Published June 26, 2024 | Version v1
Dataset Open

Comparison between 16S rRNA and shotgun sequencing in colorectal cancer, advanced colorectal lesions, and healthy human gut microbiota

  • 1. Catalan Institute of Oncology (ICO)
  • 2. ROR icon Institut d'Investigació Biomédica de Bellvitge
  • 3. Catalan Institute of Oncology
  • 4. Catalan Institute for Oncology
  • 5. Universitat de Barcelona
  • 6. Department of Clinical Science, Intervention and Technology, Karolinska Institute
  • 7. ROR icon Interaction Hôtes Agents Pathogènes
  • 8. ROR icon Université de Toulouse
  • 9. INRAE
  • 10. ROR icon École Nationale Vétérinaire de Toulouse
  • 11. ROR icon Barcelona Supercomputing Center
  • 12. Institute for Research in Biomedicine (IRB Barcelona)
  • 13. ROR icon Institució Catalana de Recerca i Estudis Avançats
  • 14. CIBERINFEC
  • 15. Gastroenterology Department, Bellvitge University Hospital,
  • 16. Gastroenterology Department, Bellvitge University Hospital
  • 17. Digestive System Service, Moisés Broggi Hospital
  • 18. Endoscopy Unit, Digestive System Service, Viladecans Hospital-IDIBELL
  • 19. ROR icon Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública
  • 20. Department of Clinical Sciences, Faculty of Medicine and health Sciences and Universitat de Barcelona Institute of Complex Systems (UBICS), University of Barcelona (UB)

Description

Background: Gut dysbiosis has been associated with colorectal cancer (CRC), the third most prevalent cancer in the world. This study compares microbiota taxonomic and abundance results obtained by 16S rRNA gene sequencing (16S) and whole shotgun metagenomic sequencing to investigate their reliability for bacteria profiling. The experimental design included 156 human stool samples from healthy controls, advanced (high-risk) colorectal lesion patients (HRL), and CRC cases, with each sample sequenced using both 16S and shotgun methods. We thoroughly compared both sequencing technologies at the species, genus, and family annotation levels, the abundance differences in these taxa, sparsity, alpha and beta diversities, ability to train prediction models, and the similarity of the microbial signature derived from these models. 

Results: As expected, the results showed that 16S detects only part of the gut microbiota community revealed by shotgun, although some genera were only profiled by 16S. The 16S abundance data was sparser and exhibited lower alpha diversity. In lower taxonomic ranks, shotgun and 16S highly differed, partially due to a disagreement in reference databases. When considering only shared taxa, the abundance was positively correlated between the two strategies. We also found a moderate correlation between the shotgun and 16S alpha-diversity measures, as well as their PCoAs. Regarding the machine learning models, only some of the shotgun models showed some degree of predictive power in an independent test set, but we could not demonstrate a clear superiority of one technology over the other. Microbial signatures from both sequencing techniques revealed taxa previously associated with CRC development, e.g., Parvimonas micra. 

Conclusions: Shotgun and 16S sequencing provide two different lenses to examine microbial communities. While we have demonstrated that they can unravel common patterns (including microbial signatures), shotgun often gives a more detailed snapshot than 16S, both in depth and breadth. Instead, 16S will tend to show only part of the picture, giving greater weight to dominant bacteria in a sample. Therefore, we recommend choosing one or another sequencing technique before launching a study. Specifically, shotgun sequencing is preferred for stool microbiome samples and in-depth analyses, while 16S is more suitable for tissue samples and studies with targeted aims. 

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