低输入饮食对不同品种影响的基因化学和免疫基因化学评估
Elisa Fonsatti1, Martina Bortoletti1, Marco Birolo2
1Department of Comparative Biomedicine and Food Science (BCA), University of Padova, Viale dell'Università, 16, 35020 Legnaro, PD, Italy.
Animals : an open access journal from MDPI
|March 13, 2025
概括
当地的品种对低投入饮食具有更强的弹性,这表明它们适合于可持续的家禽养殖. 与快速生长的品种相比,这些品种在吸收营养方面表现出更好的骨形态.
科学领域:
- 动物科学动物科学
- 可持续农业 可持续农业
- 禽类的营养 养
背景情况:
- 减少家禽养殖对环境的影响是欧洲绿色协议的关键.
- 当地的品种和低投入饮食提供了可持续的替代品.
- 饮食策略会影响禽类的肠道健康和营养利用.
研究的目的:
- 为了评估标准与低输入饮食对不同基因型的阴茎形态的影响.
- 为了比较快速生长的 (Ross 308) 和本地品种 (Bionda Piemontese,Robusta Maculata) 以及它们的交叉的肠道弹性.
- 评估饮食成分对毛高度,密室深度,杯状细胞密度和免疫细胞透的影响.
主要方法:
- 在屠宰时对来自Ross 308,本地品种 (BP,RM) 和交叉品种 (BP × SA,RM × SA) 的阴茎样本进行组织学分析.
- 测量小的高度,密室深度和杯子细胞密度.
- 免疫标记物 (CD3+和CD45+细胞) 在阴中的量化.
主要成果:
- 当地品种,特别是RM,表现出优越的毛高度与密室深度比率,这表明营养吸收能力提高.
- 罗斯的玻璃杯细胞密度较高,表明对环境压力因素的敏感性增加.
- 低输入饮食降低了小的高度和小与密码的比率,但增加了CD3+细胞密度,可能是由于fava豆的含量.
结论:
- 当地的品种具有对饮食修改的固有弹性,使他们适合可持续的家禽系统.
- 饮食变化,特别是在低投入饮食中使用豆,可以影响肠道形态和免疫反应.
- 调查结果支持将当地品种纳入与绿色倡议一致的替代家禽生产模式.
相关概念视频
Background and Environment Affect Phenotype
Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...
Principles of Pharmacogenetics: Types of Genetic Variants
The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu
Genetic variations significantly influence drug response through pharmacokinetics, receptor interactions, and biologic milieu modifications. Pharmacokinetic alterations impact drug metabolism and clearance, affecting efficacy and toxicity. Variants in drug-metabolizing enzymes, such as CYP2C9 and CYP2C19, alter drug activation and elimination. For example, CYP2C9 loss-of-function variants require lower warfarin doses to prevent excessive bleeding, while CYP2C19 variants reduce clopidogrel...
Pharmacogenetics of Drug Metabolism: Overview
Genetic polymorphism in drug metabolism is crucial to the inter-individual variability observed in drug responses. Drug metabolism primarily involves the chemical modification of drugs and other xenobiotics to enhance their elimination by increasing their polarity. Two main classes of enzymes mediate this biotransformation process: Phase I enzymes, primarily cytochrome P450s, catalyze oxidation and reduction reactions, while other enzymes, such as esterases, mediate hydrolysis, and Phase II...
Pharmacogenomics: Identification of New Drug Targets
Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
Methods of Medium Optimization
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...


