Synchrotron infrared spectral regions as signatures for foodborne bacterial typing

Synchrotron infrared spectral regions as signatures for foodborne bacterial typing 1 2 first-author 3 corresp Xue-Ling Li. E-mail address: xlli@scbit.org Xue-Ling Li. E-mail address: xlli@scbit.org xlli@scbit.org 1 1 corresp Jun-Hong Lü. E-mail address: lujunhong@sinap.ac.cn Jun-Hong Lü. E-mail address: lujunhong@sinap.ac.cn lujunhong@sinap.ac.cn Division of Physical Biology and CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences (CAS), Shanghai 201800, China. Division of Physical Biology and CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences (CAS), Shanghai 201800, China. University of Chinese Academy of Sciences, Beijing 100049, China University of Chinese Academy of Sciences, Beijing 100049, China Shanghai Center for Bioinformation Technology, Shanghai Industrial Technology Institute, Shanghai 201203, China. Shanghai Center for Bioinformation Technology, Shanghai Industrial Technology Institute, Shanghai 201203, China. Fourier transform infrared (FTIR) spectroscopy has emerged as a viable alternative to biochemical and molecular biology techniques for bacterial typing with advantages such as short analysis time, low cost, and laboratorial simplicity. In this study, synchrotron radiation-based FTIR (SR-FTIR) spectroscopy with higher spectral quality was successfully applied to 16 types foodborne pathogenic bacteria. Combined with principal component analysis (PCA) and hierarchical cluster analysis (HCA), we found that the specific spectral region is 1300-1000 cm -1 , which reflects the information of phosphate compounds and polysaccharides, and can be used as the signature region to cluster the strains into a similar groups with this genetic taxonomic method. These findings demonstrated that FTIR spectra combined with HCA have a great potential in quickly typing bacteria depending on their biochemical signatures. Synchrotron radiation FTIR Spectral signature Bacterial typing PCA HCA 1 Introduction 1 Rapid identification and typing microorganisms is an increasingly important task in food safety and epidemiological diagnosis [ 1 1 ] . Traditional bacteria classification methods, such as serological test, PCR, DNA-DNA hybridization, and DNA sequencing [ 2 2 ] , are always time-consuming and need laborious culturing procedures. Moreover, these phenotype or gene based methods have difficulty in discriminating closely related strains which have similar phenotypic or genotypic properties [ 1 1 ] . Fourier transform infrared (FTIR) spectroscopy has emerged as a real alternative of molecular biology and biochemical methods to describe microorganisms dependent on chemical components [ 3 3 - 5 5 3-5 ] . Due to the advantages of short analysis time and low cost [ 6 6 ] , since it was introduced to characterize microorganisms by Naumann and co-workers [ 7 7 , 8 8 ] , FTIR spectroscopy was successfully applied to discriminate bacteria at genus, species, and even subspecies level [ 9 9 ] . The commonly used infrared spectra (Figure S1, Supporting Information) include a lipid region, protein region, mixed region, and polysaccharide region (3000-2800, 1800-1500, 1500-1200, and 1200-900 cm -1 , respectively) [ 10 10 ] . Since infrared spectra contain complex chemical information, multivariate statistical analysis methods, such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) are usually applied to explain their diversities and to further type FTIR spectra of bacteria [ 11 11 - 13 13 11-13 ] . The development of a synchrotron source for infrared spectroscopy has greatly improved the accuracy and signal-to-noise ration of the measurements [ 14 14 ] .In the previous work, we have successfully applied synchrotron radiation based FTIR (SR-FTIR) spectroscopy to discriminate 10 bacterial strains. Herein, we tried to further classify and type 16 foodborne bacteria from seven genera by the combination of SR-FTIR and chemometric methods. Whole spectra (3000-2800, 1800-900 cm -1 ) and spectra of four divided regions (lipid region, protein region, mixed region, and polysaccharide region) were used, respectively. We found that the spectra region between 1300 and 1000 cm -1 can be recognized as the signature region to cluster the bacteria from the same taxonomic levels into one group. With high reproducibility and a low sample amount, SR-FTIR spectroscopy shows great potential in fast identifying and typing microorganisms based on the specific infrared signature. 2 Experimental Section 1 2.1 Bacterial strains 2 16 foodborne bacteria were used and their culture conditions were listed in Table 1 (Supporting Information). 2.2 Sample preparation 2 Bacterial strains were incubated overnight and collected by centrifugation, respectively. After re-suspended in 50μl ethyl alcohol, 5μl sample was dropped on the BaF 2 window and air dried at room temperature [ 9 9 , 14 14 , 15 15 ] . 2.3 Synchrotron FTIR spectroscopy 2 Transmission mode was chosen and aperture was 20 μm×20 μm. Data collecting was monitored by OMNIC 9.2 (Thermo Fisher Scientific) and the collected spectra were further processed by baseline correcting, 15-point smoothing, and normalization [ 11 11 , 16 16 ] . The Savitsky-Golay method was used to calculate the first derivative spectra. 2.4 Data analysis 2 PCA and HCA were carried out on first derivative spectra using SPSS Statistics 22.0 (IBM). For PCA, the first two PCs (PC1 and PC2) were used to draw scatter plots. For HCA, Ward’s linkage algorithm and Euclidian distance measurements (or Pearson’s correlation coefficient) were chosen [ 11 11 ] , the top horizontal axis of a dendrogram depicted the distance coefficient values, and the actual distance was assigned 0 to 25 in proportion. 3 Results and Discussion 1 Spectra were recorded from 16 different bacterial isolates (Table S1, Supporting Information). A typical SR-FTIR spectrum of Vibrio parahaemolyticus was shown in Fig.S1 (Supporting Information). The recorded spectra of these 16 bacterial strains showed similar bands in the whole spectral region (3000-2800 and 1800-900cm -1 ) and little differences were observed (Fig.S2a, Supporting Information). To further resolve the spectral differences among these strains, the first derivative transformations were performed. As shown in Fig.S2b (Supporting Information), the first derivatives of reduced FTIR spectra displayed minor differences in the mixed region (1500-1200 cm -1 ) and in the polysaccharide (1200-900 cm -1 ) region. To find out the better discriminating results, the first derivative spectra of the 16 bacterial strains were used and multivariate analysis methods PCA and HCA were carried out on the whole spectral region and specific regions (such as lipid region, protein region, mixed region and polysaccharide region), respectively. We firstly tried to carry out PCA analysis on whole spectra of 16 bacteria while performing HCA to explain their affinity-disaffinity. As shown in Fig.1 Fig.1 , it can be seen that all Listeria strains are well separated by principle component one and two. The dendrograms from HCA of the average spectra showed close clustering of these species, suggesting that Listeria strains have a similar biochemical profile and all of them are from the same order. Two Staphylococcus strains were also grouped together. However, Both Vibrio strains and Salmonella strains were decentralized and couldn't cluster into their corresponding group. These results indicated the whole spectra could be difficult to distinguish all the bacterial strains at distinct taxonomic levels. Since the infrared spectra of the microorganism reflect the information of several classes of biomolecules, PCA and HCA were carried out on these specific spectral regions, respectively. For the lipid region (3000-2800cm -1 ) and protein region (1800-1500cm -1 ), most strains with the same genus didn’t cluster correctly and only Listeria strains were partly grouped together (Fig.S3 and Fig.S4, Supporting Information). These results indicated that either the lipid region or protein region contained low specific information among these strains and couldn’t be used to classify bacteria. We further typed the bacteria based on the spectra of the mixed region 1500-1200 cm -1 ( Fig.2 Fig.2 ). PCA displayed that four Listeria strains and three Vibrio strains were significantly separated from other bacteria. HCA illustrated that Listeria , Vibrio, Staphylococcus, and most Salmonella (except S. dysenteriae ) were formed separate genus-specific clusters. Although not all strains were classified into their corresponding sub-clusters, this result was very close to the phylogenetic relatedness. Interestingly, similar results were obtained after PCA and HCA analysis of the spectral region 1200-900cm -1 , which mainly represent the information about nucleic acids and polysaccharides components ( Fig. 3 Fig. 3 ). For example, four Listeria strains were clearly differentiated from the others and three of four Salmonella species were classified into a same cluster. However, some strains, such as Staphylococcus epidermidis, S. typhimurium (CICC10420), and Vibrio vulnificus, were misclassified. The above results suggested that the spectra information in the mixed region (1500-1200cm -1 ) and polysaccharide region (1200-900cm -1 ) had relatively good discriminating capability, which have been previously reported as the specific regions for characterizing these kinds of bacterial strains [ 17 17 , 18 18 ] . To acquire the most appropriate spectra for typing bacteria within their corresponding taxonomy, the wavenumber, ranging from 1500 cm -1 to 900cm -1 , was carefully optimized and selected. We found that the region 1300-1000cm -1 were of particular interest because their proximity to the best clustering results ( Fig.4 Fig.4 ). This region mainly reflected the chemical information of P=O symmetric and asymmetric stretching vibrations of > PO 2 - in nucleic acids or phospholipids and C-O-C, C-O vibrations in glycogen and carbohydrates [ 19 19 ] . The results showed that these bacteria were typing into three larger clusters, including four. Listeria strains, three Vibrio strains and two Staphylococcus strains which were clearly classified. Although the Salmonella species, Yersinia enterocolitica and Shigella dysenteriae were grouped together, the fact that all three of them belong to members of Enterobacteriales could explain the difficulty of discrimination, which was consistent with previous reports [ 12 12 , 20 20 ] . 4 Conclusion 1 We reported on the use of SR-FTIR spectroscopy to type 16 foodborne bacteria from seven genera. A whole spectral region and four subdivided spectral regions were analyzed respectively. We found that the wavenumber range of 1300-1000 cm -1 , which mainly reflects the information of nucleic acids and polysaccharides, contained more specific information and could be referred to as the signature region. The spectral signature proceeded with PCA and HCA obtained a delineation among the bacterial classes and cluster results matched with their phylogenetic relationship. These results proved that SR-FTIR spectroscopy with high spectral resolution has the greatest potential in fast identifying and typing microorganisms based on the specific infrared signature. References J.W. Law , N.S. Ab Mutalib , K.G. Chan , et al ., Rapid methods for the detection of foodborne bacterial pathogens: principles, applications, advantages and limitations . Front. Microbiol . 5 , 770 ( 2014 ). DOI: 10.3389/fmicb.2014.00770 http://doi.org/10.3389/fmicb.2014.00770 . Rapid methods for the detection of foodborne bacterial pathogens: principles, applications, advantages and limitations C.C. Thompson , L. Chimetto , R.A. Edwards , et al ., Microbial genomic taxonomy . BMC Genomics . 14 , 913 - 913 ( 2013 ). DOI: 10.1186/1471-2164-14-913 http://doi.org/10.1186/1471-2164-14-913 . Microbial genomic taxonomy C. Quintelas , E.C. Ferreira , J.A. Lopes , et al ., An overview of the evolution of infrared spectroscopy applied to bacterial typing . Biotechnol. J . 13 ( 2017 ). DOI: 10.1002/biot.201700449 http://doi.org/10.1002/biot.201700449 . An overview of the evolution of infrared spectroscopy applied to bacterial typing J.P. Harrison and D. Berry . Vibrational Spectroscopy for Imaging Single Microbial Cells in Complex Biological Samples . Front. Microbiol . 8 , 675 ( 2017 ). DOI: 10.3389/fmicb.2017.00675 http://doi.org/10.3389/fmicb.2017.00675 . Vibrational Spectroscopy for Imaging Single Microbial Cells in Complex Biological Samples X. Lu , H.M. Al-Qadiri , M. Lin , et al ., Application of Mid-infrared and Raman Spectroscopy to the Study of Bacteria . Food Bioprocess Tech . 4 , 919 - 935 ( 2011 ). DOI: 10.1007/s11947-011-0516-8 http://doi.org/10.1007/s11947-011-0516-8 . Application of Mid-infrared and Raman Spectroscopy to the Study of Bacteria M. Wenning and S. Scherer . Identification of microorganisms by FTIR spectroscopy: perspectives and limitations of the method . Appl. Microbiol. Biotechnol . 97 ( 16 ), 7111 - 7120 ( 2013 ). DOI: 10.1007/s00253-013-5087-3 http://doi.org/10.1007/s00253-013-5087-3 . Identification of microorganisms by FTIR spectroscopy: perspectives and limitations of the method D. Naumann , D. Helm and H. Labischinski . Microbiological characterizations by FT-IR spectroscopy . Nature . 351 , 81 - 82 ( 1991 ). DOI: 10.1038/351081a0 http://doi.org/10.1038/351081a0 . Microbiological characterizations by FT-IR spectroscopy D. Helm , H. Labischinski , G. Schallehn , et al ., Classification and identification of bacteria by Fourier-transform infrared spectroscopy . J. Gen. Microbiol . 137 , 69 - 79 ( 1991 ). DOI: 10.1099/00221287-137-1-69 http://doi.org/10.1099/00221287-137-1-69 . Classification and identification of bacteria by Fourier-transform infrared spectroscopy Y.D. Wang , X.L. Li , Z.X. Liu , et al ., Discrimination of foodborne pathogenic bacteria using synchrotron FTIR microspectroscopy . Nucl. Sci. Tech . 28 , 38 - 43 ( 2017 ). DOI: 10.1007/s41365-017-0209-8 http://doi.org/10.1007/s41365-017-0209-8 . Discrimination of foodborne pathogenic bacteria using synchrotron FTIR microspectroscopy S. Passot , J. Gautier , F. Jamme , et al ., Understanding the cryotolerance of lactic acid bacteria using combined synchrotron infrared and fluorescence microscopies . Analyst . 140 , 5920 - 5928 ( 2015 ). DOI: 10.1039/c5an00654f http://doi.org/10.1039/c5an00654f . Understanding the cryotolerance of lactic acid bacteria using combined synchrotron infrared and fluorescence microscopies R. Davis and L.J. Mauer . Fourier transform infrared (FT-IR) spectroscopy: A rapid tool for detection and analysis of foodborne pathogenic bacteria . Technology and Education Topics in Applied Microbiology and Microbial Biotechnology . 1582 - 1594 ( 2010 ). Fourier transform infrared (FT-IR) spectroscopy: A rapid tool for detection and analysis of foodborne pathogenic bacteria M. Wenning , F. Breitenwieser , R. Konrad , et al ., Identification and differentiation of food-related bacteria: A comparison of FTIR spectroscopy and MALDI-TOF mass spectrometry . J. Microbiol. Methods . 103 , 44 - 52 ( 2014 ). DOI: 10.1016/j.mimet.2014.05.011 http://doi.org/10.1016/j.mimet.2014.05.011 . Identification and differentiation of food-related bacteria: A comparison of FTIR spectroscopy and MALDI-TOF mass spectrometry O.E. Preisner , J.C. Menezes , R. Guiomar , et al ., Discrimination of Salmonella enterica serotypes by Fourier transform infrared spectroscopy . Food Res. Int . 45 , 1058 - 1064 ( 2012 ). DOI: 10.1016/j.foodres.2011.02.029 http://doi.org/10.1016/j.foodres.2011.02.029 . Discrimination of Salmonella enterica serotypes by Fourier transform infrared spectroscopy X.J. Hu , Z.X. Liu , Y.D. Wang , et al ., Synchrotron FTIR spectroscopy reveals molecular changes in Escherichia coli upon Cu2+ exposure . Nucl. Sci. Tech . 27 , 45 - 52 ( 2016 ). DOI: 10.1007/s41365-016-0067-9 http://doi.org/10.1007/s41365-016-0067-9 . Synchrotron FTIR spectroscopy reveals molecular changes in Escherichia coli upon Cu2+ exposure J.P. Maity , S. Kar , C.M. Lin , et al ., Identification and discrimination of bacteria using Fourier transform infrared spectroscopy . Spectrochimica Acta Part A Mol. Biomol. Spectrosc . 116 , 478 - 484 ( 2013 ). DOI: 10.1016/j.saa.2013.07.062 http://doi.org/10.1016/j.saa.2013.07.062 . Identification and discrimination of bacteria using Fourier transform infrared spectroscopy A. Alvarez-Ordóñez , D.J.M. Mouwen , M. López , et al ., Fourier transform infrared spectroscopy as a tool to characterize molecular composition and stress response in foodborne pathogenic bacteria . J. Microbiol. Methods . 84 , 369 - 378 ( 2011 ). DOI: 10.1016/j.mimet.2011.01.009 http://doi.org/10.1016/j.mimet.2011.01.009 . Fourier transform infrared spectroscopy as a tool to characterize molecular composition and stress response in foodborne pathogenic bacteria M.A. Alruwaili . Attenuated Total Reflectance-Fourier-Transform Infrared Microspectroscopy a Rapid Method for Microbial Strain Characterization . American Journal of Agricultural and Biological Sciences . 8 , 135 - 141 ( 2013 ). DOI: 10.3844/ajabssp.2013.135.141 http://doi.org/10.3844/ajabssp.2013.135.141 . Attenuated Total Reflectance-Fourier-Transform Infrared Microspectroscopy a Rapid Method for Microbial Strain Characterization D.J.M. Mouwen , A. Hörman , H. Korkeala , et al ., Applying Fourier-transform infrared spectroscopy and chemometrics to the characterization and identification of lactic acid bacteria . Vib. Spectrosc . 56 , 193 - 201 ( 2011 ). DOI: 10.1016/j.vibspec.2011.02.008 http://doi.org/10.1016/j.vibspec.2011.02.008 . Applying Fourier-transform infrared spectroscopy and chemometrics to the characterization and identification of lactic acid bacteria D. Ami , P. Mereghetti , S.M. Doglia . Multivariate Analysis for Fourier Transform Infrared Spectra of Complex Biological Systems and Processes . Multivar. Anal. Manag. Eng. Sci ., 189 - 220 ( 2013 ). DOI: 10.5772/53850 http://doi.org/10.5772/53850 . Multivariate Analysis for Fourier Transform Infrared Spectra of Complex Biological Systems and Processes D. Naumann . Infrared spectroscopy in microbiology . Encyclopedia of Analytical Chemistry . 1 - 29 ( 2000 ). DOI: 10.1002/9780470027318.a0117 http://doi.org/10.1002/9780470027318.a0117 . Infrared spectroscopy in microbiology We thank the staff from BL01B beamline of National Center for Protein Science Shanghai (NCPSS) at Shanghai Synchrotron Radiation Facility, for assistance during data collection. 10.1007/s41365-019-0554-x This work was supported by the National Natural Science Foundation of China (Nos. U1732130 and 11474298), and Key Research Program of Frontier Sciences of the Chinese Academy Sciences (No. QYZDJ-SSW-SLH019). 2018-07-15 2018-08-25 2018-09-01 2019-01-18 2019-02-01 2021-04-10 © China Science Publishing & Media Ltd. (Science Press), Shanghai Institute of Applied Physics, the Chinese Academy of Sciences, ChineseNuclear Society and Springer Nature Singapore Pte Ltd. 2019 This is a post-peer-review, pre-copyedit version of an article published in Nuclear Science and Techniques. The final authenticated version is available online at: http://dx.doi.org/10.1007/s41365-019-0554-x http://dx.doi.org/10.1007/s41365-019-0554-x . 2019 Nuclear Science and Techniques 02 30