Data analysis was performed using MassLynx and TargetLynx software (v.4.2, Waters). Metabolites were identified using characteristic chromatographic retention time and mass spectrometric multiple-reaction monitoring (MRM) parameters, as described. Amino acid concentrations were log10-transformed and normalized to the median concentration of each metabolite. Missing values were imputed using median concentrations. PCA, clustering, heatmaps, and correlation maps were generated using MetaboAnalyst 6.063. Means and standard deviations were calculated, and the Mann-Whitney U test was applied to compare control and treatment groups. P-values were adjusted using the Benjamini-Hochberg method. Plots were generated using matplotlib.
"],"repository":["MetaboLights"],"study_status":["Public"],"ptm_modification":[""],"instrument_platform":["Liquid Chromatography MS - positive - reverse phase"],"chromatography_protocol":["The LC system comprised an Acquity I-Class UPLC (Waters) with chromatographic separation on a UPLC HSS T3 column (100 x 2.1 mm i.d.) and a 1 μL injection volume, as previously described.
"],"publication":["Metabolostasis failure thresholds are linked with network topology, metabolite solubility, and translational control."],"submitter_affiliation":["Weizmann Institute"],"submitter_name":["Alexander Brandis"],"organism_part":["Whole Organism"],"technology_type":["mass spectrometry assay"],"disease":[""],"extraction_protocol":["Metabolites were extracted by heating at 80 °C for 3 min, followed by cooling on ice for 5 min. After centrifugation (20,000 x g, 1 min, 4 °C), 0.9 mL of the supernatant was collected, centrifuged again for 10 min, and 0.8 mL of the cleared extract was stored at -80 °C until analysis.
Each sample (10 μL) was mixed with 10 μL of internal standard (Stable Isotope Labeled Amino Acid Mixture, Sigma-Aldrich), agitated for 15 min, derivatized using the AccQTag method, and filtered through 0.2 μm PES nanoFilter vials (Thomson).
"],"organism":["Saccharomyces cerevisiae"],"full_dataset_link":["https://www.ebi.ac.uk/metabolights/MTBLS12080"],"author":["Christine Lim. Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK.","Ehud Gazit. Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel. ehudga@tauex.tau.ac.il.","Alexander Brandis. alexander.brandis@weizmann.ac.il.","Dana Laor Bar-Yosef. Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel. danalaor@tauex.tau.ac.il.","Shon Levkovich. Tel Aviv University. shonl@tauex.tau.ac.il.","Metsada Pasmanik-Chor.","Emi Marzini. Tel Aviv University.","Ilana Sogolovsky-Bard.","Yasmin DeRowe.","Hanaa Adsi. Tel Aviv University.","Michele Vendruscolo. Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK. mv245@cam.ac.uk.","Myra Gartner.","Maoz Lahav.","Keila Kaplan."],"data_transformation_protocol":["Quantification was done using a standard curve (0.01-10.0 μM). Data analysis was performed using MassLynx and TargetLynx software (v.4.2, Waters). Analyses were conducted at the Weizmann Institute’s Targeted Metabolomics Unit. Results represent the average of at least four biological replicates.
"],"study_factor":["Metabolic stress","Biological replicate"],"submitter_email":["alexander.brandis@weizmann.ac.il"],"sample_collection_protocol":["Yeast cultures (5 mL) were grown to mid-log phase (OD600 ≈ 0.6) and 4 mL was quenched with 16 mL ice-cold buffer 1 (60% methanol, 10 mM tricine, pH 7.4, -20 °C). After a 5-min ice incubation, cells were pelleted (1000 x g, 3 min, 4 °C), resuspended in 1 mL cold buffer 1, and pelleted again. The supernatant was removed, and cells were resuspended in 1 mL cold buffer 2 (75% ethanol, 0.5 mM tricine, pH 7.4).
"],"omics_type":["Metabolomics"],"study_design":["ultra-performance liquid chromatography-mass spectrometry","tandem mass spectrometry","Waters Xevo TQ-S","untargeted analysis","Waters ACQUITY UPLC I-Class System","amino acid analysis","Saccharomyces cerevisiae","targeted metabolites","Whole Organism","experimental sample"],"curator_keywords":["ultra-performance liquid chromatography-mass spectrometry","tandem mass spectrometry","Waters Xevo TQ-S","untargeted analysis","Waters ACQUITY UPLC I-Class System","amino acid analysis","Saccharomyces cerevisiae","targeted metabolites","Whole Organism","experimental sample"],"mass_spectrometry_protocol":["A Xevo TQ-S triple quadrupole mass spectrometer (Waters) with electrospray ionization mode in a tandem MS regime (MRM) was used as described previously.
"],"additional_accession":[]},"is_claimable":false,"name":"Metabolostasis failure thresholds are linked with network topology, metabolite solubility, and translational control","description":"Cells maintain metabolite homeostasis (metabolostasis) by buffering fluctuations in metabolite levels, yet the limits of this buffering and the mechanisms underlying metabolic toxicity remain poorly understood. To study this, we systematically overfed metabolites in Saccharomyces cerevisiae and quantified associations with growth inhibition, intracellular aggregation, and multiomic perturbations. We identify metabolite-specific failure thresholds at which amyloid-like aggregates are observed, with graded growth inhibition detectable at sub-threshold concentrations, suggesting toxicity mechanisms beyond transporter saturation. Metabolites with higher network influence and broader pathway participation are associated with higher failure thresholds and smaller pathway disturbances. These patterns are associated with chemical properties and solubility: more soluble metabolites, while broadly tolerated, are associated with localised aggregates at their failure thresholds, whereas less soluble metabolites are associated with larger systemic pathway disruptions. Multiomic integration identifies a two-tiered translational regulatory architecture characterising cellular resilience to metabolic overfeeding. General resilience is associated with transcriptional commitment to resource conservation via attenuation of anabolic pathways. Metabolite-specific defense is characterised by high-magnitude translational regulatory events; for example, engagement of aromatic catabolism under phenylalanine overfeeding and energetic control pathways under glycine overfeeding. Together, our results operationally define metabolostasis as a cellular system associated with constraint of metabolite concentrations, coordination of network and pathway-level regulation, and buffering against amyloid-like aggregation, highlighting how network topology, pathway architecture, and chemical properties are associated with metabolic resilience and toxicity thresholds.
","dates":{"publication":"2026-09-29","submission":"2025-02-17"},"accession":"MTBLS12080","cross_references":{}}