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International Journal of Genetic Modifications and Recombinations Review Article

Diet-Gene Interactions in Farm Animals: Molecular Dynamics, Gene Expressions, Signalling Pathways and Precision Nutrition for Sustainable Productivity

  1. Md. Emran Hossain Department of Animal Science and Nutrition, Chattogram Veterinary and Animal Sciences University, Khulshi, Chattogram-4225

Abstract

Diet-gene interactions represent a central mechanism through which nutrition influences growth, health, and productivity in farm animals. Recent advances in molecular biology and genetics have revealed that nutrients act not only as metabolic substrates but also as signalling molecules capable of modulating gene expression, cellular pathways, and epigenetic regulation. This review synthesizes current knowledge on the molecular dynamics of nutrient utilization in farm animals, with emphasis on nutrigenomic responses, nutrient sensing mechanisms, and key signalling pathways involved in metabolism, immunity, and stress adaptation. Major regulatory pathways, including mTOR, AMPK, insulin, and insulin like growth factor signalling, are discussed in relation to dietary modulation and genetic variation among species and breeds. The role of epigenetic mechanisms and gut microbiome interactions in mediating diet induced genetic responses is also highlighted. Furthermore, the application of omics technologies in elucidating complex diet-gene networks and their relevance to precision nutrition strategies is critically evaluated. By integrating molecular, genetic, and nutritional perspectives, this review provides a comprehensive framework for understanding diet-gene interactions and identifies future research directions for improving efficiency, resilience, and sustainability in farm animal production systems.

Keywords

References (53)

  1. Liu Y, Mai K, Zhang W. mTOR Signaling in Aquaculture Animals: A Central Nutrient‐Sensing Hub Orchestrating Growth, Metabolism, Health and Quality. Reviews in Aquaculture. 2025;17(4). doi:10.1111/raq.70068
  2. Zhang J, Li M, Meng D, Xu S, Teame T, Yao Y, et al. Review on Omics Approaches in Aquatic Animal Nutrition: Current Status, Limitations, and Perspectives. The Journal of Nutrition. 2025;155(10):3191-3210. doi:10.1016/j.tjnut.2025.08.019
  3. Abdelrahman M, Wang W, Shaukat A, Kulyar MFEA, Lv H, Abulaiti A, et al. Nutritional Modulation, Gut, and Omics Crosstalk in Ruminants. Animals. 2022;12(8):997. doi:10.3390/ani12080997
  4. Loor JJ. Nutrigenomics in livestock: potential role in physiological regulation and practical applications. Animal Production Science. 2022;62(11):901-912. doi:10.1071/an21512
  5. Haq ZU, Saleem A, Khan AA, Dar MA, Ganaie AM, Beigh YA, et al. Nutrigenomics in livestock sector and its human-animal interface-a review. Veterinary and Animal Science. 2022;17:100262. doi:10.1016/j.vas.2022.100262
  6. Hasan MS, Feugang JM, Liao SF. A Nutrigenomics Approach Using RNA Sequencing Technology to Study Nutrient–Gene Interactions in Agricultural Animals. Current Developments in Nutrition. 2019;3(8):nzz082. doi:10.1093/cdn/nzz082
  7. Gao Z, Liu C, Mai K, He G. Nutrient Sensing for the Future of Land-Farmed Animal and Aquaculture Nutrition. Engineering. 2023;23:112-117. doi:10.1016/j.eng.2022.05.019
  8. Hassan FU, Alagawany M, Jha R. Editorial: Interplay of nutrition and genomics: Potential for improving performance and health of poultry. Frontiers in Physiology. 2022;13. doi:10.3389/fphys.2022.1030995
  9. Alagawany M, Elnesr SS, Farag MR, El-Naggar K, Madkour M. Nutrigenomics and nutrigenetics in poultry nutrition: An updated review. World's Poultry Science Journal. 2022;78(2):377-396. doi:10.1080/00439339.2022.2014288
  10. Griela E, Mountzouris KC. Nutrigenomic profiling of reduced specification diets and phytogenic inclusion effects on critical toll-like receptor signaling, mitogen-activated protein kinase-apoptosis, and PI3K-Akt-mTOR gene components along the broiler gut. Poultry Science. 2023;102(6):102675. doi:10.1016/j.psj.2023.102675
  11. Neibergs HL, Johnson KA. ALPHARMA BEEF CATTLE NUTRITION SYMPOSIUM: Nutrition and the genome1. Journal of Animal Science. 2012;90(7):2308-2316. doi:10.2527/jas.2011-4582
  12. Goul C, Peruzzo R, Zoncu R. The molecular basis of nutrient sensing and signalling by mTORC1 in metabolism regulation and disease. Nature Reviews Molecular Cell Biology. 2023;24(12):857-875. doi:10.1038/s41580-023-00641-8
  13. Zhang N. Epigenetic modulation of DNA methylation by nutrition and its mechanisms in animals. Animal Nutrition. 2015;1(3):144-151. doi:10.1016/j.aninu.2015.09.002
  14. Zhang N. Role of methionine on epigenetic modification of DNA methylation and gene expression in animals. Animal Nutrition. 2018;4(1):11-16. doi:10.1016/j.aninu.2017.08.009
  15. Department of Genetics, Breeding and Physiology of Livestock and Poultry, Faculty of Animal Science, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran., Hashemi SR, Davoodi H, Department of Immunology, Faculty of Medical Science, Golestan University of Medical Science, Gorgan, Iran., Arabiyan E, Department of Genetics, Breeding and Physiology of Livestock and Poultry, Faculty of Animal Science, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran. Nutrigenomics: A New Approach to Feed Formulation. The Journal of Qazvin University of Medical Sciences. 2020;24(1):80-93. doi:10.32598/jqums.23.5.8
  16. Wu CW, Storey KB. mTOR Signaling in Metabolic Stress Adaptation. Biomolecules. 2021;11(5):681. doi:10.3390/biom11050681
  17. R. Afruza, “Nutrigenomics: Unraveling the Gene-Diet,” Biotechnol. Adv. Heal., [Online]. Available: https://books.google.com/bookshl=en&lr=&id=y3ulEQAAQBAJ&oi=fnd&pg=PA261&dq=%22diet+gene%22+interactions+in+farm+animals+molecular+dynamics+of+nutrient+utilization+gene+expression+and+signalling+pathways&ots=7om1gjl1Wa&sig=erbAygiSW8MqAWXyvrJKPMb2b9I
  18. C. Wang et al., “… Hybrid Sturgeon (Acipenser baerii × A. schrenckii): Balancing Growth Performance, Serum Biochemistry, and Expression of Immune-Related Genes,” 2024, mdpi.com. [Online]. Available: https://www.mdpi.com/2079-7737/13/5/324
  19. S. B. Redmond, Genetic and dietary effects on chicken heterophil function and immune response to Salmonella enteritidis. search.proquest.com, 2010. [Online]. Available: https://search.proquest.com/openview/6eb0c890e96056dbec1da19600ce7ffd/1?pq-origsite=gscholar&cbl=18750
  20. J. B. LaValle, “Nutrition, Diabetes, and Cancer,” … Cancer Epidemiol. Evid. Mol. …, 2008, [Online]. Available: https://books.google.com/books?hl=en&lr=&id=4_83EQAAQBAJ&oi=fnd&pg=PA114&dq=%22diet+gene%22+interactions+in+farm+animals+molecular+dynamics+of+nutrient+utilization+gene+expression+and+signalling+pathways&ots=KAje0dOSBi&sig=Lk30rNAsis8iYeVFcsNl_viyqaA
  21. A. Lis, P. Maj, A. Świętek, and E. Romuk, “The Role of Various Types of Diets in the Treatments of Depressive Disorders,” 2025, mdpi.com. [Online]. Available: https://www.mdpi.com/1648-9144/61/10/1737
  22. M. A. Steele, J. C. Plaizier, O. AlZahal, L. Dionissopoulos, and ..., A characterization of inflammatory and structural markers within the rumen epithelium during grain-induced ruminal acidosis in lactating dairy cattle. era.library.ualberta.ca, 2012. [Online]. Available: https://era.library.ualberta.ca/items/8cf97e80-b374-4888-a347-e118c0e7416e
  23. Fan Y, Qin M, Zhu J, Chen X, Luo J, Chen T, et al. MicroRNA sensing and regulating microbiota-host crosstalk via diet motivation. Critical Reviews in Food Science and Nutrition. 2022;64(13):4116-4133. doi:10.1080/10408398.2022.2139220
  24. Fernandes AC, Reverter A, Keogh K, Alexandre PA, Afonso J, Palhares JCP, et al. Transcriptional response to an alternative diet on liver, muscle, and rumen of beef cattle. Scientific Reports. 2024;14(1). doi:10.1038/s41598-024-63619-2
  25. Naeem A, Drackley JK, Lanier JS, Everts RE, Rodriguez-Zas SL, Loor JJ. Ruminal epithelium transcriptome dynamics in response to plane of nutrition and age in young Holstein calves. Functional & Integrative Genomics. 2013;14(1):261-273. doi:10.1007/s10142-013-0351-2
  26. A. C. Fernandes, Integration of multi-tissue transcriptomics and microbiome data to assess the effects of alternative feed on methane production and other phenotypes in cattle. teses.usp.br, 2024. [Online]. Available: https://www.teses.usp.br/teses/disponiveis/11/11139/tde-10012025-081946/en.php
  27. Kwabi-Addo B. Gene-Environment Interactions in Health Disparities. Health Outcomes in a Foreign Land. 2017:233-277. doi:10.1007/978-3-319-55865-3_10
  28. H. Han, An observational investigation of gene-diet interactions on obesity outcomes. escholarship.mcgill.ca, 2025. [Online]. Available: https://escholarship.mcgill.ca/concern/theses/2r36v422w
  29. Koletzko B, Reischl E, Tanjung C, Gonzalez-Casanova I, Ramakrishnan U, Meldrum S, et al. FADS1 and FADS2 Polymorphisms Modulate Fatty Acid Metabolism and Dietary Impact on Health. Annual Review of Nutrition. 2019;39(1):21-44. doi:10.1146/annurev-nutr-082018-124250
  30. E. Reiche, A. P. Kallaur, F. V Reiche, and ..., “Genetic Polymorphisms and Gene–Nutrient Interaction in Metabolic Syndrome,” 2015, CRC Press.
  31. Pal A. Nutrigenomics. Springer Protocols Handbooks. 2021:559-569. doi:10.1007/978-1-0716-1818-9_23
  32. D. M. Ruden, P. Rasouli, L. Wang, and ..., “Nutrient-By-Genotype Interactions and Personalized Diet: What Can We Learn From Drosophila and Evolutionary Biology?,” Curr. …, 2009, [Online]. Available: https://www.ingentaconnect.com/content/ben/cppm/2009/00000007/00000003/art00005
  33. Gvozdanović K, Kralik Z, Radišić Ž, Košević M, Kralik G, Djurkin Kušec I. The Interaction between Feed Bioactive Compounds and Chicken Genome. Animals. 2023;13(11):1831. doi:10.3390/ani13111831
  34. Afruza R. Nutrigenomics: Unraveling the Gene–Diet Interactions for Personalized Nutrition and Optimizing Health Outcomes. Interdisciplinary Biotechnological Advances. 2026:261-294. doi:10.1007/978-981-95-3612-2_10
  35. J. C. Mathers, “Candidate mechanisms for interactions between nutrients and genes,” Nutr. Interact. Cancer, 2006, doi:10.1201/9780849332296-2.
  36. J. Kaput, “Diet–disease interactions at the molecular level. An experimental paradigm,” Phytochem. Nutr. Interact., 2006, [Online]. Available: https://books.google.com/books?hl=en&lr=&id=Lj_MBQAAQBAJ&oi=fnd&pg=PA23&dq=%22diet+gene%22+interactions+in+farm+animals+molecular+dynamics+of+nutrient+utilization+gene+expression+and+signalling+pathways&ots=esR2OnIkWy&sig=PB_Rz6_w3Hd1ORqGw0a8_wH2g0A
  37. Phytochemicals. 2006. doi:10.1201/9781420005905
  38. I. H. Malgwi, V. Halas, P. Grünvald, S. Schiavon, and I. Jócsák, “Genes related to fat metabolism in pigs and intramuscular fat content of pork: A focus on nutrigenetics and nutrigenomics,” 2022, mdpi.com. [Online]. Available: https://www.mdpi.com/2076-2615/12/2/150
  39. F. Prasetya and L. Hananta, “Nutrigenomics and Jamu: Integrating Nutritional Genomics with Indonesian Traditional Medicine for Precision, Preventive, and Systems-Oriented Health,” J. Trop. Pharm. …, 2025, [Online]. Available: http://jtpc.ff.unmul.ac.id/index.php/jtpc/article/view/320
  40. A. Antonacci, Improving the nutrient composition of Chlamydomonas reinhardtii to enhance the production of photosynthetic active metabolites: a molecular study on the …. dspace.unitus.it, 2010. [Online]. Available: https://dspace.unitus.it/handle/2067/1023
  41. S. Dwivedi, G. Chikara, S. Samdariya, and ..., “Molecular biotechnology for diagnostics,” Appl. Mol. …, 2016, [Online]. Available: https://api.taylorfrancis.com/content/chapters/edit/download?identifierName=doi&identifierValue=10.1201/b19543-20&type=chapterpdf
  42. R. Saif and A. Iftikhar, “Bioinformatics: Multiomics Applications in Food Science,” 2022, researchgate.net. [Online]. Available: https://www.researchgate.net/profile/Ali-Iftikhar-8/publication/360545445_Bioinformatics_Multi-Omics_Applications_in_Food_Science/links/6631de1008aa54017acfc742/Bioinformatics-Multi-Omics-Applications-in-Food-Science.pdf
  43. Xu Q, Qiao Q, Gao Y, Hou J, Hu M, Du Y, et al. Gut Microbiota and Their Role in Health and Metabolic Disease of Dairy Cow. Frontiers in Nutrition. 2021;8. doi:10.3389/fnut.2021.701511
  44. Singh Y, Sinha S, Likhita J, Arya M, Verma V. Nutrigenomics and Its Applications. Advances in Omics Technologies. 2025:311-342. doi:10.1007/978-981-95-0285-1_14
  45. Wanapat M, Dagaew G, Sommai S, Matra M, Suriyapha C, Prachumchai R, et al. The application of omics technologies for understanding tropical plants-based bioactive compounds in ruminants: a review. Journal of Animal Science and Biotechnology. 2024;15(1). doi:10.1186/s40104-024-01017-4
  46. Tekade RK, Soni N, Neetu S, Maheshwari R, Raval N, Molugulu N, et al. Omics-Driven Novel Technologies in Food and Nutrition Research. Nutrigenomics and Nutraceuticals. 2017:19-60. doi:10.1201/9781315153711-2
  47. Rahim MS, Sharma V, Pragati Yadav, Parveen A, Kumar A, Roy J, et al. Rethinking underutilized cereal crops: pan-omics integration and green system biology. Planta. 2023;258(5). doi:10.1007/s00425-023-04242-9
  48. S. Mall and A. Srivastava, “Foodomics: Integrated omics for the food and nutrition science,” Integr. Omi., 2024, [Online]. Available: https://www.sciencedirect.com/science/article/pii/B9780443160929000072
  49. K. P. Uvarajan, “Omics-Driven Insights into Nutrigenomics and Growth Optimization in Aquatic Species,” 2024, aasrresearch.com. [Online]. Available: https://www.aasrresearch.com/index.php/NJSFAI/article/download/307/329
  50. M. Kussmann, F. Raymond, and M. Affolter, “OMICS-driven biomarker discovery in nutrition and health,” J. Biotechnol., 2006, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0168165606001775
  51. Zeisel SH. Precision (Personalized) Nutrition: Understanding Metabolic Heterogeneity. Annual Review of Food Science and Technology. 2020;11(1):71-92. doi:10.1146/annurev-food-032519-051736
  52. F. H. Chilton, R. Dutta, L. M. Reynolds, S. Sergeant, and ..., “Precision nutrition and omega-3 polyunsaturated fatty acids: A case for personalized supplementation approaches for the prevention and management of …,” 2017, mdpi.com. [Online]. Available: https://www.mdpi.com/2072-6643/9/11/1165
  53. I. M. Al Zamzami, A. A. Prihanto, and ..., “Global Research Trends in Genome-Based Aquafeed for Pacific White Shrimp (Litopenaeus vannamei): A Scientometrics Analysis (1976--2025).,” Egypt. J. …, 2025, [Online]. Available: https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=11106131&AN=189314229&h=FXL3pb1suZ7nCVrg3Va6JD%2BE9qVcYwIJH4q2%2FMX%2BqG9CCDbHEBQVBJHYNqXYF5oLG2maR1X3ydimXgmlXq0ffA%3D%3D&crl=c