An Advanced Approach Based on Artificial Neural Networks to Identify Environmental Bacteria

Authors: Mauro Giacomini, Stefania Bertone, Federico Caneva Soumetz, Carmelina Ruggiero

Abstract:

Environmental micro-organisms include a large number of taxa and some species that are generally considered nonpathogenic, but can represent a risk in certain conditions, especially for elderly people and immunocompromised individuals. Chemotaxonomic identification techniques are powerful tools for environmental micro-organisms, and cellular fatty acid methyl esters (FAME) content is a powerful fingerprinting identification technique. A system based on an unsupervised artificial neural network (ANN) was set up using the fatty acid profiles of standard bacterial strains, obtained by gas-chromatography, used as learning data. We analysed 45 certified strains belonging to Acinetobacter, Aeromonas, Alcaligenes, Aquaspirillum, Arthrobacter, Bacillus, Brevundimonas, Enterobacter, Flavobacterium, Micrococcus, Pseudomonas, Serratia, Shewanella and Vibrio genera. A set of 79 bacteria isolated from a drinking water line (AMGA, the major water supply system in Genoa) were used as an example for identification compared to standard MIDI method. The resulting ANN output map was found to be a very powerful tool to identify these fresh isolates.

Keywords: Cellular fatty acid methyl esters, environmental bacteria, gas-chromatography, unsupervised ANN.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1077545

References:

[1] Environmental Protection Agency (1989). National primary drinking water rules and regulations. U.S. EPA surface water rule. Filtration, disinfection, turbidity, Giardia lamblia, viruses, Legionella, heterotrophic bacteria, Federal Register, 54, pp.27486-27541.
[2] Environmental Protection Agency (1994). National primary drinking water regulation: Enhanced Surface Water Treatment Requirements: U.S. EPA Proposed Rule, Federal Register, 59, 159, pp.38832-38858.
[3] M. Handfield, P. Simard, M. Couillard, and R. Letarte, "Aeromonas hydrophila isolated from food and drinking water: hemagglutination, hemolysis and cytotoxicity for a human intestinal cell line (HT-29)", Applied Environmental Microbiology, vol. 62, pp. 3459-3461, 1996.
[4] P. Payment, E. Coffin, and G. Paquette, "Blood agar to detect virulence factors in tap water heterotrophic bacteria", Applied Environmental Microbiology, vol. 60, no. 4, pp. 1179-1183, 1994.
[5] P. Vandamme, B. Pot, M. Gillis, P. De Vos, K. Kersters, and J. Swings, "Polyphasic taxonomy, a consensus approach to bacterial systematics", Microbiology Review, vol. 60, pp. 407-438, 1996.
[6] K. Suzuki, M. Goodfellow, and A. G. O-Donnel, Handbook of new bacterial systematics. London: Academic Press Ltd, 1993.
[7] M. Giacomini, C. Ruggiero, M. Maillard, F. B. Lillo, and O. E. Varnier "Objective evaluation of two markers of HIV-1 infection (p24 antigen concentration and CD4+ cell counts) by a self organising neural network", Medical Informatics, vol. 21, pp. 215-228, 1996.
[8] D. T. Pham, and X. Liu, Neural Networks for identification, prediction and control. London: Springer-Verlag, 1997.
[9] S. Bertone, M. Giacomini, C. Ruggiero, C. Piccarolo, and L. Calegari, "Automated systems for identification of heterotrophic marine bacteria on the basis of their fatty acid composition", Applied Environmental Microbiology, vol. 62, pp.2122-2132, 1996.
[10] M. Giacomini, C. Ruggiero, S. Bertone, and L. Calegari, "Artificial neural network identification of heterotrophic marine bacteria based on their fatty acid composition", IEEE Transactions of Biomedical Engineering, vol. 44, pp. 1185-1191, 1997.
[11] M. Giacomini, C. Ruggiero, L. Calegari, and S. Bertone, "Artificial Neural Network based identification of environmental bacteria by gaschromatographic and electrophoretic data", Journal of Microbioly Methods, vol. 1, pp. 45-54, 2000.
[12] T. Kohonen, "The self -organizing map", IEEE Proceedings, vol. 78, no. 9, pp. 1464-1480, 1990.
[13] M. P. Lechevalier, "Lipids in bacterial taxonomy: a taxonomist-s view", Clinical Microbiology Reviews, vol. 5, pp. 109-210, 1977.
[14] L. T. Miller, "Single derivatization method for routine analysis of bacterial whole-cell fatty acid methyl esters, including hydroxy acids", Journal of Clinical Microbiology, vol. 16, pp. 584-586, 1982.
[15] L. A. Briganti, and S. C. Wacker, "Fatty acid profiling and the identification of environmental bacteria for drinking water utilities", American Water Works Association Research Foundation and American Water Works Association, Denver, Colorado, 1995.
[16] C. D. Norton, and M. W. LeChevallier, "A pilot study of bacteriological population changes through potable water treatment and distribution", Applied Environmental Microbiology, vol. 66, no.1, pp.268-276, 2000
[17] D. L. Hudson and M. E. Cohen, Neural networks and artificial intelligence for biomedical engineering. New York: IEEE Press, 2000.