CRISPR-resolved virus-host interactions in a municipal landfill include non-specific viruses, hyper-targeted viral populations, and interviral conflicts

Discarded diversity: Novel megaphages, auxiliary metabolic genes, and virally encoded CRISPR-Cas systems in landfills

Background

Viruses are the most abundant microbial entity on the planet, impacting microbial community structure and ecosystem services. Despite outnumbering Bacteria and Archaea by an order of magnitude, viruses have been comparatively underrepresented in reference databases. Metagenomic examinations have illustrated that viruses of Bacteria and Archaea have been specifically understudied in engineered environments. Here we employed metagenomic and computational biology methods to examine the diversity, host interactions, and genetic systems of viruses predicted from 27 samples taken from three municipal landfills across North America.

Results

We identified numerous viruses that are not represented in reference databases, including the third largest bacteriophage genome identified to date (~678 kbp), and note a cosmopolitan diversity of viruses in landfills that are distinct from viromes in other systems. Host-virus interactions were examined via host CRISPR spacer to viral protospacer mapping which captured hyper-targeted viral populations and six viral populations predicted to infect across multiple phyla. Virally-encoded auxiliary metabolic genes (AMGs) were identified with the potential to augment hosts' methane, sulfur, and contaminant degradation metabolisms, including AMGs not previously reported in literature. CRISPR arrays and CRISPR-Cas systems were identified from predicted viral genomes, including the two largest bacteriophage genomes to contain these genetic features. Some virally encoded Cas effector proteins appear distinct relative to previously reported Cas systems and are interesting targets for potential genome editing tools.

Conclusions

Our observations indicate landfills, as heterogeneous contaminated sites with unique selective pressures, are key locations for diverse viruses and atypical virus-host dynamics.

Inferring strain-level mutational drivers of phage-bacteria interaction phenotypes

The enormous diversity of bacteriophages and their bacterial hosts presents a significant challenge to predict which phages infect a focal set of bacteria. Infection is largely determined by complementary -and largely uncharacterized- genetics of adsorption, injection, and cell take-over. Here we present a machine learning (ML) approach to predict phage-bacteria interactions trained on genome sequences of and phenotypic interactions amongst 51 Escherichia coli strains and 45 phage λ strains that coevolved in laboratory conditions for 37 days. Leveraging multiple inference strategies and without a priori knowledge of driver mutations, this framework predicts both who infects whom and the quantitative levels of infections across a suite of 2,295 potential interactions. The most effective ML approach inferred interaction phenotypes from independent contributions from phage and bacteria mutations, predicting phage host range with 86% mean classification accuracy while reducing the relative error in the estimated strength of the infection phenotype by 40%. Further, transparent feature selection in the predictive model revealed 18 of 176 phage λ and 6 of 18 E. coli mutations that have a significant influence on the outcome of phage-bacteria interactions, corroborating sites previously known to affect phage λ infections, as well as identifying mutations in genes of unknown function not previously shown to influence bacterial resistance. While the genetic variation studied was limited to a focal, coevolved phage-bacteria system, the method's success at recapitulating strain-level infection outcomes provides a path forward towards developing strategies for inferring interactions in non-model systems, including those of therapeutic significance.

Dispersal, habitat filtering, and eco-evolutionary dynamics as drivers of local and global wetland viral biogeography

Wetlands store 20-30% of the world's soil carbon, and identifying the microbial controls on these carbon reserves is essential to predicting feedbacks to climate change. Although viral infections likely play important roles in wetland ecosystem dynamics, we lack a basic understanding of wetland viral ecology. Here 63 viral size-fraction metagenomes (viromes) and paired total metagenomes were generated from three time points in 2021 at seven fresh- and saltwater wetlands in the California Bodega Marine Reserve. We recovered 12,826 viral population genomic sequences (vOTUs), only 4.4% of which were detected at the same field site two years prior, indicating a small degree of population stability or recurrence. Viral communities differed most significantly among the seven wetland sites and were also structured by habitat (plant community composition and salinity). Read mapping to a new version of our reference database, PIGEONv2.0 (515,763 vOTUs), revealed 196 vOTUs present over large geographic distances, often reflecting shared habitat characteristics. Wetland vOTU microdiversity was significantly lower locally than globally and lower within than between time points, indicating greater divergence with increasing spatiotemporal distance. Viruses tended to have broad predicted host ranges via CRISPR spacer linkages to metagenome-assembled genomes, and increased SNP frequencies in CRISPR-targeted major tail protein genes suggest potential viral eco-evolutionary dynamics in response to both immune targeting and changes in host cell receptors involved in viral attachment. Together, these results highlight the importance of dispersal, environmental selection, and eco-evolutionary dynamics as drivers of local and global wetland viral biogeography.