metabolite identification  (Chenomx Inc)

Journal: bioRxiv

Article Title: Probiotic phage steering of disease suppressive rhizosphere microbiome

doi: 10.64898/2026.01.02.697320

Figure Lengend Snippet: a , Venn diagrams displaying the overlap between ASVs connected to S. maltophilia in ‘SM+Phage’ network (Category I) and those enriched by S. maltophilia phage during pathogen invasion (Category II). b , Schematic diagram showing the effect of S. maltophilia -phage supernatants on the suppressiveness of E. ludwigii (dark red) and P. baetica (indigo) to R. solanacearum . Numbers indicate the inhibition activity based on the reduction of R. solanacearum biomass. See Fig.S9 for details. c , Effects of S. maltophilia -phage co-culture supernatant on R. solanacearum inhibition activity across different taxa. The phylogenetic neighbor-joining tree in the center shows the taxonomic relationships among 88 resident bacterial species classified belonging to four different phyla. The heatmap in the middle ring shows the inhibition activity of each bacterium to R. solanacearum treated with water (Bacteria) and S. maltophilia supernatant previously cultured in the absence (Bacteria SM ) and presence (Bacteria SM+Phage ) of phage (mean, n = 5). Eight bacteria are highlighted with triangles for their identity with ASVs whose relative abundance were increased in the presence of S. maltophilia phage during greenhouse experiment (Category II). The increase in strains’ inhibition activity to R. solanacearum in the presence of S. maltophilia -phage supernatant is shown as bars in the outer ring (mean, n = 5). The panel b was created with BioRender.com .

Article Snippet: S. maltophilia and E. ludwigii metabolite identification was performed using a combination of Human Metabolome (HMDB v5.0), MassBank of North America (MONA, https://massbank.us/ ), and Metlin ( https://metlin.scripps.edu/ ) databases with R package MetaboAnalystR .

Techniques: Inhibition, Activity Assay, Co-Culture Assay, Bacteria, Cell Culture

Journal: RMD Open

Article Title: Sjögren’s disease metabolome reveals biomarker signatures to characterise patients and assess disease activity

doi: 10.1136/rmdopen-2025-006312

Figure Lengend Snippet: Metabolite-based classification model based on UK cohort. ( a ) Metabolites selected by lasso-penalised generalised linear model that best discriminate healthy donors and patients with Sjögren’s disease (SjD) based on plasma levels (UK cohort as training set, healthy=24 and SjD=19). Bar plot showing the weights (coefficients of the model) that allows to separate healthy donors from patients with SjD (mild from severe disease). Blue bars show metabolites decreased in SjD, while red bars show increased metabolites. *Indicates non-metabolite discriminative variable. ( b ) Principal component analysis based on metabolites selected by our classification model showing prediction results when applying the metabolites-based classification model built on UK cohort. Points represent individuals in the Poland (PL) cohort (healthy=31 and SjD=34) and colours represent the true groups and prediction results. Healthy donors are all predicted as healthy (in green) except for one donor (in grey). 14 out of 34 patients with SjD are predicted as patients with SjD (in dark orange), while 20 out of 34 are predicted as healthy donors (in light orange). The latest group corresponds to a subgroup of patients with SjD with intermediate (or mild) disease severity that are located between the healthy subject’s cluster (in green) and patients with SjD with severe disease (in dark orange). The confusion matrix shows the true groups in the rows and the model’s predictions in the columns. The diagonal shows true negatives (TN, ie, healthy subjects) in green and true positives (TP, ie, SjD) in dark orange, while false negatives (FN) are shown in light orange and false positives (FP) in grey. GPA, glycerophosphoric acid; GPC, glycerophosphocholine.

Article Snippet: Proprietary software of Metabolon was used to match ions to a library of standards for metabolite identification and for metabolite quantitation by peak area integration.

Techniques: Clinical Proteomics

Journal: RMD Open

Article Title: Sjögren’s disease metabolome reveals biomarker signatures to characterise patients and assess disease activity

doi: 10.1136/rmdopen-2025-006312

Figure Lengend Snippet: Distribution of metabolites’ intensities, clinical scores across study groups in the Poland (PL) and the UK cohorts. ( a ) Box plots of metabolites’ intensities from the metabolites-based classification model show the distribution across healthy donors (n=31), patients with mild Sjögren’s disease (SjD) (n=20) and patients with severe SjD (n=14). *Indicates the only non-metabolite measurement. Differences between groups were tested via univariate linear regression models using metabolites as response variable and groups as independent variables. P value is not adjusted for multiple testing. ( b ) Box plots of clinical scores distribution across healthy donors (n=31), patients with mild SjD (n=20) and patients with severe SjD (n=14). Differences between groups were tested via univariate linear regression models using clinical scores as response variable and groups as independent variables. P value is not adjusted for multiple testing. ( c ) Box plots of selected metabolites from the metabolites-based classification model distribution in visit 1 ( V1 ) and visit 2 ( V2 ) in healthy donors (V1=12, V2=12) and patients with SjD (V1=9, V2=10). ( d ) Box plots of clinical scores distribution between the PL (mild SjD=20 patients and severe SjD=14 patients) and the UK cohorts (V1=9). ESSDAI, EULAR Sjögren’s Disease Activity Index.

Article Snippet: Proprietary software of Metabolon was used to match ions to a library of standards for metabolite identification and for metabolite quantitation by peak area integration.

Techniques: Activity Assay