Project description:The topology behind biological interaction networks has been studied for over a decade. Yet, there is no definite agreement on the theoretical models which best describe protein-protein interaction (PPI) networks. Such models are critical to quantifying the significance of any empirical observation regarding those networks. Here, we perform a comprehensive analysis of yeast PPI networks in order to gain insights into their topology and its dependency on interaction-screening technology. We find that: (1) interaction-detection technology has little effect on the topology of PPI networks; (2) topology of these interaction networks differs in organisms with different cellular complexity (human and yeast); (3) clear topological difference is present between PPI networks, their functional sub-modules, and their inter-functional "linkers"; (4) high confidence PPI networks have more "geometrical" topology compared to predicted, incomplete, or noisy PPI networks; and (5) inter-functional "linker" proteins serve as mediators in signal transduction, transport, regulation and organisational cellular processes.
Project description:RNA-binding proteins (RBPs) are key players in post-transcriptional regulation of gene expression in eukaryotic cells. To be able to unbiasedly identify RBPs in Saccharomyces cerevisiae, we developed a yeast RNA interactome capture protocol which employs RNA labeling, covalent UV crosslinking of RNA and proteins at 365nm wavelength (photoactivatable-ribonucleoside-enhanced crosslinking, PAR-CL) and finally purification of the protein-bound mRNA. The method can be easily implemented in common workflows and takes about 3days to complete. Next to a comprehensive explanation of the method, we focus on our findings about the choice of crosslinking in yeast and discuss the rationale of individual steps in the protocol.
Project description:ATP-binding cassette (ABC) transporters are a ubiquitous class of integral membrane proteins of immense clinical interest because of their strong association with human disease and pharmacology. To improve our understanding of these proteins, we used membrane yeast two-hybrid technology to map the protein interactome of all of the nonmitochondrial ABC transporters in the model organism Saccharomyces cerevisiae and combined this data with previously reported yeast ABC transporter interactions in the BioGRID database to generate a comprehensive, integrated 'interactome'. We show that ABC transporters physically associate with proteins involved in an unexpectedly diverse range of functions. We specifically examine the importance of the physical interactions of ABC transporters in both the regulation of one another and in the modulation of proteins involved in zinc homeostasis. The interaction network presented here will be a powerful resource for increasing our fundamental understanding of the cellular role and regulation of ABC transporters.
Project description:We previously reported a novel affinity purification (AP) method termed modified chromatin immunopurification (mChIP), which permits selective enrichment of DNA-bound proteins along with their associated protein network. In this study, we report a large-scale study of the protein network of 102 chromatin-related proteins from budding yeast that were analyzed by mChIP coupled to mass spectrometry. This effort resulted in the detection of 2966 high confidence protein associations with 724 distinct preys. mChIP resulted in significantly improved interaction coverage as compared with classical AP methodology for ?75% of the baits tested. Furthermore, mChIP successfully identified novel binding partners for many lower abundance transcription factors that previously failed using conventional AP methodologies. mChIP was also used to perform targeted studies, particularly of Asf1 and its associated proteins, to allow for a understanding of the physical interplay between Asf1 and two other histone chaperones, Rtt106 and the HIR complex, to be gained.
Project description:Protein interaction networks are known to exhibit remarkable structures: scale-free and small-world and modular structures. To explain the evolutionary processes of protein interaction networks possessing scale-free and small-world structures, preferential attachment and duplication-divergence models have been proposed as mathematical models. Protein interaction networks are also known to exhibit another remarkable structural characteristic, modular structure. How the protein interaction networks became to exhibit modularity in their evolution? Here, we propose a hypothesis of modularity in the evolution of yeast protein interaction network based on molecular evolutionary evidence. We assigned yeast proteins into six evolutionary ages by constructing a phylogenetic profile. We found that all the almost half of hub proteins are evolutionarily new. Examining the evolutionary processes of protein complexes, functional modules and topological modules, we also found that member proteins of these modules tend to appear in one or two evolutionary ages. Moreover, proteins in protein complexes and topological modules show significantly low evolutionary rates than those not in these modules. Our results suggest a hypothesis of modularity in the evolution of yeast protein interaction network as systems evolution.
Project description:It is hoped that comprehensive mapping of protein physical interactions will facilitate insights regarding both fundamental cell biology processes and the pathology of diseases. To fulfill this hope, good solutions to 2 issues will be essential: (i) how to obtain reliable interaction data in a high-throughput setting and (ii) how to structure interaction data in a meaningful form, amenable to and valuable for further biological research. In this article, we structure an interactome in terms of predicted permanent protein complexes and predicted transient, nongeneric interactions between these complexes. The interactome is generated by means of an associated computational algorithm, from raw high-throughput affinity purification/mass spectrometric interaction data. We apply our technique to the construction of an interactome for Saccharomyces cerevisiae, showing that it yields reliability typical of low-throughput experiments from high-throughput data. We discuss biological insights raised by this interactome including, via homology, a few related to human disease.
Project description:Scale-free networks are generically defined by a power-law distribution of node connectivities. Vastly different graph topologies fit this law, ranging from the assortative, with frequent similar-degree node connections, to a modular structure. Using a metric to determine the extent of modularity, we examined the yeast protein network and found it to be significantly self-dissimilar. By orthologous node categorization, we established the evolutionary trend in the network, from an "emerging" assortative network to a present-day modular topology. The evolving topology fits a generic connectivity distribution but with a progressive enrichment in intramodule hubs that avoid each other. Primeval tolerance to random node failure is shown to evolve toward resilience to hub failure, thus removing the fragility often ascribed to scale-free networks. This trend is algorithmically reproduced by adopting a connectivity accretion law that disfavors like-degree connections for large-degree nodes. The selective advantage of this trend relates to the need to prevent a failed hub from inducing failure in an adjacent hub. The molecular basis for the evolutionary trend is likely rooted in the high-entropy penalty entailed in the association of two intramodular hubs.
Project description:To determine whether genes retain ancestral functions over a billion years of evolution and to identify principles of deep evolutionary divergence, we replaced 414 essential yeast genes with their human orthologs, assaying for complementation of lethal growth defects upon loss of the yeast genes. Nearly half (47%) of the yeast genes could be successfully humanized. Sequence similarity and expression only partly predicted replaceability. Instead, replaceability depended strongly on gene modules: Genes in the same process tended to be similarly replaceable (e.g., sterol biosynthesis) or not (e.g., DNA replication initiation). Simulations confirmed that selection for specific function can maintain replaceability despite extensive sequence divergence. Critical ancestral functions of many essential genes are thus retained in a pathway-specific manner, resilient to drift in sequences, splicing, and protein interfaces.
Project description:Global studies of protein-protein interactions are crucial to both elucidating gene function and producing an integrated view of the workings of living cells. High-throughput studies of the yeast interactome have been performed using both genetic and biochemical screens. Despite their size, the overlap between these experimental datasets is very limited. This could be due to each approach sampling only a small fraction of the total interactome. Alternatively, a large proportion of the data from these screens may represent false-positive interactions. We have used the Genome Information Management System (GIMS) to integrate interactome datasets with transcriptome and protein annotation data and have found significant evidence that the proportion of false-positive results is high. Not all high-throughput datasets are similarly contaminated, and the tandem affinity purification (TAP) approach appears to yield a high proportion of reliable interactions for which corroborating evidence is available. From our integrative analyses, we have generated a set of verified interactome data for yeast.
Project description:Genome-wide physical protein-protein interaction (PPI) mapping remains a major challenge for current technologies. Here, we reported a high-efficiency BiFC-seq method, yeast-enhanced green fluorescent protein-based bimolecular fluorescence complementation (yEGFP-BiFC) coupled with next-generation DNA sequencing, for interactome mapping. We first applied yEGFP-BiFC method to systematically investigate an intraviral network of the Ebola virus. Two-thirds (9/14) of known interactions of EBOV were recaptured, and five novel interactions were discovered. Next, we used the BiFC-seq method to map the interactome of the tumor protein p53. We identified 97 interactors of p53, more than three-quarters of which were novel. Furthermore, in a more complex background, we screened potential interactors by pooling two BiFC libraries together and revealed a network of 229 interactions among 205 proteins. These results show that BiFC-seq is a highly sensitive, rapid, and economical method for genome-wide interactome mapping.