1. DETERMINING THE DOSE RESPONSE OF CELLULASE ENZYME COCKTAILS
UNDER VARIABLE TEMPERATURE, pH AND IONIC LIQUID CONDITIONS
A Report of a Senior Study
By
Davis Raymond Hu
Major: Biochemistry
Maryville College
Spring, 2014
Date approved____________________, by ________________________________
Faculty Supervisor
Date approved____________________, by ________________________________
Division Chair

3. iii
ABSTRACT
In today’s society, fuel technologies are becoming more expensive while
affecting the environment negatively and contributing to temperature and weather changing
patterns. The next generation of fuel technologies derives from the shift of finite fossil
sources to a development of biofuels – liquid fuels generated from solar energy stored in
plant biomass. The five-year mission of the Joint BioEnergy Institute (JBEI) in Emeryville,
California is to discover methods of harnessing solar energy in biomass that could meet the
nation’s annual transportation energy needs in ways that have less impact on global climate
change. The Deconstruction division of JBEI is working to develop a targeted cellulase
enzyme cocktail under optimized temperature, pH, and ionic liquid conditions in order to
generate fermentable glucose sugars from biofuels. Complex sugars generated from cellulase
sources are ultimately fed to engineered microbes which produce biofuels as well as other
valuable chemical products. In the experiment, the Dinitrosalicylic (DNS) assay, which
quantifies reducing sugars, was used to determine the optimal temperature and pH at which
several enzymes catalyze the production of glucose from cellulose. The catalytic efficacy of
combinations of these enzymes at different total enzyme dose volumes was also measured.
Initial experiments with enzymes Cel_9A and Cel_5A revealed optimal temperatures of
saccharification at 65°C and 85°C respectively. With commercial enzymes from Megazyme
International, Endo-Cellulase shows highest enzyme activity and is most favored at pH 5.

4. iv
TABLE OF CONTENTS
Page
Chapter I
Introduction 1
Chapter II
Materials and Methods 10
Chapter III
Results 20
Chapter IV
Discussion and Conclusions 27
Works Cited 30

5. v
LIST OF FIGURES
Figure Page
1 Figure 1. Cellulose Enzyme Breakage Types 7
2 Figure 2. Dinitrosalicylic (DNS) Colorimetric Assay 13
3 Figure 3. DNS Enzyme Reaction Schematic Diagram 14
4 Figure 4. Reaction Diagram Table 15
5 Figure 5. Whatman 350 Unifilter Plate 16
6 Figure 6. 96-well Schematic Temperature Diagram 17
7 Figure 7. DNS Enzyme 96-well Reaction 18
8 Figure 8. Temperature Variance Variable Graph for Cel_9A and Cel_5A 21
9 Figure 9. Temperature and pH 5 Variance Variables for Enzymes 22
10 Figure 10. Temperature and pH 6 Variance Variables for Enzymes 23
11 Figure 11. Temperature and pH 7 Variance Variables for Enzymes 24
12 Figure 12. Absorbance vs. Enzyme concentration of ILSG and Avicel 26
13 Figure 13. Glucose vs. Enzyme Concentration of ILSG and Avicel 26
14 Figure 14. Temperature and pH 5, 6, 7 Variance Variables for Enzymes 29

6. vi
Acknowledgements
This work was supported and made possible by the Center of Science and
Engineering Education at Lawrence Berkeley National Laboratory, U.S. Department of
Energy, Office of Science, and The Joint BioEnergy Institute. I would like to thank my
wonderful supporting mentors, University of California Berkeley Ph.D. graduate Vimalier
Reyes-Oritz and enzyme optimization scientist Kenneth Sale. I would also like to thank my
safety work lead supervisor Steve Singer. Lastly, I would also like to thank Vice President
Blake Simmons who made my internship possible by his wise selection of me for my
participation in this program. This work conducted by the Joint BioEnergy Institute was
supported by the Office of Science, Office of Biological and Environmental Research, of the
U. S. Department of Energy under Contract No. DE-AC02-05CH11231. I would also like to
thank my wonderful advisor/mentor/professor Dr. Angelia Gibson for her support by making
my thesis possible at Maryville College.

7. 1
CHAPTER I
INTRODUCTION
One of the major global issues and concerns of the 21st
century is the question on the
development of new sources of energy fuels to meet the increasing demands for generations
and beyond. Foreign oil dependence is not a solution for the future of energy sustainability
because it is a limited finite resource. In order for America to build an energy-independent
infrastructure, new strategies need to be developed and implemented to find new solutions to
solve this crisis. The desire for energy independence combined with rapid depletion of fossil
fuel reserves is a major catalyst for developing alternative fuels.
For many years, the transportation sector has relied mainly on liquid fuels, such as
gasoline and diesel because they are energy dense and fungible [1]. Fossil fuels provided 85-
95% of all energy production from 1950 to 2005 [2]. Fossil fuel energy production grew
521% over the course of fifty years from 63.9 to 396.8 quadrillion Btu (quads) [2].
Consumption of these fossil fuel resources in the United States was 28% more than it
produced; and 63% of its oil consumption was dependent on imports [3]. Of the total energy
consumed, approx. 85.1% was derived from fossil fuels, approx. 52.3% was consumed by
commerce and industry, approx. 25.8% by transportation, and approx. 21.1% in residential

8. 1
use [3]. In terms of the rate of current consumption of these fossil fuel resources, world
petroleum is projected to last another 92 years, natural gas 160 years, and coal 224 years [4].
The development of alternative renewable transportation fuels is critical to energy,
environmental, and economic security that leads to a reduced reliance on fossil fuels.
A major proportion consisting about 85% of fossil fuels and energy consumed
worldwide is derived from petroleum oil, coal and natural gas [4]. These sources of fuels are
not sustainable because they are limited in quantity and are not environmentally friendly due
to the pollution they give off, which is a threat to global climate change. The dirtiest fuel
source is coal along with petroleum ranking second, which emits sulfur dioxide, nitrogen
oxides and mercury and is used in 500 older generators that produce more than half of the
power in the United States [5]. Natural gas is a cleaner form of energy which accounts 10%
of all electricity, but it cannot be ensured with a steady fuel supply and is more expensive
than coal or petroleum [5]. This effect has stimulated interest in alternative energy from
wind, solar and biomass sources.
While there have been advances in alternative energy sources using bioethanol
produced from corn starch through hydrolysis and fermentation [1], there is a major concern
due to the nature of this fuel being derived from food products like starch, sucrose, and
oilseed feedstock. This could cause part of the food sector prices to rise at an unexpected
rate. The rising price of food fuel sources such as corn crops account for 40% of U.S.
production in 2011, up from 31% in 2008-2009 [6]. Bioethanol is also not fully “drop-in”
compatible or fungible with existing transportation energy infrastructure. This means it does
not enable maximum leverage of high capital investment for fuel production and distribution
infrastructure because of the costly expenses to run a corn bioethanol factory and it does not

9. 2
generate a maximal output in terms of fuel efficiency and cost effectiveness [5]. This implies
that ethanol fuels from corn, starch, and oilseed sources may not be the ideal carbon source
for long-term fuels.
The next big idea comes from the most basic and abundant source of fermentable
sugars in life, biomass. How can we utilize biomass and convert it into biofuels? Recently,
scientists and researchers may have found the potential fuel source derived from cellulosic
biomass, which provides a more sustainable and non-food source of fermentable sugar such
as ionic liquid pretreated switchgrass [1]. It is estimated that there are a billion tons of
biomass available annually in the United States [7]. More than half of that biomass is
composed of cellulose, which after the conversion to glucose from hydrolysis, can be
fermented into cellulosic biofuels [8, 9]. This means there is a large, untapped resource of
lignocellulosic biomass that could provide a renewable domestic source of nearly carbon-
neutral, advanced (drop-in and/or fungible) liquid fuels [10-12].
At the Joint BioEnergy Institute (JBEI) in Emeryville, California, there are four
divisions that are working synergistically to perform the basic science behind the conversion
of lignocellulosic biomass to advanced biofuels. The Feedstocks Division is where new
biomass is harvested and grown from previously obtained biomass. Then the Deconstruction
Division is responsible for breaking down the cellulolytic matter and converting it into usable
sugars. One of the difficult barriers for the conversion of cellulolytic matter to glucose sugars
is biomass recalcitrance of plant cell wall polysaccharides to enzymatic hydrolysis [13]. The
current pretreatment method being developed at JBEI is to “dissolve” lignocellulose biomass
and hydrolyze the resulting liquor into sugars using ionic liquids. These liquids are composed
of salts that are in liquid form rather than crystals at room temperature. This ionic liquid is

10. 3
known as 1-ethyl-3-methylimidazolium acetate (abbreviated as [C2mim][OAc]), which is
used to dissolve switchgrass biomass into three components – cellulose, hemicellulose, and
lignin [1]. This process has been shown to dramatically reduce biomass recalcitrance and
enhances the enzymatic hydrolysis of fermentable sugars by saccharification at high
temperatures and in presence of high ionic liquid concentrations in which this cocktail can be
converted into biodiesel (fatty acid ethyl-esters or FAEEs) by a metabolically engineered
strain of Escherichia coli. [13].
Ionic liquid pretreatment results in successful deconstruction of biomass matter. The
IL-pretreated switchgrass as recommended in a review article by Sluiter et al. describes an
experiment performed by Keasling research group about ionic liquid pretreatment of
switchgrass. The switchgrass was pretreated with [C2mim][OAc] ionic liquid at a 10:1
weight/weight ratio at 120°C for 3 hours [18], then washed multiple times with water and
ethanol. Next, the biomass was dried by using a lyophilization technique. The IL-pretreated
switchgrass end result was composed of 41% glucan and 13% xylan [18]. The composition
resembles that β-1,4-glucan cellulose have been broken into smaller fragments. The
remaining amount of ionic liquid in switchgrass was estimated using measured IL
concentrations in the hydrolyzate product after saccharification. The IL concentration in
hydrolysate was 0.05% at a 10% weight/volume loading of IL-pretreated biomass [18]. The
total saccharification product equals approximately 0.5% IL left in biomass after
pretreatment [18]. The experiment has demonstrated that IL is an effective salt in liquid
solution that effectively degrades cellulsae into fragmented sugar products.
Cellulose is then extracted from the ionic liquid pretreated biomass and sent to a
process called saccharification to generate glucose-rich sugars. But, ionic liquid pretreatment

11. 4
presents challenges to using commercial enzymes to liberate the glucose from pretreated
cellulose, because these enzymes are not active in the presence of ionic liquids.
Saccharification requires at least three enzymes to work synergistically in order for this
method to work as expected. The three enzymes used for this experiment consists of the
following: endoglucanases, cellobiohydrolases, and β –glucosidases. Cellulose is formed
from glucose polymers in which glucose molecules are linked via β-(1,4) glycosidic bonds.
Endoglucanases hydrolyze bonds at random spots on cellulose by creating, reducing and non-
reducing ends. Cellobiohydrolases catalyze hydrolysis of β-(1,4) bonds at the reducing and
non-reducing ends created by endoglucanases, creating glucose dimers called cellobiose. β –
glucosidases catalyze hydrolysis of the β-(1,4) bond of cellobiose to produce two glucose
molecules.
The Microbial Communities, Enzyme Optimization and Fungal Biotechnology
groups at JBEI are actively pursuing a mission to optimize a cellulose cocktail for the
maximum release of glucose from ionic liquid pretreated switchgrass that functions at high
temperatures (>70°C) and in 20% [C2mim][Oac] ionic liquid. The utilization of three
thermophilic enzymes from Megazyme, Ireland in a cocktail solution working synergistically
achieves the maximal glucose output: Endo-Cellulase (Endo) – Cel_9A & Cel_5A,
Cellobiohydralase (CBH) – Csac, and β-Glucoside (BG) – βG. These enzymes work
synergistically to catalyze the cleavage of the (1-4) bonds between glucose subunits to
produce the monosaccharide glucose as illustrated in Figure 1 on the next page.

12. 5
Figure 1. Enzyme cellulose fibril breakages for corresponding enzymes Endo, CBH and
βG Cellulose is a crystalline form of glucose polymers joined by β linkages (Image from
Hu, 2013).
Finally, the sugars are then fed to engineered microbes to produce biofuels in the
Fuels Synthesis Division. When new fuels have been generated successfully, the
Technologies Division then develops techniques for high yields of the biofuels. While the
production of these biofuels sounds very promising, there are roadblocks that hamper the
development of these cost and energy-efficient processes to convert lignocellulose into
advanced biofuels which include: lack of scalable and sustainable energy crops, difficulty in

13. 6
separating and breaking down biomass, expense of enzymes used to produce fermentable
sugars, and the need for microbial routes to produce advanced biofuels.
The Keasling research group at JBEI has conducted research utilizing a cellulase
cocktail solution being fed to engineered microbial strains for the production of cellulosic
biofuels. Ethanol is being used increasingly, but it has limitations for long term fuel
sustainability such as incompatibilities with existing petroleum based fuel resources. The
next generation of advanced biofuels will be derived from microorganisms such as
Escherichia coli and Saccharomyces cerevisiae [9]. The production of advanced biofuels
from these bacteria and yeast strains are developed from metabolic pathway engineering.
Once the strains have been genetically engineered, the deconstructed fermentable sugars are
fed to the bacteria which in result produce long chain isoprenoid and fatty acid alcohol based
biofuels.
The Steen experimental group examined saccharification of IL-pretreated switchgrass
hydrolysates that were generated to determine whether it would support growth of an
engineered Escherichia coli strain to produce fatty acid ethyl-ester (FAEE) biodiesel [19].
Through their method, they demonstrated the engineering of bacterial strain E. coli to
produce structurally tailored fatty esters (biodiesel), fatty alcohols, and waxes directly from
simple sugars. The end result of biofuel production includes determining the optimal
proportion of each enzyme and understanding the production of glucose as a function of the
enzyme dose and how the required dose responds to changes in the temperature and
concentration of ionic liquid in the reaction mixture. By determining the favorable
temperature, pH, and dose response conditions for the enzymatic cocktail, maximum yield of
biofuels could be produced from the genetically engineered bacteria strains.

14. 7
The enzyme optimization team’s experiment is unique in the way that the ratio
mixture of enzymes that releases a maximized output of glucose from the determined optimal
proportion, temperature and pH is specified through the dose response of the enzyme. Next,
the team measures the amount of sugar produced by absorbance spectroscopy through mixing
each tested sample in a dinitrosalicylic acid (DNS) colorimetric indicator solution. This
method tests for the presence of free carbonyl groups (C=O), also known as reducing sugars.
This involves the oxidation of the aldehyde functional group present in glucose and the
ketone functional group fructose. The aldehyde group is oxidized to carboxyl group while the
dinitrosalicylic is reduced to 3-amino, 5-nitrosalicylic acid. Side reactions such as the
decomposition of sugar competes for the availability of 3,5-dinitrosalicylic acid.
The Zhang research group uses DNS assays to test for each enzyme that delignifies
biofuels. Dinitrosalicylic acid is an effective indicator solution to measure the amount of
sugars that are produced after exposed to reaction conditions such as temperature and pH for
enzymes endoglucanases, exoglucanases, β-Glucoside [20]. The chemistry behind the
yellow/orange color shift is due to the amount of sugar content after exposed to temperature
and pH conditions. Understanding the dose response concept is important from an economic
perspective because enzymes still represent approximately $1.00 to $1.50 of the minimum
ethanol selling price per gallon. Thus, we want to be able to minimize the total amount of
enzyme used, which requires the understanding of the concept of dose response [12].

15. 1
CHAPTER II
MATERIALS AND METHODS
Biofuels are produced by fermenting the sugars derived from pretreatment and
saccharification of plant biomass to produce relatively pure glucose. At JBEI, biomass is
pretreated with ionic liquids at high temperatures and, upon dilution with water the process
produces a crystalline form of glucose polymers. Then enzymes are used to break down the
cellulose in order to obtain glucose through a process called saccharification. From there, the
glucose is fed to yeast and bacteria whose metabolisms have been engineered to utilize
glucose to produce various biofuels [1].
A key step in this process is the saccharification of cellulose to glucose, which
requires a mixture of enzymes that has been optimized to function under the pretreatment
conditions. In the case of JBEI this means optimized to perform at high temperatures and in
the presence of ionic liquids. The goal of this work is to determine the maximized release of
glucose at an optimized temperature and pH. This experiment is divided into several parts:
preparing enzymes in test tubes, reacting enzymes in ionic liquid/water source and oil,

16. 1
preparation of ionic liquid switch grass and avicel substrate, and preparation/running a DNS
assay under various temperature and pH conditions.
Materials
Thermophilic Enzymes from Megazyme Ireland
The three thermophilic enzymes used from Megazyme, Ireland are Endo-Cellulase
(Endo) – Cel_9A & Cel_5A, Cellobiohydralase (CBH) – Csac, and β-Glucoside (BG) – βG.
Reaction Conditions
The thermophilic enzymes were exposed to a reaction temperature condition range
from 50°C-80°C in order to prevent contamination and to increase the rate of the reaction.
The ionic liquid content was varied from 0%-20% concentration levels. The enzyme load
consisted 20 mg of enzyme/g of glucon.
Chemicals
The required chemicals involved in the experiment included: ionic liquid (1-n-ethyl-
3-methylimidazolium acetate), deionized water, 1M pH 5 citrate buffer, avicel and 1%
carboxymethylcellulose.
Characterization Equipment
High Performance Liquid Chromatography (HPLC) was used to separate different
components in solution/sample. The solvent that is in a reservoir gets sent to a pump solvent
manager/delivery system. Then the solvent gets separated by molecular weight of the glucose
and water. Then the sample is injected to the autosampler sample manager, which is then sent
to a high pressure column. The detector then senses and sends the signal reading to a
chromatogram to plot the data.

17. 2
2950 Biochemistry Analyzer (YSI) is used to analyze glucose content more
efficiently than HPLC by measuring liquid solution. The biochemistry analyzer analyzes
biochemical substances such as glucose and sucrose sugars, lactose and lactate, galactose,
glutamate and glutamine, choline, ethanol and methanol alcohols and peroxides [21]. An
immobilized enzyme biosensor takes a very small sample and then analyzes the amount of
chemical composition. The methodology behind YSI technology is using two membrane
layers, polycarbonate and cellulose acetate. This allows the substrate to be oxidized as it
enters an enzyme layer, which generates hydrogen peroxide. This substance is then passed
through the cellulose acetate layer to a platinum electrode where the peroxide is oxidized.
The final current is proportional to the concentration of the substrate.
SpectraMax M2 Assay Spectrophotometer is used to analyze the wavelength
absorbance of many samples in a 96-well plate. The instrument contains a rapid scanning
light beam that measures each sample within a second and generates an absorbance value
within the range of the visible light spectrum.
The Dinitrosalicylic Acid Colorimetric Assay (DNS) in conjunction with the
SpectraMax M2 Instrument measures the color intensity of a sample with insoluble biomass,
soluble sugars, and cellodextrins for the presence of reducing sugars from free carbonyl
groups.
DNS is a chemical substance used to react with reducing sugars with yellow color λ-
max = 580 nm and orange color around 620 nm. A spectrophotometer is used to measure the
amount of light absorbance in a particular sample. Refer to Figure 2 below for a colormetric
progression of using a DNS reagent on a sample.

18. 3
Figure 2. DNS colorimetric progression from yellow to red due to raised temperature
(Image from Hu, 2013).
Refer to Figure 7 below regarding how the DNS assay affects the coloration of the samples.
Figure 3. Schematic diagram the addition of enzyme and DNS reagent (Image from Hu,
2013).

19. 4
Sample Preparation and Data Collection
Preparing Enzymes in Test Tubes
The materials needed for this part are micropipettes (L10: 0.5 µL to 10 µL, L20: 2 µL
to 20 µL, L200: 20 µL to 200 µL), 17 test tube vials with caps, test tube holders, red and
green pipette tips. The enzymes tested are Endo-Cellulase 5A, Endo-Cellulase 9A,
Cellobiohydralase, and β-Glucosidase. The methods: micropipettes are used to fill each tube
with specific amounts of enzyme following the cocktail preparation and reaction preparation
table. This is done for 17 vials each for Cel_5A and Cel_9A with corresponding ratios of
other enzymes.
Reacting Enzymes in Ionic Liquid, Water Source and Oil
The reaction diagram for specified amounts of reagents for each sample well is
followed in Figure 3 below.
Figure 4. Reaction Diagram Table for indicated amounts of each specified reagent
(Image from Hu, 2013).

20. 5
Individual wells in the 96-well plate with pellets were filled with the specified
amount of enzyme, ionic liquid, sterile water and oil. 1.0 M citrate buffer with a pH of 4.8
was added without ionic liquids. This was then repeated for four sets of vials total (Cel_5A
@ 50°C, Cel_5A @ 70°C, Cel_9A @ 50°C, and Cel_9A @ 70°C).
After all of the solutions were added, VWR Type 19 oil was used to cover the layers
of the liquid in each well. This prevented liquid evaporation. Plates were placed in an
INFORS Multitron incubator/shaker at their designated temperatures for 24 hours.
After 24 hours, the plates were removed from incubator/shaker and only the sugar
solution was transferred to a Whatman 350 unifilter plate. Plates were centrifuged for 10
minutes until all sugar solution was filtered to the plate. Refer to Figure 4 below for a picture
of the filtered glucose solution. Glucose content was then measured using the YSI 2950
Biochemistry Analyzer.
Figure 5. Filtered glucose solution by using Whatman 350 unifilter plate and centrifuge
(Image from Hu, 2013).

21. 6
Preparation and Running a DNS assay for Temperature Variance with enzymes Cel_9A
and Cel_5A
The goal was to find enzyme optimal temperature. Carboxymethyl Cellulose (CMC)
is a cellulose derivative that makes up the cellulose backbone and has been used extensively
to characterize enzyme activity from endoglucanases. It is a highly specific substrate for
endo-acting cellulases, as the structure has been engineered to decrystallize cellulose and
create amorphous sites that are ideal for endoglucanase action. CMC is desirable because of
the catalysis product (glucose) as its easily measured using a reducing sugar assay such as
DNS assay. Using CMC in enzyme assays is important in regard to screening for cellulose
enzymes that are needed for more efficient cellulosic ethanol conversion. In this case it is
used as the substrate and when the enzymes are added, the sugar fiber chains break apart.
When DNS reagent was added, the solution will turn red under the presence of heat. This
involves the use of a substrate (1% CMC) and enzyme (Cel_9A and Cel_5A). We achieved a
cleaved CMC product after mixing the solutions together.
The methods: a 96-well plate were obtained filled with 165 µL of 1% CMC, 25 µL
Enzyme Cel_9A and Cel_5A into four wells column wise alternatively between the enzymes
according to schematic diagram and 10 µL buffer. Refer to Figure 5 below for temperature
and enzyme setup. A 200 µL pipette were then used to mix each well thoroughly.

22. 7
Figure 6. Schematic diagram of enzymes Cel_9A and Cel_5A placement in 96-well plate
along with specified temperatures (Image from Hu, 2013).
The wells were then covered with a metal sticker plate and placed in a Veriti 96 well
Thermal Cycler heater for 30 minutes using settings of 45°C to 70°C with 5°C increments for
each bi-column of Cel_9A and Cel_5A enzyme.
Once the initial heating was completed, the enzymes Cel_9A and Cel_5A were
transferred 60 µL to a new 96-well plate along with the DNS reagent. Color changes should
appear golden yellow. Refer to Figure 6 below for a picture of the glucose solution that
turned golden yellow upon adding DNS reagent.
Figure 7. Enzyme solution turning yellow after DNS reagent has been added (Image
from Hu, 2013).

23. 8
The plate were placed in the Veriti Thermal Cycler again for 5 minutes using DNS
reagent settings. After second heating, the orange solutions were transferred to a 96-well
transparent colorimetric reader plate and were analyzed for absorbance at 540 nm using the
SpectraMax M2 instrument. The absorbance for four samples of each enzyme at each
temperature was taken. Then the average between the four were taken and absorbance vs.
temperature was graphed along with standard error bars in Microsoft Excel.
Preparation and Running a DNS assay for Temperature and pH Variance with enzymes
Endo-cellulase, Cellobiohydrase and β-Glucosidase
The experimental procedure for creating a DNS assay is repeated similarly to that of
enzymes Cel_9A and Cel_5A. This time, three samples of each enzyme were tested at each
temperature increment 30°C to 85°C at pH 5, 6 and 7. Once plate with enzyme and DNS
reagent has been made, the 96-well transparent plate were placed in the SpectraMax M2 for
data acquisition.
Preparation and Running a DNS assay for an Enzyme Cocktail Solution
The experiment was continued by now combining the three enzymes together to form
a multi-component enzyme mixture or cocktail. The substrates, ionic liquid pretreated switch
grass (ILSG) and avicel, were used for this experiment. The experimental setup consists of
three 25 mg of ILSG for nanomolar concentration of 25 nM, 50 nM, 100 nM, 200 nM and
400 nM which means 5 different concentrations times three sets of 25 mg totals 15 samples.
The same setup goes for avicel totaling another 15 samples. Three control samples were also
included. Each sample tube were filled with specified calculated amounts of enzyme, 50 µL
of 1M citrate buffer and water. Once all samples were filled, they were placed in an

24. 9
Eppendorf Thermomixer at 60°C at 1400 rpm for 24 hours. By the next day, a DNS assay
were performed then measured with SpectraMax M2 for data acquisition.

25. 1
CHAPTER III
RESULTS
Collection of Data for Temperature Variance Variable with enzymes Cel_9A and Cel_5A
Once again, we are measuring the efficacy of saccharification of Cel_9A and Cel_5A
enzymes out of varying temperature ranges. By measuring glucose production using the DNS
assay, we were able to determine the optimal temperature for the enzymes. Four absorbance
measurements were obtained for each temperature for enzymes Cel_5A and Cel_9A. The
data from the SpectraMax M2 instrument were imported to Microsoft Excel, and a
temperature row were created from 45°C to 90°C with 5°C increments. The mean and
standard deviation of the four measurements were taken and plotted as a scatter plot of
absorbance vs. temperature. According to Figure 8, it is apparent that Cel_9A had an optimal
temperature at 65°C with an absorbance of 0.340 and Cel_5A had an optimal temperature at
85°C with an absorbance of 0.345. By the physical appearance of the graph, Cel_9A and
Cel_5A have a general inverse proportional trend crossing point at about 75°C, with Cel_9A
decreasing activity at higher temperatures and Cel_5A increasing at higher temperatures.
After crossing the 85°C threshold, Cel_9A activity significantly decreases due to extreme
temperatures.

26. 1
Figure 8. Temperature Variance Variable Graph for Cel_9A and Cel_5A. We are
measuring for determining the optimal temperature of the enzymes Cel_9A and Cel_5A
which are at 65°C and 85°C respectively.
Collection of Data for Temperature and pH Variance Variables with enzymes Endo-
cellulase, Cellobiohydrase and β-Glucosidase
Once again, we are measuring the efficacy of saccharification of enzymes endo-
cellulase, cellobiohydrase and β-Glucosidase out of varying temperature and pH ranges from
5-7. By measuring glucose production using the DNS assay, we were able to determine the
optimal temperature and pH condition for each of the enzymes.
0.340 0.345
0.000
0.050
0.100
0.150
0.200
0.250
0.300
0.350
0.400
35 45 55 65 75 85 95
Absorbanceλ=620nm
Temperature(°C)
Cel_9A
Cel_5A

27. 2
pH 5 Data
The results from the data were imported to Microsoft Excel from the SpectraMax M2
instrument. The same procedure was followed except this time with pH variance of 5. The
temperature range this time was from 30°C to 85°C. The data chart in Figure 9 shows that
endo-cellulase enzyme had a maximum temperature peak point at 40°C at pH 5 with an
absorbance of 0.455, cellobiohydralase at 35°C with an absorbance of 0.150, and β-
Glucosidase at 45°C with an absorbance of 0.148. The graph shows a consistent measure of
absorbance around 0.150 range over all temperatures for enzymes cellobiohydralase and β-
Glucosidase. By physical appearance of the graph, endo-cellulase had the most activity with
high and low peak points while cellobiohydralase and βG had similar minimal changes.
Figure 9. Temperature and pH 5Variance Variables for Endo, CBH and βG enzymes.
We are measuring for determining the optimal temperature of the enzymes endo-
cellulase, cellobiohydralase, and β-glucosidase which are 40°C, 35°C, and 45°C
respectively.
0.455
0.150 0.148
0.000
0.100
0.200
0.300
0.400
0.500
0.600
25 35 45 55 65 75 85
Absorbanceλ=620nm
Temperature (°C)
ENDO
CBH
βG

28. 3
pH 6 Data
The temperature range was the same from 30°C to 85°C. The data chart in Figure 10
shows that again, the enzyme Endo-Cellulase had the most activity where the maximum
temperature peak point is at 65°C with an absorbance of 0.401. Cellobiohydralase and β-
Glucosidase had minimal non-significant change between all temperatures. The maximum of
CBH was at 65°C with an absorbance of 0.147 and βG was at 35°C with an absorbance of
0.155. The graph shows a consistent measure of absorbance around 0.150 range over all
temperatures for enzymes cellobiohydralase and β-Glucosidase.
Figure 10. Temperature and pH 6 Variance Variables for Endo, CBH and βG enzymes.
We are measuring for determining the optimal temperature of the enzymes endo-
cellulase, cellobiohydralase, and β-glucosidase which are 65°C, 70°C, and 35°C
respectively.
0.401
0.1470.155
0.000
0.100
0.200
0.300
0.400
0.500
0.600
25 35 45 55 65 75 85
Absorbanceλ=620nm
Temperature (°C)
ENDO
CBH
βG

29. 4
pH 7 Data
The temperature range was the same from 30°C to 85°C. The data chart in Figure 11
shows that again, the enzyme Endo-Cellulase had the most activity where the maximum
temperature peak point is at 50°C with an absorbance of 0.313. The difference between pH 7
data graph and the others is that the enzymes Cellobiohydralase and β-Glucosidase had slight
more activity where CBH had a maximum peak point at 45°C with an absorbance of 0.148
and βG had a maximum peak point at 75°C with an absorbance of 0.155. It is also to note
that the enzyme absorbance begins to overlap one another starting from 75°C as opposed to
the other pH data graphs where Endo-Cellulase remained more active at higher temperatures.
Figure 11. Temperature and pH 7 Variance Variables for Endo, CBH and βG enzymes.
We are measuring for determining the optimal temperature of the enzymes endo-
cellulase, cellobiohydralase, and β-glucosidase which are 50°C, 45°C, and 75°C
respectively.
0.313
0.148 0.155
0.000
0.050
0.100
0.150
0.200
0.250
0.300
0.350
25 35 45 55 65 75 85
Absorbanceλ=620nm
Temperature (°C)
ENDO
CBH
βG

30. 5
Collection of Data for Enzyme Cocktail Solution
The data obtained for the enzyme cocktail solution for two substrates, ionic liquid
switchgrass (ILSG) and avicel, shows that glucose production appears to be a logarithmic
function of enzyme dose. With ILSG, there is a steady increase in absorbance from 0 to 400
nM because of successful saccharification of ILSG and is expected to continue grow beyond
that. To the contrary, avicel reached a plateau at 200 nM and no further growth was expected
even at a higher concentration of 400 nM was present. Refer to data charts in Figure 12 and
13. These data show that, at the same enzyme dose, the cellulose produced from ionic liquid
pretreatment (ILSG) is easier to convert to glucose; there is a 2 to 3X higher glucose yield
from the ILSG compared to that produced from avicel according to Figure 13. This is likely
due to the cellulose produced from IL pretreatment have a much lower crystallinity than the
highly crystalline structure of avicel. The glucose concentrations were calculated by taking
the averages of ILSG and avicel at each concentration then substituted into a given linear
equation by my mentor Absorbance = 0.1376x + 0.0628. The graph in Figure 13 displays the
glucose concentrations with respect to enzyme concentrations.
Figure 12. Absorbance vs. Enzyme concentration of ILSG and Avicel
-0.1
0.1
0.3
0.5
0.7
0.9
1.1
0 25 50 100 200 400
Absorbance
Enzyme Cocktail (EG, CBH, βG) and Concentration [nM]
ILSG

31. 6
Figure 13. Glucose vs. Enzyme Concentration of ILSG and Avicel
0
1
2
3
4
5
6
7
8
0 100 200 300 400
GlucoseConcentration(nM)
Enzyme Concentration [nM]
GC ILSG
GC Avicel

32. 1
CHAPTER IV
DISCUSSION AND CONCLUSION
In this project, we used the DNS assay to determine the optimal temperature and pH
at which several enzymes required to catalyze the production of glucose from cellulose
function optimally, and we then measured the performance of combinations of these enzymes
at different total enzyme doses. Through the first run of preliminary experiments with
Cel_9A and Cel_5A, we conclude that Cel_9A had optimal absorbance temperature 65°C
and Cel_5A had optimal absorbance at temperature 85°C when running the DNS assay
indicator solution for maximal glucose output. When experimenting with commercial
enzymes Endo-Cellulase, Cellobiohydralse and β-Glucosidase, it is conclusive that at pH 5,
Endo-Cellulase was the most favored which shows highest enzyme activity. The optimal
temperature conditions for each enzyme tested at three pH scales shows that Endo-Cellulase
had the highest peak at 40°C for pH 5 with 0.455 absorbance, Cellobiohydralse had the
highest peak at 45°C for pH 5 with 0.148 absorbance and β-Glucosidase had the highest peak
at 35°C for pH 6 and 75°C for pH 7 with 0.155 absorbance. The higher the absorbance value
when testing using DNS assay, the optimal ideal temperature condition is favored for the
maximal glucose output at the three pH levels.

33. 1
In summary, detection of higher absorbance from depolymerized cellulose was
present at lower pH or more acidic conditions in which pH of 5 in this case represented.
More activity was present at a lower pH range. This applies particularly for enzyme Endo-
Cellulase, but for the enzyme β-Glucosidase, slightly higher pH had a slight more absorbance
activity. Refer to Figure 14 for a complete overview graph of all the enzymes with pH
variance of 5, 6, and 7 altogether. In the next part of the experiment when a cocktail of
enzymes were mixed, the substrate ILSG had a steady increase in activity when the enzyme
concentration increased but avicel did not. Refer to Figure 12. Next the glucose concentration
was calculated for each enzyme concentration (0 nM, 25 nM, 50 nM, 100 nM, 200 nM, 400
nM) with the given formula by my mentor: Absorbance (y) = 0.1376x + 0.0628 which
therefore means x = (y-0.0628)/0.1376 where x is the glucose concentration. Then a graph of
glucose vs. enzyme concentration was plotted where shows a logarithmic trend. Refer to
Figure 13. It is apparent that ILSG had excellent activity which means at a higher enzyme
cocktail concentration, the substrate is being saccharified to glucose sugars.

34. 2
Figure 14. Complete Graph of Temperature and pH 5, 6, 7 Variance Variables for Endo,
CBH and βG enzymes.
The sources of error could have been from a mistake from tedious pipetting. Although
no significant observations are made, the results show a promising initiative for further
research on global biofuel production. With these experiments performed, we now know the
optimal temperatures for each enzyme at various pH concentrations. The limitation of
findings is that we still do not know how to produce biofuels on a massive scale at this time.
This study could be extended to more research on producing an enzyme cocktail at a specific
temperature and pH condition that would maximize the breakdown of cellulolytic biomass to
producing the highest possible yield of glucose to be fed to microbes engineered to utilize
glucose for production of biofuels, which could possibly help to solve the energy crisis in the
near future.
0.000
0.050
0.100
0.150
0.200
0.250
0.300
0.350
0.400
0.450
0.500
25 35 45 55 65 75 85
Absorbance
Temperature (°C)
ENDO pH 5
ENDO pH 6
ENDO pH 7
CBH pH 5
CBH pH 6
CBH pH 7
βG pH 5
βG pH 6
βG pH 7

35. 1
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