Comparative microbiome profiling of plant- and animal-derived traditional fermented foods from Mizoram, India using full-length 16S rRNA sequencing.

Traditional fermented foods represent complex microbial ecosystems shaped by substrate composition, indigenous processing practices, and local environmental conditions. However, the microbiomes of many traditional fermented foods from Northeast India remain poorly characterized. In this study, the bacterial communities associated with thirteen traditional fermented foods from Mizoram, India, representing both plant- and animal-derived fermentations, were characterized using full-length 16S rRNA gene sequencing on the Oxford Nanopore platform. Sequencing generated approximately 1.94 million high-quality reads, enabling high-resolution taxonomic profiling of the fermented food microbiomes. The microbial communities were predominantly composed of Bacillota, Pseudomonadota, and Cyanobacteriota, although their relative abundances varied considerably among fermented food types. Plant-derived fermented foods were enriched with fermentative bacterial genera, including Bacillus, Lactobacillus, Lacticaseibacillus, Pediococcus, and Weissella, whereas animal-derived fermented foods showed a greater abundance of anaerobic taxa, particularly Clostridium. Alpha diversity analyses demonstrated higher microbial richness in plant-derived fermented foods, while beta diversity revealed clear substrate-dependent clustering of microbial communities. Functional prediction indicated that metabolism-related pathways, particularly carbohydrate and amino acid metabolism, predominated across the fermented food microbiomes, and LEfSe analysis identified distinct microbial biomarkers associated with plant- and animal-derived fermentations. Collectively, these findings provide the first comprehensive microbiome characterization of traditional fermented foods from Mizoram and demonstrate that fermentation substrate is a major determinant of microbial community composition and predicted functional potential. Although full-length 16S rRNA gene sequencing improved taxonomic resolution, taxonomic interpretations were made cautiously at the genus level where appropriate because of the inherent limitations of 16S rRNA gene-based classification.