Phage Genome Diversity in a Biogas-Producing Microbiome Analyzed by Illumina and Nanopore GridION Sequencing.
Katharina WillenbücherDaniel WibbergLiren HuangMarius ConradyPatrice RammJulia GätckeTobias BuscheChristian BrandtUlrich SzewzykAnd Andreas SchlüterJimena Barrero CanosaIrena MausPublished in: Microorganisms (2022)
The microbial biogas network is complex and intertwined, and therefore relatively stable in its overall functionality. However, if key functional groups of microorganisms are affected by biotic or abiotic factors, the entire efficacy may be impaired. Bacteriophages are hypothesized to alter the steering process of the microbial network. In this study, an enriched fraction of virus-like particles was extracted from a mesophilic biogas reactor and sequenced on the Illumina MiSeq and Nanopore GridION sequencing platforms. Metagenome data analysis resulted in identifying 375 metagenome-assembled viral genomes (MAVGs). Two-thirds of the classified sequences were only assigned to the superkingdom Viruses and the remaining third to the family Siphoviridae , followed by Myoviridae , Podoviridae , Tectiviridae , and Inoviridae . The metavirome showed a close relationship to the phage genomes that infect members of the classes Clostridia and Bacilli . Using publicly available biogas metagenomic data, a fragment recruitment approach showed the widespread distribution of the MAVGs studied in other biogas microbiomes. In particular, phage sequences from mesophilic microbiomes were highly similar to the phage sequences of this study. Accordingly, the virus particle enrichment approach and metavirome sequencing provided additional genome sequence information for novel virome members, thus expanding the current knowledge of viral genetic diversity in biogas reactors.
Keyphrases
- anaerobic digestion
- genetic diversity
- antibiotic resistance genes
- sewage sludge
- data analysis
- municipal solid waste
- pseudomonas aeruginosa
- single cell
- microbial community
- healthcare
- sars cov
- single molecule
- genome wide
- gene expression
- machine learning
- gram negative
- multidrug resistant
- social media
- solid state
- cystic fibrosis
- transcription factor