state of the art molecular markers for fecal pollution source tracking in water

MINI-REVIEW

State of the art molecular markers for fecal pollution sourcetracking in water

Peter Roslev & Annette S. Bukh

Received: 15 December 2010 /Revised: 15 December 2010 /Accepted: 15 December 2010 /Published online: 6 January 2011# Springer-Verlag 2010

Abstract Most environmental waters are susceptible tofecal contamination from animal and/or human pollutionsources. To attenuate or eliminate such contamination, it isoften critical that the pollution sources are rapidly andcorrectly identified. Fecal pollution source tracking (FST) isa promising research area that aims to identify the origin(s)of fecal pollution in water. This mini-review focuses on thepotentials and limitations of library independent molecularmarkers that are exclusively or strongly associated withfecal pollution from humans and different animals. Fecal-source-associated molecular markers include nucleic acidsequences from prokaryotes and viruses associated withspecific biological hosts, but also sequences such asmitochondrial DNA retrieved directly from humans andanimals. However, some fecal-source-associated markersmay not be absolutely specific for a given source type, andapparent specificity and frequency established in earlystudies are sometimes compromised by new studiessuggesting variation in specificity and abundance on aregional, global and/or temporal scale. It is thereforerecommended that FST studies are based on carefullyselected arrays of markers, and that identification of humanand animal contributions are based on a multi-markertoolkit with several markers for each source category.Furthermore, future FST studies should benefit fromincreased knowledge regarding sampling strategies andtemporal and spatial variability of marker ratios. It willalso be important to obtain a better understanding ofmarker persistence and the quantitative relationship

between marker abundance and the relative contributionfrom individual fecal pollution source types. A combina-tion of enhanced pathogen screening methods, andvalidated quantitative source tracking techniques couldthen contribute significantly to future management ofenvironmental water quality including improved microbialrisk assessment.

Keywords Fecal pollution .Microbial source tracking(MST) . Host specific and host associated markers .

mtDNA . Surface water quality . Microbial risk assessment

Introduction

Fecal pollution is a primary health concern in relation toenvironmental waters used for drinking water supply,recreational activities, and food production. Fecal pollutionproblems are common to all nations regardless of economicposition although pollution level and type vary amongcountries (Stewart et al. 2007). Global estimates suggestthat swimming and bathing in fecal contaminated water,and consumption of shellfish harvested from pollutedwaters results in an excess of 175 million cases ofinfectious disease each year (Shuval 2003).

Environmental waters including lakes, streams, andcoastal marine waters are often susceptible to fecalcontamination from a range of point and nonpoint sources,with potential contributions from many individual sourcesbelonging to wildlife, domesticated animals, and/or humans(Fig. 1). Wildlife pollution sources can include fecal wastefrom avifauna and a range of different mammals. Pollutionfrom domesticated animals includes fecal waste from petsand farm animals including agricultural runoff. Humanhealth risks associated with fecal waste from wildlife and

P. Roslev (*) :A. S. BukhDepartment of Biotechnology,Chemistry and Environmental Engineering, Aalborg University,Sohngaardsholmsvej 57,DK 9000 Aalborg, Denmarke-mail: [email protected]

Appl Microbiol Biotechnol (2011) 89:1341–1355DOI 10.1007/s00253-010-3080-7

domesticated animal are generally considered to be lowercompared with human fecal waste. Human fecal waste canenter environmental water via combined sewer overflows,leaking sewer and septic systems, sewage treatment plants,and industrial wastewater outlets. As a result of thenumerous potential sources, most environmental watersincluding what appear to be pristine water bodies oftencontain traces of fecal material from several sources.

The microbiological quality of environmental watersis most often evaluated by means of fecal indicatorbacteria such as Escherichia coli, enterococci andClostridium perfringens. The presence of elevated levelsof these indicators suggest fecal pollution and potentialpublic health risks, however, it is often difficult to linkindicator bacteria such as E. coli to a particular pollutionsource because of the ubiquitous nature of these microor-ganism (Gordon 2001; Stewart et al. 2007; Field andSamadpour 2007).

Fecal pollution source tracking (FST) aims to identifythe origin(s) of fecal pollution in water. Water typestargeted by FST include ground water, resource water,finished drinking water, recreational waters, and waters inwildlife habitats (Stoeckel and Harwood 2007; Plummerand Long 2009). The underlying assumption is thatcharacteristics in, or associated with, fecal pollution canbe used to identify the feces type and the biological origin(source) (Sadowsky et al. 2007; Field and Samadpour 2007;Santo Domingo et al. 2007; Taylor and Ebdon 2007). FSTis also referred to as “microbial source tracking” or“bacterial source tracking” (Stewart et al. 2007). FST hasreceived growing attention in recent years, and the numberof methods for source tracking has increased almostexponentially during the last decade. Methods for FST cangenerally be divided into library dependent and libraryindependent methods (Stewart et al. 2007; Field and

Samadpour 2007; Santo Domingo et al. 2007; Stoeckeland Harwood 2007). The library independent methodsinclude both chemical and molecular (genetic) markers. Thecurrent status of library independent chemical markers hasbeen reviewed recently by Hagedorn and Weisberg (2009),and will not be discussed further. In this mini-review, wewill focus on the current status including potentials andlimitations of library independent molecular markers forcharacterization and identification of fecal pollution sour-ces. We define these molecular markers as unique nucleicacid sequences that are exclusively or strongly associatedwith specific biological sources (hosts). In an idealsituation, such markers are stable and universal in timeand space, and do not require generation of libraries frompotential pollution sources. Assays targeting source associ-ated molecular markers are currently considered the mostpromising among the current FST techniques, and holdmany promises for future applications (Seurinck et al.2005b; Sadowsky et al. 2007). However, there are alsolimitations and research gaps that need to be addressedbefore source-associated markers can be used for routinewater quality analysis.

Fecal source associated molecular markers

Library independent molecular markers for FST can targetsequences in host associated microorganisms or sequencesderived directly from the host. The most popular fecalsource-associated molecular (FAM) markers for FST can begrouped into:

1. Molecular markers in prokaryotes2. Molecular markers in viruses3. Molecular markers in eukaryotes

Molecular markers in prokaryotes

Library independent techniques targeting genetic markers inprokaryotes rely on the selective pressure in the gut ofanimals and humans to select for unique microbial targetsthat are specific or strongly associated with their host. Thestrength of the selective pressure is influenced by severalfactors including anatomy, physiology, diet and health ofthe host. Prokaryotic FAM markers comprise geneticsequences retrieved from aerobic, facultative, and obligateanaerobic microorganisms recovered from fecal material.Examples of FAM markers in prokaryotes are shown inTables 1 and 2.

Anaerobic bacteria often constitute the majority ofmicroorganisms in feces from humans and most animals.Very promising human- and animal-associated markershave been identified in the order Bacteroidales (Tables 1

Fig. 1 Mixed fecal pollution in surface waters, and factors affectingabundance of host associated microorganisms and molecular markers.W1–Wn, D1–Dn, and H1–Hn indicate different sources of wildlife,domesticated animal, and human fecal material, respectively

1342 Appl Microbiol Biotechnol (2011) 89:1341–1355

and 2). Bacteroidales include obligate anaerobic bacteria,and often represent a substantial fraction of the gastroin-testinal flora of many mammals. Bernhard and Fieldpioneered the identification of host associated markers inBacteroidales (Bernhard and Field 2000a, b). Additionalhost associated Bacteroidales-specific polymerase chainreactions (PCRs) have subsequently been developed andvalidated for humans (Carson et al. 2005; Seurinck et al.2005a; Layton et al. 2006; Kildare et al. 2007; Reischer etal. 2007; Okabe et al. 2007; Shanks et al. 2009; Lee andLee 2010), chickens (Lu et al. 2007), elk (Dick et al.2005a), dogs (Dick et al. 2005a; Kildare et al. 2007);Canada goose (Lu et al. 2009; Fremaux et al. 2010), horse(Dick et al. 2005b), ruminants (Reischer et al. 2006; Okabeet al. 2007; Shanks et al. 2008; Dorai-Raj et al. 2009;Mieszkin et al. 2010), and pigs (Dick et al. 2005b; Okabe etal. 2007; Mieszkin et al. 2009). Detection of host associatedmarkers in Bacteroidales is almost exclusively carried usingcultivation-independent methods, and quantitative molecu-

lar detection including use of qPCR is now possible formost host groups (Tables 1 and 2).

E. coli is used successfully for routine evaluation ofwater quality in many countries but studies documentingsuccessful identification of host associated markers in thisbacterium are more limited. An assay targeting the LTIIatoxin gene in enterotoxigenic E. coli has been suggested asa marker for cattle (Khatib et al. 2002), whereas an assaytargeting the STII toxin gene has been suggested as amarker for pigs (Khatib et al. 2003). Duck and goose-associated markers in E. coli have also been proposed(Hamilton et al. 2006). A human-associated E. coli clonewas described by Clermont et al. (2008). The clone belongsto the phylogenetic group B2 subgroup VIII, and appears tobe an avirulent commensal (Clermont et al. 2008). A PCRreaction targeting this clone has been designed but onlytested in a limited number of source tracking studies(Clermont et al. 2008; Roslev et al. 2010). In general,detection of different host associated markers in E. coli is

Table 1 Examples of human-associated molecular markers

Sourceassociation

Marker Target Detection References

Humans HF134, HF183, BACHum,B. theta, HuBac, BacH,Human-Bac1, Human M2,Human M3, Bf

Bacteroidales (C), CID, QLMD, QNMD Bernhard and Field 2000b;Carson et al. 2005; Seurincket al. 2005a; Layton et al.2006; Kildare et al. 2007;Reischer et al. 2007; Okabeet al. 2007; Shanks et al. 2009;Lee and Lee 2010

ADO, DEN Bifidobacterium dentium,Bifidobacterium adolescentis

(C), CID, QLMD, QNMD Bonjoch et al. 2004; Bonjoch et al.2007; Bonjoch et al. 2009;Gourmelon et al. 2010b

esp Enterococcus faecium C, CID, QLMD, QNMD Scott et al. 2005; Ahmed et al. 2008c

M66–M107 Enterococcus hiraeEnterococcus faecalis

C, CID, QLMD, (QNMD) Soule et al. 2006

B2 VIII/O81 Escherichia coli B2 subgroupVIII with O81 serotype

CD, (CID), QLMD Clermont et al. 2008

HFB Faecalibacterium (C), CID, QLMD Zheng et al. 2009

Mnif Methanobrevibacter smithii (C), CID, QLMD Ufnar et al. 2006

HS-AV, HAdV, HAdV-C,HAdV-F

Adenovirus CID, QLMD, QNMD Noble et al. 2003; Fong et al.2005; Hundesa et al. 2006;Wolf et al. 2010

EV, HEV Enterovirus CID, QLMD, QNMD Noble et al. 2003; Fong et al. 2005

NoVGI, NoVGII Norovirus CID, QLMD, QNMD Wolf et al. 2010

HPyV, JC, BK Polyomavirus CID, QLMD, QNMD McQuaig et al. 2006; McQuaiget al. 2009

II, III, FphGII, FphGIII F+RNA coliphages ingenogroups II and III

C, CID, QLMD, QNMD Hsu et al. 1995; Wolf et al. 2010

Humito, Human, HcytB Mitochondrial DNA CID, QLMD, QNMD Martellini et al. 2005; Caldwell et al.2007; Tobe and Linacre 2008; Schilland Mathes 2008; Kortbaoui et al.2009; Baker-Austin et al. 2010

C cultivation from environmental samples possible, CID cultivation-independent detection possible, QLMD qualitative molecular detectionpossible, QNMD quantitative molecular detection possible

Appl Microbiol Biotechnol (2011) 89:1341–1355 1343

Table 2 Examples of animal-associated molecular markers

Sourceassociation


Animals CF128 Bacteroidales (C), CID, QLMD Bernhard and Field 2000b;Gourmelon et al. 2007

Rhodococcus coprophilus R. coprophilus C, CID, QLMD, QNMD Savill et al. 2001

I, IV, FphGI, FphGIV F+RNA coliphages ingenogroups I and IV

C, CID, QLMD, QNMD Hsu et al. 1995; Wolf et al. 2010

AtAdV Adenovirus CID, QLMD, QNMD Wolf et al. 2010

Universal, Umito, CytB, AcytB Mitochondrial DNA CID, QLMD, QNMD Tobe and Linacre 2008;Kortbaoui et al. 2009;Baker-Austin et al. 2009;Baker-Austin et al. 2010

Cat Cat Mitochondrial DNA CID, QLMD, QNMD Caldwell and Levine 2009

Cattle(ruminants)

CF128, CF193, CF128,Cow-Bac, BacR, Cow M2,Cow M3, Rum, Rum 2 Bac

Bacteroidales (C), CID, QLMD, QNMD Bernhard and Field 2000b;Reischer et al. 2006;Okabe et al. 2007; Shankset al. 2008; Dorai-Raj etal. 2009; Mieszkin et al. 2010

M15, M19 Enterococcus hirae C, CID, QLMD, (QNMD) Soule et al. 2006

LTIIa E. coli C, CID, QLMD Khatib et al. 2002

Mrnif Methanobrevibacterruminantium

(C), CID, QLMD Ufnar et al. 2007b

BAV, BAdV Adenovirus CID, QLMD Maluquer de Motes et al. 2004;Hundesa et al. 2006

BEV Enterovirus CID, QLMD Ley et al. 2002; Fong et al. 2005

NoV GIII Norovirus CID, QLMD, QNMD Wolf et al. 2010

BPyV Polyomavirus CID, QLMD, QNMD Hundesa et al. 2006;Hundesa et al. 2010

Bomito, Bovine, Cow Mitochondrial DNA CID, QLMD, QNMD Martellini et al. 2005;Caldwell et al. 2007;Schill and Mathes 2008;Kortbaoui et al. 2009;Baker-Austin et al. 2010

Deer/Elk EF990 Bacteroidales (C), CID, QLMD Dick et al. 2005a

M40–M94 Enterococcus casseliflavus,Enterococcus hirae, andEnterococcus mundtii

C, CID, QLMD, (QNMD) Soule et al. 2006

White-tailed deer, Deer Mitochondrial DNA CID, QLMD, QNMD Schill and Mathes 2008;Caldwell and Levine 2009

Dog DF475, BacCan Bacteroidales (C), CID, QLMD, QNMD Dick et al. 2005a; Kildareet al. 2007

Dog Mitochondrial DNA CID, QLMD, QNMD Schill and Mathes 2008;Caldwell and Levine 2009

Duck/Goose CG-Prev f5, CGOF1-Bac,CGOF2-Bac

Bacteroidales (C), CID, QLMD, QNMD Lu et al. 2009; Fremauxet al. 2010

E2 Desulfovibrio-like (C), CID, QLMD Devane et al. 2007.

Combination of GA9, GB2,GD5, GE3, GE11, GF5,GG11

Escherichia coli C, QLMD, QNMD Hamilton et al. 2006

Gull Gull-2 Catellicoccus marimammalium CID, QLMD, QNMD Lu et al. 2008

Horse Canada goose Mitochondrial DNA CID, QLMD, QNMD Schill and Mathes 2008;Caldwell and Levine 2009

HoF597 Bacteroidales (C), CID, QLMD Dick et al. 2005b

Horse Mitochondrial DNA CID, QLMD, QNMD Schill and Mathes 2008

Pig PF163, Pig Bac Bacteroidales (C), CID, QLMD, QNMD Dick et al. 2005b; Okabeet al. 2007


often possible using both cultivation dependent pre-enrichment or directly by applying cultivation-independentamplification methods (Tables 1 and 2).

Enterococci are another group of indicator bacteriawhere human- and animal-associated markers have beenidentified (Scott et al. 2005; Soule et al. 2006; Kim et al.2010). Enterococci are gram positive facultative anaerobicbacteria, with a relatively good survival in many aquaticenvironments. One of the most popular markers inenterococci is directed towards the variant of the entero-coccal surface protein (esp) gene found in Enterococcusfaecium (Scott et al. 2005). Expression of esp is associatedwith biofilm formation and increased virulence. The espvariant targeted for source tracking is mainly associatedwith human fecal pollution (Scott et al. 2005; Ahmed et al.2008b). Soule et al. (2006) identified a series of human-and animal-associated markers in different Enterococcusspecies. The marker sequences were associated withmetabolic pathways, DNA replication, a bacteriophageprotein, and hypothetical proteins with unknown function(Soule et al. 2006). The new markers were attributed todifferent Enterococcus species including Enterococcus

casseliflavus, Enterococcus faecalis, Enterococcus hirae,and Enterococcus mundtii. These host associated markersin Enterococcus can be detected in combination withcultivation dependent enrichment or directly by cultivation-independent methods such as microarrays and different PCRtechniques (Tables 1 and 2).

Host associated strains and sequences have also beenobserved in prokaryotes other than Bacteroidales, Escherichia,and Enterococcus including human-associated Bifidobacte-rium dentium, Bifidobacterium adolescentis, Methanobrevi-bacter smithii, and Faecalibacterium (Nebra et al. 2003;Bonjoch et al. 2004; Ufnar et al. 2006; Zheng et al. 2009).Animal-associated prokaryotes and sequences include Rhodo-coccus coprophilus (Savill et al. 2001), a domesticatedruminant-associated marker in Methanobrevibacter ruminan-tium (Ufnar et al. 2007b), gull associated Catellicoccusmarimammalium (Lu et al. 2008), pig-associated markersin methanogens and Lactobaccillus sobrius/Lactobacillusamylovorus (Ufnar et al. 2007a; Marti et al. 2010), aduck-associated marker in an unknown Desulfovibrio-likebacterium (Devane et al. 2007), and a poultry associatedmarker in Brevibacterium (Weidhaas et al. 2010).

Table 2 (continued)

Sourceassociation


Pig-1-Bac, Pig-2-Bac Mieszkin et al. 2009

STII E. coli C, CID, QLMD Khatib et al. 2003

OTU171 Lactobacillus sobrius/L.amylovorus

(C), CID, QLMD, QNMD Konstantinov et al. 2005;Marti et al. 2010

P23-2 Methanogens (C), CID, QLMD Ufnar et al. 2007a

PAV, PAdV, PAdV-3, PAdV-5 Adenovirus CID, QLMD, QNMD Maluquer de Motes et al. 2004;Hundesa et al. 2006; Hundesaet al. 2009; Wolf et al. 2010

NoV GII Norovirus CID, QLMD, QNMD Wolf et al. 2010

Poultry PTV Teschovirus CID, QLMD, QNMD Jimenez-Clavero et al. 2003

Pomito, Swine Mitochondrial DNA CID, QLMD, QNMD Martellini et al. 2005;Caldwell et al. 2007; Schilland Mathes 2008; Kortbaouiet al. 2009; Baker-Austinet al. 2010

CP Bacteroidales (C), CID, QLMD Lu et al. 2007

Sheep LA35 Brevibacterium (C), CID, QLMD, QNMD Weidhaas et al. 2010

Chicken, Ckmito Mitochondrial DNA CID, QLMD, QNMD Schill and Mathes 2008;Kortbaoui et al. 2009

OAdV Adenovirus CID, QLMD, QNMD Wolf et al. 2010

NoV GIII Norovirus CID, QLMD, QNMD Wolf et al. 2010

Ovmito, Sheep Mitochondrial DNA CID, QLMD, QNMD Martellini et al. 2005; Schill andMathes 2008; Kortbaoui et al.2009; Baker-Austin et al. 2010

C cultivation from environmental samples possible, CID cultivation-independent detection possible, QLMD qualitative molecular detectionpossible, QNMD quantitative molecular detection possible


Molecular markers in viruses

More than 100 different types of pathogenic viruses areexcreted in human and animal fecal waste, and they oftenshow good persistence in environmental waters (Fong andLipp 2005). Many human and animal viruses also have arelatively stringent host association, which make themexcellent candidates for FST (Fong and Lipp 2005).Human-associated viruses for FST include members ofadenovirus (Noble et al. 2003; Fong et al. 2005; Hundesa etal. 2006; Wolf et al. 2010), enterovirus (Noble et al. 2003;Fong et al. 2005), norovirus (Wolf et al. 2010), andpolyomavirus (McQuaig et al. 2006; McQuaig et al. 2009)(Table 1). Results so far confirm that many of the human-associated viruses have a high degree of host specificity(Jimenez-Clavero et al. 2005; Fong and Lipp 2005).

Animal-associated viruses include adenovirus with rela-tively broad host specificity (Wolf et al. 2010), and also cattleassociated adenoviruses, enteroviruses, norovirus, and poly-omaviruses (Ley et al. 2002; Maluquer de Motes et al. 2004;Fong et al. 2005; Hundesa et al. 2006; Hundesa et al. 2010;Wolf et al. 2010), pig-associated adenoviruses, norovirus,and teschoviruses (Jimenez-Clavero et al. 2003; Maluquer deMotes et al. 2004; Hundesa et al. 2006; Hundesa et al. 2009;Wolf et al. 2010), and sheep-associated adenoviruses andnorovirus (Wolf et al. 2010) (Table 2). Detection of thehuman- and animal-associated viruses is carried out usingcultivation-independent methods, and quantitative moleculardetection assays including qPCR and RT-qPCR have beendeveloped for most host groups.

Coliphages are bacteriophages that infect coliformbacteria. Coliphages are nonpathogenic to humans, andhave persistence in the environment that is often compara-ble to enteric viruses. Human- and animal-associated F+RNA coliphages belonging to different genogroups havebeen suggested as potential source tracking targets (e.g.,Hsu et al. 1995; Cole et al. 2003; Lee et al. 2009; Wolf etal. 2010). F+RNA coliphages in genogroups I and IV aremainly associated with animal fecal waste (Table 2);whereas, coliphages in genogroups II and III are moreabundant in human feces (Table 1). Although many F+RNA coliphages may not be absolutely specific forindividual host groups they can be useful as part of largersource tracking toolboxes (Lee et al. 2009; Wolf et al.2010). Detection of host associated coliphages in environ-mental waters is often possible using both cultivation-independent methods and methods employing cultivationdependent enrichment (Tables 1 and 2).

Molecular markers in eukaryotes

Feces from humans and animals contain blood andintestinal cells from their host. Hence, library independent

techniques may also target host nucleic acids directly ratherthan molecular markers in the host microbiota. Aninteresting group of “direct” FAM markers is derived fromeukaryotic mitochondrial DNA sequences (mtDNA). Oneof the main advantages of targeting mtDNA is that the fecalsource organism is identified directly instead of micro-organisms it may host. Furthermore, mtDNA has manycopies per cell providing a high level of sensitivity. Theidea of using mtDNA in FST was first proposed byMartellini et al. (2005), and was based on the fact thatfeces contain large amounts of cells from the host (e.g.,epithelial cells from the intestines), and that these cells areexcreted in the environment. Martellini et al. (2005)designed primers for amplification of fecal-source-associated mtDNA markers in humans, sheep, cows, andpigs in surface waters (Tables 1 and 2). In 2007, Caldwell etal. developed primers for a triplex qPCR assay targetinghuman, bovine, and swine mtDNA in eukaryotic effluents,and qPCR assays targeting mtDNA in dogs, cows, chicken,sheep, horses, pigs, Canada geese, white-tailed deer, andhumans were developed by Schill and Mathes (2008). In2009, Caldwell and Levine developed qPCR assays fordetection of mtDNA from dogs, cats, Canada geese, anddeer in influent wastewater, and more studies havedeveloped primers for detection of mtDNA from humansand animals (Tobe and Linacre 2008; Baker-Austin et al.2009; Kortbaoui et al. 2009; Baker-Austin et al. 2010).Interestingly, these studies show that by targeting mtDNA itis possible to design primers that are highly speciesspecific, but also assays that target larger clusters ofrelevant organisms such as all mamalian species or a subsetwith selected domesticated animals (Tobe and Linacre2008; Baker-Austin et al. 2009; Kortbaoui et al. 2009).Quantitative molecular detection methods such as qPCRhave been developed for all of the human and animalmtDNA marker groups (Tables 1 and 2).

Identification and detection of source-associated markers

Identification of fecal-source-associated markers has beenbased on a range of different molecular approaches. Forexample, Bernhard and Field (2000a) characterized human-and cow-associated markers using length heterogeneity(LH) PCR and terminal restriction fragment length poly-morphism (T-RFLP). The methods utilize the variations inlength and nucleic acid composition of 16S rRNA genesbetween different genera. Subsequently, a clone library wasconstructed, and the clones that had the LH-PCR and T-RFLP patterns of interest were sequenced. Later on,Bernhard and Field (2000b) identified marker clones inwater samples collected from a bay, and they found sevenhuman- and cow-associated markers, which correspondedto their previous findings. Clones with patterns of interests


were sequenced, and primers were designed to target thehost associated sequences. Hamilton et al. (2006) identifiedgoose-associated markers in E. coli using suppressionsubtractive hybridization (SSH). In brief, SSH is atechnique that selectively amplifies target DNA andsimultaneously suppresses amplification of nontargetDNA (i.e. identical sequences in the control and testsample). The tester-specific fragments were used in a clonelibrary, and the fecal source specific clone inserts wereamplified and sequenced, and the specificity was analyzedusing Southern hybridization. The analysis revealed sevengoose-associated markers in E. coli. SSH was also used byZheng et al. 2009 to identify a molecular marker inFaecalibacterium for detection of human-associated fecalpollution in environmental samples including sewer anddomesticated animal waste. Another approach was used bySoule et al. 2006 who used DNA microarray to identifynew fecal source markers in Enterococcus. Firstly, a libraryof fecal source strains of Enterococcus was created. Theclone inserts were PCR amplified, and the products wereattached to a chip. DNA extracted from target strains ofdifferent hosts was labeled, and hybridization was carriedout. The putative fecal source markers were sequenced, andprimers were designed for the sequences. The primers wereused to screen DNA pools of multiple isolates of Enter-oococcus spp. from each host, and resulted in 15 differentmarkers associated with three different hosts.

The most used methods to identify new markers and toscreen for markers in environmental samples are nucleicacid amplification techniques including PCR, qPCR, andRT-qPCR. For these methods, a species-specific targetsequence must be chosen (e.g., Martellini et al. 2005;Reischer et al. 2007; Scott et al. 2005) or the sequence canbe found by creating a clone library using 16S rRNAuniversal primers (e.g., Mieszkin et al. 2009). In the lattercase, the clone inserts are amplified and sequenced. Thespecific sequence is then found by aligning DNA sequencesof target and non-target organisms from a database (e.g.,GenBank). Based on the alignment, a primer pair can bedesigned to amplify the target sequence. The specificity ofthe primers can be determined using BLAST. Good primerdesign is essential for PCR reactions, and several types ofsoftware can be used for designing primers (e.g., Primer3,PrimerQuest, FastPCR, Primer-BLAST). When the primersare designed, the PCR reaction can be set up, optimized,and validated. PCR products should be sequenced todetermine marker specificity (e.g., Caldwell and Levine2009; Caldwell et al. 2007; Kildare et al. 2007).

qPCR-based techniques have now been applied in manystudies for detection and quantification of FAM markers(Tables 1 and 2). In traditional qPCR, the amplification of atarget sequence is recorded in “real-time” via detection ofan unspecific florescent reporter (e.g., SYBRGreen) or a

specific probe (e.g., TaqMan probe). These techniques arevery attractive for rapid and quantitative detection of manytargets including those that are not detectable with classicalmicrobiological techniques. However, it is important toacknowledge that qPCR assays for host associated markersincluding those for Bacteroidales are not absolutely specificand sensitive for their target sequences (Wang et al. 2010).This may lead to occasional generation of false positive andfalse negative information that should be addressed in assayevaluations (Wang et al. 2010). Furthermore, qPCR doesnot discriminate between DNA from dead or living cells,hence all target DNA in the sample will be quantified.However, qPCR techniques has recently been combinedwith use of intercalating DNA-binding chemicals to inhibitamplification of nucleic acids from membrane compro-mised cells and extracellular (“naked”) nucleic acids(Nocker et al. 2006, 2007; Bae and Wuertz 2009).Intercalating chemicals such as propidium monoazide(PMA) binds to free (“naked”) DNA and DNA in deadpermeable cells. After exposure to light, PMA is photo-activated and then reacts with DNA. This prevents properPCR amplification, and it is then possible to discriminatebetween nucleic acids in intact cells, and membranecompromised cells and extracellular nucleic acids. Thistechnique can help to better understand the environmentalfate and state of nucleic acid markers used for FST.

Another challenge using qPCR-based methods for FST isthe nucleic acid extraction and recovery step. Environmentalsamples often contain various PCR inhibitors (e.g., humicacids, complex polysaccharides, inorganic ions, etc.) so samplepreparation is often critical for the outcome of the assay.Unfortunately, recovery and efficiency can vary considerablebetween protocols, and the final yield will therefore depend onboth the methods used and the matrix sampled (Girones et al.2010; Jofre and Blanch 2010; Wang et al. 2010). Theseuncertainties can, among other things, complicate directcomparison of copy numbers between studies (Girones etal. 2010; Jofre and Blanch 2010; Wang et al. 2010). Hence,estimation and documentation of the de facto efficiency andlower level of detection for individual amplification reactionsare essential for many FST applications.

Limitations of FAM markers

Although library independent FAM markers represent someof the most promising methods for FST, there are a numberof limitations that should be considered. These limitationsinclude:

▪ Lack of absolute host specificity among human- andanimal-associated microbial markers.

▪ Lack of temporal stability of some host associatedmicrobial markers in different host groups.


▪ Horizontal gene transfer of markers associated withtoxin and/or virulence genes.

▪ Low or unknown abundance of microbial markers insome host individuals and/or populations.

▪ Potential carryover of mtDNA and existence ofnonfecal mtDNA sources

Library independent molecular markers in microorgan-isms may not be present in fecal waste from all individualsof a given host type (Field and Samadpour 2007). It is alsowell known that gut flora in warm-blooded animals canchange both within individual lifetimes and betweengenerations. For example, the human gut can be colonizedby >1,000 different bacterial species (Xu and Gordon2003), some of which may only be “temporary visitors”.As a result, well-known enteric bacteria such as E. colishow little or very limited temporal stability and geograph-ical structure (Gordon 2001). Furthermore, host associatedmolecular markers including those in Bacteroidales maynot be absolutely specific for individual hosts because fecalbacteria may be transferred horizontally between organismsthat live in relative close contact (Stewart et al. 2007; Fieldand Samadpour 2007; Harwood et al. 2009). This phenom-enon has also been observed for the apparent human-associated esp gene in E. faecium (Layton et al. 2009).Originally proposed as a human specific marker (Scott et al.2005) this gene has later been detected in animals living inrelative close contact with humans including dogs, gulls,horses, seals and sea lions (Harada et al. 2005; Whitman etal. 2007; Layton et al. 2009). Despite some controversyregarding esp specificity (Whitman et al. 2007; Byappanahalliet al. 2008) the marker remains an asset in many FST studiesbased on library independent markers (Ahmed et al. 2008b;Scott et al. 2009).

The findings above emphasize that rarely is it possible tofind a marker in microorganism that is truly human oranimal “specific”. In some cases, a marker may occurtransiently in a “different” host group, and in other cases amarker may occur more permanently in “nonhosts” but at alow abundance. Regardless, the word human or animal“associated” is likely a better term for most libraryindependent genetic markers, emphasizing a preferencewithout ruling out occurrence among other host species.Furthermore, it is not always necessary to document a verynarrow and absolute host association for a marker to beattractive for FST. In many practical applications, knowl-edge about more broad source categories such as humans,ruminants and avifauna combined with probability analy-ses, is often sufficient to identify the most likely pollutionsources. Hence, library independent molecular markers inmicroorganisms remain some of the best options for FST.

Use of eukaryotic markers such as mtDNA sequencesappears promising for several reasons but there are also

limitations that should be observed. For example, mtDNA isshed via feces but also several other means including urine,saliva, blood and skin. Hence, detection of mtDNA from aparticular organism may or may not indicate direct fecalcontamination from this source. There is also potentialcarryover of DNA from food items consumed by animalsand humans, e.g., bovine mtDNA may be detected in fecesfrom beef eating humans (Caldwell et al. 2007), or avianmtDNA may be detected in chicken eating dogs and humans(J. Porter, personal communication). Hence, it is not recom-mended that detection of fecal contamination be based solelyon waterborne mtDNA markers. However, mtDNA could bevery useful in a combination with fecal indicator bacteria andFAM markers associated directly with fecal waste (Caldwellet al. 2007; Schill and Mathes 2008). A direct detection ofDNA from source organisms, and the potential for creatingprimers with both narrow and broad specificity make themtDNA technique a very promising asset for FST.

Challenges in fecal pollution source tracking

A range of studies have demonstrated that many FAMmarkers have a high degree of source association and potentialuses (Tables 1 and 2). Despite these successes, severalresearch gaps must be addressed before many FAM markerscan be adopted for routine water analysis (Gordon 2001; USEnvironmental Protection Agency 2005; Seurinck et al.2005b; Sadowsky et al. 2007; Field and Samadpour 2007;Santo Domingo et al. 2007; Stoeckel and Harwood 2007;Stapleton et al. 2009; Harwood et al. 2009). These researchneeds include further knowledge regarding:

▪ Correlation among different FAM marker typesincluding performance under diverse real life con-ditions including extensive validation with blindsamples and naturally mixed samples.

▪ Further knowledge about the de facto lower level ofdetection for FAM markers in aquatic matrices includ-ing effects of inhibitory substances on amplificationreactions, and development of suitable concentrationtechniques.

▪ Regional and global variations in specificity ofindividual FAM markers

▪ Stability, fate and persistence of FAM makers in theenvironment including temporal and spatial varia-tions in marker ratios.

▪ Quantitative relationship between abundance of FAMmarkers and the relative contribution to fecal pollu-tion from specific host groups.

▪ Knowledge about the minimum number of samplesrequired to achieve acceptable levels of correctclassification based on FAM markers.


▪ Discrimination between fresh fecal contaminationfrom primary pollution sources vs. internal con-tamination from secondary pollution sources (e.g.,sediments).

▪ Knowledge about FAM markers and potential fecalcontributions from cold-blooded animals (poikilotherms).

▪ Correlation between FAM markers and pathogenabundance.

The strength of a fecal pollution may vary bothtemporarily and spatially in response to factors such asprecipitation, hydraulic loads, local currents, wind inducedturbulence and host biology (Whitman and Nevers 2004;Boehm 2007; Roslev et al. 2008; Reischer et al. 2008;Hansen et al. 2009; Roslev et al. 2010). Concentrations offecal microorganisms in environmental waters may evenexhibit significant temporal variation on relatively shorttime scales such as minutes. This can result in fluctuatingenvironmental concentrations and an uneven distribution offecal indicators and FAM markers (Whitman and Nevers2004; Boehm 2007; Santoro and Boehm 2007; Roslev et al.2009; 2010).

An example of temporal variations in concentrations of afecal indicator at a recreational beach is shown in Fig. 2.Seawater samples were collected several times with 10-minintervals for five consecutive days during a period withcalm weather and no precipitation. Subsequent attempts todetect various FAM markers gave variable results likely dueto corresponding fluctuations in marker abundance (Roslevet al. 2010). Arbitrary detection limits for molecularmarkers corresponding to 10, 100 and 1,000 bacterial cells100 ml−1 are also shown in Fig. 2. In this theoreticalexample, a low detection limit of 10 cell equivalents100 mL−1 would result in positive detection of a FAMmarker in all 100 mL samples whereas a detection limit of100 or 1,000 cell equivalents 100 mL−1 would result in

positive marker detection in only about 50% and <5% ofthe samples, respectively. The importance of such consid-erations are underlined in a study by Stapleton et al. (2009)suggesting that concentrations of Bacteroidales sourcetracking markers in coastal marine waters can exhibit eventgreater variability than concentrations of traditional fecalindicator bacteria such as E. coli and enterococci. Hence, itis very important that temporal and spatial fluctuations areconsidered in sampling regimes, and that knowledge aboutthe de facto lower limit of detection is known for FAMmarkers applied in FST studies.

Subpopulations of fecal microorganisms can sometimessurvive extended periods in freshwater and marine sedi-ments. Resuspension of sediment can subsequently reintro-duce fecal microorganisms and potentially FAM markersinto the water column. Because sediments can act as asecondary FAM reservoir and skew correct identification ofrecent fecal pollution, the role of sediments as a potentialinternal source should be considered when studying FST inshallow environmental waters (Santo Domingo et al. 2007;Stoeckel and Harwood 2007; Roslev et al. 2008).

Monitoring for host associated prokaryotes and virusesoften require large water samples to obtain sufficient targetsfor PCR amplification. Concentration of large watersamples may result in subsequent concentration of PCRinhibitory substances that can complicate correct amplifi-cation reactions, and subsequent accurate quantification byqPCR (Girones et al. 2010; Jofre and Blanch 2010).Accurate quantification of different markers and markerratios is, however, important for establishing a quantitativerelationship between marker abundance and the relativecontribution from specific pollution sources. This isimportant because most environmental waters are affectedby mixed fecal pollution at fluctuating levels (Fig. 1).

Persistence of host associated molecular markersin the environment

In the previous section, stability and persistence of FAMmakers in the environment was identified as a potentialchallenge for marker application in source tracking studies.This is because relatively little is known about the fate inthe environment of many of the FAM markers listed inTables 1 and 2.

Knowledge has begun to accumulate regarding the fateand persistence of Bacteroidales in environmental waters(Okabe and Shimazu 2007; Seurinck et al. 2005a; Walterset al. 2009; Walters and Field 2009; Bae and Wuertz 2009;Saunders et al. 2009; Dick et al. 2010). It is well knownthat anaerobic Bacteroidales cells have a limited survival inaerobic environments outside their host including mostsurface waters and groundwater. For example, seeded

10

100

103

1 2 3 4 5

Inte

stin

al e

nter

ococ

ci

(cel

ls 1

00 m

l-1)

Day

Fig. 2 Example of temporal variations in concentrations of intestinalenterococci at a recreational beach. Water samples were collected fivetimes with 10-min intervals for five consecutive days during a periodwith calm weather and no precipitation (redrawn from Roslev et al.2010). Arbitrary detection limits for molecular markers in enterococcicorresponding to 10, 100, and 1,000 cells 100 ml−1 are shown forcomparison (dotted lines)


Bacteroidales cells lost culturability after few days in riverwater (Okabe and Shimazu 2007), seawater (Okabe andShimazu 2007), and groundwater-based drinking water(Saunders et al. 2009). In contrast, PCR amplifiable nucleicacid from Bacteroidales can persist for weeks in water(Okabe and Shimazu 2007; Seurinck et al. 2005a; Walterset al. 2009; Walters and Field 2009; Bae and Wuertz 2009;Saunders et al. 2009; Dick et al. 2010). It follows fromthese studies, that environmental PCR detection of Bacter-oidales markers mainly target nucleic acids in “viable butnonculturable cells” and/or nucleic acids associated with orreleased from dead cells.

Okabe and Shimazu 2007 studied persistence of human-,cow-, and pig-associated Bacteroidales markers in river andseawater. No major differences in persistence as determinedby qPCR were observed in river water at 10°C for thethree markers types with decay rates between −0.27and −0.32 day−1. The results suggested that Bacteroidalesmarkers persisted longer at low temperature, high salinity,and in filtered water as compared with nonfiltered water(Okabe and Shimazu 2007). The decay rates for Bacter-oidales markers in river water at 10°C were somewhatgreater than the corresponding decay rate determined forculturable fecal coliforms (−0.02 day−1). In contrast,Walters et al. (2009) observed a decay rate for the human-associated Bacteroidales marker HF183 in seawater at 17°Cthat was lower than values estimated for culturableenterococci (−0.26 and −0.91 day−1, respectively). Ahuman-associated Bacteroidales marker was detected forup to 28 days in seawater microcosms kept in the dark, butthe decay rate was affected strongly by sunlight with analmost five times shorter persistence in light compared withdark conditions (Walters et al. 2009). Interestingly, a lesspronounced effect of sunlight on Bacteroidales markerpersistence in water has been observed in other studies (Baeand Wuertz 2009; Walters and Field 2009; Dick et al.2010). Walters and Field (2009) observed decay ratesbetween −1.4 and −1.7 day−1 for human Bacteroidalesmarkers in river water microcosms exposed to sunlight at13°C whereas the corresponding values for dark micro-cosms ranged between −1.2 and −1.4 day−1. Bae andWuertz (2009) studied persistence of human, cow and dogassociated Bacteroidales markers in seawater microcosms,and found relatively limited variation in persistence amonghuman- and animal-associated markers in light and darkconditions (T99 between 160 and 193 h). The average T99

value for the Bacteroidales markers in seawater wasestimated to 177 h (Bae and Wuertz 2009). Dick et al.(2010) also observed a limited effect of sunlight onpersistence of human-associated Bacteroidales markersbut the apparent T99 was somewhat lower in the freshwatermicrocosms (37–61 h) compared with the seawater micro-cosms studied by Bae and Wuertz (2009). Bae and Wuertz

(2009) also used PMA treatment combined with qPCR todifferentiate between intact cells and dead cells or extra-cellular DNA. The authors concluded that this approachmight be explored further to estimate the age of fecalpollution from different contamination sources (Bae andWuertz 2009).

It appears that persistence of the Bacteroidales markersin freshwater, seawater, and drinking water determined byqPCR is often comparable to survival of traditional fecalindicators such as E. coli and enterococci determined bycultivation (Walters and Field 2009; Walters et al. 2009;Bae and Wuertz 2009; Saunders et al. 2009; Dick et al.2010). The persistence of Bacteroidales markers is alsocomparable to environmental persistence of molecular targetsin bifidobacteria such as B. adolescentis and B. dentium(Bonjoch et al. 2009). These findings underline the potentialfor using Bacteroidales and other prokaryotic FAM markersfor FST. However, studies have shown that ratios betweenhost associated molecular markers can change over time insome water types (Walters and Field 2009). These authorsshowed that ruminant Bacteroidales markers persisted longerin freshwater microcosms compared with human-associatedBacteroidales markers (Walters and Field 2009). These

Fig. 3 Potential roles of fecal pollution source tracking and pathogensource tracking in water quality management


results emphasize that even though individual sourceassociated markers can be detected quantitatively in envi-ronmental samples by qPCR, a differential persistence of themarkers can complicate a quantitative estimation of thecontribution from each source (Field and Samadpour 2007;Walters and Field 2009). Hence, use of marker ratios holdsmany promises, but the environmental fate and persistence ofindividual FAM markers should be studied further to allow acomprehensive usage in FST studies.

Future fecal pollution source tracking

Because environmental waters often contain traces of fecalpollution from several source groups (Fig. 1), it is notsurprising to detect molecular markers from a range ofdifferent source types during FST studies (e.g., Martellini etal. 2005; Gourmelon et al. 2007, 2010a; Ahmed et al.2008a; Reischer et al. 2008; Kortbaoui et al. 2009;Mieszkin et al. 2009; Stapleton et al. 2009; Roslev et al.2010). However, in many real world FST studies, theultimate goal of the effort is to accurately identify thequantitatively most important pollution source(s). Irrespec-tive of the source associated markers selected for FST it istherefore recommended that several quantitative markersare applied for each host category i.e., that identification ofthe relative contribution from human and animals are basedon a marker toolkit with multiple quantitative geneticmarkers for each host type. A quantitative multi-markerapproach may then benefit from recent advances in micro-array technology where a large number of targets can bescreened simultaneously in different array formats (Soule etal. 2006; Santo Domingo et al. 2007; Girones et al. 2010).Application of new markers types such as mtDNA may alsoallow establishment of hierarchical formats with over-lapping specificity (e.g., hierarchical animal, ruminant,and cattle markers). A matrix with quantitative results orratios from a combination of suitable markers couldsubsequently be used to construct better predictive FSTmodels (Ballesté et al. 2010; Wang et al. 2010).

It is also important to acknowledge that no single FSTmethod has so far emerged as sufficiently superior tobecome the “gold standard” in the field (Stewart et al.2007). Many FST studies therefore benefit from a polypha-sic (multi-tiered) approach that also includes parametersother than source associated markers. A polyphasic ap-proach may include targeted sampling for conventionalfecal indicator bacteria, cultivation of host associated fecalprokaryotes and bacteriophages, combinations of sourceassociated chemical and genetic markers, land use charac-teristics, selected ecological parameters, and differentcomputational methods (Blanch et al. 2006; Noble et al.2006; Field and Samadpour 2007; Santo Domingo et al.

2007; Stoeckel and Harwood 2007; Reischer et al. 2008;Witty et al. 2009; Stapleton et al. 2009; Ballesté et al. 2010;Gourmelon et al. 2010b). Furthermore, sampling strategiesshould be considered carefully to obtain samples that bestpossible represent the water body in question. This couldmean including hydrological and physicochemical sensors,time and flow integrated automated sampling devices, andperhaps use of filtrating fauna as natural biosamplers toobtain more representative samples (Reischer et al. 2008;Stadler et al. 2008; Roslev et al. 2009, 2010).

Current microbiological quality assessment of environ-mental waters is widely based on the concept of fecalindicator bacteria. Although this concept has clearly reducedhealth risks in many countries, the fecal indicator approachmay in the future be combined with or replaced by moredirect monitoring of genuine pathogenic microorganisms(Stewart et al. 2007; Sadowsky et al. 2007; Field andSamadpour 2007; Santo Domingo et al. 2007; Girones et al.2010; Jofre and Blanch 2010). As a consequence, currentfecal pollution source tracking techniques may evolveaccordingly to focus more on actual tracking of pathogenicmicroorganisms in the environment (“pathogen source track-ing”). A stepwise combination of rapid screening methods,and detailed source tracking techniques could then form thebasis for future management of environmental water qualityincluding improved microbial risk assessment (Fig. 3).

Acknowledgements We thank Mari Rodríguez de Evgrafov andSøren Bastholm for valuable discussions. This work was supported bythe Danish EPA (ColiBox), and the Danish Council for StrategicResearch, the project SENSOWAQ—Sensors for Monitoring andControl of Water Quality.

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state of the art molecular markers for fecal pollution source tracking in water

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