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Evaluation of a Reliable Biomarker in a Cecal Ligation and Puncture-Induced Mouse Model of Sepsis
Published on: December 9, 2022
Research article proteomics-based plasma biomarkers for predicting CRKP infection in ICU sepsis patients
Zhongan Mao1,2, Kai Yao1,2, Lei Wang1,2
1Department of Pharmacy, Shanghai 9th People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Background:
Early differentiation between carbapenem-resistant Klebsiella pneumoniae (CRKP) and carbapenem-sensitive K. pneumoniae (CSKP) infections is critical due to limited treatment options and high mortality associated with CRKP. Current diagnostic methods are slow and insufficient for timely clinical decision-making, especially in ICU settings. Identifying reliable biomarkers for rapid discrimination is urgently needed.
Methods:
We performed plasma proteomic profiling of ICU sepsis patients infected with CRKP or CSKP using data-independent acquisition (DIA) mass spectrometry. Significantly differentially expressed proteins (DEPs) underwent Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO) functional annotation and enrichment analyses. Hub proteins were identified through protein-protein interaction network analysis. Protein biomarkers for constructing a diagnostic model by logistic regression analysis were further selected by XGboost and Lasso. The model was then evaluated for discrimination, calibration, and clinical utility by area under curve (AUC), the Hosmer-Lemeshow goodness-of-fit test and calibration curve, and decision curve, respectively.
Results:
A total of 1,432 proteins and 13,482 peptides were identified in the plasma samples. Among these, 28 DEPs were detected, including 16 upregulated and 12 downregulated proteins. Functional enrichment analysis indicated that these DEPs were primarily associated with neural and cardiovascular pathways. Using a combination of XGBoost and LASSO algorithms, 10 protein biomarkers were selected to construct a diagnostic model. The proteins of the optimal diagnostic model included PLXNB1 and S100A1. Notably, a simplified two-protein model demonstrated excellent diagnostic accuracy with an AUC exceeding 0.90 in both training and testing cohorts. The Hosmer-Lemeshow goodness-of-fit test yielded p-values of 0.825 and 0.295 in the training and testing sets, respectively, indicating good model calibration.
Conclusion:
PLXNB1 and S100A1 serve as promising plasma biomarkers for early, non-culture-based differentiation of CRKP and CSKP infections. Their integration into clinical workflows could improve rapid diagnosis and guide targeted therapy in critically ill sepsis patients.
Insights
Rapidly distinguishing carbapenem-resistant Klebsiella pneumoniae (CRKP) from carbapenem-sensitive K. pneumoniae (CSKP) infections is crucial. Novel plasma biomarkers PLXNB1 and S100A1 show high accuracy for early differentiation in sepsis patients.
Area of Science:
- Proteomics
- Infectious Disease Diagnostics
- Biomarker Discovery
Background:
- Carbapenem-resistant Klebsiella pneumoniae (CRKP) infections pose a significant threat due to limited treatment options and high mortality.
- Current diagnostic methods for CRKP vs. carbapenem-sensitive K. pneumoniae (CSKP) are slow, hindering timely clinical decisions in intensive care units (ICUs).
- There is an urgent need for reliable biomarkers enabling rapid differentiation between CRKP and CSKP infections.
Purpose of the Study:
- To identify plasma protein biomarkers for the early, non-culture-based differentiation of CRKP and CSKP infections in ICU sepsis patients.
- To develop and validate a diagnostic model using these biomarkers for improved clinical decision-making.
Main Methods:
- Plasma proteomic profiling using data-independent acquisition (DIA) mass spectrometry was performed on ICU sepsis patients.
- Differentially expressed proteins (DEPs) were identified and subjected to functional enrichment analyses (GO, KEGG, DO).
- Protein-protein interaction networks were analyzed to identify hub proteins, and machine learning algorithms (XGBoost, Lasso) selected biomarkers for a diagnostic model.
Main Results:
- A total of 1,432 proteins were identified, with 28 significantly differentially expressed proteins detected.
- Functional analysis revealed DEPs associated with neural and cardiovascular pathways.
- A diagnostic model incorporating PLXNB1 and S100A1 demonstrated excellent accuracy (AUC > 0.90) in differentiating CRKP from CSKP infections, with good calibration.
Conclusions:
- Plasma proteins PLXNB1 and S100A1 are promising biomarkers for the rapid, non-culture-based differentiation of CRKP and CSKP.
- These biomarkers can aid in early diagnosis and guide targeted therapy for critically ill sepsis patients.
- Integrating these biomarkers into clinical workflows could significantly improve patient management outcomes.
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