Journal: Scientific Reports

Article Title: Exercise induces dynamic changes in intra-articular metabolism and inflammation associated with remodeling of the infrapatellar fat pad in mice

doi: 10.1038/s41598-025-86726-0

Figure Lengend Snippet: Effect of short-term wheel running exercise on synovial fluid metabolites. Synovial fluid was collected from knees of male and female mice following 0, 1, 3, or 5 days of voluntary wheel running exercise and intra-articular injections, as described in Fig. . Samples were analyzed by Metabolon’s Global Metabolomic Profiling Analysis, which identified 202 biochemicals confirmed by authenticated library standards. Peak area data were normalized to extracted volume and then median scaled. ( A ) 2-way hierarchical clustering analysis was used to identify patterns in synovial fluid metabolite abundance across experimental groups. Heatmap color legend signifies standardized metabolite values calculated by subtracting the mean and dividing by the standard deviation. Columns represent mean values per experimental group, and rows represent individual metabolites. Note that 3- and 5-day exercise conditions clustered together in the third and fourth columns. Filled cells in right-hand column indicate metabolites significantly altered by exercise (Generalized Linear Model including exercise, sex, and treatment effects). Green rectangles designate clusters (C1 – C5) with distinct changes in metabolite abundance versus days of exercise. ( B ) Pie chart of relative percent of all detected metabolites categorized by Metabolon’s Metabolic Super Pathway assignment. Numbers in parentheses indicate absolute number of metabolites detected per category. ( C ) Pie charts of cluster-specific metabolite composition based on Metabolic Super Pathway assignments. Donut charts indicate the relative proportion of metabolites within a given cluster that were significantly altered by exercise, biological sex, or treatment ( p < 0.05). Line graphs of metabolites significantly altered by exercise, expressed as abundance fold-change relative to day 0 values (log2) and color coded according to Metabolic Super Pathway (Supplemental Table 7).

Article Snippet: Green rectangles designate clusters (C1 – C5) with distinct changes in metabolite abundance versus days of exercise. ( B ) Pie chart of relative percent of all detected metabolites categorized by Metabolon’s Metabolic Super Pathway assignment.

Techniques: Standard Deviation

Journal: Research Square

Article Title: The Metabolome-Wide Signature of Major Depressive Disorder

doi: 10.21203/rs.3.rs-3127544/v1

Figure Lengend Snippet: Forest plot of the 34 replicated metabolites (standardized betas for the associations with current MDD at baseline).

Article Snippet: Enrichment analysis was done using pathways pre-assigned to the metabolites by Metabolon (Table S1).

Techniques:

Journal: Nature Microbiology

Article Title: Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome

doi: 10.1038/s41564-022-01293-8

Figure Lengend Snippet: a, b , UMAP ordination of metabolomics data (N = 232), same as Fig. , colored by Pos Early, Pos Late, and Polar platform batches ( a ; 2 batches) and by Neg platform batches ( b ; 3 batches). See Supplementary Table for which metabolites were measured by each platform. Limited batch effect is noted, which is statistically significant only for the 3 batches (PERMANOVA P = 0.09 and P = 0.023 for 2 and 3 batches, respectively). c , The fraction of samples from each batch (y-axis; top, Pos Early, Pos Late, and Polar platform batches; bottom, Neg platform batches) whose metabolite profiles clustered to each metabolite cluster (MC; x-axis), shown for each MC separately. No significant batch effect was detected in MC assignments (Two-sided Fisher’s exact P > 0.05 for all without FDR correction). d , Heatmap showing odds ratio for sPTB (color bar) for each metabolite from Fig. (x-axis) using a logistic regression model adjusting for batch (according to the appropriate platform for the metabolite, Supplementary Table ), stratified by maternal race (y-axis). The exact odds ratio and confidence interval are written in the cell for all statistically significant associations (FDR < 0.1). e , sPTB classification accuracy (auROC, x-axis) for a prediction model similar to those used for the entire cohort (Fig. , ), that is: trained and evaluated in cross validation on batch 1 (N = 114; orange; auROC = 0.66; one-sided permutation P = 0.44 for lower accuracy than random draw); trained on batch 1 (N = 114) and evaluated on batch 2 (N = 118; violet; auROC = 0.66; P = 0.46); trained and evaluated in cross validation on batch 2 (N = 118; magenta; auROC = 0.66; P = 0.44); and trained on batch 2 (N = 118) and evaluated on batch 1 (N = 114; brown; auROC = 0.69; P = 0.66). Gray histogram (black line, KDE) shows accuracy of models evaluated in cross-validation on random samples (N = 116) from this cohort (mean auROC = 0.67). This analysis demonstrates that a prediction model trained on one of the two batches generalizes well to the other batch, and that both accuracies are to be expected given the limited sample size.

Article Snippet: Metabolite super pathway assignments were provided by Metabolon. b , Distribution of metabolite prevalences across samples.

Techniques: Biomarker Discovery

Journal: Nature Microbiology

Article Title: Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome

doi: 10.1038/s41564-022-01293-8

Figure Lengend Snippet: a – c , UMAP ordination of microbiome ( a , N = 503) and metabolomics data ( b and c , N = 232), coloured by CSTs ( a and b ) or de novo clustering of metabolites data ( c , MCs; ). The vaginal microbiome and metabolome are significantly separated by CSTs (PERMANOVA P < 0.001 for both), yet the separation is less clear in the metabolome. For similar plots coloured by maternal race, see also Extended Data Fig. . d , The fraction of women whose metabolite profiles clustered to each MC, shown for each CST separately. e , Similar to d but shown for Black and White women separately. f , The fraction of White (top) and Black (bottom) women whose microbiomes belonged to each CST, separated by pregnancy outcome. g , Similar to f , for the fraction of women whose metabolomes clustered to each MC. We show a significant association of sPTB with MCs A, B and D among Black women ( P = 0.047, P = 0.025 and P = 0.006, respectively, q < 0.1). Number above horizontal lines in d – g is two-sided Fisher’s exact P , q < 0.1.

Article Snippet: Metabolite super pathway assignments were provided by Metabolon. b , Distribution of metabolite prevalences across samples.

Techniques:

Journal: Nature Microbiology

Article Title: Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome

doi: 10.1038/s41564-022-01293-8

Figure Lengend Snippet: a , Distribution of CSTs within each metabolite cluster, for all (top; N = 232), White (middle; N = 51) and Black (bottom; N = 173) women. Each group of bars corresponds to a single metabolite cluster and bars within a group sum to 100%. b , Same as Fig. , stratified by race. P - two-sided Fisher’s exact p -values, q < 0.1. c, d , Same as Fig. , colored by maternal race. P - PERMANOVA. e,f , Same as Fig. , performed for all women combined. g , Same as Fig. , for association with early sPTB (gestational age at birth < 32).

Article Snippet: Metabolite super pathway assignments were provided by Metabolon. b , Distribution of metabolite prevalences across samples.

Techniques:

Journal: Nature Microbiology

Article Title: Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome

doi: 10.1038/s41564-022-01293-8

Figure Lengend Snippet: a, b , Within cluster sum of squared distances ( a ) and gap statistic ( b ) for k-medoids clustering using Canberra distances with k from 1 to 15. A shoulder ( a ) and peak ( b ) are visible for k = 6. c , Heatmap showing metabolite levels for each subject (rows) and metabolite (columns). Subjects are sorted by their assigned metabolites cluster (MC) and metabolites are clustered hierarchically using Canberra distance and Ward linkage. The color above each column reflects metabolite annotations (legend to the right). d-f , Same as Fig. , using PCA ( d ), Canberra distance-based PCoA ( e ) and t-SNE ( f ). g , Histogram of consistency of MC assignment, defined as the fraction of samples assigned to the same MC (x-axis) in 100 iterations in which we randomly selected 90% (209 women) of the cohort, and generated 6 metabolite clusters de novo. The analysis shows that many of the iterations (36 iterations, 36%) had over 95% consistency, with an overall mean consistency of 86%.

Article Snippet: Metabolite super pathway assignments were provided by Metabolon. b , Distribution of metabolite prevalences across samples.

Techniques: Generated

Journal: Nature Microbiology

Article Title: Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome

doi: 10.1038/s41564-022-01293-8

Figure Lengend Snippet: a , Heat map showing statistically significant associations (two-sided Mann–Whitney P < 0.05) between specific metabolite measurements and birth outcomes, stratified by maternal race, and coloured by significance and direction of association. Only metabolites with at least one association with FDR <0.1 are shown. Metabolites are sorted by their average signed (direction of fold change) log P value. b , Box and swarm plots (line, median; box, IQR; whiskers, 1.5× IQR) of three metabolites with significant associations with sPTB. P, two-sided Mann–Whitney U . c , Illustration summarizing some of the literature regarding the three metabolites shown in b . DEA, which is associated with sPTB, was shown to inhibit choline uptake . Choline and betaine, both associated with TB, are important for membrane lipid synthesis and osmoregulation , . d , Same as a , with stratification by GAB, performed among Black women. Middle legend applies to a and d ; NS, not significant; q < 0.1 indicated by bright colours (legend).

Article Snippet: Metabolite super pathway assignments were provided by Metabolon. b , Distribution of metabolite prevalences across samples.

Techniques: MANN-WHITNEY, Membrane

Journal: Nature Microbiology

Article Title: Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome

doi: 10.1038/s41564-022-01293-8

Figure Lengend Snippet: a , Box and swarm plots (line, median; box, IQR; whiskers, 1.5*IQR) of the levels of metabolites associated with sPTB, comparing preterm and term deliveries and stratifying by maternal self-identified race. P – two-sided Mann-Whitney U . b , Distribution (kernel density estimation) of four xenobiotics associated with sPTB or early sPTB across this cohort. Samples with no metabolite detected are excluded. c , Same as Fig. women not treated with progesterone. d , Heatmap showing metabolite sets altered in sPTB in various subsets of this cohort. Colors correspond to two-sided p -value of metabolite set enrichment analysis . Only associations with FDR < 0.1 are shown. e , Raw intensity levels measured across samples for the same four xenobiotics as in b, compared to measures from plate negative process controls. Box mid-line, median; box, IQR; whiskers, 1.5*IQR; vertical line, min:max range; dot, mean; N.D., not detected. N = 232 for Diethanolamine; N = 230 for ethyl glucoside; N = 221 for tartrate; N = 232 for EDTA. f , Mass error for spectral matching (y-axis) for the same xenobiotics, compared to the mean mass error for all non-xenobiotic, tier 1 metabolites, showing that the four xenobiotic metabolites had very good identification quality.

Article Snippet: Metabolite super pathway assignments were provided by Metabolon. b , Distribution of metabolite prevalences across samples.

Techniques: MANN-WHITNEY

Journal: Nature Microbiology

Article Title: Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome

doi: 10.1038/s41564-022-01293-8

Figure Lengend Snippet: a , Same as Fig. , but with each microbial taxa represented as an individual node. b , Volcano plot where every point represents a microbe–metabolite association. X-axis displays the difference between spearman ρ’s calculated separately among Black and White women. Y-axis displays the significance of the difference, using the two-sided Fisher’s R-to-z transform. Horizontal maroon line designates p = 0.05. Gold points indicate associations where there is a difference in sign between the correlations among Black and White women. c, d , Same as a , for associations only among Black ( c ) and White ( d ) women. e , Same as a , for metabolites associated with extremely or very PTB among Black women. f , Same as b , for difference in associations between Black women who delivered extremely or very preterm and the rest of the Black women in the cohort.

Article Snippet: Metabolite super pathway assignments were provided by Metabolon. b , Distribution of metabolite prevalences across samples.

Techniques:

Journal: Nature Microbiology

Article Title: Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome

doi: 10.1038/s41564-022-01293-8

Figure Lengend Snippet: a – c , Putrescine ( a ), histamine ( b ), and tyramine ( c ) predictions derived from microbiome metabolic models (NMPC; ; y-axis) plotted against measured metabolite levels (x-axis), showing good accuracy for all (Spearman ρ = 0.64; ρ = 0.54; and ρ = 0.62, respectively, P < 10 −10 for all). d , Model coverage (y-axis; line, median; box, IQR; whiskers, 1.5*IQR), described as the fraction of total sample abundance represented by metabolic models, for each subgroup separately. Samples from White women had higher model coverage compared to samples from Black women, despite the lower accuracy for tyramine prediction in the former group. N = 173 for Black women; N = 21 for White women with sPTB; N = 30 for White women with TB. e , Spearman ρ between metabolic model predictions (NMPCs) and metabolite measurements (y-axis) for models that only contain a maximum of N most abundant species (x-axis). As our metabolic models account for the abundance of each microbe, and as the vaginal microbiome has a skewed distribution, our models are robust to lack of representation of low-abundance microbes.

Article Snippet: Metabolite super pathway assignments were provided by Metabolon. b , Distribution of metabolite prevalences across samples.

Techniques: Derivative Assay

Journal: Nature Microbiology

Article Title: Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome

doi: 10.1038/s41564-022-01293-8

Figure Lengend Snippet: a , Receiver operating characteristic (ROC) curve comparing the performance of different sPTB prediction algorithms on metabolomics data. LightGBM (auROC = 0.81) outperforms logistic regression (auROC = 0.78, P = 0.017 for auROC comparison against LightGBM), support vector classification (auROC = 0.76, P = 2.9 × 10 −4 ) and elastic net (auROC = 0.72, P = 0.004). b , ROC curve comparing the performance of a composite model stratified for race against a model trained on all samples. A model trained on samples from all women achieves the same accuracy as a model trained only on samples from Black women when evaluated in 10-fold cross-validation on sPTB prediction for Black women (auROC of 0.83 and 0.82, respectively). However, a model trained on samples from all women significantly underperforms a model trained only on samples from women who do not identify as Black when evaluated in 10-fold cross-validation on the same subgroup (auROC of 0.64 vs. 0.80, P = 4 × 10 −7 for auROC comparison). Demonstrating that a different model is learned on each subgroup, models trained separately on each subgroup do not generalize as well to the other subgroup (auROC of 0.64 and 0.65). c, d , ROC ( c ) and precision-recall (PR; d) curves, evaluated in nested cross-validation, comparing sPTB prediction accuracy for models based on metabolomics data alone (auROC = 0.78, auPR = 0.61), and on metabolomics data combined with microbiome and clinical data (‘combination’; auROC = 0.76, auPR = 0.62; P = 0.44). e , SHAP -based effect on total prediction (x-axis) for the top 10 features used in our combination models, sorted with descending importance. Each dot represents a sample, with the color corresponding to the metabolite level in the sample compared to all samples. f, g , ROC curves for the same metabolome-based ( f ) and microbiome-based ( g ) models as in Fig. , when prediction is evaluated for extremely (<28 weeks of gestation) and very (<32 weeks) PTB. The microbiome-based models show increasing accuracy for predicting extremely and very PTB (auROC of 0.69 and 0.62, respectively, compared to auROC of 0.55 for all sPTB, P = 0.03 and P = 0.49, respectively). h, i , PR curve for sPTB prediction on two external cohorts, obtained using our metabolome-based predictor without retraining or adaptation. j , Same as ( e ) for the microbiome-based model. Shaded lines in a–d, f, g show results from five independent 10-fold cross validation draws . p -values for comparisons between ROC curves are based on the two-sided test described in ref. .

Article Snippet: Metabolite super pathway assignments were provided by Metabolon. b , Distribution of metabolite prevalences across samples.

Techniques: Comparison, Plasmid Preparation, Biomarker Discovery

Journal: Nature Microbiology

Article Title: Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome

doi: 10.1038/s41564-022-01293-8

Figure Lengend Snippet: a , b , Receiver operating characteristic (ROC, a ) and precision-recall (PR, b ) curves comparing sPTB prediction accuracy for models based on clinical (auROC = 0.59, auPR = 0.46), microbiome (auROC = 0.55, auPR = 0.41) and metabolomics (auROC = 0.78, auPR = 0.61) data (legend), evaluated in nested cross-validation . N = 232 for all. Shaded lines show results from five independent outer 10-fold cross-validation draws . c , ROC curve evaluating the performance of our metabolomics-based predictor on two external cohorts. Despite a challenging replication setting, with different inclusion criteria, measured metabolites and batch effects, our predictor obtains relatively accurate predictions without retraining (auROC = 0.66, auROC = 0.65, for the Ghartey 2017 ( N = 50) and 2015 ( N = 20) cohorts, respectively; ). d , Effect on total prediction (SHAP-based ; X axis) for the ten most predictive metabolites in our metabolome-based predictor, sorted with descending importance. Each dot represents a specific sample, with the colour corresponding to the relative level of the metabolite in the sample compared with all other samples.

Article Snippet: Metabolite super pathway assignments were provided by Metabolon. b , Distribution of metabolite prevalences across samples.

Techniques: Biomarker Discovery