domain: spi sdv sdv.bio

text:
Aras Ahmadi

Etienne Severac

Nelly Monties

Marion Claverie

Magali Remaud-Simeon

Claire Moulis

Ligia Tiruta-Barna

Eco-design approach for an innovative production process of low molar mass dextran

Keywords: Dextransucrase, Life Cycle Assessment, process modeling, polymerization modeling

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

Introduction

Dextrans are homopolysaccharides of D-glucosyl units produced from sucrose, a cheap and abundant agroresource, by lactic acid bacteria, such as Leuconostoc, Streptococcus, Lactobacillus, or Weissella sp. They generally have a very high molar mass (10 5 -10 8 g.mol -1 ) and contain a majority of -1,6 osidic linkages. Those containing more than 95% -1,6 osidic bonds were the first microbial polysaccharides to find industrial applications in the 1950's, essentially for pharmaceutical applications or in fine chemistry. Due to their low viscosity in saline solutions and their limited antigenic characteristics, they are frequently used in the medical field, for example, as blood plasma extenders, in the prevention of post-operative shocks, for healing burns, or as anti-coagulant agents [START_REF] Svensen | Intravascular volume replacement therapy[END_REF] . In analytical chemistry, they factor in the composition of versatile chromatographic supports (Sephadex® gels) [START_REF] Vettori | Dextran: effect of process parameters on production, purification and molecular weight and recent applications[END_REF] . However, these applications concern dextran fractions with controlled molar mass between 1,000 g.mol -1 and 70,000 g.mol -1 produced by the acid hydrolysis of native dextran chains obtained via the fermentation of sucrose by lactic acid bacteria such as L. mesenteroides NRRL B-512F, followed by successive alcohol precipitation steps. To the best of our knowledge, these processes are still relatively costly today and of low or medium yield [START_REF] Vettori | Dextran: effect of process parameters on production, purification and molecular weight and recent applications[END_REF][START_REF] Leathers | Chapter Dextran. in: Polysaccharides and Polyamides in the Food Industry, Major Reference Works[END_REF] .

In lactic acid bacteria, the enzymes involved in dextran formation are called dextransucrases and are classified in glycoside hydrolase family 70 (GH70; www.cazy.org) [START_REF] Lombard | The carbohydrate-active enzymes database (CAZy) in 2013[END_REF] . These enzymes work following a two-step mechanism. First, a covalent glucosyl-enzyme intermediate is formed, with the concomitant release of the fructosyl moiety issued from sucrose. Then, the glucosyl unit is transferred onto various molecules containing hydroxyl functions (Figure 1): (i) water leading to the hydrolysis of sucrose, (ii) sucrose itself at the beginning of the reaction, (iii) gluco-oligosaccharides, which grow as soon as they are available in the reaction medium, and (iv) fructose released leading to the isomerization of sucrose into leucrose. Secondly, leucrose can itself play the role of acceptor molecule (Figure 1). The growing gluco-oligosaccharide chains rapidly lead to high molar mass polymers of thousands or millions of glucosyl residues primarily linked via -1,6 osidic bonds. As the dextran molecules contain either glucose, sucrose or leucrose at their reducing end, they will be named Gi, GjS and GkL respectively, in this study. Recently, a new dextransucrase gene of interest was discovered in the genome of the strain Leuconostoc citreum NRRL B-1299. Indeed, after gene cloning and heterologous expression in E. coli, the resulting enzyme, called DSR-M, was shown to use sucrose to produce only low molar mass dextrans of approximately 10 3 g.mol -1 , exclusively composed of -1,6 osidic linkages [START_REF] Passerini | Inventory of the GH70 enzymes encoded by Leuconostoc citreum NRRL B-1299identification of three novel α-transglucosylases[END_REF] . This enzyme is the sole dextransucrase known to date to produce only low molar mass dextrans without the concomitant synthesis of higher molar mass polymers and with a non-processive mechanism of polymerization [START_REF] Claverie | Investigations on the Determinants Responsible for Low Molar Mass Dextran Formation by DSR-M Dextransucrase[END_REF] . Moreover, the dextran size can be finely modulated according to the initial sucrose concentration ranging from 50 g.kg -1 to 500 g.kg -1 and/or the temperature of the reaction ranging from 20°C up to 45°C. Therefore, size-controlled dextrans, i.e., with controlled molar mass ranging from 5 kg.mol -1 to 25 kg.mol -1 , can be produced in a single step (without acidic hydrolysis and alcohol fractionation) with advantageous yields of approximately 80% [START_REF] Vuillemin | Protein with Dextran-Saccharase Activity, and Uses[END_REF] . As these dextran fractions are particularly well suited for industrial applications, it was tempting to develop an approach of process eco-design in order to identify the most promising development pathways and crucial unit operations that require, as a matter of priority, further investigation and experimentation.

It is now widely recognized that new processes or technologies need to be evaluated for their environmental performances via, for example, a life cycle assessment (LCA) in the early stages of design before they are placed on the market. Industry and stakeholders have also begun to incorporate the assessment of potential environmental impacts as a new criterion, in addition to the cost, to evaluate the coherence of any innovation. Indeed, the eco-design of a process or product implies the integration of the environmental performances over the life cycle of the studied process, as well as other technical or economic performance criteria. The purpose of such an approach is to explore the real benefit of a new process at a larger scale, to identify from a theoretical or practical point of view the most promising development pathways, and to focus funding on crucial process units or steps requiring, as a matter of priority, further investigation and experimentation.

The LCA methodology is based on a comprehensive analysis that assesses the cumulative environmental impacts of a process, product, or service, taking into account all the stages of its life cycle, namely, extraction of natural resources, manufacturing, transportation, use and maintenance, end-of-life, and disposal. The LCA methodology is guided by ISO 14040-14044 8,[START_REF][END_REF] , which is comprised of the following four steps: (1) goal and scope definition, (2) life cycle inventory (LCI), (3) life cycle impact assessment (LCIA), and (4) result interpretation; it can therefore be used as a tool for diagnosis and decision-making.

Building an LCI or quantification of all inputs and outputs of the processes involved in the life cycle of a system is by far the most crucial step for determining the relevance of the end results of an LCA. The inventory is divided into foreground and background processes. The foreground usually refers to a new system, e.g., an innovative technology or process for which new experiments need to be conducted and/or high-fidelity models need to be developed. Conversely, the background refers to support processes (energy, transportation, and extraction of natural resources), which are considered to be upstream in the value chain and life cycle system for which data actually exist as generic datasets, e.g. ecoinvent or ELCD [START_REF] Ecoinvent | [END_REF][START_REF]ELCD[END_REF] . Several studies have already pointed out the importance of an adaptive modeling approach for scaling and LCA in the early stages of design in biotechnology. Examples include engineering tools for the development of some biocatalytic processes [START_REF] Tufvesson | Process considerations for the scale-up and implementation of biocatalysis[END_REF] , simulation-based design applied to bio-resins [START_REF] Kikuchi | Integration of CAPE and LCA Tools in Environmentally-Conscious Process Design: A Case Study on Biomass-Derived Resin[END_REF] , and tools for the LCA assessment of succinic acid production from biomass [START_REF] Morales | Sustainability assessment of succinic acid production technologies from biomass using metabolic engineering[END_REF] .

The application of LCA to biotechnology is relatively recent, even though the concept has been widely adopted in various sectors of production and the economy [START_REF] Hatti-Kaul | Industrial biotechnology for the production of bio-based chemicals -a cradle-to-grave perspective[END_REF] . For example, the environmental assessment of biofuels with the development of decision-making supports for public institution funding grants, as well as European Community regulations [START_REF] Rathore | Key issues in estimating energy and greenhouse gas savings of biofuels: challenges and perspectives[END_REF][START_REF] Soimakallio | How to ensure greenhouse gas emission reductions by increasing the use of biofuels? -Suitability of the European Union sustainability criteria[END_REF][START_REF] Tu | Meta-analysis and Harmonization of Life Cycle Assessment Studies for Algae Biofuels[END_REF] , and LCAs in the growing bioplastics sector can be mentioned [START_REF] Spierling | Bio-based plastics -A review of environmental, social and economic impact assessments[END_REF][START_REF] Yates | Life cycle assessments of biodegradable, commercial biopolymers-A critical review[END_REF] .

Despite the accelerated growth of the biotechnology industry, the situation is different for low tonnage products, such as pharmaceuticals (drugs and additives), food components (proteins, dietary supplements, and vitamins), oleochemicals (lubricants, surfactants, and coatings), molecular platforms (used to synthesize various chemicals), and materials and molecules obtained from bio-resources rather than fossil resources. In general, reported LCA studies in the above cited sectors are usually based on near full-scale/industrial processes to compare their environmental performance to older technologies, for example, LCAs on wood coatings from biological resources [START_REF] Hatti-Kaul | Industrial biotechnology for the production of bio-based chemicals -a cradle-to-grave perspective[END_REF] , the industrial production of enzymes for pharmaceutical applications [START_REF] Kim | Enzymes for pharmaceutical applications-a cradle-to-gate life cycle assessment[END_REF] , and innovation in the production of active pharmaceutical ingredients and basic chemicals [START_REF] Wernet | Life cycle assessment of fine chemical production: a case study of pharmaceutical synthesis[END_REF] . However, a huge effort is needed for environmental performance assessments and improvements in the case of innovative, low technology readiness level (TRL) processes in these fields.

In this context, the present study aims to perform comprehensive modeling for a detailed environmental analysis and eco-design of the production of dextran with a controlled molar mass (10-20 kg.mol -1 ) from sucrose via the DSR-M enzyme. The originality of this study is twofold: (i) new synthesis and separation pathways are proposed and studied from the viewpoint of design using a multi-fidelity modeling approach, i.e., an adaptively rigorous and more flexible and practical method, therefore broadening the scope for extension to other application areas in biotechnology, and (ii) the process of dextran synthesis and purification is investigated from both energetic and environmental points of view with integrated LCA to allow a thorough assessment of the overall performance and a coherent process design.

Materials and methods

Early-stage integrated process modeling and LCA -a methodology for eco-design

The concept of eco-design entails the notion of the life cycle of the studied process and implies consideration in the early design stages of the environmental impacts generated directly by the process and indirectly by the overall life cycle system (or background processes). In this sense, eco-design needs to consider (i) a complete definition of the process with extended boundaries to its life cycle, (ii) an ecological choice of raw materials and energy resources, (iii) a global spirit of waste reduction via revalorization and reuse, and (iv) case-specific identification and adjustments of key operating parameters to maximize the overall efficiency and to minimize the environmental footprint.

As illustrated in Figure 2, the methodology is first composed of a multi-level and multi-fidelity process modeling structure comprised of unit operations and their layout with a judicious degree of complexity. At this level, the process needs to be envisaged and placed at an industrial scale, so that the modeled system is as close as possible to a real industrial process. In the case of ongoing development and a low TRL for an innovative process, scale-up should be considered in direct feedback with the LCA guidelines, as illustrated in Figure 2, by studying the environmental impacts generated by the material and energy flows at the desired scale and by checking whether the overall process configuration remains the same at a larger scale of production. A process model provides material and energy inventories at the boundaries of the studied unit operation and can be associated with an LCA software package with general LCA databases for the background component of the life cycle system (e.g., raw material extraction, energy production, chemical production, transportation, and waste treatment). This type of simulation tool for the eco-design of biotechnologies does not currently exist on the market for the following reasons: (i) the complexity related to high fidelity modeling of certain unit operations, their parameterization, and their combination with other operations; (ii) the difficulty of fully quantifying elementary process flows, i.e., the release and emission of substances and residues to the environment; (iii) the diversity of unit operations and their modes of operation involved in a process, from the raw material to the final product, including core transformation processes (e.g., reactors), processes for utilities (e.g., steam, electricity, and water), and waste treatment and advanced separation units used in biotechnology that are not generally available in simulation software (e.g., chromatography, advanced driers, and filtration); and (iv) several technical difficulties associated with coupling or integrating process simulators into LCA software. However, note that examples of such integrated tools in the fields of biomass engineering and wastewater engineering have already been proposed and investigated in the literature [START_REF] Morales | Sustainability assessment of succinic acid production technologies from biomass using metabolic engineering[END_REF][START_REF] Bisinella De Faria | Evaluation of new alternatives in wastewater treatment plants based on dynamic modelling and life cycle assessment (DM-LCA)[END_REF][START_REF] Bisinella De Faria | Feasibility of rigorous multi-objective optimization of wastewater management and treatment plants[END_REF] .

When only laboratory-scale experimental data (TRL2-4) exists for an innovative biotechnology process, the core transformation process needs to be accurately modeled at high fidelity with parameterizations and the possibility of a scale-up analysis, while the background operations can be related more flexibly with lower fidelity and higher genericity. Nevertheless, the technical compatibility between the foreground and background processes needs to be verified. The core transformation model for the foreground production unit is the most decisive step because it characterizes the productivity, and therefore this model needs to be predictive to reflect its sensitivity to key influential operating conditions. For example, regarding dextran synthesis, the polymerization reaction needs to be formulated and resolved with relevant thermodynamic and kinetic parameters to clearly describe the performance according to the main driving conditions (e.g., the temperature, concentrations, and pH).

The coupled methodology for the process modeling-LCA framework in biotechnology includes the following steps (Figure 3). 2. High-fidelity modeling, characterization, and parameterization of the synthesis model and identification of the main process inducers.

3. Flexible-fidelity development or the use of background unit operations potentially involved in the overall life cycle system and integration with an LCA calculator (in this work, the Umberto software for flowsheeting and LCA). At this level, the models provide essentially accurate mass and energy balances per operation, as well as efficiency calculations with the required degree of accuracy. They may also involve advanced aspects such as in-depth thermodynamic modeling and the integration of hydraulics, if necessary. 4. Benchmarking or comparative analyses of alternative process flowsheets and an assessment of the LCA results building the global process flowsheet and linking with the background processes of the ecoinvent database for a global calculation of the LCI following the LCA methodology. Different unit operations are used and different configurations of recycling and waste treatment, as well as energy integration, can be involved and evaluated. At this level, different process goals are defined and evaluated together with the environmental performance via LCIA.

5. LCA results interpretation, sensitivity analysis, and decision-making. Predictive inventories are generated and compared to provide design and performance insights. A predictive tool with appropriate interconnections between the unit operations (feasibility and fidelity) and their respective environmental footprints throughout the LCIA allows the performances to be studied, as well as the influences of the key parameters of the process, the choice of unit processes involved, and the choice of raw materials, energy sources, and transportation in the background system. This methodology was specifically applied to dextran synthesis and conditioning. The key operation is the enzymatic synthesis of size-controlled dextrans, for which experiments were conducted at the lab-scale.

Then, a global process, including product separation and conditioning, was built based on the lab-scale assays and theoretical modeling.

Accordingly, the relevance of the unit operations and their interconnections in the overall flowsheet were explored and evaluated by means of mass and energy balances, life cycle inventories (elementary flows such as natural resource consumption and substance emissions), and associated environmental impacts. These results were used in a comparative manner to identify the main impact contributions from the design, raw materials, utilities, or operating conditions and to provide indications for a technically relevant and environmentally friendly production path.

LCA application

In this study, the environmental impacts were calculated according to the LCA method on the basis of 1 kg of produced dextran (one functional unit). The Python scripts for the unit processes were implemented in the Umberto® 5.6 LCA software, linked with the ecoinvent 2.2 database, and laid out in process flowsheets (representing the foreground system for the LCA). In addition, regarding the raw materials, utilities, and waste treatment options, the corresponding standard lifecycle inventories of ecoinvent were selected and linked to the foreground system to calculate the overall mass and energy balances and provide a global LCI of the process. With regard to the foreground processes (the studied processes), only the operation and functioning aspects were considered, i.e., the consumption of raw materials and utilities, while the infrastructure (buildings and equipment) was not included due to the lack of relevant data. However, the contribution of infrastructure and of the equipment maintenance was investigated through a sensitivity analysis using generic datasets from ecoinvent database.

The environmental impacts were calculated using the ReCiPe method [START_REF] Goedkoop | A life cycle impact assessment method which comprises harmonised category indicators at the midpoint and the endpoint level[END_REF] , which was used at the midpoint level with an hierarchist perspective, as well as at the endpoint level (damage oriented) focusing on three categories of damage: human health, ecosystem quality, and resources (fossil and metal depletion). With regard to the endpoint impact, the average of the three perspectives (hierarchist, individualist, and egalitarian) was used to weigh the impacts (40% for human health, 40% for ecosystem quality, and 20% for resources). In addition, the impact of climate change was calculated using the IPCC 2013 method [START_REF] Stocker | Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of IPCC the Intergovernmental Panel on Climate Change[END_REF] .

Process description for the synthesis and conditioning of dextran polymers

The production of high-quality size-controlled dextrans involves the following main steps (Figure 3), applicable at the laboratory or industrial scale:

1. Synthesis of dextran, primarily based on a single in-vitro enzymatic polymerization using sucrose as a substrate (a cheap and abundant agro-resource) and dextransucrase expressed recombinantly by E. coli (details of the experimental synthesis protocol and its specificities are provided in Section 2.3); 2. Inhibition of the enzyme and separation from the reaction medium; 3. Separation of dextran polymers from oligomers and monosaccharides; 4. Conditioning of the dextran mixture as a fine-powdered product; and 5. Recovery or treatment of waste effluents. Each step is based on one or more unit operations chosen for their proven technical relevance at the laboratory scale, as advised by experts and selected using a heuristic approach [START_REF] Yenkie | A roadmap for the synthesis of separation networks for the recovery of bio-based chemicals: Matching biological and process feasibility[END_REF] . The scale of production is defined in agreement with the current production capacities of dextran; a synthesis batch reactor with a nominal volume of 100 L (the reaction volume) was considered.

The sucrose initially enters the batch reactor with the enzymes where the polymerization reaction is conducted under the same conditions as at the laboratory scale, i.e., 30°C, atmospheric pressure, and pH 5.75. At the end of the reaction, the enzymes are inactivated at 95°C over 5 min in the same container. The reaction medium is transferred for the enzyme separation operation, which may consist of membrane filtration.

The solution obtained after inactivation and the exclusion of the enzymes contains dextrans (polymers), fructose, and other monosaccharides and oligosaccharides. At this level, the dextrans need to be separated from the other small compounds to obtain a product containing polymers in the desired size range. The separation can be carried out via size exclusion chromatography or via an appropriate membrane separation process. The dextran aqueous fraction is then treated for the precipitation of dextran via a thermal/cryogenic process, which may be lyophilization or spray drying. The effluents are obtained with a composition depending on the efficiency of the unit operation used. The inactivated enzyme is separated into an aqueous phase, which also contains all the other molecules in small amounts. The dextran separation generates an aqueous solution containing monosaccharides and oligosaccharides, the concentrations of which depend on the type of separation. From an LCA perspective, the design needs to take into account the associated treatment processes, which may be elimination (e.g., waste water treatment plants and incineration), open loop valorization (e.g., monosaccharides and oligosaccharides in another process), or closed loop recycling (e.g., the reuse of residual water in the process).

Relevant modeling levels are necessary for the description of multiple-fidelity unit operations; these levels include the following:

 A rigorous base model to simulate the enzymatic synthesis of size-controlled dextran polymers that represents the critical stages of production, ensuring the required quality of the final product; and  Flexible-fidelity modeling blocks for the other unit operations including mass and heat balances, calculations of the utility requirements, hydraulic and efficiency models (for the chromatography, filtration, and membrane processes), and a thermodynamic model (for drying).

Python scripts were therefore developed in the present study to generate predictive material and energy flows for each unit operation. A full description of these modeling blocks is provided in Section 2.5 as well as in the Supplementary Information.

Laboratory-scale experiments with the DSR-M enzyme

The DSR-M 1 enzyme is recombinantly produced in E. coli as previously described [START_REF] Claverie | Investigations on the Determinants Responsible for Low Molar Mass Dextran Formation by DSR-M Dextransucrase[END_REF] . Briefly, the DSR-M 1 gene is cloned in a PET-55-DEST vector (Novagen) in fusion with a 6xHis tag at the C-terminal extremity and a Strep-tag at the N-terminal extremity. The transformed E. coli BL21 star DE3 cells are grown in Erlenmeyer flask at 21°C with agitation (150 rpm) in a modified ZYM5052 culture medium (with the following changes: 0.1% lactose, 0% glucose, and 1% glycerol) [START_REF] Studier | Protein production by auto-induction in high-density shaking cultures[END_REF] supplemented with ampicillin (100 μg.mL -1 ). After 26 h of incubation, the cells are harvested via centrifugation, newly suspended in a lysis buffer (50 mM sodium acetate with pH 5.75) at a final OD600 nm = 80, and disrupted via sonication. After centrifugation (30 min at 11,000 rpm), recombinant enzymes are recovered in the soluble fraction of the crude cell extract, ready for dextran syntheses. The enzyme activity of these crude cell extracts is estimated using the 3,5-dinitrosalicylic method [START_REF] Miller | Use of Dinitrosalicylic Acid Reagent for Determination of Reducing Sugar[END_REF] , for which one unit of DSR-M activity is defined as the amount of enzyme that catalyzes the production of 1 μmol of fructose per minute at 30°C from 100 g.L -1 sucrose in a 50 mM sodium acetate buffer with pH 5.75. The level of enzyme production is about 20,000 units per liter of culture under these conditions. Dextran syntheses were performed at 30°C in a volume of 250 mL with sucrose concentrations varying from 100 g.L -1 to 400 g.L -1 in a 50 mM sodium acetate buffer with pH 5.75 and using 1 U.mL -1 of enzyme (corresponding to 0.014 mg.mL -1 of DSR-M proteins). For the kinetic analyses, 500-µL samples were withdrawn at regular time intervals until total sucrose consumption. Reactions were stopped via a 5-min incubation at 95°C.

To assay the sucrose depletion and determine the production of the glucose, fructose, and leucrose byproducts, the reaction media were analyzed via high performance liquid chromatography on a Dionex UltiMate 3000 system and using an Aminex® HPX-87K carbohydrate analysis column (300 × 7.8 mm; Bio-Rad).

The column oven temperature was set to 65°C. The compounds were separated isocratically using ultrapure water as an eluent at a flow rate of 0.6 mL.min -1 .

The size and concentration of the dextran were determined via high performance size exclusion chromatography on a Fisher Ultimate 3000 system using two Shodex OH-Pak SB-802.5 and SB-805 columns in series coupled with a Shodex OH-Pak SB-G guard column and placed in an oven at 70°C. Elution was performed using 0.45 M NaNO3 and 1% ethylene glycol (vol.) as eluent at a flow rate of 0.3 mL.min -1 . The samples were diluted to 20 g.L -1 of dry matter, also in 0.45 M NaNO3 and 1% ethylene glycol. The weightaverage molar masses at peak apex of synthesized dextrans were determined using a calibration curve with standards of fructose, sucrose, and dextrans of 1.5, 11.3, 39.1 and 68.4 kg.mol -1 (Sigma-Aldrich), at 10 g.kg -1 each.

Monitoring of dextran production versus time were performed thanks to HPAEC PAD (high performance anion exchange chromatography with pulsed ampero-metric detection) analyses using a CarboPac TM PA100 analytical column (5 mm x 250 mm) coupled with a CarboPacTM PA100 guard (5 mm x 50 mm). A gradient of Na acetate in 150 mM NaOH was applied at a flow rate of 1 mL.min -1 as follows: 0-2 min, 0 mM; 2-110 min, 0-225 mM; 110-130 min, 225-450 mM. Detection was performed using a Dionex ED40 module with a gold working electrode and an Ag/AgCl pH reference. The samples were diluted in water to a final dry matter of 1 g.L -1 before analysis.

These analyses revealed the simultaneous presence of three different populations of dextrans, namely Gi, GjS and GkL, evolving in parallel during the synthesis as illustrated in Figure 4. These three populations of dextrans are involved in the three possible polymerization pathways described in section 1.2, were Gi is the dextran chains formed from elongation of free glucose, GjS correspond to dextran polymerized from sucrose and GkL form Leucrose. The three polymerization pathways will be discussed in Section 2.5.1 throughout the introduction of the PolyEnz model. As shown in Figure 5 for a representative synthesis experiment, the dextran solution after the inactivation and exclusion of enzymes contains dextran polymers, fructose and traces of glucose and other oligosaccharides with low polymerization degree. The dextran product was purified from monosaccharides (glucose and fructose) and short oligosaccharides (polymerization degree <3) using a 2-L DOWEX™ MONOSPHERE™ Ca 2+ chromatography column with deionized and degassed water as eluent. The flow rate was set to 15 mL.min -1 and the temperature to 50°C.

Aliquots of 15 mL were taken until total polysaccharide recovery. The dextran was then dried via lyophilization using a Telstar LyoBeta 2.0. The freezing temperature was set to -70°C. The primary drying was performed at -30°C under 0.2 mBar with the secondary drying at +20°C under 0.2 mBar. The total freezedrying time was approximately 49 h.

2.5 Predictive modeling of dextran synthesis using the DSR-M enzyme in a batch reactor

PolyEnz model

The enzymatic polymerization of glucosyl units to form dextran chains is a key step in the process for which rigorous modeling is required. To this end, experiments were conducted at the laboratory scale, as described in Section 2.3, to generate the data necessary for the development of a kinetic-based predictive model for dextran polymerization using the DSR-M enzyme, namely, the PolyEnz model. This model provides a comprehensive description of the enzymatic polymerization of glucosyl units from a sucrose substrate via the non-processive mechanism illustrated in Figure 6. More specifically, the PolyEnz model describes and determines in a predictive manner (i) the pathway of synthesis for different initial concentrations of sucrose, (ii) the three simultaneous polymerization mechanisms during the growth phase, (iii) the size distribution and the degree of polymerization of the resulting polymers, and (iv) the accurate amount of side products. The previous analyzes in Figure 4 showed the simultaneous growth of the three populations of dextran polymers using the DSR-M enzyme: the first represents glucosyl residues bonded together (Gi), the second represents glucosyl residues bonded together with a sucrose molecule at the reducing end (GjS) [START_REF] Claverie | Investigations on the Determinants Responsible for Low Molar Mass Dextran Formation by DSR-M Dextransucrase[END_REF] , and the third represents glucosyl residues bonded together with a leucrose molecule at the reducing end (GkL). The indices i, j, and k in Figure 6 indicate the number of glucosyl residues bonded to the corresponding acceptor.

Accordingly, the dextran product is composed of the three above-mentioned polymer formations, where i is varied from 1 to n while j and k are varied from 1 to n-2 to obtain the same length of polymer whatever the acceptor. Here, n represents the maximum polymerization degree. As illustrated in Figure 6, glucose released in the starting phase reacts with the EG complex to form G2 polymers, which are again polymerized later during the growth phase to form Gi (the left polymerization mechanism in Figure 6). Conversely, the GS molecules released during the starting phase are polymerized in parallel via consecutive reactions with the EG complex to form GjS polymers (the right polymerization mechanism in Figure 6). Leucrose, when produced, also acts as an acceptor and induces the third polymerization chain (the bottom growth chain in Figure 6) resulting in the formation of GkL polymers. As a result, during dextran synthesis using DSR-M, three polymerization paths occur simultaneously.

The start phase in Figure 6 is comprised of 3 reactions, while the growth phase results in the formation of Gi, GjS, and GkL and involves 3n -6 chain reactions. Only one reaction is needed to represent the formation of leucrose. Therefore, the synthesis schema involves overall 3n-2 reactions for which 3n-2 kinetic constants need to be defined and fitted. The overall system of reaction rate equations describing the dynamic mass balances in the batch reactor and the corresponding kinetic data are summarized in Appendix A.

Batch reactor utilities and mixing

The reactions take place in a batch stirred tank reactor with free enzymes, in which the mixing is maintained via mechanical agitation. A batch system (with temperature control via a heating/cooling jacket) is the most commonly used reactor in biocatalysis [START_REF] Tufvesson | Process considerations for the scale-up and implementation of biocatalysis[END_REF] . A reactor volume of 138 L (according to the standard DIN28136) can be used for a solution of 100 L (the nominal volume). Details related to the calculation of the utilities (mixing and heating) in a batch reactor are provided in the Supplementary Information (Table A.1).

2.6 Flexible-fidelity modeling of unit operations for purification and separation

Inactivation of the DSR-M enzyme

After synthesis, the enzymes need to be inactivated to prevent side reactions. This can be achieved by heating the mixture to 95°C for 1 h in the reactor. Inactivation by heating the mixture at 95°C for 1 h in the reactor was preferred to other methods such as medium acidification or basification, in order to prevent the risk of osidic linkage cleavage, and thus of the deterioration of the product. Table A.2 in the Supplementary Information offers an overall evaluation of the utility requirements for agitation and heating in a batch reactor for enzyme inactivation.

Ultrafiltration for enzyme separation

Ultrafiltration (UF), a pressure-driven membrane process, is often used throughout downstream processing for different applications in the production of bio-based chemicals. The process is smooth, fast, and comparatively inexpensive. High concentration factors and high yields (85-99%) could be achieved [START_REF] Yenkie | A roadmap for the synthesis of separation networks for the recovery of bio-based chemicals: Matching biological and process feasibility[END_REF] with the condition to countering fouling issues. UF membranes are available with different cut-off limits for the separation of molecules ranging from 1 kDa up to 300 kDa. The typical filtrate flux in UF ranges from 25 L.m -2 .h -1 (LMH) to 150 LMH. Typical transmembrane pressures (TMP) in UF are within 0.2-4 10 5 Pa. UF can be carried out in biochemistry in varying modes of operation [START_REF] Van Reis | Bioprocess membrane technology[END_REF] such as direct flow filtration or tangential flow filtration (TFF) in batch, feed batch, or diafiltration modes, which are chosen according to the context and the separation objectives.

To separate out the enzymes, direct filtration (dead-end) in batch mode was chosen, the objective being to recover all the solution containing the saccharide molecules and to eliminate the enzymes. Given the enzyme properties (size 140 kDa), a polyethersulfone membrane with a molar mass cut-off of 50 kDa (not retaining the produced dextrans) could be used due to its low-to-moderate binding properties to proteins and its repellency with regard to saccharides [START_REF] Galanakis | Separation of functional macromolecules and micromolecules: From ultrafiltration to the border of nanofiltration[END_REF] . The concentration of the enzyme in the reaction media was very low, approximately 0.01 g.L -1 . A filtration flux of 30 L.m -3 .h -1 is expected, which represents a third of the maximal flux for this membrane. A temperature of 40°C can improve the efficiency of the filtration by reducing the viscosity of the solution. Heating of the solution may not be necessary because the previous operation (enzyme inactivation) takes place at 95°C. Water can be used for the backwashing of the membranes with a flux higher than the filtration flux, for 1 min, and with a relatively high frequency, e.g., every 15 min. In-place cleaning and membrane sanitation occur at the end of the batch filtration with a hydrogen peroxide solution [START_REF] Shi | Fouling and cleaning of ultrafiltration membranes: A review[END_REF] . The determination of the main operating conditions for the enzyme separation by UF, as well as the corresponding utility requirements, is given in the Supplementary Information (Table A.3).

Dextran separation and concentration via UF

UF can be used successfully for the fractionation of polysaccharide mixtures, as is widely acknowledged in the literature [START_REF] Pinelo | Membrane technology for purification of enzymatically produced oligosaccharides: Molecular and operational features affecting performance[END_REF] . The most commonly used filtration modes are diafiltration for fractionation and batch filtration for concentration both with tangential flow. In addition to the molar mass cut-off, the separation is conditioned by the chemical properties of the membranes, i.e., their hydrophobic characteristics. For example, it is known that polyethersulfone membranes exhibit repellency with respect to proteins and polysaccharides, therefore avoiding solute adsorption on the membrane surface and pores [START_REF] Pinelo | Membrane technology for purification of enzymatically produced oligosaccharides: Molecular and operational features affecting performance[END_REF] . Optimal combinations of the hydrophobicity of a material and the molar mass cut-off can be selected (e.g., from laboratory-scale experiments) to achieve efficient separation. A membrane with rather hydrophobic properties and a cut-off size of 3 kDa can satisfy the current separation objective.

An optimal temperature for UF operation is observed near 40°C. Indeed, a high temperature decreases the viscosity of the fluid by promoting the permeate flux while reducing the retention of molecules and therefore the recovery of the product [START_REF] Pinelo | Membrane technology for purification of enzymatically produced oligosaccharides: Molecular and operational features affecting performance[END_REF][START_REF] Sun | Fractionation of polysaccharides from rapeseed by ultrafiltration: Effect of molecular pore size and operation conditions on the membrane performance[END_REF][START_REF] Barker | Fractionation, purification and concentration of dextran solutions by ultrafiltration[END_REF] . With regard to the transmembrane pressure, a high pressure considerably increases the permeate flux but, at the same time, decreases the efficiency of the separation. If fractionation is the main objective, the membranes need to operate at low pressure. Fouling is prevented by backwashing.

For example, for hollow fiber UF in tangential mode, a frequency of a few minutes with a washing duration of several seconds has been tested and has provided good results [START_REF] Pinelo | Membrane technology for purification of enzymatically produced oligosaccharides: Molecular and operational features affecting performance[END_REF] .

The goal of separation in this study is to eliminate monosaccharides and oligosaccharides with an overall size of less than 3 kDa from the solution. All polymers of higher sizes constitute the so-called dextran product.

The purity of the obtained product depends on the type of membrane used and the filtration mode. The usual filtration mode used for the size-dependent separation of molecules is tangential: diafiltration with constant or variable volume (in a feed tank) and recirculation of the retentate. Assuming that the selectivity of the chosen membrane is satisfactory (under operating conditions of a temperature of 40°C, a TMP of 1.10 5 Pa, and a permeate flux of 30 L.m -2. h -1 ) with a negligible permeability for medium-sized dextrans, the purity (fraction of polymers) of the product depends on the dilution conditions [START_REF] Li | Study on nanofiltration for purifying fructo-oligosaccharides: I. Operation modes[END_REF] (the calculation is presented in the Supplementary Information).

The washing of the membranes consists of a backflush with water at a reverse transmembrane pressure higher than the filtration pressure. Membrane sanitation can be performed with a solution of hydrogen peroxide (and optionally 0.1 M NaOH). All operations are considered at 40°C. The characteristics of the product, the utilities necessary for filtration and cleaning (energy, water, and reactant consumption), and the effluent produced (permeate composition) are calculated according to the model presented in the Supplementary Information (Tables A. 4 and A.5).

In the case of solutions that are too dilute, a concentration operation could be envisaged. Concentration operations use the principle of UF or nanofiltration in tangential flow in batch mode, i.e., the retentate is recirculated in the feed tank with no addition of water. The same operating parameters are used in terms of the temperature, permeate flux, retentate flux, and TMP. However, the membrane molar mass cut-off needs to be adapted to the dextran size, a smaller cut-off size is preferred, e.g., 1 kDa. The general scheme of UF in separation mode or concentration mode is shown in Figure 7.

Chromatography for dextran purification

Column chromatography has been shown to be an extremely efficient technique for separating saccharide molecules in biological extracts. There are several weaknesses in industrial-scale chromatography, such as designing and scaling difficulties, the dilution of the obtained product, and its high cost. Nevertheless, this process is still applied in the case of high-value products [START_REF] Yenkie | A roadmap for the synthesis of separation networks for the recovery of bio-based chemicals: Matching biological and process feasibility[END_REF][START_REF] Mikeš | Chapter 11 Polysaccharides and Oligosaccharides, in: High-Performance Liquid Chromatography of Biopolymers and Biooligomers: Separation of Individual Compound Classes[END_REF] .

Size exclusion chromatography can be used for the purification of dextrans. In a chromatography column packed with porous particles of an ion-exchange resin, the dextran molecules are not retained but are rinsed with the elution solvent (e.g., water). Conversely, the monomers and oligomers (small molecules) are retained in the porous media and are eluted later. A maximum resolution can be achieved with sample volumes ranging from 0.5% to 2% of the total volume of the column. However, volumes up to 5% can result in acceptable separation and, based on preliminary laboratory experiments, this value was chosen in this study. The scale-up follows the operating conditions of the laboratory; in addition, similar geometry and hydrodynamics are adopted for the operation of the column. The proposed process is based on a single column of 200 L, which is 10 times longer than its diameter (for hydrodynamic reasons); however, several columns could also be implemented in parallel. The dextran polymers are eluted with two column void volumes, while the monomers and small oligomers are recovered in the following four void volumes. The temperature in the column is maintained at approximately 50°C to reduce the viscosity of the liquid. A detailed determination of the main operating parameters, as well as the utility requirements, is given in the Supplementary Information (Table A.6).

Drying pharmaceutical grade dextran polymers

Almost all biotechnology and pharmaceutical processes include a drying stage to improve the stability of the final product. Lyophilization and spray drying are two widely used techniques for drying heat sensitive materials. In fact, the elimination of water allows the degradation and the mobility of the product in the solid state to be decreased [START_REF] Chen | Drying Technologies in Food Processing[END_REF] .

Spray drying or atomization of liquid droplets is a continuous process consisting of a liquid feed spray in a hot medium, where the liquid content (water) of the droplets is continuously evaporated to the desired moisture level of the particles or until a crystalline state of the powder with the desired texture is obtained.

Lyophilization is often used at lab/pilot scales for a wide range of products. A freeze-dried product has a solid, friable, and porous appearance and is characterized in particular by its great avidity for water. Lyophilization (freeze drying), associated with the manipulations that lead to this state, is a process based on the use of both cold (brutal freezing) and dehydration via sublimation of the ice formed. Indeed, lyophilization can be decomposed into two main phases: the freezing phase (requiring cooling) and the dehydration phase (requiring heating and vacuum pumping). The latter is subdivided into two stages, i.e., the sublimation and desorption phases. Schematically, it can be said that the product is first frozen and then the remaining solvent on the surface of the product is removed via sublimation. Details concerning the thermodynamic modeling of the drying stage are given in the Supplementary Information (Table A.7).

Results and discussion

Predictive description of dextran synthesis using the DSR-M enzyme for different substrate concentrations

The experimental results for dextran synthesis at the laboratory scale and the simulations obtained using the PolyEnz model (Section 2.5) are presented in Figures 8 and9. Several series of curve-fittings and numerical optimizations were carried out on the provided experimental data for initial sucrose concentrations between 10 g.L -1 and 400 g.L -1 to determine the kinetic constants and their correlations as a function of the initial sucrose concentration. Table 1 summarizes the concentration of the obtained dextran, as well as the average molar mass of the produced dextran polymers, as a function of the initial selected sucrose concentration.

To demonstrate the predictive behavior of the PolyEnz model, the results of two different initial concentrations of sucrose, 25 g.L -1 and 200 g.L -1 , were compared, as illustrated in Figures 8 and9, respectively. The concentration profiles of sucrose, fructose, and leucrose over time are shown in the top rows of these figures. Meanwhile, the graphs on the bottom left show the formation of the dextran polymers over time, namely, Gi, GjS, and GkL. The increase in the total dextran concentration (the red curve) results from the addition of these three components. For a given initial substrate concentration, the PolyEnz model allows a predictive description of the polymerization quality, as shown in Table 1, in terms of mass average molar mass (Mw) and concentration of dextrans in the batch synthesis reactor (17.9 kg.mol -1 and 88 g.L -1 , respectively, for an initial sucrose concentration of 200 g.L -1 , for instance). Additionally, in order to reflect the influence of the initial sucrose concentration on the quality and dispersity of the final dextran products after oligomer separation and cutoff at 3 kg.mol -1 , Table 1 also provides data on the quality and dispersity of the end dextrans (Mw = 19.9 kg.mol -1 , Mn = 16.8 kg.mol -1 , and PDI = 1.18, for an initial sucrose concentration of 200 g.L -1 ).

The sucrose concentration can be chosen simply depending on the desired length of dextran. However, from a quantitative point of view, the production of dextran increases with increasing initial substrate concentration while, in parallel, the length of the polymers decreases with increasing initial substrate concentration. For example, to produce 1,000 kg of "20-kDa dextran" from a 200-g.L -1 sucrose solution (Table 1), 11,364 L of the substrate solution needs to be synthesized and then treated given a final dextran concentration of 88 g.L -1 . Conversely, fixing the same amount of dextran, i.e., 1,000 kg, but aiming for a slightly different dextran quality with an average molar mass of 25 kg.mol -1 , which can be obtained from a 100-g.L -1 sucrose solution as shown in Table 1, 21,277 L of the substrate solution needs to be synthesized and further separated throughout the process; the cost in this case will be nearly double that of the previous case from both the treatment and energy perspectives. Therefore, the size of the future plant depends not only on the desired product quantity but also on the type of dextran.

The product obtained from 200-g.L -1 sucrose (highlighted in Table 1) has a mass average molar mass of approximately 20 kg.mol -1 , after separation and purification, when estimated at the peak apex of the chromatogram from size exclusion chromatography coupled with refractive index detection. The production of dextran using DSR-M directly from 200-g.L -1 sucrose is further investigated via the eco-design approach. This dextran size is of pharmaceutical interest (eye drops, blood volume expander, organ preservation, vaccines, see www.dextran.com) as well as a carrier for biophysic probes after functionalization (FITC-dextran 20, Biotin-dextran 20, DEAE-dextran 20, see www.finabio.net).

Synthesis product in batch reactor

Final dextran product after cut-off at 3 kDa Mw [START_REF] Svensen | Intravascular volume replacement therapy[END_REF] (kg.mol -1 ) Mw [START_REF] Svensen | Intravascular volume replacement therapy[END_REF] (kg.mol -1 ) Mn [START_REF] Vettori | Dextran: effect of process parameters on production, purification and molecular weight and recent applications[END_REF] (kg.mol -1 ) PDI [START_REF] Leathers | Chapter Dextran. in: Polysaccharides and Polyamides in the Food Industry, Major Reference Works[END_REF] 12.9 10.9 1.18 [START_REF] Svensen | Intravascular volume replacement therapy[END_REF] Mass average molar mass [START_REF] Vettori | Dextran: effect of process parameters on production, purification and molecular weight and recent applications[END_REF] Number average molar mass [START_REF] Leathers | Chapter Dextran. in: Polysaccharides and Polyamides in the Food Industry, Major Reference Works[END_REF] Polydispersity index

Variants for product separation and purification at an industrial scale: Benchmarks 1-3

Three flowsheets were compared, namely, Benchmarks 1, 2, and 3, to indicate the choice and combination of the most relevant operations for the optimal production of high-quality dextran (Figure 10). For the three benchmarks studied in this section, the comparative analysis is based on an initial substrate solution of 100

L with an initial sucrose concentration of 200 g.L -1 giving the same quality of dextran-20. As explained previously in Section 3.1, this choice was motivated by the mass average molar mass of the final product in this case (Mw = 19.9 kg.mol -1 after purification, Table 1) and by the considerable savings made in terms of treatment and energy compared with the use of lower sucrose concentrations (< 200 g.L -1 ). The separation steps, shown in Figure 10, were organized to converge to the same quality of products (See Section 2.6 and Supplementary Information A.). By ensuring the same quality of dextrans at the end of process after purification (Mw = 19.9 kg.mol -1 , Mn = 16.8 kg.mol -1 , PDI = 1.18, Table 1), the comparison becomes environmental and energy-wise, as presented later in this section.

Benchmark 1 combines the synthesis and purification/concentration processes currently used at the laboratory scale, which includes, as shown in Figure 10 In all scenarios, ultra-distilled water is used in the batch reactor during the enzymatic synthesis of the dextrans and softened water is used as process water in the other operations (elution in chromatography, cleaning of membranes, and membrane operation). A water reuse strategy is put in place for the entire process. The condensed water in the freeze-drying operation is theoretically pure and can be used, e.g., as an elution solvent in chromatography. The accumulated residual water is considered to have been treated

prior to release to the environment, as indicated in the process flowsheets (Figure 10).

The inventory data including the ecoinvent processes considered in LCA modelling are detailed in Supplementary Information, Table C.1 and C.2.

Table 2 presents the overall performance results for the three benchmarks compared in terms of yield, specific energy, and water consumption and the life cycle impacts per kg of dextran. The environmental impacts are presented in Table 2 as the total endpoint and endpoints for human health, ecosystems, and resources (the ReCiPe method) and as the global warming potential for their climate change impacts (kg eq-CO2). For a similar production yield (0.42 kg of dextran per kg of sucrose) in Table 2, significant discrepancies are observed in terms of the energy and water consumption. Details concerning the distribution of the energy and water consumption per unit process are shown in Figure 11. The main differences between the three benchmarks occur at the separation and conditioning stage, owing to its high energy and water consumption and its environmental impact.

The freeze dryer (lyophilizer) represents the highest energy demand, followed by chromatography, while chromatography is the primary consumer of water. As shown in Figure 11 In this way, less water needs to be removed in the last stage of freeze drying, followed by a marked reduction in energy consumption: 10 times less energy in Benchmark 2 compared to Benchmark 1. Conversely, more water is needed for the chromatography due to the lower amount of recycling from the lyophilization to the chromatography; in this case, a fraction of the water is rejected in the form of the TFF permeate to a wastewater treatment system.

As a result, the water consumption in Benchmark 2 is 1.5 times higher than in Benchmark 1. Total ReCiPe endpoint impact (*) (per FU [START_REF] Svensen | Intravascular volume replacement therapy[END_REF] )

Ecosystem quality [START_REF] Vettori | Dextran: effect of process parameters on production, purification and molecular weight and recent applications[END_REF] Human health [START_REF] Leathers | Chapter Dextran. in: Polysaccharides and Polyamides in the Food Industry, Major Reference Works[END_REF] (*) Normalized impact in points, [START_REF] Svensen | Intravascular volume replacement therapy[END_REF] Functional unit (FU): 1 kg of dextran polymer obtained, [START_REF] Vettori | Dextran: effect of process parameters on production, purification and molecular weight and recent applications[END_REF] Main damage observed: climate change, [START_REF] Leathers | Chapter Dextran. in: Polysaccharides and Polyamides in the Food Industry, Major Reference Works[END_REF] The LCA results are summarized in Table 2 and Figure 12. The total ReCiPe endpoint score is shared between the three areas of protection, namely, human health, ecosystem quality, and resources. Unambiguously, Benchmark 3 presents the best environmental performance, followed by Benchmark 2 and, with a much lower performance, Benchmark 1. The same ranking is obtained for the climate change impact category.

Therefore, the detailed results of the ReCiPe method (presented in the Supplementary Information B.)

demonstrate that climate change is the main contributor to the potential damages to human health and ecosystem quality. The contribution analysis of the LCA results shown in Figure 12 highlights again the net impact of energy consumption for all the benchmarks, corroborating the results previously presented in Figure 11. These results were obtained in a French economic context considering the French electricity mix as an electricity type close to reality. Note that, due to the different energy configurations in Europe, the choice of electricity is country dependent and therefore the origin of the electricity significantly influences the type and amount of the resulting impacts. The second largest contributor to the environmental impacts is the production of sugar, which is used as a raw material in the synthesis of dextran. Because the production yields of the three benchmarks are similar, sugar production has identical impacts in each benchmark.

Negligible impacts are generated by the water treatment (process water and wastewater), enzyme production, and the production of chemicals consumed in the process. The low contribution of enzyme production is explained by the small quantity necessary for the synthesis of dextran, i.e. 1.4 10 -5 kg of enzyme for 1 kg of dextran, and by the small corresponding culture medium volume (maximum 500 L for 1 t of dextran produced with non-optimized culture in Erlenmeyer flask, a value that will be reduced with large-scale productions in fermenter).

Sensitivity analysis and identification of the main production factors

As indicated in Table 3, various factors related to production, in particular the initial concentration of the substrate, the type of substrate, the type of water, the use of chromatography, and the amount of freeze drying expressed by the ratio of the freeze-dried moisture content to the total water content of the solution at the dextran separation stage were subjected to an additional sensitivity analysis to indicate the main factors favoring energy-efficient production with a reduced impact on the environment. The relative sensitivities in Table 3 are reported in terms of the global energy demand and the ReCiPe total endpoint to cover all categories of potential environmental damage.

The choice of the sucrose concentration as a substrate is undoubtedly the most significant determining factor in the entire production chain because it directly determines the quantity and length of the dextran polymers and, consequently, the scale of the process and the number of separation and drying steps involved. Earlier in Section 3.1, the study of the influence of the initial substrate concentration on the quality of the dextran in the batch polymerization reactor, as shown in Table 1, was intended to emphasize the importance of avoiding low concentrations of sucrose as a substrate due to the resulting large energy demands for separation and drying. The sensitivity of the results to the initial concentration of sucrose provided in Table 3 is further evidence of the importance of this choice. In fact, given the size of the targeted dextran polymers and based on the same production yield, the optimal substrate concentration to be synthetized should be selected first, as explained in Section 3.1. Figure 13 also offers benchmark simulations for sucrose concentrations between 25 g.L -1 and 400 g.L -1 to better illustrate the direct effect of the substrate concentration on the different performance indicators in terms of utilities and environmental impacts for 1 kg of dextran produced. Figure 13 includes the results of Benchmarks 1 and 3; however, those of Benchmark 2 are not indicated because the performance of Benchmark 2 was delimited by those of the other two benchmarks. It can easily be seen that the utility requirements and the environmental footprints are very sensitive to the choice of the initial substrate solution due to the different solution volumes to be processed in the different unit operations on the basis of the same dextran yield.

As shown in Table 4, to achieve a targeted production yield, for example, 1 t of dextran per year, depending on the initial substrate concentration, a different number of batches of equal size (e.g., reactors for 100 L of solution) need to be involved. Assuming that the synthesis and inactivation operations involve a one-day batch reactor operation (the separation/conditioning operations last for one day) and that 200 days per year are actually productive, the production of 1 t of dextran will naturally require several lines of production in parallel, as suggested in Table 4, independent of the benchmark. Note that, for syntheses using different sucrose concentrations, the sizes of the obtained dextrans are different (Table 1) and, therefore, the present comparison needs to be regarded from the perspective of the specific use of the targeted dextrans and their market. In the second row of Table 3, the amount of moisture removal achieved via lyophilization, namely, via freeze drying, becomes crucial. Note that at least a small amount of freeze drying needs to be maintained at the end of the chain to achieve the high texture quality of the obtained dextran powder. Nevertheless, the majority of the water content can be removed prior to the freeze drying via filtration because freeze drying is expensive at an industrial scale.

Of the existing drying technologies, freeze drying (lyophilization) is one of the most energy-intensive, as acknowledged in the literature, with an average specific energy demand (Es) of 2 kWh per kg of water removed; other widely used technologies have lower specific energy demands, e.g., 1.3 kWh.kg -1 for vacuum drying, 1.6 kWh.kg -1 for spray drying, and 1.9 kWh.kg -1 for air drying [START_REF] Chen | Drying Technologies in Food Processing[END_REF][START_REF] Först | Advances in Probiotic Technology[END_REF] . Using a more energy-efficient drying technology can significantly reduce the amount of energy consumed in all the studied benchmarks. In addition, a lateral sensitivity analysis was performed for a specific energy demand of drying ranging from 1.3 kWh to 2 kWh per kg of water removed. In summary, for all of the benchmarks, the total energy consumption could be improved by 1.3-fold for Es = 2 and by 0.9-fold for Es = 1.3 with respect to the theoretical value used in the modeling. However, note that the improvement yielded from the selection of a more energy-efficient drying technology remains relatively minor compared to the energy demand associated with the dextran separation strategy implemented in the three benchmarks, i.e., the replacement of intensive waterconsuming chromatography by membrane separation (Benchmark 3) or, to a lesser extent, by combining chromatography with a concentration step (Benchmark 2). The purpose of the latter was to minimize the water content prior to the final drying step.

For the experiments, refined commercial grade sucrose was used as a raw material for the dextran synthesis.

However, other sucrose-rich raw materials could be envisaged, for example, molasses (the residual substrate in Table 3). Estimating the process performance using molasses instead of sugar could help identify the potential value of this resource and guide future laboratory-scale experimental designs for process adaptation. Sugar beet molasses contains up to 50% sugar and other compounds (e.g., monosaccharides and salts). A replacement was therefore calculated on the basis of 3 kg of molasses per kg of sugar. The impact contribution of the raw material is reduced by a factor of 1.56 (for all impact categories), resulting in a total impact reduction of up to 36% for Benchmark 3, where the raw material contribution was predominant. In this scenario, the performance of Benchmark 2 could be improved by up to 10%, while Benchmark 1 should be discarded due to its huge energy consumption via freeze drying (calculation details in Supplementary Information D.). Similar assessments were also conducted with different sucrose sources, e.g., from sugarcane, without significant changes in the initial performances (e.g., the transport from remote sugar factories offsets the environmental benefit of sugar production on other continents).

Process water consumption is very important, particularly in Benchmarks 1 and 2 (Table 2); however, its contribution to the total impact remains very low, at less than 1% (Figure 12). The type of water treatment can affect the environmental performances of consumer processes. Simulations were performed with tap water in a batch reactor (replacing ultrapure water) for synthesis and softened water for other operations.

As expected, the results were not significantly different considering the relatively low contribution of the water treatment to the overall impact. Water as a natural renewable resource is not considered in current LCIA methods even though its consumption represents a real environmental and societal issue. This is why water consumption was calculated as an independent indicator in the present study.

As specified in Section 2.4, the infrastructure was not included in the LCA. However, an estimate of the infrastructure contribution (buildings and equipment) is possible using the generic data available in the ecoinvent database. The construction of a chemical plant for the production of organic chemicals (ecoinvent 2.2 database) generally contributes little to the life cycle impact of the produced substances; the operation stage represents approximately 95% of the life cycle impacts. According to the generic data, the infrastructure would likely contribute less than 3% to the impacts calculated per functional unit (1 kg of dextran produced) for the three benchmarks and for any initial sucrose solution. According to the results in Table 4, the infrastructure impact is much larger when 1 t of dextran is produced from 25-g.L -1 sucrose solutions than when 1 t of dextran is produced from 200-g.L -1 solutions. However, the relative contribution to the environmental impact remains at the same level due to the higher total impact of diluted solutions (Figure 13) (more details are presented in Supplementary Information D.).

The cleaning and backwashing operations for the membranes and the chromatography resin were included in the process operations, and thus in the system inventory (Supplementary Information A.). The influence of equipment maintenance over the lifetime of the infrastructure was assessed by sensitivity analysis using generic data from ecoinvent (details of the inventory are presented in Supplementary Information C.).

Replacement of used membranes and of used chromatography resin was considered with a maximum frequency of 1 year to evaluate the upper limit of their contribution. The impact contribution for the three benchmarks was less than 2% for membrane and resin replacements taken altogether (details are presented in Supplementary Information D.).

Finally, the LCA results were analyzed relative to common organic chemicals provided by the chemical industry to validate the order of magnitude of the results. In the LCA database (ecoinvent), an average score is calculated for organic chemicals derived from abiotic resources as a ReCiPe endpoint of 0.27 points and climate change for 2 kg eq-CO2 per 1 kg of substance produced. The impact results for Benchmark 3 were of the same order of magnitude, which constitutes a validation support for the approach proposed in this study.

In addition, the unusually high impacts assessed for Benchmark 1 prove that this option cannot be adapted in its basic form at an industrial scale.

Conclusions

A new process for the production of size-controlled dextran polymers was proposed in this study.

Experimental studies at the laboratory scale have demonstrated the feasibility of enzymatic synthesis from an aqueous sucrose solution while using the patented DSR-M enzyme. Experiments provided synthesis kinetics and demonstrated that the dextran characteristics (the size distribution of the polymers) strongly depend on the initial sucrose concentration and the reaction time. In the laboratory, the separation and purification of dextran polymers requires a few operations, in particular chromatography, prior to the final step of drying the dextran polymers to fine regularly structured solid crystals. This promising production path needs to be evaluated from the viewpoint of the overall technical and energetic performance and environmental perspectives before being implemented at an industrial scale. In this study, an eco-design approach was applied to the synthesis and purification of dextran polymers based on multi-fidelity process modeling, scale-up principles, and LCA. Three process benchmarks were proposed and compared to determine the appropriate and reasonable purification and scale-up processes. Various production factors, including the initial concentration and type of substrate, the type of process water, the use of ion-exchange chromatography for size exclusion separation, and the amount of freeze drying used for the final stage of production were subject to a sensitivity analysis with the criteria being the overall energy demand, the total aggregated potential environmental damages expressed in terms of the ReCiPe total endpoint, and the global warming potential.

The substrate concentration appears to be a determining factor because it directly shapes the production scale (and the related investment) for the same targeted production yield. It was clearly observed that the synthesis needs to be orientated according to the choice of the targeted dextran length and that, overall, the use of low concentrations of sucrose of less than 100 g.L -1 is not favored because it requires an additional number of batch operations and purification steps with their associated extra energy requirements. The latter again showed the importance of the rigorous modeling of dextran synthesis as a function of the initial substrate concentration to substantially guide all the subsequent separation and purification steps. Via a comparative analysis (Benchmarks 1-3), ion-exchange chromatography and membrane filtration were compared, both standalone and combined, for the separation of dextran polymers from smaller saccharides with molar mass less than 3 kDa.

Even though its effectiveness is widely acknowledged, ion-exchange chromatography naturally causes a significant dilution of the product and the water content must then be removed via intensive drying facilities.

The use of ion-exchange chromatography at an industrial scale would therefore induce a marked increase in the energy consumption for the final drying stage. The replacement of chromatography by membrane filtration can overcome this bottleneck. A new process layout, as suggested in Benchmark 3, used TFF instead of chromatography and resulted in a radical reduction in the utility requirements, a nearly 35-fold smaller energy demand and 20-fold less elution by water. Optimal environmental performances and the lowest utility demands were reported for Benchmark 3. A further reduction in the energy consumption is possible via the optimization of the operating conditions or via the replacement of freeze drying with more efficient drying approaches. Replacing commercial sugar with a sucrose-rich residue, such as molasses, could also improve the environmental footprint; nevertheless, the feasibility of such a replacement needs to be studied experimentally.

The benefits of an LCA-based performance study on the synthesis and purification of dextrans in the early stages of process design was demonstrated in this study with the aim of identifying and quantifying the most promising production routes and research and development requirements. With regards to biotechnology, it is very much a priority today to show, first and foremost, the potential and behavior of an innovative process at a larger scale and then to highlight the main challenges at that larger scale, which are usually met at the separation and purification stage. According to the reaction rates when using the DSR-M enzyme and in accordance with the notations and rate equations mentioned above, the following mathematical correlations for kinetic constants have been identified to fit the experimental kinetic data at varying initial concentration of substrate [S0]. Indeed, decreasing trends with the initial sucrose concentration were taken into account and curve-fitted to reflect the inhibition of the substrate at higher concentrations of initial sucrose solution due to the influence of water content on the structure of sucrose in solutions under these conditions. Size-dependent dextran polymerization Slow polymerization [START_REF] Svensen | Intravascular volume replacement therapy[END_REF] (ℓ < 8):

𝑘 ,ℓ = 6 565 (ℓ) + 46

Fast polymerization [START_REF] Vettori | Dextran: effect of process parameters on production, purification and molecular weight and recent applications[END_REF] (ℓ > 8):

𝑘 ,ℓ = 𝑘 . Formation of Leucrose (L) 𝑘 = 49.90 [S0] -0.9964 𝑘 in L 2 .mol -2 .min -1 and [S0] in mol.L -1 [START_REF] Svensen | Intravascular volume replacement therapy[END_REF] The degree of polymerization ℓ = 8 represents the length (ℓ) of the polymer below which the kinetics increases linearly but at relatively lower values. [START_REF] Vettori | Dextran: effect of process parameters on production, purification and molecular weight and recent applications[END_REF] Above the ℓ = 8 polymer length, the DSR-M enzyme suddenly accelerates elongation, as longer polymers are easier to capture and to synthetize. However, at this point the value of 𝑘 ,ℓ should decrease for an increase of ℓ because the molar number of dextran polymers decreases considerably with rapid growth of the length of polymers.

Figure 1 .

 1 Figure 1. Reactions catalyzed from sucrose by dextransucrase DSR-M issued from Leuconostoc citreum NRRL B-1299.

Figure 2 .

 2 Figure 2. Venn diagram of integrated LCA-process modeling for eco-design.

1 .

 1 A description and understanding of the main transformation unit (the leading foreground process of dextran synthesis in this work) based on representative laboratory experiments.

Figure 3 .

 3 Figure 3. General process diagram for high-quality dextran synthesis, purification, and drying.

Figure 4 .

 4 Figure 4. Representation of the three different dextran populations, Gi, GjS, and GkL, using an HPAEC-PAD analysis on the synthesis products at different time intervals obtained from an initial sucrose concentration of 200 g.L -1 .

Figure 5 .

 5 Figure 5. SEC analysis of the reaction products after enzyme exclusion based on an initial sucrose concentration of 200 g.L -1 .

Figure 6 .

 6 Figure 6. Mechanism for dextran synthesis from sucrose using the DSR-M enzyme: E = enzyme, S = sucrose, F = fructose, EG = glucosyl-enzyme complex, Gi = growing dextran (glucosyl chains only), GjS= growing dextran (elongated sucrose), and GkL = growing dextran (elongated leucrose).

Figure 7 .

 7 Figure 7. Tangential flow filtration (TFF) set-up in concentration mode (solid lines) or separation mode (includes dotted area).

Figure 8 .

 8 Figure 8. The PolyEnz model compared to the experimental data for dextran synthesis using the DSR-M enzyme with an initial sucrose concentration of 25 g.L -1 : (A) sucrose concentration; (B) fructose formation; (C) leucrose formation; (D) dextran polymerization; (E) evolution of the average dextran molar mass over time; and (F) dextran length distribution over time.

Figure 9 .

 9 Figure 9. The PolyEnz model compared to the experimental data for dextran synthesis using the DSR-M enzyme with an initial sucrose concentration of 200 g.L -1 : (A) sucrose concentration; (B) fructose formation; (C) leucrose formation; (D) dextran polymerization; (E) evolution of the average dextran molar mass over time; and (F) dextran length distribution over time.

  (a), polymerization (the enzymatic synthesis of dextran), enzyme inactivation via heating and separation (diafiltration-frontal ultrafiltration), ion-exchange chromatography to separate the small oligomers from the dextran polymers, and finally freeze drying to obtain excellent pharmaceutical quality dextran with relevant textures after drying. Benchmark 2 follows nearly the same production pattern except that an intermediate UF with TFF is introduced prior to the freeze dryer to concentrate the solution eluted from the chromatography column. Reducing the moisture content reduces the high energy requirements during freeze drying (Figure10(b)). In both Benchmarks 1 and 2, the use of chromatography for separation induces a marked dilution of the dextran solution due to the elution step. This step directly impacts the energy requirements of the final drying phase. Therefore, an exclusive multi-stage filtration strategy is proposed in Benchmark 3 (Figure10(c)), instead of separation via ionexchange chromatography, for the removal of small oligosaccharides and monosaccharides, that is, molecules with molar mass of less than 3 kg.mol -1 .

  (c), the replacement of chromatography by TFF in Benchmark 3 results in a radical reduction in the utility demands: 35 times less energy and 20 times less water is needed in Benchmark 3 compared to Benchmark 1. The intermediate Benchmark 2 involves a concentration step via TFF to reduce the water content of the chromatography effluent; the chromatography elution step strongly dilutes the dextran solution.

Figure 10 .

 10 Figure 10. Overall process diagram and its alternatives: (a) Benchmark 1: separation via chromatography/freeze drying; (b) Benchmark 2: separation via chromatography, TFF ultrafiltration, and freeze drying; and (c) Benchmark 3: separation via standalone TFF ultrafiltration and freeze drying.

Figure 11 .

 11 Figure 11. Contribution of specific energy and water requirements per kg dextran product for (a) Benchmark 1, (b) Benchmark 2, and (c) Benchmark 3 (results are given on a logarithmic scale, and for a dextran production chain based on a 100-L initial solution with a sucrose concentration of 200 g.L -1 ).

Figure 12 .

 12 Figure 12. Life cycle impact assessment (LCIA) for different utilities and services: (a) the total normalized ReCiPe endpoint and aggregated impacts and (b) the IPCC2007 climate change impact (results are given for a dextran production chain based on an initial substrate solution of 100 L with an initial sucrose concentration of 200 g.L -1 ).

Figure 13 .

 13 Figure 13. Sensitivity of the overall performances to the initial sucrose concentration: (a) Benchmark 1 and (b) Benchmark 3 (the performance indicators involved are the total ReCiPe-endpoint impact and the climate change impact on the left axis and the energy demand and the total water consumption on the right axis).

  The synthesis of dextrans via the DSR-M enzyme was represented by a multilayer polymerization model, namely, PolyEnz, via a non-processive mechanism. The PolyEnz model predicted (i) the pathway of synthesis for different initial concentrations of sucrose, (ii) three simultaneous polymerization mechanisms during the growth phase, (iii) the size distribution and the degree of polymerization of the resulting polymers over time, and (iv) the exact amount of byproducts requiring additional purification steps. The model satisfactorily fitted experimental data obtained for various sucrose concentrations in the range of 10-400 g.L -1 .

  Appendix A. Rate equations for the synthesis of dextran polymers using the DSR-M enzyme Notations E: Enzyme, EG: Glucosyl-enzyme complex, S: Sucrose, G: Glucose, F: Fructose, L: Leucrose, Gi: Glucosyl chains only, GjS= Elongated sucrose with glucosyl chains, GkL: Elongated leucrose with glucosyl chains.𝑑[𝑆] 𝑑𝑡 = -𝑘 [𝐸][𝑆] -𝑘 [𝐸𝐺][𝑆] 𝑑[𝐹] 𝑑𝑡 = 𝑘 [𝐸][𝑆] -𝑘 [𝐸𝐺][𝐹] 𝑑[𝐸𝐺] 𝑑𝑡 = 𝑘 [𝐸][𝑆] -𝑘 [𝐸𝐺] -𝑘 [𝐸𝐺][𝑆] -𝑘 [𝐸][𝐸𝐺] -𝑘 , [𝐺 ][𝐸𝐺] -𝑘 , [𝐸𝐺] 𝐺 𝑆 -𝑘 , [𝐸𝐺][𝐺 𝐿] 𝑑[𝐺] 𝑑𝑡 = 𝑘 [𝐸𝐺] -𝑘 , [𝐸𝐺][𝐺] (𝑖 =1) 𝑑[𝐺 ] 𝑑𝑡 = 𝑘 , [𝐸𝐺][𝐺 ] -𝑘 , [𝐸𝐺][𝐺 ] (𝑖 = 2, … , 𝑛 -1) 𝑑[𝐺 ] 𝑑𝑡 = 𝑘 , [𝐸𝐺][𝐺 ] (𝑖 = 𝑛) 𝑑[𝐺 𝑆] 𝑑𝑡 = 𝑘 [𝐸𝐺][𝑆] -𝑘 , [𝐸𝐺][𝐺 𝑆] (𝑗 = 1) 𝑑 𝐺 𝑆 𝑑𝑡 = 𝑘 , [𝐸𝐺] 𝐺 𝑆 -𝑘 , [𝐸𝐺] 𝐺 𝑆 (𝑗 = 2, … , 𝑛 -3) 𝑑[𝐺 𝑆] 𝑑𝑡 = 𝑘 , [𝐸𝐺][𝐺 𝑆] (𝑗 = 𝑛 -2) 𝑑[L] 𝑑𝑡 = 𝑘 [𝐸𝐺][𝐹] -𝑘 , [𝐸𝐺][𝐿] 𝑑[𝐺 L] 𝑑𝑡 = 𝑘 , [𝐸𝐺][𝐿] -𝑘 , [𝐸𝐺][𝐺 L] (𝑘 = 1) 𝑑[𝐺 𝐿] 𝑑𝑡 = 𝑘 , [𝐸𝐺][𝐺 𝐿] -𝑘 , [𝐸𝐺][𝐺 𝐿] (𝑘 = 2, … , 𝑛 -3) 𝑑[𝐺 𝐿] 𝑑𝑡 = 𝑘 , [𝐸𝐺][𝐺 𝐿] (𝑘 = 𝑛 -2)

Table A1 .

 A1 Kinetic correlations for synthesis of dextrans using DSRin L.mol -1 .min -1 and [S0] in mol.L -1 a = 3.543×10 -5 , b = 2.066×10 -4 , c = 3.780×10 -3 , d = 1.1090×10 -2 Hydrolysis of Glucosylenzyme complex (EG) 𝑘 = 1.001 𝑘 in min -1 Formation of elongated sucrose (GS) 𝑘 = 3.277 [S0] -0.2110 𝑘 in L.mol -1 .min -1 and [S0] in mol.L -1

  with 𝑘 = a'.exp(b'. [S0]) + c' 𝑘 , = 𝑘 , = 𝑘 , = 𝑘 ,ℓ in L.mol -1 .min -1 and [S0] in mol.L -1 n : maximum polymerization degree 𝑘 : maximum polymerization kinetics a' = 2.782×10 5 , b' = -3.749, c' = 1.293×10 4

Table 1 .

 1 Influence of initial substrate concentration on dextran quality in batch synthesis reactor and in end product after purification.

	Sucrose	Synthesis	Dextran
	concentration	time	concentration
	(g.L -1 )	(min)	(g.L -1 )

Table 2 :

 2 Overall utility demand, production yield, and environmental assessment for Benchmarks 1, 2, and 3 (results are given for a dextran production chain based on a 100-L initial solution with a sucrose concentration of 200 g.L -1 ).

	Benchmark 1	Benchmark 2	Benchmark 3

Table 3 .

 3 Relative sensitivity of the energy costs and environmental impacts to certain production factors in dextran synthesis and separation.

			Relative sensitivity coefficients
	Factor	Range	Energy demand Total ReCiPe Endpoint
	Sucrose concentration	25-400 g.L -1	3.02	2.95
	Freeze-drying use	5-95%	0.93	0.87
	Chromatography use	(1) Yes,	0.56	0.39
		(2) No
	Substrate type	(1) Commercial,	0.39	0.12
		(2) Residual
	Water type	(1) Ultra-distillated,

Table 4 .

 4 Influence of the substrate solution on the process scale to produce 1 t of dextran polymer (for a batch reactor with 100 L of solution).

	710
	711
	712
	713
	714
	715
	716

Acknowledgments

This research was partially supported by the Carnot Institute 3BCAR. The authors thank the ICEO facility dedicated to enzyme screening and discovery, and part of the Integrated Screening Platform of Toulouse (PICT, IBiSA) for providing access to their chromatographic equipment. Héctor Javier Vazquez and Jawaher Houmani are also acknowledged for their helpful participation to the project.

Conflicts of interest

There are no conflicts to declare.