Obesity and accompanying metabolic disease is negatively correlated with lung health yet the exact mechanisms by which obesity affects the lung are not well characterized. Since obesity is associated with lung diseases as chronic bronchitis and asthma, we designed a series of experiments to measure changes in lung metabolism in mice fed obesogenic diets. Mice were fed either control or high fat/sugar diet (45%kcal fat/17%kcal sucrose), or very high fat diet (60%kcal fat/7% sucrose) for 150 days. We performed untargeted metabolomics by GC-TOFMS and HILIC-QTOFMS and lipidomics by RPLC-QTOFMS to reveal global changes in lung metabolism resulting from obesity and diet composition. From a total of 447 detected metabolites, we found 91 metabolite and lipid species significantly altered in mouse lung tissues upon dietary treatments. Significantly altered metabolites included complex lipids, free fatty acids, energy metabolites, amino acids and adenosine and NAD pathway members. While some metabolites were altered in both obese groups compared to control, others were different between obesogenic diet groups. Furthermore, a comparison of changes between lung, kidney and liver tissues indicated few metabolic changes were shared across organs, suggesting the lung is an independent metabolic organ. These results indicate obesity and diet composition have direct mechanistic effects on composition of the lung metabolome, which may contribute to disease progression by lung-specific pathways.

Alzheimer's disease (AD) is a major public health priority with a large socioeconomic burden and complex etiology. The Alzheimer Disease Metabolomics Consortium (ADMC) and the Alzheimer Disease Neuroimaging Initiative (ADNI) aim to gain new biological insights in the disease etiology. We report here an untargeted lipidomics of serum specimens of 806 subjects within the ADNI1 cohort (188 AD, 392 mild cognitive impairment and 226 cognitively normal subjects) along with 83 quality control samples. Lipids were detected and measured using an ultra-high-performance liquid chromatography quadruple/time-of-flight mass spectrometry (UHPLC-QTOF MS) instrument operated in both negative and positive electrospray ionization modes. The dataset includes a total 513 unique lipid species out of which 341 are known lipids. For over 95% of the detected lipids, a relative standard deviation of better than 20% was achieved in the quality control samples, indicating high technical reproducibility. Association modeling of this dataset and available clinical, metabolomics and drug-use data will provide novel insights into the AD etiology. These datasets are available at the ADNI repository at http://adni.loni.usc.edu/.

BACKGROUNDDysregulation of l-arginine metabolism has been proposed to occur in patients with severe asthma. The effects of l-arginine supplementation on l-arginine metabolite profiles in these patients are unknown. We hypothesized that individuals with severe asthma with low fractional exhaled nitric oxide (FeNO) would have fewer exacerbations with the addition of l-arginine to their standard asthma medications compared with placebo and would demonstrate the greatest changes in metabolite profiles.METHODSParticipants were enrolled in a single-center, crossover, double-blind l-arginine intervention trial at UCD. Subjects received placebo or l-arginine, dosed orally at 0.05 mg/kg (ideal body weight) twice daily. The primary end point was moderate asthma exacerbations. Longitudinal plasma metabolite levels were measured using mass spectrometry. A linear mixed-effect model with subject-specific intercepts was used for testing treatment effects.RESULTSA cohort of 50 subjects was included in the final analysis. l-Arginine did not significantly decrease asthma exacerbations in the overall cohort. Higher citrulline levels and a lower arginine availability index (AAI) were associated with higher FeNO (P = 0.005 and P = 2.51 × 10-9, respectively). Higher AAI was associated with lower exacerbation events. The eicosanoid prostaglandin H2 (PGH2) and Nα-acetyl-l-arginine were found to be good predictors for differentiating clinical responders and nonresponders.CONCLUSIONSThere was no statistically significant decrease in asthma exacerbations in the overall cohort with l-arginine intervention. PGH2, Nα-acetyl-l-arginine, and the AAI could serve as predictive biomarkers in future clinical trials that intervene in the arginine metabolome.TRIAL REGISTRATIONClinicalTrials.gov NCT01841281.FUNDINGThis study was supported by NIH grants R01HL105573, DK097154, UL1 TR001861, and K08HL114882. Metabolomics analysis was supported in part by a grant from the University of California Tobacco-Related Disease Research Program program (TRDRP).

Urine metabolites are used in many clinical and biomedical studies but usually only for a few classic compounds. Metabolomics detects vastly more metabolic signals that may be used to precisely define the health status of individuals. However, many compounds remain unidentified, hampering biochemical conclusions. Here, we annotate all metabolites detected by two untargeted metabolomic assays, hydrophilic interaction chromatography (HILIC)-Q Exactive HF mass spectrometry and charged surface hybrid (CSH)-Q Exactive HF mass spectrometry. Over 9,000 unique metabolite signals were detected, of which 42% triggered MS/MS fragmentations in data-dependent mode. On the highest Metabolomics Standards Initiative (MSI) confidence level 1, we identified 175 compounds using authentic standards with precursor mass, retention time, and MS/MS matching. An additional 578 compounds were annotated by precursor accurate mass and MS/MS matching alone, MSI level 2, including a novel library specifically geared at acylcarnitines (CarniBlast). The rest of the metabolome is usually left unannotated. To fill this gap, we used the in silico fragmentation tool CSI:FingerID and the new NIST hybrid search to annotate all further compounds (MSI level 3). Testing the top-ranked metabolites in CSI:Finger ID annotations yielded 40% accuracy when applied to the MSI level 1 identified compounds. We classified all MSI level 3 annotations by the NIST hybrid search using the ClassyFire ontology into 21 superclasses that were further distinguished into 184 chemical classes. ClassyFire annotations showed that the previously unannotated urine metabolome consists of 28% derivatives of organic acids, 16% heterocyclics, and 16% lipids as major classes.

Bis(monoacylglycero)phosphates (BMPs), a class of lipids highly enriched within endolysosomal organelles, are key components of the lysosomal intraluminal vesicles responsible for activating sphingolipid catabolic enzymes. While BMPs are understudied relative to other phospholipids, recent reports associate BMP dysregulation with a variety of pathological states including neurodegenerative diseases and lysosomal storage disorders. Since the dramatic lysosomal remodeling characteristic of cellular transformation could impact BMP abundance and function, we employed untargeted lipidomics approaches to identify and quantify BMP species in several in vitro and in vivo models of breast cancer and comparative non-transformed cells and tissues. We observed lower BMP levels within transformed cells relative to normal cells, and consistent enrichment of docosahexaenoic acid (22:6) fatty acyl chain-containing BMP species in both human- and mouse-derived mammary tumorigenesis models. Our functional analysis points to a working model whereby 22:6 BMPs serve as reactive oxygen species scavengers in tumor cells, protecting lysosomes from oxidant-induced lysosomal membrane permeabilization. Our findings suggest that breast tumor cells might divert polyunsaturated fatty acids into BMP lipids as part of an adaptive response to protect their lysosomes from elevated reactive oxygen species levels, and raise the possibility that BMP-mediated lysosomal protection is a tumor-specific vulnerability that may be exploited therapeutically.