Comparative metabolomics studies of blood collected in streck and heparin tubes from lung cancer patients

Sodium fluoride preserves blood metabolite integrity for biomarker discovery in large-scale, multi-site metabolomics investigations

Background: Metabolite profiling of blood by nuclear magnetic resonance (NMR) is invaluable to clinical biomarker discovery. To ensure robustness, biomarkers require validation in large cohorts and across multiple centres. However, collection procedures are known to impact on the stability of biofluids that may, in turn, degrade biomarker signals. We trialled three blood collection tubes with the aim of solving technical challenges due to preanalytical variation in blood metabolite levels that are common in cohort studies. Methods: We first investigated global NMR-based metabolite variability between biobanks, including the large-scale UK Biobank and TwinsUK biobank of the general UK population, and more targeted biobanks derived from multicentre clinical trials relating to inflammatory bowel disease. We then compared the blood metabolome of 12 healthy adult volunteers when collected into either sodium fluoride/potassium oxalate, lithium heparin, or serum blood tubes using different pre-processing parameters. Results: Preanalytical variation in the method of blood collection strongly influences metabolite composition within and between biobanks. This variability can largely be attributed to glucose and lactate. In the healthy control cohort, the fluoride oxalate collection tube prevented fluctuation in glucose and lactate levels for 24 hours at either 4 °C or room temperature (20 °C). Conclusions: Blood collection into a fluoride oxalate collection tube appears to preserve the blood metabolome with delayed processing up to 24 hours at 4 °C. This method may be considered as an alternative when rapid processing is not feasible.

Metabolomics Analysis and Diagnosis of Lung Cancer: Insights from Diverse Sample Types

Lung cancer is a highly fatal disease that poses a significant global health burden. The absence of characteristic clinical symptoms frequently results in the diagnosis of most patients at advanced stages of lung cancer. Although low-dose computed tomography (LDCT) screening has become increasingly prevalent in clinical practice, its high rate of false positives continues to present a significant challenge. In addition to LDCT screening, tumor biomarker detection represents a critical approach for early diagnosis of lung cancer; unfortunately, no tumor marker with optimal sensitivity and specificity is currently available. Metabolomics has recently emerged as a promising field for developing novel tumor biomarkers. In this paper, we introduce metabolic pathways, instrument platforms, and a wide variety of sample types for lung cancer metabolomics. Specifically, we explore the strengths, limitations, and distinguishing features of various sample types employed in lung cancer metabolomics research. Additionally, we present the latest advances in lung cancer metabolomics research that utilize diverse sample types. We summarize and enumerate research studies that have investigated lung cancer metabolomics using different metabolomic sample types. Finally, we provide a perspective on the future of metabolomics research in lung cancer. Our discussion of the potential of metabolomics in developing new tumor biomarkers may inspire further study and innovation in this dynamic field.

Mass Spectrometric Detection of Formaldehyde-Crosslinked PBMC Proteins in Cell-Free DNA Blood Collection Tubes

Streck tubes are commonly used to collect blood samples to preserve cell-free circulating DNA. They contain imidazolidinyl urea as a formaldehyde-releasing agent to stabilize cells. We investigated whether the released formaldehyde leads to crosslinking of intracellular proteins. Therefore, we employed a shotgun proteomics experiment on human peripheral blood mononuclear cells (PBMCs) that were isolated from blood collected in Streck tubes, EDTA tubes, EDTA tubes containing formaldehyde, or EDTA tubes containing allantoin. The identified crosslinks were validated in parallel reaction monitoring LC/MS experiments. In total, we identified and validated 45 formaldehyde crosslinks in PBMCs from Streck tubes, which were also found in PBMCs from formaldehyde-treated blood, but not in EDTA- or allantoin-treated samples. Most were derived from cytoskeletal proteins and histones, indicating the ability of Streck tubes to fix cells. In addition, we confirm a previous observation that formaldehyde crosslinking of proteins induces a +24 Da mass shift more frequently than a +12 Da shift. The crosslinking capacity of Streck tubes needs to be considered when selecting blood-collection tubes for mass-spectrometry-based proteomics or metabolomic experiments.

Investigation of pathogenesis of hyperuricemia based on untargeted and targeted metabolomics

Hyperuricemia (HUA) seriously harms human health but the exact etiology and pathogenesis of HUA are not fully understood. Therefore, it is still of great significance to find effective biomarkers and explore the pathogenesis of HUA. Metabolomics reflects the influence of internal and external factors on system metabolism, explains the changes in metabolite levels during the development of diseases, and reveals the molecular mechanism of pathogenesis. Metabolomics is divided into untargeted metabolomics and targeted metabolomics according to different research modes. Each other's advantages can be fully utilized by combining the two so that the results of metabolomics research can be consummated. 20 HUA patients and 20 healthy individuals participated in the experiment, and untargeted metabolomics was employed to find 50 differential metabolites in HUA serum samples. Twelve candidate biomarkers were screened based on literature research and ROC Curve analysis for subsequent verification. Based on the UPLC-TQ-MS analysis platform, the targeted metabolomics detection methods were established and the content of 12 candidate biomarkers was precisely quantified. Compare with the results of untargeted metabolomics, the targeted metabolomics results were considered more reliable.

Improvement in Mung Bean Peptide on High-Fat Diet-Induced Insulin Resistance Mice Using Untargeted Serum Metabolomics

This study aimed to elucidate the potential regulatory mechanism of mung bean peptides (MBPs) on glucolipid metabolism in insulin-resistant mice induced by high-fat diet (HFD) using untargeted serum metabolomics, enzyme linked immunosorbent assay (ELISA), intraperitoneal injection glucose tolerance test (IPGTT), insulin tolerance test (IPITT), and hematoxylin-eosin staining (H&E). The regulatory effect of MBPs for alleviating insulin resistance was studied by measuring body weight, fasting blood glucose (FBG) and serum insulin levels, C-Peptide levels, inflammatory and antioxidant factors, and histopathological observation of C57BL/6 mice. The experimental results showed that dietary intervention with MBPs (245 mg/kg/d) for 5 weeks significantly relieved insulin resistance in HFD mice. The body weight, insulin resistance index, and the levels of FBG, C-Peptide, IL-6, TNF-α, and MDA in the serum of HFD mice significantly decreased (P < 0.05). Conversely, SOD content and pancreatic β cell function index significantly increased (P < 0.05), and the damaged pancreatic tissue was repaired. One biomarker associated with insulin resistance was glycine. In addition, there were four important differential metabolites: pyroglutamate, D-glutamine, aminoadipic acid, and nicotinamide, involved in 12 metabolic pathway changes. It was found that MBPs may regulate amino acid, glycerol phospholipid, fatty acid, alkaloid, and nicotinamide metabolism to regulate the metabolic profile of HFD mice in a beneficial direction.

Patterns of Alpha-Linolenic Acid Incorporation into Phospholipids in H4IIE Cells

Alpha linolenic acid (ALA) is an 18-carbon essential fatty acid found in plant-based foods and oils. While much attention has been placed on conversion of ALA to long chain polyunsaturated fatty acids, alternative routes of ALA metabolism exist and may lead to formation of other bioactive metabolites of ALA. The current study employed a non-targeted metabolomics approach to profile ALA metabolites that are significantly upregulated by ALA treatment. H4IIE hepatoma cells (n=3 samples per time point) were treated with 60 μM ALA or vehicle for 0, 0.25, 0.5, 1, 2, 3, 4, 6, 8, and 12 hours. Samples were then extracted with methanol and analyzed using high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry. We observed selective changes in ALA incorporation into phospholipid classes and subclasses over the 12 hours following ALA treatment. While levels of specific molecular species of ALA-containing phosphatidylcholine, phosphatidylethanolamine, phosphatidylserine, and lysophospholipids were elevated with ALA treatment, others were not affected. Of the phospholipids that were increased, some [e.g. PC(18:3/18:1), PC(18:3/18:4), PE(18:3/18:2), PE(18:3/18:3)] were elevated almost immediately after exposure to ALA, while others (e.g. PE(18:1/18:3) PA(18:3/22:6), and PA(18:3/18:2)] were not elevated until several hours after ALA treatment. Overall, these results suggest that ALA incorporation into phospholipids is selective and support a metabolic hierarchy for ALA incorporation into specific phospholipids. Given the functionality of phospholipids based on their fatty acid composition, future studies will need to investigate the implications of ALA incorporation into specific phospholipids on cell function.

Analyses of serum and urinary metabolites in individuals with peripheral artery disease (PAD) consuming a bean-rich diet: Relationships with drug metabolites

Peripheral artery disease (PAD) has high morbidity and mortality rates. A metabolomics approach was employed to determine whether consumption of bean-rich diets for 8 weeks would impact the metabolomic profile of PAD individuals. Serum and urine, collected from 54 participants with clinical PAD at baseline and after 8 weeks on 0.3 cups beans/d (n=19), 0.6 cups beans/d (n= 20), or control (n=23) diet, and the beans were extracted and analyzed using LC-QTOF-MS. As a result, PGE2 p-acetamidophenyl ester, PGF2α diethyl amide and 5-L-glutamyl-L-alanine were significantly changed in the serum or urine of bean groups compared to control. Significant changes (P<0.05) in the profile and/or levels of 22 flavonoids present in bean extracts showed the potential importance of the mixture of beans used in this study. In a subset of participants taking metoprolol, after 8 weeks the bean-rich diets significantly elevated metoprolol in the serum while reducing it in urine compared to baseline. In addition, the diets significantly enhanced the urinary excretion of metformin. In conclusion, several biochemical pathways including prostaglandins and glutathione were affected by bean consumption. Significant changes in the metabolism of metoprolol and metformin with bean consumption suggested the presence of diet-drug interactions that may require adjustment of the prescribed dose. ClinicalTrials.gov Identifier: NCT01382056 Novelty: • Bean consumption by people with PAD alters the levels of certain metabolites in serum and urine • Different bean types (black, red kidney, pinto, navy) have unique flavonoid profiles • Metabolomics revealed potential diet-dug interactions as serum and/or urinary levels of metoprolol and metformin are modified by bean consumption.