Modeling Progression of Fluorescent Probes in Bioinspired

Article pubs.acs.org/Biomac

Modeling Progression of Fluorescent Probes in Bioinspired Lignocellulosic Assemblies Gabriel Paes̈ ,*,†,‡ Sally Burr,†,‡ Marie-Belle Saab,†,‡,§ Michael̈ Molinari,§ Véronique Aguié-Béghin,†,‡ and Brigitte Chabbert†,‡ †

INRA, UMR614 Fractionnement des AgroRessources et Environnement, Reims, France Université de Reims Champagne-Ardenne, UMR614 Fractionnement des AgroRessources et Environnement, Reims, France § Université de Reims Champagne-Ardenne, Laboratoire de Recherche en Nanosciences LRN EA4682, Reims, France ‡

S Supporting Information *

ABSTRACT: Progression of enzymes in lignocellulosic biomass is a crucial parameter in biorefinery processes, and it appears to be one of the limiting factors in optimizing lignocellulose degradation. In order to assay the importance of the chemical and structural features of the substrate matrix on enzyme mobility, we have designed bioinspired model assemblies of secondary plant cell walls, which have been used to measure the mobility of fluorescent probes while modifying different parameters (probe size, water content, polysaccharide concentration). The results were used to construct a model of probe mobility and to rank the parameters in order of importance. Water content and probe size were shown to have the greatest effect. Although these assemblies are simplified templates of the plant cell walls, our strategy paves the way for proposing new approaches for optimizing biomass saccharification, such as selecting enzymes with suitable properties.

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INTRODUCTION Lignocellulosic biomass is a potentially sustainable alternative source of fuels, chemicals, and materials that has the potential to free the global economy from dependence on fossil fuels.1 Lignocellulose (LC) from wood or grass biomass is mainly composed of three polymers that account for more than 90% of the dry weight: cellulose, hemicellulose, and lignin.2 Because of its high chemical and structural complexity at different scales,1,3 LC biomass deconstruction is not economically viable yet. In particular, optimal transformation of all LC components requires robust and efficient lignocellulolytic enzymes to control its deconstruction.2,4 One of the challenges is to produce cheap enzyme cocktails that fit a specific biomass and have the following ideal properties: - High catalytic activity performance, which implies low susceptibility to inhibition and to inactivation by residual lignins5−7 or other coproducts.8,9 Since LC substrates are partially insoluble, catalysis occurs at a solid/liquid interface with low water content, involving adsorption of enzymes onto the surface of the substrate, which can limit catalysis. - Easy access to their substrate, which means that polymer network pore size must be large enough for enzymes10 and that nonspecific interactions do not occur on their way to substrate.6,8 Since tests of enzyme accessibility to substrate are generally performed on isolated polymers (like cellulase adsorption on cellulose11 or mobility analysis12,13), they are not representative © 2013 American Chemical Society

of the mobility of enzymes in a 3D network of interconnected polymers like those in plant cell walls. Overall, enzyme access to substrate is a critical feature,14 since enzyme progression is limited by physical barriers and unproductive chemical interactions.15 Thus, a better understanding, characterization, and quantification of the features that control enzyme progression in LC, in particular at the nanoscale,2 is essential. Chemical and structural features limiting enzyme progression in plant cell walls are numerous and difficult to isolate and to quantify properly. We believe that a relevant alternative is to design some model LC assemblies with well-defined physicochemical properties. The goal is not to mimic the plant cell wall but to prepare in vitro model assemblies containing a limited range of the polymers (and of covalent and noncovalent interactions connecting them), which are encountered in the plant cell walls. Then, these model assemblies can be used as templates to evaluate the mobility of various probes of different sizes representative of the size range of lignocellulose-degrading enzymes. This depends on features belonging either to the assemblies or the probes, such as molecular weight (of the probe) or pore size (of the assembly). Here, to prepare our model assemblies, we have decided to select polymers isolated from grass secondary cell walls, since they constitute at least Received: March 6, 2013 Revised: May 18, 2013 Published: May 30, 2013 2196

dx.doi.org/10.1021/bm400338b | Biomacromolecules 2013, 14, 2196−2205

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(Käfer GmbH, Villingen, Germany). Preparation of gels and films is schematically presented in Figure 1.

50% of the cell wall weight16 and represent one of the largest sources of LC available. Their hemicelluloses are mainly arabinoxylans (AXs), which are substituted with ferulic acid residues (FAs) and the AXs are covalently bonded to each other or to lignin to make lignin carbohydrate complexes (LCCs). We have previously studied the organization of synthetic lignin/heteroxylan nanoparticles;17 the interactions between xylan and synthetic lignins were evaluated as a model to study LCC formation18,19 and to evaluate how the interactions could impact enzymatic degradation.20 Recently, we have developed the preparation of arabinoxylan gels as a model assembly to study the mobility of fluorescent probes.21 Relying on these results, our model assemblies are based on feruloylated arabinoxylans (FAX). Indeed, FAs are of primary chemical importance in the organization of unlignified cell walls by di- and multiferulic linkages,22 and may act as nucleation sites when lignification begins to form LCCs.23 The role of FA consists of creating the intra- and inter-AX cross-linkages under the action of a laccase, leading to the formation of gels.24 If cellulose nanocrystals (CNCs) are mixed with FAX before triggering gelation, model assemblies contain two of the LC polymers and can be therefore considered as bioinspired assemblies of plant cell wall. We have also set up a protocol to prepare micrometer thick FAX films in which water content is drastically lowered. After characterizing gel and film properties, the mobility of fluorescent probes has been examined by FRAP. The whole experimental data set obtained by varying different parameters related to the probes and the assemblies has then been used to prepare a mathematical model of the mobility of probes in bioinspired plant cell wall assemblies. This model has allowed us to evaluate the relative importance of the parameters modified, and it can be used as a predictive tool for optimizing LC biomass accessibility to enzyme treatment.

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Figure 1. Preparation of gels and films with a schematic representation of the FAX and CNC arrangement. Preparation of Fluorescent Probes. Dextran-FITC probes of 10, 70, and 250 kDa (DF10, DF70, and DF250), fluorescein isothiocyanate (FITC) were purchased from Sigma-Aldrich (SaintQuentin Fallavier, France). According to the manufacturer’s information, a FITC molecule is branched every 50 to 333 glucose residues on the dextran chains. FAX was conjugated to 5-(4,6dichlorotriazinyl)-aminofluorescein (5-DTAF) (Life Technologies, Saint-Aubin, France), a FITC derivative, by preparing a mix of FAX/5-DTAF with a 80:1 ratio (w/w) in 100 mM borate buffer at pH 10.0. Under these conditions, the dichlorotriazines can react covalently with hydroxyl groups on the polysaccharide, producing fluorescently labeled FAX (FAX−FITC). The reaction mixture was left for 6 h under magnetic stirring, extensively dialyzed against double-distilled water overnight, and then frozen, lyophilized, and stored at +4 °C. The product obtained was soluble in water and in citrate buffer, and the labeling ratio was determined by elemental analysis of N and C atoms, indicating that, on average, one molecule of 5-DTAF is bound to every 25 molecules of xylose. FAX−FITC gels were prepared by the same method as FAX gels, as described above. Rheological Measurements. A cone−plate geometry (5.0 cm diameter, 0.04 rad angle) rheometer (ARES, Rheometric Scientific, Champ sur Marne, France) equipped with a Peltier temperaturecontrol system maintained at 25 °C was used for all measurements. FAX-based solutions were placed onto the plate, and intrinsic viscosity (η) measurements were performed. Then, dynamic strain sweep tests were used to determine the range where solutions showed a linear behavior, between 0.0125 and 100%, with a frequency of 10 rad·s−1. For all solutions, 10% strain was used for the measurements, in particular, for recording of G′ (storage modulus) and G″ (loss modulus). Rapid addition of laccase was done directly onto the plate, followed by a short mix of the solution with the cone. G′ and G″ were monitored before, during, and after gelation until they were constant, typically after 120 min. Finally, frequency sweep tests were used to obtain mechanical spectra of gels from 1 to 100 rad·s−1. Confocal Laser Scanning Microscopy (CLSM)-Fluorescence Recovery After Photobleaching (FRAP) Analysis. The CLSM system was a Leica TCS SP2 (Manheim, Germany) with a 63× oilimmersion objective and a numerical aperture of 1.4, equipped with a 488 nm argon laser, in a controlled-temperature room (22 ± 2 °C). Gel samples in the 8-well chamber or films placed under cover-glass were directly positioned onto the objective. Acousto-optical tunable filters (AOTF) selected light from 493 to 600 nm. Images were collected using the following parameters: 1× zoom factor, 512 × 512 pixels size at a frequency of 400 Hz, one acquisition, with a circular region of interest (ROI) of 10 μm diameter. FRAP experiments were conducted as follows: Prebleaching: laser at 10% of its power, acquisition of five reference images Bleaching: laser at 100%, 10 images

MATERIALS AND METHODS

Preparation and Characterization of Model Assemblies. Water-extractable FAXs from maize bran were purchased from Cambridge Biopolymers Ltd. (Cambridge, U.K.). Monosaccharide composition was determined as in previous work.19 Arabinose and xylose sugars represent more than 92% of total monosaccharides. The arabinose/xylose ratio was 0.58, which implies a high degree of substitution of the xylan backbone. The FA content (determined by HPLC25) was 4.2 μg of FA/mg of FAX, so that one FA is bound every 200 xylose residues, which is above the threshold of one FA per 2000 xylose residues required for gelation.24 This FA content is roughly 3 times higher than that of the wheat-extracted FAX used in our previous study.21 FAX polymer weight-average molar mass M̅ w, number-average molar mass M̅ n, and dispersity M̅ w/M̅ n were measured by size exclusion chromatography as was done previously.20 CNCs were prepared from ramie fibers as previously described.21 Laccase (EC 1.10.3.2) from Pycnoporus cinnabarinus26 (Uniprot ID: Q9UVQ2) was provided by the UMR Biotechnologie des Champignons Filamenteux (INRA and Aix-Marseille University, Marseille, France). FAX solutions were prepared at the desired concentrations by adding FAX powder to 5 mM citrate phosphate buffer at pH 5.5 (filtered on 0.22 μm filter), and the other components (buffer, fluorescent probe, CNCs) were added to the required concentration. Gelation was triggered by adding 1% (v/v) of the mix volume of laccase at 40 IU·mL−1 at 25 °C and the solution immediately poured into an eight-chamber coverglass mold (Lab-Tek II, Thermo Scientific, Courtabœuf, France) and allowed to set at least 2 h. Films were produced according to Yoshiharu et al.27 After laccase addition, the solution was poured into a horizontal tube rotating at 400 rpm at room temperature and dried under a nitrogen flow overnight. Film thickness was measured with a dual thickness gauge (precision of ±0.001 mm) 2197


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Postbleaching: laser at 10%, acquisition of images until the bleached ROI intensity was constant. FRAP experiments were repeated at least five times for each sample, at different XY positions. FRAP analysis was performed first by measuring in each image the intensity value of the ROI bleached and of that of a reference ROI far outside for correcting photobleaching, using ImageJ software (http://rsbweb.nih.gov/ij/). Corrected intensity values It were then normalized from 0 and 1 by taking into account the first intensity obtained immediately after photobleaching I0 and the intensity obtained before photobleaching Ipre as a reference in order to calculate the fluorescence recovery

R(t ) =

I t − I0 Ipre − I0

Moisture uptake = 100 ×

Wmoist − Wdry Wdry

(5)

where Wmoist is the sample weight equilibrated at a given RH and Wdry is the sample weight of the dry sample (obtained after the sequence 360 min at 60 °C). Full Factorial Experiment Modeling. For each response D and MF, experimental data (values + standard errors) were computed in a full 24 = 16 factorial experiment designed in Minitab 15.1 (Minitab SARL, Paris, France) with four principal factors Xn (n = 1−4), and all possible coefficients An and interaction coefficients Ann(n)(n) (n = 1−4) were calculated, using a first degree model equation:

(1)

D or MF = A 0 + A1· X1 + A 2 · X 2 + A3 ·X3 + A4 ·X4 + A12 ·X1·X 2

Considering that diffusion of the probe mainly occurs in the two XY lateral dimensions, fluorescence recovery data R(t) were fitted to a double exponential equation with four parameters a, b, c, and d (eq 2) which were determined subsequently (SigmaPlot 11.0, Systat Software, Chicago, IL, United States).

+ A13 ·X1·X3 + A14 ·X1·X4 + A 23 ·X 2 ·X3 + A 24 ·X 2 ·X4

R(t ) = a ·(1 − e−b·t ) + c·(1 − e−d·t )

+ A34 · X3· X4 + A123 · X1· X 2 · X3 + A124 · X1· X 2 · X4 + A134 · X1· X3· X4 + A 234 · X 2 · X3· X4 + A1234 ·X1·X 2 ·X3· X4

(2)

In addition to all absolute (or natural) coefficients, standardized coefficients were also calculated to allow comparison of coefficient influence. Fitting of the model on experimental data was performed by determining the correlation coefficient R2 to decide model accuracy.

The coefficient of determination R2 of the fitness was also calculated and was always above 0.99. τ was defined as the recovery time constant, for which R(τ) = 0.5 × R∞ so that the experimental diffusion coefficient D can be calculated from28

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RESULTS AND DISCUSSION FAX Gels and FAX/CNC Gels Characterization. FAX polymers were analyzed by size exclusion chromatography to measure their dispersity: M̅ n = 124 900 and M̅ w = 308 300 (results not shown), resulting in a high dispersity of 2.5, indicating that the FAX polymers are fairly long and their size range is wide. Schematically, taking into account the arabinose/ xylose ratio of 0.58, the FAX backbone has an average length of 1300 xylose residues, half of them bear an arabinose moiety, and there are between 5 to 6 FA esters per chain, which allows many possible inter- and even intraconnections during gelation through di-FA linkages. The polymer chain density in the gels prepared depends on both the FA content and the FAX concentration. Only the latter was modulated: gels were prepared using 0.5, 1.0, 1.5, and 2.0% FAX solutions (w/v). This means that the water content of these hydrogels varies from 99.5 to 98.0% and the density from 25 to 100 mg·cm−3. In order to prepare more complex LC bioinspired assemblies, we have also embedded CNCs in the FAX gels. In plant secondary cell walls, AXs interact with cellulose via noncovalent interactions.29 In grasses, the cellulose-to-AX ratio (in dry weight) in secondary cell wall is between 2:1 and 1:1.16 To account for this natural variability, we have prepared some FAX gels at a constant concentration of 1% and CNC concentrations ranging from 0.5 to 2.0%, in order to get CNC/FAX ratios of 1:2, 1:1, and 2:1, respectively. FAX Chains Mobility Measured by FRAP. In order to gain an understanding of the FAX chain mobility in gels, we have synthesized FAX labeled with FITC molecules and prepared FAX−FITC gels. The diffusion coefficient D and mobile fraction MF of FAX−FITC chains were measured by FRAP before and after gelation for concentrations ranging from 0.5 to 2.0% (Figure 2). With increasing FAX−FITC concentration, D proportionally decreased in solution but increased in gel. Furthermore, D values were higher in gels than in solution, which does not seem coherent. The MF values help to explain this “anomalous” diffusion. MF values also decreased in solution with increasing FAX concentration, which is consistent with the fact that high polymer content favors noncovalent

2

D=

r 4τ

(3)

where r is the radius of the bleach spot which is 5 μm in our case. The mobile fraction MF is defined as the probe fraction that contributes to the fluorescence recovery and is equal to the plateau value obtained when the recovery value becomes unchanged. Mathematically speaking, MF is expressed as the normalized fluorescence recovery in % for which t → ∞ (to reach the plateau value) so that R(t → ∞) = a + c = MF according to eq 2, while the immobile fraction IF is defined as IF = 1 − MF. The relationship between the theoretical diffusion coefficient Dth and the hydrodynamic radius RH of the probe (in m) follows the Stokes−Einstein equation:

Dth =

k·T 6π · η· RH

(4) −23

(6)

−1

J·K ), T is the where k is the Boltzmann constant (1.38 × 10 absolute temperature (in K), and η is the viscosity of the solution (in Pa·s). To analyze the statistical significance of experimental values (diffusion coefficient values, MF values, or others), a one-way ANOVA followed by a Tukey test (SigmaPlot 11.0) was performed. Scanning Electronic Microscopy (SEM). Gels were analyzed as previously described.21 Atomic Force Microscopy (AFM). Pieces of each film were cut and fixed onto a microscope slide using a double-sided tape. Topography images were recorded using a Biocatalyst AFM (BrukerNano Instruments, Santa Barbara, CA, USA). Scans were performed in contact mode with triangular silicon nitride probes with a nominal spring constant of 0.35 N·m−1. All measurements were made in air. Height and phase images were recorded for each sample. Several scans over a given surface area were measured to ensure reproducible images. Typically, the images were taken at 1 Hz scan rate and digitized in 512 × 512 pixels. Different image sizes (10 × 10, 5 × 5, and 1 × 1 μm2) were recorded for each sample at different positions to be sure of the homogeneity of each film. Water Sorption. Film samples (1−2 mg) were placed into the sorption microbalance (Hiden Isochema, IGA Dynamic Vapor Sorption). Relative humidity (RH) was increased from 10 to 90% (10% steps) at 25 °C, and moisture uptake was recorded after the sample was equilibrated at each step and calculated according to eq 5: 2198


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(Supporting Information), looking similar to those previously described.30 Contrarily, the presence of CNCs in FAX/CNC gels (Figure 3) gradually favors the apparition of a structured

Figure 3. SEM images of 1.0% FAX gels with 0, 0.5, 1.0, and 2.0% CNC (insets A, B, C, and D, respectively). The scale bar is 100 μm.

network with increasing concentration, with pores having preferred orientations and more regular shapes but with decreasing sizes21,31 (Supporting Information). CNCs appear to create a honeycomb-like structure in FAX gels that shrinks when their concentration increases, until pores are partially closed by sheets (Figure 3). Overall, combined results on gels from FRAP, rheology, and SEM demonstrate that a higher FAX concentration creates dense gels containing polymer chains that cannot easily rearrange due to high FA availability which can cause entanglements for molecules that might diffuse in these systems. The presence of CNCs in these gels promotes global and even orientation of pores, thus favoring the formation of homogeneous networks. Mobility of Probes in FAX Gels and FAX/CNC Gels. In order to assay the importance of the size of the diffusing molecules in the gels, three different fluorescent probes have been embedded: DF10, DF70, and DF250, whose RH values are 2.3, 6.0, and 11.1 nm, respectively. The probes RH have been selected so that they vary by nearly 1 order of magnitude and are representative of the majority of lignocellulose-degrading enzymes in their monomeric or multimeric state (with or without one or multiple additional modules connected by linker modules like carbohydrate binding modules), for example, like the small GH11 xylanase (RH = 1.9 nm)32 and the bigger GH7 cellulase Cel 7a (RH = 3.0 nm),33,34 which are both isolated from the fungus Trichoderm reesei widely used in biomass degradation processes. Consistently with our previous study,21 diffusion measured by FRAP (Figure 4A) depends more on probe size than on FAX concentration: for DF70 and DF250 taken separately, calculated diffusion coefficients are not statistically different in the gels; however, D decreased with increasing FAX concentration for DF10. Contrarily, diffusion between DF10 and DF250 varies by 4-fold. Considering each gel FAX concentration separately, the plot of D against RH−1 passing through the origin (Figure 5) shows a perfect proportionality between these two parameters (R2 = 0.97). This means that the Stokes−Einstein relationship applies (eq 4), so the probe RH is below the gel mesh size for all concentrations, even if the FAX gel network complexity creates entanglements and sieving effects that slow down probe diffusion, in particular for large probes. This is consistent

Figure 2. Diffusion coefficients and mobile fractions of FAX−FITC at 0.5, 1.0, 1.5, and 2.0% (w/v) in 5 mM citrate buffer (A) in solution and (B) after gelation.

interactions, creating some entanglements which slightly alter their diffusion coefficient and decrease MF. After gel formation, MF values are decreased by 4-fold at any FAX−FITC concentration: nearly all the FAX chains are crosslinked, and they have become like rigid bodies. This is in accordance with the high number of FA on the FAX chains (5-6 per chain) which allows the formation of many covalent bonds during gelation. Since the FITC abundance is higher (around 50 molecules per FAX chain), FITC can be considered as a good reporter of the mobility of the FAX chains and sections between FAs. Nonetheless, some chain sections are unconnected loose ends; they become highly mobile in comparison to the longer FAX chains in solutions because they are much shorter. This can explain why D is increased for higher FAX concentrations, because this result refers in fact to a small FAX population. FAX Chains Mobility Measured by Rheology. Mechanical spectra of FAX and FAX + CNC gels were recorded during gelation of the different systems for the various FAX and CNC concentrations, and after reaching a plateau, the final storage modulus G′ and loss modulus G″ have been compared to initial moduli (Supporting Information). Results are comparable to those obtained previously with FAX containing less FA.21 FAX chains are less mobile in more concentrated FAX, while CNCs only increase viscosity and not elasticity, which shows that they probably have few interactions with FAX due to high substitution and they do not modify the gel network but their presence merely increases entanglements. Morphological Analysis by SEM. SEM images of FAX gels show an increasing complexity with FAX concentration 2199


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substitution (3-fold) has a strong impact on the network density increasing sieving effect, in accordance with the rheological analysis which has demonstrated that higher FAX concentration created more entanglements. CNCs at different concentrations were added to 1% FAX before forming gels. Despite a 3-fold increase of total polysaccharide concentration (from 1% FAX to 1% FAX + 2% CNC), they have no impact on the diffusion (Figure 6A).

Figure 4. (A) Diffusion coefficients and (B) mobile fractions of DF10, DF70, and DF250 probes in 0.5, 1.0, 1.5, and 2.0% FAX gels.

Figure 6. (A) Diffusion coefficients and (B) mobile fractions of DF10, DF70, and DF250 probes in 1.0% FAX gels containing 0.5, 1.0, and 2.0% CNC.

With CNCs, MF values are constant for each probe and unaffected by the FAX concentration (Figure 6B), except for the DF250 probe for which MF increases with CNC concentration. In agreement with SEM images, the chain mobility analysis by FRAP and rheology suggests that CNCs do not increase entanglements in the gels and may even make it more organized. FAX and FAX/CNC Films Characterization. In order to evaluate the impact of water content on the mobility of fluorescent probes in the assemblies prepared, gels were dried into films. The homogeneity of the films obtained was evaluated: the FAX film thickness was 32 ± 3 μm. Since their volume was drastically reduced compared to gels, their density increased from 1500 to 6000 g·cm−3, i.e., 60 times higher if one considers the same initial polymer concentrations. The water sorption isotherms of FAX and FAX/CNC films were recorded (Supporting Information) to obtain the water content of the films in the 50% controlled-humidity room in which the probe mobility in films was analyzed. For all films, the water content was between 17 and 19% which is approximately 5 times lower than in gels.

Figure 5. Diffusion coefficients of probes as a function of RH−1 in 0.5, 1.0, 1.5, and 2.0% FAX gels.

with results from the previous section which have shown that an increase in FAX concentration made the polymer network more complex. Regarding the MF values (Figure 4B), they seem to be affected by both FAX concentration and probe size. MF varies from 92 to 97% (5 points range) for DF10, from 72 to 87% (15 points) for DF70, and from 46 to 66% (20 points) for DF250: the range variation increases with probe size and with FAX concentration for probes taken individually. In comparison to the mobility measurements previously made,21 diffusion coefficients are not very different, whereas mobile fractions are greatly reduced, confirming that a higher level of FA 2200


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Topographical Analysis by AFM. FAX and FAX + CNC films were imaged in the air. Several images were recorded for each film to be sure of the homogeneity of the samples. Figure 7 shows representative 5 × 5 μm2 height images of FAX films at

Figure 7. AFM 5 μm × 5 μm height images of FAX films (left) and FAX + CNC films (right) (scale is in nm). Only images with the DF70 probe are shown for clarity and are representative of film spread and roughness also obtained for DF10 and DF250. Figure 8. Relationship between RMS roughness and FAX or CNC concentrations in (A) FAX films and (B) FAX + CNC films. Mean and standard deviation RMS values are calculated at each concentration, taking the three probes together.

different concentrations and containing DF10, DF70, and DF250 probes. Film tightness appears to increase with FAX concentration. Surface RMS roughness was then calculated for each film for each probe and mean values calculated at each concentration (Figure 8A): probe size has no influence on roughness, whereas it decreases linearly with increasing FAX concentration with statistical significance, which means that the surface becomes smoother, which agrees with the image observation. The same analysis was performed on FAX + CNC films in which FAX concentration was constant and CNC concentration varied from 0.5 to 2.0% (Figure 7). As with the FAX films, the surface topography becomes more irregular and less smooth when the CNC concentration increases, whichever size probe is used. Taking 1.0% FAX film with no CNC as a reference, surface RMS roughness (Figure 8B) increases by more than 60% from 0 to 1.0% CNC, and then is stable for 2.0% CNC with no statistical difference. Thus, contrarily to FAX, the presence of CNC mainly diminishes surface smoothness. Mobility of Probes in FAX and FAX/CNC Films. As with the gels, the films have been prepared from FAX solutions containing the three probes DF10, DF70, and DF250. In comparison to FAX gels, D was drastically decreased by 130- to 300-fold depending on the probes and concentrations concerned (Figure 9A). Interestingly, the ratio between the maximum and minimum D values (between DF10 and DF250 probes) was below 2.0 in films, whereas it was close to 4.0 in gels. Moreover, an increase in FAX concentration seemed to slightly enhance D in films, whereas it had no or little influence

in gels. MF values (Figure 9B) were very similar at 1.0 and 1.5% FAX whichever probe was used. However, in 0.5% FAX films, MF increased with the probe size: it was more than 40% for DF250 and less than 30% for DF10. This means that high FAX concentration favors diffusion of probes but reduces MF values, which is in accordance with AFM analysis where RMS roughness decreased with increasing FAX concentration. In the presence of CNCs at various concentrations and constant FAX concentration, D appeared to be maximal with 0.5% CNC, at least for DF70 and DF250 and with no clear statistical difference for DF10 (Figure 10A). Taking each condition and comparing probe diffusion leads one to note that the ratio between maximal and minimal D values increased with CNC concentrations: CNCs have more influence at high concentration than at low concentration. It is the same for MF profiles (Figure 10B): they are very similar for every probe but higher without CNC. In FAX/CNC films, the increase of CNC concentration had overall a negative impact on D and MF, by probably creating more entanglements (due to the increase of global polysaccharide concentration) and compartments (due to the formation of an organized network). Accordingly, film roughness measured by AFM (Figure 8) increased with higher CNC concentration, whatever the probe size was. In gels, CNCs allowed the network to be better organized, which was characterized by higher MF values. This divergence can be ascribed to the fact that in denser systems 2201


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Figure 9. (A) Diffusion coefficients and (B) mobile fractions of DF10, DF70, and DF250 probes in 0.5, 1.0, and 1.5% FAX films.

Figure 10. (A) Diffusion coefficients and (B) mobile fractions of DF10, DF70, and DF250 probes in 1.0% FAX films containing 0.5, 1.0, and 2.0% CNC.

like films (1500−6000 g·cm−3 vs 25−100 mg·cm−3 in gels), less water molecules are available to permit an easy and rapid reorganization of the polymers. Modeling the Progression of Probes. Our experiments have shown that D and MF were variously influenced by the four principal factors that have been modulated: probe size (RH), FAX and CNC concentrations, and water content (or system density). In order to evaluate quantitatively the importance of each factor on D and MF, we have designed a full factorial experiment. In this frame, D and MF are called responses, influenced by the factors modulated and named principal factors Xn (n = 1−4), which are associated with principal coefficients An and interaction coefficients Ann(n)(n) (n = 1−4) in a first degree model equation (see eq 6). For each principal factor Xn, two levels have been defined (high and low levels, Table 1). Using the data set obtained for D and MF, principal coefficients An and all possible interaction coefficients Ann(n)(n) for both D and MF have been calculated so that these coefficients can be compared. First, the validity of the model has been checked by calculating the correlation coefficients for each response D and MF: they are respectively 0.99 and 0.98, which means that the linear model of eq 6 can accurately predict each response. Absolute (or natural) coefficients computed are shown in Figure 11A and can be used in eq 6 to predict D and MF. However, the number of terms is important, so the equation is not useful as it is. In order to be simplified, it is necessary to decide the relative importance of the coefficients to unravel those which have the most influence on the responses. To be compared, the coefficients have thus been standardized (see the

Table 1. Values of the High (+) and Low (−) Levels Used for Each Principal Factor in the Full Factorial Experiment principal factors X1 X2 X3 X4

= = = =

probe RH (nm) FAX concentration (%) CNC concentration (%) water content (%)

high level (+)

low level (−)

ratio between high and low levels

11.0 2.0 2.0 99.5

2.3 0.5 0.5 18.0

4.8 4.0 4.0 6.2

Materials and Methods section), and only those which are above twice the value of the average experimental standard error obtained for each response are considered as significant and can be conserved in the model eq 6. This threshold is 2 × 0.998 ≈ 2 for D and 2 × 2.53 ≈ 5 for MF (Figure 11B). Thus, for the response D, the most significant coefficients are A1, A4, and A14, with a positive effect for A4 (water content) approximately twice as important as the negative effects of A1 (probe size) and A14 (interaction of water content and probe size). Other significant interaction coefficients include A12, A23, A24 and A124, A234. Several of these contain one of the most important principal coefficients A1 and A4; however, the most important one A12 has a value nearly 13 times smaller than A14. This implies that D is mainly influenced by water content and probe size; their interaction is also significant. The concentration in FAX (A2) has more influence on D than the CNC concentration (A3), but both parameters are not as critical as one could expect; changes have a low impact on D. By 2202


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concentration only impacts MF. Water availability appears essential for optimizing the progression of probes, while the hydrodynamic radius of the probe directly affects their mobility. In addition to quantifying the relative influence of the parameters tested, modeling the probe progression can be used to predict the influence of the factors in ranges not measured experimentally. Taking eqs 7 and 8, contour plots have been traced by setting FAX and CNC at 1.0%, which are median values of the data set, considering that their influence has been demonstrated to be weak compared to other factors. The two contour plots obtained (Figure 12) are slightly different for D and MF, and in order to go deeper in the optimization of the

Figure 11. Values of coefficients An and their interactions Ann(n)(n) (n from 0 to 4) for D and MFs (A) before and (B) after standardization. Values before standardization can be introduced directly in the model to calculated D and MF values, whereas standardized values are calculated to determine whether coefficients can have a statistically significant influence on the model (marked with *).

conserving only significant coefficients, the response for D can be reformulated and simplified from eq 6 into D = −24.7 + 1.8X1 + 2.5X 2 + 1.6X4 − 0.20X1·X 2 − 0.11X1·X4 − 0.74X 2 ·X3 − 0.16X 2 ·X4 + 0.01X1 ·X 2 ·X4 + 0.05X 2 ·X3·X4

(7)

For MF, the same principal factors are meaningful (A1 and A4), but only two interaction coefficients are significant (A14 and A34). They have also different relative influences (Figure 11B): A4 is 3 times higher than A1 and A14; thus, water content is even more critical for MF than for D, and CNC concentration has an impact only in interaction with water content. Taking into account these results, eq 6 becomes MF = 7.1 + 3.4X1 + 0.98X4 − 0.06X1·X4 + 0.009X3·X4 (8)

Overall, in the data range tested for the four principal factors, water content has more effect on MF than on D, so water has more influence on the formation of entanglements in the network which limit MF than the propensity for the probe to diffuse in this network. Polymer concentrations have only moderate importance compared with other factors: FAX concentration has only little influence on D, while CNC

Figure 12. Contour plots of (A) diffusion coefficient and (B) mobile fraction vs probe size (X1) and water content (X4) using the simplified eqs 7 and 8, respectively. FAX (X2) and CNC (X3) concentrations have been set to the median values 1.0%. Labels on contour lines indicate the diffusion coefficient values in μm2·s−1 in part A and the mobile fraction as % in part B. 2203


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studying the progression of some probes more closely related or involved in the biomass degradation (CBMs, enzymes), which would introduce additional parameters in our model (affinity, hydrophobicity, potential catalytic activity, ...). Indeed, dextran polymers do not have the same chemical functions as proteins on their surface: proteins are more prone to interact (specifically or not) with the polymers of the lignocellulosic assemblies and can be slowed down. Despite these limitations inherent to the use of a model, our strategy demonstrates that bioinspired assemblies can easily be used to emphasize the relative importance of some selected parameters influencing the diffusion of molecules. Of course, the more parameters are introduced in the model, the more relevant information can be obtained and possibly transferred to the optimization of saccharification processes. In the future, such modeling studies should help to propose new ways to optimize enzymes and to select modifications to genetically manipulate plants and improve deconstruction of LC,36 as has been performed on the branching modifications of AX to decrease recalcitrance.37

progression of probes, it is more convenient to set one of the two parameters varied (probe size and water content). Starting with water content, it is important to note that the dry matter content in saccharification processes is likely to be in the range 15−30% to be economically viable;35 thus, water content can be set to 80%, for instance, in the assemblies. So, following the progression of a small probe (RH = 2 nm), D is 80 μm2·s−1 and MF = 80%; if the probe size is more than doubled (RH = 5 nm), D only diminishes to 60 μm (−25%), while MF decreases to 71%; the increase in probe size is thus modest. However, doubling again the probe size to 10 nm has a dramatic effect: D falls to 29 μm2·s−1 (−50%), and MF plummets to 56%. This example strengthens the importance of the probe size on progression, and shows that D diminishes a bit faster than MF when RH increases. As we know now how to model the behavior of probes of known size, a typical cellulase from Trichoderma reesei can be selected as a model probe to test the progression of an enzyme classically involved in the degradation of LC. Since the enzyme size has been determined to be close to 6 nm for CBHI (Cel 7a),33,34 its RH can be approximated to 3 nm. In this case, both D and MF increase linearly with water content from 60 to 95%: D goes from 51 to 91 μm2·s−1 and MF from 61 to 89%, demonstrating that D increases here more rapidly than MF. The augmentation of water content is directly correlated to D and also favors the access to the network, which can be explained by the hydration of the polymers that facilitates their solvation and makes their structure less compact and more relaxed.

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ASSOCIATED CONTENT

S Supporting Information *

FAX gel SEM images and rheological measurements; water sorption isotherms of FAX and FAX/CNC films. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

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*E-mail: [email protected]. Phone: +33 (0)3 26 77 36 08. Fax: +33 (0)3 26 77 35 99.

CONCLUSIONS To our knowledge, this is the first time that bioinspired model assemblies of secondary plant cell walls have been designed to serve as templates to assess progression of fluorescent probes, in order to evaluate the importance of different parameters and to introduce them in an empirical mathematical model. This strategy has permitted us to demonstrate that the four features modulated and compared had different levels of influence in this decreasing order of importance: water content, probe hydrodynamic radius, and polysaccharide concentrations. In the case of industrial processes involving enzymes that act on lignocellulose, their progression is of course considered as critical,10,15 but which of these features can be easily optimized? The role of water content is of course known to be central:35 if it is too high, the cost of treating water effluent is prohibitive, but if it is too low, enzyme progression (and so activity) is reduced. An optimal balance between cost and efficiency is essential to define. Our modeling can thus help finding the optimal balance. Regarding the enzymes, their hydrodynamic radius is a parameter more easily changed and optimized. For example, low molecular weight enzymes should be selected when several ones with similar activities are available and/or enzymes that typically oligomerize should be avoided, or modified to prevent oligomerization when possible, by engineering surface residues. Of course, our model systems are highly simplified templates of the plant cell walls; assembly complexity should be increased by introducing lignin which is the third major polymer of lignocellulosic biomass and known as a key recalcitrance feature of LC biomass.2 More relevant systems might also contain more chemical interactions between polymers (covalent and noncovalent interactions) that would be representative of the plant tissues. Moreover, we would gain more information by

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors would like to thank L. Foulon for preparation and characterization, D. Crônier for SEC analysis, O. Delfosse for elemental analysis, and M. Pernes for water sorption analysis. B. Rogé (Université de Reims Champagne-Ardenne) provided access to the rheological instrument. The authors thank J.-C. Sigoillot (Aix-Marseille Université/INRA, Marseille) for providing laccase. SEM observations were performed at the Cellular and Tissular Imaging Platform (Université de Reims Champagne-Ardenne) with the much appreciated assistance of C. Terryn. AFM analysis were made in the Nano’Mat platform (Université de Reims Champagne-Ardenne). This work was supported by a grant from INRA (Enzydam project).

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REFERENCES

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Modeling Progression of Fluorescent Probes in Bioinspired

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