Metabolomics, spring 06

Hans BohnertERML 196

bohnerth@life.uiuc.edu

265-5475333-5574

http://www.life.uiuc.edu/bohnert/

class May 2

Metabolomics Essentiality

Today’s discussion topics

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. PlantPhysiol. 137, 611-622.

Whole plantsOrgans TissuesCells

Fluids

related speciesEcotypes Mutants

TransgenicsRILs

To find a metabolic character that can serve asthe Rosetta stone explaining phenotype

To find unknown signals, new pathways, better drugs

Our technical ability to isolate and identify metabolites, Our technical ability to isolate and identify metabolites, even to obtain data on metabolic flux,even to obtain data on metabolic flux,is not matched by our understanding is not matched by our understanding

of plant metabolism, of plant metabolism, cell-specific biochemical eventscell-specific biochemical events

or the structure of metabolic pathwaysor the structure of metabolic pathwaysand their integration across cells and tissues!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

Correlationmaps forselected

metabolites

a fingerprintof differentnetworks

A simplified Calvin Cycle plus cytosolic SPS Ch – chloroplastM - cytosol

• constant conditions• make light variable• fluctuations propagate through pathway

pairwisemetabolite

comparisons

light as atime-dependent

randomvariable

this pathwaygoes towards

sucrose

Potato tuber (43 samples), leaf (34), Arabi leaf (240, tobacco leaf (29)

P – 0.001

preserved correlationsDivergence by species/tissue

PCA analysis of the same dataset

1st three p.c

1st component – glucose weighted

A model simulationwith 2 steady-state solutions

B inhibitsits degradation

dep. [B]

sink source

Technical error compared with variability

s.d./ mean

Technicalvariability of

Arabi leafmaterial

(many repeats)vs. variability

in the datasets

mMDH down

biomass and yield up

Simple story!

Why is it important?

Nunes-Nesi et al. (2005a)Plant Physiol. 137, 611.

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

(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.

(C) consequencesof deficiency

GLDH (l-galactono-1,4-lactone DH) up

Aco1 – unknown – has increased adenylate levels

- how?

Nunes-Nesi et al (2005) Enhancing cropyield in Solanaceous species through thegenetic manipulation of energy metabolism.Biochem. Soc. Transactions 33, 1430-1434

Other approaches –

Sedoheptulose BPaseictB (E. coli) (CO2-accumulation)

TPSPARP

plants deficient in stromal adenylate kinase (ADK)

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

miracleenzyme?

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 - reproducibilityand quantitative comparisons across platforms -size and complexity of metabolite libraries

• plant volatiles – tri-trophic interactions• static vs. dynamic metabolite profiling;

stable isotopes - flux determinationssugars 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