Reactome pathway analysis to enrich biological discovery in proteomics data sets

Reactome (http://www.reactome.org) is an open-source, expert-authored, peer-reviewed, manually curated database of reactions, pathways and biological processes. We provide an intuitive web-based user interface to pathway knowledge and a suite of data analysis tools. The Pathway Browser is a Systems Biology Graphical Notation-like visualization system that supports manual navigation of pathways by zooming, scrolling and event highlighting, and that exploits PSI Common Query Interface web services to overlay pathways with molecular interaction data from the Reactome Functional Interaction Network and interaction databases such as IntAct, ChEMBL and BioGRID. Pathway and expression analysis tools employ web services to provide ID mapping, pathway assignment and over-representation analysis of user-supplied data sets. By applying Ensembl Compara to curated human proteins and reactions, Reactome generates pathway inferences for 20 other species. The Species Comparison tool provides a summary of results for each of these species as a table showing numbers of orthologous proteins found by pathway from which users can navigate to inferred details for specific proteins and reactions. Reactome's diverse pathway knowledge and suite of data analysis tools provide a platform for data mining, modeling and analysis of large-scale proteomics data sets. This Tutorial is part of the International Proteomics Tutorial Programme (IPTP 8).     

PATHLOGIC-S: A Scalable Boolean Framework for Modelling Cellular Signalling

Curated databases of signal transduction have grown to describe several thousand reactions, and efficient use of these data requires the development of modelling tools to elucidate and explore system properties. We present PATHLOGIC-S, a Boolean specification for a signalling model, with its associated GPL-licensed implementation using integer programming techniques. The PATHLOGIC-S specification has been designed to function on current desktop workstations, and is capable of providing analyses on some of the largest currently available datasets through use of Boolean modelling techniques to generate predictions of stable and semi-stable network states from data in community file formats. PATHLOGIC-S also addresses major problems associated with the presence and modelling of inhibition in Boolean systems, and reduces logical incoherence due to common inhibitory mechanisms in signalling systems. We apply this approach to signal transduction networks including Reactome and two pathways from the Panther Pathways database, and present the results of computations on each along with a discussion of execution time. A software implementation of the framework and model is freely available under a GPL license.     

A guide to the Proteomics Identifications Database proteomics data repository

The Proteomics Identifications Database (PRIDE, www.ebi.ac.uk/pride ) is one of the main repositories of MS derived proteomics data. Here, we point out the main functionalities of PRIDE both as a submission repository and as a source for proteomics data. We describe the main features for data retrieval and visualization available through the PRIDE web and BioMart interfaces. We also highlight the mechanism by which tailored queries in the BioMart can join PRIDE to other resources such as Reactome, Ensembl or UniProt to execute extremely powerful across‐domain queries. We then present the latest improvements in the PRIDE submission process, using the new easy‐to‐use, platform‐independent graphical user interface submission tool PRIDE Converter. Finally, we speak about future plans and the role of PRIDE in the ProteomExchange consortium.     

蛋白质相互作用数据库

随着“蛋白质组学”的蓬勃发展和人类对生物大分子功能机制的知识积累,涌现出海量的蛋白质相互作用数据。随之,研究者开发了300多个蛋白质相互作用数据库,用于存储、展示和数据的重利用。蛋白质相互作用数据库是系统生物学、分子生物学和临床药物研究的宝贵资源。本文将数据库分为3类：（1）综合蛋白质相互作用数据库;（2）特定物种的蛋白质相互作用数据库;（3）生物学通路数据库。重点介绍常用的蛋白质相互作用数据库,包括BioGRID、STRING、IntAct、MINT、DIP、IMEx、HPRD、Reactome和KEGG等。     

Human and chicken TLR pathways: manual curation and computer-based orthology analysis

The innate immune responses mediated by Toll-like receptors (TLR) provide an evolutionarily well-conserved first line of defense against microbial pathogens. In the Reactome Knowledgebase we previously integrated annotations of human TLR molecular functions with those of over 4000 other human proteins involved in processes such as adaptive immunity, DNA replication, signaling, and intermediary metabolism, and have linked these annotations to external resources, including PubMed, UniProt, EntrezGene, Ensembl, and the Gene Ontology to generate a resource suitable for data mining, pathway analysis, and other systems biology approaches. We have now used a combination of manual expert curation and computer-based orthology analysis to generate a set of annotations for TLR molecular function in the chicken (Gallus gallus). Mammalian and avian lineages diverged approximately 300 million years ago, and the avian TLR repertoire consists of both orthologs and distinct new genes. The work described here centers on the molecular biology of TLR3, the host receptor that mediates responses to viral and other doubled-stranded polynucleotides, as a paradigm for our approach to integrated manual and computationally based annotation and data analysis. It tests the quality of computationally generated annotations projected from human onto other species and supports a systems biology approach to analysis of virus-activated signaling pathways and identification of clinically useful antiviral measures.     

HEPNet: A Knowledge Base Model of Human Energy Pool Network for Predicting the Energy Availability Status of an Individual

HEPNet is an electronic representation of metabolic reactions occurring within human cellular organization focusing on inflow and outflow of the energy currency ATP, GTP and other energy associated moieties. The backbone of HEPNet consists of primary bio-molecules such as carbohydrates, proteins and fats which ultimately constitute the chief source for the synthesis and obliteration of energy currencies in a cell. A series of biochemical pathways and reactions constituting the catabolism and anabolism of various metabolites are portrayed through cellular compartmentalization. The depicted pathways function synchronously toward an overarching goal of producing ATP and other energy associated moieties to bring into play a variety of cellular functions. HEPNet is manually curated with raw data from experiments and is also connected to KEGG and Reactome databases. This model has been validated by simulating it with physiological states like fasting, starvation, exercise and disease conditions like glycaemia, uremia and dihydrolipoamide dehydrogenase deficiency (DLDD). The results clearly indicate that ATP is the master regulator under different metabolic conditions and physiological states. The results also highlight that energy currencies play a minor role. However, the moiety creatine phosphate has a unique character, since it is a ready-made source of phosphoryl groups for the rapid synthesis of ATP from ADP. HEPNet provides a framework for further expanding the network diverse age groups of both the sexes, followed by the understanding of energetics in more complex metabolic pathways that are related to human disorders.     

A multivariate approach for integrating genome-wide expression data and biological knowledge

MOTIVATION: Several statistical methods that combine analysis of differential gene expression with biological knowledge databases have been proposed for a more rapid interpretation of expression data. However, most such methods are based on a series of univariate statistical tests and do not properly account for the complex structure of gene interactions. RESULTS: We present a simple yet effective multivariate statistical procedure for assessing the correlation between a subspace defined by a group of genes and a binary phenotype. A subspace is deemed significant if the samples corresponding to different phenotypes are well separated in that subspace. The separation is measured using Hotelling's T(2) statistic, which captures the covariance structure of the subspace. When the dimension of the subspace is larger than that of the sample space, we project the original data to a smaller orthonormal subspace. We use this method to search through functional pathway subspaces defined by Reactome, KEGG, BioCarta and Gene Ontology. To demonstrate its performance, we apply this method to the data from two published studies, and visualize the results in the principal component space.     

Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis

Over-representation analysis (ORA) is one of the commonest pathway analysis approaches used for the functional interpretation of metabolomics datasets. Despite the widespread use of ORA in metabolomics, the community lacks guidelines detailing its best-practice use. Many factors have a pronounced impact on the results, but to date their effects have received little systematic attention. Using five publicly available datasets, we demonstrated that changes in parameters such as the background set, differential metabolite selection methods, and pathway database used can result in profoundly different ORA results. The use of a non-assay-specific background set, for example, resulted in large numbers of false-positive pathways. Pathway database choice, evaluated using three of the most popular metabolic pathway databases (KEGG, Reactome, and BioCyc), led to vastly different results in both the number and function of significantly enriched pathways. Factors that are specific to metabolomics data, such as the reliability of compound identification and the chemical bias of different analytical platforms also impacted ORA results. Simulated metabolite misidentification rates as low as 4% resulted in both gain of false-positive pathways and loss of truly significant pathways across all datasets. Our results have several practical implications for ORA users, as well as those using alternative pathway analysis methods. We offer a set of recommendations for the use of ORA in metabolomics, alongside a set of minimal reporting guidelines, as a first step towards the standardisation of pathway analysis in metabolomics.     

PhosphaBase: an ontology-driven database resource for protein phosphatases

PhosphaBase is an ontology-driven database resource containing information on the protein phosphatase family. It is the first public resource dedicated to protein phosphatases, which are enzymes that perform dephosphorylation reactions. In conjunction with the phosphorylation action of protein kinases, phosphatases are involved in important control and communication mechanisms in the cell. They have also been implicated in many human diseases, including diabetes and obesity, cancers, and neurodegenerative conditions. PhosphaBase aims to centralize the growing base of knowledge in the phosphatase research domain. The resource is built around a formal, domain-specific DAML+OIL ontology, and the data are collected from heterogeneous biological sources using Gene Ontology terms as a means of data extraction. The overall ontology-driven architecture provides a robust structure with distinct advantages for sustainability and provides the potential for the development of diagnostic tools, as well as a data repository.     

Whole-Genome Pathway Analysis on 132,497 Individuals Identifies Novel Gene-Sets Associated with Body Mass Index

Whole genome pathway analysis is a powerful tool for the exploration of the combined effects of gene-sets within biological pathways. This study applied Interval Based Enrichment Analysis (INRICH) to perform whole-genome pathway analysis of body-mass index (BMI). We used a discovery set composed of summary statistics from a meta-analysis of 123,865 subjects performed by the GIANT Consortium, and an independent sample of 8,632 subjects to assess replication of significant pathways. We examined SNPs within nominally significant pathways using linear mixed models to estimate their contribution to overall BMI heritability. Six pathways replicated as having significant enrichment for association after correcting for multiple testing, including the previously unknown relationships between BMI and the Reactome regulation of ornithine decarboxylase pathway, the KEGG lysosome pathway, and the Reactome stabilization of P53 pathway. Two non-overlapping sets of genes emerged from the six significant pathways. The clustering of shared genes based on previously identified protein-protein interactions listed in PubMed and OMIM supported the relatively independent biological effects of these two gene-sets. We estimate that the SNPs located in examined pathways explain ∼20% of the heritability for BMI that is tagged by common SNPs (3.35% of the 16.93% total).     

Estimating resource preferences of a native bumblebee: the effects of availability and use–availability models on preference estimates

Identifying resource preference is considered essential for developing targeted conservation plans but, for many species, questions remain about the best way to estimate preference. Resource preferences for bees are particularly difficult to determine as the resources they collect, nectar and pollen, are challenging to estimate availability and collection. Resources are traditionally measured at the flower or inflorescence level, but these measures of availability do not correspond to the resources actually used by bees. Additionally, it is unclear as to whether common models including availability are appropriate for bees which may target resources regardless of available quantities. Here we first compare two common hypotheses of resource use – the ‘random use hypothesis’ and the ‘linear preferences hypothesis’ – using three different measures of availability (pollen, flower and inflorescence) – to determine if one measure of availability was better for understanding bee pollen use. Next, the superior model using availability was compared to a novel model of bee pollen use the ‘target use hypothesis’. This model assumes that bees target some resources regardless of how much of each resource is available (but assuming resources are present at a site), and thus models preference without availability data. Of the models including availability, the linear preference model using inflorescence availability best explained the pollen use data. This suggests that bumblebee pollen use is non‐random and that cues to identify and locate resources (i.e. display size and quantity) may be more important than the quantity of the resource available (i.e. pollen availability). Additionally, in most cases the target use model explained the data equal to or better than the other models suggesting bee resource use may be better modeled without measured availability data compared to linear models. These results could be important for expanding resource use analysis of bees that are difficult to quantify availability.     

Trends in Austrian Resource Efficiency: An Exergy and Useful Work Analysis in Comparison to Material Use,CO2 Emissions,and Land Use

In the past few years, resource use and resource efficiency have been implemented in the European Union (EU) environmental policy programs as well as international sustainable development programs. In their programs, the EU focuses on four resource types that should be addressed: materials, energy (or carbon dioxide [CO₂] emissions), water, and land. In this article, we first discuss different perspectives on energy use and present the results of a long‐term exergy and useful work analysis of the Austrian economy for the period 1900–2012, using the methodology developed by Ayres and Warr. Second, we discuss Austrian resource efficiency by comparing the presented exergy and useful work data with material use, CO₂ emissions, and land‐use data taken from statistical sources. This comparison provides, for the first time, a long‐term analysis of Austrian resource efficiency based on a broad understanding thereof and evaluates Austrian development in relation to EU and Austrian policy targets.     

Can Individual and Social Patterns of Resource Use Buffer Animal Populations against Resource Decline?

Species in many ecosystems are facing declines of key resources. If we are to understand and predict the effects of resource loss on natural populations, we need to understand whether and how the way animals use resources changes under resource decline. We investigated how the abundance of arboreal marsupials varies in response to a critical resource, hollow-bearing trees. Principally, we asked what mechanisms mediate the relationship between resources and abundance? Do animals use a greater or smaller proportion of the remaining resource, and is there a change in cooperative resource use (den sharing), as the availability of hollow trees declines? Analyses of data from 160 sites surveyed from 1997 to 2007 showed that hollow tree availability was positively associated with abundance of the mountain brushtail possum, the agile antechinus and the greater glider. The abundance of Leadbeater’s possum was primarily influenced by forest age. Notably, the relationship between abundance and hollow tree availability was significantly less than 1∶1 for all species. This was due primarily to a significant increase by all species in the proportional use of hollow-bearing trees where the abundance of this resource was low. The resource-sharing response was weaker and inconsistent among species. Two species, the mountain brushtail possum and the agile antechinus, showed significant but contrasting relationships between the number of animals per occupied tree and hollow tree abundance. The discrepancies between the species can be explained partly by differences in several aspects of the species’ biology, including body size, types of hollows used and social behaviour as it relates to hollow use. Our results show that individual and social aspects of resource use are not always static in response to resource availability and support the need to account for dynamic resource use patterns in predictive models of animal distribution and abundance.     

A general framework for the analysis of animal resource selection from telemetry data

Summary .   We propose a general framework for the analysis of animal telemetry data through the use of weighted distributions. It is shown that several interpretations of resource selection functions arise when constructed from the ratio of a use and availability distribution. Through the proposed general framework, several popular resource selection models are shown to be special cases of the general model by making assumptions about animal movement and behavior. The weighted distribution framework is shown to be easily extended to readily account for telemetry data that are highly autocorrelated; as is typical with use of new technology such as global positioning systems animal relocations. An analysis of simulated data using several models constructed within the proposed framework is also presented to illustrate the possible gains from the flexible modeling framework. The proposed model is applied to a brown bear data set from southeast Alaska.     

Validation of elk resource selection models with spatially independent data

Knowledge of how landscape features affect wildlife resource use is essential for informed management. Resource selection functions often are used to make and validate predictions about landscape use; however, resource selection functions are rarely validated with data from landscapes independent of those from which the models were built. This problem has severely limited the application of resource selection functions over larger geographic areas for widely distributed species. North American elk (Cervus elaphus) is an example of a widely-distributed species of keen interest to managers and for which validation of resource selection functions over large geographic areas is important. We evaluated the performance of resource selection functions developed for elk on one landscape in northeast Oregon with independent data from a different landscape in the same region. We compared predicted versus observed elk resource use for 9 monthly or seasonal periods across 3 yr. Results showed strong, positive agreement between predicted and observed use for 2 spring and 3 late summer-early fall models (3-yr r = 0.81–0.95). Predicted versus observed use was negatively or weakly positively correlated for 3 summer models and 1 mid-fall model (3-yr r = −0.57–0.14). Predicted and observed use correlated well when forage was limited (spring and late summer or early fall), corresponding to important biological stages for elk (parturition and breeding seasons). For these seasonal periods, model covariates such as rate of motorized traffic and canopy closure often were effective predictors of elk resource selection. The models we validated for spring and late summer-early fall may be used to evaluate management activities in areas with similar landscape characteristics. © 2010 The Wildlife Society.     

Equipment-Free Incubation of Recombinase Polymerase Amplification Reactions Using Body Heat

The development of isothermal amplification platforms for nucleic acid detection has the potential to increase access to molecular diagnostics in low resource settings; however, simple, low-cost methods for heating samples are required to perform reactions. In this study, we demonstrated that human body heat may be harnessed to incubate recombinase polymerase amplification (RPA) reactions for isothermal amplification of HIV-1 DNA. After measuring the temperature of mock reactions at 4 body locations, the axilla was chosen as the ideal site for comfortable, convenient incubation. Using commonly available materials, 3 methods for securing RPA reactions to the body were characterized. Finally, RPA reactions were incubated using body heat while control RPA reactions were incubated in a heat block. At room temperature, all reactions with 10 copies of HIV-1 DNA and 90% of reactions with 100 copies of HIV-1 DNA tested positive when incubated with body heat. In a cold room with an ambient temperature of 10 degrees Celsius, all reactions containing 10 copies or 100 copies of HIV-1 DNA tested positive when incubated with body heat. These results suggest that human body heat may provide an extremely low-cost solution for incubating RPA reactions in low resource settings.     

Material Flows and Material Productivity in China,Australia, and Japan

This article presents material flows and material productivity data and indicators for Australia, China, and Japan for the period 1970 to 2005. The main data used come from a new material flows database for the Asia‐Pacific region that was assembled using up‐to‐date standardized methodologies of material flow accounting and significantly extends the knowledge base available for studies on resource use dynamics in the region. We show that the three nations studied here have diverging patterns of resource use, and that these patterns can be linked to interdependencies between them and the very different roles each nation plays within a globalized system of natural resource exploitation. We also conduct a brief analysis of the most important drivers of changes in their resource use over the period, using an IPAT framework (Impact = Population × Affluence × Technology). The fundamentally different economic structures and trading roles of each country, that is, primary resource provider (Australia), mature and advanced manufacturer (Japan), and rapidly industrializing developing country (China), lead to starkly different contexts in which appropriate policies to encourage sustainable resource use must be formulated.     

GUILDify v2.0: A Tool to Identify Molecular Networks Underlying Human Diseases,Their Comorbidities and Their Druggable Targets

The genetic basis of complex diseases involves alterations on multiple genes. Unraveling the interplay between these genetic factors is key to the discovery of new biomarkers and treatments. In 2014, we introduced GUILDify, a web server that searches for genes associated to diseases, finds novel disease genes applying various network-based prioritization algorithms and proposes candidate drugs. Here, we present GUILDify v2.0, a major update and improvement of the original method, where we have included protein interaction data for seven species and 22 human tissues and incorporated the disease–gene associations from DisGeNET. To infer potential disease relationships associated with multi-morbidities, we introduced a novel feature for estimating the genetic and functional overlap of two diseases using the top-ranking genes and the associated enrichment of biological functions and pathways (as defined by GO and Reactome). The analysis of this overlap helps to identify the mechanistic role of genes and protein–protein interactions in comorbidities. Finally, we provided an R package, guildifyR, to facilitate programmatic access to GUILDify v2.0 (http://sbi.upf.edu/guildify2)