The integrated analysis of metabolic and protein interaction networks reveals novel molecular organizing principles

Background: The study of biological interaction networks is a central theme of systems biology. Here, we investigate the relationships between two distinct types of interaction networks: the metabolic pathway map and the protein-protein interaction network (PIN). It has long been established that successive enzymatic steps are often catalyzed by physically interacting proteins forming permanent or transient multi-enzymes complexes. Inspecting high-throughput PIN data, it was shown recently that, indeed, enzymes involved in successive reactions are generally more likely to interact than other protein pairs. In our study, we expanded this line of research to include comparisons of the underlying respective network topologies as well as to investigate whether the spatial organization of enzyme interactions correlates with metabolic efficiency. Results: Analyzing yeast data, we detected long-range correlations between shortest paths between proteins in both network types suggesting a mutual correspondence of both network architectures. We discovered that the organizing principles of physical interactions between metabolic enzymes differ from the general PIN of all proteins. While physical interactions between proteins are generally dissortative, enzyme interactions were observed to be assortative. Thus, enzymes frequently interact with other enzymes of similar rather than different degree. Enzymes carrying high flux loads are more likely to physically interact than enzymes with lower metabolic throughput. In particular, enzymes associated with catabolic pathways as well as enzymes involved in the biosynthesis of complex molecules were found to exhibit high degrees of physical clustering. Single proteins were identified that connect major components of the cellular metabolism and may thus be essential for the structural integrity of several biosynthetic systems. Conclusion: Our results reveal topological equivalences between the protein interaction network and the metabolic pathway network. Evolved protein interactions may contribute significantly towards increasing the efficiency of metabolic processes by permitting higher metabolic fluxes. Thus, our results shed further light on the unifying principles shaping the evolution of both the functional (metabolic) as well as the physical interaction network

The Dichotomy in Degree Correlation of Biological Networks

Most complex networks from different areas such as biology, sociology or technology, show a correlation on node degree where the possibility of a link between two nodes depends on their connectivity. It is widely believed that complex networks are either disassortative (links between hubs are systematically suppressed) or assortative (links between hubs are enhanced). In this paper, we analyze a variety of biological networks and find that they generally show a dichotomous degree correlation. We find that many properties of biological networks can be explained by this dichotomy in degree correlation, including the neighborhood connectivity, the sickle-shaped clustering coefficient distribution and the modularity structure. This dichotomy distinguishes biological networks from real disassortative networks or assortative networks such as the Internet and social networks. We suggest that the modular structure of networks accounts for the dichotomy in degree correlation and vice versa, shedding light on the source of modularity in biological networks. We further show that a robust and well connected network necessitates the dichotomy of degree correlation, suggestive of an evolutionary motivation for its existence. Finally, we suggest that a dichotomous degree correlation favors a centrally connected modular network, by which the integrity of network and specificity of modules might be reconciled

Macromolecular crowding and protein chemistry: views from inside and outside cells

The cytoplasm is crowded, and the concentration of macromolecules can reach ~ 300 g/L, an environment vastly different from the dilute, idealized conditions usually used in biophysical studies. Macromolecular crowding arise from two phenomena, excluded volume and nonspecific chemical interactions, until recently, only excluded volume effect has been considered. Theory predicts that this macromolecular crowding can have large effects. Most proteins, however, are studied outside cells in dilute solution with macromolecule concentrations of 10 g/L or less. In-cell NMR provides a means to assess protein biophysics at atomic resolution in living cells, but it remains in its infancy, and several potential challenges need to be addressed. One challenge is the inability to observe 15N-1H NMR spectra from many small globular proteins. 19F NMR was used to expand the application of in-cell NMR. This work suggests that high viscosity and weak interactions in the cytoplasm can make routine 15N enrichment a poor choice for in-cell NMR studies of globular proteins in Escherichia coli. To gain insight into this problem, I turned to in vitro experiments where conditions can be controlled with precision. Using both synthetic polymers and globular proteins, I studied the effects of crowding on the diffusion of the test protein, chymotrypsin inhibitor 2. The results not only pinpoint the source of the problem - nonspecific chemical interactions - but also suggest that proteins are more suitable mimics of the intracellular environment. I also measured the stability of ubiquitin in solutions crowded with synthetic polymers or globular proteins to further elucidate the role of nonspecific chemical interactions under crowded conditions. The increased stability observed in synthetic crowders was consistent with a dominant entropic role for excluded volume, but the effect of protein crowders depended on charge. Protein-induced crowding increased stability when the sign of the net charge of the crowder was the same as that of ubiquitin, but decreased stability when the proteins were oppositely charged. The results indicate that synthetic polymers do not provide physiologically relevant insights and that the overall effect of macromolecular crowding depends on the winner of the near stalemate between excluded volume and nonspecific interactions

Studies of the Protein Interaction Network Required for Enterobactin Biosynthesis in Escherichia coli

Abstract Studies of the Protein Interaction Network Required for Enterobactin Biosynthesis in Escherichia coli Sofia Khalil, Ph.D. Concordia University, 2010 Siderophores are small-molecule iron chelators that many bacteria synthesize and secrete in order to survive in iron-depleted environments. In Escherichia coli, biosynthesis of the siderophore molecule enterobactin requires the activities of six enzymes, EntA-EntF. These enzymes function sequentially to produce enterobactin molecule in the cytoplasm. The enterobactin biosynthesis pathway is divided into two modules. The first module involves the conversion of chorismate to 2,3-dihydroxybenzoic acid (2,3-DHB), and requires the activities of EntC, EntB (N-terminal domain) and EntA. The second module involves non-ribosomal peptide synthesis (NRPS) such that three molecules of 2,3-DHB are condensed with three molecules of L-serine to form the siderophore. The NRPS module requires the activities of EntE, EntB (C-terminal domain), EntD and EntF. The overall goal of my research project is characterization of the enterobactin biosynthetic enzyme EntE. EntE catalyzes the activation of 2,3-DHB via adenylation producing DHB-AMP. I am interested in addressing the following questions: (i) How does EntE bind its 2,3-DHB substrate? (ii) How does it interact with its upstream and downstream partner proteins: EntA, which produces 2,3-DHB, and EntB, which uses the EntE product (DHB-AMP) as a substrate, respectively? My thesis is divided into three research-related chapters: The first research chapter focused on the interaction of EntE with its substrate, 2,3-DHB, as well as the characterization of EntE-EntB interaction in the presence and absence of 2,3-DHB. A significant change in EntB conformation was observed upon the interaction with EntE when in the presence of 2,3-DHB. In the pull-down assay, EntB as bait protein pulled down more EntE in the presence of exogenous 2,3-DHB. We conclude from this chapter that the ligand-loaded state of the protein was necessary for efficient protein-protein interaction. The second research chapter involves the characterization of a novel interaction between EntE and its upstream partner protein EntA. A significant increase in EntE activity was observed upon adding EntA. Furthermore, EntA reduces the FRET signal of EntE-bound 2,3-DHB in a saturable manner with increasing EntA concentrations. Using this fluorescence binding assay at 20 °C revealed a positive cooperativity in EntA-EntE interaction with Hill coefficient greater than one. The AUC experiments showed that EntA conformation is highly dependent on its concentration. In conclusion, the results of this chapter suggest that EntA-EntE interaction likely induces remodeling of EntE active site, resulting in the observed increase in EntE catalysis. In the third research chapter, the EntA-EntE interaction interface was characterized using phage display. Based on the interaction interface predicted by phage display data, EntA variants containing single-site and double-site mutations were created (Q64A, A68Q, and Q64A/A68Q). Our in vitro biophysical techniques and growth phenotype experiments revealed that EntA (Q64A) and EntA (Q64A/A68Q) mutations have a more pronounced effect than EntA (A68Q) mutation on disruption of the EntA-EntE interaction interface

Searching for novel gene functions in yeast : identification of thousands of novel molecular interactions by protein-fragment complementation assay followed by automated gene function prediction and high-throughput lipidomics

La compréhension de processus biologiques complexes requiert des approches expérimentales et informatiques sophistiquées. Les récents progrès dans le domaine des stratégies génomiques fonctionnelles mettent dorénavant à notre disposition de puissants outils de collecte de données sur l’interconnectivité des gènes, des protéines et des petites molécules, dans le but d’étudier les principes organisationnels de leurs réseaux cellulaires. L’intégration de ces connaissances au sein d’un cadre de référence en biologie systémique permettrait la prédiction de nouvelles fonctions de gènes qui demeurent non caractérisées à ce jour. Afin de réaliser de telles prédictions à l’échelle génomique chez la levure Saccharomyces cerevisiae, nous avons développé une stratégie innovatrice qui combine le criblage interactomique à haut débit des interactions protéines-protéines, la prédiction de la fonction des gènes in silico ainsi que la validation de ces prédictions avec la lipidomique à haut débit. D’abord, nous avons exécuté un dépistage à grande échelle des interactions protéines-protéines à l’aide de la complémentation de fragments protéiques. Cette méthode a permis de déceler des interactions in vivo entre les protéines exprimées par leurs promoteurs naturels. De plus, aucun biais lié aux interactions des membranes n’a pu être mis en évidence avec cette méthode, comparativement aux autres techniques existantes qui décèlent les interactions protéines-protéines. Conséquemment, nous avons découvert plusieurs nouvelles interactions et nous avons augmenté la couverture d’un interactome d’homéostasie lipidique dont la compréhension demeure encore incomplète à ce jour. Par la suite, nous avons appliqué un algorithme d’apprentissage afin d’identifier huit gènes non caractérisés ayant un rôle potentiel dans le métabolisme des lipides. Finalement, nous avons étudié si ces gènes et un groupe de régulateurs transcriptionnels distincts, non préalablement impliqués avec les lipides, avaient un rôle dans l’homéostasie des lipides. Dans ce but, nous avons analysé les lipidomes des délétions mutantes de gènes sélectionnés. Afin d’examiner une grande quantité de souches, nous avons développé une plateforme à haut débit pour le criblage lipidomique à contenu élevé des bibliothèques de levures mutantes. Cette plateforme consiste en la spectrométrie de masse à haute resolution Orbitrap et en un cadre de traitement des données dédié et supportant le phénotypage des lipides de centaines de mutations de Saccharomyces cerevisiae. Les méthodes expérimentales en lipidomiques ont confirmé les prédictions fonctionnelles en démontrant certaines différences au sein des phénotypes métaboliques lipidiques des délétions mutantes ayant une absence des gènes YBR141C et YJR015W, connus pour leur implication dans le métabolisme des lipides. Une altération du phénotype lipidique a également été observé pour une délétion mutante du facteur de transcription KAR4 qui n’avait pas été auparavant lié au métabolisme lipidique. Tous ces résultats démontrent qu’un processus qui intègre l’acquisition de nouvelles interactions moléculaires, la prédiction informatique des fonctions des gènes et une plateforme lipidomique innovatrice à haut débit , constitue un ajout important aux méthodologies existantes en biologie systémique. Les développements en méthodologies génomiques fonctionnelles et en technologies lipidomiques fournissent donc de nouveaux moyens pour étudier les réseaux biologiques des eucaryotes supérieurs, incluant les mammifères. Par conséquent, le stratégie présenté ici détient un potentiel d’application au sein d’organismes plus complexes.Understanding complex biological processes requires sophisticated experimental and computational approaches. The advances in functional genomics strategies provide powerful tools for collecting diverse types of information on interconnectivity of genes, proteins and small molecules for studying organizational principles of cellular networks. Integration of that knowledge into a systems biology framework enables prediction of novel functions of uncharacterized genes. For performing such predictions on a genome-wide scale in the yeast Saccharomyces cerevisiae, we have developed a novel strategy that combines high-throughput interactomics screen for protein-protein interactions, in silico gene function prediction, and validation of predictions with high-throughput lipidomics. We started by performing a large-scale screen for protein-protein interactions using a protein-fragment complementation assay. The method allowed to monitor interactions in vivo between proteins expressed from their natural promoters. Furthermore, the method did not suffer from bias against membrane interactions comparing to established genome-wide techniques for detecting protein interactions. As a result, we detected many novel interactions and increased coverage of an interactome of lipid homeostasis that has not been yet comprehensively explored. Next, we applied a machine learning algorithm to identify eight previously uncharacterized genes with a potential role in lipid metabolism. Finally, we investigated whether these genes and a set of distinct transcriptional regulators, not implicated previously with lipids, have a role in lipid homeostasis. For that purpose, we analyzed lipidome of deletion mutants of the selected genes. In order to probe a large number of strains, we have developed a high-throughput platform for high-content lipidomic screening of yeast mutant libraries that consists of high-resolution Orbitrap mass spectrometry and a dedicated data processing framework to support lipid phenotyping across hundreds of Saccharomyces cerevisiae mutants. Lipidomics experiments confirmed functional predictions by demonstrating differences of the lipid metabolic phenotypes of deletion mutants lacking YBR141C and YJR015W genes predicted to be involved in lipid metabolism. An altered lipid phenotype was also observed for a deletion mutant of the transcription factor KAR4 that has not been linked previously with lipid metabolism. These results demonstrate that a workflow that integrates the acquisition of novel molecular interactions, computational gene function prediction and novel high-throughput shotgun lipidomics platform is a valuable contribution to an arsenal of methods for systems biology. The developments of functional genomic methods and lipidomics technologies provide means to study biological networks of higher eukaryotes, including mammals. Therefore, the presented workflow has a potential to find its applications in more complex organisms

Λειτουργική ανάλυση του γονιδίου FRA10AC1 του ανθρώπου στο πλαίσιο του δικτύου πρωτεϊνικών αλληλεπιδράσεων του σωματίου συναρμογής

Σημείωση: διατίθεται συμπληρωματικό υλικό σε ξεχωριστό αρχείο