Potato (Solanum tuberosum) is an important food crop consumed all over the world, but it is generally sensitive to drought conditions. One of the major physiological processes affected by drought stress is carbon partitioning: the plant's choice of where to allocate its photoassimilates. Our aim was to investigate the molecular factors and possible bottlenecks affecting carbon partitioning during drought. We studied potato cultivars with contrasting drought responses in the greenhouse in the years 2013-2015, and further investigated the expression of genes involved in carbon partitioning and metabolite levels. Our results indicate that one of the most severe effects of drought stress on potato is the arrest of stolon differentiation and formation of tubers. We also identified some physiological traits like stomatal conductance and chlorophyll content as affecting carbon assimilation, partitioning and eventual tuber yield. The gene expressions and biochemical analyses highlight the various tissues prioritized by the plant for assimilate transport during drought stress, and give indications of what distinguishes drought tolerance and sensitivity of cultivated potato. Some of the key genes studied (like Sucrose synthase and Sucrose transporters) may be inclusive breeding targets for drought tolerance in potato.
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http://dx.doi.org/10.1016/j.plaphy.2019.11.019 | DOI Listing |
Front Plant Sci
December 2024
BIODYNE Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liege, Gembloux, Belgium.
Introduction: The identification of the physiological processes limiting carbon assimilation under water stress is crucial for improving model predictions and selecting drought-tolerant varieties. However, the influence of soil water availability on photosynthesis-limiting processes is still not fully understood. This study aimed to investigate the origins of photosynthesis limitations on potato () during a field drought experiment.
View Article and Find Full Text PDFBMC Genomics
December 2024
Division of Plant Science and Technology, University of Missouri, Columbia, MO, USA.
Background: Efficient capture and use of resources is critical for optimal plant growth and productivity. Both shoot and root growth are essential for resource acquisition, namely light and CO by the shoot and water and mineral nutrients by roots. Soybean [Glycine max (L.
View Article and Find Full Text PDFJ Hazard Mater
December 2024
State Key Laboratory of Hydraulics and Mountain River Engineering, College of Architecture and Environment, Sichuan University, Chengdu 610065, China.
Carbon nanotubes-driven persulfates oxidation processes (CNTs/PS) have been extensively studied for environmental remediation. Solution pH is one of the main factors affecting wastewater treatment, but it is often overlooked. Herein, we report the effect laws of pH on the mechanism of peroxymonosulfate (PMS) or peroxydisulfate (PDS) activation by CNTs.
View Article and Find Full Text PDFmSystems
December 2024
Department of Computational Biology, Scientific Center for Genetics and Life Sciences, Sirius University of Science and Technology, Sochi, Russia.
Unlabelled: Context-specific genome-scale model (CS-GSM) reconstruction is becoming an efficient strategy for integrating and cross-comparing experimental multi-scale data to explore the relationship between cellular genotypes, facilitating fundamental or applied research discoveries. However, the application of CS modeling for non-conventional microbes is still challenging. Here, we present a graphical user interface that integrates COBRApy, EscherPy, and RIPTiDe, Python-based tools within the BioUML platform, and streamlines the reconstruction and interrogation of the CS genome-scale metabolic frameworks via Jupyter Notebook.
View Article and Find Full Text PDFJ Am Soc Mass Spectrom
December 2024
Department of Earth and Environmental Sciences, University of Illinois Chicago, Chicago, Illinois 60607, United States.
The spatial distribution of organics in geological samples can be used to determine when and how these organics were incorporated into the host rock. Mass spectrometry (MS) imaging can rapidly collect a large amount of data, but ions produced are mixed without discrimination, resulting in complex mass spectra that can be difficult to interpret. Here, we apply unsupervised and supervised machine learning (ML) to help interpret spectra from time-of-flight-secondary ion mass spectrometry (ToF-SIMS) of an organic-carbon-rich mudstone of the Middle Jurassic of England (UK).
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