Clostridioides difficile is a Gram-positive, spore-forming, obligate anaerobic bacterium and a major cause of antibiotic-associated diarrhea and life-threatening colitis worldwide. Infection is strongly associated with prior antibiotic exposure, and the pathogen is recognized as a significant public health threat due to increasing antimicrobial resistance.
Bacterial genes are frequently organized into operons, where multiple genes are co-transcribed as a single mRNA, enabling coordinated regulation of functionally related pathways. In C. difficile, operons regulate essential biological processes including:
- Toxin production (TcdA and TcdB)
- Sporulation
- Antibiotic resistance
- Stress response
- Biofilm formation
- Virulence regulation
Understanding operon structure and condition-specific expression is critical for revealing how C. difficile adapts to antibiotic pressure and hostile host environments.
Accurate prediction of operons in C. difficile remains challenging due to:
- Complex regulatory networks
- Incomplete genome annotations
- Strain-specific transcriptional variability
- Dynamic gene expression under environmental stress
- High transcriptional background noise
Traditional computational tools provide partial insights and often fail to prioritize operons most relevant to bacterial adaptation and pathogenicity. Therefore, improved computational frameworks integrating genomic and transcriptomic data are required.
This project investigates operon prediction in C. difficile strain 630 using RNA-Seq data under:
- Control conditions
- Vancomycin stress
- Ramoplanin stress
The study applies the COSMO (Condition-Specific Mapping of Operons) algorithm, a machine learning-based operon prediction framework, to identify transcriptionally active operons and evaluate their functional relevance in antibiotic stress adaptation.
- Can COSMO improve operon prediction accuracy using RNA-Seq data?
- What are the functional implications of predicted operons on antibiotic resistance and virulence in C. difficile?
The computational workflow includes:
- RNA-Seq quality control and preprocessing
- Read alignment to the reference genome (NC_009089.1)
- BAM processing and coverage analysis
- Operon prediction using COSMO
- Comparative analysis across antibiotic conditions
- Functional annotation of predicted operons
- Enrichment and statistical analysis
This work:
- Enhances condition-specific operon prediction using transcriptomic data
- Identifies operons potentially involved in resistance and virulence
- Improves understanding of bacterial adaptation under antibiotic stress
- Supports future strategies targeting antimicrobial resistance
All scripts, workflow documentation, and computational environments are provided in this repository to ensure transparency and reproducibility.