article

A high-throughput zebrafish screening method for visual mutants by light-induced locomotor response

Editor: Ying Xu Authors:

Rosa H. M. Chan,

Tommy W. S. Chow,

Sylvia Bonilla,

Yuk Fai LeungAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 11, Issue 4

Pages 693 - 701

https://doi.org/10.1109/TCBB.2014.2306829

Published: 01 July 2014 Publication History

Abstract

Normal and visually-impaired zebrafish larvae have differentiable light-induced locomotor response (LLR), which is composed of visual and non-visual components. It is recently demonstrated that differences in the acute phase of the LLR, also known as the visual motor response (VMR), can be utilized to evaluate new eye drugs. However, most of the previous studies focused on the average LLR activity of a particular genotype, which left information that could address differences in individual zebrafish development unattended. In this study, machine learning techniques were employed to distinguish not only zebrafish larvae of different genotypes, but also different batches, based on their response to light stimuli. This approach allows us to perform efficient high-throughput zebrafish screening with relatively simple preparations. Following the general machine learning framework, some discriminative features were first extracted from the behavioral data. Both unsupervised and supervised learning algorithms were implemented for the classification of zebrafish of different genotypes and batches. The accuracy of the classification in genotype was over 80 percent and could achieve up to 95 percent in some cases. The results obtained shed light on the potential of using machine learning techniques for analyzing behavioral data of zebrafish, which may enhance the reliability of high-throughput drug screening.

References

[1]

L. Zhang, L. Chong, J. Cho, P.-C. Liao, S. Feichen, and Y.F. Leung, "Drug Screening to Treat Early-Onset Eye Diseases: Can Zebrafish Expedite the Discovery?" The Asia-Pacific J. Ophthalmology, vol. 1, no. 6, pp. 374-383, 2012.

[2]

J.M. Fadool and J.E. Dowling, "Zebrafish: A Model System for the Study of Eye Genetics," Progress in Retinal and Eye Research, vol. 27, no. 1, pp. 89-110, 2008.

[3]

J. Gross and B. Perkins, "Zebrafish Mutants as Models for Congenital Ocular Disorders in Humans," Molecular Reproduction and Development, vol. 75, no. 3, pp. 547-555, 2008.

[4]

A.C. Morris, "The Genetics of Ocular Disorders: Insights from the Zebrafish," Birth Defects Research Part C, Embryo Today: Rev., vol. 93, no. 3, pp. 215-228, 2011.

[5]

C.A. Lessman, "The Developing Zebrafish (Danio rerio): A Vertebrate Model for High-Throughput Screening of Chemical Libraries," Birth Defects Research Part C, Embryo Today: Rev., vol. 93, no. 3, pp. 268-280, 2011.

[6]

D. Kokel, J. Bryan, C. Laggner, R. White, C.Y.J. Cheung, R. Mateus, D. Healey, S. Kim, A.A. Werdich, S.J. Haggarty, C.A. Macrae, B. Shoichet, and R.T. Peterson, "Rapid Behavior-Based Identification of Neuroactive Small Molecules in the Zebrafish," Nature Chemical Biology, vol. 6, no. 3, pp. 231-237, 2010.

[7]

J. Rihel, D.A. Prober, A. Arvanites, K. Lam, S. Zimmerman, S. Jang, S.J. Haggarty, D. Kokel, L.L. Rubin, R.T. Peterson, and A.F. Schier, "Zebrafish Behavioral Profiling Links Drugs to Biological Targets and Rest/Wake Regulation," Science, vol. 327, no. 5963, pp. 348-351, 2010.

[8]

F. Emran, J. Rihel, and J. Dowling, "A Behavioral Assay to Measure Responsiveness of Zebrafish to Changes in Light Intensities," J. Visualized Experiments, no. 20, pp. 1-6, 2008.

[9]

F. Emran, J. Rihel, A.R. Adolph, K.Y. Wong, S. Kraves, and J.E. Dowling, "OFF Ganglion Cells Cannot Drive the Optokinetic Reflex in Zebrafish," Proc. Nat'l Academy of Sciences USA, vol. 104, no. 48, pp. 19 126-19 131, 2007.

[10]

L. Zhang, L. Chong, J. Cho, W. Zhong, K.M. Ko, and Y.F. Leung, "Schisandrin B Enhanced the Visual Motor Response and Protected the Rod Photoreceptors of the Zebrafish pde6c Retinal Degeneration Mutant," submitted for publication, 2013.

[11]

R. K.-M. Ko and D.H.F. Mak, "Schisandrin B and Other Dibenzocy-clooctadiene Lignans," Herbal and Traditional Medicine: Biomolecular and Clinical Aspects, pp. 289-314, CRC Press, 2004.

[12]

G. Stearns, M. Evangelista, J.M. Fadool, and S.E. Brockerhoff, "A Mutation in the Cone-Specific pde6 Gene Causes Rapid Cone Photoreceptor Degeneration in Zebrafish," J. Neuroscience, vol. 27, no. 50, pp. 13 866-13 874, 2007.

[13]

A. Lewis, P. Williams, O. Lawrence, R.O.L. Wong, and S.E. Brockerhoff, "Wild-Type Cone Photoreceptors Persist Despite Neighboring Mutant Cone Degeneration," J. Neuroscience, vol. 30, no. 1, pp. 382-389, 2010.

[14]

Y.F. Leung, L. Zhang, L. Chong, J. Cho, and K.M. Ko, "Schisandrin B Improved the Visual Moter Response and Preserves Photoreceptors in the Zebrafish pde6c Cone Dystrophy Mutant," Investigative Ophthalmology and Visual Science, vol. 54, p. 1943, ARVO E-Abstract, 2013.

[15]

A.M. Fernandes, K. Fero, A.B. Arrenberg, S.A. Bergeron, W. Driever, and H.A. Burgess, "Deep Brain Photoreceptors Control Light-Seeking Behavior in Zebrafish Larvae," Current Biology, vol. 22, no. 21, pp. 2042-2047, Nov. 2012.

[16]

F. Emran, J. Rihel, A.R. Adolph, and J.E. Dowling, "Zebrafish Larvae Lose Vision at Night," Proc. Nat'l Academy of Sciences USA, vol. 107, no. 13, pp. 6034-6039, Mar. 2010.

[17]

M. Kabra, A.A. Robie, M. Rivera-Alba, S. Branson, and K. Branson, "JAABA: Interactive Machine Learning for Automatic Annotation of Animal Behavior," Nature Methods, vol. 10, no. 1, pp. 64-67, 2013.

[18]

O. Mirat, J.R. Sternberg, K.E. Severi, and C. Wyart, "ZebraZoom: An Automated Program for High-Throughput Behavior Analysis and Categorization," Frontiers in Neural Circuits, vol. 7, pp. 107:1-107:12, 2013.

[19]

X.P. Burgos-Artizzu, P. Dollár, D. Lin, D.J. Anderson, and P. Perona, "Social Behavior Recognition in Continuous Video," Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp. 1322-1329, 2012.

[20]

C.M. Bishop, Pattern Recognition and Machine Learning. Springer, 2006.

[21]

A. Franke, T. Caelli, and R.J. Hudson, "Analysis of Movements and Behavior of Caribou (Rangifer tarandus) Using Hidden Markov Models," Ecological Modelling, vol. 173, no. 2-3, pp. 259-270, 2004.

[22]

Y. Liu, S.-H. Lee, and T.-S. Chon, "Analysis of Behavioral Changes of Zebrafish (Danio rerio) in Response to Formaldehyde Using Self-Organizing Map and a Hidden Markov Model," Ecological Modelling, vol. 222, no. 14, pp. 2191-2201, 2011.

[23]

Y. Li, J.-M. Lee, T.-S. Chon, Y. Liu, H. Kim, M.-J. Bae, and Y.-S. Park, "Analysis of Movement Behavior of Zebrafish (Danio rerio) under Chemical Stress Using Hidden Markov Model," Modern Physics Letters B, vol. 27, no. 2, pp. 1 350 014:1-1 350 014:13, 2013.

[24]

"Zebrabox," http://www.vplsi.com/content.php?content.72, 2014.

[25]

J.J. Ingebretson and M.A. Masino, "Quantification of Locomotor Activity in Larval Zebrafish: Considerations for the Design of High-Throughput Behavioral Studies," Frontiers in Neural Circuits, vol. 7, pp. 109:1-109:9, 2013.

[26]

H.M. Ashtawy and N.R. Mahapatra, "A Comparative Assessment of Ranking Accuracies of Conventional and Machine-Learning-Based Scoring Functions for Protein-Ligand Binding Affinity Prediction," IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 9, no. 5, pp. 1301-1313, Sept./Oct. 2012.

[27]

K. Murphy, Machine Learning: A Probabilistic Perspective. The MIT Press, 2012.

[28]

C.-C. Chang and C.-J. Lin, "LIBSVM: A Library for Support Vector Machines," ACM Trans. Intelligent Systems and Technology, vol. 2, no. 3, pp. 27:1-27:27, 2011.

[29]

"ZFIN: The Zebrafish Model Organism Database," http://zfin.org/ZDB-GENO-960809-7, 2014.

[30]

M. Westerfield, The Zebrafish Book: A Guide for the Laboratory Use of Zebrafish (Danio rerio). Univ. of Oregon Press, 2000.

[31]

Y.-Y. Huang and S.C.F. Neuhauss, "The Optokinetic Response in Zebrafish and Its Applications," Frontiers in Bioscience: A J. and Virtual Library, vol. 13, pp. 1899-1916, 2008.

[32]

S.E. Brockerhoff, "Measuring the Optokinetic Response of Zebrafish Larvae," Nature Protocols, vol. 1, no. 5, pp. 2448-2451, 2006.

[33]

C. Nusslein-Volhard and R. Dahm, Zebrafish: A Practical Approach. Oxford Univ. Press, 2002.

[34]

J. Lin, E. Keogh, and L. Wei, "Experiencing SAX: A Novel Symbolic Representation of Time Series," Data Mining and Knowledge Discovery, vol. 15, no. 2, pp. 107-144, 2007.

[35]

J.S. Richman and J.R. Moorman, "Physiological Time-Series Analysis Using Approximate Entropy and Sample Entropy," Am. J. Physiology--Heart and Circulatory Physiology, vol. 278, pp. H2039-H2049, 2000.

Cited By

Gao YZhang GJelfs BCarmer RVenkatraman PGhadami MBrown SPang CLeung YChan RZhang M(2016)Computational classification of different wild-type zebrafish strains based on their variation in light-induced locomotor responseComputers in Biology and Medicine10.1016/j.compbiomed.2015.11.01269:C(1-9)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1016/j.compbiomed.2015.11.012

Index Terms

A high-throughput zebrafish screening method for visual mutants by light-induced locomotor response
1. Applied computing
  1. Life and medical sciences

Recommendations

In silico predicted essential genes required for zebrafish (Danio rerio) steroid hormone production
BCB '10: Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology

Genome-scale metabolic models associate genes and transcript enzymes involved in biochemical reactions. We present a genome-scale stoichiometric reconstruction of the zebrafish (Danio rerio) steroidogenic network and apply linear programming methods to ...
Effect of Astragalus, Ganoderm A Lucidum and Lycium Polysacchride on Zebrafish Senescence
ICBEB '12: Proceedings of the 2012 International Conference on Biomedical Engineering and Biotechnology

Polysaccharides were extracted from astragal us, ganoderm lucidum and lyceum, which are commonly used to increase organism's immunity. Several earlier studies in vitro have indicated that astragal us polysaccharide (AP), ganoderm a lucidum ...
Searching for Glycomics Role in Stem Cell Development
Computational Intelligence Methods for Bioinformatics and Biostatistics

We present a methodology to analyze zebrafish knock-out experiment replicated time series microarray data. The knock-out experiment aimed to elucidate the transcriptomal regulators underlying the glycocalyx-regulation of vasculogenesis by performing ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 11, Issue 4

July/August 2014

160 pages

ISSN:1545-5963

Editor:
Ying Xu

Issue’s Table of Contents

Publisher

IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 July 2014

Accepted: 21 January 2014

Revised: 20 January 2014

Received: 18 June 2013

Published in TCBB Volume 11, Issue 4

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
82
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 20 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Gao YZhang GJelfs BCarmer RVenkatraman PGhadami MBrown SPang CLeung YChan RZhang M(2016)Computational classification of different wild-type zebrafish strains based on their variation in light-induced locomotor responseComputers in Biology and Medicine10.1016/j.compbiomed.2015.11.01269:C(1-9)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1016/j.compbiomed.2015.11.012

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents