Multi-Region 16S rRNA Sequencing Enhances Mapping Resolution of Sponge Microbial Communities

Comprehensive profiling of microbial communities is fundamental to understanding ecological processes. Yet conventional 16S rRNA gene sequencing, typically targeting only one or two hypervariable regions, suffers from primer bias and limitations in reference databases. While metagenomic and metatranscriptomic approaches offer broader insights, their high cost and labor intensity hinder their scalability for comparative studies of large sets of samples. These limitations are particularly pronounced in holobiont systems, which harbor distinct and highly diverse microbial consortia critical to host functions such as nutrient cycling or chemical defenses. Marine sponges, as ancient holobionts with taxonomically complex microbiomes, exemplify these limitations.In this study, we applied CoSMIC, a method that combines sequencing of six 16S rRNA regions with a niche-specific augmented reference database, to microbiome profiling in seven sponge species spanning the four Porifera classes. As a first step, we assessed the completeness of standard databases: over 45 percent of short reads from sponge samples remained unassigned using the SILVA database alone, highlighting its limited coverage of sponge-associated microbes. Augmenting the reference database with full-length 16S rRNA sequences from the same samples, which is part of the CoSMIC pipeline, improved assignment rates by up to 48 percent. Subsequently, the use of CoSMIC's multi-region approach significantly reduced taxonomic ambiguity by approximately eightfold, compared to the V3V4 region analysis, and revealed greater microbial richness. Importantly, the six-region framework allowed the resolution of distinct ASVs that the standard V3V4 region alone failed to differentiate, refining core community composition and enhancing taxonomic and quantitative precision. This higher resolution improved the separation of microbial communities between sponge species and revealed temporal shifts in microbial composition that single-region approaches failed to detect. These results demonstrate the advantages of multi-region 16S profiling with a niche-specific augmented database in revealing hidden microbial diversity and structure.