Comparative assessment of human and farm animal faecal microbiota using real-time quantitative PCR


  • Editor: Julian Marchesi

Correspondence: Jean-Pierre Furet, INRA, U910, Unité d'Ecologie et de Physiologie du Système Digestif, 78350 Jouy-en-Josas, France. Tel.: +33 1 34 65 29 29; fax: +33 1 34 65 24 62; e-mail:


Pollution of the environment by human and animal faecal pollution affects the safety of shellfish, drinking water and recreational beaches. To pinpoint the origin of contaminations, it is essential to define the differences between human microbiota and that of farm animals. A strategy based on real-time quantitative PCR (qPCR) assays was therefore developed and applied to compare the composition of intestinal microbiota of these two groups. Primers were designed to quantify the 16S rRNA gene from dominant and subdominant bacterial groups. TaqMan® probes were defined for the qPCR technique used for dominant microbiota. Human faecal microbiota was compared with that of farm animals using faecal samples collected from rabbits, goats, horses, pigs, sheep and cows. Three dominant bacterial groups (Bacteroides/Prevotella, Clostridium coccoides and Bifidobacterium) of the human microbiota showed differential population levels in animal species. The Clostridium leptum group showed the lowest differences among human and farm animal species. Human subdominant bacterial groups were highly variable in animal species. Partial least squares regression indicated that the human microbiota could be distinguished from all farm animals studied. This culture-independent comparative assessment of the faecal microbiota between humans and farm animals will prove useful in identifying biomarkers of human and animal faecal contaminations that can be applied to microbial source tracking methods.


Faecal pollution in coastal or fresh waters leads to human disease and economic losses such as closure of commercial shellfish harvesting and recreational and bathing areas. Recent incidents include the isolation of human enteric viruses and bacteria such as norovirus, hepatitis A virus, and Salmonella from coastal waters and shellfish, which were implicated in shellfish-borne outbreaks after oyster consumption (Potasman et al., 2002; Martinez-Urtaza et al., 2004). In light of this risk to health and safety, it is important to identify the source of faecal contamination to better facilitate resource management and remediation.

Faecal contamination of water resources is currently evaluated by employing culturing methods to detect and enumerate living facultative-anaerobic bacteria, such as Escherichia coli, enterococci, or faecal coliforms. Samples are normally obtained from shellfish or directly from bathing waters (Directives 2006/113/CE; 2006/7/CE). The species traditionally used as faecal indicators, however, have limitations owing to several factors, including (1) their short survival time in an open-water environment, (2) their ability to proliferate in soil, sand or sediments absent in any point-source faecal contamination, (3) the low levels of correlation with the actual presence of pathogens, (4) the underestimation of true bacterial presence through omission of noncultivable bacteria, (5) their inability to track the source of faecal contamination because coliforms and enterococci are common to all mammalian hosts (Roszak & Colwell, 1987; Pommepuy et al., 1996; Gordon & Cowling, 2003; Wheeler et al., 2003; Hörman et al., 2004; Savichtcheva & Okabe, 2006). In order to overcome these shortcomings, alternative methods and indicators must be developed. Potential alternative indicators of faecal contamination could be anaerobic bacteria such as Bacteroides and Bifidobacterium that are more abundant in the faeces of warm-blooded animals than E. coli (Fiksdal et al., 1985; Suau et al., 1999). Importantly, these species have been shown to exhibit host-specific adaptation on the genetic level (Dick et al., 2005). While these bacteria are fastidious to enumerate with conventional culture techniques, they can nonetheless be easily detected using current molecular methods. Because uncultivated bacteria represent 70–80% of the total human microbiota, culture-independent methods of analysis based on 16S rRNA gene have been developed (Suau et al., 1999; Eckburg et al., 2005). These studies showed that the most highly represented bacterial groups in human stools were the Clostridium leptum and the Clostridium coccoides groups of the Firmicutes followed by the Bacteroides/Prevotella group and the Bifidobacterium genus (Harmsen et al., 2002; Lay et al., 2005a). Studies involving domestic animal microbiota are less numerous and are mainly focused on the phylogenetic diversity of the intestinal bacterial community in pigs, cattle and chicken (Lan et al., 2002; Leser et al., 2002; Ozutsumi et al., 2005). Recently, specific quantitative PCR (qPCR) approaches were used to estimate a limited number of bacterial species or groups of faecal microbiota (Matsuki et al., 2004; Seurinck et al., 2005; Reischer et al., 2006).

The work presented here seeks to establish a more comprehensive dataset in comparing human and farm animal microbiota. To this end, we developed and optimized a qPCR-based approach, which was subsequently applied to analyse faecal samples collected from humans and farm animals. Using such molecular techniques, we overcome the limits of traditional faecal indicators, including culturing methods, which consistently underestimate faecal population. The development and application of our qPCR systems quantifies faecal bacteria groups in human and animal faecal samples and provides essential information concerning potential alternative faecal indicators and host-specific bacterial groups.

Materials and methods

DNA extraction from faecal samples

The DNA extracts from faecal samples of 21 human stools were prepared as described previously (Godon et al., 1997; Lay et al., 2005b). Faecal samples from five individual animals were collected for each of six farm species (rabbit, goat, horse, pig, sheep and cow) and stored at −80 °C immediately after sampling. Total cellular DNA was extracted from 0.2 g of animal faecal material using the G'NOME® kit (BIO 101, La Jolla, CA) with modifications. Faecal samples were homogenized in the supplied cell suspension solution. Cell lysis/denaturing solution was then added and the samples incubated at 55 °C for 2 h. To improve cellular lysis, 750 μL of 0.1-mm-diameter silica beads were added, and agitation carried out at maximum speed for 10 min in a Beadbeater (Biospec, Bartlesville, OK). Polyvinylpolypyrrolidone (15 mg) was added to ensure removal of polyphenol contamination that could inhibit subsequent qPCR reactions. Samples were vortexed and centrifuged at 20 000 g for 3 min and the supernatant was recovered. The remaining pellet was washed with 400 μL of TENP [50 mM Tris (pH 8), 20 mM EDTA (pH 8), 100 mM NaCl, 1% polyvinylpolypyrrolidone] and centrifuged at 20 000 g for 3 min. The washing step was repeated once more and the resulting supernatants pooled. Nucleic acids were precipitated by addition of one volume isopropanol, storage at −20 °C for 20 min, and centrifugation at 20 000 g for 10 min. The pellet was resuspended in 400 μL of distilled water plus 100 μL of salt-out mixture and incubated at 4 °C for 10 min. Samples were spun for 10 min at maximum speed, and the supernatant containing the DNA was transferred to a clean 1.5-mL microcentrifuge tube. DNA was precipitated with two volumes of 100% ethanol at room temperature for 5 min followed by centrifugation at 16 000 g for 5 min. DNA was resuspended in 150 μL of TE buffer. DNA solutions were stored at −20 °C for later analysis.

Validation of the G'NOME DNA extraction method

We compared our DNA extraction method with our former reference (Godon et al., 1997). Two series of DNA extracts from 12 human faecal samples were prepared by each method. The all-bacteria primers (Table 1) were used to perform PCR to compare both DNA extraction protocols and to validate our method.

Table 1.   Group and species-specific 16S rRNA gene-targeted primers and probes used in this study
Target organismPrimers and probesSequence 5′–3′Sources or references
  • Probe sequences are in bold.

  • *

    Modified from reference.

All bacteria*F_Bact 1369CGG TGA ATA CGT TCC CGGSuzuki et al. (2000)
C. leptumF_Clept 09CCT TCC GTG CCG SAG TTAThis study
BifidobacteriumF_Bifid 09cCGG GTG AGT AAT GCG TGA CCThis study
C. coccoidesF_Ccoc 07GAC GCC GCG TGA AGG AThis study
P_Erec482*VIC-CGG TAC CTG ACT AAG AAGFranks et al. (1998)
Bacteroides/F_Bacter 11CCT WCG ATG GAT AGG GGT TThis study
PrevotellaR_Bacter 08CAC GCT ACT TGG CTG GTT CAG 
P_Bac303*VIC-AAG GTC CCC CAC ATT GManz et al. (1996)
E. coliE.coli FCAT GCC GCG TGT ATG AAG AAHuijsdens et al. (2002)
Lactobacillus/Leuconostoc/PediococcusF_Lacto 05AGC AGT AGG GAA TCT TCC AThis study
S. salivariusStherm 03TTA TTT GAA AGG GGC AAT TGC TFuret et al. (2004)
EnterococcusF_EnteroCCC TTA TTG TTA GTT GCC ATC ATTRinttiläet al. (2004)

Performance of the real-time qPCR protocol in artificial mixtures

To validate the performance of our modified G'NOME DNA extraction protocol and to facilitate real-time qPCR methods, we employed an approach whereby individual samples were spiked with a measured quantity of a known bacterial species. Briefly, several tubes (1 mL) of pure culture Lactococcus lactis were centrifuged. Pelleted cells were either stored pure at −80 °C or used to spike otherwise lactococci-free faecal samples before storage. Total bacterial DNA from six pellets and 12 spiked faecal samples was extracted. The resulting levels of L. lactis were assessed by real-time qPCR using species-specific 16S rRNA gene primers (Llac05-F: AGCAGTAGGGAATCTTCGGCA and Llac02-R: GGGTAGTTACCGTCACTTGATGAG). The quantitative results from bacterial pellets and spiked faecal samples were compared to validate the performance of our protocol.

Oligonucleotide primers and probes

TaqMan® qPCR was adapted to quantify total bacteria population in addition to the dominant (>1% of faecal bacteria population) bacterial species C. coccoides, C. leptum, Bacteroides/Prevotella and Bifidobacterium. Quantitative PCR using SYBR-Green® was performed for the subdominant bacterial species E. coli, Streptococcus salivarius, for the previously described Enterococcus group, and for the Lactobacillus/Leuconostoc/Pediococcus group. Primers and probes used in this study (Table 1) were designed based on 16S rRNA gene sequences (EMBL database) aligned with the program clustal w (Thompson et al., 1994). Primer design was carried out using primer-express version 2.0 (Applied-Biosystems). The specificity of the primers and probes was tested by submitting the sequences to the probe match program (Ribosomal Database Project II; Maidak et al., 2001). Before laboratory testing, OligoCheck ( was used to examine the in silico performance of the PCR systems against 5127 sequences of 16S rRNA gene from type strains of intestinal bacteria. The TaqMan® probes were synthesized by Applied-Biosystems Applera-France. Primers were purchased from MWG (MWG-Biotech AG, Ebersberg, Germany). Primer and probe specificities were further assessed using the real-time qPCR protocol against a series of selected cultured strains (Table 3).

Table 3.   Specificity of oligonucleotide primers and probes in real-time PCR assessed using pure bacterial culture DNA
StrainOrigin*PCR results with each primer set
BacteriaC. leptumC. coccoidesBacteroides/PrevotellaBifidobacteriumE. coliS. salivariusLactobacillus/Leuconostoc/PediococcusEnterococcus
  • *

    ATCC, DSM, VPI and NCTC referred to the strain names in commercial collections. UEPSD and CNRZ corresponded to two INRA collections in Jouy-en-Josas.

  • +, positive; −, negative.

Clostridium leptumATCC 29065++
Faecalibacterium prausnitziiUEPSD L43++
Ruminococus albusUEPSD M30++
Clostridium coccoidesATCC 29236++
Ruminococcus gnavusATCC 29149++
Ruminococcus hanseniiDSM 20583T++
Eubacterium rectaleUEPSD A4++
Bacteroides fragilisATCC43185++
Bacteroides ovatusATCC 8483++
Bacteroides thetaiotaomicronATCC 29148++
Bacteroides uniformisATCC 8492++
Bacteroides vulgatusATCC 8482++
Bacteroides caccaeATCC 43185++
Bacteroides eggerthiiUEPSD L78++
Prevotella oralisDSM 20702T++
Prevotella buccaeDSM 20615++
Prevotella albensisDSM 11730T++
Bifidobacterium adolescentisATCC15703++
Bifidobacterium breveATCC15700++
Bifidobacterium infantisATCC 15697++
Escherichia coliUEPSD S123++
Streptococcus salivariusDSM 20067++
Streptococcus thermophilusDSM 20259++
Streptococcus vestibularisDSM 5636T++
Lactobacillus acidophilusUEPSD R52++
Lactobacillus caseiCNRZ++
Lactobacillus paracaseiCNRZ++
Lactobacillus delbrueckiiCNRZ++
Lactobacillus fermentumCNRZ++
Lactobacillus johnsoniiCNRZ++
Lactobacillus plantarumCNRZ++
Lactobacillus rhamnosusUEPSD R11++
Lactobacillus helveticusCNRZ++
Lactobacillus crispatusDSM 20584T++
Lactobacillus salivariusDSM 20555T++
Lactobacillus gasseriDSM 20243T++
Lactobacillus mucosaeDSM 13345T++
Leuconostoc mesenteroidesCNRZ++
L. pseudomesenteroidesCNRZ++
Enterococcus faeciumUEPSD L99++
Enterococcus faecalisUEPSD L98++
Clostridium perfringensATCC 13124+
Clostridium sordeliiVPI 9048+
Atopobium parvulumUEPSD B69+
Atopobium vaginaeDSM 15829T+
Atopobium rimaeDSM 7090T+
Clostridium bifermentansNCTC 506+
Streptococcus gordoniiDSM 6777T+

Real-time qPCR

Real-time qPCR was performed using an ABI 7000 Sequence Detection System with software version 1.2.3 (Applied-Biosystems). Amplification and detection were carried out in 96-well plates with TaqMan® Universal PCR 2 × Master Mix (Applied-Biosystems) or with SYBR-Green® PCR 2 × Master Mix (Applied-Biosystems). Each reaction was run in duplicate in a final volume of 25 μL with 0.2 μM final concentration of each primer, 0.25 μM final concentration of each probe and 10 μL of appropriate dilutions of DNA samples. Amplifications were carried out using the following ramping profile: 1 cycle at 95 °C for 10 min, followed by 40 cycles of 95 °C for 30 s, 60 °C for 1 min. For SYBR-Green® amplifications, a melting step was added to improve amplification specificity.

Bacterial strains and growth conditions

The various bacterial strains used to control for the specificity of the primers and probes in this study are shown in Table 3. Bacterial strains were either available in our laboratory collection or were otherwise obtained from the German Collection of Microorganisms and Cell Cultures (DSMZ). Bacteria were cultured aerobically or anaerobically on selective broth as recommended by DSMZ. For each culture, the total number of bacteria, in terms of CFU, was determined by plating. Aliquots of 1 mL of culture were centrifuged at 12 000 g for 3 min and the bacterial pellets were stored at −80 °C before use.

Bacterial DNA extraction, standard curves and quantification

Bacterial genomic DNA used to generate standard curves was extracted twice with the Wizard Genomic DNA Purification Kit (Promega) following the manufacturer's instructions. For the quantification of bacterial species and groups, standard curves were generated from serial dilutions of a known concentration of genomic DNA from each species or group. Standard curves were generated by plotting threshold cycles (Ct) vs. bacterial quantity (CFU). The total number of bacteria (CFU) was interpolated from the averaged standard curves as described previously (Lyons et al., 2000). When PCR was performed on unknown faecal samples, we used these standard curves to quantify each bacterial population. The lower limit for detection for bacterial enumeration with good precision is 106 bacteria per gram of stool.

Normalization of qPCR results

In human and animal microbiota, all-bacteria results are presented as the mean of the log10 value ± SEM. To overcome the fact that faecal samples may contain more or less water, we have normalized the data for each faecal sample. The level for each bacterial species or group was subtracted by the level of all-bacteria content. The data are given as the log number of bacteria per gram of faecal sample.


On comparing the human microbiota with those of animals, a one-way anova test was performed using jmp® software (Abacus Concepts, Berkeley, CA). When anova indicated a significant result, values were subsequently compared using nonparametric tests (Wilcoxon). Statistical significance was accepted at P<0.05 (P value adjustment method, Holm). Partial least squares (PLS) regression was also used (Moulin-Schouleur et al., 2006) to assess the differences between human and farm animal microbiota (variables Y) on the basis of the qPCR results (variables X). PLS-predictive models using PLS regression were established using the simca software, version 8.1 (Umetri, Umeå, Sweden). The PLS regression between variables X and variables Y yielded the PLS components. These components described the variables X and explained the variables Y. The number of useful PLS components was determined by cross-validation (SIMCA-P 9.0, 2001). The X loadings and the Y loadings were noted as w* and c, respectively. Groups of strains were presented as situated on a plane defined by the PLS components. The predictive quality of the model was evaluated using the R2Y coefficient, which corresponded to the proportion of the variance of variables Y explained by variables X.


Validation and performance of DNA extraction

Total bacteria counts, as measured by qPCR, performed on DNA extractions obtained using the former reference method of Godon et al. (1997) and our modified G'NOME method were highly similar. Total bacteria levels in the two series of DNA preparations were 11.55 ± 0.1 and 11.44 ± 0.1 logs of enumerated bacterial for the Godon and G'NOME methods, respectively, with no statistical difference. This result indicates that the performance of our technique is equivalent to that of Godon et al. (1997).

Performance of the real-time qPCR protocols in artificial mixtures

Population levels of L. lactis determined using qPCR on L. lactis bacterial pellets and spiked faecal samples were 9.31 ± 0.35 and 9.05 ± 0.39 logs of bacteria, respectively. No significant difference between the two was observed. This result further confirmed the robust nature of the real-time qPCR assay coupled with our DNA extraction method for quantification of bacterial population levels in faecal samples.

Validation of primers and probes

The specificity of all PCR systems (Table 1) was tested by submitting each oligonucleotide sequence to the probe match program (Ribosomal Database Project II) (Maidak et al., 2001). This program identifies the target species, if any, matching each PCR system (Table 2). The results from a complementary program, oligocheck details the number and position of any mismatches (Table 2; positions of mismatches are provided in Supporting Information, Table S1).

Table 2.   Bacterial target species for group or species-specific primers
PCR systemsTarget species*
  • *

    Target species were obtained by using probe match program (Ribosomal Database Project II) (Maidak et al., 2001) by checking each probe and primers with the following data set options: strain, type; source, isolates; size, ≥1200 and <1200 nt; quality, good.

  • Homology of the TaqMan probe was absolute as described previously (Holland et al., 1991). oligocheck v. 1 ( was used to assist in primer design and to confirm the specificity of primers and probes. The maximum mismatch number determined by oligocheck for the type-strain sequences is shown in parentheses. The positions of mismatches are shown in Table S1.

  • Species tested as control in real-time qPCR (c.f. Table 3).

C. leptum groupClostridium leptum (1), C. methylpentosum (2), C. sporosphaeroides (2), Faecalibacterium prausnitzii (1)
Ruminococcus albus (0), R. callidus (0), R. flavefaciens (0), R. bromii (1)
Others: see Table S1
C. coccoides groupClostridium coccoides (0), C. aerotolerans (3), C. indolis (4), C. algidixylanolyticum (4), C. aminophilum (2), C. aminovalericum (5), C. amygdalium (4), C. bolteae (5), C. celerecrescens (4), C. clostridioforme (2), C. hathewayi (3), C. herbivorans (2), C. hylemonae (2), C. jejuense (2), C. lentocellum (5), C. nexile (2), C. oroticum (7), C. populeti (2), C. proteoclasticum (2), C. scindens (2), C. saccharolyticum (4), C. sphenoides (4), C. symbiosum (2), C. xylanolyticum (4), C. xylanovorans (2)
Eubacterium rectale (2), E. hallii (3), E. ruminantium (2), E. cellulosolvens (3), E. contortum (3), E. eligens (4), E. ramulus (4), E. xylanophilum (3)
Ruminococcus gnavus (2), R. hansenii (0), R. luti (0), R. obeum (2), R. hydrogenotrophicus (3), R. lactaris (2), R. schinkii (2), R. torques (3)
Others: see Table S1
Bacteroides/Prevotella groupBacteroides fragilis (0), B. vulgatus (1), B. uniformis (2), B. eggerthii (2), B. ovatus (1), B. thetaiotaomicron (0), B. caccae (1), B. acidifaciens (2), B. stercoris (0), B. plebeius (0), B. splanchnicus (5), B. salyersiae (0), B. nordii (0), B. plebeius (0), B. coprocola (0), B. massiliensis (1), B. intestinalis (2), B. finegoldii (0), B. dorei (2), Parabacteroides distasomis (1)
Prevotella albensis (4), P. bivia (5), P. bryantii (4), P. buccalis (5), P. denticola (5), P. disiens (5), P. enoeca (5), P. heparinolytica (0), P. intermedia (4), P. melaninogenica (5), P. multiformis (4), P. nigrescens (5), P. oris (6), P. oulorum (5), P. pallens (5), P. salivae (5), P. tannerae (1), P. veroralis (5), P. zoogleoformans (0)
Bifidobacterium genusBifidobacterium adolescentis (0), B. longum XX bv. infantis (0), B. animalis (0), B. breve (1), B. choerinum (0), B. gallicum (0), B. thermacidophilum (0), B. boum (0), B. merycicum (0), B. ruminantium (0), B. angulatum (0), B. pseudocatenulatum (0), B. dentium (0), B. gallinarum (0), B. saeculare (0), B. pullorum (0), B. longum (0), B. pseudolongum (0), B. indicum (1), B. bifidum (1), B. catenulatum (2), B. asteroides (1), B. coryneforme (0), B. cuniculi (1), B. minimum (0), B. scardovii (0), B. psychraerophilum (2), B. subtile (0)
Others: see Table S1
Lactobacillus/Leuconostoc/Pediococcus groupLactobacillus acidophilus (0), L. casei (0), L. paracasei (0), L. delbrueckii (0), L. fermentum (0), L. helveticus (0), L. johnsonii (0), L. plantarum (0), L. rhamnosus (0), L. crispatus (0), L. salivarius (0), L. gasseri (0), L. mucosae (0), L. acetotolerans (0), L. acidifarinae (0), L. acidipiscis (0), L. agilis (0),
L. alimentarius (0), L. amylophilus (0), L. amylovorus (0), L. antri (0), L. aviarius (0), L. bifermentans (0), L. brevis (0), L. buchneri (0), L. coleohominis (0), L. collinoides (0), L. concavus (0), L. coryniformis (0), L. curvatus (0), L. durianis (0), L. equi (0), L. farciminis (0), L. fornicalis (0), L. fructivorans (0), L. frumenti (0), L. fuchuensis (0), L. gallinarum (2), L. gastricus (0), L. graminis (0), L. hammesii (0), L. harbinensis (0), L. hilgardii (0), L. homohiochii (1), L. ingluviei (0), L. intestinalis (0), L. jensenii (0), L. kalixensis (0), L. keferi (0), L. kefiranofaciens (0), L. kimchii (0), L. kitasatonis (0), L. kunkeei (0), L. lindneri (2), L. malefermentans (0), L. mali (0), L. manihotivorans (0), L. mindensis (0), L. murinus (0), L. pontis (0) L. oligofermentans (0), L. oris (0), L. panis (0), L. pantheris (0), L. parabrevis (0), L. parabuchneri (0), L. paracollinoides (0), L. parakefiri (0), L. paralimentarius (0), L. paraplantarum (0), L. pentosus (0), L. perolens (0), L. rennini (0), L. reuteri (0), L. pseudomesenteroides (0), L. rossii (0), L. ruminis (0), L. sakei (0), L. saerimneri (0), L. salivarius (0), L. sanfranciscensis (2), L. vini (0), L. satsumensis (0), L. sharpeae (0), L. siligionis (0), L. sobrius (0), L. spicheri (0), L. suebicus (0), L. vaccinostercus (0), L. vaginalis (1), L. versmoldensis (0), L. zeae (0)
Leuconostoc mesenteroides (0), L. pseudomesenteroides (1), L. durionis (1), L. fructosum (1), L. ficulneum (1), L. gelidum (1), L. gasicomitatum (1), L. inhae (1), L. gelidum (1), L. kimchii (1), L. lactis (0), L. pseudoficulneum (1), L. fallax (1)
Pediococcus inopinatus (0), P. parvulus (0), P. celliocola (0), P. acidilactici (0), P. pentosaceus (0), P. claussenii (0), P. stilesii (0), P. dextrinicus (0)

We tested the resulting PCR systems specificity against DNA extracted from pure cultures of 48 different strains of bacterial (Table 3). All positive and negative PCR assay results corroborated our in silico predictions. For the Lactobacillus group, it was not possible to design genus-specific primers because Leuconostoc was also detected by the PCR system (Table 3).

Composition of human faecal microbiota assessed by qPCR

For the different targeted bacterial groups, qPCR systems were validated using genomic DNA extracted from the faecal microbiota of healthy human subjects. These results defined a ‘standard’ profile for dominant and subdominant groups present in the human intestinal microbiota. Dominant species or groups are defined as those found to represent 1% (−2.0 log no. of bacteria) or more of the faecal bacteria population. Clostridium leptum, C. coccoides and Bacteroides/Prevotella groups are dominant populations (Table 4). Thus, the Bifidobacterium population, having a value of −2.4, suggests a subdominant population of human microbiota. This microbiota profile was subsequently used in comparisons against that of farm animals.

Table 4.   Composition of human faecal microbiota compared with farm animal microbiota
 nTaqMan detectionSYBR-Green detection
All-bacteria*C. leptum
C. coccoides
E. coliS. salivarius
  • n represents the numbers of studied samples.

  • The reference for the statistics is with human faecal samples. The nonparametric Wilcoxon test was performed if the one-way anova for the bacterial group was significant.

  • Data not sharing the same letter within a column are significantly different to the human population, at P<0.05.

  • *

    All-bacteria results obtained by qPCR were expressed as the mean of the log10 value ± SEM.

  • Results were expressed as the mean of the log10 value ± SEM of normalized data, calculated as the log no. of targeted bacteria minus the log of all-bacteria number.

Human2111.5 ± 0.1−0.7 ± 0.05 (A)−1.3 ± 0.08 (A)−1.5 ± 0.06 (A)−2.4 ± 0.33 (A)−3.9 ± 0.13 (A)−3.8 ± 0.34 (A)−3.1 ± 0.12 (A)−5.0 ± 0.15
Horse511.5 ± 0.1−1.5 ± 0.06 (B)−2.2 ± 0.18 (B)−2.3 ± 0.04 (B)−4.8 ± 0.13 (B)−2.4 ± 0.82 (A)−5.0 ± 0.03 (B)−5.2 ± 0.20 (B)Not detected
Cow511.4 ± 0.1−1.0 ± 0.03 (B)−2.6 ± 0.03 (B)−2.3 ± 0.01 (B)−3.6 ± 0.37 (B)−3.1 ± 0.06 (A)−5.0 ± 0.31 (B)−5.0 ± 0.04 (B)Not detected
Goat512.0 ± 0.1−1.0 ± 0.07 (B)−2.2 ± 0.11 (B)−2.4 ± 0.19 (B)−1.8 ± 0.26 (A)−3.2 ± 0.78 (A)−4.5 ± 0.48 (A)−4.3 ± 0.43 (B)Not detected
Rabbit511.7 ± 0.1−0.7 ± 0.03 (A)−1.9 ± 0.03 (B)−1.2 ± 0.09 (A)−1.6 ± 0.07 (A)−5.1 ± 0.59 (A)Not detectedNot detectedNot detected
Sheep511.9 ± 0.1−1.0 ± 0.05 (B)−2.7 ± 0.08 (B)−2.4 ± 0.08 (B)−4.2 ± 0.11 (B)−5.3 ± 0.60 (A)−4.1 ± 0.52 (A)Not detectedNot detected
Pig511.9 ± 0.1−1.2 ± 0.11 (B)−1.7 ± 0.35 (A)−1.9 ± 0.17 (A)−3.4 ± 0.69 (B)−1.2 ± 0.54 (B)−2.7 ± 0.06 (B)Not detectedNot detected

Comparison of bacterial populations in stools from human and farm animals

Differences in the bacterial composition of animal stool samples compared with those found in the human faecal microbiota were assessed using qPCR (Table 4). Global one-way anova testing showed significant differences in bacterial compositions between the two groups.

The nonparametric Wilcoxon test was used to reveal whether each qPCR system allows for discrimination of the bacterial population of humans and animals. This statistical test can also show how animal microbiota differs from human. The C. leptum qPCR system revealed several significant differences between human and horse, cow, goat, and sheep microbiota (Table 4). When comparing results between human and rabbit microbiota for the C. leptum group, no significant difference was observed (Table 4).

Although unable to distinguish between the microbiota of human and pig, the C. coccoides group qPCR system produced significantly different results for all other animals, with values being higher than that of human (Table 4).

The Bacteroides/Prevotella group displayed the same type of enrichment as C. coccoides for horse, cow, goat and sheep microbiota. Two exceptions were noted, however, in rabbit and pig, where no statistical difference with respect to human samples was observed (Table 4).

We also found the Bifidobacterium genus to vary significantly in the faeces of horse, cow, sheep and pig compared with human (Table 4). The Bifidobacterium population in goat and rabbit faeces were similar in relation to human and showed the lowest normalized data (Table 4).

The Lactobacillus/Leuconostoc/Pediococcus group failed to discriminate the microbiota of animals and human, with the sole exception being for pig samples. It is important to note that the targeted lactobacilli population in pig microbiota showed the lowest normalized result (Table 4).

The E. coli species qPCR system could distinguish human and animal microbiota except in the cases of goat and sheep. Our study showed that the E. coli value in pig microbiota is the lowest (−2.7 log no. of bacteria) when compared with those of animals and humans, and was not detected in the faecal samples of rabbit (Table 4). Streptococcus salivarius species was also not detected in faecal samples of rabbit, in addition to being absent from both sheep and pig. Nevertheless, the results show that S. salivarius can be used to distinguish the human microbiota from those of horse, cow and goat (Table 4). Streptococcus salivarius was more abundant in human faecal samples than in the other faecal samples. The Enterococcus species could not be detected in any animal faecal sample in contrast to its presence in human samples (Table 4).

PLS regression analysis based on faecal microbiota composition assessed using real-time qPCR confirmed that the human faecal microbiota could be clearly differentiated from that of farm animals in the 95% probability region (Fig. 1a). The first two components of the PLS model explained 85% of the variation of the Y-matrix, indicating a good separation of the human group compared with the groups of farm animals. The X loadings (w*) corresponding to faecal microbiota quantifications and the Y loadings (c) corresponding to the human and farm animal groups are presented in Fig. 1b. PLS regression analysis demonstrated that the C. coccoides group, Enterococcus genus and S. salivarius species characterize the human faecal microbiota and Lactobacillus/Leuconostoc/Pediococcus characterize the pig faecal microbiota.

Figure 1.

 PLS discrimination between microbiota of human and farm animals. (a) Relationship between faecal microbiota (variables X) and human or farm animals (variables Y) using PLS regression. The cross-validation led to two components represented here as t(1) and t(2). The corresponding PLS model explains 80.0% of the variation of the Y-matrix. The 95% probability region defined by the model is delimited by the ellipse. The human (▴) group (n=21) can be distinguished and is delimited by the black square. ○, cow (n=5); □, horse (n=5); ▿, pig (n=5); inline image, rabbit (n=5); inline image, sheep (n=5); inline image, goat (n=5). (b) The window shows the X loadings (w*) of the X variables (faecal microbiota quantifications) and the Y loadings (c) of the Y variables (human and animal groups), and thereby shows the correlation between X and Y. The X (black triangles) and Y (black circles) variables combine in the projections, and the X variables relate to the Y variables, as shown in the figure. The Clostridium coccoides group, Streptococcus salivarius species and Enterococcus genus, significant for the discrimination of human and farm animals, and the Lactobacillus/Leuconostoc/Pediococcus group, characterizing the pig microbiota, are denoted by large black triangles (small black triangles represent less significant X variables).


Pollution by human and animal faeces harbouring potential human pathogens represents a serious environmental threat that affects many natural waters. Waters contaminated with human faeces, in particular, are generally considered to represent a greater risk for human health as they contain human-specific enteric pathogens (Baudart et al., 2000; Koopmans & Duizer, 2004; Godfree & Farrell, 2005). Animals can also serve as reservoirs for numerous enteric pathogens (Hancock et al., 2001; Brown et al., 2004; Cox et al., 2005). Given this complex situation, the ability to accurately track faecal contamination in the environment and identify its origin is of great importance. The key points of such a technique are the choice of reliable and differential faecal indicators and the development of quantitative microbial source tracking methods.

To address these requirements, a robust and reproducible protocol is required to quantify bacterial species and groups in faecal samples originating from different possible contamination sources. Matsuki et al. (2004) were the first to apply qPCR, based on 16S rRNA gene quantification, to analyse the diversity of human intestinal Bifidobacterium. In our work, employing an optimized protocol, we quantified equivalent numbers of Bifidobacterium in human samples, compared with Matsuki and colleagues. This corroborative result gave us confidence in expanding the use of the qPCR technique to compare the whole human faecal microbiota with that of animals.

One additional variable that, in some cases, could influence the measurement and comparison of different groups of bacteria is the water content of each faecal sample. Low water content, for example, could contribute to the high bacterial concentration observed in goat and sheep samples. To overcome this potential variable, we normalized our data using all-bacteria populations.

As discussed below, our data are consistent with a number of smaller-scale investigations which focused on individual farm species or targeted groups of bacteria. In our study, we observed that the pig faecal microbiota is characterized by a population of Lactobacillus/Leuconostoc/Pediococcus higher than that found in other animals or humans. Given the value of −1.2 log no. of bacteria, this population could be considered dominate in pig microbiota. These data are in agreement with the observation by Castillo et al. (2006) showing a high level of Lactobacillus in the upper gastrointestinal tract of pig. These results, combined with those obtained for E. coli, suggest that both populations can be considered important in pig microbiota.

Canzi et al. (2000) enumerated Bacteroides and Clostridium in rabbit faeces. We also found the same range of populations for the Bacteroides/Prevotella group. However, for Clostridium populations, our study indicated higher colonization levels (about 6 logs higher) than those observed by these authors. This discrepancy could be due to methodological differences as Canzi and colleagues used spore enumerations for their Clostridia estimation. The fact that our technique enumerates vegetative cells as well as noncultivable bacteria is the most likely explanation for the higher concentration observed. Moreover, our PCR system also detected Eubacteria and ruminococci species which are part of the Clostridium group.

For equine microbiota, our results are consistent with a previous study (Daly & Shirazi-Beechey, 2003) where the authors used oligonucleotide probes in hybridization assays. Daly and Shirazi-Beechey found no Bifidobacterium and observed that the Eubacterium rectaleC. coccoides group, combined with Spirochaetaceae and the Cytophaga–Flexibacter–Bacteroides assemblage, represented the largest colonized populations (10–30%). The authors further noted that the Bacillus–Lactobacillus–Streptococcus group with Fibrobacter constituted 1–10% of the total microbiota in horse samples.

It is likely that the bacterial biodiversity of the equine microbiota compared with human contributes to the significant differences in bacterial quantification. Quantitative PCR developed to detect intestinal bacteria in human samples further highlight the species specificity of our protocols and the fact that the bacterial biodiversity of the equine microbiota is notably different from that of human.

Several studies have also reported on the bovine intestinal microbiota. Stahl et al. (1988) used species- and group-specific 16S rRNA gene-targeted probes for enumeration of two species (Fibrobacter succinogenes and Lachnospira ruminicola) in the rumen of animals treated with antibiotics. Tajima et al. (2001) used qPCR to quantify several Prevotella and some Ruminococcus, Fibrobacter, and Eubacterium species in the rumen. In 2005, An et al. estimated the prokaryote diversity in the rumen of yak (Bos grunniens) and Jinnan cattle (Bos taurus) by 16S rRNA gene sequence homology analysis. Their results showed a prevalence of Bacteroides; however, no sequence was related to Ruminococcus albus (a species of the C. leptum group) in the yak and cow rumen. In our study, the level of Bacteroides/Prevotella population presents a normalized difference of −2.3 log number of bacteria and cannot be regarded as a dominant population, while C. leptum group shows only −1.0 log number of bacteria and is part of the dominant population. Whitford et al. (1998) and Ozutsumi et al. (2005) presented a phylogenetic analysis of rumen bacteria by comparative sequence analysis of cloned 16S rRNA gene. Approximately 30% of the sequences were related to bacteria of the Bacteroides/Prevotella group, most of which clustered with Prevotella ruminicola. The remaining sequences clustered with members of the Clostridium genus. The differences observed with our findings are likely due to different technical approaches and/or diversity of microbiota among bovine herds.

To our knowledge, no previous study has used qPCR techniques to describe and compare the intestinal microbiota between animal and human. Our qPCR systems, checked in silico by oligocheck against RDP databases, were successfully able to discriminate different intestinal microbiota.

Our global comparison between human and farm animal microbiota provides data to select host-specific bacterial groups and alternative faecal indicators from all hosts considered.

Our PLS regression analysis showed that the C. coccoides group, Enterococcus genus and S. salivarius species could be considered as specific markers for human faecal microbiota and that Lactobacillus/Leuconostoc/Pediococcus can be used as a specific marker of pig microbiota.

The C. leptum group was found to have the lowest normalized data in humans and animals and thus represents a promising candidate for use as a reliable faecal indicator. It is largely distributed among animal species and in humans and has also been linked with diseases (Manichanh et al., 2006; Sokol et al., 2006). Our study also shows high concentrations of Bacteroides/Prevotella and Bifidobacterium in all host faecal samples tested. Such anaerobic bacteria do not persist for long periods of time in aerobic waters and are generally unable to multiply in such conditions (Fiksdal et al., 1985; Kreader, 1998). These inherent physiological characteristics make the Bacteroides and Bifidobacterium excellent candidates for detecting faecal contamination in the environment. Integrated within these two dominant bacterial groups are several species that were found to be host-specific in several studies (Bernhard & Field, 2000a; Bonjoch et al., 2004; Dick et al., 2005). Host-specific Bacteroides markers were developed (Bernhard & Field, 2000b; Dick et al., 2005) and applied in a watershed in the United States (Shanks et al., 2006). They were also validated on French faecal and environmental samples (Gourmelon et al., 2007). Quantitative PCR assays are currently in progress and some results have already been published for human and bovine-specific Bacteroides (Seurinck et al., 2005; Reischer et al., 2006).

Among the teams who have studied the microbiota of animals over the last decade none, up to now, has presented a global comparison of the faecal microbiota composition of humans and animals. Our results are thus promising in advancing the goal to define a discrete set of host-specific faecal microbiota biomarkers. Additional investigations are continuing to refine a set of comprehensive, reliable, and predictive host-specific markers.


The authors thank Valeria Dellaretti Guimarães and Sean P. Kennedy for critical reading of this manuscript.