A Peptide-Based Checkpoint Immunomodulator Alleviates Immune Dysfunction in Murine Polymicrobial Sepsis
Bibliographic record
Abstract
ABSTRACT: Sepsis-induced immunosuppression involves both innate and adaptive immunity and is associated with the increased expression of checkpoint inhibitors, such as programmed cell-death protein 1 (PD-1). The expression of PD-1 is associated with poor outcomes in septic patients, and in models of sepsis, blocking PD-1 or its ligands with antibodies increased survival and alleviated immune suppression. While inhibitory antibodies are effective, they can lead to immune-related adverse events (irAEs), in part due to continual blockade of the PD-1 pathway, resulting in hyperactivation of the immune response. Peptide-based therapeutics are an alternative drug modality that provide a rapid pharmacokinetic profile, reducing the incidence of precipitating irAEs. We recently reported that the potent, peptide-based PD-1 checkpoint antagonist, LD01, improves T-cell responses. The goal of the current study was to determine whether LD01 treatment improved survival, bacterial clearance, and host immunity in the cecal-ligation and puncture (CLP)-induced murine polymicrobial sepsis model. LD01 treatment of CLP-induced sepsis significantly enhanced survival and decreased bacterial burden. Altered survival was associated with improved macrophage phagocytic activity and T-cell production of interferon-γ. Further, myeloperoxidase levels and esterase-positive cells were significantly reduced in LD01-treated mice. Taken together, these data establish that LD01 modulates host immunity and is a viable therapeutic candidate for alleviating immunosuppression that characterizes sepsis and other infectious diseases.
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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) | 0.001 | 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) | 0.001 | 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".