Metagenomic and Metabolomic Analysis of Traditional Organic Manures with Reference to Isolation and Phenetic Characterization of Potent Plant Growth Promoting Bacteria

Abstract

Traditional organic manures (TOMs) are widely recognized for their newlinesustainable role in agriculture, offering a reservoir of beneficial microorganisms that newlinepromote plant growth and soil health due to their metabolic activities. In the present newlinestudy, the TOMs are characterized based on whole-genome metagenomics in order to newlineunderstand the huge microbial diversities available within and further metabolomic newlinetools such as gas chromatography mass spectrophotometry (GC-MS), which offers a newlineholistic understanding of the metabolomic profiling and its functional potential to newlineidentify key bioactive compounds that influence plant health and soil fertility. Eight newlinetraditional organic manure samples were used for metagenomic analyses in the newlineIllumina 6000 sequencing platform, with results generating metagenomic data read newlinecounts of 2.64 GB to 4.95 GB, showing the variability among organic manure newlinesamples. Average scaffold length ranges from 641 bp to 1,028.6 bp, which bridges the newlinegap between contigs. These predicted genes were then taken further for taxonomic newlineand functional analysis. newlineThe average gene size range is 426.5 bp (vermicompost) to 531 bp (Matka- newlinekhad). Taxonomic abundance studies further revealed that Ghan-jeevamrut was newlinedominated by Actinomycetota, Panchagavya showed Bacillota as the dominant newlinephylum, and BD-500 and BD-501 highlighted Bacillota and Pseudomonadota as the newlinemost abundant phyla. Brahmastra and Matka-khad both identified Bacillota and newlineBacilli as the dominant phylum and vermicompost showed Pseudomonadota as the newlinemost abundant phylum. Overall, these analyses highlight the ecological diversity and newlinefunctional significance of microbial communities in these organic fertilizers. Further, newlinethe clusters of orthologous groups (COG) analysis provided valuable insights into newlinemicrobial diversity, metabolic functions and non-parametric analysis of diversity indices, such as Abundance-based Coverage Estimator (ACE), CHAO1 index, newlineSimpson evenness, Shannon, and Fisher indicated higher bacterial diversity in