Dataset Information

UPLC-Q-TOF/MS-Based Plasma and Urine Metabolomics Contribute to the Diagnosis of Sepsis

ABSTRACT: In this study, we aimed to identify potential metabolic biomarkers that can improve the diagnostic accuracy of sepsis. Sixty-six patients including 30 septic and 36 nonsepsis patients from an intensive care unit were recruited. The global plasma and urine metabolomic profiles were determined by ultraperformance liquid chromatography coupled with a quadrupole time-of-flight mass spectrometry-based methodology. The risk factors, including both traditional physiological indicators and metabolic biomarkers, were investigated by binary logistic regression analysis and used to build a least absolute shrinkage and selection operator (Lasso) regression model to evaluate the ability of diagnosis. Fifty-five metabolites in plasma and 11 metabolites in urine were identified through orthogonal projections to latent structures discriminant analysis (OPLS-DA). Among them, ten (PE (20:4(5Z, 8Z, 11Z, 14Z)/P-18:0), harderoporphyrinogen, chloropanaxydiol, (Z)-2-octenal, N¹,N⁸-diacetylspermidine, 1-nitroheptane, venoterpine, α-CEHC, LysoPE (20:0/0:0), corticrocin) metabolites were identified as risk factors. The Lasso regression model incorporating these ten metabolic biomarkers and five traditional physiological indicators displayed better differentiation than the traditional model, represented by the elevated area under receiver operating characteristic curve (AUROC) from 96.80 to 100.0%. Furthermore, patients with septic shock presented a significantly lower level of PE-Cer (d16:1(4E)/19:0). This study suggests that metabolomic profiling could be an effective tool for sepsis diagnosis.

INSTRUMENT(S): Liquid Chromatography MS – NEG, Liquid Chromatography MS – POS

SUBMITTER: liu kun

PROVIDER: MTBLS3505 | MetaboLights | 2023-04-18

REPOSITORIES: MetaboLights

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Dataset's files

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			Action	DRS
	MTBLS3505	Other
	FILES	Other
	a_MTBLS3505_plasma_lip_neg_metabolite_profiling_mass_spectrometry.txt	Txt
	a_MTBLS3505_plasma_lip_pos_metabolite_profiling_mass_spectrometry.txt	Txt
	a_MTBLS3505_plasma_met_neg_metabolite_profiling_mass_spectrometry.txt	Txt

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UPLC-Q-TOF/MS-Based Plasma and Urine Metabolomics Contribute to the Diagnosis of Sepsis.

Guan Su S Liu Kun K Liu Zimeng Z Zhou Liping L Jia Bingjie B Wang Zichen Z Nie Yao Y Zhang Xuyu X

Journal of proteome research 20211223 1

In this study, we aimed to identify potential metabolic biomarkers that can improve the diagnostic accuracy of sepsis. Sixty-six patients including 30 septic and 36 nonsepsis patients from an intensive care unit were recruited. The global plasma and urine metabolomic profiles were determined by ultraperformance liquid chromatography coupled with a quadrupole time-of-flight mass spectrometry-based methodology. The risk factors, including both traditional physiological indicators and metabolic bio ...[more]

PMID: 34941272

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Project description:Background: Sepsis-associated encephalopathy (SAE) is a common and severe complication of sepsis. While several studies have reported the proteomic alteration in plasma, urine, heart, etc. of sepsis, few research focused on the brain tissue. This study aims at discovering the differentially abundant proteins in the brains of septic rats to identify biomarkers of SAE. Methods: The Prague-Dawley rats were randomly divided into sepsis (n = 6) or sham (n = 6) groups, and then the whole brain tissue was dissected at 24 h after surgery for further protein identification by TMT-LC–MS/MS-based proteomics. Ingenuity pathway analysis, Gene ontology knowledgebase, and STRING database are used to explore the biological significance of proteins with altered concentration. Results: Among the total of 3163 proteins identified in the brain tissue, 57 were increased while 38 were decreased in the sepsis group compared to the sham group. Bioinformatic analyses suggest that the differentially abundant proteins are highly related to cellular microtubule metabolism, energy production, nucleic acid metabolism, neurological disease, etc. Additionally, acute phase response signaling was possibly activated and PI3K/AKT signaling was suppressed during sepsis. An interaction network established by IPA revealed that Akt1, Gc-globulin, and ApoA1 were the core proteins. The increase of Gc-globulin and the decrease of Akt1 were confirmed by Western blot. Conclusions: Based on the multifunction of these proteins in several brain diseases, we first propose that Gc-globulin, ApoA1, PI3K/AKT pathway, and acute phase response proteins (hemopexin and cluster of alpha-2-macroglobulin) can potentially be diagnosis biomarkers, therapeutic targets, and prognosis indicators of SAE. These results may provide new insights into the pathologic mechanism of SAE, yet further research is required to explore the functional implications and clinical applications of the differentially abundant proteins in the brains of sepsis group.