Discovery and functional prioritization of Parkinson's disease candidate genes from large-scale whole exome sequencing.

BACKGROUND: Whole-exome sequencing (WES) has been successful in identifying genes that cause familial Parkinson's disease (PD). However, until now this approach has not been deployed to study large cohorts of unrelated participants. To discover rare PD susceptibility variants, we performed WES in 1148 unrelated cases and 503 control participants. Candidate genes were subsequently validated for functions relevant to PD based on parallel RNA-interference (RNAi) screens in human cell culture and Drosophila and C. elegans models. RESULTS: Assuming autosomal recessive inheritance, we identify 27 genes that have homozygous or compound heterozygous loss-of-function variants in PD cases. Definitive replication and confirmation of these findings were hindered by potential heterogeneity and by the rarity of the implicated alleles. We therefore looked for potential genetic interactions with established PD mechanisms. Following RNAi-mediated knockdown, 15 of the genes modulated mitochondrial dynamics in human neuronal cultures and four candidates enhanced α-synuclein-induced neurodegeneration in Drosophila. Based on complementary analyses in independent human datasets, five functionally validated genes-GPATCH2L, UHRF1BP1L, PTPRH, ARSB, and VPS13C-also showed evidence consistent with genetic replication. CONCLUSIONS: By integrating human genetic and functional evidence, we identify several PD susceptibility gene candidates for further investigation. Our approach highlights a powerful experimental strategy with broad applicability for future studies of disorders with complex genetic etiologies

GRAAL: Growth, Architecture, ALlocation: a functional-structural model to analyse the interactions between growth and assimilates allocation integrating processes from organ to whole plant

An integrated functional-structural model, called GRAAL, has been developed to simulate and analyse the interactions between morphogenetic processes and assimilate partitioning during the vegetative development of individual plants. GRAAL associates models of plant morphogenesis and models simulating the growth of plant compartments as related to assimilate availability. Using objectoriented methods, knowledge is formalized at the organ level (i.e., local rules of development and functioning). The behaviour of the plant arises from interactions between the organs and the integration of the processes within the whole plant. Shoot and root organs are initiated as a function of temperature. Using the source–sink concept, organ growth is calculated from its potential growth and assimilate availability within the whole plant. In the case of maize plants as regards carbon assimilates, simulation results indicate that the model reproduces the main features of plant functioning (e.g., kinetics of root:shoot ratio for carbon, changes in priority between organs and plant plasticity to carbon availability). More generally, the model is a generic framework for testing and sorting hypotheses on processes involved in plant development. In this respect, it is an integrated ecophysiological tool for analysing the interactions between genotypic and environmental characteristics affecting plant behaviour

A model for simulating the timelines of field operations at a European scale for use in complex dynamic models

Complex dynamic models of carbon and nitrogen are often used to investigate the consequences of climate change on agricultural production and greenhouse gas emissions from agriculture. These models require high temporal resolution input data regarding the timing of field operations. This paper describes the Timelines model, which predicts the timelines of key field operations across Europe. The evaluation of the model suggests that while for some crops a reasonable agreement was obtained in the prediction of the times of field operations, there were some very large differences which need to be corrected. Systematic variations in the date of harvesting and in the timing of the first application of N fertiliser to winter crops need to be corrected and the prediction of soil workability and trafficability might enable the prediction of ploughing and applications of solid manure in preparation for spring crops. The data concerning the thermal time thresholds for sowing and harvesting underlying the model should be updated and extended to a wider range of crops

Effects of farm heterogeneity and methods for upscaling on modelled nitrogen losses in agricultural landscapes

The aim of this study is to illustrate the importance of farm scale heterogeneity on nitrogen (N) losses in agricultural landscapes. Results are exemplified with a chain of N models calculating farm-N balances and distributing the N-surplus to N-losses (volatilisation, denitrification, leaching) and soil-N accumulation/release in a Danish landscape. Possible non-linearities in upscaling are assessed by comparing average model results based on (i) individual farm level calculations and (ii) averaged inputs at landscape level. Effects of the non-linearities that appear when scaling up from farm to landscape are demonstrated. Especially in relation to ammonia losses the non-linearity between livestock density and N-loss is significant (p > 0.999), with around 20–30% difference compared to a scaling procedure not taking this non-linearity into account. A significant effect of farm type on soil N accumulation (p > 0.95) was also identified and needs to be included when modelling landscape level N-fluxes and greenhouse gas emission

Farm nitrogen balances in six European landscapes as an indicator for nitrogen losses and basis for improved management

Improved management of nitrogen (N) in agriculture is necessary to achieve a sustainable balance between the production of food and other biomass, and the unwanted effects of N on water pollution, greenhouse gas emissions, biodiversity deterioration and human health. To analyse farm N-losses and the complex interactions within farming systems, efficient methods for identifying emissions hotspots and evaluating mitigation measures are therefore needed. The present paper aims to fill this gap at the farm and landscape scales. Six agricultural landscapes in Poland (PL), the Netherlands (NL), France (FR), Italy (IT), Scotland (UK) and Denmark (DK) were studied, and a common method was developed for undertaking farm inventories and the derivation of farm N balances, N surpluses and for evaluating uncertainty for the 222 farms and 11 440 ha of farmland included in the study. In all landscapes, a large variation in the farm N surplus was found, and thereby a large potential for reductions. The highest average N surpluses were found in the most livestock-intensive landscapes of IT, FR, and NL; on average 202±28, 179±63 and 178±20 kg N ha−1 yr−1, respectively. All landscapes showed hotspots, especially from livestock farms, including a special UK case with large-scale landless poultry farming. Overall, the average N surplus from the land-based UK farms dominated by extensive sheep and cattle grazing was only 31±10 kg N ha−1 yr−1, but was similar to the N surplus of PL and DK (122±20 and 146±55 kg N ha−1 yr−1, respectively) when landless poultry farming was included. We found farm N balances to be a useful indicator for N losses and the potential for improving N management. Significant correlations to N surplus were found, both with ammonia air concentrations and nitrate concentrations in soils and groundwater, measured during the period of N management data collection in the landscapes from 2007–2009. This indicates that farm N surpluses may be used as an independent dataset for validation of measured and modelled N emissions in agricultural landscapes. No significant correlation was found with N measured in surface waters, probably because of spatial and temporal variations in groundwater buffering and biogeochemical reactions affecting N flows from farm to surface waters. A case study of the development in N surplus from the landscape in DK from 1998–2008 showed a 22% reduction related to measures targeted at N emissions from livestock farms. Based on the large differences in N surplus between average N management farms and the most modern and Nefficient farms, it was concluded that additional N-surplus reductions of 25–50 %, as compared to the present level, were realistic in all landscapes. The implemented N-surplus method was thus effective for comparing and synthesizing results on farm N emissions and the potentials of mitigation options. It is recommended for use in combination with other methods for the assessment of landscape N emissions and farm N efficiency, including more detailed N source and N sink hotspot mapping, measurements and modelling