Cross-Platform Comparison of Untargeted and Targeted Lipidomics Approaches on Aging Mouse Plasma.

Lipidomics - the global assessment of lipids - can be performed using a variety of mass spectrometry (MS)-based approaches. However, choosing the optimal approach in terms of lipid coverage, robustness and throughput can be a challenging task. Here, we compare a novel targeted quantitative lipidomics platform known as the Lipidyzer to a conventional untargeted liquid chromatography (LC)-MS approach. We find that both platforms are efficient in profiling more than 300 lipids across 11 lipid classes in mouse plasma with precision and accuracy below 20% for most lipids. While the untargeted and targeted platforms detect similar numbers of lipids, the former identifies a broader range of lipid classes and can unambiguously identify all three fatty acids in triacylglycerols (TAG). Quantitative measurements from both approaches exhibit a median correlation coefficient (r) of 0.99 using a dilution series of deuterated internal standards and 0.71 using endogenous plasma lipids in the context of aging. Application of both platforms to plasma from aging mouse reveals similar changes in total lipid levels across all major lipid classes and in specific lipid species. Interestingly, TAG is the lipid class that exhibits the most changes with age, suggesting that TAG metabolism is particularly sensitive to the aging process in mice. Collectively, our data show that the Lipidyzer platform provides comprehensive profiling of the most prevalent lipids in plasma in a simple and automated manner

Metabolomics-based biomarker discovery for bee health monitoring : a proof of concept study concerning nutritional stress in Bombus terrestris

Bee pollinators are exposed to multiple natural and anthropogenic stressors. Understanding the effects of a single stressor in the complex environmental context of antagonistic/synergistic interactions is critical to pollinator monitoring and may serve as early warning system before a pollination crisis. This study aimed to methodically improve the diagnosis of bee stressors using a simultaneous untargeted and targeted metabolomics-based approach. Analysis of 84 Bombus terrestris hemolymph samples found 8 metabolites retained as potential biomarkers that showed excellent discrimination for nutritional stress. In parallel, 8 significantly altered metabolites, as revealed by targeted profiling, were also assigned as candidate biomarkers. Furthermore, machine learning algorithms were applied to the above-described two biomarker sets, whereby the untargeted eight components showed the best classification performance with sensitivity and specificity up to 99% and 100%, respectively. Based on pathway and biochemistry analysis, we propose that gluconeogenesis contributed significantly to blood sugar stability in bumblebees maintained on a low carbohydrate diet. Taken together, this study demonstrates that metabolomics-based biomarker discovery holds promising potential for improving bee health monitoring and to identify stressor related to energy intake and other environmental stressors

Intracellular metabolites in marine microorganisms during an experiment evaluating microbial mortality

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Longnecker, K., & Kujawinski, E. B. Intracellular metabolites in marine microorganisms during an experiment evaluating microbial mortality. Metabolites, 10(3), (2020): 105, doi: 10.3390/metabo10030105.Metabolomics is a tool with immense potential for providing insight into the impact of biological processes on the environment. Here, we used metabolomics methods to characterize intracellular metabolites within marine microorganisms during a manipulation experiment that was designed to test the impact of two sources of microbial mortality, protozoan grazing and viral lysis. Intracellular metabolites were analyzed with targeted and untargeted mass spectrometry methods. The treatment with reduced viral mortality showed the largest changes in metabolite concentrations, although there were organic compounds that shifted when the impact of protozoan grazers was reduced. Intracellular concentrations of guanine, phenylalanine, glutamic acid, and ectoine presented significant responses to changes in the source of mortality. Unexpectedly, variability in metabolite concentrations were not accompanied by increases in microbial abundance which indicates that marine microorganisms altered their internal organic carbon stores without changes in biomass or microbial growth. We used Weighted Correlation Network Analysis (WGCNA) to identify correlations between the targeted and untargeted mass spectrometry data. This analysis revealed multiple unknown organic compounds were correlated with compatible solutes, also called osmolytes or chemical chaperones, which emphasizes the dominant role of compatible solutes in marine microorganisms.This research was funded by the US National Science Foundation (OCE-1154320 to EBK and KL, OCE-1634016 to EBK) and WHOI’s Ocean Life Institute (to EBK and KL). The mass spectrometry samples were analyzed at the WHOI FT-MS Users’ Facility with instrumentation funded by the National Science Foundation (OCE-0619608 and OCE-1058448)

Nearby supernova host galaxies from the CALIFA Survey: II. SN environmental metallicity

The metallicity of a supernova (SN) progenitor, together with its mass, is one of the main parameters that rules their outcome. We present a metallicity study of 115 nearby SN host galaxies (0.005<z<0.03) which hosted 142 SNe using Integral Field Spectroscopy (IFS) from the CALIFA survey. Using O3N2 we found no statistically significant differences between the gas-phase metallicities at the locations of the three main SN types (Ia, Ib/c and II) all having ~8.50$\pm$0.02 dex. The total galaxy metallicities are also very similar and we argue that this is because our sample consists only of SNe discovered in massive galaxies (log(M/Msun)>10 dex) by targeted searches. We also found no evidence that the metallicity at the SN location differs from the average metallicity at the GCD of the SNe. By extending our SN sample with published metallicities at the SN location, we studied the metallicity distributions for all SN subtypes split into SN discovered in targeted and untargeted searches. We confirm a bias toward higher host masses and metallicities in the targeted searches. Combining data from targeted and untargeted searches we found a sequence from higher to lower local metallicity: SN Ia, Ic, and II show the highest metallicity, which is significantly higher than SN Ib, IIb, and Ic-BL. Our results support the picture of SN Ib resulting from binary progenitors and, at least part of, SN Ic being the result of single massive stars stripped of their outer layers by metallicity driven winds. We studied several proxies of the local metallicity frequently used in the literature and found that the total host metallicity allows for the estimation of the metallicity at the SN location with an accuracy better than 0.08 dex and very small bias. In addition, weak AGNs not seen in total spectra may only weakly bias (by 0.04 dex) the metallicity estimate from integrated spectra. (abridged)Comment: 24 pages, 16 Figures, 13 Tables, Accepted in A&

Enhanced Characterization of Drug Metabolism and the Influence of the Intestinal Microbiome: A Pharmacokinetic, Microbiome, and Untargeted Metabolomics Study.

Determining factors that contribute to interindividual and intra-individual variability in pharmacokinetics (PKs) and drug metabolism is essential for the optimal use of drugs in humans. Intestinal microbes are important contributors to variability; however, such gut microbe-drug interactions and the clinical significance of these interactions are still being elucidated. Traditional PKs can be complemented by untargeted mass spectrometry coupled with molecular networking to study the intricacies of drug metabolism. To show the utility of molecular networking on metabolism we investigated the impact of a 7-day course of cefprozil on cytochrome P450 (CYP) activity using a modified Cooperstown cocktail and assessed plasma, urine, and fecal data by targeted and untargeted metabolomics and molecular networking in healthy volunteers. This prospective study revealed that cefprozil decreased the activities of CYP1A2, CYP2C19, and CYP3A, decreased alpha diversity and increased interindividual microbiome variability. We further demonstrate a relationship between the loss of microbiome alpha diversity caused by cefprozil and increased drug and metabolite formation in fecal samples. Untargeted metabolomics/molecular networking revealed several omeprazole metabolites that we hypothesize may be metabolized by both CYP2C19 and bacteria from the gut microbiome. Our observations are consistent with the hypothesis that factors that perturb the gut microbiome, such as antibiotics, alter drug metabolism and ultimately drug efficacy and toxicity but that these effects are most strongly revealed on a per individual basis

Metabolomic analysis of human oral cancer cells with adenylate kinase 2 or phosphorylate glycerol kinase 1 inhibition.

The purpose of this study was to use liquid chromatography-mass spectrometry (LC-MS) with XCMS for a quantitative metabolomic analysis of UM1 and UM2 oral cancer cells after knockdown of metabolic enzyme adenylate kinase 2 (AK2) or phosphorylate glycerol kinase 1 (PGK1). UM1 and UM2 cells were initially transfected with AK2 siRNA, PGK1 siRNA or scrambled control siRNA, and then analyzed with LC-MS for metabolic profiles. XCMS analysis of the untargeted metabolomics data revealed a total of 3200-4700 metabolite features from the transfected UM1 or UM2 cancer cells and 369-585 significantly changed metabolites due to AK2 or PGK1 suppression. In addition, cluster analysis showed that a common group of metabolites were altered by AK2 knockdown or by PGK1 knockdown between the UM1 and UM2 cells. However, the set of significantly changed metabolites due to AK2 knockdown was found to be distinct from those significantly changed by PGK1 knockdown. Our study has demonstrated that LC-MS with XCMS is an efficient tool for metabolomic analysis of oral cancer cells, and knockdown of different genes results in distinct changes in metabolic phenotypes in oral cancer cells