Assessment of the potential impacts of plant traits across environments by combining global sensitivity analysis and dynamic modeling in wheat

A crop can be viewed as a complex system with outputs (e.g. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A large set of traits (90) was evaluated in a wide target population of environments (4 sites x 125 years), management practices (3 sowing dates x 2 N fertilization) and $CO_2$ (2 levels). The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait x environment x management landscape ($\sim$ 82 million individual simulations in total). The patterns of parameter x environment x management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference. Main (i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identifcation of 42 parameters substantially impacting yield in most target environments. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model) improvement.Comment: 22 pages, 8 figures. This work has been submitted to PLoS On

Using numerical plant models and phenotypic correlation space to design achievable ideotypes

Numerical plant models can predict the outcome of plant traits modifications resulting from genetic variations, on plant performance, by simulating physiological processes and their interaction with the environment. Optimization methods complement those models to design ideotypes, i.e. ideal values of a set of plant traits resulting in optimal adaptation for given combinations of environment and management, mainly through the maximization of a performance criteria (e.g. yield, light interception). As use of simulation models gains momentum in plant breeding, numerical experiments must be carefully engineered to provide accurate and attainable results, rooting them in biological reality. Here, we propose a multi-objective optimization formulation that includes a metric of performance, returned by the numerical model, and a metric of feasibility, accounting for correlations between traits based on field observations. We applied this approach to two contrasting models: a process-based crop model of sunflower and a functional-structural plant model of apple trees. In both cases, the method successfully characterized key plant traits and identified a continuum of optimal solutions, ranging from the most feasible to the most efficient. The present study thus provides successful proof of concept for this enhanced modeling approach, which identified paths for desirable trait modification, including direction and intensity.Comment: 25 pages, 5 figures, 2017, Plant, Cell and Environmen

Influence of the variation of geometrical and topological traits on light interception efficiency of apple trees: sensitivity analysis and metamodelling for ideotype definition

Background and AimsThe impact of a fruit tree's architecture on its performance is still under debate, especially with regard to the definition of varietal ideotypes and the selection of architectural traits in breeding programmes. This study aimed at providing proof that a modelling approach can contribute to this debate, by using in silico exploration of different combinations of traits and their consequences on light interception, here considered as one of the key parameters to optimize fruit tree production.MethodsThe variability of organ geometrical traits, previously described in a bi-parental population, was used to simulate 1- to 5-year-old apple trees (Malus × domestica). Branching sequences along trunks observed during the first year of growth of the same hybrid trees were used to initiate the simulations, and hidden semi-Markov chains previously parameterized were used in subsequent years. Tree total leaf area (TLA) and silhouette to total area ratio (STAR) values were estimated, and a sensitivity analysis was performed, based on a metamodelling approach and a generalized additive model (GAM), to analyse the relative impact of organ geometry and lateral shoot types on STAR.Keys ResultsA larger increase over years in TLA mean and variance was generated by varying branching along trunks than by varying organ geometry, whereas the inverse was observed for STAR, where mean values stabilized from year 3 to year 5. The internode length and leaf area had the highest impact on STAR, whereas long sylleptic shoots had a more significant effect than proleptic shoots. Although the GAM did not account for interactions, the additive effects of the geometrical factors explained >90% of STAR variation, but much less in the case of branching factors.ConclusionsThis study demonstrates that the proposed modelling approach could contribute to screening architectural traits and their relative impact on tree performance, here viewed through light interception. Even though trait combinations and antagonism will need further investigation, the approach opens up new perspectives for breeding and genetic selection to be assisted by varietal ideotype definition

A phase II multicentre, open-label, proof-of-concept study of tasquinimod in hepatocellular, ovarian, renal cell, and gastric cancers

Background: Tasquinimod is a small molecule with immunomodulatory, anti-angiogenic, and anti-metastatic properties that targets the tumor microenvironment. This study aimed to obtain a clinical proof of concept that tasquinimod was active and tolerable in patients with advanced solid tumors. Patients and Methods: This early stopping design, open-label, proof-of-concept clinical trial evaluated the clinical activity of tasquinimod in four independent cohorts of patients with advanced hepatocellular (n = 53), ovarian (n = 55), renal cell (n = 38), and gastric (n = 21) cancers. Tasquinimod was given orally every day (0.5 mg/day for at least 2 weeks, with dose increase to 1 mg/day) until radiological progression according to Response Evaluation Criteria in Solid Tumor (RECIST) 1.1 criteria, intolerable toxicity, or patient withdrawal. The primary efficacy endpoint was progression-free survival (PFS) rate according to RECIST 1.1 by central assessment. Results: Interim futility analyses at 8 weeks (6 weeks for the gastric cancer cohort) found adequate clinical activity of tasquinimod only in the hepatocellular cohort and recruitment to the other three cohorts was stopped. PFS rates were 26.9% at 16 weeks, 7.3% at 24 weeks, 13.2% at 16 weeks, and 9.5% at 12 weeks, respectively, in hepatocellular, ovarian, renal cell, and gastric cancer cohorts. The pre-defined PFS threshold was not reached in the hepatocellular cancer cohort at the second stage of the trial. The most common treatment-related adverse events were fatigue (48.5%), nausea (34.1%), decreased appetite (31.7%), and vomiting (24.6%). Conclusions: This study failed to demonstrate clinical activity of tasquinimod in heavily pre-treated patients with advanced hepatocellular, ovarian, renal cell, and gastric cancer. Trial registration: NCT01743469

Physiological stress does not increase with urbanization in European blackbirds:Evidence from hormonal, immunological and cellular indicators

Urbanization changes the landscape structure and ecological processes of natural habitats. While urban areas expose animal communities to novel challenges, they may also provide more stable environments in which environmental fluctuations are buffered. Species´ ecology and physiology may determine their capacity to cope with the city life. However, the physiological mechanisms underlying organismal responses to urbanization, and whether different physiological systems are equally affected by urban environments remain poorly understood. This severely limits our capacity to predict the impact of anthropogenic habitats on wild populations. In this study, we measured indicators of physiological stress at the endocrine, immune and cellular level (feather corticosterone levels, heterophil to lymphocyte ratio, and heat-shock proteins) in urban and non-urban European blackbirds (Turdus merula) across 10 European populations. Among the three variables, we found consistent differences in feather corticosterone, which was higher in non-urban habitats. This effect seems to bedependent on sex, being greater in males. In contrast, we found no significant differences between urban and non-urban habitats in the two other physiological indicators. The discrepancy between these different measurements of physiological stress highlights the importance of including multiple physiological variables to understand the impact of urbanization on species' physiology. Overall, our findings suggest that adult European blackbirds living in urban and non-urban habitats do not differ in terms of physiological stress at an organismal level. Furthermore, we found large differences among populations on the strength and direction of the urbanization effect, which illustrates the relevance of spatial replication when investigating urban-induced physiological responses

Sensitivity analysis to evaluate a new spatialized process-oriented model of water and pesticide transfers at the catchment scale

International audienceIntensive use of pesticides in agricultural catchments leads to a widespread contamination of rivers and groundwater. Pesticides applied on fields are transfered at surface and in subsurface to waterbodies. Such transfers are highly influenced by landscape elements that can accelerate or slow ownand dissipate water and contaminant flows. The PESHMELBA model has been developed to simulate pesticide fate on small agricultural catchments and to represent the landscape elements in an explicit way. It is characterized by a process-oriented approach and an original spatial discretization that make it particularly suitable to simulate complex agricultural catchments. In the long run, we aim at setting up and comparing different landscape organization scenarios for decision-making support. However, before considering such operationnal use of PESHMELBA, it must be strongly valuated and uncertainties must be quantified and reduced. In that respect, we performed uncertainty and sensitivity analysis of the model. As such evaluation had never been performed earlier on PESHMELBA, we first set a small virtual hillslope composed of plots, vegetative filter strips and river reaches. Even basic, this configuration led to a large set of parameters as the model is fully distributed and physically-based, implying, for example, orizontal and vertical heterogeneities of soil characteristics. Due to the large number of parameters, we firstly performed Morris-type screening to iscard non-influential parameters with a limited number of model evaluations. Then, we sequentially performed a computer-intensive Sobol procedure on a reduced number of input factors. Preliminary results improved the understanding we have about the model functioning. Some guidelines about critical representation of processes or potential simplifications also arose. Above all, this study was the preliminary step of a data assimilation project. It allowed us to choose variables to be potentially assimilated and a suitable data assimilation method

Chitosan hydrogel micro-bio-devices with complex capillary patterns via reactive-diffusive self-assembly

International audienceWe present chitosan hydrogel microfluidic devices with self-assembled complex microcapillary patterns, conveniently formed by a diffusion-reaction process. These patterns in chitosan hydrogels are formed by a single-step procedure involving diffusion of a gelation agent into the polymer solution inside a microfluidic channel. By changing the channel geometry, it is demonstrated how to control capillary length, trajectory and branching. Diffusion of nanoparticles (NPs) in the capillary network is used as a model to effectively mimic the transport of nano-objects in vascularized tissues. Gold NPs diffusion is measured locally in the hydrogel chips, and during their two-step transport through the capillaries to the gel matrix and eventually to embedded cell clusters in the gel. In addition, the quantitative analyses reported in this study provide novel opportunities for theoretical investigation of capillary formation and propagation during diffusive gelation of biopolymers.Statement of SignificanceHydrogel micropatterning is a challenging task, which is of interest in several biomedical applications. Creating the patterns through self assembly is highly beneficial, because of the accessible and practical preparation procedure. In this study, we introduced complex self-assembled capillary patterns in chitosan hydrogels using a microfluidic approach. To demonstrate the potential application of these capillary patterns, a vascularized hydrogel with microwells occupied by cells was produced, and the diffusion of gold nanoparticles travelling in the capillaries and diffusing in the gel were evaluated. This model mimics a simplified biological tissue, where nanomedicine has to travel through the vasculature, extravasate into and diffuse through the extracellular matrix and eventually reach targeted cells

Investigating the Influence of Geometrical Traits on Light Interception Efficiency of Apple Trees: a Modelling Study with MAppleT

UMR AGAP - équipe AFEF - Architecture et fonctionnement des espèces fruitièresInternational audienceMAppleT is an in silico functional-structural plant model that has been built for simulating architectural development of apple trees. It has the capability of representing tree growth within a virtual space where the development of individual organs depends on geometrical traits. The purpose of this research is to investigate the influence of apple trees x architectural variability on their light interception efficiency. The STAR, namely the silhouette to total area ratio, of leaves, was chosen to evaluate the level of such efficiency. The strategy is to integrate MAppleT with the light interception model provided by the fractalysis module of the VPlants software library. Target values of four major traits (internode length, leaf area, branching angle and top shoot diameter), are varied in range previously observed in a segregating population of apple hybrids. A sensitivity analysis based on polynomial and generalised additive models was performed for highlighting the most influential trait on light interception and suggesting the optimal combination(s) of traits leading to the highest STAR. The contribution of stochastic processes that pilot tree topology in MAppleT is also investigated in the sensitivity analysis. This study not only provides a time- and resource-saving alternative for data collection, but also sets a methodology for ideotype definition and further genetic improvement of apple trees