Download IB496-May 2

Metabolomics, spring 06
Hans Bohnert
ERML 196
Metabolomics Essentiality
Today’s discussion topics
[email protected]
265-5475
333-5574
http://www.life.uiuc.edu/bohnert/
class May 2
Whole plants
Organs
Tissues
Cells
Fluids
related species
Ecotypes
Mutants
Transgenics
RILs
Morgenthal et al. (2006) Metabolic Networks in Plants:
Transitions from pattern recognition to biological
Interpretation. BioSystems 83, 108-117.
Nunes-Nesi A et al. (2005) Enhanced photosynthetic
Performance and growth as a consequence of
Decreasing mitochondrial malate dehydrogenase
Activity in transgenic tomato plants. Plant
Physiol. 137, 611-622.
To find a metabolic character that can serve as
the Rosetta stone explaining phenotype
To find unknown signals, new pathways, better drugs
Our technical ability to isolate and identify metabolites,
even to obtain data on metabolic flux,
is not matched by our understanding
of plant metabolism,
cell-specific biochemical events
or the structure of metabolic pathways
and their integration across cells and tissues!
• The challenge is to understand in vivo metabolite dynamics
in complex mixtures and to reconcile the data with the
structure of metabolism.
• As in “transcriptomics”, we need ways to analyze the data in a
statisticallly sound way by computational methods
• PCA, LDA, and other unsupervised learning algorithms
• Biomarkers for disease deficiency, transgenic modification – signature events
• Correlations to unravel nodes in networks
• Correlations between metabolites
Correlation
maps for
selected
metabolites
a fingerprint
of different
networks
• constant conditions
• make light variable
• fluctuations propagate
through pathway
A simplified Calvin Cycle plus cytosolic SPS
Ch – chloroplast
M - cytosol
pairwise
metabolite
comparisons
light as a
time-dependent
random
variable
this pathway
goes towards
sucrose
Potato tuber (43 samples), leaf (34), Arabi leaf (240, tobacco leaf (29)
Divergence by species/tissue
preserved correlations
P – 0.001
PCA analysis of the same dataset
1st three p.c
1st component – glucose weighted
A model simulation
with 2 steady-state solutions
sink
source
B inhibits
its degradation
dep. [B]
Technical error compared with variability
Technical
variability of
Arabi leaf
material
(many repeats)
vs. variability
in the datasets
s.d./ mean
Nunes-Nesi et al. (2005a)
Plant Physiol. 137, 611.
mMDH down
biomass and yield up
Simple story!
Why is it important?
Nunes-Nesi A, Carrari F, Lytovchenko A, Fernie AR (2005b) Enhancing crop yield
in Solanaceous species through the genetic manipulation of energy metabolism.
Biochem. Soc. Transactions 33, 1430
- how?
(C) consequences
of deficiency
GLDH (l-galactono1,4-lactone DH) up
(A) assimilation rate and (B) fruit yield in the antisense mitochondrial
malate dehydrogenase lines (AL) of S. lycopersicum and in the Aco1
mutant of S. pennellii.
Aco1 – unknown – has increased adenylate levels
Nunes-Nesi et al (2005) Enhancing crop
yield in Solanaceous species through the
genetic manipulation of energy metabolism.
Biochem. Soc. Transactions 33, 1430-1434
plants deficient in stromal
adenylate kinase (ADK)
Other approaches –
Sedoheptulose BPase
ictB (E. coli) (CO2-accumulation)
TPS
PARP
mMDH
down
respiration
down
Metabolites
lots of changes
ascorbate
up
precursor ASC biosynthesis – L-galactono-lactone
Where lies the crucial difference that leads to increases in metabolites?
Interpretations?
metabolite competition ??
metabolite channeling ??
protection ????
protection of what ????
PARP –
the
miracle
enzyme?
PARP functions not only in the nucleus, it regulates the activity
of an increasing number of enzymes.
PARP –
Its activity in non-repair functions of DNA/chromatin
leads to the destruction of adenine nucleotide (phosphates)
which leads to a decline in NAD/NADP.
This, in a photosynthetically-active organism,
leads to a decline in energy production.
However, there must be more to the story – there are multiple
PARP-like genes in mammals – fewer, but still a family, in plants.
An eclectic Syllabus
• metabolomics technologies
• GC-MS profiling – six steps:
extraction – derivatization – separation –
ionization – detection – acquisition/evaluation
• relative advantages of different technologies (LC, GC, TOF, MS-MS, NMR)
• challenges:
automation – analytic scope – trace compound calling - reproducibility
and quantitative comparisons across platforms size and complexity of metabolite libraries
• plant volatiles – tri-trophic interactions
• static vs. dynamic metabolite profiling;
stable isotopes - flux determinations
sugars to fatty acids (Rubisco in green seeds), TPs to amino acids
• integration of transcriptomics and metabolomics
• the cold-metabolome – certainty from highly variable datasets (ecotypes/lines)
• cell-specific reactions [animal] (how can we use plant cell cultures?)
• fluids, cell, tissues, organs, species – different types of information
• long-distance transport metabolomics
• metabolomics – transcriptomics – QTLs (tomato – wild tomato crosses)
• metabolic network construction
• transgenic manipulations in energy-generating pathways
• towards systems understanding
• some discussions developed; is metabolomics just biochemistry under a different name?
Have a great summer!
HJB