Comprehensive plasma steroidomics reveals subtle alterations of systemic steroid profile in patients at different stages of prostate cancer disease

The steroid submetabolome, or steroidome, is of particular interest in prostate cancer (PCa) as the dependence of PCa growth on androgens is well known and has been routinely exploited in treatment for decades. Nevertheless, the community is still far from a comprehensive understanding of steroid involvement in PCa both at the tissue and at systemic level. In this study we used liquid chromatography/high resolution mass spectrometry (LC/HRMS) backed by a dynamic retention time database DynaSTI to obtain a readout on circulating steroids in a cohort reflecting a progression of the PCa. Hence, 60 relevant compounds were annotated in the resulting LC/HRMS data, including 22 unknown steroid isomers therein. Principal component analysis revealed only subtle alterations of the systemic steroidome in the study groups. Next, a supervised approach allowed for a differentiation between the healthy state and any of the stages of the disease. Subsequent clustering of steroid metabolites revealed two groups responsible for this outcome: one consisted primarily of the androgens, whereas another contained corticosterone and its metabolites. The androgen data supported the currently established involvement of a hypothalamic-pituitary–gonadal axis in the development of PCa, whereas biological role of corticosterone remained elusive. On top of that, current results suggested a need for improvement in the dynamic range of the analytical methods to better understand the role of low abundant steroids, as the analysis revealed an involvement of estrogen metabolites. In particular, 2-hydroxyestradiol-3-methylether, one of the compounds present in the disease phenotype, was annotated and reported for the first time in men

Characterization and Detection of Erythropoietin Fc Fusion Proteins Using Liquid Chromatography–Mass Spectrometry

Erythropoietin Fc (EPO-Fc) fusion proteins are potential drug candidates that have been designed for the treatment of anemia in humans by stimulating erythrocyte production. Such compounds can be considered performance-enhancing agents that may be used by athletes in endurance sports. This study describes the primary structure of commercially available EPO-Fc based on comprehensive liquid chromatography coupled with mass spectrometry (LC–MS) analysis. A bottom-up approach and the intact molecular weight (MW) measurement of deglycosylated protein and its IdeS proteolytic fractions was used to determine the amino acid sequence of EPO-Fc. Using multiple proteases, peptides covering unknown fusion breakpoints (spacer peptides) were identified. We demonstrated that “spacer peptides” can be used in the determination of EPO-Fc fusion proteins in biological samples using common LC–tandem MS methods

Pharmacokinetic Properties of the Novel Synthetic Cannabinoid 5F-APINAC and Its Influence on Metabolites Associated with Neurotransmission in Rabbit Plasma

The strong psychoactive effects of synthetic cannabinoids raise the need for the deeper studying of their neurometabolic effects. The pharmacokinetic properties of 5F-APINAC and its influence on metabolomics profiles associated with neurotransmission were investigated in rabbit plasma. Twelve rabbits divided into three groups received 1-mL 5F-APINAC at 0.1, 1 and 2 mg/kg. The intervention groups were compared with the controls. Sampling was performed at nine time points (0–24 h). Ultra-high-performance liquid chromatography–tandem mass spectrometry was used. The pharmacokinetics were dose-dependent (higher curve at a higher dose) with a rapid biotransformation, followed by gradual elimination within 24 h. The tryptophan concentrations abruptly decreased (p < 0.05) in all tested groups, returning to the basal levels after 6 h. 5-hydroxylindole acetic acid increased (p < 0.05) in the controls, but this trend was absent in the treated groups. The aspartic acid concentrations were elevated (p < 0.001) in the treated groups. L-kynurenine was elevated (p < 0.01) in the intervention groups receiving 1 mg/kg to 2 mg/kg. Dose-dependent elevations (p < 0.01) were found for kynurenic acid, xanthurenic acid and quinolinic acid (p < 0.01), whereas the anthranilic acid trends were decreased (p < 0.01). The indole-3-propionic acid and indole-3-carboxaldehyde trends were elevated (p < 0.05), whereas the indole-3-lactic acid trajectories were decreased (p < 0.01) in the intervention groups. 5F-APINAC administration had a rapid biotransformation and gradual elimination. The metabolites related to the kynurenine and serotonergic system/serotonin pathways, aspartic acid innervation system and microbial tryptophan catabolism were altered

Misdiagnosis of diabetic foot ulcer in patients with undiagnosed skin malignancies

A growing number of studies report dermal malignancies mimicking diabetic foot ulcers (DFUs). We reviewed clinical cases reporting malignant tumours misdiagnosed to be DFU aiming to identify factors contributing to misdiagnosis. We systematically searched in PubMed for clinical cases reporting on misdiagnosis of DFU in patients with cancer. A chi-square analysis was conducted to show the link between the incidence of initial DFU misdiagnosis and patient age, gender and wound duration. Lesions misdiagnosed to be DFU were subsequently diagnosed as melanoma (68% of the cases), Kaposi's sarcoma (14%), squamous cell carcinoma (11%), mantle cell lymphoma, and diffuse B-cell lymphoma (both by 4%). Older age (≥65 years) was associated with a significantly increased risk of malignancy masked as DFU (OR: 2.452; 95% CI: 1.132 to 5.312; P value = .019). The risk of such suspicion in older patients (age ≥ 65 years) was 145% higher than in younger patients (age < 65 years). Clinicians should maintain a high level of awareness towards potentially malignant foot lesions in elderly patients with diabetes (age ≥ 65)

Alkylresorcinols as New Modulators of the Metabolic Activity of the Gut Microbiota

Alkylresorcinols (ARs) are polyphenolic compounds with a wide spectrum of biological activities and are potentially involved in the regulation of host metabolism. The present study aims to establish whether ARs can be produced by the human gut microbiota and to evaluate alterations in content in stool samples as well as metabolic activity of the gut microbiota of C57BL, db/db, and LDLR (−/−) mice according to diet specifications and olivetol (5-n-pentylresorcinol) supplementation to estimate the regulatory potential of ARs. Gas chromatography with mass spectrometric detection was used to quantitatively analyse AR levels in mouse stool samples; faecal microbiota transplantation (FMT) from human donors to germ-free mice was performed to determine whether the intestinal microbiota could produce AR molecules; metagenome sequencing analysis of the mouse gut microbiota followed by reconstruction of its metabolic activity was performed to investigate olivetol’s regulatory potential. A significant increase in the amounts of individual members of AR homologues in stool samples was revealed 14 days after FMT. Supplementation of 5-n-Pentylresorcinol to a regular diet influences the amounts of several ARs in the stool of C57BL/6 and LDLR (−/−) but not db/db mice, and caused a significant change in the predicted metabolic activity of the intestinal microbiota of C57BL/6 and LDLR (−/−) but not db/db mice. For the first time, we have shown that several ARs can be produced by the intestinal microbiota. Taking into account the dependence of AR levels in the gut on olivetol supplementation and microbiota metabolic activity, AR can be assumed to be potential quorum-sensing molecules, which also influence gut microbiota composition and host metabolism

Targeted metabolomic profiling as a tool for diagnostics of patients with non-small-cell lung cancer

Abstract Lung cancer is referred to as the second most common cancer worldwide and is mainly associated with complex diagnostics and the absence of personalized therapy. Metabolomics may provide significant insights into the improvement of lung cancer diagnostics through identification of the specific biomarkers or biomarker panels that characterize the pathological state of the patient. We performed targeted metabolomic profiling of plasma samples from individuals with non-small cell lung cancer (NSLC, n = 100) and individuals without any cancer or chronic pathologies (n = 100) to identify the relationship between plasma endogenous metabolites and NSLC by means of modern comprehensive bioinformatics tools, including univariate analysis, multivariate analysis, partial correlation network analysis and machine learning. Through the comparison of metabolomic profiles of patients with NSCLC and noncancer individuals, we identified significant alterations in the concentration levels of metabolites mainly related to tryptophan metabolism, the TCA cycle, the urea cycle and lipid metabolism. Additionally, partial correlation network analysis revealed new ratios of the metabolites that significantly distinguished the considered groups of participants. Using the identified significantly altered metabolites and their ratios, we developed a machine learning classification model with an ROC AUC value equal to 0.96. The developed machine learning lung cancer model may serve as a prototype of the approach for the in-time diagnostics of lung cancer that in the future may be introduced in routine clinical use. Overall, we have demonstrated that the combination of metabolomics and up-to-date bioinformatics can be used as a potential tool for proper diagnostics of patients with NSCLC

Target Metabolome Profiling-Based Machine Learning as a Diagnostic Approach for Cardiovascular Diseases in Adults

Metabolomics is a promising technology for the application of translational medicine to cardiovascular risk. Here, we applied a liquid chromatography/tandem mass spectrometry approach to explore the associations between plasma concentrations of amino acids, methylarginines, acylcarnitines, and tryptophan catabolism metabolites and cardiometabolic risk factors in patients diagnosed with arterial hypertension (HTA) (n = 61), coronary artery disease (CAD) (n = 48), and non-cardiovascular disease (CVD) individuals (n = 27). In total, almost all significantly different acylcarnitines, amino acids, methylarginines, and intermediates of the kynurenic and indolic tryptophan conversion pathways presented increased (p< 0.05) in concentration levels during the progression of CVD, indicating an association of inflammation, mitochondrial imbalance, and oxidative stress with early stages of CVD. Additionally, the random forest algorithm was found to have the highest prediction power in multiclass and binary classification patients with CAD, HTA, and non-CVD individuals and globally between CVD and non-CVD individuals (accuracy equal to 0.80 and 0.91, respectively). Thus, the present study provided a complex approach for the risk stratification of patients with CAD, patients with HTA, and non-CVD individuals using targeted metabolomics profiling

Alkylresorcinols as a New Type of Gut Microbiota Regulators Influencing Immune Therapy Efficiency in Lung Cancer Treatment

Background. Alkylresorcinols (ARs) are polyphenolic compounds of microbial origin with a wide spectrum of biological activities and are potentially involved in host immune functioning. The present study is aimed at evaluating alterations in AR content in blood serum and faeces from healthy donors and patients with lung cancer in connection with response to immune checkpoint inhibitor (ICI) therapy to estimate the regulatory potential of AR. Methods. Quantitative analysis of AR levels, as well as other microbial metabolites in blood serum and faeces, was performed using gas chromatography with mass spectrometric detection; estimation of lymphocyte subsets was performed by flow cytometry; faecal microbiota transplantation (FMT) from lung cancer patients after ICI therapy to germ-free mice was performed to explore whether the intestinal microbiota could produce AR molecules. Results. AR concentrations in both faeces and serum differ dramatically between healthy and lung cancer donors. The significant increase in AR concentrations in mouse faeces after FMT points to the microbial origin of ARs. For several ARs, there were strong positive and negative correlations in both faeces and serum with immune cells and these interrelationships differed between the therapy-responsive and nonresponsive groups. Conclusions. The content of ARs may influence the response to ICI therapy in lung cancer patients. ARs may be considered regulatory molecules that determine the functioning of antitumor immunity

Metabolomics of head and neck cancer in biofluids: an integrative systematic review

Introduction: head and neck cancer (HNC) is the fifth most common cancer globally. Diagnosis at early stages are critical to reduce mortality and improve functional and esthetic outcomes associated with HNC. Metabolomics is a promising approach for discovery of biomarkers and metabolic pathways for risk assessment and early detection of HNC.Objectives: to summarize and consolidate the available evidence on metabolomics and HNC in plasma/serum, saliva, and urine.Methods: a systematic search of experimental research was executed using PubMed and Web of Science. Available data on areas under the curve was extracted. Metabolic pathway enrichment analysis were performed to identify metabolic pathways altered in HNC. Fifty-four studies were eligible for data extraction (33 performed in plasma/serum, 15 in saliva and 6 in urine).Results: metabolites with high discriminatory performance for detection of HNC included single metabolites and combination panels of several lysoPCs, pyroglutamate, glutamic acid, glucose, tartronic acid, arachidonic acid, norvaline, linoleic acid, propionate, acetone, acetate, choline, glutamate and others. The glucose-alanine cycle and the urea cycle were the most altered pathways in HNC, among other pathways (i.e. gluconeogenesis, glycine and serine metabolism, alanine metabolism, etc.). Specific metabolites that can potentially serve as complementary less- or non-invasive biomarkers, as well as metabolic pathways integrating the data from the available studies, are presented.Conclusion: the present work highlights utility of metabolite-based biomarkers for risk assessment, early detection, and prognostication of HNC, as well as facilitates incorporation of available metabolomics studies into multi-omics data integration and big data analytics for personalized health