Molecular Characterization of Copper Resistance Bacterial Strains and its Optimization Using Statistical Methods

Abstract

Heavy metal contamination is one of the key environmental complications. Due to some disadvantages of conventional methods, the use of active organisms is becoming more popular technique to remove it. In the present study, primarily 35 bacterial strains were discovered in metal containing media. After being identified resistance power to different copper concentrations (100–1000 mg/l), JRHM33 had the highest level of resistance up to 1000 mg/l of copper. Using the 16S rRNA sequencing, bacterial strain JRHM33 was discovered and revealed 99% similarity to pseudomonas aeruginosa. Sequencing and bioinformatics study using conserved domain analysis supported the laccase gene is present in JRHM33 and has classification as a member of the multicopper oxidase superfamily, which has reduction capacity of metal ions. Analysis of phenotype microarray (PM) technology provides an insight into the metabolic profiling of microbial cell into Pseudomonas aeruginosa JRHM33. Furthermore, Using the central composite design of response surface methodology (CCD-RSM), the successive optimization of the process parameters was attempted for the maximum reduction of the copper. Maximum 68.71% Cu reduction was achieved at 6.71 pH, 90.61 min of incubation time, 5 ml of inoculum size, and 113 rpm of agitation. The generated model has R2 value of 0.9834, indicating that the ANOVA gave it a very significant result. The findings of the validation experiment showed a remarkable similarity between the projected and experimental results. It is determined that bacterial strains isolated from metal-contaminated effluent employ their natural capacity to change toxic heavy metals into less harmful or nontoxic forms.