aplicar el principi GBA a xarxes d’interaccions proteiques.