Guy A Channell

Gary G Adams

Yudong Lu

Richard B Gillis

Vlad Dinu

L Grundy

Balazs Bajka

Peter J Butterworth

Peter R Ellis

Alan Mackie

Simon Ballance

Stephen E Harding
email: steve.harding@nottingham.ac.uk

Use of the Extended Fujita method for representing the molecular weight and molecular weight distributions of native and processed oat beta-glucans

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Use of the Extended Fujita method for representing the molecular weight and molecular weight distributions of native and processed oat beta-glucans

Beta 1-3, 1-4 glucans, "beta-glucans", are one of the key polysaccharides of the cell walls of oats and other cereals, complementing the main structural component cellulose [START_REF] Mantovani | Beta-glucans in promoting health: prevention against mutation and cancer[END_REF][START_REF] Havrlentová | Can α-D-glucan protect oat seeds against a heat stress?[END_REF][START_REF] Harding | An Introduction to Polysaccharide Biotechnology, 2nd Edition[END_REF] . Beta-glucans are also an important source of water soluble dietary fibre in oat-containing foods with claims of other beneficial nutritional properties such as promoting plasma cholesterol reduction and lowering postprandial glycaemia [START_REF] Braaten | High beta-glucan oat bran and oat gum reduce postprandial blood glucose and insulin in subjects with and without type 2 diabetes[END_REF][START_REF] Wang | Oat β-glucan: physico-chemical characteristics in relation to its blood-glucose and cholesterol-lowering properties[END_REF] . Besides their chemical structure (Fig. 1), the key to the function of beta-glucans in the plant, and their use (or potential use) commercially or in biomedicine is their molecular weight and, because of their high polydispersity -their molecular weight distribution. In some cases, preparations of beta-glucan can be readily soluble and have fairly unimodal distributions of size. In other cases, insoluble components or impurities are present [START_REF] Wang | Microwave assisted dissolution of β-glucan in water -implications for the characterisation of this polymer[END_REF] and unless removed can cause problems with several techniques used to characterise the physical properties of the soluble components. A recent study [START_REF] Grundy | Impact of hydrothermal and mechanical processing on dissolution kinetics and rheology of oat β-glucan[END_REF] has considered solubility and dissolution kinetics and linked this to the biological activity of beta-glucan. An approach for polydisperse polymers was recently published 8 based on the matrix-free technique of sedimentation velocity in the analytical ultracentrifuge and converts a distribution of sedimentation coefficient g(s) vs s plot into a distribution of molecular weight utilising the power-law or scaling relationship between the sedimentation coefficient and molecular weight [START_REF] Tsvetkov | Structure of Macromolecules in Solution[END_REF][START_REF] Smidsrød | [END_REF][START_REF] Harding | Molecular weight determination of polysaccharides[END_REF]

: = κ s M (1a) w b s
where s is the sedimentation coefficient, M w is the weight average molecular weight and κ s and b are characteristic coefficients related to conformation. For example, b = 0.4-0.5 for a coil type of conformation, ~0.15-0.2 for a rod conformation and ~0.67 for a spherical conformation.

The sedimentation coefficient s depends on the size and shape of the macromolecule 12 but if κ s and b are known then M w can be found from:

= κ M s ( / ) (1b) w b s 1/
The method has been applied to alginate, pectins, and mucin glycoproteins from a wide variety of sources [START_REF] Gillis | Molecular weight distribution analysis by ultracentrifugation: adaptation of a new approach for mucins[END_REF] . We establish and apply the power law relation for the first time for beta-glucans based on work on oat beta-glucan BG90. Then for a series of beta-glucans of lower degree of purity we illustrate the simple application of the scaling equation, Eq. (1b) to identify the weight average molecular weights from the sedimentation coefficients. Finally, we use the Extended Fujita approach [START_REF] Harding | Extended Fujita approach to the molecular weight distribution of polysaccharides and other polymeric systems[END_REF] to evaluate the molecular weight distributions of beta-glucans extracted from oats that had been processed by roller milling and subject to long-time storage for 3 years at 21 °C. A major development in the molecular weight determination of polysaccharides occurred 3 decades ago with the introduction of the SEC-MALS method (size exclusion chromatography coupled to multi-angle light scattering) [START_REF] Wyatt | Multi-angle light scattering from separated samples (MALS with SEC or FFF)[END_REF][START_REF] Harding | Classical light scattering for the determination of absolute molecular weights and gross conformation of biological macromolecules[END_REF][START_REF] Harding | Light Scattering[END_REF] , which provides separation and absolute molecular weight analysis. The first application of this method to polysaccharides -namely alginates -was in 1991 [START_REF] Harding | Molecular weight determination of polysaccharides[END_REF][START_REF] Horton | Gel permeation chromatography -multi angle laser light scattering characterization of the molecular mass distribution of "Pronova" sodium alginate[END_REF] , and to glycoconjugates in 1996 [START_REF] Jumel | Rapid size distribution and purity analysis of gastric mucus glycoproteins by size exclusion chromotography/multi angle laser light scattering[END_REF] , and is a technique which has now become the chosen method for many polymer systems.

However, there are two major limitations which restricts the types of molecule that can be successfully analysed, (i) the separation limit for the columns (with generally an upper limit of ~2-3 × 10 6 g/mol), and (ii) lack of inertness of the columns used. Some beta-glucans [START_REF] Wang | Oat β-glucan: physico-chemical characteristics in relation to its blood-glucose and cholesterol-lowering properties[END_REF] have very large molecular weights (either native or through aggregation phenomena) that exceed the molecular weight limit for separation on the columns, and many offer solubility problems that can affect the inertness of the columns. Issues of solubility and non-unimodality can also affect other methods such as sedimentation equilibrium in the analytical ultracentrifuge and our recently developed SEDFIT-MSTAR method [START_REF] Schuck | SEDFIT-MSTAR: molecular weight and molecular weight distribution analysis of polymers by sedimentation equilibrium in the ultracentrifuge[END_REF] . The Extended Fujita approach based on sedimentation velocity at high speeds is more suited and supra-molecular impurities are automatically removed. The approach offers a complementary alternative to SEC-MALS and sedimentation equilibrium and can be applied to situations in which other methods cannot be employed.

The Extended Fujita approach of Harding, Schuck & coworkers. Fujita 20 provided the basis for converting a (differential) distribution g(s) of the sedimentation coefficient s into a (differential) distribution f(M) of the molecular weight M for linear polymers, based on the assumption that the polymers behave as randomly coiled polymers in solution, with b = 0.5 in Eq. (1). Harding, Schuck and colleagues 8 provided a generalisation of this method to cover any conformation type (including spheres, rods and coils). g(s) is defined as the weight fraction of the species with a sedimentation coefficient between s and s + ds and f(M) is defined as the weight fraction of species with a molecular weight between M and M + dM. To transform g(s) vs. s to f(M) vs M firstly:

. = . s ds M dM g( ) f ( ) (2)
and so

= M s ds dM f( ) g( )( / ) (3)
where

= . κ . - ds dM b s / (4) b b b s 1/ ( 1)/
Therefore, to perform the transformation the conformation type or b needs to be known under the particular solvent conditions and at least one pair of s-M values is needed to define the κ s from Eq. ( 4). Care needs to be expressed concerning thermodynamic/hydrodynamic non-ideality [START_REF] Harding | The concentration-dependence of macromolecular parameters[END_REF] , but these effects can be avoided by working at low polymer concentrations, taking advantage of the fact that sedimentation velocity experiments can be performed at concentrations as low as 0.1-0.2 mg/ml, where such effects are usually negligible. The method has now been built into the popular sedimentation velocity analysis platform known as SEDFIT [START_REF] Dam | Calculating sedimentation coefficient distributions by direct modeling of sedimentation velocity concentration profiles[END_REF][START_REF] Brown | Macromolecular size-and-shape distributions by sedimentation velocity analytical ultracentrifugation[END_REF] . In the transformation from g(s) vs s to f(M) vs M a diffusion corrected variant of g(s) known as c(s) is sometimes used [START_REF] Dam | Calculating sedimentation coefficient distributions by direct modeling of sedimentation velocity concentration profiles[END_REF][START_REF] Brown | Macromolecular size-and-shape distributions by sedimentation velocity analytical ultracentrifugation[END_REF] .

Evaluating the scaling parameters κ s and b for oat beta glucans. We can now seek to develop the procedure for oat beta-glucans by establishing the κ s and b parameters. To do that, we select a beta-glucan, which is fully soluble and has a unimodal distribution of sedimentation coefficient. One such material is a substance known as oat beta-glucan BG90, supplied by F. Prothon (Swedish Oat Fibre, Bua, Sweden) that has previously been well characterised chemically (see ref. [START_REF] Grundy | Impact of hydrothermal and mechanical processing on dissolution kinetics and rheology of oat β-glucan[END_REF] , where it is referred to as "BG2"). Its g(s) vs s distribution for a series of concentrations (where s is the apparent sedimentation coefficient, i.e. not corrected for non-ideality, at concentration c) is shown in Fig. 2a. We then obtain the b and κ s in the following way:

Defining b. We use SEC-MALS coupled to an on-line viscometer. Figure 3a shows the SEC elution concentration profile recorded using the on-line differential refractometer and one of the light scattering detectors. We then record, as a function of elution volume V e , the intrinsic viscosity [η](V e ) versus weight average molecular weight M w (V e ) profile and from the slope of a plot of log-log plot (Fig. 3b) across the main peak of elution volumes (V e ) evaluate the viscosity power law (scaling) coefficient a. It should also be remarked that across the selected peak the very accurate linear fit is indicative of a pure component within this range. From Fig. 3b a value of a = 0.62 corresponds to a flexible coil conformation. We can then use the Tsvetkov relation 9 linking the sedimentation and viscosity power law coefficients to obtain a value of 0.45 for b:

= - b a (2 )/3 ( 5)
We then repeated the whole procedure on a different SEC-MALS instrument in a different institution and re-assuringly obtained a similar value b = 0.45. This gave us a working value for b = (0.455 ± 0.010).

Defining κ s . To do this, we simply use Eq. ( 1) with the weight average sedimentation coefficient s, the weight average M w from SEC-MALS and a b value of (0.455 ± 0.010). The weight average sedimentation coefficient after extrapolation to zero concentration (Fig. 2b) to remove non-ideality effects was s° = (4.82 ± 0.10)S. The values for M w from both instruments were 634,000 and 651,000 g/mol so we take M w = (642,000 ± 10,000) g/mol, and because of the very low concentrations after dilution on the columns, non-ideality effects can be ignored. This gives a value for κ s under high dilution conditions (c < 0.15 mg/ml):

κ = = . = .
. s M ( / ) (4 82/64000

) 0 01098 (6)

s w b 0 455
Figure 4 shows the distributions obtained in this way for oat beta-glucan BG90. One can see the broad distribution and the large amount of high molecular weight material >500,000 g/mol.

+ k s c)
where k s is the concentration dependence or 'Gralén' coefficient (Gralén, 1944; Harding & Johnson, 1985). From the fit a value of s° = (4.82 ± 0.10)S and k s = (420 ± 40) ml/g are obtained.

Exploring the concentration limit for transformation of a g(s) vs s plot to an f(M) vs M plot. Where distributions g(s) vs s cannot be obtained under high dilution conditions (i.e. where s ~ s°) an approximation can be made that the weight average s value at that concentration is substituted into Eq. ( 6) and the appropriate κ s value selected (Table 1). The appropriate sedimentation coefficient value, s, for that concentration, c, can be found using the Gralén relation 24 :

= . + s s k c (1/ ) (1/ ) (1 ) (7)
o s

where k s is the concentration dependence or 'Gralén' coefficient (see refs [START_REF] Harding | The concentration-dependence of macromolecular parameters[END_REF][START_REF] Gralén | Measurements on Cellulose and Derivates[END_REF][START_REF] Harding | Physicochemical studies on turnip-yellow-mosaic virus. Homogeneity, relative molecular masses, hydrodynamic radii and concentration-dependence of parameters in non-dissociating solvents[END_REF] ), where (Fig. 2b) k s = (420 ± 40) ml/g. Note that the Gralén coefficient should not be confused with the power-law scaling coefficient κ s .

Figure 5a shows the corresponding estimated apparent molecular weight distributions of BG90 beta-glucan for different concentrations. It is seen that at 1.0 mg/ml significant hypersharpening effects due to the combined effects of polydispersity and non-ideality are evident but for concentrations c < 0.5 mg/ml distortions of the distribution are not significant and the approximation appears to be reasonable, as is evident from the normalised distribution plots of Fig. 5(b). We recommend working at low concentrations unless specific concentration effects such as possible aggregation phenomena are being explored.

Application to beta-glucans from oats processed by roller milling and long-term storage.

To further illustrate application of the method we looked at two preparations of beta glucans extracted from rolled oats and stored for a long period (>3 yrs at 21 °C); oat flakes and flour were prepared from the Matilda variety (for details see Methods section). The sedimentation coefficient distributions were recorded at the low concentration of 0.2 mg/ml. The value of κ s is adjusted accordingly (Table 1), and the corresponding distributions shown in Fig. 6 show the broad range of sizes. The low weight average molecular weights observed in these samples may be due to residual beta-glucanase activity and prolonged storage although the distribution follows a similar log-normal form as the BG90 purified beta-glucan. This would also account for the lower molecular weight in the 200 μm vs 710 μm samples due to the increase in accessibility of the beta-glucan to the beta-glucanase. In addition, it is possible that small quantities of mainly solubilised amylose, arising from leaching of swollen, gelatinised starch granules in the original oat flour, may have contributed to the molecular weight distribution profiles.

Simple power-law application. If simply the (whole distribution) weight average molecular weights M w rather than the distributions of f(M) vs M are sought then these can be obtained directly from the sedimentation coefficient from Eq. (1b). Table 2 [START_REF] Wang | Microwave assisted dissolution of β-glucan in water -implications for the characterisation of this polymer[END_REF][START_REF] Rieder | Cereal β-glucan quantification with calcofluorapplication to cell culture supernatants[END_REF][START_REF] Beer | Molecular weight distribution and (1-3)(1-4)-β-D-glucan content of consecutive extracts of various oat and barley cultivars[END_REF][START_REF] Beer | Effect of cooking and storage on the amount and molecular weight of (1-3)(1-4)-β-Dglucan extracted from oat products by an in vitro digestion system[END_REF] shows values of some other oat beta glucans obtained in this way, again at a loading concentration of 0.2 mg/ml.

Discussion

The inherent fractionation ability of the sedimentation velocity method without the need for columns or membranes, together with the automatic removal of supramolecular impurities appears to be useful for impure or incompletely soluble materials. The Extended Fujita method has been incorporated into the SEDFIT suite of analytical ultracentrifuge algorithms, developed by Schuck [START_REF] Brown | Macromolecular size-and-shape distributions by sedimentation velocity analytical ultracentrifugation[END_REF] ) and is readily useable. The method is seen as complementary to SEC (or FFF) -MALS, which have Table 1. Scaling b, κ s values for transforming sedimentation coefficient distributions for oat beta-glucans. been established as the method of choice for many polymeric molecular weight distribution analyses, and, for polysaccharides since the first analyses in 1991 [START_REF] Horton | Gel permeation chromatography -multi angle laser light scattering characterization of the molecular mass distribution of "Pronova" sodium alginate[END_REF] . However, the latter methods are not useful in cases where non-inertness of the columns (SEC) or membranes (FFF) are suspected, there is poor solubility or the separation range has been exceeded. The Calcofluor fluorescence-labelled chromatography method 7,29 -specific for β(1-4) bonds -has also proved useful for the analysis of the molecular weight of beta-glucans in the presence of non β(1-4) containing impurities, although for distributions containing substantial amounts of high molar mass material >500,000 g/mol the Calcofluor method results in an increasing underestimation of the molecular weight [START_REF] Grundy | Impact of hydrothermal and mechanical processing on dissolution kinetics and rheology of oat β-glucan[END_REF][START_REF] Rieder | Viscosity based quantification of endogenous β-glucanase activity in flour[END_REF] . For such systems the Extended Fujita method also provides a useful complementary approach. We are now exploring the extension of our approach to other beta-glucans, most notably from barley and wheat.

Methods

Beta-glucans. Oat beta-glucan BG90 was solubilised according to the following method: 0.0280 g of Oat BG90 β-glucan was accurately weighed into the base of a pre-weighed 50 ml dry pyrex conical flask and 5 ml of 0.1 M pH 7 PBS (I = 0.1 M) buffer was added to pre-wet the sample. A further 15 ml aliquot of buffer was then added ensuring all solid material was washed below the liquid surface. A pre-weighed magnetic stirrer bar was added with a pre-weighed '3-port PTFE Duran bottle insert' (port 1-thermocouple wire, port 2-sealed injection, port3 -blank), designed to prevent evaporative loss. The mixture was then placed on a hot-plate magnetic stirrer at 80.0 °C for 1 hr. A final 5 ml aliquot of buffer was added again ensuring any solid material was washed below the liquid surface, and heated for a further 1 hr at 80.0 °C until dissolved. Oat BG90 solution was then extensively dialysed (>24 hr, 1 change) against the 0.10 M pH 7.0 phosphate-chloride buffer [START_REF] Green | The preparation of acetate and phosphate buffer solutions of known pH and ionic strength[END_REF] . It was then made up to a stock concentration, c, of 1.0 mg/ml (measured refractometrically). No significant protein contaminant was evident (from the absence of uv-absorption at a wavelength of 280 nm).

Oat flakes from the Matilda variety were obtained from Lantmännen Cerealia, Moss, Norway. Oat flour was produced at Campden BRI (UK) by milling the same oat flakes on a laboratory hammer mill (Christy, 8 Lab Mill, Christy & Norris Limited, Chelmsford, UK) fitted with either a 5.0 or 0.25 mm sieve. Two different particle size fractions were obtained using two hand sieves of 200 and 710 μm aperture. The oat flours were hydrated in PBS buffer to solubilise the beta-glucan as with BG90. Again, from the absence of uv-absorption at 280 nm, no significant protein contaminant was evident. Analytical Ultracentrifugation. Sedimentation coefficient distributions were evaluated using the Beckman Optima XL-I analytical ultracentrifuge (Beckman Instruments, Indianapolis, USA). A volume of 400 µl of beta-glucan solution and matching amounts of buffer were injected into appropriate channels of 12 mm double sector aluminium epoxy cells with sapphire windows. Solutions were centrifuged at 40000 rpm at a temperature of (20.0 ± 0.1)°C. The weight average sedimentation coefficient 's' (in Svedbergs, S) for a particular component was then corrected to standard solvent conditions (the density and viscosity of water at a temperature of 20.0 °C). A partial specific volume of 0.61 ml/g was used [START_REF] Lewis | Ultracentrifugal analysis of molecular mass of glycoproteins of unknown or ill-defined carbohydrate composition[END_REF] .

SEC-MALS.

The SEC consisted of a Postnova Analysis PN7505 degassing unit (Landsberg am Lech Germany), Shimadzu LC-10AD HPLC Pump (Shimadzu UK, Milton Keynes, UK.), fitted with a Spark-Holland Marathon Basic autosampler (Spark Holland, Emmen, The Netherlands) combined with a TSK Gel guard column (7.5 × 75 mm) and TSK Gel G5000, G6000 columns (7.5 × 300 mm) connected in series (Tosoh Biosciences, Tokyo, Japan). Light scattering intensity was detected using a DAWN ® HELEOS ™ II, light scattering photometer connected in series to a ViscoStar ® II on-line differential viscometer, an Optilab ® rEX refractive index detec- tor (Wyatt Technology Corporation, California, U.S.A.). The stock solution of 1.0 mg/ml was filtered through a 0.45 µm syringe filter (Whatman, Maidstone, England) -to remove any insoluble material or dust prior to injection -and then injected into the autosampler. A 100 µL aliquot of each solution was injected onto the columns at ambient temperature. The eluent employed was the PBS dialysate at a flow rate of 0.8 mL/min. ASTRA ™ (Version 6) software (Wyatt Technology Corporation, Santa Barbara, U.S.A.) was used to estimate the weight average M w and z-average M z molecular weights with a 1 st order Zimm extrapolation [START_REF] Gillis | Molecular weight distribution analysis by ultracentrifugation: adaptation of a new approach for mucins[END_REF][START_REF] Wyatt | Multi-angle light scattering from separated samples (MALS with SEC or FFF)[END_REF] . Because of the low solute concentrations after dilution on the columns non-ideality effects were assumed as negligible. A refractive increment (dn/dc) ~0.146 mL/g was used [START_REF] Brummer | Textural and rheological properties of oat beta-glucan gels with varying molecular weight composition[END_REF] .

  Guy A. Channell, Gary G. Adams, Yudong Lu, Richard B. Gillis, Vlad Dinu, Myriam M.-L. Grundy, Balazs Bajka, Peter J. Butterworth, Peter R. Ellis, Alan Mackie, et al.To cite this version:Guy A. Channell, Gary G. Adams, Yudong Lu, Richard B. Gillis, Vlad Dinu, et al.. Use of the Extended Fujita method for representing the molecular weight and molecular weight distributions of native and processed oat beta-glucans. Scientific Reports, 2018, 8, 10.1038/s41598-018-29997-0. hal-03232013

Figure 2 .

 2 Figure 2. Analytical ultracentrifugation of oat beta-glucan BG90. (a) Sedimentation coefficient distribution plots g(s) vs s in phosphate-chloride buffer pH = 6.8, I = 0.10 M, at 3 serial dilutions from 1.0 mg/ml. A rotor speed of 40000 rpm was used. (b) Reciprocal plot of s versus concentration, fitted to (1/s) = (1/s°).(1+ k s c)where k s is the concentration dependence or 'Gralén' coefficient(Gralén, 1944; Harding & Johnson, 1985). From the fit a value of s° = (4.82 ± 0.10)S and k s = (420 ± 40) ml/g are obtained.

Figure 3 .

 3 Figure 3. SEC-MALS of oat beta-glucan BG90. (a) Elution profile with the beta-glucan peak limits selected in grey. Blue line: refractrometric (concentration) signal. Red line (light scattering signal recorded at a scattering angle of 90°). Both profiles normalized to a maximum of 1.0. (b) Mark Houwink-Kuhn Sakurada (MHKS) plot of intrinsic viscosity [η](V e ) values versus molecular weight M w (V e ) corresponding to elution volume values V e within the marked limits of (a). Fit parameters shown in the inset.

Figure 4 .

 4 Figure 4. Molecular weight distribution f(M) vs M for oat beta-glucan BG90. After transformation from the g(s) vs s distribution for c = 0.125 mg/ml (Fig. 2a), with coefficients b = 0.455 and κ s = 0.01908.

Figure 5 .

 5 Figure 5. Apparent molecular weight distributions for oat beta-glucan BG90. (a) obtained at 0.125 mg/ml, 0.25 mg/ml, 0.5 mg/ml and 1 mg/ml; (b) as (a) but normalized so the maximum value for f(M) = 1.0.

Figure 6 .

 6 Figure 6. Molecular weight distribution f(M) vs M for beta-glucans in two roller milled and stored (3 years at 21 °C) oat samples (Matilda variety). Loading concentration c = 0.2 mg/ml. b = 0.455, κ s = 0.01037. Inset: corresponding sedimentation coefficient distribution. Black line and squares: 200 μm aperture sieve used in the processing. Red line and circles: 710 μm.

  & coworkers (see Harding et al. 2011 8 , Brown & Schuck

Table 2 .

 2 Sedimentation coefficients of oat beta-glucans from the Oatwell 32 series (0.2 mg/ml), and corresponding molecular weights a .

a Evaluated from Eq. (1b). b This may represent part of the supra-molecular aggregate or non-beta-glucan material.

SCieNtiFiC REPORTS | (2018) 8:11809 | DOI:10.1038/s41598-018-29997-0

Acknowledgements

This work was supported by the Biotechnology and Biological Sciences Research Council [grant numbers BB/ L025477/1 and BB/L025272/1] to S.E.H., G.G.A., G.C., A.M., M.M.L.G., B.B., P.B. and P.E. We thank Frédéric Prothon of Swedish Oat Fiber AB (Bua, Sweden) and Lantmännen Cerealia (Moss, Norway) for kindly supplying the samples. S.B. acknowledges funding from the Foundation for the Research Levy on Agricultural Products (Research Council of Norway Grant 262300).

Author Contributions

Additional Information

Competing Interests: The authors declare no competing interests.

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Jeanne Combes

Erika Clavijo Rivera
email: erika.clavijorivera@univ-nantes.fr

Violaine Athès-Dutour

Clémentine Fojcik

Florent Allais
email: florent.allais@agroparistech.fr

Marwen Moussa

Tiphaine Clément
email: tiphaine.clement@inrae.fr

Violaine Athès

Solvent selection strategy for an

Keywords: Solvent extraction, in situ/in stream product recovery, p-hydroxycinnamic acids, distribution coefficient, biocompatibility

Introduction

p-Hydroxycinnamic acids (p-HCAs) are widely used for their biological properties such as antioxidant [START_REF] Graf | Antioxidant potential of ferulic acid[END_REF]- [START_REF] Mathew | Reactivity of phenolic compounds towards free radicals under in vitro conditions[END_REF], anti-UV [START_REF] Saija | In vitro and in vivo evaluation of caffeic and ferulic acids as topical photoprotective agents[END_REF]- [START_REF] Dean | Plant Sunscreens in the UV-B: Ultraviolet Spectroscopy of Jet-Cooled Sinapoyl Malate, Sinapic Acid, and Sinapate Ester Derivatives[END_REF], anti-inflammatory [START_REF] Chao | Anti-inflammatory and anti-coagulatory activities of caffeic acid and ellagic acid in cardiac tissue of diabetic mice[END_REF], [START_REF] Pragasam | Immunomodulatory and Antiinflammatory Effect of p-Coumaric Acid, a Common Dietary Polyphenol on Experimental Inflammation in Rats[END_REF] and antimicrobial activities [START_REF] Baranowski | INHIBITION OF Saccharomyces cerevisiae BY NATURALLY OCCURRING HYDROXYCINNAMATES[END_REF]- [START_REF] Lou | p-Coumaric acid kills bacteria through dual damage mechanisms[END_REF]. As a result, they are considered as high value molecules and precursors of other molecules of interest for cosmetic [START_REF] Taofiq | Hydroxycinnamic Acids and Their Derivatives: Cosmeceutical Significance, Challenges and Future Perspectives, a Review[END_REF], pharmaceutical and food industries. Moreover, recent developments in polymers present p-HCAs as building blocks of interest for the plastics industry [15]- [START_REF] Fonseca | Cinnamic acid derivatives as promising building blocks for advanced polymers: synthesis, properties and applications[END_REF]. p-HCAs are plant and fungi secondary metabolites. They can be produced by chemical synthesis such as Knoevenagel-Doebner condensation from p-hydroxybenzaldehydes [START_REF] Peyrot | Proline-Mediated Knoevenagel-Doebner Condensation in Ethanol: A Sustainable Access to p-Hydroxycinnamic Acids[END_REF] or extracted directly from plants using aqueous solution of ethanol, methanol and acetone as extractants [START_REF] Velioglu | Antioxidant Activity and Total Phenolics in Selected Fruits, Vegetables, and Grain Products[END_REF]- [START_REF] Do | Effect of extraction solvent on total phenol content, total flavonoid content, and antioxidant activity of Limnophila aromatica[END_REF]. Nevertheless, those techniques present many drawbacks such as the reagents cost, the availability of natural raw materials at low cost and large quantities, the low content of such molecules in the biomass, the use of hazardous solvents and the production of organic waste. On the other hand, synthetic biology and fermentation technologies showed promising results in producing p-HCAs using different simple carbon substrate such as glucose. Hence, there is an upsurge interest for the biotechnological of p-HCAs using microorganisms [START_REF] Vargas-Tah | Production of Cinnamic and p-Hydroxycinnamic Acids in Engineered Microbes[END_REF], [START_REF] Liu | Rewiring carbon metabolism in yeast for high level production of aromatic chemicals[END_REF].

The viability of biotechnological p-HCAs production depends not only on the fermentation step, but also on the development of a clean and efficient downstream process, on its technical feasibility and its economic viability. In this paper, we are interested in the production of pcoumaric acid (p-hydroxycinnamic acid, p-CA), the precursor of other p-HCAs (e.g., caffeic acid, ferulic acid, sinapic acid) and of various valuable secondary metabolites, using a modified strain of Saccharomyces cerevisiae (S. cerevisiae), ABG010 (details in section 2.4.1).

Microbial p-HCAs production still faces several challenges. First, p-CA and other p-HCAs are slightly soluble in water (0.78 g.L -1 for ferulic acid and 0.98 g.L -1 for caffeic acid, at 25 °C) [START_REF] Mota | Aqueous Solubility of Some Natural Phenolic Compounds[END_REF].

As no water solubility value has yet been published for p-CA, we determined it experimentally and found 0.838 ±0.003 g.L -1 at 30 °C. Their hydrophilic-lipophilic features can be deduced from the partition coefficient, usually measured between octanol and water (Ko/w) [START_REF] Sangster | Octanol-Water Partition Coefficients of Simple Organic Compounds[END_REF].

Databases or estimation software can be used to find or predict Ko/w in order to get an idea about the solute nature. For p-CA, Log(Ko/w) is 1.59 when estimated using KOWWIN TM v 1.68, indicating its lipophilicity. As the fermentation medium is an aqueous-based solution, the hydrophobicity of p-CA is a production limiting factor. Secondly, as stated above, p-HCAs such as p-CA have anti-microbial properties that could harshly limit their production by microorganisms. [START_REF] Baranowski | INHIBITION OF Saccharomyces cerevisiae BY NATURALLY OCCURRING HYDROXYCINNAMATES[END_REF], studied the inhibition of a S. cerevisiae strain by p-HCAs, and observed an increase of the growth lag phase with only 100 mg.L -1 p-CA in the culture medium, and a total inhibition at 1 g.L -1 of p-CA in the culture medium [START_REF] Baranowski | INHIBITION OF Saccharomyces cerevisiae BY NATURALLY OCCURRING HYDROXYCINNAMATES[END_REF]. [START_REF] Herald | Antibacterial Activity of Selected Hydroxycinnamic Acids[END_REF], studied p-HCAs antibacterial activity as well but on different strains:

Escherichia coli (E. coli), Staphylococcus aureus (S. aureus) and Bacillus cereus (B. cereus).

They found that p-CA caused more than 99.9% inhibition of E. coli at 1 g.L -1 (at pH 5.0 and after 48 hours), S. aureus at 500 mg.L -1 (at pH 5.0 and after 48 hours), and B. cereus at 500 mg.L -1 (at pH 7.0 and after 9 hours) [START_REF] Herald | Antibacterial Activity of Selected Hydroxycinnamic Acids[END_REF].

Continuous removal of a fermentation product using an in situ or in stream product recovery (ISPR, also called extractive bioconversion) process allows avoiding product retro-inhibition as well as antimicrobial effect of the product if applicable, as demonstrated for several molecules produced by fermentation [START_REF] Dafoe | In situ product removal in fermentation systems: improved process performance and rational extractant selection[END_REF] such as alcohols (ethanol [START_REF] Weilnhammer | Continuous fermentation with product recovery by in-situ extraction[END_REF], butanol [START_REF] Roffler | In-situ recovery of butanol during fermentation[END_REF], [START_REF] Khedkar | Solvent extraction of butanol from synthetic solution and fermentation broth: Batch and continuous studies[END_REF]) and organic acids [START_REF] Gössi | In-situ recovery of carboxylic acids from fermentation broths through membrane supported reactive extraction using membrane modules with improved stability[END_REF] (3-hydroxypropionic acid [START_REF] Chemarin | Towards an in situ product recovery of bio-based 3-hydroxypropionic acid: influence of bioconversion broth components on membrane-assisted reactive extraction: Towards an in situ product recovery of bio-based 3-hydroxypropionic acid[END_REF], [START_REF] Chemarin | New insights in reactive extraction mechanisms of organic acids: An experimental approach for 3-hydroxypropionic acid extraction with tri-n-octylamine[END_REF], lactic acid [START_REF] Yabannavar | Extractive fermentation for lactic acid production[END_REF]). In the case of biotechnological production of p-HCAs, continuous product removal is expected to detoxify the fermentation medium, remove the solubility limitation and enhance the productivity. For hydrophobic molecules such as p-HCAs, produced by fermentation and thus in an aqueous medium, liquidliquid extraction using a water-immiscible solvent seems to be a promising extraction technique [START_REF] Freeman | In Situ Product Removal as a Tool for Bioprocessing[END_REF].

The keystone of a liquid-liquid extraction process is the choice of solvent. It is necessary to establish beforehand the properties that the solvent must have for the process implementation. Some solvent selection strategies for extractive fermentations have been published, using theoretical or experimental data. First, [START_REF] Kollerup | Screening and identification of extractive fermentation solvents using a database[END_REF], and then Bruce and Daugulis, presented the first systematic and comprehensive screening strategy based on predicted solvent properties [START_REF] Kollerup | Screening and identification of extractive fermentation solvents using a database[END_REF], [START_REF] Bruce | Solvent selection strategies for extractive biocatalysis[END_REF]. They, among other things, developed a computer program allowing to screen a large quantity of solvent prior to experimental assessment. Dafoe and Daugulis, 2014, presented a review on extractant selection for direct removal of target molecules from the bioreactor over the last five years, and a part of this work reviews liquidliquid extraction system using organic solvents and their selection [START_REF] Dafoe | In situ product removal in fermentation systems: improved process performance and rational extractant selection[END_REF]. In this paper, an original and simple score enabling solvent selection is proposed for an indirect removal of bioconversion product. The developed methodology is based on different criteria weighted according to their significance for the process with experimental demonstrations and assessments. This method is adaptable and applicable under process specifications and requires a pre-selection based on literature in order not to assess unreasonable solvents. For example, the first part of Grundtvig et al. selection proposition in 2018, regarding tabulated properties, could be used as so and would allow to narrow the list of pre-selected solvents [START_REF] Rosinha Grundtvig | Screening of organic solvents for bioprocesses using aqueous-organic two-phase systems[END_REF]. Some of the criteria are essential for an extractive fermentation using liquid-liquid extraction.

The first criterion is solvent biocompatibility towards the microorganism. Two toxicity mechanisms must be taken into account when using solvent to extract a fermentation product:

(i) the contact toxicity, related to the direct contact of the microorganism with the solvent, and (ii) the molecular toxicity caused by solvent saturation of the aqueous fermentation medium (i.e. solvent maximal water solubility) [START_REF] Bar | Acid Fermentation in Water-Organic Solvent Two-Liquid Phase Systems[END_REF]. Several technologies, such as membrane contactors for a membrane-based solvent extraction or two-phase partitioning bioreactor with immobilized cells, allow limiting direct contact between the solvent and the fermentation medium. However, such an approach cannot prevent molecular toxicity. Therefore, the present work will focus only on the molecular toxicity of the tested solvents. This toxicity depends on several characteristics of the solvent, such as its nature, hydrosolubility, polarity and molecular weight. Laane et al., 1985, [41], implied that Log(Ko/w) is a meaningful parameter reflecting solvent polarity that can be used to predict biocompatibility. According to the authors, solvents with a Log(Ko/w) > 4 are more likely to be biocompatible. A low hydrosolubility, a low polarity and a high molecular weight tend to favor a good biocompatibility but solvent chemical nature is also a crucial factor [START_REF] Frenz | Hydrocarbon recovery and biocompatibility of solvents for extraction from cultures of Botryococcus braunii[END_REF]. Moreover, as microorganisms have different solvent tolerances, it is therefore necessary to experimentally assess the solvent biocompatibility with the strain of interest.

The second, but equally important criterion, is the solvent capacity to extract selectively the molecule of interest. For the present application, a water-immiscible solvent is needed.

Research on microporous membrane/solvent microextraction (MPMSME), a novel analytical method used for p-HCAs, gives a good indication of which water-immiscible solvents could be a good extractant for p-CA. Two research teams screened solvents and obtained the best results with long chain alcohols such as 1-heptanol, 1-octanol, and 1-decanol [START_REF] Xing | Novel Microporous Membrane/Solvent Microextraction for Preconcentration of Cinnamic Acid Derivatives in Rhizoma Typhonii[END_REF], [START_REF] Li | Two-phase/Three-phase Hollow Fibre Liquid-Phase Simultaneous Microextraction Combined with HPLC for Analysis of Phenolic Acids and Flavonoids in Traditional Chinese Medicine[END_REF]. Other authors found hexyl acetate as the best p-HCAs extractant [START_REF] Saraji | Use of hollow fibre-based liquid-liquid-liquid microextraction and high-performance liquid chromatography-diode array detection for the determination of phenolic acids in fruit juices[END_REF]. A good p-HCAs extractant will be a polar solvent, protic or aprotic. It can be explained by the presence of the carboxyl group on p-HCAs, which confers polarity to these apolar molecules and thus a capacity to form hydrogen bonds. In order to experimentally measure the solvent's capacity to extract selectively the molecule of interest, the partition coefficient also called distribution coefficient (Log(KD)) is a suitable parameter. As p-CA is a phenolic acid that possesses 2 pKa values, pH of the aqueous phase is an important factor for the extraction success. pKa of p-CA carboxylic acid function is 4.65 and that of the phenol is 9.92 [START_REF] Benvidi | Experimental and computational study of the p K a of coumaric acid derivatives[END_REF], therefore at pH < 4.65 p-CA will be mostly in its undissociated form and less soluble in water, thus the extraction with an organic solvent would be more efficient. On the other hand, fermentation process allows a small window of working pH, depending on the microorganism, in consequence a trade-off should be made between fermentation and extraction processes and must be studied in detail. For example, S. cerevisiae, the strain used in this work, is an acidophilic microorganism, with optimal growth at pH 4.5-5 [START_REF] Reed | Yeast Technology[END_REF]. For these reasons, p-CA Log(KD) will be measured between solvents and water at four initial different pH (before the addition of p-CA): pH 1 in order to show the best case scenario for the extraction (99% of p-CA is in its undissociated form [START_REF] Ronco | Chapter 3 -Aqueous-Organic Biphasic Systems: Extraction of Organic Compounds[END_REF]), pH 3 to further assess the effect of more acidic pH than p-CA pKA, pH 7 as the reference and pH 11.55, conversely, to show the effect of a pH higher than p-CA pKa on the extraction (water at pH 11.55 with addition of 400 mg.L -1 p-CA equate to a pH of 7.09, more information in section 2.2). Assessments at these pHs will show the beginning of the inflection point of the sigmoid function of extraction as function of pH due to deprotonation of p-CA carboxylic acid moiety.

The last equally essential criterion is material compatibility. The solvent must be harmless to process equipment. Besides those three criteria, in order to rank solvents, other criteria such as safety, sourcing and price were chosen. These criteria are important but non-essential for the process implementation.

To the best of our knowledge, no work has been published on p-HCAs separation from an engineered strain of S. cerevisiae fermentation. This paper presents an original strategy to select solvents for an ISPR process to recover p-CA produced by an engineered microorganism, considering the aforementioned criterion. The results presented here are potentially relevant to work on other p-HCAs due to their similarity in structure and pKas, and this strategy is applicable with adjustments for other extractive bioconversions.

Materials & Methods

Solvent selection score

Beforehand, literature search was made in order to get indications of which solvents are good candidates for p-CA extraction so as to reduce the initial solvents number (around fifty articlesbooks were studied). Based on this literature (presented partially in section 1), 10 solvents were thus selected: 9 good potential candidates, namely 2-methyltetrahydrofuran (99% form Alfa Aesar), ethyl acetate (>99.7% from Sigma-Aldrich), hexyl acetate (99% from Sigma-Aldrich), n-butyl acetate (>99% from Alfa Aesar), 1-heptanol (98% from Acros organics), 1octanol (99% from Acros Organics), 1-decanol (>98% from Alfa Aesar), 1-dodecanol (>99% from TCI) and oleyl alcohol (80-85% from Alfa Aesar). The last solvent selected as a negative control for the extraction capacity was n-hexane (>99% from Merck KGaA) as it is an apolar solvent.

The first step was an exclusion step. Essential criteria, namely extraction performance, biocompatibility and material compatibility were assessed in this step. For each essential criterion, the solvent is graded between 0 and 1.

For biocompatibility and material compatibility, solvents get 0 or 1: 0 if the solvent does not meet the expectation and 1 if it does. A grade equal to zero forces the solvent to be removed from the candidates list. In consequence, it will not be studied further. Biocompatibility was determined experimentally (cf. section 2.3) and for material compatibility, information was obtained from suppliers. For this work, the equipment consisted in a membrane contactor pilot plant manufactured by Seprosys (La Rochelle, France) with 3M™ Liqui-Cel™ EXF-2.5x8 membrane contactor. The pilot plant materials are stainless steel, polytetrafluoroethylene, polypropylene and polyethylene.

For the extraction performance, grades are between 0 and 1 as a result of experimental distribution coefficient determination (cf. section 2.2). Solvents are compared based on results obtained at pHexp 3.66 and the score 1 is fixed with the best solvent on extraction performance.

Then, the other solvents grades are expressed as ratio of their Log(KD) to the best one. As for the first two criteria, a grade equal to zero forces the solvent to be withdrawn from the list of candidates.

The next step consists in the weighting of extraction performance score and not essential criteria depending on their importance for the present application, particularly on process and production requirements. Table 1 presents this second step with the non-essential criteria and the defined weighting, with a maximum total score equal to 50. As for extraction performances, the best score, here 20, is fixed with the best distribution coefficient obtained. Then, for the other solvents, rates are expressed as percentage regarding the best one. For the non-essential criteria, scores are set based on scientific literature. For measuring solvent safety, Globally Harmonized System of classification and labelling of chemicals (GHS) was used [START_REF]Economic Commission for Europe, Globally Harmonized System of Classification and Labelling of Chemicals (GHS[END_REF]. More details on the method of calculation are presented in experimental supplementary information.

Extraction performance: p-CA distribution coefficient determination in water and in real fermentation medium

The distribution coefficient measurement method used in this work is based on shake-flask method [START_REF] Sangster | Octanol-Water Partition Coefficients of Simple Organic Compounds[END_REF]. p-CA was purchased from TCI (Tokyo Chemical Industry, purity: ≥ 98.0%).

For distribution coefficient determination, initial pH of water was first adjusted to 1.03, 3.01, All experiments, for each pHexp and each solvent were made in triplicate. 2.00 ±0.03 mg of p-CA were added in a Pyrex falcon tube with 5 mL of saturated water at pHexp and 30 °C (to reach an initial concentration of p-CA of 400 mg.L -1 ). Then, 5 mL of solvent was added. The mixture was manually shaken for 2 minutes and then let to settle for a minimum of 3 hours at 30 °C and atmospheric pressure to reach equilibrium. For oleic alcohol experiments, a centrifugation was carried out at 4000 rpm and 30 °C for 10 min in order to perfectly separate both phases.

For the determination of distribution coefficient in real fermentation media (section 3.4), the method was the same but instead of water, sterilized fermentation media were used. YEPD was made of 20 g.L -1 yeast extract, 10 g.L -1 peptone, 20 g.L -1 D-glucose and yeast nitrogen base without amino acids (YNB), purchased from Sigma-Aldrich. For YEPD, pHi was 6.81 and pHexp was 6.50. For YNB, pHi was 5.40 and pHexp 4.05.

The organic phase was discarded and the resulting p-CA concentration in the aqueous phase was measured (see section 2.5). Then, Log(KD) was calculated as follows (equation 1):

Log(𝐾 & ) = Log( [𝑝𝐶𝐴] /0 1 -[𝑝𝐶𝐴] /0 30 [𝑝𝐶𝐴] /0 30 ) (1)
where [𝑝𝐶𝐴] /0 1 is the initial concentration of p-CA in the aqueous phase and [𝑝𝐶𝐴] /0 30 is the p-CA concentration in the aqueous phase at equilibrium. Concentrations were measured according to analytical method presented in section 2.5.

Statistical analysis for distribution coefficients

An analysis of variance (ANOVA) was conducted for distribution coefficient results for each solvent. The achievement of the one-way ANOVA requirements, the normal distribution of the residuals and the homogeneity of variance, were tested by the Shapiro-Wilk's and the Bartlett's tests, respectively. In the cases where statistical significance differences were identified, the dependent variables were compared using Tukey HSD (honestly significant difference) test (p<0.05).

Biocompatibility experiments with an engineered S. cerevisiae

strain (ABG10)

ABG010: Engineered S. cerevisiae relevant characteristics

Saccharomyces cerevisiae S288C [START_REF] Mortimer | Genealogy of Principal Strains of the Yeast Genetic Stock Center[END_REF], uracil, tryptophan and leucine auxotrophic was used.

This yeast model strain was engineered in order to produce p-CA with optimized performances.

Towards this ultimate objective, ARO10 (YDR380W) and Thi3(YDL080C) genes are deleted.

Biocompatibility assessment method

The studied strain, ABG010, producing p-CA was maintained at -80 °C in 30% glycerol-YEPD medium. ABG10 was inoculated in a 250 mL baffled flask containing 20 mL of YEPD medium (20 g.L -1 yeast extract, 10 g.L -1 peptone, 20 g.L -1 D-glucose), and cultivated overnight in a Thermo MaxQ 4000 shaker set at 30 °C and 220 rpm.

This culture was used to inoculate two new 250 mL baffled flasks to an initial OD620 nm of 0.1: one containing 20 mL of YEPD (the control) and one with 20 mL of solvent saturated YEPD.

The flasks were grown in the same conditions as described above. Four samples were taken for each flask over time.

For each sample, membrane cells integrity and esterasic activity were analysed by flow cytometry using a double cell staining with propidium iodide (PI) and carboxyfluorescein diacetate succinimidyl ester (CFDA-SE). This method uses the particularity of PI to fix to DNA after penetrating damaged cell membranes. On the opposite, unaffected cells will not integrate In addition, optical density at 620 nm was measured for each sample and for the last sample, (at 24 h) p-CA production was measured (see section 2.5).

Analytical methods:

p-CA concentrations in aqueous phase were determined by high-performance liquid chromatography (HPLC) (Thermo scientific, Ultimate 3000) coupled with a diode array detector (DAD) using an accucore aQ C18 column (100x3 mm, Thermo scientific). The injection volume was 2.5 μL, the oven temperature was 48 °C and the flow rate was 0.8 mL.min -1 . The elution method was a 10 min gradient with acetonitrile (A) and formic acid 0.1% (B) as mobile phases: 0 min: 2% of A -98% of B, 3 min: 10% of A -90% of B, 8 min: 30% A -70% of B and 9 min: 2% of A -98% of B. The retention time of p-CA was 4.73 min. A p-CA calibration curve with 5 points was made using commercial p-coumaric acid (≥ 98% from TCI). Samples were diluted

(1:1) in acetonitrile and filtered using regenerated cellulose 0.2 µm filters.

Results and discussion

Extraction performance and biocompatibility of ten solvents were experimentally assessed.

Results are presented in Table 2 and discussed below. *** at 20 °C

Solvent extraction performances

Distribution coefficients of p-CA in five alcohols are presented in Figure 1 and in Table 2. Alcohols are protic solvents, p-CA experimental distribution coefficients (Log(KD)) for pHexp lower than p-CA pKa (i.e. 4.64) obtained were strictly above 1, which confirms that they are good candidates for the extraction. As expected, acidic pHexp led to higher distribution coefficients due to protonation of p-CA carboxylic acid moiety. It must be emphasized that there were no statistically significant differences between pHexp 1.01 and pHexp 2.98 overall and that p-CA Log(KD) at pHexp 3.66 while statistically lower, was still above 1. For pHexp 7.09 p-CA Log(KD) were negatives, meaning that p-CA distribution is predominant in the aqueous phase, that can be explained by the large proportion of dissociated p-CA at this pH. Broadly, p-CA distribution coefficients decreased when pH increased due to the proportion of undissociated p-CA, but the trend is relatively weak at pH lower than p-CA pKa. It seems that alcohols with longer carbon chains have lower Log(KD) and the effect of pH on the extraction yield is smoothed (e.g. no significant differences between pHexp 1.01, 2.98 and 3.66 Log(KD) for 1decanol). The reduced capacity of solvents to extract p-CA with the increase of the carbon chain length may be explained by the difficulty to form the solvation shell around p-CA due to steric hindrance. The smoothing of the pH effect could be a result of the solvent hydrosolubility diminution. This hypothesis is discussed further below in this section.

pH is a parameter influencing not only the extraction performance but also the fermentation process. Thus, it must be considered and discussed. At pH 1.01, S. cerevisiae will not grow and at pH 2.98 its growth will be affected as its optimal growth pH is 4.5-5 [START_REF] Reed | Yeast Technology[END_REF]. As shown in Figure 1, for pHexp lower than p-CA pKa, pH does not seem limiting for the extraction performance with protic solvents; whereas for pHexp higher than p-CA pKa, a liquid-liquid extraction may not be suitable as an ISPR. If a pH higher than p-CA pKa is desired for the fermentation process, a reactive liquid-liquid extraction using tertiary amine may be a good alternative [START_REF] Krzyżaniak | Novel extractants for the recovery of fermentation derived lactic acid[END_REF], [START_REF] Chemarin | Recovery of 3hydroxypropionic acid from organic phases after reactive extraction with amines in an alcohol-type solvent[END_REF]. Here, as S. cerevisiae can tolerate pH lower than 4.64 (p-CA pKa), pH would be chosen as a compromise between extraction and fermentation needs, for example the process could be conducted at pH 4.5.

Aprotic and polar solvents were tested as extractants, among them three esters (ethyl acetate, butyl acetate, hexyl acetate) and 2-methyltetrahydrofuran (2-MeTHF). Obtained p-CA distribution coefficients are presented in Figure 2 and in Table 2. As for the tested alcohols, p-CA Log(KD) of the tested esters, for pHexp more acidic than p-CA pKa, are all strictly above 1 and decrease broadly as the pH increases. Solvents such as esters, aprotic and polar, are good candidates for p-CA extraction and these results are consistent with the work of Saraji and Mousavi, 2010 [START_REF] Saraji | Use of hollow fibre-based liquid-liquid-liquid microextraction and high-performance liquid chromatography-diode array detection for the determination of phenolic acids in fruit juices[END_REF] presented in section 1. In this case, the length of carbon chain is not associated with smoothing of pH effect since with ethyl acetate, the ester with the shortest carbon chain tested, there is no statistically significant differences between pHexp 1.01 and pHexp 2.98 Log(KD). Results for esters at pHexp 7.09 could not be obtained due to saponification reaction. This could limit their use, depending on the process.

The last aprotic and polar solvent tested was 2-MeTHF. Experimental results (see Figure 2 and Table 2) showed that this solvent is the best p-CA extractant at pHexp 1.01 but on the contrary, the worst at pHexp 3.66 compared to the other tested aprotic solvents. For this solvent, pH has an important effect on p-CA distribution, the hydrosolubility of 2-MeTHF may be the reason. The solubility of 2-MeTHF in water at 20 °C is 140 g.L -1 (see Table 2), the highest hydrosolubility among the tested solvents. This could increase p-CA affinity with the aqueous phase and the differences between the different pH values could be due to different 2-MeTHF hydrosolubility with each tested pH. A high hydrosolubility suggests also a high solvent loss, which could impact the economic viability of the process. These results are consistent with those obtained for long carbon chain alcohols, for which the impact of pH might be limited by the low hydrosolubility of the solvent. Similarly to alcohols, for pHexp higher than p-CA pKa, p-CA distribution is predominant in the aqueous phase due to the proportion of undissociated p-

CA

To conclude on aprotic and polar solvent, as discussed for the protic ones, fermentation must be taken into consideration at this step. As observed for alcohols, in the case of esters, for pHexp lower than p-CA pKa, pH does not seem to be a limiting factor for the extraction, but for more alkaline pHexp, esters are not good candidates as saponification reaction can occur and this implies a solvent loss and a more problematic implementation of the process. For 2-MeTHF, although there is a high pH effect, at a pHexp of 3.66: the Log(KD) still higher than 0 and so its score will be higher than 0 (see Table 3). For a process for which acidic pH is not a limit, 2-MeTHF can be a good candidate.

As expected, the last tested solvent, n-hexane, was not suitable for p-CA extraction due to its apolarity. The determination of Log(KD) was not feasible due to p-CA crystallisation at the water/hexane interface. This phenomenon may be explained by p-CA carboxylic acid moiety that imparts a localised polarity to this phenolic and apolar molecule. Indeed p-CA needs an organic solvent with a certain polarity to be extracted. n-Hexane and water have each an affinity for a part of the molecule (hexane: aromatic ring, water: carboxylic acid moiety) and this could explain the appearance of this interfacial phenomenon. As a result, p-CA accumulates at the interface and when p-CA saturation is reached, the accumulation leads to crystallisation of the molecule. Even though this phenomenon is interesting, it raises the question of the implementation of an ISPR using hexane as the extractant phase for the recovery of p-CA.

The extraction performance scores are based on p-CA Log(KD) at pHexp 3.66, used as the reference pH and corresponding to a pHi of 7.01. The Table 3 presents the solvent score for this criterion, taking as a reference 1-heptanol, the solvent with the highest p-CA Log(KD) at pH 3.66. Alcohols with a low molecular weight have the best scores, followed by esters and high molecular weight alcohols having similar score, then 2-MeTHF and n-hexane with a score of 0. Accordingly, n-hexane is the only solvent that will be excluded from the ranking for the first selection step due to its very poor extraction performance results.

The distribution of glucose, substrate of the fermentation process, was assessed with each solvent. It was not extracted by any of them (data not shown).

Solvent biocompatibility:

In order to determine whether solvents are biocompatible or not toward the strain of interest, the growth behavior of S. cerevisiae ABG10 in each solvent saturated cultivation medium was compared to the one obtained in the control medium (without solvent saturation). Strain growth with or without solvent saturation were compared according to four parameters measured after medium inoculation: (1) density of cells (measured by flow cytometry and correlated with optical density at 620 nm), (2) the produced p-CA concentration in the supernatant, (3) the percentage of PI labelled cells (membrane damaged cells), and ( 4) the percentage of CFDA-SE labelled cells (viable, enzymatically active cells). Figure 3 presents an example of growth parameters comparison between the oleyl alcohol saturated aqueous medium and the control medium. In this example, the strain growth as well as the cell viability are similar, which leads to the conclusion that oleyl alcohol is biocompatible towards S. cerevisiae ABG10. On the opposite, growth parameters presented on Figure 4 show that no growth occurs in 2-MeTHF saturated medium (cells density remains stable over 24h) compared to the control.

There is a high proportion of PI-labelled cells in the presence of 2-MeTHF directly after inoculation, which reveals the high toxicity of this solvent to the strain in this cultivation configuration. The results obtained are consistent with a toxicity towards the strain of interest. Indeed, the conditions leading to low biomass production were also the ones exhibiting lower p-CA production in comparison with the control fermentation, as well as higher PI labelled cells (nonviable). As for the CFDA labelling, toxicity is not as obvious as for PI labelling if only chart C from Figure 5 is considered. This can be explained by the nature of results format, labelling are presented as percentage of cells population and so CFDA-SE labelling has to be compared with cell density and PI labelling. The percentage of viable cells will attest of the biocompatibility of a solvent solely if the density of cells and the percentage of PI labelled cells are similar to the control.

Among the ten assessed, four solvents were found biocompatible with the strain of interest: nhexane, oleyl alcohol, butyl acetate and hexyl acetate. Contrary to the results obtained in this work, Minier and Goma, 1982 [65], found dodecanol as a biocompatible solvent in an extractive fermentation with S. cerevisiae UG5, however S. cerevisiae ABG10 seems highly sensitive to this solvent. Likewise, in this work, n-Hexane was found biocompatible whereas Kollerup and Daugulis, 1985 [START_REF] Kollerup | Screening and identification of extractive fermentation solvents using a database[END_REF], found it toxic toward S. cerevisiae NCYC716. In contrast, oleyl alcohol is a well-known and widely used biocompatible solvent for different strains [66]- [START_REF] Daugulis | Continuous fermentation of high-strength glucose feeds to ethanol[END_REF], which is in accordance with this study. These results highlight that solvent biocompatibility is strain dependent and must be assessed experimentally.

As stated in section 1, it can be observed that there is broadly a trend towards biocompatibility increasing with solvent Log(Ko/w) (see in Table 2) but Log(Ko/w) has limits as a biocompatibility indicator. 1-Decanol and 1-dodecanol have a Log(Ko/w) close to 4 but in Figure 5 it can be seen that they have a significant negative impact on both the strain development and production.

The solvent chemical nature seems to have an important role in the solvent toxicity. Butyl acetate and hexyl acetate esters Log(Ko/w) are 1.85, 2.83, respectively, lower than those of 1decanol and 1-dodecanol, though the strain production is not impacted when growing in a saturated media with these solvents. Furthermore, the effect on the growth and viability of the cells seems negligible.

Organic solvents tend to increase microorganism membrane fluidity. The amount of solvent solubilised in the fermentation medium is a key factor, and as demonstrated by Osborne et al., 1990 [69], it may be the solvent volume in the membrane that is critical, and when it is reached, it leads to loss of bioactivity. Solvents with high hydrosolubility reach more easily the critical threshold leading to complete loss of activity. Furthermore, it is dependent on solvent chemical nature, and its distribution between the membrane and the fermentation medium. This can explain the differences observed between the different class of solvent and between strains.

Therefore, at this step, only four solvents get a score of 1 for biocompatibility criterion, namely hexane, oleyl alcohol, butyl acetate and hexyl acetate. 4 the ones with a total score strictly higher than 2, in bold text).

Outcome for the exclusion step for the solvent selection

Assessment of the extraction performances in fermentation media

p-CA distribution coefficient was measured between the three previously selected solvents and two fermentation media in order to go further in the assessment and process implementation.

One complex medium, YEPD and one defined synthetic medium, YNB were assessed. Figure 6 presents the p-CA distribution coefficients obtained.

These results are used in the second part of the strategy to rank the selected solvents in the next section. due to the proportion of undissociated acid. YEPD is a strongly buffered medium as pHi (6.81) and pHexp (6.50) illustrate, whereas YNB has a lower buffering capacity. This characteristic of YNB can explain partially the good capacity of extraction of solvents with this medium, even if pHi were not the same at first. The assessment of p-CA distribution between oleyl alcohol and YEPD was very arduous due to a strong emulsion and showed the limits of a complex medium for the process implementation.

As assumed by Zhang et Greasham, 1999 [START_REF] Zhang | Chemically defined media for commercial fermentations », A@ppl[END_REF], these data indicate that the less complex the fermentation medium is, the the product recovery, this would be the case of p-CA recovery. In biotechnological production there is always a compromise to make between the fermentation process and the downstream process to achieve the highest production and working on the definition of an optimized and better-defined medium is one of the potential strategies. Medium complexity serves the interests of the fermentation, by providing biosynthetic precursors to the strain while allowing energy savings. Those media are inexpensive ones, and this explains their large industrial use. Nevertheless, with the increase of research in fermentation biotechnology, there is a growing interest in defined medium, allowing replication and simpler product recovery.

Final scores

The final scoring step for the solvent selection is presented in Table 5. For the implementation of extraction performance criteria, a score of 20 was given to hexyl acetate, having the highest distribution coefficient for p-CA extraction when using YNB medium (Figure 6). The score of the two other solvents were obtained as a function of the hexyl acetate score and their corresponding distribution coefficient (cf. section 2.1). Safety and sourcing scores were given using the following information: oleyl alcohol is considered as the safest of the three solvent candidates [START_REF]Economic Commission for Europe, Globally Harmonized System of Classification and Labelling of Chemicals (GHS[END_REF] and is produced from animal and vegetable fats and oils.

Butyl acetate can be bio-sourced from n-butanol and acetic acid, two chemicals that can be produced by fermentation. Hexyl acetate is produced from acetic acid and 1-hexanol.

Nevertheless, 1-hexanol is synthesized generally from ethylene, which is mostly produced by steam cracking of hydrocarbons, so petro-sourced. Price score was given following suppliers information. According to the total score (Table 5), the most suitable solvent for an ISPR process to continuously extract p-CA from a fermentation medium having the YNB characteristics, is the oleyl alcohol followed by butyl acetate and then hexyl acetate.

Conclusion

The reported strategy allows a quick, simple and adaptable selection of solvents for an ISPR coupled with a fermentation process limited by end-product accumulation. Methodologies to

Table 1 :15 10 5 For

 15 Weighting of extraction performance and non-essential criteria Criteria Implementation of extraction performance Safety Sourcing Price Weighting 20 implementation of extraction performances, the results are experimental ones: solvents are compared on p-CA distribution results with yeast nitrogen base without amino acids (YNB).

ARO4

  (NP_009808) and ARO7 (NP_015385) were amplified by PCR from the genomic DNA of S. cerevisiae and mutated to resist to feedback inhibition (FBR: Feed Back Resistance)[START_REF] Gold | Metabolic engineering of a tyrosine-overproducing yeast platform using targeted metabolomics[END_REF].TAL, PAL, C4H and Cpr1 were optimized for yeast codon usage bias then synthesized by DC Biosciences, Dundee, UK. Characteristics of the final strain ABG010 are: MATα, ura3-52, trp1∆63, leu2∆1, GAL2+, LEU2+, aro10Δ0, thi3Δ0, FAT3~MTR2::(ARO4 fbr -ARO7 fbr -RgTAL)::URA3, NCA3~ASF1::(AtPAL-AtC4H-CrCPR1)::TRP1. ABG010 was engineered and provided by Abolis, France.

  PI and will not display any PI related fluorescence. On the other hand, CFDA-SE penetrates viable cells and becomes fluorescent when cleaved by esterase activity. Esterases are ubiquitous enzymes that are used here as a marker of cells viability. Yeasts suspension was diluted to approximately 10 6 cells.mL -1 in pH 4 McIlvaine buffer. One millilitre of the cell suspension was stained by adding 10 µL of 1 mg.mL -1 commercial PI solution (Sigma-Aldrich) and 10 µL CFDA-SE (Chemchrom V8, Biomérieux) diluted ten times in acetone. After incubation for 10 min at 30 °C, the stained suspension was analysed with a Sysmex CyFlow® Space flow cytometer (Partec, France) equipped with an argon 488 nm laser and four filters: a forward-angle light scatter (FSC), a side-angle light scatter (SSC), both combined with a diode collector, a 536 nm band-pass filter (526 to 546 nm) to collect the green fluorescence of carboxyfluorescein (FL1 channel) and a 670 nm band-pass filter to collect the red fluorescence of PI (FL2 channel) with photomultiplier tubes. Data acquisition, instrument control and data analysis were controlled and performed with FloMax® software (version 2.9, Partec, France).

  standard deviation. Different letters (a, b) correspond to mean values statistically different within each row by analysis of variance (ANOVA) and Tukey HSD (P< 0.05). **Calculated from kinematic viscosity (m 2 .s -1 )

Figure 1 :

 1 Figure 1: p-coumaric acid distribution coefficient between alcohols and water at 30 °C and different pHexp. Different letters correspond to mean values statistically different within each pattern by analysis of variance (ANOVA) and Tukey HSD (p < 0.05).

Figure 2 :

 2 Figure 2: p-CA distribution coefficient between aprotic and polar solvents and water at 30 °C and different pHexp. Different letters correspond to mean values statistically different within each pattern by analysis of variance (ANOVA) and Tukey HSD (p < 0.05).

Figure 3 :

 3 Figure 3: Oleyl alcohol (OA) biocompatibility assessment using cells labelling and flow cytometry. Numbers on the pie chart indicate the percentage of enzymatically active cells.

Figure 4 : 5 .

 45 Figure 4: 2-methyltetrahydrofuran (2-MeTHF) biocompatibility assessment using cells labelling and flow cytometry. Numbers on the pie chart indicate the percentage of enzymatically active cells.

Figure 5 :

 5 Figure 5: Results of solvents biocompatibility with the strain of interest.

Figure 6 :

 6 Figure 6: p-CA distribution coefficients at 30 °C with two different fermentation media (YEPD: complex medium, pHexp = 6.50; YNB: defined synthetic medium, pHexp = 4.05). Log(KD) values for pHexp 3.66 and pHexp 7.09 aqueous phase are shown for comparison purposes.

  7.01 and 11.56 with solutions of HCl 1 M and KOH 1 M. Solvents were pre-saturated with water and water was also pre-saturated with the respective solvent at the initial pH (pHi) and 30 °C.It is noteworthy to mention that, for ethyl acetate, butyl acetate and hexyl acetate, experimental results for initial pH of 11.56 could not be accurately obtained due to saponification reaction during the pre-saturation, therefore data are not presented. The pre-saturation step was

performed in order to avoid volume bias and for this purpose initial pH was used. This means that initial pH do not correspond to those of water with p-CA solubilized inside. Instead, corresponding pH with 400 mg.L -1 of p-CA solubilized are : 1.01 (for pHi 1.03), 2.98 (for pHi 3.01), 3.66 (for pHi 7.01) and 7.09 (for pHi 11.56) and thereafter, those pH will be used as they are more representative of the process and referred to as pHexp. The use of buffer was rejected as it could affect the distribution of p-CA by a salting-out effect.

Table 2 :

 2 Solvent physico-chemical properties and distribution coefficients

	Solvent	CAS number	Molecula r weight (g.mol -1 )	Density (g.cm -3 ) at 25 °C	Dynamic viscosity (mPa.s) at 25 °C	Solubility in water (g.L -1 ) at 25 °C	Log (Ko/w)	pHexp 1.01	Log (KD) solvent-water of p-coumaric acid: mean ± SD* pHexp pHexp 2.98 3.66	pHexp 7.09
	2-MeTHF	96-47-9	86.13	0.849	0.475**	140***	1.35	2.22 ±	1.83 ±	0.43 ±	-0.36 ±
				[7]	[52]	[53]		0.08 a	0.24 b	0.14 c	0.04 d
	Ethyl acetate 141-78-6	88.11	0.895	0.430	68.81	0.86	1.70 ±	1.64 ±	1.61 ±	-
				[54]	[54]	[55]		0.01 a	0.04	0.02 b
									a,b
	Butyl acetate 123-86-4	116.16	0.875	0.662	8.330	1.85	1.65 ±	1.57 ±	1.43 ±	-
				[54]	[54]	[55]		0.01 a	0.03 b	0.01c
	Hexyl	142-92-7	144.22	0.868	1.11	0.5105	2.83	1.69 ±	1.23 ±	1.23 ±	-
	acetate			[56]	[56]	[55]		0,03 a	0.06 b	0.09 b
	1-heptanol	111-70-6	116.21	0.819	6.00	1.797	2.31	2.07 ±	1.98 ±	1.76 ±	-0.17 ±
				[57]	[57]	[55]		0.02 a	0,11 a	0,05 b	0.03 c
	1-octanol	111-87-5	130.23	0.822	7.60	0.5353	2.81	1.84 ±	1.88 ±	1.56 ±	-0.32 ±
				[57]	[57]	[55]		0,07 a	0.10 a	0.04 b	0.01 c
	1-decanol	112-30-1	158.29	0.827	11.8	0.03704	3.79	1.63 ±	1.58 ±	1.47±	-0.69 ±
				[57]	[57]	[55]		0.12 a	0.17 a	0.03 a	0.01 b
	1-dodecanol	112-53-8	186.34	0.828	16.1	0.004286	4.77	1.16 ±	1.33 ±	1.18 ±	-0.76 ±
				[58]	[59]	[55]		0.08 b	0.04 a	0.03 a	0.11 b
	Oleyl alcohol 143-28-2	268.49	0.849
				[60]

Table 3 :

 3 Extraction performance score obtained at pHexp 3.66

	Solvent	Extraction performance score
	1-heptanol	1
	1-octanol	0.89
	1-decanol	0.84
	Butyl acetate	0.81
	Oleyl alcohol	0.73
	Hexyl acetate	0.70
	Ethyl acetate	0.70
	1-dodecanol	0.67
	2-MeTHF	0.25
	n-Hexane	0

Table 4

 4 presents solvents ranking for the first step of the selection strategy.

	20

Table 4 :

 4 Exclusion step scores

		Extraction performance	Biocompatibility	Material compatibility	Total
	Butyl acetate	0.81	1	1	2.81
	Oleyl alcohol	0.73	1	1	2.73
	Hexyl acetate	0.70	1	1	2.70
	n-Hexane	0	1	1	2
	1-heptanol	1	0	1	2
	1-octanol	0.89	0	1	1.89
	1-decanol	0.84	0	1	1.84
	Ethyl acetate	0.70	0	1	1.70
	1-dodecanol	0.67	0	1	1.67
	2-MeTHF	0.25	0	0	0.25
	After this step of exclusion, only three solvents among the ten initially studied are still in the
	candidates list (see in Table

Table 5 :

 5 Final solvent classification

		Implementation of extraction	Safety	Sourcing	Price	Total
		performances (/20)	(/15)	(/10)	(/5)	(/50)
	Oleyl alcohol	14	15	10	5	44
	Butyl acetate	17	10	8	5	40
	Hexyl acetate	20	12	4	3	39

Funding:

This work was supported by the Agence Nationale de la Recherche (ANR, Grant ANR-17-CE07-0046), Région Grand Est, Conseil Départemental de la Marne, and Grand Reims.

assess the solvent capacity of recovery and its compatibility with microorganisms are described and can easily be implemented for a similar process. If the molecule chemical nature is different than that of p-CA, adjustment must be made and the pre-selected list of solvent should be adapted. As for the strain, the implementation of biocompatible assessment has to be adjusted to strain properties (i.e. temperature, medium, pH). Towards the ISPR of p-CA produced by an engineered strain of S. cerevisiae using liquid-liquid extraction, three solvents can be used when working with an acidic aqueous phase, by preference order: oleyl alcohol, butyl acetate and hexyl acetate. In order to achieve the set-up of an effective downstream process, it has been highlighted that a trade-off between liquid-liquid extraction and fermentation regarding the pH must be made and definition and optimization of fermentation medium can be necessary.