ABSTRACT
Liver transplantation (LT) is crucial for end-stage liver disease, but it is linked to infection risks. Pathobionts, microorganisms potentially harmful under specific conditions, can cause complications posttransplant. Monitoring such pathogens in fecal samples can be challenging and therefore remains underexplored post-LT. This study aimed to analyze the gut microbiome before and after LT, tracking pathobionts and correlating clinical data. The study involved 17 liver transplant recipients, 17 healthy relatives (spouses), and 13 donors. Gut samples collected pretranplantation and posttransplantation underwent bacterial and fungal profiling through DNA sequencing. Quantitative polymerase chain reaction was used to assess microbial load. Statistical analyses included alpha and beta diversity measures, differential abundance analysis, and correlation tests between microbiome and clinical parameters. Microbiome analysis revealed dynamic changes in diversity posttransplant. Notably, high-severity patients showed persistent and greater dysbiosis during the first months post-LT compared with low-severity patients, partly due to an antibiotic treatment pre-LT. The analysis identified a higher proportion of pathogens such as Escherichia coli/Shigella flexneri in high-severity cases posttransplant. Furthermore, butyrate producers including Roseburia intestinalis, Anaerostipes hadrus, and Eubacterium coprostanoligenes were positively correlated with levels of albumin. This study offers valuable insights into post-LT microbiome changes, shedding light on the need for tailored prophylactic treatment post-LT.
ABSTRACT
Amplicon-based 16S ribosomal RNA sequencing remains a widely used method to profile microbial communities, especially in low biomass samples, due to its cost-effectiveness and low-complexity approach. Reference databases are a mainstay for taxonomic assignments, which typically rely on popular databases such as SILVA, Greengenes, Genome Taxonomy Database (GTDB), or Ribosomal Database Project (RDP). However, the inconsistency of the nomenclature across databases and the presence of shortcomings in the annotation of these databases are limiting the resolution of the analysis. To overcome these limitations, we created the GSR database (Greengenes, SILVA, and RDP database), an integrated and manually curated database for bacterial and archaeal 16S amplicon taxonomy analysis. Unlike previous integration approaches, this database creation pipeline includes a taxonomy unification step to ensure consistency in taxonomical annotations. The database was validated with three mock communities, two real data sets, and a 10-fold cross-validation method and compared with existing 16S databases such as Greengenes, Greengenes 2, GTDB, ITGDB, SILVA, RDP, and MetaSquare. Results showed that the GSR database enhances taxonomical annotations of 16S sequences, outperforming current 16S databases at the species level, based on the evaluation of the mock communities. This was confirmed by the 10-fold cross-validation, except for Greengenes 2. The GSR database is available for full-length 16S sequences and the most commonly used hypervariable regions: V4, V1-V3, V3-V4, and V3-V5.IMPORTANCETaxonomic assignments of microorganisms have long been hindered by inconsistent nomenclature and annotation issues in existing databases like SILVA, Greengenes, Greengenes2, Genome Taxonomy Database, or Ribosomal Database Project. To overcome these issues, we created Greengenes-SILVA-RDP database (GSR-DB), accurate and comprehensive taxonomic annotations of 16S amplicon data. Unlike previous approaches, our innovative pipeline includes a unique taxonomy unification step, ensuring consistent and reliable annotations. Our evaluation analyses showed that GSR-DB outperforms existing databases in providing species-level resolution, especially based on mock-community analysis, making it a game-changer for microbiome studies. Moreover, GSR-DB is designed to be accessible to researchers with limited computational resources, making it a powerful tool for scientists across the board. Available for full-length 16S sequences and commonly used hypervariable regions, including V4, V1-V3, V3-V4, and V3-V5, GSR-DB is a go-to database for robust and accurate microbial taxonomy analysis.
