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Background <p>Sepsis is a leading cause of critical illness and mortality, yet substantial heterogeneity limits risk stratification and biomarker translation. Mitochondrial dysfunction is widely implicated in sepsis, but genetically supported, multi-layer regulatory features and their clinical relevance remain incompletely characterized.</p> Methods <p>We integrated publicly available sepsis GWAS summary statistics (general sepsis: 1634 cases/454,714 controls; gram-positive sepsis: 168/456,180; gram-negative sepsis: 383/455,965) with blood-based molecular QTL resources (including GTEx v8 whole blood, <i>n</i> = 670) to prioritize mitochondrial genes and infer regulatory cascades. Independent whole-blood transcriptomic cohorts (the GAinS cohort, GSE65682, n = 802; GSE54514, n = 163) were used for clinical and pathogen-specific expression characterization. We developed machine learning models using mitochondrial gene features and evaluated performance by internal tenfold cross-validation.</p> Results <p>We identified mitochondrial genes with convergent genetic, epigenetic, and transcriptional regulatory evidence, showing stronger effects in inner membrane and matrix compartments. Transcriptomic analyses supported clinically relevant dysregulation and pathogen-associated patterns. In predictive modeling, aggregating mitochondrial gene features improved discrimination, with the best-performing random forest model achieving an AUC of 0.91 under internal cross-validation. These results require validation in independent external cohorts.</p> Conclusions <p>This study provides a genetically supported, multi-omics framework linking compartment-specific mitochondrial dysregulation to sepsis heterogeneity and nominates candidate biomarkers for prioritization. The reported model performance reflects internal resampling and requires validation in independent clinical cohorts and future multi-omics profiling (including metabolomics) before translational implementation.</p>

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Integrative multi-omics and machine learning identify mitochondrial biomarkers for pathogen-specific sepsis stratification and translational prioritization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月6日

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  • Chaoyuan Jin,
  • Ruijinlin Hao,
  • Bate Gonggaoang,
  • Qingxia Dai,
  • Xingxing Ren,
  • Jie Shen

摘要

Background

Sepsis is a leading cause of critical illness and mortality, yet substantial heterogeneity limits risk stratification and biomarker translation. Mitochondrial dysfunction is widely implicated in sepsis, but genetically supported, multi-layer regulatory features and their clinical relevance remain incompletely characterized.

Methods

We integrated publicly available sepsis GWAS summary statistics (general sepsis: 1634 cases/454,714 controls; gram-positive sepsis: 168/456,180; gram-negative sepsis: 383/455,965) with blood-based molecular QTL resources (including GTEx v8 whole blood, n = 670) to prioritize mitochondrial genes and infer regulatory cascades. Independent whole-blood transcriptomic cohorts (the GAinS cohort, GSE65682, n = 802; GSE54514, n = 163) were used for clinical and pathogen-specific expression characterization. We developed machine learning models using mitochondrial gene features and evaluated performance by internal tenfold cross-validation.

Results

We identified mitochondrial genes with convergent genetic, epigenetic, and transcriptional regulatory evidence, showing stronger effects in inner membrane and matrix compartments. Transcriptomic analyses supported clinically relevant dysregulation and pathogen-associated patterns. In predictive modeling, aggregating mitochondrial gene features improved discrimination, with the best-performing random forest model achieving an AUC of 0.91 under internal cross-validation. These results require validation in independent external cohorts.

Conclusions

This study provides a genetically supported, multi-omics framework linking compartment-specific mitochondrial dysregulation to sepsis heterogeneity and nominates candidate biomarkers for prioritization. The reported model performance reflects internal resampling and requires validation in independent clinical cohorts and future multi-omics profiling (including metabolomics) before translational implementation.