Bhutia MO, Thapa N, Shangpliang HNJ, Tamang JP. High-throughput sequence analysis of bacterial communities and their predictive functionalities in traditionally preserved fish products of Sikkim, India.
Food Res Int 2020;
143:109885. [PMID:
33992337 DOI:
10.1016/j.foodres.2020.109885]
[Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 10/30/2020] [Accepted: 10/31/2020] [Indexed: 12/31/2022]
Abstract
Traditionally preserved fish products viz. suka ko maccha, a smoked fish product, sidra and sukuti, sun-dried fish products are commonly consumed in Sikkim state in India. Bacterial communities in these fish products were analysed by high-throughput sequence (HTS) method supported by bioinformatics tool. Metataxonomic of the overall bacterial communities in samples revealed the abundance of phylum Firmicutes followed by Proteobacteria. Psychrobacter was abundant genus in all traditionally preserved fish products of Sikkim, followed by Bacillus, Staphylococcus, Serratia, Clostridium, Enterobacter, Pseudomonas, Rummeliibacillus, Enterococcus, Photobacterium, Myroides, Peptostreptococcus, Plesiomonas and Achromobacter. Product-wise distribution showed that Bacillus was abundant in suka ko maacha and sidra samples, whereas Psychrobacter was abundant in sukuti samples. Unique genus to each product was observed on the basis of analysis of shared operational-taxonomic-unit (OTU) contents, Alpha diversity indices showed significantly differences among the samples, and also showed maximum coverage as per Good's coverage (0.99). Beta diversity showed clustering of bacterial compositions between suka ko maacha and sidra, whereas sukuti showed scattering pattern among the other samples, indicating a diverse population in suka ko maacha and sidra samples. Non-parametric analysis of abundant genera and predictive functionalities showed the complex bacterial inter-dependencies with predictive functionalities mostly in metabolism (79.88%).
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