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Record W4388529124 · doi:10.1093/jas/skad281.446

PSI-13 Effects of a Postbiotic Program Consisting of Liquid and Solid <i>Saccharomyces Cerevisiae</i> Fermentation Products on Ruminal Microbiome Composition and Microbial Functions of Finishing Beef Steers

2023· article· en· W4388529124 on OpenAlexaff
Oluwaseun Odunfa, Anjan Dhungana, Alex Golinski, Maria Sardi, S. Ali Naqvi, I. Yoon, Fredrick Bebe, Ehsan Khafipour, Yun Jiang

Bibliographic record

VenueJournal of Animal Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsCargill (Canada)
Fundersnot available
KeywordsRumenFermentationAnimal scienceBiologyComposition (language)Food scienceChemistry

Abstract

fetched live from OpenAlex

Abstract We assessed the effects of a postbiotic program consisting of liquid and solid Saccharomyces cerevisiae fermentation products (SCFP) on the ruminal microbiome and fermentation characteristics of beef steers. Eight Holstein steers (BW 467 ± 13.9 kg) with rumen cannulas were used in a crossover design study with 21-d per period and 7-d washout in between periods. Steers were blocked based on initial BW and assigned to one of two treatments: 1) Control, basal diet, or 2) SCFP, basal diet plus one day feeding of LiquiCare RTU (Diamond V, Cedar Rapids, IA; dosed via rumen cannula at 11 mL/100 kg BW), followed by daily feeding of 12 g/d NaturSafe (Diamond V, top-dressed). Rumen fluid samples were collected on d 0, 1, 2, 3, 5 and 21 at 0, 4, 8, or 12 h post morning feeding via rumen cannula for pH, VFA and NH3-N analyses. Samples collected at 4 h post morning feeding on d 0, 2 and 21 were subjected to DNA extraction and shotgun Nanopore sequencing to determine microbiome composition and carbohydrate active enzymes (CAZy) functional potential. Statistical model included the fixed effects of treatment, period, day and their interactions and random effect of animal. Feeding SCFP tended to reduce ruminal lactate (0.09 vs. 0.02 mM, P = 0.10), and reduced ruminal NH3-N (4.86 vs. 6.83 mg/dL, P = 0.04) on d 21. Ruminal total VFA concentration was increased (P = 0.01) by feeding SCFP 4-h post-feeding on d 5 and numerically increased 4-h post-feeding on day 21 (133.3 vs. 151.2 mM, P = 0.12). Treatments did not affect species or CAZy alpha- and beta-diversity. Steers received liquid postbiotic on d 2 exhibited a positive correlation (P < 0.05) between propionate molar proportion and glycosyl transferases (GT), glycoside hydrolases (GH), carbohydrate esterases, and carbohydrate binding modules (CBM); and a negative correlation (P < 0.05) between acetate molar proportion and polysaccharide lyases, GT, GH, and CBM, which was not observed in Control. Additionally, SCFP steers had higher (P = 0.05) CBM functional potential on d 21 than on d 0, while CAZy class abundances did not change over time in Control steers. Differential network analysis indicated a greater normalized degree for GT in the SCFP steers compared with Control on d 21. There were 2, 6 and 12 differently abundant species (P < 0.01) between SCFP and Control on d 0, 2 and 21, respectively, suggesting supplementing SCFP altered the abundances of more species over time. Our results showed that the postbiotic program altered the metabolic pathways of ruminal microbes toward greater propionate production, and improved abundance of carbohydrate binding modules. Our results also showed that glycosyl transferases had a greater promotive role in the ruminal enzymatic network of SCFP steers.

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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.266
Teacher spread0.248 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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