Section 3.1. Metagenomic Approach (from DOI: 10.3390/v12020211)

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Article	Current Trends in Diagnostics of Viral Infections of Unknown Etiology (DOI: 10.3390/v12020211)
Sections in this Publication
Section	Section 1. Introduction (from DOI: 10.3390/v12020211)
Section	Section 2. Traditional Methods of Diagnosing Infections (from DOI: 10.3390/v12020211)
Section	Section 3. Studying Viral Pathogens with High Throughput Sequencing (HTS) (from DOI: 10.3390/v12020211)
Section	Section 3.1. Metagenomic Approach (from DOI: 10.3390/v12020211)
Section	Section 3.2. Problems of Metagenomic Approach (from DOI: 10.3390/v12020211)
Section	Section 3.3. Methods for Improving Sequencing Output (from DOI: 10.3390/v12020211)
Section	Section 3.3.1. Nucleic Acids Depletion (from DOI: 10.3390/v12020211)
Section	Section 3.3.2. Hybridization-Based Enrichment (from DOI: 10.3390/v12020211)
Section	Section 3.3.3. Target Amplification (from DOI: 10.3390/v12020211)
Section	Section 3.4. Whole Viral Genome Sequencing (from DOI: 10.3390/v12020211)
Section	Section 3.5. Methods of Sequencing Data Analysis (from DOI: 10.3390/v12020211)
Section	Section 4. Long Read Sequencing (from DOI: 10.3390/v12020211)
Section	Section 5. Obstacles to Overcome in the Nearest Future (from DOI: 10.3390/v12020211)
Section	Section 6. Conclusions (from DOI: 10.3390/v12020211)
Section	Author Contributions (from DOI: 10.3390/v12020211)
Section	Funding (from DOI: 10.3390/v12020211)
Section	Conflicts of Interest (from DOI: 10.3390/v12020211)
Section	References (from DOI: 10.3390/v12020211)
Named Entities in this Section

From publication: "Current Trends in Diagnostics of Viral Infections of Unknown Etiology" published as Viruses; 2020 Feb 14 ; 12 (2); DOI: https://doi.org/10.3390/v12020211

Section 3.1. Metagenomic Approach

Metagenomics allows for identification and in-depth studies of unknown viral pathogens by using shotgun sequencing, which has been extensively applied in clinical and environmental research. Nucleic acids from a sample undergo virtually unbiased sequencing, i.e., with minimum prejudice towards specific organisms; in theory, the method can be used to analyze a potentially unlimited range of targets. Nevertheless, there is evidence that metagenomic sequencing is restricted by multiple pitfalls and biases, based on the pathogen's structure, extraction method, GC content, and other factors. Therefore, there are five major limitations: (1) sequences of interest should share at least low identity with the analogous sequences in a reference genome, ensuring correct mapping; (2) the analytical complexity of obtained data often requires employment of a qualified bioinformatician and the use of a specialized computational infrastructure (including both hardware and software; (3) hidden experimental and methodological prejudices towards certain taxa; (4) defining the method's sensitivity and properly measuring it against a relevant reference; (5) dealing with the abundance of host-cell, bacterial and fungal nucleic acids.

Unlike traditional tests, metagenomics removes the necessity of designing and synthesizing PCR primers and probes. This reduces time consumption, which is critical during outbreaks of viral infections, such as Zika virus or Ebola virus, when fast unbiased pathogen identification is crucial for effective disease containment. Unbiased HTS can also aid in investigating cases of unknown clustered viral infections, when other diagnostic tools do not provide sufficient information, like in the case of the new Arenavirus.

A metagenomic approach is deemed most helpful when heterogeneous infections share an identical clinical presentation or when genetic markers of antibiotic resistance are in question. Data supplied using NGS also augment diagnostics of respiratory infections: whereas modern multiplex molecular assays reveal the etiology of respiratory infections in approximately 40-80% of cases, metagenomics successfully tackles this limitation.

Furthermore, some respiratory viruses (e.g., Rhinoviruses) frequently appear to be the only or the most abundant pathogens in samples from patients with respiratory infections. This pattern implies that either the diagnostic tools are erroneous or the groups of related viruses cause infections with a variable clinical manifestation. Metagenomics reveals actual etiological factors, allowing for genotyping, identifying antibiotic resistance markers and supplying data molecular epidemiology. Recently, the applicability of HTS protocols to analyzing clinical samples has been evaluated, demonstrating high potential benefits for clinical studies.

Graf et al. (2016) compared the sensitivity of two pathogen identification methods: metagenomic RNA sequencing (RNA-Seq) with further Taxonomer processing and commercial FDA-approved respiratory virus panel (RVP) for GenMark eSensor. They used 42 virus-positive controls from pediatric patients and 67 unidentified samples. Metagenomic analysis revealed 86% of known respiratory pathogens, with supplementary PCR corroborating the finding in a mere third of discordant samples. However, for unknown samples, consistency between the two methods reached as high as 93%. Still, metagenomic analysis uncovered 12 extra viruses that were either not targeted by the RVP or failed to bind to complementary nucleotide strands on the chip because of significantly mutated genome sequences. A metagenomic approach not only aids in identification of a pathogen, but also grants an insight into its nucleotide sequence. Although viral sequences constituted only a small fraction of total reads, sufficient data for 84% of samples were gathered to allow for high-resolution genotyping.

Therefore, despite technological encumbrances, a metagenomic approach has a great potential for clinical application. It could provide the means to identify both known and novel pathogens, establish phylogenetic connections and detect drug-resistant and highly virulent strains of common viruses.