MetaboLights

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and MS/MS spectral information was matched against the Human Metabolome Database (HMDB), Metlin, and an in-house metabolite database for metabolite annotation.MetaboLightsPublicGas Chromatography MS - positive - low-polarityGas Chromatography MS - negative - low-polarityExtracellular metabolites were analyzed using an ultra-high-performance liquid chromatography–tandem mass spectrometry system (UHPLC–Orbitrap Exploris 240; Thermo Fisher Scientific, USA) coupled with an HSS T3 column (100 × 2.1 mm i.d., 1.8 μm; Waters, USA). A 3 μL aliquot of each sample was injected for chromatographic separation. Mobile phase A consisted of water/acetonitrile (95:5, v/v) containing 0.1% formic acid, while mobile phase B consisted of acetonitrile/isopropanol/water (47.5:47.5:5, v/v/v) containing 0.1% formic acid. The flow rate was 0.40 mL/min and the column temperature was maintained at 40 °C. Mass spectrometric data were acquired in both positive- and negative-ion modes over an m/z range of 70–1050. The spray voltages were 3500 V and −3000 V for positive- and negative-ion modes, respectively. The sheath gas and auxiliary gas were set to 50 and 13 arbitrary units, respectively, and the ion-source temperature was 450 °C. A cyclic collision energy of 20, 40, and 60 V was applied.Microbiota-derived metabolites act as ecological effectors of colonization resistance against Salmonella Enteritidis.Zhiqiang HuangUniversity of Shanghai for Science and Technologyfecesmass spectrometry assayFollowing centrifugation at 14,000 × g for 10 min, the culture supernatants were collected and used as the extracellular metabolite samples. No additional extraction or derivatization was performed prior to analysis. The samples were prepared under the same conditions for all FMs and IMs treatment groups to ensure comparability.Mus musculushttps://www.ebi.ac.uk/metabolights/MTBLS15505Zhiqiang Huang. University of Shanghai for Science and Technology. hisak911@gmail.com.Raw LC–MS data were processed using Progenesis QI software (Waters Corporation, Milford, USA). Data preprocessing included baseline filtering, peak detection and integration, retention-time correction, and peak alignment to generate a data matrix containing retention time, mass-to-charge ratio (m/z), and peak intensity.Treatmenthisak911@gmail.comS. Enteritidis was cultured in Nutrient Broth (NB) at 37 °C with shaking at 180 rpm for 24 h under the corresponding FMs and IMs treatment conditions. After incubation, the cultures were centrifuged at 14,000 × g for 10 min, and the resulting supernatants were collected for extracellular metabolite analysis.MetabolomicsSalmonella infectionMetabolomicsMus musculusuntargeted analysisAgilent 5973 MSDGastrointestinal MicrobiomeAgilent 5975T LTM GCexperimental blankfecesexperimental sampleuntargeted metabolite profilingSalmonella infectionMetabolomicsMus musculusuntargeted analysisAgilent 5973 MSDGastrointestinal MicrobiomeAgilent 5975T LTM GCexperimental blankfecesexperimental sampleuntargeted metabolite profilingData were acquired in both positive- and negative-ion modes, with an m/z range of 70–1050. The electrospray ionization source was operated at a spray voltage of 3500 V in positive-ion mode and −3000 V in negative-ion mode. The sheath gas and auxiliary gas were set to 50 and 13 arbitrary units, respectively, and the ion-source temperature was maintained at 450 °C. A cyclic collision energy of 20, 40, and 60 V was applied for MS/MS acquisition. The chromatographic flow rate was 0.40 mL/min, with the column maintained at 40 °C. A 3 μL injection volume was used.falseMicrobiota-derived metabolites act as ecological effectors of colonization resistance against Salmonella EnteritidisColonization resistance is a fundamental ecological function of the gut microbiota that limits the expansion of enteric pathogens. Yet, the microbial effectors linking ecosystem homeostasis to pathogen control remain poorly understood. Here, we demonstrate that Salmonella Enteritidis (S. Enteritidis) reshapes the intestinal ecosystem by reprogramming microbial functions and metabolic profiles. Fecal microbiota transplantation (FMT) markedly alleviated disease, restored the functional capacity of the microbiome, and attenuated host inflammatory responses despite incomplete reconstruction of donor microbial composition. Microbiota-derived metabolites emerged as key functional mediators of these protective effects. These metabolites inhibited Salmonella biofilm formation and epithelial invasion by remodeling bacterial metabolic programs. Furthermore, metabolites produced by two commensal isolates, GK-6 and GK-11, recapitulated the protective effects of the complete fecal metabolite pool and exhibited conserved metabolic signatures associated with biofilm suppression. These findings establish microbiota-derived metabolites as ecological effectors of colonization resistance and provide a framework for developing defined metabolite-based therapeutics as an alternative to FMT. arrow_drop_uphide2026-08-312026-08-31MTBLS15505