Differential protein expression and enriched pathways in pediatric sepsis: identification of novel brain-associated biomarkers revealed through proteomic profiling
Notice bibliographique
Résumé
BACKGROUND: Sepsis, defined by confirmed or suspected infection with systemic inflammatory response syndrome, requires robust biomarker identification. Proteomics enables protein quantification and expression analysis across disease states. This study investigated differential protein expression patterns, particularly brain-associated proteins, between sepsis patients and healthy controls, while evaluating temporal variations and relevant molecular pathways. METHODS: Study participants were prospectively enrolled based on established pediatric sepsis criteria with clinical and blood samples collected. Plasma protein concentrations were quantified using Nucleic Acid Linked Immuno-Sandwich Assay methodology. Statistical analyses incorporated conventional statistics, bioinformatics and machine learning approaches. RESULTS: The study cohorts comprised 23 age- and sex-matched participants: pediatric sepsis patients (median 11 years, IQR 9.5–14) and healthy controls (median 11 years, IQR 7.8–13; P = 0.809). Analyses revealed 59 differentially expressed proteins (DEPs) on Pediatric Intensive Care Unit Day 1 (PICU D1). Random Forest Classification (RFC) with Boruta feature selection identified 29 proteins that facilitated distinct group stratification. Comparison between PICU D1 and D3 samples yielded 34 DEPs, with RFC and Boruta feature selection isolating 9 discriminatory proteins. Multiple proteins were correlated with PELOD-2 scores and mortality (P < 0.05). Novel brain-associated proteins demonstrated significant differential expression patterns between PICU D1 and healthy controls, and over 3 days of PICU stay in sepsis patients. PICU D1 samples demonstrated significant pathway upregulation when compared to healthy controls, including “Signaling by Interleukins”, “Cytokine Signaling in Immune system”, and “Interleukin-10 signaling”. By PICU D3, pathways associated with “Generic Transcription Pathway”, “RNA Polymerase II Transcription”, and “Gene expression (Transcription)” exhibited significant downregulation. Protein-protein interaction network analysis revealed TNF and IL1B as critical bridging proteins linking inflammatory and neurological processes. Disease enrichment analysis demonstrated significant over-representation of respiratory pathology-associated genes, with respiratory failure and adult respiratory distress syndrome as the most enriched categories. CONCLUSIONS: Our investigation revealed distinct proteomic signatures in inflammatory and transcriptional pathways, including brain-associated processes, that differentiated pediatric sepsis patients from healthy control participants and exhibited temporal dynamics. The identification of TNF and IL1B as bridging proteins between systemic inflammation and neurological processes, combined with respiratory-centric disease enrichment patterns, provides mechanistic insights into sepsis pathophysiology. These alterations may provide insight into the mechanisms underlying sepsis-associated encephalopathy and lingering cognitive impairment in sepsis survivors, warranting further investigation in future studies. The identified molecular signatures present potential diagnostic and prognostic biomarkers for pediatric sepsis management.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».