Mathematical Modeling Approaches in Plant Metabolomics

Probing the in situ volumes of Arabidopsis leaf plastids using three-dimensional confocal and scanning electron microscopy

Leaf plastids harbor a plethora of biochemical reactions including photosynthesis, one of the most important metabolic pathways on Earth. Scientists are eager to unveil the physiological processes within the organelle but also their interconnection with the rest of the plant cell. An increasingly important feature of this venture is to use experimental data in the design of metabolic models. A remaining obstacle has been the limited in situ volume information of plastids and other cell organelles. To fill this gap for chloroplasts, we established three microscopy protocols delivering in situ volumes based on: (i) chlorophyll fluorescence emerging from the thylakoid membrane, (ii) a CFP marker embedded in the envelope, and (iii) calculations from serial block-face scanning electron microscopy (SBFSEM). The obtained data were corroborated by comparing wild-type data with two mutant lines affected in the plastid division machinery known to produce small and large mesophyll chloroplasts, respectively. Furthermore, we also determined the volume of the much smaller guard cell plastids. Interestingly, their volume is not governed by the same components of the division machinery which defines mesophyll plastid size. Based on our three approaches, the average volume of a mature Col-0 wild-type mesophyll chloroplasts is 93 μm3 . Wild-type guard cell plastids are approximately 18 μm3 . Lastly, our comparative analysis shows that the chlorophyll fluorescence analysis can accurately determine chloroplast volumes, providing an important tool to research groups without access to transgenic marker lines expressing genetically encoded fluorescence proteins or costly SBFSEM equipment.

Optimization of the incubation parameters for biogenic synthesis of WO3 nanoparticles using Taguchi method

Green synthesis of metal nanoparticles is gathering attention due to eco-friendly processing. Tungsten oxide (WO₃) nanoparticles have immense applications as semiconductors, antimicrobials and photo thermal materials but their synthesis using biological systems is hitherto unpublicized. The paper discusses synthesis of WO₃ nanoparticles using Stenotrophomonas maltophilia and the optimization of physico-chemical parameters of incubation which influence the growth and metabolism of the bacterium and consequently the size of the WO₃ nanoparticles. The biogenic synthesis of WO₃ nanoparticles was confirmed by ATR-FTIR and X-ray diffraction analysis. Taguchi and analysis of variance method was applied to optimize the physico-chemical parameters (pH, temperature, time, aeration rate and concentration), considering particle size and poly dispersity index (PDI) of the nanoparticles as the experimental responses. Under the design of experiments technique, Taguchi's L27 array was selected to determine the optimal process parameters which could significantly reduce the particle size and PDI of WO₃ nanoparticles. Statistical analysis by signal-to-noise ratio, regression analysis and ANOVA (95% confidence level) on experimental responses confirmed pH and aeration as most influential while temperature and time as least influential parameters. pH 8, Temperature 40 °C, aeration 200 RPM, time 3 days and concentration of sodium tungstate at 1 mM (p3t3r3d3c1) was the most effective level and parameters combination for smallest particle size and PDI of WO₃ nanoparticles. Regression models developed for particle size and PDI exhibited a linear regression of 97.80% and 90.89% respectively, while the confirmation test validated the size and PDI of the experimental values against predicted results. SEM image of WO₃ nanoparticles illustrated the same particle size as that predicted, further validating the model. The study can be applied to optimize any process parameters in the industry or on biological systems.

Mass spectrometry-based forest tree metabolomics

Research in forest tree species has advanced slowly when compared with other agricultural crops and model organisms, mainly due to the long-life cycles, large genome sizes, and lack of genomic tools. Additionally, trees are complex matrices, and the presence of interferents (e.g., oleoresins and cellulose) challenges the analysis of tree tissues with mass spectrometry (MS)-based analytical platforms. In this review, advances in MS-based forest tree metabolomics are discussed. Given their economic and ecological significance, particular focus is given to Pinus, Quercus, and Eucalyptus forest tree species to better understand their metabolite responses to abiotic and biotic stresses in the current climate change scenario. Furthermore, MS-based metabolomics technologies produce large and complex datasets that require expertize to adequately manage, process, analyze, and store the data in dedicated repositories. To ensure that the full potential of forest tree metabolomics data are translated into new knowledge, these data should comply with the FAIR principles (i.e., Findable, Accessible, Interoperable, and Re-usable). It is essential that adequate standards are implemented to annotate metadata from forest tree metabolomics studies as is already required by many science and governmental agencies and some major scientific publishers. © 2019 John Wiley & Sons Ltd. Mass Spec Rev 40:126-157, 2021.

Mathematical Modeling of Plant Metabolism in a Changing Temperature Regime

Changes in environmental temperature regimes significantly affect plant growth, development and reproduction. Within a multigenic process termed acclimation, many plant species of the temperate region are able to adjust their metabolism to low and high temperature. Temperature-induced metabolic reprogramming is a nonlinear process affecting numerous enzyme kinetic reactions and pathways. The analysis of metabolic reprogramming during temperature acclimation is essentially supported by mathematical modeling which enables the study of nonlinear enzyme kinetics in context of metabolic networks and pathway regulation. This chapter introduces mathematical modeling of plant metabolism during a dynamic environmental temperature regime. A focus is laid on kinetic modeling and thermodynamic constraints.

Dynamics of Plant Metabolism during Cold Acclimation

Plants have evolved strategies to tightly regulate metabolism during acclimation to a changing environment. Low temperature significantly constrains distribution, growth and yield of many temperate plant species. Exposing plants to low but non-freezing temperature induces a multigenic processes termed cold acclimation, which eventually results in an increased freezing tolerance. Cold acclimation comprises reprogramming of the transcriptome, proteome and metabolome and affects communication and signaling between subcellular organelles. Carbohydrates play a central role in this metabolic reprogramming. This review summarizes current knowledge about the role of carbohydrate metabolism in plant cold acclimation with a focus on subcellular metabolic reprogramming, its thermodynamic constraints under low temperature and mathematical modelling of metabolism.