Download Research Proposal

Transcriptomic study of
mitochondrial stress responses in
Arabidopsis thaliana
OVERVIEW
• Introduction
– Plant stress
– Plant mitochondria during stress
– Research outline
• Methodology
– DNA microarrays
– Cluster analysis
• Results
– Stress-induced changes in expression of genes
encoding mitochondrial proteins
– Regulation of these changes in gene expression
HEAT
SHADE
DROUGHT
PLANT
STRESSES
INSECTS
PLANT STRESS RESPONSE
STRESS
Changes
in cell
RECOGNITION
STRESS SIGNALLING
ACTIVATION OF TFs
EXPRESSION OF STRESS
RESPONSE GENES
STRESS TOLERANCE
Plants react to changes in their environment:
• Changing environmental conditions
• Biotic stresses (cold, salt, drought)
•Pathogen attack
•Various abiotic stresses
Appropriate responses to such stresses must be induced for
survival and proliferation.
How plants respond to various stresses can be complex,
involving multiple components and levels of regulation.
Plant responses are complex:
The ABA example
> 50 At loci function in aspects of ABA response
•Their products include:
1. Transcription factors
2. Protein phosphatases and kinases
3. RNA binding proteins
4. Proteins involved in regulating trafficking or
localization of specific proteins
• Some loci have been Ided multiple times independently
 do these genes represent points of ‘cross-talk’
Regulatory mechanisms
The plasticity of plant metabolism is achieved through a
combination of regulatory mechanisms.
These include:
1. Transcriptional regulation:
• Transcriptional activation
• mRNA stability
2. Post-transcriptional regulation:
• Protein expression
• Protein activation and activity
• Degradation
Regulating gene expression
• Every cell contains a full genome
 gene expression patterns are characteristic of cell state
 changes in cell state correlate with changes in mRNA levels
Gene expression:
• highly complex
• tightly regulated
 allows a cell to respond dynamically
The mechanism
1. "on/off" switch:
to control which genes are expressed
2. "volume control”:
alters the level of expression of particular genes
Mitochondria and Microarrays
1. Investigating the role of mitochondria in stress responses
1. General to specific approaches
2. Specific to general approaches
2. Using microarrays:
1. Array technology
2. Background to our array experiment
3. Analysing array data
3. From microarrays to mitochondria biology
> putting the data in context
Aim: to investigate
A) how mitochondria are involved in the plant
responses to stress
B) how mitochondria and mitochondrial
components are regulated in response to stress
Mitochondria = power house of the cell
mitochondria in a stress context
Pathways involving mitochondria:
•
•
•
•
•
•
Oxidative phosphorylation
TCA cycle
Amino acid biosynthesis
Vitamin biosynthesis - including folate and ascorbate
Cell death
Photorespiration
Plant mitochondria are influenced by their environment:
Plant mitochondria populations are heterogeneous
structure
activity
protein composition
metabolic activity
function
developmental stage
Stress and mitochondria?
Many systems in the cell must be coordinated to facilitate
mitochondria responding to stress.
For example:
• Over 95% of mitochondrial proteins are nuclear encoded
> regulated expression, targeting, and import
• multi-subunit complexes of the mETC are composed of
nuclear and mitochondrial encoded subunits
> regulated expression of 2 genomes
• Many pathways that involve the mitochondria also
involve other organelles, such as the glycoxosome and
the chloroplast
> communication between organelles
What makes plant mitochondria so interesting?
Mitochondrial electron transport chain:
• Alternative pathway (alternative oxidase)
• Additional internal and external NAD(P)H dehydrogenases
cytosol
outer mitochondrial membrane
inter membrane space
NAD(P)H
NAD(P)+
H+
EXT
inner
mitochondrial
membrane
UQ
CI
INT
matrix
NADH NAD+
Cyt c
C III
C II
succinate
NAD(P)H
fumarate
NAD(P)+
H+
AOX
O2 H2O
CV
C IV
O2 H O
2
ADP + Pi ATP
These components are thought to play protective roles, allowing
continued mitochondrial function during situations of high
energy turnover or environmental stress
e.g Aox is induced under almost all studies ‘stress’ situations
Investigating stress responses from
a mitochondrial perspective
Two main approaches:
1. Specific to general:
focus on specific genes or proteins
traditional molecular biology approaches
‘one gene, one experiment’
1. General to specific:
aimed at facilitating ‘whole picture’ understanding
take a whole transcriptome or proteome approach
traditionally needed high-throughput large-scale technologies
Using Microarrays to get inside
Mitochondria
How can you study the complex interplay of all genes
involved in the stress response simultaneously?
> microarray technology
Promises of microarrays:
• infer probable functions of new genes based on
similarities in expression patterns with those of known
genes
• promise to expand the size of existing gene families
• reveal new patterns of coordinated gene expression
across gene families
• uncover entirely new categories of genes.
Microarray technology options
The options:
1. Oligo-based arrays
2. cDNA (spotted) arrays
Basic technology is the same:
1.
2.
3.
4.
5.
Make DNA complementary to genes of interest
Microscopic quantities of compDNA laid out on solid surfaces
DNA from samples is eluted over the surface
Complementary DNA binds
Bound DNA detected by fluorescence following laser excitation
Differ in:
• How the sequences are laid down:
spotting / photolithography
• Length of sequence used:
complete cDNAs or fragments
Microarray experimental design
Use MIAMI framework:
1. Experimental design:
the set of the hybridization experiments as a whole
2. Array design:
each array used and each spot on the array
3. Samples:
samples used, the extract preparation, and labeling
4. Hybridizations:
procedures and parameters
5. Measurements:
images, quantitation, and specifications
6. Controls:
types, values, and specifications
Arabidopsis and arrays
genome sequenced
cell culture
GeneChip® Arabidopsis ATH1 Genome Array
Specifications:
• number of sequences represented:
> 24 000 gene sequences
• feature size:
18 µm
• oligo length:
25 mer
• probe pairs / sequence:
11
• sensitivity:
1:100 000
• control sequences:
E.coli genes: bioB, bioC, BioD, cre
B. subtilis gene lysA.
Phage P1 cre gene.
Arabidopsis maintenance genes, GAPDH, actin and ubiquitin
Monitoring affects of treatments
Experimental set-up
Arabidopsis
cell culture
control
treated
chemical
Collect samples:
0, 30 min, 1 h, 3 h, 12 h, 24 h
Monitor:
• cell viability
- vital stains (Acridine orange/ Ethidium bromide)
• cell respiration
- Respiratory measurements: O2 electrode
• expression of key mitochondrial proteins
- real time PCR (iCycler)
Concentration is important:
want response of target mitochondrial genes
minimal cell death (>75 % viability)
minimal effect on respiration
Affymetrix GeneChip technology
Quality control steps
1. RNA quality
260/280 ratio
2. Array image inspection
3. Hybridization controls
4. % genes present
5. Scaling and normalization factors
Microarrays of Stress in At cell culture
Biotic
•Flagellin (bacterial elicitor)
•Chitin (fungal elicitor)
•Salicylic acid
Abiotic
•H2O2
•Anoxia
•Mannitol (dehydration)
•Cold (22o-10o C)
Organelle targeted
•Chloramphenicol (Organelle protein synthesis inhib)
•Rotenone (Mito ETC)
•Antimycin A (Mito ETC)
•Oligomycin (Mito ATP synthesis)
Low complexity cell type
•Norfluroson (Chloroplast biogenesis inhib)
After 3 hours
Ath1 Affymetrix 22K arrays
40 array experiment
Arabidopsis Suspension Cells
cystine
redox
carotenoids
tetrapyrrols
paraquat
PSII
NUCLEUS
norflurazon
chloroplast
CHLOROPLAST protein synthesis
chloramphenicol
erythromycin
carotenoid
biosynthesis
Treatments indicated at
site of action in RED
Genes studied by quantitative
real-time PCR indicated in blue
(blue box indicates mitochondrially
encoded genes)
keto acids
ROS
C
H+
III
AA
rotenone NDB1,2,3,4
NDC1
EXT
UCP
I
UQ
UCP1,2
H+
H+
Cyt C
H+
IV
AOX1a,1c,2
succinyl
CoA synthase
ANT
BKA
PTP
pyruvate
translocator
fumarate
fumarase
L-malate
succinyl-CoA
MITOCHONDRIA
MDH
IMS
a-ketoglutatate
IDH1,2
OMM
pyruvate
PDH
matrix
IMM
OAA
citrate
synthase
isocitrate aconitase citrate
Acon1,2,3
SA
H2O2
mannitol
ROS
H+
II malonate
succinate
IDH
glucose
oligomycin
SDH
-KDH
pyruvate
F1alpha
V F1beta
Aox
INT
NDA1,2
Signalling pathways
mechanisms indicated in green
sugars
FCCP
acetylCoA
CoA-SH
mitochondrial
protein synthesis
chloramphenicol
cytoplasm
Monitoring affects of treatments
Experimental set-up
Arabidopsis
cell culture
control
treated
chemical
Collect samples:
0, 30 min, 1 h, 3 h, 12 h, 24 h
Monitor:
• cell viability
- vital stains (Acridine orange/ Ethidium bromide)
• cell respiration
- Respiratory measurements: O2 electrode
• expression of key mitochondrial proteins
- real time PCR (iCycler)
Concentration is important:
want response of target mitochondrial genes
minimal cell death (>75 % viability)
minimal effect on respiration
Microarrays of Stress in At cell culture
Biotic
•Flagellin (bacterial elicitor)
•Chitin (fungal elicitor)
•Salicylic acid
Abiotic
•H2O2
•Anoxia
•Mannitol (dehydration)
•Cold (22o-10o C)
Organelle targeted
•Chloramphenicol (Organelle protein synthesis inhib)
•Rotenone (Mito ETC)
•Antimycin A (Mito ETC)
•Oligomycin (Mito ATP synthesis)
Low complexity cell type
•Norfluroson (Chloroplast biogenesis inhib)
After 3 hours
Ath1 Affymetrix 22K arrays
40 array experiment
Arabidopsis Suspension Cells
cystine
redox
carotenoids
tetrapyrrols
paraquat
PSII
NUCLEUS
norflurazon
chloroplast
CHLOROPLAST protein synthesis
chloramphenicol
erythromycin
carotenoid
biosynthesis
Treatments indicated at
site of action in RED
Genes studied by quantitative
real-time PCR indicated in blue
(blue box indicates mitochondrially
encoded genes)
keto acids
ROS
C
H+
III
AA
rotenone NDB1,2,3,4
NDC1
EXT
UCP
I
UQ
UCP1,2
H+
H+
Cyt C
H+
IV
AOX1a,1c,2
succinyl
CoA synthase
ANT
BKA
PTP
pyruvate
translocator
fumarate
fumarase
L-malate
succinyl-CoA
MITOCHONDRIA
MDH
IMS
a-ketoglutatate
IDH1,2
OMM
pyruvate
PDH
matrix
IMM
OAA
citrate
synthase
isocitrate aconitase citrate
Acon1,2,3
SA
H2O2
mannitol
ROS
H+
II malonate
succinate
IDH
glucose
oligomycin
SDH
-KDH
pyruvate
F1alpha
V F1beta
Aox
INT
NDA1,2
Signalling pathways
mechanisms indicated in green
sugars
FCCP
acetylCoA
CoA-SH
mitochondrial
protein synthesis
chloramphenicol
cytoplasm
Oxidative Stress Response (H2O2)
viewed by Sub-cellular Location Data
Mitochondrion (440)
Nucleus (163)
Chloroplast (620)
PM (235)
Peroxisome (30)
Tonoplast (58)
CLUSTER ANALYSIS
RESULTS: CLUSTER ANALYSIS
• Co-expressed gene
clusters
• Associations not
previously reported
• Functionally related
genes co-induced:
– evidence for induction of
specific biological pathways
ALTERNATIVE RESPIRATORY
PATHWAY WAS INDUCED
SUBSTRATE TRANSPORT
WAS REPRESSED
v| Entry of substrates into mt
Limit availability of substrates for ETC?
…limit flux through ETC
…limit ROS production/oxidative damage
AMINO ACID METABOLIC
PATHWAYS WERE INDUCED
Amino acid metabolism
BRANCHED CHAIN AMINO ACID
PATHWAYS INDUCED
Branched chain amino acids
BCAT
Branched chain a-keto acids
BCKDC
* Branched chain
amino acid metabolism
Acyl-CoA
IVD
MCCAse
3-methyl-gluteconyl-CoA
E-CoAH
Acetoacetate
+ Acetyl-CoA
*
Enoyl-CoA
*
*
E-CoAH
Hydroxy-acyl-CoA
Val
Leu
Ile
HIBDH
*
Propionyl-CoA
Propionyl-CoA
+ Acetyl-CoA
Image adapted from: Taylor et. al. (2004) Plant Physiol 134(2): 838-48.
Expression responses of the complexes of the mitochondrial
electron transport chain to a range of abiotic stresses
For each complex the fold change in expression relative to untreated cells
was averaged for all the subunits under each treatment. Error bars indicate
variation between the fold changes of subunits within a complex.
Treatment Key:
M = mannitol 3% w/v
R = Rotenone 40 uM
S = salicylic acid 100 uM
Co = cold
An = anoxia
Ob = oligomycin 1.25 uM
S 10 = salicyclic acid 10 uM
H = H2O2 10 mM
O = oligomycin 0.125 uM
C = chloramphenicol 200 uM
Transcript responses of the alternative oxidase gene family to stress
Quantitative real-time PCR data
expressed as a ratio of pretreated
20
18
U = untreated
C = chloramphenicol
E = erythromycin
P = paraquat
R = rotenone
S = salicylic acid
16
AOX1a
AOX1c
AOX2
14
12
10
8
6
4
2
0
Treatment
Time (h)
3
12
24
The response profiles of specific genes cluster in response to
certain treatments
GeneCluster2.0 outputs derived from quantitative real-time PCR data of all genes examined
expressed as a ratio of pretreated entered per treat
Treatment Key:
A = antimycin A
C = chloramphenicol
Ci = citrate
E = erythromycin
F = FCCP
G = glucose
M = mannitol
Ma = malonate
N = norflurazon
P = paraquat
R = rotenone
S = salicylic acid
PROBLEMS WITH CLUSTERING
• Good at identifying genes that are
co-expressed in response to all/most
treatments
• Not good at identifying genes that
are co-expressed in response to
specific subsets of treatments
PROBLEMS WITH CLUSTERING
Treatments
Genes
Genes
Treatments
NEW ALGORITHM: ModuleFinder
• Choose an initial
subset of
treatments
• Identify subset of
genes that are
co-expressed in
response to
these treatments
• Cluster into
groups
NEW ALGORITHM: ModuleFinder
• Look for other
treatments under which
these gene clusters are
co-expressed
RESULTS: ModuleFinder
NDB2
Amino acid
synthesis
AOX1a
carriers
All associated with:
flux through ETC
ATP synthase activity
ROS
Classical
ETC
components
PROMOTER ANALYSIS
• Interested in whether clustered genes
share common transcription factors
• Analyse promoter sequences of clustered
genes for common motifs
• Traditional methods:
– Slow (finding motifs is computer-intensive)
– Lots of clusters  lots of analyses to run
– Ignore relationships between clusters
NEW METHOD: CoREG
• Co-Regulation of co-Expressed Genes
• Principle 1: genes that are co-expressed are
likely to be co-regulated
• Principle 2: clustering not only splits genes into
modules but captures relationships between
those modules
• Strategy: use clustering approach to find
promoter motifs that can explain the related
expression patterns of the modules
NEW METHOD: CoREG
• Extract promoter sequences for all genes in
modules
– Upstream regions
• Assemble list of possible motifs
– e.g. known TF binding sites, 6-bp sequences
• Calculate frequencies of each possible motif in
the promoters of module genes
• Look for motifs whose frequencies vary
significantly between differentially expressed
modules
RESULTS: IDENTIFICATION OF
REGULATORY MOTIFS
MODEL OF MODULE GENE
EXPRESSION REGULATION
Model of module promoters:
HYPOTHETICAL MODEL OF
REGULATORY ELEMENTS