Characterization of a Thermophilic, Cellulolytic Microbial Culture
DISSERTATION
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of
Philosophy in the Graduate School of The Ohio State University
By
Sarah Marie Carver
Graduate Program in Microbiology
The Ohio State University
2011
Dissertation Committee: Dr. Olli H. Tuovinen, advisor Dr. Zhongtang Yu Dr. Ann D. Christy Dr. Hua Wang
Abstract
Microorganisms have evolved to degrade and hydrolyze complex matrixes and extreme environmental conditions such as higher temperature, salinity, or pH.
With the appropriate inoculum and selective conditions, organisms can be enriched using selective conditions and in order to efficiently degrade a compound of interest. Cellulosic biomass is a renewable resource explored as a feedstock for bioenergy and the microbial mechanisms to hydrolyze the material are discussed in Chapter 1. This research focuses on cellulose and elevated temperatures (52 - 60 °C) as a way to select for a microbial consortium able to degrade many plant polymers and generate products of interest, including biohydrogen.
The versatility of the consortium, henceforth called TC52 or TC60
(different by their enrichment temperature) was analyzed by observing growth and metabolic profiles with a variety of substrates. The first portion of the research focused on utilizing the consortium in a microbial fuel cell, Chapter 4.
Unfortunately, MFC designs are not sustainable at elevated temperatures so a new design was developed and tested. TC60 was unstable at 60 °C with cellulose as a substrate but produced 375 mW/m2 when fed glucose.
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The second portion of this research monitored the ability of TC52 and
TC60 to adapt and degrade a variety of substrates. In order to monitor short chain
fatty acids , a solid phase extraction method (Chapter 2) was developed in order to
clean culture samples prior to metabolite analysis with high-performance liquid
chromatography. Chapter 3 describes a study where TC60 was enriched on a
combination of microalgal biomass and cellulose in order to increase hydrogen
yields. Dunaniella tertiolecta and Chlorella vulgaris, two microalgae species, were tested with several ratios of cellulose. Cultures fed a 1:2 ratio of D. tertiolecta and cellulose generated higher hydrogen yields due to lysed microalgae cells. Chapters 7 and 8 show the difference in TC52 metabolite production when grown on commercial paper samples and polysaccharides. Pyrosequencing results indicate that Lutispora thermophilia, Clostridium thermocellum, and Clostridium stercorarium were the dominant microorganisms in many conditions. It must be noted that enrichment was not done on individual substrates, rather, immediate reactions were monitored.
The third portion of this research concerned the effect of the type of substrate, concentration of substrate, and temperature on the metabolism of TC60.
Initial substrate concentration (2, 4, 8, 12, 16, 20 g/l), temperature (50, 55, 60 °C), and cellulosic substrates (microcrystalline cellulose Sigmacell Type 20 and 50, long fibrous cellulose, and 5 x 5 mm pieces of filter paper) were tested in all possible combinations. Data analyses showed that each individual effect can have a significant affect on metabolite production rates and yields. Also, combined
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environmental factors can have a combined effect. Statistical analyses were able
to reveal which factors played a significant affect on production rates and yields of H2, CO2, ethanol, and acetate. The research and results are outlined and described in Chapters 5 and 6.
This research explored the ability of a consortium to quickly adapt to a
change in substrate and the affect this had on metabolite production. This study
showed that it is feasible to enrich for a consortium able to generate different
forms of bioenergy by changing environmental conditions.
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Dedication
To all those who put up with my eccentric nature
And encouraged it
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Acknowledgments
There are so many wonderful co-workers and friends I must acknowledge.
First of all, family and friends, because the two are very similar. My mother and father for supporting me unconditionally and for knocking me off my ‘humble post’ sometimes. My brother for reminding me that science isn’t everyone’s truth.
Friends, especially Srujana Samhita Yadavalli, Liang-Chun Liu, and Kiley Dare: thank you for the support through the stress, fun breaks to take my mind off of work, and for listening when I needed to vent.
Secondly, I want to thank my awesome advisor, Dr. Olli H. Tuovinen. Not only have I learned microbiology, I’ve learned how to think in a whole new light.
You gave me a wonderful opportunity by going to Finland to work and I will always appreciate it. Finland is a second home to me; I cannot wait to visit again.
Thank you for putting up with my weird sense of humor and my rambling at times. I have grown so much through graduate school and you have been a great mentor.
In addition to my advisor, I must thank my doctoral committee without whom I would not be completing this document. Dr. Ann D. Christy, you have always been sweet, supportive, and great for engineering questions. Dr.
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Zhongtang Yu, thank you for keeping me on my toes and forcing me to think twice as hard as I usually do. Dr. Hua Wang, while I did not get to work with you much, you have been extremely insightful during meetings and the dissertation
process.
A large part of my research was completed in Finland and I cannot forget
to thank the wonderful people I met there. The Department of Chemistry and
Bioengineering at Tampere University of Technology was extremely supportive,
financially and mentally while I carried out my studies. Of particular thanks, Dr.
Jaakko Puhakka, Pertti Vuoriranta, Raghida Lepistö, and Uwe Münster for being
mentors in their own way. The biohydrogen researchers, including two of my
best-friends-forever ever Aino-Maija Lakaniemi and Marika Nisillä, helped me
polish my communication skills and increased my interested and appreciation of bioenergy research. Aino-Maija and Marika, you two will always hold a special place for being the best officemates a crazy American like I could have. Paper samples used in this research were supplied by the paper companies UPM and E-
Real (Tampere, Finland). Special thanks go to Suvi Nieminenat at UPM who facilitated the transfer. Chris Hulatt from the University of Wales for collaboration on the co-degradation study, you are the only person as crazy as me that I have yet to find in science. Good luck on all your endeavors.
While researching at Ohio State University, I have collaborated and
learned much from a diverse bunch of people. Of particular note is the Yu lab in
the Animal Sciences Department. Mike Nelson, Jill Stephens Stiverson, and Wen
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Lv have all taught me a variety of molecular techniques necessary to carry out this research. Also, you’ve taught me patience, each in a different way. Mike, thank you for your extensive help with sequencing and analysis. Trent Bower, Alan
Yost, and C.J. Morabito, thank you for assisting with the final changes to the microbial fuel cell design and Trent for the time and effort of making Figure 1 for the thermophilic microbial fuel cell manuscript. I would like to acknowledge
Sandy Jones for assistance with XRD and surface area analysis. Thank you to
Sasha Bai and the Statistical Consulting Service at OSU for teaching me analyses that I would have struggled with alone. Also, Dr. Sukhbir Grewal and Dr.
Fredrick Michel at the OSU-Wooster extension campus for the time to teach me
T-RFLP even though another method was eventually chosen. Last, but not least, thank you to the Department of Microbiology and the Graduate School for technical support throughout my doctoral studies.
If there is anyone I have missed in this acknowledgement section, please track me down and I will thank you personally.
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Vita
2002...... Olivet High School
2006...... B.A. Biology, Albion College
2006-present ...... Graduate Teaching Associate, Department of Microbiology, The Ohio State University
June 2008-December 2009 ...... Research Associate, Department of Chemistry and Bioengineering, Tampere University of Technology, Finland
Publications
Carver, S.M., Vuoriranta, P., and O.H. Tuovinen. 2011. A Thermophilic Microbial Fuel Cell Design. J Power Sources 196:3757-3760. Carver, S.M., Hulatt, C.J., Thomas, D.N., and O.H. Tuovinen. 2011. Thermophilic,
Anaerobic Co-Digestion of Microalgal Biomass and Cellulose for H2 Production. Biodegradation. doi:10.1007/s10532-010-9419-2 Carver, S.M., Lepistö, R., and O.H. Tuovinen. 2010. Hydrolysis and Metabolism of Cellulose by an Anaerobic, Thermophilic Consortium. Proceedings of the 3rd International Symposium on Energy from Biomass and Waste, paper no 436, p.9. Rismani-Yazdi, H., Christy, A.D., Carver, S.M., Yu, Z., Dehority, B.A., and O.H. Tuovinen. 2011. Effect of External Resistance on Bacterial Diversity and Metabolism in Microbial Fuel Cells. Bioresour Technol 102:278-283.
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Rismani-Yazdi, H., Carver, S.M., Christy, A.D., and O.H. Tuovinen. 2008. Cathodic Limitations in Microbial Fuel Cells: An Overview. J Power Sources 180: 683-694.
Fields of Study
Major Field: Microbiology
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Table of Contents
Abstract ...... ii
Acknowledgments...... vi
Vita ...... ix
List of Tables ...... xviii
List of Figures ...... xx
CHAPTER 1: Anaerobic Cellulose Degradation by Microorganisms ...... 1
ABSTRACT ...... 1
CELLULOSE IN PLANTS ...... 2
CELLULOSE HYDROLYZING ENZYMES ...... 3
GENES AND REGULATION ...... 6
ENERGETICS ...... 6
MECHANISMS OF CELLULOSE DEGRADATION ...... 7
Mechanism I: Free Cellulases...... 7
Mechanism II: Individual, Cell-Anchored Cellulases ...... 8
Mechanism III. Disruption/Threading Complex ...... 9 xi
Mechanism IV: Cellulosome ...... 10
UNTAPPED CELLULASE RESERVOIRS ...... 12
Rumen ...... 12
Insects ...... 12
Compost ...... 13
Plant pathogens ...... 13
CHAPTER 2: A Solid Phase Extraction Technique for HPLC Analysis of Short
Chain Fatty Acid Fluxes during Microbial Degradation of Plant Polymers ...... 19
INTRODUCTION ...... 20
MATERIAL AND METHODS ...... 22
Reagents...... 22
Test culture and media ...... 23
SPE method ...... 24
HPLC ...... 24
Size exclusion chromatography (SEC) ...... 25
Calculations ...... 25
RESULTS AND DISCUSSION ...... 26
Optimization of SPE ...... 26
Repeatability and Recovery ...... 27
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SPE of samples of cellulose-grown cultures ...... 28
SPE of cultures grown on other polymers ...... 29
CONCLUSIONS ...... 30
CHAPTER 3: Thermophilic, Anaerobic Co-Digestion of Microalgal Biomass and
Cellulose for H2 Production ...... 34
INTRODUCTION ...... 35
MATERIALS AND METHODS ...... 38
Cultivation and harvesting of microalgae ...... 38
Microbial consortium ...... 39
Analytical methods ...... 40
RESULTS AND DISCUSSION ...... 42
Enrichment improves H2 production ...... 42
Analysis of the fifth enrichment ...... 43
Assessment of microalgal biomass digestion ...... 46
CONCLUSIONS...... 48
CHAPTER 4: A Thermophilic Microbial Fuel Cell Design ...... 56
INTRODUCTION ...... 57
MATERIALS AND METHODS ...... 59
Thermophilic MFC design...... 59
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MFC setup ...... 60
Performance analysis ...... 62
RESULTS AND DISCUSSION ...... 62
CHAPTER 5: Hydrogen and Metabolite Production during Batch Anaerobic
Growth on Cellulose by a Thermophilic Microbial Consortium at 50 and 60 °C 70
ABSTRACT ...... 70
INTRODUCTION ...... 71
MATERIALS AND METHODS ...... 73
Culture and experimental set-up ...... 73
Analysis of chemical oxygen demand and soluble sugars ...... 74
Metabolite analyses ...... 75
Statistical models and analyses...... 76
RESULTS AND DISCUSSION ...... 77
Enrichment phase ...... 77
Product yields at 50 and 60 °C ...... 78
Rate of product formation at 50 and 60 °C ...... 81
CONCLUSIONS ...... 82
CHAPTER 6: The Influence of Substrate and Concentration on Batch
Fermentation by an Anaerobic, Thermophilic Consortium ...... 91
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ABSTRACT ...... 91
INTRODUCTION ...... 92
MATERIALS AND METHODS ...... 95
Culture and experimental set-up ...... 95
COD and soluble sugar analyses ...... 96
Metabolite analyses ...... 97
Substrate analysis ...... 98
Statistical analysis ...... 98
RESULTS AND DISCUSSION ...... 100
Substrate characterization ...... 101
Substrate concentration...... 101
Effects of substrate ...... 102
Effect of substrate and concentration in relation to temperature ...... 104
CONCLUSIONS ...... 106
CHAPTER 7: Fermentation of Carbohydrates and Polysaccharides by a
Cellulolytic, Thermophilic Consortium ...... 116
ABSTRACT ...... 116
INTRODUCTION ...... 117
MATERIAL AND METHODS ...... 119
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Substrates ...... 119
Culture conditions ...... 119
Headspace gas analysis ...... 120
Analysis of short chain fatty acids ...... 120
Microbial diversity analysis ...... 121
RESULTS AND DISCUSSION ...... 123
Soluble substrates: Monosaccharides and disaccharides ...... 123
Insoluble substrates: Polysaccharides ...... 126
Substrate effect on bacterial diversity ...... 128
CONCLUSIONS ...... 129
CHAPTER 8: Commercial Paper Composition Affects Its Hydrolysis and
Fermentation by a Cellulolytic, Thermophilic Consortium ...... 141
ABSTRACT ...... 141
INTRODUCTION ...... 141
MATERIAL AND METHODS ...... 144
Paper samples ...... 144
Culture conditions ...... 144
Headspace gas analysis ...... 145
Analysis of short chain fatty acids ...... 145
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Microbial Diversity Analysis...... 146
RESULTS AND DISCUSSION ...... 148
Metabolite Production ...... 148
Paper composition and degradation ...... 150
Microbial diversity ...... 153
CONCLUSIONS ...... 154
REFERENCES ...... 163
APPENDIX ...... 202
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List of Tables
Table 1.1. Enzymatic classification (EC) of cellulolytic enzymes………… 15
Table 2.1. Recovery and standard deviation of repeatability for individual
SCFAs……………………………………………………………………… 31
Table 3.1. H2:CO2 Ratios (± standard error) for TC60 grown on various
substrates…………………………………………………………………… 50
Table 3.2. Maximum lactate, acetate, butyrate, and ammonium
concentrations (± standard error) during the 5th enrichment……………….. 51
Table 5.1. Metabolite ratios (mol:mol) at 12, 24, and 48 hr of growth of
TC60 on two types of cellulose at 50 and 60 °C…………………………... 84
Table 5.2. ANOVA results of product yields at 24 and 48 hr……………... 85
Table 5.3. ANOVA results of the effect of temperature and interactions
containing temperature on product rates…………………………………… 86
Table 6.1. Physical characteristics of the cellulose substrates used in this
study………………………………………………………………………... 107
Table 6.2. Tukey’s test of confidence for the product yields as affected by
different cellulose types in the two models………………………………… 108
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Table 6.3. Tukey’s test of confidence for the product formation rates as
affected by different cellulose types in the two models…………………… 109
Table 6.4. P-values for the four models. Shaded areas indicate significance
(p value < 0.05)…………………………………………………………….. 110
Table 6.5. Differences between inocula…………………………………… 111
Table 7.1. Classification of TC52 organisms when fed polymers at a diversity cutoff of 0.03…………………………………………………….. 139
Table 8.1. Relative composition (% w/w) of the paper samples tested in this study…………………………………………………………………… 155
Table 8.2. Classification of TC52 organisms when fed three paper samples at a diversity cutoff of 0.03………………………………………………… 162
Table A.1. Pairwise Tukey analysis for the affect of concentration on metabolite yields at 12, 24, and 48 hr with 55 °C…………………………. 205
Table A.2. Pairwise Tukey analysis of the affect of concentration on metabolite production rates at 55 °C………………………………………. 206
Table A.3. Pairwise Tukey analysis for the affect of concentration on metabolite yields in the 50 and 60 °C set. 207
Table A. 4. Pairwise Tukey analysis of metabolite production rates for the
50 and 60 °C set…….……………………………………………………… 208
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List of Figures
Figure 1.1. The structure of cellulose fibrils………………………………. 16
Figure 1.2. Cellulose degradation by endoglucanase, exoglucanase, and
cellobiohydrolase enzymes………………………………………………… 17
Figure 1.3. Four mechanisms of cellulose degradation seen in anaerobic microorganisms……………………………………………………………. 18
Figure 2.1. HPLC chromatograms of supernatants of cellulose-grown cultures……………………………………………………………………... 32
Figure 2.2. HPLC chromatograms of supernatants of microbial cultures grown with plant polymers………………………………………………… 33
Figure 3.1. Improvement of H2 yields over sequential enrichment
cultures……………………………………………………………………... 52
Figure 3.2. Cumulative gas yields during the first three days of the fifth
enrichment of TC60 with different substrate conditions…………………... 53
Figure 3.3. Changes in lactate, acetate, butyrate and total SCFA
concentrations over time for single substrate conditions…………………... 54
Figure 3.4. C and N analysis of solid fractions and the corresponding C:N
ratios……………………………………………………………………….. 55
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Figure 4.1. Design of the thermophilic microbial fuel cell reactor and the
cathode assembly…………………………………………………………... 67
Figure 4.2. Potential over time generated in the thermophilic MFC………. 68
Figure 4.3. Power and polarization curves at 450 hr and 570 hr for
thermophilic MFC fed glucose…………………………………………….. 69
Figure 5.1. Gas yields after 24 hr of TC52 growth in a temperature
gradient incubator between 35 and 75 °C………………………………….. 87
Figure 5.2. Gas yields over time by TC52 and TC60……………………… 87
Figure 5.3. Metabolite production over time of TC60 incubated with 4 g/l
microcrystalline cellulose (Sigmacell Type 20) at two temperatures……… 88
Figure 5.4. H2, CO2, ethanol, and acetate yields at 12, 24, and 48 hr with
growth on 4 g/l microcrystalline cellulose (Sigmacell Type 20) at two
temperatures………………………………………………………………... 89
Figure 5.5. Yields at 48 hr and rates of TC60 grown on 4 g/l microcrystalline cellulose (Sigmacell Type 20) at two temperatures……… 90
Figure 6.1. X-ray diffractograms of substrates A, B, and C……………….. 112
Figure 6.2. Gaseous product yields at 48 hr, 55 °C with TC60 growth on substrate A…………………………………………………………………. 113
Figure 6.3. The effect of substrate on H2, CO2, ethanol, and acetate
production over time at 55 °C with 4 g/l concentration…………………… 114
Figure 6.4. The effect of substrate on production rates at 55 °C with 4 g/l
substrate concentration…………………………………………………….. 115
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Figure 7.1. Production of H2, CH4, and CO2 by TC52 growth on glucose,
cellobiose, cellulose, hemicellulose, pectin, and starch…………………… 131
Figure 7.2. Gas metabolite yields after 48 hrs of growth on various
monosaccharides and disaccharides……………………………………….. 132
Figure 7.3. SCFA metabolite yields after 48 hrs of growth on various
monosaccharides and disaccharides……………………………………….. 133
Figure 7.4. Gas metabolite yields after 48 hrs of growth on various AC
and NAC polysaccharides…………………………………………………. 134
Figure 7.5. SCFA metabolite yields after 48 hrs of growth on various AC
and NAC polysaccharides…………………………………………………. 135
Figure 7.6. DGGE banding patterns of monosaccharide and disaccharide
fed cultures………………………………………………………………… 136
Figure 7.7. DGGE banding patterns of AC and NAC polysaccharide fed
cultures……………………………………………………………………... 137
Figure 7.8. Relative abundance of species in different genera in hemicelluloses (A) and pectin (B) samples………………………………... 138
Figure 8.1. Gas production over time by TC52 fed eight different paper samples…………………………………………………………………….. 156
Figure 8.2. SCFA production over time by TC52…………………………. 157
Figure 8.3. Paper degradation following 17 days of incubation with TC52 based on dry weight………………………………………………………... 158
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Figure 8.4. Metabolite yields of the three most degraded papers in
comparison to microcrystalline cellulose after 48 hrs of incubation………. 159
Figure 8.5. Bacterial diversity of TC52 fed different types of paper………. 160
Figure 8.6. Relative abundance of sequences from three paper samples….. 161
Figure A.1. Gram stain of TC60 with cellulose…………………………… 202
Figure A.2. Gram stain of Chorella vulgaris without TC60 inoculum. ….. 203
Figure A.3. Gram stain of Chorella vulgaris without TC60 inoculum
showing heterotrophic organisms present with microalgal biomass. ……... 203
Figure A.4. Gram stain of Dunaliella tertiolecta without TC60 inoculum... 204
Figure A.5. Gram stain of Dunaliella tertiolecta without TC60 inoculum
showing heterotrophic organisms present in t he microalgal biomass. …… 204
Figure A.6. Bacterial DGGE and dendogram results for the TC52
duplicate samples grown on monosaccharides and disaccharides………… 210
Figure A.7. Bacterial DGGE and dendogram results for the TC52
duplicate samples grown on polysaccharides……………………………… 211
Figure A.8. Bacterial DGGE and dendogram results for the TC52
duplicate samples grown on commercial paper samples………………….. 212
Figure A.9. Bacterial DGGE and dendogram results for TC52, TC60, and
glycerol samples…………………………………………………………… 213
Figure A.10. Archeal DGGE and dendogram results TC52 samples grown
on monosaccharides and disaccharides……………………………………. 214
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Figure A.11. Archeal DGGE and dendogram results for the TC52 duplicate samples grown on monosaccharides and disaccharides………… 215
Figure A.12. Archeal DGGE and dendogram results for TC52 samples grown on polysaccharides…………………………………………………. 216
Figure A.13. Archeal DGGE and dendogram results for the TC52 duplicate samples grown on polysaccharides……………………………… 217
Figure A.14. Archeal DGGE and dendogram results for TC52 samples grown on commercial paper samples……………………………………… 218
Figure A.15. Rarefaction curves for TC60 samples. ……………………… 219
Figure A.16. Rarefaction curves for TC52 grown on hemicellulose……… 220
Figure A.17. Rarefaction curves for TC52 grown on pectin……………… 221
Figure A.18. Rarefaction curves of TC52 grown on Paper D……………... 222
Figure A.19. Rarefaction curves of TC52 grown on Paper G………...... 223
Figure A.20. Rarefaction curves of TC52 grown on Paper H……………. 224
Figure A.21. Classification of sequences from TC60 samples……………. 225
Figure A.22. Classification of sequences from TC52 grown on hemicellulose……………………………………………………………… 226
Figure A.23. Classification of sequences from TC52 grown on pectin samples. …………………………………………………………………… 227
Figure A.24. Classification of sequences from TC52 grown on three paper samples (D, G, H)…………………………………………………………. 228
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Figure A.25. Diversity and richness analysis of TC60 and TC52 grown on pectin, hemicelluloses, and three paper samples at 0.03 and 0.05 cutoffs…. 229
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CHAPTER 1: Anaerobic Cellulose Degradation by Microorganisms
ABSTRACT
Renewable resources are a necessary portion of the sustainable energy research and cellulosic biomass is of particular interest. The mechanisms of cellulose degradation can be separated into four categories: excreted cellulases, cell-anchored cellulases, disruption/threading complex, and multienzymatic complexes, i.e. cellulosomes. Each mechanism requires further research to
understand their advantages and disadvantages to the hydrolytic microorganism.
Also reviewed are the nomenclature, classification, and environmental sources of
cellulolytic enzymes. In highly selective conditions, such as anaerobic
environments, evolution of cellulases would be driven towards the most
energetically efficient mechanisms. Bacteria, fungi, and protozoan species have
all shown cellulolytic capabilities in anaerobic environments but this manuscript
will focus on bacterial mechanisms.
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CELLULOSE IN PLANTS
Plant cell walls consist of parallel layers of cellulose microfibrils (40-60 % dry weight) with hemicelluloses and lignin filling structural voids, maintaining structural integrity, and protecting the plant from biodegradation. The precise orientation of each polymer to one another within the plant cell wall can only be estimated. Although cellulose is relatively easy to degrade, lignin, an aromatic polymer, prevents easy degradation of cellulose and hemicelluloses which can be degraded once purified (Shoham et al., 1999). Therefore, work in vitro has focused on digestion of independent cellulose, eliminating unavoidable complications. With the important discovery of cellulosomes, a highly cellulolytic multiproteinous compound, in the 1980s, research has grown exponentially on cellulose degradation (Lamed et al., 1983).
A third of all plant matter is made of cellulose. As shown in Figure 1.1, the cellulose macrostructure is similar to a rope, consisting of many chains in a fibril form and multiple microfibrils combined together for larger cellulose fibrils.
High intra and intermolecular hydrogen bonds allow for high tensile strength with the cellulose macrostructure (Keshwani, 2010). Some inherent difficulty lies in studying a substrate like cellulose: cellulose contains regions that are crystalline and amorphous, the necessary enzymes are heterogeneous, and multiple enzymes are required to degrade cellulose.
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CELLULOSE HYDROLYZING ENZYMES
Three types of cellulases exist: endoglucanases, exoglucanase, and
cellobiohydrolase. Endoglucanase enzymes hydrolyze within a chain of β1-4
linked glucose monomers while exoglucanases hydrolyze one or two glucose
units at a time from the end. Cellobiohydrolase hydrolyzes a cellobiose, a disaccharide of glucose, into two glucose units (Figure 1.2). Cellulose hydrolysis yields a mixture of oligosaccharides, cellobiose, and glucose monomers. Further metabolism by cellulolytic organisms occurs through known glucose catabolic
pathways. Cellulolytic enzymes are part of a larger class of enzymes called
glycoside hydrolases (Hashimoto, 2006; Vuong and Wilson, 2010).
Defining and classifying cellulases has been standardized in two ways:
sequence homology and activity. Enzyme classification (EC) is based on the
protein’s activity; cellulases are a part of the glycosidases group (3.2.1) which
contains both cellulolytic and other plant polysaccharide degrading enzymes
(Table 1.1). In addition to EC, carbohydrate-active enzymes database (CAZy) is a
database which contains annotated sequences along with their corresponding
protein function and biochemical classification. CAZy includes enzymes
associated with carbohydrates including glycoside hydrolases,
glycosyltransferases, polysaccharide lyases, carbohydrate esterases, and
carbohydrate binding modules (Cantarel et al., 2009).
Cellulases and other hydrolases have been found to have genetic motifs
present in all known enzymes. With recent advances in automated sequencing
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technology, known genetic motifs of glycoside hydrolases, especially cellulases,
can be analyzed independently of culturing (Ferrer et al., 2009; Vieites et al.,
2009). The most common motif is carbohydrate binding modules (CBM). CBM contain specific folding patterns with the ability to bind a polysaccharide, including cellulose (Boraston et al., 2004). The majority of cellulase proteins contain a CBM in addition to their catalytic domain. These structures are not limited to cellulases but can be seen across all domains of life and classes of enzymes (Guillén et al., 2010). Enzymatic structure, binding motifs, and cellulose
disruption of CBM have been studied in cellulases (Hashimoto et al., 2004;
Hildén and Johansson, 2004). The region of the protein that binds polysaccharide
contains aromatic and polar amino acids (Lehtiö, 2001). The distance of the
residues are approximately 10.3 Å, the exact distance between glycosidic bonds in cellulose (Tomme et al., 1995a). With high affinity and this molecular ‘zipper’
formation, CBM allow for specificity with the cellulosic substrate. While
attachment is not required for cellulose degradation, it increases the efficiency of degradation. Attachment is mediated by a CBM or another structure, such as the pili in Ruminococcus albus (Morrison and Miron, 2000; Devillard et al., 2004; Xu
et al., 2004).
There are several genetic motifs related to cellulases and especially the
multiproteinous cellulosome. The dockerin is a non-catalytic protein which
connects the cell, directly or indirectly, to another common protein motif called a
cohesin. In turn, the cohesin is attached to the catalytic enzymes which hydrolyze
4
glycosidic bonds. Therefore, a chain of protein interactions attach the catalytic subunits to the microorganism’s surface. Dockerins, cohesins, and some scaffoldins show sequence homology and can be used to target analyses. These components are common in cellulosomes but have been seen with other simplier
multienzymatic structures, and some cell-anchored cellulases. The genome of
Clostridium thermocellum contains 74 genes with dockerin sequences but whether
these gene products are parts of the cellulosome has not been confirmed (Berger
et al., 2007). Although these motifs are seen with cellulolytic structures,
metagenomic analyses revealed that the dockerin/cohesin interaction is prevalent
in all domains of life and does not equate cellulose degradation (Peer et al., 2009).
Many studies have shown that the synergistic ability of multiple cellulases
greatly increases hydrolysis of cellulose. With aerobic fungi, specific activity
increased 15 times with a mixture of enzymes over a single type of cellulase
(Irwin et al., 1993; Nidetzky et al., 1994). Similar results have been seen with
anaerobic, cellulolytic bacteria with free cellulases (Murashima et al., 2002;
2003). Cellulosomes, which contain many cellulases and glycoside hydrolases,
have also shown increased activity in comparison to single enzymes. Clostridium
thermocellum, a known cellulosomal organism, exhibits the highest rates of
cellulolytic activity known, 50 times higher than Trichoderma reesei, an aerobic
fungus, with only secreted cellulases (Demain et al., 2005).
5
GENES AND REGULATION
The majority of research on cellulose regulation at the transcriptional has
been studied with the well-studied aerobic fungi, Trichoderma reesei (Mach and
Zeilinger, 2003; Stricker et al., 2008). Carbon catabolite repression allows for
hydrolysis products, cellobiose and glucose, to inhibit cellulase synthesis, which
has been shown in Clostridium thermocellum (Zhang and Lynd, 2004; Abdou et al., 2008). Recent research on the cellulosome-producing organism, C. thermocellum, has shown that not only is catabolite repression used but also alternative sigma and anti-sigma factors sensing extracytoplasmic polysaccharides
(Kahel-Raifer et al., 2010; Nataf et al., 2010). Beyond these few recent studies, cellulase regulation has been poorly studied and technology has only recently caught up with the questions scientists have had concerning cellulose hydrolysis.
ENERGETICS
Little research has focused on the amount of energy necessary to synthesize cellulases or cellulosomes and the energy balance for a cellulolytic cell. One way to study the energetic is to look for evolutionary relationships and enzyme mechanisms that have been widespread and used in certain selective conditions. Another method is to specifically study individual enzyme energetic in order to piece together a larger story (Newcomb and Wu, 2010). Only one study has started to elude energetic choices in a cellulolytic organism, Clostridium cellulolyticum (Desvaux, 2006). This will help understand the relationships within
6
a microbiome and the energetic advantages of certain cellulolytic mechanisms. If the energy necessary to generate and maintain cellulases, especially cellulosomes, was known then advantages and disadvantages of different cellulolytic mechanisms could be understood. This would work towards more efficient designs of artificial cellulosomes for industrial purposes.
MECHANISMS OF CELLULOSE DEGRADATION
Mechanism I: Free Cellulases
Free cellulases are the common mechanism for cellulose degradation by
aerobic microorganisms and multicellular fungal species (Baldrain and
Valášková, 2008; Wilson, 2008a; Dashtban et al., 2009; Sánchez, 2009). In
general, the enzymes consist of three regions: a CBM, flexible linker, and
catalytic domain (Tomme et al., 1995b; Wilson, 2004). As shown in Figure 1.3,
the organism doesn’t require direct attachment for hydrolysis but the substrate
must be within free floating range. In this way, soluble sugars are released by the
enzyme into the general environment and could be a disadvantage to cellulolytic
organisms. Competing organisms can enter the environment and utilize the
abundant sugars. In comparison to the other mechanisms, increased interspecies
and intraspecies competition with the surrounding microbial population is present.
These cellulases are not possessive so after hydrolysis, the enzyme will be
released and reattach to another portion of cellulose.
7
Anaerobic microorganisms have been found to also utilize this technique
to a lesser extent. Clostridium cellulovorans has only been found to have free
enzymes while Clostridium thermocellum has been found to have both free
enzymes and cellulosomes (Gilad et al., 2003; Han et al., 2005). Anaerobic fungi,
e.g. Neocallimastix frontalis, Piromyces communis, and Caecyces communis,
often secrete cellulases into their surrounding environment as seen in vitro (Orpin,
1977; Hebraud and Fevre, 1990; Li and Calza, 1991a; 1991b; Chen et al., 1994;
Wilson et al., 1994; Rabinovich et al., 2002). Protozoa have also been shown to
actively secrete enzymes when the environment is stressful but it is unknown
whether this includes cellulases (Williams and Coleman, 1992).
Mechanism II: Individual, Cell-Anchored Cellulases
Cell-anchored, single cellulases are similar to free cellulases in that they
have a CBM, linker, and catalytic domain (Tomme et al., 1995b; Wilson, 2004)
but are attached to the cellulolytic microorganism (Figure 1.3). Following
hydrolysis, the soluble sugars released are in close proximity to the hydrolytic organism, minimizing interspecies competition. Direct attachment is not necessary for hydrolysis but the substrate must be within reach of the catalytic domain. Therefore, the location of digestible cellulose is limited by the linker length and flexibility. The cellulase polypeptide can contain more than one catalytic domain as seen with Anaerocellum thermophilum (Zverlov et al., 1998)
or a chimeric combination of glycoside hydrolases as seen with
Caldicellulosiruptor saccharolyticum (Cooper and Salmond, 1993; Te’o et al.,
8
1995; Gibbs et al., 2000). Cell-anchored cellulases, like cellobiohydrolase in N. frontalis, can detach from the surface due to unknown triggers (Wilson and
Wood, 1992a; 1992b). Most anaerobic microorganisms contain a combination of several mechanisms rather than any single process. Two microorganisms known to utilize cell-anchored cellulases, Clostridium stercorarium and C. thermocellum, only differ by the presence of a cellulosome in the later (Bronnenmeier et al.,
1990; Schwarz et al., 1995; Burrell et al., 2004; Berger et al., 2007; Zverlov and
Schwarz, 2008). All of these examples exemplify the diversity of cellulolytic capabilities.
Mechanism III. Disruption/Threading Complex
A model for the disruption/threading complex in gram negative organisms was recently proposed by Wilson (Figure 1.3). This mechanism utilizes an extracellular “disrupting complex” to gradual release a single cellulose fibril chain from the larger structure, threading it through the outer membrane. Within the periplasm, the cellulose is degraded with exocellulases (Wilson, 2008b; 2009).
Little is understood about how cellulose is degraded by gram negative organisms such as Fibrobacter succinogenes, one of the most well known cellulolytic organisms. F. succinogenes has no CBM, dockerin, or scaffoldin motifs within the genome (Xie et al., 2007; Zverlov and Schwarz, 2008; Wilson, 2009) but cellulase expression has been observed within the periplasmic space (Malburg et al., 1997; Qi et al., 2007). Similar mechanisms appear for starch degradation by
Bacteroides thetaiotaomicron (Cho and Salyers, 2001; Carvalho et al., 2004) and
9
alginate degradation in Sphingomonas species (Hashimoto et al., 2004). To the organism’s advantage, the soluble sugars are trapped within the periplasmic space and allow for exclusive access. Further research is necessary to validate this model.
Mechanism IV: Cellulosome
Three decades ago, a multiproteinous complex was found to be extremely cellulolytic even when separated from the original organism, Clostridium thermocellum (Lamed et al., 1983). Shown in Figure 1.3, the cellulosome complex has been reviewed many times (Bayer et al., 1994; 1998; 2000; 2004;
Béguin and Lemaire, 1996; Shoham et al., 1999; Doi and Tamaru, 2001; Doi et al., 2003; Gilbert, 2007; Fontes and Gilbert, 2010). The cellulosome is a complex of catalytic enzymes attached to a non-catalytic framework, composed of scaffoldin. In addition, the cellulosome contains dockerin/cohesin motifs, cell anchoring proteins with S-layer homology, and CBM. The scaffoldin is critical to the cellulosome because it allows for multiple enzymes to be bound and systematically arranged while staying attached to the cell surface via S-layer homology. Clostridium stercorarium does not have scaffoldin proteins but C. thermocellum does which allows the organism to generate cellulosomes.
Cellulosomes have been shown to dissociate from the cell and continue hydrolysis after detachment in C. thermocellum (Bayer and Lamed, 1986; Mayer et al., 1987) but further organisms have not been tested for this ability. While C. thermocellum has been the most extensively studied cellulosomal microorganism (Lynd et al.,
10
2010), many others exist including C. cellulovorans (Doi, 2010), C.
cellulolyticum (Tardif et al., 2010), Ruminococcus albus (Karita et al., 2010), and
Ruminococcus flavefaciens (Flint and Rincón, 2010).
R. flavefaciens has a highly variable cellulosome composition due to the
lowered cohesin specificity, which allows for one cohesin to interact with various
dockerins (Rincon et al., 2003; Flint and Rincón, 2010). CBM and dockerin sequences have been difficult to find for R. albus, a known cellulosomal
organism, until recently (Wood et al., 1982; Ohara et al., 2000; Taguchi et al.,
2004; Karita et al., 2010). Cloning and identification of cellulosomal genes and proteins can be difficult as seen with R. albus and presents an additional research limitation.
Anaerobic fungi also use cellulosomes for cellulose hydrolysis and are very similar to those seen in bacteria (Ljungdahl et al., 2010). The first fungal isolate was Neocallimastix frontalis (Orpin, 1975) but isolation of the first cellulosome from the same species did not occur for many years (Wilson and
Wood, 1992b). The majority of fungal cellulolytic enzymes occurs at the mycelium tip and penetrates plant particles (Akin et al., 1989; Mountfort and
Orpin, 1994; Li et al., 1997). Additional anaerobic fungal species known to make cellulases include Neocallimastix patriciarum, Orpinomyces sp. PC-2 (Akin et al.,
1989; Li et al., 1997; Lee et al., 2000; Ljungdahl, 2008), and Piromyces E2
(Teunissen et al., 1993; Ali et al., 1995; Fanutti et al., 1995; Dijkerman et al.,
1996; 1997). Most cellulolytic anaerobic fungi have been studied in the context of
11
the rumen, leaving additional sources unexplored (Wubah et al., 1991; Mountfort
and Orpin, 1994). Also, the cellulolytic efficiency of fungal and bacterial
anaerobic species has not been compared; fungal cellulosomes could be better for
plant biomass degradation in vivo and industrial applications due to their known ability to extensively penetrate plant biomass.
UNTAPPED CELLULASE RESERVOIRS
Rumen
A large amount of research concerning cellulolytic organisms has been in numerous reservoirs of highly cellulolytic organisms, including the rumen of bovine and other ruminants. Not only does the rumen contain anaerobic, cellulolytic bacteria but also fungi and protozoa species able to hydrolyze biomass
(Weimer, 1992; Gordon and Phillips, 1998; Dehority, 2003; Krause et al., 2003;
Wereszka et al., 2004; Ricard et al., 2006; Morrison et al., 2009; Weimer et al.,
2009). These organisms attach and invade fibrous material in the rumen readily, providing exposed cellulose for cellulolytic microorganisms. Unfortunately, these organisms can be difficult to culture and study in vitro. In contrast, some of the
well-studied anaerobic microorganisms have been found: R. albus, R. flavefaciens, and F. succinogenes.
Insects
Additional reservoirs of cellulolytic microorganisms include the gastrointestinal system of plant digesting insects such as the termites,
12
Macrotermes natalensis and Mastotermes darwiniensis, the wood wasp Sirex
cyaneus, detritvores, and cerambycid beetles. The most extensively studied insect
harboring cellulolytic microorganisms is lower termites with bacteria, flagellates,
and yeasts present in their gastrointestinal tract (Breznak and Brune, 1994; Varm et al., 1994; König et al., 2002; Li et al., 2006; Scharf and Boucias, 2010;
Husseneder, 2010; Watanabe and Tokuda, 2010). The hindgut environment is very different from other fibrolytic environments due to the high flow rate (24 hours for completion passage of food), a pH between 6.0 and 7.5, and a reducing potential between -230 to -270 (Breznak, 1984). These conditions would enrich for organisms able to degrade cellulose in conditions easy to mimic in a laboratory or industrial setting.
Compost
Anaerobic, cellulolytic microorganisms are also present in compost heaps
(Stutzenberger, 1991; Leschine 1995; Tuomela et al., 2000). Deep inside compost heaps, thermophilic and anaerobic or microaerobic conditions can be found.
These organisms would be tolerant of environmental changes in temperature,
substrate, and moisture. Also, cellulolytic microorganisms present in this
environment would be able to handle degradation of complex biomass and
possibly hold capabilities for hydrolysis of plant polysaccharides.
Plant pathogens
Although plant pathogens are likely not anaerobic, microorganisms within
the rhizosphere, the soil surrounding the roots, could be anaerobic or facultative
13 aerobic biomass degraders. The search for cellulases within the rhizosphere or with plant pathogens has been a secondary analysis rather than the purpose of exploring these communities. In plant biology, the analysis of plant pathogens is a way to prevent further disease but the same organisms have not been researched from a biomass degradation standpoint. For example, analysis of the bacterial diversity on the surface of potato tubers showed cellulase activity. Also, the potato genotypes affected the bacterial diversity and could prove useful in enriching for microorganisms with specific capabilities (Weinert et al., 2010).
Another study of the biodiversity and metabolic activity of microorganisms in the rice rhizosphere showed cellulase, pectinase, and even chitinase activity
(Ayyadurai et al., 2007). Endophytes, bacterial and fungal symbiotes of living plant tissue, have previously been shown to have cellulolytic activity (Oses et al.,
2006).
CONCLUSIONS
Highly selective environments, such as anaerobic conditions, select for efficient enzymes, including cellulases. Over the last twenty years, the research on cellulose hydrolysis and the enzymes involved has progressed rapidly. This review discusses the general action of cellulases, nomenclature, and the mechanisms of action. To date, four mechanisms for cellulose degradation have been proposed in the literature: free cellulases, cellulosomes, single anchored cellulases, and a disruption/threading complex.
14
Table 1.1. Enzymatic classification (EC) of cellulolytic enzymes.
Enzyme Classification Enzyme Name Action 3.2 Glycosylases 3.2.1 Glycosidases hydrolyze O- and S-glycosyl compounds 3.2.1.4 Cellulase endo glucanase 3.2.1.6 endo-1,3(4)-β-glucanase endo glucanase (hydrolyze 1-4 or 1-3 bond) 3.2.1.21 β-glucosidase exo glucanase (non reducing end) 3.2.1.74 glucan 1,4-β-glucosidase exo glucanase (glucose unit at a time) 3.2.1.91 cellulose 1,4-β-cellobiosidase
15
15
Figure 1.1. The structure of cellulose fibrils.
16
Figure 1.2. Cellulose degradation by endoglucanase, exoglucanase, and cellobiohydrolase enzymes.
17
Figure 1.3. Four mechanisms of cellulose degradation seen in anaerobic microorganisms. A: free, soluble cellulases; B: individual, cell-anchored; C: disruption/threading complex; D: cellulosome (C is modified from Wilson, 2009).
18
CHAPTER 2: A Solid Phase Extraction Technique for HPLC Analysis of Short
Chain Fatty Acid Fluxes during Microbial Degradation of Plant Polymers
ABSTRACT
The purpose of this work was to develop a solid phase extraction (SPE)
technique to remove interfering compounds that resulted in non-specific, long
retention time peaks in HPLC chromatographs. In addition to filtration (0.2 µm),
SPE shortened the HPLC run times from 140 to 70 min for analysis of short chain
fatty acids (SCFAs). Samples contained partially degraded cellulose,
hemicellulose, pectin, starch, or chitin following incubation with a microbial
consortium. The SPE technique was based on retention of large hydrophobic
compounds but the results indicated an entrapment of < 0.2 µm particles.
Repeatability varied between individual SCFAs but the standard deviations
remained below 10 %. HPLC samples that contain colloids and solutes stemming
from the biodegradation of natural, insoluble polymers can be prepared with this
SPE protocol. Thus the technique is useful for examining metabolite fluxes in microbial cultures with suspended solids and in solid phase fermentation.
19
INTRODUCTION
The biodegradation of plant polymers is of substantial interest to the sustainable energy field. Microbial metabolism can hydrolyze polymers to soluble metabolites which in turn serve as precursors to CH4 or H2 formation by anaerobic microorganisms. Central to the multiple metabolic pathways involved is the flux of short chain fatty acids (SCFAs). Monitoring of SCFAs can give insight into potential problems in the biodegradation of a feedstock. Gas chromatography is commonly used for SCFA analysis of cellulose degrading cultures but HPLC methods also exist (Loh et al., 1984; Siegfried et al., 1984; Weimer et al., 1991).
Without sample pretreatment, liquid analysis in HPLC is subject to interference by non-target compounds and biomolecules. Therefore, sample preparation and pretreatment is of great importance prior to HPLC analysis in order to remove interference, improve accuracy, and shorten the individual assay time. Sample preparation for HPLC analysis has been extensively reviewed (Smith, 2003; Sanz and Martínez-Castro, 2007).
Filtration is the most common technique for HPLC sample preparation
(Majors, 2003). Filtration with a pore size of 0.2 µm is recommended prior to
HPLC analysis. Even though further filtration is not required, more extensive types of ultrafiltration (1-50 nm) and nanofiltration (<1 nm) are available
(Pellegrino, 2000; Jakubowska et al., 2005). Ultrafiltration has been used for samples containing hydrolysates of dextrin and dextran (Confer and Logan,
1997). Microfiltration (0.05-10 µm) may not be sufficient to eliminate microbial
20
metabolites, oligosaccharides, and suspended solids of partially degraded plant
polymers. Nanofiltration has only been used specifically for salt removal in large
scale water purification systems. Hydrolytic decomposition of plant polymers by
microorganisms results in the formation of even smaller oligosaccharides as seen
with dextrin and dextran (Confer and Logan, 1997). While filtration of samples
prior to HPLC analysis is required, it is debatable whether this pretreatment is
sufficient to prevent oligosaccharides (polymers of 3 or more simple sugars) from
entering the HPLC column.
Solid phase extraction (SPE) is based on the phase distribution between
the sorbent (stationary phase) and eluent (mobile phase). For C18 (octadecyl
silane), which was the stationary phase in this study, the phase distribution is
mostly affected by sorption and partitioning but entrapment, ionic, non-polar, and dipole interactions also play a minor role (Fritz, 1999; Hennion, 1999; Thurman and Mills, 1999; Poole et al., 2000). The sorbent is cleaned to remove impurities and conditioned before the sample is applied. The target analyte is either retained or repelled through the SPE column. In the case of C18, a reverse phase matrix, the sample is applied and polar solutes flow through the sorbent along with an eluent. Non-polar compounds are retained within the matrix (Poole et al., 2000).
SPE applications have been studied extensively and additional reviews are available in the literature (Fritz, 1999; Hennion, 1999; Thurman and Mills, 1999).
In this study, microfiltration was deemed insufficient to properly clean samples of bacterial cultures grown with cellulose and other plant polymers.
21
HPLC run times of samples were 210 min after 0.45 µm filtration but were reduced to 140 min following 0.2 µm filtration. To date, other studies with similar samples containing partially biodegraded plant polymers have not addressed problems of extremely long run times in HPLC analysis. Polymer biodegradation products, i.e., oligosaccharides, have been previously characterized based on their size (Confer and Logan, 1997) and size exclusion chromatography (Dupont and
Mortha, 2004). Both techniques eliminate particles much smaller than 0.2 µm but neither has been used for sample pretreatment prior to HPLC.
In our research on the microbial degradation of cellulose, non-specific peaks appear in the HPLC chromatograms of filtered (0.2 µm) samples between
60 and 140 min. Under such conditions, additional stress is placed on the system to continue the necessary flow rate. These impurities are detrimental over time to
HPLC systems if no pretreatment technique is used. The purpose of this study was to develop an inexpensive pretreatment technique to eliminate long-retention time compounds in bacterial culture filtrates. This SPE technique was used for analysis of SCFAs in anaerobic bacterial cultures growing with plant polymers.
MATERIAL AND METHODS
Reagents
The HPLC standards (≥ 98 %) were lactic, formic, acetic, propionic, isobutyric, n-butyric, and isovaleric acid (Sigma-Aldrich). The mobile phase was prepared with HPLC-grade o-phosphoric acid (85 %) and MQ water. Powdered
22
cellulose, chitin from crab shell, pectin from apples, and starch (99 %) were
obtained from Sigma-Aldrich. Hemicellulose was obtained from a paper mill and
homogenized using a mortar and pestle.
Test culture and media
The microbial consortium used in this study was moderately thermophilic
(growth at 50-60 °C) and originated from the interior of a compost heap (Carver
et al., 2010; 2011). Subcultures had been continually maintained anaerobically
(N2 headspace) with cellulose in medium that contained (per liter): 2 g trypticase,
1 g yeast extract, 4 g Na2CO3, 0.23 g K2HPO4, 0.18 g KH2PO4, 0.36 g NH4Cl,
0.04 g NaCl, 0.09 g MgSO4·7H2O, 0.06 g CaCl2·2H2O, 5.66 g acetic acid, 1.62 g propionic acid, 0.68 g n-butyric acid, 0.23 g isobutyric acid, 0.20 g isovaleric acid, 0.20 g n-valeric acid, 0.20 g 2-methyl-butyric acid, 0.001 g rezasurin, 0.25 g cysteine-HCl, and 0.25 g Na2S·9H2O. Each substrate was tested at a final concentration of 4 g/l. The inoculum was added to 10 % (v/v) of the medium volume. Limited PCR-based 16S rDNA sequence analysis indicated the presence of Clostridium spp., Bacillus spp., and Lutispora spp. in this consortium. A more comprehensive sequence analysis is currently in progress. Chromatographs in this study were from the culture incubated at 52 °C on a shaker at 150 rev/min. The formation of SCFAs in anaerobic cultures was monitored over time and samples were collected anaerobically daily. Samples of cultures were centrifuged at approximately 16,000 g for 10 min in 2 ml microcentrifuge tubes and the supernatants were stored at 4 °C for SPE.
23
SPE method
The SPE columns were made in cotton-plugged, glass Pasteur pipettes
(150 mm long, 7 mm ID). The matrix was Sepra C18-T (50 µm; encapped)
purchased in bulk (Phenomenex). The C18 matrix was dry-packed carefully in the
glass pipettes to ensure constant capillary flow. The total length of packed C18
was approximately 25 mm.
The SPE protocol was based on a previously published procedure
(Horspool and McKellar, 1991) with some modifications. The matrix was rinsed
with 2 ml methanol, followed by a sequence of 2 ml 10 mM HCl (pH 2.0), 1 ml
sample (S16), and 1.75 ml 10 mM PBS (pH 7.0). Each addition was allowed to
absorb onto the C18 matrix before the next step. The elution was collected as
soon as the sample was applied to the column and was combined with the final
PBS rinse. Total volume of elution, 2.75 ml (sample + PBS), was mixed and
stored at 4 °C for dilution and HPLC analysis.
HPLC
Prior to HPLC analysis, the eluent from SPE was diluted with MQ water to the appropriate concentration range (0.001-1 mM) and filtered through a 0.2
µm PTFE syringe filter (Pall Corp.). A guard column, 5 cm x 4.6 mm ID
(Supelguard H, 9 μm particle size) and a cation-exchange column, 30 cm x 7.8
mm ID (Supelcogel C-610H, 9 µm particle size) were used with an autosampler
(Spectra-Physica AS 3000) and a UV detector set to 210 nm (Spectra-Physics
SP100). A Beckman 114M HPLC pump maintained a flow rate of 0.5 ml/min of
24
the mobile phase, HPLC-grade o-phosphoric acid diluted to 0.1 % with MQ water
(Peu et al., 2004). Run times were 65 min per sample with 100 µl injections at 70
min intervals. Chromatographs were analyzed through a computer interface
equipped with the Clarity Chromatography Station (DataApex).
Size exclusion chromatography (SEC)
SEC was initially tested as a possible pretreatment for HPLC samples.
Samples of media were filtered through 0.2 µm filters and 20 µl aliquots were
injected onto a TSKgel column (6 µm particle size, G3000PW XL, TosoHaas).
The autosampler, pump, UV detector (254 nm), and computer interface were the
same as used in the HPLC protocol. The flow rate was 1 ml/min and the mobile
phase consisted of 50 mM NaH2PO4, 50 mM NaHPO4, 0.15 M NaCl, and 10 mM
NaN3 (pH 6.3). The standard mix included thyroglobulin, bovine gamma globulin, chicken ovalbumin, equine myoglobin, and vitamin B-12 with masses of 670,
158, 44, 17, and 1.35 kDa, respectively (Bio-Rad).
Calculations
The repeatability of the HPLC system, recovery of SCFAs following the
SPE technique, and the variation of the complete method were measured.
Repeatability of the HPLC system was estimated with a vial of unfiltered standard
sample containing all SCFAs of interest. This sample was analyzed through
HPLC six times. The standard deviation was calculated from the six
measurements. The standard deviation of the complete method, including the SPE
treatment and filtration, was determined with a mix of 1 mM SCFA standards
25
which was applied to three separate SPE columns. The eluent from each column
was filtered and analyzed with HPLC three times. Recovery of SCFAs was
determined by comparing the HPLC analysis of the standard mix with or without
SPE treatment.
RESULTS AND DISCUSSION
Optimization of SPE
All samples were initially centrifuged for 10 min at 16,000 g and the
supernatants were retrieved for further pretreatments. SEC was tested as a candidate for sample pretreatment but was eliminated during initial experiments
because it did not remove long-retention time impurities from the sample matrix.
SPE was tested successfully with Sep-Pak C18 cartridges (Waters Associates)
prior to HPLC analysis. Subsequently, due to the large number of samples to be prepared for HPLC analysis, SPE columns were prepared in the laboratory with
bulk C18-T matrix. Regardless of the supplier, savings of over 50 % in expenses
were achieved by generating the SPE columns with bulk C18-T instead of using
individual, commercially available cartridges.
The amount of the C18 packing material necessary to separate the SCFAs
from non-specific compounds was tested by varying the length of the packed
column between 18, 25, and 30 mm with matrix weights of 0.20, 0.32, and 0.38 g,
respectively. For supernatants of cellulose grown cultures, the results demonstrated that a 25 mm C-18 column length (0.32 g packing material) yielded
26 the best recovery of SCFAs via HPLC analysis. This column length was used through the rest of the experiments.
In the initial experiments, a wash step with 1 to 2 ml of 0.01 M HCl was tested following sample application to the C18 column. In this case, lactic, formic, acetic, and propionic acids were eluted with the HCl wash, while isobutyric, butyric, and isovaleric acids eluted with PBS. In subsequent experiments, the wash step with HCl was eliminated because it was deemed unnecessary.
Similarly, Horspool and McKellar (1991) reported that lactic and acetic acids were eluted from the C18 phase within 0.5 ml of washing, regardless of KCl-HCl
(pH 2.0) or PBS buffer. The C18 matrix contains covalently bound octadecyl silane which functions as a non-polar sorbent. The silanol groups are endcapped during manufacturing to prohibit strong interactions of the matrix with polar compounds. Polar compounds, such as SCFAs, should readily elute from the matrix without the need for a wash step. Therefore, the final protocol here was to collect the eluted sample and the 1.75 ml PBS wash from the SPE column. The
SPE columns were used only once and no effort was made to regenerate the C18 matrix.
Repeatability and Recovery
Variation in HPLC repeatability ranged between 8 and 24 %; the larger the
SCFA, the higher the variation (Table 2.1). Analysis of isovaleric acid with the current configuration was the least quantitative because of approximately 75 % repeatability. The low repeatability with isovaleric acid may be due to longer
27
SCFAs behaving increasingly non-polar in the HPLC columns and in the capillary
tubings. The longer SCFAs are then retained within the system and give rise to
broad peaks leading to higher integration and calculation errors.
The recovery of SCFAs from the SPE column in comparison to the initial concentrations applied onto the column ranged from 67.7 to 90.2 %. The standard deviation of the complete method, including SPE treatment, was between 3.5 and
13.6 %. Excluding isovaleric acid, the reproducibility of the whole technique was
above 90 % (Table 2.1). The sources of this variation were not examined further
because this level of precision was sufficient for metabolic studies of cellulose
biodegradation and fermentation. If further precision is desired, internal standards
can be used to decrease the variation in repeatability.
SPE of samples of cellulose-grown cultures
Initially, supernatants of cultures grown with microcrystalline cellulose
were analyzed for SCFAs after centrifugation and 0.45 µm filtration. Analysis
with an HPLC column required run times of 210 min to elute all UV absorbing
compounds. Several broad, non-specific peaks were present after 60 min.
Replacing 0.45 with 0.2 µm filtration reduced HPLC run times to 140 min (Figure
2.1). The broad unspecific peaks are due to compounds passing 0.2 µm membrane
filtration. The Supelguard H guard column has a packing material (9 µm particle
size) and frits on each end with an inner diameter of 2 µm. The resin within the
HPLC column, sulfonated polystyrene/divinylbensene, could act as a size
exclusion technique in addition to its cation exchange capabilities. Possible
28
compounds reaching the HPLC column could include high-molecular weight fatty acids, other metabolites, oligosaccharides, or colloidal particles of cellulose.
Oligosaccharides and partially degraded polymers could range from as small as two glucose units, cellobiose, to physical diameters in the nano and submicron
range.
By adding the SPE technique prior to dilution and 0.2 µm filtration, the
HPLC run times were shortened to 65 min for each sample, as shown in Figure
2.1. In most samples, a relatively broad, unidentifiable peak occurred at 70 min.
By setting a run time of 65 min with the injection via the autosampler at 70 min, the unknown peak eluted between samples. The C18 matrix did not elute any
SCFAs when MQ water was applied, indicating the absence of SCFA
contamination from the bulk matrix.
SPE of cultures grown on other polymers
The test culture was also grown with starch, pectin, hemicellulose, and chitin. These are complex polymers that are hydrolytically degraded to monomeric sugars. Similarly to samples containing microbially degraded cellulose, supernatants of cultures growing with these polymers were either
filtered (0.2 µm), or treated with SPE and filtered prior to HPLC analysis.
Without SPE treatment, similar non-specific peaks occurred after 70 min as shown in Figure 2.2. As with cellulose, the run times were shortened with SPE
and the removal of the long retention time peaks.
29
CONCLUSIONS
Samples of bacterial cultures growing with insoluble plant polymers are
inherently problematic for HPLC analysis because they contain abundant
metabolites of catabolic pathways as well as nano size particles or colloids
resulting from incomplete hydrolysis and biodegradation. The specific problems
associated with the sample matrix following cellulose biodegradation have not
been addressed in the literature. In this study, SPE with C18-T was successfully used to remove long-retention time degradation products prior to HPLC analysis of SCFAs. Recovery ranged from 68 to 90 % with standards and was dependent on the individual SCFA. The SPE technique is not recommended for ≥ C5 SCFAs
because of low repeatability. For other SCFAs analyzed in this study, the standard
deviation of the whole method remained under 10 %, i.e. > 90 % reproducibility.
The lifespan of an HPLC column can be increased by preventing the entry of
oligosaccharides and large metabolites into the system. The SPE step described
here is relatively inexpensive because of the use of bulk SPE and Pasteur pipettes.
It has been used successfully to monitor SCFA fluxes over time in cultures
growing with plant biomass polymers (Carver et al., 2010; 2011). Savings in time
and expense are notable here because these experiments typically generate
hundreds of samples in time course and optimization studies.
30
Table 2.1. Recovery and standard deviation of repeatability for individual SCFAs.
SCFA Standard Deviation of Standard Deviation Recovery
Repeatability (%) of the Whole HPLC (%)
Method (%)
Lactic Acid 7.6 3.6 68.2
Formic Acid 8.4 4.1 74.3
Acetic Acid 9.1 6.9 72.0
Propionic Acid 9.8 4.8 81.3
Isobutyric Acid 11.5 6.5 90.2
Butyric Acid 11.3 6.0 86.4
Isovaleric Acid 24.2 13.1 67.7
31
Figure 2.1. HPLC chromatograms of supernatants from cellulose-grown cultures. The supernatant was either filtered (0.2 µm) or processed with the SPE protocol and then filtered (0.2 µm). MQ water was applied through a SPE column and is shown as a reference. Identification and retention times of major peaks are noted as the dissociated form of SCFA.
32
Figure 2.2. HPLC chromatograms of supernatants from microbial cultures grown with plant polymers. The samples were all prepared with filtration only (0.2 µm). Peaks with a retention time > 70 min (dashed line) are eliminated when using the SPE technique. Identification and retention times of major peaks are noted as the dissociated form of SCFA.
33
CHAPTER 3: Thermophilic, Anaerobic Co-Digestion of Microalgal Biomass and
Cellulose for H2 Production
ABSTRACT
Microalgal biomass has been a focus in the sustainable energy field,
especially biodiesel production. The purpose of this study was to assess the
feasibility of treating microalgal biomass and cellulose by anaerobic digestion for
H2 production. A microbial consortium, TC60, known to degrade cellulose and other plant polymers, was enriched on a mixture of cellulose and green microalgal
biomass of Dunaliella tertiolecta, a marine species, or Chlorella vulgaris, a freshwater species. After five enrichment steps at 60 °C, hydrogen yields increased at least 10 % under all conditions. Anaerobic digestion of D. tertiolecta
and cellulose by TC60 produced 7.7 mmol H2/g volatile solids (VS) which were
higher than the levels (2.9-4.2 mmol/g VS) obtained with cellulose and C.
vulgaris biomass. Both microalgal slurries contained satellite prokaryotes. The C.
vulgaris slurry, without TC60 inoculation, generated H2 levels equal to that of
TC60 on cellulose alone. The biomass-fed anaerobic degradation resulted in large
shifts in short chain fatty acid concentrations and increased ammonium levels.
34
Growth and H2 production increased when TC60 was grown on a combination of
D. tertiolecta and cellulose due to nutrients released from algal cells via lysis. The
results indicated that satellite heterotrophs from C. vulgaris produced H2 but the
Chlorella biomass was not substantially degraded by TC60. To date, this is the first study to examine H2 production by anaerobic digestion of microalgal
biomass. The results indicate that H2 production is feasible but higher yields could
be achieved by optimization of the bioprocess conditions including biomass
pretreatment.
INTRODUCTION
Microalgal biomass ties into multiple areas of bioenergy production, such
as photosynthetic H2 and anaerobic biogas production. Microalgae can produce
H2 by coupling photosynthesis with hydrogenases or nitrogenases present in
intracellular membranes (Benemann, 2000; Melis and Happe, 2001; Ghirardi et
al., 2009). Similar biophotolysis can also occur in cyanobacteria. The efficiency
of converting light energy into chemical energy in H2 ranges between 3-15 %, but
under ambient environmental conditions conversions are less than 3 %
(Benemann, 2000; Ghirardi et al., 2009). Direct H2 production from microalgae as
a source of sustainable energy is unlikely due to low efficiencies.
Multiple approaches have been tested for microalgae-coupled H2
production. Some microalgae, such as the often studied green alga
Chlamydomonas reinhardtii, can be grown under conditions known to encourage
35
intracellular accumulation of starch (Ike et al., 1996; 1997; Kawaguchi et al.,
2001). The biomass is then digested through acid or heat hydrolysis and fed to
starch hydrolyzing heterotrophs, such as Rhodobacter sphaeroides, which can
produce H2 fermentatively (Ike et al., 1996). Kawaguchi et al. (2001) tested a
three-phase approach whereby C. reinhardtii biomass was first grown to
accumulate starch, followed by conversion of the starch to lactic acid by bacteria,
and lastly, the fermentative production of H2 from lactic acid by undefined
bacteria. These approaches have produced H2 yields too low to be feasible for
industrial applications (Levin et al., 2004).
Microalgae have been used in wastewater treatment, specifically during secondary treatment processes to assimilate nutrients into biomass (Kojima and
Lee, 2001). Several decades ago, bulk microalgal biomass from wastewater treatment was recognized as a readily accessible feedstock for anaerobic digestion
(Golueke et al., 1957; Oswald and Golueke, 1960). Hernández and Córdoba
(1993) demonstrated that biogas could be produced from Chlorella vulgaris biomass upon anaerobic digestion. Of the total biogas, 68 to 76 % was CH4 and
total gas yields ranged between 0.40 and 0.45 l/g COD removed. Yen and Brune
(2007) showed the feasibility of anaerobic co-digestion of waste paper and microalgal sludge, yielding up to 1.6 l (ca. 70 mmol) CH4/l·d. Effluents from
anaerobic digesters, especially those in the olive oil industry, have also been used
as a medium for microalgal production (Hodaifa et al., 2008; Córdoba et al.,
2008). Currently, the economic sustainability of biodiesel production from
36 microalgal lipids is believed to be dependent on anaerobic digestion of residual biomass for additional biofuels (Sialve et al., 2009).
Biodiesel production from microalgae has four major phases: mass growth of microalgal biomass in photobioreactors, dewatering of biomass, lipid extraction, and processing of the lipid fraction for biodiesel production (Lardon et al., 2009; Mata et al., 2010). Residual biomass following lipid extraction has no further use for biodiesel production but could provide a feedstock for additional, sustainable energy production via anaerobic digestion processes. The ratio of carbon to nitrogen (C:N) in the microalgal biomass is relatively low (< 10) which could be an issue for anaerobic digestion (Parkin and Owen, 1986). Therefore, additional biodegradable C-rich compounds may be beneficial as a co-substrate with spent algal biomass (Hernández and Córdoba, 1993; Yen and Brune, 2007).
Plant biomass, especially cellulose, provides an abundant feedstock that has been extensively studied and promoted in the sustainable energy field (Perlack et al.,
2005; DOE, 2007). As cellulose feedstock is C-rich, it provides an ideal co- substrate for anaerobic digestion of microalgal biomass. Anaerobic digestion can be directed toward CH4 or H2 production but only H2 provides a potential energy source that is sustainable and carbonless (Ren et al., 2009).
The purpose of this study was to examine the feasibility of H2 production via anaerobic digestion of microalgal biomass. Dunaliella tertiolecta and
Chlorella vulgaris, both green algae, were grown for mass harvest in this study. A thermophilic, cellulolytic microbial consortium was initially enriched with
37
mixtures of the feedstocks, cellulose and microalgal biomass, before final testing for biogas and metabolite production.
MATERIALS AND METHODS
Cultivation and harvesting of microalgae
The freshwater microalga Chlorella vulgaris (strain 211/11B, Culture
Collection of Algae and Protozoa, Dunstaffnage Marine Laboratory, Oban,
Argyll, UK) and marine Dunaliella tertiolecta (strain 13.86, Sammlung von
Algenkulturen des Instituts für Pflanzenwissenschaften der Universität Göttingen,
Germany) were selected for this study. Both green algae have been the subject of
recent research endeavors and discussed as potential sources of biofuel. C.
vulgaris has a typical eukaryotic algal cell wall, whereas D. tertiolecta has a thin
cell wall (Sialve et al., 2009; Ben-Amotz et al., 2010).
The algae were cultured autotrophically in 20 l photobioreactors.
Photobioreactors were cylindrical (∅ 0.16 m), constructed of polyethylene, and sparged with 0.5 l/l·min filtered air (0.3 μm, Whatman Hepa-Vent). Light was provided by cool white fluorescent tubes and the incident photosynthetic photon flux density averaged 225 μmol photons of photosynthetically active radiation m2/s. C. vulgaris was grown in Jaworski’s medium
(http://www.ccap.ac.uk/media/documents/JM.pdf) prepared with Milli-Q water.
D. tertiolecta was cultured in Walne’s medium
(http://www.ccap.ac.uk/media/documents/Walnes.pdf) made of sterilized
38
seawater (~3.5 % salinity).
Microalgal biomass was harvested via flocculation and centrifugation. For
D. tertiolecta, the pH was adjusted with NaOH to approximately pH 9.5 for
flocculation (Horiuchi et al., 2003). C. vulgaris was flocculated by adding 0.08 g
chitosan/l and adjusting the pH to 7.0. Biomass was concentrated by
centrifugation at 1000 g for 10 min and removing the supernatant. The thick
slurry was stored at -20 °C. The pH of each slurry was adjusted to pH 7.0. Slurry
volatile solid (VS) concentrations were 0.094 and 0.14 g/l for D. tertiolecta and
C. vulgaris, respectively.
Microbial consortium
The microbial consortium, designated TC60, originated from the interior
of a compost pile and subcultures had been maintained with cellulose. The culture
can also grow on hemicellulose, pectin, and starch. The identification of the
dominant species based on 16S rRNA gene sequences shows a diversity of
Thermoanaerobacter and Clostridium spp. Based on qualitative PCR-DGGE analysis, the dominant species vary with substrate and other incubation
conditions.
The TC60 culture was maintained anaerobically (N2 headspace) in
medium that contained (per liter): 2 g trypticase, 1 g yeast extract, 4 g Na2CO3,
0.23 g K2HPO4, 0.18 g KH2PO4, 0.36 g NH4Cl, 0.04 g NaCl, 0.09 g
MgSO4·7H2O, 0.06 g CaCl2·2H2O, 0.25 g cysteine-HCl, 0.25 g Na2S·9H2O, 2 mg
CoCl2·6H2O, 0.16 mg Na2SeO4, and 0.09 mg NiCl2·6H2O. Cellulose (Sigmacell,
39
Type 20) was purchased from Sigma-Aldrich. Microalgal biomass and cellulose
were added to a combined concentration of 4 g volatile solids (VS)/l. Cultures (50
ml) in 125 ml serum bottles were inoculated (10 % v/v) and degassed with N2
prior to incubation at 60 °C with 180 rev/min. Samples were withdrawn
anaerobically, centrifuged at 16,000 g for 10 min, and the fractions were stored at
-20 °C until further analysis.
Four consecutive enrichment passages were completed before the fifth
enrichment was analyzed in detail. The initial four enrichment stages were
incubated in duplicate as follow: D. tertiolecta to cellulose (1:1 VS/VS), D.
tertiolecta only, and the same two conditions with C. vulgaris. Appropriate
controls included D. tertiolecta without TC60, C. vulgaris without TC60, TC60
with cellulose, and TC60 with medium only. The fourth enrichment of 1:1
microalgal biomass to cellulose was used to inoculate 1:2, 2:1, and 1:0 (VS/VS) ratios of substrate in the fifth enrichment. Each condition was tested in duplicate.
At the end of the incubation, total and volatile solids (TS and VS, respectively) were measured according to standard methods (Eaton et al., 2005). For the fifth enrichment, TS and VS were measured prior to incubation and after 10 days.
Analytical methods
Supernatants for HPLC analysis of short chain fatty acids (SCFAs) were cleaned via solid phase extraction (C18-T), diluted with Milli-Q water, and filtered through a 0.2 µm PTFE filter (Pall) as discussed in Chapter 2. A guard column, 5 cm x 4.6 mm ID (Supelguard H) and a cation-exchange column, 30 cm
40
x 7.8 mm ID (Supelcogel C-610H) were used with an autosampler (Spectra-
Physica AS 3000) and a UV detector set to 210 nm (Spectra-Physics SP100). A
Beckman 114M HPLC pump maintained a flow rate of 0.5 ml/min of the mobile phase, 0.1 % o-phosphoric acid (Peu et al., 2004). Run times were 65 min per sample with 100 µl injections at 70 min intervals. Chromatographs were analyzed through a computer interface equipped with the Clarity Chromatography Station
(DataApex).
Overpressures were measured with a sterile syringe immediately after removal from the incubator and headspace samples were manually injected into the GC. Gases were analyzed with a Shimadzu GC-2014 equipped with a thermal
conductivity detector and a Porapak N (2 m length x 2 mm ID) column (Sigma-
Aldrich). The carrier gas, nitrogen, was maintained at 20 ml/min. Temperatures were 110 °C for the injector and detector while the column oven was kept at 80
°C. The chromatographs were analyzed with GC Solution Analysis software
(Shimadzu).
The ammonium concentration in supernatants was measured fluorimetrically according to Holmes et al. (1999). Samples were diluted with
Milli-Q water and analysis was conducted using a Hitachi F2000 fluorometer.
Excitation was measured at 360 nm and emission at 420 nm.
Solids for carbon and nitrogen analysis (C and N, respectively) were dried at 80 °C for 72 h followed by measurement with a Thermo Electron FlashEA
1112 analyzer (Thermo Scientific). The instrument was calibrated using
41
sulfanilamide, 2,5-bis(5-tert-butyl-benzoxazol-2-yl)thiophene, L-cystine, and DL-
methionine as standards.
RESULTS AND DISCUSSION
Enrichment improves H2 production
By the fourth enrichment, headspace H2 and CO2 levels had increased
(Figure 3.1). CH4 was not detected under any experimental conditions in this
work. Slight changes in gas yields were seen between TC60 enrichments when
grown on cellulose. The TS and VS levels were relatively constant from one
enrichment to another (16.0-17.8 g TS/l and 8.2-9.3 g VS/l for Dunaliella; 9.2-
11.2 g TS/l and 3.8-5.3 g VS/l for Chlorella). Following enrichment, H2 production increased from 0.2 to 2.1 mmol H2/g VS for D. tertiolecta and 0.3 to
4.2 mmol H2/g VS for C. vulgaris when inoculated with TC60 (Figure 3.1C). H2
levels also increased when the microalgal biomass was not inoculated with TC60
(Figure 3.1D). Autoclaved anaerobic controls showed no gas production, ruling
out abiotic H2 production.
Microscopic examination revealed a diverse microbial population associated with the microalgal biomass controls after enrichment. Both the
Dunaliella and Chlorella slurries contained bacteria and protozoa, which included
microorganisms capable of producing H2 under the thermophilic, anaerobic conditions, specifically with C. vulgaris biomass. Heterotrophic contamination may have been associated with the original stock cultures or introduced during the
42
cultivation and handling of microalgal biomass.
Analysis of the fifth enrichment
The fourth enrichment of the 1:1 (VS/VS) ratio of microalgal biomass to
cellulose was used to inoculate the fifth enrichment, which received 1:2, 2:1, or
1:0 ratios of microalgal biomass to cellulose. Most gas production occurred within
the first three days under all conditions. In addition to H2, CO2 was monitored in
order to observe the growth of TC60. Direct measurement of microalgal or
bacterial biomass was not possible due to unknown quantities of incomplete
hydrolysis products and lysed cells.
As shown in Figure 3.2, cellulose-fed TC60 yielded 5.2 mmol H2 and 8.9
mmol CO2/g VS by day 3. When grown on a ratio of 1:2 D. tertiolecta biomass
and cellulose, the H2 levels increased whereas the CO2 levels were relatively
constant and similar to those in cellulose-fed cultures (Figure 3.2A, B). The
H2:CO2 ratio increased from 0.8 with cellulose to 1.1 (Table 3.1). With a 2:1 ratio,
the gas yields were comparable to those in cellulose-fed cultures (7.7 mmol H2
and 8.6 mmol CO2/g VS) and the H2:CO2 ratio increased to 1.8. TC60 fed only D.
tertiolecta without cellulose yielded a very high H2:CO2 ratio but individual gas
yields were relatively low (Table 3.1, Figure 3.2). When D. tertiolecta biomass was not inoculated, both the H2 and CO2 yields remained low. These results
indicated that D. tertiolecta biomass served as an additional source of nutrients
for TC60 and enhanced H2 production when cellulose was present. The heterotrophs associated with the D. tertiolecta biomass were relatively inactive
43
under conditions in this study. Heterotrophs in the D. tertiolecta culture are most likely halophiles, or at least salt-tolerant (~3.5 % salinity), and their activity would be negligible in the low salinity medium used in this study.
In the C. vulgaris-fed TC60 culture, growth remained low according to the
CO2 levels, and the H2 levels under all conditions were within standard error
measurements, averaging approximately 3.0 mmol H2 and 2.5 mmol CO2/g VS
(Figure 3.2D). These H2 levels were comparable to those observed when TC60
was fed cellulose. Microscopic examination showed a diverse prokaryotic
population in the C. vulgaris slurry. H2 was produced without inoculation of
TC60, indicating anaerobic activity of satellite heterotrophs associated with C. vulgaris. Due to the comparable H2 yields regardless of the substrate ratio, it is
concluded that these heterotrophs used organic compounds in the medium, e.g.,
yeast extract, trypticase, and microalgal excreta.
The ammonium and SCFA concentrations were also monitored in the
anaerobic digestion experiments. Formation of ammonium from protein
degradation has been cited as a possible concern for microalgal digestion (Tam
and Wong, 1996; Yen and Brune, 2007; Sialve et al., 2009). However, ammonium
concentrations, up to 17.7 mM, did not have adverse effects on growth of TC60 and gas production (data not shown). When TC60 was fed cellulose, the concentrations of ammonium remained below 10 mM (Table 3.2). Without cellulose or microalgal biomass, the ammonium yields increased to 30 mM.
Ammonium concentrations increased with the concentration of D. tertiolecta
44
biomass, indicating enhanced ammonification of N-containing compounds in the
medium. When the ratio of D. tertiolecta to cellulose was 1:2, the ammonium
concentration was 16.3 mM and increased to 21.5 mM when TC60 was supplied
with D. tertiolecta without cellulose. With C. vulgaris biomass, ammonium concentrations were relatively high under all experimental conditions (24.3-27.4 mM). These results indicated ammonification by satellite heterotrophs regardless of TC60 inoculation.
The concentrations and trends in SCFA profiles varied with experimental conditions. When TC60 was grown only on cellulose, the dominant SCFAs were lactate (22.1 mM), butyrate (18.1 mM) and acetate (10.9 mM) as seen inTable
3.2. In the presence of D. tertiolecta or C. vulgaris biomass, lactate concentration was < 4 mM, whereas both acetate and butyrate levels increased (Table 3.2,
Figure 3.3). Lactic acid fermentation was suppressed by the presence of microalgal biomass, therefore allowing for increased H2 yields. In the presence of
D. tertiolecta and cellulose, the SCFA profiles shifted towards acetate and
butyrate pathways which are known to be coupled with hydrogenases.
Total SCFA concentrations increased with the amount of C. vulgaris
biomass (Table 3.2). This association appeared to be the result of heterotrophic
microorganisms present in the microalgal slurry rather than TC60. The controls
without TC60 showed acid production at levels four times higher with C. vulgaris
than with the D. tertiolecta control (Figure 3.3). The relatively high levels of
SCFAs in the presence of C. vulgaris, even without TC60 inoculation, suggest
45
that the heterotrophs in the algal slurry were active in producing butyrate (50 % of
total SCFA). D. tertiolecta without TC60 had lower acid production than TC60
grown in medium only, indicating utilization of acetate and butyric acid by the
satellite organisms.
Assessment of microalgal biomass digestion
To assess the biodegradability of microbial biomass, samples of solids
were analyzed for C, N, TS, and VS concentrations. These results showed relatively little change in either the TS or VS over incubation. The initial C concentration in the solids was comparable across all substrate ratios in C.
vulgaris-fed samples, suggesting that the biomass remained more or less intact
under all conditions. In contrast, the initial C content of D. tertiolecta-fed samples
varied (Figure 3.4). D. tertiolecta cells were prone to lysis as seen
microscopically, releasing soluble nutrients and lowering the C concentration in
the solid fraction. Therefore, the Dunaliella-fed TC60 cultures contained
cytosolic compounds which contributed to the increased gas production and
reduced lag time. The thin cell wall and tendency of lysis of Dunaliella biomass
when removed from high salinity medium are useful attributes for anaerobic
digestion.
The C content in the solid fraction decreased after incubation in samples
with a combination of D. tertiolecta and cellulose (Figure 3.4). When TC60 was
fed D. tertiolecta only, the C content did not change after 10 days of incubation,
indicating negligible digestion of the insoluble biomass. These results suggest that
46
the soluble compounds rather than the solid fraction of the D. tertiolecta biomass were the source for increased gas production by TC60. The level of C in solids did not change after 10 days of incubation in samples containing C. vulgaris
(Figure 3.4). C. vulgaris biomass appeared to suppress cellulose utilization by
TC60, consistent with the low production of gas and other indicators of poor growth.
After incubation, the N content of solids increased from 0.7 to 6.2 mmol/g dry wt and 1.3 to 3.6 mmol/g dry wt for 1:2 and 2:1 D. tertiolecta to cellulose, respectively (Figure 3.4). This trend was consistent with the utilization of cellulose for biomass synthesis by TC60 during anaerobic digestion. When only
D. tertiolecta was fed to TC60, the N content in the solids did not increase, again indicating solid N utilization and bacterial biomass growth gave a net balance.
When TC60 was grown in the presence of C. vulgaris biomass, the level of N was
relatively unaffected (Figure 3.4), consistent with the apparent recalcitrance of
Chlorella biomass to anaerobic digestion.
When TC60 was grown on cellulose, decreases in the C:N ratios of solids
were attributed to biomass growth. The C content stayed constant whereas the N levels in solids increased by day 10. Similarly, decreases in the C:N ratios were
also seen in solids from TC60 cultures fed 1:2 and 2:1 of D. tertiolecta to
cellulose (Figure 3.4). Ratios did not change in TC60 fed D. tertiolecta only or in
uninoculated D. tertiolecta, agreeing with the lack of metabolic activity in these
samples. In the case of Chlorella, the C:N ratios of solids changed relatively little
47
from day 0 to 10. These data suggested that growth of TC60 and satellite
heterotrophs took place at the expense of soluble substrates. Metabolic data
indicated that it was the satellite heterotrophs, not TC60, that were the active
microorganisms in C. vulgaris-fed samples. Microscopic examination showed that
the C. vulgaris biomass remained intact and accounts for a large percentage of the
dry weight. Therefore, the microalgal biomass would mask the relatively minor
increase of N due to satellite heterotrophic growth.
CONCLUSIONS
This study focused on H2 generation through anaerobic degradative
metabolism and dark fermentative pathways. The overall energy balance of the
bioprocess was not compiled because this was an initial feasibility study with no
optimization. The calorific yields calculated from the maximum H2 yields were
equal to 1.86 and 1.01 kJ/g VS for the 1:2 D. tertiolecta to cellulose and 4 g/l C. vulgaris, respectively. These yields indicate major differences in the biodegradability of the two algal biomass substrates. The marine algae, Dunaliella tertiolecta, lysed readily and thereby provided additional nutrients for cellulolytic activity and H2 accumulation by TC60. Heterotrophs associated with the marine species were deemed to have a negligible effect on the digestion. In contrast, the
freshwater Chlorella vulgaris biomass remained recalcitrant and suppressed TC60
activity. In spite of the thermophilic conditions, heterotrophs associated with
Chlorella biomass produced H2 yields similar to those obtained with TC60.
48
Hydrolytic pretreatment of microalgal slurries was not tested for Chlorella biomass. The yields obtained in this study indicate the need for improvement of
H2 yields through biomass pretreatment and process optimization.
49
Table 3.1. H2:CO2 ratios (± standard error) for TC60 grown on various substrates.
Condition H2:CO2 Day 2 Day 6 No Added Substrate 1.98 1.16 4 g/l Cellulose 0.83 ± 0.02 0.61 ± 0.01 1:2 Dunaliella:Cellulose 1.13 ± 0.01 0.90 ± 0.02 2:1 Dunaliella:Cellulose 1.81± 0.25 1.24 ± 0.20 4 g/l Dunaliella 3.69 ± 0.03 1.93 ± 0.10 4 g/ Dunaliella (no TC60) 0.00 0.82 1:2 Chlorella:Cellulose 4.71 ± 0.59 1.67 ± 0.40 2:1 Chlorella:Cellulose 3.03 ± 1.45 1.41 ± 0.07 4 g/l Chlorella 3.00 ± 0.84 1.60 ± 0.02 4 g/l Chlorella (no TC60) 2.35 2.32
50
Table 3.2. Maximum lactate, acetate, butyrate, and ammonium concentrations (± standard error) during the 5th enrichment.
Condition Maximum Metabolite Concentration (mM)
Lactate Acetate Butyrate Total Ammonium
No Added Substrate 0.24 7.53 17.64 37.65 28.89
4 g/l Cellulose 22.06 ± 2.86 10.93 ± 2.74 18.10 ± 0.69 67.33 9.84 ± 5.70
1:2 Dunaliella:Cellulose 3.75 ± 1.55 16.34 ± 3.32 19.75 ± 1.97 66.33 16.31 ± 1.88
51 2:1 Dunaliella:Cellulose 1.38 ±0.89 15.48 ± 0.80 17.89 ± 0.74 50.71 18.28 ± 1.44
4 g/l Dunaliella 1.06 8.17 ± 0.84 16.37 ± 0.40 50.97 21.52 ± 0.59
4 g/ Dunaliella (no TC60) 1.13 5.28 3.51 23.90 14.86
1:2 Chlorella:Cellulose 0.70 8.82 ± 1.12 22.61 ± 2.80 53.62 24.31 ± 2.82
2:1 Chlorella:Cellulose 0.82 ± 0.02 10.43 ± 2.13 29.42 ± 2.11 63.30 26.84 ± 2.45
4 g/l Chlorella 1.88 11.32 ± 1.49 35.36 ± 0.85 72.80 27.45 ± 3.80
4 g/l Chlorella (no TC60) 0.97 12.68 34.09 80.19 25.76
51
10 A C 4 g/l D, First 4 g/l D, Fifth 8 4 g/l Ch, First 4 g/l Ch, Fifth 6 1:1 D:C, First 1:1 D:C, Second 4 1:1 D:C, Third 1:1 D:C, Fourth Yield (mmol/g VS) (mmol/g Yield
2 2 H
0 10 B 1:1 Ch:C, First D 4 g/l D, First 1:1 Ch:C, Second 4 g/l D, Fifth 8 1:1 Ch:C, Third 4 g/l Ch, First 1:1 Ch:C, Fourth 4 g/l Ch, Fifth
52 6
4 Yield (mmol/g VS) (mmol/g Yield
2 2 H
0
012345 0123456 Time (d) Time (d)
Figure 3.1. Improvement of H2 yields over sequential enrichment cultures. (A) 1:1 (VS/VS) D. tertiolecta to cellulose with TC60; (B) 1:1 C. vulgaris to cellulose with TC60; (C) first and fifth enrichments of TC60 with only microalgal biomass; (D) first and fifth enrichments of microalgal biomass without TC60. Vertical bars indicate the standard error. Abbreviations: C, cellulose; Ch, C. vulgaris; D, D. tertiolecta. 52
10 4 g/l C AC1:2 MA:C 2:1 MA:C 8 4 g/l MA 4 g/l MA, no TC60 6
4 Yield (mmol/g VS) (mmol/g Yield
2 2 H
0 10 B D
53 8
6
4 Yield (mmol/g VS) (mmol/g Yield 2 2 CO
0
01230123 Time (d) Time (d)
Figure 3.2. Cumulative gas yields during the first three days of the fifth enrichment of TC60 with different substrate conditions. (A) H2 and (B) CO2 yields for samples with D. tertiolecta; (C) H2 and (D) and CO2 yields for samples with C. vulgaris. As a reference, gas yields for cellulose-fed TC60 are included (dashed line). H2 and CO2 yields are included for uninoculated microalgal biomass. Abbreviations: C, cellulose; MA, microalgal biomass. 53
80 A D Lactic Acetic Butyric 60 Total SCFA
40
20
0
80 B E
60
40
20 SCFA Concentration (mM) 0
80 C F
60
40
20
0 0246810 0246810 Time (d) Time (d)
Figure 3.3. Changes in lactate, acetate, butyrate and total SCFA concentrations over time for single substrate conditions. (A) 4 g/l cellulose with TC60; (B) 4 g/l D. tertiolecta with TC60; (C) 4 g/l C. vulgaris with TC60; (D) TC60 without added substrate; (E) 4 g/l D. tertiolecta without TC60; (F) 4 g/l C. vulgaris without TC60.
54
50 A Day 0 D 40 Day 10
30
20
C (mmol/g dry wt) dry (mmol/g C 10
0 C :C :C D 0 h 0 g/l D D g/l C6 h:C h:C l C C6 4 1:2 2:1 4 o T 2 C 1 C g/ T , n 1: 2: 4 , no /l D Ch 4 g g/l 10 4 B E 8
6
4
N (mmol/g dry wt) dry N (mmol/g 2
0 C C C D 0 /l D: D: /l C6 h:C h:C Ch 60 4 g :2 :1 4 g T C C g/l TC 1 2 , no 1:2 2:1 4 no l D h, g/ /l C 4 4 g 70 C F 60
50
40
30 C:N Ratio 20
10
0 0 h 0 /l C D:C D:C /l D C6 h:C h:C C 6 4 g :2 :1 4 g T C C g/l TC 1 2 no 1:2 2:1 4 no D, h, g/l /l C 4 4 g
Figure 3.4. C and N analysis of solid fractions and the corresponding C:N ratios for D. tertiolecta (A-C) and C. vulgaris (D-F). The cultures were inoculated with TC60 unless otherwise noted. Abbreviations: C, cellulose; Ch, C. vulgaris; D, D. tertiolecta.
55
CHAPTER 4: A Thermophilic Microbial Fuel Cell Design
ABSTRACT
Microbial fuel cells (MFCs) are reactors able to generate electricity by
capturing electrons from the anaerobic respiratory processes of microorganisms.
While the majority of MFCs have been tested at ambient or mesophilic temperatures, thermophilic systems warrant evaluation because of the potential for increased microbial activity rates on the anode. MFC studies at elevated temperatures have been scattered, using designs that are already established, specifically air-cathode single chambers and two-chamber designs. This study was prompted by our previous attempts that showed an increased amount of evaporation in thermophilic MFCs, adding unnecessary technical difficulties and causing excessive maintenance. In this paper, we describe a thermophilic MFC design that prevents evaporation. The design was tested at 57 °C with an anaerobic, thermophilic consortium that respired with glucose to generate a power density of 375 mW/m2 after 590 hrs. Polarization and voltage data showed that the
design works in the batch mode but the design allows for adaption to continuous operation.
56
INTRODUCTION
In the last few years, interest in alternative energy sources has increased
greatly due to greenhouse gases and changes in fossil fuel-based policies and economics. Microbial fuel cells (MFCs) are one alternative that take advantage of the ability of microorganisms to couple anaerobic respiration to the reduction of external electron acceptors (Franks and Nevin, 2010; Hamelers et al., 2010). In
MFCs the electrons travel through the anode and an external resistor, which generates a current, to the cathode where the circuit is completed by pairing with protons. Although these first-generation MFC systems are unable to support high power demand, they have been shown to run small electric devices such as light bulbs, calculators, clocks, and cell phones (Franks and Nevin, 2010; Hamelers et al., 2010).
Thermophilic metabolism offers many advantages over its mesophilic counterparts and could prove the same for MFC technology. Thermophiles, while still vastly unknown, have great potential in bioprocesses for wastewater treatment and bioenergy production. For example, cellulose biodegradation occurs faster between 50 and 65 °C than at lower temperatures (Kumar et al., 2008).
Arguments for thermophilic MFC applications are similar to the reasons for thermophilic anaerobic digestion: increased rates, improved efficiency, and the elimination of many human and animal pathogens (Suryawanshi et al., 2010).
Only scattered themophilic MFC studies have been reported, due to limitations in
57
the reactor design. Our previous modular MFC design (Rismani-Yazdi et al.,
2007), shown to work under mesophilic conditions, 39 °C, was not usable at 60
°C. This design was not a closed system and permitted evaporation, specifically
from the cathode chamber. As the catholyte evaporated anolyte diffused through
the proton permeable membrane into the cathode compartment and evaporated. In
addition to all of the catholyte, between 50 and 75 % of the anode working volume was lost within 2 days. The concentrated anolyte could be detrimental to microbial metabolism and activity due to enrichment of metabolites and cell debris.
Thermophilic studies have not addressed these problems other than to note periodic anolyte or catholyte replacement (Marshall and May, 2009; Mathis et al.,
2008). Jong and co-workers (2006) utilized continuous flow, rather than batch or fed-batch, which allowed for a constant replacement of anolyte and catholyte in their thermophilic MFC. The best MFC performance was with 338 ml/hr and 11 ml/hr for the catholyte and anolyte flow rates, respectively (Jong et al., 2006). The catholyte required a higher flow rate likely due to the continuous evaporation of liquid from the open cathode chamber. While this prevents drastic liquid loss, electricity production then relies on the electrochemically active biofilm alone since suspended cells are removed with the continuous flow of the anolyte.
Several MFC studies have tested a range of operation temperatures and
demonstrated consistently higher power densities with higher temperatures,
within the limits of the microbial populations (Choi et al., 2004; Moon et al.,
58
2006; Min et al., 2008; Cercado-Quezada et al., 2010).
The inadequacy of previous MFC designs at elevated temperatures prompted this study to develop an MFC design that prevents liquid evaporation.
This technical problem has hindered research towards effective utilization of thermophilic MFC technology. The design in this study was tested successfully with a thermophilic consortium and glucose as a substrate.
MATERIALS AND METHODS
Thermophilic MFC design
The MFC design presented here is based on the original concept presented by Min and Angeladaki (2008). As shown in Figure 4.1A, a custom-designed glass reactor (158 mm OD x 137 mm height) with an inner chamber (90 mm ID x
115 mm height) was used as the anode chamber (Laborexin Oy, Tampere,
Finland). The reactor was closed using a glass lid with four access ports. The lid had a ground flange and was clamped to the reactor to ensure a tight fit. The working volume was 450 ml in this study. The reactor was surrounded by a glass water jacket (94 mm height) to maintain a constant temperature in the anode chamber. The glass lid and reactor were cleaned thoroughly with ethanol prior to
MFC setup.
The cathode chamber was designed from a polyacrylamide tube (45 mm
OD x 41 mm ID x 60 mm height) (Figure 4.1B). The electrode consisted of two graphite discs, a disc of stainless steel foil between in order to facilitate charge
59
transfer, and two stainless steel screws to hold the layers together. A high temperature graphite rod (50.8 mm diameter) was sliced and shaved for the
electrode (McMaster-Carr, Elmhurst, IL). The first graphite disc was 6 mm height
x 41 mm diameter with two holes of 3 mm diameter, 20 mm apart. The second
graphite disc was 12 mm height x 41 mm diameter with an inset ring of 4 mm
height and 3.75 mm depth. This disc had two threaded (3 x 0.5 mm thread) holes of 8 mm depth at 20 mm apart. Within the inset of the second disc, a rubber o-ring
gasket (standard size AS568A-202) was placed to prevent leaks during operation.
The three layers were held together with stainless steel screws to ensure a tight connection. With slight heating of the polyacrylamide tube, the three-layer electrode could be gently pushed up into the tube. The polyacrylamide lid was made from two pieces of polyacrylamide glued together containing two holes of 6 mm diameter, 20 mm apart. The holes allowed for norprene tubing (L/S 15,
Masterflex, Vernon Hills, IL) to enter the chamber. One of the tubes was connected to a fish tank pump while the other allowed for excess air to escape without contaminating the anodic headspace. A copper wire was connected to a wire terminal that surrounded one screw. The other end of the wire was extended through the outlet air tube to the potentiostat after the external resistor, 100 Ω.
MFC setup
An anaerobic microbial consortium (TC60) was used as the inoculum for thermophilic MFCs. TC60 originated from the interior of thermophilic compost and subcultures had been maintained with cellulose at 60 °C. This anaerobic
60
consortium has been previously used for decomposition of cellulose and other
biomass constituents (Carver et al., 2010; 2011). Dominant species in the TC60
consortium consist of Thermoanaerobacter, Clostridium, and Lutispora spp. The
culture was maintained anaerobically (N2 headspace) in a medium previously
described (Carver et al., 2010). The TC60 inoculum had been grown at 60 °C for
48 hrs on 4 g/l cellulose (Sigmacell Type 20, Sigma-Aldrich, St. Louis, MO).
TC60 was added (10 % v/v) to the anode chamber with a combination of 1.66
mM acetate and 25 mM glucose as the substrate.
The anode and cathode graphite was prepared according to previous
studies (Rismani-Yazdi et al., 2007). The anode (surface area of 40 cm2) was
suspended in the anolyte as the wire was threaded through a septum port in the
reactor lid. The cathode chamber was positioned with the air inlet and outlet tubes
threaded with o-rings through two separate ports. The fourth lid port was sealed
during this study. At this point, assembly and manipulations to the MFC were
completed in an anaerobic chamber with N2/CO2 head space. After adding the
nutrient medium, inoculum, and substrate in the MFC, the lid was secured using
silicon grease and a metal clamp. Silicon grease was also placed around cathode
chamber connections exposed to the MFC headspace. Upon removal from the
anaerobe chamber, ports on the lid were reinforced with additional silicon grease to prevent ingress of oxygen. The water jacket was connected to a water bath to maintain an internal MFC temperature of 57 ± 1 °C. The anolyte was continuously stirred using a magnetic stir plate and adjusted daily to
61 approximately 6.5 pH using 1 M NaOH. Semi-batch feeding provided a final concentration of 25 or 50 mM glucose.
Performance analysis
The electrical output of the thermophilic MFC was monitored by measuring the potential difference (voltage) at one minute intervals using a data acquisition unit (DATAQ Instruments, Akron, OH). The power density (W/m2) was calculated according to the equation P = I×V/A, where V is the voltage (V), I is the current (amps), and A is the surface area of the electrode (m2). Polarization tests were completed as previously described (Rismani-Yazdi et al., 2007).
RESULTS AND DISCUSSION
The thermophilic, cellulolytic consortium, TC60, with optimal growth at
60 °C was of interest for electrochemical analysis of the MFC design in this work.
Initial attempts in two-chamber MFC modules (Rismani-Yazdi et al., 2007) were unsuccessful because of rapid, extensive evaporation. Half of the anode working volume, 75 ml, was lost within two days, necessitating frequent interruptions because of anolyte and catholyte replacement in addition to pH adjustment and semi-batch feeding.
In a previous study conducted at 30 °C, Min and Angeladaki (2008) utilized an anaerobic, glass reactor design in combination with a cathode chamber submersed in the anolyte. The design was a foundation for the thermophilic MFC reactor described in this study. The cathode chamber design was simplified to
62 facilitate easy access and modular construction (Figure 4.1B). Rather than extensive layers of gaskets, membrane, carbon paper, and polycarbonate as in the previous design (Min and Angelidaki, 2008), this cathode chamber had a single rubber o-ring able to prevent liquid or air crossover. The components of the cathode assembly, including the stainless steel screws, foil, and graphite discs, have all been shown to be conductive and were securely connected (Dumas et al.,
2007).
The design was tested in two MFCs runs, each over 500 hrs and without liquid loss. Thus, this design eliminated evaporation. The first system was operated with 4 g/l cellulose as a substrate at 60 °C. The glass reactor achieved and maintained anaerobiosis within 30 min of inoculation as shown by no color change following resazurin addition. While it is possible that some trace amount of O2 was momentarily present in the headspace and exchanged at the gas-liquid interface, this would have been quickly consumed by facultative anaerobes and was, therefore, considered to have an insignificant effect on the overall MFC performance.
TC60 did not produce a stable, reproducible current in the MFC with cellulose as the substrate. Following a power density at 250 hrs of 337 mW/m2, the current dropped rapidly and recovery was not achieved even after 800 hrs of operation (data not shown). The system failure was considered to be due to biological constraints of the consortium; the cellulose degraders present were likely fermentative and unable to complete electron transfer with the anode. Thus
63 a build-up of metabolites, such as volatile fatty acids, would have prevented utilization of glucose for anode-coupled respiration. In addition, accumulation of glucose and cellobiose could have triggered feedback inhibition of cellulase activity (Andrić et al., 2010).
The second MFC test was run with TC60 and used glucose as a substrate.
This trial was successful as indicated by spiking power outputs following pH adjustment and five consecutive substrate additions (Figure 4.2). The lag time was
250 hrs with the highest potential recorded prior the second performance analysis at 590 hrs, 387 mV. This potential is comparable to the range of values, 300 - 500 mV, obtained with glucose-fed mesophilic MFCs (Logan, 2009). However, the range is usually broad because each MFC study differs in experimental parameters such as the glucose concentration, inoculum, MFC design, and external resistence, which all affect the power density. The system was successfully run for approximately 600 hrs at which point the experiment was terminated. It should be noted that that extensive acid formation and inadequate buffering in the medium required pH adjustment daily.
Two performance analyses of the glucose-fed thermophilic MFC showed improved performance over the 120 hr difference with an increased maximum power of 3.3 to 4.5 mW/m2 (Figure 4.3A). The polarization curve has three distinct sections of irreversible voltage losses: activation loss, ohmic loss, and mass transfer loss (O’Hayre et al., 2006). The typical initial and drastic voltage drop was not apparent, indicating lower than normal activation losses (Figure
64
4.3B). This is attributed to increased reaction rates at thermophilic temperatures that lowered the activation energy and therefore the voltage necessary to maintain active, anaerobic metabolism. Ohmic loss can be observed in the center of the polarization curve with the gradual decrease of voltage as current density increases (Figure 4.3B). The slope of this overpotential section, equivalent to voltage over current, yielded an internal resistance of 9.25 ± 0.15 Ω. This value is in the general range reported for other MFCs, although the experimental conditions are not comparable among the studies reviewed in the literature (Du et al., 2007; He et al., 2006).
The voltage overpotential resulting from mass transfer processes could not be assessed due to a power overshoot curve (Min et al., 2008; Ieropoulos et al.,
2010; Nien et al., 2011; Watson and Logan, 2011). Initially, Min et al. (2008) suggested that the overshoot effect was related to mass transfer, but increased agitation of the anolyte and catholyte did not modify the unusual curve.
Ieropoulos et al. (2010) hypothesized that the phenomenon was a result of overwhelming the anodic microorganisms, temporarily slowing their ability to transfer electrons. The most recent research (Nien et al., 2011; Watson and Logan,
2011) confirms that the overshoot is a limitation of microbially mediated electron transfer at the anode. Watson and Logan (2011) eliminated the power overshoot by utilizing a polarization method with a multi-cycle technique. In this method, the MFC is left at an individual resistance until the substrate is depleted, between
1 and 2 days. At that time, the system is switched to a new resistance and fed
65
substrate again. Whether the power overshoot is exaggerated under thermophilic conditions or with the present design requires further research. The overshoot in this study is probably due to limitations of the biofilm at the anodic surface. It should be noted that the anodic biofilm was not enriched completely as indicated by the increased overshoot current density from 25.5 to 28.9 mA/m2 after 120 hrs
(Figure 4.3). Testing of the further enrichment of TC60 was outside of the scope
of this study.
This study provides the first step towards studying thermophilic MFCs by
supplying a design for stable current while eliminating evaporation at elevated temperatures. Although this study describes an initial design for R&D, the results suggest the potential for stable, thermophilic MFC operation. The optimization of biological and engineering components is necessary prior to application of the
design. In comparison to the mesophilic counterparts, the thermophilic MFCs
could demonstrate increased metabolic and current production rates as indicated
by other complex bioprocesses at elevated temperatures.
66
A
B
Figure 4.1. Design of the thermophilic microbial fuel cell reactor (A) and the cathode assembly (B). Note the difference in scale.
67
7 6 pH 5
400
300
200 Potential (mV) Potential 100
0
0 100 200 300 400 500 600 700 Time (hr)
Figure 4.2. Potential over time generated in the thermophilic MFC. Glucose additions to a final concentration of 25 mM ( ) or 50 mM ( ) are marked with dotted lines. Changes in the pH are indicated with open circles.
68
5 450 hr A 4 570 hr ) 2
3
2
1 Power(mW/m Density
0
500 B 400
300
200
Potential (mV) 100
0
0 5 10 15 20 25 30 35 2 Current Density (mA/m )
Figure 4.3. Power (A) and polarization (B) curves at 450 hr and 570 hr for thermophilic MFC fed glucose.
69
CHAPTER 5: Hydrogen and Metabolite Production during Batch Anaerobic
Growth on Cellulose by a Thermophilic Microbial Consortium at 50 and 60 °C
ABSTRACT
Hydrogen and other metabolites can be utilized as energy or value-added products. This study focuses on an anaerobic fermentative consortium, TC60, and the effect of temperature on metabolite production following cellulose hydrolysis.
TC60 was inoculated into fresh media with different substrates (microcrystalline cellulose Sigmacell Type 20 and 50, long fibrous cellulose, and filter paper) and concentrations (2 - 12 g/l) and types of cellulose. Triplicates were completed of each combination for incubation at two temperatures, 50 or 60 °C. The main products were monitored over time and consisted of H2, CO2, ethanol, and acetate.
The data were analyzed with ANOVA and Tukey’s test of confidence to clarify
differences between the experimental conditions. Increased temperature promoted
higher H2, CO2, and ethanol yields while acetate yields were only affected prior to
24 hrs. The ANOVA model for production rates showed a significant temperature
effect on all products, including acetate (P 0.0034).
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INTRODUCTION
Hydrogen is a possible alternative form of energy with the advantage of being carbon neutral and having a high heat index. There are multiple biological processes to generate hydrogen including dark fermentation by anaerobes (Nath
and Das, 2004; Kapdan and Kargi, 2006; Hallenbeck, 2009; Hallenbeck and
Ghosh, 2009; Lee et al., 2010). Dark fermentation entails the formation of short
chain fatty acids following glycolysis and the coupled recycling of reducing
equivalents with the generation of H2. Many fermentative pathways are coupled
with hydrogenases but some pathways are sensitive to undissociated acids, pH, H2
partial pressure, and metal ions (Nath and Das, 2004; Chong et al., 2009). Dark
fermentation especially provides a feasible route for maximizing H2 production by
genetic engineering of microorganisms and bioprocess optimization. Tying H2
production with the fermentation of cellulosic feedstocks can provide a feasible
route for efficient generation from a renewable resource.
H2 production has been characterized with pure cultures as well as with enriched consortia for H2 production. Clostridium spp., with C. thermocellum
being perhaps the most studied, are well known cellulosic H2 producers (Levin et al., 2006). A mixed consortium provides an advantage to pure culture counterparts: a wide array of hydrolytic enzymes and a diverse metabolome, including hydrogenases (Ueno et al., 1995; Cheong and Hansen 2007; Cui et al.,
2009; Lo et al., 2009; Kongjan et al., 2010). To select for the appropriate
71
consortium, selective pressure must be applied and maintained to generate the most efficient cellulolytic organisms in combination with H2 producers.
Many H2 producing microorganisms live alongside hydrogenotrophic, H2 consuming, organisms such as methanogens, sulfate reducing bacteria, and lactic acid bacteria. These organisms are all present in a synergistic, diverse anaerobic community. In vitro these H2 consuming anaerobes must be inhibited or
eliminated in order to maximize H2 yields (Nath and Das, 2004). The first step is
to choose the appropriate medium that eliminates electron acceptors that are
associated with the use of H2 as an electron donor. One such electron acceptor,
CO2, cannot be eliminated from the headspace; hydrogenotrophic methanogens utilize CO2 and H2 to produce CH4. In addition to media design other selective
measures are used such as heat treatment. This most common technique eliminates all non-sporeformers, including methanogens, by pretreating the inoculum at 100 °C for 10-60 min. Many known H2 producers, such as
Clostridium and Bacillus spp., are spore-formers and can survive heat treatment.
Another way to eliminate unwanted organisms is to inhibit their growth with low
pH. For example, many methanogens grow only between pH 6.0 and 8.0, whereas
H2 producing clostridia can grow well outside this range (Whitman et al., 2006).
Lin and Hung (2008) reported that by enrichment at a slightly elevated
temperature, 55 °C for 4 days, non-sporeforming H2 producers were maintained.
Moreover, maximum H2 yields are obtained by minimizing sources of inhibition
72
such as butyric and acetic acids (Van Ginkel and Logan, 2005; Zheng and Yu,
2005) and H2 partial pressure (Claassen et al., 1999; Logan et al., 2002).
This study focuses on an H2 producing microbial consortium, TC60 that
had been maintained at 60 °C. The effect of temperature on batch culture growth and fermentative product formation of H2, CO2, ethanol, and acetate was
monitored along with selective analysis of reducing sugars. Product yields and formation rates, along with statistical analyses, were examined to characterize the
effect of temperature on fermentative metabolism.
MATERIALS AND METHODS
Culture and experimental set-up
The microbial consortium (TC60) originated from the interior of an active
compost pile. The culture was routinely maintained at 60 °C in medium that
contained (per liter): 2 g trypticase, 1 g yeast extract, 4 g Na2CO3, 0.23 g K2HPO4,
0.18 g KH2PO4, 0.36 g NH4Cl, 0.04 g NaCl, 0.09 g MgSO4·7H2O, 0.06 g
CaCl2·2H2O, 2 mg CoCl2·6H2O, 0.09 mg NiCl2·6H2O, 0.16 mg Na2SeO·5H2O,
0.25 g cysteine-HCl, and 0.25 g Na2S·9H2O. Microcrystalline cellulose (4 g/l
Sigmacell, Type 20, Sigma-Aldrich, St. Louis, MO) was the substrate. Serum bottles (120 ml) were filled with 60 ml medium including cellulose, degassed
with N2, sealed with butyl rubber stoppers, and inoculated with TC60 (10 % v/v).
73
Subcultures were maintained anaerobically under N2 headspace on a shaker at 180 rev/min (Carver et al., 2010; 2011).
H2 and CO2 production was monitored over a temperature span of 35 to 75
°C using a thermal gradient incubator (Terratec Australia, Margate, TAS,
Australia) with temperature increments of 1.5-2 °C.
In order to test the effect of temperature, substrate, and concentration, batch bottles containing medium was varied by concentration (2, 4, 8, 12 g/l) and cellulosic substrates (microcrystalline cellulose Sigmacell Type 20 and 50, fibrous cellulose, and 5 x 5 mm pieces of filter paper). Then, each combination was run in triplicates at 50 °C and another triplicate set at 60 °C. In addition, a triplicate set of controls, containing no substrate, was run at each temperature. Undefined components, including yeast extract and trypticase, contained 1.12 g carbon/l and
0.26 g nitrogen/l which contributed to background metabolite production.
Analysis of chemical oxygen demand and soluble sugars
Chemical oxygen demand (COD) was monitored using a standardized kit for measurements at 150-1000 mg COD/l (LCK114, Hach Lange). Digestion at
148 °C (Hach Lange LT200 thermostat) and measurement of absorbance (Hach
Lange DR2800 spectrophotometer) were completed according to the manufacturer’s instructions (Hach Co., Loveland, CO).
Soluble sugars were determined using a phenol-sulfuric acid method designed for samples containing suspended solids (Finger and Strutynski, 1975).
Filtered (0.2 µm) samples were diluted to a final volume of 2 ml, combined with 1
74
ml of 5 % phenol and 5 ml concentrated H2SO4, mixed well, cooled to ambient
temperature, and absorbance was measured at 490 nm.
Metabolite analyses
Gas overpressure in serum bottle cultures was measured immediately upon
removal from the incubator and then wasted. Samples of headspace gas (0.2 ml)
were manually injected with a sterile syringe. To minimize gas composition
changes or drastic changes in the temperature, samples were tested within 2 min
of overpressure measurements. Gaseous metabolites were tested as described
previously with a Shimadzu GC-2014 equipped with a Porapak N column (2 m
length x 2 mm ID, Sigma-Aldrich) and a thermoconductivity detector (Carver et al., 2010; 2011). Chromatographs were analyzed with GC Solution Analysis software (Shimadzu Corp., Columbia, MD).
Metabolites in the liquid phase were monitored via a Thermo Scientific
Focus GC equipped with an AS 3000 Series II autosampler, flame ionization detector, and Trace TR-FFAP capillary column (30 m x 0.32 mm x 0.25 µm,
Thermo Scientific, Rockford, IL). Filtered supernatants were diluted 50 % with deionized water and acidified to pH < 2 with concentrated formic acid. Crotonate
(100 mg/100 ml) and n-propanol (60 µl/100 ml) were used as internal standards.
Samples were stored at -20 °C until analyzed. Prior to injection, samples were equilibrated to 22±2 °C. The GC was run in a split mode (1:40, flow rate 100 ml/min) with He as carrier gas (2.5 ml/min). Injector and detector temperatures were set at 200 and 230 °C, respectively. The oven program was 90 °C for 1.5
75 min, 30 °C/min ramp to 180 °C, and held at 180 °C for 2 min. Ethanol and butanol along with acetic, propionic, isobutyric, butyric, isovaleric, and valeric acid standards were prepared with GC quality (≥ 98 %) reagents (Sigma-Aldrich) and analyzed in all samples. Only ethanol and acetate production are reported due to their dominant presence throughout all samples. Other fatty acids were present
< 2 mM.
Statistical models and analyses
Production rates for metabolites were determined using linear regression in JMP8 software (SAS Institute). All data points were used unless they had a negative value due to a limitation in the standard curve. H2 production over time did not follow a linear trend but with a square transformation of individual H2 measurements, linear regression was completed.
Production rates and apparent yields (mmol/g substrate added), henceforth called yields, were analyzed via separate analysis of variance (ANOVA) models and multiple comparative pairwise Tukey tests (confidence of 95 %) in JMP8.
The effect of temperature, substrate, and concentration on product yields at 12,
24, and 48 hr for H2, CO2, ethanol, and acetate were analyzed separately (by time and product) with the following simplified ANOVA model:
Yijkl = µ + αj + βk + γl + (αβ)jk 1
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Yijkl is the overall expression of the model for yields, µ is the overall mean of response, αj the effect of temperature (T), βk the effect of substrate (S), γl the
effect of initial concentration (C), εijk the overall error, and only one two-way
interaction, S*T, was included in the model, (αβ)jk. Tukey’s method (confidence
of 95 %) was used to analyze yields at separate time points.
The effect of temperature, substrate, and concentration on production rates was analyzed with a three-way ANOVA model with all two-way interactions:
Rijkl = µ + αj + βk + γl + (αβ)jk + (αγ)jl + (βγ)kl + εijkl 2
where Rijkl is the overall expression of the model for rates and other variables are
the same as noted above in eqn 1. Two-way interactions are noted as (αβ)jk
,(αγ)jl,and (βγ)kl in the model (eqn 2) are abbreviated henceforth S*T, C*T, and
S*C.
RESULTS AND DISCUSSION
Enrichment phase
The original consortium was maintained at 55 °C over a period of years on
microcrystalline cellulose and corn stover. Months prior to this experiment, the
consortium was maintained with 4 g/l microcrystalline cellulose (Type 20) at 52
°C, known as TC52. Analysis of gas production between 35 and 75 °C revealed
an optimal temperature near 60 °C after 24 hr of incubation (Figure 5.1).
77
Following months of enrichment at 60 °C, gas yields and rates increased greatly
and lag times shortened (Figure 5.2). H2 yields were ten-fold higher with TC60 in
comparison to TC52, 2.0 vs. 0.2 mmol H2/g cellulose added, respectively.
Methane formation was eliminated as the temperature was increased to 60
°C; this allowed for further H2 accumulation and higher yields. Prevention of
methanogenesis may be attributed to short chain fatty acid accumulation under
batch conditions which lowered the pH to about 5.5 (data not shown).
Methanogens are known to prefer pH conditions between 6.5 and 8.5 (Whitman et al., 2006). In addition, several studies have noted that H2 production was optimized at pH 5.5-6.0 (Lay, 2001; Lee et al., 2008; Gómez et al., 2009).
Whether this pH optimal was due to the optimal growth or enzymatic activity, specifically hydrogenases, of the culture has yet to be tested. It is also plausible that the methanogens in this consortium were unable to grow at 60 °C. The loss of methane formation was considered advantageous for H2 production and therefore,
further experiments were carried out with TC60.
Product yields at 50 and 60 °C
TC60 was used as an inoculum for fresh media containing different
combinations of cellulosic substrates (S) and concentrations (C) and then
incubated at 50 or 60 °C. Product formation trends were similar regardless of
temperature (T): H2 and acetate concentrations increased early, CO2 started to
increase once H2 reached a maximum, and ethanol showed a gradual increase
(Figure 5.3). Initial soluble sugar and COD concentrations indicated that all
78
conditions started with similar conditions, 0.18 ± 0.02 g/l and 3.7 ± 0.9 g/l, respectively. Therefore, differences between product formation rates and yields
were a result of temperature, directly or indirectly, but not the initial inoculum
conditions.
Yields were calculated for each metabolite at 12, 24, and 48 hr unless they
were below the detection limits, i.e., 12 hr ethanol and acetate yields at 50 °C.
Yields were calculated based on the amount of cellulose initially added because
the amount of cellulose consumed was not measured. Cellulose degradation was
not quantified because the large number of samples and problems associated with
non-homogenous systems. Thus the yields presented in this study are lower than actual yields based on cellulose depletion. Temperature had a positive correlation
with H2, CO2, and ethanol yields but not acetate. Of interest, H2 yields increased
more between 12 and 48 hr at 60 °C in comparison to 50 °C (Figure 5.4). CO2
production occurred mostly between 24 and 48 hr while ethanol concentration
increased gradually (Figure 5.2). Acetate peaked within 12 hr and appeared unaffected by the incubation temperature.
Another way to analyze the effect of temperature on metabolites is to compare the molar ratios of metabolites (Table 5.1). At both temperatures, the ratio of H2 to CO2 decreased over time due to early H2 accumulation and CO2
production later during growth. The decrease in the H2:CO2 ratio was somewhat
larger at 60 °C, 1.98 to 0.43 (78 %), than at 50 °C, 1.57 to 0.62 (60 %). Previous
research has shown that H2 reaches a maximum earlier than CO2 (Logan et al.,
79
2002). The results suggest that, regardless of the temperature, H2 should be
removed early during dark fermentation. The acetate:CO2 ratios were higher than
the ethanol:CO2 ratios, but both decreased over time due to the overwhelming buildup of CO2. Acetate fermentation was dominant regardless of temperature.
The acetate:ethanol ratios were around 3.0 at 50 °C but lower with 60 °C, 1.38.
The shifts in the metabolite ratios were due to changes in the microbial
community composition and metabolism. The effect of temperature on H2 production has been previously characterized, often over a wide range and outside the temperature range of the original source sample (Lin and Hung, 2008; Lee et al., 2008; Tang et al., 2008; Pan et al., 2009; Kim and Lee, 2010). Such large temperature changes lead to drastic shifts in the microbial composition and
therefore, changes in H2 production reflect population variation. Since no
enrichment was used to acclimate TC60 to growth at 50 °C, the differences are
taken to reflect the effect of temperature on the net metabolism of cellulose
hydrolysis and fermentation by this consortium under batch conditions. Further
elucidation would include a more incremental temperature experiment but was
outside the scope of this study.
In order to confirm the influence of temperature on metabolite yields, an
ANOVA model (eqn 1) was applied separately to each product at 24 and 48 hr.
H2, CO2, and ethanol yields were all significantly different (P < 0.0001), but the
2 association between H2 yields and temperature was low (R = 0.360), likely due to
high variation at the initial stages of growth. The ANOVA model (eqn 1) showed
80
that acetate yields were significantly different (Table 5.2) at 24 and 48 hr in contrast to the lack of temperature effect shown in Figure 5.4. Examination of individual effects in the ANOVA model confirmed that there was a significant effect of the temperature at 24 hr (P < 0.0001). At 48 hr, H2, CO2, ethanol, and
even acetate were significantly different at 50 and 60 °C (Table 5.2.). Analysis of
individual effects within the model showed that temperature did not have a
significant effect on acetate (P 0.770) but the S*T interaction was significant (P
0.0002). The overall P-value of the model at 48 hr was significant because the
interaction between temperature and substrate was overwhelmed in the model.
When a model contains an interaction, e.g., S*T, the effects cannot be analyzed
separated. This combined effect has yet to be noted in the literature on hydrogen
production or the fermentation of cellulose.
Rate of product formation at 50 and 60 °C
Temperature had a positive correlation with production rates similarity to
yields with acetate being an exception (Figure 5.5). CO2 production rates doubled
from 0.0134 ± 0.001 to 0.0227 ± 0.001 mmol/g substrate·hr with cultures fed 4 g/l microcrystalline cellulose (Type 20). H2 and ethanol formation appeared less
responsive between the two temperatures (Figure 5.5). Biomass was not measured
directly in these experiments due to the problem of ensuring homogeneous
sampling and therefore, accurate measurements. The effect of the temperature on
CO2 formation rates reflected faster biomass growth in addition to increased metabolic activity. Acetate production rates did not increase with the temperature,
81
indicating the presence of a microbial subpopulation active in acetate
fermentation regardless of temperature (Figure 5.5).
In order to clarify the effect of temperature on metabolite formation rates,
a three-way ANOVA model (eqn 2) was applied separately for each metabolite
(Table 5.3). CO2 and ethanol were significantly affected by all temperature interactions as well as the temperature alone (P < 0.0001). The production rate of
H2 was not affected by temperature according to this model. This goes against results seen with direct temperature comparison as shown in Figure 5.4 and 5.5.
The square transformation that was necessary to linearize the H2 data was the
likely reason for minimizing any differences seen with the temperature. Acetate
formation rates were significantly affected by temperature alone but when in
association with the concentration or type of substrate, the effect was lessened.
This is in disagreement with yield results; the effect of temperature was seen with
acetate rates but not yields, indicating that temperature plays a role on enzymes
associated with acetate fermentation. A secondary substrate, e.g., glucose or
medium components, which was available in equal quantities across all conditions was used, resulted in similar yields except that the cultures grown at 50 °C produced acetate slower.
CONCLUSIONS
Product formation rates and yields were affected by the incubation temperature (50 and 60 °C) of an enriched cellulolytic consortium (TC60). While
many metabolites were analyzed, only four products were found across all
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conditions: H2, CO2, ethanol, and acetate. Analysis of the temperature effect was carried out in parallel with different combinations of cellulosic substrates and concentrations. Statistical analyses were used to elude differences with a confidence of 95 %. While H2, CO2, and ethanol yields and rates were affected by
temperature, acetate was less responsive. Acetate yields were not affected by the
experimental conditions, indicating metabolism of non-cellulolytic substrates, but
rates increased with temperature. Interactions S*T and C*T had a significant
effect on CO2 and ethanol production rates (P < 0.0001). The interactions between type of substrate, concentration, and temperature revealed that a fraction of the H2 formation was associated with acetate fermentation.
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Table 5.1. Molar ratios (mol:mol) of major metabolites at 12, 24, and 48 hr of growth of TC60 on two types of cellulose at 50 and 60 °C.
Substrate Temperature H2:CO2 Acetate:Ethanol Acetate:CO2 Ethanol:CO2 (°C) 12 24 48 12 24 48 12 24 48 12 24 48 Type 20 (20 µm) 50 1.57 1.05 0.62 3.42 3.00 2.78 0.99 0.81 0.33 Type 20 (20 µm) 60 1.98 1.39 0.43 2.32 1.88 1.38 2.28 1.10 0.38 0.98 0.58 0.28 Filter paper 50 1.24 1.31 0.63 3.78 2.27 4.95 1.14 1.31 0.50 Filter paper 60 2.49 1.28 0.40 1.83 2.14 1.12 1.75 0.97 0.32 0.96 0.45 0.29
84
84
Table 5.2. ANOVA results of product yields at 24 and 48 hr.
24 hr 48 hr Yielda Overallb Yield Overall Metabolite 50 °C 60 °C R2 P-value 50 °C 60 °C R2 P-value
H2 0.55 1.87 0.360 < 0.0001* 1.28 2.14 0.847 < 0.0001*
CO2 0.52 1.35 0.822 < 0.0001* 2.07 4.93 0.856 < 0.0001* Ethanol 0.42 0.79 0.882 < 0.0001* 0.68 1.36 0.871 < 0.0001*
85 Acetate 1.45 1.48 0.838 < 0.0001* 2.04 1.87 0.879 < 0.0001*
aYield (mmol/g substrate added) based the average of triplicate runs with 2 g/l microcrystalline cellulose (Type 20). bANOVA results indicate overall significance of the model on product yields. Significance is P < 0.05 and is indicated by *.
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Table 5.3. ANOVA results of the effect of temperature and interactions containing temperature on product rates.
Ratea Metabolite 50 °C 60 °C Effectb R2 P-value T 0.8082 S*T 0.2913 H2 0.00193 0.00474 C*T 0.8938 Whole Modelc 0.320 0.5220 T < 0.0001* S*T < 0.0001* CO2 0.0134 0.0227 C*T < 0.0001* Whole Model 0.983 < 0.0001* T < 0.0001* S*T < 0.0001* Ethanol 0.00258 0.00547 C*T < 0.0001* Whole Model 0.931 < 0.0001* T 0.0034* S*T 0.0142* Acetate 0.00713 0.0069 C*T 0.7180 Whole Model 0.905 < 0.0001*
a Rates (mmol/g substrate·hr) for CO2, ethanol, and acetate were calculated using linear regression of conditions using microcrystalline cellulose (Type 20) at 2 g/l. 2 H2 production rate (mmol /g substrate·hr) was calculated from linear regression of the square of H2 time point measurements bT: temperature; S: substrate; C: concentration cANOVA results indicate overall significance of the whole model
86
12
H2 10 CO2
8
6
4
2 Yield (mmol gas/g substrate added)
0 35 40 45 50 55 60 65 70 75 Temperature (°C)
Figure 5.1. Gas yields after 24 hr of TC52 growth between 35 and 75 °C.
A B H2 CH 6 4 6 CO2
4 4
2 2 Yield (mmol/g substrateYield (mmol/g added)
0 0
0 50 100 150 200 250 300 350 0 50 100 150 200 250 300 350 Time (hr) Time (hr) Figure 5.2. Gas yields over time by TC52 (A) and TC60 (B).
87
50 °C 60 °C 6 6 H2
CO2 Ethanol 4 Acetate 4
2 2 Yield (mmol/g substrate added) substrate (mmol/g Yield 0 0
88 0 20406080100 0 20406080100 Time (hr) Time (hr)
Figure 5.3. Metabolite production over time of TC60 incubated with 4 g/l microcrystalline cellulose (Type 20) at 50 °C (A) and 60 °C (B). n=3
88
5 6 12 hr H2 CO2 5 4 24 hr 48 hr 4 3
3
2 2
1 1 Yield (mmol/g sustrate added) sustrate (mmol/g Yield
0 0 50 60 50 60 5 5 Ethanol Acetate
4 4
3 3
2 2
1 1 Yield (mmol/g substrate added) Yield (mmol/g
0 0 50 60 50 60 Temperature (°C) Temperature (°C)
Figure 5.4. H2 (A), CO2 (C), ethanol (B), and acetate (D) yields at 12, 24, and 48 hr with growth on 4 g/l microcrystalline cellulose (Type 20) at 50 and 60 °C. n=3
89
6
50 °C 5 60 °C
4
l/g substrate added) l/g substrate 3
2
1
Yield at 48 hr (mmo 48 hr at Yield 0 CO2 H2 Acetate Ethanol 0.025
0.020
0.015
0.010
0.005 Rates (mmol/hr·g substrate)
0.000 CO2 H2 Acetate Ethanol
Figure 5.5. Yields at 48 hr (A) and rates (B) of TC60 grown on 4 g/l microcrystalline cellulose (Type 20) at 50 and 60 °C. n=3
90
CHAPTER 6: The Influence of Substrate and Concentration on Batch
Fermentation by an Anaerobic, Thermophilic Consortium
ABSTRACT
Cellulosic substrates and their initial concentration are known to affect enzymatic activity in vitro but the extent of which remains unknown. The ability of a thermophilic, anaerobic consortium known to degrade cellulose was tested for metabolite yields and production rates. Four types of cellulosic substrates
(microcrystalline cellulose Sigmacell Type 20 and 50, long fibrous cellulose, and
5 x 5 pieces of filter paper) were tested at several concentrations (4, 8, 12, 16, and
20 g/l) and incubated at 55 °C. In addition, another experiment was completed with all substrates but more restricted concentrations (2, 4, 8, and 12 g/l) and at 50 and 60 °C. ANOVA models and Tukey’s confidence intervals (95 %) were run separately for yields and production rates of four metabolites (H2, CO2, ethanol,
and acetate). The substrate was shown to affect metabolite formation; these
differences cannot be attributed to crystallinity as X-ray diffraction showed
similar patterns. Substrates with the highest specific surface area did not promote
the highest metabolite yields and formation rates, likely due to batch conditions.
Further research is necessary to understand the effects of temperature, 91
concentration and substrates, and their combined effect on anaerobic fermentation.
INTRODUCTION
Cellulosic biomass as a renewable resource is advantageous for sustainable energy due to high availability and versatility of plant matter that could fulfill the purpose. Cellulose is a polysaccharide composed of glucose monomers linked via β1-4 glycosidic linkages. Unlike the α1-4 glycosidic bonds in starch, cellulose hydrolysis requires a variety of enzymes able to handle the unusual bond and glucose orientations. A combination of endoglucanases, exoglucanases, and cellobiohydrolases is necessary to completely hydrolyze cellulose (Doi, 2008; Wilson, 2008b). The characteristics of cellulose affect the extent and kinetics of hydrolysis, including the crystallinity, specific surface area, degree of polymerization, and porosity (Sinitsyn et al., 1991; Zhang and Lynd,
2004; Huang et al., 2010).
The activity of purified cellulases is affected by the specific surface area and, therefore, the particle size of cellulose. As particle size increases, the specific surface area should decrease, yielding lower rates of cellulolytic hydrolysis (Peri et al., 2007; Lin et al., 2010). Both studies indicated through different approaches that surface area affects the rate of cellulose hydrolysis and consequently the fermentation of carbohydrates. Experiments with mixtures of two or more purified cellulases have also indicated that surface area affects the kinetics of
92
cellulose hydrolysis with synergistic enzyme activity (Sinitsyn et al., 1991; Zhang
et al., 1999; Chen et al., 2007; Gupta and Lee, 2009; Huang et al., 2010; Pedersen
and Meyer, 2009).
The specific surface area accessible to the enzymes, not total specific surface area had the most influential effect on hydrolysis (Sinitsyn et al., 1991).
Enzymes cannot access all internal pore spaces of cellulose but proteins between
40-90 Å, the size of known cellulolytic enzymes, can access these spaces (Stone et al., 1969; Lin et al., 1985; Weimer and Weston, 1985; Burns et al., 1989;
Sinitsyn et al., 1991). Sinitsyn et al. (1991) used peroxidase and chymotrypsin to
determine the accessible surface area and showed a linear relationship between
initial hydrolysis rates and “rough” surface area, i.e., protein accessible surface
area as shown recently (Palmowski and Müller, 2003; Pinto et al., 2008; Huang et
al., 2010). In addition to accessible surface area, crystallinity is closely tied to
accessible surface area and therefore, hydrolysis. With lower crystallinity, the
surface area increases due to an ‘unraveling’ effect of cellulose microfibrils;
therefore, as crystallinity decreases the accessible surface area increases (Sinitsyn et al., 1991; Walker and Wilson, 1991; Ramos et al., 1993; Al-Zuhair, 2008).
Commercial cellulose substrates are not homogenous; many studies have noted that commercial cellulose covers a range of particle sizes, between 10 and
1000 µm, and therefore a range of surface areas (Shewale and Sadana, 1979; Fan et al., 1980; Rivers and Emert, 1988; Sinitsyn et al., 1991; Zhang et al., 1999; Das
et al., 2010). Although the majority of particle sizes are around the manufacturers’
93
descriptive size, the extreme sizes of cellulose particles may play a large role in
hydrolysis rates. With cellulosic biomass, the fiber size and crystallinity has
varied even more than seen with commercial substrates (Chen et al., 2007;
Hendriks and Zeeman 2009; Huang et al., 2010). Pedersen and Meyer (2009) used
sieves to separate particles of wheat straw following ball milling. Differences in
particle size distribution correlated with increased glucose and xylose release
although other factors such as inorganic material also affected cellulose
hydrolysis. Other studies with mixed consortia have shown an inverse relationship
between accessible surface area and hydrolysis rates (Chyi and Dague 1994; Hu et
al., 2005). Many mixed cultures studies have not used accessible surface area techniques to assess cellulolytic activity; rather, the effect of surface area on cellulose hydrolysis has been tested by varying the substrate concentration and pretreatment (Kato et al., 2004; Chen et al., 2007; Lo et al., 2008; Gómez et al.,
2009; Hendriks and Zeeman, 2009; Kongjan et al., 2010). Conclusions that can be drawn from these studies are inconsistent and equivocally convoluted. Detailed, directed studies are necessary to elucidate the effect of surface area on cellulose hydrolysis and product formation by mixed cultures.
Cellulose concentration has shown a consistent, reproducible effect on hydrolysis by purified cellulases but not with consortia. Increasing the amount of enzyme or inoculum increase the amount of reducing sugar released per unit of time (Al-Zuhair, 2008; Lo et al., 2008; Gómez et al., 2009). Other factors such as surface area have more prominent effects on product formation at lower cellulose
94
concentrations (Hu et al., 2005), but these effects can also be varied and unpredictable (Islam et al., 2006; Levin et al., 2006). Several studies have looked
at concentrations of cellulose between 0.1 and 50 g/l with the majority ≥ 10 g/l
(Lay 2001; Hu et al., 2005; Levin et al., 2006; Al-Zuhair 2008; Liao et al., 2008;
Lo et al., 2008; Gómez et al., 2009). Ratios of substrate to biomass have been
shown to affect metabolite production, including H2 formation (Chen et al., 2007).
It has yet to be elucidated whether thermophilic microorganisms at elevated
temperatures are able to consume cellulose at higher concentrations, increasing
yields.
The purpose of this study was to characterize the effect of different
cellulosic substrates and concentrations on metabolite production by a microbial
consortium under moderately thermophilic conditions. Metabolite yields and
production rates were monitored and analyzed using statistical methods in order
to understand the relationship between fermentation, substrate, and concentration.
MATERIALS AND METHODS
Culture and experimental set-up
The microbial consortium (TC60) originated from the interior of an active compost pile and subcultures were maintained at 60 °C on 4 g/l microcrystalline
cellulose (Sigmacell Type 20, Sigma-Aldrich). The medium was degassed under a
N2 headspace as previously described (Carver et al., 2010; 2011). Anaerobic,
serum bottles were filled with medium, the appropriate substrate and
95
concentration, degassed with N2, sealed with butyl rubber stoppers, and
inoculated with TC60 (10 % v/v) to a final volume of 60 ml.
Two temperatures (50 and 60 °C) were tested in combination with
different cellulose concentrations (2, 4, 8, 12 g/l) and cellulosic substrates
(microcrystalline cellulose Sigmacell Type 20 and 50, fibrous cellulose, and 5 x 5
mm pieces of filter paper). The substrates are abbreviated in the text as A, B, C, or
D, respectively (Table 6.1). A third temperature, 55 °C, was also tested with a larger range of substrate concentrations (4, 8, 12, 16, 20 g/l) and the above substrates. Each combination was incubated in triplicate at 180 rev/min along with appropriate controls, medium lacking the substrate.
COD and soluble sugar analyses
Chemical oxygen demand (COD) was monitored using a standardized kit for measurements between 150 and 1000 mg/l (LCK114, Hach Lange). Digestion at 148 °C (Hach Lange LT200 Thermostat) and absorbance measurements (Hach
Lange DR2800 Spectrophotometer) were completed according to the manufacturer’s instructions.
Soluble sugars were determined using the phenol-sulfuric acid method originally established by Dubois and co-workers (1956) with later modifications for samples containing solids (Finger and Strutynski, 1975). Filtered supernatants
(0.2 µm) were diluted to a final volume of 2 ml, combined with 1 ml of 5 % phenol and 5 ml concentrated H2SO4, mixed well, cooled to room temperature,
and absorbance was measured at 490 nm.
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Metabolite analyses
Gas overpressure was measured immediately upon removal from
incubators and wasted. To minimize gas composition changes or drastic changes in temperature, samples were tested within two minutes of overpressure
measurements. A sample of headspace gas (0.2 ml) was manually injected into a
Shimadzu GC-2014 with a sterile syringe and analyzed as previously described
(Carver et al., 2010; 2011). Chromatographs were analyzed with GC Solution
Analysis software (Shimadzu).
Short chain fatty acids (SCFAs) in the liquid phase were monitored via a
Thermo Scientific Focus GC equipped with an AS 3000 Series II autosampler, flame ionization detector, and Trace TR-FFAP capillary column (30 m x 0.32 mm
x 0.25 µm, Thermo Scientific). Filtered supernatants (0.2 µm) were diluted 50 %
with milli-Q water and acidified to pH < 2 with concentrated formic acid.
Crotonate (100 mg/100 ml) and n-propanol (60 µl/100 ml) were used as internal
standards for acids and alcohols, respectively, and 100 µl of each was added to
every vial. Samples were stored at -20 °C until analyzed. Prior to injection,
samples were equilibrated to 22 ± 2 °C. The GC was run in a split mode (1:40,
flow rate 100 ml/min) with He as carrier gas (2.5 ml/min). Injector and detector
temperatures were set at 200 and 230 °C, respectively, and the oven program was
90 °C for 1.5 min, 30 °C/min ramp to 180 °C, and held at 180 °C for 2 min.
Ethanol and butanol along with acetic, propionic, iso-butyric, n-butyric, iso-
97
valeric, and n-valeric acids were prepared with GC quality (≥ 98 %) reagents
(Sigma-Aldrich) and monitored in all samples.
Production rates for headspace gasses and SCFAs were determined using
linear regression in JMP8 software (SAS Institute). All data points were used
unless they were a negative value due to a standard curve limitation. H2
production over time did not follow a linear trend but with a square
transformation of individual H2 measurements, linear regression was completed.
Substrate analysis
Substrates were analyzed for total surface area and crystallinity. The total surface area was determined by the adsorption and desorption of N2 using the
BET method (Brunauer et al., 1938). Baseline detection by the thermal conductivity detector on the Micromeritics FlowSorb II 2300 was determined using liquid N2 and He as a carrier gas. BET analysis was completed in five
replicates of each substrate. X-ray diffraction was completed with a Bruker D8
Advance X-Ray Diffractometer (Bruker, Germany). Substrates A, B, and C were
scanned between 2 and 80 °2θ CuKα at 0.05 increments. Substrate D, filter paper,
could not be analyzed with the BET method or X-ray diffraction due to physical
constraints.
Statistical analysis
Production rates and yields (mmol/g cellulose added) were analyzed via
separate analysis of variance (ANOVA) models and multiple comparative
pairwise Tukey tests in JMP8. Product yields at 24 and 48 hrs for H2, CO2,
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ethanol, and acetate were analyzed separately (by time and product) and analyzed with a simple ANOVA model:
Yjkl = µ + βk + γl + εjk 1
where µ is the overall mean of the scores, βk the effect of substrate (S), γl the
effect of concentration (C), and εjk the overall error.
At 55 °C, production rates were analyzed with the following ANOVA model similar to the one in eqn 1 :
Rjkl = µ + βk + γl + εjkl 2
with variables equivalent to the above definitions.
Confidence intervals of 95 % were calculated using a multiple
comparative pairwise Tukey test of individual metabolites. Rates and yields were
also analyzed separately.
Cultures grown at 50 and 60 °C were analyzed with models different than
eqn 1 and 2:
Yijkl = µ + αj + βk + γl + (αβ)jk 3
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Rijkl = µ + αj + βk + γl + (αβ)jk + (αγ)jl + (βγ)kl + εijkl 4
where µ is the overall mean of response (production rates), αj the effect of
temperature (T), βk the effect of substrate (S), γl the effect of initial concentration
(C), and εijk the overall error. In addition to substrate and concentration, the model for production rates (eqn 3) contained temperature as a variable. All two way interactions were represented in the rate model as (αβ)jk, (αγ)jl, and (βγ)kl but will be discussed in the text as S*T, C*T, and S*C, respectively. The yield model (eqn
4) only consisted of one interaction, S*T.
The 50 and 60 °C models (eqn 3 and 4) were also run in parallel with
multiple comparative Tukey’s test in order to elude differences between specific
conditions with 95 % confidence. All metabolites were analyzed separately.
RESULTS AND DISCUSSION
Initially the experiment was set up to explore the effect of substrate and
concentration on product formation by TC60 at 50, 55, and 60 °C. Analysis of
soluble COD revealed that the inoculum conditions for the 50 and 60 °C
incubations were similar. The 55 °C inoculum was from a different stock culture
and contained nearly twice as much COD (7.06 ± 0.21 g/l) than the other inocula
(3.7 ± 0.3 g/l). Therefore, in order to analyze the effect of substrate and
concentration, the 50 and 60 °C sets were analyzed together and the 55 °C
conditions separately.
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Substrate characterization
Four cellulosic substrates were tested in this study, defined as A, B, C, and
D for microcrystalline cellulose Sigmacell Type 20and 50, long fibrous cellulose,
and 5 x 5 mm squares of filter paper, respectively. The specific surface area was
determined using the BET method (Brunauer et al., 1938) which monitors total
surface area. With increased particle size, the specific surface area decreased
(Table 6.1) but measurements for substrates C and D were unreliable due to the
0.5 m2/g detection limit even though the trend continues. Figure 6.1 shows a composite of X-ray diffractograms of substrates A, B, and C. Substrate D was not in powder form and thus unsuitable for powder XRD. The other three substrates types had similar X-ray patterns, indicating that no major differences in the crystallinity existed with these samples.
Substrate concentration
Fermentative product formation did not increase with initial substrate concentration. Yields and production rates were comparable regardless of substrate concentration; therefore, normalization decreased the results at high substrate amounts (Figure 6.2). At 55 °C, the highest yields were obtained with 4 g/l substrate added within an individual temperature set. Higher yields were achieved with 2 g/l for the 50 and 60 °C experiment. The yields gradually decreased up to 8 or 12 g/l cellulose, and at higher cellulose concentrations, the
normalized yields leveled off and became comparable (Figure 6.2A). This pattern
was more pronounced at 48 hr than at 12 or 24 hr and was evident with all
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substrates and temperatures. Analysis of Tukey’s confidence intervals (95 %
confidence) confirmed this pattern. The effect of cellulose concentration on gas
yields was significant within 12 hr, suggesting an effect on initial hydrolysis. The rates of product formation were also normalized to the initial substrate concentration and showed a similar trend. These findings suggested that substrate saturation occurred at 4 g/l or even less under batch conditions.
Effects of substrate
55 °C experiment
All product yields increased over time, with the onset of H2, ethanol, and
acetate production occurring before CO2 accumulation. As shown in Figure 6.3,
the product yields at 55 °C were unaffected by the substrate when the
concentration (4 g/l) was kept constant. However, confidence intervals with
Tukey’s test, which takes all concentrations into consideration, indicated
statistically significant differences between the different cellulosic substrates
(Table 6.2 and 6.3). The H2 yields were highest with substrate D (filter paper
squares) ≤ 24 hr while at 48 hr, the H2 levels were the highest in the two smallest
sizes, A and B, in addition to D.
Since high concentrations of suspended solids were present in the cultures,
CO2, the ultimate product of carbohydrate catabolism, was used as a surrogate measure of biomass. The CO2 concentrations varied between cellulosic substrates
but CO2 accumulation did not occur until ≥ 12 hr, indicating an overall effect of
substrate on fermentation (Table 6.2 and 6.3). Initially, cultures with long fibers
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(C) and filter paper squares (D) clearly produced more CO2 than microcrystalline
cellulose (A and B), but the differences between the substrates leveled out as
TC60 reached a similar growth stage. Since these experiments were carried out
under batch conditions, it is possible that all cultures reached similar environmental conditions such as low pH values, which prevented further growth.
Statistical data analysis showed that the substrate had no effect on CO2 formation rates, but higher rates of H2 formation were obtained with the microcrystalline (A
and B) and filter paper (D) substrates.
Ethanol and acetate yields at 48 hr were the highest for substrates C and
D. Tukey’s test of significance showed that both ethanol and acetate yields were higher in substrates with larger particle sizes, C and D, than microcrystalline cellulose, A and B (Tables 6.2 and 6.3), regardless of standard deviations (Figure
6.3). The substrate did not affect the acetate:ethanol molar ratios which remained around 1.5 at 12, 24, and 48 hr, indicating that the two metabolites were affected equally. In the majority of conditions, ratios of H2:CO2, ethanol:CO2, and acetate:CO2 decreased over time due to the accumulation of CO2. In contrast,
larger particle sizes, C and D, had higher molar ratios of H2:CO2 and acetate:CO2 as compared to the smaller sizes, regardless of the temperature. These trends suggested that the substrate had on overriding effect on cellulose metabolism and was particularly pronounced with the two larger particle sizes.
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50 and 60 °C experiment
The type of substrate also affected product formation in the 50 and 60 °C experimental set (Table 6.2 and 6.3). At 60 °C, CO2, H2, and ethanol formation rates were all at their lowest with substrate A, the smallest particle size. This trend was most prominent with rates at 2 g/l but was also evident at 4 (CO2, ethanol) and 8 g/l (CO2) substrates. In comparison, substrates A and B (microcrystalline) promoted higher formation rates with CO2, ethanol, and acetate at 50 °C.
Temperature-dependent shifts in microbial population or metabolic pathways caused such dynamic and drastic changes. The two larger particle sizes, C and D, consistently showed higher rates at 60 °C with substrates concentrations up to 8 g/l (Table 6.3). Acetate was unaffected by substrate except at 50 °C where 2 g/l of cellulose A or B showed increased acetate production rates (Table 6.3).
Effect of substrate and concentration in relation to temperature
In the 50 and 60 °C sets, the effect of concentration (C), cellulosic substrate (S), and temperature (T) was analyzed as two-way interactions (C*S,
C*T, and S*T) via ANOVA. The 55 °C set was relatively simplistic and showed the effect of individual factors, S and C, on product formation. The 50 and 60 °C sets indicated more complex trends and interactions among effects and environmental conditions. As shown in Table 6.4, all effects and two-way interactions influenced CO2 and ethanol production rates. At the other extreme, the rate of H2 formation was not subject to any of the effects or interactions. The effect of substrate was conflicting between the two temperature-based
104
experiments (Table 6.4). At 55 °C, substrate had no effect on product yields but did have an effect with the other set. In the 50 and 60 °C set it was determined
that in the absence of cellulose substrate the acetate yields were affected possibly
by media composition or residual sugars present in the inoculum. All these data
suggested fundamentally differences between the two temperature experiments.
The experiments were carried out in four parts, each with its own TC60
inoculum. The characteristics such as initial COD, reducing sugar, and SCFA
concentrations of the inocula are summarized in Table 6.5. The 55 °C experiment
was carried out in two parts due to the large range of conditions and therefore, a
large number of culture bottles. Although similar growth conditions (60 ± 0.5 °C)
and incubation time (36 ± 1 hr) were used for all inocula, the inoculum for
substrates A and B at 55 °C was at a different stage of growth. The concentration
of soluble COD was twice as high as the other 55 °C set, 7.06 ± 0.21 and 3.46 ±
0.59 g/l, respectively. Carryover of residual carbohydrates present in the inoculum
was eliminated as a possible cause due to similar initial reducing sugar concentrations. Instead, differences were due to downstream metabolites, i.e., ethanol and acetate, which were significantly higher in one 55 °C set (Table 6.5).
TC60 inocula grown at 50 and 60 °C had initially similar soluble COD
concentrations, 3.80 ± 0.64 and 3.58 ± 0.83 g/l, respectively. The importance of
inoculum on cellulose hydrolysis and fermentation has been previously noted and
the inoculum should be characterized previous to fermentation studies
105
(O’Sullivan et al., 2008; Jensen et al., 2009). The inoculums difference affected
metabolite production, which previously remained unnoticed.
CONCLUSIONS
The anaerobic, thermophilic consortium, TC60, was readily adaptable to
changes in the growth temperature and easily affected by the cellulosic substrate
and concentration. The substrate affected the formation of H2, CO2, acetate, and
ethanol, suggesting relative shifts in fermentation pathways depending on the
substrate. Substrates were characterized by XRD and surface area measurements; all substrates had similar crystallinity but differed in total surface area. Increased surface area was not a dominant factor in determining the rate of fermentation.
Many product yields and production rates decreased with increased specific surface area. Rate differences suggested that microorganisms involved in initial cellulose hydrolysis in TC60 cultures varied with the substrate but the underlying reasons remain cryptic. While this study shows potential for converting cellulose to biohydrogen and value-added chemicals, the interactions between the cellulosic
substrate, concentration, and temperature should be explored at more depth via
continuous culture conditions.
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Table 6.1. Physical characteristics of the cellulosic substrates used in this study.
BET analysis Calculated Designation Description Particle Sizea Sp Surface Area (m2/g) (µm2) Ave SD A Sigmacell, Type 20 1,260 2.43 0.01
107 B Sigmacell, Type 50 7,850 1.26 0.61
C Long Fibrous Cellulose 1.3 x 105 0.45 0.23
D 5x5mm Whatman's filter paper #1 2.5 x 107 0.40
a Particle size calculated for A, B, and C by assuming spherical shape and using diameters 20, 50, and 200 µm, respectively. The surface area of a sphere = 4πr2.
107
Table 6.2. Tukey’s test of confidence for the product yields as affected by different substrates in the two models.
50 and 60 °C set 55 °C set 12 hr
H2 D > A, B, C
CO2 D > A, B Ethanol C, D > A, B Acetate C, D > A, B 24 hr
H2 D > A, B, C
CO2 D > C > A, B Ethanol 50 °C all A, B > C Acetate 50 °C all A, B > C 48 hr
H2 60 °C all D > A A, B, D > C
CO2 60 °C all D > A, B n/a Ethanol 60 °C all C, D > A C, D > A, B D > B Acetate 50 °C all B > C C, D > A, B 60 °C all D > A
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Table 6.3. Tukey’s test of confidence for the product formation rates as affected by different cellulose types in the two models.
50 and 60 °C set 55 °C Rates
H2 60 °C 2 g/l B, C, D > A A, B, D > C
CO2 50 °C 2 g/l A, B, D > C n/a 60 °C 2 g/l D > A, B, C 60 °C 4 g/l C > A D > A, B 8 g/l C > A D > A, B Ethanol 50 °C 2 g/l A > C C, D > A, B B > C, D 60 °C 2 g/l C, D > A 4 g/l C, D > A D > B
Acetate 50 °C 2 g/l A, B > C C, D > A, B
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Table 6.4. P-values for the four models. Shaded areas indicate significance (P value < 0.05).
50 and 60 °C Model 55 °C Model
H2 CO2 Ethanol Acetate H2 CO2 Ethanol Acetate S 0.2292 <0.0001 0.0003 0.0326 S <0.0001 0.0956 <0.0001 <0.0001 C 0.9416 <0.0001 <0.0001 <0.0001 C 0.0003 <0.0001 <0.0001 <0.0001 S*C 0.3775 <0.0001 0.0121 0.0101 T 0.8082 <0.0001 <0.0001 0.0034 110 Rates S*T 0.2913 <0.0001 <0.0001 0.0142 C*T 0.8938 <0.0001 <0.0001 0.718
S 0.5403 0.2172 0.0202 0.111 S <0.0001 0.0624 <0.0001 0.0002 C <0.0001 <0.0001 <0.0001 <0.0001 C <0.0001 <0.0001 <0.0001 <0.0001 T <0.0001 <0.0001 <0.0001 0.7697 Yield Yield (48hr) S*T 0.0004 <0.0001 <0.0001 0.0002
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Table 6.5. Differences between inocula.
50 and 60 °C Model 55 °C Model
Inoculum for set: 50 °C 60 °C A & B C & D
Initial Reducing Sugar (g/l) 0.166 ± 0.01 0.200 ± 0.03 0.174 ± 0.03 0.278 ± 0.02
Initial Soluble COD (g/l) 3.80 ± 0.64 3.58 ± 0.83 7.06 ± 0.21 3.46 ± 0.59 111
Ethanol (mmol) 0.062 ± 0.01 0.081 ± 0.02 0.121 ± 0.01 0.082 ± 0.01
Acetate (mmol) 0.148 ± 0.01 0.131 ± 0.01 0.206 ± 0.05 0.183 ± 0.01
Total Solids (g/l) 10.07 ± 0.38 10.47 ± 0.15 9.93 ± 0.18 10.74 ± 0.13
Volatile Solids (g/l) 4.2 ± 0.03 4.5 ± 0.13 4.2 ± 0.14 5.1 ± 0.08
Yields Yijkl = µ + αj + βk + γl + (αβ)jk Yjkl = µ + βk + γl + εjk
Rates Rijkl = µ + αj + βk + γl + (αβ)jk + (αγ)jl + (βγ)kl + εijkl Rjkl = µ + βk + γl + εjkl
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Intensity 100
Substrate C
Substrate B
Substrate A
020406080 °2α CuKα
Figure 6.1. X-ray diffractograms of substrates A, B, and C.
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5 2.0 ABH2 H2 CO2 CO2 4 1.5
3
1.0
2
0.5 1 Yield (mmol gas/g substrate added) gas/g substrate Yield (mmol 0 bottle) Gas (mmol/culture Cumulative 0.0
113 5101520 5101520 Concentration (g/l) Concentration (g/l)
Figure 6.2. Gaseous product yields at 48 hr, 55 °C with TC60 growth on substrate A. Yields were normalized to the amount of cellulose added (A) and the results are also presented in mmol/culture bottle (B).
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5 7 CO H2 6 2 4 5
3 4
2 3 2 1 A 1 B C 0 0 D Yield (mmol/g substrate added) substrate (mmol/g Yield 1.0 1.2 Ethanol Acetate 1.0 0.8
0.8 0.6 0.6 0.4 0.4
0.2 0.2 Yield (mmol/g substrate added) substrate (mmol/g Yield 0.0 0.0 0 1020304050 0 1020304050 Time (hr) Time (hr)
Figure 6.3. The effect of substrate on H2, CO2, ethanol, and acetate production over time at 55 °C with 4 g/l concentration.
114
0.05
H2 CO2 0.04 Ethanol Acetate
0.03
0.02
0.01 Rates (mmol/hr·g substrate added) 0.00 ABCD
Figure 6.4. The effect of substrate type on production rates at 55 °C with 4 g/l substrate concentration.
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CHAPTER 7: Fermentation of Carbohydrates and Polysaccharides by a
Cellulolytic, Thermophilic Consortium
ABSTRACT
The growth of a known cellulolytic consortium, TC52, was monitored
with a variety of substrates including heterogeneous hemicellulose, starch, pectin,
and chitin. H2, CH4, CO2, acetate, and butyrate were the main metabolites on all substrates but the metabolite profiles changed with the substrate. Similar yields at
48 hr were obtained with all monosaccharides and disaccharides for H2, 2-3
mmol/mmol substrate but CO2 yields were higher for disaccharides, 4.5 vs 2
mmol/mmol substrate. Gas metabolite yields were low for TC52 grown on
glyceraldehyde, glycerol, and arabinose indicating little to no growth. The effect
of autoclaving on polysaccharide degradation was monitored but only the H2
yields with chitin, hemicelluloses, and starch were affected. Autoclaving
decreased the yields likely due to decomposition of free simple sugars present in
the substrates. Analysis of bacterial diversity showed that Lutispora thermophila
and Clostridium spp. were present in different relative abundances in TC52
depending on the experimental conditions.
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INTRODUCTION
Recent research into sustainable energy has focused on cellulose and cellulosic biomass as a renewable resource. However, agricultural residues, wood waste, and other forms of plant biomass contain more than just cellulose, including hemicelluloses, pectin, and lignin. The complexity of the cell wall allows the whole plant to withstand environmental conditions and pathogens.
Cellulose microfibrils are buried within this multifaceted matrix of covalently bound hemicelluloses, pectin, starch, and lignin (Somerville et al., 2004; Harris and Stone, 2008; Gilbert et al., 2008; McCann and Carpita, 2008). Much remains unknown of plant tissues, and lignin has not been shown to be degraded by microorganisms (Masai et al., 2007; Akin, 2008; Field and Sierra-Alvarez, 2008;
Arora and Sharma, 2010). The other polysaccharides available in plant cell walls are all possible targets for renewable energy via microbial degradation.
The molst readily biodegradabale plant polysaccharide is starch, which is composed of amylose, a chain of glucose with α1-4 glycosidic bonds, and amylopectin, amylose with α1-4 glycosidic glucose branches (Nigam and Singh,
1995). In comparison, hemicellulose is a heterogeneous substrate with a backbone of β1-4 linked xylose, called xylan, or a combination of xylan and glucose, xyloglucan. Attached to the backbone is a variation of pentoses and hexoses such as arabinose, mannose, and glucuronic acid to yield a plethora of hemicellulosic names: arabinogalactans, glucomannans, and galactoglucomannans. Often,
117
saccharides within the hemicellulose structure are esterified or modified in some
way to promote stability (Tombs and Harding, 1998; Dodd and Cann, 2009).
Pectin is more heterogeneous than known hemicelluloses. The polymer has a α1-4
galacturonic acid backbone with side chains including fucose, xylose, and
rhamnose with a high amount of methylation and acetylation (Tombs and
Harding, 1998; Willats et al., 2001; Caffall and Mohnen, 2009).
Microbial degradation of lignocellulosic biomass has been studied through two techniques: analysis of growth on purified substrates or biomass solids.
Wastes, especially water saturated, have been shown to be good substrates for value-added products such as ethanol and biohydrogen (Kapdan and Kargi, 2006;
Weber et al., 2010). Dry biomass waste is relatively recalcitrant but once
submerged in media it is biodegradable by cellulolytic microorganisms.
Anaerobic microorganisms containing cellulosomes, a multiproteinous enzyme
complex, have been shown to contain xylanases and other glycoside hydrolases
(Bayer et al., 2004). An efficient way to degrade biomass is to tap a variety of
microorganisms with a wide diversity of enzymes able to hydrolyze multiple
portions of the biomass. If a consortium is utilized to degrade biomass, a variety
of value-added products can also be produced, including biohydrogen
(Hallenbeck and Benemann, 2002; Chong et al., 2009; Hallenbeck, 2009).
A thermophilic, anaerobic consortium, TC52, has been used in previous studies on cellulose biodeardation. For the present work, the consortium was was grownwith simple sugars, disaccharides, and plant polysaccharides.
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MATERIAL AND METHODS
Substrates
Simple sugar substrates were ≥ 98 % pure forms of glyeraldehyde,
glycerol, ribose, arabinose, xylose, glucose, galactose, fructose, mannose, lactose,
sucrose, cellobiose, and maltose. Polysaccharides analyzed were chitin from crab
shells, amylose, pectin from apples, and a heterogeneous hemicellulose (unknown
composition, UPM, Tampere, Finland). The hemicellulose was pulverized with a
mortar and pestle before use.
Culture conditions
The microbial consortium, designated TC52, originated from the interior
of a compost heap and subcultures had been maintained with cellulose for years
(Carver et al., 2010; 2011). The culture was grown anaerobically in medium containing (per liter): 2 g trypticase, 1 g yeast extract, 4 g Na2CO3, 0.23 g
K2HPO4, 0.18 g KH2PO4, 0.36 g NH4Cl, 0.04 g NaCl, 0.09 g MgSO4·7H2O, 0.06
g CaCl2·2H2O, 5.66 g acetic acid, 1.62 g propionic acid, 0.68 g n-butyric acid,
0.23 g isobutyric acid, 0.20 g isovaleric acid, 0.20 g n-valeric acid, 0.20 g 2-
methyl-butyric acid, 0.001 g rezasurin, 0.25 g cysteine-HCl, 0.25 g Na2S·9H2O,
and 4 g/l microcrystalline cellulose (Sigmacell Type 20).
Monosaccharides and disaccharides were prepared anaerobically as 50
mM stock solutions and added to anaerobic serum bottles, containing autoclaved
medium to a final concentration of 5 mM. Polysaccharides were added in one of
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two ways: substrate was added to the medium, sealed anaerobically, and
autoclaved (AC) or the medium was sealed anaerobically, autoclaved, and
substrate was added after cooling to room temperature (NAC). Both conditions were tested in duplicate to a final concentration of 4 g/l. Following media preparation the inoculum, TC52, was added (5% v/v) and incubated at 52 °C at
180 rev/min. Daily sampling of the headspace gases was completed on all cultures. Liquid samples of cultures, sampled every 48 hr, were centrifuged at
16,000 g for 10 min. The supernatant and pellet were separated and stored at -20
°C until further analysis.
Headspace gas analysis
Immediately after removal from the incubator, overpressures were measured and wasted with a sterile syringe. Then, a sample of headspace gas (0.2 ml) was manually injected into a GC-2014 Shimadzu gas chromatograph equipped with a thermal conductivity detector and a Porapak N (2 m length x 2 mm ID) column (Sigma-Aldrich). The carrier gas, N2, was maintained at 20 ml/min. Temperatures for the injector, detector, and column oven were 110, 110, and 80 °C, respectively. The chromatographs were analyzed with GC Solution
Analysis software (Shimadzu).
Analysis of short chain fatty acids
Short chain fatty acids (SCFAs) were analyzed in sample supernatants via
HPLC following cleaning with solid phase extraction (C18-T) and filtration
120
through a 0.2 µm PTFE filter (Pall) as described in Chapter 2. The HPLC
consisted of a guard column (5 cm x 4.6 mm ID, Supelguard H), a cation-
exchange column (30 cm x 7.8 mm ID, Supelcogel C-610H), an autosampler
(Spectra-Physica AS 3000), and a UV detector set to 210 nm (Spectra-Physics
SP100). A Beckman 114M HPLC pump was used to maintain a flow rate of 0.5
ml/min of the mobile phase, 0.1 % o-phosphoric acid. Run times were 65 min per sample with 100 µl injections at 70 min intervals. Standards were made from high purity lactic, formic, acetic, propionic, iso-butyric, n-butyric, and iso-valeric acids
(≥ 98 %, Sigma-Aldrich). Chromatographs were analyzed through a computer interface equipped with the Clarity Chromatography Station (DataApex).
Microbial diversity analysis
Pellets obtained from 2 ml of endpoint liquid samples were suspended in
300 µl of 1X phosphate buffered saline. The RBB+C method was used as previously described (Yu and Morrison, 2004a) to extract DNA from the suspended pellets with only minor modifications. These changes were made to increase DNA concentration: less elution buffer and repeated elution by re- application of elution buffer to the column. The extraction was checked on a 0.8
% agarose gel. Extracted DNA was quantified in triplicate using the Quant-iT kit
(2-1000 ng, Invitrogen) and used as a template for hot start PCR (Platinum Taq
DNA polymerase, Invitrogen). Two sets of primers were used: EUB357f with a
GC clamp and EUB519r for bacteria 16S rDNA and ARC344f with a GC clamp and EUB519r for archaeal 16S rDNA as described previously (Lane, 1991; Yu
121
and Morrison, 2004a; 2004b). The PCR program was 94°C for 4 min, 10 cycles of
hot start PCR (94 °C for 30 sec, 61 °C with a decrease of 0.5 °C/sec for 30
seconds, and 72 °C for 30 sec), 25 cycles of PCR (94 °C for 30 sec, 56 °C for 30
sec, 72 °C for 30 sec), with a final extension at 72 °C for 30 sec. PCR products were checked on a 1.5 % agarose gel and quantified in triplicate with the Quant-
IT kit (2-1000 ng, Invitrogen kit).
Prior to denaturing gradient gel electrophoresis (DGGE), bacterial PCR products were diluted to 25 ng/µl and approximately 300 ng of bacterial DNA was loaded per well. For archaeal PCR products, samples were not diluted prior to loading, approximately 150 ng of DNA. Each sample was run twice to ensure reproducibility. The polyacrylamide gel (7.5 %, 37.5:1) was made with a denaturing gradient of 40-60 % acrylamide (100 % consisting of 40 % (v/v) formamide and 7 M urea) (Yu and Morrison, 2004a). The gel was run at 60 °C with a program of 80 V for 1 hr and 160 V for 15 hr in 0.5 % TAE buffer in the
INGENYphorU system (Ingeny). The gel was dyed with 2X SYBR Green I
(Invitrogen) for 30 mins with gentle rocking in the dark. Banding patterns were analyzed using Bionumerics software and similarity dendrograms determined with Dice and UPGMA algorithms.
DNA extracts from duplicate cultures of hemicellulose, pectin, and starch
(AC) were amplified with 27f and 519r primers (Lane, 1991) including unique barcodes for each sample (Hamady et al., 2008). PCR was completed using
Platinum Taq-Hi Fidelity Polymerase (Invitrogen) and MgSO4, instead of MgCl2,
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were run with the program: 95 °C for 5 min, (95 °C for 30 sec, 60 °C for 30 sec,
72 °C for 1 min) for 35 cycles, 72°C for 10 min. PCR products were run on a 1 %
agarose gel, the bands extracted, and purified using the QIAquick Gel Extraction
Kit (Qiagen). Starch AC samples could not be amplified with these primers.
Hemicellulose and pectin purified PCR products were quantified, diluted to 3 ng/µl, and combined in a 1:1 ratio. The W.M. Keck Center for Comparative and
Functional Genomics at the University of Illinois at Urbana-Champaign performed 454 Roche titanium pyrosequencing of the approximately 500 bp PCR products. Sequences were analyzed using Mothur software (Schloss et al., 2009) and the Ribosomal Database Project, RDP (Cole et al., 2009). Coverage was analyzed using rarefaction curves developed through Mothur’s software and compared to the maximum theoretical sequences observed at 0.03 diversity cutoff as calculated by the Gauss-Newton method. Following diversity and richness analysis using Mothur, unique sequences at the 0.03 diversity cutoff were sequenced using RDP SEQMATCH software.
RESULTS AND DISCUSSION
Soluble substrates: Monosaccharides and disaccharides
TC52 was grown on a variety of substrates in duplicate but often standard errors were high between duplicate conditions. This indicated that the culture was diverse and easily adapted to the substrate, leading to the different gas and SCFA profiles. As shown in Figure 7.1 with six sample substrates, H2 production
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occurred quickly, ≤ 24 hr, and accumulated until day 3 when TC52 switched to
hydrogenotrophic methane production. The highest theoretical H2 yields are
between 2 and 4 mol/mol substrate for butyrate and acetate fermentation, respectively (Kapdan and Kargi, 2006; Chong et al., 2009; Hallenbeck, 2009).
The majority of growth occurred within three days according to CO2
concentrations. By day 5, maximum apparent yields for CH4 and CO2 were 0.5
and 4 mmol/mmol substrate added, respectively. Similar gas yields occurred with
all substrates except for glyceraldehyde, glycerol, and arabinose where yields
obtained were similar to those when no substrate was added (data not shown).
Maximizing metabolite yields was outside the scope of this study.
SCFAs were analyzed on days 2, 4, and 6 using HPLC. The results
showed similar SCFA profiles in most cultures with butyrate as the predominant
SCFA at 48 hrs. The majority of monosaccharides showed a decrease in butyrate
concentration by day 6, in some cases a 50 % reduction (data not shown). In
contrast, the experiments with disaccharides showed stable levels of butyrate
throughout the 7 days incubation, indicating a balance between production and
oxidation. Acetate concentrations increased over time with the highest quantities
at day 6 for TC52 growth regardless of substrate. This corresponded to the
gradual increase in CO2 throughout incubation. Butyrate oxidation, as seen with
decreasing butyrate concentrations, was often seen in cultures with low substrate
concentrations (monosaccharides with 5 mM concentration) or poorly utilized
substrates, e.g. arabinose or glyceraldehyde. Other SCFA concentrations
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remained similar in the three sampling points. Of interest is the comparable
concentration of metabolites from common substrates, i.e., glucose, to typically more recalcitrant pentoses, i.e., ribose and xylose (Figure 7.2 and 3).
TC52 growth was also analyzed on disaccharides (sucrose, cellobiose, lactose, and maltose). Both gaseous and liquid metabolites were produced over time similar to monosaccharides except for two large differences: shorter lag times and higher yields. At 48 hrs, the disaccharides had twice as much CO2 but
similar levels of H2 as monosaccharides (Figure 7.2). This indicated that H2
production was not directly dependent on saccharide concentration. The SCFA
yields showed more nuances to the growth on various substrates than gaseous
metabolites alone (Figure 7.2 and 7.3). TC52 grown on lactose showed the best growth according to high yields of H2, CO2, formate, acetate, and butyrate: 3.0,
4.5, 2.0, 1.75, and 2.0 mmol/mmol substrate added, respectively. Both lactose and
maltose promoted high acetate and formate yields which correlate with high H2
yields; likely, acetate formation was closely associated with H2 production.
The pH values and optical density (660 nm) were determined for each
culture after 7 days of incubation. All monosaccharides had a pH of ≤ 7.0 while
disaccharides promoted lower pHs of 6.34, 6.7, 6.8, and 6.8 for lactose, maltose,
sucrose, and cellobiose, respectively. Although the initial pH of the medium was
6.7, the metabolism was largely fermentative and yielded higher concentration of
acids than the monosaccharide-fed cultures. The OD660 measurements for TC52
fed monosaccharides ranged between 0.149 and 0.193, while disaccharide fed
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cultures were twice as high, between 0.228 and 0.311. This confirmed the metabolite analysis for more extensive growth, twice as much biomass for disaccharide-fed cultures.
A reference culture, TC52 fed cellulose, was run in parallel to the substrate analysis experiment and metabolite production. This culture was much slower as compared to other test substrates, including polysaccharides. As shown in Figure 7.1, more CH4 was produced than H2 after 5 days, approximately 1 mmol/g substrate of CH4. CO2 accumulation was much slower and lower than the majority of substrates. In comparison, SCFA accumulation was similar as seen after 48 hrs of growth on all simple sugars (Figure 7.3). The pH values after incubation were similar to disaccharide conditions, 6.42 and 6.78, but lower than other polysaccharides.
Insoluble substrates: Polysaccharides
The metabolism of polymers was assessed by monitoring gas composition,
SCFA, and endpoint pH values. The polymers analyzed in these experiments were chitin, pectin, starch, and heterogeneous hemicellulose in addition to cellulose.
Autoclaving at 121 °C and under high pressure was also tested as it may affect polymer degradation and the subsequent fermentation. The effect of autoclaving,
AC, can be seen at 48 hrs in gas and SCFA yields (Figures 7.4 and 7.5). While
CH4 and CO2 yields from polymers were similar regardless of autoclaving, the formation of H2 was greatly affected by the autoclaving. Not autoclaved, NAC, conditions for chitin, hemicelluloses, and starch show at least 50 % higher yields
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(Figure 7.4). Previous studies have shown that autoclaving destroys simple sugars
and oligosaccharides otherwise available as a substrate. These simple substrates
would decrease lag time and increase overall yields in the NAC bottles, as seen
with H2 production over time (data not shown). Although there clearly were
differences in gaseous metabolites occurred at 48 hrs, the same cannot be said for
SCFA which were similar between AC and NAC (Figure 7.5). The simple
substrates available in NAC conditions provide a unique advantage for the H2
producing organisms in the culture.
There were differences in TC52 growth and metabolism between
polysaccharide substrates and simple sugars. Not only was lag time shorter and
yields higher, but also the CH4 and CO2 yields were twice as high with polymers.
The H2 yields were minimal, < 1 mmol/g substrate (Figures 7.2 and 7.4). This was
to be expected since 4 g/l is a much higher concentration of substrate than 5 mM
used for simple sugars. By comparison to cellulose, growth on pectin, starch, and
hemicellulose was much faster and the yields were higher (Figure 7.1). The chitin
yields were comparable to those in the negative control with no added substrate
(data not shown). Lactate accumulated more in polysaccharide conditions than
simple sugars. In addition, starch promoted high formate production than other
substrates, about 5 vs 1 mmol/g substrate, respectively. Otherwise, SCFA and gas
products were similar.
Analysis of the final pH values confirmed that chitin was a poor substrate,
7.6, with starch, pectin, and hemicellulose yielding much lower pH values: 5.2,
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6.1, and 6.1, respectively. The lower pH values indicate higher acid production
due to increased fermentation even in comparison to the cellulose reference, pH
6.6. By comparison with cellulose, starch, pectin, and hemicellulose were good substrates for TC52, each involving different metabolic pathways.
Substrate effect on bacterial diversity
Bacterial DGGE banding patterns of monosaccharide- and disaccharide-
fed TC52 indicated a diverse consortium adapted to each new substrate (Figure
7.6). The similarity between the patterns was 68 % or higher. Duplicate cultures
were not always similar to one another and the most unusual banding pattern
observed with arabinose as the substrate was only 59 % similar to the sucrose and glucose sample. Archaeal DGGE showed the same banding pattern regardless of substrate (100 % similarity) indicating a core group of methanogens that were present even if CH4 yields were low.
Polysaccharide fed TC52 was analyzed also through bacterial and archaeal
DGGE (Figure 7.7). Bacterial results show again adaptation of the diverse
consortium, ≥ 65 % similarity. Cultures fed substrate regardless of autoclaving
and their duplicates matched well except for two exceptions, i.e., hemicellulose
AC and starch NAC. Again, archaeal DGGE banding patterns were the same
regardless of substrate or CH4 production (data not shown). In order to understand
some of the diversity in the polysaccharide samples, sequencing of a 500 bp
region of the 16S rRNA bacterial gene of hemicelluloses, starch, and pectin AC
samples was completed. The DNA extracted from starch AC samples could not be
128
amplified by the primers used in this study and therefore, sequencing was not completed on these samples.
Pyrosequencing revealed that the dominant genus was Lutispora and specificially the organism, Lutispora thermophila. L. thermophila does not utilize carbohydrates and likely played a secondary role in the culture by utilizing medium components (Shiratori et al., 2008; Izquierdo et al., 2010; Sizova et al.,
2011). As shown in Figure 7.7, hemicellulose-fed samples were similar at the genus level to their pectin counterparts except for some variation in the
Clostridium and Acetivibrio genera. When looking at a species level (Table 7.1) the effect of substrate on the microbial diversity can be seen. Hemicellulose-fed samples, while dominated by Lutispora thermophila, had a much larger population of Clostridium xylanovorans. In comparison, pectin samples had fewer
C. xylanovorans but other Clostridium sp. and Sporobacterium olearium were present.
CONCLUSIONS
The cellulolytic, thermophilic, and anaerobic TC52 consortium was shown to degrade a variety of monosaccharides, disaccharides, and polysaccharides.
Metabolite production on each of the substrates was monitored over time. H2 yields were similar regardless of simple sugar substrate indicated a versatile population able to utilize a variety of saccharides. CO2 yields were twice as high with disaccharides as their monosaccharide counterparts and polysaccharides mineralization was even higher. Autoclaving of polysaccharides decreased H2
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production with hemicelluloses, chitin, and somewhat in starch due to the destruction of free sugars present with these substrates. Acetate and butyrate composed the majority of SCFA produced. The bacterial diversity of the consortium allowed for a quick adaption to new substrates and their hydrolysis.
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4 A B 3
2
1
0
3 C D
2
1
0
12 E F 10 Glucose Yield (mmol/mmol or g substrate added) g substrate or (mmol/mmol Yield Cellobiose 8 Cellulose
6
4 Hemicellulose 2 Pectin Starch 0 01234560123456 Time (day) Time (day)
Figure 7.1. Production of H2 (A, B), CH4 (C, D), and CO2 (E, F) by TC52 growth on glucose, cellobiose, and cellulose (A, C, E). Growth on autoclaved hemicellulose, pectin, and starch (B, D, F) is also shown.
131
5
4 A
3
2
1 5 0 4 B
132 3 Yield substrate (mmol/g added) 2
1
0 se e io se yd l o h o ul de er se ll al yc o e e er l in s C c G b bo se ly ra i lo e G A R y os e X c s lu no se G an to se ac to e M al c os e G ru ct s F a lto se Figure 7.2. Gas metabolite yields after 48 hrs of growth on various monosacchL aridesa and disaccharides.ro A: H2. B:CO2 M c u b S lo el C 132
3
A Lactate 2 Formate Propionate
1
3 0 B 2
1
4 0 133 3 C Yield (mmol/mmol or g substrate added) Yield (mmol/mmol 2
1
0 se de io se y ol lo eh er se lu ld c o se el ra ly in o se C e G b ib o se c ra R l o e ly A y c os e G X lu n os e G n t s e a ac to s M l c to se a ru c o e G F a lt os L a cr Figure 7.3. SCFA metabolite yields after 48 hrs of growth on various monosM accharidesu b and disaccharides. A: lactate, S lo el formate, and propionate. B: acetate. C: butyrate. C
133
3 A
2
1
0 2 B
1
0
Yield (mmol/g substrate added) substrate (mmol/g Yield 12 C 10
8
6
4
2
0
C C C C C C C C C - A - A A - A A - A A - A A e N e N N N s tin - s - tin - ch - lo i in lo se c in ar h u h it u lo e ct t rc ll C h ll u P e S ta e C ce ll P S C i ce em i H em H
Figure 7.4. Gas metabolite yields after 48 hrs of growth on various AC and NAC polysaccharides. A: H2. B:CH4. C: CO2.
134
5
4 A Lactate Formate 3 Propionate
2
1
50
4 B
3
2
1 135
05
Yield (mmol/g substrate added) Yield (mmol/g 4 C
3
2
1
0 C C C A C ) - A C - A m e) e - A A C C N lu at s n N - A A C - cu tr lo ti - e N A C h o bs lu hi in s - - A rc in u el C it lo e n N ta o s C h lu os ti - S (n no C el ul ec in ch l ( ic ll P ct ar ro ol m ce e St nt tr e i P o on H em C C H Figure 7.5. SCFA metabolite yields after 48 hrs of growth on various AC and NAC polysaccharides. A: lactate, formate, and propionate. B: acetate. C: butyrate. 135
136
Figure 7.6. DGGE banding patterns of monosaccharide and disaccharide fed cultures. Similarity is indicated with a dendrogram (UPGMA, DICE). 136
137
Figure 7.7. DGGE banding patterns of AC and NAC polysaccharide fed cultures. Similarity is indicated with a dendrogram (UPGMA, DICE).
137
138
Figure 7.8. Relative abundance of species in different genera with hemicellulose (A) and pectin (B) samples.
138
Table 7.1. Classification of TC52 organisms when fed polymers at a diversity cutoff of 0.03. Relative abundance is determined by the percent of total sequences in the samples.
Hemicellulose Pectin
Relative Abundance Classification Similarity (% of total sequences) RDP-Seqmatch (1.0 = 100%)
51.1 71.9 Lutispora thermophila 0.558-0.98
28.6 2.3 Clostridium xylanovorans 0.573-0.628
5.6 3.4 Clostridium stercorarium (subsp. thermolacticum, leptospartum) 0.575-0.608
5.3 Sporobacterium olearium 0.536-0.598
4.8 Clostridium sp. CM-C81 0.673-0.838
3.6 1.0 Sporanaerobacter acetigenes
or Sporanaerobacter sp. SN28 0.714-0.805
1.9 0.7 Clostridium thermobutyricum 0.885-0.913
1.4 0.4 Clostridium thermopalmarium
or Clostridium thermobutyricum 0.886-0.933
0.5 Peptostreptococcus sp. P4P_31P3
or Parvimonas sp. oral taxon 393 0.543
0.3 Sporanaerobacter acetigenes 0.687
0.3 Clostridium leptum
or Clostridium aminovalericum 0.577
0.3 0.6 Clostridium thermopalmarium 0.615-0.869
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0.4 Sinorhizobium sp. (LILM4H41, LILM2009) 1.0
0.4 Clostridium sartagoforme
or Clostridium baratii
or Clostridium sp. CM-C81 0.739
0.3 Clostridium aminovalericum 0.556-0.578
0.2 Clostridium stercorarium subsp. leptospartum
or Clostridium sporogenes 0.679
0.2 Lutispora thermophila
or Sporobacterium olearium
or Clostridium aminovalericum 0.576
0.2 Sporobacterium olearium
or Clostridium xylanovorans 0.519
6.6 6.4 ≤ 2 or more sequences so not classified
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CHAPTER 8: Commercial Paper Composition Affects Its Hydrolysis and
Fermentation by a Cellulolytic, Thermophilic Consortium
ABSTRACT
Waste paper, which contains cellulose, has been shown to serve as a substrate for value-added products. This study focuses on a cellulolytic, anaerobic consortium (TC52) fed 5 x 5 mm pieces of eight different types of commercial paper. TC52 was grown anaerobically and sampled daily for metabolite quantification in the gas and liquid phases. The composition of paper samples was compared to fermentative products and loss of substrate (dry wt). Short chain fatty acids, H2, CH4, and CO2 were monitored over time in two separate experiments, one for 5 days and another for 17 days. Papers containing high amounts of chemical pulp showed the highest metabolite yields and degradation of 74 and 52 % of total dry weight. The results showed that pulp composition and the amount of inorganic material influenced especially the fermentative metabolism.
INTRODUCTION
141
In recent years, there has been extensive research on generating sustainable energy from renewable resources. Potential cellulosic feedstocks include crop and farm residues and solid wastes generated from industrial processes. Waste from pulp and paper mills contain high amounts of cellulosic residues which have been tested as sustainable energy resources (Thompson et al.,
2001, Pokhrel and Viraraghavan, 2004, Monte et al., 2009). In addition to wastewater from mills, solid waste from papermaking and waste paper provide cellulosic substrates available for biodegradation. In the US, paper and cardboard in landfills have decreased from 38 to 28.2 % of the total solid waste and the recycling rate has increased from 42 to 74.2 % between 1999 and 2009 (US
Environmental Protection Agency 2001; 2010). The portion of waste paper that cannot be recycled could provide a large source of renewable biomass for bioenergy purposes. In addition, anaerobic digestion on recycled paper and the papermaking effluents has been shown to produce CH4 yields of 141 ml/g dry wt
(Pommier et al., 2010).
The first step to papermaking is the process of generating pulp from wood through a mechanical, chemical, or a combination of the processes. Mechanical pulping utilizes physical forces and the presence of water to strip fibers from wood and suspend them in water. This process recovers 90-95 % of the wood fiber but the pulp is of a low quality, as indicated by the presence of high amounts of hemicelluloses and recalcitrant lignin (Smook, 2002; Pokhrel and
- Viraraghavan, 2004). Chemical pulping utilizes either acidic, H2SO3 and HSO3 ,
142
or alkaline, NaOH and NaS2, chemicals along with heat and/or pressure to
separate the fibers. While the pulp yield is much lower than mechanical
processing, 40-50 %, the pulp is of higher quality with lignin dissolved during the
chemical process (Thompson et al., 2001). Following pulping, the slurry is diluted
and mixed with the mineral fillers necessary for mechanical strength and integrity
before application to a thin wire cloth, dewatering, and pressing. Bleaching of the pulp prior to dewatering is required for certain paper types. Finally, the product is
surface coated to achieve the desired final paper quality.
In an effort to improve the stability of paper products, research has
focused on treatments that affect the degradability of paper such as surfactants
(Wu and Ju, 1998; Kim and Chun, 2004; Kim et al., 2007), wet oxidation (Fox
and Noike, 2004), and biotreatment, i.e., enzymatic (Kurakake et al., 2007;
Yunqin et al., 2010). Other studies have looked at waste paper as a source for
simultaneous saccharification and fermentation processes for ethanol (Scott et al.,
1994; Brooks and Ingram, 1995; Park et al., 2010) or lactic acid production
(Schmidt and Padukone, 1997; Park et al., 2004; Marques et al., 2008). Ntaikou
and co-workers (2009) tested a known cellulolytic anaerobe, Ruminococcus albus,
and showed a positive relationship between total carbohydrates in the paper and
higher H2 yields. Currently, however, the pulp composition of paper has not been considered in paper biodegradation studies.
The purpose of this study was to monitor growth of a thermophilic, anaerobic consortium, TC52, on different types of commercial paper.
143
Fermentation products were monitored over time in an effort to determine the
effects of paper composition on microbial metabolism.
MATERIAL AND METHODS
Paper samples
Paper samples were obtained from two separate paper mills (UPM and E-
Real, Finland). The bulk paper composition was standardized at the mills, as
shown in Table 8.1. Samples were manually cut to approximately 5 x 5 mm
before addition to medium as a substrate.
Culture conditions
The microbial consortium, designated TC52, originated from the interior
of a compost heap and subcultures had been maintained with cellulose for years
(Carver et al., 2010; 2011). The culture was grown anaerobically in medium containing (per liter): 2 g trypticase, 1 g yeast extract, 4 g Na2CO3, 0.23 g
K2HPO4, 0.18 g KH2PO4, 0.36 g NH4Cl, 0.04 g NaCl, 0.09 g MgSO4·7H2O, 0.06
g CaCl2·2H2O, 5.66 g acetic acid, 1.62 g propionic acid, 0.68 g n-butyric acid,
0.23 g isobutyric acid, 0.20 g isovaleric acid, 0.20 g n-valeric acid, 0.20 g 2-
methyl-butyric acid, 0.001 g rezasurin, 0.25 g cysteine-HCl, and 0.25 g
Na2S·9H2O. Under a N2 headspace, paper was added to autoclaved medium at a
final concentration of 4 g/l with TC52 (5% v/v) and cultured in duplicate. The cultures were incubated in serum bottles at 52 °C at 180 rev/min.
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Two experiments were completed. In the first experiment, the sampling
was extended over 17 days of incubation; the second experiment comprised of
daily sampling for 5 days. Endpoint samples of the longer experiment were
analyzed for total solid (TS) and volatile solid (VS) (Eaton et al., 2005). Daily
sampling of the headspace gasses was completed on all cultures. Samples of
cultures were centrifuged at 16,000 g for 10 min. The supernatant and pellet were
separated and stored at -20 °C until further analysis.
Headspace gas analysis
Immediately after removal from the incubator, overpressures were
measured and wasted with a sterile syringe. Then, a sample of headspace gas (0.2
ml) was manually injected into a GC-2014 Shimadzu gas chromatograph
equipped with a thermal conductivity detector and a Porapak N (2 m length x 2
mm ID) column (Sigma-Aldrich). The carrier gas, N2, was maintained at 20 ml/min. Temperatures for the injector, detector, and column oven were 110, 110, and 80 °C, respectively. The chromatographs were analyzed with GC Solution
Analysis software (Shimadzu).
Analysis of short chain fatty acids
Short chain fatty acids (SCFAs) were analyzed in sample supernatants via
HPLC following cleaning with solid phase extraction (C18-T) and filtration through a 0.2 µm PTFE filter (Pall) as detailed in Chapter 2. The HPLC consisted of a guard column (5 cm x 4.6 mm ID, Supelguard H), a cation-exchange column
145
(30 cm x 7.8 mm ID, Supelcogel C-610H), an autosampler (Spectra-Physica AS
3000), and a UV detector set to 210 nm (Spectra-Physics SP100). A Beckman
114M HPLC pump was used to maintain a flow rate of 0.5 ml/min of the mobile phase, 0.1 % o-phosphoric acid. Run times were 65 min per sample with 100 µl injections at 70 min intervals. Standards were made from high purity lactic, formic, acetic, propionic, iso-butyric, n-butyric, and iso-valeric acids (≥ 98 %,
Sigma-Aldrich). Chromatographs were analyzed through a computer interface
equipped with the Clarity Chromatography Station (DataApex).
Microbial Diversity Analysis
Pellets obtained from 2 ml of endpoint liquid samples were suspended in
300 µl of 1X phosphate buffered saline. The RBB+C method was used as
previously described (Yu and Morrison, 2004a) to extract DNA from the
suspended pellets with only minor modifications. These changes were made to
increase DNA concentration: less elution buffer and repeated elution by re-
application of elution buffer to the column. The extraction was checked on a 0.8
% agarose gel. Extracted DNA was quantified in triplicate using the Quant-iT kit
(2-1000 ng, Invitrogen) and used as a template for hot start PCR (Platinum Taq
DNA polymerase, Invitrogen). Two sets of primers were used: EUB357f with a
GC clamp and EUB519r for bacteria 16S rDNA and ARC344f with a GC clamp
and EUB519r for archaeal 16S rDNA as described previously (Lane, 1991; Yu
and Morrison, 2004a; 2004b). The PCR program was 94°C for 4 min, 10 cycles of
hot start PCR (94 °C for 30 sec, 61 °C with a decrease of 0.5 °C/sec for 30
146
seconds, and 72 °C for 30 sec), 25 cycles of PCR (94 °C for 30 sec, 56 °C for 30
sec, 72 °C for 30 sec), with a final extension at 72 °C for 30 sec. PCR products were checked on a 1.5 % agarose gel and quantified in triplicate with the Quant-
IT kit (2-1000 ng, Invitrogen kit).
Prior to denaturing gradient gel electrophoresis (DGGE), bacterial PCR products were diluted to 25 ng/µl and approximately 300 ng of bacterial DNA was loaded per well. For archaeal PCR products, samples were not diluted prior to loading, approximately 150 ng of DNA. Each sample was run twice to ensure reproducibility. The polyacrylamide gel (7.5 %, 37.5:1) was made with a denaturing gradient of 40-60 % acrylamide (100 % consisting of 40 % (v/v) formamide and 7 M urea) (Yu and Morrison, 2004a). The gel was run at 60 °C with a program of 80 V for 1 hr and 160 V for 15 hr in 0.5 % TAE buffer in the
INGENYphorU system (Ingeny). The gel was dyed with 2X SYBR Green I
(Invitrogen) for 30 mins with gentle rocking in the dark. Banding patterns were analyzed using Bionumerics software and similarity dendrograms determined with Dice and UPGMA algorithms.
DNA extracts from duplicate cultures of hemicellulose, pectin, and starch
(AC) were amplified with 27f and 519r primers (Lane, 1991) including unique barcodes for each sample (Hamady et al., 2008). PCR was completed using
Platinum Taq-Hi Fidelity Polymerase (Invitrogen) and MgSO4, instead of MgCl2,
were run with the program: 95 °C for 5 min, (95 °C for 30 sec, 60 °C for 30 sec,
72 °C for 1 min) for 35 cycles, 72°C for 10 min. PCR products were run on a 1 %
147
agarose gel, the bands extracted, and purified using the QIAquick Gel Extraction
Kit (Qiagen). Starch AC samples could not be amplified with these primers.
Hemicellulose and pectin purified PCR products were quantified, diluted to 3 ng/µl, and combined in a 1:1 ratio. The W.M. Keck Center for Comparative and
Functional Genomics at the University of Illinois at Urbana-Champaign performed 454 Roche titanium pyrosequencing of the approximately 500 bp PCR products. Sequences were analyzed using Mothur software (Schloss et al., 2009) and the Ribosomal Database Project, RDP (Cole et al., 2009). Coverage was analyzed using rarefaction curves developed through Mothur’s software and compared to the maximum theoretical sequences observed at 0.03 diversity cutoff as calculated by the Gauss-Newton method. Following diversity and richness analysis using Mothur, unique sequences at the 0.03 diversity cutoff were sequenced using RDP SEQMATCH software.
RESULTS AND DISCUSSION
Metabolite Production
With all substrates, gas production was relatively rapid and the metabolite trends were similar. As seen in Figure 8.1, the formation of H2 peaked in many
cases by 24 hrs at 3 mmol/g substrate while CH4 production slowly peaked at 0.4
and 0.6 mmol/g substrate as H2 was consumed (day 2 to 5). CO2 levels peaked
later, around 120 hrs, between 1 and 5 mmol/g substrate but some substrates
showed continued growth up to 6 mmol/g substrate with further incubation. The
148
metabolic patterns were similar in both time course experiments. Paper H yielded
the highest H2 and CO2 amounts at 72 hrs with 6 and 9 mmol/g substrate,
respectively, and remained relatively constant with further incubation. Moreover,
minimal or no CH4 was associated with paper H and similar patterns were seen
with paper G (Figure 8.1). The consumption of H2 correlated with high levels of
CH4, indicating that methanogens were hydrogenotrophic.
SCFAs were also monitored over time but the largest differences were
seen with lactic, acetic, and butyric acid (Figure 8.2). By 48 hrs, lactate levels
either increased, usually with papers yielding high H2 concentrations, or decreased, especially with methane production (Figure 8.1). By day 5, TC52 fed papers A, B, D, G, and H produced lactic acid but at lower concentrations than with microcrystalline cellulose, 1.1 mmol/g substrate. With longer incubation, lactic acid production phased out, demonstrating a shift in the bacterial population. The second highest SCFA was consistently butyrate with yields between 2.5 and 3.0 mmol/g substrate at 24 hrs and its yield was comparable to that with cellulose, 2.5 mmol/g substrate. Regardless of paper substrate, butyrate concentration decreased with further incubation and therefore, butyrate oxidation was common with the consortium under these conditions. Only the TC52 culture grown on paper H showed stable butyrate levels at 2 mmol/g substrate through the
17 days of incubation. The most abundant SCFA, acetate, was present in similar amounts with all substrates, 2 to 3 mmol/g substrate after several weeks.
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After the 17 day time course experiment, it was concluded that the majority of metabolic activity occurred within the first 5 days. Consistent metabolic activity was acetate fermentation and butyrate oxidation. Both acetate and butyrate fermentation are coupled with hydrogenases. Because butyrate was oxidized, H2 production was mainly attributed to acetate fermentation. In addition, lactic acid producing organisms can utilize H2 as an electron donor, decreasing the maximum H2 yields. Since lactic acid correlated with high H2 yields, either the lactic acid bacteria required high H2 yields to grow or they did not utilize H2 as an electron donor. The highest metabolite yields occurred with paper samples containing only chemical pulp, papers G and H. Chemical pulping produced a more readily biodegradable paper which lacked lignin and other recalcitrant polymers. Growth, according to both gas and acid metabolites, was quicker even on the most recalcitrant paper than on microcrystalline cellulose.
Paper composition and degradation
Visually, the paper samples lost their shape within a day or two of incubation with TC52; only papers A and B remained intact throughout the experiment. TC52 was capable of degrading the cellulose in spite of the presence of more recalcitrant components such as clay filler materials. In order to quantify paper degradation following TC52, traditional TS/VS methods were utilized. The amount of mass (dry wt) loss following 17 days of incubation was determined by subtracting the TS from the amount of paper added (4 g/l). VS encompassed any solids that were volatile between 105 and 550 °C. This fraction includes residual
150
cellulose, recalcitrant polymers, and microbial biomass. Inert material was
considered to be the material remaining following 550 °C such as fillers,
pigments, and any other inorganic material. While this procedure was not precise,
it provided a way to compare the biodegradability of the eight commercial paper
samples.
As seen in Figure 8.3, the biodegradability of all paper types was
estimated for other samples except paper A. This culture could not be sampled
due to incomplete hydrolysis of paper A and the inability to obtain a homogenous
sample. Inert material was largest in paper G with F, C, E, H, B, and D having
decreasing amounts. This pattern can be correlated with the composition of each
paper type, specifically the amount of pigments and fillers (Table 8.1). The VS
measurements include microbial biomass and recalcitrant, but volatile, components present in mechanical and recycled pulp. Mechanical pulp is known to contain high amounts of hemicelluloses and lignin. Papers E, C, B, F, D, H, and
G had decreasing amounts of VS. Papers E, C, and B have high levels of mechanical pulp, 60, 56, and 41 %, respectively, while F and D have recycled fiber content of 46 and 92 %, respectively. G and H both had approximately 10 %
VS which would consist of mostly microbial biomass or any polysaccharides.
Other than papers G and H, a large amount of biodegradable material remained unused even after 17 days of growth, especially portions of mechanical pulp and recycled fiber.
151
In accordance with the VS and inert material results, biodegradable
material varied between paper types with the highest levels seen with sample H
and decreasing amounts with papers G, D, B, F, C, and E (Figure 8.3). High levels
of chemical pulp in papers H and G, 68 and 44 % correlated closely the loss of
paper, 74 and 52 % (dry wt), respectively. Chemical pulp is composed of mostly
cellulose and simplified saccharides present in plant biomass (Smook, 2002). This fraction was easy for TC52 to degrade in comparison to papers containing mechanical pulp such as B, F, and C. Paper D contained no chemical pulp but was the third most degraded sample. This paper contained mostly recycled fiber (92
%) but could still be degraded as indicated by the 40 % loss of dry weight. The
high volatile solids 47 %, indicates that a large amount of the recycled fiber could
not be degraded by TC52.
The metabolic patterns of the three most biodegradable papers (D, G, and
H), were similar to those of cellulose-fed TC52 (Figure 8.4). The H2 yields were highest in these three samples at 48 hrs, 2.5-3.5 mmol/g substrate added. When the cultures were fed paper, CO2 levels were higher, 1-2 mmol/g substrate, than
those with cellulose, < 0.5 mmol/g substrate. According to CO2 accumulation,
growth of TC52 was faster and yields higher on paper D, G, and H than with microcrystalline cellulose. Samples G and H had negligible CH4 production
during the first 5 days. In contrast, paper D was a source of CH4 production due to
recycled fiber (Figure 8.1). Efficient biodegradation of paper as a substrate would
be hindered by the presence of lignin. Lignin, an aromatic polymer is known for
152
its recalcitrance to bacterial degradation (Masai et al., 2007; Field and Sierra-
Alvarez, 2008; Arora and Sharma, 2010) and therefore chemical pulping, which
eliminates lignin, produced a paper that was more readily biodegradable.
Microbial diversity
DNA was extracted from TC52 endpoint samples of the different paper
types. PCR was used to amplify an approximately 170 bp region of the bacterial
and archaeal 16S rRNA gene. Bacterial products showed similar DGGE banding
patterns in all samples (Figure 8.5). Only the patterns for papers G and H were
slightly different with 88 % similarity to the other samples. These results showed
that the consortium was able to degrade all commercial paper samples. DGGE of
archaeal PCR products revealed banding patterns with no differences between
samples (100 % similarity). The methanogenic, archaeal community was
relatively simple and ubiquitous in the cultures regardless of CH4 production.
The three most readily biodegradable papers (D, G, and H) were further analyzed using 454 pyrosequencing. This method allowed identification of the main organisms involved in paper degradation. As shown in Figure 8.6., papers D and G had similar relative abundance with the family Lachnospiraceae and the genus Lutispora being dominant. With paper H, Lutispora spp. were more
dominant, composing ≥ 50 % of the identified sequences. This is in disagreement with classifications of unique sequences at the 0.03 cutoff. Clostridium
stercorarium subsp. thermobacticum and leptospartum were dominant in papers
153
D, G, and H. This discrepancy is likely due to the low levels of similarity, often between 0.50 and 0.75.
CONCLUSIONS
TC52 was grown on eight commercial paper samples and showed higher
levels of metabolism than when grown with microcrystalline cellulose. Two of the
paper samples, G and H, were copy paper with or without surface coating,
respectively; both were subject to relatively fast hydrolysis and high metabolic activity. The third paper with high degradability, D, was a form of newsprint containing 92 % recycled fiber but approximately half of its available fiber
content was unused. Lignocellulose was likely present especially in the recycled
fibers and mechanical pulp, lowering the biodegradability of some paper samples.
Fillers, pigments, binders, and coating material, all inorganic material, are
compounds that cannot be mineralized. TC52 readily degraded the majority of
paper samples produced by chemical pulping but only portions of the mechanical pulp and recycled fiber. The composition of commercial paper samples affected
the microbial activity, including growth and product formation.
154
Table 8.1. Relative composition (% w/w) of the paper samples tested in this study.
Mechanical Chemical Pulp from Pigments Pulp Pulp Recovered Fiber and Fillers Moisture Binders A 48.6 32.4 0 7.1 8.5 3.4 B 41.1 36 0 10.7 8.5 3.6 C 60 3 0 28 6 3 D 0 0 92 0 8 0 E 56 12 0 25 6 1 F 18 2 46 28 5 1 G 0 44 0 46 4 6 H 0 68 0 22 5 5
155
8
6 A
4
2
0
1.0 A 0.8 B B C 0.6 D E 0.4 F G 0.2 H
0.0
15
Cumulative Gas (mmol/g substrate added) substrate (mmol/g Gas Cumulative C
10
5
0
0123456 Time (day)
Figure 8.1. Gas production over time by TC52 fed eight different paper samples. A: H2, B: CH4, C: CO2.
156
1.5 A
1.0
0.5
0.0
4 B 3 A B 2 C D E 1 F G H
Yield (mmol/g substrate added) substrate (mmol/g Yield 0 Cellulose 4 C 3
2
1
0 0123456 Time (day) Figure 8.2. SCFA production over time by TC52. A: lactate, B: acetate, C: butyrate. 157
B C D E
21 % 40 %
5 % 4 %
F G H
12 % 52 % 74 % 158
Biodegraded VS Inert Material
Figure 8.3. Paper degradation following 17 days of incubation with TC52 based on dry weight. Percentages are presented for the amount of paper biodegradation.
158
5 H2 CO2 4
3
2
1 Yield (mmol/g substrate added) substrate (mmol/g Yield
0
3.5 Lactate 3.0 Formate Acetate Propionate 2.5 Isobutyrate Butyrate 2.0 Isovalerate
1.5
1.0
0.5 Yield (mmol/g substrate added) substrate (mmol/g Yield
0.0 D G H e los llu Ce
Figure 8.4. Metabolite yields of the three most degraded papers in comparison to microcrystalline cellulose after 48 hrs of incubation.
159
160
Figure 8.5. Bacterial diversity of TC52 fed different types of paper. Similarity is indicated in the dendrogram (UPGMA, DICE).
160
161
Figure 8.6. Relative abundance of sequences from three paper samples. Unclassified sequences were high (≥ 50 %) and were taken out prior to making this calculations. A: paper D; B: paper G; C: paper C.
161
Table 8.2. Classification of TC52 organisms when fed three paper samples at a diversity cutoff of 0.03.
Paper Sample D G H Relative Abundance Classification Similarity (% of total sequences) RDP-Seqmatch (1.0 = 100%) 52.8 0.28 64.5 Clostridium stercorarium subsp. thermobacticum, leptospartum 0.500-0.778 4.7 53.2 0.7 Clostridium stercorarium subsp. leptospartum 0.484-0.689 11.5 14.9 15.0 Lutispora thermophila 0.609-0.976 7.1 19.4 9.4 Clostridium xylanovorans 0.560-0.639 7.4 0.9 Clostridiaceae bacterium FH052 0.619-0.689 5.9 1.8 0.4 Clostridium thermocellum ATCC27405 0.639-0.985 0.9 1.3 1.1 Sporanaerobacter acetigenes Sporanaerobacter sp. SN28 0.801-0.877 0.5 Thermotalea metallivorans 0.512 0.3 1.1 Clostridium thermopalmarium 0.959-0.972 0.7 Bacillus thermoamylovorans 0.96 0.5 Clostridium thermosuccinogenes 0.934 0.3 Sporobacterium olearium or Parasporobacterium paucivorans 0.554 0.3 Thermosyntropha sp. L-60 0.392 0.8 Clostridium thermobutyricum 0.959 8.2 6.6 7.1 ≤ 2 sequences so not classified
162
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APPENDIX
Cellulose
Figure A.1. Gram stain of TC60 with cellulose. Bacteria are visible around a piece of cellulose (labeled). x1000
202
Figure A.2. Gram stain of Chorella vulgaris without TC60 inoculum. Microalgae biomass still intact following incubation. x1000
Figure A.3. Gram stain of Chorella vulgaris without TC60 inoculum showing heterotrophic organisms present with microalgal biomass. x1000
203
Figure A.4. Gram stain of Dunaliella tertiolecta without TC60 inoculum. Heterotrophic organisms are present. Unusual cell types present but not D. tertiolecta. x1000
Figure A.5. Gram stain of Dunaliella tertiolecta without TC60 inoculum showing heterotrophic organisms present in t he microalgal biomass. x1000
204
Table A.1. Pairwise Tukey analysis for the affect of concentration on metabolite yields at 12, 24, and 48 hr with 55 °C.
Effect Time Metabolite Affect (hr) Concentration
12 H2 4>8,12,16,20 8,12>20
CO2 Ethanol 4>8,12>16,20 Acetate 4>8,12,16,20 8>16,20 12>20
24 H2 4>8,12,16,20 8>16,20 12>20
CO2 4>8,16,20 Ethanol 4>8,12,16,20 Acetate 4>8,12,16,20 8>16,20 12>20
48 H2 4>8,12,16,20 8>16,20
CO2 4>8,12,16,20 8>16,20 Ethanol 4>8,12,16,20 8>16,20 Acetate 4>8>12,16,20
205
Table A.2. Pairwise Tukey analysis of the affect of concentration on metabolite production rates at 55 °C.
Effect Metabolite Affect
Concentration
H2 4 > 12, 16, 20
CO2 4 > 8, 12, 16, 20
8 > 16, 20
Ethanol 4 > 8, 12, 16, 20
8 > 20
Acetate 4 > 12, 16, 20
8 > 16
206
Table A.3. Pairwise Tukey analysis for the affect of concentration on metabolite yields in the 50 and 60 °C set.
Effect Time Metabolite Affect
(hr)
Concentration
12 H2 2 > 4 > 8, 12
CO2 2 > 4 > 8, 12
24 H2 2 > 4, 8, 12
CO2 2 > 4 > 8, 12
Ethanol 2 > 4 > 8, 12
Acetate 2 > 4 > 8, 12
48 H2 2 > 4, 8, 12
CO2 2 > 4 > 8 > 12
Ethanol 2 > 4 > 8, 12
Acetate 2 > 4 > 8 > 12
207
Table A. 4. Pairwise Tukey analysis of metabolite production rates for the 50 and 60 °C set.
Metabolite Temperature Substrate Concentration (C) (g/l)
H2 60 B, C, D > A 2
CO2 50 A > B, D > C 2 60 C > A 4, 8 D > A, B, C 2 D > A, B 4, 8 50 A 2 > 4 > 8, 12 B 2 > 4 > 8, 12 C 4 > 2, 8, 12 D 2, 4 > 8, 12 60 A 2 > 4 > 8 > 12 B 2 > 4 > 8 > 12 C 2 > 4 > 8 > 12 D 2 > 4 > 8 > 12 Ethanol 50 A > C 2 B > C, D 2 60 C > A 2,4 D > A 2,4 D > B 4 50 A 2 > 8, 12 B 2 > 4, 8, 12 60 A 2 > 8, 12 4 > 12 208
B 2 > 8, 12 4 > 12 C 2, 4 > 8, 12 D 2, 4 > 8, 12 Acetate 50 A > C 2 B > C 60 50 A 2 > 8, 12 B 2 > 4, 8, 12 C 4 > 8, 12 D 2 > 4, 8, 12 60 A 2 > 8, 12 B 2 > 4, 8, 12 4 > 12 C 2 > 8, 12 4 > 12 D 2 > 4, 8, 12 4 > 12
209
210
Figure A.6. Bacterial DGGE and dendogram results for the TC52 duplicate samples grown on monosaccharides and disaccharides.
210
211
Figure A.7. Bacterial DGGE and dendogram results for the TC52 duplicate samples grown on polysaccharides.
211
212
Figure A.8. Bacterial DGGE and dendogram results for the TC52 duplicate samples grown on commercial paper samples.
212
213
Figure A.9. Bacterial DGGE and dendogram results for TC52, TC60, and glycerol samples.
213
Dice (Tol 1.0%-1.0%) (H>0.0% S>0.0%) [36.9%-71.9%] [86.7%-86.8%] dgge dgge 0 20 40 60 80 100 glucose1Arch xylose2Arch glycerol1Arch Maltose2Arch maltose1Arch sucrose2Arch cellobiose2Arch cellobiose1Arch fructose2Arch 214 fructose1Arch mannose2Arch mannose1Arch galactose2Arch galactose1Arch glucose2Arch xylose1Arch ribose2Arch ribose1Arch glycerol2Arch
Figure A.10. Archeal DGGE and dendogram results TC52 samples grown on monosaccharides and disaccharides.
214
Dice (Tol 1.0%-1.0%) (H>0.0% S>0.0%) [36.9%-71.9%] [86.7%-86.8%] dgge dgge 100 0 20 40 60 80 glycerol1Arch glycerol2Arch ribose1Arch ribose2Arch xylose1Arch xylose2Arch glucose1Arch
215 glucose2Arch galactose1Arch galactose2Arch mannose1Arch mannose2Arch fructose1Arch fructose2Arch cellobiose1Arch cellobiose2Arch sucrose2Arch maltose1Arch maltose2Arch
Figure A.11. Archeal DGGE and dendogram results for the TC52 duplicate samples grown on monosaccharides and disaccharides.
215
Dice (Tol 1.0%-1.0%) (H>0.0% S>0.0%) [36.9%-71.9%] [86.7%-86.8%] dgge dgge 0 20 40 60 80 100 pec1ACArch pec2ACArch pec1NACArch pec2NACArch hemi1ACArch hemi2ACArch hemi1NACArch hemi2NACArch 216 sta1ACArch sta2ACArch sta1NACArch sta2NACArch chi1ACArch chi2ACArch chi1NACArch cellulose1ACArch cellulose2ACArch
Figure A.12. Archeal DGGE and dendogram results for TC52 samples grown on polysaccharides.
216
Dice (Tol 1.0%-1.0%) (H>0.0% S>0.0%) [36.9%-71.9%] [86.7%-86.8%] dgge dgge 0 20 40 60 80 100 pec1ACArch pec2ACArch pec1NACArch pec2NACArch hemi1ACArch hemi2ACArch 217 hemi1NACArch hemi2NACArch sta1ACArch sta2ACArch sta1NACArch sta2NACArch chi1ACArch chi2ACArch chi1NACArch cellulose1ACArch cellulose2ACArch
Figure A.13. Archeal DGGE and dendogram results for the TC52 duplicate samples grown on polysaccharides.
217
Dice (Tol 1.0%-1.0%) (H>0.0% S>0.0%) [36.9%-71.9%] [86.7%-86.8%] dgge dgge 100 0 50 PaperA1Arch PaperA2Arch PaperB1Arch PaperB2Arch PaperC1Arch PaperC2Arch
218 PaperD1Arch PaperD2Arch PaperE1Arch PaperE2Arch PaperF1Arch PaperF2Arch PaperG1Arch PaperG2Arch PaperH1Arch PaperH2Arch
Figure A.14. Archeal DGGE and dendogram results for TC52 samples grown on commercial paper samples.
218
A 100
90
80
70
60 0.03 50 0.05 40 0.1 0.2 30
20
10
0 0 100 200 300 400 500 600 700 Sample No
B 100 90 80 70 60 0.03 0.05 50 0.1 40 0.2 30 20 10 0 0 100 200 300 400 500 600 700 SampleNo
Figure A.15. Rarefaction curves for TC60 samples. Duplicates are shown (A and B). 219
A 80
70
60
50 0.03 40 0.05 0.1 30 0.2 20
10
0 0 200 400 600 800 1000
B 80
70
60
50 0.0 3 40 0.0 30 5
20
10
0 0 200 400 600 800 1000 Sample No
Figure A.16. Rarefaction curves for TC52 grown on hemicellulose. Duplicate samples shown (A and B).
220
A
160
140
120
100 0.03 80 0.05 0.1 60 0.2 40
20
0 0 500 1000 1500
B
60
40
20
00 0.03 80 0.05
60 0.1 0.2 40
20
0 ‐100 100 300 500 700 900 1100 1300 1500
Figure A.17. Rarefaction curves for TC52 grown on pectin. Duplicates are shown (A and B).
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A 120
100
80 0.03 0.05 60 0.1 0.2 40
20
0 0 200 400 600 800 1000
B
120
100
80 0.03 60 0.05 0.1 40 0.2
20
0 0 200 400 600 800 1000
Figure A.18. Rarefaction curves of TC52 grown on Paper D. Duplicate samples are shown (A and B). 222
A
70
60
50
40 0.03 0.05 30 0.1 0.2 20
10
0 0 200 400 600 800
B 70
60
50
40 0.03 0.05 30 0.1 0.2 20
10
0 0 200 400 600 800
Figure A.19. Rarefaction curves of TC52 grown on Paper G. Duplicate samples are shown (A and B).
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A 70
60
50
40 0.03 0.05 30 0.1 0.2 20
10
0 0 100 200 300 400 500 600 700
B 70
60
50
40 0.03 0.05 30 0.1 0.2 20
10
0 0 100 200 300 400 500 600 700
Figure A.20. Rarefaction curves of TC52 grown on Paper H. Duplicate samples are shown (A and B)..
224
Bacteria – 871 total sequences Phylum Firmicutes 99.9 Unclassified <0.1 Class Clostridia Unclassified Order Clostridiales 11.6 Unclassified 3.3 Family Incertae Sedis XI 0.1 Genus Tepidimicrobium 0.1 Family Ruminococcaceae 3.4 Unclassified 0.6 Genus Acetivibrio 2.9 Family Clostridiaceae Genus Clostridium 4.7 Order Thermoanaerobacterales Family Thermoanaerobacteraceae Genus Thermoanaerobacter 85.6
Figure A.21. Classification of sequences from TC60 samples. Percent of total sequences is given.
225
Bacteria – 1081 Total sequences Unclassified 4.8 Phylum Firmicutes 95.2 Unclassified 0.3 Class Bacilli Order Bacillales Unclassified Family Bacillaceae Genus Bacillus 0.3 Class Clostridia Unclassified 0.2 Order Clostridiales 94.4 Unclassified 5.0 Family Incertae Sedis XI Genus Sporanaerobacter 4.2 Family Ruminococcaceae Genus Acetivibrio 0.1 Family Lachnospiraceae Unclassified 27.3 Family Clostridiaceae Unclassified 0.2 Genus Clostridium 4.3 Family Gracilibacteraceae Unclassified 0.5 Genus Lutispora 52.9
Figure A.22. Classification of sequences from TC52 grown on hemicellulose. Percent of total sequences is given.
226
Bacteria – 2601 total sequences Unclassified 2.7 Phylum Firmicutes 97.3 Unclassified 0.2 Class Bacilli Order Bacillales Family Bacillaceae Genus Bacillus 0.1 Order Clostridiales 96.9 Unclassified 4.8 Family Incertae Sedis XI 1.3 Unclassified < 0.1 Genus Sporanaerobacter 1.2 Family Lachnospiraceae Unclassified 7.2 Family Clostridiaceae 8.2 Unclassified 0.5 Genus Clostridium 7.7 Genus Sarcina < 0.01 Family Gracilibacteraceae 75.4 Unclassified 0.2 Genus Lutispora 75.3
Figure A.23. Classification of sequences from TC52 grown on pectin samples. Percent of total sequences is given.
227
Bacteria – 3475 total sequences Unclassified 42.5 Phylum Firmicutes 54.8 Unclassified Firmicutes 2.4 Class Bacilli Order Bacillales Family Bacillaceae Genus Bacillus 0.4 Class Clostridia 51.9 Unclassified 1.0 Order Clostridiales 50.9 Unclassified 15.6 Family Incertae Sedis XI Genus Sporanaerobacter 1.2 Family Ruminococcaceae 2.3 Unclassified 0.6 Family Lachnospiraceae Unclassified 14.9 Family Clostridiaceae Genus Clostridium 1.0 Family Gracilibacteraceae Unclassified 0.2 Genus Lutispora 15
Figure A.24. Classification of sequences from TC52 grown on three paper samples (D, G, H). Percent of total sequences is given. 228
A 25 20 15 10 0.03 5 0 0.05
B 400 300 200 100 0.03 0 0.05
C
3.50 3.00 2.50 2.00 1.50 0.03 1.00 0.05
Figure A.25. Diversity and richness analysis of TC60 and TC52 grown on pectin, hemicelluloses, and three paper samples at 0.03 and 0.05 cutoffs. A: sequences observed; B: Chao richness estimater; C: Shannon diversity index.
229