mSystemsBioinformaticsPeer-reviewed journal article
Deciphering active prophages from metagenomes
Abstract
Temperate phages (prophages) are ubiquitous in nature and persist as dormant components of host cells (lysogenic stage) before activating and lysing the host (lytic stage). Actively replicating prophages contribute to central community processes, such as enabling bacterial virulence, manipulating biogeochemical cycling, and driving microbial community diversification. Recent advances in sequencing technology have allowed for the identification and characterization of diverse phages, yet no approaches currently exist for identifying if a prophage has activated. Here, we present PropagAtE (Prophage Activity Estimator), an automated software tool for estimating if a prophage is in the lytic or lysogenic stage of infection. PropagAtE uses statistical analyses of prophage-to-host read coverage ratios to decipher actively replicating prophages, irrespective of whether prophages were induced or spontaneously activated. We demonstrate that PropagAtE is fast, accurate and sensitive, regardless of sequencing depth. Application of PropagAtE to prophages from 348 complex metagenomes from human gut, murine gut and soil environments identified distinct spatial and temporal prophage activation signatures, with the highest proportion of active prophages in murine gut samples. Among the soil habitats evaluated (bog, fen and palsa), we identified unique populations of Myxococcales, Acetobacteraceae and Acidimicrobiaceae prophages to be active in fen, palsa and bog habitats, respectively. Within the human gut, 11 prophage populations, some encoding the sulfur metabolism gene cysH or a rhuM-like virulence factor, were consistently present over time but not active. Overall, PropagAtE will facilitate accurate representations of viruses in microbiomes by associating prophages with their active roles in shaping microbial communities in nature.
- organism: human
- organism: mouse
- organism: bacteria (other)
- assay: metagenomics
- research focus: benchmarking
- research focus: variant calling
- research focus: gene regulatory network inference
- research focus: cell-cell communication
- approach: statistical model
- Citations
- 77
- FWCI
- 7.86
- Field percentile
- top 1%
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