Biology subjects

Moshelion, M.

Publications and source records attributed to Moshelion, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Designed nutrient-gradient phenotyping enables predictive ionomics of tropical leafy greens

Accurate prediction of edible-leaf mineral composition is challenging because tissue ionomes integrate nutrient supply, plant identity, growth dilution, water-balance regulation and cumulative environmental exposure. We combined a designed nutrient-gradient experimental framework with machine learning to predict leaf ionome using 1,163 Chinese spinach (Amaranthus dubius) and Chinese broccoli (Brassica oleracea Alboglabra Group) plants grown across 16 designed nutrient-gradient experiments in a semi-controlled tropical greenhouse. Targeted perturbations of N, P, K, Ca, Mg, Fe and Mo were combined with cumulative transpiration, integrated microclimate, shoot dry weight and laboratory quantification of 12 harvested-leaf mineral targets. The resulting ionome was broad, non-Gaussian and species dependent, with coordinated off-target shifts also evident for minerals whose supplied concentrations were held constant. We benchmarked AutoGluon and TabPFN using five-fold leakage-free, distribution-balanced grouped cross-validation, in which all biological replicates from each dosing condition were withheld together. Across previously unseen nutrient conditions, pooled out-of-fold R2 ranged from 0.72 to 0.92, with broadly comparable performance between architectures. Learning curves showed rapid early gains for several targets but persistent condition-level generalization gaps for total reduced nitrogen, K, Mn and Zn. Model-agnostic Shapley additive explanations identified target-specific combinations of nutrient inputs, plant type, substrate, cumulative environment and physiological traits, while cross-model attribution agreement varied among minerals. Finally, reduced-feature models were tested on an independent grower-operated cohort of 99 plants. Calibration was closest for NO3, Mg and B, whereas Mn and Na showed substantial external bias. These results establish leakage-aware predictive ionomics as a robust framework for post-harvest lab-free crop mineral estimation while defining the calibration and sensing requirements for commercial deployment.

plant biology↗

bioRxiv2026

Adventitious roots facilitate surface water uptake but only partially sustain transpiration under waterlogging in tomato (Solanum lycopersicum)

waterlogging constrains terrestrial plants by limiting oxygen diffusion in the rhizosphere and altering root-zone physical and chemical properties. However, the extent to which whole-plant responses to waterlogging can be reproduced by oxygen deficiency alone remains unresolved. In tomato (Solanum lycopersicum), waterlogging is commonly associated with adventitious-root formation, yet the functional contribution of these roots to whole-plant water relations has rarely been quantified. Here, we experimentally separated root-zone hypoxia from waterlogging and quantified the contribution of surface-associated adventitious roots to whole-plant transpiration. Using high-resolution gravimetric lysimeters, we monitored transpiration dynamics under two conditions: (i) N2-driven displacement of root-zone O2 under near-field-capacity conditions and (ii) root-zone waterlogging. These measurements were complemented by analyses of soil redox potential and pH, mineral composition, stem anatomy, and genotypic variation among M82, IL11-4, and IL8-1. N2-driven oxygen depletion rapidly reduced rhizosphere O2 concentration and induced a moderate decline in redox potential, accompanied by changes in rhizosphere chemistry and mineral relations. Whole-plant transpiration, however, declined only progressively over several days. Under waterlogging, transpiration declined rapidly in all genotypes, with strong genotype dependence. A transient partial recovery coincided with the appearance of adventitious roots at the soil surface and was followed by renewed decline after drainage. Quantitative analysis indicated that adventitious roots contributed only a limited fraction of daily water uptake, approximately 15% to 20%, which was insufficient to restore pre-waterlogging transpiration or growth. Together, these results show that waterlogging responses were not reproduced by rapid oxygen deprivation alone and that adventitious roots provide limited hydraulic compensation.

plant biology↗

bioRxiv2025

Vacuolar invertase knockout enhances drought tolerance in potato plants

Drought stress is one of the most critical abiotic constraints limiting crop productivity worldwide, exacerbated by ongoing climate change and increasingly frequent extreme weather events. Stomatal regulation and osmoprotective sugar accumulation are critical adaptive mechanisms for plant survival under drought stress. Here, we characterize the enhanced drought resilience observed in CRISPR/Cas9-mediated potato, mutants in their vacuolar invertase gene (StVInv). Knockout plants exhibited improved performance under progressive drought stress and during rewatering drought, maintaining higher stomatal conductance, elevated transpiration rates, and superior photosynthetic efficiency compared to wild-type (WT) plants. These improved performance under similar transpiration rate led to higher agronomic water-use efficiency (AWUE) in stvinv plants resulting in greater biomass production despite reduced water availability. Metabolomic profiling revealed distinct adaptive strategies; stvinv plants preferentially accumulated galactinol and raffinose, indicating enhanced raffinose family oligosaccharide (RFO) metabolism. Furthermore, stvinv plants displayed lower levels of abscisic acid (ABA) and its catabolites under drought, suggesting a moderated ABA response facilitating a more risk-taking growth strategy that supports sustained growth and physiological stability. Our findings identify targeted metabolic and hormonal adjustments underlying drought resilience in potato plant, offering promising strategies for enhancing crop performance under water-limited conditions.

plant biology↗

bioRxiv2025

Wireless Sensor Network: New Concept of Spatial-Temporal Monitoring Plant-Environment Interactions

We present a low-cost, standards-based wireless sensor network (WSN) for continuous, canopy-integrated monitoring of plant-environment interactions. Each plant carries in-canopy microclimate sensors (temperature, relative humidity, illuminance) paired with nearby ambient references, yielding real-time canopy-ambient differentials. The system is easy to install: at planting or sowing, sensors are fixed at positions that will lie within the developing canopy, and a separate ambient reference area is designated and kept free of vegetation. As plants grow, they envelop the sensors, thereby capturing growth dynamics over time. The sensors accuracy was validated against a commercial weather station and portable system that measures gas exchange, temperature and light (LI-COR 6800/6400), and the systems ability to resolve plant physiological activity was confirmed using the PlantArray functional phenotyping platform with independent whole-plant transpiration and biomass references. Under controlled growth-room conditions and across two contrasting Cannabis cultivars, daily transpiration strongly predicted biomass gain (R{superscript 2} > 0.9). Microclimate signals mirrored physiology: midday canopy air was cooler by 4-7 {degrees}C, more humid by 18-25 % RH, and increasingly shaded as biomass accumulated, with temperature, RH, and light attenuation showing saturating logarithmic relationships with growth. The network operated for months unattended with low packet loss and predictable power use. It provides 4D (x-y-z-time) coverage, where x and y denote horizontal location, z the vertical position within the canopy, and time the dynamics, enabling resolution of where changes occur and how they evolve, and supplying high-frequency labeled data. This system complements, rather than replaces, precision instruments and high-end phenotyping platforms, providing a scalable layer for continuous tracking across wide areas. We outline practical constraints and next steps toward field pilots, modest energy harvesting, expanded sensor suites, and integration with machine learning for predictive crop management.

bioengineering↗

bioRxiv2025

Effects of Drought on Inflorescence Yield, and Secondary Metabolites in Cannabis sativa L.

O_LIThe use of medical products derived from Cannabis sativa L. has increased significantly in recent years. While drought is known to negatively affect the yields of many crops, growers often recommend controlled periods of drought for cannabis cultivation to increase concentrations of secondary metabolites. This is especially pertinent when considering the relationship between medicinal effects and the secondary-metabolite profile. C_LIO_LIWe examined the effects of tightly controlled drought treatments on biochemical (111 phytocannabinoids and 132 terpenoids), physiological, and anatomical responses of three Type-I chemotype cultivars, specifically the THCA-dominant cultivars Odem, MVA, and 187. C_LIO_LIOur results revealed strong correlations between inflorescence and phytocannabinoid yields, on the one hand, and cumulative transpiration on the other (0.96 < r2 < 0.99). Drought treatment reduced canopy conductance, with inflorescence weight decreasing by 40% and total fresh weight decreasing by 48%. The concentrations of the major phytocannabinoids, THCA and CBGA, decreased over increasing levels of drought stress (by 26% and 61%, respectively). Interestingly, terpene concentrations showed greater stress-induced variation, and that variation was genotype-dependent. C_LIO_LIOur findings suggest that the decreases in inflorescence weight and concentrations of major phytocannabinoids under drought conditions are mainly due to a lack of biochemical-production processes, as opposed to metabolic degradation. C_LI

plant biology↗

bioRxiv2024

Transpiration Insights: Estimating Transpiration Through Advanced Modeling

O_LIWe conducted research to predict daily transpiration in crops by utilizing a combination of machine learning (ML) models combined with extensive transpiration data from gravimetric load cells and ambient sensors. Our aim was to improve the accuracy of transpiration estimates. C_LIO_LIData were collected from hundreds of plant specimens growing in two semi-controlled greenhouses over seven years, automatically measuring key physiological traits (serves as our ground truth data) and meteorological variables with high temporal resolution and accuracy. We trained Decision tree, Random Forest, XGBoost, and Neural Network models on this dataset to predict daily transpiration. C_LIO_LIThe Random Forest and XGBoost models demonstrated high accuracy in predicting the whole plant transpiration, with R{superscript 2} values of 0.89 on the test set (cross-validation) and R2 = 0.82 on holdout experiments. Ambient temperature was identified as the most influential environmental factors affecting transpiration. C_LIO_LIOur results emphasize the potential of ML for precise water management in agriculture, and simplify some of the complex and dynamic environmental forces that shape transpiration. C_LI

plant biology↗

bioRxiv2024

Harnessing Smartphone RGB Imagery and LiDAR Point Cloud for Enhanced Leaf Nitrogen and Shoot Biomass Assessment - Chinese Spinach as a Case Study

Accurate estimation of leaf nitrogen concentration and shoot dry-weight biomass in leafy vegetables is crucial for crop yield management, stress assessment, and nutrient optimization in precision agriculture. However, obtaining this information often requires access to reliable plant physiological and biophysical data, which typically involves sophisticated equipment, such as high-resolution in-situ sensors and cameras. In contrast, smartphone-based sensing provides a cost-effective, manual alternative for gathering accurate plant data. In this study, we propose an innovative approach for estimating leaf nitrogen concentration and shoot biomass by integrating smartphone RGB imagery with Light Detection and Ranging (LiDAR) data, using Amaranthus dubius (Chinese spinach) as a case study. The influence of varying nitrogen dosages on individual spectral and structural features derived from smartphone RGB imagery and LiDAR data was modeled. Additionally, the spectral indices from RGB imagery and structural indices from LiDAR data were combined to model both leaf nitrogen concentration and shoot biomass. The performance of crop parameter modeling was evaluated using support vector regression, random forest regression, and lasso regression. Results demonstrate that the combined use of smartphone RGB imagery and LiDAR data can accurately estimate leaf total reduced nitrogen concentration, leaf nitrate concentration, and shoot dry-weight biomass, with average relative root mean square errors as low as 0.06, 0.16, and 0.05, respectively. Furthermore, the optimal nitrogen dosage for maximizing biomass yield in Chinese spinach was also estimated using the smartphone data. This study lays the groundwork for smartphone-based estimate leaf nitrogen concentration and shoot biomass, supporting accessible precision agriculture practices.

bioinformatics↗

bioRxiv2024

Nitrogen ionome dynamics on leafy vegetables in tropical climate

Nitrogen is known to be a critical macro-nutrient influencing plant physiology, growth, and mineral composition. In tropical conditions, which are challenging for leafy vegetable farming, the nitrogen delivery effect is unclear. In this study, we aimed to investigate the effect of nitrogen application on key physiological traits and the mineral composition of the plants, the plant ionome. Experiments were conducted under tropical conditions greenhouse with varying levels of nitrogen supply to examine the effect on plant transpiration, yield, use efficiency of water and nitrogen, and nutrient uptake dynamics followed by cross-correlation analysis, trying to understand the physiological behavior-uptake dynamics relationships. The results demonstrated that transpiration, yield and WUE theoretic optimum curve, which peaking in nitrogen concentration of 120 mg/L for Chinese spinach and 200 mg/L for Chinese broccoli. Conversely, NUE reduce significantly with increasing nitrogen delivery which reflected on antagonistic increase of excess nitrogen. In terms of mineral composition, nitrogen application resulted in an increase I nitrogen content in the plant leaf tissue, while concentration of certain macronutrients and micronutrients were affected, including potassium, phosphorus, calcium, magnesium, iron, zinc, and molybdenum. Part of the minerals exhibited decreasing pattern due to potential competitive uptake mechanism, iron revealed increasing pattern that correlated with nitrogen delivery, and some minerals correlated with the measured physiological parameters. These results underscore the importance of optimizing nitrogen fertilization to balance plant growth, physiological processes, and plant nutrient homeostasis. The study offers valuable insights for sustainable nitrogen management in agricultural systems aimed at maximizing crop yield while maintaining nutritional quality.

plant biology↗

bioRxiv2024

A missense mutation in the gene encoding the Mg-chelatase subunit I leads to a viable pale green line with phenotypic features of potential interest for barley breeding programs

The pale green trait, i.e. reduced chlorophyll content, has been shown to increase the efficiency of photosynthesis and biomass accumulation when photosynthetic microorganisms and tobacco plants are cultivated at high densities. Thus, the hus1 barley mutant is defective in photosystem antenna biogenesis, and exhibits a 50% reduction in leaf chlorophyll content. Nevertheless, its agronomical performance under standard field conditions is comparable to that of the wild-type. This supports the notion that crops can decrease their investment in antenna proteins and chlorophyll biosynthesis without detrimental effects on productivity. Here, we assess the effects of reducing leaf chlorophyll content in barley by altering the chlorophyll biosynthesis pathway (CBP). To this end, we have isolated and characterised the pale green barley mutant xan-h.chli-1, which carries a missense mutation in the Xan-h gene for subunit I of Mg-chelatase (HvCHLI), the first enzyme in the CBP. Intriguingly, xan-h.chli-1 is the only known viable homozygous mutant at the Xan-h locus in barley. The Arg298Lys amino-acid substitution in the ATP-binding cleft causes a slight decrease in HvCHLI protein abundance, and a marked reduction in Mg-chelatase activity. Under controlled growth conditions, mutant plants display reduced accumulation of antenna and photosystem core subunits, together with reduced photosystem II yield relative to wild type under moderate illumination, and consistently higher than wild-type levels at high light intensities. Moreover, the reduced content of leaf chlorophyll is associated with a stable reduction in daily transpiration rate, and slight decreases in total biomass accumulation and water-use efficiency. These traits are reminiscent of phenotypic features of wild barley accessions and landraces that thrive under arid climatic conditions. Overall, our findings make the xan-h.chli-1 allelic variant of potential interest for tailoring barley, and other crop plants, for growth in harsh environments.

plant biology↗

bioRxiv2024

Whole-Plant Physiological Identification and Quantification of Disease Progression

Visual estimates of plant symptoms are traditionally used to quantify disease severity. Yet, the methodologies used to assess these phenotypes are often subjective and do not allow tracking of disease progression from very early stages. Here, we hypothesized that quantitative analysis of whole-plant physiological vital functions can be used to objectively determine plant health, providing a more sensitive way to detect disease. We studied the tomato wilt that is caused by Fusarium oxysporum f. sp. lycopersici. Physiological performance of infected and non-infected tomato plants was compared using a whole-plant pot-based lysimeter functional-phenotyping system in a semi-environmentally controlled greenhouse. Water-balance traits of the plants were measured continuously and simultaneously in a quantitative manner. Infected plants exhibited early reductions in transpiration and biomass gain, which preceded visual disease symptoms. These changes in transpiration proved to be effective quantitative indicators for assessing both plant susceptibility to infection and virulence of the fungus. Physiological changes linked to fungal outgrowth and toxin release contributed to reduced hydraulic conductance during initial infection stages. The functional-phenotyping method objectively captures early-stage disease progression, advancing plant disease research and management. This approach emphasizes the potential of quantitative whole-plant physiological analysis over traditional visual estimates for understanding and detecting plant diseases.

plant biology↗

bioRxiv2024

Precision phenotyping of a barley diversity set reveals distinct drought response strategies

Plants exhibit a wide array of responses and adaptive mechanisms to drought. During drought, the trade-off between water loss and CO2 uptake for growth is mediated by the regulation of stomatal aperture in response to soil water content (SWC), among other factors. We earlier identified, in a few reference varieties of barley that differed by the SWC at which transpiration was curtailed, two divergent water use strategies: water-saving ("isohydric") and water-spending ("anisohydric"). We proposed that an isohydric strategy may reduce risk from early droughts in climates where the probability of precipitation increases during the growing season, whereas an anisohydric strategy is consistent with environments having terminal droughts, or with those where dry periods are short and show little seasonal variation. Here, we have examined drought response in an 81-line barley diversity set that spans 20th century European barley breeding and identified a several lines with a third, dynamic transpirational response to drought. We found a strong positive correlation between vigor and transpiration, the dynamic group being highest for both. However, these lines curtailed daily transpiration at a higher SWC than the isohydric group. While the dynamic lines, particularly cv Hydrogen and Baronesse, were not the most resilient in terms of restoring initial growth rates, their strong initial vigor and high return to initial transpiration rates meant that their growth nevertheless surpassed more resilient lines during recovery from drought. The results will be of use for defining barley physiological ideotypes suited to future climate scenarios.

plant biology↗

bioRxiv2024

Evaluating Evapotranspiration in a Commercial Greenhouse: A Comparative Study of Microclimatic Factors and Machine-Learning Algorithms

The FAO-56 Penman-Monteith equation (FPME) is commonly used to calculate evapotranspiration and apply necessary irrigation, based on environmental data usually taken from a single measuring station. In this study, we hypothesized that the accuracy of the FPME is affected by microclimatic changes over time and space within the target area. Therefore, we tested the impact of numerous spatial and temporal environmental measurement points in a commercial greenhouse on the accuracy of the FPME, by comparing its evapotranspiration evaluation to the actual evaporation measured by dozens of weighing lysimeters throughout the year. Additionally, we harnessed the capabilities of machine-learning algorithms to utilize the extensive data acquired for predicting evapotranspiration. Our results revealed that the daily FPME exhibited a -22% to +22% discrepancy in accuracy, as compared to the lysimeters, with overestimation in the winter and underestimation in the summer. Interestingly, using more data points per day led to less accurate FPME evaluation. The conflict between an increased number of data points and a reduction in accuracy was explained by daily hysteresis. Machine-learning algorithms (Decision Tree, Random Forest, XGBoost and Neural Network) showed impressive accuracy in predicting evapotranspiration, when the model dataset contained temporal parameters (R2> 0.918). Furthermore, we demonstrated that spatial sampling had a stronger effect on the accuracy of predictions than the amount of the data collected. Specifically, when we used 10% of the original dataset (3.01e5 entries) with high consideration of spatial measurements, the best-performing models (Random Forest and XGBoost) were highly accurate (R2 = 0.913 and R2 = 0.935, respectively). The top three most influential features of all models were light, day and hour, underscoring the importance of the temporal dimension. This approach allowed us to explore the potential of leveraging advanced computational methods to improve the estimation of water loss under various environmental conditions.

plant biology↗

bioRxiv2023

From Selfies to Science - Precise 3D Leaf Measurement with iPhone 13 and Its Implications for Plant Development and Transpiration

Advanced smartphone technology now integrates sophisticated sensors, increasing access to high-precision data acquisition. This study tested the hypothesis that the iPhone 13-Pro camera, with LiDAR technology, can accurately estimate maize leaf surface area (Zea mays). 3D point cloud models enabled non-destructive data collection, and four methods for canopy area extraction were evaluated in relation to plant transpiration rates. Results showed a strong correlation (R2=0.92, RMSE=49.78) between manually scanned and iPhone-estimated plant surface areas. Additionally, the stem-to-plant surface area ratio was found to be 12.3% (R2=0.9, RMSE=28.42). Using this ratio to predict canopy area showed a significant correlation (R2=0.83) with actual canopy measurements. The iPhones surface area measurement tool offers an advantage by scanning the entire plant surface, unlike traditional leaf area index measurements, which often cannot penetrate the canopy. Moreover, real-size surface measurement of the canopy correlated strongly (R2=0.83) with whole canopy transpiration rates measured gravimetrically. This study introduces a novel method for analyzing 3D plant traits using a portable, affordable, and accurate tool, which has the potential to enhance plant breeding and agricultural practices. 0. How to Use This TemplateThe template details the sections that can be used in a manuscript. Note that each section has a corresponding style, which can be found in the "Styles" menu of Word. Sections that are not mandatory are listed as such. The section titles given are for articles. Review papers and other article types have a more flexible structure. Remove this paragraph and start section numbering with 1. For any questions, please contact the editorial office of the journal or support@mdpi.com.

plant biology↗

bioRxiv2023

A tale of two pumps: Blue light and ABA alter Arabidopsis leaf hydraulics via bundle sheath cells H+-pumps and channels

This study focuses on the cellular mechanism underlying the co-regulation of the leaf hydraulic conductance (Kleaf) by blue light (BL) and the stress hormone ABA in Arabidopsis thaliana. Our previous work has demonstrated that (1) Kleaf increased by BL signaling within the leaf bundle sheath cells (BSCs), which activated their plasmalemma (PM) H+-ATPase (AHA2), acidifying the xylem sap; (2) external acidification enhanced the BSCs Kleaf and their osmotic water permeability (Pf); (3) ABA decreased both Kleaf and Pf by reducing the BSCs PM aquaporins activity. We now show, using pH and EM (membrane potential) probes combined with H+-pumps inhibitors and manipulations of cytosolic and external Ca2+ concentrations ([Ca2+]CYT, [Ca2+]EXT, respectively), that, in the BSCs: (a) under BL, ABA inhibits AHA2, depolarizing the BSCs and alkalinizing the xylem sap, (b) ABA stimulates the BSCs vacuolar H+-ATPase (VHA), alkalinizing their cytosol; (c) each pump stimulation, AHA2 by BL and VHA by ABA, requires [Ca2+]CYT elevation. ABA-effect-mimicking conditions in patch-clamp experiments activate the BSCs K+-release channels (SKOR and/or GORK). ABA decreased the Kleaf of skor mutants less than WTs, while during water deprivation stress, skor plants transpired more and their leaves lost relatively less K+ than WT. This suggests a role for SKOR in water conservation under drought.

plant biology↗

bioRxiv2023

Phenotypic plasticity in response to drought stress: Comparisons of domesticated tomato and a wild relative

Phenotypic plasticity is the ability of an organism to undergo reversible behavioral, morphological or physiological changes in response to environmental conditions. Phenotypic plasticity enables plants to cope with uncertain environmental conditions, such as drought. A primary plastic trait is the rate of stomatal response to changes in ambient conditions, which determines the amount of water lost via transpiration, as well as levels of CO2 absorption, growth and productivity. Here, we examined the differences between domesticated and wild tomato species and their responses to drought stress. We found that the domesticated tomato had a higher transpiration rate and higher stomatal conductance (gs). The domesticated tomato also had greater biomass and greater leaf area under drought conditions, as compared to the wild tomato. Despite the domesticated tomatos higher transpiration rate and higher gs, there was no difference between the photosynthetic rates of the two lines. Moreover, the wild tomato had a higher maximum rate of rubisco activity, which might explain its greater molecular and whole canopy water-use efficiency. The domesticated tomatos higher transpiration rate and greater leaf area led to its earlier exposure to drought stress, as compared to the wild tomato, which maintained higher levels of soil water, enabling it to maintain steady rates of whole-canopy stomatal conductance (gsc) for extended periods. The wild tomato was also more sensitive to the soil water availability and lowered its maximum transpiration rate at a higher soil-water-content level. Our results suggest that the domestication process of tomatoes favored morphological/anatomical performance traits over physiological efficiency.

plant biology↗

bioRxiv2023

Drought and recovery in barley: key gene networks and retrotransposon response.

O_LIDuring drought, plants close their stomata at a critical soil water content (SWC), together with diverse physiological, developmental, and biochemical responses. C_LIO_LIUsing precision-phenotyping lysimeters, we imposed pre-flowering drought on four barley varieties (Arvo, Golden Promise, Hankkija 673 and Morex) and followed their physiological responses. For Golden Promise, we carried out RNA-seq on leaf transcripts before and during drought, and during recovery, also examining retrotransposon BARE1 expression. Transcriptional data were subjected to network analysis. C_LIO_LIThe varieties differed by their critical SWC, Hankkija 673 responding at the highest and Golden Promise at the lowest. Pathways connected to drought and salinity response were strongly upregulated during drought; pathways connected to growth and development were strongly downregulated. During recovery, growth and development pathways were upregulated; altogether 117 networked genes involved in ubiquitin-mediated autophagy were downregulated. The differential response to SWC suggests adaptation to distinct rainfall patterns. C_LIO_LIWe identified several strongly differentially expressed genes not earlier associated with drought response in barley. BARE1 transcription is strongly transcriptionally upregulated by drought and downregulated during recovery unequally between the investigated cultivars. The downregulation of networked autophagy genes suggests a role for autophagy in drought response; its importance to resilience should be further investigated. C_LI

plant biology↗

bioRxiv2022

Limitations of Solar-Induced Chlorophyll Fluorescence (SIF) for Estimating Photosynthesis Under Stress

High-throughput measurements of photosynthesis of plants grown under various conditions may provide important insights into the plasticity of the photosynthetic performance of plants. Remote sensing of photosynthetic activity is the next generation of fast scanning techniques, enabling high-throughput photosynthesis measurements under controlled conditions. We hypothesized that by measuring SIF simultaneously with whole-plant water relations in a standardized controlled drought experiment, we would be able to quantify photosynthetic activity and to detect water stress at an early stage. A functional-phenotyping platform was used to apply the controlled drought treatment and to monitor the growth and water balance of tomato introgression lines (ILs). A new SIF-derived index, electron transport rate (RS-ETRi), was found to be negatively correlated with whole-plant stomatal conductance (Gsc) under non-stressed conditions. No significant relationships were found between SIF and plant biomass or Gsc. SIF687 responded to drought earlier than any of the other measured vegetation indices. SIF based indices could not differentiate between introgressed lines of tomato; whereas differences between Introgression Lines were clearly identified by the water-relations measurements. We concluded that SIF did not provide any advantage over commonly used methods for detecting physiological differences between the Introgression Lines. Overall, although SIF plays a significant role in photosynthesis, the relationship between SIF and photosynthesis is complex and we believe it would be an oversimplification to use SIF to quantify photosynthetic activity on close canopy spatial resolution level.

plant biology↗

bioRxiv2022

Leaf hydraulic maze; Differential effect of ABA on vascular bundle-sheath, palisade, and spongy mesophyll controlling hydraulic conductance

Leaf hydraulic conductance (Kleaf) facilitates the movement of water for transpiration, enabling continual CO2 uptake while the plant maintains its water status. We hypothesized that bundle-sheath and mesophyll cells play key roles in regulating the radial flow of water out of the xylem under optimal and stress conditions. To examine that hypothesis, we generated transgenic Arabidopsis plants that were insensitive to abscisic acid (ABA) in their bundle sheath (BSabi) or mesophyll (MCabi) cells. Both BSabi and MCabi plants showed greater Kleaf and transpiration under optimal conditions. Yet, the stomatal apertures, stomatal indices and vein densities of the BSabi plants were similar to those of WT plants. MCabi plants had larger stomatal apertures, a higher stomatal index and greater vascular diameter and biomass, relative to the WT and BSabi. In response to xylem-fed ABA, both transgenic and WT plants reduced their Kleaf and transpiration. However, leaf water potential was reduced only in the WT. The membrane osmotic water permeability (Pf) of the WTs spongy mesophyll was higher than that of its palisade mesophyll. Moreover, only the spongy cells reduced their Pf in response to ABA. ABA-insensitive spongy mesophyll cells had a low Pf; whereas ABA-insensitive bundle-sheath cells had a higher Pf. Palisade cells maintained a low Pf at all ABA levels. ABA increased the symplastic water pathway, but its contribution to Kleaf was negligible. We suggest that the bundle sheath-spongy mesophyll pathway may control Kleaf to maintain steady-state conditions in the palisade cells and optimal whole-leaf water-use efficiency.

plant biology↗