Evolve and resequence experiments have provided us a tool to understand bacterial adaptation to antibiotics by the gain of genomic mutations. In our previous work, we used short term evolution to isolate mutants resistant to the ribosome targeting antibiotic kanamycin. We had reported the gain of resistance to kanamycin via multiple different point mutations in the translation elongation factor G (EF-G). Furthermore, we had shown that the resistance of EF-G mutants could be increased by second site mutations in the genes rpoD / cpxA / topA / cyaA. In this work we expand on our understanding of these second site mutations. Using genetic tools we asked how mutations in the cell envelope stress sensor kinase (CpxA F218Y ) and adenylate cyclase (CyaA N600Y ) could alter their activities to result in resistance. We found that the mutation in cpxA most likely results in an active Cpx stress response. Further evolution of an EF-G mutant in a higher concentration of kanamycin than what was used in our previous experiments identified the cpxA locus as a primary target for a significant increase in resistance. The mutation in cyaA results in a loss of catalytic activity and probably results in resistance via altered CRP function. Despite a reduction in cAMP levels, the CyaA N600Y mutant has a transcriptome indicative of increased CRP activity, pointing to an unknown non-catalytic role for CyaA in gene expression. From the transcriptomes of double and single mutants we describe the epistasis between EF-G mutant and these second site mutations. We show that the large scale transcriptomic changes in the topoisomerase I (FusA A608E -TopA S180L ) mutant likely result in supercoiling changes in the cell. Finally, genes with known roles in aminoglycoside resistance were present among the misregulated genes in the mutants.Subsequently the R (R Core Team, 2016) package EdgeR (Robinson, McCarthy & Smyth, 2010) was used to call differentially expressed genes using a P value cutoff of 0.001 (using the Benjamini Hochberg method to control the false discovery rate in multiple testing).Gene ontology analysis was carried out using the R package topGO (Alexa & Rahnenfuhrer, 2010). E. coli gene annotations were obtained from Ecocyc (Karp et al., 2014) and gene ontology terms were obtained from the Gene Ontology Consortium (Gene Ontology Consortium, 2015). In topGO, the Fisher test was used to assess significance of enriched gene sets and terms with P values < 0.01 were considered significant.