MétaCan
Menu
Back to cohort
Record W2518178890 · doi:10.1186/s12864-016-2935-4

Transcriptome profiling of the rumen epithelium of beef cattle differing in residual feed intake

2016· article· en· W2518178890 on OpenAlexafffund
Rebecca S. G. Kong, Guanxiang Liang, Yanhong Chen, Paul Stothard, Le Luo Guan

Bibliographic record

VenueBMC Genomics · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture Food and Rural DevelopmentUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Livestock and Meat AgencyUniversity of Alberta
KeywordsResidual feed intakeBiologyAdherens junctionTranscriptomeRumenGene expression profilingCell biologyRNA-SeqGene expressionGeneBiochemistryFeed conversion ratioCadherinCellEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: Feed efficient cattle consume less feed and produce less environmental waste than inefficient cattle. Many factors are known to contribute to differences in feed efficiency, however the underlying molecular mechanisms are largely unknown. Our study aimed to understand how host gene expression in the rumen epithelium contributes to differences in residual feed intake (RFI), a measure of feed efficiency, using a transcriptome profiling based approach. RESULTS: The rumen epithelial transcriptome from highly efficient (low (L-) RFI, n = 9) and inefficient (high (H-) RFI, n = 9) Hereford x Angus steers was obtained using RNA-sequencing. There were 122 genes differentially expressed between the rumen epithelial tissues of L- and H- RFI steers (p < 0.05) with 85 up-regulated and 37 down-regulated in L-RFI steers. Functional analysis of up-regulated genes revealed their involvement in acetylation, remodeling of adherens junctions, cytoskeletal dynamics, cell migration, and cell turnover. Additionally, a weighted gene co-expression network analysis (WGCNA) identified a significant gene module containing 764 genes that was negatively correlated with RFI (r = -0.5, p = 0.03). Functional analysis revealed significant enrichment of genes involved in modulation of intercellular adhesion through adherens junctions, protein and cell turnover, and cytoskeletal organization that suggest possible increased tissue morphogenesis in the L-RFI steers. Additionally, the L-RFI epithelium had increased expression of genes involved with the mitochondrion, acetylation, and energy generating pathways such as glycolysis, tricarboxylic acid cycle, and oxidative phosphorylation. Further qPCR analysis of steers with different RFI (L-RFI, n = 35; M-RFI, n = 34; H-RFI, n = 35) revealed that the relative mitochondrial genome copy number per cell of the epithelium was positively correlated with RFI (r = 0.21, p = 0.03). CONCLUSIONS: Our results suggest that the rumen epithelium of L-RFI (efficient) steers may have increased tissue morphogenesis that possibly increases paracellular permeability for the absorption of nutrients and increased energy production to support the energetic demands of increased tissue morphogenesis compared to those of H-RFI (inefficient) animals. Greater expression of mitochondrial genes and lower relative mitochondrial genome copy numbers suggest a greater rate of transcription in the rumen epithelial mitochondria of L-RFI steers. Understanding how host gene expression profiles are associated with RFI could potentially lead to identification of mechanisms behind this trait, which are vital to develop strategies for the improvement of cattle feed efficiency.

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 head0.030
GPT teacher head0.223
Teacher spread0.193 · 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".

Quick stats

Citations127
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueBMC GenomicsSame topicRuminant Nutrition and Digestive PhysiologyFrench-language works237,207