Introduction

Myxobacteria are a promising source of natural products exhibiting potent biological activities, and several myxobacterial metabolites are currently under investigation as potential leads for novel drugs. However, the myxobacteria are also a striking example of the contrast between the genetic capacity for the production of secondary metabolites and the number of compounds that could be characterized to date [1,2]. In conventional analyses, the number of identified metabolites is usually significantly lower than that predicted from genome sequence information. Thus, the discovery of novel secondary metabolites from genetically proficient myxobacterial producers currently constitutes a substantial bottleneck. Improved analytical methods, based on the combined use of LC-coupled high-resolution mass spectrometry and statistical data evaluation, can significantly enhance the process of uncovering these “hidden” bacterial secondary metabolomes [3,4].

Here, we present maXis ESI-UHR-Q-TOF based analysis of myxobacterial secondary metabolites, which enables several challenges frequently encountered in metabolite profiling studies to be solved. These challenges comprise the simultaneous need for fast, robust, and sensitive analysis

Application Note # ET-21

Challenges in Metabolomics addressed by targeted and untargeted UHR-Q-TOF analysis

with high resolution, accuracy and excellent reproducibility. Analytical solutions for targeted and untargeted metabolomics experiments using ESI-UHR-Q-TOF-MS are discussed.

Experimental

The sample set consisted of five different Myxococcus xanthus strains which were each cultivated as four biological replicates. Four of the strains harbored a different genetic knockout (Mut.1 to Mut.4) and DK1622 served as a reference strain. For performance evaluation and quality control (QC) purposes a pooled sample of all extracts was created (5µL of each extract). Samples were diluted 1:10 with methanol prior to injection. Two technical replicates of each sample were injected.Myxococcus xanthus extracts were separated using a RP C18 column (50 x 2 mm, 1.7 µm particle size) on an UltiMate 3000™ rapid separation liquid chromatography system RSLC (Dionex).The following gradient was applied for separation with a flow rate of 400 µL/min using (A) Water + 0.1% HCOOH (B) Acetonitrile + 0.1% HCOOH as mobile phase.Gradient: 0 min 1% B; 1 min 1% B; 10 min 99% B; 12 min 99% B; 12.5 min 1% B; 14 min 1% B

Verification / Identification

O

NH

OMe

O

O

OH

OH

OH

OTargeted / untargeted Analysis

Fast LC separation coupled to high-resolution mass

spectrometry

Compounds across a wide dynamic range were detected

The achieved accuracy is independent of the acquisition speed

1 2 3 4 5 6 7 8Time [min]

3Hz

5Hz

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Myxochelin B

Myxalamid A

Myxovirescin A

Cittilin A

404.1814

404.1814

404.1815

404.1813

404.1816

405.1849406.1883

404.0 404.5 405.0 405.5 406.0 406.5 407.0 m/z

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0.5 ppm; 6.0 mSigma; Res. 38647

0.4 ppm; 0.6 mSigma; Res. 38036

0.2 ppm; 0.8 mSigma; Res. 38383

0.8 ppm; 8.8 mSigma; Res. 39034

Simulated isotope pattern C20H26N3O6

A B

Fig.1: Basepeak chromatogram of Myxococcus xanthus extract with hrEIC traces of known metabolites and mass spectrum of Myxalamid A (inset).

Fig.2: A: High-resolution extracted ion chromatogram traces of 1mDa width of various metabolites; B: mass spectra of Myxochelin B, both at different acquisition speeds.

ESI-MS measurements were performed using positive ionization on the maXis UHR-Q-TOF-MS m/z range: 100-1200 m/z, acquisition rate: 3, 5, 10, 20Hz.Targeted screening was carried out using high-resolution EIC traces (hrEIC), retention time and isotope pattern evaluation via SigmaFit™ with TargetAnalysis™.Statistical interpretation using PCA including all data pre-processing was performed with ProfileAnalysisTM 2.0 using FindMolecularFeatures (FMF) compounds and advanced bucketing (Bucket filter 75%; Pareto scaling).

Results

Sensitivity and dynamic range

The extracts from myxobacteria represent a complex mixture of matrix components from the growth medium and bacterial secondary metabolites. For these samples a very sensitive method with a high dynamic range is required to detect the low-concentration metabolites that are often masked by highly abundant matrix components.Figure 1 shows the base peak chromatogram (BPC) of a quality control sample composed of all extracts used in this study. The colored traces represent high-resolution extracted ion chromatogram (hrEIC) traces of known metabolite classes from Myxococcus xanthus. Thus, due

to the mass stability of the maXis, compounds across a wide dynamic range can be detected and characterized, as highlighted by the inset example for Myxalamid A.

Selectivity, Resolution, Mass accuracy at high MS acquisition Speed

When screening complex mixtures, it is important that selectivity and speed do not compromise each other. The selectivity in accurate mass and high-resolution LC-MS measurements can be visualized with hrEIC traces. Figure 2A shows hrEIC traces of 1mDa width for four metabolites acquired at acquisition rates from 3Hz up to 20Hz where basically no difference between the EIC traces is visible. The achieved accuracy is thus independent of the acquisition speed. The mass spectra of Myxochelin B are shown in Figure 2B. The mass accuracy, resolution and quality of the isotopic patterns are also not influenced by the change in acquisition rate. The mSigma-Value is a measure for the goodness of fit between measured and theoretical isotopic pattern: the smaller the mSigma-Value the better the isotopic matching.

Targeted Analysis

A block of three replicate pooled QC samples was injected randomly between samples and before and after the samples, resulting in a total of 19 technical replicates.

Clear display of screening results

Name Formula[M+H]+

m/z theo. Error [ppm]

mSigma N

Cittilin A C34H39N4O8 631.2762 0.34 6.07 19

DKxanthene 534 C29H35N4O6 535.2551 0.79 5.01 19

Myxalamid A C26H42NO3 416.3159 0.26 4.46 19

Myxochelin B C20H36N3O6 404.1816 0.47 3.49 19

Myxovirescin A C35H62NO8 624.447 0.29 6.19 18

Fig.3: A: Result of target screening as pre-sented in TargetAnalysis™; B: summary of averaged mass accuracy and mSigma values for different secondary metabolites (N = number of Samples).

A

B

Identification of compounds whose abundance differed

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 PC 1

-0.5

0.0

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PC 2

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 PC 1

-0.5

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PC 2Scores plot Loadings plot

QC

Mut. 2Mut. 2Mut. 3Mut. 4DK1622

Bucket: 4.8min : 549.27m/z

0 5 10 15 20 25 30 35 40 Analysis0

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nsity

x 1

0 4Bucket: 7.5min : 416.32m/z

0 5 10 15 20 25 30 35 40 Analysis1.0

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tens

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Myxalamid A DKxanthene-548

A

B

Fig.4: A: PCA analysis of M. xanthus wild type (DK 1622) and different mutant knockout strains – scores and loadings plots PC1 vs PC2 (expl. variance 43%). Symbols represent technical replicates; QC: Quality control samples;B: Bucket statistics plot (without the QC samples) of selected loadings visualizing the relative production of the metabolites Myxalamid A and DKxanthen-548

To screen for known metabolites, a targeted analysis was performed making use of retention time, accurate mass and isotopic pattern information. The selectivity for screening is achieved by hrEIC traces. Screening results are displayed in TargetAnalysis in a way that allows for a quick survey — compound name, molecular formula, retention time (RT), mass accuracy and mSigma fit values are given as well as peak area, intensity values and RT deviation (see Figure 3A). Figure 3B highlights the averaged mass accuracies and mSigma values for a selected subset of target compounds detected within the QC samples and demonstrates the long–term stability of the instrument performance.

Untargeted analysis using PCA

Full scan MS data used for targeted analysis can also be used for an untargeted profiling.The data set of the different Myxococcus xanthus strains was subjected to Principle Component Analysis (PCA) to investigate different production patterns and metabolites whose absence is potentially due to the genetic knockout mutations. One important step prior to the statistical analysis is the extraction of compounds using FindMolecularFeatures (FMF) for data reduction and comprehensive detection of all compounds in the LC-MS run.

The PCA result for all strains is presented in Figure 4A: the different strains cluster in the PCA scores plot and the technical replicates are close to one another. The QC samples form a cluster in the middle of the PCA plot, confirming the long-term stability of the instrument and reproducibility of the generated data. From the loadings plot, the compounds related to the clustering are derived. Figure 4B gives two examples of loadings with different production patterns in metabolites. These compounds could be identified as Myxalamid A and DKxanthene-548, whose abundance differed between the strains.

Identification

The identification of unknown compounds is one of the most challenging tasks in the metabolomics workflow. True Isotopic Patten and accurate mass information in MS and MS/MS data can help to reduce the number of possible sum formulae for target metabolites. The identification with SmartFormula3D™ is demonstrated for Myxalamid A: based on sum formulae calculated for precursor and product ions it is possible to limit the number of candidates and achieve an assignment of formulae to fragments. (Fig. 5)

Fig.5: SmartFormula3D result for Myxalamid A with MS/MS spectrum (inset).

Identification with SmartFormula3D

Conclusion

Common challenges in Metabolomics workflows can be addressed by hyphenating high resolving U-HPLC with maXis UHR-Q-TOF analysis.The desired sample throughput necessary for Metabolomics projects can be achieved by reducing the analysis time for complex samples with fast LC separations. Since mass accuracy, resolution, isotopic fidelity and selectivity (hrEIC-traces) are independent of the maXis acquisition speed (up to 20Hz), this instrument is the perfect choice for coupling to U-HPLC separations.The high-resolution and accurate mass LC-MS data sets acquired enable both targeted and untargeted analysis. Due to the dynamic range of mass accuracy provided, high- and low-abundance compounds can be studied within the same dataset – even within the same mass spectrum.SmartFormula3D, which automatically combines exact mass and isotopic pattern information in MS and MS/MS spectra for sum formula generation, enables the identification of unknown compounds – one of the most challenging tasks in the Metabolomics workflow.

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Keywords

Metabolomics

Metabolic profiling

Principle Component Analysis

Small molecule identification

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

References

[1] Wenzel SC, Müller R (2009). Curr. Opin. Drug Disc. Dev. 12 (2), 220-230[2] Garcia RO, Krug D, Müller R (2009). Methods in Enzymology 458, 59-91[3] Krug D, Zurek G, Revermann O, Vos M, Velicer GJ, Müller R (2008). Appl. Environ. Microbiol. 74, 3058-3068[4] Krug D, Zurek G, Schneider B, Garcia R, Müller R (2008). Anal. Chim. Acta 624, 97-106

Authors

Aiko Barsch1, Gabriela Zurek1, Daniel Krug², Niña Cortina2 , Rolf Müller² (1) Bruker Daltonik GmbH, Bremen, Germany(2) Helmholtz-Institute for Pharmaceutical Research Saarland (HIPS) & Universität des Saarlandes, 66123 Saarbrücken, Germany

Instrumentation & Software

maXis UHR-TOF

Dionex UltiMate 3000 RSLC

ProfileAnalysis 2.0

Bruker Daltonik GmbH

Bremen · GermanyPhone +49 (0)421-2205-0 Fax +49 (0)421-2205-103 sales@bdal.de

Bruker Daltonics Inc.

Billerica, MA · USAPhone +1 (978) 663-3660 Fax +1 (978) 667-5993 ms-sales@bdal.com

www.bruker.com/metabolicprofiler