La génomique comparative est un sujet essentiel pour éclaircir la biologie évolutionnaire. La première étape pour dépasser une connaissance seulement descriptive est de développer une méthode pour représenter le contenu du génome. Nous avons choisi la représentation modulaire des génomes pour étudier les lois quantitatives qui réglementent leur composition en unités élémentaires de type fonctionnel ou évolutif. La première partie de la thèse se fonde sur l'observation que le nombre de domaines ayant la même fonction est lié à la taille du génome par une loi de puissance. Puisque les catégories fonctionnelles sont des agrégats de familles de domaines, on se demande comment le nombre de domaines dans la même catégorie fonctionnelle est lié à l'évolution des familles. Le résultat est que les familles suivent également une loi de puissance. Le deuxième partie présente un modèle positif qui construit une réalisation à partir des composants liés dans un réseau de dépendance. L'ensemble de toutes les réalisations reproduit la distribution des composants partagés et la relation entre le nombre de familles distinctes et la taille du génome. Le dernier chapitre étend l'approche modulaire aux écosystèmes microbiens. Sur la base des constatations que nous avons faites sur les lois de puissance pour les familles de domaines, nous avons analysé comment le nombre de familles dans un metagénome en est influencé. Par conséquence, nous avons défini une nouvelle observable dont la forme fonctionnelle comprend des informations quantitatives sur la composition originelle du metagénome. / Comparative genomics is as a fundamental discipline to unravel evolutionary biology. To overcome a mere descriptive knowledge of it the first challenge is to develop a higher-level description of the content of a genome. Therefore we used the modular representation of genomes to explore quantitative laws that regulate how genomes are built from elementary functional and evolutionary ingredients. The first part sets off from the observation that the number of domains sharing the same function increases as a power law of the genome size. Since functional categories are aggregates of domain families, we asked how the abundance of domains performing a specific function emerges from evolutionary moves at the family level. We found that domain families are also characterized by family-dependent scaling laws. The second chapter provides a theoretical framework for the emergence of shared components from dependency in empirical component systems with non-binary abundances. We defined a positive model that builds a realization from a set of components linked in a dependency network. The ensemble of resulting realizations reproduces both the distribution of shared components and the law for the growth of the number of distinct families with genome size. The last chapter extends the component systems approach to microbial ecosystems. Using our findings about families scaling laws, we analyzed how the abundance of domain families in a metagenome is affected by the constraint of power-law scaling of family abundance in individual genomes. The result is the definition of an observable, whose functional form contains quantitative information on the original composition of the metagenome.

Investigations into the origin and evolution of regulatory mechanisms require quantitative estimates of the abundance and co-occurrence of functional protein domains among distantly related genomes. The metabolic and regulatory capabilities of an organism are implicit in its protein content. Currently available methods suffer for strong ascertainment biases, requiring methods for unbiased approaches to protein domain contents at genome-wide scales. The discussion will be highlighted on large scale patterns of similarities and differences of domain contains between phylum-level or even higher level taxonomic groups. This provides insights into large-scale evolutionary trends. The complement of recognizable functional protein domains and their combinations convey essentially the same information and at the same time are much more readily accessible, although protein domain models trained for one phylogenetic group frequently fail on distantly related sequences. Transcription factors (TF) typically cooperate to activate or repress the expression of genes. They play a critical role in developmental processes. While Chromatin Regulation (CR) facilitates DNA organization and prevent DNA aggregation and tangling which is important for replication, segregation, and gene expression. To compare the set of TFs and CRs between species, the genome annotation of equal quality was employed. However, the existing annotation suffers from bias in model organism. The similar count of transcripts are expected to be similar in mammals, but model organism such as human has more annotated transcripts than non model such as gorilla. Moreover, closely related species (e.g, dolphin and human) show a dramatically different distribution of TFs and CRs. Within vertebrates, this is unreasonable and contradicts phylogenetic knowledge. To overcome this problem, performing gene prediction followed by the detection of functional domains via HMM-based annotation of SCOP domains were proposed. This methods was demonstrated to lead toward consistent estimates for quantitative comparison. To emphasize the applicability, the protein domain distribution of putative TFs and CRs by quantitative and boolean means were analyzed. In particular, systematic studies of protein domain occurrences and co-occurrences to study avoidance or preferential co-occurrence of certain protein domains within TFs and CRs were utilized. Pooling related domain models based on their GO-annotation in combination with de novo gene prediction methods provides estimates that seem to be less affected by phylogenetic biases. it was shown for 18 diverse representatives from all eukaryotic kingdoms that a pooled analysis of the tendencies for co-occurrence or avoidance of protein domains is indeed feasible. This type of analysis can reveal general large-scale patterns in the domain co-occurrence and helps to identify lineage-specific variations in the evolution of protein domains. Somewhat surprisingly, strong ubiquitous patterns governing the evolutionary behavior of specific functional classes were not found. Instead, there are strong variations between the major groups of Eukaryotes, pointing at systematic differences in their evolutionary constraints. Species-specific training is required, however, to account for the genomic peculiarities in many lineages. In contrast to earlier studies wide-spread statistically significant avoidance of protein domains associated with distinct functional high-level gene-ontology terms were found.

Measuring the amount of information and of shared information in biological strings, as well as relating information to structure, function and evolution, are fundamental computational problems in the post-genomic era. Classical analyses of the information content of biosequences are grounded in Shannon's statistical telecommunication theory, while the recent focus is on suitable specializations of the notions introduced by Kolmogorov, Chaitin and Solomonoff, based on data compression and compositional redundancy. Symmetrically, classical estimates of mutual information based on string editing are currently being supplanted by compositional methods hinged on the distribution of controlled substructures. Current compositional analyses and comparisons of biological strings are almost exclusively limited to short sequences of contiguous solid characters. Comparatively little is known about longer and sparser components, both from the point of view of their effectiveness in measuring information and in separating biological strings from random strings, and from the point of view of their ability to classify and to reconstruct phylogenies. Yet, sparse structures are suspected to grasp long-range correlations and, at short range, they are known to encode signatures and motifs that characterize molecular families. In this thesis, we introduce and study compositional measures based on the repertoire of distinct subsequences of any length, but constrained to occur with a predefined maximum gap between consecutive symbols. Such measures highlight previously unknown laws that relate subsequence abundance to string length and to the allowed gap, across a range of structurally and functionally diverse polypeptides. Measures on subsequences are capable of separating only few amino acid strings from their random permutations, but they reveal that random permutations themselves amass along previously undetected, linear loci. This is perhaps the first time in which the vocabulary of all distinct subsequences of a set of structurally and functionally diverse polypeptides is systematically counted and analyzed. Another objective of this thesis is measuring the quality of phylogenies based on the composition of sparse structures. Specifically, we use a set of repetitive gapped patterns, called motifs, whose length and sparsity have never been considered before. We find that extremely sparse motifs in mitochondrial proteomes support phylogenies of comparable quality to state-of-the-art string-based algorithms. Moving from maximal motifs -- motifs that cannot be made more specific without losing support -- to a set of generators with decreasing size and redundancy, generally degrades classification, suggesting that redundancy itself is a key factor for the efficient reconstruction of phylogenies. This is perhaps the first time in which the composition of all motifs of a proteome is systematically used in phylogeny reconstruction on a large scale. Extracting all maximal motifs, or even their compact generators, is infeasible for entire genomes. In the last part of this thesis, we study the robustness of measures of similarity built around the dictionary of LZW -- the variant of the LZ78 compression algorithm proposed by Welch -- and of some of its recently introduced gapped variants. These algorithms use a very small vocabulary, they perform linearly in the input strings, and they can be made even faster than LZ77 in practice. We find that dissimilarity measures based on maximal strings in the dictionary of LZW support phylogenies that are comparable to state-of-the-art methods on test proteomes. Introducing a controlled proportion of gaps does not degrade classification, and allows to discard up to 20% of each input proteome during comparison.

No description available.

Lacking experimental data, protein function is often predicted from evolutionary and protein structure theory. Under the 'domain grammar' hypothesis the function of a protein follows from the domains it encodes. Under the 'orthology conjecture', orthologs, related through species formation, are expected to be more functionally similar than paralogs, which are homologs in the same or different species descended from a gene duplication event. However, these assumptions have not thus far been systematically evaluated. To test the 'domain grammar' hypothesis, we built models for predicting function from the domain combinations present in a protein, and demonstrated that multi-domain combinations imply functions that the individual domains do not. We also developed a novel gene-tree based method for reconstructing the evolutionary histories of domain architectures, to search for cases of architectures that have arisen multiple times in parallel, and found this to be more common than previously reported. To test the 'orthology conjecture', we first benchmarked methods for homology inference under the obfuscating influence of low-complexity regions, in order to improve the InParanoid orthology inference algorithm. InParanoid was then used to test the relative conservation of functionally relevant properties between orthologs and paralogs at various evolutionary distances, including intron positions, domain architectures, and Gene Ontology functional annotations. We found an increased conservation of domain architectures in orthologs relative to paralogs, in support of the 'orthology conjecture' and the 'domain grammar' hypotheses acting in tandem. However, equivalent analysis of Gene Ontology functional conservation yielded spurious results, which may be an artifact of species-specific annotation biases in functional annotation databases. I discuss possible ways of circumventing this bias so the 'orthology conjecture' can be tested more conclusively. / At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 6: Epub ahead of print.

Investigations into the origin and evolution of regulatory mechanisms require quantitative estimates of the abundance and co-occurrence of functional protein domains among distantly related genomes. The metabolic and regulatory capabilities of an organism are implicit in its protein content. Currently available methods suffer for strong ascertainment biases, requiring methods for unbiased approaches to protein domain contents at genome-wide scales. The discussion will be highlighted on large scale patterns of similarities and differences of domain contains between phylum-level or even higher level taxonomic groups. This provides insights into large-scale evolutionary trends. The complement of recognizable functional protein domains and their combinations convey essentially the same information and at the same time are much more readily accessible, although protein domain models trained for one phylogenetic group frequently fail on distantly related sequences. Transcription factors (TF) typically cooperate to activate or repress the expression of genes. They play a critical role in developmental processes. While Chromatin Regulation (CR) facilitates DNA organization and prevent DNA aggregation and tangling which is important for replication, segregation, and gene expression. To compare the set of TFs and CRs between species, the genome annotation of equal quality was employed. However, the existing annotation suffers from bias in model organism. The similar count of transcripts are expected to be similar in mammals, but model organism such as human has more annotated transcripts than non model such as gorilla. Moreover, closely related species (e.g, dolphin and human) show a dramatically different distribution of TFs and CRs. Within vertebrates, this is unreasonable and contradicts phylogenetic knowledge. To overcome this problem, performing gene prediction followed by the detection of functional domains via HMM-based annotation of SCOP domains were proposed. This methods was demonstrated to lead toward consistent estimates for quantitative comparison. To emphasize the applicability, the protein domain distribution of putative TFs and CRs by quantitative and boolean means were analyzed. In particular, systematic studies of protein domain occurrences and co-occurrences to study avoidance or preferential co-occurrence of certain protein domains within TFs and CRs were utilized. Pooling related domain models based on their GO-annotation in combination with de novo gene prediction methods provides estimates that seem to be less affected by phylogenetic biases. it was shown for 18 diverse representatives from all eukaryotic kingdoms that a pooled analysis of the tendencies for co-occurrence or avoidance of protein domains is indeed feasible. This type of analysis can reveal general large-scale patterns in the domain co-occurrence and helps to identify lineage-specific variations in the evolution of protein domains. Somewhat surprisingly, strong ubiquitous patterns governing the evolutionary behavior of specific functional classes were not found. Instead, there are strong variations between the major groups of Eukaryotes, pointing at systematic differences in their evolutionary constraints. Species-specific training is required, however, to account for the genomic peculiarities in many lineages. In contrast to earlier studies wide-spread statistically significant avoidance of protein domains associated with distinct functional high-level gene-ontology terms were found.

Der Transkriptionsfaktor CCAAT/enhancer-binding Protein β (C/EBPβ) reguliert die Expression zahlreicher Gene, welche die Proliferation, Differenzierung und Seneszenz in hämatopoietischen Zellen, Adipozyten und Leukämiezellen kontrollieren. Um diese mannigfaltigen Aufgaben zu erfüllen interagiert C/EBPβ mit zahlreichen Kofaktoren und Proteinen der Transkriptionsregulations-Maschinerie. Da das funktionale Netzwerk von C/EBPβ und seinen zahlreichen Kooperationspartnern bis heute nicht vollständig entziffert ist, ist es das Ziel dieser Arbeit das Netzwerk aus Interaktionspartnern und C/EBPβ regulierten Proteinen in Leukämiezelllinien und darüber hinaus zu erforschen und aufzudecken. Das Interaktom von C/EBPβ wurde mittels einer Kombination aus einem membranbasierten Peptid-Interaktions Testverfahrens (APS) und endogener Immunprezipitationen mit gekoppelter MS-Analyse untersucht. Außerdem wurde die Proteinmenge von C/EBPβ und von potentiell von C/EBPβ regulierten Proteinen mittels proteomischer MS-Analyse in C/EBPβ Knock-out- und Leukämiezelllinien untersucht. Die Protein-Interaktionsversuche ergaben epigenetische und allgemeine transkriptionsregulierende Proteine, sowie Chromatinstruktur modellierende Faktoren, die mit C/EBPβ interagieren. Zusätzlich konnten neue Interaktionen von C/EBPβ mit Kondensin- und Kinetochorproteinen beobachtet werden. Die Versuchsergebnisse eröffnen überdies neue Interaktionen von C/EBPβ mit DNA Reparatur und Apoptose assoziierten Proteinen. Interessanterweise konnten auch Komponenten des Spliceosomes und RNA-prozessierende Proteine als Interaktoren von C/EBPβ identifiziert werden. Zusammenfassend ermöglicht diese Studie nicht nur die Verifikation von bereits bekannten Proteininteraktionen von C/EBPβ, sondern eröffnet zahlreiche weitere zukünftige Forschungsfelder bezüglich des Interaktionsnetzwerkes von C/EBPβ in Leukämien, sowie anderen Zellarten und Geweben. / The basic leucine zipper transcription factor CCAAT/enhancer-binding protein β (C/EBPβ) regulates the expression of various genes that control the proliferation, differentiation and senescence of haematopoietic cells, adipocytes and leukemia cells. To facilitate its multifaceted functions C/EBPβ interacts with a collection of cofactors and proteins of the transcription regulation machinery. As the functional network of C/EBPβ and its numerous cooperation partners is still incomplete this study attempted to analyze interaction partners and downstream proteins of C/EBPβ in leukemia cells and beyond. A combinatory approach of an array based peptide-interaction screening (APS) and endogenous shotgun IP-MS from leukemia cell lines was applied to elucidate the interactome of C/EBPβ. Moreover, C/EBPβ abundance and potential C/EBPβ regulated proteins were determined by MS proteomics in C/EBPβ knockout and leukemia cell lines. The interaction screenings revealed proteins associated with the general and epigenetic regulation of transcription, with chromatin remodeling and mitotic chromatin organization as well as cell cycle regulation. Additionally, new interactions of C/EBPβ with condensin and kinetochore proteins could be elucidated. The data reports of novel C/EBPβ interactors involved in DNA repair and apoptosis. In addition, components of the spliceosome and RNA-processing were detected. Altogether this study verifies known and reveals various novel interactions of the transcription factor C/EBPβ and augments the network of previous reported interactions and potential cooperation partners. The here collected data discloses new subjects for further research concerning the interaction network of C/EBPβ during cell differentiation and in leukemia.

Cette thèse présente: 1) le développement d'une nouvelle approche pour trouver des associations directes entre des paires d'éléments liés indirectement à travers diverses caractéristiques communes, 2) l'utilisation de cette approche pour associer directement des fonctions biologiques aux domaines protéiques (ECDomainMiner et GODomainMiner) et pour découvrir des interactions domaine-domaine, et enfin 3) l'extension de cette approche pour annoter de manière complète à partir des domaines les structures et les séquences des protéines. Au total, 20 728 et 20 318 associations EC-Pfam et GO-Pfam non redondantes ont été découvertes, avec des F-mesures de plus de 0,95 par rapport à un ensemble de référence Gold Standard extrait d'une source d'associations connues (InterPro). Par rapport à environ 1500 associations déterminées manuellement dans InterPro, ECDomainMiner et GODomainMiner produisent une augmentation de 13 fois le nombre d'associations EC-Pfam et GO-Pfam disponibles. Ces associations domaine-fonction sont ensuite utilisées pour annoter des milliers de structures de protéines et des millions de séquences de protéines pour lesquelles leur composition de domaine est connue mais qui manquent actuellement d'annotations fonctionnelles. En utilisant des associations de domaines ayant acquis des annotations fonctionnelles inférées, et en tenant compte des informations de taxonomie, des milliers de règles d'annotation ont été générées automatiquement. Ensuite, ces règles ont été utilisées pour annoter des séquences de protéines dans la base de données TrEMBL / This thesis presents: 1) the development of a novel approach to find direct associations between pairs of elements linked indirectly through various common features, 2) the use of this approach to directly associate biological functions to protein domains (ECDomainMiner and GODomainMiner), and to discover domain-domain interactions, and finally 3) the extension of this approach to comprehensively annotate protein structures and sequences. ECDomainMiner and GODomainMiner are two applications to discover new associations between EC Numbers and GO terms to protein domains, respectively. They find a total of 20,728 and 20,318 non-redundant EC-Pfam and GO-Pfam associations, respectively, with F-measures of more than 0.95 with respect to a “Gold Standard” test set extracted from InterPro. Compared to around 1500 manually curated associations in InterPro, ECDomainMiner and GODomainMiner infer a 13-fold increase in the number of available EC-Pfam and GO-Pfam associations. These function-domain associations are then used to annotate thousands of protein structures and millions of protein sequences for which their domain composition is known but that currently lack experimental functional annotations. Using inferred function-domain associations and considering taxonomy information, thousands of annotation rules have automatically been generated. Then, these rules have been utilized to annotate millions of protein sequences in the TrEMBL database

One of the major outcomes of a genome sequencing project is the availability of amino acid sequences of all the proteins encoded in the genome of the organism concerned. However, most commonly, for a substantial proportion of the proteins encoded in the genome no information in function is available either from experimental studies or by inference on the basis of homology with a protein of known function. Even if the general function of a protein is known, the region of the protein corresponding to the function might be a domain and there may be additional regions of considerable length in the protein with no known function. In such cases the information on function is incomplete. Lack of understanding of the repertoire of functions of proteins encoded in the genome limits the utility of the genomic data. While there are many experimental approaches available for deciphering functions of proteins at the genomic scale, bioinformatics approaches form a good early step in obtaining clues about functions of proteins at the genomic scale (Koonin et al, 1998). One of the common bioinformatics approaches is recognition of function by homology (Bork et al, 1994). If the evolutionary relationship between two proteins, one with known function and the other with unknown function, could be established it raises the possibility of common function and 3-D structure for these proteins(Bork and Gibson, 1996). While this approach is effective its utility is limited by the ability of the bioinformatics approach to identify related proteins when their evolutionary divergence is high leading to low amino acid sequence similarity which is typical of two unrelated proteins (Bork and Koonin, 1998). Use of 3-D structural information, obtained by predictive methods such as fold recognition, has offered approaches towards increasing the sensitivity of remote homology detection 9e.g., Kelley et al, 2000; Shi et al, 2001; Gough et al, 2001). The work embodied in this thesis has the general objective of analysis of evolution of structural features and functions of families of proteins and design of new bioinformatics approaches for recognizing distantly related proteins and their applications. After an introductory chapter, a few chapters report analysis of functional and structural features of homologous protein domains. Further chapters report development and assessment of new remote homology detection approaches and applications to the proteins encoded in two protozoan organisms. A further chapter is presented on the analysis of proteins involved in methylglyoxal detoxification pathways in kinetoplastid organisms. Chapter I of the thesis presents a brief introduction, based on the information available in the literature, to protein structures, classification, methods for structure comparison, popular methods for remote homology detection and homology-based methods for function annotation. Chapter 2 describes the steps involved in the update and improvements made in this database. In addition to the update, the domain structural families are integrated with the homologous sequences from the sequence databases. Thus, every family in PALI is enriched with a substantial volume of sequence information from proteins with no known structural information. Chapter 3 reports investigations on the inter-relationships between sequence, structure and functions of closely-related homologous enzyme domain families. Chapter 4 describes the investigations on the unusual differences in the lengths of closely-related homologous protein domains, accommodation of additional lengths in protein 3-D structures and their functional implications. Chapter 5 reports the development and assessment of a new approach for remote homology detection using dynamic multiple profiles of homologous protein domain families. Chapter 6 describes development of another remote homology detection approach which are multiple, static profiles generated using the bonafide members of the family. A rigorous assessment of the approach and strategies for improving the detection of distant homologues using the multiple profile approach are discussed in this chapter. Chapter 7 describes results of searches made in the database of multiple family profiles (MulPSSM database) in order to recognize the functions of hypothetical proteins encoded in two parasitic protozoa. Chapter 8 describes the sequence and structural analyses of two glyoxalase pathway proteins from the kinetoplastid organism Leishmania donovani which causes Leishmaniases. An alternate enzyme, which would probably substitute the glyoxalase pathway enzymes in certain kinetoplastid organisms which lack the glyoxalase enzymes are also discussed. Chapter 9 summarises the important findings from the various analyses discussed in this thesis. Appendix describes an analysis on the correlation between a measure of hydrophobicity of amino acid residues aligned in a multiple sequence alignment and residue depth in 3-D structures of proteins.

Interactions between proteins give rise to many functions in cells. In the lastdecade, highthroughput experiments have identified thousands of protein interactions, which are often represented together as large protein interaction networks. However, the classical way of representing interaction networks, as nodes and edges, is too limited to take dynamic properties such as compatible and mutually exclusive interactions into account. In this work, we study protein interaction networks using structural information. More specifically, the analysis of protein interfaces in threedimensional protein structures enables us to identify which interfaces are compatible and which are not. Based on this principle, we have implemented a method, which aims at the analysis of protein interaction networks from a structural point of view by (1) predicting possible binary interactions for proteins that have been found in complex experimentally and (2) identifying possible mutually exclusive and compatible complexes. We validated our method by using positive and negative reference sets from literature and set up an assay to benchmark the identification of compatible and mutually exclusive structural interactions. In addition, we reconstructed the protein interaction network associated with the G proteincoupled receptor Rhodopsin and defined related functional submodules by combining interaction data with structural analysis of the network. Besides its established role in vision, our results suggest that Rhodopsin triggers two additional signaling pathways towards (1) cytoskeleton dynamics and (2) vesicular trafficking. / Las funciones de las proteínas resultan de la manera con la que interaccionan entre ellas. Los experimentos de alto rendimiento han permitido identificar miles de interacciones de proteínas que forman parte de redes grandes y complejas. En esta tesis, utilizamos la información de estructuras de proteínas para estudiar las redes de interacciones de proteínas. Con esta información, se puede entender como las proteínas interaccionan al nivel molecular y con este conocimiento se puede identificar las interacciones que pueden ocurrir al mismo tiempo de las que están incompatibles. En base a este principio, hemos desarrollado un método que permite estudiar las redes de interacciones de proteínas con un punto de vista mas dinámico de lo que ofrecen clásicamente. Además, al combinar este método con minería de la literatura y Los datos de la proteomica hemos construido la red de interacciones de proteínas asociada con la Rodopsina, un receptor acoplado a proteínas G y hemos identificado sus sub--‐módulos funcionales. Estos análisis surgieron una novel vıa de señalización hacia la regulación del citoesqueleto y el trafico vesicular por Rodopsina, además de su papel establecido en la visión.