SUPPORTING INFORMATION
for
Assessing Metabolomic and Chemical Diversity of a Soybean
Lineage Representing 35 Years of Breeding
Miyako Kusano1, Ivan Baxter2,*, Atsushi Fukushima1, Akira Oikawa1, Yozo Okazaki1, Ryo
Nakabayashi1, Denise J. Bouvrette2, Frederic Achard3, Andrew R. Jakubowski3, Joan M.
Ballam3, Jonathan R. Philips3, Angela H. Culler3, Kazuki Saito1,4,*, George G. Harrigan3,*
1
RIKEN Center for Sustainable Resource Science, Tsunumi-ku, Yokohama 230-00405, Japan.
2
Agricultural Research Service Plant Genetics Research Unit, Donald Danforth Plant Science
Center, United States Department of Agriculture, St. Louis, MO 63132, USA
3
Monsanto Company, 800 North Lindbergh Boulevard; St. Louis, MO 63167, USA.
4Graduate School of Pharmaceutical Science, Chiba University, Chuo-ku, Chiba 260-8675,
Japan.
.
GENOTYPE DATA
SUPPORTING TABLE 1. SIMILARITY MATRIX
CX375
(A3733/
CX329)
CX366
A3469
A3127
WILLIAMS
AG3701
AG3705
A3555
AG3803
100%
86%
68%
87%
82%
68%
73%
70%
73%
86%
100%
75%
89%
90%
75%
80%
77%
79%
A3469
68%
75%
100%
77%
71%
82%
83%
83%
81%
A3127
87%
89%
77%
100%
80%
80%
80%
76%
78%
WILLIAMS
82%
90%
71%
80%
100%
71%
77%
76%
77%
AG3701
68%
75%
82%
80%
71%
100%
78%
75%
80%
AG3705
73%
80%
83%
80%
77%
78%
100%
80%
80%
A3555
70%
77%
83%
76%
76%
75%
80%
100%
88%
AG3803
73%
79%
81%
78%
77%
80%
80%
88%
100%
CX375
(A3733/
CX329)
CX366
Genetic similarity of different soybean varieties based on genetic fingerprint data
Supporting Figure A (Genotype Data)
Supporting Figure A. PCA (principal components one and two) based on the genotypic data of 1,484
pre-commercial and commercial proprietary Monsanto lines. CX375 (A3733/CX329) was not included
due to missing data. Principal component one (PC1) explained 8.9% of the variability, while principal
component two (PC2) explained 7.4 %. Color coding refers to the maturity group for each line; Red:
Maturity Group 2, Black: Maturity Group 3, Blue: Maturity Group 4.
Method for Genetic Fingerprint Analysis: A total of 1,484 public, pre-commercial and commercial
Monsanto proprietary lines were genotyped to place the germplasm sampled in this study in context of
the scope of Monsanto and public soybean germplasm from the past 40 years. The genotyped lines
included 387 samples from maturity group two, 853 lines from maturity group three, and 246 lines from
maturity group four. Monsanto lines were genotyped at approximately 30K bi-allelic single nucleotide
polymorphisms (SNPs) using the Illumina Infinium™ platform. The Infinium™ assay relies on direct
hybridization of genomic targets to array-bound sequences. Each allele-specific single base extension is
associated with a fluorescent dye for SNP allele detection. The markers were developed by Monsanto.
Approximately 10,000 markers were dropped due to missing data in one of the included lines, leaving
20,156 markers for further analysis. Allelic scores were converted to -1, 0, and 1 for homozygous one,
heterozygous, and homozygous two scores, respectively. A principal component analysis was performed
using the base package in R 3.0.1. CX375 (A3733/CX329) was fingerprinted using the Golden Gate™
SNP platform at approximately 3,000 markers resulting in a significantly reduced overlap with the
Infinium™ marker set. This variety was therefore excluded from this analysis due to missing data.
METABOLOMICS AND IONOMICS DATA
File S1. Full metabolomic and ionomic data: summonsoy2_ion.xlsx
File S2. The 681 metabolite names: annotMets_v2.xlsx
File S3. Supp_Table 1. Ionomics_DATA.xlsx
File S4. Metadata: Metadatav2.docx
File S5. Correlation data: ILJA_ILJE_Spearman_corr.xlsx
SUPPORTING TABLES
Supporting Table 2. Metabolite Coverage of Analytical Platforms
Identified
Annotated Annotated
Platform
Metabolite Class
Metabolitesa Metabolitesb
Peaksc
CE-TOF-MS
134
139
228
Ionic compounds
Low-molecular mass
compounds including sugars,
GC-TOF-MS
74
74
97
amino acids, and organic
acids
Heavier molecules with large
polar surface area e.g.
soyasaponins
LC-TOF-MS
8
21
312
Lipid profiling
0
0
95
Di- and tri-glycerides,
phospholipids
TOTAL
216
234
732
1182
aIdentified
metabolites refer to those confirmed by authentic standards and by database matching.
metabolites refer to those identified by database matching but not confirmed by internal
bAnnotated
standards.
cAnnotated peaks refers to peaks identified at the structure class level.
SupportingTable 3. Summary of Statistically Significant Differences in Ionomic Profiles
Conventional and
Element
Site
Group A and B
Transgenic
Boron
2.90E-09
ns
ns
Sodium
ns
ns
ns
Magnesium
ns
4.10E-09
ns
Aluminum
ns
ns
ns
Phosphorus
ns
ns
ns
4.50E-07
ns
ns
Sulfur
ns
ns
ns
Potassium
ns
ns
ns
Calcium
4.30E-15
1.60E-06
6.40E-06
Manganese
2.50E-05
7.00E-14
1.20E-10
Iron
2.70E-07
7.50E-08
ns
Cobalt
6.50E-17
ns
ns
Nickel
2.40E-23
4.40E-05
5.70E-05
Copper
Zinc
1.40E-42
2.80E-08
ns
Arsenic
ns
ns
ns
Selenium
1.10E-50
ns
ns
Rubidium
3.80E-25
ns
7.50E-07
Strontium
9.40E-49
ns
ns
Molydenum
3.60E-51
ns
ns
8.40E-05
ns
ns
Cadmium
p-values are adjusted by Bonferroni correction. Ns= not significant at a=0.05
SUPPORTING FIGURES
Color versions of Figure 3 of Main Text
Supporting Fig. 1. Evaluation of the achieved coverage of metabolite profile data. PCA was
performed on the predicted physicochemical properties of the detected metabolites and the metabolites in
the SoyCyc database. (a) The score plots show that the distribution of the metabolites identified or
annotated in our study occupies a similar space as the reference database metabolites. The inset barplot
shows the ratio of variance among the reference metabolites covered by each of the individual platforms
and the summarized data set. For instance, the 65% coverage of the LC-TOF-MS data implies that the
seven annotated peaks having different physic-chemical properties from each other can occupy 65% of
chemical space of the soybean metabolome. (b) The loading plots show that PC1 is dominated by size
related- and PC2 by solubility-related properties.
Coverage of chemical diversity was estimated by fetching all available predicted physicochemical
properties from the ChemSpider database (http://www.chemspider.com) for identified metabolites and the
metabolites listed in the SoyCyc database (ver. 3.0) (http://www.plantcyc.org/) (see ref. 19 in main text).
All properties were scaled to unit variance (autoscaling). Chemical coverage was defined as the
percentage of variance among the SoyCyc metabolites that could be predicted using a PCA model of the
properties of a given subset of metabolites. Specifically, chemical coverage was defined as:
2
∑[(𝑋𝑆𝑜𝑦𝐶𝑦𝑐 𝑃𝑠𝑢𝑏 )𝑃′𝑠𝑢𝑏 − 𝑋𝑆𝑜𝑦𝐶𝑦𝑐 ]
Coverage = [1 −
]
2
∑ 𝑋𝑆𝑜𝑦𝐶𝑦𝑐
where XSoyCyc are the unit-variance scaled physicochemical properties of metabolites in SoyCyc, and Psub
the loadings matrix from the PCA model of the properties of a subset of metabolites (e.g. those from an
individual platform). Missing value robust PCA was with the pcaMethods package (Stackles et al. 2007).
Stackles W, Redestig H, Scholz M, Walther D, & Selbig J (2007) pcaMethods--a bioconductor package
providing PCA methods for incomplete data. Bioinformatics 23(9):1164-1167.
Supporting Fig. 2. Principal component analysis of the identified or annotated metabolites/ peaks.
Number of observations, 106; number of variables, 681. PC1 (t[1]), 15%; PC2 (t[2]), 9.8%.
Supporting Fig. 3. Principal component analysis of the identified or annotated metabolites/peaks and
including the ionomics data. Number of observations, 106; number of variables, 701. PC1 (t[1]), 14.9%;
PC2 (t[2]), 9.8%.
Supporting Fig. 4. The score scatter plot of OPLS-DA using the identified or annotated metabolites/
peaks and including the ionomics data. The plot is color-and shape-coded by site (ILJA:blue, ILJE:green)
(a) and by variety (b). A site location effect was observed for all varieties. We focused on the differences
in predictive components 1 and 2 (t[1] and t[2]) according to the discriminant scores related to varietydependent differences. The number of each component in the score plots represents the sum of squares
of all the annotated metabolites explained by the extracted components (t[1], 10.5%; t[2], 8.5%). The
model was validated by analysis of variance testing of cross-validated predictive residuals (CV-ANOVA)
pCV < 0.01). Number of observations, 106; number of variables, 719 (X = 701; Y = 18).
Supporting Fig. 5. Graphic representation of nodes of the first neighbors in the yield-to-metabolite
correlation networks of samples harvested at ILJA (above) and ILJE (below). We applied Spearman’s
correlation analysis to generate the correlation networks. Solid and dot links represent a positive and a
negative correlation (p < 0.01)
To evaluate the correlation between the metabolite or ion level and yield at ILJA/ILJE from Table 1 (main
text) we calculated the Spearman’s correlation coefficient (r). The statistical significance of the correlation
coefficient was tested against the hypothesis of no correlation (r = 0) according to t-statistics; where n
indicates the sample size. The significance level was set at p < 0.05.
.