CALIFORNIA STATE UNIVERSITY, NORTHRIDGE
ANALYSIS OF AN ETHANOL INDUCIBLE PROMOTER IN ESCHERICHIA COLI AND
TOBACCO FOR PRODUCTION OF CELLULASE
A thesis submitted in partial fulfillment of the requirement
For the degree Master of Science in Biology
By
Homa Hemmati
December 2014
Signature Page
The thesis of Homa Hemmati is approved:
Dr. Ernest Kwok
Date
Dr. Michael Summers
Date
Dr. Chhandak Basu, Chair
Date
California State University, Northridge
ii
Dedication
I would like to extend my gratitude to Dr. Summers for helpful comments, to Dr. Ernest Kwok
for his assistance in using microscope, and to my mentor, Dr. Chhandak Basu for his great
support and helpful comments. I would also like to thank my father for his support and spiritual
encouragement. Finally, to my boyfriend who has not only put up with me but supported me
every day.
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Table of contents
Signature Page ........................................................................................................................... ii
Dedication................................................................................................................................. iii
List of Tables ............................................................................................................................ vi
List of Figures .......................................................................................................................... vii
List of Abbreviations............................................................................................................... viii
Abstract .................................................................................................................................... ix
Chapter I: Introduction ................................................................................................................1
Ethanol production from lignocellulosic biomass .....................................................................2
Cellulose a main component of the plant cell wall ...................................................................3
Cellulases ................................................................................................................................5
Recombinant ethanologenic E. coli ..........................................................................................6
Bacterial expression system for heterologous protein production .............................................8
Ethanol inducible gene expression ......................................................................................... 10
Application of inducible promoter in plant ............................................................................. 13
The aim of this study ............................................................................................................. 14
Chapter II: Materials and Methods ............................................................................................ 16
Bacterial strains and expression vectors ................................................................................. 16
Ethanol induction condition ................................................................................................... 18
Total RNA extraction and cDNA Synthesis ........................................................................... 19
Reverse Transcription PCR (RT-PCR)................................................................................... 19
Quantitative Real-Time PCR ................................................................................................. 20
Protein extraction and SDS-PAGE analysis ........................................................................... 20
Analysis of cellulase enzyme activity with CMCase activity assay ........................................ 21
Transient expression experiment in tobacco ........................................................................... 22
Epifluorescence Microscopy .................................................................................................. 23
Statistical analysis ................................................................................................................. 23
Chapter III: Results ................................................................................................................... 24
Confirmation of heterologous DNA cloning in transformed E. coli ........................................ 24
Ethanol induction condition ................................................................................................... 25
iv
E. coli strains and vectors analysis ......................................................................................... 27
Analysis of cellulase gene expression regulated by different promoters ................................. 28
Analysis of alcR gene expression in palcA-cel-alcR strain ..................................................... 33
Analysis of protein expression ............................................................................................... 34
Analysis of cellulase enzyme activity .................................................................................... 36
Transient expression in tobacco ............................................................................................. 37
Chapter IV: Discussion ............................................................................................................. 40
Conclusion ................................................................................................................................ 49
References ................................................................................................................................ 50
Appendix A: Supplemental Figures ........................................................................................... 58
Appendix B: Sequence Data ...................................................................................................... 68
v
List of Tables
Table 1. Structural components of plant cell wall and their building monomers ...........................5
Table 2. Bacterial/plant strains, plasmids, and primers used in this study ................................... 17
Table 3. Comparison between Ct values of control and transgenic samples ............................... 30
Table 4. The magnitude of fold differences in transcription of cellulase gene between all
upregulated strains. ................................................................................................................... 33
vi
List of Figures
Figure 1. Ethanol utilization pathway in Aspergilus nidulans..................................................... 11
Figure 2. DNA binding regions of AlcR in alcA promoter ......................................................... 12
Figure 3. Various inducers of ethanol utilization pathway in A. nidulans ................................... 13
Figure 4. E. coli and Agrobacterium strains constructed in this study ........................................ 18
Figure 5. Confirmation of DNA cloning in transgenic E. coli by PCR ....................................... 24
Figure 6. Ethanol toxicity assay................................................................................................. 26
Figure 7. Differences in transcription level among cells harboring pT5-cel construct in presence
or absence of IPTG ................................................................................................................... 28
Figure 8. Analysis of Reverse Transcriptase PCR with gel electrophoresis ................................ 29
Figure 9. Expression comparison of cellulase genes between different E. coli strains ................ 32
Figure 10. Expression comparison of alcR gene under different ethanol concentrations ............. 34
Figure 11. Results for SDS-PAGE analysis ............................................................................... 35
Figure 12. CMCase activity analysis of cellulase positive E. coli cells stained with Congo red.. 37
Figure 13. Epifluorescence micrographs of GFP fluorescence in tobacco leaves. ....................... 39
Figure 14. Proposed model for regulation of alcA-alcR system in presence of ethanol ............... 46
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List of Abbreviations
C- : PCR negative control
Cel: Cellulase gene
CAI: Codon Adaptation Index
CMC: Carboxylmethyl cellulose
GFP: Green fluorescent protein
IPTG: Isopropyl-β-D-thiogalactopyranoside
NRT: No reverse transcriptase
NTC: No template control
PET plasmid: Production of ethanol operon carrying adhII and pdc genes
RNAP: RNA polymerase
SDS-PAGE: Sodium dodecyl sulfate-polyacrylamide gel electrophoresis
SEM: Standard error of the mean
SSF: Simultaneous saccharification and fermentation (SSF)
viii
ABSTRACT
ANALYSIS OF AN ETHANOL INDUCIBLE PROMOTER IN ESCHERICHIA COLI AND
TOBACCO FOR PRODUCTION OF CELLULASE
By
Homa Hemmati
Master of Science in Biology
Cellulase is widely used as an enzyme for saccharification of cellulosic materials. However,
commercially available cellulase is an expensive enzyme. Recombinant ethanologenic bacteria
have also become an important tool for production of bioethanol from lignocellulosic biomass.
Heterogolous gene expression in transgenic microorganisms can be improved by utilization of an
efficient promoter system to regulate genes encoding for cellulase or ethanol production
pathway. Here, we evaluated the feasibility of bioengineering E. coli cells with an ethanol
inducible promoter system driving a cellulase gene. We tested the strength of an ethanol
inducible native alcA promoter from Aspergillus nidulans in E. coli and compared this promoter
with T5, a strong promoter commonly used for gene expression in bacteria, and cauliflower
mosaic virus 35S (CaMV 35S), a strong and widely used promoter in plants. The activity of alcA
promoter was also compared to CaMV 35S promoter in tobacco by transiently expressing a GFP
gene under the control of 35S and alcA promoters. E. coli cells were transformed with various
plasmid constructs and quantitative PCR was performed to analyze promoter activities. The
highest transcription level was observed for alcA promoter when it was expressed along with
AlcR transcription factor in absence of ethanol in which alcA expression was 11 times higher
than T5 promoter. The alcA promoter showed basal level expression in absence of AlcR,
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although, its expression level was higher in presence of AlcR. All cassettes harboring alcA
promoter showed non-significant ethanol dose-dependent activity. Conducting SDS-PAGE and
CMCase activity assay displayed no or low protein expression and no enzyme activity. Lack of
protein expression might be due to the presence of 14 rare codons in cellulase DNA sequence
inhibiting protein translation in E. coli. In contrast to E.coli, the inducible expression of alcA
promoter system was tightly regulated in tobacco. We suggest further study for the utilization of
ethanol inducible system in ethanol-tolerant ethanologenic E. coli strains which may lead to
significant success in production of bioethanols and other biofuels. We hope, our work on
inducible promoter system for in vivo cellulase production will open new avenues of research in
the fields of developments of biofuel from prokaryotic and eukaryotic systems.
x
Chapter I: Introduction
The worldwide demand for energy is growing every day, and nonrenewable fossil fuels may
not last long enough to respond to this demand. Furthermore, the recent rise in oil prices, along
with growing concern about global warming, has prompted scientists to delve deeper into the
field of alternative energy research. Currently, bioethanol and biodiesel are two biofuels that
present the most interesting options. Bioethanol production can be achieved through
bioconversion of lignocellulosic materials, which is the fermentation of a high mass of sugars
present in plant cell walls. Lignocellulosic biomass is the most abundant natural resource that are
renewable and stably available with a low cost energy source according to energy content ($34/GJ) (Himmel et al., 2007; Lynd et al., 2008; Y. H. P. Zhang, 2009). Annually, more than one
billion tons of plant biomass are produced in the United States, which can be used in the creation
of over 100 billion gallons of bioethanol (Gu, 2013). Biofuel production from lignocellulosic
biomass holds many advantages over food crops, such as lower costs and fewer environmental
impacts, while posing no geographical restriction or raising food security concerns (Alvira,
Tomás-Pejó, Ballesteros, & Negro, 2010; Himmel et al., 2007). However strong associations
between long chains of polysaccharides in plant cell wall are a challenge, as they require harsh
conditions to break down the recalcitrant cell wall (Alvira et al., 2010; Liu, Kang, Sun, & Zhou,
2012). The addition of lignocellulosic degrading enzymes (including cellulases, hemicellulases,
and pectinases) have been found useful in converting biomass into fermentable monomeric
sugars, however, their large-scale applications are costly (Edwards et al., 2011; Y. Zhang, Mian,
& Bouton, 2006; Zhu, Sathitsuksanoh, & Percival Zhang, 2009). It has been shown that cellulase
production is the most expensive step of the ethanol production procedure from lignocellulosic
materials, accounting for approximately 40% of the total cost (Spano, Medeiros, & Mandels,
1
1976). Significant cost reduction in the cellulase production system is required in order to
develop an economically feasible process for bioconversion of lignocellulosic biomass to
ethanol. In this chapter, an introduction of the main subjects in this study has been provided to
foster a better understanding of research objectives.
Ethanol production from lignocellulosic biomass
Obtaining high ethanol yield from lignocellulosic biomass depends upon two factors: first,
having access to strains capable of metabolizing all major sugars present in cellulosic biomass to
ethanol with few side byproducts, and second, removing biomass recalcitrance through an
economical procedure. So far, the enzymatic digestion of cellulose has been the most effective
method in obtaining the highest ethanol yield (B. S. Dien, Cotta, & Jeffries, 2003). The
lignocellulosic biomass needs to be pretreated prior to enzymatic digestion to remove its
recalcitrance. “Biomass recalcitrance” is commonly defined as the natural resistance of plant cell
walls to microbial and enzymatic deconstruction. To achieve sustainable energy production, it
is necessary to overcome the chemical and structural properties that have evolved in biomass
to prevent its disassembly (Himmel et al., 2007). These pre-treatment processes include physical
forces, chemical reactions, and heat which lead to the disruption of chemical bonds between the
cell wall components that make them amenable for conversion to glucose by the enzyme
(Brunecky et al., 2011). Pretreatment process increases the cellulase enzyme access to its target
by partial or complete hydrolysis of hemicellulose, removal of lignin and de-crystallization of the
cellulose (B. S. Dien et al., 2003). Cellulose hydrolysis by cellulase enzyme can occur prior to
fermentation, or together, as simultaneous saccharification and fermentation (SSF) (B. S. Dien et
al., 2003). Higher ethanol production and lower enzyme consumption can be achieved by the
SSF procedure. However, SSF needs compatible pH and temperature conditions for both enzyme
2
producing microbes and ethanologenic microorganism. For instance utilization of Trichoderma
reesei, the most commonly used microbe as cellulase producer, with optimal activity at pH 4.5
and 55 oC is not compatible with ethanologenic E. coli strains with optimum activity at pH 6 and
below 42 oC (B. S. Dien et al., 2003). Developing bacterial co-cultures with genetically
engineered microorganisms capable of conducting the entire task from start to finish would solve
the compatibility problem and lead to significant cost reduction in ethanol production procedure.
Cellulose a main component of the plant cell wall
Plant cells are enclosed by a cell wall, a complex structure primarily made of cellulose, which
provides mechanical support to the cell. The cell wall structure restricts accessibility of the sugar
substrate to the enzyme, due to the strong associations between its components (Brunecky et al.,
2011). The plant cell wall structure is not uniform and may differ in thickness and chemical
composition, depending on cell type, species, and environmental stresses (Dickison, 2000).
Generally, the plant cell wall is made of two main categories: carbohydrate and noncarbohydrate
polymers (Table 1). The main carbohydrate involved in cell wall structure is cellulose. In
addition to cellulose, hemicellulose and pectin polymers occur along with cellulose fibers. The
cell wall also contains noncarbohydrate substances such as lignin, cutin, and chitin. Certain
minerals such as calcium and magnesium are also present in the cell wall (Sethaphong et al.,
2013).
The cellulose is a polymer made of repeating β-1, 4- linked D-glucose monomers assembled
together through interchain hydrogen bonds. Long chains of cellulose are arranged side-by-side,
to form microfibrils. Many cellulose microfibrils attach to form large-sized bundles known as
macrofibrils, subsequently forming main bundles of cellulose by joining to other macrofibrils
(Sethaphong et al., 2013).
3
Hemicelluloses are heterogeneous polysaccharides typically composed of β-1, 4
- D-
glycan backbone attached with variable monosaccharide or/and oligosaccharide sidechains,
occurring between cellulose fibers. They become deposited in the cell wall through Golgi
apparatus secretion. Hemicellulosic backbone polymers can be about 200 to 500 residues long,
with a slightly branched structure. The high structural similarity allows hemicellulosic polymers
to strongly attach to the cellulose fibers through hydrogen bond interactions (Dickison, 2000).
Hemicelluloses do not form any crystalline structures like cellulose polymers do and are soluble
in low concentration alkali solutions (Gu, 2013).
Pectins are a group of acidic polysaccharides comprising a minor component in the plant cell
wall composition. These polysaccharide groups hardly interfere in cellulose accessibility to
cellulase enzyme. They are composed of D-galacturonic acid monomers with a negative charge
that makes pectin soluble in hot water. As the cell wall grows, released Golgi vesicles secrete the
pectin precursors within the growing walls. Pectin acts as an adhesive component to glue
cellulose and hemicellulose fibers to each other (Dickison, 2000; Gu, 2013).
Lignin is the most abundant biopolymer composed of three different phenylpropanoids: phydroxyphenyl, guaiacyl, and syringyl monomers. However, lignin composition may differ
depending upon cell type and species (Demirbas, 2008). This biopolymer acts as a strong glue,
holding cellulose-hemicelluloses fibers together and preventing tissue damage, water loss, and
pathogen attacks. Thus, lignin is the main obstacle in bioethanol production, limiting the
accessibility of cellulase enzymes to cellulose polymers and hindering enzymatic hydrolysis
(Dickison, 2000; Mosier et al., 2005).
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Table 1. Structural components of plant cell wall and their building monomers.
Plant cell wall component
Cellulose
Hemicelluloses
Pectin
Lignin
Cutin
Chitin
Building monomer
D-glucose
Arabinose, xylose, mammose, galactose
Glucuronic and galacturonic acid
Coniferyl alcohols
Different fatty acids
Glucosamine
Cellulases
Cellulases are enzymes mainly produced by fungi, bacteria, protozoan, and plants which
catalyze the hydrolysis of β-1,4-glucosidic bonds in cellulose fibers (Henrissat, 1991). These
microorganisms secret cellulases to the extracellular space that are either free or bound to the cell
surface. They are composed of three classes of enzymes including endoglucanases (EC 3.2.1.4),
exoglucanases (cellobiohydrolases) (EC 3.2.1.91), and β-glucosidases (EC 3.2.1.21).
Endoglucanases cut randomly at internal regions in the cellulose chains releasing
oligosaccharides with various lengths. Exoglucanases cut at the reducing or non-reducing ends
(able/unable to reduce oxidizing agents) of cellulose chains generating either glucose or
cellubiose. β-glucosidases catalyze the conversion of cellobiose and cellodextrins to glucose
from non-reducing ends (Ratledge, Wood, & Garcia-Campayo, 1991).
Cellulases have broad applications in industry for brightening, beer brewing, improving the
quality of animal feed, as detergent for color removal, and utilization in textile, pulp and paper
industries (Bayer, Lamed, & Himmel, 2007; Himmel, Ruth, & Wyman, 1999; Pang et al., 2009;
Zaldivar, Nielsen, & Olsson, 2001).
In recent decades, cellulases have had a significant influence in the industrial enzyme market
due to their use in bioethanol production from lignocellulosic biomass. However, for
5
economically feasible production of bioethanol, high costs of cellulases need to be reduced and
their catalytic efficiency should be increased (Sheehan & Himmel, 1999).
Commercial cellulases are produced from both fungi and bacteria, although utilization of
fungi has been more common due to their ability to produce copious amounts of enzymes with
less complexity that can be easily extracted and purified. Isolation of novel bacterial cellulases is
now becoming widely exploited because of higher growth rate of bacteria over fungi, generation
of multi-enzyme complexes which increased function and synergy, and capability to grow under
extreme conditions resulting in development of stress-resistance cellulases (Maki, Leung, & Qin,
2009). Therefore bacterial cellulases may increase the rates of enzymatic hydrolysis and
fermentation leading to the production of more economically feasible bioethanol.
Recombinant ethanologenic E. coli
Saccarification of cellulosic biomass results in the production of various carbohydrates,
including hexose and pentose sugars, as well as some sugar acids which can be converted into
the biofuel by microbial fermentation. Since commercial ethanologenic microorganisms such as
Zymomonas mobilis and Saccaromyces cerevisiae are not able to utilize pentose sugars for
ethanol production, many studies have focused on the development of highly efficient,
genetically modified microorganisms for bioconversion of lignocellulosic materials (Bokinsky et
al., 2011; Edwards et al., 2011; Jarboe, Grabar, Yomano, Shanmugan, & Ingram, 2007; Steen et
al., 2010).
Currently, the most promising ethanologenic bacteria for commercial use are Zymomonas
mobilis, Klebsiella oxytoca and Escherichia coli. Among all these candidates, E. coli shows
wider substrate ranges and higher optimal fermentation temperature. E. coli has been selected as
a tool for biofuel production in many studies, because of its high amenability to metabolic
6
modification, as well as its ability to ferment a wide variety of sugars into a broad range of shortchain alcohols (Atsumi et al., 2008; Hanai, Atsumi, & Liao, 2007; Ibrahim, Jones, & Hossenya,
2013; Ingram et al., 1999). E. coli lacks the native ability of ethanol production; therefore
ethanol production pathways from natural ethanologenic microorganisms have been introduced
to E. coli (Alterthum & Ingram, 1989; Ingram & Conway, 1988). Early transgenic E. coli strains
were developed by transferring plasmid (PET) carrying genes encoding alcohol dehydrogenase II
(ADHII; adhB) and pyruvate decarboxylase (PDC; pdc) (Ingram & Conway, 1988; Ingram,
Conway, Clark, Sewell, & Preston, 1987; Ohta, Alterthum, & Ingram, 1990). Later, strain KO11
was constructed by the integration of these two genes in E. coli chromosome within or near the
pyruvate formate-lysate gene (pfl) to improve the stability of recombinant genes (Ohta, Beall,
Mejia, Shanmugam, & Ingram, 1991). During the last decade, E. coli KO11 has been further
modified by introducing various mutants to increase ethanol yields produced from different types
of substrates such as gluconate (Hildebrand, Schlacta, Warmack, Kasuga, & Fan, 2013), xylose
(Chiang et al., 2013; Qureshi, Dien, Nichols, Saha, & Cotta, 2006), and arabinose (Asghari,
Bothast, Doran, & Ingram, 1996). It has been reported that continuous fermentation by KO11
results in a decline of ethanol yield (Dumsday, Zhou, Yaqin, Stanley, & Pamment, 1999). Unlike
KO11, FBR5, another strain of E. coli, maintains a high ethanol yield during continuous
fermentation. FBR5 is constructed by transferring PET operon and mutants of pfl and ldh genes
that blocks reduction of pyruvate, and the recycling of NADH and H+ generated from glycolysis
(Bruce S. Dien, Hespell, Wyckoff, & Bothast, 1998; Hespell, Wyckoff, Dien, & Bothast, 1996).
Along with the improvement of ethanol yield, ethanol tolerance is also considered an
important factor in bioethanol production, as ethanol toxicity can limit the final product
concentrations (Yomano, York, & Ingram, 1998). High product concentrations exceeding 90 g/L
7
in commercial ethanologenic yeast benefits from its ethanol tolerance capability (Thomas,
Hynes, & Ingledew, 1996). So far, development of an ethanol-tolerant E. coli strain (LY01), a
mutant of strain KO11, has increased ethanol yield to over 60 g/L (Yomano et al., 1998). The
increased ethanol tolerance in LY01 is influenced by multiple changes including increased
metabolism of glycine, serine, and pyruvate, decreased production of organic acids, increased
production of betaine and mar drug resistance proteins and loss of FNR function (Gonzalez et
al., 2003). The changes in metabolism of betaine and glycine as osmoprotectants may indicate
linkage between ethanol tolerance and osmotolerance (Gonzalez et al., 2003).
Bacterial expression system for heterologous protein production
Bacterial expression systems are the most common tools for heterologous protein production
due to their rapid growth, low cost, well characterized genetics, and their broad range of cloning
vectors and mutant host strains. In order to produce high levels of recombinant proteins, efficient
bacterial systems are required to be equipped with strong, regulated promoters. Utilization of an
inducible expression system is an approach to regulate the amount and the timing of protein
expression. Inducible promoters can be activated by environmental stresses, such as hot or cold
temperatures, or by chemical inducers, such as IPTG or arabinose. An efficient inducible
expression system must be strong, possess a low basal expression level, and be inducible with a
simple, cost-effective substrate (Schmidt, 2004).
Many inducible expression systems are known to drive heterologous gene expression in
bacteria, specifically in E. coli. Lactose utilization system is one of the commonly used inducible
systems in E. coli. However, lac promoter is quite weak and is rarely used for high level
production of heterologous proteins. Instead, utilization of this promoter may be useful in the
production of toxic compounds (Polisky, Bishop, & Gelfand, 1976). tac and trc promoters 8
functional hybrids derived from the trp and lac promoters - are other IPTG inducible expression
systems that are stronger than lac promoter and allow the production of up to 15 to 30% of total
cell proteins (Quick & Wright, 2002). Phage promoter pL is another tightly-regulated expression
system, with a moderately high expression level. This promoter system is regulated by a
temperature-sensitive cI repressor which becomes deactivated at 42°C resulting in transcription
of downstream gene (Elvin et al., 1990; Love, Lilley, & Dixon, 1996). The promoter is also
constitutively activated at low temperatures, as this cI repressor becomes fully functional at 29°C
and temperatures below 42°C (Lowman & Bina, 1990). L-arabinose (araPBAD) and L-rhamnose
(rhaPBAD) operon are two additional highly-inducible expression systems, regulated by catabolic
repression (Wilms et al., 2001). The expression of the former promoter is induced by an AraC
activator in the presence of arabinose and is repressed by a media containing glucose. Although
araPBAD has basal expression while under a repressed state, this system allows high-level gene
expression with an inexpensive inducer. The function of a rhaPBAD promoter is very similar to
araPBAD, although two activators RhaR and RhaS are required to activate the regulon
(Haldimann, Daniels, & Wanner, 1998). One of the strongest widely used promoters for
overexpression of recombinant proteins is T7 RNA polymerase system (Studier & Moffatt,
1986). The T7 RNA polymerase is able to function five times faster than E. coli RNA
polymerase. Full induction of this promoter leads to an overexpression of downstream genes to
more than 50% of the total cell proteins within a few hours. The commercial model of this
system comprises the T7 RNA polymerase gene under the control of the lac promoter and
expresses the recombinant protein only in the presence of IPTG (Pan & Malcolm, 2000). The in
vivo strength of well-known promoters commonly used in E. coli can be placed in a hierarchy as
follows: T7 ≥ T5 > PL > Ptac > Plac > PlacUV5 (Deuschle, Kammerer, Gentz, & Bujard, 1986).
9
Even though many of these inducible systems are sufficiently functional, selecting an
adequate expression system for production of specific recombinant proteins remains difficult.
Therefore, identification of new, strong expression systems for bacterial hosts would benefit
large-scale protein production. Since E. coli is still the most commonly used bacterial host for
industrial protein production (Schmidt, 2004), development of new expression systems in E. coli
is of interest.
Ethanol inducible gene expression
One of the strongest inducible fungal expression systems is alc regulon from Aspergillus
nidulans which encodes for an ethanol utilization pathway (Flipphi, Kocialkowska, & Felenbok,
2002). In Aspergilus nidulans, ethanol can be used as a sole carbon source by the activity of two
enzymes, alcohol dehydrogenase (ADHI) and aldehyde dehydrogenase (ALDH), encoded by
alcA and aldA genes respectively (Felenbok & Sealy-Lewis, 1994; Pateman et al., 1983). These
two genes are regulated under the control of the activator AlcR, in the presence of a coinducer
(ethanol) and a repressor CreA (Figure 1) (Flipphi et al., 2002; Nikolaev, Mathieu, van de
Vondervoort, Visser, & Felenbok, 2002). The alc expression system in Aspergilus nidulans is
characterized with high inducibility due to high expression of the alcR gene, mediating by AlcR
positive autoregulation of its own gene, and extraordinary strength of alc promoters (Felenbok et
al., 1988; Lockington, Scazzocchio, Sequeval, Mathieu, & Felenbok, 1987). AlcR belongs to
zinc cluster proteins and binds to DNA regions with either inverted or direct repeats containing a
common 5’–CCGCA-3‘ motif via direct interaction (Figure 2) (Sirenko, Ni, & Needleman, 1995;
Strauss, Muro-Pastor, & Scazzocchio, 1998; L. Zhang & Guarente, 1996).
10
Figure 1. Ethanol utilization pathway in Aspergilus nidulans. The regulation of alc genes is
conducted with CreA and AlcR proteins. The brown arrows indicate the AlcR transcriptional
activation of the alcR (positively autoregulated), alcA, and aldA genes in the presence of a coinducer (ethanol). Two latter genes encode for alcohol dehydrogenase I (ADHI) and aldehyde
dehydrogenase (ALDH). The black bars indicate repression of both alcR and alcA by CreA
(Adapted from (Felenbok, Flipphi, & Nikolaev, 2001)).
In Aspergilus nidulans the presence of inducer molecules is absolutely required for induction
of the alc gene system (Flipphi, Mathieu, Cirpus, Panozzo, & Felenbok, 2001; Mathieu &
Felenbok, 1994; Panozzo, Cornillot, & Felenbok, 1998). Transcriptional study of the induction
spectrum of alc promoters has revealed high induction levels for certain small ketones (acetone
and ethyl methyl ketone), secondary alcohols and primary monoamines (ethylamine and Lthreonine) compared to a marginal induction of primary alcohols (ethanol) (Felenbok, 1991;
Felenbok et al., 1988; Lockington et al., 1987). Further studies have confirmed that all these
compounds can promote the expression of the alc system due to their conversion into
11
acetaldehyde, the physiological inducer of alc operon (Figure 3 ) (Felenbok et al., 2001; Flipphi
et al., 2002).
Figure 2. DNA binding regions of AlcR in alcA promoter. The 5’ region of the alcA promoter
in A. nidulans. Base pairs are numbered relative to the translation start site. The transcription
start site is shown by * and the putative TATA-box by #. Note that this promoter has two
transcription start sites (Adapted from (Kulmburg et al., 1992)). The promoter sequence used in
this study starts from base pair -63 to -400 and contains only one transcription start site,
indicated by red rectangle.
The alcR-alcA system, composed of alcA promoter and alcR gene - the main elements of alc
regulon - are widely used to express heterologous proteins at a high level (Battaglia, Brambilla,
Colombo, Stuitje, & Kater, 2006; Roslan et al., 2001). It has been shown that alc regulon has low
basal activity and a high induced level of gene expression in plants (Caddick et al., 1998).
12
Figure 3. Various inducers of ethanol utilization pathway in A. nidulans. In addition to primary
alcohols, primary monoamines, such as ethylamine and L-threonine can be converted into
acetaldehyde which is the main inducer of the alc regulon (Adapted from (Felenbok et al.,
2001)).
Application of inducible promoter in plant
Other studies have focused on developing genetically engineered plants capable of high
production of lignocellulosic degrading enzymes; particularly, cellulases (Brunecky et al., 2011;
Klose, Günl, Usadel, Fischer, & Commandeur, 2013; Klose, Röder, Girfoglio, Fischer, &
Commandeur, 2012). It has been noted that a constant expression of cellulases at a high level
affects the composition and structure of plant cell walls through alteration in cellulose fibers
(Takahashi et al., 2009). Therefore, it is not suitable to use a constitutive promoter for the
expression of lignocellulosic degrading enzymes, since the expressed cellulase can interfere with
13
plant regeneration, growth, and development (Ziegelhoffer, Raasch, & Austin-Phillips, 2001).
An inducible gene expression system is an alternative method that can be used in many
circumstances; for example this system can be employed when expression of foreign gene
product is required in a specific condition or developmental stage.
Several numbers of regulated gene expression systems that have been reported in plants
follow two different strategies: one, inducible plant promoters that respond to a specific
chemical, however, the promoters can trigger expressions of other native plant genes besides the
transgene; and two, inducible regulatory elements derived from other organisms that respond to
chemicals that are not common to plants (Gatz, 1997). One of the efficient inducible systems for
plants is the alc regulon, which has been shown to have low basal activity and a high induced
level of gene expression (Caddick et al., 1998). The alc system has been modified for better
functionality in plants (Caddick et al., 1998). The plant optimized system is composed of two
elements: one is the alcAmin 35S promoter, which is derived from native alcA gene promoter.
The other one is the alcR gene which is controlled by the CaMV 35S promoter. The activator
protein AlcR binds to specific regions within the alcA promoter and induce transcription in
presence of the inducer molecule (ethanol or ethyl methyl ketone) (Caddick et al., 1998;
Panozzo, Capuano, Fillinger, & Felenbok, 1997; Roslan et al., 2001). This system is useful for
transgene expression in plants, as it is inexpensive and effective for dose-dependent expression,
and suitable for use both in the laboratory and in the field (Roslan et al., 2001).
The aim of this study
E. coli characteristics, including its high amenability to metabolic modification and ability to
ferment a wide variety of sugars to alcohol compounds, have made E. coli a valuable tool to be
used for ethanol production. Moreover, development of a genetically engineered microorganism
14
with the ability to complete the entire tasks for ethanol production from cellulosic biomass can
be a promising tool for the biofuel industry. This goal may be accomplished by development of
ethanologenic and cellulase producing E. coli. Several efficient ethanologenic strains of E. coli
have been developed in last two decades (B. S. Dien, Nichols, O'Bryan, & Bothast, 2000; Ohta et
al., 1990; Yomano et al., 1998). Although E. coli has been used for cellulase production, an
optimization of gene expression is still required to benefit large-scale production of cellulase for
biofuel industry (Pang et al., 2009). Conducting a promoter analysis study to identify strong
promoters for E. coli can open new avenues for cellulase production. One of the strongest
inducible fungal expression systems is alc regulon from Aspergillus nidulans. Although
efficiency of alc regulon has frequently been studied in fungi and plants, to the best of our
knowledge, there has been no report of such study in E. coli. Here, we characterize in detail the
efficacy of alc system in comparison with two other promoters for their suitability to express
foreign genes in E. coli and Nicotiana tabacum. Three potentially useful promoters have been
included in this comparison: native alcA promoter from Aspergillus nidulans, inducible lacI-T5
promoter from coliphage and constitutive 35S promoter from cauliflower mosaic virus. All these
three promoters were used to drive the endo-1, 4-beta-D-glucanase (Umcel9B) (Pang et al.,
2009) gene expression in Escherichia coli. The alcA and 35S promoters were used to drive green
fluorescent protein (GFP) gene expression in tobacco (Nicotiana tabacum). The Umcel9B gene
(DQ235086) (referred as cellulase herein) which belongs to the glycoside hydrolase family
(GHF) 9, is a novel cellulase enzyme identified from metagenome of compost soil. It has been
reported that recombinant protein of this cellulase has high endoactive enzyme activity in E. coli
(Pang et al., 2009). This cellulase gene was expressed in E. coli under the control of various
promoters.
15
Chapter II: Materials and Methods
Bacterial strains and expression vectors
Escherichia coli DH5α and One Shot TOP10 (Invitrogen, USA) strains were used as
expression hosts and cultured in Luria-Bertani (LB) medium. TOP 10 strain (Invitrogen, USA),
which is similar to DH10B strain, was used as host for pT5 (empty vector) and pT5-cel
constructs due to incompatibility of pQE-30 vector with the DH5α strain. The cellulase gene
(DQ235086) (Pang et al., 2009) was synthesized by GenScript, USA in pUC57 vector with
BamHI and SacI restriction sites at the ends. The gene was used to construct four vectors using
three different types of carriers; pBIN mgfp5ER (Haseloff, Siemering, Prasher, & Hodge, 1997),
pQE-30 Xa (Qiagen, USA), pCAMBIA1200 (Marker Gene, USA). The pBIN mgfp5ER
originally contains a constitutive promoter (CAMV35S) and GFP as a reporter gene. The pUC57
vector was digested with BamHI and SacI releasing the cellulase gene, which was then inserted
into the pBIN, and pQE-30 (pQE-30 carries T5 promoter) vectors. The following cassettes were
made through this process, p35S-cel (Figure A. 1), and pT5-cel (Figure A. 2) (‘Cel’ is referred to
as ‘cellulase’ throughout the thesis). The p35S-gfp (pBIN mgfp5ER) vector was treated with
HindIII and BamHI to remove 35S promoter and replacing it with alcA promoter (generously
provided by Syngenta, USA) to make palcA-gfp cassette (Figure A. 3). The palcA-cel construct
(Figure A. 4) was made using palcA-gfp through digestion with BamHI and SacI to release the
gfp gene and insert cellulase gene. The alcR gene (Syngenta, USA), was digested with EcoRI
and HindIII and cloned into the pCAMBIA vector to make p35S-alcR cassette (Figure A. 5).
PalcA-cel-alcR refers to bacterial cells harboring both palcA-cel and p35S-alcR plasmids. The
presence of genes in all constructs were confirmed by PCR. The correct orientation of the genes
were also confirmed by sequencing (Laragen, USA). The details of different strains and their
16
vector construct/s are shown in Figure 4. All bacterial strains, plasmids and primers used in this
paper are listed in Table 2.
Relevant properties
Source
Strains
E. coli DH5α
E. coli Top10
Agrobacterium LB4404
Nicotiana tabacum petite Havana
Plasmids
Expression host
Expression host
Used for transgene expression
Expression host
Lab stock
Invitrogen, USA
Invitrogen, USA
Lab stock
pBIN
Ori ColE1, KanR, P35S, Low-copy-number
pCAMBIA 1200
Ori pBR322, CmrR, P35S, High-copy-number
(Haseloff et al.,
1997)
Marker Gene, USA
pQE-10 Xa
Primers
Cellulase qPCR-F
Cellulase qPCR-R
CysG qPCR-F
CysG qPCR-R
alcR qPCR-F
alcR qPCR-R
Cellulase-F
Cellulase-R
alcA-F
alcA-R
alcR-F
alcR-R
Ori ColE1, AmpR , PT5, Low-copy-number
Qiagen, USA
5'-GTGGCGTTTACCACAAACTTAC-3'
5'-GCTGTACTCTTCTCCACCATATAA-3'
5'-GGCGAAGAGCTGGAAACA-3'
5'-CGTGAGTGGAATACCCGAATAG-3'
5'-GAGGAACGCCTGGAATGAA-3'
5'-CACACTCTGCGTGAGAGAAA-3'
5'-AAACACTTCCCTGGTAGGGCAGAA-3'
5'-GCGCATTCCAGTTAATCGCAACCT-3'
5'-ACCTAGGATTGGATGCATGCGGAA-3'
5'-ATGTTGGTGAGACTGAGAACCTTGGG-3'
5'-AAACGCCGATCAACCTAGAC-3'
5'-GCTTTGGCAAACGAGTGAATAA-3'
This work
This work
This work
This work
This work
This work
This work
This work
This work
This work
This work
This work
Table 2. Bacterial/plant strains, plasmids, and primers used in this study
E. coli cells were transformed with each plasmid using standard chemical transformation
(Chan, Verma, Lane, & Gan, 2013). Agrobacterium cells were transformed using standard
electroporation protocol (Mattanovich et al., 1989). Sequential plasmid transformation for palcAcel-alcR cassette was performed using transformed palcA-cel strain. Selected palcA-cel
transformants were made chemically competent before subsequent transformations.
17
Figure 4 E. coli and Agrobacterium strains constructed in this study. E. coli TOP10 strain was
used as expression host for pT5 (empty vector) and pT5-cel constructs. DH5α strain was used as
host for p35S-cel, palcA-cel, and palcA-cel-alcR constructs. Agrobacterium LB4404 was used as
host for p35S-gfp, palcA-gfp, and palcA-gfp-alcR constructs. Each strain made in this study was
named after its vector/s. All strains carry one plasmid except palcA-cel-alcR and palcA-gfp-alcR
strains that carry two constructs with pBIN and pCAMBIA vector backbone. Kan = Kanamycin,
Cmr = Chloramphenicol, Amp = Ampicillin
Ethanol induction condition
Both E. coli and Agrobactrium strains harboring alcA promoter were tested for ethanol
toxicity in liquid medium. E. coli cells were grown in LB medium at 37°C for 24 hours and
Agrobactrium cells in YEB media at 28°C for two days with various ethanol concentrations (0,
1, 2, 3, 4, and 5%). Liquid cultures were incubated in a shaking incubator at an agitation of 200
rpm. Cell viability assay was also performed for E. coli cells grown on agar plates containing
different ethanol concentrations. DH5α cells were grown at 37°C overnight, then the cultures
were serially diluted. 100 µl of 10-6 and 10-7 diluted cultures were spread on LB plates with
different ethanol concentrations ranging from 0% to 10% with 1% increment. Colonies were
counted after overnight incubation at 37°C. Three technical replications were conducted for each
18
dilution per each ethanol concentration. The colony forming unit (CFU) per ml of culture was
calculated using following formula:
Total RNA extraction and cDNA Synthesis
Total RNA was extracted from E. coli cells using Quick-RNA MiniPrep kit (Zymo Research,
USA) according to manufacturer’s instruction. The RNA was then treated with DNase I and
Turbo DNase (Life technologies, USA) several times to remove genomic contamination
completely and was purified using RNA clean-up kit (Zymo Research, USA). Complementary
DNA (cDNA) was synthesized from 1µg total RNA extracted from overnight grown E. coli cell
culture using iScript cDNA Synthesis Kit (Bio-Rad, USA) according to manufacturer’s
instruction. A negative control, NRT (no reverse transcriptase), was made for each strain which
contained the same amount of RNA and iScript master mix but lacked reverse transcriptase.
Genomic DNA contamination was checked through screening NRT (no reverse transcriptase)
samples using gel electrophoresis (1% agarose) after reverse transcription-PCR. cDNAs of more
than three biological samples were prepared as mentioned above and stored in -20°C.
Reverse Transcription PCR (RT-PCR)
RT-PCR was conducted using 0.3 and 0.5 µl of cDNA, 1 µl of each forward and reverse
primers, and PCR master mix (Fermentas, USA). PCR negative controls (C-) and NRT controls
were included for each test. PCR was performed at an initial denaturation at 95°C for 2 min, 40
19
cycles with initial denaturation at 95°C for 30 s, annealing at 55°C for 30 s, and extension at
72°C for 2 min, with a final extension at 72°C for 15 min.
Quantitative Real-Time PCR
Real-Time PCR reaction was conducted using iTaq Universal SYBR Green Supermix (BioRad, USA) in qPCR tubes. The cysG (cysteine) gene was used as reference gene for E. coli in
this study (Zhou et al., 2011). The reaction mixture consisted of 1 µl cDNA samples, 1 µl each of
the forward and reverse primers (2 µM), 5 µl iTaq Universal SYBR Green Supermix (2×), and 2
µl nuclease free water in a final volume of 10 µl. Three technical replicates were performed for
each biological sample, reference gene, NRT and NTC (no template control). The RT-PCR
protocol was as follows: 30 s initial denaturation at 95°C, 39 cycles of denaturation at 95°C for 5
s, and annealing at 60°C for 30 s followed by a melting-curve analysis at 65°C -95°C with 0.5°C
increment for 2-5 s per step. Melting-curve analysis was performed to verify the specificity of
each primer. The threshold cycle (Ct) values were calculated for each reaction by the CFX96
Touch Real-Time PCR (Bio-Rad, USA) set with default parameters. Quantification of
normalized expression (ΔΔCt) method was used to determine gene expression (Pfaffl, 2001).
This method is defined as evaluation of a target gene expression relative to an endogenous
control gene (Pfaffl, 2001). The normalized relative expression values were analyzed relative to
zero by Bio-Rad CFX Manager 3.1.
Protein extraction and SDS-PAGE analysis
Total proteins were extracted from E. coli cells using E. coli protein extraction kit (Open
Biotechnology, USA) according to manufacturer’s instruction. Different concentrations of
ethanol were added to the cultures with cells containing alcA promoter. 5 ml of each overnight
20
culture was centrifuged and the pellet was resuspended in 500 µl of lysis buffer. Next, 5 µl of
dissolved lysozyme (5 umol) was added into the homogeneous suspension. Cell suspension was
then frozen completely to damage the cell wall, giving lysozyme access to its targets. After three
hours cell suspension was thawed to activate lysozyme and let it digest the cell wall. At last,
DNAse treatment was performed at 37°C for five minutes by adding 5µl of each DNAse and
CaCl2 to reduce the viscosity caused by genomic DNA. The lysate was then prepared for SDSPAGE analysis using Laemmli sample buffer (BioRad, USA). Laemelli sample buffer was made
using 950 µl 2× Laemelli buffer and 50 µl of 2-mercaptoethanol. Protein lysates for each studied
strains were mixed with prepared 2× Laemelli sample buffer in 1:1 ratio and heated at 95°C for
five minutes. Protein gel electrophoresis apparatus was filled up with 1x running buffer and then
50 µl of prepared samples were loaded into each well of precast polyacrylamide gel (Bio-Rad,
USA). The SDS-PAGE was run for 45 minutes at 100 V. The stain was removed by several
washings with water. The gel was also left in a water container on the shaker overnight to
completely remove the stain and achieve clear bands.
Analysis of cellulase enzyme activity with CMCase activity assay
Congo red clearing zone assay is a qualitative method to evaluate cellulase enzyme activity.
This method is conducted using a soluble cellulose derivative, carboxymethylcellulose (CMC),
also called as CMCase activity assay. Several approaches were used to perform this assay,
including E. coli cells, total protein lysate, and supernatant from liquid culture. Screening was
carried out using two types of agar plates: CMC plus glucose and CMC without glucose. CMC
plates were prepared using 0.5 g CMC, 0.1 g NaNO3, 0.1 g K2HPO4, 0.1 g KCl, 0.05 g MgSO4,
0.05 g yeast extracts, 0.1 g glucose, and 1.7% w/v agar in 100 ml of water. To prepare plates
without glucose, the glucose was removed from the formula. Congo red solution was prepared
21
with 1 mg powder per 1 ml of water. CMC-agar plates with different ethanol concentrations (1%
and 2%) used for E. coli cells harboring alcA promoter. E. coli cells were grown on the plate by
overnight incubation at 37°C after spreading 100 µl of overnight liquid culture all over the plate
or by adding to the small wells made inside the media (Figure A. 6). Plates with supernatant
were prepared using 1 ml overnight cultures (made by LB broth with or without ethanol) or
supernatant (cells were separated using centrifugation). Plates with protein extracts were
prepared using 500 µl of protein lysate (prepared as mentioned above). Pure cellulase enzyme
(Sigma, USA) was used as the positive control for CMCase activity. Plates made with pure
cellulase enzyme, supernatant, and protein lysate were incubated at 30°C overnight. Plates were
then washed with 0.1% Congo red three times, incubating for 15 minutes each time. After
incubation with Congo red, plates were washed with 1M NaCl to reveal clear zones against
background. Some plates were further rinsed with 1N HCl in order to increase the clarity of the
clear zone.
Transient expression experiment in tobacco
Tobacco (Nicotiana tabacum petite Havana) used for transient expression experiment, was
kindly provided by Dr. Ernest Kwok, California State University, Northridge. The palcA-GFP
and p35S-gfp were transferred into Agrobacterium tumefaciens strain LBA4404 through
electroporation. Cells containing palcA-gfp cassette were further used for transferring p35S-alcR
cassette, in which one cell contains both vectors. Agrobacteria were then grown in 5 ml YEB
medium (10g/l peptone, 10g/l yeast extract, 5g/l NaCl) supplemented with antibiotics: rifampicin
(100µg/ml), kanamycin (50µg/ml), and chloramphenicol (35µg/ml) at 28°C for 2 days. Bacterial
suspension was adjusted to the OD600 of 0.8. 1ml of cell culture was pelleted by centrifugation
for 15 min at 3000 g. Then cells were resuspended in 1.2 ml of 10mM MES (2-(N-morpholino)
22
ethanesulfonic acid, PH 5.5) plus MS basal medium (Murashige & Skoog, 1962) and
supplemented with 100 µM acetosyringone (Yang, Li, & Qi, 2000). Agroinfiltration was
conducted on fully expanded mature leaves using a 1ml plastic syringe. Each spot (about 3-4 cm2
surrounding infiltrated area) was targeted by infiltrating 100 µl of bacterial suspension. Leaves
treated with cells containing alcA promoter, were immediately infiltrated with 2% ethanol
solution after cells infiltration. The infiltrated areas were marked and plants were maintained in
green house at 22°C. This experiment was performed twice and 4-5 sections from different parts
of the leaves were screened for each strain.
Epifluorescence Microscopy
Several 3-mm wide sections of marked areas on tobacco leaves were cut and mounted on the
slide. Images were generated using confocal laser scanning microscope (Zeiss Observer.Z1
microscope) with
40 objective lens. Fluorescence visualization in tobacco leaves was
monitored every 24 hours for 10 days after infiltration.
Statistical analysis
The transcriptional analysis by Real-Time PCR and ethanol toxicity assay were further
analyzed for identifying significant differences between different groups. Data were analyzed
using One-way Analysis of Variance (ANOVA) with Bonferroni Multiple Comparisons Test and
Tukey-Kramer Multiple Comparisons Test. Data were analyzed using GraphPad InStat version
3.0a (GraphPad Software, USA).
23
Chapter III: Results
Confirmation of heterologous DNA cloning in transformed E. coli
The presence of DNA sequences encoding alcA promoter, alcR and cellulase genes were
confirmed using different methods including digestion with restriction enzyme, PCR and
sequencing. Figure 5 displays the results of PCR confirming the presence of related gene in
transformed E. coli. Since detection primers were used for PCR, DNA bands don’t represent the
exact size of the genes.
Figure 5. Confirmation of DNA cloning in transgenic E. coli by PCR. Related detection primers
were used for amplification of alcA promoter, alcR and cellulase genes. C- = PCR negative
control with no DNA template.
24
Ethanol induction condition
Ethanol induction with various concentrations was performed for both E. coli and
Agrobacterium strains harboring alcA promoter to check for ethanol toxicity in liquid medium.
Both bacterial strains generated the same results. Cell growth was observed in overnight liquid
cultures with 1 and 2% ethanol with OD600 above 2.5, but not in other ethanol concentrations.
Cell viability test for E. coli cultures grown on agar plates with 0% to 7% ethanol exhibited
growth of colonies although significant decrease in growth rate was observed in higher ethanol
concentrations (Figure 6). Optimum growth was obtained on agar plates with 1, and 2% ethanol
concentrations. According to our data and another study (Lee et al., 2010), considering both
liquid and solid cultures, the lowest ethanol toxicity associating optimum growth can be obtained
using 1% and 2% ethanol concentrations which were used for further experiments.
A)
25
Ethanol Con.
0%
1%
2%
3%
4%
5%
6%
7%
8%
B)
0%
# of cells
2.46E+09
2.19E+09
2.32E+09
1.9E+09
1.66E+09
1.12E+09
6.13E+08
4.1E+08
0
SEM
44226898.1
168062213
181330282
90495037.7
3742276
14142135.6
250392285
167381799
0
1%
2%
4%
5%
N(n)
3(18)
3(18)
3(18)
3(18)
3(18)
3(18)
3(18)
3(18)
3(18)
C)
3%
6%
7%
8%
Figure 6. Ethanol toxicity assay. A) Collected data imply that cell viability is decreased
significantly with increase in ethanol concentrations. Error bars represent standard error of the
mean. Data are statistically compared to control (0% ethanol). *** P<0.001, ** P<0.01, *
P<0.05, NS = Not significant. B) Data represent 18 technical replicates (n) from three biological
samples (N). One-way Analysis of Variance (ANOVA) was conducted using these data. SEM=
standard error of the mean. C) E. coli cells are declined by increase in ethanol concentration.
Plates with 1% and 2% ethanol concentration show optimum growth similar to control (0%
ethanol).
26
E. coli strains and vectors analysis
Transcription analysis was conducted using two different E. coli strains in order to overcome
the difficulty of transferring pQE-30 vector into DH5α strain which was made competent in our
laboratory. TOP 10 chemically competent E. coli strain (Invitrogen, USA), which is similar to
DH10B strain, was used as host for pT5 (empty vector) and pT5-cel constructs. TOP 10 strain
has higher efficiency for cloning and plasmid propagation compared with DH5α from our
laboratory. This strain contains ϕ80lacZΔM15 and ΔlacX74 mutations which result in deletion
of the entire lac operon (Durfee et al., 2008). Hence, although pQE-30 expression vector has
lacO site downstream of T5 promoters, the transcription of downstream gene is not repressed.
Our results demonstrated that cellulase expression is down-regulated when IPTG was added to
the media. Invitrogen, the source company, also confirmed that TOP 10 strain does not require
IPTG induction. Data correlating with uninduced IPTG samples (which showed 10 fold higher
expression of cellulase gene) were used in transcript analysis (Figure 7).
A)
27
B)
Sample
T5
T5/IPTG
T5-cel
T5-Cel/IPTG
Expression
1.00
31.44
1760167.96
171352.91
SEM
0.11
3.95
148208.87
8629.57
N(n)
2(6)
2(6)
2(6)
2(6)
P
P>0.05
P>0.05
P<0.001
P>0.05
Figure 7. Differences in transcription level among cells harboring pT5-cel construct in presence
or absence of IPTG. A) Expression of cellulase gene was 10 fold higher in pT5-cel compared
with pT5-cell/IPTG. B) One-way Analysis of Variance (ANOVA) was conducted using this data.
Cellulase expression is significant at *** P<0.001 for T5-cel compared with other groups. cel =
cellulase, n=number of technical replicates, N= number of biological replicates, SEM= standard
error of the mean.
Analysis of cellulase gene expression regulated by different promoters
Preliminary reverse transcription PCR tests were conducted to check the absence or presence
of cellulase gene expressions in the following strains: p35S-cel, pT5-cel, pT5 (empty), p35SGFP, palcA-cel and palcA-cel-alcR. Gel electrophoresis data revealed expression for all samples
except p35S-cel, p35S-GFP, and pT5 (empty) (Figure 8). Although p35S-cel didn’t show any
detectable expression with 0.3 μl cDNA, increasing cDNA amount to 0.5 μl resulted in a very
faint band on the gel, indicating very low expression of cellulase in p35S-cel strain. Expression
of cellulase in pT5-cel strain is also raised by increase in amount of cDNA as it can be seen in
Figure 8B. Similar reverse transcription PCR was also performed with alcR primers to check for
alcR gene expression in p35S-alcR (Figure 8A). In contrast with cellulase expression in p35S-cel
strain, strong amplification of alcR gene can be observed in p35S-alcR strain. Since the
expression of both genes is regulated with 35S promoter, utilization of different plasmids may
have affected the transcription efficiency.
28
Figure 8. Analysis of Reverse Transcriptase PCR with gel electrophoresis. A) The transcription
of three genes were targeted by correlating detection primers. 0.3 µl of cDNA was used for this
test. No expression can be detected for p35S-cel strain, while p35S-alcR cassette exhibits strong
amplification for alcR gene. Although alcR gene is regulated by the 35S promoter, utilization of
pCAMBIA (a high-copy-number plasmid) seems to improve the transcription efficiency. B)
Transcription of cellulase gene for various strains (1% and 2% indicate ethanol concentration
added to the culture). 0.5 µl of cDNA was used for this test. All strains show transcriptional
expression of cellulase gene except p35S-gfp and pT5 (negative controls). Very low expression
can be detected for p35S-cel strain. T5-cel displays higher expression compared with previous
RT-PCR test (A).C- = PCR negative control with no DNA template. White color labels= Samples
with added cDNA. Yellow color labels = NRT (no reverse transcriptase) controls containing
RNA, primers and PCR master mix.
29
To accurately determine the efficiency of transcriptional signals, the cellulase gene expression
was studied in E. coli using Real-Time PCR. A common method for comparative gene
expression analysis is evaluation of a target gene expression relative to an endogenous control
gene using the quantification of normalized expression (ΔΔCt) method (Pfaffl, 2001). Expression
of cellulase gene in E.coli was determined using cysG gene (encoding uroporphyrin III Cmethyltransferase) as endogenous control selected for normalization. A critical aspect in gene
expression studies is utilization of appropriate reference gene that remains stable throughout
experimental process (Wong & Medrano, 2005). According to a recent study cysG expression
was found to be highly invariant for transcription analysis in E.coli (Zhou et al., 2011). Although
cysG is a newly identified reference gene, but comparison between Ct values of control and
transgenic samples in this study also showed pretty stable expression for the endogenous control
gene indicating the reliability of this gene for transcription studies (Table 3).
Target
Strain
Mean Ct
CysG
p35S-cel
p35S-gfp
pT5
pT5-cel
palcA-cel
palcA-cel 1%
palcA-cel 2%
palcA-cel-alcR
palcA-cel-alcR 1%
palcA-cel-alcR 2%
23.2
24.0
23.8
25.6
25.8
29.7
30.1
30.2
30.5
28.6
Table 3. Comparison between Ct values of control and transgenic samples. p35s-gfp and pT5
strains were considered as negative controls in this study. The difference in Ct values of negative
controls and transgenic strains is about 2-7 increment. Ct= Threshold cycle
30
The studied constructs that are outlined in Figure 4 consists of: three derivatives of plasmid
pBIN carrying alcA and CaMV 35S promoter (palcA-cel, p35S-cel, and p35S-gfp), and pQE-30
carrying coliphages T5 promoter (pT5, and pT5-cel). According to the gene expression data,
alcA promoter in palcA-cel-alcR strain exhibited the highest intensity in absence of ethanol and
35S promoter displayed the lowest expression level between all samples (Figure 9). The
expression of cellulase gene in all constructs with T5 and alcA promoters was upregulated. We
observed low non-induced basal level of alcA expression in absence of AlcR. This is while, data
show high ethanol dose-dependent expression of alcA promoter in absence of AlcR activator.
However, the induced expression of alcA promoter was higher in presence of AlcR. No
expression was observed for 35S promoter. Table 4 summarizes the magnitude of fold
differences between all the samples.
A)
31
B)
Strain
p35S-cel
p35S-gfp
palcA-cel
palcA-cel 1%
palcA-cel 2%
palcA-cel-alcR
palcA-cel-alcR1%
palcA-cel-alcR2%
pT5
pT5-cel
Expression
0.00
0.18
193.71
501.21
658.91
7160.70
1203.10
1822.74
0.020
652.38
SEM
0.00
0.030
43.30
101.42
224.47
3452.44
291.92
704.37
0.03
272.80
N(n) P
3(9)
P>0.05
3(9)
P>0.05
3(9)
P>0.05
4(12) P>0.05
4(12) P>0.05
5(15) P<0.05
4(12) P>0.05
4(12) P>0.05
4(12) P>0.05
4(12) P>0.05
Figure 9. Expression comparison of cellulase genes between different E. coli strains. A) In
clustergram all samples are sorted based on expression intensity (red demonstrate the highest
expression level and green the lowest level). The percentages refer to concentration of ethanol
used in the experiment. Variation between samples are shown by error bars. Higher variations
between different biological samples may arise according to the difference in physiological states
of the cell. Only palcA-cel-alcR value is significant at * P<0.05 compared to all other lines. B)
One-way Analysis of Variance (ANOVA) was conducted using these data. n=number of
technical replicates, N= number of biological replicates, SEM= standard error of the mean.
32
Magnitude of fold-difference
ΔΔCt
Strain
alcA-cel
alcA-cel 1%
alcA-cel 2%
alcA-cel-alcR
alcA-cel-alcR 1%
alcA-cel-alcR 2%
T5-cel
alcA-cel
alcA-cel
1%
alcA-cel
2%
alcA-cel-alcR
alcA-cel-alcR
1%
alcA-cel-alcR
2%
T5-cel
193.71
1.00
0.39
0.29
0.03
0.16
0.11
0.30
501.21
2.59
1.00
0.76
0.07
0.42
0.27
0.77
658.91
3.40
1.31
1.00
0.09
0.55
0.36
1.01
7160.70
36.97
14.29
10.87
1.00
5.95
3.93
10.98
1203.10
6.21
2.40
1.83
0.17
1.00
0.66
1.84
1822.74
9.41
3.64
2.77
0.25
1.52
1.00
2.79
652.38
3.37
1.30
0.99
0.09
0.54
0.36
1.00
Table 4. The magnitude of fold differences in transcription of cellulase gene between all
upregulated strains. palcA-cel-alcR strain shows the highest expression level compared with all
other strains. The values are generated by dividing the respective ΔΔCt values (right column
over top row). The percentages refer to concentration of ethanol used in the experiment.
Analysis of alcR gene expression in palcA-cel-alcR strain
Transcription of alcR gene was also studied by Real-Time PCR to evaluate the expression
level of alcR under the control of 35S promoter and investigate the correlations between alcR
expression and alcA activity. Results show that expression pattern of alcR gene in the palcA-celalcR strain in absence/presence of ethanol is very similar to the expression pattern of cellulase
gene in this strain (Figure 10). These data may display the direct correlation between AlcR
concentrations and alcA activity indicating the key role of activator AlcR in regulation of alcA
promoter.
33
A)
B)
Sample
palcA-cel-alcR
palcA-cel-alcR1%
palcA-cel-alcR2%
Expression
1
0.40
0.70
SEM
0.15
0.03
0.46
N(n)
3(9)
3(9)
3(9)
P
P>0.05
P>0.05
P>0.05
Figure 10. Expression comparison of alcR gene under different ethanol concentrations. A)
Expression pattern of alcR gene in the palcA-cel-alcR strain in absence/presence of ethanol is
very similar to the expression pattern of cellulase gene in this strain indicating the key role of
activator AlcR in regulation of the alcA activity. The percentages refer to concentrations of
ethanol used in the experiment. Error bars represent standard error of the mean. Higher variations
between different biological samples may arise according to the differences in physiological
states of the cells. B) Data represent 9 technical replicates (n) from three biological samples (N).
One-way Analysis of Variance (ANOVA) was conducted using these data. SEM= standard error
of the mean.
Analysis of protein expression
To investigate the effects of studied promoters on protein synthesis, SDS-PAGE analysis was
performed. The total proteins from E. coli cell lines harboring different promoters were extracted
and run on polyacrylamide gel. The size of cellulase proteins was calculated using ExPASy
compute protein molecular weight tool using cellulase protein sequence. The calculated
34
theoretical weight is 66.88 kDa. The comparison between negative controls (p35S-gfp and pT5)
and positive cellulase strains on the polyacrylamide gel didn’t reveal any significant difference
between 55 to 72 kDa (Figure 11). These results may indicate very low or no expression of
cellulase protein.
Figure 11. Results for SDS-PAGE analysis. Crude protein extracts were extracted from all E.
coli strains and run on polyacrylamide gel stained with Coomassie Blue. No tangible difference
can be detected at 66.88 kDa between negative controls (p35S-gfp and pT5) and positive
cellulase strains.
In order to examine the factors involved in no/low cellulase protein expression, sequence of
the cellulase gene used in this study was compared with the sequence reported by Pang et al. The
reason for this comparison was due to the codon optimization of the cellulase gene used in this
study for both E. coli and plants. Codon Usage analysis was conducted to check if codon
optimization of cellulase sequence is the main cause for absence of any detectable protein
expression. This analysis shows that CAI (Codon Adaptation Index) value is reduced from 0.73
35
to 0.68 in codon-optimized cellulase sequence (Figure A. 7). CAI value correlates with the
possibility of high protein expression level. A CAI of 1.0 is considered to be ideal while a CAI of
>0.8 is rated as good for expression in the desired expression host (GenScript, USA). It has been
shown that expression of original sequence of this gene using the pQE-30 plasmid can result in
production of active cellulase enzyme in E. coli (Pang et al., 2009). In order to investigate the
possibility of codon usage hypothesis, the original cellulase sequence (DQ235086) excluding the
N-terminal signal peptide (Pang et al., 2009) was compared with codon-optimized sequence used
in this study for eukaryotic rare codons. This comparison revealed the presence of 14 eukaryotic
rare codons including 5 AGG and 9 AGA codons in codon-optimized cellulase sequence
compared with original one. These data suggest that the presence of rare codons including AGG
and AGA in codon-optimized sequence might has stalled the protein synthesis.
Analysis of cellulase enzyme activity
To identify any cellulase enzyme activity for transgenic E. coli cells containing cellulase
gene, CMCase activity assay was performed for cells, supernatant of liquid culture and protein
lysate on different CMC agar plates. No CMCase activity was observed for any cellulase positive
lines. Only positive control (pure cellulase enzyme) showed large clear zone indicating CMCase
activity (Figure 12). Since used cellulase gene in this study has a signal peptide encoding for
extracellular space, the culture supernatant was also screened for presence of any cellulase
activity. The same results (no CMCase activity) were observed for both supernatant and protein
lysate.
36
Figure 12. CMCase activity analysis of cellulase positive E. coli cells stained with Congo red.
Only positive control shows clear zone. No CMCase activity observed for transgenic strains.
Transient expression in tobacco
Application of alcA promoter has been already well-established in plants (Caddick et al.,
1998; Deveaux et al., 2003; Roslan et al., 2001). The alcR-alcA system has been modified to
increase the efficiency of this system in transgenic plants. This modification comprises
regulation of alcR gene under the control of CaMV 35S promoter, and construction of alcAmin
35S promoter, in which two AlcR binding sites are attached to the CaMV 35S minimal promoter
(Caddick et al., 1998). It has been reported that this inducible system is tightly regulated and
shows negligible basal activity in plants (Caddick et al., 1998; Roslan et al., 2001). In this study
we showed that alcA promoter in E. coli has high ethanol-induced expression level in absence of
AlcR. To detect the possibility of similar occurrence in plants, we performed transient expression
assay using native alcA promoter from Aspergilus nidulans in tobacco. We also characterized the
time course of induction and gene expression of alcA compared with 35S promoter. Tobacco
37
leaves were infiltrated with Agrobacterium carrying both palcA-gfp and p35S-alcR constructs as
well as Agrobacteria harboring individual cassette of p35S-gfp, palcA-gfp, and p35S-alcR. It has
been reported that the best concentration of ethanol for induction of the promoter in plants is 2%
ethanol (v/v) (Klose et al., 2013; Roslan et al., 2001), which was used in this study as well.
Some leaves treated with alcA promoter were induced with 2% ethanol immediately after cell
infiltration while others were kept uninduced as control. The fluorescent activity was observed
for leaves treated with CaMV 35S promoter after 3 days and for alcA promoter induced with
ethanol after 5 days from infiltration time (Figure 13). The GFP expression lasted for more than
10 days in leaves treated with p35S-gfp and alcA-gfp-alcR 2% strains. Like the wild type, no
fluorescent activity was observed in treated leaves with either palcA-gfp or p35S-alcR
irrespective of the presence of ethanol. No fluorescent was also observed in palcA-gfp-alcR with
no ethanol induction. These data are consistent with our expectation and may demonstrate that
the native alcA regulon is fully functional and inducible by ethanol in tobacco. Results showed
no fluorescent activity for alcA promoter when alcR gene is not present. This may indicate no or
negligible basal activity for this promoter. Our results revealed faster expression of constitutive
promoter over inducible promoter. It has been previously shown that the ethanol induction of
alcA promoter in transgenic plant is fast (Caddick et al., 1998; Roslan et al., 2001). In our
experiments, the delayed expression of alcA promoter may be due to the time requirement for the
production of AlcR protein and this delay may not be due to lack of minimal 35S sequence in
alcA. Further research is needed to confirm the reason for delay in alcA promoter activity.
38
Figure 13. Epifluorescence micrographs of GFP fluorescence in tobacco leaves expressing p35Sgfp at day 3 (D-F), and palcA-gfp-alcR at day 5 (G-I). Fluorescent activity of these lines is
compared to wild type leaves (A-C) which show no fluorescence. DIC images (A, D, and G), and
fluorescence images (B, E, and H) are overlapped together (C, F, and I) to display occurrence of
fluorescent activity in cellular contour.
39
Chapter IV: Discussion
One aim of this study was to accurately analyze the in vivo strength of three well-defined
promoters in E. coli: T5 as a very strong bacterial promoter, alcA as an inducible promoter, and
35S as a constitutive promoter (the latter one is well-known for use in plant transformation
experiments). In this study, a system was established to measure alcA promoter activity. A
prerequisite for this goal was a reliable and accurate method for quantifying transcription
efficiency. At present, quantitative Real-Time PCR (qPCR) is the most common reliable method
for quantitative analysis of nucleic acids (Sochivko, Fedorov, Varlamov, Kurochkin, & Petrov,
2010). It has become the benchmark technology for the analysis of gene expression by its
specificity, simplicity, and sensitivity (Ginzinger, 2002). The qPCR method was used in this
study to measure and compare the promoter efficiency in E. coli.
Transcription analysis for pT5-cel strain carrying pQE-30 vector was conducted without using
any IPTG induction. This was mainly due to the utilization of TOP 10 E. coli strain as host
which lacks the entire lac operon. Hence, although pQE-30 expression vector has lacO site
downstream of T5 promoters, the transcription of downstream gene is not repressed.
The three examined promoters can be arranged in a hierarchy, based on their activities in vivo
under defined conditions (Figure 9). The 35S promoter is located at the low end of this hierarchy,
the lowest expression level among all the promoters in E. coli. Previous studies have reported
that CaMV35S promoter (the most common promoter used for generation of transgenic plants)
can direct gene expression in E. coli (Assaad & Signer, 1990; Lewin, Jacob, Freytag, & Appel,
1998), although theoretically plant specific regulatory systems are not supposed to function in
bacteria due to differences in transcription regulatory systems. Despite our RT-PCR results that
showed very low cellulase expression for p35S-cel strain, no detectable transcription was found
40
for p35S-cel in our qPCR experiment which is more accurate technique. This is while alcR gene
expression regulated by 35S promoter was observed in p35S-alcR strain. This was confirmed by
a preliminary reverse transcription PCR as well as qPCR test indicating the absence of
expression for p35S-cel strain, while exhibiting alcR gene expression in p35S-alcR strain (Figure
8A, 9). It seems that utilization of pCAMBIA vector in p35S-alcR strain instead of pBIN vector
in p35S-cel strain improves the transcription efficiency of downstream gene. The reason for this
difference might be due to the plasmid copy number. pBIN is a low-copy-number plasmid and
pCAMBIA is a high-copy-number plasmid, which may lead to higher expression of alcR gene in
the 35S-alcR strain.
The strongest promoter identified in this study is alcA in palcA-cel-alcR strain, which is 11
times stronger than the T5 promoter. The presence of the AlcR transcription activator appears
critical for the functionality of the alcA promoter. This is mainly deduced from the data
reflecting the cellulase gene expression level in palcA-cel-alcR strain is 37 times higher than the
palcA-cel strain. The activity of alcA promoter seems to be directly related to the AlcR
concentrations (Figure 10). Comparison between qPCR data for cellulase and alcR genes in
palcA-cel-alcR strain shows similar expression pattern of these genes in absence/presence of
ethanol. These data suggest the key role of activator AlcR in regulation of the alcA activity.
These observations meet our expectations and are consistent with the reported data from
previous studies in Aspergillus nidulans (Felenbok, 1991; Felenbok et al., 2001).
It has been reported that ethanol induction of bacterial promoters are dose-dependent (Chong
et al., 2013; Haft et al., 2014; Lee et al., 2010). Our transcription data also show dose-dependent
expression of alcA promoter using ethanol, where cellulase gene expression in palcA-cel 2% and
palcA-cel-alcR 2% is respectively 1.3 and 1.5 times higher than palcA-cel 1% and palcA-cel-
41
alcR 1% strains, although these data are not statistically significant. These results are very
similar to the data reported by Haft et al. paper. There is 1.3 times increase in gene expression by
1% increase in ethanol concentration (from 1% to 2%) reported by Haft paper and this study.
In addition, palcA-cel-alcR 1% and palcA-cel-alcR 2% strains exhibit, respectively, 2.4 and
2.8 times higher expression than palcA-cel 1% and palcA-cel 2%. Role of AlcR in inducing alcA
promoter might be the reason for such increase in transcription level. These results reflect that
the alcA promoter in E. coli is able to function in the absence of AlcR activator with lower
efficiency. On the other hand, presence of AlcR increased the transcription level 37 times more
in absence of ethanol, although this effect was significantly reduced when ethanol was added.
According to studies on Aspergillus nidulans, very low non-induced basal level expression of
alcA promoter was expected (Felenbok et al., 2001; Nikolaev et al., 2002). We only observed
this non-induced low expression in palcA-cel strain but not in palcA-cel-alcR strain. Our data
indicating high non-induced basal level of alcA in presence of AlcR is consistent with the
reported data from the study of heterologous alcR-alcA system in transformed Aspergillus niger
(Nikolaev et al., 2002). Their study shows clear differences in non-induced expression level at
both transcription and translation between Aspergillus nidulans and transformed Aspergillus
niger. As they have suggested, this effect may be due to the accumulation of acetaldehyde from
the regular metabolism (Nikolaev et al., 2002). The same explanation may be applied to our
results in E. coli wherein acetaldehyde can be produced through many other cellular pathways
including ethanolamine utilization, preQ0 biosynthesis, threonine degradation IV, etc (Keseler et
al., 2013). Utilization of LB broth for culturing E. coli cells might have provided a source of
various amino acids including ethanolamine and threonine that can be converted into
acetaldehyde by E. coli cells. Furthermore, high inducibility of alc system in Aspergillus
42
nidulans may arise from the fine-tuning of alcA and aldA expression through maintaining an
optimal acetaldehyde level between inducing and toxic concentrations (Felenbok et al., 2001).
Constitutive overexpression of the alcR gene, occurring in the gpdA:alcR transformants, for
example (Nikolaev et al., 2002), may disturb this balance and result in high non-induced basal
expression.
Reduction in ethanol-induced gene expression in alcR-alcA strains may be explained by
deleterious effects of ethanol mainly on alcR expression. It has been shown that ethanol inhibits
mRNA synthesis by slowing RNA polymerase, and increases Rho-dependent transcription
termination. Ethanol also accelerates translational misreading that leads to ribosomal stalling and
termination. These changes on transcription and translation may be due to the direct influence of
ethanol on the RNA polymerase and ribosome to alter their conformations (Haft et al., 2014).
Furthermore, increasing acetaldehyde concentrations also causes rapid reduction in general
transcription efficiency and growth arrest. The toxic effects of acetaldehyde becomes apparent at
concentrations twice as low as ethanol (Crebelli, Conti, Conti, & Carere, 1989).
Another explanation to these results might be due to the effects of ethanol to induce heat
shock response in bacteria by induction of RNA polymerase sigma factor σ32 (Van Dyk et al.,
1994; Van Dyk, Reed, Vollmer, & LaRossa, 1995). Lower expression of alcR-aclA system in
presence of ethanol might also be associated with competition between different transcription
sigma factors, specifically σ 70 and σ32. During the heat shock response, sigma factor 32 might
have a greater affinity for RNA polymerase core enzyme than sigma 70 (Blaszczak, Zylicz,
Georgopoulos, & Liberek, 1995). In unstressed cells, cellular concentration of sigma 32 is very
low; however, its abundance increases following the heat shock stress (Jishage, Iwata, Ueda, &
Ishihama, 1996; Raffaelle, Kanin, Vogt, Burgess, & Ansari, 2005). In a study, the effects of heat
43
stress on cellular concentrations of sigma 70 and 32 has been investigated (Raffaelle et al.,
2005). According to this study, during growth at 30°C sigma 70 binds to ribosomal RNA
operons (rrn) and transcribes it at high level while the concentration of sigma32 is very low.
However, within five minutes after heat stress, the concentrations of sigma 70 along with RNA
polymerase drops dramatically while sigma 32 concentrations increases. Finally after 20 minutes
sigma 70 concentrations can be recovered but at a much lower amount than the unstressed level
(Raffaelle et al., 2005). The substitution of sigma 70 by sigma 32 at the rrn promoters after heat
induction provides further evidence to support lower expression of alcR-aclA system in presence
of ethanol. Increase in the transcription level of cellulase in palcA-cel-alcR 2% strain compared
to palcA-cel-alcR 1% strain may also be related to the increase in the activity of sigma 32.
Furthermore, abundance of heat shock sigma factors even during heat shock are significantly less
than sigma 70 (Haft et al., 2014; Jishage et al., 1996). This evidence may support the lower
expression of cellulase gene in palcA-cel-alcR 1% and palcA-cel-alcR 2% strains compared to
palcA-cel-alcR.
In addition to sigma 32, other heat shock related sigma factors including sigma 24, 38, and 28
may also be involved in ethanol induced alcA transcription. Sigma 24 is involved in the heat
shock response following temperature upshift and with regulating genes with extracytoplasmic
functions (Danese, Snyder, Cosma, Davis, & Silhavy, 1995; Raina, Missiakas, & Georgopoulos,
1995). It plays an important role in responses to the effects of stresses on membrane and
periplasmic protein. It can also activate the transcription of sigma 32 (Keseler et al., 2013).
Sigma 38 acts as a regulator of genes associated with secondary metabolisms and stress
responses, specially during transition between the exponential and stationary phases (Maciag et
al., 2011). Sigma 28 regulates transcription of genes involved in flagellar synthesis, motility and
44
chemotaxis (Arnosti & Chamberlin, 1989; Komeda, 1986). This is important since ethanol is
considered a repellent for E. coli and leads to activating negative chemotaxis response (Tso &
Adler, 1974). The promoter sequence analysis shows the presence of similar sequence to the
consensus region of sigma 28 at -35 region upstream of alcA promoter (Figure A. 8). There is
also the possibility of regulating adhP gene encoding for alcohol dehydrogenase/acetaldehyde
reductase by sigma 28 in E. coli (Keseler et al., 2013). The adhP gene can be induced by ethanol
(Shafqat et al., 1999). According to these evidences, it seems that alcA and adhP genes are very
similar in their function and regulation of promoter activity. Thus, it can be speculated that sigma
28 can bind to alcA promoter during stress-response by ethanol.
Here, we propose a model for regulation of alcA-alcR system in presence of ethanol in E. coli
(Figure 14). According to this model, in absence of ethanol, RNA polymerase (RNAP) can bind
to σ70 and activate the transcription of downstream gene. However when ethanol is present, it
induces the heat shock response leading to an increase in sigma 32 concentration. Increase in
expression of sigma 32 protein causes competition between this sigma factor and σ70. Hence,
sigma factor 32 can bind more efficiently to RNA polymerase compared to sigma 70 due to its
greater affinity for RNA polymerase during the heat shock response. Increase in the ethanol
induced transcription level may also be related to the increase in the activity of sigma 32 caused
by increase in ethanol concentration. Furthermore, lower gene expression in presence of ethanol
is related to the lower abundance of heat shock sigma factors compared to sigma 70. In addition,
ethanol slows RNAP by changing its conformation leading to increase in Rho-dependent
transcription termination. All these factors might cause reduction in both alcR and cellulase
transcription. Reduction in activator AlcR also leads to subsequence reduction in alcA promoter
45
due to its key role in regulation of alcA activity. In addition to sigma 32, sigma 28 may also be
involved in ethanol induced alcA transcription.
Figure 14. Proposed model for regulation of alcA-alcR system in presence of ethanol. A) In
absence of ethanol, RNA polymerase (RNAP) can bind to σ70 and activate the transcription of
downstream gene. B) Ethanol induces the heat shock response leading to competition between
sigma factors including σ70 and σ32. Ethanol also slows RNAP by changing its conformation
leading to increase in Rho-dependent transcription termination. All these factors might cause
reduction in both alcR and cellulase transcription. Reduction in activator AlcR leads to
subsequence reduction in alcA promoter due to its key role in regulation of alcA activity.
Analysis of cellulase protein expression displayed low or no expression of cellulase protein.
One reason can be due to the absence of bacterial ribosomal binding site (Shine-Dalgarno
sequence) downstream of alcA promoters. Although there is Shine-Dalgarno sequence
(AGAGGA) downstream of 35S promoter, no detectable protein expression may be expected
due to the low efficiency of this promoter in E. coli. However, this reason is not applicable for
46
T5 promoter because of its efficient transcription activity and presence of a strong ribosomal
binding site located before ATG codon in pQE-30 plasmid. In addition, the translation of DNA
sequence to protein for T5-cel strain showed that the cellulase gene is in frame with the vector
ATG site (Figure A. 9). The in-frame translation of cellulase sequence to protein is also
predicted in other strains harboring 35S and alcA promoters (Figure A. 10).
Lack of tangible cellulase protein synthesis may be better explained by difference in codon
usage between eukaryotes and prokaryotes, since the cellulase gene used in this study was codon
optimized for both plant and E. coli. The comparison between cellulase gene sequence used in
this study and the sequence reported by Pang et al. revealed the presence of 14 eukaryotic rare
codons in codon-optimized cellulase sequence compared with original one. These data may
suggest that the presence of rare codons in codon-optimized sequence might has led to no or very
low protein synthesis.
We also showed that the native alcA regulon from A. nidulan is fully functional and tightly
regulated in tobacco. This alcA induction was only obtained in the presence of both AlcR and
ethanol. These findings are consistent with previous studies for utilization of alcA promoter in
plants (Caddick et al., 1998; Klose et al., 2013; Roslan et al., 2001). It has been suggested that
ethanol itself doesn’t induce the alc regulon, instead acetaldehyde triggers the activation of AlcR
(Flipphi et al., 2001). This finding has been supported through demonstrations of promoting the
induction of alc regulon by other external inducers that are converted to acetaldehyde (Flipphi et
al., 2002). Although it has been suggested that alcA expression in tobacco is affected by a direct
interaction of the inducer with AlcR (Caddick et al., 1998), still it is not clear whether
acetaldehyde binds directly to AlcR or the inducing signal is mediated through auxiliary proteins
(Flipphi et al., 2002).
47
Further experiments are required to support mentioned hypotheses and clarify the mechanism
of the alcR-alcA system in E. coli. There are several approaches that can be suggested by this
study. According to deleterious effects of ethanol and possible induction of heat shock response
by ethanol, evaluation of this system in ethanol tolerant E. coli strains such as LY01 may lead to
reduction of such negative effects. Moreover, we speculate that the high non-induced gene
expression in palcA-cel-alcR strain is due to the cellular concentrations of acetaldehyde.
Measurement of acetaldehyde level in wild type and transgenic E. coli by gas chromatography
method may answer this question. It is also suggested to evaluate gene expression by utilization
of culture media with different concentrations of amino acids since they can be the source for
acetaldehyde production. This study also suggest to perform further analysis of alcA induction
with other inducers specifically acetaldehyde. The effects of each inducer should be evaluated
for both gene expression and toxicity aspects to identify a better substitute for ethanol in E. coli.
Furthermore, we assume that lack of any significant protein expression and enzyme activity is
due to the presence of eukaryotic rare codons in cellulase sequence. Therefore, utilization of
codon-optimized sequence specifically for E. coli with ideal CAI value over 0.8 may provide
better results. Our data also showed more efficient expression of 35S promoter when a highcopy-number plasmid was used. Therefore, application of this type of vectors for expression of
heterologous gene may lead to higher production of the product.
48
Conclusion
The ethanol utilization pathway in A. nidulans is a highly inducible regulatory system for
consumption of ethanol as carbon source. The pathway requires specific activator (AlcR)
(encoded by alcR gene) mediating the transcription in presence of an inducing compound (e.g.
ethanol) (Mathieu, Fillinger, & Felenbok, 2000). We showed that the native alcA regulon from
A. nidulan is fully functional and tightly regulated in tobacco. This alcA induction was only
obtained in the presence of both AlcR and ethanol. The clearest result based on this study is the
higher efficiency of alcA promoter over T5 and 35S promoter in E. coli. The palcA-cel-alcR
strain exhibits the highest transcription level among all strains, irrespective of ethanol induction.
Except palcA-cel and palcA-cel-alcR 1%, all other strains carrying alcA promoter showed higher
transcription levels compared with the T5 promoter. No significant expression of cellulase
protein was observed. Lack of cellulase enzyme activity might be due to the presence of rare
codons in codon-optimized cellulase sequence which can lead to the interruption of protein
synthesis.
This study suggests that high levels of expression of heterologous genes can be obtained in E.
coli using alcR-alcA system of A. nidulans. It can also be concluded that slightly different
mechanisms may be involved in the regulation of alc system in A.nidulans and plants, compared
with E. coli. We recommend the broader application of alcR-alcA system in E.coli using highcopy-number plasmids for mass production of cellulase enzyme and bioethanol.
49
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Appendix A: Supplemental Figures
Figure A. 1. Schematic diagram of p35S-cel vector construction.
58
Figure A. 2. Schematic diagram of pT5-cel vector construction.
59
Figure A. 3. Schematic diagram of palcA-gfp vector construction.
60
Figure A. 4. Schematic diagram of palcA-cel vector construction.
61
Figure A. 5. Schematic diagram of p35S-alcR vector construction.
62
Figure A. 6. Different approaches for screening CMCase activity. A) E. coli cells were added
into wells, stained with Congo red. B) E. coli cells were spread on the media, stained with Congo
read and HCl.
63
A)
B)
CAI: 0.73
CAI: 0.68
Figure A. 7. The distribution of codon usage frequency between the original and codonoptimized cellulase sequences in E. coli. Possibility of high protein expression level is correlated
to the value of CAI. A CAI of 1.0 is considered to be ideal while a CAI of >0.8 is rated as good
for expression in the desired expression organism. A) Original cellulase sequence B) Codonoptimized cellulase sequence for both plant and E. coli.
64
A)
B)
Figure A. 8. Predicted promoter regions at -35 and -10 for binding of σ28 and σ32. Comparison
between 20-25 promoter biding sites was conducted using EcoCyc database. A) alcA promoter
displays same σ28 binding site at -35 region compared to tarp promoter. B) alcA promoter
displays similar motifs for σ32 binding site at -35 and -10 region compared to htpGp1 promoter.
Yellow = similar motifs for -35 region, Blue = Similar motifs for -10 regions
65
A)
B)
E F I K E E K L T Met R G S H H H H H H G S G S G S G I E G R P Y N G T G S Met I N A W N I A R W L R R T S F V L G
VAAVYCLPLP ALAASESAP AAAKIHLNQLGFLPESGKIAVVPNTPSGEFWLVDAKTG
SEVLRGP LSQAQSWDVAGETVKIADFTAF NKPGHYRIRVAEAGESP AFHIQENVFDP
VLAAAIKYFYFNRASAP IDADYAGPF ARPAGHPDTIVYIHPS AASAARP AGTIVSSPK
GWYDAGDYNKYSVNSGISTYTLLAALADFPRLFADVRLKIPESSDAVPDLLNEILWN
L D W Y I T Met Q D P H D G G V Y H K L T D L K F S G A G Met P H E Q N T K R Y Met V E K S T A A T L N F A A V
F A Y A S V V Met R D Y Q K H F P G R A E Q Y L Q A A E K A W A W A K K N P K A L Y T Q P A E V N T G A Y
A Met L N E T L E D E W F W A A A E L L R A S D K K T Y Met T G I N L P A K P R V P E W S A V E S L G L Y A L A
R Q N N A P A K Met Q Q G A S E L L I E Met A D R Met V R E Y W Q S A Y L V P L V E A D F R W G S N A V A Met N
K A Met V L L A A E R L R P N K D Y R P A A R A L L D Y V L G R N P T G Y S F V T G F G S K S A L Y P H H R I S H
Y D G V A A P V P G Met L V G G P Q P G W Q D K C S Y P S R L P A K S Y L D D W C S Y S T N E V A I N W N A P L
V Y V L A A L R E Stop
Figure A. 9. Confirmation of in-frame insertion of cellulase gene in pQE-30 Xa vector. A) pQE30 vector map. B) The results of Expasy translation for cellulase sequence inserted in MCS of
pQE-30 vector. Translation of related DNA sequences shows that cellulase gene is in frame with
the vector translation start site (ATG site). Text highlighted in red represents vector protein
sequence and grey represents cellulase protein sequence.
66
A)
R P P R F S V S P T S S T G S Met I N A W N I A R W L R R T S F V L G V A A V Y C L P L P A L A A S E S A P A A A K
IHLNQLGFLPESGKIAVVPNTPSGEFWLVDAKTGSEVLRGPLSQAQSWDVAGETVKI
ADFT AFNKPGHYRIRVAEAGESPAFHIQEN VFDPVLAAAIKYFYFNRASAPID ADYAG
PFARPAGHPDTIVYIHPSAASAARPAGTIVSSPKGWYDAGDYNKYSVNSGISTYTLLA
A L A D F P R L F A D V R L K I P E S S D A V P D L L N E I L W N L D W Y I T Met Q D P H D G G V Y H K L T D L K
F S G A G Met P H E Q N T K R Y Met V E K S T A A T L N F A A V F A Y A S V V Met R D Y Q K H F P G R A E Q Y L
Q A A E K A W A W A K K N P K A L Y T Q P A E V N T G A Y A Met L N E T L E D E W F W A A A E L L R A S D K K
T Y Met T G I N L P A K P R V P E W S A V E S L G L Y A L A R Q N N A P A K Met Q Q G A S E L L I E Met A D
R Met V R E Y W Q S A Y L V P L V E A D F R W G S N A V A Met N K A Met V L L A A E R L R P N K D Y R P A A R
A L L D Y V L G R N P T G Y S F V T G F G S K S A L Y P H H R I S H Y D G V A A P V P G Met L V G G P Q P G W Q
D K C S Y P S R L P A K S Y L D D W C S Y S T N E V A I N W N A P L V Y V L A A L R E Stop E L
B)
R T I P L S F A R P F L Y I R K F I S F G E N T G D S R G S G S Met I N A W N I A R W L R R T S F V L G V A A V Y C
LPLPALAASESAPAAAKIHLNQLGFLPESGKIAVVPNTPSGEFWLVDAKTGSEVLRGP
LSQAQSWDVAGETVKIADFTAFNKPGHYRIRVAEAGESP AFHIQENVFDPVLAAAIK
YFYF NR AS AP IDADYAGPF ARP AGHP DT IV YIHPS AAS AARP AGT IVSS PKGWYD AGD
YNKYSVNSGISTYTLLAALADFPRLFADVRLKIPESSDAVPDLLNEILWNLDWYI
T Met Q D P H D G G V Y H K L T D L K F S G A G Met P H E Q N T K R Y Met V E K S T A A T L N F A A V F A Y A S
V V Met R D Y Q K H F P G R A E Q Y L Q A A E K A W A W A K K N P K A L Y T Q P A E V N T G A Y AMet L N E T
L E D E W F W A A A E L L R A S D K K T Y Met T G I N L P A K P R V P E W S A V E S L G L Y A L A R Q N N A P A
K Met Q Q G A S E L L I E Met A D R Met V R E Y W Q S A Y L V P L V E A D F R W G S N A V A Met N K A Met V L
LAAERLRPNKDYRPAARALLDYVLGRNPTGYSFVTGFGSKSALYPHHRISHYDGVAA
P V P G Met L V G G P Q P G W Q D K C S Y P S R L P A K S Y L D D W C S Y S T N E V A I N W N A P L V Y V L A A
L R E Stop E L
Figure A. 10. Confirmation of in-frame insertion of cellulase gene in pBIN vector. The results of
Expasy translation are shown for A) cellulase gene inserted downstream of alcA promoter, B)
cellulase gene inserted downstream of 35S promoter. Text highlighted in red represents cellulase
protein sequence.
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Appendix B: Sequence Data
Codon-optimized cellulase gene DNA sequence
GGATCCATGATTAACGCCTGGAACATCGCCCGCTGGCTGAGAAGAACCTCCTTTGTC
CTTGGTGTGGCTGCTGTCTACTGCTTGCCTCTGCCTGCTCTGGCTGCATCCGAATCTG
CACCTGCCGCGGCTAAAATTCATTTGAATCAGCTGGGTTTTCTTCCGGAGTCAGGCA
AGATCGCTGTTGTGCCTAACACCCCGAGCGGAGAATTCTGGCTGGTGGATGCAAAA
ACTGGATCAGAGGTCTTGAGAGGTCCTCTGAGCCAGGCTCAAAGTTGGGATGTGGC
AGGTGAAACCGTCAAAATTGCCGACTTTACTGCGTTCAATAAGCCGGGACATTATCG
TATTCGCGTGGCAGAAGCAGGAGAGTCCCCAGCTTTTCACATCCAAGAAAATGTTTT
CGATCCAGTGCTGGCAGCCGCGATTAAGTATTTTTACTTCAACCGTGCGAGCGCTCC
GATCGATGCCGACTATGCGGGCCCATTTGCTCGCCCTGCAGGACATCCGGATACTAT
TGTTTACATCCACCCATCTGCTGCATCAGCAGCAAGGCCAGCCGGTACAATTGTGTC
TTCACCTAAAGGATGGTATGATGCGGGTGACTATAATAAGTACAGCGTTAACAGTG
GCATCTCCACCTACACTCTGCTTGCTGCACTGGCCGATTTTCCACGTCTTTTCGCGGA
CGTTCGCTTGAAAATTCCTGAAAGCAGTGATGCTGTGCCGGACTTGCTGAATGAGAT
TTTGTGGAACCTGGATTGGTATATCACAATGCAAGATCCACATGACGGTGGCGTTTA
CCACAAACTTACGGACTTGAAGTTTTCTGGCGCAGGAATGCCACATGAACAAAATA
CCAAACGTTATATGGTGGAGAAGAGTACAGCCGCGACGCTGAACTTTGCTGCAGTCT
TCGCATATGCCTCCGTCGTTATGAGAGATTACCAGAAACACTTCCCTGGTAGGGCAG
AACAGTACTTGCAAGCCGCGGAGAAAGCGTGGGCTTGGGCAAAAAAGAATCCAAA
GGCCCTGTATACACAACCTGCGGAAGTGAATACGGGCGCCTACGCGATGCTTAACG
AGACTTTGGAAGATGAGTGGTTTTGGGCTGCAGCCGAACTTTTGAGAGCTAGTGACA
AAAAGACCTATATGACTGGAATTAACCTGCCAGCTAAACCTAGAGTTCCGGAATGG
TCCGCAGTGGAGTCTCTGGGCCTTTACGCTCTTGCAAGGCAGAATAACGCCCCAGCG
AAGATGCAGCAAGGAGCTAGCGAACTGCTTATCGAGATGGCAGATCGTATGGTTCG
CGAATATTGGCAATCTGCCTACCTGGTGCCTCTTGTCGAGGCGGATTTTCGTTGGGG
TTCAAATGCTGTCGCAATGAACAAAGCCATGGTTTTGCTGGCGGCTGAAAGACTTAG
GCCGAATAAGGATTATAGACCAGCAGCCAGGGCTCTTTTGGACTATGTCTTGGGCCG
TAACCCAACAGGATACAGCTTTGTTACGGGTTTCGGCTCAAAAAGCGCTCTGTATCC
TCATCACCGCATTAGTCATTACGATGGTGTCGCGGCTCCAGTTCCTGGAATGCTGGT
GGGAGGTCCACAGCCAGGATGGCAAGATAAATGTTCTTATCCATCACGCCTGCCTGC
TAAGTCATATCTTGATGACTGGTGCAGTTACTCCACAAACGAGGTTGCGATTAACTG
GAATGCGCCGCTTGTGTATGTGTTGGCAGCCTTGAGAGAATGAGAGCTC
68
Original cellulase sequence (Pang et al., 2009)
ATGATAAATGCCTGGAATATCGCGCGCTGGCTGCGCAGAACATCGTTCGTTTTGGGG
GTGGCGGCCGTCTATTGTTTGCCTTTGCCGGCGTTAGCCGCATCCGAATCTGCGCCC
GCAGCTGCGAAGATCCATTTAAATCAGCTGGGTTTTTTGCCGGAATCCGGCAAGATC
GCGGTGGTTCCTAATACTCCCAGCGGTGAATTCTGGCTGGTGGATGCAAAAACCGGT
AGCGAGGTGCTGCGTGGCCCTTTGTCTCAAGCGCAATCTTGGGATGTGGCGGGTGAG
ACGGTAAAAATCGCGGATTTCACGGCCTTCAATAAACCGGGACATTACCGCATCCG
GGTAGCCGAGGCGGGAGAATCACCGGCGTTTCATATTCAGGAAAATGTCTTCGACC
CCGTTCTCGCGGCAGCCATTAAATATTTCTATTTCAATCGCGCCAGTGCTCCCATTGA
TGCGGATTACGCAGGCCCCTTTGCCCGCCCAGCAGGTCATCCGGACACGATTGTCTA
TATCCATCCCTCTGCAGCGTCCGCCGCGCGCCCTGCCGGTACCATTGTTTCGTCACCC
AAGGGTTGGTATGACGCAGGTGATTACAACAAATACAGCGTTAATTCCGGTATCAG
CACTTATACCTTGTTGGCCGCGCTGGCGGATTTTCCGCGTTTGTTTGCTGATGTGCGT
TTGAAAATTCCCGAATCCAGCGATGCAGTGCCGGATTTGCTCAATGAAATTTTGTGG
AACCTCGATTGGTATATCACCATGCAGGATCCTCATGACGGCGGTGTCTATCACAAA
CTTACCGATCTCAAATTTTCCGGCGCGGGTATGCCCCATGAACAGAATACCAAGCGT
TATATGGTGGAGAAGAGCACTGCGGCCACATTGAATTTCGCCGCCGTATTTGCCTAT
GCCAGTGTGGTCATGCGCGATTATCAAAAACATTTTCCGGGTCGCGCTGAGCAGTAT
TTGCAGGCGGCGGAAAAAGCTTGGGCCTGGGCCAAGAAAAATCCCAAAGCCCTTTA
TACGCAGCCTGCGGAAGTGAATACCGGCGCCTATGCGATGCTCAACGAAACTCTGG
AAGATGAGTGGTTTTGGGCAGCGGCGGAATTATTGCGGGCCAGCGACAAAAAGACC
TATATGACCGGCATCAATTTGCCGGCAAAACCGCGCGTGCCTGAATGGAGTGCCGTA
GAGAGCTTGGGGCTTTACGCGTTGGCGCGACAAAATAATGCGCCTGCAAAAATGCA
GCAAGGCGCGAGCGAATTATTAATTGAGATGGCGGACCGCATGGTGCGGGAATATT
GGCAGTCCGCTTATCTCGTTCCTTTGGTGGAAGCGGATTTTCGCTGGGGCAGTAACG
CAGTGGCCATGAACAAAGCGATGGTATTGCTGGCAGCGGAGCGTTTGCGTCCCAAT
AAAGATTATCGCCCTGCCGCGCGGGCCTTATTGGATTATGTCCTCGGCCGCAATCCC
ACTGGTTACAGTTTTGTCACCGGCTTTGGCAGCAAATCCGCGCTCTATCCGCACCAT
CGCATTTCCCATTACGACGGAGTGGCAGCGCCAGTTCCCGGCATGTTAGTCGGCGGC
CCGCAACCGGGCTGGCAGGATAAATGTTCATACCCAAGCCGTTTGCCGGCGAAATC
TTATCTGGATGATTGGTGCAGCTATTCCACCAACGAAGTGGCGATTAATTGGAATGC
ACCTTTGGTTTATGTGTTGGCGGCATTGCGGGAGTAG
69
Yellow label represents transcription start site
alcA promoter sequence
AATCGATAGTTGTGATAGTTCCCACTTGTCCGTCCGCATCGGCATCCGCAGCTCGGG
ATAGTTCCGACCTAGGATTGGATGCATGCGGAACCGCACGAGGGCGGGGCGGAAAT
TGACACACCACTCCTCTCCACGCACCGTTCAAGAGGTACGCGTATAGAGCCGTATAG
AGCAGAGACGGAGCACTTTCTGGTACTGTCCGCACGGGATGTCCGCACGGAGAGCC
ACAAACGAGCGGGGCCCCGTACGTGCTCTCCTACCCCAGGATCGCATCCCCGCATA
GCTGAACATCTATATAAAGACCCCCAAGGTTCTCAGTCTCACCAACATCATCAACC
Artificial T5 promoter/lac operator element sequence
CTCGAGAAATCATAAAAAATTTATTTGCTTTGTGAGCGGATAACAATTATAATAGAT
TCAATTGTGAGCGGATAACAATTTCACACA
CaMV 35S promoter
AGATTAGCCTTTTCAATTTCAGAAAGAATGCTAACCCACAGATGGTTAGAGAGGCTT
ACGCAGCAGGTCTCATCAAGACGATCTACCCGAGCAATAATCTCCAGGAAATCAAA
TACCTTCCCAAGAAGGTTAAAGATGCAGTCAAAAGATTCAGGACTAACTGCATCAA
GAACACAGAGAAAGATATATTTCTCAAGATCAGAAGTACTATTCCAGTATGGACGA
TTCAAGGCTTGCTTCACAAACCAAGGCAAGTAATAGAGATTGGAGTCTCTAAAAAG
GTAGTTCCCACTGAATCAAAGGCCATGGAGTCAAAGATTCAAATAGAGGACCTAAC
AGAACTCGCCGTAAAGACTGGCGAACAGTTCATACAGAGTCTCTTACGACTCAATG
ACAAGAAGAAAATCTTCGTCAACATGGTGGAGCACGACACACTTGTCTACTCCAAA
AATATCAAAGATACAGTCTCAGAAGACCAAAGGGCAATTGAGACTTTTCAACAAAG
GGTAATATCCGGAAACCTCCTCGGATTCCATTGCCCAGCTATCTGTCACTTTATTGTG
AAGATAGTGGAAAAGGAAGGTGGCTCCTACAAATGCCATCATTGCGATAAAGGAAA
GGCCATCGTTGAAGATGCCTCTGCCGACAGTGGTCCCAAAGATGGACCCCCACCCA
CGAGGAGCATCGTGGAAAAAGAAGACGTTCCAACCACGTCTTCAAAGCAAGTGGAT
TGATGTGATATCTCCACTGACGTAAGGGATGACGCACAATCCCACTATCCTTCGCAA
GACCCTTCCTCTATATAAGGAAGTTCATTTCATTTGGAGAGAACACGGGGGACTCTA
GAGGATCC
70