Biovalorisation of Crude Glycerol and Xylose Into Xylitol by Oleaginous Yeast Yarrowia Lipolytica Ashish A

Prabhu et al. Microb Cell Fact (2020) 19:121 https://doi.org/10.1186/s12934-020-01378-1 Microbial Cell Factories

RESEARCH Open Access Biovalorisation of crude glycerol and xylose into xylitol by oleaginous yeast Yarrowia lipolytica Ashish A. Prabhu1†, Dominic J. Thomas1†, Rodrigo Ledesma‑Amaro2, Gary A. Leeke3, Angel Medina1, Carol Verheecke‑Vaessen1, Frederic Coulon1, Deepti Agrawal4 and Vinod Kumar1*

Abstract Background: Xylitol is a commercially important chemical with multiple applications in the food and pharmaceuti‑ cal industries. According to the US Department of Energy, xylitol is one of the top twelve platform chemicals that can be produced from biomass. The chemical method for xylitol synthesis is however, expensive and energy intensive. In contrast, the biological route using microbial cell factories ofers a potential cost-efective alternative process. The bioprocess occurs under ambient conditions and makes use of biocatalysts and biomass which can be sourced from renewable carbon originating from a variety of cheap waste feedstocks. Result: In this study, biotransformation of xylose to xylitol was investigated using Yarrowia lipolytica, an oleaginous yeast which was frstly grown on a glycerol/glucose for screening of co-substrate, followed by media optimisation in shake fask, scale up in bioreactor and downstream processing of xylitol. A two-step medium optimization was employed using central composite design and artifcial neural network coupled with genetic algorithm. The yeast amassed a concentration of 53.2 g/L xylitol using pure glycerol (PG) and xylose with a bioconversion yield of 0.97 g/g. Similar results were obtained when PG was substituted with crude glycerol (CG) from the biodiesel industry (titer: 50.5 g/L; yield: 0.92 g/g). Even when xylose from sugarcane bagasse hydrolysate was used as opposed to pure xylose, a xylitol yield of 0.54 g/g was achieved. Xylitol was successfully crystallized from PG/xylose and CG/xylose fermenta‑ tion broths with a recovery of 39.5 and 35.3%, respectively. Conclusion: To the best of the author’s knowledge, this study demonstrates for the frst time the potential of using Y. lipolytica as a microbial cell factory for xylitol synthesis from inexpensive feedstocks. The results obtained are competi‑ tive with other xylitol producing organisms. Keywords: Glycerol, Xylose, Yarrowia lipolytica, Biotransformation, Xylitol

Background the promising green and sustainable approach to supple- Te development of green technologies is highly desired ment the use of fossil-derived feedstocks, where a carbon to stabilise the progressive depletion of non-renewable reservoir present in the biomass is exploited for the inte- fossil-derived chemicals [1]. Te bio-refnery concept is grated production of chemicals, fuels and energy. First generation biorefneries that use edible biomass feed- *Correspondence: [email protected] stocks are well established. Te food versus fuel debate †Ashish A. Prabhu and Dominic J. Thomas contributed equally to this work gave rise to second generation biorefneries based on 1 School of Water, Energy and Environment, Cranfeld University, non-edible feedstocks and biomass wastes [2, 3]. Tese Cranfeld MK43 0AL, UK feedstocks have drawn signifcant attention as they can Full list of author information is available at the end of the article generate high value products from a renewable carbon

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source. Over the last two decades, there has been a large to reach 266,500 metric tons in 2022 with a value greater amount of research dedicated to the development of bio- than US$ 1 billion [6]. processes based on agro-industrial wastes [4]. Yarrowia lipolytica is a non-conventional, oleaginous, Lignocellulosic biomass (LCB) is the most abundant safe and robust yeast with multiple biotechnological biomass on earth and has three major components applications. It has versatile characteristics such as high namely cellulose (34-50%), hemicellulose (19–34%) and cell density cultivation, metabolic fexibility and tends to lignin (11–30%). Hemicellulose, a hetero-polysaccha- accumulate a wide array of industrially important metab- ride consists largely of xylose (~ 90%), which contrib- olites. In addition, the yeast is non-pathogenic and has a utes to 15–35% of the total dry cell weight of LCB [5, GRAS (generally regarded as safe) status [15]. Te unique 6]. Despite being the second most abundant sugar after features of Y. lipolytica make it a promising cell factory glucose, xylose valorisation through biotechnological for the production of value-added chemicals. Glycerol is routes is often overlooked. Most of the microbeslack the most preferred carbon source for Y. lipolytica and can xylose assimilation pathways as xylose is rarely present metabolize it with great efciency [16–18]. According to in the environment and in free form in natural prod- the literature, the majority of the Yarrowia strain cannot ucts. If the pathways exist, it is not a preferred carbon grow on xylose as they possess strong xylose reductase source and its uptake is suppressed in the presence of activity but have low xylitol dehydrogenase activity, how- glucose due to carbon catabolite repression [7]. Tese ever, some can biotransform xylose into xylitol [19, 20]. challenges make pentose sugar a lesser lucrative option Te current study was undertaken to investigate the as an exploitable carbon source. However, efcient uti- xylitol accumulating ability of Y. lipolytica Po1t (Ura +, lisation of xylose is essential for the commercial viabil- Leu+) [19]. Te biotransformation of xylose into xylitol ity of lignocellulosic biorefneries [8, 9]. Like xylose/ was carried out by growing Y. lipolytica on pure glycerol LCB, crude glycerol (CG) is another waste product of (PG), CG as well as glucose Te work studies the screen- interest as a carbon source for microbial conversion. ing of co-substrates for cell growth, media optimization CG is major by-product of many industrial processes, in a shake fask, scale up in bioreactor and downstream such as bioethanol, oleochemical, chemical and bio- processing of xylitol. Tis study is the frst to demonstrate diesel. Te rapid growth in the biodiesel industry has the potential of using Y. lipolytica as a microbial cell fac- led to an increase in CG production [10] with ~ 10 kg of tory for xylitol synthesis from inexpensive feedstocks. CG production for every 100 kg of biodiesel. Biodiesel production increased exponentially from 2005 to 2015 Results and is forecasted to grow by another 35% by 2025 [11]. Shake fask screening of co‑substrates for growth Te surplus of biomass derived by-product streams of Yarrowia lipolytica and subsequent biotransformation like xylose and CG has emphasised the need to utilise of xylose to xylitol them as carbon sources and avoid their disposal. It is Te biotransformation of xylose to xylitol using Y. lipol- therefore necessary to develop sustainable processes to ytica cell factory was evaluated under shake fask condi- transform these streams into promising value-added tions. Te Y. lipolytica Po1t (Ura + Leu+) strain used in products. this study can transform xylose into xylitol but it cannot Xylitol is a commercially important chemical. Accord- grow on xylose as a sole carbon source (data not shown). ing to the US Department of Energy, xylitol is one of the Two carbon sources namely glucose and glycerol were top twelve platform chemicals, which can be produced evaluated for biomass accumulation of Y. lipolytica cells from biomass [12]. Chemically, it is a polyol (sugar alco- and subsequent biotransformation of xylose into xylitol. hol) containing fve carbon atoms where a hydroxyl group Te growth profle of Y. lipolytica clearly indicated that is attached to each carbon atom. Xylitol is produced the assimilation of glycerol was faster than that of glu- chemically/biochemically by the reduction of xylose. Te cose as shown by Fig. 1. Te yeast was able to consume molecule is equivalent to sucrose in sweetness but has 20.0 g/L of glycerol within 48 h whereas more than 15% less calories and lower glycaemic index [5, 13]. Due to of glucose was left unconsumed in the same time and its high endothermicity, low glycaemic rates, cariostatic complete glucose consumption was evident by 72 h. Te properties, lack of carcinogenicity, non-involvement in highest OD 600 values obtained for glycerol and glucose the insulin metabolic pathway and non-interference with were quite similar, 29.2 and 28.0, respectively. After the food nutritional value, xylitol has many applications in majority of glucose or glycerol was exhausted (~ 48 h), the food and pharmaceutical industries [14]. Xylitol has biotransformation began and in the next 24–48 h, the a large market and a 12% share of the total polyols mar- maximum production of xylitol was recorded. However, ket, which is expanding rapidly. Te global market for owing to earlier consumption of glycerol than glucose, xylitol in 2016 was 190,900 metric tons and is anticipated the biotransformation rate was signifcantly faster in Prabhu et al. Microb Cell Fact (2020) 19:121 Page 3 of 18

the former case than the later and the maximum xylitol Yxylitol = 25.90 + 4.45X1 + 1.46X2 − 0.75X3 production was achieved in 72 h for glycerol compared 2 2 2 − 0.68X4 + 0.008X1 − 3.64X2 + 0.27X3 to 96 h for glucose. Using glycerol as a co-substrate, the − 1.35X2 + 0.36X X + 1.42X X xylitol titre was 16.0 g/L with a yield of 0.80 g/g, how- 4 1 2 1 3 ever, when glucose was used as a co-substrate the high- − 0.33X1X4 + 3.7X2X3 − 0.15X2X4 est xylitol titer and conversion yield were 12.7 g/L and − 2.8X3X4 0.64 g/g, respectively. Te pH decreased during the (1) growth phase, reducing below 4.0 after 48 h and then where Y is the xylitol concentration (g/L) and X 1, X2, X3 remained almost constant during the xylitol production and X4 are xylose (g/L), YNB (w/v %), ammonium chlo- phase. Te biotransformation yield obtained was higher ride (w/v %) and phosphate bufer (mM), respectively. with glycerol and was therefore selected as the co-sub- Te response surface plot for the interaction between strate for further experiments. the media components is shown in Fig. 2. Te 3D surface plot gives an overview of the interaction between the two Media optimization in shake fask to maximize components on xylitol production by keeping the other biotransformation of xylose to xylitol parameters at central values. Te interaction between Central composite design (CCD) xylose and NH 4Cl showed a positive efect on xylitol Statistical methods measure the efects of changing production and a progressive increment in the xylitol operating variables and their mutual interactions on the titer was observed with the increasing concentration of process through experimental design techniques. In the both components. Further, higher xylose concentrations present study, the central composite design (CCD) was caused a steep reduction in xylitol titer. Similarly, the employed to determine the optimum value of media interactions between xylose & YNB (Fig. 2b) and NH 4Cl components (xylose, YNB, NH4Cl and phosphate bufer) & YNB (Fig. 2d) showed positive efects (P < 0.05) on to maximize xylitol production in shake fask while keep- xylitol production indicating that higher concentrations ing glycerol concentration constant. Te design matrix of YNB and NH 4Cl will lead to the enhanced production and the corresponding results of observed and predicted of xylitol. On the other hand, the interaction between responses (xylitol titre) are shown in Table 1. Te experi- xylose & phosphate bufer (Fig. 2c), phosphate bufer & mental run 8 resulted in the production of 37.2 g/L xylitol NH4Cl (Fig. 2e) and YNB & phosphate bufer (Fig. 2f) for a 50.0 g/L initial concentration of xylose (Table 1). showed statistical insignifcant values, which indicate However, the best run in terms of xylitol yield was found one of the components has to be kept at a minimum to for run 9 where 0.95 gxylitol/gxylose was produced for an enhance the xylitol production. initial xylose concentration of 20.0 g/L. Te results were analysed using the Analysis of Vari- Optimization of process parameters using artifcial neural ance (ANOVA) shown in Table 2. Te error term, network linked genetic algorithm (ANN‑GA) which indicates that the amount of variation in the Te experimental design generated by the CCD was used response data, is very low. According to the ANOVA, as input feed for ANN algorithm. Te overall data set the regression model for the xylitol production showed were divided into three subsets: training (20 data points), high signifcance with a Fisher’s F value of 22.41 and validation (5 data points) and test sets (5 data points). Te explains most of the variations present in the experi- training was carried for 1000 epochs, the mean square mental design [21]. Te “p” value of 0.92 for lack of ft error (MSE) and R 2 value for data set involved in xylitol indicated that the response for xylitol concentration production are shown in Additional fle 1: Table S1. Te was not signifcant relative to the pure error. Te cor- data points apart from the training are used to exam- 2 relation coefcient (R ) between the experimental and ine the validation. During training the data over fts and model-predicted values of response variables showed substantial error will be accumulated on the validation. high statistical signifcance of 94.16%, which implies When the error on the validation reaches the threshold that only 5.84% of the total variation was not explained point the weights and biases are adjusted to minimize by the model. Te Student’s t distribution and the cor- the error [22, 23]. Network topology have a crucial role responding p values shows that most of the interaction in predicting results, the input–output neuron of ANN terms are statistically signifcant (P < 0.05), except for is the resemblance of input and output data used in this the interaction terms involving xylose with NH4Cl and study. Te number of neurons in the hidden layer was phosphate bufer which showed insignifcance. Te sec- determined by trial and error method to minimize MSE. ond-order polynomial equation for xylitol production Te MSE of xylitol production was found to be 7.425. Te by CCD is given in Eq. (1). Prabhu et al. Microb Cell Fact (2020) 19:121 Page 4 of 18

Fig. 1 Time course profles of Y. lipolytica on; a glucose and xylose, b PG and xylose. Symbols: flled square (glucose or PG), flled circle (xylose), flled

triangle (OD600), empty circle (xylitol) and flled star (pH) predicted value of ANN for xylitol is shown in Table 1. value. Te optimum values were found to be as following: Te optimum value was achieved with 4 inputs, 8 hid- xylose—55.0 g/L, NH4Cl—3.94 g/L, YNB—5.0 g/L and den layers and 1 output layer. Te simulation of ANN phosphate bufer—132.5 mM. resulted in a R2 of 0.938 between the actual experimental production values (Fig. 3a). In order to further opti- Model validation under shake fask conditions mize the solution space for global optimum, the genetic Te validation experiments were performed in shake algorithm (GA) was adapted to train ANN values. Te fasks based on the global optimum values obtained by values of GA specifc parameters used in the optimiza- ANN-GA training. Tree diferent sets of experiments tion technique were as follows: population size = 20, were conducted: PG + xylose (Fig. 4a); CG + xylose cross over probability = 0.8, mutation probability = 0.01, (Fig. 4b); PG + crude xylose (Fig. 4c). Te crude carbon No. of generation = 100. Te maximum xylitol produc- sources were included to test the ability of the Y. lipol- tion of 47.7 g/L was observed with 160 iterations. Te ytica strain to tolerate, utilize and valorise crude renew- best ftness plot of the GA for xylitol production (Fig. 3b) able sources. Te glycerol uptake rate was similar for the maps the gradual convergence of the best ftness values case of PG and CG and the major fraction of glycerol of successive generations towards the fnal optimum carbon was depleted in frst 48 h. Te cell concentration Prabhu et al. Microb Cell Fact (2020) 19:121 Page 5 of 18

Table 1 CCD and ANN design matrix of variables with experimental and predicted response for xylitol production

Experiment Xylose (g/L) NH4Cl (w/v %) YNB (w/v %) Phosphate Xylitol (g/L) CCD predicted ANN predicted no. bufer (mM)

1 20 0.2 0.2 35 17.47 18.04 18.79 2 50 0.2 0.2 35 22.20 22.80 22.48 3 20 0.5 0.2 35 13.11 11.81 14.99 4 50 0.5 0.2 35 20.05 19.51 20.45 5 20 0.2 0.5 35 10.20 10.52 4.83 6 50 0.2 0.5 35 21.77 22.48 19.70 7 20 0.5 0.5 35 18.70 20.81 18.92 8 50 0.5 0.5 35 37.22 35.73 33.50 9 20 0.2 0.2 100 18.99 26.02 23.06 10 50 0.2 0.2 100 29.53 27.94 30.42 11 20 0.5 0.2 100 17.85 17.65 19.66 12 50 0.5 0.2 100 23.30 22.52 23.14 13 20 0.2 0.5 100 4.74 5.79 5.90 14 50 0.2 0.5 100 14.07 14.91 13.92 15 20 0.5 0.5 100 15.00 13.95 14.27 16 50 0.5 0.5 100 26.08 26.03 31.47 17 35 0.35 0.35 67.5 20.71 24.25 24.96 18 35 0.35 0.35 67.5 27.18 24.25 24.96 19 35 0.35 0.35 67.5 24.53 24.25 24.96 20 35 0.35 0.35 67.5 24.84 24.25 24.96 21 5 0.35 0.35 67.5 20.57 19.33 21.37 22 65 0.35 0.35 67.5 34.98 36.16 37.22 23 35 0.05 0.35 67.5 12.41 10.67 11.56 24 35 0.65 0.35 67.5 13.88 15.55 15.66 25 35 0.35 0.05 67.5 29.69 30.82 29.96 26 35 0.35 0.65 67.5 28.00 26.80 23.25 27 35 0.35 0.35 2.5 23.62 23.15 23.75 28 35 0.35 0.35 132.5 21.03 21.43 21.08 29 35 0.35 0.35 67.5 24.87 27.46 24.96 30 35 0.35 0.35 67.5 29.78 27.46 24.96

obtained with PG (OD600: 34.2) was higher than CG Te ability of Y. lipolytica to produce xylitol from crude (OD600: 24.9). Te low cell OD 600 in comparison to PG xylose was also tested. For this, crude xylose obtained might be attributed due to the presence of some inhibi- after hydrothermal pretreatment of sugarcane bagasse tory components such as methanol present in the CG was used. Lignocellulosic material often contains com- [24]. Te results showed signifcant improvements in pounds such as phenols, furan derivatives and aliphatic xylitol titer and yield in comparison to unoptimized com- acids in large amounts which tend to inhibit microbial position. For the case of PG and xylose, 98% of xylose growth [26]. However, the cell growth (OD600: 29.1) was was transformed into xylitol, and a xylitol concentration faster and almost unafected by the presence of impuri- of 54.0 g/L was achieved. On the other hand, for the co- ties/inhibitory molecules in crude xylose. Te higher cell fermentation of CG and xylose, a xylitol titre of 48.2 g/L growth could also be contributed by other sugars such as was obtained with a conversion yield of 0.88 g/g. Tis glucose in the sugarcane bagasse (SCB) hydrolysate [27]. diference could be attributed to the composition of CG. Te conversion of crude xylose was 54% with the xylitol Te higher bufer concentration of optimized medium titre of 5.4 g/L. Te low conversion yields obtained indi- suppressed the reduction in pH, and therefore, after an cating the efect of impurities on the biotransformation initial drop, the pH was stable around 5.5. Te pH plays ability of Y. lipolytica. A plausible reason for the low bio- a crucial role in the transportation of xylose across the transformation yield from the xylose-rich SCB hydro- membrane [25]. lysate when compared to pure xylose could be due to its Prabhu et al. Microb Cell Fact (2020) 19:121 Page 6 of 18

Table 2 Analysis of variance for xylitol production Source DF Seq SS Adj SS Adj MS F p

Blocks 1 68.61 68.61 68.61 15.35 0.002 Regression 14 1402.25 1402.25 100.16 22.41 < 0.001 Linear 4 489.28 489.28 122.32 27.37 < 0.001 Xylose 1 424.91 424.91 424.91 95.08 < 0.001

NH4Cl 1 35.71 35.71 35.71 7.99 0.01 YNB 1 24.22 24.22 24.22 5.42 0.04 Phosphate 1 4.44 4.44 4.44 0.99 0.37 Square 4 404.82 404.82 101.21 22.65 < 0.001 Xylose*Xylose 1 9.54 0.14 0.14 0.03 0.86

NH4Cl*NH4Cl 1 341.93 352.75 352.75 78.93 < 0.001 YNB*YNB 1 7.65 3.14 3.14 0.7 0.42 Phosphate*Phosphate 1 45.7 45.7 45.70 10.23 0.01 Interaction 6 508.15 508.15 84.69 18.95 < 0.001

Xylose*NH4Cl 1 8.73 8.73 8.73 1.95 0.18 Xylose*YNB 1 51.99 51.99 51.99 11.63 0.004 Xylose*Phosphate 1 8.09 8.09 8.09 1.81 0.2

NH4Cl*YNB 1 273.24 273.24 273.24 61.14 <0.001

NH4Cl*Phosphate 1 4.56 4.56 4.56 1.02 0.33 YNB*Phosphate 1 161.56 161.56 161.56 36.15 < 0.001 Residual Error 14 62.57 62.57 4.47 Lack-of-Fit 10 29.03 29.03 2.90 0.35 0.92 Pure Error 4 33.54 33.54 8.38 Total 29 1533.43 DF: degree of freedom; Seq SS: sequential sum of square; Adj SS: adjusted sum of square; Adj MS: adjusted mean square; F: variance ratio (Fisher F-value); p: probability value direct use without detoxifcation. Generally hydrothermal Fig. 5. Te gradual uptake of xylose concomitant with pretreatment leads to hydrolysis of acetyl groups attached xylitol production was noticed when glycerol was almost to the hemicellulosic backbone and as a result acetic acid completely exhausted. Te maximum cell OD600 of 48.6 is formed [28]. It is likely that acetic acid and other furan was observed at 120 h with co-fermentation of PG and aldehydes were inhibitory and their presence negatively xylose, which is higher than achieved during shake fask afected the performance of Y. lipolytica during biotrans- cultivation. By the end of 168 h there was complete formation. Tis experiment also gave an insight into the consumption of xylose and a maximum 53.2 g/L xylitol importance of undertaking a detoxifcation step to remove (Fig. 5a) was produced with a yield of 0.97 g/g. Te fer- or minimize the inhibitory compounds. mentation profle of Y. lipolytica with CG and pure xylose is shown in Fig. 5b. Te maximum cell OD600 Submerged batch cultivations in bioreactor recorded was 31.8, not far from the value obtained in In order to scale up the fermentation and validate the the shake fask studies. Te yield of the xylitol was about optimized medium composition, batch cultivations 0.92 g/g with a titre of 50.5 g/L. Furthermore, pH for were carried out in a 2.5 L scale bench bioreactor with the fermentation using pure carbon source fuctuated 1 L working volume. Te process condition mimicked between 6.5 and 4.7, whereas in the fermentation with was exactly that of the shake fask studies except for the CG the pH reduced to 4.3. It is plausible that CG had aeration. Te aeration rate was maintained at 2.0 L/min some impurities which interfered with the bufer capac- for frst 48 h and then reduced to 1.0 L/min for the rest ity of the phosphate bufer, resulting in further drop in of the test. Two separate batch fermentations were run pH. In the growth phase (0–48 h) where biomass accu- with PG/xylose and CG/xylose. Te time course pro- mulation took place, aeration rate was high as glycerol fles for both the fermentations were similar to that of assimilation is known to be dependent on the oxygen the shake fask cultivations. Glycerol is the most pre- uptake rate [29]. Low aeration is maintained in the biore- ferred carbon source for Y. lipolytica and the presence actor during the xylitol production phase because excess of glycerol repressed the uptake of xylose as evident in aeration causes re-oxidation of NADH, a co-factor Prabhu et al. Microb Cell Fact (2020) 19:121 Page 7 of 18

Fig. 2 Three-dimensional response surface plot for xylitol production showing the interactive efects of a xylose & NH 4Cl, b xylose & YNB, c xylose & phosphate bufer, d YNB & NH4Cl, e phosphate bufer & NH4Cl and f YNB & phosphate bufer

ab:R=0.93887 Best: -47.7156Mean: -47.7153 -20 35 Data Fit Best fitness Y=T Meanfitness 30 -25 3. 2 25 t+ -30

20 alue -35 0.86*Targe 15 ~= Fitnes sv -40 10 Output

-45 5

0 -50 050100 150200 250300 350 400 0510 15 20 25 30 35 Generation Target Fig. 3 The prediction performance of ANN models for xylitol production: a overall regression value of simulated ANN; b best and average ftness values with successive generations showed gradual convergence to the optimum value necessary for xylitol production from xylose and NAD + Xylitol production by resting cells produced can facilitate further metabolism of xylitol for Resting cells are metabolically active non-growing cells cell growth [30]. [31]. Resting cells show an advantage over active cells such as simple operation, no requirement of nutrient Prabhu et al. Microb Cell Fact (2020) 19:121 Page 8 of 18

Fig. 4 Shake fask cultivation of Y. lipolytica using optimized media on; a PG and xylose, b CG and xylose, c PG and xylose from SCB hydrolysate.

Symbols: flled square (PG or CG), flled circle (xylose), flled triangle (OD 600), empty circle (xylitol) and flled star (pH) Prabhu et al. Microb Cell Fact (2020) 19:121 Page 9 of 18

Fig. 5 Batch kinetics of substrate assimilation, cell growth, pH and xylitol formation by Y. lipolytica in bioreactor during co-fermentation on; a PG

and xylose, b CG and xylose. Symbols: flled square (PG or CG), flled circle (xylose), flled triangle (OD 600), empty circle (xylitol) and flled star (pH)

medium and convenient downstream processing [32, 33]. the glycerol grown cell pellets in bufer containing three In the current study Y. lipolytica is carrying out a single diferent concentrations of xylose (30, 70 and 100 g/L). step biotransformation of xylose to xylitol. It is therefore It is evident from Fig. 6 that the conversion of xylose to worth exploring the potential of the yeast as a biocatalyst xylitol was not satisfactory. Te xylitol obtained for the for the continuous production of xylitol through reusabil- case of cells accumulated on CG was ~ 10 g/L regardless ity of the cells; in other words, the growth phase was split of xylose concentration. Te performance of the resting from the xylitol production phase. Te Y. lipolytica cells cells grown in PG were better and the highest amount of were grown using the PG and CG in shake fask using the xylitol recorded with PG grown cell was ~ 28 g/L from culture medium as described in “Material and methods” 30 g/L xylose. Te experimental results clearly indicated section. Te fermentation was terminated once the cell that the xylitol biotransformation yield signifcantly OD600 reached 20–25, nearly after 48 h and the cells were reduced with further increase in xylose levels. Te proba- collected through centrifugation. Te obtained cell pel- ble cause for the cessation of the biocatalytic activity, may let was suspended in bufer containing only xylose. Te be due to the lack of ability of the already built biomass feasibility of using the resting cells of Yarrowia lipolytica of Y. lipolytica cells to replenish the rate limiting cofac- for xylose biotransformation was checked by suspending tors for continuous and smooth biotransformation as Prabhu et al. Microb Cell Fact (2020) 19:121 Page 10 of 18

precipitation step further reduced the recovery of xylitol. Pure Glycerol + Xylose Te fnal crystallization step carried out at − 20 °C for 100 Crude Glycerol + Xylose 72 h resulted in 35.3 and 39.5% xylitol recovery for CG/

) xylose and PG/xylose, respectively (Table 3). Despite /L the presence of impurities, similar recovery results were (g obtained with CG and PG. Te results of xylitol recov- 70 se ery are encouraging and can be improved with further lo modifcations. Xy 30 Discussion Xylitol is a polyol of commercial interest due to its high 0 10 20 30 sweetening power and anticariogenic properties. Due Xylitol (g/L) to its multiple benefts, the market is growing with an Fig. 6 Biotransformation of xylose to xylitol by resting cells of Y. increase of 6% per year. Te industrial route requires lipolytica grown using PG and CG pure xylose and the process is operated at high temperature and pressure. Te chemical route is advantageous in terms of yield but has a number of hurdles to be over- come such as extensive purifcation, product recovery, there was no nutrient in the bioconversion stage to sup- catalyst deactivation, and energy intensity. All these fac- port cofactor supply. Te presence of xylose alone in the tors make the process expensive and non-sustainable media would not be sufcient to produce the cofactors [14]. Te biotechnological production of xylitol is envi- required for the conversion. ronmentally safe and does not use toxic catalysts. Te approach would be sustainable and create economic ben- Purifcation of xylitol from the fermentation broth efts if integrated with waste feedstocks rich in renewable Downstream processing costs are usually one of the carbon [13, 35]. Te review of the literature shows that major obstacles for the economical production of chemi- there has been two strategies for the bioproduction of cals. Crystallization is the more commonly adopted xylitol; use of xylose as sole carbon source for cell growth method in the purifcation of polyols as it allows recovery as well as xylitol accumulation; and co-feeding another of xylitol in a purifed form in a single step. In terms of carbon source along with xylose for biomass formation. energy consumption, crystallization is less energy inten- Te co-fermentation of xylose and another substrate is sive when compared to a distillation process. Crystalli- preferred over the sole use of xylose for microbial xylitol zation can be performed using various methods such as production [36–38]. In the former approach, low yield solvent evaporation, cooling and salting out [34]. Xylitol and productivities are obtained due to a number of rea- is a solid at room temperature with melting point > 90 °C. sons; slow metabolism on xylose, partitioning of xylose In the initial step, the coloured substance recovered was fux between cell growth and xylitol formation, and re- clarifed using charcoal treatment, with 5% activated consumption of produced xylitol. Terefore, it is more charcoal. Te fermentation broth almost became trans- desirable to grow cell biocatalysts on a preferred carbon lucent and the impurities such as residual xylose were source so that a high cell density could be generated in a removed. Te recovery of xylitol after charcoal treatment short time leading to higher productivities and yield. step was 76.2 and 77.1% for CG/xylose and PG/xylose, Te current study examined the potential of non-con- respectively, as shown in Table 3. A subsequent alcohol ventional yeast Y. lipolytica for xylitol production which

Table 3 Xylitol titer and percent recovery obtained during diferent steps of downstream processing of fermentation broth from batch cultivation of Y. lipolytica in shake fask on CG/xylose and pure PG/xylose Purifcation steps CG Xylose PG Xylose + + Xylitol (g/L) Recovery (%) Xylitol (g/L) Recovery (%)

Crude fermentation broth 48.2 100 54.0 100 Activated charcoal treatment 37.1 76.9 39.7 73.5 Alcohol precipitation 24.2 49.7 27.1 52.6 Crystallization 17.2 35.3 20.3 39.5 Prabhu et al. Microb Cell Fact (2020) 19:121 Page 11 of 18

is known to accumulate high levels of lipids, organic acids glucose in co-fermentations [16–18]. Workman et al. and polyols [15]. Te biochemical production of xylitol [16] found that glycerol assimilation was accompanied takes place through reduction of xylose to xylitol medi- with higher oxygen uptake rates in comparison to glu- ated by xylose reductase (XR) and the electron transfer cose and maximum growth rate of Y. lipolytica on glyc- for this reaction is facilitated through participation of the erol (0.30 h−1) was 25% higher than on glucose (0.24 h−1). redox cofactor NAD(P)H (Fig. 7). Te produced xylitol is Te hypothesis for glycerol preference/repression like further oxidized to xylulose catalysed by xylitol dehydro- efects by Y. lipolytica is that its genome contain only one genase (XDH), which is then phosphorylated to xylulose- hexose transporter but three genes linked with glycerol 5-phosphate by enzyme xylulose kinase (XKS) and enters transport. Lubuta et al. [18] performed RNA-Seq-based the central carbon metabolism for cell maintenance and transcriptome analysis in Y. lipolytica and argued that the growth. Te absence of XDH enzyme, its weak activity higher expression of several transporters could be poten- and/or imbalance in activities of XR/XDH result in accu- tially related to the phenotypic observation of glycerol mulation of xylitol [14, 37, 39]. Te majority of Y. lipo- preference. A similar kind of phenomenon can be envis- lytica strains cannot grow on xylose as documented by aged in the present strain as well. In addition, being an many literature reports [17, 19, 27]. According to Rodri- oleaginous yeast, Y. lipolytica, can consume even the CG, guez et al. [20], the complete xylose pathway exists in Y. a major industrial by-product with same the efciency as lipolytica but inability to grow robustly on xylose arises PG [42]. due to poor expression of key enzymes (XDH and XKS) Te media optimization using CCD coupled with controlling the pathway. Te Y. lipolytica Po1t (Ura + ANN-GA resulted in higher biotransformation efciency, Leu+) strain used in the current study and in a previous attaining a yield of more than 90% using PG and CG with work [19], has been shown to accumulate xylitol when xylose. Previously Pappu and Gummadi [43] adapted the cultured on xylose. Te high yield obtained was a stimu- ANN-GA model for optimizing process parameters such lus to carry out a detailed study to investigate the poten- as pH, temperature and volumetric oxygen transfer co tial of Y. lipolytica for xylitol production. efcient KLa to enhance xylitol production in Debaryo- Xylitol is an extracellular metabolite and its production myces nepalensis. With hybrid ANN-GA optimization, is afected by many factors including medium composi- they reported an optimum predicted error of 3.5% and tion, cell density and growth rate. Besides high xylose maximized xylitol yield of 0.53 g/g in batch bioreactor. concentrations, an optimal balance of other nutrients is Te results highlight the importance of nonlinear mod- necessary to achieve industrial levels of xylitol [39, 40]. elling to optimize parameters in biochemical systems. In We started growing Y. lipolytica on a mixture of glucose our study, nitrogen source (NH 4Cl) and YNB showed a or glycerol and xylose to choose better co-substrate. We momentous efect on the xylitol bioconversion. NH4Cl obtained similar results for cell growth on both the car- plays a crucial role in enhancing the protein/enzyme bon sources, however, glycerol assimilation was faster expression level as the transcription of carbon metabolis- than that of glucose. In addition, the biotransformation ing gene is relied upon as the nitrogen source [44]. On rate and yield on glycerol were signifcantly higher than the other hand, YNB comprises of essential components glucose (Fig. 1). Glycerol being a more reduced carbon such as amino acids, vitamins, salts and trace elements source than traditional carbohydrates (glucose/sucrose/ required for yeast growth. Xia et al. [45], reported excel- xylose) can provide better supply of reducing equivalents lent fermentation capability of xylose by C. shehatae in (NAD(P)H which is benefcial for xylitol formation [37, medium supplemented with YNB, whereas impaired 41]. Te preference of Y. lipolytica for glycerol is also well growth was witnessed when the medium was devoid of documented in literature. Te yeast prefers glycerol over YNB supplementation. In a recent work, YNB was found glucose and presence of glycerol but not that of glucose to be a signifcant component for xylitol production by represses the uptake of other carbon sources including Candida tropicalis JA2, when Plackett–Burman design

Fig. 7 Xylose metabolism in yeast [5, 37] Prabhu et al. Microb Cell Fact (2020) 19:121 Page 12 of 18

was done for nitrogen sources and various salts [46]. of xylitol, there is a likelihood that it not only reduced the Te results of the shake fask were replicated when the afnity of water to form H-bond with xylitol leading to experiments were scaled up in bioreactor. Te fermen- its partial precipitation but simultaneously also formed tation profles can be divided into two phases; growth H-bond with xylitol resulting in its poor recoveries. and biotransformation phase. Te biotransformation Biological production of xylitol has been studied for of xylose started only after when a large fraction of co- decades using a number of organisms including bac- substrate was consumed and may be due to carbon teria, yeasts and fungi. In general, the bioproduction of catabolite repression. Te interesting observation was xylitol by bacteria and fungi have lower performance in that cell growth was continuously increasing even after comparison to yeasts. Among yeasts, Candida species complete consumption of glycerol as probably, some of are the most researched and best organisms for biopro- the accumulated xylitol was contributing to cell growth duction of xylitol, yielding high conversion rates and pro- after exhaustion of glycerol. More work is required to ductivities [51, 52]. Table 4 compares the results obtained decode this, however, results are in agreement with those in the current work with the literature reports and large of Ledesma-Amaro et al. [19]. Te idea of the resting variation exists in terms of xylitol titer, yield and pro- cell experiment was to investigate the reusability of the ductivity. Most of the organisms shown in Table 4 have cell biocatalysts repeatedly, which will improve the bio- active xylose assimilation pathway and demonstrated process economics. Somehow, the results were not very incredible xylitol producing ability using xylose as a sole encouraging. We suspect that it could be due to dis- carbon source. Some of them have been metabolically rupted supply of reduced pyridine nucleotides and in engineered to design hyper xylitol producing cell facto- future studies the biotransformation medium could be ries. Su et al. [53] designed recombinant Escherichia coli supplemented with co-substrate at regular interval for by xylose pathway engineering and eliminating carbon uninterrupted supply of a redox cofactor to carry out the catabolite repression to allow simultaneous utilization reduction reaction [14]. of glucose and xylose. Tis recombinant strain yielded Te fnal step of recovery and purifcation of a product 172.4 g/L in 110 h. Majority of the reports including cur- in a bioprocess is challenging and determines the feasi- rent study sufers from low volumetric productivities bility of the process, especially for products synthesized restricting their application in a broader perspective. Te from crude renewable sources. Its complexity depends use of resting cells, membrane bioreactors and fed-batch on the nature of product and composition of the fermen- fermentation has resulted in remarkable improvement in tation broth. Te information on xylitol recovery from productivities in range of 3–12 g/L h [54–56]. fermentation broths is scarce in literature [13]. Tis study It is important to discuss here the report by Hallborn reafrmed that a low cost easily available adsorbent like et al. [69] where xylitol was produced from recombinant activated charcoal could efciently remove the impurities Saccharomyces cerevisiae. S. cerevisiae and Y. lipolytica and simultaneously decolorized the xylitol rich fermen- are two very diferent organisms; former is a conven- tation broth as reviewed previously [34]. Earlier, Gurgel tional yeast and later is non-conventional oleaginous et al. [47] recovered ~ 80% of xylitol when 25 g of acti- yeast. S. cerevisiae, exhibits a clear preference for glu- vated charcoal was added to 100 mL fermentation broth cose and is well adapted to assimilate it. Te ability of S. with pH adjusted to 6.0 and incubated at 80 °C for an cerevisiae to utilize glycerol is limited and therefore, the hour. Tis result is inferior to the present results where yeast is not an attractive cell factory to use glycerol, a lesser quantity of activated charcoal (25% versus 5%) for major industrial byproduct. Contrary to S. cerevisiae, Y. xylitol purifcation was used. However, when diferent lipolytica prefer glycerol over glucose and exhibits higher types of activated carbon were evaluated, Wei et al. [48] growth rates on glycerol than glucose [16–18]. Hallborn observed that 4% M1 type of activated carbon was able et al. designed the recombinant strain of S. cerevisiae by to recover 95% xylitol with 96% decolourization. Tus, overexpressing xylose reductase to enable xylitol produc- there is a scope of screening diferent forms of activated tion in the yeast whereas Y. lipolytica used in the current carbon for further improving the xylitol recovery. Te work cannot grow on xylose but has the natural ability xylitol recovery further reduced in the fnal step of alco- to transform of xylose to xylitol [19]. Te cell biomass of hol precipitation step and dropped to 35–40%. Te same recombinant S. cerevisiae was grown on glucose for the strategy used previously resulted in the recovery of 43.7% biotransformation of xylose to xylitol. Tough the xylitol xylitol [49]. When molecular dynamic computer simula- yield (g/g) obtained in both the studies were over 90%, tions were carried out for the binary mixture of polyols the titer achieved in current work (53.2 g/L) was much and water, Politi et al. [50] suggested that water forms higher than Hallborn et al. (19.0 g/L). an average number of 1.3 H-bonds with xylitol. How- Te Y. lipolytica strain used in the current work lacks ever, when excess of ethanol was used for precipitation the efective pentose pathway and require addition Prabhu et al. Microb Cell Fact (2020) 19:121 Page 13 of 18

Table 4 Bioproduction of xylitol by diferent microorganisms Microorganism Xylitol Reference Titer (g/L) Yield (g/g) Productivity (g/L h)

Corynebacterium sp. B-4247 48 0.48 2.0 [57] Corynebacterium glutamicum 166 – 7.9 [38] Escherichia coli 172.4 – 1.57 [53] Candida guilliermondii FTI-20037 77.2 0.74 – [58] Candida tropicalis 131 0.87 2.91 [59] Candida boidinii NRRL Y-17213 53.1 0.47 0.16 [60] Candida sp.559-9 173 0.87 1.44 [40] Candida athensensis SB18 256.5 0.87 0.97 [61] Candida tropicalis CCTCC M2012462 38.8 0.70 0.46 [62] Candida tropicalis SS2 220 0.93 3.3 [55] Candida tropicalis KCTC 10457 182 0.85 12.0 [54] Pichia sp. YS 54 25 0.76 0.50 [63] Debaryomyces hansenii UFV-170 76.6 0.73 0.37 [64] Hansunela anomala NCAIM Y.01499 21.7 0.47 0.23 [65] Kluyveromyces marxianus CCA510 12.3 0.50 0.17 [66] Kluyveromyces marxianus YZJ015 71.4 0.89 4.43 [56] Hansenula polymorpha 58 0.62 0.60 [67] Saccharomyces cerevisae 19 0.95 ~0.20 [69] Aspergillus niger PY11 1.14 0.101 – [68] Y. lipolyticaa 53.2 0.97 0.32 This study Y. lipolyticab 50.5 0.92 0.30 This study a Pure glycerol as co-substrate b Crude glycerol as co-substrate of co-substrate for growth. Te competitive titer and present in CG limits its application in chemical indus- yield near to the theoretical demonstrates the remark- tries as the refning costs ofset the proft and because it able potential of Y. lipolytica for xylitol production. Te has limited applications. For example, the large amount of results obtained are better than many of existing reports. CG generated in UK from biodiesel industries (Greenergy, Te volumetric productivity is not competitive with the Croda, Oil Works) has no proper use and is exported to best xylitol producers, but we are hopeful that it can be Germany, Netherland and East Europe and sold at very improved to industrial level using metabolic and process low price (£40–£150/ton). Some small manufacturers are engineering approaches as mentioned above. Te other even paying for collection of CG [72]. However, if the CG advantageous feature of Y. lipolytica is the limited ability could be used locally, then environmental and economic to cause only mild, self-limiting infections which confer benefts may be gained. To the best of our knowledge, this the GRAS status to the yeast [70]. Te main application of is the frst study where CG was used for xylitol produc- xylitol is in food and pharmaceutical industries, but patho- tion. Te results obtained with PG as well as CG were genic behaviour of most of the promising xylitol producers consistent indicating a high level of tolerance by the yeast impedes their commercialisation. For example, Candida to the impurities in CG as no inhibition was observed sp are prolifc pathogens and cause globally almost 90% of during the course of fermentation. Tese results are in fungal infections. As a result, the use of Candida sp. are agreement with many other studies where Y. lipolytica has prohibited in food industries [39, 40, 71]. Te high yield of been cultured on CG for the production of organic acids xylitol achieved with Y. lipolytica along with its safe behav- such citric acid, succinic acid etc. [15, 73, 74]. iour keep it in superior position and make it a promising microbial cell factory for xylitol production. Conclusion In the current study, the bioproduction of xylitol was Xylitol is a platform chemical with vast commercial coupled with two waste streams, CG, a major industrial potential. Tis is the frst detailed report of biopro- by-product and xylose, the second major sugar present duction of xylitol by Y. lipolytica. Te current work in the hemicellulosic fraction of biomass. Te impurities Prabhu et al. Microb Cell Fact (2020) 19:121 Page 14 of 18

demonstrates enormous potential of Y. lipolytica to con- 50 mL YPD broth. Te fnal pH of the medium prior to vert xylose to xylitol with a yield near to the theoreti- sterilization was adjusted to 7.0. Cultivation was carried cal (> 90%). It produces similar concentrations of xylitol out for 24 h at 30 °C on a rotary shaker at an agitation to some of the best xylitol producing organisms such as speed of 250 RPM. Candida strains. Moreover, it is a safe organism to use with GRAS status and exhibited high tolerance to CG Submerged cultivations in shake fask and xylose. Employment of unconventional feedstocks as Te fermentation medium had the following composi- carbon sources is highly desirable for the economic via- tion: (g/L) PG/CG/glucose, 20; xylose, 20; yeast nitrogen bility of biorefneries and becomes a good destination for base (YNB), 1.7; NH Cl, 1.5. Te medium was prepared renewable carbon-rich wastes. Te study demonstrated 4 in 50 mM phosphate bufer. Te initial pH was adjusted the feasibility of simultaneous valorisation of two major to 6.8 before inoculation by using 5 N NaOH. Te sub- wastes, CG and xylose, which can be utilized as cheaper merged cultivations were carried out in 500 mL shake feedstocks. Te strategy can be conducive towards devel- fasks containing 100 mL working volume. Te fasks opment of a bioprocess as an alternative to the commer- were inoculated with fresh inoculum at OD (optical cial chemical route and could support the sustainability 600 density) of 0.1 and kept at 30 °C under constant shak- of biodiesel industries/lignocellulosic biorefneries. More ing at 250 RPM on a rotary shaker (Excella 24, New work is required to optimise the metabolic engineering Brunswick). and process scale-up to improve the economics of the bioprocess. Central composite design (CCD) and artifcial neural network linked genetic algorithm (ANN‑GA) for media Materials and methods optimization Materials Te CCD was carried out, with the view of optimizing All chemicals used in this study were of analytical grade the variables and to give insight over the combined efect and purchased from Sigma-Aldrich and Fisher scientifc, of four variables (xylose, YNB, NH4Cl and phosphate unless stated otherwise. CG used was kindly provided bufer) at constant glycerol concentration on maximizing by Greenergy, UK. Te CG contained glycerol (72.8%), the production of xylitol concentration. Design-Expert non-glycerine material (soaps, fatty acids, esters, salts, software (version 7.0) was used to develop CCD for four other organic byproducts) (5.7%), methanol (2.0%), water independent variables and fve levels (Table 5). Te total (12.2%) and ash (9.6%). Te xylose (26.4 g/L) rich SCB number of experiments (N) was based on Eq. (1) hydrolysate was obtained from Nova Pangea Technolo- N = 2k + 2k + 6 gies, UK. (1) where k is the number of independent variables. Te Microorganism, culture maintenance and inoculum experiment comprised 2 axial points and 6 replicates for preparation centre points for the evaluation of pure error. Te sec- Te current study made use of Y. lipolytica Po1t (Ura+, ond-order polynomial for predicting the optimal levels Leu+) derived from wild-type strain W29 (ATCC20460). was expressed according to the Eq. (2). Te Y. lipolytica strain was preserved in 20% glycerol k k (v/v) at 80 °C and maintained on a petri dish contain- Y = β + β X + β X2 + β X X + ε − i 0 i i ii i ij i j ing YPD agar medium (1% yeast extract, 2% Peptone, 2% i=1 i=1 i

Table 5 Experimental codes, range and levels of the independent variables used for central composite design (CCD) experiments Independent variables Units Symbol code Coded value α 1 0 1 α + − −

Xylose g/L X1 5 20 35 50 65

YNB % (w/v) X2 0.05 0.2 0.35 0.5 0.65

NH4Cl % (w/v) X3 0.05 0.2 0.35 0.5 0.65

Phosphate bufer mM X4 2.5 35 67.5 100 132.5 Prabhu et al. Microb Cell Fact (2020) 19:121 Page 15 of 18

where, Yi is the Predicted response; β0 βi, βij, βii are con- creating a new set of individuals [76, 77]. Tis process stant and regression coefcients of the model, Xi, Xj rep- is repeated until a global optimum value is achieved. resent the independent variables in coded values and ε Te trained neural network model was used as a ft- represents the error. ness function to further optimise the input space. Te To further optimise the media components, the arti- schematic representation of ANN-GA algorithm for fcial neural network (ANN) methodology was adapted. optimisation of medium components to maximize ANN is biological inspired model, which mimics neu- xylitol production was shown in Additional fle 1: Fig ral system and tends to optimize non-linear systems. S1. Te objective function of GA is given by Eq. 5: Multi-layer perceptron method was incorporated, and Maximize Y = f(x, w),xL ≤ x ≤ xu,i= 1, 2, 3 ...P training of the network was based on feed-forward i i i back propagation method. Te network architecture (5) consisted of four input layers (xylose, YNB, NH 4Cl, where f is the objective function (ANN model), x denotes phosphate bufer), eight hidden layers and one output input vector, w denotes corresponding weight vector, Y layer representing xylitol concentration. In the feed- refers to the xylitol experimental yield, X denotes operat- L forward training system, the data was channelized from ing conditions, P denotes number of input variables, xi & U input to output via., hidden layer, which was connected xi are lower and upper bounds of xi ftness of each candi- by parameters such as weights (w) and biases (b). date solution. Transfer functions such as tan sigmoid (f1: tansig) and Pure linear (f2: purelin) were situated between hidden Model validation under shake fask conditions and output layer, respectively. Tansig sums up weighted Te integration of CCD and ANN-GA predicted some input including the biases, and the purelin carried out crucial parameters and their concentrations, which could the linearization function for the output. Te predicted give optimum xylitol yields. Terefore, it became essen- output function is represented by the Eq. (3) tial to validate the predicted values at shake fask level based on the global optimum values obtained by ANN- Y = f 2 w0 × f 1 × wH × inputvector + bH + b0 p GA training. Shake fask studies were conducted with (3) PG as co-substrate. However, simultaneously the ef- o o H H cacy of Y. lipolytica Po1t (Ura+ Leu+) using co-substrate where Yp is the predicted response, w , b and w , b are weights and biases of the output and hidden layer, combinations namely PG + xylose rich SCB hydrolysate respectively. Te network training was carried out by and CG + pure xylose was also evaluated to assess the adapting Levenberg–Marquardt (LM) backpropagation tolerance, utilization and biovalorization ability of the algorithm, which calculates error function based on the said strain for carbon sources derived from renewable diference between actual output and predicted output. feedstock. Te algorithm was trained repeatedly until subsequent Batch cultivation in bioreactor minimisation in the error between the input and output layer is met [75]. Mean squared error (MSE) was used to Te batch experiments were performed in a 2.5 L bio- calculate error function using Eq. (4). reactor (Electrolab Bioreactors, UK) with 1.0 L working volume. Te inoculum was prepared using optimised N 1 2 media and the optimum values of media components MSE = Y − Y N a p (4) were as follows (g/L): PG/CG, 20; xylose, 55; YNB, 5.0; i=1 NH4Cl, 3.94; phosphate bufer, 132.5 mM. Te starting where, Ya is the actual output, Y p is the predicted output pH was 6.8 and not controlled during the fermentation. and N is the number of data points. Te simulation of the Te temperature and agitation speed were controlled at network was carried out by in built neural network tool- 30 °C and 250 RPM, respectively, while the aeration rate box of MATLAB (version 2010a). was maintained at 2.0 L/min for initial 48 h and then Genetic algorithm (GA) is a heuristic method used to changed to 1.0 L/min for the rest of fermentation period. determine the global optimal solution for a non-linear problem and are independent of initial values; GA is Biotransformation by resting cells often coupled with ANN to achieve precise optimiza- For active cells, Y. lipolytica was grown on optimised tion values. GA follows four steps to fnd a global solu- medium with PG in 500 mL fasks containing with tion. In the frst step, initialization of the solution for 20% working volume. Te temperature, pH and agita- the population takes place followed by ftness computa- tion speed were maintained at 30 °C, 6.8 and 250 RPM, tion. Te selected individual based on the ftness com- respectively. For the second stage (biotransformation), putation then undergoes crossing over and mutation, the cells were harvested in the late exponential period Prabhu et al. Microb Cell Fact (2020) 19:121 Page 16 of 18

(after 48 h) when the OD was somewhere between 20 Acknowledgements 600 We are grateful to CBMNet and Ineuvo Ltd for funding this work. The authors and 25. Immediately after, the culture was centrifuged at express gratitude to BBSRC, Innovate UK and Department of Biotechnol‑ 2800×g for 10 min, and the resulting pellet was washed ogy, India for funding the vWa Project (Grant R1954CCE). We are thankful to with ice-cold 100 mM phosphate bufer (pH 7.0). Te Cranfeld University for providing facilities for conducting experiments. We acknowledge our industrial partner, Nova Pangaea Technologies and Biodiesel cells were resuspended in a bioconversion medium con- company Greenergy, UK for providing xylose rich SCB hydrolysate and crude taining xylose (30, 70 and 100 g/L) in phosphate bufer glycerol, respectively for this work. (100 mM). Te bioconversion experiments were carried Authors’ contributions out at 30 °C with freshly prepared biomass. AAP and DJT carried out all the experimental work. VK analyzed the data and wrote the manuscript. GAL, AM, CV-V, FC and DA were involved in proof read‑ Downstream processing of xylitol ing the manuscript. RLA provided useful suggestions for experimental design and revised the manuscript critically. All authors read and approved the fnal Te purifcation protocol for xylitol was performed manuscript. according to Rivas et al. [78]. Te 100 mL of spent fermentation broth was subjected to centrifugation at Funding The vWa Project (Grant R1954CCE) is funded by the BBSRC, Innovate UK and 20,000×g to separate the cells and the clarifed broth was Department of Biotechnology. The Master study of DJT is supported through treated with 5% activated charcoal. Te charcoal treated funding from CBMNet and Inevo Ltd. The funders had no role in study design, broth was precipitated by adding four volume of abso- data collection and analysis, decision to publish, or preparation of the article. lute ethanol and incubated at 4 °C for 1 h. After 1 h, the Availability of data and materials precipitates were removed by centrifuging the mixture at All data generated and analysed during this study are included in this pub‑ lished article and its additional fles. 4000×g for 10 min. Te supernatant was vacuum concentrated at 40 °C. Te concentrated sample and ethanol Ethics approval and consent to participate were mixed at a ratio of 1:4 and incubated at − 20 °C with Not applicable. slight agitation (50 RPM) until crystals were observed. Consent for publication To improve the crystallization about 1 g/L of xylitol was Not applicable. mixed with the concentrated sample. Competing interests Analytical methods The authors declare that they have no competing interests. Te samples were withdrawn periodically and analysed Author details 1 for OD, pH, residual glycerol/glucose, xylose and xylitol. School of Water, Energy and Environment, Cranfeld University, Cran‑ feld MK43 0AL, UK. 2 Department of Bioengineering and Imperial College Cell growth was quantifed by measuring the optical den- Centre for Synthetic Biology, Imperial College London, London SW7 2AZ, UK. sity at 600 nm wavelength in a 1 mm-path-length cuvette 3 School of Chemical Engineering, University of Birmingham, Birmingham B15 4 using a double beam spectrophotometer (Jenway 6310, 2TT, UK. Biochemistry and Biotechnology Area, Material Resource Efciency Division, CSIR-Indian Institute of Petroleum, Mohkampur, Dehradun 248005, UK). One unit of absorbance at 600 nm corresponded to a India. cell dry weight (CDW) of 0.21 g/L. Te concentrations of glycerol, glucose, xylose and xylitol were measured by high Received: 23 March 2020 Accepted: 25 May 2020 performance liquid chromatography (Agilent Technolo- gies 1200 series, USA). Te supernatants, obtained by centrifugation of the culture samples at 10,000 g for 10 min, × References were fltered through a 0.22 µm PVDF membrane (Sartori- 1. Nizami AS, Rehan M, Waqas M, Naqvi M, Ouda OKM, Shahzad K, et al. ous, Germany) and eluted using Rezex ROA-Organic Acid Waste biorefneries: enabling circular economies in developing countries. H (Phenomenex, USA) column at 60 °C attached with Bioresour Technol. 2017;241:1101–17. + 2. Naik SN, Goud VV, Rout PK, Dalai AK. Production of frst and second refractive index detector (RID). Te mobile phase and fow generation biofuels: a comprehensive review. Renew Sustain Energy Rev. rate were 0.5 mM H2SO4 and 0.4 mL/min, respectively. All 2010;14:578–97. measurements were conducted in triplicates and the values 3. Menon V, Rao M. Trends in bioconversion of lignocellulose: biofuels, platform chemicals & biorefnery concept. Prog Energy Combust Sci. were averaged. Te standard deviation was not more than 2012;38:522–50. 10%. 4. 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