Advertisement

Environmental Science and Pollution Research

, Volume 24, Issue 19, pp 16086–16096 | Cite as

Evaluation of the sensitivity spectrum of a video tracking system with zebrafish (Danio rerio) exposed to five different toxicants

  • João Amorim
  • Miguel Fernandes
  • Vitor Vasconcelos
  • Luis Oliva Teles
Research Article

Abstract

The aim of this study was to develop a biological early warning system for the detection of aquatic toxicity and test it with five toxicants with distinct chemical nature. This was done in order to verify the spectrum of sensitivities of the proposed system, as well as the potential identification capability of the tested contaminants, using only the analysis of zebrafish’s behavior. Six experimental conditions were tested: negative control and five toxicants (bleach, lindane, tributyltin, mercury, and formaldehyde). The exposure time was 45 min, and the concentrations used corresponded to 9% of LC50’s-96 h for the tested compounds, to ensure ecologically relevant results. A total of 108 fish were used, with each individual experimental condition being tested 18 times. A statistical model of diagnosis was used, combining self-organizing map and correspondence analysis. The values of sensitivity, specificity, accuracy, false positive, false negative, positive predictive value (PPV), and negative predictive value (NPV) were calculated. The objectives of the work were accomplished and the system showed a good overall diagnostic performance with 79% in accuracy, 77% in sensitivity, and 88% in specificity. The lowest result of the predictive values was 78% (lindane and mercury), in the case of the NPV, and 86% (bleach and lindane), in the case of the PPV. The best result of the predictive values was 100% (bleach and tributyltin), for the NPV, and 89% (tributyltin), for the PPV. Regarding the five tested toxicants, the system was able to correctly identify the agent responsible for the contamination in 40% of the positive diagnoses.

Keywords

Biological early warning system (BEWS) Neural network Self-organizing map Toxicant identification Video tracking Zebrafish (Danio rerio

Notes

Acknowledgements

This article is a result of project INNOVMAR-Innovation and Sustainability in the Management and Exploitation of Marine Resources (reference NORTE-01-0145-FEDER-000035), supported by North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF).

Compliance with ethical standards

This study complied with the European Directive 2010/63/EU (2010) and National Guidelines Decreto-Lei 113/2013 (2013) on animal experiments as well as with the principles of the 3R guidelines (refinement, replacement, and reduction of animal use). No animals were sacrificed in the course of this study.

Conflict of interest

The authors declare that they have no conflict of interest.

Funding

This research was partially funded by UID/Multi/04423/2013 project from Fundação para a Ciência e Tecnologia. The funding source had no involvement in any process of the experience.

References

  1. Amorim J, Fernandes M, Vasconcelos V, Oliva Teles L (2017) Stress test of a biological early warning system with zebrafish (Danio rerio). Ecotoxicology 26:13–21Google Scholar
  2. Bae M, Park Y (2014) Biological early warning system based on the responses of aquatic organisms to disturbances: a review. Sci Total Environ 466-467:635–649CrossRefGoogle Scholar
  3. Cabanes G, Bennani Y (2010) Learning the number of clusters in self-organizing map. Self-organizing maps. InTech, Shanghai, pp 15–28Google Scholar
  4. Chen T, Wang Y, Wu Y (2011) Developmental exposures to ethanol or dimethylsulfoxide at low concentrations alter locomotor activity in larval zebrafish: implications for behavioral toxicity bioassays. Aquat Toxicol 102:162–166CrossRefGoogle Scholar
  5. Cheng B, Titterington D (1994) Neural networks: a review from a statistical perspective. Stat Sci 9:2–54CrossRefGoogle Scholar
  6. Chon T, Park Y, Moon K, Cha E (1996) Patternizing communities by using an artificial neural network. Ecol Model 90:69–78CrossRefGoogle Scholar
  7. Chon T, Chung N, Kwak I, Kim J, Koh S, Lee S, Leem J, Cha E (2005) Movement behaviour of Medaka (Oryzias latipes) in response to sublethal treatments of diazinon and cholinesterase activity in semi-natural conditions. Environ Monit Assess 101:1–21Google Scholar
  8. Decreto-Lei 113/2013 do Ministério da Agricultura(2013) do Mar, do Ambiente e do Ordenamento do Território de 7 de Agosto de 2013 relativa à proteção dos animais utilizados para fins científicosGoogle Scholar
  9. Delcourt J, Denoël M, Ylieff M, Poncin P (2013) Video multitracking of fish behaviour: a synthesis and future perspectives. Fish Fish 14:186–204CrossRefGoogle Scholar
  10. Directive 2010/63/EU (2010) of the European Parlament and of the Council of 22 September 2010 on the protection of animals used for scientific purposes L 276/33Google Scholar
  11. Drobatz K (2009) Measures of accuracy and performance of diagnostic tests. J Vet Cardiol 11(Suppl. 1):33–40CrossRefGoogle Scholar
  12. Duong A, Steinmaus C, McHale C, Vaughan C, Zhang L (2011) Reproductive and developmental toxicity of formaldehyde: a systematic review. Mutat Res 728:118–138CrossRefGoogle Scholar
  13. Ensenbach U, Nagel R (1995) Toxicity of complex chemical mixtures: acute and long-term effects on different life stages of zebrafish (Brachydanio rerio). Ecotoxicol Environ Saf 30:151–157CrossRefGoogle Scholar
  14. Faucher K, Fichet D, Miramand P, Lagardere J (2008) Impact of chronic cadmium exposure at environmental dose on escape behaviour in sea bass (Dicentrarchus labrax; Teleostei, Moronidae). Environ Pollut 151:148–157CrossRefGoogle Scholar
  15. Genschow E, Spielmann H, Scholz G, Seiler A, Brown N, Piersma A, Brady M, Clemann N, Huuskonen H, Paillard F et al (2002) The ECVAM international validation study on in vitro embryotoxicity tests: results of the definitive phase and evaluation of prediction models. ATLA Altern Lab Anim 30:151–176Google Scholar
  16. Gerlai R, Fernandes Y, Pereira T (2009) Zebrafish (Danio rerio) responds to the animated image of a predator: towards the development of an automated aversive task. Behav Brain Res 201:318–324CrossRefGoogle Scholar
  17. Grillitsch B, Vogl C, Wytek R (1999) Qualification of spontaneous undirected locomotor behavior of fish for sublethal toxicity testing. Part II. Variability of measurement parameters under toxicant-induced stress. Environ Toxicol Chem 18:2743–2750CrossRefGoogle Scholar
  18. Halappa R, David M (2009) Behavioural responses of the freshwater fish, Cyprinus carpio (Linnaeus) following sublethal exposure to chlorpyrifos. Turk J Fish Aquat Sci 9:233–238CrossRefGoogle Scholar
  19. Hellou J (2011) Behavioural ecotoxicology, an “early warning” signal to assess environmental quality. Environ Sci Pollut Res Int 18:1–11CrossRefGoogle Scholar
  20. Islam R, Lynch J (2012) Mechanism of action of the insecticides, lindane and fipronil, on glycine receptor chloride channels. Br J Pharmacol 165:2707–2720CrossRefGoogle Scholar
  21. Kane A, Salierno J, Gipson G, Molteno T, Hunter C (2004) A video-based movement analysis system to quantify behavioral stress responses of fish. Water Res 38:3993–4001CrossRefGoogle Scholar
  22. Kohonen T (2001) Self-organizing maps, 3rd edn. Springer-Verlag, New York, p 501CrossRefGoogle Scholar
  23. Kokkali V, van Delft W (2014) Overview of commercially available bioassays for assessing chemical toxicity in aqueous samples. Trends Anal Chem 61:133–155CrossRefGoogle Scholar
  24. Kuklina I, Kouba A, Kozák P (2013) Real-time monitoring of water quality using fish and crayfish as bio-indicators: a review. Environ Monit Assess 185:5043–5053CrossRefGoogle Scholar
  25. Langlotz C (2003) Fundamental measures of diagnostic examination performance: usefulness for clinical decision making and research. Radiology 228:3–9CrossRefGoogle Scholar
  26. Little E, Finger S (1990) Swimming behavior as an indicator of sublethal toxicity in fish. Environ Toxicol Chem 9:13–19CrossRefGoogle Scholar
  27. Little E, Archeski R, Flerov B, Kozlovskaya V (1990) Behavioral indicators of sublethal toxicity in rainbow trout. Arch Environ Contam Toxicol 19:380–385CrossRefGoogle Scholar
  28. Liu Y, Lee S, Chon T (2011) Analysis of behavioral changes of zebrafish (Danio rerio) in response to formaldehyde using self-organizing map and a hidden Markov model. Ecol Model 222:2191–2201CrossRefGoogle Scholar
  29. Liu Y, Wu F, Ji C, Chon T (2012) Movement patterning of Daphnia magna treated with copper based on self-organizing map. Proc Environ Sci 13:994–1002CrossRefGoogle Scholar
  30. MacPhail R, Brooks J, Hunter D, Padnos B, Irons T, Padilla S (2009) Locomotion in larval zebrafish: influence of time of day, lighting and ethanol. Neurotoxicology 30:52–58CrossRefGoogle Scholar
  31. Magalhães D, Cunha R, Santos J, Buss D, Baptista D (2007) Behavioral response of zebrafish (Danio rerio, Hamilton 1822) to sublethal stress by sodium hypochlorite: ecotoxicological assay using an image analysis biomonitoring system. Ecotoxicology 16:417–422CrossRefGoogle Scholar
  32. Martins J, Oliva Teles L, Vasconcelos V (2007) Assays with Daphnia magna and Danio rerio as alert systems in aquatic toxicology. Environ Int 33:414–425CrossRefGoogle Scholar
  33. Nüßer L, Skulovich O, Hartmann S, Seiler T, Cofalla C, Schuettrumpf H, Hollert H, Salomons E, Ostfeld A (2016) A sensitive biomarker for the detection of aquatic contamination based on behavioral assays using zebrafish larvae. Ecotoxicol Environ Saf 133:271–280CrossRefGoogle Scholar
  34. Oliva Teles L, Fernandes M, Amorim J, Vasconcelos V (2015) Video-tracking of zebrafish (Danio rerio) as a biological early warning system using two distinct artificial neural networks: probabilistic neural network (PNN) and self-organizing map (SOM). Aquat Toxicol 165:241–248CrossRefGoogle Scholar
  35. Pannia E, Tran S, Rampersad M, Gerlai R (2014) Acute ethanol exposure induces behavioural differences in two zebrafish (Danio rerio) strains: a time course analysis. Behav Brain Res 259:174–185CrossRefGoogle Scholar
  36. Park Y, Céréghino R, Compin A, Lek S (2003) Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters. Ecol Model 160:265–280CrossRefGoogle Scholar
  37. Pitanga F (2011) The effect of sodium hypochlorite in different aquatic organisms. Aquatic biology. Universidade de Aveiro, Aveiro, p 58Google Scholar
  38. Pittman J, Ichikawa K (2013) iPhone(R) applications as versatile video tracking tools to analyze behavior in zebrafish (Danio rerio). Pharmacol Biochem Behav 106:137–142CrossRefGoogle Scholar
  39. Powers C, Yen J, Linney E, Seidler F, Slotkin T (2010) Silver exposure in developing zebrafish (Danio rerio): persistent effects on larval behavior and survival. Neurotoxicol Teratol 32:391–397CrossRefGoogle Scholar
  40. Qiao J, Han H (2010) An adaptative fuzzy neural network based on self-organizing map (SOM). Self-organizing maps. InTech, Shanghai, pp 1–14Google Scholar
  41. Richards F, Alderton W, Kimber G, Liu Z, Strang I, Redfern W, Valentin J, Winter M, Hutchinson T (2008) Validation of the use of zebrafish larvae in visual safety assessment. J Pharmacol Toxicol Methods 58:50–58CrossRefGoogle Scholar
  42. Richetti S, Rosemberg D, Ventura-Lima J, Monserrat J, Bogo M, Bonan C (2011) Acetylcholinesterase activity and antioxidant capacity of zebrafish brain is altered by heavy metal exposure. Neurotoxicology 32:116–122CrossRefGoogle Scholar
  43. van der Schalie W, Shedd T, Knechtges P, Widder M (2001) Using higher organisms in biological early warning systems for real-time toxicity detection. Biosens Bioelectron 16:457–465CrossRefGoogle Scholar
  44. Schreck C, Olla B, Davis M (1997) Behavioral response to stress. Fish stress and health in aquaculture. Cambridge University Press, Cambrigde, pp 145–170Google Scholar
  45. Scott G, Sloman K (2004) The effects of environmental pollutants on complex fish behaviour: integrating behavioural and physiological indicators of toxicity. Aquat Toxicol 68:369–392CrossRefGoogle Scholar
  46. Spink A, Tegelenbosch R, Buma M, Noldus L (2001) The EthoVision video tracking system - a tool for behavioral phenotyping of transgenic mice. Physiol Behav 73:731–744CrossRefGoogle Scholar
  47. StatSoft, Inc. (2012) STATISTICA (data analysis software system), version 11. http://www.statsoft.com
  48. Thresher R, Gurney R, Canning M (2011) Effects of lifetime chemical inhibition of aromatase on the sexual differentiation, sperm characteristics and fertility of medaka (Oryzias latipes) and zebrafish (Danio rerio). Aquat Toxicol 105:355–360CrossRefGoogle Scholar
  49. Tran S, Gerlai R (2014) Recent advances with a novel model organism: alcohol tolerance and sensitization in zebrafish (Danio rerio). Prog Neuro-Psychopharmacol Biol Psychiatry 55:87–93CrossRefGoogle Scholar
  50. Varusai N, Asrar S, Sultan M, Azmathullah N (2012) Toxicity of formalin on behaviour and respiration in Danio rerio. Int J Environ Sci 2:1904–1908Google Scholar
  51. Williams L, Wong K, Stewart A, Suciu C, Gaikwad S, Wu N, DiLeo J, Grossman L, Cachat J, Hart P et al (2012) Behavioral and physiological effects of RDX on adult zebrafish. Comp Biochem Physiol Part C: Toxicol Pharmacol 155:33–38Google Scholar
  52. Winter M, Redfern W, Hayfield A, Owen S, Valentin J, Hutchinson T (2008) Validation of a larval zebrafish locomotor assay for assessing the seizure liability of early-stage development drugs. J Pharmacol Toxicol Methods 57:176–187CrossRefGoogle Scholar
  53. Zhang Y, Ma J, Zhou S, Ma F (2015) Concentration-dependent toxicity effect of SDBS on swimming behavior of freshwater fishes. Environ Toxicol Pharmacol 40:77–85CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Departamento de BiologiaFaculdade de Ciências da Universidade do PortoPortoPortugal
  2. 2.CIIMAR, Centro Interdisciplinar de Investigação Marinha e AmbientalMatosinhosPortugal

Personalised recommendations