Metabolite Stability in Archived Neonatal Dried Blood Spots Used for Epidemiologic Research.

Epidemiologic studies of low-frequency exposures or outcomes using metabolomics analyses of neonatal dried blood spots (DBS) often require assembly of samples with substantial differences in duration of storage. Independent assessment of stability of metabolites in archived DBS will enable improved design and interpretation of epidemiologic research utilizing DBS. Neonatal DBS routinely collected and stored as part of the California Genetic Disease Screening Program between 1983 and 2011 were used. The study population included 899 children without cancer before age 6 years, born in California. High-resolution metabolomics with liquid-chromatography mass spectrometry was performed, and the relative ion intensities of common metabolites and selected xenobiotic metabolites of nicotine (cotinine and hydroxycotinine) were evaluated. In total, we detected 26,235 mass spectral features across 2 separate chromatography methods (C18 hydrophobic reversed-phase chromatography and hydrophilic-interaction liquid chromatography). For most of the 39 metabolites related to nutrition and health status, we found no statistically significant annual trends across the years of storage. Nicotine metabolites were captured in the DBS with relatively stable intensities. This study supports the usefulness of DBS stored long-term for epidemiologic studies of the metabolome. -Omics-based information gained from DBS may also provide a valuable tool for assessing prenatal environmental exposures in child health research.

BIOMARKERS OF MATERNAL SMOKING AND THE RISK OF RETINOBLASTOMA IN OFFSPRING.

PURPOSE: Previous studies examining the risk of retinoblastoma with maternal smoking were inconclusive, likely due in part to the reliance on self-reported maternal smoking. This study uses biomarkers of tobacco smoking in neonatal dried blood spots to investigate associations between maternal smoking and retinoblastoma in offspring. METHODS: The authors randomly selected 498 retinoblastoma cases and 895 control subjects born between 1983 and 2011 from a population-based case-control study in California. Maternal pregnancy-related smoking was measured using the following three metrics: provider or self-reported smoking during pregnancy, cotinine, and hydroxycotinine in neonatal blood. The authors used multivariable logistic regression to estimate the effects of maternal tobacco smoking on retinoblastoma. RESULTS: Using all metrics (biomarkers or self-report), maternal smoking late in pregnancy or early postpartum was related to retinoblastoma (all types; odds ratio = 1.44, 95% confidence interval: 1.00-2.09). Relying on cotinine or hydroxycotinine to ascertain smoking, maternal smoking was related to unilateral retinoblastoma (odds ratio = 1.66, 95% confidence interval: 1.08-2.57). CONCLUSION: The results indicate that maternal smoking during pregnancy may be a risk factor for retinoblastoma, particularly among unilateral cases.

Sphinganine is associated with 24-h MAP in the non-sleepy with OSA.

INTRODUCTION: Excessive daytime sleepiness is a debilitating symptom of obstructive sleep apnea (OSA) linked to cardiovascular disease, and metabolomic mechanisms underlying this relationship remain unknown. We examine whether metabolites from inflammatory and oxidative stress-related pathways that were identified in our prior work could be involved in connecting the two phenomena. METHODS: This study included 57 sleepy (Epworth Sleepiness Scale (ESS) ≥ 10) and 37 non-sleepy (ESS < 10) participants newly diagnosed and untreated for OSA that completed an overnight in-lab or at home sleep study who were recruited from the Emory Mechanisms of Sleepiness Symptoms Study (EMOSS). Differences in fasting blood samples of metabolites were explored in participants with sleepiness versus those without and multiple linear regression models were utilized to examine the association between metabolites and mean arterial pressure (MAP). RESULTS: The 24-h MAP was higher in sleepy 92.8 mmHg (8.4) as compared to non-sleepy 88.8 mmHg (8.1) individuals (P = 0.03). Although targeted metabolites were not significantly associated with MAP, when we stratified by sleepiness group, we found that sphinganine is significantly associated with MAP (Estimate = 8.7, SE = 3.7, P = 0.045) in non-sleepy patients when controlling for age, BMI, smoking status, and apnea-hypopnea index (AHI). CONCLUSION: This is the first study to evaluate the relationship of inflammation and oxidative stress related metabolites in sleepy versus non-sleepy participants with newly diagnosed OSA and their association with 24-h MAP. Our study suggests that Sphinganine is associated with 24 hour MAP in the non-sleepy participants with OSA.

Intratumoral delivery of TransCon™ TLR7/8 Agonist promotes sustained anti-tumor activity and local immune cell activation while minimizing systemic cytokine induction.

BACKGROUND: Intratumoral (IT) delivery of toll-like receptor (TLR) agonists has shown encouraging anti-tumor benefit in preclinical and early clinical studies. However, IT delivery of TLR agonists may lead to rapid effusion from the tumor microenvironment (TME), potentially limiting the duration of local inflammation and increasing the risk of systemic adverse events. METHODS: To address these limitations, TransCon™ TLR7/8 Agonist-an investigational sustained-release prodrug of resiquimod that uses a TransCon linker and hydrogel technology to achieve sustained and predictable IT release of resiquimod-was developed. TransCon TLR7/8 Agonist was characterized for resiquimod release in vitro and in vivo, in mice and rats, and was assessed for anti-tumor efficacy and pharmacodynamic activity in mice. RESULTS: Following a single IT dose, TransCon TLR7/8 Agonist mediated potent tumor growth inhibition which was associated with sustained resiquimod release over several weeks with minimal induction of systemic cytokines. TransCon TLR7/8 Agonist monotherapy promoted activation of antigen-presenting cells in the TME and tumor-draining lymph nodes, with evidence of activation and expansion of CD8+ T cells in the tumor-draining lymph node and TME. Combination of TransCon TLR7/8 Agonist with systemic immunotherapy further promoted anti-tumor activity in TransCon TLR7/8 Agonist-treated tumors. In a bilateral tumor setting, combination of TransCon TLR7/8 Agonist with systemic IL-2 potentiated tumor growth inhibition in both injected and non-injected tumors and conferred protection against tumor rechallenge following complete regressions. CONCLUSIONS: Our findings show that a single dose of TransCon TLR7/8 Agonist can mediate sustained local release of resiquimod in the TME and promote potent anti-tumor effects as monotherapy and in combination with systemic immunotherapy, supporting TransCon TLR7/8 Agonist as a novel intratumoral TLR agonist for cancer therapy. A clinical trial to evaluate the safety and efficacy of TransCon TLR7/8 Agonist, as monotherapy and in combination with pembrolizumab, in cancer patients is currently ongoing (transcendIT-101; NCT04799054).

Child serum metabolome and traffic-related air pollution exposure in pregnancy.

BACKGROUND: Maternal exposure to traffic-related air pollution during pregnancy has been shown to increase the risk of adverse birth outcomes and childhood disorders. High-resolution metabolomics (HRM) has previously been employed to identify metabolic responses to traffic-related air pollution in adults, including pregnant women. Thus far, no studies have examined metabolic effects of air pollution exposure in utero on neonates. METHODS: We retrieved stored neonatal blood spots for 241 children born in California between 1998 and 2007. These children were randomly selected from all California birth rolls to serve as birth-year matched controls for children with retinoblastoma identified from the California cancer registry for a case control study of childhood cancer. We estimated prenatal traffic-related air pollution exposure (particulate matter less than 2.5 µm (PM2.5)) during the third-trimester using the California Line Source Dispersion Model, version 4 (CALINE4) based on residential addresses recorded at birth. We employed untargeted HRM to obtain metabolic profiles, and metabolites associated with air pollution exposure were identified using partial least squares (PLS) regression and linear regressions. Biological effects were characterized using pathway enrichment analyses adjusting for potential confounders including maternal age, race/ethnicity, and education. RESULTS: In total we extracted 4038 and 4957 metabolite features from neonatal blood spots in hydrophilic interaction (HILIC) chromatography (positive ion mode) and C18 reverse phase columns (negative ion mode), respectively. After controlling for confounding factors, partial least square regression (Variable Importance in Projection (VIP) ≥ 2) selected 402 HILIC positive and 182 C18 negative features as statistically significantly associated with increasing third trimester PM2.5 exposure. Using pathway enrichment analysis, we identified metabolites in oxidative stress and inflammation pathways as being altered, primarily involving lipid metabolism. CONCLUSION: The metabolite features and pathways associated with air pollution exposure in neonates suggest that maternal exposure during late pregnancy contributes to oxidative stress and inflammation in newborn children.

Prenatal Exposure to Perfluoroalkyl Substances Associated With Increased Susceptibility to Liver Injury in Children.

BACKGROUND AND AIMS: Per- and polyfluoroalkyl substances (PFAS) are widespread and persistent pollutants that have been shown to have hepatotoxic effects in animal models. However, human evidence is scarce. We evaluated how prenatal exposure to PFAS associates with established serum biomarkers of liver injury and alterations in serum metabolome in children. APPROACH AND RESULTS: We used data from 1,105 mothers and their children (median age, 8.2 years; interquartile range, 6.6-9.1) from the European Human Early-Life Exposome cohort (consisting of six existing population-based birth cohorts in France, Greece, Lithuania, Norway, Spain, and the United Kingdom). We measured concentrations of perfluorooctane sulfonate, perfluorooctanoate, perfluorononanoate, perfluorohexane sulfonate, and perfluoroundecanoate in maternal blood. We assessed concentrations of alanine aminotransferase, aspartate aminotransferase, and gamma-glutamyltransferase in child serum. Using Bayesian kernel machine regression, we found that higher exposure to PFAS during pregnancy was associated with higher liver enzyme levels in children. We also measured child serum metabolomics through a targeted assay and found significant perturbations in amino acid and glycerophospholipid metabolism associated with prenatal PFAS. A latent variable analysis identified a profile of children at high risk of liver injury (odds ratio, 1.56; 95% confidence interval, 1.21-1.92) that was characterized by high prenatal exposure to PFAS and increased serum levels of branched-chain amino acids (valine, leucine, and isoleucine), aromatic amino acids (tryptophan and phenylalanine), and glycerophospholipids (phosphatidylcholine [PC] aa C36:1 and Lyso-PC a C18:1). CONCLUSIONS: Developmental exposure to PFAS can contribute to pediatric liver injury.

Metabolomics analysis of maternal serum exposed to high air pollution during pregnancy and risk of autism spectrum disorder in offspring.

BACKGROUND: Previously, numerous epidemiologic studies reported an association between autism spectrum disorder (ASD) and exposure to air pollution during pregnancy. However, there have been no metabolomics studies investigating the impact of pregnancy pollution exposure to ASD risk in offspring. OBJECTIVES: To identify differences in maternal metabolism that may reflect a biological response to exposure to high air pollution in pregnancies of offspring who later did or did not develop ASD. METHODS: We obtained stored mid-pregnancy serum from 214 mothers who lived in California's Central Valley and experienced the highest levels of air pollution during early pregnancy. We estimated each woman's average traffic-related air pollution exposure (carbon monoxide, nitric oxides, and particulate matter <2.5 µm) during the first trimester using the California Line Source Dispersion Model, version 4 (CALINE4). By utilizing liquid chromatography-high resolution mass spectrometry, we identified the metabolic profiles of maternal serum for 116 mothers with offspring who later developed ASD and 98 control mothers. Partial least squares discriminant analysis (PLS-DA) was employed to select metabolic features associated with air pollution exposure or autism risk in offspring. We also conducted extensive pathway enrichment analysis to elucidate potential ASD-related changes in the metabolome of pregnant women. RESULTS: We extracted 4022 and 4945 metabolic features from maternal serum samples in hydrophilic interaction (HILIC) chromatography (positive ion mode) and C18 (negative ion mode) columns, respectively. After controlling for potential confounders, we identified 167 and 222 discriminative features (HILIC and C18, respectively). Pathway enrichment analysis to discriminate metabolic features associated with ASD risk indicated various metabolic pathway perturbations linked to the tricarboxylic acid (TCA) cycle and mitochondrial function, including carnitine shuttle, amino acid metabolism, bile acid metabolism, and vitamin A metabolism. CONCLUSION: Using high resolution metabolomics, we identified several metabolic pathways disturbed in mothers with ASD offspring among women experiencing high exposure to traffic-related air pollution during pregnancy that were associated with mitochondrial dysfunction. These findings provide us with a better understanding of metabolic disturbances involved in the development of ASD under adverse environmental conditions.

Reference Standardization for Quantification and Harmonization of Large-Scale Metabolomics.

Reference standardization was developed to address quantification and harmonization challenges for high-resolution metabolomics (HRM) data collected across different studies or analytical methods. Reference standardization relies on the concurrent analysis of calibrated pooled reference samples at predefined intervals and enables a single-step batch correction and quantification for high-throughput metabolomics. Here, we provide quantitative measures of approximately 200 metabolites for each of three pooled reference materials (220 metabolites for Qstd3, 211 metabolites for CHEAR, 204 metabolites for NIST1950) and show application of this approach for quantification supports harmonization of metabolomics data collected from 3677 human samples in 17 separate studies analyzed by two complementary HRM methods over a 17-month period. The results establish reference standardization as a method suitable for harmonizing large-scale metabolomics data and extending capabilities to quantify large numbers of known and unidentified metabolites detected by high-resolution mass spectrometry methods.

Metabolomics and adductomics of newborn bloodspots to retrospectively assess the early-life exposome.

PURPOSE OF REVIEW: Exposomics studies can measure health-relevant chemical exposures during a lifetime and estimate the 'internal' environment. However, sampling limitations make these features difficult to capture directly during the critical neonatal time period. RECENT FINDINGS: We review the use of newborn dried bloodspots (DBS) archived from newborn screening programs for exposomic analysis in epidemiological children's health studies. Emerging 'omics technologies such as adductomics and metabolomics have been adapted for DBS analysis, and these technologies can now provide valuable etiological information on the complex interplay between exposures, biological response, and population phenotypes. SUMMARY: Adductomics and metabolomics of DBS can provide robust measurements for retrospective epidemiological investigations. With extensive bioarchiving programs in the United States and other countries, DBS are poised to substantially aid epidemiological studies, particularly for rare and low-frequency childhood diseases and disorders.

Lipidome signatures of metastasis in a transgenic mouse model of sonic hedgehog medulloblastoma.

Medulloblastoma (MB), the most common malignant pediatric brain tumor, has high propensity to metastasize. Currently, the standard treatment for MB patients includes radiation therapy administered to the entire brain and spine for the purpose of treating or preventing against metastasis. Due to this aggressive treatment, the majority of long-term survivors will be left with permanent and debilitating neurocognitive impairment, for the 30-40% patients that fail to respond to treatment, all will relapse with terminal metastatic disease. An understanding of the underlying biology that drives MB metastasis is lacking, and is critically needed in order to develop targeted therapeutics for its prevention. To examine the metastatic biology of sonic hedgehog (SHH) MB, the human MB subgroup with the worst clinical outcome in children, we first generated a robust SmoA1-Math-GFP mouse model that reliably reproduces human SHH MB whereby metastases can be visualized under fluorescence microscopy. Lipidome alterations associated with metastasis were then investigated by applying ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) under positive ionization mode to primary tumor samples collected from mice without (n = 18) and with (n = 7) metastasis. Thirty-four discriminant lipids associated with SHH MB metastasis were successfully annotated, including ceramides (Cers), sphingomyelins (SMs), triacylglycerols (TGs), diacylglycerols (DGs), phosphatidylcholines (PCs), and phosphatidic acids (PAs). This study provides deeper insights into dysregulations of lipid metabolism associated with SHH MB metastatic progression, and thus serves as a guide toward novel targeted therapies.

CERC: an interactive content extraction, recognition, and construction tool for clinical and biomedical text.

BACKGROUND: Automated summarization of scientific literature and patient records is essential for enhancing clinical decision-making and facilitating precision medicine. Most existing summarization methods are based on single indicators of relevance, offer limited capabilities for information visualization, and do not account for user specific interests. In this work, we develop an interactive content extraction, recognition, and construction system (CERC) that combines machine learning and visualization techniques with domain knowledge for highlighting and extracting salient information from clinical and biomedical text. METHODS: A novel sentence-ranking framework multi indicator text summarization, MINTS, is developed for extractive summarization. MINTS uses random forests and multiple indicators of importance for relevance evaluation and ranking of sentences. Indicative summarization is performed using weighted term frequency-inverse document frequency scores of over-represented domain-specific terms. A controlled vocabulary dictionary generated using MeSH, SNOMED-CT, and PubTator is used for determining relevant terms. 35 full-text CRAFT articles were used as the training set. The performance of the MINTS algorithm is evaluated on a test set consisting of the remaining 32 full-text CRAFT articles and 30 clinical case reports using the ROUGE toolkit. RESULTS: The random forests model classified sentences as "good" or "bad" with 87.5% accuracy on the test set. Summarization results from the MINTS algorithm achieved higher ROUGE-1, ROUGE-2, and ROUGE-SU4 scores when compared to methods based on single indicators such as term frequency distribution, position, eigenvector centrality (LexRank), and random selection, p < 0.01. The automatic language translator and the customizable information extraction and pre-processing pipeline for EHR demonstrate that CERC can readily be incorporated within clinical decision support systems to improve quality of care and assist in data-driven and evidence-based informed decision making for direct patient care. CONCLUSIONS: We have developed a web-based summarization and visualization tool, CERC ( https://newton.isye.gatech.edu/CERC1/ ), for extracting salient information from clinical and biomedical text. The system ranks sentences by relevance and includes features that can facilitate early detection of medical risks in a clinical setting. The interactive interface allows users to filter content and edit/save summaries. The evaluation results on two test corpuses show that the newly developed MINTS algorithm outperforms methods based on single characteristics of importance.

xMWAS: a data-driven integration and differential network analysis tool.

Summary: Integrative omics is a central component of most systems biology studies. Computational methods are required for extracting meaningful relationships across different omics layers. Various tools have been developed to facilitate integration of paired heterogenous omics data; however most existing tools allow integration of only two omics datasets. Furthermore, existing data integration tools do not incorporate additional steps of identifying sub-networks or communities of highly connected entities and evaluating the topology of the integrative network under different conditions. Here we present xMWAS, a software for data integration, network visualization, clustering, and differential network analysis of data from biochemical and phenotypic assays, and two or more omics platforms. Availability and implementation: https://kuppal.shinyapps.io/xmwas (Online) and https://github.com/kuppal2/xMWAS/ (R). Contact: kuppal2@emory.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Myeloperoxidase oxidation of methionine associates with early cystic fibrosis lung disease.

Cystic fibrosis (CF) lung disease progressively worsens from infancy to adulthood. Disease-driven changes in early CF airway fluid metabolites may identify therapeutic targets to curb progression.CF patients aged 12-38 months (n=24; three out of 24 later denoted as CF screen positive, inconclusive diagnosis) received chest computed tomography scans, scored by the Perth-Rotterdam Annotated Grid Morphometric Analysis for CF (PRAGMA-CF) method to quantify total lung disease (PRAGMA-%Dis) and components such as bronchiectasis (PRAGMA-%Bx). Small molecules in bronchoalveolar lavage fluid (BALF) were measured with high-resolution accurate-mass metabolomics. Myeloperoxidase (MPO) was quantified by ELISA and activity assays.Increased PRAGMA-%Dis was driven by bronchiectasis and correlated with airway neutrophils. PRAGMA-%Dis correlated with 104 metabolomic features (p<0.05, q<0.25). The most significant annotated feature was methionine sulfoxide (MetO), a product of methionine oxidation by MPO-derived oxidants. We confirmed the identity of MetO in BALF and used reference calibration to confirm correlation with PRAGMA-%Dis (Spearman's ρ=0.582, p=0.0029), extending to bronchiectasis (PRAGMA-%Bx; ρ=0.698, p=1.5×10-4), airway neutrophils (ρ=0.569, p=0.0046) and BALF MPO (ρ=0.803, p=3.9×10-6).BALF MetO associates with structural lung damage, airway neutrophils and MPO in early CF. Further studies are needed to establish whether methionine oxidation directly contributes to early CF lung disease and explore potential therapeutic targets indicated by these findings.

Selenium Supplementation Alters Hepatic Energy and Fatty Acid Metabolism in Mice.

Background: Human and animal studies have raised concerns that supplemental selenium can increase the risk of metabolic disorders, but underlying mechanisms are unclear. Objective: We used an integrated transcriptome and metabolome analysis of liver to test for functional pathway and network responses to supplemental selenium in mice. Methods: Male mice (8-wk-old, C57BL/6J) fed a standard diet (0.41 ppm Se) were given selenium (Na2SeO4, 20 µmol/L) or vehicle (drinking water) for 16 wk. Livers were analyzed for selenium concentration, activity of selenoproteins, reduced glutathione (GSH) redox state, gene expression, and high-resolution metabolomics. Transcriptomic and nontargeted metabolomic data were analyzed with biostatistics, bioinformatics, pathway enrichment analysis, and combined transcriptome-metabolome-wide association study (TMWAS). Results: Mice supplemented with selenium had greater body mass gain from baseline to 16 wk (55% ± 5%) compared with controls (40% ± 3%) (P < 0.05); however, no difference was observed in liver selenium content, selenoenzyme transcripts, or enzyme activity. Selenium was higher in the heart, kidney, and urine of mice supplemented with selenium. Gene enrichment analysis showed that supplemental selenium altered pathways of lipid and energy metabolism. Integrated transcriptome and metabolome network analysis showed 2 major gene-metabolite clusters, 1 centered on the transcript for the bidirectional glucose transporter 2 (Glut2) and the other centered on the transcripts for carnitine-palmitoyl transferase 2 (Cpt2) and acetyl-CoA acyltransferase (Acaa1). Pathway analysis showed that highly associated metabolites (P < 0.05) were enriched in fatty acid metabolism and bile acid biosynthesis, including acylcarnitines, triglycerides and glycerophospholipids, long-chain acyl-coenzyme As, phosphatidylcholines, and sterols. TMWAS of body weight gain confirmed changes in the same pathways. Conclusions: Supplemental selenium in mice alters hepatic fatty acid and energy metabolism and causes increases in body mass. A lack of effect on hepatic selenium content suggests that signaling involves an extrahepatic mechanism.

Western diet induces colonic nitrergic myenteric neuropathy and dysmotility in mice via saturated fatty acid- and lipopolysaccharide-induced TLR4 signalling.

KEY POINTS: A high-fat diet (60% kcal from fat) is associated with motility disorders inducing constipation and loss of nitrergic myenteric neurons in the proximal colon. Gut microbiota dysbiosis, which occurs in response to HFD, contributes to endotoxaemia. High levels of lipopolysaccharide lead to apoptosis in cultured myenteric neurons that express Toll-like receptor 4 (TLR4). Consumption of a Western diet (WD) (35% kcal from fat) for 6 weeks leads to gut microbiota dysbiosis associated with altered bacterial metabolites and increased levels of plasma free fatty acids. These disorders precede the nitrergic myenteric cell loss observed in the proximal colon. Mice lacking TLR4 did not exhibit WD-induced myenteric cell loss and dysmotility. Lipopolysaccharide-induced in vitro enteric neurodegeneration requires the presence of palmitate and may be a result of enhanced NO production. The present study highlights the critical role of plasma saturated free fatty acids that are abundant in the WD with respect to driving enteric neuropathy and colonic dysmotility. ABSTRACT: The consumption of a high-fat diet (HFD) is associated with myenteric neurodegeneration, which in turn is associated with delayed colonic transit and constipation. We examined the hypothesis that an inherent increase in plasma free fatty acids (FFA) in the HFD together with an HFD-induced alteration in gut microbiota contributes to the pathophysiology of these disorders. C57BL/6 mice were fed a Western diet (WD) (35% kcal from fat enriched in palmitate) or a purified regular diet (16.9% kcal from fat) for 3, 6, 9 and 12 weeks. Gut microbiota dysbiosis was investigated by fecal lipopolysaccharide (LPS) measurement and metabolomics (linear trap quadrupole-Fourier transform mass spectrometer) analysis. Plasma FFA and LPS levels were assessed, in addition to colonic and ileal nitrergic myenteric neuron quantifications and motility. Compared to regular diet-fed control mice, WD-fed mice gained significantly more weight without blood glucose alteration. Dysbiosis was exhibited after 6 weeks of feeding, as reflected by increased fecal LPS and bacterial metabolites and concomitant higher plasma FFA. The numbers of nitrergic myenteric neurons were reduced in the proximal colon after 9 and 12 weeks of WD and this was also associated with delayed colonic transit. WD-fed Toll-like receptor 4 (TLR4)-/- mice did not exhibit myenteric cell loss or dysmotility. Finally, LPS (0.5-2 ng·ml-1 ) and palmitate (20 and 30 µm) acted synergistically to induce neuronal cell death in vitro, which was prevented by the nitric oxide synthase inhibitor NG-nitro-l-arginine methyl ester. In conclusion, WD-feeding results in increased levels of FFA and microbiota that, even in absence of hyperglycaemia or overt endotoxaemia, synergistically induce TLR4-mediated neurodegeneration and dysmotility.

xMSannotator: An R Package for Network-Based Annotation of High-Resolution Metabolomics Data.

Improved analytical technologies and data extraction algorithms enable detection of >10 000 reproducible signals by liquid chromatography-high-resolution mass spectrometry, creating a bottleneck in chemical identification. In principle, measurement of more than one million chemicals would be possible if algorithms were available to facilitate utilization of the raw mass spectrometry data, especially low-abundance metabolites. Here we describe an automated computational framework to annotate ions for possible chemical identity using a multistage clustering algorithm in which metabolic pathway associations are used along with intensity profiles, retention time characteristics, mass defect, and isotope/adduct patterns. The algorithm uses high-resolution mass spectrometry data for a series of samples with common properties and publicly available chemical, metabolic, and environmental databases to assign confidence levels to annotation results. Evaluation results show that the algorithm achieves an F1-measure of 0.8 for a data set with known targets and is more robust than previously reported results for cases when database size is much greater than the actual number of metabolites. MS/MS evaluation of a set of randomly selected 210 metabolites annotated using xMSannotator in an untargeted metabolomics human data set shows that 80% of features with high or medium confidence scores have ion dissociation patterns consistent with the xMSannotator annotation. The algorithm has been incorporated into an R package, xMSannotator, which includes utilities for querying local or online databases such as ChemSpider, KEGG, HMDB, T3DB, and LipidMaps.

Metabolic pathways of lung inflammation revealed by high-resolution metabolomics (HRM) of H1N1 influenza virus infection in mice.

Influenza is a significant health concern worldwide. Viral infection induces local and systemic activation of the immune system causing attendant changes in metabolism. High-resolution metabolomics (HRM) uses advanced mass spectrometry and computational methods to measure thousands of metabolites inclusive of most metabolic pathways. We used HRM to identify metabolic pathways and clusters of association related to inflammatory cytokines in lungs of mice with H1N1 influenza virus infection. Infected mice showed progressive weight loss, decreased lung function, and severe lung inflammation with elevated cytokines [interleukin (IL)-1ß, IL-6, IL-10, tumor necrosis factor (TNF)-α, and interferon (IFN)-γ] and increased oxidative stress via cysteine oxidation. HRM showed prominent effects of influenza virus infection on tryptophan and other amino acids, and widespread effects on pathways including purines, pyrimidines, fatty acids, and glycerophospholipids. A metabolome-wide association study (MWAS) of the aforementioned inflammatory cytokines was used to determine the relationship of metabolic responses to inflammation during infection. This cytokine-MWAS (cMWAS) showed that metabolic associations consisted of distinct and shared clusters of 396 metabolites highly correlated with inflammatory cytokines. Strong negative associations of selected glycosphingolipid, linoleate, and tryptophan metabolites with IFN-γ contrasted strong positive associations of glycosphingolipid and bile acid metabolites with IL-1ß, TNF-α, and IL-10. Anti-inflammatory cytokine IL-10 had strong positive associations with vitamin D, purine, and vitamin E metabolism. The detailed metabolic interactions with cytokines indicate that targeted metabolic interventions may be useful during life-threatening crises related to severe acute infection and inflammation.

Computational Metabolomics: A Framework for the Million Metabolome.

"Sola dosis facit venenum." These words of Paracelsus, "the dose makes the poison", can lead to a cavalier attitude concerning potential toxicities of the vast array of low abundance environmental chemicals to which humans are exposed. Exposome research teaches that 80-85% of human disease is linked to environmental exposures. The human exposome is estimated to include >400,000 environmental chemicals, most of which are uncharacterized with regard to human health. In fact, mass spectrometry measures >200,000 m/z features (ions) in microliter volumes derived from human samples; most are unidentified. This crystallizes a grand challenge for chemical research in toxicology: to develop reliable and affordable analytical methods to understand health impacts of the extensive human chemical experience. To this end, there appears to be no choice but to abandon the limitations of measuring one chemical at a time. The present review looks at progress in computational metabolomics to provide probability-based annotation linking ions to known chemicals and serve as a foundation for unambiguous designation of unidentified ions for toxicologic study. We review methods to characterize ions in terms of accurate mass m/z, chromatographic retention time, correlation of adduct, isotopic and fragment forms, association with metabolic pathways and measurement of collision-induced dissociation products, collision cross section, and chirality. Such information can support a largely unambiguous system for documenting unidentified ions in environmental surveillance and human biomonitoring. Assembly of this data would provide a resource to characterize and understand health risks of the array of low-abundance chemicals to which humans are exposed.

High-resolution mapping of h1 linker histone variants in embryonic stem cells.

H1 linker histones facilitate higher-order chromatin folding and are essential for mammalian development. To achieve high-resolution mapping of H1 variants H1d and H1c in embryonic stem cells (ESCs), we have established a knock-in system and shown that the N-terminally tagged H1 proteins are functionally interchangeable to their endogenous counterparts in vivo. H1d and H1c are depleted from GC- and gene-rich regions and active promoters, inversely correlated with H3K4me3, but positively correlated with H3K9me3 and associated with characteristic sequence features. Surprisingly, both H1d and H1c are significantly enriched at major satellites, which display increased nucleosome spacing compared with bulk chromatin. While also depleted at active promoters and enriched at major satellites, overexpressed H1(0) displays differential binding patterns in specific repetitive sequences compared with H1d and H1c. Depletion of H1c, H1d, and H1e causes pericentric chromocenter clustering and de-repression of major satellites. These results integrate the localization of an understudied type of chromatin proteins, namely the H1 variants, into the epigenome map of mouse ESCs, and we identify significant changes at pericentric heterochromatin upon depletion of this epigenetic mark.

Disturbed flow induces systemic changes in metabolites in mouse plasma: a metabolomics study using ApoE⁻/⁻ mice with partial carotid ligation.

Disturbed blood flow (d-flow) occurring in branched and curved arteries promotes endothelial dysfunction and atherosclerosis, in part, by altering gene expression and epigenomic profiles in endothelial cells. While a systemic metabolic change is known to play a role in atherosclerosis, it is unclear whether it can be regulated by local d-flow. Here, we tested this hypothesis by carrying out a metabolomics study using blood plasma samples obtained from ApoE(-/-) mice that underwent a partial carotid ligation surgery to induce d-flow. Mice receiving sham ligation were used as a control. To study early metabolic changes, samples collected from 1 wk after partial ligation when endothelial dysfunction occurs, but before atheroma develops, were analyzed by high-resolution mass spectrometry. A metabolome-wide association study showed that 128 metabolites were significantly altered in the ligated mice compared with the sham group. Of these, sphingomyelin (SM; m/z 703.5747), a common mammalian cell membrane sphingolipid, was most significantly increased in the ligated mice. Of the 128 discriminatory metabolites, 18 and 41 were positively and negatively correlated with SM, respectively. The amino acids methionine and phenylalanine were increased by d-flow, while phosphatidylcholine and phosphatidylethanolamine were decreased by d-flow, and these metabolites were correlated with SM. Other significantly affected metabolites included dietary and environmental agents. Pathway analysis showed that the metabolic changes of d-flow impacted broad functional networks. These results suggest that signaling from d-flow occurring in focal regions induces systemic metabolic changes associated with atherosclerosis.