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Identification and detection sensitivity of Microcystis aeruginosa from mixed and field samples using MALDI-TOF MS

  • Li-Wei Sun
  • Wen-Jing Jiang
  • Jun-Yi Zhang
  • Wen-Qian Wang
  • Yang Du
  • Hiroaki Sato
  • Masanobu Kawachi
  • Ran Yu
Article

Abstract

To verify the applicability of identifying Microcystis aeruginosa by matrix-assisted laser desorption-ionization-time-of-flight mass spectrometry (MALDI-TOF MS), mixed and field samples were employed to study the sensitivity and the analysis power, respectively. Series diluted samples and artificially mixed samples by the M. aeruginosa NIES-843 strain were designed to verify the sensitivity. The lowest detection limit was 1.955 × 106 cells in pure samples, while for mixed samples, the lowest detection limit and ratio of NIES-843 strain were 2.88 × 106 cells and 33.7%, respectively. The results provided a reference for the reasonable volume of the water sample in which the M. aeruginosa could be detected. Ribosomal protein biomarkers for identifying M. aeruginosa which were successfully detected from the field samples in Taihu Lake, indicated that the identification of M. aeruginosa by MALDI-TOF MS could be applied in field samples. Furthermore, different genetic types of M. aeruginosa strains were also detected at different locations in Taihu Lake, which revealed the diversity of M. aeruginosa and the detection power of MALDI-TOF MS at the strain level for the field samples. The sensitivity and detection power in the analysis of M. aeruginosa by the MALDI-TOF MS demonstrated the applicability of this method in routine environmental monitoring.

Keywords

Microcystis aeruginosa MALDI-TOF MS Cyanobacterial bloom Ribosomal protein 

Notes

Acknowledgements

The present study was supported by the National Key Research and Development Program - China (2016YFB0601003). We would like to express our gratitude toward Dr. Kosei Yumoto at MCC-NIES, Japan, for providing cultures of all cyanobacteria strains. We thank Dr. Noriko Takamura for her cooperation in the present research.

Author contributions

Li-Wei Sun and Wen-Jing Jiang conceived and designed the experiments; Li-Wei Sun, Wen-Jing Jiang, and Yang Du performed the experiment; Jun-Yi Zhang and Wen-Qian Wang partly performed the experiments; Wen-Jing Jiang, Masanobu Kawachi, and Li-Wei Sun analyzed the data; Ran Yu conceived part of the experiment and prepared part of the manuscript; Hiroaki Sato contributed to the analysis of data and improved the manuscript; Li-Wei Sun wrote the paper.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Australian and New Zealand Environment and Conservation Council (ANZECC) (2000). Australian and New Zealand guidelines for fresh and marine water quality. Canberra: ANZECC.Google Scholar
  2. Backer, L. C., Landsberg, J. H., Melissa, M., Kevin, K., & Taylor, T. K. (2013). Canine cyanotoxin poisonings in the United States (1920s–2012): review of suspected and confirmed cases from three data sources. Toxins, 5(9), 1597–1628.CrossRefGoogle Scholar
  3. Bizzini, A., Durussel, C., Bille, J., Greub, G., & Prod'Hom, G. (2010). Performance of matrix-assisted laser desorption ionization-time of flight mass spectrometry for identification of bacterial strains routinely isolated in a clinical microbiology laboratory. Journal of Clinical Microbiology, 48(5), 1549–1554.CrossRefGoogle Scholar
  4. Briand, E., Escoffier, N., Straub, C., Sabart, M., Quiblier, C., & Humbert, J. F. (2009). Spatiotemporal changes in the genetic diversity of a bloom-forming Microcystis aeruginosa (cyanobacteria) population. ISME Journal, 3(4), 419–429.CrossRefGoogle Scholar
  5. Carmichael, W. W. (1994). The toxins of cyanobacteria. Scientific American, 270(1), 78–86.CrossRefGoogle Scholar
  6. Chorus, I., & Bartram, J. (1999). Toxic cyanobacteria in water: A guide to their public health consequences, monitoring and management. CRC Press.Google Scholar
  7. Chorus, I., Falconer, I. R., Salas, H. J., & Bartram, J. (2000). Health risks caused by freshwater cyanobacteria in recreational waters. Journal of Toxicology and Environmental Health Part B, 3(4), 323–347.CrossRefGoogle Scholar
  8. Ciccimaro, E., & Blair, I. A. (2010). Stable-isotope dilution lc–ms for quantitative biomarker analysis. Bioanalysis, 2(2), 311–341.CrossRefGoogle Scholar
  9. Dong, G., Zhu, X., Han, D., Yang, Y., Song, L., & Xie, S. (2009). Effects of dietary cyanobacteria of two different sources on growth and recovery of hybrid tilapia (Oreochromis niloticus x O. aureus). Toxicon, 54(3), 208–216.CrossRefGoogle Scholar
  10. Fagerquist, C. K., Bates, A. H., Heath, S., King, B. C., Garbus, B. R., Harden, L. A., & Miller, W. G. (2006). Sub-speciating Campylobacter jejuni by proteomic analysis of its protein biomarkers and their post-translational modifications. Journal of Proteome Research, 5(10), 2527–2538.CrossRefGoogle Scholar
  11. Francis, G. (1978). Poisonous Australian lake. Nature, 18(444), 11–12.CrossRefGoogle Scholar
  12. Gekenidis, M. T., Studer, P., Wüthrich, S., Brunisholz, R., & Drissner, D. (2014). Beyond the matrix-assisted laser desorption ionization (MALDI) biotyping workflow: in search of microorganism-specific tryptic peptides enabling discrimination of subspecies. Applied and Environmental Microbiology, 80(14), 4234–4241.CrossRefGoogle Scholar
  13. Gonçalves, A., Poeta, P., Monteiro, R., Marinho, C., Silva, N., Guerra, A., Petrucci-Fonseca, F., Rodrigues, J., Torres, C., Vitorino, R., Domingues, P., & Igrejas, G. (2014). Comparative proteomics of an extended spectrum β-lactamase producing Escherichia coli strain from the Iberian wolf. Journal of Proteomics, 104(6), 80–93.CrossRefGoogle Scholar
  14. Hai-Yan, Y. U. (2009). Study on correlation between chlorophyll a and algal density of biological monitoring. Environmental Monitoring in China.Google Scholar
  15. Humbert, J. F., Barbe, V., Latifi, A., Gugger, M., Calteau, A., Coursin, T., Lajus, A., Castelli, V., Oztas, S., Samson, G., Longin, C., Medigue, C., & de Marsac, N. T. (2013). A tribute to disorder in the genome of the bloom-forming freshwater cyanobacterium, microcystis aeruginosa. PLoS One, 8(8), e70747.CrossRefGoogle Scholar
  16. Janse, I., Kardinaal, W. E. A., Meima, M., Fastner, J., Visser, P. M., & Zwart, G. (2004). Toxic and nontoxic Microcystis colonies in natural populations can be differentiated on the basis of rRNA gene internal transcribed spacer diversity. Applied and Environmental Microbiology, 70(7), 3979–3987.CrossRefGoogle Scholar
  17. Kaneko, T., Nakajima, N., Okamoto, S., Suzuki, I., Tanabe, Y., Tamaoki, M., et al. (2008). Complete genomic structure of the bloom-forming toxic cyanobacterium Microcystis aeruginosa NIES-843. DNA Research, 14(6), 247–256.CrossRefGoogle Scholar
  18. Kasai, F., & Kawachi, M. (2004). NIES-collection, list of strains, microalgae and protozoa (7th ed.). Tsukuba: National Institute for Environmental Studies.Google Scholar
  19. Krásný, L., Hynek, R., & Hochel, I. (2013). Identification of bacteria using mass spectrometry techniques. International Journal of Mass Spectrometry, 353(1), 67–79.CrossRefGoogle Scholar
  20. Lawton, L., Marsalek, B., Padisák, J., Chorus, I. (1999). Determination of cyanobacteria in the laboratory. Toxic cyanobacteria in water: A guide to their public health consequences, monitoring and management, pp. 1–28.Google Scholar
  21. Li, X. Y., Liu, Y. D., & Song, L. R. (2010). Cytological alterations in isolated hepatocytes from common carp (Cyprinus carpio L.) exposed to microcystin-LR. Environmental Toxicology, 16(6), 517–522.CrossRefGoogle Scholar
  22. Linscheid, M. W., Ahrends, R., Pieper, S., & Kühn, A. (2009). Liquid chromatography–mass spectrometry-based quantitative proteomics. Methods in Molecular Biology, 564, 189–205.CrossRefGoogle Scholar
  23. Maier, H. R., Dandy, G. C., & Burch, M. D. (1998). Use of artificial neural networks for modelling Cyanobacteria anabaena spp. in the River Murray, South Australia. Ecological Modelling, 105(2), 257–272.CrossRefGoogle Scholar
  24. Mankiewiczboczek, J., Palus, J., Gagała, I., Izydorczyk, K., Jurczak, T., Dziubałtowska, E., et al. (2011). Effects of microcystins-containing cyanobacteria from a temperate ecosystem on human lymphocytes culture and their potential for adverse human health effects. Harmful Algae, 10(4), 356–365.CrossRefGoogle Scholar
  25. Oe, T., Maekawa, M., Satoh, R., Lee, S. H., & Goto, T. (2010). Combining [13c6]-phenylisothiocyanate and the Edman degradation reaction: a possible breakthrough for absolute quantitative proteomics together with protein identification. Rapid Communications in Mass Spectrometry, 24(2), 173–179.CrossRefGoogle Scholar
  26. Paerl, H. W., Hall, N. S., & Calandrino, E. S. (2011a). Controlling harmful cyanobacterial blooms in a world experiencing anthropogenic and climatic-induced change. Science of the Total Environment, 409(10), 1739–1745.CrossRefGoogle Scholar
  27. Paerl, H. W., Xu, H., Mccarthy, M. J., Zhu, G., Qin, B., Li, Y., et al. (2011b). Controlling harmful cyanobacterial blooms in a hyper-eutrophic lake (Lake Taihu, China): the need for a dual nutrient (N & P) management strategy. Water Research, 45(5), 1973–1983.CrossRefGoogle Scholar
  28. Pantelić, D., Svirčev, Z., Simeunović, J., Vidović, M., & Trajković, I. (2013). Cyanotoxins: characteristics, production and degradation routes in drinking water treatment with reference to the situation in Serbia. Chemosphere, 91(4), 421–441.CrossRefGoogle Scholar
  29. Qin, B., Zhu, G., Gao, G., Zhang, Y., Li, W., Paerl, H. W., & Carmichael, W. W. (2010). A drinking water crisis in Lake Taihu, China: linkage to climatic variability and lake management. Environmental Management, 45(1), 105–112.CrossRefGoogle Scholar
  30. Qiujin, X. U. (2001). Ecological simulation of algae growth in Taihu Lake. Journal of Lake Science.Google Scholar
  31. Recknagel, F. (1997). Anna – artificial neural network model for predicting species abundance and succession of blue-green algae. Hydrobiologia, 349(1–3), 47–57.CrossRefGoogle Scholar
  32. Sauer, S., & Kliem, M. (2010). Mass spectrometry tools for the classification and identification of bacteria. Nature Reviews Microbiology, 8(1), 74–82.CrossRefGoogle Scholar
  33. Schindler, D. W. (1978). Factors regulating phytoplankton production and standing crop in the world’s freshwaters. Limnology and Oceanography, 23(3), 478–486.CrossRefGoogle Scholar
  34. Shi, K., Zhang, Y., Zhu, G., Liu, X., Zhou, Y., Xu, H., Qin, B., Liu, G., & Li, Y. (2015). Long-term remote monitoring of total suspended matter concentration in lake taihu using 250 m modis-aqua data. Remote Sensing of Environment, 164(2), 43–56.CrossRefGoogle Scholar
  35. Song, L., Sano, T., Li, R., Watanabe, M. M., Liu, Y., & Kaya, K. (2010). Microcystin production of Microcystis viridis (cyanobacteria) under different culture conditions. Phycological Research, 46(s2), 19–23.CrossRefGoogle Scholar
  36. Srivastava, A., Singh, S., Ahn, C. Y., Oh, H. M., & Asthana, R. K. (2013). Monitoring approaches for a toxic cyanobacterial bloom. Environmental Science & Technology, 47(16), 8999–9013.CrossRefGoogle Scholar
  37. Sun, L., Teramoto, K., Sato, H., Torimura, M., Tao, H., & Shintani, T. (2010). Characterization of ribosomal proteins as biomarkers for matrix-assisted laser desorption/ionization mass spectral identification of Lactobacillus plantarum. Rapid Communications in Mass Spectrometry, 20(24), 3789–3798.CrossRefGoogle Scholar
  38. Sun, L. W., Jiang, W. J., Sato, H., Kawachi, M., & Lu, X. W. (2016). Rapid classification and identification of Microcystis aeruginosa strains using MALDI–TOF MS and polygenetic analysis. PLoS One, 11(5), e0156275.CrossRefGoogle Scholar
  39. Teramoto, K., Sato, H., Sun, L., Torimura, M., Tao, H., Yoshikawa, H., Hotta, Y., Hosoda, A., & Tamura, H. (2007). Phylogenetic classification of Pseudomonas putida strains by MALDI-MS using ribosomal subunit proteins as biomarkers. Analytical Chemistry, 79(22), 8712–8719.CrossRefGoogle Scholar
  40. World Health Organization. (1998). Guidelines for drinking-water quality. Vol. 2, health criteria and other supporting information: addendum (No. WHO/EOS/98.1). Geneva: World Health Organization.Google Scholar
  41. Xiang, F., Anderson, G. A., Veenstra, T. D., And, M. S. L., & Smith, R. D. (2000). Characterization of microorganisms and biomarker development from global ESI-MS/MS analyses of cell lysates. Analytical Chemistry, 72(11), 2475–2481.CrossRefGoogle Scholar
  42. Xie, F., Liu, T., Qian, W. J., Petyuk, V. A., Smith, R. D. (2011). Liquid chromatography-mass spectrometry-based quantitative proteomics. Journal of Biological Chemistry, jbc-R110.Google Scholar
  43. Ying, L. I., Shi, Z., Zhang, Y., Zhao, Q., Aijun, L. I., Jin, Y., et al. (2014). Evaluation method and application on cyanobacteria bloom degree classification with algal density. Environment & Sustainable Development.Google Scholar
  44. Zamyadi, A., Macleod, S. L., Fan, Y., Mcquaid, N., Dorner, S., SauvÉ, S., et al. (2012). Toxic cyanobacterial breakthrough and accumulation in a drinking water plant: a monitoring and treatment challenge. Water Research, 46(5), 1511–1523.CrossRefGoogle Scholar
  45. Zhou, J., Qin, B., Han, X., & Zhu, L. (2016). Turbulence increases the risk of microcystin exposure in a eutrophic lake (Lake Taihu) during cyanobacterial bloom periods. Harmful Algae, 55, 213–220.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Li-Wei Sun
    • 1
    • 2
  • Wen-Jing Jiang
    • 1
    • 2
  • Jun-Yi Zhang
    • 3
  • Wen-Qian Wang
    • 1
    • 2
  • Yang Du
    • 1
    • 2
  • Hiroaki Sato
    • 4
  • Masanobu Kawachi
    • 5
  • Ran Yu
    • 1
    • 2
  1. 1.School of Energy & EnvironmentSoutheast UniversityNanjingChina
  2. 2.Taihu Lake Water Environment Engineering Research Center (Wuxi)Southeast UniversityWuxiChina
  3. 3.Wuxi Environmental Monitoring CenterWuxiChina
  4. 4.Polymer Chemistry Group, Research Institute for Sustainable ChemistryNational Institute of Advanced Industrial Science and TechnologyTsukubaJapan
  5. 5.Biodiversity Resource Conservation Section, Center for Environmental Biology and Ecosystem StudiesNational Institute for Environmental StudiesTsukubaJapan

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