1 2 3 Supplementary Materials for 4 5 Did a -herbivore arms race drive chemical diversity in ? 6 7 M. Ernst1,2,3, L.-F. Nothias2,3, J. J. J. van der Hooft2,3,4, R. R. Silva2,3, C. H. Saslis-Lagoudakis1, 8 O. M. Grace5, K. Martinez-Swatson1, G. Hassemer1, L. A. Funez7, H. T. Simonsen6, M. H. 9 Medema4, D. Staerk8, N. Nilsson9, P. Lovato9, P. C. Dorrestein2,3,10∗ & N. Rønsted1∗

10 11 *Correspondence to: [email protected] and [email protected] 12 13 14 This PDF file includes: 15 16 Materials and Methods 17 Supplementary Text 18 Table S1 19 Fig S1-S12 20 URL S1 21 Captions for Data S1 and S2 22 23 Other Supplementary Materials for this manuscript include the following: 24 25 Data S1 and S2 26 27 ● List of Euphorbia sampled. 28 ● High-resolution TNF-α modulation profiles. 29

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30 Materials and Methods

31 32 Collection of plant material 33 34 Pooled extracts of specimens for each Euphorbia species 35 36 43 Euphorbia species (Data S1) were collected from the greenhouses of the Living Collections 37 of the Botanical Garden in Copenhagen. Live were sampled for xerophytic species, 38 whereas herbaceous perennials were grown from seeds originating from the seed bank of the 39 Botanical Garden or collections performed in southern Brazil (Species 11-14 and 16-18). E. 40 myrsinites and E. amygdaloides were purchased as live plants from Jespers Planteskole, 41 Holstebro A/S, Harrestrupvej 64, 7500 Holstebro and Kridtvejs Planter, Kridtvej 18, 7980 42 Vils, Denmark and kept in the greenhouse with the other herbaceous species until harvest. To 43 get representative samples of the specialized metabolite profile in the living plants, the plants 44 were sampled as whole intact specimens with the roots included. Over six specimens or as 45 many available from the collections were sampled for each species (Data S1). Some tree 46 species or rare species could not be sampled as whole plants, in these cases, branches of 47 representative parts of the living specimen were sampled (Data S1). The fresh plant material 48 was then stored at -20◦C until extract preparation. Voucher specimens were prepared for 49 species with enough material available from the Living Collections and deposited at the 50 General herbarium of vascular plants, Natural History Museum of Denmark, Copenhagen. 51 Taxonomic species identity was confirmed for all species sampled based on the voucher 52 specimens, the living specimens in the greenhouse or photographs. 53 54 3D mass spectral molecular cartography 55 56 Individual plant parts from one to three specimens of one representative species of each 57 subgeneric clade of Euphorbia were sampled (E. horrida, Athymalus, 3 specimens; E. hirta, 58 Chamaesyce, 2 specimens; E. lathyris, Esula, 2 specimens; E. milii, Euphorbia, 2 specimens). 59 Approximately 200 mg fresh plant material of each individual plant part was collected in 1.5 60 ml Eppendorf tubes and flash frozen under liquid nitrogen. The samples were stored at -80◦C 61 until further analysis. 62 63 Extract preparation 64 65 Pooled extracts of specimens for each Euphorbia species 66 67 Individual specimens of the same species were pooled, and the frozen plant material was 68 disrupted with a pestle and mortar in liquid nitrogen and app. 75 g was extracted with 975 ml 69 ethyl acetate (VWR Chemicals, HiPerSolv Chromanorm) under sonication (Fisher Scientific) 70 at 40◦C during 2 h. The extracts were filtered and evaporated to dryness on a rotary evaporator. 71 The dried extracts were then resuspended in acetonitrile (VWR Chemicals, HiPerSolv 72 Chromanorm), extracted under sonication (Fisher Scientific) at 40◦C during 15 min, filtered 73 and evaporated to dryness.

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74 75 3D mass spectral molecular cartography 76 77 The frozen plant material was disrupted in plastic tubes (Qiagen, RB 2 mL) in 1.3 ml 50/50 78 vol/vol methanol (Fisher Scientific, HPLC Grade)/acetonitrile (Fisher Scientific, Optima 79 LC/MS) with stainless steel beads (VWR International, 5 mm) using a tissue lyser (Qiagen, 80 TissueLyser II) at 25 Hz during 10 min. The samples were then extracted under sonication 81 (Fisher Scientific) at 40◦C during 15 min and centrifuged (Eppendorf Centrifuge 5418) for 10 82 min at 11,000 r.p.m. The extract supernatant was transferred to new plastic tubes and 83 lyophilized to dryness (Labcono, Acid Resistant CentriVap Concentrator). 84 85 LC-MS/MS analysis 86 87 Pooled extracts of specimens for each Euphorbia species 88 89 Extracts were transferred to a 96-well plate (Falcon, 96-well plates, 0.34 ml, polypropylene) 90 and dried with a vacuum centrifuge. Samples were redissolved in 3/7 vol/vol methanol (Fisher 91 Scientific, HPLC Grade)/acetonitrile (Fisher Scientific, Optima LC/MS) to a concentration of 92 10 mg/ml, in a volume of 150 µL, with a 100 mM concentration of ammonium formate, 93 sealed with Zone-Free Sealing Film (Excel Scientific) and centrifuged for 30 min. at 2000 94 r.p.m. at 4◦C. Ammonium formate was used to promote the ionisation of diterpene esters as 95 ammonium adducts rather than sodiated adducts, thus inducing a richer MS/MS fragmentation 96 pattern as described by Vogg and collaborators (31). MS analysis was performed on a qTOF 97 Maxis II (Bruker Daltonics) mass spectrometer with an electrospray ionization (ESI) source, 98 controlled by OTOF control and Hystar. Each sample was analyzed separately two times with 99 two different MS-methods for MS acquisition. For the first MS-method, 5 µL were injected, 100 auto-MS/MS was turned off, whereas for the second MS-method, 20 µL were injected and 101 auto-MS/MS was activated. For both MS-methods, MS spectra were acquired in positive ion 102 mode over a mass range of 75-1,000 m/z. An external calibration with sodium formate was 103 performed before data acquisition and hexakis(1H,1H,3H- tetrafluoropropoxy)phosphazene 104 (Synquest Laboratories) m/z 922.009798 was used as a lock mass internal calibrant during 105 data acquisition. The following instrument settings were used for data acquisition: end plate 106 Offset 500 V, capillary voltage of 5,000 V, nebulizer gas (nitrogen) pressure of 2.0 bar, ion 107 source temperature of 200◦C, dry gas flow of 9 l min−1, source temperature and spectra 108 acquisition rate of 4 Hz for MS1 and MS2. Tune parameters were set as follows: Funnel RF1 109 200 Vpp, ion energy 3.0 V, Hexapole RF 80 Vpp, Pre pulse storage 7 µs. Minutes 0-0.7 were 110 sent to waste. For the MS-method with auto-MS/MS turned on, the three most intense ions per 111 MS1 scan were selected and subjected to collision-induced dissociation if absolute intensity 112 reached 12205 counts. The following fragmentation and isolation lists were used (values are 113 m/z, isolation width and collision energy, respectively): 100, 2, 10; 250, 2, 15; 300, 2, 20; 400, 114 2, 20; 500, 2, 20; 600, 2, 30; 700, 2, 30; 800, 2, 30; 1000, 2, 40. In addition, the advanced 115 stepping function was used with time, collision radiofrequency (RF), transfer time stepping 116 (µs) and collision energy set to: 0, 800, 85, 75; 400, 65, 100, 100; 50, 400, 65, 100; 75, 150, 117 45, 150. The MS/MS active exclusion parameter was set to 1 and released after 0.25 min, and 118 reseted if ion intensity was three times higher. The injected samples were chromatographically 119 separated using an Agilent 1290 Infinity Binary LC System (Agilent Technologies) controlled

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120 by Hystar software (Bruker Daltonics), using a 100 x 2.1 mm Kinetex 1.7 µM, C18, 100 Å 121 chromatography column (Phenomenex), 40◦C column temperature, 0.5 ml min−1 flow rate, 122 mobile phase A 99.9% water (Fisher Scientific, Optima LC/MS)/0.1% formic acid (Fisher 123 Scientific, Optima LC/MS)/10 mM ammonium formate (Fluka, LC-MS Ultra), mobile phase B 124 99.9% acetonitrile (Fisher Scientific, Optima LC/MS)/0.1% formic acid (Fisher Scientific, 125 Optima LC/MS)/10 mM ammonium formate (Fluka, LC-MS Ultra), with the following 126 gradient: 0-0.5 min 55% B, 0.5-15 min 100% B, 15-18.5 min 100% B, 18.5-20 min 100% B. 127 Blanks were injected between each analyzed sample, and the column was equilibrated prior to 128 any injection with the following gradient 20-20.2 min 55% B, 20.2-20.4 min 100% B, 20.4-23 129 min 55% B, 23-25 min 55%. Blank injections - 20 µL methanol:acetonitrile (3:7) used for 130 extraction were used as negative controls. A representative extract was used as a quality 131 control (QC) sample, and this QC sample was analyzed every twelve samples to monitor 132 retention time shift and intensity shift. 133 134 3D mass spectral molecular cartography 135 136 Dried extracts were dissolved in 50/50 vol/vol methanol (Fisher Scientific, HPLC 137 Grade)/acetonitrile (Fisher Scientific, Optima LC/MS) to a concentration of 1 mg/ml, 100 µL 138 of each extract were transferred to a 96-well plate (Falcon, 96-well plates, 0.34 ml, 139 polypropylene), sealed with Zone-Free Sealing Film (Excel Scientific) and centrifuged for 30 140 min. at 2000 r.p.m. at 4◦C. 20 µL of each extract were injected into the LC-MS/MS equipment. 141 MS analysis was performed on a micrOTOF-Q II (Bruker Daltonics) mass spectrometer with 142 an electrospray ionization (ESI) source, controlled by OTOF control and Hystar. MS spectra 143 were acquired in positive ion mode over a mass range of 75-1,000 m/z. An external calibration 144 with sodium formate was performed before data acquisition and hexakis(1H,1H,3H- 145 tetrafluoropropoxy)phosphazene (Synquest Laboratories) m/z 922.009798 was used as a lock 146 mass internal calibrant during data acquisition. The following instrument settings were used 147 for data acquisition: capillary voltage of 4,500 V, nebulizer gas (nitrogen) pressure of 2 bar, 148 ion source temperature of 200◦C, dry gas flow of 9 l min−1, source temperature and spectra 149 acquisition rate of 3 Hz for MS1 and MS2. Minutes 0-0.5 and 20-25 were sent to waste. 150 Minutes 0.5-20 were recorded with auto MS/MS turned on. The three most intense ions per 151 MS1 scan were selected and subjected to collision-induced dissociation according to the 152 following fragmentation and isolation list (values are m/z, isolation width and collision energy, 153 respectively): 100, 2, 10; 250, 3, 15; 300, 4, 15; 400, 4, 15; 500, 4, 15; 600, 5, 20; 700, 5, 20; 154 800, 5, 20; 1000, 7, 35; 100, 0, 5. In addition, the advanced stepping function was used with 155 time, collision radiofrequency (RF), transfer time stepping (µs) and collision energy set to: 0, 156 550, 125, 75; 25, 400, 90, 100; 50, 250, 65, 125; 75, 150, 50, 150. The MS/MS active 157 exclusion parameter was set to 1 and released after 0.25 min. The injected samples were 158 chromatographically separated using an Agilent 1290 Infinity Binary LC System (Agilent 159 Technologies) controlled by Hystar software (Bruker Daltonics), using a 50 x 2.1 mm Kinetex 160 1.7 µM, C18, 100 Å chromatography column (Phenomenex), 40◦C column temperature, 0.5 161 ml min-1 flow rate, mobile phase A 99.9% water (Fisher Scientific, Optima LC/MS)/0.1% 162 formic acid (Fisher Scientific, Optima LC/MS)/10 mM ammonium formate (Fluka, LC-MS 163 Ultra), mobile phase B 99.9% acetonitrile (Fisher Scientific, Optima LC/MS)/0.1% formic acid 164 (Fisher Scientific, Optima LC/MS)/10 mM ammonium formate (Fluka, LC-MS Ultra), with 165 the following gradient: 0-0.5 min 55% B, 0.5-15 min 100% B, 15-18.5 min 100% B, 18.5-20

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166 min 100% B, 20-20.2 min 55% B, 20.2-20.4 min 100% B, 20.4-23 min 55% B, 23-25 min 167 55% B. Blank injections - 20 µL methanol:acetonitrile (1:1) used for extraction - were used as 168 controls. A representative extract was used as a quality control (QC) sample, and this QC 169 sample was analyzed every twelve samples to monitor retention time shift and intensity shift. 170 171 High-Resolution TNF-α modulation profiling 172 173 The modulation of PKC activity has been suggested to be a key mechanism behind the anti- 174 herbivore biological activities of Euphorbia diterpenoids. PKC has a ubiquitous distribution in 175 both mammalian and insect tissues, and thus represents a good biological target for the defense 176 against herbivory (5, 8, 27, 32). We evaluated the modulation of PKC by measuring the 177 capacity of compounds or compound groups in Euphorbia that modulate in vitro TNF-α 178 release from peripheral blood mononuclear cells (PBMCs). TNF-α is an immunomodulatory 179 cytokine, which is released upon activation of PKC (26, 27). Both vertebrates and 180 invertebrates produce cytokines, and it is likely that they exhibit similar functions both in 181 insect as well as mammalian cells (33). First, we subjected the same 43 plant extracts we had 182 previously characterized chemically to high-resolution microplate-based extract collection, 183 resulting in 144 individual fractions per extract. Then we used all the individual fractions to 184 treat human peripheral blood mononuclear cells (hPBMCs), which are activated to release 185 TNF-α by stimulation with anti-CD3 and anti-CD28 coated beads. Finally, we assessed the 186 modulation of TNF-α by evaluating the level of TNF-α released in culturing media in the 187 HPLC trace (34) (Data S2, Figs. 2-45). 188 189 High-resolution microplate-based extract collection 190 191 HPLC analyses of the extracts were performed on an Agilent 1200 series chromatograph 192 consisting of a G1311A quaternary pump, a G1322A degasser, a G1316A thermostated 193 column compartment, a G1315C photodiode-array detector, a G1367C high-performance 194 autosampler, and a G1364C fraction collector (Santa Clara, CA). Separations were performed 195 with a Phenomenex Luna C18(2) column (150 x 4.6 mm i.d., 3 µm particle size, 100 Å pore 196 size) maintained at 40◦C. HPLC solvent A consisted of water-acetonitrile 95:5 with 0.1% 197 formic acid added, and solvent B consisted of acetonitrile-water 95:5 with 0.1% formic acid 198 added. The dried extracts were resuspended in acetonitrile and 0.8 mg of extract was injected 199 and separated by use of the following gradient elution profile at 0.5 ml/min: 0 min, 0% B; 22.5 200 min, 100% B; 40 min, 100% B; 41 min, 0% B; 48 min, 0% B. The chromatograms were 201 monitored at 254, 226, 235, 280 and 360 nm. The chromatographic system was controlled by 202 the Agilent ChemStation revision B.03.02 software. Fractionation was performed from 10 to 203 45 min in 144 wells. Microplates were evaporated to dryness under reduced pressure in an 204 SPD121P Savant SpeedVac. 205 206 TNF-α inhibition assay using human peripheral blood mononuclear cells 207 208 Human peripheral blood mononuclear cells (hPBMCs) were isolated from human buffy coats 209 of healthy donors using Lymphoprep (Medinor) according to manufacturer’s instructions and 210 frozen at 5 · 107 cells/ml in (RPMI cell medium with 20% Fetal Calf Serum (FCS), (Gibco) 211 and 5% DMSO). The fractionated dry extracts were resuspended in 30 µL DMSO, and two

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212 times 70 nl was transferred to 384-well assay plates using the Echo (Labcyte) liquid handling 213 equipment. Anti-Biotin MACSiBead Particles loaded with biotinylated antibodies against 214 human CD3 and CD28 were used to mimic antigen-presenting cells and activate resting T cells 215 from PBMCs. The beads were loaded with biotinylated antibodies according to the 216 manufacturer’s instructions (human T Cell Activation/Expansion Kit, Milteny Biotec). The 217 cells were thawed, and beads in a 1:1 bead-to-cell ratio were added before seeding 70 µl of 218 73.5 · 103 cells per well to the assay plates. The assay plates were incubated 24 h overnight 219 after which 5 µl of supernatant were transferred to a 384-well detection plate to measure the 220 level of TNF-α by using the AlphaLisa kit for human TNF-α (Perkin-Elmer) according to the 221 manufacturer’s protocol. Percentage effect on TNF-α inhibition was calculated by using 222 DMSO and T cell activated PBMCs as 0% effect for no activity, i.e. maximal TNF-α release 223 and 10 µM terfenadine and T cell activated PBMCs as 100% effect for full inhibition, i.e. no 224 TNF-α detected. 10 µM dexamethasone was added to each assay plate as a control for a potent 225 compound reducing TNF-α levels without exhibiting effects on cell viability. To evaluate 226 possible cytotoxicity, we also measured inhibition of cell viability. The inhibition of cell 227 viability was determined by measuring fluorescence after adding PrestoBlue (LifeScience) 228 ready-to-use reagent to each well and incubating 24 h in the dark at room temperature 229 according to the manufacturer’s protocol. In order to evaluate the quality and resolution of 230 each assay plate we calculated the Z’-factor (35) for TNF-α modulation as well as cell viability 231 (Data S1). The Z’-factor is defined as 232 233 Z’=1-3(σc++σc-)/|µc+-µc-| 234 235 where σc+ indicates the standard deviation of 100% effect, σc- indicates the standard deviation 236 of 0% effect, µc+ indicates the mean value of 100% effect and µc- indicates the mean value of 237 0% effect. Assay performance was considered very good for Z’ > 0.5 (36). 238 Ingenol mebutate is a Euphorbia diterpenoid with known anti-herbivore activity (6). 239 Therefore, we subjected ingenol mebutate to the same assay setup as the plant extracts. Ingenol 240 mebutate (Data S2, Fig. 1) did not show a dose-dependent inhibition or increase of TNF-α 241 release. Nevertheless, significant modulation of TNF-α was observed, demonstrating that 242 ingenol mebutate like diterpenoids can be pinpointed with the chosen assay setup. We fit a 243 symmetric log-logistic model to our raw data of ingenol mebutate as well as the crude plant 244 extracts (Data S2), none of the extracts however showed a significant fit to the model. 245 Calculated values for model fits are therefore not shown. TNF-α modulatory activity was 246 assessed for each fraction, with two replicate experiments per fraction. TNF-α modulatory 247 activity and cell viability were plotted against chromatographic retention time to give a high- 248 resolution biochromatogram (Data S2), with individual measurements represented as dots and 249 mean values as bars. Fractions with mean values for TNF-α modulation of > 35% and < -35% 250 respectively and cell viability not exceeding (-)30% were considered as significantly 251 modulating the release of TNF-α without having a significant effect on cell viability. Cell 252 viability baseline was observed to be lower than -30% in some of the experiments. In these 253 cases, we also considered fractions as significantly modulating TNF-α if their percentage of 254 modulation was 1.5 times higher than of the cell viability. Fractions considered significantly 255 modulating TNF-α are highlighted in Data S2, Figs. 2-45.

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256 Data Analysis 257 258 Mass spectral molecular networking 259 260 Pooled extracts of specimens for each Euphorbia species 261 262 LC-MS/MS data of the pooled extracts was converted to mzML data, and lock mass correction 263 was performed using Compass Data Analysis (Bruker Daltonics). Each sample was analyzed 264 with two different acquisition methods, and the two LC-MS/MS data were merged using 265 MassFuser (https://github.com/alexandrovteam/MassFuser), a dedicated software that 266 combines the MS1 data from one run, with the MS2 data from the other run. Two 267 complementary acquisition methods and different injection volumes were employed as 268 described in the LC-MS/MS analysis section to acquire paired runs. This approach enabled 269 acquisition of high-quality MS1 features with the first method (with a high frequency of MS1 270 scan per chromatographic peak), and MS2 scans for a high number of MS1 features with the 271 second method (including low intensity features). The corresponding paired data were 272 combined with MassFuser. Merging failed for 6 out of 43 extracts, due to insufficient 273 matching peaks in the low concentrated extracts (E. acanthothamnos, E. aeruginosa, E. 274 helioscopia, E. lagascae, E. ornithopus and E. balsamifera). For these 6 extracts we used only 275 the highly concentrated extracts for further analysis. The merged mzML files were then 276 processed using Optimus v1.1.0, a processing workflow for LC-MS/MS untargeted 277 metabolomics based on OpenMS algorithms (37, 38) 278 (https://github.com/MolecularCartography/Optimus) with parameters set to: MS polarity 279 mode positive, m/z tolerance 30 ppm, Noise threshold 20000, Half of MS/MS isolation 280 window: 0.02 Da, RT tolerance of MS/MS acquisition 5 s, RT tolerance 20 s, Enable re- 281 integration of missing features off, Enable pose clustering alignment off, Intensity factor as 282 compared to blanks 3, Minimal occurrence number 1, Presence of MS/MS on, Variation in 283 pooled QC runs and replicates off, Enable feature normalization off, Save as MGF, MS1 noise 284 threshold 250, MS2 noise threshold 150. Features detected in blank samples were discarded 285 from the samples using the Optimus workflow. Subsequently, the ms2.mgf output file was 286 uploaded to the Global Natural Products Social Molecular Networking webserver 287 (http://gnps.ucsd.edu) and submitted to network analysis using the following settings: 288 Precursor Ion Mass Tolerance 0.02 Da, Fragment Ion Mass Tolerance 0.02 Da, Min Pairs Cos 289 0.5, Min Matched Fragment Ions 6, Network TopK 10, Minimum Cluster Size 1, Maximum 290 Connected Component Size 200, Run MS Cluster off. The molecular networks were visualized 291 using Cytoscape version 3.4.0 (39). The data are publicly accessible at http://gnps.ucsd.edu 292 under the MassIVE accession no. MSV000081082 and network exploration options and views 293 are available and networking parameters described at: 294 295 https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=26326c233918419f8dc80e8af984cdae 296 297 3D mass spectral molecular cartography 298 299 LC-MS/MS data of the extracts for the 3D mass spectral molecular cartography were 300 converted to mzML data, and lock mass correction was performed using Compass Data 301 Analysis (Bruker Daltonics). The mzML files were then preprocessed using MZmine 2.3 (40)

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302 with parameters set to: Peak detection/Mass detection/Mass detector, Centroid, MS1 noise 303 level 6.0E3, MS2 noise level 100, Chromatogram builder/MS1 level, Min time span (min) 304 0.01, Min height 2.0E4, m/z tolerance 0.02 m/z or 20 ppm, Chromatogram 305 deconvolution/Algorithm, Baseline cut-off, m/z range for MS2 scan pairing (Da) 0.03, RT 306 range for MS2 scan pairing (min) 0.5, Min peak height, 2.0E4, Peak duration range (min) 307 0.01-4, Baseline level 6.0E3. Isotopic peak grouper, m/z tolerance 0.02 m/z or 20 ppm, 308 Retention time tolerance 0.4 (min), Maximum charge 2, Representative isotopes, Most intense, 309 Feature alignment, m/z tolerance 0.03 m/z or 30 ppm, Weight for m/z 75, Retention time 310 tolerance 0.5, Weight for RT 25, Gap filling, Intensity tolerance 10%, m/z tolerance 0.02 m/z 311 or 20 ppm, Retention time tolerance 0.4 (min), RT correction Off. Subsequently, the .mgf 312 output file was uploaded to the Global Natural Products Social Molecular Networking 313 webserver (http://gnps.ucsd.edu) and submitted to network analysis using the following 314 settings: Precursor Ion Mass Tolerance 0.02 Da, Fragment Ion Mass Tolerance 0.02 Da, Min 315 Pairs Cos 0.5, Min Matched Fragment Ions 6, Network TopK 10, Minimum Cluster Size 1, 316 Maximum Connected Component Size 200, Run MS Cluster off. The molecular networks 317 were visualized using Cytoscape version 3.4.0 (39). The data are publicly accessible at 318 http://gnps.ucsd.edu under the MassIVE accession no. MSV000081083 and network 319 exploration options and views are available and networking parameters described at: 320 321 https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=cbd1ea3bc17045188d2184c4289f3023 322 323 324 Integration of in silico annotation, automated chemical classification and substructure 325 recognition with mass spectral molecular networks 326 327 Pooled extracts of specimens for each Euphorbia species 328 329 In silico structure annotation was performed by submitting the preprocessed ms1-2.mgf output 330 file of the pooled extracts from Optimus to Sirius+CSI:FingerID (11, 12) with m/z tolerance set 331 to 20 ppm. Additionally, data was submitted to Network Annotation Propagation (NAP) (13). 332 For NAP, both [M+NH4]+ and [M+H]+ adducts were searched with m/z tolerance set to 15 ppm 333 and parameters described at: 334 335 https://proteomics.ucsd.edu/ProteoSAFe/status.jsp?task=184a80db74334668ae1d0c0f852cb7 336 7c 337 https://proteomics2.ucsd.edu/ProteoSAFe/status.jsp?task=2cfddd3b8b1e469181a13e7d3a867 338 a6f 339 340 We matched a custom database of molecular structures against our samples’ preprocessed 341 mass spectral data using both Sirius+CSI:FingerID and NAP. This database was compiled 342 manually from literature (3, 4) and the dictionary of natural products (DNP) 343 (http://dnp.chemnetbase.com). 344 Subsequently, in silico structure matches from Sirius+CSI:FingerID and NAP were submitted 345 to automated chemical classification using ClassyFire (http://classyfire.wishartlab.com/) (17) 346 and consensus classifications at each hierarchical level of the chemical per mass 347 spectral molecular subnetwork were calculated. Consensus classifications and molecular

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348 structures were then visualized on the molecular networks using Cytoscape version 3.4.0 (39). 349 350 Substructure recognition of the crude extracts was performed by submitting the preprocessed 351 ms2.mgf output file from Optimus to MS2LDA (14, 15). Data and parameters used are 352 publically accessible at http://ms2lda.org/basicviz/short_summary/390/. Subsequently 353 substructures (Mass2Motifs) were mapped on the nodes of the mass spectral molecular 354 network, and Mass2Motifs shared among different nodes were mapped on the edges 355 connecting the nodes and visualized using Cytoscape version 3.4.0 (39). 356 357 3D mass spectral molecular cartography 358 359 Data from 3D mass spectral molecular cartography were submitted to Network Annotation 360 Propagation (NAP) (13). Both [M+NH4]+ and [M+H]+ adducts were searched with m/z 361 tolerance set to 20 ppm and parameters described at: 362 363 https://proteomics2.ucsd.edu/ProteoSAFe/status.jsp?task=6d58b49a645d408e9471f82d1b308 364 f6a 365 https://proteomics2.ucsd.edu/ProteoSAFe/status.jsp?task=425dd24f42d54b4ba58aba69f2f1df 366 ac 367 368 The same custom database of molecular structures as described for the pooled extracts was 369 matched against our samples’ mass spectral data. Subsequently, in silico structure matches 370 from NAP were submitted to automated chemical classification using ClassyFire 371 (http://classyfire.wishartlab.com/) (17) and consensus classifications at each hierarchical level 372 of the chemical taxonomy per mass spectral molecular subnetwork were calculated. Consensus 373 classifications and molecular structures were then visualized on the molecular networks using 374 Cytoscape version 3.4.0 (39). 375 376 3D modeling and visualization 377 378 We converted app. 250 photos taken from one to three specimens of one representative species 379 of each subgeneric clade of Euphorbia into high-definition 3D meshes using Autodesk 380 Remake (https://remake.autodesk.com/). The 3D meshes were then exported to .obj format and 381 edited using Meshmixer (http://www.meshmixer.com/). Point coordinates for sampled plant 382 parts were added using Meshlab (41), and the 3D Models were exported to .stl format. 383 Representative samples were taken of individual plant parts (e.g., roots, upper and lower 384 leaves and stems, fruits), rather than exact sample locations (e.g., 3-5 representative pieces of 385 one inflorescence were taken and pooled together for LC-MS/MS analysis, the entire 386 inflorescence was subsequently mapped with the pooled LC-MS data). The 3D mass spectral 387 mapping thus shows data obtained from representative pools of individual plant parts, rather 388 than being accurate point representatives of plant parts sampled. Point coordinates and LC-MS 389 data were combined into a .csv file and were then mapped on the 3D Models using ’ili 390 (https://github.com/MolecularCartography/ili) (37, 42). URL links of cartographical snapshots, 391 which can be opened in a web browser can be found in URL S1, links 1-15. Each URL will 392 open the ‘ili web application, and load data, settings, and visualizations as shown in Fig. 3 and 393 Fig. S2-S9. Additionally, the visualization is interactive, and the models can be rotated,

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394 visualization parameters changed, and other molecules selected by their retention time and m/z 395 value (37). 396 397 Calculation of the chemical structural compositional similarity and chemogram 398 399 To assess specialized metabolite diversity in relation to the evolutionary history of Euphorbia, 400 we calculated the chemical structural and compositional similarity (CSCS) per Euphorbia 401 subgeneric clade (25). CSCS provides a more accurate estimate of phytochemical diversity 402 compared to traditional dissimilarity measures (e.g., Bray Curtis) by integrating the cosine 403 scores retrieved from the mass spectral molecular network analysis and thus also accounting 404 for chemical structural similarity among specialized metabolites detected by mass 405 spectrometry (25, 43). To illustrate chemical similarity among species we created a 406 chemogram by using the pairwise chemical structural and compositional dissimilarities 407 (CSCD), corresponding to 1-CSCS, as input for a hierarchical cluster analysis with the 408 complete agglomeration method. 409 410 Phylogenetic hypothesis and comparative methods 411 412 We produced a phylogenetic hypothesis of Euphorbia compiling DNA sequences from 10 413 markers spanning all three plant genomes (cp accD, cp rbcL-accD, cp ndhF, cp rbcL, cp rpl16, 414 cp trnL-F, mt nad1B-C, mt rps3, nu EMB2765, nu ITS) of a publicly available dataset (1, 2). 415 Our matrix included sequences (11587 base pairs) of 38 out of the 43 Euphorbia species 416 investigated chemically. We produced a Bayesian phylogenetic hypothesis using parameters 417 described in Horn and collaborators (1). Subsequently, we added missing species (E. 418 sarcoceras, E. hyssopifolia, E. ophthalmica, E. prostrata, E. thymifolia) based on their 419 subgeneric classification (44, 45) using the bind.tip function of the R package phytools (46) to 420 the 50% majority rule consensus tree. We performed a phylogenetic generalized least squares 421 regression analysis (PGLS) using the pgls function of the R package caper (47) to test whether 422 the number of putatively annotated molecules within a chemical subclass and the number of 423 TNF-α modulating fractions are significantly associated over evolutionary time. PGLS is a 424 modification of the generalized least squares regression, taking into account non-independence 425 of species sharing an evolutionary history by weighing the generalized least squares regression 426 by the amount of expected correlation between species based on their phylogenetic 427 relationships (48). We only assessed chemical subclasses containing at least 5 molecules and 428 non-zero values for at least 20 species out of the 43 species within the phylogenetic tree. Table 429 S1 shows chemical subclasses and associated p-values. Fig. 1 and Fig. S12 show the number 430 of TNF-α modulating fractions versus the number of putatively annotated molecules within the 431 chemical subclasses with significant p-values (Euphorbia diterpenoids, overall diterpenoids, 432 glycosylglycerols) across the phylogenetic tree. The best fit was observed for the Euphorbia 433 diterpenoids (Fig. 1), with species of the American clade within subgenus Chamaesyce being 434 almost entirely deprived of these compounds, corresponding to the TNF-α modulating 435 properties of these species. 436 437 Species occurrences of sampled Euphorbia and Euphorbia-feeding Hyles 438 439 We downloaded species occurrences of sampled Euphorbia species and Euphorbia-feeding

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440 Hyles from the Global Biodiversity Information Facility (GBIF, https://www.gbif.org). Before 441 mapping species occurrences, we reduced the datasets to the native distributions of the 442 respective species (49-51). Species names of Euphorbia-feeding Hyles were retrieved from 443 Hundsdoerfer and collaborators (28). URL links to GBIF species occurrence downloads are 444 found below: 445 Species of subgenus Euphorbia: GBIF.org (5th January 2018) GBIF Occurrence 446 Download https://doi.org/10.15468/dl.llmwkb 447 Species of subgenus Esula: GBIF.org (5th January 2018) GBIF Occurrence 448 Download https://doi.org/10.15468/dl.avy89i 449 Species of subgenus Chamaesyce: GBIF.org (5th January 2018) GBIF Occurrence 450 Download https://doi.org/10.15468/dl.9ntc6n 451 Species of subgenus Athymalus: GBIF.org (5th January 2018) GBIF Occurrence 452 Download https://doi.org/10.15468/dl.ug6cnm 453 Hyles euphorbiae: GBIF.org (5th January 2018) GBIF Occurrence 454 Download https://doi.org/10.15468/dl.cgwykr 455 Other Euphorbia-feeding Hyles species: GBIF.org (24th January 2018) GBIF Occurrence 456 Download https://doi.org/10.15468/dl.ahwr3w 457 458 Code availability 459 460 All scripts used for data analysis were written in R version 3.3.2 (http:// www.R-project.org/) 461 and are publicly accessible at https://github.com/DorresteinLaboratory/supplementary- 462 GlobalEuphorbiaStudy. 463 464 Data availability 465 466 LC-MS/MS data are publicly accessible on GNPS (see links provided in the Material and 467 Methods section). The custom database of chemical structures are publicly 468 accessible at https://github.com/DorresteinLaboratory/supplementary-GlobalEuphorbiaStudy. 469 Bayesian trees will be deposited on dryad during the publication process.

470 Supplementary Text 471 472 Extended description of mass spectral molecular network analysis 473 474 The mass spectral molecular network of the pooled extracts consisted of 5,652 nodes, organized 475 in 512 independent molecular families, comprising a total of 2,739 nodes (two or more 476 connected components of a graph) (Fig. S1). Automated chemical classification of the in silico 477 annotated structures through ClassyFire (17) resulted in putatively identified compound classes 478 for over 1,800 nodes in the network, corresponding to a level 3 metabolite identification 479 according to the Metabolomics Standard Initiative’s reporting standards (18). We manually 480 validated in silico structure annotation at the subclass level and the direct parent level for the 481 diterpenoids, which led to reclassification in some cases. Detailed description of the 482 interpretation of the in silico annotated compound classes is found in 483 Cytoscape_SummaryTable.csv under the column headers ‘Subclass_Interpretation’ and

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484 ‘Diterpenoid_Subclass_Interpretation’ available at 485 https://github.com/DorresteinLaboratory/supplementary-GlobalEuphorbiaStudy. 486 487 The mass spectral molecular network of the extracts used for 3D mass spectral molecular 488 cartography consisted of 5,335 nodes, organized in 435 independent molecular families, 489 comprising a total of 3,318 nodes (two or more connected components of a graph). Automated 490 chemical classification of the in silico annotated structures through ClassyFire (17) resulted in 491 putatively identified compound classes for over 2,000 nodes in the network, corresponding to a 492 level 3 metabolite identification according to the Metabolomics Standard Initiative’s reporting 493 standards (18). We manually validated in silico structure annotation for the Euphorbia 494 diterpenoids, which led to reclassification in some cases. Manually validated Euphorbia 495 diterpenoids can be found in Cytoscape_SummaryTable_3DMolecularCartography.csv under the 496 column header ‘Diterpenoid_Subclass_Interpretation’ available at 497 https://github.com/DorresteinLaboratory/supplementary-GlobalEuphorbiaStudy.

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498 Table S1. Phylogenetic generalized least squares regression analysis (PGLS) between the 499 number of TNF-α modulating fractions and the number of compounds per chemical subclass. 500 Chemical subclass p-value Adjusted R-squared

Benzoic acids and derivatives 0.79 -0.02 Cholestane steroids 0.54 -0.01 Diterpenoids 0.02 0.11 * Glycosphingolipids 0.95 -0.02 Glycosylglycerols 0.02 0.10 * Stigmastanes and derivatives 0.16 0.02 Triterpenoids 0.63 -0.02 Regular diterpenoids 0.06 0.06 Euphorbia diterpenoids 0.02 0.10 * 501

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502 503 Fig. S1. Euphorbia global mass spectral molecular network: visualization of Euphorbia 504 subgeneric clades. Pie charts represent total ion current (TIC) observed per subgeneric clade, 505 node size represents TIC of all samples, the thickness of the lines connecting the nodes (edges) is 506 a representative of the cosine score. Nodes showing no similarity to any other node are not 507 shown.

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508 509 Fig. S2. Euphorbia global mass spectral molecular network with major chemical classes 510 highlighted. In silico annotation resulted in putative identification of over 30% of the nodes 511 within the mass spectral molecular network up to the level of chemical subclasses.

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512 513 Fig. S3. 3D molecular map of a Euphorbia diterpene in Euphorbia horrida with predominant 514 occurrence in the roots. A. Euphorbia horrida Model 1. B. Euphorbia horrida Model 2. C. 515 Euphorbia horrida Model 3. D. MS/MS spectrum of ingenol or deoxyphorbol ester with 516 hexanoyl and putatively annotated features 517 (http://gnps.ucsd.edu//ProteoSAFe/result.jsp?task=cbd1ea3bc17045188d2184c4289f3023&view 518 =cluster_details&protein=2854). For interactive cartographical snapshots see URL S1, links 1-3.

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519 520 Fig. S4. 3D molecular map of a Euphorbia diterpene in Euphorbia hirta with predominant 521 occurrence in the fruits/seeds. A. Euphorbia hirta Model 1. B. Euphorbia hirta Model 2. C. 522 MS/MS spectrum exhibiting diterpene spectral fingerprint of type A (DSF-A) with m/z 311, 293, 523 283 and partially annotated features 524 (http://gnps.ucsd.edu//ProteoSAFe/result.jsp?task=cbd1ea3bc17045188d2184c4289f3023&view 525 =cluster_details&protein=529). For interactive cartographical snapshots see URL S1, links 4-5. 526

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527 528 Fig. S5. 3D molecular map of a Euphorbia diterpene in Euphorbia lathyris with predominant 529 occurrence in the young stems. A. Euphorbia lathyris Model 1. B. Euphorbia lathyris Model 2. 530 C. MS/MS spectrum exhibiting diterpene spectral fingerprint of type A (DSF-A) with m/z 311, 531 293, 283 532 (http://gnps.ucsd.edu//ProteoSAFe/result.jsp?task=cbd1ea3bc17045188d2184c4289f3023&view 533 =cluster_details&protein=2894). For interactive cartographical snapshots see URL S1, links 6-7.

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534 535 Fig. S6. 3D molecular map of a milliamine C derivative in Euphorbia milii var. hislopii with 536 predominant occurrence in the roots. A. Euphorbia milii var. hislopii Model 1. B. Euphorbia 537 milii var. hislopii Model 2. Additionally to the roots, low occurrence of milliamine C derivative 538 was also observed in the young stems and leaves. C. MS/MS spectrum annotated as milliamine 539 C; 3-O-Deacyl-5-O-acyl isomer, de(dimethylamino), 20-Ac. The MS/MS spectrum can be 540 accessed at:

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541 http://gnps.ucsd.edu//ProteoSAFe/result.jsp?task=cbd1ea3bc17045188d2184c4289f3023&view= 542 cluster_details&protein=2213. For interactive cartographical snapshots see URL S1, links 8-9.

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543

544 545 Fig. S7. 3D molecular map of a milliamine C derivative in Euphorbia milii var. hislopii with 546 predominant occurrence in the roots. A. Euphorbia milii var. hislopii Model 1. B. Euphorbia 547 milii var. hislopii Model 2. C. MS/MS spectrum annotated as milliamine C; 20-Ac, N-de-Me. 548 The MS/MS spectrum can be accessed at: 549 http://gnps.ucsd.edu//ProteoSAFe/result.jsp?task=cbd1ea3bc17045188d2184c4289f3023&view= 550 cluster_details&protein=1214. For interactive cartographical snapshots see URL S1, links 10-11.

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551 552 Fig. S8. 3D molecular map of milliamine C derivative in Euphorbia milii var. hislopii with 553 predominant occurrence in the roots. A. Euphorbia milii var. hislopii Model 1. B. Euphorbia 554 milii var. hislopii Model 2. Additionally, to the roots, low occurrence of milliamine C was also 555 observed in the young stems. C. MS/MS spectrum annotated as miliiamine C; 20-Ac, N-de-Me 556 (or positional isomer). The MS/MS spectrum can be accessed at:

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557 http://gnps.ucsd.edu//ProteoSAFe/result.jsp?task=cbd1ea3bc17045188d2184c4289f3023&view= 558 cluster_details&protein=1134. For interactive cartographical snapshots see URL S1, links 12-13.

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559 560 Fig. S9. 3D molecular map of milliamine C derivative in Euphorbia milii var. hislopii with 561 predominant occurrence in the roots. A. Euphorbia milii var. hislopii Model 1. B. Euphorbia 562 milii var. hislopii Model 2. C. MS/MS spectrum annotated as milliamine C; 20-Ac. The MS/MS 563 spectrum can be accessed:

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564 (http://gnps.ucsd.edu//ProteoSAFe/result.jsp?task=cbd1ea3bc17045188d2184c4289f3023&view 565 =cluster_details&protein=1210). For interactive cartographical snapshots see URL S1, links 14- 566 15.

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567 568 Fig. S10. Differential expression (MS1 intensities) of molecules annotated as Euphorbia 569 diterpenoids in different plant parts of one representative species per Euphorbia subgeneric 570 clade. Euphorbia diterpenoid production is reduced throughout the whole plant in Euphorbia 571 hirta, a representative of the American radiation within subgenus Chamaesyce (blue). In 572 Euphorbia horrida (subgenus Athymalus, red) and Euphorbia milii var. hislopii (subgenus 573 Euphorbia, purple) Euphorbia diterpenoids were predominantly found in the roots (Fig. S3 and 574 S6-S15) and in Euphorbia lathyris (Subgenus Esula, green) they were abundant in several plant 575 parts, among others in the young leaves and stems (Fig. S5).

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576 577 Fig. S11. Illustration of sampling locations for 3D mass spectral molecular cartography. 578 Individual plant parts from one to three specimens of one representative species of each 579 subgeneric clade of Euphorbia were sampled. A.- C. E. horrida, subgenus Athymalus, 3 580 specimens. LC-MS/MS data were mapped on three 3D Models of three individual specimens. D. 581 E. hirta, Chamaesyce, 2 specimens. LC-MS/MS data were mapped on one representative 3D 582 Model of one specimen; E. E. lathyris, Esula, 2 specimens. LC-MS/MS data were mapped on 583 one representative 3D Model of one specimen. F. E. milii var. hislopii, Euphorbia, 2 specimens. 584 LC-MS/MS data were mapped on one representative 3D Model of one specimen.

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585 586 Fig. S12. Compound classes showing significant association to the number of TNF-α modulating 587 fractions using phylogenetic generalized least squares regression analysis. Euphorbia 588 phylogenetic tree (50% majority rule consensus tree from Bayesian analysis of 11587 bps of 589 DNA markers spanning all three plant genomes) and number of TNF-α modulating fractions 590 versus number of putatively annotated diterpenoids and glycosylglycerols per species analyzed.

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591 URL S1. 592 593 The URL links listed below can be opened in a web browser. Each of the links stores a snapshot 594 of the view shown in Fig.S2-S9. Clicking on the URL opens the ’ili application and loads the 595 data settings. The visualizations are interactive: one can rotate the model, change the parameters 596 of the visualization or visualize different molecular features based on m/z and retention time 597 values (37). 598 599 1. Euphorbia horrida, Model 1 600 601 https://ili.embl.de/?ftp://massive.ucsd.edu/MSV000081081/updates/2017-05- 602 15_mernst_9ac10437/peak/EHorrida_Model1_WithRoots.stl;ftp://massive.ucsd.edu/MSV00008 603 1081/updates/2018-03- 604 21_mernst_c88e7520/peak/X604.4476_10.9182_ID..2854_Horrida1.json;ftp://massive.ucsd.edu/ 605 MSV000081081/updates/2018-03- 606 21_mernst_c88e7520/peak/EHorrida_Model1_features_MZmine.csv 607 608 2. Euphorbia horrida, Model 2 609 610 https://ili.embl.de/?ftp://massive.ucsd.edu/MSV000081081/updates/2017-05- 611 15_mernst_9ac10437/peak/EHorrida_20160915_Model2_Clone_withroots.stl;ftp://massive.ucsd 612 .edu/MSV000081081/updates/2018-03- 613 21_mernst_c88e7520/peak/X604.4476_10.9182_ID..2854_Horrida2.json;ftp://massive.ucsd.edu/ 614 MSV000081081/updates/2018-03- 615 21_mernst_c88e7520/peak/EHorrida_Model2_features_MZmine.csv 616 617 3. Euphorbia horrida, Model 3 618 619 https://ili.embl.de/?ftp://massive.ucsd.edu/MSV000081081/updates/2017-05- 620 15_mernst_9ac10437/peak/EHorrida_20160915_Model3_withroots.stl;ftp://massive.ucsd.edu/M 621 SV000081081/updates/2018-03- 622 21_mernst_c88e7520/peak/X604.4476_10.9182_ID..2854_Horrida3.json;ftp://massive.ucsd.edu/ 623 MSV000081081/updates/2018-03- 624 21_mernst_c88e7520/peak/EHorrida_Model3_features_MZmine.csv 625 626 4. Euphorbia hirta, Model 1 627 628 https://ili.embl.de/?ftp://massive.ucsd.edu/MSV000081081/updates/2017-05- 629 15_mernst_9ac10437/peak/EHirta_20160908_Model2.stl;ftp://massive.ucsd.edu/MSV00008108 630 1/updates/2018-03- 631 21_mernst_c88e7520/peak/X850.5811_13.8857_ID..529_Hirta1.json;ftp://massive.ucsd.edu/MS 632 V000081081/updates/2018-03- 633 21_mernst_c88e7520/peak/EHirta_Model1_features_MZmine.csv 634 635 5. Euphorbia hirta, Model 2 636

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637 https://ili.embl.de/?ftp://massive.ucsd.edu/MSV000081081/updates/2017-05- 638 15_mernst_9ac10437/peak/EHirta_20160908_Model2.stl;ftp://massive.ucsd.edu/MSV00008108 639 1/updates/2018-03- 640 21_mernst_c88e7520/peak/X850.5811_13.8857_ID..529_Hirta2.json;ftp://massive.ucsd.edu/MS 641 V000081081/updates/2018-03- 642 21_mernst_c88e7520/peak/EHirta_Model2_features_MZmine.csv 643 644 6. Euphorbia lathyris, Model 1 645 646 https://ili.embl.de/?ftp://massive.ucsd.edu/MSV000081081/peak/ELathyris_Final.stl;ftp://massiv 647 e.ucsd.edu/MSV000081081/updates/2018-03- 648 21_mernst_c88e7520/peak/X630.3619_4.8084_ID..2894_Lathyris1.json;ftp://massive.ucsd.edu/ 649 MSV000081081/updates/2018-03- 650 21_mernst_c88e7520/peak/ELathyris_Model1_features_MZmine.csv 651 652 7. Euphorbia lathyris, Model 2 653 654 https://ili.embl.de/?ftp://massive.ucsd.edu/MSV000081081/peak/ELathyris_Final.stl;ftp://massiv 655 e.ucsd.edu/MSV000081081/updates/2018-03- 656 21_mernst_c88e7520/peak/X630.3619_4.8084_ID..2894_Lathyris2.json;ftp://massive.ucsd.edu/ 657 MSV000081081/updates/2018-03- 658 21_mernst_c88e7520/peak/ELathyris_Model2_features_MZmine.csv 659 660 8. Euphorbia milii, Model 1, milliamine C, Fig. S6 661 662 https://ili.embl.de/?ftp://massive.ucsd.edu/MSV000081081/updates/2017-05- 663 15_mernst_9ac10437/peak/EMilii_withRoots.stl;ftp://massive.ucsd.edu/MSV000081081/updates 664 /2018-03- 665 21_mernst_c88e7520/peak/X749.3005_4.4042_ID..2213_Milii1.json;ftp://massive.ucsd.edu/MS 666 V000081081/updates/2018-03- 667 21_mernst_c88e7520/peak/EMilii_Model1_features_MZmine.csv 668 669 9. Euphorbia milii, Model 2, milliamine C, Fig. S6 670 671 https://ili.embl.de/?ftp://massive.ucsd.edu/MSV000081081/updates/2017-05- 672 15_mernst_9ac10437/peak/EMilii_withRoots.stl;ftp://massive.ucsd.edu/MSV000081081/updates 673 /2018-03- 674 21_mernst_c88e7520/peak/X749.3005_4.4042_ID..2213_Milii2.json;ftp://massive.ucsd.edu/MS 675 V000081081/updates/2018-03- 676 21_mernst_c88e7520/peak/EMilii_Model2_features_MZmine.csv 677 678 10. Euphorbia milii, Model 1, milliamine C, Fig. S7 679 680 https://ili.embl.de/?ftp://massive.ucsd.edu/MSV000081081/updates/2017-05- 681 15_mernst_9ac10437/peak/EMilii_withRoots.stl;ftp://massive.ucsd.edu/MSV000081081/updates 682 /2018-04-

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683 30_mernst_ec3691ad/peak/X778.33_3.7326_ID..1214_Milii1.json;ftp://massive.ucsd.edu/MSV0 684 00081081/updates/2018-03-21_mernst_c88e7520/peak/EMilii_Model1_features_MZmine.csv 685 686 11. Euphorbia milii, Model 2, milliamine C, Fig. S7 687 688 https://ili.embl.de/?ftp://massive.ucsd.edu/MSV000081081/updates/2017-05- 689 15_mernst_9ac10437/peak/EMilii_withRoots.stl;ftp://massive.ucsd.edu/MSV000081081/updates 690 /2018-04- 691 30_mernst_ec3691ad/peak/X778.33_3.7326_ID..1214_Milii2.json;ftp://massive.ucsd.edu/MSV0 692 00081081/updates/2018-03-21_mernst_c88e7520/peak/EMilii_Model2_features_MZmine.csv 693 694 12. Euphorbia milii, Model 1, milliamine C, Fig. S8 695 696 https://ili.embl.de/?ftp://massive.ucsd.edu/MSV000081081/updates/2017-05- 697 15_mernst_9ac10437/peak/EMilii_withRoots.stl;ftp://massive.ucsd.edu/MSV000081081/updates 698 /2018-04- 699 30_mernst_ec3691ad/peak/X778.3291_5.1918_ID..1134_Milii1.json;ftp://massive.ucsd.edu/MS 700 V000081081/updates/2018-03- 701 21_mernst_c88e7520/peak/EMilii_Model1_features_MZmine.csv 702 703 13. Euphorbia milii, Model 2, milliamine C, Fig. S8 704 705 https://ili.embl.de/?ftp://massive.ucsd.edu/MSV000081081/updates/2017-05- 706 15_mernst_9ac10437/peak/EMilii_withRoots.stl;ftp://massive.ucsd.edu/MSV000081081/updates 707 /2018-04- 708 30_mernst_ec3691ad/peak/X778.3291_5.1918_ID..1134_Milii2.json;ftp://massive.ucsd.edu/MS 709 V000081081/updates/2018-03- 710 21_mernst_c88e7520/peak/EMilii_Model2_features_MZmine.csv 711 712 14. Euphorbia milii, Model 1, milliamine C, Fig. S9 713 714 https://ili.embl.de/?ftp://massive.ucsd.edu/MSV000081081/updates/2017-05- 715 15_mernst_9ac10437/peak/EMilii_withRoots.stl;ftp://massive.ucsd.edu/MSV000081081/updates 716 /2018-04- 717 30_mernst_ec3691ad/peak/X792.3451_2.6119_ID..1210_Milii1.json;ftp://massive.ucsd.edu/MS 718 V000081081/updates/2018-03- 719 21_mernst_c88e7520/peak/EMilii_Model1_features_MZmine.csv 720 721 15. Euphorbia milii, Model 2, milliamine C, Fig. S9 722 723 https://ili.embl.de/?ftp://massive.ucsd.edu/MSV000081081/updates/2017-05- 724 15_mernst_9ac10437/peak/EMilii_withRoots.stl;ftp://massive.ucsd.edu/MSV000081081/updates 725 /2018-04- 726 30_mernst_ec3691ad/peak/X792.3451_2.6119_ID..1210_Milii2.json;ftp://massive.ucsd.edu/MS 727 V000081081/updates/2018-03- 728 21_mernst_c88e7520/peak/EMilii_Model2_features_MZmine.csv

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729 Data S1. (separate file) 730 List of Euphorbia species sampled. 731

732 Data S2. (separate file) 733 High-resolution TNF-α modulation profiles.

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