Metabolomics @ BrukerAll the pieces to solve the Metabolomics Puzzle

Poster Hall Edition 2

Innovation with IntegrityMass Spectrometry, NMR

Table of content

Food MetabolomicsUrinary excretion of phenolic compounds from Hibiscus sabdariffa in Humans by advanced analytical techniques 4

RP-HPLC-DAD-ESI-TOF for the pharmacokinetic study of quercetin in adipocytes 5

Metabolomic consequences of aronia juice intake by human volunteers 6

Bacterial MetabolomicsComprehensive workflow for Metabolite profiling and identification of secondary metabolites from myxobacteria 7

The impact of high-resolution MS and NMR techniques on discovery of novel products from myxobacteria 8

Discovery of novel myxobacterial secondary metabolites by mass spectrometry – based metabolome mining 9

Metabolic profiling of a Corynebacterium glutamicum ∆prpD2 by GC-APCI high-resolution Q-TOF analysis 10

GC-APCI-MS/MS based identification of metabolites in bacterial cell extracts 11

Yeast MetabolomicsUntargeted metabolic profiling of yeast extracts by UHR-Q-TOF analysis to study arginine biosynthesis mutants 12

Clinical MetabolomicsA workflow for metabolic profiling and determination of the elemental compositions using MALDI-FT-ICR MS 13

NMR-based screening of neonate urines 14

NMR-based metabolomics in clinical studies of human population: Quantification 15

Plant MetabolomicsStructure elucidation and confirmation for plant metabolomics: Novel approaches 16

MS-based dereplication and classification of diterpene glycoside profiles of 23 Solanaceous plant species 17

GC-APCI-QTOF-MS: An innovative technique for metabolite profiling studies 18

Improved method for characterization of phenolic compounds from rosemary extracts using ESI-Qq-TOF MS 19

Sulfur-containing metabolite-targeted analysis using liquid chromatography – mass spectrometry 20

Invertebrate Metabolomics – Drosophila melanogaster and Caenorhabditis elegansMetabolic characterization of Sod1 null mutant flies using liquid chromatography – mass spectrometry 21

A mass spectrometry based metabolomic platform for the analysis of Caenorhabditis elegans 22

Complementary GC-EI-MS and GC-APCI-TOF-MS analysis of a short-lived mitochondrial knockdown of C. elegans to study metabolic changes during aging 23

Structure ElucidationThe Fastest Way to a Molecular Formula: How fast can it be? TLC-UHPLC-ESI-QTOF MS Coupling for Molecular Formula Determination of New Natural Products 24

MALDI ImagingAutomated tissue state assignment for high-resolution MALDI-FTMS Imaging data 25

References – Further Reading 26-27

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„Putting Together the Pieces to Solve the Metabolomics Puzzle...“

Metabolomics has emerged as an exciting new area of biological research and gained substantial attention in recent years. Previously, significant efforts have been made to probe and understand the genomes, transcriptomes and proteomes of a variety of organisms. Despite massive investments to sequence and identify cellular DNA, RNA and proteins, our understanding of many key biological functions remains rudimentary and incomplete. Many researchers believe that perhaps more complete answers can be gleaned from studying small molecules that are utilized for a variety of metabolic and cellular functions. Because of this belief, in many laboratories, Metabolomics is catching up with traditional Proteomics and Transcriptomics efforts and has been christened “Biochemistry’s new look” [1].

Typically, during a Metabolomics study, a large number of cell or patient samples are analyzed and compared to identify potential biomarker compounds which correlate to a disease, drug toxicity, or genetic or environmental variation. Validated biomarkers can form the basis for new diagnostic assays, and lead the way to help establish truly personalized medicine, since changes in small molecule concentrations are closely related to the observed phenotype.

Answering the Challenges of Metabolomics

Metabolomics researchers are regularly confronted with the daunting tasks of acquiring large sets of data and then analyzing them in minute detail. Due to the chemical diversity of small molecule metabolites, it is impossible to study the entire metabolome using a single analytical technique or technology.

Bruker’s comprehensive solution for metabolomics is based on putting together “the pieces of the puzzle” to give a complete picture: The Metabolome’s chemical space can be fully explored by utilizing Bruker’s high performance LC-MS, GC-MS, CE-MS and NMR systems in conjunction with dedicated software for data evaluation.

One of the most challenging tasks in the Metabolomics studies is the identification of unknown compounds. Exact mass and isotopic pattern information in MS and MS-MS spectra acquired by the solariX (FT-MS) or micrOTOF and maXis series

(Q)-TOF-MS instruments can be used to definitively identify unknown compounds. The intrinsic capabilities of these instruments in conjunction with the SmartFormula3D software enables accurate and reliable molecular formula generation fora wide variety of unknowns. Subsequent database queries identify likely compound structures, which can be confirmed by complementary information derived from NMR spectra.

In order to enhance separation and increase throughput, many laboratories have turned to utilization of UHPLC for Metabolomics studies. Since the mass accuracy, isotopic fidelity and resolution of maXis and micrOTOF-series ESI-(Q)-TOF instruments are independent of the acquisition rate, they are perfectly suited for a coupling to UHPLC separations.

By utilizing a Bruker solution, their high-resolution and accurate mass LC-MS capabilities enable both targeted and untargeted Metabolomics workflows. In addition, the broad dynamic range characteristics of these systems allows both, high- and low-abundance compounds to be studied within the same dataset – even within the same mass spectrum.

Finally, Bruker offers the capability to integrate both LC-MS and NMR techniques into a single analytical system for maximum metabolite coverage and molecular identification. The MetabolicProfilerTM is a powerful combination of techniques and is exclusively designed to address all analytical needs for metabolic profiling and structure elucidation.

This poster hall is a “snapshot” of some of the current work utilizing a variety of Bruker solutions for Metabolomics studies. It nicely illustrates Bruker’s aim of providing and putting togehter the “puzzle pieces” to give a complete picture of the metabolome. We would like to thank all of our partners and customers for their constant input and feedback. This information has served as an invaluable guide for us in our solution development efforts. A special thanks are directed to everyone who has worked to generate the outstanding posters which we have assembled in this poster hall for your benefit.

Sincerely yours,

Dr. Aiko BarschMarket Manager Metabolomics

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Metabolomics Posterbook 2012

I. Borrás‐Linares (1, 2), R. Beltrán‐Debón (3), Gabriela Zurek (4) , Jorge Joven (3) , D. Arráez‐Román (1, 2) , A. Segura‐Carretero (1, 2) , A. Fernández‐Gutiérrez (1, 2)

(1) Department of Analytical Chemistry, Faculty of Sciences, University of Granada, Granada, Spain.(2) Functional Food Research and Development Center (CIDAF), Health Science Technological Park, Granada, Spain.(3) Centre de Recerca Biomèdica, Hospital Universitari de Sant Joan, IISPV, Universitat Rovira i Virgili, Reus, Spain.(4) Bruker Daltonik GmbH, Bremen, Germany.

Hibiscus sabdariffa (HS) L. (family: Malvaceae), usually named bissap, karkade or roselle is a tropical plant commonly used as local soft drink. This plant contains organicand phenolic acids, anthocyanins, and flavonols as main compounds [1,2]. Traditionally the aqueous extracts of HS have been used in folk medicine to treathypertension, fever, inflammation, liver disorders, and obesity.

In recent years, this plant has been studied in depth because it exerts a great number of pharmacological activities. Previous studies have shown that HS possesses anti‐tumor and antioxidant properties [3, 4]. Recent data also indicate that aqueous extracts of HS might ameliorate metabolic disturbances [5, 6]. The antioxidant andantihyperlipemic properties of HS aqueous extract have been previously reported using a mouse model [7]. Furthermore, the hypolipemic properties of PEHS have beenrecently reported in a cell model for adipogenesis and insulin resistance [8]. However, to establish conclusive evidence for the effectiveness of phenolic compoundsfrom this plant on health, it is very important to define the bioavailability and the excretion of these compounds in humans. The objective of this study was to identifythe urinary excretion of phenolic compounds in humans after the ingestion of a phenolic‐enriched Hibiscus sabdariffa extract (PEHS).

[1] Salah, A. M., Gathaumbi, J., Vierling, W. (2002) Phytother. Res. 16:283‐285.[2] Tseng T. H., Hsu J., Dong L., Ming H., Chu C. Y. (1998) J. Agric. Food Chem. 46: 1101‐1105.[3] Chang Y. C., Huang H. P., Hsu J. D. (2005) Toxicol Appl Pharmacol. 205: 201‐207.[4] Ochani P. C., D´Mello P. (2009). Indian J. Exp. Biol. 47: 276‐282.[5] Carvajal‐Zarrabal O., Waliszewski S. M., Barradas‐Dermitz D. M. A. (2005) Plant Food Hum. Nutr. 60: 153‐159.[6] Alarcon‐Aguilar F. J., Zamilpa A., Pérez‐García M.D. (2007) J. Ethnopharmacol. 114: 66‐72.[7] Fernández‐Arroyo S., Rodríguez‐Medina I.C., Beltrán‐Debón R. (2011) Food Res. Int. 44: 1490‐1495.[8] Herranz‐López M., Fernández‐Arroyo S., Pérez‐Sánchez A. (2012) Phytomed. 19: 253‐261.

The authors are grateful to the Spanish Ministry of Science and Innovation for theproject AGL2011‐29857‐C03‐02, Andalusian Regional Government Council ofInnovation and Science for the excellence project P10‐FQM‐6563 and P11‐CTS‐7625 andto GREIB.PT.2011.18 project. IBL acknowledges financial support from the SpanishMinistry of Education and Science (FPI grant, BES‐2009‐028128).

UHPLC

•Instrument: Dionex Ultimate 3000 UHPLC

•Column: Zorbax Eclipse Plus C18, 3x250 mm, 5 μm

•Flow: 0.3 mL/min•Temperature: 20 °C•Injection volume:  5  μl•Phase A: H2O/AcN 90:10 + 0.5% Formic acid

•Phase B: AcN•Gradient: :•from 0 to 20 min, from 5% B to 20% B,

•from 20 to 25 min, from 20%B to 40%B,

•from 25 to 30 min, from 40% B to 5% B,

•from 30 to 35 min, to hold 5% B. UHR‐Q‐TOF

•Instrument: Bruker DaltonicsMaXis 4GUHR‐Q‐TOF•Mode: Negative•Mass range: 50‐1000 m/z•Nebulizing gas pressure: 2 bar•Drying gas flow: 8 L/min•Drying gas temp: 200 ºC•Calibrant: sodium formate 5mM•End Plate offset: ‐500 V•Capillary: ‐ 4500V•Funnel RF: 300,0 Vpp•Ion Energy: 4,0 eV•Collision Energy: 8,0 eV•Collision RF: 400,0 Vpp•Transfer time: 65 μs•PrePulseStorage time: 6 μs

500 μl Sample

1000 μl AcOEt10 μl HCl 1M 

Centrifuge18800 g 5 min

Supernatant Dryness

250 μl Mobil phase A UHPLC‐ESI‐UHR‐Q‐TOF

analysis

Vortex

This powerful analytical techniquerevealed the presence of severalphenolic compounds from the PEHSin the urine samples. The maincompounds detected previouslydescribed in the extract werehibiscus acid, hibiscus aciddimethylesther, hydroxycitric acid,chlorogenic acid isomers, methyldigallate, cumaroylquinic acid andN‐feruloyltyramine. The maximumexcretions of these compoundswere found at 2 and 4 hours afterthe intake of PEHS.

In this study a phenolic‐enriched Hibiscus sabdariffa extract(PEHS) has been obtained and encapsulated. Urine samplesfrom a healthy volunteer were collected before and 2, 4 and 6hours after intake of 500 mg of the PEHS for the evaluation ofthe urinary excretion of the phenolic compounds present in thisplant extract.

BPC OBTA

INED BY 

UHPLC‐ESI‐U

HR‐Q‐TOF 

Compound Retention time (min) [M‐H ]‐ Molecular Formula

Hydroxycitric acid 3,40 207,0146 C6H8O8

Hibiscus acid 3,60 189,0040 C6H6O7

Chlorogenic acid Isom I 5,30 353,0878 C16H18O9

Hibiscus acid dimethylesther 6,00 217,0353 C9H10O7

Chlorogenic acid 6,90 353,0878 C16H18O9

Chlorogenic acid Isom II 7,10 353,0878 C16H18O9

Methyldigallate 7,60 335,0408 C15H12O9

Coumaroylquinic acid 9,60 337,0928 C16H18O8

N‐Feruloyltyramine 25,60 312,1241 C18H20NO4

1

2

3

Hibiscus sabdariffaflowers

PEHS

PEHS pills

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Food Metabolomics

Metabolomics Posterbook 2012

I. Borrás‐Linares (1, 2), M. Herranz‐López (3), E.Barrajón‐Catalán (3) , D. Arráez‐Román (1, 2) , V. Micol (3) , A. Segura‐Carretero (1, 2) , A. Fernández‐Gutiérrez (1, 2)

(1) Department of Analytical Chemistry, Faculty of Sciences, University of Granada, Granada, Spain.(2) Research and Development of Functional Food Centre (CIDAF), Health Science Technological Park, Granada, Spain.(3) Institute of Molecular & Cell Biology (IMCB), Miguel Hernández University, Elche, Spain.

Hibiscus sabdariffa (HS) L. (family: Malvaceae), usually named bissap, karkade or roselle is a tropical plant commonly used as localsoft drink. The aqueous extracts of HS have been used in folk medicine to treat hypertension, fever, inflammation, liver disorders,and obesity. This plant contains organic and phenolic acids, anthocyanins, and flavonols as main compounds. One of these majorcompounds found in this plant is quercetin, a flavonol commonly present in fruits and vegetables.

In recent years, this plant has been studied in depth because it exerts a great number of pharmacological activities. Previous studiesshowed that HS possesses anti‐tumor and antioxidant properties [1]. In humans, HS elicits a significant decrease in MCP‐1 plasmaconcentration, suggesting that HSmay be a valuable traditional herbal medicine for the treatment of chronic inflammatory diseases[2]. Recent data also indicate that HS aqueous extracts might ameliorate metabolic disturbances [3]. Furthermore, the antioxidantand antihyperlipemic properties of HS aqueous extract have been previously reported by our research group using a mouse model[4]. Moreover, the strong antioxidative and hypolipidemic properties of a HS polyphenolic extract have been recently reported in acell model for adipogenesis and insulin resistance [5]. In this studies, we suggested that flavonol conjugated forms (such asquercetin) could be the responsible compounds for the observed antioxidant effects and also contribute to the salutary effects ofHS. In this sense, a study of the molecular and cellular targets of these compounds in different cellular models deserves furtherattention. For this reason, a pharmacokinetic study of quercetin in a model of adipogenesis (3T3‐L1 cells) has been carried out.

[1] Farombi EA, Fakoya A (2005) Free radical scavenging and antigenotoxic activities of natural phenolic compounds in dried flowers of Hibiscus sabdariffa L. Molecular Nutrition & Food Research 49:1120 [2] Beltran‐Debon R, Alonso‐Villaverde C, Aragones G et al (2010) The aqueous extract of Hibiscus sabdariffa calices modulates the production of monocyte chemoattractant protein‐1 in humans. Phytomedicine 17:186 [3] Carvajal‐Zarrabal O, Waliszewski SM, Barradas‐Dermitz DMA et al (2005) The consumption of Hibiscus sabdariffa dried calyx ethanolic extract reduced lipid profile in rats. Plant Foods for Human Nutrition 60:153 [4] Fernández‐Arroyo S, Rodríguez‐Medina IC, Beltrán‐Debón R et al (2011) Quantification of the polyphenolic fraction and in vitro antioxidant and in vivo anti‐hyperlipemic activities of Hibiscus sabdariffa aqueous extract. Food Res Int 44:1490 [5] Herranz‐López M, Fernández‐Arroyo S, Pérez‐Sánchez A et al. Synergism of plant‐derived polyphenols in adipogenesis: perspectives and implications. Phytomedicine 

The authors are grateful to the Spanish Ministry ofScience and Innovation for the project AGL2011‐29857‐C03‐02, Andalusian Regional GovernmentCouncil of Innovation and Science for the excellenceproject P10‐FQM‐6563 and P11‐CTS‐7625 and toGREIB.PT.2011.18 project. IBL acknowledges financialsupport from the Spanish Ministry of Education andScience (FPI grant, BES‐2009‐028128).

Quercetin was detected in all analyzedsamples, except in the control. From theobtained data, the maximum concentrationfound in cytoplasm was at 3 hour after thequercetin addition. Nevertheless, it isimportant to note that at 6 hours time pointthe concentration of quercetin is still high. Atthe end of the assay, 18 hours after theaddition, the level of quercetin is minimum. Inall samples except in the control, quercetin‐3‐O‐glucuronide has been detected.

1In this study 3T3‐L1 pre‐adipocytes were propagatedand differentiated accordingto conventional procedures.Quercetin was added at 100µM to the media at thebeginning of the study.Subsequently, samples ofcytoplasm were taken atdifferent time (3, 6, 12 and18 hours) after quercetinaddition.

HPLC

• Instrument: Agilent 1200 RRLC•Column: Zorbax Eclipse Plus C18, 4.6x150 mm, 1.8 μm• Flow: 0.3 mL/min•Temperature: 25 °C• Injection volume: 10  μl•Phase A: H2O/AcN 90:10 + 0.5% Formic acid•Phase B: AcN•Gradient: :• from 0 to 20 min, from 5% B to 20% B,• from 20 to 25 min, from 20%B to 40%B,• from 25 to 40 min, from 40% B to 5% B,• from 40 to 45 min, to hold 5% B.

ESI‐TO

F

• Instrument: Bruker DaltonicsmicrOTOF•Mode: Negative•Mass range: 50‐1000m/z•Nebulizing gas pressure: 2 bar•Drying gas flow: 9 L/min•Drying gas temp: 200 ºC•Calibrant: sodium formate 5mM•Capillary Exit: ‐120V•Skimmer 1: ‐40V•Hexapole 1: ‐23V•Hexapole RF: 100Vpp•Skimmer 2: ‐22,5V• Transfer time: 50 μs•PrePulseStorage time: 3 μs

Quercetin

Quercetin‐3‐O‐GlucuronideBPC OBTA

INED 

BY HPLC‐ESI‐TOF 

3

100 μl Cytoplasm

500 μl MeOH‐EtOH(50:50)

2 hours

Centrifuge18800 g 5 min

Supernatant Dryness

50 μl MeOH

2

HPLC‐DAD‐ESI‐TOFanalysis

0

5

10

15

20

25

30

35

40

45

0 0,5 1 1,5 2 2,5 3 3,5 4 4,5

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tration (ppm

)

Time after incubation (h)

Quercetin

3 6 12 18

-5-

Food Metabolomics

Food Metabolomics

Bruker Daltonics

Metabolomic consequences of aronia juice intake by human volunteers Medina, S.1; García-Viguera, C.1; Ferreres, F.1; Savirón, M.2; Orduna, J.2; Zurek, G.3; Gil-Izquierdo, A.1*

1Research Group on Quality, Safety and Bioactivity of Plant Foods, CEBAS-CSIC, Murcia, Spain 2Instituto de Ciencias de Materiales de Aragón. CSIC- Universidad de Zaragoza, Zaragoza, Spain 3Bruker Daltonik GmbH, 28359 Bremen, Germany. *corresponding author: angelgil@cebas.csic.es

Metabolomics is a powerful technique which focuses in highthroughput characterization of metabolome (the complete set of low-MW metabolites in biological samples) in order to obtain a molecular profile. This approach has shown a considerable potential in the field of nutrition, so, there is an increasing interest in the use of metabolomics as a tool for the understanding of the interaction between diet and metabolome and to be a promising technique for biomarker discovery related to food intake [1]. Our study was conducted with the aronia fruit (Aronia melanocarpa) common name black chokeberry which belongs to the Rosaceae family. This berry stands out in high amount of flavanols, quercetin (71.13 mg Kg-1; 93.07%) and kaempferol (5.3 mg Kg-1; 6.9%). Aronia berries are one of richest plant sources of anthocyanins, mainly containing cyanidin glycosides [2]. In a recent study, anthocyanins showed significant potency of antiobesity and ameliorate adipocyte function in vitro and in vivo systems, as well as, antioxidant, hypoglycemic and hypolipidomic activities and important implications for preventing metabolic syndrome of a chokeberry juice, indicate their contribution to the potential health benefits [3,4]. The aim of this study was to observe the single and five days changes in the metabolic profile of healthy volunteers following the consumption of aronia juice and to identify the key metabolites.

Introduction

Number of patients: 8 (4 men and 4 women) Age: 28-47 Race: Caucasian BMI means: 24.5 Kg/m2

Assay design

Wash out

2 days

200 mL 8:00 a.m

200 mL 18:00 p.m

Day 1 Day 2 Day 3 Day 4

Diet absent of fruit-vegetables and food derived products

24 h urine collection and frozen at -80º C

200 mL 8:00 a.m

200 mL 18:00 p.m

200 mL 8:00 a.m

200 mL 18:00 p.m

200 mL 8:00 a.m

200 mL 18:00 p.m

Urine samples (treatment and control)

Multivariate statistical analysis

PCA (Principal Component Analysis) with Pareto scaling algorith and Student´s t-test (P-value <0.05 was considered significant)

Biomarker discovery

Future identification of metabolites: MS/MS analysis and Databases (HMDB, KEGG, Massbank)

Results Data LC-MS from treatment samples (after aronia juice intake: ) and control samples (before aronia consumption: O) were analyzed with multivariate statistical analysis, PCA (Principal Component Analysis) and Student´s t-test (Figure 1). In this analysis six hundred masses were aproximately generated of which six were selected as the most significative ones (three in positive mode and three in negative mode) (P-value <0.05) (Table 1 and 2). We have chosen the ion at m/z 581.2308 as an example to show the score and loading plot. In these graphs, the separation between treatment and control groups can be displayed showing an interesting dispersion in the loading plot.

Bucket: 11.5min : 581.50m/z

5 10 Analysis

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-1.0

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1.0PC 6

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-1.0

-0.5

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1.0PC 6

Bucket Exact m/z Possible formula [M-H]+

11.5 min; 581.50 m/z

581.2308 C37H31N3O4

4.5 min; 696.50 m/z

696.9621 C18H5N10O21 C19H11N3O26

12.5 min; 373.50 m/z

373.2671 C15H33N8O3 C16H39N1O8

Bucket Exact m/z Possible formula [M-H]-

10.5 min; 399.50 m/z

399.1641 C17H25N3O8

C15H23N6O7

10.5 min; 470.50 m/z

470.1343 C24H18N6O5 C23H12N13

10.5 min; 560.50 m/z

560.0889 C21H22N1O17

Figure 1. PCA (Pareto scaling algorithm) score and loading plots.

Table 1. Significative features in positive mode. Table 2. Significative features in negative mode.

It was observed that aronia juice had an effect on the intensity of six metabolites. Some ions increased due to aronia juice consumption (e.g. 470.1343 m/z), and others reduced their intensity (e.g. 373.2671 m/z) (Figure 2).

Bucket: 11.5min : 581.50m/z

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a) b)

Figure 2. Intensity of metabolites in positive mode (a) and in negative mode (b).

After statistical analysis, the signicative ions were processed by SmartFormula (Figure 3). These molecular formula of the target features were generated based on the MS data.

Databases of compounds

and

MS/MS pattern

Future identification of metabolites Figure 3. SmartFormula result of formula for bucket 11.5 min; 581.50 m/z.

Conclusions • Metabolomics approach with HPLC-q-TOF analysis is a powerfull tool for the discovery of metabolic changes in nutritional interventions with humans.

• Four days of aronia juice intake alters the urinary metabolic profile.

• New markers of aronia juice consumption were isolated.

• Metabolomics can be used to distinguish the group consuming the aronia juice, moreover it can be able to separate of control group.

[1]. Lodge, K. 2010. Proceeding of the Nutrition Society. 69, 95-102. [2]. Jakobek, L.; Seruga, M.; Medvidovic-Kosanovic, M.; Novak, I. 2007. Agriculturae Conspectus Scientificus. Vol 72, No 4: 301-306. [3]. Tsuda, T. 2008. J. Agric. Food Chem. 56, 642-646. [4]. Valcheva- Kuzmanova, S.; Kuzmanov, K.; Tancheva, S.; Belcheva, A. 2007. Methods and Findings in Experimental and Clinical Pharmacology, 29, 101-105.

References

This work has been fulfilled by financial support of the Spanish CICYT (AGL2007-61694) and CONSOLIDER-INGENIO 2010 (CSD2007-00063) projects. SM wish to thank also Spanish CSIC for PhD fellowship grant, partially funded by the European Social Fund. Authors are grateful to Hero España, S.A for supplying the aronia juice.

Acnowledgement

Data adquisition LC-MS-q-TOF (Bruker Daltonics, Bremen, Germany)

Column ACE 3 C18: 150 x 0.075 mm

Fase A: H2O/0.1% HCOOH

Fase B: ACN/0.1% HCOOH

Flow rate: 312.50 nL/min

Injection volume: 6.25 nL

Positive and negative mode

Full scan: 50-900 m/z

Data preprocessing Profileanalysis 2.0. (Bruker Daltonics, Bremen, Germany) Software for profiling and statistical evaluation of LC/MS data set.

-3 -2 -1 0 1 2 PC 1

-1

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PC 5

-3 -2 -1 0 1 2 PC 1

-1

0

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PC 5

• Workflow for metabolomic analysis:

Metabolomics Posterbook 2012

-6-

For research use only. Not for use in diagnostic procedures.

COMPREHENSIVE WORKFLOW FOR METABOLIC PROFILING AND IDENTIFICATION OF SECONDARY METABOLITES FROM MYXOBACTERIA

Conclusions

ESI-UHR-QTOF-MS

• High resolution accurate mass LC-MS data sets allow for both targeted and untargeted analysis.

• Complexity is reduced by statistical data evaluation.

• Parent ion selection is significantly improved by a scheduled precursor list (SPL)

• 63 out of 176 filtered features from myxobacterial extracts could be identified based on MS2 data, thereby effectively supporting de-replication during screening

Name Formula [M+X]+ m/z theo. m/z meas. Error [ppm] mSigma

Ambruticin A [M+H]+

C28H40O6 473.28977 473.28964 0.3 8.3

Ambruticin A [M+NH4]+ C28H43N1O6 490.31632 490.31572 1.2 4.5

Ambruticin A [M-H2O+H]+

C28H38O5 455.27920 455.27909 0.2 23.6

Thomas Hoffmann1,2, Daniel Krug1, 2, Aiko Barsch3, Gabriela Zurek3, Rolf Müller1,2 1 Pharmaceutical Biotechnology, Saarland University, Saarbruecken, Germany 2 Helmholtz-Institute for Pharmaceutical Research Saarland (HIPS), Saarbruecken, Germany 3 Bruker Daltonik GmbH, Bremen, Germany

Introduction Extracting the relevant information from complex data sets is an important bottleneck in (microbial) metabolomics research. Compared to a focused targeted approach, this objective becomes even more challening in untargeted metabolomics, aiming for the identification of all compounds produced by a particular bacterial strain. Besides the identification of all known compounds it is important to assign the subset of interesting compounds that are “worth” further examination. Here, we present the combination of targeted and untargeted metabolomics utilizing statistical methods of data mining. ESI-UHR-Q-TOF based analysis of myxobacterial extracts allows for fast generation of high resolution MS as well as MS² spectra which support our screening workflow for the discovery of novel secondary metabolites.

Methods

• Sorangium cellulosum extracts were separated using a Waters BEH C18 column (100 x 2.1 mm, 1.7 µm particle size) on an UltiMate 3000TM RSLC system (Dionex). Gradient elution was performed at 600 µl/min and 45 °C using H2O + 0.1 % FA (A) and ACN + 0.1 % FA (B). The gradient started at 5 % B for 0.5 min before increasing to 95 % B in another 19 min.

• ESI-MS measurements were performed using positive ionization on a maXis UHR-Q-TOF-MS (Bruker Daltonik) m/z range: 150-1500, repetition rate: 2 Hz for MS, 3 Hz for MS².

• Targeted screening was carried out using precise extracted ion chromatograms (EICs), retention time and isotope pattern evaluation via SigmaFitTM with TargetAnalysisTM (Bruker Daltonik).

• Pre-processing of data and statistical interpretation using t-test was performed with ProfileAnalysisTM 2.0 (Bruker Daltonik). Scheduled precursor lists (SPL) for targeted MS/MS experiments were created by the same software tool. SPL derived MS² data was processed with MZmine 2.2[1]. Features were extracted from raw data using the FindMolecularFeatures (FMF) algorithm in DataAnalysisTM 4.1.

Sample Set

• The sample set consisted of 6 biological replicates of a Sorangium cellulosum strain and 6 replicates of growth medium blank. Extracts were prepared by methanol extraction of an adsorber resin which was added to the cultivation broth (Amberlite XAD-16). Samples were diluted 5x with methanol prior to injection. For each sample two technical replicates were measured.

Fig. 3: A: Result of target compound identification as presented in TargetAnalysis. B: Example: Assignment of several adducts for the same compound gives more reliable hits.

A

B

[1] T. Pluskal, S. Castillo, A. Villar-Briones, M. Orešič, BMC Bioinformatics 11:395 (2010).

References

Funding from BMB+F, DFG and DAAD is gratefully acknowledged.

Bruker Daltonics & HIPS

6 x extract

6 x blank

80,000 features (24 x 3500)

Important features

assigned by t-test analysis

Targeted search against

in-house database

Fig. 2: Features that are only present within bacterial extracts are assigned by means of a t-test. An SPL of 176 features was exported automatically for further CID analysis.

Fig. 1: Overall 24 runs were analyzed. The feature finding algorithm assigned ~ 3500 features per run.

Unknown identification

• The number of features representing potentially novel metabolites was reduced further by matching specific neutral losses within coeluting features.

Features in one MS file 3500 Features after t-test 176 - fragmented with SPL method 176 - fragmented with auto-MS² 110 Related to known compounds - 22 Assigned by online database search - 0 Identified as neutral loss - 26 Remaining unknown features 128

Assignment using MS/MS data

Peptides - 4 found due to similar fragmentation - 11 - related to 3 Thuggacins - related to 8 Ambruticins

New features for further investigation 113

Targeted Analysis

• An in-house database (Myxobase) covering ~700 myxobacterial secondary metabolites representing more than 100 distinct compound classes with retention time and sum formula information was used to identify the known compounds (Fig. 3A).

• The 176 filtered features obtained by the untargeted workflow were then compared to the TargetAnalysis result to assign known metabolites. 22 features are related to TA hits or fragments thereof.

• To increase the confidence in compound identification by targeted analysis common adduct information was additionally taken into account. The Myxobase system enables the export of a “screening file” for TargetAnalysis which automatically creates common adduct ions for a given sum formula (Fig. 3B).

176 features (22 assigned)

Feature Finding

• Myxobacterial extracts represent a complex mixture of small molecules derived from the medium and bacterial secondary metabolites covering several magnitudes in dynamic range. The feature finding algorithm (FMF) assigned approximately 3500 features for each measurement (Fig. 1).

• Overall 24 samples gave ~80,000 features.

• The FMF algorithm is able to automatically detect and combine adduct ions and common neutral losses like [M+NH4]+, [M+Na]+, or [2M+H]+ to one feature. This approach lowered the total number of features by around 10 % to ~70,000 features.

MS/MS

Untargeted Analysis

• The result was evaluated by a t-test comparing extracts and blanks and value-count filtering for features which were present in at least 11 out of 12 measurements in the extract.

• A resulting subset of 176 features was exported as a scheduled precursor list (SPL) for directed precursor selection in a subsequent MS/MS experiment (Fig. 2).

Parent ion selection for MS²

• Automatic precursor selection during acquisition routinely allows for the fragmentation of dominant species. However, compounds of interest may be low abundant and coeluting with matrix compounds from cultivation media. In such cases, the preselection of parent ions can increase the number of MS² spectra for compounds of interest.

• Manual definition of hundreds of precursors is time consuming. Therefore, the subset of 176 features was used for directed precursor selection via an SPL (Fig. 2).

• 37 % of the precursors of interest were not fragmented previously during an auto-MS² run. Using SPL lists generated based on t-test results could improve the MS² coverage to 100 % for the target compounds.

# rt [min] m/z Extract Blank

1 1.1 383.2 12 0

2 2.2 298.1 12 0

3 2.3 293.1 11 0

4 2.4 180.1 12 0

… … … … …

176 17.9 253.25 11 0 Distribution of 176 SPL features

t-test result

224.12721+

250.14341+

271.2439 288.15871+

304.19141+

333.22311+

352.2979

370.31021+

381.2747

396.32361+

423.31881+

440.31341+

458.32631+

476.33571+

C28

H46

NO

5, [I

+H],

+2.8

ppm

C24

H38

N7O

, [I+

H],

-0.4

ppm

C22

H42

N3O

3, [I

+H],

-3.9

ppm

C18

H26

NO

3, [I

+H],

-2.1

ppm

C25

H40

NO

, [I+

H],

+0.7

ppm

NH2

O

O

NH2

OO

OH

O

OHNH

O

+MS2(Ambruticin new01), 29.4eV, 11.87min #1401

0

500

1000

1500

2000

Intens.

250 300 350 400 450 m/z

Putative ambruticin derivatives:

Fragmentation proposal:

High accurate m/z of CID peaks allow for structural identification

Peak 10

Fig. 4: Unknown identification: Calculation of sum formulae suggests the presence of new ambruticin derivatives. Subsequent MS² spectra evaluation using Fragment Explorer allows for the annotation of highly accurate MS² peaks, supporting the identification.

• Guided MS/MS acquisition allows for the assignment of interesting features and facilitates their identification (Fig. 4).

• Searching in publicly accessible databases did not yield any hits.

• 113 features re-mained for further evaluation by MS and NMR experiments.

Bacterial Metabolomics

Metabolomics Posterbook 2012

-7-

For research use only. Not for use in diagnostic procedures.

The impact of high-resolution MS and NMR techniques on the discovery of novel natural products from myxobacteria

Summary

ESI-UHR-QTOF-MS & LC-SPE-NMR

Name Formula [M+X]+ m/z theo. m/z meas. Error [ppm] mSigma

Ambruticin A [M+H]+

C28H40O6 473.28977 473.28964 0.3 8.3

Ambruticin A [M+NH4]+ C28H43N1O6 490.31632 490.31572 1.2 4.5

Ambruticin A [M-H2O+H]+

C28H38O5 455.27920 455.27909 0.2 23.6

Daniel Krug1,2, Thomas Hoffmann1, Kirsten Harmrolfs2, Alberto Plaza1, Aiko Barsch3, Gabriela Zurek3, Rolf Müller1,2

1 Helmholtz-Institute for Pharmaceutical Research Saarland (HIPS), 66123 Saarbrücken/Germany

2 Saarland University, 66123 Saarbrücken/Germany 3 Bruker Daltonik GmbH, 28359 Bremen, Germany Introduction

Microbial natural products represent a rich and still largely untapped resource for novel chemical scaffolds. Their discovery enables characterization of the underlying biosynthetic pathways and at the same time chances are good to find compounds with potent biological activity. UHPLC-coupled UHR-TOF mass spectrometry and LC-NMR represent most valuable analytical tools for the discovery of novel natural products, efficient dereplication, com-prehensive mining of metabolomes and rapid structure elucidation of novel metabolites.

Fig. 2: A: Result of target compound identification as presented in TargetAnalysisTM. B: Example: Assignment of several adducts for the same compound gives more reliable hits.

A

B

References

Bruker Daltonics & HIPS

Fig. 1 Chromatogram: separation of a crude extract from a Sorangium strain. Right inset: comparison of measured and calculated spectra for Ambruticin, a myxobacterial compound that exhibits potent antifungal activity.

439.2847 1+

457.2953 1+

475.3059 1+

493.3160

510.3428

360 380 400 420 440 460 480 500 520 m/z

475.3059

476.3091

477.3111

0.0

0.2

0.4

0.6

0.8

1.0

5 x10

475 476 477 478

475.3054

476.3088

477.3117

measured calculated

C28H43O6 Dm/z = 0.9 ppm

14.8 mSigma

C28H41O5 Dm/z = 1.1 ppm

6.8 mSigma

C28H39O4 Dm/z = 0.9 ppm

8.9 mSigma

Ambruticin S C28H42O6

[M+H]+ = 475.3054 Dm/z = 0.9 ppm

14.8 mSigma

+MS, 11.20min

Chondromyces crocatus Sorangium cellulosum

Myxoprincomide c506 C45H74O16N10

[M+2H]2+ 506.2714 m/z

506.2708 2+

506.7725 2+

507.2738 2+

507.7743 2+

+MS, 2.97min #483

0.0

0.5

1.0

1.5

2.0

2.5

4 x10

Intens.

506 507 508 m/z

Scores & Loadings plot

Molecular Features

wt mutant

Samples

BPC (500-600 m/z)

506.2708 m/z 2.97 min

2 3 4 5 min

+M

S, 2

.97m

in #

483

1

2

3

4

#3

1

2 3

4

#3

506.271 m/z 2.97 min

PCA

4 x M. xanthus wildtype

5 10 15

4 x M. xanthus mutant

5 10 15

652.4039 1+

506.2708 2+

652.4018 1+

500 600 700 m/z

215.1403 1+

506.2711 2+ 797.4045

1+

910.4891 1+

+MS2(506.271), 25-30 eV

0

2000

4000

6000

8000

Intens.

200 300 400 500 600 700 800 900 m/z

N-MeSer-Leu + H+ D = 1.1 ppm

M+H+- N-MeSer-Leu D = 0.7 ppm M+H+- N-MeSer

D = 1.2 ppm

• Myxobacteria were cultivated in complex media and extracts were separated by RP chromatography using an U-HPLC system. MS measurements were performed in ESI positive mode (scan range 100-2000 m/z) using an ultra high resolution Q-TOF mass spectrometer (Bruker maXis 4G).

• A specialized database system – MYXOBASE - was developed to support targeted screening. Identi-fication of known compounds was accomplished using precise EICs and considering exact mass, retention time and isotope pattern for increased confidence (Bruker TargetAnalysis 1.3 software).

• In addition, feature-extracted HR-MS data were used for untargeted profiling by PCA (Principle Component Analysis) using the Bruker Profile Analysis 2.1 software.

• LC-SPE-NMR analysis was performed using a VWR 2400 series solvent delivery system, with a Phenomenex Luna C-18 150 x 4.6 mm (2.5 μm) column, SPE unit and Bruker Biospin Avance 700 NMR platform with cryoprobe.

Methods

LCMS profiling & dereplication

Fig. 3 Statistical treatment of extracts derived from wild-type and gene-knockout strains of Mycococcus xanthus DK1622. Principle component analysis (PCA, plot shows PC1 vs. PC2) facilitates the discovery of a novel compound class, the myxoprincomides [2], by using a combined genome- and metabolome-mining strategy (left). Full scan and MS2 spectra for myxoprincomide-c506 (right).

Genome- & metabolome-

mining

Identification & structure elucidation

MS-guided collection of LC-SPE fractions

1H-NMR p3919gp, TD: 360, NS: 16

HSQC hsqcetgpprsisp2.2 , TD: 128, NS: 16

1H-NMR p3919gp, TD: 360, NS: 16

Fig. 4: Rapid structure elucidation of metabolites from myxobacterial crude extracts enabled by LC-SPE-NMR, following semi-preparative liquid chromatography coupled to MS- and UV/Vis detection. The NMR spectrum (right path) corresponds to a new derivative of Myxochelin.

212.1 269.2 374.3 387.2

405.167

432.2

+MS, 7.7min #457

0.5

1.0

1.5

2.0

5 x10

200 300 400 500

UV, l= 220nm

+MS, 8.6min #512

205.1 223.1 267.2 283.1

334.7 343.7

402.2

419.182

472.2

0.00

0.25

0.50

0.75

1.00

1.25

1.50 5 x10

200 300 400 500

Myxochelin A C20H24N2O7

[M+H]+ 405.1656 m/z

Myxochelin A New Myxochelin derivative

Natural product discovery

- Myxobacteria strain collection - Chemical compounds database - Analytical observations library - Screening results archive - Metabolome data repository - MS2 reference spectra collection

Metabolome Mining

• The number of known compounds identified to date from many myxobacterial strains is often significantly lower than expected from genome sequence information [1, 2].

• The comparison of feature-extracted secondary metabolomes using statistical tools thus has the potential to uncover previously "hidden" natural products (Fig. 3).

• This comprehensive genome- and metabolome-mining approach is exemplified by the recent discovery of myxoprincomides, an intriguing family of novel secondary metabolites from Myxococcus xanthus [2, 3].

• LC-UHR-QTOF-MS and database-assisted strategies enhance the natural products identification workflow

• Metabolome-mining using statistical tools enable novel metabolite discovery

• LC-SPE-NMR/MS for rapid dereplication and structure elucidation

[1] “The biosynthetic potential of myxobacteria and their impact on drug discovery”, Curr. Opin Drug Disc Dev., 2009, 12(2), p. 220-230. [2] “Myxoprincomide; a natural product from Myxococcus xanthus discovered by comprehensive analysis of the secondary meta bolome”, Angew. Chem. Int. Ed., 2012, 51(3), p. 811-816. [3] “Discovering the hidden secondary metabolome of Myxococcus xanthus: a study of intraspecific diversity”, Appl. Environ. Microbiol., 2008, 74, p. 3058-3068.

Myxobase Identification • Accurate-mass MS2 fragmentation is used for the identification of compounds differentiating bacterial strains, for evaluation of chemical novelty and as starting point for structure elucidation.

• LC-SPE-NMR is an important key for the rapid identification of known and novel compound classes due to its ability to generate a partial or even complete structure based on NMR information early in the course of the discovery workflow (Fig. 4).

Myxochelin 419 C20H22N2O8

[M+H]+ 419.1454 m/z

Targeted Profiling

• Full scan spectra enable the targeted screening for known myxobacterial compounds based on highly selective EIC traces, using MYXOBASE as analyte database (Fig. 1, 2).

•Profiling reports are parsed into MYXOBASE with all relevant analytical performance qualifiers, enabling collaborative evaluation of results and crosslinking with bioactivity assays and strain-related data.

• Our screening campaign enables the in-depth comparison of high numbers of complex myxo-bacterial metaboilte profiles and the identification of alternative producers across all myxobacterial strains from our world-wide collection.

Bacterial Metabolomics

Metabolomics Posterbook 2012

-8-

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ant

PCA

I x 1

06

?

M. x

anth

usD

K16

22

• H

igh-

reso

lutio

n LC

-MS

mea

sure

men

ts o

fsa

mpl

es fr

om k

nock

out m

utan

t stra

ins

(rep

licat

es)

and

Prin

cipa

l Com

pone

nt A

naly

sis

(PC

A).

• Id

entif

icat

ion

of a

low

-ab

unda

nce

cand

idat

eco

mpo

und

(506

.271

2+ m

/z)

in L

C-M

S d

atas

ets.

• S

elec

tion

of a

n al

tern

ativ

e pr

oduc

er a

nd y

ield

impr

ovem

ent

by p

rom

otor

inse

rtion

prio

r to

upsc

alin

g fo

r fer

men

tatio

n, c

om-

poun

d is

olat

ion

and

full

stru

ctur

e el

ucid

atio

n.

H NN H

HO

OH N

OO

H

N H

H NO

HO

H N

O

O

ON H

OH

OH N

OH

O

H N

O NH

2

OH

O

Myx

oprin

com

ide

c506

• no

myx

oprin

com

ides

with

acy

l sta

rter

uni

t obs

erve

d

• ho

wev

er, p

rom

otor

inse

rtion

ups

tream

of m

odul

e 1

resu

lts i

n lo

ss o

f pro

duct

ion

• in

sert

ion

of O

HV

al-a

ctiv

atin

g A

-dom

ain

2 in

Mxp

897 an

d s

peci

ficity

cha

nge

to P

he in

mod

ule

3

• A

-dom

ain

prom

iscu

ity in

Mxp

1622

mod

ule

2 (L

eu, M

et &

Mxp

897 m

odul

e 6

(Val

, Leu

)

• m

odul

e 6

is in

volv

ed in

for-

mat

ion

of th

e ce

ntra

l α-o

xo m

oiet

y fro

m V

al a

nd m

CoA

• 13

C a

ceta

te fe

edin

g re

veal

ed t

hat o

nly

C2

is m

aint

aine

d in

myx

oprin

com

ides

• bi

osyn

thet

ic in

term

edia

tes

are

rele

ased

from

the

asse

mbl

y l

ine

(LC

-MS

and

NM

R d

ata)

• ac

tive-

site

mut

atio

n of

the

TE d

omai

n do

es n

ot a

lter t

he o

b- s

erve

d pr

oduc

t pro

file

• m

odul

es 8

and

9 in

Mxp

1622

in-

cor

pora

te T

yr a

nd β

-Lys

, res

- p

ectiv

ely

• th

ese

mod

ules

are

som

etim

es u

sed

itera

tivel

y as

a „m

ixed

dou

ble“

, a to

dat

e un

desc

ribed

fea

ture

in N

RP

S b

iosy

nthe

sis

• A

-dom

ain

10 in

Mxp

1622

occ

as-

sio

nally

inco

rpor

ates

Leu

, but

is a

lso

skip

ped

frequ

ently

• sk

ippi

ng a

ppar

ently

alw

ays

occ

urs

in th

e co

rres

pond

ing

mod

ule

11 o

f Mxp

897

c708

(14

14 D

a)

c515

(10

28 D

a)

c649

(648

Da)

c651

(650

Da)

c382

(381

Da)

c683

(682

Da)

c562

(11

22 D

a)

c587

(11

72 D

a)

M. x

anth

us A

2M

XA

N_3

779

T7A1

C45

H74

N10

O16

ACL

AC

PC

?

3

4 5 6

1 2Sta

rter u

nit

12

43

56

ACP

CA

AN

-MT

PCP

CA

PCP

CA

PCP

CA

PCP

KS

ATAC

PC

APC

PC

APC

PC

AP

CPC

AP

CPC

APC

PC

AP

CPTE

CO

xC

LA Mxp

1622

pro

tein

(~1.

6 M

Da)

Mod

ule

2 &

3M

odul

e 6

Mod

ule

8 &

9M

odul

e 10

Term

inat

ion

AA

N-M

TPC

PC

AP

CPC

AP

CPC

APC

PC

AA

N-M

TPC

PC

AP

CPC

AP

CPC

APC

PC

Myx

oprin

com

ide

c506

(101

0 D

a)

Myx

oprin

com

ide

c580

(115

8 D

a)

c534

c649

c382

c683

c798

H NN H

HO

OH N

OO

H

N H

H NOH

OH N

O

O

ON H

OH

OH N

OH

O

H N

O NH

2

OH

O

ACL

AC

PC

AA

N-M

TPC

PC

AP

CPC

AP

CPC

APC

PKS

AT

ACP

CA

PCP

CA

PCP

CA

PCP

CA

PCP

CA

PCP

CA

PCP

TEC

Ox

SS

SS

NH

O

NH

O

HO

HN

O

NH

O

HO

HN

ON

H

OHO

NH

O

HO

HN

ON

H

OHO

HN

OH

O

NH

NHO

HO

HN

ON

HOH

O

HN

OH

O

S

NH NH

O

HO

HN

ONH

OH

O

HN

OH

O

OO

NH

SO

HO

NH

NH

O

HO

HN

ON

H

OH

O

HNO

H

O

OO

NH

O

HO

HN

SO

OH

NH NH

O

HO

HN

ONH

OH

O

HN

OH

O

OO

NH

O

HO

HN

NHO

OH

H2N

SO

NH NH

O

HO

HN

ONH

OH

O

HN

OH

O

OO

NH

O

HO

HN

NHO

OH

H2N

NHO

SO

NH

NHO

HO

HN

ON

HOH

O

HN

OH

O

S

??

O

OO

HO

AC

LA

CP

CA

AN

-MT

PCP

CA

PCP

CA

PCP

CA

PCP

KS

ATAC

PC

APC

PC

APC

PC

APC

PC

APC

PC

APC

PC

APC

PTE

CO

xS

SS

S

NH

O

NH

O

HO

HN

OS

OH

N

SO

OH

NH

SO

NH

2

SS

??

APC

PC

S

OH

NH

O

HO

HN

OO

H

NH

O

HN

OO

HH

O

OH

N

OO

H

NH

HO

O

NH

O

HO

HN

OO

H

NH

O

NH

O

HO

HN

OO

H

NHO

HN

OO

H

NH

HO

O

NH

O

HO

HN

OO

H

NH

HN

O

OHN

OO

H

NH

HOO

NH

O

HO

HN

OO

H

NH

HN

OO

OHN

OO

H

NH

HOO

NH

O

HO

HN

OO

H

NH

HN

O

OH

N

O

OH

OH

N

OO

H

NH

HO

OH

N

O

OH

N

OO

H

NH

HO

NH

O OH

N

O

OH

OH

N

OO

H

NH

HO

OH

N

O

NH

O

HO

HN

OO

H

NH

HO

HNS

O

NH

2

NH

O OH

N

O

OH

OH

N

OO

H

NH

HO

OH

N

O

NH

O

HO

HN

OO

H

NH

HO

HN

OH

N

O

OH

NH

2

H NO

O

N HO

HOO

N HO

OH

H N

OHN H

O

H NO

HO

N HO

OH

H N

OH

N H

O

N HO

O

M. x

anth

us D

K16

22 M

. xan

thus

DK

897

Ser

Phe

Val

Ser

Cys

mC

oASe

rPh

eβ-

Lys

IleSe

rpr

edic

ted:

mxp

1622

gen

e (M

XA

N_3

779,

42.

8 kb

p)

mxp

897 g

ene

(46.

3 kb

p)

obse

rved

:Se

rLe

uor

Met

β-O

HVa

lSe

rVa

lSe

rTy

r- -

-or

Leu

Ala

β-Ly

sm

CoA

Ser

Val

Orn

Ser

Cys

mC

oASe

rPh

eβ-

Lys

IleSe

rpr

edic

ted:

obse

rved

:Se

rP

heβ-

OH

Val

Val

β-O

HVa

lSe

rVa

lSe

rTy

r- -

-or

Leu

Ala

β-Ly

sm

CoA

1 2

3

4

5

6

7

8

9

1

0

11

1 2

3

4

5

6

7

8

9

1

0 1

1

12

tn5

H NN H

HO

OH N

OO

H

N H

H NOH

OH N

O

O

ON H

OH

O

N H

OH

O

N H

O

H2N

O

H NO

OH

H N

OH

O

H N

O

H2N

H NN H

HO

OH N

OO

H

N H

H NOH

O

O

O

MIC

(M. h

iem

alis

): 60

µg

O

ON

MeS

er –

Leu

–O

HV

al –

Ser

–V

alS

er

O

ON

MeS

er –

Leu

Ser

1409

0

–O

HV

al –

Ser

–Va

lS

er

OH O

NM

eSer

–Le

u –

OH

Val

–S

er –

Val

Ser

–

Tyr

–βL

ys –

Leu

–A

la

O

ON

MeS

er –

Met

–O

HV

al –

Ser

–V

alS

er –

Tyr –

βLys

–A

la

O

ON

MeS

er –

OH

Val

P

he

–

NM

eSer

–O

HVa

l Ph

e –

–O

HV

al –

Ser

–Le

u

NM

eSer

–O

HV

al

Phe

–

–O

HV

al –

Ser

–Va

l

Ser

O

ON

MeS

er –

OH

Val

P

he

––

OH

Val

–S

er –

Leu

Ser

–Ty

r –βL

ys –

Ala

10

11

Ser

Val

*

Ser

NM

e-S

er

OH

Val

Leu

βLys

Tyr

Ser

Val

*

Ser

NM

e-S

er

OH

Val

Leu

βLys

Tyr

APC

PC

APC

PC

APC

PTE

SS

S

Ser

Val

*

Ser

NM

e-S

er

OH

Val

Leu

βLys

Tyr

Ala

10

11

Ser

Val

*

Ser

NM

e-S

er

OH

Val

Leu

βLys

Tyr

APC

PC

APC

PC

APC

PTE

SS

S

10

9 8

11

APC

PC

APC

PC

CA

PCP

CA

PCP

TE

CA

PCP

CA

PCP

CA

NH

SO

NH

2

HO

PCP

SH

NH

O

HO

HNS

O

Ser

Val

*

Ser

8 9

NM

e-S

er

OH

Val

Leu

Ser

Val

*

Ser

NM

e-S

er

OH

Val

Leu

Ser

Val

*

Ser

NM

e-S

er

OH

Val

Leu

Ser

Val

*

Ser

NM

e-S

er

OH

Val

Leu

CA

PCP

CA

PCP

CA

NH

SO

NH

2

HO

NH

O

HO

HNS

O

βLys

Tyr

8 9

βLys Ty

r

A

Ala

Ser

Val

*

Ser

NM

e-S

er

OH

Val

Leu

βLys

Tyr

Leu

Ser

Val

*

Ser

NM

e-S

er

OH

Val

Leu

βLys

Tyr

Leu

KSA

TAC

PC

APC

PC

CO

xA

CP NH

O

S

OH

OO

CA

PCP

H2N

SO

OH

H2N

SO

OH

CA

PCP N

H

SO

HO

NH

O

OH

O

ACP NH

O

S

OH

OO

CA

PCP

H2N

SO

OH

CA

PCP N

H

SO

HO

NH

O

OO

[Ox]

[Ox]

ACP N

HO

S

OOHO

NH

O

S

OO

ACP NH

O

S

OH

OO

ACP NH

O

S

OOH

O

67

APC

PC

APC

PC

CA

PCP

CA

PCP

TE

∗

OH

∗

∗

Ala

Stra

ins

and

bio-

activ

ity p

rofil

es

Met

abol

ome

min

ing

Gen

ome-

min

ing

LCM

S lib

rary

dere

plic

atio

n

.CGCGATCGAGATC

CGCTAGATCCGTGG

TGCGCGATAGCGA.

??

?????

AB

CD

E

AB

C

D

E

Ret

.

506.

271

m/z

@ 2

.9 m

in

Myx

obas

e

HZI

col

lect

ion:

~900

0 m

yxob

acte

ria

AC

LA

CP

CA

AN

-MT

PCP

CA

PCP

CA

PCP

CA

PCP

CA

PCP

C

• R

elat

ive

prod

uctio

n of

myx

oprin

com

ide

“c50

6” a

s de

ter-

min

ed fr

om th

e se

cond

ary

met

abol

omes

of 9

8 M

. xan

-th

us s

train

s, u

sing

dat

a fro

m H

R L

C-M

S m

easu

rem

ents

.

• Id

entif

icat

ion

of a

ltern

ativ

e pr

oduc

ers

repr

esen

ts a

n im

-po

rtant

key

for t

he is

olat

ion

and

stru

ctur

e el

ucid

atio

n of

no

vel m

yxob

acte

rial m

etab

olite

s w

hich

are

initi

ally

pro

-du

ced

in lo

w y

ield

s on

ly.

Myx

alam

id A

O

ON

MeS

er –

OH

Val

P

he

––

OH

Val

–S

er –

Val

Ser

INTR

OD

UC

TIO

NM

yxop

rinco

mid

e bi

osyn

thes

is: a

mol

ecul

ar a

ssem

bly

line

“und

er c

onst

ruct

ion”

Dis

cove

ry o

f myx

oprin

com

ide

by c

ompr

ehen

sive

ana

lysi

s of

the

Myx

o-co

ccus

xan

thus

DK

1622

sec

onda

ry m

etab

olom

e

Dis

cove

ry o

f myx

obac

teria

l nat

ural

pro

duct

s su

ppor

ted

by M

yxob

ase

Bacterial Metabolomics

Metabolomics Posterbook 2012

-9-

For research use only. Not for use in diagnostic procedures.

Metabolic profiling of a Corynebacterium glutamicum ΔprpD2 by GC-APCI high resolution Q-TOF analysis

Aiko Barsch1, Marcus Persicke2, Jens Plassmeier2, Karsten Niehaus2, Gabriela Zurek1 1 Bruker Daltonik GmbH, Bremen, Germany 2 Centrum für Biotechnologie, Universität Bielefeld, Germany

Introduction Metabolomics studies based on Gas chromatography – Mass spectrometry (GC-MS) are well established and typically employ electron impact (EI) ionisation. Currently, many possible biomarkers remain “unknowns” in these studies. This is due to the lack of reference spectra for a majority of biologically relevant compounds. Hyphenating GC with ESI-(Q)-TOF technology by atmospheric pressure chemical ionisation (APCI) permits the characterization of compounds which can not easily be identified by classical GC-EI-MS.

Corynebacterium glutamicum, a gram positive bacterium, is used in the industrial production of amino acids and can be grown on different carbon sources. Glucose is metabolized via glycolysis and tricarboxylic acid cycle (TCA) whereas propionate is catabolized through the methylcitric acid pathway (see Fig. 1). An involvement of the prpD2 gene, encoding 2-methylcitrate dehydratase, in the degradation of propionate has previously been shown by Plassmeier et. al [1].

Here, we used a GC-APCI-MS based metabolic profiling approach to analyse metabolite extracts of a prpD2 mutant C. glutamicum strain grown on glucose, with a propionate pulse in the exponential growth phase. The benefits of high resolution MS data in combination with GC separations to facilitate structure elucidation will be demonstrated.

C. glutamicum ΔprpD2 strains were cultivated and harvested as described in [1].

• two biological replicates were grown on glucose, with a propionate pulse in the exponential growth phase.

• Two technical replicates of each culture were harvested by centrifugation before and 1 h after the propionate pulse.

• Dried methanolic metabolite extracts and reference standards were derivatized by methoxymation and trimethylsilylation.

• Gas chromatography conditions: Injection volume: 1µl; Column: HP-5MS (30 m x 0.25 mm i.d.; 0.25 µm); Injector temperature: 250°C; Helium carrier gas: 1 ml / min; Temp. Gradient: 3 min @ 80°C, 5°C / min to 325°C.

• MS: Bruker Daltonics micrOTOF-Q IITM interfaced via a Bruker GC-APCI source. Data was acquired in positive ionization mode from 85 – 750m/z at 4 spectra per second.

• Relevant features were extracted using the Find Molecular Features algorithm. Feature intensities were normalized to the intensity of the internal standard ribitol. Principal Component Analysis (PCA) as well as sum formula generation by SmartFormulaTM were performed using ProfileAnalysisTM 2.0. MS/MS data was correlated with structural hypothesis using the FragmentExplorerTM in DataAnalysis 4.1TM.

Results

Methods

•High resolution MS data acquisition by coupling of gas chromatography via an APCI source to a micrOTOF-Q II instrument enabled to use the same data (pre-)processing methods established for LC-TOF-MS data.

•A PCA based on all extracted features revealed a clear separation of samples according to the carbon sources used for cultivating the C. glutamicum ΔprpD2 cells (Fig.2 A scores plot) .

•Peak intensities for all samples of two selected compounds are displayed as bucket statistics in Fig. 2 B. Both show a higher abundance in cells metabolizing propionate.

• Utilizing exact mass and isotopic pattern information SmartFormula generated 18 possible elemental formulae for compound 27.2min : 495.21m/z (Fig.3 A). C19H43O7Si4 was ranked as first sum formulae according to the goodness of the isotopic fit - the SigmaRank.

• Combining accurate mass and isotopic pattern information of MS and MS/MS spectra, sum formulae suggestions for this compound could be reduced to a single hit (Fig.3 B). The formula C19H43O7Si4, is in accordance to derivatized 2-methylcitric acid (Fig. 3B inset) and could be confirmed in comparison to the reference standard.

• An accumulation of 2-methylcitrate in C. glutamicum cells metabolizing propionate could be expected in cells lacking 2-methylcitrate dehydratase (see Fig.1). A higher concentration for this compound in prpD2 mutant C. glutamicum grown on propionate was also observed by Plassmeier et al. based on “classical low resolution” GC-EI-(Iontrap)-MS measurements [1].

• The molecular formula of Compound 9.3min : 234.13m/z (Fig. 2 B) could be limited to C9H24NO2Si2 by SmartFormula 3D (not shown). A Sum formula query in public databases using the Compound CrawlerTM returned TMS derivatized alanine as a likely structure (Fig.4 A). The top part of Fig. 4 B displays the MS/MS spectrum of derivatized alanine with assigned sum formulae for neutral losses. The automatically calculated SmartFormula 3D spectrum (Fig.4 B bottom) includes annotations for the observed fragment ions.

• Correlating accurate mass MS/MS data with possible fragments of the likely precursor structure using the FragmentExplorerTM further substantiated this hypothesis (Fig. 4 C), which could be confirmed by comparison to the reference standard.

Conclusions

GC-APCI-QTOF-MS

References [1] J. Plassmeier, A. Barsch, M. Persicke, K. Niehaus, J. Kalinowski, J. Biotechnol. 130 (2007) 354-363

•PCA analysis of GC-APCI-TOF-MS data revealed several compounds elevated in C. glutamicum extracts following an addition of propionate

•2-methylcitric acid and alanine were identified using accurate mass and isotopic pattern information in MS and MS/MS spectra - proof of concept for the identification of target compounds using high resolution MS technology

•TOF instrument performance (mass accuracy, resolution and isotopic fidelity) are independent of the acquisition rate. Therefore these systems are perfectly suited for coupling to gas chromatography.

Fig. 1: Metabolic pathways of the glycolysis, tricarboxylic acid (TCA) and methylcitric acid cycles highlighting the step catalyzed by PrpD2, which is not present in the C. glutamicum ΔprpD2 deletion mutant - adapted from [1].

Propionate

Propionyl-P

Propionyl-CoA

2-Methylcitrate

2-Methyl-cis-aconitate

2-Methylisocitrate

Oxaloacetate

Pyruvate

Acetyl-CoA

Citrate

Malate

Fumarate

Succinate

Succinyl-CoA

2-Oxo-glutarate

Isocitrate

cis-Aconitate

Pyruvate

PrpD2

TCA cycle

PEP

Glucose

Fig. 2: A: PCA scores and loading plot (Explained Variance PC1 vs. PC2: 44.4% + 29.9%) of C. glutamicum ΔprpD2 mutant strains grown on glucose before and after a propionate pulse. The scores plot revealed a clustering of samples according to the carbon sources used for cultivation. B: Bucket statistics plots for two selected loadings mainly contributing to the grouping of samples observed in A.

-1.0 -0.5 0.0 0.5 PC 1

-0.5

0.0

0.5

1.0

PC 2

-1.0 -0.5 0.0 0.5 PC 1

-0.5

0.0

0.5

1.0

PC 2 Scores

-Prop

Loadings

+Prop

Bucket: 9.3min : 234.13m/z

2 4 6 Analysis

1

2

3

4

5

Inte

nsity

x 1

05

Bucket: 27.2min : 495.21m/z

2 4 6 Analysis 0.0

0.5

1.0

1.5

Inte

nsity

x 1

05

A

B

Fig. 3: Identification of compound 27.2min: 495.21m/z (see Fig.2 B): Molecular Formula generation by SmartFormula (A) and SmartFormula3D (B). Combining accurate mass and isotopic pattern information in MS and MS/MS spectra one candidate formula was generated which is in accordance to derivatized 2-methycirate (inset).

A

B

TMSO-

O

CH3

TMS-O

TMS-O OO

O-TMS

Elemental formulae of precursor ion combining MS and MS/MS information

Fragment and neutral loss elemental formulae

A

B

116.0894

144.0833

234.1336 C3 H10 O1 Si1 - 0.22 mDa

C4 H10 2 Si1 + 0.83 mDa

C4 H14 O1 Si1 + 0.09 mDa

C1 H4 + 0.30 mDa

C1 H4 - 0.32 mDa

+MS2(234.1327)

116.0890

144.0839 234.1340

C5 H14 N1 Si1, -0.39 mDa

C5 H10 N1 O1 Si1, 0.36 mDa,

C6 H14 N1 O1 Si1, 0.66 mDa,

C 8 H 20 N 1 O 2 Si 2, 0.76 mDa,

C9 H24 N1 O2 Si2, 0.38 mDa,

SmartFormula3D spectrum: C 9 H 24 N 1 O 2 Si 2 0

2

4

6

0.0

0.5

1.0

1.5

2.0

2.5

120 140 160 180 200 220 240 m/z

Inte

nsity

x 1

04

Inte

nsity

x 1

04

128.0523 218.1019

Fig. 4: Compound 9.3min:234.13m/z (see Fig.2 B) – Identification as alanine - 2TMS. A: Compound Crawler database query. B: SmartFormula 3D spectrum with annotated sum formulae for fragment ions and neutral losses. C: FragmentExplorerTM - correlation of fragments of likely precursor structure with MS/MS data.

C

Bacterial Metabolomics

Metabolomics Posterbook 2012

-10-

GC-APCI-MS/MS based identification of metabolites in bacterial cell extracts

Christian Jäger, Maike Sest & Dietmar Schomburg Department of Bioinformatics & Biochemistry, TU Braunschweig, Germany ch.jaeger@tu-braunschweig.de

cell lysis metabolite extraction

derivatization (MeOX & MSTFA)

untargeted compound analysis2

GC-MS measurement

identified derivates

selected unknowns identification

pipeline harvesting washing

cell cultivation

Metabolome analysis

Introduction In order to analyze and understand the metabolism of microorganisms, it is important to know all the components and their interaction. The goal of metabolomic research is to measure concentrations of as many metabolites as possible. It is estimated that about 800 different chemical entities, covering a wide range of polarities, reactive behavior and sizes, are involved in a microbial metabolism. Conventionally used ionization techniques in gas chromatography – mass spectrometry, like Electron Ionization, generate reliable and unique fragment patterns. Commercial and in-house libraries can be used to identify the separated compounds. For identification of unknown compounds additional information is needed.

Identification strategy We screened GC-MS chromatograms of bacteria especially for unknown substances with biological relevance. The microorganisms were cultivated on glucose and stable isotope-labeled [U-13C]-glucose to allow unambiguous identification of biologically related compounds. Sum formulae were calculated with high resolution - mass spectrometry and structural information were gained by MS/MS fragmentation. We present the evaluation of GC-APCI-MS chromatograms using the example of Pseudomonas putida and the structural analysis of a compound from the polar cell extract of Yersinia pseudotuberculosis. This compound does not match to already known derivatives from our in-house library, the Golm Metabolome Database1 (GMD) and the NIST08 database.

Conclusions ● GC-APCI-MS/MS data provides information of the molecular ion and enables the assignment of the elemental composition to the TMS derivatives and its fragments. Additionally, functional groups can be derived from specific neutral losses.

● The developed identification strategy is a valuable supplementing method in combination with GC-EI-MS. It can be used for a fast, reliable and comprehensive evaluation of metabolic profiles on following terms: ► sum formulae of the metabolite ► biological / non-biological origin ► confirmation of ambiguous EI–spectra ► improvement of deconvolution

References 1 Golm Metabolome Database (GMD), http://gmd.mpimp-golm.mpg.de (download, 2011) Schauer, N., et al. (2005) 'GC-MS libraries for the rapid identification of metabolites in complex biological samples', FEBS letters, 579, pp 1332-1337 2 Hiller, K., et al. (2009), 'MetaboliteDetector: Comprehensive Analysis Tool for Targeted and Nontargeted GC/MS Based Metabolome Analysis', Anal. Chem., 81, pp 3429–3439 GC-APCI-MS system Agilent 7890A gas chromatograph equipped with a ZB-5MS column (Phenomenex), 29 min run Bruker micrOTOF-QII with APCI, in full scan mode m/z 50...1550, 0.4 Hz repetition rate

Mass spectrometry based structure elucidation The analysis of 70 eV fragmentation pattern of (derivatized) unknown compounds is difficult without specific information. For instance, the molecular ion is of low abundance or not detectable.

GC-APCI-MS chromatogram (deconvoluted) of a polar cell extract (P. putida)

[M+H]+

C16H33O6Si3

C13H23O5Si2

Top GC-APCI-MS(/MS) – spectra. One metastable ion (m/z 315.1075) appears. Bottom MS/MS - fragment pattern of m/z 405.1579 plus possible neutral losses.

- TMS-OH - 2 TMS-OH

- 2 TMS-OH - CO

[M-15]+

TMS+H

TMS-OH+H

MS/MS

C6H4O3 C7H5O4

● molecular ion [M+H]+ → m/z 405.1579 ● molecular ion [M+H]+ → m/znominal 412 (13C) ● sum formuladerivative → C16H33O6Si3 ● sum formulametabolite → C7H8O6 (- 3TMS groups) ● rings and double bonds → 4

Electron Ionization – mass spectrum of 'unknown YPY / RI 1846'. Loss of one methyl group is a common reaction of derivatized metabolites with MSTFA.

With GC-APCI-MS/MS measurements we could determine the molecular weight, sum formulae of almost all measured ions/fragments and the presence of functional groups.

- CH4

- TMS-OH - CO

Metabolic profiling with GC-APCI-MS Normally, 250-300 targets were detected in GC-EI-MS chromatograms of P. putida. Only 100 derivatives could be identified. With GC-APCI-MS measurements it is possible to detect up to 350 chemical species with accurate mass. We used the same chromatographic methods on every GC-MS system for a fast compound recovery.

All library entries can gradually be updated with new information concerning the chemical nature of the unknown compounds.

The presence/absence of a mass shift between the 12C- and the co-eluting 13C- peak indicates the biological / non-biological origin and the number of carbon atoms of the measured compound. Almost all of the identified derivatives from EI - measurements could be confirmed by sum formula comparison. In addition, many compounds below matching factor (0,75) were identified as the first suggested possible hit.

● deconvoluted targets GC-EI-MS: 265 → 96 identified derivatives (36%) ● deconvoluted targets GC-APCI-MS: 343

●biological origin: 220 (64%) ●non-biological origin: 43 (13%) ●no assignment: 80

12C

C-source: labeled / unlabeled

glucose 13C

Bacterial Metabolomics

Metabolomics Posterbook 2012

-11-

For research use only. Not for use in diagnostic procedures.

Untargeted metabolic profiling of yeast extracts by UHR-Q-TOF analysis to study arginine biosynthesis mutants

Sandy Yates1; Manhong Wu2, Aiko Barsch3; Gabriela Zurek3; Bob St. Onge²; Sundari Suresh², Ron Davis², Gary Peltz² 1 Bruker Daltonics, Fremont, CA 2 Stanford University, CA 3 Bruker Daltonik GmbH, Bremen, Germany Introduction Many primary polar metabolites, such as amino acids, are poorly retained by reversed phase LC column chemistries which are widely used for untargeted metabolic profiling studies. Dansylation of primary amines, secondary amines and phenolic hydroxyl functional groups in these compounds alters the chromatographic behaviour, thus enabling a standard RPLC separation with simultaneously enhancement of the signal intensities in electrospray ionization [1].

We analysed dansylated metabolite extracts from yeast wild type and deletion mutants in the arginine biosynthetic pathway using a novel UHR-Q-TOF instrument. To prove the hypothesis that upstream or downstream metabolites are altered in abundance in mutants we applied an untargeted Metabolomics workflow. Target compounds could be identified based on accurate mass and isotopic pattern information as well as based on fragment ions of the dansylated target compounds.

HPLC: U3000 RSLC, (Dionex). Column: Kinetex C18 2.1x100mm; 2.6µm (Phenomenex). Column temp. 30 °C. Flow rate: 0.5 mL/min. Injection volume: 5 µL. Mobile phase: A = H2O, B =AcN (each containing 0.1% HCOOH). Gradient: 0.5min 5% B; linear gradient 5 – 60% B in 20 min, linear gradient 60 – 95% B in 4.5 min hold 5 min. MS: maXis Impact (UHR-Q-TOF MS, Bruker Daltonik GmbH). Ionization: ESI(+). Scan range: m/z 50-1000. Acquisition rate: 2.5 Hz Data processing: SmartFormula3D and the FragmentExplorer used for sum formula generation and assignment of fragment structures are part of DataAnalysis 4.1. ProfileAnalysis 2.1 was used for statistical data evaluation (Bruker Daltonik GmbH). Sample: Yeast wild type as well as arg4 and arg7 gene deletion mutant strains were grown on a synthetic growth medium. Six biological replicates were harvested by centrifugation at the same growth phase and washed with PBS buffer. Metabolites were extracted using the Folch method and dansylated as described in [1]. 21 amino acid standards (2.5 µmol/ml each peptide) were mixed and dansylated yielding a final concentration of 1 µmol amino acid / ml.

Results

Methods

Conclusions

UHR-QqTOF

•Dansylation of metabolites enables a RP-LC separation of primary metabolites like amino acids

•Data acquired on the maXis Impact instrument permits an unambiguous molecular formula generation for known and unknown compounds

•An untargeted metabolomics workflow, analysing arginine biosynthesis yeast mutant strains, uncovered expected and unexpected metabolic changes

Fig. 1. Dansylation reaction schema

Fig. 6. A: Volcano plot based on t-test comparing arg7 mutant and wild type samples; B: Mass spectrum for the selected target compound with overlaid simulated spectrum for C19H26N3O5S. The calculated sum formula fits to N-acetylornithine. The inset presents hrEIC s for the target compound in wild type and arg7 mutant samples.

Dns

-Gly

cine

Dns

-Ala

nine

D

ns-G

ABA

Dns

-Ser

ine

Dns

-Pro

line

Dns

-Val

ine

Dns

-Thr

eoni

ne

Dns

-Leu

cine

D

ns-Is

oleu

cine

Dns-Norleucine

Dns

-Asp

arag

ine

Dns

-Asp

arta

te

Dns

-Glu

tam

ine

Dns

-Glu

tam

ate

Dns

-Met

hion

ine

Dns

-His

tidin

e

Dns

-Phe

nyla

lani

ne

Dns

-Arg

inin

e

Dns

-Try

ptop

han

Dns

-Lys

ine

Dns

-Tyr

osin

e

4 6 8 10 12 14 16 18 Time [min] 0.0

0.5

1.0

1.5

2.0

5 x10

Intens. Fig. 3. Example - Dansylmethionine A: MS spectrum measured on a maXis Impact; B: MS/MS spectrum with annotated fragment structures, including the 170.0997 m/z significant reporter ion indicating a danylated compound; C: SmartFormula3D: sum formula generation based on precursor and fragment ion information. D: FragmentExplorer links molecular formulae from SmartFormula3D result with structural suggestions for fragment ions and enables a quick annotation of the MS/MS spectrum.

Dansyl-Methionine [M+H]+ C17H23N2O4S2 Error 1.0 ppm mSigma 6.7 Res. 23921

1+ 383.1090

1+

0

2

4

6

4 x10 Intens.

382.5 383.0 383.5 384.0 384.5 385.0 385.5 386.0 386.5 387.0 m/z

170.0966

234.0587 383.1096

N

+MS2(383.1090)

0

2

4

6

4 x10

Intens.

150 200 250 300 350 400 m/z

MS

MS/MS

A

B

C

D

Fig. 4. Arginine biosynthesis highlighting steps blocked in yeast arg7 and arg4 deletion mutants.

Comparison of arg7 vs. Wild Type In order to determine statistically significant differences between sample groups, a student’s t-test was performed comparing wild type vs. arg 4 and wild type vs. arg7 samples. Fig. 6 presents the Volcano plot of the t-test result, plotting the log2 of fold changes vs. -log10 of the p-Value, for the arg 7 vs. wild type comparison. The compound with the highest fold change of 60 fold increase in arg7 samples and a p-Value of 0.0000026 had an accurate mass of 408.1588 m/z. Fig. 6 B shows the overlaid measured spectrum and the simulated spectrum for C19H26N3O5S, which was ranked #1 with the best isotopic fit by SmartFormula. This sum formula fits to Dansylated N-acetylornithine which is the metabolite upstream of the reaction catalysed by the arg7 gene product. EIC traces for the target compound were plotted in ProfileAnalysis which could validate the accumulation of Dansyl N-acetylornithine in arg7 mutant samples (Fig. 6 B inset).

[M+H]+ C19H26N3O5S Error -0.4 ppm mSigma 6.9 Res. 25895

+MS, 7.3-7.4min 408.1588 C19H26N3O5S

0.00

0.25

0.50

0.75

1.00

1.25

1.50

5 x10 Intens.

408.0 408.5 409.0 409.5 410.0 410.5 411.0 411.5 m/z

7.3min:408.16mz P-Value:0.0000026 Fold change: 60 Arg7/WT: 60.03

A

B

Fig. 5. Unsupervised clustering: Dendrogram reveals a class separation according to the analysed yeast wild type and mutants.

[1] Guo K., Li L. (2009). Anal. Chem. 81, 3919-3932

References

Dansylation of purified amino acids – method development A mix of pure amino acids was derivatized by dansylation (see Fig. 1) and separated by C18 RP chromatography. The high mass accuracy of the acquired full scan MS data enabled to create 2mDa trace width high resolution Extracted Ion Chromatograms for the target compounds, providing a very high selectivity (Fig. 2).

As shown for the example of Dansyl-methionine (Fig. 3) the correct sum formula could be generated with a mass error of 1ppm and a very good mSigma value which provided a measure for the goodness of the measured and theoretical isotopic fit. The MS/MS spectrum of dansylmethionine contained a characteristic fragment ion near m/z 171. The SmartFormula3D tool correctly assigned the sum formulae for the observed fragment ions. This information was linked to structural information using the FragmentExplorer, which also enabled a quick annotation of the MS/MS spectrum.

Untargeted Metabolomics of Yeast Mutants in the Arginine Synthesis Pathway

The established method was applied to study yeast mutants blocked in the arginine biosynthetic pathway. Fig. 4 shows the biochemical pathway of arginine with the reactions catalysed by the proteins encoded by arg4 and arg7 highlighted.

Arg4 arg4

arg7

WT

Arg7 Wild type

Comparison of arg4 vs. Wild Type Table 1 presents several selected compounds which were significantly different (p-Value <0.005) comparing arg4 and wild type samples. Using accurate mass and isotopic pattern information ememental formula were calculated by Smart Formula. Based on these sum formulae compounds were tentatively identified also taking into account retention time information for available standards.

An accumulation of argininosuccinic acid and N-acetylornithine could be expected in arg4 mutant yeast cells. Additionally a decrease in aspartate was observed, which possibly could be based on feedback inhibition triggered by the accumulation of a biosynthetic precursor of the arginine biosynthesis.

Feature p-value (t-Test)

fold change arg4 /

WT

calculated sum Formula tentative ID

5.1min : 262.59m/z (2+) 1.73E-07 42.61 C22H31N5O8S

Dns-Argininosuccinic

acid 7.3min :

408.16m/z (1+) 0.00001 2.48 C19H26N3O5S Dns-N-Acetylornithine

3.8min : 383.11m/z (1+) 8.96E-07 -3.16 C17H23N2O4S2 Dns-Methionine

6.3min : 367.10m/z (1+) 0.00006 -1.81 C16H19N2O6S Dns-Aspartate

Table 1: Selected differences comparing arg4 and wild type Yeast samples

All features were extracted from the raw data using “Find Molecular Features”. This algorithm can combine isotopic peaks, charge states and adducts which belong to the same compound into one feature. Based on these extracted features an unsupervised clustering could clearly separate the derivatized metabolite extracts from wild type and both mutant strains (Fig. 5).

Fig. 2. 2mDa trace width high resolution EICs for dansylated standards reveal a significant retention for derivatized amino acids on a reversed phase column.

maXis Impact

Yeast Metabolomics

Metabolomics Posterbook 2012

-12-

For research use only. Not for use in diagnostic procedures.

A workflow for metabolic profiling and determination of the elemental compositions using MALDI-FT-ICR MS

Kazunori Saito1; Tatsuhiko Nagao2; Daisuke Miura2; Takashi Nirasawa1; Hiroyuki Wariishi2

1Bruker Daltonics K.K., Yokohama, Japan; 2Kyushu University, Fukuoka, Japan;

Introduction Metabolomic studies can lead to the enhanced understanding of mechanisms for drug or xenobiotic effects and an increased ability to predict individual variations in drug response phenotypes. Tandem mass spectrometry (MS) has been extensively used for this purpose, however, an identification of metabolites in this approach is dependent on spectral databases or comparisons with authentic reference standard spectra. This is hampered by the fact that only about 20% of all expected metabolites are commercially available. Since Fourier transform ion cyclotron resonance (FT-ICR) MS provides an ultrahigh mass resolution and accuracy which enables isotopic fine structure analysis it allows for highly confident chemical annotations. In addition, due to this high resolving power a separation of complex samples by liquid chromatography is often not essential. Here we present a metabolic profiling workflow based on matrix assisted laser desorption ionization (MALDI)-FT-ICR MS which offers rapid profiling capabilities for metabolomic analyses.

HepG2 cells, a human liver carcinoma cell, were treated for 24 h with the anticancer drug 5-fluorouracil (5FU) at IC50 (10 µM; 5FU-Low) and excess (50 µM; 5FU-High) concentrations. Intracellular metabolites of five biological replicates (n=5) were extracted by a biphasic extraction method using methanol/water/chloroform=2/2/1. A solariX FT-ICR MS (Bruker Daltonik GmbH, Bremen, Germany) has been used for MS measurements. The MS system was equipped with a 9.4 tesla superconductive magnet and an ESI/MALDI dual ion source.

Results and Discussion

Methods

5FU acts as a pyrimidine analogue and is transformed inside the cell into different cytotoxic metabolites which can be incorporated into DNA and RNA, finally inducing cell cycle arrest and apoptosis by inhibition of cellular DNA synthesis. Although these drugs are well-known and well-established anticancer drugs that inhibit nucleotide biosynthesis, there is little information concerning the metabolic response of cancer cells against 5FU. Metabolic responses of HepG2 cells to this anticancer drug were studied by comparing extracts of treated and untreated cells by metabolic profiling. For comprehensive identification of metabolites, FT-ICR MS was selected as analytical platform.

MALDI-FT-ICR MS analysis revealed thousands of peaks in the spectra. We evaluated the complex FT-ICR MS data by PCA and OPLS-DA to differentiate metabolic states of HepG2 cell treated with different concentrations of 5FU. As shown in Fig. 1A, metabolic profiles of 5FU-high and control groups could be separated. The OPLS S-plot comparing 5FU-High and control samples is shown in Fig. 1B. Several loadings were responsible for this separation. Elemental compositions for these compounds were calculated and queried in public databases to retrieve possible structures. As shown in Table 1, those could be nucleotides and amino acids. Figure 2 reveals that the peak intensities of 328.045 and 540.054Da were reduced in a dose-dependent manner.

To enhance the confidence of the formula assignments, isotopic fine structures were compared with the theoretical ones. Figure 3 shows that the ultrahigh resolution spectrum for two compounds is in accordance to the theoretical spectra of the corresponding sum formulae which were suggested by elemental composition analyses. The high spectral resolution of >400,000 FWHM was required not only for separation of both monoisotopic peaks close each other but also for the isotopic fine structure separation. Since metabolomics samples are typically highly complex mixtures, this level of ‘ultrahigh resolution’ is one of the key features for non-targeted metabolomics and biomarker discovery.

Conclusions

FT-ICR MS

•High throughput metabolic profiling was achieved by MALDI-FT-ICR MS

•Ultrahigh resolution mass spectra can provide more reliable elemental composition analysis by resolving isotopic fine structures

•Candidate compounds were found through database search

•Metabolic profiling results indicate that 5FU seems to inhibit the biosynthesis of the nucleotides and amino acids in human liver carcinoma cell.

Fig. 1. PCA scores plot between 5FU-High, 5FU-Low, and control groups (A) and OPLS S-plot of 5FU-High versus control (B).

Fig. 3. Measured FT-ICR mass spectrum and theoretical spectra of [C10H11N5O6P]- and [C10H15N3O6SNa]-. Resolving power was 400,000 FWHM. m/z 328-330 (A), 329 (B), 330 (C) are indicated. Isotope fine structures are in accordance to the theoretical ones.

MALDI-FT-ICR MS analysis was successfully employed for metabolic profiling of HepG2 cells treated with the drug 5FU. PCA and OPLS-DA of ultrahigh resolution MS data could differentiate sample groups and find several responsible loadings. Elemental composition analysis provided candidates which could be confirmed by the ultrahigh resolution data permitting a comparison of measured and theoretical isotopic fine structures. The presented method enables rapid and non-targeted metabolic profiling.

Summary

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

7

-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8

t[2]

t[1]

5FU-High

5FU-Low Control

-1.0

-0.9

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

-0.22 -0.20 -0.18 -0.16 -0.14 -0.12 -0.10 -0.08 -0.06 -0.04 -0.02 -0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26

p(co

rr)[

1]

p[1]

110513_OPLS2.M1 (OPLS/O2PLS-DA)p[Comp. 1]/p(corr)[Comp. 1]Colored according to Var ID (Primary)

R2X[1] = 0.40124 SIMCA-P+ 12.0.1 - 2011-05-18 11:44:06 (UTC+9)

12

53

6119

710

8

4

1

0.5

1.0

0.0

-0.5

-1.0

p(co

rr)[1

]

-0.20 -0.10 0.00 0.10 0.20p[1]

▲

2

(A) (B)

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

7

-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8

t[2]

t[1]

5FU-High

5FU-Low Control

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

7

-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8

t[2]

t[1]

5FU-High

5FU-Low Control

-1.0

-0.9

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

-0.22 -0.20 -0.18 -0.16 -0.14 -0.12 -0.10 -0.08 -0.06 -0.04 -0.02 -0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26

p(co

rr)[

1]

p[1]

110513_OPLS2.M1 (OPLS/O2PLS-DA)p[Comp. 1]/p(corr)[Comp. 1]Colored according to Var ID (Primary)

R2X[1] = 0.40124 SIMCA-P+ 12.0.1 - 2011-05-18 11:44:06 (UTC+9)

12

53

6119

710

8

4

1

0.5

1.0

0.0

-0.5

-1.0

p(co

rr)[1

]

-0.20 -0.10 0.00 0.10 0.20p[1]

▲

2

(A) (B)

No m/z Formula Compound 1 328.045 C10H11N5O6P cAMP 2 294.071 C10H13N3O6Na

3 540.054 C15H20N5O13P2 cyclic-ADP-ribose

4 328.059 C10H15N3O6SNa Glutathione 5 228.099 C9H14N3O4 6 606.074 C17H26N3O17P2 UDP-GlcNAc 7 448.004 C10H13N5O10P2Na ADP 8 306.077 C10H16N3O6S Glutathione 9 429.994 C10H11N5O9P2Na ADP-H2O

10 408.012 C10H12N5O9P2 ADP-H2O 11 272.089 C10H14N3O6

12 146.046 C5H8NO4 Glutamic acid

5FU-High 5FU-Low Control

(A)

(B)

No.1. 328.045

No.3. 540.054

5FU-High 5FU-Low Control

5FU-High 5FU-Low Control

(A)

(B)

No.1. 328.045

No.3. 540.054

5FU-High 5FU-Low Control

329.04888329.05574

329.06205

329.04860

329.05551

329.06183

329.040 329.050 329.060 m/z

330.04763

330.05448

330.06283

330.04949

330.05427

330.05886

330.06272

330.050 330.060 m/z

328.05860

329.06205 330.05448

328.04524

329.04860

328.05847

329.06183 330.05427

328.0 328.5 329.0 329.5 330.0 m/z

328.04541

328.05860

328.040 328.060 m/z

Δ13mDa

Measured Spectrum

Theoretical Spectrumfor (C10H11N5O6P)-

Theoretical Spectrumfor (C10H15N3O6SNa)-

400,000 FWHM(A) (B) (C)

13C15N

13C

15N33S

13C218O

13C2

18O

34S

13C15N

329.04888329.05574

329.06205

329.04860

329.05551

329.06183

329.040 329.050 329.060 m/z

330.04763

330.05448

330.06283

330.04949

330.05427

330.05886

330.06272

330.050 330.060 m/z

329.04888329.05574

329.06205

329.04860

329.05551

329.06183

329.040 329.050 329.060 m/z

329.04888329.05574

329.06205

329.04860

329.05551

329.06183

329.040 329.050 329.060 m/z

330.04763

330.05448

330.06283

330.04949

330.05427

330.05886

330.06272

330.050 330.060 m/z

330.04763

330.05448

330.06283

330.04949

330.05427

330.05886

330.06272

330.050 330.060 m/z

328.05860

329.06205 330.05448

328.04524

329.04860

328.05847

329.06183 330.05427

328.0 328.5 329.0 329.5 330.0 m/z

328.04541

328.05860

328.040 328.060 m/z

Δ13mDa

Measured Spectrum

Theoretical Spectrumfor (C10H11N5O6P)-

Theoretical Spectrumfor (C10H15N3O6SNa)-

400,000 FWHM(A) (B) (C)

13C15N

13C

15N33S

13C218O

13C2

18O

34S

13C15N

328.05860

329.06205 330.05448

328.04524

329.04860

328.05847

329.06183 330.05427

328.0 328.5 329.0 329.5 330.0 m/z

328.04541

328.05860

328.040 328.060 m/z

328.04541

328.05860

328.040 328.060 m/z

Δ13mDa

Measured Spectrum

Theoretical Spectrumfor (C10H11N5O6P)-

Theoretical Spectrumfor (C10H15N3O6SNa)-

400,000 FWHM(A) (B) (C)

13C15N

13C

15N33S

13C218O

13C2

18O

34S

13C15N

Fig. 2. Peak intensities of the metabolites no. 1 and 3. Bars show standard deviation (n=5).

Table 1. Summary of elemental composition analysis and database search.

Measurements were carried out in MALDI mode. 9-aminoacridine (9AA) was applied as matrix in negative ion mode. Data were acquired within 30 seconds per spectrum and external calibration was applied. Acquired data were evaluated using multivariate statistical analysis. Principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were carried out using SIMCA-P+ ver. 12.0.1. software (Umetrics AB, Umeå, Sweden). Elemental compositions were calculated by SmartFormula software (Bruker Daltonik GmbH). The derived formula candidates were queried in the public database Chemspider using the CompoundCrawler software tool (Bruker Daltonik GmbH).

Clinical Metabolomics

Metabolomics Posterbook 2012

-13-

Bruker BioSpin

NMR

The ability to quantify a large number of compounds in every sample allows valid concentration distributions to be accumulated. Some were found to be different from the typical value sets published in textbooks and literature about inborn error analysis, where the number of samples previously used has been typically limited to small cohorts. Fig. 3 shows distributions of two examples with the textbook ranges drawn in.

The idea of getting references ranges for relevant metabolites can be extended to the complete spectroscopic NMR-pattern of a urine. This leads to models which can even detect deviations of unknown or not yet recognized compounds, a so called “untargeted analysis”. Figure 4 illustrates this methodology on the example of 4-Hydroxyphenyllactic Acid (marker for Tyrosinemia I). The coloured model shows the natural variance of more than 700 urine samples. The spectrum with the

4-Hydroxyphenyllactic Acid does obviously not fit into this cohort. This approach allows an automated screening procedure for the detection of diseases without any previous knowledge.

References [1] N. Blau, M. Duran, M. E. Blaskovics, K. M. Gibson, Physican’s Guide to the Laboratory Diagnosis of Metabolic Diseases, Springer 2005, p. 13.

NMR is a quantitative technique which can be used for identification of clinically relevant metabolites after a simple preparation of body fluids. A routine 1D proton spectrum contains hundreds to thousands of spectroscopic lines. (Fig. 1: typical 1H-NMR neonate urine spectrum). Hence from a single unspecific measurement it is possible to obtain information on a plethora of metabolites in a multi-marker approach.

NMR can deliver targeted and nontargeted results from one measurement of neonate urine in short time, thus combining quantification of a large set of compounds indicative of inborn errors with the statistical analysis of deviations from normality.

700 neonate urine samples from 8 centers in Turkey were collected and Spectra were recorded in full automation in two different laboratories (INFAI GmbH and Bruker BioSpin GmbH).

Typically, analysis of bodyfluids relies on direct quantification of preselected diagnostic biomarkers. The NMR signals of such biomarkers can be described in knowledgebases and quantified in urine spectra. Two examples of quantification are illustrated in Fig. 2. These

examples based on a real spiking study and, as a result, we can compare the quantified values with the real concentration values.

Introduction

NMR-based screening of neonate urines C.Cannet1, D.Krings1, M. Spraul1 ,H. Schäfer1 , B. Schütz1 , F.Fang1 ,S.Aygen2 ,U.Dürr2 ,P.Tremmel2 1Bruker BioSpin GmbH, Rheinstetten, Germany 2INFAI Institute for Biomedical Analysis and NMR Imaging GmbH, Köln, Germany

Study Design

Untargeted Analysis Figure 1: 1H-NMR spectrum (~4 minutes) of a turkish neonate urine and two enlargements (magnification of factor 10 and 50) with some signal assignments.

Targeted Analysis: Quantification

Figure 2: Quantification of 2-Hydroxyisovaleric Acid (top) and Phenylalanine (bottom). Left panel: Comparison of quantified values with real spiked concentration values. Right panel: Illustration of quantification of 2-Hydroxyisovalecic Acid (quantified value: 52 mmol/mol creatinine, spiked value: 50 mmol/mol creatinine) and Phenylanlanine (quantified value: 588 mmol/mol creatinine, spiked value: 500 mmol/mol creatinine) in NMR spectra.

Figure 3: Comparison of Glycine and Alanine concentrations by NMR to textbook ranges [1].

Figure 4: Automated detection of deviating spectroscopical regions. The model is plotted as a quantile-plot, showing the natural variance of each chemical shift. The overlaid black spectrum is detected as non-compliant since the signals of 4-Hydroxyphenyllactic Acid (1052 mmol/mol creatinine) do not fit in the model.

2-Hydroxyisovaleric Acid

Phenylalanine

Clinical Metabolomics

Metabolomics Posterbook 2012

-14-

Clinical Metabolomics

Bruker BioSpin

NMR

After successful detection of a metabolite we start a quantification procedure. Again the variability of the chemical background in different spectra can influence the results. A statistical analysis of such experiments delivers distributions of possible true concentrations values when measured a certain concentration value. Such distributions are exemplarily shown in Fig. 3 for Methylmalonic Acid.

Finally, real spiking experiments can be used to verify the implementation that based on the virtual spiking studies. A process of a complete quantification procedure is illustrated in Fig. 4. After a successful detection we fit the curve of the concentration and obtain a quantification result. If the result reaches or exceeds the detection limit a distribution with

possible real concentration values can be generated. The distribution based again on the results of a virtual spiking study in 700 urine spectra.

NMR is a quantitative technique which can be used for identification of clinically relevant metabolites after a simple preparation of body fluids. With the resulting NMR spectra it is possible to detect and quantify selected metabolites and thus creating a snapshot of the current metabolic phenotype.

Taking into account the natural variability in urine NMR-profiles, we perform a virtual spiking study on the basis of 700 urine spectra of Turkish neonates. Consequently, we obtain ranges for reliable quantification of the respective metabolites. In Fig. 1 we show results of the detection of Methylmalonic Acid and 3-Phenyllactic Acid produced in 50000 spiking experiments for each metabolite.

In each spiking experiment we store the true and false detections. The curves in Fig. 1 are functions that represent the true and false detection rate in dependency on the spiked concentration value. The courses of such curves are determined by several characteristics of the searched signal like the chemical background of the search region and the complexity

of the signal pattern. Fig. 2 shows a comparison of three spectra after spike experiments with Methylmalonic Acid and 3-Phenyllactic Acid.

Introduction

NMR Based Metabolomics in Clinical Studies of Human Population : Quantification D.Krings, C. Cannet, B. Schütz, H. Schäfer, M. Spraul , F. Fang

Bruker BioSpin GmbH, Rheinstetten, Germany

Preliminary Studies

Figure 4: After successful detection of the desired pattern the quantification procedure starts and produces a distribution of possible concentration values (Example of L-Isoleucine)

Quantification Accuracy

Figure 1: Detection rate of a spiking experiment in a cohort of newborns. The plot shows results of Methylmalonic Acid and 3-Phenyllactic Acid.

Detection and Quantification Example

Detection Limits

Methylmalonic Acid

3-Phenyllactic Acid

Figure 3: Measured concentration value and distribution of the true spiked concentration values.

Quantification accuracy of Methylmalonic Acid

Figure 2: Two different metabolites spiked with 50 mmol/mol Creatinine virtually in three spectra. In addition to the concentration, the area of the signals depends on the number of protons (Methylmalonic Acid:3 protons, 3-Phenyllactic Acid:1 proton).

Detection

Quantification

Result 527 mmol/mol creatinine

Detection Limit

reached

Distribution of possible Values

Metabolomics Posterbook 2012

-15-

Plant Metabolomics

For research use only. Not for use in diagnostic procedures.

Structure elucidation and confirmation for plant metabolomics: novel approaches

Sven Heiling1, Gabriela Zurek2 Emmanuel Gaquerel1, Matthias Schöttner1, Bernd Schneider1, Marina Behrens2, Aiko Barsch2, Ian Baldwin1 1 Max-Planck-Inst. for Chemical Ecology, Jena, Germany 2 Bruker Daltonik GmbH, Bremen, Germany

Introduction Presently, the key bottleneck of plant metabolomics is structural confirmation and elucidation of secondary metabolites.

Nicotiana attenuata is a well-established model system to monitor plant-herbivore interactions with metabolomics being a novel approach to investigate the underlying biology [1].

17-Hydroxygeranyllinallool diterpene glycosides (HGL-DTGs) are abundant direct defense compounds with their mode of action being largely unknown [1-3]. New acyclic HGL-DTGs (attenoside, nicotianoside I, II, III) were characterized using MS and NMR (1H, 13C) after extraction of several hundred grams of raw plant material [2,3]. Such scale is not compatible to the analytical scope of metabolomics.

Here, we present novel tools facilitating the identification process of natural products when mass spectral libraries are not yet available and the sample amount is limited.

[1] Gaquerel, E., J. Agric. Food Chem. 58 (2010), 9418-9427. [2] Jassbi, A., Z. Naturforsch. 61b (2006), 1138-1142. [3] Heiling, S., Plant Cell 22 (2010), 273-292.

MS detection was performed using a maXis UHR-Qq-TOF mass spectrometer (Bruker Daltonik) operated in ESI positive and negative mode in a scan range from 50-1500m/z (MS fullscan & AutoMS/MS). Mass calibration was achieved with sodium formate clusters.

Data was evaluated with DataAnalysis 4.1 software (Bruker Daltonik) using the FindMolecularFeatures (FMF) and Dissect algorithms for deconvolution. Molecular formula determination was carried out by combined evaluation of mass accuracy, isotopic patterns, adduct and fragment information using SmartFormula3D and the FragmentExplorer. Library spectra were stored in the LibraryEditor.

Results

UHR-Qq-TOF-MS

HGL-DTGs in complex samples can be recognized by

• distinct adduct pattern of [M+H]+, [M+NH4]+ and [M+Na]+ in ESI positive mode

• rich in-source fragmentation with neutral losses of rhamnose & glucose

• backbone fragment 271.242m/z

Deconvolution algorithms (FMF, Dissect) have been successfully applied in the early identifcation steps.

Dedicated MS/MS interpretation incorporating structural assignments was performed leading to a well characterized MS/MS library.

All presented tools for identification of HGL-DTGs will be applied now to other Solanum species.

Fig. 1 Core structure of HGL-DTGs (Glc= Glucose, Rh= Rhamnose) with the respective sugar and malonylgroups and identification from one selected Nicotiana extract.

R1 Sugar 1

R1 Sugar 2

R2 Sugar 1

R2 Sugar 2

Malonyl -groups

Formula detected

NH4-Adduct

Mass Accuracy

[ppm]

milli Sigma

Lyciumoside I Glc Glc C32H58NO12 -0.10 3.8 Lyciumoside II Glc Glc Glc C38H68NO17 -0.05 7.9 Lyciumoside IV Glc Rh Glc C38H68NO16 -0.16 10.5 Nicotianoside I Glc Rh Glc 1 C41H70NO19 0.27 3.4 Nicotianoside III Glc Rh Glc Rh C44H78NO20 0.21 5.8 Nicotianoside IV Glc Rh Glc Rh 1 C47H80NO23 0.13 6.3 Attenoside Glc Rh Glc Glc C44H78NO21 -0.37 6.4 Nicotianoside VI Glc Rh Glc Glc 1 C47H80NO24 -0.28 4.8

OR1

OR2

Methods Especially enriched samples in HGL-DTGs from Nicotiana leaves were generated by extraction with 80% methanol, subsequent washing and preparative chromatography [2,3]. Samples were subjected to LC-MS analysis and detailed fragmentation studies by means of direct infusion measurements (3µL/min; dilution 1:50). Standard extracts of various Nicotiana species were prepared as described in [1].

Chromatographic separation was carried out using an RSLC system (Dionex) with a 100x2mm Acclaim RSLC 120 C18 column, flow rate 0.3mL/min, Solvent A: water + 0.1% HCOOH, Solvent B: acetonitrile + 0.1% HCOOH, linear gradient from 5%B to 80% B in 10min.

Meas. m/z Ion Formula Sum Formula Adduct m/z err [ppm] mSigma

921.4691 C44H73O20 C44 H74 O21 M-H2O+H 921.469 -0.14 6.7 939.4798 C44H75O21 M+H 939.4795 -0.33 15.4 956.5065 C44H78NO21 M+NH4 956.5061 -0.42 6.8 961.4616 C44H74NaO21 M+Na 961.4615 -0.10 5.9

937.4654 C44H73O21 C44 H74 O21 M-H 937.465 -0.39 10.6 983.4713 C45H75O23 M+HCOOH-H 983.4705 -0.87 9.9

Fig. 2 LC-MS Basepeak chromatogram of plant extract enriched in HGL-DTGs; FMF spectra & formula assignment of Attenoside () in positive and negative mode.

939.4798 956.5065

M-H2O+H

M+H M+NH4

M+Na

+MS, MolFeature, 6.40-6.65min

0.0

0.5

1.0

1.5

Inte

nsity

x10

6

920 925 930 935 940 945 950 955 960 m/z

921.4691

FMF: Positive Mode

937.4654

983.4713 M-H

M+HCOOH-H -MS, MolFeature, Line, 6.36-6.65min

0

1

2

3

4

930 940 950 960 970 980 990 m/z

FMF: Negative Mode

Deconvolution- FMF

HGL-DTGs often show a very distinct pattern of adduct formation in ESI positive mode. In many cases the ammonium adduct is the most prominent ion in a series of the [M+H]+, [M+NH4]+ and [M+Na]+ adducts, however the adduct ion ratios are easily changed by different salt concentrations. In ESI negative mode, only the [M-H]- and the formic acid adduct [M+HCOO]- are observed. Adduct information can be combined with isotopes and charge states by the FindMolecularFeatures (FMF) peak finding algorithm. The corroboration of a sum formula by evaluating multiple adducts is achieved in SmartFormula.

The FMF spectra of Attenoside are presented in Fig. 2 in combination with the SmartFormula results. Peaks assigned with a formula are highlighted by a yellow circle. In metabolomics, the FMF peak finder is the first step of data pre-processing prior to statistical analysis.

Deconvolution- Dissect HGL-DTGs measured in MS positive mode exhibit many in-source fragments due to cleavages of the glycosidic bonds even at very soft ion transfer conditions. In complex samples such as the Nicotiana extracts, a peak deconvolution algorithm combining the adduct and fragments to individual compounds is one possible processing strategy in the identification process.

The Dissect algorithm for deconvolution combines all ions with a similar chromatographic elution profile to one compound.

A set of Dissect chromatogram traces is shown in Fig. 3 with an example spectrum of Attenoside. Combining precursor ion information with in-source fragments enabled sum formula annotation for neutral losses. The neutral losses of glucose and rhamnose are observed. In general MS spectra with in-source fragments and MS/MS spectra from CID fragments (Fig. 4) have a very similar fingerprint.

6.48 min

6.2 6.4 6.6 6.8 7.0 7.2 7.4 Time [min] 0.0

0.2

0.4

0.6

0.8

1.0 In

tens

ity x

107

417.3000

471.1710

597.3633

759.4164

956.5064

Glu

c - 0

.000

2

Glu

c +

0.00

03

H2O

+ 0

.000

2

Rha

H2O

- 0.

0001

Glu

c +

0.00

01

+MS, Dissect, 6.33-6.65min #377-396

0.0

0.2

0.4

0.6

0.8

1.0

Inte

nsity

x 1

06

300 400 500 600 700 800 900 m/z

271.2420

939.4798

921.4691

In a direct infusion experiment, the [M+H]+ ion of Attenoside at 939.4795m/z was submitted to an MS/MS experiment. As the glycosidic bonds fragment easily, very low collision energies of only 10-12eV are applied. The combined evaluation of mass accuracy and isotopic pattern in MS and MS/MS in SmartFormula 3D provides a fast way of safely assigning molecular formulae to the precursor and fragments as shown in Fig. 4. The SmartFormula 3D results can be combined with structural information using the FragmentExplorer. The structure of Attenoside and possible resulting fragment structures are linked to the MS/MS spectrum.

Based on the identified HGL-DTGs an MS/MS library was then created. The identification results of different HGL-DTGs in extracts (no special enrichment) from Nicotiana using the library search are summarized in Fig. 5.

Library Search MS/MS Effective Purity

Nicotiana attenuata

Nicotiana attenuata

Nicotiana obtusifolia

Nicotiana x sanderae

leaf flower leaf leaf Lyciumoside II 725(MS) - - Lyciumoside IV 984 977 - - Nicotianoside I 965 926 - 551 Nicotianoside II 983 976 963 - Nicotianoside III 652 - - - Nicotianoside IV 802 823 - Nicotianoside V - 934(MS) 873 - Attenoside 807 899 - - Nicotianoside VI 863 - 848 - Nicotianoside VII 914 968 867 -

3 4 5 6 7 8 9 Time [min] 0.0

0.2

0.4

0.6

0.8

Inte

nsity

x 1

06

BPC of pooled Flower Extract EIC 271.2420±0.001 of pooled Flower Extract

Fig. 5 Chromatogram of pooled flower & reproductive organ extracts from Nicotiana attenuata in ESI positive mode, BPC and EIC for m/z 271; Table with effective purity scores from MS/MS library search for different Nicotiana samples.

Identification

MS/MS Interpretation

O H

O

O H

O H

HO

O

O H

O H

HO

O

O H O H

O H

O

O O

O H

O

O H

O

HO

Attenoside

271.2420

417.2999

471.1708

597.3633 633.2237

759.4161 939.4795

C 20 H 31 , -0.21 mDa

C 2

0 H

33

O 1

, -0

.25

mD

a

C 1

2 H

21

O 9

, -0

.2 m

Da

C 1

2 H

21

O 1

0 , -

0.25

mD

a

C 2

6 H

41

O 4

, -0

.09

mD

a

C 18 H 31 O 14 , -0.27 mDa,

C 1

8 H

33

O 1

5 , -

0.29

mD

a

C 3

2 H

53

O 1

0 , -

0.11

mD

a

C 2

4 H

41

O 1

9 , -

0.47

mD

a

C 3

8 H

63

O 1

5 , -

0.47

mD

a

C 3

8 H

65

O 1

6 , -

0.44

mD

a

C 4

4 H

73

O 2

0 , -

0.74

mD

a

C 4

4 H

75

O 2

1 , -

0.44

mD

a

SmartFormula3Dspectrum:C44H75O21, 939.48, -0.44 mDa, -0.47 ppm, mSigma: 12.0

0.00

0.25

0.50

0.75

1.00

1.25

Inte

nsity

x10

5

300 400 500 600 700 800 900 m/z

H+

O

O H O

O H

H O

O

O

O H

H O

H O

H+

H+

B

C

Fig. 3 Dissect chromatogram traces after deconvolution and Dissect MS spectrum grouping the in-source fragments of Attenoside.

A

Characteristic for the fragmentation of the HGL-DTGs are successive neutral losses of the sugar moieties glucose (Dm= 162.0528 =C6H10O6) and rhamnose (Dm= 146.0579 =C6H10O5). The fragment at 271.242 m/z with the formula C20H31 represents the hydroxygeranylinalool backbone after losing the hydroxyl groups.

This fragment in combination with the neutral losses of sugars and malonyl groups is a very good indicator for presence of a HGL-DTG.

The table in Fig. 1 lists all HGL-DTGs that were successfully identified in a selected enriched Nicotiana extract by means of accurate mass and isotopic pattern only.

Conclusions Fig. 4 (A) SmartFormula 3D result for Attenoside showing the proposed sum formulae for precursor (left) and fragment (right) ions; (B) SmartFormula 3D MS/MS spectrum of Attenoside with formula assignments within the spectrum; (C) Fragment Editor for MS/MS interpretation linking molecular formulae, structural suggestions for fragments & the MS/MS spectrum.

Metabolomics Posterbook 2012

-16-

Plant MetabolomicsM

S-b

ased

der

eplic

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n an

d cl

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ficat

ion

of d

iterp

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gl

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of 2

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man

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atth

ias

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Inst

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in #

(92-

100)

0 1 2 3 4 5 6 Intens. ×104

300

400

500

600

700

800

m/z

Glc

Glc

G

lc

Glc

G

lc

Rha

Rha

G

lc

H2O

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men

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ith a

glyc

one

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2(86

3.43

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10.0

eV, 3

.2-3

.6m

in #

(92-

100)

0 1 2 3 4 5 6 Intens. ×104

300

400

500

600

700

800

m/z

Glc

G

lc

Ma M

a R

ha

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ha

863.

4278

Sug

ar fr

agm

enta

tion

1 2 3 5 7 8 10

13 14

18 20

18

20

22

24

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min

] 0.

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0 2.

5 3.

0

Intens. ×105

Dis

sect

clu

ster

ing

LC/M

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atog

ram

5 10

15

20

25

30

35

40

[m

in]

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1.5

2.5

Intens. ×105

18

20

22

24

26 [m

in]

0

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EIC

trac

e 27

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2(86

3.43

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10.0

eV, 3

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.6m

in #

(92-

100)

0 1 2 3 4 5 6 Intens. ×104

300

400

500

600

700

800

m/z

683.

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iana

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a. T

hese

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TGs

act a

s po

tent

an

ti-he

rbiv

ore

com

poun

ds,

who

se

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regu

late

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the

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ffect

can

be

seen

in m

alon

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ion

of th

ese

com

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early

no

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onyl

ated

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are

desc

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far.

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trodu

ctio

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al.

(201

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Than

ks to

Tho

mas

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n fo

r the

tech

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port

and

the

MP

I and

DAA

D fo

r fun

ding

.

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otia

na

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nuat

a sa

mpl

es

wer

e pr

epar

ed

as

desc

ribed

pr

evio

usly

[1].

Chr

omat

ogra

phic

se

para

tion

of

extra

cts

was

ca

rrie

d ou

t us

ing

a U

HP

LC

syst

em

com

bine

d w

ith

ultra

-hi

gh-r

esol

utio

n Q

TOF

MS

de

tect

ion.

Due

to th

e pr

esen

ce o

f mul

tiple

labi

le g

lyco

sidi

c bo

nds,

H

GL-

DTG

s ex

hibi

t in

tens

ive

fragm

enta

tion

whi

ch

prov

ides

inf

orm

atio

n va

luab

le f

or s

truct

ure

eluc

idat

ion

[2].

Dec

onvo

lutio

n of

IS-

CID

clu

ster

s w

as p

erfo

rmed

us

ing

the

Dis

sect

alg

orith

m,

mol

ecul

ar f

orm

ulae

wer

e de

term

ined

by

Sm

artF

orm

ula

3D (B

ruke

r Dal

toni

cs).

60

20

37

13

13

0

0

Glc

R

ha

Ma

Suga

r 1 S

ugar

2 S

ugar

1 S

ugar

2

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umos

ide

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lc

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cium

osid

e II

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G

lc

Glc

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cium

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G

lc

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N

icot

iano

side

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lc

Rh

Glc

1

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otia

nosi

de II

G

lc

Rh

Glc

2

Nic

otia

nosi

de II

I G

lc

Rh

Glc

R

h N

icot

iano

side

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R

h G

lc

Rh

1 N

icot

iano

side

V

Glc

R

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side

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lc

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G

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otia

nosi

de V

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d fro

m t

ypic

al

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s D

m (

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= H

2O, A

) M

a =

m/z

86

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4 =

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B)

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= m

/z 1

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579

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O4,

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9) a

nd c

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cter

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uct

form

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n ([M

+NH

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nd [M

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ce

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: 451

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rmul

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m/z

: 597

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ccur

ate

mas

s: 5

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ppm

: 0.3

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rmul

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m/z

: 417

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ccur

ate

mas

s: 4

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ppm

: 0.0

0 Fo

rmul

a: C

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2

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+MS

, Dis

sect

, 22.

0-22

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in #

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0

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200

300

400

500

600

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800

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sect

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ies

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ns

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II

and

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is U

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ems

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ass

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+NH

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a m

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]+),

12.0

eV, 2

0.4m

in #

1207

300

400

500

600

700

800

900

1000

110

0 m

/z

Hex

D

Hex

DH

ex

DH

ex

DH

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DH

ex

DH

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in #

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0 0.

2 0.

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6 0.

8 1.

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400

600

800

1000

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sect

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ster

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rofil

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23

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spec

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2 O

+

O

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O

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3

Frag

men

ter

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rary

con

stru

cted

with

11

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ied

and

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s at

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t CID

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tage

s (1

0 –

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sitiv

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n m

ode.

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e an

nota

ted

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ents

an

d fo

und

176

non-

redu

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t fra

gmen

ts.

Venn

di

agra

m

of

all

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r co

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fra

gmen

ts.

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paris

on

show

s t

hat

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onyl

-moi

etie

s ar

e ju

st p

rese

nt,

whe

n gl

ucos

e is

par

t of

the

fragm

ent.

W

e de

velo

ped

an e

ffici

ent p

ipel

ine

for t

he d

erep

licat

ion

and

clas

s pr

edic

tion

of H

GL-

DTG

s pr

oduc

ed b

y di

ffere

nt

sola

nace

ous

spec

ies

This

pip

elin

e is

bas

ed o

n th

e ex

tract

ion

of h

igh

reso

lutio

n IS

-CID

spe

ctra

con

tain

ing

the

char

acte

ristic

fra

gmen

ts fo

r HG

L-D

TG a

glyc

one

and

thei

r cla

ssifi

catio

n ba

sed

on d

iagn

ostic

frag

men

t ana

lysi

s an

d al

ignm

ent

with

kno

wn

HG

L-D

TG ta

ndem

MS

.

App

lyin

g th

is a

nnot

atio

n pi

pelin

e to

the

anal

ysis

of t

he

met

abol

omic

s pr

ofile

of 2

3 so

lana

ceae

iden

tifie

s i.

non

-uni

form

HG

L-D

TG b

iosy

nthe

tic c

apac

ities

in th

e fo

ur m

ain

genu

s an

alyz

ed a

nd

ii. m

alon

ylat

ion

as a

hig

hly

cons

erve

d re

actio

n.

Dee

p an

nota

tion

of ta

ndem

MS

frag

men

ts s

uppo

rts

that

mal

onyl

atio

n ta

kes

plac

e on

glu

cose

moi

etie

s an

d fu

ture

wor

k w

ill e

xam

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the

biol

ogic

al re

leva

nce

of th

is

reac

tion.

Metabolomics Posterbook 2012

-17-

Plant Metabolomics

GC-APCI-QTOF-MS: An innovative Technique for Metabolite Profiling Studies

8. References

Strehmel N, Boettcher C, Bernstein C, Scheel D

Leibniz Institute of Plant Biochemistry, Department of Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale)

5. Mapping of unknown MSTs

1. Motivation

2. Concept

3. Materials and Methods

4. MST Annotation

6. Elemental Composition

7. Conclusions

9. Acknowledgement

1. Metabolite Profiling by GC-EI-QUAD-MS 2. Multivariate Statistics (ICA, ANOVA, t-test)

Mass Spectral

Tag (m/z; RI)

3. MST Annotation by Library Search 4. Structure Elucidation of unknown MSTs

Electron Impact Ionization

Atmospheric Pressure Chemical Ionization

- Molecular Fingerprint - Elemental Composition - CID Mass Spectra

Samples

RI,

m/z

- 30 mg fresh weight - 300 µL MeOH - 15 min at 70°C - 200 µL CHCl3

- 400 µL H2O

2. Extraction of Roots 3. Derivatization

- 40 µL MeOX - 60 µL BSTFA - 10 µL FAMES - 10 µL Alkanes

1. Plant Cultivation 4. Measurement

1. Metabolite Profiling: GC-EI-QUAD-MS

2. Structure Elucidation: GC-APCI-QTOF-MS

RI Transfer Molecular Ion

TCA

Sugar Breakdown and Glycolysis Amino Acid Biosynthesis

Miscellaneous Metabolites

GC-EI-QUAD-MS-based metabolite profiling of roots (At and AtPi) revealed 29 differential compounds (t-test: control vs. co-cultivated, p < 0.05), of which 15 could be identified. Thereof, 13 could be classified into metabolic pathways.

[1] Hummel J, Strehmel N, Bölling C, Schmidt S, Walther D, Kopka J: Mass Spectral Search and Analysis using the Golm Metabolome Database (2012) Metabolomics and More, in press. [2] Horai H et al.: MassBank: a public repository for sharing mass spectral data for life sciences (2010) Journal of Mass Spectrometry 45(7) 703-714. [3] Strehmel N, Hummel J, Erban A, Strassburg K, Kopka J: Retention index thresholds for compound matching in GC-MS metabolite profiling (2008) Journal of Chromatography B 871(2) 182-90. [4] Kind T, Wohlgemuth G, Lee DY, Lu Y, Palazoglu M, Shahbaz S, Fiehn O: FiehnLib: Mass Spectral and Retention Index Libraries for Metabolomics Based on Quadrupole and Time-of-Flight Gas Chromatography/Mass Spectrometry (2009) Analytical Chemistry 81 (24) 10038-10048.

GC-MS-based metabolite profiling of crude plant extracts reveals more than 100 compounds. Thereof, more than half can be identified using comprehensive mass spectral libraries [1,2], the rest remains unknown. In order to identify the unknown compounds we applied GC-APCI-QTOF-MS in succession to our metabolite profiling studies since this technique allows for the determination of elemental compositions of quasi-molecular and fragment ions. For the mapping of spectral features of both techniques we used the retention index (based on Fatty Acid Methyl Ester) and the molecular ion as long as it could be derived from metabolite profiles. As proof of concept, we cocultivated Arabidopsis thaliana for two weeks with Piriformospra indica with the objective to study the metabolic phenotype of the growth promoting effect.

GC-APCI-QTOF-MS can complement GC-EI-MS-based metabolite profiling. Its soft ionisation manner, high mass accuracy and resolution make it feasible. In addition we could show that GC-APCI-QTOF-MS is much more sensitive for most substance classes relevant for metabolite profiling studies than GC-EI-MS and can not only be used for structure elucidation purposes of so-far unidentified compounds, but also for the profiling of low-abundant compounds.

6. GC-APCI-QTOF-MS vs. GC-EI-QUAD-MS

Retention Index GC-EI-QUAD-MS

Ret

entio

n In

dex

GC

-AP

CI-Q

TOF-

MS

In order to identify the remaining MSTs we used the retention index, as this is a useful measure to map peaks from one chromatographic system to another [3]. Often alkanes are used as retention index markers. However, they do not ionize under atmospheric pressure chemical ionization conditions. Consequently, we had to switch to fatty acid methyl esters and estimated the retention index according to Fiehn [4]. Besides the retention index we consulted the molecular ion for the MST mapping as long as it was available.

Roots of Arabidopsis thaliana (At)

Piriformospora indica co-cultivated roots of Arabidopsis thaliana (AtPi)

MST MAPPING Retention Index + Molecular Ion

LIBRARY SPECTRUM

EXPERIMENTAL SPECTRUM

The authors thank Jessica Thomas for the preparation of extracts and acknowledge Aiko Barsch as well as Thomas Arthen-Engeland from Bruker Daltonics for technical support during the setup of the system.

126.0424144.0553

169.0732

191.0956217.1099

243.1273271.1234

319.1632

331.1636

361.1755

1. +MS2(594.0000), 20-20eV, 25.2-25.3min #(4469-4480)

0

2

4

6

8

5x10Intens.

150 200 250 300 350 400 450 500 550 m/z

C11H23O2Si2+ C13H31O3Si3+ C14H31O3Si3+

APCI (+)-CID-MS (594 @20 eV)

Thymine (2TMS) Kinetin (2TMS)

Putrescine (4TMS) Spermidine (5TMS)

Fumaric acid (2TMS) Isocitric acid (4TMS) Tartaric acid (4TMS)

IAA (2TMS) ABA (1MeOX, 1TMS) JA (1MeOX, 1TMS) GA1 (3TMS)

Glucose (1MeOX, 5TMS) Ribitol (5TMS) Maltose (1MeOX, 8TMS) myo Inositol (6TMS)

100 µL standard stock solution (39 nM – 20 µM) were dried down and derivatized according to the routine protocol. GC-EI-QUAD-MS resulted in lower limits of detection (10 µM – 100 µM) than GC-APCI-QTOF-MS, i.e. GC-APCI providing the better sensitivity.

EI

At

AtPi

Two week old hydroponically grown plantlets were inoculated with fungal mycelium (Piriformo-spora indica; Pi). After two weeks of co-cultivation roots were harvested (control: At; cocultivated: AtPi) and analyzed for metabolic differences.

Retention Index

[FAMES]

Molecular Ion

[m/z]

Differential Analytes

Analyte Composition

Metabolite Composition

1016361 530 At > AtPi C28H42O6Si2 C22H26O6

726948 374 At > AtPi C13H30N4O3Si3 C4H6N4O3

644769 324 At > AtPi C15H24O4Si2 C9H8O4

501849 329 At > AtPi C13H27N3OSSi2 C7H11N3OS

905598 593 At < AtPi C24H51NO6SSi4 C12H19NO6S

476838 232 At < AtPi C9H20O3Si2 C3H4O3

Metabolomics Posterbook 2012

-18-

Plant Metabolomics

For research use only. Not for use in diagnostic procedures.

Improved method for characterization of phenolic compounds from rosemary extracts using ESI-Qq-TOF MS

Isabel Borrás-Linares1, Gabriela Zurek2, David Arráez-Román1, Antonio Segura-Carretero1, Alberto Fernández-Gutiérrez1

1 CIDAF, Granada, Spain 2 Bruker Daltonik, Bremen, Germany

Introduction Rosemary (Rosmarinus officinalis L.) is a shrub traditionally used as culinary spice, as well as in folk medicine, being a greatly valued medicinal herb. In fact, this plant exerts a great number of pharmacological activities: hepatoprotective, antibacterial, antithrombotic, antiulcerogenic, diuretic, antidiabetic, antinociceptive, anti-inflammatory, antitumor and antioxidant activities. Most of these observed effects are linked to its phenolic content. The characterization of phenolic compounds (Fig. 1) in the Rosemary extracts is an important step towards a better understanding of the pharmacological action.

Recently there has been a growing interest in the use of natural antioxidants in the food industry as a preservation method. Due to its high antioxidant capacity, rosemary turned out being one of the most used natural antioxidant because of its value for preservation and its beneficial effects mentioned above.

Two rosemary extracts obtained using supercritical fluid extraction (SFE, CO2 at 150 bar) and pressurized liquid extraction (PLE, H2O at 200°C) were dissolved in ethanol at 10 mg/mL. Chromatographic separation was performed on an Ultimate 3000 UHPLC with a Kinetex C18 column (2x100mm, 2.6µm particles) using as mobile phase A 0.1% aqueous formic acid and as mobile phase B acetonitrile. A 30 min linear gradient from 5 to 100 % solvent B at a flow rate of 0.5 ml/min was used. Extracts were analyzed in ESI positive and negative mode using a maXis impact ESI-Qq-TOF system (Bruker Daltonik) in the mass range 50-1000m/z operated at 3Hz, in MS full scan and MS/MS mode.

Compounds were characterized by taking into account retention time, elemental formulae derived from accurate mass and isotopic pattern information (calculated in the DataAnalysis software) as well as both in-house and public databases (SciFinder, ChemSpider, MassBank) and literature.

Results

Methods

Conclusions

UHR-QqTOF

• The novelty of this work is the development of a faster and more sensitive method for characterization of rosemary phenolic extracts compared to previous studies.

• The presented methodology has proven to be a useful tool for the separation of a wide range of phenolic compounds in rosemary extracts: phenolic diterpenes, flavonoids, phenolic acids, among other classes of polyphenols.

• Due to the higher sensitivity of the novel ESI-Qq-TOF system used, we were able to find and tentatively identify new compounds which were not detected previously in these extracts.

The presented methodology has proven to be a useful tool for the separation of a wide range of phenolic compounds in rosemary extracts: phenolic diterpenes (carnosol, carnosic acid, rosmanol and its isomers epirosmanol /epiisorosmanol), flavonoids (apigenin, genkwanin, ladanein, scutellarein, cirsiliol), phenolic acids (artepillin C), among others classes of phenolic compounds such as rosmarinic acid or glycoside/glucuronide conjugates of flavonoids (nepitrin, hesperidin, luteolin-7-glucuronide). Due to the higher sensitivity of the ESI-Qq-TOF system, we were able to find new compounds which were not detected previously in these extracts such as cirsiliol, 5-methoxy-6,7-methylenedioxyflavone, 12-O-methylcarnosol, 12-O-methylcarnosic acid. Table 1 presents a summary how many compounds have been detected in the different extract types as well as the mass accuracy and mSigma values obtained. mSigma values provide a measure for the goodness of the isotopic fit (ranging from 0-1000, the lower the value the better the fit), Fig. 3 shows the mass spectrum of scutellarein with its simulated spectrum as an example. In total, 51 phenolic compounds were tentatively identified. Table 2 presents the identification results in ESI negative mode as a representative excerpt for all characterized metabolites.

Fig. 1 Structures of selected phenolic diterpenes from Rosemary.

Carnosic acid

Rosmanol

Carnosol

References: [1] Borrás-Linares et al, J. Chromatography A, 1218 (2011) 7682-7690. [2] http://msbi.ipb-halle.de/MetFrag/

Extract Polarity Number of identified phenolic compounds

Average mass accuracy [ppm]

Average mSigma

SFE positive 13 -0.6 7.7

SFE negative 15 -0.86 13.9

PLE positive 15 -0.11 10.3

PLE negative 34 -0.52 9.7

RT [min] Meas. m/z Ion Formula

[M-H]- m/z err [ppm] mSigma Extract Proposed compound

...

8,1 269,046 C15H9O5 269,0455 -1,8 3,6 SFE /PLE Apigenin

8,2 299,056 C16H11O6 299,0561 0,4 15,9 PLE Diosmetin / Hispidulin

8,4 329,0671 C17H13O7 329,0667 -1,3 4,3 SFE Cirsiliol

9,4 345,1711 C20H25O5 345,1707 -1 4,3 SFE/PLE Rosmanol

9,5 299,0561 C16H11O6 299,0561 0,2 10,7 PLE Diosmetin / Hispidulin

9,6 313,0715 C17H13O6 313,0718 0,7 8,2 SFE/PLE Cirsimaritin

9,6 313,0721 C17H13O6 313,0718 -1,1 12,3 SFE/PLE Ladanein

9,9 345,1705 C20H25O5 345,1707 0,7 9,5 SFE/PLE Epiisorosmanol

10,4 345,171 C20H25O5 345,1707 -0,8 31,5 SFE/PLE Epirosmanol

10,8 283,0619 C16H11O5 283,0612 -1,4 4,7 SFE/PLE Genkwanin

12,7 319,192 C19H27O4 319,1915 -1,5 10,6 SFE Ubiquinol 10

13 299,1654 C19H23O3 299,1653 -0,4 14,5 SFE Artepillin C

13,3 329,1761 C20H25O4 329,1758 -0,7 2,9 SFE/PLE Carnosol

14 343,1557 C20H23O5 343,1551 -1,8 10,8 SFE Rosmadial

14,3 373,2015 C22H29O5 373,202 1,6 8,9 SFE 7-Ethoxyrosmanol

14,9 331,1918 C20H27O4 331,1915 -1,1 22 SFE/PLE Carnosic Acid

Tab. 2 Excerpt of identification results from ESI negative mode.

Tab. 1 Summary of identification results.

This work aims for the comprehensive characterization of bioactive secondary metabolites present in two rosemary extracts obtained by SFE and PLE. Both extracts were analyzed in positive and negative ionization modes in order to obtain a complete picture. Compared to a previous study [1], the chromatographic runtime was shortened to 30 min without significant loss of chromatographic resolution. The four resulting base peak chromatograms (BPCs) after spectral background subtraction are depicted in Fig. 2. The elution profiles indicate that the PLE extraction method yields compounds of higher polarity compared to the SFE extraction.

Target compounds were tentatively identified taking into account retention time, accurate mass and isotopic pattern (yielding the molecular formula) and subsequent database and literature searches. In this particular work, the data was compared to the previous characterization of rosemary metabolite extracts by HPLC-DAD-ESI-TOF-MS [1].

Fig. 2 Base peak chromatograms (BPC) of SFE and PLE Rosemary extracts in electrospray positive and negative mode.

PLE negative mode

PLE positive mode

SFE negative mode

SFE positive mode

For further characterization MS/MS experiments were carried out with a special focus on the negative ionization mode. Some general observations could be made: at medium collision energies of approx. 20eV most fragmentation occurs in the periphery of the phenolic compounds. The phenolic diterpenes, such as rosmanol, primarily exhibit a neutral loss of CO2 from the carboxylic acid group and H2O. SmartFormula 3D allows for the combined evaluation of MS and MS/MS spectra and supports the direct link to the in-silico fragmentation in MetFrag [2]. Out of 815 hits in PubChem for the elemental composition C20H26O5, rosmanol was returned as the 3. hit (Fig. 4) .

Fig. 3 Measured and simulated mass spectrum of Scutellarein in ESI negative mode.

285.0407

286.0439

21. -MS, 7.0-7.3min #1242-1294, -Peak Bkgrnd

285.0405 1-

286.0438

21. C15H9O6, -285.0405 0.0

0.5

1.0

1.5

5 x10

Intens.

0.0

0.5

1.0

1.5

5 x10

284 285 286 287 288 289 290 m/z

Scutellarein

Mass Accuracy -0.7ppm Isotopic Pattern Fit 7.1mSigma Resolution 21926

182.9888 221.1191 248.9601 283.1711

313.0725

345.1712 7. -MS, 9.4-9.5min #1117-1135

283.1707

301.1814

345.1708

7. -MS2(345.1712), 22.4eV, 9.4-9.5min #1118-1137 0.0

0.2

0.4

0.6

0.8

5 x10

Intens.

0.0

0.2

0.4

0.6

0.8

5 x10

180 200 220 240 260 280 300 320 340 m/z

Fig. 4 MS and MS/MS spectrum of Rosmanol in ESI negative mode with SmartFormula 3D and MetFrag result.

815hits in PubChem

3. hit Rosmanol

maXis Impact

Metabolomics Posterbook 2012

-19-

Plant Metabolomics

Sulfur-containing metabolite-targeted analysis using liquid chromatography-mass spectrometry

Ryo Nakabayashi1, Yuji Sawada1, Makoto Suzuki1, Masami Yokota Hirai1, Noriya Masamura2, Masayoshi Shigyo3 and Kazuki Saito1,4

1 RIKEN Plant Science Center, 2 Somatech Center, House Foods Corporation, 3 Faculty of Agriculture, Yamaguchi University, 4 Graduate School of Pharmaceutical Sciences, Chiba University

lants accumulate human beneficial metabolites such as amino acids, flavonoids and sulfur-containing metabolites (S-metabolites) with a wide range of biological activities related to antioxidation, platelet aggregation inhibition, and anticancer benefit. Profiling methods of S-metabolites with high accuracy and throughput are being required for efficient phytochemical functional genomics and crop breeding. Here, we describe the S-metabolite-targeted analysis using liquid chromatography (LC)-fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) with U-13C-labeled onion bulb. Natural abundance of 32S (31.972072 Da) and its isotope 34S (33.967868 Da) is 95.02 % and 4.21 %, respectively. These facts are obviously reflected on molecular ion peaks of S-metabolites. Thus, the use of such information allows us to efficiently and accurately extract the peaks related to S-metabolites. By using this method, finally, S-alk(en)ylcysteine sulfoxides together with their intermediates were chemically annotated in onion bulb. It means to realize S-omics using LC-FT-MS for screening of biomarkers in any other organisms.

P Summary

Profiling of S-metabolites using LC-FT-MS in non-labeled and 13C-labeled onion bulb

Genomics Transcriptomics

Next generation sequencer etc

Phytochemical genomics Crop breeding

ATGC

Metabolomics LC-MS etc

Health-promoting crops

Amino acids Vitamines Lipids Terpenoids

Phenylpropanoids Flavonoids Sulfur-metabolites Alkaloids

High throughput analysis Highly accurate analysis

Integration

Diverse population of plants/chromosomes

mQTL

Selection of potential inbred parents of higher-yielding hybrids

Crossing of inbreds to make hybrids

Selection and evaluation of heteroic hybrids for commerce

min

Sign

al in

tens

ity

Extraction of S-peaks

BPI

m/z

Sign

al in

tens

ity

0.99703 Da

1.99579 Da

0.0063 Da 0.0084 Da

M1 ion Monoisotopic ion M2 ion

0.0024 Da

12Cv1Hw

14Nx16Oy

32Sz

15N-substituted

13C- 34S- 18O- 13C2-

FT-ICR-MS (Solarix 7.0 T)

Q

Magnet

Magnet Q

ICR cell

Dual ion funnel

Hexapol ion transfer

S-trans-1-prenylcysteine sulfoxide (PRENCSO)

1

-glutamyl-S-1-carboxy propylcysteine sulfoxide

2 S-2-carboxypropylglutathione

3

-glutamyl-S-1-propenylcysteine 4

S-1-propenyl mercaptoglutathione 5

S-1-propyl mercaptoglutathione 6

13C-labeled onion bulb was purchased from SI Science (http://www.si-science.co.jp/)

S-1-propenyl mercaptoglutathione (5) m/z 380.09438; resolution 330,000

5 elemental compositions

13C-labeled

1 elemental composition C13H22N3O6S2

Determination of elemental composition

C13H21N3O6S2

Comparison of peak pattern

S-peak picking C0-50H0-100N0-5O0-50S0-5 < 1 mDa

MS/MS analysis

Characterization

min

13C-

labe

led

Non

-labe

led 10.20

10.20

380.09438

381.09773

382.09018

392.13473

393.13805

392.13056

C13

m/z

380.09438

380.09445

381.09773

381.09149

381.09132

381.09781

Non

-labe

led

Theo

retic

al

M M+1

382.09018

382.09025 382.09870

382.10116

382.09849

382.10100

M+2

m/z

18O 13C2

34S 13C

15N

EIC MS

m/z

MS/MS

125.

5264

7

188.

0198

2

234.

0254

0

251.

0519

5

287.

0520

8

380.

0948

1 C2H5N1O2 C2H7N1O3

C5H7N1O3 C5H10N2O3 C6H12N2O5

C8H13N1O3S1 C9H20N2O4S1

C5H9N1O3S1

C4H2

N1O

2S1

127.

9800

8

C5H9

N2O

3S1

177.

0328

2 C7

H10N

1O1S

2 18

8.01

983

C8

H13N

2O3S

1 21

7.06

414

C8

H12N

1O3S

2 23

4.02

531

C8

H15N

2O3S

2 25

1.05

186

C1

1H15

N2O

3S2

287.

0518

6

C11H

17N

2O4S

2 30

5.06

242

C13H

22N

3O6S

2 38

0.09

445

217.

0647

4

177.

0327

4

125.

5264

7

188.

0198

2

234.

0254

0

251.

0519

5

287.

0520

8

305.

0626

6

380.

0948

1

195.

0433

3

242.

0522

5

259.

0788

0

316.

0994

1

393.

1385

3

298.

0889

9

177.

0327

4 18

2.04

983

129.

5398

6

C13 C11 C11

C8

C8 C7 C5 C4

305.

0626

6

m/z

Non

-labe

led

Sim

ulat

ed

13C-

labe

led

Non

-labe

led

Peak picking of S-metabolite

LC-FT-MS is a strong tool for extraction and characterization of S-metabolites. Onion bulb includes quite rare S-

metabolites which have strong anti-inflammatory activity.

Strategy of biomarker screening will be

changed by this method with approaches of natural products chemistry

Conclusion

Acknowledgements

MS/MS

Strategic International Collaborative Research Program (SICORP) for JP-NZ and JP-US, JST Japan Advanced Plant Science Network

0 2 4 6 8 10 12 14

1

2

3

4

5

6

min

EICs of 6 representative S-metabolites

Image of picking S-metabolite peaks Theoretical ion peak pattern

Metabolomics Posterbook 2012

-20-

Invertebrate Metabolomics:Drosophila melanogaster and Caenorhabditis elegans

Metabolic Characterization of Sod1 null mutant flies using Liquid Chromatography/Mass Spectrometry Jose M. Knee1, Teresa1 Z. Rzezniczak1, Kevin Kun Guo2, Aiko Barsch3 & Thomas J. S. Merritt1, 1Department of Chemistry & Biochemistry Laurentian University Sudbury, Ontario, Canada tmerritt@laurentian.ca 2Bruker Daltonics Inc., Billerica, MA 3Bruker Daltonics Inc., Bremen, Germany

Introduction We are characterizing the metabolomic impact of knocking out (KO) cytosolic Superoxide dismutase (Sod1) in Drosophila melanogaster using the Sod1 KO allele cSODn108. Sod1 is responsible for scavenging the O-. ion, a highly reactive chemical that contributes to oxidative stress(1). cSODn108 mutants have a variety of phenotypes including an accumulation of oxidative damage, decreased lifespan, male infertility and an overall enfeebled phenotype(2). Here we describe our initial steps to use a liquid chromatography coupled mass spectrometry (LC-MS) based approach to characterize the broad metabolomic impact of this KO. Methods

Adult Sod1 null (cSODn108) and transgenic rescue control Drosophila melanogaster were homogenized in cold methanol and protein and debris were removed by centrifugation. Supernatant was brought to approximately 80% water before injection into a micrOTOF QII mass spectrometer (Bruker Daltonics) with an ESI source coupled to a UltiMate 3000 rapid separation liquid chromatography system (Dionex). The LC was run with diamylammonium acetate at pH 4.95 using a water-methanol multi-step gradient on a Kinetex C18 RP 100x2.1mm, 1.7µm column (Phenomenex). The MS was run under negative ionization mode. Chromatograms were analyzed using DataAnalysis software (Bruker Daltonics). Principal component analysis (PCA) and t-tests were performed with ProfileAnalysis software (Bruker Daltonics). Results • Polar molecules are notoriously difficult to separate with C-18

reversed phase columns and an alternative, hydrophilic interaction chromatography (HILIC), generates less than optimal peak shapes and reproducibility(3). We have recently focused on establishing a C-18 reversed phase column based separation with a basic ion pairing reagent to improve resolution. Fig 1 and Table 1 show a series of polar standards we are currently able to resolve. Polar basic compounds are still poorly retained. • LC-MS raw data from fly samples was subject to peak finding using the FindMolecularFeatures (FMF) algorithm which can efficiently differentiate real signals and background noise. FMF features from all samples were assigned to buckets, a combination of time and m/z, in a bucket table as basis for PCA or t-test analysis. • PCA loadings plot (Fig 2 A) and bucket statistics (Fig 2 D) reveal how the FMF compounds contribute to the variation between groups. Each point in the loadings plot represents a bucket and the bucket statistics displays the levels at which each sample expresses the bucket. • PCA scores plot (Fig 2 B: PC1 vs PC2) demonstrates separate grouping between the two fly sample types. As expected, genetic differences between the groups results in downstream alterations of the metabolic profile.

Figure 1: LC-MS chromatograms of A) 0.05mM prepared standards B) Fly homogenate with extracted chromatograms of targeted compounds

A

B

[M-H]- = C6H9N3O2 Histidine

Sod1-null Control 1.28 fold Change p=0.002

Figure 2: PCA analysis of cSODn108 flies and controls (A) Loadings plot (B) Scores plot (C) Chromatograms and spectrum of differentially expressed metabolite, Histidine (D) Bucket statistics of histidine across all samples (E) Histidine structure and elemental formula

• Fig 2 C and D represent information of the loading highlighted in green in Fig 2 A. Fig 2 C shows the extracted ion chromatogram (EIC) of bucket (0.8min; 154.06m/z) and the mass spectrum. On average there is a 1.28 fold decrease of this compound in Sod1-null flies. Fig 2 E presents the identity of the bucket at 0.8min; 154.06m/z: histidine as revealed in comparison to the standard. • t-test results generated 30 FMF compounds differentially expressed with p≤0.05 between the cSODn108 flies and the controls and 15 differentially expressed with p≤0.01. There were a total of 336 detected compounds. The first 11 most varying features are represented in Table 2. • Analysis of an unknown bucket is demonstrated in Figure 3. An EIC and spectrum of bucket 0.8 min; 376.10 m/z is depicted in Fig 3 A and Fig 3 B, respectively. The SmartFormula feature uses accurate mass measurement, isotopic pattern, and, potentially, fragmentation pattern to generate a candidate formula. Fig 3 C reveals the SmartFormula results for the selected bucket ranked according to the best isotopic fit. Unknown metabolites can then be identified using the CompoundCrawler software, which searches online metabolite databases such as KEGG(4,5) for possible structures.

Figure 3: Analysis of Bucket 0.8 min; 376.10 m/z a) Extracted ion chromatogram B) Mass spectra of unknown compound C) SmartFormula result

Table 2: MS t-Test results of Sod-null flies vs. controls of the first 11 significant metabolites. Blue highlighted metabolite was identified as Histidine. Red highlighted metabolite is shown as example in figure 3.

Table 1: Targeted Standards. (*) denotes standards not detected in our fly samples

C

E

(m/z)

References (1) Zelko, Mariani, & Folz. (2002). Free Radic Biol Med 33 (3), 337-349. (2) Phillips, Campbell, Michaud, et al. (1989). Proc. Natl. Acad. Sci. 86, 2761-2765. (3) Luo, Groenke, Takors, et al. (2007). J. Chromatogr. A. 1147, 153- 164. (4) Kanehisa, Sato, Furumichi, & Tanabe. (2012). Nucleic Acids Res. 40, D109-D114. (5) Kanehisa & Goto. (2000). Nucleic Acids Res. 28, 27-30.

A

B

D

Sod-null

Control

Bucket P-Value "Sodnull/Control” Fold Change “Sodnull /control”

0.8min : 376.10m/z 0.00008 0.53 -1.88 5.3min : 421.07m/z 0.00026 1.62 1.62

23.4min : 354.22m/z 0.00047 2.18 2.18 23.7min : 356.24m/z 0.00077 2.46 2.46 0.8min : 152.08m/z 0.00085 0.64 -1.57 0.8min : 718.21m/z 0.00123 0.58 -1.74 1.2min : 168.04m/z 0.00127 0.71 -1.4

23.6min : 362.24m/z 0.00158 1.81 1.81 0.8min : 154.06m/z 0.00232 0.78 -1.28

23.1min : 460.92m/z 0.00236 0.74 -1.35 7.5min : 393.04m/z 0.00312 0.72 -1.39

(m/z)

These are preliminary results from LC-MS analysis aimed at characterizing the metabolic impact of Sod-null genotypes and oxidative stress in D. melanogaster . This poster also demonstrates the applicability of LC-MS analysis for D. melanogaster metabolomics. The future directions, outlined below, will allow us to more completely characterize the SOD1 metabolic fingerprint and to move into a broader suite of metabolites and genes of interest. • Acquire MS/MS on all known standards to confirm

compound identity in fly samples • MS/MS on unknown compounds to identify structure

and compare to metabolite databases • Refining metabolite extraction procedures to detect

currently undetected compounds • Optimize resolution and analysis of basic analytes • Analysis of relatively non-polar compounds • Pathway reconstruction and biological significance

D B

C

Sod-null Control

Time (min)

Conclusions

Prepared Standards

Fly Homogenate

Loadings

Scores

1 Arginine 13 valine 25 Fructose 6-phosphate 35 α-ketoglutaric acid

2 Ornithine 14 methionine 25 Mannose 6-phosphate 36 Fumarate

3 Lysine 15 tyrosine 25 Glucose 1-phosphate 37 6-phosphogluconoic acid*

4 Dopamine* 16 leucine 26 Pyroglutamic acid 38 Fructose 1,6-biphosphate

5 Trehalose 17 isoleucine 27 Glycerol 1-phosphate 39 Citric acid

6 Asparagine 18 phenylalanine 28 Glutathione (red) 39 Isocitric acid

7 Glutamine 19 Glutamic acid 29 β-glycerophosphate* 40 Phosphoenolpyruvate*

8 Histidine 20 Aspartic acid 30 NAD+ 41 NADP+*

9 Threonine 21 Uric acid 31 AMP 42 ADP

10 Serine 22 Tryptophan 32 Succinic Acid 43 NADH

11 Proline 23 Ascorbic Acid 33 Malic acid 44 NADPH

12 Cysteine 24 Glucose 6-phosphate 34 Glutathione (ox) 45 ATP

A

Control Sod-null

ATP

ADP

Metabolomics Posterbook 2012

-21-

Invertebrate Metabolomics:Drosophila melanogaster and Caenorhabditis elegans

Analytical BioGeoChemistry

A mass spectrometry based metabolomic platform for the analysis of Caenorhabditis elegans

Witting Michael 1,2, Ruf Alexander 1, Garvis Steve 3, Wägele Brigitte 2,4, Voulhoux Romé 3, Schmitt-Kopplin Philippe 1,5

1 Department of BioGeoChemistry and Analytics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstaedter Landstrasse 1, 85764 Neuherberg, Germany 2 Department of Genome Oriented Bioinformatics, Life and Food Science Center Weihenstephan, Technische Universität München, 85354 Freising Weihenstephan, Germany 3 Centre National de la Recherche Scientifique, 31 Chemin Joseph Aiguier, 13402 Marseille Cedex 20, France 4 Institute of Bioinformatics and Systems biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstaedter Landstrasse 1, 85764 Neuherberg, Germany 5 Chair of Analytical Food Chemistry, Life and Food Science Center Weihenstephan, Technische Universität München, 85354 Freising Weihenstephan, Germany

michael.witting@helmholtz-muenchen.de schmitt-kopplin@helmholtz-muenchen.de

Caenorhabditis elegans is a widely used model organism, that was introduced in the 1960s by Sydney Brenner. Its sequenced genome, the easy cultivation, the short reproduction cycle and transparency makes it an ideal model organism.

It is routinely used for studying host-pathogen interactions, neurobiology, physiology, ecology and many more. Despite its popularity, surprisingly few metabolomics studies, mostly using NMR have been carried out using C. elegans [1]. Here we present the setup of a MS based metabolomic platform for C. elegans studies.

UPLC-MS:

Waters Acquity UPLC

Bruker maXis UHR-ToF-MS

Resolution > 50,000 error < 2 ppm

Advion NanoMate for Chip-ESI and fraction collection

Size: ~ 1 mm x 65 µm Cells: 959 (hermaphrodite) 1031 (male) Lifespan: 2-3 weeks Food: bacteria

ICR-FT-MS:

Bruker solariX with 12 T magnet

Resolution > 300,000 error < 100 ppb

Gilson 223 sample changer, 6-port valve and Agilent 1100 quad pump for automated sample infusion

Sample preparation:

Metabolite extraction from worm pellets (~1000 worms) with 50% MeOH at -80°C

Non-targeted metabolomics using ICR-FT-MS:

Dilution of sample, measurements in positive and negative mode

Data analysis / Bioinformatics:

Internal spectra recalibration, masslist export and automated metabolite annotation using MassTRIX server [2,3]

172.09437

212.56868

261.13090

300.35351

393.29757442.30089

500.27451 589.47997

630.87865674.40266

745.46711

803.09675

834.54253

960.78217

200 300 400 500 600 700 800 900 m/z0

2

4

6

8

8x10Intens.

Cel Ecoli OP50 fed 2M 300s pos 001_000001.d: +MS

Non-targeted metabolomics using UPLC-MS:

RP method for non- and mid-polar metabolites:

Acquity BEH C8, 1.7 µm particles, 150 x 2.1 mm

A: 10% MeOH + 0.1% formic acid B: 100 % MeOH + 0.1% formic acid

HILIC method for polar metabolites:

Acquity BEH Amide, 1.7 µm particles, 150 x 2.1 mm

A: 95 % ACN + 10 mM ammonium acetate + 0.1 % formic acid B: 50 % ACN + 10 mM ammonium acetate + 0.1 % formic acid

Measurements in positive and negative mode

Calibration of individual UPLC-MS runs through injection of standard mix via 6-port valve at the beginning of each run and lock mass calibration

Data pre-processing I:

Automated internal recalibration using standard mix and lock mass and export to net-CDF format using Bruker Data Analysis 4.0 and VB scripting

Data pre-processing II:

Raw data import into MZmine 2.1 [4], chromatogram deconvolution, peak picking, identification of isotopes and adducts, alignment and database searching for metabolite annotation (All steps can be automated by the MZmine batch function)

Multivariate statistic / Data analysis

Unsupervised methods (PCA and HCA) and supervised methods (PLS) for pattern recognition and identification of masses contributing to groups separation

Comparison and Combination of ICR-FT-MS and UPLC-MS results

Collection and identification of unknown peaks using NanoMate based fraction collection, exact mass from ICR-FT-MS, MS/MS experiments and NMR

Further development:

• Development of metabolite extraction method using 96-well plate format and “fast” UPLC-MS for high throughput screening and deep phenotyping

• Identification of novel compounds using a LC-SPE-NMR-MS system with a 800 MHz NMR (Bruker Metabolic Profiler)

• Mass difference networking [5] Our presented MS based metabolomic platform allows the measurement of both, polar and non-polar, metabolites in one platform. The developed methods are currently applied to different projects using C. elegans as model organism in the fields of host-pathogen interactions, nutritional studies, probiotic research and the analysis of knock-out mutants.

Ongoing developments in sample preparation and chromatographic methods, in combination with the implemented automated data pre-processing will allow us to use this platform for high throughput metabolome analysis of bigger sample amounts and studies. Literature: [1] Blaise BJ, Giacomotto J, Elena Bnd, Dumas M-E, Toulhoat P, et al. (2007) Metabotyping of Caenorhabditis elegans reveals latent phenotypes. Proceedings of the National Academy of Sciences 104: 19808-19812. [2] Suhre K, Schmitt-Kopplin P (2008) MassTRIX: mass translator into pathways. Nucleic Acids Research 36: W481-484. [3] Wägele B, Witting M, Schitt-Kopplin P, Suhre K (2012) MassTRIX Reloaded: Combinded Analysis and Visualization of Transcriptome and Metabolome Data. PLoS ONE 7(7). doi:10.1371/journal.pone.0039860 [4] Pluskal T, Castillo S, Villar-Briones A, Oresic M (2010) MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics 11: 395. [5] Tziotis D, Hertkorn N, Schmitt-Kopplin P (2011) Kendrick-analogous network visualisation of ion cyclotron resonance Fourier transform mass spectra: improved options for the assignment of elemental compositions and the classification of organic molecular complexity. European Journal of Mass Spectrometry 17(4):415-421

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Metabolomics Posterbook 2012

-22-

Invertebrate Metabolomics:Drosophila melanogaster and Caenorhabditis elegans

For research use only. Not for use in diagnostic procedures.

Complementary GC-EI-MS and GC-APCI-TOF-MS analysis of a short-lived mitochondrial knockdown of C. elegans to study Metabolic changes during aging

Verena Tellström1, Aiko Barsch1, Gabriela Zurek1, Carsten Jaeger1, Bernd Kammerer1 1Bruker Daltonik GmbH, Bremen, GERMANY 2Albert-Ludwigs-Universität Freiburg, Freiburg, GERMANY

Introduction Non-targeted GC-EI-MS profiling is a core metabolomics technique for studying central metabolism in a wide range of organisms. However, many detected metabolites cannot be identified and unknown identification is mostly impossible due to lack of molecular ion information. By contrast, GC-APCI-TOF-MS readily yields pseudomolecular ions, facilitating generation of molecular formulae and thereby partial or complete compound identification. As a test case, we studied primary metabolism in the nematode worm Caenorhabditis elegans, a popular model organism for aging processes and mitochondrial dysfunction. Profiling with GC-EI-MS revealed biochemical differences between control and mitochondrially deficient animals, but many metabolites including important ones for phenotype differentiation could not be identified. We therefore compared GC-APCI-TOF-MS to GC-EI-MS profiles of C. elegans with respect to unknown identification and also phenotype differentiation.

For metabolite profiling, worms were homogenized in cold methanol/water by bead beating and extracted metabolites were derivatized with methoxyamine/pyridine and MSTFA (Fig. 1). 1-µL aliquots of the reaction mixture were injected into GC-EI-MS and GC-APCI-TOF-MS instruments (Fig. 4), respectively. Identical column type and gas chromatographic conditions were used to ensure the same compound elution order.

Peak identification of EI data was achieved by mass spectral and retention index matching using AMDIS 2.68 and NIST05/Golm Metabolite Library (GMD: http://gmd.mpimp-golm.mpg.de/Default.aspx) as databases. High resolution TOF-MS data from micrOTOF-Q II (Bruker Daltonik) was processed with Bruker DataAnalysis 4.0 software. Molecular formulae were derived from accurate mass and isotopic pattern using the SmartFormula algorithm. Corresponding structures were searched in public databases (PubChem, KEGG, Chemspider) with Bruker CompoundCrawler.

C. elegans worms were maintained on NGM plates seeded with E. coli OP50 as food source. To study the mitochondrial phenotype, animals were subjected to RNAi-mediated knockdown of prohibitin, a 1 MDa protein complex acting as a molecular chaperone at the respiratory chain. Depletion of this protein affects mitochondrial function and reduces lifespan. Changes in metabolite abundances after treatment were compared against non-treated control animals.

Results

Methods

Conclusions

GC-APCI-TOF-MS

•GC-EI-MS and GC-APCI-TOF-MS were successfully applied as complementary platforms for metabolic profiling and identification in C. elegans.

•The potential of GC-APCI-TOF-MS for the identification of unknown metabolites has been demonstrated with an unidentified compound from EI-MS measurements.

•Further investigations with GC-APCI acquiring high resolution Q-TOF MS/MS data will extend the capabilities for structural elucidation.

Initial profiling with GC-EI-MS yielded 212 reconstructed mass spectra, of which 134 could be assigned structures at a match factor of ≥800. To evaluate GC-APCI-TOF-MS as identification platform, we tested if EI matched metabolites could also be found in TOF-MS chromatograms. Searching for the respective mass traces, successful molecular formula confirmation was possible for 57 out of 60 test candidates, with measured m/z values deviating in average 1.52 ppm from theoretical values (Fig. 1). GC-APCI-TOF-MS also allowed identification of unknown metabolites. By comparing peak elution order, EI-MS peaks without database match were located in the APCI-MS chromatogram. Molecular formulae were generated, in part allowing structural assignment (Fig. 2) after searching public databases. Differentiation of metabolic phenotypes was tested by principal component analysis (PCA) carried out on both EI-MS and APCI-MS datasets. Both technologies indicated different metabolic patterns between knockdown and control animals (Fig. 3).

Summary

Fig. 1 Metabolite profiling by GC-EI-MS and GC-APCI-TOF-MS in C. elegans. (A) Metabolite extraction and derivatization. (B) EI-MS and APCI-TOF-MS profiles. (C) Identified metabolites with measured TOF-MS m/z values.

Fig. 2 Unknown identification by GC-APCI-TOF-MS. (A) Peak #3 could not be identified by EI-MS but its location in the APCI-TOF-MS chromatogram could be derived from known neighboring peaks #1, 2 and 4. (B) Exact mass and isotopic pattern allowed for molecular formula generation and structure proposal.

Fig. 3 Discrimination of biochemical phenotypes by GC-EI-MS and GC-APCI-TOF-MS. Metabolic features (GC-EI-MS: 212, GC-APCI-TOF-MS: 455) were extracted from profiling data and submitted to principal component analysis (PCA). Two treatment groups (P1, P2) cluster separately from the control (C) in the PCA scores plot.

In a test set of 60 metabolites identified by GC-EI-MS, 95% of the compounds were confirmed via their molecular formula by GC-APCI-TOF-MS. Identification of unknown compounds from GC-EI-MS measurements is enabled by GC-APCI-TOF-MS. A GC-APCI interface preserves the precursor ion information and enables molecular formula generation based on accurate mass and isotopic pattern information. Both techniques yield similar clustering of treated and control animals in principal component analysis.

Fig. 4 micrOTOF QII (QTOF) coupled to 450-GC via APCI source

Metabolomics Posterbook 2012

-23-

Structure Elucidation

For

rese

arch

use

onl

y. N

ot f

or u

se in

dia

gnos

tic p

roce

dure

s.

160.1

079

284.2

221

383.1

238

+M

S2(8

25.4

396),

3.9

-3.9

min

#886-9

02

01235x1

0In

tens

.

100

200

300

400

500

600

700

800

m/z

Ou

tloo

k

Dir

k W

un

der

lich

1,

Sve

n M

eyer

1,

Ste

ph

anie

Gro

nd

2,

Torb

enH

ofed

itz2

, an

d

Tilm

ann

Web

er2

1Bru

ker

Dal

toni

kG

mbH

, Fa

hren

heits

traß

e4,

28

359

Bre

men

, G

erm

any

2 Fac

ulty

of

Sci

ence

, Eb

erha

rd-K

arls

-Uni

vers

ität

Tübi

ngen

, Auf

der

M

orge

nste

lle18

, 72

076

Tübi

ngen

, G

erm

any

Intr

odu

ctio

nTh

eco

uplin

gof

(U)H

PLC

with

high

reso

lutio

nQ

TOF

mas

ssp

ectr

o-m

eter

sal

low

sfa

stde

term

inat

ion

ofsu

mfo

rmul

aefo

rbo

thkn

own

and

unkn

own

com

poun

dsin

aN

atur

alPr

oduc

tScr

eeni

ng.

This

appr

oach

isof

ten

appl

ied

tocr

ude

extr

acts

oren

rich

edfr

actio

ns.

The

com

bina

tion

ofU

HPL

Csy

stem

sw

ithfa

stsc

anni

ngQ

TOF

mas

ssp

ectr

omet

ers

has

the

pote

ntia

lto

perf

orm

the

anal

ysis

ina

few

min

utes

.

Her

e,w

ein

vest

igat

edif

the

anal

ysis

time

coul

dbe

furt

her

redu

ced

byco

uplin

gTL

C-M

Sw

ithdi

rect

sum

form

ula

dete

rmin

atio

nfr

omTL

Csp

ots

byhi

ghre

solu

tion

MS

anal

ysis

.

Mas

s S

pec

trom

eter

: Bru

ker

Dal

toni

km

aXis

4G a

nd m

aXis

IMPA

CT

HP

LC:

Dio

nex

RSLC

C

olu

mn

: W

ater

s, B

EHC18

, 1.

7 µm

, 2.

1 x

50 m

mA:

H2O

, B:

ACN

, bo

th0.

1% H

CO

OH

5m

inut

esgr

adie

ntfr

om5

to90

%B

TLC

-MS

: CAM

AG

TLC

-MS I

nter

face

Pla

tes:

Mer

ck,

TLC S

ilica

60 F

254

Sof

twar

eSm

artF

orm

ula3

D;

Com

poun

d Cra

wle

r an

d Fr

agm

enta

tion

Expl

orer

, Bru

ker

Dal

toni

k.

Met

hod

s

•TLC

-MS a

naly

sis

requ

ires

hig

h sa

mpl

e am

ount

s (5

-50

µg o

n pl

ate)

•Hig

h ba

ckgr

ound

fro

m T

LC p

late

s:

a) c

halle

ngin

g to

fin

d co

mpo

und

peak

b) c

ompo

unds

with

no

or o

nly

one

nitr

ogen

ato

m io

nize

as

sodi

um

addu

cts

(mos

t M

S/M

S s

pec

not

usef

ul)

c) o

nly

mai

n co

mpo

und

ioni

zes,

di

ffic

ultie

s in

det

ectio

n of

sid

e pr

oduc

ts

•TLC

-MS a

naly

sis

wel

l sui

ted

for

pure

com

poun

ds a

nd s

ampl

es o

f lo

w

com

plex

ity.

•Onl

y fe

w c

ompo

unds

fou

nd b

y TL

C-M

S o

f irom

ycin

e ex

trac

t, b

ut m

any

by (

U)H

PLC-

MS (

sepa

ratio

n, le

ss io

n su

pres

sion

)

•Sof

twar

e Fr

agm

enta

tionE

xplo

rer

help

ful f

or M

S/M

S s

pect

rum

in

terp

reta

tion

Fig.

3 a

) TL

C o

f cr

ude

extr

act

b) M

S s

pect

rum

of

TLC s

pot

04 (

new

oxi

dize

d irom

ycin

e)

in

dica

tes

only

1 c

ompo

und

as s

odiu

m a

dduc

t

c

) LC

-MS T

otal

Ion

Chr

omat

ogra

m o

f m

anua

lly e

xtra

cted

TLC

spo

t 04

rev

eals

a c

ompl

ex

m

ixtu

re m

easu

red

as p

roto

nate

d sp

ecie

s

Fig.

2 I

nter

pret

atio

n of

ary

lom

ycin

eM

S/M

S s

pect

rum

by

Frag

men

tatio

n Ex

plor

er

Fig.

4 a

) Str

uctu

re p

ropo

sal f

or t

he n

ew L

Lp-W

R1

natu

ral p

rodu

ct o

f th

e en

gine

ered

PKSII

;

give

s on

ly 1

fra

gmen

t in

MS/M

S (

m/z

403

, C

22H

11O

8, [

M-H

]-)

b) M

SM

S s

pect

rum

for

the

sec

ond,

pur

ple

colo

red

com

poun

d LL

p-W

L1 in

the

sam

e ex

trac

t

•Str

uctu

reel

ucid

atio

nof

com

poun

dsw

ithsi

mila

rsu

mfo

rmul

aas

irom

ycin

e.Are

they

alld

eriv

ativ

esof

irom

ycin

s?

•Str

uctu

reel

ucid

atio

nof

addi

tiona

lary

lom

ycin

esfo

und

byH

R-M

S/M

S.

•Add

ition

ofa

shor

tco

lum

nbe

twee

nTL

Can

dM

Sfo

rsa

ltre

mov

al.

UH

R-Q

qTO

F

The

Fast

est

Way

to

a M

olec

ula

r Fo

rmu

la:

How

fas

t ca

n it

b

e? T

LC-U

HP

LC-E

SI-

QTO

F M

S C

oup

ling

for

Mol

ecu

lar

Form

ula

Det

erm

inat

ion

of

New

Nat

ura

l Pro

du

cts

Res

ult

s

164.

9207

342.

2040

617.

3047

+M

S, 0.

41-0

.49m

in #

24-2

9

012345x1

0In

tens

.

200

400

600

m/z

C19

H29

NO

3Na

0.1

ppm

Iro S

pot

4 po

s M

S_1

-C,6

_01_

2502

.d:

TIC

+A

ll M

S

0.00

0.25

0.50

0.75

1.00

1.25

7x1

0In

tens

.

2.0

2.5

3.0

3.5

Tim

e [m

in]

C19

H30

NO

3

C18

H30

NO

4

C19

H28

NO

3

C19

H30

NO

8

C19

H30

NO

6

C19

H32

NO

5

C20

H32

NO

4

C20

H32

NO

3

C20

H30

NO

4

LC-M

S o

f spo

t 04

TLC

-MS

of s

pot 0

4

•Sem

i-au

tom

atio

n w

ith T

LC-M

S

•Mas

s ac

cura

cy o

f TL

C-M

S a

s go

od

as w

ith (

U)H

PLC-

MS:

~ 1

ppm

•Sum

For

mul

a de

term

inat

ion

as

relia

ble

as w

ith (

U)H

PLC-M

S

Sev

eral

Nat

ural

Pro

duct

s w

ere

anal

yzed

by

a co

mbi

natio

n of

(U

)HPL

C-M

S a

nd T

LC-M

S:

Rese

rpin

e, c

urcu

rmin

e, e

met

ine,

ba

filom

ycin

, se

vera

l iro

myc

ines

, co

llino

keto

n, c

ollin

olac

ton,

he

xacy

clin

ic a

cid,

ary

lom

ycin

, PK

S

prod

ucts

of LL

p-se

ries

and

thr

ee

Str

epto

myc

escr

ude

extr

acts

.

•TLC

-MS a

naly

sis

only

30

sec a)

b) c)

C25

H18

O9

M =

462

a)

401.0

667

1-

443.0

772

1-

469.0

565

1-48

7.067

11-

C 23 H 13 O 7 , -0.24 mDa,

C 25

H 15

O 8

, -0.4

mDa,

C 26 H 13 O 9 , 0.19 mDa,

C 26 H 15 O 10 , -0.2 mDa,

SmartFormula₃Dspectrum:C₂

₆H₁₅O

₁₀, 4

87.07

, -0.20

mDa

, -0.42

ppm,

mSig

ma: 1

6.3

0.00.20.40.60.8

5x1

0Int

ens.

400

420

440

460

480

500

m/z

b)

m/z

403

Irom

ycin

Fig.

1

Str

uctu

ral f

orm

ulae

of se

vera

l ana

lyze

d m

etab

olite

s fo

r th

is s

tudy

and

inst

rum

enta

l set

up o

f TL

C-U

HR M

S

Engi

neer

ed P

KS p

rodu

ctLL

p-W

R1

O

O

OO

H

CO

OH

OH

O

O

Hex

acyc

linic

Aci

d

71

OO

O15

9O

HO

OH

O21

OH

OH

H

Baf

ilom

ycin

A1

O

OO

O

OH

H

H

H

H

Col

linol

acto

n

Emet

ine

Ary

lom

ycin

e A

2

Metabolomics Posterbook 2012

-24-

MALDI Imaging

For

rese

arch

use

onl

y. N

ot for

use

in d

iagn

ostic

pro

cedu

res.

Au

tom

ated

Tis

sue

Sta

te A

ssig

nm

ent

for

Hig

h R

esol

uti

on M

ALD

I-FT

MS

Im

agin

g D

ata

AS

MS

20

12

, #

WP

39

6

Intr

odu

ctio

n

Res

ult

s

Su

mm

ary

J. P

aul

Sp

eir1

; S

oere

n-O

liver

D

ein

ing

er2;

Jen

s Fu

chse

r2;

Kat

her

ine

A.

Kel

lers

ber

ger

1;

D.

Sh

ann

on C

orn

ett1

; C

rist

ine

Qu

iaso

n3;

Sh

eeri

n K

. S

hah

idi-

Lath

am3;

Mic

hae

l B

ecke

r2;

Rai

ner

P

aap

e2

1 Bru

ker

Dal

toni

cs,

Inc.

, Bill

eric

a, M

A

2 Bru

ker

Dal

toni

k G

mbH

, Bre

men

, G

erm

any

3 Gen

ente

ch,

San

Fra

ncis

co,

CA

Mul

tivar

iate

fea

ture

ext

ract

ion

met

hods

suc

h as

pr

inci

pal c

ompo

nent

ana

lysi

s (P

CA)

and

segm

enta

tion

met

hods

are

rou

tinel

y us

ed in

M

ALD

I-TO

F im

agin

g. T

hese

met

hods

pro

vide

a

conc

ise

over

view

of th

e m

ain

stru

ctur

e in

M

ALD

I im

agin

g da

tase

ts.

Hig

h m

ass

reso

lutio

n im

agin

g da

tase

ts

gene

rate

d fr

om M

ALD

I-FT

MS im

agin

g ty

pica

lly

are

muc

h m

ore

com

plex

tha

n M

ALD

I-TO

F da

ta,

cont

aini

ng t

hous

ands

of m

ass

sign

als.

Alth

ough

FT

MS im

agin

g da

tase

ts s

houl

d, t

here

fore

, be

nefit

hig

hly

from

con

cise

rep

rese

ntat

ions

, no

t m

uch

wor

k ha

s be

en d

one

in t

his

field

so

far.

Her

e w

e in

vest

igat

ed t

he u

se o

f PC

A a

nd

hier

arch

ical

clu

ster

ing

for

FTM

S im

agin

g da

ta.

To a

sses

s th

e pa

ram

eter

s fo

r th

e PC

A a

nd t

he

clus

teri

ng, ca

lcul

atio

ns w

ere

perf

orm

ed o

n a

subs

et o

f da

ta (

each

64

spec

tra

from

sel

ecte

d or

gans

). F

igur

e 2

show

s th

e sc

ores

plo

ts for

th

ree

para

met

er s

ettin

gs in

the

PCA.

It is

see

n th

at P

CA o

n th

e ra

w d

ata

lead

s to

sca

tter

ed

and

over

lapp

ing

clus

ters

(Fi

g. 2

a) .

Aft

er

norm

aliz

atio

n, t

he c

lust

ers

are

bett

er

sepa

rate

d, b

ut s

till o

verl

ap t

o so

me

exte

nt (

Fig.

2b

). S

patia

l sm

ooth

ing

(all

peak

inte

nsiti

es a

re

repl

aced

with

the

aver

age

of a

3x3

pix

el

win

dow

) le

ads

to w

ell s

epar

ated

clu

ster

s (F

ig.

2c).

This

effec

t is

als

o vi

sibl

e in

the

who

le d

atas

et.

The

scor

es o

f th

e fir

st P

C a

lign

wel

l with

the

anat

omy

of t

he s

ampl

e (F

ig. 3a

) as

is e

xpec

ted

whe

n th

e m

ain

vari

ance

in t

he d

ata

com

es fro

m

real

fea

ture

s in

the

sam

ple.

How

ever

, a

slig

ht

gran

ular

ity (

salt-

and-

pepp

er-n

oise

) ca

n be

ob

serv

ed.

This

noi

se le

ads

to t

he fac

t th

at t

he c

lust

ers

of

spec

tra

are

not

perf

ectly

sep

arat

ed. Th

e cl

uste

ring

sho

ws

mai

n an

atom

ical

feat

ures

(s

uch

as li

ver,

brai

n, c

ecum

) bu

t w

ith a

lot

of

gran

ular

ity t

hat

prev

ents

a p

erfe

ct

segm

enta

tion

(Fig

. 3b

).

Spa

tial s

moo

thin

g ca

n ef

fect

ivel

y re

mov

e th

e no

ise.

The

PC s

core

s sh

ow a

muc

h sm

ooth

er

dist

ribu

tion

afte

r sp

atia

l sm

ooth

ing

of t

he

data

set

(Fig

. 3c

).

Hie

rarc

hica

l clu

ster

ing

of t

he d

atas

et a

fter

no

rmal

izat

ion

and

smoo

thin

g le

ads

to a

ver

y de

taile

d an

d go

od s

egm

enta

tion,

and

all

clus

ters

can

be

attr

ibut

ed t

o ac

tual

ana

tom

ical

fe

atur

es (

Fig.

4).

Var

ious

clu

ster

s w

ere

assi

gned

a c

olor

to

aid

in v

isua

lizat

ion,

and

in

gene

ral o

ne c

lust

er p

er o

rgan

or

regi

on o

f an

or

gan

(suc

h as

med

ulla

and

cor

tex

of t

he

kidn

ey)

is o

bser

ved.

Prin

cipa

l com

pone

nt a

naly

sis

and

clus

teri

ng

enab

le e

ffic

ient

and

con

cise

inte

rpre

tatio

n of

sm

all m

olec

ule

MALD

I-FT

MS d

ata.

Bes

t re

sults

ar

e ob

tain

ed w

hen

a sp

atia

l sm

ooth

ing

step

is

inco

rpor

ated

.

Fig.

1 O

ptic

al im

age

of t

he w

hole

rat

se

ctio

n

Con

clu

sion

s

•H

iera

rchi

cal c

lust

erin

g an

d PC

A o

f FT

MS-i

mag

ing

data

•Au

tom

ated

ass

ignm

ent

of t

issu

e st

ates

•Spa

tial s

moo

thin

g cl

uste

ring

re

sults

•D

etai

led

anno

tatio

n of

ana

tom

ical

fe

atur

es

MALD

I-FT

MS-I

mag

ing

Met

hod

s Th

e M

ALD

I im

agin

g da

tase

t of

a w

hole

bod

y ra

t se

ctio

n (1

9275

pix

els,

500

µm s

patia

l re

solu

tion)

was

acq

uire

d on

a S

olar

ix 9

.4T

mas

s sp

ectr

omet

er (

Bru

ker)

. Th

e sa

mpl

e w

as

sect

ione

d at

20

um t

hick

ness

and

mou

nted

on

a po

lishe

d st

eel t

arge

t. M

atri

x (D

HB,

40

mg/

mL

in 5

0% m

etha

nol)

was

app

lied

usin

g th

e H

TX

Spr

ayer

(H

TX).

The

peak

list

s (m

ass

rang

e 40

0 D

a to

900

Da)

of

the

indi

vidu

al s

pect

ra w

ere

binn

ed in

to o

ne

buck

et t

able

(bi

n w

idth

=0.

02 D

a).

In a

ll ca

lcul

atio

ns P

aret

o sc

alin

g on

the

pea

k in

tens

ities

was

use

d. D

ata

wer

e no

rmal

ized

to

RM

S in

tens

ity. All

calc

ulat

ions

wer

e do

ne in

R

(ww

w.r

-pro

ject

.orf

). T

he r

esul

ts o

f th

e PC

A a

nd

hier

arch

ical

clu

ster

ing

wer

e sa

ved

in t

he

Clin

ProT

ools

form

at a

nd im

port

ed in

to

flexI

mag

ing

3.0

(Bru

ker)

for

vis

ualiz

atio

n.

A

B C

Fig.

2 P

CA-

scor

es o

f a

subs

et o

f sp

ectr

a fr

om 7

reg

ions

in t

he w

hole

bod

y da

tase

t A)

raw

dat

a, B

) af

ter

norm

aliz

atio

n to

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-25-

References – Further Reading

Original papers

Plant MetabolomicsDevelopment and validation of a liquid chromatography-electrospray ionization-time-of-flight mass spectrometry method for induced changes in Nicotiana attenuata leaves during simulated herbivory.E. Gaquerel, S. Heiling, M. Schoettner, G. Zurek, I.T. Baldwin; J Agric Food Chem. 2010 Sep 8;58(17):9418-27.

17-Hydroxygeranyllinalool glycosides are major resistance traits of Nicotiana obtusi-folia against attack from tobacco hornworm larvae. A.R. Jassbi, S. Zamanizadehnajari, I.T. Baldwin; J Chem Ecol. 2010; 36(12):1398-407.

Jasmonate and ppHsystemin Regulate Key Malonylation Steps in the Biosynthe-sis of 17-Hydroxygeranyllinalool Diterpene Glycosides, an Abundant and Effective Direct Defense against Herbivores in Nicotiana attenuataS. Heiling, M. C. Schuman, M. Schoettner, P. Mukerjee, B. Berger, B. Schneider,A. R. Jassbi, I. T. Baldwin; The Plant Cell 2010; 22: 273–292

The multifunctional enzyme CYP71B15 (PHYTOALEXIN DEFICIENT3) converts cysteine-indole-3-acetonitrile to camalexin in the indole-3-acetonitrile metabolic network of Arabidopsis thaliana. C. Böttcher, L. Westphal, C. Schmotz, E. Prade, D. Scheel and E. Glawischnig; The Plant Cell 2009 Jun 30; 21: 1830-1845

LC-MSMS Profiling of Flavonoid Conjugates in Wild Mexican Lupine, Lupinus reflexus. M. Stobiecki, A. Staszkow, A. Piasecka, P. M. Garcia-Lopez, F. Zamora-Natera and P. Kachlicki; J. Nat. Prod. 2010; 73, 1254–1260

Fragmentation Pathways of Acylated Flavonoid Diglucuronides from Leaves of Medicago truncatula.L. Marczak, M. Stobiecki, M. Jasin´ski, W. Oleszekb and P. Kachlickic; Phytochem. Anal. 2010; 21, 224–233

Evaluation of glycosylation and malonylation patterns in flavonoid glycosides during LC/MS/MS metabolite profilingP. Kachlicki, J. Einhorn, D. Muth, L. Kerhoas, M. Stobiecki; J. Mass Spectrom. 2008; 43: 572–586

Differential metabolic response of narrow leafed lupine (Lupinus angustifolius) leaves to infection with Colletotrichum lupiniD. Muth, P. Kachlicki, P. Krajewski, M. Przystalski, M. Stobiecki; Metabolomics 2009; 5:354–362

LC/MS profiling of flavonoid glycoconjugates isolated from hairy roots, suspension root cell cultures and seedling roots of Medicago truncatula A. Staszków, B. Swarcewicz, J. Banasiak, D. Muth, M. Jasinski, M. Stobiecki; Metabolomics 2011; 7:604–613

Combined NMR and LC-MS analysis reveals the metabonomic changes in Salvia miltiorrhiza Bunge induced by water depletion.H. Dai, C. Xiao, H. Liu, H. Tang; J Proteome Res. 2010 Mar 5; 9(3):1460-75.

Overexpression of Sinapine Esterase BnSCE3 in Oilseed Rape Seeds Triggers Global Changes in Seed Metabolism K. Clauß, E. von Roepenack-Lahaye, C. Boettcher, M. R. Roth, R. Welti, A. Erban, J. Kopka, D. Scheel, C. Milkowski, D. Strack; Plant Phys.2011 March; 155: 1127–1145

Combined NMR and flow injection ESI-MS for Brassicaceae metabolomicsJ.M. Baker, J.L. Ward, M.H. Beale; Methods Mol Biol. 2012; 860:177-91.

Chemical identification strategies using liquid chromatography-photodiode array-solid-phase extraction-nuclear magnetic resonance/mass spectrometry. S. Moco, J. Vervoort; Methods Mol Biol. 2012; 860:287-316

Bacterial / Sponge MetabolomicsEfficient mining of myxobacterial metabolite profiles enabled by liquid chromatogra-phy-electrospray ionisation-time-of-flight mass spectrometry and compound-based principal component analysis.D. Krug, G. Zurek, B. Schneider, R. Garcia, R. Müller; Anal Chim Acta. 2008 Aug 22; 624(1):97-106.

Discovering the Hidden Secondary Metabolome of Myxococcus xanthus: a Study of Intraspecific DiversityD. Krug, G. Zurek, O. Revermann, M. Vos, G.J. Velicer, R. Müller; Appl Environ Microbiol. 2008 May; 74(10):3058-68.

Myxoprincomide, a novel natural product from Myxococcus xanthus discovered by a comprehensive secondary metabolome mining approachN.S. Cortina, D. Krug, A. Plaza, O. Revermann, R. Müller; Angewandte Chemie International Edition 2012; 51(3): 811–816.

Methods to optimize myxobacterial fermentations using off-gas analysisS. Hüttel, R. Müller; Microbial Cell Factories 2012; 11:59 

Microbial Strain Prioritization Using Metabolomics Tools for the Discovery of Natural ProductsY. Hou, D.R. Braun, C.R. Michel, J.L. Klassen, N. Adnani, T.P. Wyche, T.S. Bugni;Anal Chem. 2012 May 15; 84(10): 4277–4283.

Metabolic fingerprinting as an indicator of biodiversity: towards understanding inter-specific relationships among Homoscleromorpha spongesJ. Ivaniševi, O. P. Thomas, C. Lejeusne, P. Chevaldonné, T. Pérez; Metabolomics 2011; 7(2): 289-304

Synthesis of 7-(15)N-Oroidin and evaluation of utility for biosynthetic studies of pyrrole-imidazole alkaloids by microscale (1)H-(15)N HSQC and FTMS.Y.G. Wang, B.I. Morinaka, J.C. Reyes, J.J. Wolff, D. Romo, T.F. Molinski.;J Nat Prod. 2010 Mar 26; 73(3):428-34. GC-APCI based MetabolomicsGas chromatography/atmospheric pressure chemical ionization-time of flight mass spectrometry: analytical validation and applicability to metabolic profiling.A. Carrasco-Pancorbo , E. Nevedomskaya , T. Arthen-Engeland, T. Zey, G. Zurek, C. Baessmann, A.M. Deelder, O. Mayboroda; Anal Chem. 2009 Dec 15; 81(24):10071-9.

Evaluation of GC-APCI/MS and GC-FID As a Complementary Platform.T. Pacchiarotta, E. Nevedomskaya, A. Carrasco-Pancorbo, A.M. Deelder, and O.A. Mayboroda; J Biomol Tech. 2010 Dec; 21(4): 205–213.

Performance Evaluation of Gas Chromatography Atmosphericc Pressure Chemical Ionization Time-of-Flight Mass Spectrometry for Metabolic Fingerprinting and ProfilingC. J. Wachsmuth M. F. Almstetter, M. C. Waldhier, M. A. Gruber, N. Nuernberger, P. J. Oefner, K. Dettmer; Anal. Chem., 2011; 83 (19): 7514–7522 LipidomicsCombined Reversed Phase HPLC, Mass Spectrometry, and NMR Spectroscopy for a Fast Separation and Efficient Identification of Phosphatidylcholines.J. Willmann, H. Thiele, and D. Leibfritz; J Biomed Biotechnol. 2011; 2011: 385786.

Time-Dependent Oxidation during Nano-Assisted Laser Desorption Ionization Mass Spectrometry: A Useful Tool for Structure Determination or a Source of Possible Confusion?K. Pavlaskova, M. Strnadova, M. Strohalm, V. Havlicek, M. Sulc, M. Volny;Anal. Chem., 2011, 83 (14): 5661–5665 Food MetabolomicsUnraveling different chemical fingerprints between a champagne wine and its aerosols.G. Liger-Belair, C. Cilindre, R. D. Gougeon, M. Lucio, I. Gebefügi, P. Jeandet, P. Schmitt-Kopplin; PNAS. 2009 Sept. 29; 106(39):16545–16549

Expressing forest origins in the chemical composition of cooperage oak woods and corresponding wines by using FTICR-MS.R.D. Gougeon, M. Lucio, A. De Boel, M. Frommberger, N. Hertkorn, D. Peyron, D. Chassagne, F. Feuillat, P. Cayot, A. Voilley, I. Gebefügi, P. Schmitt-Kopplin;Chemistry. 2009; 15(3):600-11.

Exploratory analysis of human urine by LC-ESI-TOF MS after high intake of olive oil: understanding the metabolism of polyphenols. R. Garcia-Villalba, A. Carrasco-Pancorbo, E. Nevedomskaya, O. Mayboroda, A. Deelder, A. Segura-Carretero, and A. Fernandez-Gutiérrez; Analytical and bioanalytical chemistry. 2010 Sep; 398(1):463-75

Automated identification of phenolics in plant-derived foods by using library search approach. M. Gómez-Romero, G. Zurek, B. Schneider, C. Baessmann, A. Segura-Carretero, and A. Fernandez-Gutiérrez; Food Chemistry. 2011 Jan; 124(1): 379-386

Scope and limitations of principal component analysis of high resolution LC-TOF-MS data: the analysis of the chlorogenic acid fraction in green coffee beans as a case studyN. Kuhnert, R. Jaiswal, P. Eravuchira, R. M. El-Abassy, B. von der Kammer;Anal. Methods 2011; 3, 144-155

Apple Peels, from Seven Cultivars, Have Lipase-Inhibitory Activity and Contain Numerous Ursenoic Acids As Identified by LC-ESI-QTOFHRMST. K. McGhie, S. Hudault, R. C. M. Lunken, J. T. Christeller; J. Agric.Food Chem. 2012; 60: 482−491

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Unknown IdentificationA strategy for the determination of the elemental composition by fourier transform ion cyclotron resonance mass spectrometry based on isotopic peak ratios.D. Miura, Y. Tsuji, K. Takahashi, H. Wariishi, K. Saito; Anal Chem. 2010 Jul 1; 82(13): 5887-91

Bruker Daltonics – Application Notes

ET-19Metabolic profiling of tea extracts by high-resolution LC in combination with maXis UHR-TOF MS analysis

ET-21Challenges in metabolomics addressed by targeted and untargeted UHR-Q-TOF analysis

ET-22Metabolic profiling of a Corynebacterium glutamicum ∆prpD2 by GC-APCI high-resolution Q-TOF analysis

ET-23Accurate molecular formula determination and identification of molecules with > 1100 m/z with UHR-TOF

ET-26Novel approaches for small molecule identification in metabolomics research

ET-29Screening for novel natural products from myxobacteria using LC-MS and LC-NMR

ET-30Metabolic profiling of Arabidopsis thaliana secondary metabolites using a maXis impact

TN-23Certainty in small molecule identification by applying SmartFormula3D on a UHR-TOF mass spectrometer

CA #271518Using gas chromatography – tandem mass spectrometry to improve the reliability and accuracy of organic acid analyses in urine

Ultra high performance liquid chromatography-time of flight mass spectrometry for analysis of avocado fruit metabolites: Method evaluation and applicability to the analysis of ripening degreesE. Hurtado-Fernandez, T. Pacchiarotta, M. Gómez-Romero, B. Schoenmaker, R. Derks, A. M. Deelder, O. A. Mayboroda, A. Carrasco-Pancorbo, A. Fernandez-Gutiérrez; Journal of Chromatography A 211; 1218(42): 7723–7738

Human / Clinical MetabolomicsAmino acid profiling in urine by capillary zone electrophoresis - mass spectrometry. O.A. Mayboroda, C. Neusüss, M. Pelzing, G. Zurek, R. Derks, I. Meulenbelt, M. Kloppenburg, E. Slagboom, and A. Deelder; Journal of chromatography A. 2007 Aug; 1159(1-2):149-53

CE-MS for metabolic profiling of volume-limited urine samples: application to accelerated aging TTD mice.E. Nevedomskaya, R. Ramautar, R. Derks, I. Westbroek, G. Zondag, I. van der Pluijm, A. M. Deelder, O. A. Mayboroda; J. Proteome Res. 2010; 9 (9): 4869–4874

Cross-platform analysis of longitudinal data in metabolomicsE. Nevedomskaya, O. A. Mayboroda, A. M. Deelder; Mol. BioSyst., 2011; 7: 3214–3222

Explorative Analysis of Urine by Capillary Electrophoresis-Mass Spectrometry in Chronic Patients with Complex Regional Pain SyndromeR. Ramautar, A. A. van der Plas, E. Nevedomskaya, R. J. E. Derks, G. W. Somsen, G. J. de Jong, J. J. van Hilten, A. M. Deelder, O. A. Mayboroda;Journal of Proteome Research 2009; 8: 5559–5567

Metabolic profiling of human urine by CE-MS using a positively charged capillary coating and comparison with UPLC-MSR. Ramautar, E. Nevedomskaya, O. A. Mayboroda, A. M. Deelder, I. D. Wilson, H. G. Gika, G. A. Theodoridis, G. W. Somsena, G. J. de Jonga;Mol. BioSyst., 2011; 7: 194–199

Metabolite profiling of blood plasma of patients with prostate cancerP. G. Lokhov, M. I. Dashtiev, S. A. Moshkovskii. A. I. Archakov;Metabolomics 2012; 6(1): 156-163

Potential Effect of Diaper and Cotton Ball Contamination on NMR- and LC/MS-Based Metabonomics Studies of Urine from Newborn BabiesA. M. Goodpaster, E. H. Ramadas, M. A. Kennedy; Anal. Chem. 2011; 83: 896–902

Metabolomics Reveals Metabolic Biomarkers of Crohn’s DiseaseJ. Jansson, B. Willing, M. Lucio, A. Fekete, J. Dicksved, J. Halfvarson, C. Tysk, P. Schmitt-Kopplin; PLoS ONE 4(7): e6386

Holistic metabonomic profiling of urine affords potential early diagnosis for bladder and kidney cancersZ. Huang, Y. Chen, W. Hang, Y. Gao, L. Lin, D. Y. Li, J. Xing, X. Yan;Metabolomics 2012, DOI: 10.1007/s11306-012-0433-5

Single Cell MetabolomicsCapillary electrophoresis with electrospray ionization mass spectrometric detection for single-cell metabolomics.T. Lapainis, S. S. Rubakhin, J. V. Sweedler; Anal Chem. 2009; 81(14): 5858-64.

Metabolic Differentiation of Neuronal Phenotypes by Single-cell Capillary Electrophoresis Electrospray Ionization-Mass SpectrometryP. Nemes, A. M. Knolhoff, S. S. Rubakhin, J. V. Sweedler; Anal. Chem. 2011; 83: 6810–6817

Environmental MetabolomicsMetabolic evidence for biogeographic isolation of the extremophilic bacterium Salinibacter ruberR. Rossello´-Mora, M. Lucio, A. Pena, J. Brito-Echeverrıa, A. Lopez-Lopez, M. Valens-Vadell, M. Frommberger, J. Anto´n, P. Schmitt-Kopplin;The ISME Journal 2008; 2: 242–253

High molecular diversity of extraterrestrial organic matter in Murchison meteorite revealed 40 years after its fallP. Schmitt-Kopplina, Z. Gabelicab, R. D. Gougeonc, A. Feketea, B. Kanawatia, M. Harira, I. Gebefuegia, G. Eckeld, N. Hertkorn; PNAS 2010; 107(7): 2763–2768

Localization of secondary metabolites in marine invertebrates: contribution of MALDI MSI for the study of saponins in Cuvierian tubules of H. forskali.S. Van Dyck, P. Flammang, C. Meriaux, D. Bonnel, M. Salzet, . Fournier, M. Wisztorski; PLoS One. 2010 Nov 10; 5(11):e13923.

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