Enzyme efficiency but not thermostability drives cefotaxime resistance evolution in TEM-1 β-lactamase

Abstract

A leading intellectual challenge in evolutionary genetics is to identify the specific phenotypes that drive adaptation. Enzymes offer a particularly promising opportunity to pursue this question, because many enzymes’ contributions to organismal fitness depend on a comparatively small number of experimentally accessible properties. Moreover, on first principles the demands of enzyme thermostability stand in opposition to the demands of catalytic activity. This observation, coupled with the fact that enzymes are only marginally thermostable, motivates the widely held hypothesis that mutations conferring functional improvement require compensatory mutations to restore thermostability. Here, we explicitly test this hypothesis for the first time, using four missense mutations in TEM-1 β-lactamase that jointly increase cefotaxime Minimum Inhibitory Concentration (MIC) ∼1500-fold. First, we report enzymatic efficiency (kcat/KM) and thermostability (Tm, and thence ΔG of folding) for all combinations of these mutations. Next, we fit a quantitative model that predicts MIC as a function of kcat/KM and ΔG. While kcat/KM explains ∼54% of the variance in cefotaxime MIC (∼92% after log transformation), ΔG does not improve explanatory power of the model. We also find that cefotaxime MIC rises more slowly in kcat/KM than predicted. Several explanations for these discrepancies are suggested. Finally, we demonstrate substantial sign epistasis in MIC and kcat/KM, and antagonistic pleiotropy between phenotypes, in spite of near numerical additivity in the system. Thus constraints on selectively accessible trajectories, as well as limitations in our ability to explain such constraints in terms of underlying mechanisms are observed in a comparatively “well-behaved” system.

Beyond stability constraints: a biophysical model of enzyme evolution with selection for stability and activity

“…Further, activationenergy factors are needed to explain, for instance, the effect of temperature on the growth rate, thus fitness, of viruses and bacteria [8,20,21]. Moreover, in some cases selection on activity is stronger than selection on stability [31]. Despite the extensive evidence of selection on protein activity, there is a general lack of biophysical models of evolution that consider activity constraints explicitly [13].…”

Predicting the Viability of Beta-Lactamase: How Folding and Binding Free Energies Correlate with Beta-Lactamase Fitness

“…Although the community's understanding is still evolving, recent research points to the significant impact the kinetics of catalysis [46], protein quality control [10], protein aggregation and degradation [6], and post-translational modifications [78], among other non-thermodynamic factors, have on fitness. Accurately modeling protein kinetics still represents a formidable challenge for simulators as doing so either requires the ability to simulate out to long enough times to capture all relevant protein dynamics or models that can reliably project out to these long times [79].…”

“…The relatively slow temperature ramp and small cuvettes helped to ensure that the samples attained equilibrium at each temperature. Changes in the ellipticity at 223 nM were recorded from 20 to 90 • C. The experiment was performed in triplicate and the melting April 15, 2020 5/26 temperature (T m ) and van't Hoff enthalpy (∆H) were calculated by fitting resultant data to a two-state transition as described in previous work [24,46].…”

“…We then analyze how predictive these experimental and computational free energies are of TEM-1 β-lactamase fitness. The TEM-1 β-lactamase protein is a model enzyme [46] that hydrolyzes such essential β-lactam drugs as penicillins (including the ampicillin modeled here) and cephalosporins, and is therefore directly responsible for the evolution of many common forms of bacterial drug resistance (see the Supporting Information for further information about β-lactamases, including TEM-1) [47,48]. Beyond basic research interest, being able to predict the fitness of TEM-1's many possible mutants is therefore also helpful for predicting and ultimately combating the mutants that will lead to the next-generation of drug-resistant, "superbug" bacteria.…”

Inferring Genetic Interactions From Comparative Fitness Data

“…On the other hand, empirical findings suggest that the opposite is true for many other systems (Szendro et al 2013;Neidhart, Szendro, and Krug 2013;Sailer and Harms 2017a). For example, Weinreich, Lan, et al (2013) argue that three-way and four-way interactions can be as strong as pairwise epistasis referring to various empirical fitness studies, and Knies, Cai, and Weinreich (2017) find many epistatic interactions in a numerically near-additive fitness landscape, reducing dramatically the number of accessible evolutionary trajectories. Although the significance of higher order interactions may vary between systems, the topic has not been thoroughly investigated.…”