Characterization of the White-Rot Fungus, Phanerochaete Carnosa , Through Proteomic Methods and Compositional Analysis of Decayed Wood Fibre

Characterization of the White-rot Fungus, Phanerochaete carnosa , through Proteomic Methods and Compositional Analysis of Decayed Wood Fibre

Sonam Mahajan

A thesis submitted in conformity with the requirements for the degree of Doctorate of Philosophy Department of Chemical Engineering and Applied Chemistry University of Toronto

Characterization of the white-rot fungus, Phanerochaete carnosa , through proteomic methods and compositional analysis of decayed wood fibre

Sonam Mahajan

Doctorate of Philosophy

Department of Chemical Engineering and Applied Chemistry

University of Toronto

2011

Abstract

Biocatalysts are important tools for harnessing the potential of wood fibres since they can perform specific reactions with low environmental impact. Challenges to bioconversion technologies as applied to wood fibres include low accessibility of plant cell wall polymers and the heterogeneity of plant cell walls, which makes it difficult to predict conversion efficiencies.

White-rot fungi are among the most efficient degraders of plant fibre (lignocellulose), capable of degrading cellulose, hemicellulose and lignin. Phanerochaete carnosa is a white-rot fungus that, in contrast to many white-rot fungi that have been studied to date, was isolated almost exclusively from fallen coniferous trees (softwood). While several studies describe the lignocellulolytic activity of the hardwood-degrading, model white-rot fungus Phanerochaete chrysosporium , the lignocellulolytic activity of P. carnosa has not been investigated.

An underlying hypothesis of this thesis is that P. carnosa encodes enzymes that are particularly well suited for processing softwood fibre, which is an especially recalcitrant feedstock, though a major resource for Canada. Moreover, given the phylogenetic similarity of P. carnosa and P. chrysosporium , it is anticipated that the identification of pertinent enzymes for softwood degradation can be more easily conducted. In particular, this project describes the characterization of P. carnosa in terms of the growth conditions that support lignocellulolytic

activity, the effect of enzymes secreted by P. carnosa on the chemistry of softwood feedstocks,

and the characterization of the corresponding secretome using proteomic techniques. Through

this study, cultivation methods for P. carnosa were established and biochemical assays for

protein activity and quantification were developed. Analytical methods, including FTIR and

ToF-SIMS were used to characterize wood samples at advancing stages of decay, and revealed

preferential degradation of lignin in the early stages of growth on all softwoods analyzed.

Finally, an in depth proteomic analysis of the proteins secreted by P. carnosa on spruce and

cellulose established that similar sets of enzyme activities are elicited by P. carnosa grown on different lignocellulosic substrates, albeit to different expression levels.

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Acknowledgements

I would like to express my deepest gratitude to Dr. Emma Master for accepting me as her PhD student 5 some years ago, in spite of the sufficient lack of background that I brought with me in this demanding field. Over the years, I have acquired technical skills and developed the ability to learn newer information better - it would certainly not have been possible without her kind patience throughout my learning stages. I am very grateful for her constant guidance, intellectually stimulating discussions, understanding of my personal and technical challenges, and encouragement during the trying times.

I am also very grateful to Dr. Dragica Jeremic for all her technical assistance, and more importantly, for her friendly guidance throughout the later years of my PhD. It would have been very difficult for me to see the end of my PhD if it were not for her un-ending support.

I am thankful to the members of my committee, Dr. Elizabeth Edwards and Dr. Krishna Mahadevan, for the feedback and direction they provided for this project.

I am also grateful to Dr. Robyn Goacher for her technical assistance and contribution to the fibre characterization studies, and to Peter Brodersen for his help in the early phases of fibre characterization experiments with ToF-SIMS. I would also like to express my thanks to Dr. Eric Yang from Sunnybrooke for his collaboration through the Proteomic Studies and to Dr. Tony Ung for his assistance in sugar analysis and kind offering of all technical resources whenever required.

I will always remember Jacqueline, for our friendly PhD pep-talks and for the technical and moral support in designing the mammoth fibre characterization experiments. Finally, my huge appreciation for all the members of the Master Lab and BioZone for their cooperation, and for accepting me as a relatively inert member of the group, especially during the final stages of my PhD.

I am very grateful to my parents, Dr. Ravi Mahajan and Dr. Kalpana Mahajan, who inspired me to begin this marathon journey in life. Over the last 5 years, I’ve learnt a significant amount and in spite of the challenges, I would have never taken it up, if it were not for them. My loving thanks to my sister, Samridhi for adding a fresh breathe of non-academic humor to my sometimes humdrum life.

I am indebted to my husband, Ateet, for his never-ending patience, gentle encouragement, kind technical assistance and acceptance of all sloppy standards at home. And to all my dearest friends, whose warmth created for me a home away from home, whose company made dull moments bright, and whose smiles, encouraging emails, texts and generous home visits with food, kept me alive through the most trying times…we did it!

Finally, my heartfelt gratitude to all my spiritual teachers and mentors, who have carved on my heart determination and faith in the absolute will, the ability to discern the temporary from the eternal, and the desire to serve with perfection…

Things that are very difficult to do become easy to execute if one somehow or other simply remembers Lord Caitanya Mahaprabhu. But if one does not remember Him, even easy things become very difficult. To this Lord Caitanya Mahaprabhu I offer my respectful obeisances. Caitanya Caritamrita Adi Lila 14.1

Table of Contents

Table of Contents ...... vi

List of Figures ...... x

List of Abbreviations ...... xi

Chapter 1 : Overview ...... 1

Chapter 2 : Literature Review ...... 7

2.1 Lignocellulose: A Valuable Resource ...... 7

2.2 Composition and Structure of Plant Cell Walls ...... 8

2.3 Bioconversion of Lignocellulose ...... 9

2.4 Lignocellulose-degrading Bacteria ...... 9

2.5 Lignocellulose-degrading Fungi ...... 10

2.5.1 White-Rot Fungi ...... 11

2.5.2 Brown-Rot Fungi ...... 11

2.6 Lignocellulose Active Enzymes ...... 12

2.6.1 Carbohydrate Active Enzymes ...... 12

2.6.1.1 Cellulases ...... 13

2.6.1.2 Hemicellulases ...... 13

2.6.1.3 Fungal Oxidative Lignin Enzymes ...... 14

2.7 Investigative Approaches to Improve Lignocellulose Bioconversion ...... 14

2.7.1 Analytical Characterization of Wood Fibre ...... 15

2.7.2 Biochemical Characterization of Wood-degrading Fungi ...... 18

Chapter 3 : Effect of Cultivation Conditions on the Expression of Cellulolytic Activity by Phanerochaete species P. chrysosporium and P. carnosa ...... 30

3.1 Abstract ...... 30

3.2 Introduction ...... 30

3.3 Review of P. chrysosporium Cultivation and Lignocellulolytic Activity ...... 32

3.4 Materials and Methods ...... 41

3.4.1 Microorganism and Materials ...... 41

3.4.2 Cultivation Conditions ...... 41

3.4.3 Growth Measurements ...... 42

3.4.4 Biochemical Assays ...... 43

3.5 Results ...... 45

3.5.1 Effect of Cultivation Condition on the Extent of P. carnosa Growth on Microcrystalline Cellulose...... 45

3.5.2 Effect of Nitrogen Concentration and Agitation on Lignocellulolytic Expression by P. carnosa ...... 48

3.6 Discussion ...... 51

Chapter 4 : Mode of Coniferous Wood Decay by the White Rot Fungus Phanerochaete carnosa as Confirmed by FT-IR and ToF-SIMS ...... 56

4.1 Abstract ...... 56

4.2 Introduction ...... 56

4.3 Materials and Methods ...... 59

4.3.1 Fungal Strain and Cultivation Conditions ...... 59

4.3.2 Fourier Transform Infrared Spectroscopy ...... 59

4.3.3 Transmission Electron Microscropy - Energy-Dispersive X-ray Analysis (TEM-EDXA) ...... 60

4.3.4 Time of Flight Secondary Ion Mass Spectroscopy ...... 60

4.3.5 Statistical Analysis ...... 61

4.4 Results ...... 61

4.4.1 FTIR Spectroscopy ...... 62

4.4.2 TEM-EDXA ...... 68

4.4.3 ToF-SIMS ...... 68

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4.5 Discussion ...... 71

Chapter 5 : Proteomic Characterization of Lignocellulose-degrading Enzymes Secreted by Phanerochaete carnosa Grown on Spruce and Microcrystalline Cellulose ...... 79

5.1 Abstract ...... 79

5.2 Introduction ...... 79

5.3 Materials and Methods ...... 81

5.3.1 Cultivation Conditions ...... 81

5.3.2 Protein Extraction ...... 81

5.3.3 Protein Preparation and Analysis by Mass Spectrometry ...... 82

5.3.4 Peptide Sequence Annotation ...... 82

5.4 Results ...... 83

5.4.1 Preparation and Analysis of Peptide Samples ...... 83

5.4.2 Cellulases and Hemicellulases in Cellulose and Spruce Cultivations ...... 84

5.4.3 Oxidoreductases in Cellulose and Spruce Cultivations ...... 91

5.4.4 Carbohydrate Esterases, Proteases and Other Glycoside Hydrolases ...... 95

5.4.5 Peptides Annotated to Hypothetical Proteins Identified in Proteomic Analyses of P. chrysosporium ...... 100

5.5 Discussion ...... 101

Chapter 6: Synthesis and Conclusions ...... 107

Chapter 7: Engineering Relevance ...... 110

Appendix 1 : B3 Medium ...... 113

Appendix 2: Supplemental Information for Chapter 4 ...... 115

Appendix 3: Supplemental Information for Chapter 5 ...... 119

Appendix 4: Substrate recognition and hydrolysis by a fungal xyloglucan-specific family 12 hydrolase ...... 126

Appendix 6: Additional Information Collected During ResearchError! Bookmark not defined.

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List of Tables

Table 3.1 Summary of carbohydrate active enzyme production by Phanerochaete chrysosporium

Table 3.2 Expression of lignocellulolytic activity by Phanerochaete chrysosporium

Table 3.3 Cultivation of P. carnosa in B3 medium at 27˚C with 1 % microcrystalline cellulose, variable nitrogen, pH 4.5

Table 3.4 Protein concentration in P. carnosa culture supernatant during growth on microcrystalline cellulose

Table 3.5 Chitin content of P. carnosa cultivations after 24 days

Table 3.6 Endoglucanase activity in P. carnosa culture supernatant during growth on microcrystalline cellulose

Table 3.7 β-glucosidase activity in P. carnosa culture supernatant during growth on microcrystalline cellulose

Table 3.8 Total cellulolytic activity in P. carnosa culture supernatant during growth on microcrystalline cellulose

Table 3.9 MnP activity in P. carnosa culture supernatant during growth on microcrystalline cellulose

Table 4.1 Assignment of FTIR peaks that distinguish control and decayed wood samples

Table 5.1 Analysis of peptides from extracellular filtrates of P. carnosa grown on cellulose and spruce that correspond to predicted cellulases and hemicellulases

Table 5.2 Analysis of peptides from extracellular filtrates of P. carnosa grown on cellulose and spruce that correspond to extracellular oxidases and proteins involved in lignin degradation

Table 5.3 Analysis of peptides from extracellular filtrates of P. carnosa grown on cellulose and spruce that correspond to extracellular carbohydrate-active enzymes

Table 5.4 Peptides from extracellular filtrates of P. carnosa grown on cellulose that were annotated to proteins from P. chrysosporium with unknown function

List of Figures

Figure 3.1 SDS-PAGE of P.carnosa culture supernatants

Figure 4.1 Spruce decay described by FTIR analysis

Figure 4.2 Fir decay described by FTIR analysis

Figure 4.3 Pine decay described by FTIR analysis

Figure 4.4 PCA of normalized and mean-centered ToF-SIMS spectra: A) Fir, B) Pine, and C) Spruce

Figure 4.5 PCA of mean-centered ToF-SIMS image of decayed pine (time point 5)

Figure 5.1 Distribution of peptide annotations from proteins produced by P. carnosa grown on (A) crystalline cellulose, and (B) spruce wood chips

Figure 6.1 Synthesis of information flow between the three key objectives of this thesis

List of Abbreviations

FTIR - Fourier Transform InfraRed ToF-SIMS - Time-of-Flight Secondary Ion Mass Spectrometry TEM-EDXA - Transmission Electron Microscope with Energy Dispersive X-ray Analysis SEM - scanning electron microscopy CAZymes - Carbohydrate Active Enzymes FOLymes - Fungal Oxidative Lignin enzymes GH - Glycoside Hydrolase CE - Carbohydrate esterase LiP - Lignin peroxidase MnP - Manganese peroxidase ABTS - 2,2'-azino-bis(3-ethylbenzthiazoline-6-sulphonic acid) EGase - Endoglucanase CBH - Cellobiohydrolase CBD - Cellobiose Dehydrogenase BGL - beta-glucosidase FPU - Filter Paper Units XYN - Xylanase MAN - Mannanase PCR - Polymerase chain reaction Ribosomal ITS sequences – ribosomal internal transcribed spacer sequences BSA - Bovine Serum Albumin LN - Low nitrogen, stationary cultivations LNS - Low nitrogen cultivations with shaking HN - Low nitrogen, stationary cultivations PCA - Principal Component Analysis PLS Toolbox - Partial Least Squares Toolbox PDMS - Polydimethylsiloxane MWCO – Molecular weight cut off

Chapter 1 : Overview

Terrestrial plants synthesize nearly 200 billion tons of lignocellulosic biomass per year (Zhang 2008). Lignocellulose comprises the structural component of plant cell walls and is composed of cellulose, hemicellulose, and lignin, as well as relatively low amounts of aliphatic and alicyclic compounds, phenolic compounds, and proteins. The “lignocellulose biorefinery” aims to produce renewable fuels, chemicals and new polymers from all the constituents of lignocellulose. As the non-consumable portion of plant biomass, bioproducts from lignocellulose do not directly compete with food resources. Moreover, lignocellulose can be supplied as a residual of agricultural and forest industries.

Despite the advantages of lignocellulose, the complex and variable composition of this feedstock creates significant technical barriers that can be broadly grouped into 1) the production of fermentable sugars and technical polymers, and 2) the conversion of mixed sugars to fuels and chemicals. Current research towards efficient production of fermentable sugars and polymeric materials is aimed at improving the integration of pretreatment technologies and downstream enzyme applications, and minimizing the production of compounds that inhibit fermentation processes (Kabel et al. 2006). Still, the yield of fermentable sugars from enzyme treatments is variable and incomplete, and the effect of commercially available enzymes often depends on the nature of the substrate, rather than advertised enzyme activity (Lynd et al. 2008).

White-rot fungi are the most efficient degraders of lignocellulose as they can degrade cellulose and hemicellulose, as well as lignin (Schmidt 2006). Many white-rot fungi have been isolated from hardwoods. By contrast, Phanerochaete carnosa is a white-rot fungus that has been isolated predominantly from softwood (Burdsall 1985). While several studies describe the lignocellulolytic behaviour of the hardwood-degrading, model white-rot fungus P. chrysosporium (Sato et al. 2007; Vanden Wymelenberg et al. 2005a; Vanden Wymelenberg et al. 2005b), the lignocellulolytic activity of P. carnosa has not been investigated. As a softwood degrader, P. carnosa might encode enzymes that are particularly well suited for processing softwood fibre, which consists of different lignin and hemicellulose composition, compared to hardwood. Given the abundance and comparatively high recalcitrance of softwood fibre (Mosier

et al. 2005), enzymes that effectively process this material would benefit forest sectors in northern countries, including Canada, where boreal forests occupy 77% of the forest land (Natural Resources Canada 2011).

Accordingly, the main aim of this PhD thesis is to characterize the secretome of P. carnosa during growth on lignocellulosic substrates, and to evaluate the pattern of wood decay by this fungus over time. In addition to increasing our fundamental understanding of bioprocesses that have evolved to transform recalcitrant biomass, the analyses described herein assesses the potential of P. carnosa to be used for biopulping and as a supply of industrially relevant enzymes.

In particular, the following specific research hypotheses were tested: 1. Cultivation conditions that promote the expression of lignocellulolytic enzymes in P. chrysosporium will also induce the expression of lignocellulolytic enzymes in P. carnosa . 2. P. carnosa will exhibit selective degradation of lignin at early stages of softwood decay, and having been isolated from softwood species, P. carnosa will efficiently degrade guaiacyl lignin, which dominates in softwood. 3. The profile of proteins secreted by P. carnosa during growth on lignocellulose will depend on the source of the substrate, and could be used to predict enzyme formulations that optimally transform a particular biomass feedstock.

As summarized below, the research hypotheses listed above were tested as described in chapters three, four and five, respectively.

Chapter 3: Effect of cultivation conditions on the expression of cellulolytic activity by Phanerochaete species, P. chrysosporium and P. carnosa This study describes the first reported effort to cultivate and biochemically characterize Phanerochaete carnosa . Accordingly, the primary aim of this study was to establish growth conditions that induce the expression of lignocellulolytic enzymes by this organism, and to investigate the effect of nitrogen concentration, temperature and agitation on the production of cellulolytic and lignin-degrading activities. Protein concentration in culture supernatants, fungal

growth, and biochemical assays for cellulases, lignin peroxidases and manganese peroxidases, were measured. By comparing the effect of cultivation condition on the production of lignocellulolytic activity by P. carnosa and P. chrysosporium , differences in enzyme activities and their regulation were also evaluated.

Chapter 4: Mode of coniferous wood decay by the white-rot fungus Phanerochaete carnosa as elucidated by FTIR and ToF-SIMS (a version of this chapter has been accepted for publication ) The objective of this study was to investigate the effect of conifer species and stage of degradation on the mode of softwood decay by P. carnosa (i.e., selective vs. simultaneous decay) . The three wood species investigated were white spruce, lodgepole pine and balsam fir, all of which are abundant in the North American boreal forests, and comprise common mixed feedstocks for pulp and paper industries. FTIR and ToF-SIMS were used to monitor changes in lignocellulose composition resulting from fungal activity. Additionally, TEM and SIMS imaging were used to evaluate the selectivity of the degradation across cell walls by determining the localization of lignin prior and post-decay.

Chapter 5: Proteomic analysis of the secretome of P. carnosa grown on microcrystalline cellulose and spruce wood chips (a version of this chapter was published in Applied and Microbial Biotechnology ) The objective of this research was to describe the secretome of P. carnosa while growing on cellulose and spruce. A comparative study of the extracellular proteins expressed in both cultivation conditions was performed to determine whether a model cellulose substrate elicited a similar profile of lignocellulose-active enzymes as a complex lignocellulosic substrate. Since the genome sequence of P. carnosa was not available, the proteomic protocols were modified to accommodate de novo sequencing and manual validation of peptides along with sequence- similarity based annotations. In this way, the regulation of lignocellulose-active enzyme expression in P. carnosa was investigated, as was the possibility to define enzyme formulations that are optimal for particular feedstocks. Finally, the proteomic data resulting from this study were analyzed to predict enzyme activities that may have evolved in P. carnosa to promote softwood degradation.

As summarized in Chapters 6 and 7, the results of the research described herein elucidate the growth conditions that support lignocellulolytic activity of P. carnosa , the differential effects of P. carnosa on the chemistry of softwood feedstocks, and the expression profile of enzymes secreted by P. carnosa during growth on cellulose and a lignocellulosic feedstocks. As a result, the softwood-degrading activity of P. carnosa was confirmed and the applied potential of P. carnosa and encoded enzymes was explored.

Summary of Scholarly Contributions:

A. Peer-reviewed Publications:

MASc

1. S. Mahajan , S.K. Konar and D.G.B. Boocock. Standard Biodiesel from Soybean Oil by a Single Chemical Reaction. Journal of American Oil Chemists’ Society. 2006. 84: 641-644.

2. S. Mahajan , S.K. Konar and D.G.B. Boocock. Determining acid number for biodiesel. Journal of American Oil Chemists’ Society. 2006. 83: 567-570.

3. S. Mahajan , S.K. Konar and D.G.B. Boocock. Variables Affecting the Production of Standard Biodiesel. Journal of American Oil Chemists’ Society. 2007. 84: 189-195.

PhD

4. J. Powlowski, S. Mahajan , M. Schapira and E.R. Master. Substrate Recognition and Hydrolysis by a Fungal Xyloglucan-Specific Family 12 Hydrolase. Carbohydrate research. 2009. 344(10):1175-9. (see Appendix 4). ( Contribution: Performed enzymatic digestion for xyloglucans and detected products through HPLC )

5. S. Mahajan and E.R. Master. Proteomic Characterization of Lignocellulose-degrading Enzymes Secreted by Phanerochaete carnosa Grown on Spruce and Microcrystalline Cellulose. Applied Microbiology and Biotechnology. 2010. 86(6):1903-14. (see Chapter 5)

6. S. Mahajan , D. Jeremic, R. E. Goacher and E. R. Master. Mode of coniferous wood decay by the white-rot fungus Phanerochaete carnosa as elucidated by FTIR and ToF-SIMS. 2011 (accepted with revisions; see Chapter 4). ( Contribution: established growth conditions for P. carnosa on softwood species; harvested samples and performed FTIR analysis; prepared

4 samples for ToF-SIMS and performed data analyses in collaboration with D. Jeremic and R.E. Goacher. )

B. Conference Presentations (Presenter is underlined) :

MASc

1. S. Mahajan , S.K. Konar and D.G.B. Boocock. ‘Production of Standard Biodiesel from Soybean Oil: A one-step process using NaOH’ (speaker + poster). 55th Canadian Chemical Engineering Conference, Toronto, ON, October 2005

PhD

2. S. Mahajan and E.R. Master. ‘Proteomic Analysis of Softwood-degrading Fungi’ (speaker). 56 th Canadian Chemical Engineering Conference, Sherbrooke, QC, October 2006

3. S. Mahajan and E.R. Master. ‘Proteomic Analysis of Softwood-degrading Fungi’ (poster). 56 th Canadian Chemical Engineering Conference, Sherbrooke, QC, October 2006

4. S. Mahajan . ‘Unit Operations Laboratory: Problem based learning’ (speaker). 56 th Canadian Chemical Engineering Conference, Sherbrooke, QC, October 2006

5. S. Mahajan and E.R. Master. ‘Proteomic Analysis of Softwood-degrading Fungi towards Biomimetic Enzyme Applications’ (poster). The World Congress on Industrial Biotechnology and Bioprocessing, Toronto, ON, July 2006

6. S. Mahajan and E.R. Master. ‘Proteomic Analysis of Softwood-degrading Fungi’ (poster). 107th General Meeting of the American Society for Microbiology.Toronto, ON May 2007

7. S. Mahajan and E.R. Master. ‘Refining models of Lignocellulose Biotransformation towards Biomimetic Enzyme Applications’ (poster). 10th International Congress on Biotechnology in the Pulp and Paper Industry, Madison WI, June 2007

8. J. MacDonald, S. Mahajan and E. R. Master ‘Transcription Profiles and Proteomic Analysis of P. carnosa Grown on Softwood Feedstocks for Tailored Applications of Hydrolytic Enzymes.’ MIE BioForum Mie, Japan. Sep 2008

9. S. Mahajan and E.R. Master. ‘Proteomic Analysis of the Secretome of Softwood-Degrading Fungi’ (poster). 4 th annual conference US HUPO North Bethesda, MD, March 2008

10. S Mahajan , E Yang and E.R. Master . ‘The secretome of the softwood-degrading Basidiomycete, Phanerochaete carnosa , grown on cellulose and spruce.’

Gordon Research Conference (GRC): Cellulosomes, Cellulases & Other Carbohydrate Modifying Enzymes. Proctor Academy, NH, USA. July 26-31, 2009.

11. J. MacDonald, S. Mahajan and E. R. Master . ‘Proteomic and Transcriptomic Analysis of Lignocellulose-degrading enzymes in softwood-degrading fungus, Phanerochaete carnosa’ Lignobiotech One Symposium, Reims, France, March 2010

Chapter 1 References

Burdsall HHJ (1985) A contribution to the taxonomy of the genus Phanerochaete (Corticiaceae, Aphyllophorales) . Mycological Memoir No. 10. Braunschweig.

Kabel MA, van der Maarel MJEC, Klip G, Voragen AGJ, Schols HA (2006) Standard assays do not predict the efficiency of commercial cellulase preparations towards plant materials. Biotechnology and Bioengineering 93 (1):56-63.

Lynd LR, Laser MS, Bransby D, Dale BE, Davison B, Hamilton R, Himmel M, Keller M, McMillan JD, Sheehan J, Wyman CE (2008) How biotech can transform biofuels. Nature Biotechnology 26 (2):169-172

Mosier N, Wyman C, Dale B, Elander R, Lee YY, Holtzapple M, Ladisch M (2005) Features of promising technologies for pretreatment of lignocellulosic biomass. Bioresource Technology 96 (6):673-686

Sato S, Liu F, Hasan K, Tien M (2007) Expression analysis of extracellular proteins from Phanerochaete chrysosporium grown on different liquid and solid substrates. Microbiology 153:3023-3033

Schmidt O (2006) Wood and Tree Fungi. Biology, Damage, Protection and Use. Springer- Verlag, New York

Vanden Wymelenberg A, Sabat G, Martinez D, Rajangam AS, Teeri TT, Gaskell J, K KPJ, Cullen D (2005a) The Phanerochaete chrysosporium secretome: Database predictions and initial mass spectrometry peptide identifications in cellulose-grown medium Journal of Biotechnology 118 (1):17-34

Vanden Wymelenberg A, Sabat G, Martinez D, Rajangam AS, Teeri TT, Gaskell J, Kersten PJ, Cullen D (2005b) The Phanerochaete chrysosporium secretome: database predictions and initial mass spectrometry peptide identifications in cellulose-grown medium. Journal of Biotechnology 118:17-34

Zhang YH (2008) Reviving the carbohydrate economy via multi-product lignocellulose biorefineries. Journal of Industrial Microbiology Biotechnology 35 (5):367-375.

Chapter 2 : Literature Review

2.1 Lignocellulose: A Valuable Resource

Lignocellulose is the major structural component of woody plants. It is a network of lignin, cellulose and hemicellulose that is chemically bonded through non-covalent forces and covalent cross-linkages (Perez et al. 2002).

Terrestrial plants synthesize nearly 200 billion tons of lignocellulosic biomass per year (Zhang 2008). Forests, which are the primary source of lignocellulosic biomass, cover approximately 30% of the total land area, and comprise a significant source of energy, timber, and pulp (Global Forest Resources Assessment 2005). Protective functions of forests include soil and water conservation, biological diversity and mitigation of climate change. While conventional research in wood fibre structure and composition focused on properties that improve timber, pulp and paper qualities, present research has expanded to include bioconversion of wood fibre to platform sugars that can be fermented to fuels and chemicals, and bioprocessing techniques that increase the sustainability of processes used to convert wood fibre to high-quality pulp and other value-added materials.

Large amounts of lignocellulosic residues (6 to 3849 million t and from 64 to 561 million t, for US and Canada) are generated through forestry and agricultural practices, with the most common disposal being combustion (Magdalena 2008). This is not only an environmental concern but also represents an under-utilization of a renewable resource that can be converted to energy and biochemicals. For instance, over 75% of organic chemicals are produced from five primary base chemicals: ethylene, propylene, benzene, toluene and xylene. The aromatic compounds could be produced from lignin, while the low molecular weight aliphatic compounds could be obtained from alcohols that result from fermentation of sugars generated from bioconversion of hemicellulose and cellulose (Howard et al. 2003). Moreover, the alcohols produced could be utilized as a biofuel. And chemicals like vanillin, xylitol, and furfural from lignocellulosic wastes can be used in industrial products including herbicides, pharmaceuticals, and household products (Howard et al. 2003; Ragauskas et al. 2006).

2.2 Composition and Structure of Plant Cell Walls The main components of plant cell walls are cellulose, lignin, and hemicellulose. Cellulose and hemicellulose are polysaccharides, whereas lignin is an aromatic polymer synthesized from phenylpropanoid precursors (Sjostrom 1983). Wood from different tree species is typically composed of 40–50% α-cellulose, 20–35% hemicellulose and 15–35% lignin (Perez et al. 2002). With the exception of cellulose, these polymers are synthesized inside the cell and then organized outside the cell membrane. The primary cell wall is deposited first and is characterized by relatively amorphous cellulose structure. During cell differentiation, the primary cell wall expands and elongates, then secondary cell walls are synthesized, which are characterized by cellulose microfibrils with higher crystallinity and altered hemicellulose content (Ding and Himmel, 2006). Differentiated xylem cells with a secondary cell wall are called secondary xylem or wood.

Cellulose is the most abundant organic polymer on earth, and is composed of repeating cellobiose subunits that consist of β-1,4-linked glucose. The degree of polymerization (DP) of wood-derived cellulose is typically greater than 10,000 and adjacent cellulose polymers interact through hydrogen bonds, forming highly stable structures that contain both amorphous and crystalline regions (Lerouxel et al. 2006; McCann and Carpita 2008). Cellulose is synthesized by a cellulose synthase complex that is located within the cytoplasmic membrane of plant cells. In plant cells, the cellulose synthase is comprised of many enzymes that include 36 cellulose synthase enzymes assembled as rosette structure (Taylor et al. 2000). Accordingly, 36 cellulose molecules are thought to emerge from each rosette structure.

Like cellulose, hemicellulose backbones are composed of β-1,4-linked sugars. However, the particular sugar composition of hemicellulose depends on the source of the polysaccharide. For instance, xyloglucan is the main hemicellulose of primary cell walls and consists of a β-1,4- linked glucose backbone that is decorated with xylose, galactose and sometimes fucose branching sugars (Hayashi and Kaida 2011). By contrast, the main hemicellulose in secondary cells walls of hardwoods and softwoods is xylan and galactoglucomannan, respectively. Xylan is composed of β-1,4-linked xylose that can be substituted by arabinose and glucuronic acid; xylan

can also be acetylated. By contrast, wood-derived mannans are composed of β-1,4-linked mannose and glucose that can be substituted by galactose (Stenius and Vuorinen 1999). These polymers have a lower DP than cellulose, averaging between 100 and 200 and have a lower crystallinity (Rowell 2005a). While cellulose is synthesized by cellulose synthases located within the cytoplasmic membrane, hemicelluloses are generated in the Golgi complex, and then secreted to the plant cell wall (Keegstra 2010).

Lignin is a complex polymer that is composed of phenylpropane units linked together by carbon- carbon (C-C) and ether (C-O-C) linkages. Lignin provides structural support to plant fibres, as well as resistance against microbial attack and improved water transport in xylem and phloem tissues. Precursors of lignin biosynthesis are p-coumaryl alcohol, coniferyl alcohol, and sinapyl alcohol, and while p-coumaryl alcohol predominates in grasses to form H-lignin, coniferyl alcohol is the main monolignol in softwood G-lignin, and hardwood G/S-lignin contains both sinapyl and coniferyl monolignols (Humphreys and Chapple 2002).

Besides carbohydrates and lignin, the cell wall contains hydroxyproline-rich glycoproteins, arabinogalactan proteins, glycine-rich proteins, and proline-rich proteins (Showalter 2001). Other extraneous compounds that do not contribute to the structure of cell walls are grouped as extractives and ash (Pettersen 1984).

2.3 Bioconversion of Lignocellulose Physical and chemical processes have been developed to pretreat wood fibres to separate cellulose, hemicellulose, and lignin; however, these processes can decrease the quality of the polymers and create by-products that inhibit the fermentation of resulting sugars. Alternatively, biocatalysts (enzymes) can perform specific reactions under comparatively mild reaction conditions, and could be used to improve the pretreatment process (Lynd et al. 2008).

2.4 Lignocellulose-degrading Bacteria Evidence of bacterial degradation of lignin is sparse. Streptomycete species have been shown to degrade low levels of lignin (Crawford, 1978; Watanabe et al. 2003), and multicopper oxidases

with laccase activity have been isolated from bacteria, although these enzymes are expected to mainly participate in sporulation (Claus 2004; Malherbe and Cloete 2002).

Accordingly, bacteria are generally considered secondary lignocellulose degraders, and can degrade cellulose and hemicellulose both aerobically and anaerobically (Walker and Wilson 1991). Examples of aerobic (hemi)cellulose degraders include Thermobifida fusca and Cellulomonas composti , as well as several other bacteria (Béguin and Aubert 1994). In these cases, degradation is initiated by the concerted activity of cell-associated and free extracellular cellulases and hemicellulases (Walker and Wilson 1991). By contrast, anaerobic cellulose- degrading bacteria typically anchor relevant hydrolase activities to the cell through a cellulosome complex. Cellulosomes are multienzyme complexes that bind to the bacterial cell wall and promote the uptake of solubilized sugars by the hydrolytic organism (Bayer et al. 2008). Clostridium thermocellum and C. cellulolyticum are among the best-studied anaerobic, cellulose- degrading bacteria (Bayer et al. 2008). Notably, these organisms have been extensively evaluated for their potential to promote simultaneous saccharification and fermentation (SSF) or consolidated bioprocessing (CBP) (Lynd et al. 2002).

2.5 Lignocellulose-degrading Fungi Fungi are the primary degraders of lignocellulose (Rabinovich et al. 2002; Sanchez 2009). In addition to secreting enzymes that are critical to lignocellulose decomposition, fungal growth on lignocellulose is promoted by the formation of mycelia that allow filamentous fungi to transport nutrients, including nitrogen and iron, to the carbon-rich lignocellulosic substrate (Hammel 1997). Many fungi are also more resistant to wood-derived biocides that limit bacterial growth. These compounds include tannins and various phenolic compounds (terpenes, stilbenes, flavonoids and tropolones) that are particularly abundant in the heartwood of fallen trees.

The majority of wood-degrading fungi that have been characterized to date are members of the phylum Basidiomycota and are characterized by either brown-rot or white-rot decay.

2.5.1 White-Rot Fungi Fungi causing white-rot decay secrete enzymes that degrade lignin, hemicellulose and cellulose, leaving the residual wood fibre with a bleached appearance. Two main patterns of white-rot decay have been distinguished by microscopic and ultra structural investigations (Liese 1970). Simultaneous white-rot (“corrosion rot ”) is exemplified by combined degradation of carbohydrates and lignin at early and late stages of decay. Examples of fungi that elicit simultaneous white-rot include Fomes fomentarius , Phellinus robustus , and Trametes versicolor (Blanchette 1984, Blanchette 1994). By contrast, selective (sequential) white-rot is exemplified by early degradation of lignin and hemicelluloses followed by cellulose degradation. Ceriporiopsis subvermispora and Phlebia radiata are perhaps the best studied fungi to elicit selective white-rot decay (Ander and Eriksson, 1977; Blanchette, 1991; Fackler et al., 2007). Often, the sequential white-rot fungi “selectively” degrade lignin and hemicellulose in small elongated cavities within a wood tissue such that decayed regions are surrounded by tissue that appears sound (Blanchette 1984). With advancing decay and delignification of the middle lamella and primary cell wall, the wood sample typically acquires a fibrous texture (Schmidt 2006). Importantly, elicitation of simultaneous or sequential white-rot decay can depend on the wood that is being degraded, the stage of wood degradation, and the particular fungal strain used in the study (Messner and Srebonik, 1994). For instance, certain strains of Phanerochaete chrysosporium (e.g. BKM-F-1767), cause selective decay of deciduous wood samples, while many other strains cause simultaneous wood decay (Blanchette 1992).

2.5.2 Brown-Rot Fungi Brown-rot decay is characterized by rapid degradation of cellulose and hemicellulose, and retention of a modified lignin residue (Yelle et al. 2008). To date, brown-rot fungi were mainly isolated from coniferous (softwood) trees and represent approximately 7 % of isolated wood- rotting basidiomycetes (Martínez et al. 2005). Brown-rot decay can cause rapid loss in the structural strength of construction wood (Green III and Highley 1997), and decayed woods are typically characterized by reddish brown color and dry, crumbly and brittle consistency (Martínez et al. 2005). Examples of brown-rot fungi include Fomitopsis lilacino-gilva, Laetiporus portentosus , Postia placenta, Gloeophyllum trabeum and Serpula lacrymans . In contrast to the numerous enzymes secreted by white-rot fungi, brown-rot fungi appear to initiate

the degradation of wood polysaccharides using Fenton chemistry, whereby diffusible hydroxyl radicals penetrate the wood surface and depolymerize cellulose and hemicelluloses while leaving lignin intact (Jensen Jr. et al. 2001).

Studies on the phylogeny and substrate preference of wood decaying fungi suggest that the brown-rot fungi have evolved multiple times from white-rot fungi. Hibbet et al. (2001) predict that white-rot, tetrapolar mating systems, and ability to degrade conifers and hardwoods is a primitive characteristic of wood-degrading fungi, whereas brown-rot, bipolar mating systems, and exclusive decay of conifers, evolved subsequently from these ancestral characteristics (Hibbett and Donoghue 2001).

2.6 Lignocellulose Active Enzymes Given the polymeric and complex composition of lignocellulose, it is not surprising that the initial attack of this substrate is achieved through the concerted activity of several extracellular microbial enzymes. Lignocellulose-active enzymes that are produced by white-rot fungi are particularly valuable for biomass conversion, since they can be used to selectively transform both lignin and polysaccharides (Kirk and Cullen 1998). The enzymes that contribute to this activity can be broadly classified as Carbohydrate-Active enzymes (CAZymes) and Fungal Oxidative Lignin enzymes (FOLymes) (Cantarel et al. 2009; Levasseur et al. 2008). The following is a summary of the main enzymes involved in these degradation processes.

2.6.1 Carbohydrate Active Enzymes A sequence-based classification scheme for carbohydrate-active enzymes was developed in 1991, called the CAZy database (CArbohydrate enZYme database) (Cantarel et al. 2009; Henrissat 1991). At present, this database is comprised of 125 glycoside hydrolase families, 92 glycoside transfer families, 22 polysaccharide lyase families and 16 carbohydrate esterase families. Glycoside hydrolases hydrolyze the glycosidic bonds between α-linked or β-linked sugars, using a retaining or inverting mechanism (Davies and Henrissat 1995). Polysaccharide lyases cleave polysaccharide chains via a β-elimination mechanism resulting in the formation of a double bond at the newly formed non-reducing end, whereas carbohydrate esterases catalyze the deacetylation and demethylation of substituted polysaccharides. Efficient degradation of

polysaccharides requires cooperation or synergistic interactions between enzymes responsible for cleaving the different linkages. Significant research has been done to demonstrate and understand synergy between various isolated enzymes for degradation of microcrystalline cellulose (Avicel) and commercial xylans (de Vries and Visser 2001). For instance, hydrolysis of xylan by an Aspergillus xylanase was increased in the presence of accessory enzymes that catalyze the hydrolysis of xylan side chains (Paszczynski et al. 1988).

2.6.1.1 Cellulases Hydrolysis of cellulose to glucose requires the activity of endoglucanases (endo-cellulases), cellobiohydrolases and β-glucosidases. While endo-cellulases hydrolyze glycosidic linkages at internal positions within cellulose molecules, cellobiohydrolases release cellobiose from the reducing or non-reducing end of cellulose and β-glucosidases hydrolyze cellobiose to glucose. These enzymes work synergistically to degrade both amorphous and crystalline cellulose, and endo-cellulases and cellobiohydrolases are often associated with cellulose-binding modules to promote their activity on polymeric substrates (Kirk and Cullen 1998). Of the 125 GH families, fungal cellulases belong to GH families 5, 6, 7, 9, 12, 44, 45, 48, 61 and 74 (Dashtban et al. 2009). In addition to the hydrolytic enzymes, oxidative enzymes also participate in cellulose degradation (Kirk and Cullen 1998). For example, quinone oxidoreductase (cellobiose dehydrogenase) reduces quinones and phenoxy radicals in the presence of cellobiose, which is oxidized to cellobiono-δ-lactone. And cellobiose oxidase uses molecular oxygen to oxidize cellobiose and longer cello-oligomers to corresponding acids.

2.6.1.2 Hemicellulases Wood hemicelluloses include xylan, (galacto)glucomannan, and xyloglucan. These polysaccharides contain a β-1,4-linked sugar backbone that can be acetylated or substituted by sugar branches (Scheller and Ulvskov 2010). Given the diversity of hemicelluloses, many glycoside hydrolases, as well as carbohydrate esterases participate in their degradation. For instance, xylan degradation requires the activity of xylanases, xylosidases, arabinofuranosidases, galactosidases, deacetylases, glucuronidases, glucuronyl esterases, and feruloyl esterases. Like cellulases, many of these activities function synergistically and are associated with carbohydrate- binding modules that promote enzyme activity on polymeric substrates (de Vries et al. 2000;

Hervé et al. 2010). So far, fungal hemicellulases were identified in nineteen GH families: 1, 2, 3, 5, 10, 11, 26, 27, 36, 39, 43, 51, 53, 54, 62, 67, 74,115, and 116, and nine CE families: 1, 2, 3, 4, 5, 6, 12, 15 and 16.

2.6.1.3 Fungal Oxidative Lignin Enzymes Similar to carbohydrate-active enzymes, enzymes involved in lignin catabolism have also been grouped into sequence-based families and integrated in a database, named the Fungal Oxidative Lignin enzymes (FOLy) (Levasseur et al. 2008).

Laccases and peroxidases are extracellular, lignolytic enzymes that are key to enzymatic lignin degradation (Perez et al. 2002; ten Have and Teunissen, 2001). The peroxidases include lignin peroxidase (LiPs) and manganese-dependent peroxidase (MnP). Both LiP and MnP oxidize corresponding substrates through two consecutive one-electron oxidation steps with intermediate cation radical formation (Sanchez 2009). LiP degrades non-phenolic lignin units (up to 90% of the polymer) whereas MnP generates Mn 3+ , which acts as a diffusible oxidant of phenolic or non-phenolic lignin units likely mediated through lipid peroxidation reactions (Cullen and Kersten 2004; Moen and Hammel, 1994).

Laccases are blue copper oxidases that catalyze the one-electron oxidation of phenolics, aromatic

amines, and other electron-rich substrates with the concomitant reduction of O 2 to H 2O (d'Souza et al. 1999). Like Mn(III) chelates, laccases oxidize the phenolic units in lignin to phenoxy radicals, which can lead to aryl-C cleavage (Kawai et al. 1988). Laccase can also oxidize non- phenolic substrates in the presence of certain auxiliary substrates such as 2,2´-azino-bis-3- ethylthiazoline-6- sulfonate (Call and Muncke 1997).

2.7 Investigative Approaches to Improve Lignocellulose Bioconversion Challenges associated with bioconversion of lignocellulose can be attributed to the distinct composition and structure of different plant cell walls. For instance, the accessibility of enzymes to fibre polymers can vary, and the enzymes required to depolymerize cell wall components depends on the source of fibre. Moreover, since plant cell walls are a composite of different

polymers, multiple enzymes are necessary to process fibres, and the enzyme requirement is likely to change as the fibre is treated.

Methods for increasing the efficiency of biocatalysts include screening for organisms with novel enzymes, improvement of existing industrial strains, enzyme engineering and development of enzyme cocktails that are tailored to particular lignocellulosic feedstocks. Considerable progress has been made concerning the biochemistry of purified, fungal enzymes that participate in wood degradation. However, the effect of enzyme activities on natural lignocellulosic feedstocks is limiting. It is also unclear how the profile of enzymes secreted by wood-degrading fungi depends on the composition of the lignocellulosic feedstock.

It is anticipated that by characterizing the effect of lignocellulose composition on the expression of lignocellulolytic enzymes by fungi, and enhanced analytical tools to characterize the impact of these enzymes on the lignocellulose substrate, the development of more efficient bioprocesses can be developed. The following is a summary of some of the analytical and molecular biology tools that have recently been used to increase our understanding of lignocellulose bioconversion by fungi.

2.7.1 Analytical Characterization of Wood Fibre Degradation patterns associated with advanced stages of white-rot and brown-rot decay can be identified macroscopically and microscopically. However, a precise analysis of the effect of degradation requires chemical assessment of the residual fibre. Lignin in wood is traditionally measured by the Klason method, which is based on total acid hydrolysis of polysaccharides and gravimetric estimation of the precipitated lignin. This, however, is a time consuming method and not particularly suited to characterizing partially decayed or modified wood (Martínez et al. 2005). Alternatively, the direct characterization of fibre chemistry facilitates the analysis of fungal decay, since these methods require minimal sample preparation and quantity (Stenius and Vuorinen 1999). These methods typically use dry fibre samples and enable either surface analysis or bulk analysis of fibre chemistry. Examples of analytical techniques for fibre surface analysis include X-ray photoelectron spectroscopy (XPS) and secondary ion mass spectroscopy (SIMS), as well as atomic force microscopy (AFM) and attenuated total reflectance (ATR).

Common analytical techniques for bulk analysis of fibre chemistry include nuclear magnetic resonance (NMR), Fourier transform infrared (FTIR), Raman and ultraviolet/visible (UV/Vis) spectroscopy, as well as pyrolysis gas chromatography (Py-GC) (Stenius and Vuorinen 1999). Additionally, microscopic methods, including transmission electron microscopy (TEM), have been used to detect morphological changes in wood cell walls that occur during wood decay. In the present thesis, FTIR, ToF-SIMS and TEM were used to analyze residual fibre composition and anatomy, and so these methods will be further described below.

ToF-SIMS is a powerful technique that provides chemical information about a solid surface by ionizing the sample surface with a primary ion beam and determining the mass-to-charge ratio of the secondary ion fragments (Benninghoven 1994). ToF-SIMS has been used to characterize pulp fibre (Rowell 2005), as well as spatially resolve different components on the surface of wood, providing compositional information across cell walls (Jung et al. 2010). Further, Kangas et al., (2004) compared different types of fines separated from thermomechanical pulp based on the chemical composition as determined by ToF-SIMS (Kangas and Kleen 2004). In another study, Saito et al., (2008) used ToF-SIMS to investigate the distribution of trace elements and lignin in contiguous rings to highlight differences between sapwood and heartwood of Chamaecyparis obtusa. ToF-SIMS imaging was also used to conclude that the ray parenchyma cells play a role in defining the state of elements during the transition from sapwood to heartwood (Saito et al. 2008). Another application of this technology has been to determine the surface distribution of lignin, carbohydrates and metal for aspen and spruce wood tissue-sections (Tokareva et al. 2007). Additionally, effects of processing techniques like refining and pulping on fibre surfaces have also been determined using ToF-SIMS (Fardim and Duran 2003). Importantly, in a recent publication, researchers from our own group conducted a detailed characterization of wood samples to improve ToF-SIMs analysis of lignocellulosic materials by substantially increasing the annotation of corresponding secondary ions (Goacher et al. 2011).

FTIR has been used to distinguish angiosperms and gymnosperms (Schultz et al. 1985), determine the lignin, glucose and xylose content of different woods (McDonald 1976), and characterize the variation in fibre chemistry of natural and transgenic plants (Mouille et al. 2006). FTIR has also been used to detect compositional changes in cell wall polymers as a result

of chemical extraction (McCann et al. 1992). FTIR is especially suited to rapid chemical characterization of small quantities of wood with minimum sample preparation and has been used to analyze changes in wood fibre chemistry after degradation by different brown and white- rot fungi. The recently published investigation of spruce degradation by the brown-rot fungi G. trabeum and P. placenta , describes for the first time, the application of FTIR imaging microscopy coupled with multivariate analysis (Fackler et al. 2010). In their study, Fackler et al (2010) concluded that the brown-rot decay was most significant in the outer cell wall regions in the early stages of decay. This was accompanied by loss in glycosidic bonds and pectic substances.

Standard wavelengths that describe chemical bonds characteristic of lignin and cellulose have been reported (Faix 1992; Faix et al. 1991; Kacurakova et al. 2000; Kotilainena et al. 2000; Labbe et al. 2005; Pandey and Nagveni 2007; Pandey and Pitman 2003; Schwanninger et al. 2004). Commonly, visual comparison of spectra and lignin to carbohydrate peak area ratios have been used to characterize the activity of brown-rot and white-rot fungi on wood fibre (Pandey and Nagveni 2007). However, the utilization of chemometric tools would allow more efficient, complete and un-biased assessment of large sets of FTIR data.

Typically, delignification is not uniformly distributed throughout wood samples, and so bulk chemical methods to characterize the mode of fungal decay (i.e., selective or simultaneous) can be misleading. Conventional SEM and TEM have been used as effective tools to confirm the ability of fungi to degrade wood cell walls, and specific morphological features observed during selective and simultaneous wood decay have been established. Simultaneous white-rot results in thinning of wood cell walls along the circumference of the lumen, while selective white-rot leads to gradual delignification of middle lamella and cell walls without any observable changes in cell morphology (Blanchette and Reid 1986; Blanchette et al. 1985; Ruel et al. 1981; Ruel et al.

1986). Visualization of lignin by staining wood samples with either Bromine or KMnO 4 has greatly enhanced the capability of TEM to decipher the type and extent of wood decay caused by microbes (Fromm et al. 2003; Saka et al. 1978). Notably, an extensive review on the utility of microscopic techniques to characterize wood biodegradation has been previously published (Daniel 1994; Daniel 2003).

2.7.2 Biochemical Characterization of Wood-degrading Fungi 2.7.2.1. Techniques to Measure Fungal Growth . Monitoring fungal growth is often used to assess degradation potential. Because fungal mycelium is difficult to separate from solid substrates like wood, rapid methods for measuring fungal growth are by necessity indirect, and measure either constituents of fungal cells or factors relating to their metabolic processes (Gottlieb and van Etten 1964). These indirect methods however, have their own limitations. While visual inspection is the simplest method to assess fungal growth, poor visibility of hyphae limits its application for detecting small differences. Additionally, hyphae can be obscured by substrate and living mycelium can not be differentiated from the dead (Boyle and Kropp 1992). In their study to develop methods to measure growth of filamentous fungi on wood, Boyle et al., (1992) compared visual inspection, substrate dry weight loss, rate of fluroescein diacetate hydrolysis, extractable protein content and chitin content of the colonized substrate. The authors concluded that each assay measured a different aspect of the growth and chitin gave the best measure of biomass.

There are several methods available for determination of chitin content (Ekblad and Nisholm 1996; Zamani et al. 2008). An assay of glucosamine based on acidic hydrolysis is considered sensitive and to have low susceptibility to interfering compounds. Hydrolysis of chitin in 6 M HCl for 16 hours at 80˚C is approximately 90 % complete and yields acetyl and glucosamine residues. Alternative methods include freeze-drying and evaporation under reduced pressure, which are comparatively arduous. Further, alkaline hydrolysis typically releases only 50 % of glucosamine residues and requires numerous washings to remove excess base. However as a cautionary note, the slight release of other sugars by HCl used in this assay can create minor interferences during the color reaction, leading to slight over estimations of chitin concentration. This disadvantage could be avoided by using alkaline hydrolysis, but it appears to be largely compensated by simplicity of this method (Plassard et al. 1982).

2.7.2.2. Techniques to Characterize the Secreted Enzymes that Participate in Lignocellulose Degradation. Traditionally, biochemical assays have been used to characterize lignocellulose degrading enzymes. These include assays that use model and often chromogenic substrates to determine specific cellulolytic, hemicellulolytic and ligninolytic activities (Himmel et al. 1997). Biochemical characterization of enzymes through activity assays requires previous knowledge of expected activities, and is often limited by the availability of commercial substrates. As a result, biochemical characterization of culture supernatant through conventional enzyme activity studies limits the possibility to discover new enzymes and proteins, as well as novel protein combinations that promote lignocellulose degradation.

Phanerochaete chrysosporium was the first lignocellulose degrading fungus to have its genome sequenced (Martinez et al. 2004). Since then, the genomes of over 25 basidiomycetes have been sequenced (http://genome.jgi-psf.org/). With the sequencing of genomes from lignocellulose degrading microorganisms, advances in instrumentation for protein separation, and protein identification by mass-spectrometry, new proteins that contribute to lignocellulose transformation can now be identified (Vanden Wymelenberg et al. 2005b). For example, proteomic analyses of Gloeophyllum trabeum grown on spruce sawdust identified secreted enzymes and proteins that were not distinguished by previous biochemical assays of culture supernatant (Abbas et al. 2005; Varela et al. 2003). Notably, less than 15 % of the proteins secreted by G. trabeum had been previously identified by biochemical or sequence data. This potential of genomic and proteomic approaches to identify new enzymes is further highlighted by the systematic analysis of the P. placenta genome, transcriptome and proteome (Martinez et 2+ al. 2009). While oxidases predicted to generate hemicellulases and extracellular Fe and H 2O2 were identified, perhaps most striking was the relatively low number of cellulolytic glycoside hydrolases (GH) encoded by the P. placenta genome, and apparent lack of GHs containing family 1 carbohydrate-binding modules.

Genomic and proteomic analyses of the white-rot basidiomycete Phanerochaete chrysosporium have revealed many glycoside hydrolases and other carbohydrate-active proteins that were not differentiated by previous biochemical assays of culture supernatant (Vanden Wymelenberg et

al. 2006; Vanden Wymelenberg et al. 2005a). Notably, more than 65% of the proteins secreted by P. chrysoporium grown on crystalline cellulose corresponded to previously uncharacterized glycoside hydrolases, or proteins with unknown function. Genomic analyses indicate that P. chrysosporium is distinguished from other eukaryotic genomes by encoding more than twice as many carbohydrate hydrolyzing enzymes than synthesis enzymes. Since less than 20 % of predicted glycoside hydrolases encoded by P. chrysosporium were characterized before the genome sequence was published, this example clearly illustrates the potential for genome sequencing to increase the arsenal of industrially relevant enzymes (Martinez et al. 2004; Vanden Wymelenberg et al. 2006).

As might be predicted from biomass composition, the characterization of extracellular proteins from P. chrysosporium grown on cellulose and wood substrates consistently detect the expression of GH3 β-glycosidases, GH6 and GH7 cellobiohydrolases, GH10 xylanases, and GH12 endoglucanases; polygalacturonases, α-galactosidase, aspartic acid proteases, and GH88 d-4,5-unsaturated glucuronyl hydrolases are also frequently detected (Abbas et al. 2005; Ravalason et al. 2008; Sato et al. 2007; Vanden Wymelenberg et al. 2009; Vanden Wymelenberg et al. 2005a). Notably, lignin peroxidases and manganese peroxidases were often missed in proteomic analyses of P. chrysosporium grown on cellulose and wood substrates, although detectable by RT-PCR (Sato et al. 2009; Vanden Wymelenberg et al. 2009). Proteomic analyses of P. chrysosporium have also revealed substrate dependent expression profiles of hypothetical proteins, identified secreted proteins not predicted from initial bioinformatics analyses, and have improved gene models for this organism (Vanden Wymelenberg et al. 2006). More recently, pyrosequencing technology was used to sequence an expression library from P. chrysosporium grown on oak (Sato et al. 2009). These data were consistent with previously reported proteomic analyses, and underscore the significance of gene expression levels in microbial responses to substrate composition.

For several years, a main focus in lignocellulose degradation research has been to isolate and identify organisms that are hyper-producers of stable lignocellulolytic enzymes. Now, the application of genomic and proteomic techniques can be used to reveal new enzymes from un- cultured organisms, or enzymes that are difficult to detect using conventional biochemical

assays. The challenge, however, is to couple these advanced molecular techniques with analytical methods that characterize the effects of new enzymes and enzyme mixtures on complex, industrially relevant substrates and predict which proteins directly participate in lignocellulose utilization.

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Chapter 3 : Effect of Cultivation Conditions on the Expression of Cellulolytic Activity by Phanerochaete species P. chrysosporium and P. carnosa

3.1 Abstract

This chapter describes the first biochemical and growth study of the white-rot fungus Phanerochaete carnosa. A range of simple compounds (glucose, glycerol, cellulose) and lignocellulosic substrates (coniferous softwoods: pine, fir, spruce; and deciduous hardwoods: maple and poplar) were validated as potential substrates for P. carnosa . In-depth characterization of P. carnosa cultivation was pursued using microcrystalline cellulose. Culture conditions and activity studies indicate that P. carnosa grows optimally at temperatures between 27ºC and 30ºC, and confirmed that lignocellulolytic activity is present in extracellular filtrates of cultures grown on cellulose. Low nitrogen (2.5 mM), stationary cultures led to highest growth measurements (as determined by chitin content) and final protein concentration in culture supernatant. However, while high nitrogen condition (25 mM) was consistently correlated to low specific cellulase and MnP activity, shaking at 150 rpm did not appear to significantly effect the secretion of lignocellulose-degrading enzyme in P. carnosa .

3.2 Introduction Efficient biotransformation of wood components is critical for the production of renewable energy and chemicals from this abundantly available resource. Fungi are the major decomposers of higher plants, and the largest group of wood-degrading fungi is the Basidiomycetes. The terms brown-rot and white-rot have been used to classify two types of decay by these fungi. White-rot fungi enzymatically degrade all major components of wood (i.e., hemicellulose, cellulose and lignin). By contrast, brown-rot fungi typically initiate rapid degradation of cellulose and hemicellulose through Fenton chemistry, and modify the residual lignin without substantial depolymerization. A different type of decay that is similar in macroscopic appearance to brown- rotted wood is caused by soft-rot fungi. This form of wood decay is usually important in extremely wet environments, or environments that undergo frequent wet-dry cycles, where more aggressive wood degrading fungi cannot survive. Of all these, the white-rot fungi are known to cause most wide-spread and significant decay (Eriksson 1990; Otjen et al. 1987).

Within the white-rot fungi, there are selective and simultaneous degraders, that is, fungi that preferentially degrade lignin before attacking cellulose and hemicellulose (e.g. Ceriporiopsis subvermispora) and those that degrade all components at similar rates (e.g. Trametes versicolor ), respectively. The fungi that can selectively remove lignin without extensive cellulose degradation are of prime interest specifically for the pulping process as well as bioremediation due to their established ability to degrade the recalcitrant lignin.

Phanerochaete chrysosporium is commonly used to study the production and regulation of lignocellulolytic enzymes by white-rot fungi. This model white-rot fungus belongs to the wood inhabiting Agaricomycete class of the Basidiomycota phylum. There are over 90 species in this genus that are currently described all over the world (Wu et al. 2010). P. chrysosporium was first isolated from wood chip piles and was given the name Chrysosporium lignorum (Bergman and Nilsson 1966). This name was later changed to Sporotichum pulverulentum and finally the present name was assigned to it based on its perfect state (the stage at which spores are formed after nuclear fusion). The fungus is characterized as thermotolerant and has an optimum growth

rate at around 38-39˚C (Eriksson 1978).

Phanerochaete carnosa is a comparatively poorly characterized member of the family Phanerochaete sensu strictum , and was previously isolated from balsam fir ( Abies balsamea ) as well as other softwoods (de Koker et al. 2003). The phylogenetic analysis reported by de Koker et al. (2003) groups P. carnosa with P. arizonica, P. burtii and P. sanguinea , and reveals that the ribosomal ITS sequences of P. carnosa and P. chrysosporium share 89% identity. However, the isolation of P. carnosa being primarily from softwoods is different from other well-studied white-rot fungi like P. chrysosporium , which were primarily isolated from hardwoods. Accordingly, it is possible that P. carnosa has evolved to express unique lignocellulolytic activities that effectively transform softwoods, which would constitute a valuable source of

enzymes, given the typical recalcitrance and abundance of this biomass feedstock.

Since the cultivation and biochemical characterization of P. carnosa has not been described, the primary aim of this first study was to establish growth conditions that induce the expression of lignocellulolytic enzyme by this organism. This information will provide a basis for further investigation of lignocellulose conversion by P. carnosa to utilize and enhance its potentially

unique lignocellulolytic activity. Moreover, by comparing the effect of cultivation conditions on the production of lignocellulolytic activity by P. carnosa and P. chrysosporium , differences in enzyme activities and their regulation can be evaluated.

Given the phylogenetic similarity of P. carnosa and P. chrysosporium , growth conditions that promote lignocellulolytic activity in P. chrysosporium were predicted to also promote lignocellulolytic activity by P. carnosa . Therefore, in order to establish the growth conditions and investigate the lignocellulolytic enzyme profiles of P. carnosa in the present study, published literature on P. chrysosporium was used as a basis for defining initial experimental conditions.

3.3 Review of P. chrysosporium Cultivation and Lignocellulolytic Activity General maintenance and sporulation of P. chrysosporium has been achieved using potato dextrose agar (PDA). Tien and Kirk (1988) reported a medium containing 1% glucose, 1% malt extract, 0.2% peptone, 0.2% yeast extract, 0.1%, aspargine, 0.2% KH 2PO 4, 0.1% MgSO 4.7H 2O, 0.0001% thiamine, and 2% agar for maintenance and sporulation. In P. chrysosporium , cellulose degradation occurs during primary metabolism, while lignin mineralization has been characterized as a secondary metabolic event that is triggered by carbon, nitrogen or sulfur starvation (Jeffries et al 1981; Kirk and Farrell 1987).

The P. chrysosporium genome sequence reveals that this organism encodes at least 10 LiPs, 5 MnPs and 6 copper radical oxidases, in addition to several other gene products that might contribute to lignin degradation, including glyoxal oxidases, (Martinez et al. 2004). Growth conditions that trigger the production of lignin degrading enzymes by P. chrysosporium were recently reviewed (Singh and Chen 2008). To summarize, the expression of lignin-degrading enzymes by submerged liquid cultures of P. chrysosporium is promoted by limiting carbon, nitrogen and/or sulfur availability, as well as increasing organic acid ( α-hydroxy acids) and trace element concentration. Increased agitation, temperature and pH appeared to reduce lignin degrading activity. The effect of agitation was more pronounced in large flasks with high shaking, which was predicted to generate shear forces that can denature secreted enzymes. It has also been proposed that enzyme inactivation may occur due to higher level of dissolved oxygen

in agitated cultures than in stationary cultures. This has been attributed to the higher level of active oxygen content in the culture which can oxidize, fragment, and cross-link proteins (Wolff et al. 1986) . Further, high oxygen concentrations can inhibit fungal growth at low glucose concentrations (Leisola et al. 1984).

Decades worth of studies now mean that P. chrysosporium is typically cultivated between pH 4.2-6.2 (optimal below 6.0) at 25-39˚C (optimal 37˚C) in either stationary or shaking cultivations (up to 300 rpm; optimal below 200 rpm). The growth medium is also amended with 0.02% ammonium tartarate and 1% glucose or glycerol, as well as trace metals including calcium, manganese, iron, zinc, copper and magnesium. Notably, adding detergents such as Tween 80 or non-reactive foam-stabilizing agents such as polyethylene glycol (PEG) to the culture medium can also increase lignolytic activity in culture supernatants (Asther et al. 1987; Venkatadri and Irvine 1993). Moreover, the highest LiP production of 3,800 U/ml was obtained by immobilizing fungal mycelia in polyurethane foam, whereas the highest MnP production observed was 1,375 U/ml with solid-state fermentation using steam-exploded straw as substrate. Some effective repressors of lignin-degrading enzyme production are glutamic acid, glutamine and ammonium ion.

While the effect of cultivation conditions on the expression of lignolytic activity by P. chrysosporium was recently reviewed, corresponding effects on the expression of cellulolytic activities are more dispersed in the published literature. Similar to lignolytic enzymes, carbohydrate active enzyme expression is highly dependent on the nitrogen and carbon source supplied to the culture medium, pH, temperature, and presence of inhibitors (Table 3.1 and Table 3.2). For instance, early studies of white-rot fungi indicated that cellulases are induced in the presence of cellulosic substrates, and that cellulase expression is regulated through catabolite repression (Eriksson 1978; Jensen 1971; Johansson 1966; Smith and Gold 1979). These studies employed viscometric measurements to determine endoglucanase activity on carboxymethylcellulose, and colorimetric assays to measure glucosidase activity on p- nitrophenyl-D-glycopyranosides. In general, early biochemical analyses of P. chrysosporium confirmed that cellulose is a good substrate to investigate cellulolytic and hemicellulolytic activity by this fungus (Bao et al. 1994; Eriksson and Rzedowski 1969). Early studies also

determined that asparagine is an effective nutrient source and that aeration with oxygen enhances the activity of the cellulase systems (Ander and Eriksson 1977; Levonen-Munoz and Bone 1984). No clear impact of agitation on cellulolytic activity has been reported, and the pH optimum for cellulolytic activity in basidiomycetes is typically between 3.5 to 5.5 and the temperature optima are between 35-50˚C (Baldrian and Valaskova 2008; Eriksson 1978; Smith and Gold 1979; Eriksson and Hamp 1978).

More recently, the effect of substrate, inducing compounds, and cultivation conditions on lignocellulolytic expression by P. chrysosporium has been evaluated using molecular techniques, including quantitative PCR and proteomic methods. PCR is particularly useful for monitoring microbial activity when corresponding enzymes are not easily accessible or distinguished by activity alone. One of the early studies to use PCR to investigate P. chrysosporium activity grew the fungus on four different carbon sources: ball-milled straw, microcrystalline cellulose (Avicel), high glucose concentration (2 %), and low glucose concentration (0.2 %); all under nitrogen limiting conditions (0.23 g/L) (Broda et al. 1995). This research revealed differential expression of cellulase gene families, depending on the substrate and stage of cultivation. Specifically, the expression of genes that encode lignin-degrading and cellulose-degrading enzymes was observed on straw and microcrystalline cellulose, suggesting lignocellulose- dependent expression of these enzymes (Broda et al. 1995).

RT-PCR was also used to investigate the regulation of LiPs and MnPs by P. chrysosporium , (Janse et al. 1998), and several more recent studies have used RT-PCR to identify inducers and repressors of lignocellulolytic genes. For example, Suzuki et al., (2008) used real-time quantitative RT-PCR to investigate carbon catabolite repression in P. chrysosporium . They concluded that cellobiohydrolase genes ( cel6A, cel7d and cdh ) were drastically influenced by increased levels of glucose concentration, whereas no significant changes were observed in β- glucosidase gene (bgl3A) (Suzuki et al. 2008). Recent advances in RT-PCR techniques that improve the quantification of gene transcripts produced at low levels, were also used to quantify transcript levels of glycoside hydrolases genes ( cel6A and cel7A to cell 7F/G ) in cultures containing glucose, cellulose and cellooligosaccharides (Suzuki et al. 2009). Cellulose, cellotriose and cellotetraose demonstrated clear upregulation of transcript levels as compared to

Table 3.1. Summary of carbohydrate active enzyme production by Phanerochaete chrysosporium Strain Cultivation Agitation Substrate Enzyme Activity (Units per mL) a,b Ref Conditions EGase CBH CBD FPU BGL XYN/ MAN ATCC Submerged Yes 1 % Cellulose 6.8 8.9 77 105 ND ND ND ND (Ander 32629) 0.25% aspargine (no powder and 1 L or 250 mL Eriksson details) 23.0 46.3 227 263 1977) 11 days and 14 days Submerged Yes 1 % Cellulose 0.9 1.1 8.5 11.7 ND ND ND ND (Ander 0.25% (no powder and Eriksson NH 4H2PO 4 details) 1977) 1 L or 250 mL 2.2 1.6 23 50 11 days and 14 days Submerged Yes (no 1 % pine wood 0.40 12.5 ND ND ND ND (Ander 0.25% aspargine details) meal and Eriksson 18 days 0.50 19.4 1977) and 25 days Submerged Yes (no 1 % pine wood 0.06 5.4 ND ND ND ND (Ander 0.25% details) meal and NH H PO Eriksson 4 2 4 0.04 4.0 1977) 18 days and 25 days MTCC Submerged No Paddy straw 0.11 ND ND 0.40 0.33 0.50 c (Mishra et 787 7 days al. 2007) c and 15 days 0.43 0.88 1.2 0.67 NCIM Submerged No Paddy straw 0.22 ND ND 0.44 0.34 0.65 c (Mishra et 1073 7 days al. 2007) and 15 days 0.37 0.56 1.1 0.64 c

Strain Cultivation Agitation Substrate Enzyme Activity (Units per mL) a,b Ref Conditions EGase CBH CBD FPU BGL XYN/ MAN NCIM Submerged No Paddy straw 0.10 ND ND 0.30 0.38 0.43 c (Mishra et al. 2007) 1106 7 days c and 15 days 0.15 0.36 1.1 0.47 NCIM Submerged No Paddy straw 0.16 ND ND 0.31 0.37 0.46 c (Mishra et al. 2007) 1197 7 days c and 15 days 0.18 0.41 0.98 0.54 OGC Submerged 150 rpm Cotton linters ND 138±5 74± ND 237± ND (Bao et al. 101 500 mL 10.0 20.5 1994) 14 days succinate buffer protein concentration: 939 ± 24.5 mg OGC Submerged 150 rpm Microcrystalline ND 75±9 90±7.1 ND 82± ND (Bao et al. 101 500 mL cellulose 15.5 1994) 14 days succinate buffer protein concentration: 694 ± 53.7 mg OGC Submerged 150 rpm Filter-paper ND 29±5 50.7± ND 155± ND (Bao et al. 101 500 mL 12.0 10.8 1994) 14 days succinate buffer protein concentration: 583 ± 25.5 mg

Strain Cultivation Agitation Substrate Enzyme Activity (Units per mL) a,b Ref Conditions EGase CBH CBD FPU BGL XYN/ MAN OGC Submerged 150 rpm Acid-treated ND 35±4 8.9± ND 8.9± ND (Bao et al. 101 500 mL cellulose 12.6 2.5 1994) 14 days succinate buffer protein concentration: 421 ± 39.3 mg OGC Submerged 150 rpm Microcrystalline ND 141±16 53±17 ND 237±2 ND (Bao et al. 101 14 days cellulose 1 1994) succinate Max (pH 4.5) on day protein 14 concentration: 1.08± 0.14g/l OGC Submerged 150 rpm Microcrystalline ND 20 ± 1.4 184 ND 23 ±1 ND (Bao et al. 101 14 days cellulose ±20 Max 1994) acetate (pH 4.5) on day protein 6 concentration 0.59 ± 0.02 g/l OGC Submerged 150 rpm Microcrystalline ND 6 ± 1 75 ± ND 7 ± 1 ND (Bao et al. 101 14 days cellulose 15 Max 1994) phosphate on day (pH 5.8) 3 protein concentration 0.31 ± 0.01 g/l OGC Submerged 150 rpm Microcrystalline ND 58 ± 6 58 ± 2 ND 45 ± 9 ND (Bao et al. 101 14 days cellulose Max 1994) phosphate on day

Strain Cultivation Agitation Substrate Enzyme Activity (Units per mL) a,b Ref Conditions EGase CBH CBD FPU BGL XYN/ MAN (pH 7.0) 3 protein concentration 0.64 ± 0.17 g/l OGC Submerged 150 rpm Microcrystalline ND 77±3 129± ND 81±6 ND (Bao et al. 101 14 days cellulose 20 Max 1994) phosphate on day (pH 8.0) 9 protein concentration 1.39 ± 0.29 g/l

a. EG: Endoglucanase, CBH: cellobiohydrolase, CDH: cellobiose dehydrogenase, BGL: beta-glucosidase, XYN: xylanase, MAN: mannanase. b. Split cells indicate activities in 1L and 250 mL cultures (left to right) and first and second time point (top to bottom), respectively. c. Activity was expressed as IU/ml (one unit = 0.1 unit change in OD per min).

Table 3.2. Expression of lignocellulolytic activity by Phanerochaete chrysosporium Strain Cultivation Agitation Substrate Summary of Results a Ref Conditions n/a 300 mL Yes Powdered No induction of BGL activity was observed when glucose was used as (Deshpande 13 days cellulose the carbon source. When cellobiose and cellulose were used, BGL et al. 1978) conical shake flasks activity was detected in fungal cell wall fractions after 15-20 min. modified Norkrans’ Extracellular enzyme activity was detected with cellulose as the sole medium carbon source. pH optima 4-4.5. Localization of BGL activity depended on the carbon source. (Smith and ME Submerged 150 rpm 0.25 % Extracellular BGL was induced by cellulose but repressed by glucose. Gold 1979b)

446 1 L cotton or Intracellular BGL was induced by cellobiose but repressed by modified Vogel CMC cycloheximide. Optimum temperature was 45˚C. pH optima for medium intracellular and extracellular activities were pH 7.0 and 5.5, respectively.

Strongest inducers of extracellular BGL were cellulosic polymers (highest for CMC – 14.9 U/mg), wherease cellobiose was a very poor inducer. Intracellular BGL activity was 27.5 U/mg protein in cellobiose cultivations.

The soluble intracellular enzyme represented approximately 90 and 75% of the total BGL activity in cells induced with cellobiose for 10 and 25 hours, respectively (Igarashi et Strain Investigation with No Bacterial Cellobiose acted as a competitive inhibitor of cellobiohydrolase. al. 1998) K3 purified enzymes micro- Decomposition of bacterial microcrystalline cellulose by CBH I was crystalline enhanced in the presence of CDH/ferricyanide redox-system. It was cellulose concluded that the reason for the enhancement of CBH I activity in the presence of CDH redox system was that it relieves competitive inhibition of cellobiose by its oxidation to cellobionolactone.

Strain Cultivation Agitation Substrate Summary of Results a Ref Conditions

ATCC Modified Norkrans 200 rpm Glucose The effect of several monosaccharides and disaccharides in different (Eriksson and Hamp 1978) 32629 medium (pH 5.0) (5g/L) concentrations on expression of EGs was investigated. Induction of EGs occurred at cellobiose concentrations as low as 1 mg/mL, catabolite repression was not obtained until the glucose or mannose concentration reached 50 mg/L. Xylose, galactose and arabinose did not repress EG expression even at concentrations as high as 1g/L. (Eriksson and n/a Modified Norkrans No Powdered Cellulase, xylanase, mannanase, BGL and aryl-β-glucosidase activities Rzedowski 1969) medium cellulose/ were detected in cultures containing cellulose. The production of 27˚C xylan/guar cellulase and mannanase was not observed in xylan cultivations, and powder only traces of all enzymes were found in mannan cultivations. (Khalil 2002) BKM- 500 ml conical 200 rpm Sugarcane The optimum pH values were 4.6, 4.2, 5.0 and 5.0 for EG, BGL, F- flasks bagasse xylanase, and β-xylosidase activities, respectively; optimum 1767 100 ml mineral temperatures were 60, 70, 65, and 60 ˚C, respectively. Significant loss medium (pH 5.0) in activity was reported after 5 hours heating at 60˚C. Addition of 1% 37˚C glucose inhibited the production of cellulolytic and xylanolytic enzymes at the beginning of the cultivation; activity increased after the total consumption of glucose.

a. EG: Endoglucanase, CBH: cellobiohydrolase, CDH: cellobiose dehydrogenase, BGL: beta-glucosidase

glucose for most genes, at all time points, while cellobiose was a weak inducer of cellulase gene transcription by comparison (Suzuki et al. 2010).

The specific objective of the current study was to establish conditions and methods to cultivate P. carnosa , as well as monitor the growth and the profile of proteins secreted by this fungus. In addition to enabling subsequent investigations and growth studies on wood, comparison of optimum cultivation conditions to those defined for other wood-degrading Basidiomycetes, will help elucidate evolutionary adaptations among the white-rot fungi.

3.4 Materials and Methods 3.4.1 Microorganism and Materials P. carnosa strains (FP-135508-Sp, HHB-10118-Sp, RLG-7412-Sp) were obtained from the US Department of Agriculture (USDA) Forest Products Laboratory, Madison, WI. All strains were maintained on YMPG agar media; yeast (2 % w/v), maltose (10 % w/v), peptone (2 % w/v), glucose (1 % w/v) at 27°C. Strain HHB-10118 was selected for detailed cultivation studies, given its consistent growth rate and phenotype. The internal transcribed spacer (ITS) region was sequenced to confirm the identity of the fungus (de Koker et al. 2003). Avicel (Fluka 11365), which is a commercial preparation of wood-derived microcrystalline cellulose, and contains approximately 30 % of amorphous structure, was used as the carbon source in liquid cultivations, and ammonium tartarate (Sigma A4767) was used as the nitrogen source.

3.4.2 Cultivation Conditions Liquid cultures were prepared in 200 mL B3 medium (Appendix 1) (Hon, D.N-S 1984; Jai, M., et. al., 1996). Liquid media were then inoculated with 0.1 mg/mL blended mycelia from 10-day liquid YMPG cultures. Cultivation temperatures were 22 °C, 27 °C, 30 °C, and 37 °C, and cultures were incubated for up to 18 days under stationary conditions or with shaking (150 rpm). The impact of nitrogen concentration on the secretion of lignocellulolytic activity was tested in stationary cultivations grown for 27 days at 27 °C with 1 % Avicel and 2.5 or 25 mM ammonium tartrate (Table 3.2).

3.4.3 Growth Measurements Chitin measurement was used to compare the extent of growth in all cultivation conditions (Plassard et al. 1982). Briefly, three replicate cultivations were harvested after five time points (Table 3.3), and the mycelia were separated from the culture supernatant using Mira Cloth. The mycelia were then oven dried overnight and the final dry weight of the mycelia was recorded. Precisely weighed amounts (~0.2 g) of each mycelial sample were transferred to a glass tube with a screw-cap lid, incubated in 5 mL of 6 M HCI at room temperature for 3 h with the lid open, capped and then hydrolyzed for 16 h at 80°C. Aliquots of 1 mL hydrolysate were transferred to 5 mL of 1.25 M NaOAc to bring the pH of the solution to approximately pH 3, and then further processed at room temperature. Specifically, 1 mL of the solution at pH 3 was mixed with 1 mL of 5 % KHSO 4 and 1 mL of 5 % NaNO 2. After shaking for 5 min, the mixture was allowed to stand for 15 min to allow for deamination, and then 1 mL of 12.5 % ammonium sulfonate was added to release excess nitric acid (note: This step is accompanied by vigorous mixing to prevent over-flow of the solution from reaction vial due to effervescence. ) After shaking for 5 min, 1 mL of 0.5 % MBTH (Methylbenzthiazolinone-2-hydrazone) was added. The reaction was allowed to proceed for 1 h without any shaking. For colorimetric detection of the

anhydromannose-MBTH-FeCl 3 complex at 653 nm; 1 mL of 0.5 % FeCl 3 was added and the solution was left to develop for 30 min. The absorbance was stable up to 20 h.

For protein concentration measurements, culture supernatants from the three biological replicates prepared for each cultivation condition were pooled to obtain adequate amounts of protein for all biochemical assays. Prior to pooling, the biological reproducibility of protein profiles was determined using SDS-page gels. The pooled samples were then centrifuged for 20 min at 4500 rpm and concentrated at 4 °C using Pall’s Jumbosep (FD010K65) centrifugal units at a cutoff of 10 kDa. Protein concentration was measured using the Bio-rad Protein Assay according to the manufacturer’s instructions. The absorption measurements were performed at 595 nm. A BSA stock solution of 2 mg/ml was used to generate a standard curve from 0.05-0.5 mg/mL of protein.

3.4.4 Biochemical Assays The standard endoglucanase assay contained 50 mM sodium acetate (pH 4.5) and 0.3 wt % of carboxymethyl cellulose (CMC). The final reaction volume was 100 µL and reactions were incubated at 27°C for up to 1 h. The reaction products were developed using the BCA assay for reducing ends (Doner et al. 1992), and measured at 565 nm. Glucose was used to generate a standard curve.

β-glucosidase activity was determined by measuring the amount of p-nitrophenol released from p-nitrophenol-β-D-glucoside ( pNPG) using 0.6 % pNPG by weight in 50 mM sodium acetate (pH 4.5) (Bowers et al. 1980). The reaction products were developed using 100 µL of 1M

Na 2CO 3 and measured at 410 nm; pNP (0-0.05 µmol) was used to generate a standard curve.

Filter paper units (FPUs) were measured using Whatman No.1 filter paper. An office hole- puncher was used to produce equal sized filter paper samples (~ 1 mm 2), which were submerged in 50 mM sodium acetate (pH 4.5). The total reaction volume was 200 µL and reactions were incubated at 27°C for up to 1 hour. The reaction products were again developed using the BCA assay, and glucose was used to generate a standard curve (Ghose 1987).

Lignin peroxidase activity was measured by monitoring oxidation of 3,5-dimethoxybenzyl alcohol to veratraldehyde at 310 nm (Tien and Kirk 1984). An extinction coefficient of 9.3 mmol -1 cm -1 was used to determine product formation (Kapich et al. 2004). Manganese peroxidase (MnP) activity was measured by monitoring the oxidation of ABTS at 415 nm

(Katagiri et al. 1995). The reaction mixture contained 7.8 mM ABTS, 0.1 mM MnSO 4, 50 mM

sodium lactate, 0.1 mM H 2O2, and 20 mM sodium succinate buffer (pH 4.5). An extinction coefficient of 3.6 mmol -1 cm -1 was used to determine product formation. The assay was performed in the presence and absence of MnSO 4 to determine Mn-dependent and Mn- independent activity.

All assays were performed in triplicate at the temperature and pH of the cultivation. Enzyme activities are expressed as units (U) per ml of culture supernatant, where one unit of enzyme activity is defined as the amount of enzyme generating 1 µmol of product per minute.

Table 3.3 Cultivation of P. carnosa in B3 medium at 27˚C with 1 % microcrystalline cellulose, variable nitrogen, pH 4.5 Nomenclature Nitrogen (Ammonium Agitation Incubation Analyses Tartarate) Time LN (low 2.5 mM No 3 days, 6 Mycelial weight, protein concentration in culture nitrogen) days, 12 days, supernatant, protein profile (SDS-PAGE). 18 days and 24 days Cellulase (CMC, pNP and FPU), LiP and MnP activity for each time point (at pH 4.5 and 27 °C).

HN (high 25 mM No 3 days, 6 Mycelial weight, protein concentration in culture nitrogen) days, 12 days, supernatant, protein profile (SDS-PAGE). 18 days and 24 days Cellulase (CMC, pNP and FPU), LiP and MnP activity for each time point (at pH 4.5 and 27 °C).

LNS (low 2.5 mM 150 rpm 3 days, 6 Mycelial weight, protein concentration in culture nitrogen with days, 12 days, supernatant, protein profile (SDS-PAGE). shaking) 18 days and 24 days Cellulase (CMC, pNP and FPU), LiP and MnP activity for each time point (at pH 4.5 and 27 °C).

3.5 Results Initial growth studies performed to evaluate the range of compounds that could be used to cultivate P. carnosa confirmed that P. carnosa grew on commercial substrates including glycerol, glucose and microcrystalline cellulose (Avicel), as well as chips of lodgepole pine, white spruce, balsam fir, maple and poplar, submerged in B3 medium. To characterize the growth of P. carnosa in more detail, cultivations were prepared using Avicel as the carbon source.

3.5.1 Effect of Cultivation Condition on the Extent of P. carnosa Growth on Microcrystalline Cellulose. Preliminary cultivations of P. carnosa were grown on micro-crystalline cellulose (Avicel) at 22˚C, 27°C, 30°C and 37°C. From visual inspection of cultivations and weight of collected mycelia, it was concluded that cultures incubated at 27°C led to the highest fungal growth. The air-dried weight of mycelia collected after 5 weeks at 22˚C, 27˚C, 30˚C and 37˚C were 0.99g, 0.95g, 0.70 and 0.74g, respectively. In contrast to P. chrysosporium , which grows optimally at 37°C, growth of P. carnosa was not visible at 37°C,

As reviewed above, nitrogen concentration and agitation affects the expression of lignocellulolytic enzymes by P. chrysosporium . Therefore, the impact of these two parameters on the secretion of lignocellulolytic activity by P. carnosa grown at 27°C on Avicel was investigated (Table 3.3). P. carnosa grew well in low and high nitrogen conditions as well as in cultivations agitated at 150 rpm. At each of the time points indicated in Table 3.3, the extent of fungal growth was determined by protein concentrations in culture supernatants, and by measuring chitin content (Plassard et al. 1982).

The final protein concentrations for high nitrogen (HN) and low nitrogen with shaking (LNS) cultivations were similar, while the low nitrogen, stationary (LN) cultivations had the highest protein concentration at the last time point (Table 3.4). After 12 days of cultivation, protein concentrations in both HN and LNS cultivations appeared to decrease, suggesting proteolysis. By constrast, the protein concentration in LN cultivations increased steadily over 24 days.

Table 3.4 Protein concentration in P. carnosa culture supernatant during growth on microcrystalline cellulose Days (protein concentrations are indicated as µg/ml) Cultivation 3 6 12 18 24 LN 1.0 ± 0.3 1.8 ± 0.2 4.0 ± 0.1 4.1 ± 0.1 8.0 ± 0.0 LNS n/a 2.3 ± 0.3 3.4 ± 0.8 1.7 ± 0.1 3.2 ± 0.4 HN 3.4± 0.7 2.6 ± 0.1 8.3 ± 0.6 3.1 ± 0.1 4.5 ± 0.0

LN: low nitrogen, stationary; LNS: low nitrogen, shaking; HN: high nitrogen, stationary. n = 3; errors correspond to standard deviations.

The effect of growth condition and cultivation time on the profile of proteins secreted by P. carnosa was evalutated by SDS gel electrophoresis (Fig. 3.1). The protein profiles from the different cultivations appeared similar at early time points, and the complexity of the protein profiles from stationary cultivations appeared to increase with time (Fig 3.1). Consistent with protein concentration measurements (Table 3.4), the intensity of proteins in the LNS cultivations decreased after time point 3.

1 2 3 4 5 kDa 250 150 100 75 50

Figure 3.1 Silver stained 10% SDS-PAGE gel of P.carnosa culture supernatants . LN: low nitrogen, stationary; LNS: low nitrogen, shaking; HN: high nitrogen, stationary. Numbers refer to time points specified in Table 3.3. Sample volumes were 10 µL, and ranged from 0.01 to 0.08 µg.

Indirect methods including visual inspection, substrate dry weight loss, rate of diacetate hydrolysis, extractable protein content and chitin content have previously been used to quantify fungal growth on substrates like wood chips, where separation between mycelia and substrate is challenging. In a comparative study of these methods, Boyle and Kropp (1992) concluded that chitin (a polymer of N-acetyl-p-glucosamine, the major cell wall component in fungi) is the most suitable means of measuring fungal growth. Ergosterol, which is a fungus specific membrane component, is another means to quantify fungal growth. While ergosterol measurements can give an estimate of the living fungal biomass at time of harvest, chitin content is a measure of both living and dead fungal biomass, and so can be used to estimate total growth (Ekblad and Nisholm 1996). Literature cites several methods to determine chitin content (Ekblad and Nisholm 1996; Francois 2006; Zamani et al. 2008). The method developed by Plassard et al. (1982) has been used to determine chitin content in similar cultivations, and so was used in the current study (Papinutti and Lechner 2008; Levin et al. 2008). As anticipated, low nitrogen stationary cultivations resulted in the highest fungal growth and protein concentration. FTIR of the mycelial samples was also investigated as a means to obtain relative mycelial growth. However, due to the low mycelial density, the FTIR spectra of the test samples were very similar to the controls containing Avicel alone.

While chitin was initially measured from 0.3-30 mg (dry weight) of mycelia recovered from cellulose cultivations, the mycelial sample had to be increased to approximately 200 mg before reliable absorption measurements could be obtained. As a result, chitin was measured in oven dried mycelial samples collected from 24-day cultures, which corresponded to comparatively high visible growth. Microcrystalline cellulose that remained in the 24-day P. carnosa cultivations was aggretated with fungal mycelia, and so could not be removed prior to chitin measurements. Notably, up to 0.8 mg of cellulose was found to contribute to the calorimetric reading. However, since cellulose in exceess of 0.8 mg did not increase background readings, the absorption of 0.8 mg of microcrystalline cellulose was used as a reference in the assay. Chitin measurements were then used to estimate the growth of P. carnosa in the different cultures; this analysis was consistent with protein concentrations measured in the culture media, where the highest chitin content was observed for LN cultures (Table 3.5).

Table 3.5. Chitin content of P. carnosa cultivations pooled after 24 days Cultivation Total dry weight (mg) Total chitin (mg) Negative control (cellulose only) 0.8 0.4 LN 2.9 2.8 LNS 2.1 2.1 HN 1.6 1.6

LN: low nitrogen, stationary; LNS: low nitrogen, shaking; HN: high nitrogen, stationary.

Taken together, the protein concentration measurements and the chitin measurements suggest that stationary cultivations containing low nitrogen resulted in the highest fungal growth. The impact of cultivation conditions on the expression lignocellulolytic activity was then assessed using standard enzyme activity assays.

3.5.2 Effect of Nitrogen Concentration and Agitation on Lignocellulolytic Expression by P. carnosa . All cellulytic activities (CMC, pNP and FPU activities) were observed in all cultivation conditions at all time points, while LiP activity was not observed at any time point (Tables 3.6 to 3.8). MnP activity (both Mn dependent and independent) was observed at most time points, but there was a considerable variation over the time course (Table 3.9). The apparent lack of LiP activity is distinct from cultivation studies using P. chrysosporium (Kirk et al. 1978), but consistent with recent transcriptional analysis of P. carnosa grown on various wood and simple substrates (MacDonald et al. 2011). As will be discussed in Chapter 4, this difference in enzyme expression compared to P. chrysosporium , could reflect distinct lignin-degrading activity by these fungi.

Specific endoglucanase activity was initially highest in the LN cultivations, and then decreased with increasing cultivation time (Table 3.6). This could be explained by increasing protein concentrations in the culture supernatant that resulted from lysis of aging cells. The highest specific endoglucanase activity was detected in LNS cultivations, and the lowest was initially detected in HN cultivations.

Table 3.6. Endoglucanase activity in P. carnosa culture supernatant during growth on microcrystalline cellulose Cultivation Days (specific activities are indicated as nmol reducing ends/ µµµg/min) 3 6 12 18 24 LN 20.9 ± 0.6 10.2±0.2 1.3 ± 0.03 3.4 ± 0.1 1.5 ± 0.05 LNS n/a 7.7 ± 0.2 2.4 ± 0.05 16.2 ± 0.4 9.9 ± 0.1 HN 3.5±0.2 0.9 ± 0.3 1.3 ± 0.01 2.4 ± 0.02 3.9 ± 0.2

LN: low nitrogen, stationary; LNS: low nitrogen, shaking; HN: high nitrogen, stationary. n = 3; errors correspond to standard deviations.

Other researchers have shown that product inhibition of cellulases by cellobiose is minimized by the presence of β-glucosidase. Accordingly, β-glucosidase activity was also measured. Similar to cellulase profiles, β-glucosidase activity was highest in LNS cultivations (Table 3.7). Further, β-glucosidase activity in P. carnosa was generally lower than endo-glucanase activity, which is consistent with recently published proteomic and transcriptomic analyses of P. carnosa (MacDonald et al, 2011; Mahajan and Master, 2010). Total cellulolytic activity was then measured using the Filter Paper Assay (Table 3.8).

Table 3.7. βββ-glucosidase activity in P. carnosa culture supernatant during growth on microcrystalline cellulose Cultivation Days (specific activities are indicated as nmol reducing ends/ µµµg/min) 3 6 12 18 24 LN 0.9 ± 0.01 0.3 ± 0.01 0.2 ± 0.01 0.3 ± 0.001 0.1 ± 0.01 LNS n/a 0.6 ± 0.01 0.5 ± 0.01 1.4 ± 0.01 2.1 ± 0.01 HN 0.04 ± 0.01 0.1 ± 0.001 0.14 ± 0.01 0.09 ± 0.01 0.03 ± 0.01

LN: low nitrogen, stationary; LNS: low nitrogen, shaking; HN: high nitrogen, stationary. n = 3; errors correspond to standard deviations.

Table 3.8. Total cellulolytic activity in P. carnosa culture supernatant during growth on microcrystalline cellulose Cultivation Days (specific activities are indicated as nmol reducing ends/ µµµg/min) 3 6 12 18 24 LN 0.67 ± 0.01 0.81±0.01 0.26±0.01 0.15±0.02 0.01 ± 0.001 LNS n/a 1.10±0.01 0.15±0.01 0.16±0.17 0.73 ± 0.01 HN 0.21 ± 0.01 1.08±0.01 0.33±0.01 0.98±0.06 0.75 ± 0.001

LN: low nitrogen, stationary; LNS: low nitrogen, shaking; HN: high nitrogen, stationary. n = 3; errors correspond to standard deviations.

MnP activities were detected in both the presence and absence of MnSO 4. The values obtained were not significantly different, implying that manganese is not required as an inducer for MnP expression in P. carnosa cultivations grown on cellulose. The highest specific activity was observed in LNS cultivations (Table 3.9), although MnP was initially higher in LN and HN cultivations. The initial rise and the decline in MnP activity is consistent with recent transcriptional analysis of P. carnosa where transcripts predicted to encode MnP activity were quantified using RT-PCR. Specifically, transcripts predicted to encode MnP activity were highest in mycelia collected from early cultivations of P. carnosa grown on various wood samples, and then decreased at later growth stages (personal communication with J. MacDonald, a Ph.D.candidate in E. Master’s lab).

Table 3.9. MnP activity in P. carnosa culture supernatant during growth on microcrystalline cellulose Cultivation Days (specific activities are indicated as nmol reducing ends/ µµµg/min) 3 6 12 18 24 LN 0.3 ± 0.01 n/a n/a n/a 0.01 ± 0.001 LNS n/a n/a 2.4 ± 0.01 n/a 0.2 ± 0.001 HN 0.05 ± 0.02 n/a 0.01 ± 0.01 n/a 0.03 ± 0.001

LN: low nitrogen, stationary; LNS: low nitrogen, shaking; HN: high nitrogen, stationary. n = 3; errors correspond to standard deviations. 50

3.6 Discussion Similar to P. chrysosporium , the cellulolytic enzyme activity expressed by P. carnosa was lowest in HN (high nitrogen) stationary cultivations (Barclay et al. 1993). While β-glucosidase activity in P. chrysosporium was typically higher than endoglucanase activity (Tables 3.1 and 3.2), the opposite trend was observed in P. carnosa cultivations (Tables 3.6 and 3.7). As described in Chapter 5, comparatively low β-glucosidase activity in P. carnosa cultures is consistent with the comparatively few peptides corresponding to GH3 enzymes detected in P. carnosa culture supernatants.

The highest lignin degrading activity was measured in LNS cultivations using the MnP assay. Low-nitrogen concentration also induces the expression of lignolytic activity by P. chrysosporium , where nutrient limiting conditions are thought to induce secondary metabolism (Kirk and Farrell 1987). Decreased MnP activity in later time points sampled from P. carnosa cultivations was consistent with recent transcription analyses performed by J. MacDonald (a PhD candidate in E. Master’s lab), where it was observed that MnP expression was highest during early growth on wood samples, and then declined (personal communication with J. MacDonald).

Shaking at 150 rpm did not have a detrimental effect on lignocellulolytic activity, and in fact increased the activities measured in this study. Notably, early studies with P. chrysosporium revealed that agitation (rpm = 125, 150) of submerged cultures can suppress lignin degrading activity (Kirk et al. 1978), perhaps as a result of pellet formation (Shimada 1981). However, other studies report increased lignin degrading activity in P. chrysosporium cultivations shaking at 150 rpm, due to increased diffusion of oxygen in the culture medium (Leisola et al. 1984). Providing oxygen while reducing shear stress might be key (Venkatadri and Irvine 1993).

It is evident that final lignocellulolytic activity measurements were low, despite the complex extracellular enzyme profiles observed by SDS-PAGE (Fig 3.1). This can be attributed to several factors, including the presence of proteases or enzyme inhibitors. Based on the review of P. chrysosporium , it is possible that changes to cultivation conditions including pH, nitrogen source, carbon source, or addition of inducers including oligosaccharides and cellobiose oxidase, could increase the production of lignocellulolytic enzymes by P. carnosa . Further, the addition 51

of surfactants like Tween, or organic acids is likely to impact production of lignin degrading peroxidases. Still, it is important to remember that biochemical assays of enzyme activity are limited to the availability of appropriate substrates, and so can underestimate the range of lignincellulolytic activities present in culture supernatants. Accordingly, as described in Chapters 4 and 5, further characterization of lignocellulose conversion by P. carnosa applied analytical tools that could retrieve more detailed descriptions of enzymes secreted by P. carnosa during growth on lignocellulosic substrates, and the corresponding impact on wood fibre composition and structure.

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Chapter 4 : Mode of Coniferous Wood Decay by the White Rot Fungus Phanerochaete carnosa as Confirmed by FT-IR and ToF-SIMS

4.1 Abstract The softwood degrading white-rot fungus, Phanerochaete carnosa was investigated for its ability to degrade three coniferous woods: balsam fir, white spruce and lodgepole pine. P. carnosa grew similarly on these three wood species, and like the hardwood-degrading white-rot fungi Ceriporiopsis subvermispora and Phanerochaete chrysosporium BKM-F-1767, P. carnosa demonstrated selective degradation of lignin, as observed by FTIR and ToF-SIMS analyses. Lignin degradation across-the-wall was determined by the ratio of lignin concentration between distinct cell wall regions as measured by TEM and by ToF-SIMS, and was shown to be uniform. This study evaluates the ability of a white-rot fungus to utilize softwood lignin and reveals the industrial potential of the lignocellulolytic activity elicited by this fungus.

4.2 Introduction Basidiomycetes are among the most important degraders of lignocellulosic biomass (Lundell et al. 2010). The two most common modes of wood degradation by basidiomycetes are brown-rot and white-rot decay. Brown-rot decay is characterized by extensive degradation of cellulose and hemicellulose with only limited modification of lignin. Examples of well-studied brown-rot basidiomycetes include Gloeophyllum trabeum, Serpula lacrymans , and Postia placenta (Hogberg et al. 2006; Jensen et al. 2001; Martinez et al. 2009). These fungi appear to initiate the depolymerization of wood polysaccharides using Fenton chemistry, whereby diffusible hydroxyl radicals penetrate the wood surface, leaving the lignin as a modified residue (Jensen et al. 2001; Yelle et al. 2008). By contrast, white-rot fungi secrete numerous glycoside hydrolases, carbohydrate esterases and lignin-degrading peroxidases that target all of the main components of wood, including cellulose, hemicellulose and lignin, leaving residual wood with a bleached appearance (Hatakka 1994; Kersten and Cullen 2007; Tuor et al. 1995).

Certain white-rot fungi, such as Trametes versicolor , appear to simultaneously degrade lignin and polysaccharides, while other white-rot fungi, including Ceriporiopsis subvermispora, appear to selectively degrade lignin and then only slowly hydrolyze cellulose (Blanchette 1991; Blanchette et al. 1997; Fackler et al. 2006; Pandey and Pitman 2003). The ability of fungi to selectively degrade lignin has been used to pretreat fibre and defibrillate wood in the production of mechanical pulp (Fackler et al. 2006; Scott et al. 2002).

While many white-rot fungi characterized to date have been isolated from hardwoods (Hibbett and Donoghue 2001), Phanerochaete carnosa, studied here, was isolated almost exclusively from softwood species (Burdsall 1985). As boreal forests comprise more than 45% of the North America’s forested area and softwoods are comparatively recalcitrant sources of lignocellulosic biomass (Birdsey et al. 2006), the glycoside hydrolases and lignin-degrading peroxidases produced by P. carnosa constitute an important source of industrially relevant enzymes. The secretome and transcriptome of the P. carnosa grown on lignocellulosic substrates were recently described, and show that similar sets of enzymes are elicited by P. carnosa grown on different lignocellulosic substrates although to different expression levels, suggesting that adaptations to degrade lignocellulose might result from fine-tuning the expression of similar enzyme families (Mahajan and Master 2010; MacDonald et al., 2011). Although the production of lignocellulolytic activities by P. carnosa has been studied, the pattern of wood decay by this fungus over time is not known. By characterizing the progression of chemical and morphological changes of softwood decayed by P. carnosa , the aim of the current study was to predict whether the expression of secreted hemicellulases and cellulases likely follows initial lignin degradation by this organism. That is, to determine if P. carnosa can be classified as a selective or simultaneous degrader.

Previous reports describe several analytical methods that can be used to investigate anatomical and chemical changes introduced in wood fibres upon microbial decay. Electron microscopy techniques, including scanning electron microscopy (SEM) and transmission electron microscopy (TEM) have been used to determine micro-morphological degradation patterns in plant cell walls (Blanchette 1991). Lignin-specific dyes such as phloroglucinol and safranin are used for the visualization of lignin by light microscopy (Hakala et al. 2004). Changes in fibre

chemistry have been monitored directly using Fourier transform infrared spectroscopy (FTIR), nuclear magnetic resonance spectroscopy (NMR), pyrolysis mass spectrometry (Pyr-MS), ion chromatography, and thermochemolysis (Blanchette 1995; Davis et al. 1994; Faix et al. 1991; Irbe et al. 2006; Kelley et al. 2002). FTIR is especially suited to rapid chemical characterization of small quantities of wood with minimum sample preparation and has been used to analyze changes in wood fibre chemistry after degradation by different brown and white-rot fungi (Faix et al. 1991; Pandey and Nagveni 2007; Pandey and Pitman 2003). For example, Pandey and Pitman (2003) use FTIR to analyze the chemical composition of pine (softwood) and beech (hardwood) following treatment with the brown-rot Coniophora puteana , the non-selective white-rot T. versicolor and the selective white-rot P. chrysosporium . In their study, brown-rot decay was indicated by a relative increase in the lignin-to-carbohydrate peak area ratio, selective white-rot decay was demonstrated by a relative decrease in lignin and xylan content, and the simultaneous decay led to similar reduction in carbohydrate and lignin peaks.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is another versatile technique that has previously been applied to characterize surfaces of pulp, fibres and wood samples (Rowell 2005). In addition to parallel analysis of organic and inorganic constituents of wood fibre, ToF- SIMS provides chemical images with a mass spectrum at each pixel. As a result, ToF-SIMS is capable of spatially resolving different components on the surface of wood, providing information about the composition across cell walls (Jung et al. 2010). ToF-SIMS analysis of lignin and carbohydrate polymers in wood had been limited to a few wood peaks assigned in literature until the recent identification of approximately 40 additional confirmed lignocellulose- descriptive peaks through ToF-SIMS analysis of pine species (Goacher et al. 2011).

The current study investigates the effect of conifer species and stage of degradation on the mode of softwood decay by P. carnosa. Specifically, P. carnosa was grown on wood chips prepared from white spruce, lodgepole pine and balsam fir. These three wood species are not only of prime commercial value in the North American boreal forests, but they also comprise common mixed feedstocks for pulp and paper industries. Specifically, FTIR was employed to monitor temporal changes in lignocellulose composition resulting from fungal activity. To evaluate the mode of degradation across cell walls and regions of lignin degradation (i.e. cell wall or middle

lamella), TEM was then used to determine the localization of lignin prior and post-decay. Finally, ToF-SIMS analysis was performed to simultaneously describe the chemical composition and spatial resolution of lignocellulose components and to validate results obtained using FTIR and TEM.

4.3 Materials and Methods 4.3.1 Fungal Strain and Cultivation Conditions Phanerochaete carnosa strain HHB-10118-sp was obtained from the US Department of Agriculture (USDA) Forest Products Laboratory, Madison, WI, and was maintained on YMPG

medium containing 2 g yeast extract, 10 g malt extract, 2 g peptone, 10 g glucose, 2 g KH 2PO 4, 1 g MgSO 4·7H 2O, 1 g asparagine per 1 L in H 2O. Wood cultivations were prepared by using a Waring blender to grind balsam fir ( Abies balsamea ), lodgepole pine ( Pinus contorta ), white spruce ( Picea glauca ), and then sifting air-dried samples through US standard mesh size 6 (3.35 mm 2) and 14 (1.5 mm 2) sieves. Wood particles that were retained on mesh size 14 were recovered, and 4 g samples were transferred to a total of sixty (3 species x 5 time points x 3 test replicates and one control) 500 mL beakers containing 10 mL of B3 buffer (Appendix 1; Kirk et al. 1978). Thiamine-HCl, ammonium tartrate, and 2,2- dimethylsuccinic acid were added as filter-sterilized solutions, while all other media were steam sterilized for 20 to 30 min.

Each culture medium was inoculated with an 11 mm circular agar plug taken from the growing edge of P. carnosa cultivated on solid YMPG. Cultivations were incubated under stationary conditions at 27 °C until the diameter of the surface mycelial mat was 1 cm, 4 cm, 6 cm and 8 cm; the final sample was taken after 12 weeks of cultivation. At each growth point, a metal cutter with 2.8 cm diameter was used to isolate residual wood particles from the centre of each cultivation. Wood samples were rinsed with milli-Q water to remove mycelia and were air dried before analysis.

4.3.2 Fourier Transform Infrared Spectroscopy A minimum of three wood slivers from each time point for each wood species were randomly chosen and powdered using a bench-top ball mill (WIG-L-BUG model, Dentsply Rinn). A small amount of the milled sample (~ 1 mg) was mixed with KBr (200 mg) and the mixture pelletized

using a die (1.3 cm diameter) and a hydraulic press. A Bruker Tensor 27 FTIR was used to record the absorbance between 4000 and 400 cm -1 with a resolution of 4 cm -1. The spectra representing the average of 32 scans were corrected for atmospheric vapor compensation; baseline was corrected using the rubber band method (Opus software, v. 5.0). Spectra were normalized for unit-vector and mean-centred prior to the principal component analysis (PCA) in Matlab v. 7.8.0 software.

The following compounds were run as standards to assist lignocellulose FTIR assignments for unreported peaks: microcrystalline cellulose (Avicel PH-101), tamarind xyloglucan, birchwood xylan, arabinogalactan, guar (galactomannan), pectin, and mannose were obtained from Sigma- Aldrich; konjac glucomannan was obtained from Megazyme, and Klason lignin was prepared by Dr. D. Jeremic (PDF in E. Master’s lab) from white spruce according to TAPPI standards (T222 om-83). The FTIR spectra were obtained and analyzed as described above.

4.3.3 Transmission Electron Microscropy - Energy-Dispersive X-ray Analysis (TEM- EDXA)

Lignin is usually visualized with TEM-EDXA by labeling lignin with KMnO 4 or bromine (Sjöström 1983; Stein et al. 1992). In this study, six late decay wood samples (time point 5) and six early decay wood samples (time point 1) from spruce cultivations were brominated following the protocol of Saka et al (1978). The brominated samples were then embedded in low viscosity embedding medium (Modification E) (Spurr 1968). Thin sections (100 nm) of the embedded samples were obtained by using a Leica EM UC 6 microtome, and Diatome Ultra 35˚ knife (MC 12734), and analyzed using an HD-2000 transmission electron microscope (Hitachi High Technologies America, Inc) equipped with an Oxford Instruments Inca X-sight detector.

4.3.4 Time of Flight Secondary Ion Mass Spectroscopy Since samples observed under TEM-EDXA showed very similar patterns of lignin distribution across the cell walls, cross-section surfaces of a fibre sample from each wood species representing growth points one and five were prepared using a microtome (Leica EM UC 6). ToF-SIMS spectra of these cross-sections were collected by Dr. R. Goacher (PDF in C. Mims + and E. Edwards’ lab) using a ToF-SIMS IV (IonTof, Muenster, Germany). 25 keV Bi 3 primary

ions were utilized to generate high mass resolution spectra in the bunched mode (0.2 pA pulsed current) and high spatial resolution images in burst alignment mode. Charge compensation was applied with a low energy electron beam. Base vacuum was 5 × 10 -9 mbar. Spectra were + + + + calibrated to the CH 3 , H 3O , C 2H3 and C 3H5 ions using IonSpec v.4.1 software. At m/z 91, the mass resolution (M/ ∆M) was ~5,000 in bunched mode and ~250-300 in burst alignment mode; depending on sample roughness. To determine local variations in degradation, one spot on each sample (100×100 to 200×200 µm 2) was analyzed and split into three to four regions of interest to generate replicates. Prior to PCA, peak intensities were Poisson-corrected to compensate for minor detector saturation (Stephan et al. 1994). A peak list consisting of 36 definitive lignocellulosic m/z ratios was employed for the analyses in both spectral and spatial models. These peaks comprised groups 1 and 2 of Goacher et al. (2011), minus peaks at m/z 19 and 31 due to suspicion of moisture effects and at m/z 45, 59, 73, 131, and 147 because of PDMS (polydimethylsiloxane) interferences. ToF-SIMS high mass resolution spectra were normalized to unit area and mean-centred. ToF-SIMS images were mean-centred without normalization.

4.3.5 Statistical Analysis PLS Toolbox v. 5.8.2 (Eigenvector Research, Inc.) was used with MATLAB v. 7.8.0 (The MathWorks, Inc.) to perform PCA of spectra obtained from FTIR and ToF-SIMS. MIA Toolbox (Eigenvector Research, Inc.) was used for PCA analysis of ToF-SIMS nominal mass resolution ion images. PCA loadings were analyzed to determine the specific lignocellulose components that changed significantly over time.

4.4 Results Radial extension of fungal mycelia has been used to measure growth and compare growth profiles on solid substrates. Previous reports also describe that visual estimates of fungal growth on wood chips are consistent with indirect methods including substrate dry weight loss, rate of diacetate hydrolysis, or extractable protein content and chitin content, where separation of mycelia and substrate is challenging (Boyle and Kropp 1992; Marin et al. 2005). To monitor growth in the present study, radial extension of P. carnosa from the agar plug was measured, and wood samples from the centre of each cultivation were harvested at pre-defined radial measurements. The time required to reach each radial extension was similar between cultivation 61

replicates and across wood species, indicating a similar growth progression on all the three species selected (Appendix 2, Fig. S4.1). Gradual darkening of wood chips was observed in all cultivations. This darkening has been correlated to chemical changes due to lignin decay (Fackler et al. 2007; Fackler et al. 2003). These decay products and compositional changes induced in the material were investigated using FTIR and ToF-SIMS spectral analysis and the abundance of lignin in different cell wall layers was monitored by TEM-EDXA and ToF-SIMS imaging.

4.4.1 FTIR Spectroscopy The PCA of wood FTIR spectra in the fingerprint region (800-1800 cm -1) clearly separated the advanced decayed samples from the controls (Fig. 4.1 to 4.3). Considering the natural variability of wood samples, Hotelling’s T-square statistics were used to initially identify samples from the same time point and species that generated scans with high leverage as well as residual variance (i.e., dangerous outliers). Despite the removal of the obvious outliers, the initial principal component (PC1) of the analysis of spruce and fir identified variability among the data due to sample origin.

PC1 of pine and subsequent PCs of fir and spruce (PC2 and PC3, respectively) revealed progressive lignin decay by P. carnosa (Fig. 4.1 to 4.3). All control samples exhibited relatively high peak intensity at ~1512 and ~1262 cm -1 as determined by PCA (Table 4.1). The 1512 cm -1 peak corresponds to aromatic skeletal vibrations of lignin in softwoods (Faix and Beinhoff 1988; Pandey and Pitman 2003). The PCA loading at 1262 cm-1 does not correspond to an actual peak in the spectra, but rather the difference in the slope of the peak with 1268 cm -1 maxima (guaiacyl ring breathing). It has been previously shown that the 1263 cm -1 peak appears in a first derivative IR spectra of lignin, confirming its suggested relation to 1268 cm -1 peak (Faix 1992). Therefore, for all of the examined species, the significant aromatic assignments for lignin were distinguished by PCA as higher in control samples than the decayed samples.

Table 4.1 Assignment of FTIR peaks that distinguish control and decayed wood samples

Peaks assigned in literature (* sh = shoulder) SPRUCE PINE FIR Wavenumber PCA actual PCA actual PCA actual Peak description (cm-1) loading peak loading peak loading peak 1768 1761 1767 Unconjugated carbonyl stretches; characteristic of 1728 1800-1700 both lignin and carbohydrates (Faix 1992) 1722 1711 1709 1711 Conjugated carbonyl stretches, conjugated and 1696 1692 1700-1550 nonconjugated C=C stretches; characteristic of 1683 both lignin and carbohydrates (Faix 1992) 1646 1400 1401 Aromatical skeletal vibrations (Faix and Beinhoff, 1511 1512 1511 1512 1511 1512 1511 1988) Aromatical skeletal vibrations (Faix and Beinhoff, 1493, 1498 1496 1495 1988) C-H deformations, asym in -CH and -CH (Faix 1466 1460-1470 3 2 1466 and Beinhoff, 1988, Faix 1992) sh* Seen as deconvoluted wavelengths for 1465 cm-1 1453 1453 1453 peak (Faix and Beinhoff, 1988) Aromatic skeletal vibrations combined with C-H 1427-1428 in-plane deformations (Faix and Beinhoff, 1988, 1427 1426 sh Faix 1992) 1400 1401 Guaiacyl ring breathing with carbonyl stretching 1268-1273 1261 1269 1261 1268 1263 1268 (Faix and Beinhoff, 1988) Common to lignin and cellulose (Hobro et al. 1223-1226 1228 1233 2010) C-O stretching from the pyranosidic ring in carbohydrates and lignin (Denes et al. 1999, 1057-1058 1061 1061 Pandey and Pitman 2003, Faix and Beinhoff, 1988) C-H in-plane deform. guaiacyl ring and C-O deform. primary alcohols and possibly influence of 1031- 63 1031-1033 1033 non-conjugated C=O groups (Faix and Beinhoff, 1034 1988)

Fig. 4.1 Spruce decay described by FTIR analysis . A) Grouping on PCs 2 and 3 for normalized and mean- centered FTIR data. Closed symbols represent test data; open symbols represent control data. Symbol shapes represent time progression: time point

1 ( O), 2 ( ⁪), 3 ( ), 4 ( ), 5 ( ◊). B) Loadings for PC 3, separating more and less decayed samples. Horizontal dotted lines at magnitude |0.05| represent thresholds for loading significance. Peaks with highest loadings for the control samples denotes their loss in the decayed samples. C) Mean baseline corrected FTIR data normalized to total intensity between 800 and 1800 cm -1: control time point 1 (thick black line), test time point 2 (thin black line) and test time point 5 (thin grey line). Grey vertical dashed lines are visual guides to compare significant loadings in (B) with FTIR spectra in (C).

Fig. 4.2 Fir decay described by FTIR analysis . A) Grouping on PCs 1 and 2 for normalized and mean- centered FTIR data. B) Loadings for PC 2, separating more and less decayed samples. C) Mean baseline corrected FTIR data for control and test data. Symbols and lines as described in Fig. 4.1.

Fig. 4.3 Pine decay described by FTIR analysis. A) Grouping on PCs 1 and 2 for normalized and mean-centered FTIR data. B) Loadings for PC 1, separating more and less decayed samples. C) Mean baseline corrected FTIR data for control and test data. Symbols and lines as described in Fig 4.1.

Nearly all of the peaks that corresponded to other significant loadings (>|0.05|) in control samples were previously reported in milled-wood lignin (MWL) and/or G and GS lignin dehydrogenation polymers by Faix and Beinhoff (1988). Exceptions include certain peaks in the fir control samples that are not exclusive to lignin (Table 4. 1). For example, the peak at 1228 can be attributed to both lignin and hemicelluloses (Hobro et al. 2010). Still, the only peak not associated with lignin that showed high peak loading in a control sample, was at 1400 cm -1 for spruce. This peak was also highest in the 1480-1300 cm -1 region of non-cellulosic carbohydrates, including arabinogalactan, guar galactomannan, mannose, pectin, tamarind xyloglucan and birchwood xylan (Appendix 2, Table S4.1).

The PC loadings that describe the decayed samples indicate either bonds that were not attacked by the fungus or else were generated through biological modification of the substrate. With the exception of the loading at 1400 cm -1 that described decayed pine, PCA of all samples showed high loadings in the 1800-1700 cm -1 region and the 1700-1550 cm -1 region. The PCA loading at ~1767 cm -1 and ~1711 cm -1 described decayed spruce, pine and fir, but were not detected in raw spectra of the samples or standards and instead were correlated to shoulders (Fig. 4.1 to 4.3; Appendix 2, Fig. S4.2). Similarly, the loading at 1722 cm -1 in decayed pine samples and loading at 1728 cm -1 in decayed fir corresponded to peaks that were not detected in the raw spectra. All of these loadings describe slopes of peaks in the 1800-1700 cm -1 region of the corresponding spectra, where the 1734-1732 cm -1 region defines the peak maximum. This region describes unconjugated carbonyl stretches that can be formed either in lignin or hemicelluloses (Faix 1992). For instance, three peaks in the 1737-1710 cm -1 region (1737, 1720 and 1710 cm -1) are detected in lignin spectra after deconvolution (Faix and Beinhoff 1988) and like the peak at 1228 cm -1, a peak maximum at 1734 cm -1 has also been attributed to both lignin and hemicelluloses (Hobro et al. 2010; Pandey and Pitman 2003)

The decayed samples were also characterized by significant loadings in the 1700-1550 cm -1 region, which were identified as slopes of peaks that are characteristic of conjugated carbonyl stretches (Table 4.1). For instance, aromatic rings of milled wood lignin absorb at ~1595 cm -1 (Colom et al. 2003; Faix and Beinhoff 1988; Pandey and Pitman 2003), while Klason lignin displays carbon stretching at 1682 cm -1 and aromatic skeletal vibration breathing with C=O

stretching at 1603 cm -1 (Kubo and Kadla 2005). Although outside of the range of important loadings that describe the data reported here, conjugated carbonyl stretch in degraded cellulose has been reported at 1620 cm -1(Lojewska et al. 2005).

In summary, the PCA of FTIR spectra described the decayed samples by peaks characteristic of lignin and carbohydrates, while the same analysis distinguished the control samples primarily by lignin-specific peaks that were lost upon degradation. Accordingly, the PCA of FTIR spectra indicated that progressive decay by P. carnosa was mainly described by change in lignin composition. TEM and ToF-SIMS were performed next to evaluate the localization of lignin decay and to characterize the biochemical transformations in more detail.

4.4.2 TEM-EDXA Lignin concentration in different regions of the cell wall differs considerably, with the highest reported concentration being in cell corners and middle lamellae (Fromm et al. 2003; Saka et al. 1982). Here, the spatial degradation of lignin was investigated to describe microscopic progression of P. carnosa attack on the softwood species.

Spruce samples from growth point one and five were selected for TEM-EDXA image analyses given the clear compositional transformations detected in these samples as characterized by FTIR. In this study, line scans of brominated samples across the cell wall and cell corners demonstrated that the ratio of lignin in the cell walls to lignin in the middle lamella did not change significantly with decay (Appendix 2, Table S4.1). No apparent change in lignin concentration between cell walls suggests that P. carnosa simultaneously degraded lignin present in the cell walls and middle lamella. Further, wood decay by P. carnosa did not appear to cause morphological changes in the decayed samples.

4.4.3 ToF-SIMS ToF-SIMS analysis was used to validate the FTIR results of lignin degradation and also the lignin concentration pattern across fibre cell walls as observed by TEM-EDXA. Chemical changes due to decay were assessed using an extended list of peak annotations for lignocellulosic materials (Goacher et al. 2011).

A clear progression of lignin degradation in each of the three wood species tested was revealed when comparing ToF-SIMS spectra of samples from early and late P. carnosa cultivations (Fig 4.4). The lignin peaks described by Goacher et al. (2011) characterized the samples at early growth points (except m/z 67 for spruce and fir), while peaks characteristic of polysaccharides were more prevalent in the later growth points.

However, the contributions of each lignin and polysaccharide peak depended on the wood species. For example, degraded spruce samples exhibited lower enhancement of high-mass polysaccharide peaks, suggesting that bioconversion activity had progressed further in these samples. Also, the m/z 137 peak characteristic of guaiacyl lignin (Saito et al. 2005) was particularly influential in separating degraded spruce and fir samples from corresponding controls, but less influential for the pine samples. Finally, the prevalence of the lignin peak at m/z 67 at the later time points of decayed spruce and fir samples may indicate a degradation product that arose at this mass.

For all wood species, the PCA models of ToF-SIMS cross-sections of control and decayed samples demonstrated higher concentrations of lignin in middle lamella and cell corners relative to the intensities in the cell walls (Fig. 4.5, Appendix 2 Fig. S4.3 and S4.4), further supporting the TEM-EDXA observations that P. carnosa simultaneously degrades lignin in the cell walls and middle lamella.

Fig 4.4 PCA of normalized and mean-centered ToF-SIMS spectra: A) Fir, B) Pine, and C) Spruce. Left column depicts scores on PC1 separating controls (squares) and test samples (diamonds). Right column depicts corresponding loadings with peaks identified by Goacher et al. (2011) labeled for polysaccharides (stars) and lignin (triangles). Grey horizontal lines at loading magnitudes |0.05| represent thresholds for loading significance

Fig. 4.5 PCA of mean-centered ToF-SIMS image of decayed pine (time point 5): image of scores (left) and loadings (right) for PC 2. Image dimensions are in pixels where 50 pixels equal 19.1 microns.

4.5 Discussion P. carnosa grew on wood particles prepared from lodgepole pine, balsam fir and white spruce; in all cases, radial growth of 8 cm was reached on average in 45-50 days. The darkening of wood chips observed during the decay by P. carnosa was similarly reported for growth of white-rot fungi C. subservmispora, P. chrysosporium and T. versicolor on spruce, and has been correlated to the formation of free lignin radicals and corresponding decay products (Fackler et al. 2007).

Significant changes in fibre chemical composition were detected by FTIR analysis after growth point three, which was reached after approximately 25 days of cultivation. The time required to reach this stage of decay might be reduced in cultivations that do not contain additional nutrient medium (Singh and Chen 2008). In general, ToF-SIMS and FTIR analyses revealed the degradation of lignin accompanied by partial hemicellulose degradation, reminiscent of selective white-rot fungi (Blanchette et al. 1985).

PCA of FTIR spectra indicated the disappearance of lignin in decayed samples by revealing the dominance of vibrations at 1512 and 1267 cm -1 in the control samples, which correspond to softwood lignin. While the PCA loadings descriptive of control samples pointed to actual peaks observed in the spectra and could be attributed either to lignin or hemicelluloses, the loadings of

decayed samples were not as clear and described changes in slopes in regions between 1800 and 1590 cm -1 overall suggesting changes in residual lignin and hemicelluloses. Notably, the PCA of FTIR and ToF-SIMS also revealed that the progression of decay was most aggressive in spruce samples, since similar levels of decay were achieved after the third growth point. Further, the PCA of ToF-SIMS spectra composed of previously assigned peaks more clearly separated early and late decay by lignin degradation. Given the clarity of the ToF-SIMS data and the capability of this technique to give both spectral and spatial information of the samples, these analyses reinforce the utility of ToF-SIMS for chemical analysis of complex biomass samples.

During delignification or selective decay, cell walls retain their morphology, but gradually become un-reactive to lignin stains and increasingly reactive to cellulose stains as the lignin is removed (Kirk and Cullen 1998). For instance, morphological alterations of cell walls were not detected in pinewood chips degraded by the selective white-rot C. subvermispora , even though staining with uranyl acetate indicated that the permeability of the secondary wall had changed (Blanchette et al. 1997). Similarly, changes in the cell morphology and the ratio of lignin in cell walls and middle lamellae were not detected when comparing early and late decayed samples by TEM-EDXA and ToF-SIMS in the present study.

Most white-rot fungi that have been characterized to date typically degrade syringyl (S) lignin in preference to guaiacyl (G) lignin (Faix et al. 1985). For instance, the secondary cell wall layer in hardwoods is primarily composed of syringyl units and is typically more receptive to white-rot attack than the cell corners, which are rich in guaiacyl units (Blanchette et al. 1987). By contrast, the current results indicated that P. carnosa is proficient at attacking and modifying the guaiacyl lignin component in softwood (as denoted by ToF-SIMS as m/z 137, and by FTIR as 1512 and 1268 cm -1). Though not detected in the samples characterized here, syringyl lignin is described by a peak at 1128-1125 cm -1 in FTIR spectra and m/z 167 and 181 in ToF-SIMS spectra (Saito et al. 2005)..

The degradation of guaiacyl lignin by P. carnosa is consistent with recent transcriptomic analyses of P. carnosa grown on woody substrates (MacDonald et al. 2011). MacDonald et al. (2011) revealed that the expression of transcripts encoding manganese peroxidases (MnP) in P.

carnosa were higher than those predicted to encode lignin peroxidases (LiP), and that the P. carnosa genome encoded more genes that were predicted to encode MnPs than LiPs (MacDonald et al. 2011). This is in contrast to P. chrysosporium , which encodes 10 LiPs and 5 MnPs (Martinez et al. 2004). Since the oxidants produced by MnP, including Mn 3+ , are smaller than LiPs and associated oxidants, it is possible that products of MnP activity can diffuse more easily through denser guaiacyl lignin structures present in softwood fibre, and promote softwood decay by P. carnosa (Fackler et al. 2007; Hammel and Cullen 2008). As described in the next Chapter, the similar pattern of decay observed for lodgepole pine, balsam fir and white spruce is also consistent with the proteomic analysis of P. carnosa , which highlighted the secretion of similar lignocellulolytic enzymes during growth on spruce and cellulose, implying the elicitation of a similar decay strategy for different lignocellulosic substrates (Mahajan and Master 2010).

To summarize, P. carnosa can degrade guaiacyl lignin moieties and elicits selective white-rot decay of lodgepole pine, balsam fir and white spruce. These results suggest that P. carnosa could benefit bioprocess treatments used to reduce the recalcitrance of softwood species that predominate in many boreal forests. Still, it is important to bear in mind that the pattern of white- rot decay (i.e., selective or simultaneous decay) by wood-degrading fungi can vary depending on cultivation conditions. For example, Ganoderma applanatum elicits simultaneous decay when grown on Acer saccharum, Populus alba, Populus tremuloides , as does Ischnaderma resinosum when grown on Populus deltoides (Blanchette 1984). However, both of these organisms were correlated with selective decay of other tree species (Blanchette 1984). Accordingly, although P. carnosa elicited selective degradation of lignin in lodgepole pine, balsam fir and white spruce, it is plausible that the mode of decay may differ when grown on significantly different feedstocks, including hardwood species.

Chapter 4 References

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Chapter 5 : Proteomic Characterization of Lignocellulose- degrading Enzymes Secreted by Phanerochaete carnosa Grown on Spruce and Microcrystalline Cellulose

5.1 Abstract Proteins secreted by the white-rot, softwood degrading fungus Phanerochaete carnosa during growth on cellulose and spruce were analyzed using tandem mass spectrometry and de novo sequencing. Homology driven proteomics was applied to compare P. carnosa peptide sequences to proteins in P. chrysosporium using MS BLAST and non-gapped alignment. In this way, 665 and 365 peptides from cellulose and spruce cultivations, respectively, were annotated. Predicted activities included endoglucanases from glycoside hydrolase (GH) families 5, 16, and 61, cellobiohydrolases from GH6 and GH7, GH3 β-glucosidases, xylanases from GH10 and GH11, GH2 β-mannosidases, and de-branching hemicellulases from GH43 and CE15. Peptides corresponding to glyoxal oxidases, peroxidases, and glycopeptides that could participate in lignin degradation were also detected. Overall, predicted activities detected in extracellular filtrates of spruce cultures represented a subset of those identified in cellulose cultures, suggesting that the adaptation of P. carnosa to growth on lignocellulose might result from fine-tuning the expression of similar enzyme families.

5.2 Introduction Genomic and proteomic analyses of basidiomycetes are providing more complete descriptions of enzyme combinations that play a critical role in carbon-cycling and can be applied in the production of energy and chemicals from lignocellulosic biomass. Progress in this area is exemplified by recent genomic and proteomic analyses of the white-rot fungus Phanerochaete chrysosporium and the brown-rot fungus Postia placenta (Martinez et al. 2004; Vanden Wymelenberg et al. 2009; Martinez et al. 2009). For instance, less than 20 % of predicted glycoside hydrolases encoded by P. chrysosporium were characterized before the genome sequence was published. And while proteomic analysis of P. chrysosporium grown on cellulose and wood substrates consistently detect the expression of GH3 β-glycosidases, GH6 and GH7

cellobiohydrolases, GH10 xylanases, and GH12 endoglucanases, more than 65 % of the proteins secreted by P. chrysoporium grown on crystalline cellulose corresponded to previously uncharacterized glycoside hydrolases, esterases and proteases, as well as proteins with unknown function (Abbas et al. 2005; Ravalason et al. 2008; Sato et al. 2007; Wymelenberg et al. 2005; Vanden Wymelenberg et al. 2006; Vanden Wymelenberg et al. 2009). By contrast, genomic and proteomic analysis of P. placenta identified oxidases predicted to generate extracellular Fe 2+ and

H2O2 and relatively few cellulolytic glycoside hydrolases (GH) (Martinez et al. 2009). The apparent lack of GHs encoded by P. placenta that contain carbohydrate-binding modules was particularly striking.

As described in previous chapters, Phanerochaete carnosa is a white-rot fungus isolated from balsam fir ( Abies balsamea ) as well as other softwoods, and shown to belong to the family Phanerochaete sensu strictum (de Koker et al. 2003). Given the abundance of softwood and the unique challenges associated with its hydrolysis, enzymes that have evolved to efficiently transform softwood feedstocks are particularly relevant both industrially and for fundamental investigation. To ascertain the diversity and regulation of carbohydrate-active and lignolytic enzymes secreted by P. carnosa , a proteomic approach was applied to compare the secretome of this fungus while growing on microcrystalline cellulose and spruce wood chips. This study represents the first detailed characterization of the carbohydrate-active and lignin-degrading enzymes produced by P. carnosa . Further, comparative proteomics was performed to reveal enzymes and proteins secreted by P. carnosa that can accelerate the bioconversion of softwood feedstocks to fermentable sugars. Since the P. carnosa genome sequence was not available when this study was performed, the genome sequence of P. chrysosporium was used to facilitate the annotation of peptide sequences retrieved. In this way, this study describes a proteomic approach to investigate the enzymatic basis for feedstock preference, while also contributing to the still limited number of investigations that have applied homology driven proteomics to annotate peptides from organisms that lack genome sequence information (Junqueira et al. 2008).

5.3 Materials and Methods 5.3.1 Cultivation Conditions As previously described, Phanerochaete carnosa strain HHB-10118 was obtained from the US Department of Agriculture (USDA) Forest Products Laboratory, Madison, WI. The strain was grown on liquid or solid medium under stationary conditions at 27 °C, and was maintained on YMPG agar slants (yeast extract (2 % w/v), maltose (10 % w/v), peptone (2 % w/v), glucose (1 % w/v) and agar (2 %w/v)) at 4 °C.

Cellulose medium was prepared in 1 L flasks and contained 1 % wood-derived microcrystalline cellulose (Avicel PH-101, Fluka) submerged in 200 mL B3 medium (low nitrogen, 0.1 mg L -1 thiamine HCl, 0.2 mg L -1 ammonium tartrate, 10 mM dimethyl succinate, pH 4.5) (Kirk et al. 1978). Wood cultivations were prepared in autoclavable 22 X 28 cm polypropylene bags containing a gas exchange filter (Unicorn Imp & Mfg Corp TX USA) (Sato et al. 2007). Briefly, chips of white spruce ( Picea glauca ) measuring approximately 0.1 cm X 0.2 cm X 1.2 cm were washed with milliQ water, steam sterilized for 30 min, and 225 g of the chips were submerged in 350 mL of B3 medium. The inoculum was obtained from P. carnosa cultivations grown for 7 days at 27 °C in liquid YMPG medium. The mycelium was filtered through sterile Miracloth, rinsed with B3 buffer (pH 4.5), and then blended three times for 10 s using a sterile Waring blender. The mycelium suspension was transferred to each culture to obtain an initial mycelium concentration of 40 mg (fresh weight) 100 mL -1. Cellulose and spruce cultivations were grown at 27 °C under stationary conditions for 4 and 12 weeks, respectively. Triplicate cultivations were prepared for each growth condition; cellulose and spruce growth medium without fungal inoculation served as abiotic controls. Extents of fungal growth on cellulose was determined by measuring final dry weight and total chitin in each cultivation (Plassard et al. 1982).

5.3.2 Protein Extraction Following vacuum filtration of cultivation supernatants through Miracloth, mycelium was rinsed with B3 buffer and the filtrate was recovered then pooled with the corresponding culture supernatant. Liquid fractions were centrifuged at 5300 x g for 20 min at 4 °C, and then filtered using 0.45 µm syringe filters. The filtrate was concentrated approximately 20 times using 81

Amicon Centrifugal devices (5000 MWCO polyethersulfone membrane; Centricon-20 cat: UFC2BCC24). Protein concentration was measured by the Bio-Rad Bradford assay, and concentrated samples were stored at -80 ˚C for proteomic analysis.

5.3.3 Protein Preparation and Analysis by Mass Spectrometry Protein preparations (20 µg each) were fractionated by 1-D SDS-PAGE and each lane was excised into 10 fractions using a clean razor blade. In-gel trypsin digestion was then performed by treating each gel segment with 40 µL of 50 mM NH 4HCO 3 and 12.5 ng µL-1 of modified trypsin (Promega, sequencing grade) (Shevchenko et al. 1996). Peptides were extracted from the gel by shaking for 20 min at room temperature with 50 µL of 5 % formic acid twice, and then with 50 µL of 5 % formic acid in 30 % acetonitrile. The peptide extracts were concentrated to roughly 20 µL by vacuum centrifuge. Concentrated peptide samples were loaded onto a HPLC_Chip (160 nL high capacity sample enrichment column and 75-µm x 150 mm SB-C18 separation column, Agilent Technologies, Santa Clara, CA, USA) and separated by flow rate at 300 nL min -1, with solvent A (0.2 (v/v) % formic acid in water) and solvent B (100 % acetonitrile) and the following gradients: 3 %, 35 %, 80 %, 100 % of solvent B at 0, 50, 54 and 56 min after injection, respectively. The LC-MS/MS analysis was carried out by an Agilent 1100 HPLC-chip and 6340 ion trap system with MS scan range from 300 to 1,300 m/z. Three precursor ions were selected from each MS scan for back-to-back collision induced dissociation and electron transfer dissociation (CID/ETD). A 30 s dynamic exclusion period was applied to the precursor previously selected twice for MS/MS, i.e., two rounds, each with CID/ETD. (SunnyBrook Health Science Centre, Proteomics Core Facility).

5.3.4 Peptide Sequence Annotation Since the P. carnosa genome sequence was not available at the time of analysis, it was necessary to identify peptide spectra through de novo sequencing. The SHERENGA de novo sequencing tool (version A.03.03.084, Agilent Technologies) was used to explicitly read peptide sequences from high-quality fragment ion spectra (Dancik et al. 1999). Peptide sequences that received a SHERENGA score greater than 100 were matched to all predicted proteins from Phanerochaete chrysosporium (v.2.1) using MS BLAST ((Shevchenko et al. 2001), http://genetics.bwh.harvard.edu/msblast/index.html). To increase the confidence of protein 82

matches based on a homology-driven approach, only alignments receiving an MS BLAST score of 55 or above were considered, which is consistent with other scoring schemes based on MS BLAST analyses (Shevchenko et al. 2001, Junqueira et al. 2008). Peptide sequences were also compared to predicted proteins from Phanerochaete chrysosporium using BLASTp and the following parameter settings: no gaps, word length 5, and gap extension cost 100. The PAM30 substitution matrix was selected given the short peptide sequences that were queried in the BLAST search. Initial analyses were automated using freely available BioPython source code (Cock et al. 2009), and peptide matches were manually inspected. Percent identity was computed as the number of contiguous amino acid matches divided by the total peptide length. To avoid over prediction of protein hits, protein sequences receiving only one peptide match were ignored if the corresponding peptide aligned to another protein with additional peptide matches. The MS/MS spectra of peptides that were annotated were then manually interpreted to verify the validity of de novo sequences predicted using SHERENGA. Non-gapped direct alignments using BLASTp was also performed to compare peptide sequences from extracellular filtrates of cellulose and spruce cultivations.

5.4 Results 5.4.1 Preparation and Analysis of Peptide Samples Cellulose and spruce cultivations prepared for proteomic analyses of P. carnosa extracellular filtrates were grown at 27 °C. While cellulose cultivations were harvested after four weeks of incubation, spruce cultivations were grown for an additional eight weeks given the comparative recalcitrance of the substrate.

Protein secreted from Phanerochaete carnosa grown on cellulose and spruce was fractionated by SDS-PAGE to confirm that concentrated proteins from triplicate cellulose and spruce cultivations generated reproducible protein profiles. Approximately 5.4 mg and 9.8 mg of protein were recovered from 200 mL of cellulose-grown and 350 mL of spruce-grown culture supernatant, respectively. Proteins were fractionated by one-dimensional gel electrophoresis, each lane was cut into at least ten segments, and each section was processed by in-gel trypsin digestion. Peptides eluted from each gel segment were then analyzed by LC-MS/MS. This procedure enabled the characterization of proteins that might not have been detected by two- 83

dimensional gel electrophoresis. This approach also avoided reported difficulties associated with proteins extracted from wood-grown cultivations, which produce streaks in electrophoresis gels that mask protein spots (Sato et al. 2007).

De novo sequencing was performed to explicitly read peptide sequences. In total, 3222 and 1763 peptide sequences were recovered from cellulose and spruce cultivations of P. carnosa , respectively. Of these, 665 and 365 could be annotated based on sharing at least 70 % sequence identity to predicted proteins from the white-rot fungus Phanerochaete chrysosporium . Notably, direct non-gapped alignments also indicated that only 16 % (284) of the peptides obtained from extracellular filtrates of spruce cultures shared greater than 70 % identity to peptides extracted from cellulose cultivations.

Peptide sequences with best matches to predicted glycoside hydrolases, esterases, oxidoreductases, monooxygenases, and proteins with unknown function were manually inspected; all hits reported here correspond to proteins with predicted secretory signal sequences and that lack predicted transmembrane sequences (Fig. 5.1). In most cases, non-gapped direct alignments were able to validate the MS BLAST results, since many of the same peptide matches were predicted in both cases (Tables 5.1 to 5.3). However, the percent identity of peptide and protein sequences from P. carnosa and P. chrysosporium , respectively, did not correlate well to MS BLAST score. Therefore, matches to enzyme families identified through both MS BLAST and non-gapped alignment are reported in Tables 5.1 and 5.2, while enzyme families that were predicted using non-gapped alignment only are summarized in the supplemental material (Appendix 3, Table S5.1). Since P. carnosa peptide sequences were annotated using predicted protein sequences from a different, albeit related, organism, P. carnosa peptide annotations are discussed below in terms of predicted enzyme activity rather than expression of a specific protein.

5.4.2 Cellulases and Hemicellulases in Cellulose and Spruce Cultivations Cellulose degradation by white-rot fungi is mediated by the concerted activity of three cellulolytic enzymes: endoglucanases, cellobiohydrolases and β-glucosidases (Baldrian and Valaskova 2008). Evidence of these cellulolytic activities was found in the secretome

A. Unknowns (56, 24%) Glycoside Hydrolase (49, 21%)

Carbohydrate Esterase, Lipase (10, 4 %)

Transport (37, 16%)

Peptidase (26, 11%)

Glycosyl Transferase (7, 3 %)

Oxidoreductase (20, 9 %) Extracellular - Structural (17, 7%) Monooxygenase (8, 3%)

Unknowns (14, 14.5 %) B. Glycoside Hydrolase (16, 16.5%)

Transport (11, 11%)

Carbohydrate Esterase, Lipase(7,7%)

Extracellular - Structural (5, 5 %)

Monooxygenase (3, 3%) Peptidase (19, 20%)

Oxidoreductase (19, 20 %) Glycosyl Transferase (3, 3 %)

Fig. 5.1. Distribution of peptide annotations from proteins produced by P. carnosa grown on (A) crystalline cellulose, and (B) spruce wood chips. Unique peptide sequences annotated to proteins with predicted signal sequences using both MS BLAST and non-gapped alignment are summarized.

of P. carnosa grown on cellulose (Table 5.1). While over ten unique peptide sequences corresponding to GH7 cellobiohydrolases were detected, only one peptide corresponding to a GH6 cellobiohydrolase was detected. Most of the peptide matches to family 7 cellobiohydrolases

were distributed over conserved regions of this GH family. This result confounds the ability to predict the number of unique GH7 enzymes secreted by P.carnosa . However, since only those protein hits containing at least one unique peptide match are listed in Table 5.1, the higher number of peptides corresponding to a GH7 cellobiohydrolase likely results from the secretion of multiple GH7 isozymes. MS BLAST analyses identified three unique peptide sequences corresponding to GH3 glycoside hydrolases in the culture of P. carnosa grown on cellulose. GH family 3 enzymes exhibit a broad range of substrate specificity, including β-glucosidases that hydrolyze cellobiose to glucose.

Comparatively few peptide sequences corresponding to GH families with uniquely endo- cellulolytic activities were identified in extracellular filtrates of P. carnosa grown on cellulose or spruce. Non-gapped alignments identified peptide sequences that were matched to GH5 and GH74 enzymes in P. chrysosporium ; so it is conceivable that GH5 and GH74 enzymes secreted by P. carnosa catalyze additional cellulolytic activity (Appendix 3 Table S5.1). Five peptide sequences corresponding to GH61 endoglucanases were also identified in cellulose cultivations (two by both MS BLAST and BLASTp). The Cel61A enzyme from Aspergillus kawachii has endoglucanase activity, and so it is tempting to conclude that GH61 enzymes could play a role in cellulose degradation by P. carnosa . However, the biological function of GH61 enzymes remains unclear. Recent structural analyses of a GH61 enzyme could not unambiguously assign amino acid residues involved in catalysis and substrate binding (Karkehabadi et al. 2008).

Table 5.1. Analysis of peptides from extracellular filtrates of P. carnosa grown on cellulose and spruce that correspond to predicted cellulases and hemicellulases. Predicted GH Substrate MS BLAST Non-gapped Alignment Activity

Pchr P. carnosa Peptide Score b Pchr P. carnosa Peptide Pchr Sequence % ID a Sequence ID a Sequence Match ID CBH II GH6 Cellulose 133052 WGDWCNI 66 133052 WGDWCNIQ WGDWCNI 100 CBH I GH7 Cellulose 126964 DTDFFSQHGGI 321 126964 CTAGTGFCV CTSNTGFCD 75 GLCDADGCDFNSFR

NTGICDGDGCDFDS

YGTGYCDSQC Cellulose/ 127029 FGDTDFFSQHGGI c 540 127029 FGDTDFFSQHGG c FGDTNYFAQHGG 75 Spruce FVTGSNVG c TDNFCTQQQ c TDNFCSQQ 87

LMSDDSHYQMIQ c LMSDDSHYQM c LMADDTHYQM 80 c d GLCDADGCDFNSFR GLFGDGSVDFNSF GLCDADGCDFNSF 57 NTGICDGDGCDFDS c R TDNFCTQQ c c YGTGYCDSQC 137216 FGDTDFFSQHGGIQ c 461 137216 YGQGAGTQS c YGQQAGTQT 87 GTQTPETHP c GTQTPETHPSLS c GTQTAETHPQLT 75

GLCDADGCDFNSFR c c FVTGSNVG c NTGLCDGDGCDFDS YGTGYCDSQC c d VTQDTSVVIDGN GTQTPETHPSLSXQ d 137372 GLCDGDGCDFNSFR 520 137372 GLCDGDGCDFNSFR c GLCDGDGCDFNSF 100 87

Predicted GH Substrate MS BLAST Non-gapped Alignment Activity

Pchr P. carnosa Peptide Score b Pchr P. carnosa Peptide Pchr Sequence % ID a Sequence ID a Sequence Match ID R FVTGSNVG NTGLCDGDGCDFDS 78 Rc NTGLCDGDGCDFDS VDGDGCDFDS c 72 LMSDDSHYQMI FVTGSNV d FVTGSNV 100 FGDTDFFSQHGGI TDNFCTQQ YGTGYCDSQC Possible GH61 Cellulose 41650 QTDVTGLSWF 66 41650 QTDVTGLSWFQ QTDVTGLKWFK 81 Endo- 122129 DDTYTTSWAVDR 89 122129 DDTYTTSWAVDR DTYTTSWAVDR 91 glucanase 41563 QTDVTGLSWF 59

129325 VGPSGPTGE PSGPTG 100 Glyco- GH3 Cellulose 139063 ITFSVGAS 189 139063 ITFSVGAS ITFSVGAS 100 sidase / GLCLEDSPLG GLCLEDSPLG GLCLEDSPL 100 BGL HYINNEQE HYINNEQEFDR HYINNEQE 100 PIAQGSTT PIAEGSTT 87 GNIPAI GNIPAI 100

9257 IVCNPSADVV VCNASADPV 77 Spruce 133018 WEAEGFN WESEGFD 71

β-manno- GH2 Cellulose 135385 ITEPLLGINEFTQR 336 135385 ITEPLLGINEFTQR ITEPLLGINEFTQR 100 sidase SPVFIEESSLEI SPVFIEESSLEI SPIFIEESSLEIS 91

LESAVLSGQNMLR LESAVLSGQNMLR LESAVLSGQNMLR 100 88

Predicted GH Substrate MS BLAST Non-gapped Alignment Activity

Pchr P. carnosa Peptide Score b Pchr P. carnosa Peptide Pchr Sequence % ID a Sequence ID a Sequence Match ID TPGFIYGNTDR TPGFIYGNTDR TAGFIYGNSER 72 YSPPVIY YSPVVIY 85

Mannan- GH5 Cellulose 5115 EPTILGWELANE 82 5115 EPTILGWELANE EPTILAWELANE 91 ase 140501 DIAGAGSTTVR 126 140501 DIAGAGSTTVR DIANAGSTVVR 82

EPTILGWELANE EPTILGWELANE EPTIMAWELSNE 75

Xylanase GH10 Cellulose 125669 LPSTPALLQ 58 139732 DVCNEMQNEDGT 145

INDYNLDSANA

7852 VSVWGVSR VSVWGVS 100

a Cellulose/ 138345 GVDGIAIGF PAYDGIAIGF 77 Spruce PGAAGIAIGF a PAYDGIAIGF 70

ENIYNIEYA b INEYNIEYA 87

GH11 Cellulose 133788 TSDGSSYDVYE 70 133788 TSDGSSYDVYE TSDGATYDVYE 81

Endo- GH16 Cellulose 3846 YDNAYFEVR 59 3846 YDNAYFEVR YDEAYFEIR 77 1,3(4)-β- glucanase Arabino- GH43 Cellulose 4822 NDGAIEASVIW 78 4822 NDGAIEASVIWQ NNGAIEASVIW 90 furanosi- dase Cellulose/ 333 ITGGGGIGASNS a ITGTGGIGTS 80

Spruce DIVISGFAP b IISGFAP 85

Predicted GH Substrate MS BLAST Non-gapped Alignment Activity

Pchr P. carnosa Peptide Score b Pchr P. carnosa Peptide Pchr Sequence % ID a Sequence ID a Sequence Match ID NA Cellulose 3651 IAETLVEMG 133 3651 IAETLVEMG IAEALVEMG 88 PTMESSSVSAAFMT NLEGQTTATR NLGQTTATR 100

Spruce 38548 YENEVAIR 57 38548 YENEVAIR YENEVSIR 87

a Additional information for protein from P. chrysosporium can be accessed by ending the following URL with the model number, e.g. http://genome.jgi-psf.org/cgi- bin/dispGeneModel?db=Phchr1&id=6482 ; bAnnotation score from MS BLAST; c Detected in extracellular filtrates of cellulose cultivations; d Detected in extracellular filtrates of spruce cultivations. Figure S5.1 summarizes Table 5.1 as pie graphs.

Both MS BLAST and non-gapped alignments matched peptide sequences from P. carnosa to Pchr5115 and Pchr140501, which are predicted to encode mannanase activity and were detected in P. chrysosporium cultivations grown on Wood’s medium and spruce (Ravalason et al. 2008; Vanden Wymelenberg et al. 2009). Both methods also identified a GH2 mannosidase in P. carnosa cultures grown on cellulose. While a GH2 mannosidase was detected in extracellular filtrates of P. chrysosporium maintained on replete medium (Vanden Wymelenberg et al. 2009), GH2 enzymes have not been recovered by proteomic analyses of P. chrysosporium grown on cellulose or woody substrates.

Seven peptides sequences matching GH10 xylanases were detected in both cellulose and spruce cultivations. By contrast, only one peptide sequence from cellulose cultures was matched to a GH11 xylanase. Both MS BLAST and non-gapped alignments matched a similar number of peptide sequences to α-arabinofuranosidases, supporting other studies that emphasize the importance of debranching enzymes in the hydrolysis of lignocellulosic substrates (de Vries et al. 2000).

5.4.3 Oxidoreductases in Cellulose and Spruce Cultivations Peptides recovered from both cellulose and spruce cultivations were matched to Pchr4636 (Table 5.2), a manganese peroxidase (MnP) that was previously detected in P. chrysosporium cultivations grown on spruce (Ravalason et al. 2008). Non-gapped alignment also identified peptides from spruce cultivations that matched Pchr131707, a lignin peroxidase (LiP) that is encoded by a gene located on scaffold 19 of the P. chrysosporium genome (Appendix 3 Table 1). While the expression cellobiose dehydrogenease activity (CDH) was not detected, peptides from extracellular filtrates of P. carnosa grown on spruce were matched to a putative multicopper

oxidase and Cir1, a CBM-containing reductase that is related to CDH1 (Table 5.2, Appendix 3 Table 1). Multicopper oxidases are not implicated in lignin degradation by P. chrysosporium ; instead, they may function as extracellular ferroxidases with potential to modulate Fenton reactions (Kersten and Cullen 2007; Larrondo et al. 2003) . Notably, the majority of peptide matches to oxidoreductases were to enzymes thought to be involved in extracellular peroxide generation, including glyoxal oxidases and GMC oxidoreductases (Table 5.2). Further, a peptide sequence matching Pchr1923 was detected in culture filtrates of P. carnosa grown on cellulose. Pchr1923 shares over 70 % sequence identity to deduced amino acid sequence of glp2 , which encodes a glycopeptide implicated in the production of hydroxyl radicals involved in lignin and carbohydrate degradation (Baldrian and Valaskova 2008; Tanaka et al. 2007).

Peptides corresponding to cytochrome P450s and monooxygenases were also detected in both cellulose and spruce cultivations (Table 5.2, Appendix 3 Table 1). Notably, Pchr37971, Pchr663, Pchr38849 and Pchr139445 are CYP2E-type enzymes, a class of P450 monooxygenases that participate in the bioconversion of exogenous aromatic compounds. Although only a few peptides corresponding to monooxygenase activity were detected in spruce cultivations, the dependence of monooxygenase expression on biomass feedstock composition will be interesting to pursue given the potential of this enzyme family to degrade oleoresins in softwood.

Table 5.2. Analysis of peptides from extracellular filtrates of P. carnosa grown on cellulose and spruce that correspond to extracellular oxidases and proteins involved in lignin degradation. Predicted Substrate MS BLAST Non-gapped Alignment Activity Pchr P. carnosa Peptide Score b Pchr P. carnosa Peptide Pchr Sequence % ID ID a Sequence ID a Sequence Match Monooxygenase Cellulose 37971 EEQSAVTEEIN 58 P450 663 GFIFEMIGWR IFERIGW 85

38849 QTDVSAISWFQ DVSPISWF 87 139445 IFITVA IFITVA 100 138612 IVIGDGLPD IVIGDGL 100

Spruce 5081 SVGSHVATE GSHVAT 100 GMC Cellulose 6270 GGSSSIDGAAW 71 6270 GWGWSGER GWGWSG 100 AFIATCQ AFIATC 100

WVVIVGHTA VIVGGGTA 66

11098 ATYLQTAL ATYLQTAL 100 Spruce 9508 RETAHTQATF AHTQAT 100 6017 GIVGGGTSGS IVGGGTSG 100 c Cellulose/Spruce 37188 VGTAATDADIVA 183 37188 VGTAATDADIV VGTAESDAEIVAA 72

c GTAATDADIVAA GTAATDADIVAA VGGGTAGNVVA 75

c VGGGGTAGNIVA PVGTAATDADIVA 75

d VGTAATDADIVAA 67 83 SSVGGGGTAGNIVA c PVGTAATDADIVAA d 76 Glyoxal oxidase Cellulose 8882 IVAATSSTH 311 8882 TVAATSSTH VAATSSTH 100 VAATSSTHGN VAATSSTHGNPA VAATSSTHGN 100

ACYVDNA ACYVDNAD ACYVDNA 100 IGGWSLQSTQGVR

VGYYNEAR

Predicted Substrate MS BLAST Non-gapped Alignment Activity Pchr P. carnosa Peptide Score b Pchr P. carnosa Peptide Pchr Sequence % ID ID a Sequence ID a Sequence Match Cellulose/ 11068 PANSFEFF c 63 11068 PANSFEFF PANSFEFF 100 Spruce TFTAPPD c,d TFTAPP 85 Peroxidase Cellulose/ 4636 LQSDFALAR c,d 61 4636 QPATFIAADGPQ d PATFPATKGPQ 72 Spruce Spruce 36058 SGRSHAVAI GRSHAV 100 Multicopper Spruce 132237 LDIPQAIID 61 132237 LDIPQAIIDY LDIPQAIVD 88 oxidase Glycopeptides Cellulose 1932 TVTFVNNCGFGT 77 1932 VTFVDNCGFGT TVTFVDICGFGT a Additional information for protein from P. chrysosporium can be accessed by ending the following URL with the model number, e.g. http://genome.jgi-psf.org/cgi-bin/dispGeneModel?db=Phchr1&id=37971; bAnnotation score from MS BLAST; c Detected in extracellular filtrates of cellulose cultivations; d Detected in extracellular filtrates of spruce cultivations.

5.4.4 Carbohydrate Esterases, Proteases and Other Glycoside Hydrolases Approximately 10 % of the peptide sequences identified from P. carnosa culture filtrates were matched to predicted esterases in P. chrysosporium . Lipase and esterase enzymes typically demonstrate broad substrate specificity, and have been implicated in the hydrolysis of lignin- carbohydrate complexes. For instance, glucuronoyl esterases were recently identified and shown to hydrolyze linkages between lignin and glucuonate branching sugars of xylan (Spanikova et al. 2007). Both MS BLAST and non-gapped alignment identified peptides from cellulose and spruce cultivations that matched Pchr6482, a predicted glucuronoyl esterase from P. chrysosporium . Non-gapped alignment identified three additional sequences from cellulose cultivations with greater than 75 % identity to Pchr130517, a second putative glucuronoyl esterase encoded by P. chrysosporium . Notably, in a separate study, a full-length cDNA transcript isolated from P. carnosa was predicted to encode a protein that shares over 50 % and 80 % sequence identity to Pchr130517 and Pchr6482, respectively (data not shown).

Peptide sequences that were matched to proteolytic activities accounted for 25 % and 40 % of the peptides analyzed from cellulose and spruce cultures, respectively (Fig. 5.1). Secreted proteases could participate in nitrogen cycling and acquisition through hydrolysis of fungal proteins, as well as plant proteins that are bound to lignocellulose. The relative susceptibility of lignocellulose degrading enzymes to secreted fungal proteases remains to be elucidated.

In both cellulose and spruce cultivations, the majority of peptide sequences annotated to extracellular glycoside hydrolases were matched to enzymes involved in cellulose and hemicellulose degradation. Still, extracellular filtrates of P.carnosa grown on cellulose and spruce also contained several peptides that were matched to amylases involved in starch degradation (Table 5.3). MS BLAST and non-gapped alignment also identified peptides sequences corresponding to enzymes involved in pectin degradation, namely a CE8 pectin esterase and a GH28 pectinase, respectively (Table 5.3, Appendix 3, Table S5.1).

Similar to P. chrysosporium , P. carnosa appears to secrete a GH88 candidate d-4,5-unsaturated β-glycosidase during growth on cellulose and spruce (Ravalason et al. 2008; Vanden Wymelenberg et al. 2009) (Appendix 3, Table S5.1). GH88 d-4,5-unsaturated β-glycosidases 95

have been implicated in the degradation of unsaturated oligosaccharides, likely produced by polysaccharide lyases (Nankai et al. 1999). Notably, peptides corresponding to a PL14 lyase were also detected in cellulose cultivations (Table 5.3). Eight unique peptides from P.carnosa culture filtrates were matched to GH92 α-1,2-mannosidases; only one peptide sequence was matched to a GH47 α-mannosidase. While GH47 α-mannosidases are thought to participate in post-translational modification of secreted proteins (Yoshida et al. 1993), most GH92 α-1,2- mannosidases that have been identified and characterized to date are of bacterial origin. Nevertheless, P. chrysosporium is predicted to encode four GH92 enzymes, and the Pchr1930 and Pchr133585 protein matches identified in my study correspond to genes that are upregulated in P. chrysosporium during growth on cellulose (Vanden Wymelenberg et al. 2009).

Table 5.3. Analysis of peptides from extracellular filtrates of P. carnosa grown on cellulose and spruce that correspond to extracellular carbohydrate-active enzymes .

Predicted GH Substrate MS BLAST Non-gapped Alignment Activity Family Pchr ID a P. carnosa Score Pchr P. carnosa Pchr Sequence % Peptide Sequence b ID a Peptide Sequence Match ID exo-1,3- GH55 Cellulose 8072 NPDGFADTITA 103 8072 NPDGFADTITA NPNGFADTITAW 92 glucanase WTR W

α-amylase GH13 Cellulose 38357 GQTSDQALI 56 38357 GQTSDQALIQ GQTSDQSLI 88 ASDWQGR ASDWQSR 85

Spruce 130190 HDIDVLIDA 56 130190 HDIDVLIDAEQF HGIDVLIDA 89

127674 VEADINGIR VEAEIDGER 66

GH15 Cellulose 138813 IDQYTSGQDTS 62 138813 IDQYTSGQDTS VDQYAAGQDTS 72 GH31 Cellulose 35408 EQYTDVI 169 35408 EQYTDVI EQYTDVI 100 GHWLGDNY

LETMWTDID

125462 DGGVGTIQTM 233 125462 DGGVGTIQTMW DGGVGTVQTMWA 92 WAR AR R GHWLGDNY IYGLGEVVASSG IYGLGEVVASSG 100 IYGLGEVVASS GDDGGFGTIQTF IGTDGGVGTVQTM 69 G W W PEQGAISQQSN EQGAISQ 87 968 TVDPDYF 186 968 TVDPDYF TVDPDYF 100

VTYETNTR VTYETNTR VTYETNTR 100

LETMWTDI SLETMWTDIDY LETMWTDI 100 135833 GHWLGDNY 185

Predicted GH Substrate MS BLAST Non-gapped Alignment Activity Family GTIQTMWAR IYGLGEVVASS G Chitinase GH18 Cellulose 39872 LSIGGWTGAR 63 39872 LSIGGWTGAR LSIGGWTGGR 88 128098 FIAEQGLR 58 128098 FIAEQGLR FIAEQGLR 100 β-galactosidase GH35 Cellulose 9466 NPDTGAQFVIV 272 9466 DTGAQFVIVR NPDTGAQFIIVR 90 R INEGGLFGER GAQFVIVR NEGGLFGER 83 IFVDGWQY NPDEGAQFVIVR 83

LADYTFGSPA INEGGLFGER 100

NPDTGAQFVNV 91

NPDTGAQFVIVR 91

IFVDGWQY 85 DTGAQF DTGAQF 100 Spruce 134404 GITGAGGTVI GITGAG 100 α-mannosidasae GH47 Cellulose 4550 MSVAWVGS 62 4550 MSVAWVGS MSVAWVGS 100 SAIQSFQQ SAIQSFQK 87

GH92 cellulose 1930 VGIDVDEVPR 65 1930 VGIDVDEVPR VGIDVDETPR 90

133585 EYAFEDFAIR 80 133585 EYAIGDFAVR EYAFEDFAIR 70

EYAFEDFAIR 100

35714 FNAGTGFMEAR 200

SFISVDQAR

YNDYAVSV

3431 AFDQWDELL 234

EYAFEDFAI

ISPADYTDAN

YANQPGLSTQ

Predicted GH Substrate MS BLAST Non-gapped Alignment Activity Family Glucuronoyl- CE15 Cellulose 6482 MAGAFEER 120 6482 VIEVTPAAHV VIEVTPAAHV 100 esterase IEVTPAAHV MAGAFEER MAGAFEER 100

130517 WVWGVSR WAWGVSR 85

PFIFNDGT PFVFNDGS 75 Spruce 6482 MAGAFEER 62 Pectin esterase CE8 cellulose 132137 AYFYGNTIAT 59 Polysaccharide PL14 cellulose 964 YTAYFPASFD 57 lyase bAnnotation score from MS BLAST; c Detected in extracellular filtrates of cellulose cultivations; d Detected in extracellular filtrates of spruce cultivations.

5.4.5 Peptides Annotated to Proteins with Unknown Function, Identified in Proteomic Analyses of P. chrysosporium An important outcome of proteomic analysis is the detection of proteins with unknown function whose expression can be correlated to specific cultivation conditions. Seven unique peptide sequences from cellulose cultivations were assigned to six P. chrysosporium proteins that have predicted signal sequences but unknown function (Table 5.4). Matches to Pchr3328 and Pchr138739 were to proteins previously identified in culture supernatant of P. chrysosporium grown on spruce and cellulose (Vanden Wymelenberg et al. 2006; Ravalason et al. 2008; Vanden Wymelenberg et al. 2009). As noted by Vanden Wymelenberg et al. (2006), Pchr138739 shares homology to proteins also found in filamentous Ascomycetes. Further, the carboxy-terminus of Pchr138739 contains a series of proline residues and is rich in serine and threonine, reminiscent of linker sequences that connect catalytic domains and carbohydrate binding modules.

Table 5.4. Peptides from extracellular filtrates of P. carnosa grown on cellulose that were annotated to proteins from P. chrysosporium with unknown function.

Pchr ID a P. carnosa Peptide Sequence Score b 5038 SNIEDLTIR 57 4562 IPGAVQVYGIES 61 3328 ISDTDFSQR 126 QVIEWTDID 3053 TIIDGDDFT 56 1934 EDDVPSMSA 57 138739 NPADTSETDGIR 69

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5.5 Discussion Proteomic analyses of wood-degrading microorganisms reveal a rich repertoire of biomass- degrading proteins, many of which are yet to be characterized. In addition to identifying new families of carbohydrate-active enzymes, correlating substrate composition to profile of secreted proteins can inform the design of enzyme formulations that are most suitable for processing specific biomass feedstocks. Boreal forests represent one of the largest biomes on Earth, and bioprocessing softwood remains a significant challenge. Accordingly, the aim of this study was to characterize the secretome of P.carnosa grown on spruce and microcrystalline cellulose to i) uncover potential adaptations that allow this white-rot fungus to efficiently grow on softwood feedstocks, and ii) evaluate substrate-dependent protein secretion by this organism. Given the phylogenetic similarity of P. carnosa and P. chrysosporium , the genome sequence of P. chrysosporium could be used to annotate sequenced peptides from P.carnosa ; comparative proteomics was also enabled.

A comparison of peptide annotations from both cellulose and spruce cultivations of P. carnosa to proteomic data for P. chrysosporium reveals their potential to secrete similar families of cellulolytic and lignin-degrading enzymes. For example, cellobiohydrolases, endoglucanases, and β-glycosidases were identified in P. carnosa secretomes, as were peptide sequences corresponding oxidative enzymes involved in lignin degradation. The relative abundance of GH7 peptide sequences compared to GH6 peptide sequences that were identified in the P. carnosa secretome suggests that like P. chrysosporium , GH7 isozymes are more highly represented in P. carnosa than GH6. Also similar to P. chrysosporium , xylanolytic enzymes, including GH10 and GH11 xylanases, debranching hydrolases, and glucuronoyl esterases were identified in extracellular filtrates of P.carnosa cultivations. Notably, while GH2 β- mannosidases have not been detected in extracellular filtrates of P. chrysosporium grown on cellulosic substrates, a GH2 β-mannosidase was predicted in extracellular filtrates of P. carnosa grown on cellulose and spruce. Since mannan is the main hemicellulose in softwood fibre, this result raises the question whether up-regulation of mannan-degrading enzymes could contribute to preferential growth of P. carnosa on softwood.

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P. carnosa peptide sequences were also matched to a multicopper oxidase, Cir1, and two distinct glycopeptides. These proteins have been implicated in lignocellulose degradation, but have not been identified in previous proteomic analyses of P. chrysosporium . Cir1 and certain membrane- bound glycopeptides are likely involved in generating hydroxyl radicals that participate in carbohydrate and lignin degradation. Given the lack of peptide sequences from P. carnosa extracellular filtrates that were matched to exclusively endo-acting cellulases, an intriguing possibility is that oxidative enzymes play a significant role in cellulose degradation by P. carnosa .

The range of predicted glycoside hydrolases detected in the culture medium of P. carnosa grown on spruce was represented in the culture medium of P. carnosa grown on cellulose. These results are consistent with those reported by Sato et al. (2007) and Vanden Wymelenberg et al. (2005) who detect similar profiles of cellulases and hemicellulases in culture supernatants of P. chrysosporium grown on cellulose and wood substrates. Moreover, these results support the possibility that like P. chrysosporium , P. carnosa optimizes its growth on different lignocellulosic substrates by modifying the relative abundance of a similar pool of glycoside hydrolases and oxidative enzymes. Transcriptional analyses are currently underway to test this hypothesis. Quantitative transcription profiles will also complement proteomic analyses by narrowing the set of proteins with unknown function that are most important to lignocellulose degradation. Still, 1229 peptides were unique to spruce cultivations and could not be annotated based on identity to predicted proteins from P. chrysopsorium . Many of these will correspond to intracellular proteins that are detected in culture supernatants due to cell lysis. However, some will likely correspond to secreted or intracellular proteins involved in the hydrolysis or metabolism of softwood components that enable the growth of P. carnosa on this feedstock. For comparison, MS BLAST was used to align peptide sequences from P. carnosa to P. placenta protein models. This analysis did not lead to the identification of additional GH families or lignin-degrading enzymes in extracellular filtrates of P. carnosa grown on cellulose or spruce. Notably, peptides from cellulose cultivations were most frequently matched to predicted GH31 enzymes in P. placenta , while peptides from spruce cultivations were most frequently matched to predicted proteases. While the number of peptide matches to a particular protein family provides some indication of the relative abundance of that family in culture supernatants,

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differences in digestibility by trypsin, and subsequent ionization and detection by LC/MS limits quantificative comparisons of proteins families detected in the current analysis.

In summary, proteomic analysis of extracellular filtrates of P. carnosa grown on cellulose and spruce revealed a similar range of glycoside hydrolases and oxidative activities. The distribution of cellulases and xylanases predicted in extracellular filtrates of P. carnosa were also similar to those identified in cultures of P. chrysosporium . However, the profile of secreted proteins detected in cultures of P. carnosa could be distinguished by the prediction of mannan-degrading enzymes and oxidative enzymes that were not previously identified in proteomic analysis of P. chrysosporium .

Clearly, the similarity of P. carnosa and P. chrysoporium was sufficient to apply a homology driven proteomic approach to identify a core set of carbohydrate-active and lignin-degrading enzymes that are secreted by P. carnosa grown on cellulosic substrates. However, in December, 2010, the P. carnosa genome sequence was completed and annotated using automated algorithms, and so the proteomic data collected for P. carnosa was re-analyzed to determine whether additional carbohydrate-active or lignin-degrading enzymes could be identified. Through this analysis, additional 116 and 11 peptides that were predicted to originate from lignocellulose-active enzymes were annotated from cellulose and spruce samples, respectively (Appendix 3, Tables S5.2 and S5.3). Interestingly, new assignments that corresponded to lignin- degrading activities were comparatively high in peptide samples originating from spruce cultures. Is is anticipated that proteins identified using the P.carnosa genome but not by comparison to P. chrysosoporium , comprise a starting point for the discovery of novel enzymes that could accelerate the performance of a core set of lignocellulose-degrading enzymes on softwood feedstocks.

Chapter 5 References

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Chapter 6: Synthesis and Conclusions

Four hypotheses were primary addressed through this study: 1. Cultivation conditions that promote the expression of lignocellulytic enzymes in P. chrysosporium will also induce the expression of lignocellulytic enzymes in P. carnosa . 2. P. carnosa will exhibit selective decay of lignin and the extent of this decay will depend on wood fibre composition. 3. Because of its isolation from softwood species, P. carnosa will efficiently degrade guaiacyl lignin moiety, which dominates softwood lignin. 4. The extracellular protein activities secreted by P. carnosa to degrade various lignocellulosic feedbstocks will differ depending on the particular chemistry of the lignocellulosic substrate.

Figure 6.1 describes schematically the synthesis of information flow between the three research objectives.

To characterize the impact of P.carnosa growth on various lignocellulose substrates and to understand the mode of lignocellulose decay adopted by P. carnosa, there was a need to initially establish the growth conditions for P. carnosa . Through a series of experiments, it was established that P. carnosa grows well at 27˚C and is capable of lignocellulolytic activity expression under nitrogen limiting conditions. These experimental conditions were then used to investigate the impact of P. carnosa growth on various wood fibres, specifically to confirm that P.carnosa decays softwood and if so, then how.

Through FTIR and ToF-SIMS investigations, it was validated that P. carnosa initiates softwood decay through targeting G-lignin. In a recent transcriptomic analyses , the ability of P. carnosa to degrade softwood was explained by the relatively high number of MnP genes than LiP genes in P. carnosa as compared to P. chrysosporium (which is essentially a hardwood degrading white- rot fungus) (MacDonald et al. 2011). The biochemical assays in Chapter 3 also imply similar results, where significant MnP activity but no LiP activity was detected on various P. carnosa cultivations on cellulose.

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Limitations associated with activity-based enzyme discovery particularly in culture supernatants that contain multiple enzymes with similar activity, was discussed in Chapter 3. Accordingly, a proteomic method was used to compare the profiles of enzymes secreted by P. carnosa during growth on cellulose and spruce wood fibre. Notably, differences in the profile of enzymes secreted by P. carnosa grown on cellulose and spruce were subtler than initially hypothesized. In particular, activities detected in spruce cultivations were also identified in cellulose cultivations, and the profile of enzymes secreted by P. carnosa grown on cellulose was more complex than that secreted by P. carnosa grown on spurce. This result implies that P. carnosa might regulate the relative abundances of a similar set of lignocellulose-active enzymes rather than the presense of particular activities.

Chapter 3: Growth on Chapter 4: Growth on coniferous wood microcrystalline cellulose Optimal growth Optimal temperature: 27 °C conditions G-lignin is degraded. used P. carnosa Cellulolytic activity is Growth of on balsam fir, lodgepole expressed. pine and white spruce reveals similar MnP activity is expressed. degradation patterns. P.carnosa LiP activity is not detected. selectively degrades lignin prior to wood polysaccharides.

Chapter 5: Comparative proteomic analysis of proteins Optimal growth secreted by P.carnosa grown on cellulose and spruce conditions used Proteins secreted during growth on spruce were also detected in cellulose cultivations. Cellulase enzymes appear to be more abundant in culture supernatant than β- glucosidases. Glyoxal oxidases appear to be more abundant than lignin-degrading peroxidases. An additional 127 peptides were annonated to predicted CAZymes or FOLymes when the P.carnosa genome was used in the analysis.

Figure 6.1 Conclusions and information flow between the three key objectives of this thesis.

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P. chrysosporium , which is a model white-rot fungus, was used as a benchmark for results obtain through studies using P. carnosa . Cultivation conditions published for P. chrysosporium were used to define the initial experimental conditions for P. carnosa (Chapter 3) and the genome sequence for P. chrysosporium was used to assist with the protein annotation based on sequence similarity (Chapter 5). While P. carnosa selectively degrades lignin like certain strains of P. chrysosporium, these organisms differed in terms of optimal growth conditions and expression of lignin-degrading and carbohydrate-active enzymes. For example, the optimal growth temperature of P. carnosa is 27˚C, while that of P. chrysosporium is 37˚C. Unlike P. chrysosporium, P. carnosa’s lignocellulolytic enzyme expression is not severely hampered by high nitrogen and/or shaking cultivation conditions. P. carnosa is also distinguished by its ability to effectively degrade softwood lignin. In addition to lignin degradation, the proteomic analyses also suggest that P. carnosa was actively degrading softwood hemicelluloses. Softwood lignin and hemicellulose degradation by P. carnosa was supported by the detection of mannosidase and lignin-degading activities in the secretome of P.carnosa (Chapter 5), which has previously not been reported for P. chrysosporium.

To conclude, P. carnosa has been established as a softwood degrading white-rot fungus, which is capable of selectively degrading softwood-lignin. The following list summarizes recommendations for further research on the lignocellulolytic activity of P. carnosa and wood- degrading basiodiomycetes in general: 1. Systematic investigation of the effect of growth conditions (rpm, pH, oligosaccharide addition) on P. carnosa growth and lignocellulolytic activity. 2. Annotation of proteins with unknown function, identified through proteomic analysis of the secretome of P. carnosa on spruce and micro-crystalline cellulose. 3. Differential proteomics for extracellular enzymes expressed by P.carnosa on other simple substrates and hardwood species to determine if the conclusion of non-substrate specific protein expression can be extended. 4. Investigation of changes in fibre chemistry for hardwood species when degraded by P. carnosa to determine explicitly the differences between conventional white-rot degrading fungi isolated from hardwoods and P.carnosa.

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Chapter 7: Engineering Relevance Growing environmental concerns are requiring that more industrial sectors adopt eco-friendly processes. Further, with increasing competition from industries located in the Southern hemisphere, North American and European forest industries are realizing the need to diversify the range of products that can be derived from wood fibre. Accordingly, there is an opportunity for technology that harnesses the full potential of biomass for the production of fuels and other biodegradable materials.

Enzymes are renewable biocatalysts, and several enzymes are already being used by forest industries to reduce chemical consumption. For instance, approximately 10% of North American pulp and paper industries use microbial xylanases to boost fibre bleaching processes (Kenealy and Jeffries 2003; Subramaniyan and Prema 2002). Laccases and manganese peroxidases have also shown promise for improving pulp bleaching; cellulases have been used during fibre pulping and to recover fibre from recycled paper products, whereas lipases and pectinases have been used to remove pitch deposited on paper manufacturing equipment (Bajpai 2004; Kenealy and Jeffries 2003). The synergistic action of glycosyl hydrolases, including endo and exo- glucanases and glucosidases have been used to transform cellulosic material to glucose which can be fermented for production of bioethanol. Enzymes have also been used to alter the functionality of wood polymers. For example, Gustavsson et al. (2005) used a lipase from Candida antartica to link chemically modified oligosaccharides to xyloglucan (Gustavsson et al. 2005). The modified xyloglucans hydrogen bond with cellulose, thereby functionalizing cellulose microfibrils. In this way, chemically reactive and bioactive side groups were grafted to filter paper.

Despite their potential as industrial catalysts, associated costs have limited the utility of enzymes for production of commodities. These costs can be attributed to enzyme production, typically low availability of enzymes to insoluble substrates like plant fibre, and the variable composition of plant biomass. In particular, the distinct composition and structure of lignocellulosic substrates contained in plant fibre presents unique challenges for bioconversion technologies. It is possible that the efficiency of bioconversion technologies could be improved by tailoring enzyme applications to particular lignocellulosic feedstocks. One approach is to mimic the profiles of 110

enzymes secreted by fungi grown on different lignocellulosic substrates. Accordingly, it is anticipated that the proteomic analysis performed as part of the current thesis, will help to formulate ideal enzyme mixtures for softwood conversion. For instance, the various ligninolytic enzymes expressed by P. carnosa on the softwoods can be eventually purified for pre-treatment of softwood fibre used to generate mechanical pulp or otherwise sugars for subsequent fermentation. Alternatively, since P. carnosa has demonstrated ability to selectively remove lignin from multiple softwood species, this organism could be used for in-situ conversion of softwood wastes to fermentable sugars for fuels or chemical production.

In addition to potential industrial relevance, contributions to method development that have broader scientific relevance include:

1. Proteomic Analysis of Uncharacterized Microorganisms: The methodology established for differential proteomic analysis of P. carnosa can be transferred to other investigations where the genome sequence for the organism is not available. In addition, it can also be applied towards metagenomic studies, where microbial consortia are being studied using incomplete genomic data. 2. Analytical Techniques for Fibre Characterization: The method developed in this study for investigating fibre properties of wood chips (as opposed to the commonly used wood powder) through FTIR and ToF-SIMS makes a significant contribution in monitoring changes in fibre chemistry. This becomes especially relevant since most industrial processes deal with wood chips. In addition, the method development for ToF- SIMS using un-embedded samples will also contribute tremendously for ToF-SIMS based fibre analysis, while preventing unwanted interference with embedding medium. Also through this thesis, the use of ToF-SIMS for both spectral and spatial analysis of degraded wood fibre was established. These results have been validated with those from FTIR and TEM. The use of ToF-SIMS as a single method to obtain information regarding changes in fibre chemistry and localization of cell wall constituents, will improve the efficiency and ease of analyzing treated wood fibre.

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Chapter 7 References

Bajpai P (2004) Biological Bleaching of Chemical Pulps. Critical Reviews in Biotechnology 24 (1):1-58

Gustavsson MT, Persson PV, Iversen T, Martinelle M, Hult K, Teeri TT, Brumer H (2005) Modification of Cellulose Fiber Surfaces by Use of a Lipase and a Xyloglucan Endotransglycosylase. Biomacromolecules 6 (1):196-203.

Kenealy WR, Jeffries TW (2003) Wood deterioration and preservation: advances in our changing world. In: Society AC (ed), Washington, DC, Oxford University Press, pp 210- 239

Subramaniyan S, Prema P (2002) Biotechnology of microbial xylanases: enzymology, molecular biology, and application. Critical Reviews in Biotechnology 22 (1):33-64

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Appendix 1: B3 Medium

10X Buffer Recipe (per 1L) 20 g KH 2PO 4 5g MgSO 4-7H 2O 1g CaCl 2-2H 2O pH to 4.5 add milliQ water up to 1L autoclave for 30 min

Mineral Elixir (per 1L): 1.5 g nitrilotriacetate 0.5 g MnSO 4 1g NaCl 100 mg FeSO 4-7H 2O 100mg CoSO 4 (= 0.645 mM ≡ 180 mg CoSO 4-7H 2O 100 mg ZnSO 4 (= 0.619 mM ≡ 178 mg ZnSO 4-7H 2O 10 mg CuSO 4-5H 2O 10mg AlK(SO 4)2 10 mg H 3BO 3 10mg NaMoO 4 autoclave 30 min

Vitamins 10 mg/ml thiamine HCl filter sterilize

Nitrogen Source 1g/10ml ammonium tartrate filter sterilize

Carbon Source 0.5 g/ml selected carbon source (if not wood chips), ρglycerol = 1.27 g/ml autoclave 30 min

Other 0.73 g/50 ml 2,2-dimethylsuccinic acid (added as a filter sterilized solution adjusted to pH 4.5 using NaOH)

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Medium Preparation (1L in Sterilized Bottle containing specified volume of milliQ water)

Component Volume (no Volume (no Volume Volume Carbon/ low Carbon/ high (Carbon/ low (Carbon/ high Nitrogen) Nitrogen) Nitrogen) Nitrogen) Buffer 100 100 100 100 Mineral 10 10 10 10 Vitamins 50 µl 50 µl 50 µl 50 µl 2,2- 50 50 50 50 dimethylsuccinic acid Carbon Source 0 0 20 20 Nitrogen Source 2 20 2 20 MilliQ Water 838 820 818 800

References: 1.Kenealy and Dietrich. 2004. Enzyme and Microbial Technology. 34:490-498. 2. Kirk et al. 1978. Arch. Microbiol. 117:277-285.

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Appendix 2: Supplemental Information for Chapter 4

70 60 50 40 30 20 10 0 2 cm 6 cm 8 cm Growth Diameter

Figure S4.1 P. carnosa cultivation on wood samples. Number of days required to cultivate P. carnosa on to specified growth diameters on fir (black bars), pine (grey bars) and spruce (white bars). n=3, error bars indicate standard deviations.

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Figure S4.2. Mean baseline corrected FTIR spectra of nine standard materials, normalized to highest intensity peak. From top to bottom: beech xylan (dark cyan), xyloglucan (orange), pectin (grey), mannose (magenta), lignin (purple), glucomannan (blue), galactomannan (green), cellulose (red), arabinogalactan (black). Dashed vertical lines indicate selected wavelengths significant to PCA grouping of control and decayed samples (see Figs. 4.1-4.3 and Table 4.1)

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Fig. S4.3 Images of scores for ToF-SIMS analyses of Spruce at time point 1 (left) and 5 (right) resulting from the PCA model built on pine (Fig. 4. 5). Image dimensions are in pixels where 50 pixels equal 7.9 microns

Fig. S4.4 Images of scores for ToF-SIMS analyses of Fir at time point 1 (left) and 4 (right) resulting from the PCA model built on pine (Fig. 5) Image dimensions are in pixels where 50 pixels equal 7.8 microns (left) and 9.7 microns (right)

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Table S4.1. The Ratio of Bromine Concentration in the Middle Lamella to Cell Wall in Spruce Samples Analyzed by TEM-EDXA Ratio of Time point 1 Time point 5 1. cell wall: middle lamella 1.63 1.38 1.25 2.42 1.33 1.33 2. cell corner: middle lamella 1.88 2.25 1.39 1.23 1.23 0.852

* t-test was used to determine the statistical significance; p-value = 0.6590 (1) and 0.4876 (2)

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Appendix 3: Supplemental Information for Chapter 5

(A) (B)

Figure S5.1 Distribution of peptide sequences corresponding to GH families for cultivations of (A) Cellulose (B) Spruce

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Table S5.1. Non-gapped alignment of peptides from extracellular filtrates of P. carnosa grown on cellulose and spruce to probable lignocellulose-degrading enzymes from P. chrysosporium . Predicted Activity GH Family Substrate Pchr ID a P. carnosa Peptide Sequence Pchr Sequence Match % ID

Glycoside hydrolase GH5 Cellulose 3985 IIIEQGSTAV EQGSTA 100 5773 NTGTQTVIFQ TGTPTIIF 75 138313 YNGGAGAPSE YSGGSGAP 75 5607 QITGIGASF VITGIGASG 100

38870 ASTGQIEM TGQIEM 100

126220 PNQFASFI QYASFI 71 Spruce 137948 AENFEVTIQ ENFGVTI 85 ASGGSSAGVSS ASGGSSSG 87 GH74 Cellulose/S 138266 EADSISGAAGAb DSTSGAAGA 88 pruce MGGVGDNGGV c VGDNGG 100

α-D-galactosidase GH27 Cellulose 134001 FMIGSGNSP FMINTGNS 75 GH28 Cellulose 4449 NSFAQNGAR AQNGAR 100 Polygalacturonase Spruce 3805 LSVNSGAER LTVNSGAER 100 Putative chitinase Cellulose/ 444 GGMMTSGSGG b GSMMTYGSG 77 Spruce MPSIIAPTQG b IVAPTQG 85 EGAGTSGSGAT c TSASAT 87 d-4,5 unsaturated - GH88 Cellulose 1106 VNFDDATIAQ VGFNDATIA 77 glucuronyl hydrolase IADQSFAQAAR SFAQAAR 100 Spruce 840 MFIDNDXADGIFN DKNDGI 67

CBM, low similarity to NA Cellulose 131440 ADEDPNYHQY AVEDPNFHQY 87 glycoside hydrolases Monooxygenase Cellulose 1321 EGSGPQEATE SGPQEST 85 2191 QISAFV QISAFV 100 QISAFVDDV 100 120

2613 SHFTID SHFTID 100 8209 MFGAGPGGGI THFGAGPG 100 Spruce 140125 IYAGNNGIGG YDKNDGIGG 70 8208 SPTAINSR SPTAIN 100 Peroxidase LiP Cellulose 131707 SMSAANAVQT SLSAANAV 87 Catalase Cellulose 128306 YGGEMGAAE VGGEMGSAD 75 Cir1 Spruce 147 SDPADVNS SDPADVDS 87 Copper amine oxidase Cellulose 4526 QGSVSFAGGH SVSFAG 100 GFIQQGITR IQQKITR 85 Spruce 36058 SGRSHAVAI GRSHAV 100 a Additional information for protein from P. chrysosporium can be accessed by ending the following URL with the model number, e.g. http://genome.jgi-psf.org/cgi-bin/dispGeneModel?db=Phchr1&id=3985; bDetected in extracellular filtrates of cellulose cultivations; cDetected in extracellular filtrates of spruce cultivations.

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Table S5.2. Additional peptides from extracellular filtrates of P. carnosa grown on cellulose that were matched to CAZymes and oxidoreductases using the P. carnosa genome sequence.

P. carnosa Family Predicted Activity Peptide Protein ID 259050 GH2 β-galactosidase, β-mannosidase IESAVISGQNMIR ITEPIIGINEFTQR PADTSEPT SPVFIEESSIEI TPGFIYGNTDR 262713 GH3 β-glucosidase FDSPAEQPFR IVCNPSAD 266084 GH3 β-glucosidase GICIEDSPIG HYINNEQE 249799 GH3 β-glucosidase ITFSVGAS 247802 GH5 endoglucanase QGVVNAVR 248589 GH5 endoglucanase DIAGAGSTTVR EPTIIGWEIANE 255866 GH6 cellobiohydrolase WGDWCNI 264060 GH7 cellobiohydrolase GICDADGCDFNSFR 264426 GH7 cellobiohydrolase FGDTDFFSQHGGI FVTGSNVG GTQTPETHPSI IGAGITVDT NTGICDGDGCDFDS TDNFCTQQ TSSGTAITI YGTGYCDSQC 258037 GH7 cellobiohydrolase IMSDDSHYQM 252890 GH10 xylanase IPSTPAIIQ 249059 GH10 xylanase DVCNEMQNEDGT INDYNIDSANA TSSSSGIDA 258624 GH11 xylanase TSDGSSYDVYE 261445 GH13 amylase ASDWQGR GQTSDQAII YQFITDR 261990 GH15 amylase IDQYTSGQDTS 247750 GH15 amylase DPFIFNDGT MAGAFEER VIEVTPAAHV WVWGVSR 143000 GH16 xyloglucanase VYETGGGII 265415 GH16 xyloglucanase YDNAYFEV 61816 GH18 chitinase ISIGGWTGAR 122

254594 GH18 chitinase FIAEQGIR 246104 GH31 α-glucosidase, α-xylosidase DGSAIIIHA EQYTDVI GDDGGFGTIQTFW IENSGTYEVE IETMWTDID 246124 GH31 α-glucosidase, α-xylosidase GHWIGDNY GTIQTMWAR FAGAGAHV TVDPDYF VTYETNTR 95998 GH35 β-galactosidase IADYTFGSPAHS NPDTGAQFVIVR NVITVVQDHM YDYGSTIR 257303 GH35 β-galactosidase DTGAQFVIVR IADYTFGAVIHS IFVDGWQY INEGGIFGER 214333 GH37 α-trehalase IIDIFWDS 192563 GH43 α-L-arabinofuranosidase NDGAIEASVIW 256205 GH43 α-L-arabinofuranosidase, galactan 1,3- YTSTDIVNWSR β-galactosidase 252166 GH47 α-mannosidase FIESYSAY GGAISAYEIS MSVAWVGS 208092 GH51 α-L-arabinofuranosidase IAETIVEMG PTMESSSVSAAFMT 261448 GH61 cellulose-binding DDTYTTSWAVDR 263097 GH61 cellulose-binding QTDVTGISWF 253139 GH74 xyloglucanase GIVPGIVFN 257835 GH79 β-4-O-methyl-glucuronidase AAQAHAASI 249789 GH88 d-4,5 unsaturated β-glucuronyl IADQSFAQAAR hydrolase VNFDDATIA 248819 GH92 α-mannosidase FNAGTGFMEAR SFISVDQAR YNDYAVSV 255063 GH92 α-mannosidase AFDQWDEII DSITSIFAR EYAFEDFAI GAASFAASFG ISPADYTDAN YANQPGISTQ 249084 GH92 α-mannosidase EYAIGDFAVR 123

259392 GH95 fucosidase MATDFQNIR 260703 GH115 α-(4-O-methyl)-glucuronidase IGQATCVAF 198942 CE8 pectin methylesterase AYFYGNTIAT 262938 GT15 glycolipid 2-alpha-mannosyltransferase IGYEHNAN 29063 PL14 Polysaccharide Lyase GYTAYFPASFD 97090 Chitin synthase TNILFAMKEDNEK 262183 CBM1 Carbohydrate-Binding NPADTSETDGIR 254017 CBM1 Carbohydrate-Binding EDPNYHQY 261029 Lipase PTSQAEVVR SAVAIVGDSITD TYITFGDQTR 255713 Lipase DQAAVFDAR IIIEQGSTAV 179599 Glucose-methanol-choline DSWNEIITP oxidoreductase GGSSSIDGAAW GWGWSGER WVVIVGHTA 199860 galactose-1-epimerase EVDGDFIPT 206015 Cytochrome P450 AAGSVPAVW 256997 Fungal lignin peroxidase IQSDFAIAR 261553 Multicopper oxidase IINEGSED 260543 Glucose-methanol-choline oxidoreductase 258261 glyoxal oxidase FMESSVSA PANSFEFF 259359 glyoxal oxidase NEGIASSNI GTAATDADIVAA IHVIIDNTV PVGTAATDADIVA SSINFITY 263528 glyoxal oxidase ACYVDNA 263533 glyoxal oxidase IGGWSIQSTQGVR IVAATSSTH VAATSSTHGN VGYYNEAR 266369 FAD linked oxidase NNFGIVTR

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Table S5.3. Additional peptides from extracellular filtrates of P. carnosa grown on spruce that were matched to CAZymes and oxidoreductases using the P. carnosa genome sequence.

P. carnosa GH Family Predicted Activity Peptide Protein ID 260755 GH16 xyloglucanase TSGASTAGDADA 252166 GH47 α-mannosidase FIESYSAY 247750 CE15 4-O-methyl-glucuronoyl methylesterase DVIEVPTAAHVN 100639 Multicopper oxidase DIPQAIID ISGITVSGTNSDIR 262882 Fungal lignin peroxidase VDCSAVVPV 256980 Fungal lignin peroxidase IAIIGHNR 258261 glyoxal oxidase FMESSVSA 260543 Glucose-methanol-choline oxidoreductase PVGTAATDADIVAA 261029 Lipase NDGDGPNAI TYITFGDQTR

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Appendix 4: Substrate recognition and hydrolysis by a fungal xyloglucan-specific family 12 hydrolase

Justin Powlowski, Sonam Mahajan, Matthieu Schapira and Emma R. Master

Abstract

Biochemical studies to elucidate the structural basis for xyloglucan specificity among GH12 xyloglucanases are lacking. Accordingly, the substrate specificity of a GH12 xyloglucanase from Aspergillus niger (An XEG12A) was investigated using pea xyloglucan and 12 xylogluco- oligosaccharides, and data were compared to a structural model of the enzyme. The specific −1 −1 activity of An XEG12A with pea xyloglucan was 113 µmol min mg , and apparent kcat and Km values were 49 s−1 and 0.54 mg mL −1 , respectively. These values are similar to previously published results using xyloglucan from tamarind seed, and suggest that substrate fucosylation does not affect the specific activity of this enzyme. An XEG12A preferred xylogluco- oligosaccharides containing more than six glucose units, and with xylose substitution at the −3 and +1 subsites. The specific activities of An XEG12A on 100 µM XXXGXXXG and 100 µM XLLGXLLG were 60 ± 4 and 72 ± 9 µmol min −1 mg −1 , respectively. An XEG12A did not hydrolyze XXXXXXXG, consistent with other data that demonstrate the requirement for an unbranched glucose residue for hydrolysis by this enzyme.

Graphical abstract

Keywords: Xyloglucan; Oligosaccharides; Xyloglucanase; Glycoside hydrolase; Kinetics

Abbreviations: MALDI, matrix-assisted laser desorption ionization; XEG, xyloglucan endoglucanase; Glc, glucose 126