Profiling of volatile and non-volatile metabolites in Polianthes tuberosa L. flowers reveals intraspecific variation among cultivars

Phytochemistry 162 (2019) 10–20

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Profiling of volatile and non-volatile metabolites in Polianthes tuberosa L. flowers reveals intraspecific variation among cultivars

T

Nithya N Kutty, Adinpunya Mitra∗ Natural Product Biotechnology Group, Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India

A B S T R A C T

Polianthes tuberosa L. (tuberose) is a widely cultivated ornamental crop in Asian countries. Different cultivars of tuberose have been developed through breeding programs in India. However, no reports on floral fragrance and metabolite contents of these cultivars are available. In this study, an attempt has been made to evaluate the levels of both volatile and non-volatile metabolites from seven different cultivars of P. tuberosa. Presence of benzenoids, phenylpropanoids, terpenoids, and few fatty acid derivatives as emitted, endogenous and glycosylated forms were revealed from the studied cultivars. Further, chemometric analyses in both supervised and unsupervised manner led to identification of patterns among the cultivars. Among the seven cultivars, four distinct clusters were obtained linking to their volatiles, flavonoids and primary metabolite levels. Metabolic variations obtained from the cultivars also suggest cross-talks between phenylpropanoid, benzenoid, and flavonoid pathways. Thus metabolite profiling reported here may help in characterization of tuberose cultivars for perfumery utility and future breeding programme.

1. Introduction Polianthes tuberosa L. or tuberose belongs to the Amaryllidaceae family, is an economically important horticultural plant cultivated widely around the world for its sweet-smelling floral scent. Tuberose flowers are generally white in colour with single or double waxy tepals, and are borne in pairs in a long unbranched spike (Lim, 2014). These evening-blooming fragrant flowers are utilized both as cut flowers for interior decorations and as loose flowers for making garlands and bouquets. Flowers of P. tuberosa are also used in the production of essential oils and perfumes. It is believed that the warm soothing effect of oil helps in releasing stress by uplifting mood, particularly for patients suffering from insomnia (Jin et al., 2004). Based on their biosynthetic origin, floral scent volatiles can be classified into four major classes: terpenoids, benzenoids/phenylpropanoids, fatty acid derivatives and nitrogen/sulphur containing compounds (Muhlemann et al., 2014). Analyses of emitted volatiles in P. tuberosa flowers (Calcutta single cultivar) revealed presence of 24 compounds including benzenoids/phenylpropanoids (81.2%), terpenoids (13.64%) and fatty acid derivatives (Maiti et al., 2014). The emitted volatilome of both in situ and plucked flowers have also been studied using different adsorbent matrices leading to the identification of 57 volatiles (Maiti and Mitra, 2019). The volatile emission pattern of P. tuberosa when studied throughout the floral life span exhibited nocturnal rhythm. It was also observed that the maximum volatile emission occurred on the day of anthesis (Maiti and Mitra, 2017). As

∗

revealed from spatial emission study, maximum volatile emission was shown to occur in petaloid tepals. Histochemical studies along with scanning electron microscopic analyses have suggested the role of floral stomata in scent volatiles emission; further the emission mechanism has also been proposed based on transmission electron microscopic studies of different stages of petaloid tepals in P. tuberosa (Maiti and Mitra, 2017). However, the temporal storage/secretion mechanism of volatiles in floral tissue and subsequent emission of volatiles still remains unclear in P. tuberosa flower. Emission of volatiles appears to be a physical phenomenon regulated by several factors including evaporation rate, the relative amount of endogenous and emitted volatiles and activity levels of glucosidase enzymes (Sagae et al., 2008). A previous report also indicated the need to study concurrently the levels of endogenous and emitted volatiles for proper understanding of total floral scent composition (Kondo, 2006). However, no such study reporting endogenous and glycosyl-bound volatile levels is available till date in P. tuberosa flower. Although flowers of tuberose are used for culinary purposes in countries such as China, Indonesia and few other parts of the world, no thorough study has been done on floral metabolites of P. tuberosa from different cultivars (Lim, 2014). Metabolomics refers to identification and quantification of metabolites followed by further chemometric analysis (Lubes and Goodarzi, 2017). This has become a powerful tool in identifying biomarkers for understanding the phenotypic and genotypic diversity of crop plants. Metabolite status not only helps in predicting the biological status of the cell or tissue, but also can help in

Corresponding author. . E-mail addresses: [email protected], [email protected] (A. Mitra).

https://doi.org/10.1016/j.phytochem.2019.02.006 Received 21 November 2018; Received in revised form 12 February 2019; Accepted 15 February 2019 0031-9422/ © 2019 Elsevier Ltd. All rights reserved.

Phytochemistry 162 (2019) 10–20

N.N. Kutty and A. Mitra

Porapak Q 80/100 has a larger surface area i.e. around 500–600 m2 g−1 (Tholl et al., 2006) and thus contributing to its adsorption capacity of volatile compounds. The headspace scent emission rate of different cultivars was calculated in μg g−1 fr. wt h−1 (μg per gram fresh weight per hour) and are given in Table 2. During the blooming period (17:30–18:30 h), the cultivars CS and SN showed the highest emission rates (ca. 84 μg g−1 fr. wt h−1 and 44 μg g−1 fr. wt h−1), followed by SR (38 μg g−1 fr. wt h−1), PR (42 μg g−1 fr. wt h−1), UJ (30 μg g−1 fr. wt h−1), JY (16 μg g−1 fr. wt h−1), and CD (4 μg g−1 fr. wt h−1) (Fig. 1A). In all cultivars except JY and PR, aromatics and nitrogen containing compounds were found in abundance followed by terpenoids, fatty acid derivatives and other lactones (Table 2). The amounts of terpenoids and aromatics were relatively same in both JY and PR flowers. In CS cultivar, terpenoid bouquet comprised of monoterpenes including 1, 8cineole, cis-geraniol, 1-R-α-pinene, and sesquiterpenes such as germacrene D, α-farnesene, and (Z, E)-farnesol. Some other terpenes namely, (−)-β-pinene, 3-carene, D-limonene, citronellal, β-caryophyllene, βcopane, were also detected in trace amounts in other cultivars. The amount of monoterpenes were always higher than sesquiterpenes in all cultivars studied. Least amount of sesquiterpenes were detected from headspace volatiles of cultivars such as CD, JY, UJ, and SR. The amounts of benzenoids were found to be the dominating aromatic compounds in all cultivars except UJ. Among all seven cultivars, nitrogen containing compounds namely, methyl 2-amino benzoic acid (8.97 μg g−1 fr. wt h−1) and 1, H-indole (2.51 μg g−1 fr. wt h−1) were found in abundant amounts in UJ. In both CS and SN cultivars, the levels of aromatic compounds were higher thus contributing to their higher emission rates. n-Nonanal and n-decanal were the major fatty acid esters found in all cultivars. Nonalactone constituted the only emitted lactone in CS amounting to 0.87 μg g−1 fr. wt h−1 while two other lactone namely 5-hydroxy-7(Z)-deconoic acid-δ-lactone and 5hydroxydecanoic acid-δ-lactone were detected in the emitted bouquets of CD, UJ, JY, and SR cultivars. In order to elucidate complete volatilome of tuberose flowers, free endogenous and glycosyl-bound volatiles were also analysed. Endogenous volatiles from all cultivars were extracted with dichloromethane and quantified in terms of μg g−1 fr. wt (Table 3). It was seen that CD had the highest amount of total endogenous volatiles ca. 371 μg g−1 fr. wt, followed by SN (ca. 183 μg g−1 fr. wt) and UJ (ca. 173 μg g−1 fr. wt). Cultivars namely PR, JY, and SR showed a lower amount of endogenous volatiles (Fig. 1B). In case of glycosyl-bound volatiles, PR showed highest accumulation (ca. 37 μg g−1 fr. wt), followed by CD, JY and CS cultivars (Fig. 1C). Aromatic compounds were found in higher amounts among the other classes of volatiles in free endogenous and glycosyl-bound form (Table 4). All major emitted volatiles like benzyl benzoate, methyl 2-amino benzoic acid, methyl isoeugenol, 1, 8-cineole, were detectable as both free endogenous and glycosylated form. Some phenol and alcohol compounds namely benzyl

elucidating the biochemical changes under different environmental conditions (Fiehn et al., 2000; Niederbacher et al., 2015). The present investigation aims to investigate the floral metabolite status of seven P. tuberosa cultivars viz. Calcutta Single (CS), Calcutta double (CD), Bidhan Rajani-1 Shnigdha (SN), Bidhan Rajani −2 Ujwal (UJ), Bidhan Rajani-3 Jyothi (JY), Shringhar (SR) and Phule rajani (PR). To the best of our knowledge, this is the first scientific report on the targeted metabolite profiling using GC-MS and UHPLC-DAD based techniques for characterization of emitted, endogenous and glycosyl-bound volatiles along with non-volatile metabolites such as sugars, amino acids, organic acids, phenolic acids and flavonoids. The raw metabolite data were subjected to multivariate analysis in both supervised and unsupervised manners aiming at cultivar classification and biomarker identification. Such studies can help breeders and biotechnologists to understand, improve and create new cultivars for better floricultural prospects. 2. Results and discussion This study was mainly concerned on analyses of floral volatile and non-volatile metabolites followed by multivariate data analysis for pattern recognition in seven cultivars of P. tuberosa. The floral morphological features of these P. tuberosa cultivars are shown in Table 1. Among these seven cultivars, only CD has a double whorl arrangement of tepals. Days to first anthesis was calculated as number of days from planting to develop fully opened flower. Bidhan rajani varieties viz. UJ and JY took comparatively lesser time to flower than other cultivars, while CD had the longest vegetative phase (significantly different at p value < 0.05). Inflorescence length (cm) i.e. length between the lowest green bracts to the tip of the spike during anthesis was measured manually for all cultivars. The longest spike was observed for both CS and JY. Among all the cultivars, flowers of SN and PR were comparatively bigger in size. 2.1. Status of floral volatiles in P. tuberosa cultivars The emitted volatiles status of P. tuberosa flowers from the above cultivars was studied using dynamic headspace sampling technique with Porapak Q (80/100) as adsorbent matrix and dichloromethane as elution solvent according to a previous study described by Maiti et al. (2014). Volatile trapping included 1 h of saturation during the blooming stage (17:30–18:30 h) for all flowers as shown in Fig. S1. Different classes of compounds viz. aromatics including benzenoids, phenylpropanoids, nitrogen containing compounds, monoterpenes, sesquiterpenes, fatty acid derivatives, lactones were detected in the emitted floral bouquet of P. tuberosa (Table 2). Trapping of headspace volatiles in flowers led to the identification of 32 compounds, predominantly by aromatics including benzenoids and phenylpropanoids. Table 1 Floral morphological features of P. tuberosa cultivars. Parameters

Calcutta Single

Calcutta double

BR-1 Shnigdha

BR-2 Ujwal

BR-3 Jyothi

Shringhar

Phule Rajani

West Bengal 174.3 ± 6.65b 127.6 ± 10.26e 4.4 ± 0.31a 3.45 ± 0.24a

West Bengal 258 ± 15d 105.33 ± 8.38c,d,e 5.03 ± 0.15a,b 4.5 ± 0.5c,d

West Bengal 195.3 ± 10.06b,c 98.0 ± 7c,d 6.23 ± 0.25d 5 ± 0.5d

West Bengal 77.66 ± 6.65a 42 ± 4.82a 5.23 ± 0.251b,c 3.43 ± 0.115a

West Bengal 69.33 ± 5.85a 113 ± 33.8d,e 5.26 ± 0.68b,c 3.66 ± 0.115a

Karnataka 173.33 ± 11.15c 66.33 ± 13.05a,b 5.36 ± 0.55b,c 3.96 ± 0.05b,c

Maharashtra 202 ± 22.53b 78.33 ± 2.51b,c,d 5.83 ± 0.28c,d 4.26 ± 0.25c

Flower

Geographical origin Number of days to anthesis Inflorescence length (cm) Tepal dimensions Length Breadth

Note: All values are mean ± s.d. of four replicates. Values having same letters as superscript do not vary significantly at p < 0.05. Abbreviations: BR: Bidhan Rajani, s.d.: standard deviation. 11

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Table 2 Emission rate (in μg g−1 fr. wt h−1) of scent volatiles from P. tuberosa flowers of different cultivars. S. No.

Compound

Rt

Emission rate (μg g−1 fr. wt h−1) CS

CD

SN

Monoterpenes 1 α-Pinene 927 0.08 ± 0.01 – 0.16 ± 0.01 2 (−)-β-Pinene 988 – 0.07 ± 0.02 – 3 3-Carene 1007 – – – 4 α-Terpineol 1187 1.81 ± 0.56 0.07 ± 0.03 0.81 ± 0.12 5 β-Terpineol 1064 – 0.08 ± 0.03 – a 6 1,8-Cineole 1027 9.67 ± 0.22 1.45 ± 0.88 3.44 ± 1.43 7 trans-Geraniol 1255 1.47 ± 1.42 – 2.35 ± 0.90 8 D-Limonene 1025 – – Sesquiterpenes 9 β-Caryophyllene 1413 – – 10 Germacrene D 1475 2.78 ± 0.02 – 11 α –Farnesene 1510 0.97 ± 0.014 – 12 (Z,E)-Farnesol 1719 0.13 ± 0.03 – 0.74 ± 0.25 Aromatic hydrocarbons (benzenoids, phenylpropanoids, nitrogen containing compounds and others) 13 Benzaldehyde 950 0.53 ± 0.014 – 0.20 ± 0.06 14 Methyl benzoate 1092 14.73 ± 0.73 – 4.82 ± 1.63 15 Methyl salicylate 1189 5.66 ± 0.90 – 4.93 ± 0.33 16 0-Cymene 1022 – – 0.15 ± 0.04 17 Eugenola 1355 0.33 ± 0.06 – 0.23 ± 0.02 18 Methyl eugenol 1404 0.77 ± 0.03 0.08 ± 0.03 19 1, H-Indole 1289 2.37 ± 1.46 – 4.32 ± 0.87 20 Methyl 2-amino benzoic acid 1336 9.38 ± 5.61 – 8.05 ± 1.63 21 trans-Isoeugenol 1447 1.56 ± 0.74 – 1.45 ± 0.66 22 cis-Methyl isoeugenola 1497 13.60 ± 3.56 0.05 ± 0.02 10.25 ± 1.19 23 Benzyl benzoatea 1758 16.86 ± 0.81 0.57 ± 0.18 8.60 ± 0.18 24 Benzyl salicylatea 1861 0.23 ± 0.017 – 0.53 ± 0.03 Fatty acid derivatives 25 n-Decanal 1205 0.07 ± 0.01 – 0.17 ± 0.06 26 n-Nonanal 1104 0.07 ± 0.01 – 0.13 ± 0.06 27 Undecane 1100 0.009 ± 0.005 0.056 ± 0.09 – 28 Tetradecane 1200 0.03 ± 0.07 – – Others 30 Nonalactone 1270 0.87 ± 0.005 – 31 5-Hydroxy-7(Z)-decenoic acid-δ-lactone 1486 – 0.09 ± 0.03 32 5-Hydroxydecanoic acid δ-lactone 1502 – 0.05 ± 0.03 -

UJ

JY

SR

PR

– 0.2 ± 0.04 – 1.52 ± 1.12 0.02 ± 0.002 3.47 ± 3.57 0.74 ± 0.20 –

0.11 ± 0.02 0.11 ± 0.003 – 0.45 ± 0.067 – 5.4 ± 0.82 0.69 ± 0.45 –

– – 0.21 0.18 – 1.32 1.30 0.31

1.05 ± 0.19 – 2.87 ± 0.31 1.18 ± 0.42 – 11.51 ± 1.16 0.92 ± 0.07 2.60 ± 0.49

– – – 0.19 ± 0.18

0.047 ± 0.02 – – 0.27 ± 0.25

0.04 ± 0.014 – – 0.138 ± 0.01

0.12 ± 0.01 – – 0.31 ± 0.01

0.10 2.01 2.05 – 0.22 – 2.51 8.97 2.98 0.12 3.05 –

– 0.57 0.35 – 0.16 0.12 2.07 0.15 0.17 0.83 3.58 0.26

± ± ± ± ± ± ± ±

0.19 0.14 1.01 0.12 0.06 0.28 2.38 0.06

– 5.99 ± 2.49 3.39 ± 0.52 0.13 ± 0.01 0.283 ± 0.08 0.29 ± 0.009 2.89 ± 0.58 6.08 ± 1.71 1.13 ± 0.40 5.32 ± 0.3 5.91 ± 1.32 0.17 ± 0.004

– 4.45 ± 0.05 1.27 ± 0.54 3.00 ± 2.66 0.23 ± 0.01 0.49 ± 0.08 0.37 ± 0.07 – 2.14 ± 1.22 3.19 ± 1.77 4.45 ± 2.58 0/76 ± 0.07

– 0.13 ± 0.04 – –

0.11 0.18 3.45 2.24

± ± ± ±

0.02 0.01 0.67 0.89

0.07 ± 0.003 0.08 ± 0.01 – –

1.79 ± 1.64 – 0.67 ± 0.06 –

– 1.54 ± 0.52 1.01 ± 0.33

– 0.67 ± 0.09 0.08 ± 0.009

– 1.62 ± 0.55 1.41 ± 1.27

– – –

± 0.01 ± 0.04 ± 1.24 ± 0.02 ± ± ± ± ±

2.27 4.46 1.49 0.03 2.26

± 0.25 ± 0.04

± 0.15 ± 0.06 ± 0.71 ± 0.41 ± 0.03

Note: All values are mean ± s.d. of three replicates. “-“indicates not detected. Letter ‘a’ in superscript form indicates those compounds which are identified by comparing mass spectra and retention index with their respective standards. Other compounds were identified by comparing mass spectra and retention indices from NIST 14 library and available literature. Abbreviations: CS: Calcutta single, CD: Calcutta double, SN: Shnigdha, UJ: Ujwal, JY: Jyothi, SR: Shringhar, PR: Phule rajani, Rt: Retention index.

h−1) was found abundantly in its free and glycosylated form amounting to 3.71 μg g−1 fr. wt and 1.23 μg g−1 fr. wt respectively in CS cultivar. The higher boiling point (∼320 °C) of benzyl salicylate may be a contributory factor for the above observation. The roles of boiling point, vapour pressure, and temperature in affecting the emission levels of volatiles were studied in rose and petunia flowers (Picone et al., 2004; Sagae et al., 2008).

alcohol, 2-methoxy phenol, phenyl ethyl alcohol were only detected as free endogenous and glycosyl-bound form in P. tuberosa flower tissues. Lipophilic molecules are potentially toxic in nature and therefore these compounds undergo glycosylation before accumulation in the vacuoles (Ohgami et al., 2015; Winterhalter and Skouroumounis, 1997). Previous studies in tea have shown that both benzyl alcohol and geraniol were stored as glycosides before emission (Ohgami et al., 2015). Compounds involved in plant defence mechanisms such as geraniol and α-terpineol were also detected in their glycosylated form in P. tuberosa flowers (Groyne et al., 1999). Hydroxylated forms of 1, 8-cineole, 2hydroxycineole and 3-exo-hydroxy-1, 8-cineole were only found in free endogenous and glycosylated forms suggesting the role of glucosidases and hydroxylases in volatile emission. Other factors which control the emission rate of volatiles are light, boiling point, temperature, and vapour pressure (Jakobsen and Olsen, 1994; Kolosova et al., 2001). In our study, we detected 4-propyl benzaldehyde and 2-methyl benzaldehyde oxime, only as free endogenous volatiles in almost all cultivars, while benzaldehyde was detected mostly in the emitted bouquet. For example, in CS cultivar the emission rate of benzaldehyde was 0.53 μg g−1 fr. wt h−1, while 4-propyl benzaldehyde (0.92 μg g−1 fr. wt) and 2-methyl benzaldehyde oxime (0.89 μg g−1 fr. wt) were only detected in endogenous fraction. This may be due to the lower boiling point of benzaldehyde (178 °C) than that of 4-propyl benzaldehyde (240 °C). Another aromatic compound benzyl salicylate having a lower emission rate (ca. 0.23 μg g−1 fr. wt

2.2. Metabolite profiling of non-volatile compounds from P. tuberosa cultivars Methanolic extract alone as well as ethyl acetate fractions of acidified methanolic extract of P. tuberosa flowers were derivatized with N, O-Bis(trimethylsilyl) trifluoroacetamide (BSTFA) and trimethylchlorosilane (TMCS) according to a previously described protocol (Proestos et al., 2006) before subjected to GC-MS analysis. Post run processing was done using Xcalibur 3.0.63 software and a total of 42 compounds including amino acids, organic acids, sugars, organic acids, fatty acid derivatives, phenolic acids, and flavonoids were identified from floral tissue of P. tuberosa. The identities of all compounds were confirmed on basis of their mass spectra and retention indices as given in Table S1. The normalized relative abundance per gram of fresh weight of floral tissue with respect to the internal standard was used for quantitative comparison among the cultivars. Heat map representation of normalized abundances of all 12

Phytochemistry 162 (2019) 10–20

N.N. Kutty and A. Mitra

Fig. 1. Profile of floral scent volatiles in studied cultivars of P. tuberosa. Stack plots represent (A) volatiles emitted by flowers in μg g−1 fr. wt h−1, (B) free endogenous volatiles of whole flower in μg μg g−1 fr. wt and (C) glycosyl-bound volatiles of whole flower in μg μg g−1 fr. wt. Abbreviations: CD - Calcutta double, CS - Calcutta single, JY - Jyothi, UJ - Ujwal, SN - Shnigdha, SR Shringhar, and PR - Phule rajani.

compounds is shown in Fig. 2. Amino acids such as, valine, alanine, glycine, leucine, serine, and phenylalanine were found abundantly in cultivars like UJ, SN, and SR. Among the sugars and their derivatives, cultivars such as CS, SN, SR, PR had shown higher levels of sucrose, βD-glucopyranose (glucose in pyranose form), D-fructofuranose (fructose in furanose form). Three glycosides viz. methyl α-D-galactofuranoside, methyl β-D-galactopyranoside, and methyl α-D-glucopyranoside were found abundantly in CD cultivar. TCA cycle intermediates including citric acid, succinic acid, malic acid, fumaric acid were also detected. Several intermediates of phenylpropanoid and benzenoid metabolism including quinic acid, p-coumaric acid, caffeic acid, coniferyl aldehyde along with coumarins like scopoletin were detected almost in all cultivars. Upon UHPLC–DAD analysis of floral methanolic extracts, three major peaks were detected in all cultivars (Fig. S2A). Among the three peaks, rutin was identified and quantified in ng g−1 fr. wt by comparing retention time and UV–visible spectrum with that of standard compound. The other two peaks were tentatively identified as quercetin glycosides based on information available in the literature (Lin and Harnly, 2007). Cultivars such as UJ, SR, CD, and PR had shown to accumulate maximum amount of rutin (Fig. 3A). These cultivars also had a higher amount of quercetin and kaempferol as shown in Fig. 2. Previous studies had led to the identification of kaempferol and its glycosides from P. tuberosa leaves (El-Moghazy et al., 1980). The contents of phenolic acids in the cell wall were also analysed according to the previously described method (Sircar et al., 2007). Three major phenolic acids were detected viz. p-coumaric acid, ferulic acid, and caffeic acid and quantitated in terms of ng mg−1 pellet dry wt. (Fig. S2B). The compounds were identified by comparing both retention time and UV–Visible spectra to their respective standards. Cultivars such as JY, PR, and SR showed a higher amount of phenolic acids in cell wallbound form (Fig. 3B). 2.3. Multivariate analysis of floral metabolites of P. tuberosa cultivars Mutivariate analysis were applied to identify patterns from both floral volatile and non-volatile compounds of P. tuberosa cultivars. All the 127 compounds identified and quantified as stated above, were subjected to normalization (unit scaling) in MetaboAnalyst 3.0 software. Principal component analysis (PCA) was applied for preliminary evaluation of data. PCA is an unsupervised technique which helps to identify patterns among the measured data. It helps in highlighting possible similarities and differences in the data set (Steinfath et al., 2008). From the data set, around 42% of variance amongst the cultivars could be clearly explained by principal component (PC) 1 and PC 2 (Fig. S3A). The scores plot so obtained could clearly segregate CD and CS cultivars, while rest of the cultivars were grouped together (Fig. S3B). The biplot shows various features along with their loading scores obtained after analysis (Fig. S3C). However, PCA may not be able to capture maximum variance among the samples since it assumes most varying components are also biologically important. Considering the results obtained from PCA, a supervised technique namely Partial Least Square-Discriminant Analysis (PLS-DA) was applied to obtain better classification models using MetaboAnalyst 3.0. Thus, from the scores plot obtained, a better intra cultivar separation was observed (Fig. 4A). PLS-DA analysis also provided variable importance in projection (VIP) scores, which can act as a quantitative measure of discriminating power of each metabolite (Steinfath et al., 13

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Table 3 Amount of free endogenous volatiles (μg g−1 fr. wt) of P. tuberosa flowers in all cultivars. S. No.

Compound

Rt

Amount (μg g−1 fr. wt) CS

CD

SN

Monoterpenes 1027 4.51 ± 2.45 71.49 ± 4.78 16.24 ± 12.78 1 1,8-Cineolea 2 α –Terpineol 1187 0.54 ± 0.19 10.02 ± 7.16 2.23 ± 1.88 3 2-Hydroxy cineole 1219 0.06 ± 0.01 0.63 ± 0.44 0.05 ± 0.015 4 3-exo-Hydroxy-1,8-cineole 1237 0.13 ± 0.05 1.49 ± 1.12 0.03 ± 0.003 5 cis-Geraniol 1255 0.37 ± 0.16 1.16 ± 0.19 1.45 ± 1.28 Sesquiterpenes 6 β-Caryophyllene 1413 0.25 ± 0.02 – 0.81 ± 0.76 7 Germacrene D 1475 6.93 ± 5.55 – 0.19 ± 0.03 8 α-Farnesene 1510 1.67 ± 0.21 – – 9 (Z,E)-Farnesol 1719 5.29 ± 1.73 27.52 ± 5.22 6.76 ± 5.11 Aromatic hydrocarbons (benzenoids, phenylpropanoids, nitrogen containing compounds and others) 10 Benzyl alcohol 1033 0.35 ± 0.14 3.21 ± 2.37 1.59 ± 1.02 11 2-Methoxy phenol 1086 0.35 ± 0.4 5.53 ± 5.13 3.08 ± 2.93 12 Methyl benzoate 1092 1.61 ± 0.64 10.14 ± 1.03 6.82 ± 5.94 13 Phenylethyl alcohol 1125 0.15 ± 0.03 – 0.03 ± 0.002 14 2-Methyl benzaldehyde oxime 1298 0.89 ± 0.05 – – 15 Methyl salicylate 1189 0.62 ± 0.46 3.08 ± 0.41 5.42 ± 4.84 16 4-Propyl benzaldehyde 1265 0.92 ± 0.03 1.49 ± 1.12 3.67 ± 0.26 17 1, H-Indole 1289 4.78 ± 0.72 11.33 ± 8.70 20.28 ± 16.82 18 2-Nitrocumene 1269 0.58 ± 0.10 0.05 ± 0.03 2.80 ± 2.68 19 Methyl 2-amino benzoic acid 1336 5.31 ± 2.48 21.26 ± 0.81 7.94 ± 6.48 20 Eugenola 1355 0.29 ± 0.03 1.55 ± 0.21 0.55 ± 0.15 21 Methyl eugenol 1404 0.17 ± 0.06 2.28 ± 0.21 2.82 ± 2.72 22 trans-Isoeugenol 1447 4.78 ± 1.58 8.80 ± 1.32 9.38 ± 6.14 23 Benzyl benzoatea 1758 29.08 ± 26.76 133.15 ± 2.68 51.92 ± 189.87 24 Benzyl salicylatea 1861 3.71 ± 2.11 1.50 ± 1.17 134.51 ± 1.56 a 25 cis-Methyl isoeugenol 1497 7.24 ± 4.44 25.89 ± 0.19 8.03 ± 6.22 Others including fatty acid derivatives 26 5-Hydroxy-7(Z)-decenoic acid-δ1486 10.79 ± 2.50 41.99 ± 15.65 21.66 ± 18.47 lactone 27 α-Linolenic acid 2210 .83 ± .03 11.02 ± 7.71 –

UJ

JY

SR

PR

25.67 ± 5.72 5.16 ± 1.45 – – 0.73 ± 0.17

7.31 0.71 0.03 0.09 0.58

± ± ± ± ±

4.88 0.51 0.001 0.001 0.16

1.39 ± 0.23 – – – –

0.82 0.11 0.01 0.02 –

– – – –

5.81 6.28 4.50 3.51

± ± ± ±

4.57 5.15 3.23 4.68

– – – 2.50 ± 0.08

0.059 ± 0.004 0.07 ± 0.055 – 0.32 ± 0.31

– 3.57 ± 0.41 14.97 ± 6.45 – – 1.59 ± 0.38 1.99 ± 0.22 29.13 ± 8.89 1.52 ± 0.18 25.60 ± 8.52 0.55 ± 0.15 25.60 ± 8.52 6.69 ± 2.37 25.58 ± 0.77 25.58 ± 0.77 –

0.16 ± 0.06 7.43 ± 4.88 2.26 ± 1.97 – – 0.97 ± 0.06 0.92 ± 0.10 2.22 ± 1.38 0.51 ± 0.38 1.84 ± 2.05 0.29 ± 0.10 – 2.03 ± 2.18 14.22 ± 13.18 8.86 ± 6.97 2.10 ± 2.51

– 5.82 1.52 – – 1.40 2.67 5.77 2.16 2.56 0.27 – 2.07 2.26 – –

0.11 ± 0.01 0.06 ± 0.04 0.30 ± 0.15 – – 0.04 ± 0.009 – 0.56 ± 0.48 0.06 ± 0.05 0.30 ± 0.24 0.02 ± 0.02 0.008 ± 0.002 0.288 ± 0.26 4.54 ± 4.39 0.17 ± 0.16 0.19 ± 0.13

–

2.38 ± 1.17

–

0.62 ± 0.54

–

–

–

–

± 0.29 ± 0.07

± ± ± ± ± ±

0.08 0.32 0.35 0.02 0.40 0.009

± 0.27 ± 0.68

± ± ± ±

0.46 0.03 0.004 01

Note: All values are mean ± s.d. of three replicates. “-“indicates not detected. Compounds having letter ‘a’ in superscript form indicates that these are identified by comparing mass spectra and retention index with their respective standards. Other compounds were identified by comparing mass spectra and retention indices from NIST 14 library and available literature. Abbreviations: CS: Calcutta single, CD: Calcutta double, SN: Shnigdha, UJ: Ujwal, JY: Jyothi, SR: Shringhar, PR: Phule rajani, Rt: Retention index.

Interestingly, cultivars belonging to these clusters had higher scent volatiles emission rates than the CD cultivar, which was grouped in cluster A. Floral scent volatiles emission in P. tuberosa has been predicted to be an energy driven eccrine mechanism (Maiti and Mitra, 2017). TEM studies of petaloid tepals in P. tuberosa (CS cultivar) have also revealed more accumulation of mitochondria inside the cells during the blooming period. This stage also happened to be the maximum scent volatiles emission point for P. tuberosa flowers (CS cultivar). Thus higher levels of primary metabolites formation could be due to more metabolic activity in cells, which were also in support with TEM observations studied earlier in petaloid tepals of P. tuberosa (Maiti and Mitra, 2017). Metabolome profiling of Petunia axillaris had also revealed that the levels of primary metabolites varied synchronously in highly scented lines (Oyama-Okubo et al., 2013). In all the seven cultivars, several cross talks among benzenoid, phenylpropanoid and flavonoid metabolism were observed. For example, cultivars like UJ and SR (Cluster D) had higher levels of flavonoids but lower levels of endogenous and emitted phenylpropanoid volatiles. This may be due to the competition for same precursor pcoumaric acid in the cell (Falcone Ferreyra et al., 2012), while cultivars from cluster C (JY and PR) had a higher amount of p-coumaric acid and other phenolic acids in the cell wall, thus accounting for its lower levels in the cytoplasm. Benzenoids namely benzyl benzoate dominated the P. tuberosa aroma in all clusters, indicating that the levels of cinnamic acid may also affect the content of flavonoids and phenylpropanoid volatiles. A study with Phlox subtula cultivars had reported that the floral tissues experienced a competition for phenylalanine, the aromatic amino acid precursor of phenylpropanoid/benzenoid metabolites, and

2008). The top 25 metabolites having VIP scores greater than 1.5 are presented in Fig. 4B. This includes emitted volatile compounds such as, D-limonene, 1R-α-pinene, 3-carene, benzyl salicylate, Z-β-caryophyllene, β-terpeniol. Further, quinic acid and sugar derivatives viz. myo-inositol, D-mannose, D-fructofuranose were also shown to have higher VIP scores. These factors may also have contributed to cultivar segregation. Another unsupervised pattern recognition technique was used to obtain clusters from the metabolite data of P. tuberosa cultivars. Hierarchical clustering analysis was performed using Euclidean distance measurement and ward algorithm. The dendrogram so obtained from MetaboAnalyst 3.0 has been given Fig. 4C. A previous study has shown that Euclidean distance is most appropriate during clustering analysis (Uarrota et al., 2014). Four distinct clusters were obtained among the seven cultivars of P.tuberosa. The CD and CS cultivars were identified separately as clusters A and B, respectively. While cluster C incorporates JY and PR cultivars, cluster D includes SR, UJ, and SN cultivars. Similar results were also obtained from PLS-DA analysis thus confirming metabolic variations among the cultivars.

2.4. Metabolic alterations between P. tuberosa cultivars In order to understand metabolic alterations among the cultivars a summarized map of normalized relative abundances of a few important primary and specialized metabolites was constructed (Fig. 5). The map shows metabolite levels of four different clusters of P. tuberosa cultivars. Cultivars from clusters B, C and D had higher levels of glucose, fructose and TCA cycle intermediates such as citric acid and malic acid. 14

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Table 4 Amount of glycosyl-bound volatiles (in μg g−1 fr. wt) of P. tuberosa flowers in all cultivars. S. No.

Compound

Rt

Amount (μg g−1 fr. wt) CS

CD

Monoterpenes 1027 0.93 ± 0.73 1.33 ± 0.35 1 1,8-cineolea 2 α-Terpineol 1187 1.14 ± 0.01 1.17 ± 0.43 3 2-Hydroxy cineole 1219 – 0.22 ± 0.17 4 3-exo-Hydroxy-1,8-cineole 1237 – 0.36 ± 0.27 5 cis-Geraniol 1255 – 0.61 ± 0.21 Sesquiterpenes 6 Germacrene D 1.79 ± 0.25 0.46 ± 0.27 Aromatic hydrocarbons (benzenoids, phenylpropanoids, nitrogen containing compounds 7 2-Methoxy phenol 1086 – 0.44 ± 0.01 8 Benzyl alcohol 1033 1.25 ± 0.62 7.94 ± 0.53 9 Methyl salicylate 1189 0.63 ± 0.03 – 10 4-Propyl benzaldehyde 1265 1.06 ± 0.16 1.27 ± 0.52 11 2-Nitrocumene 1269 – 0.85 ± 0.30 12 1H-Indole 1289 0.31 ± 0.02 0.52 ± 0.32 13 Methyl 2-amino benzoic acid 1336 0.41 ± 0.28 3.88 ± 1.74 14 Eugenola 1355 0.15 ± 0.04 0.43 ± 0.16 15 trans-Isoeugenol 1447 0.22 ± 0.16 0.79 ± 0.25 16 cis-methyl isoeugenola 1497 1.88 ± 0.53 0.15 ± 0.06 17 Benzyl benzoatea 1758 3.57 ± 3.45 1.77 ± 0.83 18 Benzyl salicylatea 1861 1.27 ± 0.02 – Others 19 5-Hydroxy-7(Z)-decenoic acid-δ-lactone 1486 1.25 ± 0.06 1.16 ± 0.69 20 5-Hydroxydecanoic acid δ-lactone 1502 0.67 ± 0.02 0.13 ± 0.04

SN

UJ

JY

SR

PR

0.27 ± 0.21 – – – –

0.17 0.24 0.07 0.17 0.05

2.11 ± 1.79 – – – –

– – – – 0.69 ± 0.58

2.65 ± 1.30 0.91 ± 0.26 – – –

0.15 ± 0.01 and others) 0.18 ± 0.01 0.84 ± 0.15 0.41 ± 0.10 0.73 ± 0.10 0.30 ± 0.10 0.40 ± 0.08 0.41 ± 0.06 0.15 ± 0.03 0.31 ± 0.11 – 2.39 ± 0.42 –

0.58 ± 0.20

0.87 ± 0.68

–

1.61 ± 0.40

0.35 1.43 – 0.85 0.69 0.41 0.25 0.11 0.16 – 2.37 –

– 0.89 0.39 1.89 0.67 1.89 0.52 0.17 0.28 0.97 2.45 3.77

0.16 7.24 2.67 5.61 2.65 1.06 2.57 0.43 0.34 – 5.34 –

0.79 ± 0.30 0.46 ± 0.34

1.61 ± 0.79 0.33 ± 0.05

± ± ± ± ±

0.06 0.11 0.01 0.03 0.011

± 0.12 ± 0.26 ± ± ± ± ± ±

0.33 0.23 0.16 0.08 0.04 0.07

± 1.16

± ± ± ± ± ± ± ± ± ± ±

0.72 0.33 1.55 0.54 1.55 0.45 0.15 0.25 0.84 0.35 3.09

1.14 ± 1.08 –

± ± ± ± ± ± ± ± ±

0.02 6.01 2.19 1.73 0.29 0.80 2.08 0.32 0.22

± 4.86

6.36 ± 1.78 0.18 ± 0.03

– 5.22 ± 4.65 1.19 ± 0.44 3.89 ± 0.91 1.94 ± 0.38 0.88 ± 0.16 1.82 ± 0.29 0.70 ± 0.05 0.56 ± 0.08 2.2 ± 1.34 9.51 ± 5.89 1.49 ± 0.09 2.69 ± 0.24 –

Note: All values are mean ± s.d. of three replicates. “-“indicates not detected. Compounds having letter ‘a’ in superscript form indicates that these are identified by comparing mass spectra and retention index with their respective standards. Other compounds were identified by comparing mass spectra and retention indices from NIST 14 library and available literature. Abbreviations: CS: Calcutta single, CD: Calcutta double, SN: Shnigdha, UJ: Ujwal, JY: Jyothi, SR: Shringhar, PR: Phule rajani, Rt: Retention index.

profiling data, when combined with quantitative genetics, might help in identifying ‘quantitative trait loci’ that could help breeders during marker assisted breeding programme in P. tuberosa. This report also provided a comparative account of endogenous, emitted and glycosylbound volatiles of all the studied P. tuberosa cultivars, which might help perfume industries to identify superior cultivars for essential oil extractions. Upon comparison of the metabolite status of these P. tuberosa cultivars, following questions have been raised: how are the cross talks between benzenoid, phenylpropanoid, and flavonoid metabolism occurring in P. tuberosa flowers? Are transcription factors or substrate availability of precursor compounds playing role in regulating these cross talks during the floral lifespan? Is there any role of glucosidase enzymes in volatile emission? Are there any other factors such as floral wax which hinders emission of secreted volatiles from P. tuberosa flowers? These questions will be addressed in future manuscripts for better understanding of floral metabolic networking in tuberose.

causing pathway redirections (Majetic and Sinka, 2013). Several transcription factors including EOB II (Emission of Benzenoids), ODORANT 1 belonging to R2R-MYB family have been identified from petunia and other plants, were shown to play a possible role in the regulation of phenylpropanoid/benzenoid metabolism (Van Moerkercke et al., 2011; Liu et al., 2015). However, levels of scent volatile compounds such as, methyl benzoate in petunia have been regulated by substrate concentrations, and not by the levels of transcripts accumulation involved in volatile biosynthesis (Kolosova et al., 2001). Benzyl alcohol, a precursor compound of benzyl benzoate, was found only as endogenous or in its glycosylated form, thus indicating substrate utilization in P. tuberosa flowers. Our study has also identified glycosides including methylated forms of α-D-glucopyranoside and β-D-glucopyranoside, which may act as precursors for aroma compounds such as geraniol, benzyl alcohol, phenyl ethyl alcohol (Cui et al., 2016). Since majority of emitted volatiles were found in both endogenous and glycosyl-bound form, the role of glucosidases in volatile secretion, emission and storage mechanisms needs to be further studied. Another observation made from the study was that CD a cultivar having double whorl arrangement had relatively higher amount of endogenous volatiles with lower emission rate, a possible reason for this could be due to changes in surface morphology of petals, including wax accumulation and number of stomata in the adaxial surface of the petaloid tepals in the flower.

4. Experimental 4.1. Plant material, cultivation, and growth Tubers of different P. tuberosa cultivars viz. Calcutta single (CS), Calcutta double (CD), Bidhan rajani 1-Shnigdha (SN), Bidhan rajani 2Ujwal (UJ), Bidhan rajani 3- Jyothi (JY), Shringhar (SR), and Phule rajani (PR) were collected from Bidhan Chandra Krishi Viswavidyalaya (BCKV; bckv.edu.in), Kalyani, West Bengal, India. All the different cultivars were developed as a result of various breeding programs throughout the country. Cultivars viz. Calcutta single (CS) and Calcutta double (CD) are native to West Bengal, India. Cultivars such as, SN, UJ and JY were developed at BCKV through breeding programmes. SR is a hybrid cultivar developed by a cross between single and double cultivars, and released by ICAR-Indian Institute of Horticultural Research (IIHR; www.iihr.res.in), Bangalore, India. Similarly, PR cultivar was developed by a breeding program at Mahatma Phule Krishi Vidyapeeth (MPKV; mpkv.ac.in), Rahuri, Maharashtra, India. The tubers were

3. Conclusion P. tuberosa is a widely cultivated floricultural crop with ornamental and perfumery uses. Many cultivars of P. tuberosa have been developed with improved floral morphology and enhanced vase life; cultivars tolerant to various abiotic and biotic stresses were also established. This study is the first of its kind where targeted metabolite analysis between seven P. tuberosa cultivars has been reported. Several volatile compounds including benzyl benzoate, D-limonene, 3-carene along with a few primary metabolites such as, D-mannose have been identified as marker compounds after chemometric analysis. These metabolite 15

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Fig. 2. Heat map representing the diversity of nonvolatile compounds detected in P. tuberosa flowers. Floral extracts after derivatization were analysed by GC-MS. Classes 0, 1, 2, 3, 4, 5, and 6 indicates group average abundances of Calcutta double, Calcutta single, Jyothi, Ujwal, Shnigdha, Shringhar, and Phule rajani cultivars respectively. Red and green color denotes highest and lowest relative abundances of non-volatile compounds, respectively. Abbreviations: Met a-D-gal - Methyl α-D-galactofuranoside, Met b-D-gal - Methyl β-D-galactopyranoside, Met b-D-glu - Methyl β-D-glucofuranoside, 4HBALD - 4-hydroxybenzaldehyde, 4-HBA - 4-hydroxybenzoic acid, 2, 5-DHBA - 2, 5-Dihydroxybenzoic acid. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

4.2. Chemicals and reagents

planted in lateritic soil at the experimental garden of the department at Kharagpur (22.3460° N, 87.2320° E), under natural environmental conditions during the month of April 2015 when average temperature was reaching maximum up to 41 °C and minimum up to 21 °C with relative humidity of ca. 69%. Plants were regularly watered once in two days according to the seasonal requirements. Fertilizer dose of 100 kg N, 50 kg P2O5 and 70 kg K2O per hectare as recommended for tuberose production, was applied in split doses within 60 days of plantation. Sampling was done from one-year-old mature tuberose plants. Flowers from CD, CS, PR and SN cultivars were sampled during the month of May 2016 (average maximum temp. 42 °C, average minimum temp. 22 °C, relative humidity: 70%), while flowers from UJ, SR and JY cultivars were sampled during the month of July 2016 (average maximum temp. 35 °C, average minimum temp. 25 °C, relative humidity: 80%). However, the sampling points for all cultivars remained same, i.e. during its blooming stage between 17:30–18:30 h, unless stated otherwise.

Solvents like dichloromethane, hexane, acetone, methanol, acetonitrile of analytical and HPLC grade were purchased from Merck India. Adsorbent matrix Porapak™ (Porapak Q 80/100 mesh size having a surface area of 550 m2/g) was purchased from SUPELCO (Sigma Aldrich, USA). Standards compounds and derivatization reagents like BSTFA and TMCS wherever mentioned were also purchased from Sigma Aldrich.

4.3. Floral morphological study of P. tuberosa cultivars After cultivation, morphological features of the cultivars such as number of days to flower, number of true flowers, floral dimensions, and spike length were monitored regularly.

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4.4.2. Extraction of free endogenous and glycosyl-bound volatiles Endogenous volatiles were extracted according to the modified method of Sagae et al. (2008). The whole flower plucked at 19:00 h after emitted volatile trapping was crushed using liquid N2 and extracted with 1 ml of DCM. The samples were sonicated for 15 min using a sonicator and subsequently centrifuged at 15,000 rpm using Eppendorf Minispin® for 15 min. The supernatants so obtained were dehydrated with anhydrous sodium sulfate and concentrated under N2 gas purge to reduce the sample volume to 100 μl. Glycosyl-bound volatiles were extracted as previously described by Masakapalli et al. (2014). Floral tissue was crushed using liq. N2 and extracted with phosphate citrate buffer (pH 5.4). Samples were then sonicated for 15 min, followed by treatment with 200 μl of Viscozyme®L (Sigma Aldrich, USA) for the release of glycosyl-bound volatiles. After thorough vortexing, 500 μl of DCM was added to the mixture. The samples were incubated for 18 h at 37 °C in an Eppendorf thermo mixer R. The DCM layer was pooled and dehydrated using anhydrous sodium sulfate and concentrated under N2 gas purge to a final volume of 100 μl for all samples. Ethyl hexanoate (1:20 (v/v) in DCM) (1 μl) was used as internal standard for both endogenous and glycosyl-bound volatiles. 4.4.3. GC-MS analysis of volatiles GC-MS analysis was carried out according to previously described method of Barman and Mitra (2019) using TG-5 MS column (30 m × 0.32 mm, 0.25 μm thickness) in Trace 1300 gas chromatograph coupled with ISQ-QD mass spectrometer (Thermo Scientific, USA). Helium was used as the carrier gas at a flow rate of 1.5 ml/min in split mode with split ratio 10 and split flow 15 ml/min. The injection port temperature was maintained at 260 °C and initial temperature of the column was set at 50 °C followed by a hold time of 2 min. The temperature was linearly increased at a rate of 2 °C per min up to 60 °C followed by a hold time of 2 min. This was followed by an increase of column temperature to 210 °C at a rate of 3 °C/min and a hold time of 2 min. Finally, the temperature was linearly increased to 270 °C at a rate of 10 °C/min and held for 7 min. The MS transfer line temperature was maintained at 280 °C and the ion source temperature at 200 °C. The generated ion chromatograms were analysed using Thermo Excalibur 3.0.63 software (Thermo Fisher Scientific Inc.) and the mass spectra of compounds were confirmed with NIST14 (National Institute of Standards and Technology, Gaithersburg, USA) library and also with relevant authentic standards. A sample volume of 1 μl was injected into GC-MS. Retention indices for all compounds were calculated by running an alkane series C8 to C20 and compared with NIST Chemistry WebBook. Relative peak area normalized against the fresh weight of floral tissue with respect to the internal standard was used to quantify compounds for all the samples (IOFI Working Group on Methods of Analysis, 2011). For compounds which have been identified by comparing to respective standards, quantitation were done according to their response factor (IOFI Working Group on Methods of Analysis, 2011). The emission rate of compounds was also calculated in terms of μg scent/g fresh weight (g fr. wt)/hour according to Majetic and Sinka (2013). Endogenous and glycosyl-bound volatile levels were also quantified in terms of μg/g fresh weight (g fr. wt) according to Kondo et al. (2006).

Fig. 3. Soluble and wall-bound phenolics profile of tuberose cultivars. Histogram A shows the amount of rutin determined by UHPLC analysis in ng g−1 fr. wt of flower tissue, and B shows the amount of caffeic acid, p-coumaric acid and ferulic acid in ng mg−1 pellet dry wt of. Note: All values are mean ± s.d. of three replicates.

4.4. Comparative study of floral volatiles of P. tuberosa 4.4.1. Collection and preparation of emitted volatiles Headspace collection method was used to analyse the profile of emitted volatiles (Maiti et al., 2014; Bera et al., 2017). The volatile collection was done during the blooming stage (17:30–18:30 h) from an intact single flower undetached from the plant as shown in Fig. S1. The headspace consisted of a 500 ml round-bottomed glass flask having two open arms (8 mm in diameter). As shown in Fig. S1, one arm was plugged with DCM washed non-adsorbent cotton, while the other was connected to a glass column (8 cm length and 5 mm i.d.) having 30 mg of adsorption matrix i.e. Porapak Q 80/100 (polydivinylbenzene filter) via teflon tubing. The air spaces near the petioles, i.e., at the bottom of the glass chamber, were also blocked with DCM-washed non-adsorbent cotton. Volatiles were allowed to get saturated in the headspace for an hour during the blooming period (i. e. from 17:30–18:30 h). After saturation, the volatiles were trapped on to adsorption matrix by sucking the volatiles-enriched air with 60 k Pa air pressure on to the adsorption matrix for 30 min using a ROCKYVAC 400 vacuum pump (Tarsons India Pvt. Ltd., Mumbai). The volatiles were eluted from the adsorbent matrix using 200 μl of dichloromethane (DCM). Ethyl hexanoate (1:20 (v/v) in DCM (1 μl) was added as internal standard before injection.

4.5. Metabolite profiling of non-volatile compounds in tuberose flowers 4.5.1. Sample preparation and derivatization using BSTFA and TMCS Sample extraction and preparation was done according to the modified method of Proestos et al. (2006). For identification and quantitation of phenolic acids, organic acids, fatty acids, and flavonoids, acidified methanolic extraction followed by ethyl acetate fractionation was used. Floral tissues (single flower of each cultivar plucked between 17:30–18:30 h) were dried at 25 °C overnight and extracted with 2 ml of acidified methanol (80% in 6 M HCl). The extracts were sonicated for 15 min and refluxed in Eppendorf plus® thermomixer at 17

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Fig. 4. Multivariate analysis of P. tuberosa cultivars (classes 0–6). The scores plot obtained after PLS-DA analysis has been shown in A, where components 1 and 2 together can explain the maximum variance between the cultivars. Metabolites having higher VIP scores are shown in B where their relative abundances are color coded from high to low as per the scale shown. The dendrogram as shown in C was obtained after hierarchical clustering analysis of studied P. tuberosa cultivars. Classes 0, 1, 2, 3, 4, 5, and 6 represent Calcutta double, Calcutta single, Jyothi, Ujwal, Shnigdha, Shringhar and Phule rajani cultivars. Metabolites enlisted as 1–25 in B are sequentially represented as follows: quinic acid, V_D-limonene (V: detected as emitted volatile), myo-inositol, D-mannose, V_3-carene, EV_cis-methyl eugenol (EV: detected as endogenous volatile), V_Z- β-caryophyllene, V_benzyl salicylate, EV_benzyl benzoate, EV_eugenol, methyl α-D-galactofuranoside, EV_(Z, E)-farnesol, GV_2-nitrocumene (GV: detected as glycosyl-bound volatile), EV_1,8-cineole, V_1,R-α-pinene, Methyl β-D-galactopyranoside, GV_4-propyl benzaldehyde, Methyl α-Dglucofuranoside, V_β-terpineol, D-fructofuranose, L-threonine, EV_2-hydroxy cineole, EV_3-exo-hydroxy-1,8-cineole, EV_5-hydroxy-7(Z)-decenoic acid-δ-lactone and GV_5-hydroxy-7Z-decenoic acid- δ –lactone.

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Fig. 5. Metabolite map representing the normalized relative abundances of few important primary and specialized metabolites during the blooming stage of P. tuberosa flowers. The amount of metabolites from four different P. tuberosa cultivar clusters were colour coded for low to high contents as per the scale shown. Cluster A includes CD, cluster B includes CS, cluster C includes JY and PR and cluster D includes SN, UJ, and SR. Solid arrows represent single step reactions, dotted arrows represent multi step reactions and block arrows represent volatile emission or exchange within the cell. Emitted levels of volatiles are shown outside the cell, while endogenous levels are represented inside the cytosol. Glycosyl-bound volatiles are shown inside in a representative vacuolar structure. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

3.0.63 software with acceptable deconvolution parameters such as baseline offset-1, smoothing points-5 and signal to noise ratio as 10. Normalized relative abundance in respect to BHT was calculated for quantification.

80 °C for 2 h. Further, upon fractionation of the extracts with ethyl acetate (1 ml), the organic layer was treated with anhydrous Na2SO4 to remove water molecules. Butylated hydroxytoluene (BHT) (90 μg) was added as internal standard into the organic layer before drying. The organic layer of all samples were completely dried under N2 purge and used for further derivatization. Fresh floral tissues (single flower of each cultivar plucked between 17:30–18:30 h) were used for methanolic extraction (80% v/v) to detect and quantify amino acids, sugars, and its derivatives. The extracts obtained after crushing were subjected to sonication for 15 min, followed by through vortexing for 10 min. The extracts were centrifuged at 10,000 rpm for 10 min (Eppendorf Minispin® Plus). An aliquot of 50 μl of supernatant spiked with derivatization standard i.e. BHT (90 μg) was dried completely under vacuum concentrator. Derivatization of extracts was done using 200 μl of BSTFA and 100 μl of TMCS by incubating at 80 °C for 45 min. All samples were prepared and analysed in triplicate.

4.5.3. UHPLC-DAD analysis of flavonoids and wall-bound phenolics Single whole flowers of different cultivars of tuberose were plucked between 17:30–18:30 h during blooming stage and were extracted in 60% methanol and sonicated for 15 min. After sonication, for 15 min, the extracts were vortexed and centrifuged for 15 min at 10,000 rpm. The supernatants so obtained were filtered using a 0.45 μm syringe filter and used for further analysis. The pellets were used for wall-bound phenolics extraction as previously described by Sircar et al. (2007). The samples were stored at 4 °C until further studies. All the extractions and analysis were carried out in triplicate. UHPLC-DAD analysis of both soluble and wall-bound phenolic compounds were carried out using Thermo Dionex Ultimate 3000 UVDAD system having a quaternary pump with the help of Chromelon™ software. For soluble phenolics, a modified method of Lin and Harnly (2007) was followed. Phenomenex™ C18 (reverse phase) Lichrosorb 5μ (250 × 4.60 mm) column was used with a mobile phase consisting of 0.1% formic acid in water and 0.1% formic acid in acetonitrile in a gradient mode at a flow rate of 1 ml/min. UHPLC analysis of wallbound phenolics was carried out according to Sircar et al. (2007). Phenomenex™ Synergi C18 (reverse phase hydro) 4 μ (250 × 4.6 mm) column was used with a linear isocratic aqueous trifluoroacetic acid: methanol (68:32). The sample (20 μl) was injected and monitored at 254 and 310 nm along with the complete 3D field.

4.5.2. GC-MS analysis of derivatized compounds GC-MS analysis was carried out according to the modified method of Proestos et al. (2006) using Thermo Trace 1300 Gas Chromatograph coupled with ISQ QD single quadrupole mass spectrometer. TG-5MS column (column dimensions same as above) was used for separation along with carrier gas (Helium) at a flow rate of 1.5 ml/min in split less mode with a split time of 1 min. The injection port temperature was maintained at 280 °C and initial temperature in the column was set at 70 °C followed by a hold time of 1 min. The temperature was then linearly increased at a rate of 2 °C per min up to 135 °C followed by a hold time of 10 min. The temperature was then increased up to 220 °C at 4 °C per min followed by a hold time of 10 min and finally till 270 °C at 3.5 °C per min and a hold time of 20 min. The MS transfer line temperature was maintained at 290 °C and the ion source temperature at 200 °C. All chromatograms were processed using Thermo Xcalibur

4.6. Statistical analysis Statistical analysis such as Duncan's multiple range tests was carried out using SPSS Statistics 17.0 (IBM, New York, USA) at a significance 19

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level of 0.05. Multivariate analysis including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and hierarchial clustering analysis was performed for metabolite data using MetaboAnalyst 3.5 (Xia and Wishart, 2016). All graphs including stack plots and histograms were plotted using OriginPro 8.5.

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Acknowledgements This work was supported by a research grant [38(1336)/12/EMR-II to A Mitra] from the Council of Scientific and Industrial Research (CSIR), India. Ms. N N Kutty thanks CSIR, India for the award of an individual doctoral fellowship [09/081(1247)/2015-EMR-I]. The authors thank Raghunath Sadhukhan of BCKV, Mohanpur (India) for providing tubers of all six varieties other than Calcutta single used in this study. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.phytochem.2019.02.006. References Barman, M., Mitra, A., 2019. Temporal relationship between emitted and endogenous floral scent volatiles in summer‐and winter‐blooming Jasminum species. Physiol. Plantarum. http://doi:10.1111/ppl.12849. Bera, P., Mukherjee, C., Mitra, A., 2017. Enzymatic production and emission of floral scent volatiles in Jasminum sambac. Plant Sci. 256, 25–38. Cui, J., Katsuno, T., Totsuka, K., Ohnishi, T., Takemoto, H., Mase, N., Toda, M., Narumi, T., Sato, K., Matsuo, T., Mizutani, K., Yang, Z., Watanabe, N., Tong, H., 2016. Characteristic fluctuations in glycosidically bound volatiles during tea processing and identification of their unstable derivatives. J. Agric. Food Chem. 64, 1151–1157. El-Moghazy, A.M., Ali, A.A., Ross, S.A., El-Shanawany, M.A., 1980. Phytochemical studies on Polianthes tuberosa L. Fitoterapia 51, 179–181. Falcone Ferreyra, M.L., Rius, S.P., Casati, P., 2012. Flavonoids: biosynthesis, biological functions, and biotechnological applications. Front. Plant Sci. 3, 1–15. Fiehn, O., Kopka, J., Dörmann, P., Altmann, T., Trethewey, R.N., Willmitzer, L., 2000. Metabolite profiling for plant functional genomics. Nat. Biotechnol. 18, 1157–1161. Groyne, J., Lognay, G., Marlier, M., 1999. Accumulation of glycosidically bound compounds in Fragaria × ananassa cv. Elsanta fruits at various developmental stages. Biotechnol. Agron. Soc. Environ. 3, 5–9. IOFI Working Group on Methods of Analysis, 2011. Guidelines for the quantitative gas chromatography of volatile flavouring substances, from the working group on methods of analysis of the international organization of the flavor industry (IOFI). Flavour Fragrance J. 26, 297–299. Jakobsen, H., Olsen, C., 1994. Influence of climatic factors on emission of flower volatiles in situ. Planta 192, 365–371. Jin, J.M., Zhang, Y.J., Yang, C.R., 2004. Spirostanol and furostanol glycosides from the fresh tubers of Polianthes tuberosa. J. Nat. Prod. 67, 5–9. Kolosova, N., Gorenstein, N., Kish, C.M., Dudareva, N., 2001. Regulation of circadian methyl benzoate emission in diurnally and nocturnally emitting plants. Plant Cell 13, 2333–2347. Kondo, M., Oyama-Okubo, N., Ando, T., Marchesi, E., Nakayama, M., 2006. Floral scent diversity is differently expressed in emitted and endogenous components in Petunia axillaris lines. Ann. Bot. 98, 1253–1259. Lim, T.K., 2014. Polianthes tuberosa. In: Edible Medicinal and Non-medicinal Plants, vol.

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