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Ginzburg, D.

Publications and source records attributed to Ginzburg, D..

4 recordsLinked to original sources

Dim Green Light Enables Day-and-Night Monitoring of Leaf Movements

Understanding plant growth dynamics requires imaging across day-and-night cycles to quantify growth, movement and development in the aerial plant body and to capture the rhythmic nature of these processes. This requires imaging in light during the day and in darkness at night without perturbing plant physiology. Nighttime imaging has typically depended on infrared (IR) illumination, producing monochrome datasets that require specialised hardware and separate analysis pipelines when combined with daytime RGB imaging. Here, we evaluated very low-intensity green (dimG) illumination from standard LEDs as a practical alternative for colour-consistent nighttime imaging and assessed its physiological impact in Arabidopsis thaliana and Lactuca sativa (lettuce). We show that high resolution colour images can be obtained under dimG using low- cost cameras, with sufficient consistency between full-spectrum and dimG images to allow direct comparison and unified image analysis. We show that very low-fluence green light (<0.5 mol m-2 s-1) does not sustain circadian oscillations of gene activity under continuous exposure and does not perturb rhythms when applied during the dark phase of diel cycles. DimG imaging enabled accurate detection of diel leaf movement profiles in Arabidopsis circadian mutants, revealing genotype-specific phase differences under varying photoperiods. In lettuce, dimG pulses and continuous dimG enabled accurate quantification of diel leaf movement without affecting growth, stomatal opening, electron transport rate or chlorophyll content. Motion profiles under continuous dimG mirrored those under darkness. Our findings establish dim green illumination as a cost-effective solution for night-time imaging, simplifying phenotyping workflows with minimal impact on physiology.

plant biology↗

Sorghum Metabolic Atlas: Large-Scale Mapping of Subcellular Enzyme Localization in Sorghum bicolor

Plant metabolism drives traits essential for productivity and resilience, yet understanding metabolic networks requires subcellular, cellular, and tissue-level spatial context that remains limited, particularly in crop species. Experimentally-derived subcellular localization data for enzymes are sparse, constraining analyses of metabolic organization in the cell. We developed a high-throughput protoplast transformation and fluorescent protein (FP) tagging system optimized for Sorghum bicolor, a climate-resilient C4 crop. Using this platform, we experimentally determined the subcellular localization of 234 metabolic enzymes spanning 184 pathways. The sorghum enzymes we characterized localize to 12 subcellular compartments. Comparison with computational predictions highlights variable accuracy across compartments, and cross-species comparison with Arabidopsis thaliana shows partial agreement with available experimental data. All data are accessible through the Sorghum Metabolic Atlas (www.sorghummetabolicatlas.org) web platform, enabling search, visualization, and download. This study presents a large-scale experimental dataset of enzyme localization in sorghum, providing a resource for studies of plant metabolic organization and comparative analyses.

plant biology↗

Non-destructive, whole-plant phenotyping reveals dynamic changes in water use efficiency, photosynthetic efficiency, and rhizosphere acidification of sorghum cultivars under osmotic stress

Noninvasive phenotyping can quantify dynamic plant growth processes at higher temporal resolution than destructive phenotyping and can reveal phenomena that would be missed by end-point analysis alone. Additionally, whole-plant phenotyping can identify growth conditions that are optimal for both above- and below-ground tissues. However, noninvasive, whole-plant phenotyping approaches available today are generally expensive, complex, and non-modular. We developed a low-cost and versatile approach to non-invasively measure whole-plant physiology over time by growing plants in isolated hydroponic chambers. We demonstrate the versatility of our approach by measuring whole-plant biomass accumulation, water use, and water use efficiency every two days on unstressed and osmotically-stressed sorghum accessions. We identified relationships between root zone acidification and photosynthetic efficiency on whole-plant water use efficiency over time. Our system can be implemented using cheap, basic components, requires no specific technical expertise, and is suitable for any non-aquatic vascular plant species.

plant biology↗

Plant Metabolic Network: A multi-species resource of plant metabolic information

Plant metabolism is a pillar of our ecosystem, food security, and economy. To understand and engineer plant metabolism, we first need a comprehensive and accurate annotation of all metabolic information across plant species. As a step towards this goal, we previously created the Plant Metabolic Network (PMN), an online resource of curated and computationally predicted information about the enzymes, compounds, reactions, and pathways that make up plant metabolism. Here we report PMN 15, which contains genome-scale metabolic pathway databases of 126 algal and plant genomes, ranging from model organisms to crops to medicinal plants, and new tools for analyzing and viewing metabolism information across species and integrating omics data in a metabolic context. We systematically evaluated the quality of the databases, which revealed that our semi-automated validation pipeline dramatically improves the quality. We then compared the metabolic content across the 126 organisms using multiple correspondence analysis and found that Brassicaceae, Poaceae, and Chlorophyta appeared as metabolically distinct groups. To demonstrate the utility of this resource, we used recently published sorghum transcriptomics data to discover previously unreported trends of metabolism underlying drought tolerance. We also used single-cell transcriptomics data from the Arabidopsis root to infer cell-type specific metabolic pathways. This work shows the continued growth and refinement of the PMN resource and demonstrates its wide-ranging utility in integrating metabolism with other areas of plant biology. One-sentence SummaryThe Plant Metabolic Network is a collection of databases containing experimentally-supported and predicted information about plant metabolism spanning many species.

plant biology↗