Investigating and Identifying Fish Illness in Aquaculture of Raised Fish: An Overview from the Proteomics Angle
Abstract
The growing demand for fish protein throughout the world is mostly met by aquaculture. However, the sector confronts issues including disease outbreaks that can have a major effect on fish health and productivity. Recognizing anomalous behavior, physical symptoms, and changes in appearance among grown fish are all part of diagnosing fish sickness in aquaculture. To preserve the health of the aquaculture system and to perform appropriate treatments, effective diagnosis is essential. The susceptibility of farmed fish to illnesses by improper husbandry techniques, outside variables like pollution and climatic change, and even modifications to the way products are traded within this industry, be one of the primary obstacles to aquaculture production. This study underscores the significance of using proteome analysis to uncover biomarkers, since this facilitates the creation of diagnostic instruments for the early identification of illness. Aquaculturists can lessen the effects of diseases on fish populations by taking targeted actions based on the identification of certain protein signatures linked to different fish illnesses.Proteomics is an example of a high-throughput technology that may be a valuable characterization implement, concentrated on the identification of pathogens and the virulence mechanisms connected to host pathogen interactions in the study and diagnosis of diseases that enforce the control, avoidance, in addition to treating illnesses in farmed fish. One important and promising method for studying fish illness is proteomics, which plays a significant role in knowing fish responses to environmental cues such stress and temperature, as well as pathogenic processes.
References
Ahmed, M. S., Aurpa, T. T., & Azad, M. A. K. (2022). Fish disease detection using image based machine learning technique in aquaculture. Journal of King Saud University-Computer and Information Sciences, 34(8), 5170-5182. https://doi.org/10.1016/j.jksuci.2021.05.003
Assefa, A., &Abunna, F. (2018). Maintenance of fish health in aquaculture: review of epidemiological approaches for prevention and control of infectious disease of fish. Veterinary medicine international, 2018. https://doi.org/10.1155/2018/5432497
Kaur, G., Adhikari, N., Krishnapriya, S., Wawale, S. G., Malik, R. Q., Zamani, A. S., ...&Osei-Owusu, J. (2023). Recent Advancements in Deep Learning Frameworks for Precision Fish Farming Opportunities, Challenges, and Applications. Journal of Food Quality, 2023.https://doi.org/10.1155/2023/4399512
Ziarati, M., Zorriehzahra, M. J., Hassantabar, F., Mehrabi, Z., Dhawan, M., Sharun, K., ...&Shamsi, S. (2022). Zoonotic diseases of fish and their prevention and control. Veterinary Quarterly, 42(1), 95-118.https://doi.org/10.1080/01652176.2022.2080298
Mia, M. J., Mahmud, R. B., Sadad, M. S., Al Asad, H., & Hossain, R. (2022). An in-depth automated approach for fish disease recognition. Journal of King Saud University-Computer and Information Sciences, 34(9), 7174-7183.https://doi.org/10.1016/j.jksuci.2022.02.023
Lulijwa, R., Alfaro, A. C., & Young, T. (2022). Metabolomics in salmonid aquaculture research: Applications and future perspectives. Reviews in Aquaculture, 14(2), 547-577. https://doi.org/10.1111/raq.12612
Przybyla, C., Bonnefoy, J., Paounov, R., Debiol, A., Dutto, G., Mansuy, E., ...&Frippiat, J. P. (2023). Embryogenesis of an aquaculture fish (Dicentrarchuslabrax) under simulated altered gravity. Frontiers in Space Technologies, 4, 1240251. https://doi.org/10.3389/frspt.2023.1240251
Andersen, L., Rønneseth, A., Powell, M. D., &Brønstad, A. (2023). Defining piscine endpoints: Towards score sheets for assessment of clinical signs in fish research. Laboratory Animals, 00236772231156031.https://doi.org/10.1111/raq.12612
Bhat, R. A. H., Mallik, S. K., Tandel, R. S., &Shahi, N. (2023). An Overview of Cold-Water Fish Diseases and Their Control Measures. Fisheries and Aquaculture of the Temperate Himalayas, 255-283. https://doi.org/10.1007/978-981-19-8303-0_15
Medina‐Félix, D., Garibay‐Valdez, E., Vargas‐Albores, F., &Martínez‐Porchas, M. (2023). Fish disease and intestinal microbiota: A close and indivisible relationship. Reviews in Aquaculture, 15(2), 820-839.https://doi.org/10.1111/raq.12762
Sunny, B. K. (2022, July). A systematic study of climate change impact on fish and fisheries sector in Bangladesh and role of Geospatial science in mitigation. In IOP Conference Series: Earth and Environmental Science (Vol. 1064, No. 1, p. 012036). IOP Publishing. DOI 10.1088/1755-1315/1064/1/012036
Waiho, K., Afiqah‐Aleng, N., Iryani, M. T. M., &Fazhan, H. (2021). Protein–protein interaction network: An emerging tool for understanding fish disease in aquaculture. Reviews in Aquaculture, 13(1), 156-177. https://doi.org/10.1111/raq.12468
Wang, C., Li, Z., Wang, T., Xu, X., Zhang, X., & Li, D. (2021). Intelligent fish farm—the future of aquaculture. Aquaculture International, 1-31. https://doi.org/10.1007/s10499-021-00773-8
Mzula, A., Wambura, P. N., Mdegela, R. H., &Shirima, G. M. (2021). Present status of aquaculture and the challenge of bacterial diseases in freshwater farmed fish in Tanzania; A call for sustainable strategies. Aquaculture and Fisheries, 6(3), 247-253. https://doi.org/10.1016/j.aaf.2020.05.003
Corriero, A., Zupa, R., Mylonas, C. C., &Passantino, L. (2021). Atresia of ovarian follicles in fishes, and implications and uses in aquaculture and fisheries. Journal of Fish Diseases, 44(9), 1271-1291. https://doi.org/10.1111/jfd.13469
Waiho, K., Afiqah‐Aleng, N., Iryani, M. T. M., &Fazhan, H. (2021). Protein–protein interaction network: An emerging tool for understanding fish disease in aquaculture. Reviews in Aquaculture, 13(1), 156-177.https://doi.org/10.1111/raq.12468
Nombela, I., Lopez-Lorigados, M., Salvador-Mira, M. E., Puente-Marin, S., Chico, V., Ciordia, S., ...& Ortega-Villaizan, M. D. M. (2019). Integrated transcriptomic and proteomic analysis of red blood cells from rainbow trout challenged with VHSV point towards novel immunomodulant targets. Vaccines, 7(3), 63.https://doi.org/10.3390/vaccines7030063
Géron, A., Werner, J., Wattiez, R., Lebaron, P., &Matallana-Surget, S. (2019). Deciphering the functioning of microbial communities: shedding light on the critical steps in metaproteomics. Frontiers in Microbiology, 10, 2395.https://doi.org/10.3389/fmicb.2019.02395
Carrera, M., Piñeiro, C., & Martinez, I. (2020). Proteomic strategies to evaluate the impact of farming conditions on food quality and safety in aquaculture products. Foods, 9(8), 1050.https://doi.org/10.3390/foods9081050
Ormsby, M. J., Grahame, E., Burchmore, R., & Davies, R. L. (2019). Comparative bioinformatic and proteomic approaches to evaluate the outer membrane proteome of the fish pathogen Yersinia ruckeri. Journal of proteomics, 199, 135-147.https://doi.org/10.1016/j.jprot.2019.02.014
Syanya, F. J., Litabas, J. A., Mathia, W. M., &Ntakirutimana, R. (2023). Nutritional fish diseases in aquaculture: A human health hazard or mythical theory: An overview. European Journal of Nutrition & Food Safety, 15(8), 41-58.https://doi.org/10.9734/ejnfs/2023/v15i81326
Moreira, M., Schrama, D., Farinha, A. P., Cerqueira, M., Raposo de Magalhaes, C., Carrilho, R., & Rodrigues, P. (2021). Fish pathology research and diagnosis in aquaculture of farmed fish; a proteomics perspective. Animals, 11(1), 125.https://doi.org/10.3390/ani11010125
Yolanda, T. C., Clara, F. Á., & Ysabel, S. (2019). Proteomic and molecular fingerprinting for identification and tracking of fish pathogenic Streptococcus. Aquaculture, 498, 322-334.https://doi.org/10.1016/j.aquaculture.2018.08.041
Gallardo-Escárate, C., Valenzuela-Muñoz, V., Núñez-Acuña, G., Carrera, C., Gonçalves, A. T., Valenzuela-Miranda, D., ...& Roberts, S. (2019). Catching the complexity of salmon-louse interactions. Fish & Shellfish Immunology, 90, 199-209.https://doi.org/10.1016/j.fsi.2019.04.065