Is The A Test That Tells Fecal Matter From Animals And Humans

Appl Environ Microbiol. 2001 Apr; 67(4): 1503–1507.

Identification of Fecal Escherichia coli from Humans and Animals by Ribotyping

C. Andrew Carson

Department of Veterinarian Pathobiologyⁱ and Agricultural Experiment Station Statisticians,² University of Missouri, Columbia, Missouri 65211

Brian L. Shear

Department of Veterinary Pathobiologyⁱ and Agricultural Experiment Station Statisticians,² University of Missouri, Columbia, Missouri 65211

Marking R. Ellersieck

Department of Veterinary Pathobiology¹ and Agricultural Experiment Station Statisticians,^two University of Missouri, Columbia, Missouri 65211

Amha Asfaw

Department of Veterinary Pathobiology¹ and Agricultural Experiment Station Statisticians,² University of Missouri, Columbia, Missouri 65211

Received 2000 Aug 16; Accepted 2001 January xvi.

Abstract

Fecal pollution of water resources is an environmental problem of increasing importance. Identification of individual host sources of fecal Escherichia coli, such as humans, pets, production animals, and wild animals, is prerequisite to formulation of remediation plans. Ribotyping has been used to distinguish fecal E. coli of human origin from pooled fecal Eastward. coli isolates of nonhuman origin. We accept extended application of this technique to distinguishing fecal E. coli ribotype patterns from human and seven individual nonhuman hosts. Classification accuracy was best when the analysis was limited to three host sources. Awarding of this technique to identification of host sources of fecal coliforms in h2o could assist in conception of pollution reduction plans.

Fecal pollution of water resources is a trouble of increasing worldwide concern (4, xv). Man population growth, inadequate sewage systems, and direction of animal waste material (especially related to concentrated animal feeding operations) are some of the issues associated with maintenance of supplies of clean water (17). Counts of commensal coliform bacteria have traditionally been used to indicate the potential presence of pathogenic microbes of abdominal origin (one). Total coliform and fecal coliform numbers (1) are useful for estimating fecal pollution levels just give no indication of the specific sources of microbial pollution, such equally humans, product animals, pets, or migratory birds. Examples of methods which have been used as indicators of host sources include phage susceptibility (twenty) and the ratio of fecal coliforms to streptococci (5). Such indirect measurements are based on unstable parameters and may thereby lead to erroneous conclusions (11). More recently, DNA fingerprinting techniques such equally ribotyping (eleven), pulsed-field gel electrophoresis (9), PCR of repetitive intergenic sequences (iii), and 16S ribosomal DNA length heterogeneity PCR with last restriction fragment length polymorphism (2) have been described as promising for discriminating betwixt fecal-origin bacteria from humans and animals. Multiple antibiotic resistance phenotype has been used to distinguish betwixt human being and nonhuman sources of Escherichia coli (seven, x, 11, 19) and streptococci (six, 18), just genetic instability or changes in antibiotic use tin can modify the resistance profiles obtained.

Ribotyping has been compared to multiple antibiotic resistance profiles, and both approaches were reportedly complementary in discriminating between human and nonhuman (commonage) sources of fecal pollution (eleven). Ribotyping has since become a popular approach (personal communications) to the problem of differentiating between fecal E. coli pollution from humans and, in particular, that from animals and birds. We draw here the employ of ribotyping for the identification of E. coli cultured from feces of humans, cattle, swine, horses, chickens, turkeys, dogs, and migratory geese.

MATERIALS AND METHODS

Fecal E. coli.

Table 1 presents the host sources of feces, the numbers of individuals sampled, and the geographic regions from which samples were collected. Eastward. coli isolates of human origin were isolated directly from anal swabs. Feces of beefiness cattle, dairy cattle, swine, and horses served as source material for isolates from these species. Composite collections were also made from the excreta of chickens, turkeys, and migratory geese. Samples were incubated overnight in lactose broth at 37°C (Difco Laboratories, Sparks, Md.) and streaked on mEndo (Les) agar (Difco Laboratories). Colonies presenting a gold metallic sheen were transferred to mFC (Difco Laboratories) and cultured overnight at 44.five°C to select for fecal East. coli. Colonies were farther characterized equally E. coli by subculture on MacConkey-MUG (Difco Laboratories). Pinkish colonies which fluoresced under UV light were transferred to brain heart infusion (BHI; Difco Laboratories) plates. Individual East. coli isolates were finally confirmed biochemically by growth on Kligler Iron Agar, Simmons Citrate Agar, Methyl Ruby/VP, and Indol with 1% tryptose (all from Difco Laboratories). A total of 287 isolates were examined, including forty homo, 39 cattle, 44 pig, 37 horse, 29 canis familiaris, 23 craven, 26 turkey, and 49 goose isolates.

Table ane

Due east. coli isolates used in this report

Source	No. of isolates	No. of individuals sampled^a	Location in Missouri
Homo (direct)	40	15	Central
Cattle	39	24	Central
Grunter	44	30	Central
Horse	37	ten	Primal and southern
Canis familiaris	29	xv	Primal and western
Chicken	23	C	Central and southern
Turkey	26	C	Central
Goose	49	24	Cardinal

DNA extraction.

Fecal Eastward. coli isolates were grown in BHI broth (Difco Laboratories) and DNA extracted by using The Easy Deoxyribonucleic acid Kit (Invitrogen, Carlsbad, Calif.) according to manufacturer's instructions. Deoxyribonucleic acid concentration was measured spectrophotometrically, and 2.five μg was digested with HindIII (New England Biolabs, Beverly, Mass.) co-ordinate to the manufacturer's instructions. Fragments were separated in 1% agarose gels in TBE buffer (0.09 1000 Tris-borate, 0.002 M EDTA) using xxx mV for xvi h.

Southern blot analysis.

Gels were depurinated in 0.25 N HCl for fifteen min, rinsed twice with deionized water, denatured in 0.4 Thousand NaOH–0.6 M NaCl, and neutralized in 0.5 M Tris-HCl (pH seven.5)–1.5 K NaCl for xxx min (13). Deoxyribonucleic acid was transferred (16) onto nylon membranes (Boehringer Mannheim Corp., Indianapolis, Ind.) using a vacuum blotter. Membranes were broiled at eighty°C for ii h.

Probe grooming.

The probe was a BamHi (New England Biolabs) fragment from plasmid pKK 3535 containing E. coli 16S and 23S rRNA genes (12). Digested Dna was separated in 0.eight% agarose gel in TAE buffer for ii h. at 80 mV. Insert DNA was purified using the Geneclean system (Bio 101, Carlsbad, Calif.) as specified by the manufacturer. The probe was labeled with digoxigenin-dUTP (Boehringer Mannheim Corp.) according to the manufacturer's instructions.

Hybridization.

Membranes were prehybridized at 65°C for 90 min and hybridized with the probe used as 25 ng of Deoxyribonucleic acid per filter (10 by 15 cm) at 65°C for 16 h in a hybridization oven (Hybaid Instruments, Holbrook, N.Y.). Membranes were washed twice for 5 min using two× SSC (0.3 M NaCl–30 mM sodium citrate)–0.ane% sodium dodecyl sulfate (SDS). Two final 15-min washes were performed with 0.5× SSC–0.i% SDS at 65°C (13). Membranes were developed colorimetrically using nitroblue tetrazolium and 5-bromo-4-chloro-3-indolyl-phosphate (BCIP; Boehringer Mannheim Corp.) according to the manufacturer's instructions.

Statistical analysis.

The method used for the discrimination of riboprints of Eastward. coli isolated from known-host sources was based on a previously reported procedure (xi). Riboprints, developed on membranes afterwards hybridization with the riboprobe, were captured for computer analysis past placement on a flatbed scanner. Each pattern of bands was converted to a line diagram, and fragment sizes (in base pairs) were assigned to each ring using DNA Proscan software (Nashville, Tenn.). Riboprint patterns were converted to a binary code for discriminant analysis (8) performed in SAS (SAS/STAT [1989] version 6). All or selected portions of the riboprint patterns were sequentially divided into 8 to 34 equal segments (windows) extending betwixt 0 and 35 kb. The algorithm compared pattern profiles by the presence or absence and number of bands in each window. The number and width of these windows were adjusted until the accuracy of nomenclature of host patterns reached its highest attainable level. Discriminant analysis using SAS software (PROC DISCRIM) was performed as a comparing of riboprints of all eight known-host sources, human versus pooled nonhuman sources, and groups of three to five selected host classes. Cross-validation iterations were performed with each riboprint in the database, and the percent correct classification was adamant. Spatial plot display, based on the use of 24 windows, was projected into two chief variables using SAS software (PROC Can DISC). Plots represented a visual interpretation of pattern analysis. Separation of patterns with respect to the host source is an indication of accuracy of identification.

RESULTS

A total of 287 known-host riboprint patterns were generated for E. coli strains isolated from humans, cattle, pigs, horses, chickens, turkeys, migratory geese, and dogs. These riboprints were composed of half dozen to 12 bands ranging in size from approximately 0.5 to 25.0 kb. Lanes containing the patterns were divided into segments (windows) of equal size for computer analysis. Best results were obtained by dividing the 0.5- to 22.0-kb portion of each blueprint into 24 equal windows. Rates of right classification (RCC) for diverse combinations of host classes are shown in Tables 2 to 6. A comparing of human and nonhuman riboprints resulted in 95.0 and 99.19% right classifications, respectively (Table 2). The boilerplate charge per unit of right nomenclature (ARCC) for riboprints compared to all eight host classes simultaneously (Table 3) was 73.56%. When comparison was made between a more limited number of classes the ARCC improved. Examples of results obtained from discriminant analyses with three classes included in each do are represented by Tables 4 to vi.

TABLE 2

RCC of riboprints of fecal E. coli from human being and nonhuman sources

Sample source	No. of isolates	No. correctly identified	RCC (%)^a
Human	40	38	95.00
Nonhuman (pooled)	247	245	99.xix
Total	287	283

TABLE 3

RCC of riboprints of fecal Eastward. coli from eight known-host sources

Host class	No. of Isolates	No. correctly identified	RCC (%)^a	Principal source(southward) of misclassification
Homo (direct)	xl	37	92.50	Turkey
Cattle	39	29	74.36	Pig
Sus scrofa	44	29	65.91	Turkey, dog
Horse	37	eighteen	48.65	Turkey, pig
Dog	29	16	55.17	Cattle, pig
Chicken	23	22	95.65	Pig
Turkey	26	21	eighty.77	Pig
Goose	49	37	75.51	Canis familiaris
Total	287	177

TABLE 4

RCC of riboprints of fecal Eastward. coli from homo, dog, and equus caballus sources

Sample source	No. of isolates	No. correctly identified	RCC (%)^a
Human	twoscore	39	97.50
Dog	29	27	93.10
Horse	37	34	91.89
Full	84	79

TABLE 6

RCC of riboprints of fecal Eastward. coli from goose, turkey, and craven sources

Sample source	No. of isolates	No. correctly identified	RCC (%)^a
Goose	49	47	95.92
Turkey	26	25	96.fifteen
Chicken	23	22	95.65

Plots of approved variables 1 and 2 (Can1 and Can2) on the x and y axes, respectively, are presented in Fig. ane to four. The variables represent major characteristics used as criteria for the comparison of riboprints. The resulting spatial display of riboprints from all 8 host classes displayed simultaneously appears in Fig. 1. In this instance there is a complex and complicated array without singled-out clustering of host-associated patterns. However, comparison of three host classes at a time resulted in distinct separation of patterns from each class (Fig. 2 to four). These visual displays of cluster clan reflect the level of accuracy of classification of the riboprints of respective host species.

An external file that holds a picture, illustration, etc. Object name is am0411380001.jpg

Two-dimensional spatial plot of riboprints of fecal E. coli from all eight host sources studied. Hosts are identified past the following messages: h, human; p, pig; c, cattle; e, horse; d, dog; westward, chicken; t, turkey; and grand, migratory geese. Can1 is on the ten axis, and Can2 is on y axis. There were 84 hidden ascertainment points which were invisible due to overlapping.

An external file that holds a picture, illustration, etc. Object name is am0411380002.jpg

Spatial plot of riboprints of fecal Due east. coli from cattle, pigs, and humans. Positions of patterns are represented as follows: c, cattle; p, pigs; and h, humans. Can1 is on the x centrality, and Can2 is on the y centrality. There were 28 hidden observations.

An external file that holds a picture, illustration, etc. Object name is am0411380004.jpg

Spatial plot of riboprints of fecal E. coli from humans, dogs, and geese. Positions of patterns are represented as follows: h, humans; d, dogs; and g, geese. Can1 is on the x centrality, and Can2 is on the y axis. There were 24 hidden observations.

DISCUSSION

Nosotros have tested the utilise of riboprinting for identification of fecal E. coli from specific sources. This method proved to be quite accurate for discriminating betwixt riboprint patterns of human and nonhuman origin, with an ARCC of 97.10%. The RCC of riboprints, from each of the eight known-host sources studied, ranged between 48.65 and 95.65% when compared simultaneously (Table 3). However, it was shown that the accurateness of classification can be greatly increased by limiting the number of classes compared (Tables 2 to half-dozen). Similarly, the spatial analysis of all patterns simultaneously did not distinctly cluster the host sources. When each clustering iteration was limited to three host sources, such as cattle, pigs, and humans (Fig. 2), chickens, pigs, and cattle (Fig. iii), or geese, humans, and dogs (Fig. 4), it became possible to distinguish riboprints representing E. coli from detail hosts.

An external file that holds a picture, illustration, etc. Object name is am0411380003.jpg

Spatial plot of riboprints of fecal East. coli from cattle, pigs, and chickens. Positions of patterns are represented every bit follows: c, cattle; p, pigs; and w, chickens. Can1 is on the x centrality, and Can2 is on the y centrality. There were 21 hidden observations.

Awarding of this technology to testing h2o quality is our ultimate goal. This arroyo would require riboprinting of unknown-source E. coli in water samples and comparison of the resultant patterns with known-host patterns in the database. For instance, Fig. iii provides a distinct display of fecal coliform riboprints from 3 of the most common animal species involved in full-bodied feeding operations—cattle, chickens, and pigs—indicating that unknowns compared to particular known-host patterns may be accurately characterized. In analyses of municipal storm water the suspected pollution sources may include humans, dogs, or migratory geese. The associated patterns in Fig. 4 form fairly singled-out clusters, indicating good probability for correct classification. In field situations information technology is also recommended that additional samples of host sources in the local landscape be included in the database for discriminant analysis of unknown Due east. coli isolated from waters under study. This measure could prove to exist important if the geographic location affects host intestinal flora.

Undoubtedly, there are instances where fecal E. coli pollutants in water may come from a large number of contributing host sources, consequently increasing the rates of misclassification by ribotyping. Commonly, there will also be instances of application of ribotyping for microbial source tracking where there volition be only a few obvious potential sources of pollution. In these latter situations nosotros would propose that the fingerprinting scheme presented hither will be more than accurate in the rate of correct classification of riboprints.

Equally with other DNA fingerprinting methods, ribotyping produces diverse patterns that represent the genomic similarity or dissimilarity betwixt isolates. Sure fecal Eastward. coli riboprints appear to be associated with (if not unique) to certain host classes. We can merely speculate as to why this phenomenon may occur and suppose that factors affecting the microenvironment of particular host intestines, including temperature, pH, and diet may be associated with strain selection or enrichment. Seasonal, geographic, or genetic variation in natural fecal E. coli populations may as well occur, just these problems were not examined in the present study. Based on a report of host-associated E. coli strains by multiple antibiotic resistance profiles, there is reportedly little or no cross-colonization (9). It has besides been reported that unlike host classes harbor East. coli which have very similar or identical riboprints (xi), and consequently misclassification may occur in these instances.

It appears that typical enteric populations of East. coli within each host species studied are significantly dissimilar for ribotyping to serve as a valuable ways for identification of sources of fecal pollution. Reported results are based on a pocket-sized database of 287 patterns. The accuracy of results is expected to increase equally the database of known-host samples is expanded. For practical application of this engineering to water quality, it will also be valuable to include additional host sources of East. coli in our database.

Tabular array 5

RCC of riboprints of fecal E. coli from moo-cow, pig, and chicken sources

Sample source	No. of isolates	No. correctly identified	RCC (%)^a
Cow	39	33	84.62
Pig	44	41	93.eighteen
Chicken	23	21	91.thirty

ACKNOWLEDGMENTS

This work was supported by the University of Missouri Outreach and Extension, Columbia, Mo., and the U.S. Geological Survey, Rolla, Mo.

We thank Salina Parveen (University of Florida) for advice and methods information and Shaun Tyler (Health Canada) for plasmid pKK3535 used in preparing the riboprobe. We are also grateful to John Schumacher and Don Wilkison (U.Due south. Geological Survey), Mike Monda (U.S. Army Corps of Engineers), and John Knudsen and Michael Heaton (Missouri Department of Natural Resources) for help in sample collection.

REFERENCES

one. American Public Health Association. Standard methods for the examination of water and wastewater. 19th ed. Washington, D.C.: American Public Health Association; 1995. [Google Scholar]

2. Bernhard A E, Field K K. A PCR assay to discriminate human and animal feces on the basis of host differences in Bacteroides-Prevotella genes encoding 16S rRNA. Appl Environ Microbiol. 2000;66:4571–4574. [PMC free article] [PubMed] [Google Scholar]

3. Dombek P E, Johnson L K, Zimmerley South J, Sadowsky G J. Use of repetitive DNA sequences and the PCR to differentiate Escherichia coli isolates from homo and animal sources. Appl Environ Microbiol. 2000;66:2572–2577. [PMC free article] [PubMed] [Google Scholar]

iv. Fleisher J M, Kay D, Salmen R L, Jones F, Wyer Yard D, Godfree A F. Marine waters, contaminated with domestic sewage: nonenteric illnesses associated with bather exposure in the United Kingdom. Am J Public Health. 1996;86:1228–1234. [PMC complimentary article] [PubMed] [Google Scholar]

5. Geldreich E E, Kenner B A. Concepts of fecal streptococci in stream pollution. J H2o Pollut Command Fed. 1969;41:R336–R352. [PubMed] [Google Scholar]

6. Hagedorn C, Robinson Due south 50, Filtz J R, Grubbs South M, Angier T A, Reneau R B., Jr Determining sources of fecal pollution in rural Virginia watershed with antibiotic resistance patterns in fecal streptococci. Appl Environ Microbiol. 1999;65:5522–5531. [PMC free article] [PubMed] [Google Scholar]

7. Harwood V J, Whitlock J, Withington V. Classification of antibiotic resistance patterns of indicator bacteria past discriminant analysis: use in predicting the source of fecal contagion in subtropical waters. Appl Environ Microbiol. 2000;66:3698–3704. [PMC free article] [PubMed] [Google Scholar]

8. Johnson R A, Wichern D W. Applied multivariate statistical analysis. 3rd ed. Englewood Cliffs, North.J: Prentice-Hall, Inc.; 1992. [Google Scholar]

nine. Kariuki S, Gilks C, Kimari J, Obanda A, Muyodi J, Waiyaki P, Hart C A. Genotype analysis of Escherichia coli strains isolated from children and chickens living in close contact. Appl Environ Microbiol. 1999;65:472–476. [PMC free commodity] [PubMed] [Google Scholar]

10. Parveen Due south, Murphree R L, Edmiston L, Kaspar C W, Portier Thousand M, Tamplin Chiliad Fifty. Association of multiple-antibiotic resistance profiles with point and nonpoint sources of Escherchia coli in Apalachicola Bay. Appl Environ Microbiol. 1997;63:2607–2612. [PMC free article] [PubMed] [Google Scholar]

11. Parveen S, Portier Grand M, Robinson K, Edmiston L, Tamplin M L. Discriminant analysis of ribotype profiles of Escherichia coli for differentiating homo and nonhuman sources of fecal pollution. Appl Environ Microbiol. 1999;65:3142–3147. [PMC costless article] [PubMed] [Google Scholar]

12. Regnault B, Grimont F, Grimont P A D. Universal ribotyping method using a chemically labelled oligonucleotide probe mixture. Res Microbiol. 1997;148:649–659. [PubMed] [Google Scholar]

thirteen. Sambrook J, Fritsch E F, Maniatis T. Molecular cloning: a laboratory manual. 2nd ed. Cold Spring Harbor, N.Y: Cold Leap Harbor Laboratory; 1989. [Google Scholar]

14. SAS Institute, Inc. SAS/STAT user'south guide, version 6. 4th ed. Vol. one. Cary, Due north.C: SAS Constitute, Inc.; 1989. [Google Scholar]

15. Sauer T J, Daniel T C, Nichols D J, W C P, Moore P A, Jr, Wheeler One thousand 50. Runoff water quality from poultry litter-treated pasture and forest sites. J Environ Qual. 2000;29:515–521. [Google Scholar]

16. Southern East M. Detection of specific sequences amid DNA fragments separated by gel electrophoresis. J Mol Biol. 1975;98:503–517. [PubMed] [Google Scholar]

xviii. Wiggins B A. Discriminant analysis of antibiotic resistance patterns in fecal streptococci, a method to differentiate man and beast sources of fecal pollution in natural waters. Appl Environ Microbiol. 1996;62:3997–4002. [PMC free article] [PubMed] [Google Scholar]

19. Wiggins B A, Andrews R W, Conway R A, Corr C 50, Dobratz East J, Dougherty D P, Eppard F R, Knupp S R, Limjoco M C, Mettenburg J One thousand, Rinehardt J M, Sonsino J, Torrijos R 50, Zimmerman Thou E. Use of antibiotic resistance analysis to identify nonpoint sources of fecal pollution. Appl Environ Microbiol. 1999;65:3483–3486. [PMC free article] [PubMed] [Google Scholar]

xx. Zierdt C H, Robertson E A, Williams R L, MacLowry J D. Computer analysis of Staphylococcus aureus phage typing data from 1957 to 1975, citing epidemiological trends and natural development inside phage typing system. Appl Environ Microbiol. 1980;39:623–629. [PMC free article] [PubMed] [Google Scholar]

Articles from Applied and Environmental Microbiology are provided hither courtesy of American Society for Microbiology (ASM)

Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC92761/

Posted by: whatleyephimagent.blogspot.com