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Fluxomics Quantifying Metabolic Phenotypes · Alexander Braun [email protected]...
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Fluxomics – Quantifying Metabolic Phenotypes
Alexander Braun
Environmental Isotope Chemistry Institute of Groundwater Ecology
„The central dogma of
one gene, one protein, one function
died in the last years.“ (Cascante & Marin 2008)
What is Fluxomics?
„What can happen?“
„What appears to be happening?“
„What makes it happen?“
„What could have happened?“
(Dettmer et al. 2006)
What is Fluxomics?
„What did happen and how did it happen?“
„What can happen?“
„What appears to be happening?“
„What makes it happen?“
„What could have happened?“
(Dettmer et al. 2006)
What is Fluxomics?
… the „traffic of metabolites“
The entity of intracellular fluxes that comprise the
ultimate endpoint of the regulated interplay of
genome, transcriptome, proteome and
metabolome.
The science of the fluxome is called
FLUXOMICS.
What is Fluxomics?
(Dettmer et al. 2006)
(Zamboni 2009)
(Niklas et al. 2010)
FLUXOME
„qualitative snapshots of the metabolic state of a cell“
indicators for metabolic phenotype
(Winter & Krömer 2013)
? gap
(Dettmer et al. 2006)
What is Fluxomics?
FLUXOME
(Chubukov 2013)
The Proteome-To-Phenotype Gap
(Tang 2008)
Fluxomics: metabolic endpoint = high scientific output?
(Winter & Krömer 2013)
𝑣 = 𝑓(𝐸, 𝑘, 𝑆, 𝑃, 𝐼)
Principles of Fluxomics
enzymek
S
I
P v
v: metabolic flux
E: enzyme concentration
k: kinetic parameters of the enzyme
S: substrate(s) concentration
P: product(s) concentration
I : effector molecule(s) concentration
(Winter & Krömer 2013)
𝑣 = 𝑓(𝐸, 𝑘, 𝑆, 𝑃, 𝐼)
Principles of Fluxomics
k: in vitro ≠ in vivo
v must be quantified indirectly
Proteomics Metabolomics
Enzyme kinetics (in vitro!)
v: metabolic flux
E: enzyme concentration
k: kinetic parameters of the enzyme
S: substrate(s) concentration
P: product(s) concentration
I : effector molecule(s) concentration
Glcin
PEPin
Lacin Pyrin
Alain
Glcex
Directly measurable:
• intracellular metabolites and
their stable isotope label,
e.g. Glc, Lac, Ala
Principles of Fluxomics
Not directly measurable:
• intracellular fluxes,
e.g. Glc -> PEP
Glcin
PEPin
Lacin Pyrin
Alain
Glcex
Principles of Fluxomics
Stable Isotopes as Tracer:
(Niedenführ 2015)
Fluxomics
Complexity (experimental & modelling)
Example 1
(Niedenführ 2015)
Fluxomics
Complexity (experimental & modelling)
(Zhang et al. 2014)
Example 1: Isotope profiling
(Zhang et al. 2014)
healthy cell
Example 1: Isotope profiling
(Zhang et al. 2014)
cancer cell
Example 1: Isotope profiling
Example 1: Isotope profiling
Conclusions:
-probably the easiest way to determine metabolic fluxes
(label->sample->data analysis[yes or no])
allows to prove metabolic connections / disconnections,
e.g. induced by cancer / diabetes
Isotope profiles can be used as indicators for metabolic disorders and/or
environmental conditions
Example 1
(Niedenführ 2015)
Fluxomics
Complexity (experimental & modelling)
Example 2
Glcin
PEPin
Lacin Pyrin
Alain
Glcex
Alaex Lacex
Directly measurable:
• intracellular metabolites and
their stable isotope label,
e.g. Glc, Lac, Ala
• extracellular fluxes,
e.g. Glc, Lac, Ala
Example 2: 13C Metabolic Flux Analysis
Not directly measurable:
• intracellular fluxes,
e.g. Glc -> PEP
Metabolic steady state
constant growth rate,
O2 consumpition,
CO2 production,
organic intake &
output rates
Isotopic steady state
constant isotope
signatures of
metabolites over time
Metabolic network model
Example 2: 13C Metabolic Flux Analysis
Atom transitions
(Zamboni 2011)
„metabolic
phenotype“
Example 2: 13C Metabolic Flux Analysis
(Zamboni 2011)
Example 2: 13C Metabolic Flux Analysis
„Flux map“
Example 2: 13C Metabolic Flux Analysis
Conclusions:
-“sophisticated“ experiments
-only for „known networks“
-extensive(!) modelling is necessary (CPU time: days-weeks)
Absolute flux information
- Metabolic fluxes are the ultimate metabolic endpoint
- Stable Isotopes allow determination of fluxes
- Different approaches for different levels of flux information
(in principle: the more input, the more output)
For further ideas / suggestions / cooperations, please contact
Alexander Braun
Overarching Conclusions
Complexity (experimental & modelling)
Example 1
(Niedenführ 2015)
Fluxomics:
Example 3
Example 2
Example 3: Kinetic Flux Profiling
liver muscle
C & N
input
10 20 100 200 1000
Half-life (h)
urin
efe
ces
liver
kidn
ey, s
plee
n
hear
t
mus
cle
brai
nlu
ngpl
asm
a
amin
oac
idsin
mus
cle
cow (Bos taurus)rat (Rattus rattus)
milk
fat
milk
case
in
milk
lact
ose
who
lem
ilk
fece
sha
irC & N residence time (h)
-48 -24 0 24 48 72 96-31
-30
-29
-28
-27
-26
Time after isotopic switch (h)
Iso
top
icco
mp
ositio
n(0
/ 00)
Example 3: Kinetic Flux Profiling
• Metabolic and isotopic steady state
• Time resolved sampling
• (simple) modelling
grey: diet
black: liver
Example 3: Kinetic Flux Profiling
Conclusions:
-metabolic & isotopic steady states are necessary
-(simple) modelling is necessary
time-resolved sampling allows kinetic flux information (e.g. half-lifes)
Sampling
Compound specific
(Metabolomics)
Bulk tissues
Organisms
(time resolved?)
Isotope Tracer
C, N, S, O, H….
Molecules
Isotopomes
Data analysis
No modelling
Exponential decay
Isotopomer modelling
….
Principles of Fluxomics
The Metabolome-To-Phenotype Gap