Circadian rhythms regulate key physiological processes through clock genes in central and peripheral tissues. While circadian gene expression during development has been well studied, the temporal dynamics of metabolism across tissues remain less understood. Here, we present the Circadian Ontogenetic Metabolomics Atlas (COMA), which maps circadian metabolic rhythms across 16 rat anatomical structures. The brain (suprachiasmatic nuclei, medial prefrontal cortex) and periphery (liver, plasma) span developmental stages from embryonic E19 to postnatal P2, P10, P20, and P28. Fecal samples include all four postnatal stages, while additional peripheral tissues were analyzed at P20 and P28. Using a multiplatform liquid chromatography-mass spectrometry approach, we annotated 851 metabolites from 1610 samples. We identified distinct circadian shifts, particularly during the transition from nursing to solid food intake (P10-P20), with an average of 24% of metabolites exhibiting circadian oscillations across sample types, as determined by JTK_CYCLE. Our study also underscores the importance of standardized sampling, as metabolite intensities fluctuate with both circadian rhythms and development. COMA serves as an open-access resource ( https://coma.metabolomics.fgu.cas.cz ) for exploring circadian metabolic regulation and its role in developmental biology.

• Keywords
• Atlas, Circadian rhythm, Lipidomics, Metabolomics, Resource,
• MeSH
• Chromatography, Liquid MeSH
• Circadian Rhythm * physiology   MeSH
• Feces * chemistry   MeSH
• Liver metabolism   MeSH
• Rats MeSH
• Metabolome * MeSH
• Metabolomics * methods   MeSH
• Animals MeSH
• Check Tag
• Rats MeSH
• Male MeSH
• Female MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH

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
• Gram-Negative Bacteria * chemistry   MeSH
• Ion Mobility Spectrometry * methods   MeSH
• Lipid A * analysis   chemistry   MeSH
• Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization * methods   MeSH
• Tandem Mass Spectrometry methods   MeSH
• Uropathogenic Escherichia coli * chemistry   MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• 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.

• Keywords
• Fusarium, UHPLC-HRMS/MS, biomarkers, food pathogens, metabolomic fingerprinting, mycotoxins, pulsed electric field, spore viability,
• MeSH
• Electricity * MeSH
• Fusarium * metabolism   growth & development   MeSH
• Metabolome * MeSH
• Metabolomics MeSH
• Microbial Viability MeSH
• Mycotoxins * metabolism   MeSH
• Spores, Fungal metabolism   growth & development   MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Mycotoxins * 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
• Chromatography, Liquid methods   MeSH
• Data Interpretation, Statistical MeSH
• Metabolomics * methods   MeSH
• Software MeSH
• Tandem Mass Spectrometry methods   MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH
• Research Support, U.S. Gov't, Non-P.H.S. 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 * methods   MeSH
• Phosphatidylcholines metabolism   chemistry   MeSH
• HeLa Cells MeSH
• Mass Spectrometry methods   MeSH
• Humans MeSH
• Lipidomics * methods   MeSH
• Lipids chemistry   analysis   MeSH
• Metabolomics methods   MeSH
• Fatty Acids, Unsaturated metabolism   chemistry   MeSH
• Software MeSH
• Tandem Mass Spectrometry methods   MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Phosphatidylcholines MeSH
• Lipids MeSH
• Fatty Acids, Unsaturated 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.

• Keywords
• High-resolution mass spectrometry, In silico spectral library, Liquid chromatography, Metabolite identification in Eisenia fetida and Lactuca sativa, Pharmaceuticals, Software prediction,
• MeSH
• Chromatography, Liquid methods   MeSH
• Mass Spectrometry methods   MeSH
• Soil Pollutants analysis   metabolism   MeSH
• Pharmaceutical Preparations metabolism   chemistry   analysis   MeSH
• Oligochaeta * metabolism   chemistry   MeSH
• Computer Simulation MeSH
• Lactuca * metabolism   chemistry   MeSH
• Animals MeSH
• Check Tag
• Animals MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Soil Pollutants MeSH
• Pharmaceutical Preparations 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
• Diet, High-Fat adverse effects   MeSH
• Genetic Predisposition to Disease MeSH
• Haplotypes * MeSH
• Insulin Resistance genetics   MeSH
• Rats MeSH
• Metabolic Syndrome * genetics   metabolism   MeSH
• DNA, Mitochondrial genetics   metabolism   MeSH
• Mitochondria metabolism   genetics   MeSH
• RNA, Mitochondrial genetics   metabolism   MeSH
• RNA, Ribosomal * genetics   metabolism   MeSH
• Animals MeSH
• Check Tag
• Rats MeSH
• Male MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• DNA, Mitochondrial MeSH
• RNA, Mitochondrial MeSH
• RNA, Ribosomal * MeSH
• RNA, ribosomal, 12S MeSH Browser

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
• Biomarkers metabolism   MeSH
• Diabetes Mellitus, Type 1 metabolism   complications   MeSH
• Diabetes Mellitus, Type 2 * metabolism   MeSH
• Cardiovascular Diseases * metabolism   diagnosis   MeSH
• Humans MeSH
• Lipidomics * methods   MeSH
• Metabolic Syndrome * metabolism   MeSH
• Metabolomics * methods   MeSH
• Fatty Liver metabolism   MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Review MeSH
• Names of Substances
• Biomarkers 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.

• Keywords
• 3D histology, DICECT, Lugol’s iodine, adipocytes, adipose tissue, bone marrow, lipids/chemistry, muscle,
• MeSH
• Staining and Labeling methods   MeSH
• Contrast Media * chemistry   MeSH
• Mice, Inbred C57BL MeSH
• Mice MeSH
• Tomography, X-Ray Computed * methods   MeSH
• Cattle MeSH
• Adipose Tissue * diagnostic imaging   metabolism   MeSH
• Adipocytes cytology   metabolism   MeSH
• Animals MeSH
• Check Tag
• Mice MeSH
• Cattle MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Contrast Media * 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.

• Keywords
• leukocyte, lipid raft, lymphocyte, membrane, membrane proteins, peptides,
• MeSH
• Cell Membrane metabolism   chemistry   MeSH
• Cell Line MeSH
• Humans MeSH
• Maleates chemistry   MeSH
• Membrane Microdomains metabolism   chemistry   MeSH
• Membrane Proteins * chemistry   metabolism   MeSH
• Peptides * chemistry   MeSH
• Polystyrenes chemistry   MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH