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Record W2749152050 · doi:10.1186/s12864-017-4051-5

Maternal nutrient restriction in mid-to-late gestation influences fetal mRNA expression in muscle tissues in beef cattle

2017· article· en· W2749152050 on OpenAlexafffund
Francois Paradis, Katie M Wood, Kendall C Swanson, Stephen P. Miller, B.W. McBride, Carolyn Fitzsimmons

Bibliographic record

VenueBMC Genomics · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsAgriculture Food and Rural DevelopmentAgriculture and Agri-Food CanadaUniversity of GuelphUniversity of Alberta
FundersOntario Ministry of Agriculture, Food and Rural AffairsBeef Cattle Research CouncilBeef Farmers of OntarioUniversity of Guelph
KeywordsBiologyEndocrinologyInternal medicineInsulin-like growth factorInsulin-like growth factor 1 receptorGenomic imprintingFetusIntrauterine growth restrictionInsulin-like growth factor 2 receptorGrowth factorDNA methylationGene expressionReceptorPregnancyGeneticsGene

Abstract

fetched live from OpenAlex

BACKGROUND: Manipulating maternal nutrition during specific periods of gestation can result in re-programming of fetal and post-natal development. In this experiment we investigated how a feed restriction of 85% compared with 140% of total metabolizable energy requirements, fed to cows during mid-to-late gestation, influences phenotypic development of fetuses and mRNA expression of growth (Insulin-Like Growth Factor family and Insulin Receptor (INSR)), myogenic (Myogenic Differentiation 1 (MYOD1), Myogenin (MYOG), Myocyte Enhancer Factor 2A (MEF2A), Serum Response Factor (SRF)) and adipogenic (Peroxisome Proliferator Activated Receptor Gamma (PPARG)) genes in fetal longissimus dorsi (LD) and semitendinosus (ST) muscle. DNA methylation of imprinted genes, Insulin Like Growth Factor 2 (IGF2) and Insulin Like Growth Factor 2 Receptor (IGF2R), and micro RNA (miRNA) expression, were also examined as potential consequences of poor maternal nutrition, but also potential regulators of altered gene expression patterns. RESULTS: While the nutrient restriction impacted dam body weight, no differences were observed in phenotypic fetal measurements (weight, crown-rump length, or thorax circumference). Interestingly, LD and ST muscles responded differently to the differential pre-natal nutrient levels. While LD muscle of restricted fetal calves had greater mRNA abundances for Insulin Like Growth Factor 1 and its receptor (IGF1 and IGF1R), IGF2R, INSR, MYOD1, MYOG, and PPARG, no significant differences were observed for gene expression in ST muscle. Similarly, feed restriction had a greater impact on the methylation level of IGF2 Differentially Methylated Region 2 (DMR2) in LD muscle as compared to ST muscle between treatment groups. A negative correlation existed between IGF2 mRNA expression and IGF2 DMR2 methylation level in both LD and ST muscles. Differential expression of miRNAs 1 and 133a were also detected in LD muscle. CONCLUSIONS: Our data suggests that a nutrient restriction of 85% as compared to 140% of total metabolizable energy requirements during the 2nd half of gestation can alter the expression of growth, myogenic and adipogenic genes in fetal muscle without apparent differences in fetal phenotype. It also appears that the impact of feed restriction varies between muscles suggesting a priority for nutrient partitioning depending on muscle function and/or fiber composition. Differences in the methylation level in IGF2, a well-known imprinted gene, as well as differences in miRNA expression, may be functional mechanisms that precede the differences in gene expression observed, and could lead to trans-generational epigenetic programming.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score0.668

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 head0.034
GPT teacher head0.269
Teacher spread0.235 · 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 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

Citations95
Published2017
Admission routes2
Has abstractyes

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