Scalable virome enrichment methods for community detection and quantification

Viruses are important but often overlooked members of most microbial communities, including the human gut, where many remain uncharacterized. This is due to a combination of both computational and experimental limitations: viral nucleotides are difficult to enrich and extract, and once sequenced, their uniqueness and rapid evolutionary divergence can make them difficult to classify. The limitations of high-throughput sequencing approaches to address this have been noted previously, but to our knowledge, no study has evaluated the efficiency of specific protocols for retaining viral nucleotides from a community while depleting non-viral members.
Here, we present our work benchmarking varied experimental protocols to isolate virus-like particles (VLP) from gut microbial communities. Different experimental parameters drawn from multiple previous studies were evaluated to develop an optimized protocol, which was further validated in mock communities (viruses representing common gut viral families) and in spiked stool samples. The optimized VLP isolation protocol efficiently reduced bacterial signals below the limit of detection in mock viral communities. In spiked stool samples, the protocol depleted bacterial signals by approximately 100-fold – although, notably, this still left non-viral nucleotides in the majority in many cases. Different viral clades were also differentially affected by changes in experimental parameters, leading to bias relative to the ground truth. We thus provide a standardized and optimized protocol for gut VLP isolation, with known limits of detection and differential extraction efficiency among potential viral targets.
We are currently carrying out analysis of metagenomic and metatranscriptomic sequencing from VLP-treated preemie stool samples to evaluate the protocol on real-world samples at scale. We are also continuing to improve BAQLaVa (Bioinformatic Application for Quantification and Labeling of Viral taxonomy), a newly developed integrative computational method for virome profiling. Together, we hope these tools will improve experimental and bioinformatic capabilities for gut virome profiling.