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Record W4416404811 · doi:10.3389/fimmu.2025.1663704

Neutrophil dysregulation differentiates pediatric septic shock biomarker-based mortality-risk strata: insights from weighted gene co-expression network and transcriptomic analyses

2025· article· en· W4416404811 on OpenAlexaff
Leland Dunwoodie, Min Huang, A. R. Moore, Natalja L. Stanski, Stephen W. Standage, Jennifer Kaplan, Basilia Zingarelli, Kelli Harmon, Julie C. Fitzgerald, Scott L. Weiss, Michael T. Bigham, Adam J. Schwarz, Riad Lutfi, Neal J. Thomas, Bereketeab Haileselassie, Parag Jain, Timothy E. Sweeney, Rishikesan Kamaleswaran, Mihir R. Atreya, Andrew J. Lautz

Bibliographic record

VenueFrontiers in Immunology · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicNeutrophil, Myeloperoxidase and Oxidative Mechanisms
Canadian institutionsInstitute of Infection and Immunity
FundersNational Institute of General Medical SciencesNational Institutes of HealthCincinnati Children's Hospital Medical Center
KeywordsTranscriptomeSeptic shockGeneImmune dysregulationNeutrophil extracellular trapsSepsisInflammationDisease

Abstract

fetched live from OpenAlex

Background Pediatric sepsis is a leading cause of global mortality, particularly among children, with limited therapeutic options beyond antibiotics and organ support. The Pediatric Sepsis Biomarker Risk Model (PERSEVERE-II) stratifies mortality risk in pediatric septic shock, yet the molecular mechanisms underlying high mortality risk remain incompletely understood. Methods We analyzed whole blood transcriptomes collected from 81 children with septic shock on day 1 of meeting study criteria. Patients were stratified into high- and low-mortality risk groups according to the PERSEVERE-II biomarker risk model. Using weighted gene co-expression network analysis (WGCNA) and differential gene expression analyses, we identified molecular pathways and transcription factors (TFs) associated with mortality risk. Cell type differences were inferred using CIBERSORTx and using a reference single-cell dataset inclusive of neutrophils and their subsets. Findings We identified distinct molecular profiles with high-risk patients displaying significant overexpression of genes related to neutrophil degranulation and innate immunity, alongside suppressed adaptive immune responses. The predominance of developing neutrophils underscored a major role of emergency granulopoiesis. Key TFs identified, including LTF , FOXM1 , KLF1 , and CEBPB , were linked to high-risk gene expression signatures. Our findings indicate a pathological shift toward a dysregulated neutrophil-driven hyperinflammation and adaptive immune suppressive state, which together are associated with adverse outcomes. Interpretation Our results suggest that neutrophil dysregulation underpins the high mortality risk conferred by the PERSEVERE-II model. The identified transcriptional regulators may provide potential targets to mitigate neutrophil dysregulation and improve outcomes among high-risk patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.265
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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