Highly sensitive detection and discrimination of LR and YR microcystins based on protein phosphatases and an artificial neural network

Abstract

The inhibition characteristics of three different protein phosphatases by three microcystin (MC) variants—LR, YR, and RR—were studied. The corresponding K I for each enzyme–MC couple was calculated. The toxicity of MC varies in the following order: MC-LR > MC-YR > MC-RR. The sensitivity of the enzymes increased in the following order: mutant PP2A < mutant PP1 < natural PP2A. The best limit of detection obtained was 21.2 pM MC-LR using the most sensible enzyme. Methanol, ethanol, and acetonitrile up to 2 % (v/v) may be used in inhibition measurements. An artificial neural network (ANN) was used to discriminate two MC variants—LR and YR—using the differences in inhibition percentages measured with mutant PP1 and natural PP2A. The ANN is able to analyze mixtures with concentrations ranging from 8 to 98 pM MC-LR and 31 to 373 pM MC-YR.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. 1.

    Dawson RM (1998) The toxicology of microcystins. Toxicon 36:953–962

    Article  CAS  Google Scholar 

  2. 2.

    de Figueiredo DR, Azeiteiro UM, Esteves SM, Goncalves FJM, Pereira MJ (2004) Microcystin-producing blooms—a serious global public health issue. Ecotoxicol Environ Safety 59:151–163

    Article  Google Scholar 

  3. 3.

    Hastie CJ, Borthwick EB, Morrison LF, Codd GA, Cohen PTW (2005) Inhibition of several protein phosphatases by a non-covalently interacting microcystin and a novel cyanobacterial peptide, nostocyclin. Biochim Biophys Acta 1726:187–193

    Article  CAS  Google Scholar 

  4. 4.

    MacKintosh RW, Dalby KN, Campbell DG, Cohen PTW, Cohen P, MacKintosh C (1995) The cyanobacterial toxin microcystin binds covalently to cysteine-273 on protein phosphatase 1. FEBS Lett 371:236–240

    Article  CAS  Google Scholar 

  5. 5.

    Gehringer MM, Milne P, Lucietto F, Downing TG (2005) Comparison of the structure of key variants of microcystin to vasopressin. Environ Toxicol Phar 19:297–303

    Article  CAS  Google Scholar 

  6. 6.

    Yoshizawa S, Matsushima R, Watanabe MF, Hard MF, Ichihara A (1990) Inhibition of protein phosphatase by microcystin and nodularin associated with hepatotoxicity. J Cancer Res Clin Onc 116:609–614

    Article  CAS  Google Scholar 

  7. 7.

    Magalhaes VF, Soares RM, Azevedo SMFO (2001) Microcystin contamination in fish from the Jacarepagua Lagoon (Rio de Janeiro, Brazil): ecological implication and human health risk. Toxicon 39:1077–1085

    Article  CAS  Google Scholar 

  8. 8.

    Pouria S, De Andrade A, Barbosa J, Barreto VTS, Ward CJ, Preiser W, Poon GK, Neild GH, Codd GA (1998) Fatal microcystin intoxication on haemodialysis unit in Caruaru, Brazil. Lancet 352:21–26

    Article  CAS  Google Scholar 

  9. 9.

    Jochimsen EM, Carmichael WW, An J, Cardo DM, Cookson ST, Holmes CEM, Antunes MBC, De Melo Filho DA, Lyra TM, Barreto VST, Azevedo SMFO, Jarvis WR (1998) Liver failure and death after exposure to microcystins at a hemodialysis centre in Brazil. New Engl J Med 338:873–878

    Article  CAS  Google Scholar 

  10. 10.

    Lawton LA, Edwards C, Codd GA (1994) Extraction and high-performance liquid chromatography method for the determination of microcystins in raw and treated waters. Analyst 119:1525–1530

    Article  CAS  Google Scholar 

  11. 11.

    Sano T, Takagi H, Nagano K, Nishikawa M, Kaya K (2011) Accurate LC-MS analyses for microcystins using per-15N-labeled microcystins. Anal Bioanal Chem 399:2511–2516

    Article  CAS  Google Scholar 

  12. 12.

    Hiller S, Krock B, Cembella A, Luckas B (2007) Rapid detection of cyanobacterial toxins in precursor ion mode by liquid chromatography tandem mass spectrometry. J Mass Spectrom 42:1238–1250

    Article  CAS  Google Scholar 

  13. 13.

    Rivasseau C, Racaud P, Deguin A, Hennion M-C (1999) Development of a bioanalytical phosphatase inhibition test for the monitoring of microcystins in environmental water samples. Anal Chim Acta 394:243–257

    Article  CAS  Google Scholar 

  14. 14.

    Campas M, Szydlowska D, Trojanowicz M, Marty J-L (2005) Towards the protein phosphatase-based biosensor for microcystin detection. Biosens Bioelectr 20:1520–1530

    Article  CAS  Google Scholar 

  15. 15.

    Bouaïcha N, Maatouk I, Levi VY (2002) A colorimetric and fluorometric microplate assay for the detection of microcystin-LR in drinking water without preconcentration. Food Chem Toxicol 40:1677–1683

    Article  Google Scholar 

  16. 16.

    Campas M, Szydlowska D, Trojanowicz M, Marty J-L (2007) Enzyme inhibition-based biosensor for the electrochemical detection of microcystins in natural blooms of cyanobacteria. Talanta 72:179–186

    Article  CAS  Google Scholar 

  17. 17.

    Dondoi MP, Bucur B, Danet AF, Toader CN, Barthemebs L, Marty JL (2006) Organophosphorous insecticides extraction and oxidation on column for analysis with a acetylcholinesterase (AChE) biosensor. Anal Chim Acta 578:162–169

    Article  CAS  Google Scholar 

  18. 18.

    Mountfort DO, Holland P, Sprosen J (2005) Method for detecting classes of microcystins by combination of protein phosphatase inhibition assay and ELISA: comparison with LC-MS. Toxicon 45:199–206

    Article  CAS  Google Scholar 

  19. 19.

    Balabin RM, Safieva RZ, Lomakina EI (2007) Comparison of linear and nonlinear calibration models based on near infrared (NIR) spectroscopy data for gasoline properties prediction. Chemometr Intell Lab Syst 88:183–188

    Article  CAS  Google Scholar 

  20. 20.

    Zhou P, Tian F, Lv F, Shang Z (2009) Comprehensive comparison of eight statistical modelling methods used in quantitative structure–retention relationship studies for liquid chromatographic retention times of peptides generated by protease digestion of the Escherichia coli proteome. J Chromat A 1216:3107–3116

    Article  CAS  Google Scholar 

  21. 21.

    Denis-Quanquin S, Lamouroux L, Lougarre A, Maheo S, Saves I, Paquereau L, Demange P, Fournier D (2007) Protein expression from synthetic genes: selection of clones using GFP. J Biotechnol 131:223–230

    Article  CAS  Google Scholar 

  22. 22.

    Moreno-Barón L, Cartas R, Merkoci IA, Alegret S, del Valle M, Leija L, Hernandez PR, Munoz R (2006) Application of the wavelet transform coupled with artificial neural networks for quantification purposes in a voltammetric electronic tongue. Sens Actuat B1:487–499

    Google Scholar 

  23. 23.

    Montgomery DC (1997) Design and analysis of experiment. Wiley, New York

    Google Scholar 

  24. 24.

    Rocha JR, Catana R, Ferreira BS, Cabral JMS, Fernandes P (2006) Design and characterisation of an enzyme system for inulin hydrolysis. Food Chem 95:77–82

    Article  CAS  Google Scholar 

  25. 25.

    Abbot C et al. (2011) Guidelines for drinking-water quality, 4th edn. World Health Organization. ISBN 978 92 4 154815 1

  26. 26.

    Campas M, Olteanu MG, Marty J-L (2008) Enzymatic recycling for signal amplification: improving microcystin detection with biosensors. Sens Actuat B 129:263–267

    Article  Google Scholar 

  27. 27.

    Sassolas A, Catanante G, Fournier D, Marty J-L (2011) Development of a colorimetric inhibition assay for microcystin-LR detection: comparison of the sensitivity of different protein phosphatases. Talanta 85(15):2494–2503

    Google Scholar 

  28. 28.

    Yu H-W, Lee J, Kim S, Nguyen GH, Kim IS (2009) Electrochemical immunoassay using quantum dot/antibody probe for identification of cyanobacterial hepatotoxin microcystin-LR. Anal Bioanal Chem 394:2173–2181

    Article  CAS  Google Scholar 

  29. 29.

    Sun X, Shi H, Wang H, Xiao L, Li L (2010) A simple, highly sensitive, and label-free impedimetric immunosensor for detection of microcystin-LR in water. Anal Lett 43:533–544

    Article  CAS  Google Scholar 

  30. 30.

    Herranz S, Bockova M, Marazuela MD, Homola J, Moreno-Bondi MC (2010) An SPR biosensor for the detection of microcystins in drinking water. Anal Bioanal Chem 398:2625–2634

    Article  CAS  Google Scholar 

  31. 31.

    Geis-Asteggiante L, Lehotay SJ, Fortis LL, Paoli G, Wijey C, Heinzen H (2011) Development and validation of a rapid method for microcystins in fish and comparing LC-MS/MS results with ELISA. Anal Bioanal Chem 401:2617–2630

    Article  CAS  Google Scholar 

  32. 32.

    Busby WF Jr, Ackermann JM, Crespi CL (1999) Effect of methanol, ethanol, dimethyl sulfoxide, and acetonitrile on in vitro activities of cDNA-expressed human cytochromes p-450. Drug Metab Disposit 27:246–249

    CAS  Google Scholar 

  33. 33.

    Sellek GA, Chaudhuri JB (1999) Biocatalysis in organic media using enzymes from extremophiles. Enzyme Microb Technol 25:471–482

    Article  CAS  Google Scholar 

  34. 34.

    Cong L, Huang B, Chen Q, Lu B, Zhang J, Ren Y (2006) Determination of trace amount of microcystins in water samples using liquid chromatography coupled with triple quadrupole mass spectrometry. Anal Chim Acta 569:157–168

    Article  CAS  Google Scholar 

  35. 35.

    Alonso GA, Istamboulie G, Ramírez-Garcia A, Noguer T, Marty J-L, Munoz R (2010) Artificial neural network implementation in single low-cost chip for the detection of insecticides by modeling of screen-printed enzymatic sensors response. Comput Electr Agricult 74:223–229

    Article  Google Scholar 

Download references

Acknowledgments

The work was supported by the Romania–France bilateral grant Brancusi (487/2011). O.I. Covaci is a PhD student with a scholarship funded by the Sectoral Operational Programme Human Resources Development 2007–2013 of the Romanian Ministry of Labour, Family and Social Protection through the Financial Agreement POSDRU/6/1.5/S/19.

Author information

Affiliations

Authors

Corresponding author

Correspondence to J.-L. Marty.

Additional information

Published in the special paper collection Instrumental Methods of Analysis (IMA 2011) with guest editors Maria Ochsenkuehn-Petropoulou, Nikos Kallithrakas, and Panagiotis Kefalas.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Covaci, O.I., Sassolas, A., Alonso, G.A. et al. Highly sensitive detection and discrimination of LR and YR microcystins based on protein phosphatases and an artificial neural network. Anal Bioanal Chem 404, 711–720 (2012). https://doi.org/10.1007/s00216-012-6092-6

Download citation

Keywords

  • Protein phosphatase
  • Microcystin
  • Artificial neural network
  • Influence of organic solvents