APPLICATION NOTE No. 10532 Application of GC Orbitrap mass spectrometry for untargeted metabolomics of pathogenic microorganisms

Authors: Cristian Cojocariu,1 Stefan Weidt,2 Jeni Haggarty,2 Paul Silcock1 and Karl Burgess2

1Thermo Fisher Scientific, Runcorn, UK;2 Glasgow Polyomics, University of Glasgow, Glasgow, UK

Keywords: Metabolomics, Q Exactive GC, Pathogenic bacteria, Biofilms, Staphylococcus aureus, Candida albicans

Introduction Infections due to pathogenic bacteria affect a large number of people worldwide. There is increasing concern about the effectiveness of existing vaccines and antibiotics used to prevent and treat infections caused by these organisms. Pathogenic bacteria can form biofilms either as single species aggregates and/or as polymicrobial biofilms. An important biofilm-causing fungus, Candida albicans, is the In this study, the interaction between C. albicans and fourth leading cause of bloodstream infections worldwide S. aureus was investigated using an untargeted and is associated with the highest incidence of mortality.1 metabolomics approach and by applying gas Furthermore, it has been demonstrated that C. albicans chromatography (GC) coupled with high resolution mass can form biofilms in close association with the pathogenic spectrometry (HRMS). Bacterial culture media as well as bacterium Staphylococcus aureus. The combined effect of C. albicans and S. aureus cells were investigated in co- C. albicans and S. aureus result in a synergistic increase culture and separately with the aim of detecting and in virulence and mortality in mice2 and humans.3 At the identifying statistically relevant metabolites that can help metabolic level little is known about the biochemicals to elucidate these microbial interactions. responsible for this symbiotic effect. In order to elucidate the biochemical interactions between were dried prior to addition of 50 µL of 20 mg/ml (w/v) C. albicans and S. aureus and to identify the key metabolites methoxyamine HCl in pyridines and incubation at 60 °C involved in this synergistic relationship, a Thermo Scientific™ for 120 min. Following the methoximation step, 50 µL Q Exactive™ GC Orbitrap™ mass spectrometer was of MSTFA + 1 % TMCS (N-Methyl-N-(trimethylsilyl) employed. This high mass resolution bench-top analytical trifluoroacetamide + 1% trimethylchlorosilane) was added. GC-MS platform has the advantage of delivering full-scan Silylation was performed by incubation at 80 °C for a sensitivity and selectivity with consistent sub-ppm mass further 120 min. Samples were cooled to room temperature accuracy, critical for confident compound identification and and 1 µL of retention index alkane mixture was added confirmation. In addition, the large instrumental dynamic to each sample before GC-MS analysis. Retention range and fast scan rate of the Q Exactive GC system time prediction is particularly useful for untargeted allow for the detection of metabolites present at low and metabolomics as it can increase the confidencein high concentrations enabling reliable peak deconvolution compound identification when searching a spectral library.5 and compound quantification. Importantly, data can be searched against existing commercial spectral libraries GC-MS analysis (such as NIST or Wiley) and/or customized high resolution Derivatized samples were acquired in a randomized metabolites libraries for putative metabolite identification. order across the batch to reduce the statistical impact of analytical variation. A Thermo Scientific™ TriPlus™ RSH™ Experimental robotic arm was used for sample introduction and GC Samples separation was achieved using a Thermo Scientific™ Candida albicans laboratory strain SC5314 (ATCC MYA- TRACE™ 1310 gas chromatograph and a Thermo 2876) and Staphylococcus aureus Newman strain (NCTC Scientific™ TraceGOLD™ TG-5Sil MS 30 m length × 8178/ATCC) were used in all experiments described. The 0.25 mm inner diameter × 0.25 µm film thickness column samples analyzed were both individual and co-culture (Table 1). The Q Exactive GC mass spectrometer was cells of Candida albicans and Staphylococcus aureus in operated in full-scan using electron ionization and internal addition to fresh and spent media used to grow the two lock mass correction (Table 2). microorganisms, individually and in co-culture. In addition, Table 1. GC and injector conditions. injection of pooled samples in which a small aliquot of each TRACE 1310 GC Parameters biological sample are mixed together were used as quality Injection Volume (µL): 1 control samples (QC) were used to assess the variance observed in the data throughout the sample preparation, Liner: Single taper (P/N 453A1345) data acquisition and data pre-processing steps. In addition Inlet (°C): 250 to these, pure metabolite standards containing sugars, sugar phosphates, pentose phosphate pathway, and Inlet Module and Mode: SSL, split 1:100 amino acids were used to confirm candidate compounds Carrier Gas, (mL/min): He, 1.0 identified with NIST library. Details of the exact conditions Oven Temperature Program used for biofilm culture as well as the information about the Temperature 1 (°C): 70 extraction procedure of extra- and intracellular metabolites Hold Time (min): 4 are given elsewhere.4 Temperature 2 (°C): 320 Rate (°C/min): 20 Sample derivatization Hold Time (min): 8 All media and cell samples were subjected to chemical derivatization (methoximation and silylation) to increase Table 2. Mass spectrometer conditions. the volatility of the compounds present in the samples for Q Exactive GC Mass Spectrometer Parameters gas chromatography separation. A total volume of 30 µL of Transfer Line (°C): 250 13 media, cell sample was spiked with C6-Glucose (2 nmol), Ionization Type: EI

D27-Myristic Acid (2 nmol) and Scyllo-Inositol (1 nmol) used Ion Source (°C): 230 4 as internal standards to monitor and correct for injection. Electron Energy (eV): 70 For compound confirmation a series of standards mixtures Acquisition Mode: full-scan containing sugars, sugar phosphates, pentose phosphate Mass Range (Da): 50–650 pathway, and amino acids were also used following the Mass Resolution (FWHM at m/z 200): 60,000 same derivatization procedure. Samples and standards Lockmass (m/z): 207.03235 2 Data processing Results and discussion Acquired data was subjected to an untargeted metabolite The data processing workfl ow used in this study utilized detection and identifi cation workfl ow. The workfl ow uses both Compound Discoverer software and TraceFinder Thermo Scientifi ™c Compound Discoverer software for software. Compound Discoverer allowed for comparative comparing the relative abundances of metabolites in group analysis and isolation of signifi cant metabolites using multiple samples without prior identifi cation and followed parameters such as %CV of peak area repeatability in the by discriminatory statistical analysis (such as PCA and QC samples, calculated probability values (p<0.05) and Volcano plots). Metabolites isolated from statistical fold-change in concentration between the sample groups analysis were further assessed with Thermo Scientifi ™c (Figure 1). TraceFinder enabled compound deconvolution TraceFinder™ software for semi-quantitation (fold change and putative identifi cation using library search and accurate calculation) and putative metabolite identifi cation. mass information. Peak deconvolution was automatically performed with TraceFinder, where NIST 2014 mass spectral library was used to annotate the peaks with a search index threshold of >700. Compound identifi cation was made using a total confi dence score that takes into account the NIST spectral match as well as the percentage of fragment ions that can be explained from the elemental composition of the molecular ion assigned by NIST Thermo Scientifi c.6

import .raw data Experiment definition Data processing (full-scan EI/CI) (sample grouping) (peak alignment & extraction)

ID in TraceFinder and confirmation with chemical standards

Statistical analysis (trend plot, PCA)

Data export Data review further statistical analysis (based on %CV, p-values) using 3rd party software

Figure 1. Data processing workfl ow used for untargeted metabolomics. Compound Discoverer software was used to fi nd sample differences based on principal component analysis or group comparison using Volcano plots. Data from Compound Discoverer software can be exported (as *.csv or *.xls) for further statistical tests. Compound identifi cation was made using TraceFinder and/or by injection of pure chemical standards to confi rm the retention times and mass spectra.

3 Even before applying any statistical tests, noticeable PCA analysis revealed distinct sample grouping with differences in the intensities of chromatographic peaks were clear differentiation between media samples and cell observed as shown for the peak eluting at RT = 9.04 min samples were observed (Figure 3). Interestingly, the media across four media sample groups (Figure 2). samples show individual clustering with the media used for co-culturing the C. albicans and S. aureus as distinct In order to automatically extract biologically relevant group. This could be due to different uptake rates of information, the samples were investigated using the biomolecules found in the media and/or excretion of Compound Discoverer for comparative analysis and metabolites into the media from the two bacterial species. determination of group differences using PCA analysis. In addition, the QC samples clustered very closely together Compound Discoverer allows for automated, easy-to- indicating good reproducibility (<20 % RSD) of the data. setup chromatographic alignment, peak detection and data normalization in addition to robust statistical analysis. Following this analysis, a large number of chromatographic features were detected (~17,000) across all 47 samples.

9.24 9.41 NL: Figure 2. TIC chromatograms (EI, full-scan 100 2.84E7 9.04 TIC MS MO3 90 a data) of media only (a), C. albicans media (b), 9.06 80 S. aureus (c) and co-culture C. albicans and 70 S. aureus (d). Highlighted is peak at RT = 9.04 min 8.92 8.97 60 9.65 9.12 9.55 9.60 9.85 9.48 9.80 later identifi ed as glycine shows depleted levels 50 8.87 9.18 9.33 9.71 9.76 in S. aureus media as compared to the other 9.04 9.24 NL: 100 3.57E7 90 TIC MS samples. CAM3 80 9.41 b 9.06 70 60 50 9.48 9.55 9.65 8.92 8.98 9.13 9.61 9.80 40 9.18 9.34 9.70 9.24 NL: 100 3.48E7 90 TIC MS SAM2 80 9.07 c 70 9.41 60 50 8.98 9.55 9.65 8.84 8.92 9.13 9.48 9.79 40 9.19 9.35 9.62 9.71 9.24 NL: 100 3.57E7 90 TIC MS SCM3 80 9.06 d 9.41 70 60 50 9.12 9.55 9.65 8.92 8.97 9.47 9.79 9.85 8.86 40 9.18 9.35 9.61 9.71 8.9 9.0 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 Time (min)

Figure 3. Principal component analysis (PCA) clearly shows a distinct separation between the media samples and the cell samples. Media only (MO), S. aureus media (SAM), C. albicans media (CAM), and media used for the co-culture of the two species (SCM) form distinct clusters. Intracellular metabolites extracted from S. aureus cells (SAC), C. albicans cells (CAC) and co-culture biofi lm (SCC) also form separate groups.

4 Following the PCA analysis, the data was further In addition to putative compound identifi cation, TraceFinder interrogated with TraceFinder and putative compound software allows for easy data visualization of the each identifi cation was performed by searching NIST and in signifi cant metabolite intensity (p<0.05) across sample house developed high resolution metabolomics spectral groups. Examples of biomarkers with signifi cant intensity library. Compound identifi cation was made based on a total variation across the media and the cell sample groups are score calculated taking into account the spectral library shown in Figure 5. match value (SI >700) and a high resolution fi ltering score (HRF) as described elsewhere.4 An example of TraceFinder deconvolution browser is shown in Figure 4 for myo-inositol identifi ed with an average total score >98 in the media samples.

Figure 4. TraceFinder software peak deconvolution browser showing myo-inositol b c identifi cation (a) based on a total (average) a score (b) across the retention time aligned media samples (c). NIST spectral match (d), deconvoluted spectrum (e) as well as a list of the measured ions with their corresponding mass errors calculated taking into account the theoretical chemical elemental composition (f) are shown. e d

f

Figure 5. TraceFinder software browser showing cross sample retention time aligned peaks (a), peak identifi cation from the NIST a search and HRF score (b), cross sample peak overlay (c) and peak intensity trend across media samples analyzed for selected compounds (in this case 4-hydroxybutanoic). Control sample is media only, C. albicans (CAM), b S. aureus (SAM) and media used for co-culture of C. albicans and S. aureus (SCM).

c d Although the total score allows for high confi dence Assessment of cell extracts revealed obvious separation in compound identifi cation, in order to increase the between the S. aureus and C. albicans grown in co-culture confi dence even further, the retention times and mass and the cells grown separately. Sugar phosphates were spectra of putatively identifi ed metabolites were compared also commonly found in the cells samples. All spent media with the retention time and mass spectra of pure standards are characterized by the consumption of amino acids (mixture of sugars, sugar phosphates, pentose phosphates and sugars, associated with rapid growth in rich medium. and amino acids) that were derivatized following the same Interestingly, the sedoheptulose-7-phosphate was found procedure and analyzed using the same GC-MS method. at signifi cantly higher levels in the co-culture as compared An example of compound identifi cation using standards is to either of the monocultures and this is believed to be shown in Figure 6 for scyllo-Inositol 6TMS derivative found excreted by C. albicans to control the growth of S. aureus.4 in signifi cantly higher amounts in the S. aureus cell extracts as compared. All metabolites showing p-values <0.05 and signifi cant fold-changes between the sample groups were identifi ed following this workfl ow.

RT: 13.06 - 13.88 SM: 7B 13.33 NL: 318.14968 NL: 2.70E7 100 2.09E7 C 13 H 30 O 3 Si 3 SAC5#1782-1783 RT: m/z= 217.10753 -0.14909 ppm 13.33-13.33 AV: 2 SB: 62 90 100 318.14790- C 9 H 21 O 2 Si 2 13.16-13.29 , 13.37-13.52 T: 318.15108 0.31987 ppm FTMS + p EI Full lock ms 80 MS SAC5 90 [50.00-650.00]

70 S. aureus cells 80 70 60 73.04686 191.09182 C 7 H 19 O 2 Si 2 60 C 3 H 9 Si 50 0.82536 ppm 0.04795 ppm bundan ce 50 A

147.06569 40 v e

i C 5 H 15 O Si 2 t 40 a l 0.66537 ppm 343.15748 30 e R 30 C 15 H 31 O 3 Si 3 20 -0.19293 ppm 20 103.05742 265.11078 393.17653 C 4 H 11 O Si C 9 H 25 O 3 Si 3 C 15 H 37 O 4 Si 4 10 10 0.52507 ppm 0.67035 ppm 0.47883 ppm 0 0 13.33 NL: 318.14949 NL: 2.58E7 100 1.88E7 C 7 H 22 O 8 N 6 Sugar_Mix#1824 RT: 13.33 m/z= 217.10743 0.39347 ppm AV: 1 SB: 41 13.18-13.29 , 90 100 318.14790- C H O Si 13.38-13.46 T: FTMS + p EI 318.15108 9 21 2 2 Full lock ms [50.00-650.00] -0.12668 ppm 80 MS 90 Sugar mix standard Sugar_Mix 70 80 70 191.09179 60 73.04683 C H O Si C H Si 7 19 2 2 60 3 9 -0.10787 ppm 50 0.36110 ppm 50 147.06563 40 C H O Si 40 5 15 2 0.27385 ppm 343.15748 30 30 C 15 H 31 O 3 Si 3 13.65 -0.19809 ppm 20 20 103.05741 265.11072 393.17646 C 4 H 11 O Si C 9 H 25 O 3 Si 3 C H O Si 10 10 15 37 4 4 0.38883 ppm 0.46380 ppm 0.28831 ppm 0 0 13.1 13.2 13.3 13.4 13.5 13.6 13.7 13.8 50 100 150 200 250 300 350 400 450 Time (min) m/z

Figure 6. Confi rmation of scyllo-Inositol 6TMS derivative in S. aureus cell extracts, using a mixture of pure sugar standards. Library (NIST search index), retention time, spectral fi delity and sub ppm mass accuracy of measured fragment ions were used to confi rm this compound.

6 Conclusions References • The results from these experiments suggest that 1. Crump, J. A., and P. J. Collignon. 2000. Intravascular catheter- most interactions between Candida albicans and associated infections. European Journal of Clinical Microbiology & Infectious Diseases. 19:1-8. Staphylococcus aureus are related to the synthesis 2. Carlson, E. C. 1988. Synergism of Candida albicans and delta toxin and utilization of sugars as the main carbon source, in producing Staphylococcus aureus on mouse mortality and morbidity: particular to the sedoheptulose-7-phosphate metabolism. protection by indomethacin. Zentralblatt für Bakteriologie Mikrobiologie • Compound Discoverer and TraceFinder automate data und Hygiene: I. Abt. Originale C: Allgemeine, angewandte und ökologische Mikrobiologie. A 269:377-386. processing, streamlining and simplifying the detection 3. Morales DK, Hogan DA (2010) Candida albicans Interactions with and confident identification of statistically significant Bacteria in the Context of Human Health and Disease. PLoS Pathog metabolites. 6(4): e1000886. doi:10.1371/journal.ppat.1000886. • Importantly, the metabolomics workflow described here 4. Weidt, S., Haggarty, J., Kean, R., Cojocariu, C. I., Silcock, P.J., Rajendran, R., Ramage, G., and Burgess K. E.V. May facilitates timely and confident data acquisition, data submitted 2016. A novel targeted/untargeted GC-Orbitrap metabolomics processing and interpretation of the results. methodology applied to Candida albicans and Staphylococcus aureus • The results obtained from these experiments demonstrate biofilms.Metabolomics . http://link.springer.com/article/10.1007/ s11306-016-1134-2. that the Q Exactive GC system is a powerful analytical 5. Dunn, W. B., Broadhurst, D., Begley, P., Zelena, E., Francis-McIntyre, tool that can be used to understand metabolic changes S., Anderson, N., et al. (2011). Procedures for large-scale metabolic in complex bacterial interactions offering unprecedented profiling of serum and plasma using gas chromatography and liquid insights into the pathogen-pathogen interactions at the chromatography coupled to mass spectrometry. Nature Protocols, 6(7), small molecule level. 1060–1083. 6. Untargeted Metabolomics Using Orbitrap-Based GC-MS, Thermo • Taken together, the Q Exactive GC mass spectrometer Fisher Scientific Application Note 10457, 2015. [Online]: https://tools. is a unique analytical tool able to detect a large number thermofisher.com/content/sfs/brochures/AN-10457-GC-MS-Orbitrap- of metabolites with a simple setup and full-scan high Untargeted-Metabolomics-AN10457-EN.pdf. resolution experiments.

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