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Modern Molecular Taxonomy

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Updated: May 12, 2026

A Fast and Reliable Pipeline for Bacterial Transcriptome Analysis Case study: Serine-dependent Gene Regulation in Streptococcus pneumoniae

A Fast and Reliable Pipeline for Bacterial Transcriptome Analysis Case study: Serine-dependent Gene Regulation in Streptococcus pneumoniae

Published on: April 25, 2015

Sepsis Important Genes Identification Through Biologically Informed Deep Learning and Transcriptomic Analysis.

Ruichen Li^1,2, Qiushi Wang³, Ru Gao^1,2

¹University of Shanghai for Science and Technology, Shanghai, China.

Clinical and Experimental Pharmacology & Physiology

|May 13, 2025

Summary

This summary is machine-generated.

Identifying key genes in sepsis is crucial for immune response regulation. This study highlights TIMP1, GSTO1, and MYL6 as potential biomarkers and therapeutic targets for sepsis treatment.

Keywords:

biomarkers explainable artificial intelligence immune infiltration analysis molecular docking sepsis

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A Data-Driven Approach to Quantifying Immune States in Sepsis

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

Area of Science:

Immunology
Genetics
Computational Biology

Background:

Sepsis is a life-threatening condition driven by immune system dysregulation.
Identifying genes that influence the immune response in sepsis is critical for developing effective treatments.

Purpose of the Study:

To utilize a novel AI model, P-NET, to identify influential genes in sepsis.
To explore the potential of identified genes as biomarkers and therapeutic targets for sepsis.

Main Methods:

Employed P-NET, a biologically informed explainable AI model, to assess gene importance in sepsis.
Analyzed gene expression at both bulk and single-cell levels.
Utilized a drug repositioning strategy to identify potential therapeutic compounds.

Main Results:

Identified 688 important genes enriched in inflammation and immune regulation pathways (e.g., PI3K-Akt, necroptosis, NF-κB).
TIMP1, GSTO1, and MYL6 showed significant differential expression across multiple cell types and correlated with immune cell abundance (e.g., M-MDSC).
These three genes were highly expressed in sepsis patients with severe outcomes, including non-survivors and those in shock.

Conclusions:

TIMP1, GSTO1, and MYL6 are identified as key genes in sepsis pathogenesis.
These genes show promise as diagnostic biomarkers and therapeutic targets for sepsis.
Navitoclax, curcumin, and rotenone are potential drugs that could modulate these target genes.