PubMed

New Phytol. 2023 Nov 14. doi: 10.1111/nph.19378. Online ahead of print.ABSTRACTLand plants evolved multiple adaptations to restrict transpiration. However, the underlying molecular mechanisms are not sufficiently understood. We used an ozone-sensitivity forward genetics approach to identify Arabidopsis thaliana mutants impaired in gas exchange regulation. High water loss from detached leaves and impaired decrease of leaf conductance in response to multiple stomata-closing stimuli were identified in a mutant of MURUS1 (MUR1), an enzyme required for GDP-l-fucose biosynthesis. High water loss observed in mur1 was independent from stomatal movements and instead could be linked to metabolic defects. Plants defective in import of GDP-l-Fuc into the Golgi apparatus phenocopied the high water loss of mur1 mutants, linking this phenotype to Golgi-localized fucosylation events. However, impaired fucosylation of xyloglucan, N-linked glycans, and arabinogalactan proteins did not explain the aberrant water loss of mur1 mutants. Partial reversion of mur1 water loss phenotype by borate supplementation and high water loss observed in boron uptake mutants link mur1 gas exchange phenotypes to pleiotropic consequences of l-fucose and boron deficiency, which in turn affect mechanical and morphological properties of stomatal complexes and whole-plant physiology. Our work emphasizes the impact of fucose metabolism and boron uptake on plant-water relations.PMID:37964509 | DOI:10.1111/nph.19378

Molecules. 2023 Oct 30;28(21):7345. doi: 10.3390/molecules28217345.ABSTRACTTraditional strategies for the metabolic profiling of doping are limited by the unpredictable metabolic pathways and the numerous proportions of background and chemical noise that lead to inadequate metabolism knowledge, thereby affecting the selection of optimal detection targets. Thus, a stable isotope labeling-based nontargeted strategy combined with ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) was first proposed for the effective and rapid metabolism analysis of small-molecule doping agents and demonstrated via its application to a novel doping BPC-157. Using 13C/15N-labeled BPC-157, a complete workflow including automatic 13C0,15N0-13C6,15N2m/z pair picking based on the characteristic behaviors of isotope pairs was constructed, and one metabolite produced by a novel metabolic pathway plus eight metabolites produced by the conventional amide-bond breaking metabolic pathway were successfully discovered from two incubation models. Furthermore, a specific method for the detection of BPC-157 and the five main metabolites in human urine was developed and validated with satisfactory detection limits (0.01~0.11 ng/mL) and excellent quantitative ability (linearity: 0.02~50 ng/mL with R2 > 0.999; relative error (RE)% < 10% and relative standard deviation (RSD)% < 5%; recovery > 90%). The novel metabolic pathway and the in vitro metabolic profile could provide new insights into the biotransformation of BPC-157 and improved targets for doping control.PMID:37959764 | DOI:10.3390/molecules28217345

Int J Biol Macromol. 2023 Nov 12:128063. doi: 10.1016/j.ijbiomac.2023.128063. Online ahead of print.ABSTRACTWRKY transcription factors (TFs) are a superfamily of regulators involved in plant responses to pathogens and abiotic stress. Passion fruit is famous for its unique flavor and nutrient-rich juice, but its growth is limited by environmental factors and pathogens. In this study, 55 WRKY genes were identified from the Passiflora edulis genome. The structure and evolutionary characteristics of PeWRKYs were analyzed using a bioinformatics approach. PeWRKYs were classified into seven subgroups (I, IIa, IIb, IIc, IId, IIe, III) according to their homologs in Arabidopsis thaliana. Group IIa PeWRKY48 gene was highly up-regulated under cold stress by RNA expression analysis, and transgenic PeWRKY48 in yeast and Arabidopsis showed resistance exposure to cold, salt, and drought stress. Metabolome and transcriptome co-expression analysis of two different disease resistance genotypes of P. edulis identified PeWRKY30 as a key TF co-expressed with flavonoid accumulation in yellow fruit P. edulis, which may contribute to biotic or abiotic resistance. The qRT-PCR verified the expression of key genes in different tissues of P. edulis and in different species of Passiflora. This study provides a set of WRKY candidate genes that will facilitate the genetic improvement of disease and abiotic tolerance in passion fruit.PMID:37963507 | DOI:10.1016/j.ijbiomac.2023.128063

BMC Bioinformatics. 2023 Nov 14;24(1):431. doi: 10.1186/s12859-023-05525-4.ABSTRACTBACKGROUND: Liquid chromatography-mass spectrometry is widely used in untargeted metabolomics for composition profiling. In multi-run analysis scenarios, features of each run are aligned into consensus features by feature alignment algorithms to observe the intensity variations across runs. However, most of the existing feature alignment methods focus more on accurate retention time correction, while underestimating the importance of feature matching. None of the existing methods can comprehensively consider feature correspondences among all runs and achieve optimal matching.RESULTS: To comprehensively analyze feature correspondences among runs, we propose G-Aligner, a graph-based feature alignment method for untargeted LC-MS data. In the feature matching stage, G-Aligner treats features and potential correspondences as nodes and edges in a multipartite graph, considers the multi-run feature matching problem an unbalanced multidimensional assignment problem, and provides three combinatorial optimization algorithms to find optimal matching solutions. In comparison with the feature alignment methods in OpenMS, MZmine2 and XCMS on three public metabolomics benchmark datasets, G-Aligner achieved the best feature alignment performance on all the three datasets with up to 9.8% and 26.6% increase in accurately aligned features and analytes, and helped all comparison software obtain more accurate results on their self-extracted features by integrating G-Aligner to their analysis workflow. G-Aligner is open-source and freely available at https://github.com/CSi-Studio/G-Aligner under a permissive license. Benchmark datasets, manual annotation results, evaluation methods and results are available at https://doi.org/10.5281/zenodo.8313034 CONCLUSIONS: In this study, we proposed G-Aligner to improve feature matching accuracy for untargeted metabolomics LC-MS data. G-Aligner comprehensively considered potential feature correspondences between all runs, converting the feature matching problem as a multidimensional assignment problem (MAP). In evaluations on three public metabolomics benchmark datasets, G-Aligner achieved the highest alignment accuracy on manual annotated and popular software extracted features, proving the effectiveness and robustness of the algorithm.PMID:37964228 | DOI:10.1186/s12859-023-05525-4

Nat Commun. 2023 Nov 14;14(1):7348. doi: 10.1038/s41467-023-43262-7.ABSTRACTUbiquitination is a post-translational modification initiated by the E1 enzyme UBA1, which transfers ubiquitin to ~35 E2 ubiquitin-conjugating enzymes. While UBA1 loss is cell lethal, it remains unknown how partial reduction in UBA1 activity is endured. Here, we utilize deep-coverage mass spectrometry to define the E1-E2 interactome and to determine the proteins that are modulated by knockdown of UBA1 and of each E2 in human cells. These analyses define the UBA1/E2-sensitive proteome and the E2 specificity in protein modulation. Interestingly, profound adaptations in peroxisomes and other organelles are triggered by decreased ubiquitination. While the cargo receptor PEX5 depends on its mono-ubiquitination for binding to peroxisomal proteins and importing them into peroxisomes, we find that UBA1/E2 knockdown induces the compensatory upregulation of other PEX proteins necessary for PEX5 docking to the peroxisomal membrane. Altogether, this study defines a homeostatic mechanism that sustains peroxisomal protein import in cells with decreased ubiquitination capacity.PMID:37963875 | DOI:10.1038/s41467-023-43262-7

Nat Commun. 2023 Nov 14;14(1):7351. doi: 10.1038/s41467-023-43205-2.ABSTRACTProduction of secondary metabolites is controlled by a complicated regulatory network in eukaryotic cells. Several layers of regulators are involved in this process, ranging from pathway-specific regulation, to epigenetic control, to global regulation. Here, we discover that interaction of an RNA-binding protein CsdA with a regulator RsdA coordinates fungal secondary metabolism. Employing a genetic deletion approach and transcriptome analysis as well as metabolomics analysis, we reveal that CsdA and RsdA synergistically regulate fungal secondary metabolism comprehensively. Mechanistically, comprehensive genetic and biochemical studies prove that RsdA and CsdA co-localize in the nucleus and physically interact to achieve their functions. In particular, we demonstrate that CsdA mediates rsdA expression by binding specific motif "GUCGGUAU" of its pre-mRNA at a post-transcriptional level. We thus uncover a mechanism in which RNA-binding protein physically interacts with, and controls the expression level of, the RsdA to coordinate fungal secondary metabolism.PMID:37963872 | DOI:10.1038/s41467-023-43205-2

Anal Chem. 2023 Nov 14. doi: 10.1021/acs.analchem.3c03343. Online ahead of print.ABSTRACTCommonly, in MS-based untargeted metabolomics, some metabolites cannot be confidently identified due to ambiguities in resolving isobars and structurally similar species. To address this, analytical techniques beyond traditional MS2 analysis, such as MSn fragmentation, can be applied to probe metabolites for additional structural information. In MSn fragmentation, recursive cycles of activation are applied to fragment ions originating from the same precursor ion detected on an MS1 spectrum. This resonant-type collision-activated dissociation (CAD) can yield information that cannot be ascertained from MS2 spectra alone, which helps improve the performance of metabolite identification workflows. However, most approaches for metabolite identification require mass-to-charge (m/z) values measured with high resolution, as this enables the determination of accurate mass values. Unfortunately, high-resolution-MSn spectra are relatively rare in spectral libraries. Here, we describe a computational approach to generate a database of high-resolution-MSn spectra by converting existing low-resolution-MSn spectra using complementary high-resolution-MS2 spectra generated by beam-type CAD. Using this method, we have generated a database, derived from the NIST20 MS/MS database, of MSn spectral trees representing 9637 compounds and 19386 precursor ions where at least 90% of signal intensity was converted from low-to-high resolution.PMID:37963318 | DOI:10.1021/acs.analchem.3c03343

Geroscience. 2023 Nov 14. doi: 10.1007/s11357-023-01001-2. Online ahead of print.ABSTRACTHeterogeneous accumulation of senescent cells expressing the senescence-associated secretory phenotype (SASP) affects tissue homeostasis which leads to diseases, such as osteoarthritis (OA). In this study, we set out to characterize heterogeneity of cellular senescence within aged articular cartilage and explored the presence of corresponding metabolic profiles in blood that could function as representative biomarkers. Hereto, we set out to perform cluster analyses, using a gene-set of 131 senescence genes (N = 57) in a previously established RNA sequencing dataset of aged articular cartilage and a generated metabolic dataset in overlapping blood samples. Using unsupervised hierarchical clustering and pathway analysis, we identified two robust cellular senescent endotypes. Endotype-1 was enriched for cell proliferating pathways, expressing forkhead box protein O4 (FOXO4), RB transcriptional corepressor like 2 (RBL2), and cyclin-dependent kinase inhibitor 1B (CDKN1B); the FOXO mediated cell cycle was identified as possible target for endotype-1 patients. Endotype-2 showed enriched inflammation-associated pathways, expressed by interleukin 6 (IL6), matrix metallopeptidase (MMP)1/3, and vascular endothelial growth factor (VEGF)C and SASP pathways were identified as possible targets for endotype-2 patients. Notably, plasma-based metabolic profiles in overlapping blood samples (N = 21) showed two corresponding metabolic clusters in blood. These non-invasive metabolic profiles could function as biomarkers for patient-tailored targeting of senescence in OA.PMID:37962736 | DOI:10.1007/s11357-023-01001-2

Plant Physiol. 2023 Nov 14:kiad607. doi: 10.1093/plphys/kiad607. Online ahead of print.ABSTRACTIn eukaryotic cells, phosphorus is assimilated and utilized primarily as phosphate (Pi). Pi homeostasis is mediated by transporters that have not yet been adequately characterized in green algae. This study reports on PHOSPHATE TRANSPORTER 4-7 (CrPHT4-7) from Chlamydomonas reinhardtii, a member of the PHT4 transporter family, which exhibits remarkable similarity to AtPHT4; 4 from Arabidopsis (Arabidopsis thaliana), a chloroplastic ascorbate transporter. Using fluorescent protein tagging we show that CrPHT4-7 resides in the chloroplast envelope membrane. Crpht4-7 mutants, generated by the CRISPR/Cas12a-mediated single- strand templated repair, show retarded growth, especially in high light, reduced ATP level, strong ascorbate accumulation, and diminished non-photochemical quenching in high light. On the other hand, total cellular phosphorous content was unaffected, and the phenotype of the Crpht4-7 mutants could not be alleviated by ample Pi supply. CrPHT4-7-overexpressing lines exhibit enhanced biomass accumulation under high light conditions in comparison with the wild-type strain. Expressing CrPHT4-7 in a yeast (Saccharomyces cerevisiae) strain lacking Pi transporters substantially recovered its slow growth phenotype, demonstrating that CrPHT4-7 transports Pi. Even though CrPHT4-7 shows a high degree of similarity to AtPHT4; 4, it does not display any substantial ascorbate transport activity in yeast or intact algal cells. Thus, the results demonstrate that CrPHT4-7 functions as a chloroplastic Pi transporter essential for maintaining Pi homeostasis and photosynthesis in Chlamydomonas reinhardtii.PMID:37962583 | DOI:10.1093/plphys/kiad607

Environ Sci Technol. 2023 Nov 14. doi: 10.1021/acs.est.3c05814. Online ahead of print.ABSTRACTWe investigated the metabolomic profile associated with exposure to trihalomethanes (THMs) and nitrate in drinking water and with colorectal cancer risk in 296 cases and 295 controls from the Multi Case-Control Spain project. Untargeted metabolomic analysis was conducted in blood samples using ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. A variety of univariate and multivariate association analyses were conducted after data quality control, normalization, and imputation. Linear regression and partial least-squares analyses were conducted for chloroform, brominated THMs, total THMs, and nitrate among controls and for case-control status, together with a N-integration model discriminating colorectal cancer cases from controls through interrogation of correlations between the exposure variables and the metabolomic features. Results revealed a total of 568 metabolomic features associated with at least one water contaminant or colorectal cancer. Annotated metabolites and pathway analysis suggest a number of pathways as potentially involved in the link between exposure to these water contaminants and colorectal cancer, including nicotinamide, cytochrome P-450, and tyrosine metabolism. These findings provide insights into the underlying biological mechanisms and potential biomarkers associated with water contaminant exposure and colorectal cancer risk. Further research in this area is needed to better understand the causal relationship and the public health implications.PMID:37962559 | DOI:10.1021/acs.est.3c05814

Aging Dis. 2023 Oct 18. doi: 10.14336/AD.2023.1013. Online ahead of print.ABSTRACTSarcopenia, a group of skeletal muscle diseases with high prevalence in older adults, usually manifests as loss of muscle mass and strength, and/or physical performance decline. Cognitive impairment, defined as impaired function in one or more cognitive domains such as memory, language, computation, comprehension, executive, and visuospatial skills, affects the quality of life and social functioning of patients. Both sarcopenia and cognitive impairment are common geriatric syndromes, and the two disorders interact and influence each other. Declining muscle function accelerates cognitive impairment, and cognitive impairment in turn affects muscle strength. Potential common pathological mechanisms between the two include chronic inflammation, mitochondrial dysfunction and oxidative stress, and gut microbiota disorder. Additionally, neuroendocrine connections including testosterone, insulin, and growth factors have important effects on muscle and brain function. Recently, the development of applied metabolomics technologies has shown significant potential in uncovering shared biochemical pathways and exploring potential biomarkers. Exercise, nutritional, and cognitive interventions are significant as nonpharmacologic approaches in the treatment of sarcopenia and cognitive impairment. However, the specific mechanism of interaction between two diseases, biomarkers and effective therapeutic medications still has knowledge gaps that need to be further explored.PMID:37962457 | DOI:10.14336/AD.2023.1013

Nucleic Acids Res. 2023 Nov 14:gkad1041. doi: 10.1093/nar/gkad1041. Online ahead of print.ABSTRACTPathBank (https://pathbank.org) and its predecessor database, the Small Molecule Pathway Database (SMPDB), have been providing comprehensive metabolite pathway information for the metabolomics community since 2010. Over the past 14 years, these pathway databases have grown and evolved significantly to meet the needs of the metabolomics community and respond to continuing changes in computing technology. This year's update, PathBank 2.0, brings a number of important improvements and upgrades that should make the database more useful and more appealing to a larger cross-section of users. In particular, these improvements include: (i) a significant increase in the number of primary or canonical pathways (from 1720 to 6951); (ii) a massive increase in the total number of pathways (from 110 234 to 605 359); (iii) significant improvements to the quality of pathway diagrams and pathway descriptions; (iv) a strong emphasis on drug metabolism and drug mechanism pathways; (v) making most pathway images more slide-compatible and manuscript-compatible; (vi) adding tools to support better pathway filtering and selecting through a more complete pathway taxonomy; (vii) adding pathway analysis tools for visualizing and calculating pathway enrichment. Many other minor improvements and updates to the content, the interface and general performance of the PathBank website have also been made. Overall, we believe these upgrades and updates should greatly enhance PathBank's ease of use and its potential applications for interpreting metabolomics data.PMID:37962386 | DOI:10.1093/nar/gkad1041

Obesity (Silver Spring). 2023 Nov 14. doi: 10.1002/oby.23943. Online ahead of print.ABSTRACTOBJECTIVE: This observational study investigated metabolomic changes in individuals with type 2 diabetes (T2D) after weight loss. We hypothesized that metabolite changes associated with T2D-relevant phenotypes are signatures of improved health.METHODS: Fasting plasma samples from individuals undergoing bariatric surgery (n = 71 Roux-en-Y gastric bypass [RYGB], n = 22 gastric banding), lifestyle intervention (n = 66), or usual care (n = 14) were profiled for 139 metabolites before and 2 years after weight loss. Principal component analysis grouped correlated metabolites into factors. Association of preintervention metabolites was tested with preintervention clinical features and changes in T2D markers. Association between change in metabolites/metabolite factors and change in T2D remission markers, homeostasis model assessment of β-cell function, homeostasis model assessment of insulin resistance, and glycated hemoglobin (HbA1c) was assessed.RESULTS: Branched-chain amino acids (BCAAs) were associated with preintervention adiposity. Changes in BCAAs (valine, leucine/isoleucine) and branched-chain ketoacids were positively associated with change in HbA1c (false discovery rate q value ≤ 0.001) that persisted after adjustment for percentage weight change and RYGB (p ≤ 0.02). In analyses stratified by RYGB or other weight loss method, some metabolites showed association with non-RYGB weight loss.CONCLUSIONS: This study confirmed known metabolite associations with obesity/T2D and showed an association of BCAAs with HbA1c change after weight loss, independent of the method or magnitude of weight loss.PMID:37962326 | DOI:10.1002/oby.23943

Brain Behav. 2023 Nov 14:e3328. doi: 10.1002/brb3.3328. Online ahead of print.ABSTRACTBACKGROUND: The pathogenesis of moyamoya disease (MMD) is unclear. Inflammation and immune imbalance have been identified as potential factors contributing to the occurrence and progression of MMD. However, the specific proteins and metabolites responsible for triggering this process are yet to be established. The purpose of this study is to identify differentially expressed proteins and metabolites in patients with MMD and perform Kyoto Encyclopedia of Genes and Genomes pathway integration analysis to pinpoint crucial proteins and metabolites involved in the disease.METHODS: We performed untargeted metabolomic and data-independent acquisition proteomic analyses on the serum samples of individuals with MMD and healthy controls (HC).RESULTS: In patients with MMD versus HC, 24 proteins and 60 metabolites, including 21 anionic metabolites and 39 cationic metabolites, which were significantly different, were identified. In patients with MMD, several proteins involved in inflammation and immune metabolism, such as tubulin beta-6 and complement C4, were found to have significantly altered levels. Similarly, many metabolites involved in inflammation and immune metabolisms, such as dimethyl 4-hydroxyisophthalate, beta-nicotinamide mononucleotide, 2-(3-(4-pyridyl)-1H-1,2,4-triazol-5-yl)pyridine, and PC (17:1/18:2), were significantly altered. Intriguingly, these proteins and metabolites are involved in the progression of atherosclerosis through immune and inflammatory pathways, although some have never been reported in MMD. Moreover, integrated proteomics and metabolomics studies were conducted to determine shared pathways involving cholesterol metabolism, vitamin digestion, fat digestion, and absorption pathways of proteins and metabolites, which warrant further investigation.CONCLUSIONS: Significant increases in pro-inflammatory and immunosuppressive abilities have been observed in patients with MMD, accompanied by significant reductions in anti-inflammatory and immune regulation. Various metabolites and proteins implicated in these processes have been identified for the first time. These findings hold immense significance for comprehending the pathogenesis of MMD and for the development of future drug therapies.PMID:37962021 | DOI:10.1002/brb3.3328

Circulation. 2023 Nov 14. doi: 10.1161/CIRCULATIONAHA.123.066542. Online ahead of print.ABSTRACTBACKGROUND: Cardiac metabolic dysfunction is a hallmark of heart failure (HF). Estrogen-related receptors ERRα and ERRγ are essential regulators of cardiac metabolism. Therefore, activation of ERR could be a potential therapeutic intervention for HF. However, in vivo studies demonstrating the potential usefulness of ERR agonist for HF treatment are lacking, because compounds with pharmacokinetics appropriate for in vivo use have not been available.METHODS: Using a structure-based design approach, we designed and synthesized 2 structurally distinct pan-ERR agonists, SLU-PP-332 and SLU-PP-915. We investigated the effect of ERR agonist on cardiac function in a pressure overload-induced HF model in vivo. We conducted comprehensive functional, multi-omics (RNA sequencing and metabolomics studies), and genetic dependency studies both in vivo and in vitro to dissect the molecular mechanism, ERR isoform dependency, and target specificity.RESULTS: Both SLU-PP-332 and SLU-PP-915 significantly improved ejection fraction, ameliorated fibrosis, and increased survival associated with pressure overload-induced HF without affecting cardiac hypertrophy. A broad spectrum of metabolic genes was transcriptionally activated by ERR agonists, particularly genes involved in fatty acid metabolism and mitochondrial function. Metabolomics analysis showed substantial normalization of metabolic profiles in fatty acid/lipid and tricarboxylic acid/oxidative phosphorylation metabolites in the mouse heart with 6-week pressure overload. ERR agonists increase mitochondria oxidative capacity and fatty acid use in vitro and in vivo. Using both in vitro and in vivo genetic dependency experiments, we show that ERRγ is the main mediator of ERR agonism-induced transcriptional regulation and cardioprotection and definitively demonstrated target specificity. ERR agonism also led to downregulation of cell cycle and development pathways, which was partially mediated by E2F1 in cardiomyocytes.CONCLUSIONS: ERR agonists maintain oxidative metabolism, which confers cardiac protection against pressure overload-induced HF in vivo. Our results provide direct pharmacologic evidence supporting the further development of ERR agonists as novel HF therapeutics.PMID:37961903 | DOI:10.1161/CIRCULATIONAHA.123.066542

J Sci Food Agric. 2023 Nov 14. doi: 10.1002/jsfa.13124. Online ahead of print.ABSTRACTBACKGROUND: Yield, disease tolerance, and climate adaptation are important traits in grapevine genetic breeding programs. Selection for these characteristics cause unpredictable changes in primary and specialized metabolism, affecting the physicochemical properties and chemical composition of the berries and their processed products, juice and wine. In this study, we investigated the influence of the genetic distance between grapevine genotypes on the chemical signatures of the juices, by integrating comprehensive metabolic profiling to genetic analyses.RESULTS: The studied grapevine cultivars exhibited low genetic diversity. Breeding for agronomic traits promoted higher contents of soluble sugars, total phenolics, and anthocyanins in the juices. Untargeted juice metabolomics identified a total of 147 metabolites, consisting of 30 volatiles, 21 phenolics, and 96 UHPLC-MS features. Juices from grapes of the most recent cultivars exhibited increased levels of trans-resveratrol, catechin, and luteolin. The blend of volatiles from juices of later cultivars was also more complex, consisting of 29 distinct metabolites in 'BRS Magna. Grapes from 'BRS Carmem', an intermediate cultivar, gave the most divergent UHPLC-MS juice profile.CONCLUSION: Contents of soluble solids, total phenolics and anthocyanins in grape juices were increased by controlled crosses and hybrid selection. Integrative analyses demonstrated that the juices metabolic profiles accurately represent the cultivars genetic distances. Juices from 'BRS Violeta' and 'BRS Magna' show relevant positive association with health-related phenolics and a distinct set of odor volatiles, although these characteristics were not aimed at by breeding. This article is protected by copyright. All rights reserved.PMID:37961851 | DOI:10.1002/jsfa.13124

Drug Dev Res. 2023 Nov 14. doi: 10.1002/ddr.22129. Online ahead of print.ABSTRACTOsteosarcoma (OS) is a primary malignant bone tumor characterized by frequent metastasis, rapid disease progression, and a high rate of mortality. Treatment options for OS have remained largely unchanged for decades, consisting primarily of cytotoxic chemotherapy and surgery, thus necessitating the urgent need for novel therapies. Tropolones are naturally occurring seven-membered non-benzenoid aromatic compounds that possess antiproliferative effects in a wide array of cancer cell types. MO-OH-Nap is an α-substituted tropolone that has activity as an iron chelator. Here, we demonstrate that MO-OH-Nap activates all three arms of the unfolded protein response (UPR) pathway and induces apoptosis in a panel of human OS cell lines. Co-incubation with ferric chloride or ammonium ferrous sulfate completely prevents the induction of apoptotic and UPR markers in MO-OH-Nap-treated OS cells. MO-OH-Nap upregulates transferrin receptor 1 (TFR1) protein levels, as well as TFR1, divalent metal transporter 1 (DMT1), iron-regulatory proteins (IRP1, IRP2), ferroportin (FPN), and zinc transporter 14 (ZIP14) transcript levels, demonstrating the impact of MO-OH-Nap on iron-homeostasis pathways in OS cells. Furthermore, MO-OH-Nap treatment restricts the migration and invasion of OS cells in vitro. Lastly, metabolomic profiling of MO-OH-Nap-treated OS cells revealed distinct changes in purine and pyrimidine metabolism. Collectively, we demonstrate that MO-OH-Nap-induced cytotoxic effects in OS cells are dependent on the tropolone's ability to alter cellular iron availability and that this agent exploits key metabolic pathways. These studies support further evaluation of MO-OH-Nap as a novel treatment for OS.PMID:37961833 | DOI:10.1002/ddr.22129

bioRxiv. 2023 Nov 3:2023.10.13.562226. doi: 10.1101/2023.10.13.562226. Preprint.ABSTRACTEnvironmental and genetic risk factors, and their interactions, contribute significantly to the etiology of neurodevelopmental disorders (NDDs). Recent epidemiology studies have implicated pyrethroid pesticides as an environmental risk factor for autism and developmental delay. Our previous research showed that low-dose developmental exposure to the pyrethroid pesticide deltamethrin in mice caused male-biased changes in the brain and in NDD-relevant behaviors that persisted into adulthood. Here, we used a metabolomics approach to determine the broadest possible set of metabolic changes in the adult male mouse brain caused by low-dose developmental pyrethroid exposure. Using a litter-based design, we exposed mouse dams during pregnancy and lactation to deltamethrin (3 mg/kg or vehicle every 3 days) at a concentration well below the EPA-determined benchmark dose used for regulatory guidance. We raised male offspring to adulthood and collected whole brain samples for untargeted high-resolution metabolomics analysis. Developmentally exposed mice had disruptions in 116 metabolites which clustered into pathways for folate biosynthesis, retinol metabolism, and tryptophan metabolism. Perturbagen analysis on split-sample transcriptomics data from the same mice identified a folate inhibitor as the drug causing the most similar effect to DPE, thus confirming that developmental pyrethroid exposure disrupted folate metabolism. These results suggest that DPE directly disrupts folate metabolism in the brain, which may inform both prevention and therapeutic strategies.PMID:37961675 | PMC:PMC10634990 | DOI:10.1101/2023.10.13.562226

bioRxiv. 2023 Oct 31:2023.10.26.564244. doi: 10.1101/2023.10.26.564244. Preprint.ABSTRACTMOTIVATION: Metabolomics generates complex data necessitating advanced computational methods for generating biological insight. While machine learning (ML) is promising, the challenges of selecting the best algorithms and tuning hyperparameters, particularly for nonexperts, remain. Automated machine learning (AutoML) can streamline this process; however, the issue of interpretability could persist. This research introduces a unified pipeline that combines AutoML with explainable AI (XAI) techniques to optimize metabolomics analysis.RESULTS: We tested our approach on two datasets: renal cell carcinoma (RCC) urine metabolomics and ovarian cancer (OC) serum metabolomics. AutoML, using auto-sklearn, surpassed standalone ML algorithms such as SVM and random forest in differentiating between RCC and healthy controls, as well as OC patients and those with other gynecological cancers (Non-OC). Autosklearn employed a mix of algorithms and ensemble techniques, yielding a superior performance (AUC of 0.97 for RCC and 0.85 for OC). Shapley Additive Explanations (SHAP) provided a global ranking of feature importance, identifying dibutylamine and ganglioside GM(d34:1) as the top discriminative metabolites for RCC and OC, respectively. Waterfall plots offered local explanations by illustrating the influence of each metabolite on individual predictions. Dependence plots spotlighted metabolite interactions, such as the connection between hippuric acid and one of its derivatives in RCC, and between GM3(d34:1) and GM3(18:1_16:0) in OC, hinting at potential mechanistic relationships. Through decision plots, a detailed error analysis was conducted, contrasting feature importance for correctly versus incorrectly classified samples. In essence, our pipeline emphasizes the importance of harmonizing AutoML and XAI, facilitating both simplified ML application and improved interpretability in metabolomics data science.AVAILABILITY: https://github.com/obifarin/automl-xai-metabolomics Contact: facundo.fernandez@chemistry.gatech.edu.PMID:37961534 | PMC:PMC10634896 | DOI:10.1101/2023.10.26.564244

bioRxiv. 2023 Oct 23:2023.10.20.563226. doi: 10.1101/2023.10.20.563226. Preprint.ABSTRACTUrine is assayed alongside blood in medicine, yet current clinical diagnostic tests utilize only a small fraction of its total biomolecular repertoire, potentially foregoing high-resolution insights into human health and disease. In this work, we characterized the joint landscapes of transcriptomic and metabolomic signals in human urine. We also compared the urine transcriptome to plasma cell-free RNA, identifying a distinct cell type repertoire and enrichment for metabolic signal. Untargeted metabolomic measurements identified a complementary set of pathways to the transcriptomic analysis. Our findings suggest that urine is a promising biofluid yielding prognostic and detailed insights for hard-to-biopsy tissues with low representation in the blood, offering promise for a new generation of liquid biopsies.PMID:37961398 | PMC:PMC10634682 | DOI:10.1101/2023.10.20.563226