FEMS Microbiology Letters, 362, 2015, fnv060 doi: 10.1093/femsle/fnv060 Advance Access Publication Date: 13 April 2015 Research Letter
R E S E A R C H L E T T E R – Environmental Microbiology
Untargeted metabolic profiling of Vitis vinifera during fungal degradation Avinash V. Karpe1,2,∗ , David J. Beale2 , Paul D. Morrison3 , Ian H. Harding1 and Enzo A. Palombo1 1
Faculty of Science, Engineering and Technology, Swinburne University of Technology, PO Box 218, Hawthorn, VIC 3122, Australia, 2 Land and Water Flagship, Commonwealth Scientific and Industrial Research Organization, PO Box 56, Highett, VIC 3190, Australia and 3 School of Applied Sciences, RMIT University, PO Box 2547, Melbourne, VIC 3000, Australia ∗ Corresponding author: Faculty of Science, Engineering and Technology, Swinburne University of Technology, PO Box 218, Hawthorn, VIC 3122, Australia. Tel: +61-3-9214-4874; E-mail:
[email protected] One sentence summary: Metabolomics of fungi mediated winery biomass waste degradation. Editor: Rich Boden
ABSTRACT This paper illustrates the application of an untargeted metabolic profiling analysis of winery-derived biomass degraded using four filamentous fungi (Trichoderma harzianum, Aspergillus niger, Penicillium chrysogenum and P. citrinum) and a yeast (Saccharomyces cerevisiae). Analysis of the metabolome resulted in the identification of 233 significant peak features [P < 0.05; fold change (FC) > 2 and signal-to-noise ratio >50] using gas chromatography-mass spectrometry followed by statistical chemometric analysis. Furthermore, A. niger and P. chrysogenum produced higher biomass degradation due to considerable β-glucosidase and xylanase activities. The major metabolites generated during fungal degradation which differentiated the metabolic profiles of fungi included sugars, sugar acids, organic acids and fatty acids. Although, P. chrysogenum could degrade hemicelluloses due to its high β-glucosidase and xylanase activities, it could not utilize the resultant pentoses, which A. niger and P. citrinum could do efficiently, thus indicating a need of mixed fungal culture to improve the biomass degradation. Saccharomyces cerevisiae, a non-cellulose degrader, exhibited sugar accumulation during the fermentation. Penicillium chrysogenum was observed to degrade about 2% lignin, a property not observed in other fungi. This study emphasized the differential fungal metabolic behavior and demonstrated the potential of metabolomics in optimizing degradation or manipulating pathways to increase yields of products of interest. Keywords: fungi; biomass degradation; metabolomics; GC-MS; chemometrics
INTRODUCTION Plant-based biomass is one of the most abundant waste products originating from numerous processes, including agriculture and allied industries, food processing, paper and textile manufacturing and domestic waste (UCSUSA 2012). Biodegradation of these substrates has been the focus of considerable research efforts, specifically those based on production of single-cell
protein (Poulsen and Petersen 1988), biofuels (Pauly and Keegstra 2008) and metabolites of medicinal importance (Arnous and Meyer 2009; Vicens et al. 2009). Owing to its structure, microbial degradation of biomass is a very complex process which involves numerous enzymes, chemicals and other factors (e.g. pH and temperature) (Duarte et al. 2012). Species belonging to Ascomycota fungi are some of the most ´ skova´ 2008; Karpe, efficient biomass degraders (Baldrian and Valaˇ
Received: 30 September 2014; Accepted: 8 April 2015 C FEMS 2015. All rights reserved. For permissions, please e-mail:
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Harding and Palombo 2014b). However, degradation varies according to the type of biomass available and the individual species present. A thorough understanding of the molecules (metabolites) produced during the biodegradation process is necessary to generate a functional consortium of these species in order to achieve maximum bioconversion and increase product yields. Due to the differences in metabolism of each fungal species, arrays of metabolites are generated during the biodegradation process. Sugars and carbon dioxide are the major metabolites of fungal metabolism (Kumar, Singh and Singh 2008). However, it should be noted that a large number of secondary and tertiary intermediate metabolites are also produced in relatively smaller quantities that affect the rate and efficiency of biodegradation process (such as sugars and polyphenols) (Duarte et al. 2012). Therefore, their assessment becomes important in order to obtain a better understanding of the fungal biodegradation process with the goal to improve efficiencies and product yields. As such, the field of metabolomics has the potential to provide this information in order to understand and characterize the various mechanisms related to fungal biomass degradation. Among numerous areas, metabolomics has been applied to investigate bacterial processes related to preventative health (Bi et al. 2013), environmental pollution (Beale et al. 2013a; Jones et al. 2014) and fungal metabolism on various substrates such as benzoic acid and Chardonnay grape berries (Matsuzaki, Shimizu and Wariishi 2008; Hong et al. 2012). However, it has not been applied within the context of fungal-mediated biomass degradation. As such, the assessment of metabolites consumed and generated during the biodegradation process would provide a better understanding of the metabolic processes involved. This has the potential to aid in the development of novel modifications during biodegradation that minimize product inhibition of fungal enzymes and enhance yields of particular metabolites of interest (i.e. biofuels such as ethanol). In a previous study, a statistically optimized fungal mixture was used to improve winery biomass waste degradation efficiency. A metabolomic analysis performed on those samples was degraded by a mixture of different fungi (Trichoderma harzianum, Aspergillus niger, Penicillium chrysogenum and P. citrinum) (Karpe et al. 2014a). The study presented herein describes an untargeted metabolic approach applied to study the degradation of winery-derived biomass waste by individual Ascomycete fungi. Typically, this type of study is performed by examining the enzyme kinetics or specific products generated at the completion of biodegradation. However, by using metabolomic techniques, the metabolites produced and consumed during biodegradation can be identified and characterized in order to obtain a better understanding of the fungal degradation efficiency and its suitability in degrading wineryderived biomass. In addition, it is proposed that by using a metabolomic approach, a considerable amount of time could be saved in optimizing fungal biodegradation reactors. An untargeted metabolic profiling approach combined with chemometric statistical analysis will, therefore, provide a rapid and efficient solution to modify the biodegradation process to enhance biodegradation efficiency.
MATERIALS AND METHODS Biomass and fungi Post-fermentation wastes of Vitis vinifera var. Shiraz was obtained from the Australian Wine Research Institute (AWRI), Glen
Osmond, South Australia. The substrate was oven-dried at 70◦ C for 96 h and ground using a domestic blender (Philips Electronics Australia, model HR2094, North Ryde, NSW, Australia). The dried, ground substrate was further oven-dried for 96 h at 70◦ C, due to its observed minimal impact on thermolabile metabolites (data not shown), higher efficiency and previous reported works (Hideno et al. 2011; Cheng and Liu 2012; Kamei et al. 2014). Aspergillus niger (ATCC 10577), P. citrinum (ACM 4F) and Saccharomyces cerevisiae (ATCC 287) were sourced from the culture collection at Swinburne University of Technology (Hawthorn, Victoria, Australia). Trichoderma harzianum (AG46) and P. chrysogenum (AG47) were obtained from Agpath Pty. Ltd (Vervale, Victoria, Australia).
Biomass degradation American Association of Textile Chemists and Colorists mineral salts iron medium, consisting NH4 NO3 (3.0 g l−1 ), KH2 PO4 (2.5 g l−1 ), K2 HPO4 (2.0 g l−1 ) MgSO4 .7H2 O (0.2 g l−1 ) and FeSO4 .7H2 O (0.1 g l−1 ), was used for all the degradation studies herein. The pH was adjusted to 5.0 by adding 1M H2 SO4 . Ovendried grape waste was then added to this medium as the sole carbon source. The ratio between the volume of the medium and substrate was kept at 1:1. To this medium, about 1 × 108 spores mL−1 from fungi grown on Sabouraud dextrose agar plate was added to 250 mL borosilicate glass conical flasks and incubated at 30◦ C for 1 week at 250 rpm. To prevent water loss, the R . Biodegradation and control flasks were sealed with Parafilm (no fungi) cultures were prepared in triplicate from a standard single spore suspension (see supplementary materials, Fungal spore suspension preparation). The degraded biomass samples derived after 1 week of fungal treatment were then immediately frozen under liquid nitrogen and stored at –80◦ C until further analysis.
Enzyme activities and other biochemical analyses Cellulase activity was measured in terms of filter paper activity as per the IUPAC protocols (Ghose 1987) using 50 mg of Whatman no. 1 filter paper as the substrate. One International Unit (IU) of cellulase is defined as the amount of enzyme required to liberate 1 μmol glucose per minute under assay conditions. Activity of βglucosidase was determined by the p-nitrophenyl-β-D-glucoside assay according to the reported method (Kovacs et al. 2009) with slight modifications. One IU of β-glucosidase is defined as the amount of enzyme required to liberate 1 μmol p-nitrophenol per minute under assay conditions. Xylanase activity was measured by Highely’s method (Highley 1997) using Birchwood xylan (1%) as the substrate. One IU of xylanase is defined as the amount of enzyme required to liberate 1 μmol xylose per minute under assay conditions (see Supplementary materials section for more details). Protein determination was performed using Biuret assay. The quantitative determination of reducing sugars in the filtrate of degraded grape waste was performed by dinitrosalicylic acid assay (Plummer 1987). Lignins were determined as acid-soluble lignin and acid insoluble lignin by minor modifications in NREL procedure (Sluiter et al. 2011).
Silyl derivatization and gas chromatography-mass spectrometry (GC-MS) Freeze-dried samples of fungal-degraded grape biomass (40 ± 2 mg) were taken in triplicate and prepared for derivatization as previously described by Ng et al. (2012). Briefly, a 1.0 mL aliquot
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of methanol (LC grade, ScharLab, Sentemanat, Spain) was added and the mixture was vortexed for 2 min before centrifugation at 572.5 g/4◦ C for 15 min. Adonitol (10 μg mL−1 , HPLC grade, SigmaAldrich, Castle Hill, NSW, Australia) was added as an internal standard. A 50.0 μL aliquot of the supernatant was then transferred to a clean tube and dried in a RVC 2–18 centrifugal evaporator at 40◦ C/210 g (Vacubrand GMBH, Wertheim, Germany). All samples were stored at –80◦ C until further use. In order to derivatize the samples for GC-MS analysis, 40.0 μL methoxyamine HCl (20 mg mL−1 in pyridine) followed by 70.0 μL N,O-bis(trimethylsilyl)trifluoroacetamide in 1% trimethylchlorosilane was added to the dried samples. Samples were then briefly vortexed before being transferred to clean GC-MS vials. These were then derivatized in a Multiwave 3000 microwave (PerkinElmer Inc., Melbourne, Australia) for 3 min at 120◦ C/600 W before transferring to the GC-MS for analysis. Prederivatized 13 C-Sorbitol [Kovats Retention Index = 1918.76, m/z = 620.00 (10 μg mL−1 , HPLC grade, Sigma-Aldrich, Castle Hill, NSW, Australia)] was added as the second internal standard at this point in order to verify instrument stability over the run time. This increased the reliability and prevented any miscalculations arising from sample loss during GC-MS. The derivatized samples were analyzed using an Agilent 6890B GC oven coupled with a 5973A MS detector (Agilent Technologies, Mulgrave, VIC, Australia), as described previously (Beale et al. 2013b; Beale, Morrison and Palombo 2014). The GC-MS system was fitted with a 30 m DB-5MS column, 0.25 mm ID and 0.25 μm film thickness. All injections were performed in splitless mode with 1.0 μL volume; the oven was held at an initial temperature of 70◦ C for 2.0 min before increasing to 325◦ C at 7.5◦ C min−1 ; the final temperature was held for 4.5 min. The transfer line was held at 280◦ C and the detector voltages at 1054 V. Total Ion Chromatogram mass spectra were acquired from 45 to 550 m z−1 , at an acquisition frequency of 1.08 spectra s−1 . The ionization source used was electron ionization and the energy was 70 eV. The solvent delay time of 7.5 min ensured that the source filament was not saturated and damaged with derivatization reagent. Data acquisition and spectral analysis were performed using MassHunter. Qualitative identification of the compounds was performed according to the Metabolomics Standard Initiative Chemical Analysis Workgroup (Sumner et al. 2007) using standard GC-MS reference metabolite libraries of Wiley, NIST 11 and NIST EPA/NIH using Kovats retention Indices based on the referenced n-alkane retention times (C8-C40 Alkanes Calibration Standard, Sigma-Aldrich, Castle Hill, NSW, Australia). For peak integration, a 5-point detection filtering (default settings) was set with a start threshold of 0.2 and stop threshold of 0.0 for 10 scans per sample.
Chemometricstatistical analysis Chemometric statistical analysis was undertaken predominately using SIMCA 13, a chemometric software package (Umet˚ Sweden), and post-analysis fold-change (FC) rics AG, Umea, analysis using MetaboAnalyst 2.0 (Xia et al. 2012), an online statistical package (TMIC, Edmonton, Canada). Peak areas were taken in to consideration for statistical analyses. Data were normalized with respect to the internal standards (adonitol and 13C Sorbitol). Chromatography peaks were considered significant where the signal-to-noise (S/N) ratio >50, the FC was >2.0 and P-values were ≤0.05. Similarly, the metabolites with FC values < 0.5 were considered as the fungal-degraded/utilized metabolites.
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It was expected and observed that each profile analyzed would comprise a collection of putative metabolites of mixed concentrations generated during the biodegradation process. The data generated by mass spectral analyses were thus normalized with respect to internal standards (RSD = 7.39%), where a magnitude of 1 FC referred to the concentration of 10 mg L−1 . Also, the FC values of less than 0.5 were indicated as fungalutilized metabolites. An unsupervised statistical approach using principal component analysis (PCA) was undertaken on the data, with no clear separation being observed (see Figs S1 and S2, Supporting Information, for the PCA and DModX plots, respectively). To accommodate the outliers and enable differentiation between the groups based on metabolic pattern, which PCA could not fully accomplish, a partial least square-discriminant analysis (PLS-DA) was employed. PLS-DA is a supervised method used to analyze large datasets and has the ability to assess linear/polynomial correlation between variable matrices by lowering the dimensions of the predictive model, enabling easy discrimination between samples and the metabolite features that ¨ om ¨ and Eriksson 2001). cause the discrimination (Wold, Sjostr
RESULTS AND DISCUSSION The GC-MS chromatogram and spectral analysis indicated a total of ca. 585 peaks identified across all of the fungal and yeasttreated samples of which 233 were considered significant (S/N ratio >50, P-values ≤ 0.05). Table S1 (Supporting Information) details the significant unique metabolites and their P-values that relate to T. harzianum, A. niger, P. chrysogenum, P. citrinum, S. cerevisiae (negative control), and the top 25 metabolites utilized by fungi. Fig. 1A illustrates the PLS-DA score-scatter plot of the data, with clear grouping of the data evident in the upper/lower hemisphere. Whereas, the data points in left hemisphere display the control and the negative control (S. cerevisiae) samples, the data points on the left display the distribution of fungi causing biomass degradation. The metabolites responsible for this distribution have been illustrated in Fig. 1B, which displays the metabolite PLS-DA loading-scatter plot of fungal biomass degradation. Table 1 displays the enzyme activities and lignin degradation observed in all the fungal cultures utilized for this experimental setup. Although there were no considerable differences in cellulase activities following 1 week of winery biomass degradation, T. harzianum was observed to display the highest amounts of cellulase activity at 39 U mL−1 . This was expected since Trichoderma spp. is known to generate greater amounts of cellulases with respect to other fungi which are used to degrade biomass (Chahal 1985). However, its overall degradation ability was limited due to the lower β-glucosidase and xylanase activities during the experiment, as also indicated in the literature (Pollet, Delcour and Courtin 2010; Duarte et al. 2012). Penicillium chrysogenum was observed to possess a considerably higher β-glucosidase and xylanase activities of 106.3 and 936.7 U mL−1 , respectively. This explains the enhanced breakdown of hemicelluloses as also evidenced from the considerable amounts of accumulated pentose sugars in its culture medium (discussed below). The highest xylanase activity, however, was observed in A. niger culture at 1430 U mL−1 . It was also observed that even better cellulase and β-glucosidase activities were produced in further incubated cultures under solid-state fermentation conditions. For example, after 2 weeks of incubation the cellulase levels increased to about 78 U mL−1 in P. chrysogenum culture, while β-glucosidase activity spiked to 153 and 180 U mL−1 in A. niger
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Figure 1. (A) Score-scatter plot of PLS-DA during winery biomass degradation by fungi over 1 week. Each point on the scatter plot refers to single sample, with R2 X (cumulative) = 94.3%, R2 Y (cumulative) = 96.1% and Q2 (cumulative) = 88.6%. The eclipse represents level of significance with a 95% confidence level based on Hotelling’s T2 modeling. (B) PLS-DA derived loading scatter plot during winery biomass degradation by fungi over 1 week. Four pointed star labels denote the fungi. Yellow circles represent all the putative metabolites under consideration and their orientation.
and P. citrinum cultures, respectively (data not shown). Ascomycota fungi, which have been utilized in these experiments, are known as good cellulose degraders. However, they have not been reported to possess lignolytic properties. The only exception is P. chrysogenum, which under certain conditions mineralizes mi-
nor levels of low molecular weight compounds of lignin origin (Rodr´ıguez et al. 1994). A majority of fungi did not display any lignolytic activities. However, as expected, P. chrysogenum displayed minimal lignin mineralization of 2.2%. Surprisingly, T. harzianum displayed lignin degradation. However, it is more likely that it
Table 1. Enzyme activities and lignin degradation in various fungal cultures (n = 3). Values in parentheses show the standard error in each experiment. Enzyme activities (U mL−1 ) Organism
Cellulase
β-glucosidase
Xylanase
Lignin degraded
Trichoderma harzianum Aspergillus niger Penicillium chrysogenum Penicillium citrinum Saccharomyces cerevisiae
39.0 (± 0.3) 28.9 (± 0.26) 30.7 (± 0.25) 28.8 (± 0.15) –
27.9 (± 0.85) 28.7 (± 0.46) 106.3 (± 0.4) 30.4 (± 0.15) 26.3 (± 0.4)
772.7 (± 35.8) 1430.5 (± 10.1) 936.7 (± 9.9) 1100.8 (± 28.3) –
3.5% – 2.2% 1.7% –
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degraded a minor amount of tannins which are bound to lignins in grape biomass, rather than degrading lignin. The difference in biomass degradation abilities was also reflected in the metabolic outputs in the PCA score plot (Fig. S1, Supporting Information) and the PLS-DA score plot (Fig. 1A). All the plots clearly discriminated biomass-degrading fungi and S. cerevisiae (negative control and non-degrader of biomass) with respect to the control. The t-test and one way ANOVA classified the significant metabolites using Fisher’s least significant difference method (Fisher’s LSD) and Tukey’s Honestly Significant Difference (Tukey’s HSD). Aspergillus niger, P. chrysogenum and P. citrinum were also able to utilize considerable amounts of lignocellulose complex metabolites to generate either fungal biomass or pentoses and disaccharide sugars. However, noticeable amounts of other metabolites such as amino acids, fatty acids organic acids and deoxy sugars were also observed to be accumulated. Saccharomyces cerevisiae (negative control), due to its inability to degrade biomass, was expectedly observed to accumulate larger amounts of metabolites (Table S1, Supporting Information). A considerable accumulation of trehalose in the cultures of T. harzianum, A. niger, P. chrysogenum and S. cerevisiae (FC of 20.22, 8.68, 18.54 and 34.48, respectively) indicated the inability of these fungi to utilize this sugar efficiently. On the other hand, xylose was accumulated in exceptionally high concentrations in Penicillium spp. cultures (FC: 8.57 and 45.26). One of the peculiar observations was the difference in metabolic output of both Penicillium spp. P. chrysogenum displayed high efficiency in hydrolyzing hemicelluloses to pentoses, but was unable to utilize the pentoses, as evidenced from their accumulation in high concentrations. Penicillium citrinum, however, was possibly more efficient in bioconversion of those pentoses to acids. Similar observations were seen in the A. niger culture, which indicated that a composite culture mix of these fungi will be able to degrade the biomass further and prevent product inhibition of cellulolytic enzymes (Karpe et al. 2014a) The proof of concept presented in this paper demonstrates the ability of metabolomics to readily characterize the products of fungal degradation. Numerous metabolite groups such as sugars, amino acids, fatty acids, secondary metabolites, amides and organic acids were observed to be produced by fungi (see Table S1, Supporting Information). In addition, through chemometric statistical analysis, unique metabolite biomarkers were identified that relate to each fungal species and waste biomass combination. In this study, single cultures of these fungi were utilized individually in order to characterize the metabolic profiling of individual fungi for each sample under testing. With respected to the previously reported study (Karpe et al. 2014a), where sugars, sugar acids, sugar alcohols and aromatic alcohols were prominent, the present experiment showed a predominance of oligosaccharides (trehalose and cellobiose), amino acids, amides and secondary metabolites such as gallic acid. However, additional research is required to fully understand the metabolomic characteristics of the bioconversion process using various fungi and bacteria. The biodegradation process, especially under laboratory or commercial conditions, is aqueous in nature. Thus, additional metabolites, especially those of lignin origin such as phenolics, can be profiled to greater efficiencies through the utilization of aqueous analytical methods such as liquid chromatography-mass spectrometry to obtain more comprehensive metabolomic information.
CONCLUSIONS In this study, we assessed the metabolic profile of wineryderived wastes of Shiraz grapes treated with T. harzianum,
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A. niger, P. chrysogenum and S. cerevisiae. Aspergillus niger and P. chrysogenum were observed to be more efficient degraders than T. harzianum, as evident from the cellulase, β-glucosidase and xylanase enzyme activities. To assess metabolomic aspects of the degradation, analysis of the pre- and post-fungal degradation samples was performed by GC-MS. It is anticipated that understanding and characterizing biodegradation processes will assist in the development of a novel and economical bio-processing methods with enhanced degradation efficiency.
SUPPLEMENTARY DATA Supplementary data is available at FEMSLE online.
ACKNOWLEDGEMENTS The authors would like to thank Dr Jacqui McRae, the AWRI, South Australia for providing the winery grape waste and Dr Mary Cole, Agpath Pty. Ltd., Victoria for providing the fungal cultures.
FUNDING We also acknowledge the contribution of Faculty of Science, Engineering and Technology Swinburne University of Technology and the Land and Water Flagship, Commonwealth Scientific and Industrial Research Organization (CSIRO) for the provision of finance and resources towards this research. Conflict of interest. None declared.
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