Variation in Root Exudate Composition Influences Soil Microbiome Membership and Function.

Root exudation is one of the primary processes that mediate interactions between plant roots, microorganisms, and the soil matrix, yet the mechanisms by which exudation alters microbial metabolism in soils have been challenging to unravel. Here, utilizing distinct sorghum genotypes, we characterized the chemical heterogeneity between root exudates and the effects of that variability on soil microbial membership and metabolism. Distinct exudate chemical profiles were quantified and used to formulate synthetic root exudate treatments: a high-organic-acid treatment (HOT) and a high-sugar treatment (HST). To parse the response of the soil microbiome to different exudate regimens, laboratory soil reactors were amended with these root exudate treatments as well as a nonexudate control. Amplicon sequencing of the 16S rRNA gene illustrated distinct microbial diversity patterns and membership in response to HST, HOT, or control amendments. Exometabolite changes reflected these microbial community changes, and we observed enrichment of organic and amino acids, as well as possible phytohormones in the HST relative to the HOT and control. Linking the metabolic capacity of metagenome-assembled genomes in the HST to the exometabolite patterns, we identified microorganisms that could produce these phytohormones. Our findings emphasize the tractability of high-resolution multiomics tools to investigate soil microbiomes, opening the possibility of manipulating native microbial communities to improve specific soil microbial functions and enhance crop production. IMPORTANCE Decrypting the chemical interactions between plant roots and the soil microbiome is a gateway for future manipulation and management of the rhizosphere, a soil compartment critical to promoting plant fitness and yields. Our experimental results demonstrate how soil microbial community and genomic diversity is influenced by root exudates of differing chemical compositions and how changes in this microbiome result in altered production of plant-relevant metabolites. Together, these findings demonstrate the tractability of high-resolution multiomics tools to investigate soil microbiomes and provide new information on plant-soil environments useful for the development of efficient and precise microbiota management strategies in agricultural systems.

Quantitative analysis of short-chain fatty acids in human plasma and serum by GC-MS.

Short-chain fatty acids (SCFAs) are volatile fatty acids produced by gut microbial fermentation of dietary nondigestible carbohydrates. Acetate, propionate, and butyrate SCFA measures are important to clinical and nutritional studies for their established roles in promoting healthy immune and gut function. Additionally, circulating SCFAs may influence the metabolism and allied function of additional tissues and organs. The accurate quantification of SCFAs in plasma/serum is critical to understanding the biological role of SCFAs. The low concentrations of circulating SCFAs and their volatile nature present challenges for quantitative analysis. Herein, we report a sensitive method for SCFA quantification via extraction with methyl tert-butyl ether after plasma/serum acidification. The organic extract of SCFAs is injected directly with separation and detection using a polar GC column coupled to mass spectrometry. The solvent-to-sample ratio, plasma volume, and amount of HCl needed for SCFA protonation were optimized. Method validation shows good within-day and inter-day repeatability. The limit of detection was 0.3-0.6 µg/mL for acetate and 0.03-0.12 µg/mL for propionate and butyrate. Successful application of this method on clinical plasma and serum samples was demonstrated in six datasets. By simplifying the sample preparation procedure, the present method reduces the risk of contamination, lowers the cost of analysis, increases throughput, and offers the potential for automated sample preparation.

Evaluating the ability of rapid evaporative ionization mass spectrometry to differentiate beef palatability based on consumer preference.

Rapid Evaporative Ionization Mass Spectrometry (REIMS) is a type of ambient ionization mass spectrometry, which enables real-time evaluation of several complex traits from a single measurement. The objective of this study was to evaluate the capability of REIMS analysis of raw samples coupled with chemometrics to accurately identify and predict cooked beef palatability. REIMS analysis and consumer sensory evaluation were conducted for beef arm center roasts (n = 20), top loin steaks (n = 20), top sirloin steaks (n = 20), and 20% lipid ground beef (n = 20). These data were used to train predictive models for six classification sets representing different sensory traits. The maximum prediction accuracies achieved (from high to low): beefy flavor acceptance (86.25%), juiciness acceptance (83.75%), overall acceptance (81.25%), overall flavor acceptance (81.25%), grilled flavor acceptance (78.75%), and tenderness acceptance (75%). The current study demonstrates that REIMS analysis of raw meat has the potential to predict and classify cooked beef palatability. Supplementary Information: The online version contains supplementary material available at 10.1007/s13197-022-05562-6.

Feature selection and causal analysis for microbiome studies in the presence of confounding using standardization.

BACKGROUND: Microbiome studies have uncovered associations between microbes and human, animal, and plant health outcomes. This has led to an interest in developing microbial interventions for treatment of disease and optimization of crop yields which requires identification of microbiome features that impact the outcome in the population of interest. That task is challenging because of the high dimensionality of microbiome data and the confounding that results from the complex and dynamic interactions among host, environment, and microbiome. In the presence of such confounding, variable selection and estimation procedures may have unsatisfactory performance in identifying microbial features with an effect on the outcome. RESULTS: In this manuscript, we aim to estimate population-level effects of individual microbiome features while controlling for confounding by a categorical variable. Due to the high dimensionality and confounding-induced correlation between features, we propose feature screening, selection, and estimation conditional on each stratum of the confounder followed by a standardization approach to estimation of population-level effects of individual features. Comprehensive simulation studies demonstrate the advantages of our approach in recovering relevant features. Utilizing a potential-outcomes framework, we outline assumptions required to ascribe causal, rather than associational, interpretations to the identified microbiome effects. We conducted an agricultural study of the rhizosphere microbiome of sorghum in which nitrogen fertilizer application is a confounding variable. In this study, the proposed approach identified microbial taxa that are consistent with biological understanding of potential plant-microbe interactions. CONCLUSIONS: Standardization enables more accurate identification of individual microbiome features with an effect on the outcome of interest compared to other variable selection and estimation procedures when there is confounding by a categorical variable.

Tafazzin deficiency impairs CoA-dependent oxidative metabolism in cardiac mitochondria.

Barth syndrome is a mitochondrial myopathy resulting from mutations in the tafazzin (TAZ) gene encoding a phospholipid transacylase required for cardiolipin remodeling. Cardiolipin is a phospholipid of the inner mitochondrial membrane essential for the function of numerous mitochondrial proteins and processes. However, it is unclear how tafazzin deficiency impacts cardiac mitochondrial metabolism. To address this question while avoiding confounding effects of cardiomyopathy on mitochondrial phenotype, we utilized Taz-shRNA knockdown (TazKD ) mice, which exhibit defective cardiolipin remodeling and respiratory supercomplex instability characteristic of human Barth syndrome but normal cardiac function into adulthood. Consistent with previous reports from other models, mitochondrial H2O2 emission and oxidative damage were greater in TazKD than in wild-type (WT) hearts, but there were no differences in oxidative phosphorylation coupling efficiency or membrane potential. Fatty acid and pyruvate oxidation capacities were 40-60% lower in TazKD mitochondria, but an up-regulation of glutamate oxidation supported respiration rates approximating those with pyruvate and palmitoylcarnitine in WT. Deficiencies in mitochondrial CoA and shifts in the cardiac acyl-CoA profile paralleled changes in fatty acid oxidation enzymes and acyl-CoA thioesterases, suggesting limitations of CoA availability or "trapping" in TazKD mitochondrial metabolism. Incubation of TazKD mitochondria with exogenous CoA partially rescued pyruvate and palmitoylcarnitine oxidation capacities, implicating dysregulation of CoA-dependent intermediary metabolism rather than respiratory chain defects in the bioenergetic impacts of tafazzin deficiency. These findings support links among cardiolipin abnormalities, respiratory supercomplex instability, and mitochondrial oxidant production and shed new light on the distinct metabolic consequences of tafazzin deficiency in the mammalian heart.

Biochemical Characterization of Isoniazid-resistant Mycobacterium tuberculosis: Can the Analysis of Clonal Strains Reveal Novel Targetable Pathways?

Tuberculosis (TB) continues to be an important public health threat worldwide, due in part to drug resistant Mycobacterium tuberculosis (Mtb) strains. The United States recently reported a shortage of isoniazid (INH), which could drive higher INH resistance rates. Changes in the Mtb proteome before and after acquisition of INH resistance in a clean genetic background remain understudied and may elucidate alternate drug targets. Here, we focused on Mtb clonal strains to characterize the consequences of INH resistance on mycobacterial metabolism. Proteomic analysis was conducted by liquid-chromatography tandem mass spectrometry (LC-MS/MS) of cellular and secreted fractions, followed by a normalized spectral counting (NSAF) analysis (data are available via ProteomeXchange with identifier PXD009549). Two different Mtb clonal pairs representing a specific genetic lineage (one clinical and one generated in the laboratory) but sharing a katG mutation associated with INH resistance, were used in our analysis. Overall, we found 26 Mtb proteins with altered abundances after acquisition of INH resistance across both Mtb genetic lineages studied. These proteins were involved in ATP synthesis, lipid metabolism, regulatory events, and virulence, detoxification, and adaptation processes. Proteomic findings were validated by Western blotting analyses whenever possible. Mycolic acid (MA) analysis through LC/MS in the clonal Mtb pairs did not reveal a common trend in the alteration of these fatty acids across both INHr strains but revealed a significant reduction in levels of the two more abundant α-MA features in the clinical INHr strain. Interestingly, the clinical clonal pair demonstrated more variation in the abundance of the proteins involved in the FAS II pathway. Together, the proteomic and lipidomic data highlight the identification of potential drug targets such as alternative lipid biosynthetic pathways that may be exploited to combat clinically relevant Mtb INHr strains.

Correction: Effect of Insulin Resistance on Monounsaturated Fatty Acid Levels: A Multi-cohort Non-targeted Metabolomics and Mendelian Randomization Study.

[This corrects the article DOI: 10.1371/journal.pgen.1006379.].

The effects of neurectomy and hibernation on bone properties and the endocannabinoid system in marmots (Marmota flaviventris).

Hibernators have adapted a physiological mechanism allowing them to undergo long periods of inactivity without experiencing bone loss. However, the biological mechanisms that prevent bone loss are unknown. Previous studies found meaningful changes, between active and hibernating marmots, in the endocannabinoid system of many tissues, including bone. Cannabinoid receptors (CB1 and CB2) have divergent localization in bone. CB1 is predominately found on sympathetic nerve terminals, while CB2 is more abundant on bone cells and their progenitors. This study aimed to determine the contribution of innervation on endocannabinoid regulation of bone properties in hibernating (during torpor) and non-hibernating yellow-bellied marmots. Neurectomy, a model for disuse osteoporosis, was performed unilaterally in both hibernating and active marmots. Endocannabinoid concentrations were measured in bone marrow, cortical, and trabecular regions from fourth metatarsals of both hindlimbs using microflow chromatography-tandem quadrupole mass spectrometry. Trabecular bone architectural properties of fifth metatarsals were evaluated using micro-computed tomography. There were ligand-specific increases with neurectomy in active, but not hibernating, marmots. Trabecular bone architectural properties were not affected by neurectomy during hibernation, but did show some minor negative changes in active marmots. These findings suggest protection from bone loss in hibernating rodents is peripherally rather than centrally regulated. Furthermore, findings suggest even active marmots with normal metabolism are partially protected from disuse induced bone loss compared to laboratory rodents. Understanding the mechanism hibernators use to maintain bone density may guide development for novel bone loss prevention therapies.

A Multi-Cohort Metabolomics Analysis Discloses Sphingomyelin (32:1) Levels to be Inversely Related to Incident Ischemic Stroke.

BACKGROUND AND PURPOSE: To search for novel pathophysiological pathways related to ischemic stroke using a metabolomics approach. METHODS: We identified 204 metabolites in plasma by liquid chromatography mass spectrometry in 3 independent population-based samples (TwinGene, Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) and Uppsala Longitudinal Study of Adult Men). TwinGene was used for discovery and the other 2 samples were meta-analyzed as replication. In PIVUS, traditional cardiovascular (CV) risk factors, multiple markers of subclinical CV disease, markers of coagulation/fibrinolysis were measured and analyzed in relation to top metabolites. RESULTS: In TwinGene (177 incident cases, median follow-up 4.3 years), levels of 28 metabolites were associated with incident ischemic stroke at a false discover rate (FDR) of 5%. In the replication (together 194 incident cases, follow-up 10 and 12 years, respectively), only sphingomyelin (32:1) was significantly associated (HR .69 per SD change, 95% CI .57-0.83, P value = .00014; FDR <5%) when adjusted for systolic blood pressure, diabetes, smoking, low density lipoportein (LDL)- and high density lipoprotein (HDL), body mass index (BMI) and atrial fibrillation. In PIVUS, sphingomyelin (32:1) levels were significantly related to both LDL- and HDL-cholesterol in a positive fashion, and to serum triglycerides, BMI and diabetes in a negative fashion. Furthermore, sphingomyelin (32:1) levels were related to vasodilation in the forearm resistance vessels, and inversely to leukocyte count (P < .0069 and .0026, respectively). CONCLUSIONS: An inverse relationship between sphingomyelin (32:1) and incident ischemic stroke was identified, replicated, and characterized. A possible protective role for sphingomyelins in stroke development has to be further investigated in additional experimental and clinical studies.

Adaptive remodeling of skeletal muscle energy metabolism in high-altitude hypoxia: Lessons from AltitudeOmics.

Metabolic responses to hypoxia play important roles in cell survival strategies and disease pathogenesis in humans. However, the homeostatic adjustments that balance changes in energy supply and demand to maintain organismal function under chronic low oxygen conditions remain incompletely understood, making it difficult to distinguish adaptive from maladaptive responses in hypoxia-related pathologies. We integrated metabolomic and proteomic profiling with mitochondrial respirometry and blood gas analyses to comprehensively define the physiological responses of skeletal muscle energy metabolism to 16 days of high-altitude hypoxia (5260 m) in healthy volunteers from the AltitudeOmics project. In contrast to the view that hypoxia down-regulates aerobic metabolism, results show that mitochondria play a central role in muscle hypoxia adaptation by supporting higher resting phosphorylation potential and enhancing the efficiency of long-chain acylcarnitine oxidation. This directs increases in muscle glucose toward pentose phosphate and one-carbon metabolism pathways that support cytosolic redox balance and help mitigate the effects of increased protein and purine nucleotide catabolism in hypoxia. Muscle accumulation of free amino acids favor these adjustments by coordinating cytosolic and mitochondrial pathways to rid the cell of excess nitrogen, but might ultimately limit muscle oxidative capacity in vivo Collectively, these studies illustrate how an integration of aerobic and anaerobic metabolism is required for physiological hypoxia adaptation in skeletal muscle, and highlight protein catabolism and allosteric regulation as unexpected orchestrators of metabolic remodeling in this context. These findings have important implications for the management of hypoxia-related diseases and other conditions associated with chronic catabolic stress.

High-throughput quantitative analysis of phytohormones in sorghum leaf and root tissue by ultra-performance liquid chromatography-mass spectrometry.

Plant development, growth, and adaptation to stress are regulated by phytohormones, which can influence physiology even at low concentrations. Phytohormones are chemically grouped according to both structure and function as auxins, cytokinins, abscisic acid, jasmonates, salicylates, gibberellins, and brassinosteroids, among others. This chemical diversity and requirement for highly sensitive detection in complex matrices create unique challenges for comprehensive phytohormone analysis. Here, we present a robust and efficient quantitative UPLC-MS/MS assay for 17 phytohormones, including jasmonates, salicylates, abscisic acid, gibberellins, cytokinins, and auxins. Using this assay, 12 phytohormones were detected and quantified in sorghum plant tissue without the need for solid phase extraction (SPE) or liquid-liquid extraction. Variation of phytohormone profiles was explored in both root and leaf tissues between three genotypes, harvested at two different developmental time points. The results highlight the importance of tissue type, sampling time, and genetic factors when designing experiments that involve phytohormone analysis of sorghum. This research lays the groundwork for future studies, which can combine phytohormone profiling with other datasets such as transcriptome, soil microbiome, genome, and metabolome data, to provide important functional information about adaptation to stress and other environmental variables.

Effect of Insulin Resistance on Monounsaturated Fatty Acid Levels: A Multi-cohort Non-targeted Metabolomics and Mendelian Randomization Study.

Insulin resistance (IR) and impaired insulin secretion contribute to type 2 diabetes and cardiovascular disease. Both are associated with changes in the circulating metabolome, but causal directions have been difficult to disentangle. We combined untargeted plasma metabolomics by liquid chromatography/mass spectrometry in three non-diabetic cohorts with Mendelian Randomization (MR) analysis to obtain new insights into early metabolic alterations in IR and impaired insulin secretion. In up to 910 elderly men we found associations of 52 metabolites with hyperinsulinemic-euglycemic clamp-measured IR and/or ß-cell responsiveness (disposition index) during an oral glucose tolerance test. These implicated bile acid, glycerophospholipid and caffeine metabolism for IR and fatty acid biosynthesis for impaired insulin secretion. In MR analysis in two separate cohorts (n = 2,613) followed by replication in three independent studies profiled on different metabolomics platforms (n = 7,824 / 8,961 / 8,330), we discovered and replicated causal effects of IR on lower levels of palmitoleic acid and oleic acid. A trend for a causal effect of IR on higher levels of tyrosine reached significance only in meta-analysis. In one of the largest studies combining "gold standard" measures for insulin responsiveness with non-targeted metabolomics, we found distinct metabolic profiles related to IR or impaired insulin secretion. We speculate that the causal effects on monounsaturated fatty acid levels could explain parts of the raised cardiovascular disease risk in IR that is independent of diabetes development.

Impact of primary carbon sources on microbiome shaping and biotransformation of pharmaceuticals and personal care products.

Knowledge of the conditions that promote the growth and activity of pharmaceutical and personal care product (PPCP)-degrading microorganisms within mixed microbial systems are needed to shape microbiomes in biotreatment reactors and manage process performance. Available carbon sources influence microbial community structure, and specific carbon sources could potentially be added to end-of-treatment train biotreatment systems (e.g., soil aquifer treatment [SAT]) to select for the growth and activity of a range of microbial phylotypes that collectively degrade target PPCPs. Herein, the impacts of primary carbon sources on PPCP biodegradation and microbial community structure were explored to identify promising carbon sources for PPCP biotreatment application. Six types of primary carbon sources were investigated: casamino acids, two humic acid and peptone mixtures (high and low amounts of humic acid), molasses, an organic acids mixture, and phenol. Biodegradation was tracked for five PPCPs (diclofenac, 5-fluorouracil, gemfibrozil, ibuprofen, and triclosan). Primary carbon sources were found to differentially impact microbial community structures and rates and efficiencies of PPCP biotransformation. Of the primary carbon sources tested, casamino acids, organic acids, and phenol showed the fastest biotransformation; however, on a biomass-normalized basis, both humic acid-peptone mixtures showed comparable or superior biotransformation. By comparing microbial communities for the different primary carbon sources, abundances of unclassified Beijerinckiaceae, Beijerinckia, Sphingomonas, unclassified Sphingomonadaceae, Flavobacterium, unclassified Rhizobiales, and Nevskia were statistically linked with biotransformation of specific PPCPs.

Stacked Injections of Biphasic Extractions for Improved Metabolomic Coverage and Sample Throughput.

Omics technologies attempt to provide comprehensive coverage of their target analytes. Comprehensive coverage of metabolites, the aim of nontargeted metabolomics applications, is hindered by the extreme diversity in physiochemical properties of the metabolome. One approach to deal with this challenge is the use of biphasic extractions. These methods generate two largely complementary extracts from a single sample, with an organic lipid-rich fraction and an aqueous fraction containing largely primary and secondary metabolites. To improve metabolite coverage, these two fractions are then independently analyzed resulting in a doubling of the experimental time. In this manuscript, we describe a novel injection approach, stacked injections of a biphasic extraction (SIBE), which enables simultaneous analysis of the two fractions. We demonstrate that SIBE offers nearly 3-fold more total peak area than a monophasic extract without dramatically increasing instrumentation time required for the analysis. The analytical variance is very slightly increased; however, significant improvements in retention time stability are obtained with SIBE vs monophasic injections. Collectively, these data indicate that SIBE is a viable injection approach whenever comprehensive metabolomic coverage is desired.

Comprehensive Tandem-Mass-Spectrometry Coverage of Complex Samples Enabled by Data-Set-Dependent Acquisition.

Tandem mass spectrometry (MS/MS) is an invaluable experimental tool for providing analytical data supporting the identification of small molecules and peptides in mass-spectrometry-based "omics" experiments. Data-dependent MS/MS (DDA) is a real-time MS/MS-acquisition strategy that is responsive to the signals detected in a given sample. However, in analysis of even moderately complex samples with state-of-the-art instrumentation, the speed of MS/MS acquisition is insufficient to offer comprehensive MS/MS coverage of all detected molecules. Data-independent approaches (DIA) offer greater MS/MS coverage, typically at the expense of selectivity or sensitivity. This report describes data-set-dependent MS/MS (DsDA), a novel integration of MS1-data processing and target prioritization to enable comprehensive MS/MS sampling during the initial MS-level experiment. This approach is guided by the premise that in omics experiments, individual injections are typically made as part of a larger set of samples, and feedback between data processing and data acquisition can allow approximately real-time optimization of MS/MS-acquisition parameters and nearly complete MS/MS-sampling coverage. Using a combination of R, Proteowizard, XCMS, and WRENS software, this concept was implemented on a liquid-chromatograph-coupled quadrupole time-of-flight mass spectrometer. The results illustrate comprehensive MS/MS coverage for a set of complex small-molecule samples and demonstrate a strong improvement on traditional DDA.

Common bean varieties demonstrate differential physiological and metabolic responses to the pathogenic fungus Sclerotinia sclerotiorum.

Plant physiology and metabolism are important components of a plant response to microbial pathogens. Physiological resistance of common bean (Phaseolus vulgaris L.) to the fungal pathogen Sclerotinia sclerotiorum has been established, but the mechanisms of resistance are largely unknown. Here, the physiological and metabolic responses of bean varieties that differ in physiological resistance to S. sclerotiorum are investigated. Upon infection, the resistant bean variety A195 had a unique physiological response that included reduced photosynthesis and maintaining a higher leaf surface pH during infection. Leaf metabolomics was performed on healthy tissue adjacent to the necrotic lesion at 16, 24, and 48 hr post inoculation, and 144 metabolites were detected that varied between A195 and Sacramento following infection. The metabolites that varied in leaves included amines/amino acids, organic acids, phytoalexins, and ureides. The metabolic pathways associated with resistance included amine metabolism, uriede-based nitrogen remobilization, antioxidant production, and bean-specific phytoalexin production. A second experiment was conducted in stems of 13 bean genotypes with varying resistance. Stem resistance was associated with phytoalexin production, but unlike leaf metabolism, lipid changes were associated with susceptibility. Taken together, the data supports a multifaceted, physiometabolic response of common bean to S. sclerotiorum that mediates resistance.

Pregnancy-induced changes in metabolome and proteome in ovine uterine flushings.

Mass spectrometry (MS) approaches were used herein to identify metabolites and proteins in uterine flushings (UF) that may contribute to nourishing the conceptus. Ovine uteri collected on Day 12 of the estrous cycle (n = 5 ewes exposed to vasectomized ram) or Days 12 (n = 4), 14 (n = 5), or 16 (n = 5) of pregnancy (bred with fertile ram) were flushed using buffered saline. Metabolites were extracted using 80% methanol and profiled using ultraperformance liquid chromatography (LC) tandem mass spectrometry. The proteome was examined by digestion with trypsin, followed by the analysis of peptides with LC-MS/MS. Metabolite profiling detected 8510 molecular features of which 9 were detected only in UF from Day 14-16 pregnant ewes that function in fatty acid transport (carnitines), hormone synthesis (androstenedione like), and availability of nutrients (valine). Proteome analysis detected 783 proteins present by Days 14-16 of pregnancy in UF, 7 of which are as follows: annexin (ANX) A1, A2, and A5; calcium-binding protein (S100A11); profilin 1; trophoblast kunitz domain protein 1 (TKDP); and interferon tau (IFNT). These proteins function in endocytosis, exocytosis, calcium signaling, and inhibition of prostaglandins (annexins and S100A11); protecting against maternal proteases (TKDP); remodeling cytoskeleton (profilin 1); and altering uterine release of prostaglandin F2 alpha as well as inducing IFNT-stimulated genes in the endometrium and the corpus luteum (IFNT). Identifying metabolites and proteins produced by the uterus and conceptus advances our understanding of embryo/maternal signaling and provides insights into possible the causes of reproductive failure.

Large-scale metabolomic profiling identifies novel biomarkers for incident coronary heart disease.

Analyses of circulating metabolites in large prospective epidemiological studies could lead to improved prediction and better biological understanding of coronary heart disease (CHD). We performed a mass spectrometry-based non-targeted metabolomics study for association with incident CHD events in 1,028 individuals (131 events; 10 y. median follow-up) with validation in 1,670 individuals (282 events; 3.9 y. median follow-up). Four metabolites were replicated and independent of main cardiovascular risk factors [lysophosphatidylcholine 18∶1 (hazard ratio [HR] per standard deviation [SD] increment = 0.77, P-value<0.001), lysophosphatidylcholine 18∶2 (HR = 0.81, P-value<0.001), monoglyceride 18∶2 (MG 18∶2; HR = 1.18, P-value = 0.011) and sphingomyelin 28∶1 (HR = 0.85, P-value = 0.015)]. Together they contributed to moderate improvements in discrimination and re-classification in addition to traditional risk factors (C-statistic: 0.76 vs. 0.75; NRI: 9.2%). MG 18∶2 was associated with CHD independently of triglycerides. Lysophosphatidylcholines were negatively associated with body mass index, C-reactive protein and with less evidence of subclinical cardiovascular disease in additional 970 participants; a reverse pattern was observed for MG 18∶2. MG 18∶2 showed an enrichment (P-value = 0.002) of significant associations with CHD-associated SNPs (P-value = 1.2×10-7 for association with rs964184 in the ZNF259/APOA5 region) and a weak, but positive causal effect (odds ratio = 1.05 per SD increment in MG 18∶2, P-value = 0.05) on CHD, as suggested by Mendelian randomization analysis. In conclusion, we identified four lipid-related metabolites with evidence for clinical utility, as well as a causal role in CHD development.

Non-targeted metabolomics combined with genetic analyses identifies bile acid synthesis and phospholipid metabolism as being associated with incident type 2 diabetes.

AIMS/HYPOTHESIS: Identification of novel biomarkers for type 2 diabetes and their genetic determinants could lead to improved understanding of causal pathways and improve risk prediction. METHODS: In this study, we used data from non-targeted metabolomics performed using liquid chromatography coupled with tandem mass spectrometry in three Swedish cohorts (Uppsala Longitudinal Study of Adult Men [ULSAM], n = 1138; Prospective Investigation of the Vasculature in Uppsala Seniors [PIVUS], n = 970; TwinGene, n = 1630). Metabolites associated with impaired fasting glucose (IFG) and/or prevalent type 2 diabetes were assessed for associations with incident type 2 diabetes in the three cohorts followed by replication attempts in the Cooperative Health Research in the Region of Augsburg (KORA) S4 cohort (n = 855). Assessment of the association of metabolite-regulating genetic variants with type 2 diabetes was done using data from a meta-analysis of genome-wide association studies. RESULTS: Out of 5961 investigated metabolic features, 1120 were associated with prevalent type 2 diabetes and IFG and 70 were annotated to metabolites and replicated in the three cohorts. Fifteen metabolites were associated with incident type 2 diabetes in the four cohorts combined (358 events) following adjustment for age, sex, BMI, waist circumference and fasting glucose. Novel findings included associations of higher values of the bile acid deoxycholic acid and monoacylglyceride 18:2 and lower concentrations of cortisol with type 2 diabetes risk. However, adding metabolites to an existing risk score improved model fit only marginally. A genetic variant within the CYP7A1 locus, encoding the rate-limiting enzyme in bile acid synthesis, was found to be associated with lower concentrations of deoxycholic acid, higher concentrations of LDL-cholesterol and lower type 2 diabetes risk. Variants in or near SGPP1, GCKR and FADS1/2 were associated with diabetes-associated phospholipids and type 2 diabetes. CONCLUSIONS/INTERPRETATION: We found evidence that the metabolism of bile acids and phospholipids shares some common genetic origin with type 2 diabetes. ACCESS TO RESEARCH MATERIALS: Metabolomics data have been deposited in the Metabolights database, with accession numbers MTBLS93 (TwinGene), MTBLS124 (ULSAM) and MTBLS90 (PIVUS).

Enabling Efficient and Confident Annotation of LC-MS Metabolomics Data through MS1 Spectrum and Time Prediction.

Liquid chromatography coupled to electrospray ionization-mass spectrometry (LC-ESI-MS) is a versatile and robust platform for metabolomic analysis. However, while ESI is a soft ionization technique, in-source phenomena including multimerization, nonproton cation adduction, and in-source fragmentation complicate interpretation of MS data. Here, we report chromatographic and mass spectrometric behavior of 904 authentic standards collected under conditions identical to a typical nontargeted profiling experiment. The data illustrate that the often high level of complexity in MS spectra is likely to result in misinterpretation during the annotation phase of the experiment and a large overestimation of the number of compounds detected. However, our analysis of this MS spectral library data indicates that in-source phenomena are not random but depend at least in part on chemical structure. These nonrandom patterns enabled predictions to be made as to which in-source signals are likely to be observed for a given compound. Using the authentic standard spectra as a training set, we modeled the in-source phenomena for all compounds in the Human Metabolome Database to generate a theoretical in-source spectrum and retention time library. A novel spectral similarity matching platform was developed to facilitate efficient spectral searching for nontargeted profiling applications. Taken together, this collection of experimental spectral data, predictive modeling, and informatic tools enables more efficient, reliable, and transparent metabolite annotation.