Comparative Metabolomic and Proteomic Analyses Reveal the

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Bioactive Constituents, Metabolites, and Functions

Comparative metabolomic and proteomic analyses reveal the regulation mechanism underlying MeJA-induced bioactive compound accumulation in cutleaf groundcherry (Physalis angulata L.) hairy roots Xiaori Zhan, Xinyue Liao, Xiujun Luo, Yujia Zhu, Shangguo Feng, Chunna Yu, Jiangjie Lu, Chenjia Shen, and Huizhong Wang J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b02502 • Publication Date (Web): 06 Jun 2018 Downloaded from http://pubs.acs.org on June 7, 2018

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Journal of Agricultural and Food Chemistry

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Comparative metabolomic and proteomic analyses reveal the

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regulation

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compound accumulation in cutleaf groundcherry (Physalis angulata

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L.) hairy roots

mechanism

underlying

MeJA-induced

bioactive

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Xiaori Zhan1,2, Xinyue Liao1,2, Xiujun Luo1,2, Yujia Zhu1,2, Shangguo Feng1,2,

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Chunna Yu1,2, Jiangjie Lu1,2, Chenjia Shen1,2*, Huizhong Wang1,2*

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1College

of Life and Environmental Science, Hangzhou Normal University,

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Hangzhou 310036, China;

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2Zhejiang

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Medicinal Plants, Hangzhou Normal University, Hangzhou 310036, China;

Provincial Key Laboratory for Genetic Improvement and Quality Control of

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Corresponding author:

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Chenjia Shen, E-mail: [email protected];

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Huizhong Wang, E-mail: [email protected]

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College of Life and Environmental Science, Hangzhou Normal University, Hangzhou

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310036, China;

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Tel: +86-571-28865198; Fax: +86-571-28865198

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ACS Paragon Plus Environment


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Abstract

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Cutleaf groundcherry (Physalis angulata L.) is an annual plant with a number of

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medicinal ingredients. However, studies about the secondary metabolism of P.

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angulata are very limited. An integrated metabolome and proteome approach was

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used to reveal the variations in the metabolism associated with bioactive compounds

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under methyl-jasmonate (MeJA) treatment. Application of MeJA to the hairy roots

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could significantly increase the accumulation of most active ingredients. A targeted

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approach confirmed the variations in physalins D and H between MeJA treatment and

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the controls. Increases in the levels of a number of terpenoid backbone biosynthesis-

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and steroid biosynthesis-related enzymes, cytochrome P450 monooxygenases and 3β-

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hydroxysterioid dehydrogenase might provide a potential explanation for the MeJA-

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induced active ingredient synthesis. Our results may contribute to a deeper

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understanding of the regulation mechanism underlying the MeJA-induced active

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compound accumulation in P. angulata.

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Keywords: Physalis; MeJA treatment; physalins; metabolome; proteome; steroid

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1. Introduction

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Cutleaf groundcherry (Physalis angulata L.) is an annual herbaceous plant with

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potential high medicinal values, widely distributed in tropical, subtropical and warmer

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temperate areas, especially in Southeast Asia, Central and South America (1, 2). As a

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medicinal plant from the Solanaceae family and Physalis genus, P. angulata not only

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be rich in vitamins, minerals, and antioxidants, but also important pharmacologically

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active constituents including anti-bacterial, anti-inflammatory and anti-cancer

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ingredients (3-5). Recently, a variety of bioactive steroids, including physagulins A–

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Q, physangulidines A–C, withangulatins A–I, physalins B, D, F, G, and H, and

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withaminimin, have been isolated from P. angulata (6, 7). For example, physalins B

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and D exhibit significant in vitro and in vivo antitumor activities (8). Due to high

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horticultural and medicinal values, P. angulata plants have been widely cultivated

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over decades. Despite its important chemical and food properties, studies on the

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metabolism of secondary metabolites in P. angulata are very limited.

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The chemical structures of most bioactive ingredients, such as physalins D and H,

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isolated from P. angulata have been successfully deciphered (6, 9, 10). Several

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studies have been focused on the whole synthesis pathway of plant steroid backbone

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(11). Phytosteroids are synthesized from two general C5 isoprene units, including

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isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP), which are

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provided via the cytosolic mevalonate (MVA) and plastidal 2-C-methyl-d-erythritol-

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4-phosphate (MEP) pathways (12). The MVA pathway is the major route to produce

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the steroid backbone (13). In plants, the enzymes that catalyze the steps involving in 3



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steroid backbone biosynthesis have been identified, such as acetoacetyl-CoA thiolase

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(AACT) (14), 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR) (15), mevalonate

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diphosphate decarboxylase (MVD) (16), IPP isomerase (IPI) (17), squalene epoxidase

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(SQE) (18), cycloartenol synthase (CAS) (19) and lanosterol synthase (LAS) (20). In

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addition, a number of cytochrome P450 monooxygenases (P450s) have also been

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demonstrated to be involved in the biosynthesis and metabolism of steroids (21).

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The phytohormone methyl jasmonate (MeJA) was always applied to enhance the

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accumulation of secondary metabolites in various plants (22). MeJA acts as an

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intermediate signaling molecule in elicitor-induced accumulation of secondary

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metabolites, including steroid precursors (23, 24). MeJA treatment is also used to

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discover novel proteins involved in the synthesis of bioactive compounds (22). An

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artificially established hairy roots has been used for a variety of purposes over the last

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30 years, ranging from recombinant protein production to metabolic engineering host

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(25). Agrobacterium rhizogenes-mediated hairy root production has been frequently

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utilized as an important biotechnological strategy in a number of plant species to

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reveal novel biological insights (26).

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An untargeted metabolome provides an opportunity to systematically analyze the

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features of primary and secondary metabolites in plants under different conditions.

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For example, a metabolomic analysis profiled the metabolites of T. media cultures

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induced by MeJA treatment (27). In P. peruviana fruits, untargeted metabolomics

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identified two novel withanolides and one fatty acid glycoside as tentative metabolites

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(28). A gel-free MS/MS-based proteomics approach with isobaric labeling reagents, 4


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such as Tandem Mass Tags (TMT), has recently been developed for accurate

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quantification of proteins (29, 30). This newly emerging technology can be used to

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compare relative abundances of the proteins between the control and treatment groups

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(31). Therefore, an integrated metabolome and proteome approach can contribute to

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the identification of a huge number of metabolites and enzymes, revealing the

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complex processes involved in regulating plant metabolism (32).

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So far, very few proteomic or metabolomic data have been published in Physalis

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species (28). In the present study, an integrated metabolome and proteome approach

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was employed to elucidate the regulation mechanisms underlying the MeJA-induced

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bioactive compound accumulation in P. angulata using an A. rhizogenes-mediated

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hairy root system.

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2. Materials and Methods

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2.1 A. rhizogenes and plant materials

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A. rhizogenes strain C58C1 was used to infect P. angulata leaves to obtain hairy

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roots. The hairy roots of P. angulata were cultivated in a 100 ml conical flask

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containing 50 mL of Murashige & Skoog liquid medium supplemented with 30 g/L

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sucrose. The conical flask was shaken at room temperature, in dark. For each repeat,

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an amount of 0.2 g fresh hairy roots was first inoculated in the MS medium and

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cultured for 18 d. MeJA at a concentration of 100 μM was then applied to the conical

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flask and inoculated with hairy roots for another 4 d. The hairy roots added with

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ethanol solvent without MeJA were used as a control group. Thereafter, the hairy root 5



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samples from both the control and treatment groups were harvested. One half of each

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sample was frozen at -80 °C for protein extraction, and the other half was dried at

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40 °C for the determination of physalins D and H. The standards of D and H (>

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98.0%) were kindly provided by Prof. Zhongjun Ma in Zhejiang University. All the

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treatments were performed in three replicates.

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2.2 Metabolite extraction

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For metabolite extraction, 100 mg of fried hairy root samples from each group ( N

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= 15) was first transferred into a 1.5 mL microcentrifige tube, and 1.0 mL methanol

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was added to the tube. The tube was vortexed for 1 min. Then, the mixture was

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ultrasonicated for 20 min in ice bath and centrifuged at 12,000 × g for 20 min, 4 °C.

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At last, 200 μL of the supernatant was collected and transferred to sampler vials for

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MS analysis.

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2.3 Untargeted metabolomic analysis

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An Agilent 1290 Infinity UHPLC system coupled with an Agilent 6545 UHD and

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Accurate-Mass Q-TOF/MS was used for LC-MS analysis (Agilent, Santa Clara, CA,

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USA). The chromatographic column was a Waters XSelect HSS T3 column (2.5 μm

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particle size; 100 × 2.1 mm). The mobile phase consisted of solution A (aqueous

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solution with 0.1% formic acid) and solution B (acetonitrile with 0.1% formic acid).

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The flow rate was 0.35 mL/min. The column temperature was maintained at 25 °C.

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The injection volume was 2.0 μL. The gradient elution condition was as follows: 06


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2min, 5% Solution B; 2-13min, 5-95% Solution B; 13-15min, 95% Solution B. The

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column was purged for 5 min before the injection for system balance.

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Mass spectrometry was operated in both positive and negative ion modes. The

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optimized parameters were as follows: capillary voltage, 4 kV in positive mode and

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3.5 kV in negative mode; drying gas flow, 10 L/min; gas temperature, 325 °C;

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nebulizer pressure, 20 psig; fragmentor voltage, 120 V; skimmer voltage, 45 V; mass

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range, m/z 50–1500. Reference ions were used during the MS data acquisition process

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to ensure mass accuracy. The reference ions in positive ion mode: 121.0509,

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922.0098. Negative ion mode: 112.9856, 1033.9981. The differential metabolites

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were further identified by MS/MS with collision energy of 10 V, 20 V, and 40 V.

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2.4 Bioinformatics of the untargeted metabolomic dataset

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All the raw data were converted into the common format by Agilent Mass-Hunter

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Qualitative Analysis B.07.00 software (Agilent Technologies, USA). In the R

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software platform, the XCMS program was used in peak identification, retention time

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correction and automatic integration pretreatment. Then, the data were subjected to

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normalization. Visualization matrices contained sample names, m/z-RT pairs and

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peak areas. An internal standard sample was prepared for the untargeted analysis.

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Internal standard sample samples were also prepared by combining 10 μL of each

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extraction sample. The intensity of peak data was further preprocessed by an in-house

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software metaX. Those features that were detected in less than 50% of QC samples or

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80% of biological samples were removed, the remaining peaks with missing values 7



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were imputed with the k-nearest neighbor algorithm to further improve the data

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quality. In addition, the relative standard deviations of the metabolic features were

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calculated across all QC samples, and those > 30% were then removed. In total, 1677

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features were obtained in the positive mode and 844 features in the negative mode.

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After editing, the data matrices were imported into SIMCA-P 13.0 (Umetrics, Umea,

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Sweden), mean-centered and scaled to Pareto variance. Finally, a multivariate

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analysis was conducted.

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Wilcoxon tests were conducted to detect differences in metabolite concentrations

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between two sample groups. The P value was adjusted for multiple tests using an

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FDR (Benjamini–Hochberg). Supervised PLS-DA was conducted through metaX to

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discriminate the different variables between groups. The differential metabolites were

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screened out by VIP (Variable Importance in the Projection) value of PLS-DA model

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(VIP >= 1) and independent sample t-test (p < 0.05).

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2.5 Analysis of targeted metabolites

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The physalin D and physalin H standards were dissolved in methanol to prepare

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stock solutions with a final concentration of 10 mg/mL each. The working solution of

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the internal standard isofraxidin (IS) was prepared at a concentration of 1 μg.mL−1

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with the same procedure. Then, the stock solutions were diluted to a serial of

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concentrations for the construction of calibration curves. Eight concentration points of

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the two standard solutions were injected in triplicate, and the calibration curves were

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constructed by plotting the value of peak areas versus the value of concentrations of 8


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each sample.

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A total of 25 mg powder of freeze-dried samples was accurately weighed and

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transferred to a 2 mL centrifuge tube. Briefly, 200 μL of the IS working solution was

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added followed by adding 800 μL of methanol into the centrifuge tube. The centrifuge

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tube was sealed, extracted by ultrasonication for 30 min at room temperature. Then

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centrifugation at 12,000 g for 10 min, and the supernatant was collected and passed

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through a syringe filter of 0.22 μm and 1 μL of the filtrate was injected into the

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UPLC-ESI-MS/MS system for analysis.

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The UPLC-MS/MS system consisted of a Waters Acquity Ultra High

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Performance LC system (Waters, Milford, MA, USA) connected with a TSQ Vantage

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triple quadrupole tandem mass spectrometer (AB/SCIEX, USA) via electrospray

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ionization (ESI) interface operated in the positive ion mode. Chromatographic

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separations of the physalins were performed on an Acquity UPLC BEH Shield RP

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C18-column (100 mm × 2.1 mm, 1.7 μm particle size; Waters, Milford, MA, USA)

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kept at a temperature of 35°C. The mobile phase was composed of 0.1% formic acid

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aqueous solution (A) and 0.1% formic acid acetonitrile solution (B), which was

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delivered at a flow rate of 0.3 mL.min−1. The gradient elution programs were as

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follows: 32–40% B (0–3.5 min), 40–55% B (3.5–5.0 min), 55–100% B (5.0–5.2 min),

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100–100% B (5.2–6.2 min), 100–32% B (6.2–6.5 min). The autosampler was

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maintained at 10°C. The injection volume was set at 1 μL.

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The MS detection was performed with MRM mode. The parameters of the mass

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spectrometers were as follows: spray voltage, 5.5 kV; source temperature, 100°C; 9



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curtain gas pressure(nitrogen), 20 psi; gas1 (nitrogen), 50 psi; gas2 (nitrogen), 55 psi;

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entrance potential, 10 V; collision cell exit potential, 10 V. The parameters on the

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m/z, collision energy and declustering potential of the parent ions and quantitative

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daughter ions for the two physalins and IS are summarized in Table S1.

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The metabolome data were normalized to the total ion current, and the relative

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quantity of each feature was calculated using the mean area of the chromatographic

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peaks from three replicate injections. The quantities of metabolites were generated

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using an algorithm that clustered masses into spectra based on co-variation and co-

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elution in the dataset. The metabolites were annotated by searching against the KEGG

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database.

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2.6 Protein extraction

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Approximately 500 mg hair root samples were harvested and kept in liquid nitrogen

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quickly. For protein extraction, the samples were pulverized and transferred into a 5

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mL tube. Four volumes of pre-cooled lysis buffer containing 8 M urea, 2 mM

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ethylenediaminetetraacetic acid, 10 mM dithiothreitol and 1% Protease Inhibitor

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Cocktail VI were added to the powder. Then, the powder was sonicated three times on

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ice using a high intensity ultrasonic processor (Sonics & Materials, Inc., Newtown,

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USA). The remaining debris was discarded by centrifugation at 20,000 × g at 4 °C for

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10 min. At last, the protein samples were precipitated with pre-cooled 20% TCA

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buffer for 2 h at -20 °C. Again, the remaining debris was removed by centrifugation at

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20,000 g at 4 °C for 10 min. The protein precipitate was washed with cold acetone 10


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and redissolved in 8 M urea. The resulting protein was quantified by a 2-D Quant kit

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(GE Healthcare, Beijing, China) according to the manufacturer’s instructions.

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2.7 Trypsin digestion and TMT labeling

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Before digestion, the protein samples were reduced with 5 mM dithiothreitol for 30

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min at high temperature (56 °C) and alkylated with 11 mM iodoacetamide for 15 min

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at 25 °C in darkness. The protein samples were then diluted with 100 mM TEAB to a

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urea concentration lower than 2 M. Finally, trypsin was added to the sample solution

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at 1:50 trypsin to protein mass ratio for the first digestion overnight and 1:100 trypsin

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to protein mass ratio for second 4 h digestion.

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The digested peptides were desalted by adding to Strata X C18 column

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(Phenomenex, Torrance, CA, USA) and vacuum-dried. The peptide samples were

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reconstituted in 0.5 M TEA buffer and processed using a TMT kit according to its

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protocol. Briefly, one unit of TMT reagent was thawed and reconstituted in

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acetonitrile. Then, the peptides were mixed, incubated for 2 h at 25 °C and dried by

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vacuum centrifugation.

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2.8 Protein fractionation and LC-MS/MS analysis

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The tryptic peptides were fragmented into fractions by high pH reverse-phase

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HPLC using Agilent 300Extend C18 column (5 μm particles, 4.6 mm ID, 250 mm

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length, Agilent, Santa Clara, CA, USA). Briefly, the peptides were first separated with

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a gradient of 8% to 32% acetonitrile over 60 min into 60 fractions, pH 9.0. The 11



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resulting peptides were combined into 18 fractions and dried by vacuum

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centrifugation.

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The tryptic peptides were dissolved in solution A (0.1% formic acid) and directly

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loaded onto a reversed-phase analytical column (150 mm length, 75 μm, Agilent,

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Santa Clara, CA, USA). The gradient was comprised of a solution B (0.1% formic

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acid in 98% acetonitrile) increasing from 6% to 23% over 26 min, from 23% to 35%

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over 8 min, climbing to 80% within 3 min, and holding at 80% for 3 min. A constant

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flow rate was set at 400 nL/min in an EASY-nLC 1000 UPLC system.

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The peptide samples were subjected to NSI source followed by MS/MS in Q

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ExactiveTM Plus coupled online with UPLC (Thermo, Shanghai, China). The

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electrospray voltage was set at 2.0 kV and the m/z scan range from 350-1800 was

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applied for full scan. The intact peptides and fragments were detected in the Orbitrap

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at resolutions of 70,000 and 17,500, respectively. A data dependent procedure that

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alternated between one MS scan followed by 20 MS/MS scans with 15.0s dynamic

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exclusion. The automatic gain control was set at 5E4.

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2.9 Database search and annotation

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The resulting MS/MS data were searched against several protein databases,

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including NCBI non-redundant (Nr) (http://www.ncbi.nlm.nih.gov/protein/), Swiss-

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Prot protein (http://www.uniprot.org/) and Kyoto Encyclopedia of Genes and

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Genomes (KEGG) database (http://www.genome.jp/kegg/), using MaxQuant search

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engine (v.1.5.2.8). The mass tolerance for precursor ions was set at 20 ppm in the first 12


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round searching and 5 ppm in the main searching, and for fragment ions was set as

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0.02 Da. The carbamidomethyl on Cys was specified as fixed modification and

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oxidation on Met was specified as variable modifications. FDR was adjusted to 1%

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and the minimum score for the peptides was set at > 40.

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Gene Ontology (GO) annotation of P. angulata proteome was searched against the

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UniProt-GOA database (www.ebi.ac.uk/GOA). Firstly, all identified protein IDs were

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converted to UniProt IDs, which were mapped onto GO database. Then, the un-

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annotated proteins were annotated by InterProScan soft using protein sequence

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alignment method. The GO annotations of protein were classified into three

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categories, including biological process, cellular component and molecular function.

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Domain functional descriptions of the identified proteins were annotated by

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InterProScan database using protein sequence alignment method. KEGG database was

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used to annotate protein pathway. The protein’s KEGG description was annotated by

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KEGG online service tool, KAAS. The annotation results were mapped on the KEGG

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pathway using KEGG online service tool, KEGG mapper. The subcellular localization

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predication software, Wolfpsort, was used to predict subcellular localization.

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2.10 Functional enrichment analysis

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A two-tailed Fisher’s exact test was used to analyze the domain, GO and KEGG

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functional enrichments of the differential expressed proteins (DEPs). Correction for

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multiple hypothesis test was carried out using standard FDR method. Domain, GO

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and KEGG categories with a corrected P value < 0.05 was considered significant. K13



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means cluster was analyzed using the MeV software. For the bioinformatics analysis,

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including the domain-base, GO-based and KEGG-based enrichment, all the sequences

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in the database were used as the background.

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In order to meet the requirements of the hierarchical clustering method, the P value

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was transformed into Z-score after log transformation (33).

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Formula:

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𝑍 𝑠𝑎𝑚𝑝𝑙𝑒 - 𝑖 =

log2(Signalsample - i) - Mean(Log2(Signal)of all samples) Standard deviation (Log2 (Signal)of all samples)

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2.11 Homology analysis and phylogenetic tree building

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A homology analysis of the proteins from P. angulata was carried out using

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ClustalW with default parameters. The predicted full-length protein sequences of the

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key enzymes involved in the biosynthesis pathway of bioactive steroids were used for

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multiple sequence alignments. An unrooted phylogenetic tree of the P450s was

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constructed using software MEGA6.1 (http://www.megasoftware.net/) employing the

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neighbor-joining method.

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2.12 Protein-protein interaction (PPI) network analysis

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All the DEPs were searched against the STRING database ver. 10.0 for PPI

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prediction. The interactions between the proteins belonging to data set were selected.

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All the interactions with a confidence score lower than 0.7 were fetched. Cytoscape

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was used to visualize the interaction network from STRING.

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2.13 Statistical analysis

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Significant changes between two sample groups were calculated using a one-way

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analysis of variance with a Tukey’s test (P < 0.01). All of the expression analyses

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were performed for three biological replicates. All the reported values represent the

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averages of three replicates, and data are expressed as the mean plus or minus the

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standard deviation (mean ± SD).

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3. Results

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3.1 Establishment of P. angulata hairy root system

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The A. rhizogenes C58C1 infected explants were used to establish the hairy root

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system of P. angulata. The biomass of P. angulata hairy root was measured to

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investigate the growth process (Fig. 1a-f). In our study, two independent lines hairy

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root lines (line 1 and 2) were establishment. Two maker genes, rolB and rolC, were

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selected to check the insertion of T-DNA fragment from A. rhizogenes C58C1 in the

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hairy roots. The PCR results confirmed the successful establishment of P. angulata

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hairy root system (Fig. 1g). Then, an initial characterization of two independent hairy

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root lines (line 1 and line 2) was performed. A targeted profiling of Physalin D and H

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were applied to determine the uniformity in the two independent lines hairy root lines.

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There were no significant differences between line 1 and line 2, thus the line 1 was

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used for untargeted metabolomic analysis (Fig. S1). The hairy roots from the line 1

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were divided into 15 portions for untargeted metabolomic analysis. Our data showed

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that the hairy roots in the suspended culture were in the lag phase between 0 and 4 d, 15



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in the logarithmic growth phase between 4 and 32 d, and in the platform stage

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between 32 d and 60 d (Fig. S2). Furthermore, the best concentration of MeJA and

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MeJA-induced time were determined by an initial characterization. Our data showed

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that a concentration of 100 μM MeJA and an induction time of 4 d were the best

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conditions for MeJA induced bioactive compound accumulation (Fig. S3).

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3.1 Untargeted metabolomic profiling reveals the variations in the abundance

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levels of the metabolites under MeJA treatment

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To explore the comprehensive variations in the metabolomes of P. angulata under

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MeJA treatment, an untargeted approach (15 repeats for each group) was applied.

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Data were detected and collected according to the LC-MS/MS method, and the total

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ion chromatogram (TIC) is shown in Fig. S4. A PCA analysis was performed to

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produce an overview of the metabolic variations between the control and MeJA

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treatment groups. The PCA data showed two clearly separated clusters, indicating

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obvious separations between the control and treatment groups. All the quality control

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(QC) samples were gathered well and the dispersion of QC samples was obviously

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lower than the experiment samples, indicating that the system stability was

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satisfactory (Fig. S5a-b).

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A large number of metabolites, including 1677 features in the positive mode and

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844 features in the negative mode, were identified, respectively (Table S2 and Table

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S3). The differentially accumulated metabolites were characterized by a volcano plot

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analysis (Fig. S5c-d). Among these identified metabolites, a number of metabolites, 16


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including several amino acids, inorganic acids and physalins, were significantly

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changed by MeJA treatment (Table S4). Interestingly, the levels of most identified

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amino acids, including L-proline, L-tryptophan, L-valine, L-histidine, L-glutamine, L-

356

phenylalanine and L-methionine, decreased under MeJA treatment. Based on the

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KEGG annotations, 43 differentially accumulated metabolites were assigned into 11

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primary metabolic pathways, such as ‘Aminoacyl-tRNA biosynthesis’, ‘Glucosinolate

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biosynthesis’, ‘Tropane piperidine and pyridin metabolism’, ‘Arginine and proline

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metabolism’, ‘Citrate cycle’, ‘Alanine aspartate and glutamate metabolism’,

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‘Phenylalanine tyrosine metabolism’, ‘Glyoxylate and dicarboxylate metabolism’,

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‘Histidine metabolism’, ‘Nitrogen metabolism’, and ‘Pyrimidine metabolism’ (Fig.

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1h). By searching the metabolite pool, several bioactive steroids, including

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physagulins A-C, isophysalins B and G, physalins A-D, F-H and L-P, and

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withangulatin A, were identified. The abundance of most of these bioactive

366

ingredients increased under MeJA treatment (Fig. 1i and Table S5). Intereastingly,

367

the content of geranylfarnesyl diphosphate, which is the most important end-product

368

of the MVA and MEP pathways, were largely induced by MeJA treatment (Table

369

S5).

370 371

3.3 Confirmation of the variations in physalins D and H using a targeted

372

approach

373

To determine more precisely the differences in the Physalis-specific active

374

ingredients between the control and MeJA treatment groups, a targeted approach was 17



Page 18 of 53

375

used to measure the concentrations of physalins D and H using their authentic

376

standards. The untargeted analysis indicated that the contents of physalins D and H

377

under MeJA treatment were significantly greater than that under the control condition

378

(Fig. 2). The targeted analysis results were similar to those obtained from the

379

untargeted metabolomes.

380 381

3.4 Quantitative proteome analysis

382

The proteomes of P. angulata hairy roots under the control and MeJA treatment

383

were quantified using a newly developed approach involving TMT labeling-based

384

LC-MS/MS analysis (Fig. S6a). Three samples, which were used for untargeted

385

metabolomic analysis, were randomly selected for proteomic analysis. The correlation

386

coefficients of three replicates for each group indicated a good repeatability of our

387

MS data (Fig. S6b). The mass errors of all the identified peptides were lower than

388

0.02 Da and their distributions were near zero (Fig. S6c). The lengths of the majority

389

of the identified peptides varied from 7 to 17 amino acid residues, suggesting a high

390

quality of the sample preparation (Fig. S6d). Our results identified 7055 peptides,

391

among which 5900 peptides were quantified. All the peptides were grouped into

392

different categories according to their GO, KEGG, domain and subcellular

393

localization (Table S6).

394 395 396

3.5 Analysis of the DEPs under MeJA treatment Among 5900 quantified peptides, 1427 DEPs, including 733 up- and 694 down18


Page 19 of 53


397

regulated peptides in the MeJA-treated and control groups, respectively (Fig. 3a). The

398

top five significantly accumulated proteins were a miraculin (comp140502_c0), an

399

uncharacterized

400

(comp138452_c0), a polyphenol oxidase B (comp157313_c0), and a tryptophan

401

aminotransferase-related protein 4 (comp146942_c0)(34). On the contrary, the top

402

five significantly decreased proteins were an abscisic acid and environmental stress-

403

inducible protein TAS14 (comp142682), two β-galactosidases (comp159590_c0 and

404

comp158484_c2),

405

comp149746_c0) (Table S7).

protein

and

(comp142115_c0),

two

uncharacterized

a

threonine

proteins

dehydratase

(comp92680_c0

and

406

All the identified peptides and DEPs under MeJA treatment were both classified

407

into three major GO categories. For the molecular function, 39.0% of the identified

408

peptides and 45.5% of the DEPs belonged to the ‘catalytic activity’ term; 39.4% of

409

the identified peptides and 37.4% of the DEPs were classed into the ‘binding’ term;

410

and 2.6% of the identified peptides and 3.4% of the DEPs belonged to the ‘transporter

411

activity’ term. For the cellular component, 12.9% of the identified peptides and 9.1%

412

of the DEPs belonged to the ‘cell’ term; 6.9% of the identified peptides and 6.0% of

413

the DEPs were grouped to the ‘membrance’ term; and 7.7% of the identified peptides

414

and 5.5% of the DEPs belonged to the ‘organelle’ term. For the biological process,

415

36.2% of the identified peptides and 38.4% of the DEPs were grouped into the

416

‘metabolic process’ term; 20.4% of the identified peptides and 24.9% of the DEPs

417

were classed into the ‘single-organism process’ term; and 25.8% of the identified

418

peptides and 22.3% of the DEPs were grouped into the ‘cellular process’ term (Fig. 19



419

Page 20 of 53

3b).

420

Subcellular localization of the identified peptides and DEPs were predicted. For

421

the identified peptides, a total of 15 different components were identified, including

422

chloroplast- (1909 peptides), cytoplasm- (2236 peptides), nucleus- (1622 peptides),

423

cytoskeleton- (328 peptides), and plasma membrane-localized protein (377 peptides)

424

(Fig. 3c). For the DEPs, 15 components were also identified, including chloroplast-

425

(396 peptides), cytoplasm- (472 peptides), nucleus- (248 peptides), extracellular- (65

426

peptides), and plasma membrane-localized protein (83 peptides) (Fig. 3d).

427 428

3.6 Enrichment analysis of the DEPs under MeJA treatment

429

A total of 25 GO terms referring to 554 DEPs were enriched. For the ‘cellular

430

component’ category, the most enriched GO terms were ‘DNA packaging complex’,

431

‘nucleosome’, ‘chromatin’, ‘protein-DNA complex’ and ‘chromosomal part’; for the

432

‘molecular function’ category, the top five enriched GO terms were ‘peroxidase’,

433

‘antioxidant activity’, ‘heme binding’, ‘tetrapyrrole binding’ and ‘oxidoreductase

434

activity, acting on peroxide as acceptor’; and for the ‘biological process’ category, the

435

most significantly enriched GO terms were ‘response to oxidative stress’, ‘response to

436

stress’, ‘oxidation-reduction process’, ‘reactive oxygen species metabolic process’,

437

and ‘hydrogen peroxide metabolic process’ (Fig. S5).

438

KEGG pathway enrichment analysis indicated that the up-regulated peptides were

439

mostly related to three metabolic pathways, including ‘steroid biosynthesis’ and

440

‘terpenoid backbone biosynthesis’, and the down-regulated peptides were mostly 20


Page 21 of 53

441


associated with ‘phenylpropanoid biosynthesis’ (Fig. S6).

442

The protein domain analysis pointed out that the top five enriched domains were

443

‘isopenicillin N synthase-like’, ‘oxoglutarate/iron-dependent dioxygenase’, ‘haem

444

peroxidase’, ‘non-haem dioxygenase N-terminal domain’, and ‘secretory peroxidase’

445

(Fig. S7).

446 447

3.7 PPI networks for the DEPs

448

The PPI networks were analyzed to predict the biological functions of MeJA

449

responsive proteins in P. angulata. A total of 329 DEPs, including 163 up- and 166

450

down-regulated peptides, were assigned into the PPI networks. Intereastingly, four

451

important metabolism-related clusters, such as the ‘terpenoid backbone biosynthesis’,

452

‘glycolysis’, ‘GST superfamily’ and ‘steroid biosynthesis’ pathways, were identified

453

(Fig. 4). Most of the proteins associated with the above four metabolic pathways were

454

increasingly accumulated under MeJA treatment. For example, for the ‘terpenoid

455

backbone biosynthesis’ pathway, only four proteins were down-regulated, while the

456

number of the up-regulated proteins was 16. For the ‘steroid biosynthesis’ pathway,

457

the up- and down-regulated proteins under MeJA treatment were nine and three,

458

respectively (Table S8).

459 460

3.8 DEPs related to MVA and MEP pathways

461

The enzymes catalyzing the key steps in the MEP and MVA pathways were

462

identified by our proteomic data. A total of 15 key enzymes were quantified (Fig. 5a). 21



Page 22 of 53

463

In the MEP pathway, the most significantly accumulated enzyme was 1-deoxy-D-

464

xylulose 5-phosphate synthase (DXS), which was induced more than three folds under

465

MeJA treatment. Additionally, another six enzymes, including

466

erythritol 4-phosphate cytidylyltransferase (MCT), a 4-diphosphocytidyl-2-C-methyl-

467

D-erythritol kinase (CMK), a 4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase

468

(HDS), a 4-hydroxy-3-methylbut-2-enyl diphosphate reductase (HDR), a farnesyl

469

pyrophosphate synthase (FPPS) and a geranylgeranyl pyrophosphate synthase

470

(GGPPS), were significantly increased under MeJA treatment. For the MVA pathway,

471

three

472

hydroxymethylglutaryl-CoA

473

decarboxylase (MPDC), were identified (Fig. 5b and Table S9).

significantly

up-regulated synthase

enzymes, (HMGS)

including and

a

a 2-C-methyl-D-

an

AACT,

a

diphospho-mevalonate

474 475

3.9 DEPs related to the steroid biosynthesis pathway

476

The KEGG results showed that the ‘steroid biosynthesis’ term was one of the most

477

significantly enriched metabolic pathways in the DEPs under MeJA treatment (Fig.

478

S2). The enzymes involved in the steroid biosynthesis pathway have been well

479

studied in model plants, providing us an opportunity to identify steroid biosynthesis-

480

related proteins in P. angulata (Fig. 6a). A total of ten enzymes involved in the

481

steroid biosynthesis pathway were quantified, including nine up- and one down-

482

regulated proteins, respectively. These significantly up-regulated enzymes were a

483

squalene epoxidase 1 (SQLE1), a sterol 14-demethylase (CYP51), a delta(14)-sterol

484

reductase (TM7SF2), a methylsterol monooxygenase 1 (SMO1), a 3-beta22


Page 23 of 53


485

hydroxysteroid-delta(8),delta(7)-isomerase

(EBP),

a

3-beta-hydroxysteroid-

486

dehydrogenase (NSDHL), a delta(24)-sterol reductase 1 (DWF1), a cycloartenol-C-

487

24-methyltransferase (SMT), and a cycloeucalenol cycloisomerase (CPI). Only one

488

enzyme, cycloartenol synthase 1 (CAS1), was significantly down-regulated by MeJA

489

treatment (Fig. 6b and Table S9).

490 491

3.10 The later steps in the biosynthesis pathway of bioactive steroids

492

A large number of P450 superfamily members were identified, among which 30

493

P450 candidates with full-length sequences were extracted to build a phylogenetic

494

tree. These P450 proteins were grouped into five major classes: Clans 71, 72, 74, 85

495

and 97. Most of the selected P450 members were grouped into Clans 72 (eight

496

members) , 74 (nine members) and 85 (nine members). Interestingly, three flavonoid

497

biosynthesis-, one steroid biosynthesis- and one phenylpropanoid metabolism-related

498

P450s were identified based on sequence alignment analysis. All the three flavonoid

499

biosynthesis-related P450s were up-regulated and the only one steroid biosynthesis-

500

related P450 was down-regulated by MeJA treatment (Fig. 7 and Table S9). In

501

addition, one 3β-hydroxysterioid dehydrogenase (3β-HSDs) was identified as a

502

significantly up-regulated enzyme under MeJA treatment (Table S9). The steroid

503

biosynthesis pathway in P. angulata was predicted and shown in Fig. 8 (35).

504 505 506

4. Discussion The genus Physalis in the Solanaceae family contains several bioactive ingredients 23



Page 24 of 53

507

of important benefit to human health. Examples included physalins in P. angulata and

508

P. lancifolia, isophysalins in P. alkekengi, and withanolides in P. pernviana and P.

509

viscose (36, 37). Recently, the anti-tumor activities of physalins have been well

510

studied. The industrial application of physalins at large scale remains a great

511

challenge due to their low abundances in plants (35, 38).

512

MeJA can effectively increase the production of secondary metabolites in various

513

medical plants (39). In our study, the application of MeJA to P. angulata hairy roots

514

was performed. By identifying specific metabolites, our results suggested that the

515

variations, not only in physalins, but also in other secondary metabolites, exist

516

between the control and MeJA treatment groups (Fig. 1h). Interestingly, the contents

517

of physalins D and H were significantly induced by MeJA treatment over two folds. It

518

suggested that physalin biosynthesis in P. angulata hairy roots can be enhanced by

519

elicitors, such as MeJA. In plants, the terpenoid backbone is partially supplied by the

520

MEP pathway (40). Besides, 2-C-methyl-D-erythritol 2,4-cyclodiphosphate, an

521

important intermediate product of the MEP pathway, was also induced under MeJA

522

treatment. Furthermore, the content of geranylfarnesyl diphosphate were largely

523

induced by MeJA treatment, suggesting an increase in the efficiency of physalin

524

biosynthesis using the precursors from the MEP pathway (41).

525

To date, no comprehensive proteome of the genus Physalis is available. Recently,

526

several transcriptomes of the genus Physalis have been published by different

527

research groups. Firstly, using 454 GS FLX Titanium technology, 21,191 assembled

528

sequences with putative functions were identified in P. peruviana (42). Then, 263 24


Page 25 of 53


529

differentially expressed transcript-derived fragment in P. philadelphica were isolated

530

by cDNA-amplified fragment length polymorphism method (43). Lately, Saito’s

531

group reported the de novo assembly of the transcriptome of the leaves of P.

532

alkekengi and P. peruviana using Illumina RNA-seq technologies (35). These

533

transcriptomes provided massive genomic resources for the annotation and protein

534

model prediction of P. angulata. In our study, the effects of MeJA on physalin

535

biosynthesis in P. angulata have been confirmed using a hairy root system (Fig. 1).

536

Functional annotation of the DEPs responses to MeJA treatment identified a number

537

of metabolism-associated enzymes. Thus, identification of enzymes regulated by

538

MeJA treatment may give us an opportunity to clarify the regulation mechanism of

539

physalin biosynthesis in P. angulata.

540

In Physalis, enzyme candidates for each step in the terpenoid backbone- and steroid

541

biosynthesis were regarded to be involved in the synthesis of the withanolides along

542

with physalins (35). In our study, most of the enzymes associated with terpenoid

543

backbone biosynthesis and steroid biosynthesis were significantly up-regulated by

544

MeJA treatment. For example, DXS, catalyzing the first step of the MEP pathway,

545

was induced over 1.5 fold . Overexpression of the DXS gene can increase the contents

546

of end products in plants (44). Thus, MeJA-induced DXS may represent a key and

547

bottleneck step in plastidial isoprenoid biosynthesis in P. angulata hairy roots (45). In

548

addition, HDR also showed a higher expression level under MeJA treatment

549

compared with the control. HDR, a member of the NADP/NAD-dependent

550

oxidoreductase family, catalyzes the last step of the MEP pathway toward the IPP 25



Page 26 of 53

551

synthesis (46). Increased expression of HDR may enhance the metabolic flux through

552

the MEP pathway in P. angulata hairy roots(44). Intereastingly, an increasing in the

553

content of 2-C-methyl-D-erythritol 2,4-cyclodiphosphate were detected by LC-MS

554

analysis, suggesting a close connection between the proteomic and metabolomic

555

analyses. Besides, GPP, FPP and GGPP are the branch points for subsequent

556

synthesis of all isoprenoid end-products (47). The expression levels of FPS, GGPS

557

and GPS, were significantly increased by MeJA treatment. On the another hand,

558

metabolomic data showed that the content of geranylfarnesyl diphosphate was largely

559

induced, suggesting an adequate supply of precursors for the terpenoid backbone

560

synthesis.

561

SQLE, catalyzing the conversion of squalene to 2,3(S)-oxidosqualene, was reported

562

to be one of the important regulatory enzymes in the steroid biosynthesis pathway

563

(48). In Panax ginseng roots, MeJA treatment could enhance the accumulation of

564

SQLE1 mRNA resulting in increased phytosterol accumulation (49). In P. angulata

565

hairy roots, a SQLE was induced by MeJA treatment about 1.8 fold, suggesting a

566

great elevation of the steroid biosynthesis. Except for SQLE, several other steroid

567

biosynthesis-related enzymes, such as SMO1, SMT, CYP51 and EBP, were also

568

identified. Up-regulation of these enzymes might provide a potential explanation for

569

the accumulations of bioactive steroids in P. angulata hairy roots under MeJA

570

treatment.

571

To date, the later steps in the biosynthesis pathways of physalins, as well as other

572

bioactive steroids, in P. angulata were obscure (50, 51). Increasing evidences showed 26


Page 27 of 53


573

that the biosynthesis of steroidal compounds and terpenoids was regulated by the

574

P450 superfamily. Two P450 monooxygenases catalyze several hydroxylation steps in

575

the steroid glycoalkaloid biosynthetic pathway of potato. P450s play key roles in the

576

structural diversity of steroids and triterpenoid saponins (52). Recently, the

577

transcriptomes of P. alkekengi and P. peruviana have identified a large number of

578

P450 monooxygenases and dioxygenases that can modify the structures of both

579

withanolides and physalins. A P450 chloroplastic–like protein was involved in the

580

oxidations at the C15 and C18 positions of the steroid backbone required in the

581

synthesis of physalins (35). In our study, several close homologs of P450 were

582

identified in P. angulata. The KEGG annotations identified two P450s involved in the

583

steroid biosynthesis, three P450s involved in the flavonoid biosynthesis and one P450

584

involved in the

585

P450s (comp163239 and comp135358), both belonging to Clan 74, were significantly

586

up-regulated by MeJA treatment, suggesting an involvement of P450s in MeJA-

587

induced physalin synthesis.

phenylpropanoid metabolism (Fig. 7). Notably, two steroid-related

588

Together with P450s, 3β-HSDs may conduct the late steps of the biosynthesis of

589

steroidal compounds in P. angulata (53). The predicted steroidal compound

590

biosynthetic pathway provides a breakthrough in industrial application of physalins,

591

as well as other active compounds, which was limited to their low abundances in

592

plants (35).

593

In summary, an untargeted metabolomic approach was used to reveal the variations

594

in the metabolism associated with bioactive compounds under MeJA treatment. The 27



Page 28 of 53

595

proteomic analysis identified a number of metabolism associated proteins. The

596

dynamic accumulations of the enzymes involved in the terpenoid backbone- and

597

steroid biosynthesis might provide a potential explanation for the MeJA-induced

598

physalin synthesis (Table S9). Our results may contribute to a deeper understanding

599

of the regulation mechanism underlying the MeJA-induced bioactive compound

600

accumulation in P. angulata.

601 602

Abbreviations Used

603

AACT: acetoacetyl-CoA thiolase;

604

CAS: cycloartenol synthase;

605

CMK: 4-diphosphocytidyl-2-C-methyl-D-erythritol kinase;

606

CPI: cycloeucalenol cycloisomerase;

607

CYP51: sterol 14-demethylase;

608

DEP: differential expressed proteins;

609

DMAPP: dimethylallyl diphosphate;

610

DXS: 1-deoxy-D-xylulose 5-phosphate synthase;

611

DWF: delta(24)-sterol reductase;

612

EBP: 3-beta-hydroxysteroid-delta(8),delta(7)-isomerase;

613

ESI: electrospray ionization;

614

FPPS: farnesyl pyrophosphate synthase;

615

GGPPS: geranylgeranyl pyrophosphate synthase;

616

GO: Gene Ontology; 28


Page 29 of 53


617

HDS: 4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase;

618

HDR: 4-hydroxy-3-methylbut-2-enyl diphosphate reductase;

619

HMGR: 3-hydroxy-3-methylglutaryl-CoA reductase;

620

HPLC: High Performance Liquid Chromatography;

621

KEGG: Kyoto Encyclopedia of Genes and Genomes;

622

LAS: lanosterol synthase;

623

LC-MS/MS: Liquid chromatography-tandem mass spectrometry;

624

MCT: 2-C-methyl-D-erythritol 4-phosphate cytidylyltransferase;

625

MeJA: methyl jasmonate;

626

MEP: plastidal 2-C-methyl-d-erythritol-4-phosphate;

627

MVA: mevalonate;

628

MVD: mevalonate diphosphate decarboxylase;

629

NSDHL: 3-beta-hydroxysteroid-dehydrogenase;

630

Nr: NCBI non-redundant;

631

IPI: IPP isomerase;

632

IPP: isopentenyl diphosphate;

633

IS: isofraxidin;

634

P450: cytochrome P450 monooxygenase;

635

QC: quality control;

636

SMO: methylsterol monooxygenase;

637

SMT: cycloartenol-C-24-methyltransferase;

638

SQE: squalene epoxidase; 29



639

SQL1:squalene epoxidase;

640

TIC: total ion chromatogram;

641

TM7SF2: delta(14)-sterol reductase;

642

TMT: Tandem Mass Tags

Page 30 of 53

643 644

Funding

645

Our work was funded by the National Natural Science Foundation of China

646

(31470407 and 31601343); the Zhejiang Provincial Public Welfare Technology

647

Applied Research Foundation of China (2014C32090); the Hangzhou Scientific and

648

Technological Program (20150932H03 and 20150932H04); and the Research

649

Foundation of Education Bureau of Zhejiang Province (Y201533081).

650 651

The authors declare that they have no competing interests.

652 653

Supporting Information description

654

Figure S1 A targeted profiling of Physalin D and H were applied to determine the

655

uniformity in the two independent lines hairy root lines.

656

Figure S2 The accumulation of biomass of suspension cultured P. angulata L. hairy

657

root line 1 and 2.

658

Figure S3 An initial characterization of the concentration and induction time of MeJA

659

treatment.

660

Figure S4 The total ion chromatogram (TIC) for positive and negative modes. 30


Page 31 of 53


661

Figure S5 Comprehensive analysis of the untargeted metabolome data.

662

Figure S6 The proteomes of P. angulata hairy roots under the control and MeJA

663

treatment.

664

Figure S7 GO enrichment analysis of the DEPs under MeJA treatment.

665

Figure S8 KEGG enrichment analysis of the DEPs under MeJA treatment.

666

Figure S9 Protein domain enrichment analysis of the DEPs under MeJA treatment.

667

Table S1 ESI+-MS/MS parameters on the parent and daughter ions (m/z), collision

668

energy(CE) and declustering potential(DP) of the two physalins and IS.

669

Table S2 The identified features in the positive mode.

670

Table S3 The identified features in the negative mode.

671

Table S4 Partial significantly changed metabolites under MeJA treatment.

672

Table S5 The abundance of the bioactive ingredients under the control and MeJA

673

treatments.

674

Table S6 The detail information of all the identified peptides.

675

Table S7 The detail information of the DEPs under MeJA treatment.

676

Table S8 The detail information of the DEPs involved in the PPI networks.

677

Table S9 The DEPs involved in the biosynthesis pathway of bioactive steroids.

31



Page 32 of 53

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Nagafuji, S.; Okabe, H.; Akahane, H.; Abe, F., Trypanocidal constituents in

plants 4. Withanolides from the aerial parts of Physalis angulata. Biological & pharmaceutical bulletin 2004, 27, 193-7. 2.

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21. Ohnishi, T.; Yokota, T.; Mizutani, M., Insights into the function and evolution of P450s in plant steroid metabolism. Phytochemistry 2009, 70, 1918. 22. Liu, J.; Liu, Y.; Wang, Y.; Zhang, Z.-H.; Zu, Y.-G.; Efferth, T.; Tang, Z.-H., The combined effects of ethylene and MeJA on metabolic profiling of phenolic compounds in Catharanthus roseus revealed by metabolomics analysis. Frontiers in Physiology 2016, 7, 217. 23. Ciura, J.; Szeliga, M.; Grzesik, M.; Tyrka, M., Changes in fenugreek transcriptome induced by methyl jasmonate and steroid precursors revealed by RNASeq. Genomics 2017. 24. Gan, L.; Wu, H.; Wu, D.; Zhang, Z.; Guo, Z.; Yang, N.; Xia, K.; Zhou, X.; Oh, K.; Matsuoka, M.; Ng, D.; Zhu, C., Methyl jasmonate inhibits lamina joint inclination by repressing brassinosteroid biosynthesis and signaling in rice. Plant science : an international journal of experimental plant biology 2015, 241, 238-45. 25. Hughes, E. H.; Hong, S. B.; Shanks, J. V.; San, K. Y.; †, S. I. G., Characterization of an inducible promoter system in Catharanthus roseus hairy roots. Biotechnology Progress 2007, 18, 1258-1260. 26. Ricigliano, V.; Kumar, S.; Kinison, S.; Brooks, C.; Nybo, S. E.; Chappell, J.; Howarth, D. G., Regulation of sesquiterpenoid metabolism in recombinant and elicited Valeriana officinalis hairy roots. Phytochemistry 2016, 125, 43-53. 27. Ketchum, R. E.; Rithner, C. D.; Qiu, D.; Kim, Y. S.; Williams, R. M.; Croteau, R. B., Taxus metabolomics: methyl jasmonate preferentially induces production of taxoids oxygenated at C-13 in Taxus × media cell cultures. Phytochemistry 2003, 62, 35



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901-9. 28. Llano, S. M.; Muñoz-Jiménez, A. M.; Jiménez-Cartagena, C.; Londoño-Londoño, J.; Medina, S., Untargeted metabolomics reveals specific withanolides and fatty acyl glycoside as tentative metabolites to differentiate organic and conventional Physalis peruviana fruits. Food Chemistry 2018, 244, 120-127. 29. Thompson, A.; Schafer, J.; Kuhn, K.; Kienle, S.; Schwarz, J.; Schmidt, G.; Neumann, T.; Johnstone, R.; Mohammed, A. K.; Hamon, C., Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Analytical chemistry 2003, 75, 1895-904. 30. Xu, D.; Yuan, H.; Tong, Y.; Zhao, L.; Qiu, L.; Guo, W.; Shen, C.; Liu, H.; Yan, D.; Zheng, B., Comparative proteomic analysis of the graft unions in hickory (Carya cathayensis) provides insights into response mechanisms to grafting process. Frontiers in plant science 2017, 8, 676. 31. Hao, J.; Guo, H.; Shi, X.; Wang, Y.; Wan, Q.; Song, Y.; Zhang, L.; Dong, M.; Shen, C., Comparative proteomic analyses of two Taxus species (Taxus × media and Taxus mairei) reveals variations in the metabolisms associated with paclitaxel and other metabolites. Plant and Cell Physiology 2017, pcx128-pcx128. 32. Jin, J.; Zhang, H.; Zhang, J.; Liu, P.; Chen, X.; Li, Z.; Xu, Y.; Lu, P.; Cao, P., Integrated transcriptomics and metabolomics analysis to characterize cold stress responses in Nicotiana tabacum. BMC genomics 2017, 18, 496. 33. Yu, C.; Guo, H.; Zhang, Y.; Song, Y.; Pi, E.; Yu, C.; Zhang, L.; Dong, M.; Zheng, B.; Wang, H.; Shen, C., Identification of potential genes that contributed to the 36


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root cultures of Polygonum multiflorum. Applied microbiology and biotechnology 2018, 102, 199-209. 40. Eisenreich, W.; Menhard, B.; Hylands, P. J.; Zenk, M. H.; Bacher, A., Studies on the biosynthesis of taxol: the taxane carbon skeleton is not of mevalonoid origin. Proceedings of the National Academy of Sciences of the United States of America 1996, 93, 6431-6. 41. Gonzalez-Cabanelas, D.; Hammerbacher, A.; Raguschke, B.; Gershenzon, J.; Wright, L. P., Quantifying the mMetabolites of the methylerythritol 4-phosphate (MEP) pathway in plants and bacteria by liquid chromatography-triple quadrupole mass spectrometry. Methods in enzymology 2016, 576, 225-49. 42. Garzon-Martinez, G. A.; Zhu, Z. I.; Landsman, D.; Barrero, L. S.; MarinoRamirez, L., The Physalis peruviana leaf transcriptome: assembly, annotation and gene model prediction. BMC genomics 2012, 13, 151. 43. Wang, L.; Li, Z.; He, C., Transcriptome-wide mining of the differentially expressed transcripts for natural variation of floral organ size in Physalis philadelphica. Journal of experimental botany 2012, 63, 6457-6465. 44. Carretero-Paulet, L.; Cairó, A.; Botella-Pavía, P.; Besumbes, O.; Campos, N.; Boronat, A.; Rodríguez-Concepción, M., Enhanced flux through the methylerythritol 4-phosphate pathway in Arabidopsis plants overexpressing deoxyxylulose 5phosphate reductoisomerase. Plant molecular biology 2006, 62, 683-695. 45. Battilana, J.; Costantini, L.; Emanuelli, F.; Sevini, F.; Segala, C.; Moser, S.; Velasco, R.; Versini, G.; Stella Grando, M., The 1-deoxy-D: -xylulose 5-phosphate 38


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synthase gene co-localizes with a major QTL affecting monoterpene content in grapevine. TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik 2009, 118, 653-69. 46. Ma, D.; Li, G.; Zhu, Y.; Xie, D.-Y., Overexpression and suppression of Artemisia annua 4-hydroxy-3-methylbut-2-enyl diphosphate reductase 1 gene (AaHDR1) differentially regulate artemisinin and terpenoid biosynthesis. Frontiers in plant science 2017, 8, 77. 47. Vranova, E.; Coman, D.; Gruissem, W., Network analysis of the MVA and MEP pathways for isoprenoid synthesis. Annual review of plant biology 2013, 64, 665-700. 48. Ryder, N. S., Squalene epoxidase as a target for the allylamines. Biochemical Society Transactions 1991, 19, 774-777. 49. Han, J.-Y.; In, J.-G.; Kwon, Y.-S.; Choi, Y.-E., Regulation of ginsenoside and phytosterol biosynthesis by RNA interferences of squalene epoxidase gene in Panax ginseng. Phytochemistry 2010, 71, 36-46. 50. Ji, L.; Yuan, Y.; Luo, L.; Chen, Z.; Ma, X.; Ma, Z.; Cheng, L., Physalins with anti-inflammatory activity are present in Physalis alkekengi var. franchetii and can function as Michael reaction acceptors. Steroids 2012, 77, 441-447. 51. Men, R. Z.; Li, N.; Ding, W. J.; Hu, Z. J.; Ma, Z. J.; Cheng, L., Unprecedent aminophysalin from Physalis angulata. Steroids 2014, 88, 60-5. 52. Umemoto, N.; Nakayasu, M.; Ohyama, K.; Yotsu-Yamashita, M.; Mizutani, M.; Seki, H.; Saito, K.; Muranaka, T., Two cytochrome P450 monooxygenases catalyze early hydroxylation steps in the potato steroid glycoalkaloid biosynthetic pathway. 39



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Plant physiology 2016, 171, 2458-2467. 53. Day, J. M.; Tutill, H. J.; Foster, P. A.; Bailey, H. V.; Heaton, W. B.; Sharland, C. M.; Vicker, N.; Potter, B. V. L.; Purohit, A.; Reed, M. J., Development of hormonedependent prostate cancer models for the evaluation of inhibitors of 17βhydroxysteroid dehydrogenase Type 3. Molecular & Cellular Endocrinology 2009, 301, 251-258.

Figure legends Figure 1 Untargeted metabolite profiles of P. angulata hairy roots under the 40


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control and MeJA treatment conditions. The establishment of P. angulata hairy root system. Different stages of the P. angulata hairy root production, including 10 d (a), 20 d (b), and 25 d (c) under solid medium, and 30 d (d), 55 d (e), and 85 d (f) under liquid medium. (g) Checking the insertion of T-DNA fragment (from A. rhizogenes C58C1) in hairy roots. N: negative control (the root of the leaves that were not infected); L1: roots from Line 1; L2: roots from Line 2; and P: positive control (strain

C58C1).

rolB：

F-

CGAGGGGATCCGATTTGC/GACGCCCTCCTCGCCTTC-R； rolC：

F-CGCCATGCCTCACCAACTC/CTTGATCGAGCCGGGTGAG-R;

VirD5: F-TGGTTTACTGCTTCTGGGTCA/GCGATACACTTGCTGCACG-R. (h) KEGG annotations of the differential accumulated metabolites under MeJA treatment. (i) Identification of the changes in the bioactive ingredients in P. angulata under MeJA treatment.

Figure 2 Analysis of two targeted metabolites (Physalins D and H). The mass spectrograms of physalin D under the control (a) and MeJA treatment (b) conditions. The mass spectrograms of physalin H under the control (c) and MeJA treatment (d) conditions. (e) Changes in the contents of physalin D under MeJA treatment. (f) Changes in the contents of physalin H under MeJA treatment.

Figure 3 Variations in protein levels between the control and MeJA treatment. (a) The numbers of up- and down-regulated proteins in the MeJA treatment compared to 41



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the control. (b) GO analysis of all identified proteins and DEPs. All identified proteins and DEPs were classified by GO terms based on their cellular component, molecular function, and biological process. Subcellular locations of identified proteins (c) and DEPs (d).

Figure 4 Interaction networks of the DEPs analyzed by Cytoscape software (version 3.0.1). The color bar indicated protein quantitation of log2 (MeJA/control) ratio. Red indicated up-regulated proteins and green indicated down-regulated proteins. Blue cycles indicated four enriched protein-protein interaction clusters.

Figure 5 DEPs related to MVA and MEP pathways. Overview of the MVA and MEP pathways in P. angulata. Red blocks indicated up-regulated proteins. The white block indicated an undetected protein. Green block indicated down-regulated proteins. The enzyme abbreviations are: AACT: Acetoacetyl-CoA thiolase; HMGS: 3Hydroxy-3-methylglutaryl-CoA synthase; HMGR: 3-Hydroxy-3-methylglutaryl-CoA reductase; MK: MVA kinase; PMK: Phospho-MVA kinase; MPDC: DiphosphoMVA decarboxylase; DXS: 1-Deoxy-D-xylulose 5-phosphate synthase; DXR: 1Deoxy-D-xylulose 5-phosphate reductoisomerase; MCT: 2-C-methyl-D-erythritol 4phosphate cytidylyltransferase; CMK: 4-(Cytidine 5-diphospho)-2-C-methyl-Derythritol kinase; MDS: 2-C-methyl-D-erythritol 2,4-cyclodiphosphate synthase; HDS: 4-Hydroxy-3-methylbut-2-enyl-diphosphate synthase; HDR: 4-Hydroxy-3methylbut-2-enyl diphosphate reductase; IPPI: Isopentenyl diphosphate-isomerase; 42


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GPPS: Geranyl diphosphate synthase; FPPS: Farnesyl diphosphate synthase; GGPPS: Geranylgeranyl diphosphate synthase. (b) Relative expression levels of proteins involved in the MVA and MEP pathways. Significant differences in the expression levels were indicated by “*”.

Figure 6 DEPs related to the steroid biosynthesis pathway. (a) Overview of the steroid biosynthesis pathway in P. angulata. Red blocks indicated up-regulated proteins. White blocks indicated undetected proteins. The green block indicated an down-regulated proteins. The enzyme abbreviations are: SQL1:squalene epoxidase, CYP51: sterol 14-demethylase, TM7SF2: delta(14)-sterol reductase, SMO1: methylsterol

monooxygenase

1,

EBP:

3-beta-hydroxysteroid-delta(8),delta(7)-

isomerase, NSDHL: 3-beta-hydroxysteroid-dehydrogenase, DWF1: delta(24)-sterol reductase 1, SMT: cycloartenol-C-24-methyltransferase, CPI: cycloeucalenol cycloisomerase, and CAS1: cycloartenol synthase 1. (b) Relative expression levels of proteins related to the steroid biosynthesis pathway. Significant differences in the expression levels were indicated by “*”.

Figure 7 Identification and analysis of the cytochrome P450s in P. angulata. (a) A phylogenetic tree of 30 P450 candidates with full length sequences. Various background colors indicated different P450 clans. (b) Relative expression levels of these P450s under MeJA treatment were shown by a heat map. The heatmap scale ranges from -3 to +3 on a log2 scale. Significant differences in the expression levels 43



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were indicated by “*”.

Figure 8 Possible pathways of physalin biosynthesis in P. angulata. The chemical compounds were indicated by black font and the enzymes were indicated by orange font. The enzyme abbreviations are: 3β-HSD: 3β-hydroxysterioid dehydrogenase, P450: cytochrome P450.

44


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45



Figure 1 Untargeted metabolite profiles of P. angulata hairy roots under the control and MeJA treatment conditions. The establishment of P. angulata hairy root system. Different stages of the P. angulata hairy root production, including 10 d (a), 20 d (b), and 25 d (c) under solid medium, and 30 d (d), 55 d (e), and 85 d (f) under liquid medium. (g) Checking the insertion of T-DNA fragment (from A. rhizogenes C58C1) in hairy roots. N: negative control (the root of the leaves that were not infected); L1: roots from Line 1; L2: roots from Line 2; and P: positive control (strain C58C1). rolB： FCGAGGGGATCCGATTTGC/GACGCCCTCCTCGCCTTC-R； rolC： F-CGCCATGCCTCACCAACTC/CTTGATCGAGCCGGGTGAG-R; VirD5: FTGGTTTACTGCTTCTGGGTCA/GCGATACACTTGCTGCACG-R. (h) KEGG annotations of the differential accumulated metabolites under MeJA treatment. (i) Identification of the changes in the bioactive ingredients in P. angulata under MeJA treatment.

130x109mm (300 x 300 DPI)


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Figure 2 Analysis of two targeted metabolites (Physalins D and H). The mass spectrograms of physalin D under the control (a) and MeJA treatment (b) conditions. The mass spectrograms of physalin H under the control (c) and MeJA treatment (d) conditions. (e) Changes in the contents of physalin D under MeJA treatment. (f) Changes in the contents of physalin H under MeJA treatment. 99x75mm (300 x 300 DPI)



Figure 3 Variations in protein levels between the control and MeJA treatment. (a) The numbers of up- and down-regulated proteins in the MeJA treatment compared to the control. (b) GO analysis of all identified proteins and DEPs. All identified proteins and DEPs were classified by GO terms based on their cellular component, molecular function, and biological process. Subcellular locations of identified proteins (c) and DEPs (d). 129x99mm (300 x 300 DPI)


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Figure 4 Interaction networks of the DEPs analyzed by Cytoscape software (version 3.0.1). The color bar indicated protein quantitation of log2 (MeJA/control) ratio. Red indicated up-regulated proteins and green indicated down-regulated proteins. Blue cycles indicated four enriched protein-protein interaction clusters. 104x67mm (300 x 300 DPI)



Figure 5 DEPs related to MVA and MEP pathways. Overview of the MVA and MEP pathways in P. angulata. Red blocks indicated up-regulated proteins. The white block indicated an undetected protein. Green block indicated down-regulated proteins. The enzyme abbreviations are: AACT: Acetoacetyl-CoA thiolase; HMGS: 3-Hydroxy-3-methylglutaryl-CoA synthase; HMGR: 3-Hydroxy-3-methylglutaryl-CoA reductase; MK: MVA kinase; PMK: Phospho-MVA kinase; MPDC: Diphospho-MVA decarboxylase; DXS: 1-Deoxy-D-xylulose 5phosphate synthase; DXR: 1-Deoxy-D-xylulose 5-phosphate reductoisomerase; MCT: 2-C-methyl-Derythritol 4-phosphate cytidylyltransferase; CMK: 4-(Cytidine 5-diphospho)-2-C-methyl-D-erythritol kinase; MDS: 2-C-methyl-D-erythritol 2,4-cyclodiphosphate synthase; HDS: 4-Hydroxy-3-methylbut-2-enyldiphosphate synthase; HDR: 4-Hydroxy-3-methylbut-2-enyl diphosphate reductase; IPPI: Isopentenyl diphosphate-isomerase; GPPS: Geranyl diphosphate synthase; FPPS: Farnesyl diphosphate synthase; GGPPS: Geranylgeranyl diphosphate synthase. (b) Relative expression levels of proteins involved in the MVA and MEP pathways. Significant differences in the expression levels were indicated by “*”. 134x107mm (300 x 300 DPI)


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Figure 6 DEPs related to the steroid biosynthesis pathway. (a) Overview of the steroid biosynthesis pathway in P. angulata. Red blocks indicated up-regulated proteins. White blocks indicated undetected proteins. The green block indicated an down-regulated proteins. The enzyme abbreviations are: SQL1:squalene epoxidase, CYP51: sterol 14-demethylase, TM7SF2: delta(14)-sterol reductase, SMO1: methylsterol monooxygenase 1, EBP: 3-beta-hydroxysteroid-delta(8),delta(7)-isomerase, NSDHL: 3-betahydroxysteroid-dehydrogenase, DWF1: delta(24)-sterol reductase 1, SMT: cycloartenol-C-24methyltransferase, CPI: cycloeucalenol cycloisomerase, and CAS1: cycloartenol synthase 1. (b) Relative expression levels of proteins related to the steroid biosynthesis pathway. Significant differences in the expression levels were indicated by “*”. 134x132mm (300 x 300 DPI)



Figure 7 Identification and analysis of the cytochrome P450s in P. angulata. (a) A phylogenetic tree of 30 P450 candidates with full length sequences. Various background colors indicated different P450 clans. (b) Relative expression levels of these P450s under MeJA treatment were shown by a heat map. Significant differences in the expression levels were indicated by “*”. 129x114mm (300 x 300 DPI)


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Figure 8 Possible pathways of physalin biosynthesis in P. angulata. The chemical compounds were indicated by black font and the enzymes were indicated by orange font. The enzyme abbreviations are: 3β-HSD: 3βhydroxysterioid dehydrogenase, P450: cytochrome P450. 118x169mm (300 x 300 DPI)


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