Genomic characterization of Pseudomonas wenzhouensis A.M.S.S. isolated from oil-contaminated soil and its metabolic potential for bioremediation
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Abstract
Petroleum-contaminated soil is useful for environmental bioremediation studies because it contains many different types of microorganisms that can hydrolyze hydrocarbons and degrade sulfur-contaminated environments. This study included the isolation of Pseudomonas wenzhouensis A.M.S.S. from oil-contaminated soil in Mosul, Iraq; its taxonomic status and potential use were determined through extensive whole-genome sequencing. Whole-genome sequencing, Genome Assembly and Annotation, 16S rRNA gene phylogenetic tree, and in silico DNA-DNA Hybridization and Genetic analysis were performed on the isolate to assess its ability to degrade hydrocarbon and sulfur compounds. Genomic analysis showed that the GC content was 61.9 percent, 4.228 projected protein-coding sequences, and 50 tRNA genes, and the 4.65 Mb draft genome produced by de novo assembly demonstrated significant metabolic potential. A.M.S.S. is a unique strain and possibly a new species within the genus, according to anin silico DNA–DNA hybridization value of 60.9%, which is below the 70% species criterion, even though phylogenomic analysis (TYGS) placed the isolate closest to Pseudomonas wenzhouensis A20. A vast gene repertoire for co factor biosynthesis, stress adaptation, and the metabolism of amino acids and carbohydrates was identified through subsystem annotation. Notably, the genome encodes several aromatic compound degradation pathways, and reduction of inorganic sulfate, supporting its ability to catabolize pollutants derived from petroleum and indicating a genomic potential for the breakdown of contaminants produced from petroleum. The study provides important clues about the ecological breadth of this strain and points to its possible use in environmental biotechnology, specifically for the bioremediation of polluted ecosystems.
Article Details
Article Details
Aromatic compound degradation, Comparative genomics, Genomic prediction of bioremediation, Sulfur metabolism
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