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Mass Spectrometry-Based Microbial Metabolomics: Techniques, Analysis, and Applications

  • Edward E. K. BaidooEmail author
  • Veronica Teixeira Benites
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1859)

Abstract

The demand for understanding the roles genes play in biological systems has steered the biosciences into the direction the metabolome, as it closely reflects the metabolic activities within a cell. The importance of the metabolome is further highlighted by its ability to influence the genome, transcriptome, and proteome. Consequently, metabolomic information is being used to understand microbial metabolic networks. At the forefront of this work is mass spectrometry, the most popular metabolomics measurement technique. Mass spectrometry-based metabolomic analyses have made significant contributions to microbiological research in the environment and human disease. In this chapter, we break down the technical aspects of mass spectrometry-based metabolomics and discuss its application to microbiological research.

Key words

Mass spectrometry Metabolomics Microbial LC-MS GC-MS CE-MS Metabolic quenching Metabolite extraction Data analysis Microbial communities Human disease 

Notes

Acknowledgments

The authors would also like to acknowledge that this work was part of the DOE Joint BioEnergy Institute (http://www.jbei.org) supported by the US Department of Energy, Office of Science, Office of Biological and Environmental Research, through contract DE-AC02-05CH11231 between Lawrence Berkeley National Laboratory and the US Department of Energy.

References

  1. 1.
    Villas-Bôas SG, Roessner U, Hansen MAE et al (2007) Metabolome analysis: an introduction. John Wiley & Sons, Inc, Hoboken, NJCrossRefGoogle Scholar
  2. 2.
    Baidoo EEK, Benke PI, Keasling JD (2012) Mass spectrometry-based microbial metabolomics. In: Navid A (ed) Microbial systems biology: methods and protocols. Springer, New York, NY, pp 215–278CrossRefGoogle Scholar
  3. 3.
    Murzin AG, Brenner SE, Hubbard T et al (1995) SCOP: a structural classification of proteins database for the Investigation of Sequences and Structures. J Mol Biol 247:536–540PubMedGoogle Scholar
  4. 4.
    Stryer L (1995) Biochemistry. W.H. Freeman & Company, New York, NYGoogle Scholar
  5. 5.
    Lodish H, Berk A, Zipursky L et al (2000) Molecular cell biology. W.H. Freeman & Company, New YorkGoogle Scholar
  6. 6.
    Pugh BF (2000) Control of gene expression through regulation of the TATA-binding protein. Gene 255:1–14PubMedCrossRefGoogle Scholar
  7. 7.
    de KW, van DK (1992) A method for the determination of changes of glycolytic metabolites in yeast on a subsecond time scale using extraction at neutral pH. Anal Biochem 204:118–123CrossRefGoogle Scholar
  8. 8.
    Rabinowitz JD, Kimball E (2007) Acidic acetonitrile for cellular metabolome extraction from Escherichia coli. Anal Chem 79:6167–6173PubMedCrossRefGoogle Scholar
  9. 9.
    da Luz JA, Hans E, Zeng AP (2014) Automated fast filtration and on-filter quenching improve the intracellular metabolite analysis of microorganisms. Eng Life Sci 14:135–142CrossRefGoogle Scholar
  10. 10.
    Kell DB, Brown M, Davey HM et al (2005) Metabolic footprinting and systems biology: the medium is the message. Nat Rev Microbiol 3(7):557–565PubMedCrossRefGoogle Scholar
  11. 11.
    Pinu FR, Villas-Boas SG, Aggio R (2017) Analysis of intracellular metabolites from microorganisms: quenching and extraction protocols. Metabolites 7:E53PubMedCrossRefGoogle Scholar
  12. 12.
    Breil C, Abert Vian M, Zemb T et al (2017) “Bligh and dyer” and Folch methods for solid–liquid–liquid extraction of lipids from microorganisms. Comprehension of solvatation mechanisms and towards substitution with alternative solvents. Int J Mol Sci 18:1–21CrossRefGoogle Scholar
  13. 13.
    Bligh EG, Dyer WJ (1959) Can J Biochem Physiol 37:911–917PubMedCrossRefGoogle Scholar
  14. 14.
    Folch J, Lees M, Sloane Stanley GH (1957) A simple method for the isolation and purification of total lipides from animal tissues. J Biol Chem 226:497–509Google Scholar
  15. 15.
    Axelsson M, Gentili F (2014) A single-step method for rapid extraction of total lipids from green microalgae. PLoS One 9:17–20CrossRefGoogle Scholar
  16. 16.
    Oikawa A, Fujita N, Horie R et al (2011) Solid-phase extraction for metabolomic analysis of high-salinity samples by capillary electrophoresis-mass spectrometry. J Sep Sci 34:1063–1068PubMedCrossRefGoogle Scholar
  17. 17.
    Johnson WM, Kido Soule MC, Kujawinski EB (2017) Extraction efficiency and quantification of dissolved metabolites in targeted marine metabolomics. Limnol Oceanogr Methods 15:417–428CrossRefGoogle Scholar
  18. 18.
    Mousavi F, Bojko B, Pawliszyn J (2015) Development of high throughput 96-blade solid phase microextraction-liquid chromatrography-mass spectrometry protocol for metabolomics. Anal Chim Acta 892:95–104PubMedCrossRefGoogle Scholar
  19. 19.
    Wang Z, Zhu H, Huang G (2017) Ion suppression effect in DESI mass spectrometry and ESI mass spectrometry. Rapid Commun Mass Spectrom 31(23):1957–1962PubMedCrossRefGoogle Scholar
  20. 20.
    Buszewski B, Noga S (2012) Hydrophilic interaction liquid chromatography (HILIC)—a powerful separation technique. Anal Bioanal Chem 402:231–247CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Baker DR (1995) Capillary electrophoresis. John Wiley & Sons, Inc, New YorkGoogle Scholar
  22. 22.
    Harris DC (2003) Quantitative chemical analysis. W. H. Freeman and Company, New YorkGoogle Scholar
  23. 23.
    Snyder LR, Kirkland JJ, Dolan JW (2010) Introduction to modern liquid chromatography. Wiley, Hoboken, NJGoogle Scholar
  24. 24.
    Przybyciel M, Industries ES, Berlin W et al (2002) Phase collapse. ES Industries, West Berlin, NJGoogle Scholar
  25. 25.
    Bajad SU, Lu W, Kimball EH et al (2006) Separation and quantitation of water soluble cellular metabolites by hydrophilic interaction chromatography-tandem mass spectrometry. J Chromatogr A 1125:76–88PubMedCrossRefGoogle Scholar
  26. 26.
    ChromAcademy (2014) The Theory of HPLC. Chromatographic parameters. E-learning. Anal Chem Commun 1:23Google Scholar
  27. 27.
    Chawla G, Ranjan C (2016) Principle, instrumentation, and applications of UPLC: A novel technique of liquid chromatography. Open Chem J 3:1–16CrossRefGoogle Scholar
  28. 28.
    Desai TK, Mahajan AA, Thaker A (2012) Ultra performance liquid chromatography: a step ahead to HPLC. Int J Pharm Rev Res 2:61–68Google Scholar
  29. 29.
    Lake R (2007) Easy transfer of HPLC methods to UPLC. Restek Advantage 4:10–11Google Scholar
  30. 30.
    Cunliffe JM, Maloney TD (2007) Fused-core particle technology as an alternative to sub-2-microm particles to achieve high separation efficiency with low backpressure. J Sep Sci 30:3104–3109PubMedCrossRefGoogle Scholar
  31. 31.
    Abrahim A, Al-Sayah M, Skrdla P et al (2010) Practical comparison of 2.7 μm fused-core silica particles and porous sub-2 μm particles for fast separations in pharmaceutical process development. J Pharm Biomed Anal 51:131–137PubMedCrossRefGoogle Scholar
  32. 32.
    Hübschmann HJ (2008) Handbook of GC/MS: fundamentals and applications. Wiley-VCH Verlag GmbH & Co. KGaA, WeinheimCrossRefGoogle Scholar
  33. 33.
    Halket JM, Waterman D, Przyborowska AM et al (2005) Chemical derivatization and mass spectral libraries in metabolic profiling by GC/MS and LC/MS/MS. J Exp Bot 56:219–243PubMedCrossRefGoogle Scholar
  34. 34.
    Halket J, Zaikin V (2003) Review: derivatization in mass spectrometry—1. Silylation. Eur J Mass Spectrom 9:1CrossRefGoogle Scholar
  35. 35.
    Kanani H, Chrysanthopoulos PK, Klapa MI (2008) Standardizing GC-MS metabolomics. J Chromatogr B Anal Technol Biomed Life Sci 871:191–201CrossRefGoogle Scholar
  36. 36.
    Soga T, Ohashi Y, Ueno Y et al (2003) Quantitative metabolome analysis using capillary electrophoresis mass spectrometry. J Proteome Res 2:488–494PubMedCrossRefGoogle Scholar
  37. 37.
    Baidoo EEK, Benke PI, Neusüss C et al (2008) Capillary electrophoresis-Fourier transform ion cyclotron resonance mass spectrometry for the identification of cationic metabolites via a pH-mediated stacking-transient isotachophoretic method. Anal Chem 80:3112–3122PubMedCrossRefPubMedCentralGoogle Scholar
  38. 38.
    Soga T, Ueno Y, Naraoka H et al (2002) Pressure-assisted capillary electrophoresis electrospray ionization mass spectrometry for analysis of multivalent anions. Anal Chem 74:6224–6229PubMedCrossRefGoogle Scholar
  39. 39.
    Harada K, Fukusaki E, Kobayashi A (2006) Pressure-assisted capillary electrophoresis mass spectrometry using combination of polarity reversion and electroosmotic flow for metabolomics anion analysis. J Biosci Bioeng 101:403–409PubMedCrossRefGoogle Scholar
  40. 40.
    Hoffmann E d, Stroobant V (2002) Mass spectrometry: principles and applications. Wiley, ChichesterGoogle Scholar
  41. 41.
    Gabellca V, De PE (2005) Internal energy and fragmentation of ions produced in electrospray sources. Mass Spectrom Rev 24:566–587CrossRefGoogle Scholar
  42. 42.
    Skoog DA, Holler FJ, Nieman TA (1998) Principles of instrumental analysis. Brooks Cole, Pacific Grove, CAGoogle Scholar
  43. 43.
    Fjieldsted J (2011) Time-of-flight mass spectrometry technical overview this overview describes. Agilent Technologies, Santa Clara, CAGoogle Scholar
  44. 44.
    Stewart II (1999) Electrospray mass spectrometry: a tool for elemental speciation. Spectrochim Acta B Atom Spectrosc 54:1649–1695CrossRefGoogle Scholar
  45. 45.
    Wilhelm O, Mädler L, Pratsinis SE (2003) Electrospray evaporation and deposition. J Aerosol Sci 34:815–836CrossRefGoogle Scholar
  46. 46.
    Bruins a P (1998) Mechanistic aspects of electrospray ionization. J Chromatogr A 798:345–357CrossRefGoogle Scholar
  47. 47.
    Smith JN, Flagan RC, Beauchamp JL (2002) Droplet evaporation and discharge dynamics in electrospray ionization. J Phys Chem A 106:9957–9967CrossRefGoogle Scholar
  48. 48.
    Banks JF (1997) Review Recent advances in capillary electrophoresis/electrospray/mass spectrometry. Electrophoresis 18:2255–2266PubMedCrossRefGoogle Scholar
  49. 49.
    Von BA, Nicholson G, Bayer E (2001) Recent advances in capillary electrophoresis/electrospray-mass spectrometry. Electrophoresis 22:1251–1266CrossRefGoogle Scholar
  50. 50.
    Park CJ, Ahn JR (2005) A closed ion source with a cylindrical repeller for sensitivity enhancement in quadrupole mass spectrometry. Rev Sci Instrum 76:044101CrossRefGoogle Scholar
  51. 51.
    Watson JT, Sparkman OD (2007) (First published 20 June 2008) Introduction to mass spectrometry: instrumentation, applications and strategies for data interpretation. John Wiley & Sons, Inc., Chichester.  https://doi.org/10.1002/9780470516898, Print ISBN: 9780470516348, Online ISBN: 9780470516898
  52. 52.
    Hinterberger F (2006) Ion optics with electrostatic lenses. CAS, Cern Accel Sch Small Accel, Geneva, pp 27–44Google Scholar
  53. 53.
    Birkinshaw K, Hirst DM, Jarrold MF (1978) The focusing of an ion beam from a quadrupole mass filter using an electrostatic octopole lens. J Phys E 11:1037–1040CrossRefGoogle Scholar
  54. 54.
    Zhang R, Lei W, Molina LT et al (2000) Ion transmission and ion/molecule separation using an electrostatic ion guide in chemistry ionization mass spectrometry. Int J Mass Spectr 194(1):B1–B2CrossRefGoogle Scholar
  55. 55.
    Orloff J (2009) Handbook of charged particle optics. CRC Press, Boca Raton, FLGoogle Scholar
  56. 56.
    Limbach PA, Marshall AG, Wang M (1993) An electrostatic ion guide for efficient transmission of low energy externally formed ions into a Fourier transform ion cyclotron resonance mass spectrometer. Int J Mass Spectrom Ion Process 125:135–143CrossRefGoogle Scholar
  57. 57.
    Willoughby R, Sheehan E, Mitrovich S (1998) A global view of LC/MS: how to solve your most challenging analytical. Global View Publishing, PittsburgGoogle Scholar
  58. 58.
    Johnson AR, Carlson EE (2015) Collision-induced dissociation mass spectrometry: a powerful tool for natural product structure elucidation. Anal Chem 87:10668–10678PubMedCrossRefGoogle Scholar
  59. 59.
    McMaster MC (2005) LC/MS: a practical users guide. John Wiley & Sons, Inc, Hoboken, NJCrossRefGoogle Scholar
  60. 60.
    Jonscher KR, Yates JR (1997) The quadrupole ion trap mass spectrometer – a small solution to a big challenge. Anal Biochem 224:1–15CrossRefGoogle Scholar
  61. 61.
    Hu Q, Noll RJ, Li H et al (2005) The Orbitrap: a new mass spectrometer. J Mass Spectrom 40:430–443PubMedCrossRefGoogle Scholar
  62. 62.
    Perry RH, Cooks RG, Noll RJ (2008) Orbitrap mass spectrometry: instrumentation, ion motion and applications. Mass Spectrom Rev 27:661–699PubMedCrossRefGoogle Scholar
  63. 63.
    Scigelova M, Makarov A (2006) Orbitrap mass analyzer – overview and applications in proteomics. Proteomics 1:16–21CrossRefGoogle Scholar
  64. 64.
    Eliuk S, Makarov A (2015) Evolution of Orbitrap mass spectrometry instrumentation. Annu Rev Anal Chem 8:61–80CrossRefGoogle Scholar
  65. 65.
    Marshall AG, Hendrickson CL, GS J (1998) Fourier transform ion cyclotron resonance mass spectrometry: a primer. Mass SpectromRev 17:1–35CrossRefGoogle Scholar
  66. 66.
    Products C (2008) Electron multipliers for mass spectrometry. Restek, Bellefonte, PA, pp 1–4Google Scholar
  67. 67.
    Ladislas Wiza J (1979) Microchannel plate detectors. Nucl Instrum Methods 162:587–601CrossRefGoogle Scholar
  68. 68.
  69. 69.
    Link H, Fuhrer T, Gerosa L et al (2015) Real-time metabolome profiling of the metabolic switch between starvation and growth. Nat Methods 12:1091–1097PubMedCrossRefGoogle Scholar
  70. 70.
    Heinemann J, Noon B, Mohigmi MJ et al (2014) Real-time digitization of metabolomics patterns from a living system using mass spectrometry. J Am Soc Mass Spectrom 25:1755–1762PubMedPubMedCentralCrossRefGoogle Scholar
  71. 71.
    Aretz I, Meierhofer D (2016) Advantages and pitfalls of mass spectrometry based metabolome profiling in systems biology. Int J Mol Sci 17:E632PubMedPubMedCentralCrossRefGoogle Scholar
  72. 72.
    Fujimura Y, Miura D (2014) MALDI mass spectrometry imaging for visualizing in situ metabolism of endogenous metabolites and dietary phytochemicals. Metabolites 4:319–346PubMedPubMedCentralCrossRefGoogle Scholar
  73. 73.
    Shroff R, Schramm K, Jeschke V et al (2015) Quantification of plant surface metabolites by matrix-assisted laser desorption-ionization mass spectrometry imaging: glucosinolates on Arabidopsis thaliana leaves. Plant J 81:961–972PubMedCrossRefGoogle Scholar
  74. 74.
    Zaima N, Hayasaka T, Goto-Inoue N et al (2010) Matrix-assisted laser desorption/ionization imaging mass spectrometry. Int J Mol Sci 11:5040–5055PubMedPubMedCentralCrossRefGoogle Scholar
  75. 75.
    Dunham SJB, Ellis JF, Li B et al (2017) Mass spectrometry imaging of complex microbial communities. Acc Chem Res 50:96–104PubMedCrossRefGoogle Scholar
  76. 76.
    Svatos A (2011) Single-cell metabolomics comes of age new developments in mass spectrometry profiling and imaging. Anal Chem 83:5037–5044PubMedCrossRefGoogle Scholar
  77. 77.
    Passarelli MK, Newman CF, Marshall PS et al (2015) Single-cell analysis: visualizing pharmaceutical and metabolite uptake in cells with label-free 3D mass spectrometry imaging. Anal Chem 87:6696–6702PubMedCrossRefGoogle Scholar
  78. 78.
    Louie KB, Bowen BP, Cheng X et al (2013) “Replica-extraction-transfer” nanostructure-initiator mass spectrometry imaging of acoustically printed bacteria. Anal Chem 85:10856–10862PubMedCrossRefGoogle Scholar
  79. 79.
    Northen TR, Yanes O, Northen MT et al (2007) Clathrate nanostructures for mass spectrometry. Nature 449:1033–1036PubMedCrossRefGoogle Scholar
  80. 80.
    Woo HK, Northen TR, Yanes O et al (2008) Nanostructure-initiator mass spectrometry: a protocol for preparing and applying NIMS surfaces for high-sensitivity mass analysis. Nat Protoc 3:1341–1349PubMedCrossRefGoogle Scholar
  81. 81.
    Banimustafa AH, Hardy NW (2012) A strategy for selecting data mining techniques in metabolomics. Methods Mol Biol 860:317–333PubMedCrossRefGoogle Scholar
  82. 82.
    Baran R (2017) Untargeted metabolomics suffers from incomplete raw data processing. Metabolomics 13:107–110CrossRefGoogle Scholar
  83. 83.
    Roberts LD, Souza AL, Gerszten RE et al (2012) Targeted metabolomics. Curr Protoc Mol Biol 1:1–24Google Scholar
  84. 84.
    Wu L, Mashego MR, Van DJC et al (2005) Quantitative analysis of the microbial metabolome by isotope dilution mass spectrometry using uniformly 13C-labeled cell extracts as internal standards. Anal Biochem 336:164–171PubMedCrossRefGoogle Scholar
  85. 85.
    Saccenti E, Hoefsloot HCJ, Smilde AK et al (2014) Reflections on univariate and multivariate analysis of metabolomics data. Metabolomics 10:361–374CrossRefGoogle Scholar
  86. 86.
    Worley B, Powers R (2013) Multivariate analysis in metabolomics. Curr Metabolomics 1:92–107PubMedPubMedCentralGoogle Scholar
  87. 87.
    Gromski PS, Muhamadali H, Ellis DI et al (2015) A tutorial review: metabolomics and partial least squares-discriminant analysis – a marriage of convenience or a shotgun wedding. Anal Chim Acta 879:10–23PubMedCrossRefGoogle Scholar
  88. 88.
    Warth B, Spangler S, Fang M et al (2017) Exposome-scale investigations guided by global metabolomics, pathway analysis, and cognitive computing. Anal Chem 2017:acs.analchem.7b02759Google Scholar
  89. 89.
    Cai Y, Weng K, Guo Y et al (2015) An integrated targeted metabolomic platform for high-throughput metabolite profiling and automated data processing. Metabolomics 11:1575–1586CrossRefGoogle Scholar
  90. 90.
    Schwahn K, Beleggia R, Omranian N et al (2017) Stoichiometric correlation analysis: principles of metabolic functionality from metabolomics data. Front Plant Sci 8:1–12CrossRefGoogle Scholar
  91. 91.
    Robinson MD, De SDP, Keen W et al (2007) A dynamic programming approach for the alignment of signal peaks in multiple gas chromatography-mass spectrometry experiments. BMC Bioinformatics 8:419PubMedPubMedCentralCrossRefGoogle Scholar
  92. 92.
    Alves TC, Pongratz RL, Zhao X et al (2015) Integrated, step-wise, mass-isotopomeric flux analysis of the TCA cycle. Cell Metab 22:936–947PubMedPubMedCentralCrossRefGoogle Scholar
  93. 93.
    Kappelmann J, Klein B, Geilenkirchen P et al (2017) Comprehensive and accurate tracking of carbon origin of LC-tandem mass spectrometry collisional fragments for 13C-MFA. Anal Bioanal Chem 409:2309–2326PubMedPubMedCentralCrossRefGoogle Scholar
  94. 94.
    Gebreselassie NA, Antoniewicz MR (2015) 13C-metabolic flux analysis of co-cultures: a novel approach. Metab Eng 31:132–139PubMedPubMedCentralCrossRefGoogle Scholar
  95. 95.
    Leighty RW, Antoniewicz MR (2011) Dynamic metabolic flux analysis (DMFA): a framework for determining fluxes at metabolic non-steady state. Metab Eng 13:745–755PubMedCrossRefGoogle Scholar
  96. 96.
    Schumacher R, Wahl SA (2015) Effective estimation of dynamic metabolic fluxes using 13C labeling and piecewise affine approximation: from theory to practical applicability. Metabolites 5:697–719PubMedPubMedCentralCrossRefGoogle Scholar
  97. 97.
    Julien F, Georges R, Vande A et al (2016) Direct metabolic of dynamic metabolic of dynamic metabolic analysis dynamic metabolic flux of and overdetermined underdetermined and overdetermined underdetermined and overdetermined underdetermined and metabolic. Science 49:318–323Google Scholar
  98. 98.
    Liu D, Hoynes-O’Connor A, Zhang F (2013) Bridging the gap between systems biology and synthetic biology. Front Microbiol 4:1–8CrossRefGoogle Scholar
  99. 99.
    Kell DB (2006) Metabolomics, modelling and machine learning in systems biology – towards an understanding of the languages of cells. FEBS J 273:873–894PubMedCrossRefGoogle Scholar
  100. 100.
    O’Hagan S, Kell DB (2018) Analysing and navigating natural products space for generating small, diverse, but representative chemical libraries. Biotechnol J 13:1–11CrossRefGoogle Scholar
  101. 101.
    Ritchie MD, Holzinger ER, Li R et al (2015) Methods of integrating data to uncover genotype-phenotype interactions. Nat Rev Genet 16:85–97PubMedCrossRefGoogle Scholar
  102. 102.
    Trivedi DK, Hollywood KA, Goodacre R (2017) Metabolomics for the masses: The future of metabolomics in a personalized world. New Horizons Transl Med 3:294–305Google Scholar
  103. 103.
    George KW, Thompson MG, Kang A et al (2015) Metabolic engineering for the high-yield production of isoprenoid-based C5 alcohols in E. coli. Sci Rep 5:11128PubMedPubMedCentralCrossRefGoogle Scholar
  104. 104.
    Zhou K, Zou R, Stephanopoulos G et al (2012) Metabolite profiling identified methylerythritol cyclodiphosphate efflux as a limiting step in microbial isoprenoid production. PLoS One 7:e47513PubMedPubMedCentralCrossRefGoogle Scholar
  105. 105.
    Zou R, Zhou K, Stephanopoulos G et al (2013) Combinatorial engineering of 1-deoxy-D-xylulose 5-phosphate pathway using cross-lapping in vitro assembly (CLIVA) method. PLoS One 8:e79557PubMedPubMedCentralCrossRefGoogle Scholar
  106. 106.
    George KW, Thompson M, Kim J et al (2018) Integrated analysis of isopentenyl pyrophosphate (IPP) toxicity in isoprenoid-producing Escherichia coli. Metab Eng 47:60–72PubMedCrossRefGoogle Scholar
  107. 107.
    Brunk E, George KW, Alonso-Gutierrez J et al (2016) Characterizing strain variation in engineered E. coli. Cell Syst 2:335–346PubMedPubMedCentralCrossRefGoogle Scholar
  108. 108.
    Panizzon JP, Luiz H, Júnior P et al (2015) Microbial diversity: relevance and relationship between environmental conservation and human health. Braz Arch Biol Technol 58:137–145CrossRefGoogle Scholar
  109. 109.
    Nazaries L, Pan Y, Bodrossy L et al (2013) Evidence of microbial regulation of biogeochemical cycles from a study on methane flux and land use change. Appl Environ Microbiol 79:4031–4040PubMedPubMedCentralCrossRefGoogle Scholar
  110. 110.
    Stewart EJ (2012) Growing unculturable bacteria. J Bacteriol 194:4151–4160PubMedPubMedCentralCrossRefGoogle Scholar
  111. 111.
    Vanwonterghem I, Jensen PD, Ho DP et al (2014) Linking microbial community structure, interactions and function in anaerobic digesters using new molecular techniques. Curr Opin Biotechnol 27:55–64PubMedCrossRefGoogle Scholar
  112. 112.
    Beale DJ, Crosswell J, Karpe AV et al (2017) A multi-omics based ecological analysis of coastal marine sediments from Gladstone, in Australia’s Central Queensland, and Heron Island, a nearby fringing platform reef. Sci Total Environ 609:842–853PubMedCrossRefGoogle Scholar
  113. 113.
    Bargiela R, Herbst FA, Martínez-Martínez M et al (2015) Metaproteomics and metabolomics analyses of chronically petroleum-polluted sites reveal the importance of general anaerobic processes uncoupled with degradation. Proteomics 15:3508–3520PubMedPubMedCentralCrossRefGoogle Scholar
  114. 114.
    Kimes NE, Callaghan AV, Aktas DF et al (2013) Metagenomic analysis and metabolite profiling of deep-sea sediments from the Gulf of Mexico following the Deepwater Horizon oil spill. Front Microbiol 4:50PubMedPubMedCentralCrossRefGoogle Scholar
  115. 115.
    Lutz S, Anesio AM, Field K et al (2015) Integrated “Omics”, targeted metabolite and single-cell analyses of arctic snow algae functionality and adaptability. Front Microbiol 6:1–17Google Scholar
  116. 116.
    Anderson DM, Cembella AD, Hallegraeff GM (2012) Progress in understanding harmful algal blooms: paradigm shifts and new technologies for research, monitoring, and management. Annu Rev Mar Sci 4:143–176CrossRefGoogle Scholar
  117. 117.
    Parmar KM, Gaikwad SL, Dhakephalkar PK et al (2017) Intriguing interaction of bacteriophage-host association: an understanding in the era of omics. Front Microbiol 8:559PubMedPubMedCentralCrossRefGoogle Scholar
  118. 118.
    Bertrand S, Bohni N, Schnee S et al (2014) Metabolite induction via microorganism co-culture: a potential way to enhance chemical diversity for drug discovery. Biotechnol Adv 32:1180–1204PubMedCrossRefGoogle Scholar
  119. 119.
    World Health Organization. Infectious diseases. http://www.who.int/topics/infectious_diseases/en/
  120. 120.
    World Health Organization. Malaria. http://www.who.int/mediacentre/factsheets/fs094/en/
  121. 121.
    Park YH, Shi YP, Liang B et al (2015) High-resolution metabolomics to discover potential parasite-specific biomarkers in a Plasmodium falciparum erythrocytic stage culture system. Malar J 14:122PubMedPubMedCentralCrossRefGoogle Scholar
  122. 122.
    Gardinassi LG, Cordy RJ, Lacerda MVG et al (2017) Metabolome-wide association study of peripheral parasitemia in Plasmodium vivax malaria. Int J Med Microbiol 307:533–541PubMedCrossRefGoogle Scholar
  123. 123.
    Lau SK, Lam C-W, Curreem SO et al (2015) Identification of specific metabolites in culture supernatant of Mycobacterium tuberculosis using metabolomics: exploration of potential biomarkers. Emerg Microbes Infect 4:e6PubMedPubMedCentralCrossRefGoogle Scholar
  124. 124.
    Garay CD, Dreyfuss JM, Galagan JE (2015) Metabolic modeling predicts metabolite changes in Mycobacterium tuberculosis. BMC Syst Biol 9:57PubMedPubMedCentralCrossRefGoogle Scholar
  125. 125.
    Xu Y, Zhang Z, Sun Z (2015) Drug resistance to Mycobacterium tuberculosis: From the traditional Chinese view to modern systems biology. Crit Rev Microbiol 41:399–410PubMedCrossRefGoogle Scholar
  126. 126.
    Lobritz MA, Belenky P, Porter CBM et al (2015) Antibiotic efficacy is linked to bacterial cellular respiration. Proc Natl Acad Sci 112:8173–8180PubMedCrossRefGoogle Scholar
  127. 127.
    Warner DF, Arlehamn CSL, Lewinsohn D et al (2014) Mycobacterium tuberculosis metabolism. Metabolism 5:a021121Google Scholar
  128. 128.
    Luier L, Loots DT (2016) Tuberculosis metabolomics reveals adaptations of man and microbe in order to outcompete and survive. Metabolomics 12:1–9CrossRefGoogle Scholar
  129. 129.
    Mason S, van FAMT, Solomons R et al (2016) A putative urinary biosignature for diagnosis and follow-up of tuberculous meningitis in children: outcome of a metabolomics study disclosing host–pathogen responses. Metabolomics 12:1–16CrossRefGoogle Scholar
  130. 130.
    Washio J, Takahashi N (2016) Metabolomic studies of oral biofilm, oral cancer, and beyond. Int J Mol Sci 17:870PubMedCentralCrossRefPubMedGoogle Scholar
  131. 131.
    Johnson CH, Dejea CM, Edler D et al (2015) Metabolism links bacterial biofilms and colon carcinogenesis. Cell Metab 21:891–897PubMedPubMedCentralCrossRefGoogle Scholar
  132. 132.
    Shaffer M, Armstrong AJS, Phelan VV et al (2017) Microbiome and metabolome data integration provides insight into health and disease. Transl Res 189:51–64.  https://doi.org/10.1016/j.trsl.2017.07.001CrossRefPubMedPubMedCentralGoogle Scholar
  133. 133.
    Martinez KB, Leone V, and Chang EB (2017) Microbial metabolites in health and disease: Navigating the unknown in search of function. J Biol Chem 292 (21):8553–8559.  https://doi.org/10.1074/jbc.R116.752899CrossRefPubMedPubMedCentralGoogle Scholar
  134. 134.
    Li DY, Tang WHW (2017) Gut Microbiota and Atherosclerosis. Curr Atheroscler Rep 19(10):39.  https://doi.org/10.1007/s11883-017-0675-9CrossRefPubMedGoogle Scholar
  135. 135.
    He X, Ji G, Jia W et al (2016) Gut microbiota and nonalcoholic fatty liver disease: insights on mechanism and application of metabolomics. Int J Mol Sci 17:300PubMedPubMedCentralCrossRefGoogle Scholar
  136. 136.
    Li H, He J, Jia W (2016) The influence of gut microbiota on drug metabolism and toxicity. Expert Opin Drug Metab Toxicol 12:31–40PubMedCrossRefGoogle Scholar
  137. 137.
    Nichols RG, Hume NE, Smith PB et al (2016) Omics approaches to probe microbiota and drug metabolism interactions. Chem Res Toxicol 29:1987–1997PubMedCrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Edward E. K. Baidoo
    • 1
    • 2
    Email author
  • Veronica Teixeira Benites
    • 1
    • 2
  1. 1.Biological Systems and Engineering DivisionLawrence Berkeley National LaboratoryBerkeleyUSA
  2. 2.Joint BioEnergy InstituteEmeryvilleUSA

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