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A Metabolic Pathway Extension Approach for Metabolomic Biomarker Identification Lin Wang, Hui Ye, Di Sun, Tuo Meng, Lijuan Cao, Mengqiu Wu, Min Zhao, Yun Wang, Baoqiang Chen, Xiaowei Xu, Guangji Wang, and Haiping Hao Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.6b03757 • Publication Date (Web): 16 Dec 2016 Downloaded from http://pubs.acs.org on December 19, 2016

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Analytical Chemistry

1 2 3 4 5

A Metabolic Pathway Extension Approach for

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Metabolomic Biomarker Identification

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Lin Wang† ‡, Hui Ye †‡, Di Sun†, Tuo Meng†, Lijuan Cao†, Mengqiu Wu†, Min

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Zhao†, Yun Wang†, Baoqiang Chen†, Xiaowei Xu†, Guangji Wang†*, Haiping

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Hao†*.

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†

Key Lab of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Tongjiaxiang #24, Nanjing 21009, China.

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* Correspondence: Professor Haiping Hao, E-mail: [email protected], Tel: 86-25-83271179 or Professor Guangji Wang, E-mail: [email protected], Tel: 86-25-83271128

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‡ These authors contributed equally to this work.

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ACS Paragon Plus Environment


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ABSTRACT Discovery of metabolomic biomarkers represents an important task in disease diagnosis and

3

therapy.

Although the development of various analytical tools and online libraries facilitates the

4

identification of biomarkers, the fast and reliable identification of new biomarkers that are not

5

included in databases still represents a major bottleneck in the field of metabolomics.

6

developed a metabolic pathway extension (MPE) approach to the fast characterization of

7

metabolomic biomarkers.

8

metabolome is built from a limited number of initial metabolites via various kinds and multiple steps

9

of metabolic reactions, and thus, theoretically, the whole metabolome might be mapped from the

10

initial metabolites and metabolic reactions. Carnitine was used as an example of initial metabolites

11

to validate this concept and the usefulness of MPE approach.

12

mice induced a significant alternation of a total of 97 metabolites.

13

pair of metabolites were calculated and then matched with those of typical metabolic pathways

14

automatically by an in-house developed program.

15

validating the matches.

16

identified, while only half of them could be traced from the currently available online database.

17

The MPE approach was further validated by applying to the identification of carnitine-associated

18

biomarkers in a typical mice model of fasting, and extended to the development of bile acids

19

submetabolome.

20

identification of metabolically and structurally associated biomarkers.

21

KEYWORDS

Here, we

This approach was proposed based on a core concept that the whole

The intragastric dosing of carnitine to Mass differences between each

Diagnostic ions and neutral losses were used for

With this approach, 93 out of a total of 97 metabolites were putatively

Our study indicates that the MPE approach is highly useful for rapid and reliable

2


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Targeted metabolome; metabolic pathway extension approach; initial metabolites; carnitine

2

INTRODUCTION

3

Metabolomics is becoming a rapidly expanded and intensified field that enables delineation of

4

metabolic profiles and mapping of metabolic changes in response to different physiological and

5

pathological conditions.1,2

6

biomarkers and elucidating biochemical pathways to improve disease diagnosis, staging,

7

prognostication and selection of therapeutic strategies.3

8

significant progresses over the past decades in providing biomarkers for inborn errors of metabolism

9

in various diseases such as diabetes, cirrhosis and cancer in preclinical and clinical studies.4,5

10

It is predicted that a total of 29,265 endogenous metabolites exist in humans.6

Indeed, it provides a powerful platform for discovering potential

Metabolomics studies have gained

Therefore,

11

sensitive detection of metabolites lays the foundation for successful biomarker discovery from the

12

large pool of compounds.

13

hyphenation of ultra-performance liquid chromatography (UPLC) and high resolution mass

14

spectrometry (HRMS) have facilitated the separation of biologically complex metabolites and

15

increased the coverage of metabolic biomarkers, significantly facilitating the metabolite detection

16

process with improved efficiency and accuracy.7-10

17

non-redundant compounds (~50% of the human metabolome) have been detected with MS spectra. 6

18

However, the great challenge lies in the structural characterization of the metabolomics

19

biomarkers.11-14

20

biomarkers are usually of low specificity and there are many potential biomarkers remained

Recent advancements in analytical instrumentation and particularly the

Recent statistics show that 14,673

The field of metabolomics has been encountering a dilemma of that the identified

3



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structurally unknown. Current metabolite identification mainly relies on searching the acquired m/z

2

of precursors against various online metabolome databases such as human metabolome database

3

(HMDB), the METLIN metabolite database (METLIN) and Kyoto Encyclopedia of Genes and

4

Genomes (KEGG).15,16 The major challenge of this typical approach is that accurate mass matching

5

usually generates a multitude of hits for single molecular query, requiring considerable effort to

6

compare the experimentally acquired MS/MS fragment ions to the MS/MS spectra of pure standards

7

deposited in databases.

8

entries with MS/MS spectra available due to the lack of standards.

9

MS/MS spectra of metabolites (~20% of the human metabolome) can be retrieved according to the

Moreover, current databases usually contain a limited number of molecular For instance, merely 5,774

10

most updated statistics of HMDB.6

11

features extracted from comparative metabolomics data as potential biomarkers fail to be assigned to

12

known compounds.

It is thus not surprising why the vast majority of metabolic

13

Although the whole human metabolome consists of a myriad of metabolites with diverse

14

structures and various functions, the metabolites can generally be classified into several categories,

15

such as organic acids, amino acids, nucleotides, carbohydrates, and lipids.

16

metabolites in a certain category are often structurally associated and can be traced by metabolic

17

reactions.11 Based on this core concept, we reasoned that the whole metabolome can be classified

18

to sets of sub-metabolome with the initial metabolite as the network core from which various kinds

19

and sequential steps of metabolic reactions build into the sub-metabolome.

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advancements of biochemistry, the metabolic pathways and principles of biological systems have

21

been well characterized and the completely new metabolic reactions should be very scarce. 4


Moreover, the

Because of the great

We

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therefore proposed a targeted metabolomic pathway extension (MPE) approach to the fast

2

characterization of metabolomic biomarkers.

3

L-carnitine (carnitine) is an extensively-studied molecule in lipid metabolism as it functions to

4

transport the activated fatty acids from the cytosol to the mitochondria where the fatty acids undergo

5

β-oxidation to release energy.17

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metabolism and often consumed as a healthy food supplement due to its beneficial effects such as

7

lowering hepatic and blood lipid levels, improving cholesterol profiles, and reducing obesity caused

8

by high-fat diet.18

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atherosclerosis risks in humans with high circulating levels of trimethylamine-N-oxide (TMAO), a

Therefore, carnitine has long been considered to promote fat

In sharp contrast, carnitine has recently been associated with increased

10

compound metabolized from dietary intake of carnitine by gut microbiota.19,20

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balance of carnitine consumption may actually depend on the metabolic homeostasis,21 and thus the

12

elucidation of the whole carnitine metabolome is of great significance in better understanding of its

13

physiological and pathological role.

The benefit to risk

14

For this consideration, we selected carnitine as an example of one initial metabolite to validate

15

the usefulness and reliability of this approach. We started by establishing a pool of metabolites that

16

undergo significant changes in biological systems in response to the intake of carnitine.

17

Subsequently, a network that connects the metabolites via putative metabolic reactions was

18

constructed with carnitine set as “an initial compound”.

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summarized by combining the bio-transformations commonly encountered in biological metabolism

20

and the specific metabolic reactions involved in carnitine metabolism retrieved from KEGG.

21

Lastly, the metabolic reactions connected-metabolite pairs were quantitatively evaluated to confirm

The possible metabolic pathways were

5



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such matching that is assigned on the basis of acquired MS and MS/MS information.

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metabolic pathways connecting the metabolite pairs via the MPE approach merely predict the

3

association

4

biosynthesis/degradation routes.

5

targeted metabolic network of carnitine independent of metabolome databases.

6

further validated by applying to the identification of carnitine-associated biomarkers in a typical

7

mice model of fasting.

8

useful for rapid and reliable identification of metabolically and structurally associated biomarkers.

of

the

paired

metabolites

in

terms

of

structures,

not

Notably, the

necessarily

their

Using this MPE approach, we have successfully characterized the Such approach was

Combinatorially, our results demonstrate that the MPE approach is highly

9

10

11

EXPERIMENTAL SECTION

Animals.

For targeted metabolomics studies, female 8 week C57BL/6 mice were orally

12

administered by gavage of 200 µL of 100 mM carnitine.

13

water, was also given by gavage at the same dosage.

14

saline alone.

15

D3-carinitine (N-methyl-D3), dissolved in

Control mice were administered with normal

Further details of animals can be found in Supporting Information (SI).

Sample Preparation.

All the plasma and tissue samples were extracted using the protein

16

precipitation method by adding cold methanol spiked with internal standard to plasma and tissue

17

homogenate.

18

evaporated and reconstituted before analysis on an UPLC-MS/MS.

19

preparation can be found in SI.

The extracts were then vortexed and centrifuged.

6


The resulting supernatant was The details regarding sample

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Liquid Chromatography and Mass Spectrometry.

A 5 µL aliquot of the samples was

2

injected onto a LC-30A Shimadzu UPLC system (Shimadzu, Japan) using an amide column

3

(3.0×100mm i.d., 2.5µm) (Waters, Milford, MA).

4

(ESI) ion source into a quadrupole-time-of-flight (Q-TOF), an AB SCIEX TripleTOF 5600 system

5

(Framingham, MA, USA).

6

The eluent was then introduced via electrospray

More details regarding the LC-MS setup can be found in SI.

Peak features were extracted from raw data and

Data Processing and Statistical Analysis.

7

aligned inter-run to generate a data matrix that included a detailed summary of precursor m/z,

8

retention time and peak areas normalized to internal standard.

9

matrix was imported into SIMCA-P (Umetrics, Kinnelon, NJ) for orthogonal projections to latent

The generated multivariate data

10

structures discriminant analysis (OPLS-DA).

11

and S-plot were applied to select potential biomarkers between the control and carnitine-treated

12

groups.22-24

13

and possible adducts or dimers by extracting their ion chromatograms. The Student’s T-test was

14

subsequently performed to analyze the biomarker candidates, and p< 0.05 was considered as

15

significant.

16

using IBM SPSS Statistics version 19.0 (SPSS, Chicago, USA).

17

were then subjected to our MPE analysis for structural characterization.

18

Variable importance in the Project (VIP) value

The biomarker candidates have all been manually checked to remove false positives

The normal distribution of data was analyzed by the Kolmogorov-Smirnov (K-S) test The selected putative biomarkers

Metabolic Pathway Extension Approach for Metabolite Identification.

A MatLab

19

(Mathworks)-based program named Metabolic Pathway Extension Analysis (MPEA) was developed

20

to perform MPE analysis for the characterization of unknown components for targeted metabolome.

21

The metabolite biomarkers were first compiled to a list summarizing information including accurate 7



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m/z and retention time.

2

the list, with the corresponding mass differences calculated and recorded.

3

differences were then matched with the accurate mass changes of the metabolic reactions from an

4

in-house metabolic reaction database within a mass error window of 10 ppm.

5

metabolic reaction database consists of a total of 62 types of metabolic pathways.

6

including the bio-transformations commonly encountered in biological systems,11 and the metabolic

7

pathways involved in carnitine metabolism retrieved from KEGG.

8

in this study and their origins were summarized in Table S1 (SI), and can be further customized

9

depending on the specific initial metabolites based on the user’s need.

10

Each metabolite was then sequentially paired with another metabolite on The resultant mass

The in-house

It was curated by

The 62 metabolic reactions used

For MPE analysis, a single-step metabolic reaction was first queried to match the mass

11

differences of each metabolite pair to possible metabolic pathways.

If failed, the query was

12

continued by searching the mass differences against a combination of two metabolic reactions

13

(defined as “two-step match”).

14

allowed.

15

the limit of m set as ≤5, and n set as -5≤ n≤ 5.

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metabolite with the addition/subtraction of an expected group or multiple expected groups

17

(depending on the steps of metabolic reactions) from the “precursor” metabolite.

18

metabolites, product and precursor metabolites, must subsequently undergo scrutiny to verify the

19

metabolic pathway-matched pairs predicted by the MPEA program.

20

SI.

A maximum of three-step metabolic pathway extension was

A specific case exists for the lipids, with a multi-step of m*CH2-n*H2 was allowed with Therefore, each MPE route generates a “product”

8


The paired

Further details can be found in

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Carnitine Metabolite Identification and Validation.

The direct metabolites of carnitine

2

were determined by analyzing the plasma and intestine samples from the mice dosed with

3

D3-Carnitine (N-methyl-D3) and non-isotopically labeled carnitine.

4

identification of direct metabolites of carnitine can be found in SI.

More details regarding the

5

To further verify the identification results obtained via the MPE approach, all the metabolic

6

biomarkers were searched against two frequently used metabolome databases, HMDB

7

(http://www.hmdb.ca/) and METLIN (https://metlin.scripps.edu/index.php).

8

of 10 ppm was employed for accurate mass matching.

9

database searching were then compared with those obtained via the MPE approach.

A mass error window

The putatively identified metabolites by

10

11

12

RESULTS AND DISCUSSION

In this study, we proposed a core concept that the whole metabolome is derived from some

13

initial metabolites and sequential metabolic reactions.

Therefore, the whole metabolome can be

14

classified to sets of sub-metabolome with the initial metabolite as the network core from which

15

various kinds and sequential steps of metabolic reactions build into the sub-metabolome.

16

was herein selected as a representative initial metabolite, and the “carnitine metabolome” is defined

17

as the metabolites that are significantly changed in response to carnitine intake.

18

carnitine metabolome consists of compounds that are either the real and direct metabolites of

19

carnitine or other metabolites that are changed upon carnitine intake.

9


Carnitine

Theoretically,


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Construction of Carnitine Metabolome by Metabolomic Analysis.

To construct the

2

carnitine metabolome, mice were intragastrically administered with 200 µL of 100 mM carnitine and

3

the plasma samples were collected for targeted metabolomics analysis. The carnitine metabolome

4

was first characterized by processing the UPLC/Q-TOF data of the plasma samples from mice

5

treated with or without carnitine via principle components analysis (PCA).

6

injected in random orders, and exhibited good reproducibility and separation in the PCA model

7

(Figure 1A).

8

was subsequently employed to analyze the same set of the data.

9

the selection of the endogenous metabolites with VIP value> 1.

The samples were

Orthogonal partial least-squares to latent structures discriminate analysis (OPLS-DA) The OPLS-DA model allows for Subsequently, S-plot was

10

employed to validate whether the selected metabolites were statistically significant biomarkers,

11

based on their contributions to the model and their reliability as visualized in the red data points in

12

Figure 1B.

13

response to carnitine intake.

14

and those metabolites with p value< 0.05 were assigned as potential biomarkers (normal distribution

15

of data indicated by the K-S test, Table S2, SI).

16

statistically analyzed metabolite biomarkers were summarized in Figure S1, SI.

17

biomarker candidates were then defined as carnitine metabolome and summarized in Table S2, SI.

18

The heat map in Figure 1C illustrates the trend of these metabolites’ changes in Lastly, a t-test was performed to analyze these selected metabolites,

The extracted chromatograms of all the

Metabolic Pathway Extension Analysis of the Carnitine Metabolome.

The resultant 97

The carnitine

19

metabolome determined by statistical analyses were then subjected to identification using the MPE

20

approach.

21

identification.

Figure 2 illustrates the overall workflow of the MPE approach for metabolite Firstly, the accurate m/z and corresponding MS/MS were extracted from the raw 10


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data, and imported to a stand-alone program we developed for MPE analysis named “Metabolic

2

Pathway Extension Analysis (MPEA)”.

3

the carnitine metabolome to establish the targeted metabolic network of carnitine by calculating the

4

mass differences between any pairs of metabolites within the pool of carnitine metabolome, and

5

subsequently matching them against a list of metabolic reactions commonly encountered in

6

biological systems. Besides single step bio-transformations, the metabolic reactions were further

7

combined as two-step, three-step and multi-step sequential metabolic reactions for matching.

8

Consequently, the sub-metabolome of carnitine was constructed into a network with carnitine as the

9

core compound from which sequential steps of metabolic reactions connect each metabolite and

The MPEA program used the accurate m/z information of

10

build into the carnitine sub-metabolome (Figure 3).

Out of the 97 biomarkers, 93 metabolites were

11

built into the targeted metabolic network of carnitine, and connected to each other via single-step,

12

two-step, three-step and multi-step reactions.

13

connected via a single-step reaction, whereas 42 and 6 metabolites were linked by two-step and

14

three-step reactions, respectively.

15

establishment of such network bridged by sequential metabolic reactions allows for the inclusion of

16

as many metabolites with diverse structures as possible from the carnitine metabolome.

As shown in Table S3 (SI), 27 compounds were

17 metabolites were connected via a multi-step reaction.

The

17

Notably, the implementation of MPE analysis first relies on accurate mass matching to generate

18

the metabolic network for the initial compound. Therefore, mass accuracy is of primary importance

19

for MPE analysis.

20

@ m/z 300),22 and performed an external calibration injection for each 5 samples we analyzed in a

21

queue to prevent any possible mass drift due to instrumentation conditions.

We employed a high resolution Q-TOF in this study (~30,000 FWHM resolution

11


Less than 10 ppm mass


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error can generally be achieved with our settings.

Therefore, we set the mass tolerance for MPE

2

analysis as 10 ppm with the high mass accuracy and resolution provided by the MS we employed in

3

this study. We found such a narrow mass window is critical in filtering the false positive MPE

4

results that would otherwise deliver more possible metabolic reaction routes with a wide mass

5

window.

6

(m/z 236.149), which corresponds to 2 metabolic reaction routes within a mass error of 10 ppm

7

compared to 3 possible pathways with a mass error of 20 ppm.

An example is the metabolic reaction matched pair, the core compound carnitine to M35

8

Although accurate mass matching proposes possible metabolic pathways that transform the

9

“precursor” metabolite to the “product” metabolite, evaluation of the MS/MS spectra of the paired

10

metabolites validates whether such assigned bio-transformation based on m/z agrees with the identity

11

of the product metabolite (Figure 2).

12

be predicted by the addition or subtraction of certain chemical groups based on the matched

13

metabolic reactions to the possible functional groups of the precursor metabolite.

14

rapidly identify all the metabolites in the network, a step-forward approach is proposed.

15

step-forward identification process starts with the characterization of the metabolites linked closest to

16

the initial compound, carnitine in this case.

17

(highlighted in orange in Figure 3), with the initial compound serving as the precursor metabolite

18

and “Layer 1” metabolites as product metabolites.

19

metabolites can be used as acquired knowledge, allowing for the structural characterization of “Layer

20

2” metabolites (highlighted in yellow in Figure 3) that are connected to the “Layer 1” compounds.

21

Such sequential characterization strategy would consequently enable the identifications of the 93

Once validated, the structure of the product metabolite could

In order to The

These compounds are defined as “Layer 1” metabolites

Subsequently, the identities of the “Layer 1”

12


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metabolites connected into the metabolic network of carnitine (Table S3, SI).

Nevertheless, the

2

proposed identities of the metabolites by MPEA in Table S3 are representative structures putatively

3

assigned based on the accurate mass and MS/MS information.

4

nuclear magnetic resonance spectroscopy could elucidate the orientations of the substituent groups

5

and differentiate the isomers for definitive identification of the carnitine metabolome.

A more in-depth study employing

6

While some databases supports to match the MS/MS spectra of two metabolites via manual

7

interpretation, manual interpretation is a subjective process, and depends heavily on the researchers’

8

experience.

9

evaluation of the MS/MS spectra for each paired metabolites.

Therefore, we employed the program MPEA to automatically perform quantitative An index, match ratio (MR), was

10

utilized to appraise the match between the precursor and product metabolites, which considers three

11

evaluation criteria: the number of the identical MS/MS fragment ions present in the MS/MS spectra

12

of the paired metabolites (defined as IF); the number of the fragment ions which deliver the exact

13

mass difference as the pseudomolecular ions of the paired metabolites (defined as DF); the number

14

of the paired ions of the product and precursor metabolites with identical neutral losses from the

15

pseudomolecular ions (defined as NLF).

16

false matching, and increases the reliability and correctness of MPEA results.

17

automatically evaluated metabolite pairs were also manually validated for correctness.

18

Such rigorous evaluation system reduces the number of Meanwhile, all the

Based on the quantitative gauging criteria of MS/MS spectra between matched pairs, some

19

metabolites were assigned a relatively high MR value regarding the matching (Table S3, SI).

20

is illustrated by an exemplary metabolite M49 at m/z 400.3413 in Figure 4.

21

matched to the initial compound as “Layer 1” metabolite by the MPE approach, and putatively 13


This

The metabolite is first


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Page 14 of 34

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assigned as a product of carnitine undergoing the metabolic reaction of palmitoylation (mass error< 5

2

ppm) as shown in Figure 4A.

3

of the fragment ions in the MS/MS spectrum at m/z 60.0799, 85.0278 and 144.1016 indicative of the

4

carnitine backbone (Figure S2, SI) confirmed the proposed precursor metabolite.

5

signature ions at m/z 341.2668 and 239.2346 represent the palmitoyl group, validating the metabolic

6

pathway of palmitoylation predicted via the MPE approach.

7

L-carnitine was also confirmed by database searching in HMDB and METLIN (Figure 4B).

8

proposed fragmentation pathway of palmitoyl L-carnitine validates the identification made by the

9

MPE approach (Figure 4C).

Compared to the “precursor” metabolite (carnitine), the exact match

Moreover, the

The identification of palmitoyl The

10

Although the identification of a few metabolites within the network via the MPE approach can

11

be accomplished by the conventional approach of database searching, the MPE approach enabled

12

characterization of more metabolites that are not recorded by any database yet confirmed by MS/MS

13

evaluation.

14

interpretation of metabolites’ accurate mass and MS/MS information rather than limited metabolites

15

entries in databases.

16

represented by the metabolite M35 detected at m/z 236.1494 (Figure S3, SI).

17

carnitine, MS/MS fragmentation of M35 also yielded signature ions of carnitine such as ions at m/z

18

58.0649, 85.0279 and 144.1013, suggesting its structural correlation with carnitine.

19

difference between M35 and carnitine is 74.0365 Da, which can be matched to a single-step

20

metabolic reaction of [+C3H6O2] as shown in Figure S3A (SI).

21

at m/z 162.1086 suggested a substitution of an additional C3H6O2 group might happen at the site of

The advantage of the MPE approach shines in its rationale that relies on the

The identification of an unreported substance identified at high MR value is Similar as palmitoyl

The mass

The detection of the fragment ion

14


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the hydroxyl group in carnitine.

Moreover, a loss of 18.0155 Da corresponding to dehydration

2

suggests the presence of a hydroxyl group in M35 (Figure S3B, SI).

3

combinatorially revealed a metabolite [3-Carboxy-2-(2,3-dihydroxy-propoxy)-propyl]-trimethyl

4

-ammonium that cannot be located from any of the mainstream databases as we inferred via MPEA.

5

The proposed fragmentation pathway of M35 further validates the identification made by MPEA

6

(Figure S3C, SI).

These MS/MS details

7

In addition, the strength of MPEA in discovering unknown metabolites and characterizing the

8

whole metabolome is further demonstrated by the identification of metabolites that are remotely

9

connected to carnitine via a step-forward approach.

Figure S4 (SI) illustrated how MPEA enabled

10

the identification of a representative metabolite, M10, located in Layer 6 as aminobenzamide by first

11

matching M10 to a previously characterized metabolite M11 located in Layer 5 via a two-step

12

metabolic pathway of [+CH2-O], and subsequently confirmed the assigned bio-transformation via

13

comparing the paired MS/MS spectra.

14

Based on MPEA, we have identified 93 metabolites with different confidence scores by the

15

MPEA program (Table S3, SI).

The structures tentatively inferred by the matched metabolic

16

pathways were summarized in Figure S5 (SI).

17

calculated for each matched pair, which varied depending on the number of product ions of the

18

matched pairs. Noteworthy, 27 metabolites were assigned MR value as 0. We thereby performed

19

manual interpretation for these metabolites to find out the reasons why presumed chemical structures

20

via MPEA cannot be assigned.

21

27) belonging to the metabolites identified at low confidence contained too few fragment ion peaks

MR indicative of identification confidence was

Unexpectedly, we found that most of the MS/MS spectra (26 out of

15



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1

to assign a match (Table S3).

The insufficient MS/MS information could be attributed to reasons

2

such as that the metabolic biomarkers were present in the samples at relatively low concentration or

3

co-eluted with high abundance molecular species, thereby missing the chance to undergo MS/MS by

4

the flawed data-dependent acquisition mode. Despite this limitation, the higher MR value indicates

5

the higher reliability of metabolites characterization. In addition to the carnitine metabolome, the

6

MPE approach was further applied to the characterization of another sub-metabolome using bile acid

7

as a representative initial metabolite to validate its general applicability. A caecal ligation and

8

puncture (CLP) model in which perturbed bile acid homeostasis had been widely demonstrated was

9

employed for the construction of bile acid metabolome.25

Out of the 69 metabolite biomarkers

10

selected from the CLP samples, 65 compounds were connected into a metabolic network of bile acid

11

and putatively identified by MPEA. The detailed MPE network of bile acid metabolome is shown in

12

Figure S6 (SI).

13

Validation of Identified Metabolites by Stable Isotope-coded Carnitine.

To identify the

14

metabolites that directly metabolized from carnitine, we analyzed the plasma from mice treated with

15

D3-carnitine.

16

metabolites with an appropriate mass shift (+3.02 Da) as compared to the metabolites from

17

non-isotopic carnitine-dosed mice plasma were assigned as direct metabolites of carnitine.

18

retention time and MS/MS spectra of the putative direct metabolites were also matched with the

19

carnitine-dosed mice plasma metabolites for structural confirmation.

20

carnitine were identified, whereas seven metabolites were characterized in the carnitine

21

sub-metabolome,

D3-Carnitine contained three 2H atoms in the tri-methyl group.

including

3-dehydroxycarnitine,


The

Eight direct metabolites of

acetylcarnitine,

16

Therefore, the

propionyl-carnitine,

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1

dodecanoyl-carnitine, tetradecanoyl-carnitine, palmitoyl-carnitine and stearoyl-carnitine.

Only one

2

direct metabolite of carnitine, TMAO, was not included in the targeted metabolome of carnitine.

3

This could be explained by its relatively fast metabolism rate (Figure S7, SI), resulting in the

4

depletion of TMAO when the mice plasma was collected at 24 hours after the supplementation of

5

carnitine for the construction of carnitine metabolome.

6

other direct metabolites revealed by the isotopic tracing approach in the targeted metabolic network

7

of carnitine validated the correctness and powerfulness of the MPE approach in metabolite

8

identification.

Despite TMAO, the inclusion of all the

9

Meanwhile, the fact that merely seven direct metabolites were included in the metabolic

10

network of carnitine suggests that most of the metabolic biomarkers changed in response to the

11

carnitine intake are likely to result from the perturbation of the signaling network of carnitine rather

12

than the direct metabolism of “carnitine”.

13

represent the metabolic reactions that actually happen to produce the product metabolites from the

14

precursor. In fact, the matched metabolic pathways that connect the carnitine metabolome are only

15

hypothetical bio-transformations used for the characterization of the structurally-related metabolites.

16

Therefore, the proposed metabolic pathways do not

Validation of Identified Metabolites by Standard Metabolome Databases.

To further

17

validate the MPEA-enabled identification of carnitine metabolome, database searching was

18

performed against standard metabolome databases, HMDB and METLIN.

19

metabolite biomarkers using HMDB (Table S3, SI), 45 metabolites achieve possible matches,

20

whereas MS/MS spectra were only provided for 21 of them.

21

carnitine, 36 metabolites have reasonable hits, 19 of which were further confirmed by the MS/MS 17


For the queries of 93

Based on the core structure of


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1

spectra provided by HMDB.

Another extensively-employed database, METLIN, retrieved hits for

2

67 metabolites by mass matching, whereas 41 of those have reasonable structures (Table S2, SI).

3

25 of the putatively identified metabolites via database searching were provided with MS/MS spectra

4

by METLIN for structural confirmation.

5

In summary, ~40% compounds included in the metabolic network of carnitine were putatively

6

identified via mass matching by the approach of database searching, whereas ~25% of the assigned

7

metabolites can be confirmed by MS/MS matching against standard databases (Table S2-3, SI).

8

Nevertheless, such coverage is far from satisfactory for structural characterization of metabolic

9

biomarkers.

In addition, database searching via non-proprietary software is also a time-consuming

10

and labor-intensive task.

It requires either manual comparison of the experimental data with

11

available MS/MS spectra of retrieved hits from databases or extensive efforts in interpreting the

12

experimental acquired MS/MS spectra using the structures of retrieved hits when no MS/MS spectra

13

are available in the databases.

14

comprehensive and efficient identification strategy for unknown metabolic biomarker candidates.

15

allows for matching the metabolite biomarker candidates with reasonable metabolic pathways

16

independent of the currently available database entries, thereby significantly improving the coverage

17

of metabolites identifiable with the same instrumental settings.

18

MPE analysis is less than 10 min, and the analyzed results agree with the structures proposed by

19

manual analysis via database searching (Table S3, SI).

20

candidate structures for the carnitine metabolome at a high coverage of ~95%, demonstrating the

In contrast, our presently developed MPE approach presents a more It

The whole process consumed for

Moreover, the MPEA software predicted

18


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1

MPE approach is highly useful and complementary to the prevalent approach of database searching

2

for the characterization of metabolic biomarkers.

3

Applications of the Characterized Carnitine Metabolome to Biomarkers Studies.

4

comparison with the conventional database-based approach, MPEA developed in this study is

5

expected to be more powerful in the identification of metabolomic biomarkers.

6

the usefulness of MPEA, we performed a real metabolomics study by employing a biomarker

7

identification of a fasting model since changes in carnitine metabolism has been demonstrated during

8

the course of starvation.26

9

LC-MS/MS to compare the metabolome of the samples from fed and fasted mice.

10

In

To further validate

Therefore, we have collected plasma from fasted mice, and applied

An OPLS-DA model was first used to analyze the metabolic biomarker candidates by selecting

11

the compounds with VIP value> 1.

As shown in Figure 5A, multiple metabolites with VIP value>

12

1 were highlighted in red in the scatter plot of OPLS-DA of plasma ion from vehicle and fasted mice.

13

The p[1] P-values display the relative abundance of the ions, whereas the p(corr)[1] P-values show

14

the inter-class difference.

15

the plasma samples of fasted mice (normal distribution of data indicated by the K-S test, Table S4,

16

SI), 14 metabolites were matched with those in the carnitine metabolome identified via the MPE

17

approach, 5 of which cannot be identified by any databases.

18

expression level changes of the 14 targeted metabolites of carnitine from the fasted mice plasma.

19

addition, the identification of carnitine-associated biomarkers in an experimental model of ulcerative

20

colitis (UC) also validated the MPE approach.

21

reported in UC animals due to the down-regulation of carnitine/organic cation transporter (OCTN),27

Out of a total of 112 metabolites with VIP value> 1 and p value< 0.05 in

Figure 5B-D illustrates the relative In

Since systematic carnitine deficiency has often been

19



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Page 20 of 34

1

we collected plasma from experimental colitis mice induced by administration of dextran sulfate

2

sodium (DSS), and compared the metabolic changes of saline and DSS-treated mice.

3

190 potential biomarkers, 21 metabolites were matched with those in the carnitine metabolome

4

identified via the MPE approach, 8 of which cannot be identified by any metabolome

5

databases.(Figure S8, SI).

6

Out of the

Collectively, these results provide evidence for the disturbed carnitine homeostasis in the

7

fasting model and the experimental colitis model.

More importantly, such results demonstrate the

8

successful application of the MPE approach to the identification of biomarkers in real metabolomic

9

studies, strongly supporting the wide applicability of MPEA in structural characterization of

10

potential new biomarkers and particularly of those not yet included in the currently available

11

databases.

12

validation cannot suffice for the unambiguous identification of metabolite biomarkers, especially for

13

those with low MR values. The employment of orthogonal techniques such as nuclear magnetic

14

resonance spectroscopy could facilitate the structural characterization process via the MPE approach

15

by differentiating the isomers that cannot otherwise be accomplished based on the accurate MS and

16

MS/MS information. Ultimately, synthesis of pure standards could definitively validate the

17

correctness of the MPEA-assigned identities.

Nevertheless, MS-based characterization strategy without using authentic standards for

18

19

CONCLUSIONS

20


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1

Compared to the conventional database searching approach that is often limited by the number

2

of available metabolite entries, the MPE approach enables a database-independent and highly

3

efficient process that matches the mass differences of metabolites to possible metabolic reactions,

4

and maps a metabolic network that links the unknown metabolites to the specific core compound via

5

proposed metabolic pathways.

6

evaluated by quantitative gauging the MS/MS spectra of the matched metabolite pairs.

7

MPE approach, we rapidly elucidated the targeted metabolic network of carnitine by inferring the

8

structures of 93 metabolic biomarker candidates (~95% coverage), with 66 metabolites (~70%

9

coverage) confirmed by MS/MS matching.

Such metabolic pathway extension-based inferences are then Using our

In comparison, merely 42% of the metabolic

10

biomarkers can be matched to entries in current metabolome databases by accurate mass, with only

11

25% of the metabolite biomarkers further validated by MS/MS.

12

approach has been validated by the comparison with the conventional database searching approach,

13

the isotopic labeling assay, and the successful application to a real metabolomics analysis of a fasting

14

model.

15

identification of metabolically and structurally associated biomarkers.

16

that the MPE strategy will be extended to the construction of other targeted metabolome as in our

17

case of carnitine and thereafter expand the capability in the structural characterization of novel and

18

physiologically/pathologically significant biomarkers.

We thus believe the MPE approach presents a preferred solution to rapid and reliable

19

20

The reliability of the MPE

ACKNOWLEDGMENTS

21


It is reasonable to expect


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1

This study was financially supported by the National Natural Science Foundation of China (grants

2

81325025, 81430091, 91429308, 81530098, 81403005 and 81273586), the Natural Science

3

Foundation of Jiangsu province, China (grant BK20140667) and the project for Major New Drugs

4

Innovation and Development (grant 2015ZX09501010).

5

6

SUPPORTING INFORMATION

7

Supporting Information available as noted in the text.

8

the Internet at http://pubs.acs.org.

9

NOTES

10

This material is available free of charge via

The authors declare no competing financial interest.

11 12

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1

FIGURES 2

3

4

5

6 7 8 9 10 11 12 13 14

Figure 1. Non-targeted metabolomic analysis and building of carnitine sub-metabolome. (A) Scores plot of a PCA model differentiates the carnitine-treated mice plasma from the control group. Each data point corresponds to an individual mouse plasma sample. The t[1] and t[2] represents principle components 1 and 2, respectively. (B) S-plot validates the selected metabolites with VIP value> 1 were statistically significant and potentially biochemically significant biomarkers based on an OPLS-DA model. (C) A heat map illustrates the trend of how the putatively assigned metabolites change in the carnitine-treated group compared to the control group.

15

16 17 18 19 20

24


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Figure 2. The overall workflow of the metabolic pathway extension (MPE) approach. Briefly, the carnitine metabolome was first constructed by differential analysis of the plasma samples collected from mice administered with or without carnitine by LC-MS. The accurate m/z and corresponding MS/MS of the metabolites changed in response to carnitine intake were then extracted from the raw data, and imported to a stand-alone program we developed for MPE analysis named “Metabolic Pathway Extension Analysis (MPEA)”. The MPEA program used the accurate m/z information of the carnitine metabolome to establish the targeted metabolic network of carnitine by calculating the mass differences between any pairs of metabolites within the pool of carnitine metabolome, and subsequently matching them against a list of metabolic reactions commonly encountered in biological systems. The MS/MS spectra of the paired metabolites were then evaluated to validate whether such assigned bio-transformation based on m/z agrees with the identity of the product metabolite by calculating the match ratio (MR). The calculation of MR considers three evaluation criteria, including the number of the identical MS/MS fragment ions present in the MS/MS spectra of the paired metabolites (defined as IF), the number of the fragment ions which deliver the exact mass difference as the pseudomolecular ions of the paired metabolites (defined as DF) and the number of the paired ions of the product and precursor metabolites with identical neutral losses from the pseudomolecular ions (defined as NLF). Once validated, the structure of the product metabolite could be predicted by the addition or subtraction of certain chemical groups based on the matched metabolic reactions to the possible functional groups of the precursor metabolite, and manually checked by MS/MS for correctness.

25



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1 2 3 4 5 6

Figure 3. The carnitine metabolome connected by metabolic reactions via the metabolic pathway extension approach. All the metabolites are registered with a number based on m/z values, and carnitine is the initial metabolite (highlighted in red). A sequential layer-by-layer characterization strategy enables the connection of the 93 metabolites into the metabolic network of carnitine by one, two, three or multiple-step metabolic reactions.

7

8

9

10

11

12

13

14

26


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1 2 3 4 5 6 7 8 9 10

Figure 4. Identification of an exemplary metabolite via the metabolic pathway extension (MPE) approach and validation by database querying. (A) The process of characterizing M49 via the MPE approach. M49 is first matched to carnitine by accurate mass matching as a product of carnitine undergoing the metabolic reaction of palmitoylation, followed by MS/MS evaluation of the assigned identity as palmitoyl L-carnitine based on the value of MR. (B) The MS/MS spectra retrieved from METLIN shown in the insert confirmed the assignment of M49 as palmitoyl L-carnitine based on the experimentally acquired MS/MS spectrum, with the identical MS/MS fragment ions highlighted in red. (C) The proposed fragmentation pathway of palmitoyl L-carnitine validates the identification made by the MPE approach.

11

12 13 14 15 16

27



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1 2 3 4 5 6 7 8 9 10 11 12

Figure 5. Characterization of the carnitine-associated biomarkers in a fasting model. (A) Metabolites with VIP value >1 were highlighted in red in the scatter plot of OPLS-DA of plasma ion from vehicle and fasted mice. The p[1] P-values display the relative abundance of the ions, whereas the p(corr)[1] P-values show the interclass difference. (B) A heat map displays the expression level changes of the targeted metabolites of carnitine previously identified via the metabolic pathway extension approach from the plasma sample of fasted mice. (C) Downregulation and (D) upregulation of the targeted metabolites of carnitine as biomarker candidates of fasted mice plasma. Metabolites that underwent statistically significant expression level changes are denoted with * (p value< 0.05), ** (p value< 0.01) and *** (p value< 0.001). Peak area ratio was calculated by dividing the peak area of the carnitine-associated metabolite by that of the internal standard.

13 14

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Figure 1 246x218mm (300 x 300 DPI)



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Figure 2 210x297mm (300 x 300 DPI)


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Figure 3 294x197mm (300 x 300 DPI)



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Figure 4 200x295mm (300 x 300 DPI)


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Figure 5 201x152mm (300 x 300 DPI)



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