The Plant Pathology Journal

The Korean Society of Plant Pathology (한국식물병리학회)
권호 표지
DOI QR코드

DOI QR Code

Detection of Clavibacter michiganensis subsp. michiganensis Assisted by Micro-Raman Spectroscopy under Laboratory Conditions

  • Perez, Moises Roberto Vallejo (CONACyT- Universidad Autonoma de San Luis Potosi) ;
  • Contreras, Hugo Ricardo Navarro (Universidad Autonoma de San Luis Potosi. Coordinacion para la Innovacion y la Aplicacion de la Ciencia y la Tecnologia (CIACyT)) ;
  • Herrera, Jesus A. Sosa (CONACyT- Centro de Investigacion en Ciencias de Informacion Geoespacial A.C. Circuito Tecnopolo Norte 117, Col. Fraccionamiento Tecnopolo Pocitos) ;
  • Avila, Jose Pablo Lara (Universidad Autonoma de San Luis Potosi. Facultad de Agronomia y Veterinaria. Km. 14.5 Carretera San Luis Potosi, Matehuala, Ejido Palma de la Cruz, Soledad de Graciano Sanchez) ;
  • Tobias, Hugo Magdaleno Ramirez (Universidad Autonoma de San Luis Potosi. Facultad de Agronomia y Veterinaria. Km. 14.5 Carretera San Luis Potosi, Matehuala, Ejido Palma de la Cruz, Soledad de Graciano Sanchez) ;
  • Martinez, Fernando Diaz-Barriga (Universidad Autonoma de San Luis Potosi. Coordinacion para la Innovacion y la Aplicacion de la Ciencia y la Tecnologia (CIACyT)) ;
  • Ramirez, Rogelio Flores (CONACyT- Universidad Autonoma de San Luis Potosi) ;
  • Vazquez, Angel Gabriel Rodriguez (Universidad Autonoma de San Luis Potosi. Coordinacion para la Innovacion y la Aplicacion de la Ciencia y la Tecnologia (CIACyT))
  • Received : 2018.02.07
  • Accepted : 2018.05.31
  • Published : 2018.10.01
Download PDF
⟨ Previous Next ⟩

Abstract

Clavibacter michiganensis subsp. michiganesis (Cmm) is a quarantine-worthy pest in $M{\acute{e}}xico$. The implementation and validation of new technologies is necessary to reduce the time for bacterial detection in laboratory conditions and Raman spectroscopy is an ambitious technology that has all of the features needed to characterize and identify bacteria. Under controlled conditions a contagion process was induced with Cmm, the disease epidemiology was monitored. Micro-Raman spectroscopy ($532nm\;{\lambda}$ laser) technique was evaluated its performance at assisting on Cmm detection through its characteristic Raman spectrum fingerprint. Our experiment was conducted with tomato plants in a completely randomized block experimental design (13 plants ${\times}$ 4 rows). The Cmm infection was confirmed by 16S rDNA and plants showed symptoms from 48 to 72 h after inoculation, the evolution of the incidence and severity on plant population varied over time and it kept an aggregated spatial pattern. The contagion process reached 79% just 24 days after the epidemic was induced. Micro-Raman spectroscopy proved its speed, efficiency and usefulness as a non-destructive method for the preliminary detection of Cmm. Carotenoid specific bands with wavelengths at 1146 and $1510cm^{-1}$ were the distinguishable markers. Chemometric analyses showed the best performance by the implementation of PCA-LDA supervised classification algorithms applied over Raman spectrum data with 100% of performance in metrics of classifiers (sensitivity, specificity, accuracy, negative and positive predictive value) that allowed us to differentiate Cmm from other endophytic bacteria (Bacillus and Pantoea). The unsupervised KMeans algorithm showed good performance (100, 96, 98, 91 y 100%, respectively).

Keywords

References

  1. Abdi, H. and Williams, L. J. 2010. Principal component analysis. WIREs Comp. Stat. 2:433-459. https://doi.org/10.1002/wics.101
  2. Ashton, L., Lau, K., Winder, C. L. and Goodacre, R. 2011. Raman spectroscopy: lighting up the future of microbial identification. Future Microbiol. 6:991-997. https://doi.org/10.2217/fmb.11.89
  3. Athamneh, A. I. M. and Senger, R. S. 2012. Peptide-guided surface-enhanced Raman scattering probes for localized cell composition analysis. Appl. Environ. Microbiol. 78:7805-7808. https://doi.org/10.1128/AEM.02000-12
  4. Benson, D. A., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J. and Sayers, E. W. 2010. GenBank. Nucleic Acids Res. 38:D46-D51. https://doi.org/10.1093/nar/gkp1024
  5. Bentley, S. D., Corton, C., Brown, S. E., Barron, A., Clark, L., Doggett, J., Harris, B., Ormond, D., Quail, M. A., May, G., Francis, D., Knudson, D., Parkhill, J. and Ishimaru, C. A. 2008. Genome of the actinomycete plant pathogen Clavibacter michiganensis subsp. sepedonicus suggests recent niche adaptation. J. Bacteriol. 190:2150-2160. https://doi.org/10.1128/JB.01598-07
  6. Campbell, C. L. and Madden, L. V. 1990. Introduction to Plant Disease Epidemiology. John Wiley and Sons, New York, USA. 532 pp.
  7. Chang, R. J., Ries, S. M. and Pataky, J. K. 1991. Dissemination of Clavibacter michiganensis subsp. michiganensis by practices used to produce tomato transplants. Phytopahology 81:1276-1281. https://doi.org/10.1094/Phyto-81-1276
  8. Chen, T., Madey, J. M. J., Price, F. M., Sharma, S. K. and Lienert, B. 2007. Remote Raman spectra of benzene obtained from 217 meters using a single 532 nm laser pulse. Appl. Spectrosc. 61:624-629. https://doi.org/10.1366/000370207781269756
  9. Colvine, S. and Branthome, F. X. 2016. The tomato: a seasoned traveler. In: The Tomate Genome, eds. by M. Causse, J. Giovannoni, M. Bouzajen and M. Zouine, pp. 1-6. Springer-Verlag, Berlin, Germany.
  10. Eichenlaub, R. and Gartemann, K. H. 2011. The Clavibacter michiganensis subspecies: molecular investigation of gram-positive bacterial plant pathogens. Annu. Rev. Phytopathol. 49:445-464. https://doi.org/10.1146/annurev-phyto-072910-095258
  11. EPPO, 2016. PM 7/42 (3) Clavibacter michiganensis subsp. michiganensis. EPPO Bulletin 46:202-225. https://doi.org/10.1111/epp.12302
  12. FAO, 2017. Food and Agriculture Organization. Deposited on: URL http://www.fao.org/faostat/es/#rankings/countries_by_commodity/ [15 July 2017].
  13. Fletcher, J., Bender, C., Budowle, B., Cobb, W. T., Gold, S. E., Ishimaru, C. A., Luster, D., Melcher, U., Murch, R., Scherm, H., Seem, R. C., Sherwood, J. L., Sobral, B. W. and Tolin, S. A. 2006. Plant pathogen forensics: capabilities, needs and recommendations. Microbiol. Mol. Biol. Rev. 70:450-471. https://doi.org/10.1128/MMBR.00022-05
  14. Gan, Q., Wang, X., Wang, Y., Xie, Z., Tian, Y. and Lu, Y. 2017. Culture-free detection of crop pathogen at the single-cell level by Micro-Raman spectroscopy. Adv. Sci. 4:1700127. https://doi.org/10.1002/advs.201700127
  15. Gelder, J. D., Gussem, K. D., Vandenabeele, P., Vancanneyt, M., Vos, P. D. and Moens, L. 2007. Methods for extracting biochemical information from bacterial Raman spectra: focus on a group of structurally similar biomolecules-Fatty acids. Anal. Chim. Acta 603:167-175. https://doi.org/10.1016/j.aca.2007.09.049
  16. Guillen-Sanchez, D., Teliz-Ortiz, D., Mora-Aguilera, G., Mora-Aguilera, A., Sanchez-Garcia, P. and Gonzalez-Hernandez, V. 2003. Desarrollo temporal de epidemias de cenicilla (Oidium mangiferae Berthet) en huertos de mango (Mangifera indica L.) en Michoacan, Mexico. Rev. Mex. Fitopatol. 21:181-188 (in Spanish).
  17. Hartigan, J. A. and Wong, M. A. 1979. Algorithm as 136: A k-means clustering algorithm. J. Royal Stat. Soc. 28:100-108.
  18. Izenman, A. J. 2008. Linear Discriminant Analysis. In: Modern Multivariate Statistical Techniques, pp. 237-280. Springer, New York, USA.
  19. Jasso, C. C., Martinez, G. M. A., Chavez, V. J. R., Ramirez, T. J. A. and Garza, U. E. 2012. Guia para cultivar jitomate en condiciones de malla sombra en San Luis Potosi. URL http://www.inifapcirne.gob.mx/Biblioteca/Publicaciones/905.pdf/ (in Spanish)
  20. Jehlicka, J. and Oren, A. 2013. Raman spectroscopy in halophile research. Front. Microbiol. 4:380.
  21. Jolliffe, I. 2002. Principal components as a small number of interpretable variables: some examples. In: Principal Component Analysis. ed by J. Jolliffe, pp. 63-77. Springer, New York, USA.
  22. Kawaguchi, A., Tanina, K. and Inoue, K. 2010. Molecular typing and spread of Clavibacter michiganensis subsp. michiganensis in greenhouses in Japan. Plant Pathol. 59:76-83. https://doi.org/10.1111/j.1365-3059.2009.02207.x
  23. Knapp, S. and Peralta, I. E. 2016. The tomato (Solanum lycopersicum L., Solanaceae) and its botanical relatives. In: The Tomate Genome, eds. by M. Causse, J. Giovannoni, M. Bouzajen and M. Zouine, pp. 7-21. Springer-Verlag, Berlin, Germany.
  24. Lorenz, B., Wichmann, C., Stockel, S., Rosch, P. and Popp, J. 2017. Cultivation-free Raman spectroscopy investigations of bacteria. Trends Microbiol. 25:413-424. https://doi.org/10.1016/j.tim.2017.01.002
  25. Maiti, N., Kapoor, S. and Mukherjee, T. 2013. Surface-enhanced Raman Scattering (SERS) spectroscopy for trace level detection of chlorogenic acid. Adv. Mater. Lett. 4:502-506. https://doi.org/10.5185/amlett.2012.ib.121
  26. Malard, L. M., Pimenta, M. A., Dresselhaus, G. and Dresselhaus, M. S. 2009. Raman spectroscopy in grapheme. Phys. Rep. 473:51-87. https://doi.org/10.1016/j.physrep.2009.02.003
  27. Martinez-Castro, E., Jarquin-Galvez, R., Alpuche-Solis, A. G., Vallejo-Perez, M. R., Colli-Mull, J. G. and Lara-Avila, J. P. 2018. Bacterial wilt and canker of tomato: fundamentals of a complex biological system. Euphytica 214:72. https://doi.org/10.1007/s10681-018-2140-4
  28. Mirski, T., Bartoszcze, M., Bielawska-Drozd, A., Cieslik, P., Michalski, A. J., Niemcewicz, M., Kocik, J. and Chomiczewski, K. 2014. Review of methods used for identification of biothreat agents in environmental protection and human health aspects. Ann. Agric. Environ. Med. 21:224-234. https://doi.org/10.5604/1232-1966.1108581
  29. Misra, A. K., Sharma, S. K., Kamemoto, L., Zinin, P. V., Yu, Q., Hu, N. and Melnick, L. 2009. Novel micro-cavity substrates for improving the Raman signal from submicrometer size materials. Appl. Spectrosc. 63:373-377. https://doi.org/10.1366/000370209787598988
  30. Monciardini, P., Sosio, M., Cavaletti, L., Chiocchini, C. and Donadio, S. 2002. New PCR primers for the selective amplification of 16S rDNA from different groups of actinomycetes. FEMS Microbiol. Ecol. 42:419-429.
  31. Movileanu, L., Benevides, J. M. and Thomas, G. J. 1999. Temperature dependence of the Raman spectrum of DNA. Part I-Raman signatures of premelting and melting transitions of poly(dA-dT).poly(dA-dT). J. Raman Spectrosc. 30:637-649. https://doi.org/10.1002/(SICI)1097-4555(199908)30:8<637::AID-JRS431>3.0.CO;2-B
  32. Paret, M. L., Sharma, S. K., Green, L. M. and Alvarez, A. M. 2010. Biochemical Characterization of Gram-Positive and Gram-Negative Plant-Associated Bacteria with Micro-Raman Spectroscopy. Appl. Spectrosc. 64:433-441. https://doi.org/10.1366/000370210791114293
  33. Paret, M. L., Sharma, S. K. and Alvarez, A. M. 2012. Characterization of biofumigated Ralstonia solanacearum cells using Micro-Raman spectroscopy and electron microscopy. Phytopathology 102:105-113. https://doi.org/10.1094/PHYTO-12-10-0330
  34. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. 2011. Scikit-learn:machine learning in Python. J. Mach. Learn. Res. 12:2825-2830.
  35. Perez, M. R. V., Mendoza, M. G., Elias, M. G., Gonzalez, F. J., Contreras, H. R. and Servin, C. C. 2016. Raman spectroscopy an option for the early detection of citrus Huanglongbing. Appl. Spectrosc. 70:829-839. https://doi.org/10.1177/0003702816638229
  36. Polisetti, S., Bible, A. N., Morrell-Falvey, J. L. and Bohn, P. W. 2016. Raman chemical imaging of the rhizosphere bacterium Pantoea sp. YR343 and its co-culture with Arabidopsis thailiana. Analyst 141:2175-2182. https://doi.org/10.1039/C6AN00080K
  37. Pruesse, E., Peplies, J. and Glockner, F. O. 2012. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 28:1823-1829. https://doi.org/10.1093/bioinformatics/bts252
  38. Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J. and Glockner, F. O. 2013. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41:D590-D596.
  39. Sambrook, J. and Russell, D. 2001. Molecular Cloning: A Laboratory Manual. 3rd ed. Cold Spring Harbor Laboratory Press, NY, USA. 2344 pp.
  40. Santos, M. C., Morais, C. L., Nascimento, Y. M., Araujo, J. M. and Lima, K. M. 2017. Spectroscopy with computational analysis in virological studies: A decade (2006-2016). Trends Analyt. Chem. 97:244-256. https://doi.org/10.1016/j.trac.2017.09.015
  41. Saperstein, S., Starr, M. P. and Filfus, J. A. 1954. Alterations in carotenoid synthesis accompanying mutation in Corynebacterium michiganense. J. Gen. Microbiol. 10:85-92. https://doi.org/10.1099/00221287-10-1-85
  42. Sen, Y., Feng, Z., Vandenbroucke, H., van der Wolf, J., Visser, R. G. F. and van Heusden, A. W. 2013. Screening for new sources of resistance to Clavibacter michiganensis subsp. michiganensis (Cmm) in tomato. Euphytica 190:309-317. https://doi.org/10.1007/s10681-012-0802-1
  43. Sen, Y., van der Wolf, J., Visser, R. G. F. and van Heusden, S. 2015. Bacterial canker of tomato: current knowledge of detection, management, resistance and interactions. Plant Dis. 99:4-13. https://doi.org/10.1094/PDIS-05-14-0499-FE
  44. Sharabani, G., Shtienberg, D., Borenstein, M., Shulhani, R., Lofthouse, M., Sofer, M., Chalupowicz, L., Barel, V. and Manulis-Sasson, S. 2013. Effects of plant age on disease development and virulence of Clavibacter michiganensis subsp. michiganensis on tomato. Plant Pathol. 62:1114-1122. https://doi.org/10.1111/ppa.12013
  45. Siqueira, L. F. S. and Lima, K. M. G. 2016. MIR-biospectroscopy coupled with chemometrics in cancer studies. Analyst 141:4833-4847. https://doi.org/10.1039/C6AN01247G
  46. Velez, D. R., White, B. C., Motsinger, A. A., Bush, W. S., Ritchie, M. D., Williams, S. M. and Moore, J. H. 2007. A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genet. Epidemiol. 31:306-315. https://doi.org/10.1002/gepi.20211
  47. Wang, Y., Huang, W. E., Cui, L. and Wagner, M. 2016. Single cell stable isotope probing in microbiology using Raman microspectroscopy. Curr. Opin. Biotechnol. 41:34-42. https://doi.org/10.1016/j.copbio.2016.04.018
  48. Wold, S., Esbensen, K. and Geladi, P. 1987. Principal component analysis. Chemometr. Intell. Lab. 2:37-52. https://doi.org/10.1016/0169-7439(87)80084-9
  49. Xanthopoulos, P., Pardalos, P. M. and Trafalis, T. B. 2013. Linear Discriminant Analysis. In: Robust Data Mining, pp. 27-33. SpringerBriefs in Optimization. Springer, New York, USA.
  50. Xu, X., Miller, S. A., Baysal-Gurel, F., Gartemann, K. H., Eichenlaub, R. and Rajashekara, G. 2010. Bioluminescence imaging of Clavibacter michiganensis subsp. michiganensis infection of tomato seeds and plants. Appl. Environ. Microbiol. 76:3978-3988. https://doi.org/10.1128/AEM.00493-10
  51. Zhao, J., Lui, H., McLean, D. I. and Zeng, H. 2007. Automated autofluorescence background subtraction algorithm for biomedical spectroscopy. Appl. Spectrosc. 61:1225-1232. https://doi.org/10.1366/000370207782597003