Application of Proteomic Biomarkers in Livestock Disease Management

Oskoueian, Ehsan; Eckersall, P.D.; Bencúrová, Elena; Dandekar, Thomas

doi:10.1007/978-3-319-43278-6_14

Cited by 5 publications

(4 citation statements)

References 68 publications

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“…The research applications in proteomics range from early growth and development to postmortem events important for meat quality (Yarmush and Jayaraman, 2002;Bendixen, 2005). One of the major areas of interest in proteomics is finding out robust protein biomarkers that could be useful in disease surveillance, monitoring the health and wellbeing of animals, elucidating disease mechanisms, and assessing pharmacologic response to therapeutics (Oskoueian et al, 2016) (Muhanguzi et al, 2022). Proteomics can also be applied for different animal products post-harvest like meat, milk, cheese, etc., to identify genetic variants with desirable traits for selection and breeding (Almeida et al, 2015).…”

Section: Proteomicsmentioning

confidence: 99%

Applications of Omics Technology for Livestock Selection and Improvement

et al. 2022

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Conventional animal selection and breeding methods were based on the phenotypic performance of the animals. These methods have limitations, particularly for sex-limited traits and traits expressed later in the life cycle (e.g., carcass traits). Consequently, the genetic gain has been slow with high generation intervals. With the advent of high-throughput omics techniques and the availability of multi-omics technologies and sophisticated analytic packages, several promising tools and methods have been developed to estimate the actual genetic potential of the animals. It has now become possible to collect and access large and complex datasets comprising different genomics, transcriptomics, proteomics, metabolomics, and phonemics data as well as animal-level data (such as longevity, behavior, adaptation, etc.,), which provides new opportunities to better understand the mechanisms regulating animals’ actual performance. The cost of omics technology and expertise of several fields like biology, bioinformatics, statistics, and computational biology make these technology impediments to its use in some cases. The population size and accurate phenotypic data recordings are other significant constraints for appropriate selection and breeding strategies. Nevertheless, omics technologies can estimate more accurate breeding values (BVs) and increase the genetic gain by assisting the section of genetically superior, disease-free animals at an early stage of life for enhancing animal productivity and profitability. This manuscript provides an overview of various omics technologies and their limitations for animal genetic selection and breeding decisions.

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Section: Proteomicsmentioning

confidence: 99%

Applications of Omics Technology for Livestock Selection and Improvement

et al. 2022

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“…The amount of data from different origins and an increase in the reported frequency and severity of marine diseases demands that new diagnostic tools should be implemented for a more rapid and effective diagnosis [ 24 , 25 , 26 ]. Thus, several scientific advances in aquatic health continue to close the gap to veterinary medicine, and new optical, analytical chemistry, molecular biology [ 27 ], and Omics techniques are becoming a reality that offers a vast array of benefits to the aquaculture industry [ 12 , 28 ]. Proteomics techniques are one of those new tools, and one of the most interesting approaches for health management, epidemiology, and fish disease research [ 3 , 22 , 23 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

Fish Pathology Research and Diagnosis in Aquaculture of Farmed Fish; a Proteomics Perspective

Moreira

Schrama

Farinha

et al. 2021

Animals

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One of the main constraints in aquaculture production is farmed fish vulnerability to diseases due to husbandry practices or external factors like pollution, climate changes, or even the alterations in the dynamic of product transactions in this industry. It is though important to better understand and characterize the intervenients in the process of a disease outbreak as these lead to huge economical losses in aquaculture industries. High-throughput technologies like proteomics can be an important characterization tool especially in pathogen identification and the virulence mechanisms related to host-pathogen interactions on disease research and diagnostics that will help to control, prevent, and treat diseases in farmed fish. Proteomics important role is also maximized by its holistic approach to understanding pathogenesis processes and fish responses to external factors like stress or temperature making it one of the most promising tools for fish pathology research.

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“…If agriculture is to have a future it needs to be sustainable; for the dairy sector this requires zero-carbon farming (using pasture rather than indoor systems) and minimising the use of antibiotics (to avoid resistances arising). Artificial intelligence (AI) has a major role to play in these efforts (see, for example, the UN AI for Good initiative), and our work has focused on a major animal health problem, mastitis, for dairy farms (which costs US$19-32B per annum (Oskoueian et al 2016)). Current approaches to handling mastitis involve the administration of large quantities of antibiotics, which is not sustainable (Oskoueian et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Artificial intelligence (AI) has a major role to play in these efforts (see, for example, the UN AI for Good initiative), and our work has focused on a major animal health problem, mastitis, for dairy farms (which costs US$19-32B per annum (Oskoueian et al 2016)). Current approaches to handling mastitis involve the administration of large quantities of antibiotics, which is not sustainable (Oskoueian et al 2016). The proliferation of sensors on modern dairy farms, and the data that they generate, offers an opportunity to more sustainably address the mastitis issue.…”

Section: Introductionmentioning

confidence: 99%

Predicting Illness for a Sustainable Dairy Agriculture: Predicting and Explaining the Onset of Mastitis in Dairy Cows

Ryan¹,

Guéret²,

Berry³

et al. 2021

Preprint

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Mastitis is a billion dollar health problem for the modern dairy industry, with implications for antibiotic resistance. The use of AI techniques to identify the early onset of this disease, thus has significant implications for the sustainability of this agricultural sector. Current approaches to treating mastitis involve antibiotics and this practice is coming under ever increasing scrutiny. Using machine learning models to identify cows at risk of developing mastitis and applying targeted treatment regimes to only those animals promotes a more sustainable approach. Incorrect predictions from such models, however, can lead to monetary losses, unnecessary use of antibiotics, and even the premature death of animals, so it is important to generate compelling explanations for predictions to build trust with users and to better support their decision making. In this paper we demonstrate a system developed to predict mastitis infections in cows and provide explanations of these predictions using counterfactuals. We demonstrate the system and describe the engagement with farmers undertaken to build it.

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Application of Proteomic Biomarkers in Livestock Disease Management

Cited by 5 publications

References 68 publications

Applications of Omics Technology for Livestock Selection and Improvement

Applications of Omics Technology for Livestock Selection and Improvement

Fish Pathology Research and Diagnosis in Aquaculture of Farmed Fish; a Proteomics Perspective

Predicting Illness for a Sustainable Dairy Agriculture: Predicting and Explaining the Onset of Mastitis in Dairy Cows

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