Metabolomic analysis on samples from rats expressing human amylin (plasma).
Ontology highlight
ABSTRACT:
ORGANISM(S): Rattus Norvegicus
TISSUE(S): Blood
DISEASE(S): Diabetes
PROVIDER: ST000350 | MetabolomicsWorkbench |
REPOSITORIES: MetabolomicsWorkbench
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| mwtab | Other |
Items per page: 5 1 - 1 of 1 |
Similar Datasets
Project description:BackgroundHigh altitude de-acclimatization (HADA) is gradually becoming a public health concern as millions of individuals of different occupations migrate to high-altitude areas for work due to economic growth in plateau areas. HADA affects people who return to lower elevations after exposure to high altitudes. It causes significant physiological and functional changes that can negatively impact health and even endanger life. However, uncertainties persist about the detailed mechanisms underlying HADA.MethodsWe established a population cohort of individuals with HADA and assessed variations in metabolite composition. Plasm samples of four groups, including subjects staying at plain (P) and high altitude (H) as well as subjects suffering from HADA syndrome with almost no reaction (r3) and mild-to-moderate reaction (R3) after returning to plain from high altitude, were collected and analyzed by Liquid Chromatography-Mass Spectrometry metabolomic. Multivariate statistical analyses were used to explore significant differences and potential clinical prospect of metabolites.ResultAlthough significantly different on current HADAS diagnostic symptom score, there were no differences in 17 usual clinical indices between r3 and R3. Further multivariate analyses showed isolated clustering distribution of the metabolites among the four groups, suggesting significant differences in their metabolic characteristics. Through K-means clustering analysis, we identified 235 metabolites that exhibited patterns of abundance change consistent with phenotype of HADA syndrome. Pathway enrichment analysis indicated a high influence of polyunsaturated fatty acids under high-altitude conditions. We compared the metabolites between R3 and r3 and found 107 metabolites with differential abundance involved in lipid metabolism and oxidation, suggesting their potential role in the regulation of oxidative stress homeostasis. Among them, four metabolites might play a key role in the occurrence of HADA, including 11-beta-hydroxyandrosterone-3-glucuronide, 5-methoxyindoleacetate, 9,10-epoxyoctadecenoic acid, and PysoPC (20:5).ConclusionWe observed the dynamic variation in the metabolic process of HADA. Levels of four metabolites, which might be provoking HADA mediated through lipid metabolism and oxidation, were expected to be explore prospective indices for HADA. Additionally, metabolomics was more efficient in identifying environmental risk factors than clinical examination when dramatic metabolic disturbances underlying the difference in symptoms were detected, providing new insights into the molecular mechanisms of HADAS.
| S-EPMC11220191 | biostudies-literature
Project description:Many hepatocellular carcinoma (HCC) patients suffer from late stages when diagnosed, leading to dismal prospects for cure. Improving the diagnosis and treatment of HCC remains a challenge. In this work, NMR-based metabolomic techniques were used to investigate the metabolic alterations of HCC patients from different pathological backgrounds. Metabolic improvement of clinical surgical treatments or transcatheter arterial chemoembolization (TACE) for recurrent or metastatic HCC was also evaluated. HCC was characterized by enhanced lipid metabolism and high consumption in response to liver injury. Expectedly, higher consumption of glucose and lactate production in TACE group confirmed that recurrent or metastatic HCC is more active in citric acid cycle and oxidative phosphorylation. However, TACE or surgical treatments did not immediately improve the metabolic profiles of HCC patients. Combining multivariate statistical analyses with univariate t-test, a series of characteristic metabolites were identified and served as biomarkers for discrimination of HCC patients in different pathological backgrounds. The relative metabolic pathway analyses help to get insight into the underlying biochemical mechanism and extend clinical relevance. Furthermore, algorithm of support vector classification was used to identify HCC and control subjects, and diagnostic sensitivity and specificity reached to 100% and 81.08% respectively by receiver operating characteristic analysis. It is concluded that NMR-based metabolomic analysis of plasma can provide a powerful approach to discover diagnostic and therapeutic biomarkers, and subsequently contribute to clinical disease management.
| S-EPMC5216945 | biostudies-literature
Project description:Ossification of the posterior longitudinal ligament (OPLL) is formed by heterogeneous ossification of posterior longitudinal ligament. The patho-mechanism of OPLL is still largely unknown. MicroRNAs are small nucleatides that function as regulators of gene expression in almost any biological process. However, few microRNAs are reported to have a role in the pathological process of OPLL. Therefore, we performed high-throughput microRNA sequencing and transcriptome sequencing of primary OPLL and PLL cells in order to decipher the interacting network of microRNAs in OPLL. MRNA and microRNA profiles were done using primary culture cells of human ossification of the posterior longitudinal ligament (OPLL) tissue and normal posterior longitudinal ligament (PLL) tissue.
2016-04-04 | E-GEOD-69787 | biostudies-arrayexpress
Project description:Differential mobility spectrometry (DMS) has been gaining popularity in small molecule analysis over the last few years due to its selectivity towards a variety of isomeric compounds. While DMS has been utilized in targeted liquid chromatography-mass spectrometry (LC-MS), its use in untargeted discovery workflows has not been systematically explored. In this contribution, we propose a novel workflow for untargeted metabolomics based solely on DMS separation in a clinically relevant chronic kidney disease (CKD) patient population. We analyzed ten plasma samples from early- and late-stage CKD patients. Peak finding, alignment, and filtering steps performed on the DMS-MS data yielded a list of 881 metabolic features (unique mass-to-charge and migration time combinations). Differential analysis by CKD patient group revealed three main features of interest. One of them was putatively identified as bilirubin based on high-accuracy MS data and comparison of its optimum compensation voltage (COV) with that of an authentic standard. The DMS-MS analysis was four times faster than a typical HPLC-MS run, which suggests a potential for the utilization of this technique in screening studies. However, its lower separation efficiency and reduced signal intensity make it less suitable for low-abundant features. Fewer features were detected by the DMS-based platform compared with an HPLC-MS-based approach, but importantly, the two approaches resulted in different features. This indicates a high degree of orthogonality between HPLC- and DMS-based approaches and demonstrates the need for larger studies comparing the two techniques. The workflow described here can be adapted for other areas of metabolomics and has a value as a prescreening method to develop semi-targeted workflows and as a faster alternative to HPLC in large biomedical studies.
| S-EPMC6721987 | biostudies-literature
Project description: Not available
| ST000349 | MetabolomicsWorkbench
Project description:Introduction: Hepatocellular carcinoma (HCC) is the fourth most prevalent cancer in China. Transcatheter arterial chemoembolization (TACE) is a common interventional therapy for HCC. In this study, we aimed to explore specific metabolites that can accurately predict prognosis after TACE in patients with HCC. Methods: Patients with HCC and healthy volunteers (n = 20 each) were recruited to our study; plasma samples were collected from patients before and after TACE and from healthy volunteers. Plasma samples were subjected to untargeted ultra-high performance liquid chromatography-high resolution mass spectrometry metabolomics analysis, to identify metabolites significantly associated with the prognosis of patients with HCC after TACE. Results: Orthogonal filtered partial least squares discriminant analysis confirmed significant separation of the pre-TACE, post-TACE, and healthy groups, and 34 differential metabolites were identified between the pre-TACE and post-TACE groups. KEGG analysis revealed that phenylalanine, tyrosine, and tryptophan biosynthesis pathways and the phenylalanine metabolism pathway were potentially altered in HCC genesis and during TACE. Phenylalanine and tyrosine are involved in both pathways and were increased in the pre-TACE group relative to controls, with phenylalanine further increased in the post-TACE group. Receiver operating characteristic (ROC) curve analysis indicated that PC 36:4|PC 18:2_18:2 (area under the ROC curve (AUC) = 0.798) is a potential marker for assessment of prognosis in patients with HCC after TACE. Moreover, ROC curve analysis indicated that palmitoylcarnitine (AUC = 1) is a marker with potential value for HCC diagnosis. Conclusions: Limited studies had been conducted on the detection of metabolites in the plasma of HCC patients before and after TACE. PC 36:4|PC 18:2_18:2 is a potential marker for evaluation of the therapeutic effects of TACE. This finding may be beneficial for the treatment of patients with HCC after TACE.
| S-EPMC10758137 | biostudies-literature
Project description:PurposeMetabolomics is the comprehensive global study of metabolites in biological samples. In this retrospective pilot study we explored whether serum metabolomic profile can discriminate the presence of human breast cancer irrespective of the cancer subtype.MethodsPlasma samples were analyzed from healthy women (n = 20) and patients with breast cancer after diagnosis (n = 91) using a liquid chromatography-mass spectrometry platform. Multivariate statistics and a Random Forest (RF) classifier were used to create a metabolomics panel for the diagnosis of human breast cancer.ResultsMetabolomics correctly distinguished between breast cancer patients and healthy control subjects. In the RF supervised class prediction analysis comparing breast cancer and healthy control groups, RF accurately classified 100% both samples of the breast cancer patients and healthy controls. So, the class error for both group in and the out-of-bag error were 0. We also found 1269 metabolites with different concentration in plasma from healthy controls and cancer patients; and basing on exact mass, retention time and isotopic distribution we identified 35 metabolites. These metabolites mostly support cell growth by providing energy and building stones for the synthesis of essential biomolecules, and function as signal transduction molecules. The collective results of RF, significance testing, and false discovery rate analysis identified several metabolites that were strongly associated with breast cancer.ConclusionsIn breast cancer a metabolomics signature of cancer exists and can be detected in patient plasma irrespectively of the breast cancer type.
| S-EPMC5386702 | biostudies-literature
Project description:Glycosylation regulates functional responses mediated by the interaction of IgG with their receptors. Multiple analytical methods have been designed for the determination of the IgG N-glycan microheterogeneity, including MS methods for the analysis of site specific glycoforms of IgG. However, measurement of low abundant glycoforms remains challenging in complex samples like serum without enrichment of the IgG. We present a workflow for quantitative analysis of site specific glycoforms of IgG based on data independent acquisition (DIA) of Y-ions generated under "minimal" fragmentation conditions. The adjusted collision induced dissociation (CID) conditions generate specific Y-ions in the yield of up to 60% precursor ion intensity. These selective fragments, measured in high resolution, improve specificity of detection compared to the typically quantified B-ions which have higher overall intensity but lower signal-to-noise ratios. Under optimized conditions, we achieve label-free quantification of the majority of previously reported glycoforms of IgG (26 glycoforms of IgG1, 22 glycoforms of IgG 2/3, and 19 glycoforms of IgG4) directly in unfractionated samples of human plasma and we detect traces of previously unreported glycoforms of IgG1, including doubly fucosylated glycoforms. The SWATH data independent quantification of IgG glycoforms in pooled plasma samples of patients with liver cirrhosis detects reliably the expected changes in the quantity of major glycoforms compared to healthy controls. Our results show that optimized CID fragmentation enables DIA of IgG glycoforms and suggest that such workflow may enable quantitative analyses of the glycoproteome in complex matrixes.
| S-EPMC5803558 | biostudies-literature
Project description:We explored, using nuclear magnetic resonance (NMR) metabolomics and fatty acids profiling, the effects of a common nutritional complement, Curcuma longa, at a nutritionally relevant dose with human use, administered in conjunction with an unbalanced diet. Indeed, traditional food supplements have been long used to counter metabolic impairments induced by unbalanced diets. Here, rats were fed either a standard diet, a high level of fructose and saturated fatty acid (HFS) diet, a diet common to western countries and that certainly contributes to the epidemic of insulin resistance (IR) syndrome, or a HFS diet with a Curcuma longa extract (1% of curcuminoids in the extract) for ten weeks. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) on the serum NMR profiles and fatty acid composition (determined by GC/MS) showed a clear discrimination between HFS groups and controls. This discrimination involved metabolites such as glucose, amino acids, pyruvate, creatine, phosphocholine/glycerophosphocholine, ketone bodies and glycoproteins as well as an increase of monounsaturated fatty acids (MUFAs) and a decrease of n-6 and n-3 polyunsaturated fatty acids (PUFAs). Although the administration of Curcuma longa did not prevent the observed increase of glucose, triglycerides, cholesterol and insulin levels, discriminating metabolites were observed between groups fed HFS alone or with addition of a Curcuma longa extract, namely some MUFA and n-3 PUFA, glycoproteins, glutamine, and methanol, suggesting that curcuminoids may act respectively on the fatty acid metabolism, the hexosamine biosynthesis pathway and alcohol oxidation. Curcuma longa extract supplementation appears to be beneficial in these metabolic pathways in rats. This metabolomic approach highlights important serum metabolites that could help in understanding further the metabolic mechanisms leading to IR.
| S-EPMC4545834 | biostudies-literature
Project description:Epidemiological data demonstrate that bovine whole milk is often substituted for human milk during the first 12 months of life and may be associated with adverse infant outcomes. The objective of this study is to interrogate the human and bovine milk metabolome at 2 weeks of life to identify unique metabolites that may impact infant health outcomes. Human milk (n = 10) was collected at 2 weeks postpartum from normal-weight mothers (pre-pregnant BMI < 25 kg/m2) that vaginally delivered term infants and were exclusively breastfeeding their infant for at least 2 months. Similarly, bovine milk (n = 10) was collected 2 weeks postpartum from normal-weight primiparous Holstein dairy cows. Untargeted data were acquired on all milk samples using high-resolution liquid chromatography-high-resolution tandem mass spectrometry (HR LC-MS/MS). MS data pre-processing from feature calling to metabolite annotation was performed using MS-DIAL and MS-FLO. Our results revealed that more than 80% of the milk metabolome is shared between human and bovine milk samples during early lactation. Unbiased analysis of identified metabolites revealed that nearly 80% of milk metabolites may contribute to microbial metabolism and microbe-host interactions. Collectively, these results highlight untargeted metabolomics as a potential strategy to identify unique and shared metabolites in bovine and human milk that may relate to and impact infant health outcomes.
| S-EPMC10490210 | biostudies-literature