Classification and prediction of toxicity of chemicals using an automated phenotypic profiling of Caenorhabditis elegans
Abstract Background Traditional toxicological studies have relied heavily on various animal models to understand the effect of various compounds in a biological context. Considering the great cost, complexity and time involved in experiments using higher order organisms. Researchers have been explor...
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| Format: | Article |
| Language: | English |
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BMC
2018-04-01
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| Series: | BMC Pharmacology and Toxicology |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40360-018-0208-3 |
| _version_ | 1811269438861737984 |
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| author | Shan Gao Weiyang Chen Yingxin Zeng Haiming Jing Nan Zhang Matthew Flavel Markandeya Jois Jing-Dong J. Han Bo Xian Guojun Li |
| author_facet | Shan Gao Weiyang Chen Yingxin Zeng Haiming Jing Nan Zhang Matthew Flavel Markandeya Jois Jing-Dong J. Han Bo Xian Guojun Li |
| author_sort | Shan Gao |
| collection | DOAJ |
| description | Abstract Background Traditional toxicological studies have relied heavily on various animal models to understand the effect of various compounds in a biological context. Considering the great cost, complexity and time involved in experiments using higher order organisms. Researchers have been exploring alternative models that avoid these disadvantages. One example of such a model is the nematode Caenorhabditis elegans. There are some advantages of C. elegans, such as small size, short life cycle, well defined genome, ease of maintenance and efficient reproduction. Methods As these benefits allow large scale studies to be initiated with relative ease, the problem of how to efficiently capture, organize and analyze the resulting large volumes of data must be addressed. We have developed a new method for quantitative screening of chemicals using C. elegans. 33 features were identified for each chemical treatment. Results The compounds with different toxicities were shown to alter the phenotypes of C. elegans in distinct and detectable patterns. We found that phenotypic profiling revealed conserved functions to classify and predict the toxicity of different chemicals. Conclusions Our results demonstrate the power of phenotypic profiling in C. elegans under different chemical environments. |
| first_indexed | 2024-04-12T21:41:02Z |
| format | Article |
| id | doaj.art-5354bfc701a748de8b1793037a7f1895 |
| institution | Directory Open Access Journal |
| issn | 2050-6511 |
| language | English |
| last_indexed | 2024-04-12T21:41:02Z |
| publishDate | 2018-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pharmacology and Toxicology |
| spelling | doaj.art-5354bfc701a748de8b1793037a7f18952022-12-22T03:15:45ZengBMCBMC Pharmacology and Toxicology2050-65112018-04-0119111110.1186/s40360-018-0208-3Classification and prediction of toxicity of chemicals using an automated phenotypic profiling of Caenorhabditis elegansShan Gao0Weiyang Chen1Yingxin Zeng2Haiming Jing3Nan Zhang4Matthew Flavel5Markandeya Jois6Jing-Dong J. Han7Bo Xian8Guojun Li9Beijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control/Beijing Center of Preventive Medicine ResearchCollege of Information, Qilu University of Technology (Shandong Academy of Sciences)Beijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control/Beijing Center of Preventive Medicine ResearchBeijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control/Beijing Center of Preventive Medicine ResearchBeijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control/Beijing Center of Preventive Medicine ResearchSchool of Life Sciences, La Trobe UniversitySchool of Life Sciences, La Trobe UniversityKey Laboratory of Computational Biology, CAS Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of SciencesKey Laboratory of Computational Biology, CAS Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of SciencesBeijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control/Beijing Center of Preventive Medicine ResearchAbstract Background Traditional toxicological studies have relied heavily on various animal models to understand the effect of various compounds in a biological context. Considering the great cost, complexity and time involved in experiments using higher order organisms. Researchers have been exploring alternative models that avoid these disadvantages. One example of such a model is the nematode Caenorhabditis elegans. There are some advantages of C. elegans, such as small size, short life cycle, well defined genome, ease of maintenance and efficient reproduction. Methods As these benefits allow large scale studies to be initiated with relative ease, the problem of how to efficiently capture, organize and analyze the resulting large volumes of data must be addressed. We have developed a new method for quantitative screening of chemicals using C. elegans. 33 features were identified for each chemical treatment. Results The compounds with different toxicities were shown to alter the phenotypes of C. elegans in distinct and detectable patterns. We found that phenotypic profiling revealed conserved functions to classify and predict the toxicity of different chemicals. Conclusions Our results demonstrate the power of phenotypic profiling in C. elegans under different chemical environments.http://link.springer.com/article/10.1186/s40360-018-0208-3C. elegansChemicalsToxicityImage analysisPhenotype |
| spellingShingle | Shan Gao Weiyang Chen Yingxin Zeng Haiming Jing Nan Zhang Matthew Flavel Markandeya Jois Jing-Dong J. Han Bo Xian Guojun Li Classification and prediction of toxicity of chemicals using an automated phenotypic profiling of Caenorhabditis elegans BMC Pharmacology and Toxicology C. elegans Chemicals Toxicity Image analysis Phenotype |
| title | Classification and prediction of toxicity of chemicals using an automated phenotypic profiling of Caenorhabditis elegans |
| title_full | Classification and prediction of toxicity of chemicals using an automated phenotypic profiling of Caenorhabditis elegans |
| title_fullStr | Classification and prediction of toxicity of chemicals using an automated phenotypic profiling of Caenorhabditis elegans |
| title_full_unstemmed | Classification and prediction of toxicity of chemicals using an automated phenotypic profiling of Caenorhabditis elegans |
| title_short | Classification and prediction of toxicity of chemicals using an automated phenotypic profiling of Caenorhabditis elegans |
| title_sort | classification and prediction of toxicity of chemicals using an automated phenotypic profiling of caenorhabditis elegans |
| topic | C. elegans Chemicals Toxicity Image analysis Phenotype |
| url | http://link.springer.com/article/10.1186/s40360-018-0208-3 |
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