Dipartimento di Agraria, University of Sassari, Sassari 07100, Italy; Department of Animal and Dairy Science, University of Georgia, Athens 30602, GA, USA.
Department of Animal and Dairy Science, University of Georgia, Athens 30602, GA, USA.
Animal. 2024 Mar;18(3):101102. doi: 10.1016/j.animal.2024.101102. Epub 2024 Feb 7.
Microbial composition of the gastrointestinal tracts is an important factor affecting the variation in feed efficiency in ruminants. Several studies have investigated the composition of the ruminal and fecal microbiotas, as well as their impacts on feed efficiency and digestion. In addition, next-generation DNA sequencing techniques have allowed us to gain a better understanding of such microbiomes. In this study, the beef cattle microbiome data were analyzed using both a multivariate and a univariate approach and the results were compared. Moreover, a statistical procedure to classify calves in two groups with extreme Residual Feed Intake (RFI) values, using their microbiota profile, was developed. Both fecal and ruminal samples were collected from 63 Angus steers at two different time points for evaluation of their microbiomes: at the beginning and at the end of the feedlot. An additional fecal sample was collected at weaning. A total of 149 and 119 bacterial families (BFs) were retrieved from the ruminal and fecal samples, respectively. A Canonical Discriminant Analysis (CDA) was used to investigate whether BFs were able to distinguish between rumen and fecal samples. A sub-sample of 28 steers was divided in two groups based on their feed efficiency status: positive or negative for RFI. Fecal samples collected at weaning were used to assign the positive and negative RFI animals to their corresponding groups using both Stepwise Discriminant Analysis and CDA. Results revealed that CDA was able to distinguish between rumen and fecal samples. Peptostreptococcaceae was the family most associated with the fecal samples, whereas Prevotellaceae the most associated with the ruminal samples. The CDA using 19 BFs selected from the stepwise was able to correctly assign all animals to the proper RFI groups (negative or positive). Rhizobiaceae was the family most associated with negative RFI, whereas Comamonadacea was the family most linked with positive RFI. The results from this study showed that the multivariate approach can be used to improve microbiome data analysis, as well as to predict feed efficiency in beef cattle using information derived from the fecal microbiome.
胃肠道内的微生物组成是影响反刍动物饲料效率变化的一个重要因素。有几项研究调查了瘤胃和粪便微生物群的组成及其对饲料效率和消化的影响。此外，新一代 DNA 测序技术使我们能够更好地了解这些微生物组。在这项研究中，使用多元和单变量方法分析了肉牛微生物组数据，并比较了结果。此外，还开发了一种统计程序，用于根据微生物组谱将具有极端剩余饲料摄入量 (RFI) 值的小牛分为两组。在两个不同的时间点从 63 头安格斯育肥牛收集粪便和瘤胃液样本，以评估其微生物组：在开始和结束时。在断奶时收集了额外的粪便样本。从瘤胃液和粪便样本中分别获得了 149 和 119 个细菌家族 (BF)。使用典型判别分析 (CDA) 研究了 BF 是否能够区分瘤胃和粪便样本。根据其饲料效率状况，将 28 头育肥牛的亚样本分为两组：RFI 为正或负。使用逐步判别分析和 CDA 将断奶时收集的粪便样本用于将阳性和阴性 RFI 动物分配到相应的组中。结果表明，CDA 能够区分瘤胃和粪便样本。消化链球菌科是与粪便样本最相关的科，而拟杆菌科是与瘤胃样本最相关的科。使用逐步选择的 19 个 BF 进行的 CDA 能够正确地将所有动物分配到适当的 RFI 组（阴性或阳性）。根瘤菌科是与阴性 RFI 最相关的科，而贪噬菌科是与阳性 RFI 最相关的科。本研究结果表明，多元方法可用于改进微生物组数据分析，并使用来自粪便微生物组的信息预测肉牛的饲料效率。
