Virtual mitochondria : metabolic modelling and control.
Marie Aimar-Beurton1, , Bernard Korzeniewski3, Thierry Letellier1, Stéphane
Ludinard1, Jean-Pierre Mazat1 and Christine Nazaret2 (alphabetical order)
1
Inserm EMI 9929, and 2ESTBB, Université de Bordeaux 2, 146 rue Léo-Saignat, F 33076,
Bordeaux-cedex France and M.A.B. Université de Bordeaux 1.
3
Jagiellonian University, ul. Gronostajowa 7, 30-387 Krakow, Poland
Abstract
Inside the eukaryotic cell, mitochondria are internal organelles of prokaryotic origin,
responsible for energy supply in the cell. The control of the mitochondrial ATP production is
a complex problem with different patterns according to different tissues and organs.
Our aim is to continue to develop the modelling of oxidative phosphorylation in
different tissues, to model other parts of mitochondrial metabolism and to include this virtual
mitochondria in a virtual cell.
In constructing the complete metabolic map of mitochondria, we will take advantage of
the sequenced genomes of eukaryotic organism (10-15% of the yeast genome concerns
mitochondria).
Introduction
Mitochondria are internal organelles inside the eukaryotic cell; it is the place of
oxidative phosphorylation (OXPHOS), i.e. of the ATP synthesis coupled to respiratory chain
functioning (Fig. 1 in Appendix). Mitochondria play an important role not only in ATP
synthesis but also (non-exhaustive list) in some specific metabolic pathways, in cell oxidoreduction ratio upholding, in cell calcium homeostasis and signalling, in apoptosis, etc. The
mitochondrial metabolism is thus rich and varied and it is one of the aims of our work to
understand how this metabolism can account for very different functions and behaviour of
mitochondria in different tissues (Rossignol et al., 1999; Rossignol et al., 2000).
The first aim of our work is to continue the modelling of oxidative phosphorylation in
different tissues in order to simulate their functioning and to understand the basis in their
control differences.
In addition mitochondria hold a significant part of cellular metabolism: Krebs cycle, boxidation of fatty acids, etc., and the second aim of our work will be to model this
metabolism. In the third step we will include this virtual mitochondria in a virtual cell by
modelling the exchanges of metabolites, energy and signals (calcium signals) between the cell
and mitochondria.
In constructing the complete metabolic map of mitochondria, we will take advantage of
the sequenced genomes of eukaryotic organism, from which a significant part (10-15% in
yeast) concerns mitochondria. Our project will also lean on sequenced genomes of
prokaryotic organisms, which are ancestors of mitochondria. This should help to ascribe
functions to unknown ORF, possibly involved in mitochondrial metabolism.
To sum it up, our work will consist in linking sequenced genomes to mitochondrial
metabolism, in order to construct mitochondrial metabolic maps and to analyse the
mitochondrial fluxes and their regulation.
Results
Oxidative phosphorylation is probably the mitochondrial metabolic pathway that was
most frequently modelled in the quantitative way. The general scheme of oxidative
phosphorylation is presented in Fig. 1 in Appendix and a detailed metabolic map in Fig. 2 of
the Appendix. Several different kinetic (and thermodynamic) models of this process have
been developed. They are shortly summarised in Table 1 (see Appendix). Among these
models, only the model developed by Korzeniewski and co-workers has been tested for a
broad range of experimentally-measured parameter values and system properties. This model
was used to predict new properties of the system and the existence of new phenomena.
One of the most important predictions was that only a direct activation of (almost) all
oxidative phosphorylation steps, in parallel with the activation of ATP usage, by some (still
unknown) cytosolic factor X, transmitting the signal of stimulation of a cell by neural
excitation (skeletal muscle, heart) or hormones (liver) can explain the large changes in fluxes
(respiration, ATP turnover) accompanied by only moderate changes in metabolite
concentrations (ATP/ADP, NADH/NAD+) in intact tissues (Korzeniewski, & Froncisz, 1992;
Korzeniewski, 1998; Korzeniewski & Zoladz, 2001). Some other important predictions
concern the effect of inborn enzyme deficiencies and the ethiology of mitochondrial diseases
(Korzeniewski et al., 2001, Korzeniewski, 2002).
Computer models of other metabolic pathways located in mitochondria, for example
of Krebs cycle, were also developed. These models also helped significantly to understand the
control and regulation of mitochondrial metabolism.
Some of the results obtained experimentally or predicted with the help of a model are
understandable in the framework of metabolic control analysis (Kacser & Burns, 1973;
Heinrich & Rapoport, 1974; Reder, 1988). The values of control coefficients are of great
interest in the prediction of the effects of a deficiency in mitochondrial diseases; they are also
of interest in biotechnology, where some steps are amplified. The theoretical models of
oxidative phosphorylation developed so far, allow more or less easily to calculate its control
coefficients (Korzeniewski, & Froncisz, 1992; Korzeniewski, 1996a; Korzeniewski & Mazat,
1996c; Korzeniewski & Mazat, 1996d).
Conclusion :
With the help of models of mitochondrial metabolism it is possible to analyse and to
compare the metabolic organisation and functioning of different types of mitochondria. The
basic knowledge (based on already studied enzymes and on reasonable hypotheses) of the
kinetic parameters of enzymes or enzymatic complexes will enable us to predict the metabolic
fluxes, their regulation and their control. In a sense our aim is to apply to mitochondria the
method developed for whole cells in the post-genomic area, i.e. to construct and to analyse the
metabolic maps from the genes. Due to the lower number of genes involved in mitochondria
(10% of an eukaryotic genome, see table 2) this application could be easier than for whole
cells and is, in any case, a compulsory step in cell metabolism modelling, because
mitochondria are largely autonomous and independent units inside cells. This will impose to
precisely recognise these sequences, involved in mitochondria.
Acknowledgements This work was supported by the Association Française contre les
Myopathies (A.F.M.), INSERM, the Université Bordeaux II and the Région Aquitaine. B.K.
was supported by the KBN grant 0450/P04/2001/20.
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APPENDIX :
Table 1. Quantitative models of oxidative phosphorylation available in the literature.
Authors
type of model
Chance and Williams
one simple
equation
chatacteristics
references
kinetic for isolated mitochondria; Michaelis- Chance & Williams,
Menten kinetic dependence of the
1955; Chance and
respiration rate on [ADP]; black-box
description
Williams, 1996
Rottenberg; Westerhoff NET – non-equilibrium for isolated mitochondria; linear Rottenberg; 1979;
and van Dam
thermodynamics
depencence of fluxes on thermodynamic
Westerhoff & van
forces; black-box description; limited
range of application
Dam, 1987
Wilson, Erecinska and static kinetic model of for isolated mitochondria; kinetic
co-workers
one rate-limiting step
description of cytochrome oxidase
assumed to be the only rate-limiting step;
depencence on external [ATP]/[ADP]
instead of on Dp
Bohnensack and co- static kinetic model for isolated mitochondria; kinetic
workers
involving many steps
description of many (but not all)
complexes (phosphate carrier not included
explicitly, respiratory chain described as
one unit); tested for a limited set of system
properties
Holzhütter and co- dynamic kinetic model for isolated mitochondria; kinetic
workers
involving many steps
description of all complexes; several
assumptions are not justified; developed
for mitochondria working in nonphysiological temperature (8 °C); tested
for a limited set of system properties
Korzeniewski and co- dynamic kinetic model for isolated mitochondria and intact tissues
workers
involving many steps
(liver, muscle); kinetic description of all
complexes; tested for a broad set of system
properties; used for a series of new
theoretical predictions
Saks and co-workers
Wilson, et al., 1977;
Wilson et al., 1979.
Bohnensack, 1981;
Bohnensack et al.,
1982;
Gellerich et al., 1983.
Holzhütter
et
al.,
1985.
Korzeniewski &
Froncisz, 1991, 1992;
Korzeniewski, 1998;
Korzeniewski et al.,
2001; Korzeniewski,
2002; Korzeniewski,
1996a, 1996b;
Korzeniewski &
Mazat, 1996a, 1996b ;
Korzeniewski &
Zoladz, 2001.
dynamic kinetic model for intact heart; creatine kinase assumed to Aliev & Saks, 1997;
involving many steps
be essentially displaced from
Vendelin et al., 2000.
thermodynamic equilibrium; Pi assumed to
be the main metabolite regulating
oxidative phosphorylation; contradicts
several experimental data concerning the
value of, and relative changes in, [Pi]
Table 2. Number of nuclear genes known for coding mitochondrial proteins :
SwissProt was used as a data bank. The interrogation was built with two kinds of keywords: different
organism names (listed in the first column) and the prefix "mito"; the number of these occurrences is
listed in the third column with the number of mtDNA uncoded proteins in brackets. The fourth column
gives the percentage of the number of ‘mito’ occurences scaled by the total number of known proteins
in the organisms under study (second column).
Organism
Nb of
Nb of “mito”citations
%
known proteins
Arabidopsis thaliana
1513
101 (17)
6.67
Sacharomyces cerevisiae
4864
603 (18)
12.39
Drosophila melanogaster
1576
110 (13)
7.55
Caenorhabditis elegans
2214
112 (12)
5.05
Mus musculus
5070
397 (16)
7.83
Homo sapiens
7819
636 (13)
8.13
Figure 1. Scheme of oxidative phosphorylation in mitochondria. SH, respiratory substrate; 1.,
substrate dehydrogenation; 2., complex I; 3., complex III; 4., complex IV; 5., proton leak; 6., ATP
synthase, 7. ATP/ADP carrier; 8. phosphate carrier; 9., ATP usage.
Fumarate
AMP + PPi
Malate Citrate
TPr
FAMN
FeS
+
28
3GP
NADH
CI
ATP + CO 2
30
NADH + CO2
Citrulline
FAD
FADH2
31
C II
Oxaloacetate
NADH 2
H O
2
n‘H
UQ
b565, b566
FeS ; C1
32
+
FADH
12 9
10 9
10 9
NAD
5
NAD
NADH
6
Cplx TIM23
3
18
E
Glutamate
TG
36
Oxaloacetate Aspartate
_-Ketoglutarate
Malate
GDP + Pi
7
10 9
17
23
23
70 44 17
70 44
NADH + CO2
Succinyl-CoA
C III
NH
E
ATP
NAD
Succinate
19
NADH + CO2
_-Ketoglutarate
8
FAD
2 ATP + CO2
Isocitrate
Fumarate
10 9
10 9
2 ADP + Pi
4
Malate
2
Carbamoyl-P
Hs-CoA
Citrate
10
NAD
Fumarate
2e
+
22
Cplx TIM22
54
3
Succinate
9
n‘H
Ornithine
ADP + Pi
Orotate
Q
8 13
8 13
8 13
Pi
H O
2
20
DHAP
2e
Ornithine
Citrulline
Cplx
OXA1
Acetyl-CoA
2
DHODH
2e
29
G3-PDH 2e
Urea
NAD + Hs-CoA
1
Dihydrorotate
2e
H2 O
23
TT
Pyruvate
+
nH
NAD
Malate
Arginine
21
Aspartate + ATP
+
H Pyruvate
nH
Arginino succinate 22
17
20
5
6
7
40
22
70
Cplx
H TOM
+
TD
GTP
2e
H O
2
Methylmalonyl-CoA
AMP + PPi
Cyt c
+
n‘H
H
33
C IV
HO
2
H
H
+
n SH
F0
34
n
F1
S
25
H
Pi
H+
35
TP
At the end of the loop,
if nC is not par
Acetoacetyl-CoA
+
+
NAD
NADH 2
27
4ATP
3ADP
24
Hs-CoA
Hs-CoA
+
Propionyl-CoA
Acetyl-CoA
n‘
+
ATP + CO2
Acetyl-CoA
1/2 O2 + 2H+
Cu1 ; Cu2
a ; a3
Acyl-CoA (n-2C)
16
4ATP
ADP 3-
FADH
H2 O
2
HO methyl
Glutaryl CoA
FAD
Hs-CoA
Malate
Succinate
_-cetoacyl-CoA
Malate
NADH
14
_-Ketoglutarate
NAD
_-OH-acyl-CoA
Glutamate
13
H2 O
Trans enoyl CoA
Aspartate
TD
Ca 2+
12
Acyl-CoA (Cn)
Ca
_-OH Butyrate
Pi
H+
Acetyl-CoA
NADH 2
2+
Succinate
Carnitine
Hs-CoA
_-OH Butyrate
Carnitine
11
Carnitine
Made by Rachid OUHABI
& Stéphane LUDINARD
Acyl-CoA (>12C) Acyl-CoA (<12C)
Figure 2. Mitochondrial metabolism.
2-
TD
TCg
H+
TGA
E
2 Na+
+
or 2 H
CCVD
26
Acetoacetate
NAD
Pi
15
Hs-CoA
Pi
TD
2-