Revealing the genetic changes responsible for antibiotic resistance can be critical for developing novel antibiotic therapies. However, systematic studies correlating genotype to phenotype in the context of antibiotic resistance have been missing. In order to fill in this gap, we evolved 88 isogenic Escherichia coli populations against 22 antibiotics for 3 weeks. For every drug, two populations were evolved under strong selection and two populations were evolved under mild selection. By quantifying evolved populations’ resistances against all 22 drugs, we constructed two separate cross-resistance networks for strongly and mildly selected populations. Subsequently, we sequenced representative colonies isolated from evolved populations for revealing the genetic basis for novel phenotypes. Bacterial populations that evolved resistance against antibiotics under strong selection acquired high levels of cross-resistance against several antibiotics, whereas other bacterial populations evolved under milder selection acquired relatively weaker cross-resistance. In addition, we found that strongly selected strains against aminoglycosides became more susceptible to five other drug classes compared with their wild-type ancestor as a result of a point mutation on TrkH, an ion transporter protein. Our findings suggest that selection strength is an important parameter contributing to the complexity of antibiotic resistance problem and use of high doses of antibiotics to clear infections has the potential to promote increase of cross-resistance in clinics.

The lack of effective and well-tolerated therapies against antibiotic-resistant bacteria is a global public health problem leading to prolonged treatment and increased mortality. To improve the efficacy of existing antibiotic compounds, we introduce a new method for strategically inducing antibiotic hypersensitivity in pathogenic bacteria. Following the systematic verification that the AcrAB-TolC efflux system is one of the major determinants of the intrinsic antibiotic resistance levels in Escherichia coli, we have developed a short antisense oligomer designed to inhibit the expression of acrA and increase antibiotic susceptibility in E. coli. By employing this strategy, we can inhibit E. coli growth using 2- to 40-fold lower antibiotic doses, depending on the antibiotic compound utilized. The sensitizing effect of the antisense oligomer is highly specific to the targeted gene’s sequence, which is conserved in several bacterial genera, and the oligomer does not have any detectable toxicity against human cells. Finally, we demonstrate that antisense oligomers improve the efficacy of antibiotic combinations, allowing the combined use of even antagonistic antibiotic pairs that are typically not favored due to their reduced activities.

Evolutionary fitness landscapes of several antibiotic target proteins have been comprehensively mapped showing strong high-order epistasis between mutations, but understanding these effects at the biochemical and structural levels remained open. Here, we carried out an extensive experimental and computational study to quantitatively understand the evolutionary dynamics of Escherichia coli dihydrofolate reductase (DHFR) enzyme in the presence of trimethoprim-induced selection. To facilitate this, we developed a new in vitro assay for rapidly characterizing DHFR steady-state kinetics. Biochemical and structural characterization of resistance-conferring mutations targeting a total of ten residues spanning the substrate binding pocket of DHFR revealed distinct changes in the catalytic efficiencies of mutated DHFR enzymes. Next, we measured biochemical parameters ( K m , K i , and k cat ) for a mutant library carrying all possible combinations of six resistance-conferring DHFR mutations and quantified epistatic interactions between them. We found that the high-order epistasis in catalytic power of DHFR ( k cat and K m ) creates a rugged fitness landscape under trimethoprim selection. Taken together, our data provide a concrete illustration of how epistatic coupling at the level of biochemical parameters can give rise to complex fitness landscapes, and suggest new strategies for developing mutant specific inhibitors.

The antibiotic trimethoprim (TMP) is used to treat a variety of Escherichia coli infections, but its efficacy is limited by the rapid emergence of TMP-resistant bacteria. Previous laboratory evolution experiments have identified resistance-conferring mutations in the gene encoding the TMP target, bacterial dihydrofolate reductase (DHFR), in particular mutation L28R. Here, we show that 4’-desmethyltrimethoprim (4’-DTMP) inhibits both DHFR and its L28R variant, and selects against the emergence of TMP-resistant bacteria that carry the L28R mutation in laboratory experiments. Furthermore, antibiotic-sensitive E. coli populations acquire antibiotic resistance at a substantially slower rate when grown in the presence of 4’-DTMP than in the presence of TMP. We find that 4’-DTMP impedes evolution of resistance by selecting against resistant genotypes with the L28R mutation and diverting genetic trajectories to other resistance-conferring DHFR mutations with catalytic deficiencies. Our results demonstrate how a detailed characterization of resistance-conferring mutations in a target enzyme can help identify potential drugs against antibiotic-resistant bacteria, which may ultimately increase long-term efficacy of antimicrobial therapies by modulating evolutionary trajectories that lead to resistance.

Dihydrofolate reductase (DHFR) is a ubiquitous enzyme with an essential role in cell metabolism. DHFR catalyzes the reduction of dihydrofolate to tetrahydrofolate, which is a precursor for purine and thymidylate synthesis. Several DHFR targeting antifolate drugs including trimethoprim, a competitive antibacterial inhibitor, have therefore been developed and are clinically used. Evolution of resistance against antifolates is a common public health problem rendering these drugs ineffective. To combat the resistance problem, it is important to understand resistance-conferring changes in the DHFR structure and accordingly develop alternative strategies. Here, we structurally and dynamically characterize Escherichia coli DHFR in its wild type (WT) and trimethoprim resistant L28R mutant forms in the presence of the substrate and its inhibitor trimethoprim. We use molecular dynamics simulations to determine the conformational space, loop dynamics and hydrogen bond distributions at the active site of DHFR for the WT and the L28R mutant. We also report their experimental kcat, Km, and Ki values, accompanied by isothermal titration calorimetry measurements of DHFR that distinguish enthalpic and entropic contributions to trimethoprim binding. Although mutations that confer resistance to competitive inhibitors typically make enzymes more promiscuous and decrease affinity to both the substrate and the inhibitor, strikingly, we find that the L28R mutant has a unique resistance mechanism. While the binding affinity differences between the WT and the mutant for the inhibitor and the substrate are small, the newly formed extra hydrogen bonds with the aminobenzoyl glutamate tail of DHF in the L28R mutant leads to increased barriers for the dissociation of the substrate and the product. Therefore, the L28R mutant indirectly gains resistance by enjoying prolonged binding times in the enzyme-substrate complex. While this also leads to slower product release and decreases the catalytic rate of the L28R mutant, the overall effect is the maintenance of a sufficient product formation rate. Finally, the experimental and computational analyses together reveal the changes that occur in the energetic landscape of DHFR upon the resistance-conferring L28R mutation. We show that the negative entropy associated with the binding of trimethoprim in WT DHFR is due to water organization at the binding interface. Our study lays the framework to study structural changes in other trimethoprim resistant DHFR mutants.

15Evolutionary fitness landscapes of certain antibiotic target enzymes have been comprehensively

The E. coli dihydrofolate reductase (DHFR) destabilizing domain (DD), which shows promise as a biologic tool and potential gene therapy approach, can be utilized to achieve spatial and temporal control of protein abundance in vivo simply by administration of its stabilizing ligand, the routinely prescribed antibiotic trimethoprim (TMP). However, chronic TMP use drives development of antibiotic resistance (increasing likelihood of subsequent infections) and disrupts the gut microbiota (linked to autoimmune and neurodegenerative diseases), tempering translational excitement of this approach in model systems and for treating human diseases. Herein, we identified a TMP-based, non-antibiotic small molecule, termed 14a (MCC8529), and tested its ability to control multiple DHFRbased reporters and signaling proteins. We found that 14a is non-toxic and can effectively stabilize DHFR DDs expressed in mammalian cells. Furthermore, 14a crosses the blood-retinal barrier and stabilizes DHFR DDs expressed in the mouse eye with kinetics comparable to that of TMP (%6 h). Surprisingly, 14a stabilized a DHFR DD in the liver significantly better than TMP did, while having no effect on the mouse gut microbiota. Our results suggest that alternative small-molecule DHFR DD stabilizers (such as 14a) may be ideal substitutes for TMP in instances when conditional, non-antibiotic control of protein abundance is desired in the eye and beyond.

Rapid detection and phenotyping of pathogenic microbes is critical for administration of effective antibiotic therapies and for impeding the spread of antibiotic resistance. Here, we present a novel platform, rapid ultrasensitive detector (RUSD), that utilizes the high reflectance coefficient at high incidence angles when light travels from low- to high-refractive-index media. RUSD leverages a principle that does not require complex manufacturing, labeling, or processing steps. Utilizing RUSD, we can detect extremely low cell densities (optical density [OD] ≥ 5 × 10 −7 ) that correspond to approximately 20 bacterial cells or a single fungal cell in the detection volume, which is nearly 4 orders of magnitude more sensitive than standard OD methods. RUSD can measure minimum inhibitory concentrations (MICs) of commonly used antibiotics against gram-negative and gram-positive bacteria, including Staphylococcus aureus , Pseudomonas aeruginosa , and Escherichia coli , within 2 to 4 h. Here, we demonstrate that antibiotic susceptibility tests for several pathogens can rapidly be performed with RUSD using both small inoculum sizes (500 cells/mL) and larger inoculum sizes (5 × 10 5 cells/mL) used in standard antibiotic susceptibility tests. We anticipate that the RUSD system will be particularly useful for the cases in which antibiotic susceptibility tests have to be done with a limited number of bacterial cells that are available. Its compatibility with standard antibiotic susceptibility tests, simplicity, and low cost can make RUSD a viable and rapidly deployed diagnostic tool.