Metabolomics: beyond biomarkers and towards mechanisms.

Metabolomics, which is the profiling of metabolites in biofluids, cells and tissues, is routinely applied as a tool for biomarker discovery. Owing to innovative developments in informatics and analytical technologies, and the integration of orthogonal biological approaches, it is now possible to expand metabolomic analyses to understand the systems-level effects of metabolites. Moreover, because of the inherent sensitivity of metabolomics, subtle alterations in biological pathways can be detected to provide insight into the mechanisms that underlie various physiological conditions and aberrant processes, including diseases.

Growth characteristics of HCT116 xenografts lacking asparagine synthetase vary according to sex.

BACKGROUND: Sex-related differences in colorectal (CRC) incidence and mortality are well-documented. However, the impact of sex on metabolic pathways that drive cancer growth is not well understood. High expression of asparagine synthetase (ASNS) is associated with inferior survival for female CRC patients only. Here, we used a CRISPR/Cas9 technology to generate HCT116 ASNS-/- and HCT 116 ASNS+/+ cancer cell lines. We examine the effects of ASNS deletion on tumor growth and the subsequent rewiring of metabolic pathways in male and female Rag2/IL2RG mice. RESULTS: ASNS loss reduces cancer burden in male and female tumor-bearing mice (40% reduction, q < 0.05), triggers metabolic reprogramming including gluconeogenesis, but confers a survival improvement (30 days median survival, q < 0.05) in female tumor-bearing mice alone. Transcriptomic analyses revealed upregulation of G-protein coupled estrogen receptor (GPER1) in tumors from male and female mice with HCT116 ASNS-/- xenograft. Estradiol activates GPER1 in vitro in the presence of ASNS and suppresses tumor growth. CONCLUSIONS: Our study indicates that inferior survival for female CRC patients with high ASNS may be due to metabolic reprogramming that sustains tumor growth. These findings have translational relevance as ASNS/GPER1 signaling could be a future therapeutic target to improve the survival of female CRC patients.

Ferroptosis in colorectal cancer: a future target?

Colorectal cancer (CRC) is the third leading cause of cancer deaths worldwide and is characterised by frequently mutated genes, such as APC, TP53, KRAS and BRAF. The current treatment options of chemotherapy, radiation therapy and surgery are met with challenges such as cancer recurrence, drug resistance, and overt toxicity. CRC therapies exert their efficacy against cancer cells by activating biological pathways that contribute to various forms of regulated cell death (RCD). In 2012, ferroptosis was discovered as an iron-dependent and lipid peroxide-driven form of RCD. Recent studies suggest that therapies which target ferroptosis are promising treatment strategies for CRC. However, a greater understanding of the mechanisms of ferroptosis initiation, propagation, and resistance in CRC is needed. This review provides an overview of recent research in ferroptosis and its potential role as a therapeutic target in CRC. We also propose future research directions that could help to enhance our understanding of ferroptosis in CRC.

Urinary phenol concentrations and fecundability and early pregnancy loss.

STUDY QUESTION: Are urinary phenol concentrations of methylparaben, propylparaben, butylparaben, triclosan, benzophenone-3, 2,4-dichlorophenol or 2,5-dichlorophenol associated with fecundability and early pregnancy loss? SUMMARY ANSWER: 2,5-dichlorophenol concentrations were associated with an increased odds of early pregnancy loss, and higher concentrations of butylparaben and triclosan were associated with an increase in fecundability. WHAT IS KNOWN ALREADY: Phenols are chemicals with endocrine-disrupting potential found in everyday products. Despite plausible mechanisms of phenol reproductive toxicity, there are inconsistent results across few epidemiologic studies examining phenol exposure and reproductive function in non-fertility treatment populations. STUDY DESIGN, SIZE, DURATION: Specimens and data were from the North Carolina Early Pregnancy Study prospective cohort of 221 women attempting to conceive naturally from 1982 to 1986. This analysis includes data from 221 participants across 706 menstrual cycles, with 135 live births, 15 clinical miscarriages and 48 early pregnancy losses (before 42 days after the last menstrual period). PARTICIPANTS/MATERIALS, SETTING, METHODS: Participants collected daily first-morning urine specimens. For each menstrual cycle, aliquots from three daily specimens across the cycle were pooled within individuals and analyzed for phenol concentrations. To assess sample repeatability, we calculated intraclass correlation coefficients (ICCs) for each phenol. We evaluated associations between phenol concentrations from pooled samples and time to pregnancy using discrete-time logistic regression and generalized estimating equations (GEE), and early pregnancy loss using multivariable logistic regression and GEE. MAIN RESULTS AND THE ROLE OF CHANCE: ICCs for within-person variability across menstrual cycles in pooled phenol concentrations ranged from 0.42 to 0.75. There was an increased odds of early pregnancy loss with 2,5-dichlorophenol concentrations although the CIs were wide (5th vs 1st quintile odds ratio (OR): 4.79; 95% CI: 1.06, 21.59). There was an increased per-cycle odds of conception at higher concentrations of butylparaben (OR: 1.62; 95% CI: 1.08, 2.44) and triclosan (OR: 1.49; 95% CI: 0.99, 2.26) compared to non-detectable concentrations. No associations were observed between these endpoints and concentrations of other phenols examined. LIMITATIONS, REASONS FOR CAUTION: Limitations include the absence of phenol measurements for male partners and a limited sample size, especially for the outcome of early pregnancy loss, which reduced our power to detect associations. WIDER IMPLICATIONS OF THE FINDINGS: This study is the first to use repeated pooled measures to summarize phenol exposure and the first to investigate associations with fecundability and early pregnancy loss. Within-person phenol concentration variability underscores the importance of collecting repeated samples for future studies. Exposure misclassification could contribute to differences between the findings of this study and those of other studies, all of which used one urine sample to assess phenol exposure. This study also contributes to the limited literature probing potential associations between environmental exposures and early pregnancy loss, which is a challenging outcome to study as it typically occurs before a pregnancy is clinically recognized. STUDY FUNDING/COMPETING INTEREST(S): This research was supported by the National Institute of Environmental Health Sciences of the National Institutes of Health (award number F31ES030594), the Intramural Research Program of the National Institutes of Health, the National Institute of Environmental Health Sciences (project numbers ES103333 and ES103086) and a doctoral fellowship at the Yale School of Public Health. The authors declare they have no competing interests to disclose. TRIAL REGISTRATION NUMBER: N/A.

Perfluorooctanesulfonic Acid and Perfluorooctanoic Acid Promote Migration of Three-Dimensional Colorectal Cancer Spheroids.

Perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) are persistent environmental contaminants that are of increasing public concern worldwide. However, their relationship with colorectal cancer (CRC) is poorly understood. This study aims to comprehensively investigate the effect of PFOS and PFOA on the development and progression of CRC in vitro using a series of biological techniques and metabolic profiling. Herein, the migration of three-dimensional (3D) spheroids of two CRC cell lines, SW48 KRAS wide-type (WT) and SW48 KRAS G12A, were observed after exposure to PFOS and PFOA at 2 µM and 10 µM for 7 days. The time and dose-dependent migration phenotype induced by 10 µM PFOS and PFOA was further confirmed by wound healing and trans-well migration assays. To investigate the mechanism of action, derivatization-mass spectrometry-based metabolic profiles were examined from 3D spheroids of SW48 cell lines exposed to PFOS and PFOA (2 µM and 10 µM). Our findings revealed this exposure altered epithelial-mesenchymal transition related metabolic pathways, including fatty acid ß-oxidation and synthesis of proteins, nucleotides, and lipids. Furthermore, this phenotype was confirmed by the downregulation of E-cadherin and upregulation of N-cadherin and vimentin. These findings show novel insight into the relationship between PFOS, PFOA, and CRC.

Evaluation of the role of KPNA2 mutations in breast cancer prognosis using bioinformatics datasets.

Breast cancer, comprising of several sub-phenotypes, is a leading cause of female cancer-related mortality in the UK and accounts for 15% of all cancer cases. Chemoresistant sub phenotypes of breast cancer remain a particular challenge. However, the rapidly-growing availability of clinical datasets, presents the scope to underpin a data-driven precision medicine-based approach exploring new targets for diagnostic and therapeutic interventions.We report the application of a bioinformatics-based approach probing the expression and prognostic role of Karyopherin-2 alpha (KPNA2) in breast cancer prognosis. Aberrant KPNA2 overexpression is directly correlated with aggressive tumour phenotypes and poor patient survival outcomes. We examined the existing clinical data available on a range of commonly occurring mutations of KPNA2 and their correlation with patient survival.Our analysis of clinical gene expression datasets show that KPNA2 is frequently amplified in breast cancer, with differences in expression levels observed as a function of patient age and clinicopathologic parameters. We also found that aberrant KPNA2 overexpression is directly correlated with poor patient prognosis, warranting further investigation of KPNA2 as an actionable target for patient stratification or the design of novel chemotherapy agents.In the era of big data, the wealth of datasets available in the public domain can be used to underpin proof of concept studies evaluating the biomolecular pathways implicated in chemotherapy resistance in breast cancer.

Development of an Accessible Gene Expression Bioinformatics Pipeline to Study Driver Mutations of Colorectal Cancer.

Colorectal cancer (CRC) is a global cause of cancer-related mortality driven by genetic and environmental factors which influence therapeutic outcomes. The emergence of next-generation sequencing technologies enables the rapid and extensive collection and curation of genetic data for each cancer type into clinical gene expression biobanks. We report the application of bioinformatics tools for investigating the expression patterns and prognostic significance of three genes that are commonly dysregulated in colon cancer: adenomatous polyposis coli (APC); B-Raf proto-oncogene (BRAF); and Kirsten rat sarcoma viral oncogene homologue (KRAS). Through the use of bioinformatics tools, we show the patterns of APC, BRAF and KRAS genetic alterations and their role in patient prognosis. Our results show mutation types, the frequency of mutations, tumour anatomical location and differential expression patterns for APC, BRAF and KRAS for colorectal tumour and matched healthy tissue. The prognostic value of APC, BRAF and KRAS genetic alterations was investigated as a function of their expression levels in CRC. In the era of precision medicine, with significant advancements in biobanking and data curation, there is significant scope to use existing clinical data sets for evaluating the role of mutational drivers in carcinogenesis. This approach offers the potential for studying combinations of less well-known genes and the discovery of novel biomarkers, or for studying the association between various effector proteins and pathways.

Immune landscape and prognostic immune-related genes in KRAS-mutant colorectal cancer patients.

BACKGROUND: KRAS gene is the most common type of mutation reported in colorectal cancer (CRC). KRAS mutation-mediated regulation of immunophenotype and immune pathways in CRC remains to be elucidated. METHODS: 535 CRC patients were used to compare the expression of immune-related genes (IRGs) and the abundance of tumor-infiltrating immune cells (TIICs) in the tumor microenvironment between KRAS-mutant and KRAS wild-type CRC patients. An independent dataset included 566 cases of CRC and an in-house RNA sequencing dataset were served as validation sets. An in-house dataset consisting of 335 CRC patients were used to analyze systemic immune and inflammatory state in the presence of KRAS mutation. An immue risk (Imm-R) model consist of IRG and TIICs for prognostic prediction in KRAS-mutant CRC patients was established and validated. RESULTS: NF-κB and T-cell receptor signaling pathways were significantly inhibited in KRAS-mutant CRC patients. Regulatory T cells (Tregs) was increased while macrophage M1 and activated CD4 memory T cell was decreased in KRAS-mutant CRC. Prognosis correlated with enhanced Tregs, macrophage M1 and activated CD4 memory T cell and was validated. Serum levels of hypersensitive C-reactive protein (hs-CRP), CRP, and IgM were significantly decreased in KRAS-mutant compared to KRAS wild-type CRC patients. An immune risk model composed of VGF, RLN3, CT45A1 and TIICs signature classified CRC patients with distinct clinical outcomes. CONCLUSIONS: KRAS mutation in CRC was associated with suppressed immune pathways and immune infiltration. The aberrant immune pathways and immune cells help to understand the tumor immune microenvironments in KRAS-mutant CRC patients.

Beyond genomics: understanding exposotypes through metabolomics.

BACKGROUND: Over the past 20 years, advances in genomic technology have enabled unparalleled access to the information contained within the human genome. However, the multiple genetic variants associated with various diseases typically account for only a small fraction of the disease risk. This may be due to the multifactorial nature of disease mechanisms, the strong impact of the environment, and the complexity of gene-environment interactions. Metabolomics is the quantification of small molecules produced by metabolic processes within a biological sample. Metabolomics datasets contain a wealth of information that reflect the disease state and are consequent to both genetic variation and environment. Thus, metabolomics is being widely adopted for epidemiologic research to identify disease risk traits. In this review, we discuss the evolution and challenges of metabolomics in epidemiologic research, particularly for assessing environmental exposures and providing insights into gene-environment interactions, and mechanism of biological impact. MAIN TEXT: Metabolomics can be used to measure the complex global modulating effect that an exposure event has on an individual phenotype. Combining information derived from all levels of protein synthesis and subsequent enzymatic action on metabolite production can reveal the individual exposotype. We discuss some of the methodological and statistical challenges in dealing with this type of high-dimensional data, such as the impact of study design, analytical biases, and biological variance. We show examples of disease risk inference from metabolic traits using metabolome-wide association studies. We also evaluate how these studies may drive precision medicine approaches, and pharmacogenomics, which have up to now been inefficient. Finally, we discuss how to promote transparency and open science to improve reproducibility and credibility in metabolomics. CONCLUSIONS: Comparison of exposotypes at the human population level may help understanding how environmental exposures affect biology at the systems level to determine cause, effect, and susceptibilities. Juxtaposition and integration of genomics and metabolomics information may offer additional insights. Clinical utility of this information for single individuals and populations has yet to be routinely demonstrated, but hopefully, recent advances to improve the robustness of large-scale metabolomics will facilitate clinical translation.

Evaluation of potential carcinogenicity of organic chemicals in synthetic turf crumb rubber.

Currently, there are >11,000 synthetic turf athletic fields in the United States and >13,000 in Europe. Concerns have been raised about exposure to carcinogenic chemicals resulting from contact with synthetic turf fields, particularly the infill material ("crumb rubber"), which is commonly fabricated from recycled tires. However, exposure data are scant, and the limited existing exposure studies have focused on a small subset of crumb rubber components. Our objective was to evaluate the carcinogenic potential of a broad range of chemical components of crumb rubber infill using computational toxicology and regulatory agency classifications from the United States Environmental Protection Agency (US EPA) and European Chemicals Agency (ECHA) to inform future exposure studies and risk analyses. Through a literature review, we identified 306 chemical constituents of crumb rubber infill from 20 publications. Utilizing ADMET Predictor™, a computational program to predict carcinogenicity and genotoxicity, 197 of the identified 306 chemicals met our a priori carcinogenicity criteria. Of these, 52 chemicals were also classified as known, presumed or suspected carcinogens by the US EPA and ECHA. Of the remaining 109 chemicals which were not predicted to be carcinogenic by our computational toxicology analysis, only 6 chemicals were classified as presumed or suspected human carcinogens by US EPA or ECHA. Importantly, the majority of crumb rubber constituents were not listed in the US EPA (n = 207) and ECHA (n = 262) databases, likely due to an absence of evaluation or insufficient information for a reliable carcinogenicity classification. By employing a cancer hazard scoring system to the chemicals which were predicted and classified by the computational analysis and government databases, several high priority carcinogens were identified, including benzene, benzidine, benzo(a)pyrene, trichloroethylene and vinyl chloride. Our findings demonstrate that computational toxicology assessment in conjunction with government classifications can be used to prioritize hazardous chemicals for future exposure monitoring studies for users of synthetic turf fields. This approach could be extended to other compounds or toxicity endpoints.

Re-engineering and evaluation of anti-DNA autoantibody 3E10 for therapeutic applications.

A key challenge in the development of novel chemotherapeutics is the design of molecules capable of selective toxicity to cancer cells. Antibodies have greater target specificity compared to small molecule drugs, but most are unable to penetrate cells, and predominantly target extracellular antigens. A nuclear-penetrating anti-DNA autoantibody isolated from the MRL/lpr lupus mouse model, 3E10, preferentially localizes to tumors, inhibits DNA repair, and selectively kills cancer cells with defects in DNA repair. A murine divalent single chain variable fragment of 3E10 with mutations for improved DNA binding affinity, 3E10 (D31N) di-scFv, has previously been produced in P. pastoris and yielded promising pre-clinical findings, but is unsuitable for clinical testing. The present study reports the design, expression and testing of a panel of humanized 3E10 (D31N) di-scFvs, some of which contain CDR substitution. These variants were expressed in a modified CHO system and evaluated for their physicochemical attributes and ability to penetrate nuclei to selectively cause DNA damage accumulation in and kill cancer cells with DNA repair defects. Secondary structure was conserved and most variants retained the key characteristics of the murine 3E10 (D31N) di-scFv produced in P. pastoris. Moreover, several variants with CDR substitutions outperformed the murine prototype. In conclusion, we have designed several humanized variants of 3E10 (D31N) di-scFv that have potential for application as monotherapy or conjugates for targeted nuclear drug delivery.

Yale school of public health symposium on lifetime exposures and human health: the exposome; summary and future reflections.

The exposome is defined as "the totality of environmental exposures encountered from birth to death" and was developed to address the need for comprehensive environmental exposure assessment to better understand disease etiology. Due to the complexity of the exposome, significant efforts have been made to develop technologies for longitudinal, internal and external exposure monitoring, and bioinformatics to integrate and analyze datasets generated. Our objectives were to bring together leaders in the field of exposomics, at a recent Symposium on "Lifetime Exposures and Human Health: The Exposome," held at Yale School of Public Health. Our aim was to highlight the most recent technological advancements for measurement of the exposome, bioinformatics development, current limitations, and future needs in environmental health. In the discussions, an emphasis was placed on moving away from a one-chemical one-health outcome model toward a new paradigm of monitoring the totality of exposures that individuals may experience over their lifetime. This is critical to better understand the underlying biological impact on human health, particularly during windows of susceptibility. Recent advancements in metabolomics and bioinformatics are driving the field forward in biomonitoring and understanding the biological impact, and the technological and logistical challenges involved in the analyses were highlighted. In conclusion, further developments and support are needed for large-scale biomonitoring and management of big data, standardization for exposure and data analyses, bioinformatics tools for co-exposure or mixture analyses, and methods for data sharing.

PPARα regulates tumor cell proliferation and senescence via a novel target gene carnitine palmitoyltransferase 1C.

Carnitine palmitoyltransferase 1C (CPT1C), an enzyme located in the outer mitochondria membrane, has a crucial role in fatty acid transport and oxidation. It is also involved in cell proliferation and is a potential driver for cancer cell senescence. However, its upstream regulatory mechanism is unknown. Peroxisome proliferator activated receptor α (PPARα) is a ligand-activated transcription factor that regulates lipid metabolism and tumor progression. The current study aimed to elucidate whether and how PPARα regulates CPT1C and then affects cancer cell proliferation and senescence. Here, for the first time we report that PPARα directly activated CPT1C transcription and CPT1C was a novel target gene of PPARα, as revealed by dual-luciferase reporter and chromatin immunoprecipitation (ChIP) assays. Moreover, regulation of CPT1C by PPARα was p53-independent. We further confirmed that depletion of PPARα resulted in low CPT1C expression and then inhibited proliferation and induced senescence of MDA-MB-231 and PANC-1 tumor cell lines in a CPT1C-dependent manner, while forced PPARα overexpression promoted cell proliferation and reversed cellular senescence. Taken together, these results indicate that CPT1C is a novel PPARα target gene that regulates cancer cell proliferation and senescence. The PPARα-CPT1C axis may be a new target for the intervention of cancer cellular proliferation and senescence.

Exposome-Scale Investigations Guided by Global Metabolomics, Pathway Analysis, and Cognitive Computing.

Concurrent exposure to a wide variety of xenobiotics and their combined toxic effects can play a pivotal role in health and disease, yet are largely unexplored. Investigating the totality of these exposures, i.e., the "exposome", and their specific biological effects constitutes a new paradigm for environmental health but still lacks high-throughput, user-friendly technology. We demonstrate the utility of mass spectrometry-based global exposure metabolomics combined with tailored database queries and cognitive computing for comprehensive exposure assessment and the straightforward elucidation of biological effects. The METLIN Exposome database has been redesigned to help identify environmental toxicants, food contaminants and supplements, drugs, and antibiotics as well as their biotransformation products, through its expansion with over 700 000 chemical structures to now include more than 950 000 unique small molecules. More importantly, we demonstrate how the XCMS/METLIN platform now allows for the readout of the biological effect of a toxicant through metabolomic-derived pathway analysis, and further, artificial intelligence provides a means of assessing the role of a potential toxicant. The presented workflow addresses many of the methodological challenges current exposomics research is facing and will serve to gain a deeper understanding of the impact of environmental exposures and combinatory toxic effects on human health.

Determining conserved metabolic biomarkers from a million database queries.

MOTIVATION: Metabolite databases provide a unique window into metabolome research allowing the most commonly searched biomarkers to be catalogued. Omic scale metabolite profiling, or metabolomics, is finding increased utility in biomarker discovery largely driven by improvements in analytical technologies and the concurrent developments in bioinformatics. However, the successful translation of biomarkers into clinical or biologically relevant indicators is limited. RESULTS: With the aim of improving the discovery of translatable metabolite biomarkers, we present search analytics for over one million METLIN metabolite database queries. The most common metabolites found in METLIN were cross-correlated against XCMS Online, the widely used cloud-based data processing and pathway analysis platform. Analysis of the METLIN and XCMS common metabolite data has two primary implications: these metabolites, might indicate a conserved metabolic response to stressors and, this data may be used to gauge the relative uniqueness of potential biomarkers. AVAILABILITY AND IMPLEMENTATION: METLIN can be accessed by logging on to: https://metlin.scripps.edu CONTACT: siuzdak@scripps.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Metabolomics reveals mycoplasma contamination interferes with the metabolism of PANC-1 cells.

Mycoplasma contamination is a common problem in cell culture and can alter cellular functions. Since cell metabolism is either directly or indirectly involved in every aspect of cell function, it is important to detect changes to the cellular metabolome after mycoplasma infection. In this study, liquid chromatography mass spectrometry (LC/MS)-based metabolomics was used to investigate the effect of mycoplasma contamination on the cellular metabolism of human pancreatic carcinoma cells (PANC-1). Multivariate analysis demonstrated that mycoplasma contamination induced significant metabolic changes in PANC-1 cells. Twenty-three metabolites were identified and found to be involved in arginine and purine metabolism and energy supply. This study demonstrates that mycoplasma contamination significantly alters cellular metabolite levels, confirming the compelling need for routine checking of cell cultures for mycoplasma contamination, particularly when used for metabolomics studies. Graphical abstract Metabolomics reveals mycoplasma contamination changes the metabolome of PANC-1 cells.

Xenobiotic metabolomics: major impact on the metabolome.

Xenobiotics are encountered by humans on a daily basis and include drugs, environmental pollutants, cosmetics, and even components of the diet. These chemicals undergo metabolism and detoxication to produce numerous metabolites, some of which have the potential to cause unintended effects such as toxicity. They can also block the action of enzymes or receptors used for endogenous metabolism or affect the efficacy and/or bioavailability of a coadministered drug. Therefore, it is essential to determine the full metabolic effects that these chemicals have on the body. Metabolomics, the comprehensive analysis of small molecules in a biofluid, can reveal biologically relevant perturbations that result from xenobiotic exposure. This review discusses the impact that genetic, environmental, and gut microflora variation has on the metabolome, and how these variables may interact, positively and negatively, with xenobiotic metabolism.

Thermal Degradation of Small Molecules: A Global Metabolomic Investigation.

Thermal processes are widely used in small molecule chemical analysis and metabolomics for derivatization, vaporization, chromatography, and ionization, especially in gas chromatography mass spectrometry (GC/MS). In this study the effect of heating was examined on a set of 64 small molecule standards and, separately, on human plasma metabolite extracts. The samples, either derivatized or underivatized, were heated at three different temperatures (60, 100, and 250 °C) at different exposure times (30 s, 60 s, and 300 s). All the samples were analyzed by liquid chromatography coupled to electrospray ionization mass spectrometry (LC/MS) and the data processed by XCMS Online ( xcmsonline.scripps.edu ). The results showed that heating at an elevated temperature of 100 °C had an appreciable effect on both the underivatized and derivatized molecules, and heating at 250 °C created substantial changes in the profile. For example, over 40% of the molecular peaks were altered in the plasma metabolite analysis after heating (250 °C, 300s) with a significant formation of degradation and transformation products. The analysis of 64 small molecule standards validated the temperature-induced changes observed on the plasma metabolites, where most of the small molecules degraded at elevated temperatures even after minimal exposure times (30 s). For example, tri- and diorganophosphates (e.g., adenosine triphosphate and adenosine diphosphate) were readily degraded into a mono-organophosphate (e.g., adenosine monophosphate) during heating. Nucleosides and nucleotides (e.g., inosine and inosine monophosphate) were also found to be transformed into purine derivatives (e.g., hypoxanthine). A newly formed transformation product, oleoyl ethyl amide, was identified in both the underivatized and derivatized forms of the plasma extracts and small molecule standard mixture, and was likely generated from oleic acid. Overall these analyses show that small molecules and metabolites undergo significant time-sensitive alterations when exposed to elevated temperatures, especially those conditions that mimic sample preparation and analysis in GC/MS experiments.

Autonomous metabolomics for rapid metabolite identification in global profiling.

An autonomous metabolomic workflow combining mass spectrometry analysis with tandem mass spectrometry data acquisition was designed to allow for simultaneous data processing and metabolite characterization. Although previously tandem mass spectrometry data have been generated on the fly, the experiments described herein combine this technology with the bioinformatic resources of XCMS and METLIN. As a result of this unique integration, we can analyze large profiling datasets and simultaneously obtain structural identifications. Validation of the workflow on bacterial samples allowed the profiling on the order of a thousand metabolite features with simultaneous tandem mass spectra data acquisition. The tandem mass spectrometry data acquisition enabled automatic search and matching against the METLIN tandem mass spectrometry database, shortening the current workflow from days to hours. Overall, the autonomous approach to untargeted metabolomics provides an efficient means of metabolomic profiling, and will ultimately allow the more rapid integration of comparative analyses, metabolite identification, and data analysis at a systems biology level.