Abstract
Rapid and robust high-throughput identification of environmental, industrial, or clinical yeast isolates is important whenever relatively large numbers of samples need to be processed in a cost-efficient way. Nuclear magnetic resonance (NMR) spectroscopy generates complex data based on metabolite profiles, chemical composition and possibly on medium consumption, which can not only be used for the assessment of metabolic pathways but also for accurate identification of yeast down to the subspecies level. Initial results on NMR based yeast identification where comparable with conventional and DNA-based identification. Potential advantages of NMR spectroscopy in mycological laboratories include not only accurate identification but also the potential of automated sample delivery, automated analysis using computer-based methods, rapid turnaround time, high throughput, and low running costs.
We describe here the sample preparation, data acquisition and analysis for NMR-based yeast identification. In addition, a roadmap for the development of classification strategies is given that will result in the acquisition of a database and analysis algorithms for yeast identification in different environments.
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References
Sorrell TC, Himmelreich U (2008) The role of nuclear magnetic resonance in medical mycology. Curr Fungal Infect Rep 2:149–156
Pavlovic M, Mewes A, Maggipinto M et al (2014) MALDI-TOF MS based identification of food-borne yeast isolates. J Microbiol Methods 106:123–128
Croxatto A, Prod'hom G, Greub G (2012) Applications of MALDI-TOF mass spectrometry in clinical diagnostic microbiology. FEMS Microbiol Rev 36:380–407
Himmelreich U, Somorjai RL, Dolenko B et al (2003) Rapid identification of Candida species by using nuclear magnetic resonance spectroscopy and a statistical classification strategy. Appl Environ Microbiol 69:4566–4574
Allen JK, Davey HM, Broadhurst D et al (2003) High-throughput characterisation of yeast mutants for functional genomics using metabolic footprinting. Nat Biotechnol 21:692–696
Maquelin K, Kirschner C, Choo-Smith LP et al (2002) Identification of medically relevant microorganisms by vibrational spectroscopy. J Microbiol Methods 51:255–271
Dunn WB, Bailey NJC, Johnson HE (2005) Measuring the metabolome: current analytical technologies. Analyst 130:606–625
Pope GA, MacKenzie DA, Defernez M et al (2007) Metabolic footprinting as a tool for discriminating between brewing yeasts. Yeast 24:667–679
Himmelreich U, Somorjai RL, Dolenko B et al (2005) A Rapid screening test to distinguish between Candida albicans and Candida dubliniensis using NMR Spectroscopy. FEMS Microbiol Lett 251:327–332
Marklein G, Josten M, Klanke U et al (2009) J Clin Microbiol 47:2912–2917
van Veen SQ, Claas EC, Kuijper EJ (2010) High-throughput identification of bacteria and yeast by matrix-assisted laser desorption ionization-time of flight mass spectrometry in conventional medical microbiology laboratories. J Clin Microbiol 48:900–907
Bizzini A, Greub G (2010) Matrix-assisted laser desorption ionization-time of flight mass spectrometry, a revolution in clinical microbial identification. Clin Microbiol Infect 16:1614–1619
Urbanczyk-Wochniak E, Luedemann A, Kopka J (2003) Parallel analysis of transcript and metabolic profiles: a new approach in systems biology. EMBO Rep 4:989–993
Himmelreich U, Mountford CE, Sorrell TC (2004) NMR spectroscopic determination of microbiological profiles in infectious diseases. Trends Appl Spectrosc 5:269–283
Gupta RK, Lufkin RB (2002) MR imaging and spectroscopy of central nervous system infection. Kluwer Academic Publisher, New York
Sorrell TC, Wright LC, Malik R et al (2006) Application of proton nuclear magnetic resonance spectroscopy to the study of Cryptococcus and cryptococcosis. FEMS Yeast Res 6:558–566
Nath K, Agarwal M, Ramola M et al (2009) Role of diffusion tensor imaging metrics and in vivo proton magnetic resonance spectroscopy in the differential diagnosis of cystic intracranial mass lesions. Magn Reson Imaging 27:198–206
Coen M, Bodkin J, Power D et al (2006) Antifungal effects on metabolite profiles of medically important yeast species measured by nuclear magnetic resonance spectroscopy. Antimicrob Agents Chemother 50:4018–4026
Himmelreich U, Malik R, Kühn T et al (2009) Rapid etiological classification of meningitis by NMR spectroscopy based on metabolite profiles and host response. PLoS One 4:e5328
Coen M, O’Sullivan M, Bubb WA et al (2005) Proton nuclear magnetic resonance-based metabonomics for rapid diagnosis of meningitis and ventriculitis. Clin Infect Dis 41:1582–1590
Nicholson JK, Wilson ID (1989) High resolution proton magnetic resonance spectroscopy of biological fluids. In: Emsley JW, Feeney J (eds) Progress in nuclear magnetic resonance spectroscopy. Pergamon Press, Oxford, pp 449–501
Nikulin A, Dolenko B, Bezabeh T et al (1998) Near-optimal region selection for feature space reduction: novel preprocessing methods for classifying MR spectra. NMR Biomed 11:209–217
Menze B, Kelm M, Masuch R et al (2009) A comparison of random forest and its Gini importance with standard chemometric methods for a feature selection and classification of spectral data. BMC Bioinformatics 10:1–16
Croitor-Sava A, Beck V, Sandaite I et al (2015) High resolution 1H NMR spectroscopy discriminates amniotic fluid of fetuses with congenital diaphragmatic hernia from healthy controls. J Proteome Res 14:4502–4510
Baumgartner R, Somorjai RL, Bowman C et al (2004) Unsupervised feature dimension reduction for classification of MR spectra. Magn Reson Imaging 22:251–256
Somorjai RL (2009) Creating robust, reliable, clinically relevant classifiers from spectroscopic data. Biophys Rev 1:201–211
Somorjai RL (2008) Pattern recognition approaches for classifying proteomic mass spectra of biofluids. Methods Mol Biol 428:383–395
Janssens D, Arahal DR, Bizet C et al (2010) The role of public biological resource centers in providing a basic infrastructure for microbial research. Res Microbiol 161:422–429
Daniel HM, Himmelreich U, Dedeurwaerdere T (2006) Integrating different windows on reality: socio-economic and institutional challenges for culture collections. Int Soc Sci J 188:369–380
Jain AK, Chandrasekaran B (1982) Dimensionality and sample size considerations in pattern recognition practice. North Holland Publishing, Amsterdam
O’Mahony M (1987) Sensory evaluation of food: statistical methods and procedures. Marcel Dekker Cop, New York, p 487
Hollander M, Wolfe DA (1973) Nonparametric statistical methods. Wiley, New York
Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugenics 7:179–188
Pearson K (1901) On lines and planes of closest fit to systems of points in space. Phil Mag 2:559–572
Borg I, Groenen PJF (2005) Modern multidimensional scaling: theory and applications. Springer, New York
Efron B, Tibshirani R (1993) An introduction to the bootstrap. Chapman Hill, New York
Bourne R, Himmelreich U, Sharma A et al (2001) Identification of Enterococcus, Streptococcus and Staphylococcus by multivariate analysis of proton magnetic resonance spectroscopic data from plate cultures. J Clin Microbiol 39:2916–2923
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Himmelreich, U., Sorrell, T.C., Daniel, HM. (2017). Nuclear Magnetic Resonance Spectroscopy-Based Identification of Yeast. In: Lion, T. (eds) Human Fungal Pathogen Identification. Methods in Molecular Biology, vol 1508. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6515-1_17
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DOI: https://doi.org/10.1007/978-1-4939-6515-1_17
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