Regulatory mechanisms underlying C3 carbon and nitrogen metabolism under elevated CO2

Rising atmospheric CO2 concentration will affect species-specific responses to carbon (C) gain in plants, in turn impacting plant productivity in natural and agro-ecosystems in the future. Several interacting environmental and genetic factors affect species-specific response to C gain under elevated atmospheric CO2 concentration (eCO2). One of the many challenges faced in understanding these interaction responses is efficiently predicting genotype to phenotype relationships that are also needed to design future crop idiotypes. Hence this thesis aims to partly address this fundamental knowledge gap by using mathematical modeling and systems biology approaches to identify regulatory factors influencing carbon (C) and nitrogen (N) metabolism in C3 plants under eCO2 through inferences made in Soybean and Arabidopsis. This aim is achieved threefold. The first aim involves optimizing protein translation dynamics in C3 plants. It is essential to understand the processes underlying changes in protein abundance in a gene under varying physiological and developmental conditions to model and rationally engineer plants. Several protein translation models have been developed in the past that utilize genome-wide transcriptomic datasets to make predictions on protein levels in eukaryotes. Still, very few have successfully incorporated post-transcriptional and post-translational modifications in making these predictions, especially in plants, owing to the lack of experimental data capturing these mechanisms. Moreover, a large proportion of genes in plants undergo complex regulatory dynamics, some of which are different from other eukaryotes that cause a large discrepancy between mRNA expression and protein levels of individual genes, and this discrepancy changes based on the severity of the stress condition. Hence, to partly address this issue, genome-wide proteome and transcriptome datasets in Arabidopsis are utilized as a benchmark to optimize a protein transition model represented by a non-linear ordinary differential equation. The second aim of this thesis involves multiscale modeling to gain a mechanistic understanding of the regulation of photosynthesis in soybean under eCO2. Multiscale models can predict emergent plant responses to environmental perturbations by mimicking the biological flow of information across scales. Soybean is one of the major sources of plant protein and oil. Specific genotypes of soybean have shown a lower than theoretically anticipated stimulation of photosynthesis under eCO2. It is hypothesized that a guided genetic manipulation could alter the photosynthesis machinery in soybean under eCO2, which might, in turn, affect its productivity under future “C” fertilization conditions as photosynthesis is one of the fundamental processes for sustaining plant growth. Hence, to test this hypothesis, chapter 3 utilizes a multiscale modeling approach that scales from gene expression to organ-level physiology to predict robust transcription factor candidates that might influence soybean photosynthesis under eCO2. The third aim of this thesis involves the investigation of regulatory mechanisms controlling the coordination of C and N metabolism in C3 plants under eCO2. Among the many interacting environmental factors, N availability has been shown to significantly influence the extent of photosynthetic stimulation, photosynthetic acclimation, leaf N status as well as productivity in selected C3 plants under eCO2. Chapter 4 draws inferences from a C-N interaction significant gene regulatory network to predict key transcription factors that could fine-tune the genetic changes in C3 plants to coordinate C and N metabolism under eCO2. Transcription factors thus predicted could provide additional insights on regulatory mechanisms underlying C3 plant quality and productivity in a future “C” fertilization environment.LimitedAuthor requested closed access (OA after 2yrs) in Vireo ETD syste