ADAP-GC 2.0: Deconvolution of Coeluting Metabolites from GC/TOF-MS Data for Metabolomics Studies

ADAP-GC 2.0 has been developed to deconvolute coeluting metabolites that frequently exist in real biological samples of metabolomics studies. Deconvolution is based on a chromatographic model peak approach that combines five metrics of peak qualities for constructing/selecting model peak features. Prior to deconvolution, ADAP-GC 2.0 takes raw mass spectral data as input, extracts ion chromatograms for all the observed masses, and detects chromatographic peak features. After deconvolution, it aligns components across samples and exports the qualitative and quantitative information of all of the observed components. Centered on the deconvolution, the entire data analysis workflow is fully automated. ADAP-GC 2.0 has been tested using three different types of samples. The testing results demonstrate significant improvements of ADAP-GC 2.0, compared to the previous ADAP 1.0, to identify and quantify metabolites from gas chromatography/time-of-flight mass spectrometry (GC/TOF-MS) data in untargeted metabolomics studies

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<p>The luxS gene is present in a wide range of bacteria and is involved in many cellular processes. LuxS mutation can cause autoinducer(AI)-2 deficiency and methyl metabolism disorder. The objective of this study was to demonstrate that, in addition to AI-2-mediated quorum sensing (QS), methyl metabolism plays an important role in LuxS regulation in Streptococcus mutans. The sahH gene from Pseudomonas aeruginosa was amplified and introduced into the S. mutans luxS-null strain to complement the methyl metabolism disruption in a defective QS phenotype. The intracellular activated methyl cycle (AMC) metabolites [S-adenosylmethionine (SAM), S-adenosylhomocysteine (SAH), homocysteine (HCY), and methionine] were quantified in wild-type S. mutans and its three derivatives to determine the metabolic effects of disrupting the AMC. Biofilm mass and structure, acid tolerance, acid production, exopolysaccharide synthesis of multispecies biofilms and the transcriptional level of related genes were determined. The results indicated that SAH and SAM were relatively higher in S. mutans luxS-null strain and S. mutans luxS null strain with plasmid pIB169 when cultured overnight, and HCY was significantly higher in S. mutans UA159. Consistent with the transcriptional profile, luxS deletion-mediated impairment of biofilm formation and acid tolerance was restored to wild-type levels using transgenic SahH. These results also suggest that methionine methyl metabolism contributes to LuxS regulation in S. mutans to a significant degree.</p

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<p>The luxS gene is present in a wide range of bacteria and is involved in many cellular processes. LuxS mutation can cause autoinducer(AI)-2 deficiency and methyl metabolism disorder. The objective of this study was to demonstrate that, in addition to AI-2-mediated quorum sensing (QS), methyl metabolism plays an important role in LuxS regulation in Streptococcus mutans. The sahH gene from Pseudomonas aeruginosa was amplified and introduced into the S. mutans luxS-null strain to complement the methyl metabolism disruption in a defective QS phenotype. The intracellular activated methyl cycle (AMC) metabolites [S-adenosylmethionine (SAM), S-adenosylhomocysteine (SAH), homocysteine (HCY), and methionine] were quantified in wild-type S. mutans and its three derivatives to determine the metabolic effects of disrupting the AMC. Biofilm mass and structure, acid tolerance, acid production, exopolysaccharide synthesis of multispecies biofilms and the transcriptional level of related genes were determined. The results indicated that SAH and SAM were relatively higher in S. mutans luxS-null strain and S. mutans luxS null strain with plasmid pIB169 when cultured overnight, and HCY was significantly higher in S. mutans UA159. Consistent with the transcriptional profile, luxS deletion-mediated impairment of biofilm formation and acid tolerance was restored to wild-type levels using transgenic SahH. These results also suggest that methionine methyl metabolism contributes to LuxS regulation in S. mutans to a significant degree.</p

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<p>The luxS gene is present in a wide range of bacteria and is involved in many cellular processes. LuxS mutation can cause autoinducer(AI)-2 deficiency and methyl metabolism disorder. The objective of this study was to demonstrate that, in addition to AI-2-mediated quorum sensing (QS), methyl metabolism plays an important role in LuxS regulation in Streptococcus mutans. The sahH gene from Pseudomonas aeruginosa was amplified and introduced into the S. mutans luxS-null strain to complement the methyl metabolism disruption in a defective QS phenotype. The intracellular activated methyl cycle (AMC) metabolites [S-adenosylmethionine (SAM), S-adenosylhomocysteine (SAH), homocysteine (HCY), and methionine] were quantified in wild-type S. mutans and its three derivatives to determine the metabolic effects of disrupting the AMC. Biofilm mass and structure, acid tolerance, acid production, exopolysaccharide synthesis of multispecies biofilms and the transcriptional level of related genes were determined. The results indicated that SAH and SAM were relatively higher in S. mutans luxS-null strain and S. mutans luxS null strain with plasmid pIB169 when cultured overnight, and HCY was significantly higher in S. mutans UA159. Consistent with the transcriptional profile, luxS deletion-mediated impairment of biofilm formation and acid tolerance was restored to wild-type levels using transgenic SahH. These results also suggest that methionine methyl metabolism contributes to LuxS regulation in S. mutans to a significant degree.</p

Comparison of a subset of manual gates and OpenCyto automated gates for a representative sample from the HVTN080 ICS data set.

<p>The automated gates are data-driven. Each panel shows a corresponding manual and automated gate side-by-side. The left panel is the manual gate; the right panel is the OpenCyto data-driven gate. Parent population names differ between manual and automated gates for singlets and lymphocytes because the automated gating hierarchy differs from the manual gating by including boundary and boundary debris gates, respectively, before these populations. Starting at the top left and proceeding along the rows, the gates shown are singlets, live cells, lymphocytes, CD3⁺ T-cells, CD4⁺ and CD8⁺ T-cells, IFN-γ⁺ and IL2⁺ expressing CD4⁺ and CD8⁺ T-cells, and Granzyme B⁺ and CD57⁺ expressing CD8⁺ T-cells. The manual and automated gates are very comparable.</p

Comparison of OpenCyto automated gating and manual gating (performed with FlowJo and imported and reproduced in R using OpenCyto) for HVTN 080.

<p>A) Box-plots of the paired differences (post-vaccination – baseline) in proportions of cytokine-producing cells from significant cell subsets identified by the linear model (see Supplementary Methods) for each stimulation condition, gating method, and vaccine regimen. Differences between baseline and post-vaccination are background-corrected (stimulated – non-stimulated). There were no significant differences between the observed distributions for manual or OpenCyto gating (paired Wilcoxon test). B) Scatter plots comparing manual gating vs. OpenCyto gating. The per-subject, background-corrected difference between vaccine and baseline is plotted for OpenCyto and manual gating, with concordance correlation coefficients shown for all stimulations.</p

An overview of the OpenCyto infrastructure.

<p>When reproducing manual gating, raw FCS files and FlowJo workspace XML files are read into the R environment using <i>parseWorkspace</i>, creating a <i>GatingSet</i> object that represents the compensated, transformed and gated data stored in an <i>ncdfFlowSet</i> on disk. Cell populations annotated with gates can be visualized using <i>plotGate</i>, from the <i>flowViz</i> package Gating schemes can be visualized using <i>plot</i>. To perform automated gating, the user defines a <i>csv</i> representation of a gating tree, which is read by the <i>OpenCyto</i> package to generate a <i>gatingTemplate object</i>. This template can be applied to a <i>GatingSet</i> containing data, but no gates, provided the data uses the markers defined in the template. OpenCyto utilizes built-in automated gating methods, or external methods registered via a plug-in framework, to gate different cell subsets and populate the <i>GatingSet</i> with data-driven gate definitions for each sample. Manual and automated gating may be readily compared within a single framework. Cell populations and features can be extracted for further statistical analysis with other R and BioConductor software packages. Data (red boxes), software packages (blue boxes), framework functionality (gray boxes), and data flow/data structures (arrows/labeled arrows) are represented. <i>flowCore</i>, <i>flowStats</i>, and <i>flowViz</i>, are the <i>core</i> Bioconductor flow packages that benefit from the substantial infrastructure changes we have made to improve scalability and data visualization.</p

The average frequency of expression across two CyTOF samples for cytokine-producing cell subsets from four T-cell maturational states.

<p>Samples were stimulated with PMA-Ionomycin for 3 hours. Rows represent different maturational cell subsets (TN: naïve, TCM: central memory, TEF: effector, TEM: effector memory) and are clustered by Euclidean distance similarity. Columns represent different cytokine-producing cell subsets. The bottom legend defines the cell subset in a column. The legend is colored by degree of functionality of the cell subsets (light blue: degree 1, dark blue: degree 2, light green: degree 3, dark green: degree 4, salmon: degree 5, red: degree 6, orange: degree 7). The shading of individual blocks of the heatmap represents the average proportion of cells in the subset across the two samples, normalized to the total number of CD8 T-cells. Naïve cells have low polyfunctionality compared to effector, effector memory, and central memory cells.</p

The distribution of cells of each maturational state and their degree of functionality.

<p>The majority of naïve CD8 T cells (TN) do not express any cytokines (degree of functionality 0) or are mono-functional, while effector memory cells (TEM) are the most polyfunctional of the subsets (peaking at degree 5). Short-lived effector (TEF) cells have lower polyfunctionality (peaking at degree 4), and central memory (TCM) populations tend to have a constant level of polyfunctionality from degree1 through degree 7. The area under the curve for each cell subset integrates to one. The y-axis is transformed by a hyperbolic-arcsine to facilitate visualization of differences between subsets at higher degrees of polyfunctionality.</p

Performance metric of OpenCyto on the flow cytometry and CyTOF data sets, on a single-processor machine with 8 GB of RAM.

<p>OpenCyto can reproduce the FlowJo manual gates from a 16-workspace data set in 21 minutes with a peak memory usage of 1.8 GB. Once gated, the data occupies only 4.6 MB of RAM and is efficiently stored on disk in the HDF5/NetCDF format. Automated gating of the same data set using on OpenCyto GatingTemplate to generate data-driven gates for each of the 470 samples takes 1.74 hours on a single-processor. This can be parallelized across multiple cores for greater efficiency. The 420×2⁴ Boolean subsets of 4-cytokine producing cells can be generated and extracted efficiently, taking only 17 minutes for 7520 different subsets. Analogous results are shown for the CyTOF data, which has higher dimensionality. Calculating the Boolean subsets of 9 cytokine gates for the four maturation subsets in the data was extremely quick. In contrast, the 4×2⁹ Boolean subsets took 104 minutes to compute in FlowJo.</p><p>Performance metric of OpenCyto on the flow cytometry and CyTOF data sets, on a single-processor machine with 8 GB of RAM.</p