Vascular Proteome Responses Precede Organ Dysfunction in a Murine Model of Staphylococcus aureus Bacteremia

Sepsis is a life-threatening response to infection that results in immune dysregulation, vascular dysfunction, and organ failure. New methods are needed for the identification of diagnostic and therapeutic targets. ABSTRACT Vascular dysfunction and organ failure are two distinct, albeit highly interconnected, clinical outcomes linked to morbidity and mortality in human sepsis. The mechanisms driving vascular and parenchymal damage are dynamic and display significant molecular cross talk between organs and tissues. Therefore, assessing their individual contribution to disease progression is technically challenging. Here, we hypothesize that dysregulated vascular responses predispose the organism to organ failure. To address this hypothesis, we have evaluated four major organs in a murine model of Staphylococcus aureus sepsis by combining in vivo labeling of the endothelial cell surface proteome, data-independent acquisition (DIA) mass spectrometry, and an integrative computational pipeline. The data reveal, with unprecedented depth and throughput, that a septic insult evokes organ-specific proteome responses that are highly compartmentalized, synchronously coordinated, and significantly correlated with the progression of the disease. These responses include abundant vascular shedding, dysregulation of the intrinsic pathway of coagulation, compartmentalization of the acute phase response, and abundant upregulation of glycocalyx components. Vascular cell surface proteome changes were also found to precede bacterial invasion and leukocyte infiltration into the organs, as well as to precede changes in various well-established cellular and biochemical correlates of systemic coagulopathy and tissue dysfunction. Importantly, our data suggest a potential role for the vascular proteome as a determinant of the susceptibility of the organs to undergo failure during sepsis. IMPORTANCE Sepsis is a life-threatening response to infection that results in immune dysregulation, vascular dysfunction, and organ failure. New methods are needed for the identification of diagnostic and therapeutic targets. Here, we took a systems-wide approach using data-independent acquisition (DIA) mass spectrometry to track the progression of bacterial sepsis in the vasculature leading to organ failure. Using a murine model of S. aureus sepsis, we were able to quantify thousands of proteins across the plasma and parenchymal and vascular compartments of multiple organs in a time-resolved fashion. We showcase the profound proteome remodeling triggered by sepsis over time and across these compartments. Importantly, many vascular proteome alterations precede changes in traditional correlates of organ dysfunction, opening a molecular window for the discovery of early markers of sepsis progression.

[1]  James T. Sorrentino,et al.  Endothelial Heparan Sulfate Mediates Hepatic Neutrophil Trafficking and Injury during Staphylococcus aureus Sepsis , 2021, mBio.

[2]  P. Dorrestein,et al.  Mortality Risk Profiling of Staphylococcus aureus Bacteremia by Multi-omic Serum Analysis Reveals Early Predictive and Pathogenic Signatures , 2020, Cell.

[3]  Chuan-Qi Zhong,et al.  Generation of a murine SWATH-MS spectral library to quantify more than 11,000 proteins , 2020, Scientific Data.

[4]  J. Marshall,et al.  Biomarkers of sepsis: time for a reappraisal , 2020, Critical Care.

[5]  U. Völker,et al.  Early-Stage Staphylococcus aureus Bloodstream Infection Causes Changes in the Concentrations of Lipoproteins and Acute-Phase Proteins and Is Associated with Low Antibody Titers against Bacterial Virulence Factors , 2020, mSystems.

[6]  Niranjan Kissoon,et al.  Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study , 2020, The Lancet.

[7]  H. Haller,et al.  Dual Pharmacological Inhibition of Angiopoietin-2 and VEGF-A in Murine Experimental Sepsis , 2019, Journal of Vascular Research.

[8]  James T. Sorrentino,et al.  Proteomic atlas of organ vasculopathies triggered by Staphylococcus aureus sepsis , 2019, Nature Communications.

[9]  Christoph B. Messner,et al.  DIA-NN: Neural networks and interference correction enable deep proteome coverage in high throughput , 2019, Nature Methods.

[10]  W. Aird,et al.  The in vivo endothelial cell translatome is highly heterogeneous across vascular beds , 2019, Proceedings of the National Academy of Sciences.

[11]  Jeffrey W. Smith,et al.  Plasma Proteome Signature of Sepsis: a Functionally Connected Protein Network , 2019, Proteomics.

[12]  J. Vincent,et al.  Mechanisms and treatment of organ failure in sepsis , 2018, Nature Reviews Nephrology.

[13]  Ronnie H. Fang,et al.  Defining Host Responses during Systemic Bacterial Infection through Construction of a Murine Organ Proteome Atlas. , 2018, Cell systems.

[14]  K. Kain,et al.  Endothelial Activation: The Ang/Tie Axis in Sepsis , 2018, Front. Immunol..

[15]  Oliver M. Bernhardt,et al.  Optimization of Experimental Parameters in Data-Independent Mass Spectrometry Significantly Increases Depth and Reproducibility of Results* , 2017, Molecular & Cellular Proteomics.

[16]  Baozhen Shan,et al.  Complete De Novo Assembly of Monoclonal Antibody Sequences , 2016, Scientific Reports.

[17]  M. Oelgeschläger,et al.  Defining the optimal animal model for translational research using gene set enrichment analysis , 2016, EMBO molecular medicine.

[18]  H. Augustin,et al.  Amelioration of sepsis by TIE2 activation–induced vascular protection , 2016, Science Translational Medicine.

[19]  Lars Malmström,et al.  Large-scale inference of protein tissue origin in gram-positive sepsis plasma using quantitative targeted proteomics , 2016, Nature Communications.

[20]  Adil Rafiq Rather,et al.  The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) , 2015 .

[21]  J. Marshall,et al.  Why have clinical trials in sepsis failed? , 2014, Trends in molecular medicine.

[22]  Matko Bosnjak,et al.  REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms , 2011, PloS one.

[23]  R. Wunderink,et al.  A clinical evaluation committee assessment of recombinant human tissue factor pathway inhibitor (tifacogin) in patients with severe community-acquired pneumonia , 2009, Critical care.

[24]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[25]  E. Hack,et al.  Sepsis-induced coagulation in the baboon lung is associated with decreased tissue factor pathway inhibitor. , 2007, The American journal of pathology.

[26]  Sorin Draghici,et al.  Machine Learning and Its Applications to Biology , 2007, PLoS Comput. Biol..

[27]  Eric W. Deutsch,et al.  The PeptideAtlas project , 2005, Nucleic Acids Res..

[28]  Eberhard Durr,et al.  Direct proteomic mapping of the lung microvascular endothelial cell surface in vivo and in cell culture , 2004, Nature Biotechnology.

[29]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[30]  C. Sprung,et al.  Efficacy and safety of tifacogin (recombinant tissue factor pathway inhibitor) in severe sepsis: a randomized controlled trial. , 2003, JAMA.

[31]  R. Campbell,et al.  Modeling human congenital disorder of glycosylation type IIa in the mouse: conservation of asparagine-linked glycan-dependent functions in mammalian physiology and insights into disease pathogenesis. , 2001, Glycobiology.

[32]  F. Taylor Staging of the pathophysiologic responses of the primate microvasculature to Escherichia coli and endotoxin: Examination of the elements of the compensated response and their links to the corresponding uncompensated lethal variants , 2001, Critical care medicine.

[33]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[34]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[35]  Christian von Mering,et al.  STRING: a database of predicted functional associations between proteins , 2003, Nucleic Acids Res..