MétaCan
Menu
Back to cohort
Record W4388539502 · doi:10.1093/jas/skad281.611

PSX-25 The Fecal Metabolome Differs Between Nelore Bulls Fed Pre- and Post-Feedlot Diets: Initial Results

2023· article· en· W4388539502 on OpenAlexaboutno aff
Jéssica Moraes Malheiros, Matheus Henrique Vargas de Oliveira, Pollyana Ferreira da Silva, Luiz Alberto Colnago, Josineudson Augusto Vasconcelos Silva

Bibliographic record

VenueJournal of Animal Science · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsnot available
Fundersnot available
KeywordsFeedlotAnimal scienceFecesSilageBeef cattleDry matterBiology

Abstract

fetched live from OpenAlex

Abstract The objective of this study was to evaluate the fecal metabolome of Nelore bulls (Bos taurus indicus) fed pre- and post-feedlot diets. In pre-feedlot, 21 animals belonging to the Nelore Qualitas breeding program aged 666 ± 68 days old, and 534.5 ± 34.2 kg of body weight were allocated to one pen for 15 days. The pre-feedlot diet contained corn silage (80%) and sugarcane bagasse (20%). Next, the bulls (546.0 ± 41.2 kg of body weight) were submitted into a finishing stage in feedlot for 56 days. During the post-feedlot diet contained wet grain corn (44.6%), corn silage (27.6%), citrus pulp (11.9%), peanut bran (8%), sugarcane bagasse (4.9%), premix (1.8%) and urea (1.2%) twice a day with ad libitum access in Intergado electronic troughs (Intergado Ltda., Contagem, Minas Gerais, Brazil). Pre- and post-feedlot fecal samples were collected directly from the rectal ampulla of each bovine and stored in a -80°C freezer for metabolomic assays by Nuclear Magnetic Resonance analysis (1H NMR). The metabolites were identified, quantified and data processing using the Chenomx NMR Suite 8.6 (Chenomx Inc., Edmonton, AB, Canada) and MetaboAnalyst 5.0 (http://www.metaboanalyst.ca). We identified and quantified 57 and 55 metabolites in the fecal samples of the bulls fed pre- and post-feedlot diet, respectively. We observed that 39 metabolites differed (P < 0.05) between pre- and post-feedlot diet. The 2-phenylpropionate, caprylate, fumarate, deaminotyrosine, gamma-glutamyl phenylalanine, NADH, thymidine and pantothenate were observed only in the pre-feedlot diet. However, acetamide, 2-oxoglutarate, uracil, xanthine, trimethylamine and demethyl sulfone were consumed exclusively post-feedlot diet. Our multivariate analyses demonstrated that the fecal metabolite profiles of the Nelore bulls fed pre- and post-feedlot diet were different. The first two components of principal component analysis (PCA) explained 86.1% of the total data variance (PC1 = 77.6%; PC2 = 8.5%). Likewise, the partial least squares discriminant analysis (PLS-DA) explained 85.5% of the data variance with the first two components (component 1 (74.4%) vs. component 2 (8.1%)). Three fecal metabolites (acetate, butyrate, and propionate) with variable importance in the projection (VIP scores over 1.0; P<0.05) were greater in animals fed post-feedlot diet. Additionally, metabolome profile offers information on the main metabolic pathways, and five pathways were identified as different (P<0.05 and impact >2) in the fecal samples that bulls fed pre and post feedlot diets: pyruvate metabolism (bta00620); glycine, serine and threonine metabolism (bta00260); synthesis and degradation of ketone bodies (bta00088); histidine metabolism (bta00340) and biosynthesis of phenylalanine, tyrosine and tryptophan (bta00400). Our results suggest that pre- and post-feedlot diets modify the fecal metabolome profile of Nelore bulls. In addition, further studies about metabolic effects of diets are required for a better understanding of these influence on Nelore bulls.

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 score0.004
Threshold uncertainty score0.009

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)0.0020.000

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 head0.020
GPT teacher head0.295
Teacher spread0.275 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Animal ScienceSame topicGenetic and phenotypic traits in livestockFrench-language works237,207