Dataset Information

Plasma proteomics identifies molecular subtypes in sepsis

ABSTRACT: Background The heterogeneity of sepsis represents a major problem for the development of personalized sepsis therapies. Thus, sepsis subtyping emerged as an important tool to approach this problem, but little progress was made due to insufficient molecular insights. Modern proteomics techniques allow the identification of subtypes and enable molecular and mechanistical insights. Here, we analyzed a prospective multi-center sepsis cohort using plasma proteomics to describe and characterize sepsis plasma proteome subtypes. Methods Plasma samples from 333 patients collected at days 1 and 4 of sepsis were analyzed using liquid-chromatography coupled to tandem mass spectrometry. Plasma proteome subtypes were identified using K-means clustering and were characterized based on clinical routine data, cytokine measurements and proteomics data. A random forest machine learning (ML) classifier was generated to enable the assignment of patients to the subtypes in future. Results Four subtypes with different sepsis severity were identified. Cluster 0 represented the most severe sepsis with 100 % mortality. Cluster 1, 2 and 3 showed a gradual decrease of the median SOFA score, which was reflected by clinical data and cytokine measurements. On the proteome level, the subtypes were characterized by distinct molecular features. We found an alternating immune response with cluster 1 showing prominent activation of the adaptive immune system as indicated by elevated levels of immunoglobulins (Ig) that were verified using orthogonal measurements. Cluster 2 was characterized by acute inflammation and the lowest Ig levels. Cluster 3 represented the sepsis proteome baseline of the investigated cohort. We generated a ML classifier and optimized it for a minimum number of proteins that could realistically be implemented into routine diagnostics. The final model was based on 10 proteins and Ig quantities and allowed the assignment of patients to clusters 1, 2 and 3 with high confidence. Conclusion The identified plasma proteome subtypes provide insights into immune response and disease mechanisms and allow conclusions on appropriate therapeutic measures. Thus, they represent a step forward in the development of targeted therapies and personalized medicine in sepsis.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood Plasma

DISEASE(S): Bacterial Sepsis,Critical Covid-19

SUBMITTER: Thilo Bracht

LAB HEAD: Thilo Bracht

PROVIDER: PXD064125 | Pride | 2025-09-04

REPOSITORIES: Pride

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Plasma proteomics identifies molecular subtypes in sepsis.

Bracht Thilo T Kappler Kerstin K Bayer Malte M Grell Franziska F Schork Karin K Palmowski Lars L Koos Björn B Rahmel Tim T Ziehe Dominik D Unterberg Matthias M Bergmann Lars L Rump Katharina K Broecker-Preuss Martina M Limper Ulrich U Henzler Dietrich D Ehrentraut Stefan Felix SF von Groote Thilo T Zarbock Alexander A Pfaender Stephanie S Babel Nina N Marcus-Alic Katrin K Eisenacher Martin M Adamzik Michael M Sitek Barbara B Nowak Hartmuth H

Critical care (London, England) 20250902 1

<h4>Background</h4>The heterogeneity of sepsis represents a significant challenge to the development of personalized sepsis therapies. Sepsis subtyping has therefore emerged as an important approach to this problem, but its impact on clinical practice was limited due to insufficient molecular insights. Modern proteomics techniques allow the identification of subtypes and provide molecular and mechanistical insights. In this study, we analyzed a prospective multi-center sepsis cohort using plasma ...[more]

PMID: 40898225

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Project description:Background: Sepsis therapy is still limited to the treatment of the underlying infection and supportive measures. Therapeutic options that address the molecular changes of sepsis have not yet been identified. With the aim of a future individualized therapy, we used unsupervised machine learning (ML) to identify clinical phenotypes in a prospective multicenter cohort of patients with sepsis and characterized them using plasma proteomics. Methods: Routine clinical data and blood samples were collected from 384 sepsis patients. Clinical phenotypes were identified based on clinical routine measurements using the k-means algorithm. In addition, plasma samples from 276 patients were analyzed using liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS). The obtained data were analyzed and interpreted in the context of the phenotypes and supervised ML classifiers were developed to prospectively allocate patients to the clusters and to identify the most important features for discrimination of the phenotypes. Results: Three clinical phenotypes were identified. Cluster C was characterized by the highest disease severity and multi-organ failure with leading liver failure and a mortality rate of 92%. Cluster B, with a mortality rate of 45 %, also showed relevant organ failure, with renal damage being particularly prominent in comparison to cluster A. The plasma proteome reflected the clinical features of the phenotypes and revealed the excessive consumption of complement and coagulation factors in severe sepsis. Supervised ML and feature importance analysis underlined these findings and highlighted specific clinical measures and proteins. Conclusions: ML identified clinical phenotypes showed different degrees of sepsis severity and could be translated to the plasma proteome. Plasma proteomics provided novel insights into the molecular processes of sepsis and allowed the characterization of the phenotypes. This approach represents a blueprint to identify molecular features of sepsis subgroups and may pave the way for future targeted sepsis therapy.

Project description:Expression data from CD8+ T cells and CD68+ monocytes from patients with hemophagocytic lymphohistiocytosis, sepsis, and persistent systemic inflammatory response syndrome Hemophagocytic lymphohistiocytosis (HLH) is a syndrome characterized by pathologic immune activation in which prompt recognition and initiation of immune suppression is essential for survival. Children with HLH have many overlapping clinical features with critically ill children with sepsis and persistent systemic inflammatory response syndrome (SIRS) in whom alternative therapies are indicated. To determine if plasma biomarkers could differentiate HLH from other inflammatory conditions and to better define a ‘core inflammatory signature’ of HLH, concentrations of inflammatory plasma proteins were compared in 40 patients with HLH to 47 pediatric patients with severe sepsis or SIRS. Seventeen of 135 analytes were significantly different in HLH plasma compared to SIRS/sepsis, including increased interferon-gamma (IFNg)-regulated chemokines CXCL9, CXCL10 and CXCL11. Further, a 5-analyte plasma protein classifier including these chemokines was able to differentiate HLH from SIRS/sepsis. Gene expression in CD8+ T cells and CD68+ monocytes from blood were also enriched for IFNg pathway signatures in peripheral blood cells from patients with HLH compared to SIRS/sepsis. This study identifies differential expression of inflammatory proteins as a diagnostic strategy to identify critically ill children with HLH. Further, comprehensive unbiased analysis of inflammatory plasma proteins and global gene expression demonstrates that IFNg signaling is uniquely elevated in HLH. In addition to demonstrating the ability of diagnostic criteria for HLH, sepsis and SIRS to identify groups with distinct inflammatory patterns, results from this study support the potential for prospective evaluation of inflammatory biomarkers to aid in diagnosis of and optimizing therapeutic strategies for children with distinctive hyperinflammatory syndromes.

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	C1_report.pg_matrix.tsv	Tabular
	C3_report.pg_matrix.tsv	Tabular
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	EXI01832.raw	Raw