xMSannotator: An R Package for Network-Based Annotation of High-Resolution Metabolomics Data

Improved analytical technologies and data extraction algorithms enable detection of >10 000 reproducible signals by liquid chromatography–high-resolution mass spectrometry, creating a bottleneck in chemical identification. In principle, measurement of more than one million chemicals would be possible if algorithms were available to facilitate utilization of the raw mass spectrometry data, especially low-abundance metabolites. Here we describe an automated computational framework to annotate ions for possible chemical identity using a multistage clustering algorithm in which metabolic pathway associations are used along with intensity profiles, retention time characteristics, mass defect, and isotope/adduct patterns. The algorithm uses high-resolution mass spectrometry data for a series of samples with common properties and publicly available chemical, metabolic, and environmental databases to assign confidence levels to annotation results. Evaluation results show that the algorithm achieves an F1-measure of 0.8 for a data set with known targets and is more robust than previously reported results for cases when database size is much greater than the actual number of metabolites. MS/MS evaluation of a set of randomly selected 210 metabolites annotated using xMSannotator in an untargeted metabolomics human data set shows that 80% of features with high or medium confidence scores have ion dissociation patterns consistent with the xMSannotator annotation. The algorithm has been incorporated into an R package, xMSannotator, which includes utilities for querying local or online databases such as ChemSpider, KEGG, HMDB, T3DB, and LipidMaps

Hybrid Feature Detection and Information Accumulation Using High-Resolution LC–MS Metabolomics Data

Feature detection is a critical step in the preprocessing of liquid chromatography–mass spectrometry (LC–MS) metabolomics data. Currently, the predominant approach is to detect features using noise filters and peak shape models based on the data at hand alone. Databases of known metabolites and historical data contain information that could help boost the sensitivity of feature detection, especially for low-concentration metabolites. However, utilizing such information in targeted feature detection may cause large number of false positives because of the high levels of noise in LC–MS data. With high-resolution mass spectrometry such as liquid chromatograph–Fourier transform mass spectrometry (LC–FTMS), high-confidence matching of peaks to known features is feasible. Here we describe a computational approach that serves two purposes. First it boosts feature detection sensitivity by using a hybrid procedure of both untargeted and targeted peak detection. New algorithms are designed to reduce the chance of false-positives by nonparametric local peak detection and filtering. Second, it can accumulate information on the concentration variation of metabolites over large number of samples, which can help find rare features and/or features with uncommon concentration in future studies. Information can be accumulated on features that are consistently found in real data even before their identities are found. We demonstrate the value of the approach in a proof-of-concept study. The method is implemented as part of the R package apLCMS at http://www.sph.emory.edu/apLCMS/

ADAP-GC 3.2: Graphical Software Tool for Efficient Spectral Deconvolution of Gas Chromatography–High-Resolution Mass Spectrometry Metabolomics Data

ADAP-GC is an automated computational workflow for extracting metabolite information from raw, untargeted gas chromatography–mass spectrometry metabolomics data. Deconvolution of coeluting analytes is a critical step in the workflow, and the underlying algorithm is able to extract fragmentation mass spectra of coeluting analytes with high accuracy. However, its latest version ADAP-GC 3.0 was not user-friendly. To make ADAP-GC easier to use, we have developed ADAP-GC 3.2 and describe here the improvements on three aspects. First, all of the algorithms in ADAP-GC 3.0 written in R have been replaced by their analogues in Java and incorporated into MZmine 2 to make the workflow user-friendly. Second, the clustering algorithm DBSCAN has replaced the original hierarchical clustering to allow faster spectral deconvolution. Finally, algorithms originally developed for constructing extracted ion chromatograms (EICs) and detecting EIC peaks from LC–MS data are incorporated into the ADAP-GC workflow, allowing the latter to process high mass resolution data. Performance of ADAP-GC 3.2 has been evaluated using unit mass resolution data from standard-mixture and urine samples. The identification and quantitation results were compared with those produced by ADAP-GC 3.0, AMDIS, AnalyzerPro, and ChromaTOF. Identification results for high mass resolution data derived from standard-mixture samples are presented as well

Mitochondrial metabolites from AE that were found to discriminate male from female mitochondria using false discovery rate analysis (q = 0.1).

<p>Metabolites with greater ion intensity in male mitochondria include (A) leucine/isoleucine, (B) glutamate and (C) methionine. Metabolites with greater ion intensity in female mitochondria include (D) adenosine, (E) amino octadecanoic acid and (F) unknown metabolite (<i>m/z</i> 537.789). Data was analyzed using one-way ANOVA and Tukey's post hoc test (* p<0.05, ** p<0.01, *** p<0.001).</p

A breakdown of the animals used for mitochondrial isolation and metabolic profiling studies.

<p>A total of 40 animals were used, 20 wild type and 20 thiroredoxin-2 transgenic (Trx2TG). These groups were further broken down into subgroups of 5 based on age and sex.</p

NLS-hTrx1 transgenic mice have increased NF-κB activation and proinflammatory cytokine (TNF-α and IL-6) production after influenza viral infection.

<p>Lung tissues obtained from Tg and WT before (3 mice each for Tg and WT) and 3-d post infection (6 mice for Tg, 5 mice for WT) were examined for mRNA of cytokines (A) and NF-κB activity (B). mRNA levels of TNF-α and IL-6 were analyzed and quantified by real-time PCR. * p<0.05 for 3-d post infection in Tg compared to 3 d post infection in WT and 0 d in Tg. B, NF-κB activity was examined by EMSA using same tissues analyzed for cytokines shown in A. Bands indicated as NF-κB bound DNA probe show activity of NF-κB. Densitometry values shown as fold difference were obtained from measuring relative intensities compared to that in WT before infection (lane 2). [lanes 1–6 are as follows; 1, NF-κB probe alone; 2, WT before infection; 3, Tg before infection; 4, WT 3 days post infection; 5, Tg 3-d post infection; 6, Tg 3-d post infection (cold NF-κB probe incubation followed by labeled NF-κB probe incubation)]. C, NF-κB activity (NF-κB Luc) was examined by expression of NLS-hTrx1 WT or dominant negative mutant of Trx1, NLS-hTrx1 C35S by transient cotransfection with NF-κB luciferase and β-galactosidase. Quantified luciferase activity as a measure of NF-κB activity was normalized by β-galactosidase <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0018918#pone.0018918-Go4" target="_blank">[37]</a>. Western blot confirms expression of NLS-hTrx1 WT and NLS-hTrx1 C35S (C, top) and β-actin (C, middle) for equal protein loading in HeLa cells 2 d after transfection. Results of luciferase activity in bar graph are shown as means ± SE (n = 8, * p<0.05).</p

Distribution of metabolites resolved by anion exchange (A) and reverse phase (B) chromatography.

<p>To determine if a metabolite was present in the mitochondrial isolation and not a buffer contaminant, a ratio of ion intensity (sample ion intensity/buffer ion intensity) was calculated for each metabolite. A metabolite was determined to be “mitochondrial” if this ratio (s/b) was greater than or equal to 4. Additionally, employment of two chromatographic techniques resulted in the detection of 2127 mitochondrial metabolites (C).</p

Mitochondrial metabolites from C18 that were found to discriminate male from female mitochondria using false discovery rate analysis (q = 0.1).

<p>Metabolites with greater ion intensity in male mitochondria include (A) leucine/isoleucine, (B) glutamate and (C) methionine. Metabolites with greater ion intensity in female mitochondria include (D) adenosine, (E) sphinganine and (F) unknown metabolite (<i>m/z</i> 442.759). Data was analyzed using one-way ANOVA and Tukey's post hoc test (* p<0.05, *** p<0.001).</p

Most metabolic pathways are represented in metabolites isolated from liver mitochondria.

<p>Ten pathways containing the most matches are plotted.</p

A scheme for stimulation of inflammatory response in NLS-hTrx1 Tg by infection.

<p>Virus infection and other inflammatory stimuli affect cytoplasmic redox state, e.g. oxidation of E_hGSSG. Cytoplamic oxidation results in phosphorylation and degradation of I-κBα, which then translocates NF-κB to nucleus. Upon its translocation, p50 subunit (Cys62) reduced by nuclear localized Trx1 together with Ref-1 and Prx stimulates its DNA binding activity followed by subsequent gene expression, e.g. TNF-α and IL-6. Increased inflammatory cytokines further activate NF-κB as feedback stimulation.</p