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Multi-capillary Column Ion Mobility Spectrometry of Volatile Metabolites for Phenotyping of Microorganisms

  • Christoph Halbfeld
  • Jörg Ingo Baumbach
  • Lars M. BlankEmail author
  • Birgitta E. Ebert
Part of the Methods in Molecular Biology book series (MIMB, volume 1671)

Abstract

Rational strain engineering requires solid testing of phenotypes including productivity and ideally contributes thereby directly to our understanding of the genotype–phenotype relationship. Actually, the test step of the strain engineering cycle becomes the limiting step, as ever advancing tools for generating genetic diversity exist. Here, we briefly define the challenge one faces in quantifying phenotypes and summarize existing analytical techniques that partially overcome this challenge. We argue that the evolution of volatile metabolites can be used as proxy for cellular metabolism. In the simplest case, the product of interest is a volatile (e.g., from bulk alcohols to special fragrances) that is directly quantified over time. But also nonvolatile products (e.g., from bulk long-chain fatty acids to natural products) require major flux rerouting that result potentially in altered volatile production. While alternative techniques for volatile determination exist, rather few can be envisaged for medium to high-throughput analysis required for phenotype testing. Here, we contribute a detailed protocol for an ion mobility spectrometry (IMS) analysis that allows volatile metabolite quantification down to the ppb range. The sensitivity can be exploited for small-scale fermentation monitoring. The insights shared might contribute to a more frequent use of IMS in biotechnology, while the experimental aspects are of general use for researchers interested in volatile monitoring.

Key words

Ion mobility spectrometry Multi-capillary column Online analysis On-site analysis Phenotype screening Volatile metabolites Volatile organic compounds 

References

  1. 1.
    Petzold CJ, Chan LJ, Nhan M, Adams PD (2015) Analytics for metabolic engineering. Front Bioeng Biotechnol 3:135. doi: 10.3389/fbioe.2015.00135 CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Billeci K, Suh C, Di Ioia T, Singh L, Abraham R, Baldwin A, Monteclaro S (2016) Implementation of an automated high-throughput plasmid DNA production pipeline. J Lab Autom 21(6):765–778. doi: 10.1177/2211068216630547 CrossRefPubMedGoogle Scholar
  3. 3.
    Dietrich JA, McKee AE, Keasling JD (2010) High-throughput metabolic engineering: advances in small-molecule screening and selection. Annu Rev Biochem 79:563–590. doi: 10.1146/annurev-biochem-062608-095938 CrossRefPubMedGoogle Scholar
  4. 4.
    Ukibe K, Katsuragi T, Tani Y, Takagi H (2008) Efficient screening for astaxanthin-overproducing mutants of the yeast Xanthophyllomyces dendrorhous by flow cytometry. FEMS Microbiol Lett 286(2):241–248. doi: 10.1111/j.1574-6968.2008.01278.x CrossRefPubMedGoogle Scholar
  5. 5.
    Kim SW, Keasling JD (2001) Metabolic engineering of the nonmevalonate isopentenyl diphosphate synthesis pathway in Escherichia coli enhances lycopene production. Biotechnol Bioeng 72(4):408–415CrossRefPubMedGoogle Scholar
  6. 6.
    Olson ML, Johnson J, Carswell WF, Reyes LH, Senger RS, Kao KC (2016) Characterization of an evolved carotenoids hyper-producer of Saccharomyces cerevisiae through bioreactor parameter optimization and Raman spectroscopy. J Ind Microbiol Biotechnol 43(10):1355–1363. doi: 10.1007/s10295-016-1808-9 CrossRefPubMedGoogle Scholar
  7. 7.
    Kreyenschulte D, Paciok E, Regestein L, Blümich B, Büchs J (2015) Online monitoring of fermentation processes via non-invasive low-field NMR. Biotechnol Bioeng 112(9):1810–1821. doi: 10.1002/bit.25599 CrossRefPubMedGoogle Scholar
  8. 8.
    Luoma P, Golabgir A, Brandstetter M, Kasberger J, Herwig C (2016) Workflow for multi-analyte bioprocess monitoring demonstrated on inline NIR spectroscopy of P. chrysogenum fermentation. Anal Bioanal Chem 409(3):797–805. doi: 10.1007/s00216-016-9918-9 CrossRefPubMedGoogle Scholar
  9. 9.
    Levin-Karp A, Barenholz U, Bareia T, Dayagi M, Zelcbuch L, Antonovsky N, Noor E, Milo R (2013) Quantifying translational coupling in E. coli synthetic operons using RBS modulation and fluorescent reporters. ACS Synth Biol 2(6):327–336. doi: 10.1021/sb400002n CrossRefPubMedGoogle Scholar
  10. 10.
    Mutalik VK, Nonaka G, Ades SE, Rhodius VA, Gross CA (2009) Promoter strength properties of the complete sigma E regulon of Escherichia coli and Salmonella enterica. J Bacteriol 191(23):7279–7287CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Rhodius VA, Mutalik VK, Gross CA (2012) Predicting the strength of UP-elements and full-length E. coli sigmaE promoters. Nucleic Acids Res 40(7):2907–2924. doi: 10.1093/nar/gkr1190 CrossRefPubMedGoogle Scholar
  12. 12.
    Zobel S, Benedetti I, Eisenbach L, de Lorenzo V, Wierckx N, Blank LM (2015) Tn7-based device for calibrated heterologous gene expression in Pseudomonas putida. ACS Synth Biol 4(12):1341–1351. doi: 10.1021/acssynbio.5b00058 CrossRefPubMedGoogle Scholar
  13. 13.
    Binder S, Schendzielorz G, Stabler N, Krumbach K, Hoffmann K, Bott M, Eggeling L (2012) A high-throughput approach to identify genomic variants of bacterial metabolite producers at the single-cell level. Genome Biol 13(5):R40. doi: 10.1186/gb-2012-13-5-r40 CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Dietrich JA, Shis DL, Alikhani A, Keasling JD (2013) Transcription factor-based screens and synthetic selections for microbial small-molecule biosynthesis. ACS Synth Biol 2(1):47–58. doi: 10.1021/sb300091d CrossRefPubMedGoogle Scholar
  15. 15.
    Siedler S, Stahlhut SG, Malla S, Maury J, Neves AR (2014) Novel biosensors based on flavonoid-responsive transcriptional regulators introduced into Escherichia coli. Metab Eng 21:2–8CrossRefPubMedGoogle Scholar
  16. 16.
    Mustafi N, Grunberger A, Kohlheyer D, Bott M, Frunzke J (2012) The development and application of a single-cell biosensor for the detection of L-methionine and branched-chain amino acids. Metab Eng 14(4):449–457CrossRefPubMedGoogle Scholar
  17. 17.
    Paige JS, Nguyen-Duc T, Song WJ, Jaffrey SR (2012) Fluorescence imaging of cellular metabolites with RNA. Science 335(6073):1194–1194CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Ebert BE, Halbfeld C, Blank LM (2016) Exploration and exploitation of the yeast volatilome. Curr Metabolomics 4:1–17. doi: 10.2174/2213235X04666160818151119 Google Scholar
  19. 19.
    Haggarty J, Burgess KE (2016) Recent advances in liquid and gas chromatography methodology for extending coverage of the metabolome. Curr Opin Biotechnol 43:77–85. doi: 10.1016/j.copbio.2016.09.006 CrossRefPubMedGoogle Scholar
  20. 20.
    Wu C, Siems WF, Hill HH (2000) Secondary electrospray ionization ion mobility spectrometry/mass spectrometry of illicit drugs. Anal Chem 72(2):396–403. doi: 10.1021/ac9907235 CrossRefPubMedGoogle Scholar
  21. 21.
    Barrios-Collado C, García-Gómez D, Zenobi R, Vidal-de-Miguel G, Ibáñez AJ, Martinez-Lozano Sinues P (2016) Capturing in vivo plant metabolism by real-time analysis of low to high molecular weight volatiles. Anal Chem 88(4):2406–2412. doi: 10.1021/acs.analchem.5b04452 CrossRefPubMedGoogle Scholar
  22. 22.
    Fink T, Baumbach JI, Kreuer S (2014) Ion mobility spectrometry in breath research. J Breath Res 8(2):027104. doi: 10.1088/1752-7155/8/2/027104 CrossRefPubMedGoogle Scholar
  23. 23.
    Baumbach JI, Eiceman GA (1999) Ion mobility spectrometry: arriving on site and moving beyond a low profile. Appl Spectrosc 53(9):338a–355a. doi: 10.1366/0003702991947847 CrossRefPubMedGoogle Scholar
  24. 24.
    Halbfeld C, Ebert BE, Blank LM (2014) Multi-capillary column-ion mobility spectrometry of volatile metabolites emitted by Saccharomyces cerevisiae. Meta 4(3):751–774. doi: 10.3390/metabo4030751 Google Scholar
  25. 25.
    Xie Z, Sielemann S, Schmidt H, Baumbach JI (2000) A novel method for the detection of MTBE: ion mobility spectrometry coupled to multi capillary column. Int J Ion Mobil Spectrom 4:77–83Google Scholar
  26. 26.
    Baumbach JI (2006) Process analysis using ion mobility spectrometry. Anal Bioanal Chem 384(5):1059–1070. doi: 10.1007/s00216-005-3397-8 CrossRefPubMedGoogle Scholar
  27. 27.
    Cumeras R, Figueras E, Davis C, Baumbach JI, Gracia I (2015) Review on ion mobility spectrometry. Part 1: current instrumentation. Analyst 140(5):1376–1390. doi: 10.1039/c4an01100g CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Cumeras R, Figueras E, Davis C, Baumbach JI, Gracia I (2015) Review on ion mobility spectrometry. Part 2: hyphenated methods and effects of experimental parameters. Analyst 140(5):1391–1410. doi: 10.1039/c4an01101e CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2018

Authors and Affiliations

  • Christoph Halbfeld
    • 1
  • Jörg Ingo Baumbach
    • 2
  • Lars M. Blank
    • 1
  • Birgitta E. Ebert
    • 1
  1. 1.iAMB—Institute of Applied Microbiology, ABBt—Aachen Biology and BiotechnologyRWTH Aachen UniversityAachenGermany
  2. 2.Faculty of Applied ChemistryReutlingen UniversityReutlingenGermany

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