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Record W4402541871 · doi:10.1093/jas/skae234.738

PSLBI-13 Characterizing the seminal microbiota in mature rams managed on divergent planes of nutrition, and in their male offspring

2024· article· en· W4402541871 on OpenAlexaff
Justine Kilama, Devin B. Holman, Kerri A Bochantin-Winders, Jennifer L Hurlbert, Friederike Baumgaertner, Chris S Schauer, Carl R Dahlen, Samat Amat

Bibliographic record

VenueJournal of Animal Science · 2024
Typearticle
Languageen
FieldMedicine
TopicInfectious Disease Case Reports and Treatments
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsOffspringBiologyZoologyPhysiologyEcologyPregnancyGenetics

Abstract

fetched live from OpenAlex

Abstract Increasing evidence suggests that the bovine semen harbors commensal microbiota, and it may be important in male fertility. However, much is unknown regarding the seminal microbiota, its evolution and factors shaping this community in sheep. The objective of this study was to evaluate whether managing the rams on divergent planes of nutrition can influence the seminal microbiota in mature rams and their male offspring. For this, Rambouillet rams (n = 24; 1.5 to 4 yr old) were randomly assigned to one of three groups: 1) a positive plane of nutrition [target 12% gain in their initial body weight (BW; POS, n = 8); 2) a maintenance [maintain the initial BW (MNT, n = 8)]; and 3) a negative [target a 12% reduction in BW (NEG; n = 8)] plane of nutrition over 84 d. The rams were individually housed and fed a common diet, and feed allocations were adjusted weekly. Following the 84-d feeding period, these rams were used to breed 240 mature Rambouillet ewes (1:10 ram:ewe ratio) over 28 d of breeding. After lambing, the male lambs were maintained on a similar diet for 124 d. Semen samples from the mature rams (F0 generation) were collected on d 28, 56, and 84, and semen samples from offspring rams (F1 generation) were collected at 330 d of age via electroejaculation. Genomic DNA was extracted from semen samples and the seminal microbiota characterized using 16S rRNA gene (V3-V4) sequencing. Several environmental and negative extraction controls were also sequenced to assess potential contaminants in these samples. Overall community structure of the seminal microbiota in F0 rams was not affected by the divergent planes of nutrition (PERMANOVA: P ≥ 0.05) or by sampling time (P > 0.05). Microbial richness (observed ASVs) was also not different (P > 0.05) between the three groups of F0 rams at any of the sampling time points; however, microbial diversity (P < 0.05) in the F0 rams was influenced by sire dietary treatment. Microbial community structure, richness and diversity did not differ among the POS, MNT and NEG F1 generation rams (P > 0.05). Actinobacteriota, Firmicutes, Bacteroidota, Proteobacteria and Fusobacteriota were the most abundant phyla. Only Bacteroidota abundance was significantly (P < 0.05) different between three F0 groups, with POS rams had greater (18.6%) of this phylum on d 28 as compared with MNT (2.1%) and NEG (5.1%). The predominant genera identified included Fastidiosipila, Corynebacterium, Trueperella, Arthrobacter, Dietzia, Bifidobacterium, Streptobacillus, Ornithinimicrobium, and Porphyromonas, and their relative abundance was not affected (P > 0.05) by the dietary treatment in F0 rams. Overall, rams managed on positive and negative planes of nutrition harbored similar seminal microbiota to control rams, and divergent nutrition also had no effect on the offspring ram seminal microbiota.

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 score0.799
Threshold uncertainty score0.145

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 head0.012
GPT teacher head0.262
Teacher spread0.249 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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