Immunometabolic reprogramming and glycolysis-associated signatures in sepsis: insights from single-cell RNA sequencing and machine learning - Takeaways - MDSpire

Immunometabolic reprogramming and glycolysis-associated signatures in sepsis: insights from single-cell RNA sequencing and machine learning

  • By

  • Tao Li

  • Yun Liu

  • WanZhao Wang

  • QiuYue Li

  • Bing Chen

  • May 14, 2026

  • 0 min

Share

  • 1

    Sepsis is characterized by immunometabolic reconfiguration, particularly increased glycolysis in immune cells.

  • 2

    Five glycolytic biomarkers (GLRX, MDH1, MDH2, TGFBI, COPB2) were identified as potential indicators of sepsis.

  • 3

    TGFBI was notably enriched in monocytes and involved in a communication network with B cells and neutrophils.

  • 4

    The study utilized single-cell RNA sequencing and machine learning to analyze immune-cell composition in sepsis.

  • 5

    Findings suggest that glycolysis-related pathways are central to sepsis pathobiology and warrant further investigation.

Original Source(s)

Immunometabolic reprogramming and glycolysis-associated signatures in sepsis: insights from single-cell RNA sequencing and machine learning

  • by Tao Li, Yun Liu, WanZhao Wang, QiuYue Li, Bing Chen

  • May 14, 2026

Related Content

Teleology of immune system response to sepsis – failure due to dysregulation or adaptive response that sometimes fails?

Computational discovery of PGD, MAPK14, and KRAS as diagnostic biomarkers for neonatal sepsis through integrated machine learning, immune infiltration analysis, and molecular docking

Early ICU Rehab Backed, With Caveats

Invited narrative review supports early, interprofessional rehabilitation across the ICU recovery continuum while emphasizing heterogeneous evidence and inconsistent implementation worldwide.