Categorizing biases in high-confidence high-throughput protein-protein interaction data sets

Interactome Analysis of ClpX Reveals Its Regulatory Role in Metabolism and Photosynthesis in Cyanobacteria

Protein homeostasis is essential for cyanobacteria to maintain proper cellular function under adverse and fluctuating conditions. The AAA+ superfamily of proteolytic complexes in cyanobacteria plays a critical role in this process, including ClpXP, which comprises a hexameric ATPase ClpX and a tetradecameric peptidase ClpP. Despite the physiological effects of ClpX on growth and photosynthesis, its potential substrates and underlying mechanisms in cyanobacteria remain unknown. In this study, we employed a streptavidin-biotin affinity pull-down assay coupled with label-free proteome quantitation to analyze the interactome of ClpX in the model cyanobacterium Synechocystis sp. PCC 6803 (hereafter Synechocystis). We identified 503 proteins as potential ClpX-binding targets, many of which had novel interactions. These ClpX-binding targets were found to be involved in various biological processes, with particular enrichment in metabolic processes and photosynthesis. Using protein-protein docking, GST pull-down, and biolayer interferometry assays, we confirmed the direct association of ClpX with the photosynthetic proteins, ferredoxin-NADP+ oxidoreductase (FNR) and phycocyanin subunit (CpcA). Subsequent functional investigations revealed that ClpX participates in the maintenance of FNR homeostasis and functionality in Synechocystis grown under different light conditions. Overall, our study provides a comprehensive understanding of the extensive functions regulated by ClpX in cyanobacteria to maintain protein homeostasis and adapt to environmental challenges.

Discovering cellular protein-protein interactions: Technological strategies and opportunities

The analysis of protein interaction networks is one of the key challenges in the study of biology. It connects genotypes to phenotypes, and disruption often leads to diseases. Hence, many technologies have been developed to study protein-protein interactions (PPIs) in a cellular context. The expansion of the PPI technology toolbox however complicates the selection of optimal approaches for diverse biological questions. This review gives an overview of the binary and co-complex technologies, with the former evaluating the interaction of two co-expressed genetically tagged proteins, and the latter only needing the expression of a single tagged protein or no tagged proteins at all. Mass spectrometry is crucial for some binary and all co-complex technologies. After the detailed description of the different technologies, the review compares their unique specifications, advantages, disadvantages, and applicability, while highlighting opportunities for further advancements.

Mechanisms of action of Coxiella burnetii effectors inferred from host-pathogen protein interactions

Coxiella burnetii is an obligate Gram-negative intracellular pathogen and the etiological agent of Q fever. Successful infection requires a functional Type IV secretion system, which translocates more than 100 effector proteins into the host cytosol to establish the infection, restructure the intracellular host environment, and create a parasitophorous vacuole where the replicating bacteria reside. We used yeast two-hybrid (Y2H) screening of 33 selected C. burnetii effectors against whole genome human and murine proteome libraries to generate a map of potential host-pathogen protein-protein interactions (PPIs). We detected 273 unique interactions between 20 pathogen and 247 human proteins, and 157 between 17 pathogen and 137 murine proteins. We used orthology to combine the data and create a single host-pathogen interaction network containing 415 unique interactions between 25 C. burnetii and 363 human proteins. We further performed complementary pairwise Y2H testing of 43 out of 91 C. burnetii-human interactions involving five pathogen proteins. We used the combined data to 1) perform enrichment analyses of target host cellular processes and pathways, 2) examine effectors with known infection phenotypes, and 3) infer potential mechanisms of action for four effectors with uncharacterized functions. The host-pathogen interaction profiles supported known Coxiella phenotypes, such as adapting cell morphology through cytoskeletal re-arrangements, protein processing and trafficking, organelle generation, cholesterol processing, innate immune modulation, and interactions with the ubiquitin and proteasome pathways. The generated dataset of PPIs-the largest collection of unbiased Coxiella host-pathogen interactions to date-represents a rich source of information with respect to secreted pathogen effector proteins and their interactions with human host proteins.

Characterization of the Translationally Controlled Tumor Protein (TCTP) Interactome Reveals Novel Binding Partners in Human Cancer Cells

Translationally controlled tumor protein (TCTP) is a highly conserved housekeeping protein present in eukaryotic organisms. It is involved in regulating many fundamental processes and plays a critical role in tumor reversion and tumorigenesis. Increasing evidence suggests that TCTP plays a role in the regulation of cell fate determination and is a promising therapeutic target for cancer. To decipher the exact mechanisms by which TCTP functions and how all these functions are integrated, we analyzed the interactome of TCTP in HeLa cells by coimmunoprecipitation (IP) and mass spectrometry (MS). A total of 98 proteins were identified. We confirmed the in vitro and in vivo association of TCTP with six of the identified binding proteins using reciprocal IP and bimolecular fluorescence complementation (BiFC) analysis, respectively. Moreover, TCTP interacted with Y-box-binding protein 1 (YBX1), and their interaction was localized to the N-terminal region of TCTP and the 1-129 amino acid (aa) residues of YBX1. The YBX1 protein plays an important role in cell proliferation, RNA splicing, DNA repair, drug resistance, and stress response to extracellular signals. These data suggest that the interaction of TCTP with YBX1 might cooperate or coordinate their functions in the control of diverse regulatory pathways in cancer cells. Taken together, our results not only reveal a large number of TCTP-associated proteins that possess pleiotropic functions, but also provide novel insights into the molecular mechanisms of TCTP in tumorigenesis.

Using host-pathogen protein interactions to identify and characterize Francisella tularensis virulence factors

Background

Francisella tularensis is a select bio-threat agent and one of the most virulent intracellular pathogens known, requiring just a few organisms to establish an infection. Although several virulence factors are known, we lack an understanding of virulence factors that act through host-pathogen protein interactions to promote infection. To address these issues in the highly infectious F. tularensis subsp. tularensis Schu S4 strain, we deployed a combined in silico, in vitro, and in vivo analysis to identify virulence factors and their interactions with host proteins to characterize bacterial infection mechanisms.

Results

We initially used comparative genomics and literature to identify and select a set of 49 putative and known virulence factors for analysis. Each protein was then subjected to proteome-scale yeast two-hybrid (Y2H) screens with human and murine cDNA libraries to identify potential host-pathogen protein-protein interactions. Based on the bacterial protein interaction profile with both hosts, we selected seven novel putative virulence factors for mutant construction and animal validation experiments. We were able to create five transposon insertion mutants and used them in an intranasal BALB/c mouse challenge model to establish 50 % lethal dose estimates. Three of these, ΔFTT0482c, ΔFTT1538c, and ΔFTT1597, showed attenuation in lethality and can thus be considered novel F. tularensis virulence factors. The analysis of the accompanying Y2H data identified intracellular protein trafficking between the early endosome to the late endosome as an important component in virulence attenuation for these virulence factors. Furthermore, we also used the Y2H data to investigate host protein binding of two known virulence factors, showing that direct protein binding was a component in the modulation of the inflammatory response via activation of mitogen-activated protein kinases and in the oxidative stress response.

Conclusions

Direct interactions with specific host proteins and the ability to influence interactions among host proteins are important components for F. tularensis to avoid host-cell defense mechanisms and successfully establish an infection. Although direct host-pathogen protein-protein binding is only one aspect of Francisella virulence, it is a critical component in directly manipulating and interfering with cellular processes in the host cell.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2351-1) contains supplementary material, which is available to authorized users.

Neuroprotective mechanisms activated in non-seizing rats exposed to sarin

Exposure to organophosphate (OP) nerve agents, such as sarin, may lead to uncontrolled seizures and irreversible brain injury and neuropathology. In rat studies, a median lethal dose of sarin leads to approximately half of the animals developing seizures. Whereas previous studies analyzed transcriptomic effects associated with seizing sarin-exposed rats, our study focused on the cohort of sarin-exposed rats that did not develop seizures. We analyzed the genomic changes occurring in sarin-exposed, non-seizing rats and compared differentially expressed genes and pathway activation to those of seizing rats. At the earliest time point (0.25 h) and in multiple sarin-sensitive brain regions, defense response genes were commonly expressed in both groups of animals as compared to the control groups. All sarin-exposed animals activated the MAPK signaling pathway, but only the seizing rats activated the apoptotic-associated JNK and p38 MAPK signaling sub-pathway. A unique phenotype of the non-seizing rats was the altered expression levels of genes that generally suppress inflammation or apoptosis. Importantly, the early transcriptional response for inflammation- and apoptosis-related genes in the thalamus showed opposite trends, with significantly down-regulated genes being up-regulated, and vice versa, between the seizing and non-seizing rats. These observations lend support to the hypothesis that regulation of anti-inflammatory genes might be part of an active and sufficient response in the non-seizing group to protect against the onset of seizures. As such, stimulating or activating these responses via pretreatment strategies could boost resilience against nerve agent exposures.

Mining host-pathogen protein interactions to characterize Burkholderia mallei infectivity mechanisms

Burkholderia pathogenicity relies on protein virulence factors to control and promote bacterial internalization, survival, and replication within eukaryotic host cells. We recently used yeast two-hybrid (Y2H) screening to identify a small set of novel Burkholderia proteins that were shown to attenuate disease progression in an aerosol infection animal model using the virulent Burkholderia mallei ATCC 23344 strain. Here, we performed an extended analysis of primarily nine B. mallei virulence factors and their interactions with human proteins to map out how the bacteria can influence and alter host processes and pathways. Specifically, we employed topological analyses to assess the connectivity patterns of targeted host proteins, identify modules of pathogen-interacting host proteins linked to processes promoting infectivity, and evaluate the effect of crosstalk among the identified host protein modules. Overall, our analysis showed that the targeted host proteins generally had a large number of interacting partners and interacted with other host proteins that were also targeted by B. mallei proteins. We also introduced a novel Host-Pathogen Interaction Alignment (HPIA) algorithm and used it to explore similarities between host-pathogen interactions of B. mallei, Yersinia pestis, and Salmonella enterica. We inferred putative roles of B. mallei proteins based on the roles of their aligned Y. pestis and S. enterica partners and showed that up to 73% of the predicted roles matched existing annotations. A key insight into Burkholderia pathogenicity derived from these analyses of Y2H host-pathogen interactions is the identification of eukaryotic-specific targeted cellular mechanisms, including the ubiquitination degradation system and the use of the focal adhesion pathway as a fulcrum for transmitting mechanical forces and regulatory signals. This provides the mechanisms to modulate and adapt the host-cell environment for the successful establishment of host infections and intracellular spread.

Burkholderia species need to manipulate many host processes and pathways in order to establish a successful intracellular infection in eukaryotic host organisms. Burkholderia mallei uses secreted virulence factor proteins as a means to execute host-pathogen interactions and promote pathogenesis. While validated virulence factor proteins have been shown to attenuate infection in animal models, their actual roles in modifying and influencing host processes are not well understood. Here, we used host-pathogen protein-protein interactions derived from yeast two-hybrid screens to study nine known B. mallei virulence factors and map out potential virulence mechanisms. From the data, we derived both general and specific insights into Burkholderia host-pathogen infectivity pathways. We showed that B. mallei virulence factors tended to target multifunctional host proteins, proteins that interacted with each other, and host proteins with a large number of interacting partners. We also identified similarities between host-pathogen interactions of B. mallei, Yersinia pestis, and Salmonella enterica using a novel host-pathogen interactions alignment algorithm. Importantly, our data are compatible with a framework in which multiple B. mallei virulence factors broadly influence key host processes related to ubiquitin-mediated proteolysis and focal adhesion. This provides B. mallei the means to modulate and adapt the host-cell environment to advance infection.

A systems biology strategy to identify molecular mechanisms of action and protein indicators of traumatic brain injury

The multifactorial nature of traumatic brain injury (TBI), especially the complex secondary tissue injury involving intertwined networks of molecular pathways that mediate cellular behavior, has confounded attempts to elucidate the pathology underlying the progression of TBI. Here, systems biology strategies are exploited to identify novel molecular mechanisms and protein indicators of brain injury. To this end, we performed a meta-analysis of four distinct high-throughput gene expression studies involving different animal models of TBI. By using canonical pathways and a large human protein-interaction network as a scaffold, we separately overlaid the gene expression data from each study to identify molecular signatures that were conserved across the different studies. At 24 hr after injury, the significantly activated molecular signatures were nonspecific to TBI, whereas the significantly suppressed molecular signatures were specific to the nervous system. In particular, we identified a suppressed subnetwork consisting of 58 highly interacting, coregulated proteins associated with synaptic function. We selected three proteins from this subnetwork, postsynaptic density protein 95, nitric oxide synthase 1, and disrupted in schizophrenia 1, and hypothesized that their abundance would be significantly reduced after TBI. In a penetrating ballistic-like brain injury rat model of severe TBI, Western blot analysis confirmed our hypothesis. In addition, our analysis recovered 12 previously identified protein biomarkers of TBI. The results suggest that systems biology may provide an efficient, high-yield approach to generate testable hypotheses that can be experimentally validated to identify novel mechanisms of action and molecular indicators of TBI.

Molecular interaction networks for the analysis of human disease: utility, limitations, and considerations

High-throughput '-omics' data can be combined with large-scale molecular interaction networks, for example, protein-protein interaction networks, to provide a unique framework for the investigation of human molecular biology. Interest in these integrative '-omics' methods is growing rapidly because of their potential to understand complexity and association with disease; such approaches have a focus on associations between phenotype and "network-type." The potential of this research is enticing, yet there remain a series of important considerations. Here, we discuss interaction data selection, data quality, the relative merits of using data from large high-throughput studies versus a meta-database of smaller literature-curated studies, and possible issues of sociological or inspection bias in interaction data. Other work underway, especially international consortia to establish data formats, quality standards and address data redundancy, and the improvements these efforts are making to the field, is also evaluated. We present options for researchers intending to use large-scale molecular interaction networks as a functional context for protein or gene expression data, including microRNAs, especially in the context of human disease.

Global identification of O-GlcNAc transferase (OGT) interactors by a human proteome microarray and the construction of an OGT interactome

O-Linked β-N-acetylglucosamine (O-GlcNAcylation) is an important protein PTM, which is very abundant in mammalian cells. O-GlcNAcylation is catalyzed by O-GlcNAc transferase (OGT), whose substrate specificity is believed to be regulated through interactions with other proteins. There are a handful of known human OGT interactors, which is far from enough for fully elucidating the substrate specificity of OGT. To address this challenge, we used a human proteome microarray containing ~17,000 affinity-purified human proteins to globally identify OGT interactors and identified 25 OGT-binding proteins. Bioinformatics analysis showed that these interacting proteins play a variety of roles in a wide range of cellular functions and are highly enriched in intra-Golgi vesicle-mediated transport and vitamin biosynthetic processes. Combining newly identified OGT interactors with the interactors identified prior to this study, we have constructed the first OGT interactome. Bioinformatics analysis suggests that the OGT interactome plays important roles in protein transportation/localization and transcriptional regulation. The novel OGT interactors that we identified in this study could serve as a starting point for further functional analysis. Because of its high-throughput and parallel analysis capability, we strongly believe that protein microarrays could be easily applied for the global identification of regulators for other key enzymes.

Systems biology approaches for discovering biomarkers for traumatic brain injury

The rate of traumatic brain injury (TBI) in service members with wartime injuries has risen rapidly in recent years, and complex, variable links have emerged between TBI and long-term neurological disorders. The multifactorial nature of TBI secondary cellular response has confounded attempts to find cellular biomarkers for its diagnosis and prognosis or for guiding therapy for brain injury. One possibility is to apply emerging systems biology strategies to holistically probe and analyze the complex interweaving molecular pathways and networks that mediate the secondary cellular response through computational models that integrate these diverse data sets. Here, we review available systems biology strategies, databases, and tools. In addition, we describe opportunities for applying this methodology to existing TBI data sets to identify new biomarker candidates and gain insights about the underlying molecular mechanisms of TBI response. As an exemplar, we apply network and pathway analysis to a manually compiled list of 32 protein biomarker candidates from the literature, recover known TBI-related mechanisms, and generate hypothetical new biomarker candidates.

Novel Burkholderia mallei virulence factors linked to specific host-pathogen protein interactions

Burkholderia mallei is an infectious intracellular pathogen whose virulence and resistance to antibiotics makes it a potential bioterrorism agent. Given its genetic origin as a commensal soil organism, it is equipped with an extensive and varied set of adapted mechanisms to cope with and modulate host-cell environments. One essential virulence mechanism constitutes the specialized secretion systems that are designed to penetrate host-cell membranes and insert pathogen proteins directly into the host cell's cytosol. However, the secretion systems' proteins and, in particular, their host targets are largely uncharacterized. Here, we used a combined in silico, in vitro, and in vivo approach to identify B. mallei proteins required for pathogenicity. We used bioinformatics tools, including orthology detection and ab initio predictions of secretion system proteins, as well as published experimental Burkholderia data to initially select a small number of proteins as putative virulence factors. We then used yeast two-hybrid assays against normalized whole human and whole murine proteome libraries to detect and identify interactions among each of these bacterial proteins and host proteins. Analysis of such interactions provided both verification of known virulence factors and identification of three new putative virulence proteins. We successfully created insertion mutants for each of these three proteins using the virulent B. mallei ATCC 23344 strain. We exposed BALB/c mice to mutant strains and the wild-type strain in an aerosol challenge model using lethal B. mallei doses. In each set of experiments, mice exposed to mutant strains survived for the 21-day duration of the experiment, whereas mice exposed to the wild-type strain rapidly died. Given their in vivo role in pathogenicity, and based on the yeast two-hybrid interaction data, these results point to the importance of these pathogen proteins in modulating host ubiquitination pathways, phagosomal escape, and actin-cytoskeleton rearrangement processes.

Bcl2-associated athanogene 3 interactome analysis reveals a new role in modulating proteasome activity

Bcl2-associated athanogene 3 (BAG3), a member of the BAG family of co-chaperones, plays a critical role in regulating apoptosis, development, cell motility, autophagy, and tumor metastasis and in mediating cell adaptive responses to stressful stimuli. BAG3 carries a BAG domain, a WW domain, and a proline-rich repeat (PXXP), all of which mediate binding to different partners. To elucidate BAG3's interaction network at the molecular level, we employed quantitative immunoprecipitation combined with knockdown and human proteome microarrays to comprehensively profile the BAG3 interactome in humans. We identified a total of 382 BAG3-interacting proteins with diverse functions, including transferase activity, nucleic acid binding, transcription factors, proteases, and chaperones, suggesting that BAG3 is a critical regulator of diverse cellular functions. In addition, we characterized interactions between BAG3 and some of its newly identified partners in greater detail. In particular, bioinformatic analysis revealed that the BAG3 interactome is strongly enriched in proteins functioning within the proteasome-ubiquitination process and that compose the proteasome complex itself, suggesting that a critical biological function of BAG3 is associated with the proteasome. Functional studies demonstrated that BAG3 indeed interacts with the proteasome and modulates its activity, sustaining cell survival and underlying resistance to therapy through the down-modulation of apoptosis. Taken as a whole, this study expands our knowledge of the BAG3 interactome, provides a valuable resource for understanding how BAG3 affects different cellular functions, and demonstrates that biologically relevant data can be harvested using this kind of integrated approach.

Reconstituting protein interaction networks using parameter-dependent domain-domain interactions

BACKGROUND

We can describe protein-protein interactions (PPIs) as sets of distinct domain-domain interactions (DDIs) that mediate the physical interactions between proteins. Experimental data confirm that DDIs are more consistent than their corresponding PPIs, lending support to the notion that analyses of DDIs may improve our understanding of PPIs and lead to further insights into cellular function, disease, and evolution. However, currently available experimental DDI data cover only a small fraction of all existing PPIs and, in the absence of structural data, determining which particular DDI mediates any given PPI is a challenge.

RESULTS

We present two contributions to the field of domain interaction analysis. First, we introduce a novel computational strategy to merge domain annotation data from multiple databases. We show that when we merged yeast domain annotations from six annotation databases we increased the average number of domains per protein from 1.05 to 2.44, bringing it closer to the estimated average value of 3. Second, we introduce a novel computational method, parameter-dependent DDI selection (PADDS), which, given a set of PPIs, extracts a small set of domain pairs that can reconstruct the original set of protein interactions, while attempting to minimize false positives. Based on a set of PPIs from multiple organisms, our method extracted 27% more experimentally detected DDIs than existing computational approaches.

CONCLUSIONS

We have provided a method to merge domain annotation data from multiple sources, ensuring large and consistent domain annotation for any given organism. Moreover, we provided a method to extract a small set of DDIs from the underlying set of PPIs and we showed that, in contrast to existing approaches, our method was not biased towards DDIs with low or high occurrence counts. Finally, we used these two methods to highlight the influence of the underlying annotation density on the characteristics of extracted DDIs. Although increased annotations greatly expanded the possible DDIs, the lack of knowledge of the true biological false positive interactions still prevents an unambiguous assignment of domain interactions responsible for all protein network interactions.Executable files and examples are given at: http://www.bhsai.org/downloads/padds/

Plant protein interactomes

Protein-protein interactions are a critical element of biological systems, and the analysis of interaction partners can provide valuable hints about unknown functions of a protein. In recent years, several large-scale protein interaction studies have begun to unravel the complex networks through which plant proteins exert their functions. Two major classes of experimental approaches are used for protein interaction mapping: analysis of direct interactions using binary methods such as yeast two-hybrid or split ubiquitin, and analysis of protein complexes through affinity purification followed by mass spectrometry. In addition, bioinformatics predictions can suggest interactions that have evaded detection by other methods or those of proteins that have not been investigated. Here we review the major approaches to construct, analyze, use, and carry out quality control on plant protein interactome networks. We present experimental and computational approaches for large-scale mapping, methods for validation or smaller-scale functional studies, important bioinformatics resources, and findings from recently published large-scale plant interactome network maps.

Interactome mapping for analysis of complex phenotypes: insights from benchmarking binary interaction assays

Protein interactions mediate essentially all biological processes and analysis of protein-protein interactions using both large-scale and small-scale approaches has contributed fundamental insights to the understanding of biological systems. In recent years, interactome network maps have emerged as an important tool for analyzing and interpreting genetic data of complex phenotypes. Complementary experimental approaches to test for binary, direct interactions, and for membership in protein complexes are used to explore the interactome. The two approaches are not redundant but yield orthogonal perspectives onto the complex network of physical interactions by which proteins mediate biological processes. In recent years, several publications have demonstrated that interactions from high-throughput experiments can be equally reliable as the high quality subset of interactions identified in small-scale studies. Critical for this insight was the introduction of standardized experimental benchmarking of interaction and validation assays using reference sets. The data obtained in these benchmarking experiments have resulted in greater appreciation of the limitations and the complementary strengths of different assays. Moreover, benchmarking is a central element of a conceptual framework to estimate interactome sizes and thereby measure progress toward near complete network maps. These estimates have revealed that current large-scale data sets, although often of high quality, cover only a small fraction of a given interactome. Here, I review the findings of assay benchmarking and discuss implications for quality control, and for strategies toward obtaining a near-complete map of the interactome of an organism.

Diversity in genetic in vivo methods for protein-protein interaction studies: from the yeast two-hybrid system to the mammalian split-luciferase system

The yeast two-hybrid system pioneered the field of in vivo protein-protein interaction methods and undisputedly gave rise to a palette of ingenious techniques that are constantly pushing further the limits of the original method. Sensitivity and selectivity have improved because of various technical tricks and experimental designs. Here we present an exhaustive overview of the genetic approaches available to study in vivo binary protein interactions, based on two-hybrid and protein fragment complementation assays. These methods have been engineered and employed successfully in microorganisms such as Saccharomyces cerevisiae and Escherichia coli, but also in higher eukaryotes. From single binary pairwise interactions to whole-genome interactome mapping, the self-reassembly concept has been employed widely. Innovative studies report the use of proteins such as ubiquitin, dihydrofolate reductase, and adenylate cyclase as reconstituted reporters. Protein fragment complementation assays have extended the possibilities in protein-protein interaction studies, with technologies that enable spatial and temporal analyses of protein complexes. In addition, one-hybrid and three-hybrid systems have broadened the types of interactions that can be studied and the findings that can be obtained. Applications of these technologies are discussed, together with the advantages and limitations of the available assays.

Protein stickiness, rather than number of functional protein-protein interactions, predicts expression noise and plasticity in yeast

Background

A hub protein is one that interacts with many functional partners. The annotation of hub proteins, or more generally the protein-protein interaction “degree” of each gene, requires quality genome-wide data. Data obtained using yeast two-hybrid methods contain many false positive interactions between proteins that rarely encounter each other in living cells, and such data have fallen out of favor.

Results

We find that protein “stickiness”, measured as network degree in ostensibly low quality yeast two-hybrid data, is a more predictive genomic metric than the number of functional protein-protein interactions, as assessed by supposedly higher quality high throughput affinity capture mass spectrometry data. In the yeast Saccharomyces cerevisiae, a protein’s high stickiness, but not its high number of functional interactions, predicts low stochastic noise in gene expression, low plasticity of gene expression across different environments, and high probability of forming a homo-oligomer. Our results are robust to a multiple regression analysis correcting for other known predictors including protein abundance, presence of a TATA box and whether a gene is essential. Once the higher stickiness of homo-oligomers is controlled for, we find that homo-oligomers have noisier and more plastic gene expression than other proteins, consistent with a role for homo-oligomerization in mediating robustness.

Conclusions

Our work validates use of the number of yeast two-hybrid interactions as a metric for protein stickiness. Sticky proteins exhibit low stochastic noise in gene expression, and low plasticity in expression across different environments.

Inferring high-confidence human protein-protein interactions

Background

As numerous experimental factors drive the acquisition, identification, and interpretation of protein-protein interactions (PPIs), aggregated assemblies of human PPI data invariably contain experiment-dependent noise. Ascertaining the reliability of PPIs collected from these diverse studies and scoring them to infer high-confidence networks is a non-trivial task. Moreover, a large number of PPIs share the same number of reported occurrences, making it impossible to distinguish the reliability of these PPIs and rank-order them. For example, for the data analyzed here, we found that the majority (>83%) of currently available human PPIs have been reported only once.

Results

In this work, we proposed an unsupervised statistical approach to score a set of diverse, experimentally identified PPIs from nine primary databases to create subsets of high-confidence human PPI networks. We evaluated this ranking method by comparing it with other methods and assessing their ability to retrieve protein associations from a number of diverse and independent reference sets. These reference sets contain known biological data that are either directly or indirectly linked to interactions between proteins. We quantified the average effect of using ranked protein interaction data to retrieve this information and showed that, when compared to randomly ranked interaction data sets, the proposed method created a larger enrichment (~134%) than either ranking based on the hypergeometric test (~109%) or occurrence ranking (~46%).

Conclusions

From our evaluations, it was clear that ranked interactions were always of value because higher-ranked PPIs had a higher likelihood of retrieving high-confidence experimental data. Reducing the noise inherent in aggregated experimental PPIs via our ranking scheme further increased the accuracy and enrichment of PPIs derived from a number of biologically relevant data sets. These results suggest that using our high-confidence protein interactions at different levels of confidence will help clarify the topological and biological properties associated with human protein networks.

"Guilt by association" is the exception rather than the rule in gene networks

Gene networks are commonly interpreted as encoding functional information in their connections. An extensively validated principle called guilt by association states that genes which are associated or interacting are more likely to share function. Guilt by association provides the central top-down principle for analyzing gene networks in functional terms or assessing their quality in encoding functional information. In this work, we show that functional information within gene networks is typically concentrated in only a very few interactions whose properties cannot be reliably related to the rest of the network. In effect, the apparent encoding of function within networks has been largely driven by outliers whose behaviour cannot even be generalized to individual genes, let alone to the network at large. While experimentalist-driven analysis of interactions may use prior expert knowledge to focus on the small fraction of critically important data, large-scale computational analyses have typically assumed that high-performance cross-validation in a network is due to a generalizable encoding of function. Because we find that gene function is not systemically encoded in networks, but dependent on specific and critical interactions, we conclude it is necessary to focus on the details of how networks encode function and what information computational analyses use to extract functional meaning. We explore a number of consequences of this and find that network structure itself provides clues as to which connections are critical and that systemic properties, such as scale-free-like behaviour, do not map onto the functional connectivity within networks.

The analysis of gene function and gene networks is a major theme of post-genome biomedical research. Historically, many attempts to understand gene function leverage a biological principle known as “guilt by association” (GBA). GBA states that genes with related functions tend to share properties such as genetic or physical interactions. In the past ten years, GBA has been scaled up for application to large gene networks, becoming a favored way to grapple with the complex interdependencies of gene functions in the face of floods of genomics and proteomics data. However, there is a growing realization that scaled-up GBA is not a panacea. In this study, we report a precise identification of the limits of GBA and show that it cannot provide a way to understand gene networks in a way that is simultaneously general and useful. Our findings indicate that the assumptions underlying the high-throughput use of gene networks to interpret function are fundamentally flawed, with wide-ranging implications for the interpretation of genome-wide data.