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. 2018 Apr 3;5:180051. doi: 10.1038/sdata.2018.51

The natural variance of the Arabidopsis floral secondary metabolites

Takayuki Tohge 1,2,a, Monica Borghi 1, Alisdair R Fernie 1,b
PMCID: PMC5881409  PMID: 29611844

Abstract

Application of mass spectrometry-based metabolomics enables the detection of genotype-related natural variance in metabolism. Differences in secondary metabolite composition of flowers of 64 Arabidopsis thaliana (Arabidopsis) natural accessions, representing a considerable portion of the natural variation in this species are presented. The raw metabolomic data of the accessions and reference extracts derived from flavonoid knockout mutants have been deposited in the MetaboLights database. Additionally, summary tables of floral secondary metabolite data are presented in this article to enable efficient re-use of the dataset either in metabolomics cross-study comparisons or correlation-based integrative analysis of other metabolomic and phenotypic features such as transcripts, proteins and growth and flowering related phenotypes.

Subject terms: Secondary metabolism, Natural variation in plants, Metabolomics

Background and Summary

Plant secondary metabolites (so-called specialized metabolites) that have high natural diversity in their chemical structures and abundances can be identified through metabolic screening of populations even in the comparisons between ecotypes and cultivars belonging to the same species1–3. This may represent relatively recent adaptations or more phylogenetical restrictions in the evolution of such metabolisms3–5. With metabolomic screening of such populations, metabolic polymorphism in aliphatic glucosinolates6, flavonol-glycosides7 and phenylacylated-flavonols3 have been discovered in Arabidopsis. Additionally, a key gene of production of phenylacylated-flavonols for the conferral of protection towards UV irradiation3, was characterized by an integrative functional genomic approach. Since several physiological studies using Arabidopsis accessions have been reported with phenotypic analysis under stress conditions such as UV-B irradiation8, drought and salinity stress9,10 and biotic stressors11, understanding of plant secondary metabolites for the conferral of protection towards stress condition is highly important. To capture the variance of secondary metabolites across populations, liquid chromatography-mass spectrometry (LC-MS) has often been preferred to other analytical methods as it presents the technical advantage of capturing the most extensive variety of plant metabolites.

Here, data of floral secondary metabolite abundance measured in a population of 64 Arabidopsis thaliana (Arabidopsis) natural accessions are presented (Data Citation 1)(Data Citation 2). Sixty-eight secondary metabolites were measured via LC-MS, ions acquired in positive and negative ion detection mode, and compounds annotated through a combination of chemical confirmation with analytical standards and comparative analysis with flavonoids knockout and over-expresser Arabidopsis lines12,13. The list of the Arabidopsis accessions used in this study, and raw and normalized metabolomics data are provided (Data Citation 1)(Data Citation 2), respectively. This dataset can be used for cross-study comparisons of plant metabolites, investigations on the reproducibility of metabolomics data, and in-depth analysis of plant metabolism. Importantly, transcriptomics data obtained from 10 samples in this experimental set is available in the Gene Expression Omnibus (GEO) database (Data Citation 3). Correlation studies with data of metabolomics, transcriptomics, proteomics and phenomic data of floral related traits are also anticipated. In addition, the presence in this dataset of standard reference files and complex biological data files, which were acquired on the same LC-MS system, makes it useful for practical exercises on data analysis and interpretation. Finally, as several secondary compounds initially identified in model plants bring nutritional and health benefits to humans14,15, these data will be helpful in the design of future plant metabolic engineering used for translational genomics applications from model species to crops.

Methods

Plant material and sample preparation

Seeds of Arabidopsis natural accessions (Table 1 (available online only)) were germinated on 1/2 MS salts solidified with 1% of agar in a growth chamber (16 h light, 140-160 μmol m−2 s−1, 20 °C; 8 h dark, 16 °C) after vernalization (two days in the dark at 8–10 °C). Fourteen days after planting, the seedlings were transferred onto soil (GS-90 Einheitserde; Gebrueder Patzer) and grown in a greenhouse (16 h light, an average irradiance of 120 μmol m−2 s−1, 20 °C; 8 h dark, 16 °C) until flowering. Positioning of the plants was randomized during plant growth. Fully open mature flowers (first flowers) were harvested at around noon (after approximately 5 h of light) and immediately frozen in liquid nitrogen for further analysis. Flowers from three plants were individually harvested to prepare one biological replicate. Sample preparation and extraction were performed as previously described3.

Table 1. The site of origin of Arabidopsis accessions presented in this article.

No. Accession No. Name city country altitude latitude longitude
Geographical coordinates were obtained from a previous study3 and the 1001 genomes project (http://1001genomes.org/).              
1 CS28017 An-2 Antwerpen Netherlands 9.0 51.58 4.35
2 CS28018 Ang-0 Angleur Belgium 270.4 50.61 5.59
3 CS76445 Bd-0 Berlin/Dahlem Germany 47.3 52.46 13.29
4 CS28062 Be-0 Bensheim/Bergstr. Germany 99.4 49.68 8.61
5 CS28086 Bla-11 Blanes/Gerona Spain 95.5 41.69 2.80
6 CS76098 Blh-1 Bulhary Austria 248.4 48.83 16.74
7 CS28100 Bsch-2 Buchschlag/FFM Germany 95.9 50.02 8.67
8 CS28102 Bu-2 Burghaun/Rhon Germany 465.4 50.69 9.72
9 CS76105 Bur-0 Burren (Eire) ireland 186.2 53.20 -8.98
10 CS76106 C24 Lousa Portugal 146.9 40.11 -8.244
11 CS76109 Can-0 de Gran Canaria Morocco 1260.0 29.21 -13.48
12 CS28133 Cha-0 Champex Switzerland 1973.8 46.02 7.07
13 CS28164 Co-3 Coimbra Portugal 146.9 40.20 -8.42
14 CS76113 Col-0 Columbia USA 249.4 38.30 -92.30
15 CS76116 Cvi-0 Cape Verdi Islands Senegal 1150.0 15.11 -23.62
16 CS28200 Da-0 Darmstadt Germany 89.1 49.87 8.65
17 CS28206 Dijon-M   Russia 186.3 55.45 37.35
18 CS76126 Edi-0 Edinburgh UK 64.8 55.97 -3.22
19 CS76479 El-0 Ellershausen Germany 492.7 51.51 9.682
20 CS76127 Est-1 Estland Estonia 29.1 58.30 25.30
21 CS28973 Gol-1 Scotland Golspie UK 6.7 57.97 -3.97
22 CS76137 Gr-1 Graz, Austria Austria 332.0 47.00 15.50
23 CS28349 HI-3 Holtensen Germany 260.3 52.14 9.38
24 CS28351 HOG Khodga-Obi-Garm Tajikistan 1750.0 38.55 68.47
25 CS76145 Hs-0 Hannover/Stroehen Germany 39.1 52.50 9.50
26 CS28365 Je-54   Czech republic 279.0 49.30 17.00
27 CS76148 JEA St Jean Cap Ferrat France 10.8 43.68 7.33
28 CS79018 Kas-1 Kashimir India 2324.0 35.00 77.00
29 CS28389 Kl-0 Koeln Germany 414.1 50.95 6.97
30 CS28395 Kn-0 Kaunas Lithuania 87.0 54.90 23.89
31 CS77020 Ler-0 Landsberg Germany 66.5 47.98 10.87
32 CS76168 Lip-0 Lipowiec/Chrzanow Poland 240.1 50.00 19.30
33 CS76175 Lov-5 Lovvik Sweden 2.60 62.80 18.08
34 CS28922 Lovel-1 Løvel Denmark 3.0 56.57 9.48
35 CS77056 Lu Lund Sweden 13.1 55.70 13.20
36 CS28493 Mh-1 Muehen (OstPr) Poland 193.2 53.31 20.12
37 CS76192 Mt-0 Martuba/Cyrenaika Libya 283.1 32.34 22.46
38 CS28528 Nd Niederzenz Germany 49.1 47.40 8.18
39 CS76199 NFA-8 Ascot (England) UK 79.9 51.41 -0.64
40 CS28564 No-0 Nossen Germany 417.7 51.06 13.30
41 CS1402 Nok-2 Noordwijk Netherlands 7.9 52.25 4.45
42 CS28568 Nok-1 Noordwijk Netherlands 7.9 52.25 4.45
43 CS28576 Nw-3 Neuweilnau Germany 457.8 50.31 8.40
44 CS28583 Old-1 Oldenburg Germany 9.3 53.17 8.20
45 CS76203 Oy-0 Oystese Norway 31.0 60.39 6.19
46 CS76211 Petergof Petergof Russia 153.3 59.87 29.91
47 CS28648 Po-0 Poppelsdorf Germany 72.1 50.72 7.09
48   Pyl-1 Le Pyla France 45.4 44.65 -1.17
49 CS76216 Ra-0 Randan France 305.8 46.00 3.30
50 CS76588 RLD-1   Netherlands 17.8 52.15 5.30
51 CS28715 Rsch-0 Rschew/Starize Russia 231.7 56.20 34.30
52 CS28718 Rubezhno-1 Rubezhnoe Ukraine 189.2 49.00 38.30
53 CS76224 Sap-0 Slapy Czech republic 494.6 49.49 14.24
54 CS28725 Sav-0 Slavice Czech republic 701.0 49.18 15.88
55 CS28735 Shakdara Shakdara River Tajikistan 4178.1 39.25 68.24
56 CS76231 St-0 Stockholm Sweden 76.7 59.00 18.00
57 CS76605 Stw-0 Stobowa/Orel Russia 217.0 52.00 36.00
58 CS76242 Ta-0 Tabor Czech republic 398.2 49.50 14.50
59 CS28757 Te-0 Tenala Finland 30.9 60.06 23.30
60 CS28786 Ty-0 Taynuilt UK 19.3 56.43 -5.23
61 CS28817 Wei-1 Weiningen Switzerland 529.4 47.41 8.42
62 CS28819 Will Vilnius Lithuania 147.9 54.68 25.32
63 CS76303 Ws-0 Wassilewskija Russia 129.0 52.30 30.00
64 CS28847 Zue-1 Zurich Switzerland 707.6 47.37 8.55
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LC-MS analysis and flavonoid mutant-based peak annotation

Profiling of secondary metabolites was performed as previously described3,16. Briefly, flower tissues were ground with liquid nitrogen and homogenized in a mixer mill for 3 min at 25 Hz with a zirconia bead and 20 μL of extraction buffer (80% methanol, prepared with 5 μg mL−1 isovitexin as an internal standard) per mg of ground tissue (e.g., 204.0 μl extraction buffer for 10.2 mg fresh weight sample). Thereafter, the supernatant was separated from the cellular debris via centrifugation at 12,000 x G and 3 μL of the clarified supernatant directly injected in an HPLC system Surveyor (Thermo Finnigan, USA) coupled to LTQ-XP system (Thermo Finnigan, USA) for metabolite profiling described as below. All samples including flower extracts obtained from Arabidopsis mutants described in ‘Data processing and metabolite data analysis’ were analyzed together. Sample run order was determined by replicates consecutively.

Chromatography

Chromatography was performed as previously described16. Samples were run on a Surveyor HPLC system (Thermo, USA), 150×2 mm, 2.0 μm particles (Reverse Phase Luna C18(2), Phenomenex, USA), HPLC column at 28 °C oven temperature. The solvents used for the assay consisted of water containing 0.1% v/v formic acid (Solvent A) and an acetonitrile solution containing 0.1% v/v formic acid (Solvent B). Gradient [time (min)/%B] starting: 2.0/0, 4.0/15, 14.0/32, 19.0/50, 19.01/100, 21.0/100, 21.01/0, 23.0/0 at flow rate 0.20 mL min−1. Injection volume was 2 μL.

Mass spectrometry

The compounds were detected using a Thermo LTQ-XL Linear-Ion-Trap mass spectrometer (expected resolution is 0.3 u FWHM) with electrospray ionization (ESI) mode in negative (collision energy: 0 and 30 meV) and positive ion detections with a scan range from 100–2000 m/z. Main MS parameters (capillary temperature: 275 °C; source voltage: 4.00 kV(negative) and 4.50 kV (positive); capillary voltage: −50 V(negative) and 50 V(positive) were optimized for the detection of plant secondary metabolism. Other MS parameters are described in Tohge et al., 201016. The LTQ-XP used the Xcalibur software (Thermo Finnigan, USA) version 2.1.0 for data acquisition.

Data processing and metabolite data analysis

Data were processed using Xcalibur 2.1.0 software, and peak identification and annotation implemented through a combination of the following approaches: standard chemical confirmation17, MS fragmentation and retention time profiling, mutant analysis3,12,13, literature/database survey18,19. The following Arabidopsis mutants known for having altered flavonoid profiling were used as control lines for the determination of flavonoid derivatives: UDP-glucosyl transferase 78D2 (ugt78d2), decreased production of flavonoid-3-O-glucoside20; transparent testa 7 (tt7), no production of quercetin and isorhamnetin derivatives21; ugt78d1, no production of flavonol-3-O-rhamnosides22; ugt78d3, no production of flavonol-3-O-arabinosides23; O-methyltransferase 1 (omt1), no production of isorhamnetin-derivatives12; ugt89c1, no production of flavonol-7-O-rhamnosides24; tt4, no production of all flavonoids25,26; production of anthocyanin pigment 1-Dominant (pap1-D), increased accumulation of anthocyanins20,27. Peak picking was performed by Xcalibur Quan Browser (Window (sec), 30; highest peak; minimum peak height (S/N), 3.0; Baseline window, 80-150; area noise factor, 2; peak noise factor, 10; peak height (%), 5.0, tailing factor, 1.5).

Transcriptomic data

Transcriptomic analysis was performed using ATH1 microarrays as described previously3 with ten accessions (Col-0, C24, Cvi, Da, Rsch, Ler-0, Ws, Sap, Stw and RLD). Duplicate hybridizations were carried out for Col-0 and C24, and a single hybridization was performed for all the other accessions except Col-0 and C24. Data is deposited in the Gene Expression Omnibus database (Data Citation 3).

Data Records

Raw data obtained from the analysis of natural Arabidopsis accessions and mutant reference lines have been deposited in the Metabolights (Data Citation 1). Raw data contains two negative (collision energy: 0 and 30 meV) and one positive ion detections. Cdf files contain negative and positive ion detections without data of in-source fragmentation using collision energy. This dataset contains a total of 216 raw files resulting from 72 lines (64 accessions and 8 Arabidopsis mutant lines) with three biological replicates each. A dataset of floral secondary metabolite (68 compounds; 16 glucosinolates, 3 hydroxycinnamate derivatives, 42 flavonol derivatives and 7 putative polyamines) and general statistics relative to the natural accessions used in the study is provided (Data Citation 2). Metabolite data was obtained from a dataset previously published3 and reformatted for correlation-based analysis by average-scaling and log-transformation ([log2(mean(replicates)/mean(mean of all accessions)]) (Data Citation 2). The geographic coordinates of the Arabidopsis accessions provided in Table 1 (available online only) are updated accordingly with the Arabidopsis 1001 genome database (http://1001genomes.org/)28.

Technical Validation

To qualitatively and quantitatively validate metabolite data obtained from three biological samples the standard deviation was estimated (Data Citation 2).

Usage notes

Data of floral secondary metabolites are presented in Excel files (Data Citation 2). For each compound, the method used for peak identification/annotation, which includes retention time, ion detection mode and relative peak area, is specified. The value of the relative peak area was obtained from the average of three measurements (n=3) normalized by the standard deviation (SD)(Data Citation 2). Compound’s family name and reference literature are also provided. The abundance of floral metabolites, normalized by average-scaling (mean/average) and log-transformation (log2) is reported (Data Citation 2). The dataset here presented can be used for cross correlation studies to integrate metabolomics with transcriptomics, proteomics, and floral phenotypic data. Figure 1 shows an example of metabolite-metabolite correlation network analysis (r2>0.6, Pearson correlation estimated R statistical package (https://www.r-project.org/)) performed with the data reported (Data Citation 2). Visualization of network connection based on coefficient value was performed with Cytoscape (http://www.cytoscape.org/) using an organic layout style (Data Citation 2). As previously discussed3 accession-specific floral phenylacyl-flavonol glycosides (saiginols, indicated with the number 1 in Fig. 1) show a strong correlation within the saiginol clade. The following ten additional clades of compounds were also identified and these are indicated in Fig. 1 with the following numbering: 2) common flavonol mono- or di-glycosides, 3) pollen specific flavonols and pollen specific polyamines, 4) putative pollen specific polyphenolic polyamines, 5) flavonol-3-O-(2′′-O-rhamnosyl)glucoside-7-O-rhamnosides, 6) flower specific flavonol-glycosides, 7) accession-specific glucosinolate, 8) short-chain aliphatic glucosinolates, 9), long-chain aliphatic sulfinyl-glucosinolates, 10) long-chain aliphatic thio-glucosinolates, and 11) other glucosinolates as for example indolic glucosinolates. No subclades of hydroxycinnamates were found. Network analysis suggests that metabolites that belong to the same clade are produced in Arabidopsis natural accessions that share the common genetic polymorphism, transcriptionally co-regulated, or are the resulted of a similar metabolic pattern maintained by the combination of different metabolic flux changes. The data presented in this article are useful in biodiversity studies, e.g., to investigate relationships between natural metabolic diversity and accession distribution, physiological diversity and the genomic polymorphism.

Figure 1. Correlation network of Arabidopsis floral secondary metabolites.

Figure 1

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Network analysis and visualization were performed with Cytoscape using an organic layout. The Pearson correlation threshold of 0.6 was chosen to determine the connections between edges and nodes. Nodes represent metabolites and the edges the interaction between metabolites. The size of nodes and edges maps to clustering coefficient and correlation coefficient, respectively, with small nodes and thin edges representing small values. Different classes of metabolites are represented with different colors: saiginols, red; flavonols, yellow; polyamine, pink; purple, aliphatic glucosinolates; green, putative hydroxycinnamate; light blue, indole glucosinolate.

Additional information

How to cite this article: Tohge, T, et al. The natural variance of the Arabidopsis floral secondary metabolites. Sci. Data 5:180051 doi: 10.1038/sdata.2018.51 (2018).

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Material

sdata201851-isa1.zip (9.6KB, zip)

Acknowledgments

T.T. and A.R.F. were funded by the Max Planck Society. MB is supported by a Marie Skłodowska-Curie Actions Individual Fellowship Grant no. 656918.

Footnotes

The authors declare no competing interests.

Data Citations

  1. Tohge T. 2017. MetaboLights. MTBLS528
  2. Tohge T, Borghi M, Fernie A. 2018. Figshare. http://doi.org/10.6084/m9.figshare.c.3938875
  3. 2016. Gene Expression Omnibus. GSE83291

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Citations

  1. Tohge T. 2017. MetaboLights. MTBLS528
  2. Tohge T, Borghi M, Fernie A. 2018. Figshare. http://doi.org/10.6084/m9.figshare.c.3938875
  3. 2016. Gene Expression Omnibus. GSE83291

Supplementary Materials

sdata201851-isa1.zip (9.6KB, zip)

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