| Literature DB >> 31323792 |
Tasha M Santiago-Rodriguez1, Emily B Hollister2.
Abstract
The virome is comprised of endogenous retroviruses, eukaryotic viruses, and bacteriophages and is increasingly being recognized as an essential part of the human microbiome. The human virome is associated with Type-1 diabetes (T1D), Type-2 diabetes (T2D), Inflammatory Bowel Disease (IBD), Human Immunodeficiency Virus (HIV) infection, and cancer. Increasing evidence also supports trans-kingdom interactions of viruses with bacteria, small eukaryotes and host in disease progression. The present review focuses on virus ecology and biology and how this translates mostly to human gut virome research. Current challenges in the field and how the development of bioinformatic tools and controls are aiding to overcome some of these challenges are also discussed. Finally, the present review also focuses on how human gut virome research could result in translational and clinical studies that may facilitate the development of therapeutic approaches.Entities:
Keywords: microbiome; phage therapy; viral mock communities; virome
Year: 2019 PMID: 31323792 PMCID: PMC6669467 DOI: 10.3390/v11070656
Source DB: PubMed Journal: Viruses ISSN: 1999-4915 Impact factor: 5.048
Figure 1Flow gram of potential applications of high-throughput sequencing in the discovery of an unexpected eukaryotic virus in a disease, and the discovery of eukaryotic viruses in a disease of unknown etiology.
Figure 2Potential phage-mediated lysis of commensal and pathogenic bacteria in the human gut. Panel (A) shows lysis of commensal bacteria in the human gut triggered by external factors that would need to individually be evaluated for each disease phenotype (e.g., Type 1 diabetes (T1D), Inflammatory Bowel Disease (IBD) and cancer). Panel (B) shows how phages can potentially aid in pathogen lysis. This is hypothesized based on T4 phages in vitro experiments [36].
Examples of evolutionary advantages conferred through lysogenic conversion. Modified from [60].
| Evolutionary Advantage through Lysogenic Conversion | Bacterial Host | Reference |
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| Cell colonization and adhesion |
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| Promotion of cell invasion |
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| Resistance to serum and phagocytes |
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| Exotoxin production |
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| Antibiotic susceptibility |
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Figure 3Lysogenic conversion of commensal bacteria. Temperate phages can carry genes that can confer an evolutionary advantage to the bacterial host cell. Figure shows antibiotic-resistance as an example, which can further aid commensal bacteria to survive when exposed to specific antibiotics.
Figure 4Phages as potential human pathogens. This was probably first suggested with phages harboring stx, which represents a serious risk to human health. Stx B subunit targets a human endothelial cells receptor known as Gb3. Another study involving introducing phages infecting bacteria from the Enterobacteriaceae, Staphylococcaceae and Streptococcaceae families to mice also demonstrated the possibility of phages as human pathogens. An increase in lactulose/mannitol ratio and in Butyrivibrio, Oscillospira and Ruminococcus relative abundances, as well as a decrease in Blautia, Catenibacterium, Lactobacillus and Faecalibacterium relative abundances was observed. The decrease in Lactobacillus and Faecalibacterium (highlighted in red asterisks), specifically, is associated with impaired gut permeability and inflammation.
Bioinformatic tools for virome analyses. Table shows selected bioinformatic tools classified as alignment- or k-mer-based, a brief description and the source.
| Bioinformatic Tool | Description | Source | Reference |
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| VIROME | Web-application interface for the classification of Open-Reading Frames (ORF) or assembled data, which receive one classification. |
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| VirusSeeker | Linux-based for the classification of sequences at the nucleotide and amino acid level. It is used for virus characterization and discovery; the latter requires assembled reads. |
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| VirFind | Web-based tool that maps the reads to reference genomes and also performs de novo assembly to get longer contigs to identify known viruses and discover new ones. It performs Blastn and Blastx. |
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| FastViromeExplorer | Pseudo-alignment tool that maps reads to a reference virus database, filters the alignment results based on minimal coverage criteria and reports virus types and abundances along with taxonomic annotation. |
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| VirMap | Suitable for low coverage and highly divergent viruses in metagenomic datasets. Uses a mapping assembly algorithm with both nucleotide and amino-acid alignments to build virus-like super-scaffolds. It possesses a taxonomic classification algorithm based on bits-per-base scoring system. |
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| EZ-Map | Python-based to filter, align, and analyze viromes from cell-free DNA samples. |
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| MetaVir2 | Web-application interface that works with assembled reads to perform taxonomy assignments based on available sequences in RefSeq. |
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| Vipie | Web-application capable of analyzing datasets from different studies by performing de-novo assembly, followed by taxonomic classification. |
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| ViromeScan | Linux-based application that performs taxonomy classification from raw data. The tool uses hierarchical databases for eukaryotic viruses to assign reads to viral species. |
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| Vanator | Perl-based pipeline designed for metagenomics and virus discovery projects using Illumina FASTQ-formatted deep sequencing reads as the input. |
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| VirSorter | Predicts prophage and viral sequences in a reference-dependent and -independent manner. Detects circular sequences, performs gene prediction, removes poor quality protein-predicted sequences and those remaining are compared to PFAM and RefSeqABVir or Viromes databases. |
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| VirusDetect | Performs virus identification by aligning small RNA reads to a known virus reference database and also performs de novo assembly. |
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| PHASTER | The enhanced release of PHAST for the fast identification and annotation of prophage sequences from assembled metagenomic datasets. |
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| MetaVir2 | Web-application interface that works with assembled reads to perform taxonomy assignments based on available sequences in RefSeq. Provides users the option to perform the analysis based on k-mers. |
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| VIP | Developed for identification of viral pathogens from metagenomic datasets. Removes background reads, classifies reads based on nucleotide and amino acid homology, and uses k-mer based de novo assembly for evolutionary studies. |
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| VirFinder | K-mer-based tool to identify sequence signatures that distinguish viral sequences from host sequences. |
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