Journal of Chemical Technology and Biotechnology
J Chem Technol Biotechnol 81:832–840 (2006) DOI: 10.1002/jctb.1450
Mn-peroxidase production by Panus tigrinus CBS 577.79: response surface optimisation and bioreactor comparison Daniele Quaratino,1,2 Massimiliano Fenice,1 Federico Federici1 and Alessandro D’Annibale1∗ 1 Dipartimento
di Agrobiologia and Agrochimica, Universita` degli Studi della Tuscia, Via San Camillo de Lellis, snc I-01100 Viterbo, Italy Consortium ‘The Chemistry for the Environment’ (INCA), Venezia, Italy
2 Interuniversitary
Abstract: This study reports the statistical optimisation through response surface methodology of the growth medium for Panus tigrinus manganese-dependent peroxidase (MnP) production in shaken culture. Three crucial variables, including carbon source, malonic acid and Mn2+ , were optimised in a nitrogenlimited medium. Sucrose was the best carbon source for MnP production. Mn2+ ions and malonic acid significantly stimulated MnP production at an optimal concentration of 53 mg dm−3 and 8.2 mmol dm−3 , respectively, resulting in 0.83 U cm−3 . Further experiments were performed in lab-scale stirred tank (STR) and bubble-column (BCR) reactors using the previously optimised liquid medium. BCR proved to be more adequate than STR in supporting MnP production, leading to 3700 U dm−3 after 144 h with a productivity of 25.7 U dm−3 h−1 . On a comparative basis with other production data in lab-scale reactors, these results appear to be compatible with scale transfer. 2006 Society of Chemical Industry
Keywords: Mn-dependent peroxidase production; Panus tigrinus; response surface method; optimisation; shaken cultures; bioreactor
INTRODUCTION Development of suitable medium and culture conditions is an obligatory step in obtaining high enzyme yields. The factorial design and response surface methodology (RSM) is commonly used in biotechnology for the optimisation of media and culture conditions. Statistical optimisation allows one to perform a quick screening of a large experimental domain and to determine the influence of each variable and their possible interactions. Basically, this is a threestep optimisation process involving the estimation of coefficients in a mathematical model, prediction of the response and checking of the model adequacy. Among lignin-modifying extracellular enzymes, Mn-dependent peroxidase (E.C. 1.11.1.13 Mn2+ : hydrogen peroxide oxidoreductase; MnP) is gaining increasing attention owing to its possible use in wastewater treatment,1 bioremediation2 and bio bleaching applications.3 MnP is a glycosylated haem protein able to catalyse the oxidation of Mn(II) into reactive Mn(III) which is, in turn, stabilised by fungal organic acids such as oxalate, malonate
and tartrate.4 These manganic chelates are highly diffusible mediators with redox potentials ranging from 0.5 to 1.1 V4 able to oxidise phenolic and some non-phenolic lignin substructures.5 – 7 One promising source for the production of this enzyme is the whiterot fungus Panus tigrinus,8 – 10 the MnP of which has been shown to be involved in the degradation of priority pollutants.11 The present investigation deals with the optimisation by RSM of the growth medium for MnP production by P. tigrinus liquid cultures. Shaken culture experiments were performed on a chemically defined nitrogen-limited medium containing low levels of olive mill wastewater (OMW), which were shown to act as inducer.10 The attention was focused on three key variables, including carbon source, Mn2+ and malonic acid, markedly affecting the production of this enzyme in other white-rot species.7,9,12,13 Medium optimisation in shaken culture was used as a basis for the upscaling of MnP production by P. tigrinus in both mechanically agitated (i.e. stirred tank reactor) and pneumatically agitated (i.e. bubble-column reactor) systems.
∗
Correspondence to: Alessandro D’Annibale, Dipartimento di Agrobiologia and Agrochimica, Universita` degli Studi della Tuscia, Via San Camillo de Lellis, snc I-01100 Viterbo, Italy E-mail:
[email protected] Contract/grant sponsor: Consorzio Interuniversitario della Chimica per l’Ambiente (INCA) Contract/grant sponsor: EC; contract/grant number: ICA3-CT-1999-00010 (Received 11 May 2005; revised version received 22 August 2005; accepted 21 September 2005) Published online 8 March 2006
2006 Society of Chemical Industry. J Chem Technol Biotechnol 0268–2575/2006/$30.00
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Mn-peroxidase by Panus tigrinus CBS 577.79
MATERIALS AND METHODS Organism and inoculum preparation Panus tigrinus (strain 577.79) was obtained from the CBS culture collection (Baarn, The Netherlands). During the study, the strain was maintained on potato dextrose agar slants at 4 ◦ C and subcultured every month. Inocula were prepared as previously described.14 Culture conditions and optimisation by RSM in shaken flasks Experiments were carried out in shaken cultures (28 ◦ C, 140 rpm) for 14 days using 250 mL Erlenmeyer flasks containing 50 mL of a medium composed of 0.5 mmoL dm−3 ammonium sulfate (corresponding to 1 mmol L−1 nitrogen), 5% (v/v) olive mill wastewater, 0.05% Tween 80 and 0.001% yeast extract and 7 g dm−3 of the selected carbon source (Medium P). The chemical composition and storage conditions of olive mill wastewater employed in this study were reported elsewhere.15 The medium was adjusted to pH 5.0 with either 1 mol dm−3 HCl or NaOH. Composition of Medium P was further optimised by response surface methodology using a D-optimal design.16 Two independent quantitative variables Mn(II) (X1 ) and malonic acid (X2 ) concentrations and one qualitative variable, i.e. carbon source (X3 ), were investigated in this study. Among carbon sources, sucrose, mannose, maltose and glucose were compared. The experimental design included a set of 22 variable combinations and three centre points, each one replicated twice. The actual values of the variables were coded as dimensionless terms using eqn (1): Xi = (Ai − Ao ∗ )/A
(1)
where Xi is a coded value and Ai the actual value of the variable, Ao ∗ the actual value of the same variable at the centre point and A the step change of the variable. The actual and coded values of this experimental design, which was replicated twice, are shown in Table 1. Culture samples were withdrawn starting from the 24th hour of fermentation. Statistical analysis and modelling Data were subjected to analysis of variance (ANOVA) and fitted according to a second-order polynomial model shown by eqn (2): Y = βo +
βi Xi +
βii Xi 2 +
βij Xi Xj
(2)
where Y is the predicted response variable, βo is the intercept, βi and βii linear coefficient and quadratic coefficient, respectively, βij is the interaction coefficient and Xi and Xj are the coded forms of the input variables. To estimate the impact of single independent variables on the response, regardless of J Chem Technol Biotechnol 81:832–840 (2006)
the presence of the other factors, main effects were calculated using eqn (3): Y = βo + βi Xi + βii Xi 2
(3)
Statistical examination of results and generation of response surfaces were performed using the software package Modde 5.0 (Umetrics AB, Ume˚a, Sweden). Reactor experiments Fermentations were carried out in a 3 dm3 jacketed bench-top stirred tank reactor (STR) (Applikon Dependable Instruments, Schiedam, The Netherlands) and a 3 dm3 bubble-column reactor (BCR) both filled with 2 dm3 of medium. STR was equipped with a top stirrer bearing two six-blade Rushtontype turbines. The following probes were installed on the top plate: dissolved oxygen sensor (Ingold, CH), double reference pH sensor (Phoenix, AZ) and PT 100 temperature sensor. Standard bioprocess conditions were as follows: inoculum size 0.25 g dm−3 mycelium dry weight; impeller speed 500 rpm (tip speed = 118 cm s−1 ), aeration rate 1.0 vvm; temperature 28 ◦ C; initial dissolved oxygen concentration 100% of saturation. Experiments in BCR (3 dm3 total volume, diameter 12 cm, height 27 cm) were performed under the following conditions: inoculum size 0.25 g dm−3 ; aeration rate 0.3 vvm; temperature 28 ◦ C. Air was injected through a fritted glass sparger (diameter 11 cm). Fermentation parameters were monitored in both the bioreactors by an adaptative/PID digital controller, ADI 1030 (Applikon Dependable Instruments, Schiedam, The Netherlands). Each condition was tested in triplicate. Enzyme and biochemical assays Manganese-dependent peroxidase activity was determined by the method of Waarishi and coworkers.4 The assay mixture (1 cm3 ) contained 0.5 mmol dm−3 MnSO4 and 75 µmol dm−3 H2 O2 in 50 mmol dm−3 tartrate buffer at pH 4.5. The formation of Mn(III)–tartrate complex was followed at 290 nm (ε = 2860 dm3 mol−1 cm−1 ). One unit of enzyme activity (U) is defined as the amount of enzyme which produces 1 µmol of product per minute under the assay conditions. Extracellular protein was determined by the dye-binding method using bovine serum albumin as a standard.17 Microbial biomass concentration was measured by dry weight estimation: broth samples were filtered on preweighed Whatman GF/C discs (diameter, 47 mm), the harvested biomass was washed twice with distilled water and the filter was dried at 105 ◦ C for 24 h, cooled in a desiccator and weighed. Total sugars were determined by the phenol–sulphuric acid method.18 Ammonium was determined spectrophotometrically at 670 nm by its reaction with hypochlorite ions and salicylate in the presence of 833
D Quaratino et al. Table 1. D-optimal quadratic design for MnP production reporting actual values of the independent variables Mn2+ (X1 ) and malonic acid (X2 ) concentrations, carbon source (X3 ) as a qualitative factor and observed and predicted values of the response. Coded values of input variables are shown in square brackets
Mn-peroxidase activity (U cm−3 )
Run 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
X1 (mg dm−3 )
X2 (mmol dm−3 )
X3
Observed
Predicted
Observed − predicted
Confidence interval
0 [−1] 60 [1] 0 [−1] 60 [1] 30 [0] 0 [−1] 60 [1] 0 [−1] 60 [1] 0 [−1] 60 [1] 30 [0] 30 [0] 0 [−1] 60 [1] 0 [−1] 60 [1] 30 [0] 0 [−1] 60 [1] 0 [−1] 60 [1] 30 [0] 30 [0] 30 [0] 0 [−1] 60 [1] 0 [−1] 60 [1] 30 [0] 0 [−1] 60 [1] 0 [−1] 60 [1] 0 [−1] 60 [1] 30 [0] 30 [0] 0 [−1] 60 [1] 0 [−1] 60 [1] 30 [0] 0 [−1] 60 [1] 0 [−1] 60 [1] 30 [0] 30 [0] 30 [0]
0 [−1] 0 [−1] 10 [1] 10 [1] 5 [0] 0 [−1] 0 [−1] 10 [1] 10 [1] 5 [0] 5 [0] 0 [−1] 10 [1] 0 [−1] 0 [−1] 10 [1] 10 [1] 5 [0] 0 [−1] 0 [−1] 10 [1] 10 [1] 5 [0] 5 [0] 5 [0] 0 [−1] 0 [−1] 10 [1] 10 [1] 5 [0] 0 [−1] 0 [−1] 10 [1] 10 [1] 5 [0] 5 [0] 0 [−1] 10 [1] 0 [−1] 0 [−1] 10 [1] 10 [1] 5 [0] 0 [−1] 0 [−1] 10 [1] 10 [1] 5 [0] 5 [0] 5 [0]
Sucrose Sucrose Sucrose Sucrose Sucrose Mannose Mannose Mannose Mannose Mannose Mannose Mannose Mannose Maltose Maltose Maltose Maltose Maltose Glucose Glucose Glucose Glucose Glucose Glucose Glucose Sucrose Sucrose Sucrose Sucrose Sucrose Mannose Mannose Mannose Mannose Mannose Mannose Mannose Mannose Maltose Maltose Maltose Maltose Maltose Glucose Glucose Glucose Glucose Glucose Glucose Glucose
0.088 0.408 0.385 0.831 0.824 0.030 0.345 0.018 0.483 0.178 0.400 0.271 0.320 0.115 0.276 0 0.276 0.420 0.106 0.010 0.007 0.050 0.400 0.210 0.280 0.200 0.308 0.394 0.735 0.700 0.035 0.420 0.015 0.570 0.171 0.620 0.280 0.330 0.138 0.276 0 0.138 0.370 0.097 0 0.069 0.110 0.160 0.410 0.290
0.152 0.415 0.420 0.812 0.716 0.027 0.330 0.039 0.471 0.178 0.545 0.299 0.376 0.105 0.259 0 0.244 0.408 0.101 0.012 0 0.049 0.309 0.309 0.309 0.152 0.415 0.419 0.812 0.716 0.027 0.330 0.039 0.471 0.178 0.545 0.299 0.376 0.105 0.259 0 0.244 0.408 0.101 0.124 0.009 0.049 0.309 0.309 0.309
−0.064 −0.007 −0.035 0.019 0.108 0.003 0.015 −0.021 0.012 0 −0.145 −0.028 −0.056 0.010 0.017 0 0.032 0.012 0.005 −0.002 0.007 0.001 0.091 −0.099 −0.029 0.048 −0.107 −0.025 −0.077 −0.016 0.008 0.090 −0.024 0.099 −0.007 0.075 −0.019 −0.046 0.033 0.017 0 −0.106 −0.038 −0.004 −0.124 0.060 0.061 −0.149 −0.100 −0.019
0.086 0.086 0.086 0.086 0.048 0.073 0.073 0.073 0.073 0.074 0.074 0.074 0.074 0.087 0.087 0.087 0.087 0.058 0.086 0.086 0.086 0.086 0.046 0.046 0.046 0.086 0.086 0.086 0.086 0.048 0.073 0.073 0.073 0.073 0.074 0.074 0.074 0.074 0.087 0.087 0.089 0.087 0.058 0.086 0.086 0.086 0.086 0.046 0.046 0.046
sodium nitroprusside to yield a blue-green reaction product.19 Non-denaturing polyacrylamide gel electrophoresis (PAGE) was performed using the discontinuous gel system of Davis.20 Gels were cast with a 4% stacking 834
gel and 10% resolving gel. Proteins were allowed to stack at 20 mA and separated at 30 mA. Prior to activity staining, gels were rinsed three times with distilled water, equilibrated in 50 mmol dm−3 tartrate buffer pH 4.0 (buffer A) and finally stained J Chem Technol Biotechnol 81:832–840 (2006)
Mn-peroxidase by Panus tigrinus CBS 577.79
with buffer A containing 0.1 mmol dm−3 orthotolidine in the presence of 2 mmol dm−3 MnSO4 and 0.1 mmol dm−3 H2 O2 .
RESULTS Table 1 summarises results obtained with the experimental design, which was aimed both at identifying the best carbon source and at assessing the impact of Mn2+ and malonic acid concentrations on MnP production. Data were best fitted by a polynomial quadratic equation, as it can be inferred by the good agreement of experimental data with those estimated by the model (Table 1). The correlation coefficient (R2 ) was 0.939, indicating that the statistical model can explain 93.9% of variability in the response (Table 2). The model Fvalue of 42.1 (P < 0.001) indicates that model terms were highly significant. In addition, the FME value of 1.69 (P = 0.141), calculated by the ratio between mean squares of model error and replicate error, indicates that the probability for lack of fit of the model was not statistically significant (Table 2). Table 2 also shows that the intercept, first-order and second-order coefficients of independent variables were highly significant, with the exception of the first-order coefficient for mannose. With regard to interaction and quadratic coefficients, Table 2 reports only those related to sucrose, which proved to be the most suitable carbon source in supporting MnP production (Fig. 1A). The main effect due to Mn2+ and malonic acid concentrations on the response are shown in Fig. 1(B, C), respectively. Figure 1(B) shows that the addition of 30 mg dm−3 of Mn2+ resulted in a significant increase Table 2. Least square estimates of coefficients of input variables Mn2+ (X1 ), malonic acid (X2 ) concentrations and carbon source (X3 ), taken as a qualitative factor. Statistical parameters measuring the correlation and significance of the model are shown in the last columns
Coefficient Intercept βo β1 β2 β3 Sucrose Mannose Maltose Glucose β1 2 β2 2 β1 β2 β1 β3 (Sucrose) β2 β3
Valuea 0.480 ± 0.018∗∗∗ 0.113 ± 0.012∗∗∗ 0.041 ± 0.012∗∗ 0.237 ± 0.017∗∗∗
Parameterb R2 R2 adj F FME Confidence level
Value 0.939 0.917 42.11∗∗∗ 1.65 n.s. 0.95
0.0037 ± 0.016 n.s. −0.070 ± 0.018∗∗∗ −0.169 ± 0.016∗∗∗ −0.120 ± 0.027∗∗∗ −0.145 ± 0.027∗∗∗ 0.032 ± 0.012∗ 0.052 ± 0.021∗ 0.128 ± 0.021∗∗∗
Estimated coefficients ± standard error and significance levels: P < 0.05; ∗∗ P < 0.01; ∗∗∗ P < 0.001; n.s. not significant. b 2 R , correlation coefficient; R2 adj , correlation coefficient adjusted for degrees of freedom; F, ratio between mean squares of regression and residuals; FME , ratio between mean squares of model error and replicate error. a ∗
J Chem Technol Biotechnol 81:832–840 (2006)
in MnP activity, while a further increase to 60 mg dm−3 did not further improve the response. The same main effect was observed for malonic acid, the addition of which resulted in a significant increase in MnP production up to 5 mmol dm−3 (Fig. 1C). Figure 2 shows the effect of interaction of Mn2+ and malonic acid concentrations on MnP production using sucrose as a carbon source and shows that the best combination of the former and the latter variables were 52 mg dm−3 and 8.2 mmol dm−3 , respectively, leading to a production of 0.83 U cm−3 , though a combination including 35 mg dm−3 and 6.25 mmol dm−3 of the above-mentioned variables yielded MnP activity levels (0.77 U cm−3 ) that were very close to the optimal value. Validation of the model was carried out by selecting intermediate values of the input variables X1 and X2 , within the previously tested concentration range, using sucrose as a carbon source. In particular, Mn2+ was added to the medium at 15 and 45 mg dm−3 , while malonic acid at 2.5 and 7.5 mmol dm−3 and experiments with variable combinations of these levels were carried out. A high degree of correlation between predicted versus observed data was obtained (R2 = 0.945) as shown in Fig. 3, confirming the adequacy of the model. Figure 4(A) shows the time course of a typical fermentation process using the previously mentioned optimal combination of Mn2+ and malonic acid. The onset of MnP activity occurred after 72 h of cultivation in concomitance with the almost complete depletion of nitrogen in the medium and increased steeply between 72 and 96 h in parallel with extracellular protein. MnP activity peak was attained after 120 h, reaching volumetric activity of 0.83 ± 0.04 U cm−3 , specific activity of 15.7 U (mg protein)−1 and productivity of 6.91 U dm−3 h−1 . Fungal biomass increased slowly along the first 168 h. Thereafter, it increased at a higher rate for the remaining fermentation period, reaching 1.68 g dm−3 after 268 h. It is worth noting that no laccase activity was detected under this condition. Non-denaturing polyacrylamide gel electrophoresis followed by activity staining showed the presence of two MnP isoforms with electrophoretic mobility (Rf) of 0.45 and 0.51 and that the isozyme pattern did not change as a function of time (Fig. 4A, inset). With the growth medium thus optimised, further experiments were performed in STR and BCR in order to assess the possibility of upscaling the process. At the set conditions, no significant differences in volumetric mass transfer (KL a) were recorded between STR and BCR (0.137 and 0.125 s−1 , respectively). Figure 4(B) shows that, albeit with a delay of 24 h, the activity peak in BCR was markedly higher than that in shaken cultures (3.7 versus 0.83 U cm−3 ) with a productivity of 25.7 U dm−3 h−1 . In addition, the specific activity (73.2 U mg−1 protein) was about fivefold higher than that in shaken cultures. Differently from shaken culture experiments, the time course of MnP activity paralleled biomass production. In STR, the activity 835
D Quaratino et al.
Figure 1. Main effect plots of carbon source (A), manganese concentration (B) and malonic acid (C) on manganese peroxidase production. The isolated main effects for each variable have been calculated according to eqn (3).
peak was reached on day 7 (1.61 U cm−3 ) with a productivity and specific activity (9.6 U dm−3 h−1 and 34.6 U mg−1 protein, respectively) lower than those obtained in BCR (Fig. 4C). Regardless of the reactor employed, the onset of MnP activity occurred in concomitance with nitrogen depletion. The isozyme pattern of MnP did not differ from that observed in shaken cultures (data not shown).
DISCUSSION Surface response methodology has already been used to optimise the composition in lignin-modifying enzymes of extracellular crude extracts from Phanerochaete chrysosporium21 and Trametes trogii 22 in reference to the degradation of synthetic dyes. To our 836
knowledge, however, this is the first report dealing with the response surface method to specifically optimise manganese-dependent peroxidase production in fungal cultures. The mathematical model prediction of P. tigrinus CBS 577.79 MnP activity titres was in good agreement with experimental observations, as it can be inferred by the high statistical significance of the model and by the subsequent validation trials. Sucrose and glucose appeared to be the most and the least suitable carbon sources to support MnP production, respectively. These data are not in agreement with a previous study conducted with strain 8/18 of P. tigrinus, where maltose appeared to be the most effective carbon source for the production of this enzyme.9 In this study, manganese addition to the medium markedly increased MnP production and its optimal J Chem Technol Biotechnol 81:832–840 (2006)
Mn-peroxidase by Panus tigrinus CBS 577.79
concentration range was found to fluctuate widely from 35 to 53 mg dm−3 . In another study, 71 mg dm−3 was found to be the optimal Mn2+ concentration in a Kirk’s mineral medium, leading to a fourfold increase in MnP activity of P. tigrinus 8/18.9 The inducing action of Mn(II) ions on MnP production observed in this study was expected; in fact, this metal has been shown to regulate MnP production in several lignin-degrading fungi.23 – 26 In addition, it has been found that Mn(II) acts on MnP expression at a transcriptional level.27 It is interesting to note that the onset of P. tigrinus MnP activity (this study) was coincident with nutrient nitrogen depletion from the medium. This finding is
Figure 2. Response surface of manganese peroxidase production as a function of Mn2+ and malonic acid concentrations.
in agreement with other studies reporting that MnP production is a secondary metabolic event triggered by nutrient (nitrogen, in particular) limitation in P. tigrinus,9 Lentinula edodes28 and Phlebia radiata.24 This is not a general rule for white-rot fungi, since it has been shown that MnP production by Bjerkandera sp.29 and Clitocybula dusenii 30 occurred in nitrogen-rich and nitrogen-sufficient media, respectively. The mechanism of organic acid stimulation on MnP production has not been yet clarified. With regard to the stimulatory effect of malonate on MnP production by P. tigrinus CBS 577.79, it should be mentioned that the addition of either dicarboxylic or α-hydroxy acids led to a notable increase in MnP production of Bjerkandera sp.12 and the basidiomycete IZU-154.13 Addition of high Mn(II) concentration led to an increase in MnP production by Phlebia radiata only when malonate was concomitantly present.24 The role of organic acids in MnP catalysis is widely known and involves their role as chelators of enzyme-generated manganic ions, thus facilitating their stripping from the active site.7 In addition, these chelators stimulate the rate of reduction of Compound II,31 a catalytically active form of the enzyme, and stabilise manganic ions, thus preventing their dismutation.4 Aside from their role in catalysis, it has been suggested that the presence of organic acids might be required by the concomitant presence of high MnP activity and Mn2+ ions.25,32 In fact, organic acids could prevent Mn3+ dismutation, which, otherwise leads to MnO2 formation, and it has been postulated that this compound could inhibit further MnP production. However, it should be borne in mind that MnP is sensitive to high concentrations of hydrogen peroxide7 and that MnO2 could counteract peroxidase inactivation by catalytically decomposing H2 O2 in excess.33 More likely, organic acids could
Figure 3. Correlation plot between manganese peroxidase activities predicted by the model and those obtained experimentally by testing the following manganese (mg dm−3 )/malonic acid (mmol dm−3 ) combinations: 1: 15/2.5; 2: 15/7.5; 3: 45/2.5; 4: 45/7.5; 5: 45/10; 6:30/7.5; 7: 45:5; 8: 60/5.
J Chem Technol Biotechnol 81:832–840 (2006)
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ž
°
Figure 4. Time courses of manganese peroxidase activity ( ), biomass production ( ), nutrient nitrogen () and total sugars () in shaken flasks (Plot A), in a 3 dm3 bubble-column reactor (Plot B) and a 3 dm3 stirred-tank reactor (Plot C). The inset of Plot A shows the isoenzyme pattern of MnP measured at the onset of enzyme activity, at the activity peak and at the end of the fermentation. Data are the mean ± standard deviation of data.
induce MnP production at a molecular level, as hypothesised by some investigators.12 In the present study, STR was found to be less adequate than BCR in supporting MnP production by P. tigrinus CBS 577.79. These findings might be ascribable to several factors. For instance, the occurrence of mycelial agglomerates in STR has been shown to be markedly reduced in pneumatically agitated systems.34 Moreover, other studies report the negative effect of shear stress on the degradative metabolism of white-rot fungi.35 – 37 Mycelial damage and morphological changes caused by shear stress were observed when the white-rot fungus Trametes multicolor was grown in STR, leading to low laccase production.38 Nonetheless, ligninolytic peroxidases have been shown to be susceptible to denaturation caused by the mechanical forces 838
generated in high speed regimes35 and/or vigorous aeration.39 Owing to the lack of differences in mycelial growth and in KL a between STR and BCR (present study), the better adequacy of the latter reactor system might be simply due to a lower shear stressing environment rather than to a better aeration or mixing. P. tigrinus CBS 577.79 appears to be a valuable producer of MnP activity in submerged liquid fermentation. Table 3 reports maximal production and productivities of MnP by a number of white-rot fungi in several reactor types. The highest productivities of MnP were reported for the hyper-producing P. chrysosporium strain I 1512 grown both in an airlift and STR36 and for Coryolus hirsutus (strain 075) in STR.44 On a comparative basis, data obtained in the present study with BCR are among the highest values reported. J Chem Technol Biotechnol 81:832–840 (2006)
Mn-peroxidase by Panus tigrinus CBS 577.79 Table 3. Maximal activities and productivities of manganese peroxidase in lab-scale bioreactors by free pellets or immobilised mycelia of white-rot fungi
Fungus/strain Pc I-1512C Pc BKM F-1767 Pc I-1512 Pc BKM F-1767 Pt CBS 577.79 Pt CBS 577.79 Pc I-1512 Pc BKM F-1767 Nf b19 Pt CBS 577.79 Pt 8/18 Ba UAMH 8258 Ba UAMH 7308 Tv IJFMA-137 Ch 075 Cd b11
Bioreactora
Maximal activities (U dm−3 )
Tmax b (h)
Pmax c (U dm−3 h−1 )
Reference
Air-lift Air-lift Air-lift BCR BCR BCR BCR STR STR STR STR STR STR STR STR STR
350 1944 3360 1146 410 3700 3600 618 2000 1610 3300 3500 2500 27 7500 1177
120 99 99 95 168 144 95 78 192 168 216 216 216 192 120 62
2.91 19.6 34 12.06 2.44 25.7 37.5 7.9 10.4 9.6 15.27 16.2 11.57 0.14 62.5 18.8
40 36 39 36 10 This study 41 36 25 This study 9 42 42 43 44 25
a
The type of bioreactor is italicised for immobilized cell cultures. Time required to reach activity peak. c Maximal productivity calculated according to activity data reported by the authors; Pc, P. chrysosporium; Pt, Panus tigrinus; Nf, Nematoloma frowardii; Ba, Bjerkandera adusta; Tv, Trametes versicolor; Ch, Coryolus hirsutus; Cd, Clitocybula dusenii. b
ACKNOWLEDGEMENTS This work was supported by the Consorzio Interuniversitario della Chimica per l’Ambiente (INCA) within the frame of the project ‘Piano Agroalimentare Nazionale’ and by the EC contract no. ICA3-CT1999-00010, Mediterranean Usage of Biotechnological Treated Effluent Water, ‘Medusa Water’.
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