Development and Optimization of an Unbiased, Metagenomics-Based Pathogen Detection Workflow for Infectious Disease and Biosurveillance Applications

Abstract

Rapid, specific, and sensitive identification of microbial pathogens is critical to infectious disease diagnosis and surveillance. Classical culture-based methods can be applied to a broad range of pathogens but have long turnaround times. Molecular methods, such as PCR, are time-effective but are not comprehensive and may not detect novel strains. Metagenomic shotgun next-generation sequencing (NGS) promises specific identification and characterization of any pathogen (viruses, bacteria, fungi, and protozoa) in a less biased way. Despite its great potential, NGS has yet to be widely adopted by clinical microbiology laboratories due in part to the absence of standardized workflows. Here, we describe a sample-to-answer workflow called PanGIA (Pan-Genomics for Infectious Agents) that includes simplified, standardized wet-lab procedures and data analysis with an easy-to-use bioinformatics tool. PanGIA is an end-to-end, multi-use workflow that can be used for pathogen detection and related applications, such as biosurveillance and biothreat detection. We performed a comprehensive survey and assessment of current, commercially available wet-lab technologies and open-source bioinformatics tools for each workflow component. The workflow includes total nucleic acid extraction from clinical human whole blood and environmental microbial forensic swabs as sample inputs, host nucleic acid depletion, dual DNA and RNA library preparation, shotgun sequencing on an Illumina MiSeq, and sequencing data analysis. The PanGIA workflow can be completed within 24 h and is currently compatible with bacteria and viruses. Here, we present data from the development and application of the clinical and environmental workflows, enabling the specific detection of pathogens associated with bloodstream infections and environmental biosurveillance, without the need for targeted assay development.

Case report: A toxoplasmic encephalitis in an immunocompromised child detected through metagenomic next-generation sequencing

“…Due to these advantages, cerebrospinal fluid second-generation sequencing technology can serve as an auxiliary diagnostic tool for CNS infections. It can rapidly and sensitively detect pathogens, provide information on thousands of pathogens, narrow down the detection range (11), and provide reliable evidence for clinically challenging infections (28)(29)(30).…”

Diagnosis of Acute Q Fever in a Patient by Using Metagenomic Next-Generation Sequencing: A Case Report [Letter]

“…Identification of certain pathogens from the metagenomics data is very tricky since we have to consider the composition of microbial community and its interaction with the host and their surroundings. 3 Therefore, the investigators should take samples from the environment in which the patient had worked, ie, in the farm, pigs, co-workers who had similar cases if any and so forth. Metagenomics sequencing for each of these can be performed and the researchers should compare the results with those from the patient in order to see if there is any intertwined relationship.…”

“… 4 Unfortunately, the authors did not provide information on whether they retrieved complete genome sequence of Coxiella burnetii from their reads, whether they had performed normalization and concentration matching for DNA as well as RNA samples and whether they tracked down further the remaining unidentified reads as these may affect the result interpretation. 3 , 5 …”

Enhancing Clinical Utility: Utilization of International Standards and Guidelines for Metagenomic Sequencing in Infectious Disease Diagnosis

“…The challenges associated with the standardization of workflows in mNGS have a significant impact on the turnaround time and costs [ 30 , 31 ], data quality, reproducibility [ 32 ], comparability [ 33 ], and biological interpretation [ 34 , 35 , 36 ]. Therefore, recent studies have made significant progress in standardizing metagenomics workflows both in the wet laboratory (wet lab) [ 37 , 38 ] and the dry laboratory (dry lab) [ 39 , 40 , 41 ]. For instance, Parker et al (2023) developed a sample-to-answer workflow called PanGIA that includes simplified, standardized wet lab procedures and data analysis with an easy-to-use bioinformatics tool [ 38 ].…”

“…Therefore, recent studies have made significant progress in standardizing metagenomics workflows both in the wet laboratory (wet lab) [ 37 , 38 ] and the dry laboratory (dry lab) [ 39 , 40 , 41 ]. For instance, Parker et al (2023) developed a sample-to-answer workflow called PanGIA that includes simplified, standardized wet lab procedures and data analysis with an easy-to-use bioinformatics tool [ 38 ]. In addition, the International Organization for Standardization (ISO) has also published international standards for the data processing of shotgun metagenomic sequences (ISO24420:2023) [ 42 ] and for the workflow and quality evaluation of sequencing data from massively parallel sequencing (ISO20397-1:2021 and ISO20397-2:2021) [ 43 , 44 ].…”