Microsoft Word - 476hernandez.docx CHEMICAL ENGINEERINGTRANSACTIONS VOL. 43, 2015 A publication of The Italian Association of Chemical Engineering Online at www.aidic.it/cet Chief Editors:SauroPierucci, JiříJ. Klemeš Copyright © 2015, AIDIC ServiziS.r.l., ISBN 978-88-95608-34-1; ISSN 2283-9216 Detection of Parameters Enhancing the Performance of White-Rot Fungi for Degradation of Poly-Aromatic Hydrocarbons Through Design-of-Experiment Methodologies Giuliano Saiu*a, Francesca Poggib, Stefania Troncia, Massimiliano Grossoa, Antonio Lallaia, Enzo Cadonic, Nicoletta Currelib aDipartimento di Ingegneria Meccanica, Chimica e dei Materiali, Università degli Studi di Cagliari, Via Marengo 2, 09123 Cagliari bDipartimento di Scienze Biomediche, Unità di Biochimica, Università degli Studi di Cagliari, Cittadella Universitaria, 09042 Monserrato,Cagliari cDipartimento di Scienze Chimiche e Geologiche, Università degli Studi di Cagliari, Cittadella Universitaria, 09042 Monserrato, Cagliari g.saiu@dimcm.unica.it The aim of this work is to evaluate the tolerance and the growth capabilities of a white rot fungus, the Pleurotus-Sajor Caju, when exposed to Poly-Aromatic Hydrocarbons. The research was carried out by using in vitro systems developed on Petri dishes, where microbial strains are exposed to chemical pollutants, in particular pyrene and chrysene along with addition of surfactants, peptone, copper sulphate and lecithin that may promote fungal growth and tolerance. It was found that the fungal population growth is strongly inhibited by chrysene presence. On the other hand the pyrene has a mild negative impact on the micelyal growth, which seems to be positively influenced by the presence of Tween 80 and copper sulphate. 1. Introduction The need for new regulations on environmental remediation of polluted sites has produced an increase of decontamination operations for soils, groundwater and sediments. The recovery processes are often directed to remove organic contaminants that are toxic and barely biodegradable. Among the several contaminants present in the environment, Poly-Aromatic Hydrocarbons (PAHs) are considered priority pollutants because they are one of the most ubiquitous class, and most of them are mutagenic and carcinogenic. Biological treatments applied to organic compounds removal showed to be attractive, mainly because they are not expensive and fulfil the most important properties required by the current regulations (Martins et al, 2012). The main obstacles for an efficient biodegradation of PAHs are their low bioavailability and recalcitrance (Leonardi et al., 2007). Among the different microbial consortia proposed in literature to be applied for bioremediation, white rot fungi have recently captured the interest of several researchers because of their ability to degrade an extremely diverse range of very persistent or toxic environmental pollutants. In particular, their degradation ability has been assessed under laboratory conditions for pesticides (Xiao et al., 2011), chlorophenols (Rubilar et al., 2011), synthetic dyes (Zhuo et al., 2011), drugs (Rodarte-Morales, 2012) and PAHs (Wen et al., 2011). The capacity for degrading such complex compounds depends on their ability to produce extracellular enzymes with low substrate specificity, such as lignin peroxidase, laccase, aryl-alchohol-oxidase and manganese peroxidase (Tortella et al., 2013). In the present study, the ability of the white rot fungi Pleurotus sajor-caju to degrade PAHs has been investigated, focusing the research on its tolerance when exposed to pyrene and chrysene. The impact of different parameters affecting bioavailability (Tween 80) and promoting the fungal growth (lecithin, peptone and copper sulphate) have been also considered. The experimental campaign has been developed using a DOI: 10.3303/CET1543046 Please cite this article as: Saiu G., Poggi F., Tronci S., Grosso M., Lallai A., Cadoni E., Curreli N., 2015, Detection of parameters enhancing the performance of white-rot fungi for the degradation of poly-aromatic hydrocarbons through design-of-experiment methodologies, Chemical Engineering Transactions, 43, 271-276 DOI: 10.3303/CET1543046 271 fractional factorial design which allowed a rigorous assessment of the effects of the different parameters here considered. 2. Materials and methods 2.1 Organism and culture conditions Pleurotus sajor caju, a white-rot fungus growing spontaneously in eastern countries, was used in the described experimental campaign. The strain was maintained in malt-yeast-agar plates at 25 °C further mentioned as AMY; a voucher culture is permanently preserved at 4 °C in the collection of the Biochemistry Unit of the Department of ‘Scienze Biomediche’, Cittadella Universitaria di Monserrato’ University of Cagliari. Disks 6 mm in diameter were transferred under sterile conditions in 9 cm Petri dishes containing the appropriate medium and PAHs (chrysene or pyrene); in this work, the impact of the following substances was investigated: copper sulphate (Bettin et al., 2008), peptone (Hanson, 2008) and soy lecithin (Pannu et al., 2004). Furthermore, in order to enhance the bioavailability of the contaminants, the addition of a surfactant (Tween 80) was considered (Roch and Alexander, 1995). The cultivations were maintained at 25 +/- 1 °C for more than 40 days. The temperature was controlled and monitored by using an electronically controlled incubator. 2.2 Data acquisition Fungus growth was monitored taking digital photos in raw format at regular interval of 24 h using a high resolution digital camera (Canon EOS 50d – 15.1 mp sensor). Every photo was suitably cropped and processed to jpeg format. The images are then post-processed by exploiting the Adobe Camera Raw® and Adobe Photoshop® software. The areas covered by the fungal population were calculated using an in-house written Matlab® code that binarizes the images and then computes the number of black pixels corresponding to the mycelium. Figure 1 reports an example of raw picture collected during the experiments and the related processed image. Figure 1: Pleurotus sajor-caju strains in a Petri dish; (a) original picture and (b) corresponding processed binary image containing only the mycelium area in black 2.3 Experimental design The behaviour of the mycelia strains was thus monitored by jointly varying 6 factors, which are the concentrations of: (a) pyrene; (b) copper sulphate (CuSO4); (c) peptone; (d) Tween 80; (e) soy lecithin; (f) chrysene. Table 1: Levels at low (-1) and high (1) concentration. Factor/Level Low High Units A=Pyrene 0 5 mg/kg B=CuSO4 0 0.05 mM C=Peptone 0 1 % D=Tween 80 0.25 1 % E=Lecithin 0 3 % F=Chrysene 0 5 mg/kg 272 A two level fractional factorial design of the experiments was adopted in order to obtain a good compromise between number of experiments and effective reliable information. In particular, a two level, six factors, one- quarter fraction factorial design, with resolution IV (2 ) was used (for further details see e.g. Montgomery, 2013), leading to 16 different experimental conditions describing the influence of 6 parameters. Every treatment was repeated three times and three more control runs (with only AMY) have been also performed, leading to a total of 51 experiments. The concentration levels used for each compound in the experiments are shown in Table 1. Table 2 summarizes the generators of the fractional design, along with all the single effects and the second order combined effects that can be resolved and not confounded with the (2 ) fractional design (for further details on the methodology see Montgomery, 2013). Table 2: Generators, defining relations and alias structure, up to third degree interactions Terms Generators E = BCD F = ACD Defining relation I = BCDE I= ACDF I= ABEF Alias structure A + BEF + CDF D + ACF + BCE AD + CF BC + DE B + AEF + CDE D + ACF + BCE AE + BF BD + CE C + ADF + BDE F + ABE + ACD AF + BE + CD 2.4 Growth model Above all, two mathematical models were taken into account in order to describe the fungal population growth (Seber et al, 2003): the logistic and the Gompertz model. It was found that the Gompertz model (Cogoni et al, 2012), introduced in Equation 1, was the most suited to describe the growth dynamics. = μ ∙ (log − log ). (1) In Equations (1), A is the mycelial population, µ is the specific growth rate and k is the carrying capacity (i.e. the asymptotic value as t →∞). The model was compared with the experimental data provided by the protocol introduced in Section 2.2, assuming that the measured area is a reasonable estimation of the fungal population. The model parameter estimation was performed for each experimental run by resorting to the nonlinear regression libraries provided with Matlab®. To evaluate the model performance the Mean Square Error Eq.(2) is considered. = ∑ − (2) where n is the number of experimental data collected at each experimental condition, is the i-th experimental point and is the model predicted value at the i-th point. The MSE scalar is the maximum likelihood estimation of the experimental variance, therefore the quality of fit increases as this scalar tends to zero. For the objective of the present work, the interest was mainly focused on the evaluation of the specific growth rate parameter μ. 3. Results The Gompertz model provided a good description of the fungal population growth, with an average value for the MSE scalar equal to 0.863, with values ranging from MSEmin=0.032 to MSEmax=14.60. Figure 2 shows some representative comparisons between experimental data and model prediction for different experimental conditions: white circles refer to experimental points for the control condition (AMY, absence of the other compounds) compared with the model predictions (solid line); gray squares refer to experimental data collected at Tween 80 =1 %, pyrene=5 mg/kg, CuSO4=0.05 mM, absence of peptone, lecithin and chrysene, compared with the model (dashed line); gray triangles refer to data collected at Tween 80 =1 %, lecithin=3 %, pyrene=5 mg/kg, absence of peptone, lecithin and CuSO4, compared with the model (dashed line). The point estimation of the μ scalar in Eq. (1) can be regarded as a representative index of the fungal population growth, therefore a statistical analysis on the μ values estimated for the different experimental runs 273 can give information on the effects of process conditions on the biological system. This task was accomplished by means of Minitab Statistical Software, used to calculate the main statistical parameters of the fitted models. An ANOVA test was performed and the related results are reported on Table 2. In more detail, for each source of variation (reported on the column 1), the table shows the corresponding degrees of freedom d.o.f. (column 2), the sum of squares of the source variation (column 3), the adjusted Mean Square Error (column 4), the F-ratio statistics (column 5) and the p-value associated to that source of variation (column 6). The effect contribution is considered significant when p< 0.05. The significant factors (i.e. with a p- value <0.05) are highlighted with the asterisk. The main outcomes of the analysis are listed below: 1. the compounds with statistically significant effects are: (i) peptone, (ii) CuSO4, (iii) Tween 80 and (iv) chrysene concentrations. 2. interactions of pyrene with Tween 80 and lecithin are statistically significant. Figure 2: Normalized experimental growth curves compared with the model prediction. Table 2: Analysis of Variance for the fungus growth. All the scalars in table are dimensionless. Source d.o.f. SS MS F-ratio p-value Main Effects Pyrene CuSO4(*) Peptone(*) Tween 80(*) Lecithin Chrysene(*) 2-Way Interactions Pyrene·CuSO4 Pyrene·Peptone Pyrene·Tween 80(*) Pyrene·Lecithin(*) Pyrene·Chrysene CuSO4·Peptone CuSO4·Tween 80 3-Way Interactions Pyrene·CuSO4·Peptone Pyrene·CuSO4·Tween 80(*) Residual Error Pure Error Total 6 1 1 1 1 1 1 7 1 1 1 1 1 1 1 2 1 1 16 16 31 439.474 5.102 70.514 183.003 52.208 1.613 127.034 165.221 0.033 13.614 43.596 71.441 10.513 25.927 0.097 46.115 7.855 38.26 103.748 103.748 754.557 73.246 5.102 70.514 183.003 52.208 1.613 127.034 23.603 0.033 13.614 43.596 71.441 10.513 25.927 0.097 23.057 7.855 38.26 6.484 6.484 11.30 0.79 10.87 28.22 8.05 0.25 19.59 3.64 0.01 2.10 6.72 11.02 1.62 4.00 0.01 3.56 1.21 5.90 0.000 0.388 0.005 0.000 0.012 0.625 0.000 0.015 0.944 0.167 0.020 0.004 0.221 0.063 0.904 0.053 0.287 0.027 274 The dependence of the parameter μ on the process conditions can be described by the relationship, reported in Eq. (3), where only the significant effects selected with the ANOVA are taken into account. Eq. (3) can be used to optimize or estimate the mycelium growth rate, and it is written with the terms in order of significance. = 3.564 − 2.391 ∙ − 1.992 ∙ − 1.494 ∙ + 1.484 ∙ + 1.277 ∙ + 1.167 ∙ + 1.093 ∙ (3) It can be easily seen that peptone (C) and chrysene (F) strongly reduce the mycelium growth rate, whereas copper (B) and Tween 80 (D) enhanced the response. Also two (AD and AE) and three factor (ABD) interactions significantly contribute to the mycelium growth as reported on Eq. (3), written in coded units. To measure how much variability is observed in the response values, the determination coefficient R2 has been estimated, obtaining 86.25%. The results are also confirmed by the Pareto chart calculated using the Yates algorithm (Morgan, 1991) and reported in Figure 3.a. For sake of clarity, also the significance threshold value (with a significance level α=0.05) is reported with the solid line. It was confirmed that the response of peptone, chrysene, copper, Tween 80 and the interaction between pyrene and Tween 80 are the main significant effects. On the other hand pyrene and lecithin do not affect significantly the system, meaning that the former seems well tolerated by Pleur. Sajor-caju strains, while the latter does not enhance fungal growth. On the other hand the presence of pyrene combined with Tween 80 has an impact of the fungus growth, at least for the concentration ranges here investigated. For sake of completeness, the main effects are shown in Figure 3.b, where significant effects are reported with solid lines, while the effects classified as not significant are reported with dashed lines. It was confirmed that: (i) copper and Tween 80 positively affect the system response; (ii) peptone and chrysene strongly inhibit the fungus growth and (iii), pyrene (slight negative impact) and lecithin (slight positive impact) are not statistically significant. Figure 3: a) Pareto chart of standardized effects for the μ parameter; b) Main effect behaviour. Figure 4: Contour plot for second order interaction: a) pyrene-lecithin, b) pyrene-Tween 80. 275 The significant interactions between the factors are shown in Figure 4, that indicates that lecithin does not favor mycelia growth when medium is contaminated by pyrene, whereas Tween 80 does. In particular, Figure 4a shows that high pyrene concentrations lead to higher growth rate when lecithin is absent in the medium, while its addition decreases growth rate. Figure 4b indicates that addition of Tween 80 favors fungal growth rate leading to the highest values of µ when both compounds are at the highest concentration levels. 4. Conclusions An experimental campaign based on a (2 ) fractional factorial design was carried out to analyze the impact of different medium composition on the Pleurotus sajur-caju growth when exposed to PAHs contaminants. As main result it was found that the mycelia can better tolerate pyrene than chrysene, furthermore Tween 80 and copper have a significant positive impact on the growth of fungal strains when exposed to pyrene. On the other hand, addition of lecithin showed a minor effect while peptone was detrimental for the process. The statistical analysis also indicated the occurrence of interactions among the factors. In particular the influence of lecithin and Tween 80 on the process changes with the level of pyrene concentration in the medium. For the current investigation the impact of the additives to the medium does not seem to influence the ability of the mycelium to tolerate chrysene which had always a negative effect on the population growth rate. 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