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Fourier transform infrared microspectroscopy as a bacterial source tracking tool to discriminate fecal E. coli strains
Microchemical Journal 99 (2011) 15–19
Contents lists available at ScienceDirect
Microchemical Journal j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / m i c r o c
Fourier transform infrared microspectroscopy as a bacterial source tracking tool to discriminate fecal E. coli strains Camila Carlos a, Danilo A. Maretto b, Ronei J. Poppi b,⁎, Maria Inês Z. Sato c, Laura M.M. Ottoboni a a b c
Centro de Biologia Molecular e Engenharia Genética, Universidade Estadual de Campinas, UNICAMP, CP 6010, 13083-875 Campinas, SP, Brazil Instituto de Química, Universidade Estadual de Campinas, UNICAMP, CP 6154, 13084-971 Campinas, SP, Brazil Companhia de Tecnologia de Saneamento Ambiental, Av. Prof. Frederico Hermann Jr 345, 05489-900 São Paulo, SP, Brazil
a r t i c l e
i n f o
Article history: Received 20 September 2010 Received in revised form 1 March 2011 Accepted 1 March 2011 Available online 15 March 2011 Keywords: Bacterial source tracking Escherichia coli BOX-PCR Fourier transform infrared microspectroscopy
a b s t r a c t The aim of this work was to use the FT-IR microspectroscopy technique, a union of a FT-IR spectrometer with a microscope, to discriminate fecal Escherichia coli strains from cows, chickens and humans and to compare the efficiency of this method with the genomic fingerprinting method, BOX-PCR. The obtained BOX-PCR profiles were able to correctly discriminate 93.75% of the chicken strains, 80% of the cow strains and 65% of the human strains. An efficient PLS-DA model was developed, using orthogonal signal correction and the second derivate of the FT-IR spectra. This model allowed the correct discrimination, according to the animal source, of all the E. coli strains analyzed. The bands in the FT-IR spectra that were responsible for the strains discrimination were in the region between 2816 and 3026 cm−1 wavenumber, described as fatty acids. It was demonstrated that FT-IR microspectroscopy can be a suitable tool for fecal E. coli discrimination, because it is fast, easy to carry out and presents a flexible discrimination power. © 2011 Elsevier B.V. Open access under the Elsevier OA license.
1. Introduction Escherichia coli is a thermo tolerant coliform present in the intestinal flora of warm blood animals and, for this reason, this bacterium has been used as an indicator of fecal contamination in water [1]. Fecal contamination of water could indicate the presence of pathogenic bacteria and viruses. Therefore, the identification of the animal source of fecal contamination is extremely important for the effective management of water systems [2]. Several methods have been developed for bacterial source tracking (BST). The BST methods assume that it is possible to identify the source of fecal contamination based on differences among the feces characteristics of each animal source [3]. The methods that use E. coli to identify the animal source of fecal contamination are usually library-dependent [2]. These methods are based on phenotypic or genotypic profiles. The genotypic methods take into consideration the host specific genetic differences of the indicator organisms, while the phenotypic methods use the host specific biochemical properties [2]. Among the phenotypic methods are antibiotic resistance profiling [4,5], carbon source utilization tests [6,7] and, recently, Maldi-TOF/MS
⁎ Corresponding author. Tel./fax: + 55 19 35213126. E-mail address: [email protected] (R.J. Poppi). 0026-265X/© 2011 Elsevier B.V. Open access under the Elsevier OA license. doi:10.1016/j.microc.2011.03.002
profiling [8]. The genotypic methods include ribotyping [9,10], pulse field gel electrophoresis (PFGE) [11], denaturing gradient gel electrophoresis (DGGE) [12], repetitive element sequence-based PCR (rep-PCR) [13,14] and others. The rep-PCR method uses primers to amplify specific regions of the bacterial genome [15]. These genomic fingerprints can be generated by using repetitive extragenic palindromic (REP) sequences (35–40 bp), enterobacterial repetitive intergenic consensus (ERIC) sequences (124–127 bp), the 154 bp BOX elements [16] and, recently, the polytrinucleotide sequence (GTG)5 [16,17]. Informative results have been obtained by using these sequences to discriminate the animal source of fecal E. coli, especially with BOX- and (GTG)5-PCR [17]. Fourier transform infrared (FT-IR) spectroscopy has been successfully applied in the identification and classification of microorganisms at the species and strain levels [18]. This technique measures the vibration properties of the chemical bonds when excited by the absorption of the IR radiation. When applied to whole microbial cells, the infrared spectrum reflects the qualitative biochemical composition of the cells [19]. Recently, three E. coli strains were successfully discriminated by FT-IR, in spite of the high similarity among their spectra [20]. The FT-IR microspectroscopy combines a FT-IR spectrometer with a microscope. The advantage of this technique is that the spectra can be obtained from a single point or from a 2D image of the sample [21]. The aim of this work was to verify the potential of the FT-IR microspectroscopy technique to discriminate fecal E. coli from cows, chickens and humans and to compare this technique with the wellestablished BST tool BOX-PCR.
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C. Carlos et al. / Microchemical Journal 99 (2011) 15–19
Fig. 1. Average spectra of each NIR image obtained.
2. Experimental 2.1. BOX-PCR analysis Twenty E. coli strains isolated from humans [22], 15 isolated from cows [23] and 16 from chickens [24] were used in the BOX-PCR experiments. For this, genomic DNA was isolated from the different strains with the Wizard Genomic DNA Purification Kit (Promega), following the manufacturer's instructions. The BOX-PCR reactions were carried out as described by Versalovic et al. [16] with modifications. The reaction mixtures (20 μL) consisted of 10 ng of DNA, 50 pmoles of the BOXA1R primer (5′-CTACGGCAAGGCGACGCTGAGG-3′; 1x Taq polymerase buffer, 0.1 mM of each dNTP, 2 mM MgCl2 and 0.5 units of Taq polymerase (Fermentas). PCR reactions were performed in duplicate in a PT 100 thermocycler (MJ Research Inc.). The amplification conditions included an initial denaturation at 95 °C for 7 min, followed by 30 amplification cycles (94 °C for 1 min, 53 °C for 1 min, 56 °C for 4 min) and a final extension at 65 °C for 16 min. The amplification products were separated by electrophoresis on 2.0% agarose, 1x TBE buffer gels [25]. The gels containing ethidium bromide (0.5 μg mL−1) run at 5.0 V cm−1. The results were visualized and recorded with a gel documentation system (Kodak).
The BOX-PCR fingerprints were analyzed with the software GelCompar II (Applied Maths). A dendrogram was constructed by using the Jaccard similarity coefficient [26] and the UPGMA (unweigthed pair group method with arithmetic mean) algorithm. A discriminant function analysis, with the Jackknife algorithm, using the average similarities, was performed with the cluster analysis result to find out the percentage of strains of each animal that was classified in each source category. The average rate of correct classification (ARCC) was determinated by using the percentage of strains correctly classified in all the animals analyzed. 2.2. FT-IR microspectroscopy experiments The same E. coli strains used in the BOX-PCR analysis were used in the FT-IR microspectroscopy experiments. The experiments were performed in triplicate, in different days, with five to seven colonies of each strain, grown on LB agar plates (Invitrogen) for 16 h at 37 °C. The colonies were transferred to glass slides (1.4 mm thick) and dried at room temperature. Samples were analyzed on a spectrometer SPOTLIGHT 400 N (Perkin Elmer). The parameters used were image mode, transmittance, 64 scans for pixel, 4 cm−1 of resolution, 100 × 100 μm of area and spectral range from 2600 to 3300 cm−1,
Fig. 2. The preprocessed spectra.
C. Carlos et al. / Microchemical Journal 99 (2011) 15–19 Table 1 Assignment of fecal E. coli strains to the correct animal source estimated by Jackknife analysis. Animal source
Human
Chicken
Cow
Human Chicken Cow
60 35 5
0 93.75 6.25
6.67 13.33 80
Values are presented in percentage. Values in boldface indicate the rate of correct classification (RCC). The ARCC was 77.92%.
where the glass absorption is null or weak. The empty glass slides were used to obtain the background. The image mode was used to collect spectra from several bacteria cells at once and afterwards, a mean of the spectra of each image was obtained (Fig. 1). A total of 192 different spectra were obtained from each strain analyzed. 2.2.1. FT-IR data treatment, model and validation The analysis of the spectra was performed with the PLS toolbox 4.2.1 from MATLAB 7.9 (Eigenvector Research Inc.). The average spectrum for each strain was used in the analysis. In order to get the best partial least squares — discriminant analysis (PLS-DA) model [27], several pre-processes were tested. The PLS-DA algorithm can build models which allow the maximum separation among classes of objects. To accomplish this purpose, an X matrix formed by the spectra is decomposed in three other matrices: the score matrix, which is the coordinates of the samples in a new space with reduced dimensions (where the axes are named as latent variables); the loading matrix, which shows how each variable was important for the model building; and an error matrix which contains the information not explained by the PLS-DA model. A rotation of the latent variables was performed to achieve a better class separation, which was given by a Y matrix. A PLS-DA analysis consists in observation of both scores and loadings plots. While the score plot gives the idea of similarity among the samples, the loading plot shows the contribution of each variable in the modeling. The variables with greater values in the loading plot are correspondent to the infrared bands with the greatest differences among the sample groups. The best model was the one in which the spectra range was from 2600 to 3300 cm−1. The baseline adjustment was performed using the second derivative of the data and the algorithm OSC (orthogonal signal correction) [28] was used to eliminate unnecessary information from the model. In this procedure, the X matrix is corrected by the
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subtraction of the variation that is orthogonal to a vector y, which gives to each sample its correspondence class. After using OSC, the spectral range was cut again between 2816 and 3026 cm−1, which corresponds to CH2 and CH3 stretching. The data were also meancentered. The preprocessed spectra are shown in Fig. 2. The validation of the method was performed according to a supervised learning with a new PLS-DA model. The samples were divided into calibration and validation groups with the Kennard– Stone algorithm [29]. Samples 1 to 34 were used as the calibration set, and samples 35 to 51 were used as the validation set (samples 35 to 39 were the validation set for the identification of bacteria from cow feces, samples 40 to 44 were the validation set for the identification of bacteria from chicken feces and samples 45 to 51 were the validation set for the identification of bacteria from human feces). This new PLSDA model, using the calibration set, was built using the same preprocesses of the previously best model, and the validation set was used to evaluate the rate of correct assignment of the PLS-DA model in the first latent variable. 3. Results and discussion A total of 51 E. coli strains were analyzed by BOX-PCR. Three strains from chickens did not produce fingerprints and, for this reason, these strains were not included in the statistical analysis. The fingerprints obtained for 20 human, 15 cow and 13 chicken strains were compared by using the Jaccard coefficient of similarity, considering presence/ absence of the bands and the UPGMA algorithm. The similarity coefficient ranged from 14.27% to 100%. The discriminant analysis function with the Jackknife algorithm was conducted on the cluster analysis result to assess the accuracy of BOX-PCR fingerprints to predict the animal source. Table 1 shows the percentage of E. coli strains assigned to the correct animal source. The ARCC was 77.92%, i.e. 77.92% of the E. coli strains analyzed were correctly assigned to the animal source. The chicken strains presented a rate of correct classification (RCC) of 93.75%, the cow strains presented a RCC of 80% and the human strains presented a RCC of 60%. Approximately 35% of the human strains were assigned as chicken source and 5% were assigned as cow source. The average rate of correct classification, obtained with the BOXPCR analysis is similar to the ones found in the literature [30]. Until now, there is no well-established standard method to BST, and any method with a rate of correct classification from 60% to 70% is considered to be useful for the development and implementation of
Fig. 3. Plot of scores of LV1 versus scores of LV2. (■) E. coli strains from humans, (●) E. coli strains from chickens and (▼) E. coli strains from cows.
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Fig. 4. Plot of LV1 versus calibration and validation samples. (■) E. coli from humans, (●) E. coli strains from chickens (▼) E. coli strains from cows, (◆) Validation samples: 35 to 39 are cow strains, samples 40 to 44 are of chicken strains and samples 45 to 51 are human strains.
appropriate management plans to prevent further fecal contamination [6]. Therefore, BOX-PCR can be a useful tool for BST. In the FT-IR microspectroscopy analysis, the spectra were obtained from 2000 to 6000 cm−1 for human, cow and chicken strains. A preliminary analysis of the spectra indicated that the region from 2600 to 3300 cm−1 was the best one to separate the E. coli strains analyzed according to their animal source. The second derivative was applied in the spectra in order to perform baseline adjustments. Then, the OSC algorithm was used to eliminate unnecessary information on the previously selected region to provide the best classification model, which completely separated the E. coli strains from humans, cows and chickens. Fig. 3 shows the PLS-DA score plot obtained from the OSC of the second derivative spectra. The first two latent variables (LV1 and LV2) explain 83.45% of the total variance. The first variable (LV1) is responsible for the separation of E. coli strains from humans, chickens and cows. The second variable (LV2) explains the variation within each group. A supervised learning was performed to validate of the PLS-DA model as shown in Fig. 4. In this case, all validation samples presented similar scores to their respective calibration set on the LV1. The calibration and validation samples from cows presented scores
between 0.5 and −0.5 × 10−3, samples from chickens presented scores between −1.5 and −2.5 × 10−3 and samples from humans presented scores between 1.25 and 2 × 10−3. Fig. 5 shows the loadings of the PLS-DA model, providing information that FT-IR bands at 2852, 2924, 2946 and 2962 cm−1, which are all assigned in Table 2, are the most influent variables in the separation of the groups. The information contained in those bands is responsible to the group separation among the samples. These bands were assigned [31] as C―H stretching vibrations of the functional groups CH3 and CH2, which generally present spectral characteristics of fatty acid chains of the various membrane amphiphiles [18]. It is possible to suggest that the bands in this region are good phenotypic marker candidates to discriminate E. coli strains from different animals. There are many host factors that can select the gut microflora, such as immune system, body temperature and diet as well as biochemical, physiological and behavior characteristics [32]. The use of some phenotypic characteristics can be more advantageous to discriminate E. coli from different hosts than genotypic methods since it was observed that the genotypic profiles are more variable than some phenotypic profiles [33]. Recently, Grasselli et al. [34] have shown evidences for horizontal gene transfer between human and nonhuman E. coli commensal strains, which can also be difficult for the bacterial source tracking by using genotypic methods. The phenotypic methods can be more influenced by the bacteria growth conditions. For example, several factors can influence the spectral reproducibility of FT-IR microspectroscopy. Among these factors are medium, growth temperature, incubation time, sample preparation and spectrum measurement [19]. For this reason, the standardization of the spectral acquisition parameters, culture and sample preparation conditions are important for the reproducibility of the spectra. Since our experiments were done in triplicates, in different days, and the analyses were qualitative, these biases diminished in this work.
Table 2 Assignment of the most important bands in model building.
Fig. 5. Plot of loadings of LV1 versus wavenumber.
Wavenumber (cm−1)
Assignment
2852 2924, 2946 2960
C―H stretching (symmetric) of CH2 in fatty acids C―H stretching (asymmetric) of CH2 C―H stretching (asymmetric) of CH3 in fatty acids
C. Carlos et al. / Microchemical Journal 99 (2011) 15–19
The genomic fingerprint techniques, such as BOX-PCR, depend less on the cultivation conditions than the FT-IR microspectroscopy technique. However, BOX-PCR reproducibility is influenced by a large number of protocol steps and the intensities of the band patterns can vary, making the interpretation and analysis of the profiles difficult. On the other hand, in spite of the high degree of standardization and complex data processing, FT-IR is easier to perform [19]. The main advantage of the FT-IR methodology is the large range of discriminatory level that is, FT-IR can be used to discriminate bacteria from different genus [34], species of one genus [35], and single strains [20]. So, it is possible to fit the model according to the discriminatory level needed only by changing the spectral window used and the multivariate statistical technique applied. Moreover, after the library construction, bacteria identification by FTIR is feasible even in mixed cultures and in liquid samples and in addition, culturing is not required [36,37].
[10]
[11]
[12]
[13]
[14]
[15]
[16]
4. Conclusions [17]
FT-IR microspectroscopy is a powerful technique to discriminate fecal E. coli strains according to their animal source. Its performance is better than the genotypic method BOX-PCR. The discrimination of the FT-IR spectra can be accessed by using multivariate statistical techniques since the analysis of the spectral data is very complex. We propose that FT-IR microspectroscopy can be a useful technique to BST since it is a simple, easy to carry out and low-cost. Besides that, it allows one to choose the region that produces the better separation among the bacterial strains from the different hosts. Our results are a promising baseline for future BST studies.
[18] [19]
[20] [21]
[22]
Acknowledgments This work was supported by grants from Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP 2007/55312-6 and FAPESP 2006/07309-3) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq 502198/2008-7). L. M. M. O. received a research fellowship from CNPq. C. C. received fellowship from FAPESP (07/570254). The authors thank Dr. L.A. Amaral for providing E. coli strains from cow feces, Dr. T. A. T. Gomes for providing E. coli strains from human feces, and Dr. W. D. Silveira for providing E. coli from chicken feces.
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Contents lists available at ScienceDirect
Microchemical Journal j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / m i c r o c
Fourier transform infrared microspectroscopy as a bacterial source tracking tool to discriminate fecal E. coli strains Camila Carlos a, Danilo A. Maretto b, Ronei J. Poppi b,⁎, Maria Inês Z. Sato c, Laura M.M. Ottoboni a a b c
Centro de Biologia Molecular e Engenharia Genética, Universidade Estadual de Campinas, UNICAMP, CP 6010, 13083-875 Campinas, SP, Brazil Instituto de Química, Universidade Estadual de Campinas, UNICAMP, CP 6154, 13084-971 Campinas, SP, Brazil Companhia de Tecnologia de Saneamento Ambiental, Av. Prof. Frederico Hermann Jr 345, 05489-900 São Paulo, SP, Brazil
a r t i c l e
i n f o
Article history: Received 20 September 2010 Received in revised form 1 March 2011 Accepted 1 March 2011 Available online 15 March 2011 Keywords: Bacterial source tracking Escherichia coli BOX-PCR Fourier transform infrared microspectroscopy
a b s t r a c t The aim of this work was to use the FT-IR microspectroscopy technique, a union of a FT-IR spectrometer with a microscope, to discriminate fecal Escherichia coli strains from cows, chickens and humans and to compare the efficiency of this method with the genomic fingerprinting method, BOX-PCR. The obtained BOX-PCR profiles were able to correctly discriminate 93.75% of the chicken strains, 80% of the cow strains and 65% of the human strains. An efficient PLS-DA model was developed, using orthogonal signal correction and the second derivate of the FT-IR spectra. This model allowed the correct discrimination, according to the animal source, of all the E. coli strains analyzed. The bands in the FT-IR spectra that were responsible for the strains discrimination were in the region between 2816 and 3026 cm−1 wavenumber, described as fatty acids. It was demonstrated that FT-IR microspectroscopy can be a suitable tool for fecal E. coli discrimination, because it is fast, easy to carry out and presents a flexible discrimination power. © 2011 Elsevier B.V. Open access under the Elsevier OA license.
1. Introduction Escherichia coli is a thermo tolerant coliform present in the intestinal flora of warm blood animals and, for this reason, this bacterium has been used as an indicator of fecal contamination in water [1]. Fecal contamination of water could indicate the presence of pathogenic bacteria and viruses. Therefore, the identification of the animal source of fecal contamination is extremely important for the effective management of water systems [2]. Several methods have been developed for bacterial source tracking (BST). The BST methods assume that it is possible to identify the source of fecal contamination based on differences among the feces characteristics of each animal source [3]. The methods that use E. coli to identify the animal source of fecal contamination are usually library-dependent [2]. These methods are based on phenotypic or genotypic profiles. The genotypic methods take into consideration the host specific genetic differences of the indicator organisms, while the phenotypic methods use the host specific biochemical properties [2]. Among the phenotypic methods are antibiotic resistance profiling [4,5], carbon source utilization tests [6,7] and, recently, Maldi-TOF/MS
⁎ Corresponding author. Tel./fax: + 55 19 35213126. E-mail address: [email protected] (R.J. Poppi). 0026-265X/© 2011 Elsevier B.V. Open access under the Elsevier OA license. doi:10.1016/j.microc.2011.03.002
profiling [8]. The genotypic methods include ribotyping [9,10], pulse field gel electrophoresis (PFGE) [11], denaturing gradient gel electrophoresis (DGGE) [12], repetitive element sequence-based PCR (rep-PCR) [13,14] and others. The rep-PCR method uses primers to amplify specific regions of the bacterial genome [15]. These genomic fingerprints can be generated by using repetitive extragenic palindromic (REP) sequences (35–40 bp), enterobacterial repetitive intergenic consensus (ERIC) sequences (124–127 bp), the 154 bp BOX elements [16] and, recently, the polytrinucleotide sequence (GTG)5 [16,17]. Informative results have been obtained by using these sequences to discriminate the animal source of fecal E. coli, especially with BOX- and (GTG)5-PCR [17]. Fourier transform infrared (FT-IR) spectroscopy has been successfully applied in the identification and classification of microorganisms at the species and strain levels [18]. This technique measures the vibration properties of the chemical bonds when excited by the absorption of the IR radiation. When applied to whole microbial cells, the infrared spectrum reflects the qualitative biochemical composition of the cells [19]. Recently, three E. coli strains were successfully discriminated by FT-IR, in spite of the high similarity among their spectra [20]. The FT-IR microspectroscopy combines a FT-IR spectrometer with a microscope. The advantage of this technique is that the spectra can be obtained from a single point or from a 2D image of the sample [21]. The aim of this work was to verify the potential of the FT-IR microspectroscopy technique to discriminate fecal E. coli from cows, chickens and humans and to compare this technique with the wellestablished BST tool BOX-PCR.
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C. Carlos et al. / Microchemical Journal 99 (2011) 15–19
Fig. 1. Average spectra of each NIR image obtained.
2. Experimental 2.1. BOX-PCR analysis Twenty E. coli strains isolated from humans [22], 15 isolated from cows [23] and 16 from chickens [24] were used in the BOX-PCR experiments. For this, genomic DNA was isolated from the different strains with the Wizard Genomic DNA Purification Kit (Promega), following the manufacturer's instructions. The BOX-PCR reactions were carried out as described by Versalovic et al. [16] with modifications. The reaction mixtures (20 μL) consisted of 10 ng of DNA, 50 pmoles of the BOXA1R primer (5′-CTACGGCAAGGCGACGCTGAGG-3′; 1x Taq polymerase buffer, 0.1 mM of each dNTP, 2 mM MgCl2 and 0.5 units of Taq polymerase (Fermentas). PCR reactions were performed in duplicate in a PT 100 thermocycler (MJ Research Inc.). The amplification conditions included an initial denaturation at 95 °C for 7 min, followed by 30 amplification cycles (94 °C for 1 min, 53 °C for 1 min, 56 °C for 4 min) and a final extension at 65 °C for 16 min. The amplification products were separated by electrophoresis on 2.0% agarose, 1x TBE buffer gels [25]. The gels containing ethidium bromide (0.5 μg mL−1) run at 5.0 V cm−1. The results were visualized and recorded with a gel documentation system (Kodak).
The BOX-PCR fingerprints were analyzed with the software GelCompar II (Applied Maths). A dendrogram was constructed by using the Jaccard similarity coefficient [26] and the UPGMA (unweigthed pair group method with arithmetic mean) algorithm. A discriminant function analysis, with the Jackknife algorithm, using the average similarities, was performed with the cluster analysis result to find out the percentage of strains of each animal that was classified in each source category. The average rate of correct classification (ARCC) was determinated by using the percentage of strains correctly classified in all the animals analyzed. 2.2. FT-IR microspectroscopy experiments The same E. coli strains used in the BOX-PCR analysis were used in the FT-IR microspectroscopy experiments. The experiments were performed in triplicate, in different days, with five to seven colonies of each strain, grown on LB agar plates (Invitrogen) for 16 h at 37 °C. The colonies were transferred to glass slides (1.4 mm thick) and dried at room temperature. Samples were analyzed on a spectrometer SPOTLIGHT 400 N (Perkin Elmer). The parameters used were image mode, transmittance, 64 scans for pixel, 4 cm−1 of resolution, 100 × 100 μm of area and spectral range from 2600 to 3300 cm−1,
Fig. 2. The preprocessed spectra.
C. Carlos et al. / Microchemical Journal 99 (2011) 15–19 Table 1 Assignment of fecal E. coli strains to the correct animal source estimated by Jackknife analysis. Animal source
Human
Chicken
Cow
Human Chicken Cow
60 35 5
0 93.75 6.25
6.67 13.33 80
Values are presented in percentage. Values in boldface indicate the rate of correct classification (RCC). The ARCC was 77.92%.
where the glass absorption is null or weak. The empty glass slides were used to obtain the background. The image mode was used to collect spectra from several bacteria cells at once and afterwards, a mean of the spectra of each image was obtained (Fig. 1). A total of 192 different spectra were obtained from each strain analyzed. 2.2.1. FT-IR data treatment, model and validation The analysis of the spectra was performed with the PLS toolbox 4.2.1 from MATLAB 7.9 (Eigenvector Research Inc.). The average spectrum for each strain was used in the analysis. In order to get the best partial least squares — discriminant analysis (PLS-DA) model [27], several pre-processes were tested. The PLS-DA algorithm can build models which allow the maximum separation among classes of objects. To accomplish this purpose, an X matrix formed by the spectra is decomposed in three other matrices: the score matrix, which is the coordinates of the samples in a new space with reduced dimensions (where the axes are named as latent variables); the loading matrix, which shows how each variable was important for the model building; and an error matrix which contains the information not explained by the PLS-DA model. A rotation of the latent variables was performed to achieve a better class separation, which was given by a Y matrix. A PLS-DA analysis consists in observation of both scores and loadings plots. While the score plot gives the idea of similarity among the samples, the loading plot shows the contribution of each variable in the modeling. The variables with greater values in the loading plot are correspondent to the infrared bands with the greatest differences among the sample groups. The best model was the one in which the spectra range was from 2600 to 3300 cm−1. The baseline adjustment was performed using the second derivative of the data and the algorithm OSC (orthogonal signal correction) [28] was used to eliminate unnecessary information from the model. In this procedure, the X matrix is corrected by the
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subtraction of the variation that is orthogonal to a vector y, which gives to each sample its correspondence class. After using OSC, the spectral range was cut again between 2816 and 3026 cm−1, which corresponds to CH2 and CH3 stretching. The data were also meancentered. The preprocessed spectra are shown in Fig. 2. The validation of the method was performed according to a supervised learning with a new PLS-DA model. The samples were divided into calibration and validation groups with the Kennard– Stone algorithm [29]. Samples 1 to 34 were used as the calibration set, and samples 35 to 51 were used as the validation set (samples 35 to 39 were the validation set for the identification of bacteria from cow feces, samples 40 to 44 were the validation set for the identification of bacteria from chicken feces and samples 45 to 51 were the validation set for the identification of bacteria from human feces). This new PLSDA model, using the calibration set, was built using the same preprocesses of the previously best model, and the validation set was used to evaluate the rate of correct assignment of the PLS-DA model in the first latent variable. 3. Results and discussion A total of 51 E. coli strains were analyzed by BOX-PCR. Three strains from chickens did not produce fingerprints and, for this reason, these strains were not included in the statistical analysis. The fingerprints obtained for 20 human, 15 cow and 13 chicken strains were compared by using the Jaccard coefficient of similarity, considering presence/ absence of the bands and the UPGMA algorithm. The similarity coefficient ranged from 14.27% to 100%. The discriminant analysis function with the Jackknife algorithm was conducted on the cluster analysis result to assess the accuracy of BOX-PCR fingerprints to predict the animal source. Table 1 shows the percentage of E. coli strains assigned to the correct animal source. The ARCC was 77.92%, i.e. 77.92% of the E. coli strains analyzed were correctly assigned to the animal source. The chicken strains presented a rate of correct classification (RCC) of 93.75%, the cow strains presented a RCC of 80% and the human strains presented a RCC of 60%. Approximately 35% of the human strains were assigned as chicken source and 5% were assigned as cow source. The average rate of correct classification, obtained with the BOXPCR analysis is similar to the ones found in the literature [30]. Until now, there is no well-established standard method to BST, and any method with a rate of correct classification from 60% to 70% is considered to be useful for the development and implementation of
Fig. 3. Plot of scores of LV1 versus scores of LV2. (■) E. coli strains from humans, (●) E. coli strains from chickens and (▼) E. coli strains from cows.
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C. Carlos et al. / Microchemical Journal 99 (2011) 15–19
Fig. 4. Plot of LV1 versus calibration and validation samples. (■) E. coli from humans, (●) E. coli strains from chickens (▼) E. coli strains from cows, (◆) Validation samples: 35 to 39 are cow strains, samples 40 to 44 are of chicken strains and samples 45 to 51 are human strains.
appropriate management plans to prevent further fecal contamination [6]. Therefore, BOX-PCR can be a useful tool for BST. In the FT-IR microspectroscopy analysis, the spectra were obtained from 2000 to 6000 cm−1 for human, cow and chicken strains. A preliminary analysis of the spectra indicated that the region from 2600 to 3300 cm−1 was the best one to separate the E. coli strains analyzed according to their animal source. The second derivative was applied in the spectra in order to perform baseline adjustments. Then, the OSC algorithm was used to eliminate unnecessary information on the previously selected region to provide the best classification model, which completely separated the E. coli strains from humans, cows and chickens. Fig. 3 shows the PLS-DA score plot obtained from the OSC of the second derivative spectra. The first two latent variables (LV1 and LV2) explain 83.45% of the total variance. The first variable (LV1) is responsible for the separation of E. coli strains from humans, chickens and cows. The second variable (LV2) explains the variation within each group. A supervised learning was performed to validate of the PLS-DA model as shown in Fig. 4. In this case, all validation samples presented similar scores to their respective calibration set on the LV1. The calibration and validation samples from cows presented scores
between 0.5 and −0.5 × 10−3, samples from chickens presented scores between −1.5 and −2.5 × 10−3 and samples from humans presented scores between 1.25 and 2 × 10−3. Fig. 5 shows the loadings of the PLS-DA model, providing information that FT-IR bands at 2852, 2924, 2946 and 2962 cm−1, which are all assigned in Table 2, are the most influent variables in the separation of the groups. The information contained in those bands is responsible to the group separation among the samples. These bands were assigned [31] as C―H stretching vibrations of the functional groups CH3 and CH2, which generally present spectral characteristics of fatty acid chains of the various membrane amphiphiles [18]. It is possible to suggest that the bands in this region are good phenotypic marker candidates to discriminate E. coli strains from different animals. There are many host factors that can select the gut microflora, such as immune system, body temperature and diet as well as biochemical, physiological and behavior characteristics [32]. The use of some phenotypic characteristics can be more advantageous to discriminate E. coli from different hosts than genotypic methods since it was observed that the genotypic profiles are more variable than some phenotypic profiles [33]. Recently, Grasselli et al. [34] have shown evidences for horizontal gene transfer between human and nonhuman E. coli commensal strains, which can also be difficult for the bacterial source tracking by using genotypic methods. The phenotypic methods can be more influenced by the bacteria growth conditions. For example, several factors can influence the spectral reproducibility of FT-IR microspectroscopy. Among these factors are medium, growth temperature, incubation time, sample preparation and spectrum measurement [19]. For this reason, the standardization of the spectral acquisition parameters, culture and sample preparation conditions are important for the reproducibility of the spectra. Since our experiments were done in triplicates, in different days, and the analyses were qualitative, these biases diminished in this work.
Table 2 Assignment of the most important bands in model building.
Fig. 5. Plot of loadings of LV1 versus wavenumber.
Wavenumber (cm−1)
Assignment
2852 2924, 2946 2960
C―H stretching (symmetric) of CH2 in fatty acids C―H stretching (asymmetric) of CH2 C―H stretching (asymmetric) of CH3 in fatty acids
C. Carlos et al. / Microchemical Journal 99 (2011) 15–19
The genomic fingerprint techniques, such as BOX-PCR, depend less on the cultivation conditions than the FT-IR microspectroscopy technique. However, BOX-PCR reproducibility is influenced by a large number of protocol steps and the intensities of the band patterns can vary, making the interpretation and analysis of the profiles difficult. On the other hand, in spite of the high degree of standardization and complex data processing, FT-IR is easier to perform [19]. The main advantage of the FT-IR methodology is the large range of discriminatory level that is, FT-IR can be used to discriminate bacteria from different genus [34], species of one genus [35], and single strains [20]. So, it is possible to fit the model according to the discriminatory level needed only by changing the spectral window used and the multivariate statistical technique applied. Moreover, after the library construction, bacteria identification by FTIR is feasible even in mixed cultures and in liquid samples and in addition, culturing is not required [36,37].
[10]
[11]
[12]
[13]
[14]
[15]
[16]
4. Conclusions [17]
FT-IR microspectroscopy is a powerful technique to discriminate fecal E. coli strains according to their animal source. Its performance is better than the genotypic method BOX-PCR. The discrimination of the FT-IR spectra can be accessed by using multivariate statistical techniques since the analysis of the spectral data is very complex. We propose that FT-IR microspectroscopy can be a useful technique to BST since it is a simple, easy to carry out and low-cost. Besides that, it allows one to choose the region that produces the better separation among the bacterial strains from the different hosts. Our results are a promising baseline for future BST studies.
[18] [19]
[20] [21]
[22]
Acknowledgments This work was supported by grants from Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP 2007/55312-6 and FAPESP 2006/07309-3) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq 502198/2008-7). L. M. M. O. received a research fellowship from CNPq. C. C. received fellowship from FAPESP (07/570254). The authors thank Dr. L.A. Amaral for providing E. coli strains from cow feces, Dr. T. A. T. Gomes for providing E. coli strains from human feces, and Dr. W. D. Silveira for providing E. coli from chicken feces.
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