Marina Rufino Salinas Fortes
Sara Pegolo- 1School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD, Australia
- 2Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Padova, Italy
Editorial on the Research Topic
Application of Omics Technologies to improve robustness and resilience in livestock species
Omics Technologies, such as genomics, transcriptomics, or metabolomics, are used to provide an understanding of the biological processes underlying phenotypes. They enable the annotation of genomes (1, 2), the identification of genetic variants (3), and the elucidation of complex molecular mechanisms (4, 5). Genomics has also transformed the practice of selective breeding livestock: the classic practice of selective breeding based on purely quantitative models and pedigree information changed to approaches that use genome-wide molecular information (6–8). This change in practice increased the accuracy of estimated breeding values. The application of Omics Technologies has the potential to improve the robustness and resilience of production animals to various environmental stressors and diseases.
Genomic selection, the practice of selecting animals based on their DNA profile (i.e., genotypes) (7), is successful in many traits and species. Genomic Selection can further develop to target “robustness” and “resilience” once these traits are defined as phenotypes linked to animal health and welfare (9). Resilience is the ability of an animal to adapt to various disturbances and return to its “normal” physiological state (10). Robustness can be defined as the ability to face environmental challenges while maintaining high productivity, without compromising reproduction, health, and welfare (11). In short, robustness implies that the animal maintains its functionality during a stressor event, which includes resilience. Omics Technologies can improve robustness and resilience by identifying genetic variants associated with disease or parasite resistance (12). These variants can be used in Genomic Selection, which can target multiple traits to improve overall performance. In addition, genomic data can inform mating systems to promote biodiversity, while selecting for enhanced performance. Genetic biodiversity should have a positive impact on animal resilience to pathogens.
In this Research Topic, there are five articles that showcase Omics Technologies applied to livestock research. The technologies featured include transcriptomics, metabolomics, and the development of a data-driven approach to improve animal husbandry. In their paper proposing the new data-driven approach to animal health and welfare, Thomann et al. explored their methods in all of the most common livestock species: cattle, sheep, goats, pigs, and poultry. Other articles in this Research Topic focused on sheep, goats, and chickens, exploring species-specific Omics data. Overall, the research presented in this Research Topic contributes to the annotation of animal genomes.
Three articles report on transcriptome data of small ruminants, which are relevant to the functional annotation of the genomes in less studied species and breeds. Hosseini et al. studied the transcriptome of adipose tissue in fat-tail sheep. Dixon et al. studied sheep's response to nematode infections, discovering genes linked to parasite burden. Zhao et al. studied adipose tissue gene expression in newborn goats because fat protects them against the cold. These are examples of transcriptomics being used to identify gene expression patterns associated with specific traits. Such information can be used to elucidate molecular mechanisms underpinning complex traits and ultimately lead to breeding more robust and resilient livestock.
Metabolomics can provide insight into the biochemical pathways involved in various physiological processes. For example, it can identify metabolic pathways that are important for growth and development. Li et al. used a metabolomics approach to study bone quality in chickens, a trait that affects productivity. Recently, Dehau et al. reviewed the applications of Omics to poultry (13).
To fulfill its full potential, Omics Technologies will be applied to animals, including humans and plants (6, 14, 15). Improved crops lead to feed and food security. As we use Omics to target animal welfare, hoping to breed livestock that is resilient and less susceptible to disease, it is possible to draw parallels with the use of Omics in human health (16). In this context, the article from Zhao et al. pointed out the possibility to translate the results among different species. Therefore, the integration of Omics approaches is promising for livestock production, crops, and personalized medicine. Integration of Omics data to enhance livestock production benefits from international collaborations and large consortium efforts, such as FAANG (1, 2). The accumulation of knowledge about genome function creates opportunities to address the issues related to livestock disease and pathogens.
Author contributions
MF and SP edited this Research Topic together and co-authored the editorial text. All authors contributed to the article and approved the submitted version.
Acknowledgments
We thank Guilherme J. M. Rosa who introduced us to each other facilitating the creation of this Research Topic.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1. Andersson L, Archibald AL, Bottema CD, Brauning R, Burgess SC, Burt DW, et al. Coordinated international action to accelerate genome-to-phenome with FAANG, the Functional Annotation of Animal Genomes project. Genome Biol. (2015) 16:57. doi: 10.1186/s13059-015-0622-4
2. Tuggle CK, Giuffra E, White SN, Clarke L, Zhou H, Ross PJ, et al. GO-FAANG meeting: a gathering on functional annotation of animal genomes. Anim Genet. (2016) 47:528–33. doi: 10.1111/age.12466
3. Xiang R, MacLeod IM, Daetwyler HD, de Jong G, O'Connor E, Schrooten C, et al. Genome-wide fine-mapping identifies pleiotropic and functional variants that predict many traits across global cattle populations. Nat Commun. (2021) 12:860. doi: 10.1038/s41467-021-21001-0
4. Canovas A, Reverter A, DeAtley KL, Ashley RL, Colgrave ML, Fortes MRS, et al. Multi-tissue omics analyses reveal molecular regulatory networks for puberty in composite beef cattle. PLoS ONE. (2014) 9:e102551. doi: 10.1371/journal.pone.0102551
5. Fortes MRS, Zacchi LF, Nguyen LT, Raidan F, Weller MMDCA, Choo JJY, et al. Pre- and post-puberty expression of genes and proteins in the uterus of Bos indicus heifers: the luteal phase effect post-puberty. Anim Genet. (2018) 49:539–49. doi: 10.1111/age.12721
6. Meuwissen T, van den Berg I, Goddard M. On the use of whole-genome sequence data for across-breed genomic prediction and fine-scale mapping of QTL. Genet Sel Evol. (2021) 53:19. doi: 10.1186/s12711-021-00607-4
7. Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. (2001) 157:1819–29. doi: 10.1093/genetics/157.4.1819
8. Garrick DJ. The nature, scope and impact of genomic prediction in beef cattle in the United States. Genet Sel Evol. (2011) 43:17. doi: 10.1186/1297-9686-43-17
9. Chakraborty D, Sharma N, Kour S, Sodhi SS, Gupta MK, Lee SJ, et al. Applications of omics technology for livestock selection and improvement. Front Genet. (2022) 13:774113. doi: 10.3389/fgene.2022.774113
10. Urruty N, Tailliez-Lefebvre D, Huyghe C. Stability, robustness, vulnerability and resilience of agricultural systems. A review. Agron Sustain Dev. (2016) 36:5. doi: 10.1007/s13593-015-0347-5
11. Colditz IG, Hine BC. Resilience in farm animals: biology, management, breeding and implications for animal welfare. Anim Prod Sci. (2016) 56:1961–83. doi: 10.1071/AN15297
12. Porto Neto LR, Jonsson NN, D'Occhio MJ, Barendse W. Molecular genetic approaches for identifying the basis of variation in resistance to tick infestation in cattle. Vet Parasitol. (2011) 180:165–72. doi: 10.1016/j.vetpar.2011.05.048
13. Dehau T, Ducatelle R, Van Immerseel F, Goossens E. Omics technologies in poultry health and productivity—Part 1: current use in poultry research. Avian Pathol. (2022) 51:407–17. doi: 10.1080/03079457.2022.2086447
14. Yang Y, Saand MA, Huang L, Abdelaal WB, Zhang J, Wu Y, et al. Applications of multi-omics technologies for crop improvement. Front Plant Sci. (2021) 12:563953. doi: 10.3389/fpls.2021.563953
15. Matasci N, Hung LH, Yan Z, Carpenter EJ, Wickett NJ, Mirarab S, et al. Data access for the 1,000 Plants (1KP) project. Gigascience. (2014) 3:17. doi: 10.1186/2047-217X-3-17
Keywords: livestock (including poultry), genomics, genetics, selective breeding, pathogens, disease resistance
Citation: Fortes MRS and Pegolo S (2023) Editorial: Application of Omics Technologies to improve robustness and resilience in livestock species. Front. Vet. Sci. 10:1224630. doi: 10.3389/fvets.2023.1224630
Received: 18 May 2023; Accepted: 21 June 2023;
Published: 04 July 2023.
Edited and reviewed by: Seyed Abbas Rafat, University of Tabriz, Iran
Copyright © 2023 Fortes and Pegolo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Marina Rufino Salinas Fortes, bS5mb3J0ZXMmI3gwMDA0MDt1cS5lZHUuYXU=; Sara Pegolo, c2FyYS5wZWdvbG8mI3gwMDA0MDt1bmlwZC5pdA==