基于 1H-NMR 的具有不同 RFI 表型的杂交肉牛血浆代谢组图谱分析

Ruminants Pub Date : 2024-04-08 DOI:10.3390/ruminants4020012
G. Taiwo, M. Idowu, Taylor S Sidney, Emily Treon, D. Ologunagba, Yarahy Leal, Samanthia Johnson, Rhoda Olowe Taiwo, Anjola Adewoye, E. Ezeigbo, F. Eichie, I. Ogunade
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引用次数: 0

摘要

本研究的重点是探索不同残余饲料摄入量(RFI)水平的杂交肉牛的代谢组特征,这是衡量肉牛饲料效率的一个指标。对 67 头杂交生长肉牛(体重 = 277 ± 29.7 千克)进行为期 64 天的高饲喂量全混合日粮饲喂,以确定它们的 RFI 表型。在 64 天饲喂试验结束时,根据肉牛的 RFI 值将其分为两组:低(或负)RFI 肉牛(n = 28;RFI = -1.08 ± 0.88 kg/d)和高(或正)RFI 肉牛(n = 39;RFI = 1.21 ± 0.92 kg/d)。研究人员采集了血液样本,并使用核磁共振光谱分析了血浆样本,最终确定了 50 种代谢物。研究发现了与 RFI 状态相关的独特代谢组特征。包括氨基酸(酪氨酸、甘氨酸、缬氨酸、亮氨酸和蛋氨酸)和其他化合物(二甲基砜、3-羟基异戊酸、柠檬酸、肌酸和左旋肉碱)在内的八种代谢物在低 RFI 组和高 RFI 组之间显示出不同的丰度。具体来说,酪氨酸、甘氨酸和二甲基砜表现出显著的特异性和敏感性,从而产生了一个接收器操作特征曲线下面积为 0.7 的判别模型,使它们成为 RFI 的潜在标记物。包含这些生物标记物的逻辑回归模型能有效区分高RFI和低RFI牛,阈值临界点为0.48,突显了与低RFI牛高效营养利用相关的独特代谢物特征。包含这些生物标记物的逻辑回归模型有望准确划分RFI值,为了解肉牛饲料效率的代谢基础提供依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
1H-NMR-Based Plasma Metabolomic Profiling of Crossbred Beef Cattle with Divergent RFI Phenotype
This study focused on exploring the metabolomic profiles of crossbred beef cattle with varying levels of residual feed intake (RFI), a measure of feed efficiency in beef cattle. Sixty-seven crossbred growing beef steers (BW = 277 ± 29.7 kg) were subjected to a high-forage total mixed ration for 64 days to determine their RFI phenotypes. At the end of the 64d feeding trial, beef steers were divided into two groups based on their RFI values: low (or negative)-RFI beef steers (n = 28; RFI = −1.08 ± 0.88 kg/d) and high (or positive)-RFI beef steers (n = 39; RFI = 1.21 ± 0.92 kg/d). Blood samples were collected, and plasma samples were analyzed using Nuclear Magnetic Resonance spectroscopy, resulting in the identification of 50 metabolites. The study found a distinct metabolomic signature associated with RFI status. Eight metabolites, including amino acids (tyrosine, glycine, valine, leucine, and methionine) and other compounds (dimethyl sulfone, 3-hydroxy isovaleric acid, citric acid, creatine, and L-carnitine), showed differential abundance between low- and high-RFI groups. Specifically, tyrosine, glycine, and dimethyl sulfone exhibited significant specificity and sensitivity, which produced a discriminatory model with an area under the receiver operating characteristic (ROC) curve of 0.7, making them potential markers for RFI. A logistic regression model incorporating these biomarkers effectively distinguished between high- and low-RFI steers, with a threshold cutoff point of 0.48, highlighting a distinctive metabolite profile associated with efficient nutrient utilization in low-RFI cattle. The logistic regression model, incorporating these biomarkers, holds promise for accurately categorizing RFI values, providing insights into the metabolic basis of feed efficiency in beef cattle.
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