Serum proteomics reveals disorder of lipoprotein metabolism in sepsis

Liang, Xi; Wu, Tianzhou; Chen, Qi; Jiang, Jing; Jiang, Yongpo; Ruan, Yiwen; Zhang, Huaping; Zhang, Sheng; Chao, Zhang; Chen, Peng; Lv, Yuhang; Xin, Jiaojiao; Shi, Dongyan; Chen, Xin; Jun, Li; Xu, Yinghe

doi:10.26508/lsa.202101091

Cited by 8 publications

(5 citation statements)

References 55 publications

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“…And these features might serve as biomarker candidates for diagnosis and therapeutic targets. Sixteen proteins, including FIBA, SAA2, A2GL, AACT, CRP, LBP, SAA1, and S10A9, were identified as potential biomarkers in sepsis through integrative multiomics analysis, which is consistent with prior evidence [ 13 ]. These results illustrated that the multiomics analysis results had high reliability.…”

Section: Discussionsupporting

confidence: 84%

“…Proteomics analysis has been described previously [ 13 ] and used the same biological samples as metabolomics. All proteomic data from the patients with sepsis and NCs analysed in this study were obtained from the ProteomeXchange Consortium ( http://proteomecentral.proteomexchange.org ) via the iProX partner repository with the dataset identifier IPX0003225000.…”

Section: Methodsmentioning

confidence: 99%

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Integrative analysis of metabolomics and proteomics reveals amino acid metabolism disorder in sepsis

Chen

Liang

et al. 2022

J Transl Med

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Background Sepsis is defined as a systemic inflammatory response to microbial infections with multiple organ dysfunction. This study analysed untargeted metabolomics combined with proteomics of serum from patients with sepsis to reveal the underlying pathological mechanisms involved in sepsis. Methods A total of 63 patients with sepsis and 43 normal controls were enrolled from a prospective multicentre cohort. The biological functions of the metabolome were assessed by coexpression network analysis. A molecular network based on metabolomics and proteomics data was constructed to investigate the key molecules. Results Untargeted metabolomics analysis revealed widespread dysregulation of amino acid metabolism, which regulates inflammation and immunity, in patients with sepsis. Seventy-three differentially expressed metabolites (|log2 fold change| > 1.5, adjusted P value < 0.05 and variable importance in the projection (VIP) > 1.5) that could predict sepsis were identified. External validation of the hub metabolites was consistent with the derivation results (area under the receiver operating characteristic curve (AUROC): 0.81–0.96/0.62–1.00). The pentose phosphate pathway was found to be related to sepsis-associated encephalopathy. Phenylalanine metabolism was associated with sepsis-associated acute kidney injury. The key molecular alterations of the multiomics network in sepsis compared to normal controls implicate acute inflammatory response, platelet degranulation, myeloid cell activation involved in immune response and phenylalanine, tyrosine and tryptophan biosynthesis, and arginine biosynthesis. Conclusions Integrated analysis of untargeted metabolomics and proteomics revealed characteristic metabolite and protein alterations in sepsis, which were mainly involved in inflammation-related pathways and amino acid metabolism. This study depicted the pathological characteristics and pathways involved in sepsis and potential therapeutic targets.

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Section: Discussionsupporting

confidence: 84%

Section: Methodsmentioning

confidence: 99%

Integrative analysis of metabolomics and proteomics reveals amino acid metabolism disorder in sepsis

Chen

Liang

et al. 2022

J Transl Med

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“…32 A growing number of studies have concluded that disturbances in substance and energy metabolism are highly relevant to the progression of sepsis. [33][34][35] And cuproptosis is closely linked to the TCA cycle. 36 Therefore, it is reasonable to assume that cuproptosis may play a vital role in the disease progression of sepsis, multi-organ damage and other processes.…”

Section: Discussionmentioning

confidence: 99%

Cuproptosis-Related Biomarkers and Characterization of Immune Infiltration in Sepsis

Wang,

Qiu,

Liu

et al. 2024

JIR

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Introduction:Sepsis is a worldwide epidemic, with high morbidity and mortality. Cuproptosis is a form of cell death that is associated with a wide range of diseases. This study aimed to explore genes associated with cuproptosis in sepsis, construct predictive models and screen for potential targets. Methods: The LASSO algorithm and SVM-RFE model has been analysed the expression of cuproptosis-related genes in sepsis and immune infiltration characteristics and identified the marker genes under a diagnostic model. Gene-drug networks, mRNA-miRNA networks and PPI networks were constructed to screen for potential biological targets. The expression of marker genes was validated based on the GSE57065 dataset. Consensus clustering method was used to classify sepsis samples. Results: We found 381 genes associated with the development of sepsis and discovered significantly differentially expressed cuproptosis-related genes of 16 cell types in sepsis and immune infiltration with CD8/CD4 T cells being lower. NFE2L2, NLRP3, SLC31A1, DLD, DLAT, PDHB, MTF1, CDKN2A and DLST were identified as marker genes by the LASSO algorithm and the SVM-RFE model. AUC > 0.9 was constructed for PDHB and MTF1 alone respectively. The validation group data for PDHB (P=0.00099) and MTF1 (P=7.2e-14) were statistically significant. Consistent clustering analysis confirmed two subtypes. The C1 subtype may be more relevant to cellular metabolism and the C2 subtype has some relevance to immune molecules.The results of animal experiments showed that the gene expression was consistent with the bioinformatics analysis. Discussion: Our study systematically explored the relationship between sepsis and cuproptosis and constructed a diagnostic model. And, several cuproptosis-related genes may interfere with the progression of sepsis through immune cell infiltration.

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“…showed that TG/cholesterol homeostasis was associated with sepsis-related acute kidney injury [33]. However, many other studies, which involved simple comparisons between survivors and nonsurvivors, reported similar TG levels in deceased patients and survivors [13][14][15][16].…”

Section: Plos Onementioning

confidence: 99%

U-shaped association between serum triglyceride levels and mortality among septic patients: An analysis based on the MIMIC-IV database

Xiao,

Deng,

Mao

et al. 2023

PLoS ONE

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Background Sepsis is characterized by upregulated lipolysis in adipose tissue and a high blood triglyceride (TG) level. It is still debated whether serum TG level is related to mortality in septic patients. The aim of this study is to investigate the association between serum TG level and mortality in septic patients admitted to the intensive care unit (ICU). Methods Data from adult septic patients (≥18 years) admitted to the ICU for the first time were obtained from the Multiparameter Intelligent Monitoring in Intensive Care IV (MIMIC-IV) database. The patients’ serum TG levels that were measured within the first week after ICU admission were extracted for statistical analysis. The endpoints were 28-day, ICU and in-hospital mortality. Results A total of 2,782 septic patients were included. Univariate analysis indicated that the relationship between serum TG levels and the risk of mortality was significantly nonlinear. Both the Lowess smoothing technique and restricted cubic spline analyses revealed a U-shaped association between serum TG levels and mortality among septic patients. The lowest mortality rate was associated with a serum TG level of 300–500 mg/dL. Using 300∼500 mg/dL as the reference range, we found that both hypo-TG (<300 mg/dL) and hyper-TG (≥500 mg/dL) were associated with increased mortality. The result was further adjusted by Cox regression with and without the inclusion of some differential covariates. Conclusions There was a U-shaped association between serum TG and mortality in septic ICU patients. The optimal concentration of serum TG levels in septic ICU patients is 300–500 mg/dL.

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Serum proteomics reveals disorder of lipoprotein metabolism in sepsis

Cited by 8 publications

References 55 publications

Integrative analysis of metabolomics and proteomics reveals amino acid metabolism disorder in sepsis

Integrative analysis of metabolomics and proteomics reveals amino acid metabolism disorder in sepsis

Cuproptosis-Related Biomarkers and Characterization of Immune Infiltration in Sepsis

U-shaped association between serum triglyceride levels and mortality among septic patients: An analysis based on the MIMIC-IV database

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