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== Viral Metagenomics Challenges ==
== Viral Metagenomics Challenges ==
The traditional methods for discovering, characterizing, and assigning viral taxonomy to viruses were based on isolating the virus particle or its nucleic acid from samples<ref name=":0">{{Cite journal |last=Santiago-Rodriguez |first=Tasha M. |last2=Hollister |first2=Emily B. |date=2022-09-16 |title=Unraveling the viral dark matter through viral metagenomics |url=https://www.frontiersin.org/articles/10.3389/fimmu.2022.1005107/full |journal=Frontiers in Immunology |volume=13 |doi=10.3389/fimmu.2022.1005107 |issn=1664-3224 |pmc=PMC9523745 |pmid=36189246}}</ref>. The virus morphology could be visualized using electron microscopy but only if the virus could be isolated in high enough titer to be detected. The virus could be cultured in eukaryotic cell lines or bacteria but only if the appropriate host cell type was known and the nucleic acid of the virus would be detected using PCR but only if a consensus primer was known.<ref name=":0" />
The earliest metagenomic studies of viruses were carried out on ocean samples in 2002 in which the researchers found that 65% of the sequences of DNA and RNA viruses had no matches in the sequence reference databases.<ref name="Breitbart_2002">{{cite journal | vauthors = Breitbart M, Salamon P, Andresen B, Mahaffy JM, Segall AM, Mead D, Azam F, Rohwer F | display-authors = 6 | title = Genomic analysis of uncultured marine viral communities | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 99 | issue = 22 | pages = 14250–14255 | date = October 2002 | pmid = 12384570 | pmc = 137870 | doi = 10.1073/pnas.202488399 | bibcode = 2002PNAS...9914250B | doi-access = free }}</ref> The sequences that were matched to referenced sequences were double-stranded DNA bacteriophages and double-stranded algal viruses.<ref name="Breitbart_2002" /> Subsequent studies of the soil virome discovered that bacteriophages were equally as prevalent as bacteria in the soil.<ref name="Pratama_2018">{{cite journal | vauthors = Pratama AA, van Elsas JD | title = The 'Neglected' Soil Virome - Potential Role and Impact | journal = Trends in Microbiology | volume = 26 | issue = 8 | pages = 649–662 | date = August 2018 | pmid = 29306554 | doi = 10.1016/j.tim.2017.12.004 | s2cid = 25057850 }}</ref> Acknowledging the importance of viral metagenomics, the [[International Committee on Taxonomy of Viruses]] (ICTV) recognizes that genomes assembled from metagenomic data represent a virus and can be classified using the same procedures for viruses isolated via classical virology approaches.<ref>{{cite journal | vauthors = Simmonds P, Adams MJ, Benkő M, Breitbart M, Brister JR, Carstens EB, Davison AJ, Delwart E, Gorbalenya AE, Harrach B, Hull R, King AM, Koonin EV, Krupovic M, Kuhn JH, Lefkowitz EJ, Nibert ML, Orton R, Roossinck MJ, Sabanadzovic S, Sullivan MB, Suttle CA, Tesh RB, van der Vlugt RA, Varsani A, Zerbini FM | display-authors = 6 | title = Consensus statement: Virus taxonomy in the age of metagenomics | journal = Nature Reviews. Microbiology | volume = 15 | issue = 3 | pages = 161–168 | date = March 2017 | pmid = 28134265 | doi = 10.1038/nrmicro.2016.177 | s2cid = 1478314 | doi-access = free }}</ref>

Metagenomics requires no prior knowledge of the viral genome as it does not require primer or probe design, allowing for rapid response to emerging threats.<ref name="Houldcroft_2017" /> Because this method uses prediction tools to detect viral content of a sample, it can be used to identify new virus species or divergent members of known species.

The earliest metagenomic studies of viruses were carried out on ocean samples in 2002 in which the researchers found that 65% of the sequences of DNA and RNA viruses had no matches in the sequence reference databases.<ref name="Breitbart_2002">{{cite journal |display-authors=6 |vauthors=Breitbart M, Salamon P, Andresen B, Mahaffy JM, Segall AM, Mead D, Azam F, Rohwer F |date=October 2002 |title=Genomic analysis of uncultured marine viral communities |journal=Proceedings of the National Academy of Sciences of the United States of America |volume=99 |issue=22 |pages=14250–14255 |bibcode=2002PNAS...9914250B |doi=10.1073/pnas.202488399 |pmc=137870 |pmid=12384570 |doi-access=free}}</ref> The sequences that were matched to referenced sequences were double-stranded DNA bacteriophages and double-stranded algal viruses.<ref name="Breitbart_2002" />

There are seven classes of viruses based on the Baltimore classification system which groups viruses based on their genomic structure and their manner of transcription: there are double-stranded DNA viruses, single-stranded DNA viruses, double-stranded RNA viruses, and single-stranded RNA virus<ref>{{Cite journal |last=Koonin |first=Eugene V. |last2=Krupovic |first2=Mart |last3=Agol |first3=Vadim I. |date=2021-08-18 |title=The Baltimore Classification of Viruses 50 Years Later: How Does It Stand in the Light of Virus Evolution? |url=https://journals.asm.org/doi/10.1128/MMBR.00053-21 |journal=Microbiology and Molecular Biology Reviews |language=en |volume=85 |issue=3 |doi=10.1128/MMBR.00053-21 |issn=1092-2172 |pmc=PMC8483701 |pmid=34259570}}</ref>. Single-stranded RNA can be positive or negative sense. The 2002 sequences that were matched to referenced sequences were double-stranded DNA bacteriophages and double-stranded algal viruses.<ref name=":1">{{Cite journal |last=Breitbart |first=Mya |last2=Salamon |first2=Peter |last3=Andresen |first3=Bjarne |last4=Mahaffy |first4=Joseph M. |last5=Segall |first5=Anca M. |last6=Mead |first6=David |last7=Azam |first7=Farooq |last8=Rohwer |first8=Forest |date=2002-10-29 |title=Genomic analysis of uncultured marine viral communities |url=https://pnas.org/doi/full/10.1073/pnas.202488399 |journal=Proceedings of the National Academy of Sciences |language=en |volume=99 |issue=22 |pages=14250–14255 |doi=10.1073/pnas.202488399 |issn=0027-8424 |pmc=PMC137870 |pmid=12384570}}</ref> There is still a bias towards DNA viruses in reference databases. Common reasons for this bias is because RNA viruses mutate more rapidly than DNA viruses, DNA is easier to handle from samples while RNA is unstable, and more steps are needed for RNA metagenomics analysis (reverse transcription).

Acknowledging the importance of viral metagenomics, the [[International Committee on Taxonomy of Viruses]] (ICTV) recognizes that genomes assembled from metagenomic data represent a virus and can be classified using the same procedures for viruses isolated via classical virology approaches.<ref>{{cite journal | vauthors = Simmonds P, Adams MJ, Benkő M, Breitbart M, Brister JR, Carstens EB, Davison AJ, Delwart E, Gorbalenya AE, Harrach B, Hull R, King AM, Koonin EV, Krupovic M, Kuhn JH, Lefkowitz EJ, Nibert ML, Orton R, Roossinck MJ, Sabanadzovic S, Sullivan MB, Suttle CA, Tesh RB, van der Vlugt RA, Varsani A, Zerbini FM | display-authors = 6 | title = Consensus statement: Virus taxonomy in the age of metagenomics | journal = Nature Reviews. Microbiology | volume = 15 | issue = 3 | pages = 161–168 | date = March 2017 | pmid = 28134265 | doi = 10.1038/nrmicro.2016.177 | s2cid = 1478314 | doi-access = free }}</ref>


The IMG/VR system and the IMG/VR v.2.0 are the largest interactive public virus databases with over 760,000 metagenomic viral sequences and isolate viruses and serves as a starting point for the sequence analysis of viral fragments derived from metagenomic samples.<ref>{{cite journal | vauthors = Paez-Espino D, Chen IA, Palaniappan K, Ratner A, Chu K, Szeto E, Pillay M, Huang J, Markowitz VM, Nielsen T, Huntemann M, K Reddy TB, Pavlopoulos GA, Sullivan MB, Campbell BJ, Chen F, McMahon K, Hallam SJ, Denef V, Cavicchioli R, Caffrey SM, Streit WR, Webster J, Handley KM, Salekdeh GH, Tsesmetzis N, Setubal JC, Pope PB, Liu WT, Rivers AR, Ivanova NN, Kyrpides NC | display-authors = 6 | title = IMG/VR: a database of cultured and uncultured DNA Viruses and retroviruses | journal = Nucleic Acids Research | volume = 45 | issue = D1 | pages = D457–D465 | date = January 2017 | pmid = 27799466 | pmc = 5210529 | doi = 10.1093/nar/gkw1030 }}</ref><ref>{{cite journal | vauthors = Paez-Espino D, Roux S, Chen IA, Palaniappan K, Ratner A, Chu K, Huntemann M, Reddy TB, Pons JC, Llabrés M, Eloe-Fadrosh EA, Ivanova NN, Kyrpides NC | display-authors = 6 | title = IMG/VR v.2.0: an integrated data management and analysis system for cultivated and environmental viral genomes | journal = Nucleic Acids Research | volume = 47 | issue = D1 | pages = D678–D686 | date = January 2019 | pmid = 30407573 | pmc = 6323928 | doi = 10.1093/nar/gky1127 }}</ref>
The IMG/VR system and the IMG/VR v.2.0 are the largest interactive public virus databases with over 760,000 metagenomic viral sequences and isolate viruses and serves as a starting point for the sequence analysis of viral fragments derived from metagenomic samples.<ref>{{cite journal | vauthors = Paez-Espino D, Chen IA, Palaniappan K, Ratner A, Chu K, Szeto E, Pillay M, Huang J, Markowitz VM, Nielsen T, Huntemann M, K Reddy TB, Pavlopoulos GA, Sullivan MB, Campbell BJ, Chen F, McMahon K, Hallam SJ, Denef V, Cavicchioli R, Caffrey SM, Streit WR, Webster J, Handley KM, Salekdeh GH, Tsesmetzis N, Setubal JC, Pope PB, Liu WT, Rivers AR, Ivanova NN, Kyrpides NC | display-authors = 6 | title = IMG/VR: a database of cultured and uncultured DNA Viruses and retroviruses | journal = Nucleic Acids Research | volume = 45 | issue = D1 | pages = D457–D465 | date = January 2017 | pmid = 27799466 | pmc = 5210529 | doi = 10.1093/nar/gkw1030 }}</ref><ref>{{cite journal | vauthors = Paez-Espino D, Roux S, Chen IA, Palaniappan K, Ratner A, Chu K, Huntemann M, Reddy TB, Pons JC, Llabrés M, Eloe-Fadrosh EA, Ivanova NN, Kyrpides NC | display-authors = 6 | title = IMG/VR v.2.0: an integrated data management and analysis system for cultivated and environmental viral genomes | journal = Nucleic Acids Research | volume = 47 | issue = D1 | pages = D678–D686 | date = January 2019 | pmid = 30407573 | pmc = 6323928 | doi = 10.1093/nar/gky1127 }}</ref>
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=== Direct Metagenomics ===
=== Direct Metagenomics ===
Metagenomic analysis uses whole genome shotgun sequencing to characterize microbial diversity in clinical and environmental samples. Total DNA and/or RNA are extracted from the samples and are prepared on a DNA or RNA library for sequencing.<ref name="Houldcroft_2017">{{cite journal | vauthors = Houldcroft CJ, Beale MA, Breuer J | title = Clinical and biological insights from viral genome sequencing | language = En | journal = Nature Reviews. Microbiology | volume = 15 | issue = 3 | pages = 183–192 | date = March 2017 | pmid = 28090077 | pmc = 7097211 | doi = 10.1038/nrmicro.2016.182 | author-link3 = Judith Breuer }}</ref> These methods have been used to sequence the whole genome of [[Epstein–Barr virus|Epstein-Barr virus]] (EBV) and [[Hepatitis C|HCV]], however, contaminating nucleic acids can affect the sensitivity to the target viral genome with the proportion of reads related to the target sequence often being low.<ref>{{cite journal | vauthors = Depledge DP, Palser AL, Watson SJ, Lai IY, Gray ER, Grant P, Kanda RK, Leproust E, Kellam P, Breuer J | display-authors = 6 | title = Specific capture and whole-genome sequencing of viruses from clinical samples | journal = PLOS ONE | volume = 6 | issue = 11 | pages = e27805 | date = 2011-11-18 | pmid = 22125625 | pmc = 3220689 | doi = 10.1371/journal.pone.0027805 | bibcode = 2011PLoSO...627805D | doi-access = free | veditors = Jhaveri R }}</ref><ref name="Thomson_2016">{{cite journal | vauthors = Thomson E, Ip CL, Badhan A, Christiansen MT, Adamson W, Ansari MA, Bibby D, Breuer J, Brown A, Bowden R, Bryant J, Bonsall D, Da Silva Filipe A, Hinds C, Hudson E, Klenerman P, Lythgow K, Mbisa JL, McLauchlan J, Myers R, Piazza P, Roy S, Trebes A, Sreenu VB, Witteveldt J, Barnes E, Simmonds P | display-authors = 6 | title = Comparison of Next-Generation Sequencing Technologies for Comprehensive Assessment of Full-Length Hepatitis C Viral Genomes | journal = Journal of Clinical Microbiology | volume = 54 | issue = 10 | pages = 2470–2484 | date = October 2016 | pmid = 27385709 | pmc = 5035407 | doi = 10.1128/jcm.00330-16 }}</ref> Due to the uncontrollable nature of environmental DNA samples, the most abundant organisms in the environmental sample are the highest represented in the sequencing data and require large samples to achieve full coverage. That being said, shotgun sequencing ensures that these organisms that would previously go unnoticed in culture dependent methods are represented by some sequence segments.<ref>{{cite journal | vauthors = Tyson GW, Chapman J, Hugenholtz P, Allen EE, Ram RJ, Richardson PM, Solovyev VV, Rubin EM, Rokhsar DS, Banfield JF | display-authors = 6 | title = Community structure and metabolism through reconstruction of microbial genomes from the environment | journal = Nature | volume = 428 | issue = 6978 | pages = 37–43 | date = March 2004 | pmid = 14961025 | publisher = Nature Publishing Group | doi = 10.1038/nature02340 | bibcode = 2004Natur.428...37T | s2cid = 4420754 | oclc = 926320276 }}</ref>
Metagenomic analysis uses whole genome shotgun sequencing to characterize microbial diversity in clinical and environmental samples. Total DNA and/or RNA are extracted from the samples and are prepared on a DNA or RNA library for sequencing.<ref name="Houldcroft_2017">{{cite journal | vauthors = Houldcroft CJ, Beale MA, Breuer J | title = Clinical and biological insights from viral genome sequencing | language = En | journal = Nature Reviews. Microbiology | volume = 15 | issue = 3 | pages = 183–192 | date = March 2017 | pmid = 28090077 | pmc = 7097211 | doi = 10.1038/nrmicro.2016.182 | author-link3 = Judith Breuer }}</ref> These methods have been used to sequence the whole genome of [[Epstein–Barr virus|Epstein-Barr virus]] (EBV) and [[Hepatitis C|HCV]], however, contaminating nucleic acids can affect the sensitivity to the target viral genome with the proportion of reads related to the target sequence often being low.<ref>{{cite journal | vauthors = Depledge DP, Palser AL, Watson SJ, Lai IY, Gray ER, Grant P, Kanda RK, Leproust E, Kellam P, Breuer J | display-authors = 6 | title = Specific capture and whole-genome sequencing of viruses from clinical samples | journal = PLOS ONE | volume = 6 | issue = 11 | pages = e27805 | date = 2011-11-18 | pmid = 22125625 | pmc = 3220689 | doi = 10.1371/journal.pone.0027805 | bibcode = 2011PLoSO...627805D | doi-access = free | veditors = Jhaveri R }}</ref><ref name="Thomson_2016">{{cite journal | vauthors = Thomson E, Ip CL, Badhan A, Christiansen MT, Adamson W, Ansari MA, Bibby D, Breuer J, Brown A, Bowden R, Bryant J, Bonsall D, Da Silva Filipe A, Hinds C, Hudson E, Klenerman P, Lythgow K, Mbisa JL, McLauchlan J, Myers R, Piazza P, Roy S, Trebes A, Sreenu VB, Witteveldt J, Barnes E, Simmonds P | display-authors = 6 | title = Comparison of Next-Generation Sequencing Technologies for Comprehensive Assessment of Full-Length Hepatitis C Viral Genomes | journal = Journal of Clinical Microbiology | volume = 54 | issue = 10 | pages = 2470–2484 | date = October 2016 | pmid = 27385709 | pmc = 5035407 | doi = 10.1128/jcm.00330-16 }}</ref> Due to the uncontrollable nature of environmental DNA samples, the most abundant organisms in the environmental sample are the highest represented in the sequencing data and require large samples to achieve full coverage. That being said, shotgun sequencing ensures that these organisms that would previously go unnoticed in culture dependent methods are represented by some sequence segments.<ref>{{cite journal | vauthors = Tyson GW, Chapman J, Hugenholtz P, Allen EE, Ram RJ, Richardson PM, Solovyev VV, Rubin EM, Rokhsar DS, Banfield JF | display-authors = 6 | title = Community structure and metabolism through reconstruction of microbial genomes from the environment | journal = Nature | volume = 428 | issue = 6978 | pages = 37–43 | date = March 2004 | pmid = 14961025 | publisher = Nature Publishing Group | doi = 10.1038/nature02340 | bibcode = 2004Natur.428...37T | s2cid = 4420754 | oclc = 926320276 }}</ref>

Metagenomics can be used for pathogen discovery or diagnosis with the proper bioinformatic tools and databases that can evaluate the possible pathogen. Metagenomics requires no prior knowledge of the viral genome as it does not require primer or probe design, allowing for rapid response to emerging threats.<ref name="Houldcroft_2017" /> Because this method is agnostic to expected viral content of a sample, it can be used to identify new virus species or divergent members of known species. It therefore has a role in clinical diagnostics, such as identification of pathogens causing encephalitis or virus-associated cancers.<ref name="Houldcroft_2017" />


=== PCR Amplicon Enrichment ===
=== PCR Amplicon Enrichment ===
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=== Ecology ===
=== Ecology ===
Viral metagenomics contributes to viral classification without the need of culture based methodologies and has provided vast insights on viral diversity in any system. Metagenomics can be used to study viruses effects on a given ecosystem and how they effect the microbiome as well as monitoring viruses in an ecosystem for possible spillover into human populations.<ref name="Sommers_2021" /> Within the ecosystems, viruses can be studied to determine how they compete with each other as well as viral effects on functions of the host. Viral metagenomics has been used to study unculturable viral communities in marine and soil ecosystems.<ref name="Alavandi_2012" /><ref name="Pratama_2018" />
Viral metagenomics contributes to viral classification without the need of culture based methodologies and has provided vast insights on viral diversity in any system. Metagenomics can be used to study viruses effects on a given ecosystem and how they effect the microbiome as well as monitoring viruses in an ecosystem for possible spillover into human populations.<ref name="Sommers_2021" /> Within the ecosystems, viruses can be studied to determine how they compete with each other as well as viral effects on functions of the host. Viral metagenomics has been used to study unculturable viral communities in marine and soil ecosystems.<ref name="Alavandi_2012" /><ref name="Pratama_2018">{{cite journal |vauthors=Pratama AA, van Elsas JD |date=August 2018 |title=The 'Neglected' Soil Virome - Potential Role and Impact |journal=Trends in Microbiology |volume=26 |issue=8 |pages=649–662 |doi=10.1016/j.tim.2017.12.004 |pmid=29306554 |s2cid=25057850}}</ref>


=== Infectious Disease Research ===
=== Infectious Disease Research ===

Revision as of 06:38, 1 December 2023

Environmental Shotgun Sequencing (ESS)
         (A) Sampling from habitat
         (B) filtering particles, typically by size
         (C) Lysis and DNA extraction
         (D) cloning and library construction
         (E) sequencing the clones
         (F) sequence assembly into contigs and scaffolds

Viral metagenomics uses metagenomic technologies to detect viral genomic material from diverse environmental and clinical samples.[1][2] Viruses are the most abundant biological entity and are extremely diverse; however, only a small fraction of viruses have been sequenced and only an even smaller fraction have been isolated and cultured.[1][3] Sequencing viruses can be challenging because viruses lack a universally conserved marker gene so gene-based approaches are limited and can only target specific groups of viruses (such as RNA viruses that share a conserved RNA polymerase sequence).[3][4] Metagenomics can be used to study and analyze unculturable viruses and has been an important tool in understanding viral diversity and abundance and in the discovery of novel viruses.[1][5][6] For example, metagenomics methods have been used to describe viruses associated with cancerous tumors and in terrestrial ecosystems.[7]

Viral Metagenomics Challenges

The traditional methods for discovering, characterizing, and assigning viral taxonomy to viruses were based on isolating the virus particle or its nucleic acid from samples[8]. The virus morphology could be visualized using electron microscopy but only if the virus could be isolated in high enough titer to be detected. The virus could be cultured in eukaryotic cell lines or bacteria but only if the appropriate host cell type was known and the nucleic acid of the virus would be detected using PCR but only if a consensus primer was known.[8]

Metagenomics requires no prior knowledge of the viral genome as it does not require primer or probe design, allowing for rapid response to emerging threats.[9] Because this method uses prediction tools to detect viral content of a sample, it can be used to identify new virus species or divergent members of known species.

The earliest metagenomic studies of viruses were carried out on ocean samples in 2002 in which the researchers found that 65% of the sequences of DNA and RNA viruses had no matches in the sequence reference databases.[10] The sequences that were matched to referenced sequences were double-stranded DNA bacteriophages and double-stranded algal viruses.[10]

There are seven classes of viruses based on the Baltimore classification system which groups viruses based on their genomic structure and their manner of transcription: there are double-stranded DNA viruses, single-stranded DNA viruses, double-stranded RNA viruses, and single-stranded RNA virus[11]. Single-stranded RNA can be positive or negative sense. The 2002 sequences that were matched to referenced sequences were double-stranded DNA bacteriophages and double-stranded algal viruses.[12] There is still a bias towards DNA viruses in reference databases. Common reasons for this bias is because RNA viruses mutate more rapidly than DNA viruses, DNA is easier to handle from samples while RNA is unstable, and more steps are needed for RNA metagenomics analysis (reverse transcription).

Acknowledging the importance of viral metagenomics, the International Committee on Taxonomy of Viruses (ICTV) recognizes that genomes assembled from metagenomic data represent a virus and can be classified using the same procedures for viruses isolated via classical virology approaches.[13]

The IMG/VR system and the IMG/VR v.2.0 are the largest interactive public virus databases with over 760,000 metagenomic viral sequences and isolate viruses and serves as a starting point for the sequence analysis of viral fragments derived from metagenomic samples.[14][15]

Methods


Direct Metagenomics

Metagenomic analysis uses whole genome shotgun sequencing to characterize microbial diversity in clinical and environmental samples. Total DNA and/or RNA are extracted from the samples and are prepared on a DNA or RNA library for sequencing.[9] These methods have been used to sequence the whole genome of Epstein-Barr virus (EBV) and HCV, however, contaminating nucleic acids can affect the sensitivity to the target viral genome with the proportion of reads related to the target sequence often being low.[16][17] Due to the uncontrollable nature of environmental DNA samples, the most abundant organisms in the environmental sample are the highest represented in the sequencing data and require large samples to achieve full coverage. That being said, shotgun sequencing ensures that these organisms that would previously go unnoticed in culture dependent methods are represented by some sequence segments.[18]

PCR Amplicon Enrichment

PCR amplicon enrichment enriches a portion of the viral genome prior to sequencing. This is done via PCR amplification of primers that are complementary to a known, highly conserved nucleotide sequence.[9] PCR amplicon enrichment is then followed by whole genome sequencing methods and has been used to track the Ebola virus,[19] Zika Virus,[20] and COVID-19[21] epidemics. PCR amplicon sequencing is more successful for whole genome sequencing of samples with low concentrations. However, with larger viral genomes and the heterogeneity of RNA viruses multiple overlapping primers may be required to cover the amplification of all genotypes. PCR amplicon sequencing requires knowledge of the viral genome prior to sequencing, appropriate primers, and is highly dependent on viral titers, however, PCR amplicon sequencing is a cheaper evaluation method than metagenomic sequencing when studying known viruses with relatively small genomes.[9]

Target Enrichment

Target enrichment is a culture independent method that sequences viral genomes directly from clinical sample using small RNA or DNA probes complementary to the pathogens reference sequence. The probes, which can be bound to a solid phase and capture and pull down complementary DNA sequences in the sample.[9] The presence of overlapping probes increases the tolerance for primer mismatches but their design requires high cost and time so a rapid response is limited. DNA capture is followed by brief PCR cycling and shotgun sequencing. Success of this method is dependent available reference sequences to create the probes and is not suitable for characterization of novel viruses.[9] This method has been used to characterize large and small viruses such as HCV,[17] HSV-1,[22] and HCMV.[23]

Limitations

Viral metagenomics methods can produce erroneous chimerical sequences.[24][25] These can include in vitro artifacts from amplification and in silico artifacts from assembly.[25] Chimeras can form between unrelated viruses, as well as between viral and eukaryotic sequences.[25] The likelihood of errors is partially mitigated by greater sequencing depth, but chimeras can still form in areas of high coverage if the reads are highly fragmented.[24]

Applications

Agriculture

Plant viruses pose a global threat to crop production but through metagenomic sequencing and viral database creation, modified plant viruses can be used to aid in plant immunity as well as alter physical appearance.[26] Data obtained on plant virus genomes from metagenomic sequencing can be used to create clone viruses to inoculate the plant with to study viral components and biological characterization of viral agents with increased reproducibility. Engineered mutant virus strains have been used to alter the coloration and size of various ornamental plants and promote the health of crops.[27]

Ecology

Viral metagenomics contributes to viral classification without the need of culture based methodologies and has provided vast insights on viral diversity in any system. Metagenomics can be used to study viruses effects on a given ecosystem and how they effect the microbiome as well as monitoring viruses in an ecosystem for possible spillover into human populations.[1] Within the ecosystems, viruses can be studied to determine how they compete with each other as well as viral effects on functions of the host. Viral metagenomics has been used to study unculturable viral communities in marine and soil ecosystems.[7][28]

Infectious Disease Research

Viral metagenomics is readily used to discover novel viruses, with a major focus on those zoonotic or pathogenic to humans. Viral databases obtained from metagenomics provides quick response methods to determine viral infections as well as determine drug resistant variants in clinical samples.[9] The contributions of viral metagenomics to viral classification have aided pandemic surveillance efforts as well as made infectious disease surveillance and testing more affordable.[29] Since the majority of human pandemics are zoonotic in origin, metagenomic surveillance can provide faster identification of novel viruses and their reservoirs.[30]

One such surveillance program is the the Global Virome Project (GVP) an international collaborative research initiative based at the One Health Institute at the University of California, Davis.[31][32] The GVP aims to boost infectious disease surveillance around the globe by using low cost sequencing methods in high risk countries to prevent disease outbreaks and to prevent future virus outbreaks.[29][33]

Medicine

Viral metagenomics has been used to test for virus related cancers and difficult to diagnose cases in clinical diagnostics.[33] This method is most often used when conventional and advanced molecular testing cannot find a causative agent for disease. Metagenomic sequencing can also be used to detect pathogenic viruses in clinical samples and provide real time data for a pathogens presence in a population.[30]

See also

References

  1. ^ a b c d Sommers P, Chatterjee A, Varsani A, Trubl G (September 2021). "Integrating Viral Metagenomics into an Ecological Framework". Annual Review of Virology. 8 (1): 133–158. doi:10.1146/annurev-virology-010421-053015. PMID 34033501.
  2. ^ Grasis JA (2018). "Host-Associated Bacteriophage Isolation and Preparation for Viral Metagenomics". Viral Metagenomics. Methods in Molecular Biology. Vol. 1746. New York, NY: Springer New York. pp. 1–25. doi:10.1007/978-1-4939-7683-6_1. ISBN 978-1-4939-7682-9. PMID 29492882. S2CID 3637163. Retrieved 2022-12-02.
  3. ^ a b Krishnamurthy SR, Wang D (July 2017). "Origins and challenges of viral dark matter". Virus Research. 239: 136–142. doi:10.1016/j.virusres.2017.02.002. PMID 28192164.
  4. ^ Pappas N, Roux S, Hölzer M, Lamkiewicz K, Mock F, Marz M, Dutilh BE (2021). "Virus Bioinformatics". In Bamford DH, Zuckerman M (eds.). Encyclopedia of Virology (4th ed.). Elsevier. pp. 124–132. doi:10.1016/b978-0-12-814515-9.00034-5. ISBN 978-0-12-814516-6. PMC 7567488.
  5. ^ Kristensen DM, Mushegian AR, Dolja VV, Koonin EV (January 2010). "New dimensions of the virus world discovered through metagenomics". Trends in Microbiology. 18 (1): 11–19. doi:10.1016/j.tim.2009.11.003. PMC 3293453. PMID 19942437.
  6. ^ Bernardo P, Albina E, Eloit M, Roumagnac P (May 2013). "[Pathology and viral metagenomics, a recent history]". Medecine Sciences (in French). 29 (5): 501–508. doi:10.1051/medsci/2013295013. PMID 23732099.
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