Dataset Information

A Novel Yeast Extract from a Multi-Stress-Resistant Saccharomyces cerevisiae Confers Potent Antioxidant and UV-Protective Activities to Skin Cells

ABSTRACT:

Strain A154 was obtained through the method of combined mutagenesis and screening with UVC and UVA. This strain has multiple tolerances.

INSTRUMENT(S): Liquid Chromatography MS - negative - reverse-phase, Liquid Chromatography MS - positive - reverse-phase

PROVIDER: MTBLS13804 | MetaboLights | 2026-01-31

REPOSITORIES: MetaboLights

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			Action	DRS
	a_MTBLS13804_LC-MS_negative_reverse-phase_metabolite_profiling.txt	Txt
	a_MTBLS13804_LC-MS_positive_reverse-phase_metabolite_profiling.txt	Txt
	i_Investigation.txt	Txt
	m_MTBLS13804_LC-MS_negative_reverse-phase_metabolite_profiling_v2_maf.tsv	Tabular
	m_MTBLS13804_LC-MS_positive_reverse-phase_metabolite_profiling_v2_maf.tsv	Tabular

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Project description:Objectives: This study aimed to investigate plasma metabolomic and lipidomic profiles in rheumatoid arthritis (RA) to identify potential biomarkers for distinguishing treatment responses.Methods: Plasma samples were collected from 106 RA patients and 10 healthy controls, with 30 RA samples selected based on predefined inclusion criteria. Using liquid chromatography–mass spectrometry (LC-MS)-based untargeted metabolomics and lipidomics analysis, a total of 2,279 metabolites and 2,987 lipids were systematically identified.Results: Through rigorous statistical evaluation (variable importance in the projection (VIP) > 1 and false discovery rate (FDR) < 0.05), 22 metabolites and lipids were found to be positively associated with RA risk based on logistic regression analysis. These were further refined into 12 core features using least absolute shrinkage and selection operator (LASSO) regression. The 12 core features identified in the treatment non-response group included lipids Cer(d18:1/16:0), PS(16:0/20:0), and Palmitic acid; metabolites Mimosine and D-Xylose; drug-related metabolites Dihydralazine, Tridihexethyl, Acyclovir monophosphate, Indoline, and Melleolide; and pollution-related metabolites Norcotinine and 2-Chloro-1-(chloromethyl)ethyl carbamate. Machine learning models utilizing these features demonstrated robust discriminatory performance, achieving area under the curve (AUC) values exceeding 0.90, validating the efficacy of these biomarkers in differentiating treatment-responsive from non-responsive RA patients. Additionally, the identified features showed significant correlations with high disease activity, enabling differentiation across various stages of disease progression. Conclusion: The findings reveal significant metabolic and lipidomic alterations in RA and highlight the identified biomarkers as effective indicators of treatment responses and potential therapeutic targets.

Project description:Objectives: This study aimed to investigate plasma metabolomic and lipidomic profiles in rheumatoid arthritis (RA) to identify potential biomarkers for distinguishing treatment responses.Methods: Plasma samples were collected from 106 RA patients and 10 healthy controls, with 30 RA samples selected based on predefined inclusion criteria. Using liquid chromatography–mass spectrometry (LC-MS)-based untargeted metabolomics and lipidomics analysis, a total of 2,279 metabolites and 2,987 lipids were systematically identified.Results: Through rigorous statistical evaluation (variable importance in the projection (VIP) > 1 and false discovery rate (FDR) < 0.05), 22 metabolites and lipids were found to be positively associated with RA risk based on logistic regression analysis. These were further refined into 12 core features using least absolute shrinkage and selection operator (LASSO) regression. The 12 core features identified in the treatment non-response group included lipids Cer(d18:1/16:0), PS(16:0/20:0), and Palmitic acid; metabolites Mimosine and D-Xylose; drug-related metabolites Dihydralazine, Tridihexethyl, Acyclovir monophosphate, Indoline, and Melleolide; and pollution-related metabolites Norcotinine and 2-Chloro-1-(chloromethyl)ethyl carbamate. Machine learning models utilizing these features demonstrated robust discriminatory performance, achieving area under the curve (AUC) values exceeding 0.90, validating the efficacy of these biomarkers in differentiating treatment-responsive from non-responsive RA patients. Additionally, the identified features showed significant correlations with high disease activity, enabling differentiation across various stages of disease progression. Conclusion: The findings reveal significant metabolic and lipidomic alterations in RA and highlight the identified biomarkers as effective indicators of treatment responses and potential therapeutic targets.

Dataset Information

A Novel Yeast Extract from a Multi-Stress-Resistant Saccharomyces cerevisiae Confers Potent Antioxidant and UV-Protective Activities to Skin Cells

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