For stool, approximately 250 mg of wet sample was lyophilized overnight. After drying, 40 mg was weighed into microcentrifuge tubes and extracted with 8:2 methanol:H2O to a final concentration of 40 mg/mL. Samples were then vortexed for 30 sec, followed by centrifugation for 15 min at 10 000 rpm. Supernatant was then transferred to LC-MS vials for with micro-inserts for analysis.
Metabolites were extracted from plasma according to the methods of Dunn et al, 201111. Briefly, plasma samples were thawed on ice for 30 min. Once thawed, 805 µL of 8:2 methanol:H2O was added to 230 µL of plasma to make a 4.5 fold dilution. Samples were vortexed for 15 sec and centrifuged at 15 000 rpm for 15 min to pellet precipitated proteins. 370 µL of supernatant was then transferred to separate vials and dried down for LC-MS using a speedvac with no heat. Samples were then reconstituted with 90 µL ddH2O and transferred to LC-MS vials with micro-inserts for analysis.
For urine, 200 µL of sample was extracted with 800 µL 1:9 Acetonitrile:H2O as per the methods of Warth et al. 201212. After centrifugation, 500 µL of supernatant was transferred
to LC-MS vials with micro-inserts for analysis.
6.2.2
LC-MS analyses
Samples were analyzed using an Agilent 1290 Infinity HPLC coupled to a Q-Exactive Orbitrap mass spectrometer (Thermo Fisher Scientific, Waltham, USA) with a HESI (heated electrospray ionization) source. For reverse phase HPLC, 2 µL of each sample was injected into a ZORBAX Eclipse plus C18 2.1 x 50mm x 1.8 micron column. Mobile phase (A) consisted of 0.1% formic acid in water and mobile phase (B) consisted of 0.1% formic acid in acetonitrile. The initial composition of 0% (B) was held constant for 30 s and increased to 100% over 3.0 min. Mobile phase B was held at 100% for 2 minutes and returned to 0% over 30s for a total run time of 6 min. For normal phase HPLC, 2 µL of each sample was injected into a ZORBAX RRHD HILIC plus 2.1 x 50mm x 1.8 micron column. Mobile phase (A) consisted of 0.1% formic acid in water and mobile phase (B) consisted of 0.1% formic acid in acetonitrile. The initial composition of 95% (B) was held constant for 30 s and decreased to 5% over 3.0 min. Mobile phase B was held at 5% for 1 minute and returned to 95% over 30s and held for 1 minute for a total run time of 6 min.
Full MS scanning between the ranges of m/z 50-750 was performed at 140 000 res–olution. The HESI source was operated under the following conditions: nitrogen flow of 30 and 8 arbitrary units for the sheath and auxiliary gas respectively, probe temperature and capillary temperature of 450 °C and 250 °C respectively and spray voltage of 3.9 kV and 3.5 kV in positive and negative mode respectively. The automatic gain control (AGC) target and maximum injection time were 1e6 and 500 ms respectively. For experiments testing the affect of sweep gas on cluster formation, sweep gas was set to 2 arbitrary units. Blanks of pure methanol were run between every sample to limit carryover. After data acquisition Thermo .RAW files were converted to .MZML format and centroided using ProteoWizard13. Files were then imported into R using the XCMS package14 for chromatogram alignment and deconvolution. Features were detected with the “xcmsSet” function using the “centWave” method and a ppm tolerance of 1. Prefilter was set to 3- 5000, noise 1E5, and signal to noise threshold was set to 5. Due to a lower overall noise and signal in negative mode, noise was set to 1E3 for this mode. Retention time correction was conducted using the “obiwarp” method, grouping included features present in at least one samples, allowable retention time deviation was 5 seconds, and m/z width set to 0.015. Areas of features below the signal to noise threshold in the data were integrated using the “fillPeaks” function with default settings.
6.3
References
(1) Zhou, S.; Hamburger, M. Formation of Sodium Cluster Ions in Electrospray Mass Spectrometry. Rapid Commun. Mass Spectrom. 1996, 10 (7), 797–800.
(2) Zhang, D.; Cooks, R. G. Doubly Charged Cluster Ions [(NaCl)m(Na)2]2+: Magic
Numbers, Dissociation, and Structure. Int. J. Mass Spectrom. 2000, 195-196, 667– 684.
(3) Hao, C.; March, R. E.; Croley, T. R.; Smith, J. C.; Rafferty, S. P. Electrospray Ionization Tandem Mass Spectrometric Study of Salt Cluster Ions. Part 1--
Investigations of Alkali Metal Chloride and Sodium Salt Cluster Ions. J. Mass
Spectrom. 2001, 36 (1), 79–96.
(4) Konermann, L.; McAllister, R. G.; Metwally, H. Molecular Dynamics Simulations of the Electrospray Process: Formation of NaCl Clusters via the Charged Residue Mechanism. J. Phys. Chem. B 2014, 118 (41), 12025–12033.
(5) Wishart, D. S.; Tzur, D.; Knox, C.; Eisner, R.; Guo, A. C.; Young, N.; Cheng, D.; Jewell, K.; Arndt, D.; Sawhney, S.; Fung, C.; Nikolai, L.; Lewis, M.; Coutouly, M.- A.; Forsythe, I.; Tang, P.; Shrivastava, S.; Jeroncic, K.; Stothard, P.; Amegbey, G.; Block, D.; Hau, D. D.; Wagner, J.; Miniaci, J.; Clements, M.; Gebremedhin, M.; Guo, N.; Zhang, Y.; Duggan, G. E.; Macinnis, G. D.; Weljie, A. M.; Dowlatabadi, R.; Bamforth, F.; Clive, D.; Greiner, R.; Li, L.; Marrie, T.; Sykes, B. D.; Vogel, H. J.; Querengesser, L. HMDB: The Human Metabolome Database. Nucleic Acids Res.
2007, 35 (Database issue), D521–D526.
(6) Barri, T.; Dragsted, L. O. UPLC-ESI-QTOF/MS and Multivariate Data Analysis for Blood Plasma and Serum Metabolomics: Effect of Experimental Artefacts and Anticoagulant. Anal. Chim. Acta 2013, 768, 118–128.
(7) Hernandes, M. Z.; Cavalcanti, S. M. T.; Moreira, D. R. M.; de Azevedo Junior, W. F.; Leite, A. C. L. Halogen Atoms in the Modern Medicinal Chemistry: Hints for the Drug Design. Curr. Drug Targets 2010, 11 (3), 303–314.
(8) Jeschke, P. The Unique Role of Halogen Substituents in the Design of Modern Agrochemicals. Pest Manag. Sci. 2010, 66 (1), 10–27.
(9) Benjamini, Y.; Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological). 1995, pp 289–300.
(10) Bonferroni, Carlo, E. Teoria Statistica Delle Classi E Calcolo Delle Probabilità.
(11) Dunn, W. B.; Broadhurst, D.; Begley, P.; Zelena, E.; Francis-McIntyre, S.; Anderson, N.; Brown, M.; Knowles, J. D.; Halsall, A.; Haselden, J. N.; Nicholls, A. W.; Wilson, I. D.; Kell, D. B.; Goodacre, R. Procedures for Large-Scale Metabolic Profiling of Serum and Plasma Using Gas Chromatography and Liquid Chromatography Coupled to Mass Spectrometry. Nat. Protoc. 2011, 6 (7), 1060– 1083.
(12) Warth, B.; Sulyok, M.; Fruhmann, P.; Mikula, H.; Berthiller, F.; Schuhmacher, R.; Hametner, C.; Abia, W. A.; Adam, G.; Fröhlich, J.; Krska, R. Development and Validation of a Rapid Multi-Biomarker Liquid Chromatography/tandem Mass Spectrometry Method to Assess Human Exposure to Mycotoxins. Rapid Commun.
Mass Spectrom. 2012, 26 (13), 1533–1540.
(13) Kessner, D.; Chambers, M.; Burke, R.; Agus, D.; Mallick, P. ProteoWizard: Open Source Software for Rapid Proteomics Tools Development. Bioinformatics 2008, 24 (21), 2534–2536.
(14) Patti, G. J.; Tautenhahn, R.; Siuzdak, G. Meta-Analysis of Untargeted Metabolomic Data from Multiple Profiling Experiments. Nat. Protoc. 2012, 7 (3), 508–516.
Chapter 7
7
General discussion
The composition of the microbiome has only recently become accessible, resulting from advancements in NGS techniques. Due to the infancy of the field, as well as the cost of metagenomics/transcriptomics, the majority of microbiome studies to date have been limited to “who is there”. Metagenomics has become increasingly more affordable and common, but validation of predicted products is still extremely rare, and conclusions are usually drawn from metagenomics alone1–3. Our work on BV in Chapters 2 & 3 has
discovered that many metabolites discriminating BV from health are of unknown origin (i.e. 2HV, GHB, 2-hydroxyisocaproate, 2-hydroxyglutarate), with the genes responsible for their production either being poorly characterized or completely unknown. Indeed, none of the above mentioned metabolites were predicted based on meta-transcriptome analysis4. Our work therefore serves as a cautionary tale for those tempted to draw sweeping conclusions from metagenomic data alone. As demonstrated by the case of the amines in BV, a single metabolite can have a large impact on phenotype5,6. Conversely, it is often overlooked that genes and transcripts identified by NGS experiments are merely predictions based on sequence similarity to genes whose functions have been proven experimentally. In the absence of classical microbiology methods, metabolomics offers some confirmation of these predictions. The findings within Chapters 2 & 3 highlight this fact, and advocate that a combination of techniques is absolutely necessary to obtain a complete picture of the function of microbial communities.