Understanding the regulatory grammar of sepsis-causing Staphylococcus aureus bacteria using contexualised DNA...

Area of Science:

• Systems biology
• Computational biology
• Genomics

Background:

• Sepsis understanding requires detailed insight into biological systems.
• Conventional methods lack a comprehensive view of functional and regulatory elements in pathogenic infections.

Purpose of the Study:

• To develop an efficient and accurate method for understanding, detecting, and treating sepsis.
• To create a systems-level view of sepsis-causing bacteria by integrating multi-omics data and genome NLP.

Main Methods:

• Multi-omics integration using sparse Partial Least Squares Discriminant Analysis (sPLSDA) to generate functional-omics signatures at the single molecule level.
• Genome Natural Language Processing (genomeNLP) with specialized language models trained on DNA sequences to identify correlated features and discover regulatory motifs in bacterial promoters.
• Analysis of five sepsis-causing Staphylococcus aureus strains.

Main Results:

• Pinpointed multi-omics signatures with single molecule-level precision.
• Uncovered novel and established regulatory patterns governing the omics signature.
• Revealed hierarchical gene regulation in genome structure, ncRNA control, metabolism, and antibiotic resistance in Staphylococcus aureus.

Conclusions:

• The novel approach provides a comprehensive systems-level view of bacterial responses in sepsis.
• The method is organism-agnostic and applicable beyond sepsis research.
• This approach enhances understanding for potential improvements in sepsis detection and treatment.