Molecular interaction databases

cLD: Rare-variant linkage disequilibrium between genomic regions identifies novel genomic interactions

Linkage disequilibrium (LD) is a fundamental concept in genetics; critical for studying genetic associations and molecular evolution. However, LD measurements are only reliable for common genetic variants, leaving low-frequency variants unanalyzed. In this work, we introduce cumulative LD (cLD), a stable statistic that captures the rare-variant LD between genetic regions, which reflects more biological interactions between variants, in addition to lack of recombination. We derived the theoretical variance of cLD using delta methods to demonstrate its higher stability than LD for rare variants. This property is also verified by bootstrapped simulations using real data. In application, we find cLD reveals an increased genetic association between genes in 3D chromatin interactions, a phenomenon recently reported negatively by calculating standard LD between common variants. Additionally, we show that cLD is higher between gene pairs reported in interaction databases, identifies unreported protein-protein interactions, and reveals interacting genes distinguishing case/control samples in association studies.

Differential analysis of the dentin soluble proteomic

OBJECTIVES

To perform a differential analysis of the dentin soluble proteomic and assess the effects of tissue health state and protocol for protein extraction. We hypothesized the dentin soluble proteomic varies according to the tissue physiopathological state (intact vs. caries-affected) and protocol used to extract its proteins.

METHODS

Dentin from freshly extracted non-carious and carious teeth were randomly assigned for protein extraction using either guanidine-HCl/ethylenediaminetetraacetic acid (EDTA) or acetic acid. Protein extracts from intact and caries-affected dentin were processed and digested with trypsin for shotgun label-free proteomic analysis (nLC-ESI-MS/MS). Peptides identification was performed on a nanoACQUITY UPLC-Xevo Q-Tof MS system. Peptides identified with scores of confidence greater than 95% were included in the quantitative statistical analysis embedded in the PLGS software. Differences between experimental conditions were calculated using Student test-t with significance pre-set at α=0.05.

RESULTS

A total of 158 human proteins were identified. Approximately one-sixth of proteins (24/158) were present in at least two different extracts. Conversely, the greatest number of proteins (134/158) was identified uniquely in only one of the extracts. Overall, a larger number of soluble proteins was retrieved from caries-affected than intact dentin (86/158). Likewise, a greater number of proteins was extracted by the guanidine-HCl/EDTA (106/158) in comparison to acetic acid protocol. Several proteins detected in dentin extracts, mainly those from caries-affected teeth, are biological and/or metabolically involved with tissue turnover/remodeling.

CONCLUSION

The identity/abundance of soluble proteins retrieved from and remained in dentin noticeably depend on this tissue physiopathological state and protocol used to remove its minerals.

CLINICAL SIGNIFICANCE

The present findings brought new insight into the proteomic phenotype of human dentin and may provide targets for the development of novel caries disease-prevention therapies.

Proteomic profiling reveals mitochondrial dysfunction in the cerebellum of transgenic mice overexpressing DYRK1A, a Down syndrome candidate gene

Introduction

DYRK1A is a dual-specificity kinase that is overexpressed in Down syndrome (DS) and plays a key role in neurogenesis, neuronal differentiation and function, cognitive phenotypes, and aging. Dyrk1A has also been implicated in cerebellar abnormalities observed in association with DS, and normalization of Dyrk1A dosage rescues granular and Purkinje cell densities in a trisomic DS mouse model. However, the underlying molecular mechanisms governing these processes are unknown.

Methods

To shed light on the effects of Dyrk1A overexpression in the cerebellum, here we investigated the cerebellar proteome in transgenic Dyrk1A overexpressing mice in basal conditions and after treatment with green tea extract containing epigallocatechin-3-gallate (EGCG), a DYRK1A inhibitor.

Results and Discussion

Our results showed that Dyrk1A overexpression alters oxidative phosphorylation and mitochondrial function in the cerebellum of transgenic mice. These alterations are significantly rescued upon EGCG-containing green tea extract treatment, suggesting that its effects in DS could depend in part on targeting mitochondria, as shown by the partially restoration by the treatment of the increased mtDNA copy number in TG non-treated mice.

Semantic Representation of the Intersection of Criminal Law & Civil Tort

The more complex and globalized social structures become, the greater the need for new ways of exchanging information and knowledge. Legal science is a field that needs to be codified to allow the interoperability between people and states, as well as between humans and machines. The objective of this work is to develop an ontology in order to describe two different pillars of codified law (civil and criminal) and be able to depict the interaction between them. To answer the above question, we examine the Greek Criminal Law as depicted in the Greek Penal Code (ΠΚ) and the way its articles can be analyzed. Then we examine Tort as described in the Greek Civil Code (AΚ) and link the two codifications through the concepts of illegality and damage, both being prerequisites of tortious liability. Following that, through the Protégé application, a legal ontology is created in the OWL semantic language, while finally, four articles of the Penal Code are codified in the ontology and a presentation of their relation to the civil tort is required from a reasoning algorithm.

Amaral S, Buzalaf MAR. Effect of Physical Exercise and Genetic Background on Glucose Homeostasis and Liver/Muscle Proteomes in Mice

We compared the parameters related to glucose homeostasis, and liver and muscle proteomes in fluorosis-susceptible (A/J; S) and fluorosis-resistant (129P3/J; R) mice in response to fluoride (F) exposure and exercise. Ninety male mice (45 R-mice and 45 S-mice) were randomized into three groups: (SI; RI) No-F, No-Exercise, (SII; RII) 50 ppm F, No-Exercise, (SIII; RIII) 50 ppm F, Exercise. Overall, mean F concentrations in the plasma and femur were significantly higher in R-mice compared with S-mice. In R-mice, exercise resulted in an increase in F accumulation in the femur. In S-mice, the mean plasma glucose level was significantly higher in Group II compared with Groups I and III. There was an increase in liver proteins involved in energy flux and antioxidant enzymes in non-exercise groups (I, II) of S-mice in comparison with the corresponding groups of R-mice. The results also showed a decrease in muscle protein expression in Group I S-mice compared with their R-mice counterparts. In conclusion, the findings suggest an increased state of oxidative stress in fluorosis-susceptible mice that might be exacerbated by the treatment with F. In addition, fluorosis-susceptible mice have plasma glucose levels higher than fluorosis-resistant mice on exposure to F, and this is not affected by exercise.

Open Science Resources for the Mass Spectrometry-Based Analysis of SARS-CoV-2

The SARS-CoV-2 virus is the causative agent of the 2020 pandemic leading to the COVID-19 respiratory disease. With many scientific and humanitarian efforts ongoing to develop diagnostic tests, vaccines, and treatments for COVID-19, and to prevent the spread of SARS-CoV-2, mass spectrometry research, including proteomics, is playing a role in determining the biology of this viral infection. Proteomics studies are starting to lead to an understanding of the roles of viral and host proteins during SARS-CoV-2 infection, their protein-protein interactions, and post-translational modifications. This is beginning to provide insights into potential therapeutic targets or diagnostic strategies that can be used to reduce the long-term burden of the pandemic. However, the extraordinary situation caused by the global pandemic is also highlighting the need to improve mass spectrometry data and workflow sharing. We therefore describe freely available data and computational resources that can facilitate and assist the mass spectrometry-based analysis of SARS-CoV-2. We exemplify this by reanalyzing a virus-host interactome data set to detect protein-protein interactions and identify host proteins that could potentially be used as targets for drug repurposing.

Increase of complex I and reduction of complex II mitochondrial activity are possible adaptive effects provoked by fluoride exposure

Fluoride (F) can induce changes in the expression of several liver proteins, most of them localized in the mitochondria and its effect is dose- and time-dependent. This study analyzed the effect of distinct F concentrations and exposure periods on the mitochondrial activity of complex I-III and II-III in the liver. Thirty-six 21-day-old male Wistar rats were divided into 2 groups (n = 18) according to the duration of the treatment (20 or 60 days). They were subdivided into 3 subgroups (n = 6) according to the concentration of F (0 mg/L, 15 mg/L or 50 mg/L). After the experimental periods, the animals were anesthetized, liver mitochondria were isolated and stored for activity analyses. The determination of complexes II-III and I-III was based on the reduction of cytochrome c³⁺ to cytochrome c²⁺ performed spectrophotometrically. Bioinformatics analyses were performed using data from a previous study (Pereira et al., 2018). The mitochondrial complex I-III was significantly activated in the groups treated with 50 mgF/L for 20 days and 15 mgF/L for 60 days. The complex II-III was significantly reduced in the group treated with the higher F dose for 60 days. The networks indicated more changes in mitochondrial proteins in the group treated with the higher dose for 20 days; the reduction is probably linked to the activation of the complex I-III. The reduction in the complex II-III upon exposure to the higher F dose in the long term might be part of an adaptative mechanism of the body to counteract the deleterious effects of this ion on the energy metabolism.

Effects of acute fluoride exposure on the jejunum and ileum of rats: Insights from proteomic and enteric innervation analysis

Fluoride (F) is largely employed in dentistry, in therapeutic doses, to control caries. However, excessive intake may lead to adverse effects in the body. Since F is absorbed mostly from the gastrointestinal tract (GIT), gastrointestinal symptoms are the first signs following acute F exposure. Nevertheless, little is known about the mechanistic events that lead to these symptoms. Therefore, the present study evaluated changes in the proteomic profile as well as morphological changes in the jejunum and ileum of rats upon acute exposure to F. Male rats received, by gastric gavage, a single dose of F containing 0 (control) or 25 mg/Kg for 30 days. Upon exposure to F, there was a decrease in the thickness of the tunic muscularis for both segments and a decrease in the thickness of the wall only for the ileum. In addition, a decrease in the density of HuC/D-IR neurons and nNOS-IR neurons was found for the jejunum, but for the ileum only nNOS-IR neurons were decreased upon F exposure. Moreover, SP-IR varicosities were increased in both segments, while VIP-IR varicosities were increased in the jejunum and decreased in the ileum. As for the proteomic analysis, the proteins with altered expression were mostly negatively regulated and associated mainly with protein synthesis and energy metabolism. Proteomics also revealed alterations in proteins involved in oxidative/antioxidant defense, apoptosis and as well as in cytoskeletal proteins. Our results, when analyzed together, suggest that the gastrointestinal symptoms found in cases of acute F exposure might be related to the morphological alterations in the gut (decrease in the thickness of the tunica muscularis) that, at the molecular level, can be explained by alterations in the gut vipergic innervation and in proteins that regulate the cytoskeleton.

Effects of low-level fluoride exposure on glucose homeostasis in female NOD mice

Water fluoridation is an important public health measure for the control of dental caries. Recent animal studies have shown that low doses of fluoride (F) in the drinking water, similar to those found in public water supplies, increase insulin sensitivity and reduce blood glucose. In the present study we evaluated the effects of low-level F exposure through the drinking water on glucose homeostasis in female NOD mice. Seventy-two 6-week mice were randomly divided into 2 groups according to the concentration of F in the drinking water (0-control, or 10 mg/L) they received for 14 weeks. After the experimental period the blood was collected for analyses of plasma F, glucose and insulin. Liver and gastrocnemius muscle were collected for proteomic analysis. Plasma F concentrations were significantly higher in the F-treated than in the control group. Despite treatment with fluoridated water reduced plasma levels glucose by 20% compared to control, no significant differences were found between the groups for plasma glucose and insulin. In the muscle, treatment with fluoridated water increased the expression of proteins related to muscle contraction, while in the liver, there was an increase in expression of antioxidant proteins and in proteins related to carboxylic acid metabolic process. Remarkably, phosphoenolpyruvate carboxykinase (PEPCK) was found exclusively in the liver of control mice. The reduction in PEPCK, a positive regulator of gluconeogenesis, thus increasing glucose uptake, might be a probable mechanism to explain the anti-diabetic effects of low doses of F, which should be evaluated in further studies.

Network reconstruction from infection cascades

Accessing the network through which a propagation dynamics diffuses is essential for understanding and controlling it. In a few cases, such information is available through direct experiments or thanks to the very nature of propagation data. In a majority of cases however, available information about the network is indirect and comes from partial observations of the dynamics, rendering the network reconstruction a fundamental inverse problem. Here we show that it is possible to reconstruct the whole structure of an interaction network and to simultaneously infer the complete time course of activation spreading, relying just on single epoch (i.e. snapshot) or time-scattered observations of a small number of activity cascades. The method that we present is built on a belief propagation approximation, that has shown impressive accuracy in a wide variety of relevant cases, and is able to infer interactions in the presence of incomplete time-series data by providing a detailed modelling of the posterior distribution of trajectories conditioned to the observations. Furthermore, we show by experiments that the information content of full cascades is relatively smaller than that of sparse observations or single snapshots.

DYRK1A Overexpression Alters Cognition and Neural-Related Proteomic Pathways in the Hippocampus That Are Rescued by Green Tea Extract and/or Environmental Enrichment

Down syndrome (DS), caused by trisomy of chromosome 21, is the most common genetic cause of intellectual disability. We recently discovered that green tea extracts containing epigallocatechin-3-gallate (EGCG) improve cognition in mice transgenic for Dyrk1a (TgDyrk1A) and in a trisomic DS mouse model (Ts65Dn). Interestingly, paired with cognitive stimulation, green tea has beneficial pro-cognitive effects in DS individuals. Dual Specificity Tyrosine-Phosphorylation-Regulated Kinase 1A (DYRK1A) is a major candidate to explain the cognitive phenotypes of DS, and inhibiting its activity is a promising pro-cognitive therapy. DYRK1A kinase activity can be normalized in the hippocampus of transgenic DYRK1A mice administering green tea extracts, but also submitting the animals to environmental enrichment (EE). However, many other mechanisms could also explain the pro-cognitive effects of green tea extracts and EE. To underpin the overall alterations arising upon DYRK1A overexpression and the molecular processes underneath the pro-cognitive effects, we used quantitative proteomics. We investigated the hippocampal (phospho)proteome in basal conditions and after treatment with a green tea extract containing EGCG and/or EE in TgDyrk1A and control mice. We found that Dyrk1A overexpression alters protein and phosphoprotein levels of key postsynaptic and plasticity-related pathways and that these alterations were rescued upon the cognitive enhancer treatments.

Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences

Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research.

Low-level fluoride exposure reduces glycemia in NOD mice

Water fluoridation is the most widespread measure to prevent dental caries but its relationship with the development of type-1 diabetes (T1D), which has been increasing by 2-5% worldwide, is not quite well understood.

AIM

This study evaluated if fluoride (F) administered in the drinking water can prevent or reduce the development of T1D in non-obese diabetic (NOD) mice, as well as to explore the underlying mechanisms.

MATERIALS AND METHODS

Twenty-four weaning NOD mice received water containing 0, 10 or 50 ppm F for 21 days. Plasma glucose and insulin were analyzed. Quantitative proteomic analysis was conducted in the liver and gastrocnemius muscle.

RESULTS

Animals treated with 10 ppm F had significantly lower glucose levels than the control group, but there was no significant difference among the groups in relation to insulin. The % of β-cell function was significantly higher in the 10 ppm F group. Changes in the proteomic profile of muscle and liver were seen among the groups. In the muscle, the 10 ppm F group presented, when compared with control, increased expression of proteins involved in energy metabolism. The 50 ppm F group, compared with control, presented increased expression of proteins related to muscle contraction, differentiation of brown adipose tissue and apoptosis. For the liver, the 10 ppm F group had increase in proteins involved in energy metabolism and protein synthesis, in respect to control. There was also an increase in isoforms of Glutathione S transferase, which was confirmed by Western blotting. In the group treated with 50 ppm F, proteins related to ROS metabolism and energetic metabolism were altered.

CONCLUSION

Increased expression of antioxidant proteins by treatment with low F concentration may possibly help to explain protection against the development of T1D, which should be better explored in future mechanistic studies.

Liver Proteome of Mice with Distinct Genetic Susceptibilities to Fluorosis Treated with Different Concentrations of F in the Drinking Water

Appropriate doses of fluoride (F) have therapeutic action against dental caries, but higher levels can cause disturbances in soft and mineralized tissues. Interestingly, the susceptibility to the toxic effects of F is genetically determined. This study evaluated the effects of F on the liver proteome of mice susceptible (A/J) or resistant (129P3/J) to the effects of F. Weanling male A/J (n = 12) and 129P3/J (n = 12) mice were housed in pairs and assigned to two groups given low-F food and drinking water containing 15 or 50 ppm F for 6 weeks. Liver proteome profiles were examined using nano-LC-ESI-MS/MS. Difference in expression among the groups was determined using the PLGS software. Treatment with the lower F concentration provoked more pronounced alterations in fold change in liver proteins in comparison to the treatment with the higher F concentration. Interestingly, most of the proteins with fold change upon treatment with 15 ppm F were increased in the A/J mice compared with their 129P3/J counterparts, suggesting an attempt of the former to fight the deleterious effects of F. However, upon treatment with 50 ppm F, most proteins with fold change were decreased in the A/J mice compared with their 129P3/J counterparts, especially proteins related to oxidative stress and protein folding, which might be related to the higher susceptibility of the A/J animals to the deleterious effects of F. Our findings add light into the mechanisms underlying genetic susceptibility to fluorosis.

Proposed mechanism for understanding the dose- and time-dependency of the effects of fluoride in the liver

Fluoride (F) can induce changes in the expression of several liver proteins. It is suggested that these changes are dose- and time-dependent. The objective of this study was to analyze the effect of different F concentrations and exposure times to this ion on the pattern of protein expression in the liver of rats. Thirt-six 21-day-old male Wistar rats were divided into 2 groups (n = 18) according to the treatment duration (20 or 60 days). Each of these groups was then divided into 3 subgroups (n = 6) according to the concentration of F administered in drinking water, as follows: 0 mg/L (control), 15 mg/L or 50 mg/L. After the experiment periods, the animals were anesthetized and the liver and blood were collected. F was analyzed in plasma and liver. Part of the liver was fixed for histological analysis. Liver proteins were extracted and prepared for quantitative label-free mass spectrometry analysis. F concentrations in plasma and liver were significantly higher in the group treated with 50 mg /L in comparison with control, regardless the time of exposure. Histological alterations in the liver were more evident in the subgroups treated for 20 days. The proteomic analysis revealed changes in proteins related to endoplasmic reticulum and mitochondrial oxidative stress, mitochondrial alteration, apoptosis and cellular respiration upon exposure to F. The results reinforce previous findings showing that the effects of F in the liver are dose- and time-dependent and provide the molecular basis for understanding the evolution of these effects.

Chronic treatment with fluoride affects the jejunum: insights from proteomics and enteric innervation analysis

Gastrointestinal symptoms are the first signs of fluoride (F) toxicity. In the present study, the jejunum of rats chronically exposed to F was evaluated by proteomics, as well as by morphological analysis. Wistar rats received water containing 0, 10 or 50 mgF/L during 30 days. HuC/D, neuronal Nitric Oxide (nNOS), Vasoactive Intestinal Peptide (VIP), Calcitonin Gene Related Peptide (CGRP), and Substance P (SP) were detected in the myenteric plexus of the jejunum by immunofluorescence. The density of nNOS-IR neurons was significantly decreased (compared to both control and 10 mgF/L groups), while the VIP-IR varicosities were significantly increased (compared to control) in the group treated with the highest F concentration. Significant morphological changes were seen observed in the density of HUC/D-IR neurons and in the area of SP-IR varicosities for F-treated groups compared to control. Changes in the abundance of various proteins correlated with relevant biological processes, such as protein synthesis, glucose homeostasis and energy metabolism were revealed by proteomics.

The effect of fluoride on the structure, function, and proteome of intestinal epithelia

Fluoride exposure is widespread, with drinking water commonly containing natural and artificially added sources of the ion. Ingested fluoride undergoes absorption across the gastric and intestinal epithelia. Previous studies have reported adverse gastrointestinal effects with high levels of fluoride exposure. Here, we examined the effects of fluoride on the transepithelial ion transport and resistance of three intestinal epithelia. We used the Caco-2 cell line as a model of human intestinal epithelium, and rat and mouse colonic epithelia for purposes of comparison. Fluoride caused a concentration-dependent decline in forskolin-induced Cl^- secretion and transepithelial resistance of Caco-2 cell monolayers, with an IC₅₀ for fluoride of about 3 mM for both parameters. In the presence of 5 mM fluoride, transepithelial resistance fell exponentially with time, with a t_1/2 of about 7 hours. Subsequent imaging by immunofluorescence and scanning electron microscopy showed structural abnormalities in Caco-2 cell monolayers exposed to fluoride. The Young's modulus of the epithelium was not affected by fluoride, although proteomic analysis revealed changes in expression of a number of proteins, particularly those involved in cell-cell adhesion. In line with its effects on Caco-2 cell monolayers, fluoride, at 5 mM, also had profound effects on Cl^- secretion and transepithelial resistance of both rat and mouse colonic epithelia. Our results show that treatment with fluoride has major effects on the structure, function, and proteome of intestinal epithelia, but only at concentrations considerably higher than those likely to be encountered in vivo, when much lower fluoride doses are normally ingested on a chronic basis.

High-throughput proteomics and the fight against pathogens

Pathogens pose a major threat to human and animal welfare. Understanding the interspecies host-pathogen protein-protein interactions could lead to the development of novel strategies to combat infectious diseases through the rapid development of new therapeutics. The first step in understanding the host-pathogen crosstalk is to identify interacting proteins in order to define crucial hot-spots in the host-pathogen interactome, such as the proposed pharmaceutical targets by means of high-throughput proteomic methodologies. In order to obtain holistic insight into the inter- and intra-species bimolecular interactions, apart from the proteomic approach, sophisticated in silico modeling is used to correlate the obtained large data sets with other omics data and clinical outcomes. Since the main focus in this area has been directed towards human medicine, it is time to extrapolate the existing expertise to a new emerging field: the 'systems veterinary medicine'. Therefore, this review addresses high-throughput mass spectrometry-based technology for monitoring protein-protein interactions in vitro and in vivo and discusses pathogen cultivation, model host cells and available bioinformatic tools employed in vaccine development.

Enteric innervation combined with proteomics for the evaluation of the effects of chronic fluoride exposure on the duodenum of rats

Ingested fluoride (F) is absorbed mainly in the small intestine, which is controlled by the Enteric Nervous System (ENS). Although important intestinal symptomatology has been described after excessive F exposure, there have been no studies reporting the effects of F on the ENS. In this study, the effects of chronic F exposure were evaluated on the duodenums of rats through proteomic and morphological analyses. Concentrations of 0, 10, or 50 ppm of F were applied to the drinking water for 30 days. Immunofluorescence techniques were performed in the myenteric plexus of the duodenum to detect HuC/D, neuronal nitric oxide (nNOS), vasoactive intestinal peptide (VIP), calcitonin gene related peptide (CGRP), and substance P (SP). The 50 ppm F group presented a significant decrease in the density of nNOS-IR neurons. Significant morphological alterations were also observed in HUC/D-IR and nNOS-IR neurons; VIP-IR, CGRP-IR, and SP-IR varicosities for both groups (10 and 50 ppm F). Proteomic analysis of the duodenum demonstrated alterations in the expression of several proteins, especially those related to important biological processes, such as protein polymerization, which helps to explain the downregulation of many proteins upon exposure to 50 ppm of F.

The effect of fluoride on the structure, function, and proteome of a renal epithelial cell monolayer

High concentrations of fluoride in the body may cause toxic effects. Here, we investigated the effects of fluoride on the structure, function, and proteome of a cortical collecting duct epithelium in vitro. Kidney tubule cells (M-1) were chosen because the concentration of fluoride in the kidney is 4-5-fold higher than that in plasma. Mouse M-1 cell monolayers were incubated in fluoride-containing media, and the amiloride-sensitive short-circuit current and transepithelial resistance were measured. The Young's modulus of the epithelium was determined using atomic force microscopy, and the effect of fluoride on epithelial structure was assessed using scanning and transmission electron microscopy, and immunofluorescence. Differences in the expression of membrane proteins were evaluated using proteomics and bioinformatics. Fluoride exposure reduced both transepithelial Na⁺ transport and resistance. The IC₅₀ for fluoride was ∼300 µM for both effects, and the half-times for the decays of ion transport and resistance were 8.4 h and 3.6 days, respectively. Fluoride treatment did not affect the sensitivity of Na⁺ transport to amiloride. The Young's modulus of the epithelium was also unaffected by fluoride; however, the functional effects of fluoride were accompanied by marked structural effects. Proteomic analysis revealed changes in expression of a number of proteins, and particularly mitochondrial proteins. Treatment with fluoride had profound effects on the structure, function and proteome of a model cortical collecting duct epithelium. Significantly, however, these effects were produced only at concentrations considerably higher than those likely to be encountered in vivo. © 2016 Wiley Periodicals, Inc. Environ Toxicol 32: 1455-1467, 2017.

Weighted Protein Interaction Network Analysis of Frontotemporal Dementia

The genetic analysis of complex disorders has undoubtedly led to the identification of a wealth of associations between genes and specific traits. However, moving from genetics to biochemistry one gene at a time has, to date, rather proved inefficient and under-powered to comprehensively explain the molecular basis of phenotypes. Here we present a novel approach, weighted protein–protein interaction network analysis (W-PPI-NA), to highlight key functional players within relevant biological processes associated with a given trait. This is exemplified in the current study by applying W-PPI-NA to frontotemporal dementia (FTD): We first built the state of the art FTD protein network (FTD-PN) and then analyzed both its topological and functional features. The FTD-PN resulted from the sum of the individual interactomes built around FTD-spectrum genes, leading to a total of 4198 nodes. Twenty nine of 4198 nodes, called inter-interactome hubs (IIHs), represented those interactors able to bridge over 60% of the individual interactomes. Functional annotation analysis not only reiterated and reinforced previous findings from single genes and gene-coexpression analyses but also indicated a number of novel potential disease related mechanisms, including DNA damage response, gene expression regulation, and cell waste disposal and potential biomarkers or therapeutic targets including EP300. These processes and targets likely represent the functional core impacted in FTD, reflecting the underlying genetic architecture contributing to disease. The approach presented in this study can be applied to other complex traits for which risk-causative genes are known as it provides a promising tool for setting the foundations for collating genomics and wet laboratory data in a bidirectional manner. This is and will be critical to accelerate molecular target prioritization and drug discovery.

Clustering and Network Analysis of Reverse Phase Protein Array Data

Molecular profiling of proteins and phosphoproteins using a reverse phase protein array (RPPA) platform, with a panel of target-specific antibodies, enables the parallel, quantitative proteomic analysis of many biological samples in a microarray format. Hence, RPPA analysis can generate a high volume of multidimensional data that must be effectively interrogated and interpreted. A range of computational techniques for data mining can be applied to detect and explore data structure and to form functional predictions from large datasets. Here, two approaches for the computational analysis of RPPA data are detailed: the identification of similar patterns of protein expression by hierarchical cluster analysis and the modeling of protein interactions and signaling relationships by network analysis. The protocols use freely available, cross-platform software, are easy to implement, and do not require any programming expertise. Serving as data-driven starting points for further in-depth analysis, validation, and biological experimentation, these and related bioinformatic approaches can accelerate the functional interpretation of RPPA data.

Liver proteome of mice with different genetic susceptibilities to the effects of fluoride

OBJECTIVE

In this study, we investigated the differential pattern of protein expression in the liver of these mice to provide insights on why they have different responses to F.

MATERIAL AND METHODS

Weanling male A/J and 129P3/J mice (n=10 from each strain) were pared and housed in metabolic cages with ad libitum access to low-F food and deionized water for 42 days. Liver proteome profiles were examined using nLC-MS/MS. Protein function was classified by GO biological process (Cluego v2.0.7 + Clupedia v1.0.8) and protein-protein interaction network was constructed (PSICQUIC, Cytoscape).

RESULTS

Most proteins with fold change were increased in A/J mice. The functional category with the highest percentage of altered genes was oxidation-reduction process (20%). Subnetwork analysis revealed that proteins with fold change interacted with Disks large homolog 4 and Calcium-activated potassium channel subunit alpha-1. A/J mice had an increase in proteins related to energy flux and oxidative stress.

CONCLUSION

This could be a possible explanation for the high susceptibility of these mice to the effects of F, since the exposure also induces oxidative stress.

Gene regulation knowledge commons: community action takes care of DNA binding transcription factors

A large gap remains between the amount of knowledge in scientific literature and the fraction that gets curated into standardized databases, despite many curation initiatives. Yet the availability of comprehensive knowledge in databases is crucial for exploiting existing background knowledge, both for designing follow-up experiments and for interpreting new experimental data. Structured resources also underpin the computational integration and modeling of regulatory pathways, which further aids our understanding of regulatory dynamics. We argue how cooperation between the scientific community and professional curators can increase the capacity of capturing precise knowledge from literature. We demonstrate this with a project in which we mobilize biological domain experts who curate large amounts of DNA binding transcription factors, and show that they, although new to the field of curation, can make valuable contributions by harvesting reported knowledge from scientific papers. Such community curation can enhance the scientific epistemic process.

Database URL: http://www.tfcheckpoint.org

Quantitative Proteome Profiling of Street Rabies Virus-Infected Mouse Hippocampal Synaptosomes

It is well established now that neuronal dysfunction rather than structural damage may be responsible for the development of rabies. In order to explore the underlying mechanisms in rabies virus (RABV) and synaptic dysfunctions, a quantitative proteome profiling was carried out on synaptosome samples from mice hippocampus. Synaptosome samples from mice hippocampus were isolated and confirmed by Western blot and transmission electron microscopy. Synaptosome protein content changes were quantitatively detected by Nano-LC-MS/MS. Protein functions were classified by the Gene Ontology (GO) and KEGG pathway. PSICQUIC was used to create a network. MCODE algorithm was applied to obtain subnetworks. Of these protein changes, 45 were upregulated and 14 were downregulated following RABV infection relative to non-infected (mock) synaptosomes. 28 proteins were unique to mock treatment and 12 were unique to RABV treatment. Proteins related to metabolism and synaptic vesicle showed the most changes in expression levels. Furthermore, protein-protein interaction (PPI) networks revealed that several key biological processes related to synaptic functions potentially were modulated by RABV, including energy metabolism, cytoskeleton organization, and synaptic transmission. These data will be useful for better understanding of neuronal dysfunction of rabies and provide the foundation for future research.

Using biological networks to integrate, visualize and analyze genomics data

Network biology is a rapidly developing area of biomedical research and reflects the current view that complex phenotypes, such as disease susceptibility, are not the result of single gene mutations that act in isolation but are rather due to the perturbation of a gene’s network context. Understanding the topology of these molecular interaction networks and identifying the molecules that play central roles in their structure and regulation is a key to understanding complex systems. The falling cost of next-generation sequencing is now enabling researchers to routinely catalogue the molecular components of these networks at a genome-wide scale and over a large number of different conditions. In this review, we describe how to use publicly available bioinformatics tools to integrate genome-wide ‘omics’ data into a network of experimentally-supported molecular interactions. In addition, we describe how to visualize and analyze these networks to identify topological features of likely functional relevance, including network hubs, bottlenecks and modules. We show that network biology provides a powerful conceptual approach to integrate and find patterns in genome-wide genomic data but we also discuss the limitations and caveats of these methods, of which researchers adopting these methods must remain aware.

A Computational Network Biology Approach to Uncover Novel Genes Related to Alzheimer's Disease

Recent advances in the fields of genetics and genomics have enabled the identification of numerous Alzheimer's disease (AD) candidate genes, although for many of them the role in AD pathophysiology has not been uncovered yet. Concomitantly, network biology studies have shown a strong link between protein network connectivity and disease. In this chapter I describe a computational approach that, by combining local and global network analysis strategies, allows the formulation of novel hypotheses on the molecular mechanisms involved in AD and prioritizes candidate genes for further functional studies.

Fundamentals of protein interaction network mapping

Studying protein interaction networks of all proteins in an organism (“interactomes”) remains one of the major challenges in modern biomedicine. Such information is crucial to understanding cellular pathways and developing effective therapies for the treatment of human diseases. Over the past two decades, diverse biochemical, genetic, and cell biological methods have been developed to map interactomes. In this review, we highlight basic principles of interactome mapping. Specifically, we discuss the strengths and weaknesses of individual assays, how to select a method appropriate for the problem being studied, and provide general guidelines for carrying out the necessary follow‐up analyses. In addition, we discuss computational methods to predict, map, and visualize interactomes, and provide a summary of some of the most important interactome resources. We hope that this review serves as both a useful overview of the field and a guide to help more scientists actively employ these powerful approaches in their research.

SIGNOR: a database of causal relationships between biological entities

Assembly of large biochemical networks can be achieved by confronting new cell-specific experimental data with an interaction subspace constrained by prior literature evidence. The SIGnaling Network Open Resource, SIGNOR (available on line at http://signor.uniroma2.it), was developed to support such a strategy by providing a scaffold of prior experimental evidence of causal relationships between biological entities. The core of SIGNOR is a collection of approximately 12,000 manually-annotated causal relationships between over 2800 human proteins participating in signal transduction. Other entities annotated in SIGNOR are complexes, chemicals, phenotypes and stimuli. The information captured in SIGNOR can be represented as a signed directed graph illustrating the activation/inactivation relationships between signalling entities. Each entry is associated to the post-translational modifications that cause the activation/inactivation of the target proteins. More than 4900 modified residues causing a change in protein concentration or activity have been curated and linked to the modifying enzymes (about 351 human kinases and 94 phosphatases). Additional modifications such as ubiquitinations, sumoylations, acetylations and their effect on the modified target proteins are also annotated. This wealth of structured information can support experimental approaches based on multi-parametric analysis of cell systems after physiological or pathological perturbations and to assemble large logic models.

Low-Level Fluoride Exposure Increases Insulin Sensitivity in Experimental Diabetes

The effect of chronic fluoride (F) exposure from the drinking water on parameters related to glucose homeostasis was investigated. Wistar rats were randomly distributed into 2 groups (diabetic [D] and nondiabetic [ND]; n = 54 each). In D, diabetes was induced with streptozotocin. Each group was further divided into 3 subgroups (0, 10, or 50 mgF/L in drinking water). After 22 days of treatment, plasma and liver samples were collected. No alterations in glycemia, insulinemia, KITT, and HOMA2-IR (homeostasis model assessment 2 of insulin resistance) were seen for ND. F-exposure of D rats led to significantly lower insulinemia, without alterations in glycemia (increased %S). Proteomic analysis detected 19, 39, and 16 proteins differentially expressed for the comparisons D0 vs. D10, D0 vs. D50, and D10 vs. D50, respectively. Gene Ontology with the most significant terms in the comparisons D0 vs. D10, D0 vs. D50, and D50 vs. D10 were organic acid metabolic process and carboxylic acid metabolic process, organic acid metabolic process, and cellular ketone metabolic process. Analysis of subnetworks revealed that proteins with fold changes interacted with GLUT4 in comparison D0 vs. D10. Among these proteins, ERj3p was present in D10. Upregulation of this protein in the presence of F might help to explain the higher %S found in these animals. These data suggest that fluoride might enhance glucose homeostasis in diabetes and identify specific biological mechanisms that merit future studies.

Computational analysis of the LRRK2 interactome

LRRK2 was identified in 2004 as the causative protein product of the Parkinson’s disease locus designated PARK8. In the decade since then, genetic studies have revealed at least 6 dominant mutations in LRRK2 linked to Parkinson’s disease, alongside one associated with cancer. It is now well established that coding changes in LRRK2 are one of the most common causes of Parkinson’s. Genome-wide association studies (GWAs) have, more recently, reported single nucleotide polymorphisms (SNPs) around the LRRK2 locus to be associated with risk of developing sporadic Parkinson’s disease and inflammatory bowel disorder. The functional research that has followed these genetic breakthroughs has generated an extensive literature regarding LRRK2 pathophysiology; however, there is still no consensus as to the biological function of LRRK2. To provide insight into the aspects of cell biology that are consistently related to LRRK2 activity, we analysed the plethora of candidate LRRK2 interactors available through the BioGRID and IntAct data repositories. We then performed GO terms enrichment for the LRRK2 interactome. We found that, in two different enrichment portals, the LRRK2 interactome was associated with terms referring to transport, cellular organization, vesicles and the cytoskeleton. We also verified that 21 of the LRRK2 interactors are genetically linked to risk for Parkinson’s disease or inflammatory bowel disorder. The implications of these findings are discussed, with particular regard to potential novel areas of investigation.

Fourteen years of plant proteomics reflected in Proteomics: moving from model species and 2DE-based approaches to orphan species and gel-free platforms

In this article, the topic of plant proteomics is reviewed based on related papers published in the journal Proteomics since publication of the first issue in 2001. In total, around 300 original papers and 41 reviews published in Proteomics between 2000 and 2014 have been surveyed. Our main objective for this review is to help bridge the gap between plant biologists and proteomics technologists, two often very separate groups. Over the past years a number of reviews on plant proteomics have been published . To avoid repetition we have focused on more recent literature published after 2010, and have chosen to rather make continuous reference to older publications. The use of the latest proteomics techniques and their integration with other approaches in the "systems biology" direction are discussed more in detail. Finally we comment on the recent history, state of the art, and future directions of plant proteomics, using publications in Proteomics to illustrate the progress in the field. The review is organized into two major blocks, the first devoted to provide an overview of experimental systems (plants, plant organs, biological processes) and the second one to the methodology.

Merging and scoring molecular interactions utilising existing community standards: tools, use-cases and a case study

The evidence that two molecules interact in a living cell is often inferred from multiple different experiments. Experimental data is captured in multiple repositories, but there is no simple way to assess the evidence of an interaction occurring in a cellular environment. Merging and scoring of data are commonly required operations after querying for the details of specific molecular interactions, to remove redundancy and assess the strength of accompanying experimental evidence. We have developed both a merging algorithm and a scoring system for molecular interactions based on the proteomics standard initiative–molecular interaction standards. In this manuscript, we introduce these two algorithms and provide community access to the tool suite, describe examples of how these tools are useful to selectively present molecular interaction data and demonstrate a case where the algorithms were successfully used to identify a systematic error in an existing dataset.

Protomers of protein hetero-oligomers tend to resemble each other more than expected

A large fraction of the proteome is made by proteins that are not permanently monomeric but form oligomeric assemblies, which can be either homo- or hetero-oligomeric. Here it is described that protomers of hetero-oligomeric proteins tend to resemble each other more than expected. This is verified by comparing the level of similarity of pairs of hetero-oligomeric protein protomers and of pairs of proteins that do not interact with each other. This observation, interesting per se, might reflect the evolution of hetero-oligomers from ancestral homo-oligomers, through gene duplication and paralogs divergence. However, other hypotheses cannot be excluded and the observed structural similarity might result from several causes.

Proteomic analysis of gastrocnemius muscle in rats with streptozotocin-induced diabetes and chronically exposed to fluoride

Administration of high doses of fluoride (F) can alter glucose homeostasis and lead to insulin resistance (IR). This study determined the profile of protein expression in the gastrocnemius muscle of rats with streptozotocin-induced diabetes that were chronically exposed to F. Male Wistar rats (60 days old) were randomly distributed into two groups of 18 animals. In one group, diabetes was induced through the administration of streptozotocin. Each group (D-diabetic and ND-non-diabetic) was further divided into 3 subgroups each of which was exposed to a different F concentration via drinking water (0 ppm, 10 ppm or 50 ppm F, as NaF). After 22 days of treatment, the gastrocnemius muscle was collected and submitted to proteomic analysis (2D-PAGE followed by LC-MS/MS). Protein functions were classified by the GO biological process (ClueGO v2.0.7+Clupedia v1.0.8) and protein-protein interaction networks were constructed (PSICQUIC, Cytoscape). Quantitative intensity analysis of the proteomic data revealed differential expression of 75 spots for ND0 vs. D0, 76 for ND10 vs.D10, 58 spots for ND50 vs. D50, 52 spots for D0 vs. D10 and 38 spots for D0 vs. D50. The GO annotations with the most significant terms in the comparisons of ND0 vs. D0, ND10 vs. D10, ND50 vs. D50, D0 vs. D10 and D0 vs. D50, were muscle contraction, carbohydrate catabolic processes, generation of precursor metabolites and energy, NAD metabolic processes and gluconeogenesis, respectively. Analysis of subnetworks revealed that, in all comparisons, proteins with fold changes interacted with GLUT4. GLUT4 interacting proteins, such as MDH and the stress proteins HSPB8 and GRP78, exhibited decreased expression when D animals were exposed to F. The presence of the two stress proteins indicates an increase in IR, which might worsen diabetes. Future studies should evaluate whether diabetic animals treated with F have increased IR, as well as which molecular mechanisms are involved.

Hox protein interactions: screening and network building

Understanding the mode of action of Hox proteins requires the identification of molecular and cellular pathways they take part in. This includes to characterize the networks of protein-protein interactions involving Hox proteins. In this chapter we propose a strategy and methods to map Hox interaction networks, from yeast two-hybrid and high-throughput yeast two-hybrid interaction screening to bioinformatic analyses based on the software platform Cytoscape.

Protein-protein interaction networks studies and importance of 3D structure knowledge

Protein-protein interaction networks (PPINs) are a powerful tool to study biological processes in living cells. In this review, we present the progress of PPIN studies from abstract to more detailed representations. We will focus on 3D interactome networks, which offer detailed information at the atomic level. This information can be exploited in understanding not only the underlying cellular mechanisms, but also how human variants and disease-causing mutations affect protein functions and complexes' stability. Recent studies have used structural information on PPINs to also understand the molecular mechanisms of binding partner selection. We will address the challenges in generating 3D PPINs due to the restricted number of solved protein structures. Finally, some of the current use of 3D PPINs will be discussed, highlighting their contribution to the studies in genotype-phenotype relationships and in the optimization of targeted studies to design novel chemical compounds for medical treatments.

Drug-Like Protein-Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology

[Formula: see text] Fundamental processes in living cells are largely controlled by macromolecular interactions and among them, protein-protein interactions (PPIs) have a critical role while their dysregulations can contribute to the pathogenesis of numerous diseases. Although PPIs were considered as attractive pharmaceutical targets already some years ago, they have been thus far largely unexploited for therapeutic interventions with low molecular weight compounds. Several limiting factors, from technological hurdles to conceptual barriers, are known, which, taken together, explain why research in this area has been relatively slow. However, this last decade, the scientific community has challenged the dogma and became more enthusiastic about the modulation of PPIs with small drug-like molecules. In fact, several success stories were reported both, at the preclinical and clinical stages. In this review article, written for the 2014 International Summer School in Chemoinformatics (Strasbourg, France), we discuss in silico tools (essentially post 2012) and databases that can assist the design of low molecular weight PPI modulators (these tools can be found at www.vls3d.com). We first introduce the field of protein-protein interaction research, discuss key challenges and comment recently reported in silico packages, protocols and databases dedicated to PPIs. Then, we illustrate how in silico methods can be used and combined with experimental work to identify PPI modulators.

Functional molecular units for guiding biomarker panel design

The field of biomarker research has experienced a major boost in recent years, and the number of publications on biomarker studies evaluating given, but also proposing novel biomarker candidates is increasing rapidly for numerous clinically relevant disease areas. However, individual markers often lack sensitivity and specificity in the clinical context, resting essentially on the intra-individual phenotype variability hampering sensitivity, or on assessing more general processes downstream of the causative molecular events characterizing a disease term, in consequence impairing disease specificity. The trend to circumvent these shortcomings goes towards utilizing multimarker panels, thus combining the strength of individual markers to further enhance performance regarding both sensitivity and specificity. A way of identifying the optimal composition of individual markers in a panel approach is to pick each marker as representative for a specific pathophysiological (mechanistic) process relevant for the disease under investigation, hence resulting in a multimarker panel for covering the set of pathophysiological processes underlying the frequently multifactorial composition of a clinical phenotype.Here we outline a procedure of identifying such sets of disease-specific pathophysiological processes (units) delineated on the basis of disease-associated molecular feature lists derived from literature mining as well as aggregated, publicly available Omics profiling experiments. With such molecular units in hand, providing an improved reflection of a specific clinical phenotype, biomarker candidates can then be assigned to or novel candidates are to be selected from these units, subsequently resulting in a multimarker panel promising improved accuracy in disease diagnosis as well as prognosis.

Controlled vocabularies and ontologies in proteomics: overview, principles and practice

This paper focuses on the use of controlled vocabularies (CVs) and ontologies especially in the area of proteomics, primarily related to the work of the Proteomics Standards Initiative (PSI). It describes the relevant proteomics standard formats and the ontologies used within them. Software and tools for working with these ontology files are also discussed. The article also examines the "mapping files" used to ensure correct controlled vocabulary terms that are placed within PSI standards and the fulfillment of the MIAPE (Minimum Information about a Proteomics Experiment) requirements. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

The MIntAct project--IntAct as a common curation platform for 11 molecular interaction databases

IntAct (freely available at http://www.ebi.ac.uk/intact) is an open-source, open data molecular interaction database populated by data either curated from the literature or from direct data depositions. IntAct has developed a sophisticated web-based curation tool, capable of supporting both IMEx- and MIMIx-level curation. This tool is now utilized by multiple additional curation teams, all of whom annotate data directly into the IntAct database. Members of the IntAct team supply appropriate levels of training, perform quality control on entries and take responsibility for long-term data maintenance. Recently, the MINT and IntAct databases decided to merge their separate efforts to make optimal use of limited developer resources and maximize the curation output. All data manually curated by the MINT curators have been moved into the IntAct database at EMBL-EBI and are merged with the existing IntAct dataset. Both IntAct and MINT are active contributors to the IMEx consortium (http://www.imexconsortium.org).

Integrating in silico resources to map a signaling network

The abundance of publicly available life science databases offers a wealth of information that can support interpretation of experimentally derived data and greatly enhance hypothesis generation. Protein interaction and functional networks are not simply new renditions of existing data: they provide the opportunity to gain insights into the specific physical and functional role a protein plays as part of the biological system. In this chapter, we describe different in silico tools that can quickly and conveniently retrieve data from existing data repositories and we discuss how the available tools are best utilized for different purposes. While emphasizing protein-protein interaction databases (e.g., BioGrid and IntAct), we also introduce metasearch platforms such as STRING and GeneMANIA, pathway databases (e.g., BioCarta and Pathway Commons), text mining approaches (e.g., PubMed and Chilibot), and resources for drug-protein interactions, genetic information for model organisms and gene expression information based on microarray data mining. Furthermore, we provide a simple step-by-step protocol for building customized protein-protein interaction networks in Cytoscape, a powerful network assembly and visualization program, integrating data retrieved from these various databases. As we illustrate, generation of composite interaction networks enables investigators to extract significantly more information about a given biological system than utilization of a single database or sole reliance on primary literature.

Plant proteomics: from genome sequencing to proteome databases and repositories

Proteomic approaches are useful for the identification of functional proteins. These have been enhanced not only by the development of proteomic techniques but also in concert with genome sequencing. In this chapter, 30 databases and Web sites relating to plant proteomics are reviewed and recent technologies relating to data collection and annotation are surveyed.

Protein-Protein Interactions: Gene Acronym Redundancies and Current Limitations Precluding Automated Data Integration

Understanding protein interaction networks and their dynamic changes is a major challenge in modern biology. Currently, several experimental and in silico approaches allow the screening of protein interactors in a large-scale manner. Therefore, the bulk of information on protein interactions deposited in databases and peer-reviewed published literature is constantly growing. Multiple databases interfaced from user-friendly web tools recently emerged to facilitate the task of protein interaction data retrieval and data integration. Nevertheless, as we evidence in this report, despite the current efforts towards data integration, the quality of the information on protein interactions retrieved by in silico approaches is frequently incomplete and may even list false interactions. Here we point to some obstacles precluding confident data integration, with special emphasis on protein interactions, which include gene acronym redundancies and protein synonyms. Three human proteins (choline kinase, PPIase and uromodulin) and three different web-based data search engines focused on protein interaction data retrieval (PSICQUIC, DASMI and BIPS) were used to explain the potential occurrence of undesired errors that should be considered by researchers in the field. We demonstrate that, despite the recent initiatives towards data standardization, manual curation of protein interaction networks based on literature searches are still required to remove potential false positives. A three-step workflow consisting of: (i) data retrieval from multiple databases, (ii) peer-reviewed literature searches, and (iii) data curation and integration, is proposed as the best strategy to gather updated information on protein interactions. Finally, this strategy was applied to compile bona fide information on human DREAM protein interactome, which constitutes liable training datasets that can be used to improve computational predictions.

A new reference implementation of the PSICQUIC web service

The Proteomics Standard Initiative Common QUery InterfaCe (PSICQUIC) specification was created by the Human Proteome Organization Proteomics Standards Initiative (HUPO-PSI) to enable computational access to molecular-interaction data resources by means of a standard Web Service and query language. Currently providing >150 million binary interaction evidences from 28 servers globally, the PSICQUIC interface allows the concurrent search of multiple molecular-interaction information resources using a single query. Here, we present an extension of the PSICQUIC specification (version 1.3), which has been released to be compliant with the enhanced standards in molecular interactions. The new release also includes a new reference implementation of the PSICQUIC server available to the data providers. It offers augmented web service capabilities and improves the user experience. PSICQUIC has been running for almost 5 years, with a user base growing from only 4 data providers to 28 (April 2013) allowing access to 151 310 109 binary interactions. The power of this web service is shown in PSICQUIC View web application, an example of how to simultaneously query, browse and download results from the different PSICQUIC servers. This application is free and open to all users with no login requirement (http://www.ebi.ac.uk/Tools/webservices/psicquic/view/main.xhtml).

Information content and scalability in signal transduction network reconstruction formats

One of the first steps towards holistic understanding of cellular networks is the integration of the available information in a human and machine readable format. This network reconstruction process is well established for metabolic networks, and numerous genome wide metabolic reconstructions are already available. Extending these strategies to signalling networks has proven difficult, primarily due to the combinatorial nature of regulatory modifications. The combinatorial nature of possible protein-protein interactions and post translational modifications affects both network size and the correspondence between the reconstructed network and the underlying empirical data. Here, we discuss different approaches to reconstruction of signal transduction networks. We divide the current approaches into topological, specific state based and reaction-contingency based, and discuss their different information content and scalability. The discussion focusses on graphical formats but the points are in general applicable also to mathematical models and databases. While the formats have complementary strengths especially for small networks, reaction-contingency based formats have a number of advantages in the light of global network reconstruction. In particular, they minimise the need for assumptions, maximise the congruence with empirical data, and scale efficiently with network size.