Download Supplementary Text 2: Extensions to the prototype model

Supplementary Text 2: Extensions to the prototype model.
The prototype model1 was developed in order to integrate major pathway
components with other pertinent information on sphingolipid metabolism. As typical in
modeling, this model design was based on abstraction and simplification. In order to
facilitate the direct experimental testing of model predictions, several issues peculiar to
yeast sphingolipid metabolism became important and suggested further refinements of
the model.
Complex sphingolipids.
The complex sphingolipids inositolphosphoceramide (IPC),
mannosylinositolphosphoceramide (MIPC), and mannosyl(inositol phosphate)2ceramide
(M(IP)2C) are metabolized in different compartments, move between these
compartments, and the accumulate over time. To capture this in the model, IPC, MIPC
and M(IP)2C were elevated from independent to dependent variables, and their pools
were split into plasma membrane and other compartments. The ratio of this split was
determined by accounting for the contribution of membrane synthesis to cell growth,
which is directly accompanied by a net positive flux of IPC, MIPC, and M(IP)2C to the
plasma membrane (Fig. 1 and Supplementary Fig. 1A). It should be noted that other
possible compartmentalizations were not included due to paucity of data.
The relative amount of lipids in each component was determined based on a report
by Patton and Lester3. A concentration of 90% of complex sphingolipids for the plasma
membrane was used with the remaining 10% present in other organelles. The values for
the fluxes were estimated from Wu et al. (1995, Fig. 7)4 . The flux towards the plasma
membrane was based on the 30 min pulse-label experiment. The recycling flux
percentage was calculated by a comparison of complex sphingolipid concentrations
during exponential growth versus steady state after five generations growth (~90 min per
generation).
The transfer of complex sphingolipids into compartments inaccessible to IPCase is
consistent with results showing that ISC1 is not in the plasma membrane5. Instead, the
enzyme appears to move from the endoplasmic reticulum, where it is in early log phase,
to the mitochondria, where it is found in late phase. This suggests that the calculation of
percentages in plasma membrane, as taken from Patton and Lester3, is appropriate.
Fatty Acid Synthesis and Elongation.
Another model refinement concerned the availability and metabolism of fatty acids
at their metabolic entry points into sphingolipid synthesis at serine palmitoyl transferase
(SPT) and ceramide synthase. Supplementary Fig. 1B shows the expansion of fatty acid
synthesis of SPT substrate and elongation to the C26-CoAs necessary for ceramide
acylation.
Kobayashi and Nagiec6 showed the importance of very long chain fatty acids
(VLCFAs) in regulating the flow of de novo sphingolipid synthesis towards ceramide,
complex sphingolipids, and free sphingoid bases and their phosphates. Modeling this
observation requires the inclusion of C26-CoA as a dynamically changing (rather than
constant input) variable. The elongation of fatty acids beyond C16/C18 proceeds two
carbons at a time, and to model this repetitive elongation process, ELO1p alone was used
to represent the independent variable for the catalysis of the elongation of fatty acids to
the C26-CoA product. This decision was based on two factors. First, it has been shown in
rat7,8 and swine9 that this condensation step is at least one of the rate-limiting steps in the
overall elongation of very-long-chain fatty acyl-CoA. In yeast, it has been shown that
mutants with disruption of ELO1 and FAS2 must be supplied with fatty acids of at least
16 carbons to grow10, indicating the importance of this initial elongation step. Second,
from the modeling perspective, a linear process can be collapsed from several steps to
one without penalty, except for possible dynamic inaccuracies caused by time delays
between steps11.
Sphingosine-phosphate flux.
An equally important parameter concerns the ultimate breakdown of sphingolipids
through the action of the lyase on long chain base phosphates. The computation of fluxes
to and from the sphingosine-phosphate pools was accomplished by taking into
consideration that, in vivo, the lyase functions as the major clearance pathway for the
long chain base phosphates (LCB-Ps). Thus, the effluxes from the LCB-P pool were
partitioned with the majority (90%) processed through the lyase and 10% through the
phosphatase. Using stoichiometric arguments, the specific values of the fluxes were then
derived from the kinase flux, which was based on Michaelis-Menten kinetics with ATP
as secondary substrate (Supplementary Fig. 1C).
A.
IPC-g
X8
0.02
MIPC-g
X18
MIPC
Synthase
0.02  0.75
M(IP)2C-g
X19
M(IP)2C
Synthase
0.02  0.75
0.02
0.01
PI
X15
DAG
X14
M(IP)2C-m
X22
MIPC-m
X21
IPC-m
X20
B.
Palmitate
X12
Acetate,
ACSp
CoA
ATP
X12, X23
C26-CoA
X23
G3P Acylt.
Ac-CoA
X25
ACCp
0.01  0.5
FAS
Mal-CoA
X24
Pal-CoA
X12
SPT
Serine
X13
ELO1p
ACBP,
Metabolism
X10
KDHS
X1
C.
0.000011
0.000107
DHS-1-P
DHS
0.00011
Pal-CoA CDP-Eth
0.01857
PHS-1-P
PHS
0.00185
0.0167
Supplementary Figure 1. Model development. A) Fluxes between complex
sphingolipid components. B) Fatty acid metabolism. C) Fluxes into and out of
the LCB phosphates.
Effect of CDP-DAG and PI on phosphatidate phosphatase.
We included the allosteric regulation of phosphatidate phosphatase by
phospholipids. Wu and Carman (1996. Figures 4A and 5A)2 report for yeast a positive
effect of cardiolipin (CL), CDP-diacylglycerol (CDP-DG), phosphatidylinositol (PI), and
to a minor degree of phosphatidylglycerol (PG) and phosphatidylserine (PS), on PAPPase activity.
We computed the corresponding kinetic orders of the model variables CDP-DG and
PI. As recommended in the literature 12, we used a log-log representation (see
Supplementary Figure 2) of the phospholipid concentrations vs. PA-PPase activity at 3
mol% PA.
Also, since we are interested in the response of sphingolipid metabolism to acute
perturbations, we eliminated the long-term regulation of PA-PPase by inositol13,14 that
was included in the prototype model1.
1.2
Log (Relative PA-PPase activity)
PA-PPase activity (U/mg)
12
10
8
y = 0.3269x + 0.9877
1.1
1
0.9
0.8
y = 0.3038x + 0.7254
0.7
0.6
0.5
6
0
1
2
3
4
Phospholipid (mol%)
5
6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
Log (Phospholipid (mol%))
Supplementary Figure 2: Dependence of relative PA-PPase activity on
concentration of phospholipids CDP-DAG () and PI (). Left panel: Original data,
redrawn for 3 mol% PA from Wu and Carman (1996) 2. Right panel: Data shown in
logarithmic coordinates, where the kinetic orders are determined as slopes of the
linear regression.
1
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