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Record W2503867347 · doi:10.1371/journal.pone.0160751

Transcriptome Analysis Reveals Regulation of Gene Expression for Lipid Catabolism in Young Broilers by Butyrate Glycerides

2016· article· en· W2503867347 on OpenAlexafffund
Fugui Yin, Haibo Yu, Dion Lepp, Xuejiang Shi, Xiaojian Yang, Jielun Hu, S. Leeson, Chengbo Yang, Shaoping Nie, Yongqing Hou, Joshua Gong

Bibliographic record

VenuePLoS ONE · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPeroxisome Proliferator-Activated Receptors
Canadian institutionsUniversity of ManitobaUniversity of GuelphAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of CanadaChina Scholarship CouncilMinistry of Education, IndiaAustralian Government
KeywordsLipid metabolismTranscriptomeBiologyBroilerButyrateInternal medicineGene expressionPeroxisome proliferator-activated receptorEndocrinologyCatabolismMetabolismGeneBiochemistryAnimal scienceMedicine

Abstract

fetched live from OpenAlex

BACKGROUND & AIMS: Butyrate has been shown to potently regulate energy expenditure and lipid metabolism in animals, yet the underlying mechanisms remain to be fully understood. The aim of this study was to investigate the molecular mechanisms of butyrate (in the form of butyrate glycerides, BG)-induced lipid metabolism at the level of gene expression in the jejunum and liver of broilers. METHODOLOGY/PRINCIPAL FINDINGS: Two animal experiments were included in this study. In Experiment 1, two hundred and forty male broiler chickens were equally allocated into two groups: 1) basal diet (BD), 2) BG diets (BD + BG). Growth performance was compared between treatments for the 41-day trial. In Experiment 2, forty male broiler chickens were equally allocated into two groups. The general experimental design, group and management were the same as described in Experiment 1 except for reduced bird numbers and 21-day duration of the trial. Growth performance, abdominal fat deposition, serum lipid profiles as well as serum and tissue concentrations of key enzymes involved in lipid metabolism were compared between treatments. RNA-seq was employed to identify both differentially expressed genes (DEGs) and treatment specifically expressed genes (TSEGs). Functional clustering of DEGs and TSEGs and signaling pathways associated with lipid metabolism were identified using Ingenuity Pathways Analysis (IPA) and DAVID Bioinformatics Resources 6.7 (DAVID-BR). Quantitative PCR (qPCR) assays were subsequently conducted to further examine the expression of genes in the peroxisome proliferator-activated receptors (PPAR) signaling pathway identified by DAVID-BR. Dietary BG intervention significantly reduced abdominal fat ratio (abdominal fat weight/final body weight) in broilers. The decreased fat deposition in BG-fed chickens was in accordance with serum lipid profiles as well as the level of lipid metabolism-related enzymes in the serum, abdominal adipose, jejunum and liver. RNA-seq analysis indicated that dietary BG intervention induced 79 and 205 characterized DEGs in the jejunum and liver, respectively. In addition, 255 and 165 TSEGs were detected in the liver and jejunum of BG-fed group, while 162 and 211 TSEGs genes were observed in the liver and jejunum of BD-fed birds, respectively. Bioinformatic analysis with both IPA and DAVID-BR further revealed a significant enrichment of DEGs and TSEGs in the biological processes for reducing the synthesis, storage, transportation and secretion of lipids in the jejunum, while those in the liver were for enhancing the oxidation of ingested lipids and fatty acids. In particular, transcriptional regulators of THRSP and EGR-1 as well as several DEGs involved in the PPAR-α signaling pathway were significantly induced by dietary BG intervention for lipid catabolism. CONCLUSIONS: Our results demonstrate that BG reduces body fat deposition via regulation of gene expression, which is involved in the biological events relating to the reduction of synthesis, storage, transportation and secretion, and improvement of oxidation of lipids and fatty acids.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.016
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

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

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 head0.016
GPT teacher head0.226
Teacher spread0.210 · 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 teacher head, 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".

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Citations46
Published2016
Admission routes2
Has abstractyes

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