Project description:Metabolomics has been increasingly used in animal and food sciences. Animal health is one of the most important factor that can also alter animal integrity and welfare. Some studies have already investigated the link between health and metabolic profile of dairy animals. These studies in metabolomics often consider a single type of sample using a single analytical platform (nuclear magnetic resonance or mass spectrometry). Only few studies with multi-platform approaches are also used with a single or a multi type of sample, but they mainly consider dairy cows' metabolome although dairy goats present similar diseases, that it could be interesting to detect early to preserve animal health and milk production. This study aims to create a metabolic atlas of goat plasma, milk and feces, based on healthy animals. Our study describes a standard operating procedure for three goat matrices: blood plasma, milk, and feces using multiple platforms (NMR (1H), UHPLC (RP)-MS and UHPLC (HILIC)-MS) that follows a unique sample preparation procedure for each sample type to be analyzed on multi-platforms basis. Our method was evaluated for its robustness and allowed a better characterization of goat metabolic profile in healthy conditions.
Project description:Quantification of malondialdehyde (MDA) as a marker of lipid peroxidation is relevant for many research fields. We describe a new sensitive and selective method to measure free and total plasmatic MDA using derivatization with 2,4-dinitrophenylhydrazine (DNPH) and ultra-HPLC-high-resolution MS. Free and total MDA were extracted from minute sample amounts (10 μl) using acidic precipitation and alkaline hydrolysis followed by acidic precipitation, respectively. Derivatization was completed within 10 min at room temperature, and the excess DNPH discarded by liquid-liquid extraction. Quantification was achieved by internal standardization using dideuterated MDA as internal standard. The method's lowest limit of quantification was 100 nM and linearity spanned greater than three orders of magnitude. Intra- and inter-day precisions for total MDA were 2.9% and 3.0%, respectively, and those for free MDA were 12.8% and 24.9%, respectively. Accuracy was 101% and 107% at low and high concentrations, respectively. In human plasma, free MDA levels were 120 nM (SD 36.26) and total MDA levels were 6.7 μM (SD 0.46). In addition, we show the applicability of this method to measure MDA plasma levels from a variety of animal species, making it invaluable to scientists in various fields.
Project description:Lipidic metabolites play essential roles in host physiological health and growth performance, serving as the major structural and signaling components of membranes, energy storage molecules, and steroid hormones. Bamboo, as wild giant pandas' exclusive diet, is the main determinant of giant pandas' lipidome, both as a direct source and through microbiota activity. Interestingly, the consumption of bamboo has attracted little attention from a lipidomic perspective. In the current study, we outline the lipidomic atlas of different parts of bamboo. By gas chromatography-mass spectrometry (GC-MS), we have been able to obtain the absolute quantification of 35 fatty acids pertaining to short chain fatty acids (8), medium chain fatty acids (6), long chain fatty acids (17), and very long chain fatty acids (4), while liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS/MS) allowed us to obtain the relative quantification of another 1638 lipids. Among the fatty acids quantified in absolute terms, eight showed significantly distinct concentrations among different bamboo parts. Subsequently, we investigated how the giant panda's serum and fecal lipidome adapt to the most important annual change in their diet, represented by the consumption of high amounts of bamboo shoots, typical of spring, the weight-gaining season. Five fatty acids were significantly altered in feces and two in serum, respectively, due to the different levels of bamboo shoot consumption. Furthermore, significant differences of the main bacteria strains were observed in feces between the two groups at the genus level, pertaining to Streptococcus, Leuconostoc, and Vagococcus. Correlations between giant panda fecal microbiome and lipidome were evaluated by Pearson correlation analysis. These findings suggest that a balanced diet, important for the overall lipidomic function and giant panda health, could be reached even in this remarkable case of a single food-based diet, by administering to the giant panda's combinations of different parts of bamboo, with specific lipidome profiles.
Project description:Currently, most clinical studies in metabolomics only consider a single type of sample such as urine, plasma, or feces and use a single analytical platform, either NMR or MS. Although some studies have already investigated metabolomics data from multiple fluids, the information is limited to a unique analytical platform. On the other hand, clinical studies investigating the human metabolome that combine multi-analytical platforms have focused on a single biofluid. Combining data from multiple sample types for one patient using a multimodal analytical approach (NMR and MS) should extend the metabolome coverage. Pre-analytical and analytical phases are time consuming. These steps need to be improved in order to move into clinical studies that deal with a large number of patient samples. Our study describes a standard operating procedure for biological specimens (urine, blood, saliva, and feces) using multiple platforms (1H-NMR, RP-UHPLC-MS, and HILIC-UHPLC-MS). Each sample type follows a unique sample preparation procedure for analysis on a multi-platform basis. Our method was evaluated for its robustness and was able to generate a representative metabolic map.
Project description:IntroductionOsteosarcoma (OS) is the most common primary malignant bone tumor in children and adolescents. An increasing number of studies have demonstrated that tumor proliferation and metastasis are closely related to complex metabolic reprogramming. However, there are limited data to provide a comprehensive metabolic picture of osteosarcoma.ObjectivesOur study aims to identify aberrant metabolic pathways and seek potential adjuvant biomarkers for osteosarcoma.MethodsSerum samples were collected from 65 osteosarcoma patients and 30 healthy controls. Nontargeted metabolomic profiling was performed by liquid chromatography-mass spectrometry (LC-MS) based on univariate and multivariate statistical analyses.ResultsThe OPLS-DA model analysis identified clear separations among groups. We identified a set of differential metabolites such as higher serum levels of adenosine-5-monophosphate, inosine-5-monophosphate and guanosine monophosphate in primary OS patients compared to healthy controls, and higher serum levels of 5-aminopentanamide, 13(S)-HpOTrE (FA 18:3 + 2O) and methionine sulfoxide in lung metastatic OS patients compared to primary OS patients, revealing aberrant metabolic features during the proliferation and metastasis of osteosarcoma. We found a group of metabolites especially lactic acid and glutamic acid, with AUC values of 0.97 and 0.98, which could serve as potential adjuvant diagnostic biomarkers for primary osteosarcoma, and a panel of 2 metabolites, 5-aminopentanamide and 13(S)-HpOTrE (FA 18:3 + 2O), with an AUC value of 0.92, that had good monitoring ability for lung metastases.ConclusionsOur study provides new insight into the aberrant metabolic features of osteosarcoma. The potential biomarkers identified here may have translational significance.