Modelling progression of enzymes in lignocellulosic assemblies and plant cell walls – LIGNOPROG

Measuring the progression of enzymes in lignocellulosic substrates is a challenge because only the result of enzyme catalysis and not the enzyme itself is generally observed. To go beyond, we are using fluorescence techniques, since fluorescence is a very sensitive parameter depending on its environment. By chemically grafting a fluorescent molecule to an enzyme, making a probe, we have set up a procedure to follow the progression of this probe in lignocellulosic substrates. Data collected even give access to the affinity of the enzyme for the polymers contained in the substrate. Knowing the composition of the substrate, and by varying the enzyme properties, we can decipher the role played by the different factors. In addition, the topographical and topochemcial analysis of the samples will provide information on the dynamics of substrate deconstruction at an atomic scale.

With some model lignocellulosic substrates, it becomes possible to measure the progression and the interactions of enzymes in assemblies which are representative of the architecture of plant cell walls and to determine the structural features involved in the non-specific interactions.
Also, a new developed multimodal fluorescence technique allows to use the natural autofluorescence of lignocellulose to evaluate interactions with fluorescent probes.
Finally, quantification of the deconstruction of lignocellulose at the nanometric scale can be carried out to demonstrate the role of lignin, even when present at a low content.

The possibility of identifying, quantifying and thus ranking the impact of chemical and structural factors involved in the progression of enzymes in lignocellulosic substrates should lead to the optimization of the transformation processes of lignocellulosic biomass. First, at the enzyme level, methods developed will give the possibility to select appropriately the enzymes having modularity and/or surface properties which minimize non-specific interactions. At the substrate level, the choice of pretreatment type should be done by taking into account their influence regarding the creation of disruptive chemical motifs.

5 scientific reviews have done from the results of this project.

Lignocellulosic biomass (LB) is a complex network of polymers that constitute plant cell walls (PCWs). It comes from various sources: residues from agriculture and forest or dedicated plants. Since LB is composed of various polymers such as cellulose, hemicellulose (polysaccharides) and lignin (polyphenols), LB transformation can potentially produce chemicals, materials and biofuels in dedicated biorefineries. Consequently, LB transformation is considered as a way to limit greenhouse gas emission and a sustainable alternative to fossil carbon-derived products.
However, the architectural and chemical complexity of LB is also a bottleneck to its cost-effective industrial conversion. Today, to achieve this goal, not only the cellulosic part of LB but also the hemicellulosic and lignin parts must be retrieved and transformed, otherwise the biorefineries cannot compete economically. The major challenge to overcome is the high price and the relative low efficiency of the enzymatic deconstruction step of the LB. Despite recent advances, complete biomass transformation of all LB components cannot be achieved yet. Actually, enzyme activity and progression in the PCW network are restricted: this is the so-called recalcitrance of LB. One of the critical questions to answer is: what are the features that limit the LB conversion and how can they be released? These features regard both the LB (composition, architecture,…) and the enzymes (physical progression limitations, non-specific interactions, catalysis inactivated or inhibited,…). Since LB deconstruction is a dynamical process where enzymes progress in a complex heterogeneous network, it appears critical to uncover how the features listed above are correlated to the progression and which ones are the most critical.
Since features influencing enzyme progression in PCWs are numerous and difficult to quantify properly, we propose in the first part of Lignoprog to design and use model LB assemblies which contain some polymers and interactions existing in the PCWs. We believe it is an original and relevant alternative because complexity of model assemblies is controlled and their characteristics are known. They will be used to set-up protocols aimed at measuring the mobility and interactions of a set of LB-active enzymes by state-of-the-art fluorescence microscopy techniques (FRAP and FLIM-FRET). Results obtained will be dependent on parameters that regard the enzymes (size, affinity, catalysis, …) and the assemblies (polymer type and concentration, interaction types, …). By modelling the whole dataset using statistical analysis, the parameters will be ranked quantitatively according to their influence on mobility and interaction; the most influence ones will be highlighted, in addition to the possible interactions between parameters.
In the second part of Lignoprog, the same methodology will be applied to pre-treated PCW samples from two species (one monocot and one dicot). Enzyme progression will be followed by FRAP at the tissular scale in various histological locations, and the impact of enzymatic destructuration will be observed by atomic force microscopy at a sub-cellular scale, also revealing the LB components that have been transformed and those that have appeared.
Overall, Lignoprog will combine microscopic tools, physico-chemical and spectral characterization of enzymes, polymer assemblies and PCWs to find out the chemical and structural features that limit the most the progression of enzymes in PCWs during their deconstruction. Results obtained should be of primarily importance for optimizing LB transformation: it could lead to new directions for modifying some of these features in the PCWs using plant molecular biology techniques, and for optimizing the dedicated enzymes by using our model assemblies as screening templates. Moreover, even if only applied on a small set of enzymes and LB samples, as a proof of concept, our strategy could easily be applied to a larger extent.