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The aim of this research was to optimize and investigate effects of corrosion process during cleaning of stainless-steel coatings in microbrewery tanks and to predict their resistance to nitric acid in washing solution. Experimental conditions were limited with minimal concentration of acid that satisfied requirements of sanitation protocol, and maximal concentration of acid that did not cause corrosion and release of metals higher then prescribed law limits. Process was monitored by release of metal ions and optimized by design of experiment (DOE) using software Design-Expert. This approach proved to be very beneficial, cost reductive, and effective for optimization of complex systems. The results of experiments prediction were compared to additional testing, and their matching proved the effectiveness of modeling. Therefore DOE is an effective tool for estimation of steel coating corrosion processes. In addition, optimized values satisfied required criteria, so the proposed protocol can be used in the effective sanitation of industrial tanks and pipelines in brewing industry. [ABSTRACT FROM AUTHOR]

Methamphetamine (METH) abuse has become a global public health problem. However, the potential mechanisms involving METH-induced metabolic disorders have thus far remained poorly understood. Metabolomics can provide a clue for the cause of apparent changes and consequently be used to investigate the METH-induced dysregulation of metabolite expression and the mechanism of metabolic disorder mechanism. This laboratory investigation included 80 METH abusers and 80 healthy people. The serum metabolites were detected and analysed by gas chromatography/time-of-flight mass spectrometry. Raw data were processed with the software MS DIAL, which includes deconvolution, peak alignment and compound identification. The data matrix was processed by univariate and multivariate analyses for significant metabolite screening with the criteria of variable importance in projection values > 1, fold change > 1.5 and the t test (p value < 0.05). Significant differences in 16 metabolites (deoxycholic acid, cholic acid, hydroxylamine, etc.) in serum were found between the METH abuse group and the control group. Energy metabolic pathways and several amino acid metabolic pathways (alanine, aspartic acid and glutamate metabolism and tryptophan metabolism) were primarily involved. Further analysis indicated that the area under the receiver operating characteristic curve (AUC) was 0.998 for these 16 metabolites. Among the metabolites, three carbohydrates (d-ribose, cellobiose and maltotriose) had an AUC of 0.975, which were determined as potential markers of abuse. We observed metabolic disturbances in METH abusers, particularly perturbation in energy metabolism and amino acid metabolism, which can provide new insights into the search for biomarkers and the mechanisms underlying the adverse effects of METH on human health., (© 2021 Society for the Study of Addiction.)

The integration of circadian and metabolic signals is essential for maintaining robust circadian rhythms and ensuring efficient metabolism and energy use. Using Drosophila as an animal model, we show that cellular protein O-GlcNAcylation exhibits robust 24-hour rhythm and represents a key post-translational mechanism that regulates circadian physiology. We observe strong correlation between protein O-GlcNAcylation rhythms and clock-controlled feeding-fasting cycles, suggesting that O-GlcNAcylation rhythms are primarily driven by nutrient input. Interestingly, daily O-GlcNAcylation rhythms are severely dampened when we subject flies to time-restricted feeding at unnatural feeding time. This suggests the presence of clock-regulated buffering mechanisms that prevent excessive O-GlcNAcylation at non-optimal times of the day-night cycle. We show that this buffering mechanism is mediated by the expression and activity of GFAT, OGT, and OGA, which are regulated through integration of circadian and metabolic signals. Finally, we generate a mathematical model to describe the key factors that regulate daily O-GlcNAcylation rhythm.

Gestational long-term hypoxia increases the risk of myriad diseases in infants including persistent pulmonary hypertension. Similar to humans, fetal lamb lung development is susceptible to long-term intrauterine hypoxia, with structural and functional changes associated with the development of pulmonary hypertension including pulmonary arterial medial wall thickening and dysregulation of arterial reactivity, which culminates in decreased right ventricular output. To further explore the mechanisms associated with hypoxia-induced aberrations in the fetal sheep lung, we examined the premise that metabolomic changes and functional phenotypic transformations occur due to intrauterine, long-term hypoxia. To address this, we performed electron microscopy, Western immunoblotting, calcium imaging, and metabolomic analyses on pulmonary arteries isolated from near-term fetal lambs that had been exposed to low- or high-altitude (3,801 m) hypoxia for the latter 110+ days of gestation. Our results demonstrate that the sarcoplasmic reticulum was swollen with high luminal width and distances to the plasma membrane in the hypoxic group. Hypoxic animals were presented with higher endoplasmic reticulum stress and suppressed calcium storage. Metabolically, hypoxia was associated with lower levels of multiple omega-3 polyunsaturated fatty acids and derived lipid mediators (e.g., eicosapentaenoic acid, docosahexaenoic acid, α-linolenic acid, 5-hydroxyeicosapentaenoic acid (5-HEPE), 12-HEPE, 15-HEPE, prostaglandin E3, and 19(20)-epoxy docosapentaenoic acid) and higher levels of some omega-6 metabolites ( P < 0.02) including 15-keto prostaglandin E2 and linoleoylglycerol. Collectively, the results reveal broad evidence for long-term hypoxia-induced metabolic reprogramming and phenotypic transformations in the pulmonary arteries of fetal sheep, conditions that likely contribute to the development of persistent pulmonary hypertension.

We recently demonstrated that the sympathetic nervous system can be voluntarily activated following a training program consisting of cold exposure, breathing exercises, and meditation. This resulted in profound attenuation of the systemic inflammatory response elicited by lipopolysaccharide (LPS) administration. Herein, we assessed whether this training program affects the plasma metabolome and if these changes are linked to the immunomodulatory effects observed. A total of 224 metabolites were identified in plasma obtained from 24 healthy male volunteers at six timepoints, of which 98 were significantly altered following LPS administration. Effects of the training program were most prominent shortly after initiation of the acquired breathing exercises but prior to LPS administration, and point towards increased activation of the Cori cycle. Elevated concentrations of lactate and pyruvate in trained individuals correlated with enhanced levels of anti-inflammatory interleukin (IL)-10. In vitro validation experiments revealed that co-incubation with lactate and pyruvate enhances IL-10 production and attenuates the release of pro-inflammatory IL-1β and IL-6 by LPS-stimulated leukocytes. Our results demonstrate that practicing the breathing exercises acquired during the training program results in increased activity of the Cori cycle. Furthermore, this work uncovers an important role of lactate and pyruvate in the anti-inflammatory phenotype observed in trained subjects.

The tumor microenvironment (TME) plays a pivotal role in cancer progression, and, in ovarian cancer (OvCa), the primary TME is the omentum. Here, we show that the diabetes drug metformin alters mesothelial cells in the omental microenvironment. Metformin interrupts bidirectional signaling between tumor and mesothelial cells by blocking OvCa cell TGF-β signaling and mesothelial cell production of CCL2 and IL-8. Inhibition of tumor-stromal crosstalk by metformin is caused by the reduced expression of the tricarboxylic acid (TCA) enzyme succinyl CoA ligase (SUCLG2). Through repressing this TCA enzyme and its metabolite, succinate, metformin activated prolyl hydroxylases (PHDs), resulting in the degradation of hypoxia-inducible factor 1α (HIF1α) in mesothelial cells. Disruption of HIF1α-driven IL-8 signaling in mesothelial cells by metformin results in reduced OvCa invasion in an organotypic 3D model. These findings indicate that tumor-promoting signaling between mesothelial and OvCa cells in the TME can be targeted using metformin., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)

Scope: High sodium and low potassium (HNaLK) intake increases the risk of cardiovascular disease (CVD) and metabolic syndrome. The authors investigate if the dietary minerals interact with the gut microbiota to alter circulating lipid profiles, implicated in CVD and metabolic syndrome., Methods and Results: Plasma samples from Wistar rats fed a control or HNaLK diet with or without antibiotic treatment (n = 7 each, a total of 28) are subjected to lipidomics analysis. Lipidomic data are then analyzed using statistical and bioinformatics tools, which detect numerous lipid species altered by the treatments, and consistently demonstrated interactions between the gut microbiota and the HNaLK diet in altering circulating lipids, mainly triglycerides (TGs). Two distinct TG groups differentially regulated by antibiotic treatment are identified. One group (cluster 1), representing the majority of TG species detected, is downregulated, whereas the other group (cluster 2) is upregulated by antibiotic treatment. Interestingly, cluster 2 TGs are also regulated by the diet. Cluster 2 TGs exhibit greater carbon-chain length and double-bond content and include TGs composed of very-long-chain polyunsaturated fatty acids, associated with reduced diabetes risk., Conclusion: The HNaLK diet interacts with gut bacteria to alter plasma lipid profiles, which may be related to its health effects., (© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Ozone plays an increasingly important role in food processing for its antimicrobial ability and degradation effects on mycotoxins. The mycotoxin deoxynivalenol (DON) was treated with saturated aqueous ozone for different amounts of time, and by-products were collected for compounds annotation and cytotoxicity evaluation. To investigate the cytotoxicity of ozone degradation by-products, untargeted GC-TOFMS-based metabolomics were utilized. Caco-2 cells were dosed with 0.1 μg/mL DON and saturated aqueous ozone-treated DON (treatment time: 1 min, 3 min, 5 min) for 24 h followed by cytotoxicity tests (cell viability assay, ROS assay, and apoptosis assay), and intracellular metabolic analysis. Cytotoxicity test results revealed that ozone treatment could degrade DON structure; however, its degradation products and cellular toxicity existed under different treatment time of ozone. Metabolomics analysis indicated that ozone-treated DON degradation products weakened DON-induced metabolic disorder, such as purines-related nucleotide metabolism; Krebs cycle-related fuel and energy metabolism; and lipid, alkaloid and amino acid metabolism. By contrast, the catecholamine pathway, which is related to latent inflammation and oxidative stress effects, was unaltered in the ozone-treated DON group, indicating that the potential cytotoxicity still existed. These findings provide a comprehensive safety evaluation for ozone-treated DON in vitro and propose a new strategy for studying the effects of ozone-treated food., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

High-grade serous carcinoma has a poor prognosis, owing primarily to its early dissemination throughout the abdominal cavity. Genomic and proteomic approaches have provided snapshots of the proteogenomics of ovarian cancer 1,2 , but a systematic examination of both the tumour and stromal compartments is critical in understanding ovarian cancer metastasis. Here we develop a label-free proteomic workflow to analyse as few as 5,000 formalin-fixed, paraffin-embedded cells microdissected from each compartment. The tumour proteome was stable during progression from in situ lesions to metastatic disease; however, the metastasis-associated stroma was characterized by a highly conserved proteomic signature, prominently including the methyltransferase nicotinamide N-methyltransferase (NNMT) and several of the proteins that it regulates. Stromal NNMT expression was necessary and sufficient for functional aspects of the cancer-associated fibroblast (CAF) phenotype, including the expression of CAF markers and the secretion of cytokines and oncogenic extracellular matrix. Stromal NNMT expression supported ovarian cancer migration, proliferation and in vivo growth and metastasis. Expression of NNMT in CAFs led to depletion of S-adenosyl methionine and reduction in histone methylation associated with widespread gene expression changes in the tumour stroma. This work supports the use of ultra-low-input proteomics to identify candidate drivers of disease phenotypes. NNMT is a central, metabolic regulator of CAF differentiation and cancer progression in the stroma that may be therapeutically targeted.

Metabolic profiling in Caco-2 cells was studied for the combined toxic effects of deoxynivalenol (DON), zearalenone (ZEN), and Aflatoxin B 1 (AFB 1 ) through untargeted GC-MS. The GC-MS spectra of Caco-2 cells treated with individual 6.7 μM DON, 20 μM ZEN, 20 μM AFB 1 and the combined DON + AFB 1 (6.7 + 20 μM) and DON + ZEN + AFB 1 (6.7 + 20 + 20 μM) for 24 h were deconvoluted, aligned and identified with MS DIAL. The metabolic pathway analysis was analyzed with MetaMapp and visualized with CytoScape. Results show that the combined DON + AFB 1 and DON + ZEN + AFB 1 treatment has an obvious "synergistic effect". The apoptosis-related gene mRNA test result indicates that the combined mycotoxins downregulate Bcl-2 gene and upregulate Bax, p53, caspase-3, caspase-8 and caspase-utilized 9 genes, more significantly than any individual mycotoxins group. The cellular metabolism illustrated that the combined mycotoxin groups, DON + ZEN + AFB 1 seriously effect glycine, serine and threonine metabolism, pyruvate metabolism, etc. while no metabolic disorders were presented in individual mycotoxin group. Our hypothesis was validated that the combined mycotoxins with low concentrations can have a synergistic effect in the metabolism, which may lead to cellular apoptosis or necrosis., (Copyright © 2018. Published by Elsevier Ltd.)

Urine metabolites are used in many clinical and biomedical studies but usually only for a few classic compounds. Metabolomics detects vastly more metabolic signals that may be used to precisely define the health status of individuals. However, many compounds remain unidentified, hampering biochemical conclusions. Here, we annotate all metabolites detected by two untargeted metabolomic assays, hydrophilic interaction chromatography (HILIC)-Q Exactive HF mass spectrometry and charged surface hybrid (CSH)-Q Exactive HF mass spectrometry. Over 9,000 unique metabolite signals were detected, of which 42% triggered MS/MS fragmentations in data-dependent mode. On the highest Metabolomics Standards Initiative (MSI) confidence level 1, we identified 175 compounds using authentic standards with precursor mass, retention time, and MS/MS matching. An additional 578 compounds were annotated by precursor accurate mass and MS/MS matching alone, MSI level 2, including a novel library specifically geared at acylcarnitines (CarniBlast). The rest of the metabolome is usually left unannotated. To fill this gap, we used the in silico fragmentation tool CSI:FingerID and the new NIST hybrid search to annotate all further compounds (MSI level 3). Testing the top-ranked metabolites in CSI:Finger ID annotations yielded 40% accuracy when applied to the MSI level 1 identified compounds. We classified all MSI level 3 annotations by the NIST hybrid search using the ClassyFire ontology into 21 superclasses that were further distinguished into 184 chemical classes. ClassyFire annotations showed that the previously unannotated urine metabolome consists of 28% derivatives of organic acids, 16% heterocyclics, and 16% lipids as major classes.

Scope: High-sodium and low-potassium (HNaLK) content in Western diets increases the risk of hypertension and cardiovascular disease (CVD). It is investigated if the dietary minerals interact with gut bacteria to modulate circulating levels of biogenic amines, which are implicated in various pathologies, including hypertension and CVD., Methods and Results: Using a metabolomic approach to target biogenic amines, the effects of gut bacteria depletion and HNaLK intake on circulating levels of biogenic amines in rats are examined. Forty-five metabolites whose plasma levels are significantly altered by gut bacteria depletion (p < 0.05) are found, indicating their regulation by gut bacteria. Many of them are not previously linked to gut bacteria; therefore, these data provide novel insights into physiological or pathological roles of gut bacteria. A number of plasma metabolites that are altered both by gut bacteria and HNaLK intake are also found, suggesting possible interactions of the diet and gut bacteria in the modulation of these metabolites. The diet effects are observed with significant changes in the gut bacterial taxa Porphyromonadaceae and Prevotellaceae (p < 0.05)., Conclusion: The dietary minerals may regulate abundances of certain gut bacteria to alter circulating levels of biogenic amines, which may be linked to host physiology or pathology., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Lipopolysaccharide (LPS) is the main virulence factor of Gram-negative bacteria, which can incite inflammation in tissues by inducing cells to secrete a variety of proinflammatory mediators, including cytokines, chemokines, interleukins, and prostaglandins. Herein, we chose LPS as an inducer to establish an inflammatory model of HeLa cells, and explored the effects of LPS on energy metabolism. We treated HeLa cells with different concentrations (0, 0.4, 1.0, 2.0, 4.0, and 6.0 μg/mL) of LPS for 24 h, and explored its effects on intercellular adenosine triphosphate (ATP) levels, intercellular nitrous oxide (NO) content, mitochondrial functions, and enzyme activities related to energy metabolism. Furthermore, we used metabonomics to study the metabolites that participated in energy metabolism. We found a positive correlation between LPS concentrations and intracellular ATP levels. In addition, LPS increased intracellular NO production, altered mitochondrial functions, strengthened glycolytic enzyme activities, and changed metabolites related to energy metabolism. Hence, in this study, we showed that LPS can strengthen energy metabolism by enhancing glycolysis, which could be used as an early diagnostic biomarker or a novel therapeutic target for inflammation-associated cancers.

Unknown metabolites represent a bottleneck in untargeted metabolomics research. Ion mobility-mass spectrometry (IM-MS) facilitates lipid identification because it yields collision cross section (CCS) information that is independent from mass or lipophilicity. To date, only a few CCS values are publicly available for complex lipids such as phosphatidylcholines, sphingomyelins, or triacylglycerides. This scarcity of data limits the use of CCS values as an identification parameter that is orthogonal to mass, MS/MS, or retention time. A combination of lipid descriptors was used to train five different machine learning algorithms for automatic lipid annotations, combining accurate mass ( m/ z), retention time (RT), CCS values, carbon number, and unsaturation level. Using a training data set of 429 true positive lipid annotations from four lipid classes, 92.7% correct annotations overall were achieved using internal cross-validation. The trained prediction model was applied to an unknown milk lipidomics data set and allowed for class 3 level annotations of most features detected in this application set according to Metabolomics Standards Initiative (MSI) reporting guidelines.

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included.

Urine metabolic profiling of mice was conducted utilizing gas chromatography-mass spectrometry (GC-MS) to investigate the combinatory effect of mycotoxins deoxynivalenol (DON) and zearalenone (ZEN) on the metabolism of the mice. Experiments were conducted by means of five-week-old mice which were individually exposed to 2 mg/kg DON, 20 mg/kg ZEN and the mixture of DON and ZEN (2 mg/kg and 20 mg/kg, respectively). The intragastric administration was applied for three weeks and urine samples were collected for metabolic analysis. Univariate and multivariate analysis were applied to data matrix processing along with respective pathway analysis by MetaMapp and CytoScape. The results showed that the combined DON and ZEN administration resulted in lower significant changes, compared to the individual mycotoxin treated groups verified by heatmap. Metabolic pathways network mapping indicated that the combined mycotoxins treated groups showed a little effect on the metabolites in most pathways, especially in glucose metabolism and its downstream amino acid metabolism. In glucose metabolism, the content of galactose, mannitol, galactonic acid, myo-inositol, tagatose was drastically down-regulated. Furthermore, the organic acids, pyruvate, and amino acids metabolism displayed the same phenomenon. In conclusion, the combined DON/ZEN administration might lead to an "antagonistic effect" in mice metabolism.

Lipopolysaccharide (LPS) can lead to uncontrollable cytokine production, fatal sepsis syndrome and depression/multiple organ failure, as pathophysiologic demonstration. Various toxic effects of LPS have been extensively reported, mainly on the toxicity of LPS in cellular level, macrophages or tumor cells, etc. This work aimed on the impact of LPS on mast cell metabolism, which focused on LPS-induced cellular metabolic profiles. Gas chromatography-mass spectrometry (GC-MS) based metabolomics strategy was implemented for the endo-metabolites detection in rat basophilic leukemia (RBL-2H3) cells, treated with 10 μg/mL LPS for 24 h, along with multiple time-dose tests of cells viability/apoptosis. Significantly changes metabolites were mainly involved the metabolism of glycine, serine, threonine and the biosynthesis of phenylalanine, tyrosine, tryptophan and pentose phosphate pathway. The endo-metabolism results illustrated that LPS treatment led to downregulation of glycine, serine and threonine metabolism besides pentose phosphate pathway in RBL-2H3 cells. This novel insight into LPS cellular metabolism, provides some heuristic guidance for elucidating the underlying mechanism of LPS-mediated disease., (Copyright © 2017. Published by Elsevier B.V.)

In mass spectrometry-based untargeted metabolomics, rarely more than 30% of the compounds are identified. Without the true identity of these molecules it is impossible to draw conclusions about the biological mechanisms, pathway relationships and provenance of compounds. The only way at present to address this discrepancy is to use in silico fragmentation software to identify unknown compounds by comparing and ranking theoretical MS/MS fragmentations from target structures to experimental tandem mass spectra (MS/MS). We compared the performance of four publicly available in silico fragmentation algorithms (MetFragCL, CFM-ID, MAGMa+ and MS-FINDER) that participated in the 2016 CASMI challenge. We found that optimizing the use of metadata, weighting factors and the manner of combining different tools eventually defined the ultimate outcomes of each method. We comprehensively analysed how outcomes of different tools could be combined and reached a final success rate of 93% for the training data, and 87% for the challenge data, using a combination of MAGMa+, CFM-ID and compound importance information along with MS/MS matching. Matching MS/MS spectra against the MS/MS libraries without using any in silico tool yielded 60% correct hits, showing that the use of in silico methods is still important.