New insights into protein-protein interaction data lead to increased estimates of the S. cerevisiae interactome size

<p>Abstract</p> <p>Background</p> <p>As protein interactions mediate most cellular mechanisms, protein-protein interaction networks are essential in the study of cellular processes. Consequently, several large-scale interactome mapping projects have been undertaken, and protein-protein interactions are being distilled into databases through literature curation; yet protein-protein interaction data are still far from comprehensive, even in the model organism <it>Saccharomyces cerevisiae</it>. Estimating the interactome size is important for evaluating the completeness of current datasets, in order to measure the remaining efforts that are required.</p> <p>Results</p> <p>We examined the yeast interactome from a new perspective, by taking into account how thoroughly proteins have been studied. We discovered that the set of literature-curated protein-protein interactions is qualitatively different when restricted to proteins that have received extensive attention from the scientific community. In particular, these interactions are less often supported by yeast two-hybrid, and more often by more complex experiments such as biochemical activity assays. Our analysis showed that high-throughput and literature-curated interactome datasets are more correlated than commonly assumed, but that this bias can be corrected for by focusing on well-studied proteins. We thus propose a simple and reliable method to estimate the size of an interactome, combining literature-curated data involving well-studied proteins with high-throughput data. It yields an estimate of at least 37, 600 direct physical protein-protein interactions in <it>S. cerevisiae</it>.</p> <p>Conclusions</p> <p>Our method leads to higher and more accurate estimates of the interactome size, as it accounts for interactions that are genuine yet difficult to detect with commonly-used experimental assays. This shows that we are even further from completing the yeast interactome map than previously expected.</p

Protein–Protein Interactions Essentials: Key Concepts to Building and Analyzing Interactome Networks

8 páginas, 3 figuras, 1 tabla.-- This is an open-access article distributed under the terms of the Creative Commons Attribution License.This work has been supported by funds provided by the Local Government Junta de Castilla y León (JCyL, ref. project: CSI07A09), by the Spanish Ministry of Science and Innovation (MICINN - ISCiii, ref. projects: PI061153 and PS09/00843) and by the European Commission Research Grant PSIMEx (ref. FP7-HEALTH-2007-223411).Peer Reviewe

Reuse of structural domain–domain interactions in protein networks

<p>Abstract</p> <p>Background</p> <p>Protein interactions are thought to be largely mediated by interactions between structural domains. Databases such as <it>i</it>Pfam relate interactions in protein structures to known domain families. Here, we investigate how the domain interactions from the <it>i</it>Pfam database are distributed in protein interactions taken from the HPRD, MPact, BioGRID, DIP and IntAct databases.</p> <p>Results</p> <p>We find that known structural domain interactions can only explain a subset of 4–19% of the available protein interactions, nevertheless this fraction is still significantly bigger than expected by chance. There is a correlation between the frequency of a domain interaction and the connectivity of the proteins it occurs in. Furthermore, a large proportion of protein interactions can be attributed to a small number of domain interactions. We conclude that many, but not all, domain interactions constitute reusable modules of molecular recognition. A substantial proportion of domain interactions are conserved between <it>E. coli</it>, <it>S. cerevisiae </it>and <it>H. sapiens</it>. These domains are related to essential cellular functions, suggesting that many domain interactions were already present in the last universal common ancestor.</p> <p>Conclusion</p> <p>Our results support the concept of domain interactions as reusable, conserved building blocks of protein interactions, but also highlight the limitations currently imposed by the small number of available protein structures.</p

Markov clustering versus affinity propagation for the partitioning of protein interaction graphs

<p>Abstract</p> <p>Background</p> <p>Genome scale data on protein interactions are generally represented as large networks, or graphs, where hundreds or thousands of proteins are linked to one another. Since proteins tend to function in groups, or complexes, an important goal has been to reliably identify protein complexes from these graphs. This task is commonly executed using clustering procedures, which aim at detecting densely connected regions within the interaction graphs. There exists a wealth of clustering algorithms, some of which have been applied to this problem. One of the most successful clustering procedures in this context has been the Markov Cluster algorithm (MCL), which was recently shown to outperform a number of other procedures, some of which were specifically designed for partitioning protein interactions graphs. A novel promising clustering procedure termed Affinity Propagation (AP) was recently shown to be particularly effective, and much faster than other methods for a variety of problems, but has not yet been applied to partition protein interaction graphs.</p> <p>Results</p> <p>In this work we compare the performance of the Affinity Propagation (AP) and Markov Clustering (MCL) procedures. To this end we derive an unweighted network of protein-protein interactions from a set of 408 protein complexes from <it>S. cervisiae </it>hand curated in-house, and evaluate the performance of the two clustering algorithms in recalling the annotated complexes. In doing so the parameter space of each algorithm is sampled in order to select optimal values for these parameters, and the robustness of the algorithms is assessed by quantifying the level of complex recall as interactions are randomly added or removed to the network to simulate noise. To evaluate the performance on a weighted protein interaction graph, we also apply the two algorithms to the consolidated protein interaction network of <it>S. cerevisiae</it>, derived from genome scale purification experiments and to versions of this network in which varying proportions of the links have been randomly shuffled.</p> <p>Conclusion</p> <p>Our analysis shows that the MCL procedure is significantly more tolerant to noise and behaves more robustly than the AP algorithm. The advantage of MCL over AP is dramatic for unweighted protein interaction graphs, as AP displays severe convergence problems on the majority of the unweighted graph versions that we tested, whereas MCL continues to identify meaningful clusters, albeit fewer of them, as the level of noise in the graph increases. MCL thus remains the method of choice for identifying protein complexes from binary interaction networks.</p

Interactome and Gene Ontology provide congruent yet subtly different views of a eukaryotic cell

15 pages, 6 figures.-- 19604360 [PubMed]BACKGROUND: The characterization of the global functional structure of a cell is a major goal in bioinformatics and systems biology. Gene Ontology (GO) and the protein-protein interaction network offer alternative views of that structure. RESULTS: This study presents a comparison of the global structures of the Gene Ontology and the interactome of Saccharomyces cerevisiae. Sensitive, unsupervised methods of clustering applied to a large fraction of the proteome led to establish a GO-interactome correlation value of +0.47 for a general dataset that contains both high and low-confidence interactions and +0.58 for a smaller, high-confidence dataset. CONCLUSION: The structures of the yeast cell deduced from GO and interactome are substantially congruent. However, some significant differences were also detected, which may contribute to a better understanding of cell function and also to a refinement of the current ontologiesResearch supported by grant BIO2008-05067 (Programa Nacional de Biotecnología; Ministerio de Ciencia e Innovación. Spain), awarded to IM. AM was a FPI fellow from Ministerio de Educación y Ciencia (Spain).Peer reviewe

Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism

Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases

Removal of Hepatitis B virus surface HBsAg and core HBcAg antigens using microbial fuel cells producing electricity from human urine

© 2019, The Author(s). Microbial electrochemical technology is emerging as an alternative way of treating waste and converting this directly to electricity. Intensive research on these systems is ongoing but it currently lacks the evaluation of possible environmental transmission of enteric viruses originating from the waste stream. In this study, for the first time we investigated this aspect by assessing the removal efficiency of hepatitis B core and surface antigens in cascades of continuous flow microbial fuel cells. The log-reduction (LR) of surface antigen (HBsAg) reached a maximum value of 1.86 ± 0.20 (98.6% reduction), which was similar to the open circuit control and degraded regardless of the recorded current. Core antigen (HBcAg) was much more resistant to treatment and the maximal LR was equal to 0.229 ± 0.028 (41.0% reduction). The highest LR rate observed for HBsAg was 4.66 ± 0.19 h−1 and for HBcAg 0.10 ± 0.01 h−1. Regression analysis revealed correlation between hydraulic retention time, power and redox potential on inactivation efficiency, also indicating electroactive behaviour of biofilm in open circuit control through the snorkel-effect. The results indicate that microbial electrochemical technologies may be successfully applied to reduce the risk of environmental transmission of hepatitis B virus but also open up the possibility of testing other viruses for wider implementation

Estimating the Cost of Type 1 Diabetes in the U.S.: A Propensity Score Matching Method

Diabetes costs represent a large burden to both patients and the health care system. However, few studies that examine the economic consequences of diabetes have distinguished between the two major forms, type 1 and type 2 diabetes, despite differences in underlying pathologies. Combining the two diseases implies that there is no difference between the costs of type 1 and type 2 diabetes to a patient. In this study, we examine the costs of type 1 diabetes, which is often overlooked due to the larger population of type 2 patients, and compare them to the estimated costs of diabetes reported in the literature.Using a nationally representative dataset, we estimate yearly and lifetime medical and indirect costs of type 1 diabetes by implementing a matching method to compare a patient with type 1 diabetes to a similar individual without the disease. We find that each year type 1 diabetes costs this country $14.4 billion (11.5-17.3) in medical costs and lost income. In terms of lost income, type 1 patients incur a disproportionate share of type 1 and type 2 costs. Further, if the disease were eliminated by therapeutic intervention, an estimated$10.6 billion (7.2-14.0) incurred by a new cohort and $422.9 billion (327.2-519.4) incurred by the existing number of type 1 diabetic patients over their lifetime would be avoided.We find that the costs attributed to type 1 diabetes are disproportionately higher than the number of type 1 patients compared with type 2 patients, suggesting that combining the two diseases when estimating costs is not appropriate. This study and another recent contribution provides a necessary first step in estimating the substantial costs of type 1 diabetes on the U.S

Underlying Factors Associated with Anemia in Amazonian Children: A Population-Based, Cross-Sectional Study

Background: Although iron deficiency is considered to be the main cause of anemia in children worldwide, other contributors to childhood anemia remain little studied in developing countries. We estimated the relative contributions of different factors to anemia in a population-based, cross-sectional survey. Methodology: We obtained venous blood samples from 1111 children aged 6 months to 10 years living in the frontier town of Acrelandia, northwest Brazil, to estimate the prevalence of anemia and iron deficiency by measuring hemoglobin, erythrocyte indices, ferritin, soluble transferrin receptor, and C-reactive protein concentrations. Children were simultaneously screened for vitamin A, vitamin B-12, and folate deficiencies; intestinal parasite infections; glucose-6-phosphate dehydrogenase deficiency; and sickle cell trait carriage. Multiple Poisson regression and adjusted prevalence ratios (aPR) were used to describe associations between anemia and the independent variables. Principal Findings: The prevalence of anemia, iron deficiency, and iron-deficiency anemia were 13.6%, 45.4%, and 10.3%, respectively. Children whose families were in the highest income quartile, compared with the lowest, had a lower risk of anemia (aPR, 0.60; 95% CI, 0.37-0.98). Child age (&lt;24 months, 2.90; 2.01-4.20) and maternal parity (&gt;2 pregnancies, 2.01; 1.40-2.87) were positively associated with anemia. Other associated correlates were iron deficiency (2.1; 1.4-3.0), vitamin B-12 (1.4; 1.0-2.2), and folate (2.0; 1.3-3.1) deficiencies, and C-reactive protein concentrations (&gt;5 mg/L, 1.5; 1.1-2.2). Conclusions: Addressing morbidities and multiple nutritional deficiencies in children and mothers and improving the purchasing power of poorer families are potentially important interventions to reduce the burden of anemia.Sao Paulo State Research Agency [FAPESP 07/53042-1]Sao Paulo State Research AgencyNational Research Agency of BrazilNational Research Agency of Brazil [CNPq 470573/2007-4