(436q) Using Inverse Metabolic Engineering to Restore Antibiotic Sensitivity in a Resistant P. Aeruginosa Isolate

Antibiotic
resistance is a pervasive and growing clinical problem. Bacterial infections
that prove difficult to cure are often treated with combination therapies that
simultaneously increase susceptibility and target essential genes.  Our aims
are to improve understanding and examine the potential for using combinatorial
therapies against resistant infections.  Towards this end, inverse metabolic
engineering tools were employed to create and characterized mutants displaying aminoglycoside-sensitive
phenotypes from a multi-drug resistant isolate of P. aeruginosa. 
Following random chemical mutageneisis, the frequency of finding mutants with
heightened sensitivity levels was 10^-1, several orders of magnitude
greater than frequencies reported for identifying mutations that increase
resistance (10^-5-10). Transcriptional profiles using Affymetrix
GeneChips were obtained from the resistant isolate, two sensitive mutants, as
well from the sequenced laboratory strain, PAO1.  Hierarchical clustering and
principle component analysis revealed that transcriptional profiles of the
sensitive mutants more closely resembled the sensitive laboratory strain PAO1 and
each other than the parental resistant isolate.  In particular, genes related
to cell permeability and aminoglycoside-modification had significant changes in
expression levels between the resistant isolate and the sensitive mutants. 
Minimum inhibitory concentrations performed on spheroplasts suggested that
outer membrane permeability contributed significantly to the noted changes in
resistance levels.  Altered patterns of resistance to multiple aminoglycosides
suggest that activity of aminoglycoside-modifying enzymes.  Our results
indicate that there are a large number of ways in which sensitivity could be
restored to resistant isolates and that there is large potential for the use of
combination therapies in the treatment of resistant infections.  We are
currently working on the construction of genomic libraries that will be
screened for genes conferring amikacin resistance as well as a promoter probe
with both negative and positive selection genes that will be used for
identifying genes responding to antibiotics.