Analysis of low-molecular-weight metabolites in stomach cancer cells by a simplified and inexpensive GC/MS metabolomics method

GC/MS-Based Metabolomic Analysis of A549 Cells Exposed to Emerging Organophosphate Flame Retardants

Emerging organophosphate flame retardants (eOPFRs) have attracted attention in recent times and are expected to gain extensive usage in the coming years. However, they may have adverse effects on organisms. Due to their novel nature, there are few relevant articles dealing with toxicological studies of the above eOPFRs, especially their information on the perturbation of cellular metabolism, which is, thus far, marginally understood. Our research initially assessed the cytotoxicity of eOPFRs, which include compounds like cresyl diphenyl phosphate (CDP), resorcinol bis(diphenyl phosphate) (RDP), triallyl phosphate (TAP), and pentaerythritol phosphate alcohol (PEPA). This evaluation was conducted using the methyl thiazolyl tetrazolium (MTT) assay. Subsequently, we utilized a gas chromatography/mass spectrometry (GC/MS)-based metabolomic approach to investigate the metabolic disruptions induced by these four eOPFRs in A549 cells. The MTT results showed that, at high concentrations of 1 mM, their cytotoxicity was ranked as CDP > TAP > RDP > PEPA. In addition, metabolic studies at low concentrations of 10 μM showed that the metabolic interference of CDP, TAP, and PEPA focuses on oxidative stress, amino acid metabolism, and energy metabolism, while RDP mainly affects energy metabolism-galactose metabolism and gluconeogenesis. Therefore, from the perspective of cytotoxicity and metabolic analysis, RDP may be a more promising alternative. Our experiments provide important insights into the possible metabolic effects of potential toxic substances and complement the evidence on the human health risks of eOPFRs.

Advances in mass spectrometry imaging for spatial cancer metabolomics

Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in-depth understanding of cancer-associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three-dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI-based cancer studies. The adoption of MSI in clinical research and for single-cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context.

Metabolomics study based on GC-MS reveals a protective function of luteolin against glutamate-induced PC12 cell injury

Oxidative stress response is closely related to neurodegenerative diseases. This study aimed to investigate the cytoprotective effects of luteolin on glutamate-induced oxidative stress injury in PC12 cells. GC-MS combined with multivariate statistical approaches was used to perform metabolomics studies to assess the possible mechanisms. Our results identified 23 metabolites as differential expressed metabolites in the glutamate group, including cysteine content in cells that decreased drastically. This suggests that glutathione synthesis, which balances the redox state of cells, was affected. Luteolin inhibits the reduction in viability in glutamate-induced PC12 cells and regulates 13 differential expressed metabolites in glutamate-induced cell damage. These metabolites associated with luteolin included glycine, serine, and threonine metabolism; glyoxylate and dicarboxylate metabolism; aminoacyl-tRNA biosynthesis; cysteine and methionine metabolism; inositol phosphate metabolism; and starch and sucrose metabolism. In summary, the systemic antioxidant capacity of luteolin in PC12 cells is related to its regulation of amino acid, glucose, and nucleotide metabolism pathways.

Recent Advances in Mass Spectrometry-Based Structural Elucidation Techniques

Mass spectrometry (MS) has become the central technique that is extensively used for the analysis of molecular structures of unknown compounds in the gas phase. It manipulates the molecules by converting them into ions using various ionization sources. With high-resolution MS, accurate molecular weights (MW) of the intact molecular ions can be measured so that they can be assigned a molecular formula with high confidence. Furthermore, the application of tandem MS has enabled detailed structural characterization by breaking the intact molecular ions and protonated or deprotonated molecules into key fragment ions. This approach is not only used for the structural elucidation of small molecules (MW < 2000 Da), but also crucial biopolymers such as proteins and polypeptides; therefore, MS has been extensively used in multiomics studies for revealing the structures and functions of important biomolecules and their interactions with each other. The high sensitivity of MS has enabled the analysis of low-level analytes in complex matrices. It is also a versatile technique that can be coupled with separation techniques, including chromatography and ion mobility, and many other analytical instruments such as NMR. In this review, we aim to focus on the technical advances of MS-based structural elucidation methods over the past five years, and provide an overview of their applications in complex mixture analysis. We hope this review can be of interest for a wide range of audiences who may not have extensive experience in MS-based techniques.

Development of a prognostic metabolic signature in stomach adenocarcinoma

PURPOSE

The growth and aggressiveness of Stomach adenocarcinoma (STAD) is significantly affected by basic metabolic changes. This study aimed to identify metabolic gene prognostic signatures in STAD.

METHODS

An integrative analysis of datasets from the Cancer Genome Atlas and Gene Expression Omnibus was performed. A metabolic gene prognostic signature was developed using univariable Cox regression and Kaplan-Meier survival analysis. A nomogram model was developed to predict the prognosis of STAD patients. Finally, Gene Set Enrichment Analysis (GESA) was used to explore the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways significantly associated with the risk grouping.

RESULTS

A total of 327 metabolism-related differentially expressed genes were identified. Three subtypes of STAD were identified and nine immune cell types, including memory B cell, resting and activated CD4+ memory T cells, were significantly different among the three subgroups. A risk score model including nine survival-related genes which could separate high-risk patients from low-risk patients was developed. The prognosis of STAD patients likely benefited from lower expression levels of genes, including ABCG4, ABCA6, GPX8, KYNU, ST8SIA5, and CYP19A1. Age, radiation therapy, tumor recurrence, and risk score model status were found to be independent risk factors for STAD and were used for developing a nomogram. Nine KEGG pathways, including spliceosome, pentose phosphate pathway, and citrate TCA cycle were significantly enriched in GESA.

CONCLUSION

We propose a metabolic gene signature and a nomogram for STAD which might be used for predicting the survival of STAD patients and exploring prognostic markers.

A metabolomics strategy to identify potential biomarkers associated with human laryngeal cancer based on dried blood spot mass spectrometry approach

ABSTRACT

Laryngeal cancer (LC) as one of common malignant tumors in the head and neck region accounted for 1% to 5% of new cancer cases and was ranked as the third otolaryngology cancer. However, some patients with LC were diagnosed at the advanced stage, which can cause delayed diagnosis and treatment. It is an urgent task to seek effective biomarkers for the early diagnosis of LC aimed at alleviating suffering.A combination of dried blood spot sampling and direct infusion mass spectrometry technology was applied to 39 patients with LC and 53 healthy individuals. Multiple algorithms towards 93 metabolites including amino acids and carnitine/acylcarnitines were run for selecting differential metabolites. Furthermore, leave-one-out cross-validation method was used to evaluate diagnostic performance of selected metabolite biomarkers.A biomarker panel consisting of arginine, proline, hexacosanoic carnitine, ornithine /citrulline, and 3-hydroxy-octadecenoylcarnitine exhibited potential to distinguish patients with LC from healthy individuals, with a sensitivity of 0.8974 and a specificity of 0.8302 in leave-one-out cross-validation model.The metabolomic analysis of LC patients is beneficial to screen disease-associated biomarkers and develop new diagnostic approaches.

Quantification of carbohydrates in human serum using gas chromatography–mass spectrometry with the stable isotope-labeled internal standard method

Comparison of two derivatization approaches (silylation and acylation) for carbohydrate separation based on optimizing reaction conditions by artificial neural networks.

Serum metabolomic profiling based on GC/MS helped to discriminate Diffuse Large B-cell Lymphoma patients with different prognosis

BACKGROUND

The varied clinical outcomes of patients with Diffuse Large B Cell Lymphoma (DLBCL) are attributed to the different genetic and phenotypic subtypes. The purpose of this study was to determine whether metabolic alterations were related to cell-of-origin subtypes of DLBCL and find some metabolites which are associated with the clinical outcomes.

METHODS

Pre-treatment serum samples from eighty (80) newly diagnosed DLBCL patients, including twenty-eight (28) patients with Germinal Center B cell-like (GCB) subtypes and fifty-two (52) patients with non-GCB subtypes, were tested by the Gas Chromatography-Mass Spectrometry (GC-MS) technique. Univariate and multivariate analysis methods, principal component analysis (PCA), and partial least square discriminant analysis (PLS-DA) were conducted to examine the potential differential metabolites. Overall survival (OS) was calculated.

RESULTS

Overall, 65 out of 1472 entities were identified for subsequent analysis. Unfortunately, the initial PLS-DA analysis failed to discriminate GCB from non-GCB samples. Intriguingly, further PLS-DA analysis identified two subgroups of DLBCL (named as group A and group B) and the metabolic subgroups were significantly associated with overall survival. Valine, hexadecenoic acid, and pyroglutamic acid were identified and verified as the most important altered metabolites and could be candidate biomarkers for the prognosis of DLBCL.

CONCLUSIONS

Our results demonstrated that metabolic alterations in serum could be helpful to predict different clinical outcomes of DLBCL patients. Further studies are warranted to understand whether the altered metabolites might serve as prognostic factors for DLBCL.

Metabolite profiling of chondrosarcoma cells: A robust GC-MS method for the analysis of endogenous metabolome

Chondrosarcoma is the 2nd most frequent bone sarcoma. In this study, the metabolic profiling of human chondrosarcoma SW-1353 cell line was investigated for the first time. To obtain more precise information about the metabolites from chondrosarcoma cells, pretreatment methods including washing steps/solvents, harvesting conditions, and extraction protocols for chondrosarcoma SW-1353 cells were evaluated in the context of metabolite profiling by GC-MS technique. In addition, a total of 32 representative metabolites (related to amino acid metabolism, TCA cycle, glycolysis, and fatty acid metabolism) were quantitatively determined. We found that a fast water rinse step before metabolic quenching, may reduce the contaminants and improve sensitivity. Trypsin/ethylene diamine tetraacetic acid treatment led to a large amount of metabolite leakage, which was not suitable for metabolomics research. Methanol was selected as a more suitable extraction solvent among four extraction approaches applied to SW-1353 cells. The final protocol can provide a simple, robust, and reproducible method to obtain precise information about the metabolites from chondrosarcoma cells, which is helpful to further understand the chondrosarcoma cell physiology and the mechanism of drug resistance in this disease, from the perspective of metabolomics.