Metabolomics of neonatal blood spots reveal distinct phenotypes of pediatric acute lymphoblastic leukemia and potential effects of early-life nutrition.

Early-life exposures are believed to influence the incidence of pediatric acute lymphoblastic leukemia (ALL). Archived neonatal blood spots (NBS), collected within the first days of life, offer a means to investigate small molecules that reflect early-life exposures. Using untargeted metabolomics, we compared abundances of small-molecule features in extracts of NBS punches from 332 children that later developed ALL and 324 healthy controls. Subjects were stratified by early (1-5 y) and late (6-14 y) diagnosis. Mutually-exclusive sets of metabolic features - representing putative lipids and fatty acids - were associated with ALL, including 9 and 19 metabolites in the early- and late-diagnosis groups, respectively. In the late-diagnosis group, a prominent cluster of features with apparent 18:2 fatty-acid chains suggested that newborn exposure to the essential nutrient, linoleic acid, increased ALL risk. Interestingly, abundances of these putative 18:2 lipids were greater in infants who were fed formula rather than breast milk (colostrum) and increased with the mother's pre-pregnancy body mass index. These results suggest possible etiologic roles of newborn nutrition in late-diagnosis ALL.

Untargeted lipidomic features associated with colorectal cancer in a prospective cohort.

BACKGROUND: Epidemiologists are beginning to employ metabolomics and lipidomics with archived blood from incident cases and controls to discover causes of cancer. Although several such studies have focused on colorectal cancer (CRC), they all followed targeted or semi-targeted designs that limited their ability to find discriminating molecules and pathways related to the causes of CRC. METHODS: Using an untargeted design, we measured lipophilic metabolites in prediagnostic serum from 66 CRC patients and 66 matched controls from the European Prospective Investigation into Cancer and Nutrition (Turin, Italy). Samples were analyzed by liquid chromatography-high-resolution mass spectrometry (LC-MS), resulting in 8690 features for statistical analysis. RESULTS: Rather than the usual multiple-hypothesis-testing approach, we based variable selection on an ensemble of regression methods, which found nine features to be associated with case-control status. We then regressed each selected feature on time-to-diagnosis to determine whether the feature was likely to be either a potentially causal biomarker or a reactive product of disease progression (reverse causality). CONCLUSIONS: Of the nine selected LC-MS features, four appear to be involved in CRC etiology and merit further investigation in prospective studies of CRC. Four other features appear to be related to progression of the disease (reverse causality), and may represent biomarkers of value for early detection of CRC.

SimExTargId: a comprehensive package for real-time LC-MS data acquisition and analysis.

Summary: Liquid chromatography mass spectrometry (LC-MS) is the favored method for untargeted metabolomic analysis of small molecules in biofluids. Here we present SimExTargId, an open-source R package for autonomous analysis of metabolomic data and real-time observation of experimental runs. This simultaneous, fully automated and multi-threaded (optional) package is a wrapper for vendor-independent format conversion (ProteoWizard), xcms- and CAMERA- based peak-picking, MetMSLine-based pre-processing and covariate-based statistical analysis. Users are notified of detrimental instrument drift or errors by email. Also included are two shiny applications, targetId for real-time MS2 target identification, and peakMonitor to monitor targeted metabolites. Availability and implementation: SimExTargId is publicly available under GNU LGPL v3.0 license at https://github.com/JosieLHayes/simExTargId, which includes a vignette with example data. SimExTargId should be installed on a dedicated data-processing workstation or server that is networked to the LC-MS platform to facilitate MS1 profiling of metabolomic data. Supplementary information: Supplementary data are available at Bioinformatics online.

Adductomic signatures of benzene exposure provide insights into cancer induction.

Although benzene has long been recognized as a cause of human leukemia, the mechanism by which this simple molecule causes cancer has been problematic. A complicating factor is benzene metabolism, which produces many reactive intermediates, some specific to benzene and others derived from redox processes. Using archived serum from 20 nonsmoking Chinese workers, 10 with and 10 without occupational exposure to benzene (exposed: 3.2-88.9 ppm, controls: 0.002-0.020 ppm), we employed an adductomic pipeline to characterize protein modifications at Cys34 of human serum albumin, a nucleophilic hotspot in extracellular fluids. Of the 47 measured human serum albumin modifications, 39 were present at higher concentrations in benzene-exposed workers than in controls and many of the exposed-control differences were statistically significant. Correlation analysis identified three prominent clusters of adducts, namely putative modifications by benzene oxide and a benzene diolepoxide that grouped with other measures of benzene exposure, adducts of reactive oxygen and carbonyl species, and Cys34 disulfides of small thiols that are formed following oxidation of Cys34. Benzene diolepoxides are potent mutagens and carcinogens that have received little attention as potential causes of human leukemia. Reactive oxygen and carbonyl species-generated by redox processes involving polyphenolic benzene metabolites and by Cyp2E1 regulation following benzene exposure-can modify DNA and proteins in ways that contribute to cancer. The fact that these diverse human serum albumin modifications differed between benzene-exposed and control workers suggests that benzene can increase leukemia risks via multiple pathways involving a constellation of reactive molecules.

Cys34 Adductomes Differ between Patients with Chronic Lung or Heart Disease and Healthy Controls in Central London.

Oxidative stress generates reactive species that modify proteins, deplete antioxidant defenses, and contribute to chronic obstructive pulmonary disease (COPD) and ischemic heart disease (IHD). To determine whether protein modifications differ between COPD or IHD patients and healthy subjects, we performed untargeted analysis of adducts at the Cys34 locus of human serum albumin (HSA). Biospecimens were obtained from nonsmoking participants from London, U.K., including healthy subjects (n = 20) and patients with COPD (n = 20) or IHD (n = 10). Serum samples were digested with trypsin and analyzed by liquid chromatography-high resolution mass spectrometry. Effects of air pollution on adduct levels were also investigated based on estimated residential exposures to PM2.5, O3 and NO2. For the 39 adducts with sufficient data, levels were essentially identical in blood samples collected from the same subjects on two consecutive days, consistent with the 28 day residence time of HSA. Multivariate linear regression revealed 21 significant associations, mainly with the underlying diseases but also with air-pollution exposures (p-value < 0.05). Interestingly, most of the associations indicated that adduct levels decreased with the presence of disease or increased pollutant concentrations. Negative associations of COPD and IHD with the Cys34 disulfide of glutathione and two Cys34 sulfoxidations, were consistent with previous results from smoking and nonsmoking volunteers and nonsmoking women exposed to indoor combustion of coal and wood.

compMS2Miner: An Automatable Metabolite Identification, Visualization, and Data-Sharing R Package for High-Resolution LC-MS Data Sets.

A long-standing challenge of untargeted metabolomic profiling by ultrahigh-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) is efficient transition from unknown mass spectral features to confident metabolite annotations. The compMS2Miner (Comprehensive MS2 Miner) package was developed in the R language to facilitate rapid, comprehensive feature annotation using a peak-picker-output and MS2 data files as inputs. The number of MS2 spectra that can be collected during a metabolomic profiling experiment far outweigh the amount of time required for pain-staking manual interpretation; therefore, a degree of software workflow autonomy is required for broad-scale metabolite annotation. CompMS2Miner integrates many useful tools in a single workflow for metabolite annotation and also provides a means to overview the MS2 data with a Web application GUI compMS2Explorer (Comprehensive MS2 Explorer) that also facilitates data-sharing and transparency. The automatable compMS2Miner workflow consists of the following steps: (i) matching unknown MS1 features to precursor MS2 scans, (ii) filtration of spectral noise (dynamic noise filter), (iii) generation of composite mass spectra by multiple similar spectrum signal summation and redundant/contaminant spectra removal, (iv) interpretation of possible fragment ion substructure using an internal database, (v) annotation of unknowns with chemical and spectral databases with prediction of mammalian biotransformation metabolites, wrapper functions for in silico fragmentation software, nearest neighbor chemical similarity scoring, random forest based retention time prediction, text-mining based false positive removal/true positive ranking, chemical taxonomic prediction and differential evolution based global annotation score optimization, and (vi) network graph visualizations, data curation, and sharing are made possible via the compMS2Explorer application. Metabolite identities and comments can also be recorded using an interactive table within compMS2Explorer. The utility of the package is illustrated with a data set of blood serum samples from 7 diet induced obese (DIO) and 7 nonobese (NO) C57BL/6J mice, which were also treated with an antibiotic (streptomycin) to knockdown the gut microbiota. The results of fully autonomous and objective usage of compMS2Miner are presented here. All automatically annotated spectra output by the workflow are provided in the Supporting Information and can alternatively be explored as publically available compMS2Explorer applications for both positive and negative modes ( https://wmbedmands.shinyapps.io/compMS2_mouseSera_POS and https://wmbedmands.shinyapps.io/compMS2_mouseSera_NEG ). The workflow provided rapid annotation of a diversity of endogenous and gut microbially derived metabolites affected by both diet and antibiotic treatment, which conformed to previously published reports. Composite spectra (n = 173) were autonomously matched to entries of the Massbank of North America (MoNA) spectral repository. These experimental and virtual (lipidBlast) spectra corresponded to 29 common endogenous compound classes (e.g., 51 lysophosphatidylcholines spectra) and were then used to calculate the ranking capability of 7 individual scoring metrics. It was found that an average of the 7 individual scoring metrics provided the most effective weighted average ranking ability of 3 for the MoNA matched spectra in spite of potential risk of false positive annotations emerging from automation. Minor structural differences such as relative carbon-carbon double bond positions were found in several cases to affect the correct rank of the MoNA annotated metabolite. The latest release and an example workflow is available in the package vignette ( https://github.com/WMBEdmands/compMS2Miner ) and a version of the published application is available on the shinyapps.io site ( https://wmbedmands.shinyapps.io/compMS2Example ).

Profiling the Serum Albumin Cys34 Adductome of Solid Fuel Users in Xuanwei and Fuyuan, China.

Xuanwei and Fuyuan counties in China have the highest lung cancer rates in the world due to household air pollution from combustion of smoky coal for cooking and heating. To discover potential biomarkers of indoor combustion products, we profiled adducts at the Cys34 locus of human serum albumin (HSA) in 29 nonsmoking Xuanwei and Fuyuan females who used smoky coal, smokeless coal, or wood and 10 local controls who used electricity or gas fuel. Our untargeted "adductomics" method detected 50 tryptic peptides of HSA, containing Cys34 and prominent post-translational modifications. Putative adducts included Cys34 oxidation products, mixed disulfides, rearrangements, and truncations. The most significant differences in adduct levels across fuel types were observed for S-glutathione (S-GSH) and S-γ-glutamylcysteine (S-γ-GluCys), both of which were present at lower levels in subjects exposed to combustion products than in controls. After adjustment for age and personal measurements of airborne benzo(a)pyrene, the largest reductions in levels of S-GSH and S-γ-GluCys relative to controls were observed for users of smoky coal, compared to users of smokeless coal and wood. These results point to possible depletion of GSH, an essential antioxidant, and its precursor γ-GluCys in nonsmoking females exposed to indoor-combustion products in Xuanwei and Fuyuan, China.

Evaluating Ultra-long-Chain Fatty Acids as Biomarkers of Colorectal Cancer Risk.

BACKGROUND: Cross-sectional studies reported a novel set of hydroxylated ultra-long-chain fatty acids (ULCFA) that were present at significantly lower levels in colorectal cancer cases than controls. Follow-up studies suggested that these molecules were potential biomarkers of protective exposure for colorectal cancer. To test the hypothesis that ULCFAs reflect causal pathways, we measured their levels in prediagnostic serum from incident colorectal cancer cases and controls. METHODS: Serum from 95 colorectal cancer patients and 95 matched controls was obtained from the Italian arm of the European Prospective Investigation into Cancer and Nutrition cohort and analyzed by liquid chromatography-high-resolution mass spectrometry. Levels of 8 ULCFAs were compared between cases and controls with paired t tests and a linear model that used time to diagnosis (TTD) to determine whether case-control differences were influenced by disease progression. RESULTS: Although paired t tests detected significantly lower levels of four ULCFAs in colorectal cancer cases, confirming earlier reports, the case-control differences diminished significantly with increasing TTD (7 days-14 years). CONCLUSION: Levels of several ULCFAs were lower in incident colorectal cancer cases than controls. However, because case-control differences decreased with increasing TTD, we conclude that these molecules were likely consumed by processes related to cancer progression rather than causal pathways. IMPACT: ULCFA levels are unlikely to represent exposures that protect individuals from colorectal cancer. Future research should focus on the diagnostic potential and origins of these molecules. Our use of TTD as a covariate in a linear model provides an efficient method for distinguishing causal and reactive biomarkers in biospecimens from prospective cohorts. Cancer Epidemiol Biomarkers Prev; 25(8); 1216-23. ©2016 AACR.

MetMSLine: an automated and fully integrated pipeline for rapid processing of high-resolution LC-MS metabolomic datasets.

UNLABELLED: MetMSLine represents a complete collection of functions in the R programming language as an accessible GUI for biomarker discovery in large-scale liquid-chromatography high-resolution mass spectral datasets from acquisition through to final metabolite identification forming a backend to output from any peak-picking software such as XCMS. MetMSLine automatically creates subdirectories, data tables and relevant figures at the following steps: (i) signal smoothing, normalization, filtration and noise transformation (PreProc.QC.LSC.R); (ii) PCA and automatic outlier removal (Auto.PCA.R); (iii) automatic regression, biomarker selection, hierarchical clustering and cluster ion/artefact identification (Auto.MV.Regress.R); (iv) Biomarker-MS/MS fragmentation spectra matching and fragment/neutral loss annotation (Auto.MS.MS.match.R) and (v) semi-targeted metabolite identification based on a list of theoretical masses obtained from public databases (DBAnnotate.R). AVAILABILITY AND IMPLEMENTATION: All source code and suggested parameters are available in an un-encapsulated layout on http://wmbedmands.github.io/MetMSLine/. Readme files and a synthetic dataset of both X-variables (simulated LC-MS data), Y-variables (simulated continuous variables) and metabolite theoretical masses are also available on our GitHub repository.

Normalization to specific gravity prior to analysis improves information recovery from high resolution mass spectrometry metabolomic profiles of human urine.

Extraction of meaningful biological information from urinary metabolomic profiles obtained by liquid-chromatography coupled to mass spectrometry (MS) necessitates the control of unwanted sources of variability associated with large differences in urine sample concentrations. Different methods of normalization either before analysis (preacquisition normalization) through dilution of urine samples to the lowest specific gravity measured by refractometry, or after analysis (postacquisition normalization) to urine volume, specific gravity and median fold change are compared for their capacity to recover lead metabolites for a potential future use as dietary biomarkers. Twenty-four urine samples of 19 subjects from the European Prospective Investigation into Cancer and nutrition (EPIC) cohort were selected based on their high and low/nonconsumption of six polyphenol-rich foods as assessed with a 24 h dietary recall. MS features selected on the basis of minimum discriminant selection criteria were related to each dietary item by means of orthogonal partial least-squares discriminant analysis models. Normalization methods ranked in the following decreasing order when comparing the number of total discriminant MS features recovered to that obtained in the absence of normalization: preacquisition normalization to specific gravity (4.2-fold), postacquisition normalization to specific gravity (2.3-fold), postacquisition median fold change normalization (1.8-fold increase), postacquisition normalization to urinary volume (0.79-fold). A preventative preacquisition normalization based on urine specific gravity was found to be superior to all curative postacquisition normalization methods tested for discovery of MS features discriminant of dietary intake in these urinary metabolomic datasets.

Aspirin-triggered 15-epi-lipoxin A4 predicts cyclooxygenase-2 in the lungs of LPS-treated mice but not in the circulation: implications for a clinical test.

Inhibition of cyclooxygenase (COX)-2 increases cardiovascular deaths. Identifying a biomarker of COX-2 is desirable but difficult, since COX-1 and COX-2 ordinarily catalyze formation of an identical product, prostaglandin H2. When acetylated by aspirin, however, COX-2 (but not COX-1) can form 15(R)-HETE, which is metabolized to aspirin-triggered lipoxin (ATL), 15-epi-lipoxin A4. Here we have used COX-1- and COX-2-knockout mice to establish whether plasma ATL could be used as a biomarker of vascular COX-2 in vivo. Vascular COX-2 was low but increased by LPS (10 mg/kg; i.p). Aspirin (10 mg/kg; i.v.) inhibited COX-1, measured as blood thromboxane and COX-2, measured as lung PGE2. Aspirin also increased the levels of ATL in the lungs of LPS-treated wild-type C57Bl6 mice (vehicle: 25.5±9.3 ng/ml; 100 mg/kg: 112.0±7.4 ng/ml; P<0.05). Despite this, ATL was unchanged in plasma after LPS and aspirin. This was true in wild-type as well as COX-1(-/-) and COX-2(-/-) mice. Thus, in mice in which COX-2 has been induced by LPS treatment, aspirin triggers detectable 15-epi-lipoxin A4 in lung tissue, but not in plasma. This important study is the first to demonstrate that while ATL can be measured in tissue, plasma ATL is not a biomarker of vascular COX-2 expression.

Identification of human urinary biomarkers of cruciferous vegetable consumption by metabonomic profiling.

Consumption of cruciferous vegetables (CVs) is inversely correlated to many human diseases including cancer (breast, lung, and bladder), diabetes, and cardiovascular and neurological disease. Presently, there are no readily measurable biomarkers of CV consumption and intake of CVs has relied on dietary recall. Here, biomarkers of CV intake were identified in the urine of 20 healthy Caucasian adult males using (1)H NMR spectroscopy with multivariate statistical modeling. The study was separated into three phases of 14 days: a run-in period with restricted CV consumption (phase I); a high CV phase where participants consumed 250 g/day of both broccoli and Brussels sprouts (phase II); a wash-out phase with a return to restricted CV consumption (phase III). Each study participant provided a complete cumulative urine collection over 48 h at the end of each phase; a spot urine (U0), 0-10 h (U0-10), 10-24 h (U10-24), and 24-48 h (U24-48) urine samples. Urine samples obtained after consumption of CVs were differentiated from low CV diet samples by four singlet (1)H NMR spectroscopic peaks, one of which was identified as S-methyl-l-cysteine sulfoxide (SMCSO) and the three other peaks were tentatively identified as other metabolites structurally related to SMCSO. These stable urinary biomarkers of CV consumption will facilitate future assessment of CVs in nutritional population screening and dietary intervention studies and may correlate to population health outcomes.