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Hyper-lignified root systems as a carbon sink in Arabidopsis thaliana 2009

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      HYPER‐LIGNIFIED
ROOT
SYSTEMS
AS
A
CARBON
SINK
IN
ARABIDOPSIS
THALIANA
    by   ADRIENNE JULIANA NYE  B. Sc., The University of Victoria, 2005      A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE  in  THE FACULTY OF GRADUATE STUDIES  (Plant Science) 
 
 
 
 
 
 
 THE
UNIVERSITY
OF
BRITISH
COLUMBIA

(Vancouver)





October
2009

 
 ©
Adrienne
Juliana
Nye,
2009
 
 ii
 Abstract
 
Lignified
 plant
 cell
 walls
 represent
 an
 immense
 carbon
 sink
 to
 offset
 rising
atmospheric
 carbon
 dioxide
 (CO2)
 due
 to
 the
 chemical
 stability
 and
 structural
diversity
 of
 the
 bonds
 formed
 between
 lignin
 subunits,
 making
 it
 the
 slowest
decomposing
 component
of
dead
vegetation.
This
 thesis
 explores
 the
 feasibility
of
increasing
 lignin
 deposition
 in
 roots
 through
 overexpression
 of
 SND1
 (Secondary
Wall
Associated
NAC
Domain
Protein
1),
a
key
transcriptional
activator
involved
in
regulating
secondary
wall
biosynthesis
in
fibres,
under
the
control
of
two
different
putative
root‐specific
promoters,
4‐coumarate:CoA
ligase
1
(4CL1)
and
glutathione
S‐transferase‐tau
 class
 19
 (GSTU19).
 Transgenic
 plants
 were
 analyzed
 at:
 1)
 the
molecular
level
(transcription
of
lignin
pathway
genes
and
regulatory
transcription
factors
 (TFs)
 involved
 in
 cell
 wall
 biosynthesis),
 2)
 the
 chemical
 level
 (lignin
content)
 and
 3)
 the
 plant
 growth
 and
 development
 level
 (phenotyping
 and
microscopy).
 
Results
 showed
 that:
 i)
SND1
was
overexpressed
 in
a
 tissue‐specific
manner
 in
roots,
 ii)
SND1
overexpression
caused
an
upregulation
of
 its
previously
known
 direct
 downstream
 targets,
 iii)
 SND1
 overexpression
 did
 not
 result
 in
 a
modification
 of
 indicative
 lignin
 biosynthetic
 pathway
 genes
 in
 roots,
 iv)
 plants
overexpressing
SND1
in
roots
generally
produced
plants
with
decreased
total
lignin
content,
 v)
 plants
 overexpressing
 SND1
 in
 roots
 generally
 showed
 an
 increase
 in
lateral
root
density,
and
vi)
seed
traits,
plant
growth
and
development,
plant
height
and
lignin
deposition
patterns
in
roots
remained
unaltered.

Misregulation
of
SND1
in
roots
did
not
result
in
the
predicted
increase
in
lignin
deposition
patterns
in
this
organ.
 
 iii
 Table
of
Contents
 Abstract.......................................................................................................................................ii
 Table
of
Contents................................................................................................................... iii
 List
of
Tables ............................................................................................................................. v
 List
of
Figures...........................................................................................................................vi
 Abbreviations .........................................................................................................................vii
 Acknowledgements............................................................................................................. viii
 Research
and
material
contributions.....................................................................................viii
 Support
and
guidance.....................................................................................................................ix
 1.
 Introduction ......................................................................................................................1
 1.1
 Global
climate
change
and
mitigating
global
carbon
emissions ............................1
1.1.1
 The
global
carbon
cycle
and
the
role
of
plants
as
terrestrial
carbon
sinks ............3
1.1.2
 Increasing
agricultural
soil
carbon
stocks
through
carbon
sequestration .............4
1.1.3
 Root‐derived
soil
carbon ..............................................................................................................7
1.1.4
 Arabidopsis
thaliana
as
a
model
organism............................................................................8
 1.2
 Secondary
cell
walls
and
the
importance
of
lignin
in
vascular
plant

biology...9
1.2.1
 Lignin
biosynthesis ......................................................................................................................12
1.2.2
 Lignin
as
a
carbon
sink ...............................................................................................................16
1.2.3
 Lignin
modification
via
the
monolignol
biosynthetic
pathway ................................17
 1.3
 Transcription
factors
as
tools
for
metabolic
engineering
in
plants................... 17
1.3.1
 The
role
of
transcription
factors
in
the
regulation
and
modification
of
lignin


 biosynthesis.....................................................................................................................................18
 1.4
 Root­specific
and
inducible
gene
expression
systems............................................ 23
1.4.1
 Herbicidal
safeners
as
inducers
of
root‐specific
gene
expression...........................26
 1.5
 Project
rationale
and
thesis
objectives........................................................................ 27
 2.
 Materials
and
Methods ............................................................................................... 30
 2.1
 Organ­specific
expression
of
candidate
gene
and
promoters.............................. 30
 2.2
 Cis­element
analysis
of
candidate
promoters............................................................ 31
 2.3
 Preparation
of
the
4CL1pro­SND1
gene
expression
constructs
and

transgenic
 
 plants ....................................................................................................................................... 31
 2.4
 Preparation
of
the
GSTU19pro­SND1
gene
expression
constructs
and

 
 transgenic
plants ................................................................................................................. 35
 2.5
 Molecular
analysis
of
transgenic
plants ...................................................................... 38
2.5.1
 Reverse
transcription‐PCR
of
direct
downstream
targets
of
SND1 ........................38
2.5.2
 Reverse
transcription‐PCR
of
lignin
biosynthetic
pathway
enzymes ....................39
 2.6
 Determination
of
lignin
content
in
transgenic
plants
overexpressing

SND1 . 40
2.6.1
 Plant
growth
conditions.............................................................................................................40
2.6.2
 Rapid,
micro
scale,
acetyl
bromide‐based
method
for
lignin
content
analysis..41
2.6.3
 Klason
lignin
or
72%
(v/v)
H2SO4
acid
procedure
and
carbohydrate
analysis .42
 2.7
 Starch
analysis...................................................................................................................... 43
 2.8
 Phenotypic
analysis
of
transgenic
plants.................................................................... 44
2.8.1
 Seed
phenotyping..........................................................................................................................44
2.8.2
 Root
growth
and
lateral
root
density ...................................................................................44
2.8.3
 Plant
growth
and
height.............................................................................................................45
2.8.4
 Microscopy.......................................................................................................................................45
 
 iv
 3.
 Results .............................................................................................................................. 47
 3.1
 Organ­specific
expression
of
candidate
gene
and
promoters.............................. 47
 3.2
 Cis­regulatory
element
analysis
of
candidate
promoters...................................... 51
 3.3
 SND1
overexpression
in
transgenic
plants ................................................................. 55
 3.4
 Molecular
analysis
of
transgenic
plants
overexpressing
SND1 ........................... 56
 3.5
 Determination
of
lignin
content
in
transgenic
plants
overexpressing


SND1 59
3.5.1
 Determination
of
lignin
content
in
transgenic
plants
overexpressing
SND1
by


 rapid
micro‐scale
acetyl
bromide
method .........................................................................59
3.5.2
 Cellulose,
starch
and
Klason
lignin
analysis ......................................................................60
 3.6
 Phenotypic
analysis
of
transgenic
plants
overexpressing
SND1 ......................... 63
3.6.1
 Seed
phenotyping..........................................................................................................................63
3.6.2
 Root
growth
and
lateral
root
density ...................................................................................64
3.6.3
 Plant
growth
and
height.............................................................................................................66
3.6.4
 Microscopy.......................................................................................................................................68
 4.
 Discussion ....................................................................................................................... 71
 5.
 Conclusions
and
Future
Directions ........................................................................ 98
 Bibliography.........................................................................................................................103
 Appendices ...........................................................................................................................112
 Appendix
A.
 Primary
sequences
of
gene
expression
constructs ..............................112
 Appendix
B.
 Cis­acting
DNA
regulatory
element
analysis
of
At4CL1
and
AtGSTU19
 
 promoters...........................................................................................................116
 Appendix
C.
 Primer
sequences ............................................................................................119
 Appendix
D.
 Media,
Buffers
and
Reagent
Stocks............................................................121
 Appendix
E.
 One­way
analysis
of
variance
(ANOVA)
for
average
seed
weight



and
 
 lateral
root
density..........................................................................................123




















 
 v
 List
of
Tables
 
 Table
1.
 Candidate
genes
whose
promoters
have
the
potential
to
drive
root­
 
 
 specific
transgene
expression.............................................................................48
 
 Table
2.

 Cis­acting
DNA
regulatory
elements
located
2000
bp
upstream
of
the

 
 
 transcription
start
site
of
At4CL1
(At1g51680).............................................53
 
 Table
3.
 Cis­acting
DNA
regulatory
elements
located
2000
bp
upstream
of
the

 
 
 transcription
start
site
of
AtGSTU19
(At1g78380).......................................54
 
 Table
4.

 Cell
wall
composition
of
roots
from
empty
vector
and
transgenic
lines

 
 
 overexpressing
SND1.............................................................................................62
 
 Table
5.
 Summary
of
cis­acting
regulatory
DNA
elements
associated
with
root­
 
 
 specific
gene
expression.......................................................................................78
 
 Table
6.

 Cis­acting
DNA
regulatory
element
analysis
of
At4CL1,
2000bp

 
 
 
 upstream
of
the
transcription
start
site........................................................116
 
 Table
7.

 Cis­acting
DNA
regulatory
element
analysis
of
AtGSTU19,
2000bp

 
 
 upstream
of
the
transcription
start
site........................................................117
 
 Table
8.

 List
of
all
primer
sequences
used
for
PCR,
reverse
transcription­PCR

 
 
 and
sequencing......................................................................................................119





















 
 vi
 List
of
Figures

 Figure
1.
 Monolignols
and
Lignin.........................................................................................13
 
 Figure
2.
 The
Phenylpropanoid
Pathway..........................................................................15
 
 Figure
3.
 Phylogenetic
tree
of
five
closely
related
NAC
domain
proteins...............19
 
 Figure
4.
 Schematic
diagram
of
the
SND1
overexpression
constructs
in

 
 
 
 pPZP211......................................................................................................................33
 
 Figure
5.


 Genevestigator
heat
map
of
candidate
genes
whose
promoters

 
 
 have
the
potential
to
drive
root­specific
transgene
expression..............49
 
 Figure
6.
 Organ­specific
expression
of
candidate
gene
and
promoters
from

 
 
 four­week­old
Arabidopsis
plants......................................................................51
 
 Figure
7.
 Transcriptional
analysis
of
T2
generation
plants
overexpressing

 
 
 SND1
using
RTPCR...................................................................................................55
 
 Figure
8.

 Transcriptional
analysis
of
transcription
factors
known
to
be

 
 
 
 direct
targets
of
SND1.............................................................................................57
 
 Figure
9.
 Reverse
transcription­PCR
analysis
of
genes
involved
in
lignin

 
 
 
 biosynthesis...............................................................................................................58
 
 Figure
10.
 Lignin
content
in
transgenic
Arabidopsis
plants
overexpressing

 
 
 
 SND1.............................................................................................................................60
 
 Figure
11.
 Seed­related
phenotypes
of
T3
generation
seeds
from
transgenic

 
 
 and
empty
vector
constructs...............................................................................64
 
 Figure
12.
 Primary
root
extension,
lateral
root
formation
and
number
of

 
 
 lateral
roots
per
cm
(lateral
root
density)
of
14­day­old
seedlings.......66
 
 Figure
13.

 Plant
growth
and
height
time­course
experiment
for
transgenic

 
 
 Plants
overexpressing
SND1
and
empty
vector
lines..................................67
 
 Figure
14.

 Wax­embedded
root­hypocotyl
cross
sections
of
SND1
overexpressors

 
 
 and
empty
vector
control
lines...........................................................................69
 
 Figure
15.
 Auto­fluorescence
of
lignin
in
root­hypocotyl
cross­sections..................70


 Figure
16.
 One­way
ANOVA
statistical
analysis
to
determine
differences
in


 
 
 average
seed
weight
between
genotypes......................................................123
 
 Figure
17.

 One­way
ANOVA
statistical
analysis
to
determine
differences
in


 
 
 average
number
of
lateral
roots
between
genotypes...............................124

 
 vii
 Abbreviations

 4CL
 4‐coumarate:CoA
ligase
 HCT
 p‐hydroxycinnamoyl‐CoA:
quinate/shikimate
4‐hydroxycinnamoyltransferase
4CL1
 4‐coumarate:CoA
ligase
1
 KNAT
 Knotted1‐like
TALE
homeodomain
protein
6xHis
 hexameric
histidine
tag
 LB
 Luria‐Bertani
ANACO12
 Arabidopsis
NAC
domain
containing
protein
12
 LRD
 lateral
root
density
ARF
 auxin
response
factor
 LRP
 lateral root primordium 
ASF‐1
 nuclear
activating
sequence‐1‐binding
factor
 Mya
 million
years
ago
ATAF
1/2
 arabidopsis
transcription
activation
factor
 mRNA
 messenger
RNA
bp
 base
pair
 MS
 Murashige
and
Skoog
C
 carbon
 MYB
 v‐myb
myeloblastosis
viral
oncogene
homolog
(avian)
C3H
 4‐coumarate‐3‐hydroxylase
 NAC
 NAM,
ATAF1/2
and
CUC2
C4H
 cinnamate‐4‐hydroxylase
 NAM
 no
apical
meristem
CAD
 cinnamyl
alcohol
dehydrogenase
 
 
CCR
 cinnamoyl‐CoA‐reductase
 NST1
 NAC
secondary
wall
thickening
promoting
factor
1

CCoAOMT
 caffeoyl‐CoA
3‐O‐methyltransferase
 NST2
 NAC
secondary
wall
thickening
promoting
factor
2
cDNA
 complementary

deoxyribonucleic
acid
 NST3
 NAC
secondary
wall
thickening
promoting
factor
3
CDS
 coding
sequence
 OD
 optical
density
CO2
 carbon
dioxide
 ORF
 open
reading
frame
CoA
 coenzyme
A
 PAL
 phenylalanine
ammonia‐lyase
COMT
 caffeic
acid/5‐hydroxyconiferaldehyde
O‐methyltransferase
 PCR
 polymerase
chain
reaction
CUC2
 cup‐shaped
cotyledon
2
 RNA
 ribonucleic
acid
DNA
 deoxyribonucleic
acid
 RT
 reverse
transcription
F5H
 ferulate‐5‐hydroxylase
 S
 syringyl
(lignin)
G
 guaiacyl
(lignin)
 SND1
 Secondary
Wall
Associated
NAC
Domain
Protein
1
GHGs
 greenhouse
gases
 SOC

 soil
organic
carbon
GPI
 glycosylphosphatidylinositol
 SIC
 soil
inorganic
carbon
GSH
 glutathione
 SURE
 sulfur‐responsive
element

GSTU19
 glutathione
S‐transferase‐tau
class
19
 TF
 transcription
factor
Gt
 gigatonnes
 UGT
 UDP‐glucosyltransferase
GUS
 beta‐glucuronidase
 VND6
 Vascular‐related
NAC‐Domain
6

H
 hydroxyphenyl
(lignin)
 VND7
 Vascular‐related
NAC‐Domain
7


 
 viii
 Acknowledgements
 Research
and
material
contributions
I
 would
 like
 to
 begin
 by
 expressing
my
 sincerest
 gratitude
 to
my
 supervisor,
 Dr.
Brian
Ellis,
who
has
proven
to
be
an
exceptional
mentor
in
guiding
me
through
my
master’s
 degree.
 His
 warm
 and
 approachable
 nature,
 along
 with
 his
 exceedingly
brilliant
 contributions,
 thoughtful
 insights
and
endless
encouragement
 throughout
my
research,
has
inspired
me
to
grow
and
develop
on
both
a
personal
and
academic
level.
 
 This
 work
 would
 not
 have
 been
 possible
 without
 the
 guidance,
 ideas,
suggestions
and
support
offered
by
my
committee
members,
Dr.
Leonard
Foster
and
Dr.
 James
 Kronstad,
 whose
 time
 and
 energy
 devoted
 to
 improving
 my
 research
project
 is
deeply
appreciated.
 
 I
acknowledge
Dr.
Patrick
von
Aderkas
and
the
 late
Yousuf
 Ebrahim
 for
 recognizing
my
 potential
 and
 encouraging
me
 to
 pursue
 this
master’s
 degree.
 
 In
 addition,
 I
 would
 like
 to
 thank
Margaret
 Ellis,
 our
 lovely
 lab
manager,
for
her
excellent
ordering
skills,
organizational
prowess
and
commitment
to
 ensuring
 that
 lab
 affairs
 run
 smoothly.
 
 I
 am
 indebted
 to
 my
 student
 and
 lab
colleagues,
Hardy
Hall,
QingNing
Zeng,
Apurva
Bhargava,
Ankit
Walia,
Doris
Vong,
Jia
 Cheng,
 JinSuk
 Lee,
 Earl
 Alikpala,
 and
 Dr.
 Jun
 Chen
 for
 their
 endless
 patience,
moral
support
and
generous
friendship
offered
while
teaching
me
new
techniques,
answering
 my
 many
 questions
 and
 allowing
 me
 above
 all
 to
 be
 myself.
 
 Their
contribution
 to
 stimulating
 conversations
 over
 coffee
 and
 shared
meals,
 company
during
 long
 hours
 poured
 over
 gels
 and
 petri
 dishes,
 priceless
 involvement
 in
several
 lab
adventures
and
eventful
musical
collaborations,
made
my
time
pass
by
here
in
somewhat
of
a
bliss,
providing
me
with
memories
to
last
a
lifetime.
I
would
 
 ix
 like
to
recognize
the
 following
people
 for
 their
 invaluable
contributions
to
helping
me
complete
my
thesis
research:
 Jim
Guo
of
 the
Chen
Lab
for
devoting
his
 time
to
teach
me
how
to
use
the
microscope,
Zorica
Kotur
of
the
Glass
Lab
for
her
help
and
guidance
with
the
hydroponics,
Julia
Nowak
of
the
Cronk
Lab
for
aiding
me
with
the
hand
 sectioning,
 staining
 and
 microscopy
 of
 my
 transgenic
 plant
 lines,
 Dr.
 Sarah
McKim
 of
 the
 Haughn
 Lab
 for
 assistance
 with
 the
 wax
 embedding
 protocol,
microtome
 sectioning
 and
 staining
 of
 my
 transgenic
 plant
 lines,
 Lifang
 Zhao
 for
providing
me
with
the
pPZP211
empty
vector
seed
lines,
Xue
Feng
Chang
(Harry)
of
the
Beatson
Lab
at
BCIT
for
his
 immense
involvement
 in
the
acetyl
bromide
 lignin
analysis,
 Vicki
 Maloney
 of
 the
Mansfield
 Lab
 for
 her
 amazing
 contribution
 to
 the
Starch
and
Klason
lignin
analyses,
and
to
Paul
Lee
(undergraduate
in
the
Ellis
Lab)
for
 his
 enthusiasm
 and
 painstaking
 commitment
 to
 helping
 me
 with
 seed
phenotyping,
germination
assays
and
RNA
extractions
in
the
summer
of
2009.


 Support
and
guidance

A
very
heartfelt
 thanks
goes
out
to
my
Mom
and
Dad
for
their
 immeasurable
 love,
generosity,
encouragement
and
financial
support
throughout
my
life.
Their
selfless
acts
of
kindness
and
tireless
ability
to
provide
me
with
whatever
I
need,
constantly
remind
me
 to
 remain
 humble
 and
 be
 grateful
 for
 everything
 that
 is
 given
 to
me.

Without
providing
me
with
the
safe
and
loving
environment
needed
for
growth
and
self‐discovery
as
I
was
growing
up
to
the
hour‐long
phone
conversations
and
visits
home
throughout
my
degree,
I
would
not
be
who
I
am
today
and
for
that
am
forever
in
their
debt.

I
feel
honoured
to
share
my
genes
(and
my
birth
date)
with
my
sister,
Gabrielle,
whose
love
and
support
always
seem
to
show
up
at
exactly
the
right
time.




 
 x
 Her
 belief
 in
me
 and
my
 ability
 to
 succeed
 throughout
my
 graduate
 studies
 have
given
me
 the
 strength
 to
 pull
 through
 to
 the
 end.
 
 I
 would
 like
 to
 convey
 a
 very
special
message
 to
 all
my
 friends,
who
have
made
my
 time
here
 both
memorable
and
delectably
fun.

To
the
ones
who
were
there
through
all
the
ups
and
downs,
ins
and
outs,
who
listened
when
I
needed
to
vent,
who
lifted
me
up
when
I
was
down
and
who
provided
me
with
the
courage
I
needed
to
deal
with
everyday
life,
 I
raise
my
glass
to
you!

Finally,
I
would
like
to
dedicate
this
thesis
to
my
late
grandmother,
Anyu,
for
always
believing
in
me
but
more
importantly
showing
me,
by
example,
what
it
means
to
be
a
true
Survivor.

I
am
deeply
grateful
for
the
contribution
she
has
made
to
my
life
as
a
caregiver,
friend
and
kindred
spirit.

Her
life
has
inspired
me
in
more
ways
than
I
can
express;
she
was
the
embodiment
of
strength,
self‐empowerment
and
above
all
grace.

Her
love,
wisdom
and
spirituality
have
always
taught
me
to
be
true
to
myself,
a
theme
that
has
become
the
driving
force
behind
all
 that
I
do,
all
 that
I
am
and
is
ultimately
what
propels
me
forward
into
the
future,
with
no
regrets.





 
 1
 1.
 Introduction
 1.1
 Global
climate
change
and
mitigating
global
carbon
emissions

Our
 planet
 is
 habitable
 due
 to
 its
 proximity
 to
 the
 sun
 and
 to
 the
 layer
 of
 gases
surrounding
it,
which
we
have
come
to
know
as
our
atmosphere
(Karl
&
Trenberth,
2003).
 
 
 This
 natural
 greenhouse
 effect
 results
 from
 the
 presence
 of
 a
 particular
combination
of
atmospheric
gases
including
nitrogen,
oxygen,
argon,
carbon
dioxide
(CO2)
and
other
trace
gases
(Karl
&
Trenberth
2003).
 
 
These
gases
act
as
a
sort
of
insulating
 blanket,
 trapping
 solar
 energy
 as
 heat
 and
 regulating
 average
 global
surface
temperatures
within
a
range
suitable
for
life
to
evolve
(Li
et
al.
2009).



Life
 on
 Earth
 as
 we
 know
 it
 relies
 greatly
 on
 the
 balance
 of
 these
 important
greenhouse
 gases
 (GHGs).
 
 Through
 both
 human
 endeavors
 and
 natural
 fluxes
through
the
Earth
system,
it
is
now
acknowledged
that
atmospheric
CO2
levels
have
increased
 about
 35%
 since
 the
 early
 ages
 of
 industrialization
 (Karl
 &
 Trenberth
2003;
Millard
 et
 al.
2007).
 
What’s
more,
 is
 that
 roughly
 half
 of
 the
 CO2
 released
since
the
Industrial
Revolution
remains
in
the
atmosphere
while
the
other
half
has
been
sequestered
 in
the
ocean
as
well
as
terrestrial
ecosystems
(Karl
&
Trenberth
2003;
Millard
et
al.
2007;
Raven
&
Karley
2006).

It
has
been
hypothesized
that
one
result
of
 this
 increase
 in
GHGs
 (atmospheric
CO2,
methane
and
nitrous
oxide)
 is
 a
pattern
 of
 climate
 change
 phenomena
 that
 includes
 but
 is
 not
 limited
 to:
 rises
 in
global
 surface
 temperatures,
 increased
 occurrence
 of
 extreme
 weather
 events,
increased
 incidence
 and
 intensity
 of
 wild
 fires,
 shifting
 of
 ecosystems,
 rising
 sea
 
 2
 levels
 and
 changes
 in
 disease
 transmission
 dynamics
 (Lal
 2008).
 
 In
 addition,
anthropogenic
activities
 such
as
deforestation,
 fossil
 fuel
 combustion,
 altered
 land
use
 through
 urbanization,
 wetland
 draining,
 soil
 cultivation
 and
 biomass
 burning
have
 also
 had
 a
 large‐scale
 impact
 on
 terrestrial
 surface
 characteristics
 and,
 by
extension,
on
climate
change
(Lal
2008;
Karl
&
Trenberth,
2003).

As
a
result,
there
is
growing
 interest
 in
stabilizing
 increases
 in
GHGs
with
 the
goal
of
mitigating
 the
risks
associated
with
global
climate
change
(Lal
2008).

An
introduction
to
all
of
the
mitigation
strategies
proposed
 for
 lowering
GHG
emissions
 is
beyond
the
scope
of
this
 thesis;
 therefore,
 only
 those
 related
 to
 carbon
 (CO2)
 emissions
 will
 be
mentioned.
 
 The
 reason
 being
 that
 of
 all
 the
 GHGs
 contributing
 to
 global
 climate
change,
CO2,
next
to
water
vapour,
is
considered
one
of
the
most
important
(Malhi
et
 al.
2002).




There
are
currently
three
broad
categories
of
mitigation
strategies
and
practices
for
lowering
 CO2
 emissions:
 (i)
 to
 reduce
 global
 energy
 use,
 (ii)
 to
 reduce
 emissions,
and
(iii)
to
enhance
removal
via
carbon
sequestration
(Lal
2008;
Smith
et
al.
2008).


The
 last
 of
 these
 approaches
 has
 garnered
 the
 attention
 of
 both
 scientists
 and
politicians
as
an
effective
strategy
 for
mitigating
GHG
emissions
 (Mondini
&
Sequi
2008).
 
 As
 a
 result
 of
 this
 interest,
 four
main
 types
 of
 carbon
 sequestration
 have
been
 proposed:
 ocean
 storage,
 geological
 storage,
 biomass
 storage
 and
 mineral
carbonation
 (Oelkers
 &
 Cole
 2008).
 In
 conclusion,
 carbon
 sequestration
 is
 by
 no
means
 instantaneous
 and
 consideration
must
 be
 given
 to
 the
 fact
 that
 strategies,
practices
and
techniques
take
time
to
develop
and
implement.


 
 3
 
 1.1.1 The
global
carbon
cycle
and
the
role
of
plants
as
terrestrial
carbon
sinks


In
 order
 to
 develop
 workable
 strategies
 for
 mitigating
 global
 climate
 change
 we
must
first
understand
how
the
global
carbon
(C)
cycle
works
(Lal
2008).

The
global
C
cycle
is
typically
thought
of
as
an
interconnected
flow
of
C
through
four
principal
reservoirs:
 
the
terrestrial
biosphere,
the
oceans,
fossil
carbon
and
the
atmosphere

(Schimel
1995;
Oelkers
&
Cole,
2008).
 
 
 
 In
 the
 terrestrial
biosphere,
 a
number
of
organisms
 (cyanobacteria,
 green
 algae
 and
 land
 plants)
 have
 specialized
mechanisms
 that
allow
 for
absorption
of
CO2
 into
 their
cells.
 
With
 the
addition
of
water
 and
 energy
 from
 solar
 radiation,
 they
 use
 photosynthesis
 to
 chemically
convert
 CO2
 to
 carbohydrates
 (Black
 1973).
 
 Conversely,
 CO2
 and
 energy
 can
 be
released
from
terrestrial
ecosystems
by
the
process
of
respiration.

This
involves
the
metabolic
 breakdown
 of
 C‐based
 organic
 molecules
 primarily
 into
 gaseous
 CO2,
among
other
byproducts.

Every
year,
respiration
returns
almost
half
of
the
CO2
that
is
 absorbed
 by
 photosynthesis
 to
 the
 atmosphere
 (Falkowski
 et
 al.
 2000;
 Black
1973).
 The
movement
 of
 atmospheric
 C
 through
 photosynthesis,
 respiration,
 and
back
 to
 the
 atmosphere
 is
 considerable,
 and
 this
 flux
 produces
 notable
 annual
fluctuations
in
atmospheric
CO2
concentrations
(Falkowski
et
al.
2000).


Photosynthetic
organisms
play
a
significant
role
in
the
global
C
cycle
and
over
time,
significant
amounts
of
C
can
be
stored
or
released
from
terrestrial
biomes
(Schimel
1995).
 
 For
 example,
 changes
 in
 land
 use
 can
 greatly
 contribute
 to
 carbon
source/sink
 dynamics,
 as
 demonstrated
 by
 the
 accumulation
 of
 C
 in
 the
 living
 
 4
 tissues
of
new
plant
growth
and
within
the
soil
of
regenerating
forests
following
the
abandonment
 of
 agricultural
 lands,
 causing
 a
 net
 decrease
 in
 contributions
 to
atmospheric
CO2
concentrations
(Schimel
1995).
 
 
 
 In
other
words,
as
atmospheric
CO2
 increases,
 terrestrial
 plants
 can
 become
 a
 potential
 sink
 for
 anthropogenic
carbon
 (Falkowski
 et
 al.
 2000).
 
 These
 net
 returns
 and
 losses
 (or
 fluxes)
 of
 C
between
 the
 four
 previously
 mentioned
 C
 reservoirs
 are
 known
 as
 the
 global
 C
budget
 (Schimel
 1995).
 
 Although
 terrestrial
 ecosystems
 have
 the
 potential
 to
mitigate
 rising
 atmospheric
 CO2
 levels
 in
 the
 coming
 decades,
 there
 is
 still
considerable
 uncertainty
 surrounding
 how
 these
 ecosystems
 will
 respond
 to
 the
combined
effects
of
higher
CO2
concentrations,
higher
temperatures,
and
changes
in
soil
 dynamics
 (Falkowski
et
al.
 2000).
 
 In
order
 to
predict
how
 these
 sources
 and
sinks
will
behave
 in
 the
 future,
 it
 is
 crucial
 that
we
enhance
our
understanding
of
how
 plants
 will
 respond
 to
 the
 foreseeable
 increases
 in
 human‐derived
 CO2
emissions
(Lal
2008;
Raven
&
Karley
2006).


 1.1.2
 Increasing
agricultural
soil
carbon
stocks
through
carbon
sequestration


Since
ratification
of
the
Kyoto
Protocol,
most
C
mitigation
strategies
have
focused
on
the
use
of
C
sinks
(natural
or
manmade
carbon
reservoirs)
as
a
form
of
C
offset,
and
this
 focus
has
 increased
general
awareness
of
 carbon
sink
significance
 (Lal
2008).


In
 attempting
 to
 balance
 the
 global
 C
 budget,
 future
 economic
 growth
 would
 be
based
on
a
 ‘carbon
neutral’
strategy,
or
rather
a
 ‘no
net
increase’
 in
atmospheric
C
(Lal
2008).
 
Therefore,
of
interest
to
those
involved
with
the
Kyoto
Protocol
is
any
mitigation
practice
that
increases
C
input
via
photosynthesis
or
slows
the
return
of
stored
C
via
 respiration
or
 fire
 (Smith
et
al.
2008).
 
These
strategies
consequently
 
 5
 offer
the
potential
to
 ‘sequester’
C
or
build
C
 ‘sinks’
and
will
 likely
be
a
focal
point
for
 future
 approaches
 to
mitigating
 climate
 change
 (Smith
 et
 al.
 2008).
 
 In
 short,
carbon
sequestration
is
the
process
by
which
the
terrestrial
C
sink
generates
a
net
removal
of
CO2
from
the
atmosphere.

The
capture,
transport
and
final
deposition
of
carbon,
via
carbon
sequestration,
are
largely
 dependent
 upon
 a
 complex
 set
 of
 biochemical
 and
 chemical
 processes
(Oelkers
&
 Cole
 2008).
 
 
More
 specifically,
 sequestering
 C
 represents
 a
metabolic
dead
end,
inhibiting
its
reusability
by
terminating
its
physiological
activity
(Millard
 et
al.
2007),
and
more
securely
storing
it
in
other
more
long‐lived
C
reservoirs
(Lal
2004).



In
 the
 face
of
 increasing
 carbon
emissions,
particular
emphasis
 is
being
placed
on
this
process
of
carbon
sequestration
(Lal
2008).


For
example,
roughly
a
third
of
the
terrestrial
 land
 surface
 is
 dominated
 by
 agricultural
 lands
 (crops
 or
 planted
pastures)
whose
soils
are
capable
of
acting
as
either
carbon
sources
or
sinks
(Smith
 et
al.
2008).

Of
the
~2500
gigatons
(Gt)
of
worldwide
soil
C,
there
is
roughly
1550
Gt
of
soil
organic
carbon
(SOC)
and
950
Gt
of
soil
inorganic
carbon
(carbonates)
(Lal
2004).
 
 Furthermore,
 the
 global
 soil
 C
 pool
 is
 considerable
 compared
 to
 the
atmospheric
C
pool
of
760
Gt
and
the
biotic
pool
of
560
Gt,
(Lal
2004).

However,
soil
carbon
 sequestration
 is
 a
 trickier
 long‐term
 strategy
 for
 climate
 mitigation
 as
opposed
 to
 reducing
 carbon
 emissions,
 given
 that
 it
 could
 be
 difficult
 to
measure
and
verify
the
amount
of
carbon
sequestered
below
ground
(Mondini
&
Sequi
2008).

 
 6
 Nevertheless,
 strategies
 to
 improve
 soil
 carbon
 stocks
 are
 appealing
 as
 part
 of
 an
integrated
 sustainability
 approach
 since
 enhanced
 agricultural
management
 often
brings
with
it
an
array
of
other
desirable
environmental
and
economic
outcomes
in
addition
 to
mitigating
 climate
 change
 (Mondini
&
 Sequi
 2008;
 Smith
 and
 Falloon,
2005).
 
 
 Lal
 (2008)
 summarizes
 these
 soil
 C
 sequestration
 benefits
 as
 including
enhanced
soil
quality,
improved
soil
productivity,
decreased
risk
of
soil
erosion
and
sedimentation,
 and
 reduced
 water
 contamination
 and
 eutrophication.
 These
potential
 outcomes
 also
 demonstrate
 that
 soil
 C
 sequestration
 could
 represent
 an
approach
 to
 attain
 food
 security
 (Johnson
 et
 al.
 2007).
 
 In
 addition
 to
 both
environmental
and
economic
benefits,
C
sequestration
is
attractive
for
one
another
reason:
 it
 is
 likely
 to
 be
 the
 most
 cost‐effective
 and
 feasible
 method
 to
 lower
atmospheric
 CO2
 levels
 within
 the
 first
 20–30
 years
 that
 it
 is
 implemented,
 thus
effectively
 buying
 time
 while
 other
 technologies
 aimed
 directly
 at
 reducing
 GHG
emissions
 are
 developed
 (Mondini
 &
 Sequi
 2008).
 
 
 However,
 yearly
 increases
 in
SOC
 can
 only
 be
 sustained
 perhaps
 for
 50–100
 years,
 at
which
 point
 increases
 in
SOC
are
 likely
 to
 slow
and
ultimately
 cease
as
 the
 soil
 reaches
a
new
equilibrium.


This
emphasizes
the
point
that
C
sequestration
may
even
be
a
reversible
process
if
suitable
soil
management
practices
are
not
maintained
(Lal

2004;
Mondini
&
Sequi
2008).

Given
the
sizeable
amount
of
global
carbon
contained
within
agricultural
soils,
it
is
not
 surprising
 that
 the
 possibility
 of
 partially
 offsetting
 fossil‐fuel
 emissions
 by
sequestering
 excess
 atmospheric
 C
 within
 these
 soils
 is
 now
 being
 strongly
 
 7
 advocated
 (West
 &
 Marland
 2002).
 
 Unfortunately,
 fossil‐fuel
 emissions
 over
 the
next
 100
 years
 are
 anticipated
 to
 greatly
 exceed
 even
 the
 maximum
 amount
 of
carbon
 that
 could
 potentially
 be
 sequestered.
 
 Therefore,
 carbon
 sequestration
should
simply
be
seen
as
a
modest
contribution
to
a
much
larger
mitigation
plan
and
not
as
a
replacement
for
the
development
of
new
energy
supplies,
improved
energy
use
strategies
and
technological
innovations
required
to
stabilize
concentrations
of
atmospheric
CO2
(Malhi
et
al.
2002).
 1.1.3
 Root‐derived
soil
carbon

In
 terrestrial
 plants,
 the
 rhizosphere
 (the
 soil
 that
 immediately
 surrounds
 a
 plant
root)
 encompasses
 the
 complex
 chemical,
 physical,
 and
 biological
 interactions
between
roots
and
their
surrounding
environment
(Bais
et
al.
2006).

Plant
roots
are
actively
involved
in:
soil‐microbe
interactions,
the
secretion
of
compounds
required
for
 pathogen
 defense
 and
 absorption
 of
 soil
 nutrients.
 
 Roots
 also
 play
 a
 role
 in
protecting
above
ground
tissues
 from
acidic
conditions,
heavy
metals
and
drought
(Koyama
et
al.
2005).

Studies
have
shown
that
soil
C
is
predominantly
composed
of
root
 C
 and
 that
 within
 the
 organic
 soil
 horizons,
 root‐derived
 soil
 organic
 C
generally
 decreases
 with
 depth
 (Jobbágy
 &
 Jackson
 2001;
 Rasse
 et
 al.
 2005).
 In
natural
 ecosystems,
 root‐derived
 SOC
 is
 almost
 entirely
 a
 result
 of
 materials
released
from
the
roots
of
natural
vegetation
or
crops
during
growth,
such
as
root
exudates,
 sloughed
off
 root
 tips
and
cells,
mucilage
and
by
decomposition
of
dead
roots
 (Subedi
at
 al.
 2006).
 
 There
 is
 still
 considerable
 debate
 over
 the
 amount
 of
plant
 root
 C
 that
 contributes
 to
 the
 total
 C
 pool
 in
 the
 terrestrial
 biosphere.

According
 to
 Robinson
 (2007),
 the
 best
 approximation
 of
 the
 root
 carbon
 pool
 is
 
 8
 270–280 Pg
of
 the
 total
 terrestrial
biome
C
pool
of
650 Pg
 (Subedi
at
al.
 2006).
 
A
global
root
C
reservoir
this
large
has
implications
for
land
C
sinks
as
a
response
to
a
rise
 in
 atmospheric
 CO2.
 For
 instance,
 excess
 levels
 of
 CO2
 can
 stimulate
photosynthesis
leading
to
an
estimated
20%
increase
in
plant
production,
which
in
turn
could
enhance
soil
C
input
thus
increasing
soil
C
sequestration
(De
Graaff
et
al.
2007).
 Moreover,
 this
 increase
 in
 SOC
 could
 thereby
 counterbalance
 the
 rise
 in
atmospheric
CO2
(De
Graaff
et
al.
2007).


Conversely,
an
increase
in
input
of
SOC
due
to
increased
rhizodeposition
and
root
litter
can
have
a
profound
influence
on
plant
productivity
and
root
growth
(Subedi
at
al.
2006).

It
is
worth
noting
that
more
in‐depth
measurement
 of
 the
 impacts
 of
 root‐derived
 SOC
 from
 crop
 systems
 could
make
invaluable
contributions
to
our
study
of
C
dynamics,
the
global
C
budget
and
C
sequestration
(Subedi
at
al.
2006).

 1.1.4
 Arabidopsis
thaliana
as
a
model
organism


 Arabidopsis
 thaliana,
 also
 known
 as
 thale
 cress
 or
 mouse‐ear
 cress,
 is
 a
 small
flowering
 plant
 widely
 used
 as
 a
 model
 organism
 in
 plant
 biology
 research.
 Arabidopsis
is
a
member
of
the
mustard
family
(Brassicaceae),
which
includes
many
familiar
agricultural
species
such
as
broccoli,
cabbage,
turnip,
rapeseed,
cauliflower,
brussels
 sprouts
 and
 radish.
 Arabidopsis
 itself
 is
 not
 of
 any
 major
 agricultural
importance,
 but
 it
 is
 intensively
 used
 as
 a
model
 organism
 for
 studies
 in
 genetics
and
molecular
biology
and
 is
a
close
relative
of
 canola,
a
major
 transgenic
crop
 in
Canadian
 agriculture.
 
 Arabidopsis
 can
 produce
 numerous
 self‐progeny
 in
 a
relatively
 short
 time
 period,
 and
 it
 has
 very
 limited
 growth
 space
 requirements,
which
means
that
large
populations
can
be
easily
grown
in
a
greenhouse
or
indoor
 
 9
 growth
 chamber.
 
 It
 has
 a
 relatively
 small,
 genetically
 tractable
 and
 sequenced
genome
 that
 can
 be
 manipulated
 through
 genetic
 engineering
 more
 rapidly
 and
easily
than
any
other
plant
genome
(About
Arabidopsis
2008;
Arabidopsis
thaliana
2009).
 1.2
 Secondary
cell
walls
and
the
importance
of
lignin
in
vascular
plant
 
 biology


Plant
 cell
 walls
 have
 many
 important
 functions
 such
 as,
 providing
 mechanical
strength,
 regulating
 cell
 expansion
 and
 cell
 cohesion,
 water
 conduction
 and
pathogen
 defense
 (Knox
 2008).
 
 The
 carbon‐based
 polymers,
 cellulose,
hemicellulose,
 pectin
 and
 lignin,
 are
 what
 form
 the
 strong,
 but
 flexible
macromolecular
 complexes
 of
 the
 cell
walls
 of
 higher
 plants
 
 (Weng
 et
 al.
 2008).


Cellulose,
 hemicellulose
 and
 pectin
 are
 the
 main
 carbohydrates
 comprising
 the
growing
primary
cell
wall,
while
cellulose,
xylan,
other
hemicelluloses
and
lignin
are
the
major
contributors
to
secondary
cell
walls
(Weng
et
al.
2008).

These
major
cell
wall
components
are
variable
in
their
composition
and
relative
abundance,
and
the
final
 combination
 in
 any
 given
 tissue
 often
 depends
 on
 the
 species,
 growing
 site,
climate,
age
and
part
of
the
plant
(Ko
et
al.

2009).



The
composition
of
cell
wall
components
can
be
distinguished
based
on
the
ground
tissues
that
they
are
composed
of:
i.e.
parenchyma,
collenchyma,
and
sclerenchyma.

Parenchyma
 and
 collenchyma
 cells,
 which
 possess
 primary
 cell
 walls,
 provide
structural
 support
 in
 regions
 of
 the
 plant
 body
 that
 are
 still
 growing
 whereas
sclerenchyma
tissue
has
both
primary
cell
walls
and
thickened
secondary
cell
walls.

 
 10
 For
example,
specialized
cells
involved
in
structural
support
and
water
conduction,
such
as
fibres,
are
composed
primarily
of
sclerenchyma
(Zhong
et
al.
2006;
Burk
et
 al.
2001;
Rogers
et
al.
2005;
Boerjan
et
al.
2003).

The
ability
to
resist
the
forces
of
gravity
 and/or
 tension
 associated
 with
 the
 pull
 of
 the
 water
 column
 due
 to
transpiration
 (involved
 in
 transporting
 water
 and
 solutes
 over
 long
 distances)
comes
 from
 the
 evolution
 of
 these
 specialized
 cells,
 which
 provide
 mechanical
support
to
regions
of
the
plant
body
that
have
ceased
elongation
(Rogers
et
al.
2005;
Boerjan
 et
 al.
 2003).
 
 A
 defining
 feature
 of
 these
 cells
 is
 the
 secondary
 cell
 wall,
which
 is
 formed
 in
 a
 highly
 coordinated
 manner
 by
 successive
 encrustation
 and
deposition
 of
 the
 various
 cell
wall
 constituents
 (Ko
 et
 al.
 
 2009).
 
 
 Lignin
 fills
 the
spaces
 between
 cellulose
 and
 hemicellulose,
 where
 it
 is
 covalently
 linked
 to
 the
hemicellulose
 and
 crosslinked
 to
 other
 plant
 polysaccharides
 (Weng
 et
 al.
 2008).

The
 secondary
 cell
 wall
 polysaccharides
 are
 highly
 hydrophilic
 and
 are
 easily
permeable
 to
 water
 whereas
 lignin
 is
 more
 hydrophobic.
 
 Lignification
 of
 the
secondary
 cell
wall
 thus
waterproofs
 the
 cell
wall
 and
 facilitates
 the
 transport
 of
water
and
solutes
through
the
vascular
system
(Boerjan
et
al.
2003).
 
In
summary,
lignified
secondary
cell
walls
are
essential
for
the
function
of
structurally
supportive
and
conductive
xylem
tissues.


Cell
 wall
 lignification
 emerged
 in
 the
 plant
 kingdom
 about
 430
million
 years
 ago
(Mya)
 and
 is
 considered
 to
 be
 a
 relatively
 recent
 process
 in
 the
 evolution
 of
photosynthetic
organisms,
which
developed
approximately
2000
Mya
(Boerjan
et
al.
2003).
 
 The
 ability
 to
 produce
 lignin
 is
 thought
 to
 have
 been
 crucial
 for
 the
 
 11
 adaptation
of
aquatic
plants
to
a
terrestrial
environment
where
they
were
likely
to
face
 critical
 new
 stresses
 including
 UV
 radiation,
 desiccation
 and
 attack
 by
established
and
diverse
communities
of
soil
microbes
(Emiliani
et
al.
2009).


In
fact,
deposition
of
lignin
or
rather
the
synthesis
of
monolignols,
has
been
shown
to
play
an
 essential
 role
 in
 the
 assembly
 of
 cell
 wall
 appositions
 (CWAs),
 also
 known
 as
papillae,
 which
 provide
 a
 primary
 means
 of
 defense
 against
 pathogens
 that
 are
attempting
to
penetrate
the
cell
wall
(Bhuiyan
et
al.
2009).



The
study
of
phenylpropanoid
metabolism
(the
pathway
responsible
 for
 the
 lignin
biosynthesis
as
well
as
some
other
important
secondary
metabolic
compounds)
has
been
 a
 central
 theme
 in
 plant
 biochemistry.
 In
 addition
 to
 lignin
 formation,
 the
contributions
to
plant
fitness
of
many
phenylpropanoid
pathway
intermediates
and
end
products
such
as
antioxidants,
ultra‐violet
protectants,
phytoalexins,
pigments,
aroma
 compounds
 and
 antiherbivory
 compounds,
 emphasizes
 the
 importance
 of
this
 metabolic
 system
 (Humphreys
 &
 Chapple
 2002).
 
 Moreover,
 the
phenylpropanoid
 pathway
 represents
 an
 essential
 and
 ubiquitous
 metabolic
 trait
amongst
land
plants,
since
it
supplies
vital
compounds
such
as
lignin
(essential
for
vascularization
and
xylem
formation
as
well
as
structural
support
and
stem
rigidity
out
of
water),
and
flavonoids
(essential
for
reproductive
biology
and
for
protection
against
 UV
 via
 pigment
 accumulation,
 for
 deterring
 microbial
 attack
 and
 for
modulating
 symbiotic
 plant‐microbe
 interactions
 by
 production
 of
 anti‐microbial
compounds
such
as
phytoalexins,
and
signaling
flavonoids)
(Emiliani
et
al.
2009).
 
 12
 1.2.1
 Lignin
biosynthesis


The
 coordinated
 expression
 of
 numerous
 genes
 is
 required
 for
 the
 biosynthesis,
assembly
 and
 deposition
 of
 both
 primary
 and
 secondary
 cell
 wall
 components,
including
the
determining
structural
and
chemical
specificity
of
lignified
secondary
walls
 
(Boudet
et
al.
2003).
Lignin
is
a
racemic
aromatic
polymer
that
results
from
the
oxidative
combination
of
three
p‐hydroxycinnamyl
alcohol
monomers
known
as
monolignols
 (p‐coumaryl,
 coniferyl
 and
 sinapyl
 alcohols)
 whose
 structure
 differ
only
in
the
number
of
methoxyl
groups
present
in
their
aromatic
rings
(Fig.
1
(A))
(Goujon
et
al.
2003).

While
lignins
tend
to
be
dominated
by
these
three
monolignol
components,
 there
 are
 several
 additional
monomers
 that
 are
 sometimes
 found
 in
lignin
polymers.
 
Many
naturally
 occuring
plant
 species
 contain
 lignins
derived
 in
part
from
these
other
monomers,
in
addition
to
trace
amounts
of
units
formed
from
incomplete
 or
 secondary
 reactions
 that
 occur
 during
 monolignol
 biosynthesis
(Boerjan
et
al.
2003).

 
 13
 
 Figure
1.

Molecular
structures
of
the
three
main
monolignols
and
of
a
putative
 lignin
 polymer.
 (A)
 Three
 traditional
 lignin
 precursors
 (p­coumaryl
 alcohol,
coniferyl
alcohol,
 sinapyl
alcohol)
 (Monolignol
2008)
and
(B)
a
hypothetical
 lignin
polymer
(What
Is
Wood?
2009)

Initially,
 carbon
 flux
 is
 redirected
 from
 primary
 metabolism
 to
 phenylpropanoid
biosynthesis
 through
three
enzyme‐catalyzed
reactions
(PAL,
C4H
and
4CL;
Fig.
2)
which
 transform
 L‐phenylalanine
 into
p­coumaroyl
 CoA.
 
 The
 latter
 serves
 as
 the
entry‐point
 for
 the
 two
 main
 downstream
 
 branch
 pathways,
 monolignol
 and
flavonoid
biosynthesis
(Ferrer
et
al.
2008).
 
The
synthesis
of
monolignols
 involves
consecutive
hydroxylations
of
the
aromatic
ring,

phenolic
O‐methylation
and

side‐chain
 carboxyl
 conversion
 to
 an
alcohol
 group
ultimately
 forming
 the
p‐coumaryl,
coniferyl
 and
 sinapyl
 alcohols
 (Boerjan
 et
 al.
 2003;
 Boudet
 A.‐M.
 2000).
 
 These
 
 14
 monolignols
respectively
give
rise
to
p­hydroxyphenyl
(H),
guaiacyl
(G)
and
syringyl
(S)
 lignin
 residues
 within
 the
 lignin
 polymer
 (Fig.
 2)
 (Grima‐Pettenati
 &
 Goffner
1999;
 Vanholme
 et
 al.
 2008).
 To
 produce
 the
 final
 intricate
 and
 interconnected
lignin
complex
(Fig.
1
(B)),
the
monomeric
residues
are
exported
to
the
extracellular
space
 (apoplast)
 where
 oxidative
 enzymes
 catalyze
 the
 formation
 of
 free
 radical
derivativs
 of
 the
monomers.
 
 The
 radicals
 are
 then
 coupled
 to
 the
 growing
 lignin
polymer
forming
either
carbon–carbon
or
ether
bonds
(Boudet
A.‐M.
2000;
Grima‐Pettenati
&
Goffner
1999;
Vanholme
et
al.
2008).


















 
 15
 

 
 
 
 
 Figure
 2.
 The
 Phenylpropanoid
 Pathway.
 PAL,
 phenylalanine
 ammonia‐lyase;
C4H,
cinnamate‐4‐hydroxylase;
C3H,
4‐coumarate‐3‐hydroxylase;
COMT,
caffeic
acid
3‐O‐methyltransferase;
 CCoAOMT,
 caffeic
 acid/5‐hydroxyconiferaldehyde
 O‐methyltransferase;
F5H,
ferulate‐5‐hydroxylase;
4CL,
p‐coumaroyl:CoA
ligase;
HCT,
p‐hydroxycinnamoyl‐CoA:
quinate
 shikimate
p‐hydroxycinnamoyltransferase;
CCR,
cinnamoyl‐CoA‐reductase;
 CAD,
 cinnamyl
 alcohol
 dehydrogenase;
 UGT,
 UDP‐glucosyltransferase.
(Besseau
et
al.
2007
(Figure);
Vanholme
et
al.
2008
(Caption))


 
 16
 1.2.2
 Lignin
as
a
carbon
sink


In
 addition
 to
 their
many
 important
 biological
 functions,
 lignified
 plant
 cell
 walls
represent
a
large
proportion
of
plant
biomass
in
the
terrestrial
biosphere
and
thus
an
 immense
carbon
sink
 (Boudet
et
al.
 2003).
 
Next
only
 to
cellulose,
 lignin
 is
 the
second
most
abundant
biopolymer
on
earth
(Boudet
et
al.
2003;
Grima‐Pettenati
&
Goffner
1999;
Humphreys
&
Chapple
2002).

Over
1.4x1012
kg
of
C
is
sequestered
in
terrestrial
plant
material
each
year
(Battle
et
al.
2000)
with
lignin
constituting
about
30%
 of
 that
 total
 (Humphreys
 &
 Chapple
 2002).
 
 Research
 interest
 in
 lignin
biosynthesis
and
lignin
deposition
has
been
motivated
by
the
multiple
roles
played
by
lignin
in
plant
biology,
including
management
of
abiotic
and
biotic
stress,
water
conduction,
 cell
 differentiation,
 and
 carbon
 partitioning,
 all
 of
 which
 have
 both
industrial
 and
agricultural
 importance
 (Boudet
et
al.
2003;
Humphreys
&
Chapple
2002).
 
 An
 important
 aspect
 of
 lignin
 that
 impacts
 lignocellulosic
 biomass
utilization,
 in
 both
 industry
 and
 agriculture,
 stems
 from
 the
 variable
 and
 stable
cross‐linking
of
the
various
cell
wall
components,
which
minimizes
the
accessibility
of
 cellulose
 and
hemicellulose
 to
degradative
 enzymes
 (Bhuiyan
et
al.
 2009).
 
Not
only
 is
 the
 capacity
 of
 lignin
 to
 resist
 degradation
 largely
 due
 to
 its
 unique
polymeric
structure,
but
this
structure’s
distinct
arrangement
and
representation
of
monomeric
 units
 varies
 widely
 among
 species,
 individuals
 and
 even
 within
 cell
types
of
the
same
plant
(Weng
et
al.
2008).

In
essence,
the
combination
of
chemical
stability
 and
 structural
 diversity
 of
 the
 bonds
 formed
 between
 lignin
 subunits
 is
sufficient
 to
 prevent
 complete
 degradation
 of
 the
 polymer
 by
 any
 single
 enzyme
(Weng
et
al.
2008).

This
stability
highlights
the
potential
for
lignin
to
act
as
a
long‐ 
 17
 lived
C
reservoir,
and
by
extension,
 to
serve
as
a
vehicle
 increased
carbon
storage
and
sequestration.
 1.2.3
 Lignin
modification
via
the
monolignol
biosynthetic
pathway

The
 past
 twenty
 years
 of
 research
 has
 led
 to
 significant
 insight
 into
 lignin
biosynthesis,
particularly
through
the
use
of
reverse
genetics
approaches
 in
which
expression
of
genes
encoding
individual
monolignol
and
phenylpropanoid
pathway
enzymes
has
been
altered
(Vanholme
et
al.
2008).
Generally
speaking,
in
transgenic
plants,
 the
 downregulation
 of
 PAL,
 C4H,
 4CL,
HCT,
 C3H,
 CCoAOMT,
 CCR,
 and,
 to
 a
smaller
degree,
CAD,
has
been
shown
to
have
a
major
influence
on
lignin
content
as
well
 as
 the
 ratios
 of
 H,
 G
 and
 S
 lignin,
 although
 these
 outcomes
 are
 often
accompanied
 by
 other,
 undesirable
 pleiotropic
 impacts
 on
 plant
 growth,
morphology
or
chemistry
(Anterola
&
Lewis
2002;
Vanholme
et
al.
2008).

 1.3
 Transcription
factors
as
tools
for
metabolic
engineering
in
plants

Transcriptional
 regulation
 is
 an
 important
 mechanism
 by
 which
 metabolic
pathways
and
assembly
of
cell
wall
components
in
plants
is
controlled
(Broun
2004;
Zhong
&
Ye,
2007).
Transcription
factors
(TFs)
are
regulatory
proteins
that
modify
the
 expression
 of
 specific
 sets
 of
 genes
 by
 interacting
 with
 the
 transcriptional
machinery,
 including
 chromatin
 remodeling
 proteins
 and/or
 other
 transcription
factors
 involved
 in
 transcription
 through
 sequence‐specific
 DNA
 binding
 and
protein–protein
interactions
(Broun
2004).

In
other
words,
these
proteins
are
able
to
 recognize
 and
 bind
 specific
 sequences
 in
 the
 promoter
 regions
 of
 their
 target
genes,
 thereby
 subsequently
 activating
 or
 repressing
 entire
 metabolic
 or
 
 18
 developmental
processes.
 
This
often
occurs
by
mediation
of
either
an
 increase
or
decrease
 of
 the
 encoded
 mRNA
 by
 acting
 as
 activators
 or
 repressors
 of
 gene
expression
 (Broun
 2004;
 Arce
 et
 al.
 2008).
 
 The
 role
 of
 transcription
 factors
 in
coordinated
 metabolic
 regulation
 is
 of
 great
 interest
 in
 metabolic
 engineering
because
 of
 their
 ability
 to
 control
 both
 cellular
 processes
 and
 multiple
 pathway
steps
necessary
for
metabolite
accumulation
(Broun
2004;
Petersen,
2007).

Unlike
alterations
 in
 single‐enzyme
 expression,
 the
 use
 of
 TFs
 for
metabolic
 engineering
has
 the
potential
 to
 generate
more
 complex
phenotypes
 in
 transgenic
 plants,
 as
 a
result
 of
 simultaneous
 modification
 of
 different
 transcriptionally‐regulated
pathways
(Tyo
et
al.
2007).

 1.3.1
 The
role
of
transcription
factors
in
the
regulation
and
modification
of
lignin
 
 biosynthesis

Lignin
 synthesis
 and
 deposition
 requires
 strict
 spatial
 and
 temporal
 regulation
 of
processes
occurring
during
plant
growth
and
development
(Boudet
A.‐M.
2007).

So
far,
 numerous
 studies
 suggest
 that
 several
 features
 of
 cellular
 structure
 and
metabolism,
 such
 as
 the
 cytoskeleton,
 phosphoinositide
 signaling,
glycosylphosphatidylinositol
(GPI)‐anchored
proteins,
hormones,
and
the
supply
of
sugar
nucleotides,
must
all
be
integrated
as
part
of
the
regulation
of
secondary
cell
wall
biosynthesis
and
lignin
deposition
(Zhong
&
Ye
2007).



Although
many
of
the
genes
encoding
enzymes
involved
in
lignin
biosynthesis
have
been
characterized,
little
is
known
about
the
molecular
mechanisms
underlying
the
coordinated
expression
of
 these
genes
 (Weng
et
al.
 2008).
 
However,
 the
 study
of
 
 19
 global
 patterns
 of
 gene
 expression
 by
 high‐throughput
 technologies
 has
 recently
revealed
 some
 additional
 features
 of
 the
 various
 regulatory
 networks
 through
which
this
metabolic
pathway
is
controlled
(Broun
2004).
For
example,
comparative
transcriptome
 analyses
 in
 xylem
 cells
 of
Arabidopsis
 plants
 undergoing
 secondary
growth
have
identified
a
range
of
upregulated
genes
(specifically
NAC
and
MYB
TFs)
involved
 in
 secondary
 cell
 wall
 formation,
 and
 these
 have
 provided
 an
 initial
glimpse
 of
 the
 complex
 networks
 of
 TFs
 controlling
 this
 process
 (Ko
 et
 al.
 2007;
Weng
et
al.
2008;
Zhong
&
Ye
2007;
Zhong
et
al.
2008).



A
group
of
closely
related
NAC
 domain
 proteins
 in
 Arabidopsis
 thaliana
 (Fig.
 3),
 including
 ANAC043/NST1
(NAC
 Secondary
 Wall
 Thickening
 Promoting
 Factor
 1),
 ANAC066/NST2,
ANAC012/NST3/SND1
 (Secondary
Wall
Associated
NAC
Domain
Protein
1),
VND6
(Vascular‐related
 NAC‐Domain
 6),
 and
 VND7
 are
 now
 known
 to
 be
 major
transcriptional
regulators
of
secondary
wall
biosynthesis
in
various
supporting
cell
types
in
plant
tissues
that
have
ceased
elongation
(Zhong
et
al.
2008).



 
 Figure
3.
Phylogenetic
analysis
of
five
closely
related
NAC
domain
proteins
in
 Arabidopsis
thaliana
involved
in
regulating
secondary
cell
wall
biosynthesis
in
 various
 supporting
 cell
 types.
 
 The
 full‐length
 coding
 sequences
 (CDS)
 were
aligned
using
 the
CLUSTAL
W
program
and
the
phylogenetic
 tree
was
constructed
by
 neighbor‐joining
 methods.
 
 The
 GenBank
 accession
 numbers
 for
 the
 used
sequences
are
represented
as
follows:
ANACO12/NST3/SND1
(NM_103011);
NST1
(NM_130243);
NST2


 (NM_116056);
 VND6
 (NM_125632);
 VND7
(NM_105851)
and
CUC3
(NM_106292).

 
 20
 SND1
 and
 NST1
 are
 proposed
 to
 function
 in
 a
 redundant
 manner
 to
 control
development
of
 secondary
walls
 in
 fibres
while
VND6
and
VND7,
 respectively,
are
proposed
to
regulate
metaxylem
and
protoxylem
differentiation
in
vessels
(Zhong
et
 al.
2008;
Mitsuda
et
al.
2007).
 
 In
anther
endothecium
cells,
NST1
and
NST2
were
shown
to
function
redundantly
in
regulating
secondary
wall
thickening
(Mitsuda
et
 al.
 2005).
 
 
 Overexpression
 of
 these
 NAC
 genes
 results
 in
 ectopic
 deposition
 of
secondary
walls
in
cells
not
normally
reinforced
with
lignin,
while
inhibition
of
their
functions
 via
 dominant
 repression
 or
 knockout
 results
 in
 secondary
 walls
 with
reduced
thickening
in
the
mutant
plants
(Zhong
et
al.
2008).

These
secondary
wall
NACs
are
proposed
to
act
through
a
cascade
of
downstream
TFs,
which
in
turn
lead
to
 the
 activation
 of
 secondary
 wall
 biosynthetic
 genes
 including
 SND2,
 SND3,
MYB20,
MYB42,
MYB43,
MYB46,
MYB52,
MYB54,
MYB58,
MYB63,
MYB69,
MYB85,
MYB103,
 and
 KNAT7
 (a
 Knotted1‐like
 homeodomain
 protein),
 are
 regulated
 by
SND1
(Zhong
et
al.
2006;
Zhong
et
al.
2008;
Zhong
et
al.
2007a;
Zhong
et
al.
2007b;
Zhou
et
al.
2009;
Zhong
&
Ye
2007).

Previous
 studies
 by
 Zhong
 et
 al.
 (2006)
 have
 shown
 that
 SND1
 is
 expressed
specifically
 in
 interfascicular
 fibres
 and
 xylary
 fibres
 of
 stems.
 
 Constitutive
overexpression
of
SND1
 resulted
 in
activation
of
 the
expression
of
 secondary
wall
biosynthetic
genes,
leading
to
massive
deposition
of
secondary
walls
in
cells
that
are
normally
not
lignified
(Zhong
et
al.
2006).

An
activator
is
defined
in
the
literature
as
a
DNA‐binding
protein
 that
 regulates
one
or
more
genes
by
 increasing
 the
 rate
of
transcription.
 
Ko
et
al.
(2007)
showed
that
SND1
gene
expression
was
localized
to
 
 21
 the
 procambium
 region
 of
 inflorescence
 stems
 and
 roots.
 
 They
 confirmed
 the
function
 of
 SND1
 as
 a
 transcriptional
 activator
 but
 also
 found
 that
 ectopic
overexpression
 of
 35S::SND1
 plants
 in
 Arabidopsis
 noticeably
 suppressed
secondary
 wall
 deposition
 in
 the
 xylary
 fibre.
 
 Moreover,
 they
 observed
 a
 slight
increase
in
cell‐wall
thickness
in
xylem
vessels
which
suggested
that
SND1
might
act
as
a
negative
regulator
of
secondary
wall
thickening
in
xylary
fibres.

In
contrast
to
activators,
a
negative
regulator
is
defined
in
the
literature
as
any
regulator
that
acts
to
prevent
 transcription
or
 translation.
 In
addition
 to
 the
elucidation
of
SND1
 as
a
major
transcriptional
activator
of
secondary
wall
biosynthesis,
Zhong
et
al.
(2007b)
demonstrated
 that
 the
Arabidopsis
 thaliana
MYB46
 transcription
 factor
 is
 a
 direct
target
of
SND1.
 
They
showed
that
dominant
repression
of
MYB46
caused
a
severe
decrease
in
the
secondary
wall
thickening
of
fibres
and
vessels
while
overexpression
of
this
gene
resulted
in
the
activation
of
the
cellulose,
xylan,
and
lignin
biosynthetic
pathways,
which
concurrently
 led
 to
ectopic
deposition
of
 secondary
walls
 in
cells
not
 normally
 lignified.
 
 Overexpression
 of
MYB46
 caused
 an
 upregulation
 in
 gene
expression
 among
 particular
 genes
 involved
 in
 the
 synthesis
 of
 all
 three
 major
secondary
cell
wall
components
(Weng
et
al.
2008;
Zhong
et
al.
2007b;
Zhong
et
al.
2008).

Furthermore,
the
expression
of
two
secondary
wall–associated
transcription
factors,
 MYB85
 and
 KNAT7,
 was
 highly
 upregulated
 by
 MYB46
 overexpression
demonstrating
 that
MYB46
 is
possibly
another
major
player
 in
 the
 transcriptional
network
 involved
 in
regulating
secondary
wall
biosynthesis
 in
Arabidopsis
 (Zhong
 et
al.
2007b).
 
 
In
addition,
Zhou
et
al.
(2009)
demonstrated
that
overexpression
of
MYB58
and
MYB63
 resulted
 in
 specific
 activation
of
 lignin
 biosynthetic
 genes
 and
 
 22
 simultaneous
ectopic
deposition
of
lignin
in
cells
not
normally
lignified.

MYB58
was
able
to
directly
activate
the
expression
of
lignin
biosynthetic
genes
and
a
secondary
wall–associated
laccase
(LAC4)
gene.

Furthermore,
the
SND1
homologs
NST1,
NST2,
VND6,
and
VND7
as
well
as
the
SND1
downstream
target,
MYB46,
were
also
shown
to
regulate
the
expression
of
MYB58
and
MYB63.

Their
results
suggest
that
MYB58
and
MYB63
 are
 transcriptional
 activators
 of
 lignin
 biosynthesis
 specifically
within
the
 SND1‐mediated
 transcriptional
 network
 regulating
 secondary
 cell
 wall
formation.
 
 Lastly,
 a
 recent
 high‐throughput
 study
 using
 whole‐transcriptome
analyses
by
Ko
et
al.
 (2009)
provided
 insight
 into
 the
 regulatory
 relationship
of
 a
group
 of
 transcription
 factors
 upregulated
 by
 MYB46,
 uncovering
 a
 speculative
regulatory
network
with
intricate
cross
communication.


Recently,
another
study
identified
a
novel
CCCH‐type
zinc
finger
protein,
AtC3H14,
as
 a
 potential
 master
 regulator
 of
 secondary
 wall
 biosynthesis
 operating
downstream
of
MYB46
(Ko
et
al.
2009).
 
These
studies
suggest
 that
SND1,
MYB46
and
 C3H14,
 act
 as
 key
 regulators
 of
 secondary
 cell
 wall
 deposition
 through
 their
demonstrated
ability
 to
 turn
on
 the
entire
 cellulose,
 xylan,
 and
 lignin
biosynthetic
pathways
in
transgenic
plants
(Zhong
et
al.
2008).

In
conclusion,
this
model
of
over‐arching
regulation
of
secondary
cell
wall
biosynthesis
by
SND1,
MYB46
and
C3H14,
along
with
the
discovery
of
other
TFs
upregulated
by
these
master
regulator
genes,
has
provided
an
initial
glimpse
into
the
regulatory
networks
controlling
secondary
cell
wall
formation
(Zhong
et
al.
2007a;
Zhong
et
al.
2007b;
Zhou
et
al.
2009).



 
 23
 As
 mentioned
 earlier,
 the
 amount
 of
 global
 carbon
 contained
 within
 agricultural
soils,
 offers
 the
 potential
 to
 partially
 offset
 fossil‐fuel
 emissions
 by
 sequestering
excess
atmospheric
C
in
the
roots
within
these
soils
(West
&
Marland
2002;
Subedi
 et
al.
2006).
Given
the
potential
for
lignin
to
act
as
a
C
sink
in
below‐ground
tissues,
the
recent
identification
of
specific
TFs
involved
in
regulating
lignin
deposition
is
an
important
 discovery.
 
 
 Single‐enzyme
 modifications
 that
 have
 led
 to
 changes
 in
lignin
 content
 and/or
 the
 ratios
 of
 H,
 G
 and
 S
 lignin
 (Anterola
 &
 Lewis
 2002;
Vanholme
 et
 al.
 2008)
 that
 are
 generally
 unsuitable
 for
metabolic
 engineering
 in
current
crop
systems,
due
to
their
severe
pleiotropic
phenotypes.

However,
specific
TFs
that
are
involved
in
the
regulation
of
lignin
biosynthetic
pathway
genes
may
be
important
 candidates
 for
 developing
 transgenic
 plants
 with
 enhanced
 levels
 of
lignin
 in
 their
 roots
 for
 the
 purpose
 of
 improved
 soil
 carbon
 sequestration
(Vijaybhaskar
et
al.
2008).

 1.4
 Root‐specific
and
inducible
gene
expression
systems

Identification
 of
 suitable
 tissue‐specific
 and
 inducible
 promoter
 systems
 to
 drive
target
gene
expression
is
another
important
step
in
developing
plants
that
have
the
potential
 to
 increase
 below‐ground
 carbon
 sticks.
 
 Normally,
 ectopic
 gene
expression
in
plants
is
achieved
by
using
a
broadly
active
and
constitutive
promoter
such
 as
 the
 Cauliflower
Mosaic
 Virus
 (CaMV)
 35S
 promoter
 
 (Brand
 et
 al.
 2006).

However,
ubiquitous
and
constitutive
gene
expression
can
often
be
lethal
or
lead
to
severe
defects
if
the
gene
being
overexpressed
is
of
vital
importance
to
normal
plant
development.
 
Therefore,
 the
 choice
of
promoter
and
 inducible
 expression
 system
often
 determines
 both
 the
 range
 of
 tissues
 and
 organs
 in
 which
 the
 gene
 can
 be
 
 24
 expressed,
in
addition
to
the
specific
developmental
stage
in
which
gene
expression
can
be
induced
(Moore
et
al.
2006;
Brand
et
al.
2006).


Root‐specific
promoters,
for
example,
 would
 be
 of
 particular
 interest
 in
 plant
 biotechnology
 for
 genetically
engineering
 improved
 tolerance
 to
 salt
 and
 water
 stress,
 resistance
 against
 root
pathogens,
improved
uptake
of
nutrients
and
carbon
sequestration
(Vijaybhaskar
et
 al.
2008;
Maizel
&
Weigel
2004).


The
 organ
 and
 tissue
 types
 in
 higher
 plants,
 are
 both
 temporally
 and
 spatially
controlled
 through
 the
 selective
 expression
 of
 specific
 parts
 of
 the
 genome,
 in
different
 cells,
 over
 the
 organisms
 entire
 life
 cycle
 (Ma
 et
 al.
 2005).
 
 With
 the
development
of
high
throughput
technologies,
such
as
DNA
microarrays,
 there
has
been
a
substantial
effort
made
in
recent
years
to
identify
and
determine
the
relative
abundance
 of
 transcripts
 expressed
 within
 each
 organ
 or
 tissue
 type
 (Ma
 et
 al.
2005).

The
ability
of
microarrays
to
measure
the
individual
transcript
level,
for
tens
of
thousands
of
genes
in
parallel,
provides
a
way
to
analyze
gene
expression
levels
among
different
cell
 types,
 tissues
and
even
along
developmental
gradients
(Ma
et
 al.
 2005;
 Birnbaum
 et
 al.
 2003).
 
 Furthermore,
 a
 global
 map
 of
 gene
 expression
patterns
within
an
organ,
such
as
 the
root,
can
 identify
genes
whose
expression
 is
localized
 to
 particular
 areas,
 thus
 relating
 the
 activity
 of
 individual
 genes,
 or
 co‐regulated
sets
of
genes,
 to
 tissue
specialization
and
even
cell
 fate
 (Birnbaum
et
al.
2003).

Birnbaum
et
al.
(2003)
mapped
global
gene
expression
to
15
different
zones
of
 the
developing
root
corresponding
 to
both
cell
 types
and
 tissues
at
progressive
developmental
 stages.
 
 Their
 data,
 as
 well
 as
 additional
 publicly
 available
 
 25
 microarray
 data
 from
 experiments
 conducted
 in
 other
 plant
 organs,
 allow
 plant
biologists
to
identify
candidate
genes
involved
in
specific
cell
types
within
the
root.

By
the
same
token,
this
data
could
reveal
genes
whose
promoters
may
be
useful
in
driving
root‐specific
transgene
expression.

The
 ability
 to
 turn
 on
 gene
 expression
 both
 spatially
 and
 temporally
 offers
 the
ability
 to
 fine‐tune
 ectopic
 gene
 expression
without
 compromising
 the
 viability
 of
the
organism
or
the
function
of
the
organ
being
altered.
However,
since
it
may
not
be
possible
to
easily
identify
genes
whose
expression
is
truly
restricted
to
the
time
and
place
of
interest,
researchers
have
also
sought
“inducible”
gene
promoters;
i.e.
a
promoter
whose
transcriptional
activity
is
determined
by
the
presence
(or
absence)
of
 a
 specific
 chemical
 or
 physical
 induction
 stimulus.
 
 In
 principle,
 this
 allows
expression
 of
 a
 transgene
 to
 be
 restricted
 to
 a
 given
 developmental
 stage
 for
 a
specific
 duration.
 
 So
 far
 there
 have
 been
 several
 inducible‐expression
 systems
described
in
the
literature,
generally
falling
into
three
broad
categories
based
on
the
nature
 of
 the
 “inducer”:
 Chemical‐inducible,
 hormone‐inducible
 and
 temperature‐inducible.




Since
 the
 early
 1990s,
 several
 transactivated
 and
 chemical‐inducible
 gene
expression
 systems
 have
 been
 developed
 based
 on
 transcriptional
 de‐repression,
inactivation,
 and
 activation
 of
 the
 gene
 of
 interest,
 as
 reviewed
 in
 Moore
 et
 al.
(2006).

In
the
most
popular
hormone‐inducible
systems,
the
regulatory
domains
of
the
rat
glucocorticoid
receptor,
the
human
estrogen
receptor
and
an
insect
ecdysone
 
 26
 receptor
have
been
used
to
construct
chimeric
transactivation
systems
whose
gene
expression
 activities
 are
 controlled
 by
 the
 use
 of
 specific
 hormones
 or
 chemically
similar
 compounds
 (Zuo
 et
 al.
 2001;
 Moore
 et
 al.
 2006).
 
 
 Alternatively,
 the
molecular
 responses
 to
 environmental
 temperature
 changes
 that
 have
 evolved
throughout
 living
 systems
 has
 led
 to
 cold
 tolerance
 and
 heat
 shock
 phenomena.

These
 phenomena
 have
 in
 turn
 contributed
 to
 the
 development
 of
 temperature‐inducible
 gene
 regulation
 (TIGR)
 systems
 (Weber
 et
 al.
 2003).
 
 Lastly,
 a
 further
development
towards
a
more
stringent
control
of
transgene
expression
is
the
use
of
inducible
promoters,
which
 are
 activated
by
 the
 application
of
 a
 specific
 chemical
stimulus
(Tang
et
al.
2004).

Chemical‐inducible
systems
are
appealing
compared
to
alternatives
 because
 they
 are
 generally
 dormant
 in
 the
 absence
 of
 the
 inducer,
allowing
 a
 greater
 level
 of
 flexibility.
 
 This
 in
 combination
 with
 an
 appropriate
tissue‐specific
 promoter
 to
 control
 the
 chemically‐responsive
 gene
 product
 can
increase
 the
 specificity
 of
 target
 gene
 expression
 by
 restricting
 it
 to
 particular
organs,
tissues
or
cell
types
at
a
desired
point
in
time
(Tang
et
al.
2004).

Chemicals
that
 have
 been
 used
 to
 regulate
 transgene
 expression
 include
 the
 antibiotic
tetracycline,
 the
 steroids
 dexamethasone
 (dex)
 and
 estradiol,
 copper,
 ethanol,
benzothiadiazol
 (the
 inducer
 of
 pathogen‐related
 proteins),
 the
 insecticide
methoxyfenozide
and
herbicide
safeners
(Tang
et
al.
2004).
 1.4.1
 Herbicidal
safeners
as
inducers
of
root‐specific
gene
expression

Herbicidal
 safeners
 are
 chemicals
 that
 increase
 herbicide
 tolerance
 and
 protect
monocot
crops
from
herbicide
burn
(DeRidder
&
Goldsbrough
2006;
De
Veylder
et
 al.
 1997;
DeRidder
et
 al.
2002).
Detoxification
 of
 these
 xenobiotics
 in
 plants
 is
 an
 
 27
 important
process
involving
three
enzyme‐catalyzed
phases.

Phase
one
begins
with
the
 oxidation,
 reduction,
 or
 hydrolysis
 reactions
 catalyzed
 by
 cytochrome
 P450‐dependent
monooxygenases
(De
Veylder
et
al.
1997;
DeRidder
et
al.
2002).
 
Phase
two
 involves
 the
 conjugation
 of
 the
 newly
 formed
 functional
 group
 with
 a
hydrophilic
substance
such
as
sugars
or
the
tripeptide
glutathione
(GSH).

The
GSH
conjugation
 reaction
 is
 catalyzed
 by
 a
 class
 of
 enzymes
 known
 as
 glutathione
 S‐transferases
(GSTs),
which
essentially
“tag”
these
molecules
for
excretion
or
storage.

In
 the
 final
 phase,
 these
 conjugates
 are
 recognized
 by
 appropriate
 transporters
(such
 as
ATP‐binding
 cassette
 transporters)
 and
 are
 then
 either
 excreted
 into
 the
apoplast
or
sequestered
in
the
vacuole
(DeRidder
et
al.
2002).




In
monocots,
it
was
found
that
herbicide
tolerance
can
be
markedly
enhanced
using
herbicide
safeners,
although
this
phenomenon
is
less
effective
dicotyledenous
crops
(DeRidder
 et
 al.
2002).
 
 Nevertheless,
 in
Arabidopsis,
 a
 tau‐class
 GST
 (AtGSTU19)
was
shown
to
respond
to
safeners
in
a
manner
similar
to
that
observed
in
monocot
plants,
 and
 to
 do
 so
 in
 a
 tissue‐specific
 manner.
 
 In
 response
 to
 the
 safener
benoxacor
 (and
 to
a
 lesser
extent
 fenclorim)
GSTU19
mRNA
 levels
were
 increased
30‐fold
 in
 roots
 compared
 to
 a
 relatively
 negligable
 4‐fold
 increase
 in
 shoots

(DeRidder
&
Goldsbrough
2006).


 1.5
 Project
rationale
and
thesis
objectives

It
is
important
that
we
learn
how
plants
will
respond
to
the
anticipated
increases
in
anthropogenic
 carbon
emissions
over
 the
 coming
decades
 given
 their
 vital
 role
 in
the
global
carbon
cycle
(Lal
2008).
This
information
is
critical
to
understanding
the
 
 28
 effects
of
global
climate
change
on
our
ecosystems
and
is
required
to
assess
the
role
of
 plant
 life
 in
 carbon
 sequestration
 (Raven
 &
 Karley
 2006).
 Plants
 offer
 the
potential
 to
 play
 a
 significant
 role
 in
 carbon
 sequestration,
 a
 process
 by
 which
atmospheric
 CO2
 can
 be
 transferred
 to,
 and
 securely
 stored
 in
more
 long‐lived
 C
reservoirs
(Lal
2004;
Millard
et
al.
2007).



The
 overall
 aim
 of
 my
 M.Sc.
 research
 was
 to
 design
 and
 engineer
 transgenic
Arabidopsis
plants
with
enhanced
levels
of
lignin
in
their
roots.

If
successful,
these
plants
could
then
offer
the
potential
to
increase
soil
carbon
stocks
if
implemented
in
crop
systems
such
as
canola
or
soybean.



Lignin
is,
after
cellulose,
the
second
most
abundant
terrestrial
biopolymer
and
offers
the
potential
 to
 increase
 soil
 carbon
 stocks
due
 to
 its
 ability
 to
 resist
degradation
(Humphreys
 &
 Chapple
 2002;
 Weng
 et
 al.
 2008).
 
 Lignin
 biosynthesis
 and
accumulation
is
a
highly
localized
and
regulated
process
that
requires
strict
spatial
and
 temporal
 control
 of
 the
 processes
 occurring
 during
 normal
 plant
 growth
 and
development.


The
past
twenty
years
of
research
have
led
to
the
identification
and
characterization
of
many
different
lignin
biosynthetic
and
regulatory
genes
involved
in
the
biosynthesis
of
monolignols,
control
of
the
many
genes
involved
in
catalyzing
the
reactions
of
the
lignin
biosynthetic
pathway,
ultimately
leading
to
secondary
cell
wall
deposition
(Anterola
&
Lewis
2002;
Vanholme
et
al.
2008).



Specifically,
the
objectives
of
my
project
were:
 
 29
 1. To
 identify
 suitable
 genes
 for
 overexpression
 that
 would
 result
 in
 ectopic
deposition
of
lignin
2. To
 identify
 suitable
 promoters
 needed
 to
 drive
 root‐specific
 expression
 of
the
transgene
3. To
 identify
 inducible
 systems
 that
may
be
used
 to
 turn
on
gene
expression
spatially
and
temporally

4. To
 engineer
 gene
 expression
 constructs
 designed
 to
 enhance
 lignin
deposition
in
Arabidopsis
roots
5. To
analyze
transgenic
plants
for
relevant
phenotypes



























 
 30
 2.
 Materials
and
Methods
 2.1
 Organ‐specific
expression
of
candidate
gene
and
promoters
 
Wild
 type
 Arabidopsis
 thaliana
 (Columbia
 ecotype)
 seeds
 were
 surface
 sterilized
using
20%
bleach
solution
and
several
washes
of
dH2O,
sown
in
(Sunshine
Mix
#5,
Sun
 Gro
 Horticulture
 Canada
 Ltd.,
 Seba
 Beach,
 Alberta,
 Canada)
 and
 grown
 in
 a
chamber
for
a
16hr
light/8hr
dark
photoperiod.
 
Root,
stem,
leaf
and
flower
tissue
was
harvested
from
four‐week‐old
plants,
 frozen
in
 liquid
nitrogen
and
stored
at
 ‐80˚C
for
 later
use.
For
semi‐quantitative
RT‐PCR
analyses
of
the
At4CL1,
AtGSTU19
and
 AtSND1
 genes,
 total
 RNA
 (1µg)
 was
 extracted
 from
 frozen
 tissue
 using
 the
RNeasy
Plant
Mini
Kit
(Qiagen)
and
the
purified
RNA
treated
with
DNase
I
to
remove
any
 potential
 genomic
 DNA
 contamination
 before
 use
 for
 cDNA
 synthesis.
 
 RNA
concentration
was
measured
using
a
NanoDrop
ND‐1000
Spectrophotometer
at
an
OD
 of
 260nm.
 
 cDNA
 was
 made
 via
 reverse
 transcription
 using
 qScript™
 cDNA
SuperMix
(Quanta
Biosciences),
according
to
the
specifications
of
the
manufacturer.

PCR
(polymerase
chain
reaction)
was
performed
in
a
25µl
reaction
containing
10x
PCR
 Buffer,
 2mM
 MgCl2,
 0.2mM
 dNTPs,
 0.1µl
 Taq
 DNA
 polymerase,
 0.5µl
 cDNA
template
 and
 0.5µl
 each
 of
 forward
 and
 reverse
 primers.
 
 The
 following
 program
was
used:

 Step
 Temperature
 Time
 Cycle
1
 94°C
 3
minutes
2
 94°C
 30
seconds
3
 54°C
 30
seconds
4
 72°C

 1
minute
5
 72°C

 10
minutes
6
 4°C

 Pause
 


Step
4→2
x
35
cycles
 
 
 31
 RT‐PCR
was
repeated
three
times
on
two
biological
replicates,
and
identical
results
were
obtained.
Primers
designed
to
amplify
fragments
of
these
native
genes
can
be
found
in
Table
8
in
Appendix
C
(1,
2,
3,
4,
21
and
22).
The
expression
level
of
the
β‐ Actin8
 gene
was
 used
 as
 an
 internal
 control
 (Table
 8;
 29
 and
 30).
 
 Samples
were
visualized
on
1%
agarose
gels
stained
with
SYBR
Safe
DNA
gel
stain
(Invitrogen).
Differentially
 expressed
 PCR
 products
 were
 analyzed
 using
 the
 Image
 J
 (1.42)
(ImageJ:
Image
Processing
and
Analysis
in
Java)
program
to
compare
the
expression
levels
 of
 each
 transcript.
 The
 Image
 J
 program
calculates
 the
 area
 and
pixel
 value
statistics
of
user‐defined
selections.

 2.2
 Cis‐element
analysis
of
candidate
promoters

In
order
to
investigate
the
promoter
regions
of
the
4CL1
(At1g51680)
and
GSTU19
(At1g78380)
genes
for
common
cis‐acting
root‐specific
regulatory
elements,
500bp,
1000bp,
2000bp
and
3000bp
regions
upstream
of
the
transcription
start
sites
were
analyzed
 using
 the
 PLACE
 (Plant
 Cis‐acting
 Regulatory
 DNA
 Elements)
 database
(Higo
 et
 al.
 1999).
 
 Putative
 regulatory
 elements
 that
 could
 contribute
 to
 root‐specific
 expression
 were
 identified
 from
 previously
 published
 literature
(Vijaybhaskar
et
al.
2008)
and
results
for
the
2000bp
analysis
is
listed
in
Appendix
B
(Tables
6
and
7).
 2.3
 Preparation
of
the
4CL1pro‐SND1
gene
expression
constructs
and
 
 transgenic
plants
 
A
1224bp
 fragment
 containing
 the
4CL1
 (At1g51680)
promoter
was
 amplified
via
tailed‐PCR
 from
 Arabidopsis
 (Columbia
 ecotype)
 wild
 type
 genomic
 DNA.
 
 The
reaction
was
 carried
 out
 in
 a
 25µl
 reaction
 containing
 10x
HiFi
 PCR
Buffer,
 2mM
 
 32
 MgCl2,
 0.2mM
 dNTPs,
 0.1µl
 HiFi
 Taq
 polymerase,
 1.0µl
 wild
 type
 genomic
 DNA
template
and
0.5µl
 each
of
 forward
and
reverse
primers
 (Table
8
 (Appendix
C);
5
and
6)
according
to
the
following
program:
 Step
 Temperature
 Time
 Cycle
1
 94°C
 5
minutes
2
 94°C
 30
seconds
3
 54°C
 30
seconds
4
 72°C

 1
minute
20
seconds
5
 72°C

 10
minutes
6
 4°C

 Pause
 


Step
4→2
x
35
cycles
 
The
 forward
 primer
 (5'‐GGGCACGˇAATTCTTTTCGGTCTCTAATACCTCC‐3')
 contained
 an
 EcoRI
 site
 (underlined
 and
 bolded)
 and
 the
 reverse
 primer
 (5'‐ CACGAGGˇGATCCGˇGTNACCCCGCˇGGCTGAAGGAAACAGGAGTTGTATC‐3')
 contained
 restriction
sites
 for
 BamHI
 (GˇGATCC),
 BstEII
 (GˇGTNACC)
 and
 SacII
 (CCGCˇGG)
 (underlined
 and
bolded)
 respectively.
 
 Following
 enzyme
 digestion
 with
 EcoRI
 and
 BamHI
 the
promoter
 fragment
 (4CL1pro)
was
 ligated
 into
 the
pPZP211
Agrobacterium
binary
vector
 (Hajdukiewicz
 et
 al.
 1994).
 
 The
 SND1
 (At1g32770)
 open
 reading
 frame
(ORF)
was
 amplified
 from
a
pDG2
plasmid
 (obtained
 from
Apurva
Bhargava,
 Ellis
lab)
 containing
 the
 SND1
 cDNA
 using
 a
 forward
 primer
 (5’‐ GAGCTCCCGCˇGGATGGCTGATAATAAGGTCAATCTTTCG‐3’)
 containing
 a
 SacII
 restriction
enzyme
 site
 (underlined
 and
 bolded)
 and
 a
 reverse
 primer
 (5’‐ GGGTGTGˇGATCCATGATGATGATGATGATGTCATACAGATAAATGAAGAAGTGGGTC‐3’)
 containing
a
BamHI
site
(underlined
and
bolded)
and
a
HIS
x6
tag
(bolded).
 
PCR
was
carried
out
 in
a
25µl
 reaction
containing
10x
HiFi
PCR
Buffer,
2mM
MgCl2,
0.2mM
dNTPs,
0.1µl
 HiFi
 Taq
 polymerase,
 0.5µl
 cDNA
 template
 and
 0.5µl
 each
 of
 forward
 and
reverse
primers.

Conditions
for
SND1
amplification
were
as
follows:
 
 33
 Step
 Temperature
 Time
 Cycle
1
 94°C
 5
minutes
2
 94°C
 30
seconds
3
 58°C
 30
seconds
4
 72°C

 1
minute
18
seconds
5
 72°C

 10
minutes
6
 4°C

 Pause
 


Step
4→2
x
35
cycles
 
After
digestion
with
BamHI
and
SacII
the
SND1
ORF
fragment
was
inserted
into
the
pPZP211
 vector
 (Hajdukiewicz
 et
 al.
 1994)
 already
 containing
 the
 4CL1pro
fragment.
The
recombinant
plasmid
(4CL1pro­SND1;
Fig.
4)
was
sequenced
(Applied
Biosystems,
NAPS
Unit,
UBC,
Vancouver,
Canada)
using
the
standard
M13
primers,
transferred
 into
Agrobacterium
 tumefaciens
 strain
 GV3101
 by
 heat
 shock
method
and
then
used
to
transform
Arabidopsis
wild
type
plants
via
the
floral
dip
method.

The
complete
primary
sequence
of
4CL1pro­SND1
can
be
found
in
Appendix
A.
 
 
 Figure
 4.
 
 Schematic
 diagram
 of
 the
 SND1
 overexpression
 constructs
 in
 pPZP211.
 Separate
 SND1
 overexpression
 constructs
 are
 driven
 by
 the
 4CL1
 and
GSTU19
 promoters,
 respectively
 (left
 to
 right
 the
 constructs
 are
 5’
 to
 3’).
 Both
constructs
 contain
 EcoRI,
 SacII
 and
 BamHI
 restriction
 enzyme
 sites
 as
 well
 as
 a
6xHis
tag
at
the
3’
end
(complete
primary
sequences
may
be
found
in
Appendix
A).
Genomic
 DNA
 was
 extracted
 from
 kanamycin‐resistant
 (50µg/ml)
 T1
 generation
plants
and
PCR
used
to
confirm
the
presence
of
the
transgene.
PCR
was
carried
out
 !"#$%& '()#*& !"#$%&'(')*&+ ,-.$+ +,-.*& /(,**& !"#$%&'/,-.$+0+123$+4%++ 5,67$8%&'(')*&+ ,-.$+ +,-.*& /(,**& !"#$%& '()#*& 5,67$8%&'/,-.$+0+1993+4%++ 0121344& 0121344& 56748&90:&& 56748&90:&& 
 34
 in
a
25µl
reaction
containing
10x
PCR
Buffer,
2mM
MgCl2,
0.2mM
dNTPs,
0.1µl
Taq
DNA
polymerase,
1.0µl
cDNA
template
and
0.5µl
each
of
4CL1pro
forward
(Table
8
(Appendix
 C);
 5)
 and
SND1
 reverse
 primers
 (Table
 8
 (Appendix
 C);
 10)
 using
 the
following
program:
 Step
 Temperature
 Time
 Cycle
1
 94°C
 5
minutes
2
 94°C
 30
seconds
3
 59.2°C
 30
seconds
4
 72°C

 2
minute
30
seconds
5
 72°C

 10
minutes
6
 4°C

 Pause
 


Step
4→2
x
35
cycles
 

T1
 generation
 lines
 containing
 the
 transgene
 were
 harvested
 and
 T2
 generation
seeds
screened
on
½
Murashige
and
Skoog
(MS)
media
plates
containing
50µg/ml
kanamycin.
 
 I
 selected
12
plants/line
 showing
 a
1:3
 segregation
 ratio
 indicating
 a
single
 insertion
 event
 and
 planted
 them
 in
 soil
 (Sunshine
 Mix
 #5,
 Sun
 Gro
Horticulture
 Canada
 Ltd.,
 Seba
 Beach,
 Alberta,
 Canada),
 where
 they
 were
 grown
under
 16hr
 light/8hr
 dark
 photoperiod.
 
 In
 addition,
 12
 plants/line
 were
 also
transferred
to
½
MS
media
and
roots
harvested
at
three
weeks
for
analysis
of
SND1
overexpression
 using
 RT‐PCR.
 
 Total
 RNA
 (385ng
 and
 1µg
 starting
material)
was
extracted
 from
 frozen
 tissue
 using
 the
 RNeasy
 Plant
 Mini
 Kit
 (Qiagen)
 and
 the
purified
 RNA
 treated
 with
 DNase
 I
 to
 remove
 any
 potential
 genomic
 DNA
contamination
 before
 use
 for
 cDNA
 synthesis.
 
 RNA
 concentration
was
measured
using
 a
NanoDrop
 ND‐1000
 Spectrophotometer
 at
 an
OD
 of
 260nm.
 
 cDNA
was
made
via
 reverse
 transcription
using
SuperScript™
 II
RT
(Invitrogen)
and
OligodT
(Invitrogen),
according
 to
 the
specifications
of
 the
manufacturer.
 
All
PCR
and
RT‐ 
 35
 PCR
reactions
were
visualized
on
1%
agarose
gels
stained
with
SYBR
Safe
DNA
gel
stain
(Invitrogen).

Seeds
from
8
lines
showing
SND1
overexpression
were
harvested
and
screened
for
homozygosity
on
½
MS
media
plates
containing
50µg/ml
kanamycin.

Of
the
twelve
T3
homozygous
 sub‐lines
 identified,
 seven
were
planted
 in
 soil
 (Sunshine
Mix
#5,
Sun
Gro
Horticulture
 Canada
 Ltd.,
 Seba
Beach,
Alberta,
 Canada)
 and
 grown
under
16hr
 light/8hr
 dark
 photoperiod.
 
 Seeds
 were
 harvested
 at
 approximately
 eight
weeks
and
used
for
subsequent
analyses.
 2.4
 Preparation
of
the
GSTU19pro‐SND1
gene
expression
constructs
and
 
 transgenic
plants

A
1402bp
fragment
containing
the
GSTU19
(At1g78380)
promoter
was
amplified
via
tailed‐PCR
 from
 Arabidopsis
 (Columbia
 ecotype)
 wild
 type
 genomic
 DNA.
 The
reaction
was
 carried
 out
 in
 a
 25µl
 reaction
 containing
 10x
HiFi
 PCR
Buffer,
 2mM
MgCl2,
 0.2mM
 dNTPs,
 0.1µl
 HiFi
 Taq
 polymerase,
 1.0µl
 wild
 type
 genomic
 DNA
template
and
0.5µl
each
of
forward
and
reverse
primers

according
to
the
following
program:
 Step
 Temperature
 Time
 Cycle
1
 94°C
 5
minutes
2
 94°C
 30
seconds
3
 56°C
 30
seconds
4
 72°C

 1
minute
20
seconds
5
 72°C

 10
minutes
6
 4°C

 Pause
 


Step
4→2
x
35
cycles
 
The
 forward
 primer
 (5'‐GGGTCTGˇAATTCGCTACGTGTCGTGAGATATCG‐3')
 contained
 an
 EcoRI
 site
 (underlined
 and
 bolded)
 and
 the
 reverse
 primer
 (5'‐ 
 36
 CACGAGGˇGATCCGˇGTNACCCCGCˇGGTGTTACGATCGCTAAAGCTCAC‐3')
 contained
 restriction
sites
 for
 BamHI
 (GˇGATCC),
 BstEII
 (GˇGTNACC)
 and
 SacII
 (CCGCˇGG)
 (underlined
 and
bolded)
 respectively.
 Following
 enzyme
 digestion
 with
 EcoRI
 and
 BamHI
 the
promoter
 fragment
 (GSTU19pro)
 was
 ligated
 into
 the
 pPZP211
 Agrobacterium
binary
vector
(Hajdukiewicz
et
al.
1994).



As
previously
described
in
section
2.3,
the
SND1
amplicon
was
digested
with
BamHI
and
 SacII
 and
 inserted
 into
 the
 pPZP211
 vector
 (Hajdukiewicz
 et
 al.
 1994)
containing
 the
GSTU19pro
 fragment.
 The
 recombinant
 plasmid
 (GSTU19pro­SND1;
Fig.
 4)
was
 sequenced
 
 (Applied
 Biosystems,
 NAPS
Unit,
 UBC)
 using
 the
 standard
M13
 primers,
 transferred
 into
 Agrobacterium
 tumefaciens
 strain
 GV3101
 by
 heat
shock
method
and
then
used
to
produce
transgenic
Arabidopsis
plants
via
the
floral
dip
method.
 The
 complete
 primary
 sequence
 of
GSTU19pro­SND1
 can
 be
 found
 in
Appendix
A.
 
Genomic
DNA
was
extracted
from
kanamycin‐resistant
(50µg/ml)
T1
generation
plants
and
PCR
used
to
confirm
the
presence
of
the
transgene.

PCR
was
carried
out
in
a
25µl
reaction
containing
10x
PCR
Buffer,
2mM
MgCl2,
0.2mM
dNTPs,
0.1µl
 Taq
 DNA
 polymerase,
 1.0µl
 cDNA
 template
 and
 0.5µl
 each
 of
 GSTU19pro
forward
(Table
8
(Appendix
C);
7)
and
SND1
reverse
primers
(Table
8
(Appendix
C);
10).

PCR
conditions
were
as
follows:
 Step
 Temperature
 Time
 Cycle
1
 94°C
 5
minutes
2
 94°C
 30
seconds
3
 58°C
 30
seconds
4
 72°C

 2
minute
30
seconds
5
 72°C

 10
minutes
6
 4°C

 Pause
 


Step
4→2
x
35
cycles
 
 
 37
 T2
generation
plants
were
screened
on
½
MS
media
containing
50μg/ml
kanamycin,
treated
with
100µM
benoxacor
for
24
hours
and
checked
for
SND1
overexpression
using
RT‐PCR.
 
 I
planted
12
plants/line,
showing
a
1:3
segregation
ratio
 indicating
one
 insertion
 event
were
 planted,
 in
 soil
 (Sunshine
Mix
 #5,
 Sun
 Gro
Horticulture
Canada
Ltd.,
Seba
Beach,
Alberta,
Canada)
and
grew
them
under
16hr
light/8hr
dark
photoperiod.

I
transferred
10
plants
per
line
to
½
MS
media
and
roots
harvested
at
three
weeks
 for
 analysis
 of
 SND1
 overexpression
 using
 RT‐PCR.
 
 Total
 RNA
 (1µg
starting
material)
was
extracted
from
frozen
tissue
using
the
RNeasy
Plant
Mini
Kit
(Qiagen)
 and
 the
 purified
 RNA
 treated
 with
 DNase
 I
 to
 remove
 any
 potential
genomic
DNA
contamination
before
use
for
cDNA
synthesis.

RNA
concentration
was
measured
 using
 a
 NanoDrop
 ND‐1000
 Spectrophotometer
 at
 an
 OD
 of
 260nm.

cDNA
was
made
via
reverse
transcription
using
SuperScript™
II
RT
(Invitrogen)
and
OligodT
(Invitrogen),
 according
 to
 the
specifications
of
 the
manufacturer.
 
All
PCR
and
RT‐PCR
reactions
were
visualized
on
1%
agarose
gels
stained
with
SYBR
Safe
DNA
gel
stain
(Invitrogen).

Seeds
from
eight
lines
showing
SND1
overexpression
were
harvested
and
screened
for
homozygosity
on
½
MS
media
plates
containing
50µg/ml
kanamycin.

Of
the
22
T3
 homozygous
 sub‐lines
 identified,
 eight
were
 planted
 in
 soil
 (Sunshine
Mix
 #5,
Sun
Gro
Horticulture
 Canada
 Ltd.,
 Seba
Beach,
Alberta,
 Canada)
 and
 grown
under
16hr
 light/8hr
dark
photoperiod
at
22°C.
 
Seeds
were
harvested
at
approximately
eight
weeks
and
used
for
subsequent
analyses.
 
 38
 2.5
 Molecular
analysis
of
transgenic
plants
 2.5.1
 Reverse
transcription‐PCR
of
direct
downstream
targets
of
SND1

Roots
and
shoots
(aerial
tissue
in
seedlings
that
does
not
include
stems)
from
two‐week‐old
plants
grown
on
½
MS
media
were
harvested
and
frozen
in
liquid
nitrogen
from
 three
 different
 T3
 lines
 for
 each
 construct
 as
 well
 as
 two
 different
 empty
vector
control
 lines.
 
Total
RNA
(1µg
starting
material)
was
extracted
 from
frozen
tissue
using
the
RNeasy
Plant
Mini
Kit
(Qiagen)
and
the
purified
RNA
treated
with
Dnase
I
 to
remove
any
potential
genomic
DNA
contamination
before
use
 for
cDNA
synthesis.
 
 RNA
 concentration
 was
 measured
 using
 a
 NanoDrop
 ND‐1000
Spectrophotometer
at
an
OD
of
260nm.

I
made
cDNA
via
reverse
transcription
using
SuperScript™
 II
 RT
 (Invitrogen)
 and
 OligodT
 (Invitrogen),
 according
 to
 the
specifications
 of
 the
manufacturer.
 
 PCR
was
 performed
 in
 order
 to
 amplify
 four
known
downstream
targets
of
SND1
(SND3,
MYB46,
MYB103
and
KNAT7)
as
well
as
SND1
itself.

Primers
used
can
be
found
in
Table
8
of
Appendix
C
(13,
14,
15,
16,
17,
18,
 19,
 20,
 21
 and
 22)
 and
 the
 PCR
 reaction
 carried
 out
 in
 a
 Biometra
 Tpersonal
thermocycler.
 The
 reaction
 was
 25µl
 and
 contained
 10x
 PCR
 Buffer,
 2mM
MgCl2,
0.2mM
 dNTPs,
 0.1µl
 Taq
 polymerase,
 0.5µl
 cDNA
 template
 and
 0.5µl
 each
 of
 the
appropriate
forward
and
reverse
primers
according
to
the
following
program:
 Step
 Temperature
 Time
 Cycle
1
 94°C
 5
minutes
2
 94°C
 30
seconds
3
 54°C
 30
seconds
4
 72°C

 50
seconds
5
 72°C

 10
minutes
6
 4°C

 Pause
 


Step
4→2
x
35
cycles
 
 
 39
 All
 PCR
 and
 RT‐PCR
 reactions
 were
 visualized
 on
 1%
 agarose
 gels
 stained
 with
SYBR
Safe
DNA
gel
stain
(Invitrogen).

The
PCR
reaction
was
repeated
three
times
yielding
similar
results.
 2.5.2
 Reverse
transcription‐PCR
of
lignin
biosynthetic
pathway
enzymes
Roots
and
shoots
(aerial
tissue
in
seedlings
that
does
not
include
stems)
from
two‐week‐old
plants
were
harvested
and
 frozen
 in
 liquid
nitrogen
 from
three
different
T3
lines
for
each
construct
as
well
as
two
different
empty
vector
control
lines
grown
on
½
MS
media.

Total
RNA
(1µg
starting
material)
was
extracted
from
frozen
tissue
using
the
Rneasy
Plant
Mini
Kit
(Qiagen)
and
the
purified
RNA
treated
with
Dnase
I
to
remove
any
potential
genomic
DNA
contamination
before
use
for
cDNA
synthesis.

RNA
concentration
was
measured
using
a
NanoDrop
ND‐1000
Spectrophotometer
at
an
OD
of
260nm.

I
made
cDNA
via
reverse
transcription
using
SuperScript™
II
RT
(Invitrogen)
 and
 OligodT
 (Invitrogen),
 according
 to
 the
 specifications
 of
 the
manufacturer.
 
 PCR
 was
 performed
 to
 amplify
 4CL1
 (At1g51680),
 CCR1
(At1g15950)
 and
 COMT1
 (At5g54160);
 specific
 enzymes
 involved
 in
 the
 lignin
biosynthetic
pathway.

The
primers
for
these
enzymes
along
with
the
Actin8
control
can
be
found
in
Table
8
(Appendix
C;
23‐30).

The
PCR
reaction
was
carried
out
in
a
Biometra
 Tpersonal
 thermocycler.
 The
 reaction
 was
 20µl
 and
 contained
 2x
MangoMix
(Bioline),
0.5µl
cDNA
template
and
0.5µl
each
of
the
appropriate
forward
and
reverse
primers
according
to
the
following
program:


 
 40
 Step
 Temperature
 Time
 Cycle
1
 94°C
 5
minutes
2
 94°C
 30
seconds
3
 54°C
 30
seconds
4
 72°C

 30
seconds
5
 72°C

 10
minutes
6
 4°C

 Pause
 


Step
4→2
x
35
cycles
 
All
 PCR
 and
 RT‐PCR
 reactions
 were
 visualized
 on
 1%
 agarose
 gels
 stained
 with
SYBR
Safe
DNA
gel
stain
(Invitrogen).

The
PCR
reaction
was
repeated
in
triplicate
yielding
similar
results.
 2.6
 Determination
of
lignin
content
in
transgenic
plants
overexpressing
 
 SND1
 2.6.1
 Plant
growth
conditions

T3
generation
transgenic
and
empty
vector
lines
were
grown
hydroponically
 in
an
open‐top
liquid
culture
system.

Plastic
cylinders
that
were
1.5‐cm
in
diameter
were
cut
from
the
tops
of
disposable
10mL
pipette
tips
were
lined
with
wire
mesh,
filled
with
coarse
sand,
topped
off
with
fine
sand
and
placed
in
a
0.64‐cm‐thick
Styrofoam
platform
specifically
cut
and
fitted
to
float
on
7L
of
hydroponic
nutrient
medium
in
an
8L
plastic
basin.

Each
platform
contained
25
holes
(diameter
1.6
cm),
into
which
were
fitted
the
plastic
cylinders.
Two
to
four
seeds
were
sown
in
each
cylinder
and
germinated
 in
 dH2O
 for
 the
 first
 ten
 days,
 then
 transferred
 to
 aerated
 complete
nutrient
 solution
 at
 pH
 6.1
 (1/10
 Johnson;
 see
 Appendix
 D).
 
 Nutrient
 solutions
were
 replaced
weekly,
 light
was
 provided
 from
 fluorescent
 tubes
 (150
 E
m–2
 s–1)
and
 the
 walk‐in
 environment
 chamber
 was
 maintained
 under
 the
 following
conditions:
 light/dark,
 8/16
 h;
 24/20°C;
 relative
 humidity
 =
 70%;
 photon
 flux
 of
150
to
200
uE
m‐2
s‐1.
 
Roots
 from
both
constructs
were
harvested
at
eight
weeks,
 
 41
 GSTU19pro­SND1
 lines
 treated
 for
 24hrs
 with
 100µM
 Benoxacor
 and
 tissue
 was
stored
at
‐80°C
for
later
use.
 2.6.2
 Rapid,
micro
scale,
acetyl
bromide‐based
method
for
lignin
content
analysis

Lignin
content
was
measured
using
a
modified
acetyl
bromide
method
to
enable
the
rapid
microscale
determination
of
lignin
content
in
Arabidopsis
as
outlined
in
Chang
 et
al.
 (2008).
 
Samples
(roots
 from
~10‐20
plants)
were
dried
overnight
 in
a
40°C
oven
and
ground
using
a
microball
mill
at
80‐mesh
then
transferred
to
vials,
placed
in
a
vacuum
drying
oven
at
40°C
for
48hrs
and
then
into
a
P2O5
desiccator
overnight.


Approximately
0.10g
(±0.01g)
of
oven‐dried
sample
was
weighed
and
transferred
to
a
 large
test
tube
by
adding
water.
 
Tubes
(containing
the
sample
plus
water)
were
then
 placed
 in
 a
 65°C
 water
 bath
 for
 30
 minutes
 and
 vortexed
 at
 10
 minute
intervals.
 
Samples
were
then
hot
filtrated
using
a
Millipore
filter
with
preweighed
D47mm
(0.45µm)
nylon
membrane.

 
Samples
were
washed
roughly
25
times
with
2mL
dH2O
using
a
glass
pipette.
 
 
Subsequent
washes
entailed:
25x1mL
of
ethanol,

25x1mL
acetone
and
25x1mL
of
diethyl
ether.


Membranes
were
removed
carefully
and
 transferred
 to
 preweighed
 aluminum
 pans
 and
 placed
 into
 a
 vacuum
 drying
oven
at
40°C
for
48hrs
and
then
into
the
P2O5
desiccator
overnight.


Weights
were
recorded
 and
 difference
 for
 extracted
 weights
 obtained.
 
 Samples
 were
 then
transferred
to
new
vials.

Approximately
5.00mg
(±1.00mg)
of
oven‐dried
extracted
sample
(times
three
replicates
per
line)
was
weighed
and
transferred
to
a
sealable
glass
test
tube.


Samples
were
digested
with
1.0mL
of
25%
acetyl
bromide
in
acetic
acid.

Tubes
were

capped
and
placed
in
a
70°C
water
bath
for
30
minutes,
vortexing
 
 42
 every
10
minutes.
 
 
Samples
were
then
cooled
and
stored
on
ice
for
a
minimum
of
five
minutes
up
to
two
hours.

Acetic
acid
(5mL)
was
added
to
the
tubes
containing
the
samples,
vortexed
and
centrifuged
to
spin
down
any
precipitate.

Subsequently,
300µL
of
sample
mixture
was
transferred
to
a
quartz
cuvette
followed
by
400µL
of
1.5M
NaOH,
300µL
of
0.5M
H2NOH⋅HCL
and
1.5mL
of
acetic
acid
for
a
total
volume
of
2.5mL.

Absorbance
was
measured
at
280nm
against
a
blank
and
recorded.


 2.6.3
 Klason
lignin
or
72%
(v/v)
H2SO4
acid
procedure
and
carbohydrate
analysis

Samples
were
dried
at
40°C
overnight
and
ground
using
a
microball
mill
at
80‐mesh
then
transferred
to
vials
and
stored
in
the
desiccator
until
used.

Approximately
0.2g
of
 sample
 was
 weighed
 into
 a
 test
 tube
 and
 its
 mass
 recorded.
 
 The
 separation
reaction
 was
 carried
 out
 by
 adding
 3mL
 of
 72%
 (w/w)
 H2SO4
 to
 the
 weighed
samples
and
mixing
with
a
glass
rod
every
10
minutes
 for
two
hours.
 
Contents
of
tubes
were
completely
transferred
to
serum
bottles
and
sealed
with
septa.

Samples
were
 then
autoclaved
along
with
 the
sugar
control
 (Appendix
D)
 for
one
hour
at
121oC.



For
 the
 insoluble
 lignin
 analysis,
 bottles
 were
 allowed
 to
 cool
 before
 filtering
through
 a
 pre‐weighed
 Medium
 Coarseness
 (M)
 sintered‐glass
 crucible.
 
 The
crucible
 solids
 were
 washed
 by
 filtering
 through
 200mL
 warm
 deionized
 water
followed
 by
 drying
 overnight
 at
 105oC.
 
 To
 complete
 the
 retentate
 analysis,
 after
filtration,
crucibles
containing
the
 insoluble
 lignin
were
weighed
and
recorded.
 
 In
order
 to
 determine
 the
 final
 weight
 (dry
 mass)
 of
 insoluble
 lignin,
 total
 crucible
 
 43
 weight
 (crucible
 and
 insoluble
 lignin)
was
 subtracted
 from
 the
weight
 of
 the
pre‐weighed
empty
crucible.

For
the
acid
soluble
lignin
filtrate
analysis
the
absorbance
at
205
nm
was
determined
using
a
quartz
cuvette.


For
the
carbohydrate
analysis
used
to
determine
hemicellulose
content,
the
filtrate
from
 the
 autoclaved
 samples
 was
 retained.
 The
 sugar
 analysis
 of
 the
 filtrate
required
 the
 preparation
 of
 a
 1mL
 sample
 for
 HPLC
 by
 weight
 using
 ~950
 mg
hydrolysate
+
50
mg
of
fucose
standard
(Appendix
D).



 2.7
 Starch
analysis

Roughly
 25‐50mg
 of
 dried
 ground
 tissue
 per
 sample
 (in
 duplicate)
 (see
 Klason
analysis
protocol
for
drying
and
grinding
protocol)
was
weighed
into
a
10mL
glass
culture
 tube.
 
 Following
 this,
 5mL
 of
 4%
 H2SO4
 was
 added
 to
 each
 tube,
 gently
vortexed,
then
autoclaved
for
3½
minutes.
Samples
were
cooled
and
gently
spun
for
five
minutes
at
500rpm
to
pellet
the
insoluble
matter.
 
The
supernatant
containing
the
glucose
fraction
was
retained
and
the
pellet
discarded.

Samples
were
prepared
for
HPLC
by
adding
 fucose
and
 filtered.
Using
 the
glucose
standards
 (Appendix
D)
and
regression
analysis,
the
amount
of
glucose
in
the
HPLC
vial
was
calculated
and
then
 back
 calculated
 to
 determine
 how
much
 glucose
 the
 entire
 sample
 released.

The
glucose
content
was
used
to
determine
the
relative
cellulose
composition
of
the
samples
analyzed.

 
 44
 2.8
 Phenotypic
analysis
of
transgenic
plants
 2.8.1
 Seed
phenotyping

The
average
weight
per
seed
was
determined
by
weighing
six
samples
of
100
seeds
per
 line
 and
 the
 average
 seed
 number
 per
 silique
was
measured
 by
 counting
 the
number
of
seeds
in
each
of
30
siliques.

Silique
length
was
determined
by
measuring
30
 siliques
 for
 each
 transgenic
 line.
 
 For
 the
germination
assay,
28‐36
 seeds
 from
two
 transgenic
 lines
 per
 construct
 and
 two
 empty
 vector
 controls
 were
 surface
sterilized
using
70%
and
95%
ethanol,
dried
and
then
sown
on
½
MS
media.

Plates
were
kept
in
the
dark
at
4°C
for
four
days
then
placed
in
a
walk
in
growth
chamber
under
16hr
light
and
8hr
dark.
Germinants
were
counted
24
hours
later
and
every
12
hours
after
that
up
to
48
hours.
 
 
A
one‐way
Analysis
of
Variance
(ANOVA)
was
performed
in
the
statistical
environment
'R'
(http://www.bioconductor.org/)
using
the
 function
 'aov'**
 with
 the
 balanced
 linear
 model
 function
 'lm',
 and
 contrasts
made
upon
8
levels
for
seed
weight
(A‐7,
B‐5,
D‐2,
F‐5,
F‐7,
G‐8,
EV40,
EV41)
and
5
levels
 for
 lateral
 root
 density
 (A‐7,
 B‐5,
 F‐7,
 G‐7,
 EV40)
 (see
 section
 2.8.2
 below)
(Chambers
et
al.
2002).

 2.8.2
 Root
growth
and
lateral
root
density

After
cold
treatment
for
two
days
at
4°C,
surface
sterilized
seeds
were
individually
pipetted
out
in
a
single
row
at
a
seed
density
of
15
seeds
per
plate
at
the
top
of
petri
dishes
containing
1.2%
agar
in
½
MS
media.


Plants
were
grown
vertically
in
a
walk
in
growth
chamber
at
16hrs
light/8hrs
dark
for
14
days.

I
measured
20
seedlings
of
similar
 length
 (to
 account
 for
 different
 germination
 times)
 per
 genotype
 and
 
 45
 recorded
both
the
root
 length
and
number
of
 lateral
roots.
 
GSTU19pro­SND1
 lines
were
treated
for
24hrs
with
100µM
benoxacor.
 2.8.3
 Plant
growth
and
height


Transgenic
and
empty
vector
control
seeds
were
surface
sterilized
with
20%
bleach
solution
 for
 20
 minutes
 and
 rinsed
 several
 times
 with
 distilled
 water
 then
germinated
 on
 ½
 MS
 plates
 then
 transferred
 to
 soil
 (Sunshine
 Mix
 #5,
 Sun
 Gro
Horticulture
 Canada
 Ltd.,
 Seba
 Beach,
 Alberta,
 Canada)
 and
 placed
 in
 a
 growth
chamber
at
16hrs
light/8hrs
dark
photoperiod.
 
Plants
were
photographed
weekly
with
 a
 Nikon
 Coolpix
 E3200
 digital
 camera
 to
 track
 plant
 height
 over
 a
 six
week
period.
 2.8.4
 Microscopy

Fresh
sections
of
the
lower
and
mid
part
of
the
stem
as
well
as
a
5mm
section
of
the
root‐hypocotyl
 (portion
 of
 the
 hypocotyl
 below
 the
 soil
 surface),
 from
 both
transgenic
and
empty
vector
lines,
grown
as
above,
were
obtained
using
a
fine
razor
blade
 and
 stained
with
 Phloroglucinol‐HCl.
 
 Sectioned
were
 placed
 in
water
 on
 a
slide
and
visualized
using
a
Leica
DM
6000B
fitted
with
a
Leica
DFC350
Fx
camera.
 

In
addition,
5mm
sections
of
root‐hypocotyl
from
both
transgenic
and
empty
vector
lines
were
fixed
in
20mL
vials
using
a
mix
of
ethanol,
acetic
acid,
formaldehyde
and
water
 (Appendix
D)
 then
dehydrated
with
50%,
60%,
70%,
85%,
95%
and
100%
ethanol.
 
 Tissues
 were
 then
 cleared
 to
 allow
 for
 paraffin
 permeation
 with
 100%
ethanol
 and
 then
 25%
 xylene:75%
 ethanol,
 50%
 xylene:50%
 ethanol,
 75%
 
 46
 xylene:25%
ethanol
and
100%
xylene.

Infiltration
was
achieved
slowly
in
order
to
preserve
 the
morphology
 of
 the
 tissue
 by
 incubating
 overnight
with
 a
mixture
 of
100%
 xylene
 and
 Paraplast®
 plus
 (Sigma)
 embedding
 chips.
 
 The
 vials
 were
incubated
at
42°C
for
one
hour
to
melt
the
Paraplast®
chips
and
then
incubated
at
60°C
for
at
least
four
hours.

The
xylene/wax
mixture
was
then
replaced
with
100%
molten
 Paraplast®
 embedding
 media
 and
 exchanged
 twice
 a
 day
 for
 three
 days
(total
 of
 six
wax
 changes).
 
Wax
moulds
were
made
 by
 pouring
 the
 hot
wax
 and
tissue
 into
petri
dishes,
which
were
 then
stored
at
4°C
 for
 later
use.
 
Paraffin
wax
embedded
 tissues
 were
 individually
 mounted
 on
 wooden
 blocks
 and
 sectioned
using
a
rotary
microtome
(Microm
HM
325).
The
10
µm
sections
were
heat
fixed
to
glass
 slides,
 used
 for
 phloroglucinol‐HCl
 staining
 and
 lignin
 autofluorescence
 (UV
360±40nm)
and
visualized
using
a
Leica
DM
6000B
microscope
fitted
with
a
Leica
DFC350
Fx
camera.











 
 47
 3.
 Results

 3.1
 Organ‐specific
expression
of
candidate
gene
and
promoters

Genes
involved
in
regulating
cell
fate
in
all
major
root
tissues
have
been
previously
described
 in
 Arabidopsis
 (Birnbaum
 &
 Benfey,
 2004).
 
 Birnbaum
 et
 al.
 (2003)
developed
 a
 method
 that
 measured
 high‐resolution
 spatial
 and
 temporal
 gene
expression
 profiles
 for
 more
 than
 22,000
 genes
 throughout
 the
 Arabidopsis
 root.

Using
an
Affymetrix
ATH1
GeneChip,
 they
mapped
gene
expression
 in
15
different
root
zones
(endodermis,
endodermis
and
cortex,
epidermal
atrichoblasts
and
lateral
root
 cap)
 that
 relate
 to
 cell
 types
 and
 tissues
at
progressive
developmental
 stages
(stage
1,
2
and
3)
(Birnbaum
et
al.
2003).



To
 identify
 candidate
 root‐specific
 genes,
 I
 mined
 the
 Birnbaum
microarray
 gene
expression
 data
 set
 for
 genes
 expressed
 in
 either
 the
 stele
 or
 endo‐cortex,
whose
relative
 probe
 intensity
 values
 were
 between
 1500
 and
 5000
 for
 those
 two
 cell
types.
 
Based
on
this
gene
expression
data,
suitable
candidate
genes
were
selected
whose
promoters
had
 the
potential
 to
drive
 root‐specific
 transgene
expression,
 as
summarized
in
Table
1.

Genes
 found
 within
 these
 specified
 parameters
 were
 then
 checked
 via
Genevestigator
 (Genevestigator,
 2008)
 for
 their
 relative
 expression
 in
 root
compared
to
other
plant
organs
and
tissues
(Fig.
5).




 
 48
 Table
1.
 
Candidate
genes
whose
promoters
have
the
potential
 to
drive
root­ specific
 transgene
 expression.
 These
 values
 are
 based
 on
 microarray
hybridization
 signals,
 which
 have
 no
 units.
 
 Values
 for
 each
 of
 the
 40
 candidate
genes
 expressed
 in
 two
 cell
 types
 (stele
 and
 endo‐cortex)
 along
 three
 stages
 of
development
are
summarized.


 
 

 Gene ID st el e st ag e1  st el e st ag e2 st el e st ag e3 co rte x- en do  s ta ge 1 co rte x- en do  s ta ge 2 co rte x- en do  s ta ge 3 AT5G11740 2328.20 4072.65 4187.95 AT1G02500 1533.40 2485.74 3911.02 AT5G08690 2447.19 2722.15 2262.39 AT5G19760 2130.62 2934.41 1991.17 AT5G64350 2036.62 2192.35 1815.69 AT5G64400 1951.80 2439.48 1970.78 AT5G44340 1679.18 2069.35 1915.47 AT5G42980 2388.33 2173.63 2080.07 AT3G62290 2491.67 3133.28 2949.11 AT3G55440 3077.48 3992.49 3306.27 AT3G48140 2206.00 2961.10 2688.84 AT4G37830 1950.50 2691.84 1996.96 AT4G33865 4566.83 4278.17 1712.47 AT4G27960 2935.53 3639.66 2929.31 AT4G11150 2053.33 2758.95 2240.15 AT4G09000 2233.24 3067.99 2910.17 AT4G05320 3069.37 4034.32 3232.28 AT4G01850 2554.28 3461.86 2174.89 AT1G18080 3571.20 2940.98 1540.72 AT3G52300 1663.77 1834.42 1629.61 AT3G17390 2674.72 3894.37 2358.48 AT3G09820 1851.13 2265.78 1842.36 AT3G02230 2841.93 4425.77 3271.57 AT1G13440 3409.25 4162.22 4092.73 AT1G78380 3422.04 3875.63 3367.03 AT1G49140 2004.04 2631.03 2008.53 AT1G07890 2681.04 3714.33 3476.60 AT1G65930 2429.96 3191.61 2690.26 AT1G56075 4228.00 4219.88 2731.18 AT1G78040 2328.73 3661.73 2509.98 AT1G79550 1946.76 2503.74 1927.94 AT1G04410 3069.00 3539.86 3280.20 AT2G36530 3452.06 3965.86 2905.62 AT1G09640 2944.01 2990.13 1917.17 AT1G22840 2644.99 3147.32 2588.76 AT1G08830 2355.48 2318.31 2170.68 AT2G16850 3075.62 3860.32 3456.93 AT2G47110 3795.65 3816.35 2060.36 AT2G30870 2107.32 3218.08 4080.73 AT2G33040 2228.69 2377.96 2174.81 1985.63 3473.40 3571.74 1909.86 3096.00 4871.20 2381.70 2649.31 2201.85 2273.26 3130.84 2124.47 2179.20 2345.83 1942.81 1963.30 2453.85 1982.39 2039.91 2513.89 2326.97 1943.89 1769.15 1693.00 2569.18 3230.76 3040.86 2927.79 3798.29 3145.46 1991.44 2673.10 2427.32 1677.77 2315.44 1717.73 4659.56 4365.03 1747.24 3228.73 4003.20 3221.90 1862.41 2502.42 2031.87 2006.88 2757.02 2615.20 2209.25 2903.80 2326.51 2819.90 3821.87 2401.06 3955.22 3257.23 1706.39 1775.90 1958.06 1739.45 2787.03 4057.89 2457.51 1797.13 2199.68 1788.62 2374.53 3697.89 2733.51 4073.49 4973.17 4890.14 2955.32 3347.04 2907.81 2042.08 2680.97 2046.65 2993.99 4147.88 3882.40 2577.25 3385.06 2853.33 4343.62 4335.28 2805.87 2305.95 3625.91 2485.43 2496.27 3210.46 2472.13 2943.20 3394.76 3145.74 3172.00 3644.12 2669.89 3190.15 3240.13 2077.46 2461.02 2928.41 2408.70 2290.24 2254.10 2110.56 2804.01 3519.41 3151.65 4195.35 4218.23 2277.33 1603.56 2448.79 3105.22 2017.78 2152.92 1969.00 
 49
 Figure
5.

Genevestigator
heat
map
of
candidate
genes
whose
promoters
have
 the
 potential
 to
 drive
 root­specific
 transgene
 expression.
 
 The
 diagram
represents
a
global
expression
map
depicting
major
patterns
of
gene
activity
among
candidate
 genes
 listed
 in
 Table
 1,
 in
 different
 plant
 organs
 and
 tissues
(Genevestigator,
 2009).
 Columns
 on
 the
 right
 represent
 two
 candidate
 promoters
(A=GSTU19
 and
B=4CL1)
 and
 one
 candidate
 gene
 (C=SND1)
 for
 engineering
 gene
expression
constructs
to
enhance
levels
of
lignin
in
the
roots
of
transgenic
plants.


 ABC 
 50
 Based
 on
 these
 results,
 one
 candidate
 gene,
 GSTU19,
 was
 selected
 for
 further
analysis.


For
the
second
candidate
gene,
4CL1,
previous
studies
have
showed
high
levels
 of
 4CL1
 gene
 expression
 in
 seedling
 roots,
 as
 demonstrated
 by
 analysis
 of
transgenic
Arabidopsis
 plants
 containing
 the
 4CL1
 or
 4CL2
 promoter
 fused
 to
 the
 beta‐glucuronidase
 (GUS)
 reporter
 gene.
 
 These
 GUS
 reporter
 plants
 show
developmentally
regulated
GUS
expression
in
the
xylem
tissues
of
both
the
root
and
shoot,
 although,
 At4CL1::GUS
 lines
 showed
 root‐specific
 expression
 in
 seedlings
(Soltani
et
al.
2006).

In
order
to
confirm
these
results
and
validate
the
potential
of
these
 candidates
 to
 drive
 root‐specific
 expression,
 the
 activity
 of
 both
 candidate
promoters
were
checked
using
semi‐quantitative
reverse
transcription
(RT)‐PCR
in
flower,
leaf,
stem
and
roots
of
four‐week‐old
plants
(Fig.
6).


Results
confirmed
that
 GSTU19
is,
in
fact,
expressed
at
a
noticeably
higher
level
in
roots
compared
to
other
plant
organs.

However,
4CL1,
showed
only
a
negligible
increase
in
expression
in
the
roots
of
four‐week‐old
plants
as
compared
to
other
tissues.

Although
these
results
showed
 4CL1
 to
 be
 less
 promising
 for
 root‐specific
 transgene
 expression,
 it
 was
retained
 as
 a
 candidate,
 based
 on
 the
 earlier
 published
 data.
 
 In
 addition,
 SND1
showed
expression
in
stems
but
no
detectable
expression
in
other
organs
(Figs.
5
&
6).
 
 
 Along
 with
 previous
 publications
 on
 the
 role
 of
 SND1
 in
 regulating
 lignin
biosynthesis,
 the
 combined
 data
 shown
 supports
 the
 use
 of
 these
 candidate
promoters
in
producing
transgenic
plants
with
higher
levels
of
lignin
in
their
roots.

In
 addition
 to
 the
 endogenous
 root‐specificity
 of
 the
 GSTU19
 gene,
 the
 previous
studies
in
Arabidopsis
showing
the
increased
root‐specific
expression
of
GSTU19
 in
response
 to
 the
 herbicide
 safeners,
 benoxacor
 and
 fenclorim
 (DeRidder
 &
 
 51
 Goldsbrough
 2006),
 suggested
 that
 the
 GSTU19
 promoter
 could
 be
 useful
 as
 a
chemical‐inducible
root‐specific
gene
expression
system.

 
 
 Figure
6.
Organ­specific
 gene
expression
of
 candidate
 gene
and
 root­specific
 promoters
 from
 four­week­old
 Arabidopsis
 plants.
 
 Semi‐quantitative
 reverse
transcription
 (RT)‐PCR
 analysis
 showing
 the
 relative
 gene
 expression
 of
 SND1,
 GSTU19
and
4CL1
in
flower,
leaf,
stem
and
root
tissues.
Expression
of
the
Act8
gene
was
used
as
both
an
internal
control
and
loading
control.

RT‐PCR
was
carried
out
in
triplicate
on
two
biological
replicates.
 
Differentially
expressed
PCR
products
were
analyzed
using
 the
 Image
 J
 (1.42)
(ImageJ:
 Image
Processing
and
Analysis
 in
 Java)
program
to
compare
the
expression
levels
of
each
transcript.
 
 3.2
 Cis‐regulatory
element
analysis
of
candidate
promoters

Several
 tissue‐specific
 cis‐acting
 regulatory
 elements
 have
 been
 previously
described;
 ACGTROOT1
 (Salinas
 et
 al.
 1992),
 ROOTMOTIFTAPOX1
 (Elmayan
 &
Tepfer
 1995),
WUSATAg
 (Kamiya
 et
 al.
 2003),
 OSE1ROOTNODULE
 (Vieweg
 et
 al.
 !" #" $" %" &" '" (" )*+,#-./01234" )*+,#-.5367" )*+,#-.*839" )*+,#-.:118" ! " #$ % & " '( " ) " '* + , -" .. /0 ) ' 12(34567'81269:;<;=>'' !" #" $" %" &" '" (" ;" <" -" #!" &=5#./01234" &=5#.5367" &=5#.*839" &=5#.:118" ! " #$ % & " '( " ) " '* + , -" .. /0 ) ' 12?@A6'81269B6C;=>'' !" #" $" %" &" '" (" ;" <" -" *>?#./01234" *>?#.5367" *>?#.*839" *>?#.:118" ! " #$ % & " '( " ) " '* + , -" .. /0 ) ' 123DE6'81269<F::=>' 
 52
 2004),
 OSE2ROOTNODULE
 (Vieweg
 et
 al.
 2004),
 RAV1AAT
 (Kagaya
 et
 al.
 1999),
ASF1MOTIFCAMV
 (Klinedinst
 et
 al.
 2000),
 SURECOREATSULTR11
 (Maruyama‐Nakashita
et
al.
2005),
SP8BFIBSP8BIB
(Ishiguro
&
Nakamura
1992),
ARFAT
(Inukai
 et
 al.
 2005),
 TELO
 (Tremousaygue
 et
 al.
 1999)
 and
 SORLIP1AT
 (Jiao
 et
 al.
2005).





To
 investigate
 possible
 root‐specific
 elements
 in
 the
 promoters
 of
 my
 candidate
genes,
 2kb
 regions
 of
 the
 4CL1
 and
 GSTU19
 promoters
 were
 analyzed
 using
 the
PLACE
(Plant
Cis‐acting
Regulatory
DNA
Elements)
database
(Higo
et
al.
1999).
 
 In
addition
 to
 the
 TATA‐box
 and
 CAAT‐box
 (core
 promoter
 sequences
 required
 to
properly
 initiate
 transcription),
 this
 analysis
 revealed
 the
 presence
 of
 many
elements
 that
 could
 possibly
 be
 related
 to
 root‐specific
 expression.
 
 The
 cis‐regulatory
elements
for
4CL1
are
summarized
in
Table
2
and
include
all
of
the
root
expression‐associated
 motifs
 mentioned
 above,
 with
 the
 exception
 of
 the
ACGTROOT1,
TELO
and
SORLIP1AT
elements.
 
 Similarly,
 as
 shown
 in
Table
3,
 the
 GSTU19
promoter
contained
all
the
previously
described
root
expression‐associated
motifs
 with
 the
 exception
 of
 the
 ACGTROOT1
 and
 TELO
 elements.
 
 It
 should
 be
noted
that
the
frequency
of
any
given
cis‐regulatory
motif
sequence
occurring
in
the
promoter
 region
 by
 random
 chance
 may
 be
 calculated
 based
 on
 the
 nucleotide
frequency
that
could
occur
within
a
2kb
promoter
region,
assuming
that
nucleotides
are
arranged
at
random.

The
elements
that
were
of
doubtful
statistical
significance
in
the
in
silico
GSTU19
promoter
analysis,
are
demarcated
by
an
asterisk
(Table
3).

It
is
important
to
note
that
the
sizes
of
the
promoter
fragments
that
were
amplified
for
 the
 transgenic
 constructs
 (4CL1pro
 (1224bp)
 and
GSTU19pro
 (1402bp)),
were
slightly
less
then
the
2kb
regions
analyzed
in
PLACE
but
contained
at
the
very
least
 
 53
 one
of
each
of
the
root
expression‐associated
elements
found
in
the
2kb
fragments
analyzed.


 Table
2.
Cis­acting
DNA
regulatory
elements
located
2000
bp
upstream
of
the
 transcription
 start
 site
 of
At4CL1
 (At1g51680).
The
 high
 frequency
 regulatory
elements
 are
 shown
 first
 as
well
 as
 the
 number
 of
 times
 the
 element
 appears
 on
both
 the
 (+)
 and
 (‐)
 strands
 (actual
 frequency).
 The
 third
 column
 represents
 the
number
of
 times
that
a
motif
could
occur
at
random
assuming
all
 four
nucleotides
are
 represented
equally,
 given
 the
number
of
base
pairs
 in
 the
sequence
 (i.e.
1:4x,
where
 x
 is
 the
 number
 of
 base
 pairs
 in
 the
motif
 sequence),
 in
 the
 2kb
promoter
region
 analyzed.
 This
 number
 gives
 an
 indication
 of
 the
 number
 of
 elements
 that
would
need
 to
 appear
 in
 the
promoter
 (on
 a
 single
 strand)
 in
 order
 for
 the
over‐represented
motif
to
be
statistically
significant,
based
on
the
statistical
frequency
of
occurrence
of
that
sequence.



 Putative
root
motif
 Sequence
 Statistical
frequency
of
occurrence
in
the
2kb
promoter
 fragment
analyzed
 Actual
frequency
 ROOTMOTIFTAPOX1
 ATATT
 1.95:2000
 13+;
16‐
RAV1AAT
 CAACA

 1.95:2000
 6+;
1‐
ASF1MOTIFCAMV*
 TGACG

 1.95:2000
 2+;
2‐
OSE2ROOTNODULE
 CTCTT
 1.95:2000
 4+
OSE1ROOTNODULE
 AAAGAT
 0.488:2000
 2+;
1‐
SURECOREATSULTR11
 GAGAC
 1.95:2000
 3‐
SP8BFIBSP8BIB
 TACTATT
 0.122:2000
 2‐
ARFAT
 TGTCTC
 0.5:2000
 1+
WUSATAg
 TTAATAG
 0.122:2000
 1‐
*Sequence
of
doubtful
statistical
significance
 
 
 
 
 
 
 
 54
 
 Table
3.
Cis­acting
DNA
regulatory
elements
located
2000
bp
upstream
of
the
 transcription
start
site
of
AtGSTU19
(At1g78380).
The
high
frequency
regulatory
elements
 are
 shown
 first
 as
well
 as
 the
 number
 of
 times
 the
 element
 appears
 on
both
 the
 (+)
 and
 (‐)
 strands
 (actual
 frequency).
 The
 third
 column
 represents
 the
number
of
 times
that
a
motif
could
occur
at
random
assuming
all
 four
nucleotides
are
 represented
equally,
 given
 the
number
of
base
pairs
 in
 the
 sequence
 (i.e.
1:4x,
where
 x
 is
 the
 number
 of
 base
 pairs
 in
 the
motif
 sequence),
 in
 the
 2kb
promoter
region
 analyzed.
 This
 number
 gives
 an
 indication
 of
 the
 number
 of
 elements
 that
would
need
 to
 appear
 in
 the
promoter
 (on
 a
 single
 strand)
 in
 order
 for
 the
 over‐represented
motif
to
be
statistically
significant,
based
on
the
statistical
frequency
of
occurrence
of
that
sequence.


 
 

 Putative
root
motif
 Sequence
 Statistical
frequency
of
occurrence
in
the
 2kb
promoter
 fragment
analyzed
 Actual
frequency

 ROOTMOTIFTAPOX1
 ATATT
 1.95:2000
 6+;
8‐
OSE1ROOTNODULE
 AAAGAT
 0.488:2000
 1+;
5‐
OSE2ROOTNODULE
 CTCTT
 1.95:2000
 4+;
2‐
ASF1MOTIFCAMV
 TGACG

 1.95:2000
 3+;
2‐
RAV1AAT*
 CAACA

 1.95:2000
 2+;
2‐
SORLIP1AT
 GCCAC
 1.95:2000
 4+
SURECOREATSULTR11*
 GAGAC
 1.95:2000
 1+;
1‐
ARFAT
 TGTCTC
 0.488:2000
 1+
SP8BFIBSP8BIB
 TACTATT
 0.122:2000
 1‐
WUSATAg
 TTAATAG
 0.122:2000
 1+

*Sequence
of
doubtful
statistical
significance



 
 55
 3.3
 SND1
overexpression
in
transgenic
plants

Two
 gene
 expression
 constructs
 (GSTU19pro­SND1
 and
 4CL1pro­SND1)
 were
engineered
 by
 PCR
 amplification
 and
 ligation
 of
 the
GSTU19
 and
4CL1
 promoters
and
SND1
 ORF
with
 the
 pPZP211
Agrobacterium
 binary
 vector.
 
 These
 constructs
were
 then
 introduced
 into
 Arabidopsis
 plants
 using
 Agrobacterium‐mediated
transformation.
 PCR
 analysis
 of
 genomic
 DNA
 was
 used
 to
 select
 T1
 generation
kanamycin‐resistant
 transgenic
 lines
by
 confirming
 the
presence
of
 the
 transgene.
Roots
 from
 three‐week‐old
 T2
 generation
 kanamycin‐resistant
 transgenic
 lines
were
 subsequently
 analyzed
 using
 RT‐PCR
 to
 determine
 whether
 the
 SND1
transgene
was
being
overexpressed.

The
RT‐PCR
analysis
detected
overexpression
of
SND1,
compared
to
wild
type,
in
~90%
of
the
lines
analyzed
for
both
constructs,
as
shown
in
Figure
7.

 
 
 Figure
 7.
 Transcriptional
 analysis
 of
 T2
 generation
 plants
 overexpressing
 SND1
using
RT­PCR.


Total
RNAs
were
isolated
from
three‐week–old
root
tissue
of
10
 independent
 transgenic
 plant
 lines
 from
 each
 construct
 as
 well
 as
 wild
 type
control
 plants.
GSTU19pro­SND1
 lines
were
 induced
with
 100µM
benoxacor
 on
½
MS
solid
media
for
twenty‐four
hours
prior
to
RNA
extraction.
Actin8
was
used
as
an
internal
and
loading
control
as
shown
by
comparable
expression
levels.


These
lines
represent
a
mixture
of
both
homozygous
and
heterozygous
individuals;
therefore,
 among
 the
 T2
 generation
 lines
 showing
 overexpression,
 12
 sub‐lines
 W .T .1  !"#$%&'()*"+,%- ./0%'()*"+,%- A -1  A -7  C -5  W .T .2  B -8  D -2  D -7  W .T .2  E -2  E -4  E -3  F -3  F -4  F -5  F -7  F -8  G -8  H -3  B -5  C -2  D -1  D -6  SND1 Act8 
 56
 were
 screened
 for
 homozygosity.
 
 Twenty‐two
 kanamycin‐resistant
 homozygous
sub‐lines
were
identified
for
GSTU19pro­SND1
and
twelve
for
4CL1pro­SND1.

These
T3
generation
transgenic
 lines,
homozygous
 for
a
single
active
T‐DNA
insert,
were
used
 for
 further
 experiments
 to
 determine
 the
 possible
 effects
 of
 SND1
overexpression.
 3.4
 Molecular
analysis
of
transgenic
plants
overexpressing
SND1

Given
the
recent
identification
of
SND1
as
a
master
transcriptional
switch
activating
the
developmental
program
of
secondary
cell
wall
biosynthesis
and
as
an
activator
of
several
transcription
factors
that
are
involved
in
that
process
(Zhong
et
al.
2006;
Zhong
et
al.
2008),
I
predicted
that
SND1
overexpression
would
result
in
an
increase
in
expression
of
direct
targets
of
SND1,
such
as
MYB46,
SND3,
MYB103
and
KNAT7.

Reverse
 transcription
 PCR
 analysis
 of
 these
 direct
 targets
was
 conducted
 for
 two
reasons:
1)
to
determine
whether
the
secondary
cell
wall
gene
regulatory
networks
previously
described
were
present
and
functional
in
roots,
and
2)
to
investigate
the
root‐specificity
 of
 the
 constructs.
 
 As
 shown
 in
 Figure
 8,
 SND1
 was
 found
 to
 be
upregulated
in
both
roots
and
shoots
(aerial
tissue
in
seedlings
that
does
not
include
stems)
compared
to
empty
vector
control
lines.

In
contrast,
the
other
transcription
factors
(TFs)
analyzed
showed
negligible
changes
in
gene
expression
in
shoots
but
showed
a
more
noticeable
increase
in
gene
expression
in
roots.

Given
that
these
TFs
are
 normally
 preferentially
 expressed
 in
 stems
 (Zhong
 et
 al.
 2006;
 Zhong
 et
 al.
2008),
 this
 data
 provides
 evidence
 that
 the
 SND1
 overexpression
 constructs
 are
behaving
in
a
root‐preferential
manner
and
that
SND1
overexpression
results
in
an
increase
in
gene
expression
of
its
direct
targets.


 
 57
 
 Figure
8.
Transcriptional
analysis
of
transcription
factors
known
to
be
direct
 targets
of
SND1.
Three‐week‐old
T3
generation
Arabidopsis
seedlings
grown
on
½
MS
solid
medium
and
GSTU19pro­SND1
 lines
 treated
 for
24
hours
with
benoxacor
(100
µM).

Total
RNA
was
extracted
from
roots
(R)
and
shoots
(S)
of
transgenic
and
empty
vector
 lines.
Transcription
 factors
were
analyzed
using
RT‐PCR.
Actin8
was
used
 as
 an
 internal
 and
 loading
 control
 as
 shown
 by
 comparable
 levels.
Differentially
 expressed
 PCR
 products
 were
 analyzed
 using
 the
 Image
 J
 (1.42)
(ImageJ:
Image
Processing
and
Analysis
in
Java)
program
to
compare
the
expression
levels
 of
 each
 transcript
 relative
 to
 the
 Actin8
 control.
 SND1
 (At1g32770);
 SND3
(At1g28470);
MYB46
(At5g12870);
MYB103
(At1g63910);
KNAT7
(At1g62990).



To
 determine
 whether
 the
 result
 of
 the
 ectopic
 gene
 expression
 of
 these
 TFs
specifically
 influences
 lignin
 biosynthesis
 in
 roots,
 RT‐PCR
 analysis
 was
 also
performed
on
genes
encoding
three
indicative
lignin
biosynthetic
pathway
enzymes
(4CL1,
CCR
and
COMT),
as
shown
in
Figure
9.


 !"!# $!"!# %!"!# &!"!# '!"!# (!!"!# ($!"!# (%!"!# ! " #$ % & " '( " ) " '* + , -" .. /0 ) ' 1234' !005' 16005' *7' (1894:,-0;1234'<3;=>'?@A4,-0;1234'<B;C>' !"!# (!"!# $!"!# )!"!# %!"!# *!"!# &!"!# +!"!# '!"!# ! " #$ % & " '( " ) " '* + , -" .. /0 ) ' 123D# @0)5-0#E.6005' ?@A4,-0;1234E.6005' (1894:,-0;1234E.6005' @0)5-0#E-005' ?@A4,-0;1234E-005' (1894:,-0;1234E-005' !"!# (!"!# $!"!# )!"!# %!"!# *!"!# &!"!# +!"!# ! " #$ % & " '( " ) " '* + , -" .. /0 ) ' FGH?I## !"!# (!"!# $!"!# )!"!# %!"!# *!"!# &!"!# +!"!# ! " #$ % & " '( " ) " '* + , -" .. /0 ) ' FGH4JD# !"!# *!"!# (!!"!# (*!"!# $!!"!# $*!"!# ! " #$ % & " '( " ) " '* + , -" .. /0 ) ' K2L8C# 
 58
 
 
 Figure
 9.
 Reverse
 transcription
 PCR
 analysis
 of
 genes
 involved
 in
 lignin
 biosynthesis.
 
 Three‐week‐old
 T3
 generation
 Arabidopsis
 seedlings
 grown
 on
½
MS
solid
medium
and
GSTU19pro­SND1
 lines
 treated
 for
24
hours
with
benoxacor
(100
µM).

Total
RNA
was
extracted
from
roots
(R)
and
shoots
(S)
of
transgenic
and
empty
vector
lines.
 
Act8
was
used
as
an
internal
and
loading
control
as
shown
by
comparable
 levels.
Differentially
 expressed
PCR
products
were
analyzed
using
 the
Image
J
(1.42)
(ImageJ:
Image
Processing
and
Analysis
in
Java)
program
to
compare
the
expression
levels
of
each
transcript
relative
to
the
Act8
control.


In
 contrast
 to
 the
 results
 for
 expression
 of
 the
 secondary
 cell
 wall‐related
 TFs,
 I
observed
no
difference
 in
gene
expression
among
 the
 lignin
biosynthetic
genes
or
among
 tissue
 types
 compared
 to
 empty
 vector
 controls.
 
 
 This
 data
 suggests
 that
unlike
the
previously
described
constitutive
overexpression
of
SND1,
which
showed
ectopic
 deposition
 of
 lignified
 secondary
walls
 in
 normally
 non‐sclerenchymatous
cells
 of
 flowers,
 leaves
 and
 stems
 (Zhong
 et
 al.
 2006),
 overexpression
 of
 SND1
 in
 A-7 Root A-7 Shoot G-8 Root G-8 Shoot EV Root EV Shoot !" #!" $!" %!" &!" '!!" '#!" '$!" '%!" '&!" ! " #$ % & " '( " ) " '* + , -" .. /0 ) ' A-7 Root A-7 Shoot G-8 Root G-8 Shoot EV Root EV Shoot 
 59
 roots
 had
 no
 influence
 on
 the
 expression
 of
 genes
 encoding
 certain
 key
 lignin
biosynthetic
enzymes.
 3.5
 Determination
of
lignin
content
in
transgenic
plants
overexpressing

 
 SND1
 3.5.1
 Determination
of
lignin
content
in
transgenic
plants
overexpressing
SND1
by
 
 rapid
micro‐scale
acetyl
bromide
method

To
 determine
 total
 lignin
 content,
 several
 methods
 and
 techniques
 have
 been
developed
and
adapted
in
order
to
quantitatively
determine
total
lignin
content
and
composition
 in
plant
 tissues
(Hatfield
&
Fukushima
2005).
 
To
analyze
 total
 lignin
content
(w/w)
in
the
roots
of
transgenic
plants
overexpressing
SND1,
I
first
used
a
rapid
micro‐scale
method
as
outlined
 in
Chang
et
al.
(2008).
 
This
acetyl
bromide‐based
 lignin
 micro‐scale
 assay
 was
 primarily
 developed
 to
 provide
 a
 rapid
 yet
sensitive
method
of
determining
lignin
concentration,
using
small
amounts
of
plant
material.
 
 This
 method
 is
 useful
 for
 small
 samples
 whose
 size
 is
 unsuitable
 for
procedures
 that
 rely
 on
 the
 production
 and
 gravimetric
 measurement
 of
 an
insoluble
lignin
residue,
such
as
the
Klason
lignin
analysis.



Based
on
the
previous
studies
that
had
shown
SND1
to
be
a
master
transcriptional
switch
activating
the
developmental
program
of
secondary
cell
wall
biosynthesis
in
fibres,
 I
 predicted
 that
 the
 overexpression
 of
 SND1
 and
 its
 direct
 target
 genes
 in
roots
 would
 cause
 an
 increase
 in
 total
 lignin
 content
 (Zhong
 et
 al.
 2006).

Unexpectedly,
 my
 analysis
 of
 roots
 of
 the
 transgenic
 SND1
 overexpression
 lines,
showed
a
47%
and
40%
decrease
 in
 total
 lignin
 content
 in
both
GSTU19pro­SND1
overexpression
 lines
 (A‐7
 and
 B‐5
 respectively)
 and
 a
 46%
 decrease
 in
 lignin
 
 60
 content
 in
 one
 of
 the
 two
 4CL1pro­SND1
 overexpression
 lines
 (G‐8)
 (Fig.
 10),
compared
 to
 the
 roots
of
 empty
vector
 control
plants.
 
 The
 second
4CL1pro­SND1
overexpression
line
analyzed
(F‐5)
showed
no
obvious
change
in
lignin
content
(1%
decrease)
 compared
 to
 the
 empty
 vector
 control.
 
 This
 result
 appears
 to
 be
correlated
with
 the
 lack
 of
 altered
 gene
 expression
 among
 the
 lignin
 biosynthetic
genes
observed
in
these
same
genotypes
(Fig.
9).


 Figure
 10.
 Lignin
 content
 in
 transgenic
 Arabidopsis
 plants
 overexpressing
 SND1.
 Percent
 lignin
 content
 (w/w),
 determined
 by
 the
 rapid
 microscale
 acetyl
bromide
 method,
 in
 empty
 vector
 control
 and
 transgenic
 Arabidopsis
 plants
expressing
 the
 4CL1pro­SND1
 (Grey)
 and
 GSTU19pro­SND1
 (Red)
 constructs.
Control
 plants
 contain
 pPZP211.
 Error
 bars
 indicate
 standard
 error
 from
 three
technical
replicates
(control
and
transgenic
lines
are
T3
generation).
 3.5.2
 Cellulose,
starch
and
Klason
lignin
analysis

Because
SND1
has
been
shown
to
be
a
master
transcriptional
switch
activating
the
developmental
 program
 of
 overall
 secondary
 cell
 wall
 biosynthesis
 in
 fibres,
 as
opposed
 to
 just
 lignin
biosynthesis,
 I
 reasoned
 that
 the
decrease
 in
 lignin
 content
 !"!# $"!# %"!# &"!# '"!# ("!# )"!# *"!# +"!# ,"!# $!"!# ! "# $% & $& "' ( & )* & )" +, -, ." /$%&$&"0(&)*&)" -.(# /.+# 01234# 567389# :.*# ;.(# 01234# 567389# <;6=8>?# 
 61
 and
 lack
 of
 change
 in
 expression
 of
 genes
 encoding
 lignin
 biosynthetic
 enzymes,
could
 be
 a
 result
 of
 carbon
 being
 reallocated
 to
 a
 different
 area
 of
 carbon
metabolism.



Plants
 use
 photosynthesis
 to
 chemically
 convert
 CO2
 to
 carbohydrates,
 such
 as
cellulose
and
starch.


Cellulose
is
an
important
component
of
the
cell
walls
of
higher
plants
 and
 the
world's
most
 abundant
 organic
 polymer,
 serving
 as
 another
major
carbon
sink
in
plants
(similar
to
lignin)
(Delmer
&
Haigler
2002).

One
other
major
plant
 carbon
 sink
 is
 the
 other
 major
 glucan,
 starch
 (α‐1,4‐glucan
 with
 α‐1,6
branches).
 
 As
 leaves
 (sources
 that
 export
 carbon)
 and
 storage
organs
 (sinks
 that
import
 carbon)
 expand,
 they
 enlarge
 and
 deposit
 their
 cellulose
 in
 their
 primary
walls
before
the
developmental
transition
that
leads
to
starch
deposition
(Delmer
&
Haigler
2002).



While
the
ratio
of
cellulose
to
other
cell
wall
polymers
can
change
considerably,
 until
 recently
 it
 was
 not
 clear
 from
 the
 publicly
 available
 literature
whether
 carbon
 flux
 in
 plants
 with
 altered
 lignin
 biosynthetic
 pathways
 directly
altered
 other
 carbon‐polymer
 synthetic
 pathways
 (Delmer
 &
 Haigler
 2002).


Studies
 have
 now
 shown
 that
 alterations
 in
 lignin
 deposition
 can
 cause
 relative
cellulose
 content
 to
 increase,
 as
 a
 result
 of
 these
 perturbations
 (Coleman
 et
 al.
2008).
To
 test
 my
 carbon
 reallocation
 hypothesis,
 I
 analyzed
 both
 cellulose
 and
 starch
content
 (in
 addition
 to
 insoluble
 lignin
 content).
 
 
 The
 carbohydrate
 analysis
provided
an
indirect
measure
of
the
cellulose
(quantified
as
glucose
monomers)
and
other
wall
 pollysacharides
 (pectin
 and
 hemicellulose),
 (quantified
 as
 other
 sugars
 
 62
 such
 as
 rhamnose,
 fucose,
 arabinose,
 xylose,
 mannose
 and
 galactose
 monomers)
content
of
transgenic
lines
overexpressing
SND1
(Table
4).


These
results
showed
a
29%
decrease
in
cellulose
content
and
26%
decrease
in
hemicellulose
content
in
the
 GSTU19pro­SND1
 line
 (D‐2)
 compared
 to
 the
 empty
 vector
 control.
 
 The
4CL1pro­ SND1
 line
 (G‐8)
 showed
 a
 slight
 decrease
 of
 3.5%
 in
 cellulose
 content
 and
 a
negligible
1.5%
decrease
in
hemicellulose
content.


Furthermore,
the
Klason
lignin
analysis
 revealed
 a
 23%
 decrease
 in
 lignin
 content
 in
 the
 GSTU19pro­SND1
overexpression
 line
 (D‐2)
compared
 to
 the
control,
which
was
consistent
with
 the
decrease
in
lignin
content
found
for
GSTU19pro­SND1
lines
analyzed
using
the
acetyl
bromide‐based
method.

Conversely,
the
4CL1pro­SND1
line
(G‐8)
showed
less
then
a
0.1%
increase
in
lignin
content
compared
to
the
empty
vector
control,
a
nominal
amount.
 
This
 line,
when
analyzed
by
 the
acetyl
bromide
based
method,
showed
a
46%
decrease
in
 lignin
content
as
described
in
the
previous
section.
 
Although
the
 4CL1pro­SND1
 (G‐8)
 line
 showed
 different
 results
 when
 analyzed
 using
 two
different
methods,
the
results
shown
here
using
the
Klason
procedure
are
similar
to
the
other
4CL1pro­SND1
line
(F‐5)
analyzed
using
the
acetyl‐bromide
based
method.

 Table
4.
Cell
wall
composition
of
roots
from
empty
vector
and
transgenic
lines
 overexpressing
 SND1.
Numbers
 represent
 milligrams
 of
 cellulose,
 hemicellulose
and
lignin
per
100
milligrams
of
initial
dry
weight.


Absolute
values
shown
are
from
a
single
biological
replicate.
 

 
 63
 
 3.6
 Phenotypic
analysis
of
transgenic
plants
overexpressing
SND1
 3.6.1
 Seed
phenotyping

It
 is
 desirable
 to
 avoid
 pleiotropic
 effects
 that
 might
 result
 from
 constitutive
overexpression
of
 target
genes
 in
agricultural
 systems,
which
 is
why
 the
ability
 to
drive
 transgene
 expression
 in
 a
 location‐specific
 and
 controlled
 manner
 is
important.
 
 
 I
wished
 to
 determine
whether
 the
 transgenic
 plants
 overexpressing
 SND1,
 displayed
 any
 phenotypes
 that
 might
 reflect
 an
 impact
 of
 transgene
expression
 on
 normal
 plant
 growth
 and
development.
 
 As
 one
measure
 of
 overall
growth
 and
 productivity,
 I
 decided
 to
 analyze
 seed‐related
 traits.
 
 My
 transgenic
plants
overexpressing
SND1
did
not
show
significant
deviations
from
control
plants
(empty
vector
 lines)
 in
 terms
of
 the
average
number
of
 seeds
per
 silique,
 average
silique
length,
or
average
germination
rate
when
compared
to
empty
vector
control
lines
(Fig.
11;
A,
B
and
D).

Average
seed
weights
for
all
lines
fell
into
a
range
of
18‐25µg
 per
 seed.
 The
 results
 for
 average
 weight
 per
 seed
 showed
 significant
differences
(Fig.
11;
C)
as
represented
by
the
lack
of
overlap
in
the
error
bars,
but
significant
variation
was
also
seen
for
both
control
lines
as
well.


A
one‐way
ANOVA
test
 of
 the
 overall
 model
 was
 done
 to
 determine
 if
 there
 was
 a
 statistically
significant
difference
in
means
(with
respect
to
seed
weight)
between
genotypes.

In
this
 case,
 the
 p‐value
 was
 small
 P
 <
 0.001
 (Appendix
 E),
 therefore
 there
 was
 a
statistically
significant
difference
in
seed
weight
among
genotypes.


 
 64
 
 
 Figure
 11.
 Seed­related
 phenotypes
 of
 T3
 generation
 seeds
 from
 transgenic
 and
empty
vector
constructs.
 
Average
number
of
 seeds
per
silique
 (A),
 average
silique
 length
 (B),
 average
weight
 per
 seed
 (C)
 and
 average
 germination
 rate
 (D).

Error
bars
represent
the
95%
confidence
 interval
of
an
average
of
30
samples
per
genotype
 for
 (A)
 and
 (B),
 6
 samples
 per
 genotype
 for
 (C)
 and
 3
 samples
 per
genotype
 for
 (D).
 
GSTU19pro­SND1
 lines
were
 treated
 for
 24
 hours
with
 100µM
benoxacor
in
water,
at
4‐weeks.
 
 3.6.2
 Root
growth
and
lateral
root
density

Another
 facet
 of
 plant
 development
 in
 Arabidopsis
 is
 the
 production
 of
 a
 highly
branched
root
system.

Plant
roots
are
important
tissues
involved
in
many
processes
such
 as
 uptake
 of
 water,
 interactions
 with
 soil‐microbes,
 the
 secretion
 of
compounds
required
for
defense
against
pathogens
and
absorption
of
soil
nutrients.

Furthermore,
 they
 protect
 the
 aboveground
 tissues
 against
 the
 effects
 of
 acidic
 !" !#$" !#%" !#&" !#'" (" (#$" (#%" (#&" (#'" ! " # $% & # '( )* )+ , # '- # . & /0 '1 23 4' '''''56-78$9:(;<7'''=(>?7@8$9:(;<7'''A38/B'C#2/9$' )*'"+%," -*."+%," -*/"+(!," 0*."+/," 1*/"+&," 2*$"+&," 3#4#"5%!" 3#4#"5%(" !" (!" $!" 6!" %!" /!" &!" .!" ! " # $% & # 'D '( # # E F' 8 # $' ( )* )+ , # ' '''''56-78$9:(;<7'''=(>?7@8$9:(;<7'''A38/B'C#2/9$' )*'"+%," -*."+%," -*/"+(!," 0*."+/," 1*/"+&," 2*$"+&," 3#4#"5%!" *(!#!7" !#!7" (!#!7" $!#!7" 6!#!7" %!#!7" /!#!7" &!#!7" .!#!7" '!#!7" 8!#!7" (!!#!7" ((!#!7" $%9:;" 6&9:;" %'9:;" = # $3 ). % G 9 . 'H % /# ' >)3#' 0*."+/," 1*/"+&," -*."+%," )*'"+'," 3#4#"%!" 3#4#"%(" A B C D !#!" /#!" (!#!" (/#!" $!#!" $/#!" ! " # $% & # 'I # )& 0 /' 8 # $' ( # # E '1 , & 4' '''''56-78$9:(;<7'''=(>?7@8$9:(;<7'''A38/B'C#2/9$' )*'"+%," -*."+%," -*/"+(!," 0*."+/," 1*/"+&," 2*$"+&," 3#4#"5%!" 3#4#"5%(" 
 65
 conditions
or
heavy
metals
in
the
soil,
and
against
desiccation
(Koyama
et
al.
2005).

Since
I
was
overexpressing
a
transcription
factor
in
the
roots,
which
is
not
a
tissue
in
which
it
is
usually
highly
expressed,
I
asked
whether
the
overexpression
of
SND1
in
the
roots
of
my
transgenic
plants
had
altered
their
root
development.

To
assess
this,
I
 examined
 root
 growth
 and
 architecture
 by
 measuring:
 1)
 the
 primary
 root
extention
 among
 14‐day‐old
 seedlings
 at
 a
 similar
 stage
 of
 developmental
 (i.e.
similar
primary
root
 length)
(Fig.
12A)
and
2)
the
number
of
 lateral
roots
forming
on
 these
 primary
 extentions
 (Fig.
 12B).
 
 Lateral
 root
 density
 (LRD)
 was
 then
determined
by
dividing
the
average
number
of
lateral
roots
counted,
by
the
average
length
of
the
primary
root
(Fig.
12C).



Both
of
the
GSTU19pro­SND1
 transgenic
lines
(A‐7
and
B‐5)
showed
an
increase
in
LRD
compared
to
the
empty
vector
control.


In
comparison,
one
of
the
two
4CL1pro­ SND1
transgenic
lines
analyzed,
(G‐8),
showed
higher
lateral
root
density,
whereas
the
 other
 line
 (F‐7)
 did
 not
 show
 any
 difference
 compared
 to
 the
 empty
 vector
control
line.
 
A
one‐way
ANOVA
test
of
the
overall
model
was
done
to
determine
if
there
 was
 a
 statistically
 significant
 difference
 in
 means
 (with
 respect
 to
 LRD)
between
 genotypes.
 
 In
 this
 case,
 the
 p‐value
 was
 small
 P
 =
 0.000
 (Appendix
 E),
therefore
 there
 was
 a
 statistically
 significant
 difference
 in
 lateral
 root
 density
among
genotypes.

 
 66
 
 Figure
 12.
 
 Primary
 root
 extention,
 lateral
 root
 formation
 and
 number
 of
 lateral
roots
per
cm
(lateral
root
density)
of
14­day­old
seedlings.

Left
to
right,
 4CL1pro­SND1
(red);
GSTU19pro­SND1
(grey);
empty
vector
(neutral).

Lateral
root
density
 was
 calculated
 by
 dividing
 the
 number
 of
 lateral
 roots
 by
 the
 length
 of
primary
 root
 (cm).
 Lateral
 roots
 that
 had
 emerged
 at
 least
 1.0
mm
 from
 the
 root
surface
 were
 counted.
 Error
 bars
 represent
 the
 95%
 confidence
 interval
 of
 an
average
of
20
samples
per
genotype.

 3.6.3
 Plant
growth
and
height


Plant
 growth
 and
 development
 are
 controlled
 by
 the
 combined
 action
 of
 many
different
 signaling
 pathways,
 which
 integrate
 information
 from
 the
 environment
with
 metabolic
 and
 developmental
 signals.
 
 If
 these
 normal
 developmental
pathways,
 such
 as
 the
phenylpropanoid
pathway,
 are
 disrupted
or
 altered,
 severe
consequences
to
overall
plant
growth
and
function
could
ensue.

To
investigate
the
effects
of
SND1
overexpression
in
roots
on
general
plant
growth
and
development,
I
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
 67
 examined
 transgenic
 lines
 over
 a
 six‐week
 period
 to
 look
 for
 any
 obvious
phenotypic
differences
in
normal
plant
growth
and
development,
such
as
flowering
time,
overall
plant
height
and
shape,
and
leaf
morphology.

Transgenic
plants
did
not
show
any
visible
phenotypic
differences
as
compared
to
empty
vector
control
lines
as
shown
in
Figure
13.





 

 Figure
 13.
 
 Plant
 growth
 and
 height
 time­course
 experiment
 for
 transgenic
 plants
overexpressing
SND1
and
empty
vector
lines.
 
Photographs
are
detailing
plant
growth
at
(A)
3
weeks,
(B)
4
weeks,
(C)
5
weeks
and
(D)
6
weeks.

GSTU19pro­ SND1
lines
were
treated
at
4
weeks
for
24
hours
with
100µm
benoxacor.
 

 !"#$%&# '()*+,-./0(12+# %34+-./0(12+# !"#$%&# '()*+,-./0(12+# %34+-./0(12+# !"#$%&# '()*+,-./0(12+# %34+-./0(12+# !"#$%&# '()*+,-./0(12+# %34+-./0(12+# A B C D 
 68
 3.6.4
 Microscopy

In
SND1
overexpression
plants
where
the
gene
was
under
the
control
of
the
CaM35S
promoter,
 ectopic
 secondary
wall
 thickening
was
not
always
observed
 in
 root
 cell
types
 but
was
 occasionally
 seen
 in
 the
 epidermal
 cells
 of
 hypocotyls
 and
 cortical
cells
(Zhong
et
al.
2006).

To
investigate
lignin
deposition
patterns
in
my
transgenic
lines
 overexpressing
 SND1
 under
 the
 control
 of
 more
 root‐specific
 promoters,
 I
conducted
 a
 histochemical
 analysis
 of
 the
 root‐hypocotyl
 in
 the
 various
 different
transgenic
plant
genotypes
I
had
developed.

Root‐hypocotyls
were
fixed,
embedded
in
 paraffin
wax
 and
 sectioned.
 
 For
 visualization
 of
 lignified
 secondary
walls,
 the
sections
 were
 stained
 with
 phloroglucinol‐HCl
 reagent
 (Pomar
 et
 al.
 2002).

Phloroglucinol–HCl
staining
should
identify
cell
walls
that
have
lignin
deposition,
by
staining
 them
 red.
 
 However,
 my
 transgenic
 plants
 did
 not
 show
 any
 visible
differences
 in
 lignin
 deposition
 when
 compared
 with
 empty
 vector
 controls
 (Fig.
14).
 
 
 There
 was
 notable
 variation
 in
 lignin
 content
 along
 the
 5mm
 sections
 of
hypocotyl
 analyzed,
 however,
 which
 made
 it
 difficult
 to
 establish
 developmental
equivalencies.
 Nevertheless,
 the
 histochemical
 results
 suggest
 that
 SND1
overexpression
 in
 root
 tissue
 had
 produced
 no
 observable
 difference
 in
 lignin
deposition
patterns
in
the
tissues
analyzed.
 
 69
 

 Figure
 14.
 Wax­embedded
 root­hypocotyl
 cross­sections
 of
 SND1
 overexpressors
and
empty
vector
control
lines.
The
10
µm
sections
were
stained
with
phloroglucinol‐HCl
 to
 show
 lignified
walls.
X=xylem,
P=phloem
and
NV=non‐vascular.
 Scale
 bars
 represent
 200µm
 at
 5x
 magnification.
 
 
 A=A‐7;
 B=D‐6;
 C=EV
(safener
 treated);
 D=F‐7;
 E=G‐8;
 F=EV.
 
 A,
 B
 and
 C
 were
 treated
 with
 100µM
benoxacor
for
24
hours.


To
 further
 analyze
 lignin
 deposition
 patterns,
 lignin
 autofluorescence
 was
monitored
 in
 tissues
 irradiated
 with
 UV
 light
 at
 360±40nm
 (Fig.
 15).

Autofluorescence
at
this
irradiation
wavelength
allows
an
assessment
of
the
overall
localization
 of
 lignin
 in
 tissues
 that
 are
 lignified
 (Tao
 et
 al.
 2009).
 
 
 Observations
from
 low
(5x)
 to
high
 (40x)
magnification
 (data
not
 shown)
revealed
no
apparent
differences
in
lignin
location
or
architecture.

Again,
there
was
some
variation
in
the
observed
fluorescence
along
the
5mm
developmental
gradient.

However,
as
seen
at
20x
magnification
(Fig.
15)
there
was
no
substantial
difference
in
cell
wall
thickness
or
organization
among
sections
and
tissues
analyzed.
 GSTU19pro-SND1 Empty Vector Empty Vector 4CL1pro-SND1 B C D E F A NV P X 
 70
 
 
 Figure
 15.
 Auto­fluorescence
 of
 lignin
 in
 root­hypocotyl
 cross­sections.
 UV
fluorescence
 microscopy
 (UV
 360±40nm)
 of
 10µm
 wax‐embedded
 root
 cross‐sections
visualized
at
20x
magnification.
Bars=50µm.

A=A‐7;
B=D‐6;
C=EV
(safener
treated);
D=F‐7;
E=G‐8;
F=EV.















 A B C D E F GSTU19pro-SND1 Empty Vector 4CL1pro-SND1 Empty Vector 
 71
 4.
 Discussion

Soils
 represent
 the
main
 carbon
 pool
 of
 the
 global
 carbon
 cycle.
 
 Photosynthesis
enables
plants
 to
convert
atmospheric
CO2
 into
carbohydrates
(such
as
starch
and
cellulose)
or
into
other
more
stable
organic
carbon
forms
such
as
lignin
(Zibilske
&
Bradford
 2007).
 
 Next
 to
 cellulose,
 lignin
 is
 the
 second
 most
 abundant
 carbon
biopolymer
on
earth,
accounting
for
an
estimated
30%
of
the
organic
carbon
(C)
in
the
 biosphere
 (Dungait
 et
 al.
 2008).
 
 It
 is
 known
 that
 the
 abundance,
 tissue
distribution
and
composition
of
this
important
plant
cell
wall
polymer
can
have
an
important
 effect
 on
plant
health,
 as
well
 as
 agro‐industrial
 processing
 and
 carbon
sequestration
potential
(Saballos
et
al.
2009).


In
fact,
the
decomposition
of
lignin
in
roots
and
plant
residues
in
soils
used
for
agriculture,
forestry
and
land
reclamation
has
been
recognized
as
a
potential
option
 to
sequester
carbon
and
mitigate
global
change
 by
 trapping
 carbon
 into
 longer‐lived
 pools
 (Kumar
 et
 al.
 2006).


Furthermore,
 a
 high
 content
 of
 polyphenolic
 compounds,
 such
 as
 lignin,
 in
 plant
residues
 can
prolong
 the
 retention
 of
 C
 in
 soils
 (Zibilske
&
Bradford
2007).
 
 
 Soil
organic
carbon
is
an
essential
component
of
healthy
soils
and
has
been
reported
to
increase
 the
water‐holding
 capability
 of
 sandy
 soil
 and
 to
 improve
 the
 structural
stability
 of
 clay
 loam
 soils
 by
 helping
 to
 form
 particle
 aggregates
 (Zibilske
 &
Bradford
 2007).
 
 
 Soil
 organic
 carbon
 is
 an
 effective
medium
 for
 sequestration
 of
inorganic
nutrients;
it
can
bind
both
cations
and
trace
elements
that
can
affect
crop
growth
and
yield.
 
This
 yield
 enhancement
 can
 involve
 either
 the
direct
 supply
of
nutrients
to
plant
root
systems,
or
indirectly
alter
the
physical
properties
of
the
soil,
 
 72
 thus
 improving
 the
 root
 environment
 and
 stimulating
 plant
 growth
 (Hati
 et
 al.
2007).
 
 
 The
 sequestration
 capacity
 of
 organic
 carbon
 in
 soils
 is
 advantageous
 to
plants
 when
 it
 comes
 to
 plant
 stress
 because
 roots
 serve
 as
 the
 proverbial
 foot
soldiers
 in
the
plant’s
battle
to
survive
 in
an
often
hostile
environment.
 
Roots
are
the
 first
 and
most
 critical
 plant
 organ
 to
 experience
 nutrient
 deficiency,
 drought,
osmotic
and
 ionic
stress,
soil
salinization,
heavy
metal
accumulation
and
pathogen
interactions.
 
 In
 response
 to
 these
 various
 stresses,
 plants
 undergo
 physiological
and
metabolic
changes
underpinned
by
alterations
in
gene
expression
that
produce,
among
other
things,
complex
mixtures
of
biologically
active
secondary
metabolites
involved
 in
 important
 processes
 such
 as
 cellular
 protection
 and
 ion
 homeostasis
(Jones
et
al.
2008).
 
For
example,
 the
production
of
 secondary
metabolites
via
 the
phenylpropanoid
 pathway
 provides
 intermediates
 for
 the
 synthesis
 of
 UV
protectants
 (flavonols),
 defense
 compounds
 (isoflavonoids),
 pigments
(anthocyanins/flavonols),
nodulation
inducers
(flavones)
and
lignins
(monolignols)
(Kumar
et
al.
2006;
Nessler
1994).



As
 a
 model
 for
 engineering
 increases
 in
 soil
 carbon
 stocks
 (if
 implemented
 in
 a
widely
planted
crop
system),
I
proposed
to
create
transgenic
Arabidopsis
plants
with
the
 ability
 to
 produce
 enhanced
 levels
 of
 lignin
 in
 their
 roots.
 
 To
 engineer
transgenic
plants
with
a
desired
phenotype,
such
as
enhanced
root
lignin,
the
choice
of
promoter
is
a
crucial
factor.

Strong
promoters
are
needed
for
effective
transgene
expression
in
plant
cells,
but
regrettably,
most
of
the
widely
used
constitutive
gene
expression
 systems,
 like
 the
 35S
 promoter
 from
 the
 Cauliflower
 Mosaic
 Virus
 
 73
 (CaMV35S),
 can
 produce
 undesirable
 pleiotropic
 effects
 due
 to
 spatially
 and/or
temporally
 inappropriate
 ectopic
 gene
 expression
 patterns
 (Yoshida
 &
 Shinmyo
2000).


For
my
project,
it
was
desirable
to
restrict
transgene
expression
exclusively
to
 root
 tissues.
 
 So
 far,
 only
 a
 handful
 of
 root‐specific
 gene
 promoters
 have
 been
identified
in
plant
species
such
as
Arabidopsis,
rice,
tomato
and
tobacco.
 
However,
these
promoters
are
often
limited
in
their
applicability
due
to:
a)
restricted
activity
in
specific
developmental
stages,
regions
or
tissues
within
the
root
structure,
b)
to
undesirable
 effects
 of
 biotic
 and
 abiotic
 factors
 on
 their
 regulation,
 or
 c)
 to
 a
requirement
 for
 specific
 growth
 conditions
 (Jones
 et
 al.
 2008).
 
 Genes
 controlling
cell
 fate
 in
 Arabidopsis
 in
 15
 different
 root
 zones
 (endodermis,
 endodermis
 and
cortex,
 epidermal
 atrichoblasts
 and
 lateral
 root
 cap)
 that
 relate
 to
 cell
 types
 and
tissues
at
progressive
developmental
stages
(stage
1,
2
and
3)
have
been
previously
described
 (Birnbaum
&
 Benfey,
 2004).
 
 
 Data
mining
 of
 the
 complete
microarray
gene
expression
data
set
 from
these
studies
enabled
me
to
develop
my
own
list
of
candidate
 genes
 whose
 promoters
 could
 be
 used
 to
 drive
 SND1
 gene
 expression.

These
 candidate
 genes
were
 then
examined
within
 the
Genevestigator
microarray
database
 (Hruz
 et
 al.
 2008,
 https://www.genevestigator.com)
 for
 relative
expression
 in
 roots
 compared
 to
 other
 plant
 organs
 and
 tissues.
 
 Based
 on
 these
results,
one
candidate
gene,
GSTU19,
was
selected
for
further
analysis.



The
 second
 candidate
 gene,
 4CL1,
 was
 selected
 based
 on
 previous
 studies
 that
reported
 high
 levels
 of
 4CL1
 gene
 expression
 in
 seedling
 roots.
 Specifically,
transgenic
Arabidopsis
 plants
 containing
 the
 4CL1
 or
 4CL2
 promoter
 fused
 to
 the
 
 74
 beta‐glucuronidase
 (GUS)
 reporter
 gene
 showed
 developmentally
 regulated
 GUS
expression
 in
 the
xylem
tissues
of
both
the
root
and
shoot,
with
At4CL1::GUS
 lines
showing
its
highest
levels
of
gene
expression
in
seedling
roots
(Soltani
et
al.
2006).

In
order
to
confirm
these
4CL1
results
and
validate
the
potential
of
these
candidate
promoters
 to
 drive
 transgene
 expression
 in
 the
 roots,
 the
 expression
 of
 both
candidate
 genes
 was
 checked
 using
 semi‐quantitative
 reverse
 transcription
 (RT)‐PCR
 in
 flower,
 leaf,
 stem
 and
 roots
 of
 four‐week‐oldplants.
 4CL1
 showed
 only
 a
negligible
 increase
 in
 gene
 expression
 in
 the
 roots
 compared
 to
 other
 tissues
 but
was
retained
as
a
candidate
based
on
earlier
studies
of
the
organ‐specific
expression
pattern
of
4CL1,
which
detected
the
highest
4CL1
mRNA
levels
in
3‐day‐old
seedling
roots
and
in
bolting
stems
of
mature
plants
(Soltani
et
al.
2006;
Ehlting
et
al.
2002).


This
difference
 in
gene
expression
patterns
among
plant
organs,
and
among
 these
organs
 at
 different
 stages
 of
 development,
 suggests
 that
 4CL1
 may
 exhibit
 some
root‐specificity
but
only
at
a
given
point
in
the
plant’s
growth
cycle.

As
a
side
note,

the
 4CL1
 promoter
 was
 an
 attractive
 candidate
 due
 to
 its
 active
 involvement
 in
channeling
carbon
flow
into
branch
pathways
of
phenylpropanoid
metabolism.


This
 RT‐PCR
 analysis
 also
 demonstrated
 that
 GSTU19
 is
 in
 fact
 expressed
 at
 a
noticeably
 higher
 level
 in
 roots
 compared
 to
 other
 organs.
 
 These
 results
 were
consistent
with
the
Genevestigator
heat
map
profile
as
well
as
with
previous
studies
showing
that,
under
control
conditions,
expression
of
GSTU19
mRNA
was
higher
in
roots
than
in
shoots
(DeRidder
&
Goldsbrough
2006).


In
summary,
the
data
shown
for
the
expression
of
4CL1
and
GSTU19
in
different
plant
organs
along
with
evidence
 
 75
 from
the
previous
studies
mentioned,
suggests
that
the
promoters
from
these
genes
could
both
be
good
candidates
to
drive
SND1
transgene
expression
in
roots,
but
for
different
 reasons.
 
 Based
 on
 my
 results
 from
 the
 RT‐PCR
 analysis,
 the
 4CL1
promoter
does
not
seem
to
be
a
good
candidate
for
driving
root‐specific
expression
but
 conversely,
 GSTU19
 does
 seem
 to
 have
 the
 potential
 to
 drive
 root‐specific
expression,
which
is
further
supported
by
this
promoters
ability
to
be
induced
in
a
root
specific
manner
when
treated
with
the
herbicide
safener
benoxacor.


The
 ability
 to
 turn
 on
 gene
 expression
 both
 spatially
 and
 temporally
 offers
 the
opportunity
 to
 fine‐tune
 ectopic
 gene
 expression
 without
 compromising
 the
viability
of
the
organism
or
the
function
of
the
organ
being
altered,
in
this
case
the
roots.
 
 
 Plant
 promoters
 that
 impart
 root‐specific
 expression
 are
 of
 interest
 for
improving
tolerance
to
abiotic
stresses
such
as
drought
and
salinity,
for
engineering
pathogen
resistance
and
 for
 improving
nutrient
uptake
(Vijaybhaskar
et
al.
2008),
due
to
their
potential
to
express
recombinant
proteins,
such
as
the
Cry
toxins,
in
the
root
 (Nitz
 et
 al.
 2001;
 Vijaybhaskar
 et
 al.
 2008;
 Maizel
 &
 Weigel
 2004).

Interestingly,
 a
 considerable
 number
 of
 root‐specific
 promoters
 have
 been
characterized,
 including:
 
 Pyk10
 from
 Arabidopsis
 thaliana
 (Nitz
 et
 al.
 2001),
 a
glycosyltransferase
gene
(At1g73160)
from
Arabidopsis
thaliana
(Vijaybhaskar
et
al.
2008),
 the
 PHT1
 gene
 from
 Arabidopsis
 thaliana
 (Koyama
 et
 al.
 2005),
 the
mannopine
synthase
2'
(mas2')
promoter
from
Agrobacterium
tumefacians
(Ni
et
al.
1996),
 the
 iron
 deficiency
 specific
 clone
 no.
 2
 (IDS2)
 promoter
 from
 barley
(Kobayashi
et
al.
2003),
putrescine
N‐methyltransferase
(PMT)
gene
(Mizusaki
et
al.
 
 76
 1971)
and
TobRB7
promoter
from
tobacco
(Yamamoto
et
al.
1991)
and
SlREO
gene
from
Solanum
lycopersicum.

Despite
these
examples,
strong
root‐specific
promoters
(i.e.
promoters
that
provide
for
a
high
level
of
gene
expression)
that
can
be
used
for
various
crop
improvements
are
still
thought
to
be
limited
(Cai
et
al.
2007).

Indeed,
when
 I
 examined
 the
 expression
 level
 of
 the
 so‐called
 “root‐specific”
 Arabidopsis
promoters
 (mentioned
 above)
within
 the
Birnbaum
 et
 al.
 (2003)
 data
 set
 used
 to
identify
GSTU19,
 I
 found
 that
 their
 relative
probe
 intensity
values
within
 the
 stele
and
endo‐cortex,
 fell
 below
my
 chosen
 cutoff
 of
 1500‐5000.
 
They
were
 therefore
excluded
 from
 this
 project,
 but
 that
 does
 not
 mean
 they
 should
 be
 rejected
 as
candidate
promoters
 to
drive
 root‐specific
 transgenes
 in
 general.
 
 Further
 studies
could
 test
 the
 strength
of
 these
promoters
 experimentally
 by
quantifying
 the
GUS
activity
expressed
in
promoter::GUS
transgenic
lines.


Moreover,
when
considering
the
 use
 of
 these
 promoters
 for
 genetic
 engineering,
 it
 may
 be
 important
 to
determine
 (via
 the
 data
 set
 in
 Birnbaum
 et
 al.
 (2003)
 or
 by
 promoter::GUS
expression
 patterns)
 in
 which
 tissues
 these
 promoters
 are
 predominantly
expressed,
so
that
their
usefulness
to
drive
transgene
expression
can
be
assessed
in
the
context
of
particular
biological
questions
and
objectives.



The
identification
of
the
afore‐mentioned
root‐specific
promoters
from
the
primary
literature,
 along
 with
 the
 various
 other
 candidates
 that
 I
 screened,
 raises
 an
interesting
 question:
 What
 makes
 a
 promoter
 root‐specific?
 
 The
 answer
 to
 this
question
 remains
 somewhat
 inconclusive,
 but
 there
 is
 some
 evidence
 suggesting
that
gene
expression
is
determined,
at
least
in
part,
by
motifs
or
cis‐elements,
within
 
 77
 the
promoter
 sequence
of
 regulated
genes
 (Cai
et
al.
2007).
 In
plants,
 distinct
cis‐regulatory
elements
have
been
 linked
 to
 specific
 responses
 to
various
 treatments,
and
analysis
of
the
associated
DNA
sequence
motifs
has
resulted
in
the
elucidation
of
 a
 number
 of
 promoter
 sequence
 motifs
 related
 to
 stress
 responses,
developmental
 and
 organ‐specific
 regulation
 (Ma
 &
 Bohnert
 2007).
 
 The
characteristics
of
some
of
these
root‐specific
cis‐acting
regulatory
DNA
elements
are
summarized
 in
 Table
 5.
 
 
 In
my
 in
 silico
 analysis
 of
 the
4CL1
 promoter
 using
 the
PLACE
 (Plant
Cis‐acting
Regulatory
DNA
Elements)
database,
 almost
all
of
 the
cis‐regulatory
 motifs
 mentioned
 in
 Table
 5
 were
 present,
 with
 the
 exception
 of
 the
ACGTROOT1,
TELO
and
SORLIP1AT
elements.

Similarly,
my
analysis
of
the
GSTU19
promoter
showed
 that
 it
 contained
almost
all
 the
motifs
with
 the
exception
of
 the
ACGTROOT1
and
TELO
elements.

These
findings
suggest
that
these
elements
could
play
 a
 role
 in
 conferring
 the
 root‐specificity
 previously
 described
 for
 these
 genes,
albeit
at
different
stages
of
plant
growth
and
development.

For
this
reason,
I
chose
the
largest
possible
promoter
region
sequences
for
my
constructs
that
excluded
any
upstream
 genes,
 yet
 included
 as
 many
 of
 the
 putative
 root‐specific
 regulatory
elements
as
possible.
 
 
 
 
 
 
 
 
 
 
 
 
 
 78
 Table
5.
Summary
of
cis­acting
regulatory
DNA
elements
associated
with
root­ specific
gene
expression.
 
 Putative
root­specific
 element
 Sequence
 Description
 Reference
 ARFAT
 TGTCTC
 ARF
binding
site
found
in
the
promoters
of
primary/early
auxin
response
genes
of
Arabidopsis
thaliana.
 (Inukai
et
al.
2005)
 ASF1MOTIFCAMV
 TGACG
 A
xenobiotic
stress‐activated
transcription
factor
that
binds
to
the
TGACG
motif
and
is
expressed
preferentially
in
root
apical
meristems.
 (Klinedinst
et
al.
2000),

 OSE1ROOTNODULE
 AAAGAT
 A
consensus
sequence
motif
of
organ‐specific
elements
characteristic
of
activated
promoters
found
in
the
infected
cells
of
root
nodules.
 (Vieweg
et
al.
2004)
 OSE2ROOTNODULE
 CTCTT
 A
consensus
sequence
motif
of
organ‐specific
elements
characteristic
of
activated
promoters
found
in
the
infected
cells
of
root
nodules.
 (Vieweg
et
al.
2004)
 RAV1AAT
 CAACA
 Binds
specifically
to
DNA
with
bipartite
motifs
of
RAV1‐A
(CAACA)
and
RAV1‐B
(CACCTG).
Expression
levels
of
RAV1
were
reported
to
be
high
in
rosette
leaves
and
roots.
 (Kagaya
et
al.
1999)
 ROOTMOTIFTAPOX1
 ATATT
 Motif
found
in
rolD
promoters.
The
rol
A,
B,
C
and
D
genes
have
been
identified
as
the
main
determinants
of
the
hairy
root
disease
caused
on
dicots
by
Agrobacterium
rhizogenes
(Bettini
et
 al.
2003).

 (Elmayan
&
Tepfer
1995)

 SORLIP1AT
 GCCAC
 Sequences
Over‐Represented
in
Light‐Induced
Promoters
(SORLIPs)
in
Arabidopsis.
Over‐represented
in
light‐induced
cotyledon
and
root
common
genes
and
root‐specific
genes.

 (Jiao
et
al.
2005)

 SP8BFIBSP8BIB
 TACTATT
 A
nuclear
factor
that
binds
to
the
5′
upstream
regions
of
three
different
genes
coding
for
major
proteins
of
sweet
potato
tuberous
roots.
 (Ishiguro
&
Nakamura
1992)
 SURECOREATSULTR11
 GAGAC
 Core
of
sulfur‐responsive
element
(SURE)
found
in
the
promoter
of
SULTR1;1
high‐affinity
sulfate
transporter
gene
in
Arabidopsis.

SURE
contains
auxin
response
factor
(ARF)
binding
sequence
(GAGACA)
 (Maruyama‐Nakashita
et
al.
2005)
 TELO
 AAACCCTAA
 Found
in
the
Arabidopsis
eEF1A
A1gene
promoter
as
well
as
in
the
5′
region
of
genes
encoding
components
of
the
translational
apparatus.
Implicated
in
the
activation
of
gene
expression
in
root
primordia
and
root
meristems.
 (Tremousaygue
et
 al.
1999)

 WUSATAg
 TTAATAG
 Target
sequence
of
WUS
in
the
intron
of
AGAMOUS
gene
in
Arabidopsis.
WUSCHEL‐type
homoebox
gene
that
is
specifically
expressed
in
the
central
cells
of
a
quiescent
center
in
the
root
apical
meristem.
 (Kamiya
et
al.
2003)
 
 79
 I
chose
to
use
a
chemical‐inducible
system
to
turn
on
gene
expression
of
SND1
at
a
specific
 time
 point
 in
 order
 to
 avoid
 the
 possible
 negative
 effects
 of
 constitutive
gene
expression.

The
benoxacor‐inducible
system
used
to
induce
SND1
expression
from
 the
 GSTU19pro­SND1
 construct,
 offers
 an
 advantage
 over
 other
 available
chemical‐inducible
 gene
 expression
 systems.
 
My
 results
 showed
 that
 the
GSTU19
promoter
was
already
root‐specific
in
its
expression
and
that
this
expression
could
be
further
 induced
by
the
herbicide
safener
causing
an
additional
 increase
 in
gene
expression
within
that
organ.


These
results
were
confirmed
by
the
transcriptional
analysis
 of
 SND1
 and
 its
 downstream
 targets
 in
 T3
 generation
 transgenic
 plants,
which
caused
a
marked
increase
in
gene
expression
preferentially
in
roots.



These
 results
 show
 that
when
driven
by
 the
GSTU19
 promoter,
 benoxacor
may
 in
fact
be
an
excellent
 inducer
of
transgene
expression
but
there
are
some
important
points
to
consider
(such
as
induction
time,
concentration
and
application
methods)
when
examining
 the
potential
of
 this
 safener‐induction
system
to
be
used
 in
 root‐specific
crop
biotechnology
applications.

The
use
of
herbicidal
safeners
as
chemical‐inducible
gene
expression
systems
 in
Arabidopsis,
was
previously
examined
by
De
Veylder
et
al.
(1997)
who
expressed
the
In2­2
promoter
from
maize
in
Arabidopsis
and
 induced
 its
 expression
 by
 treatment
 with
 benzenesulfonamide
 herbicide
safeners.
 
 
Similar
to
 later
studies
done
on
the
 induction
of
GSTs
 in
Arabidopsis
by
herbicide
 safeners
 (DeRidder
 &
 Goldsbrough
 2006;
 DeRidder
 et
 al.
 2002),
 GUS
staining
of
the
In2­2
transgenic
lines
was
visible
exclusively
in
the
root
as
soon
as
24
hours
after
induction.

In
addition,
the
authors
conducted
a
time‐course
experiment
 
 80
 on
 two‐week‐old
 In2­2
 transgenic
 plants
 containing
 the
 GUS
 reporter
 gene
 by
transferring
 seedlings
 from
 safener‐free
 media
 to
 media
 containing
 safener
(50mg/L).

After
transfer
of
the
plants
back
to
safener‐free
medium,
they
found
that
GUS
staining
disappeared
within
three
days,
indicating
a
strong
correlation
between
the
presence
of
 the
 safener
 and
 In2‐2
expression.
They
also
 found
 that
prolonged
induction
by
safeners
(at
a
concentration
of
50mg/L)
resulted
in
inhibition
of
root
growth,
indicating
that
the
amount
of
time
the
plant
was
exposed
to
the
chemical
at
that
 concentration
 was
 critical.
 
 Therefore,
 the
 majority
 of
 studies
 involving
herbicide
safeners
use
an
induction
time
of
24
hours.


It
was
not
immediately
clear
in
 the
 literature
why
 the
 standard
 induction
concentration
now
used
among
most
research
 groups
 for
 herbicide
 safeners
 is
 100µM
 but
 it
 appears
 that
 this
concentration
is
thought
to
serve
as
an
“antidotally
effective
amount”
(Mccutchen
et
 al.
 2008)
 that
 is
 the
 amount
 that
 should
 be
 added
 to
 an
 herbicide
 formulation
 in
order
to
eliminate
or
reduce
the
phytotoxic
effects
of
the
herbicide
to
certain
crops.




Although
studies
have
suggested
that
herbicide
safeners
could
be
potentially
useful
as
a
tissue‐specific
transient
expression
system
where
inducible
transcription
in
the
root
 is
required,
 there
have
been
no
studies
reported
where
this
system
had
been
optimized
with
 respect
 to
 safener
 concentration
 and
 time
 of
 induction
within
 the
context
 of
 driving
 transgene
 expression.
 
 In
 addition
 to
 the
 time
 of
 induction
 and
concentration
 of
 reagent,
 the
 type
 of
 application
 method
 may
 be
 an
 important
component
of
 a
 safener‐inducible
gene
expression
 system.
 
 For
example,
previous
studies
 have
 shown
 that
 adding
 the
 safeners
 to
 hydroponically
 grown
 plants
 
 81
 resulted
in
consistent
induction
patterns
among
all
safeners
tested,
whereas,
foliar
application
did
not
induce
any
GUS
activity
(De
Veylder
et
al.
1997).
 
Later
studies
using
 three‐week‐old
 Arabidopsis
 plants
 treated
 with
 safeners
 (100µM)
 by
 foliar
application
required
treatment
once
per
day
for
four
consecutive
days
to
achieve
the
desired
 level
 of
 gene
 induction
 (DeRidder
 &
 Goldsbrough
 2006).
 
 These
 results
provide
 some
 insight
 into
 the
 efficacy
 of
 a
 particular
 application
 method
 with
respect
 to
 the
 time
 of
 induction
 of
 the
 inducible
 promoter.
 
 Absorption
 of
 the
safener
 via
 the
 roots
 seems
 to
 result
 in
 a
much
 faster
 and
more
 direct
 induction
whereas
 to
 achieve
 similar
 results
 via
 foliar
 application
 longer
 exposure
 to
 the
inducer
 at
 similar
 concentrations
 is
 required.
 
 Further
 studies
 are
 needed
 to
optimize
this
system
if
safeners
are
to
be
more
widely
used
as
root‐specific
chemical
induction
systems.



The
reverse
transcription
PCR
analysis
in
flower,
leaf,
stem
and
roots
also
detected
 AtSND1
 expression
 exclusively
 in
 stems
 of
 four‐week‐oldplants.
 
 Given
 that
 the
lignin
 biosynthetic
 pathway
 seems
 to
 be
 regulated
 by
 a
 network
 of
 TFs,
 such
 as
 SND1,
 it
 is
 important
to
consider
the
implications
of
 introducing
a
regulatory
gene
into
 an
 environment
 in
which
 it
 is
 usually
 not
 expressed.
 
 
 Previous
 studies
 have
shown
 that,
 in
 roots,
 the
 expression
 level
 of
 a
 cohort
 of
 TF
 genes
 working
downstream
 of
 SND1,
 as
 well
 as
 of
 SND1
 itself,
 was
 largely
 restricted
 to
 the
developing
secondary
xylem
but
this
expression
was
at
very
low
levels
compared
to
their
 expression
 in
 stems
 (Zhong
 
 2008).
 
 At
 the
 outset
 of
 this
 project,
 it
was
not
known
how
 root‐specific
 overexpression
 of
SND1
might
 affect
 secondary
 cell
wall
 
 82
 thickening
in
roots,
or
if
the
regulatory
network
activated
by
SND1
would
function
the
same
way
 in
 this
organ
as
 it
does
 in
stems.
 
 
 It
 is
possible
 that
 transcriptional
activators,
 such
 as
 SND1
 and
 its
 downstream
 targets,
 might
 be
 able
 to
 regulate
secondary
cell
wall
formation
in
non‐sclerenchymatous
tissues
of
the
growing
plant
by
 acting
 as
 repressors
 of
 gene
 expression
 in
 order
 to
 prevent
 any
 pleiotropic
effects
associated
with
 the
ectopic
expression
of
genes
controlling
and
 involved
 in
lignin
biosynthesis.

However,
only
a
limited
number
of
expression
repressors
have
been
identified
in
plants
thus
far.




 


Secondary
 wall
 formation
 is
 a
 highly
 coordinated
 process
 that
 results
 from
 the
subsequent
deposition
of
cellulose,
hemicelluloses
and
lignin
as
soon
as
primary
cell
growth
 has
 ceased.
 The
 proportion
 of
 each
 of
 these
 major
 components
 is
 highly
variable
depending
on
the
climate,
geographic
location,
species,
age
and
part
of
the
plant.


Knowledge
of
how
the
coordinated
regulation
of
genes
leading
to
secondary
cell
wall
formation
and
how
this
regulation
leads
to
the
relative
composition
of
the
main
 constituents,
 is
 still
 growing
 (Ko
et
al.
2009).
 
However,
 there
are
 still
 some
gaps
 in
 our
 understanding
 and
 as
 a
 result,
 it
 was
 difficult
 to
 predict
 how
 SND1
overexpression
would
influence
lignin
deposition
in
roots,
a
tissue
in
which
only
low
levels
 of
 the
 TFs
 involved
 in
 regulating
 secondary
 cell
 wall
 formation
 have
 been
previously
 described
 (Zhong
 et
 al.
 2008).
 
 I
 created
 two
 different
 root‐specific
overexpression
constructs
(4CL1pro­SND1
and
GSTU19pro­SND1)
in
Arabidopsis
and
results
from
the
transcriptional
analysis
of
SND1
gene
expression
in
T2
generation
plants
confirmed
that
SND1
was
indeed
overexpressed
in
the
roots
in
almost
all
of
 
 83
 the
lines
analyzed
within
each
overexpression
construct.

Transcriptional
analysis
of
 SND1
 in
T3
generation
 transgenic
plant
 lines,
however,
 showed
overexpression
 in
both
roots
and
shoots
compared
to
empty
vector
control
lines,
indicating
transgene
expression
was
observed
in
both
tissues
and
that
expression
in
the
roots
was
only
slightly
 higher
 in
 shoots.
 
 The
 promoters
 selected
 to
 drive
 transgene
 expression
(4CL1pro
and
GSTU19pro)
are
not
necessarily
“root‐specific”
in
the
sense
that
their
native
expression
pattern
 indicate
 that
 they
are
expressed
elsewhere
 in
 the
plant,
which
may
be
why
SND1
was
seen
to
be
overexpressed
in
shoots
as
well
as
roots
in
transgenic
plants.
 
On
 the
other
hand,
given
 that
 the
native
expression
analysis
 in
different
plant
organs
in
addition
to
the
data
obtained
from
Genevestigator,
showed
that
 SND1
 was
 expressed
 somewhat
 exclusively
 in
 stems,
 the
 fact
 that
overexpression
 of
 SND1
 was
 seen
 in
 roots
 of
 transgenic
 lines
 indicates
 that
 the
promoters
 are
 functioning
 in
 their
 ability
 to
 drive
 expression
 of
 the
 transgene
 in
roots,
albeit
not
in
a
comparatively
restricted
manner.

 SND1
 has
 been
 previously
 shown
 to
 upregulate
 the
 expression
 of
 several
transcription
 factors
 that
are
highly
expressed
 in
 fibres
during
secondary
cell
wall
biosynthesis
 (Zhong
et
al.
2006).
 
Therefore,
 it
was
not
 surprising
 that
my
results
indicated
an
increase
in
gene
expression
(specifically
 in
roots)
of
the
transcription
factors
 acting
 downstream
 of
 SND1
 (MYB46,
 SND3,
MYB103
 and
 KNAT7).
 
 Given
that
these
transcription
factors
have
been
previously
shown
to
be
expressed
at
very
low
levels
in
roots
(Zhong
et
al.
2006;
Zhong
et
al.
2008),
my
data
further
confirms
that
 the
 4CL1pro­SND1
 and
 GSTU19pro­SND1
 constructs
 are
 behaving
 in
 a
 root‐ 
 84
 specific
 manner.
 
 These
 results
 correlate
 with
 the
 previously
 characterized
hierarchical
 organization
 of
 these
 transcription
 factors
 acting
 as
 direct
 targets
 of
SND1,
 therefore
 it
 seems
as
 though
 the
 interactions
previously
described
 in
aerial
tissues,
behave
in
a
similar
fashion
in
root
tissues
(Ko
et
al.
2009;
Zhong
et
al.
2008).



However,
 there
 is
 still
 much
 that
 we
 do
 not
 know
 about
 the
 organization,
association
 and
 interrelation
 of
 the
 entire
 regulatory
 cascade
 involved
 in
 the
activation
 and
 regulation
 of
 lignin
 biosynthetic
 genes
 during
 secondary
 cell
 wall
formation
in
stems,
let
alone
in
the
roots.
This
could
be
problematic
when
trying
to
determine
 and
 interpret
 what
 is
 happening
 downstream
 of
 these
 master
transcriptional
switches,
such
as
SND1
and
MYB46,
and
how
the
lignin
biosynthetic
pathway
 is
being
specifically
altered
 in
roots
of
 transgenic
plants,
an
environment
within
which
these
TFs
do
not
normally
operate.


The
growing
amount
of
data
(and
many
 different
 interpretations
 of
 this
 data)
 being
 generated
 and
 subsequently
presented
in
the
literature
is
usually
studied
within
stems
and
leaf
protoplast
and
is
often
 confusing
 and
 sometimes
 conflicting.
 
 Further
 studies
 are
 needed
 to
characterize
 all
 the
 putative
 TFs
 involved
 in
 regulating
 secondary
 cell
 wall
formation,
 in
addition
to
studies
aimed
at
determining
associations
between
these
factors
 and
 with
 biosynthetic
 genes.
 
 These
 studies
 should
 clarify
 some
 of
 the
missing
links
in
our
current
knowledge,
at
 least
within
aerial
tissues.
 
Significantly
more
work
would
be
required
in
Arabidopsis
root
systems
in
order
to
determine
the
effects
 of
 overexpressing
 regulatory
 factors
 involved
 in
 secondary
 cell
 wall
formation
 in
 tissues
 not
 normally
 heavily
 lignified.
 
 This
 is
 an
 important
 
 85
 consideration
for
future
attempts
at
inducing
hyper‐lignification
in
Arabidopsis
root
systems,
 before
 attempts
 can
 be
made
 at
 increasing
 soil
 carbon
 stocks
 in
 a
 large‐scale
crop
system
through
similar
approaches
and
methods.



The
genes
involved
in
cellulose,
xylan,
and
lignin
biosynthesis
need
to
be
turned
on
in
order
to
make
lignified
secondary
cell
walls
in
Arabidopsis.

The
RT‐PCR
analysis
of
 three
 phenylpropanoid
 pathway
 enzymes
 leading
 to
 the
 production
 of
monolignols
 (4CL1,
 CCR
 and
 COMT)
 showed
 no
 observable
 difference
 in
 gene
expression
among
these
 lignin
biosynthetic
genes
or
among
tissue
types
(root
and
shoot).
 
 
Several
possibilities
could
explain
this
finding,
despite
the
overexpression
of
SND1
and
its
direct
targets:
(i)
they
are
not
involved
in
the
transcriptional
control
of
 these
 particular
 lignin
 biosynthetic
 genes,
 (ii)
 they
 require
 the
 involvement
 of
other
 transcription
 factor(s)
 to
 function,
 or
 (iii)
 they
 are
 not
 directly
 involved
 in
secondary
wall
formation
(Ko
et
al.
2009).


The
first
explanation
could
certainly
be
true
where
 SND3
 and
MYB103
 are
 concerned,
 since
 they
were
 recently
 shown
 to
induce
 the
 GUS
 reporter
 gene
 expression
 driven
 by
 the
 CesA8
 promoter,
 from
 a
cellulose
synthase
gene
required
for
cellulose
synthesis
during
secondary
cell
wall
formation
 (Zhong
 et
 al.
 2008).
 This
 proves
 that
 SND1
 is
 involved
 in
 regulating
certain
 genes
 involved
 in
 other
 aspects
 of
 the
 secondary
 cell
 wall
 biosynthetic
program,
in
addition
to
that
of
lignin.
 
On
the
other
hand,
the
MYB46
transcription
factor
was
shown
to
be
a
direct
target
of
SND1
and
both
TFs
were
previously
shown
to
be
capable
of
turning
on
a
whole
set
of
genes
involved
in
secondary
wall
synthesis
in
 general
 (Zhong
 et
 al.
 2007b;
 Ko
 et
 al.
 2009).
 
 Therefore,
 it
 is
 puzzling
 that
overexpression
 of
 this
 gene
 did
 not
 activate
 key
 lignin
 biosynthetic
 enzymes
 in
 
 86
 either
 the
 root
 or
 shoot,
 where
 previous
 studies
 have
 shown
 this
 to
 occur.
 
 For
example,
 MYB85
 gene
 expression
 was
 previously
 shown
 to
 be
 upregulated
 by
 MYB46
 overexpression,
 and
MYB85
was
shown
 to
be
able
 to
 induce
expression
of
the
 GUS
 reporter
 gene,
 when
 driven
 by
 the
 4CL1
 promoter
 in
 leaf
 protoplasts.


Studies
have
shown
that
overexpression
of
MYB85
led
to
ectopic
deposition
of
lignin
in
epidermal
and
cortical
cells
in
stems
(Zhong
et
al
2007a;
Zhong
et
al.
2006;
Zhong
 et
al.
2008).

Therefore,
since
MYB46
has
been
shown
to
be
a
direct
target
of
SND1,
I
am
unable
 to
explain
 (within
 the
 current
model
of
 this
SND1‐mediated
 regulatory
network)
why
the
overexpression
of
SND1
and
MYB46
did
not
specifically
cause
the
 4CL1
gene
to
be
turned
on
through
induction
of
the
4CL1
promoter
by
MYB85.

The
gene
 expression
 level
 of
MYB85
 was
 not
 examined
 in
 the
 roots
 of
 my
 transgenic
lines,
 therefore
 transcriptional
 analysis
 of
 this
 gene
 by
 RT‐PCR,
 could
 provide
further
 information
 into
 determining
 why
 the
 4CL1
 gene
 was
 not
 turned
 on
 in
response
 to
 SND1
 overexpression.
 
 Nevertheless,
 there
 are
 several
 other
transcription
factors
that
have
been
previously
reported
to
regulate
secondary
wall
biosynthesis
 including,
 KNAT7,
MYB52,
MYB54,
MYB58
 and
MYB63.
 
 
 KNAT7,
 for
example,
was
overexpressed
in
the
roots
of
my
transgenic
plants
but
did
not
seem
to
influence
the
secondary
lignin
biosynthetic
genes
tested
here.

This
could
be
because
KNAT7
is
not
 involved
in
activating
 lignin
biosynthetic
genes
directly.
 
To
test
this
theory,
the
characterization
of
KNAT7
using
reverse
genetics
approaches
along
with
the
 yeast
 two‐hybrid
 system
 for
 determining
 protein
 interactions,
 may
 provide
some
 insight
 into
 its
 specific
 function
 and
 interacting
 partners.
 
 In
 short,
 TFs
 in
general
have
diverse
roles
in
regulating
gene
transcription.

For
instance,
they
may
 
 87
 act
as
part
of
a
complex
with
other
TFs
or
regulatory
proteins,
which
together
might
be
 involved
 in
 directly
 regulating
 gene
 expression
 in
 a
 particular
 biosynthetic
pathway.

Others,
however,
might
be
involved
in
enhancing
or
fine‐tuning
the
level
of
expression
of
different
metabolic
pathway
genes
(Zhong
et
al.
2008).



Interest
 in
 lignin
biosynthesis
and
 lignin
deposition
 is
mainly
due
to
 the
extensive
involvement
 of
 lignin
 in
 plant
 biology
 (Boudet
et
 al.
2003;
Humphreys
&
Chapple
2002).

Lignin
can
be
defined
two
ways:
1)
from
a
chemical
point
of
view
(chemical
composition
and
structure)
or
2)
from
a
functional
point
of
view
(what
lignin
does
within
the
plant)
(Hatfield
&
Fukushima
2005).

Regardless
of
these
definitions,
it
is
important
 to
 be
 able
 to
 determine
 the
 concentration
 of
 lignin
 within
 a
 broad
assortment
 of
 cell
wall
 varieties.
 
 One
would
 think
 that
 lignin
would
 be
 relatively
easy
to
measure,
given
that
it
is
somewhat
resistant
to
both
chemical
and
biological
degradation.
 
However,
there
have
been
several
methods
and
techniques
that
have
been
developed
and
adapted
throughout
the
years
to
quantitatively
determine
total
lignin
 content
 and
 composition
 in
 different
 types
 of
 plant
 samples,
 yet
 not
 one
 of
them
has
been
deemed
as
a
 standard
clear‐cut
method
 for
all
 samples
 (Hatfield
&
Fukushima
 2005).
 Worth
 mentioning,
 however,
 for
 the
 determination
 of
 lignin
content
 in
 plant
 samples,
 are
 non‐invasive
 approaches
 such
 as:
 near
 infrared
spectroscopy
 (NIRS)
 and
 nuclear
magnetic
 resonance
 spectroscopy
 (NMR).
 These
methods
 of
 lignin
 content
 determination
 offer
 an
 advantage
 over
 more
 invasive
methods
in
that
they
ultimately
leave
the
lignin
in
the
sample
chemically
unaltered
(Hatfield
 &
 Fukushima
 2005).
 
 Alternatively,
 two
 procedures
 (thioglycolate
 and
 
 88
 acetyl
 bromide)
 rely
 on
 the
 solubilization
 of
 lignin
 in
 an
 appropriate
 solvent
whereby
 the
 lignin
 in
 solution
 can
 be
 measured
 (Hatfield
 &
 Fukushima
 2005).

Lastly,
 various
methods
have
been
proposed
using
mineral
 acids
 to
 solubilize
 and
hydrolyze
carbohydrates
leaving
the
lignin
residue
to
be
measured
and
determined
gravimetrically,
such
as
the
Klason
lignin
method
(Hatfield
&
Fukushima
2005).
 
Is
seems
 that
 the
most
 commonly
 used
method
 for
 determining
 lignin
 is
 the
 Klason
lignin
or
72%
(v/v)
H2SO4
acid
procedure.


Given
the
relatively
low
amount
of
 lignin
present
in
roots
to
begin
with,
as
well
as
the
limited
amount
of
root
material
available
working
in
the
Arabidopsis
system,
it
was
 important
 to
 be
 prudent
 and
 judicial
 with
 the
 choice
 of
 lignin
 content
determination
method.
 
 
Results
 from
the
acetyl
bromide
analysis
of
soluble
 lignin
content,
 showed
 a
 marked
 decrease
 in
 total
 lignin
 content
 (~40‐50%)
 in
 both
 GSTU19pro­SND1
 lines
but
only
one
of
the
4CL1pro­SND1
 lines.
 
 
The
Klason
lignin
analysis
 supported
 the
 data
 obtained
 from
 the
 acetyl
 bromide‐based
 method
 by
confirming
 a
 decrease
 in
 insoluble
 lignin
 content
 in
 the
 GSTU19pro­SND1
overexpression
line.

On
the
other
hand,
one
4CL1pro­SND1
line
in
each
of
the
lignin
content
 analyses
 showed
 negligible
 changes
 in
 lignin
 content.
 
 
 Nevertheless,
 I
reasoned
that
the
decreases
seen
in
lignin
content
in
the
majority
of
lines
analyzed
(and
 also
 lack
 of
 change
 in
 gene
 expression
 of
 indicative
 lignin
 biosynthetic
enzymes),
 could
 be
 a
 result
 of
 carbon
 reallocation
 to
 a
 different
 area
 of
 carbon
metabolism
(such
as
production
of
cellulose
and
starch).


Results
from
the
cellulose
and
starch
content
analyses,
disproved
this
theory
by
showing
a
similar
decrease
in
 
 89
 cellulose
and
hemicellulose
content
in
the
GSTU19pro­SND1
line
analyzed.


I
should
note,
that
results
from
the
cellulose,
starch
and
Klason
lignin
analyses
were
absolute
values
 from
 a
 single
 biological
 replicate
 making
 the
 data
 somewhat
 unreliable,
however,
 given
 that
 they
 correlate
 to
 certain
 degree
 with
 results
 seen
 using
 the
acetyl
bromide‐based
method,
I
have
included
them
in
this
thesis.



An
 overall
 trend
 of
 decreased
 cell
 wall
 composition
 (lignin,
 cellulose
 and
hemicellulose)
 was
 seen
 in
 both
 SND1
 overexpression
 constructs
 analyzed.


Previous
 studies
 have
 shown
 that
 although
 SND1
 overexpression
 induces
 ectopic
secondary
 wall
 deposition
 in
 cells
 that
 are
 normally
 not
 lignified,
 excess
 SND1
apparently
inhibits
normal
secondary
wall
thickening
in
fibres
(Zhong
et
al.
2006).

In
 these
 studies,
 SND1
 overexpression
 was
 seen
 to
 induce
 secondary
 cell
 wall
production
 in
 many
 parenchyma
 cells
 in
 leaves
 and
 floral
 organs
 as
 well
 as
epidermal
 cells
 in
 stems;
 however,
 ectopic
 secondary
wall
 deposition
was
 seldom
seen
 in
 the
 parenchyma
 cells
 of
 other
 organs.
 
 Moreover,
 SND1
 overexpressors
showed
 that
 ectopic
 secondary
 wall
 thickening
 was
 rarely
 observed
 in
 the
epidermal
 cells
of
hypocotyls
 and
cortical
 cells
of
 roots
but
was
not
 seen
 in
other
root
cell
types
(Zhong
et
al.
2006).

This
finding
suggests
that
different
cell
types
in
different
 organs
 might
 exhibit
 differential
 competence
 to
 induction
 of
 secondary
wall
 thickening
 by
 SND1.
 
 Equally,
 this
 differential
 induction
 could
 be
 a
 case
 of
substrate
 availability,
 meaning
 that
 the
 required
 precursors
 for
 monolignol
biosynthesis
may
be
 in
short
 supply
 in
 the
root,
given
 that
Arabidopsis
root
 tissue
does
 not
 normally
 contain
 high
 levels
 of
 lignin.
 
 Either
way,
 any
 of
 these
 reasons
 
 90
 could
account
for
the
fact
that
overexpression
of
SND1
in
the
root‐specific
(4CL1pro­ SND1)
 and
 inducible
 (GSTU19pro­SND1)
 constructs
 did
 not
 result
 in
 the
 direct
activation
of
 lignin
biosynthetic
genes
as
demonstrated
using
the
RT‐PCR
analysis,
which
 should
 have
 resulted
 in
 an
 increase
 in
 root
 lignin
 content
 instead
 of
 the
observed
decrease
in
lignin
content.




As
 for
 the
 other
 cell
 wall
 constituents
 analyzed,
 Ko
 et
 al.
 (2007)
 reported
 that
cellulose
compositions
of
the
cell
wall
were
decreased
in
the
inflorescent
stems
and
roots
of
plants
overexpressing
SND1
driven
by
the
CaMV35S
promoter,
most
likely
resulting
 from
defects
 in
 xylary
 fibre
 formation.
 
However,
my
 results
 showed
 an
increase
 in
 the
 relative
 gene
 expression
 of
 SND3
 and
 MYB103
 as
 seen
 in
 the
transcriptional
 analysis
 of
 T3
 generation
 transgenic
 plants,
 which
 should
 have
resulted
in
an
increase
in
cellulose
content
given
that
these
downstream
targets
of
SND1
were
 recently
 shown
 to
 induce
 the
GUS
reporter
gene
expression
driven
by
the
CesA8
promoter
(Zhong
et
al.
2008).
Instead,
I
generally
observed
a
decrease
in
cellulose
and
hemicellulose
content,
similar
to
that
observed
for
lignin.



Overexpression
of
mRNA
can
sometimes
 lead
 to
a
drastic
reduction
 in
 the
 level
of
expression
of
the
endogenous
genes
concerned,
i.e.
host
genes
can
be
silenced
as
a
consequence
of
the
presence
of
a
homologous
transgene,
thus
limiting
the
potential
application
 of
 genetic
 transformation;
 a
 phenomenon
 called
 co‐suppression
(Vaucheret
et
al.
2001).
 
One
way
of
understanding
this
phenomenon
is
that
when
RNA
 transcripts
 accumulate
 beyond
 a
 critical
 threshold,
 they
 are
 selectively
 
 91
 degraded
 by
 ribonucleases
 (RNases),
 a
 type
 of
 nuclease
 that
 catalyzes
 the
degradation
of
RNA.

An
accumulation
of
elevated
levels
of
mRNAs
might
lead
to
the
production
 of
 abnormal
 sense
 RNA
 transcripts
 of
 the
 transgene
 (Vaucheret
 et
 al.
2001)
and
accumulation
of
these
anomalous
RNA
transcripts
is
proposed
to
activate
the
 RNA‐dependent
 RNA
 polymerase,
 which
 transcribes
 the
 RNA
 transcripts
 to
produce
 antisense
 RNA.
 The
 antisense
 RNA
 transcripts
 then
 bind
 to
 the
accumulated
normal
and
abnormal
RNA
transcripts
of
the
transgene
as
well
as
the
endogenous
 gene,
 producing
 RNA
 duplexes
 that
 are
 then
 targeted
 by
 double‐stranded
RNA
specific
RNases.

This
often
leads
to
a
radical
reduction
in
the
level
of
transgene
 expression
 as
 well
 as
 the
 expression
 of
 the
 endogenous
 gene
 and
sometimes
homologous
genes
as
well.

This
series
of
events
are
collectively
referred
to
 as
 gene
 silencing
 and
 are
 defined
 by
 predominantly
 taking
 place
 at
 the
 post‐transcriptional
 level,
 where
 RNA
 does
 not
 accumulate
 even
 though
 transcription
occurs
(Vaucheret
et
al.
2001).
 
The
degradation
of
RNA
via
gene
silencing
may
be
why
I
observed
a
decrease
in
total
lignin
content
in
my
transgenic
plants
but
it
does
not
explain
why
I
observed
an
increase
in
mRNA
transcripts
in
my
transgenic
lines.

One
reason
for
this
could
be
because
the
transcriptional
analysis
was
performed
on
three‐week‐old
plants
and
the
lignin
analysis
was
performed
on
mature
plants
that
were
roughly
eight‐weeks
old.

It
is
possible
that
gene
silencing
is
occurring
during
secondary
cell
wall
 formation
 in
plants
that
are
older
then
three‐weeks.
Using
RT‐PCR
to
analyze
SND1
and
its
targeted
TFs
in
the
root
tissue
from
transgenic
lines
at
different
developmental
stages
could
test
this
hypothesis.



 
 92
 An
 alternative
 possibility
 for
 this
 observed
 difference
 in
 increased
 mRNA
 versus
decreased
lignin
could
be
some
kind
of
regulation
at
the
translational
level,
but
the
mechanisms
for
this
type
of
control
are
poorly
understood.

It
is
clear
that
there
are
many
mechanisms
in
place
to
control
and
maintain
normal
levels
of
plant
cell
wall
constituent
 biosynthesis
 and
 deposition.
 
 This
 presents
 a
 significant
 challenge
 to
overcome
 when
 designing
 and
 engineering
 genetic
 constructs
 for
 crop
improvement.
 
 As
 more
 knowledge
 is
 gained
 regarding
 the
 mechanisms
 that
regulate
transcription
of
secondary
cell
wall
components
as
a
whole
(as
well
as
the
coordinated
 expression
 of
 the
 cohort
 of
 transcription
 factors
 and
 proteins
regulating
 the
 lignin
 biosynthetic
 pathway),
 we
 will
 undoubtedly
 be
 able
 to
 gain
new
 insight
 that
 will
 help
 us
 to
 develop
 more
 complex
 and
 fine‐tuned
 gene
expression
 systems
 that
 could
 complement
 or
 counteract
 any
 other
 regulatory
mechanisms
present
that
may
prevent
us
from
achieving
the
desired
end‐product
or
phenotype.


It
 is
 imperative
 to
 the
 process
 of
 genetic
 engineering
 for
 agricultural
 purposes
 to
drive
transgene
expression
in
a
manner
that
evades
health
costs
to
the
plant
caused
by
the
constitutive
expression
of
target
genes.

It
was
therefore
important
to
survey
a
variety
of
different
key
plant
physiological
traits
that
could
have
a
dire
impact
on
the
 efficacy
 of
 crop
 production.
 
 Overexpression
 of
 key
 genes
 involved
 in
 normal
plant
 growth
 and
 development,
 such
 as
 secondary
 cell
 wall
 pathways,
 could
 be
implicated
in
normal
agricultural
activities
such
as
seed
production
and
crop
yield,
thus
 resulting
 in
 major
 economic
 consequences
 if
 altered
 inappropriately.
 
 The
phenotypic
analysis
of
seed‐related
traits
revealed
that
overexpression
of
SND1
did
 
 93
 not
 cause
 any
 undesirable
 pleiotropic
 effects
 in
 seed
 production
 and/or
 viability
among
my
 transgenic
 plants.
 
 Given
 that
GSTU19pro­SND1
 lines
were
 induced
 by
herbicide
 safener
 at
 four‐weeks
 post‐flowering,
 it
 was
 expected
 that
 these
 lines
would
not
 result
 in
 a
phenotype
 involving
 seed‐related
 traits.
 
 This
data
 supports
my
 previous
 analyses
 showing
 that
 both
 construct
 promoters
 were
 shown
 to
 be
root‐specific,
 which
 means
 that
 SND1
 overexpression
 in
 the
 roots
 should
 not
activate
gene
expression
of
secondary
cell
wall
biosynthetic
genes
in
seeds.

To
determine
whether
overexpression
of
SND1
in
roots
caused
any
variation
in
root
architecture,
 lateral
 root
 density
 (LRD)
 was
 analyzed
 for
 two
 lines
 in
 each
constructs
and
showed
an
increase
in
LRD
in
three
out
of
the
four
lines
analyzed.

It
was
interesting
that
the
transgenic
lines
showing
an
increase
in
LRD
were
the
same
transgenic
 lines
corresponding
 to
a
decrease
 in
 total
 lignin
content,
as
seen
 in
 the
chemical
lignin
analysis.

It has been previously shown, that SND1
is
a
member
of
the
NAC
 domain
 protein
 family,
 which
 comprises
 approximately
 100
 genes
 in
 the
 Arabidopsis
genome
and
function
as
plant‐specific
transcriptional
 factors.
 
To
date,
only
a
small
number
of
NAC
domain
genes
have
been
characterized
and
NAC
domain
proteins
 have
 been
 implicated
 in
 a
 wide
 variety
 of
 processes,
 including
 the
establishment
 of
 the
 shoot
 apical
 meristem,
 the
 signaling
 pathway
 involved
 in
abiotic
 stress,
 defense
 responses
 and
 lateral
 root
 formation.
 
 Specifically,
AtNAC1

(At1g56010)
has
been
shown
to
mediate
auxin
signaling
and
promote
 lateral
 root
formation
(Xie
et
al.
2000).

A
multiple
sequence
alignment
of
various
NAC
domain
genes,
 from
a
previous
study,
has
shown
that
AtNAC1
 is
a
distant
relative
of
SND1
(Zhong
et
al.
2006).

Plant
roots
have
a
distinct
organization
that
is
fundamental
to
 
 94
 the
 formation
 of
 lateral
 roots.
 
 The
 outer
 tissues
 of
 dicot
 plant
 roots
 (epidermis,
cortex,
and
endodermis)
are
organized
into
separate
concentric
layers
whereas
the
vascular
 tissues
of
 the
central
 stele
have
a
more
bilateral
 symmetry
 (Parizot
et
al.
2008).


The
outermost
layer
of
the
stele,
known
as
the
pericycle,
is
composed
of
two
different
 cell
 types:
 one
 subset
 is
 associated
with
 the
 xylem,
whereas
 the
 other
 is
associated
with
 the
 phloem.
 
 The
 former
 has
 the
 strong
 capability
 to
 initiate
 cell
division
 but
 the
 latter
 appears
 to
 remain
 inactive
 (Parizot
 et
 al.
 2008).
 
 
 The
formation
of
lateral
roots
is
a
result
of
a
subset
of
pericycle
cells
(called
the
pericycle
founder
 cells)
 that
 are
 positioned
 at
 the
 xylem
 poles
 within
 parent
 root
 tissues.
Subsequently,
 the
 mature
 pericycle
 cells
 form
 lateral
 root
 primordium
 (LRP)
 via
dedifferentiation,
which
then
undergoes
consistent
cell
divisions
to
generate
a
well‐organized
LRP.

Cell
expansion
causes
the
LRP
to
emerge
from
the
parent
root,
and
the
 lateral
 root
meristem
becomes
 activated
 resulting
 in
 continued
 growth
 of
 the
lateral
root
(Lee
et
al.
2009).
 
The
positioning
of
 the
pericycle
 founder
cells
 to
 the
xylem
poles
may
provide
a
testable
hypothesis
regarding
the
decrease
in
lignin
and
increase
in
LRD
in
transgenic
lines
overexpressing
SND1
and
its
downstream
target
 MYB46.
 
Previously,
ectopic
secondary
wall
thickening
in
the
parenchymatous
cells
of
leaves,
floral
organs
and
inflorescence
stems
was
seen
in
MYB46
overexpressors
(Ko
et
al.
2009).
 
 In
addition,
SND1
overexpression
showed
a
small
 increase
in
the
wall
 thickness
 of
 vessels
 (Zhong
 
 2006).
 
 Although
 I
 did
 not
 specifically
 look
 at
ectopic
secondary
wall
thickening
in
my
transgenic
lines,
a
possible
increase
in
wall
thickening
due
to
SND1
and
MYB46
overexpression
in
root
xylem
vessels
may
have
caused
 a
movement
 in
 auxin
 pools
 near
 the
 xylem
 poles,
 causing
 lateral
 roots
 to
 
 95
 form.

One
way
to
test
this
hypothesis
would
be
to:
i)
to
confirm
that
secondary
wall
thickness
 was
 in
 fact
 perturbed
 and
 ii)
 to
 transform
 the
 SND1
 root‐specific
overexpression
 constructs
 (GSTU19pro­SND1
 and
 4CL1pro­SND1)
 with
 the
promoter‐marker
 gene
 fusion
 DR5::GUS
 activated
 by
 auxins
 to
 visualize
 auxin
response
patterns
in
the
root.



Another
possible
 explanation
 for
 this
 increase
 in
 lateral
 root
 density
 found
 in
 the
 GSTU19pro­SND1
 transgenic
 lines
 in
 particular,
 could
 be
 due
 to
 the
 fact
 that,
 in
addition
to
high
levels
of
expression
in
the
stele
and
endo‐cortex,
GSTU19
was
found
to
have
an
even
higher
level
of
expression
in
lateral
root
cap
tissues
as
seen
by
the
Genevestigator
heat
map
that
I
generated
and
the
relative
probe
intensities
from
the
Birnbaum
and
Benfey
dataset
(2004).

The
root
cap
has
been
shown
to
be
a
complex
and
dynamic
plant
organ.
 
Root
 caps
are
 responsible
 for
 sensing
and
 transmitting
environmental
 signals,
 synthesizing
 and
 secreting
 small
 molecules
 and
macromolecules,
and
in
some
species
shedding
metabolically
active
cells
(Tsugeki
&
Federoff
1999).

One
study
reported
the
identification
and
use
of
a
root
cap‐specific
promoter
to
genetically
destroy
root
caps
by
directing
root
cap‐specific
expression
of
a
diphtheria
 toxin
A‐chain
gene.
 
The
roots
of
 these
transgenic
plants
had
more
highly
 branched
 lateral
 roots
 than
 those
 of
 wild‐type
 control
 plants.
 
 
 Root
 cap
ablation
 (where
 individual
 cells
 are
 destroyed
 for
 experimental
 purposes)
 in
 this
study
 was
 shown
 to
 alter
 root
 architecture
 both
 by
 inhibiting
 root
 meristematic
activity
and
by
stimulating
 lateral
root
 initiation.
 
These
observations
 implied
that
 
 96
 root
caps
contain
essential
components
of
the
signaling
system
that
determines
root
architecture
(Tsugeki
&
Federoff
1999).


If
 SND1
 overexpression
 in
 GSTU19pro­SND1
 lines
 caused
 a
 similar
 ablation
 or
alteration
in
lateral
root
caps
this
could
certainly
explain
the
observed
increases
in
LRD
seen
among
the
two
transgenic
lines
analyzed
for
this
particular
construct.

One
way
 to
 test
 this
 hypothesis
would
be
 to
 visualize
 longitudinal
 sections
of
 primary
root
 tips
 using
 electon
 or
 confocal
 microscopy
 in
 order
 to
 determine
 the
differentiation
 of
 root
 cells
 in
 my
 transgenic
 and
 empty
 vector
 control
 plants.


Another
 interesting
observation
 from
Tsugeki
&
Federoff
 (1999)
was
 that
despite
the
abnormal
 root
 structure
of
 their
 transgenic
 lines,
 the
appearance
of
 the
aerial
parts
of
the
transgenic
plants
was
normal
on
both
MS
agar
medium
and
in
soil.


The
normal
aerial
phenotype
was
also
observed
in
my
transgenic
lines,
including
those
showing
 increased
 lateral
 root
 formation
 and
 a
 decrease
 in
 total
 lignin
 content.

According
 to
 Tsugeki
 &
 Federoff
 (1999),
 these
 results
 could
 indicate
 that
 the
formation
 of
more
 lateral
 roots
might
 compensate
 for
 the
 effect
 of
 the
 short‐root
phenotype
seen
in
previous
studies
involving
the
SHORT­ROOT
(SHR)
gene,
which
is
typified
by
the
absence
of
gravitropic
response
in
shoots
and
exhibits
a
determinate
root
growth
pattern
(Benfey
et
al.
1993).


Multiple
 signaling
 pathways
 are
 responsible
 for
 controlling
 normal
 plant
 growth
and
 development.
 
 These
 pathways
 are
 able
 to
 integrate
 information
 from
 the
environment
 using
 metabolic
 and
 developmental
 signals.
 
 If
 these
 normal
developmental
and
signaling
pathways,
such
as
 the
phenylpropanoid
pathway,
are
 
 97
 disrupted
 or
 altered,
 consequences
 to
 overall
 plant
 growth
 and
 function
 could
result.
 
 In
 order
 to
 determine
 any
 phenotypes
 involving
 flowering
 time,
 overall
height
 and
 shape
 (leaf
 and
 plant),
 plant
 growth
 was
 examined
 over
 a
 six‐week
period.
 
 My
 transgenic
 plants
 overexpressing
 SND1
 did
 not
 show
 any
 observable
phenotype
 among
 aerial
 plant
 tissues,
 which
 could
 mean
 that:
 a)
 my
 transgenic
constructs
were
sufficiently
 root‐specific
 that
overexpression
of
SND1
 in
 roots
did
not
seem
to
interefere
with
normal
plant
growth
and
development
or
b)
there
is
no
alteration
 in
 secondary
 cell
 wall
 composition
 that
 could
 cause
 an
 observable
phenotype
 in
 aerial
 tissues.
 
 Either
 of
 these
 reasons
 could
 explain
 why
 the
pendulous
phenotype
 (as
well
 as
 other
 severe
phenotypes
 in
 flowers
 and
 leaves),
previously
 seen
 in
 SND1
 overexpressors
 under
 the
 control
 of
 the
 constitutive
CaMV35S
promoter,
was
not
observed
in
my
transgenic
plants
(Zhong
et
al.
2006).



Histochemical
staining
and
UV
autofluorescence
of
lignin
in
root‐hypocotyls
did
not
show
significant
visible
phenotypic
changes
even
 though
considerable
variation
 in
lignin
content
was
seen
along
the
5mm
sections
of
hypocotyl
analyzed,
which
could
be
due
to
differences
in
developmental
equivalencies.

It
is
possible
that
visualizing
wall
thickness
and
lignin
content
at
this
magnification
using
this
particular
type
of
microscopy,
was
not
sufficient
to
observe
any
changes
in
cell
wall
thickness
or
lignin
deposition
 patterns
 in
 the
 roots.
 
 A
 more
 sensitive
 method
 might
 be
 needed
 to
distinguish
more
 subtle
 differences
 in
 cell
 wall
 thickness
 among
 transgenic
 lines,
such
as
transmission
electron
microscopy.




 
 98
 5.
 Conclusions
and
Future
Directions


To
 my
 knowledge,
 this
 is
 the
 first
 investigation
 into
 the
 manipulation
 of
 lignin
deposition
 in
 Arabidopsis
 roots
 for
 the
 end‐use
 of
 increasing
 carbon
 stocks
 in
agricultural
root
systems,
such
as
canola
or
soybean.

Using
a
metabolic
engineering
approach,
 SND1,
 a
 key
 transcriptional
 activator
 controlling
 secondary
 cell
 wall
biosynthesis
 and
 deposition
 in
Arabidopsis,
 was
 identified
 as
 a
 suitable
 candidate
gene
to
alter
the
expression
of
several
endogenous
genes
and
transcription
factors
involved
 in
 lignin
 biosynthesis,
 through
 overexpression
 in
 root
 tissues.
 
 In
 my
transgenic
plant
 lines
overexpressing
SND1
 in
roots
 (driven
by
 two
different
root‐specific
 candidate
 gene
 promoters,
 4CL1
 and
 SND1),
 I
 found
 that
 SND1
overexpression
upregulated
previously
known
downstream
targets
of
SND1,
did
not
result
 in
 a
modification
 of
 lignin
 biosynthetic
 pathway
 genes,
 generally
 showed
 a
decrease
in
total
lignin
and
carbohydrate
content,
showed
an
increase
in
lateral
root
density
and
did
not
exhibited
any
visible
phenotypes
regarding
seed‐related
traits,
plant
growth
and
development,
plant
height
or
lignin
deposition
patterns
in
roots.


SND1
 did
 not
 behave
 in
 a
 predictable
 manner
 when
 overexpressed
 in
 an
environment
 that
 it
does
not
normally
operate
 in.
 
There
 is
 still
much
 to
discover
about
 the
 organization,
 association
 and
 interrelation
 of
 the
 entire
 regulatory
cascade
 of
 TFs
 (along
 with
 regulatory
 proteins
 and
 cofactors)
 involved
 in
 the
activation
 or
 supression
 of
 lignin
 biosynthetic
 genes
 during
 secondary
 wall
formation
 in
 shoots,
 let
 alone
 in
 the
 roots.
 
 
 Further
 studies
 are
 underway,
 in
 
 99
 Arabidopsis,
 to
 characterize
 the
 TFs
 involved
 in
 the
 SND1‐mediated
 regulation
 of
secondary
cell
wall
formation
(probably
through
reverse
genetics
approaches).

The
mechanisms
 in
 place
 to
 control
 and
 maintain
 normal
 levels
 of
 plant
 cell
 wall
biosynthesis
 and
 deposition
 present
 a
 significant
 challenge
 to
 overcome
 when
designing
 and
 engineering
 genetic
 constructs
 to
 ectopically
 express
 transcription
factors
that
regulate
secondary
cell
wall
metabolic
pathways,
in
plant
organs
where
these
 factors
 do
 not
 normally
 regulate
 this
 process.
 
 TFs
 in
 general
 have
 very
diverse
roles
in
regulating
gene
transcription
and
may
act
as
part
of
a
complex
with
other
 TFs
 or
 regulatory
 proteins,
 which
 together
 might
 be
 involved
 in
 directly
regulating
gene
expression
 in
 a
particular
biosynthetic
pathway.
 
Others
might
be
involved
 in
enhancing
or
 fine‐tuning
 the
 level
of
 expression
of
different
metabolic
pathway
genes.

Therefore,
studies
are
needed
to
determine
the
specific
associations
between
these
factors
and
with
cell
wall
biosynthetic
genes
(in
vivo,
 in
vitro
and
 in
 planta)
 could
 also
 provide
more
 insight
 into
 how
 this
 particular
 lignin
metabolic
pathway
 is
 controlled
 as
 well
 as
 possibly
 present
 new
 candidate
 genes
 whose
overexpression
might
 induce
ectopic
 lignification
 in
root
 tissues.
 
All
 these
studies
combined
 should
 clarify
 some
 of
 the
 missing
 links
 in
 our
 current
 knowledge
 of
secondary
cell
wall
formation,
within
above
ground
tissues.

Significantly
more
work
is
 required
 in
 Arabidopsis
 root
 systems
 to
 determine
 how
 (and
 even
 which)
secondary
cell
wall
regulatory
 factors
operate
 in
 these
tissues.
 
These
studies
may
even
 elucidate
 new
 candidate
 genes
 controlling
 lignin
 deposition
 specifically
 in
roots.




 
 100
 Suitable
promoters
and
a
safener‐inducible
gene
expression
system
were
identified
in
 this
 project
 and
 used
 to
 induce
 root‐specific
 expression
 in
 transgenic
 plants.

Experimentally
 testing
 the
 strength
 and
 tissue‐specificity
 of
 all
 the
 other
 putative
root‐specific
promoters
 that
have
been
previously
 identified
 in
Arabidopsis
 can
be
used
to
assess
their
ability
to
drive
transgene
expression
in
the
context
of
particular
biological
questions
and
objectives.
 
 
Since
 in
silico
analysis
of
regulatory
motifs
or
 cis‐elements
in
promoter
regions
indicates
that
these
binding
sequences
could
play
an
important
role
in
conferring
root‐specificity,
as
previously
described
for
these
so‐called
“root‐specific”
genes,
it
may
be
valuable
to
determine
which
of
these
putative
motifs
 are
 in
 fact
 directly
 linked
 to
 root‐specific
 gene
 expression
 by
 more
 direct
experimental
 approaches.
 
 For
 example,
 recapitulation
 studies
 using
 intact
 and
mutated
 versions
 of
 the
 predicted
 cis­element
 driving
 a
 reporter
 gene
 (such
 as
luciferase)
in
transgenic
plants
could
be
used
to
validate
the
hypothesized
function
of
 the
 cis­acting
 regulatory
 element
 in
 vivo.
 
 
 The
 additional
 information
 gathered
from
 these
 future
 studies,
 could
 provide
 us
 with
 more
 ways
 to
 fully
 explore
 the
various
 gene
 expression
 resources
 available
 for
manipulating
 lignin
 deposition
 in
roots,
thereby
enabling
us
to
develop
new
highly
specific
gene
expression
constructs
for
 enhancing
 lignin
 deposition
 in
 roots.
 
 Furthermore,
 the
 root‐specific
 gene
induction
system
 in
dicots
using
benoxacor
and
 fenclorim
as
 chemical
 inducers
of
the
 GSTU19
 promoter
 used
 to
 drive
 root‐specific
 transgene
 expression,
 showed
some
promise
in
conferring
spatial
and
temporal
control
of
transgene
expression
in
the
roots
of
 transgenic
plants
analyzed
 in
 this
project
but
 this
 system
needs
 to
be
optimized
 with
 respect
 to
 safener
 concentration,
 induction
 time
 and
 application
 
 101
 method
 if
 it
 is
 to
be
used
widely
as
an
acceptable
 chemical‐inducible
 root‐specific
gene
expression
for
various
root‐related
biotechnology
applications.

 
For
example,
direct
induction
of
transgene
expression
in
hydroponically
grown
plants
is
the
most
effective
way
 to
 induce
 transgene
expression
 in
 roots
by
allowing
direct
access
 to
the
 safener
 by
 root
 systems,
 a
method
 that
may
 not
 be
well‐suited
 to
 large‐scale
crop
systems.

Studies
with
the
overall
aim
of
modifying
lignin
content
and
composition
in
plants
have
many
potential
economic
and
environmental
benefits
to
humans.

As
a
result
of
this
importance,
in
just
over
a
decade,
a
number
of
studies
have
been
conducted
to
manipulate
 gene
 expression
 in
 the
 monolignol
 pathway
 within
 phenylpropanoid
metabolism.
 
 For
 instance,
 cheaper
 and
more
 easily
 processed
 trees
 for
 pulp
 and
paper
manufacture
that
could
decrease
pollution,
more
readily
digestible
forage
for
livestock
 and
 improved
 feedstock
 for
 fuel/chemical
 production
 (Anterola
&
Lewis
2002).
 
These
research
endeavors,
along
with
high
 throughput
 transcriptional
and
metabolic
profiling
studies,
have
produced
an
immense
collection
of
scientific
data.

These
 studies
 are
 important
 in
 gaining
 significant
 insight
 into:
 1)
 the
 overall
dynamics
 of
 phenylpropanoid
 metabolism
 (i.e.
 how
 carbon
 flux
 through
 various
pathways
 is
 differentially
 controlled)
 and
 2)
 how
 genetic
manipulations
 can
 alter
and
disrupt
programmed
lignin
assembly
in
a
predictable
manner
without
affecting
overall
 plant
 viability
 (Anterola
&
 Lewis
 2002).
 
 In
 fact,
metabolic
 engineering
 in
general
is
now
beginning
to
take
over
from
single‐gene
engineering
as
the
best
way
to
manipulate
metabolic
 flux
 in
 transgenic
 plants.
 
 
 The
 ability
 to
 control
 several
 
 102
 points
 in
 a
 given
 metabolic
 pathway
 at
 the
 same
 time
 either
 by
 overexpressing
and/or
 suppressing
several
enzymes
 through
 the
use
of
 transcriptional
 regulators
controlling
endogenous
genes
is
a
powerful
tool
in
developing
complex
phenotypes
resulting
 from
 modifications
 of
 entire
 pathways.
 Our
 knowledge
 of
 metabolic
pathways
 continues
 to
 expand
 via
 the
 use
 of
 applied
 genomics,
 proteomics
 and
metabolomics,
while
 advances
 in
 systems
 biology
 help
 us
 to
model
 the
 impact
 of
different
modifications.

In
conclusion,
these
more
recent
biotechnological
advances
are
 greatly
 increasing
 our
 understanding
 of
 the
 regulatory
 processes
 involved
 in
controlling
secondary
cell
wall
biosynthesis
and
deposition.


























 
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 112
 Appendices

 
 Appendix
A.
 Primary
sequences
of
gene
expression
constructs
 
 4CL1pro­SND1
(2381
bp)
                                              EcoR IForward primer -1280                                         5’-GAATTCTTTTCGGTCTCTAA -1260 TACCTCCGGTTTTAAAAAAAAACATATCAGTTGAAGGATGAGTTTGGTGAAGGCTATATTGTC -1197 CATTGATTTTGGAGATATATGTATTATGGTCATGATTATTACGATTTTTATATAAAAGAATAT -1134 TAAAAATGGTGGGGTTGGTGAAGAAATGAAGATTTATCGTCAAATATTTCAATTTTTACTTGG -1071 ACTATTGCTTCGGTTATATCGTCAACATGGGCCCACTCTTCCACCAAAGCCCAATCAATATAT -1008 CTCTCGCTATCTTCACCAACCCACTCTTCTTCTCTTACCAAACCCATTTCCTTTATTTCCAAC -945 CCTACCCCTTTATTTCTCAAGCTTTACACTTTTAGCCCATAACTTTCTTTTTATCCAAATGGA -882 TTTGACTGGTCTCCAAAGTTGAATTAAATGGTTGTAGAAATAAAATAAAATTATACGGGTTCA -819 ATTGTTCAATTGTTCATATACCGTTGACGTTCAATTGTTCATATACGGGTTCCGTGGTCGTTG -756 GTAATATATATGTCTTTTATGGAACCAAAATAGACCAAATCAACAACAAATGAAGAAATTGTT -693 AGAGTATGATACACTCATATATACCCAAATATAGCATATATTTATAATATAACTTTTGGCTAT -630 GTCATTTTACATGATTTTTTTGGCTTATCTATTAAAAGTATCATACAAACTGTTTTTACTTCT -567 TTTTTTTCTTAGAATATATATGCCCAAAATGGAAAAGAACATATGCCAAGGTTGATTTTATCG -504 CTTATATGGTAAAAATTGGAAAAACATACAAATCATTACTTTATTTAATTAAATCATGTGAAG -441 AAACATATTCAATTACGGTAATACGTTATCAAAACATTTTTTTTTACATTAATTGTTACATTT -378 TTTTTTTTTGCAAATATTCTTAAATAACCATTCTTTTTTTATTTACTATAATTAACATAAAAA -315 TAAATAAAATATAACATTTCAACAAAGAAATTTGCTTATGAAAAATACAAAATCCAGTTAATT -252 TTTCAGAAAAATACAAATTTGCTTATAAATATATTACCACTAGTTTATGTGATTTTAAAAGAA -189 AGAAATGCAGCTTACCAAACGCAACGTGAAAATTTGAGAAACCCATACTCAAAAAAGATTAAA -126 TGACAAAATCACCCTCAGCAAAATCATGAAACAACAACACTAACATTTTCACCAACCCCACCG -63 TCTACTCCGGTGAATTGTCTATATGAACTCCTCCGATACAACTCCTGTTTCCTTCAGCCGCGG                                        Reverse primer     Sac II    +1    MetAlaAspAsnLysValAsnLeuSerIleAsnGlyGlnSerLysValProProGlyPheArg    ATGGCTGATAATAAGGTCAATCTTTCGATTAATGGACAATCAAAAGTGCCTCCAGGTTTCAGA 63            Forward Primer    PheHisProThrGluGluGluLeuLeuHisTyrTyrLeuArgLysLysValAsnSerGlnLys    TTCCATCCCACCGAAGAAGAACTTCTCCATTACTATCTCCGTAAGAAAGTTAACTCTCAAAAG 126     IleAspLeuAspValIleArgGluValAspLeuAsnLysLeuGluProTrpAspIleGlnGlu    ATCGATCTTGATGTCATTCGTGAAGTTGATCTAAACAAGCTTGAGCCTTGGGATATTCAAGAG 189     GluCysArgIleGlySerThrProGlnAsnAspTrpTyrPhePheSerHisLysAspLysLys    GAATGTAGAATCGGTTCAACGCCACAAAACGACTGGTACTTCTTCAGCCACAAGGACAAGAAG 252     TyrProThrGlyThrArgThrAsnArgAlaThrValAlaGlyPheTrpLysAlaThrGlyArg    TATCCAACCGGGACCAGGACGAACCGGGCAACAGTCGCTGGATTCTGGAAAGCTACCGGACGT 315     AspLysIleIleCysSerCysValArgArgIleGlyLeuArgLysThrLeuValPheTyrLys    GACAAAATCATCTGCAGTTGTGTCCGGAGAATTGGACTGAGGAAGACACTCGTGTTCTACAAA 378     GlyArgAlaProHisGlyGlnLysSerAspTrpIleMetHisGluTyrArgLeuAspAspThr    GGAAGAGCTCCTCACGGTCAGAAATCCGACTGGATCATGCATGAGTATCGCCTCGACGATACT 441     ProMetSerAsnGlyTyrAlaAspValValThrGluAspProMetSerTyrAsnGluGluGly    CCAATGTCTAATGGCTATGCTGATGTTGTTACAGAAGATCCAATGAGCTATAACGAAGAAGGT 504 
 113
     TrpValValCysArgValPheArgLysLysAsnTyrGlnLysIleAspAspCysProLysIle    TGGGTGGTATGTCGAGTGTTCAGGAAGAAGAACTATCAAAAGATTGACGATTGTCCTAAAATC 567     ThrLeuSerSerLeuProAspAspThrGluGluGluLysGlyProThrPheHisAsnThrGln    ACTCTATCTTCTTTACCTGATGACACGGAGGAAGAGAAGGGGCCCACCTTTCACAACACTCAA 630     AsnValThrGlyLeuAspHisValLeuLeuTyrMetAspArgThrGlySerAsnIleCysMet    AACGTTACCGGTTTAGACCATGTTCTTCTCTACATGGACCGTACCGGTTCTAACATTTGCATG 693     ProGluSerGlnThrThrThrGlnHisGlnAspAspValLeuPheMetGlnLeuProSerLeu    CCCGAGAGCCAAACAACGACTCAACATCAAGATGATGTCTTATTCATGCAACTCCCAAGTCTT 756     GluThrProLysSerGluSerProValAspGlnSerPheLeuThrProSerLysLeuAspPhe    GAGACACCTAAATCCGAGAGCCCGGTCGACCAAAGTTTCCTGACTCCAAGCAAACTCGATTTC 819     SerProValGlnGluLysIleThrGluArgProValCysSerAsnTrpAlaSerLeuAspArg    TCTCCCGTTCAAGAGAAGATAACCGAAAGACCGGTTTGCAGCAACTGGGCTAGTCTTGACCGG 882     LeuValAlaTrpGlnLeuAsnAsnGlyHisHisAsnProCysHisArgLysSerPheAspGlu    CTCGTAGCTTGGCAATTGAACAATGGTCATCATAATCCGTGTCATCGTAAGAGTTTTGATGAA 945     GluGluGluAsnGlyAspThrMetMetGlnArgTrpAspLeuHisTrpAsnAsnAspAspAsn    GAAGAAGAAAATGGTGATACTATGATGCAGCGATGGGATCTTCATTGGAATAATGATGATAAT 1008     ValAspLeuTrpSerSerPheThrGluSerSerSerSerLeuAspProLeuLeuHisLeuSer    GTTGATCTTTGGAGTAGTTTCACTGAGTCTTCTTCGTCTTTAGACCCACTTCTTCATTTATCT 1071                                                 Reverse Primer    Val   HisHisHisHisHisHis     
    GTATGACATCATCATCATCATCATGGATCC-3’                               1101               6xHis tag    BamH I  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 114
 
 
 GSTU19pro­SND1
(2558
bp)
     EcoR I -1457      Forward primer                                    5’GAATTCGC -1449   TACGTGTCGTGAGATATCGAACCCAACGCAGATATGAGTATGTTGAGCTAGTTTCTTCTTATG -1386   AAACAATCATATATGTCTATAATGAATAGATCACATTATCTGCCTGAAAAAAATCCCGTATAT -1323   TACTCGACGAAATATAAATACCCAATGTAGCTGATTTTGCTTTCTCTGGTGACATATCCAATT -1260   TGGCTAAATTTGTTAACTAGTCTATTATAGGTTTATAATAGATCTAGCTATGTTAAAGATACT -1197   AAAGCATCAGTTACATAAATTTTTGGCGCGAGTTTATATCTTTTGGAATTAAAAATAAGAGAA -1134   TTTAAAAATAAGAAGATCATTTTGTTTGGCCACAGGAGTTCTGAAAGGTCAGGTATGATTTTT -1071   TTCTTGCTCGCTCTTATGATTTTGTTTTTATTAATGGGTTTTCAAATAAGAAAAACTGTTTTT -1008   CGAAGCCCGGTTCAGATCCATTGTTTTTTGTAAAATATAGGCCCAATTCACCATAAGTCCATG -945    ACCAAAACAAAAATAAGATAGAACCAATACTGAACCAGGATCTTCTCTCGCTTTCGTGATCAA -882    TGTCGCCAAGCTTCTCGAGATCATGTGGTCACGTCAATTGTATAAATACAATTATTGACGTAA -819    CACAATCTCTACAGTTCCATCGAAATATCTCGAAAATTTCCAGTTAATTCTGGTAACGTGAAC -756    GTATCTTCCACCTCTTCAACCTACACAGCTTTCTAGAAATTTGGCTCGCTTTTCTAAGTCCTC -693    TGTATTTTTTTGCACGTTTTTCAACTAAGTTTCAATATGAATCATTTCTTCTATAAATAAATG -630    ATATTTTCATCAGGTAATGATACATTGTGCCGAAATAAAACGTCAATACTCATTAGTCAAATT -567    AATTGTTCACATAATTTAAAACTGTGTTAATCCATCCAGTTATTTTCTTACAACAAAATAATC -504    TTTTCCATCAACTTTTAAAATAATTAAACGCAGTGCTAAGAAATCTAAAATCTTGATTTAGAA -441    ATCCATTATGGTTTCTGGTCAACTGAAATCCATAATTTCCTTTAACATCCAAAATCCAAATTT -378    GCTACTATGATAATAGATTTCAGACGATTTTTTTTCTTTTTTCAATCATAGAGTCCACACGAA -315    TATTTGCAAGTTACTATATAAAACACTATAATGGTCAACAGATAAAAAAAAGGCGAATGAAGA -252    TATGTTACGTAAAAAGAAAATACTGTAATTATAAATTATTACTTTAAAAAGCTTTAAAATCTG -189    GCCACATGTTTTTAAAGAGTGGTGTGACGTAACGACTAGAGTCAGCACAATCCATTATTGTAT -126    CATAAATATTCTCATCTATAAATTACCTAAACCCTTACAGGTAGTGTCCCAACCAAACAAATC -63     GAGAAAGACGAACACTTACAAAAAAAAATCTCTTTGTGAGCTTTAGCGATCGTAACACCGCGG                                           Reverse primer     SacII    +1    MetAlaAspAsnLysValAsnLeuSerIleAsnGlyGlnSerLysValProProGlyPheArg    ATGGCTGATAATAAGGTCAATCTTTCGATTAATGGACAATCAAAAGTGCCTCCAGGTTTCAGA 63            Forward Primer    PheHisProThrGluGluGluLeuLeuHisTyrTyrLeuArgLysLysValAsnSerGlnLys    TTCCATCCCACCGAAGAAGAACTTCTCCATTACTATCTCCGTAAGAAAGTTAACTCTCAAAAG 126     IleAspLeuAspValIleArgGluValAspLeuAsnLysLeuGluProTrpAspIleGlnGlu    ATCGATCTTGATGTCATTCGTGAAGTTGATCTAAACAAGCTTGAGCCTTGGGATATTCAAGAG 189     GluCysArgIleGlySerThrProGlnAsnAspTrpTyrPhePheSerHisLysAspLysLys    GAATGTAGAATCGGTTCAACGCCACAAAACGACTGGTACTTCTTCAGCCACAAGGACAAGAAG 252     TyrProThrGlyThrArgThrAsnArgAlaThrValAlaGlyPheTrpLysAlaThrGlyArg    TATCCAACCGGGACCAGGACGAACCGGGCAACAGTCGCTGGATTCTGGAAAGCTACCGGACGT 315     AspLysIleIleCysSerCysValArgArgIleGlyLeuArgLysThrLeuValPheTyrLys    GACAAAATCATCTGCAGTTGTGTCCGGAGAATTGGACTGAGGAAGACACTCGTGTTCTACAAA 378     GlyArgAlaProHisGlyGlnLysSerAspTrpIleMetHisGluTyrArgLeuAspAspThr    GGAAGAGCTCCTCACGGTCAGAAATCCGACTGGATCATGCATGAGTATCGCCTCGACGATACT 441     ProMetSerAsnGlyTyrAlaAspValValThrGluAspProMetSerTyrAsnGluGluGly    CCAATGTCTAATGGCTATGCTGATGTTGTTACAGAAGATCCAATGAGCTATAACGAAGAAGGT 504     TrpValValCysArgValPheArgLysLysAsnTyrGlnLysIleAspAspCysProLysIle    TGGGTGGTATGTCGAGTGTTCAGGAAGAAGAACTATCAAAAGATTGACGATTGTCCTAAAATC 567 
 115
     ThrLeuSerSerLeuProAspAspThrGluGluGluLysGlyProThrPheHisAsnThrGln    ACTCTATCTTCTTTACCTGATGACACGGAGGAAGAGAAGGGGCCCACCTTTCACAACACTCAA 630     AsnValThrGlyLeuAspHisValLeuLeuTyrMetAspArgThrGlySerAsnIleCysMet    AACGTTACCGGTTTAGACCATGTTCTTCTCTACATGGACCGTACCGGTTCTAACATTTGCATG 693     ProGluSerGlnThrThrThrGlnHisGlnAspAspValLeuPheMetGlnLeuProSerLeu    CCCGAGAGCCAAACAACGACTCAACATCAAGATGATGTCTTATTCATGCAACTCCCAAGTCTT 756     GluThrProLysSerGluSerProValAspGlnSerPheLeuThrProSerLysLeuAspPhe    GAGACACCTAAATCCGAGAGCCCGGTCGACCAAAGTTTCCTGACTCCAAGCAAACTCGATTTC 819     SerProValGlnGluLysIleThrGluArgProValCysSerAsnTrpAlaSerLeuAspArg    TCTCCCGTTCAAGAGAAGATAACCGAAAGACCGGTTTGCAGCAACTGGGCTAGTCTTGACCGG 882     LeuValAlaTrpGlnLeuAsnAsnGlyHisHisAsnProCysHisArgLysSerPheAspGlu    CTCGTAGCTTGGCAATTGAACAATGGTCATCATAATCCGTGTCATCGTAAGAGTTTTGATGAA 945     GluGluGluAsnGlyAspThrMetMetGlnArgTrpAspLeuHisTrpAsnAsnAspAspAsn    GAAGAAGAAAATGGTGATACTATGATGCAGCGATGGGATCTTCATTGGAATAATGATGATAAT 1008     ValAspLeuTrpSerSerPheThrGluSerSerSerSerLeuAspProLeuLeuHisLeuSer    GTTGATCTTTGGAGTAGTTTCACTGAGTCTTCTTCGTCTTTAGACCCACTTCTTCATTTATCT 1071                                                 Reverse Primer    Val   HisHisHisHisHisHis
    GTATGACATCATCATCATCATCATGGATCC-3’                               1101            6XHis tag       BamH I 




















 
 116
 Appendix
B.
 Cis‐acting
DNA
regulatory
element
analysis
of
At4CL1
and
 AtGSTU19
promoters

 Table
 6.
 Cis­acting
 DNA
 regulatory
 element
 analysis
 of
 At4CL1,
 2000bp
 upstream
of
the
transcription
start
site.

 Putative
root
motifs
(Vijaybhaskar
et
al.
2008)
 Location
 Strand
 Signal
sequence
 Description
(Higo
et
al.
1999;
Prestridge
1991)

ARFAT
 ‐424
 (+)
 TGTCTC

 ARF
 binding
 site
 found
 in
 the
 promoters
 of
primary/early
 auxin
 response
 genes
 of
Arabidopsis
thaliana

ASF1MOTIFCAMV
 ‐341
 (+)
 TGACG

 ASF‐1
binding
site
involved
in
transcriptional
activation
of
several
genes
by
auxin
and/or
salicylic
acid
ASF1MOTIFCAMV
 ‐1214
 (+)
 TGACG

 ASF‐1
binding
site

ASF1MOTIFCAMV
 ‐912
 (–)
 TGACG

 ASF‐1
binding
site
ASF1MOTIFCAMV
 ‐957
 (–)
 TGACG

 ASF‐1
binding
site
OSE1ROOTNODULE
 ‐2873
 (+)
 AAAGAT
 A
 consensus
 sequence
 motif
 of
 organ‐specific
elements
 characteristic
 of
 activated
 promoters
found
in
the
infected
cells
of
root
nodules
OSE1ROOTNODULE
 ‐1873
 (+)
 AAAGAT
 organ‐specific
elements

OSE1ROOTNODULE
 ‐494
 (–)
 AAAGAT
 organ‐specific
elements
OSE2ROOTNODULE
 ‐427
 (+)
 CTCTT
 organ‐specific
elements
OSE2ROOTNODULE
 ‐973
 (+)
 CTCTT
 organ‐specific
elements
OSE2ROOTNODULE
 ‐1024
 (+)
 CTCTT
 organ‐specific
elements
OSE2ROOTNODULE
 ‐1032
 (+)
 CTCTT
 organ‐specific
elements
RAV1AAT
 ‐960
 (+)
 CAACA

 RAV1
 transcription
 factor
 binding
 consensus
sequence
RAV1AAT
 ‐1293
 (+)
 CAACA

 RAV1
transcription
factor
binding
consensus
sequence
RAV1AAT
 ‐1296
 (+)
 CAACA

 RAV1
transcription
factor
binding
consensus
sequence
RAV1AAT
 ‐1713
 (+)
 CAACA

 RAV1
transcription
factor
binding
consensus
sequence
RAV1AAT
 ‐1914
 (+)
 CAACA

 RAV1
transcription
factor
binding
consensus
sequence
RAV1AAT
 ‐1917
 (+)
 CAACA

 RAV1
transcription
factor
binding
consensus
sequence
RAV1AAT
 ‐198
 (–)
 CAACA

 RAV1
transcription
factor
binding
consensus
sequence
ROOTMOTIFTAPOX1

 ‐64
 (+)
 ATATT
 Motif
found
in
rolD
promoters;
organ
specificity
and
strength
ROOTMOTIFTAPOX1

 ‐92
 (+)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐307
 (+)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐337
 (+)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐366
 (+)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐687
 (+)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐804
 (+)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐871
 (+)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐918
 (+)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐1353
 (+)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐1572
 (+)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐1644
 (+)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐1787
 (+)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐91
 (–)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐115
 (–)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐159
 (–)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐169
 (–)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐469
 (–)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐686
 (–)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐870
 (–)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐917
 (–)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐994
 (–)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐1255
 (–)
 ATATT
 Motif
found
in
rolD
promoters
 
 117
 ROOTMOTIFTAPOX1

 ‐1343
 (–)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐1361
 (–)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐1454
 (–)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐1643
 (–)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐1701
 (–)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐1784
 (–)
 ATATT
 Motif
found
in
rolD
promoters
SP8BFIBSP8BIB
 ‐275
 (–)
 TACTATT
 SPBF
binding
site
SP8BFIBSP8BIB
 ‐510
 (–)
 TACTATT
 SPBF
binding
site
SURECOREATSULTR11
 ‐425
 (–)
 GAGAC
 Core
of
SURE
found
in
the
promoter
of
SULTR1;
sulfate
uptake
and
transport
SURECOREATSULTR11
 ‐741
 (–)
 GAGAC
 Core
of
SURE
found
in
the
promoter
of
SULTR1;
sulfate
uptake
and
transport
SURECOREATSULTR11
 ‐1135
 (–)
 GAGAC
 Core
of
SURE
found
in
the
promoter
of
SULTR1;
sulfate
uptake
and
transport
WUSATAg

 ‐416
 (+)
 TTAATGG
 Target
 sequence
 of
 WUS
 in
 the
 intron
 of
AGAMOUS
gene
in
Arabidopsis

 
 Table
 7.
 Cis­acting
 DNA
 regulatory
 element
 analysis
 of
 AtGSTU19,
 2000bp
 upstream
of
the
transcription
start
site.

 Putative
root
motifs
(Vijaybhaskar
et
al.
2008)
 Location
 Strand
 Signal
sequence
 Description
(Higo
et
al.
1999;
Prestridge
1991)

ARFAT
 ‐359
 (+)
 TGTCTC

 ARF
 binding
 site
 found
 in
 the
 promoters
 of
primary/early
 auxin
 response
 genes
 of
Arabidopsis
thaliana

ASF1MOTIFCAMV
 ‐371
 (+)
 TGACG

 ASF‐1
binding
site
involved
in
transcriptional
activation
of
several
genes
by
auxin
and/or
salicylic
acid
ASF1MOTIFCAMV
 ‐1267
 (+)
 TGACG

 ASF‐1
binding
site

ASF1MOTIFCAMV
 ‐1929
 (+)
 TGACG

 ASF‐1
binding
site
ASF1MOTIFCAMV
 ‐1243
 (–)
 TGACG

 ASF‐1
binding
site
ASF1MOTIFCAMV
 ‐1504
 (–)
 TGACG

 ASF‐1
binding
site
OSE1ROOTNODULE
 ‐888
 (+)
 AAAGAT
 A
 consensus
 sequence
 motif
 of
 organ‐specific
elements
 characteristic
 of
 activated
 promoters
found
in
the
infected
cells
of
root
nodules
OSE1ROOTNODULE
 ‐34
 (–)
 AAAGAT
 organ‐specific
elements

OSE1ROOTNODULE
 ‐127
 (–)
 AAAGAT
 organ‐specific
elements
OSE1ROOTNODULE
 ‐177
 (–)
 AAAGAT
 organ‐specific
elements
OSE1ROOTNODULE
 ‐934
 (–)
 AAAGAT
 organ‐specific
elements
OSE1ROOTNODULE
 ‐1587
 (–)
 AAAGAT
 organ‐specific
elements
OSE2ROOTNODULE
 ‐47
 (+)
 CTCTT
 organ‐specific
elements
OSE2ROOTNODULE
 ‐158
 (+)
 CTCTT
 organ‐specific
elements
OSE2ROOTNODULE
 ‐1033
 (+)
 CTCTT
 organ‐specific
elements
OSE2ROOTNODULE
 ‐1349
 (+)
 CTCTT
 organ‐specific
elements
OSE2ROOTNODULE
 ‐953
 (–)
 CTCTT
 organ‐specific
elements
OSE2ROOTNODULE
 ‐1919
 (–)
 CTCTT
 organ‐specific
elements
RAV1AAT
 ‐1577
 (+)
 CAACA

 RAV1
 transcription
 factor
 binding
 consensus
sequence
RAV1AAT
 ‐1814
 (+)
 CAACA

 RAV1
transcription
factor
binding
consensus
sequence
RAV1AAT
 ‐639
 (–)
 CAACA

 RAV1
transcription
factor
binding
consensus
sequence
RAV1AAT
 ‐685
 (–)
 CAACA

 RAV1
transcription
factor
binding
consensus
sequence
ROOTMOTIFTAPOX1

 ‐336
 (+)
 ATATT
 Motif
found
in
rolD
promoters;
organ
specificity
and
strength
ROOTMOTIFTAPOX1

 ‐605
 (+)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐767
 (+)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐1464
 (+)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐1778
 (+)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐1973
 (+)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐267
 (–)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐391
 (–)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐781
 (–)
 ATATT
 Motif
found
in
rolD
promoters
 
 118
 ROOTMOTIFTAPOX1

 ‐1119
 (–)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐1298
 (–)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐1434
 (–)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐1777
 (–)
 ATATT
 Motif
found
in
rolD
promoters
ROOTMOTIFTAPOX1

 ‐1972
 (–)
 ATATT
 Motif
found
in
rolD
promoters
SORLIP1AT
 154
 (+)
 GCCAC
 One
 of
 "Sequences
 Over‐Represented
 in
 Light‐Induced
 Promoters
 (SORLIPs)
 in
 Arabidopsis;
Computationally
 identified
 phyA‐induced
motifs;
SORLIP
1
is
most
over‐represented,
and
most
statistically
significant
SORLIP1AT
 435
 (+)
 GCCAC
 One
of
"Sequences
Over‐Represented
in
Light‐Induced
Promoters
(SORLIPs)
SORLIP1AT
 988
 (+)
 GCCAC
 One
of
"Sequences
Over‐Represented
in
Light‐Induced
Promoters
(SORLIPs)
SORLIP1AT
 1905
 (+)
 GCCAC
 One
of
"Sequences
Over‐Represented
in
Light‐Induced
Promoters
(SORLIPs)
SP8BFIBSP8BIB
 ‐330
 (–)
 TACTATT
 SPBF
binding
site
SURECOREATSULTR11
 ‐496
 (+)
 GAGAC
 Core
of
SURE
found
in
the
promoter
of
SULTR1;
sulfate
uptake
and
transport
SURECOREATSULTR11
 ‐360
 (–)
 GAGAC
 Core
of
SURE
found
in
the
promoter
of
SULTR1;
sulfate
uptake
and
transport
WUSATAg

 ‐1053
 (+)
 TTAATGG
 Target
 sequence
 of
 WUS
 in
 the
 intron
 of
AGAMOUS
gene
in
Arabidopsis






























 
 119
 Appendix
C.
 Primer
sequences


 Table
8.
List
of
all
primer
sequences
used
for
PCR,
reverse
transcription­PCR
 and
sequencing.

 No.
 Name
 Primer
Sequence
 Comments
 1
 4CL1
Forward


 5’-TCCAGAGGTGTAAAGTGACGGTGGC-3’ Native
gene
expression
 2
 4CL1
Reverse

 5’-CCGTCATTCCGTATCCCTGACCGAG-3’ Native
gene
expression
 3
 GSTU19
Forward

 5’-AGGTGTGGGCGACAAAGGGTG-3’ Native
gene
expression
 4
 GSTU19
Reverse

 5’-CCACGCTCTCCCTCTGCAAACAC-3’ Native
gene
expression
 5
 4CL1pro
Forward

 5'-GGGCACGˇAATTCTTTTCGGTCTCTAATACCTCC- 3’ EcoRI
RE
site
 6
 4CL1pro
Reverse
 5’CACGAGGˇGATCCGˇGTNACCCCGCˇGGCTGAAGGA AACAGGAGTTGTATC-3’ BamHI,
BstEII
and
SacII
RE
sites
 7
 GSTU19pro
Forward

 5’-GGGTCTGˇAATTCGCTACGTGTCGTGAGATATCG- 3’ EcoRI
RE
site
 8
 GSTU19pro
Reverse
 5’- CACGAGGˇGATCCGˇGTNACCCCGCˇGGTGTTACGAT CGCTAAAGCTCAC-3’ BamHI,
BstEII
and
SacII
RE
sites
 9
 SND1
Forward


 5’GAGCTCCCGCˇGGATGGCTGATAATAAGGTCAATCTTTCG-3’ SacII
RE
site
 10
 SND1
Reverse


 5’GGGTGTGˇGATCCATGATGATGATGATGATGTCATACAGATAAATGAAGAAGTGGGTC-3’ BamHI
RE
site
and
HIS
x6
tag
 11
 4CL1pro­SND1
Rev
(mid‐insert)
 5’-GTCACGTCCGGTAGCTTTCC-3’ For
sequencing
from
the
middle
of
the
insert
 12
 GSTU19pro­SND1
Rev
(mid‐insert)
 5’-TCTCCGGACACAACTGCAGATG-3’ For
sequencing
from
the
middle
of
the
insert
 13
 MYB46
Forward

 5’-CTGGTCGGACCGATAACGAG-3’ 300bp
fragment
 14
 MYB46
Reverse

 5’-GGTGGCTGATCATGTTTCCC-3’  300bp
fragment
 15
 SND3
Forward

 5’-ACGCTTGAAGGAGAGAATGG-3’ 300bp
fragment
 16
 SND3
Reverse

 5’-CTGATGCATCACCCAATTCG-3’ 300bp
fragment
 17
 MYB103
Forward

 5’-AGGTGGGCTCATATAGCTAG-3’ 400bp
fragment
 18
 MYB103
Reverse

 5’-CTCTTCCTCCTCTTTGCGTG-3’  400bp
fragment
 19
 KNAT7
Forward

 5’-CAGCACGTGAGGGTTCATGC-3’ 300bp
fragment
 20
 KNAT7
Reverse

 5’-CCCAGCCCTTCTCTTCCTCA-3’  300bp
fragment
 21
 SND1
Forward

 5’-GATCATGCATGAGTATCGCC-3’ 200bp
fragment
 22
 SND1
Reverse

 5’-CGGGCTCTCGGATTTAGGTG-3’ 200bp
fragment
 
 120
 23
 4CL1
L1
 5’-TCAACCCGGTGAGATTTGTA-3’ From
Apurva
Bhargava
(Ellis
Lab)
 24
 4CL1
R1
 5’-TCGTCATCGATCAATCCAAT-3’ From
Apurva
Bhargava
(Ellis
Lab)
 25
 CCR1
L1
 5’-GTGCAAAGCAGATCTTCAGG-3’ From
Apurva
Bhargava
(Ellis
Lab)
 26
 CCR1
R1
 5’-GCCGCAGCATTAATTACAAA-3’ From
Apurva
Bhargava
(Ellis
Lab)
 27
 COMT1
L1
 5’-GTGCAAAGCAGATCTTCAGG-3’ From
Apurva
Bhargava
(Ellis
Lab)
 28
 COMT1
R1
 5’-CATGGTGATTGTGGAATGGT-3’ From
Apurva
Bhargava
(Ellis
Lab)
 29
 ACT8F(QRT)
 5’-TCTAAGGAGGAGCAGGTTTGA-3’ From
Apurva
Bhargava
(Ellis
Lab)
 30
 ACT8R(QRT)
 5’-TTATCCGAGTTTGAAGAGGCTAC-3’
  From
Apurva
Bhargava
(Ellis
Lab)

 




























 
 121
 Appendix
D.
 Media,
Buffers
and
Reagent
Stocks

 LB
broth
(1L)
 
 • Tryptone
 
 
 
 10
g
 • Yeast
Extract
 
 
 5
g
 • NaCl
 
 
 
 10
g
*For
plates
add
15g
agar


 ½
MS
media
(1L)
 
 • MS
salt
plus
vitamin
 
 2.2
g
 • MES
hydrate
 
 
 0.5
g
 • Sucrose
 
 
 
 10
g
(phenotyping)

 
 
 
 
 20
g
(growth)
*For
plates
add
7g
agar
*Adjust
pH
to
5.7
using
1M
KOH


 1/10
Johnson
solution
(20L)

 • 20mM
Fe‐EDTA
(use
3mL/20L
solution)
 • 10mM
CaSO4
(use
800mL/20L
solution)
 • Macro
stock
(mix:
20mL/L
of
1M
MgSO4,
40mL/L
of
1M
KH2PO4,
80mL/L
of
0.5M
K2SO4)
(use
100mL/20L
solution)
 • Micro
stock
(mix:
25mM
H3BO3,
2mM
MnSO4
x
H20,
2mM
ZnSO4
x
H20,
0.5mM
CuSO4
x
5H2O,
0.5mM
NaMoO4)
(use
3mL/20mL
solution)
 • 2
spoons
of
CaCO3
powder
 • Add
NH2NO3
directly
to
a
final
concentration
of
1mM



 
 
 Benoxacor
100mM
Stock
Solution
(1000x)

 • Benoxacor
 125
mg

 • Acetone
 
 4.81
mL

 
 Fenclorim
100mM
Stock
Solution
(1000x)

 • Fenclorim

 125
mg
 • Acetone
 
 5.55
mL


 
 122
 Klason
lignin
procedure
solutions
 
 72%
H2SO4
665
mL
conc.
H2SO4
300
mL
DI
H2O
cool,
bring
to
1L

 4%
H2SO4
37
mL
conc.
H2SO4
950
mL
DI
H2O
cool,
bring
to
1L
 
 Sugar
Control
(in
50
mL
DI
H2O)
arabinose
 10
mg
galactose
 10
mg
glucose
 200
mg
xylose

 60
mg
mannose
 60
mg
rhamnose
 50
mg

High
standard:
 sugar
stock
 30
mL

 
 
 DI
H2O
82
mL

 
 
 72%
H2SO4
 3
mL
Medium
Standard:
 sugar
stock
 10
mL

 
 
 DI
H2O
102
mL

 
 
 72%
H2SO4
 3
mL
Low
Standard:
 sugar
stock
 5
mL

 
 
 DI
H2O
107
mL

 
 
 72%
H2SO4

 3
mL

 Internal
Standard
 
fucose

 10
mg/mL









 
 123
 Appendix
E.
 One‐way
analysis
of
variance
(ANOVA)
for
average
seed
weight

 
 
 and
lateral
root
density

 
 Figure
16.
 One­way
ANOVA
statistical
analysis
to
determine
differences
in

 
 
 
 average
seed
weight
between
genotypes

 !"# $%&#$'# ()*+#$'# ,#-*.%)# /012,3# -*.%)# $))4#5)6789# :# ;<=>;?@# A<B;C:C# ?<;A=># A<AA;:=@# D)E64%*.E# ?A# B<;>;@:# A<A=>BF# ! 40 41 A!7_5 B!5_6 D!2_6 F!5_10 F!7_4 G!8_4 1 .6 1 .8 2 .0 2 .2 2 .4 Seed Weight (ug)  ANOVA Report list(Df = c(7, 40), ‘Sum Sq‘ = c(1.53145833333333, 2.13166666666667), ‘Mean Sq‘ = c(0.218779761904761, 0.0532916666666668), ‘F value‘ = c(4.10532782307604, NA), ‘Pr(>F)‘ = c(0.00175643763379917, NA)) Genotype L a te ra l R o o t D e n s it y  ( #  r o o ts /c m ) 
 124
 



 
 
 Figure
17.

 One­way
ANOVA
statistical
analysis
to
determine
differences
in

 
 
 
 average
number
of
lateral
roots
between
genotypes
 !  roots_cmA!75  roots_cmF!74 roots_cmEV40 0 .0 0 .2 0 .4 0 .6 0 .8 1 .0 1 .2 1 .4 Lateral Root Density (LRD)  ANOVA Report list(Df = c(4, 95), ‘Sum Sq‘ = c(3.70348645871960, 10.2922317372679), ‘Mean Sq‘ = c(0.925871614679899, 0.108339281444925), ‘F value‘ = c(8.54603798669804, NA), ‘Pr(>F)‘ = c(6.19424094405413e!06, NA)) Genotype L a te ra l R o o t D e n s it y  ( #  r o o ts /c m ) A-7 B-5 F-7 G-8 EV40 !"# $%&#$'# ()*+#$'# ,#-*.%)# /012,3# 45!# 6# 789:7;# :8<=;<# >8;6?# ?8@<6)A:?# 5)BCD%*.B# <;# @:8=<==# :8@:>7#

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