Download Supplement I

Supplementary Materials:
Table of Contents:
Supplement I: Biomass Composition and 13C labeling Results
Figure SIa. Amino acid profile of soybean embryo storage protein
Figure SIb. Fatty acid profile of soybean embryo triacylglycerol
Figure SIc. Starch labeling from [U-13C5]-glutamine experiment
Figure SId. Cell wall, protein glycan, and starch labeling for [U-13C6]-glucose
experiment
Table SIa. Biomass accumulation in soybean embryos
Table SIb. Free and protein amino acid average labeling data
Table SIc. 13C isotopomer abundances for [U-13C5]-glutamine and glucose labeling
experiments measured by GCMS
Table SId. 13C enrichments and bond-connectivity for [U-13C5]-glutamine and glucose
labeling experiments measured by NMR
Supplement II: Redox Balance of Seed Metabolism
Figure SIIa. Redox state of glutamine
Table SIIa. Amino acid redox/oxidation states in soybeans
Table SIIb. Oxidation state for soybean storage triacylglycerol
Supplement III: ATP Calculations
Table SIIIa. ATP polymerization events
Table SIIIb. Redox requirements for biosynthesis of amino acids from primary
metabolism using imported glutamine and asparagine
Supplement IV: OPPP vs. Calvin Cycle
Figure SIVa. Flux map with OPPP
Figure SIVb. Flux map with Calvin cycle
Supplement V: Metabolic Network
Supplement VI: Extended Experimental Procedures
Supplement VII: Flux Analysis in Complex Systems
Supplement VIII: Potential Limitations of Using MFA to Analyze
Plant Tissues
References
1
Supplement I: Biomass Composition and 13C labeling Results
Table SIa. Biomass accumulation in soybean embryos
Description
Biomass (mg
DW/day/seed)
Oil content
%
Protein content
%
In planta
5-7*
19.2 +/- 3.3
40.4 +/- 1.2
Culture [U-12C]
7.8 +/- 1.6
18.8 +/- 1.9
39.9 +/- 2.5
Culture [U-13C]
glucose study
6.3 +/- 0.8
17.7 +/- 1.1
38.6 +/- 1.6
Culture [U-13C]
glutamine study
6.8 +/- 0.7
18.5 +/- 0.9
39.3 +/- 3.6
Culture [U-14C]
carbon balance
study
4.9 +/- 0.3
18.6 +/- 2.0
35.6 +/- 2.5
*Represents an average range from (Rubel et al., 1972; Hsu and Obendorf, 1982; Egli et
al., 1985).
Percentage
30
In planta seeds
U13C glc expt
L
T
U13C gln expt
20
10
0
R
H
I
K
M
F
V
A
D/N
C
E/Q
G
P
S
Y
Amino Acid
Figure SIa. Amino acid profile of soybean embryo storage protein. In planta results from
(Yazdi-Samadi et al., 1977).
2
80%
In planta (Fehr et al)
In planta
U12 unlabeled expt
U13C glc expt
60%
40%
U13C gln expt
U14C Balance expt
20%
0%
c16:0
c18:0
c18:1
c18:2
c18:3
Figure SIb. Fatty acid profile of soybean embryo triacylglycerol. An independent set of
in planta data were taken from (Fehr et al., 1971).
C4
(a)
C1
NMR Data:
(b)
Starch Glucose C1-C6 as MAG
Average label = 0.0%
(Shown in picture of NMR)
C2
C3
C5
C6
Triose from Glycerol-TAG
12
C-12C-12C = 99% fully unlabeled
(Average label ~0.0%)
GCMS Data:
(c)
Starch Glucose C1-C6 fragment
M0=99%, Sum(M1..M6)=1%
Starch Glucose C1-C5 fragment
M0=100%, Sum(M1-M5)=0%
Figure SIc. Starch labeling from [U-13C5]-glutamine experiment. Lack of gluconeogenic
flux is reflected by GCMS/NMR data of starch glucose and glycerol from fatty acids that
represent hexose and triose pools of glycolysis. a). NMR of monoacetone glucose
derivative of starch glucose. b). Summary of NMR data for starch glucose monomers and
glycerol labeling. c). GCMS data for starch glucose monomers. Note: the boxed labeling
results have been corrected for natural abundance of carbon.
3
Fractional Enrichment
(a)
(c)
(b)
0.6
Cytosol cell wall glucose
Cytosol cell wall rhamnose
Cytosol protein galactose
Cytosol protein mannose
Plastid starch
0.4
0.2
Starch glucose (NMR)
positional enrichment
34.5%
34.3%
33.7%
O
35.3%
35.0%
O
33.7%
n
0
[M]+
[M+1]+ [M+2]+ [M+3]+ [M+4]+ [M+5]+
Molecule
Carbons
Cell Wall
Protein glycan
Starch
+
+
1-3/3-5
36.6 /-3.3
38.3 /-1.0
Arabinose
1-4/2-5
36.2+/-3.3
38.2+/-1.0
Arabinose
1-4/3-6
38.1+/-3.5
38.8+/-1.0
Galactose
+
1-5/2-6
37.7 /-3.6
Galactose
38.7+/-1.0
+
1-4/3-6
35.6 /-1.7
Glucose
1-5/2-6
Glucose
35.6+/-1.7
1-5
Glucose
34.7+/-2.2
1-6
36.6+/-2.4
Glucose
1-6
Glucose (NMR)
34.4+/-0.7
+
1-4/3-6
40.1 /-1.1
Mannose
1-5/2-6
Mannose
40.0+/-1.0
1-4
36.8+/-3.0
Rhamnose
2-6
Rhamnose
36.5+/-3.0
1-4/2-5
37.3+/-3.2
38.0+/-2.1
Xylose
Average
---------36.7+/-1.1
38.9+/-0.5
35.2+/-1.1
13
Figure SId. Cell wall, protein glycan, and starch labeling for [U- C6]-glucose
experiment. Measurements of compartment-specific metabolites indicate high exchange
between cytosol and plastid and that pools are in near-equilibrium. Data are taken from
the [U-13C6]-glucose labeling experiment. (a) GCMS fragments from compartmentspecific metabolites have very similar profiles even though they reflect different carbon
compositions, indicating that all carbons are similarly labeled. (b) Labeling of different
carbons was analyzed by NMR for starch glucose confirming similar enrichments for all
six carbons. (c) Table of the average enrichments of other measurements from cell wall,
starch, and glycans from protein all show similar average labeling levels regardless of
cytosolic or plastidic biosynthetic origin.
4
Table SIb. Free and protein amino acid average labeling data. Comparison of average
label in proteinaceous and free amino acids for the [U-13C6]-glucose (Glc) and [U-13C5]glutamine (Gln) labeling experiments.
13C
% labeled amino acid
Amino Acid
carbons
Expt
Protein
Free
1,2,3
Gln
10.4
11.0
Alanine
1,2,3
Glc
36.9
42.6
2,3
Gln
10.7
11.0
2,3
Glc
37.4
42.7
1,2
Gln
6.5
7.8
Glycine
1,2
Glc
36.5
37.6
2
Gln
5.4
5.5
2
Glc
37.9
38.7
1,2,3,4,5
Gln
10.6
12.5
Valine
1,2,3,4,5
Glc
34.0
39.1
2,3,4,5
Gln
10.5
10.5
2,3,4,5
Glc
34.1
38.5
2,3,4,5,6
Gln
9.9
8.0
Leucine
1,2,3,4,5,6
Gln
32.6
31.6
Isoleucine
2,3,4,5,6
Gln
33.5
31.4
2,3,4,5,6
Gln
36.8
36.2
2,3,4,5,6
Glc
22.0
22.9
1,2,3
Glc
38.7
42.6
Serine
2,3
Glc
38.8
44.7
1,2
Glc
38.4
42.1
1,2,3,4
Gln
48.8
48.7
Threonine
1,2,3,4
Glc
14.9
16.8
2,3,4
Gln
49.4
48.9
2,3,4
Glc
15.7
16.1
1.2
2.0
Phenylalanine 1,2,3,4,5,6,7,8,9 Gln
1,2,3,4,5,6,7,8,9 Glc
34.6
45.0
2,3,4,5,6,7,8,9
Gln
2.0
2.2
1,2,3,4,5,6
Gln
32.6
32.7
Lysine
2,3,4,5,6,7,8,9
Gln
2.5
4.5
Tyrosine
1,2,3,4,5,6
Gln
2.1
1.5
Histidine
Most of the protein amino acid labeling measurements are within 10% of the free amino
acid levels, with the variation tending to be larger at low labeling levels that are
associated with larger technical uncertainties (i.e. phenylalanine, tyrosine, histidine).
Similarity between these pools representing a highly turned-over pool and a storage
product is consistent with the system having reached near isotopic steady state. .
5
Table SIc. 13C isotopomer abundances for [U-13C5]-glutamine and [U-13C6]-glucose labeling experiments measured by GCMS. Note:
results represent the modeled values with the incorporation of natural abundance in the carbon backbone.
100% [U-13C6]-glucose
100% [U-13C5]-glutamine
Metabolite +
carbons
Number of
Expt 1
Expt 2
Expt 3
Expt 1
Expt 2
Expt 3
13
fragments
C atoms
1,2
0
0.677
0.695
0.684
0.870
0.868
0.868
Acetyl-CoA
Butylamide
1
0.074
0.072
0.076
0.045
0.046
0.047
C2 Product
2
0.249
0.233
0.240
0.085
0.085
0.084
1,2,3
0
0.481
0.510
0.467
0.811
0.792
0.786
2xAcetyl-CoA
Butylamide
1
0.235
0.221
0.242
0.120
0.130
0.132
C3 Product
2
0.184
0.171
0.191
0.060
0.011
0.012
3
0.100
0.099
0.100
0.010
0.011
0.012
1,2,3
0
0.516
0.548
0.534
0.852
0.850
0.854
Alanine
[TBDMS-57]+
(protein)
1
0.107
0.102
0.109
0.056
0.057
0.057
2
0.087
0.078
0.086
0.018
0.018
0.018
3
0.289
0.272
0.271
0.074
0.074
0.072
1,2,3
0
0.475
0.458
0.488
0.847
0.846
[TBDMS-57]+
(free)
1
0.102
0.100
0.110
0.056
0.056
2
0.094
0.094
0.097
0.020
0.020
3
0.329
0.348
0.305
0.077
0.078
[TBDMS-85]+
2,3
0
0.572
0.599
0.592
0.871
0.867
0.870
(protein)
1
0.077
0.075
0.077
0.045
0.049
0.048
2
0.351
0.326
0.331
0.084
0.084
0.081
+
[TBDMS-85]
2,3
0
0.531
0.515
0.551
0.863
0.862
(free)
1
0.080
0.080
0.083
0.055
0.056
2
0.389
0.406
0.366
0.082
0.082
1,2,3,4
0
0.812
0.802
0.800
0.693
0.691
0.698
Asparagine/
(protein)
1
0.093
0.094
0.096
0.075
0.073
0.075
Aspartate
[TBDMS-57]+
2
0.044
0.054
0.050
0.031
0.035
0.034
3
0.036
0.036
0.040
0.037
0.032
0.031
4
0.014
0.014
0.016
0.164
0.169
0.162
6
[TBDMS-85]+
2,3,4
(protein)
[TBDMS-159]+
2,3,4
(protein)
[TBDMS 302]+
1,2
(protein)
Citrate
[TBDMS-15]+
1,2,3,4,5,6
Fumarate
[TBDMS-57]+
1,2,3,4
Glutamine/
Glutamate
[TBDMS-85]+
2,3,4,5
(protein)
[TBDMS-159]+
2,3,4,5
(protein)
0
1
2
3
0
1
2
3
0
1
2
0
1
2
3
4
5
6
0
1
2
3
4
0
1
2
3
4
0
1
2
0.852
0.082
0.041
0.024
0.849
0.080
0.042
0.030
0.857
0.087
0.056
0.449
0.184
0.232
0.080
0.037
0.017
0.004
0.733
0.119
0.076
0.053
0.019
0.840
0.068
0.070
0.012
0.010
0.839
0.069
0.072
0.860
0.081
0.039
0.021
0.839
0.085
0.046
0.030
0.865
0.081
0.054
0.418
0.163
0.249
0.086
0.050
0.025
0.008
0.764
0.103
0.073
0.044
0.016
0.801
0.082
0.089
0.016
0.011
0.813
0.077
0.085
7
0.844
0.084
0.046
0.026
0.845
0.079
0.044
0.032
0.858
0.086
0.056
0.469
0.178
0.221
0.073
0.038
0.015
0.005
0.733
0.112
0.076
0.058
0.020
0.837
0.069
0.070
0.013
0.011
0.833
0.071
0.074
0.694
0.076
0.046
0.184
0.700
0.071
0.042
0.186
0.725
0.067
0.208
0.293
0.130
0.103
0.080
0.216
0.136
0.043
0.109
0.023
0.080
0.000
0.812
0.119
0.028
0.081
0.714
0.073
0.038
0.175
0.681
0.076
0.046
0.198
0.716
0.067
0.217
0.326
0.109
0.099
0.073
0.248
0.109
0.036
0.087
0.020
0.079
0.005
0.810
0.116
0.029
0.087
0.719
0.075
0.037
0.168
0.690
0.078
0.044
0.188
0.724
0.067
0.209
0.329
0.144
0.114
0.071
0.214
0.096
0.033
0.115
0.029
0.085
0.000
0.786
0.138
0.035
0.092
Glycine
[TBDMS-57]+
1,2
(protein)
Glycine
[TBDMS-57]+
1,2
(free)
[TBDMS-85]+
2
(protein)
2
(free)
2,3,4,5,6
[TBDMS-85]+
Hexose
(Cell Wall)
(Rhamnose)
[Red. Alditol
Acetate-73]
[Red. Alditol
Acetate-87]
1,2,3,4
(Starch)
(glucose)
[Alditol
Acetate-59]
1,2,3,4,5,6
[Alditol
1,2,3,4,5
3
4
0
1
2
0
1
2
0
1
0
1
0
1
2
3
4
5
0
1
2
3
4
0
1
2
3
4
5
6
0
0.011
0.009
0.536
0.182
0.283
0.537
0.176
0.287
0.613
0.387
0.613
0.387
0.397
0.125
0.099
0.093
0.085
0.201
0.409
0.154
0.103
0.125
0.210
0.411
0.082
0.067
0.132
0.058
0.054
0.194
0.435
0.014
0.010
0.557
0.175
0.268
0.536
0.176
0.288
0.629
0.371
0.613
0.387
0.462
0.125
0.086
0.081
0.071
0.176
0.473
0.150
0.091
0.103
0.183
0.453
0.076
0.060
0.120
0.045
0.051
0.197
0.486
8
0.012
0.010
0.541
0.186
0.273
0.526
0.194
0.280
0.620
0.379
0.612
0.388
0.425
0.091
0.074
0.137
0.055
0.049
0.169
0.451
0.039
0.734
0.906
0.067
0.027
0.874
0.095
0.031
0.950
0.050
0.948
0.052
0.929
0.064
0.004
0.002
0.001
0.001
0.939
0.054
0.003
0.001
0.002
0.933
0.052
0.011
0.002
0.000
0.000
0.000
0.956
0.039
0.730
0.901
0.069
0.030
0.886
0.071
0.043
0.946
0.054
0.945
0.055
0.931
0.064
0.003
0.001
0.000
0.000
0.941
0.055
0.003
0.001
0.000
0.925
0.058
0.015
0.002
0.000
0.000
0.000
0.952
0.036
0.700
0.896
0.068
0.036
0.940
0.060
0.941
0.059
0.923
0.063
0.010
0.002
0.000
0.000
0.000
0.948
Acetate-73]
Histidine
[TBDMS-57]+
1,2,3,4,5,6
(protein)
Histidine
[TBDMS-57]+
1,2,3,4,5,6
(free)
Isoleucine
[TBDMS-15]+
1,2,3,4,5,6
(protein)
Isoleucine
[TBDMS-15]+
1,2,3,4,5,6
(free)
1
2
3
4
5
0
1
2
3
4
5
6
0
1
2
3
4
5
6
0
1
2
3
4
5
6
0
1
2
3
4
0.089
0.120
0.110
0.059
0.188
0.440
0.137
0.262
0.086
0.043
0.023
0.008
-
0.085
0.102
0.093
0.054
0.181
0.323
0.174
0.109
0.124
0.088
0.103
0.079
0.453
0.133
0.259
0.081
0.044
0.022
0.008
-
9
0.100
0.126
0.110
0.055
0.157
0.322
0.200
0.134
0.142
0.088
0.076
0.038
0.437
0.138
0.256
0.089
0.046
0.025
0.009
-
0.041
0.001
0.001
0.000
0.001
0.916
0.074
0.006
0.003
0.000
0.000
0.000
0.926
0.072
0.000
0.002
0.000
0.000
0.000
0.326
0.104
0.090
0.090
0.329
0.025
0.036
0.319
0.111
0.119
0.081
0.301
0.045
0.001
0.000
0.001
0.001
0.922
0.070
0.002
0.003
0.001
0.001
0.001
0.327
0.103
0.096
0.080
0.335
0.025
0.034
-
0.051
0.000
0.001
0.001
0.001
0.889
0.088
0.014
0.005
0.000
0000
0.000
0.328
0.104
0.093
0.081
0.333
0.025
0.036
-
[TBDMS-85]+
2,3,4,5,6
(protein)
[TBDMS-85]+
2,3,4,5,6
(free)
[TBDMS-159]+
2,3,4,5,6
(protein)
[TBDMS-159]+
2,3,4,5,6
(free)
Leucine
[TBDMS-15]+
1,2,3,4,5,6
(protein)
5
6
0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
4
0.482
0.128
0.278
0.069
0.027
0.016
0.465
0.132
0.289
0.070
0.028
0.016
0.463
0.130
0.279
0.077
0.034
0.017
0.281
0.089
0.301
0.079
0.174
0.496
0.127
0.271
0.066
0.027
0.014
0.481
0.129
0.280
0.067
0.028
0.015
0.462
0.131
0.279
0.0778
0.033
0.017
0.310
0.092
0.293
0.049
0.175
10
0.482
0.129
0.274
0.071
0.028
0.016
0.472
0.129
0.280
0.072
0.029
0.017
0.458
0.134
0.276
0.078
0.035
0.019
0.274
0.090
0.306
0.084
0.174
0.041
0.029
0.381
0.102
0.110
0.352
0.022
0.035
0.401
0.130
0.071
0.329
0.032
0.038
0.378
0.101
0.111
0.352
0.022
0.036
0.383
0.123
0.113
0.319
0.026
0.037
0.670
0.096
0.192
0.016
0.024
0.387
0.105
0.104
0.351
0.021
0.032
0.385
0.104
0.107
0.349
0.021
0.032
0.376
0.114
0.108
0.339
0.025
0.038
0.677
0.101
0.180
0.019
0.021
0.382
0.107
0.106
0.350
0.021
0.034
0.395
0.117
0.107
0.320
0.028
0.033
0.381
0.106
0.107
0.350
0.022
0.034
0.408
0.118
0.106
0.314
0.023
0.030
0.686
0.102
0.182
0.005
0.022
[TBDMS-85]+
2,3,4,5,6
(protein)
Leucine
[TBDMS-85]+
2,3,4,5,6
(free)
Lysine
[TBDMS-57]+
1,2,3,4,5,6
(protein)
Lysine
[TBDMS-57]+
1,2,3,4,5,6
(free)
[TBDMS-159]+
2,3,4,5,6
(protein)
5
6
0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
4
5
6
0
1
2
3
4
5
6
0
1
2
0.028
0.048
0.308
0.171
0.239
0.151
0.079
0.052
0.428
0.140
0.192
0.162
0.042
0.024
0.011
0.481
0.127
0.278
0.028
0.053
0.329
0.166
0.228
0.144
0.078
0.055
0.449
0.136
0.188
0.154
0.040
0.023
0.010
0.486
0.125
0.274
11
0.028
0.044
0.305
0.174
0.244
0.150
0.078
0.048
0.429
0.143
0.191
0.160
0.043
0.025
0.011
0.480
0.127
0.273
0.001
0.002
0.699
0.137
0.131
0.022
0.009
0.002
0.746
0.160
0.058
0.024
0.011
0.002
0.344
0.109
0.089
0.227
0.178
0.018
0.034
0.345
0.118
0.101
0.206
0.167
0.031
0.033
0.382
0.107
0.105
0.001
0.001
0.709
0.137
0.124
0.021
0.008
0.001
0.341
0.109
0.092
0.224
0.184
0.018
0.033
0.345
0.113
0.099
0.211
0.180
0.016
0.036
0.390
0.112
0.101
0.001
0.002
0.711
0.135
0.123
0.021
0.009
0.002
0.359
0.112
0.090
0.218
0.173
0.015
0.032
0.379
0.112
0.102
Malate
[TBDMS-15]+
1,2,3,4
Methionine
[TBDMS-57]+
1,2,3,4,5
(protein)
[TBDMS-85]+
2,3,4,5
(protein)
[TBDMS-159]+
2,3,4,5
(protein)
Phenylalanine
[TBDMS-57]+
1,2,3,4,5,6,7,8,9
(protein)
3
4
5
0
1
2
3
4
0
1
2
3
4
5
0
1
2
3
4
0
1
2
3
4
0
1
2
3
4
5
6
0.069
0.029
0.017
0.715
0.115
0.097
0.055
0.017
0.422
0.336
0.099
0.078
0.051
0.015
0.465
0.340
0.089
0.076
0.031
0.469
0.345
0.087
0.071
0.027
0.232
0.116
0.107
0.142
0.099
0.083
0.086
0.068
0.030
0.017
0.662
0.131
0.106
0.074
0.027
0.439
0.317
0.098
0.079
0.052
0.016
0.489
0.324
0.086
0.072
0.028
0.490
0..28
0.087
0.070
0.026
0.264
0.115
0.101
0.134
0.091
0.077
0.081
12
0.072
0.030
0.078
0.625
0.145
0.115
0.083
0.032
0.422
0.323
0.100
0.082
0.054
0.016
0.477
0.334
0.088
0.074
0.028
0.473
0.336
0.091
0.073
0.027
0.221
0.118
0.113
0.150
0.104
0.086
0.085
0.345
0.023
0.038
0.426
0.130
0.099
0.045
0.300
0.399
0.103
0.063
0.081
0.345
0.009
0.464
0.100
0.076
0.354
0.005
0.459
0.102
0.077
0.356
0.006
0.900
0.094
0.001
0.004
0.001
0.000
0.000
0.341
0.022
0.034
0.418
0.120
0.102
0.047
0.314
0.400
0.101
0.072
0.069
0.349
0.010
0.470
0.105
0.073
0.348
0.005
0.458
0.105
0.076
0.356
0.005
0.904
0.092
0.000
0.003
0.000
0.000
0.000
0.345
0.024
0.039
0.455
0.130
0.101
0.042
0.271
0.400
0.103
0.070
0.069
0.347
0.010
0.467
0.105
0.074
0.349
0.005
0.461
0.107
0.074
0.352
0.006
0.903
0.093
0.000
0.004
0.001
0.000
0.000
Phenylalanine
[TBDMS-57]+
1,2,3,4,5,6,7,8,9
(free)
[TBDMS-159]+
2,3,4,5,6,7,8,9
(protein)
[TBDMS-159]+
2,3,4,5,6,7,8,9
(free)
7
8
9
0
1
2
3
4
5
6
7
8
9
0
1
2
3
4
5
6
7
8
0
1
2
3
4
5
6
7
8
0.052
0.037
0.047
0.153
0.089
0.096
0.134
0.103
0.095
0.097
0.061
0.091
0.081
0.176
0.092
0.147
0.135
0.124
0.110
0.104
0.048
0.065
-
0.050
0.037
0.050
0.152
0.088
0.096
0.133
0.103
0.095
0.096
0.063
0.090
0.086
0.205
0.095
0.144
0.126
0.115
0.102
0.100
0.046
0.066
-
13
0.049
0.034
0.040
0.168
0.094
0.152
0.141
0.128
0.111
0.102
0.046
0.057
-
0.000
0.000
0.000
0.871
0.098
0.011
0.009
0.002
0.001
0.000
0.001
0.006
0.002
0.873
0.101
0.021
0.003
0.001
0.000
0.000
0.000
0.000
0.882
0.089
0.015
0.002
0.001
0.002
0.001
0.001
0.007
0.000
0.000
0.000
0.888
0.098
0.009
0.004
0.000
0.001
0.001
0.000
0.000
0.000
0.871
0.103
0.021
0.003
0.000
0.000
0.000
0.000
0.000
0.893
0.089
0.013
0.003
0.000
0.002
0.000
0.000
0.000
0.000
0.000
0.000
0.875
0.101
0.020
0.003
0.000
0.000
0.000
0.000
0.000
-
[TBDMS 302]+
1,2
(protein)
Proline
[TBDMS-57]+
1,2,3,4,5
(protein)
[TBDMS-85]+
2,3,4,5
(protein)
[TBDMS-159]+
2,3,4,5
(protein)
Serine
[TBDMS-57]+
1,2,3
(protein)
Serine
[TBDMS-57]+
1,2,3
(free)
[TBDMS-159]+
2,3
(protein)
[TBDMS-159]+
2,3
0
1
2
0
1
2
3
4
5
0
1
2
3
4
0
1
2
3
4
0
1
2
3
0
1
2
3
0
1
2
0
0.545
0.148
0.307
0.804
0.080
0.080
0.021
0.010
0.005
0.817
0.076
0.086
0.011
0.010
0.822
0.069
0.087
0.012
0.010
0.420
0.200
0.161
0.220
0.394
0.187
0.159
0.260
0.475
0.257
0.268
0.433
0.565
0.135
0.301
0.780
0.085
0.025
0.025
0.011
0.006
0.800
0.080
0.095
0.014
0.010
0.799
0.076
0.099
0.015
0.011
0.449
0.185
0.145
0.221
0.395
0.187
0.159
0.259
0.506
0.232
0.262
0.446
14
0.550
0.151
0.300
0.801
0.080
0.081
0.023
0.010
0.006
0.821
0.073
0.084
0.018
0.010
0.820
0.070
0.087
0.013
0.011
0.422
0.204
0.161
0.212
0.382
0.205
0.172
0.240
0.482
0.259
0.259
0.424
0.969
0.030
0.001
0.124
0.032
0.027
0.086
0.042
0.689
0.137
0.031
0.102
0.036
0.695
0.138
0.031
0.100
0.039
0.693
0.929
0.061
0.009
0.002
0.953
0.041
0.006
-
0.967
0.032
0.002
0.127
0.035
0.029
0.098
0.042
0.670
0.140
0.035
0.113
0.036
0.676
0.143
0.035
0.112
0.039
0.671
0.925
0.065
0.010
0.001
0.953
0.042
0.005
-
0.966
0.032
0.002
0.138
0.038
0.031
0.097
0.042
0.654
0.153
0.038
0.113
0.035
0.661
0.155
0.038
0.112
0.038
0.658
0.923
0.066
0.011
0.001
0.956
0.040
0.004
-
(free)
[TBDMS 302]+
1,2
(protein)
[TBDMS 302]+
1,2
(free)
Succinate
[TBDMS-15]+
1,2,3,4
Threonine
[TBDMS-57]+
1,2,3,4
(protein)
Threonine
[TBDMS-57]+
1,2,3,4
(free)
[TBDMS-85]+
2,3,4
(protein)
[TBDMS-85]+
2,3,4
(free)
1
2
0
1
2
0
1
2
0
1
2
3
4
0
1
2
3
4
0
1
2
3
4
0
1
2
3
0
1
2
3
0.229
0.338
0.526
0.168
0.306
0.485
0.183
0.332
0.730
0.087
0.143
0.024
0.016
0.691
0.137
0.078
0.069
0.025
0.673
0.129
0.099
0.071
0.028
0.731
0.126
0.088
0.055
0.720
0.131
0.096
0.053
0.235
0.320
0.541
0.157
0.301
0.493
0.177
0.330
0.716
0.092
0.151
0.030
0.011
0.703
0.129
0.083
0.063
0.022
0.673
0.129
0.099
0.071
0.028
0.731
0.129
0.089
0.050
-
15
0.249
0.327
0.530
0.171
0.298
0.478
0.202
0.320
0.721
0.090
0.145
0.026
0.018
0.688
0.136
0.079
0.071
0.026
0.642
0.148
0.098
0.077
0.035
0.720
0.129
0.094
0.058
-
0.943
0.046
0.011
0.277
0.071
0.167
0.034
0.451
0.371
0.107
0.069
0.085
0.368
0.369
0.106
0.101
0.055
0.369
0.404
0.104
0.089
0.403
0.399
0.126
0.085
0.390
0.940
0.048
0.013
0.221
0.065
0.177
0.035
0.502
0.375
0.105
0.077
0.071
0.371
0.398
0.108
0.087
0.407
-
0.939
0.049
0.012
0.307
0.083
0.169
0.031
0.410
0.382
0.108
0.076
0.071
0.363
0.413
0.110
0.085
0.392
-
Tyrosine
[TBDMS-159]+
2,3,4,5,6,7,8,9
(protein)
Tyrosine
[TBDMS-159]+
2,3,4,5,6,7,8,9
(free)
Valine
[TBDMS-57]+
1,2,3,4,5
(protein)
Valine
[TBDMS-57]+
1,2,3,4,5
(free)
[TBDMS-85]+
2,3,4,5
0
1
2
3
4
5
6
7
8
0
1
2
3
4
5
6
7
8
0
1
2
3
4
5
0
1
2
3
4
5
0
0.193
0.098
0.156
0.136
0.121
0.104
0.095
0.041
0.055
0.375
0.103
0.202
0.174
0.045
0.100
0.325
0.099
0.203
0.182
0.054
0.137
0.409
0.210
0.096
0.150
0.129
0.117
0.100
0.096
0.042
0.059
0.391
0.099
0.192
0.169
0.044
0.105
0.322
0.098
0.201
0.180
0.054
0.145
0.422
16
0.170
0.095
0.157
0.141
0.129
0.110
0.100
0.043
0.055
0.373
0.105
0.204
0.176
0.045
0.097
0.316
0.106
0.211
0.188
0.058
0.122
0.408
0.850
0.110
0.034
0.005
0.000
0.000
0.000
0.000
0.000
0.856
0.086
0.017
0.004
0.001
0.010
0.001
0.005
0.020
0.747
0.078
0.088
0.072
0.005
0.010
0.737
0.085
0.086
0.073
0.007
0.012
0.762
0.844
0.113
0.037
0.005
0.000
0.000
0.000
0.000
0.000
0.858
0.086
0.012
0.003
0.000
0.005
0.020
0.005
0.012
0.752
0.081
0.084
0.068
0.005
0.009
0.733
0.082
0.082
0.067
0.007
0.028
0.766
0.854
0.109
0.032
0.004
0.000
0.000
0.000
0.000
0.000
0.864
0.089
0.013
0.002
0.000
0.009
0.034
0.000
0.000
0.753
0.080
0.084
0.069
0.005
0.010
0.724
0.081
0.076
0.063
0.006
0.049
0.766
(protein)
[TBDMS-85]+
2,3,4,5
(free)
1
2
3
4
0
1
2
3
4
0.090
0.340
0.044
0.117
0.346
0.088
0.360
0.053
0.153
0.087
0.327
0.044
0.120
0.367
0.090
0.349
0.051
0.143
0.091
0.343
0.045
0.113
0.354
0.097
0.355
0.058
0.137
0.074
0.147
0.007
0.011
0.749
0.086
0.143
0.010
0.011
0.080
0.140
0.005
0.009
0.765
0.083
0.134
0.008
0.010
0.077
0.139
0.007
0.011
0.771
0.085
0.128
0.006
0.010
Fragments were selected by first running standards that after correction for natural abundance were compared with theoretical values.
All selected fragments reflect standards that corrected well, with over 97% of all measurements giving <1% error from theoretical
values, and all selected fragment standards correcting to within 3% of theoretical values. These criteria are slightly more stringent
than selection reported by others (Dauner and Sauer, 2000) and result in fragment selection that is highly similar to (Antoniewicz et
al., 2007). Additional fragments are reported here for which greater abundances were available, or when different derivatizations led
to acceptable results. TBDMS: tert-butyl-dimethylsilyl derivatization, [TBDMS-57]+ implies the TBDMS derivative of the metabolite
that has been reduced in weight by 57 a.m.u. due to fragmentation and loss of part of the compound during mass spectrometry.
Details of TBDMS, alditol acetate and butylamide derivatizations are given in the methods and in (Allen et al., 2007).
17
Table SId. 13C enrichments and bond-connectivity for [U-13C5]-glutamine and [U-13C6]-glucose labeling experiments measured by
NMR. Note: results represent the modeled values with the incorporation of natural abundance in the carbon backbone. For the
positional labeling descriptions, a cumomer representation is used (Wiechert et al., 1999) where “0” implies unlabeled carbon
positions, “1” implies a labeled carbon position, and “x” implies unknown labeling.
100% [U-13C6]-glucose
100% [U-13C5]-glutamine
metabolite
carbons
position
Expt 1
Expt 2
Expt 3
3 Combined Expt Samples
1
1xxxxx
0.36
0.35
0.33
0.01
Hexose
(Starch glucose)
2
x1xxxx
0.35
0.35
0.35
0.01
MAG
3
xx1xxx
0.34
0.34
0.35
0.01
4
xxx1xx
0.35
0.34
0.33
0.01
5
xxxx1x
0.34
0.34
0.34
0.01
6
xxxxx1
0.35
0.35
0.35
0.01
Bond
11xxxx
0.31
Connectivity
10xxxx
0.03
110xxx
0.07
011xxx
0.01
111xxx+010xxx
0.27
x110xx
0.08
x011xx
0.03
x111xx+x010xx
0.23
xx110x
0.01
xx011x
0.09
xx111x+xx010x
0.24
xxx110
0.00
xxx011
0.03
xxx111+xxx010
0.31
xxxx11
0.33
xxxx01
0.02
1/3
000
0.96
Triose
1/3
1xx+xx1
0.06
1/3
1x1
0.30
-
18
Supplement II: Redox balance of seed metabolism
Conservation of the redox state of a system implies that the amount of substrates
taken up (including oxygen if it is consumed) and the amount of products generated must
preserve an overall balanced oxidation state. In the following paragraphs we outline
calculations of the redox balance of soybean embryo metabolism.
Seeds that store larger amounts of reserves with highly reduced carbon, such as
oil, will be more reduced overall. Accumulation of oil is typically accompanied by the
production of carbon dioxide that is highly oxidized. Thus the redox state of embryo
metabolism represents a balance between generated embryo products that are more
reduced and carbon dioxide production that is highly oxidized. Furthermore calculating
the redox balance can corroborate values for carbon dioxide and oxygen consumption or
production
H H
C1
C2
C3 C 4 C5
H H
H
Figure SIIa. Redox state of glutamine. The redox state of carbon is a reflection of
attached chemical groups. Following basic redox rules, atoms of nitrogen, hydrogen ,
and oxygen have oxidation states of: -3, +1, and -2 when they are present in organic
compounds (i.e. not in an elemental form). An example of this calculation process for
glutamine is presented in the footnote to Table SIIa.
Oxidation state of embryo storage components: To calculate the redox state of the
major storage components of soybean, first, the measured amino acid composition of
soybean embryo protein was converted to mol carbon percent (Table SIIa). Then the
average oxidation/redox state of all carbon contained in soybean protein was calculated.
The oxidation state per molecule is calculated for each amino acid and, for a given
protein composition, can be used to calculate a net oxidative/redox state per carbon.
19
Table SIIa. Amino acid redox/oxidation states in soybean
Elemental
Composition
Description of Amino Acid
C H N O S
?
1 -3 -2 -2
3
7
1 2 0
Alanine
6 14 4 2 0
Arginine
4
7
1 4 0
Aspartate
4
8
2 3 0
Asparagine
5
9
1 4 0
Glutamate
5 10 2 3 0
Glutamine
2
5
1 2 0
Glycine
6
9
3 2 0
Histidine
6 13 1 2 0
Isoleucine
6 13 1 2 0
Leucine
3
7
1 2 1
Cysteine
6 14 2 2 0
Lysine
5 11 1 2 1
Methionine
9 11 1 2 0
Phenylalanine
5
9
1 2 0
Proline
3
7
1 3 0
Serine
4
9
1 3 0
Threonine
9 11 1 3 0
Tyrosine
11 12 2 2 0
Tryptophan
5 11 1 2 0
Valine
Relative carbon %
Average
Soybean
carbon
protein
Ox/AAcarb
oxidation composition
Soybean
Total
%
state
mol %
carbon
carbon
0
7.0
0.21
4.0
0
0.33
6.7
0.40
8.0
0.03
1
12.1
0.48
10.0
0.1
1
*
0.4
16.1
0.80
17.0
0.07
0.4
*
1
7.7
0.15
3.0
0.03
0.66
2.3
0.14
3.0
0.02
-1
4.6
0.28
6.0
-0.06
-1
8.1
0.49
10.0
-0.1
0.66
0.4
0.01
0.0
0.00
-0.66
6.3
0.38
8.0
-0.05
-0.4
0.8
0.04
1.0
-0.00
-0.44
4.1
0.37
8.0
-0.03
-0.4
6.1
0.30
6.0
-0.03
0.66
5.7
0.17
4.0
0.02
0
4.2
0.17
3.0
0
-0.22
1.8
0.16
3.0
-0.00
†
-0.182
-0.8
6.0
0.30
6.0
-0.05
Total:
100.0
4.86
100
-0.06
As an example, the oxidation state of glutamine is calculated as follows using Figure SIIa.: The first carbon, must balance the -2 state of the
carbonyl oxygen, as well as the overall -1 formal charge that remains in the amino group after two hydrogens (+2) are added to the one
nitrogen (-3). Therefore, the first carbon has a redox state of: +3. In a similar fashion the other carbons must balance their connected atoms
and carbons 2 through 5 have redox states of: -2, -2, 0, and +3 respectively. Thus the average redox/oxidative state of glutamine on a per
carbon basis is: +0.4. Similarly, the average oxidation/redox state per carbon can be calculated for all amino acids as well as other organic
compounds. In the next column the reported amino acid composition (mol %) that was experimentally measured is reported. By multiplying
the mol % for each amino acid by the number of carbons contained for that amino acid a fraction of the total protein carbon in each amino
acid is next given (i.e. alanine total carbon is 3 atoms x 7% of total protein = 0.21). The carbon quantities can be rescaled to a percent of total
as reported in the “% carbon” column. Finally, the percent carbon multiplied by the oxidation state gives a contribution of each amino acid to
20
the average oxidation state of protein on a per carbon basis as given in the last column, and summed to give the total average oxidation state
per carbon in protein of -0.06.
* Glutamine and asparagine are converted to glutamate and aspartate during the hydyrolysis process, but this does not impact the
calculation because the oxidation/redox states of the acid and base compounds are equivalent.
†
Tryptophan content in soybean is too low to be accurately quantitated and is therefore not used in this calculation.
21
Similarly, the redox state of triacylglycerol in soybean can be calculated as shown in
Table SIIb.
Table SIIb. Oxidation state for soybean storage triacyglycerol
Description
of chemical
group
CH2
CH3
COOCH
Glycerol
CH2O
CHO
16:0
18:0
18:1
18:2
18:3
Number
of
carbons
14
(for 16:0)
1
1
1
2
1
Relative
Oxidation
oxidation
state/Fatty
contribution/
acidcarbon
fatty acid
Oxidation
state of
carbon
Number
per mole
fatty acid
% of
oil§
-2
-28
-
-
-3
3
-1
-1
0
Total:
-3
3
0 for 16:0
-2
0
-28.66‡
-32.66‡
-30.66‡
-28.66‡
-26.66‡
13.7
3.3
24.4
47.5
10.6
-
-3.93
-0.23
-1.08
-0.06
-7.48
-0.39
-13.62
-0.72
-2.82
-0.15
Total:
-1.55
‡
rd
Note this is the average state for a single fatty acid plus 1/3 of the glycerol
contribution, so that the glycerol backbone is included in the calculation on a per carbon
basis.
§
Measured fatty acid composition for soybean embryos in this work
Oil and protein have net negative oxidation states, relative to the amino acid substrates
(glutamine and asparagine) that have net positive oxidation states. This difference
implies that balancing requires the inclusion of carbon dioxide and oxygen. Carbon
dioxide impacts the redox state of the products dramatically (CO2 has an oxidative state
of +4/carbon). Alternatively, for oxygen consumption, elemental oxygen with oxidative
state of 0 goes to a reduced form of -2 per oxygen (i.e. change of -4 per elemental O2
molecule). Therefore the effect of converting oxygen to water in metabolism has an
oxidizing effect on the redox balance of biomass production and accounts for the
difference in oxidation state between products and substrates.
Calculation of overall redox balance of seed metabolism: Soybean embryos store 18%
oil, 39% protein and 43% carbohydrate by weight. On a carbon basis, these are 26%,
40%, 34%. In addition to this stored carbon, the production of carbon dioxide must be
included. This results in carbon percentages of oil (21%), protein (33%) and CO2 (18%)
with the remainder as carbohydrate on a carbon basis. These are used to calculate the
overall oxidation state of the embryo. Multiplying each of these percentages by its
calculated redox state and summing gives: -0.06x33% + -1.55x21% + 4x18% + 0x28% =
+0.37.
22
Oxidation state of substrates consumed: In a similar fashion the oxidative/redox state of
substrates consumed during embryo metabolism was calculated: sucrose (0), glucose (0),
glutamine (+0.4), and asparagine (+1.0). Measurements of the depletion of the medium
indicated the amount of different substrates used by the seed on a per carbon basis to be:
carbohydrates (78.2%), glutamine (18.3%) and asparagine (3.5%). The oxidation state of
the consumed substrates was calculated per carbon: 0x78.2% + 0.4x18.3% +1.0x3.5% =
+0.11.
The comparison of substrate oxidation state to the embryo products (including carbon
dioxide) indicates the products are more oxidized (i.e. 0.37-0.11= 0.26) and the
difference must be offset by the consumption of oxygen. By multiplying the 0.26
difference in oxidation state by the amount of carbon taken up, the total excess oxidation
(electrons) is calculated at 0.26 x 391 = 102 µmol/day/seed. The conversion of elemental
oxygen to water would require 102/4 = 25µmol of O2 consumed/day/seed. The oxygen
consumption, calculated by redox balancing, is nearly equivalent to the value measured
by both parametric means and GCMS (see main text – value of 28.5 µmol O2
consumed/day seed) and closes the redox balance. Because the balance is in close
agreement with measurements it also serves as a validation for the different gas and
composition flux measurements.
Comparison to Flux model values The flux model presented in the text can also be
partially validated by the oxygen balance measurement. Presuming that all metabolically
generated reductant that is not used in biosynthesis of storage products is oxidized, then
60 µmol reducing equivalents produced would be directed towards electron transport and
would consume half as much oxygen according to the two half-reactions:
NADH  H   NAD   2 H   2e 
1
O2  2 H   2e   H 2 O
2
Therefore, 30 µmol oxygen/day/embryo would be consumed in oxidative
phosphorylation. (A slightly lesser number of 26 µmol oxygen/day/embryo results if
only oxidation of cofactors produced in the citric acid cycle is considered). Furthermore
the desaturation of fatty acids would increase oxygen consumption to a maximum of 33
µmol/day/embryo.
23
Supplement III: ATP Calculations
Calculation of ATP demand for storage product synthesis: The generation of protein,
oil, and carbohydrate require substantial amounts of ATP for polymerization events.
Protein requires 4.3 moles of ATP equivalents for each elongation by one amino acid
(Stephanopoulos et al., 1998), and in fatty acid biosynthesis 1 mole of ATP is required to
extend the fatty acid polymer by a two carbon acetyl group. Additionally, the production
of starch and cell wall requires two ATP equivalents for each polymerization of a hexose
unit (i.e. ADP-glucose pyrophosphorylase. glucokinase/hexokinase/adenyltransferase
reactions). Using these stoichiometries and the measured composition and amounts of
protein, oil and carbohydrate contained in embryo, an estimated 168 μmol of ATP are
required per day per embryo.
Table SIIIa. ATP polymerization events
Description
ATP
equivalents/
polymerization
event
Protein
4.3
Oil
1.0
Carbohydrate
2.0
µmol ATP
Total
equivalents
per embryo
per day
22.8
98.2
35.2
35.2
17.5
35.0
Total:
168.4*
*This estimate represents only ATP demand for polymerization. No accounting is made
for uptake of substrates that are partially active processes. Futile cycling, including
polymer turnover, maintenance of cellular gradients and transport of metabolites across
organelle membranes is also excluded. These processes require ATP and commonly
represent significant burdens for cellular metabolism.
Estimate of maximum ATP production: We can estimate the maximal amount of ATP
produced by oxidative and substrate level phosphorylation. Two independent sets of
culturing experiments with different experimental measurement strategies (paramagnetic
response and GCMS) were used to estimate oxygen consumption. The experimental
methods resulted in approximately 28.5 μmol oxygen consumed per day per embryo,
with 6 μmol attributed to fatty acid desaturation leaving a maximum of 22 μmol oxygen
that could be oxidized for ATP production without the aid of light. The stoichiometry in
equation SIIIa indicates that for every 6 moles of oxygen consumed; one mole of glucose
is converted to 6 moles of carbon dioxide. One mole of glucose oxidation through
glycolysis and TCA results in the production of 30-32 ATP (Garrett and Grisham, 1999).
C 6 H 12O6  6O2  6CO2  6H 2 O
[Eqn SIIIa]
Therefore the complete oxidation of carbon by growing embryos results in approximately
110-117 μmol ATP production per day per embryo (i.e. 22 μmol O2 completely oxidizes
1/6th this amount of glucose or 3.66 μmol of glucose, and this process creates 3.66 x 32 =
117 μmol ATP equivalents). Substrate level phosphorylation is calculated by
24
determining the maximum amount of substrate that can transit glycolysis. Subtracting
the cell wall/starch flux from the measured uptake of carbohydrates that enter glycolysis
(i.e.[glucose uptake (16.9) + 2* sucrose uptake (17.0) – storage carbohydrate production
(17.5)] = 33.5 μmol hexose or 67 μmol triose phosphate. Further subtracting triose that is
used for storage products (i.e. does not get converted from phosphoenolpyruvate to
pyruvate through pyruvate kinase): glycerol (1.30), serine (1.30), glycine (1.76),
cysteine(0.10), phenylalanine (0.94), and tyrosine (0.40) results in 61.2 μmol ATP per
day per embryo. The combination of substrate and oxidative phosphorylation events
would result in 61 + 110 = 171-178 μmol ATP production per day per embryo - only 16% more than the minimal requirements of polymerization for storage compounds. The
small surplus of ATP, does not include significant drains in metabolism like futile cycles
or maintenance and implies that some other source of ATP is necessary.
Comparison to flux model: We have also calculated the ATP balance for our optimal
flux model including the conversion of excess reductant to ATP. Cofactor requirements
for the model are accounted for up to the point of amino acid biosynthesis by the fluxes
of the network, therefore only the storage biosynthetic pathways need to be additionally
addressed here. The redox equivalents consumed in the transamination reactions that
form amino acids are balanced by those produced by the transamination reactions
involved in the consumption of glutamine and asparagine.
Table SIIIb. Redox requirements for biosynthesis of amino acids from primary
metabolism using imported glutamine and asparagine
Precursor(s)
Product(s)
net transaminase Others Overall NAD(P)H
Required
pyruvate (+2)
Alanine (0)
-2
+2
0
0
Glutamate (+2)
Arginine (+2)
0
+2
-2
0
0
Asparagine (+4)
Aspartate (+4)
0
-0
0
Glutamine (+2)
Glutamate (+2)
0
-0
0
Serine (+2)
Glycine (+2)
0
-0
0
P5P (0)
Histidine (+4)
+4
+2
-2
+4
-2
Pyr(+2)
Isoleucine (-6)
-8
+2
-6
3
/Aspartate(+4)
/CO2 (+4)
2Pyr(+4)
Leucine (-6)
-2
+2
0
0
/AcCoA(0)
/2CO2 (+8)
Serine(+2)
Cysteine (+2)
0
-0
0
Pyr(+2)
Lysine (-4)
-6
+2
-4
2
/Asp(+4)
/CO2(+4)
Aspartate (+4)
Methionine
-6
-+2
-4
2
(-2)
E4P (0)/
Phenylalanine
-4
+2
-2
1
2PEP(+4)
(-4)/CO2(+4)
Glutamate (+2)
Proline (-2)
-4
--4
2
PGA(+2)
Serine (+2)
0
+2
+2
-1
Aspartate (+4)
Threonine (0)
-4
--4
2
E4P (0)/
Tyrosine (-2)
-2
+2
0
0
25
2PEP(+4)
2Pyr (+4)
/CO2(+4)
Valine (-4)
/CO2(+4)
-4
+2
-2
1
Glutamine and asparagine are supplied to cultures and therefore not included, tryptophan
concentration is negligible. As an example, the redox difference between the primary
metabolism precursor (pyruvate) and the amino acid (alanine) is (-2), and is offset by the
oxidation of glutamate to alpha ketoglutarate (i.e. transamination). There is no further
requirement for reductant in generating alanine from pyruvate as indicated in the last
column of Table SIIIb. These reductant stoichiometries used with their corresponding
biosynthetic fluxes, along with primary carbon metabolic fluxes that generate or consume
energy or reductant in the model, give an approximate accounting for the ATP status of
the system, but exclude maintenance. If all reductant not used for fatty acid or protein
amino acid biosynthesis is used to make ATP, there remains a significant deficit of
approximately 27-39 μmol ATP per day per embryo. The deficit reflects reported P/O
ratios (Hinkle, 2005). Additionally, the conversion of glutamate to alpha-ketoglutarate in
the model could be used as an alternative to the tranamination events reported in Table
SIIIb, but would result in a slightly greater ATP deficit, making the reported one
conservative. The deficit, that does not include any maintenance activities, implies that
alternate means of generating energy are necessary.
Estimate of ATP from light: To approximate the amount of energy that could be
produced photosynthetically by the embryo, we first determined the surface area of the
embryo by measuring seed dimensions. For a partially opened embryo during culturing,
the embryo shape can be approximated as the combination of four ellipses, giving a total
embryo surface area of roughly 226 mm2 seed-1 during the midpoint of culturing. For
seeds that received 30 µmol photons m-2s-1 during culturing, each embryo would receive
586 µmol of light per day for continuous light provided by culturing, or 342 µmol
photons day-1 for a 14 hour period. Of course in planta, the exposure to light will be
influenced by other parameters including the seed size that changes with growth, pod
location relative to leaf canopy, and the degree of constriction by the pod that could
decrease the surface area by as much as a factor of 2.
The ATP yield by light is a consequence of the amount of cyclic and non-cyclic
photosynthesis that can produce energy as well as reductant. Using a range of
stoichiometries of 1 ATP per 3-4.7 photons (Arnon, 1984; Steigmiller et al., 2008; Zhu et
al., 2008), 586 µmol of photons per day would correspond to 125-195 µmol of ATP per
day per embryo, excluding any reductant production (i.e. NADPH production would lead
to an overall surplus of reductant and require more oxidative electron transport). This
number is roughly equivalent to the demands of storage reserve polymerization and is
significantly higher than the ATP deficit calculated above. Though some light is reflected
and may reduce the actual ATP produced (Long et al., 2006), the embryo may serve as an
efficient sink for incident light that is transmitted through tissues, indicated by the
greenness of even the inner embryo tissues.
26
Supplement IV: OPPP vs. Calvin Cycle
Soybean embryos are green, receive light, and contain significant amounts of
Rubisco in an active enzymatic state (Ruuska et al., 2004) implying that the Calvin cycle
may be active. Also well characterized in the literature is the inactivated state of glucose
6-phosphate dehydrogenase (G6PDH) under these conditions(Werdan et al., 1975;
Buchanan, 1980). Thioredoxin modulates the activity of chloroplast enzymes that are
involved in both pentose phosphate and Calvin cycle activity by altering the catalytic
center conformationally through disulfide bridge formation (Buchanan, 1980). Coupled
with light-induced changes in pH for thylakoid and stroma, pentose phosphate systems
are efficiently governed by multiple biochemical mechanisms (Werdan et al., 1975).
Here we investigated the differences in modeling based upon utilization of OPPP
or Rubisco activity pathway descriptions. Both pathways provide a means for carbon
rearrangements that are necessary to obtain optimal fit of the 13C-glucose labeling data.
Our labeling measurements are equally well fitted by either model. The errors associated
with either model are well distributed and not largely associated with a single
measurement, and the flux values outside the PPP branch are similar (Figure SIVa, SIVb)
reflecting the contribution of many measurements. Moreover, increased
compartmentation and different metabolic descriptions were considered but did not
improve the overall ability to simulate the experimental data. However, the net carbon
dioxide flux measured is much better accounted for by the model that includes Rubisco
activity, supporting this modeling alternative and greatly restricting the flux through
G6PDH for the OPPP model (i.e. in an OPPP model the flux through G6PDH is ~1/10th
of the hexose taken up).
As OPPP produces carbon dioxide while Rubisco assimilates carbon dioxide the
measurement of carbon dioxide is critical to modeling efforts. Here, the carbon dioxide
efflux was measured using a radiolabeling experiment and confirmed through closure of a
redox balance.
27
Sugars
2.3
[-1.1, 5.6]
X5P
E4P
T3P
X5P
R5P
S7P
T3P
44.8
Ru5P
0.5
1.8
1.8
1.8
[-1.2, 2.2]
[0.1, 3.4]
[0.1, 3.4]
[0.1, 3.4]
Cell Wall
Starch
HP
28.5
4.5
[26.5, 30.5]
OPPP
1.2
85.5
[1.1, 1.4]
Glycerol
CO2
C1, CO2
[82.3, 88.7]
[3.7, 4.0]
Gly, Ser, Cys
[0.5, 0.5]
X CO
PGA
Isoleucine
[50.0, 55.9]
2
1.3
[3.9, 6.0]
[1.0, 1.1]
[1.2, 1.4]
PEP
4.9
1.0
45.0
CO2
Phe, Tyr
7.8
[7.5, 8.2]
[43.6, 46.3]
Ala, Val, Leu
CO2
Pyruvate
10.1
Lysine
+ CO2
2.1
[8.9, 11.3]
7.9
[7.6, 8.2]
1.4
[2.0, 2.2]
[35.2, 38.9]
Fatty Acid Elongation
7.7
[7.3, 8.1]
7.7
[7.3, 8.1]
Malate
2.5
[0.2, 1.8]
C1
15.0
0.2
[14.6, 15.6]
[0.1, 0.2]
Methionine
[13.3, 14.1]
7.4
[7.2, 7.6]
15.0
Glutamate
6.2
[5.9, 6.7]
[14.6, 15.6]
Succinate
13.7
CO2
2-oxoglutarate
[1.6, 3.3]
Asx
Glutamine
Isocitrate
[1.3, 1.5]
1.0
Fatty Acids
37.0
CO2
Citrate
CO2
Threonine
Asparagine
His
C1
52.4
CO2
[0.1, 0.1]
0.5
X5P
Ru5P
R5P
[53.7, 58.8]
3.8
[0.8, 1.0]
0.1
S7P
T3P
X5P
E4P
T3P
[0, 9.5]
56.3
0.9
Fatty Acid
Elongation
[42.4, 47.2]
14.0
[11.5, 16.5]
CO2
Pro, Arg, Glx
Fig. SIVa. Flux map with OPPP. Flux model of soybean embryo metabolism
including oxidative pentose phosphate pathway without contribution of Rubisco or
Calvin cycle activity.
28
X5P
E4P
T3P
Sugars
5.5
T3P
T3P
[5.1, 5.8]
X5P
R5P
S7P
44.4
Ru5P
3.4
2.1
2.1
2.1
[3.2, 3.6]
[2.0, 2.2]
[2.0, 2.2]
[2.0, 2.2]
Cell Wall
Starch
HP
[24.5, 26.9]
[1.2, 1.3]
Glycerol
CO2
C1, CO2
[68.9, 75.9]
[3.9, 4.0]
Gly, Ser, Cys
S7P
0.5
X5P
Ru5P
R5P
[0.5, 0.5]
7.0
[6.6, 7.5]
52.7
CO2
1.3
1.0
[4.9, 7.1]
[1.0, 1.1]
[1.2, 1.4]
PEP
6.0
44.2
CO2
Phe, Tyr
7.8
[7.5, 8.2]
[42.7, 45.7]
Ala, Val, Leu
CO2
Pyruvate
10.7
Lysine
+ CO2
2.1
[9.5, 11.9]
7.7
[7.2, 8.2]
1.4
[2.0, 2.2]
[35.2, 38.9]
Fatty Acid Elongation
7.5
[7.0, 8.0]
7.5
Malate
[7.0, 8.0]
2.6
[0.9, 1.5]
C1
14.4
0.2
[13.6, 15.0]
[0.1, 0.2]
Methionine
[12.8, 13.7]
6.9
[6.7, 7.0]
14.4
Glutamate
6.3
[5.9, 6.7]
[13.6, 15.0]
Succinate
13.2
CO2
2-oxoglutarate
[2.2, 3.0]
Asx
Glutamine
Isocitrate
[1.3, 1.5]
1.2
Fatty Acids
37.0
CO2
Citrate
CO2
Threonine
Asparagine
His
C1
Calvin Cycle
[50.1, 55.4]
CO2
[0.1, 0.1]
T3P
PGA
Isoleucine
0.1
X5P
E4P
[40.5, 44.7]
3.9
[0.9, 1.1]
Fatty Acid
Elongation
T3P
T3P
42.6
1.0
X
25.7
1.3
72.4
[42.6, 46.2]
15.3
[13.6, 17.1]
CO2
Pro, Arg, Glx
Fig. SIVb. Flux map with Rubisco activity. Flux model including Rubisco and
Calvin cycle activity without the oxidative component of pentose phosphate pathways.
29
Supplement V: Metabolic Network
The network is described as a set of reactions. Each reaction or flux name is given
in the first column followed in subsequent columns by the substrates and products
involved in the reaction. The individual carbon atoms are positionally mapped from the
substrates to products by tracking them using alphabetical representation. For glucose
(GLCU) that contains six carbons, the letter “A” in the representation ABCDEF
represents the first of 6 carbons. In the glucose uptake reaction (uptU), the first carbon of
glucose is converted to the first carbon positionally in hexose phosphate (HP), therefore
the HP representation (ABCDEF) also maintains “A” in the first position. For reactions
with multiple substrates or products the reactions are mapped in a case sensitive manner.
For further details to the software implementation see (Wiechert et al., 2001)
NETWORK
FLUX NAME Substrate 1
// Input
uptU
Substrate 2
Product 1
GLCU
#ABCDEF
HP
#ABCDEF
SuSINVg
SUCR
#ABCDEF
HP
#ABCDEF
SuSINVf
SUCR
#ABCDEF
HP
#ABCDEF
uptGln
GLNex
#ABCDE
GLN
#ABCDE
uptAsn
ASNex
#ABCD
ASN
#ABCD
// Embden Meyerhof Pathway
ALDO
HP
#ABCDEF
TP
#CBA
GAPDH
TP
#ABC
PGA
#ABC
GLYC
TP
#ABC
Glycer
#ABC
ENO
PGA
#ABC
PEP
#ABC
30
Product 2
TP
#DEF
PEPC
PEP
#ABC
CO2
#a
MOAA
#ABCa
ME
MOAA
#ABCD
PYR
#ABC
PK
PEP
#ABC
PYR
#ABC
// Glutamine metabolism
GLXn
GLN
#ABCDE
GLX
#ABCDE
GLXu
GLU
#ABCDE
GLX
#ABCDE
GS
GLN
#ABCDE
GLU
#ABCDE
GLXout
GLX
#ABCDE
GLXout
#ABCDE
GDH
GLU
#ABCDE
aKG
#ABCDE
PRO
GLU
#ABCDE
PRO
#ABCDE
ARG
CO2
#A
ARGout
ARG
#ABCDEF
// ppp
TK1
CO2
#D
GLU
#abcde
ARG
#abcdeA
ARGout
#ABCDEF
S7P
#abcdefg
TP
#CDE
XP
#abCDE
TA
E4P
#DEFG
TP
#CBA
S7P
#ABCDEFG
TK2
TP
#CDE
HP
#abcdef
XP
#abCDE
PPISO
RP
#ABCDE
RuP
#ABCDE
31
RP
#cdefg
E4P
#cdef
PPEPI
XP
#ABCDE
Rub
RuP
#ABCDE
//AcCoA
PDH
CO2
#a
PYR
#ABC
FA
ACA
#AB
FAtwo
ACAtwo
#ABCD
//TCA
CitSyn
RuP
#ABCDE
PYR
#ABC
ACA
#ab
PGA
#aBA
PGA
#CDE
ACA
#BC
CO2
#A
ACAtwo
#ABab
FAtwout
#ABCD
MOAA
#abcd
Cit
#BCbcda
CO2
#A
Aco
Cit
#ABCDEF
Icit
#EDCBAF
ICitDH
Icit
#ABCDEF
aKG
#ABCDE
CO2
#F
ACL
Cit
#ABCDEF
MOAA
#FCDE
cACA
#AB
FAE
cACA
#AB
FAEout
cACA2
#ABCD
cACA2out
#ABCD
aKGDH
aKG
#ABCDE
SUCC
#BCDE
CO2
#A
aKGDH2
aKG
#ABCDE
SUCC
#EDCB
CO2
#A
FUM
SUCC
#ABCD
MOAA
#ABCD
FUM2
SUCC
MOAA
cACA
#ab
cACA2
#ABab
32
#ABCD
#DCBA
//amino acid products
SER
PGA
#ABC
SER
#ABC
SERout
SER
#ABC
SERout
#ABC
CYS
SER
#ABC
CYS
#ABC
CYSout
CYS
#ABC
CYSout
#ABC
aKIV
PYR
#ABC
VALout
aKIV
#ABCDE
LEU
aKIV
#ABCDE
LEUout
LEU
#ABCDEF
LEUout
#ABCDEF
CW
HP
#ABCDEF
CW
#ABCDEF
PCR
RuP
#ABCDE
HISout
HIS
#ABCDEF
Shikim
E4P
#ABCD
PEP
#abc
Shikimat
#abcABCD
Shikim2
E4P
#ABCD
PEP
#abc
Shikimat
#abDCBAc
Aromat
Shikimat
#ABCDEFG
PEP
#abc
Aro
CO2
#abcBCDEFG #A
Aroout
Aro
PYR
#abc
aKIV
#abBCc
CO2
#A
VALout
#ABCDE
ACA
#ab
LEU
#abBCDE
C1
#a
CO2
#A
HIS
#EDCBAa
HISout
#ABCDEF
Aroout
33
#ABCDEFG
HI
#ABCDEFG
HI
DHP1
MOAA
#ABCD
PYR
#abc
LYS
#ABCDcb
CO2
#a
DHP2
MOAA
#ABCD
PYR
#abc
LYS
#abcDCB
CO2
#A
LYSout
LYS
#ABCDEF
LYSout
#ABCDEF
THR
MOAA
#ABCD
THR
#ABCD
THRout
THR
#ABCD
THRout
#ABCD
THRALDO
THR
#ABCD
GLY
#AB
MET
MOAA
#ABCD
METout
MET
#ABCDE
METout
#ABCDE
ASP
ASN
#ABCD
MOAA
#ABCD
ASXn
ASN
#ABCD
ASX
#ABCD
ASXp
MOAA
#ABCD
ASX
#ABCD
ASXout
ASX
#ABCD
ASXout
#ABCD
ILE
THR
#ABCD
ILEout
ILE
#ABCDEF
ILEout
#ABCDEF
ALA
PYR
ALA
C1
#a
cACA
#CD
MET
#ABCDa
PYR
#abc
ILE
#ABbCDc
34
CO2
#a
#ABC
#ABC
ALAout
ALA
#ABC
ALAout
#ABC
C1metab
C1
#A
C1ext
#A
GLY
SER
#ABC
GLY
#AB
C1
#C
GLYD
GLY
#AB
C1
#B
CO2
#A
GLYout
GLY
#AB
GLYout
#AB
CO2
#A
CO2_ext
#A
// CO2 output
CO2_out
35
Supplement VI: Extended Experimental Procedures
Plant Material and Culturing Conditions
Soybean seeds, cv Amsoy were potted in 3:1 (v:v) mixture of soil and vermiculite
(Therm-O-Rock, New Eagle, PA) and grown in a greenhouse at 27°C. Greenhouse
conditions, seed harvest, and aseptic culturing have been previously described (Allen et
al. 2007). Briefly, pods harvested from plants (R5-R5.5 stage) were surface sterilized
using 5% sodium hypochlorite for several minutes and thoroughly rinsed in water. Seed
dissection and immediate transfer of cotyledons to medium occurred aseptically under a
laminar flow bench. Each culture contained one cotyledon and 15mL of filter-sterilized
medium in a 250mL Erlenmeyer flask that was capped with a foam plug and maintained
in a growth chamber at 27°C under continuous light (30-35μE m-2s-1) to develop steady
state conditions for seed expansion metabolism. Culturing experiments were performed
in triplicate or more for each condition. In several instances, to increase the amount of
available biomass for analysis, three randomly chosen embryo cultures were combined
after culturing, therefore requiring nine cultures initially to get triplicate results.
Growth media contained inorganic constituents according to a modified
Linsamaier-Skoog medium (Thompson et al., 1977; Hsu and Obendorf, 1982) along with
Gamborg’s vitamins. Specifically, medium contained: 8mM MgSO4, 10mM KCl, 3mM
CaCl2, 1.25mM KH2PO4, 0.5mM MnSO4, 0.15mM ZnSO3, 0.1mM Na2EDTA, 0.1mM
FeSO4, 0.1mM H3BO3, 27μM glycine, 2.5μM CuSO3, 5μM KI, 1μM Na2MoO4, 4μM
nicotinic acid, 1μM thiamine HCl, 0.5μM pyridoxine HCl, 0.56mM myo-inositol and
5mM MES to buffer the final solution to ph 5.8. Sucrose, glucose, glutamine and
asparagine were provided at levels of 140mM, 70mM, 35mM and 12.6mM respectively.
As necessary, 100% [U-13C6] glucose or [U-13C5] glutamine carbon forms replaced
unlabeled forms for respective stable isotope labeling experiments. Incubation for a 14
day period at 27°C ensured 3.5-4 doublings of biomass in all cultures. Harvested
cotyledons were rinsed in water, sliced in to small chunks to facilitate drying and
immediately frozen in liquid nitrogen and lyophilized for 72 hrs at -80°C and 1.0 Pa and
stored at -20°C until further use.
For the radiolabeled carbon balance study, cultures were pre-incubated in
unlabeled medium for 5 days and then transferred aseptically to equivalently radiolabeled
medium and maintained under gas tight conditions using 250 mL Erlenmeyer flasks
sealed with septum closures following the methods of Goffman et al. 2005. Briefly,
uniformly 14C-labeled carbon sources were added to each flask to establish a 10 μCi/flask
total radioactivity, with molar adjustment so that carbon atoms in the medium were
equivalently represented by 14C carbon (specific activity = 0.29 mCi/mol carbon). The
cultures were maintained in this environment for 5 days and the depletion of oxygen was
monitored. Since levels of up to 2.5% carbon dioxide have been documented in the seed
atmosphere (Johnson-Flanagan and Spencer, 1994; King et al., 1998; Goffman et al.,
2004; Goffman et al., 2005). IRGA measurements of less than this amount of carbon
dioxide accumulation were deemed acceptable.
36
CO2 Capture from 14C labeled substrates
Immediately after culturing the flasks were placed on ice, and the pH was dropped
by addition of 1mL 0.2N HCL to stop metabolism and release inorganic carbon to the
flask headspace. The CO2 was captured by flushing the flasks with nitrogen for several
hours, collecting the exhausted CO2 in a 250 mL gas washing bottle filled with 140 mL
of 1 N KOH (see Goffman et al. 2005 for more details). After checking with IRGA that
all CO2 had been captured, the seeds were rinsed, sliced, and lyophilized and the l4C-label
depletion within the medium as well as the captured CO2 in KOH solution was measured
by scintillation counting. For all radiolabeling measurements, samples were prepared
with combination of 10-15ml scintillation cocktail and analysis was duplicated with
correction for quenching and background. Note, the CO2 measurement reflects the net
production of all carbon dioxide during the labeling experiment and is not merely a
reflection of carbon dioxide exchange events between the embryo and the flask
headspace, therefore it is indeed the true flux to carbon dioxide production. The carbon
conversion efficiency (CCE) was calculated by two methods. First scintillation counting
of 14CO2 was compared to the change in counts for taken up carbon substrates. Carbon
uptake was calculated from the difference in initial and final radiolabel counts of culture
medium. Of the carbon uptake, the radiolabel not accounted for by carbon dioxide, as a
percentage, represents the conversion to biomass (i.e. % CCE = [1-14CO2/14C uptake] x
100). Second the direct measurement of biomass was experimentally determined with
scintillation counting and used for CCE calculation as: % CCE= [14C biomass x 100]/[14C
biomass + 14CO2]. The average and standard deviation of these two methods is reported.
Preparation and Quantitation of Analytes
Oil was removed with repeated extractions of 2:1 hexane:isopropanol, dried under
nitrogen at 45°C and either redissolved in dueterated chloroform for NMR analysis or
derivatized to FAME for lipid quantitation via GC-FID or derivatized to the butyl amide
(Kopka et al., 1995; Allen et al., 2007) for evaluation of fatty acetyl carbon labeling.
Seed oil content was established from fatty acid methyl esters (FAME) generated by
transmethylation with triheptadecanoin internal standard. FAME were quantified by gas
chromatography (GC) using a DB-23 capillary column (30 m length x 0.25 mm inner
diameter, 0.25 µ film thickness; J&W) coupled to flame ionization detection.
Alcohol soluble components including: free amino acids, sugars and organic acids
were extracted with an aqueous 80% ethanol solution and separated using cation (Dowex
50) and anion (Dowex 1) exchange resin.
Protein quantification was measured by C:N (Duke Environmental Stable Isotope
Laboratory) and amino acid analysis at MSU Macromolecular Structure, Sequencing and
Synthesis Facility. Labeling patterns in proteinaceous-amino acids were determined as
follows. First proteins were extracted in a 10mM tris (pH 8.0) buffer containing 138mM
NaCl, 2.7mM KCl, 1mM EDTA, 10mM 2-mercaptoethanol, 0.02% sodium azide and
0.0125% sodium dodecyl sulfate, then precipitated with 5% TCA, hydrolyzed with HCl
under vacuum at 150°C for 1.5 hours or 24 hours at 110°C at atmospheric conditions and
derivatized to the TBDMS-derivatives (Das Neves and Vasconcelos, 1987).
Radiolabeled amino acids were separated on a Hitachi L-8800 HPLC analyzer
using a gradient of lithium buffers and detected by ninhydrin reaction. Fractions were
collected for each peak and analyzed by scinitillation counting. All recorded
37
concentrations were within the linear range of the instrument and specific activity was
determined by interpolation from a set of standards at 5 different concentrations for each
amino acid. To account for differences in metabolism for the differing light conditions,
the specific activities were scaled for the intracellular bicarbonate that was established as
the difference between radioactive counts for arginine and glutamate.
Starch was extracted at 121°C in 5ml of 0.1M acetate buffer (pH 4.8) and
enzymatically digested to release glucose units followed by derivatization to
monoacetone glucose form for NMR or to the alditol acetates for GCMS analysis (Allen
et al., 2007). Cell wall that remained in the pellet was hydrolyzed by trifluoracetic acid
and also analyzed as the alditol acetate derivative (Allen et al., 2007).
GC/MS Analysis of Analytes
GC/MS analyses were performed at the MSU Mass Spectrometry Facility as
described previously (Allen et al., 2007). Typical conditions included helium as a carrier
gas at 1 mL/min with 30m x 0.25mm DB23 or DB5 columns for lipid or amino acid
analyses. Sugar acetates were processed on a SP2330 column (J&W). Fragment
candidates for isotopomer quantitation were chosen based upon several criteria. All
selected fragments were distinct from other overlapping fragments, correctly reflected
labeling in monitored standards, and could be derived compositionally by simple losses
from the original compound that had a retention time consistent with standards for that
compound. All ions of interest were selectively monitored (SIM) and several
concentrations of each sample were tested to ensure linear dynamic operation of the
instrument before quantitation. Correction for the occurrence of heavy isotopes in the
derivative and heteroatoms was performed as described by others (Dauner and Sauer,
2000; van Winden et al., 2002). Duplicate technical replicates were performed for all
analyses.
NMR Analysis of Analytes
One dimensional 1H-NMR and 13C-NMR was performed for lipids, derivatized
starch, and measurement of the medium depletion using a Varian Unity Plus 500 MHz
instrument. Relaxation times of 20 seconds and gated decoupling were standardly used
to ensure full recovery of all carbon groups and 1000-2000 scans generated during an
overnight operation of 12 hours was sufficient for data quantitation. Further details on
NMR methods can be found in (Allen et al., 2007).
38
Supplement VII: Flux Analysis in Complex Systems
Most prokaryotic MFA studies use a single labeling experiment to estimate
fluxes. However this may not be adequate for complex systems and/or more detailed
networks (Schwender et al., 2006; Libourel et al., 2007; Chang et al., 2008), a fact that is
not obvious as confidence intervals are often not established rigorously (Antoniewicz et
al., 2006). Obtaining measurements of sufficient quality, number, and independence is
important to making estimates of fluxes throughout the network (van der Heijden et al.,
1994; Isermann and Wiechert, 2003; Klapa et al., 2003). Accordingly, we used multiple
labeling experiments with labeled carbon precursors entering metabolism at different
nodes and measurements of a wide range of metabolic products (707 label measurements
plus 48 uptake and efflux measurements) to create a highly over-determined, system to
better identify flux values throughout the network (Allen et al., 2007). Additional
measurements/calculations were reserved for independent validation, as this can identify
errors and check consistency (Shirai et al., 2006).
Using measurements to establish fluxes is a typical example of an inverse
problem (Isermann and Wiechert, 2003) that is solved numerically and results in a best fit
solution in overdetermined cases. One cannot be sure that a best fit solution is a global
minimum so we used 600 unique feasible starting points to explore the solution space for
each of the replicate experimental datasets (n=3). The differences among the three
resulting optimized flux maps therefore represent both biological variation between
samples as well as any technical differences (see methods).
Two previous MFA studies have documented fluxes through primary metabolism
of soybean, (Sriram et al., 2004; Iyer et al., 2008). Our work considers the role of light
and recently documented role of Rubisco (Schwender et al., 2004) utilizing a highly
overdetermined system of multiple labeling experiments and flux measurements. In
contrast with our findings, the previous studies concluded that there were substantial
fluxes through the OPPP rather than Calvin cycle enzymes, and that malate made no
significant contribution to fatty acid synthesis. We found that without using the data from
13
C sugar and 13C amino acid labeling experiments and the direct measurement of CO2
efflux, it was not possible to resolve these alternate metabolic routes. Similar difficulties
in identifying anaplerotic fluxes from a single labeling experiment have been previously
noted (Klapa et al., 2003). As discussed in Supplement IV, using an alternative model
including OPPP but without Rubisco activity leads to a G6PDH flux of 4.5 µmol day-1
embryo-1, which is ~10% of sugar uptake. Thus our modeling suggests that OPPP flux is
not a major source of NADPH, with NAD(P)-GAPDH (Muller, 1970) and malic enzyme
(Smith et al., 1992; Pleite et al., 2005) likely meeting this need.
Clearly, discernment of reversible pentose phosphate fluxes alone is a challenging
topic given the multiple reactions catalyzed by transketolase and transaldolase enzymes
(Williams et al., 1987). The difficulties are exacerbated by the oxidative portion of the
pathway and Rubisco fluxes that require novel measurements (such as the CO2 efflux)
that are more challenging to obtain accurately. Further metabolic resolution of this region
in heterotrophic tissues will warrant continued exploration with unique labels that do not
adversely impact cellular metabolism.
39
Supplement VIII: Potential Limitations of Using MFA to Analyze
Plant Tissues
In vitro culture conditions are an imperfect mimic of in planta growth. Here, the
culture conditions are based on analyses of the light and substrates available to soybean
embryos in planta. It has recently been shown in rapeseed that the bulk endosperm may
itself be sub-compartmentalized, and therefore not accurately represent the substrates
provided to the embryo (Morley-Smith et al., 2008). However, since developing
soybeans do not have a liquid endosperm during the period of seed filling analyzed here,
this concern is reduced, supporting the use of culture medium whose composition is
based on apoplastic fluid analyses.
Different light levels result in different patterns of metabolism in green seeds;
(this study and (Goffman et al., 2005)). However the use of MFA requires metabolic
steady state so embryos were cultured under continuous light and constant temperature.
Embryos cultured under these conditions had growth rates and compositions very similar
to ones grown in planta; so it is likely that the patterns of flux through central
metabolism are similar to those in planta.
In addition modeling tissue metabolism neglects cellular heterogeneity. Others
(Borisjuk et al., 2005) have reported and we have confirmed spatial gradients for storage
products by soybeans (data not shown). However, our anlaysis is limited to early to midstorage seed development that is consistent with the uniform spatial quantum yields for
this time frame established by others (Borisjuk et al., 2005). To the extent that gradients
in metabolism represent light or oxygen availability, these will be reduced for cultured
cotyledons that are partially opened, and receive light from multiple directions.
Nonetheless, analysis of the whole embryos must represent an average of cells.
MFA in eukaryotes employs simplifications of network structure, sacrificing
detailed biological verisimilitude to obtain a model that aims to capture essential features
and whose parameters (the fluxes) can be well defined by the data (Schmidt et al., 1999).
Often this generalization does not practically impact the results (Hellerstein and Neese,
1999), particularly when it is supported by labeling data that shows nearly equilibrated
pools. Thus compartmentation of metabolism between hexose and triose pools was
omitted due to the similarity of labeling in plastidic and cytosolic carbohydrate pools.
Under high light conditions, labeling differences in cytosolic and plastidic carbohydrate
pools were enhanced in the work of Sriram et al. (2004), and with the exclusion of
Rubisco allowed a more compartmented model for the hexose and pentose pools in
glycolysis. In our model, adding parallel fluxes (i.e. plastidic/cytosolic OPP and
glycolysis) did not improve the fit to the data, and reduced flux identifiability.
We also considered the possible transient nature of starch in soybean seed
metabolism. The model was robust to the removal of starch measurements and/or other
label measurements, reflected by only insignificant changes in the resulting flux map. As
the amount of sugars taken up is much greater than any starch produced, the dilution of a
hexose phosphate pool by recycled starch is anticipated to be small. Moreover, the
similar labeling in starch, cell wall and protein glycan pools indicated that these pools are
made from precursor pools who’s labeling is the same and should be modeled together.
Given that carbohydrates such as cell walls do not turnover significantly, it is unlikely
that starch turnover is a source of error for flux analysis. Modeled in this way, they
40
represent a redundant set of measurements, further resolution of starch turnover events
would not be resolved from our labeling data and thus our model would not report or be
largely impacted by this process.
41
References
Allen, D.K., Shachar-Hill, Y. and Ohlrogge, J.B. (2007) Compartment-specific
labeling information in 13C metabolic flux analysis of plants. Phytochemistry, 68,
2197-2210.
Antoniewicz, M.R., Kelleher, J.K. and Stephanopoulos, G. (2006) Determination of
confidence intervals of metabolic fluxes estimated from stable isotope
measurements. Metab. Eng., 8, 324-337.
Antoniewicz, M.R., Kelleher, J.K. and Stephanopoulos, G. (2007) Accurate
assessment of amino acid mass isotopomer distributions for metabolic flux
analysis. Anal. Chem., 79, 7554-7559.
Arnon, D.I. (1984) The discovery of photosynthetic phosphorylation. Trends Biochem.
Sci., 9, 258-262.
Borisjuk, L., Nguyen, T.H., Neuberger, T., Rutten, T., Tschiersch, H., Claus, B.,
Feussner, I., Webb, A.G., Jakob, P., Weber, H., Wobus, U. and Rolletschek,
H. (2005) Gradients of lipid storage, photosynthesis and plastid differentiation in
developing soybean seeds. New Phytol., 167, 761-776.
Buchanan, B.B. (1980) Role of light in the regulation of chloroplast enzymes. Annu.
Rev. Plant Physiol. Plant Mol. Biol., 31, 341-374.
Chang, Y., Suthers, P.F. and Maranas, C.D. (2008) Identification of optimal
measurement sets for complete flux elucidation in metabolic flux analysis
experiments. Biotechnol. Bioeng., 100, 1039-1049.
Das Neves, H.J.C. and Vasconcelos, A.M.P. (1987) Capillary gas-chromatography of
amino-acids, including asparagine and glutamine - sensitive gas-chromatographic
mass-spectrometric and selected ion monitoring gas-chromatographic massspectrometric detection of the N,O(S)-tert-butyldimethylsilyl derivatives. Journal
of Chromatography, 392, 249-258.
Dauner, M. and Sauer, U. (2000) GC-MS analysis of amino acids rapidly provides rich
information for isotopomer balancing. Biotechnol. Prog., 16, 642-649.
Egli, D.B., Guffy, R.D., Meckel, L.W. and Leggett, J.E. (1985) The effect of source
sink alterations on soybean seed growth. Ann. Bot., 55, 395-402.
Fehr, W.R., Thorne, J.C. and Hammond, E.G. (1971) Relationship of fatty acid
formation and chlorophyll content in soybean seed. Crop Sci., 11, 211-&.
42
Garrett, R.H. and Grisham, C.M. (1999) Biochemistry, Second edn. Fort Worth:
Saunders College Publishing.
Goffman, F.D., Ruckle, M., Ohlrogge, J. and Shachar-Hill, Y. (2004) Carbon dioxide
concentrations are very high in developing oilseeds. Plant Physiol. Biochem., 42,
703-708.
Goffman, F.D., Alonso, A.P., Schwender, J., Shachar-Hill, Y. and Ohlrogge, J.B.
(2005) Light enables a very high efficiency of carbon storage in developing
embryos of rapeseed. Plant Physiol., 138, 2269-2279.
Hellerstein, M.K. and Neese, R.A. (1999) Mass isotopomer distribution analysis at eight
years: Theoretical, analytic, and experimental considerations. Am. J. Physiol.
Endocrinol. Metab., 276, E1146-E1170.
Hinkle, P.C. (2005) P/O ratios of mitochondrial oxidative phosphorylation. Biochim.
Biophys. Acta, Bioenerg., 1706, 1-11.
Hsu, F.C. and Obendorf, R.L. (1982) Compositional analysis of in vitro matured
soybean seeds. Plant Sci. Lett., 27, 129-135.
Isermann, N. and Wiechert, W. (2003) Metabolic isotopomer labeling systems. Part II:
Structural flux identifiability analysis. Math. Biosci., 183, 175-214.
Iyer, V.V., Sriram, G., Fulton, D.B., Zhou, R., Westgate, M.E. and Shanks, J.V.
(2008) Metabolic flux maps comparing the effect of temperature on protein and
oil biosynthesis in developing soybean cotyledons. Plant Cell Environ., 31, 506517.
Johnson-Flanagan, A.M. and Spencer, M.S. (1994) Ethylene production during
development of mustard (brassica-juncea) and canola (brassica-napus) seed.
Plant Physiol., 106, 601-606.
King, S.P., Badger, M.R. and Furbank, R.T. (1998) CO2 refixation characteristics of
developing canola seeds and silique wall. Aust. J. Plant Physiol., 25, 377-386.
Klapa, M.I., Aon, J.C. and Stephanopoulos, G. (2003) Systematic quantification of
complex metabolic flux networks using stable isotopes and mass spectrometry.
Eur. J. Biochem., 270, 3525-3542.
Kopka, J., Ohlrogge, J.B. and Jaworski, J.G. (1995) Analysis of in-vivo levels of acylthioesters with gas-chromatography mass-spectrometry of the butylamide
derivative. Anal. Biochem., 224, 51-60.
43
Libourel, I.G.L., Gehan, J.P. and Shachar-Hill, Y. (2007) Design of substrate label for
steady state flux measurements in plant systems using the metabolic network of
brassica napus embryos. Phytochemistry, 68, 2211-2221.
Long, S.P., Zhu, X.G., Naidu, S.L. and Ort, D.R. (2006) Can improvement in
photosynthesis increase crop yields? Plant Cell Environ., 29, 315-330.
Morley-Smith, E.R., Pike, M.J., Findlay, K., Koeckenberger, W., Hill, L.M., Smith,
A.M. and Rawsthorne, S. (2008) The transport of sugars to developing embryos
is not via the bulk endosperm in oilseed rape seeds. Plant Physiol., 147, 21212130.
Muller, B. (1970) On mechanism of light-induced activation of NADP-dependent
glyceraldehyde phosphate dehydrogenase. Biochim. Biophys. Acta, 205, 102-&.
Pleite, R., Pike, M.J., Garces, R., Martinez-Force, E. and Rawsthorne, S. (2005) The
sources of carbon and reducing power for fatty acid synthesis in the heterotrophic
plastids of developing sunflower (helianthus annuus l.) embryos. J. Exp. Bot., 56,
1297-1303.
Rubel, A., Rinne, R.W. and Canvin, D.T. (1972) Protein, oil, and fatty-acid in
developing soybean seeds. Crop Sci., 12, 739-741.
Ruuska, S.A., Schwender, J. and Ohlrogge, J.B. (2004) The capacity of green oilseeds
to utilize photosynthesis to drive biosynthetic processes. Plant Physiol., 136,
2700-2709.
Schmidt, K., Nørregaard, L.C., Pedersen, B., Meissner, A., Duus, J.Ø., Nielsen, J.Ø.
and Villadsen, J. (1999) Quantification of intracellular metabolic fluxes from
fractional enrichment and 13C-13C coupling constraints on the isotopomer
distribution in labeled biomass components. Metab. Eng., 1, 166-179.
Schwender, J., Goffman, F.D., Ohlrogge, J.B. and Shachar-Hill, Y. (2004) Rubisco
without the Calvin cycle improves the carbon efficiency of developing green
seeds. Nature, 432, 779-782.
Schwender, J., Shachar-Hill, Y. and Ohlrogge, J.B. (2006) Mitochondrial metabolism
in developing embryos of Brassica napus. J. Biol. Chem., 281, 34040-34047.
Shirai, T., Matsuzaki, K., Kuzumoto, M., Nagahisa, K., Furusawa, C., Shioya, S.
and Shimizu, H. (2006) Precise metabolic flux analysis of Coryneform bacteria
by gas chromatography-mass spectrometry and verification by nuclear magnetic
resonance. J. Biosci. Bioeng., 102, 413-424.
44
Smith, R.G., Gauthier, D.A., Dennis, D.T. and Turpin, D.H. (1992) Malate-dependent
and pyruvate-dependent fatty-acid synthesis in leukoplastids from developing
castor endosperm. Plant Physiol., 98, 1233-1238.
Sriram, G., Fulton, D.B., Iyer, V.V., Peterson, J.M., Zhou, R.L., Westgate, M.E.,
Spalding, M.H. and Shanks, J.V. (2004) Quantification of compartmented
metabolic fluxes in developing soybean embryos by employing biosynthetic ally
directed fractional C-13 labeling, [C-13, H-1] two-dimensional nuclear magnetic
resonance, and comprehensive isotopomer balancing. Plant Physiol., 136, 30433057.
Steigmiller, S., Turina, P. and Graber, P. (2008) The thermodynamic H+/ATP ratios of
the H+-atpsynthases from chloroplasts and Escherichia coli. Proc. Natl. Acad.
Sci. U. S. A., 105, 3745-3750.
Stephanopoulos, G., Aristidou, A. and Nielsen, J. (1998) Metabolic engineering:
Principles and methodologies. San Diego: Academic Press.
Thompson, J.F., Madison, J.T. and Muenster, A.M.E. (1977) In vitro culture of
immature cotyledons of soya bean (Glycine-max-L-merr). Ann. Bot., 41, 29-39.
van der Heijden, R.T.J.M., Heijnen, J.J., Hellinga, C., Romein, B. and Luyben,
K.C.A.M. (1994) Linear constraint relations in biochemical reaction systems: I.
Classification of the calculability and the balanceability of conversion rates.
Biotechnol. Bioeng., 43, 3-10.
van Winden, W.A., Wittmann, C., Heinzle, E. and Heijnen, J.J. (2002) Correcting
mass isotopomer distributions for naturally occurring isotopes. Biotechnol.
Bioeng., 80, 477-479.
Werdan, K., Heldt, H.W. and Milovancev, M. (1975) Role of pH in regulation of
carbon fixation in chloroplast stroma - studies on CO2 fixation in light and dark.
Biochim. Biophys. Acta, 396, 276-292.
Wiechert, W., Mollney, M., Isermann, N., Wurzel, W. and de Graaf, A.A. (1999)
Bidirectional reaction steps in metabolic networks: III. Explicit solution and
analysis of isotopomer labeling systems. Biotechnol. Bioeng., 66, 69-85.
Wiechert, W., Mollney, M., Petersen, S. and de Graaf, A.A. (2001) A universal
framework for C-13 metabolic flux analysis. Metab. Eng., 3, 265-283.
Williams, J.F., Arora, K.K. and Longenecker, J.P. (1987) The pentose pathway - a
random harvest - impediments which oppose acceptance of the classical (F-type)
pentose cycle for liver, some neoplasms and photosynthetic tissue - the case for
the L-type pentose pathway. Int. J. Biochem., 19, 749-817.
45
Yazdi-Samadi, B., Rinne, R.W. and Seif, R.D. (1977) Components of developing
soybean seeds - oil, protein, sugars, starch, organic-acids, and amino-acids.
Agron. J., 69, 481-486.
Zhu, X.G., Long, S.P. and Ort, D.R. (2008) What is the maximum efficiency with
which photosynthesis can convert solar energy into biomass? Curr. Opin.
Biotechnol., 19, 153-159.
46