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Using studies of gene expression to investigate species radiations in the New Zealand alpine flora
Southern Connection 2010
Claudia Voelckel, Peter B Heenan, Peter J Lockhart
DNA
mRNA
proteins
Transcription
Translation
provide structure & drive metabolism
substrate product
Why Gene Expression Studies?
Genomics
Transcriptomics
Proteomics
Metabolomics
*Comparative transcript profiling within & between species
* Evolutionary
2
1. Transcriptomics and species radiation – a case study
2. New tool in town – sequencing based methods replace microarrays
3. Putting the new tool to the test – case study revisited
4. Systems biology and species radiation
Outline
3
1. Transcriptomics and species radiation – a case study
2. New tool in town – sequencing based methods replace microarrays
3. Putting the new tool to the test – case study revisited
4. Systems biology and species radiation
Outline
4
Pachycladon (Brassicaceae)
Pachycladon super-network, S. Joly, unpubl.
stellatum
fastigiatum
enysii
enysii
cheesemanii exile
novae-zealandiae
wallii
latisiliqua
Diversification in New Zealand Alpine Cress
Habitat Rosette
Flowering Fruiting
Habitat Rosette
Flowering Fruiting
vs.
6
Pachycladon fastigiatum Pachycladon enysii
Sampling in the New Zealand Southern Alps
7
P. enysiiP. fastigiatum
8
DNA chip
with gene probesAAAAAA3’TTTTTT5’
TTTTTT5’green-labeled cDNA
AAAAAA3’TTTTTT5’
TTTTTT5’ red-labeled cDNA
Microarrays (DNA chips)
Sample 1
AAAAAA3’mRNA
Sample 2
AAAAAA3’ mRNA
DATA ANALYSISintensity 1intensity 2
Expression ratio: log
P. enysiiP. fastigiatum
Prob
abili
ty o
f diff
eren
tial e
xpre
ssio
n ( l
og o
dds
ratio
)
Magnitude of differential expression (log fold change)
ESM1 ESP
Arabidopsis microarray (20,468 genes)
310 genes (1.5%) up in P. fastigiatum 324 genes (1.6%) up in P. enysii
up-regulation of ESM1 and ESP predict P. fastigiatum to produce isothiocyanates and P. enysii to produce nitriles
Results
Voelckel et al. 2008, Molecular Ecology, 17: 4740–4753 9
Methionine Chain elongation pathway
Homomethionine (C3 GLS)Dihomomethionine(C4 GLS)
Methylthioalkyl GLS
Methylsulfinylalkyl GLS
Alkenyl GLS Hydroxalkyl GLS
Hydroxalkenyl GLS
GLS core pathway
Glucosinolate hydrolysis
Thiocyanates Nitriles (Eithionitriles)
Isothiocyanates Oxazolidine-2-thione
Side
cha
in m
odifi
catio
n
(Aliphatic) Glucosinolates (GLS) – Synthesis and hydrolysis genes
MAM, MAM-I, MAM-D, BCAT4
CYP79, CYP83, C-S lyase, SGT, SOT
FMO
AOP2 AOP3
GS-OH
myrosinase
ESM1 ESP
0
2
4
6
8
10
12
14
Isothiocyanates Nitriles/Epithionitriles
Allyl 3MTP
01234567
Isothiocyanates Nitriles/Epithionitriles
3MSOP
ESP (At1g54040)
ESM 1 (At3g14210)
6.29
- 4.62
Nitriles in P. enysii
Isothiocyanatesin P. fastigiata
P. enysii
P. fastigiata
HP
(μ m
ol/g
fw)
HP
(μ m
ol/g
fw)
Gene Prediction Regulation (log ratio)
Test
HPLC Test of Microarray Prediction
Voelckel et al. 2008, Molecular Ecology, 17: 4740–4753
Hypothesis: Role for herbivory in species diversification?
1. Transcriptomics and species radiation – a case study
2. New tool in town – sequencing based methods replace microarrays
3. Putting the new tool to the test – case study revisited
4. Systems biology and species radiation
Outline
12
NEXT-GEN Sequencing
Inexpensive production of large volumes of sequence data
Several platforms (Roche/454, Illumina/Solexa, ABI/SOLiD)
Many applications (de-novo assembly, re-sequencing, epigenetics and chromatin structure, metagenomics)
Revolutionary tools for gene expression analysis (e.g. Tag profiling, RNA-seq)
14
Tag Profiling
12
21
11
count 1count 2
log
STATISTICAL ANALYSIS
Solexa Genome Analyzer
Sample 1 mRNA
AAA3’AAA3’
AAA3’AAA3’
Sample 2mRNAAAA3’
AAA3’AAA3’
AAA3’
18 bp tag library
AAA3’
AAA3’
AAA3’
AAA3’
18 bp tag library
AAA3’
AAA3’
AAA3’
AAA3’
Sample 1
Sample 2
Reference
TAG MAPPING
Advantages & Challenges of Tag Profiling
open to any organism
any expressed transcript detectable (1 copy/cell)
less RNA needed (tag profiling = 1µg, microarrays = 100 µg)
minor data normalization/no background
Advantages
Challenges
mapping 18 bp tags (sequence differences Pachycladon/Arabidopsis)
counting tags per gene (noise, location, abundance)
statistical analysis of differential expression (proportion data)
15
1. Transcriptomics and species radiation – a case study
2. New tool in town – sequencing based methods replace microarrays
3. Putting the new tool to the test – case study revisited
4. Systems biology and species radiation
Outline
16
Tag Profiling Results
17423 A. thaliana loci (noise filter 10, count most abundant tag per gene)
2654 genes (15.2%) up in P. fastigiatum 1857 genes (10.7%) up in P. enysii
(tagwise normalization, -log2(1.5) < logfc < log2 (1.5))
P. enysiiP. fastigiatum
17
Microarrays (MA) vs. Tag Profiling (TP)
more differentially expressed genes in TP (10.7-15.2% ) than with MA (1.5-1.6% )
310 up in PF324 up in PE
2654 up in PF1857 up in PE
PF
MA TP41269 2613
PE
50274 1807MA TP
13.2% (PF) and 15.4 % (PE) of MA results confirmed by TP results
18
biological inferences from both studies identical
Locus lfc MA lfc TPAT1G54040, ESP 6.3 7.0
Locus lfc MA lfc TPAT3G14210, ESM1 -4.6 -35.0
MA: 20,468 genes
TP: 17,423 genes116058863 5818
“...not a popular product, too expensive, tricky chemistry.. instead use:
RNA-Seq!”
Tag Profiling is dead, long live RNA-Seq!
2 Oct 09: “Illumina is discontinuing the support of Tag Profiling and will no longer be manufacturing the reagent kits for this application.”
One year later: Tag profiling works for a non-model plant with a distant reference transcriptome! Let’s do more experiments!
19
20
RNA-Sequencing
Sample 1
AAA3’AAA3’
AAA3’AAA3’
Sample 2mRNA mRNA
Solexa Genome Analyzer
AAA3’
AAA3’AAA3’
AAA3’
cDNA library cDNA library
Sample 1
Sample 2
ReferenceREAD MAPPING
12
21
11
count 1count 2
log
STATISTICAL ANALYSIS
gene length
read mapping (reference transcriptome)
quantification of reads (lack of software, but packages evolve: e.g. edgeR)
Advantages & Challenges of RNA-Seq
Advantages
Challenges
whole transcriptome coverage and longer reads
large dynamic range of expression levels
base-resolution expression profiles for each gene
multiplex-compatible
sequence variation in transcribed regions (e.g. SNPs)
splicing isoforms, gene boundaries, novel transcribed regions
21
Great for non-model organisms!
Planned RNA Sequencing Projects
EST library for Pachcladon fastigiatum (31,116 genes, 79% of Arabidopsis)
22
Allopolyploidy and genome bias in Pachycladon
Adaptation to warmer climates in Pachycladon
SNP development in Pachycladon
1. Transcriptomics and species radiation – a case study
2. New tool in town – sequencing based methods replace microarrays
3. Putting the new tool to the test – case study revisited
4. Systems biology and species radiation
Outline
23
DNA
mRNA
proteins
Transcription
Translation
provide structure & drive metabolism
substrate product
How about System Biology?
Genomics
Transcriptomics
Proteomics
Metabolomics
* Evolutionary
24
* Evolutionary
* Evolutionary
*Comparative transcript, protein and metabolite profiling within & between species
Q: Ecological drivers of diversification?
A: Comparative gene and protein expression profiling in common gardens
FA
ST
EN
EN
LA
CHEX
NZ
WA
Questions & Approach
P. cheesemanii (CH)
P. exile (EX)
P. novae-zelandiae(NZ)
CHEX
NZ
LincolnPlant growth
Peter Heenan Murray Dawson
AucklandMicroarray analysis
Bart Janssen Luke Luo Silvia Schmidt
Jena Glucosinolate
analysis
Michael Reichelt
PalmyLink all data
Claudia VoelckelPete Lockhart
SydneyProtein analysis
Paul A. Haynes Mehdi Mirzai Dana Pascovici
People who helped:
Submitted
9601 loci 1489 loci
Overall correlation:
8527 4151074
T PTP
CH EX NZ
CH 0.52 0.43 0.30
EX 0.47 0.45 0.32
NZ 0.40 0.36 0.34
TP
T = transcript profiling, P = protein profiling
similar to other non-plant systems (0.2-0.5)
Interconversion of carbon dioxide and bicarbonate (carbonic anhydrase)
Interconversion of carbon dioxide and bicarbonate (carbonic anhydrase)
Draught response
Draught response
Serine racemase
Vegetative storage proteins
97 6129*
18 814
14 2288
T PTP
T PTP
T PTP
EX+NZ
CH+NZ
CH+EX
23%32%
18%4%
36%3%
CH
EX
NZ
Specific Genes Found by T AND P
EX+NZ
CH -
CH+NZ
EX iso
CH+EX
NZ -
EX+NZ
CH -
CH+NZ
EX -
CH+EX
NZ nitriles
Testing Predictions from T & P: Glucosinolate Hydrolysis
Prediction
P. cheesemanii
P. novae-zelandiae
Test
T CH EX NZ
CH 1 0.91 0.74
EX 1 0.83
NZ 1
P CH EX NZ
CH 1 0.75 0.59
EX 1 0.72
NZ 1
= =
Profiling Patterns Through the Phylogenetic Lens:
3MSO
P4M
SOB
3-Bu
teny
l4M
TB8M
SOO
6MSO
H
4OH
I3M
7MTH
1MO
I3M
4MO
I3M
7MSO
H
3MTP
S-2O
H3-
But.
Ally
l
EX
CH*
NZ*
CHEX
NZ≠
Glucosinolates
Thanks to:
YOU!
FundingMarsden & Humboldt Foundation
New ZealandLandcare: Peter Heenan, Kerry Ford, Murray Dawson, Kat Trought
Plant and Food: Bart Janssen, Luke Luo, Silvia SchmidtAWC Genome Service: Pete Lockhart, Patrick Biggs, Lorraine Berry, Lesley Collins, Maurice Collins
Students: Christine Reinsch, Hanna Daniel, Helene Kretzmer
GermanyMPICE: Michael Reichelt, Jonathan Gershenzon
AustraliaMacquarie University: Mehdi Mirzai, Dana Pascovici, Paul Haynes, Mark Westoby