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Host-specific Genetic Markers of Fecal Bacteria for Fecal Source
Journal of Food: Microbiology, Safety & Hygiene

Journal of Food: Microbiology, Safety & Hygiene
Open Access

ISSN: 2476-2059

+44 1478 350008

Review Article - (2018) Volume 3, Issue 1

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Host-specific Genetic Markers of Fecal Bacteria for Fecal Source Tracking in Food and Water

Guolu Zheng1* and Zhenyu Shen2
1Cooperative Research Program, Lincoln University, Jefferson, MO 65101, USA
2Veterinary Diagnostic Laboratory, University of Missouri, Columbia, MO 65211, USA
*Corresponding Author: Guolu Zheng, Lincoln University, Jefferson City, 211 Frost Hall, 902 Chestnut Street MO 65101, USA, Tel: 573-681-5964, Fax: 573-681-5955 Email:

Abstract

Fecal pollution in food and water is the major cause of disease outbreaks, so accurate identification of this fecal pollution in these locations is critical to prevent such outbreaks. Fecal source tracking (FST) is an effective tool to identify the sources (human vs. animal) of any particular instance of fecal pollution. Many FST technologies were developed over the past two decades and used mainly to monitor and manage water quality, but their applications have recently attracted more attention in food systems. With the great advances of technologies in DNA sequencing and related bioinformatic tools, a significant number of novel genetic markers of fecal indicator bacteria (FIB) have been identified to be associated with particular host species, and thus, suitable for FST. The host-specific genetic markers were originally limited to 16S rDNA sequences of FIB, which are relatively conserved. Novel genetic markers include genes of the host-bacterium interaction and intervening sequences within the 16S- and 23S-rDNA of fecal bacteria. However, most of the genetic markers were only evaluated in laboratory settings, and their compete values in the monitoring and management of food and water safety need further assessments in the field.

Keywords: Fecal source tracking (FST); Food safety; Water safety; Fecal contamination; Monitoring; Foodborne illness; Waterborne illness; Fecal indicator bacteria (FIB)

Introduction

The feces of human and food animals are the main sources of pathogens associated with foodborne and waterborne illness outbreaks. Not only ill people and animals can shed pathogens in their feces; healthy animals can be reservoirs of pathogens. Cattle have been determined to be the major reservoir for enterohemorrhagic Escherichia coli O157:H7 [1], chicken and turkey for Salmonella spp. and Campylobacter jejuni [2], and pig for Campylobacter coli [3]. Thus, one of the main concerns regarding the microbiological quality of food and water is the presence of pathogens associated with human and animal feces. It is impossible to monitor all pathogens on a routine basis because each of these microorganisms requires a specific test method. Therefore, fecal indicator bacteria (FIB) were introduced to evaluate the food and water systems to assess the microbiological quality and to predict the presence of pathogens therein [4,5].

While the presence of FIB is indicative of fecal pollution, it provides no information about the sources of pollution. That information is needed so that effective steps can be taken for food and water safety. To identify fecal pollution sources, fecal source tracking (FST) or microbial source tracking (MST) methods have been developed. Although current FST methods are primarily used in the field of water monitoring and management to reduce outbreaks of waterborne diseases, it is suggested that MST methods can be applied to track fecal pollution sources in food systems to better prevent the contamination of food by feces-carrying pathogens [6]. The principle of FST is to compare the characteristics of microorganisms isolated from the polluted water with those of fecal microorganisms in host-known feces [6,7]. The assumption of FST is that the distribution of fecal microorganisms in human or animal intestinal tracts is not random, i.e., some fecal microorganisms are host-specific. Obviously, the goal of FST is to determine which fecal microorganism(s) or associated trait(s) (i.e., phenotypic or genotypic) is/are specific to an animal species.

Over the last two decades, FST technologies have expanded greatly, from phenotype-bases to genotype-bases, from detection of standard FIB to alternative FIB, from differentiations of bacterial 16S-RNA genes to other less conserved genes, and from fecal bacteria to viruses. There have been several comprehensive review papers on FST technologies and their application in the monitoring and management of water quality [6-11]. This review evaluates the recent developments in FST technologies, with an emphasis on those using host-specific genetic markers of fecal bacteria and their application in the management of food and water safety.

Fecal Indicator Bacteria

Historically, total and fecal coliforms, E. coli , and enterococci bacteria were used as standard FIB in many countries to monitor the microbiological quality of food and water. Recently, E. coli has been proven to be a better fecal indicator bacterium than total and fecal coliforms for fresh water, while enterococci are optimal when testing marine water [12]. The Beaches Environmental Assessment and Coastal Health (BEACH) Act of 2000 requires states with marine or Great Lakes to adopt enterococci as the fecal indicator to monitor recreational water quality [13].

However, the standard FIB (E. coli and enterococci) have at least two drawbacks. First, they are found in relatively low numbers in human and animal intestinal tracts (feces), resulting in low detection sensitivity. In human and animal intestines, prokaryotes are the dominant microbes, with approximately 1011 cells/g feces [14], but the phylum Proteobacteria, which contains E. coli , only represents less than 2% of the prokaryotic populations. Second, under certain conditions, E. coli and enterococci can survive and multiply in water environments [15,16], beach sand, or sediments [17-20], resulting in false alarms of fecal pollution. To circumvent the limitations of the standard FIB, other fecal microorganisms, mainly obligate anaerobic bacteria, have been proposed as alternative FIB, including bacteria in Bacteroidales [21-23], Catellicoccus spp. [24], Faecalibacterium spp. [25-28], Lactobacillus spp. [29], Bifidobacterium spp. [30-32] Brevibacterium spp. [33], Rhodococcus spp. [34], and Desulfovibrio spp. [35]. Among the aforementioned alternative FIB, bacteria of Bacteroidales is the most intensively studied and those of Faecalibacterium spp. and Brevibacterium spp. are the most recently reported.

The alternative FIB is relatively abundant in human or animal feces compared with the standard FIB. For example, about 25% of bacteria in the human colon belong to the Bacteroides spp. [36]. The Faecalibacterium is the most abundant bacterium in chicken feces [37]. In human infant stool samples, the Bifidobacteriales order accounts for 80.6% of bacterial populations [38]. Lactobacillales is among the top six most abundant bacterial groups in swine feces [39]. Bacteria of the genera Faecalibacterium , Bacteroides , and Bifidobacterium , and some members of the genera Lactobacillus and Prevotella , are obligate anaerobes. They can only survive for a short time and hardly multiply, if any, in non-host environments, due to their low oxygen tolerance. Their fast decay in the environment makes these anaerobes suitable for the detection of fresh and extensive fecal pollution [40].

Other biological fecal indicators have been proposed and used mainly in tracking the sources of fecal pollution. These include hostassociated archaea and viruses as well as the mitochondria of the epithermal cells of human and animal intestinal tracts. However, the genetic indicators of these categories are beyond the scope of this review

Genetic Marker Based FST Methods

With advances in science and the advent of new technologies, many FST methods have been developed and used to track fecal pollution in water. Categorically, these methods can be divided into librarydependent and library-independent methods [6]. The librarydependent methods require the construction of a library or database containing a set of either phenotypic or genotypic characteristics of FIB, commonly E. coli or enterococci bacteria, isolated from the feces of known host sources. To determine the fecal sources of a fecespolluted site, strains of E. coli or enterococci are isolated from the site and their phenotypic or genotypic characteristics are then compared with those in the library to find the matches [6]. On the other hand, library-independent FST methods do not require a reference library, relying instead on the detection of host-specific markers associated with FIB or animal feces.

A host-specific marker can be either a genetic or chemical marker that is unique to the feces of an animal species or human being. A host-specific genetic marker refers to a unique nucleic acid (i.e., DNA or RNA) sequence that is exclusively or strongly associated with particular host sources. This sequence can be either a nucleic acid sequence of fecal cells (e.g., bacteria and viruses) or of host cells. A number of chemicals and metabolites have been found to be associated with humans or animals because of different life styles and digestion systems. Chemical marker-based methods have been comprehensively reviewed recently [41], so this review will focus on the host-specific genetic makers of fecal bacteria.

Host-specific Genetic markers of Fecal Bacteria

Theoretically, genes in microorganisms involved in host-microbe interactions are ideal potential host genetic markers. However, most fecal microorganisms are uncultured, and their genomes remain largely unknown. For many years, bacterial 16S rDNA were the only genetic markers used in FST.

16S rDNA

The pioneer work of developing host-specific markers derived from16S rDNA sequences of fecal bacteria is credited to research by Field and others [42]. Since 2000, a considerable number of hostspecific genetic markers for the detection of major sources of fecal pollution have been identified in 16S rDNA Table 1. Among them, host-specific genetic markers of Bacteroidales 16S rDNA have been the most intensively studied, including through field studies, as these markers are the most promising for FST [43]. Therefore, 16S rDNA has become the most widely used genetic marker in FST.

Target Host Target bacteria Marker Reference
Human Bacteroidales HF134F, HF183F Walters and Field  [85]
HuBac Layton et al. [49]
BacHum Kildare et al. [86]
BacH Reischer et al. [87]
Human-Bac1 Okabe et al. [51]
BuniF2, BfragF1, BvulgF1, PcopriF1, BsteriF1, BthetaF2 Haugland et al. [88]
Bifidobcterium adolescents and B. dentium Bi-AOD, Bi-DEN Bonjoch et al. [30]
Bifidobcterium spp. HM Gómez-Doñate et al. [89]
Faecalibacterium spp. HFB-F3/R5 Zheng et al. [28]
Cattle Bacteroidales CF128F, CF193F Bernhard and Field [21]
BacCow Kildare et al. [86]
Cow-Bac Okabe and Shimazu [90]
CI125f Stricker et al. [91]
YCF Jeong et al. [92]
Bifidobcterium spp. CW Gómez-Doñate et al. [89]
Deer/Elk Bacteroidales EF447F/990R Dick et al. [93]
Dog Bacteroidales DF475F Dick et al. [93]
Bacteroidales BacCan Kildare et al. [86]
Duck Bacteroidales CG-Prev f5 Lu et al. [94]
Desulfovibrio-like E2 Devane et al. [35]
Faecalibacterium Sun et al. [27]
Goose Bacteroidales CGOF1-Bac, CGOF2-Bac Fremaux et al. [95]
CG-Prev f5 Lu et al. [94]
Gull Catellicoccus marimammalium Gull-2, Gull-4 Lu et al. [24]
Herbivore Rhodococcus coprophilus Savill et al. [34]
Horse Bacteroidales HoF597 Dick et al. [96]
Pig Bacteroidales PF163F Dick et al. [96]
Pig-Bac1, Pig-Bac2 Okabe et al. [51]
Pig-Bac, Pig-2-Bac Mieszkin et al. [97]
Bifidobcterium sp. PG Gómez-Doñate et al. [89]
Faecalibacterium Duan et al. [25]
Lactobacillus sp. OTU171 Konstantinov et al. [98]
Poultry Bifidobcterium spp. PL Gómez-Doñate et al. [89]
Brevibacterium avium LA35 Weidhaas et al. [33]
Faecalibacterium spp. IVS-p Shen et al. [26]
Ruminants Bacteroidales BacR Reischer et al. [99]
Rum-2-Bac Mieszkin et al. [100]

Table 1: Host-specific genetic markers derived from bacterial 16S rDNA.

Although 16S rDNA is highly conserved across species of the same genus of bacteria, variable regions of 16S rDNA provide a tool to discriminate between bacteria at the subspecies level, according to their host [44]. In addition, 16S rDNA has multiple copies in most bacterial cells [45]. For example, each E. coli cell has seven copies of almost identical 16S rDNA in its genome [46]. Multiple numbers of markers in cells can increase the detection sensitivity.

Recently, studies of the physiology, ecology, and biodiversity of intestinal flora have resulted in an enormous number of 16S rDNA sequences of microorganisms from human and animal guts or feces. Those sequences are available via public databases, such as the Ribosomal Database Project (RDP) [47] and Genbank [48]. By comparing these sequences using bioinformatics approaches, hostspecific markers can be found in 16S rDNA [26].

However, 16S rDNA-based genetic markers are not without drawbacks. Cross-reaction is a common issue [49-52].

Bacterial virulent genes

Several virulent genes of pathogenic strains of the standard FIB have been reported to be host associated Table 2. The heat labile toxin IIa (LTIIa) gene and the heat stable toxin II (STII) gene in enterotoxigenic E. coli (ETEC) have been found to be responsible for diarrheal diseases of cattle and pig [53]. Sequence analysis of LTIIa and STII in addition to PCR reactions showed the two markers were 100% specific to cattle and pig feces, respectively [54-56]. Other studied but less successful FST markers in the toxin genes of E. coli include a heat-stable enterotoxin gene (STIb) for humans [57], a major fimbrial subunit gene (ralG) for rabbits, a P fimbrial adhesin gene (papG) for dogs, and a temperature-sensitive hemagglutinin gene (tsh) for birds [58].Seven goose/duck specific genetic markers were identified in E. coli isolates by the suppression subtractive hybridization (SSH) method, and their specificities were tested with the colony hybridization method. Among the markers, sequences of GA9 and GC11 are homologues to the genes encoding type III secretion proteins in E. coli O157:H7, and both GB2 and GE11 have been found to encode adhesion-like proteins in E. coli O157:H7 [59].

Target host Target bacteria Target gene Gene function Reference
Human E. coli STIb Enterotoxin Field et al. [57]
Enterococcus faecium esp Surface protein Scott et al. [64]
Bird E. coli tsh Hemagglutinin Jiang et al. [58]
Cattle E. coli LTIIa Enterotoxin Khatib et al. [55]
Co2 Putative adhesion Gomi et al. [101]
Dog E. coli papG P fimbrial adhesin Jiang et al. [58]
Duck/goose E. coli GA9, GG11 Putative type III secretion proteins Hamilton et al. [59]
GB2, GE11 Putative adhesion-like proteins Hamilton et al. [59]
Pig E. coli STII Enterotoxin Khatib et al. [56]
Ch7 Putative adhesin Gomi et al. [101]
Ch13 Putative minor fimbrial Gomi et al. [101]
Rabbit E. coli ralG Fimbrial Jiang et al. [58]
GB2, GE11 Adhesion-like proteins Hamilton et al. [59]

Table 2: Host-specific genetic markers derived from bacterial virulence genes

In Enterococcus faecium , a putative virulence gene encoding the enterococcal surface protein (esp) was also proposed for identification of human fecal pollution, with controversial results [60-64]. The detection sensitivity of virulent genes has also been an issue because healthy humans and animals usually do not shed such pathogens in their feces, and the pathogens are present in low numbers in ill humans and animals.

Bacterium-host interaction genes as markers

As host-specific genetic markers, genes involved in bacterium-host interactions are superior to other genetic markers. Although knowledge of such genes is very limited for most intestinal microbes, studies have identified some host-specific genetic markers that might be associated with bacterium-host interactions, primarily through gene annotations Table 3.

Target host Target bacteria Target gene Gene function Reference
Human Bacteroides thetaiotaomicron α-1-6, mannanase Degradation of polysaccharides Yampara-Iquise et al. [67]
hum39 Putative outer membrane protein Shanks et al. [68]
hum163 Putative remodeling of bacterial surface polysaccharides and lipopolysaccharides Shanks et al. [68]
hum336 Putative efflux protein precursor Shanks et al. [68]
E. coli ycjM Putative degradation of polysaccharides Deng et al. [102]
H8 Sodium/hydrogen exchanger precursor Gomi et al. [101]
H12 Putative phage protein Gomi et al. [101]
H14 ATP/GTP-binding protein Gomi et al. [101]
Cattle Bacteroidales Bac1, Bac2, Bac3 Putative membrane secretion protein Shanks et al. [69]
E. coli Co3 Putative integrase Gomi et al. [101]
Pig E. coli P1 F1C fimbrial usher Gomi et al. [101]
P3 DNA fragment Gomi et al. [101]
P4 Hypothetical protein Gomi et al. [101]
Poultry E. coli Ch9 Hypothetical protein Gomi et al. [101]
Ch12 Type I restriction-modification system Gomi et al. [101]

Table 3: Host-specific genetic markers derived from bacterium-host interaction or other genes

Bacteroides thetaiotaomicron is a symbiont living in the human gut, and it contributes to degrading indigestible polysaccharides to provide calories and maintain the health of the intestinal ecosystem. The gene encoding α-1,6-mannanase is believed to be involved in this degradation [65], and this gene has been found to be highly associated with human feces [66,67].

There are several genetic markers reportedly associated with domestic animals. By the method of genome fragment enrichment of fecal metagenomes, Shanks et al. [68] identified 26 genetic markers in B. thetaiotaomicron highly associated with humans. Three of them showed high similarities to the genes encoding outer membrane protein (hum39), genes encoding the outer membrane efflux protein precursor (hum336), and genes of a protein associated with remodeling bacterial surface polysaccharides and lipopolysaccharides (hum163) [68]. With the same metagenomic approach, a series of cattle-specific markers were identified, and three Bacteroidales-like markers (i.e., Bac1, Bac2, and Bac3) were randomly selected for PCR assay development. The markers are predicted to be associated with membrane protein and secretion [69].

Other bacterial genes as markers

There are various bacterial genes, whose functions are unknown or not directly involved in bacterium-host interactions, found to be associated with particular host sources Table 3. With DNA microarray hybridization, Soule et al. [70] identified eight genetic markers in the Enterococcus genome specific to cattle (M15 and M19), two to elk/ deer (M40 and M48), and four to humans (M67, M68, M77, and M81). M15 is homologous to the helicase gene, M40 belongs to the MutS2 family, M68 is related to carbohydrate kinase PfkB, M77 encodes a transcriptional regulator, and the other markers do not have significant similarity to function-known genes [70].

Using the metagenomic method, Lu et al. [71] identified 21 chickenspecific genetic markers having various predictive functions, including cellular process, metabolism, and information storage. The three markers (i.e., CP2-9, CP3-49, and CB-R2-42) that performed best were associated with bacterial metabolism.

Recently, research by Zheng et al. [26] used the approach of bioinformatics in the comparative analysis of 7,458 sequences of Faecalibacterium 16S rDNA, reportedly associated with human and animal species. They identified an intervening sequence (IVS), IVS-p, within Faecalibacterium 16S rDNA. IVS-p appeared to be specific to poultry (chicken and turkey) feces. IVS was considered to be a bacterial adaptation to a close working relationship with the host species [72].

Applications of FST in Food Systems

Seafood

Using FST technologies in tracking fecal pollution sources in foods has attracted increasing interest recently [73-75]. Pathogens carried by feces can enter the food production chain through various paths, from farm to table. Food crops can be polluted in the field, by untreated human or animal manure used as fertilizer or by feces-polluted irrigation water. Pathogens can enter a water body through point (i.e. specific) pollution sources, such as effluent from wastewater treatment plants, lagoons, and septic tanks, or through non-point (i.e. nonspecific) pollution sources, such as wildlife or storm runoff from urban and agricultural areas [76]. FST methods were first introduced into aquaculture and then into vegetable production for food safety management.

Molluscan shellfish, including oysters, clams, mussels, and scallops, are an important food commodity in the United States. Shellfish can accumulate waterborne pathogens in their bodies, posing health risks to consumers [7]. Mauffret et al. [77] investigated the presence of hostspecific genetic markers in oysters, cockles, and clams grown in water artificially or naturally polluted by feces. HF183 (human-specific, Bacteroides ), Rum2Bac (cattle-specific, Bacteroides ), Swine1Bac (pigspecific, Bacteroides ), and Gull2 (gull-specific, C. marimallium ) were quantified in 100% of intravalvular liquid samples of oysters living in artificially polluted water, while HF183 and Rum2Bac were quantified in 31% and 23% of those shellfish living in naturally polluted water. However, none of the host-specific markers was detected in the cockle or clam. This study suggests that FST methods can be used to manage at least some seafood products.

Fresh produce

Fresh produce has been associated with rising numbers of foodborne disease outbreaks in the United States, as the products are increasingly consumed as a part of healthy diets. Most of the pathogens causing outbreaks are of fecal origin. Feces-polluted irrigation water and/or improperly composted human/animal wastes can be the sources of fecal pollution in produce [78,79]. The work of Ravaliya et al. [74] in Northern Mexico has provided evidence that Bacteroidales 16S rDNA markers may serve to distinguish both general and humanfeces contamination in the production environment. Furthermore, FST can be used for the rapid identification of human or livestock fecal contamination in fresh produce [73]. It is generally believed that FST technologies can be a powerful tool to manage food safety in produce production.

Conclusion

Although 16S rDNA are currently the dominant FST genetic markers, their high degree of conserved sequences makes the crossreaction an inherent issue. Genetic markers with higher hostspecificity may be found in microbial genes involved with microbehost interactions. In fact, the known human-feces marker, the α-1,6- mannanase gene of B. thetaiotaomicron , is highly associated with humans [66,67]. However, the determination of microbe-host interaction genes relies much on the current knowledge of the microbial genome, which excludes most fecal microbes. It has been proposed to use the ribosomal intervening sequence (IVS) of fecal bacteria as genetic markers for FST, which would combine the advantages of the enormous data about 16S rDNA available in public databases with the desirable host specificity of the genes (DNA fragments) involved in microbe-host interactions. Ribosomal IVSs are insertion sequences in the 16S or 23S rDNA of prokaryotes. They are post-transcriptionally excised by RNase III without religation, which causes rRNA fragmentation [80]. The fragmentation may enhance the rRNA degrading rate by creating more targets for certain ribonucleases [80]. It is known that bacterial cells adjust their rRNA levels based on environmental changes. The fact that IVSs are mainly found in symbionts and pathogens of eukaryotic hosts also supports the conclusion that IVSs may contribute to the host-microbe interaction [72]. The presence of ribosomal IVS in prokaryotes is relatively uncommon but does occur in many bacterial species [81,82]. IVS is more commonly found in 23S rDNA than in 16S rDNA [80,81,83]. With the development of next-generation sequencing (NGS) methods, a vast amount of 23S rDNA data has been increasingly accumulated, which will facilitate the identification of novel, host-specific IVSs in microbial 23S rDNA.

Phylogenetic analysis of fecal microbes from different host species is a common method for the identification of host-specific genetic markers. This method is based on alignment analysis against the DNA sequences of potential FST molecules of the target fecal microorganisms. Before the availability of NGS technologies, DNA cloning and Sanger DNA sequencing were the necessary steps for phylogenetic analysis. However, DNA cloning is time-consuming and subject to cloning bias. NSG provides a high-throughput, timeefficient, and cost-effective tool for the identification of host-specific FST markers.

The major challenge of using FST technologies for the safety of water and food is that the correlation between the FST markers/ indicators and pathogens/diseases has not been well established [6,84]. Nevertheless, the future of FST should not be underestimated, especially in the area of food safety.

Acknowledgement

This work was supported by a USDA NIFA’s Evans-Allen Grant

References

  1. Pruimboom-Brees IM, Morgan TW, Ackermann MR, Nystrom ED, Samuel JE, et al. (2000) Cattle lack vascular receptors for Escherichia coli O157:H7 Shiga toxins. Proc Natl Acad Sci U S A 97: 10325-10329.
  2. Bryan FL, Doyle MP (1995) Health risks and consequences of Salmonella and Campylobacter jejuni in raw poultry. J Food Prot 58: 326-344.
  3. Weijtens MJ, Bijker PG, Van der Plas J, Urlings HA, Biesheuvel MH (1993) Prevalence of campylobacter in pigs during fattening; an epidemiological study. Vet Q 15: 138-143.
  4.   Jay JM, Loessner MJ, Golden DA (2005) Modern food microbiology, Springer, New York.
  5. Mara D, Horan NJ (2003) Handbook of water and wastewater microbiology, Academic Press.
  6.  Hagedorn C, Blanch AR, Harwood VJ (2011) Microbial source tracking: Methods, applications, and case studies, Springer, New York.
  7. Santo Domingo JW, Sadowsky MJ (2007) Microbial source tracking, ASM Press, Washington, D.C.
  8. Field KG, Samadpour M (2007) Fecal source tracking, the indicator paradigm, and managing water quality. Water Res 41: 3517-3538.
  9. Chase E, Hunting J, Staley C, Harwood VJ (2012) Microbial source tracking to identify human and ruminant sources of faecal pollution in an ephemeral Florida river. J Appl Microbiol 113: 1396-1406.
  10. Meays CL, Broersma K, Nordin R, Mazumder A (2004) Source tracking fecal bacteria in water: A critical review of current methods. J Environ Manage 73: 71-79.
  11. Scott TM, Rose JB, Jenkins TM, Farrah SR, Lukasik J (2002) Microbial source tracking: Current methodology and future directions. Appl Environ Microbiol 68: 5796-5803.
  12. US EPA (2005) Microbial source tracking guide document, U.S. Environmental Protection Agency, Office of Research and Development, National Risk Management Research Laboratory, Cincinnati, OH.
  13. Beaches Environmental Assessment and Coastal Health Act of 2000 (October 10, 2000) Public Law 106-284.
  14. Whitman WB, Coleman DC, Wiebe WJ (1998) Prokaryotes: The unseen majority. Proc Natl Acad Sci U S A 95: 6578-6583.
  15. Byappanahalli M, Fowler M, Shively D, Whitman R (2003) Ubiquity and persistence of Escherichia coli in a Midwestern coastal stream. Appl Environ Microbiol 69: 4549-4555.
  16. Pote J, Haller L, Kottelat R, Sastre V, Arpagaus P, et al. (2009) Persistence and growth of faecal culturable bacterial indicators in water column and sediments of Vidy Bay, Lake Geneva, Switzerland. J Environ Sci 211: 62-69.
  17. Byappanahalli M, Fujioka R (2004) Indigenous soil bacteria and low moisture may limit but allow faecal bacteria to multiply and become a minor population in tropical soils. Water Sci Technol 50: 27-32.
  18. Byappanahalli MN, Whitman RL, Shively DA, Sadowsky MJ, Ishii S (2006) Population structure, persistence, and seasonality of autochthonous Escherichia coli in temperate, coastal forest soil from a Great Lakes watershed. Environ Microbiol 8: 504-513.
  19. Byappanahalli MN, Whitman RL, Shively DA, Ting WT, Tseng CC, et al. (2006) Seasonal persistence and population characteristics of Escherichia coli and enterococci in deep backshore sand of two freshwater beaches. J Water Health 4: 313-320.
  20. Solo-Gabriele HM, Wolfert MA, Desmarais TR, Palmer CJ (2000) Sources of Escherichia coli in a coastal subtropical environment. Appl Environ Microbiol 66: 230-237.
  21. Bernhard AE, Field KG (2000) A PCR assay to discriminate human and ruminant feces on the basis of host differences in Bacteroides-Prevotella genes encoding 16S rRNA. Appl Environ Microbiol 66: 4571-4574.
  22. Carson CA, Christiansen JM, Yampara-Iquise H, Benson VW, Baffaut C, et al. (2005) Specificity of a Bacteroides thetaiotaomicron marker for human feces. Appl Environ Microbiol 71: 4945-4949.
  23. Seurinck S, Defoirdt T, Verstraete W, Siciliano SD (2005) Detection and quantification of the human-specific HF183 Bacteroides 16S rRNA genetic marker with real-time PCR for assessment of human faecal pollution in freshwater. Environ Microbiol 7: 249-259.
  24. Lu J, Santo Domingo JW, Lamendella R, Edge T, Hill S (2008) Phylogenetic diversity and molecular detection of bacteria in gull feces. Appl Environ Microbiol 74: 3969-3976.
  25. Duan C, Cui Y, Zhao Y, Zhai J, Zhang B, et al. (2016) Evaluation of Faecalibacterium 16S rDNA genetic markers for accurate identification of swine faecal waste by quantitative PCR. J Environ Manage 181: 193-200.
  26. Shen Z, Duan C, Zhang C, Carson A, Xu D, et al. (2013) 16S rDNA to identify poultry Using an intervening sequence of Faecalibacterium feces. Water Res 47: 6415-6422.
  27. Sun D, Duan C, Shang Y, Ma Y, Tan L, et al. (2016) Application of Faecalibacterium 16S rDNA genetic marker for accurate identification of duck faeces. Environ Sci Pollut Res Int. 23: 7639-7647.
  28. Zheng G, Yampara-Iquise H, Jones JE, Carson AC (2009) Development of Faecalibacterium 16S rRNA gene marker for identification of human faeces. J Appl Microbiol 106: 634-641.
  29. Konstantinov SR, Smidt H, de Vos WM (2005) Representational difference analysis and real-time PCR for strain-specific quantification of Lactobacillus sobrius sp. nov. Appl Environ Microbiol 71: 7578-7581.
  30. Bonjoch X, Balleste E, and Blanch AR (2004) Multiplex PCR with 16S rRNA gene-targeted primers of Bifidobacterium spp. to identify sources of fecal pollution. Appl Environ Microbiol 70: 3171-3175.
  31. Bonjoch X, Lucena F, Blanch AR (2009) The persistence of bifidobacteria populations in a river measured by molecular and culture techniques. J Appl Microbiol 107: 1178-1185.
  32. Gourmelon M, Caprais MP, Mieszkin S, Marti R, Wery N, et al. (2010) Development of microbial and chemical MST tools to identify the origin of the faecal pollution in bathing and shellfish harvesting waters in France. Water Res 44: 4812-4824.
  33. Weidhaas JL, Macbeth TW, Olsen RL, Sadowsky MJ, Norat D, et al. (2010) Identification of a Brevibacterium marker gene specific to poultry litter and development of a quantitative PCR assay. J Appl Microbiol 109: 334-347.
  34. Savill MG, Murray SR, Scholes P, Maas EW, McCormick RE, et al. (2001) Application of polymerase chain reaction (PCR) and TaqMan PCR techniques to the detection and identification of Rhodococcus coprophilus in faecal samples. J Microbiol Methods 47: 355-368.
  35. Devane ML, Robson B, Nourozi F, Scholes P, Gilpin BJ (2007) A PCR marker for detection in surface waters of faecal pollution derived from ducks. Water Res 41: 3553-3560.
  36. Salyers AA (1984) Bacteroides of the human lower intestinal tract. Annu Rev Microbiol 38: 293-313.
  37. Bjerrum L, Engberg R, Leser T, Jensen B, Finster K, Pedersen K (2006) Microbial community composition of the ileum and cecum of broiler chickens as revealed by molecular and culture-based techniques. Poult Sci 85: 1151-1164.
  38. Turroni F, Peano C, Pass DA, Foroni E, Severgnini M, et al. (2012) Diversity of Bifidobacteria within the infant gut Microbiota. PLoS One 7: e36957.
  39. Lamendella R, Domingo JW, Ghosh S, Martinson J, Oerther DB (2011) Comparative fecal metagenomics unveils unique functional capacity of the swine gut. BMC Microbiol 11: 103.
  40. Oladeinde A, Bohrmann T, Wong K, Purucker ST, Bradshaw K, et al. (2013) Decay of fecal indicator bacterial populations and bovine-associated source tracking markers in freshly deposited cowpats. Appl Environ Microbiol 80: 110-118.
  41. Hagedorn C, Blanch, AR, Harwood VJ (2011) Microbial source tracking : methods, applications, and case studies, Springer, New York.
  42. Bernhard AE, Field KG (2000) Identification of nonpoint sources of fecal pollution in coastal waters by using host-specific 16S Ribosomal DNA genetic markers from fecal anaerobes. Appl Environ Microbiol 66: 1587-1594.
  43. Harwood VJ, Staley C, Badgley BD, Borges K, Korajkic A (2013) Microbial source tracking markers for detection of fecal contamination in environmental waters: Relationships between pathogens and human health outcomes. FEMS Microbiol Rev 38: 1-40.
  44. Zhang C, Zheng G, Xu SF, Xu D (2012) Computational challenges in characterization of bacteria and bacteria-host interactions based on genomic data. J Comput Sci Technol 27: 225-239.
  45. Acinas SG, Marcelino LA, Klepac-Ceraj V, Polz MF (2004) Divergence and redundancy of 16S rRNA sequences in genomes with multiple rrn operons. J Bacteriol 186: 2629-2635.
  46. Stevenson BS, Schmidt TM (2004) Life history implications of rRNA gene copy number in Escherichia coli. Appl Environ Microbiol 70: 6670-6677.
  47. Cole JR, Chai B, Farris RJ, Wang Q, Kulam SA, et al. (2005) The Ribosomal Database Project (RDP-II): sequences and tools for high-throughput rRNA analysis. Nucleic Acids Res 33: 294-296.
  48. Benson D, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler D L (2008) GenBank. Nucleic Acids Res 36 : 25-30.
  49. Layton A, McKay L, Williams D, Garrett V, Gentry R, et al. (2006) Development of Bacteroides 16S rRNA gene TaqMan-based real-time PCR assays for estimation of total, human, and bovine fecal pollution in water. Appl Environ Microbiol 72: 4214-4224.
  50. McLain JE, Ryu H, Kabiri-Badr L, Rock CM, Abbaszadegan M (2009) Lack of specificity for PCR assays targeting human Bacteroides 16S rRNA gene: Cross-amplification with fish feces. FEMS Microbiol Lett 299: 38-43.
  51. Okabe S, Okayama N, Savichtcheva O, Ito T (2007) Quantification of host-specific Bacteroides-Prevotella 16S rRNA genetic markers for assessment of fecal pollution in freshwater. Appl Microbiol Biotechnol 74: 890-901.
  52. Silkie SS, Nelson KL (2009) Concentrations of host-specific and generic fecal markers measured by quantitative PCR in raw sewage and fresh animal feces. Water Res 43: 4860-4871.
  53. Shin SJ, Chang YF, Timour M, Lauderdale TL, Lein DH (1994) Hybridization of clinical Escherichia coli isolates from calves and piglets in New York State with gene probes for enterotoxins (STaP, STb, LT), Shiga-like toxins (SLT-1, SLT-II) and adhesion factors (K88, K99, F41, 987P). Vet Microbiol 38: 217-225.
  54. Chern EC, Tsai YL, Olson BH (2004) Occurrence of genes associated with enterotoxigenic and enterohemorrhagic Escherichia coli in agricultural waste lagoons. Appl Environ Microbiol. 70: 356-362.
  55.  Khatib L, Tsai Y, Olson B (2002) A biomarker for the identification of cattle fecal pollution in water using the LTIIa toxin gene from enterotoxigenic Escherichia coli. Appl Microbiol Biotechnol 59: 97-104.
  56. Khatib LA, Tsai YL, Olson BH (2003) A biomarker for the identification of swine fecal pollution in water, using the STII toxin gene from enterotoxigenic Escherichia coli. Appl Microbiol Biotechnol 63: 231-238.
  57. Field KG, Chern EC, Dick LK, Fuhrman J, Griffith J, et al. (2003) A comparative study of culture-independent, library-independent genotypic methods of fecal source tracking. J Water Health 1: 181-194.
  58. Jiang SC, Chu W, Olson BH, He JW, Choi S, et al. (2007) Microbial source tracking in a small southern California urban watershed indicates wild animals and growth as the source of fecal bacteria. Appl Microbiol Biotechnol 76: 927-934.
  59. Hamilton MJ, Yan T, Sadowsky MJ (2006) Development of goose- and duck-specific DNA markers to determine sources of Escherichia coli in waterways. Appl Environ Microbiol 72: 4012-4019.
  60. Ahmed W, Stewart J, Gardner T, Powell D (2008) A real-time polymerase chain reaction assay for quantitative detection of the human-specific enterococci surface protein marker in sewage and environmental waters. Environ Microbiol 10: 3255-3264.
  61. Ahmed W, Stewart J, Powell D, Gardner T (2008) Evaluation of the host-specificity and prevalence of enterococci surface protein (esp) marker in sewage and its application for sourcing human fecal pollution. J Environ Qual 37: 1583-1588.
  62. Byappanahalli MN, Przybyla-Kelly K, Shively, DA, Whitman RL (2008) Environmental occurrence of the enterococcal surface protein (esp) gene is an unreliable indicator of human fecal contamination. Environ Sci Technol 42: 8014-8020.
  63. Layton BA, Walters SP, Boehm AB (2009) Distribution and diversity of the enterococcal surface protein (esp) gene in animal hosts and the Pacific coast environment. J Appl Microbiol 106: 1521-1531.
  64. Scott TM, Jenkins TM, Lukasik J, Rose JB (2005) Potential use of a host associated molecular marker in Enterococcus faecium as an index of human fecal pollution. Environ Sci Technol 39: 283-287.
  65. Xu J, Bjursell MK, Himrod J, Deng S, Carmichael LK, et al. (2003) A genomic view of the human-Bacteroides thetaiotaomicron symbiosis. Science 299: 2074-2076.
  66. Aslan A, Rose JB (2013) Evaluation of the host specificity of Bacteroides thetaiotaomicron alpha-1-6, mannanase gene as a sewage marker. Lett Appl Microbiol 56: 51-56.
  67. Yampara-Iquise H, Zheng G, Jones JE, Carson CA (2008) Use of a Bacteroides thetaiotaomicron-specific α-1-6, mannanase quantitative PCR to detect human faecal pollution in water. J Appl Microbiol 105: 1686-1693.
  68. Shanks OC, Domingo JWS, Lu J, Kelty CA, Graham JE (2007) Identification of bacterial DNA markers for the detection of human fecal pollution in water. Appl Environ Microbiol 73: 2416-2422.
  69. Shanks OC, Santo Domingo JW, Lamendella R, Kelty CA, Graham JE (2006) Competitive metagenomic DNA hybridization identifies host-specific microbial genetic markers in cow fecal samples. Appl Environ Microbiol 72: 4054-4060.
  70. Soule M, Kuhn E, Loge F, Gay J, Call DR (2006) Using DNA microarrays to identify library-independent markers for bacterial source tracking. Appl Environ Microbiol 72: 1843-1851.
  71. Lu J, Santo Domingo J, Shanks OC (2007) Identification of chicken-specific fecal microbial sequences using a metagenomic approach. Water Res 41: 3561-3574.
  72. Baker BJ, Hugenholtz P, Dawson SC, Banfield JF (2003) Extremely acidophilic protists from acid mine drainage host Rickettsiales-lineage endosymbionts that have intervening sequences in their 16S rRNA genes. Appl Environ Microbiol 69: 5512-5518.
  73. Lee CS, Lee J (2013) Application of host-specific source-tracking tools for rapid identification of fecal contamination in fresh produce by humans and livestock. J Sci Food Agric 93: 1089-1096.
  74. Ravaliya K, Gentry-Shields J, Garcia S, Heredia N, de Aceituno AF, et al. (2014) Use of Bacteroidales microbial source tracking to monitor fecal contamination in fresh produce production. Appl Environ Microbiol 80: 612-617.
  75. Symonds EM, Young S, Verbyla ME, McQuaig-Ulrich SM, Ross E, et al. (2017) Microbial source tracking in shellfish harvesting waters in the Gulf of Nicoya, Costa Rica. Water Res. 15: 177-184.
  76. Savichtcheva O, Okabe S (2006) Alternative indicators of fecal pollution: Relations with pathogens and conventional indicators, current methodologies for direct pathogen monitoring and future application perspectives. Water Res 40: 2463-2476.
  77. Mauffret A, Mieszkin S, Morizur M, Alfiansah Y, Lozach S, et al. (2013) Recent innovation in microbial source tracking using bacterial real-time PCR markers in shellfish. Marine Poll Bull 68: 21-29.
  78. Martellini A, Payment P, Villemur R (2005) Use of eukaryotic mitochondrial DNA to differentiate human, bovine, porcine and ovine sources in fecally contaminated surface water. Water Res 39: 541-548.
  79. Mukherjee A, Speh D, Dyck E, Diez-Gonzalez F (2004) Preharvest evaluation of coliforms, Escherichia coli, Salmonella, and Escherichia coli O157:H7 in organic and conventional produce grown by Minnesota farmers. J Food Prot 67: 894-900.
  80. Evguenieva-Hackenberg E (2005) Bacterial ribosomal RNA in pieces. Mol Microbiol 57: 318-325.
  81. Pronk LM, Sanderson KE (2001) Intervening sequences in rrl genes and fragmentation of 23S rRNA in genera of the family Enterobacteriaceae. J Bacteriol 183: 5782-5787.
  82. Villemur R, Constant P, Gauthier A, Shareck M, Beaudet R (2007) Heterogeneity between 16S ribosomal RNA gene copies borne by one Desulfitobacterium strain is caused by different 100-200 bp insertions in the 5´ region. Can J Microbiol 53: 116-128.
  83. Pei AY, Oberdorf WE, Nossa CW, Agarwal A, Chokshi P, et al. (2010) Diversity of 16S rRNA genes within individual prokaryotic genomes. Appl Environ Microbiol 76: 3886-3897.
  84. Bower PA, Scopel CO, Jensen ET, Depas MM, McLellan SL (2005) Detection of genetic markers of fecal indicator bacteria in Lake Michigan and determination of their relationship to Escherichia coli densities using standard microbiological methods. Appl Environ Microbiol 71: 8305-8313.
  85. Walters SP, Field KG (2006) Persistence and growth of fecal Bacteroidales assessed by bromodeoxyuridine immunocapture. Appl Environ Microbiol 72: 4532-4539.
  86. Kildare BJ, Leutenegger CM, McSwain BS, Bambic DG, Rajal VB, et al. (2007) 16S rRNA-based assays for quantitative detection of universal, human-, cow-, and dog-specific fecal Bacteroidales: A Bayesian approach. Water Res 41: 3701-3715.
  87. Reischer GH, Kasper DC, Steinborn R, Farnleitner AH, Mach RL (2007) A quantitative real-time PCR assay for the highly sensitive and specific detection of human faecal influence in spring water from a large alpine catchment area. Lett Appl Microbiol 44: 351-356.
  88. Haugland RA, Varma M, Sivaganesan M, Kelty C, Peed L, et al. (2010) Evaluation of genetic markers from the 16S rRNA gene V2 region for use in quantitative detection of selected Bacteroidales species and human fecal waste by qPCR. Syst Appl Microbiol. 33: 348-357.
  89. Gómez-Doñate M, Ballesté E, Muniesa M, Blanch AR (2012) New molecular quantitative PCR assay for detection of host-specific Bifidobacteriaceae suitable for microbial source tracking. Appl Environ Microbiol 78: 5788-5795.
  90. Okabe S, Shimazu Y (2007) Persistence of host-specific Bacteroides-Prevotella 16S rRNA genetic markers in environmental waters: Effects of temperature and salinity. Appl Microbiol Biotechnol 76: 935-944
  91. Stricker AR, Wilhartitz I, Farnleitner AH, Mach RL (2008) Development of a Scorpion probe-based real-time PCR for the sensitive quantification of Bacteroides sp. ribosomal DNA from human and cattle origin and evaluation in spring water matrices. Microbiol Res 163: 140-147.
  92. Jeong JY, Park HD, Lee KH, Hwang JH, Ka JO (2010) Quantitative analysis of human and cow-specific 16S rRNA gene markers for assessment of fecal pollution in river waters by real-time PCR. J Microbiol Biotechnol 20: 245-253.
  93. Dick LK, Simonich MT, Field KG (2005) Microplate subtractive hybridization to enrich for Bacteroidales genetic markers for fecal source identification. Appl Environ Microbiol 71: 3179-3183.
  94. Lu J, Santo Domingo JW, Hill S, Edge TA (2009) Microbial diversity and host-specific sequences of Canada goose feces. Appl Environ Microbiol 75: 5919-5926.
  95. Fremaux B, Boa T, Yost CK (2010) Quantitative real-time PCR assays for sensitive detection of Canada goose-specific fecal pollution in water sources. Appl Environ Microbiol 76: 4886-4889.
  96. Dick LK, Bernhard AE, Brodeur TJ, Santo Domingo JW, Simpson JM, et al. (2005) Host distributions of uncultivated fecal Bacteroidales bacteria reveal genetic markers for fecal source identification. Appl Environ Microbiol 71: 3184-3191.
  97. Mieszkin S, Furet J-P, Corthier G, Gourmelon M (2009) Estimation of pig fecal contamination in a river catchment by real-time PCR using two pig-specific Bacteroidales 16S rRNA genetic markers. Appl Environ Microbiol 75: 3045-3054.
  98. Konstantinov SR, Poznanski E, Fuentes S, Akkermans AD, Smidt H, et al. (2006) Lactobacillus sobrius sp. nov., abundant in the intestine of weaning piglets. Int J Syst Evol Microbiol 56: 29-32.
  99. Reischer GH, Kasper DC, Steinborn R, Mach RL, Farnleitner AH (2006) Quantitative PCR method for sensitive detection of ruminant fecal pollution in freshwater and evaluation of this method in Alpine karstic regions. Appl Environ Microbiol 72: 5610-5614.
  100. Mieszkin S, Yala JF, Joubrel R, Gourmelon, M (2010) Phylogenetic analysis of Bacteroidales 16S rRNA gene sequences from human and animal effluents and assessment of ruminant faecal pollution by real-time PCR. J Appl Microbiol 108: 974-984.
  101. Gomi R, Matsuda T, Matsui Y, Yoneda M (2014) Fecal source tracking in water bynext-generation sequencing technologies using host-specific Escherichia coligenetic markers. Environ Sci Technol 48: 9616-23.
  102. Deng D, Zhang N, Xu D, Reed M, Liu F, et al. (2015) Polymorphism of the glucosyltransferase gene (ycjM) in Escherichia coli and its use for tracking human fecal pollution in water. Sci Total Environ 537: 260-267.
Citation: Zheng G, Shen Z (2018) Host-specific Genetic Markers of Fecal Bacteria for Fecal Source Tracking in Food and Water . J Food Microbiol Saf Hyg 3: 135.

Copyright: © 2018 Zheng G, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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