Genes and metabolites to phenotypes:
QTL mapping of glucosinolate production in Arabidopsis
A major quantitative metabolic trait:
•  Specialist insects like glucosinolates - attractant
•  Generalist insects hate glucosinolates
Fluctuating selection and genetic constraint
•  Positive selection and negative selection
bounces around by year
•  Force glucosinolates to occupy middle
ground but also be diverse to change
Glucosinolate Biosynthetic Pathway
A
Tryptophan
CYP
79F
CYP
83A1
C-S
Lyase
GST
F11
SGT
74B1
ST5C
ST5B
Elongated
Methionine
S G lc
S
Methionine
N OSO3-
3-methylthio
C
2-oxo
acid
FMO
O
S
3-malate
derivative
S Glc
N O SO3-
3-methylsulfinyl
2-malate
derivative
AOP2
Aconitase
OH
B
S Glc
S Glc
N O SO 3-
3-hydroxypropyl
N O SO3 -
Allyl
Glucosinolate
Variation in total methionine-derived glucosinolate content
16
14
12
10
8
6
4
Accessions
•  Two genotypes recreate species?
Cvi-1
Col-0
Bay-0
0
Shahdara
2
Ler-0
Total Aliphatic Glucosinolate
(µmol mg-1)
18
It is possible to rapidly recreate the diversity in the wild with a
single outcross
•  Genetic and phenotypic variance not linearly associated
Glucosinolates are phenotypically constrained in the wild
16 Total Alipha?c Glucosinolate (μmol mg-­‐1) •
14 12 10 8 6 4 2 0 Set of accessions Genotypes 96 Bay-­‐0 x Sha 2 Ler x Col-­‐0 2 Ler x Cvi 2 0%
Heritability
1 .0 0
0 .9 5
GLS
0 .9 0
0 .8 5
0 .8 0
0 .7 5
0 .7 0
0 .6 5
Metabolites
0 .6 0
0 .5 5
0 .5 0
0 .4 5
0 .4 0
0 .3 5
0 .3 0
0 .2 5
0 .2 0
0 .1 5
40%
0 .1 0
0 .0 5
Percent of traits
Transcripts and metabolites have different genetics
Transcripts
30%
20%
10%
GLS
Mapping glucosinolate content and regulation
Glucosinolate
Bay-0
Glucosinolate
x
Sha
•
Expression QTLs controlling transcript
level of biosynthetic genes identified
•  Test for association between
polymorphisms controlling enzymeencoding gene & resulting metabolites
•
self x5
F7 RILs
•
Controlling factors are a mix of enzymes
and regulatory factors
Regulatory connections can feedback
from metabolism to transcripts
•  Cloning novel glucosinolate regulators
QTL mapping of glucosinolate production in Arabidopsis
•
In Wentzell et al. (2007) the well-studied glucosinolate gene network was
used to test the feasibility of an a priori-defined gene network approach to
map QTLs and eQTLs in 148 Bay-0 x Sha RILs
•
Bay-0 and Sha are two Arabidopsis thaliana ecotypes (natural variants) - they
are the same species but have distinct genotypes and phenotypes
•  A Bay-0
x Sha cross was made (parents that have divergent glucosinolate
content), then recombinant inbred lines (RILs) generated. Each line is
homozygous for a (different) mixture of Sha and Bay-0 genes
•
In each RIL: measured ~48 glucosinolate metabolites
measured ~95 genetic markers
measured expression of ~25k genes with microarrays (for eQTLs)
•
(1) Used QTL-mapping methods to identify parts of the genome (genes) that
were associated with different levels of the different glucosinolates
(2) Used eQTL-mapping methods to identify glucosinolate-regulatory networks
•
Tryptophan
Elongated
Methionine
CYP
79F
CYP
83A1
GST
F11
C-S
Lyase
SGT
74B1
ST5C
ST5B
S G lc
S
Methionine
Glucosinolate
N OSO3-
2-oxo
acid
3-methylthio
Glucosinolate
GSL.Elong Biosynthetic
QTL
Pathway
FMO
O
S Glc
S
3-malate
derivative
GSL.OX
QTL
2-malate
derivative
N O SO3-
3-methylsulfinyl
AOP2
Aconitase
OH
S Glc
Results suggest that natural variation in
transcripts may significantly impact
phenotypic variation, but that natural
variation in metabolites or their enzymatic
loci can feed back to affect the transcripts
N O SO 3-
3-hydroxypropyl
GSL.ALK
QTL
S Glc
N O SO3 -
Allyl
Tryptophan
Elongated
Methionine
CYP
79F
CYP
83A1
GST
F11
C-S
Lyase
SGT
74B1
ST5C
ST5B
S G lc
S
Methionine
Glucosinolate
N OSO3-
2-oxo
acid
3-methylthio
Glucosinolate
GSL.Elong Biosynthetic
QTL
Pathway
FMO
O
S Glc
S
3-malate
derivative
GSL.OX
QTL
2-malate
derivative
N O SO3-
3-methylsulfinyl
AOP2
Aconitase
OH
S Glc
•  Analysis of Arabidopsis natural variants
N O SO 3-
3-hydroxypropyl
GSL.ALK
QTL
S Glc
N O SO3 -
Allyl
detected several QTLs (-> identify genes)
affecting glucosinolate content
•  mRNA levels for these genes should be different in these ecotypes
•  We will test their predictions by extracting RNA and testing with Quantitative PCR
Quantitative PCR principles
- a type of PCR reaction that enables detection and quantification of gene expression
- use gene-specific primers to amplify your gene of interest
- the product is fluorescently-labelled by incorporating a dye into the reaction; the dye
tissue
extract
RNA
binds to the newly-synthesised dsDNA
- measuring how much fluorescence tells you how much dsDNA there is
- fluorescence is measured at the end of each PCR cycle (see graph on next slide)
- often called real-time PCR since the data generated shows progression of the
reverse
transcribe
cDNA
reaction over time (rather than just at the end as you have for PCR)
- the amount of dsDNA produced is dependent on how much cDNA was present for
your specific gene, and thus how much of your gene was expressed in the starting
RNA sample
QPCR
Quantitative PCR output graph:
•  QPCR cycle number on the x-axis, fluorescence level on the y-axis.
•  To compare the fluorescence of different samples (e.g. 1-4 below), the cycle number at which the
lines cross an (arbitrary) fluorescence cutoff (threshold cycle) is determined...
crossing points: 1 27.3 2 27.1 3 28.0 4 28.5 -­‐ control -­‐-­‐-­‐ 1+2 = more mRNA of gene being detected since the cutoff is crossed at a lower cycle number 1 2 3 4 crossing cutoff 5 = nega?ve control QPCR standard curve
•
... and then compared to a standard curve that tells you what the threshold cycle (y-axis below)
will be for a given starting amount of gene expression (x-axis below)
The standard curve will be different for different
genes since PCR products for genes will
be amplified at different efficiencies. WHY?
Have to normalise between different samples
•  The QPCR reaction will give you a threshold cycle for your gene which you can compare to your
standard curve to find out how much of your gene was expressed
•  To compare between samples you need to know that you started with the same amount of cDNA
•  To assess the amount of cDNA we use a control/
housekeeping gene (e.g. Tubulin) for which the
level of gene expression is ALWAYS proportional to
the amount of cDNA present
- can t just rely on [RNA] values (esp. if the
nanodrop didn t even work!!)
•
So you can normalise your gene expression
quantification results against the Tubulin results to
allow you to compare e.g. the amount of AOP2
expression between different Bay-0 and Sha samples
SAMPLE 1
Primers designed
to detect specific
transcripts (genes)
SAMPLE 2
Primers designed
to detect specific
transcripts (genes)
QPCR Melt curve/peak: check that the primers are amplifying just one
product (specific assay) - gives one peak
Write-up for practical on
QTL mapping of glucosinolate production in Arabidopsis
•  You
ll write this practical up as if you had carried out this work:
- a brief introduction (2 pages max)
- explain the principle of QTL mapping and how it can be used in agriculture to
find genes that control phenotypes (traits) of interest for crop improvement (find
some examples of this)
- explain why glucosinolates are a trait of interest to study
- a brief methods section (2 pages max)
- explain what the genotype and phenotype data that you used was, and where
it came from (refer to the Wentzell et al paper)
Write-up for practical on
QTL mapping of glucosinolate production in Arabidopsis
- results and discussion section:
•  What type of information can you find out about the QTLs that are detected in the
analysis that you carried out? Why might the QTLs that you detect be different when
you use different QTL mapping methods (Haley-Knott etc). (2 pages max)
•  Plot an eQTL LOD for AOP3. What does this plot tell you, explain what it shows in
detail. Are there cis or trans (or both) effects? Which genes are predicted to regulate
AOP3 expression (identify the genes from the LOD plots and use
www.arabidopsis.org (TAIR) to search for information on them. List all of the genes
that you think might control AOP3 and suggest which ones you think are most likely (2
pages max).