Article pubs.acs.org/jpr
Study of Phanerochaete chrysosporium Secretome Revealed Protein Glycosylation as a Substrate-Dependent Post-Translational Modification Sunil S. Adav, Anita Ravindran, and Siu Kwan Sze* School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551 S Supporting Information *
ABSTRACT: Lignocellulosic biomass is a potential sustainable resource of mixed sugars that can be exploited for biofuel and other biomaterials. Phanerochaete chrysosporium (P. chrysosporium) produce an arsenal of extracellular enzymes, the secretome, for efficiently degrading lignocellulosic biomass. Post-translational modifications (PTMs) of these biomass-degrading enzymes generate remarkable diversity, complexity, heterogeneity and also alter physiological behavior, function, and activities. Identification of PTMs and the sites of modifications of these secreted proteins remain as an essential but unexploited step to understand the biomass degradation mechanism. Therefore, this study applied electrostatic repulsion hydrophilic interaction chromatography (ERLIC) for glycopeptides enrichment and coupled with tandem mass spectrometry (LC-MS/MS) analysis for glycosylated secreted enzymes of P. chrysosporium during glucose, cellulose, and lignin degradation. Varied groups of enzymes, including cellulases, glycoside hydrolases, hemicellulases, lignindegrading enzymes, were glycosylated. The comparisons of the glycosylated secreted enzymes of P. chrysosporium in glucose, cellulose, and lignin culture conditions revealed glycosylation as substrate-dependent PTMs. KEYWORDS: Phanerochaete chrysosporium, bioenergy and biorefinery, post-translational modifications, cellulases, ERLIC
1. INTRODUCTION Different from the limited availability of fossil fuels, lignocellulosic biomass like crop residues, agricultural, and forest waste are continuously produced, and it is one of the biggest sinks for global carbon. The main components of lignocellulosic biomass including cellulose, hemicellulose, pectin, and lignin form a very complex and robust networkbuilding crystalline structure. Due to complex structure, direct biotechnological processing of biomass into fuel and valueadded products remains a challenge. To achieve efficient biomass hydrolysis, lignocellulose structure needs to be deconstructed so that carbohydrates can be accessible for enzymatic hydrolysis. Using nature’s tool box of microbial enzymes1 to deconstruct biomass structure and hydrolyze carbohydrates could be an intriguing idea because enzymatic biomass hydrolysis is efficient, cost-effective, high yielding, and environmentally friendly. Many microorganisms have the potential to degrade biomass, but relatively few fungi can selectively remove lignin. Enzymes from these fungi are useful not only in biofuel but also in a wide range of biotechnological applications including paper industry, textile and bioremediation.2 Due to foreseeable applications of fungal lignocellulolytic enzymes in bioenergy and biorefinery, genome of several fungal strains including Aspergillus clavatus, 3 A. flavus, A. fumigatus, 4 A. nidulans, 5 A. niger, 6 A. oryzae,7 A. terreus, Botrytis cinerea,8 Chaetomium globosum,8 Coprinus cinereus,8 Fusarium graminearum,8 Fusarium verticillioides,9 Magnaporthe grisea,10 Neurospora crassa,,11 Phanerochaete chrysosporium, 2 Rhizopus oryzae, Sclerotinia sclerotiorum, Stagonospora nodorum, and many more © 2014 American Chemical Society
have been completed and several are yet in the pipeline. Details of the 1000 fungal genome project12 can be found at http:// genome.jgi.doe.gov/programs/fungi/1000fungalgenomes.jsf. Genome sequencing has revolutionized the protein database that has been used to explore the protein expression profile of enzymes by fungal strains during biomass degradation.13−21 Although both genomics and proteomics studies of potential lignocellulose-degrading fungal strains have been documented, a required breakthrough has not yet been achieved. Phanerochaete chrysosporium, white rot basidiomycete, is extensively studied because of its specialized ability to degrade lignin, leaving behind the white cellulose.2 P. chrysosporium secretes an array of lignin degrading peroxidases, unique extracellular oxidative enzymes, and cellulolytic enzymes.2,13−16,22−24 Genomics of P. chrysosporium emphasized lignocellulolytic genes, whereas proteomics focused on determining protein expression levels during biomass utilization.2,14,15 However, numerous processes during different substrate utilization are governed not only by the relative abundance of proteins but also by their regulation, localization, protein−protein interactions, and post-translational modifications (PTMs). According to Janson,25 PTMs serve as “on” and “off” switches of protein activity, protein targeting and also control protein−ligand, protein−protein, and protein−nucleic acid interactions. These PTMs include phosphorylation, glycosylation, acylation, alkylation, oxidation, and many more. Received: April 16, 2014 Published: August 27, 2014 4272
dx.doi.org/10.1021/pr500385y | J. Proteome Res. 2014, 13, 4272−4280
Journal of Proteome Research
Article
2.2. Protein Digestion and ERLIC Fractionation
Glycosylation has been exploited in a variety of biochemical and cellular processes such as protein secretion, protein stability and translocation, maintenance of cell structure, receptor−ligand interactions, and cell signaling.26,27 Genomics and proteomics studies have highlighted fungal and bacterial lignocellulolytic enzymes and their possible role in biomass degradation.2,13,14,17−20,28−30 But several questions cannot be answered without considering PTMs of these proteins. Fungal glycoside hydrolases are often glycosylated and carry both O-linked and N-linked glycans. These glycoside hydrolases consists of catalytic module, flexible peptide linker, and a carbohydratebinding module. The linker peptides are rich in Ser and Thr residues and are typically O-glycosylated, whereas Nglycosylation seems to be restricted to the catalytic modules.31,32 Again, modifications of one or more amino acids or the side chain of a given protein changes the intrinsic properties of the protein, and can alter its biological function, determines its localization, interactions with other proteins; and also play major role in signaling, metabolic pathways and many more processes.33 Despite their importance, very few studies have been performed on the glycoprotein analysis of fungal secretome. P. chrysosporium has been extensively studied due to its potential to degrade lignin but no literature exits on PTMs of its secretome. Therefore, this study aimed to analyze secretome of P. chrysosporium for Glycosylation. This study applied ERLIC technique for analysis of glycoproteins from secretome of P. chrysosporium. Further, the comparisons of glycoprotein in glucose, cellulose and lignin culture conditions were presented.
The protein digestion and peptide extraction were performed as described previously.17,19 In brief, an equal amount of secretory protein (80 μg) of each secretome was dissolved in 8 M urea ammonium acetate buffer, pH 6.0, reduced with dithiothreitol (DTT) (10 mM), and then alkylated using iodoacetamide (55 mM) at pH 6.0. Then, proteins were digested with sequencing grade modified trypsin (Promega, Madison, WI) at 37 °C for 12 h. The reaction was quenched by adding 1% acetic acid, and peptides were vacuum-dried. The tryptic peptides of samples were fractionated using a PolyWAX LP anion-exchange column (4.6 × 200 mm, 5 μm, 300 Å, PolyLC, Columbia, MD) using Shimadzu Prominence UFLC system as described earlier.34 Mobile phase A (70% ACN/0.1% FA) and mobile phase B (30% ACN/2% FA) were used to establish 60 min gradient consisting of 5−16% B over 35 min and 16−100% B over 10 min followed by 5 min at 100% B, at a flow rate of 0.9 mL/min and eluted fractions were collected. The fractions were dried using vacuum centrifuge, resuspended in acetate buffer (100 mM, pH 6.0), and treated with PNGase F at 37 °C for 6 h.35 The resultant peptides were dried using vacuum centrifuge and analyzed by LC-MS/MS. 2.3. LC-MS/MS Analysis
The samples were separated and analyzed on a Dionex UHPLC system coupled to a LTQFT Ultra (Thermo Electron, Bremen, Germany). The samples were redissolved in 0.1% formic acid and loaded to autosampler, and then injected onto a Zorbax peptide trap column (Agilent, CA) for concentration and desalting. The peptides were separated in a capillary column (200 μm × 10 cm) packed with C18 AQ (5 μm, 300 Å; BrukerMichrom, Auburn, CA) at flow rate 500 nL/min. Mobile phase A (0.1% FA in H2O) and mobile phase B (0.1% FA in ACN) were utilized to establish 60 min gradient comprised of 45 min of 5−35% B, 8 min of 35−50% B, and 2 min of 80% B followed by re-equilibration at 5% B for 5 min. The eluted peptides were analyzed on the LTQFT Ultra with an ADVANCE CaptiveSpray Source (Bruker-Michrom) at an electrospray potential of 1.5 kV. The gas flow was set at 2, ion transfer tube temperature at 180 °C and collision gas pressure at 0.85 mTorr. The LTQFT Ultra was set to perform data acquisition in the positive ion mode as previously described.36 A full MS scan (350−1600m/z range) was acquired in the FT-ICR cell at a resolution of 1 000 000 and a maximum ion accumulation time of 1000 ms. Peptides with charge of +2 to +4 were selected for MS/MS. The linear ion trap was used to collect peptide ions and to measure peptide ion fragments generated by collisioninduced dissociation (CID). Protein fragments generated by collision-activated dissociation (CAD) were captured and measured by the linear ion trap with a maximum ion accumulation time of 100 ms, isolation width of 2 Da. The 10 most intense ions above a 500 counts threshold were selected for fragmentation in CID (MS2). An MS3 scan was followed after each MS2 scan when neutral losses of 98 Da for 1+, 49 Da for 2+, or 32.7 Da for 3+ions were detected.
2. MATERIALS AND METHODS 2.1. Strain Growth Conditions
All reagents used were purchased from Sigma-Aldrich (St. Louis, U.S.A.) unless otherwise stated. P. chrysosporium strain (ATCC 20696) was procured from American Type Culture Collection U.S.A. and maintained following suppliers protocol. The initial mycelium culture was prepared in malt extract. The mycelium was collected by centrifugation at 7000g, for 15 min at 4 °C, washed with sterilized water, and inoculated into minimal media consisting 3.1 g L−1 (NH4)2SO4, 2.0 g L−1 glucose, 1.5 g L−1 NaCl, 0.9 g L−1 KH2PO4, 0.91 g L−1 K2HPO4, and micronutrients 0.200 g L−1 MgSO4·7H2O, 0.008 g L−1 ZnSO4·7H2O, 0.02 g L−1 FeSO4·7H2O, 0.015 g L−1 MnSO4·H2O, and 0.026 g L−1 CaCl2·2H2O. The cell biomass was collected and inoculated into test flask that contained 3.1 g L−1 (NH4)2SO4, 1.5 g L−1 NaCl, 0.9 g L−1 KH2PO4, 0.91 g L−1 K2HPO4, and micronutrients 0.200 g L−1 MgSO4·7H2O, 0.008 g L−1 ZnSO4·7H2O, 0.02 g L−1 FeSO4· 7H2O, 0.015 g L−1 MnSO4·H2O, and 0.026 g L−1 CaCl2·2H2O with 5 g L−1 and glucose (5, g L−1), crystalline cellulose (5 g L−1, Sigma-Aldrich no. C6663), 2 g L−1 lignin (Sigma-Aldrich no. 471003) as a major carbon source. The growth behavior of the strain was monitored by analyzing total protein contents. For proteomics analysis, secretome was harvested at mid exponential phase (i.e., 5 days) by centrifugation at 10 000g for 15 min at 4 °C and further clarified by filtration through a 0.25 μm filter (Nalgene, U.S.A.). The filtered secretome was concentrated by freeze-drying. The experimental design contained three biological replicates for each substrate. The proteins were precipitated by using ice cold acetone and used for further studies.
2.4. Data Analysis
The raw data were converted into dta files using extract-msn, and then to Mascot generic file format using an in-house program. A concatenated target decoy database (20 096 sequences and 9 138 356 residues) of P. chrysosporium proteins obtained from the organism’s genome project (http://genome. jgi-psf.org/Phchr1/Phchr1.home.html, database version 2.1) was used. The protein sequence database downloaded from 4273
dx.doi.org/10.1021/pr500385y | J. Proteome Res. 2014, 13, 4272−4280
Journal of Proteome Research
Article
biomass degradation have been described elsewhere2,14,15,37 and were not within the scope of this study. The main focus of this work is glycosylation of the secretome by P. chrysosporium. ERLIC peptide elution profile of the secreted enzymes of P. chrysosporium during glucose, cellulose, and lignin utilization is shown in Figure 1. In ERLIC technique, required gradient was
the organism’s genome project Web site contained only the accession number and amino acid sequence. To obtain the protein function by homology, the amino acid sequence of each protein entry in the database was automatically BLASTp to the NCBInr protein database. The first four hits from the BLAST were used as the protein name. If there was not a good hit, the protein was named as hypothetic protein. The database search was performed using an in-house Mascot server (version 2.3.02, Matrix Science, Boston, MA). In search, enzyme limits were set at full tryptic cleavage at both ends and a maximum of two missed cleavages. Mass tolerance of 0.8 Da was set for fragment ions in Mascot searches with MS tolerance of 5.0 ppm and MS/ MS tolerance of 0.8 Da.35 Two missed cleavage sites of trypsin were allowed. Carbamidomethylation (C) was set as a fixed modification, whereas oxidation (M), phosphorylation (S, T, and Y), and deamidation (N and Q) were variable modifications. Peptides identified with a consensus NX(S/T) (with X not proline) and a modification of deamidation at the Asn were regarded as N-linked glycopeptides. The deamidation (Asn to Asp) can be wrongly assigned from database searches if an isotopic peak of a precursor is incorrectly assigned as a “monoisotopic peak.” This occurs occasionally when a precursor ion signal is weak. To eliminate such false positive assignments, we integrated the area of the peak located immediately before the assigned monoisotopic peak and filtered out the assignments when the peak area over the monoisotopic peak was above 1%. Furthermore, a set of nonredundant glycoproteins was reported by inclusion of only the protein isoform with a higher protein score assigned by the database search engines when the protein isoforms share the same glycopeptides. The peptide/protein lists obtained were either exported to Microsoft Excel or processed using in-house scripts for further analysis. The peptide/protein list for each fraction was then exported to Microsoft Excel. The FDR of peptide identification was set to be less than 1% (FDR = 2.0 × decoy_hits/total_hits). Glycosylated peptides with ion score > identity score and expect identity score and expect identity score and expect identity score and expect
