abundant metabolites  (IROA Technologies LLC)

Journal: BMC Bioinformatics

Article Title: MIMI: Molecular Isotope Mass Identifier for stable isotope-labeled Fourier transform ultra-high mass resolution data analysis

doi: 10.1186/s12859-025-06348-1

Figure Lengend Snippet: MIMI Workflow : Detailed workflow illustrating input and output file formats for Preprocessing (mimi_cache_create) and Mass Analysis (mimi_mass_analysis) steps. Snippets of input abundance files and cache files for two independent Preprocessing runs are shown for the default natural atomic isotope ratios (blue headers; to identify metabolites in the test sample) and for an override file specifying \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$95\%$$\end{document} 13 C-labeled compounds (green headers; for the IROA-IS spike-in). In the cache files, natural minor isotopes for H and N are indicated in blue font; minor isotopes for C are in red font ( 13 C for natural ratios; 12 C for the spike-in). One or more cache files and MS peak lists (after calibration and peak picking; pink/orange headers) may be used as input for a single Mass Analysis run. A partial results table shows the layout for a run using both cache files and two sample replicates (data for the second replicate would appear as a second set of columns to the right). The same color scheme highlights output columns for corresponding inputs. The top row of the final output file contains the file path and name of the log file, which records metadata on how each workflow was run

Article Snippet: Fig. 3 Effect of varying ppm error thresholds on peak assignments: Number of a monoisotopic and b fine-structure peak matches for peak lists from two technical replicates of a diatom sample (testdata1.asc and testdata1.asc) containing naturally abundant metabolites (blue/light blue bars) with a 13 C-labeled IROA-IS spike-in (orange/pink bars), when compared with a list of 8,529 unique chemical formulas for 16,089 distinct KEGG compounds ranging between 40–1000 Daltons.

Techniques: Labeling

Journal: BMC Bioinformatics

Article Title: MIMI: Molecular Isotope Mass Identifier for stable isotope-labeled Fourier transform ultra-high mass resolution data analysis

doi: 10.1186/s12859-025-06348-1

Figure Lengend Snippet: Effect of varying ppm error thresholds on peak assignments: Number of a monoisotopic and b fine-structure peak matches for peak lists from two technical replicates of a diatom sample (testdata1.asc and testdata1.asc) containing naturally abundant metabolites (blue/light blue bars) with a 13 C-labeled IROA-IS spike-in (orange/pink bars), when compared with a list of 8,529 unique chemical formulas for 16,089 distinct KEGG compounds ranging between 40–1000 Daltons. Comparisons were performed across a range of error thresholds against the theoretical masses of metabolites with either natural isotopic abundance (nat_nist) or 95 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} 13 C-labeling (C13_95). a ) Number of distinct molecular features (monoisotopic masses) identified at varying -p settings of 0.1, 0.5, 1 ppm, with -vp held constant at 0.5 ppm. b ) Average number of minor isotopic variants detected per matched chemical formula at a -p setting of 0.5 and varying -vp values of 0.1, 0.5, 1 ppm

Article Snippet: Fig. 3 Effect of varying ppm error thresholds on peak assignments: Number of a monoisotopic and b fine-structure peak matches for peak lists from two technical replicates of a diatom sample (testdata1.asc and testdata1.asc) containing naturally abundant metabolites (blue/light blue bars) with a 13 C-labeled IROA-IS spike-in (orange/pink bars), when compared with a list of 8,529 unique chemical formulas for 16,089 distinct KEGG compounds ranging between 40–1000 Daltons.

Techniques: Labeling

Journal: Breast Cancer : Targets and Therapy

Article Title: Immune and Metabolic Reprogramming Induced by Paclitaxel, Capecitabine and Eribulin in Breast Cancer: Insights into Therapeutic Targets

doi: 10.2147/BCTT.S498070

Figure Lengend Snippet: Metabolic assessment of serum at baseline and time of progression reveals distinct metabolic alterations between three different treatment groups at time of progression. Matched serum samples from Luminex assays were obtained from patients at baseline and time of progression on either Capecitabine, Eribulin, or Paclitaxel. ( A ) Bar graph shows the distribution of measurable metabolites (n=581) spanning 20 biochemical classes. ( B ) Principal component analysis shows some separation of samples, based on their metabolic profiles and drug treatment. Volcano plots of differentially abundant immune factors in the ( C ) Capecitabine, ( D ) Eribulin and ( E ) Paclitaxel groups reveal significantly differential metabolites (p<0.05, and |logFC|>0.58) between progression and baseline for these 3 treatments, some of which were significant by adjusted p-value, however p<0.05 is being shown. However, many of these metabolites did not overlap between the groups. ( F ) Metabolite set enrichment analysis was implemented on the differential metabolites, to highlight the pathways associated with differentially abundant metabolites ( Supplementary Figure 2 ). The lack of overlap is further described in the venn diagram which details the few metabolic pathways that were significant (p val <0.1), with their direction of change (red=increased at progression, blue=decreased at progression).

Article Snippet: As we did not assume normality of the data, simple Spearman correlation R and p values of top differentially abundant metabolites (Biocrates) and immune factors (Luminex), for each of the treatments at time of progression were calculated, using the “corrplot” function in R, and visualized using “pheatmap”.

Techniques: Luminex

Journal: Breast Cancer : Targets and Therapy

Article Title: Immune and Metabolic Reprogramming Induced by Paclitaxel, Capecitabine and Eribulin in Breast Cancer: Insights into Therapeutic Targets

doi: 10.2147/BCTT.S498070

Figure Lengend Snippet: Correlation of metabolites with immune factors, at time of progression, in patients treated with eribulin, capecitabine or paclitaxel. Spearman correlation R and p-values were calculated for the top differential metabolites and top variable immune factors for ( A ) Eribulin, ( B ) Capecitabine and ( C ) Paclitaxel. Significant positive (red) and negative (blue) correlations between metabolite abundance and immune factors are indicated by a * (p < 0.05) in the heatmaps.

Article Snippet: As we did not assume normality of the data, simple Spearman correlation R and p values of top differentially abundant metabolites (Biocrates) and immune factors (Luminex), for each of the treatments at time of progression were calculated, using the “corrplot” function in R, and visualized using “pheatmap”.

Techniques: