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Record W2281347736 · doi:10.2527/jas.2015-8975

Transcriptomic analysis by RNA sequencing reveals that hepatic interferon-induced genes may be associated with feed efficiency in beef heifers1

2015· article· en· W2281347736 on OpenAlexafffund
Francois Paradis, Siqing Yue, Jason R. Grant, Paul Stothard, J. A. Basarab, Carolyn Fitzsimmons

Bibliographic record

VenueJournal of Animal Science · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsUniversity of AlbertaAgriculture Food and Rural DevelopmentAgriculture and Agri-Food Canada
FundersAlberta Livestock and Meat Agency
KeywordsResidual feed intakeFeed conversion ratioBeef cattleBiologyAnimal scienceBiotechnologyBody weightEndocrinology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.039
GPT teacher head0.303
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations88
Published2015
Admission routes2
Has abstractyes

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