Fabrile Maria Pia, Ghidini Sergio, Caligiani Augusta, Scali Federico, Varrà Maria Olga, Lolli Veronica, Alborali Giovanni Loris, Ianieri Adriana, Zanardi Emanuela
Department of Food and Drug, University of Parma, Strada del Taglio, 10, 43126 Parma, Italy.
Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia-Romagna, Via A. Bianchi 9, 25124 Brescia, Italy.
Foods. 2023 Nov 25;12(23):4259. doi: 10.3390/foods12234259.
An untargeted Nuclear Magnetic Resonance (NMR) spectroscopy-based metabolomics approach was applied as a first attempt to explore the metabolome of pigs treated with antibiotics. The final goal was to investigate the possibility of discriminating between antibiotic-treated (TX group) and untreated pigs (CTRL group), with the further perspective of identifying the authentication tools for antibiotic-free pork supply chains. In particular, 41 samples of pig liver were subjected to a biphasic extraction to recover both the polar and the non-polar metabolites, and the H NMR spectroscopy analysis was performed on the two separate extracts. Unsupervised (principal component analysis) and supervised (orthogonal partial least squares discriminant analysis) multivariate statistical analysis of H NMR spectra data in the range 0-9 ppm provided metabolomic fingerprinting useful for the discrimination of pig livers based on the antibiotic treatment to which they were exposed. Moreover, within the signature patterns, significant discriminating metabolites were identified among carbohydrates, choline and derivatives, amino acids and some lipid-class molecules. The encouraging findings of this exploratory study showed the feasibility of the untargeted metabolomic approach as a novel strategy in the authentication framework of pork supply chains and open a new horizon for a more in-depth investigation.
首次尝试采用基于非靶向核磁共振(NMR)光谱的代谢组学方法来探索用抗生素处理过的猪的代谢组。最终目标是研究区分抗生素处理组(TX组)和未处理猪(CTRL组)的可能性,并进一步确定无抗生素猪肉供应链的认证工具。具体而言,对41份猪肝样本进行双相萃取,以回收极性和非极性代谢物,并对两份单独的提取物进行1H NMR光谱分析。对0-9 ppm范围内的1H NMR光谱数据进行无监督(主成分分析)和有监督(正交偏最小二乘判别分析)多变量统计分析,提供了代谢组学指纹图谱,有助于根据猪肝所接受的抗生素处理来区分它们。此外,在特征模式中,在碳水化合物、胆碱及其衍生物、氨基酸和一些脂质类分子中鉴定出了显著的区分性代谢物。这项探索性研究的令人鼓舞的结果表明,非靶向代谢组学方法作为猪肉供应链认证框架中的一种新策略是可行的,并为更深入的研究开辟了新的视野。