Systematic interpretation of genetic interactions using protein networks

Genetic interaction analysis,in which two mutations have a combined effect not exhibited by either mutation alone, is a powerful and widespread tool for establishing functional linkages between genes. In the yeast Saccharomyces cerevisiae, ongoing screens have generated >4,800 such genetic interaction data. We demonstrate that by combining these data with information on protein-protein, prote in-DNA or metabolic networks, it is possible to uncover physical mechanisms behind many of the observed genetic effects. Using a probabilistic model, we found that 1,922 genetic interactions are significantly associated with either between- or within-pathway explanations encoded in the physical networks, covering approximately 40% of known genetic interactions. These models predict new functions for 343 proteins and suggest that between-pathway explanations are better than within-pathway explanations at interpreting genetic interactions identified in systematic screens. This study provides a road map for how genetic and physical interactions can be integrated to reveal pathway organization and function.     

A predicted interactome for Arabidopsis

The complex cellular functions of an organism frequently rely on physical interactions between proteins. A map of all protein-protein interactions, an interactome, is thus an invaluable tool. We present an interactome for Arabidopsis (Arabidopsis thaliana) predicted from interacting orthologs in yeast (Saccharomyces cerevisiae), nematode worm (Caenorhabditis elegans), fruitfly (Drosophila melanogaster), and human (Homo sapiens). As an internal quality control, a confidence value was generated based on the amount of supporting evidence for each interaction. A total of 1,159 high confidence, 5,913 medium confidence, and 12,907 low confidence interactions were identified for 3,617 conserved Arabidopsis proteins. There was significant coexpression of genes whose proteins were predicted to interact, even among low confidence interactions. Interacting proteins were also significantly more likely to be found within the same subcellular location, and significantly less likely to be found in conflicting localizations than randomly paired proteins. A notable exception was that proteins located in the Golgi were more likely to interact with Golgi, vacuolar, or endoplasmic reticulum sorted proteins, indicating possible docking or trafficking interactions. These predictions can aid researchers by extending known complexes and pathways with candidate proteins. In addition we have predicted interactions for many previously unknown proteins in known pathways and complexes. We present this interactome, and an online Web interface the Arabidopsis Interactions Viewer, as a first step toward understanding global signaling in Arabidopsis, and to whet the appetite for those who are awaiting results from high-throughput experimental approaches.     

Defining the role of essential genes in human disease

A greater understanding of the causes of human disease can come from identifying characteristics that are specific to disease genes. However, a full understanding of the contribution of essential genes to human disease is lacking, due to the premise that these genes tend to cause developmental abnormalities rather than adult disease. We tested the hypothesis that human orthologs of mouse essential genes are associated with a variety of human diseases, rather than only those related to miscarriage and birth defects. We segregated human disease genes according to whether the knockout phenotype of their mouse ortholog was lethal or viable, defining those with orthologs producing lethal knockouts as essential disease genes. We show that the human orthologs of mouse essential genes are associated with a wide spectrum of diseases affecting diverse physiological systems. Notably, human disease genes with essential mouse orthologs are over-represented among disease genes associated with cancer, suggesting links between adult cellular abnormalities and developmental functions. The proteins encoded by essential genes are highly connected in protein-protein interaction networks, which we find correlates with an over-representation of nuclear proteins amongst essential disease genes. Disease genes associated with essential orthologs also are more likely than those with non-essential orthologs to contribute to disease through an autosomal dominant inheritance pattern, suggesting that these diseases may actually result from semi-dominant mutant alleles. Overall, we have described attributes found in disease genes according to the essentiality status of their mouse orthologs. These findings demonstrate that disease genes do occupy highly connected positions in protein-protein interaction networks, and that due to the complexity of disease-associated alleles, essential genes cannot be ignored as candidates for causing diverse human diseases.     

Valine 181 is critical for the nucleotide exchange activity of human mitochondrial ADP/ATP carriers in yeast

We isolated yeast Saccharomyces cerevisiae cells transformed with one of the three human adenine nucleotide carrier genes (HANC) that exhibited higher growth capacity than previously observed. The HANC genes were isolated from these clones, and we identified two independent mutations of HANC that led to replacement of valine 181 located in the fourth transmembrane segment by methionine or phenylalanine. Tolerance of this position toward substitution with various amino acids was systematically investigated, and since HANC/V181M was among the most efficient in growth complementation, it was more extensively studied. Here we show that increased growth capacities were associated with higher ADP/ATP exchange activities and not with higher human carrier amount in yeast mitochondria. These results are discussed in the light of the bovine Ancp structure, that shares more than 90% amino acid identity with Hancps, and its interaction with the lipid environment.     

Cloning and molecular characterization of three ubiquitin fusion degradation 1 (Ufd1) ortholog genes from Xenopus laevis, Gallus gallus and Drosophila melanogaster.

The yeast ubiquitin fusion degradation 1 (Ufd1) protein is involved in a degradation pathway for ubiquitin fused products. The human ortholog gene (UFD1-like, UFD1L) is deleted in patients affected by the DiGeorge/velocardiofacial syndromes. We report the cloning of UFD1L orthologs from Drosophila melanogaster (dufd1l), Xenopus laevis and Gallus gallus. The 1,125-bp Drosophila cDNA encodes a protein of 316 amino acids, showing 60% identity with the human and murine proteins. The identity to the G. gallus, X. laevis, C. elegans and S. cerevisiae proteins is 95%, 83%, 32%, and 36%, respectively. Northern expression data in Drosophila indicate that dufd1l is expressed through embryonic, larval and pupal development, as well as in the adult fly.     

Microarray image analysis: background estimation using quantile and morphological filters

Background

Identification of protein interaction networks has received considerable attention in the post-genomic era. The currently available biochemical approaches used to detect protein-protein interactions are all time and labour intensive. Consequently there is a growing need for the development of computational tools that are capable of effectively identifying such interactions.

Results

Here we explain the development and implementation of a novel Protein-Protein Interaction Prediction Engine termed PIPE. This tool is capable of predicting protein-protein interactions for any target pair of the yeast Saccharomyces cerevisiae proteins from their primary structure and without the need for any additional information or predictions about the proteins. PIPE showed a sensitivity of 61% for detecting any yeast protein interaction with 89% specificity and an overall accuracy of 75%. This rate of success is comparable to those associated with the most commonly used biochemical techniques. Using PIPE, we identified a novel interaction between YGL227W (vid30) and YMR135C (gid8) yeast proteins. This lead us to the identification of a novel yeast complex that here we term vid30 complex (vid30c). The observed interaction was confirmed by tandem affinity purification (TAP tag), verifying the ability of PIPE to predict novel protein-protein interactions. We then used PIPE analysis to investigate the internal architecture of vid30c. It appeared from PIPE analysis that vid30c may consist of a core and a secondary component. Generation of yeast gene deletion strains combined with TAP tagging analysis indicated that the deletion of a member of the core component interfered with the formation of vid30c, however, deletion of a member of the secondary component had little effect (if any) on the formation of vid30c. Also, PIPE can be used to analyse yeast proteins for which TAP tagging fails, thereby allowing us to predict protein interactions that are not included in genome-wide yeast TAP tagging projects.

Conclusion

PIPE analysis can predict yeast protein-protein interactions. Also, PIPE analysis can be used to study the internal architecture of yeast protein complexes. The data also suggests that a finite set of short polypeptide signals seem to be responsible for the majority of the yeast protein-protein interactions.     

Combining different 'omics' technologies to map and validate protein-protein interactions in humans.

The mapping of protein-protein interactions is key to understanding biological processes. Many technologies have been reported to map interactions and these have been systematically applied in yeast. To date, the number of reported yeast protein interactions that have been truly validated by at least one other approach is low. The mapping of human protein interaction networks is even more complicated. Thus, it is unreasonable to try to map the human interactome; instead, interaction mapping in human cell lines should be focused along the lines of diseases or changes that can be associated with specific cells. In this paper, an approach for combining different 'omics' technologies to achieve efficient mapping and validation of protein interactions in human cell lines is presented.     

双杂交和质谱技术在蛋白质-蛋白质相互作用研究中的应用

基因的功能是由蛋白质来执行的,而蛋白质要通过与其他生物分子相互作用来完成其各种生物功能。因此,如果能够快速做出蛋白质在不同时间、空间和不同环境中的相互作用图谱,就会帮助我们了解这些蛋白质的功能,进而了解许多生命活动的机制。目前,用于大规模研究蛋白质间相互作用的方法主要有酵母双杂交系统及其衍生系统、亲和纯化与质谱分析联用技术,前者用于研究蛋白分子间的两两相互作用,后者用于研究蛋白质复合物间的相互作用。本文主要阐述了酵母双杂交、细菌双杂交、哺乳动物细胞双杂交、亲和纯化与质谱联用技术在大规模蛋白质相互作用研究中的应用。     

Three-dimensional reconstruction of protein networks provides insight into human genetic disease

To better understand the molecular mechanisms and genetic basis of human disease, we systematically examine relationships between 3,949 genes, 62,663 mutations and 3,453 associated disorders by generating a three-dimensional, structurally resolved human interactome. This network consists of 4,222 high-quality binary protein-protein interactions with their atomic-resolution interfaces. We find that in-frame mutations (missense point mutations and in-frame insertions and deletions) are enriched on the interaction interfaces of proteins associated with the corresponding disorders, and that the disease specificity for different mutations of the same gene can be explained by their location within an interface. We also predict 292 candidate genes for 694 unknown disease-to-gene associations with proposed molecular mechanism hypotheses. This work indicates that knowledge of how in-frame disease mutations alter specific interactions is critical to understanding pathogenesis. Structurally resolved interaction networks should be valuable tools for interpreting the wealth of data being generated by large-scale structural genomics and disease association studies.     

Complete Protein Linkage Map of Poliovirus P3 Proteins: Interaction of Polymerase 3Dpol with VPg and with Genetic Variants of 3AB

Poliovirus has evolved to maximize its genomic information by producing multifunctional viral proteins. The P3 nonstructural proteins harbor various activities when paired with different binding partners. These viral polypeptides regulate host cell macromolecular synthesis and function as proteinases, as RNA binding proteins, or as RNA-dependent RNA polymerase. A cleavage product of the P3 region is the genome-linked protein VPg that is essential in the initiation of RNA synthesis. We have used an inducible yeast two-hybrid system to analyze directly protein-protein interactions among P3 proteins. Sixteen signals of homo- or heterodimer interactions have been observed and have been divided into three groups. Of interest is the newly discovered affinity of VPg to 3D^pol that suggests direct interaction between these molecules in genome replication. A battery of 3AB variants (eight clustered-charge-to-alanine changes and five single-amino-acid mutations) has been used to map the binding determinants of 3AB-3AB interaction which were found to differ from the amino acids critical for the 3AB-3D^pol interaction. The viral proteinase 3C^pro was not found to interact with other 3C^pro molecules or with any other P3 polypeptide in yeast cells, a result confirmed by glutaraldehyde cross-linking. The weak apparent interaction between 3AB and 3CD^pro scored in the yeast two-hybrid system was in contrast to a strong signal by far-Western blotting. The results elucidate, in part, previous results of biochemical and genetic analyses. The role of the interactions in RNA replication is addressed.     

The rad16 gene of Schizosaccharomyces pombe: a homolog of the RAD1 gene of Saccharomyces cerevisiae.

The rad10, rad16, rad20, and swi9 mutants of the fission yeast Schizosaccharomyces pombe, isolated by their radiation sensitivity or abnormal mating-type switching, have been shown previously to be allelic. We have cloned DNA correcting the UV sensitivity or mating-type switching phenotype of these mutants and shown that the correcting DNA is encompassed in a single open reading frame. The gene, which we will refer to as rad16, is approximately 3 kb in length, contains seven introns, and encodes a protein of 892 amino acids. It is not essential for viability of S. pombe. The predicted protein is the homolog of the Saccharomyces cerevisiae RAD1 protein, which is involved in an early step in excision-repair of UV damage from DNA. The approximately 30% sequence identity between the predicted proteins from the two yeasts is distributed throughout the protein. Two-hybrid experiments indicate a strong protein-protein interaction between the products of the rad16 and swi10 genes of S. pombe, which mirrors that reported for RAD1 and RAD10 in S. cerevisiae. We have identified the mutations in the four alleles of rad16. They mapped to the N-terminal (rad10), central (rad20), and C-terminal (rad16 and swi9) regions. The rad10 and rad20 mutations are in the splice donor sequences of introns 2 and 4, respectively. The plasmid correcting the UV sensitivity of the rad20 mutation was missing the sequence corresponding to the 335 N-terminal amino acids of the predicted protein. Neither smaller nor larger truncations were, however, able to correct its UV sensitivity.     

A protein interaction map for cell polarity development

Many genes required for cell polarity development in budding yeast have been identified and arranged into a functional hierarchy. Core elements of the hierarchy are widely conserved, underlying cell polarity development in diverse eukaryotes. To enumerate more fully the protein-protein interactions that mediate cell polarity development, and to uncover novel mechanisms that coordinate the numerous events involved, we carried out a large-scale two-hybrid experiment. 68 Gal4 DNA binding domain fusions of yeast proteins associated with the actin cytoskeleton, septins, the secretory apparatus, and Rho-type GTPases were used to screen an array of yeast transformants that express approximately 90% of the predicted Saccharomyces cerevisiae open reading frames as Gal4 activation domain fusions. 191 protein-protein interactions were detected, of which 128 had not been described previously. 44 interactions implicated 20 previously uncharacterized proteins in cell polarity development. Further insights into possible roles of 13 of these proteins were revealed by their multiple two-hybrid interactions and by subcellular localization. Included in the interaction network were associations of Cdc42 and Rho1 pathways with proteins involved in exocytosis, septin organization, actin assembly, microtubule organization, autophagy, cytokinesis, and cell wall synthesis. Other interactions suggested direct connections between Rho1- and Cdc42-regulated pathways; the secretory apparatus and regulators of polarity establishment; actin assembly and the morphogenesis checkpoint; and the exocytic and endocytic machinery. In total, a network of interactions that provide an integrated response of signaling proteins, the cytoskeleton, and organelles to the spatial cues that direct polarity development was revealed.     

Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data

MOTIVATION: Biological processes in cells are properly performed by gene regulations, signal transductions and interactions between proteins. To understand such molecular networks, we propose a statistical method to estimate gene regulatory networks and protein-protein interaction networks simultaneously from DNA microarray data, protein-protein interaction data and other genome-wide data. RESULTS: We unify Bayesian networks and Markov networks for estimating gene regulatory networks and protein-protein interaction networks according to the reliability of each biological information source. Through the simultaneous construction of gene regulatory networks and protein-protein interaction networks of Saccharomyces cerevisiae cell cycle, we predict the role of several genes whose functions are currently unknown. By using our probabilistic model, we can detect false positives of high-throughput data, such as yeast two-hybrid data. In a genome-wide experiment, we find possible gene regulatory relationships and protein-protein interactions between large protein complexes that underlie complex regulatory mechanisms of biological processes.     

Predicting protein-protein interactions from protein domains using a set cover approach

One goal of contemporary proteome research is the elucidation of cellular protein interactions. Based on currently available protein-protein interaction and domain data, we introduce a novel method, maximum specificity set cover (MSSC), for the prediction of protein-protein interactions. In our approach, we map the relationship between interactions of proteins and their corresponding domain architectures to a generalized weighted set cover problem. The application of a greedy algorithm provides sets of domain interactions which explain the presence of protein interactions to the largest degree of specificity. Utilizing domain and protein interaction data of S. cerevisiae, MSSC enables prediction of previously unknown protein interactions, links that are well supported by a high tendency of coexpression and functional homogeneity of the corresponding proteins. Focusing on concrete examples, we show that MSSC reliably predicts protein interactions in well-studied molecular systems, such as the 26S proteasome and RNA polymerase II of S. cerevisiae. We also show that the quality of the predictions is comparable to the maximum likelihood estimation while MSSC is faster. This new algorithm and all data sets used are accessible through a Web portal at http://ppi-cse.nd.edu     

A protein-protein interaction network for human inherited ataxias and disorders of Purkinje cell degeneration

Many human inherited neurodegenerative disorders are characterized by loss of balance due to cerebellar Purkinje cell (PC) degeneration. Although the disease-causing mutations have been identified for a number of these disorders, the normal functions of the proteins involved remain, in many cases, unknown. To gain insight into the function of proteins involved in PC degeneration, we developed an interaction network for 54 proteins involved in 23 inherited ataxias and expanded the network by incorporating literature-curated and evolutionarily conserved interactions. We identified 770 mostly novel protein-protein interactions using a stringent yeast two-hybrid screen; of 75 pairs tested, 83% of the interactions were verified in mammalian cells. Many ataxia-causing proteins share interacting partners, a subset of which have been found to modify neurodegeneration in animal models. This interactome thus provides a tool for understanding pathogenic mechanisms common for this class of neurodegenerative disorders and for identifying candidate genes for inherited ataxias.