Protein Complex Identification by Integrating Protein-Protein Interaction Evidence from Multiple Sources

Background

Understanding protein complexes is important for understanding the science of cellular organization and function. Many computational methods have been developed to identify protein complexes from experimentally obtained protein-protein interaction (PPI) networks. However, interaction information obtained experimentally can be unreliable and incomplete. Reconstructing these PPI networks with PPI evidences from other sources can improve protein complex identification.

Results

We combined PPI information from 6 different sources and obtained a reconstructed PPI network for yeast through machine learning. Some popular protein complex identification methods were then applied to detect yeast protein complexes using the new PPI networks. Our evaluation indicates that protein complex identification algorithms using the reconstructed PPI network significantly outperform ones on experimentally verified PPI networks.

Conclusions

We conclude that incorporating PPI information from other sources can improve the effectiveness of protein complex identification.     

COMBINE archive and OMEX format: one file to share all information to reproduce a modeling project

Background

With the ever increasing use of computational models in the biosciences, the need to share models and reproduce the results of published studies efficiently and easily is becoming more important. To this end, various standards have been proposed that can be used to describe models, simulations, data or other essential information in a consistent fashion. These constitute various separate components required to reproduce a given published scientific result.

Results

We describe the Open Modeling EXchange format (OMEX). Together with the use of other standard formats from the Computational Modeling in Biology Network (COMBINE), OMEX is the basis of the COMBINE Archive, a single file that supports the exchange of all the information necessary for a modeling and simulation experiment in biology. An OMEX file is a ZIP container that includes a manifest file, listing the content of the archive, an optional metadata file adding information about the archive and its content, and the files describing the model. The content of a COMBINE Archive consists of files encoded in COMBINE standards whenever possible, but may include additional files defined by an Internet Media Type. Several tools that support the COMBINE Archive are available, either as independent libraries or embedded in modeling software.

Conclusions

The COMBINE Archive facilitates the reproduction of modeling and simulation experiments in biology by embedding all the relevant information in one file. Having all the information stored and exchanged at once also helps in building activity logs and audit trails. We anticipate that the COMBINE Archive will become a significant help for modellers, as the domain moves to larger, more complex experiments such as multi-scale models of organs, digital organisms, and bioengineering.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0369-z) contains supplementary material, which is available to authorized users.     

From microbial gene essentiality to novel antimicrobial drug targets

Background

Bacterial respiratory tract infections, mainly caused by Streptococcus pneumoniae, Haemophilus influenzae and Moraxella catarrhalis are among the leading causes of global mortality and morbidity. Increased resistance of these pathogens to existing antibiotics necessitates the search for novel targets to develop potent antimicrobials.

Result

Here, we report a proof of concept study for the reliable identification of potential drug targets in these human respiratory pathogens by combining high-density transposon mutagenesis, high-throughput sequencing, and integrative genomics. Approximately 20% of all genes in these three species were essential for growth and viability, including 128 essential and conserved genes, part of 47 metabolic pathways. By comparing these essential genes to the human genome, and a database of genes from commensal human gut microbiota, we identified and excluded potential drug targets in respiratory tract pathogens that will have off-target effects in the host, or disrupt the natural host microbiota. We propose 249 potential drug targets, 67 of which are targets for 75 FDA-approved antimicrobials and 35 other researched small molecule inhibitors. Two out of four selected novel targets were experimentally validated, proofing the concept.

Conclusion

Here we have pioneered an attempt in systematically combining the power of high-density transposon mutagenesis, high-throughput sequencing, and integrative genomics to discover potential drug targets at genome-scale. By circumventing the time-consuming and expensive laboratory screens traditionally used to select potential drug targets, our approach provides an attractive alternative that could accelerate the much needed discovery of novel antimicrobials.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-958) contains supplementary material, which is available to authorized users.     

Tamoxifen Enhances the Hsp90 Molecular Chaperone ATPase Activity

Background

Hsp90 is an essential molecular chaperone that is also a novel anti-cancer drug target. There is growing interest in developing new drugs that modulate Hsp90 activity.

Methodology/Principal Findings

Using a virtual screening approach, 4-hydroxytamoxifen, the active metabolite of the anti-estrogen drug tamoxifen, was identified as a putative Hsp90 ligand. Surprisingly, while all drugs targeting Hsp90 inhibit the chaperone ATPase activity, it was found experimentally that 4-hydroxytamoxifen and tamoxifen enhance rather than inhibit Hsp90 ATPase.

Conclusions/Significance

Hence, tamoxifen and its metabolite are the first members of a new pharmacological class of Hsp90 activators.     

Interactively illustrating polymerization using three-level model fusion

Background

Research in cell biology is steadily contributing new knowledge about many aspects of physiological processes, both with respect to the involved molecular structures as well as their related function. Illustrations of the spatio-temporal development of such processes are not only used in biomedical education, but also can serve scientists as an additional platform for in-silico experiments.

Results

In this paper, we contribute a new, three-level modeling approach to illustrate physiological processes from the class of polymerization at different time scales. We integrate physical and empirical modeling, according to which approach best suits the different involved levels of detail, and we additionally enable a form of interactive steering, while the process is illustrated. We demonstrate the suitability of our approach in the context of several polymerization processes and report from a first evaluation with domain experts.

Conclusion

We conclude that our approach provides a new, hybrid modeling approach for illustrating the process of emergence in physiology, embedded in a densely filled environment. Our approach of a complementary fusion of three systems combines the strong points from the different modeling approaches and is capable to bridge different spatial and temporal scales.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-345) contains supplementary material, which is available to authorized users.     

Jerarca: Efficient Analysis of Complex Networks Using Hierarchical Clustering

Background

How to extract useful information from complex biological networks is a major goal in many fields, especially in genomics and proteomics. We have shown in several works that iterative hierarchical clustering, as implemented in the UVCluster program, is a powerful tool to analyze many of those networks. However, the amount of computation time required to perform UVCluster analyses imposed significant limitations to its use.

Methodology/Principal Findings

We describe the suite Jerarca, designed to efficiently convert networks of interacting units into dendrograms by means of iterative hierarchical clustering. Jerarca is divided into three main sections. First, weighted distances among units are computed using up to three different approaches: a more efficient version of UVCluster and two new, related algorithms called RCluster and SCluster. Second, Jerarca builds dendrograms based on those distances, using well-known phylogenetic algorithms, such as UPGMA or Neighbor-Joining. Finally, Jerarca provides optimal partitions of the trees using statistical criteria based on the distribution of intra- and intercluster connections. Outputs compatible with the phylogenetic software MEGA and the Cytoscape package are generated, allowing the results to be easily visualized.

Conclusions/Significance

The four main advantages of Jerarca in respect to UVCluster are: 1) Improved speed of a novel UVCluster algorithm; 2) Additional, alternative strategies to perform iterative hierarchical clustering; 3) Automatic evaluation of the hierarchical trees to obtain optimal partitions; and, 4) Outputs compatible with popular software such as MEGA and Cytoscape.     

No Association between Oxytocin Receptor (OXTR) Gene Polymorphisms and Experimentally Elicited Social Preferences

Background

Oxytocin (OXT) has been implicated in a suite of complex social behaviors including observed choices in economic laboratory experiments. However, actual studies of associations between oxytocin receptor (OXTR) gene variants and experimentally elicited social preferences are rare.

Methodology/Principal Findings

We test hypotheses of associations between social preferences, as measured by behavior in two economic games, and 9 single nucleotide polymorphisms (SNPs) of the OXTR gene in a sample of Swedish twins (n = 684). Two standard economic games, the dictator game and the trust game, both involving real monetary consequences, were used to elicit such preferences. After correction for multiple hypothesis testing, we found no significant associations between any of the 9 single nucleotide polymorphisms (SNPs) and behavior in either of the games.

Conclusion

We were unable to replicate the most significant association reported in previous research between the amount donated in a dictator game and an OXTR genetic variant.     

Vitamin E supplementation increases the attractiveness of males' scent for female European green lizards

Background

In spite that chemoreception is important in sexual selection for many animals, such as reptiles, the mechanisms that confer reliability to chemical signals are relatively unknown. European green lizards (Lacerta viridis) have substantial amounts of α-tocopherol ( = vitamin E) in their femoral secretions. Because vitamin E is metabolically important and can only be attained from the diet, its secretion is assumed to be costly. However, its role in intraspecific communication is unknown.

Methodology/Principal Findings

Here, we experimentally show that male European green lizards that received a dietary supplement of vitamin E increased proportions of vitamin E in their femoral secretions. Furthermore, our experiments revealed that females preferred to use areas scent marked by males with experimentally increased vitamin E levels in their secretions. Finally, female preferences were stronger when vitamin E differences between a pair of males'' secretions were larger.

Conclusions/Significance

Our results demonstrate that female green lizards are able to discriminate between males based on the vitamin E content of the males'' femoral secretions. We suggest that the possible cost of allocating vitamin E to secretions, which might be dependent on male quality, may be a mechanism that confers reliability to scent marks of green lizards and allows their evolution as sexual signals.     

Efficient prediction of human protein-protein interactions at a global scale

Background

Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods.

Results

On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments.

Conclusions

The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0383-1) contains supplementary material, which is available to authorized users.     

Computational Design of Protein-Based Inhibitors of Plasmodium vivax Subtilisin-Like 1 Protease

Background

Malaria remains a major global health concern. The development of novel therapeutic strategies is critical to overcome the selection of multiresistant parasites. The subtilisin-like protease (SUB1) involved in the egress of daughter Plasmodium parasites from infected erythrocytes and in their subsequent invasion into fresh erythrocytes has emerged as an interesting new drug target.

Findings

Using a computational approach based on homology modeling, protein–protein docking and mutation scoring, we designed protein–based inhibitors of Plasmodium vivax SUB1 (PvSUB1) and experimentally evaluated their inhibitory activity. The small peptidic trypsin inhibitor EETI-II was used as scaffold. We mutated residues at specific positions (P4 and P1) and calculated the change in free-energy of binding with PvSUB1. In agreement with our predictions, we identified a mutant of EETI-II (EETI-II-P4LP1W) with a Ki in the medium micromolar range.

Conclusions

Despite the challenges related to the lack of an experimental structure of PvSUB1, the computational protocol we developed in this study led to the design of protein-based inhibitors of PvSUB1. The approach we describe in this paper, together with other examples, demonstrates the capabilities of computational procedures to accelerate and guide the design of novel proteins with interesting therapeutic applications.     

Functional Validation and Comparison Framework for EIT Lung Imaging

Introduction

Electrical impedance tomography (EIT) is an emerging clinical tool for monitoring ventilation distribution in mechanically ventilated patients, for which many image reconstruction algorithms have been suggested. We propose an experimental framework to assess such algorithms with respect to their ability to correctly represent well-defined physiological changes. We defined a set of clinically relevant ventilation conditions and induced them experimentally in 8 pigs by controlling three ventilator settings (tidal volume, positive end-expiratory pressure and the fraction of inspired oxygen). In this way, large and discrete shifts in global and regional lung air content were elicited.

Methods

We use the framework to compare twelve 2D EIT reconstruction algorithms, including backprojection (the original and still most frequently used algorithm), GREIT (a more recent consensus algorithm for lung imaging), truncated singular value decomposition (TSVD), several variants of the one-step Gauss-Newton approach and two iterative algorithms. We consider the effects of using a 3D finite element model, assuming non-uniform background conductivity, noise modeling, reconstructing for electrode movement, total variation (TV) reconstruction, robust error norms, smoothing priors, and using difference vs. normalized difference data.

Results and Conclusions

Our results indicate that, while variation in appearance of images reconstructed from the same data is not negligible, clinically relevant parameters do not vary considerably among the advanced algorithms. Among the analysed algorithms, several advanced algorithms perform well, while some others are significantly worse. Given its vintage and ad-hoc formulation backprojection works surprisingly well, supporting the validity of previous studies in lung EIT.     

Cyclosporin A associated helicase-like protein facilitates the association of hepatitis C virus RNA polymerase with its cellular cyclophilin B

Background

Cyclosporin A (CsA) is well known as an immunosuppressive drug useful for allogeneic transplantation. It has been reported that CsA inhibits hepatitis C virus (HCV) genome replication, which indicates that cellular targets of CsA regulate the viral replication. However, the regulation mechanisms of HCV replication governed by CsA target proteins have not been fully understood.

Principal Findings

Here we show a chemical biology approach that elucidates a novel mechanism of HCV replication. We developed a phage display screening to investigate compound-peptide interaction and identified a novel cellular target molecule of CsA. This protein, named CsA associated helicase-like protein (CAHL), possessed RNA-dependent ATPase activity that was negated by treatment with CsA. The downregulation of CAHL in the cells resulted in a decrease of HCV genome replication. CAHL formed a complex with HCV-derived RNA polymerase NS5B and host-derived cyclophilin B (CyPB), known as a cellular cofactor for HCV replication, to regulate NS5B-CyPB interaction.

Conclusions

We found a cellular factor, CAHL, as CsA associated helicase-like protein, which would form trimer complex with CyPB and NS5B of HCV. The strategy using a chemical compound and identifying its target molecule by our phage display analysis is useful to reveal a novel mechanism underlying cellular and viral physiology.     

A p-Median approach for predicting drug response in tumour cells

Background

The complexity of biological data related to the genetic origins of tumour cells, originates significant challenges to glean valuable knowledge that can be used to predict therapeutic responses. In order to discover a link between gene expression profiles and drug responses, a computational framework based on Consensus p-Median clustering is proposed. The main goal is to simultaneously predict (in silico) anticancer responses by extracting common patterns among tumour cell lines, selecting genes that could potentially explain the therapy outcome and finally learning a probabilistic model able to predict the therapeutic responses.

Results

The experimental investigation performed on the NCI60 dataset highlights three main findings: (1) Consensus p-Median is able to create groups of cell lines that are highly correlated both in terms of gene expression and drug response; (2) from a biological point of view, the proposed approach enables the selection of genes that are strongly involved in several cancer processes; (3) the final prediction of drug responses, built upon Consensus p-Median and the selected genes, represents a promising step for predicting potential useful drugs.

Conclusion

The proposed learning framework represents a promising approach predicting drug response in tumour cells.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0353-7) contains supplementary material, which is available to authorized users.     

Organization and evolution of transposable elements along the bread wheat chromosome 3B

Background

The 17 Gb bread wheat genome has massively expanded through the proliferation of transposable elements (TEs) and two recent rounds of polyploidization. The assembly of a 774 Mb reference sequence of wheat chromosome 3B provided us with the opportunity to explore the impact of TEs on the complex wheat genome structure and evolution at a resolution and scale not reached so far.

Results

We develop an automated workflow, CLARI-TE, for TE modeling in complex genomes. We delineate precisely 56,488 intact and 196,391 fragmented TEs along the 3B pseudomolecule, accounting for 85% of the sequence, and reconstruct 30,199 nested insertions. TEs have been mostly silent for the last one million years, and the 3B chromosome has been shaped by a succession of bursts that occurred between 1 to 3 million years ago. Accelerated TE elimination in the high-recombination distal regions is a driving force towards chromosome partitioning. CACTAs overrepresented in the high-recombination distal regions are significantly associated with recently duplicated genes. In addition, we identify 140 CACTA-mediated gene capture events with 17 genes potentially created by exon shuffling and show that 19 captured genes are transcribed and under selection pressure, suggesting the important role of CACTAs in the recent wheat adaptation.

Conclusion

Accurate TE modeling uncovers the dynamics of TEs in a highly complex and polyploid genome. It provides novel insights into chromosome partitioning and highlights the role of CACTA transposons in the high level of gene duplication in wheat.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0546-4) contains supplementary material, which is available to authorized users.     

Cost-Effectiveness of Buprenorphine and Naltrexone Treatments for Heroin Dependence in Malaysia

Aims

To aid public health policymaking, we studied the cost-effectiveness of buprenorphine, naltrexone, and placebo interventions for heroin dependence in Malaysia.

Design

We estimated the cost-effectiveness ratios of three treatments for heroin dependence. We used a microcosting methodology to determine fixed, variable, and societal costs of each intervention. Cost data were collected from investigators, staff, and project records on the number and type of resources used and unit costs; societal costs for participants’ time were estimated using Malaysia’s minimum wage. Costs were estimated from a provider and societal perspective and reported in 2004 US dollars.

Setting

Muar, Malaysia.

Participants

126 patients enrolled in a randomized, double-blind, placebo-controlled clinical trial in Malaysia (2003–2005) receiving counseling and buprenorphine, naltrexone, or placebo for treatment of heroin dependence.

Measurements

Primary outcome measures included days in treatment, maximum consecutive days of heroin abstinence, days to first heroin use, and days to heroin relapse. Secondary outcome measures included treatment retention, injection drug use, illicit opiate use, AIDS Risk Inventory total score, and drug risk and sex risk subscores.

Findings

Buprenorphine was more effective and more costly than naltrexone for all primary and most secondary outcomes. Incremental cost-effectiveness ratios were below $50 for primary outcomes, mostly below $350 for secondary outcomes. Naltrexone was dominated by placebo for all secondary outcomes at almost all endpoints. Incremental treatment costs were driven mainly by medication costs, especially the price of buprenorphine.

Conclusions

Buprenorphine appears to be a cost-effective alternative to naltrexone that might enhance economic productivity and reduce drug use over a longer term.