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Record W4416048491 · doi:10.1093/jas/skaf388

Effects of dietary fungal lysozyme levels on growth performance, body composition, serum biochemical profile, and microbiota interaction in growing pigs

2025· article· en· W4416048491 on OpenAlexaff
Renée M. Petri, Bruna Schroeder, Jennifer Ronholm, Sara Ricci, Jeffery Escobar, Inês Andretta, Adrian Tsang, C. Pomar, Aline Remus

Bibliographic record

VenueJournal of Animal Science · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsConcordia UniversityUniversité de SherbrookeMcGill UniversityAgriculture and Agri-Food Canada
Fundersnot available
KeywordsLysozymeNutrientSerum ureaComposition (language)UreaBody weightFeed conversion ratio

Abstract

fetched live from OpenAlex

Understanding the response to enzyme supplementation on body composition and nutrient balance creates the potential to increase nutrient utilization efficiency and, consequently, maximize growth performance in growing pigs. This study aimed to evaluate the effect of fungal lysozyme supplementation on body composition and nutrient balance in growing pigs. Seventy-two barrows [(40.6 kg ± 2.6; (Yorkshire × Landrace ♀) × Duroc ♂)] were used in this experiment. Pigs were distributed in a completely randomized design with 12 replicates within six treatments (0, 16, 32, 48, 64, and 80 mg of lysozyme/kg of diet). Pigs were group-housed in the same pen, and each pig was equipped with a transponder that enabled feeders to record individual feed intake and dispense feed according to the assigned treatment. Growth performance was evaluated through average daily feed intake (ADFI, kg/d), average daily gain (ADG, kg/d), gain-to-feed ratio (G: F, kg/kg) over 21 days. Body composition, nutrient balance and metabolic changes were measured using dual-energy X-ray absorptiometry and serum sampling. At the end of the experiment, samples of the digesta in the jejunum and cecum, as well as tissue from the jejunum, cecum, and liver, were taken for microbiota analysis and gene expression, respectively. Differences among treatments were analyzed using polynomial contrasts, and adjusted means were obtained with R. Optimal inclusion level was determined by non-linear models' analysis. The ADFI decreased linearly (P ≤ 0.01), while the body weight (BW), ADG, and G: F increased linearly (P ≤ 0.01) as the level of lysozyme in the diet increased. Increased urea and protein deposition and nitrogen -utilization efficiency were observed (P ≤ 0.05). Changes in the gut ecology included decreases in Gram-positive bacteria and inflammatory gene targets. The largest changes were observed in the cecum and were supported by gene expression in the liver. Based on the results of this experiment, the ideal lysozyme inclusion level for growing pigs is 60 mg/kg based on G: F, and 48 mg/kg based on ADG and evenness in jejunum digesta. Thus, dietary lysozyme supplementation improves pig growth performance, likely through immune response modulation initiated in the gastrointestinal tract due to minor changes in microbiota along with decreased expression of genes such as NF-kB1 and TLR2. This likely improved protein metabolism where increases in lysozyme supplementation enhanced nitrogen and amino acid utilization efficiency. Although the ideal dose for lysozyme supplementation will depend on the stage of production and the economic gain achieved from increasing enzyme supplementation, the results of this study support fungal lysozyme supplementation in growing pigs.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.244
Teacher spread0.232 · 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 designBench or experimental
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
Published2025
Admission routes1
Has abstractyes

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