Understanding the functional and genomic architecture of the rumen microbiome affecting performance traits in bovines

到2050年，人口将增长到90亿人以上，同时，全球肉类产量将增长73%。为了应对这一挑战，需要提高动物生产的效率和可持续性，减少食物链中的浪费，并确保安全和有营养的饮食。瘤胃微生物赋予了一种独特的能力，可以将人类不可食用的高纤维饲料转化为动物可以吸收的营养物质，以生产肉和牛奶等高质量的蛋白质。然而，集约化的粮食生产给环境带来了压力，需要以不损害环境的方式合乎道德地生产更多的粮食。该项目通过揭示影响牛生产性能的瘤胃微生物组的功能和基因组结构来解决这些挑战。这些信息将被用来确定微生物组或其基因与动物性能特征和甲烷排放之间的基本联系。在这项研究中，我们将对所有微生物基因组--元基因组--进行测序，以描述微生物群落及其功能基因的组成。分析将基于288头实验肉牛的独特数据集，包括瘤胃DNA样本和大量性能信息(例如饲料转化效率、生长、身体成分和肉类质量)。这些数据按品种和父代子代群体进行组织，以估计动物宿主对微生物组和微生物基因的遗传影响。这些实验数据已经成为许多出版物的基础，这些出版物表明，在动物性能水平和甲烷排放方面，品种、父代后代群体和饮食之间存在很大差异。对其中8只动物的初步分析表明，微生物群落或微生物基因的丰度与动物的性能特征和甲烷排放之间存在联系。然而，为了了解瘤胃微生物组的功能和基因组结构，对整个样本集的分析是必要的。将开发算法来预测动物的性能，例如饲料转化效率和微生物群落和基因丰富的甲烷排放。然后，可以通过分析瘤胃微生物群(通过活体动物的胃管或屠宰场采样)来预测这些高价值但测量成本高的特征。然而，为了验证瘤胃微生物组和生产性能之间的联系，我们需要关于微生物组的功能和基因组结构的基本知识。此外，还可以确定预测饲料转化效率的微生物生物标记物。由于父本后代群体和饲粮数据的独特结构，我们将能够预测宿主遗传和营养对微生物群落和微生物基因的影响。这种结构还可以用于网络分析，以确定动物遗传对微生物组功能和基因组结构的影响。该项目将提供有关微生物组的基因组和功能结构及其对性能特征和甲烷排放的影响以及与动物遗传学和营养相互作用的前所未有的新知识。我们将比较肉牛和其他物种微生物组的功能和遗传结构，以提供对不同物种的微生物组的见解，特别是人类。通过了解宿主遗传对瘤胃微生物区系的影响以及与身体成分的关系，我们希望为人类个性化药物治疗减少肥胖提供新的见解。

By 2050, the human population will grow to over 9 billion people, and in the same time frame, global meat production is set to increase by 73%. There is a need to increase the efficiency and sustainability of animal production, reduce waste in the food chain and ensure safe and nutritious diets in order to address this challenge. Rumen microbes confers a unique ability to convert human inedible high-fibre forage into nutrients the animal can absorb to produce high-quality proteins as meat and milk. However, intensive food production puts a strain on the environment, and there is a need to produce more food ethically and in a way that does not harm the environment. The project addresses these challenges by unravelling the functional and genomic architecture of the ruminal microbiome affecting performance traits of cattle. This information will be used to identify fundamental associations between the microbiome or its genes with animal performance traits and methane emissions. In this study we will sequence all microbial genomes - the metagenome - to describe the composition of the microbial community and its functional genes. The analysis will be based on a unique dataset of 288 experimental beef cattle, with rumen DNA samples and a large array of performance information (e.g. feed conversion efficiency, growth, body composition and meat quality) available. These data are structured by breeds and sire progeny groups to estimate the animal host genetic effects on the microbiome and microbial genes. The experimental data have been the basis of numerous publications in which it was shown that at the animal performance level, and for methane emissions, there are large differences between breeds, sire progeny groups and diets. Preliminary analysis for 8 of these animals suggests that there is a link between the abundance of the microbial community or microbial genes and animal performance traits and methane emissions. However, to understand the function and genomic architecture of the ruminal microbiome, analysis of the full sample set is necessary. Algorithms will be developed to predict animal performance, e.g. feed conversion efficiency and methane emissions from the abundance of the microbial community and genes. These high value, but costly-to-measure traits could then be predicted by analysing the rumen microbiome (sampled via stomach tube on live animals or in the abattoir). However, to verify the associations between the rumen microbiome and performance traits, we need basic knowledge about the functional and genomic architecture of the microbiome. Additionally, microbial biomarkers to predict e.g. feed conversion efficiency could be identified. Due to the unique structure of the data in sire progeny groups and diets, we will be able to predict the host genetic and nutritional effect on the microbial community and microbial genes. This structure can also be used in the network analysis to identify animal genetic effects on the functional and genomic architecture of the microbiome. The project will provide unprecedented new knowledge of the genomic and functional architecture of the microbiome and its impact on performance traits and methane emissions as well as the interaction with animal genetics and nutrition. We will compare the functional and genetic architecture of the microbiome in beef cattle with that of other species to provide insights about the microbiome of different species, in particular humans. By understanding host genetic effects on the rumen microbiota and associations with body composition, we expect to provide new insights for human personalised medicine approaches to reduce obesity.