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Role of Multi-Omics in Functional Genomic, Transcriptomic, Proteomic and Metabolic Mechanisms for Milk Production, Growth, Fertility and Health in Livestock Animals

Year 2025, Volume: 6 Issue: 3, 38 - 46, 01.08.2025
https://doi.org/10.55549/zbs.1680746

Abstract

The integration of multi-omics technologies has significantly advanced our understanding of functional genomic, transcriptomic, proteomic, and metabolic mechanisms in livestock animals. These approaches provide a comprehensive framework for exploring the intricate biological processes underlying milk production, growth, fertility, and overall health. By leveraging genomic data, researchers can identify key genetic variants and regulatory elements that influence phenotypic traits. Transcriptomic studies reveal dynamic gene expression patterns, while proteomics and metabolomics offer insights into protein interactions and metabolic pathways that are critical for optimal physiological functions. Integrative multi-omics not only enhances precision breeding strategies but also facilitates the development of tailored nutritional and management practices designed to improve productivity and resilience under diverse environmental conditions. This review highlights recent advancements in multi-omics applications, emphasizing their transformative potential in advancing livestock research and enhancing animal welfare in sustainable agricultural systems.

References

  • Alexandre, P.A., Kogelman, L.J., Santana, M.H., Passarelli, D., Pulz, L.H., Fantinato-Neto, P., . . . Coutinho, L.L. (2015). Liver transcriptomic networks reveal main biological processes associated with feed efficiency in beef cattle. BMC genomics, 16, 1-13.
  • Bahlo, C., Dahlhaus, P., Thompson, H., & Trotter, M. (2019). The role of interoperable data standards in precision livestock farming in extensive livestock systems: A review. Computers and electronics in agriculture, 156, 459-466.
  • Bernard, L., Bonnet, M., Delavaud, C., Delosiere, M., Ferlay, A., Fougere, H., & Graulet, B. (2018). Milk fat globule in ruminant: Major and minor compounds, nutritional regulation and differences among species. European journal of lipid science and technology, 120(5), 1700039.
  • Bhat, S.A., Ahmad, S.M., Ibeagha-Awemu, E.M., Bhat, B.A., Dar, M.A., Mumtaz, P.T., . . . Ganai, N.A. (2019). Comparative transcriptome analysis of mammary epithelial cells at different stages of lactation reveals wide differences in gene expression and pathways regulating milk synthesis between jersey and kashmiri cattle. PLoS One, 14(2), e0211773.
  • Bora, S.S., Hazarika, D.J., Gogoi, R., Dullah, S., Gogoi, M., & Barooah, M. (2022). Long-term pruning modulates microbial community structure and their functional potential in tea (camellia sinensis l.) soils. Applied Soil Ecology, 176, 104483.
  • Braun, T., & Gautel, M. (2011). Transcriptional mechanisms regulating skeletal muscle differentiation, growth and homeostasis. Nature reviews Molecular cell biology, 12(6), 349-361.
  • Brito, L., Bédère, N., Douhard, F., Oliveira, H., Arnal, M., Peñagaricano, F., . . . Miglior, F. (2021). Genetic selection of high-yielding dairy cattle toward sustainable farming systems in a rapidly changing world. Animal, 15, 100292.
  • Choudhary, R.K., Kumar BV, S., Sekhar Mukhopadhyay, C., Kashyap, N., Sharma, V., Singh, N., . . . Singh Malik, Y. (2024). Animal wellness: The power of multiomics and integrative strategies: Multiomics in improving animal health. Veterinary Medicine International, 2024(1), 4125118.
  • Chovatiya, R., & Medzhitov, R. (2014). Stress, inflammation, and defense of homeostasis. Molecular cell, 54(2), 281-288.
  • Feil, R. (2006). Environmental and nutritional effects on the epigenetic regulation of genes. Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis, 600(1-2), 46-57.
  • Ghildiyal, K., Nayak, S.S., Rajawat, D., Sharma, A., Chhotaray, S., Bhushan, B., . . . Panigrahi, M. (2023). Genomic insights into the conservation of wild and domestic animal diversity: A review. Gene, 886, 147719.
  • Glazier, A.M., Nadeau, J.H., & Aitman, T.J. (2002). Finding genes that underlie complex traits. science, 298(5602), 2345-2349.
  • Groeneveld, L., Lenstra, J., Eding, H., Toro, M., Scherf, B., Pilling, D., . . . Groeneveld, E. (2010). Genetic diversity in farm animals–a review. Animal genetics, 41, 6-31.
  • Gulati, G.S., D’Silva, J.P., Liu, Y., Wang, L., & Newman, A.M. (2024). Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics. Nature Reviews Molecular Cell Biology, 1-21.
  • Hoffmann, I. (2010). Climate change and the characterization, breeding and conservation of animal genetic resources. Animal genetics, 41, 32-46.
  • Hume, D., Whitelaw, C., & Archibald, A. (2011). The future of animal production: Improving productivity and sustainability. The Journal of Agricultural Science, 149(S1), 9-16.
  • Jabbar, A., Zulfiqar, F., Mahnoor, M., Mushtaq, N., Zaman, M.H., Din, A.S.U., . . . Ahmad, H.I. (2021). Advances and perspectives in the application of crispr-cas9 in livestock. Molecular Biotechnology, 63(9), 757-767. Karlebach, G., & Shamir, R. (2008). Modelling and analysis of gene regulatory networks. Nature reviews Molecular cell biology, 9(10), 770-780.
  • Kirgiafini, D., Kyrgiafini, M.-A., Gournaris, T., & Mamuris, Z. (2024). Understanding circular rnas in health, welfare, and productive traits of cattle, goats, and sheep. Animals, 14(5), 733.
  • Lemay, D.G., Hovey, R.C., Hartono, S.R., Hinde, K., Smilowitz, J.T., Ventimiglia, F., . . . Silva, P.I. (2013). Sequencing the transcriptome of milk production: Milk trumps mammary tissue. BMC genomics, 14, 1-17.
  • Leroy, J.L., De Bie, J., Jordaens, L., Desmet, K., Smits, A., Marei, W.F., . . . Van Hoeck, V. (2018). Negative energy balance and metabolic stress in relation to oocyte and embryo quality: An update on possible pathways reducing fertility in dairy cows. Animal Reproduction (AR), 14(3), 497-506.
  • Loue, S. (2016). Therapeutic farms: Recovery from mental illness: Springer.
  • Lowe, R., Shirley, N., Bleackley, M., Dolan, S., & Shafee, T. (2017). Transcriptomics technologies. PLoS computational biology, 13(5), e1005457.
  • Lu, Y., Li, M., Gao, Z., Ma, H., Chong, Y., Hong, J., . . . Deng, W. (2024). Innovative insights into single-cell technologies and multi-omics integration in livestock and poultry. International Journal of Molecular Sciences, 25(23), 12940.
  • Majumder, P. (2024). Golden opportunities: Harnessing bioinformatics to revolutionize plant research and unleash the power of golden rice in crop breeding. Bioinformatics for Plant Research and Crop Breeding, 505-537. Meuwissen, T., Hayes, B., & Goddard, M. (2016). Genomic selection: A paradigm shift in animal breeding. Animal frontiers, 6(1), 6-14.
  • Nibbe, R.K., Chowdhury, S.A., Koyutürk, M., Ewing, R., & Chance, M.R. (2011). Protein–protein interaction networks and subnetworks in the biology of disease. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 3(3), 357-367.
  • Renaudeau, D., Collin, A., Yahav, S., De Basilio, V., Gourdine, J.-L., & Collier, R. (2012). Adaptation to hot climate and strategies to alleviate heat stress in livestock production. Animal, 6(5), 707-728.
  • Roco, M.C., & Bainbridge, W.S. (2003). Overview converging technologies for improving human performance: Nanotechnology, biotechnology, information technology, and cognitive science (nbic) Converging technologies for improving human performance: Nanotechnology, biotechnology, information technology and cognitive science (pp. 1-27): Springer.
  • Sammad, A., Wang, Y.J., Umer, S., Lirong, H., Khan, I., Khan, A., . . . Wang, Y. (2020). Nutritional physiology and biochemistry of dairy cattle under the influence of heat stress: Consequences and opportunities. Animals, 10(5), 793.
  • Santos, R., Ursu, O., Gaulton, A., Bento, A.P., Donadi, R.S., Bologa, C.G., . . . Oprea, T.I. (2017). A comprehensive map of molecular drug targets. Nature reviews Drug discovery, 16(1), 19-34.
  • Shashank, C.G., Sejian, V., Silpa, M.V., Devaraj, C., Madhusoodan, A.P., Rebez, E.B., . . . Dunshea, F.R. (2024). Climate resilience in farm animals: Transcriptomics-based alterations in differentially expressed genes and stress pathways. BioTech, 13(4), 49.
  • Shi, Y.B., Wong, J., Puzianowska‐Kuznicka, M., & Stolow, M. (1996). Tadpole competence and tissue‐specific temporal regulation of amphibian metamorphosis: Roles of thyroid hormone and its receptors. Bioessays, 18(5), 391-399.
  • Silpa, M.V., König, S., Sejian, V., Malik, P.K., Nair, M.R.R., Fonseca, V.F., . . . Bhatta, R. (2021). Climate-resilient dairy cattle production: Applications of genomic tools and statistical models. Frontiers in Veterinary Science, 8, 625189.
  • Singh, V.K., Seed, T.M., & Olabisi, A.O. (2019). Drug discovery strategies for acute radiation syndrome. Expert Opinion on Drug Discovery, 14(7), 701-715.
  • Smith, J.A. (2018). Regulation of cytokine production by the unfolded protein response; implications for infection and autoimmunity. Frontiers in immunology, 9, 422.
  • Spitzer, A.J. (2019). Endotoxin increases oxidative stress and oxygen tension while reducing milk protein gene expression in the mammary gland: The University of Vermont and State Agricultural College.
  • Strianese, O., Rizzo, F., Ciccarelli, M., Galasso, G., D’Agostino, Y., Salvati, A., . . . Rusciano, M.R. (2020). Precision and personalized medicine: How genomic approach improves the management of cardiovascular and neurodegenerative disease. Genes, 11(7), 747.
  • Strzezek, J., Wysocki, P., Kordan, W., Kuklinska, M., Mogielnicka, M., Soliwoda, D., & Fraser, L. (2005). Proteomics of boar seminal plasma–current studies and possibility of their application in biotechnology of animal reproduction. Reprod Biol, 5(3), 279-290.
  • Sun, H., Plastow, G., & Guan, L. (2019). Invited review: Advances and challenges in application of feedomics to improve dairy cow production and health. Journal of dairy science, 102(7), 5853-5870.
  • Tolani, P., Gupta, S., Yadav, K., Aggarwal, S., & Yadav, A.K. (2021). Big data, integrative omics and network biology. Advances in protein chemistry and structural biology, 127, 127-160.
  • Tona, G.O. (2018). Current and future improvements in livestock nutrition and feed resources. Animal husbandry and nutrition, 18, 73088.
  • Vaissière, T., Sawan, C., & Herceg, Z. (2008). Epigenetic interplay between histone modifications and DNA methylation in gene silencing. Mutation Research/Reviews in Mutation Research, 659(1-2), 40-48.
  • Wang, S., Ren, J., Jing, Y., Qu, J., & Liu, G.-H. (2024). Perspectives on biomarkers of reproductive aging for fertility and beyond. Nature Aging, 4(12), 1697-1710.
  • Yang, Q., Fu, W., Wang, Y., Miao, K., Zhao, H., Wang, R., . . . An, L. (2020). The proteome of ivf-induced aberrant embryo-maternal crosstalk by implantation stage in ewes. Journal of Animal Science and Biotechnology, 11, 1-17.
  • Yang, X., Li, Q., Wang, Y., Wang, J., Hu, J., Ji, Z., & Chao, T. (2024). Research progress on genomic regions and candidate genes related to milk composition traits of dairy goats based on functional genomics: A narrative review. Genes, 15(10), 1341.
  • Yin, M., Ma, R., Luo, H., Li, J., Zhao, Q., & Zhang, M. (2023). Non-contact sensing technology enables precision livestock farming in smart farms. Computers and Electronics in Agriculture, 212, 108171.
There are 45 citations in total.

Details

Primary Language English
Subjects Epigenetics
Journal Section Review
Authors

Tanveer Nasir 0009-0008-7232-1053

Abdul Rehman 0009-0000-7588-0127

Muhammad Tariq 0000-0001-5539-0454

Khadija Tul Kubra 0009-0001-0504-4040

Naghman Ashraf 0009-0002-5290-0169

Farrukh Jamal Nasir 0009-0006-7103-7611

Early Pub Date August 1, 2025
Publication Date August 1, 2025
Submission Date April 21, 2025
Acceptance Date May 5, 2025
Published in Issue Year 2025 Volume: 6 Issue: 3

Cite

EndNote Nasir T, Rehman A, Tariq M, Kubra KT, Ashraf N, Nasir FJ (August 1, 2025) Role of Multi-Omics in Functional Genomic, Transcriptomic, Proteomic and Metabolic Mechanisms for Milk Production, Growth, Fertility and Health in Livestock Animals. Zeugma Biological Science 6 3 38–46.