Parallel reductions in phenolic constituents resulting from the domestication of eggplant

Phytochemistry xxx (2015) xxx–xxx

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Parallel reductions in phenolic constituents resulting from the domestication of eggplant Rachel S. Meyer a,b,c,⇑, Bruce D. Whitaker d, Damon P. Little a, Shi-Biao Wu e, Edward J. Kennelly b,e, Chun-Lin Long f, Amy Litt a a

The New York Botanical Garden, 2900 Southern Blvd, Bronx, NY 10458, United States The Graduate Center, The City University of New York, 365 Fifth Avenue, New York, NY 10016, United States New York University, Center for Genomics and Systems Biology, 12 Waverly Place, New York, NY 10003, United States d Food Quality Laboratory, Building 002, Room 117, Beltsville Agricultural Research Center-West, Agricultural Research Service, USDA, 10300 Baltimore Avenue, Beltsville, MD 20705, United States e Department of Biological Sciences, Lehman College, The City University of New York, 250 Bedford Park Boulevard West, Bronx, NY 10468, United States f College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, PR China b c

a r t i c l e

i n f o

Article history: Received 11 December 2013 Received in revised form 21 December 2014 Available online xxxx Keywords: Eggplant Solanum melongena Solanaceae Caffeic acid derivatives Domestication Domestication syndrome Phenolics Food crop Crop wild relatives

a b s t r a c t Crop domestication is often accompanied by changes in metabolite compositions that alter traits such as flavor, color, or other beneficial properties. Fruits of eggplants (Solanum melongena L.) and related species are abundant and diverse in pharmacologically interesting phenolic compounds, particularly hydroxycinnamic acid (HCA) conjugates such as the antioxidant caffeoylquinic acids (CQA) and HCA-polyamine amides (HCAA). To understand metabolite variability through the lens of natural and artificial selection, HPLC-DAD was used to generate phenolic profiles for 32 compounds in fruits from 93 accessions representing 9 Solanum species. Profiles were used for identification of species-level and infraspecific chemical patterns across both genetic distance and landscape. Sampling of plant lines included the undomesticated progenitor of eggplant and Asian landraces with a genetic background associated with three Asian regions near proposed separate centers of domestication to test whether chemical changes were convergent despite different origins. Results showed ten compounds were unique to species, and ten other compounds varied significantly in abundance among species. Five CQAs and three HCApolyamine conjugates were more abundant in wild (undomesticated) versus domesticated eggplant, indicating that artificial selection may have led to reduced phenolic levels. No chemical abundance patterns were associated with site-origin. However, one genetically distinct lineage of geographicallyrestricted SE Asian eggplants (S. melongena subsp. ovigerum) had a higher HCAA content and diversity than other lineages, which is suggested to be related to artificial selection for small, firm fruit. Overall, patterns show that fruit size, palatability and texture were preferentially selected over health-beneficial phytochemical content during domestication of several nightshade crops. Ó 2015 Published by Elsevier Ltd.

1. Introduction Domestication is an ongoing process that changes plants through conscious and unconscious selection according to humanity’s needs, tools, technologies and unintended impacts. One of the major changes accompanying domestication is with secondary or ‘‘specialized’’ metabolites (Meyer et al., 2012a). Where, for instance, the edible portion of the plant becomes more palatable ⇑ Corresponding author at: New York University, Center for Genomics and Systems Biology, 12 Waverly Place, New York, NY 10003, United States. Tel.: +1 206 351 7997. E-mail address: [email protected] (R.S. Meyer).

and less toxic. Study of how metabolite profiles have been altered as a result of natural and artificial selection provide highly useful data. It can lead to determinations of the most important regulators of variation produced by phytochemical biosynthetic pathways. Such work can also help identify which species are the richest in particular compounds useful for the crop improvement industry. Additionally, basic evolutionary biology can be better understood through illumination of the nature of human–plant interactions. One example of this is the examination of parallel and convergent evolution under selection by different societies across the globe (Fuller et al., 2014). Numerous pharmacologically valuable bioactive secondary metabolites are found in food crops, and the nightshade family

http://dx.doi.org/10.1016/j.phytochem.2015.02.006 0031-9422/Ó 2015 Published by Elsevier Ltd.

Please cite this article in press as: Meyer, R.S., et al. Parallel reductions in phenolic constituents resulting from the domestication of eggplant. Phytochemistry (2015), http://dx.doi.org/10.1016/j.phytochem.2015.02.006

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R.S. Meyer et al. / Phytochemistry xxx (2015) xxx–xxx

(Solanaceae) is among the richest sources. High phytochemical diversity has led to the economic value of hundreds of Solanaceae species as foods and medicines (Hawkes, 1999). However, the effect of selection on metabolite presence and abundance is obscure in the Solanaceae, as it is for most food crops. This has limited our understanding of the domestication process and underemphasized the importance of metabolic shifts as a major trait of the domestication syndrome (Hammer, 1984). This study focuses on how metabolite compositions vary in the context of domestication of the eggplant (Solanum melongena L.), a globally important health-promoting food (Plazas et al., 2013). Some of the earliest written evidence of eggplant use is actually

a description of its health-beneficial properties from the ancient Ayurvedic texts written ca. 2100 years ago, the Charaka and Sushruta Samhitas (Bhishagratna, 1907; Sharma and Dash, 1983), suggesting bioactive metabolites may have been intentionally selected at the start of domestication along with gross morphological traits such as larger fruit size and non-prickliness (Hurtado et al., 2012). Eggplant is still used for medicine (Meyer et al., 2014). Interestingly, it has been proposed that eggplant was independently domesticated in multiple regions of tropical Asia. Several researchers have proposed possible multiple domestications in South and SE Asia (reviewed in Lester and Daunay, 2001; Wang et al., 2008). Meyer et al. (2012b) proposed centers in India,

HO

OH NH(CH 2)3NH(CH 2) 4NH

HO

7

10 9 : 11 : 12 :

O

HO

OH

7'

O 7' 7 7

2: 5: 8: 13 :

O

(E)-caffeoyl : R1 = OH, R 2 = H (E)-feruloyl : R1 = OH, R 2 = OMe (E)-p-coumaroyl : R1 = R 2 = H (E)-sinapoyl : R1 = R 2 = OMe

7' OH

NH(CH 2)3N(CH 2) 4NH(CH 2)3NH 7

O

R1

R

HO

R2 HO

OH

7'

O

R=H R=H 7 R=H 7 7' R = dihydrocaffeoyl

HO

O

HO

O OH

O

malonyl

(Z)-caffeoyl HO R

NH(CH 2) 4NH 2

R

7

O

1 : R = OH 3 : R = OH 7 4: R =H 7 7 : R = OMe 7

O

NH 2(CH 2)3N(CH 2) 4N(CH 3)3NH

R OH NH 2

HN

6 : R = caffeoyl

32: tryptophan

OH OR3

O

OR2

R 4O OR1

R1 = (E)-caffeoyl R 3 = (E)-caffeoyl R 2 = (E)-caffeoyl HO R 3 = (Z)-caffeoyl HO R1 = R 3 = (E)-caffeoyl R 2 = R 3 = (E)-caffeoyl R1 = R 2 = (E)-caffeoyl R1 = malonyl, R 3 = (E)-caffeoyl R 2 = (E)-caffeoyl, R 3 = malonyl R 3 = (E)-p-coumaroyl R1 = (E)-feruloyl R 3 = (E)-feruloyl R1 or R 3 = (E)-feruloyl, R 2 = hexoside R1 = R 3 = (E)-feruloyl

O

O

HO OH

14: 15: 16: 17: 18: 19: 20: 21: 22: 23: 24: 25: 26: 27:

O

HO

O OH

O OH O

O

OH HO HO

O

O OH

O OH

OH

O

O

O

O

O

OH

HO OH

OR

O

O

OR3

R1 O OH

28 : R = H 29 : R = malonyl

OR2

30 : R1 = R 3 = H, R 2 = malonyl 32 : R1 = R 2 = H, R 3 = malonyl

Fig. 1. Structures of the 32 compounds for which relative abundance was quantified in 93 accessions of eggplant (Solanum melongena L.) and related Solanum.

Please cite this article in press as: Meyer, R.S., et al. Parallel reductions in phenolic constituents resulting from the domestication of eggplant. Phytochemistry (2015), http://dx.doi.org/10.1016/j.phytochem.2015.02.006

R.S. Meyer et al. / Phytochemistry xxx (2015) xxx–xxx

southern China, and around Malaysia, and Cericola et al. (2013) also found a population structure that supports multiple domestications in South and East Asian centers. Although the specific origins remain unresolved, eggplant’s long domestication history in floristically distinct candidate geographic centers of origin is a platform to test a central question in crop evolutionary biology: was selection under domestication unique or parallel among centers? And further, can the desired traits be determined that drove selection on such metabolites, leading to a better understanding of how domestication proceeds? The metabolites focused upon in this study belong to a group of phenolic compounds known as hydroxycinnamic acid (HCA) conjugates. These possess health-beneficial activity (Whitaker and Stommel, 2003; Ma et al., 2010), contribute to the flavor of the fruit (Frank et al., 2006), and assist in cellular processes that can affect fruit texture or other physical attributes (Facchini et al., 2002; Bassard et al., 2010). The composition and abundance of HCA conjugates (Fig. 1) in fruit extracts, referred to herein as the phenolic profile, were quantified using HPLC-DAD and the chemical variance was explored in the context of the evolutionary relationships of nine Solanum species (Fig. 2). Eggplant is the most important Solanum food crop after the tomato (Solanum lycopersicum L.) and potato (Solanum tuberosum L.). Among these three crops, eggplant has the highest free radical scavenging potential, largely due to its high content of 5-O-[E]caffeoylquinic acid (15) (5-CQA (see Fig. 1 for structures); chlorogenic acid) which typically accounts for P70% of the total phenolic compounds in the fruit pulp (Winter and Herrmann, 1986; Whitaker and Stommel, 2003). Dozens of other less common phenolic compounds, mostly caffeic acid derivatives, have been reported in the pulp of eggplant and its close relatives (Winter and Herrmann, 1986; Whitaker and Stommel, 2003), and, like 5-CQA (15), they contribute to flavor and health beneficial qualities (Ma et al., 2010). These phenolics include derivatives of 5-CQA, other HCA quinate esters, and HCA amides of polyamines (HCAAs). Only scant information is available on the biological activity and presence of most of these compounds. Therefore, another aim of this work was to compile a library of HCA conjugates in eggplant and closely-related species using a sampling strategy that favored capturing intraspecific diversity through sampling 62 S. melongena accessions (mostly Asian landraces hailing from the putative domestication centers) rather than characterizing the stability of compound abundance among plants of the same accession.

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2. Results Nine species within Solanum subgenus Leptostemonum were selected for phytochemical profiling. Material included 62 accessions of domesticated eggplant (S. melongena), many of them landraces of historic or recent germplasm collections, and 31 accessions representing the other species of close and distant relatives of eggplant (Table S1). The ingroup of this study was S. melongena and its wild progenitor, Solanum insanum. Three African species, Solanum richardii Dunal, Solanum macrocarpon L. (the domesticated gboma eggplant), and Solanum linnaeanum Hepper & P.-M.L. Jaeger, were included. Also included were two species of African or Asian origin from section Oliganthes (Fig. 2), a section that is phylogenetically close to section Melongena. These were Solanum aethiopicum, (the domesticated scarlet eggplant), and Solanum violaceum Orteg., a close wild relative. Distantly-related New World Solanum species belonging to section Acanthophora were included: Solanum capsicoides All., occasionally cultivated as an ornamental, and Solanum viarum Dunal, a worldwide invasive weed. Most outgroup species were only represented by one accession; however, multiple accessions were included of the cultivated outgroups, S. aethiopicum, S. macrocarpon, and S. capsicoides.

2.1. HPLC profiles of Solanum HCA conjugates from fruit quantified 32 therapeutic compounds HPLC analysis of HCA conjugates from the fruit pulp of the 93 accessions enabled relative quantification of 32 compounds (Tables 1, S1; Fig. 1). The 32 compounds were assigned to eight groups based on structural similarity (See Table 1 for full names and group associations; Fig. 3). Identification was based on C18-HPLC retention times, UV absorbance spectra, and where standards were not available and peaks did not coelute with compounds previously identified, on ES-MS and 1H-NMR spectroscopic analyses (Table S2). The groups of compounds and their health-related associations are as follows: Group 1 included thirteen compounds identified as HCA amides of polyamines (HCAA), consisting of either a caffeic and/or dihydrocaffeic acid N-linked with a polyamine (spermidine, spermine, or putrescine), or with ferulic acid substituting the caffeic acid. In planta functions of HCAAs include a proposed role in cell wall modification (Facchini et al., 2002; Edreva et al.,

Fig. 2. Simplified phylogeny based on recent molecular phylogenetic analyses (Levin et al., 2006; Weese and Bohs, 2010; Meyer et al., 2012b), of species within Solanum included in the present study, as well as S. incanum, whose phytochemistry has been studied together with domesticated eggplant (e.g., Prohens et al., 2013). * = subgenus Leptostemonum; # = Old World clade.

Please cite this article in press as: Meyer, R.S., et al. Parallel reductions in phenolic constituents resulting from the domestication of eggplant. Phytochemistry (2015), http://dx.doi.org/10.1016/j.phytochem.2015.02.006

Abbreviation

kmax (nm)

ES-MS [M 1]-ion

Confirmation

References

Group 1 – Hydroxycinnamic acid – polyamine amides (HCAA) 1 1_N-dhcaff-put N-dihydrocaffeoylputrescine 2 2_KukA N1,N14-bis(dihydrocaffeoyl)spermine 3 3_Caff-put N-caffeoylputrescine 4 4_Co-put N-p-coumaroylputrescine 5 5_Caff-dhcaff-spm N1-caffeoyl-N14-dihydrocaffeoylspermine 6 6_N5-Bis-caff-spm N1,N5-bis(caffeoyl)spermine 7 7_Fer-put N-Feruloylputrescine 8 8_N14-Bis-caff-spm N1,N14-bis(caffeoyl)spermine 9 9_Caff/dhcaff-spd-1 N1-caffeoyl-N10-dihydrocaffeoylspermidine

7.1 7.2 7.6 8.4 10.4 11 11.7 12 18.1

280 280 292, 297, 294, 294, 291, 294, 289,

317 306 318 318 318 318 320

251 529 249 233 527 525 263 525 470

UV, HR-TOF-MSa NMRb NMR, UV, HR-TOF-MS Co-injection Co-injection, HR-TOF-MS UV, HR-TOF-MS & NMR UV, Co-injection UV, Co-injection NMR, HR-TOF-MS

10 11 12 13

15.9 19.2 20.6 22.5

280 289, 319 296, 320 280

472 470 468 693

NMR UV, Co-injection, HR-TOF-MS NMR, HR-TOF-MS NMR

Wu et al. (2013) Wu et al. (2013) Wu et al. (2013), Table S2 Wu et al. (2013) Wu et al. (2013), Table S2 Stommel and Whitaker (2003) Bruce Whitaker (unpublished data) Wu et al. (2013) Whitaker and Stommel (2003) and Stommel and Whitaker (2003) Wu et al. (2013), Table S2 Wu et al. (2013) and Stommel and Whitaker (2003) Wu et al. (2013), Table S2 Wu et al. (2013)

Group 2 – Monocaffeoylquinic acid esters 14 14_3-CQA 3-O-[E]-caffeoylquinic acid

17.8

353

HR-TOF-MS & NMR

15

15_5-CQA

5-O-[E]-caffeoylquinic acid (chlorogenic acid)

21.6

353

HR-TOF-MS & NMR

16

16_4-CQA

4-O-(E)-caffeoylquinic acid

22.9

353

HR-TOF-MS & NMR

17

17_5Z-CQA

5-O-(Z)-caffeoylquinic acid

24.2

296 (sh), 326 296 (sh), 326 296 (sh), 326 319

353

HR-TOF-MS & NMR

3,5-di-O-[E]-caffeoylquinic acid 4,5-di-O-[E]-caffeoylquinic acid 3,4-di-O-[E]-caffeoylquinic acid

28.4 28.9 29.5

296, 328 296, 328 300, 329

515 515 515

NMR, HR-TOF-MS NMR, HR-TOF-MS NMR, HR-TOF-MS

Wu et al. (2013), Table S2 Wu et al. (2013), Table S2 Wu et al. (2013), Table S2

Group 4 – Malonylcaffeoylquinic acid esters 21 21_3-M-5-CQA 3-O-malonyl-5-O-[E]-caffeoylquinic acid 22 22_4-C-5-MQA 4-O-[E]-caffeoyl-5-O-malonylquinic acid

24.7 26.1

300, 328 300, 329

439 439

NMR, HR-TOF-MS, Co-injection NMR, HR-TOF-MS, Co-injection

Ma et al. (2011) Ma et al. (2011)

Group 5 – Mono-coumaroylquinic acid esters 23 23_5-p-CoQA 5-O-[E]-p-coumaroylquinic acid

25.2

298, 313

337

UV, HR-TOF-MS

Wu et al. (2013)

Group 6 – Mono- or Di-feruloylquinic acid esters 24 24_3-FQA 3-O-[E]-feruloylquinic acid 25 25_5-FQA 5-O-[E]-feruloylquinic acid 26 26_FQA-hexo 3 or 5-O-(E)-feruloylquinic acid-4-hexoside 27 27_-3,5-diFQA 3,5-di-O-[E]-feruloylquinic acid

22.1 25.7 25.9 30.3

299, 300, 299, 302,

330 328 330 330

367 367 529 543

UV, Co-injection UV, Co-injection HR-TOF-MS UV, MS fragment, tentatively identified

Prohens et al. (2013) Prohens et al. (2013) Wu et al. (2013) ShiBiao Wu (unpublished data)

25.6 26.9 29.1

296, 324 296, 324 221, 320

851 937 807

NMR, HR-TOF-MS NMR, HR-TOF-MS NMR, HR-TOF-MS

Wu et al. (2012) Wu et al. (2012) Ma et al. (2010)

30.1

221, 320

807

NMR, HR-TOF-MS

Ma et al. (2010)

12.8

253, 288

203

HR-TOF-MS & NMR

Wu et al. (2013)

Group 3 – Dicaffeoylquinic acid esters 18 18_3,5-diCQA 19 19_4,5-diCQA 20 20_3,4-diCQA

N1,N10-bis(dihydrocaffeoyl)spermidine N1-dihydrocaffeoyl-N10-caffeoylspermidine N1,N10-bis(caffeoyl)spermidine N1,N5,N14-tris(dihydrocaffeoyl)spermine

Group 7 – Glucoside – caffeoylquinic acid esters 28 28_ViaA Viarumacid A 29 29_ViaB Viarumacid B 30 30_3-M-5-CSGQA 3-O-malonyl-5-O-(E)-caffeoyl[4-(6sinapoyl)glucosyl]quinic acid 31 31_4-M-5-CSGQA 4-O-malonyl-5-O-(E)-caffeoyl[4-(6sinapoyl)glucosyl]quinic acid Group 8 – Other 32 32_Tryptophan a b

Tryptophan

HR-TOF-MS: high resolution time of flight mass. NMR: 1H-Nuclear magnetic resonance.

Prohens (2003) Prohens (2003) Prohens (2003) Prohens (2003)

et al. (2013) and Whitaker and Stommel et al. (2013) and Whitaker and Stommel et al. (2013) and Whitaker and Stommel et al. (2013) and Whitaker and Stommel

R.S. Meyer et al. / Phytochemistry xxx (2015) xxx–xxx

RT (min)

10_Bis-dhcaff-spd 11_Caff/dhcaff-spd-2 12_Bis-caff-spd 13_Tris-dhCaff-spm

Structure

4

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Table 1 Compounds identified from nine species of Solanum by HPLC-UV that were used in statistical analyses.

R.S. Meyer et al. / Phytochemistry xxx (2015) xxx–xxx

5

Fig. 3. Graph depicting each species’ (x-axis) average relative abundance (y-axis) of compounds as ions in negative mode per group assigned based on structural similarity (Table 1). The average relative abundance is taken from all accessions within each species.

2007; Bassard et al., 2010). In humans, compounds within this group such as N1,N14-bis(dihydrocaffeoyl)spermine (Table 1: compound 2) (Kukoamine A; KukA) have been reported to have antihypertensive, lipoxygenase inhibitory, and antiparasitic activity (Funayama et al., 1980; Garnelis et al., 2005; Parr et al., 2005; Hadjipavlou-Litina et al., 2009). Group 2 consisted of four monocaffeoyl quinic acid esters (14– 17) (monoCQAs), which included 5-O-[E]-caffeoylquinic acid (5-CQA) (15) and three of its isomers (14, 16, 17). These compounds have anticancer activity and anti-inflammatory activity related to bowel disease, and stimulate proper bile flow (Mori et al., 1986; Huang et al., 1988; Kasai et al., 2000; Monteiro et al., 2007; Qin et al., 2010). Group 3 included three dicaffeoylquinic acid (diCQA) esters, also known as isochlorogenic acids A, B, and C (18–20). Although less studied than monoCQAs, all three of these substitutions of diCQA have been shown to have antiviral (Robinson et al., 1996; Takemura et al., 2012) and antihepatotoxic (Choi et al., 2005) activity. Group 4 consisted of two malonylcaffeoylquinic acid esters, 3-M-5-CQA (21) and 4-C-5-MQA (22) which have moderate antioxidant and strong iron chelation activity (Ma et al., 2011). Group 5 only contained one compound, 5-O-[E]-p-coumaroylquinic acid (23), which has been shown to inhibit LDL cholesterol oxidation in addition to having hydroxyl-quenching antioxidant (Zang et al., 2000) and neuroprotective (Vauzour et al., 2010) capacity. Group 6 contained four feruloylquinic acid (FQA) esters (24–27) identified as 3-FQA (24), 5-FQA (25), FQA-hexoside (26), and 3,5diFQA (27), which are antioxidant and anticancer compounds widely reported in angiosperms (Alonso-Salces et al., 2009; Clifford et al., 2006a,b; Siracusa et al., 2011). Group 7 consisted of four complex glucosides of CQA: 3-Mal-5-Caff[4-(6-Sin)Gluc]-QA (30), 4-Mal-5-Caff[4-(6-Sin)Gluc]-QA (31) (Table 1; Ma et al., 2010), and Viarumacids A (28) and B (29) (Wu et al., 2012). Other minor peaks occurred in HPLC profiles, but LC–MS separation was poor or too little of the isolate was available to use NMR spectroscopy. No studies on the biological activity of these compounds have been reported. Group 8 contained tryptophan (32), which is an essential amino acid of the human diet; although this compound is not an HCA

derivative it was present in all profiles, therefore, it was included in the analyses. 2.2. Diversity and species-specificity of profiled compounds High diversity of compound compositions and relative abundance levels were observed (Fig. 3). Ten of the 32 compounds were only detected in one of the nine species (Table S1: 1, 2, 4, 5, 6, 8, 28–31). Surprisingly, no compounds were exclusively found in (called ‘unique’ in this study) eggplant or found only in eggplant and its wild progenitor, S. insanum (for more information on taxonomy see Meyer et al., 2013; Knapp et al., 2013). In the study herein, N-p-coumaroylputrescine (4) was only detected in S. insanum. However, more sensitive detection methods (LC-TOFMS) did trace this compound in minor abundance in other Solanum species Wu et al. (2013). Higher levels were only reported in species distantly related to eggplant (e.g., Solanum parcistrigosum). Of the other species in section Melongena (Fig. 2), S. richardii contained three unique HCA-spermine amides, (2, 5–6), and S. macrocarpon posessed two unique compounds, N-dihydrocaffeoylputrescine and N1,N14-bis(caffeoyl)spermine (1, 8). The two species from section Oliganthes, the domesticated S. aethiopicum and wild relative S. violaceum, did not contain any unique compounds. Within section Acanthophora, S. capsicoides did not contain any unique compounds, but its relative S. viarum contained four unique compounds including the 6-sinapoylglucosides of CQA (28–31; Group 7; Table 1). Thus, at least one unique compound was found in four species, which were therefore not used in downstream comparisons of relative abundance between species. These are potentially useful as marker compounds to support taxonomic identification based on morphology or molecular characters. 2.3. Distinguishing species by compound abundance The remaining 22 compounds found in more than one species were subjected to ANOVA and Tukey’s HSD tests to identify significant differences in relative abundance among species (Table 2). ANOVA results indicated that the abundance of the above ten compounds and the total abundance of all compounds profiled were significantly different (p < 0.05) among the nine species.

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7, 32, 12 12 13 7

12 12 12

7, 32, 13 —

21, 26, 22, T

7, 14 7, 14 7, 14 7 32, 14 7, 14 7, 13 — 13 13 13 13 7, 32, 13 13 —

S. violaceum S. viarum S. richardii S. melongena

10,136 ± 3552 5027 ± 3099 27,418 ± 12,072 34,074 9859 ± 3401 16,887 ± 7702 11,203 34,479 4919

Two of the compounds with significant levels of variance, N1,N10bis(dihydrocaffeoyl)spermidine (10) and 5-O-[Z]-CQA (17) (Table 1), were not significantly different between any two pairs of species in Tukey’s HSD analysis (Table 2) and were not considered further. The eight compounds that differed in abundance among species by Tukey’s HSD, were also candidate markers of species. S. insanum had significantly different abundance of three compounds compared to S. melongena (discussed below). In S. macrocarpon, tryptophan (32) abundance was higher than in all other species, N-feruloylputrescine (7) was higher than all species except for S. viarum, and N1,N5,N14-tris(dihydrocaffeoyl)spermine (13) was higher in amounts than four other species. S. viarum had a significantly higher abundance of compound 7 than all other species except for S. macrocarpon, and also had a higher abundance of 3-O-[E]-CQA (14) than all but the three outgroup species. In S. richardii, N1,N5,N14-tris(dihydrocaffeoyl)spermine (13) was significantly more abundant than in all other species. S. violaceum contained significantly higher amounts of N1,N10-bis(caffeoyl)spermidine (12) than in five species. Regarding this compound, it is notable is that N1,N10-bis(caffeoyl)spermidine (12) was the most abundant, and made up circa half of the total sum of compounds profiled in S. violaceum.

32, 13 32, 13 32, 13 32 7, 7, 7, 7, —

S. insanum

T T — —

To explore the differences in metabolite contents that were caused by domestication, accessions of S. insanum and S. melongena were used as proxies for ‘before’ and ‘after’ onset of domestication, and to test whether any compounds differed in abundance. Student’s t-tests (Table 3) showed eight compounds (4, 10, 13, 15, 18, 21–22, 26) were lower in abundance in S. melongena and 5-Z-CQA (17; Table S1) was at a higher level in S. melongena. Three of these were also indicated by Tukey’s HSD tests (Table 2): the malonylcaffeoylquinic acid esters (21–22) and the FQA-hexoside (26) were lower in S. melongena from 6 to 16-fold compared to S. insanum. Prohens et al. (2013) reported levels of monoferuloylquinic acid esters [24, 25, and 4-O-[E]-feruloylquinic acid (4-FQA)] that were six times higher in S. incanum, a close relative of S. insanum (Fig. 2), relative to S. melongena. To compare, a sum of the four feruloylquinic acid esters (24–27) making up group 6 was used in a t-test, and the results here similarly showed significantly lower levels in S. melongena (Table 3) but, instead abundance was only five times higher in S. insanum. Also in contrast to Prohens et al. (2013), 4-FQA was not detected in our samples, raising the question of whether it is produced in S. insanum or S. melongena at detectable levels. These results suggest that S. incanum is more chemically distant from S. melongena than the proposed progenitor, S. insanum, and that there are chemical markers that may complement the molecular, morphological, or geographic distinctiveness of these species, which alone have had their discrepancies (see Meyer et al., 2013 for discussion). 2.5. The abundance of some compounds correlates with genetic distance

—

S. aethiopicum

S. capsicoides

—

S. linnaeanum

S. macrocarpon

2.4. Relative abundance distinguishes wild from domesticated eggplant

(B) S. aethiopicum S. capsicoides S. insanum S. linnaeanum S. macrocarpon S. melongena S. richardii S. viarum

92 ± 112 188 ± 89 88 ± 98 0 631 ± 363 148 ± 155 0 0 0 0 0 168 ± 280 267 0 13 ± 47 0 0 0 264 ± 339 0 1022 1788 107 ± 151 161 ± 130 320 0 170 79 ± 158 0 2654 ± 3801 5124 352 ± 105 174 ± 180 782 1197 0 90 ± 103 83 ± 72 119 ± 95 115 0 178 ± 108 0 0 0 29 ± 58 50 ± 87 117 ± 196 0 0 54 ± 159 0 700 120 71 ± 82 33 ± 58 316 ± 275 273 0 393 ± 741 0 0 2771 29 ± 58 0 575 ± 445 0 273 ± 32 312 ± 316 0 0 247 0 0 7 ± 28 0 617 ± 588 8 ± 40 0 475 0

0 0 28 ± 88 0 1300 ± 1838 0 291 0 0

12_Bis-caff-spd 10_Bis-dhcaff-spd

(A) S. aethiopicum S. capsicoides S. insanum S. linnaeanum S. macrocarpon S. melongena S. richardii S. viarum S. violaceum

7_Fer-put

13_Tris_dhCaff-spm

14_3-CQA

17_5Z-CQA

26_FQA-hexo 22_4-C-5-MQA 21_3-M-5-CQA

32_Tryptophan

Total

R.S. Meyer et al. / Phytochemistry xxx (2015) xxx–xxx Table 2 (A) Analysis of Variance (ANOVA) results indicating ten compounds and the total phenolic levels were significantly variable (p < 0.05) among groups. Compound abundance for each species reported as means ± standard deviation (s.d.). Headers are abbreviated compound names from Table 1. (B) Tukey’s HSD results showing significant difference in compound abundance between pairs of species; Compound #s are indicated (see Table 1 for full names), T = total compound abundance.

6

Because there was substantial variation in compound abundance within S. melongena, the distribution of each compound’s level was compared with genetic relatedness using AFLP data sourced from Meyer et al. (2012b). The abundance of individual compounds in different accessions was plotted as smooth variable contours of ordination fit to the surface of the principle coordinates analysis (PCoA) plot based on the AFLP data (SURF analysis). Results showed that of the 20 compounds found in the 42

Please cite this article in press as: Meyer, R.S., et al. Parallel reductions in phenolic constituents resulting from the domestication of eggplant. Phytochemistry (2015), http://dx.doi.org/10.1016/j.phytochem.2015.02.006

R.S. Meyer et al. / Phytochemistry xxx (2015) xxx–xxx Table 3 Student’s t-test results for compounds occurring in significantly different (p < 0.05) abundance between S. melongena and S. insanum. Amounts are reported as the mean ± s.d. Degrees of freedom (d.f.) for all comparisons = 78. 5-O-[Z]-CQA (17) was the only compound significantly more abundant in S. melongena than S. insanum. Compound

S. insanum

S. melongena

t-Value

p-Value

4_Co-put 10_Bis-dhcaff-spd 15_5-CQA 13_Tris_dhCaff-spm 17_5Z-CQA 21_3-M-5-CQA 26_FQA-hexo 22_4-C-5-MQA 18_3,5-diCQA Group 6 totals TOTAL

49.1 ± 138 575 ± 445 19,700 ± 7756 27.9 ± 87.6 119 ± 94.9 2654 ± 3801 168 ± 280 1022 ± 1685 305 ± 227 482 ± 453 27,418 ± 12,072

0±0 312 ± 316 13,300 ± 5926 0±0 178 ± 108 174 ± 180 13 ± 47.3 161 ± 130 275 ± 284 94 ± 170 16,887 ± 7702

2.85 2.38 3.75 2.55 2.08 5.2 4.23 4.05 2.35 4.048 4.47

0.0056 0.006 0.0003 0.013 0.041 <0.0001 <0.0001 <0.0001 0.021 <0.0001 <0.0001

accessions where both AFLP and chemical data were available, 11 compounds exhibited some correlation with genetic relatedness (Mahalanobis’ Distance = D; D2 > 0.15; Table 4). These included five HCAAs (7, 9–12), the diCQA isomers (18–20), two feruloylquinic acids (26–27), and tryptophan (32) (Table 4). However, the high D2 value was a result of rare occurrence of some compounds (N 6 4); therefore, there is only weak support that these compounds might be genetically predictable. The analysis of 16 more accessions was then repeated for which AFLP data was available (4 S. aethiopicum, 1 S. capsicoides, 7 S. insanum, 1 S. linnaeanum, 1 S. richardii, 1 S. viarum, and 1 S. violaceum). The expectation here was whether there was less correlation for those that varied across the S. melongena genetic diversity landscape because these distant species would compress multiple clusters of the same species in the PCoA. Results indicated that eight compounds and their total abundance were correlated with

Table 4 List of compounds that were fitted to the AFLP PCoA that included all species with a minimum Mahalanobis’ D2 > 0.5 or for S. melongena with a D2  0.15. GCV is the deviation from the degrees of freedom (d.f.); the total d.f. is reported. N = total accessions included in the calculation. Compound #

All species 4_Co-put 7_Fer-put 10_Bis-dhcaff-spd 15_5-CQA 18_3,5-diCQA 22_4-C-5-MQA 24_3-FQA 26_FQA-hexo Total

ns = <0.5 D2

GCV

d.f.

N

0.99 0.80 0.66 0.69 0.71 0.90 0.90 0.77 0.74

5 2416 81,154 34,592,533 46,292 29,143 420 2737 58,502,906

30 20 20 21 21 28 29 25 23

2 4 45 58 47 46 3 8 58

D2

GCV

d.f.

N

0.79 0.97 0.81 0.93 0.98 0.82 1 1 0.19 0.97 0.15

1551 239,400 132,196 17,463 166,862 53395.96 0.6830539 31.641 2885.96 1205.03 26523.13

19 28 24 25 28 24 30 30 5.5 21.3 3

2 26 33 27 28 31 2 3 4 4 27

ns = <0.15

Solanum melongena only 7_Fer-put 9_Caff/dhcaff-Spd-1 10_Bis-dhcaff-Spd 11_Caff/dhcaff-Spd-2 12_Bis-caff-spd 18_3,5-diCQA 19_4,5-diCQA 20_3,4-diCQA 26_FQA-hexo 27_3,5-diFQA 32_Tryptophan

7

genetic diversity with considerable support (D2 > 0.50; Table 4; Fig. 4). Four compounds of significance were also found to have a genetic distance relationship, based on the S. melongena SURF analysis (7, 10, 18, 26), and two of these were quite common: N1,N10-bis(dihydrocaffeoyl)spermidine (10) occurred in 45 of the 58 accessions, and 3,5-diCQA (18) occurred in 47 accessions. These results suggest that the natural selection which led to speciation, and the artificial selection that led to production of Asian cultivated eggplant diversity, have both acted on levels of these compounds and that their abundance is tightly genetically regulated and not stochastic. 2.6. Differences in S. melongena profiles among genetic clusters To assess whether the eggplants from lineages of mainland origins (India and China) were chemically distinct from each other or from the lineage largely made up of island (Malesian) landraces, chemical differences, if any, were explored among the clusters in the PCoA remade from the AFLP data (Meyer et al., 2012b) containing only S. melongena samples used in this study. The PCoA resolved three clusters, in which the Chinese eggplants fell into both of the more populated clusters but were overrepresented in one (R), the Indian eggplants fell into the other of these clusters (L), and the small-fruited S. melongena subsp. ovigerum accessions, from SE Asia and Malesia, fell into the third, smaller, cluster (T) (Fig. 5). ANOVA was used to assess whether there were differences in the abundance of any compounds among the three clusters, which would characterize the clusters for differences attributed to divergent selection on the lineages defined by genetic distance and geographic structure. ANOVA was performed for each of the 20 compounds that were present in at least one S. melongena accession. Results established six compounds that were significantly different in abundance (p < 0.05) among the three clusters (Table 5), but these differences were between S. melongena subsp. ovigerum and the two mainland lineages; the two mainland lineages were not distinct from each other. Four of these compounds were the HCAAs (7, 9, 11, 12) and the remaining two compounds were tryptophan (32) and 5O-[Z]-CQA (17). Five of these compounds [all but tryptophan (32)] were the most abundant in cluster T, representing S. melongena subsp. ovigerum and the Malesian lineage (Fig. 5, Table 5). The finding that the island lineage of eggplant was distinguished by relatively high abundance of HCAAs, whereas mainland eggplant lineages were not chemically distinct, suggests this subspecies underwent a strikingly different evolutionary trajectory. 2.7. Correlation of compound abundance with site-origin of eggplant landraces Artificial selection for local preferences of fruit qualities such as flavor, as well as adaptation to local agricultural conditions and ecologies, may contribute to eggplant phenolic diversity just as genetic history does. To test this potential influence from landscape, another ANOVA was performed using groups based on floristic region, as defined by Takhtajan (1986), of collection locales (site-origin) of S. melongena. Similar to previous tests, in this ANOVA abundance levels of all 21 compounds present in the 62 S. melongena samples were evaluated in addition to the total abundance levels. Since 95% of all samples were from four floristic regions that together span the range of all three possible domestication events, only those four regions were analyzed: the Eastern Asiatic floristic region (n = 17); the Indian region (n = 12); the Indochinese region (n = 15), and the Malesian region (n = 15). Only 3,5-di-O-[E]-feruloylquinic acid (27) was significantly different in abundance among groups. This compound, found only in four eggplants of Indonesian origin, is also minor (Table S1), and

Please cite this article in press as: Meyer, R.S., et al. Parallel reductions in phenolic constituents resulting from the domestication of eggplant. Phytochemistry (2015), http://dx.doi.org/10.1016/j.phytochem.2015.02.006

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R.S. Meyer et al. / Phytochemistry xxx (2015) xxx–xxx

Fig. 4. Spatial ordination of the amounts of four selected compounds in an AFLP principle coordinates analysis (PCoA) plot (Meyer et al., 2012b). Topographic lines indicate different compound amounts smoothed over the plot points. Plot points are indicated by hollow circles. Plot A: N1,N10-bis-dihydrocaffeoylspermidine (10). Plot B: 5-O-[E]caffeoylquinic acid (15). Plot C: 4-O-[E]-caffeoyl-5-O-malonylquinic acid (22). Plot D: 3,5-di-O-[E]-caffeoylquinic acid (18).

was not consistently produced in any species. Therefore, site-origin appears to have no clear relationship to the metabolite complements of these landraces. 3. Discussion This study aimed to examine correlations of the distribution of 32 compounds occurring in HPLC profiles with the genetic and geographic relatedness among samples of eggplant landraces and related species. The 8 structural groups these compounds spanned have been shown to exhibit biological activity indicating they may contribute to the flavor, health benefits, and even texture of eggplant fruit. Identifications were largely consistent with reported profiles of eggplant and relatives (Whitaker and Stommel, 2003; Stommel and Whitaker, 2003; Prohens et al., 2013; Wu et al., 2013), except that in our strategy of sampling a diverse collection, more infraspecific compositional diversity was found, and many domestication-related questions could be tested with the data.

3.1. Utility of markers and phenolic profiles in applied settings A high level of variation in phenolic composition and abundance was observed among species (Fig. 1), suggesting many of these compounds can serve as markers useful in breeding, conservation, and in supplementing morphological and molecular characters for identification. Expanding on this point, first, the species analyzed are used in hybridization and grafting to increase stress tolerance in eggplant (Collonnier et al., 2001; Prabhu et al., 2009; Vageeshbabu and Bhalewar, 2011). Initiatives to improve eggplant nutritional quality are paying increasing attention to the beneficial alleles in wild relatives (Prohens et al., 2007; Rotino et al., 2014). As shown in this work, understudied wild relatives such as S. richardii hold promise for introgression to increase antihypertensive metabolites in eggplant. Second, ex-situ conservation efforts for crop landraces, wild crop relatives, and particularly crop progenitors, are highly important (see Maxted et al., 2007; Kole, 2011), and they face challenges to prioritize

Please cite this article in press as: Meyer, R.S., et al. Parallel reductions in phenolic constituents resulting from the domestication of eggplant. Phytochemistry (2015), http://dx.doi.org/10.1016/j.phytochem.2015.02.006

R.S. Meyer et al. / Phytochemistry xxx (2015) xxx–xxx

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S. aethiopicum, S. macrocarpon, and S. melongena, an interesting trend related to domestication was observed. Two of these, S. aethiopicum and S. melongena, were indistinguishable from each other phytochemically (Table 2), in accord with previous studies (Stommel and Whitaker, 2003). However, these species belong to different sections of the genus Solanum (Oliganthes and Melongena, respectively), and therefore it is surprising that they are more chemically similar to each other than to other species in their own sections. S. aethiopicum and S. melongena were artificially selected for larger fruits (Lester and Seck, 2004). In contrast, S. macrocarpon, which is also in section Melongena, had a substantially different profile from that of S. melongena (Table S1 and 2), reflecting both unique compounds and significantly different abundance of three compounds (7, 13, 32) However, during domestication, S. macrocarpon plants were selected for palatable (spinach-like) glabrous leaves, and, therefore, the predominant changes that occurred may have only been to leaves. Consumption of the fruit is only a secondary use (Bukenya-Ziraba and Bonsu, 2004), and therefore relaxed selection pressure on fruit palatability may be the reason S. macrocarpon is phytochemically different. Fig. 5. AFLP plot of only S. melongena samples. L, R, and T indicate delimitations for left, right, and top clusters that are subsets of the AFLP clusters in Meyer et al. (2012b), which represent hypothesized common lineages of three separate domestication events. Clusters are hypothesized to represent the following lineages: L = Indian background, R = Chinese background, T = Malesian background (S. melongena subsp. ovigerum).

Table 5 One-way Analysis of Variance (ANOVA) results for compounds significantly different (p < 0.05) among left, right, and top regions of the S. melongena AFLP plot shown in Fig. 5. ‘‘Gr.’’ corresponds to group of left, right, or top as L, R, T, respectively. Sample sizes are as follows: L: n = 30; R: n = 7; T: n = 5. Compound 7_Fer-put

32_Tryptophan

9_Caff/dhcaff-spd-1

11_Caff/dhcaff-spd-2

12_Bis-caff-spd

17_5Z CQA

Mean

Gr.

p-Value

3.9 0.0 54.0

L R T

0.0373

131.1 324.1 91.0

L R T

0.0051

268.4 577.6 1949.2

L R T

0.0003

86.8 169.6 407.0

L R T

0.0009

293.9 428.7 1488.8

L R T

0.0065

153.4 199.1 342.0

L R T

0.0014

collections for conservation and for creating core research collections. Chemical diversity observed in phenolic profiles can serve as cost effective data to show variation within a species. In addition, many of these species possess unique or unusual compounds (see also Wu et al., 2013), detection of which could aid in weeding out unintentionally admixed or mislabeled germplasm.

3.2. Phytochemical patterns displaying a general Solanum fruit crop domestication trait By assessing common elements of HCA profiles among many accessions of three domesticated species included in this study,

3.3. Landscape and genetic influences on Asian eggplant metabolites Despite the many historical medicinal applications of eggplants that overlap with the reported activity for many HCA conjugates (Raghunatha, 1956; Lad, 1984; Sudheesh et al., 1997; Guimarães et al., 2000; Cong et al., 2001; Lad and Lad, 2005; Kwon et al., 2008; Wang et al., 2010), it is apparent that human selection on all three lineages resulted in reduced phenolic levels (both caffeic and ferulic acid conjugates), which does not support the notion of intentional selection for increased health-beneficial compounds in the fruit pulp. This decrease is consistent with results reported by Prohens et al. (2013) showing higher levels of major and minor compounds in the wild eggplant relative. Specifically, the analyses herein lead to the conclusion that chemical changes that accompanied the domestication and diversification processes in multiple regions in Asia were parallel: all involved a reduction in levels of 5-CQA (15) and other derivatives including the malonated CQA esters (21–22) and 3,5-diCQA (18) (Tables 3 and S1), provoking the question of why parallel selection took place. We speculate that some thermal degradation products of CQA derivatives (groups 2– 4, Table) may have a bitter taste (Frank et al., 2006), so reduction of certain CQA derivatives may have been a result of selection for increased palatability, which would not be surprising to be selected on in parallel. However, phenolics in eggplant were shown to be fairly thermally stable (Lo Scalzo et al., 2010), and other saponins or glycoalkaloids have been shown to largely be responsible for the bitter flavor (Aubert et al., 1989). Phenolic levels could alternatively have been lowered to reduce unattractive browning of exposed fruit pulp, although only weak positive correlations were found (Prohens et al., 2007). Nonetheless, selection may also have acted on polyphenolic oxidase genes (PPOs) or other genes in the CQA biosynthesis pathway (Gramazio et al., 2014); PPO activity was found to differ among cultivars (Doganlar et al., 2002; Mennella et al., 2012). Even though it can only be speculated that specific changes would be historically relevant, it is known that a change in fruit flavor was a domestication trait, along with size: Wang et al. (2008) inferred a change in flavor from historical documents. In 6th Century China, eggplant was said to have a flavor akin to ‘‘small beans’’ (Jia, 6th Century), whereas three centuries later the flavor was reported as ‘‘very delicious’’ (Duan, 9th Century). Nonetheless, there are chemical differences that are correlated with genetic background. The domestication event that gave rise to S. melongena subsp. ovigerum produced a smaller fruit than the

Please cite this article in press as: Meyer, R.S., et al. Parallel reductions in phenolic constituents resulting from the domestication of eggplant. Phytochemistry (2015), http://dx.doi.org/10.1016/j.phytochem.2015.02.006

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R.S. Meyer et al. / Phytochemistry xxx (2015) xxx–xxx

wild progenitor, with lower total HCA abundance (Table S1) but increased levels of certain HCAAs (7, 9, 11, 12) compared to other domesticated lineages, particularly caffeic acid-spermidine conjugates (9, 11) (Table 5). In spite of the therapeutic activity of these compounds, it can be proposed that the higher abundance and diversity of HCAAs in S. melongena subsp. ovigerum has no relation to selection for taste or health benefits, but rather is related to selection for smaller fruit size and firmer, crisper texture. HCAAs (1–13) have been implicated both in limiting extensibility of cell walls via cross-linking (Facchini et al., 2002; Edreva et al., 2007; Bassard et al., 2010), and in promoting cell wall loosening by inhibiting calcium binding (Nance, 1973; Messiaen and Van Cutsem, 1999; Bassard et al., 2010). S. melongena subsp. ovigerum fruits are both smaller and spongier than fruits of the wild progenitor, and are much smaller than fruits from the mainland lineages, Given that HCAAs are implicated in various aspects of cell wall formation and degradation, they could be playing key roles in fruit texture or perhaps size. HCAAs (1–13) have desirable therapeutic activities, such as antihypertensive properties (Funayama et al., 1980; Han et al., 2002). While some of these compounds have been found in the pollen of numerous species (Meurer et al., 1986, 1988; Bokern et al., 1995; Facchini et al., 2002; Negri et al., 2011; Fellenberg et al., 2012), reports of their abundance in fruits of food crops are not as common. Therefore, S. melongena subsp. ovigerum may be a good source for obtaining these compounds for the natural products industry. In addition, S. melongena subsp. ovigerum is an excellent candidate for functional studies on the HCAA biosynthesis pathway. It is small, yields fruit quickly and abundantly, and is amenable to agrobacterium-mediated stable and transient transformation (Franklin and Lakshmi, 2003; Meyer, unpublished data). 4. Conclusions Little is understood about how selection in different regions shapes extant crop diversity, or what signatures multiple independent onsets of domestication in a single crop species can leave after hundreds of years of widespread or even global trade. This study is one of few to characterize the pattern of how flavor-related metabolites changed under domestication, to test for convergent evolution of traits across lineages from multiple domestication events, and to explore the geographic structure of metabolite production in a food crop. Through combined phytochemical, genetic and landscape correlation analyses, the most influential factors on eggplant cultivar phenolic profiles were determined, which were genetic background and palatability (i.e., convergent selection for lower health beneficial but potentially bitter HCA derivatives across origins). However, one genetic lineage, S. melongena subsp. ovigerum, was found that had a significantly different HCA-polyamine content. It can be hypothesized that this trait resulted from selection for a decrease in fruit size. These results highlight the extent to which cultural associations can steer evolution of traits. 5. Experimental 5.1. Preliminary analyses to select growth conditions and sample material Preliminary experiments were conducted to test whether the inclusion of seeds in fruit samples altered the HCA content detectable by HPLC-DAD in phenolic extracts (see below for fruit tissue isolation, extraction, and chemical quantification methods). Nine landraces were used in a paired test of extracts with seeds included (n = 9, mean HCA content = 21,170 relative units, s.d. = 9344) and

with seeds removed (n = 9, mean = 9462 relative units, s.d. = 5339). Student’s t-test indicates that extracts including seeds had significantly higher phenolics (p = 0.005; t-value = 3.82; d.f. = 8); inclusion of seeds raises HCA content by ca. 2-fold. Although seeds contribute to HCA content, the number of seeds is highly variable among fruits, and they were therefore removed before making extracts to make different samples comparable. Outdoor-grown versus indoor-grown plants may produce different amounts of total phenolics because of different environmental factors; therefore, preliminary experiments were conducted to assess potential differences in phenolic levels of outdoor- and indoor-grown fruits. Four biological replicates were grown in each environment. The total phenolic content of fruits grown indoors (mean = 20,180 relative units, s.d. = 6301) was not significantly different from the outdoor group (mean = 11,630 relative units, s.d. = 2416), based on a paired Student’s t-test (p = 0.136; t-value = 2.03; d.f. = 3). Indoor-grown plots were therefore chosen for this study because they could be continuously grown under controlled conditions. 5.2. Plant material Seeds were obtained from sources described in Meyer et al. (2012b), and were germinated from each accession with two seedlings of each accession grown in 1-gallon pots spaced 0.5 meters apart in a single house in the Nolen Greenhouses of The New York Botanical Garden. In some cases, seeds from one accession produced plants with variable morphological phenotypes. In those cases, more seeds were germinated, with the plants grouped by phenotype into sub-accessions, and unique identifiers assigned consisting of the accession name followed by a letter. Two fruits from each of the two biological replicates per accession were harvested 25–50 days after anthesis, when fruits were full size but seeds were still immature. With peduncles removed, fruits were sliced to isolate the middle section (1/3 of the fruit), and any tissue containing seeds was discarded. Immediately after slicing, isolated tissue was frozen in liquid N2 and stored at 80 °C until it was lyophilized. All freeze-dried tissue for each accession was then pooled and pulverized immediately prior to extraction. 5.3. Sample preparation and HPLC analysis Powdered samples (0.2 g each) were individually extracted as follows: samples were stirred for 15 min with MeOH-H2O (10 mL, 4:1, v/v), followed by centrifugation at 4000g for 5 min. This first extract was decanted, set aside, and the process was repeated on the same powdered sample. The first and second extracts were combined and samples (4 mL) were passed through a Whatman PTFE syringe filter (0.2 lm pore size). An aliquot (1 mL) of each filtered extract was transferred to an amber HPLC vial and the solvent was evaporated under a stream of N2 at 35 °C. The residue was then resuspended in H2O-MeOH (1 mL, 4:1, v/v containing 0.02% H3PO4). The sample was flushed with N2 before it was sealed with a Teflon-lined septum cap. Samples were stored at 20 °C and analyzed within 4 days. Compounds were separated and quantified by RP-HPLC in 20 lL injections onto a Luna C18(2) column (5 lm particle size, 250 mm long, 4.6 mm i.d.) from Phenomenex (Torrance, CA) using an HP 1100 Series instrument with a quaternary pump, autosampler, and photodiode array detector (Agilent Technologies). Data were analyzed with Agilent ChemStation software (Revision B.03.01). The method implemented was a modification of that used by Whitaker and Stommel (2003). The binary gradient consisted of 0.02% H3PO4 in H2O (A) and MeOH (B) as follows: 0 min, 90A:10B at 1.0 mL/min; 0–15 min, linear increase to 25% B at 1.0 mL/min;

Please cite this article in press as: Meyer, R.S., et al. Parallel reductions in phenolic constituents resulting from the domestication of eggplant. Phytochemistry (2015), http://dx.doi.org/10.1016/j.phytochem.2015.02.006

R.S. Meyer et al. / Phytochemistry xxx (2015) xxx–xxx

15–25 min, linear increase to 50% B at 1.0 mL/min; 25–28 min, linear increases to 80% B and 1.2 mL/min; 28–30 min, linear increase to 100% B at 1.2 mL/min; 30–32 min, 100% B at 1.2 mL/ min; 32–35 min, decrease to 10% B at 1.2 mL/min; 35–38 min, 10% B with linear decrease to 1.0 mL/min. Relative quantification was based on absorbance at 325 nm and 280 nm. 5.4. HR-ESI-MS and 1H-NMR High-resolution electrospray ionization mass spectrometry (HR-ESI-MS) was used to obtain the mass ([M 1] and [M+1]) for identification of the following compounds: 3, 5, 10, 12–13, 18–20 (Table 1; Table S2). HR-ESI-MS was performed using an LCT premier XE TOF mass spectrometer (Waters, Manifold, MA) equipped with an ESI interface and controlled by MassLynx V4.1 software. Mass spectra were acquired in both positive and negative modes over the range m/z 100–1000. The capillary voltages were set at 3000 V (positive mode) and 2800 V (negative mode), respectively, and the cone voltage was 20 V. N2 gas was used for both the nebulizer and in desolvation. The desolvation and cone gas flow rates were 300 and 20 L/h, respectively. The desolvation temperature was 400 °C, and the source temperature was 120 °C. For the dynamic range enhancement (DRE) lockmass, a solution of leucine enkephalin (Sigma–Aldrich, Steinheim, Germany) was infused by a secondary reference probe at 200 pg/mL in CH3CN:H2O (1:1 containing 0.1% HCO2H) with a second LC pump (Waters 515 HPLC pump). The reference mass was scanned once every five scans for each positive and negative data collection. Both positive and negative ESI data were collected using a scan time of 0.2 s, with an interscan time of 0.01 s, and a polarity switch time of 0.3 s. 1 H NMR spectra for compounds 18–20 (Table S2) were obtained using a Bruker Avance 300 NMR spectrometer, equipped with broadband inverse probe (300.1312 MHz), in the deuterated solvents CD3OD or DMSO-d6. Chemical shifts are expressed in d values (ppm). 1H NMR chemical shifts were referenced to the residual solvent signal [dH 3.31 (CD3OD) or dH 2.50 (DMSO-d6)]. 5.5. Data analysis HPLC raw data were entered into a spreadsheet using the area under the curve as relative units. The area was reported for either the 325 nm or 280 nm reading, depending on which was closer to the primary absorbance maximum of the compound above 250 nm. Any compound amounts under 100 area units were considered negligible and therefore were reported as 0. The raw data table was transformed into matrices for statistical tests in R version 2.3.10 (R Development Core Team, 2011; Ripley, 2001). To test for differences in compound abundance among different species or among landraces from different regions, one-way Analysis of Variance (ANOVA), Tukey’s HSD, and two-tailed Student’s t-tests were conducted according to Sokal and Rohlf (1995). For ANOVA, settings used were Gabriel’s comparison intervals with a = 0.05 (Sokal and Rohlf, 1995). Student’s t-tests for preliminary analyses to select growth conditions and sample material were performed based on paired groups of equal variance. All other Student’s t-tests in this study were unpaired, assuming equal variance. AFLP data obtained from Meyer et al. (2012b) in cases where the same accession was used in both studies and chemical data was fit to these genetic data using principal coordinates analysis (PCoA) and the SURF function in packages LABDSV (Roberts, 2010) and VEGAN (Dixon, 2003; Oksanen et al., 2011). SURF was performed using the default conditions of Gaussian function. The first and second principal coordinates were plotted as x- and y-axes, respectively. In cases where one accession was divided into sub-accessions, the average of these was taken and used in the analysis with AFLP data for the accession.

11

Acknowledgments We thank the following people for their contributions to this work: Dr. J.R. Stommel for help with extractions and experimental design, G. Phillips for help with data analysis, the students and researchers at the laboratory of Dr. E.J. Kennelly for help with sample preparation, F. Trouth for assistance with laboratory facilities, faculty and staff at the Kunming Institute of Botany and Philippines National Museum for help with sample collections, people at the germplasm centers and field sites that provided seeds, and staff at the NYBG Nolen Greenhouses for growing the plants. We gratefully acknowledge funders of the research: The New York Botanical Garden Genomics Program, the CUNY Research Grant, the Botany in Action Grant, and the Chatham Fellowship in Medicinal Botany.

Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.phytochem.2015. 02.006.

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