Revisiting Date and Party Hubs: Novel Approaches to Role Assignment in Protein Interaction Networks

The idea of 'date' and 'party' hubs has been influential in the study of protein-protein interaction networks. Date hubs display low co-expression with their partners, whilst party hubs have high co-expression. It was proposed that party hubs are local coordinators whereas date hubs are global connectors. Here we show that the reported importance of date hubs to network connectivity can in fact be attributed to a tiny subset of them. Crucially, these few, extremely central, hubs do not display particularly low expression correlation, undermining the idea of a link between this quantity and hub function. The date/party distinction was originally motivated by an approximately bimodal distribution of hub co-expression; we show that this feature is not always robust to methodological changes. Additionally, topological properties of hubs do not in general correlate with co-expression. Thus, we suggest that a date/party dichotomy is not meaningful and it might be more useful to conceive of roles for protein-protein interactions rather than individual proteins. We find significant correlations between interaction centrality and the functional similarity of the interacting proteins.Comment: 27 pages, 5 main figures, 4 supplementary figure

Predicting and Validating Protein Interactions Using Network Structure

Protein interactions play a vital part in the function of a cell. As experimental techniques for detection and validation of protein interactions are time consuming, there is a need for computational methods for this task. Protein interactions appear to form a network with a relatively high degree of local clustering. In this paper we exploit this clustering by suggesting a score based on triplets of observed protein interactions. The score utilises both protein characteristics and network properties. Our score based on triplets is shown to complement existing techniques for predicting protein interactions, outperforming them on data sets which display a high degree of clustering. The predicted interactions score highly against test measures for accuracy. Compared to a similar score derived from pairwise interactions only, the triplet score displays higher sensitivity and specificity. By looking at specific examples, we show how an experimental set of interactions can be enriched and validated. As part of this work we also examine the effect of different prior databases upon the accuracy of prediction and find that the interactions from the same kingdom give better results than from across kingdoms, suggesting that there may be fundamental differences between the networks. These results all emphasize that network structure is important and helps in the accurate prediction of protein interactions. The protein interaction data set and the program used in our analysis, and a list of predictions and validations, are available at http://www.stats.ox.ac.uk/bioinfo/resources/PredictingInteractions

PETALS: Proteomic Evaluation and Topological Analysis of a mutated Locus' Signaling

<p>Abstract</p> <p>Background</p> <p>Colon cancer is driven by mutations in a number of genes, the most notorious of which is <it>Apc</it>. Though much of <it>Apc</it>'s signaling has been mechanistically identified over the years, it is not always clear which functions or interactions are operative in a particular tumor. This is confounded by the presence of mutations in a number of other putative cancer driver (CAN) genes, which often synergize with mutations in <it>Apc</it>.</p> <p>Computational methods are, thus, required to predict which pathways are likely to be operative when a particular mutation in <it>Apc </it>is observed.</p> <p>Results</p> <p>We developed a pipeline, PETALS, to predict and test likely signaling pathways connecting <it>Apc </it>to other CAN-genes, where the interaction network originating at <it>Apc </it>is defined as a "blossom," with each <it>Apc</it>-CAN-gene subnetwork referred to as a "petal." Known and predicted protein interactions are used to identify an Apc blossom with 24 petals. Then, using a novel measure of bimodality, the coexpression of each petal is evaluated against proteomic (2 D differential In Gel Electrophoresis, 2D-DIGE) measurements from the <it>Apc</it>^{<it>1638N</it>+/-}mouse to test the network-based hypotheses.</p> <p>Conclusions</p> <p>The predicted pathways linking <it>Apc </it>and <it>Hapln1 </it>exhibited the highest amount of bimodal coexpression with the proteomic targets, prioritizing the <it>Apc-Hapln1 </it>petal over other CAN-gene pairs and suggesting that this petal may be involved in regulating the observed proteome-level effects. These results not only demonstrate how functional 'omics data can be employed to test in <it>silico </it>predictions of CAN-gene pathways, but also reveal an approach to integrate models of upstream genetic interference with measured, downstream effects.</p

Semantic integration to identify overlapping functional modules in protein interaction networks

<p>Abstract</p> <p>Background</p> <p>The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms.</p> <p>Results</p> <p>We have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches.</p> <p>Conclusion</p> <p>The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification.</p

Patient-directed self-management of pain (PaDSMaP) compared to treatment as usual following total knee replacement; a randomised controlled trial

Background Self-administration of medicines by patients whilst in hospital is being increasingly promoted despite little evidence to show the risks and benefits. Pain control after total knee replacement (TKR) is known to be poor. The aim of the study was to determine if patients operated on with a TKR who self-medicate their oral analgesics in the immediate post-operative period have better pain control than those who receive their pain control by nurse-led drug rounds (Treatment as Usual (TAU)). Methods A prospective, parallel design, open-label, randomised controlled trial comparing pain control in patient-directed self-management of pain (PaDSMaP) with nurse control of oral analgesia (TAU) after a TKR. Between July 2011 and March 2013, 144 self-medicating adults were recruited at a secondary care teaching hospital in the UK. TAU patients (n = 71) were given medications by a nurse after their TKR. PaDSMaP patients (n = 73) took oral medications for analgesia and co-morbidities after two 20 min training sessions reinforced with four booklets. Primary outcome was pain (100 mm visual analogue scale (VAS)) at 3 days following TKR surgery or at discharge (whichever came soonest). Seven patients did not undergo surgery for reasons unrelated to the study and were excluded from the intention-to-treat (ITT) analysis. Results ITT analysis did not detect any significant differences between the two groups’ pain scores. A per protocol (but underpowered) analysis of the 60% of patients able to self-medicate found reduced pain compared to the TAU group at day 3/discharge, (VAS -9.9 mm, 95% CI -18.7, − 1.1). One patient in the self-medicating group over-medicated but suffered no harm. Conclusion Self-medicating patients did not have better (lower) pain scores compared to the nurse-managed patients following TKR. This cohort of patients were elderly with multiple co-morbidities and may not be the ideal target group for self-medication

PathFinder: mining signal transduction pathway segments from protein-protein interaction networks

<p>Abstract</p> <p>Background</p> <p>A Signal transduction pathway is the chain of processes by which a cell converts an extracellular signal into a response. In most unicellular organisms, the number of signal transduction pathways influences the number of ways the cell can react and respond to the environment. Discovering signal transduction pathways is an arduous problem, even with the use of systematic genomic, proteomic and metabolomic technologies. These techniques lead to an enormous amount of data and how to interpret and process this data becomes a challenging computational problem.</p> <p>Results</p> <p>In this study we present a new framework for identifying signaling pathways in protein-protein interaction networks. Our goal is to find biologically significant pathway segments in a given interaction network. Currently, protein-protein interaction data has excessive amount of noise, e.g., false positive and false negative interactions. First, we eliminate false positives in the protein-protein interaction network by integrating the network with microarray expression profiles, protein subcellular localization and sequence information. In addition, protein families are used to repair false negative interactions. Then the characteristics of known signal transduction pathways and their functional annotations are extracted in the form of association rules.</p> <p>Conclusion</p> <p>Given a pair of starting and ending proteins, our methodology returns candidate pathway segments between these two proteins with possible missing links (recovered false negatives). In our study, <it>S. cerevisiae </it>(yeast) data is used to demonstrate the effectiveness of our method.</p

Dynamics of extracellular matrix in ovarian follicles and corpora lutea of mice

Despite the mouse being an important laboratory species, little is known about changes in its extracellular matrix (ECM) during follicle and corpora lutea formation and regression. Follicle development was induced in mice (29 days of age/experimental day 0) by injections of pregnant mare’s serum gonadotrophin on days 0 and 1 and ovulation was induced by injection of human chorionic gonadotrophin on day 2. Ovaries were collected for immunohistochemistry (n=10 per group) on days 0, 2 and 5. Another group was mated and ovaries were examined on day 11 (n=7). Collagen type IV α1 and α2, laminin α1, β1 and γ1 chains, nidogens 1 and 2 and perlecan were present in the follicular basal lamina of all developmental stages. Collagen type XVIII was only found in basal lamina of primordial, primary and some preantral follicles, whereas laminin α2 was only detected in some preantral and antral follicles. The focimatrix, a specialised matrix of the membrana granulosa, contained collagen type IV α1 and α2, laminin α1, β1 and γ1 chains, nidogens 1 and 2, perlecan and collagen type XVIII. In the corpora lutea, staining was restricted to capillary sub-endothelial basal laminas containing collagen type IV α1 and α2, laminin α1, β1 and γ1 chains, nidogens 1 and 2, perlecan and collagen type XVIII. Laminins α4 and α5 were not immunolocalised to any structure in the mouse ovary. The ECM composition of the mouse ovary has similarities to, but also major differences from, other species with respect to nidogens 1 and 2 and perlecan

Near-Native Protein Loop Sampling Using Nonparametric Density Estimation Accommodating Sparcity

Unlike the core structural elements of a protein like regular secondary structure, template based modeling (TBM) has difficulty with loop regions due to their variability in sequence and structure as well as the sparse sampling from a limited number of homologous templates. We present a novel, knowledge-based method for loop sampling that leverages homologous torsion angle information to estimate a continuous joint backbone dihedral angle density at each loop position. The φ,ψ distributions are estimated via a Dirichlet process mixture of hidden Markov models (DPM-HMM). Models are quickly generated based on samples from these distributions and were enriched using an end-to-end distance filter. The performance of the DPM-HMM method was evaluated against a diverse test set in a leave-one-out approach. Candidates as low as 0.45 Å RMSD and with a worst case of 3.66 Å were produced. For the canonical loops like the immunoglobulin complementarity-determining regions (mean RMSD <2.0 Å), the DPM-HMM method performs as well or better than the best templates, demonstrating that our automated method recaptures these canonical loops without inclusion of any IgG specific terms or manual intervention. In cases with poor or few good templates (mean RMSD >7.0 Å), this sampling method produces a population of loop structures to around 3.66 Å for loops up to 17 residues. In a direct test of sampling to the Loopy algorithm, our method demonstrates the ability to sample nearer native structures for both the canonical CDRH1 and non-canonical CDRH3 loops. Lastly, in the realistic test conditions of the CASP9 experiment, successful application of DPM-HMM for 90 loops from 45 TBM targets shows the general applicability of our sampling method in loop modeling problem. These results demonstrate that our DPM-HMM produces an advantage by consistently sampling near native loop structure. The software used in this analysis is available for download at http://www.stat.tamu.edu/~dahl/software/cortorgles/

Innate Immune Response of Human Plasmacytoid Dendritic Cells to Poxvirus Infection Is Subverted by Vaccinia E3 via Its Z-DNA/RNA Binding Domain

Plasmacytoid dendritic cells (pDCs) play important roles in antiviral innate immunity by producing type I interferon (IFN). In this study, we assess the immune responses of primary human pDCs to two poxviruses, vaccinia and myxoma virus. Vaccinia, an orthopoxvirus, was used for immunization against smallpox, a contagious human disease with high mortality. Myxoma virus, a Leporipoxvirus, causes lethal disease in rabbits, but is non-pathogenic in humans. We report that myxoma virus infection of human pDCs induces IFN-α and TNF production, whereas vaccinia infection does not. Co-infection of pDCs with myxoma virus plus vaccinia blocks myxoma induction effects. We find that heat-inactivated vaccinia (Heat-VAC; by incubating the virus at 55°C for 1 h) gains the ability to induce IFN-α and TNF in primary human pDCs. Induction of IFN-α in pDCs by myxoma virus or Heat-VAC is blocked by chloroquine, which inhibits endosomal acidification required for TLR7/9 signaling, and by inhibitors of cellular kinases PI3K and Akt. Using purified pDCs from genetic knockout mice, we demonstrate that Heat-VAC-induced type I IFN production in pDCs requires the endosomal RNA sensor TLR7 and its adaptor MyD88, transcription factor IRF7 and the type I IFN feedback loop mediated by IFNAR1. These results indicate that (i) vaccinia virus, but not myxoma virus, expresses inhibitor(s) of the poxvirus sensing pathway(s) in pDCs; and (ii) Heat-VAC infection fails to produce inhibitor(s) but rather produces novel activator(s), likely viral RNA transcripts that are sensed by the TLR7/MyD88 pathway. Using vaccinia gene deletion mutants, we show that the Z-DNA/RNA binding domain at the N-terminus of the vaccinia immunomodulatory E3 protein is an antagonist of the innate immune response of human pDCs to poxvirus infection and TLR agonists. The myxoma virus ortholog of vaccinia E3 (M029) lacks the N-terminal Z-DNA/RNA binding domain, which might contribute to the immunostimulating properties of myxoma virus

Network Compression as a Quality Measure for Protein Interaction Networks

With the advent of large-scale protein interaction studies, there is much debate about data quality. Can different noise levels in the measurements be assessed by analyzing network structure? Because proteomic regulation is inherently co-operative, modular and redundant, it is inherently compressible when represented as a network. Here we propose that network compression can be used to compare false positive and false negative noise levels in protein interaction networks. We validate this hypothesis by first confirming the detrimental effect of false positives and false negatives. Second, we show that gold standard networks are more compressible. Third, we show that compressibility correlates with co-expression, co-localization, and shared function. Fourth, we also observe correlation with better protein tagging methods, physiological expression in contrast to over-expression of tagged proteins, and smart pooling approaches for yeast two-hybrid screens. Overall, this new measure is a proxy for both sensitivity and specificity and gives complementary information to standard measures such as average degree and clustering coefficients