<p>In order to derive reliable protein networks it has recently been suggested that the combination of information from both gene and protein level is required. In this thesis a combination of gene expression and protein chip analysis was performed when constructing protein networks. Proteins with high affinity to the same substrates and encoded by genes with high correlation is here thought to constitute reliable protein networks. The protein networks derived are unfortunately not as reliable as were hoped for. According to the tests performed, the method derived in this thesis does not perform more than slightly better than chance. However, the poor results can depend on the data used, since mismatching and shortage of data has been evident.</p>

In order to derive reliable protein networks it has recently been suggested that the combination of information from both gene and protein level is required. In this thesis a combination of gene expression and protein chip analysis was performed when constructing protein networks. Proteins with high affinity to the same substrates and encoded by genes with high correlation is here thought to constitute reliable protein networks. The protein networks derived are unfortunately not as reliable as were hoped for. According to the tests performed, the method derived in this thesis does not perform more than slightly better than chance. However, the poor results can depend on the data used, since mismatching and shortage of data has been evident.

The very large G-protein-coupled receptor 1 (VLGR1/ADGRV1) is the largest member of the adhesion G-protein-coupled receptor (ADGR) family. Mutations in VLGR1/ADGRV1 cause human Usher syndrome (USH), a form of hereditary deaf-blindness, and have been additionally linked to epilepsy. In the absence of tangible knowledge of the molecular function and signaling of VLGR1, the pathomechanisms underlying the development of these diseases are still unknown. Our study aimed to identify novel, previously unknown protein networks associated with VLGR1 in order to describe new functional cellular modules of this receptor. Using affinity proteomics, we have identified numerous new potential binding partners and ligands of VLGR1. Tandem affinity purification hits were functionally grouped based on their Gene Ontology terms and associated with functional cellular modules indicative of functions of VLGR1 in transcriptional regulation, splicing, cell cycle regulation, ciliogenesis, cell adhesion, neuronal development, and retinal maintenance. In addition, we validated the identified protein interactions and pathways in vitro and in situ. Our data provided new insights into possible functions of VLGR1, related to the development of USH and epilepsy, and also suggest a possible role in the development of other neuronal diseases such as Alzheimer’s disease.

In this study, an algorithm named ClustOrth is proposed for determining and matching functionally orthologous protein clusters in different species. The algorithm requires protein interaction networks of the organisms to be compared and GO terms of the proteins in these interaction networks as prior information. After determining the functionally related protein groups using the Repeated Random Walks algorithm, the method maps the identified protein groups according to the similarity metric defined. In order to evaluate the similarities of protein groups, graph theoretical information is used together with the context information about the proteins. The clusters are aligned using GO-Term-based protein similarity measures defined in previous studies. These alignments are used to evaluate cluster similarities by defining a cluster similarity metric from protein similarities. The top scoring cluster alignments are considered as orthologous. Several data sources providing orthology information have shown that the defined cluster similarity metric can be used to make inferences about the orthological relevance of protein groups. Comparison with a protein orthology prediction algorithm named ISORANK also showed that the ClustOrth algorithm is successful in determining orthologies between proteins. However, the cluster similarity metric is too strict and many cluster matches are not able to produce high scores for this metric. For this reason, the number of predictions performed is low. This problem can be overcomed with the introduction of different sources of information related to proteins in the clusters for the evaluation of the clusters. The ClustOrth algorithm also outperformed the NetworkBLAST algorithm which aims to find orthologous protein clusters using protein sequence information directly for determining orthologies. It can be concluded that this study is one of the leading studies addressing the protein cluster matching problem for identifying orthologous functional modules of protein interaction networks computationally.

Orientador: Jorg Kobarg / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Biologia / Made available in DSpace on 2018-08-18T17:04:45Z (GMT). No. of bitstreams: 1 Meirelles_GabrielaVaz_D.pdf: 11269390 bytes, checksum: bfd22a079ffc68a78b96b3e50dd60b1c (MD5) Previous issue date: 2011 / Resumo: A proteína NIMA foi identificada e caracterizada funcionalmente em Aspergillus nidulans como sendo uma serina/treonina cinase critica para a progressão do ciclo celular. As Neks (NIMA-related kinases) constituem uma família de cinases composta por 11 membros em mamíferos, que compartilham 40-45% de identidade com a proteína NIMA no domínio catalítico N-terminal. As Neks estão associadas a funções do ciclo celular e diversas patologias, o que as torna potenciais alvos quimioterápicos. Mutações no gene da Nek1 levam ao desenvolvimento da doença renal policistica e ao aparecimento de diversos efeitos pleiotrópicos, sugerindo sua participação em vias reguladoras de vários processos celulares. A Nek6, por sua vez, e ativada durante a mitose, e a super-expressão de mutantes inativos ou a sua depleção por RNAi produz células exibindo defeitos no fuso, anormalidades nucleares, parada na metáfase e apoptose. A Nek6 humana foi recentemente associada a carcinogênese, mas, assim como para a maioria das Neks, sua estrutura molecular, parceiros de interação e vias de sinalização permanecem ainda desconhecidos. Nesse trabalho, introduzimos a hNek6 como uma hub no interactoma humano. Uma extensa comparação de bancos de dados baseada em analises de conectividade mostrou que o quinoma humano e enriquecido em hubs. Nossas redes de interação incluem um amplo espectro de novos parceiros de interação para a hNek6 identificados em screenings de duplo - hibrido em levedura, classificados em 18 categorias funcionais. Alguns novos parceiros de interação da hNek6 são também possíveis substratos e, ainda, colocalizam com a hNek6 e ?-tubulina em células humanas, apontando para uma possível interação centrossomal. Os diversos parceiros de interação conectam a hNek6 a novas vias, como a sinalização de Notch e a regulação do citoesqueleto de actina, ou fornecem novas pistas de como a hNek6 poderia regular vias previamente propostas, como ciclo celular, reparo de DNA e sinalização do NF-?B. Alem disso, obtivemos o primeiro modelo estrutural de baixa resolução para a hNek6 a partir de SAXS. Analises estruturais revelaram que a hNek6 e um monômero em solução, apresentando uma conformação predominantemente globular, mas levemente alongada. Particularmente, a curta região N-terminal desordenada da hNek6 e importante para mediar as interações com seus parceiros. No caso da hNek1, observamos que ela interage com Fez1 e Clasp2 através de seus motivos coiled-coil, e colocaliza com essas proteínas em uma região candidata ao centrossomo / Abstract: NIMA was identified and functionally characterized in Aspergillus nidulans as a critical Ser/Thr kinase for cell cycle progression. The mammalian Neks (NIMA-related kinases) represent an evolutionarily conserved family of 11 serine/threonine kinases that share 40-45% identity with NIMA N-terminal domain. Neks are associated to cell cyclerelated functions and diverse pathologies, which highlight them as potential chemotherapeutic targets. Nek1 gene mutations lead to the development of polycystic kidney disease and the emergence of several pleiotropic effects, suggesting its involvement in pathways regulating various cellular processes. Nek6, in turn, is activated during mitosis, and overexpression of inactive mutants or its depletion by iRNA produces cells exhibiting mitotic spindle defects, nuclear abnormalities, metaphase arrest and apoptosis. Human Nek6 was recently found to be linked to carcinogenesis, but as for the majority of Neks, the molecular structure, interacting partners and signaling pathways remain elusive. Here we introduce hNek6 as a hub kinase in the human interactome. We performed a broad databank comparison based on degree distribution analysis and found that the human kinome is enriched in hubs. Our networks include a large set of novel hNek6 interactors identified in our yeast two-hybrid screens, classified into 18 functional categories. Some novel interactors are also putative substrates and colocalized with hNek6 and ?-tubulin in human cells, pointing to a possible centrosomal interaction. The interacting proteins link hNek6 to novel pathways, e.g. Notch signaling and actin cytoskeleton regulation, or give new insights on how hNek6 may regulate previously proposed pathways such as cell cycle, DNA repair and NF-?B signalings. Furthermore, we obtained the first low-resolution structural model of hNek6 by SAXS. Structural analysis revealed that hNek6 is a monomer in solution with a mostly globular, though slightly elongated conformation. Notably, we found that hNek6 unfolded short N-terminal region is important to mediate the interactions with its partners. In the case of hNek1, we found that it interacts with Fez1 and Clasp2 through coiled-coil motifs and colocalizes with these proteins in a candidate centrosomal region / Doutorado / Bioquimica / Doutor em Biologia Funcional e Molecular

No description available.

Cell functions are steered by complex interactions of gene products, like forming a temporary or stable complex, altering gene expression or catalyzing a reaction. Mapping these interactions is the key in understanding biological processes and therefore is the focus of numerous experiments and studies. Small-scale experiments deliver high quality data but lack coverage whereas high-throughput techniques cover thousands of interactions but can be error-prone. Unfortunately all of these approaches can only focus on one type of interaction at the time. This makes experimental mapping of the genome-wide network a cost and time intensive procedure. However, to overcome these problems, different computational approaches have been suggested that integrate multiple data sets and/or different evidence types. This widens the stringent definition of an interaction and introduces a more general term - functional association.  FunCoup is a database for genome-wide functional association networks of Homo sapiens and 16 model organisms. FunCoup distinguishes between five different functional associations: co-membership in a protein complex, physical interaction, participation in the same signaling cascade, participation in the same metabolic process and for prokaryotic species, co-occurrence in the same operon. For each class, FunCoup applies naive Bayesian integration of ten different evidence types of data, to predict novel interactions. It further uses orthologs to transfer interaction evidence between species. This considerably increases coverage, and allows inference of comprehensive networks even for not well studied organisms.  BinoX is a novel method for pathway analysis and determining the relation between gene sets, using functional association networks. Traditionally, pathway annotation has been done using gene overlap only, but these methods only get a small part of the whole picture. Placing the gene sets in context of a network provides additional evidence for pathway analysis, revealing a global picture based on the whole genome. PathwAX is a web server based on the BinoX algorithm. A user can input a gene set and get online network crosstalk based pathway annotation. PathwAX uses the FunCoup networks and 280 pre-defined pathways. Most runs take just a few seconds and the results are summarized in an interactive chart the user can manipulate to gain further insights of the gene set's pathway associations. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 2: Manuscript.</p>

No description available.

Mapping of the human genome has been instrumental in understanding diseasescaused by changes in single genes. However, disease mechanisms involvingmultiple genes have proven to be much more elusive. Their complexityemerges from interactions of intracellular molecules and makes them immuneto the traditional reductionist approach. Only by modelling this complexinteraction pattern using networks is it possible to understand the emergentproperties that give rise to diseases.The overarching term used to describe both physical and indirect interactionsinvolved in the same functions is functional association. FunCoup is oneof the most comprehensive networks of functional association. It uses a naïveBayesian approach to integrate high-throughput experimental evidence of intracellularinteractions in humans and multiple model organisms. In the firstupdate, both the coverage and the quality of the interactions, were increasedand a feature for comparing interactions across species was added. The latestupdate involved a complete overhaul of all data sources, including a refinementof the training data and addition of new class and sources of interactionsas well as six new species.Disease-specific changes in genes can be identified using high-throughputgenome-wide studies of patients and healthy individuals. To understand theunderlying mechanisms that produce these changes, they can be mapped tocollections of genes with known functions, such as pathways. BinoX wasdeveloped to map altered genes to pathways using the topology of FunCoup.This approach combined with a new random model for comparison enables BinoXto outperform traditional gene-overlap-based methods and other networkbasedtechniques.Results from high-throughput experiments are challenged by noise and biases,resulting in many false positives. Statistical attempts to correct for thesechallenges have led to a reduction in coverage. Both limitations can be remediedusing prioritisation tools such as MaxLink, which ranks genes using guiltby association in the context of a functional association network. MaxLink’salgorithm was generalised to work with any disease phenotype and its statisticalfoundation was strengthened. MaxLink’s predictions were validatedexperimentally using FRET.The availability of prioritisation tools without an appropriate way to comparethem makes it difficult to select the correct tool for a problem domain.A benchmark to assess performance of prioritisation tools in terms of theirability to generalise to new data was developed. FunCoup was used for prioritisationwhile testing was done using cross-validation of terms derived fromGene Ontology. This resulted in a robust and unbiased benchmark for evaluationof current and future prioritisation tools. Surprisingly, previously superiortools based on global network structure were shown to be inferior to a localnetwork-based tool when performance was analysed on the most relevant partof the output, i.e. the top ranked genes.This thesis demonstrates how a network that models the intricate biologyof the cell can contribute with valuable insights for researchers that study diseaseswith complex genetic origins. The developed tools will help the researchcommunity to understand the underlying causes of such diseases and discovernew treatment targets. The robust way to benchmark such tools will help researchersto select the proper tool for their problem domain. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 5: Manuscript. Paper 6: Manuscript.</p>