Metabolomic profiling of cancer-related fatigue involved in cachexia and chemotherapy.

Patients with advanced cancer are frequently burdened with a severe sensation of fatigue called cancer-related fatigue (CRF). CRF is induced at various stages and treatments, such as cachexia and chemotherapy, and reduces the overall survival of patients. Objective and quantitative assessment of CRF could contribute to the diagnosis and prediction of treatment efficacy. However, such studies have not been intensively performed, particularly regarding metabolic profiles. Here, we conducted plasma metabolomics of 15 patients with urological cancer. The patients with and without fatigue, including those with cachexia or chemotherapy-induced fatigue, were compared. Significantly lower concentrations of valine and tryptophan were observed in fatigued patients than in non-fatigued patients. In addition, significantly higher concentrations of polyamine pathway metabolites were observed in patients with fatigue and cachexia than in those without cachexia. Patients with exacerbated fatigue due to chemotherapy showed significantly decreased cysteine and methionine metabolism before chemotherapy compared with those without fatigue exacerbation. These findings suggest that plasma metabolic profiles could help improve the diagnosis and monitoring of CRF.

Targeted Metabolomic Profiling of Plasma Samples in Gastric Cancer by Liquid Chromatography-Mass Spectrometry.

INTRODUCTION: As the high mortality rate of gastric cancer (GC) is due to delayed diagnosis, early detection is vital for improved patient outcomes. Metabolic deregulation plays an important role in GC. Although various metabolite-level biomarkers for early detection have been assessed, there is still no unified early detection method. We conducted a plasma metabolome study to assess metabolites that may distinguish GC samples from non-GC samples. METHODS: Blood samples were collected from 72 GC patients and 29 control participants (non-GC group) at the Tokyo Medical University Hospital between March 2020 and November 2020. Hydrophilic metabolites were identified and quantified using liquid chromatography-time-of-flight mass spectrometry. Differences in metabolite concentrations between the GC and non-GC groups were evaluated using the Mann-Whitney test. The discrimination ability of each metabolite was evaluated by the area under the receiver operating characteristic curve. A radial basis function (RBF) kernel-based support vector machine (SVM) model was developed to assess the discrimination ability of multiple metabolites. The selection of variables used for the SVM utilized a step-wise regression method. RESULTS: Of the 96 quantified metabolites, 8 were significantly different between the GC and non-GC groups. Of these, N1-acetylspermine, succinate, and histidine were used in the RBF-SVM model to discriminate GC samples from non-GC samples. The area under the curve (AUC) of the RBF-SVM model was higher (0.915; 95% CI: 0.865-0.965, p < 0.0001), indicating good performance of the RBF-SVM model. The application of this RBF-SVM to the validation dataset resulted from the AUC of the RBF-SVM model was (0.885; 95% CI: 0.797-0.973, p < 0.0001), indicating the good performance of the RBF-SVM model. The sensitivity of the RBF-SVM model was better (69.0%) than those of the common tumor markers carcinoembryonic antigen (CEA) (10.5%) and carbohydrate antigen 19-9 (CA19-9) (2.86%). The RBF-SVM showed a low correlation with CEA and CA19-9, indicating its independence. CONCLUSION: We analyzed plasma metabolomics, and a combination of the quantified metabolites showed high sensitivity for the detection of GC. The independence of the RBF-SVM from tumor markers suggested that their complementary use would be helpful for GC screening.

Metabolomics Analysis of Blood, Urine, and Saliva Samples Based on Capillary Electrophoresis-Mass Spectrometry.

Capillary electrophoresis-mass spectrometry (CE-MS) is an ideal method for analyzing various metabolites in biological samples. CE-MS can simultaneously identify and quantify hundreds of charged metabolites using only two acquisition methods for positively and negatively charged metabolites. Furthermore, CE-MS is commonly used for analyzing biological samples to understand the pathology of diseases at the metabolic level and biofluid samples, such as blood and urine, to explore biomarkers. Here, we introduce a protocol that delineates the handling of clinical samples to ensure that the CE-MS analysis yields reproducible quantified data. We have focused on sample collection, storage, processing, and measurement. Although the implementation of rigorous standard operating protocols is preferred for enhancing the quality of the samples, various limitations in an actual clinical setting make it difficult to adhere to strict rules. Therefore, the effect of each process on the quantified metabolites needs to be evaluated to design a protocol with acceptable tolerances. Furthermore, quality controls and assessments to handle clinical samples are introduced.

Data Processing and Analysis in Liquid Chromatography-Mass Spectrometry-Based Targeted Metabolomics.

Mass spectrometry (MS)-based metabolomics provides high-dimensional datasets; that is, the data include various metabolite features. Data analysis begins by converting the raw data obtained from the MS to produce a data matrix (metabolite × concentrations). This is followed by several steps, such as peak integration, alignment of multiple data, metabolite identification, and calculation of metabolite concentrations. Each step yields the analytical results and the accompanying information used for the quality assessment of the anterior steps. Thus, the measurement quality can be analyzed through data processing. Here, we introduce a typical data processing procedure and describe a method to utilize the intermediate data as quality control. Subsequently, commonly used data analysis methods for metabolomics data, such as statistical analyses, are also introduced.

Long-Term Mastication Changed Salivary Metabolomic Profiles.

Saliva is an ideal biofluid for monitoring oral and systemic health. Repeated mastication is a typical physical stimulus that improves salivary flow and oral hygiene. Recent metabolomic studies have shown the potential of salivary metabolomic components for various disease monitoring systems. Here, we evaluated the effect of long-term mastication on salivary metabolomic profiles. Young women with good oral hygiene (20.8 ± 0.3 years, n = 17) participated. They were prohibited from chewing gum during control periods (4 weeks each) and were instructed to chew a piece of gum base seven times a day for 10 min each time during the intervention period. Paired samples of unstimulated whole saliva collected on the last day of the control and intervention period were compared. Liquid chromatography−time-of-flight mass spectrometry successfully quantified 85 metabolites, of which 41 showed significant differences (p < 0.05, Wilcoxon paired test corrected by false discovery rate). Except for a few metabolites, such as citrate, most metabolites showed lower concentrations after the intervention. The pathways related to glycogenic amino acids, such as alanine, arginine, and glutamine, altered considerably. This study suggests that long-term mastication induces unstimulated salivary component-level changes.

Shotgun proteomic investigation of methyltransferase and methylation profiles in lipopolysaccharide stimulated RAW264.7 murine macrophages.

Arginine methylation is a common post-translational modification which functions as an epigenetic regulator of transcription and plays a key role in various cell signaling pathways. The methylation of arginine residues is catalyzed by protein arginine methyltransferase (PRMT). However, the expression pattern and underlying mechanism of PRMTs and protein methylation profile in lipopolysaccharide (LPS)-induced innate immune responses are poorly understood. Using a shotgun proteomic approach, we found that LPS stimulation increased arginine and proline metabolism and responses to inflammation and bacterial infections. In comparison, cysteine and methionine metabolism, the pentose phosphate pathway, purine metabolism, and protein methylation factors were also decreased in LPS stimulated murine macrophage cell lines. We revealed that LPS stimulation downregulated PRMT1, PRMT5, and protein arginine methylation profiles in RAW264.7 cells using western blot analysis. Additionally, this phenomenon occurred in parallel with nitric oxide accumulation in LPS-induced macrophages. Using inflammation models, we demonstrate for the first time that LPS stimulation decreases PRMTs, leading to the decreasing of arginine methylation in macrophages.

Biomarkers for Alzheimer's Disease in the Current State: A Narrative Review.

Alzheimer's disease (AD) has become a problem, owing to its high prevalence in an aging society with no treatment available after onset. However, early diagnosis is essential for preventive intervention to delay disease onset due to its slow progression. The current AD diagnostic methods are typically invasive and expensive, limiting their potential for widespread use. Thus, the development of biomarkers in available biofluids, such as blood, urine, and saliva, which enables low or non-invasive, reasonable, and objective evaluation of AD status, is an urgent task. Here, we reviewed studies that examined biomarker candidates for the early detection of AD. Some of the candidates showed potential biomarkers, but further validation studies are needed. We also reviewed studies for non-invasive biomarkers of AD. Given the complexity of the AD continuum, multiple biomarkers with machine-learning-classification methods have been recently used to enhance diagnostic accuracy and characterize individual AD phenotypes. Artificial intelligence and new body fluid-based biomarkers, in combination with other risk factors, will provide a novel solution that may revolutionize the early diagnosis of AD.

Iron deficiency negatively regulates protein methylation via the downregulation of protein arginine methyltransferase.

Iron is an essential trace metal for all biological processes and plays a role in almost every aspect of body growth. Previously, we found that iron-depletion downregulated the expression of proteins, arginine methyltransferase-1 and 3 (PRMT1 and PRMT3), by an iron-specific chelator, deferoxamine (DFO), in rat liver FAO cell line using DNA microarray analysis (unpublished data). However, regulatory mechanisms underlying the association between iron deficiency and PRMT expression are unclear in vitro and in vivo. In the present study, we revealed that the treatment of cells with two iron-specific chelators, DFO and deferasirox (DFX), downregulated the gene and protein expression of PRMT1 and 3 as compared with the untreated cells. Subsequently, DFO and DFX treatments decreased protein methylation. Importantly, these effects were attenuated by a holo-transferrin treatment. Furthermore, weanling Wistar-strain rats were fed a control diet or an iron-deficient diet for 4 weeks. Dietary iron deficiency was found to decrease the concentration of hemoglobin and liver iron while increasing the heart weight. PRMT and protein methylation levels were also significantly reduced in the iron-deficient group as compared to the control group. To our knowledge, this is the first study to demonstrate that PRMT levels and protein methylation are reduced in iron-deficient models, in vitro and in vivo.

Global metabolic reprogramming of colorectal cancer occurs at adenoma stage and is induced by MYC.

Cancer cells alter their metabolism for the production of precursors of macromolecules. However, the control mechanisms underlying this reprogramming are poorly understood. Here we show that metabolic reprogramming of colorectal cancer is caused chiefly by aberrant MYC expression. Multiomics-based analyses of paired normal and tumor tissues from 275 patients with colorectal cancer revealed that metabolic alterations occur at the adenoma stage of carcinogenesis, in a manner not associated with specific gene mutations involved in colorectal carcinogenesis. MYC expression induced at least 215 metabolic reactions by changing the expression levels of 121 metabolic genes and 39 transporter genes. Further, MYC negatively regulated the expression of genes involved in mitochondrial biogenesis and maintenance but positively regulated genes involved in DNA and histone methylation. Knockdown of MYC in colorectal cancer cells reset the altered metabolism and suppressed cell growth. Moreover, inhibition of MYC target pyrimidine synthesis genes such as CAD, UMPS, and CTPS blocked cell growth, and thus are potential targets for colorectal cancer therapy.

Metabolic abnormalities induced by mitochondrial dysfunction in skeletal muscle of the renal carcinoma Eker (TSC2+/-) rat model.

Tuberous sclerosis complex 2 (TSC2) is a mediator of insulin signal transduction, and a loss of function in TSC2 induces hyperactivation of mTORC1 pathway, which leads to tumorigenesis. We have previously demonstrated that Eker rat model, which is heterozygous for a TSC2 mutation, exhibits hyperglycemia and hyperketonemia. The present study was to investigate whether these changes also can affect metabolism in skeletal muscle of the Eker rat. Wild-type (TSC2+/+) and Eker (TSC2+/-) rats underwent an oral glucose tolerance test, and the latter showed decrease in whole-body glucose utilization. Additionally, reductions in the expression of glycolysis-, lipolysis-, and ketone body-related genes in skeletal muscle were observed in Eker rats. Furthermore, ATP content and mitochondrial DNA copy number were lower in skeletal muscle of Eker rats. These data demonstrate that heterozygous to mutation TSC2 not only affects the liver metabolism, but also skeletal muscle metabolism, via mitochondrial dysfunction.

The tuberous sclerosis complex model Eker (TSC2+/-) rat exhibits hyperglycemia and hyperketonemia due to decreased glycolysis in the liver.

Tuberous sclerosis complex (TSC) presents as benign tumors that affect the brain, kidneys, lungs and skin. The inactivation of TSC2 gene, through loss of heterozygosity is responsible for tumor development in TSC. Since TSC patients are carriers of heterozygous a TSC2; mutation, to reveal the risk factors which these patients carry prior to tumor development is important. In this experiment, Eker rat which carry a mutation in this TSC2 gene were analyzed for their metabolic changes. Wild-type (TSC2+/+) and heterozygous mutant TSC2 (TSC2+/-) Eker rats were raised for 100 days. As a result, the Eker rats were found to exhibit hyperglycemia and hyperketonemia. However the high ketone body production in the liver was observed without accompanying increased levels of plasma free fatty acids or insulin. Further, production of the ketone body ß-hydroxybutyrate was inhibited due to the low NADH/NAD(+) ratio resulting from the restraint on glycolysis, which was followed by inhibition of the malate-aspartate shuttle and TCA cycle. Therefore, we conclude that glycolysis is restrained in the livers of TSC2 heterozygous mutant rats, and these defects lead to abnormal production of acetoacetate.