1. Anicet R. Blanch Department of Microbiology Microbiología del Agua Relacionada con la Salud (MARS) Intérêts et limites des traceurs de sources microbiennes Advantages and limitations of Microbial Source Tracking indicators UNIVERSITAT DE BARCELONA Brest, October 29 th, 2010

2. High concentration at point source Different water matrices Prevalence Universal (geographic, diets, etc.) Steps needed to develop MST models What tracer ? What method ? Candidate tracer POTENTIAL DIFFERENTIAL TRACER DEVELOPMENT OF PREDICTIVE MODELS DECISION SUPORT SYSTEMS Chemical Microbial Cellular Quantitative Qualitative Sensitivity Specificity Robustness Human Porcine Ruminants Birds NUMERICAL ANALYSES Host specificity Correlation to other parameters Environmental persistence Resistance to water treatments Usefulness for fecal pollution mixtures

3. 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 Pitfalls of MST studies

4. Methodical Step by step Parsimonious From simple to complex Our conceptual bases

5. Tracers High differential capacity (host specificity) Presence in high concentration Good extra-intestinal persistence Feasible methods (difficulties and costs) Numerical methods What we need? What we need?

6. Chemical: faecal sterols, caffeine, fluorescent whitening, etc. Microbial: pathogens and commensals Cellular: animal cells (mitochondrial DNA) Tracers: What we have Tracers: What we have

7. 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 Classification of methods

8. 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 First step European Commission

9. Inductive learning methods:  Euclidean k-nearest-neighbour  Linear Bayesian classifiers  Quadratic Bayesian classifiers  Support Vector Machine Statistical and machine learning methods Belanche & Blanch 2008. Environmental Modelling & Software 23: 741-750

10. Tracers # variables % correct classification* Somatic coliphages Phages infecting Bacteroides spp. 2100 Faecal coliforms Phages infecting Bacteroides spp. 2100 Bif. adolescentis, F-RNA II phages 297.1 F-RNA II + F-RNA III phages Phages infecting Bacteroides spp. 399 F-RNA I phages F-RNAPH II phages Faecal coliforms 397.1 Somatic coliphages F-RNA II phages F-RNA I phages 397.1 F-RNA I phages F-RNA II phages E. coli Ph-P phenotypes 399 E. coli Ph-P phenotypes F-RNA II phages Bif. adolescentis Sorbitol-fermenting bifidobacteria 499 Somatic coliphages F-RNA II phages Bif. adolescentis Sorbitol-fermenting bifidobacteria 4100 Models at point source *LOOCV: Leave One Out Cross-Validation

11. Blanch et al. 2006. Appl. Environ. Microbiol. 72: 5915-5926 Human Animal Somatic coliphages Somatic coliphages / human Bacteroides phages 2D scatter plot of the first predictive model

12. Occurrence and densities Dilution Persistence Mixtures Limiting factors

13. Occurrence and densities Concentration of tracer should be detectable for any matrix of water World wide distributed Intestine microbial commensals vs. pathogens

14. Dilution Disposal of wastewater (fecal pollution) to surface water Reduction of concentration of tracer by water treatments Blanch et al. 2008. Journal of Environmental Detection 1: 2-21 1 – 2 log units 5 – 6 log units

15. Tracers # variables% correct classification* Somatic coliphages Phages infecting Bacteroides spp. 2100 Fecal coliforms Phages infecting Bacteroides spp. 2100 Bif. adolescentis Phages infecting Bacteroides spp. 2100 F-RNA II Bif. adolescentis 297.1 Sorbitol-fermenting bifidobacteria Total bifiobacteria Phages infecting Bacteroides spp. 3100 Second step *LBC: Lineal Bayesian Classifier Models including dilution effects

16. Second step Optimal predictive models at point source are useful when dilution effects are included. Approach: 1.Many models are defined. 2.Given a new sample described by certain tracers, a model is selected among a “bag of models”. 3.The model could be different for each sample. 4.The model is selected according to different criteria: accuracy (confidence and support), cost, size and number of variables at detection limit.

17. Dilution Other potential tracers:  Bifidobacterium spp.10 7 – 10 8 cultivable cells / 100 mL at point source (wastewater)  Bacteroides spp. spp. 10 6 – 10 7 cultivable cells / 100 mL at point source (wastewater)  Bacteroidetes group (marker equivalent concentrations by qPCR approaches) 10 9 – 10 10 copies/ g feces

18. Dialysis membrane Sun radiation River flow  At least two assays by season  Duplicate analyses by sample  Dialysis tubing Persistence

19. ■, sulfite-reducing clostridia; ■, fecal coliforms, ■, somatic coliphages; ■, human specific Bacteroides phages, ■, bifidobacteria Diluted wastewater WinterSummer Persistence Log reduction 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)

20. Survival of Bif. adolescentis ■, real-time PCR, winter ■, real-time PCR, summer ▲, Beerens medium, winter ▲, Beerens medium, summer Time (h) Persistence Log reduction

21. Persistence Deaging approach Summer sample Adjustment of values at point source Use of the best model for these measured variables ■, sulfite-reducing clostridia ■, fecal coliforms ■, somatic coliphages ■, human specific Bacteroides phages ■, bifidobacteria

22. 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

23. 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

24. Mixtures  Bacteroides host strains to the enumeration of bacteriophages specific to porcine fecal pollution: 10 5 PFU/ 100 mL in porcine abattoir wastewaters.  Multiplex PCR Bif. adolescentis – Bif. dentium: up to 99% animal source and 1% human source (detection limit 10 1 CFU/ml).  Bacteroidetes / Bacteroidales host specific q-PCR: human versus ruminant / pigs. Detection limit 10 3 – 10 5 gene copies/ 100 ml Bonjoch et al. 2004. AEM 70: 3171-3175  qPCR Bif. adolescentis – Bif. dentium: detection limit 10 3 CFU/ml. Bonjoch et al. 2009. JAM 70: 1178 - 1185 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 10 7 gene copies/l. Weidhass et al. 2010. JAM 109: 334- 347 Payán et al. 2006. AEM 71: 5659-5662 Payán,A. 2006. Ph.D. Thesis. University of Barcelona

25. High concentration at point source Different water matrices Prevalence Universal (geographic, diets, etc.) What tracer ? What method ? Candidate tracer POTENTIAL DIFFERENTIAL TRACER DEVELOPMENT OF PREDICTIVE MODELS DECISION SUPORT SYSTEMS Chemical Microbial Cellular Quantitative Qualitative Sensitivity Specificity Robustness NUMERICAL ANALYSES Host specificity Correlation to other parameters Environmental persistence Resistance to water treatments Usefulness for fecal pollution mixtures Research on MST models: where we are At point source Dilution Mixtures Deaging WORKING ON … DONE Initial steps

26. 1.No single indicator. At least two parameters: one which discriminates sources and one which does not. 2.Combining several discriminating indicators for different faecal sources could provide the relative contribution to the total faecal load from each source. 3.The concentrations of indicators (tracers) should be detectable by the respective method of measurement for any matrix of water analyzed. Minimal requirements for MST indicators (tracers) in the development of predictive modelsConclusions

27. 4.The persistence in the environment and the resistance to water treatments of the different indicators used in predictive models should be similar. 5.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

28. 6.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. 7.The indicators and their parameters should be accessible without incurring large economic or logistic costs.Conclusions

29. 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 Spanish Government Supported by:Acknowledgements

30. UNIVERSITAT DE BARCELONA ablanch@ub.edu