Advertisement

Phenotyping Microarrays for the Characterization of Environmental Microorganisms

  • Etienne Low-DécarieEmail author
  • Andrea Lofano
  • Pedram Samani
Protocol
Part of the Springer Protocols Handbooks book series (SPH)

Abstract

Culture-based methods for the characterization of microorganisms remain essential to advances in microbiology. Phenotyping arrays and microplates in which each well represents a different selective growth environment are important tools (1) in the identification of microbial isolates, (2) in the characterization of the phenotypic fingerprint of microbial communities, (3) for linking specific functions with specific organisms or genes, and (4) for the identification of evolutionary trade-offs in the establishment of phenotypes. The use of phenotyping arrays in the study of hydrocarbon and lipid degradation by microbial isolates or communities is an emerging application. The application of phenotyping arrays requires careful selection of substrates, growth medium, and dyes and consideration of the intrinsic limitations of the approach. The use of phenotyping arrays leads to the production of large amounts of data, which require specific approaches for summarization and analysis. Liquid handling automation will increase the feasibility of custom phenotyping arrays that include hydrocarbons and lipids.

Keywords:

Biodegradation Biolog™ Culturomics Ecotype High throughput Microtiter Phenomics Phenotyping microarray (PM) Substrate 

Notes

Acknowledgments

We thank Graham Bell, from McGill University, for supporting this research. Andrea Lofano and Pedram Samani were supported by a Discovery Grant from NSERC awarded to Graham Bell. Pedram Samani was also supported by a scholarship from FRQNT.

References

  1. 1.
    Wainwright M, Lederberg J (1992) History of microbiology. In: Encyclopedia of microbiology. Academic Press, Waltham, Massachusetts, pp 358–362Google Scholar
  2. 2.
    Viti C, Decorosi F, Marchi E, Galardini M, Giovannetti L (2015) High-throughput phenomics. In: Bacterial pangenomics. Springer, New York, pp 99–123. doi: 10.1007/978-1-4939-1720-4_7
  3. 3.
    Lagier J-C, Hugon P, Khelaifia S, Fournier P-E, La Scola B, Raoult D (2015) The rebirth of culture in microbiology through the example of culturomics to study human gut microbiota. Clin Microbiol Rev 28(1):237–264CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Bochner B (1989) Sleuthing out bacterial identities. Nature 339:157–158CrossRefPubMedGoogle Scholar
  5. 5.
    Bochner BR, Savageau MA (1977) Generalized indicator plate for genetic, metabolic, and taxonomic studies with microorganisms. Appl Environ Microbiol 33(2):434–444PubMedPubMedCentralGoogle Scholar
  6. 6.
    Garland JL, Mills AL (1991) Classification and characterization of heterotrophic microbial communities on the basis of patterns of community-level sole-carbon-source utilization. Appl Environ Microbiol 57(8):2351–2359PubMedPubMedCentralGoogle Scholar
  7. 7.
    Garland J (1997) Analysis and interpretation of community level physiological profiles in microbial ecology. FEMS Microbiol Ecol 24(4):289–300CrossRefGoogle Scholar
  8. 8.
    Atanasova L, Druzhinina IS (2010) Review: global nutrient profiling by Phenotype MicroArrays: a tool complementing genomic and proteomic studies in conidial fungi. J Zhejiang Univ Sci B 11(3):151–168CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Maila MP, Randima P, Drønen K, Cloete TE (2006) Soil microbial communities: influence of geographic location and hydrocarbon pollutants. Soil Biol Biochem 38:303–310CrossRefGoogle Scholar
  10. 10.
    Kaufmann K, Christophersen M, Buttler A, Harms H, Höhener P (2004) Microbial community response to petroleum hydrocarbon contamination in the unsaturated zone at the experimental field site Værløse, Denmark. FEMS Microbiol Ecol 48:387–399CrossRefPubMedGoogle Scholar
  11. 11.
    Wünsche L, Brüggemann L, Babel W (1995) Determination of substrate utilization patterns of soil microbial communities: an approach to assess population changes after hydrocarbon pollution. FEMS Microbiol Ecol 17:295–305CrossRefGoogle Scholar
  12. 12.
    Juck D, Charles T, Whyte LG, Greer CW (2000) Polyphasic microbial community analysis of petroleum hydrocarbon-contaminated soils from two northern Canadian communities. FEMS Microbiol Ecol 33:241–249CrossRefPubMedGoogle Scholar
  13. 13.
    Mansur A, Adetutu EM, Kadali KK, Morrison PD, Nurulita Y, Ball AS (2014) Assessing the hydrocarbon degrading potential of indigenous bacteria isolated from crude oil tank bottom sludge and hydrocarbon-contaminated soil of Azzawiya oil refinery, Libya. Environ Sci Pollut Res 2014:10725–10735Google Scholar
  14. 14.
    McKew B, Coulon F, Osborn M, Timmis KN, McGenity TJ (2007) Determining the identity and roles of oil-metabolizing marine bacteria from the Thames estuary, UK. Environ Microbiol 9(1):165–176Google Scholar
  15. 15.
    Johnsen AR, Bendixen K, Karlson U (2002) Detection of microbial growth on polycyclic aromatic hydrocarbons in microtiter plates by using the respiration indicator WST-1. Applied and Environmental Microbiology 68(6):2683–2689. doi: 10.1128/AEM.68.6.2683-2689.2002
  16. 16.
    Samani P, Low-Decarie E, McKelvey K et al (2015) Metabolic variation in natural populations of wild yeast. Ecol Evol 5(3):722–732CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Lee C, Russell NJ, White GF (1995) Rapid screening for bacterial phenotypes capable of biodegrading anionic surfactants: development and validation of a microtitre plate method. Microbiology 141(11):2801–2810CrossRefPubMedGoogle Scholar
  18. 18.
    Oberhardt M, Puchałka J, Fryer KE, Martins dos Santos VP, Papin J (2008) Genome-scale metabolic network analysis of the opportunistic pathogen Pseudomonas aeruginosa PAO1. J Bacteriol 190(8):2790–2803Google Scholar
  19. 19.
    Preston-Mafham J, Boddy L, Randerson PF (2002) Analysis of microbial community functional diversity using sole-carbon-source utilisation profiles – a critique. FEMS Microbiol Ecol 42(1):1–14PubMedGoogle Scholar
  20. 20.
    Ritz K (2007) The plate debate: cultivable communities have no utility in contemporary environmental microbial ecology. FEMS Microbiol Ecol 60(3):358–362CrossRefPubMedGoogle Scholar
  21. 21.
    Ling LL, Schneider T, Peoples AJ et al (2015) A new antibiotic kills pathogens without detectable resistance. Nature 517:455–459CrossRefPubMedGoogle Scholar
  22. 22.
    Rogers GW, Brand MD, Petrosyan S et al (2011) High throughput microplate respiratory measurements using minimal quantities of isolated mitochondria. PLoS One 6(7), e21746CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Deshpande RR, Koch-Kirsch Y, Maas R, John GT, Krause C, Heinzle E (2005) Microplates with integrated oxygen sensors for kinetic cell respiration measurement and cytotoxicity testing in primary and secondary cell lines. Assay Drug Dev Technol 3(3):299–307CrossRefPubMedGoogle Scholar
  24. 24.
    Berridge MV, Herst PM, Tan AS (2005) Tetrazolium dyes as tools in cell biology: new insights into their cellular reduction. Biotechnol Annu Rev 11(05):127–152CrossRefPubMedGoogle Scholar
  25. 25.
    Tachon S, Michelon D, Chambellon E et al (2009) Experimental conditions affect the site of tetrazolium violet reduction in the electron transport chain of Lactococcus lactis. Microbiology 155(Pt 9):2941–2948CrossRefPubMedGoogle Scholar
  26. 26.
    Anderson SA, Sissons CH, Coleman MJ, Wong L (2002) Application of carbon source utilization patterns to measure the metabolic similarity of complex dental plaque biofilm microcosms. Appl Environ Microbiol 68(11):5779–5783CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Tracy BS, Edwards KK, Eisenstark A (2002) Carbon and nitrogen substrate utilization by archival Salmonella typhimurium LT2 cells. BMC Evol Biol 2:14CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Altmann FP (1969) The use of eight different tetrazolium salts for a quantitative study of pentose shunt dehydrogenation. Histochemie 19(4):363–374Google Scholar
  29. 29.
    Vera-Jimenez NI, Pietretti D, Wiegertjes GF, Nielsen ME (2013) Comparative study of β-glucan induced respiratory burst measured by nitroblue tetrazolium assay and real-time luminol-enhanced chemiluminescence assay in common carp (Cyprinus carpio L.). Fish Shellfish Immunol 34(5):1216–1222CrossRefPubMedGoogle Scholar
  30. 30.
    Mosmann T (1983) Rapid colorimetric assay for cellular growth and survival: application to proliferation and cytotoxicity assays. J Immunol Methods 65(1-2):55–63CrossRefPubMedGoogle Scholar
  31. 31.
    Scudiero DA, Shoemaker RH, Paull KD et al (1988) Evaluation of a soluble tetrazolium/formazan assay for cell growth and drug sensitivity in culture using human and other tumor cell lines. Cancer Res 48(17):4827–4833PubMedGoogle Scholar
  32. 32.
    Meshulam T, Levitz SM, Christin L, Diamond RD (1995) A simplified new assay for assessment of fungal cell damage with the tetrazolium dye, (2,3)-bis-(2-methoxy-4-nitro-5-sulphenyl)-(2H)-tetrazolium-5-carboxanil ide (XTT). J Infect Dis 172(4):1153–1156CrossRefPubMedGoogle Scholar
  33. 33.
    Kuhn DM, Balkis M, Chandra J, Mukherjee PK, Ghannoum MA (2003) Uses and limitations of the XTT assay in studies of Candida growth and metabolism. J Clin Microbiol 41(1):506–508CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Guckert J, Carr G, Johnson T (1996) Community analysis by Biolog: curve integration for statistical analysis of activated sludge microbial habitats. J Microbiol Methods 27:183–197Google Scholar
  35. 35.
    Smith A, Sterba-Boatwright B, Mott J (2010) Novel application of a statistical technique, Random Forests, in a bacterial source tracking study. Water Res 44(14):4067–4076CrossRefPubMedGoogle Scholar
  36. 36.
    R Development Core Team (2009) R: a language and environment for statistical computing. R Development Core Team. Is software. Vienna, AustriaGoogle Scholar
  37. 37.
    Vaas LI, Sikorski J, Hofner B et al (2013) Opm: an R package for analysing OmniLog® phenotype microarray data. Bioinformatics 29(14):1823–1824Google Scholar
  38. 38.
    Jacobsen JS, Joyner DC, Borglin SE, Hazen TC, Arkin AP, Bethel EW (2007) Visualization of growth curve data from phenotype microarray experiments. In: Proceedings of the international conference on information visualisation. Zürich, Switzerland, pp 535–544Google Scholar
  39. 39.
    Vaas LI, Sikorski J, Michael V, Göker M, Klenk H-P (2012) Visualization and curve-parameter estimation strategies for efficient exploration of phenotype microarray kinetics. PLoS One 7(4):e34846Google Scholar
  40. 40.
    Galardini M, Mengoni A, Biondi EG et al (2014) DuctApe : a suite for the analysis and correlation of genomes and Omnilog TM phenotype microarray data. Genomics 103(1):1–10CrossRefPubMedGoogle Scholar
  41. 41.
    Bell G (2013) Experimental evolution of heterotrophy in a green alga. Evolution 67(2):468–476CrossRefPubMedGoogle Scholar
  42. 42.
    Stein JR (1979) Handbook of physiological methods: culture methods and growth measurements. Cambridge University Press, CambridgeGoogle Scholar
  43. 43.
    De Visser JGM, Hoekstra RF, Van Ende H Den, De Visser A (1997) An experimental test for synergistic epistasis and its application in Chlamydomonas. Genetics 145(3):815–819Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Etienne Low-Décarie
    • 1
    Email author
  • Andrea Lofano
    • 2
  • Pedram Samani
    • 2
  1. 1.School of Biological Sciences, University of EssexColchesterUK
  2. 2.Department of BiologyMcGill UniversityMontrealCanada

Personalised recommendations