Transcript IB496-April 27

Metabolomics, spring 06
Hans Bohnert
ERML 196
Metabolomics Essentiality
Today’s discussion topic
[email protected]
265-5475
333-5574
http://www.life.uiuc.edu/bohnert/
class April 27
Schauer N, Zamir D, Fernie, AR (2005) Metabolic
profiling of leaves and fruit of wild species
tomato: a survey of the Solanum
lycopersicum complex.
J Exp Bot. 56: 297-307.
Schauer N, Semel Y Roessner Um Gur A, Balbo I,
Carrari F, Pleban T, Perez-Melis A,
Bruedigam C, Kopka J, Willmitzer L,
Zamir D, Fernie AR (2006) Comprehensive
metabolic profiling and phenotyping of
interspecific introgression lines for tomato
improvement. Nat Biotechnol. 24: 447-454.
From single genes to multi-gene traits
1. ‚Systems biology‘
2. Use of natural diversity
Mark Stitt lecture
From ‚biased‘ inhibition of candidate gene function
to multisite ‚unbiased‘ analysis of change-of-function alleles:
Natural diversity is a central resource for the
analysis of regulation and gene function
Alleles are the key to breeding, fitness‘ and evolution
Leaf (A) and fruit (B) phenotypes of the S. lycopersicum complex.
(I)
S. chmielewskii, (II) S. habrochaites, (III) S. lycopersicum,
(IV) S. pimpinellifolium, (V) S. neorickii, and (VI) S. pennellii.
Protein and starch levels of fruits of the S.
lycopersicum complex.
Six independent fruit samples were measured.
Fruits were harvested 45 DAF 6 h into the light.
Protein values (dark bars) are presented as mg
protein/g FW. Starch values (grey bars) are
presented as umol hexose/g FW.
Metabolite composition in leaves from species of the S. lycopersicum complex
Single leaf samples of six plants were measured. Leaves were harvested 6 h into
the light period from fully-expanded mature leaves of 6-week-old plants.
Values are presented as the mean 6SE of six independent biological
determinations. Those metabolites that are significantly different to S.
lycopersicum are in bold type. Metabolites in italics represent relative differences
with respect to S. lycopersicum, nd indicates metabolites were not detected.
A part of a very long table
Repeat at different ages of fruit development
Crosses
Mapping
Grow under field conditions
Repeat in several seasons
Lycopersicon pennellii x Lycopersicon esculentum
Wild relative
Elite cultivar
Cross between the modern
tomato cultivar M82 and a
related wild species
ca. 100 Introgression Lines
Each contains a small part of
the genome from the ‚donor‘
(here, the wild species) in the
background of the otherwise
unaltered genome from the
acceptor (elite cultivar)
Dani Zamir, HU Jerusalem
Metabolite profiles for each introgression line
Chromosome 9
9-1
+GP39
GP39
-GP39
+TG254
IL9-1
IL9-1-2
9-A
TG254
5.5
9-B
1
2
3
4
CT143
-CT143
+TG223A
9-1-2
5
+GP263
-CT32
1.1
3.3
9-H
0.0
-CT208
+TG390
-TG551
+TG404
IL9-2-6
9-G
+TG186
-TG348
9-I
-GP129
+CT198
5
9-J
TG568
(TG3A,CD8,CT215B,CT215C)
GP125A
TG79
TG207,CT17,TG486,TG589,TG640,Tm2a
TG101,TG291
PC6
CT208
CT208.,CT235,TG79,TG415,CT17,CD3,TG591B
TG390
4.0
2.8
1.9
2.0
1.0
3.0
1.0
2.0
1.7
2.2
3.9
9-1-3
IL9-3-1
4
IL9-3
IL9-3-2
3
CP44
CD32A,CT215A,CT284B
5.3
9-F
2
TG223A
10.1 (TG225,TG10)
4.7
+CD32A
-TG591B
1
6.9 (CT283A)
CT32
9-E
0
TG9
4.0
IL9-2
0
+TG9
-CT143
IL9-1-3
9-D
TG18
3.6
IL9-2-5
9-C
8.9 (CT225A)
3.8
TG551
(CT279,TG35,CT183,TG558,TG409)
TG404
(TG144)
TG186,CT236
TG429
(Est-2)
TG348,TG347
TG248
GP94B
CT74,CT177
GP129
CT198
(GP123A)
CT218
TG421
5.5
(TG8, Nr)
TG424
5.9
+GP101
-CT112A
9-K
4.5
2.2
1.6
2.7
GP101, CHS4
(CT96)
CT112A
(CT210)
TG328,GP41,TG591A
CT71
CT220
+CT220
Dani Zamir
HU Jerusalem
0
1
2
3
4
5
Field grown plants
Massively parallel identification of QTL’s for
metabolite levels in tomato introgression lines
Example: high sugars. This is an important yield trait (BRIX)
Old Hypothesis:
Introgression lines
GC-MS
Metabolite
-profiling
Cell wall invertase hydrolyses
sucrose and increases import
into growing organs
500 mM
sucrose
Metabolites, 1 .......n
351 QTL‘s
Ally Fernie, MPI-MP
Dani Zamir HU Jerusalem
INV
Glc
Fru
Massively parallel identification of QTL’s for
metabolite levels in tomato introgression lines
…. showed that a mutation leading to changed
kinetic parameters of LIN5 - encoding Invertase
is the molecular basis of a
sugar content ( BRIX ) QTL
Metabolites, 1 .......n
Fine
mapping
+GP39
IL9-1
IL9-1-2
IL9-1-3
IL9-2
IL9-2-5
Introgression lines
GC-MS
Metaboliteprofiling
GP39
8.9(CT225A)
TG254
5.5
TG18
3.6
TG9
4.0
+TG9
-CT143
CT143
-CT143
(CT283A)
6.9
+TG223A TG223A
+GP26310.1(TG225,TG10)
9-A -GP39
+TG254
9-B
9-C
9-D
-CT32
9-J
Fridman et al. , Science 2004
Ally Fernie, MPI-MP
Dani Zamir HU Jerusalem
IL9-2-6
9-F
9-G
9-H
9-I
IL9-3
IL9-3-2
IL9-3-1
351 QTL‘s
LIN5
CT32
4.7
1.1 CP44
5.3
+CD32A
-TG591B
3.3 TG568
TG79
0.0 GP125A
TG101,TG291
CT208
-CT208 PC6
TG390
+TG390
4.0
TG551
-TG551
+TG404 2.8
(TG144)
1.9 TG404
TG186,CT236
2.0
+TG186
TG429
1.0
(Est-2)
TG348,TG347
-TG348
3.0 TG248
1.0
2.0 GP94B
CT74,CT177
GP129
-GP129 1.7 CT198
+CT198 2.2
3.9 (GP123A)
CT218
3.8
TG421
5.5 (TG8, Nr)
TG424
5.9
GP101, CHS4
+GP101 4.5 (CT96)
-CT112A
CT112A
2.2 (CT210)
1.6 CT71
2.7 CT220
9-E
Invertase
kinetics
M82
.…....
___
0,3
1/activity
9-2-5
Lee
0,2
9-K
+CT220
0,1
1/mM sucrose
-0,2
-0,1
0
0,1
0,2
Use of diversity in crop plant populations and wild
populations for analysis of gene function:
A ‘strategic advantage’ for plant science
• Long experience of breeders in creating and phenotyping populations
• Large resources already available in the reference species Arabidopsis
• A wide range of resources are becoming available in several crops
• Their production will be aided by developments in genotyping
• Their production will be aided by information about genome sequences
• Analysis benefit from the analytics technologies developed in plant
genomics will depend on and be driven by development of
bioinformatics capabilities
• A tight link from basic science into the application realm
Overlay heat map of the metabolite profiles and other traits of the ILs in comparison to the
parental control (S. lycopersicum).
Large sections of each map are white or pale in color, reflecting the fact that many of the
chromosomal segment substitutions do not have a great effect on the amount of every
metabolite. Regions of red or blue indicate that the metabolite content is increased or decreased,
respectively, after the introgression of S. pennellii segments. Very dark coloring indicates that a
large change in metabolite content was conserved across harvests; purple indicates that, relative
to S. lycopersicum, the metabolite was increased in one harvest but decreased in the other. For
each harvest, gas chromatography/mass spectrometry was used to quantify 74 metabolites,
including amino acids, organic acids, fatty acids, sugars, sugar alcohols and vitamins.
What are we
looking at?
Overlay heat map
of the metabolite
(total 74)
profiles and other
traits of the ILs
metabolite analysis
white – no effect
red (up) & blue (down)
in presence of
L. pen. segment
& purple (up in one,
down in other harvest)
dark – two seasons
light – one season
Cartography of a network
Morphology
associated &
independent
metabolites
Fine evaluation of genomic regions containing morphologically
associated and independent metabolite QTLs.
Robustness of cartography algorithm
each pair of nodes
(20 partitions in network)
how often in ILs
classified the same
always together – red
never together –
dark blue
GC–MS libraries for the rapid identification of
metabolites in complex biological samples
Needed are dbs that allow
data transfer between
instruments and labs
BRENDA – relational database
for enzymes and their metabolites
Schomburg et al. (2004)
Nuc. Acids Res. 32, database issue
D431-433.
Corynebacterium polar extract
– >600 peaks, >50% unknown
Spike extracts with known set
of metabolites (that are NOT in
the organism you study).
Calibrate reference data sets
Downloadable files from MPI Golm
MIPS – protein db
Mewes et al. (2004) Nuc Acids Res. 32
database issue D41-44.
KEGG
TAIR
Golm Metabolome Database
Oliver Fiehn lab, UC Davis
MSRI library downloadble files (for different
Technologies GC/LC/TOF:
www.csbdb.mpimp-golm.mpg.de/gmd.html
merge with NIST02 or own libraries.
NIST02/AMDIS
www.chemdata.nist.gov/mass-spc/amdis
www.chemdata.nist.gov/mass-spc/index.html
Golm libraries are
(a) manually analyzed, assigned to ID libraries
(b) automated deconvolution libraries, assigned NS libraries
Identify deconvolution errors:
multiple mass spectra for single components
accidental deconvolutions based on random fluctuations
background noise (spill-over)
chimeric mass spectra (mixed mass positions)
Provide data on experiment, samples, sources of reference, investigator
Provide both mass spectrum and retention time
Q_MSRI_ID library with
1166 annotated, identified MSTs for 574 non-redundant compounds
but only 306 are unambiguously identified
Also a non-supervised collection of
>30,000 MSTs from a range of plant species, models and crops,
and different plant organs
Schauer et al. (2005) FEBS Lett 579, 1332-1337.
Test cases - Spike animal/bacterial samples with plant-specific metabolites
(kaempferol, phytosterol, a-tocopherol)
How accurate are unsupervised analyses?
Chlorogenic acid – typical 2nd metabolite in Solanaceae
Caffeic acid – precursor for chlorogenic acid
Quinic acid – ubiquitous in plants
supplemental slides
documenting variability of field data.
Supplem. Figure 1 (next slide)
Heat maps of the metabolite profiles of the introgression lines in comparison to that of the
parental control (S. lycopersicum) from the individual data sets of A) 2001 and B) 2003.
Large sections of each map are white or pale in coloor reflecting the fact that many of the
chromosomal segment substitutions do not have a large affect on the level of every
metabolite.
Regions of red or blue indicate that the metabolite content is either increased or decreased
respectively following the introgression of S. pennellii segments. A total of 74 metabolites
were quantified by gas chromatography-mass spectrometry for each harvest, including
amino acids, organic acids, fatty acids, sugars, sugar alcohols and vitamins.
Variability
(a) Data set 2001
Variability
(b) Data set 2003