With age comes a decline in the dynamic regulation of a balanced and functional mitochondrial proteome (proteostasis) that leads to an increase in oxidative stress and macromolecule damage, with a decline in ATP production. Compromised protein networks and reduced available energy leaves an organism susceptible to accelerated aging and the onset of age-related disease. Since mitochondrial respiratory complexes are composed of protein subunits from both mitochondria and nuclear genomes, their assembly relies on the coordination of mitochondrial and cytoplasmic translation machinery. Disruption of mitochondrial translation generates an imbalance in the ratio of mitochondrial (mtDNA) to nuclear DNA (nDNA) encoded proteins, which is called a mitonuclear protein imbalance. In response to the protein imbalance, a retrograde stress signal is sent from the mitochondria to the nucleus, invoking the mitochondrial unfolded protein response (UPRᵐᵗ) to resolve the mitoproteostatic stress. In a young healthy cell, the UPRᵐᵗ upregulates protein folding chaperones and proteases to resolve the consequences of a mitonuclear protein imbalance. In the early stages of aging, the UPRᵐᵗ appears to be upregulated in response to age-related mitochondrial proteostatic stress. In aged senescent cells however, the UPRᵐᵗ response is blunted. There is cross-species evidence that induction of the UPRᵐᵗ through moderate-intensity exercise or through genetic disruption of the mitochondrial translation machinery will act as a hormetic - resulting in health benefits in the long term. Caenorhabditis elegans longevity models demonstrate that a reduction in mitochondrial ribosomal protein (Mrp) gene expression or disturbed mitochondrial translation will function as a hormetic. The disruption of the mitochondrial ribosome leads to a mitonuclear protein imbalance, invokes the nematode UPRᵐᵗ, which then robustly extends C. elegans lifespan and health span. To determine whether the hormetic effects of mild mitochondrial ribosome disruption can be recapitulated in a mammalian model, this thesis tests a C57/BL6/NTaconic mouse model altered in the germline to have reduced Mrpl54 expression through heterozygous mutation. Mice were metabolically tested at ages 6-, 18-, and 24-months and followed through their natural lifespan to determine whether reduction in the expression of a critical Mrp (Mrpl54) impacts lifespan or metabolic health span. While Mrpl54 mRNA expression was ~50% of wildtype in all Mrpl54⁺ᐟ⁻ tissues tested, there were no differences observed in metabolic health with age or lifespan in either male or female mice. Cultured Mrpl54⁺ᐟ⁻ primary myoblasts had lower absolute levels of nDNA- and mtDNA-encoded respiratory complex subunits relative to wildtype; however, the ratio between nDNA- and mtDNA-encoded protein subunits remained like wildtype. Further testing of the model revealed that Mrpl54⁺ᐟ⁻ males had weaker grip strength by age 12-months, which was also found in the data from multiple heterozygous Mrp (Mrp⁺ᐟ⁻) mouse models available at the International Mouse Phenotyping Consortium. 12-month-old Mrpl54⁺ᐟ⁻ males displayed reduced tetanic force and better fatigue recovery in ex vivo skeletal muscles, and the transmission electron micrographs of skeletal muscle sarcomeres revealed an early aging phenotype. Unlike the C. elegans reduced Mrp longevity model, reduced expression of a critical Mrp did not result in lifespan or metabolic health span benefits in a mouse model. In contrast, the Mrpl54⁺ᐟ⁻ male model showed evidence of premature skeletal muscle aging. While the results of this research do not support the role of Mrpl54 reduced expression in mammalian lifespan or health span extension, they do point to a premature aging phenotype for certain muscle parameters that may be relevant to people living with heterozygous mitochondrial protein mutations. Typically, these individuals are regarded as carriers and free of phenotype associated with their mitochondrial protein mutation. The results in this thesis suggest that those with a heterozygous mitochondrial protein gene mutation may manifest a phenotype as they grow older and are less resilient to external or internal challenges.

No description available.

Submitted by Angela Maria de Oliveira (amolivei@uepg.br) on 2017-11-30T10:57:51Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) Douglas Tomachewski.pdf: 4287227 bytes, checksum: 4ee4e1b519755860efa6f01d55b3569f (MD5) / Made available in DSpace on 2017-11-30T10:57:51Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) Douglas Tomachewski.pdf: 4287227 bytes, checksum: 4ee4e1b519755860efa6f01d55b3569f (MD5) Previous issue date: 2017-09-04 / A identificação de microrganismos, nas áreas da saúde e agricultura, é essencial para compreender a composição e o desenvolvimento do meio. Novas técnicas estão buscando identificar estes microrganismos com mais acurácia, rapidez e com menor custo. Uma técnica cada vez mais estudada e utilizada atualmente é a identificação de microrganismos através de espectros de massa, gerados por uma espectrometria de massa. Os espectros de massa são capazes de gerar um perfil para reconhecimento de um microrganismo, utilizando os picos referentes às mais abundantes massas moleculares registradas nos espectros. Analisando os picos pode-se designar um padrão, como uma impressão digital, para reconhecer um microrganismo, esta técnica é conhecida como PMF, do inglês Peptide Mass Fingerprint. Outra forma de identificar um espectro de massa, é através dos picos que são esperados que se apresentem no espectro, modelo qual este trabalho utilizou. Para prever os picos esperados no espectro, foram calculados os pesos moleculares estimados de proteínas ribossomais. Essas proteínas são denominadas house keeping, ou seja são presentes para o próprio funcionamento celular. Além de apresentarem grande abundância no conteúdo procariótico, elas são altamente conservadas, não alterando sua fisiologia para diferentes meios ou estágios celulares. Os pesos estimados formaram uma base de dados presumida, contendo todas as informações obtidas do repositório do NCBI. Esta base de dados presumida foi generalizada para taxonomia a nível de espécie, e posteriormente submetida à um aprendizado de máquina. Com isso foi possível obter um modelo classificatório de microrganismos baseado em valores de proteínas ribossomais. Utilizando o modelo gerado pelo aprendizado de máquina, foi desenvolvido um software chamado Ribopeaks, capaz classificar os microrganismos a nível de espécie com acurácia de 94.83%, considerando as espécies correlatas. Também foram observados os resultados a nível taxonômico de gênero, que obteve 98.69% de assertividade. Valores de massas moleculares ribossomais biológicas retiradas da literatura também foram testadas no modelo obtido, obtendo uma assertividade total de 84,48% para acertos em nível de espécie, e 90,51% de acerto em nível de gênero. / Identification of microorganisms in health and agriculture areas is essential to understand the composition and development of the environment. New techniques are seeking to identify these microorganisms with more accuracy, speed and at a lower cost. Nowadays, a technique that is increasingly studied and used is the identification of microorganisms through mass spectra, generated by mass spectrometry. The mass spectra are able to generate a recognition profile from a microorganism, using the referring peaks to the most abundant molecular masses recorded in the spectrum. By analyzing the peaks, it is possible to designate a pattern, such as a fingerprint, to recognize a microorganism; this technique is known as the Peptide Mass Fingerprint (PMF). Another way to identify a mass spectrum is through the peaks that are expected to appear in the spectrum, which model this work used. To predict the expected peaks in the spectrum, the estimated molecular weights of ribosomal proteins were calculated. These proteins are responsible for the cellular functioning itself, so-called housekeeping. Besides they being abundant in the prokaryotic content, they are highly conserved, not altering their physiology to different environments or cell stage. The estimated weights formed a presumed database, containing all the information obtained from the NCBI’s repository. This presumed database was generalized at the specie level and later submitted to a machine learning algorithm. With this, it was possible to obtain a microorganism’s classificatory model based on ribosomal proteins values. Using the generated model by the machine learning, a software called Ribopeaks was developed to classify the microorganisms at the specie level with an accuracy of 94.83%, considering the related species. It was also observed the results at genus level, which obtained 98.69% of assertiveness. Values of biological ribosomal molecular masses from the literature were also tested in the acquihired model, obtaining a total assertiveness of 84.48% at the specie level, and 90.51% at the genus level.

Dissertation (Ph.D.)--University of South Florida, 2008. / Title from PDF of title page. Document formatted into pages; contains 197 pages. Includes vita. Includes bibliographical references.

Submitted by Ana Maria Fiscina Sampaio (fiscina@bahia.fiocruz.br) on 2014-08-11T13:24:14Z No. of bitstreams: 1 Diego Moura Santos. Avaliação... 2014.pdf: 3934510 bytes, checksum: 06fa6fa5250655c119370a03b641e2d0 (MD5) / Made available in DSpace on 2014-08-11T13:24:14Z (GMT). No. of bitstreams: 1 Diego Moura Santos. Avaliação... 2014.pdf: 3934510 bytes, checksum: 06fa6fa5250655c119370a03b641e2d0 (MD5) Previous issue date: 2014 / Fundação Oswaldo Cruz. Centro de Pesquisa Gonçalo Moniz. Salvador, BA, Brasil / A leishmaniose é uma doença de escala global, que afeta 12 milhões de pessoas e pode causar um espectro de doenças que vai desde a forma cutânea localizada, que tende para a cura espontânea, até a forma visceral que é fatal. Apesar da gravidade da doença, até o momento não existe uma vacina efetiva para prevenir a leishmaniose. Dentre os antígenos promissores para o desenvolvimento de uma vacina, destacam-se as proteínas ribossomais (S4, S6, L3 e L5) e a KMP-11, uma proteína de superfície presente nos membros da família tripanosomatidae. Nosso estudo consistiu em avaliar os efeitos da imunização com estes antígenos frente ao desafio com L. major e com L. braziliensis, empregando modelos experimentais de infecção. Primeiramente, avaliamos a capacidade protetora dos antígenos ribossomais frente à infecção por L. major. Dos quatro antígenos avaliados, apenas L3 ou L5 foram capazes de prevenir o desenvolvimento da lesão e de diminuir a carga parasitária. A vacinação de camundongos com estes antígenos, na presença de CpG, induziu um perfil de resposta Th1, com elevada produção de IFN-γ, baixa produção de IL-10 e presença de anticorpos IgG2a. Em seguida, avaliamos a capacidade protetora dos antígenos L3 e L5 contra o desafio por L. braziliensis, na presença da saliva do vetor. A imunização com os antígenos L3 e/ou L5 também induziu uma elevada produção de IFN-γ, resultando em significativa redução na espessura da lesão e menor carga parasitária. Com relação ao antígeno KMP-11, investigamos a sua capacidade protetora utilizando duas estratégias vacinais: a estratégia homóloga que consistiu na imunização de camundongos com um plasmídeo de DNA que codifica KMP11 (DNA KMP-11) e a estratégia heteróloga que consistiu na imunização com nanopartículas de PLGA contendo DNA KMP-11, seguido de um reforço com nanopartículas contendo a proteína KMP-11 sob forma recombinante, na presença de CpG. As nanopartículas protegem o antígeno da degradação enzimática e promovem a liberação controlada deste, além de atuar como um adjuvante. Ambas as estratégias não impediram o desenvolvimento da lesão, após o desafio com L. braziliensis e na presença de saliva do vetor. Entretanto, os animais imunizados com a estratégia heteróloga apresentaram uma maior redução da carga parasitária comparado com o grupo imunizado pela estratégia homóloga. Este efeito foi associado com uma maior produção de IFN-γ e de TNF-α no sítio da infecção. Por fim, avaliamos a indução da resposta imune inata em macrófagos estimulados com KMP-11 encapsulados em nanopartículas. Observamos que a estimulação de macrófagos murinos com KMP-11, encapsulada em nanopartículas de PLGA, reduziu a carga parasitária intracelular e aumentou a produção de oxido nítrico, superóxido, TNF-α, IFN-γ, IL-6, IL-1β, IL-18, CCL2/MCP-1, CXCL-1/KC sugerindo a indução de uma potente resposta imune inata. Assim, concluímos que os antígenos L3 e/ou L5 mostraram ser promissores para o desenvolvimento de uma vacina que proteja contra as principais espécies de Leishmania e que o encapsulamento de antígenos em nanopartículas é capaz de induzir uma forte resposta imune. Essa estratégia deve ser considerada quanto ao desenvolvimento de vacinas para a leishmaniose. / Leishmaniasis is a global disease affecting 12 million people and can cause diseases that range from self-healing localized cutaneous leishmaniasis to fatal visceral leishmaniasis. Despite the severity of the disease, there is no effective vaccine to prevent leishmaniasis. Among the promising antigens for the development of a vaccine, stand out the ribosomal proteins (S4, S6, L3, and L5) and KMP-11, a surface protein, widely found in the members of family Trypanosomatidae. Our study evaluated the effects of immunization with these antigens upon challenge with L. major and L. braziliensis, employing the experimental models of infection. First, we evaluated the protective ability of ribosomal antigens to infection by L. major. Among the four antigens examined only L3 or L5 were able to prevent lesion development and decrease the parasite load. Mice vaccinated with these antigens, plus CpG, developed a Th1-type response with high production of IFN-γ, low production of IL-10 and presence of IgG2a antibodies. Next, we evaluated the protective capacity of L3 and L5 antigens against challenge by L. braziliensis, in the presence of sand fly saliva. Vaccination with L3 or L5 also induced a high production of IFN-γ, resulting in significant inhibition of lesion development and lower parasite load. Regarding KMP-11, we investigated its protective capacity using two immunization strategies: the homologous strategy, which consisted in immunizing mice with a plasmid DNA encoding KMP-11(DNA KMP-11) while the heterologous immunization strategy consisted of inoculation of PLGA nanoparticles (NPs) containing DNA KMP-11 followed by a booster inoculation with nanoparticles containing recombinant KMP-11, in the presence of CpG. Nanoparticles protect the antigen from enzymatic degradation and promote controlled release, in addition to acting as an adjuvant. Lesion development was not inhibited following either immunization strategy, after challenge with L. braziliensis in the presence of sand fly saliva. However, animals immunized with the heterologous strategy showed a greater reduction in parasite load compared with the group immunized by the homologous strategy. This effect was associated with increased production of IFN-γ e TNF-α at the infection site. Finally, we evaluated the induction of the innate response in macrophages stimulated with KMP-11 encapsulated in NPs. We observed that stimulation of murine macrophages with KMP-11 encapsulated in NPs reduced the parasitic load and increased production of nitric oxide, superoxide, TNF-α, IFN-γ, IL-6, IL-1β, IL-18, CCL2/MCP-1, CXCL-1/KC suggesting the induction of a potent innate immune response. We conclude that the L3 and/or L5 are promising antigens for the development of a vaccine that protects against the main species of Leishmania and that encapsulation of antigens into nanoparticles induces strong immune response. This strategy should be considered for the development of vaccines against leishmaniasis.

CSIR / Translation initiation is a rate limiting step during protein biosynthesis. Initiation occurs by formation of an initiation complex comprising 30S subunit of ribosome, mRNA, initiator tRNA, and initiation factors. The initiator tRNA has a specialized function of binding to ribosomal P site whereas all the other tRNAs are selected in the ribosomal A site. The presence of a highly conserved 3 consecutive G-C base pairs in the anticodon stem of the initiator tRNA has been shown to be responsible for its P-site targeting. The exact molecular mechanism involved in the P-site targeting of the initiator tRNA is still unclear and focus of our study. Using genetic methods, we obtained mutant E. coli strains where initiator tRNA mutants lacking the characteristic 3-GC base pairs can also initiate translation. One such mutant strain, A30, was selected for this study. Using standard molecular genetic tools, the mutation was mapped and identified to be a mutation in a transcription remodeling factor, RapA (A511V). RapA is a transcription recycling factor and it displaces S1 when it performs its transcription recycling activity. We found this mutation to cause an increase in the S1-depleted ribosomes leading to decreased fidelity of translation initiation as the mutant RapA inefficiently displaces S1 from RNA polymerase complex. The mutation in the RapA was also found to cause changes in the transcriptome which leads to downregulation of major genes important for methionine and purine metabolism. Using mass spectrometric analysis, we identified deficiencies of methionine and adenine in the strain carrying mutant RapA. Our lab had previously reported that methionine and S-adenosyl methionine deficiency cause deficiency of methylations in ribosome which in turn decreases the fidelity of protein synthesis initiation. We used strains deleted for two newly identified methyltransferases, namely RlmH and RlmI, for our study and these strains also showed decreased fidelity of initiation. RlmH and RlmI methylate 1915 and 1962 positions of 23S rRNA respectively. We found that deletion of these methyltransferases also caused defects in ribosome biogenesis and compromised activity of ribosome recycling factor. We constructed phylogenetic trees of the initiator tRNA from 158 species which distinctly assembled into three domains of life. We also constructed trees using the minihelix or the whole sequence of species specific tRNAs, and iterated our analysis on 50 eubacterial species. We identified tRNAPro, tRNAGlu, or tRNAThr (but surprisingly not elongator tRNAMet) as probable ancestors of tRNAi. We then determined the factors imposing selection of methionine as the initiating amino acid. Overall frequency of occurrence of methionine, whose metabolic cost of synthesis is the highest among all amino acids, remains almost unchanged across the three domains of life. Our results indicate that methionine selection, as the initiating amino acid was possibly a consequence of the evolution of one-carbon metabolism, which plays an important role in regulating translation initiation. In conclusion, the current study reveals the importance of methylations in ribosome biogenesis and fidelity of translation initiation. It also strongly suggests a co-evolution of the metabolism and translation apparatus giving adaptive advantage to the cells where presence of methionine in the environment can be a signal to initiate translation with methionine initiator tRNA.

Indiana University-Purdue University Indianapolis (IUPUI) / The MDM2-p53 feedback loop is a well-characterized pathway. p53 is a transcription factor and regulates the transcriptional expression of genes that encode proteins responsible for cellular senescence, cell cycle arrest, apoptosis, and DNA repair. Various cellular stresses can result in p53 activation, including hypoxia, DNA damage by agents such as UV or IR, oncogenic signaling, nucleotide depletion and nucleolar stress from perturbation of ribosomal biogenesis. Under normal conditions, MDM2’s role in the pathway is to inhibit p53 function by directly binding to this protein and facilitating its ubiquitylation and 26S proteasome-mediated degradation. Under stressful cellular conditions, certain proteins interact with and rescue MDM2’s inhibition of p53. For example, upon exposure to small amounts of Actinomycin D, rRNA transcript synthesis is stalled resulting in the release of various ribosomal proteins including RPL5, RPL11 and RPL23; each of which has been shown to bind MDM2 within its central acidic domain and inhibit its ability to destabilize p53. Although the RPL5 binding region of MDM2 have been mapped in prior investigations, the MDM2-binding region(s) of RPL5 have yet to be characterized. By employing RPL5 deletion mutagenesis and in vitro GST-fusion protein-protein association assays with purified proteins, this dissertation attempts to elucidate those regions of RPL5 that may interact with MDM2. Normalizing RPL5-WT to 1.00, our study reveals that the basic N and C-terminals of RPL5 appear to bind with MDM2 while RPL5’s central region displays negligible binding to the central acidic domain of MDM2. Also, the possible meanings of these RPL5 MDM2 binding domains are discussed along with their utilization in potential future applications.

Les objectifs de cette thèse étaient d'apporter une percée conceptuelle pour une compréhension en profondeur des mécanismes moléculaires des protéines intrinsèquement désordonnées (IDPs) et de leurs rôles dans la physiologie cellulaire de Chlamydomonas reinhardtii. La combinaison d’approches expérimentale et bioinformatique m’a permis d’identifier 682 protéines thermorésistantes chez C. reinhardtii. Parmi celles-ci, 299 protéines sont systématiquement prédites comme potentielles IDP par quatre algorithmes de prédiction de désordre. Nos résultats indiquent que le pourcentage désordonné moyen de ces protéines prédites comme étant des IDPs est d'environ 20%, et la plupart d'entre elles (~70%) sont adressées à d'autres compartiments que la mitochondrie et le chloroplaste. Leur composition en acides aminés est biaisée par rapport à d'autres IDPs de la base de données de protéines désordonnées (DisProt). Ces IDPs potentielles jouent des fonctions moléculaires diverses, et 54% d'entre elles sont des cibles de phosphorylation.Notre travail a également augmenté l’état des connaissances sur l'adénylate kinase 3 (ADK3), une enzyme contenant une région intrinsèquement désordonnée (IDR). Cette enzyme a été isolée par notre approche globale pour caractériser les IDPs de l’algue verte. L’extension C-terminale désordonnée (CTE) de cette enzyme lui confère de nouvelles fonctions comme par exemple, la formation d’un complexe bi-enzymatique avec la glycéraldéhyde-3-phosphate déshydrogénase (GAPDH), la régulation (négative) de l'activité GAPDH avec le NADPH comme cofacteur, et le rôle de chaperon pour la GAPDH en la protégeant de la dénaturation par traitement thermique et de l’agrégation. / The objectives of this work were to bring a conceptual breakthrough for an in-depth understanding of the molecular mechanisms of intrinsically disordered proteins (IDPs) and their roles in the cellular physiology of Chlamydomonas reinhardtii. Using experimental approaches, 682 heat-resistant proteins were identified as putative IDPs. Among them, 299 proteins were consistently predicted as IDPs by all four disordered predictors. The mean percentage of disordered residues content of these IDPs is about 20%, and most of them (~70%) are addressed to other compartments than mitochondrion and chloroplast. These newly identified IDPs from C. reinhardtii have a biased amino acid composition as regard to other IDPs from the Database of protein disorder (DisProt). Furthermore, they play diverse molecular functions, and 54% of them are the targets for phosphorylation. Our work also revealed more knowledge of the IDR-containing protein adenylate kinase 3 (ADK3) that was extracted by heat-treatment. Its disordered C-terminal extension (CTE) brought new functions to this protein. For instance, via its CTE, ADK3 can form a bi-enzyme complex with glyceraldehyde-3-phosphate dehydrogenase (GAPDH), down-regulates the NADPH-dependent GAPDH activity, and behaves as a chaperone for GAPDH against its aggregation and inactivation under heat-treatment.

Functional interaction of proteins is a broad term encompassing many different types of associations that are observed amongst proteins. It includes direct non-covalent interactions where the interacting proteins physically associate using an interface. There are also many protein-protein interactions where the proteins concerned are not involved in direct physical interactions but affect each other’s functions. Central focus of this thesis is to understand the various aspects of functionally interacting proteins. Chapter 1 of this thesis provides an introduction to functional interactions between proteins and discusses the key developments available in the literature. This chapter discusses the different types of functional associations observed commonly between proteins. Various approaches developed over time to elucidate such interactions have also been discussed. This chapter highlights how functional interactions between proteins have been helpful in understanding different cellular processes such as organization of metabolic pathways. The chapter emphasizes the importance of functional interactions between proteins, providing a motivation for development of methods with enhanced accuracy and sensitivity for the prediction of functional interactions. In this thesis, domain families which are found to co-exist in multidomain proteins have been used to understand and subsequently predict functional associations amongst proteins. Domains in proteins typically serve as modules associated with specific functions. There exist proteins with a single domain which describes the entire function of a protein, while there also exist proteins containing multiple domains, where various domains in unison describe the complete function of the multidomain protein. Therefore, by virtue of “guilt by association” domain families found together in multidomain proteins are functionally linked. This forms the basic premise for understanding functional association amongst proteins and is explained in great detail in the Introduction chapter. Using domain families which co-occur in multidomain proteins as the basis for functional association has many merits. First, as stated before, constituent domain families act as effective descriptors of function(s) of proteins. For example, members of SH3 domain family mediate protein-protein interactions by binding to regions with polyproline conformation irrespective of the multidomain protein in which it occurs. Thus, studies of domain families co-existing in multidomain proteins act as an accurate resource of functional associations between proteins. Also, assignment of domains to a protein relies on homology detection which has achieved a high level of reliability, thus, resulting in reasonably accurate prediction of functions. Such approaches enable exhaustive coverage of many diverse proteins including many multidomain proteins leading to detection of large numbers of functional associations between domains of multidomain proteins. Given the advantages attributed to functionally linked domain families in further understanding of functional associations, it is imperative to exhaustively enumerate all possible pairs of functionally linked domain families in multidomain proteins and study their various properties. This aspect is covered in the second chapter of the thesis. In the second chapter, analysis of domain families which co-occur in multidomain proteins, termed as 'tethered domain families', has been reported. For this analysis, a large dataset of multidomain proteins was considered from a diverse set of fully sequenced genomes from many eukaryotic and prokaryotic organisms. In every multidomain protein, all possible pairs of unique domain family pairs have been considered and they are assumed to be under the same functional/evolutionary constraint. Thus, from the entire dataset of multidomain proteins, all possible pairs of tethered domain families are obtained. For a given domain family, the number of other uniquely tethered families is referred to as the tethering number of a domain family. Therefore, tethering number of a domain family is an indicator of the diverse functional contexts in which a particular domain family is involved. Further analysis was carried out to understand various other attributes of domain families and its relation to tethering number. The results are summarized in the following points: 1) Distribution of tethering numbers of domain families in the entire dataset is found to be highly heterogeneous. Nearly 88% of domain families (10783 out of 12249 domain families) have tethering number of 10 or less and only 78 domain families show more than 100 unique associations. Further analysis reveals bias in functions of families showing high and low tethering numbers. The domain families with high tethering numbers are involved in processes such as signaling and protein-protein interactions. The domain families with low tethering numbers are often found to be involved in metabolic processes. 2) Differences are also observed in the type of organisms containing the domain families and their tethering numbers. Typically, domain families with high tethering numbers are ubiquitously found across almost all the kingdoms of life. In contrast, most of the domain families exclusively found in a kingdom have low tethering numbers. Furthermore, for the ubiquitously occurring domain families with high tethering numbers, the number of associations made and the type of associations are not strictly conserved across the kingdoms. Thus, the tethering preferences of such domain families vary across the kingdoms depending on their function. For instance, the protein kinase domain family which is a key regulator of signaling processes in eukaryotes, has a high tethering number in eukaryotes (270), and low tethering number in prokaryotes (96). 3) Tethering number of domain families is found to be correlated with the number of members (population) comprising a family. A Pearson correlation coefficient of 0.78 at a p-value ≤0.001 is obtained for the correlation between tethering number of domain families and their population. 4) Tethering numbers of domain families are also found to be well correlated with sequence and functional diversity within families. Thus, domain families with high tethering numbers comprise of members showing diversity in both sequence and functions. Thus, the work presented in second chapter provides a framework for understanding the tethering preferences of domain families. The use of tethered domain families to identify functional association amongst proteins is the central theme of third and fourth chapters of this thesis. The use of tethered domain families for the prediction of functionally interacting proteins originates from the initial idea of “Rosetta stone” approach, which was proposed by Ouzounis and coworkers and Eisenberg and coworkers in 1999. Rosetta stone approach demonstrated the use of fused genes in predicting functional interaction. It stems from the observation that in many organisms, genes corresponding to proteins acting in a metabolic pathway are found fused in another organism. Thus, enumeration of 'fused genes' in a template database could provide a good basis for prediction of functionally interacting proteins in target organisms in which the homologous genes are not found to be fused. The method has been shown, by others, to work quite effectively in prokaryotes, especially in the identification of interactions between metabolic proteins. Chapter 3 of this thesis explores the idea of “Rosetta stones” at the level of domain families, by considering tethered domain families as analogs to the fused genes. In this analysis, tethered domain families derived from multidomain proteins comprises the template dataset. If members of two domain families occurring in a multidomain protein are found to occur independently in two different proteins in the target organism then an interaction is predicted between these two proteins (collection of such predicted interactions is henceforth referred as TEDIP database, Tethered Domain-based Interaction Prediction). During this analysis, care is taken such that none of the proteins in the template dataset belongs to the target organisms. The entire analysis has been conducted on 6 model organisms which act as the target dataset where functional interactions between proteins are predicted. The effectiveness of tethered domain families in functional interaction prediction is compared with two other datasets 1) all experimentally known interactions and 2) interactions predicted on the basis of their homology with interacting domain families with known structure. Subsequently, an attempt has been made to answer these questions: 1) how effective is the information on tethered domain families in predicting functional linkages amongst proteins operating in pathways in eukaryotic organisms? 2) what is the false positive rate of the predictions? The above mentioned datasets show very little overlap in the coverage of functional interactions. This is largely attributed to insufficient sampling and inherent bias existing in each of the methods. The TEDIP datasets in the six organisms led to an average three-fold more functional interaction predictions in cellular pathways than the other two datasets. Nearly 90% of the predicted interactions derived from tethered domain families are amongst proteins across different pathways. In yeast, more than 60% of such interactions were found to be overlapping with a recent large scale genetic interaction screen based on synthetic lethality especially performed for metabolic proteins, thus establishing the effectiveness of this approach in understanding pathway crosstalk. Along with efficacy in identifying functional interactions, an assessment based on co-localization, co-expression and overall functional similarity based on Gene Ontology (GO) terms was carried out. It was found that the TEDIP predictions and experimentally found interactions show poor correspondence with co-expression and co-localization data (10% and 20% respectively for the two methods). Additionally, it was found that functional similarity between predicted interacting proteins in TEDIP dataset is low (5%) and is comparable to experimentally known interactions that shows 10% similarity in functions based on a scoring function for GO term similarity. From Chapter 3, it was concluded that the use of tethered domain families is effective in exhaustive enumeration of functionally associated proteins. However, the low co-expression and functional similarity measures are a cause for concern. On the one hand, co-expression and GO functional similarity have been found to be weak predictors of functional interactions, explaining the low values obtained for both predictions in the TEDIP datasets and experimentally known interactions. On the other hand, the poorer values shown for predictions in the TEDIP datasets suggest that further improvement in prediction accuracy is possible. Chapter 4 explores the use of machine learning in improving the accuracy of functional interaction prediction based on TEDIP dataset. In Chapter 4, two distinct machine learning approaches have been employed on a training dataset derived exclusively from yeast. Since the objective of the work is to improve the accuracy of prediction of functional interactions, the GO based functional similarity measures have been used to define positive and negative datasets. Thus, in the training dataset, positive interactions comprises of protein pairs which show high GO similarity in functions as defined in chapter 3 and 10% of this data overlaps with experimentally known interactions, while the negative dataset consists of protein pairs with no or insignificant similarity in their functions and additionally do not show similarity to any experimentally known interactions. Two machine learning approaches, namely Support vector machine (SVM) and Random forest, have been used on this training dataset. Use of two distinct approaches helps in addressing the weakness, if any, of these methods. Fourteen carefully chosen features have been utilized during the training process to aid in the process of distinguishing potentially correctly predicted interactions from incorrect predictions. Out of 14 features, some of the features chosen for the analysis are involved in quantifying the extent of similarity between the template proteins containing the fused domain families and the target protein pairs predicted to interact. The analysis also incorporates graph theory based parameters which are derived from a domain family based graph. In such a graph, each of the domain families which are involved in forming multidomain proteins represents the nodes and an edge is constructed between domain families which are found to co-exist in at least one multidomain protein. Graph theory based parameters such as clustering coefficient, degree and topological overlap have been employed. These are useful in down weighting appropriately the domain family pairs showing large number of associations which are expected to be promiscuous in their functions. These features also enable in identifying domain family pairs which are functionally related. Apart from the above mentioned features, coevolution and phylogenetic profiling of tethered domain families is also utilized to identify functionally related domain family pairs. Utilizing all these features in training, the machine learning approach yielded an accuracy of 94% using SVM and 92% using Random forest against the training data. Furthermore, the importance of using all these features has been addressed by performing principle component analysis, training both SVM and Random forest by removing one feature at a time and by quantifying the sensitivity by using only one feature. All of these suggest that the features used provide non-redundant information and contributed significantly to the classification. The models so generated were finally used on all the predicted functional interactions after the removal of the training dataset in yeast. The true positives observed were 56% using SVM and 63% using Random forest with around 80% of the interactions common between the two methods. Further analysis has been carried out on these interactions by first imparting a confidence score to these interactions using support vector regression that provides a probabilistic measure for SVM classification. Based on a cutoff of 0.5, 62455 interactions in total were termed as high confidence interactions. Further analysis was carried out for the high confidence interactions. Out of these, in 2855 interactions, both the proteins predicted to interact could be associated with a pathway in KEGG database. In-depth case studies have been performed on this dataset of 2855 interactions. Literature mining suggested that many known cross-pathway interactions such as between TCA and glycolysis are captured as high confidence interactions using TEDIP dataset. A few other case studies of high confidence interactions with supporting literature evidence are also presented in the chapter. These predictions could further aid in experimental characterization of pathway cross-talk between important metabolic and signaling pathways. So far, the thesis discussed analyses involving functional interactions and their prediction. In the subsequent chapters, analyses pertaining to two different types of functional interactions are discussed. Chapters 5 and 6 involve analyses incorporating metabolic proteins in diverse pathways in the pathogenic organism Plasmodium falciparum. Chapter 5 attempts to improve the coverage of the repertoire of metabolic proteins in P.falciparum while in Chapter 6 interactions and pathways prevalent in different stages in the life cycle of the parasite are deciphered and discussed. Apart from functionally interacting proteins in metabolic pathways, physically and transiently interacting proteins have been analyzed and discussed in Chapters 7 and 8. In Chapter 5, metabolic proteins participating in pathways in Plasmodium falciparum have been analyzed. P.falciparum is the causative agent of malaria, a disease which affects large populations in the subtropical regions. P.falciparum genome is atypical and is rich in Adenine/Thymine pairs, and there is presence of large stretches of amino acid repeats encoded in protein coding regions. Various sequence-related features of P.falciparum proteins when compared with those of other organisms show extensive divergence. All of these have made reliable function prediction, by homology to other proteins with known functions, daunting. Like other proteins in P.falciparum, metabolic proteins have also diverged significantly from their functional counterparts in model eukaryotes such as yeast. Metabolic pathways play an important role in the survival of the organism and hence are amenable towards the identification of proteins susceptible to drugs, thereby combating pathogenesis. Chapter 5 of the thesis aims at furthering knowledge pertaining to metabolic proteins by first quantifying the extent of divergence observed in the already characterized metabolic proteins. This knowledge is further used in identification of potential metabolic proteins which are not identified as proteins involved in metabolic pathways by other annotation efforts undertaken for P.falciparum. In the first part of the chapter, the extent of divergence in the sequences of metabolic proteins in P.falciparum has been determined by comparing the P.falciparum proteins with their functional counterparts from 34 completely sequenced unicellular eukaryotic organisms. Comparison of domain architectures between the P.falciparum proteins with their functional counterparts reveals that in nearly 54% of metabolic pathways, proteins show nearly the same domain architecture as the other functional counterparts. Inversion, deletion and duplication of domains are observed in rest of the proteins. Further analysis reveals that P.falciparum proteins are longer than their functional counterparts. It was also observed in nearly 15% of the cases, the domains are characterized by the presence of large non-conserved or plasmodium genus specific inserts within the domain assigned regions. There is also prevalence of unassigned regions in the N- and C- terminal regions in P.falciparum proteins when compared with their functional counterparts. Finally, it was also observed that metabolic proteins of P.falciparum show significantly low sequence similarity when compared with other functional counterparts. From this analysis, it can be clearly seen that metabolic proteins of P.falciparum have significantly diverged from such proteins in other organisms, thus making function prediction by homology very difficult. There are several steps in metabolic pathways in P.falciparum which are expected to be active based on experimental analysis. However, some of these proteins with expected functions have not been identified so far. One of the reasons for this apparent incompleteness is the high divergence observed in the metabolic proteins of P. falciparum. To overcome this limitation, in the second part of the chapter, a sensitive approach based on domain family assignment (MulPSSM), developed in-house, has been used to identify proteins which are potentially involved in metabolic pathways. The approach is based on reverse PSI–BLAST, where multiple sequence profiles for each family are used to search against sequence databases. This approach has been shown to be better or at-par with other remote homology detection procedures. Using this approach, 15 P. falciparum proteins have been identified which can potentially function as metabolic proteins and were not characterized in P.falciparum so far. All the proteins identified by the approach show low sequence similarity to other well characterized proteins and contain significant fractions of unassigned regions thus, making function recognition non-trivial. Supporting literature and other data is provided to demonstrate the robustness of the homology-based annotation of the identified pathway proteins. Chapter 6 is an analysis of the dynamic changes occurring in the metabolic network of P.falciparum during its life cycle. In this chapter, two aspects of P. falciparum metabolic proteins have been integrated and analyzed. First, the dataset of protein-protein interactions derived from experimental studies and second, the datasets of microarray analysis providing information on stage specific expression of P. falciparum genes corresponding to the metabolic proteins. As a first step, protein-protein interaction information for the metabolic proteins was gathered. A total of 810 interactions have been obtained, where one or both proteins are involved in a pathway. Subsequently, these interactions were compared with 14070 interactions involving metabolic proteins from free-living and non-pathogenic unicellular eukaryote yeast. Comparison across the two organisms shows wide discrepancy in the number of proteins involved in interactions and also the pathways in which they participate. Out of the 810 interactions in P.falciparum, 173 are found uniquely in plasmodium where both or one of the protein have no identifiable homolog in yeast. Insufficient sampling of interactions made by proteins in P.falciparum in comparison to yeast, is one of the reasons for the observed discrepancy. However, the differences due to the parasitic lifestyle of P.falciparum could also be a potential reason. Further analysis of the protein-protein interactions by the metabolic proteins revealed that a large fraction of interactions are made between a metabolic protein and a non-metabolic protein. For instance, interaction observed between glycolytic protein phospoglycerate kinase with MAP kinase. This trend is observed in both plasmodium and yeast where 65% and 77% of the interactions, respectively, involve proteins not directly participating in metabolic pathways. Further, interactions between proteins belonging to different pathways and lastly, interactions between proteins in the same pathway are uncovered. All of these interactions depict the different modes by which metabolic pathways are regulated through protein-protein interactions. Another aspect explored in this analysis is the stage specific expression of genes encoding these metabolic proteins. The analysis is especially relevant in the parasite because its entire life cycle is divided into seven distinct stages. Upon integrating the protein-protein interactions with the gene expression data, it became apparent that the trophozoite, schizont and gametocyte stages show large fractions of co-expressed genes encoding proteins involved in protein-protein interactions within metabolic pathways. The high preponderance of co-expressed genes encoding for interacting protein pairs in these stages is also consistent with metabolic requirement of plasmodium in the various stages. Glycolytic pathway is central to energy production in the parasite and is discussed at length in this chapter. Members of this pathway are involved in interactions with other glycolytic proteins (9 such interactions), they also interact with proteins involved in other pathways (30 interactions) and with proteins not involved directly in any metabolic pathway (75 interactions). Nearly 70% of the interactions made by the glycolytic proteins are encoded by genes found to be co-expressed across the various stages. Integration of gene expression data along with protein-protein interaction information for metabolic pathways such as the glycolytic pathway thus, highlights the complex mode of regulation underlying this pathway. The analysis carried out in this chapter emphasizes on the intricacies involved in the regulation of metabolic proteins in P.falciparum. Chapter 7 describes an in-depth analysis carried out to understand the basis for interaction specificity between small monomeric GTPases and their regulators, the Guanine nucleotide Exchange Factors (GEFs). Monomeric GTPases are involved in binding to guanine nucleotide. These proteins can bind to both GTP and GDP. However, transition from GDP bound to GTP bound form occurs with large conformational changes and requires binding of the GEFs. The conformational changes that arise due to the nucleotide exchange are required for the GTPases to bind to its various effectors. For the analysis carried out in Chapter 7, GTPases belonging to the Ras superfamily have been considered. The superfamily is further subdivided into 5 distinct families based on their functions. The 5 families are Ras, Ran, Rab, Arf and Rho. Members belonging to each of these families are involved in a wide array of cellular processes such as signaling and cytoskeletal remodeling. Members of each of these GTPase families bind to structurally distinct GEFs, and in some cases, multiple GEFs are involved in nucleotide exchange within a family. It is intriguing therefore, to understand how GTPases belonging to the same structural family maintain specificity across the highly dissimilar GEFs and this forms the main objective of this analysis. So far, 13 distinct complexes between GTPases and their cognate GEFs have been solved using X-ray crystallography. This set of structural complexes forms the starting point of the analysis. As a first step, pairwise structural comparison of the interfaces has made between various pairs of complex structures. Based on these comparisons, it is apparent that most of the interfaces in the GTPase and GEF complexes comprise of residue positions which are topologically not equivalent suggesting different modes of binding across these complexes. Further analysis was carried out to probe the extent of specificity underlying these complexes. This is achieved by determining interface residues which are found to be conserved in a family specific manner. Such residue positions have been obtained by using a statistically robust algorithm Contrast Hierarchical Alignment and Interaction Network (CHAIN) that extracts sequence patterns most distinguishing two sets of homologous sequences. The analysis indicated the presence of family specific residues at the GTPase and GEF interface. Such residues could be implicated in maintaining the specific interactions between the GTPases and the GEFs. The robustness in the specificity of the interactions was further interrogated by providing an energetic basis to the specificity in the interactions mediated by the cognate GTPases and the GEFs and also understanding how crosstalk is prevented across the non-cognate complexes. For each of the 13 cognate complexes, empirical interaction energies have been estimated using FoldX. The interaction energy is compared to non-cognate complexes which are obtained by swapping the interface residues of the cognate GTPase with the non-cognate GTPase residues. For most of the complexes, it was observed that the interaction energies for the cognate complexes are much lower than the non-cognate complexes. Energy values across the non-cognate complexes are usually indicative of reduced stability, thereby precluding such interactions from occurring. Such large energy differences between cognate and non-cognate interactions arise due to drastic substitutions at the interface patch due to difference in the charge or other stereochemical aspects of the amino acids. Both evolutionary and energy based analysis indicates the presence and importance of few family specific residues in the cognate complexes and also the presence of unfavorable residues in the non-cognate complexes thus preventing crosstalk. However, apart from changes at the interfaces, many positions outside the interface also undergo changes across the various homologs within the same family/subfamily of GTPase. Coevolutionary analysis of GTPase and GEFs from multiple eukaryotic organisms has been carried out in these complexes and it was observed that most of the coevolving positions are not found at the interface. Many of these residue positions are near the active site or near the interface. Identification of such coevolving positions, where residue variations in the GTPase are strongly coupled to the GEF, may provide initial clues to the possible allosteric path adopted in connecting the binding of GEF to the vast structural changes observed during GTP exchange in GTPases. Thus, the analysis provides a comprehensive framework to understand how interaction specificity has evolved between the GTPase and GEF complexes. Chapter 8 discusses another example of transient protein-protein interaction observed between proteins implicated in signaling process in Dictyostelium discoideum. The work reported in this chapter was carried out in collaboration with Prof. Nanjundaiah and coworkers from Molecular Reproduction and Developmental Genetics department, Indian Institute of Science. All the experimental analyses mentioned in this chapter were carried out by Prof. Nanjundaiah and coworkers and the author carried out all the computational analysis. Experimental analysis indicated the presence of a ribosomal protein S4 in D. discoideum which mediates interactions with CDC24 and CDC42. The protein is speculated to be a functional analog of yeast scaffolding protein Bem1. However, the exact structural and sequence features of the protein which can accommodate its non-ribosomal function as a scaffold by mediating protein-protein interactions are not clearly understood. With the aid of structural modeling, a 3-D structure was generated for the C-terminal regions of D. discoideum protein S4. The modeled structure, as in the template used for modelling, resembled the fold of SH3 domain which has been shown to be involved in protein-protein interactions. Structural and sequence analyses were carried out to evaluate the potential mode by which interactions could be mediated by this protein. The hypothesis generated was further corroborated by experimental analysis. Thus, both experimental and computational analysis provide evidence for the functional role of the ribosomal protein S4 from Dictyostelium discoideum as a scaffold. Chapter 9 summarizes the conclusions reached in various chapters of the thesis. The thesis embodies analyses probing various aspects of functional interactions between proteins. A frame work has been provided to elucidate functional interactions using tethered domain families in multidomain proteins. Further, the role of these functional interactions have been explored in different scenarios by exhaustively analyzing metabolic proteins and their regulation in pathogenic organism Plasmodium falciparum and by also analyzing two distinct types of transient protein-protein interactions.

L’oncogène v-erbA transforme les progéniteurs érythrocytaires primaires aviaires (T2EC) en bloquantleur engagement d’un programme d’auto-renouvellement vers un programme de différenciation. Unecomparaison trancriptomique de T2EC exprimant soit v-erbA, soit une forme non transformante de verbAa été réalisée par SAGE et RT-qPCR. Seuls quelques uns, mais pas tous les messagers codant lesprotéines ribosomiques sont réprimés. Ces résultats suggèrent que v-erbA pourrait moduler lacomposition des ribosomes et/ou moduler les fonctions extra-ribosomiques de protéines ribosomiquesspécifiques. Ainsi, nous avons décidé d’analyser le taux des protéines ribosomiques associées auxribosomes par 2D-DIGE à partir des ribosomes purifiés. L’analyse statistique effectuée sur 4expériences indépendantes avec des marquages inversées a montré de manière significative que letaux de RPL11 est inférieur dans les T2EC exprimant v-erbA comparé à ceux exprimant la forme nontransformante de v-erbA. Ces données indiquent l’existence de ribosomes dépourvus de RPL11 dansles T2EC sous l’effet de v-erbA. Les résultats des expériences d’immunoprécipitation ont conforté cettehypothèse. L’ensemble des résultats obtenus suggèrent l’implication des protéines ribosomiques, etspécialement celle de RPL11, dans les processus de transformation induite par l’oncogène v-erbA, à lafois au niveau de la traduction, et probablement par sa fonction extra-ribosomique. L’analyse de lafonction biologique de RPL11 a montré qu’une sur-expression de RPL11 dans les T2EC retarderait laprolifération cellulaire. / The v-erbA oncogene transforms chicken erythroid progenitors by blocking their differentiation andpreventing them to exit a state of self-renewal. The transcriptome of primary avian erythroidprogenitors cells (T2EC) expressing either v-erbA or a non-transforming form of v-erbA werecompared by SAGE. Only some, but not all, mRNAs encoding ribosomal proteins were shown to beaffected. These results suggest that v-erbA could modulate the composition of ribosomes and/ormodulate the extraribosomal functions of specific ribosomal proteins. We therefore decided to analyzethe level of ribosomal proteins associated to ribosomes by 2D-DIGE performed on purified ribosomes.A statistical analysis performed on 4 independent flip-flop experiments demonstrated that the level ofRPL11 is significantly lower in T2EC expressing v-erbA as compared to the non-transforming form ofv-erbA. These data suggest the presence of ribosomes without RPL11 in T2EC expressing v-ErbA.Results obtained from immunoprecipitation experiments were strengthened this hypothesis. The set ofthese data evoke the involvement of ribosomal proteins, and specially RPL11, in the v-erbAtransformation process both at the translational level and possibly in its extra-ribosomal function.Overexpression of RPL11 in T2EC showed a decrease of cell proliferation.