Integrative Analysis of Transgenic Alfalfa (Medicago sativa L.) Suggests New Metabolic Control Mechanisms for Monolignol Biosynthesis

Abstract

The entanglement of lignin polymers with cellulose and hemicellulose in plant cell walls is a major biological barrier to the economically viable production of biofuels from woody biomass. Recent efforts of reducing this recalcitrance with transgenic techniques have been showing promise for ameliorating or even obviating the need for costly pretreatments that are otherwise required to remove lignin from cellulose and hemicelluloses. At the same time, genetic manipulations of lignin biosynthetic enzymes have sometimes yielded unforeseen consequences on lignin composition, thus raising the question of whether the current understanding of the pathway is indeed correct. To address this question systemically, we developed and applied a novel modeling approach that, instead of analyzing the pathway within a single target context, permits a comprehensive, simultaneous investigation of different datasets in wild type and transgenic plants. Specifically, the proposed approach combines static flux-based analysis with a Monte Carlo simulation in which very many randomly chosen sets of parameter values are evaluated against kinetic models of lignin biosynthesis in different stem internodes of wild type and lignin-modified alfalfa plants. In addition to four new postulates that address the reversibility of some key reactions, the modeling effort led to two novel postulates regarding the control of the lignin biosynthetic pathway. The first posits functionally independent pathways toward the synthesis of different lignin monomers, while the second postulate proposes a novel feedforward regulatory mechanism. Subsequent laboratory experiments have identified the signaling molecule salicylic acid as a potential mediator of the postulated control mechanism. Overall, the results demonstrate that mathematical modeling can be a valuable complement to conventional transgenic approaches and that it can provide biological insights that are otherwise difficult to obtain. Cellulose-based biofuels presently offer the most environmentally attractive and technologically promising alternative to fossil fuels. To be viable, biofuels must be derived from non-food crops, such as grasses, wood, bark, and plant residues. Techniques for releasing the energy stored in these renewable materials must first untangle a very recalcitrant scaffold of interlinking molecules inside the plant cell walls, which is very costly. Much of the recalcitrance is due to the natural polymer lignin, which hardens the cell walls and is composed of three different building blocks, called monolignols. Modern transgenic techniques have yielded plant lines whose cell walls are easier to break down, but some of these modified plants have exhibited unexplained and undesired features. Here, we present new computational methods for analyzing monolignol biosynthesis in unprecedented detail. The analysis simultaneously accounts for lignin biosynthesis in various transgenic lines and different developmental stages and yields six novel, testable postulates regarding the metabolic control of the pathway. The results suggest new, targeted experiments towards a better understanding of monolignol biosynthesis and issues of recalcitrance reduction. More generally, the results highlight the genuine benefits of using computational methods as companions and complements to experimental studies.