Lipid A, a crucial component of lipopolysaccharides (LPS), plays a pivotal role in the pathogenesis of Gram-negative bacteria. Lipid A patterns are recognized by mammals and can induce immunostimulatory effects. However, the outcome of the interaction is highly dependent on the chemical composition of individual lipid A species. The diversity of potential fatty acyl and polar headgroup combinations in this complex saccharolipid presents a significant analytical challenge. Current mass spectrometry (MS)-based lipid A methods are focused on either direct matrix-assisted laser desorption/ionization (MALDI)-MS screening or comprehensive structural elucidation by tandem mass spectrometry (MS/MS) hyphenated with separation techniques. In this study, we developed an alternative workflow for rapid lipid A profiling covering the entire analysis pipeline from sample preparation to data analysis. This workflow is based on microextraction and subsequent MALDI-MS/MS analysis of uropathogenic Escherichia coli utilizing trapped ion mobility spectrometry (TIMS), followed by mzmine data processing. The additional TIMS dimension served for enhanced sensitivity, selectivity, and structural elucidation through mobility-resolved fragmentation via parallel accumulation-serial fragmentation (PASEF) in parallel reaction monitoring (prm)-mode. Furthermore, mzmine enabled automated MS/MS acquisition by adapting the spatial ion mobility-scheduled exhaustive fragmentation (SIMSEF) strategy for MALDI spot analysis. It also facilitated robust lipid A annotation through a newly developed extension of the rule-based lipid annotation module, allowing for the custom generation of lipid classes, including specific fragmentation rules. In this study, the first publication of lipid A species' collision cross section (CCS) values is reported, which will enhance high-confidence lipid A annotation in future studies.

• MeSH
• gramnegativní bakterie * chemie   MeSH
• iontová mobilní spektrometrie * metody   MeSH
• lipid A * analýza   chemie   MeSH
• spektrometrie hmotnostní - ionizace laserem za účasti matrice * metody   MeSH
• tandemová hmotnostní spektrometrie metody   MeSH
• uropatogenní Escherichia coli * chemie   MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• lipid A * MeSH

Fusarium fungi are widespread pathogens of food crops, primarily associated with the formation of mycotoxins. Therefore, effective mitigation strategies for these toxicogenic microorganisms are required. In this study, the potential of pulsed electric field (PEF) as an advanced technology of increasing use in the food processing industry was investigated to minimize the viability of Fusarium pathogens and to characterize the PEF-induced changes at the metabolomic level. Spores of four Fusarium species (Fusarium culmorum, Fusarium graminearum, Fusarium poae, and Fusarium sporotrichioides) were treated with PEF and cultured on potato dextrose agar (PDA) plates. The viability of the Fusarium species was assessed by counting the colony-forming units, and changes in the mycotoxin content and metabolomic fingerprints were evaluated by using UHPLC-HRMS/MS instrumental analysis. For metabolomic data processing and compound identification, the MS-DIAL (v. 4.80)-MS-CleanR-MS-Finder (v. 3.52) software platform was used. As we found out, both fungal viability and the ability to produce mycotoxins significantly decreased after the PEF treatment for all of the species tested. The metabolomes of the treated and untreated fungi showed statistically significant differences, and PEF-associated biomarkers from the classes oxidized fatty acid derivatives, cyclic hexapeptides, macrolides, pyranocoumarins, carbazoles, and guanidines were identified.

• Klíčová slova
• Fusarium, UHPLC-HRMS/MS, biomarkers, food pathogens, metabolomic fingerprinting, mycotoxins, pulsed electric field, spore viability,
• MeSH
• elektřina * MeSH
• Fusarium * metabolismus   růst a vývoj   MeSH
• metabolom * MeSH
• metabolomika MeSH
• mikrobiální viabilita MeSH
• mykotoxiny * metabolismus   MeSH
• spory hub metabolismus   růst a vývoj   MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• mykotoxiny * MeSH

Feature-based molecular networking (FBMN) is a popular analysis approach for liquid chromatography-tandem mass spectrometry-based non-targeted metabolomics data. While processing liquid chromatography-tandem mass spectrometry data through FBMN is fairly streamlined, downstream data handling and statistical interrogation are often a key bottleneck. Especially users new to statistical analysis struggle to effectively handle and analyze complex data matrices. Here we provide a comprehensive guide for the statistical analysis of FBMN results, focusing on the downstream analysis of the FBMN output table. We explain the data structure and principles of data cleanup and normalization, as well as uni- and multivariate statistical analysis of FBMN results. We provide explanations and code in two scripting languages (R and Python) as well as the QIIME2 framework for all protocol steps, from data clean-up to statistical analysis. All code is shared in the form of Jupyter Notebooks ( https://github.com/Functional-Metabolomics-Lab/FBMN-STATS ). Additionally, the protocol is accompanied by a web application with a graphical user interface ( https://fbmn-statsguide.gnps2.org/ ) to lower the barrier of entry for new users and for educational purposes. Finally, we also show users how to integrate their statistical results into the molecular network using the Cytoscape visualization tool. Throughout the protocol, we use a previously published environmental metabolomics dataset for demonstration purposes. Together, the protocol, code and web application provide a complete guide and toolbox for FBMN data integration, cleanup and advanced statistical analysis, enabling new users to uncover molecular insights from their non-targeted metabolomics data. Our protocol is tailored for the seamless analysis of FBMN results from Global Natural Products Social Molecular Networking and can be easily adapted to other mass spectrometry feature detection, annotation and networking tools.

• MeSH
• chromatografie kapalinová metody   MeSH
• interpretace statistických dat MeSH
• metabolomika * metody   MeSH
• software MeSH
• tandemová hmotnostní spektrometrie metody   MeSH
• Publikační typ
• časopisecké články MeSH

Lipidomics and metabolomics communities comprise various informatics tools; however, software programs handling multimodal mass spectrometry (MS) data with structural annotations guided by the Lipidomics Standards Initiative are limited. Here, we provide MS-DIAL 5 for in-depth lipidome structural elucidation through electron-activated dissociation (EAD)-based tandem MS and determining their molecular localization through MS imaging (MSI) data using a species/tissue-specific lipidome database containing the predicted collision-cross section values. With the optimized EAD settings using 14 eV kinetic energy, the program correctly delineated lipid structures for 96.4% of authentic standards, among which 78.0% had the sn-, OH-, and/or C = C positions correctly assigned at concentrations exceeding 1 μM. We showcased our workflow by annotating the sn- and double-bond positions of eye-specific phosphatidylcholines containing very-long-chain polyunsaturated fatty acids (VLC-PUFAs), characterized as PC n-3-VLC-PUFA/FA. Using MSI data from the eye and n-3-VLC-PUFA-supplemented HeLa cells, we identified glycerol 3-phosphate acyltransferase as an enzyme candidate responsible for incorporating n-3 VLC-PUFAs into the sn1 position of phospholipids in mammalian cells, which was confirmed using EAD-MS/MS and recombinant proteins in a cell-free system. Therefore, the MS-DIAL 5 environment, combined with optimized MS data acquisition methods, facilitates a better understanding of lipid structures and their localization, offering insights into lipid biology.

• MeSH
• data mining * metody   MeSH
• fosfatidylcholiny metabolismus   chemie   MeSH
• HeLa buňky MeSH
• hmotnostní spektrometrie metody   MeSH
• lidé MeSH
• lipidomika * metody   MeSH
• lipidy chemie   analýza   MeSH
• metabolomika metody   MeSH
• nenasycené mastné kyseliny metabolismus   chemie   MeSH
• software MeSH
• tandemová hmotnostní spektrometrie metody   MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• fosfatidylcholiny MeSH
• lipidy MeSH
• nenasycené mastné kyseliny MeSH

Pharmaceuticals released into the aquatic and soil environments can be absorbed by plants and soil organisms, potentially leading to the formation of unknown metabolites that may negatively affect these organisms or contaminate the food chain. The aim of this study was to identify pharmaceutical metabolites through a triplet approach for metabolite structure prediction (software-based predictions, literature review, and known common metabolic pathways), followed by generating in silico mass spectral libraries and applying various mass spectrometry modes for untargeted LC-qTOF analysis. Therefore, Eisenia fetida and Lactuca sativa were exposed to a pharmaceutical mixture (atenolol, enrofloxacin, erythromycin, ketoprofen, sulfametoxazole, tetracycline) under hydroponic and soil conditions at environmentally relevant concentrations. Samples collected at different time points were extracted using QuEChERS and analyzed with LC-qTOF in data-dependent (DDA) and data-independent (DIA) acquisition modes, applying both positive and negative electrospray ionization. The triplet approach for metabolite structure prediction yielded a total of 3762 pharmaceutical metabolites, and an in silico mass spectral library was created based on these predicted metabolites. This approach resulted in the identification of 26 statistically significant metabolites (p < 0.05), with DDA + and DDA - outperforming DIA modes by successfully detecting 56/67 sample type:metabolite combinations. Lettuce roots had the highest metabolite count (26), followed by leaves (6) and earthworms (2). Despite the lower metabolite count, earthworms showed the highest peak intensities, closely followed by roots, with leaves displaying the lowest intensities. Common metabolic reactions observed included hydroxylation, decarboxylation, acetylation, and glucosidation, with ketoprofen-related metabolites being the most prevalent, totaling 12 distinct metabolites. In conclusion, we developed a high-throughput workflow combining open-source software with LC-HRMS for identifying unknown metabolites across various sample types.

• Klíčová slova
• High-resolution mass spectrometry, In silico spectral library, Liquid chromatography, Metabolite identification in Eisenia fetida and Lactuca sativa, Pharmaceuticals, Software prediction,
• MeSH
• chromatografie kapalinová metody   MeSH
• hmotnostní spektrometrie metody   MeSH
• látky znečišťující půdu analýza   metabolismus   MeSH
• léčivé přípravky metabolismus   chemie   analýza   MeSH
• Oligochaeta * metabolismus   chemie   MeSH
• počítačová simulace MeSH
• salát (hlávkový) * metabolismus   chemie   MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• látky znečišťující půdu MeSH
• léčivé přípravky MeSH

Metabolic syndrome is a growing concern in developed societies and due to its polygenic nature, the genetic component is only slowly being elucidated. Common mitochondrial DNA sequence variants have been associated with symptoms of metabolic syndrome and may, therefore, be relevant players in the genetics of metabolic syndrome. We investigate the effect of mitochondrial sequence variation on the metabolic phenotype in conplastic rat strains with identical nuclear but unique mitochondrial genomes, challenged by high-fat diet. We find that the variation in mitochondrial rRNA sequence represents risk factor in the insulin resistance development, which is associated with diacylglycerols accumulation, induced by tissue-specific reduction of the oxidative capacity. These metabolic perturbations stem from the 12S rRNA sequence variation affecting mitochondrial ribosome assembly and translation. Our work demonstrates that physiological variation in mitochondrial rRNA might represent a relevant underlying factor in the progression of metabolic syndrome.

• MeSH
• dieta s vysokým obsahem tuků škodlivé účinky   MeSH
• genetická predispozice k nemoci MeSH
• haplotypy * MeSH
• inzulinová rezistence genetika   MeSH
• krysa rodu Rattus MeSH
• metabolický syndrom * genetika   metabolismus   MeSH
• mitochondriální DNA genetika   metabolismus   MeSH
• mitochondrie metabolismus   genetika   MeSH
• RNA mitochondriální genetika   metabolismus   MeSH
• RNA ribozomální * genetika   metabolismus   MeSH
• zvířata MeSH
• Check Tag
• krysa rodu Rattus MeSH
• mužské pohlaví MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• mitochondriální DNA MeSH
• RNA mitochondriální MeSH
• RNA ribozomální * MeSH
• RNA, ribosomal, 12S MeSH Prohlížeč

Metabolomics and lipidomics have emerged as tools in understanding the connections of metabolic syndrome (MetS) with cardiovascular diseases (CVD), type 1 and type 2 diabetes (T1D, T2D), and metabolic dysfunction-associated steatotic liver disease (MASLD). This review highlights the applications of these omics approaches in large-scale cohort studies, emphasizing their role in biomarker discovery and disease prediction. Integrating metabolomics and lipidomics has significantly advanced our understanding of MetS pathology by identifying unique metabolic signatures associated with disease progression. However, challenges such as standardizing analytical workflows, data interpretation, and biomarker validation remain critical for translating research findings into clinical practice. Future research should focus on optimizing these methodologies to enhance their clinical utility and address the global burden of MetS-related diseases.

• MeSH
• biologické markery metabolismus   MeSH
• diabetes mellitus 1. typu metabolismus   komplikace   MeSH
• diabetes mellitus 2. typu * metabolismus   MeSH
• kardiovaskulární nemoci * metabolismus   diagnóza   MeSH
• lidé MeSH
• lipidomika * metody   MeSH
• metabolický syndrom * metabolismus   MeSH
• metabolomika * metody   MeSH
• ztučnělá játra metabolismus   MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH
• Názvy látek
• biologické markery MeSH

Contrast-enhanced computed tomography offers a nondestructive approach to studying adipose tissue in 3D. Several contrast-enhancing staining agents (CESAs) have been explored, whereof osmium tetroxide (OsO4) is the most popular nowadays. However, due to the toxicity and volatility of the conventional OsO4, alternative CESAs with similar staining properties were desired. Hf-WD 1:2 POM and Hexabrix have proven effective for structural analysis of adipocytes using contrast-enhanced computed tomography but fail to provide chemical information. This study introduces isotonic Lugol's iodine (IL) as an alternative CESA for adipose tissue analysis, comparing its staining potential with Hf-WD 1:2 POM and Hexabrix in murine caudal vertebrae and bovine muscle tissue strips. Single and sequential staining protocols were compared to assess the maximization of information extraction from each sample. The study investigated interactions, distribution, and reactivity of iodine species towards biomolecules using simplified model systems and assesses the potential of the CESA to provide chemical information. (Bio)chemical analyses on whole tissues revealed that differences in adipocyte gray values post-IL staining were associated with chemical distinctions between bovine muscle tissue and murine caudal vertebrae. More specific, a difference in the degree of unsaturation of fatty acids was identified as a likely contributor, though not the sole determinant of gray value differences. This research sheds light on the potential of IL as a CESA, offering both structural and chemical insights into adipose tissue composition.

• Klíčová slova
• 3D histology, DICECT, Lugol’s iodine, adipocytes, adipose tissue, bone marrow, lipids/chemistry, muscle,
• MeSH
• barvení a značení metody   MeSH
• kontrastní látky * chemie   MeSH
• myši inbrední C57BL MeSH
• myši MeSH
• počítačová rentgenová tomografie * metody   MeSH
• skot MeSH
• tuková tkáň * diagnostické zobrazování   metabolismus   MeSH
• tukové buňky cytologie   metabolismus   MeSH
• zvířata MeSH
• Check Tag
• myši MeSH
• skot MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• kontrastní látky * MeSH

Styrene-maleic acid (SMA) and similar amphiphilic copolymers are known to cut biological membranes into lipid nanoparticles/nanodiscs containing membrane proteins apparently in their relatively native membrane lipid environment. Our previous work demonstrated that membrane raft microdomains resist such disintegration by SMA. The use of SMA in studying membrane proteins is limited by its heterogeneity and the inability to prepare defined derivatives. In the present paper, we demonstrate that some amphiphilic peptides structurally mimicking SMA also similarly disintegrate cell membranes. In contrast to the previously used copolymers, the simple peptides are structurally homogeneous. We found that their membrane-disintegrating activity increases with their length (reaching optimum at 24 amino acids) and requires a basic primary structure, that is, (XXD)n, where X represents a hydrophobic amino acid (optimally phenylalanine), D aspartic acid, and n is the number of repeats of these triplets. These peptides may provide opportunities for various well-defined potentially useful modifications in the study of membrane protein biochemistry. Our present results confirm a specific character of membrane raft microdomains.

• Klíčová slova
• leukocyte, lipid raft, lymphocyte, membrane, membrane proteins, peptides,
• MeSH
• buněčná membrána metabolismus   chemie   MeSH
• buněčné linie MeSH
• lidé MeSH
• maleáty chemie   MeSH
• membránové mikrodomény metabolismus   chemie   MeSH
• membránové proteiny * chemie   metabolismus   MeSH
• peptidy * chemie   MeSH
• polystyreny chemie   MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Liquid chromatography with mass spectrometry (LC-MS)-based metabolomics detects thousands of molecular features (retention time-m/z pairs) in biological samples per analysis, yet the metabolite annotation rate remains low, with 90% of signals classified as unknowns. To enhance the metabolite annotation rates, researchers employ tandem mass spectral libraries and challenging in silico fragmentation software. Hydrogen/deuterium exchange mass spectrometry (HDX-MS) may offer an additional layer of structural information in untargeted metabolomics, especially for identifying specific unidentified metabolites that are revealed to be statistically significant. Here, we investigate the potential of hydrophilic interaction liquid chromatography (HILIC)-HDX-MS in untargeted metabolomics. Specifically, we evaluate the effectiveness of two approaches using hypothetical targets: the post-column addition of deuterium oxide (D2O) and the on-column HILIC-HDX-MS method. To illustrate the practical application of HILIC-HDX-MS, we apply this methodology using the in silico fragmentation software MS-FINDER to an unknown compound detected in various biological samples, including plasma, serum, tissues, and feces during HILIC-MS profiling, subsequently identified as N1-acetylspermidine.

• Klíčová slova
• hydrogen/deuterium exchange, liquid chromatography, mass spectrometry, metabolomics, unknown identification,
• MeSH
• chromatografie kapalinová metody   MeSH
• deuterium MeSH
• hydrofobní a hydrofilní interakce MeSH
• metabolomika * metody   MeSH
• vodík/deuteriová výměna a hmotnostní spektrometrie * MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• deuterium MeSH