Stress phenotyping analysis leveraging autofluorescence image sequences with machine learning.

Background: Autofluorescence-based imaging has the potential to non-destructively characterize the biochemical and physiological properties of plants regulated by genotypes using optical properties of the tissue. A comparative study of stress tolerant and stress susceptible genotypes of Brassica rapa with respect to newly introduced stress-based phenotypes using machine learning techniques will contribute to the significant advancement of autofluorescence-based plant phenotyping research. Methods: Autofluorescence spectral images have been used to design a stress detection classifier with two classes, stressed and non-stressed, using machine learning algorithms. The benchmark dataset consisted of time-series image sequences from three Brassica rapa genotypes (CC, R500, and VT), extreme in their morphological and physiological traits captured at the high-throughput plant phenotyping facility at the University of Nebraska-Lincoln, USA. We developed a set of machine learning-based classification models to detect the percentage of stressed tissue derived from plant images and identified the best classifier. From the analysis of the autofluorescence images, two novel stress-based image phenotypes were computed to determine the temporal variation in stressed tissue under progressive drought across different genotypes, i.e., the average percentage stress and the moving average percentage stress. Results: The study demonstrated that both the computed phenotypes consistently discriminated against stressed versus non-stressed tissue, with oilseed type (R500) being less prone to drought stress relative to the other two Brassica rapa genotypes (CC and VT). Conclusion: Autofluorescence signals from the 365/400 nm excitation/emission combination were able to segregate genotypic variation during a progressive drought treatment under a controlled greenhouse environment, allowing for the exploration of other meaningful phenotypes using autofluorescence image sequences with significance in the context of plant science.

Cover crop performance under a changing climate in continuous corn system over Nebraska.

Fall-planted cover crop (CC) within a continuous corn (Zea mays L.) system offers potential agroecosystem benefits, including mitigating the impacts of increased temperature and variability in precipitation patterns. A long-term simulation using the Decision Support System for Agrotechnology Transfer model was made to assess the effects of cereal rye (Secale cereale L.) on no-till continuous corn yield and soil properties under historical (1991-2020) and projected climate (2041-2070) in eastern Nebraska. Local weather data during the historical period were used, while climate change projections were based on the Canadian Earth System Model 2 dynamically downscaled using the Canadian Centre for Climate Modelling and Analysis Regional Climate Model 4 under two representative concentration pathways (RCP), namely, RCP4.5 and RCP8.5. Simulations results indicated that CC impacts on corn yield were nonsignificant under historical and climate change conditions. Climate change created favorable conditions for CC growth, resulting in an increase in biomass. CC reduced N leaching under climate change scenarios compared to an average reduction of 60% (7 kg ha- 1 ) during the historical period. CC resulted in a 6% (27 mm) reduction in total water in soil profile (140 cm) and 22% (27 mm) reduction in plant available water compared to no cover crop during historical period. CC reduced cumulative seasonal surface runoff/soil evaporation and increased the rate of soil organic carbon buildup. This research provides valuable information on how changes in climate can impact the performance of cereal rye CC in continuous corn production and should be scaled to wider locations and CC species.

Rapid online plant leaf area change detection with high-throughput plant image data.

High-throughput plant phenotyping (HTPP) has become an emerging technique to study plant traits due to its fast, labor-saving, accurate and non-destructive nature. It has wide applications in plant breeding and crop management. However, the resulting massive image data has raised a challenge associated with efficient plant traits prediction and anomaly detection. In this paper, we propose a two-step image-based online detection framework for monitoring and quick change detection of the individual plant leaf area via real-time imaging data. Our proposed method is able to achieve a smaller detection delay compared with some baseline methods under some predefined false alarm rate constraint. Moreover, it does not need to store all past image information and can be implemented in real time. The efficiency of the proposed framework is validated by a real data analysis.

EmergeNet: A novel deep-learning based ensemble segmentation model for emergence timing detection of coleoptile.

The emergence timing of a plant, i.e., the time at which the plant is first visible from the surface of the soil, is an important phenotypic event and is an indicator of the successful establishment and growth of a plant. The paper introduces a novel deep-learning based model called EmergeNet with a customized loss function that adapts to plant growth for coleoptile (a rigid plant tissue that encloses the first leaves of a seedling) emergence timing detection. It can also track its growth from a time-lapse sequence of images with cluttered backgrounds and extreme variations in illumination. EmergeNet is a novel ensemble segmentation model that integrates three different but promising networks, namely, SEResNet, InceptionV3, and VGG19, in the encoder part of its base model, which is the UNet model. EmergeNet can correctly detect the coleoptile at its first emergence when it is tiny and therefore barely visible on the soil surface. The performance of EmergeNet is evaluated using a benchmark dataset called the University of Nebraska-Lincoln Maize Emergence Dataset (UNL-MED). It contains top-view time-lapse images of maize coleoptiles starting before the occurrence of their emergence and continuing until they are about one inch tall. EmergeNet detects the emergence timing with 100% accuracy compared with human-annotated ground-truth. Furthermore, it significantly outperforms UNet by generating very high-quality segmented masks of the coleoptiles in both natural light and dark environmental conditions.

Drought stress prediction and propagation using time series modeling on multimodal plant image sequences.

The paper introduces two novel algorithms for predicting and propagating drought stress in plants using image sequences captured by cameras in two modalities, i.e., visible light and hyperspectral. The first algorithm, VisStressPredict, computes a time series of holistic phenotypes, e.g., height, biomass, and size, by analyzing image sequences captured by a visible light camera at discrete time intervals and then adapts dynamic time warping (DTW), a technique for measuring similarity between temporal sequences for dynamic phenotypic analysis, to predict the onset of drought stress. The second algorithm, HyperStressPropagateNet, leverages a deep neural network for temporal stress propagation using hyperspectral imagery. It uses a convolutional neural network to classify the reflectance spectra at individual pixels as either stressed or unstressed to determine the temporal propagation of stress in the plant. A very high correlation between the soil water content, and the percentage of the plant under stress as computed by HyperStressPropagateNet on a given day demonstrates its efficacy. Although VisStressPredict and HyperStressPropagateNet fundamentally differ in their goals and hence in the input image sequences and underlying approaches, the onset of stress as predicted by stress factor curves computed by VisStressPredict correlates extremely well with the day of appearance of stress pixels in the plants as computed by HyperStressPropagateNet. The two algorithms are evaluated on a dataset of image sequences of cotton plants captured in a high throughput plant phenotyping platform. The algorithms may be generalized to any plant species to study the effect of abiotic stresses on sustainable agriculture practices.

A non-destructive approach for measuring rice panicle-level photosynthetic responses using 3D-image reconstruction.

BACKGROUND: Our understanding of the physiological responses of rice inflorescence (panicle) to environmental stresses is limited by the challenge of accurately determining panicle photosynthetic parameters and their impact on grain yield. This is primarily due to the lack of a suitable gas exchange methodology for panicles and non-destructive methods to accurately determine panicle surface area. RESULTS: To address these challenges, we have developed a custom panicle gas exchange cylinder compatible with the LiCor 6800 Infra-red Gas Analyzer. Accurate surface area measurements were determined using 3D panicle imaging to normalize the panicle-level photosynthetic measurements. We observed differential responses in both panicle and flag leaf for two temperate Japonica rice genotypes (accessions TEJ-1 and TEJ-2) exposed to heat stress during early grain filling. There was a notable divergence in the relative photosynthetic contribution of flag leaf and panicles for the heat-tolerant genotype (TEJ-2) compared to the sensitive genotype (TEJ-1). CONCLUSION: The novelty of this method is the non-destructive and accurate determination of panicle area and photosynthetic parameters, enabling researchers to monitor temporal changes in panicle physiology during the reproductive development. The method is useful for panicle-level measurements under diverse environmental stresses and is sensitive enough to evaluate genotypic variation for panicle physiology and architecture in cereals with compact inflorescences.

Joint effects of climate, tree size, and year on annual tree growth derived from tree-ring records of ten globally distributed forests.

Tree rings provide an invaluable long-term record for understanding how climate and other drivers shape tree growth and forest productivity. However, conventional tree-ring analysis methods were not designed to simultaneously test effects of climate, tree size, and other drivers on individual growth. This has limited the potential to test ecologically relevant hypotheses on tree growth sensitivity to environmental drivers and their interactions with tree size. Here, we develop and apply a new method to simultaneously model nonlinear effects of primary climate drivers, reconstructed tree diameter at breast height (DBH), and calendar year in generalized least squares models that account for the temporal autocorrelation inherent to each individual tree's growth. We analyze data from 3811 trees representing 40 species at 10 globally distributed sites, showing that precipitation, temperature, DBH, and calendar year have additively, and often interactively, influenced annual growth over the past 120 years. Growth responses were predominantly positive to precipitation (usually over ≥3-month seasonal windows) and negative to temperature (usually maximum temperature, over ≤3-month seasonal windows), with concave-down responses in 63% of relationships. Climate sensitivity commonly varied with DBH (45% of cases tested), with larger trees usually more sensitive. Trends in ring width at small DBH were linked to the light environment under which trees established, but basal area or biomass increments consistently reached maxima at intermediate DBH. Accounting for climate and DBH, growth rate declined over time for 92% of species in secondary or disturbed stands, whereas growth trends were mixed in older forests. These trends were largely attributable to stand dynamics as cohorts and stands age, which remain challenging to disentangle from global change drivers. By providing a parsimonious approach for characterizing multiple interacting drivers of tree growth, our method reveals a more complete picture of the factors influencing growth than has previously been possible.

Wild emmer introgression alters root-to-shoot growth dynamics in durum wheat in response to water stress.

Water deficit during the early vegetative growth stages of wheat (Triticum) can limit shoot growth and ultimately impact grain productivity. Introducing diversity in wheat cultivars to enhance the range of phenotypic responses to water limitations during vegetative growth can provide potential avenues for mitigating subsequent yield losses. We tested this hypothesis in an elite durum wheat background by introducing a series of introgressions from a wild emmer (Triticum turgidum ssp. dicoccoides) wheat. Wild emmer populations harbor rich phenotypic diversity for drought-adaptive traits. To determine the effect of these introgressions on vegetative growth under water-limited conditions, we used image-based phenotyping to catalog divergent growth responses to water stress ranging from high plasticity to high stability. One of the introgression lines exhibited a significant shift in root-to-shoot ratio in response to water stress. We characterized this shift by combining genetic analysis and root transcriptome profiling to identify candidate genes (including a root-specific kinase) that may be linked to the root-to-shoot carbon reallocation under water stress. Our results highlight the potential of introducing functional diversity into elite durum wheat for enhancing the range of water stress adaptation.

Soil properties limiting vegetation establishment along roadsides.

Roadside vegetation provides a multitude of ecosystem services, including pollutant remediation, runoff reduction, wildlife habitat, and aesthetic scenery. Establishment of permanent vegetation along paved roads after construction can be challenging, particularly within 1 m of the pavement. Adverse soil conditions could be one of the leading factors limiting roadside vegetation growth. In this study, we assessed soil physical and chemical properties along a transect perpendicular to the road at six microtopographic positions (road edge, shoulder, side slope, ditch, backslope, and field edge) along two highway segments near Beaver Crossing and Sargent, NE. At the Beaver Crossing site, Na concentration was 81 times, exchangeable Na 66 times, and cone index (compaction parameter) six times higher at the road-edge position (closest to the paved road and with sparse vegetation) compared to positions with abundant vegetation (ditch or field edge). At the Sargent site, Na concentration was 111 times, exchangeable Na 213 times, and cone index up to two times higher at the road-edge position compared with ditch or field-edge positions. Likewise, electrical conductivity was higher and macroaggregation and water infiltration were lower at the road edge than at the ditch or field-edge positions. Soil properties improved with increasing distance from the road. Exchangeable Na percentage and cone index at the road-edge position exceeded threshold levels for the growth of sensitive plants. Thus, high Na concentration and increased compaction at the road edge appear to be the leading soil properties limiting vegetation establishment along Nebraska highways.

Leveraging Image Analysis to Compute 3D Plant Phenotypes Based on Voxel-Grid Plant Reconstruction.

High throughput image-based plant phenotyping facilitates the extraction of morphological and biophysical traits of a large number of plants non-invasively in a relatively short time. It facilitates the computation of advanced phenotypes by considering the plant as a single object (holistic phenotypes) or its components, i.e., leaves and the stem (component phenotypes). The architectural complexity of plants increases over time due to variations in self-occlusions and phyllotaxy, i.e., arrangements of leaves around the stem. One of the central challenges to computing phenotypes from 2-dimensional (2D) single view images of plants, especially at the advanced vegetative stage in presence of self-occluding leaves, is that the information captured in 2D images is incomplete, and hence, the computed phenotypes are inaccurate. We introduce a novel algorithm to compute 3-dimensional (3D) plant phenotypes from multiview images using voxel-grid reconstruction of the plant (3DPhenoMV). The paper also presents a novel method to reliably detect and separate the individual leaves and the stem from the 3D voxel-grid of the plant using voxel overlapping consistency check and point cloud clustering techniques. To evaluate the performance of the proposed algorithm, we introduce the University of Nebraska-Lincoln 3D Plant Phenotyping Dataset (UNL-3DPPD). A generic taxonomy of 3D image-based plant phenotypes are also presented to promote 3D plant phenotyping research. A subset of these phenotypes are computed using computer vision algorithms with discussion of their significance in the context of plant science. The central contributions of the paper are (a) an algorithm for 3D voxel-grid reconstruction of maize plants at the advanced vegetative stages using images from multiple 2D views; (b) a generic taxonomy of 3D image-based plant phenotypes and a public benchmark dataset, i.e., UNL-3DPPD, to promote the development of 3D image-based plant phenotyping research; and (c) novel voxel overlapping consistency check and point cloud clustering techniques to detect and isolate individual leaves and stem of the maize plants to compute the component phenotypes. Detailed experimental analyses demonstrate the efficacy of the proposed method, and also show the potential of 3D phenotypes to explain the morphological characteristics of plants regulated by genetic and environmental interactions.

Leveraging Image Analysis for High-Throughput Plant Phenotyping.

The complex interaction between a genotype and its environment controls the biophysical properties of a plant, manifested in observable traits, i.e., plant's phenome, which influences resources acquisition, performance, and yield. High-throughput automated image-based plant phenotyping refers to the sensing and quantifying plant traits non-destructively by analyzing images captured at regular intervals and with precision. While phenomic research has drawn significant attention in the last decade, extracting meaningful and reliable numerical phenotypes from plant images especially by considering its individual components, e.g., leaves, stem, fruit, and flower, remains a critical bottleneck to the translation of advances of phenotyping technology into genetic insights due to various challenges including lighting variations, plant rotations, and self-occlusions. The paper provides (1) a framework for plant phenotyping in a multimodal, multi-view, time-lapsed, high-throughput imaging system; (2) a taxonomy of phenotypes that may be derived by image analysis for better understanding of morphological structure and functional processes in plants; (3) a brief discussion on publicly available datasets to encourage algorithm development and uniform comparison with the state-of-the-art methods; (4) an overview of the state-of-the-art image-based high-throughput plant phenotyping methods; and (5) open problems for the advancement of this research field.

Holistic and component plant phenotyping using temporal image sequence.

BACKGROUND: Image-based plant phenotyping facilitates the extraction of traits noninvasively by analyzing large number of plants in a relatively short period of time. It has the potential to compute advanced phenotypes by considering the whole plant as a single object (holistic phenotypes) or as individual components, i.e., leaves and the stem (component phenotypes), to investigate the biophysical characteristics of the plants. The emergence timing, total number of leaves present at any point of time and the growth of individual leaves during vegetative stage life cycle of the maize plants are significant phenotypic expressions that best contribute to assess the plant vigor. However, image-based automated solution to this novel problem is yet to be explored. RESULTS: A set of new holistic and component phenotypes are introduced in this paper. To compute the component phenotypes, it is essential to detect the individual leaves and the stem. Thus, the paper introduces a novel method to reliably detect the leaves and the stem of the maize plants by analyzing 2-dimensional visible light image sequences captured from the side using a graph based approach. The total number of leaves are counted and the length of each leaf is measured for all images in the sequence to monitor leaf growth. To evaluate the performance of the proposed algorithm, we introduce University of Nebraska-Lincoln Component Plant Phenotyping Dataset (UNL-CPPD) and provide ground truth to facilitate new algorithm development and uniform comparison. The temporal variation of the component phenotypes regulated by genotypes and environment (i.e., greenhouse) are experimentally demonstrated for the maize plants on UNL-CPPD. Statistical models are applied to analyze the greenhouse environment impact and demonstrate the genetic regulation of the temporal variation of the holistic phenotypes on the public dataset called Panicoid Phenomap-1. CONCLUSION: The central contribution of the paper is a novel computer vision based algorithm for automated detection of individual leaves and the stem to compute new component phenotypes along with a public release of a benchmark dataset, i.e., UNL-CPPD. Detailed experimental analyses are performed to demonstrate the temporal variation of the holistic and component phenotypes in maize regulated by environment and genetic variation with a discussion on their significance in the context of plant science.

Dominant plant species shape soil bacterial community in semiarid sandy land of northern China.

Plant species affect soil bacterial diversity and compositions. However, little is known about the role of dominant plant species in shaping the soil bacterial community during the restoration of sandy grasslands in Horqin Sandy Land, northern China. We established a mesocosm pots experiment to investigate short-term responses of soil bacterial diversity and composition, and the related soil properties in degraded soils without vegetation (bare sand as the control, CK) to restoration with five plant species that dominate across restoration stages: Agriophyllum squarrosum (AS), Artemisia halodendron (AH), Setaria viridis (SV), Chenopodium acuminatum (CA), and Corispermum macrocarpum (CM). We used redundancy analysis (RDA) to analyze the association between soil bacterial composition and soil properties in different plant species. Our results indicated that soil bacterial diversity was significantly lower in vegetated soils independent of plant species than in the CK. Specifically, soil bacterial species richness and diversity were lower under the shrub AH and the herbaceous plants AS, SV, and CA, and soil bacterial abundance was lower under AH compared with the CK. A field investigation confirmed the same trends where soil bacteria diversity was lower under AS and AH than in bare sand. The high-sequence annotation analysis showed that Proteobacteria, Actinobacteria, and Bacteroidetes were the most common phyla in sandy land irrespective of soil plant cover. The OTUs (operational taxonomic units) indicated that some bacterial species were specific to the host plants. Relative to bare sand (CK), soils with vegetative cover exhibited lower soil water content and temperature, and higher soil carbon and nitrogen contents. The RDA result indicated that, in addition to plant species, soil water and nitrogen contents were the most important factors shaping soil bacterial composition in semiarid sandy land. Our study from the pot and field investigations clearly demonstrated that planting dominant species in bare sand impacts bacterial diversity. In semiarid ecosystems, changes in the dominant plant species during vegetation restoration efforts can affect the soil bacterial diversity and composition through the direct effects of plants and the indirect effects of soil properties that are driven by plant species.

Enhancing soybean photosynthetic CO2 assimilation using a cyanobacterial membrane protein, ictB.

Soybean C3 photosynthesis can suffer a severe loss in efficiency due to photorespiration and the lack of a carbon concentrating mechanism (CCM) such as those present in other plant species or cyanobacteria. Transgenic soybean (Glycine max cv. Thorne) plants constitutively expressing cyanobacterial ictB (inorganic carbon transporter B) gene were generated using Agrobacterium-mediated transformation. Although more recent data suggest that ictB does not actively transport HCO3-/CO2, there is nevertheless mounting evidence that transformation with this gene can increase higher plant photosynthesis. The hypothesis that expression of the ictB gene would improve photosynthesis, biomass production and seed yield in soybean was tested, in two independent replicated greenhouse and field trials. Results showed significant increases in photosynthetic CO2 uptake (Anet) and dry mass in transgenic relative to wild type (WT) control plants in both the greenhouse and field trials. Transgenic plants also showed increased photosynthetic rates and biomass production during a drought mimic study. The findings presented herein demonstrate that ictB, as a single-gene, contributes to enhancement in various yield parameters in a major commodity crop and point to the significant role that biotechnological approaches to increasing photosynthetic efficiency can play in helping to meet increased global demands for food.

Expression of Chlorovirus MT325 aquaglyceroporin (aqpv1) in tobacco and its role in mitigating drought stress.

MAIN CONCLUSIONS: A Chlorovirus aquaglyceroporin expressed in tobacco is localized to the plastid and plasma membranes. Transgenic events display improved response to water deficit. Necrosis in adult stage plants is observed. Aquaglyceroporins are a subclass of the water channel aquaporin proteins (AQPs) that transport glycerol along with other small molecules transcellular in addition to water. In the studies communicated herein, we analyzed the expression of the aquaglyceroporin gene designated, aqpv1, from Chlorovirus MT325, in tobacco (Nicotiana tabacum), along with phenotypic changes induced by aqpv1 expression in planta. Interestingly, aqpv1 expression under control of either a constitutive or a root-preferred promoter, triggered local lesion formation in older leaves, which progressed significantly after induction of flowering. Fusion of aqpv1 with GFP suggests that the protein localized to the plasmalemma, and potentially with plastid and endoplasmic reticulum membranes. Physiological characterizations of transgenic plants during juvenile stage growth were monitored for potential mitigation to water dry-down (i.e., drought) and recovery. Phenotypic analyses on drought mimic/recovery of juvenile transgenic plants that expressed a functional aqpv1 transgene had higher photosynthetic rates, stomatal conductance, and water use efficiency, along with maximum carboxylation and electron transport rates when compared to control plants. These physiological attributes permitted the juvenile aqpv1 transgenic plants to perform better under drought-mimicked conditions and hastened recovery following re-watering. This drought mitigation effect is linked to the ability of the transgenic plants to maintain cell turgor.

Distinct Pseudomonas type-III effectors use a cleavable transit peptide to target chloroplasts.

The pathogen Pseudomonas syringae requires a type-III protein secretion system and the effector proteins it injects into plant cells for pathogenesis. The primary role for P. syringae type-III effectors is the suppression of plant immunity. The P. syringae pv. tomato DC3000 HopK1 type-III effector was known to suppress the hypersensitive response (HR), a programmed cell death response associated with effector-triggered immunity. Here we show that DC3000 hopK1 mutants are reduced in their ability to grow in Arabidopsis, and produce reduced disease symptoms. Arabidopsis transgenically expressing HopK1 are reduced in PAMP-triggered immune responses compared with wild-type plants. An N-terminal region of HopK1 shares similarity with the corresponding region in the well-studied type-III effector AvrRps4; however, their C-terminal regions are dissimilar, indicating that they have different effector activities. HopK1 is processed in planta at the same processing site found in AvrRps4. The processed forms of HopK1 and AvrRps4 are chloroplast localized, indicating that the shared N-terminal regions of these type-III effectors represent a chloroplast transit peptide. The HopK1 contribution to virulence and the ability of HopK1 and AvrRps4 to suppress immunity required their respective transit peptides, but the AvrRps4-induced HR did not. Our results suggest that a primary virulence target of these type-III effectors resides in chloroplasts, and that the recognition of AvrRps4 by the plant immune system occurs elsewhere. Moreover, our results reveal that distinct type-III effectors use a cleavable transit peptide to localize to chloroplasts, and that targets within this organelle are important for immunity.

Activity of the Arabidopsis RD29A and RD29B promoter elements in soybean under water stress.

The constitutive and drought-induced activities of the Arabidopsis thaliana RD29A and RD29B promoters were monitored in soybean (Glycine max (L.) Merr.] via fusions with the visual marker gene ß-glucuronidase (GUS). Physiological responses of soybean plants were monitored over 9 days of water deprivation under greenhouse conditions. Data were used to select appropriate time points to monitor the activities of the respective promoter elements. Qualitative and quantitative assays for GUS expression were conducted in root and leaf tissues, from plants under well-watered and dry-down conditions. Both RD29A and RD29B promoters were significantly activated in soybean plants subjected to dry-down conditions. However, a low level of constitutive promoter activity was also observed in both root and leaves of plants under well-watered conditions. GUS expression was notably higher in roots than in leaves. These observations suggest that the respective drought-responsive regulatory elements present in the RD29X promoters may be useful in controlling targeted transgenes to mitigate abiotic stress in soybean, provided the transgene under control of these promoters does not invoke agronomic penalties with leaky expression when no abiotic stress is imposed.

Comparative transcriptome analysis of two olive cultivars in response to NaCl-stress.

BACKGROUND: Olive (Olea europaea L.) cultivation is rapidly expanding and low quality saline water is often used for irrigation. The molecular basis of salt tolerance in olive, though, has not yet been investigated at a system level. In this study a comparative transcriptomics approach was used as a tool to unravel gene regulatory networks underlying salinity response in olive trees by simulating as much as possible olive growing conditions in the field. Specifically, we investigated the genotype-dependent differences in the transcriptome response of two olive cultivars, a salt-tolerant and a salt-sensitive one. METHODOLOGY/PRINCIPAL FINDINGS: A 135-day long salinity experiment was conducted using one-year old trees exposed to NaCl stress for 90 days followed by 45 days of post-stress period during the summer. A cDNA library made of olive seedling mRNAs was sequenced and an olive microarray was constructed. Total RNA was extracted from root samples after 15, 45 and 90 days of NaCl-treatment as well as after 15 and 45 days of post-treatment period and used for microarray hybridizations. SAM analysis between the NaCl-stress and the post-stress time course resulted in the identification of 209 and 36 differentially expressed transcripts in the salt-tolerant and salt-sensitive cultivar, respectively. Hierarchical clustering revealed two major, distinct clusters for each cultivar. Despite the limited number of probe sets, transcriptional regulatory networks were constructed for both cultivars while several hierarchically-clustered interacting transcription factor regulators such as JERF and bZIP homologues were identified. CONCLUSIONS/SIGNIFICANCE: A systems biology approach was used and differentially expressed transcripts as well as regulatory interactions were identified. The comparison of the interactions among transcription factors in olive with those reported for Arabidopsis might indicate similarities in the response of a tree species with Arabidopsis at the transcriptional level under salinity stress.

Effects of grazing exclusion on soil properties and on ecosystem carbon and nitrogen storage in a sandy rangeland of Inner Mongolia, northern China.

The Horqin sandy rangeland of northern China is a seriously desertified region with a fragile ecology. The sandy alluvial and aeolian sediments have a coarse texture and loose structure and are therefore vulnerable to damage caused by grazing animals and wind erosion. We investigated whether grazing exclusion could enhance ecosystem carbon (C) and nitrogen (N) storage and thereby improve overall soil quality. We compared soil properties, C and N storage in biomass (aboveground and below-ground), and the total and light fraction soil organic matter between adjacent areas with continuous grazing and a 12-year grazing exclosure. The soil silt + clay content, organic C, total Kjeldahl N, available N and K, and cation-exchange capacity were significantly (P < 0.05) greater in the exclosure. We found that to a depth of 100 cm, the exclosure plots had greater light fraction C storage (by 267.2 g m(-2) = 73.3 %), light fraction N storage (by 16.6 g m(-2) = 105.7 %), total soil C storage (by 1174.4 g m(-2) = 43.9 %), and total N storage (by 91.1 g m(-2) = 31.3 %). Biomass C and N storage were also 205.0 and 8.0 g m(-2) greater (154.8 and 181.8 %, respectively). The increase was greatest in the light fraction organic matter and biomass and decreased with increasing depth in the soil. The results suggest that light fraction C and N respond more rapidly than total soil C and N to grazing exclusion and that vegetation recovers faster than soil. Our results confirmed that the degraded sandy rangeland is recovering and sequestering C after the removal of grazing pressure.

Metabolic and gene expression changes triggered by nitrogen deprivation in the photoautotrophically grown microalgae Chlamydomonas reinhardtii and Coccomyxa sp. C-169.

Microalgae are emerging as suitable feedstocks for renewable biofuel production. Characterizing the metabolic pathways involved in the biosynthesis of energy-rich compounds, such as lipids and carbohydrates, and the environmental factors influencing their accumulation is necessary to realize the full potential of these organisms as energy resources. The model green alga Chlamydomonas reinhardtii accumulates significant amounts of triacylglycerols (TAGs) under nitrogen starvation or salt stress in medium containing acetate. However, since cultivation of microalgae for biofuel production may need to rely on sunlight as the main source of energy for biomass synthesis, metabolic and gene expression changes occurring in Chlamydomonas and Coccomyxa subjected to nitrogen deprivation were examined under strictly photoautotrophic conditions. Interestingly, nutrient depletion triggered a similar pattern of early synthesis of starch followed by substantial TAG accumulation in both of these fairly divergent green microalgae. A marked decrease in chlorophyll and protein contents was also observed, including reduction in ribosomal polypeptides and some key enzymes for CO2 assimilation like ribulose-1,5-bisphosphate carboxylase/oxygenase. These results suggest that turnover of nitrogen-rich compounds such as proteins may provide carbon/energy for TAG biosynthesis in the nutrient deprived cells. In Chlamydomonas, several genes coding for diacylglycerol:acyl-CoA acyltransferases, catalyzing the acylation of diacylglycerol to TAG, displayed increased transcript abundance under nitrogen depletion but, counterintuitively, genes encoding enzymes for de novo fatty acid synthesis, such as 3-ketoacyl-ACP synthase I, were down-regulated. Understanding the interdependence of these anabolic and catabolic processes and their regulation may allow the engineering of algal strains with improved capacity to convert their biomass into useful biofuel precursors.