A Shotgun-metagenomics approach for laboratory diagnostics in clinical microbiology

Abstract

Shotgun-metagenomics (SMg) has a huge potential, particularly in clinical microbiology where conventional diagnostic methods have limitations such as diagnosis of prosthetic joint infection (PJI). The main objective of this project was to explore the use of SMg in clinical diagnostics of PJI directly from blood culture bottles (BCBs) inoculated with PJT specimens. First, we assessed the use of a BacT/Alert® Virtuo blood culture system for culturing PJT specimens. We showed that the BCB method detected a wider range of bacteria more rapidly than the conventional method making it a convenient approach for diagnosis of PJI. Then, we developed a proof-of-concept study to evaluate the use of SMg on BCBs inoculated with PJT for pathogen identification for PJI diagnosis. We developed a method for preparation of high-quality bacterial DNA from PJT for downstream SMg, established a bioinformatics pipeline, and compared SMg results with conventional culture method results. Our DNA preparation method resulted in high-quality microbial DNA from all samples allowing downstream SMg. All bacteria identified by the culture method were also identified by SMg. We obtained an acceptable high number of bacterial reads, genome coverage, and genome sequencing depth for the identification of PJI pathogens, allowing us to explore the application of SMg further. Our SMg approach also allowed the strain-level typing of Staphylococcus aureus, antimicrobial resistance (AMR), and virulence gene prediction from both monomicrobial and polymicrobial samples. However, the use of this approach for the detection of AMR to help guide clinical antibiotic usage needs to be further elucidated. Precise AMR prediction is required for the mainstream adoption of SMg into the clinical microbiology laboratory. In conclusion, these studies present an alternative approach for the application of SMg in PJI diagnosis, complementing the currently available tools. Our results might be useful in further validation and standardization for solving challenges presented in PJI diagnoses.