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Article Abstract
This study evaluated the efficacy of a multi-protease on the performance, egg quality and digestibility of laying hen. The study had four treatments: Control (without multi-protease, CON), Pro 1.2 (CON + 0.1% multi-protease), Pro 2.4 (CON + 0.2% multi-protease) and Pro 3.6 (CON + 0.3% multi-protease). Each treatment was replicated six times (replicate = experimental unit = one pen with 15 hens) to give a total of 360 layer hens of the Hy-line breed. The study lasted for a total of 3 months (14 day adaptation period + 84 days experimental period). The effects of the additive were assessed on: the performance variables, egg quality and ileal amino acid (AA) digestibility. At the end of the study, dietary supplementation with Pro 2.4 and Pro 3.6 improved (p < 0.05) hen-day egg production, egg mass and eggshell thickness compared with CON at the peak phase. Further improvements (p < 0.05) were observed in the digestibility of crude protein and AAs such as isoleucine, lysine, threonine and cysteine at Pro 2.4 and Pro 3.6 protease supplementation levels compared with CON, while arginine and alanine were higher (p < 0.05) at Pro 3.6 compared with CON. No differences were reported for other performances such as body weight, average daily feed intake, average egg weight, feed conversion ratio, eggshell hardness and all the egg qualities measured. Overall, the results from this study showed better efficacy at Pro 2.4 and Pro 3.6 on the performance of laying hen during the peak phase.
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Article Synopsis
- Studying protein isoforms is crucial for biomedical research, but current methods using bottom-up mass spectrometry often face challenges like noisy detection and shared peptides, making it hard to analyze individual isoforms.
- A new statistical method is introduced to enhance protein isoform analysis by combining mass spectrometry and transcriptomics data in a Bayesian framework, addressing uncertainties in peptide detection and abundance allocation.
- The method shows strong performance in simulations and real datasets, accurately inferring protein isoform presence, estimating their abundance, and detecting differences between protein and transcript levels; it is available as a free Bioconductor R package with usage examples.