Anicet R. Blanch

Department of MicrobiologyMicrobiología 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 29th, 2010

High concentration at point source

Different water matrices

Prevalence

Universal (geographic, diets, etc.)

Steps needed to develop MST modelsSteps 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

• 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 studiesPitfalls of MST studies

• Methodical

Step by stepStep by step

• Parsimonious

From simple to complex From simple to complex

Our conceptual basesOur conceptual bases

• TracersHigh 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?

Chemical: faecal sterols, caffeine, fluorescent whitening, etc.

Microbial: pathogens and commensals

Cellular: animal cells (mitochondrial DNA)

(CH 2)3

Me

Me

Me

CHMe 2

HO

H

H H

H

H

R

S

R

R

R

S

S

RS

Tracers: What we haveTracers: What we have

• 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 methodsClassification of methods

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 stepFirst step

European CommissionEuropean Commission

• Inductive learning methods:

Euclidean k-nearest-neighbourEuclidean k-nearest-neighbour

Linear Bayesian classifiersLinear Bayesian classifiers

Quadratic Bayesian classifiersQuadratic Bayesian classifiers

Support Vector MachineSupport Vector Machine

Statistical and machine learning methodsStatistical and machine learning methods

Belanche & Blanch 2008. Environmental Modelling & Software 23: 741-750

Tracers#

variables% correct

classification*Somatic coliphagesPhages infecting Bacteroides spp.

2 100

Faecal coliformsPhages infecting Bacteroides spp.

2 100

Bif. adolescentis, F-RNA II phages 2 97.1

F-RNA II + F-RNA III phagesPhages infecting Bacteroides spp.

3 99

F-RNA I phagesF-RNAPH II phagesFaecal coliforms

3 97.1

Somatic coliphagesF-RNA II phagesF-RNA I phages

3 97.1

F-RNA I phagesF-RNA II phagesE. coli Ph-P phenotypes

3 99

E. coli Ph-P phenotypesF-RNA II phagesBif. adolescentisSorbitol-fermenting bifidobacteria

4 99

Somatic coliphagesF-RNA II phagesBif. adolescentisSorbitol-fermenting bifidobacteria

4 100

Models at point sourceModels at point source

*LOOCV: Leave One Out Cross-Validation

Blanch et al. 2006. Appl. Environ. Microbiol. 72: 5915-5926

HumanAnimal

Somatic coliphages

Somatic coliphages / human Bacteroides phages

2D scatter plot of the first predictive model2D scatter plot of the first predictive model

• Occurrence and densities

• Dilution

• Persistence

• Mixtures

Limiting factorsLimiting factors

Occurrence and densitiesOccurrence and densities

Concentration of tracer should be detectable for any matrix of water

World wide distributed

Intestine microbial commensals vs. pathogens

DilutionDilution

Disposal of wastewater (fecal pollution) to surface water Reduction of concentration of tracer by water treatments

FC

(log CFU/100ml) SOMCPH

(log PFU/100ml) BTHPH

(log PFU/100ml)

Average SD Average SD Average SD

Raw urban sewage 7,31 0,14 6,80 0,30 5,00 0,37

Secondary effluent 5,18 0,82 5,29 0,82 3,14 1,00

Tertiary effluent 0,83 1,12 3,53 0,80 1,72 1,11

Raw sludge ND ND 5,87 0,30 4,51 0,15

Pig abattoir wastewater 7,73 0,78 7,57 0,54 1,85 0,36

Cattle abattoir wastewater 6,81 0,33 6,60 0,47 <1,70 -

Poultry abattoir wastewater 7,67 0,50 5,81 1,11 <1,70 -

River2 3,52 0,30 3,80 0,26 2,63 0,37

Sea2, 3 1,001 0,931 1,86 0,57 0,44 0,35

1

Table 3. Average of log number per 100 ml and Standard Deviation (SD) of faecal coliforms (FC), somatic 1 coliphages (SOMCPH) and phages infecting Bacteroides tethaiotaomicron GA17 (BTHPH) in waters with 2 faecal contaminants of different origins. 3

1: Values corresponded to E. coli 1 2 Mostly influence by disposal of treated urban sewage 2 3 Data extracted from Mocé-Llivina et al., 2005. 3 ND: Not determined 4

Blanch et al. 2008. Journal of Environmental Detection 1: 2-21

1 – 2 log units

5 – 6 log units

Tracers#

variables % correct classification*Somatic coliphagesPhages infecting Bacteroides spp.

2 100

Fecal coliformsPhages infecting Bacteroides spp.

2 100

Bif. adolescentisPhages infecting Bacteroides spp.

2 100

F-RNA IIBif. adolescentis

2 97.1

Sorbitol-fermenting bifidobacteriaTotal bifiobacteriaPhages infecting Bacteroides spp.

3 100

Second stepSecond step

*LBC: Lineal Bayesian Classifier

Models including dilution effects

Second stepSecond 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.

DilutionDilution

Other potential tracers: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

At least two assays by season

Duplicate analyses by sample

Dialysis tubing

PersistencePersistence

■, sulfite-reducing clostridia; ■, fecal coliforms, ■, somatic coliphages;

■, human specific Bacteroides phages, ■, bifidobacteria

Diluted wastewater

WinterWinterSummerSummer

PersistencePersistence

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)

Survival of Survival of Bif. adolescentisBif. adolescentis

■, real-time PCR, winter

■ , real-time PCR, summer

▲, Beerens medium, winter

▲, Beerens medium, summer-4,00

-3,00

-2,00

-1,00

0,00

0 24 48 72 96 120 144 168 192 216 240 264 288

Log

(N

t/N

o)

Time (h)Time (h)

PersistencePersistence

Log reduction

PersistencePersistence

Deaging approachSummer sample

Adjustment of values at point source

Use of the best modelfor these measured variables

■, sulfite-reducing clostridia■, fecal coliforms

■, somatic coliphages■, human specific Bacteroides phages

■, bifidobacteria

MixturesMixtures

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

MixturesMixtures

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

MixturesMixtures

Bacteroides host strains to the enumeration of bacteriophages specific to porcine fecal pollution: 105 PFU/ 100 mL in porcine abattoir wastewaters.

Multiplex PCR Bif. adolescentis – Bif. dentium: up to 99% animal source and 1% human source (detection limit 101 CFU/ml).

Bacteroidetes / Bacteroidales host specific q-PCR: human versus ruminant / pigs. Detection limit 103 – 105 gene copies/ 100 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

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

Payán et al. 2006. AEM 71: 5659-5662

Payán ,A. 2006. Ph.D. Thesis. University of Barcelona

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 areResearch on MST models: where we are

At point sourceDilutionMixturesDeagingWORKING ON …

DONE

DONE

Initial steps

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 models

ConclusionsConclusions

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.

ConclusionsConclusions

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.

ConclusionsConclusions

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:

AcknowledgementsAcknowledgements

UNIVERSITAT DE BARCELONA

ablanch@ub.edu