Identification and characterization of molecular entities differentially expressed in bacteria genome upon treatment with glyphosate shock.

Antimetabolites developed from enzymes in the shikimate pathway are appealing targets. There are, however, certain unidentified molecular entities that show bacterial sensitivity to glyphosate shock. This study aims to identify the expression pattern of such entities following treatment with glyphosate shock and to characterize them structurally and functionally. Understanding such entities' catalytic structure and modulatory role guides the design and development of novel antibiotics. This study's functional profiling of 16S rRNA sequencing data and transcriptome analysis of glyphosate-exposedE. coli revealed that two genes were upregulated and twenty-eight were downregulated after glyphosate shock. We discovered the differential expression of some processes based on functional gene analysis, such as global and overview maps (4.2195 on average), carbohydrate metabolism (0.6858 on average), amino acid metabolism (0.5032 on average), and co-factor and vitamin metabolism (0.5032 on average) (0.2876 on average). After examining the two data sets, we discovered that some unidentified proteins were strongly expressed after glyphosate treatment. After examining the two datasets, we discovered a protein with no unique features expressed when treated with glyphosate. The Ecs2020 model looks to be the most stable in structural modeling investigations, while the catalytic residues sought in drug development are anticipated. Furthermore, biological processes and cellular component enrichment analysis revealed that the differentially expressed genes were strongly related to the trehalose manufacturing process and represented the cell membrane's outer membrane component. To estimate the functional gene content of soil sample metagenomics based on 16S rRNA, predictive functional analysis was done with R using the Tax4Fun2 package. On the other hand, transcriptome analysis was carried out using the R tool GEO2R. The results could be a good starting point for making new antibiotic medicines.