Biophysical principles predict fitness landscapes of drug resistance - PubMed (original) (raw)

Biophysical principles predict fitness landscapes of drug resistance

João V Rodrigues et al. Proc Natl Acad Sci U S A. 2016.

Erratum in

Abstract

Fitness landscapes of drug resistance constitute powerful tools to elucidate mutational pathways of antibiotic escape. Here, we developed a predictive biophysics-based fitness landscape of trimethoprim (TMP) resistance for Escherichia coli dihydrofolate reductase (DHFR). We investigated the activity, binding, folding stability, and intracellular abundance for a complete set of combinatorial DHFR mutants made out of three key resistance mutations and extended this analysis to DHFR originated from Chlamydia muridarum and Listeria grayi We found that the acquisition of TMP resistance via decreased drug affinity is limited by a trade-off in catalytic efficiency. Protein stability is concurrently affected by the resistant mutants, which precludes a precise description of fitness from a single molecular trait. Application of the kinetic flux theory provided an accurate model to predict resistance phenotypes (IC50) quantitatively from a unique combination of the in vitro protein molecular properties. Further, we found that a controlled modulation of the GroEL/ES chaperonins and Lon protease levels affects the intracellular steady-state concentration of DHFR in a mutation-specific manner, whereas IC50 is changed proportionally, as indeed predicted by the model. This unveils a molecular rationale for the pleiotropic role of the protein quality control machinery on the evolution of antibiotic resistance, which, as we illustrate here, may drastically confound the evolutionary outcome. These results provide a comprehensive quantitative genotype-phenotype map for the essential enzyme that serves as an important target of antibiotic and anticancer therapies.

Keywords: DHFR; drug resistance; fitness landscapes; molten globule; protein stability.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.

Fig. 1.

DHFR mutations associated with TMP resistance and mapping of phenotypic and molecular effects in a combinatorially complete set of mutations. (A) Resistance-conferring mutations in DHFR are found close to binding pocket of dihydrofolate (in yellow). (B) Color-code scheme of all possible combination made out of the three mutations studied in this work. (C) IC50 values determined for E. coli transformed with pFLAG plasmid harboring different DHFR mutants. (D) Stability data measured for different DHFR mutants. (Top) Δ_T_m values were determined from thermal denaturation experiments monitored by the change in tryptophan fluorescence upon unfolding. (Bottom) bis-ANS fluorescence upon binding to different DHFR mutants measured after incubation at 37 °C for 5 min. (E) Catalytic parameters determined for all DHFR mutants. (Top) Catalytic efficiency measured at 25 °C from full progress reaction curves. (Bottom) TMP inhibition constants (_K_i) determined at 25 °C. (F) Catalytic efficiency trades-off with increase in _K_i. (Inset) A similar trade-off observed for orthologous DHFR mutants from L. grayi and C. muridarum.

Fig. S1.

Fig. S1.

(A) Chromatogram of E. coli DHFR mutants separated on a size-exclusion chromatography column. Red starts indicate expected partition coefficient (_K_av) of multiples of DHFR theoretical molecular mass (18 kDa). (B) Thermal denaturation of E. coli DHFR mutants followed by intrinsic tryptophan fluorescence. The WT denaturation curve is represented in gray for comparison.

Fig. S2.

Fig. S2.

Comparison of the catalytic parameters determined for DHFR mutants from E. coli, L. grayi, and C. muridarum.

Fig. S3.

Fig. S3.

Sequence alignment of DHFR from E. coli, L. grayi, and C. muridarum. Sequence identity (percent) matrix is shown.

Fig. S4.

Fig. S4.

Effect of mutations associated with TMP resistance in E. coli on kinetic and biophysical properties of DHFRs from (A) C. muridarum and (B) L. grayi.

Fig. 2.

Fig. 2.

Mutations associated with TMP resistance affect the intracellular abundance of DHFR. (A) Intracellular abundance of E. coli DHFR mutants expressed from pFLAG plasmid in the absence of inducer (color scheme as in Fig. 1_B_). Abundance was determined from total catalytic activity measurements in cell lysates prepared from cultures of different mutants grown at 37 °C in M9 minimal media. Values are normalized to WT DHFR expressed from plasmid at the same conditions. (B) Protein abundance is inversely correlated with propensity to form molten-globule intermediates, as assessed by bis-ANS fluorescence. The represented data (mean ± SEM) were obtained from abundance measurements of E. coli DHFR mutants and orthologous DHFRs from L. grayi and C. muridarum including the cognate TMP-resistance mutations. The fit that is shown was obtained using the equation y = A/(γ_·_ANS), where A = 4.7 × 105 molecules/cell and γ = 1.5.

Fig. S5.

Fig. S5.

(A) Relative protein abundances determined for DHFR mutants from E. coli, L. grayi, and C. muridarum. Values were normalized to pFLAG-expressed WT E. coli DHFR abundance. (B) Protein abundances do not correlate with Δ_T_m.

Fig. 3.

Fig. 3.

Prediction of IC50 from molecular parameters. (A) Normalized rate of DHFR reaction(V norm) determines fitness in E. coli. Growth measurements were performed at 37 °C in M9 minimal media and under varying concentrations of TMP. The V norm values were computed at any given TMP concentration using Eq. 4, in which α was set to 0.1, and with input from experimentally determined molecular quantities (protein abundance and catalytic constants) for each DHFR mutant. The results shown include data from TMP inhibition determined for all E. coli, L. grayi, and C. muridarum DHFR mutants expressed from pFLAG plasmid (gray) and nontransformed E. coli MG1655 strain expressing solely its endogenous chromosomal DHFR (blue). The solid line represents the best fit of the data using Eq. 1 with a = 1, and from which B was determined from nonlinear regression (1.3 ± 0.1 SE). Also shown for comparison are the fitness data obtained in previous works where orthologous DHFRs have been incorporated in E. coli chromosome (20) and where chromosomal DHFR was under IPTG-controlled expression (13) (red and orange points, respectively). (B) Comparison of experimental vs. predicted IC50 for TMP in strains expressing DHFR mutants from pFLAG. Predicted IC50 were calculated from Eq. 5 using estimates of cellular DHFR abundance and catalytic parameters. Increasing the pFLAG-based expression of a particular mutant DHFR by means of adding IPTG results in corresponding increase in IC50 as predicted from Eq. 5. Data obtained for parent strain MG1655 E. coli expressing only its chromosomal DHFR are shown. (C) Prediction of fitness from protein biophysics. IC50 was predicted using Eq. 6, in which the protein abundance term in Eq. 5 was replaced by 1/ANS from the reciprocal relationship shown in Fig. 2. (Inset) The decrease in the correlation coefficient if the protein abundance term, or its predictor (1/ANS), is omitted in the equation.

Fig. S6.

Fig. S6.

Dependence of normalized growth with V norm calculated from Eq. 4 using α = 1 (Left) and 0.1 (Right).

Fig. S7.

Fig. S7.

Fitness definition does not change the shape of the curve in Fig. 3_A_. (A) Replot of Fig. 3_A_ using the maximal growth rate as a proxy for fitness instead of the area under the growth curve (AUC). (B) Comparison of fitness data defined by both the area under the curve and maximal growth rates.

Fig. S8.

Fig. S8.

(A) Prediction of IC50 from isolated molecular traits. (B) Change in IC50 upon induction of protein expression with increasing amounts of IPTG. Each color set represents three different IPTG concentrations (control and 10 and 50 μM).

Fig. 4.

Fig. 4.

Resistance to TMP is influenced by components of the PQC system. (A) Impact of GroEL overexpression and Lon protease deletion on IC50 (mean ± SEM) determined for E. coli and orthologous DHFR mutants from L. grayi and C. muridarum. *P < 0.05, **P < 0.005. (B) Effect of GroEL overexpression and Lon deletion affect IC50 mostly through their effect on protein abundance. Protein abundances were determined for various E. coli, L. grayi, and C. muridarum DHFR mutants under GroEL overexpression or Lon deletion. The dotted line shows a theoretical linear dependence with slope = 1.

Fig. S9.

Fig. S9.

Magnitude of the effect of GroEL and ΔLon on IC50 correlates with ANS properties of DHFR variants.

Fig. 5.

Fig. 5.

Quantitative dissection of fitness into multiple microscopic landscapes of different molecular traits. (A) Relative resistance to TMP computed by the product [_DHFR_] × _k_cat/_K_m × _K_i. (B) Relative contribution of protein abundance and catalysis to overall fitness. (C) Catalysis term is decomposed into catalytic efficiency and _K_i.

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