Skip to main content
SpringerLink
Log in

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

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Australian and New Zealand Environment and Conservation Council (ANZECC) (2000). Australian and New Zealand guidelines for fresh and marine water quality. Canberra: ANZECC.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Carmichael, W. W. (1994). The toxins of cyanobacteria. Scientific American, 270(1), 78–86.

    Article  CAS  Google Scholar 

  • Chorus, I., & Bartram, J. (1999). Toxic cyanobacteria in water: A guide to their public health consequences, monitoring and management. CRC Press.

  • 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.

    Article  CAS  Google Scholar 

  • Ciccimaro, E., & Blair, I. A. (2010). Stable-isotope dilution lc–ms for quantitative biomarker analysis. Bioanalysis, 2(2), 311–341.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Francis, G. (1978). Poisonous Australian lake. Nature, 18(444), 11–12.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hai-Yan, Y. U. (2009). Study on correlation between chlorophyll a and algal density of biological monitoring. Environmental Monitoring in China.

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Kasai, F., & Kawachi, M. (2004). NIES-collection, list of strains, microalgae and protozoa (7th ed.). Tsukuba: National Institute for Environmental Studies.

    Google Scholar 

  • Krásný, L., Hynek, R., & Hochel, I. (2013). Identification of bacteria using mass spectrometry techniques. International Journal of Mass Spectrometry, 353(1), 67–79.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • Linscheid, M. W., Ahrends, R., Pieper, S., & Kühn, A. (2009). Liquid chromatography–mass spectrometry-based quantitative proteomics. Methods in Molecular Biology, 564, 189–205.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Qiujin, X. U. (2001). Ecological simulation of algae growth in Taihu Lake. Journal of Lake Science.

  • Recknagel, F. (1997). Anna – artificial neural network model for predicting species abundance and succession of blue-green algae. Hydrobiologia, 349(1–3), 47–57.

    Article  CAS  Google Scholar 

  • Sauer, S., & Kliem, M. (2010). Mass spectrometry tools for the classification and identification of bacteria. Nature Reviews Microbiology, 8(1), 74–82.

    Article  CAS  Google Scholar 

  • Schindler, D. W. (1978). Factors regulating phytoplankton production and standing crop in the world’s freshwaters. Limnology and Oceanography, 23(3), 478–486.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

  • 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.

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

Download references

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 information

Authors and Affiliations

  1. School of Energy & Environment, Southeast University, Nanjing, Jiangsu, China

    Li-Wei Sun, Wen-Jing Jiang, Wen-Qian Wang, Yang Du & Ran Yu

  2. Taihu Lake Water Environment Engineering Research Center (Wuxi), Southeast University, Wuxi, Jiangsu, China

    Li-Wei Sun, Wen-Jing Jiang, Wen-Qian Wang, Yang Du & Ran Yu

  3. Wuxi Environmental Monitoring Center, Wuxi, Jiangsu, China

    Jun-Yi Zhang

  4. Polymer Chemistry Group, Research Institute for Sustainable Chemistry, National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki, Japan

    Hiroaki Sato

  5. Biodiversity Resource Conservation Section, Center for Environmental Biology and Ecosystem Studies, National Institute for Environmental Studies, Tsukuba, Ibaraki, Japan

    Masanobu Kawachi

Authors
  1. Li-Wei Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wen-Jing Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun-Yi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wen-Qian Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yang Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hiroaki Sato
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Masanobu Kawachi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ran Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Corresponding authors

Correspondence to Li-Wei Sun or Ran Yu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, LW., Jiang, WJ., Zhang, JY. et al. Identification and detection sensitivity of Microcystis aeruginosa from mixed and field samples using MALDI-TOF MS. Environ Monit Assess 190, 712 (2018). https://doi.org/10.1007/s10661-018-7093-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-018-7093-5

Keywords

Search

Navigation