DJ-1/PARK7 Impairs Bacterial Clearance in Sepsis
Bibliographic record
Abstract
Abstract Rationale Effective and rapid bacterial clearance is a fundamental determinant of outcomes in sepsis. DJ-1 is a well-established reactive oxygen species (ROS) scavenger. Objectives Because cellular ROS status is pivotal to inflammation and bacterial killing, we determined the role of DJ-1 in bacterial sepsis. Methods We used cell and murine models with gain- and loss-of-function experiments, plasma, and cells from patients with sepsis. Measurements and Main Results Stimulation of bone marrow–derived macrophages (BMMs) with endotoxin resulted in increased DJ-1 mRNA and protein expression. Cellular and mitochondrial ROS was increased in DJ-1–deficient (−/−) BMMs compared with wild-type. In a clinically relevant model of polymicrobial sepsis (cecal ligation and puncture), DJ-1−/− mice had improved survival and bacterial clearance. DJ-1−/− macrophages exhibited enhanced phagocytosis and bactericidal activity in vitro, and adoptive transfer of DJ-1−/− bone marrow–derived mononuclear cells rescued wild-type mice from cecal ligation and puncture–induced mortality. In stimulated BMMs, DJ-1 inhibited ROS production by binding to p47phox, a critical component of the NADPH oxidase complex, disrupting the complex and facilitating Nox2 (gp91phox) ubiquitination and degradation. Knocking down DJ-1 (siRNA) in THP-1 (human monocytic cell line) and polymorphonuclear cells from patients with sepsis enhanced bacterial killing and respiratory burst. DJ-1 protein levels were elevated in plasma from patients with sepsis. Higher levels of circulating DJ-1 were associated with increased organ failure and death. Conclusions These novel findings reveal DJ-1 impairs optimal ROS production for bacterial killing with important implications for host survival in sepsis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".