Clinical metagenomic next-generation sequencing (mNGS) is the comprehensive analysis of microbial and host genetic material (DNA or RNA) in clinical samples from patients by next-generation sequencing. It uses the techniques of metagenomics to identify and characterize the genome of bacteria, fungi, parasites, and viruses without the need for a prior knowledge of a specific pathogen directly from clinical specimens. The capacity to detect all the potential pathogens in a sample makes metagenomic next generation sequencing a potent tool in the diagnosis of infectious disease especially when other more directed assays, such as PCR, fail. Its limitations include clinical utility, laboratory validity, sense and sensitivity, cost and regulatory considerations.
Outside of clinical medicine, similar work is done to identify genetic material in environmental samples, such as ponds or soil.
Definition Next-generation sequencing uses the techniques of metagenomics to identify and characterize the genome of bacteria, fungi, parasites, and viruses without the need for a prior knowledge of a specific pathogen directly from clinical specimens. The capacity to detect all the potential pathogens in a sample makes metagenomic next generation sequencing a potent tool in the diagnosis of infectious disease especially when other more directed assays, such as PCR, fail.
Laboratory workflow A typical mNGS workflow consists of the following steps: Sample acquisition: the most commonly used samples for metagenomic sequencing are blood, stool, cerebrospinal fluid (CSF), urine, or nasopharyngeal swabs. Among these, blood and CSF are the cleanest, having less background noise, while the others are expected to have a great amount of commensals and/or opportunistic infections and thus have more background noise. Samples should be collected with much caution as surgical specimens could be contaminated during handling of the biopsy; for example, lumbar punctures to obtain CSF specimens may be contaminated during the procedure. RNA/DNA extraction: the DNA and the RNA of the sample is extracted by using an extraction kit. If there is a strong previous suspicion of the pathogen genome composition and since the amount of pathogen nucleic acid in more noise samples is overwhelmed by the RNA/DNA of other organisms, selecting an extraction kit of only RNA or DNA would be a more specific and convenient approach. Some commerciable available kits are for example RNeasy PowerSoil Total RNA kit (Qiagen), RNeasy Minikit (Qiagen), MagMAX Viral Isolation kit (ABI), Viral RNA Minikit (Qiagen). Optimization strategies for library preparation: because of high levels of background noise in metagenomic sequencing, several target enrichment procedures have been developed that aim to increase the probability of capturing pathogen-derived transcripts and/or genomes. Generally there are two main approaches that can be used to increase the amount of pathogen signal in a sample: negative selection and positive enrichment.
Negative selection (background depletion or subtraction) targets and eliminates the host and microbiome genomic background, while aiming to preserve the nucleic acid derived from the pathogens of interest. Degradation of genomic background can be performed through broad-spectrum digestion with nucleases, such as DNase I for DNA background, or by removing abundant RNA species (rRNA, mtRNA, globin mRNA) using sequence-specific RNA depletion kits. Also CRISPR-Cas9-based approaches can be performed to target and deplete human mitochrondrial RNA for example. Generally, however, subtraction approaches lead to a certain degree of loss of the targeted pathogen genome, as poor recovery may occur during the cleanup. Positive enrichment is used to increase pathogen signal rather than reducing background noise. This is commonly done through hybridization-based target capture by probes, which are used to pull out nucleic acid of interest for downstream amplification and sequencing. Panviral probes have been shown to successfully identify diverse types of pathogens in different clinical fluid and respiratory samples, and have been used for sequencing and characterization of novel viruses. However, the probe approach includes extra hybridization and cleanup steps, requiring higher sample input, increasing the risk of losing the target, and increasing the cost and hands-on time. High-throughput sequencing: all the nucleic acids fragments of the library are sequenced. The sequencing platform to be used is chosen depending on different factors such as laboratory's research objectives, personal experience and skill levels. So far, the Illumina MiSeq system has proven to be the most commonly used platform for infectious disease research, pathogen surveillance, and pathogen discovery in research and public health. The instrument is compact enough to fit on a laboratory bench, has a fast runtime as compared to other similar platforms, and has a strong user support community. However, with further improvements of this technology and with additional error reduction and software stabilization, the MinION may be an excellent addition to the arsenal of current sequencing technologies for routine surveillance, especially in smaller laboratories with limited resources. For instance, the MinION was successfully used in the ZiBRA project for real-time Zika virus surveillance of mosquitoes and humans in Brazil, and in Guinea to perform real-time surveillance during the ongoing Ebola outbreak. In general, for limited resources IlluminaMiSeq, iSeq, Ion Torrent PGM, Oxford Nanopore, MinION are used. While for substantial resources Illumina NextSeq, NovaSeq, PacBio Sequel, Oxford Nanopore and PromethION are preferred. Moreover, for pathogen sequencing the use of controls is of fundamental importance ensuring mNGS assay quality and stability over time; PhiX is used as sequencing control, then the other controls include the positive control, an additional internal control (e.g., spiked DNA or other known pathogen) and a negative control (usually water sample). Bioinformatic analysis: Whereas the sequencing itself has been made widely accessible and more user friendly, the data analysis and interpretation that follows still requires specialized bioinformatics expertise and appropriate computational resources. The raw data from a sequencing platform is usually cleaned, trimmed, and filtered to remove low-quality and duplicate reads. Removal of the host genome/transcriptome reads is performed to decrease background noise (e.g., host and environmental reads) and increase the frequency of pathogen reads. This step will also decrease downstream analysis time. Further background noise removal is achieved by mapping of sample reads to the reads from the negative control to ensure elimination of any contaminating reads, such as those associated with the reagents or sampling storage medium. The remaining reads are usually assembled de novo to produce long stretches of sequences called contigs. Taxonomic identification of the resulting contigs is performed by matching them to the genomes and sequences in nucleotide or protein databases; for this, various versions of BLAST are most commonly used. Advanced characterization of bacterial organisms can be also performed, allowing to obtain the necessary depth and breadth of coverage for genetic characterization results. Gene calling can be performed in a variety of ways, including RAST or using NCBI services at the time of full genome submission. Results of multiple annotation tools can be compared for accuracy and completeness and, if necessary, merged using BEACON. For characterization of antibiotic resistance genes, the Resistance Gene Identifier from the Comprehensive Antibiotic Resistance Database (CARD) is commonly used. To characterize virulence factor genes, ShortBRED offers analyses with a customized database from the Virulence Factor Database.
Applications in Infectious diseases One way to detect these pathogens is detect part of their genome by metagenomics sequencing (Next Generation Sequencing-mNGS), which can be targeted or untargeted.
Targeted Because of that, the sensitivity to detect microorganisms that are being targeted usually increases, but this comes with a limitation of the amount of identifiable pathogens.
Untargeted The untargeted analysis is a metagenomic Shotgun sequencing approach. The whole DNA and/or RNA is sequenced with this approach using universal primers. The resultant mNGS reads can be assembled into partial or complete genomes. These genome sequences allow to monitor hospital outbreaks to facilitate infection control and public health surveillance. Also, they can be used for subtyping (identificate a specific genetic variant of a microorganism).
Untargeted mNGS is the most promising approach to analyse clinical samples and provide a comprehensive diagnosis of infections. Various groups have validated mNGS in Clinical Laboratory Improvement Amendments (CLIA), such as meningitis or encephalitis, sepsis and pneumonia. This method can be very helpful in the settings where no exact infectious etiology is suspected. For example, in patients with suspected pneumonia, identification of the underlying infectious etiology as in COVID-19 has important clinical and public health implications.
The traditional method consists on formulating a differential diagnosis on the basis of the patient's history, a clinical presentation, imaging findings and laboratory testing. But here it is suggested a different way of diagnosis; metagenomic next-generation sequencing (NGS) is a promising method because a comprehensive spectrum of a potential causes (viral, bacterial; fungus and parasitic) can be identify by a single assay.
Below are some examples of the metagenomic sequencing application in infectious diseases diagnosis.
Examples
Diagnosis of meningitis and encephalitis The traditional method used to the diagnosis of infectious diseases has been challenged in some cases: neuroinflammatory diseases, lack of diagnostic tests for rare pathogens and the limited availability and volume of the Central Nervous System (CNS) samples, because of the requirement for invasive procedures. Owing to these problems, some assays suggest a different way of diagnosis, which is the metagenomic next-generation sequencing (NGS). Summarising, NGS can identify a broad range of pathogens in a single test.
Some studies evaluate the clinical usefulness of metagenomic NGS for diagnosis neurologic infections, in parallel with conventional microbiologic testing. It has been seen that the highest diagnostic yield resulted from a combination of metagenomics NGS of CSF and conventional testing, including serologic testing and testing of sample types other than CSF. Sometimes neurologic infections remain undiagnosed in a proportion of patients despite conventional testing.
The results of metagenomic NGS can also be valuable even when concordant with results of conventional testing, not only providing reassurance that the conventionally obtained diagnosis is correct but also potentially detecting or ruling out coinfections, specially in immunocompromised patients.
Study of antimicrobial resistance Nowadays to detect resistances of different microbes is used a technique called Antibiotic Sensitivity (AST), but several studies have discovered that bacterial resistance is in the genoma and it is transferred by horizontal way (HGT), so sequencing methods are being developed to ease the identification and characterization of those genomes and metagenomes. For the moment exist the following methods to detect antimicrobial resistances: Antibiotic sensitivity: an advantage about this method is that it gives information for patients treatment. There are also some disadvantages, one of them is that this technique is only useful in cultivable bacteria for which competent personal is required.