Mapping Plant Interactomes Using Literature Curated and Predicted Protein–Protein Interaction Data Sets

Most cellular processes are enabled by cohorts of interacting proteins that form dynamic networks within the plant proteome. The study of these networks can provide insight into protein function and provide new avenues for research. This article informs the plant science community of the currently available sources of protein interaction data and discusses how they can be useful to researchers. Using our recently curated IntAct Arabidopsis thaliana protein–protein interaction data set as an example, we discuss potentials and limitations of the plant interactomes generated to date. In addition, we present our efforts to add value to the interaction data by using them to seed a proteome-wide map of predicted protein subcellular locations.For well over two decades, plant scientists have studied protein interactions within plants using many different and evolving approaches. Their findings are represented by a large and growing corpus of peer-reviewed literature reflecting the increasing activity in this area of plant proteomic research. More recently, a number of predicted interactomes have been reported in plants and, while these predictions remain largely untested, they could act as a useful guide for future research. These studies have allowed researchers to better understand the function of protein complexes and to refine our understanding of protein function within the cell (Uhrig, 2006; Morsy et al., 2008). The extraction of protein interaction data from the literature and its standardized deposition and representation within publicly available databases remains a challenging task. Aggregating the data in databases allows researchers to leverage visualization, data mining, and integrative approaches to produce new insights that would be unachievable when the data are dispersed within largely inaccessible formats (Rodriguez et al., 2009).Currently, there are three databases that act as repositories of plant protein interaction data. These are IntAct (http://www.ebi.ac.uk/intact/; Aranda et al., 2010), The Arabidopsis Information Resource (TAIR; http://www.Arabidopsis.org/; Poole, 2007), and BioGRID (http://www.thebiogrid.org/; Breitkreutz et al., 2008). These databases curate experimentally established interactions available from the peer-reviewed literature (as opposed to predicted interactions, which will be discussed below). Each repository takes its own approach to the capture, storage, and representation of protein interaction data. TAIR focuses on Arabidopsis thaliana protein–protein interaction data exclusively; BioGRID currently focuses on the plant species Arabidopsis and rice (Oryza sativa), while IntAct attempts to capture protein interaction data from any plant species. Unlike the other repositories, IntAct follows a deep curation strategy that captures detailed experimental and biophysical details, such as binding regions and subcellular locations of interactions using controlled vocabularies (Aranda et al., 2010). While the majority of plant interaction data held by IntAct concern protein–protein interaction data in Arabidopsis, there is a small but growing content of interaction data relating to protein–DNA, protein–RNA, and protein–small molecule interactions, as well as interaction data from other plant species.Using the IntAct Arabidopsis data set as an example, we outline how the accumulating knowledge captured in these repositories can be used to further our understanding of the plant proteome. We compare the characteristics of predicted interactomes with the IntAct protein–protein interaction data set, which consists entirely of experimentally measured protein interactions, to gauge the predictive accuracy of these studies. Finally, we show how the IntAct data set can be used together with a recently developed Divide and Conquer k-Nearest Neighbors Method (DC-kNN; K. Lee et al., 2008) to predict the subcellular locations for most Arabidopsis proteins. This data set predicts high confidence subcellular locations for many unannotated Arabidopsis proteins and should act as a useful resource for future studies of protein function. Although this article focuses on the IntAct Arabidopsis protein–protein interaction data set, readers are also encouraged to explore the resources offered by our colleagues at TAIR and BioGRID.Each database employs its own system to report molecular interactions, as represented in the referenced source publications, and each avoids making judgments on interaction reliability or whether two participants in a complex have a direct interaction. Thus, the user should carefully filter these data sets for their specific purpose based on the full annotation of the data sets. In particular, the user should consider the experimental methods and independent observation of the same interaction in different publications when assessing the reliability and type of interaction of the proteins (e.g., direct or indirect). Confidence scoring schemes for interaction data are discussed widely in the literature (Yu and Finley, 2009).     

Can Data Science Inform Environmental Justice and Community Risk Screening for Type 2 Diabetes?

Background

Having the ability to scan the entire country for potential “hotspots” with increased risk of developing chronic diseases due to various environmental, demographic, and genetic susceptibility factors may inform risk management decisions and enable better environmental public health policies.

Objectives

Develop an approach for community-level risk screening focused on identifying potential genetic susceptibility hotpots.

Methods

Our approach combines analyses of phenotype-genotype data, genetic prevalence of single nucleotide polymorphisms, and census/geographic information to estimate census tract-level population attributable risks among various ethnicities and total population for the state of California.

Results

We estimate that the rs13266634 single nucleotide polymorphism, a type 2 diabetes susceptibility genotype, has a genetic prevalence of 56.3%, 47.4% and 37.0% in Mexican Mestizo, Caucasian, and Asian populations. Looking at the top quintile for total population attributable risk, 16 California counties have greater than 25% of their population living in hotspots of genetic susceptibility for developing type 2 diabetes due to this single genotypic susceptibility factor.

Conclusions

This study identified counties in California where large portions of the population may bear additional type 2 diabetes risk due to increased genetic prevalence of a susceptibility genotype. This type of screening can easily be extended to include information on environmental contaminants of interest and other related diseases, and potentially enables the rapid identification of potential environmental justice communities. Other potential uses of this approach include problem formulation in support of risk assessments, land use planning, and prioritization of site cleanup and remediation actions.     

Bayesian Risk Mapping and Model-Based Estimation of Schistosoma haematobium–Schistosoma mansoni Co-distribution in C?te d′Ivoire

Background

Schistosoma haematobium and Schistosoma mansoni are blood flukes that cause urogenital and intestinal schistosomiasis, respectively. In Côte d′Ivoire, both species are endemic and control efforts are being scaled up. Accurate knowledge of the geographical distribution, including delineation of high-risk areas, is a central feature for spatial targeting of interventions. Thus far, model-based predictive risk mapping of schistosomiasis has relied on historical data of separate parasite species.

Methodology

We analyzed data pertaining to Schistosoma infection among school-aged children obtained from a national, cross-sectional survey conducted between November 2011 and February 2012. More than 5,000 children in 92 schools across Côte d′Ivoire participated. Bayesian geostatistical multinomial models were developed to assess infection risk, including S. haematobium–S. mansoni co-infection. The predicted risk of schistosomiasis was utilized to estimate the number of children that need preventive chemotherapy with praziquantel according to World Health Organization guidelines.

Principal Findings

We estimated that 8.9% of school-aged children in Côte d′Ivoire are affected by schistosomiasis; 5.3% with S. haematobium and 3.8% with S. mansoni. Approximately 2 million annualized praziquantel treatments would be required for preventive chemotherapy at health districts level. The distinct spatial patterns of S. haematobium and S. mansoni imply that co-infection is of little importance across the country.

Conclusions/Significance

We provide a comprehensive analysis of the spatial distribution of schistosomiasis risk among school-aged children in Côte d′Ivoire and a strong empirical basis for a rational targeting of control interventions.     

Cleaning Products,Environmental Awareness and Risk Perception in Mérida,Mexico

Cleaning products are associated with many health and environmental problems. Contamination of water resources by cleaning products is more likely to occur with septic tanks as sewage treatment systems especially in karstic terrains. We explored women’s ideas about water sources and the risk cleaning products pose to health and sewage in Mérida, a city in the Yucatán peninsula of Mexico. Women were unaware of the city’s water management system. We found a positive and statistically significant association between risk perception and environmental awareness, education level and employment status. We suggest developing education and risk communication strategies to inform residents about the hydro-geological features in the Yucatán, the vulnerability of its karstic aquifer and the health and environmental risks associated with cleaning agents.     

Reconstruction of coral reef fisheries catches in American Samoa, 1950–2002

Fisheries catches from Pacific Island coral reefs are rarely recorded in official statistics. Reconstruction of catch estimates with limited hard data requires interpolation and assumptions, justifiable only by the unsatisfactory alternative of continued substitution of zero catches, a common policy interpretation for ‘no data’. Uncertainties associated with reconstructions are high, requiring conservative estimation. American Samoan domestic fisheries consist of an artisanal, small-boat sector, whose commercial catches are reported, and a shore-based subsistence sector, with no regular reporting. Our catch reconstruction (with large pelagic species removed) suggested a 79% decrease in catches between 1950 (752 t) and 2002 (155 t). Accounting for rapid human population growth on the main island, the per capita catch rate may have declined from 36.3 kg·person⁻¹ year⁻¹ in 1950 to 1.3 kg·person⁻¹ year⁻¹ by 2002, while the catch rate for the inhabited outer islands has been independently reported as 58.6 kg·person⁻¹ year⁻¹. Catch per area of coral reef (to 50-m depth) may have declined from 5.5 to 0.7 t km⁻² year⁻¹ for the main island, and from 9.1 to 4.9 t km⁻² year⁻¹ for the outer islands, for 1950 and 2002, respectively. Summed for 1950–2002, our reconstruction suggested a 17-fold difference between reconstructed estimates and reported statistics.     

Evaluation of a Community’s Risk for Canine Parvovirus and Distemper Using Antibody Testing and GIS Mapping of Animal Shelter Intakes

This cross-sectional study aimed to identify where dogs with negative antibody tests to canine parvovirus (CPV) and canine distemper virus (CDV) originated when entering a community shelter, using a commercially available ELISA antibody test and Geographic Information Systems mapping. Of 2745 canines entering during a three-month period, 1056 test results were obtained. Dogs or puppies weighing over 2 lb were eligible if they could be humanely, nonchemically restrained for phlebotomy. Age and minor health issues weren't exclusions. Dogs were excluded if trained personnel were concerned health would be compromised by phlebotomy. Blood samples were collected within 24 hours of entry. Four hundred and twenty-seven (40%) dogs had positive antibody test results for both viruses, 422 (40%) were positive for CPV, 37 (4%) were positive for CDV, and 170 (16%) were negative for both. Mapping revealed geographic patterns for dogs with negative antibody tests. This shelter admitted dogs with negative CPV and/or CDV antibody tests from defined community areas. Targeting vaccination efforts in communities to areas where dogs with negative antibody tests originate could be an effective wellness strategy.     

Do We Know Whether Researchers and Reviewers are Estimating Risk and Benefit Accurately?

Accurate estimation of risk and benefit is integral to good clinical research planning, ethical review, and study implementation. Some commentators have argued that various actors in clinical research systems are prone to biased or arbitrary risk/benefit estimation. In this commentary, we suggest the evidence supporting such claims is very limited. Most prior work has imputed risk/benefit beliefs based on past behavior or goals, rather than directly measuring them. We describe an approach – forecast analysis – that would enable direct and effective measure of the quality of risk/benefit estimation. We then consider some objections and limitations to the forecasting approach.     

Open Data in Global Environmental Research: The Belmont Forum’s Open Data Survey

This paper presents the findings of the Belmont Forum’s survey on Open Data which targeted the global environmental research and data infrastructure community. It highlights users’ perceptions of the term “open data”, expectations of infrastructure functionalities, and barriers and enablers for the sharing of data. A wide range of good practice examples was pointed out by the respondents which demonstrates a substantial uptake of data sharing through e-infrastructures and a further need for enhancement and consolidation. Among all policy responses, funder policies seem to be the most important motivator. This supports the conclusion that stronger mandates will strengthen the case for data sharing.     

Convergence and contingency in production–precipitation relationships in North American and South African C4 grasslands

Mesic grasslands in North America and South Africa share many structural attributes, but less is known of their functional similarities. We assessed the control of a key ecosystem process, aboveground net primary production (ANPP), by interannual variation in precipitation amount and pattern via analysis of data sets (15- and 24-year periods) from long-term research programs on each continent. Both sites were dominated by C₄ grasses and had similar growing season climates; thus, we expected convergence in precipitation–ANPP relationships. Lack of convergence, however, would support an alternative hypothesis—that differences in evolutionary history and purportedly greater climatic variability in South Africa fundamentally alter the functioning of southern versus northern hemisphere grasslands. Neither mean annual precipitation nor mean ANPP differed between the South African and North American sites (838 vs. 857 mm/year, 423.5 vs. 461.4 g/m² respectively) and growing season precipitation–ANPP relationships were similar. Despite overall convergence, there were differences between sites in how the seasonal timing of precipitation affected ANPP. In particular, interannual variability in precipitation that fell during the first half of the growing season strongly affected annual ANPP in South Africa (P < 0.01), but was not related to ANPP in North America (P = 0.098). Both sites were affected similarly by late season precipitation. Divergence in the seasonal course of available soil moisture (chronically low in the winter and early spring in the South African site vs. high in the North American site) is proposed as a key contingent factor explaining differential sensitivity in ANPP to early season precipitation in these two grasslands. These long-term data sets provided no support for greater rainfall, temperature or ANPP variability in the South African versus the North American site. However, greater sensitivity of ANPP to early season precipitation in the South African grassland suggests that future patterns of productivity may be more responsive to seasonal changes in climate compared with the North American site.