Effects of severe water stress on partitioning of 14C-assimilates in tomato plants

Tomato plants (Lycopersicon esculentum Mill. cv Nikita) were grown hydroponically and subjected to severe water stress induced by addition of PEG-6000 to the nutrient solution. The PEG-treatment clearly impaired growth. Leaf photosynthesis decreased during the experiment. Moreover, the decrease in photosynthesis was associated with a decrease in dry weight of the shoot compared to the root. Also leaf area expansion, stomatal conductance and transpiration decreased. Water stress enhanced the transport of 14C-assimilates from the source leaf to the lower parts of the plant where the assimilates were incorporated in the lower stem, the leaves below the source leaf and the roots. It was observed that 14C was much more concentrated in the roots compared to the other plant parts

Supporting dune management by quantitative estimation of evapotranspiration

Research was conducted in the nature reserve De Westhoek (B) in order to estimate the hydrological impact of shrub removal in favour of the recolonisation and development of herbaceous vegetation types in the dune slacks. Dune slacks are one of the most rare ecotopes in Europe. Therefore, the evapotranspiration of herbaceous and shrub vegetation types was estimated based on experimentally obtained data and modelling. Analysis of the experimentally obtained stomatal resistance values revealed that there was no difference in the minimal stomatal resistance values (in absence of any stress) between herbs and shrubs. Stomatal resistance was modelled based as a function of climatic variables. Maximal rooting depth was similar in both vegetation types, and was maximal 60cm. For both vegetation types more than 60% of the roots were found in the upper 15cm. The mean leaf-area-index (LAI) of herbaceous and shrub vegetations is respectively 2.11±0.34 and 3.27±0.20 m2.m-2. Evapotranspiration of both vegetation types was modelled with a multi-layer dynamic vegetation model FORUG and seasonal evapotranspiration amounted roughly 200 and 550mm for the herbaceous and shrub vegetation types respectively. Although these estimates can be somewhat refined, from these results it can be concluded that shrub removal, and the replacement of this vegetation type by a herbaceous vegetation type, will not result in a lowering of the groundwater table. This knowledge can help managing hydrologically disturbed dune ecosystems

LAI determination in dune vegetation: a comparison of different techniques

Research was conducted in the nature reserve De Westhoek (De Panne, Belgium) in order to determine leaf-area-index (LAI) in different dune vegetation types by both direct (destructively) and indirect optical measurements. The destructive LAI determination was conducted in herbaceous and shrub vegetation types. It was found that the LAI of herbaceous vegetation ranges between 0.87 and 4.60 and the LAI of shrub vegetation between 2.25 and 3.58. Ground-based optical determination of LAI was only conducted in the shrub vegetation, by means of the SunScan (Delta-T Devices Ltd, Cambridge, UK). This indirect LAI method systematically overestimated direct LAI. Another applied optical method is the hemispherical photography (Nikon Coolpix 5000 camera). Airborne remote sensing data are used to establish a relationship between direct LAI and some vegetation indices. Based on the above established relationship a map of the horizontal LAI distribution in the nature reserve De Westhoek will be produced

Atmospheric drivers of storage water use in Scots pine

International audienceIn this study we determined the microclimatic drivers of storage water use in Scots pine (Pinus sylvestris L.) growing in a temperate climate. The storage water use was modeled using the ANAFORE model, integrating a dynamic water flow and ? storage model with a process-based transpiration model. The model was calibrated and validated with sap flow measurements for the growing season of 2000 (26 May?18 October). Because there was no severe soil drought during the study period, we were able to study atmospheric effects. Incoming radiation was the main driver of storage water use. The general trends of sap flow and storage water use are similar, and follow more or less the pattern of incoming radiation. Nevertheless, considerable differences in the day-to-day pattern of sap flow and storage water use were observed, mainly driven by vapour pressure deficit (VPD). During dry atmospheric conditions (high VPD) storage water use was reduced. This reduction was disproportionally higher than the reduction in measured sap flow. Our results suggest that the trees did not rely more on storage water during periods of atmospheric drought, without severe soil drought. A third important factor was the tree water deficit. When storage compartments were depleted beyond a threshold, storage water use was limited due to the low water potential in the storage compartments. The maximum relative contribution of storage water to daily transpiration was also constrained by an increasing tree water deficit

flowCore: a Bioconductor package for high throughput flow cytometry

<p>Abstract</p> <p>Background</p> <p>Recent advances in automation technologies have enabled the use of flow cytometry for high throughput screening, generating large complex data sets often in clinical trials or drug discovery settings. However, data management and data analysis methods have not advanced sufficiently far from the initial small-scale studies to support modeling in the presence of multiple covariates.</p> <p>Results</p> <p>We developed a set of flexible open source computational tools in the R package flowCore to facilitate the analysis of these complex data. A key component of which is having suitable data structures that support the application of similar operations to a collection of samples or a clinical cohort. In addition, our software constitutes a shared and extensible research platform that enables collaboration between bioinformaticians, computer scientists, statisticians, biologists and clinicians. This platform will foster the development of novel analytic methods for flow cytometry.</p> <p>Conclusion</p> <p>The software has been applied in the analysis of various data sets and its data structures have proven to be highly efficient in capturing and organizing the analytic work flow. Finally, a number of additional Bioconductor packages successfully build on the infrastructure provided by flowCore, open new avenues for flow data analysis.</p

Knowledge based identification of essential signaling from genome-scale siRNA experiments

<p>Abstract</p> <p>Background</p> <p>A systems biology interpretation of genome-scale RNA interference (RNAi) experiments is complicated by scope, experimental variability and network signaling robustness. Over representation approaches (ORA), such as the Hypergeometric or z-score, are an established statistical framework used to associate RNA interference effectors to biologically annotated gene sets or pathways. These methods, however, do not directly take advantage of our growing understanding of the interactome. Furthermore, these methods can miss partial pathway activation and may be biased by protein complexes. Here we present a novel ORA, protein interaction permutation analysis (PIPA), that takes advantage of canonical pathways and established protein interactions to identify pathways enriched for protein interactions connecting RNAi hits.</p> <p>Results</p> <p>We use PIPA to analyze genome-scale siRNA cell growth screens performed in HeLa and TOV cell lines. First we show that interacting gene pair siRNA hits are more reproducible than single gene hits. Using protein interactions, PIPA identifies enriched pathways not found using the standard Hypergeometric analysis including the FAK <it>cytoskeletal remodeling pathway</it>. Different branches of the <it>FAK </it>pathway are distinctly essential in HeLa versus TOV cell lines while other portions are uneffected by siRNA perturbations. Enriched hits belong to protein interactions associated with cell cycle regulation, anti-apoptosis, and signal transduction.</p> <p>Conclusion</p> <p>PIPA provides an analytical framework to interpret siRNA screen data by merging biologically annotated gene sets with the human interactome. As a result we identify pathways and signaling hypotheses that are statistically enriched to effect cell growth in human cell lines. This method provides a complementary approach to standard gene set enrichment that utilizes the additional knowledge of specific interactions within biological gene sets. </p