Literature DB >> 26440002

Transcriptomic analysis by RNA sequencing reveals that hepatic interferon-induced genes may be associated with feed efficiency in beef heifers.

F Paradis, S Yue, J R Grant, P Stothard, J A Basarab, C Fitzsimmons.   

Abstract

In beef cattle, production feedstuffs are the largest variable input cost. Beef cattle also have a large carbon footprint, raising concern about their environmental impact. Unfortunately, only a small proportion of dietary energy is directed toward protein deposition and muscle growth whereas the majority supports body maintenance. Improving feed efficiency would, therefore, have important consequences on productivity, profitability, and sustainability of the beef industry. Various measures of feed efficiency have been proposed to improve feed utilization, and currently, residual feed intake (RFI) is gaining popularity. However, the cost associated with measuring RFI and the limited knowledge of the biology underlying improved feed efficiency make its adoption prohibitive. Identifying molecular mechanisms explaining divergence in RFI in beef cattle would lead to the development of early detection methods for the selection of more efficient breeding stock. The objective of this study was to identify hepatic markers of metabolic feed efficiency in replacement beef heifers. A group of 87 heifers were tested for RFI adjusted for off-test backfat thickness (RFIfat). Preprandial liver biopsies were collected from 10 high- and 10 low-RFIfat heifers (7 Hereford–Aberdeen Angus and 3 Charolais–Red Angus–Main Anjou per group) and gene expression analysis was performed using RNA sequencing and quantitative real-time PCR. The heifers used in this study differed in RFIfat averaging 0.438 vs. –0.584 kg DM/d in high- and low-RFIfat groups, respectively. As expected, DMI was correlated with RFIfat and ADG did not differ between high- and low-RFIfat heifers. Through a combination of whole transcriptome and candidate gene analyses, we identified differentially expressed genes involved in inflammatory processes including hemoglobin β (HBB), myxovirus resistance 1 interferon-inducible protein p78 (MX1), ISG15 ubiquitin-like modifier (ISG15), hect domain and RLD 6 (HERC6), and interferon-induced protein 44 (IFI44) whose mRNA abundance was lower (HBB) or higher (MX1, ISG15, HERC6, and IFI44) in low-RFIfat heifers. These genes have been shown to be directly or indirectly modulated by interferon signaling and involved with innate immunity. Our results suggest that more efficient heifers respond differently to hepatic proinflammatory stimulus, potentially expending less energy toward combating systemic inflammation and redirecting nutrients toward growth and protein accretion.

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Year:  2015        PMID: 26440002     DOI: 10.2527/jas.2015-8975

Source DB:  PubMed          Journal:  J Anim Sci        ISSN: 0021-8812            Impact factor:   3.159


  26 in total

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Review 3.  Application of Genetic, Genomic and Biological Pathways in Improvement of Swine Feed Efficiency.

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4.  Liver transcriptome profiling of beef steers with divergent growth rate, feed intake, or metabolic body weight phenotypes1.

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Journal:  J Anim Sci       Date:  2019-11-04       Impact factor: 3.159

5.  Genetic potential for residual feed intake and diet fed during early- to mid-gestation influences post-natal DNA methylation of imprinted genes in muscle and liver tissues in beef cattle.

Authors:  Julia Devos; Amir Behrouzi; Francois Paradis; Christina Straathof; Changxi Li; Marcos Colazo; Hushton Block; Carolyn Fitzsimmons
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6.  Identification of Gene Networks for Residual Feed Intake in Angus Cattle Using Genomic Prediction and RNA-seq.

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8.  Transcriptomic Profiles of Brain Provide Insights into Molecular Mechanism of Feed Conversion Efficiency in Crucian Carp (Carassius auratus).

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9.  A transcriptome multi-tissue analysis identifies biological pathways and genes associated with variations in feed efficiency of growing pigs.

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10.  Liver transcriptomic networks reveal main biological processes associated with feed efficiency in beef cattle.

Authors:  Pamela A Alexandre; Lisette J A Kogelman; Miguel H A Santana; Danielle Passarelli; Lidia H Pulz; Paulo Fantinato-Neto; Paulo L Silva; Paulo R Leme; Ricardo F Strefezzi; Luiz L Coutinho; José B S Ferraz; Joanie P Eler; Haja N Kadarmideen; Heidge Fukumasu
Journal:  BMC Genomics       Date:  2015-12-18       Impact factor: 3.969

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