PubMed

Semin Immunol. 2023 May 2;67:101767. doi: 10.1016/j.smim.2023.101767. Online ahead of print.NO ABSTRACTPMID:37141767 | DOI:10.1016/j.smim.2023.101767

Food Chem. 2023 Apr 28;422:136231. doi: 10.1016/j.foodchem.2023.136231. Online ahead of print.ABSTRACTAn integrated metabolomics approach based on UPLC-QTOF-MS and HS-SPME-GC-orbitrap-MS was performed to investigate the dynamic changes of metabolite profiling in chickpeas, red speckled kidney beans, and mung beans during soaking. There were 23, 23, 16 non-volatile metabolites, and 18, 21, 22 volatile metabolites were identified as differential metabolites in chickpeas, red speckled kidney beans, and mung beans during soaking, respectively. These metabolites mainly included flavonoids, lysophosphatidylcholines (LPCs), lysophosphatidylethanolamines (LPEs), fatty acids, alcohols, aldehydes, and esters. The key time points responsible for the significant changes in metabolites and quality of the three pulses were 4, 8, and 24 h of soaking. Results revealed that the variations of some metabolites could attribute to oxidation and hydrolysis reactions. These results contribute to a better understanding of how soaking affects pulses quality, and provide useful information for determining soaking time according to nutritional and sensory requirements of their final products or dishes.PMID:37141754 | DOI:10.1016/j.foodchem.2023.136231

Trials. 2023 May 4;24(1):308. doi: 10.1186/s13063-023-07317-w.ABSTRACTBACKGROUND: Major depressive disorder (MDD) with atypical features, namely depression with atypical features (AFD), is one of the most common clinical specifiers of MDD, closely associated with bipolar disorder (BD). However, there is still a lack of clinical guidelines for the diagnosis, treatment, and prognosis of AFD. Our study mainly focuses on three issues about how to identify AFD, what is the appropriate individualized treatment for AFD, and what are the predictive biomarkers of conversion to BD.METHODS: The Study of Individualized Diagnosis and Treatment for Depression with Atypical Features (iDoT-AFD) is a multicenter, prospective, open-label study consisting of a 12-week randomized controlled trial (RCT) and a continued follow-up until 4 years or reaching the study endpoint. It is enrolling 480 patients with AFD (120 per treatment arm), 100 patients with BD, and 100 healthy controls (HC). Multivariate dimension information is collected including clinical features, cognitive function, kynurenine pathway metabolomics, and multimodal magnetic resonance imaging (MRI) data. Firstly, multivariate informatics analyses are performed to recognize patients with AFD from participants including the first-episode and recurrent atypical depression, patients with BD, and patients with HC. Secondly, patients with atypical depression are randomly allocated to one of the four treatment groups including "single application of selective serotonin reuptake inhibitor (SSRI) or serotonin-noradrenaline reuptake inhibitor (SNRI)", "SSRI/SNRI combined with mood stabilizer," "SSRI/SNRI combined with quetiapine (≥ 150 mg/day)," or "treatment as usual (TAU)" and then followed up 12 weeks to find out the optimized treatment strategies. Thirdly, patients with atypical depression are followed up until 4 years or switching to BD, to explore the risk factors of conversion from atypical depression to BD and eventually build the risk warning model of conversion to BD.DISCUSSION: The first enrolment was in August 2019. The iDoT-AFD study explores the clinical and biological markers for the diagnosis, treatment, and prognosis of AFD and further provides evidence for clinical guidelines of AFD.TRIAL REGISTRATION: ClinicalTrials.gov NCT04209166. Registered on December 19, 2019.PMID:37143128 | DOI:10.1186/s13063-023-07317-w

Chin Med. 2023 May 4;18(1):48. doi: 10.1186/s13020-023-00750-8.ABSTRACTBACKGROUND: Cold-dampness Syndrome (RA-Cold) and Hot-dampness Syndrome (RA-Hot) are two distinct groups of rheumatoid arthritis (RA) patients with different clinical symptoms based on traditional Chinese medicine (TCM) theories and clinical empirical knowledge. However, the biological basis of the two syndromes has not been fully elucidated, which may restrict the development of personalized medicine and drug discovery for RA diagnosis and therapy.METHODS: An integrative strategy combining clinical transcriptomics, phenomics, and metabolomics data based on clinical cohorts and adjuvant-induced arthritis rat models was performed to identify novel candidate biomarkers and to investigate the biological basis of RA-Cold and RA-Hot.RESULTS: The main clinical symptoms of RA-Cold patients are joint swelling, pain, and contracture, which may be associated with the dysregulation of T cell-mediated immunity, osteoblast differentiation, and subsequent disorders of steroid biosynthesis and phenylalanine metabolism. In contrast, the main clinical symptoms of RA-Hot patients are fever, irritability, and vertigo, which may be associated with various signals regulating angiogenesis, adrenocorticotropic hormone release, and NLRP3 inflammasome activation, leading to disorders of steroid biosynthesis, nicotinamide, and sphingolipid metabolism. IL17F, 5-HT, and IL4I1 were identified as candidate biomarkers of RA-Cold, while S1P and GLNS were identified as candidate biomarkers of RA-Hot.CONCLUSIONS: The current study presents the most comprehensive metabonomic and transcriptomic profiling of serum, urine, synovial fluid, and synovial tissue samples obtained from RA-Cold and RA-Hot patients and experimental animal models to date. Through the integration of multi-omics data and clinical independent validation, a list of novel candidate biomarkers of RA-Cold and RA-Hot syndromes were identified, that may be useful in improving RA diagnosis and therapy.PMID:37143094 | DOI:10.1186/s13020-023-00750-8

Methods Mol Biol. 2023;2644:193-209. doi: 10.1007/978-1-0716-3052-5_12.ABSTRACTCellular health, functionality, response to environment, and other variables affecting cell, tissue, or organ viability are reflected in the cellular proteomes and metabolomes. These "omic" profiles are in constant flux even during normal cellular functioning, to maintain cellular homeostasis, in response to small environmental changes and maintenance of optimal cell viability. However proteomic "fingerprints" can also provide insight into cellular ageing, response to disease, adjustment to environmental changes, and other variables that impact cellular viability. A variety of proteomic methods can be used to determine qualitative and quantitative proteomic change. In this chapter, we will focus on a labeling method called isobaric tags for relative and absolute quantification (iTRAQ), which is frequently used to identify and quantify proteomic expression changes in cells and tissues.PMID:37142923 | DOI:10.1007/978-1-0716-3052-5_12

Cell Death Dis. 2023 May 5;14(5):306. doi: 10.1038/s41419-023-05806-z.ABSTRACTThe major underlying cause for the high mortality rate in colorectal cancer (CRC) relies on its drug resistance, to which intratumor heterogeneity (ITH) contributes substantially. CRC tumors have been reported to comprise heterogeneous populations of cancer cells that can be grouped into 4 consensus molecular subtypes (CMS). However, the impact of inter-cellular interaction between these cellular states on the emergence of drug resistance and CRC progression remains elusive. Here, we explored the interaction between cell lines belonging to the CMS1 (HCT116 and LoVo) and the CMS4 (SW620 and MDST8) in a 3D coculture model, mimicking the ITH of CRC. The spatial distribution of each cell population showed that CMS1 cells had a preference to grow in the center of cocultured spheroids, while CMS4 cells localized at the periphery, in line with observations in tumors from CRC patients. Cocultures of CMS1 and CMS4 cells did not alter cell growth, but significantly sustained the survival of both CMS1 and CMS4 cells in response to the front-line chemotherapeutic agent 5-fluorouracil (5-FU). Mechanistically, the secretome of CMS1 cells exhibited a remarkable protective effect for CMS4 cells against 5-FU treatment, while promoting cellular invasion. Secreted metabolites may be responsible for these effects, as demonstrated by the existence of 5-FU induced metabolomic shifts, as well as by the experimental transfer of the metabolome between CMS1 and CMS4 cells. Overall, our results suggest that the interplay between CMS1 and CMS4 cells stimulates CRC progression and reduces the efficacy of chemotherapy.PMID:37142595 | DOI:10.1038/s41419-023-05806-z

Nat Commun. 2023 May 4;14(1):2581. doi: 10.1038/s41467-023-38335-6.ABSTRACTMany signaling and other genes known as "hidden" drivers may not be genetically or epigenetically altered or differentially expressed at the mRNA or protein levels, but, rather, drive a phenotype such as tumorigenesis via post-translational modification or other mechanisms. However, conventional approaches based on genomics or differential expression are limited in exposing such hidden drivers. Here, we present a comprehensive algorithm and toolkit NetBID2 (data-driven network-based Bayesian inference of drivers, version 2), which reverse-engineers context-specific interactomes and integrates network activity inferred from large-scale multi-omics data, empowering the identification of hidden drivers that could not be detected by traditional analyses. NetBID2 has substantially re-engineered the previous prototype version by providing versatile data visualization and sophisticated statistical analyses, which strongly facilitate researchers for result interpretation through end-to-end multi-omics data analysis. We demonstrate the power of NetBID2 using three hidden driver examples. We deploy NetBID2 Viewer, Runner, and Cloud apps with 145 context-specific gene regulatory and signaling networks across normal tissues and paediatric and adult cancers to facilitate end-to-end analysis, real-time interactive visualization and cloud-based data sharing. NetBID2 is freely available at https://jyyulab.github.io/NetBID .PMID:37142594 | DOI:10.1038/s41467-023-38335-6

Nat Commun. 2023 May 4;14(1):2587. doi: 10.1038/s41467-023-38304-z.ABSTRACTMulti-enzymatic cascades with enzymes arranged in close-proximity through a protein scaffold can trigger a substrate channeling effect, allowing for efficient cofactor reuse with industrial potential. However, precise nanometric organization of enzymes challenges the design of scaffolds. In this study, we create a nanometrically organized multi-enzymatic system exploiting engineered Tetrapeptide Repeat Affinity Proteins (TRAPs) as scaffolding for biocatalysis. We genetically fuse TRAP domains and program them to selectively and orthogonally recognize peptide-tags fused to enzymes, which upon binding form spatially organized metabolomes. In addition, the scaffold encodes binding sites to selectively and reversibly sequester reaction intermediates like cofactors via electrostatic interactions, increasing their local concentration and, consequently, the catalytic efficiency. This concept is demonstrated for the biosynthesis of amino acids and amines using up to three enzymes. Scaffolded multi-enzyme systems present up to 5-fold higher specific productivity than the non-scaffolded ones. In-depth analysis suggests that channeling of NADH cofactor between the assembled enzymes enhances the overall cascade throughput and the product yield. Moreover, we immobilize this biomolecular scaffold on solid supports, creating reusable heterogeneous multi-functional biocatalysts for consecutive operational batch cycles. Our results demonstrate the potential of TRAP-scaffolding systems as spatial-organizing tools to increase the efficiency of cell-free biosynthetic pathways.PMID:37142589 | DOI:10.1038/s41467-023-38304-z

J Plant Physiol. 2023 Apr 27;285:153996. doi: 10.1016/j.jplph.2023.153996. Online ahead of print.ABSTRACTContinuous cropping of ginseng leads to serious declines in yield and quality because of self-toxicity of allelochemicals and other factors in soil. However, because of the long growth cycle and low survival rate of ginseng, rapid screening of autotoxic activity is difficult. Therefore, it is important to analyze the allelochemicals and identify a model plant with autotoxic responses similar to those of ginseng. In this study, UPLC-Orbitrap-HRMS targeted metabolomics and verification of autotoxic activity were used to analyze a problem soil from continuously cropped ginseng. Allelochemical markers were screened by OPLS-DA. Seeds and seedlings of maize, Chinese cabbage, cucumber, green beans, wheat, sunflower, and oats were selected to identify potential model plants. Model plants with autotoxic responses similar to those of ginseng were evaluated by comparing morphological, physiological, and biochemical characteristics. The n-butanol extract of the continuously cropped problem soil had the most significant autotoxic activity. Twenty-three ginsenosides and the contributions to autotoxic effects were screened and evaluated. Of potential model plants, seeds and seedlings of cucumber showed similar growth inhibition to that of ginseng under the action of allelochemicals. Thus, metabolomics can be used to screen allelochemicals in soil and predict the autotoxic effects, and the cucumber plant model can be used to rapidly screen allelopathic activity of ginseng. The study will provide reference for methodology in allelopathy research on ginseng.PMID:37141674 | DOI:10.1016/j.jplph.2023.153996

Biotechnol Prog. 2023 May 4:e3352. doi: 10.1002/btpr.3352. Online ahead of print.ABSTRACTThe strategy of temperature downshift has been widely used in the biopharmaceutical industry to improve antibody production and cell-specific production rate (qp ) with Chinese hamster ovary cells (CHO). However, the mechanism of temperature-induced metabolic rearrangement, especially important intracellular metabolic events, remains poorly understood. In this work, in order to explore the mechanisms of temperature-induced cell metabolism, we systematically assessed the differences in cell growth, antibody expression, and antibody quality between high-producing (HP) and low-producing (LP) CHO cell lines under both constant temperature (37°C) and temperature downshift (37°C→33°C) settings during fed-batch culture. Although the results showed that low-temperature culture during the late phase of exponential cell growth significantly reduced the maximum viable cell density (p < 0.05) and induced cell cycle arrest in the G0/G1 phase, this temperature downshift led to a higher cellular viability and increased antibody titer by 48% and 28% in HP and LP CHO cell cultures, respectively (p < 0.001), and favored antibody quality reflected in reduced charge heterogeneity and molecular size heterogeneity. Combined extra- and intra-cellular metabolomics analyses revealed that temperature downshift significantly downregulated intracellular glycolytic and lipid metabolic pathways while upregulated tricarboxylic acid (TCA) cycle, and particularly featured upregulated glutathione metabolic pathways. Interestingly, all these metabolic pathways were closely associated with the maintenance of intracellular redox state and oxidative stress-alleviating strategies. To experimentally address this, we developed two high-performance fluorescent biosensors, denoted SoNar and iNap1, for real-time monitoring of intracellular nicotinamide adenine dinucleotide/nicotinamide adenine dinucleotide + hydrogen (NAD+ /NADH) ratio and nicotinamide adenine dinucleotide phosphate (NADPH) amount, respectively. Consistent with such metabolic rearrangements, the results showed that temperature downshift decreased the intracellular NAD+ /NADH ratio, which might be ascribed to the re-consumption of lactate, and increased the intracellular NADPH amount (p < 0.01) to scavenge intracellular reactive oxygen species (ROS) induced by the increased metabolic requirements for high-level expression of antibody. Collectively, this study provides a metabolic map of cellular metabolic rearrangement induced by temperature downshift and demonstrates the feasibility of real-time fluorescent biosensors for biological processes, thus potentially providing a new strategy for dynamic optimization of antibody production processes.PMID:37141532 | DOI:10.1002/btpr.3352

Environ Health Perspect. 2023 May;131(5):57002. doi: 10.1289/EHP11139. Epub 2023 May 4.ABSTRACTBACKGROUND: Exposure to traffic-related air pollution (TRAP) has been associated with increased risks of respiratory diseases, but the biological mechanisms are not yet fully elucidated.OBJECTIVES: Our aim was to evaluate the respiratory responses and explore potential biological mechanisms of TRAP exposure in a randomized crossover trial.METHODS: We conducted a randomized crossover trial in 56 healthy adults. Each participant was exposed to high- and low-TRAP exposure sessions by walking in a park and down a road with high traffic volume for 4 h in random order. Respiratory symptoms and lung function, including forced expiratory volume in the first second (FEV1), forced vital capacity (FVC), the ratio of FEV1 to FVC, and maximal mid-expiratory flow (MMEF), were measured before and after each exposure session. Markers of 8-isoprostane, tumor necrosis factor-α (TNF-α), and ezrin in exhaled breath condensate (EBC), and surfactant proteins D (SP-D) in serum were also measured. We used linear mixed-effects models to estimate the associations, adjusted for age, sex, body mass index, meteorological condition, and batch (only for biomarkers). Liquid chromatography-mass spectrometry was used to profile the EBC metabolome. Untargeted metabolome-wide association study (MWAS) analysis and pathway enrichment analysis using mummichog were performed to identify critical metabolomic features and pathways associated with TRAP exposure.RESULTS: Participants had two to three times higher exposure to traffic-related air pollutants except for fine particulate matter while walking along the road compared with in the park. Compared with the low-TRAP exposure at the park, high-TRAP exposure at the road was associated with a higher score of respiratory symptoms [2.615 (95% CI: 0.605, 4.626), p=1.2×10-2] and relatively lower lung function indicators [-0.075L (95% CI: -0.138, -0.012), p=2.1×10-2] for FEV1 and -0.190L/s (95% CI: -0.351, -0.029; p=2.4×10-2) for MMEF]. Exposure to TRAP was significantly associated with changes in some, but not all, biomarkers, particularly with a 0.494-ng/mL (95% CI: 0.297, 0.691; p=9.5×10-6) increase for serum SP-D and a 0.123-ng/mL (95% CI: -0.208, -0.037; p=7.2×10-3) decrease for EBC ezrin. Untargeted MWAS analysis revealed that elevated TRAP exposure was significantly associated with perturbations in 23 and 32 metabolic pathways under positive- and negative-ion modes, respectively. These pathways were most related to inflammatory response, oxidative stress, and energy use metabolism.CONCLUSIONS: This study suggests that TRAP exposure might lead to lung function impairment and respiratory symptoms. Possible underlying mechanisms include lung epithelial injury, inflammation, oxidative stress, and energy metabolism disorders. https://doi.org/10.1289/EHP11139.PMID:37141245 | DOI:10.1289/EHP11139

PLoS One. 2023 May 4;18(5):e0284315. doi: 10.1371/journal.pone.0284315. eCollection 2023.ABSTRACTMachine learning (ML) models are used in clinical metabolomics studies most notably for biomarker discoveries, to identify metabolites that discriminate between a case and control group. To improve understanding of the underlying biomedical problem and to bolster confidence in these discoveries, model interpretability is germane. In metabolomics, partial least square discriminant analysis (PLS-DA) and its variants are widely used, partly due to the model's interpretability with the Variable Influence in Projection (VIP) scores, a global interpretable method. Herein, Tree-based Shapley Additive explanations (SHAP), an interpretable ML method grounded in game theory, was used to explain ML models with local explanation properties. In this study, ML experiments (binary classification) were conducted for three published metabolomics datasets using PLS-DA, random forests, gradient boosting, and extreme gradient boosting (XGBoost). Using one of the datasets, PLS-DA model was explained using VIP scores, while one of the best-performing models, a random forest model, was interpreted using Tree SHAP. The results show that SHAP has a more explanation depth than PLS-DA's VIP, making it a powerful method for rationalizing machine learning predictions from metabolomics studies.PMID:37141218 | DOI:10.1371/journal.pone.0284315

Angew Chem Int Ed Engl. 2023 May 4:e202302110. doi: 10.1002/anie.202302110. Online ahead of print.ABSTRACTHyperpolarized nuclear magnetic resonance (NMR) offers an ensemble of methods that remarkably address the sensitivity issues of NMR. Dissolution Dynamic Nuclear Polarization (d-DNP) provides a unique and general way to detect 13C NMR signals with a sensitivity enhanced by several orders of magnitude. The expanding application scope of d-DNP now encompasses the analysis of complex mixtures at natural 13C abundance. However, it has in this area been limited to metabolite extracts. Here, we report the first d-DNP-enhanced 13C NMR analysis of a biofluid -urine- at natural abundance, offering unprecedented resolution and sensitivity for this challenging type of sample. We also show that accurate quantitative information on multiple targeted metabolites can be retrieved through a standard addition procedure.PMID:37141160 | DOI:10.1002/anie.202302110

Am J Physiol Renal Physiol. 2023 May 4. doi: 10.1152/ajprenal.00301.2022. Online ahead of print.ABSTRACTAutosomal dominant polycystic kidney disease (ADPKD) is characterized by the formation of numerous fluid-filled cysts that lead to progressive loss of functional nephrons. Currently, there is an unmet need for diagnostic and prognostic indicators of early-stage ADPKD. Metabolites were extracted from the urine of early-stage ADPKD patients (n=48) and age- and sex-matched normal controls (n=47) and analyzed by liquid chromatography-mass spectrometry. Orthogonal partial least squares-discriminant analysis was employed to generate a global metabolomic profile of early ADPKD for the identification of metabolic pathway alterations and discriminatory metabolites as candidate diagnostic and prognostic biomarkers. The global metabolomic profile exhibited alterations in metabolism of steroids, fatty acids, pyruvate, amino acids, and the urea cycle. A panel of 46 metabolite features were identified as candidate diagnostic biomarkers. Notable putative identities of candidate diagnostic biomarkers for early detection include creatinine, cAMP, dCMP, various androgens, betaine aldehyde, phosphoric acid, choline, 18-hydroxycorticosterone, and cortisol. Metabolic pathways associated with variable rates of disease progression included metabolism of steroids, vitamin D3, fatty acids, amino acids, sialic acid, the pentose phosphate pathway, the tricarboxylic acid cycle, and chondroitin sulfate and heparin sulfate degradation. A panel of 41 metabolite features were identified as candidate prognostic biomarkers. Notable putative identities of candidate prognostic biomarkers include ethanolamine, C20:4 anandamide phosphate, progesterone, various androgens, betaine aldehyde, inflammatory lipids, and choline. Our exploratory data support metabolic reprogramming in early ADPKD and demonstrate the ability of mass spectrometry-based global metabolomic profiling to detect metabolic pathway alterations as new therapeutic targets and biomarkers of ADPKD.PMID:37141147 | DOI:10.1152/ajprenal.00301.2022

Environ Sci Pollut Res Int. 2023 May 4. doi: 10.1007/s11356-023-27051-9. Online ahead of print.ABSTRACTChronic exposure to excessive environmental fluoride has caused fluorosis to become a major public health problem worldwide. Although studies on stress pathways, signaling pathways, and apoptosis induced by fluoride have provided an in-depth understanding of the mechanism of this disease, its exact pathogenesis remains unclear. We hypothesized that the human gut microbiota and metabolome are associated with the pathogenesis of this disease. To get further insight into the profiles of intestinal microbiota and metabolome in coal-burning-induced endemic fluorosis patients, we conducted 16S rRNA sequencing of the intestinal microbial DNA and carried out non-targeted metabolomics of fecal samples from 32 patients with skeletal fluorosis and 33 matched healthy controls in Guizhou, China. We found that the gut microbiota of coal-burning endemic fluorosis patients displayed significant differences in composition, diversity, and abundance compared with healthy controls. This was characterized by an increase in the relative abundance of Verrucomicrobiota, Desulfobacterota, Nitrospirota, Crenarchaeota, Chloroflexi, Myxococcota, Acidobacteriota, Proteobacteria, and unidentified_Bacteria, and a significant decrease in the relative abundance of Firmicutes and Bacteroidetes at the phylum level. Additionally, at the genus level, the relative abundance of some beneficial bacteria, such as Bacteroides, Megamonas, Bifidobacterium, and Faecalibacterium, was significantly reduced. We also demonstrated that, at the genus level, some gut microbial markers, including Anaeromyxobacter, MND1, oc32, Haliangium, and Adurb.Bin063_1, showed potential for identifying coal-burning endemic fluorosis. Moreover, non-targeted metabolomics and correlation analysis revealed the changes in the metabolome, particularly the gut microbiota-derived tryptophan metabolites such as tryptamine, 5-hydroxyindoleacetic acid, and indoleacetaldehyde. Our results indicated that excessive fluoride might cause xenobiotic-mediated dysbiosis of human gut microbiota and metabolic disorders. These findings suggest that the alterations in gut microbiota and metabolome play vital roles in regulating disease susceptibility and multi-organ damage after excessive fluoride exposure.PMID:37140865 | DOI:10.1007/s11356-023-27051-9

Methods Mol Biol. 2023;2668:57-68. doi: 10.1007/978-1-0716-3203-1_6.ABSTRACTExtracellular vesicle (EV) release and their content are influenced by diverse clinical conditions. EVs participate in inter-cellular communication and have been postulated as reflectors of the pathophysiology of the cells, tissues, organs or the whole system with which they are in contact. Urinary EVs have been proved to reflect pathophysiology not only of renal system related diseases constituting an additional source of potential biomarkers easily accessible in a non-invasive way. The interest in EVs cargo has been mostly focused on proteins and nucleic acids and more recently it has been extended to metabolites. Metabolites represent the downstream changes in the genome, transcriptome, and proteome as a reflection of processes occurring in living organisms. For their study, nuclear magnetic resonance (NMR) and mass spectrometry in tandem (LC-MS/MS) are widely used. NMR is a reproducible and non-destructive technique and we show here methodological protocols for the metabolomics analysis of urinary EVs by NMR. Additionally, we also describe the workflow for a targeted LC-MS/MS analysis that is extensible to untargeted studies.PMID:37140790 | DOI:10.1007/978-1-0716-3203-1_6

Pediatr Nephrol. 2023 May 4. doi: 10.1007/s00467-023-05963-5. Online ahead of print.ABSTRACTBACKGROUND: Nocturnal enuresis (NE) is a common disease with multiple pathogenic mechanisms. This study aimed to compare levels of metabolites and proteins between wet and dry nights in urine samples from children with monosymptomatic NE (MNE).METHODS: Ten boys with MNE and nocturnal polyuria (age: 7.6 ± 1.3 years) collected their total nighttime urine production during a wet and a dry night. Untargeted metabolomics and proteomics were performed on the urine samples by liquid chromatography coupled with high-mass accuracy tandem mass spectrometry (LC-MS/MS).RESULTS: On wet nights, we found reduced urine osmolality (P = 0.025) and increased excretion of urinary potassium and sodium by a factor of, respectively, 2.1 (P = 0.038) and 1.9 (P = 0.19) compared with dry nights. LC-MS identified 59 metabolites and 84 proteins with significantly different levels between wet and dry nights (fold change (FC) < 0.67 or > 1.5, P < 0.05). Some compounds were validated by different methodologies. During wet nights, levels of compounds related to oxidative stress and blood pressure, including adrenalin, were increased. We found reduced levels of aquaporin-2 on wet nights. The FCs in the 59 metabolites were positively correlated to the FCs in the same metabolites identified in urine samples obtained during the evening preceding wet and dry nights.CONCLUSIONS: Oxidative stress, which in the literature has been associated with nocturia and disturbances in sleep, might be increased during wet nights in children with MNE. We further found evidence of increased sympathetic activity. The mechanisms related to having wet nights in children with MNE seem complex, and both free water and solute handling appear to be important. A higher resolution version of the Graphical abstract is available as Supplementary information.PMID:37140712 | DOI:10.1007/s00467-023-05963-5

Elife. 2023 May 4;12:e86336. doi: 10.7554/eLife.86336. Online ahead of print.ABSTRACTEndocytosis through Drosophila glia is a significant determinant of sleep amount and occurs preferentially during sleep in glia of the blood brain barrier (BBB). To identify metabolites whose trafficking is mediated by sleep-dependent endocytosis, we conducted metabolomic analysis of flies that have increased sleep due to a block in glial endocytosis. We report that acylcarnitines, fatty acids conjugated to carnitine to promote their transport, accumulate in heads of these animals. In parallel, to identify transporters and receptors whose loss contributes to the sleep phenotype caused by blocked endocytosis, we screened genes enriched in barrier glia for effects on sleep. We find that knockdown of lipid transporters LRP1&2 or of carnitine transporters ORCT1&2 increases sleep. In support of the idea that the block in endocytosis affects trafficking through specific transporters, knockdown of LRP or ORCT transporters also increases acylcarnitines in heads. We propose that lipid species, such as acylcarnitines, are trafficked through the BBB via sleep-dependent endocytosis, and their accumulation reflects an increased need for sleep.PMID:37140181 | DOI:10.7554/eLife.86336

J Natl Cancer Inst Monogr. 2023 May 4;2023(61):12-29. doi: 10.1093/jncimonographs/lgad014.ABSTRACTThe obesity pandemic currently affects more than 70 million Americans and more than 650 million individuals worldwide. In addition to increasing susceptibility to pathogenic infections (eg, SARS-CoV-2), obesity promotes the development of many cancer subtypes and increases mortality rates in most cases. We and others have demonstrated that, in the context of B-cell acute lymphoblastic leukemia (B-ALL), adipocytes promote multidrug chemoresistance. Furthermore, others have demonstrated that B-ALL cells exposed to the adipocyte secretome alter their metabolic states to circumvent chemotherapy-mediated cytotoxicity. To better understand how adipocytes impact the function of human B-ALL cells, we used a multi-omic RNA-sequencing (single-cell and bulk transcriptomic) and mass spectroscopy (metabolomic and proteomic) approaches to define adipocyte-induced changes in normal and malignant B cells. These analyses revealed that the adipocyte secretome directly modulates programs in human B-ALL cells associated with metabolism, protection from oxidative stress, increased survival, B-cell development, and drivers of chemoresistance. Single-cell RNA sequencing analysis of mice on low- and high-fat diets revealed that obesity suppresses an immunologically active B-cell subpopulation and that the loss of this transcriptomic signature in patients with B-ALL is associated with poor survival outcomes. Analyses of sera and plasma samples from healthy donors and those with B-ALL revealed that obesity is associated with higher circulating levels of immunoglobulin-associated proteins, which support observations in obese mice of altered immunological homeostasis. In all, our multi-omics approach increases our understanding of pathways that may promote chemoresistance in human B-ALL and highlight a novel B-cell-specific signature in patients associated with survival outcomes.PMID:37139973 | DOI:10.1093/jncimonographs/lgad014

J Agric Food Chem. 2023 May 4. doi: 10.1021/acs.jafc.2c08637. Online ahead of print.ABSTRACTDisturbance of the gut microbiota plays a critical role in the development of nonalcoholic fatty liver disease (NAFLD). Increasing evidence supports that natural products may serve as prebiotics to regulate the gut microbiota in the treatment of NAFLD. In the present study, the effect of nobiletin, a naturally occurring polymethoxyflavone, on NAFLD was evaluated, and metabolomics, 16S rRNA gene sequencing, and transcriptomics analysis were performed to determine the underlying mechanism of nobiletin, and the key bacteria and metabolites screened were confirmed by in vivo experiment. Nobiletin treatment could significantly reduce lipid accumulation in high-fat/high-sucrose diet-fed mice. 16S rRNA analysis demonstrated that nobiletin could reverse the dysbiosis of gut microbiota in NAFLD mice and nobiletin could regulate myristoleic acid metabolism, as revealed by untargeted metabolomics analysis. Treatment with the bacteria Allobaculum stercoricanis, Lactobacillus casei, or the metabolite myristoleic acid displayed a protective effect on liver lipid accumulation under metabolic stress. These results indicated that nobiletin might target gut microbiota and myristoleic acid metabolism to ameliorate NAFLD.PMID:37139957 | DOI:10.1021/acs.jafc.2c08637