Optimized Lytic Polysaccharide Monooxygenase Action Increases Fiber Accessibility and Fibrillation by Releasing Tension Stress in Cellulose Cotton Fibers.

Lytic polysaccharide monooxygenase (LPMO) enzymes have recently shaken up our knowledge of the enzymatic degradation of biopolymers and cellulose in particular. This unique class of metalloenzymes cleaves cellulose and other recalcitrant polysaccharides using an oxidative mechanism. Despite their potential in biomass saccharification and cellulose fibrillation, the detailed mode of action of LPMOs at the surface of cellulose fibers still remains poorly understood and highly challenging to investigate. In this study, we first determined the optimal parameters (temperature, pH, enzyme concentration, and pulp consistency) of LPMO action on the cellulose fibers by analyzing the changes in molar mass distribution of solubilized fibers using high performance size exclusion chromatography (HPSEC). Using an experimental design approach with a fungal LPMO from the AA9 family (PaLPMO9H) and cotton fibers, we revealed a maximum decrease in molar mass at 26.6 °C and pH 5.5, with 1.6% w/w enzyme loading in dilute cellulose dispersions (100 mg of cellulose at 0.5% w/v). These optimal conditions were used to further investigate the effect of PaLPMO9H on the cellulosic fiber structure. Direct visualization of the fiber surface by scanning electron microscopy (SEM) revealed that PaLPMO9H created cracks on the cellulose surface while it attacked tension regions that triggered the rearrangement of cellulose chains. Solid-state NMR indicated that PaLPMO9H increased the lateral fibril dimension and created novel accessible surfaces. This study confirms the LPMO-driven disruption of cellulose fibers and extends our knowledge of the mechanism underlying such modifications. We hypothesize that the oxidative cleavage at the surface of the fibers releases the tension stress with loosening of the fiber structure and peeling of the surface, thereby increasing the accessibility and facilitating fibrillation.

Determining whether granule structural or surface features govern the wheat starch digestion, a kinetic analysis.

Deciphering the determinants of starch digestion from multiple interrelated properties is a challenge that can benefit from multifactorial data analysis. The present study investigated the digestion kinetic parameters (rate, final extent) of size-fractions from four commercial wheat starches with different amylose contents. Each size-fraction was isolated and characterized comprehensively using a large range of analytic techniques (FACE, XRD, CP-MAS NMR, time-domain NMR, DSC…). A statistical clustering analysis applied on the results revealed that the mobility of water and starch protons measured by time-domain NMR was consistently related to the macromolecular composition of the glucan chains and to the ultrastructure of the granule. The final extent of starch digestion was determined by the granule structural features. The digestion rate coefficient dependencies, on the other hand, changed significantly with the range of granule size, i.e. the accessible surface for initial binding of α-amylase. The study particularly showed the molecular order and the chains mobility predominantly limiting or accelerating the digestion rate depending on the accessible surface. This result confirmed the need to differentiate between the surface and the inner-granule related mechanisms in starch digestion studies.

Water mobility and microstructure of gluten network during dough mixing using TD NMR.

This work aimed to establish the relationships between flour components, dough behaviour and changes in water distribution at mixing. TD NMR was used to track water distribution in dough during mixing for different mixing times and hydration levels. Four commercial wheat flours with distinct characteristics were expressly selected to exhibit various dough behaviours at mixing. TD NMR measurements of mixed dough samples revealed four to five water mobility domains depending on the flour type and the mixing modality. A classification tree procedure was used to identify characteristic patterns of water mobility in dough, called hydration states (HS). The HS changes with experimental conditions are highly dependent on flour characteristics, and HS were assigned to physical/chemical changes in the gluten network during dough formation. This study proposes an interpretation of the water distribution in dough based on gluten network development. This will help to adapt the mixing process to the flour characteristics.

Beyond amylose content, selecting starch traits impacting in vitro α-amylase degradability in a wheat MAGIC population.

A major challenge faced when studying the "structure-degradability" interaction of native starch is deciphering the interdependency between different structural levels, especially when experimental conditions limit the number of samples. To tackle this challenge, 224 wheat starches from a 4-way multiparent advanced generation inter-cross population were screened for structural features and degradation profiles by porcine pancreatic α-amylase. A hierarchical clustering on principal components (HCPC) were used as multifactorial analysis to explore the data structure. The degradation procedure was proved to be robust and sensible enough to screen a large collection of starches. The HCPC highlighted the combined effects of granule size distribution (GSD), amylopectin chain length distribution (CLD), amylose content and endogenous α-amylase activity on degradation kinetics. Especially the GSD and amylopectin CLD showed high co-occurrences with specific hydrolysis profiles. These findings provide an innovative screening method and structural factors to be primarily considered for wheat starch selection in breeding programs.

How Does Starch Structure Impact Amylolysis? Review of Current Strategies for Starch Digestibility Study.

In vitro digestibility of starch is a common analysis in human nutrition research, and generally consists of performing the hydrolysis of starch by α-amylase in specific conditions. Similar in vitro assays are also used in other research fields, where different methods can be used. Overall, the in vitro hydrolysis of native starch is a bridge between all of these methods. In this literature review, we examine the use of amylolysis assays in recent publications investigating the complex starch structure-amylolysis relation. This review is divided in two parts: (1) a brief review of the factors influencing the hydrolysis of starch and (2) a systematic review of the experimental designs and methods used in publications for the period 2016-2020. The latter reports on starch materials, factors investigated, characterization of the starch hydrolysis kinetics and data analysis techniques. This review shows that the dominant research strategy favors the comparison between a few starch samples most frequently described through crystallinity, granule type, amylose and chain length distribution with marked characteristics. This strategy aims at circumventing the multifactorial aspect of the starch digestion mechanism by focusing on specific features. An alternative strategy relies on computational approaches such as multivariate statistical analysis and machine learning techniques to decipher the role of each factor on amylolysis. While promising to address complexity, the limited use of a computational approach can be explained by the small size of the experimental datasets in most publications. This review shows that key steps towards the production of larger datasets are already available, in particular the generalization of rapid hydrolysis assays and the development of quantification approaches for most analytical results.

Annotation data about multi criteria assessment methods used in the agri-food research: The french national institute for agricultural research (INRA) experience.

This data article contains annotation data characterizing Multi Criteria Assessment (MCA) Methods proposed in the agri-food sector by researchers from INRA, Europe's largest agricultural research institute (INRA, http://institut.inra.fr/en). MCA can be used to assess and compare agricultural and food systems, and support multi-actor decision making and design of innovative systems for crop production, animal production and processing of agricultural products. These data are stored in a public repository managed by INRA (https://data.inra.fr/; https://doi.org/10.15454/WB51LL).

Multi-scale characterization of thermoplastic starch structure using Second Harmonic Generation imaging and NMR.

Starch granules can be extruded to obtain a thermoplastic material. Thermoplastic starch (TPS) usually requires a significant break down of the starch granular organization to form a continuous polysaccharide matrix. In this work, we extrude potato starch with and without a plasticizer and store samples at high humidity to generate recrystallization. A multi-scale investigation of the microstructure is performed by combining different techniques: WAXS and solid-state NMR to describe macromolecule organization and Second Harmonic Generation (SHG) imaging to provide spatial information. Finally, the ability of the material to swell and remain sound in water is assessed. Glycerol-plasticized samples swell the least despite many granules with native-like structure embedded in the starch matrix. Glycerol limits the fragmentation and melting of the granules and crystallites during extrusion but also reduces the proportion of starch molecules in constrained conformations, enabling the formation of a polymer network that can sustain the penetration of water.

Testing scientific models using Qualitative Reasoning: Application to cellulose hydrolysis.

With the accumulation of scientific information in natural science, even experts can find difficult to keep integrating new piece of information. It is critical to explore modelling solutions able to capture information scattered in publications as a computable representation form. Traditional modelling techniques are important in that regard, but relying on numerical information comes with limitations for integrating results from distinct studies, high-level representations can be more suited. We present an approach to stepwise construct mechanistic explanation from selected scientific papers using the Qualitative Reasoning framework. As a proof of concept, we apply the approach to modelling papers about cellulose hydrolysis mechanism, focusing on the causal explanations for the decreasing of hydrolytic rate. Two explanatory QR models are built to capture classical explanations for the phenomenon. Our results show that none of them provides sufficient explanation for a set of basic experimental observations described in the literature. Combining the two explanations into a third one allowed to get a new and sufficient explanation for the experimental results. In domains where numerical data are scarce and strongly related to the experimental conditions, this approach can aid assessing the conceptual validity of an explanation and support integration of knowledge from different sources.

Integration of Basic Knowledge Models for the Simulation of Cereal Foods Processing and Properties.

Cereal processing (breadmaking, extrusion, pasting, etc.) covers a range of mechanisms that, despite their diversity, can be often reduced to a succession of two core phenomena: (1) the transition from a divided solid medium (the flour) to a continuous one through hydration, mechanical, biochemical, and thermal actions and (2) the expansion of a continuous matrix toward a porous structure as a result of the growth of bubble nuclei either by yeast fermentation or by water vaporization after a sudden pressure drop. Modeling them is critical for the domain, but can be quite challenging to address with mechanistic approaches relying on partial differential equations. In this chapter we present alternative approaches through basic knowledge models (BKM) that integrate scientific and expert knowledge, and possess operational interest for domain specialists. Using these BKMs, simulations of two cereal foods processes, extrusion and breadmaking, are provided by focusing on the two core phenomena. To support the use by non-specialists, these BKMs are implemented as computer tools, a Knowledge-Based System developed for the modeling of the flour mixing operation or Ludovic®, a simulation software for twin screw extrusion. They can be applied to a wide domain of compositions, provided that the data on product rheological properties are available. Finally, it is stated that the use of such systems can help food engineers to design cereal food products and predict their texture properties.

Multivariate model to characterise relations between maize mutant starches and hydrolysis kinetics.

The many studies about amylolysis have collected considerable information regarding the contribution of the starch physico-chemical properties. But the inherent elaborate and variable structure of granular starch and, consequently, the multifactorial condition of the system hinders the interpretation of the experimental results. The immediate benefit of multivariate statistical analysis approaches with that regard is twofold: considering the factors, possibly interrelated, all together and not independently, providing a first estimation of the magnitude and confidence level of the relations between factors and amylolysis kinetic parameters. Based on data of amylolysis of 13 starch samples from wild type, single and double mutants of maize by porcine pancreatic α-amylase (PPA), a multivariate analysis is proposed. Amylolysis progress-curves were fitted by a Weibull function, as proposed in a previous work, to extract three kinetic parameters: the reaction rate coefficient during the first time-unit, k, the reaction rate retardation over time, h, and the final hydrolysis extent, X∞. Multivariate models relate the macromolecular composition and the fractions of crystalline polymorphic types to the kinetic parameters. h and X∞ are found to be highly related to the measured properties. Thus the amylose content appears to be significantly correlated to the hydrolysis rate retardation, which sheds some light on the probable contribution of the amylose molecules contained in the granules. The multivariate models give correct prediction performances except for k whose a part of variability remains unexplained. A further analysis points out the extent of the characterisation effort of the granule structure needed to extend the fraction of explained variability.

Amylolysis of maize mutant starches described with a fractal-like kinetics model.

Two empirical models, a conventional first-order kinetics and a fractal-like first-order kinetic model were tested for analysing the hydrolysis of 13 wild type, single and double mutants of maize starch by porcine pancreatic α-amylase (PPA). The major difference between the two models is an additional parameter, the fractal kinetics exponent h, which enables to characterise a decrease of the reaction rate coefficient over time. The fractal-like kinetic model should be preferred to characterise the amylolysis for 10 mutants out of 13 as sugary-2 and amylose-extender curves exhibit clear reaction rate retardation, unlike normal maize and waxy maize. Analysis of the model parameter values reveals two groups of kinetics for the maize mutants: amylose-extender, normal and waxy on one hand, sugary-2 on the other hand. Possible relations between the parameters of the model and granule composition and structure are discussed.

Predicting the quality of wheat flour dough at mixing using an expert system.

Modelling the links between the mixing process conditions of wheat flour dough and the properties of the dough is a challenge. This paper presents a systematic modelling approach based on qualitative algebra to represent human expertise in this domain. Qualitative models of wheat dough mixing have been implemented as an expert system, called Ascopain. The relations between the process conditions - flour specifications and kneading conditions - and the dough sensory properties, have been formalised by means of qualitative functions. An extensive evaluation of Ascopain is provided by comparing the simulation results, first to experts' predictions, and second, to experimental results of sensory evaluation of mixed dough properties. The good matching level proves the accuracy and the robustness of the expert-system and, overall, its ability to implement a reasoning on the influences of process conditions to predict actual dough properties, starting from ingredient characteristics.