Speed, accuracy, sensitivity and quality control choices for detecting clinically relevant microbes in whole blood from patients.

Abstract

Infections are a serious health concern worldwide, particularly in vulnerable populations such as the immunocompromised, elderly, and young. Advances in metagenomic sequencing availability, speed, and decreased cost offer the opportunity to supplement or replace culture-based identification of pathogens with DNA sequence-based diagnostics. Adopting metagenomic analysis for clinical use requires that all aspects of the pipeline are optimized and tested, including data analysis. We tested the accuracy, sensitivity, and resource requirements of Centrifuge within the context of clinically relevant bacteria. Binary mixtures of bacteria showed Centrifuge reliably identified organisms down to 0.1% relative abundance. A staggered mock bacterial community showed Centrifuge outperformed CLARK while requiring less computing resources. Shotgun metagenomes obtained from whole blood in three febrile neutropenia patients showed Centrifuge could identify both bacteria and viruses as part of a culture-free workflow. Finally, Centrifuge results changed minimally by eliminating time-consuming read quality control and host screening steps. AUTHOR SUMMARYImmunocompromised patients, such as those with febrile neutropenia (FN), are susceptible to infections, yet cultures fail to identify causative organisms ~80% of the time. High-throughput metagenomic sequencing offers a promising approach for identifying pathogens in clinical samples. Mining through metagenomes can be difficult given the volume of reads, overwhelming human contamination, and lack of well-defined bioinformatics methods. The goal of our study was to assess Centrifuge, a leading tool for the identification and quantitation of microbes, and provide a streamlined bioinformatics workflow real-word data from FN patient blood samples. To ensure the accuracy of the workflow we carefully examined each step using known bacterial mixtures that varied by genetic distance and abundance. We show that Centrifuge reliably identifies microbes present at just 1% relative abundance and requires substantially less computer time and resource than CLARK. Moreover, we found that Centrifuge results changed minimally by quality control and host-screening allowing for further reduction in compute time. Next, we leveraged Centrifuge to identify viruses and bacteria in blood draws for three FN patients, and confirmed suspected pathogens using genome coverage plots. We developed a web-based tool in iMicrobe and detailed protocols to promote re-use.