Dataset Information

Lipid metabolism impairment in patients with sepsis secondary to hospital acquired pneumonia, a proteomic analysis.

ABSTRACT:

Background

Sepsis is a dysregulated host response to infection and a major cause of death worldwide. Respiratory tract infections account for most sepsis cases and depending on the place of acquisition, i.e., community or hospital acquired infection, differ in etiology, antimicrobial resistance and outcomes. Accordingly, the host response may be different in septic patients secondary to community-acquired pneumonia and hospital acquired pneumonia (HAP). Proteomic analysis is a useful approach to evaluate broad alterations in biological pathways that take place during sepsis. Here we evaluated plasma proteome changes in sepsis secondary to HAP.

Methods

Plasma samples were obtained from patients (n?=?27) at admission and after 7 days of follow-up, and were analyzed according to the patients' outcomes. The patients' proteome profiles were compared with healthy volunteers (n?=?23). Pooled plasma samples were labeled with isobaric tag for relative and absolute quantitationand analyzed by LC-MS/MS. We used bioinformatics tools to find altered functions and pathways. Results were validated using biochemical estimations and ELISA tests.

Results

We identified 159 altered proteins in septic patients; most of them were common when comparing patients' outcomes, both at admission and after 7 days. The top altered biological processes were acute inflammatory response, response to wounding, blood coagulation and homeostasis. Lipid metabolism emerged as the main altered function in patients, with HDL as a central node in the network analysis, interacting with downregulated proteins, such as APOA4, APOB, APOC1, APOL1, SAA4 and PON1. Validation tests showed reduced plasma levels of total cholesterol, HDL-C, LDL-C, non-HDL cholesterol, apolipoproteins ApoA1 and ApoB100, and Paraoxonase 1 in HAP patients.

Conclusion

Proteomic analysis pointed to impairment of lipid metabolism as a major change in septic patients secondary to HAP, which was further validated by the reduced levels of cholesterol moieties and apolipoproteins in plasma. Our results stress the involvement of lipids in the pathogenesis of sepsis, which is in accordance with previous reports supporting the role of lipid moieties in pathogen toxin clearance and in modulating inflammatory responses.

SUBMITTER: Sharma NK

PROVIDER: S-EPMC6631513 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Lipid metabolism impairment in patients with sepsis secondary to hospital acquired pneumonia, a proteomic analysis.

Sharma Narendra Kumar NK Ferreira Bianca Lima BL Tashima Alexandre Keiji AK Brunialti Milena Karina Colo MKC Torquato Ricardo Jose Soares RJS Bafi Antonio A Assuncao Murillo M Azevedo Luciano Cesar Pontes LCP Salomao Reinaldo R

Clinical proteomics 20190716

<h4>Background</h4>Sepsis is a dysregulated host response to infection and a major cause of death worldwide. Respiratory tract infections account for most sepsis cases and depending on the place of acquisition, i.e., community or hospital acquired infection, differ in etiology, antimicrobial resistance and outcomes. Accordingly, the host response may be different in septic patients secondary to community-acquired pneumonia and hospital acquired pneumonia (HAP). Proteomic analysis is a useful app ...[more]

PMID: 31341447

Similar Datasets

Project description:Mechanisms governing the inflammatory response during sepsis have been shown to be complex, involving cross-talk between diverse signaling pathways. Current knowledge regarding the mechanisms underlying sepsis provides an incomplete picture of the syndrome, justifying additional efforts to understand this condition. Microarray-based expression profiling is a powerful approach for the investigation of complex clinical conditions such as sepsis. In this study, we investigate whole-genome expression profiles in mononuclear cells from survivors (n = 5) and non-survivors (n = 5) of sepsis. To circumvent the heterogeneity of septic patients, only patients admitted with sepsis caused by community-acquired pneumonia were included. Blood samples were collected at the time of sepsis diagnosis and seven days later to evaluate the role of biological processes or genes possibly involved in patient recovery. Principal Components Analysis (PCA) profiling discriminated between patients with early sepsis and healthy individuals. Genes with differential expression were grouped according to Gene Ontology, and most genes related to immune defense were up-regulated in septic patients. Additionally, PCA in the early stage was able to distinguish survivors from non-survivors. Differences in oxidative phosphorylation seem to be associated with clinical outcome because significant differences in the expression profile of genes related to mitochondrial electron transport chain (ETC) I-V were observed between survivors and non-survivors at the time of patient enrollment. Global gene expression profiles after seven days of sepsis progression seem to reproduce, to a certain extent, patterns collected at the time of diagnosis. Gene expression profiles comparing admission and follow-up samples differed between survivors and non-survivors, with decreased expression of genes related to immune functions in non-survivors. In conclusion, genes related to host defense and inflammatory response ontology were up-regulated during sepsis, consistent with the need for a host response to infection, and the sustainability of their expression in follow-up samples was associated with outcomes.

Project description:IntroductionCommunity-acquired pneumonia (CAP) and hospital-acquired pneumonia (HAP) are common complications in idiopathic inflammatory myopathy (IIM) patients, and are frequently associated with unfavorable outcome as well as prolonged antibiotic therapy. In this study, we intended to clarify whether clinical pulmonary infection score (CPIS) and multiple serum biomarkers are valuable in predicting unfavorable outcomes and prolonged antibiotic therapy in adult IIM patients complicated with CAP or HAP.MethodsData of IIM patients with CAP or HAP who were admitted to three tertiary centers from December 2010 to November 2019 were retrospectively collected. Cox proportional hazards regression analysis and logistic regression analysis were adopted to identify risk factors for unfavorable outcomes and prolonged antibiotic therapy in these patients. The predictive values of potential predictors were assessed using receiver operating characteristic analysis.ResultsThe mortality rate was 60.6% in 109 IIM patients complicated with CAP or HAP. Myositis Disease Activity Assessment Visual Analogue Scales (MYOACT) score, CPIS and timely adjustment to antibiotics based on drug susceptibility test (DST-based antibiotic) were significantly associated with long-term outcome in these patients. With an optimal cutoff value of 6.5 and area under the curve (AUC) of 0.813, CPIS was a more satisfying predictor compared with MYOACT score. The peak C-reactive protein (CRP) level, DST-based antibiotics, and complication of interstitial lung disease (ILD) were also significantly correlated with prolonged antibiotic therapy.ConclusionsIIM patients complicated with CAP or HAP frequently suffer from unfavorable outcomes. Compared with IIM disease activity, CPIS worked as a better predictor of outcome in these patients. Also, the peak CRP level during hospitalization might be valuable in predicting prolonged antibiotic therapy. The existence of ILD might impede early discontinuation of antibiotics. Timely adjustment to antibiotics based on drug susceptibility testing would decrease the mortality rate and reduce the incidence of prolonged antibiotic therapy.

Dataset Information

Lipid metabolism impairment in patients with sepsis secondary to hospital acquired pneumonia, a proteomic analysis.

Background

Methods

Results

Conclusion

Publications

Lipid metabolism impairment in patients with sepsis secondary to hospital acquired pneumonia, a proteomic analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets