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P3029 Identification of regulatory genes involved in longissimus dorsi transcriptomic differences between pig genotypes

2016· article· en· W2602597735 on OpenAlexaff
Miriam Ayuso, J. Garrayo, Almudena Fernández, Yaira Nunez, Rita Benítez, B. Isabel, Ana I. Fernández, Amanda Rey, Antonio Gonzalez‐Bulnes, Juan F. Medrano, Ángela Cánovas, C.J. López-Bote, C. Óvilo

Bibliographic record

VenueJournal of Animal Science · 2016
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBiologyTranscriptomeIntramuscular fatGeneGene expressionGenotypeGeneticsPhenotypeAndrologyAnimal science

Abstract

fetched live from OpenAlex

Iberian pig production is based on both purebred Iberian (IB) and crossbred Duroc X Iberian (DUxIB) pigs. These two genetic types show important differences in growth, fattening and tissue composition. This study was conducted to assess Longissimus dorsi muscle gene expression profiles and to identify regulatory genes potentially responsible for gene expression and phenotypic differences between pig genotypes. Nine IB and 10 DUxIB piglets were slaughtered at birth, and 7 IB and 10 DUxIB were slaughtered at 4 mo of age (growing stage). Carcass traits were measured and samples from Longissimus dorsi were taken to study intramuscular fat (IMF) content and composition and to analyze the muscle transcriptome with RNA-seq technology. Differences in growth and fatness patterns were observed between genotypes. Genetic type significantly affected expression of 261 genes (P < 0.01 and Fold change > 1.5) at birth and 113 genes at growing stage. To understand the molecular mechanisms underlying gene expression differences between IB and IBxDU, a regulatory gene examination was conducted following three different approaches. Identification of regulators was based on biological data mining (Ingenuity Pathways Analysis software), coexpression data (Regulatory Impact Factor study) and differential expression data (DE regulators). Regulatory genes identified at both ages were deemed to have a deeper impact in the final phenotype. Some of the genes were closely related to muscle development (MYOD1, BHLHE40 and HDAC2) and adipogenesis and fat accumulation (NFKBIA, ATF4 or CEBPA). Regulators identified in more than one approach were considered to be the most robust results. In newborns, these regulators were mainly involved in muscle cell differentiation (MEF2C, MEF2D, MYOG, SOX4) and protein degradation (CREB3L1, HSF1 and CREBBP), thus playing a role in muscle development. FOS and FOXO regulatory genes control muscle differentiation but also adipogenic genes expression and IMF accumulation. In growing animals, three regulators were identified by complementary approaches, being involved in the regulation of the cell cycle (EN1 and IRF2) and lipid metabolism (EN1 and TCF7L2). Moreover, a functional analysis was performed combining the DE and the regulators studies information. Several pathways were enriched at both stages. Among them, pathways involved in adipocyte differentiation and protein degradation (the PPAR signaling, the adipogenesis, the Wnt and the unfolded protein response) are of special relevance. The present work identifies regulatory genes potentially involved in differences in metabolism and productive traits between IB and IBxDU pigs.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.035
GPT teacher head0.295
Teacher spread0.260 · 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 designObservational
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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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