Understanding the liver under heat stress with statistical learning: an integrated metabolomics and transcriptomics computational approach
| Author(s) | Hubbard, Allen H. | |
| Author(s) | Zhang, Xiaoke | |
| Author(s) | Jastrebski, Sara | |
| Author(s) | Singh, Abhyudai | |
| Author(s) | Schmidt, Carl J. | |
| Date Accessioned | 2025-02-20T19:46:56Z | |
| Date Available | 2025-02-20T19:46:56Z | |
| Publication Date | 2019-06-17 | |
| Description | This article was originally published in BMC Genomics. The version of record is available at: https://doi.org/10.1186/s12864-019-5823-x. © The Author(s). 2019. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. | |
| Abstract | Background We present results from a computational analysis developed to integrate transcriptome and metabolomic data in order to explore the heat stress response in the liver of the modern broiler chicken. Heat stress is a significant cause of productivity loss in the poultry industry, both in terms of increased livestock morbidity and its negative influence on average feed efficiency. This study focuses on the liver because it is an important regulator of metabolism, controlling many of the physiological processes impacted by prolonged heat stress. Using statistical learning methods, we identify genes and metabolites that may regulate the heat stress response in the liver and adaptations required to acclimate to prolonged heat stress. Results We describe how disparate systems such as sugar, lipid and amino acid metabolism, are coordinated during the heat stress response. Conclusions Our findings provide more detailed context for genomic studies and generates hypotheses about dietary interventions that can mitigate the negative influence of heat stress on the poultry industry. | |
| Sponsor | This project was supported by the Agriculture and Food Research Initiative Competitive Grant 2011–67003-30228 from the United States Department of Agriculture National Institute of Food and Agriculture. | |
| Citation | Hubbard, A.H., Zhang, X., Jastrebski, S. et al. Understanding the liver under heat stress with statistical learning: an integrated metabolomics and transcriptomics computational approach. BMC Genomics 20, 502 (2019). https://doi.org/10.1186/s12864-019-5823-x | |
| ISSN | 1471-2164 | |
| URL | https://udspace.udel.edu/handle/19716/35835 | |
| Language | en_US | |
| Publisher | BMC Genomics | |
| dc.rights | Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| Keywords | high throughput sequencing | |
| Keywords | transcriptome | |
| Keywords | metabolome | |
| Title | Understanding the liver under heat stress with statistical learning: an integrated metabolomics and transcriptomics computational approach | |
| Type | Article |
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