A scale of functional divergence for yeast duplicated genes revealed from analysis of the protein-protein interaction network

BACKGROUND: Studying the evolution of the function of duplicated genes usually implies an estimation of the extent of functional conservation/divergence between duplicates from comparison of actual sequences. This only reveals the possible molecular function of genes without taking into account their cellular function(s). We took into consideration this latter dimension of gene function to approach the functional evolution of duplicated genes by analyzing the protein-protein interaction network in which their products are involved. For this, we derived a functional classification of the proteins using PRODISTIN, a bioinformatics method allowing comparison of protein function. Our work focused on the duplicated yeast genes, remnants of an ancient whole-genome duplication. RESULTS: Starting from 4,143 interactions, we analyzed 41 duplicated protein pairs with the PRODISTIN method. We showed that duplicated pairs behaved differently in the classification with respect to their interactors. The different observed behaviors allowed us to propose a functional scale of conservation/divergence for the duplicated genes, based on interaction data. By comparing our results to the functional information carried by GO annotations and sequence comparisons, we showed that the interaction network analysis reveals functional subtleties, which are not discernible by other means. Finally, we interpreted our results in terms of evolutionary scenarios. CONCLUSIONS: Our analysis might provide a new way to analyse the functional evolution of duplicated genes and constitutes the first attempt of protein function evolutionary comparisons based on protein-protein interactions

Clustering proteins from interaction networks for the prediction of cellular functions

BACKGROUND: Developing reliable and efficient strategies allowing to infer a function to yet uncharacterized proteins based on interaction networks is of crucial interest in the current context of high-throughput data generation. In this paper, we develop a new algorithm for clustering vertices of a protein-protein interaction network using a density function, providing disjoint classes. RESULTS: Applied to the yeast interaction network, the classes obtained appear to be biological significant. The partitions are then used to make functional predictions for uncharacterized yeast proteins, using an annotation procedure that takes into account the binary interactions between proteins inside the classes. We show that this procedure is able to enhance the performances with respect to previous approaches. Finally, we propose a new annotation for 37 previously uncharacterized yeast proteins. CONCLUSION: We believe that our results represent a significant improvement for the inference of cellular functions, that can be applied to other organism as well as to other type of interaction graph, such as genetic interactions

Relationships between predicted moonlighting proteins, human diseases, and comorbidities from a network perspective

International audienceMoonlighting proteins are a subset of multifunctional proteins characterized by their multiple, independent, and unrelated biological functions. We recently set up a large-scale identification of moonlighting proteins using a protein-protein interaction (PPI) network approach. We established that 3% of the current human interactome is composed of predicted moonlighting proteins. We found that disease-related genes are over-represented among those candidates. Here, by comparing moonlighting candidates to non-candidates as groups, we further show that (7 they are significantly involved in more than one disease, (ii) they contribute to complex rather than monogenic diseases, (iii) the diseases in which they are involved are phenotypically different according to their annotations, finally, (iv) they are enriched for diseases pairs showing statistically significant comorbidity patterns based on Medicare records. Altogether, our results suggest that some observed comorbidities between phenotypically different diseases could be due to a shared protein involved in unrelated biological processes

GOToolBox: functional analysis of gene datasets based on Gene Ontology

We have developed methods and tools based on the Gene Ontology (GO) resource allowing the identification of statistically over- or under-represented terms in a gene dataset; the clustering of functionally related genes within a set; and the retrieval of genes sharing annotations with a query gene. GO annotations can also be constrained to a slim hierarchy or a given level of the ontology. The source codes are available upon request, and distributed under the GPL license

Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network

We here describe PRODISTIN, a new computational method allowing the functional clustering of proteins on the basis of protein-protein interaction data. This method, assessed biologically and statistically, enabled us to classify 11% of the Saccharomyces cerevisiae proteome into several groups, the majority of which contained proteins involved in the same biological process(es), and to predict a cellular function for many otherwise uncharacterized proteins

Phloem sap exudates as a criterion for sink strength appreciation in Vitis vinifera cv. Pinot noir grapevines

The temporal evolution of the main compounds present in the phloem sap feeding the cluster of Vitis vinifera Pinot noir has been determined from the beginning of flowering until fruit set, after improvement of the facilitated exudation technique. The retained composition for the dipping solution was: HEPES (10 mM, pH 7.5), EDTA (10 mM). The first ramification of the cluster, maintained in situ, was sectionned then immersed in the dipping solution in order to favour the phloem exudation. The major organic components of the phloem sap were carbohydrates, amino acids and organic acids (i.e. sucrose, glutamine and tartrate, respectively). For each metabolic group, the mean exuded quantities correspond to 300 nmol per cluster in 4 h. The sharp increase in both organic compounds and potassium released over the flowering time-course reflects the rise of the mass flow supplying the cluster and underline the increasing sink strength of this organ. Moreover, the increasing contents of glutamine and hexoses in the exudate suggest a regulation in the allocation of assimilates to the reproductive organs.Les exsudats de sève phloémique comme critère d'appreciation de la force de puits de la grappe chez Vitis vinifera cv. Pinot noirL'évolution temporelle des principaux constituants de la sève libérienne alimentant la grappe de Vitis vinifera Pinot noir a été étudiée au cours de la floraison, après adaptation d'une technique de prélèvement par exsudation facilitée. La composition de la solution d'exsudation retenue est la suivante: HEPES (10 mM, pH 7,5), EDTA (10 mM). Sur la grappe maintenue in situ, l'extrémité de la première ramification est sectionnée puis immergée dans le milieu précédemment défini pour permettre la récupération des assimilats. Les composés organiques prédominants dans la sève libérienne sont les glucides solubles, les acides aminés et les acides organiques (saccharose, glutamine et tartrate respectivement). Pour chacun de ces groupes métaboliques, les quantités moyennes exsudées sont voisines de 300 nmol par grappe en 4 heures. Les quantités croissantes de glucides, d'acides aminés et de potassium collectées entre Je début de l'anthèse et la nouaison reflètent l'augmentation de flux de masse parvenant à la grappe et soulignent l'évolution de la force d'appel de ce puits. De plus, la part croissante de la glutamine et des hexoses dans les exsudats suggère une régulation dans la distribution des assimilats aux organes reproducteurs

Functional Integrative Levels in the Human Interactome Recapitulate Organ Organization

Interactome networks represent sets of possible physical interactions between proteins. They lack spatio-temporal information by construction. However, the specialized functions of the differentiated cell types which are assembled into tissues or organs depend on the combinatorial arrangements of proteins and their physical interactions. Is tissue-specificity, therefore, encoded within the interactome? In order to address this question, we combined protein-protein interactions, expression data, functional annotations and interactome topology. We first identified a subnetwork formed exclusively of proteins whose interactions were observed in all tested tissues. These are mainly involved in housekeeping functions and are located at the topological center of the interactome. This ‘Largest Common Interactome Network’ represents a ‘functional interactome core’. Interestingly, two types of tissue-specific interactions are distinguished when considering function and network topology: tissue-specific interactions involved in regulatory and developmental functions are central whereas tissue-specific interactions involved in organ physiological functions are peripheral. Overall, the functional organization of the human interactome reflects several integrative levels of functions with housekeeping and regulatory tissue-specific functions at the center and physiological tissue-specific functions at the periphery. This gradient of functions recapitulates the organization of organs, from cells to organs. Given that several gradients have already been identified across interactomes, we propose that gradients may represent a general principle of protein-protein interaction network organization

In vivo RNA localization of I factor, a non-LTR retrotransposon, requires a cis-acting signal in ORF2 and ORF1 protein

According to the current model of non-LTR retrotransposon (NLR) mobilization, co-expression of the RNA transposition intermediate, and the proteins it encodes (ORF1p and ORF2p), is a requisite for the formation of cytoplasmic ribonucleoprotein complexes which contain necessary elements to complete a retrotransposition cycle later in the nucleus. To understand these early processes of NLR mobilization, here we analyzed in vivo the protein and RNA expression patterns of the I factor, a model NLR in Drosophila. We show that ORF1p and I factor RNA, specifically produced during transposition, are co-expressed and tightly co-localize with a specific pattern (Loc+) exclusively in the cytoplasm of germ cells permissive for retrotransposition. Using an ORF2 mutated I factor, we show that ORF2p plays no role in the Loc+ patterning. With deletion derivatives of an I factor we define an RNA localization signal required to display the Loc+ pattern. Finally, by complementation experiments we show that ORF1p is necessary for the efficient localization of I factor RNA. Our data suggest that ORF1p is involved in proper folding and stabilization of I factor RNA for efficient targeting, through Loc+ patterning, to the nuclear neighborhood where downstream steps of the retrotransposition process occur