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Proteomics. 2019 Mar;19(5):e1800389. doi: 10.1002/pmic.201800389. Epub 2019 Feb 20.

Plasma Proteome Signature of Sepsis: a Functionally Connected Protein Network.

Author information

1
Cancer Metabolism and Signaling Networks Program, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, 9207, USA.
2
Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, CA, 93106, USA.
3
Center for Nanomedicine, University of California, Santa Barbara, CA, 93106, USA.
4
Proteomics Facility, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, 92037, USA.
5
Department of Cellular and Molecular Medicine, Glycobiology Research and Training Center, University of California, San Diego, La Jolla, CA, 92093, USA.

Abstract

Sepsis is an extreme host response to infection that leads to loss of organ function and cardiovascular integrity. Mortality from sepsis is on the rise. Despite more than three decades of research and clinical trials, specific diagnostic and therapeutic strategies for sepsis are still absent. The use of LFQ- and TMT-based quantitative proteomics is reported here to study the plasma proteome in five mouse models of sepsis. A knowledge-based interpretation of the data reveals a protein network with extensive connectivity through documented functional or physical interactions. The individual proteins in the network all have a documented role in sepsis and are known to be extracellular. The changes in protein abundance observed in the mouse models of sepsis have for the most part the same directionality (increased or decreased abundance) as reported in the literature for human sepsis. This network has been named the Plasma Proteome Signature of Sepsis (PPSS). The PPSS is a quantifiable molecular readout that can supplant the current symptom-based approach used to diagnose sepsis. This type of molecular interpretation of sepsis, its progression, and its response to therapeutic intervention are an important step in advancing our understanding of sepsis, and for discovering and evaluating new therapeutic strategies.

KEYWORDS:

label-free quantification; mouse models; plasma proteomics; protein networks; sepsis

PMID:
30706660
PMCID:
PMC6447370
[Available on 2020-03-01]
DOI:
10.1002/pmic.201800389

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