Critical Assessment of Whole Genome and Viral Enrichment Shotgun Metagenome on the Characterization of Stool Total Virome in Hepatocellular Carcinoma Patients

Viruses. 2022 Dec 24;15(1):53. doi: 10.3390/v15010053.

Abstract

Viruses are the most abundant form of life on earth and play important roles in a broad range of ecosystems. Currently, two methods, whole genome shotgun metagenome (WGSM) and viral-like particle enriched metagenome (VLPM) sequencing, are widely applied to compare viruses in various environments. However, there is no critical assessment of their performance in recovering viruses and biological interpretation in comparative viral metagenomic studies. To fill this gap, we applied the two methods to investigate the stool virome in hepatocellular carcinoma (HCC) patients and healthy controls. Both WGSM and VLPM methods can capture the major diversity patterns of alpha and beta diversities and identify the altered viral profiles in the HCC stool samples compared with healthy controls. Viral signatures identified by both methods showed reductions of Faecalibacterium virus Taranis in HCC patients' stool. Ultra-deep sequencing recovered more viruses in both methods, however, generally, 3 or 5 Gb were sufficient to capture the non-fragmented long viral contigs. More lytic viruses were detected than lysogenetic viruses in both methods, and the VLPM can detect the RNA viruses. Using both methods would identify shared and specific viral signatures and would capture different parts of the total virome.

Keywords: deep sequencing; hepatocellular carcinoma; metagenome; total virome.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Carcinoma, Hepatocellular* / genetics
  • Ecosystem
  • Genome, Viral
  • Humans
  • Liver Neoplasms* / genetics
  • Metagenome
  • Metagenomics / methods
  • Virome
  • Viruses* / genetics

Grants and funding

The research was supported by grants from the Federal Government through the St George and Sutherland Medical Research Foundation, and Hong Kong Research Grants Council (RGC) General Research Fund (GRF) 11206819 and Hong Kong Innovation and Technology Fund (ITF) MRP/071/20X.