BACKGROUND AND OBJECTIVE: Metabolomic interaction networks provide critical insights into the dynamic relationships between metabolites and their regulatory mechanisms. This study introduces MInfer, a novel computational framework that integrates outputs from MetaboAnalyst, a widely used metabolomic analysis tool, with Jacobian analysis to enhance the derivation and interpretation of these networks. METHODS: MInfer combines the comprehensive data processing capabilities of MetaboAnalyst with the mathematical modeling power of Jacobian analysis. This framework was applied to various metabolomic datasets, employing advanced statistical tests to construct interaction networks and identify key metabolic pathways. RESULTS: The application of MInfer revealed significant metabolic pathways and potential regulatory mechanisms across multiple datasets. The framework demonstrated high precision, sensitivity, and specificity in identifying interactions, enabling robust network interpretations. CONCLUSIONS: MInfer enhances the interpretation of metabolomic data by providing detailed interaction networks and uncovering key regulatory insights. This tool holds significant potential for advancing the study of complex biological systems.

• Klíčová slova
• Dynamic relationships, Metabolite interactions, Metabolomic data analysis, Systems biology,
• MeSH
• algoritmy MeSH
• lidé MeSH
• metabolické sítě a dráhy * MeSH
• metabolomika * MeSH
• software MeSH
• výpočetní biologie MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Modern science takes into account phenotype complexity and establishes approaches to track changes on every possible level. Many "omics" studies have been developed over the last decade. Metabolomic analysis enables dynamic measurement of the metabolic response of a living system to a variety of stimuli or genetic modifications. Important targets of metabolomics is biomarker development and translation to the clinic for personalized diagnosis and a greater understanding of disease pathogenesis. The current review highlights the major aspects of metabolomic analysis and its applications for the identification of relevant predictive, diagnostic and prognostic biomarkers for some ocular diseases including dry eye, keratoconus, retinal diseases, macular degeneration, and glaucoma. To date, possible biomarker candidates for dry eye disease are lipid metabolites and androgens, for keratoconus cytokeratins, urea, citrate cycle, and oxidative stress metabolites. Palmitoylcarnitine, sphingolipids, vitamin D related metabolites, and steroid precursors may be used for distinguishing glaucoma patients from healthy controls. Dysregulation of amino acid and carnitine metabolism is critical in the development and progression of diabetic retinopathy. Further work is needed to discover and validate metabolic biomarkers as a powerful tool for understanding the molecular mechanisms of ocular diseases, to provide knowledge on their etiology and pathophysiology and opportunities for personalized clinical intervention at an early stage.

• Klíčová slova
• biomarkers, metabolites, metabolomics, ocular diseases,
• MeSH
• biologické markery metabolismus   MeSH
• lidé MeSH
• metabolomika metody   MeSH
• oční nemoci diagnóza   patofyziologie   MeSH
• oční proteiny metabolismus   MeSH
• oftalmologie metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH
• Názvy látek
• biologické markery MeSH
• oční proteiny MeSH

The widespread occurrence of cyanobacteria blooms damages the water ecosystem and threatens the safety of potable water and human health. Exogenous L-lysine significantly inhibits the growth of a dominant cyanobacteria Microcystis aeruginosa in freshwater. However, the molecular mechanism of how lysine inhibits the growth of M. aeruginosa is unclear. In this study, both non-target and target metabolomic analysis were performed to investigate the effects of algicide L-lysine. The results showed that 8 mg L- 1 lysine most likely disrupts the metabolism of amino acids, especially the arginine and proline metabolism. According to targeted amino acid metabolomics analysis, only 3 amino acids (L-arginine, ornithine, and citrulline), which belong to the ornithine-ammonia cycle (OAC) in arginine metabolic pathway, showed elevated levels. The intracellular concentrations of ornithine, citrulline, and arginine increased by 115%, 124%, and 19.4%, respectively. These results indicate that L-lysine may affect arginine metabolism and OAC to inhibit the growth of M. aeruginosa.

• Klíčová slova
• Arginine metabolism, L-lysine, Metabolomic analysis, Microcystin, Microcystis aeruginosa, Ornithine-ammonia cycle,
• MeSH
• amoniak MeSH
• arginin chemie   metabolismus   MeSH
• citrulin metabolismus   MeSH
• ekosystém MeSH
• herbicidy * metabolismus   MeSH
• lidé MeSH
• lysin toxicita   metabolismus   MeSH
• Microcystis * metabolismus   MeSH
• mikrocystiny metabolismus   MeSH
• ornithin toxicita   metabolismus   MeSH
• sinice * metabolismus   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• amoniak MeSH
• arginin MeSH
• citrulin MeSH
• herbicidy * MeSH
• lysin MeSH
• mikrocystiny MeSH
• ornithin MeSH

Metabolomics has become an important tool in clinical research and diagnosis of human diseases. In this work we focused on the diagnosis of inherited metabolic disorders (IMDs) in plasma samples using a targeted metabolomic approach. The plasma samples were analyzed with the flow injection analysis method. All the experiments were performed on a QTRAP 5500 tandem mass spectrometer (AB SCIEX, U.S.A.) with electrospray ionization. The compounds were measured in a multiple reaction monitoring mode. We analyzed 50 control samples and 34 samples with defects in amino acid metabolism (phenylketonuria, maple syrup urine disease, tyrosinemia I, argininemia, homocystinuria, carbamoyl phosphate synthetase deficiency, ornithine transcarbamylase deficiency, nonketotic hyperglycinemia), organic acidurias (methylmalonic aciduria, propionic aciduria, glutaric aciduria I, 3-hydroxy-3-methylglutaric aciduria, isovaleric aciduria), and mitochondrial defects (medium-chain acyl-coenzyme A dehydrogenase deficiency, carnitine palmitoyltransferase II deficiency). The controls were distinguished from the patient samples by principal component analysis and hierarchical clustering. Approximately 80% of patients were clearly detected by absolute metabolite concentrations, the sum of variance for first two principle components was in the range of 44-55%. Other patient samples were assigned due to the characteristic ratio of metabolites (the sum of variance for first two principle components 77 and 83%). This study has revealed that targeted metabolomic tools with automated and unsupervised processing can be applied for the diagnosis of various IMDs.

• MeSH
• analýza hlavních komponent MeSH
• dítě MeSH
• dospělí MeSH
• lidé MeSH
• metabolom MeSH
• metabolomika metody   MeSH
• mladiství MeSH
• předškolní dítě MeSH
• průtoková injekční analýza MeSH
• reprodukovatelnost výsledků MeSH
• shluková analýza MeSH
• tandemová hmotnostní spektrometrie MeSH
• vrozené poruchy metabolismu aminokyselin krev   diagnóza   MeSH
• Check Tag
• dítě MeSH
• dospělí MeSH
• lidé MeSH
• mladiství MeSH
• mužské pohlaví MeSH
• předškolní dítě MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Clinical metabolomics aims at finding statistically significant differences in metabolic statuses of patient and control groups with the intention of understanding pathobiochemical processes and identification of clinically useful biomarkers of particular diseases. After the raw measurements are integrated and pre-processed as intensities of chromatographic peaks, the differences between controls and patients are evaluated by both univariate and multivariate statistical methods. The traditional univariate approach relies on t-tests (or their nonparametric alternatives) and the results from multiple testing are misleadingly compared merely by p-values using the so-called volcano plot. This paper proposes a Bayesian counterpart to the widespread univariate analysis, taking into account the compositional character of a metabolome. Since each metabolome is a collection of some small-molecule metabolites in a biological material, the relative structure of metabolomic data, which is inherently contained in ratios between metabolites, is of the main interest. Therefore, a proper choice of logratio coordinates is an essential step for any statistical analysis of such data. In addition, a concept of b-values is introduced together with a Bayesian version of the volcano plot incorporating distance levels of the posterior highest density intervals from zero. The theoretical background of the contribution is illustrated using two data sets containing samples of patients suffering from 3-hydroxy-3-methylglutaryl-CoA lyase deficiency and medium-chain acyl-CoA dehydrogenase deficiency. To evaluate the stability of the proposed method as well as the benefits of the compositional approach, two simulations designed to mimic a loss of samples and a systematical measurement error, respectively, are added.

• Klíčová slova
• Bayesian inference, Compositional data, High-dimensional data, Multiple hypotheses testing, Untargeted metabolomics, Volcano plot,
• MeSH
• acetyl-CoA-C-acetyltransferasa nedostatek   metabolismus   MeSH
• acyl-CoA-dehydrogenasa nedostatek   metabolismus   MeSH
• Bayesova věta * MeSH
• datové soubory jako téma MeSH
• lidé MeSH
• metabolomika * MeSH
• vrozené poruchy metabolismu aminokyselin metabolismus   MeSH
• vrozené poruchy metabolismu tuků metabolismus   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• acetyl-CoA-C-acetyltransferasa MeSH
• acyl-CoA-dehydrogenasa MeSH

BACKGROUND: The search for effective biomarkers for ovarian cancer (OC) early diagnosis is an urgent task of modern oncogynecology. Metabolic profiling by ultra-high performance liquid chromatography and mass spectrometry (UHPLC-MS) provides information on the totality of all low molecular weight metabolites of patient's biological fluids sample, reflecting the processes occurring in the body. The aim of the study was to research blood plasma and urine metabolomic profile of patients with serous ovarian adenocarcinoma by UHPLC-MS. MATERIAL AND METHODS: To perform metabolomic analysis, 60 blood plasma samples and 60 urine samples of patients diagnosed with serous ovarian carcinoma and 20 samples of apparently healthy volunteers were taken. Chromatographic separation was performed on a Vanquish Flex UHPLC System chromatograph (Thermo Scientific, Germany). Mass spectrometric analysis was performed on an Orbitrap Exploris 480 (Thermo Scientific, Germany) equipped with an electrospray ionization source. Bioinformatic analysis was performed using Compound Discoverer Software (Thermo Fisher Scientific, USA), statistical data analysis was performed in the Python programming language using the SciPy library. RESULTS: Using UHPLC-MS, 1,049 metabolites of various classes were identified in blood plasma. In patients with OC, 8 metabolites had a significantly lower concentration (P < 0.01) compared with conditionally healthy donors, while the content of 19 compounds, on the contrary, increased (P < 0.01). During the metabolomic profiling of urine samples, 417 metabolites were identified: 12 compounds had a significantly lower concentration compared to apparently healthy individuals, the content of 14 compounds increased (P < 0.01). In patients with ovary serous adenocarcinoma, a significant change in the metabolome of blood plasma and urine was found, expressed in abnormal concentrations of lipids and their derivatives, fatty acids and their derivatives, acylcarnitines, phospholipids, amino acids and their derivatives, derivatives of nitrogenous bases and steroids. At the same time, kynurenine, myristic acid, lysophosphatidylcholine and L-octanoylcarnitine are the most promising markers of this disease. CONCLUSION: The revealed changes in the metabolome can become the basis for improving approaches to the diagnosis of serous ovarian adenocarcinoma.

• Klíčová slova
• Urine, blood plasma, metabolomic profile, serous ovarian adenocarcinoma, ultra-high performance liquid chromatography and mass spectrometry, urine,
• MeSH
• hmotnostní spektrometrie MeSH
• lidé středního věku MeSH
• lidé MeSH
• metabolom MeSH
• metabolomika * metody   MeSH
• nádorové biomarkery krev   moč   MeSH
• nádory vaječníků * metabolismus   krev   moč   diagnóza   MeSH
• serózní cystadenokarcinom metabolismus   diagnóza   krev   moč   MeSH
• vysokoúčinná kapalinová chromatografie MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• nádorové biomarkery MeSH

INTRODUCTION: Shiga toxin 2a (Stx2a) induces hemolytic uremic syndrome (STEC HUS) by targeting glomerular endothelial cells (GEC). OBJECTIVES: We investigated in a metabolomic analysis the response of a conditionally immortalized, stable glomerular endothelial cell line (ciGEnC) to Stx2a stimulation as a cell culture model for STEC HUS. METHODS: CiGEnC were treated with tumor necrosis factor-(TNF)α, Stx2a or sequentially with TNFα and Stx2a. We performed a metabolomic high-throughput screening by lipid- or gas chromatography and subsequent mass spectrometry. Metabolite fold changes in stimulated ciGEnC compared to untreated cells were calculated. RESULTS: 320 metabolites were identified and investigated. In response to TNFα + Stx2a, there was a predominant increase in intracellular free fatty acids and amino acids. Furthermore, lipid- and protein derived pro-inflammatory mediators, oxidative stress and an augmented intracellular energy turnover were increased in ciGEnC. Levels of most biochemicals related to carbohydrate metabolism remained unchanged. CONCLUSION: Stimulation of ciGEnC with TNFα + Stx2a is associated with profound metabolic changes indicative of increased inflammation, oxidative stress and energy turnover.

• Klíčová slova
• Conditionally immortalized glomerular endothelial cells, Hemolytic uremic syndrome, Metabolomics, Shiga toxin,
• MeSH
• endoteliální buňky cytologie   účinky léků   metabolismus   MeSH
• glomerulus cytologie   MeSH
• kultivované buňky MeSH
• lidé MeSH
• lipopolysacharidy MeSH
• metabolomika * MeSH
• multivariační analýza MeSH
• počet buněk MeSH
• shiga toxin 2 metabolismus   farmakologie   MeSH
• viabilita buněk účinky léků   MeSH
• zánět chemicky indukované   metabolismus   patologie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• lipopolysacharidy MeSH
• shiga toxin 2 MeSH

Urea, as an end product of protein metabolism and an abundant polar compound, significantly complicates the metabolomic analysis of urine by GC-MS. We developed a sample preparation method removing urea from urine samples prior the GC-MS analysis. The method based on urease immobilized on magnetic microparticles was compared with the others that are conventionally used (liquid-liquid extraction, free urease protocol), and samples without any treatment. To study the impact of sample preparation approaches on the quality of analytical data, we employed comprehensive metabolomic analysis (using both GC-MS and LC-MS/MS platforms) of standard material based on human urine. Multivariate statistical analysis has shown that immobilized urease treatment provides similar results to a free urease approach. However, significant alterations in the profiles of metabolites were observed in the samples without any treatment and after the extraction. Compared to other approaches that were tested, the immobilization of urease on microparticles reduces both the number of artifacts and the variability of the metabolites (average CV of extraction 19.7%, no treatment 11.4%, free urease 5.0%, and immobilized urease 2.5%). The method that was developed was applied in a GC-MS metabolomic experiment of glutaric aciduria type I, where both known diagnostically important biomarkers and unknowns, as the most discriminating compounds, were found.

• Klíčová slova
• GC–MS, Immobilized urease, Metabolomics, Urine sample preparation,
• MeSH
• analýza hlavních komponent MeSH
• chromatografie kapalinová metody   MeSH
• enzymy imobilizované moč   MeSH
• glutaryl-CoA-dehydrogenasa nedostatek   metabolismus   MeSH
• lidé MeSH
• magnetické jevy * MeSH
• metabolické nemoci mozku metabolismus   MeSH
• metabolom MeSH
• metabolomika metody   MeSH
• metody pro přípravu analytických vzorků * MeSH
• močovina metabolismus   MeSH
• plynová chromatografie s hmotnostně spektrometrickou detekcí metody   MeSH
• reprodukovatelnost výsledků MeSH
• studie proveditelnosti MeSH
• tandemová hmotnostní spektrometrie MeSH
• ureasa moč   MeSH
• vrozené poruchy metabolismu aminokyselin metabolismus   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• enzymy imobilizované MeSH
• glutaryl-CoA-dehydrogenasa MeSH
• močovina MeSH
• ureasa MeSH

BACKGROUND: Metabolomics is becoming an important tool in clinical research and the diagnosis of human diseases. It has been used in the diagnosis of inherited metabolic disorders with pronounced biochemical abnormalities. The aim of this study was to determine if it could be applied in the diagnosis of inherited metabolic disorders (IMDs) with less clear biochemical profiles from urine samples using an untargeted metabolomic approach. METHODS: A total of 14 control urine samples and 21 samples from infants with cystinuria, maple syrup urine disease, adenylosuccinate lyase deficiency and galactosemia were tested. Samples were analyzed by liquid chromatography on aminopropyl column in aqueous normal phase separation system using gradient elution of acetonitrile/ammonium acetate. Detection was performed by time-of-flight mass spectrometer fitted with electrospray ionisation in positive mode. The data were statistically processed using principal component analysis (PCA), principal component discriminant function analysis (PCA-DFA) and partial least squares (PLS) regression. RESULTS: All patient samples were first distinguished from controls using unsupervised PCA. Discrimination of the patient samples was then unambiguously verified using supervised PCA-DFA. Known markers of the diseases in question were successfully confirmed and a potential new marker emerged from the PLS regression. CONCLUSION: This study showed that untargeted metabolomics can be applied in the diagnosis of mild IMDs with less clear biochemical profiles.

• Klíčová slova
• inherited metabolic disorders, mass spectrometry, untargeted metabolomics,
• MeSH
• adenylsukcinátlyasa nedostatek   MeSH
• analýza hlavních komponent MeSH
• autistická porucha diagnóza   MeSH
• biologické markery moč   MeSH
• cystinurie diagnóza   MeSH
• dítě MeSH
• dospělí MeSH
• galaktosemie diagnóza   MeSH
• hmotnostní spektrometrie metody   MeSH
• kojenec MeSH
• lidé MeSH
• metabolické nemoci diagnóza   MeSH
• metabolomika metody   MeSH
• mladiství MeSH
• mladý dospělý MeSH
• nemoc s močí javorového sirupu diagnóza   MeSH
• poruchy metabolismu purinů a pyrimidinů diagnóza   MeSH
• studie případů a kontrol MeSH
• vysokoúčinná kapalinová chromatografie metody   MeSH
• Check Tag
• dítě MeSH
• dospělí MeSH
• kojenec MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• adenylsukcinátlyasa MeSH
• biologické markery MeSH

A simple analytical workflow is described for gas chromatographic-mass spectrometry (GC-MS)-based metabolomic profiling of protic metabolites, particularly amino-carboxylic species in biological matrices. The sample preparation is carried out directly in aqueous samples and uses simultaneous in situ heptafluorobutyl chloroformate (HFBCF) derivatization and dispersive liquid-liquid microextraction (DLLME), followed by GC-MS analysis in single-ion monitoring (SIM) mode. The protocol involves ten simple pipetting steps and provides quantitative analysis of 132 metabolites by using two internal standards. A comment on each analytical step and explaining notes are provided with particular attention to the GC-MS analysis of 112 physiological metabolites in human urine.

• Klíčová slova
• Chloroformate derivatization, Dispersive liquid-liquid microextraction, GC-MS, Metabolomic profiling, Quantitative analysis, Urine,
• MeSH
• analýza moči metody   MeSH
• biologické markery moč   MeSH
• fluorokarbony chemie   MeSH
• formiáty chemie   MeSH
• lidé MeSH
• metabolomika metody   MeSH
• mikroextrakce kapalné fáze metody   MeSH
• plynová chromatografie s hmotnostně spektrometrickou detekcí metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• biologické markery MeSH
• fluorokarbony MeSH
• formiáty MeSH
• heptafluorobutyl chloroformate MeSH Prohlížeč