plant physiology - rmd filtering for specialized …...2015/02/06 · †current address: mpi...

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RMD filtering for specialized metabolite discovery

A. Daniel Jones

Department of Biochemistry and Molecular Biology

603 Wilson Road, Biochemistry Room 212

Michigan State University

East Lansing, MI 48824 USA

E-mail: [email protected]

Phone: (517) 432-7126

FAX: (517) 353-9334

Plant Physiology Preview. Published on February 6, 2015, as DOI:10.1104/pp.114.251165

Copyright 2015 by the American Society of Plant Biologists

www.plantphysiol.orgon September 9, 2020 - Published by Downloaded from Copyright © 2015 American Society of Plant Biologists. All rights reserved.

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Relative mass defect filtering of mass spectra: a path to discovery of plant specialized

metabolites

E. A. Prabodha Ekanayaka,1† Mary Dawn Celiz2*, and A. Daniel Jones1,2

1Department of Chemistry, Michigan State University, East Lansing, MI 48824, USA

2Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing,

MI 48824, USA

Metabolite masses measured using LC-MS can be sorted into structural classes using relative

mass defect filtering, and such classifications accelerate annotation of novel specialized

metabolites.



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Footnotes

Financial support from:

National Science Foundation (Grant number IOS-1025636 and DBI-0604336)

National Institutes of Health (Grant 1RC2 GM092521)

Michigan AgBioResearch (Project MICL02143).

†Current address: MPI Research, 54943 North Main St., Mattawan, MI 49071 USA

*Current address: Maryland Department of Health and Mental Hygiene, Baltimore, MD 21201

USA



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Abstract

Rapid identification of novel plant metabolites and assignments of newly-discovered substances

to natural product classes present the main bottlenecks to defining plant specialized phenotypes.

Although mass spectrometry provides powerful support for metabolite discovery by measuring

molecular masses, ambiguities in elemental formulas often fail to reveal the biosynthetic origins

of specialized metabolites detected using liquid chromatography-mass spectrometry (LC-MS).

A promising approach for mining LC-MS metabolite profiling data for specific metabolite

classes is achieved by calculating relative mass defects (RMDs) from molecular and fragment

ions. This strategy enabled rapid recognition of an extensive range of terpenoid metabolites in

complex plant tissue extracts and is independent of retention time, abundance, and elemental

formula. Using RMD filtering and MS/MS data analysis, 24 novel elemental formulas

corresponding to glycosylated sesquiterpenoid metabolites were identified in extracts of the

wild tomato Solanum habrochaites LA1777 trichomes. Extensive isomerism was revealed by

ultrahigh performance liquid chromatography, leading to evidence of more than 200 distinct

sesquiterpenoid metabolites. RMD filtering led to recognition of the presence of glycosides of

two unusual sesquiterpenoid cores that bear limited similarity to known sesquiterpenes in the

genus Solanum. In addition, RMD filtering is readily applied to existing metabolomics

databases, and correctly classified the annotated terpenoid metabolites in the public metabolome

database for Catharanthus roseus.



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1. Introduction

Plant metabolic networks generate amazing chemical diversity, but our understanding of the

genetic factors responsible for plant chemistry remains primitive. Discovery and identification of

metabolites has posed the greatest bottleneck in recent efforts to exploit metabolomics to address

questions about the basis for biosynthetic diversity in the plant kingdom (Ji et al., 2009; Zhou et

al., 2012). Since specialized metabolism of non-model plants is taxonomically-restricted,

metabolite databases offer poor representation of plant chemical diversity, and de novo

recognition and discovery of metabolite chemistry is necessary. A common strategy for

metabolite discovery has often started with generation of tandem mass (MS/MS) spectra, usually

beginning with the most abundant metabolites, and uses characteristic fragment ions to assign

metabolites to a particular class of compounds. Flavonoid identification from MS/MS spectra is

often successful because most flavonoids yield MS/MS fragment ions characteristic of their

flavonoid cores (Ma et al., 1997; Li et al., 2013). However, when MS/MS spectra fail to display

class-characteristic fragment ions, recognition of a metabolite’s structural class is less obvious.

Specialized plant metabolites are often grouped as polyphenolic, terpenoid, alkaloid, polyketide,

or fatty acid metabolites based upon biosynthesis of their core scaffolds, which often undergo

subsequent metabolic decoration such as glycosylation. Among phytochemicals, terpenoids

offer perhaps the greatest structural diversity. This feature makes them useful as chemical

defenses and as the foundation for candidate drugs (Ajikumar et al., 2008; Goodger and

Woodrow, 2011), and the commercial importance of terpenes makes their discovery and

synthesis an important research focus (Zwenger and Basu, 2008). Terpenoids exhibit

remarkable structural diversity resulting from varied metabolic cyclizations, oxidations,

rearrangements, and branching reactions (Chappell, 1995; Mizutani and Ohta, 2010) and from

diversity in glycosylation (Dembitsky, 2006; Goodger and Woodrow, 2011). Such structural

diversity challenges investigators to recognize novel terpenoids in a complex matrix (Pfander

and Stoll, 1991; Fraga, 2012) because few features in MS/MS spectra of nonvolatile terpenoids

provide reliable keys for their annotation as terpenoids. As a result, nonvolatile terpenoids

represent an underappreciated group of plant specialized metabolites.

Advances in chromatography and mass spectrometry have enabled detection of a broad range of

natural products, and characteristic ions in mass spectra have been useful for distinguishing



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compound classes. While GC-MS has enabled identification of volatile and semivolatile

terpenes for decades, it is not a suitable approach for nonvolatile conjugated terpenoids unless

they are first cleaved to form volatile products or derivatized to increase volatility. Furthermore,

MS/MS fragment ions characteristic of terpenoid glycosides have yet to be documented, and

characterization of conjugated terpenoids has been limited largely to saponins that share a

common steroidal or triterpenoid core (Challinor and De Voss, 2013). In contrast to other

specialized metabolite classes, the diversity of terpenoid cores dictates that fragment ions

specific to terpenoids often fail to provide for universal recognition of metabolites within this

class, particularly for two situations: (1) when terpenoids are glycosylated and MS/MS spectra

are dominated by fragment ions derived from the carbohydrate and (2) when mass spectra are

generated in negative-ion mode, which often yields limited cleavage of carbon-carbon bonds in

the terpenoid core that might serve as terpenoid indicators. The structural diversity of the

terpenoid cores yields different fragments in MS/MS spectra of different non-volatile

terpenoids, as has been demonstrated for a series of saponins (Huhman and Sumner, 2002).

Therefore, annotations of terpene glycosides in a metabolite profile have been driven by the

absence of fragment ions in mass spectra that represent other classes of molecules (Ward et al.,

2011).

Despite its limited capabilities in differentiating stereoisomers, mass spectrometry plays

important roles in discovery of natural products and elucidation of their structures (Lei et al.,

2011). Modern medium- to high-resolution mass spectrometers have provided greater (low

ppm) mass measurement accuracy. Such errors may be more pronounced than measurements for

an individual sample when they represent an average mass extracted from large metabolomics

data sets. For metabolites of relatively low molecular mass, such measurements provide

sufficient information to assign molecular formulas, but for metabolites of higher (> 500 Da)

molecular masses, formula assignments are often ambiguous owing to the large number of

formulas consistent with a molecular mass (Kind and Fiehn, 2007). Moreover, assignments of

molecular formulas often fail to yield reliable assignments of metabolites to specific

biosynthetic origins.



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In this report, we examine specialized metabolites of the wild tomato Solanum habrochaites

LA1777, which has been extensively studied for its plant defense compounds including volatile

sesquiterpenoids and acyl sugars (Coates et al., 1988; Ghosh et al., 2014). Our recent discovery

of a few glycosylated sesquiterpenoids in this accession suggested the metabolic capacity to

form such metabolites in the genus (Ekanayaka et al., 2014). It is the intent of this report to

present a framework for accelerated discovery of terpenoid glycosides from mass spectra

generated using common instruments such as time-of-flight mass spectrometers that provide

intermediate mass resolution and low ppm mass accuracy, using S. habrochaites LA1777 as an

example.

1.1 Foundations of relative mass defect filtering for mining plant metabolomes for novel

terpenoid glycosides

The power of ‘high-resolution’ or ‘exact mass’ MS for metabolite identification lies in the

unique mass of each element and isotope. A common approach that reflects this derives from

the idea of a mass defect, which is defined as the deviation of each atom’s mass from the

integer-rounded mass, (nominal mass). The absolute value of the mass defect of an ion reflects

the ion’s elemental composition because each element has a unique mass defect. A positive

absolute mass defect usually reflects a large number of hydrogen atoms because the atomic

mass of hydrogen is slightly greater than the rounded-off integer (by +7.83 mDa), and carbon

(exactly 12 Da) does not contribute to mass defect. Oxygen has a smaller negative mass defect

(-5.09 mDa) and nitrogen has a small positive defect (+3.07 mDa), and these elements are often

fewer in number than hydrogen in specialized metabolites. The absolute mass defect of an ion

represents the sum of the mass defects for all atoms in the molecule. Absolute mass defects

serve as the basis for assigning elemental formulas from high resolution mass spectra, but mass

measurement accuracy often falls short of providing unambiguous formula assignments,

particularly for higher molecular weight substances where the number of elemental formulas

within mass measurement error can be large.

An alternative and promising strategy relies on normalizing the absolute mass defect to an ion’s

mass, known as the relative mass defect (RMD). Since absolute mass defect largely reflects the



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total hydrogen content, RMD serves as a measure of fractional hydrogen content (Stagliano et

al., 2010), which in turn reflects the reduced state of carbon that derives from the contributions

of metabolic precursors. RMD is calculated in ppm as ((mass defect/measured monoisotopic

mass) × 106). For the terpene building block isoprene (C5H8), the RMD of 920 ppm reflects its

high hydrogen content (11.8 wt %H). This value remains constant for larger mono-, di-, and

triterpene oligomers, which share the same fractional hydrogen content. This demonstrates how

RMD values aid grouping of metabolites based on common biosynthetic precursors, despite

differences in molecular mass and absolute mass defect. Metabolic oxidations of a

sesquiterpene decrease RMD values, as shown by the shift in RMD to 830, 752, and 692 ppm

upon addition of a one, and two oxygen atoms and subsequent oxidative dehydrogenation (e.g.

C15H24O, C15H24O2, and C15H22O2). Terpenoid metabolites usually require one or more oxygen

atoms in order to be detected by LC-MS using electrospray ionization, and in many organisms,

they are conjugated to more polar groups (e.g. glycosides or phosphates). Such conjugations

decrease a terpenoid’s RMD value further: each glucosylation adds C6H10O5, so glucosylation

of a sesquiterpene alcohol (to form C21H34O6) would decrease RMD to 616 ppm. Additional

oxidation or conjugation by malonate (addition of C3H2O3) would decrease the RMD value of

terpenoid metabolites yet further, and acylation by aliphatic acids (e.g. acetylation) may

increase RMD if the fractional hydrogen content of the acyl group is greater than the core

molecule. Since conjugated terpenoids usually consist of a terpenoid core that is rich in reduced

carbon and conjugate groups (carbohydrates, malonate esters) of low hydrogen content, RMD

values of glycosylated sesquiterpenoids range from ~400 to ~600 ppm. In contrast,

polyphenolic metabolites have lower hydrogen content, and their RMD is usually less than 300

ppm (e.g. 230 ppm for salicylic acid, 167 ppm for kaempferol).

Terpenoid glycosides represent a diverse class of phytochemicals that have been understudied

due to the challenges in their identification and structure elucidation (Pfander and Stoll, 1991;

Sahu and Achari, 2001). While some of these compounds display biological activity (Chang et

al., 2002; da Silva et al., 2008), much remains to be learned about their synthesis and

functionality in plants (Maier et al., 1995). In this report, application of RMD filtering is

presented as a quickly-calculated measure that can advance annotation of novel plant

metabolites from metabolite profiling analyses and databases.



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2. Results and discussion

2.1 Recognition of sesquiterpene glycosides from ion relative mass defects

Analysis of leaf extracts of S. habrochaites LA1777 using LC-multiplexed CID MS in negative

ion mode yielded evidence of complex mixtures of metabolites, dominated by acylsucroses and

flavonoid glycosides (Figure 1). Automated peak detection, deisotoping, integration, and

retention time alignment using Waters MarkerLynx XS software yielded a total of 3280 m/z-

retention time pairs, which are estimated to represent more than 1000 distinct metabolites owing

to formation of multiple adduct ions, noncovalent dimer ions, and fragment ions.

Sorting of the RMD values for the entire automated pick picking data set revealed that 3199 (98

%) of the ions had positive absolute mass defects, but 2% had negative absolute mass defects

typical of inorganic salt cluster ions and instrument contaminants (e.g. trifluoroacetate,

NaHPO4-), and these were filtered from further consideration. The ions with positive mass

defects were divided into bins, with 1805 (55% of total) falling in the RMD range of 400 to 650

ppm and 1177 (36% of total) with RMD from 200 to 400 ppm, the latter range being typical of

polyphenols. Since the objective of this exercise was annotation of sesquiterpene glycosides

from this data set, three boundary conditions satisfied by sesquiterpenoid glycosides were

proposed: 1) the maximum RMD for a sesquiterpene glycoside is estimated as 636 ppm based

on the theoretical m/z of 383.2439 for [M-H]- of farnesol monoglycoside (C21H35O6-); 2) the

minimum RMD a sesquiterpene glycoside (maximum of four hexose moieties) can display is

463 ppm calculated from the theoretical m/z of 869.4024 for [M-H]- of farnesol tetraglycoside;

3) minimum nominal m/z of a sesquiterpenoid monoglycoside should be 383 based on farnesol

monoglycoside. It is notable that some terpenoid compounds are esterified to malonate as

evidenced by the malonylated diterpene glycosides of Nicotiana attenuata and malonylated

sesquiterpenes of Panax ginseng (Guangzhi et al., 2005; Heiling et al., 2010; Ruan et al., 2010;

Sun et al., 2011) . To account for acetate or malonate esters of sesquiterpenoid glycosides and to

allow for experimental error in mass measurement, we propose that nearly all compounds in this

class should fall in the RMD range of 440 to 640 ppm. The number of detected ions with RMD

440 to 640 ppm detected was 1280 (38% of total), and this number was reduced to 1076 (33%

of total) after application of the low mass (m/z 383) cutoff. Next, this list of putative



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sesquiterpenoid glycosides was sorted by descending peak area and the 200 most abundant ions

were selected for further processing (Supplemental Information Table S1).

Distinguishing terpenoid glycosides from other compounds: Applying the RMD and molecular

mass criteria described allows for inclusion of nearly all sesquiterpene glycosides, but the final

list may also include some non-terpenoids (Supplemental Information Table S1). To distinguish

these, calculation of RMD of fragment ions generated using either MS/MS or non-mass

selective CID can provide additional discriminating information. Fragment ion RMD values

distinguish terpenoid glycosides from other compounds since losses of all carbohydrate

moieties will yield a fragment ion corresponding to a terpenoid core, yielding RMD values >

800 ppm. Furthermore, even if all sugars are not removed during fragmentation, RMD values of

fragment ions formed by terpenoid glycosides will be greater than that of the pseudomolecular

ion because RMDs are less than 350 ppm for the neutral hexose substructures and their

fragments, values much less than for terpenoid cores (Table 1). Therefore, terpenoid glycosides

are characterized by fragment ions that display increasing RMD as their masses decrease from

removal of glycoside groups. This phenomenon is illustrated in Figure 2, which shows the

MS/MS spectra of several abundant metabolites with pseudomolecular ion RMD falling in the

440 to 636 ppm range. Among the fragment ions of these compounds, only the fragments m/z

199 (RMD ~ 840 ppm; Figure 2A and 2B) display RMD close to that of isoprene or its

oligomers (919 ppm), but, m/z 199 has a mass too low to be an oxygenated sesquiterpenoid core

(C15H24 would be 204 Da). In addition, there is no systematic increase of RMD as fragment

masses decrease among the fragment ions in any of these compounds, suggesting the groups

being lost have high hydrogen contents similar to, or greater than, the intact molecule. These

findings suggest the molecules are not terpenoid glycosides, and in fact, the metabolites whose

MS/MS spectra are depicted in Figure 2A, 2B, and 2C are all acylsucroses. In contrast, the

MS/MS spectra of glycosides of the sequiterpenoid campherenane diol (discussed below)

shown in Figure 2D and 2E show a systematic increase in RMD as major fragment masses

decrease, consistent with a hydrogen-rich terpenoid core and neutral mass losses of glycosides.

Only the less-abundant carbohydrate-derived product ions of m/z 161 and 323 (Fig. 2D) and m/z

179 and 323 (Fig. 2E) deviate from this trend.



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2.2 Annotation of sesquiterpene diol glycosides from S. habrochaites LA1777

An example workflow for metabolite annotation follows. In the list of the 200 S. habrochaites

LA1777 metabolite ions with greatest peak areas within RMD 440 to 640 ppm (as discussed in

Section 2.4.1), a metabolite m/z of 609 was ranked 17th in peak area (Supplemental Information

Table S1). Negative-ion mode multiplexed CID mass spectra of these compounds yielded

fragment ions that displayed a systematic increase of RMD with decreasing mass, consistent

with losses of neutral fragments lower in hydrogen content than the intact molecule. This

observation flagged the metabolite as a potential terpenoid glycoside. In order to ensure that

these fragment ions were derived from the proposed pseudomolecular ion, MS/MS spectra were

generated.

Since multiplexed CID results can be complicated by formation of fragments arising from other

co-eluting metabolites, the MS/MS spectrum of products of m/z 609 (Figure 2D) was generated.

A fragment ion at m/z 563 was observed, corresponding to loss of HCOOH. Since formic acid

was in the mobile phase, m/z 609 was annotated as [M+formate]- of a metabolite of 564 Da.

Such ionization behavior is common for glycosides that lack acidic functional groups. The next

most abundant fragment was m/z 401, corresponding to one less hexose moiety than m/z 563.

RMD values of both of these fragments (539 ppm and 631 ppm for m/z 563 and 401

respectively) increased as the m/z of fragment decreased (Figure 2D), consistent with annotation

as a terpenoid glycoside. However, no prominent fragment ions with high RMD typical of

terpenoid cores were observed with m/z < 401. The fragment ion mass of m/z 401.2532

suggested a formula of C21H37O7-, which has 6 more than the 15 carbon atoms that are usually

found in sesquiterpenes, suggesting the presence of an additional hexose not released during

fragmentation. A fragment ion of m/z 323.098 (RMD=303 ppm) was tentatively assigned as

[dihexose-H-H2O]- (C12H19O10-) suggesting a diglucoside where the two hexose groups are

linked to one another. Additional evidence for a glycoside was provided by the fragment at m/z

161.04 (RMD=271 ppm), corresponding to C6H9O5-. Further characterization of this compound

required purification and structure determination using 1D and 2D NMR, since further

fragmentation of the core was not observed in negative ion mode and the mass spectra were not

consistent with previously-known metabolites. The NMR spectra confirmed the structure as the



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sesquiterpenoid glycoside campherenane diol diglucoside as we reported earlier (Ekanayaka et

al., 2014).

Another example of how RMD values guide discovery of more complex terpenoid glycosides is

presented in the form of a metabolite detected in negative-ion profiling of S. habrochaites

LA1777 leaf trichomes. The metabolite was detected as m/z 811.3587 (RMD=442 ppm),

perhaps higher in molecular mass and with lower RMD than expected for a sesquiterpenoid

glycoside. Its MS/MS product ion spectrum (Figure 2E) shows fragments formed by loss of

CO2 to give m/z 767.3707 (RMD=483 ppm) followed by loss of C2H2O to give m/z 725.3587

(RMD=495 ppm). Both fragments are characteristic of malonate esters. Further fragmentation

generated ions of m/z 563.3057 (RMD=543 ppm), m/z 401.2516 (RMD=627 ppm), and m/z

239.2017 (RMD=841 ppm). With the exception of common carbohydrate fragment ions, RMD

values increased as fragment ion mass decreased, again consistent with a terpenoid glycoside.

The fragment ion at m/z 239.2017 was annotated as a sesquiterpenoid core (C15H27O2-) as it did

not undergo further fragmentation. Based on these observed characteristics this compound can

be annotated as a sesquiterpenoid triglycoside malonate ester.

The application of RMD analysis is not limited to sesquiterpenoid metabolites, but is readily

extended to other related and unrelated substances. MS/MS spectra of the triterpenoid

glycoalkaloid tomatine displayed similar behavior. RMD of the [M-H]- ion of tomatine (m/z

1032.5) is 520 ppm. The major fragments display an increasing RMD from 520 ppm to 674

ppm with decreasing product ion mass, and correspond to neutral losses of relatively hydrogen-

deficient carbohydrate moieties (Figure 2F). The relationship between fragment ion RMD value

and ion mass (m/z) for an tetraacylsucrose, a sesquiterpenoid glycoside, and the triterpenoid

glycoside tomatine shows how terpenoid glycosides can be distinguished from other compounds

based on fragment ions RMD (Figure 3). For the two glycosides that possess a terpenoid core,

RMD values of fragment ions that contain the terpenoid core are greater than for the precursor

ion, whereas the reverse is true, with the exception of the fatty acyl anion at m/z 199, for the

tetraacylsucrose.

The utility of RMD filtering was then assessed by applying RMD-based filtering criteria for

sesquiterpenoid glycosides, as described above, to the most abundant 200 metabolite ions in the



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list of S. habrochaites LA1777 metabolites detected by nontargeted LC/MS profiling. There

were 224 peaks annotated as sesquiterpene glycosides, including multiple isomers for each

elemental formula, as presented in Table 2. Three different sesquiterpenoid core formulas were

established from MS/MS spectra including the campherenane diol core (C15H28O2). The MS/MS

data generated for each of these compounds and the RMD of each fragment ion are presented in

Supplemental Information Figures S2-S22.

2.3 Discovery of conjugated terpenoid glycosides from S. habrochaites LA1777

RMD filtering of the list of m/z-retention time pairs extracted from nontargeted metabolite

profiling of S. habrochaites LA1777 revealed numerous m/z values consistent with

sesquiterpenoid glycosides. Among those giving the greatest integrated peak areas were three

nominal masses (m/z 661, 633, and 591) that gave CID mass spectra suggestive of glycosides,

yielded multiple chromatographic peaks consistent with several isomers (Figure 4), and were

judged to represent metabolites in sufficient abundance for their isolation and characterization

by NMR spectroscopy.

Three individual isomers designated with formulas 1, 2, and 22 (Table 2) were selected based

on RMD criteria and were purified using HPLC. Their NMR spectra (presented in Supplemental

Information Table S2) revealed structures with methyl branching consistent with isoprenoid

precursors, though the aglycone cores differed in structure from known volatile or nonvolatile

sesquiterpene metabolites within the genus Solanum. The core for the isolated compound

(Figure 4A, peak 8a; detected as m/z 661 in negative ion mode) was consistent with an oxidized

core of formula C15H24O3, and this core formula was designated as “sesquiterpene I”. NMR

spectra revealed the purified isomer to be an acyclic metabolite with a ketone group near the

center of the carbon chain. NMR spectra of two additional metabolites (Figure 4B, peak 3b, m/z

663 and Figure 4C, peak 6c, m/z 591) suggested both were glycosides of a common aglycone

core of C15H26O, and this was designated “sesquiterpene II”. The two masses were consistent

with the compounds differing by the attachment of one acetyl group. Structures of the three

metabolites are presented in Figure 4.

Neither terpenoid core displays much structural similarity to volatile sesquiterpenes of S.

habrochaites or other documented metabolites within the genus, nor do their structures suggest



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they are formed by the action of common terpene synthase enzymatic transformations. RMD

filtering accelerated recognition of the presence of these unusual compounds, but it is clear that

more investigation is needed to determine biosynthetic origins and the biological functions of

these metabolites.

2.4 Application of RMD filtering for mining of intermediates in terpene indole alkaloid

biosynthesis in Catharanthus roseus

The original metabolome data set from the Medicinal Plants Consortium database has 3229 m/z

–retention time pairs derived from nontargeted LC-MS analysis of C. roseus tissue extracts.

The distribution of RMD values for all detected ions is presented in Figure 5. Applying the

boundary conditions discussed in Section 4.6 resulted in 2109 m/z-retention time pairs (65 %)

that satisfy the criteria as potential terpene alkaloid pathway intermediates, and this finding

suggests that a large fraction of the specialized metabolome may be derived from common or

similar precursors (Supplemental Information Table S2). All 12 monoterpene indole alkaloids

annotated in the Medicinal Plants Consortium database were correctly assigned as lying within

the RMD search criteria. The only annotated metabolite falling outside this range was sucrose,

reflecting the success of the filtering in excluding metabolites from different structural classes.

For distinguishing potential terpene indole alkaloids from the nonterpenoid compounds, RMD

of fragment ions can be used. Fragment ions of terpene indole alkaloids that possess the

terpenoid component in it display a characteristic increase of RMD compared to the parent ion

as the fragment ion m/z values decrease. This can be inferred based on the relationship between

RMD and fragment ion masses reported for vinblastine, vindoline and catharanthine (Figure 6).

Furthermore, terpene indole alkaloids display the presence of a number of even mass fragment

ions as observed in their CID mass spectra (Supplemental Information Figures S23-S25) that

are characteristic of fragment ions containing an odd number of nitrogen atoms.

The applications discussed in this report demonstrate the applicability of RMD filtering for

discovering terpenoid compounds even when the terpenoid component represents only a

minority fraction of mass of the molecules of interest and has been subjected to a number of

biotransformations, as in the case of vinblastine. However, RMD filtering still allows for

distinguishing vinblastine among other compounds, and the gradual increase of the RMD of



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fragment ions with decreasing ion mass suggests of the presence of a terpenoid core. Similar to

the case with sesquiterpene triglycoside malonate esters in S. habrochaites, the sesquiterpene

component was a minor fraction of metabolite mass.

3. Conclusions

The range of specialized metabolites in the plant kingdom is astounding, yet deep explorations

into the metabolomes of nonmodel plants face enormous data sets and would benefit from tools

that guide a focus on specific biosynthetic classes. Analyses using LC-MS with multiplexed

CID generate molecular and fragment mass information for all ionized metabolites providing

the ion signal is sufficient. When coupled with medium- to high-resolution mass

measurements, this approach reduces the need for separate MS/MS analyses for all metabolites,

and generates information about molecular and fragment masses with sufficient accuracy to

allow for useful RMD measurements. Analysis of the RMD variation among precursor ions and

product/fragment ions accelerates metabolite discovery by eliminating signals with RMD values

that are inconsistent with a target class of metabolites. For this investigation, while we cannot

exclude the possibility that the RMD values consistent with terpenoid conjugates may include

non-terpenoids, this sorting removes many non-terpenoid metabolites and guides a focus on

candidate terpenoid conjugates. This strategy enabled annotation of more than 200 novel

sesquiterpene glycosides from a plant system that has been studied for several decades. We

anticipate that RMD filtering of nontargeted metabolite profiles will propel annotation and

identification of terpenoid glycosides that have been underappreciated owing to the lack of a

systematic method for recognition of their presence.

The research discussed here used the negative ion mode MS/MS for the annotation of terpene

glycosides from complex matrices, and provides the first evidence for a remarkably extensive

and diverse group of sesquiterpenoid glycosides in S. habrochaites LA1777. We recognize that

RMD values for molecular mass alone yield limited resolution of compound classes, and many

metabolites are derived from multiple precursors (e.g. prenylated polyphenols and terpenoid

glycosides). For more refined annotations, examinations of RMD values of molecular and

fragment ions as well as neutral mass losses provide evidence of multiple biosynthetic

precursors, as was demonstrated for the terpenoid glycosides described in this report. In

addition, RMD filtering is equally applicable to positive ion mode data sets as demonstrated by



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its application in the correct annotation of known terpene indole alkaloids and the assignment of

more than 1000 ions as candidate terpenoid intermediates from C. roseus. We envision that

development of algorithms that provide automated classification of metabolites based on

molecular and fragment RMD values will accelerate discoveries of gene functions that regulate

plant chemistry.

4. Materials and Methods

4.1 Plant material

Sample preparation for metabolite profiling: Solanum habrochaites LA1777 plants were grown

in Michigan State University plant growth chambers (28 °C, 16:8 h day/night cycle, 150 µmol

m-2s-1, 96% humidity) for six weeks from seeds obtained from the C. M. Rick Tomato Genetics

Resource Center at the University of California-Davis. Ten leaflets harvested from each plant

(six weeks post germination) were extracted by dipping in 5 mL of methanol: water (80:20 v/v)

for about 30 s. Three biological replicates were used for profiling. Extracts were concentrated

by drying under a stream of N2 gas at room temperature, and the residues were redissolved in

0.5 mL methanol: water (80:20 v/v). The extract was centrifuged (10000g for 10 min at 25 °C)

to remove debris; the supernatant was transferred to an autosampler vial for LC-MS and

MS/MS analyses.

4.2 LC-MS and MS/MS analyses

Initial exploration of complexity of S. habrochaites LA1777 extracts was performed using a

Waters LCT Premier time-of-flight mass spectrometer coupled to Shimadzu LC-20AD pumps.

Separations were performed using an Ascentis Express C18 UHPLC column (2.1 × 100 mm,

2.7 µm; Supelco, USA) and metabolites were detected using electrospray ionization in

negative-ion mode. Solvents used were 0.15% aqueous formic acid - pH 2.85 (A) and methanol

(B). The LC gradient was as follows: 0-1.00 min (99:1), 1.01-100.00 (linear ramp to 20:80),

100.01 – 101.00 (linear ramp to 1:99) hold at (1:99) 101-105 min, 105 – 106 min (linear ramp

to 99:1) and hold at (99:1) over 106-110 min. Flow rate was 0.30 mL/min and column

temperature was held at 35 °C. Sample volume injected to the column was 10 µL. Mass spectra

were acquired over m/z 50-1500 using dynamic range extension. Mass resolution (M/ΔM, full

width-half maximum) was approximately 10000. Five parallel collision energy functions were



17

used, by switching the Aperture 1 voltage between 5, 20, 40, 60, and 80 V with 0.1 s per

function. Other parameters include capillary voltage of 2.50 kV, desolvation temperature of 350

°C, source temperature of 100 °C, cone gas (N2) at 40 L/h and desolvation gas (N2) at 350 L/h.

All LC-MS/MS experiments used for the characterization of novel terpenoid metabolites from

S. habrochaites LA1777 were performed using a Waters Xevo G2-S QToF mass spectrometer

coupled to a Waters Acquity ultra-high pressure liquid chromatography system. The same

chromatographic column and solvents used in experiments performed on the LCT Premier were

used here. The solvent gradient (A:B) was as follows: 0-1.00 min (99:1), 1.01-4.00 (linear ramp

to 55:45), hold at (55:45) 4.01-9.00 min, step to (50:50) and hold at (50:50) over 9.01-14.00

min, step to (1:99) and held at this ratio over 14.01-17.00 min, followed by a step to (99:1) and

held at this composition over 17.01-20.00 min. Flow rate was 0.30 mL/min and column

temperature was held at 35 °C. Mass spectra were acquired using negative-ion mode

electrospray ionization and dynamic range extension over m/z 50-1500, with mass resolution

(M/ΔM, full width-half maximum) approximately 20000. Five parallel collision energy

functions were used, with 0.1 s per function. Collision cell potentials used for negative-ion

mode fragmentation for each function were 5, 15, 25, 35 and 60 V. Other parameters include

capillary voltage of 2.14 kV, desolvation temperature of 280 °C, source temperature of 90 °C,

cone gas (N2) at 0 L/hr and desolvation gas (N2) at 800 L/hr.

4.3 Data processing

Automated peak detection, integration, and retention time alignment were performed using

Waters MarkerLynx XS software, and lists of m/z values, retention times, and extracted ion

chromatogram peak areas were exported as text files and processed further using Microsoft

Excel software. The lowest collision energy function (function 1) was used for peak detection,

integration, retention time alignment, and deisotoping. The parameters used with MarkerLynx

processing were as follows: marker intensity threshold: 800 counts, mass window 0.05 Da,

retention time window: 0.25 min, m/z range: 100 – 1500, retention time range 0.5 – 20.0 min.

Peak smoothing was not applied.

4.4 Structure elucidation of candidate sequiterpenoid glycosides from S. habrochaites

LA1777



18

Details of experimental procedures used to isolate sesquiterpenoid glycoside metabolites and

determine their structures using HPLC-MS/MS and NMR spectroscopy are presented in

Supplemental Information. Metabolite structures, NMR chemical shifts (Supplemental Table

S1), and accurate mass MS and MS/MS data are included. NMR assignments were made based

on one-dimensional 1H and 13C spectra and two-dimensional 1H-1H COSY and NOESY, 1H-13C

HSQC, HMBC, and TOCSY spectra. Since none of the metabolite identities have been

confirmed by synthesis, structures should be considered putatively annotated compounds, or

level 2 based on the Metabolomics Standards Initiative guidelines (Sumner et al., 2007).

4.5 LC-MS analysis of Catharanthus roseus metabolites

Catharanthus roseus tissue extracts were analyzed by LC-MS, and the data are available to the

public at the Medicinal Plants Consortium Metabolome database http://metnetdb.org/PMR/

(Wurtele et al., 2012). Analyses were performed using a Waters LCT Premier time-of-flight

mass spectrometer coupled to Shimadzu LC-20AD pumps. Separations were performed using

an Ascentis Express C18 UHPLC column (2.1 × 50 mm, 2.7 µm; Supelco, USA) and the

ionized compounds were detected in positive ion mode electrospray ionization. Solvents used

were 10 mM aqueous ammonium formate, pH 2.85 (A) and 1:1 mixture of methanol and

acetonitrile (B). The solvent gradient (A:B) was as follows: 0-1.00 min (90:10), 1.01-23.00

(linear ramp to 10:90), hold at (10:90) 23.01 - 27.00 min, linear ramp to (90:10) by 28 min, and

hold at (90:10) over 28.00 – 32.00 min. Flow rate was 0.30 mL/min and column temperature

was held at 40 °C. Mass spectra were acquired using positive-ion mode electrospray ionization

and dynamic range extension over m/z 50-1500, with mass resolution (M/ΔM, full width-half

maximum) approximately 8500. Four parallel collision energy functions were used, with 0.15 s

per function. Collision cell potentials used for positive-ion mode fragmentation for each

function were 20, 40, 60 and 80 V. Other parameters include capillary voltage of 3.0 kV,

desolvation temperature of 350 °C, source temperature of 100 °C, cone gas (N2) at 40 L/hr and

desolvation gas (N2) at 350 L/hr.

4.6 Annotation of Catharanthus roseus metabolomes

Performance of the RMD filtering approach was evaluated by applying it to annotate metabolites

from C. roseus, which accumulates monoterpene indole alkaloids (Svoboda et al., 1959;



19

O'Connor and Maresh, 2006). This was performed by establishing appropriate boundary

conditions for monoterpene metabolites and assessing whether proposed monoterpene-derived

intermediates from C. roseus documented in the Medicinal Plants Consortium Metabolome

database were correctly classified. The precursors of monoterpene indole alkaloids are

tryptamine (theoretical m/z 161.1073 ([M+H]+); RMD=666 ppm) and the iridoid glycoside,

secologanin (theoretical m/z 389.1442 ([M+H]+); RMD=371 ppm). One proposed precursor of

secologanin is iridotrial (theoretical m/z 183.1016 ([M+H]+); RMD=555 ppm) (Miettinen et al.,

2014). The biosynthetic pathway dictates that the indole component of these compounds

originated from the tryptamine group while the iridotrial acts as a precursor of the monoterpene

component (El-Sayed and Verpoorte, 2007). Direct condensation of iridotrial with tryptamine

would generate the simplest form of terpene indole alkaloid with elemental formula C20H25N2O2+

(theoretical m/z 325.1911 ([M+H]+); RMD=588 ppm). Strictosidine (theoretical m/z 531.2337

([M+H]+); RMD=440 ppm), is formed by condensation of the monoterpenoid glycoside

secologanin with tryptamine (El-Sayed and Verpoorte, 2007). Additional biosynthetic steps

result in formation of more complicated terpene indole alkaloids including vinblastine, which is

consistent with formation of a strictosidine dimer (theoretical m/z 1061.46 ([M+H]+) and RMD

of 434 ppm). Based on this information, the minimum RMD for mining for terpene indole

alkaloids can be proposed as about 420 ppm, and the maximum RMD can be estimated as about

588 ppm. To account for errors in mass measurements, the RMD range of 350 – 600 ppm range

was employed, and the lower and the mass range was estimated as about m/z 325 to 1061 for

[M+H]+ ions. Therefore, compounds with RMDs ranging from 350 to 600 ppm and m/z ranging

from 320 to 1100 were selected as representing potential pathway intermediates involved in

monoterpene indole alkaloid biosynthesis in C. roseus.

5. Acknowledgments

We thank Drs. Robert Last, Tony Schilmiller, Eran Pichersky, and Dean DellaPenna for

valuable suggestions and comments, Ms. Lijun Chen of the Michigan State University RTSF

Mass Spectrometry and Metabolomics Core staff for assistance with analyses performed on the

Xevo G2-S QTof mass spectrometer, Dr. Joseph Chappell, Scott Kinison, and Yunsoo Yeo of

the University of Kentucky for processing C. roseus tissues, and Dr. Eve Wurtele, Manhoi Hur,

Nick Ransom, and Luda Rizshsky for development and organization of the online PMR



20

database that includes the C. roseus metabolome data set used in this report, and the entire

group of participants in the NIH Medicinal Plants Consortium for access to extracts of

numerous medicinal plant tissues.



21

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List of Figures

Figure 1. Complexity of a plant extract is evident from the number of peaks in a UHPLC-MS base peak intensity (BPI) chromatogram generated from a leaf dip extract of S. habrochaites LA1777. Automated peak detection yielded 3280 retention time-mass pair features. Analysis was performed using a 110 min chromatographic gradient and detected in negative-ion mode.

Figure 2. Negative ion mode multiplexed CID mass spectra of S. habrochaites LA1777 metabolites: (A) acylsugar S4:22, RMD = 492 ppm for [M+formate]-. (B) acylsugar S4:23, RMD = 402 ppm for [M+formate]-. (C) acylsugar S4:17, RMD = 440 ppm for [M+formate]-. (D) negative ion mode MS/MS spectrum of products of m/z 609 ([M+formate]- of campherenane diol diglucoside; (E) MS/MS spectrum of products of m/z 811 ([M-H]-) from campherenane diol triglycoside malonate ester. (F) Negative ion mode multiplexed CID mass spectrum of the triterpenoid glycoalkaloid tomatine from S. habrochaites LA1777. All chromatographically-resolved isomers displayed fragments of the same m/z values. Values for RMD of the major fragment ions are presented. All displayed negative-ion mode CID mass spectra were obtained using a collision potential of -60 V, and MS/MS spectra were obtained using a collision potential of -50 V. All chromatographically-resolved isomers displayed fragments of the same m/z values.

Figure 3. Relationships between negative-ion mode MS/MS fragment ion masses (m/z values) and RMD values for a triterpenoid glycoalkaloid (tomatine, indicated by �), a sesquiterpene

triglycoside malonate ester (sesquiterpene triglycoside-811; indicated by � solid line connector), and a tetraacylsucrose (AS2-765; indicated by � alternating line/dash connector) from S. habrochaites LA1777 leaf dip extract.

Figure 4. HPLC-MS extracted ion chromatogram profiles for masses of three compounds purified from S. habrochaites LA1777 showing evidence of (A) 10 isomers of [M-H]- (m/z 661) of sesquiterpene I diol diglucoside malonate ester (Compound 22 formula from Table 2), (B) three isomers of [M+formate]- (m/z 633) of sesquiterpene II alcohol diglucoside acetate ester (Compound 2 formula from Table 2), and (C) 8 isomers of [M+formate]- (m/z 591) of sesquiterpene II alcohol diglucoside (Compound 1 formula from Table 2). Structures of Compounds of formulas 1, 2, and 22 determined by NMR and MS/MS are presented. Carbon atoms are numbered in accordance with NMR assignments in Table S2.

Figure 5. Histogram of relative mass defect values for C. roseus metabolites extracted from the Medicinal Plants Consortium metabolite database (available at http://metnetdb.org/PMR). The highlighted region corresponds to the range of RMD values anticipated for monoterpene indole alkaloid pathway intermediates. Data were generated by UHPLC/TOF-MS in positive-ion mode.

Figure 6. Relative mass defect filtering relationships between pseudomolecular and fragment ion masses for the terpene indole alkaloids A) vinblastine, B) vindoline and C) catharanthine from C. roseus. A slight increase of RMD of fragment ions is observed as their m/z decreases, consistent with a relatively hydrogen-rich terpenoid core.



25

List of Tables

Table 1: Characteristic fragment ions observed in negative-ion mode MS/MS spectra for various sugar oligosaccharides and monosaccharides. The masses shown correspond to [M-H]- formed by each sugar group. The MS/MS spectra of candidate terpenoid compounds were examined for the presence of these fragment ions for identification of the presence of these oligosaccharides in the terpenoids.

Table 2: Groups of metabolites identified as sesquiterpenoid glycosides from S. habrochaites LA1777 based on RMD filtering of molecular and fragment ions.



26

Table 1.

Negative ion mode fragment ion m/z

(Theoretical exact mass)

RMD

(ppm) Common sugar

moiety

503.1618 321 Trihexoside

(Hexose-Hexose-hexose; C18H31O16

-)

485.1512 312 Trihexoside - H2O

(C18H29O15-)


malonate ester (C21H33O19

-)


malonate ester - H2O

341.1089 319 Dihexoside

(hexose-hexose; C12H21O11

-)

323.0984 304 Dihexoside - H2O

(C12H19O10-)

179.0561 313 Monohexoside

(Hexose; C6H11O6-)

221.0667 302 Monohexoside acetate ester

(C8H13O7-)

161.0455 283 Monohexoside - H2O (C6H9O5

-)

101.0232 230 Fragment ion from hexoses (C4H5O3

-)

113.0228 202 Fragment ion from

hexoses

125.0244 195 Fragment ion from

hexoses



27

Table 2.

Compound # Exptl. m/z

RMD (ppm)

Proposed elemental formula of

neutral molecule

∆m (ppm) Compound type

Measured m/z of

terpenoid core

fragment ion

Elemental formula of terpenoid

core fragment

ion ∆m

(ppm)

RMD of terpenoid

core fragment

(ppm) # of

Isomers

1 545.2932 538 C27H46O11 -1 Sesquiterpene II

diglycoside 221.1527 C14H21O2- -9 691 8


diglycoside acetate ester

221.1529 C14H21O2- -8 692 1

3 631.2929 464 C37H44O13 3 Sesquiterpene II

diglycoside malonate ester

221.1565 C14H21O2- 8 708 2


triglycoside 221.1539 C14H21O2- -4 696 4

5 793.3474 438 C36H58O19 1 Sesquiterpene II diol triglycoside malonate

ester 221.1527 C14H21O2

- -9 691 11

6 401.2520 628 C21H38O7 -6 Campherenane diol monoglycoside 239.1997 C15H27O2

- -8 836 6

7 443.2632 594 C23H40O8 -4 Campherenane diol

monoglycoside acetate ester

239.1994 C15H27O2- -9 834 14


monoglycoside derivative

239.1999 C15H27O2- -7 836 5

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monoglycoside malonate ester

239.1994 C15H27O2- -9 834 9


diglycoside 239.2048 C15H27O2- 13 857 7

11 605.3152 521 C29H50O13 -4 Campherenane diol diglycoside acetate

ester 239.1993 C15H27O2

- -10 834 9



239.1993 C15H27O2- -10 834 14

13 725.3607 497 C33H58O17 2 Campherenane diol

triglycoside 239.2007 C15H27O2- -4 840 6


triglycoside malonate ester

239.2006 C15H27O2- -4 839 13

15 411.1986 483 C21H32O8 -9 Sesquiterpene III monoglycoside 249.1481 C15H21O3

- -4 595 13

16 453.2082 459 C23H34O9 -9 Sesquiterpene III monoglycoside acetate ester

249.1483 C15H21O3- -3 596 14

17 497.2011 404 C24H34O11 -3 Sesquiterpene III monoglycoside malonate ester

249.1475 C15H21O3- -6 592 11

18 413.2143 519 C21H34O8 -9 Sesquiterpene I monoglycoside 251.1634 C15H23O3

- -8 651 17

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19 455.2270 499 C22H36O9 -4 Sesquiterpene I monoglycoside acetate ester

251.1638 C15H23O3- -6 653 21

20 499.2162 433 C24H36O12 -5 Sesquiterpene I monoglycoside malonate ester

251.1643 C15H23O3- -4 655 11

21 617.2795 453 C29H45O14 -2 Sesquiterpene I

diglycoside acetate ester

251.1636 C15H23O3- -7 652 4

22 661.2693 407 C30H46O16 -3 Sesquiterpene I


251.1639 C15H23O3- -6 653 8

23 823.3245 394 C36H56O21 1 Sesquiterpene I

triglycoside malonate ester

251.1640 C15H23O3- -5 653 5

24 985.3742 380 C30H50O26 -3 Sesquiterpene I tetraglycoside malonate ester

251.1641 C15H23O3- -5 654 11

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