Exploring the microbiomes of freshwater and marine ecosystems as key players in aquatic species health - Sahu, Ma’ruf Karimov, Rame Riadhusin, Ibragimov Ulmas Rakhmanovich, Yeshwanth Raj, Deepak Kumar (International Journal of Aquatic Research and Environmental Studies, 2025)
Microbial communities consist of bacteria, archaea, fungi, and viruses, and are critical to the health, resiliency, and ecological balance in both marine and freshwater systems. They help maintain environmental equilibrium. There is increasing curiosity about the differences, at a gross level, in the structure and function of freshwater and marine microbiomes, although comparative studies remain limited. This study analyzes the compositional and functional diversity of the microbial consortia in different aquatic systems within the spectrum of their contributions to enhanced immunity, nutrient cycling, and possible disease antagonism... An integrated framework of Metagenomic Host Interaction Profiling (MHIP) is introduced, where community structure is determined by 16S rRNA gene sequencing and shotgun metagenomic sequencing, and metagenomic analysis is followed by machine learning correlation mapping to enable the understanding of community dynamics and their impact on specific host physiological attributes. Sampling was performed to capture spatial variability and the impact of anthropogenic activities, ranging from the freshwater environments (lakes, rivers) to the marine environments (estuaries, coastal zones). Preliminary analysis shows marine microbiomes to be taxonomically more complex with functional redundancy than freshwater systems, where more specialized symbiotic relationships are encountered and driven by environmental factors. The MHIP framework can be described as an exceptional diagnostic tool in species health evaluation based on microbial signatures and can aid targeted aquaculture and biodiversity maintenance.
Keywords: Aquatic microbiome, Metagenomics, Host microbial interaction, Freshwater, marine ecosystems, Biodiversity, Species health, Machine learning, Environmental microbiology, MHIP framework