Association of proteome and metabolome signatures with severity in patients with community-acquired pneumonia.


Journal

Journal of proteomics
ISSN: 1876-7737
Titre abrégé: J Proteomics
Pays: Netherlands
ID NLM: 101475056

Informations de publication

Date de publication:
01 03 2020
Historique:
received: 19 09 2019
revised: 29 11 2019
accepted: 22 12 2019
pubmed: 4 1 2020
medline: 22 6 2021
entrez: 4 1 2020
Statut: ppublish

Résumé

A combined OMICS screening approach of human plasma and serum was used to characterize protein and metabolome signatures displaying association to severity of Community-acquired pneumonia (CAP). 240 serum and BD P100 EDTA plasma samples from patients diagnosed with CAP, collected during the day of enrolment to the hospital, were analyzed by a metabolomic and proteomic approach, respectively. Disease severity of CAP patients was stratified using the Sequential Organ Failure Assessment (SOFA) score. Quantitative proteome and metabolome data, derived by LC-MS/MS, were associated to SOFA and specific parameters of SOFA using linear regression models adjusted for age, BMI, sex, smoking and technical variables. Both proteome and metabolome profiling revealed remarkable strong changes in plasma and serum composition in relation to severity of CAP. Proteins and metabolites displaying SOFA associated levels are involved in immune response, particularly in processes of lipid metabolism. Proteins, which show an association to SOFA score, are involved in acute phase response, coagulation, complement activation and inflammation. Many of these metabolites and proteins displayed not only associations to SOFA, but also to parameters of SOFA score, which likely reflect the strong influence of lung-, liver-, kidney- and heart-dysfunction on the metabolome and proteome patterns. SIGNIFICANCE: Community-acquired pneumonia is the most frequent infection disease with high morbidity and mortality. So far, only few studies focused on the identification of proteins or metabolites associated to severity of CAP, often based on smaller sample sets. A screening for new diagnostic markers requires extensive sample collections in combination with high quality clinical data. To characterize the proteomic and metabolomics pattern associated to severity of CAP we performed a combined metabolomics and proteomic approach of serum and plasma sample from a multi-center clinical study focused on patients with CAP, requiring hospitalization. The results of this association study of omics data to the SOFA score enable not only an interpretation of changes in molecular patterns with severity of CAP but also an assignment of altered molecules to dysfunctions of respiratory, renal, coagulation, cardiovascular systems as well as liver.

Identifiants

pubmed: 31899367
pii: S1874-3919(19)30399-9
doi: 10.1016/j.jprot.2019.103627
pii:
doi:

Substances chimiques

Proteome 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

103627

Informations de copyright

Copyright © 2020 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors have declared no conflict of interest.

Auteurs

Manuela Gesell Salazar (M)

Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Felix-Hausdorff-Str. 8, 17475 Greifswald, Germany.

Sophie Neugebauer (S)

Jena University Hospital, Institute of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), Am Klinikum 1, 07740 Jena, Germany.

Tim Kacprowski (T)

Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Felix-Hausdorff-Str. 8, 17475 Greifswald, Germany; Junior Research Group on Computational Systems Medicine, Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising-Weihenstephan, Germany.

Stephan Michalik (S)

Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Felix-Hausdorff-Str. 8, 17475 Greifswald, Germany.

Peter Ahnert (P)

University of Leipzig, Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Härtelstr. 16-18, 04107, Leipzig, Germany.

Petra Creutz (P)

Department of Infectious Disease and Respiratory Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Campus Virchowklinikum, Augustenburgerplatz 1, 13353 Berlin, Germany.

Maciej Rosolowski (M)

University of Leipzig, Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Härtelstr. 16-18, 04107, Leipzig, Germany.

Markus Löffler (M)

University of Leipzig, Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Härtelstr. 16-18, 04107, Leipzig, Germany.

Michael Bauer (M)

Department of Anesthesiology and Intensive Care Medicine, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany.

Norbert Suttorp (N)

Department of Infectious Disease and Respiratory Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Campus Virchowklinikum, Augustenburgerplatz 1, 13353 Berlin, Germany.

Michael Kiehntopf (M)

Jena University Hospital, Institute of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), Am Klinikum 1, 07740 Jena, Germany.

Uwe Völker (U)

Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Felix-Hausdorff-Str. 8, 17475 Greifswald, Germany. Electronic address: voelker@uni-greifswald.de.

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