Biology subjects

Sonawane, B. V.

Publications and source records attributed to Sonawane, B. V..

2 recordsLinked to original sources

bioRxiv2023

Altered cell wall hydroxycinnamate composition impacts leaf and canopy-level CO2-uptake and water-use in rice

Cell wall properties can play a major role in determining photosynthetic carbon-uptake and water-use through impacts on mesophyll conductance (CO2 diffusion from substomatal cavities into photosynthetic mesophyll cells) and leaf hydraulic conductance (water movement from xylem, through leaf tissue to stomata). Consequently, modification of cell wall properties is proposed as a major path for improving photosynthesis and crop water-use efficiency. We tested this using two independent transgenic rice lines that overexpress the rice OsAT10 gene (a "BAHD" CoA acyltransferase) which altered cell wall hydroxycinnamic acid content (greater para-coumaric acid and lower ferulic acid). Plants were grown under high and low water-levels and traits related to leaf anatomy, cell wall composition, gas exchange and hydraulics, plant biomass, and canopy-level water-use were measured. Alteration of hydroxycinnamic acid content led to significant decreases in mesophyll cell wall thickness (-14%), and increased mesophyll conductance (+120%) and photosynthesis (+22%). However, concomitant increases in stomatal conductance negated increased photosynthesis, resulting in no change in intrinsic water-use efficiency (ratio of photosynthesis/stomatal conductance). The leaf hydraulic conductance was also unchanged; however, the transgenics showed small, but significant increase in above-ground biomass (+12.5%), and canopy-level water-use efficiency (+8.8%; ratio of above-ground biomass/ water-used) and performed better under low water-level. Our results demonstrate that changes in cell wall composition, specifically hydroxycinnamic acid content, can increase mesophyll conductance and photosynthesis in C3 cereal crops like rice. However, attempts to improve photosynthetic water-use efficiency will need to enhance mesophyll conductance and photosynthesis whilst maintaining or decreasing stomatal conductance.

plant biology↗

bioRxiv2021

Increased signal to noise ratios within experimental field trials by regressing spatially distributed soil properties as principal components.

Environmental variability poses a major challenge to any field study. Researchers attempt to mitigate this challenge through replication. Thus, the ability to detect experimental signals is determined by the degree of replication and the amount of environmental variation, noise, within the experimental system. A major source of noise in field studies comes from the natural heterogeneity of soil properties which create micro-treatments throughout the field. To make matters worse, the variation within different soil properties is often non-randomly distributed across a field. We explore this challenge through a sorghum field trial dataset with accompanying plant, microbiome and soil property data. Diverse sorghum genotypes and two watering regimes were applied in a split-plot design. We describe a process of identifying, estimating, and controlling for the effects of spatially distributed soil properties on plant traits and microbial communities using minimal degrees of freedom. Importantly, this process provides a tool with which sources of environmental variation in field data can be identified and removed, improving our ability to resolve effects of interest and to quantify subtle phenotypes. IMPORTANCEData from field experiments are notoriously noisy. Proper field designs with high replication aid in mitigating this challenge, yet true biological correlations are still often masked by environmental variability. This work identifies soil property composition as a spatially distributed source of variance to three types of characteristics: plant phenotype, microbiome composition, and leaf traits. We show that once identified, spatial principal component regression was able to account for these effects so that more precise estimates of experimental factors were obtained. This generalizable method is applicable to diverse field experiments.

systems biology↗