Reduced ribosome activity influences the non-uniform evolution of 16S rRNA hypervariable regions

Abstract

16S rRNA gene sequences are commonly analyzed for taxonomic and phylogenetic studies because they contain hypervariable regions that can help distinguish different genera. However, intra-genus distinction is often difficult due to high sequence identities among closely related species. Although common tools for 16S sequence taxonomic classification weight residue variations equally during comparisons, specific residues within hypervariable regions have not drifted evenly through evolution, suggesting that portions of them may be biologically important. We developed an in vivo test system where 16S variants coexisted among natural ribosome populations which allowed their fitness to be evaluated. We found that versions with evolutionarily disparate hypervariable regions were underpopulated in ribosomes and active translation pools, even for a single nucleotide polymorphism (SNP), which indicates functional constraints to the free evolutionary drift of hypervariable regions. Using an in silico method (positional relative entropy), we analyzed over 12,000 16S V3-V4 sequences across Escherichia and Shigella strains and identified species that can be distinguished by position-specific SNPs present in multiple 16S alleles in a genome. When we evaluated these informative SNPs with our in vivo system, we discovered that ribosomes harboring them were compromised, suggesting that their evolution is indeed biologically constrained. Overall, this study demonstrates that SNPs within hypervariable regions are not necessarily inconsequential and that common computational approaches for taxonomic 16S rRNA sequence classification should not assume an even probability of residues at each position.

Multi-Omics Insights into the Impact of Increased Litter Size on Sow Gut Microbiota: Unveiling the Role of Serotonin-Associated Microbiota in Fertility Enhancement

“…The taxonomy of each 16S rRNA gene sequence was analyzed by UCLUST against the Silva 16S rRNA database (v138) using a con dent threshold of 90% [12]. Finally, an OTU table was produced for abundance information and further analysis.…”