Silverj, A. | Search bioRxiv

bioRxiv2024

Discovering and exploring the hidden diversity of human gut viruses using highly enriched virome samples

Viruses are an abundant and crucial component of the human microbiome, but accurately discovering them via metagenomics is still challenging. Currently, the available viral reference genomes poorly represent the diversity in microbiome samples, and expanding such a set of viral references is difficult. As a result, many viruses are still undetectable through metagenomics even when considering the power of de novo metagenomic assembly and binning, as viruses lack universal markers. Here, we describe a novel approach to catalog new viral members of the human gut microbiome and show how the resulting resource improves metagenomic analyses. We retrieved >3,000 viral-like particles (VLP) enriched metagenomic samples (viromes), evaluated the efficiency of the enrichment in each sample to leverage the viromes of highest purity, and applied multiple analysis steps involving assembly and comparison with hundreds of thousands of metagenome-assembled genomes to discover new viral genomes. We reported over 162,000 viral sequences passing quality control from thousands of gut metagenomes and viromes. The great majority of the retrieved viral sequences ([~]94.4%) were of unknown origin, most had a CRISPR spacer matching host bacteria, and four of them could be detected in >50% of a set of 18,756 gut metagenomes we surveyed. We included the obtained collection of sequences in a new MetaPhlAn 4.1 release, which can quantify reads within a metagenome matching the known and newly uncovered viral diversity. Additionally, we released the viral database for further virome and metagenomic studies of the human microbiome.

microbiology↗

bioRxiv2020

A step-by-step sequence-based analysis of virome enrichment protocol for freshwater and sediment samples

Cultivation-free metagenomic analysis afforded unprecedented details on the diversity, structure and potential functions of microbial communities in different environments. When employed to study the viral fraction of the community that is recalcitrant to cultivation, metagenomics can shed light into the diversity of viruses and their role in natural ecosystems. However, despite the increasing interest in virome metagenomics, methodological issues still hinder the proper interpretation and comparison of results across studies. Virome enrichment experimental protocols are key multi-step processes needed for separating and concentrating the viral fraction from the whole microbial community prior to sequencing. However, there is little information on their efficiency and their potential biases. To fill this gap, we used metagenomic and amplicon sequencing to examine the microbial community composition through the serial filtration and concentration steps commonly used to produce viral-enriched metagenomes. The analyses were performed on water and sediment samples from an Alpine lake. We found that, although the diversity of the retained microbial communities declined progressively during the serial filtration, the final viral fraction contained a large proportion (from 10% to 40%) of non-viral taxa, and that the efficacy of filtration showed biases based on taxonomy. Our results quantified the amount of bacterial genetic material in viromes and highlighted the influence of sample type on the enrichment efficacy. Moreover, since viral-enriched samples contained a significant portion of microbial taxa, computational sequence analysis should account for such biases in the downstream interpretation pipeline. ImportanceFiltration is a commonly used method to enrich viral particles in environmental samples. However, there is little information on its efficiency and potential biases on the final result. Using a sequence-based analysis on water and sediment samples, we found that filtration efficacy is dependent on sample type and that the final virome contained a large proportion of non-viral taxa. Our finding stressed the importance of downstream analysis to avoid biased interpretation of data.

microbiology↗