Brest, October 29th, 2010 Intérêts et limites des traceurs de sources microbiennes Advantages and limitations of Microbial Source Tracking indicators Anicet R. Blanch Department of Microbiology Microbiología del Agua Relacionada con la Salud (MARS) UNIVERSITAT DE BARCELONA
Steps needed to develop MST models Human Chemical Microbial Cellular Quantitative Qualitative Birds Ruminants Porcine Sensitivity Specificity Robustness What tracer ? What method ? High concentration at point source Different water matrices Prevalence Universal (geographic, diets, etc.) Candidate tracer Host specificity Correlation to other parameters Environmental persistence Resistance to water treatments Usefulness for fecal pollution mixtures POTENTIAL DIFFERENTIAL TRACER NUMERICAL ANALYSES DEVELOPMENT OF PREDICTIVE MODELS DECISION SUPORT SYSTEMS
Pitfalls of MST studies • Assays based on non-significant number or non- appropriate samples • Approaches too local • Focussing in methods rather than in tracers • Trying to solve the selection of appropriated tracers and methods at one time
Our conceptual bases • Methodical • Step by step • Parsimonious • From simple to complex
What we need? • Tracers High differential capacity (host specificity) Presence in high concentration Good extra-intestinal persistence • Feasible methods (difficulties and costs) • Numerical methods
Tracers: What we have Chemical: faecal sterols, caffeine, fluorescent whitening, etc. Microbial: pathogens and commensals Cellular: animal cells (mitochondrial DNA)
Classification of methods • Methods needing or not cultivation - Culture-dependent methods - Culture-independent methods • Methods needing reference data - Library-independent methods - Library-dependent methods • Providing data for numerical treatment - Qualitative - Quantitative
First step • To work at point source • To differentiate Human from Non-Human fecal sources • To improve, search and select the most differential indicators (tracers) • To look for the best differential subset of tracers • To evaluate statistical and machine learning methods • To assay procedures for development of models • To use quality assurance schema • To sample a wide geographical area European Commission
Statistical and machine learning methods • Inductive learning methods: • Euclidean k-nearest-neighbour • Linear Bayesian classifiers • Quadratic Bayesian classifiers • Support Vector Machine Belanche & Blanch 2008. Environmental Modelling & Software 23: 741-750
Models at point source *LOOCV: Leave One Out Cross-Validation
2D scatter plot of the first predictive model Human Animal Somatic coliphages Somatic coliphages / human Bacteroides phages Blanch et al. 2006. Appl. Environ. Microbiol. 72: 5915-5926
Limiting factors • Occurrence and densities • Dilution • Persistence • Mixtures
Occurrence and densities • Concentration of tracer should be detectable for any matrix of water • World wide distributed • Intestine microbial commensals vs. pathogens
Dilution • Disposal of wastewater (fecal pollution) to surface water • Reduction of concentration of tracer by water treatments 1 – 2 log units 5 – 6 log units Blanch et al. 2008. Journal of Environmental Detection 1: 2-21
Second step Models including dilution effects *LBC: Lineal Bayesian Classifier
Second step • Optimal predictive models at point source are useful when dilution effects are included. • Approach: • Many models are defined. • Given a new sample described by certain tracers, a model is selected among a “bag of models”. • The model could be different for each sample. • The model is selected according to different criteria: accuracy (confidence and support), cost, size and number of variables at detection limit.
Dilution Other potential tracers: • Bifidobacterium spp.107 – 108 cultivable cells / 100 mL at point source (wastewater) • Bacteroides spp. spp. 106 – 107 cultivable cells / 100 mL at point source (wastewater) • Bacteroidetes group (marker equivalent concentrations by qPCR approaches) 109 – 1010 copies/ g feces
Dialysis membrane Sun radiation River flow Persistence • At least two assays by season • Duplicate analyses by sample • Dialysis tubing
Persistence Log reduction Summer Winter ■, sulfite-reducing clostridia;■, fecal coliforms, ■, somatic coliphages; ■, human specific Bacteroides phages, ■, bifidobacteria Diluted wastewater Bonjoch et al. 2009.The persistence of bifidobacteria populations in a river measured by molecular and culture techniques. Journal of Appl. Microbiol. 107: 1178 – 1185 Ballesté and Blanch 2010. Persistence of Bacteroides spp. populations in a river measured by molecular and culture techniques. Appl. Environ. Microbiol. (on-line, in press)
Persistence Log reduction Survival of Bif. adolescentis ■, real-time PCR, winter ■ , real-time PCR, summer ▲, Beerens medium, winter ▲, Beerens medium, summer Time (h)
Persistence Deaging approach Summer sample Adjustment of values at point source ■, sulfite-reducing clostridia ■, fecal coliforms ■, somatic coliphages ■, human specific Bacteroides phages ■, bifidobacteria Use of the best model for these measured variables
Mixtures Predictive models to detect 4 different sources. Ballesté et al. 2010. Molecular indicators used in the development of predictive models for microbial source trackingAEM 76: 1789 - 1795
Mixtures Predictive models to detect H - NH sources. Ballesté et al. 2010. Molecular indicators used in the development of predictive models for microbial source trackingAEM 76: 1789 - 1795
Mixtures • Bacteroides host strains to the enumeration of bacteriophages specific to porcine fecal pollution: 105 PFU/ 100 mL in porcine abattoir wastewaters. Payán et al. 2006. AEM 71: 5659-5662 Payán ,A. 2006. Ph.D. Thesis. University of Barcelona • Multiplex PCR Bif. adolescentis – Bif. dentium: up to 99% animal source and 1% human source (detection limit 101 CFU/ml). Bonjoch et al. 2004. AEM 70: 3171-3175 • qPCR Bif. adolescentis – Bif. dentium: detection limit 103 CFU/ml. Bonjoch et al. 2009. JAM 70: 1178 - 1185 • Bacteroidetes / Bacteroidales host specific q-PCR: human versus ruminant / pigs. Detection limit 103 – 105 gene copies/ 100 ml Reischer et al. 2007. Lett. Appl. Microbiol.: 44, 351 – 356 / Reischer et al. 2006. AEM 72: 5610-5614 Mieszkin et al. 2009. AEM 79: 3045 – 3054 / Mieszkin et al. 2010. JAM 108: 974 – 984 • qPCR Brevibacterium to poultry: detection limit 107 gene copies/l. Weidhass et al. 2010. JAM 109: 334- 347
Research on MST models: where we are Chemical Microbial Cellular Quantitative Qualitative Sensitivity Specificity Robustness What tracer ? What method ? • DONE High concentration at point source Different water matrices Prevalence Universal (geographic, diets, etc.) Candidate tracer Host specificity Correlation to other parameters Environmental persistence Resistance to water treatments Usefulness for fecal pollution mixtures • DONE POTENTIAL DIFFERENTIAL TRACER • At point source NUMERICAL ANALYSES • Dilution • Mixtures WORKING ON … DEVELOPMENT OF PREDICTIVE MODELS • Deaging Initial steps DECISION SUPORT SYSTEMS
Conclusions Minimal requirements for MST indicators (tracers) in the development of predictive models No single indicator. At least two parameters: one which discriminates sources and one which does not. Combining several discriminating indicators for different faecal sources could provide the relative contribution to the total faecal load from each source. The concentrations of indicators (tracers) should be detectable by the respective method of measurement for any matrix of water analyzed.
Conclusions The persistence in the environment and the resistance to water treatments of the different indicators used in predictive models should be similar. Numerical analyses (inductive learning methods) other than traditional statistical methods are reliable tools for the selection of variables (tracers and their parameters) and the development of predictive models.
Conclusions Ideally, the parameters selected should be consistent with the development of MST predictive models and independent of geography, climate, pathogen’s prevalence or dietary habits. The indicators and their parameters should be accessible without incurring large economic or logistic costs.
Acknowledgements Spanish Government • Prof. J. Jofre. Dept. Microbiology at UB. • Prof. F. Lucena. Dept. Microbiology at UB. • Associate Prof. M. Muniesa. Dept. Microbiology at UB. • Prof. L. Belanche. Dept. Software at Polytechnical University of Catalonia. • Dr. X. Bonjoch • Dr. E. Ballesté • A. Casanova Supported by:
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