Overexpression of arogenate dehydratase reveals an upstream point of metabolic control in phenylalanine biosynthesis

Out of the three aromatic amino acids, the highest flux in plants is directed towards phenylalanine, which is utilized to synthesize proteins and thousands of phenolic metabolites contributing to plant fitness. Phenylalanine is produced predominantly in plastids via the shikimate pathway and subsequent arogenate pathway, both of which are subject to complex transcriptional and post-transcriptional regulation. Previously, it was shown that allosteric feedback inhibition of arogenate dehydratase (ADT), which catalyzes the final step of the arogenate pathway, restricts flux through phenylalanine biosynthesis. Here, we show that in petunia (Petunia hybrida) flowers, which typically produce high phenylalanine levels, ADT regulation is relaxed, but not eliminated. Moderate expression of a feedback-insensitive ADT increased flux towards phenylalanine, while high overexpression paradoxically reduced phenylalanine formation. This reduction could be partially, but not fully, recovered by bypassing other known metabolic flux control points in the aromatic amino acid network. Using comparative transcriptomics, reverse genetics, and metabolic flux analysis, we discovered that transcriptional regulation of the d-ribulose-5-phosphate 3-epimerase gene in the pentose phosphate pathway controls flux into the shikimate pathway. Taken together, our findings reveal that regulation within and upstream of the shikimate pathway shares control over phenylalanine biosynthesis in the plant cell.

Multiscale plant modeling: from genome to phenome and beyond

Plants are complex organisms that adapt to changes in their environment using an array of regulatory mechanisms that span across multiple levels of biological organization. Due to this complexity, it is difficult to predict emergent properties using conventional approaches that focus on single levels of biology such as the genome, transcriptome, or metabolome. Mathematical models of biological systems have emerged as useful tools for exploring pathways and identifying gaps in our current knowledge of biological processes. Identification of emergent properties, however, requires their vertical integration across biological scales through multiscale modeling. Multiscale models that capture and predict these emergent properties will allow us to predict how plants will respond to a changing climate and explore strategies for plant engineering. In this review, we (1) summarize the recent developments in plant multiscale modeling; (2) examine multiscale models of microbial systems that offer insight to potential future directions for the modeling of plant systems; (3) discuss computational tools and resources for developing multiscale models; and (4) examine future directions of the field.

Multiscale computational models can guide experimentation and targeted measurements for crop improvement

Computational models of plants have identified gaps in our understanding of biological systems, and have revealed ways to optimize cellular processes or organ-level architecture to increase productivity. Thus, computational models are learning tools that help direct experimentation and measurements. Models are simplifications of complex systems, and often simulate specific processes at single scales (e.g. temporal, spatial, organizational, etc.). Consequently, single-scale models are unable to capture the critical cross-scale interactions that result in emergent properties of the system. In this perspective article, we contend that to accurately predict how a plant will respond in an untested environment, it is necessary to integrate mathematical models across biological scales. Computationally mimicking the flow of biological information from the genome to the phenome is an important step in discovering new experimental strategies to improve crops. A key challenge is to connect models across biological, temporal and computational (e.g. CPU versus GPU) scales, and then to visualize and interpret integrated model outputs. We address this challenge by describing the efforts of the international Crops in silico consortium.

Plant Networks as Traits and Hypotheses: Moving Beyond Description

Biology relies on the central thesis that the genes in an organism encode molecular mechanisms that combine with stimuli and raw materials from the environment to create a final phenotypic expression representative of the genomic programming. While conceptually simple, the genotype-to-phenotype linkage in a eukaryotic organism relies on the interactions of thousands of genes and an environment with a potentially unknowable level of complexity. Modern biology has moved to the use of networks in systems biology to try to simplify this complexity to decode how an organism's genome works. Previously, biological networks were basic ways to organize, simplify, and analyze data. However, recent advances are allowing networks to move beyond description and become phenotypes or hypotheses in their own right. This review discusses these efforts, like mapping responses across biological scales, including relationships among cellular entities, and the direct use of networks as traits or hypotheses.

"Hit-and-Run" leaves its mark: catalyst transcription factors and chromatin modification

Understanding how transcription factor (TF) binding is related to gene regulation is a moving target. We recently uncovered genome‐wide evidence for a “Hit‐and‐Run” model of transcription. In this model, a master TF “hits” a target promoter to initiate a rapid response to a signal. As the “hit” is transient, the model invokes recruitment of partner TFs to sustain transcription over time. Following the “run”, the master TF “hits” other targets to propagate the response genome‐wide. As such, a TF may act as a “catalyst” to mount a broad and acute response in cells that first sense the signal, while the recruited TF partners promote long‐term adaptive behavior in the whole organism. This “Hit‐and‐Run” model likely has broad relevance, as TF perturbation studies across eukaryotes show small overlaps between TF‐regulated and TF‐bound genes, implicating transient TF‐target binding. Here, we explore this “Hit‐and‐Run” model to suggest molecular mechanisms and its biological relevance.

Reduction of benzenoid synthesis in petunia flowers reveals multiple pathways to benzoic acid and enhancement in auxin transport

In plants, benzoic acid (BA) is believed to be synthesized from Phe through shortening of the propyl side chain by two carbons. It is hypothesized that this chain shortening occurs via either a beta-oxidative or non-beta-oxidative pathway. Previous in vivo isotope labeling and metabolic flux analysis of the benzenoid network in petunia (Petunia hybrida) flowers revealed that both pathways yield benzenoid compounds and that benzylbenzoate is an intermediate between L-Phe and BA. To test this hypothesis, we generated transgenic petunia plants in which the expression of BPBT, the gene encoding the enzyme that uses benzoyl-CoA and benzyl alcohol to make benzylbenzoate, was reduced or eliminated. Elimination of benzylbenzoate formation decreased the endogenous pool of BA and methylbenzoate emission but increased emission of benzyl alcohol and benzylaldehyde, confirming the contribution of benzylbenzoate to BA formation. Labeling experiments with 2H5-Phe revealed a dilution of isotopic abundance in most measured compounds in the dark, suggesting an alternative pathway from a precursor other than Phe, possibly phenylpyruvate. Suppression of BPBT activity also affected the overall morphology of petunia plants, resulting in larger flowers and leaves, thicker stems, and longer internodes, which was consistent with the increased auxin transport in transgenic plants. This suggests that BPBT is involved in metabolic processes in vegetative tissues as well.