ABSTRACT
Diagnostic metagenomics is a rapidly evolving laboratory tool for culture-independent tracing of foodborne pathogens. The method has the potential to become a generic platform for detection of most pathogens and many sample types. Today, however, it is still at an early and experimental stage. Studies show that metagenomic methods, from sample storage and DNA extraction to library preparation and shotgun sequencing, have a great influence on data output. To construct protocols that extract the complete metagenome but with minimal bias is an ongoing challenge. Many different software strategies for data analysis are being developed, and several studies applying diagnostic metagenomics to human clinical samples have been published, detecting, and sometimes, typing bacterial infections. It is possible to obtain a draft genome of the pathogen and to develop methods that can theoretically be applied in real-time. Finally, diagnostic metagenomics can theoretically be better geared than conventional methods to detect co-infections. The present review focuses on the current state of test development, as well as practical implementation of diagnostic metagenomics to trace foodborne bacterial infections in fecal samples from animals and humans.
ABSTRACT
In microbial food safety, molecular methods such as quantitative PCR (qPCR) and next-generation sequencing (NGS) of bacterial isolates can potentially be replaced by diagnostic shotgun metagenomics. However, the methods for pre-analytical sample preparation are often optimized for qPCR, and do not necessarily perform equally well for qPCR and sequencing. The present study investigates, through screening of methods, whether qPCR can be used as an indicator for the optimization of sample preparation for NGS-based shotgun metagenomics with a diagnostic focus. This was used on human fecal samples spiked with 10³ or 106 colony-forming units (CFU)/g Campylobacter jejuni, as well as porcine fecal samples spiked with 10³ or 106 CFU/g Salmonella typhimurium. DNA was extracted from the samples using variations of two widely used kits. The following quality parameters were measured: DNA concentration, qPCR, DNA fragmentation during library preparation, amount of DNA available for sequencing, amount of sequencing data, distribution of data between samples in a batch, and data insert size; none showed any correlation with the target ratio of the spiking organism detected in sequencing data. Surprisingly, diagnostic metagenomics can have better detection sensitivity than qPCR for samples spiked with 10³ CFU/g C. jejuni. The study also showed that qPCR and sequencing results may be different due to inhibition in one of the methods. In conclusion, qPCR cannot uncritically be used as an indicator for the optimization of sample preparation for diagnostic metagenomics.
ABSTRACT
BACKGROUND: The development of diagnostic metagenomics is driven by the need for universal, culture-independent methods for detection and characterization of pathogens to substitute the time-consuming, organism-specific, and often culture-based laboratory procedures for epidemiological source-tracing. Some of the challenges in diagnostic metagenomics are, that it requires a great next-generation sequencing depth and unautomated data analysis. RESULTS: DNA from human fecal samples spiked with 7.75 × 101-7.75 × 107 colony forming unit (CFU)/ml Campylobacter jejuni and chicken fecal samples spiked with 1 × 102-1 × 106 CFU/g Campylobacter jejuni was sequenced and data analysis was done by the metagenomic tools Kraken and CLARK. More hits were obtained at higher spiking levels, however with no significant linear correlations (human samples p = 0.12, chicken samples p = 0.10). Therefore, no definite detection limit could be determined, but the lowest spiking levels found positive were 7.75 × 104 CFU/ml in human feces and 103 CFU/g in chicken feces. Eight human clinical fecal samples with estimated Campylobacter infection loads from 9.2 × 104-1.0 × 109 CFU/ml were analyzed using the same methods. It was possible to detect Campylobacter in all the clinical samples. CONCLUSIONS: Sensitivity in diagnostic metagenomics is improving and has reached a clinically relevant level. There are still challenges to overcome before real-time diagnostic metagenomics can replace quantitative polymerase chain reaction (qPCR) or culture-based surveillance and diagnostics, but it is a promising new technology.
