The mixture toxicity of heavy metals on Photobacterium phosphoreum and its modeling by ion characteristics-based QSAR


Autoři: Jianjun Zeng aff001;  Fen Chen aff001;  Mi Li aff001;  Ligui Wu aff001;  Huan Zhang aff001;  Xiaoming Zou aff001
Působiště autorů: School of Life Science, Jinggangshan University, Ji’an, China aff001
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226541

Souhrn

Organisms are frequently exposed to mixtures of heavy metals because of their persistence in the environment. The mixture toxicity of heavy metals should therefore be evaluated to perform a rational environmental risk assessment for organisms. In this study, we determined the inhibition toxicity of five heavy metals (Cu2+, Co2+, Zn2+, Fe3+ and Cr3+) and their binary mixtures to Photobacterium phosphoreum (P. phosphoreum). We obtained the following results: (1) the order of individual toxicity was Zn2+>Cu2+>Co2+>Cr3+>Fe3+, and (2) different combined effects (additive, synergistic and antagonistic) were observed in the binary mixtures of heavy metals, with toxicity unit (TU) values ranging from 0.15 to 3.50. To predict the mixture toxicity of heavy metals, we derived the ion characteristic parameters of heavy metal mixtures and explored the ion-characteristic-based quantitative structure–activity relationship (QSAR) model (R2 = 0.750, Q2 = 0.649). The developed QSAR model indicated that the mixture toxicity of heavy metals is related to the change in ionization potential ((ΔIP)mix), the first hydrolysis constant (log(KOH)mix) and the formation constant value (logKfmix).

Klíčová slova:

Environmental impacts – Heavy metals – Hydrolysis – Pollutants – Predictive toxicology – Toxicity – Toxicology – Toxicity testing


Zdroje

1. Xu X, Li Y, Wang Y, Wang Y. Assessment of toxic interactions of heavy metals in multi-component mixtures using sea urchin embryo-larval bioassay[J]. Toxicology in Vitro, 2011, 25(1): 294–300. doi: 10.1016/j.tiv.2010.09.007 20854890.

2. Schnug L, Leinaas H P, Jensen J. Synergistic sub-lethal effects of a biocide mixture on the springtail Folsomia fimetaria[J]. Environmental pollution, 2014, 186: 158–164. doi: 10.1016/j.envpol.2013.12.004 24374376.

3. Utgikar VP, Chaudhary N, Koeniger A, Tabak HH, Haines JR, Govind R. Toxicity of metals and metal mixtures: analysis of concentration and time dependence for zinc and copper[J]. Water Research, 2004, 38(17): 3651–3658. doi: 10.1016/j.watres.2004.05.022 15350416.

4. Uwizeyimana H, Wang M, Chen W, Khan K. The eco-toxic effects of pesticide and heavy metal mixtures towards earthworms in soil[J]. Environmental toxicology and pharmacology, 2017, 55: 20–29. doi: 10.1016/j.etap.2017.08.001 28806580.

5. Expósito N, Kumar V, Sierra J, Schuhmacher M, Papiol GG. Performance of Raphidocelis subcapitata exposed to heavy metal mixtures[J]. Science of The Total Environment, 2017, 601: 865–873. doi: 10.1016/j.scitotenv.2017.05.177 28578244.

6. Madoni P, Romeo MG. Acute toxicity of heavy metals towards freshwater ciliated protists[J]. Environmental Pollution, 2006, 141(1): 1–7. doi: 10.1016/j.envpol.2005.08.025 16198032.

7. Wang H, Wang XJ, Zhao JF, Chen L. Toxicity assessment of heavy metals and organic compounds using CellSense biosensor with E. coli[J]. Chinese Chemical Letters, 2008, 19(2): 211–214. https://doi.org/10.1016/j.cclet.2007.10.053 PMID: 19545031.

8. Karri V, Kumar V, Ramos D, Oliveira E. An in vitro cytotoxic approach to assess the toxicity of heavy metals and their binary mixtures on hippocampal HT-22 cell line[J]. Toxicology letters, 2018, 282: 25–36. doi: 10.1016/j.toxlet.2017.10.002 28988819.

9. Cleuvers M. Chronic mixture toxicity of pharmaceuticals to Daphnia–the example of nonsteroidal anti-inflammatory drugs[M]//Pharmaceuticals in the Environment. Springer, Berlin, Heidelberg, 2008: 277–284. https://doi.org/10.1007/978-3-540-74664-5_17.

10. Bliss C I. The toxicity of poisons applied jointly 1[J]. Annals of applied biology, 26(3): 585–615. doi: 10.1111/j.1744-7348.1939.tb06990.x

11. Plackett RL, Hewlett PS. Quantal responses to mixtures of poisons[J]. Journal of the Royal Statistical Society: Series B (Methodological), 1952, 14(2): 141–154. https://doi.org/10.2307/2983865.

12. Backhaus T, Arrhenius Å, Blanck H. Toxicity of a mixture of dissimilarly acting substances to natural algal communities: predictive power and limitations of independent action and concentration addition[J]. Environmental science & technology, 2004, 38(23): 6363–6370. doi: 10.1021/es0497678 15597893.

13. Khan FR, Keller W, Yan ND, Welsh PG, Wood CM, McGeer JC. Application of biotic ligand and toxic unit modeling approaches to predict improvements in zooplankton species richness in smelter-damaged lakes near Sudbury, Ontario[J]. Environmental science & technology, 2012, 46(3): 1641–1649. doi: 10.1021/es203135p 22191513.

14. Le TTY, Vijver MG, Hendriks AJ, Peijnenburg WJ. Modeling toxicity of binary metal mixtures (Cu2+–Ag+, Cu2+–Zn2+) to lettuce, Lactuca sativa, with the biotic ligand model[J]. Environmental toxicology and chemistry, 2013, 32(1): 137–143. doi: 10.1002/etc.2039 23109233

15. Altenburger R, Nendza M, Schüürmann G. Mixture toxicity and its modeling by quantitative structure‐activity relationships[J]. Environmental Toxicology and Chemistry: An International Journal, 2003, 22(8): 1900–1915. doi: 10.1897/01-386 12924589.

16. Zou X, Lin Z, Deng Z, Yin D, Zhang Y. The joint effects of sulfonamides and their potentiator on Photobacterium phosphoreum: Differences between the acute and chronic mixture toxicity mechanisms[J]. Chemosphere, 2012, 86(1): 30–35. doi: 10.1016/j.chemosphere.2011.08.046 21944043.

17. Newman MC, McCloskey JT, Tatara CP. Using metal-ligand binding characteristics to predict metal toxicity: quantitative ion character-activity relationships (QICARs)[J]. Environmental health perspectives, 1998, 106(suppl 6): 1419–1425. doi: 10.1289/ehp.98106s61419 9860900.

18. Mathews AP. The relation between solution tension, atomic volume, and the physiological action of the elements[J]. American Journal of Physiology-Legacy Content, 1904, 10(6): 290–323. https://doi.org/10.1152/ajplegacy.1904.10.6.290.

19. Tatara CP, Newman MC, McCloskey JT, Williams PL. Use of ion characteristics to predict relative toxicity of mono-, di-and trivalent metal ions: Caenorhabditis elegans LC50[J]. Aquatic toxicology, 1998, 42(4): 255–269. https://doi.org/10.1016/s0166-445x(97)00104-5.

20. Ownby DR, Newman MC. Advances in quantitative ion character‐activity relationships (QICARs): Using metal‐ligand binding characteristics to predict metal toxicity[J]. QSAR & Combinatorial Science, 2003, 22(2): 241–246. https://doi.org/10.1002/qsar.200390018.

21. Wang X, Qu R, Wei Z, Yang X, Wang Z. Effect of water quality on mercury toxicity to Photobacterium phosphoreum: Model development and its application in natural waters[J]. Ecotoxicology and environmental safety, 2014, 104: 231–238. doi: 10.1016/j.ecoenv.2014.03.029 24726934.

22. Tsiridis V, Petala M, Samaras P, Hadjispyrou S, Sakellaropoulos G, Kungolos A. Interactive toxic effects of heavy metals and humic acids on Vibrio fischeri[J]. Ecotoxicology and environmental safety, 2006, 63(1): 158–167. doi: 10.1016/j.ecoenv.2005.04.005 15939470.

23. McCloskey JT, Newman MC, Clark S B. Predicting the relative toxicity of metal ions using ion characteristics: Microtox® bioluminescence assay[J]. Environmental Toxicology and Chemistry: An International Journal, 1996, 15(10): 1730–1737. https://doi.org/10.1002/etc.5620151011.

24. Backhaus T, Froehner K, Altenburger R, Grimme L. Toxicity testing with Vibrio fischeri: A comparison between the long term (24 H) and the short term (30 min) bioassay[J]. Chemosphere, 1997, 35(12):2925–2938. https://doi.org/10.1016/S0045-6535(97)00340-8.

25. Lundholt BK, Scudder KM, Pagliaro L. A simple technique for reducing edge effect in cell-based assays[J]. Journal of biomolecular screening, 2003, 8(5): 566–570. doi: 10.1177/1087057103256465 14567784.

26. Broderius SJ, Kahl MD, Hoglund MD. Use of joint toxic response to define the primary mode of toxic action for diverse industrial organic chemicals[J]. Environmental Toxicology and Chemistry, 1995, 14(9):1591–1605. https://doi.org/10.1002/etc.5620140920.

27. Magwood S, George S. In vitro alternatives to whole animal testing. Comparative cytotoxicity studies of divalent metals in established cell lines derived from tropical and temperate water fish species in a neutral red assay[J]. Marine Environmental Research, 1996, 42(1–4): 37–40. https://doi.org/10.1016/0141-1136(95)00058-5.

28. Can C, Jianlong W. Correlating metal ionic characteristics with biosorption capacity using QSAR model. [J]. Chemosphere, 2007, 69(10):0–1616. doi: 10.1016/j.chemosphere.2007.05.043 17624405.

29. Wang B, Yu G, Zhang Z, Hu H, Wang L. Quantitative structure-activity relationship and prediction of mixture toxicity of alkanols[J]. Chinese Science Bulletin, 2006, 51(22): 2717–2723. https://doi.org/10.1007/s11434-006-2168-z.

30. Zou X, Lin Z, Deng Z, Yin D. Novel approach to predicting hormetic effects of antibiotic mixtures on Vibrio fischeri[J]. Chemosphere, 2013, 90(7): 2070–2076. doi: 10.1016/j.chemosphere.2012.09.042 23200841.

31. Cassee FR, Groten JP, Bladeren PJ, Feron VJ. Toxicological evaluation and risk assessment of chemical mixtures[J]. Critical Reviews in Toxicology, 1998, 28(1): 73–101. doi: 10.1080/10408449891344164 9493762

32. Hastings JW, Balny C, Le Peuch C, Douzou P. Spectral properties of an oxygenated luciferase—flavin intermediate isolated by low-temperature chromatography[J]. Proceedings of the National Academy of Sciences, 1973, 70(12): 3468–3472. doi: 10.1073/pnas.70.12.3468 16592121.

33. Lee RT, Denburg JL, McElroy WD. Substrate-binding properties of firefly luciferase: II. ATP-binding site[J]. Archives of biochemistry and biophysics, 1970, 141(1): 38–52. doi: 10.1016/0003-9861(70)90103-7 5480123.

34. Riahi S, Abdolahzadeh S, Faridbod F, Chaichi MJ, Ganjali MR, Norouzi P. Complexation study of luciferin with metal ions in acetonitrile employing theoretical and experimental methods[J]. Journal of Molecular Liquids, 2010, 157(1): 51–56. https://doi.org/10.1016/s0166-445x(97)00104-5.

35. Golbraikh A, Tropsha A. Beware of q2![J]. Journal of molecular graphics and modelling, 2002, 20(4): 269–276. doi: 10.1016/s1093-3263(01)00123-1 11858635.

36. Eriksson D, Fransén E, Zilberter Y, Lansner A. Effects of short-term synaptic plasticity in a local microcircuit on cell firing[J]. Neurocomputing, 2003, 52: 7–12. https://doi.org/10.1016/S0925-2312(02)00757-9.

37. Wang Y, Chen J, Li F, Qin H, Qiao X, Hao C. Modeling photoinduced toxicity of PAHs based on DFT-calculated descriptors[J]. Chemosphere, 2009, 76(7): 999–1005. doi: 10.1016/j.chemosphere.2009.04.010 19427664.

38. Umetrics AB. SIMCA‐P and SIMCA‐P+ 10 user guide[J]. 2002.

39. Preston S, Coad N, Townend J, Killham K, Paton GI. Biosensing the acute toxicity of metal interactions: are they additive, synergistic, or antagonistic?[J]. Environmental Toxicology and Chemistry: An International Journal, 2000, 19(3): 775–780. https://doi.org/10.1002/etc.5620190332.

40. Marr JCA, Hansen JA, Meyer J S, Cacela D, Podrabsky T, Lipton J, et al. Toxicity of cobalt and copper to rainbow trout: application of a mechanistic model for predicting survival[J]. Aquatic toxicology, 1998, 43(4): 225–238. https://doi.org/10.1016/s0166-445x(98)00061-7.

41. Fulladosa E, Murat JC, Villaescusa I. Study on the toxicity of binary equitoxic mixtures of metals using the luminescent bacteria Vibrio fischeri as a biological target[J]. Chemosphere, 2005, 58(5): 551–557. doi: 10.1016/j.chemosphere.2004.08.007 15620748.

42. Weltje L. Mixture toxicity and tissue interactions of Cd, Cu, Pb and Zn in earthworms (Oligochaeta) in laboratory and field soils: a critical evaluation of data[J]. Chemosphere, 1998, 36(12): 2643–2660. doi: 10.1016/s0045-6535(97)10228-4 9570111.

43. Kümmerer K. Antibiotics in the aquatic environment–a review–part I[J]. Chemosphere, 2009, 75(4): 417–434. doi: 10.1016/j.chemosphere.2008.11.086 19185900.

44. Tong W, Hong H, Xie Q, Shi L, Fang H, Perkins R. Assessing QSAR limitations-A regulatory perspective[J]. Current Computer-Aided Drug Design, 2005, 1(2): 195–205. https://doi.org/10.2174/1573409053585663.


Článek vyšel v časopise

PLOS One


2019 Číslo 12