Protein complexes are central in the yeast genetic landscape

If perturbing two genes together has a stronger or weaker effect than expected, they are said to genetically interact. Genetic interactions are important because they help map gene function, and functionally related genes have similar genetic interaction patterns. Mapping quantitative (positive and negative) genetic interactions on a global scale has recently become possible. This data clearly shows groups of genes connected by predominantly positive or negative interactions, termed monochromatic groups. These groups often correspond to functional modules, like biological processes or complexes, or connections between modules. However it is not yet known how these patterns globally relate to known functional modules. Here we systematically study the monochromatic nature of known biological processes using the largest quantitative genetic interaction data set available, which includes fitness measurements for ～5.4 million gene pairs in the yeast Saccharomyces cerevisiae. We find that only 10% of biological processes, as defined by Gene Ontology annotations, and less than 1% of inter-process connections are monochromatic. Further, we show that protein complexes are responsible for a surprisingly large fraction of these patterns. This suggests that complexes play a central role in shaping the monochromatic landscape of biological processes. Altogether this work shows that both positive and negative monochromatic patterns are found in known biological processes and in their connections and that protein complexes play an important role in these patterns. The monochromatic processes, complexes and connections we find chart a hierarchical and modular map of sensitive and redundant biological systems in the yeast cell that will be useful for gene function prediction and comparison across phenotypes and organisms. Furthermore the analysis methods we develop are applicable to other species for which genetic interactions will progressively become more available.     

Qualitative and Quantitative Protein Complex Prediction Through Proteome-Wide Simulations

Despite recent progress in proteomics most protein complexes are still unknown. Identification of these complexes will help us understand cellular regulatory mechanisms and support development of new drugs. Therefore it is really important to establish detailed information about the composition and the abundance of protein complexes but existing algorithms can only give qualitative predictions. Herein, we propose a new approach based on stochastic simulations of protein complex formation that integrates multi-source data—such as protein abundances, domain-domain interactions and functional annotations—to predict alternative forms of protein complexes together with their abundances. This method, called SiComPre (Simulation based Complex Prediction), achieves better qualitative prediction of yeast and human protein complexes than existing methods and is the first to predict protein complex abundances. Furthermore, we show that SiComPre can be used to predict complexome changes upon drug treatment with the example of bortezomib. SiComPre is the first method to produce quantitative predictions on the abundance of molecular complexes while performing the best qualitative predictions. With new data on tissue specific protein complexes becoming available SiComPre will be able to predict qualitative and quantitative differences in the complexome in various tissue types and under various conditions.     

Functional maps of protein complexes from quantitative genetic interaction data

Recently, a number of advanced screening technologies have allowed for the comprehensive quantification of aggravating and alleviating genetic interactions among gene pairs. In parallel, TAP-MS studies (tandem affinity purification followed by mass spectroscopy) have been successful at identifying physical protein interactions that can indicate proteins participating in the same molecular complex. Here, we propose a method for the joint learning of protein complexes and their functional relationships by integration of quantitative genetic interactions and TAP-MS data. Using 3 independent benchmark datasets, we demonstrate that this method is >50% more accurate at identifying functionally related protein pairs than previous approaches. Application to genes involved in yeast chromosome organization identifies a functional map of 91 multimeric complexes, a number of which are novel or have been substantially expanded by addition of new subunits. Interestingly, we find that complexes that are enriched for aggravating genetic interactions (i.e., synthetic lethality) are more likely to contain essential genes, linking each of these interactions to an underlying mechanism. These results demonstrate the importance of both large-scale genetic and physical interaction data in mapping pathway architecture and function.     

Hierarchical modularity and the evolution of genetic interactomes across species

To date, cross-species comparisons of genetic interactomes have been restricted to small or functionally related gene sets, limiting our ability to infer evolutionary trends. To facilitate a more comprehensive analysis, we constructed a genome-scale epistasis map (E-MAP) for the fission yeast Schizosaccharomyces pombe, providing phenotypic signatures for ~60% of the nonessential genome. Using these signatures, we generated a catalog of 297 functional modules, and we assigned function to 144 previously uncharacterized genes, including mRNA splicing and DNA damage checkpoint factors. Comparison with an integrated genetic interactome from the budding yeast Saccharomyces cerevisiae revealed a hierarchical model for the evolution of genetic interactions, with conservation highest within protein complexes, lower within biological processes, and lowest between distinct biological processes. Despite the large evolutionary distance and extensive rewiring of individual interactions, both networks retain conserved features and display similar levels of functional crosstalk between biological processes, suggesting general design principles of genetic interactomes.     

Visualization and analysis of the complexome network of Saccharomyces cerevisiae

Most processes in the cell are delivered by protein complexes, rather than individual proteins. While the association of proteins has been studied extensively in protein-protein interaction networks (the interactome), an intuitive and effective representation of complex-complex connections (the complexome) is not yet available. Here, we describe a new representation of the complexome of Saccharomyces cerevisiae. Using the core-module-attachment data of Gavin et al. ( Nature 2006 , 440 , 631 - 6 ), protein complexes in the network are represented as nodes; these are connected by edges that represent shared core and/or module protein subunits. To validate this network, we examined the network topology and its distribution of biological processes. The complexome network showed scale-free characteristics, with a power law-like node degree distribution and clustering coefficient independent of node degree. Connected complexes in the network showed similarities in biological process that were nonrandom. Furthermore, clusters of interacting complexes reflected a higher-level organization of many cellular functions. The strong functional relationships seen in these clusters, along with literature evidence, allowed 44 uncharacterized complexes to be assigned putative functions using guilt-by-association. We demonstrate our network model using the GEOMI visualization platform, on which we have developed capabilities to integrate and visualize complexome data.     

From proteomes to complexomes in the era of systems biology

Protein complexes carry out almost the entire signaling and functional processes in the cell. The protein complex complement of a cell, and its network of complex–complex interactions, is referred to here as the complexome. Computational methods to predict protein complexes from proteomics data, resulting in network representations of complexomes, have recently being developed. In addition, key advances have been made toward understanding the network and structural organization of complexomes. We review these bioinformatics advances, and their discovery‐potential, as well as the merits of integrating proteomics data with emerging methods in systems biology to study protein complex signaling. It is envisioned that improved integration of proteomics and systems biology, incorporating the dynamics of protein complexes in space and time, may lead to more predictive models of cell signaling networks for effective modulation.     

The mitochondrial complexome of Arabidopsis thaliana

Mitochondria are central to cellular metabolism and energy conversion. In plants they also enable photosynthesis through additional components and functional flexibility. A majority of those processes relies on the assembly of individual proteins to larger protein complexes, some of which operate as large molecular machines. There has been a strong interest in the makeup and function of mitochondrial protein complexes and protein–protein interactions in plants, but the experimental approaches used typically suffer from selectivity or bias. Here, we present a complexome profiling analysis for leaf mitochondria of the model plant Arabidopsis thaliana for the systematic characterization of protein assemblies. Purified organelle extracts were separated by 1D Blue native (BN) PAGE, a resulting gel lane was dissected into 70 slices (complexome fractions) and proteins in each slice were identified by label free quantitative shot‐gun proteomics. Overall, 1359 unique proteins were identified, which were, on average, present in 17 complexome fractions each. Quantitative profiles of proteins along the BN gel lane were aligned by similarity, allowing us to visualize protein assemblies. The data allow re‐annotating the subunit compositions of OXPHOS complexes, identifying assembly intermediates of OXPHOS complexes and assemblies of alternative respiratory oxidoreductases. Several protein complexes were discovered that have not yet been reported in plants, such as a 530 kDa Tat complex, 460 and 1000 kDa SAM complexes, a calcium ion uniporter complex (150 kDa) and several PPR protein complexes. We have set up a tailored online resource ( https://complexomemap.de/at_mito_leaves ) to deposit the data and to allow straightforward access and custom data analyses.     

AGAPE (Automated Genome Analysis PipelinE) for Pan-Genome Analysis of Saccharomyces cerevisiae

The characterization and public release of genome sequences from thousands of organisms is expanding the scope for genetic variation studies. However, understanding the phenotypic consequences of genetic variation remains a challenge in eukaryotes due to the complexity of the genotype-phenotype map. One approach to this is the intensive study of model systems for which diverse sources of information can be accumulated and integrated. Saccharomyces cerevisiae is an extensively studied model organism, with well-known protein functions and thoroughly curated phenotype data. To develop and expand the available resources linking genomic variation with function in yeast, we aim to model the pan-genome of S. cerevisiae. To initiate the yeast pan-genome, we newly sequenced or re-sequenced the genomes of 25 strains that are commonly used in the yeast research community using advanced sequencing technology at high quality. We also developed a pipeline for automated pan-genome analysis, which integrates the steps of assembly, annotation, and variation calling. To assign strain-specific functional annotations, we identified genes that were not present in the reference genome. We classified these according to their presence or absence across strains and characterized each group of genes with known functional and phenotypic features. The functional roles of novel genes not found in the reference genome and associated with strains or groups of strains appear to be consistent with anticipated adaptations in specific lineages. As more S. cerevisiae strain genomes are released, our analysis can be used to collate genome data and relate it to lineage-specific patterns of genome evolution. Our new tool set will enhance our understanding of genomic and functional evolution in S. cerevisiae, and will be available to the yeast genetics and molecular biology community.     

Dosage suppression genetic interaction networks enhance functional wiring diagrams of the cell

Dosage suppression is a genetic interaction in which overproduction of one gene rescues a mutant phenotype of another gene. Although dosage suppression is known to map functional connections among genes, the extent to which it might illuminate global cellular functions is unclear. Here we analyze a network of interactions linking dosage suppressors to 437 essential genes in yeast. For 424 genes, we curated interactions from the literature. Analyses revealed that many dosage suppression interactions occur between functionally related genes and that the majority do not overlap with other types of genetic or physical interactions. To confirm the generality of these network properties, we experimentally identified dosage suppressors for 29 genes from pooled populations of temperature-sensitive mutant cells transformed with a high-copy molecular-barcoded open reading frame library, MoBY-ORF 2.0. We classified 87% of the 1,640 total interactions into four general types of suppression mechanisms, which provided insight into their relative frequencies. This work suggests that integrating the results of dosage suppression studies with other interaction networks could generate insights into the functional wiring diagram of a cell.     

Characterization of multiprotein complexes of the Borrelia burgdorferi outer membrane vesicles

Among bacterial cell envelopes, the Borrelia burgdorferi outer membrane (OM) is structurally unique in that the identities of many protein complexes remain unknown; however, their characterization is the first step toward our understanding of membrane protein interactions and potential functions. Here, we used two-dimensional blue native/SDS-PAGE/mass spectrometric analysis for a global characterization of protein-protein interactions as well as to identify protein complexes in OM vesicles isolated from multiple infectious sensu stricto isolates of B. burgdorferi. Although we uncovered the existence of at least 10 distinct OM complexes harboring several unique subunits, the complexome is dominated by the frequent occurrence of a limited diversity of membrane proteins, most notably P13, outer surface protein (Osp) A, -B, -C, and -D and Lp6.6. The occurrence of these complexes and specificity of subunit interaction were further supported by independent two-dimensional immunoblotting and coimmunoprecipitation assays as well as by mutagenesis studies, where targeted depletion of a subunit member (P66) selectively abolished a specific complex. Although a comparable profile of the OM complexome was detected in two major infectious isolates, such as B31 and 297, certain complexes are likely to occur in an isolate-specific manner. Further assessment of protein complexes in multiple Osp-deficient isolates showed loss of several protein complexes but revealed the existence of additional complex/subunits that are undetectable in wild-type cells. Together, these observations uncovered borrelial antigens involved in membrane protein interactions. The study also suggests that the assembly process of OM complexes is specific and that the core or stabilizing subunits vary between complexes. Further characterization of these protein complexes including elucidation of their biological significance may shed new light on the mechanism of pathogen persistence and the development of preventative measures against the infection.     

The balance of weak and strong interactions in genetic networks

Genetic interactions are being quantitatively characterized in a comprehensive way in several model organisms. These data are then globally represented in terms of genetic networks. How are interaction strengths distributed in these networks? And what type of functional organization of the underlying genomic systems is revealed by such distribution patterns? Here, I found that weak interactions are important for the structure of genetic buffering between signaling pathways in Caenorhabditis elegans, and that the strength of the association between two genes correlates with the number of common interactors they exhibit. I also determined that this network includes genetic cascades balancing weak and strong links, and that its hubs act as particularly strong genetic modifiers; both patterns also identified in Saccharomyces cerevisae networks. In yeast, I further showed a relation, although weak, between interaction strengths and some phenotypic/evolutionary features of the corresponding target genes. Overall, this work demonstrates a non-random organization of interaction strengths in genetic networks, a feature common to other complex networks, and that could reflect in this context how genetic variation is eventually influencing the phenotype.     

Systematic identification and correction of annotation errors in the genetic interaction map of Saccharomyces cerevisiae

The yeast mutant collections are a fundamental tool in deciphering genomic organization and function. Over the last decade, they have been used for the systematic exploration of ∼6 000 000 double gene mutants, identifying and cataloging genetic interactions among them. Here we studied the extent to which these data are prone to neighboring gene effects (NGEs), a phenomenon by which the deletion of a gene affects the expression of adjacent genes along the genome. Analyzing ∼90,000 negative genetic interactions observed to date, we found that more than 10% of them are incorrectly annotated due to NGEs. We developed a novel algorithm, GINGER, to identify and correct erroneous interaction annotations. We validated the algorithm using a comparative analysis of interactions from Schizosaccharomyces pombe. We further showed that our predictions are significantly more concordant with diverse biological data compared to their mis-annotated counterparts. Our work uncovered about 9500 new genetic interactions in yeast.     

Arabidopsis TRANSCURVATA1 Encodes NUP58, a Component of the Nucleopore Central Channel

The selective trafficking of proteins and RNAs through the nuclear envelope regulates nuclear-cytoplasmic segregation of macromolecules and is mediated by nucleopore complexes (NPCs), which consist of about 400 nucleoporins (Nups) of about 30 types. Extensive studies of nucleoporin function in yeast and vertebrates showed that Nups function in nucleocytoplasmic trafficking and other processes. However, limited studies of plant Nups have identified only a few mutations, which cause pleiotropic phenotypes including reduced growth and early flowering. Here, we describe loss-of-function alleles of Arabidopsis TRANSCURVATA1 (TCU1); these mutations cause increased hypocotyl and petiole length, reticulate and asymmetrically epinastic leaf laminae of reduced size, and early flowering. TCU1 is transcribed in all of the organs and tissues examined, and encodes the putative ortholog of yeast and vertebrate Nup58, a nucleoporin of the Nup62 subcomplex. Nup58 forms the central channel of the NPC and acts directly in translocation of proteins through the nuclear envelope in yeast and vertebrates. Yeast two-hybrid (Y2H) assays identified physical interactions between TCU1/NUP58 and 34 proteins, including nucleoporins, SCF (Skp1/Cul1/F-box) ubiquitin ligase complex components and other nucleoplasm proteins. Genetic interactions were also found between TCU1 and genes encoding nucleoporins, soluble nuclear transport receptors and components of the ubiquitin-proteasome and auxin signaling pathways. These genetic and physical interactions indicate that TCU1/NUP58 is a member of the Nup62 subcomplex of the Arabidopsis NPC. Our findings also suggest regulatory roles for TCU1/NUP58 beyond its function in nucleocytoplasmic trafficking, a hypothesis that is supported by the Y2H and genetic interactions that we observed.     

Predicting Physical Interactions between Protein Complexes

Protein complexes enact most biochemical functions in the cell. Dynamic interactions between protein complexes are frequent in many cellular processes. As they are often of a transient nature, they may be difficult to detect using current genome-wide screens. Here, we describe a method to computationally predict physical interactions between protein complexes, applied to both humans and yeast. We integrated manually curated protein complexes and physical protein interaction networks, and we designed a statistical method to identify pairs of protein complexes where the number of protein interactions between a complex pair is due to an actual physical interaction between the complexes. An evaluation against manually curated physical complex-complex interactions in yeast revealed that 50% of these interactions could be predicted in this manner. A community network analysis of the highest scoring pairs revealed a biologically sensible organization of physical complex-complex interactions in the cell. Such analyses of proteomes may serve as a guide to the discovery of novel functional cellular relationships.Protein complexes are central to nearly all biochemical processes in the cell (1). In physiologically relevant states, their protein members assemble with varying degrees of stability, over time and under different cellular conditions, to carry out specific cellular functions (1). Although it is a dynamic and tightly regulated process, there is much evidence to support the notion that protein complex assembly results in discrete signaling macromolecules (2). According to the modular organization of molecular networks of the cell (3), protein complexes cooperate in functional networks through dynamic physical interactions with other macromolecules, including other protein complexes (4–6). These physical interactions between pairs of protein complexes may form the backbone of cellular processes (7), such as the recruitment of complexes by other complexes to sites of genome reorganization or in signaling networks. In this study, we attempted to predict these physical interactions between all pairs of known protein complexes, using the manually curated protein complex databases in CORUM and CYC2008 for humans and yeast, respectively.The physical protein interactions that may occur between pairs of complexes have been reported to be more transient, compared with the combination of both permanent and transient interactions that occur within complexes (8). Indeed, the very stability of protein interactions within a protein complex lies between the two extremes of either transient or permanent states (9). Consequently, the experimental identification in a genome-wide manner of the physical interactions between pairs of complexes is very difficult. This challenge has recently been addressed (7, 10) by experiments where the weak interactions were preserved during affinity purifications, followed by inference of the less stable interactions of proteins with the core proteins within the complex. Guided by a computational method to predict the list of protein members in the complexes (10), this allowed a screen of putative inter-complex relationships from human cell lines (7). This adds to the many landmark developments in recent years to characterize protein complexes in a genome-wide manner (7, 11–13). However, in these experiments it is not always easy to infer accurately what constitutes the protein members of a protein complex. Because of various experimental limitations (14) and the dynamic nature of complex assembly in the cell (15), the protein members of the complexes must be predicted from thousands of purification measurements (10–12, 16). As a result, there are surprisingly large differences in the protein complexes inferred in these studies, depending on the algorithm used (17, 18). Hence, the inference of protein complexes from genome-wide screens (11, 12) is likely to contain significant noise from false-positives resulting from methodological uncertainty (9). This noise would in turn cause ambiguity when attempting to predict, genome-wide, interactions that may occur between protein complexes. One solution to this problem, as applied in this study, is the use of comprehensive databases of the so-called “gold standard” community definitions of protein complexes (19–22). In these resources, critical reading of the scientific literature by trained experts leads to definitions of the lists of protein members that are experimentally verified to form complexes. Each of these manually curated protein complexes are assigned functional annotations and a unique identifier. It is our assumption that this approach will allow for a more accurate resolution of the physical interactions between protein complexes.Based on this reasoning, we utilized all protein complex pairs from 1216 human protein complexes in CORUM (21) and 471 in the yeast CYC2008 databases (22, 23), and we attempted to predict physical interactions between them.To this end, we integrated only binary physical protein interactions that were experimentally verified and supported by Medline references, from the iRefIndex database (24, 25), and we developed a statistical method that compared the number of observed physical protein interactions between pairs of protein complexes versus the number of protein interactions expected to be present in pairs of randomized protein complexes. The highest scoring predicted pairs formed a network that was analyzed to identify communities of physically interacting protein complexes. Such higher order perspectives of cellular proteomes may aid discovery of novel functional relationships and lead to an improved understanding of cellular behavior.One recent study utilized manually curated protein complexes-complex interactions in yeast (23) as part of a machine learning strategy to identify complex-complex interactions. However, they added to the training data complex pairs enriched with protein interactions under the assumption that these were likely to contain complex-complex interactions but without a clear statistical argument to assess the reliability of these. Our aim has been to provide a more rigorous statistical approach applied to yeast and human, in which the main confounding factors, protein degrees and protein similarities within the complexes, have been taken into account.We used only the manually curated yeast complex-complex interactions from Ref. 23 as the reference set to evaluate our method after verifying with the authors that the manual curation had not been guided by enrichment in the protein network. Of these interactions, we predicted half at a 10% false discovery rate. Thus, although improvements in data as well as methods are still required for a more complete prediction of complex-complex interactions, a fair portion of these interactions can be reliably predicted now by using our method.     

Development of cell polarity in budding yeast

The development of cell polarity involves virtually every aspect of cell biology. Yeast are less complex than cells traditionally used for studies on cell polarity and are amendable to sophisticated genetic analysis. This has resulted in a growing number of molecular markers for yeast cell polarity and an increasingly well-defined progression of molecular events required for bud formation. Together, these factors provide a favorable context in which to understand how the interplay between a large number of processes can polarize a cell. Many genes required for morphogenesis have been identified, and genetic interactions provide evidence that the products of these genes function together. Studies on cell polarity development in S. cerevisiae have demonstrated a requirement for small GTP-binding proteins and have established functional relationships between temporally coincident events. With the continued identification and analysis of genes required for morphogenesis, and the pursuit of these studies on a cytological and biochemical level, studies on yeast will continue to contribute to our understanding of cell polarity development.