Introducing Molecular Hypernetworks for Discovery in Multidimensional Metabolomics Data.

Orthogonal separations of data from high-resolution mass spectrometry can provide insight into sample composition and address challenges of complete annotation of molecules in untargeted metabolomics. "Molecular networks" (MNs), as used in the Global Natural Products Social Molecular Networking platform, are a prominent strategy for exploring and visualizing molecular relationships and improving annotation. MNs are mathematical graphs showing the relationships between measured multidimensional data features. MNs also show promise for using network science algorithms to automatically identify targets for annotation candidates and to dereplicate features associated with a single molecular identity. This paper introduces "molecular hypernetworks" (MHNs) as more complex MN models able to natively represent multiway relationships among observations. Compared to MNs, MHNs can more parsimoniously represent the inherent complexity present among groups of observations, initially supporting improved exploratory data analysis and visualization. MHNs also promise to increase confidence in annotation propagation, for both human and analytical processing. We first illustrate MHNs with simple examples, and build them from liquid chromatography- and ion mobility spectrometry-separated MS data. We then describe a method to construct MHNs directly from existing MNs as their "clique reconstructions", demonstrating their utility by comparing examples of previously published graph-based MNs to their respective MHNs.

Fire Impacts on the Soil Metabolome and Organic Matter Biodegradability.

Global wildfire activity has increased since the 1970s and is projected to intensify throughout the 21st century. Wildfires change the composition and biodegradability of soil organic matter (SOM) which contains nutrients that fuel microbial metabolism. Though persistent forms of SOM often increase postfire, the response of more biodegradable SOM remains unclear. Here we simulated severe wildfires through a controlled "pyrocosm" approach to identify biodegradable sources of SOM and characterize the soil metabolome immediately postfire. Using microbial amplicon (16S/ITS) sequencing and gas chromatography-mass spectrometry, heterotrophic microbes (Actinobacteria, Firmicutes, and Protobacteria) and specific metabolites (glycine, protocatechuate, citric cycle intermediates) were enriched in burned soils, indicating that burned soils contain a variety of substrates that support microbial metabolism. Molecular formulas assigned by 21 T Fourier transform ion cyclotron resonance mass spectrometry showed that SOM in burned soil was lower in molecular weight and featured 20 to 43% more nitrogen-containing molecular formulas than unburned soil. We also measured higher water extractable organic carbon concentrations and higher CO2 efflux in burned soils. The observed enrichment of biodegradable SOM and microbial heterotrophs demonstrates the resilience of these soils to severe burning, providing important implications for postfire soil microbial and plant recolonization and ecosystem recovery.

Current Practices in LC-MS Untargeted Metabolomics: A Scoping Review on the Use of Pooled Quality Control Samples.

Untargeted metabolomics is an analytical approach with numerous applications serving as an effective metabolic phenotyping platform to characterize small molecules within a biological system. Data quality can be challenging to evaluate and demonstrate in metabolomics experiments. This has driven the use of pooled quality control (QC) samples for monitoring and, if necessary, correcting for analytical variance introduced during sample preparation and data acquisition stages. Described herein is a scoping literature review detailing the use of pooled QC samples in published untargeted liquid chromatography-mass spectrometry (LC-MS) based metabolomics studies. A literature query was performed, the list of papers was filtered, and suitable articles were randomly sampled. In total, 109 papers were each reviewed by at least five reviewers, answering predefined questions surrounding the use of pooled quality control samples. The results of the review indicate that use of pooled QC samples has been relatively widely adopted by the metabolomics community and that it is used at a similar frequency across biological taxa and sample types in both small- and large-scale studies. However, while many studies generated and analyzed pooled QC samples, relatively few reported the use of pooled QC samples to improve data quality. This demonstrates a clear opportunity for the field to more frequently utilize pooled QC samples for quality reporting, feature filtering, analytical drift correction, and metabolite annotation. Additionally, our survey approach enabled us to assess the ambiguity in the reporting of the methods used to describe the generation and use of pooled QC samples. This analysis indicates that many details of the QC framework are missing or unclear, limiting the reader's ability to determine which QC steps have been taken. Collectively, these results capture the current state of pooled QC sample usage and highlight existing strengths and deficiencies as they are applied in untargeted LC-MS metabolomics.

Secreted metabolome of porcine blastocysts encapsulated within in vitro 3D alginate hydrogel culture systems undergoing morphological changes provides insights into specific mechanisms involved in the initiation of porcine conceptus elongation.

CONTEXT: The exact mechanisms regulating the initiation of porcine conceptus elongation are not known due to the complexity of the uterine environment. AIMS: To identify contributing factors for initiation of conceptus elongation in vitro , this study evaluated differential metabolite abundance within media following culture of blastocysts within unmodified alginate (ALG) or Arg-Gly-Asp (RGD)-modified alginate hydrogel culture systems. METHODS: Blastocysts were harvested from pregnant gilts, encapsulated within ALG or RGD or as non-encapsulated control blastocysts (CONT), and cultured. At the termination of 96h culture, media were separated into blastocyst media groups: non-encapsulated control blastocysts (CONT); ALG and RGD blastocysts with no morphological change (ALG- and RGD-); ALG and RGD blastocysts with morphological changes (ALG+ and RGD+) and evaluated for non-targeted metabolomic profiling by liquid chromatography (LC)-mass spectrometry (MS) techniques and gas chromatography-(GC-MS). KEY RESULTS: Analysis of variance identified 280 (LC-MS) and 1 (GC-MS) compounds that differed (P <0.05), of which 134 (LC-MS) and 1 (GC-MS) were annotated. Metabolites abundance between ALG+ vs ALG-, RGD+ vs RGD-, and RGD+ vs ALG+ were further investigated to identify potential differences in metabolic processes during the initiation of elongation. CONCLUSIONS: This study identified changes in phospholipid, glycosphingolipid, lipid signalling, and amino acid metabolic processes as potential RGD-independent mechanisms of elongation and identified changes in lysophosphatidylcholine and sphingolipid secretions during RGD-mediated elongation. IMPLICATIONS: These results illustrate changes in phospholipid and sphingolipid metabolic processes and secretions may act as mediators of the RGD-integrin adhesion that promotes porcine conceptus elongation.

Reference materials for MS-based untargeted metabolomics and lipidomics: a review by the metabolomics quality assurance and quality control consortium (mQACC).

INTRODUCTION: The metabolomics quality assurance and quality control consortium (mQACC) is enabling the identification, development, prioritization, and promotion of suitable reference materials (RMs) to be used in quality assurance (QA) and quality control (QC) for untargeted metabolomics research. OBJECTIVES: This review aims to highlight current RMs, and methodologies used within untargeted metabolomics and lipidomics communities to ensure standardization of results obtained from data analysis, interpretation and cross-study, and cross-laboratory comparisons. The essence of the aims is also applicable to other 'omics areas that generate high dimensional data. RESULTS: The potential for game-changing biochemical discoveries through mass spectrometry-based (MS) untargeted metabolomics and lipidomics are predicated on the evolution of more confident qualitative (and eventually quantitative) results from research laboratories. RMs are thus critical QC tools to be able to assure standardization, comparability, repeatability and reproducibility for untargeted data analysis, interpretation, to compare data within and across studies and across multiple laboratories. Standard operating procedures (SOPs) that promote, describe and exemplify the use of RMs will also improve QC for the metabolomics and lipidomics communities. CONCLUSIONS: The application of RMs described in this review may significantly improve data quality to support metabolomics and lipidomics research. The continued development and deployment of new RMs, together with interlaboratory studies and educational outreach and training, will further promote sound QA practices in the community.

Oocyte metabolic function, lipid composition, and developmental potential are altered by diet in older mares.

Dietary supplementation is the most feasible method to improve oocyte function and developmental potential in vivo. During three experiments, oocytes were collected from maturing, dominant follicles of older mares to determine whether short-term dietary supplements can alter oocyte metabolic function, lipid composition, and developmental potential. Over approximately 8 weeks, control mares were fed hay (CON) or hay and grain products (COB). Treated mares received supplements designed for equine wellness and gastrointestinal health, flaxseed oil, and a proprietary blend of fatty acid and antioxidant support (reproductive support supplement (RSS)) intended to increase antioxidant activity and lipid oxidation. RSS was modified for individual experiments with additional antioxidants or altered concentrations of n-3 to n-6 fatty acids. Oocytes from mares supplemented with RSS when compared to COB had higher basal oxygen consumption, indicative of higher aerobic metabolism, and proportionately more aerobic to anaerobic metabolism. In the second experiment, oocytes collected from the same mares prior to (CON) and after approximately 8 weeks of RSS supplementation had significantly reduced oocyte lipid abundance. In the final experiment, COB was compared to RSS supplementation, including RSS modified to proportionately reduce n-3 fatty acids and increase n-6 fatty acids. The ability of sperm-injected oocytes to develop into blastocysts was higher for RSS, regardless of fatty acid content, than for COB. We demonstrated that short-term diet supplementation can directly affect oocyte function in older mares, resulting in oocytes with increased metabolic activity, reduced lipid content, and increased developmental potential.

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.

Equine maternal aging affects oocyte lipid content, metabolic function and developmental potential.

Advanced maternal age is associated with a decline in fertility and oocyte quality. We used novel metabolic microsensors to assess effects of mare age on single oocyte and embryo metabolic function, which has not yet been similarly investigated in mammalian species. We hypothesized that equine maternal aging affects the metabolic function of oocytes and in vitro-produced early embryos, oocyte mitochondrial DNA (mtDNA) copy number, and relative abundance of metabolites involved in energy metabolism in oocytes and cumulus cells. Samples were collected from preovulatory follicles from young (≤14 years) and old (≥20 years) mares. Relative abundance of metabolites in metaphase II oocytes (MII) and their respective cumulus cells, detected by liquid and gas chromatography coupled to mass spectrometry, revealed that free fatty acids were less abundant in oocytes and more abundant in cumulus cells from old vs young mares. Quantification of aerobic and anaerobic metabolism, respectively measured as oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) in a microchamber containing oxygen and pH microsensors, demonstrated reduced metabolic function and capacity in oocytes and day-2 embryos originating from oocytes of old when compared to young mares. In mature oocytes, mtDNA was quantified by real-time PCR and was not different between the age groups and not indicative of mitochondrial function. Significantly more sperm-injected oocytes from young than old mares resulted in blastocysts. Our results demonstrate a decline in oocyte and embryo metabolic activity that potentially contributes to the impaired developmental competence and fertility in aged females.

Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations.

KEY MESSAGE: Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

Differential Stem Proteomics and Metabolomics Profiles for Four Wheat Cultivars in Response to the Insect Pest Wheat Stem Sawfly.

Common wheat (Triticum aestivum L.) is a global staple crop, and insect pests can impact grain yield. The wheat stem sawfly (Cephus cinctus, WSS) is a major wheat pest, and while partial resistance has been deployed by breeding for a solid-stem trait, this trait is affected by environment. Here, a proteomics and metabolomics study was performed on four wheat cultivars to characterize a molecular response to WSS infestation. The cultivars Hatcher (hollow-stem partially tolerant), Conan (semisolid-stem-resistant), and Denali and Reeder (hollow-stem-susceptible) were infested with WSS, and changes in stem proteins and metabolites were characterized using liquid chromatography-mass spectrometry. The proteome was characterized as 1830 proteins that included five major biological processes, including metabolic processes and response to stimuli, and the metabolome (1823 metabolites) spanned eight chemical superclasses, including alkaloids, benzenoids, and lipids. All four varieties had a molecular response to WSS following infestation. Hatcher had the most distinct changes, whereby 62 proteins and 29 metabolites varied in metabolic pathways involving enzymatic detoxification, proteinase inhibition, and antiherbivory compound production via benzoxazinoids, neolignans, and phenolics. Taken together, these data demonstrate variation in the wheat stem molecular response to WSS infestation and support breeding for molecular resistance in hollow-stem cultivars.

A comprehensive time-course metabolite profiling of the model cyanobacterium Synechocystis sp. PCC 6803 under diurnal light:dark cycles.

Cyanobacteria are a model photoautotroph and a chassis for the sustainable production of fuels and chemicals. Knowledge of photoautotrophic metabolism in the natural environment of day/night cycles is lacking, yet has implications for improved yield from plants, algae and cyanobacteria. Here, a thorough approach to characterizing diverse metabolites-including carbohydrates, lipids, amino acids, pigments, cofactors, nucleic acids and polysaccharides-in the model cyanobacterium Synechocystis sp. PCC 6803 (S. 6803) under sinusoidal diurnal light:dark cycles was developed and applied. A custom photobioreactor and multi-platform mass spectrometry workflow enabled metabolite profiling every 30-120 min across a 24-h diurnal sinusoidal LD ('sinLD') cycle peaking at 1600 µmol photons m-2 sec-1 . We report widespread oscillations across the sinLD cycle with 90%, 94% and 40% of the identified polar/semi-polar, non-polar and polymeric metabolites displaying statistically significant oscillations, respectively. Microbial growth displayed distinct lag, biomass accumulation and cell division phases of growth. During the lag phase, amino acids and nucleic acids accumulated to high levels per cell followed by decreased levels during the biomass accumulation phase, presumably due to protein and DNA synthesis. Insoluble carbohydrates displayed sharp oscillations per cell at the day-to-night transition. Potential bottlenecks in central carbon metabolism are highlighted. Together, this report provides a comprehensive view of photosynthetic metabolite behavior with high temporal resolution, offering insight into the impact of growth synchronization to light cycles via circadian rhythms. Incorporation into computational modeling and metabolic engineering efforts promises to improve industrially relevant strain design.

Metabolic compounds within the porcine uterine environment are unique to the type of conceptus present during the early stages of blastocyst elongation.

The objective of this study was to identify metabolites within the porcine uterine milieu during the early stages of blastocyst elongation. At Days 9, 10, or 11 of gestation, reproductive tracts of White cross-bred gilts (n = 38) were collected immediately following harvest and flushed with Roswell Park Memorial Institute-1640 medium. Conceptus morphologies were assessed from each pregnancy and corresponding uterine flushings were assigned to one of five treatment groups based on these morphologies: (a) uniform spherical (n = 8); (b) heterogeneous spherical and ovoid (n = 8); (c) uniform ovoid (n = 8); (d) heterogeneous ovoid and tubular (n = 8); and (e) uniform tubular (n = 6). Uterine flushings from these pregnancies were submitted for nontargeted profiling by gas chromatography-mass spectrometry (GC-MS) and ultra performance liquid chromatography (UPLC)-MS techniques. Unsupervised multivariate principal component analysis (PCA) was performed using pcaMethods and univariate analysis of variance was performed in R with false discovery rate (FDR) adjustment. PCA analysis of the GC-MS and UPLC-MS data identified 153 and 104 metabolites, respectively. After FDR adjustment of the GC-MS and UPLC-MS data, 38 and 59 metabolites, respectively, differed (p < .05) in uterine flushings from pregnancies across the five conceptus stages. Some metabolites were greater (p < .05) in abundance for uterine flushings containing earlier stage conceptuses (i.e., spherical), such as uric acid, tryptophan, and tyrosine. In contrast, some metabolites were greater (p < .05) in abundance for uterine flushings containing later stage conceptuses (i.e., tubular), such as creatinine, serine, and urea. These data illustrate several putative metabolites that change within the uterine milieu during early porcine blastocyst elongation.

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.].

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.

GreenCut protein CPLD49 of Chlamydomonas reinhardtii associates with thylakoid membranes and is required for cytochrome b6 f complex accumulation.

The GreenCut encompasses a suite of nucleus-encoded proteins with orthologs among green lineage organisms (plants, green algae), but that are absent or poorly conserved in non-photosynthetic/heterotrophic organisms. In Chlamydomonas reinhardtii, CPLD49 (Conserved in Plant Lineage and Diatoms49) is an uncharacterized GreenCut protein that is critical for maintaining normal photosynthetic function. We demonstrate that a cpld49 mutant has impaired photoautotrophic growth under high-light conditions. The mutant exhibits a nearly 90% reduction in the level of the cytochrome b6 f complex (Cytb6 f), which impacts linear and cyclic electron transport, but does not compromise the ability of the strain to perform state transitions. Furthermore, CPLD49 strongly associates with thylakoid membranes where it may be part of a membrane protein complex with another GreenCut protein, CPLD38; a mutant null for CPLD38 also impacts Cytb6 f complex accumulation. We investigated several potential functions of CPLD49, with some suggested by protein homology. Our findings are congruent with the hypothesis that CPLD38 and CPLD49 are part of a novel thylakoid membrane complex that primarily modulates accumulation, but also impacts the activity of the Cytb6 f complex. Based on motifs of CPLD49 and the activities of other CPLD49-like proteins, we suggest a role for this putative dehydrogenase in the synthesis of a lipophilic thylakoid membrane molecule or cofactor that influences the assembly and activity of Cytb6 f.

The effector AvrRxo1 phosphorylates NAD in planta.

Gram-negative bacterial pathogens of plants and animals employ type III secreted effectors to suppress innate immunity. Most characterized effectors work through modification of host proteins or transcriptional regulators, although a few are known to modify small molecule targets. The Xanthomonas type III secreted avirulence factor AvrRxo1 is a structural homolog of the zeta toxin family of sugar-nucleotide kinases that suppresses bacterial growth. AvrRxo1 was recently reported to phosphorylate the central metabolite and signaling molecule NAD in vitro, suggesting that the effector might enhance bacterial virulence on plants through manipulation of primary metabolic pathways. In this study, we determine that AvrRxo1 phosphorylates NAD in planta, and that its kinase catalytic sites are necessary for its toxic and resistance-triggering phenotypes. A global metabolomics approach was used to independently identify 3'-NADP as the sole detectable product of AvrRxo1 expression in yeast and bacteria, and NAD kinase activity was confirmed in vitro. 3'-NADP accumulated upon transient expression of AvrRxo1 in Nicotiana benthamiana and in rice leaves infected with avrRxo1-expressing strains of X. oryzae. Mutation of the catalytic aspartic acid residue D193 abolished AvrRxo1 kinase activity and several phenotypes of AvrRxo1, including toxicity in yeast, bacteria, and plants, suppression of the flg22-triggered ROS burst, and ability to trigger an R gene-mediated hypersensitive response. A mutation in the Walker A ATP-binding motif abolished the toxicity of AvrRxo1, but did not abolish the 3'-NADP production, virulence enhancement, ROS suppression, or HR-triggering phenotypes of AvrRxo1. These results demonstrate that a type III effector targets the central metabolite and redox carrier NAD in planta, and that this catalytic activity is required for toxicity and suppression of the ROS burst.

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.