Evidence map›Paper›PMID 40596376›Full record

ArticleScientific reports2025

Proteomic profiling of plasma extracellular vesicles identifies signatures of innate immunity, coagulation, and endothelial activation in septic patients.

Chanhee Park, Taekyung Ryu, Rashida Mohamed-Hinds, Kyungdo Kim, Jin Hyeok Kim, Lin Zou, Brittney Williams, Chan Hyun Na, Wei Chao

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Observational
  4. Article
  5. Review
  6. Rethinking Extracellular Vesicle Signaling.Advanced materials (Deerfield Beach, Fla.) · 2026
    Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Chanhee Park *Translational Research Program, Department of Anesthesiology, University of Maryland School of Medicine, Baltimore, MD, USA.
Taekyung Ryu *Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Rashida Mohamed-HindsTranslational Research Program, Department of Anesthesiology, University of Maryland School of Medicine, Baltimore, MD, USA.
Kyungdo KimDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Jin Hyeok KimDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Lin ZouTranslational Research Program, Department of Anesthesiology, University of Maryland School of Medicine, Baltimore, MD, USA.
Brittney WilliamsTranslational Research Program, Department of Anesthesiology, University of Maryland School of Medicine, Baltimore, MD, USA.
Chan Hyun NaDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA. chanhyun@jhmi.edu.
Wei ChaoTranslational Research Program, Department of Anesthesiology, University of Maryland School of Medicine, Baltimore, MD, USA. wchao@som.umaryland.edu.

Funding

Function and mechanisms of extracellular microRNAs in sepsis-induced lung injuryR35GM124775 · NIGMS · UNIVERSITY OF MARYLAND BALTIMORE · PI Lin Zou · 2017 to 2026
$3.2M
Extracellular miRNAs, innate immunity, and critical illnessR35GM140822 · NIGMS · UNIVERSITY OF MARYLAND BALTIMORE · PI WEI CHAO · 2021 to 2026
$2.5M
Fourier Transform Orbitrap Fusion Lumos Tribrid Mass Spectrometer with ETDS10OD021844 · OD · JOHNS HOPKINS UNIVERSITY · PI PANDEY, AKHILESH · 2016 to 2016
$1.1M
Role of miRNA-TLR7 signaling in platelet activation and dysfunction in sepsisK08HL153784 · NHLBI · UNIVERSITY OF MARYLAND BALTIMORE · PI WILLIAMS, BRITTNEY · 2020 to 2024
$782k
NHLBI NIH HHS K08 HL153784NIGMS NIH HHS R35 GM124775NIGMS NIH HHS R35 GM140822NIH HHS K08-HL153784NIH HHS R35-GM124775NIH HHS R35-GM140822NIH HHS S10 OD021844
6 · The paper itself

Abstract

Plasma extracellular vesicles (EVs) are cell-derived lipid particles and reportedly play a role in sepsis pathogenesis. This study aimed to identify EV cargo proteins in septic patients and explore their association with key sepsis pathophysiology. Plasma EVs were subjected to Tandem Mass Tag (TMT)-based quantitative proteomic analysis. We identified 522 differentially expressed (DE) EV proteins in septic patients (n = 15) compared to the healthy controls (n = 10). The KEGG analysis of the DE proteins revealed multiple functional pathways linked to sepsis, e.g., complement/coagulation, platelet activation, phagosome, inflammation, and neutrophil extracellular trap formation. Weighted Gene Coexpression Network Analysis of 1,642 EV proteins identified nine unique protein modules, some of which were highly correlated with the sepsis diagnosis and diverse endotype markers including organ injury, inflammation, coagulopathy, and endothelial activation, and mortality. ROC analysis revealed a list of novel EV proteins that exhibited strong diagnostic performance. Cell type-specific enrichment analysis revealed the cellular origins of EVs, including immune and epithelial cells, neurons, and glial cells. Thus, the current study discovered complex proteomic signatures in plasma EVs that are closely associated with key pathophysiological responses in sepsis. These findings support the importance of EV cargo proteins in the patients' immune responses, coagulation, and endothelial activation and lay the foundation for future mechanistic study of plasma EVs and their clinical application as potential diagnostic and prognostic markers.

Indexed as

Blood CoagulationExtracellular VesiclesImmunity, InnateProteomeProteomicsSepsisAdultAgedBiomarkersCase-Control StudiesFemaleHumansMaleMiddle AgedBiomarkersProteomeExtracellular vesicles (EVs)Mass spectrometryProteomicsSepsisTandem mass Tag (TMT)

Identifiers

PMID40596376
PMCPMC12215025

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.