¹H NMR based serum metabolic profiles associated with pathological progression of pancreatic islet β cell tumor in Rip1-Tag2 mice

Serum Metabolic Characterization of Vitamin E Deficiency in Holstein Cows during the Transition Period Based on Proton Nuclear Magnetic Resonance Spectroscopy

Vitamin E, a potent antioxidant, is a necessary and complex micronutrient for cows. During the transition period, vitamin E deficiency (VED) is among the highest prevalent micronutrient deficits in dairy cows. It may eventually result in oxidative stress and immunological malfunction, and it increases the risk of peripartum disorders. At present, detailed data on blood metabolites in VED cows are limited. Consequently, the purpose of this research was to examine the alterations in the serum metabolic profile of VED cows throughout the early postpartum period. Using comprehensive 1H nuclear magnetic resonance (1H NMR), the alterations in serum metabolic activities of VED cows were analyzed. In total, 28 multiparous Holstein cows were assigned according to serum α-tocopherol (α-Toc) concentrations into normal (α-Toc ≥ 4 μg/mL, n = 14) and VED (α-Toc < 3 μg/mL, n = 14) groups at 21 days postpartum, and their blood samples were collected for biochemical and 1H NMR analyses. A t-test on independent samples as well as multivariate statistics were used to assess the findings. In comparison with normal cows, VED cows showed significantly worse body condition scores, milk yield, and dry matter intake (p < 0.05). Significantly higher levels of serum non-esterified fatty acids, aspartate aminotransferase, low-density lipoprotein, and malonaldehyde were found in VED-affected cows, as well as lesser concentrations of serum albumin, high-density lipoprotein, and total antioxidant capacity in comparison with normal cows (p < 0.01), while other vitamins and minerals concentrations showed no distinction between the groups (p > 0.05). Furthermore, 24 upregulated serum metabolites were identified under VED conditions. The metabolomics pathway analysis of these metabolites demonstrated that a global metabolic response to VED in cows was represented by changes in 11 metabolic pathways, comprising energy, carbohydrate, and amino acid metabolism. From these results, we conclude that VED cows were more likely to experience a negative energy balance characterized by alterations of common systemic metabolic processes and develop oxidative stress, inflammation, and ultimately liver injury. This study provides the first evidence of metabolic changes in cows with VED.

Identifying target ion channel-related genes to construct a diagnosis model for insulinoma

Background: Insulinoma is the most common functional pancreatic neuroendocrine tumor (PNET) with abnormal insulin hypersecretion. The etiopathogenesis of insulinoma remains indefinable. Based on multiple bioinformatics methods and machine learning algorithms, this study proposed exploring the molecular mechanism from ion channel-related genes to establish a genetic diagnosis model for insulinoma. Methods: The mRNA expression profile dataset of GSE73338 was applied to the analysis, which contains 17 insulinoma samples, 63 nonfunctional PNET (NFPNET) samples, and four normal islet samples. Differently expressed ion channel-related genes (DEICRGs) enrichment analyses were performed. We utilized the protein-protein interaction (PPI) analysis and machine learning of LASSO and support vector machine-recursive feature elimination (SVM-RFE) to identify the target genes. Based on these target genes, a nomogram diagnostic model was constructed and verified by a receiver operating characteristic (ROC) curve. Moreover, immune infiltration analysis, single-gene gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) were executed. Finally, a drug-gene interaction network was constructed. Results: We identified 29 DEICRGs, and enrichment analyses indicated they were primarily enriched in ion transport, cellular ion homeostasis, pancreatic secretion, and lysosome. Moreover, the PPI network and machine learning recognized three target genes (MCOLN1, ATP6V0E1, and ATP4A). Based on these target genes, we constructed an efficiently predictable diagnosis model for identifying insulinomas with a nomogram and validated it with the ROC curve (AUC = 0.801, 95% CI 0.674-0.898). Then, single-gene GSEA analysis revealed that these target genes had a significantly positive correlation with insulin secretion and lysosome. In contrast, the TGF-beta signaling pathway was negatively associated with them. Furthermore, statistically significant discrepancies in immune infiltration were revealed. Conclusion: We identified three ion channel-related genes and constructed an efficiently predictable diagnosis model to offer a novel approach for diagnosing insulinoma.

Application of NMR metabolomics to search for human disease biomarkers in blood

Recently, nuclear magnetic resonance spectroscopy (NMR)-based metabolomics analysis and multivariate statistical techniques have been incorporated into a multidisciplinary approach to profile changes in small molecules associated with the onset and progression of human diseases. The purpose of these efforts is to identify unique metabolite biomarkers in a specific human disease so as to (1) accurately predict and diagnose diseases, including separating distinct disease stages; (2) provide insights into underlying pathways in the pathogenesis and progression of the malady and (3) aid in disease treatment and evaluate the efficacy of drugs. In this review we discuss recent developments in the application of NMR-based metabolomics in searching disease biomarkers in human blood samples in the last 5 years.

High-Throughput Metabolomics by 1D NMR

Metabolomics deals with the whole ensemble of metabolites (the metabolome). As one of the -omic sciences, it relates to biology, physiology, pathology and medicine; but metabolites are chemical entities, small organic molecules or inorganic ions. Therefore, their proper identification and quantitation in complex biological matrices requires a solid chemical ground. With respect to for example, DNA, metabolites are much more prone to oxidation or enzymatic degradation: we can reconstruct large parts of a mammoth's genome from a small specimen, but we are unable to do the same with its metabolome, which was probably largely degraded a few hours after the animal's death. Thus, we need standard operating procedures, good chemical skills in sample preparation for storage and subsequent analysis, accurate analytical procedures, a broad knowledge of chemometrics and advanced statistical tools, and a good knowledge of at least one of the two metabolomic techniques, MS or NMR. All these skills are traditionally cultivated by chemists. Here we focus on metabolomics from the chemical standpoint and restrict ourselves to NMR. From the analytical point of view, NMR has pros and cons but does provide a peculiar holistic perspective that may speak for its future adoption as a population-wide health screening technique.

Metabonomic analysis of metastatic lung tissue in breast cancer mice by an integrated NMR-based metabonomics approach

This study identified the common potential biomarkers for early lung metastasis of breast cancer in two models.