From FASTQ to Function to Phylogeny: In Silico Methods for Antimicrobial Resistance and Outbreak Investigation.

Area of Science:

• Microbiology
• Genomics
• Bioinformatics

Background:

• Clostridioides difficile infections pose a significant public health threat.
• Whole genome sequencing (WGS) offers powerful insights into bacterial isolates.
• Standardized WGS data processing is crucial for accurate analysis.

Purpose of the Study:

• To present a comprehensive method for processing Clostridioides difficile WGS data.
• To enable draft genome assembly, gene annotation, and multilocus strain type (MLST) identification.
• To demonstrate the utility of WGS in antimicrobial resistance (AMR) profiling and phylogenetic analysis for outbreak investigations.

Main Methods:

• Utilized publicly available Clostridioides difficile whole genome sequencing data.
• Applied a standardized pipeline for draft genome assembly and gene annotation.
• Performed multilocus strain typing (MLST) and antimicrobial resistance (AMR) profiling.
• Generated a relationship phylogeny to infer isolate relatedness.

Main Results:

• Successfully processed WGS data for multiple Clostridioides difficile isolates.
• Achieved draft genome assembly and gene annotation for each isolate.
• Identified specific MLST profiles and AMR markers.
• Constructed a phylogenetic tree illustrating isolate relationships, useful for mock outbreak analysis.

Conclusions:

• The presented method provides a robust framework for Clostridioides difficile WGS data analysis.
• WGS data can yield critical information for understanding C. difficile epidemiology and transmission.
• This approach facilitates comprehensive isolate characterization, aiding in outbreak detection and management.