Identification of pre-diagnostic lipid sets associated with liver cancer risk using untargeted lipidomics and chemical set analysis: A nested case-control study within the ATBC cohort.

In pre-disposed individuals, a reprogramming of the hepatic lipid metabolism may support liver cancer initiation. We conducted a high-resolution mass spectrometry based untargeted lipidomics analysis of pre-diagnostic serum samples from a nested case-control study (219 liver cancer cases and 219 controls) within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. Out of 462 annotated lipids, 158 (34.2%) were associated with liver cancer risk in a conditional logistic regression analysis at a false discovery rate (FDR) <0.05. A chemical set enrichment analysis (ChemRICH) and co-regulatory set analysis suggested that 22/28 lipid classes and 47/83 correlation modules were significantly associated with liver cancer risk (FDR <0.05). Strong positive associations were observed for monounsaturated fatty acids (MUFA), triacylglycerols (TAGs) and phosphatidylcholines (PCs) having MUFA acyl chains. Negative associations were observed for sphingolipids (ceramides and sphingomyelins), lysophosphatidylcholines, cholesterol esters and polyunsaturated fatty acids (PUFA) containing TAGs and PCs. Stearoyl-CoA desaturase enzyme 1 (SCD1), a rate limiting enzyme in fatty acid metabolism and ceramidases seems to be critical in this reprogramming. In conclusion, our study reports pre-diagnostic lipid changes that provide novel insights into hepatic lipid metabolism reprogramming may contribute to a pro-cell growth and anti-apoptotic tissue environment and, in turn, support liver cancer initiation.

Metabolomic Evidence for Peroxisomal Dysfunction in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic and debilitating disease characterized by unexplained physical fatigue, cognitive and sensory dysfunction, sleeping disturbances, orthostatic intolerance, and gastrointestinal problems. People with ME/CFS often report a prodrome consistent with infections. Using regression, Bayesian and enrichment analyses, we conducted targeted and untargeted metabolomic analysis of plasma from 106 ME/CFS cases and 91 frequency-matched healthy controls. Subjects in the ME/CFS group had significantly decreased levels of plasmalogens and phospholipid ethers (p < 0.001), phosphatidylcholines (p < 0.001) and sphingomyelins (p < 0.001), and elevated levels of dicarboxylic acids (p = 0.013). Using machine learning algorithms, we were able to differentiate ME/CFS or subgroups of ME/CFS from controls with area under the receiver operating characteristic curve (AUC) values up to 0.873. Our findings provide the first metabolomic evidence of peroxisomal dysfunction, and are consistent with dysregulation of lipid remodeling and the tricarboxylic acid cycle. These findings, if validated in other cohorts, could provide new insights into the pathogenesis of ME/CFS and highlight the potential use of the plasma metabolome as a source of biomarkers for the disease.

Systematic Error Removal Using Random Forest for Normalizing Large-Scale Untargeted Lipidomics Data.

Large-scale untargeted lipidomics experiments involve the measurement of hundreds to thousands of samples. Such data sets are usually acquired on one instrument over days or weeks of analysis time. Such extensive data acquisition processes introduce a variety of systematic errors, including batch differences, longitudinal drifts, or even instrument-to-instrument variation. Technical data variance can obscure the true biological signal and hinder biological discoveries. To combat this issue, we present a novel normalization approach based on using quality control pool samples (QC). This method is called systematic error removal using random forest (SERRF) for eliminating the unwanted systematic variations in large sample sets. We compared SERRF with 15 other commonly used normalization methods using six lipidomics data sets from three large cohort studies (832, 1162, and 2696 samples). SERRF reduced the average technical errors for these data sets to 5% relative standard deviation. We conclude that SERRF outperforms other existing methods and can significantly reduce the unwanted systematic variation, revealing biological variance of interest.

Arginine reprogramming in ADPKD results in arginine-dependent cystogenesis.

Research into metabolic reprogramming in cancer has become commonplace, yet this area of research has only recently come of age in nephrology. In light of the parallels between cancer and autosomal dominant polycystic kidney disease (ADPKD), the latter is currently being studied as a metabolic disease. In clear cell renal cell carcinoma (RCC), which is now considered a metabolic disease, we and others have shown derangements in the enzyme arginosuccinate synthase 1 (ASS1), resulting in RCC cells becoming auxotrophic for arginine and leading to a new therapeutic paradigm involving reducing extracellular arginine. Based on our earlier finding that glutamine pathways are reprogrammed in ARPKD, and given the connection between arginine and glutamine synthetic pathways via citrulline, we investigated the possibility of arginine reprogramming in ADPKD. We now show that, in a remarkable parallel to RCC, ASS1 expression is reduced in murine and human ADPKD, and arginine depletion results in a dose-dependent compensatory increase in ASS1 levels as well as decreased cystogenesis in vitro and ex vivo with minimal toxicity to normal cells. Nontargeted metabolomics analysis of mouse kidney cell lines grown in arginine-deficient versus arginine-replete media suggests arginine-dependent alterations in the glutamine and proline pathways. Thus, depletion of this conditionally essential amino acid by dietary or pharmacological means, such as with arginine-degrading enzymes, may be a novel treatment for this disease.

Dietary flavonoid intake and colorectal cancer risk in the European prospective investigation into cancer and nutrition (EPIC) cohort.

Flavonoids have been shown to inhibit colon cancer cell proliferation in vitro and protect against colorectal carcinogenesis in animal models. However, epidemiological evidence on the potential role of flavonoid intake in colorectal cancer (CRC) development remains sparse and inconsistent. We evaluated the association between dietary intakes of total flavonoids and their subclasses and risk of development of CRC, within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. A cohort of 477,312 adult men and women were recruited in 10 European countries. At baseline, dietary intakes of total flavonoids and individual subclasses were estimated using centre-specific validated dietary questionnaires and composition data from the Phenol-Explorer database. During an average of 11 years of follow-up, 4,517 new cases of primary CRC were identified, of which 2,869 were colon (proximal = 1,298 and distal = 1,266) and 1,648 rectal tumours. No association was found between total flavonoid intake and the risk of overall CRC (HR for comparison of extreme quintiles 1.05, 95% CI 0.93-1.18; p-trend = 0.58) or any CRC subtype. No association was also observed with any intake of individual flavonoid subclasses. Similar results were observed for flavonoid intake expressed as glycosides or aglycone equivalents. Intake of total flavonoids and flavonoid subclasses, as estimated from dietary questionnaires, did not show any association with risk of CRC development.

compMS2Miner: An Automatable Metabolite Identification, Visualization, and Data-Sharing R Package for High-Resolution LC-MS Data Sets.

A long-standing challenge of untargeted metabolomic profiling by ultrahigh-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) is efficient transition from unknown mass spectral features to confident metabolite annotations. The compMS2Miner (Comprehensive MS2 Miner) package was developed in the R language to facilitate rapid, comprehensive feature annotation using a peak-picker-output and MS2 data files as inputs. The number of MS2 spectra that can be collected during a metabolomic profiling experiment far outweigh the amount of time required for pain-staking manual interpretation; therefore, a degree of software workflow autonomy is required for broad-scale metabolite annotation. CompMS2Miner integrates many useful tools in a single workflow for metabolite annotation and also provides a means to overview the MS2 data with a Web application GUI compMS2Explorer (Comprehensive MS2 Explorer) that also facilitates data-sharing and transparency. The automatable compMS2Miner workflow consists of the following steps: (i) matching unknown MS1 features to precursor MS2 scans, (ii) filtration of spectral noise (dynamic noise filter), (iii) generation of composite mass spectra by multiple similar spectrum signal summation and redundant/contaminant spectra removal, (iv) interpretation of possible fragment ion substructure using an internal database, (v) annotation of unknowns with chemical and spectral databases with prediction of mammalian biotransformation metabolites, wrapper functions for in silico fragmentation software, nearest neighbor chemical similarity scoring, random forest based retention time prediction, text-mining based false positive removal/true positive ranking, chemical taxonomic prediction and differential evolution based global annotation score optimization, and (vi) network graph visualizations, data curation, and sharing are made possible via the compMS2Explorer application. Metabolite identities and comments can also be recorded using an interactive table within compMS2Explorer. The utility of the package is illustrated with a data set of blood serum samples from 7 diet induced obese (DIO) and 7 nonobese (NO) C57BL/6J mice, which were also treated with an antibiotic (streptomycin) to knockdown the gut microbiota. The results of fully autonomous and objective usage of compMS2Miner are presented here. All automatically annotated spectra output by the workflow are provided in the Supporting Information and can alternatively be explored as publically available compMS2Explorer applications for both positive and negative modes ( https://wmbedmands.shinyapps.io/compMS2_mouseSera_POS and https://wmbedmands.shinyapps.io/compMS2_mouseSera_NEG ). The workflow provided rapid annotation of a diversity of endogenous and gut microbially derived metabolites affected by both diet and antibiotic treatment, which conformed to previously published reports. Composite spectra (n = 173) were autonomously matched to entries of the Massbank of North America (MoNA) spectral repository. These experimental and virtual (lipidBlast) spectra corresponded to 29 common endogenous compound classes (e.g., 51 lysophosphatidylcholines spectra) and were then used to calculate the ranking capability of 7 individual scoring metrics. It was found that an average of the 7 individual scoring metrics provided the most effective weighted average ranking ability of 3 for the MoNA matched spectra in spite of potential risk of false positive annotations emerging from automation. Minor structural differences such as relative carbon-carbon double bond positions were found in several cases to affect the correct rank of the MoNA annotated metabolite. The latest release and an example workflow is available in the package vignette ( https://github.com/WMBEdmands/compMS2Miner ) and a version of the published application is available on the shinyapps.io site ( https://wmbedmands.shinyapps.io/compMS2Example ).

MetMSLine: an automated and fully integrated pipeline for rapid processing of high-resolution LC-MS metabolomic datasets.

UNLABELLED: MetMSLine represents a complete collection of functions in the R programming language as an accessible GUI for biomarker discovery in large-scale liquid-chromatography high-resolution mass spectral datasets from acquisition through to final metabolite identification forming a backend to output from any peak-picking software such as XCMS. MetMSLine automatically creates subdirectories, data tables and relevant figures at the following steps: (i) signal smoothing, normalization, filtration and noise transformation (PreProc.QC.LSC.R); (ii) PCA and automatic outlier removal (Auto.PCA.R); (iii) automatic regression, biomarker selection, hierarchical clustering and cluster ion/artefact identification (Auto.MV.Regress.R); (iv) Biomarker-MS/MS fragmentation spectra matching and fragment/neutral loss annotation (Auto.MS.MS.match.R) and (v) semi-targeted metabolite identification based on a list of theoretical masses obtained from public databases (DBAnnotate.R). AVAILABILITY AND IMPLEMENTATION: All source code and suggested parameters are available in an un-encapsulated layout on http://wmbedmands.github.io/MetMSLine/. Readme files and a synthetic dataset of both X-variables (simulated LC-MS data), Y-variables (simulated continuous variables) and metabolite theoretical masses are also available on our GitHub repository.

Environmental Tobacco Smoke Alters Metabolic Systems in Adult Rats.

Human exposure to environmental tobacco smoke (ETS) is associated with an increased incidence of pulmonary and cardiovascular disease and possibly lung cancer. Metabolomics can reveal changes in metabolic networks in organisms under different physio-pathological conditions. Our objective was to identify spatial and temporal metabolic alterations with acute and repeated subchronic ETS exposure to understand mechanisms by which ETS exposure may cause adverse physiological and structural changes in the pulmonary and cardiovascular systems. Established and validated metabolomics assays of the lungs, hearts. and blood of young adult male rats following 1, 3, 8, and 21 days of exposure to ETS along with day-matched sham control rats (n = 8) were performed using gas chromatography time-of-flight mass spectrometry, BinBase database processing, multivariate statistical modeling, and MetaMapp biochemical mapping. A total of 489 metabolites were measured in the lung, heart, and blood, of which 142 metabolites were identified using a standardized metabolite annotation pipeline. Acute and repeated subchronic exposure to ETS was associated with significant metabolic changes in the lung related to energy metabolism, defense against reactive oxygen species, substrate uptake and transport, nucleotide metabolism, and substrates for structural components of collagen and membrane lipids. Metabolic changes were least prevalent in heart tissues but abundant in blood under repeated subchronic ETS exposure. Our analyses revealed that ETS causes alterations in metabolic networks, especially those associated with lung structure and function and found as systemic signals in the blood. The metabolic changes suggest that ETS exposure may adversely affects the mitochondrial respiratory chain, lung elasticity, membrane integrity, redox states, cell cycle, and normal metabolic and physiological functions of the lungs, even after subchronic ETS exposure.

System response of metabolic networks in Chlamydomonas reinhardtii to total available ammonium.

Drastic alterations in macronutrients are known to cause large changes in biochemistry and gene expression in the photosynthetic alga Chlamydomonas reinhardtii. However, metabolomic and proteomic responses to subtle reductions in macronutrients have not yet been studied. When ammonium levels were reduced by 25-100% compared with control cultures, ammonium uptake and growth rates were not affected at 25% or 50% nitrogen-reduction for 28 h. However, primary metabolism and enzyme expression showed remarkable changes at acute conditions (4 h and 10 h after ammonium reduction) compared with chronic conditions (18 h and 28 h time points). Responses of 145 identified metabolites were quantified using gas chromatography-time of flight mass spectrometry; 495 proteins (including 187 enzymes) were monitored using liquid chromatography-ion trap mass spectrometry with label-free spectral counting. Stress response and carbon assimilation processes (Calvin cycle, acetate uptake and chlorophyll biosynthesis) were altered first, in addition to increase in enzyme contents for lipid biosynthesis and accumulation of short chain free fatty acids. Nitrogen/carbon balance metabolism was found changed only under chronic conditions, for example in the citric acid cycle and amino acid metabolism. Metabolism in Chlamydomonas readily responds to total available media nitrogen with temporal increases in short-chain free fatty acids and turnover of internal proteins, long before nitrogen resources are depleted.

In utero metabolomic signatures of refined grain intake and risk of gestational diabetes: A metabolome-wide association study.

BACKGROUND: Epidemiologic evidence has linked refined grain intake to a higher risk of gestational diabetes (GDM), but the biological underpinnings remain unclear. OBJECTIVES: We aimed to identify and validate refined grain-related metabolomic biomarkers for GDM risk. METHODS: In a metabolome-wide association study of 91 cases with GDM and 180 matched controls without GDM (discovery set) nested in the prospective Pregnancy Environment and Lifestyle Study (PETALS), refined grain intake during preconception and early pregnancy and serum untargeted metabolomics were assessed at gestational weeks 10-13. We identified refined grain-related metabolites using multivariable linear regression and examined their prospective associations with GDM risk using conditional logistic regression. We further examined the predictivity of refined grain-related metabolites selected by least absolute shrinkage and selection operator regression in the discovery set and validation set (a random PETALS subsample of 38 individuals with and 336 without GDM). RESULTS: Among 821 annotated serum (87.4% fasting) metabolites, 42 were associated with refined grain intake, of which 17 (70.6% in glycerolipids, glycerophospholipids, and sphingolipids clusters) were associated with subsequent GDM risk (all false discovery rate-adjusted P values <0.05). Adding 7 of 17 metabolites to a conventional risk factor-based prediction model increased the C-statistic for GDM risk in the discovery set from 0.71 (95% CI: 0.64, 0.77) to 0.77 (95% CI: 0.71, 0.83) and in the validation set from 0.77 (95% CI: 0.69, 0.86) to 0.81 (95% CI: 0.74, 0.89), both with P-for-difference <0.05. CONCLUSIONS: Clusters of glycerolipids, glycerophospholipids, and sphingolipids may be implicated in the association between refined grain intake and GDM risk, as demonstrated by the significant associations of these metabolites with both refined grains and GDM risk and the incremental predictive value of these metabolites for GDM risk beyond the conventional risk factors. These findings provide evidence on the potential biological underpinnings linking refined grain intake to the risk of GDM and help identify novel disease-related dietary biomarkers to inform diet-related preventive strategies for GDM.

Sexual Dimorphism of the Mouse Plasma Metabolome Is Associated with Phenotypes of 30 Gene Knockout Lines.

Although metabolic alterations are observed in many monogenic and complex genetic disorders, the impact of most mammalian genes on cellular metabolism remains unknown. Understanding the effect of mouse gene dysfunction on metabolism can inform the functions of their human orthologues. We investigated the effect of loss-of-function mutations in 30 unique gene knockout (KO) lines on plasma metabolites, including genes coding for structural proteins (11 of 30), metabolic pathway enzymes (12 of 30) and protein kinases (7 of 30). Steroids, bile acids, oxylipins, primary metabolites, biogenic amines and complex lipids were analyzed with dedicated mass spectrometry platforms, yielding 827 identified metabolites in male and female KO mice and wildtype (WT) controls. Twenty-two percent of 23,698 KO versus WT comparison tests showed significant genotype effects on plasma metabolites. Fifty-six percent of identified metabolites were significantly different between the sexes in WT mice. Many of these metabolites were also found to have sexually dimorphic changes in KO lines. We used plasma metabolites to complement phenotype information exemplified for Dhfr, Idh1, Mfap4, Nek2, Npc2, Phyh and Sra1. The association of plasma metabolites with IMPC phenotypes showed dramatic sexual dimorphism in wildtype mice. We demonstrate how to link metabolomics to genotypes and (disease) phenotypes. Sex must be considered as critical factor in the biological interpretation of gene functions.

MetaMapp: mapping and visualizing metabolomic data by integrating information from biochemical pathways and chemical and mass spectral similarity.

BACKGROUND: Exposure to environmental tobacco smoke (ETS) leads to higher rates of pulmonary diseases and infections in children. To study the biochemical changes that may precede lung diseases, metabolomic effects on fetal and maternal lungs and plasma from rats exposed to ETS were compared to filtered air control animals. Genome- reconstructed metabolic pathways may be used to map and interpret dysregulation in metabolic networks. However, mass spectrometry-based non-targeted metabolomics datasets often comprise many metabolites for which links to enzymatic reactions have not yet been reported. Hence, network visualizations that rely on current biochemical databases are incomplete and also fail to visualize novel, structurally unidentified metabolites. RESULTS: We present a novel approach to integrate biochemical pathway and chemical relationships to map all detected metabolites in network graphs (MetaMapp) using KEGG reactant pair database, Tanimoto chemical and NIST mass spectral similarity scores. In fetal and maternal lungs, and in maternal blood plasma from pregnant rats exposed to environmental tobacco smoke (ETS), 459 unique metabolites comprising 179 structurally identified compounds were detected by gas chromatography time of flight mass spectrometry (GC-TOF MS) and BinBase data processing. MetaMapp graphs in Cytoscape showed much clearer metabolic modularity and complete content visualization compared to conventional biochemical mapping approaches. Cytoscape visualization of differential statistics results using these graphs showed that overall, fetal lung metabolism was more impaired than lungs and blood metabolism in dams. Fetuses from ETS-exposed dams expressed lower lipid and nucleotide levels and higher amounts of energy metabolism intermediates than control animals, indicating lower biosynthetic rates of metabolites for cell division, structural proteins and lipids that are critical for in lung development. CONCLUSIONS: MetaMapp graphs efficiently visualizes mass spectrometry based metabolomics datasets as network graphs in Cytoscape, and highlights metabolic alterations that can be associated with higher rate of pulmonary diseases and infections in children prenatally exposed to ETS. The MetaMapp scripts can be accessed at http://metamapp.fiehnlab.ucdavis.edu.

Extending biochemical databases by metabolomic surveys.

Metabolomics can map the large metabolic diversity in species, organs, or cell types. In addition to gains in enzyme specificity, many enzymes have retained substrate and reaction promiscuity. Enzyme promiscuity and the large number of enzymes with unknown enzyme function may explain the presence of a plethora of unidentified compounds in metabolomic studies. Cataloguing the identity and differential abundance of all detectable metabolites in metabolomic repositories may detail which compounds and pathways contribute to vital biological functions. The current status in metabolic databases is reviewed concomitant with tools to map and visualize the metabolome.

Human gut microbiome adopts an alternative state following small bowel transplantation.

Small bowel transplants provide an exceptional opportunity for long-term study of the microbial ecology of the human small bowel. The ileostomy created at time of transplant for ongoing monitoring of the allograft provides access to samples of ileal effluent and mucosal biopsies. In this study, we used qPCR to assay the bacterial population of the small bowel lumen of 17 small bowel transplant patients over time. Surprisingly, the posttransplant microbial community was found to be dominated by Lactobacilli and Enterobacteria, both typically facultative anaerobes. This represents an inversion of the normal community that is dominated instead by the strictly anaerobic Bacteroides and Clostridia. We found this inverted community also in patients with ileostomies who did not receive a transplant, suggesting that the ileostomy itself is the primary ecological determinant shaping the microbiota. After surgical closure of the ileostomy, the community reverted to the normal structure. Therefore, we hypothesized that the ileostomy allows oxygen into the otherwise anaerobic distal ileum, thus driving the transition from one microbial community structure to another. Supporting this hypothesis, metabolomic profiling of both communities demonstrated an enrichment for metabolites associated with aerobic respiration in samples from patients with open ileostomies. Viewed from an ecological perspective, the two communities constitute alternative stable states of the human ileum. That the small bowel appears to function normally despite these dramatic shifts suggests that its ecological resilience is greater than previously realized.

Serum unsaturated phosphatidylcholines predict longitudinal basal forebrain degeneration in Alzheimer's disease.

Basal forebrain cholinergic neurons are among the first cell types affected by Alzheimer's disease pathology, but the cause of their early vulnerability is unknown. The lipid phosphatidylcholine is an essential component of the cell membrane, and phosphatidylcholine levels have been shown to be abnormal in the blood and brain of Alzheimer's disease patients. We hypothesized that disease-related changes in phosphatidylcholine metabolism may disproportionately affect basal forebrain cholinergic neurons due to their extremely large size, plasticity in adulthood and unique reliance on phosphatidylcholine for acetylcholine synthesis. To test this hypothesis, we examined whether serum phosphatidylcholine levels predicted longitudinal basal forebrain degeneration in Alzheimer's disease. All data were collected by the Alzheimer's Disease Neuroimaging Initiative. Participants were divided into a normal CSF group (controls; n = 77) and an abnormal CSF group (preclinical and clinical Alzheimer's disease; n = 236) based on their CSF ratios of phosphorylated tau and amyloid beta at baseline. Groups were age-matched (t = 0.89, P > 0.1). Serum lipidomics data collected at baseline were clustered by chemical similarity, and enrichment analyses were used to determine whether serum levels of any lipid clusters differed between the normal and abnormal CSF groups. In a subset of patients with longitudinal structural MRI (normal CSF n = 62, abnormal CSF n = 161), two timepoints of MRI data were used to calculate grey matter annual percent change for each participant. Multivariate partial least squares analyses tested for relationships between neuroimaging and lipidomics data which are moderated by CSF pathology. Our clustering analyses produced 23 serum lipid clusters. Of these clusters, six were altered in the abnormal CSF group, including a cluster of unsaturated phosphatidylcholines. In the subset of participants with longitudinal structural MRI data, a priori nucleus basalis of Meynert partial least squares analyses detected a relationship between unsaturated phosphatidylcholines and degeneration in the nucleus basalis which is moderated by Alzheimer's disease CSF pathology (P = 0.0008). Whole-brain grey matter partial least squares analyses of all 23 lipid clusters revealed that only unsaturated phosphatidylcholines and unsaturated acylcarnitines exhibited an Alzheimer's disease-dependent relationship with longitudinal degeneration (P = 0.0022 and P = 0.0018, respectively). Only the unsaturated phosphatidylcholines predicted basal forebrain degeneration in the whole-brain analyses. Overall, this study provides in vivo evidence for a selective relationship between phosphatidylcholine and basal forebrain degeneration in human Alzheimer's disease, highlighting the importance of phosphatidylcholine to basal forebrain grey matter integrity.

Predictive Metabolomic Markers in Early to Mid-pregnancy for Gestational Diabetes Mellitus: A Prospective Test and Validation Study.

Gestational diabetes mellitus (GDM) predisposes pregnant individuals to perinatal complications and long-term diabetes and cardiovascular diseases. We developed and validated metabolomic markers for GDM in a prospective test-validation study. In a case-control sample within the PETALS cohort (GDM n = 91 and non-GDM n = 180; discovery set), a random PETALS subsample (GDM n = 42 and non-GDM n = 372; validation set 1), and a case-control sample within the GLOW trial (GDM n = 35 and non-GDM n = 70; validation set 2), fasting serum untargeted metabolomics were measured by gas chromatography/time-of-flight mass spectrometry. Multivariate enrichment analysis examined associations between metabolites and GDM. Ten-fold cross-validated LASSO regression identified predictive metabolomic markers at gestational weeks (GW) 10-13 and 16-19 for GDM. Purinone metabolites at GW 10-13 and 16-19 and amino acids, amino alcohols, hexoses, indoles, and pyrimidine metabolites at GW 16-19 were positively associated with GDM risk (false discovery rate <0.05). A 17-metabolite panel at GW 10-13 outperformed the model using conventional risk factors, including fasting glycemia (area under the curve: discovery 0.871 vs. 0.742, validation 1 0.869 vs. 0.731, and validation 2 0.972 vs. 0.742; P < 0.01). Similar results were observed with a 13-metabolite panel at GW 17-19. Dysmetabolism is present early in pregnancy among individuals progressing to GDM. Multimetabolite panels in early pregnancy can predict GDM risk beyond conventional risk factors.

Evidence for Peroxisomal Dysfunction and Dysregulation of the CDP-Choline Pathway in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.

BACKGROUND: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic and debilitating disease that is characterized by unexplained physical fatigue unrelieved by rest. Symptoms also include cognitive and sensory dysfunction, sleeping disturbances, orthostatic intolerance, and gastrointestinal problems. A syndrome clinically similar to ME/CFS has been reported following well-documented infections with the coronaviruses SARS-CoV and MERS-CoV. At least 10% of COVID-19 survivors develop post acute sequelae of SARS-CoV-2 infection (PASC). Although many individuals with PASC have evidence of structural organ damage, a subset have symptoms consistent with ME/CFS including fatigue, post exertional malaise, cognitive dysfunction, gastrointestinal disturbances, and postural orthostatic intolerance. These common features in ME/CFS and PASC suggest that insights into the pathogenesis of either may enrich our understanding of both syndromes, and could expedite the development of strategies for identifying those at risk and interventions that prevent or mitigate disease. METHODS: Using regression, Bayesian and enrichment analyses, we conducted targeted and untargeted metabolomic analysis of 888 metabolic analytes in plasma samples of 106 ME/CFS cases and 91 frequency-matched healthy controls. RESULTS: In ME/CFS cases, regression, Bayesian and enrichment analyses revealed evidence of peroxisomal dysfunction with decreased levels of plasmalogens. Other findings included decreased levels of several membrane lipids, including phosphatidylcholines and sphingomyelins, that may indicate dysregulation of the cytidine-5’-diphosphocholine pathway. Enrichment analyses revealed decreased levels of choline, ceramides and carnitines, and increased levels of long chain triglycerides (TG) and hydroxy-eicosapentaenoic acid. Elevated levels of dicarboxylic acids were consistent with abnormalities in the tricarboxylic acid cycle. Using machine learning algorithms with selected metabolites as predictors, we were able to differentiate female ME/CFS cases from female controls (highest AUC=0.794) and ME/CFS cases without self-reported irritable bowel syndrome (sr-IBS) from controls without sr-IBS (highest AUC=0.873). CONCLUSION: Our findings are consistent with earlier ME/CFS work indicating compromised energy metabolism and redox imbalance, and highlight new abnormalities that may provide insights into the pathogenesis of ME/CFS. ONE SENTENCE SUMMARY: Plasma levels of plasmalogens are decreased in patients with myalgic encephalomyelitis/chronic fatigue syndrome suggesting peroxisome dysfunction.

The volatile compound BinBase mass spectral database.

BACKGROUND: Volatile compounds comprise diverse chemical groups with wide-ranging sources and functions. These compounds originate from major pathways of secondary metabolism in many organisms and play essential roles in chemical ecology in both plant and animal kingdoms. In past decades, sampling methods and instrumentation for the analysis of complex volatile mixtures have improved; however, design and implementation of database tools to process and store the complex datasets have lagged behind. DESCRIPTION: The volatile compound BinBase (vocBinBase) is an automated peak annotation and database system developed for the analysis of GC-TOF-MS data derived from complex volatile mixtures. The vocBinBase DB is an extension of the previously reported metabolite BinBase software developed to track and identify derivatized metabolites. The BinBase algorithm uses deconvoluted spectra and peak metadata (retention index, unique ion, spectral similarity, peak signal-to-noise ratio, and peak purity) from the Leco ChromaTOF software, and annotates peaks using a multi-tiered filtering system with stringent thresholds. The vocBinBase algorithm assigns the identity of compounds existing in the database. Volatile compound assignments are supported by the Adams mass spectral-retention index library, which contains over 2,000 plant-derived volatile compounds. Novel molecules that are not found within vocBinBase are automatically added using strict mass spectral and experimental criteria. Users obtain fully annotated data sheets with quantitative information for all volatile compounds for studies that may consist of thousands of chromatograms. The vocBinBase database may also be queried across different studies, comprising currently 1,537 unique mass spectra generated from 1.7 million deconvoluted mass spectra of 3,435 samples (18 species). Mass spectra with retention indices and volatile profiles are available as free download under the CC-BY agreement (http://vocbinbase.fiehnlab.ucdavis.edu). CONCLUSIONS: The BinBase database algorithms have been successfully modified to allow for tracking and identification of volatile compounds in complex mixtures. The database is capable of annotating large datasets (hundreds to thousands of samples) and is well-suited for between-study comparisons such as chemotaxonomy investigations. This novel volatile compound database tool is applicable to research fields spanning chemical ecology to human health. The BinBase source code is freely available at http://binbase.sourceforge.net/ under the LGPL 2.0 license agreement.

Genomics of Postprandial Lipidomics in the Genetics of Lipid-Lowering Drugs and Diet Network Study.

Postprandial lipemia (PPL) is an important risk factor for cardiovascular disease. Inter-individual variation in the dietary response to a meal is known to be influenced by genetic factors, yet genes that dictate variation in postprandial lipids are not completely characterized. Genetic studies of the plasma lipidome can help to better understand postprandial metabolism by isolating lipid molecular species which are more closely related to the genome. We measured the plasma lipidome at fasting and 6 h after a standardized high-fat meal in 668 participants from the Genetics of Lipid-Lowering Drugs and Diet Network study (GOLDN) using ultra-performance liquid chromatography coupled to (quadrupole) time-of-flight mass spectrometry. A total of 413 unique lipids were identified. Heritable and responsive lipid species were examined for association with single-nucleotide polymorphisms (SNPs) genotyped on the Affymetrix 6.0 array. The most statistically significant SNP findings were replicated in the Amish Heredity and Phenotype Intervention (HAPI) Heart Study. We further followed up findings from GOLDN with a regional analysis of cytosine-phosphate-guanine (CpGs) sites measured on the Illumina HumanMethylation450 array. A total of 132 lipids were both responsive to the meal challenge and heritable in the GOLDN study. After correction for multiple testing of 132 lipids (α = 5 × 10-8/132 = 4 × 10-10), no SNP was statistically significantly associated with any lipid response. Four SNPs in the region of a known lipid locus (fatty acid desaturase 1 and 2/FADS1 and FADS2) on chromosome 11 had p < 8.0 × 10-7 for arachidonic acid FA(20:4). Those SNPs replicated in HAPI Heart with p < 3.3 × 10-3. CpGs around the FADS1/2 region were associated with arachidonic acid and the relationship of one SNP was partially mediated by a CpG (p = 0.005). Both SNPs and CpGs from the fatty acid desaturase region on chromosome 11 contribute jointly and independently to the diet response to a high-fat meal.

Hydrocarbon phenotyping of algal species using pyrolysis-gas chromatography mass spectrometry.

BACKGROUND: Biofuels derived from algae biomass and algae lipids might reduce dependence on fossil fuels. Existing analytical techniques need to facilitate rapid characterization of algal species by phenotyping hydrocarbon-related constituents. RESULTS: In this study, we compared the hydrocarbon rich algae Botryococcus braunii against the photoautotrophic model algae Chlamydomonas reinhardtii using pyrolysis-gas chromatography quadrupole mass spectrometry (pyGC-MS). Sequences of up to 48 dried samples can be analyzed using pyGC-MS in an automated manner without any sample preparation. Chromatograms of 30-min run times are sufficient to profile pyrolysis products from C8 to C40 carbon chain length. The freely available software tools AMDIS and SpectConnect enables straightforward data processing. In Botryococcus samples, we identified fatty acids, vitamins, sterols and fatty acid esters and several long chain hydrocarbons. The algae species C. reinhardtii, B. braunii race A and B. braunii race B were readily discriminated using their hydrocarbon phenotypes. Substructure annotation and spectral clustering yielded network graphs of similar components for visual overviews of abundant and minor constituents. CONCLUSION: Pyrolysis-GC-MS facilitates large scale screening of hydrocarbon phenotypes for comparisons of strain differences in algae or impact of altered growth and nutrient conditions.