Skeletal muscle and liver gene expression profiles in finishing steers supplemented with Amaize

Elolimy, Ahmed A.; Moisá, Sonia J.; Brennan, K. M.; Smith, Allison; Graugnard, Daniel E.; Shike, Daniel W; Loor, Juan J.

doi:10.1111/asj.13041

Cited by 6 publications

(4 citation statements)

References 72 publications

(77 reference statements)

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“…Dietary modifications in ruminant rations alter gene expression patterns and improve body growth performance and product quality of animals (da Costa et al, 2013;Elolimy et al, 2018;Wang et al, 2016). High silage content in cattle rations was proposed to mitigate polyunsaturated fatty acid (PUFA) production by decreasing the expression and activity of lipoprotein lipase, fatty acid binding protein 4, and stearoyl CoA desaturase genes (da Costa et al, 2013).…”

Section: Nutrigenomics Of Growth Productivity Quality and Feed Efficiency In Ruminantsmentioning

confidence: 99%

“…On the other hand, alpha-amylase is a frequently used supplement in beef cattle feeding to enhance the fermentation of starch. Intensive use of this enzyme adversely affects daily body weight gains and feed efficiency by suppressing the expression levels of genes (PPARGC1A, ABRA, FOXO1) responsible for adipogenesis in muscle tissue and the genes (FAB1, BDH1) responsible for lipid catabolism in liver tissue (Elolimy et al, 2018).…”

Section: Nutrigenomics Of Growth Productivity Quality and Feed Efficiency In Ruminantsmentioning

confidence: 99%

See 1 more Smart Citation

Genome-wise engineering of ruminant nutrition – nutrigenomics: applications, challenges, and future perspectives – A review

Kızılaslan¹,

Arzık²,

Çınar

et al. 2022

Annals of Animal Science

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Use of genomic information in ruminant production systems can help relieve concerns related to food security and sustainability of production. Nutritional genomics (i.e., Nutrigenomics) is a field of research that is interested in all types of reciprocal interactions between nutrients and genomes of organisms, i.e., variable patterns of gene expression and effect of genetic variations on the nutritional environment. Devising a revolutionizing analytical approach to traditional ruminant nutrition research, the relatively novel area of ruminant nutrigenomics has several studies concerning different aspects of animal production systems. This paper aims to review the current nutrigenomics research in the frame of how nutrition of ruminants can be modified accounting for individual genetic backgrounds and gene/diet relationships behind productivity, quality, efficiency, disease resistance, fertility, and GHG emissions. Furthermore, current challenges facing ruminant nutrigenomics are evaluated and future directions for the novel area are strongly argued by this review.

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Section: Nutrigenomics Of Growth Productivity Quality and Feed Efficiency In Ruminantsmentioning

confidence: 99%

Section: Nutrigenomics Of Growth Productivity Quality and Feed Efficiency In Ruminantsmentioning

confidence: 99%

Genome-wise engineering of ruminant nutrition – nutrigenomics: applications, challenges, and future perspectives – A review

Kızılaslan¹,

Arzık²,

Çınar

et al. 2022

Annals of Animal Science

View full text Add to dashboard Cite

show abstract

“…On the other hand, alpha-amylase enzyme is often added to beef cattle diets to improve animal performance through improving starch fermentation. A study of this practice was recently described by Elolimy et al (2018): in this work, finishing steers were supplemented with amylase to determine its effect on performance and on carcass features in relation to liver and muscle global gene expression profiles. In the experimental group, the animals had reduced average daily gain and gain/feed ratio, while no effect was observed on carcass traits or serum metabolites.…”

Section: Beef Ruminants Postnatal Feeding: Starch Sourcementioning

confidence: 99%

“…Muscle tissue showed upregulation of the genes involved in adipogenesis-namely, peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PPARGC1A), actin-binding rho activating protein (ABRA), and forkhead box O1 (FOXO1). The reduced expression of fatty acid binding protein 1 (FABP1) and 3-hydroxybutyrate dehydrogenase 1 (BDH1) in the liver suggested a potential reduction in hepatic lipid catabolism in the amylase-supplemented animals (Elolimy et al 2018).…”

Section: Beef Ruminants Postnatal Feeding: Starch Sourcementioning

confidence: 99%

Nutrigenomics in livestock—recent advances

Nowacka‐Woszuk

2019

J Appl Genetics

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The study of the effects of nutrients on genome functioning, in terms of gene transcription, protein levels, and epigenetic mechanisms, is referred to as nutrigenomics. Nutrigenomic studies in farm animals, as distinct from rodents, are limited by the high cost of keeping livestock, their long generational distance, and ethical aspects. Yet farm animals, and particularly pigs, can serve as valuable animal models for human gastrological diseases, since they possess similar size, physiology, and nutritional habits and can develop similar pathological states. In livestock, the effects of dietary modifications have mostly been studied with reference to effective breeding and their influence on production traits and animal health. The majority of such studies have looked at the impact of various sources and quantities of fat and protein, supplementation with microelements, and plant-derived additives. The period of life of the animal-whether prenatal, neonatal, or mature-is typically considered when a modified diet is used. This review presents a summary of recent nutrigenomic studies in livestock.

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Use of a graph neural network to the weighted gene co-expression network analysis of Korean native cattle

Lee

Chung

Chung³

et al. 2022

Sci Rep

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In the general framework of the weighted gene co-expression network analysis (WGCNA), a hierarchical clustering algorithm is commonly used to module definition. However, hierarchical clustering depends strongly on the topological overlap measure. In other words, this algorithm may assign two genes with low topological overlap to different modules even though their expression patterns are similar. Here, a novel gene module clustering algorithm for WGCNA is proposed. We develop a gene module clustering network (gmcNet), which simultaneously addresses single-level expression and topological overlap measure. The proposed gmcNet includes a “co-expression pattern recognizer” (CEPR) and “module classifier”. The CEPR incorporates expression features of single genes into the topological features of co-expressed ones. Given this CEPR-embedded feature, the module classifier computes module assignment probabilities. We validated gmcNet performance using 4,976 genes from 20 native Korean cattle. We observed that the CEPR generates more robust features than single-level expression or topological overlap measure. Given the CEPR-embedded feature, gmcNet achieved the best performance in terms of modularity (0.261) and the differentially expressed signal (27.739) compared with other clustering methods tested. Furthermore, gmcNet detected some interesting biological functionalities for carcass weight, backfat thickness, intramuscular fat, and beef tenderness of Korean native cattle. Therefore, gmcNet is a useful framework for WGCNA module clustering.

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Skeletal muscle and liver gene expression profiles in finishing steers supplemented with Amaize

Cited by 6 publications

References 72 publications

Genome-wise engineering of ruminant nutrition – nutrigenomics: applications, challenges, and future perspectives – A review

Genome-wise engineering of ruminant nutrition – nutrigenomics: applications, challenges, and future perspectives – A review

Nutrigenomics in livestock—recent advances

Use of a graph neural network to the weighted gene co-expression network analysis of Korean native cattle

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