Metabolomics- and proteomics-based multi-omics integration reveals early metabolite alterations in sepsis-associated acute kidney injury

BMC Medicine, Journal Year: 2025, Volume and Issue: 23(1)

Published: Feb. 11, 2025

Abstract Background Sepsis-associated acute kidney injury (SA-AKI) is a frequent complication in patients with sepsis and associated high mortality. Therefore, early recognition of SA-AKI essential for administering supportive treatment preventing further damage. This study aimed to identify validate metabolite biomarkers assist clinical diagnosis. Methods Untargeted renal proteomic metabolomic analyses were performed on the tissues LPS-induced mice. Glomerular filtration rate (GFR) monitoring technology was used evaluate real-time function To elucidate distinctive characteristics SA-AKI, multi-omics Spearman correlation network constructed integrating core metabolites, proteins, function. Subsequently, metabolomics analysis explore dynamic changes metabolites serum mice at 0, 8, 24 h. Finally, cohort (28 vs. 28 sepsis) quantitative carried out build diagnostic model via logistic regression (LR). Results Thirteen differential 112 proteins identified through highlight five i.e., 3-hydroxybutyric acid, 3-hydroxymethylglutaric creatine, myristic inosine, which then observed time series experiments The levels creatine increased significantly h, acid 8 while inosine decreased Ultimately, based we recruited 56 named IC3, using (AUC = 0.90). Conclusions We proposed blood consisting screening SA-AKI. Future studies will observe performance these other populations their role.

Language: Английский

Accurate identification of medulloblastoma subtypes from diverse data sources with severe batch effects by RaMBat

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 28, 2025

As the most common malignant pediatric brain cancer, medulloblastoma (MB) accounts for around 20% of all central nervous system (CNS) neoplasms. MB includes a complex array distinct molecular subtypes, mainly including SHH, WNT, Group 3 and 4. Accurate identification subtypes enables improved downstream risk stratification tailored therapeutic treatment design. Existing methods demonstrated feasibility leveraging transcriptomics data identifying subtypes. However, their performance may be poor due to limited cohorts severe batch effects when integrating various sources. To overcome these limitations, we propose novel accurate approach called RaMBat subtype from diverse sources with effects. Specifically, leverages intra-sample gene expression ranking information instead absolute levels, which can efficiently tackle across cohorts. By rank analysis, reversal ratio feature selection, select subtype-specific features finally accurately identify Benchmarking tests based on 13 datasets suggested that achieved median accuracy 99%, significantly outperforming other state-of-the-art subtyping approaches conventional machine learning classifiers. In addition, in terms visualization, could remove clearly separate samples according whereas visualization like tSNE suffered We believe is promising tool would have direct positive impacts facilitate use RaMBat, developed an R package freely available at https://github.com/wan-mlab/RaMBat .

Language: Английский

SAA1 as a Potential Early Diagnostic Biomarker for Sepsis Through Integrated Proteomics and Metabolomics

Immunology, Journal Year: 2025, Volume and Issue: unknown

Published: April 10, 2025

ABSTRACT Sepsis is characterised by fatal organ dysfunction resulting from a dysfunctional host response to infection, imposing substantial economic burden on families and society. Therefore, identifying biomarkers for early sepsis diagnosis improving patient prognosis are critical. This study recruited 59 patients 35 healthy volunteers the Department of Critical Care Medicine at Harbin Medical University Affiliated First Hospital between March December 2021. Through combination non‐targeted targeted proteomics metabolomics sequencing, along with various analytical methods, we initially identified validated serum amyloid A1 (SAA1) as diagnostic biomarker sepsis. Our found that SAA1 was significantly elevated in group, demonstrating its value ( AUC : 0.95, 95% CI 0.88–1). Additionally, positive correlation observed disease severity, indicated Sequential Organ Failure Assessment SOFA ) score R = 0.51, p 0.004) Acute Physiology Chronic Health Evaluation II APACHE 0.52, 0.003). suggests potentially effective reliable marker diagnosing predicting severity.

Language: Английский