Capturing the mutational landscape of the beta-lactamase TEM-1

Abstract

Adaptation proceeds through the selection of mutations. The distribution of mutant fitness effect and the forces shaping this distribution are therefore keys to predict the evolutionary fate of organisms and their constituents such as enzymes. Here, by producing and sequencing a comprehensive collection of 10,000 mutants, we explore the mutational landscape of one enzyme involved in the spread of antibiotic resistance, the beta-lactamase TEM-1. We measured mutation impact on the enzyme activity through the estimation of amoxicillin minimum inhibitory concentration on a subset of 990 mutants carrying a unique missense mutation, representing 64% of possible amino acid changes in that protein reachable by point mutation. We established that mutation type, solvent accessibility of residues, and the predicted effect of mutations on protein stability primarily determined alone or in combination changes in minimum inhibitory concentration of mutants. Moreover, we were able to capture the drastic modification of the mutational landscape induced by a single stabilizing point mutation (M182T) by a simple model of protein stability. This work thereby provides an integrated framework to study mutation effects and a tool to understand/define better the epistatic interactions.

Keywords: adaptive landscape; distribution of fitness effects; epistasis.

Fig. 1.

Distribution of mutation effects on the MIC to amoxicillin in mg/L. (A) For each amino acid along the protein, excluding the signal peptide, the average effect of mutations on MIC is presented in the gene box with a color code, and the effect of each individual amino acid change is presented above. The color code corresponds to the color used in B. Gray bars represent amino acid changes reachable through a single mutation that were not recovered in our mutant library. Amino acids considered in the extended active site are associated with a blue bar beneath the gene box. (B) Distribution of mutation effects on the MIC is presented in color bars (n = 990); white bars illustrate the distribution of MIC of the wild-type clones (n = 1,594), in other words the noise in MIC measurement. (C) Representation of the average effect of mutations on MIC for each residue on the 3D structure of the protein.

Fig. 2.

Determinants of mutations effects on MIC. (A) Average effect of amino acid changes on MIC is presented as a matrix. The color code is identical to the one in Fig. 1. (B) Matrix BLOSUM62, representing amino acid penalty used in protein alignments using a color gradient of the same range as in A. In both matrices, only amino acid changes observed in the mutant library are colored. (C) Impact of accessibility to the solvent on mutant’s MIC. The distribution of accessibility of amino acids (buried = 0, fully accessible = 100) is plotted for different categories of mutants sharing the same MIC. Large effect mutations are enriched for buried sites. (D) Impact of predicted effect of mutations on protein stability (∆∆G estimated by PopMusic software) on mutant’s MIC. The distribution of ∆∆G of mutants (∆∆G > 0 is destabilizing, ∆∆G < 0, stabilizing) is plotted for different categories of mutants sharing the same MIC. Large effect mutations are enriched for destabilizing mutations. In C and D, hatched fractions represent amino acids included in the active site. The color code is similar to that of Fig. 1.

Fig. 3.

Epistatic interactions due to the stabilizing mutation M182T. (A) Distribution of mutation effects on MIC in M182T, for mutants also found in the TEM-1 library (n = 167). The color of the bars represents the MIC in the TEM-1 background of the mutants. A much larger fraction of mutants with no effect on MIC is found in M182T and is composed of mutants found to have some deleterious effects in TEM-1 background. (B) Plot of the MIC score in the two different backgrounds. The size of dots represents the number of mutants in that spot. The large fraction of points in the upper diagonal illustrates the compensating effect of mutation M182T. (C and D) Observed (colored bars) and predicted (white bars) distributions of mutant MICs in TEM-1 (C) and M182T backgrounds (D), using a three-parameter biophysical model of stability and excluding the active site.