No time to waste : transcriptome study reveals that drought tolerance in barley may be attributed to stressed-like expression patterns that exist before the occurrence of stress

Plant survival in adverse environmental conditions requires a substantial change in the metabolism, which is reflected by the extensive transcriptome rebuilding upon the occurrence of the stress. Therefore, transcriptomic studies offer an insight into the mechanisms of plant stress responses. Here, we present the results of global gene expression profiling of roots and leaves of two barley genotypes with contrasting ability to cope with drought stress. Our analysis suggests that drought tolerance results from a certain level of transcription of stress-influenced genes that is present even before the onset of drought. Genes that predispose the plant to better drought survival play a role in the regulatory network of gene expression, including several transcription factors, translation regulators and structural components of ribosomes. An important group of genes is involved in signaling mechanisms, with significant contribution of hormone signaling pathways and an interplay between ABA, auxin, ethylene and brassinosteroid homeostasis. Signal transduction in a drought tolerant genotype may be more efficient through the expression of genes required for environmental sensing that are active already during normal water availability and are related to actin filaments and LIMdomain proteins, which may function as osmotic biosensors. Better survival of drought may also be attributed to more effective processes of energy generation and more efficient chloroplasts biogenesis. Interestingly, our data suggest that several genes involved in a photosynthesis process are required for the establishment of effective drought response not only in leaves, but also in roots of barley. Thus, we propose a hypothesis that root plastids may turn into the anti-oxidative centers protecting root macromolecules from oxidative damage during drought stress. Specific genes and their potential role in building upa drought-tolerant barley phenotype is extensively discussedwith special emphasis on processes that take place in barley roots. When possible, the interconnections between particular factors are emphasized to drawa broader picture of the molecular mechanisms of drought tolerance in barle

Prioritization of Candidate Genes in QTL Regions for Physiological and Biochemical Traits Underlying Drought Response in Barley (Hordeum vulgare L.)

Drought is one of the most adverse abiotic factors limiting growth and productivity of crops. Among them is barley, ranked fourth cereal worldwide in terms of harvested acreage and production. Plants have evolved various mechanisms to cope with water deficit at different biological levels, but there is an enormous challenge to decipher genes responsible for particular complex phenotypic traits, in order to develop drought tolerant crops. This work presents a comprehensive approach for elucidation of molecular mechanisms of drought tolerance in barley at the seedling stage of development. The study includes mapping of QTLs for physiological and biochemical traits associated with drought tolerance on a high-density function map, projection of QTL confidence intervals on barley physical map, and the retrievement of positional candidate genes (CGs), followed by their prioritization based on Gene Ontology (GO) enrichment analysis. A total of 64 QTLs for 25 physiological and biochemical traits that describe plant water status, photosynthetic efficiency, osmoprotectant and hormone content, as well as antioxidant activity, were positioned on a consensus map, constructed using RIL populations developed from the crosses between European and Syrian genotypes. The map contained a total of 875 SNP, SSR and CGs, spanning 941.86 cM with resolution of 1.1 cM. For the first time, QTLs for ethylene, glucose, sucrose, maltose, raffinose, α-tocopherol, γ-tocotrienol content, and catalase activity, have been mapped in barley. Based on overlapping confidence intervals of QTLs, 11 hotspots were identified that enclosed more than 60% of mapped QTLs. Genetic and physical map integration allowed the identification of 1,101 positional CGs within the confidence intervals of drought response-specific QTLs. Prioritization resulted in the designation of 143 CGs, among them were genes encoding antioxidants, carboxylic acid biosynthesis enzymes, heat shock proteins, small auxin up-regulated RNAs, nitric oxide synthase, ATP sulfurylases, and proteins involved in regulation of flowering time. This global approach may be proposed for identification of new CGs that underlies QTLs responsible for complex traits

Hydrological Performance and Runoff Water Quality of Experimental Green Roofs

Within the scope of the conducted experiment the authors analysed the efficiency of runoff reduction by the system of extensive type green roofs. The observations were based on storm events in the area of Lower Silesia at the Agro and Hydrometeorology Station Wrocław-Swojec. The authors analysed the thickness of plant substrate, and also estimated the quality of runoff waters under the conditions of periodic atmospheric deposition. Also considered were such indicators as electrolytic conductivity, N, NO3−, NO2−, NH4+, P, PO43−. The experiment included roof substrates designed in two variants, with known hydraulic and physical properties of the soil material. The analysis was performed for models with vegetation layer based on pumice and zelolite, covered with five plant species from the sedum family. The modelling of the hydraulic properties was conducted with variably saturated medium, using the Hydrus 1D software. The performance of systems with primary layer thickness of 11 cm and 9, 8, 7, 6 and 5 cm was estimated. The designed models reduced the average peak flows to 89%, and in addition they caused a delay in the initiation of the runoff which was dependent on the intensity and distribution of rainfalls in time, and on the initial moisture of the profiles. Simulations, performed for variable substrate thickness, permit the conclusion that in the case of thin-layer profiles (5 cm), the relative retention index was decidedly lower and amounted to 35.9% for the substrate with zeolite (originally 60.6%) and 41% for the substrate with pumice (originally 65.7%). In the case of total nitrogen and phosphates, statistical analysis revealed significant differences (p < 0.05) in relation to specific concentrations in the rainwater and in the control surface. The total nitrogen in the runoff from the green roof was nearly twice as high as that in the rainwater and amounted to, on average, 8.3 mg L−1

Pluvial Flood Risk Assessment Tool (PFRA) for Rainwater Management and Adaptation to Climate Change in Newly Urbanised Areas

The aim of this research is to develop the Pluvial Flood Risk Assessment tool (PFRA) for rainwater management and adaptation to climate change in newly urbanised areas. PFRA allows pluvial hazard assessment, as well as pluvial flood risk mapping. The original model was created using ArcGIS software with the ArcHydro extension, and the script was written using the Python programming language. The PFRA model effectively combines information about land cover, soils, microtopography (LiDAR data), and projected hydro-meteorological conditions, which enables the identification of the spatial and temporal distribution of pluvial flood risks in newly developed areas. Further improvements to the PFRA concern the quantification of pluvial flood-related damages, the application of high resolution precipitation data, and the optimisation of coding

A Location Intelligence System for the Assessment of Pluvial Flooding Risk and the Identification of Storm Water Pollutant Sources from Roads in Suburbanised Areas

The interplay of an ever-growing number of inhabitants, sprawl development, soil sealing, changes in urban traffic characteristics, as well as observed climate trends gives rise to more frequent pluvial flooding in cities, a higher run-off of water, and an increasing pollution of surface water. The aim of this research is to develop a location intelligence system for the assessment of pluvial flooding risks and the identification of storm water pollutant sources from roads in newly-developed areas. The system combines geographic information systems and business intelligence software, and it is based on the original Pluvial Flood Risk Assessment tool. The location intelligence system effectively identifies the spatial and temporal distribution of pluvial flood risks, allows to preliminarily evaluate the total run-off from roads, and helps localise potential places for new water management infrastructure. Further improvements concern the modelling of a flow accumulation and drainage system, the application of weather radar precipitation data, and traffic monitoring and modelling