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Structure–activity analysis of harmful algae inhibition by congeneric compounds: case studies of fatty acids and thiazolidinediones

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Abstract

The occurrence of harmful algal blooms has been increasing significantly around the world. In order to ensure the safety of drinking water, procedures to screen potential materials as effective algicides are needed, and predictive methods which save both the labor and time compared with traditional experimental approaches, are particularly desirable. In this study, data from previous studies on the algal-growth inhibitory action of two kinds of compounds, namely, the action of fatty acids and thiazolidinediones on the harmful algae Heterosigma akashiwo and Chattonella marina, were modeled using multiple linear regression (MLR) based on quantitative structure–activity relationships (QSAR). The models were shown to have highly predictive ability and stability, and provided insight into the inhibitory mechanisms of congeneric compounds. The main descriptors in the fatty-acid models were the Connolly accessible area and the number of rotatable bonds, illustrating that molecular surface area and shape are important in their algicidal actions. In the thiazolidinedione models, the critical volume, octanol–water partition coefficient (LogP), and Connolly solvent-excluded volume were found to be significant, indicating that hydrophobicity, substituent group size, and mode of action are mechanistically important. Our results showed the algicidal activity of a series of compounds on different algae could be modeled, and each model is efficacious for compounds that fall into the application domain of the QSAR model. This work demonstrates how reliable predictions of the algicidal activity of novel compounds and explanations of their inhibitory mechanisms can be obtained.

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References

  • Alamsjah MA, Hirao S, Ishibashi F, Oda T, Fujita Y (2008) Algicidal activity of polyunsaturated fatty acids derived from Ulva fasciata and U.pertusa (Ulvaceae, Chlorophyta) on phytoplankton. J Appl Phycol 20(5):713–720

    Article  CAS  Google Scholar 

  • Azen R, Budescu DV (2003) The dominance analysis approach for comparing predictors in multiple regression. Psychol Methods 8(2):129

    Article  Google Scholar 

  • Beman JM, Arrigo KR, Matson PA (2005) Agricultural runoff fuels large phytoplankton blooms in vulnerable areas of the ocean. Nature 434(7030):211–214

    Article  Google Scholar 

  • Budescu DV (1993) Dominance analysis: a new approach to the problem of relative importance of predictors in multiple regression. Psychol Bull 114(3):542

    Article  Google Scholar 

  • Chatterjee S, Hadi AS (2006) Regression analysis by example, 4th edn. John Wiley & Sons, Hoboken, NJ, USA

    Book  Google Scholar 

  • Chirico N, Gramatica P (2011) Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient. J Chem Inf Model 51(9):2320–2335

    Article  CAS  Google Scholar 

  • Chirico N, Gramatica P (2012) Real external predictivity of QSAR models: Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection. J Chem Inf Model 52(8):2044–2058

    Article  CAS  Google Scholar 

  • Connolly ML (1983) Solvent-accessible surfaces of proteins and nucleic acids. Science 221(4612):709–713

    Article  CAS  Google Scholar 

  • Consonni V, Ballabio D, Todeschini R (2009) Comments on the definition of the Q 2 parameter for QSAR validation. J Chem Inf Model 49(7):1669–1678

    Article  CAS  Google Scholar 

  • Erturk MD, Sacan MT, Novic M, Minovski N (2012) Quantitative structure–activity relationships (QSARs) using the novel marine algal toxicity data of phenols. J Mol Graph Model 38:90–100

    Article  Google Scholar 

  • Ghasemi J, Saaidpour S (2007) Quantitative structure–property relationship study of n-octanol–water partition coefficients of some of diverse drugs using fmultiple linear regression. Anal Chim Acta 604(2):99–106

    Article  CAS  Google Scholar 

  • Gramatica P (2007) Principles of QSAR models validation: internal and external. QSAR Comb Sci 26(5):694–701

    Article  CAS  Google Scholar 

  • Gramatica P, Cassani S, Roy PP, Kovarich S, Yap CW, Papa E (2012) QSAR modeling is not “push a button and find a correlation”: a case study of toxicity of (benzo-)triazoles on algae. Mol Inform 31(11–12):817–835

    Article  CAS  Google Scholar 

  • Gross EM, Meyer H, Schilling G (1996) Release and ecological impact of algicidal hydrolysable polyphenols in Myriophyllum spicatum. Phytochemistry 41(1):133–138

    Article  CAS  Google Scholar 

  • Hansch C, Muir RM, Fujita T, Maloney PP, Geiger F, Streich M (1963) The correlation of biological activity of plant growth regulators and chloromycetin derivatives with Hammett constants and partition coefficients. J Am Chem Soc 85(18):2817–2824

    Article  CAS  Google Scholar 

  • Haranczyk M, Puzyn T, Sadowski P (2008) ConGENER — a tool for modeling of the congeneric sets of environmental pollutants. QSAR Comb Sci 27(7):826–833

    Article  CAS  Google Scholar 

  • Hirao S, Tara K, Kuwano K, Tanaka J, Ishibashi F (2012) Algicidal activity of glycerolipids from brown alga Ishige sinicola toward red tide microalgae. Biosci Biotechnol Biochem 76(2):372–374

    Article  CAS  Google Scholar 

  • Ivan D, Crisan L, Funar-Timofei S, Mracec M (2013) A quantitative structure–activity relationships study for the anti-HIV-1 activities of 1- (2-hydroxyethoxy)methyl-6-(phenylthio)thymine derivatives using the multiple linear regression and partial least squares methodologies. J Serb Chem Soc 78(4):495–506

    Article  CAS  Google Scholar 

  • Izadiyan P, Fatemi MH, Izadiyan M (2013) Elicitation of the most important structural properties of ionic liquids affecting ecotoxicity in limnic green algae; a QSAR approach. Ecotoxicol Environ Saf 87:42–48

    Article  CAS  Google Scholar 

  • Jančula D, Maršálek B (2011) Critical review of actually available chemical compounds for prevention and management of cyanobacterial blooms. Chemosphere 85(9):1415–1422

    Article  Google Scholar 

  • Jančula D, Maršálek B (2012) The toxicity of phthalocyanines to the aquatic plant Lemna minor (duckweed) — testing of 31 compounds. Chemosphere 88(8):962–965

    Article  Google Scholar 

  • Jančula D, Gregorová J, Maršálek B (2010) Algicidal and cyanocidal effects of selected isoquinoline alkaloids. Aquacult Res 41(4):598–601

    Article  Google Scholar 

  • Jing GH, Zhou ZM, Zhuo J (2012) Quantitative structure–activity relationship (QSAR) study of toxicity of quaternary ammonium compounds on Chlorella pyrenoidosa and Scenedesmus quadricauda. Chemosphere 86(1):76–82

    Article  CAS  Google Scholar 

  • Kar S, Roy K (2010) QSAR modeling of toxicity of diverse organic chemicals to Daphnia magna using 2D and 3D descriptors. J Hazard Mater 177(1–3):344–351

    Article  CAS  Google Scholar 

  • Kim YM, Wu Y, Duong TU, Ghodake GS, Kim SW, Jin ES, Cho H (2010) Thiazolidinediones as a novel class of algicides against red tide harmful algal species. Appl Biochem Biotechnol 162(8):2273–2283

    Article  CAS  Google Scholar 

  • Kim YM, Wu Y, Duong TU, Jung SG, Kim SW, Cho H, Jin E (2012) Algicidal activity of thiazolidinedione derivatives against harmful algal blooming species. Mar Biotechnol 14(3):312–322

    Article  CAS  Google Scholar 

  • Li X, Yu LM, Jiang XH, Xia SW, Zhao HZ (2009) Synthesis, algal inhibition activities and QSAR studies of novel gramine compounds containing ester functional groups. Chin J Oceanol Limnol 27(2):309–316

    Article  CAS  Google Scholar 

  • Moulton MP, Schultz TW (1986) Structure–activity-relationships of selected pyridines: II. Principal components-analysis. Chemosphere 15(1):59–67

    Article  CAS  Google Scholar 

  • Mracec M, Oprea TI, Mracec M (2011) Models of the algistatic activity of 5-amino-1-aryl-1H-tetrazoles. Rev Roum Chim 56(4):373–379

    CAS  Google Scholar 

  • Mullins L (1954) Some physical mechanisms in narcosis. Chem Rev 54(2):289–323

    Article  CAS  Google Scholar 

  • Nalewajko C, Murphy TP (2001) Effects of temperature, and availability of nitrogen and phosphorus on the abundance of Anabaena and Microcystis in Lake Biwa, Japan: an experimental approach. Limnology 2(1):45–48

    Article  Google Scholar 

  • Neuwoehner J, Zilberman T, Fenner K, Escher BI (2010) QSAR-analysis and mixture toxicity as diagnostic tools: influence of degradation on the toxicity and mode of action of diuron in algae and daphnids. Aquat Toxicol 97(1):58–67

    Article  CAS  Google Scholar 

  • Park SC, Lee JK, Kim SW, Park Y (2011) Selective algicidal action of peptides against harmful algal bloom species. PLoS ONE 6(10):e26733

    Article  CAS  Google Scholar 

  • Perkins R, Fang H, Tong WD, Welsh WJ (2003) Quantitative structure–activity relationship methods: perspectives on drug discovery and toxicology. Environ Toxicol Chem 22(8):1666–1679

    Article  CAS  Google Scholar 

  • Ren S, Frymier PD (2009) Modeling the toxicity of polar and nonpolar narcotic compounds to luminescent bacterium SHK1. Environ Toxicol Chem 21(12):2649–2653

    Article  Google Scholar 

  • Schelenz T (2000) Relationships between algistatic activity and physicochemical properties of 5-amino-1-aryl-1H-tetrazoles. J Prakt Chemie-Chemiker-Zeitung 342(2):205–210

    Article  CAS  Google Scholar 

  • Todeschini R, Consonni V (2008) Handbook of molecular descriptors. Wiley-VCH Verlag Weinheim, Germany

    Google Scholar 

  • Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 22(1):69–77

    Article  CAS  Google Scholar 

  • Wu JT, Chiang YR, Huang WY, Jane WN (2006) Cytotoxic effects of free fatty acids on phytoplankton algae and cyanobacteria. Aquat Toxicol 80(4):338–345

    Article  CAS  Google Scholar 

  • Xiao X, Chen Y, Liang X, Lou L, Tang X (2010) Effects of Tibetan hulless barley on bloom-forming cyanobacterium (Microcystis aeruginosa) measured by different physiological and morphologic parameters. Chemosphere 81(9):1118–1123

    Article  CAS  Google Scholar 

  • Xiao X, Huang H, Ge Z, Rounge TB, Shi J, Xu X, Li R, Chen Y (2013) A pair of chiral flavonolignans as novel anti-cyanobacterial allelochemicals derived from barley straw (Hordeum vulgare): characterization and comparison of their anti-cyanobacterial activities. Environ Microbiol. doi:10.1111/1462-2920.12226

    Google Scholar 

  • Xue C, Zhang R, Liu H, Yao X, Liu M, Hu Z, Fan B (2004) QSAR models for the prediction of binding affinities to human serum albumin using the heuristic method and a support vector machine. J Chem Inf Comput Sci 44(5):1693–1700

    Article  CAS  Google Scholar 

  • Yim EC, Park IT, Han HK, Kim SW, Cho H, Kim SJ (2010) Acute toxicity assessment of new algicides of thiazolidinediones derivatives, TD53 and TD49, using Ulva pertusa Kjellman. Environ Health Toxicol 25(4):273–278

    Google Scholar 

  • Young D, Martin T, Venkatapathy R, Harten P (2008) Are the chemical structures in your QSAR correct? QSAR Comb Sci 27(11–12):1337–1345

    Article  CAS  Google Scholar 

  • Zeng M, Lin ZF, Yin DQ, Zhang YL, Kong DY (2011) A K ow-based QSAR, model for predicting toxicity of halogenated benzenes to all algae regardless of species. Bull Environ Contam Toxicol 86(6):565–570

    Article  CAS  Google Scholar 

  • Zhu H, Tropsha A, Fourches D, Varnek A, Papa E, Gramatica P, Oberg T, Dao P, Cherkasov A, Tetko IV (2008) Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis. J Chem Inf Model 48(4):766–784

    Article  CAS  Google Scholar 

  • Zhu H, Ye L, Richard A, Golbraikh A, Wright FA, Rusyn I, Tropsha A (2009) A novel two-step hierarchical quantitative structure–activity relationship modeling work flow for predicting acute toxicity of chemicals in rodents. Environ Health Perspect 117(8):1257–1264

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This study was supported by the National Natural Science Foundation of China (Grant NO. 21307106), China Postdoctoral Science Fund (Grant NO. 2012 M521181), and Public Welfare Research Projects of Zhejiang Province of China (Grant No. 2013C33003). We thank Assoc. Prof. Ashton Shortridge at Michigan State University and Ms. Stephanie Wong at the University of British Columbia for critically reading the manuscript, and Assoc. Prof. Shulin Zhuang at Zhejiang University for his valuable suggestions and kind help on the use of software.

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Authors and Affiliations

  1. Institute of Environmental Science and Technology, College of Environmental and Resource Science (CERS), Zhejiang University, Nongshenghuan Building B323, Number 388 Yuhangtang Road, Zijingang Campus, Hangzhou, 310058, China

    Haomin Huang, Xi Xiao, Jiyan Shi & Yingxu Chen

  2. Zhejiang Province Key Laboratory for Water Pollution Control and Environmental Safety, Hangzhou, 310058, China

    Yingxu Chen

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  1. Haomin Huang
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  2. Xi Xiao
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  3. Jiyan Shi
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  4. Yingxu Chen
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Correspondence to Xi Xiao.

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Responsible editor: Michael Matthies

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Huang, H., Xiao, X., Shi, J. et al. Structure–activity analysis of harmful algae inhibition by congeneric compounds: case studies of fatty acids and thiazolidinediones. Environ Sci Pollut Res 21, 7154–7164 (2014). https://doi.org/10.1007/s11356-014-2626-0

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  • DOI: https://doi.org/10.1007/s11356-014-2626-0

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