Microbiome data reveal significant differences in the bacterial diversity in freshwater rohu (Labeo rohita) across the supply chain in Dhaka, Bangladesh.
A Q M Robiul KawserMd Javed FoysalEng-Guan ChuaMd Hazrat AliAdnan MannanMuhammad A B SiddikSulav Indra PaulMd Mahbubur RahmanAlfred Chin-Yen TayPublished in: Letters in applied microbiology (2022)
The present study aimed to characterize and compare the skin and gut microbial communities of rohu at various post-harvest stages of consumption using quantitative real-time polymerase chain reaction and 16S rRNA-based amplicon sequencing. Real-time PCR amplification detected higher copy numbers for coliform bacteria-Escherichia coli, Salmonella enterica and Shigella spp. in the marketed fish-compared to fresh and frozen samples. The 16S rRNA data revealed higher alpha diversity measurements in the skin of fish from different retail markets of Dhaka city. Beta ordination revealed distinct clustering of bacterial OTUs for the skin and gut samples from three different groups. At the phylum level, Proteobacteria was most abundant in all groups except the Fusobacteria in the control fish gut. Although Aeromonas was found ubiquitous in all types of samples, diverse bacterial genera were identified in the marketed fish samples. Nonetheless, low species richness was observed for the frozen fish. Most of the differentially abundant bacteria in the skin samples of marketed fish are opportunistic human pathogens enriched at different stages of postharvest handling and processing. Therefore, considering the microbial contamination in the aquatic environment in Bangladesh, post-harvest handling should be performed with proper methods and care to minimize bacterial transmission into fish.
Keyphrases
- escherichia coli
- single cell
- soft tissue
- wound healing
- healthcare
- endothelial cells
- palliative care
- gene expression
- drinking water
- mass spectrometry
- high resolution
- microbial community
- big data
- real time pcr
- multidrug resistant
- machine learning
- pseudomonas aeruginosa
- artificial intelligence
- cystic fibrosis
- klebsiella pneumoniae
- data analysis