Enzyme Efficiency but Not Thermostability Drives Cefotaxime Resistance Evolution in TEM-1 β-Lactamase

Quantifying higher-order epistasis: beware the chimera

Epistasis, or interactions in which alleles at one locus modify the fitness effects of alleles at other loci, plays a fundamental role in genetics, protein evolution, and many other areas of biology. Epistasis is typically quantified by computing the deviation from the expected fitness under an additive or multiplicative model using one of several formulae. However, these formulae are not all equivalent. Importantly, one widely used formula - which we call the chimeric formula - measures deviations from a multiplicative fitness model on an additive scale, thus mixing two measurement scales. We show that for pairwise interactions, the chimeric formula yields a different magnitude, but the same sign (synergistic vs. antagonistic) of epistasis compared to the multiplicative formula that measures both fitness and deviations on a multiplicative scale. However, for higher-order interactions, we show that the chimeric formula can have both different magnitude and sign compared to the multiplicative formula - thus confusing negative epistatic interactions with positive interactions, and vice versa. We resolve these inconsistencies by deriving fundamental connections between the different epistasis formulae and the parameters of the multivariate Bernoulli distribution . Our results demonstrate that the additive and multiplicative epistasis formulae are more mathematically sound than the chimeric formula. Moreover, we demonstrate that the mathematical issues with the chimeric epistasis formula lead to markedly different biological interpretations of real data. Analyzing multi-gene knockout data in yeast, multi-way drug interactions in E. coli , and deep mutational scanning (DMS) of several proteins, we find that 10 - 60% of higher-order interactions have a change in sign with the multiplicative or additive epistasis formula. These sign changes result in qualitatively different findings on functional divergence in the yeast genome, synergistic vs. antagonistic drug interactions, and and epistasis between protein mutations. In particular, in the yeast data, the more appropriate multiplicative formula identifies nearly 500 additional negative three-way interactions, thus extending the trigenic interaction network by 25%.

Evolutionary druggability for low-dimensional fitness landscapes toward new metrics for antimicrobial applications

The term 'druggability' describes the molecular properties of drugs or targets in pharmacological interventions and is commonly used in work involving drug development for clinical applications. There are no current analogues for this notion that quantify the drug-target interaction with respect to a given target variant's sensitivity across a breadth of drugs in a panel, or a given drug's range of effectiveness across alleles of a target protein. Using data from low-dimensional empirical fitness landscapes composed of 16 β-lactamase alleles and 7 β-lactam drugs, we introduce two metrics that capture (i) the average susceptibility of an allelic variant of a drug target to any available drug in a given panel ('variant vulnerability'), and (ii) the average applicability of a drug (or mixture) across allelic variants of a drug target ('drug applicability'). Finally, we (iii) disentangle the quality and magnitude of interactions between loci in the drug target and the seven drug environments in terms of their mutation by mutation by environment (G x G x E) interactions, offering mechanistic insight into the variant variability and drug applicability metrics. Summarizing, we propose that our framework can be applied to other datasets and pathogen-drug systems to understand which pathogen variants in a clinical setting are the most concerning (low variant vulnerability), and which drugs in a panel are most likely to be effective in an infection defined by standing genetic variation in the pathogen drug target (high drug applicability).

Epistasis arises from shifting the rate-limiting step during enzyme evolution of a β-lactamase

Epistasis, the non-additive effect of mutations, can provide combinatorial improvements to enzyme activity that substantially exceed the gains from individual mutations. Yet the molecular mechanisms of epistasis remain elusive, undermining our ability to predict pathogen evolution and engineer biocatalysts. Here we reveal how directed evolution of a β-lactamase yielded highly epistatic activity enhancements. Evolution selected four mutations that increase antibiotic resistance 40-fold, despite their marginal individual effects (≤2-fold). Synergistic improvements coincided with the introduction of super-stochiometric burst kinetics, indicating that epistasis is rooted in the enzyme's conformational dynamics. Our analysis reveals that epistasis stemmed from distinct effects of each mutation on the catalytic cycle. The initial mutation increased protein flexibility and accelerated substrate binding, which is rate-limiting in the wild-type enzyme. Subsequent mutations predominantly boosted the chemical steps by fine-tuning substrate interactions. Our work identifies an overlooked cause for epistasis: changing the rate-limiting step can result in substantial synergy that boosts enzyme activity.

Positive epistasis drives clavulanic acid resistance in double mutant libraries of BlaC β-lactamase

Phenotypic effects of mutations are highly dependent on the genetic backgrounds in which they occur, due to epistatic effects. To test how easily the loss of enzyme activity can be compensated for, we screen mutant libraries of BlaC, a β-lactamase from Mycobacterium tuberculosis, for fitness in the presence of carbenicillin and the inhibitor clavulanic acid. Using a semi-rational approach and deep sequencing, we prepare four double-site saturation libraries and determine the relative fitness effect for 1534/1540 (99.6%) of the unique library members at two temperatures. Each library comprises variants of a residue known to be relevant for clavulanic acid resistance as well as residue 105, which regulates access to the active site. Variants with greatly improved fitness were identified within each library, demonstrating that compensatory mutations for loss of activity can be readily found. In most cases, the fittest variants are a result of positive epistasis, indicating strong synergistic effects between the chosen residue pairs. Our study sheds light on a role of epistasis in the evolution of functional residues and underlines the highly adaptive potential of BlaC.

Epistasis and pleiotropy shape biophysical protein subspaces associated with drug resistance

Protein space is a rich analogy for genotype-phenotype maps, where amino acid sequence is organized into a high-dimensional space that highlights the connectivity between protein variants. It is a useful abstraction for understanding the process of evolution, and for efforts to engineer proteins towards desirable phenotypes. Few mentions of protein space consider how protein phenotypes can be described in terms of their biophysical components, nor do they rigorously interrogate how forces like epistasis-describing the nonlinear interaction between mutations and their phenotypic consequences-manifest across these components. In this study, we deconstruct a low-dimensional protein space of a bacterial enzyme (dihydrofolate reductase; DHFR) into "subspaces" corresponding to a set of kinetic and thermodynamic traits [k_{cat}, K_{M}, K_{i}, and T_{m} (melting temperature)]. We then examine how combinations of three mutations (eight alleles in total) display pleiotropy, or unique effects on individual subspace traits. We examine protein spaces across three orthologous DHFR enzymes (Escherichia coli, Listeria grayi, and Chlamydia muridarum), adding a genotypic context dimension through which epistasis occurs across subspaces. In doing so, we reveal that protein space is a deceptively complex notion, and that future applications to bioengineering should consider how interactions between amino acid substitutions manifest across different phenotypic subspaces.

Evolutionary druggability: leveraging low-dimensional fitness landscapes towards new metrics for antimicrobial applications

The term "druggability" describes the molecular properties of drugs or targets in pharmacological interventions and is commonly used in work involving drug development for clinical applications. There are no current analogues for this notion that quantify the drug-target interaction with respect to a given target variant's sensitivity across a breadth of drugs in a panel, or a given drug's range of effectiveness across alleles of a target protein. Using data from low-dimensional empirical fitness landscapes composed of 16 β-lactamase alleles and seven β-lactam drugs, we introduce two metrics that capture (i) the average susceptibility of an allelic variant of a drug target to any available drug in a given panel ("variant vulnerability"), and (ii) the average applicability of a drug (or mixture) across allelic variants of a drug target ("drug applicability"). Finally, we (iii) disentangle the quality and magnitude of interactions between loci in the drug target and the seven drug environments in terms of their mutation by mutation by environment (G × G × E) interactions, offering mechanistic insight into the variant variability and drug applicability metrics. Summarizing, we propose that our framework can be applied to other datasets and pathogen-drug systems to understand which pathogen variants in a clinical setting are the most concerning (low variant vulnerability), and which drugs in a panel are most likely to be effective in an infection defined by standing genetic variation in the pathogen drug target (high drug applicability).

Epistasis meets pleiotropy in shaping biophysical protein subspaces associated with antimicrobial resistance

Protein space is a rich analogy for genotype-phenotype maps, where amino acid sequence is organized into a high-dimensional space that highlights the connectivity between protein variants. It is a useful abstraction for understanding the process of evolution, and for efforts to engineer proteins towards desirable phenotypes. Few framings of protein space consider how higher-level protein phenotypes can be described in terms of their biophysical dimensions, nor do they rigorously interrogate how forces like epistasis-describing the nonlinear interaction between mutations and their phenotypic consequences-manifest across these dimensions. In this study, we deconstruct a low-dimensional protein space of a bacterial enzyme (dihydrofolate reductase; DHFR) into "subspaces" corresponding to a set of kinetic and thermodynamic traits [(kcat, KM, Ki, and Tm (melting temperature)]. We then examine how three mutations (eight alleles in total) display pleiotropy in their interactions across these subspaces. We extend this approach to examine protein spaces across three orthologous DHFR enzymes (Escherichia coli, Listeria grayi, and Chlamydia muridarum), adding a genotypic context dimension through which epistasis occurs across subspaces. In doing so, we reveal that protein space is a deceptively complex notion, and that the process of protein evolution and engineering should consider how interactions between amino acid substitutions manifest across different phenotypic subspaces.

Experimental and In Silico Analysis of TEM β-Lactamase Adaptive Evolution

Multiple mutations often have non-additive (epistatic) phenotypic effects. Epistasis is of fundamental biological relevance but is not well understood mechanistically. Adaptive evolution, i.e., the evolution of new biochemical activities, is rich in epistatic interactions. To better understand the principles underlying epistasis during genetic adaptation, we studied the evolution of TEM-1 β-lactamase variants exhibiting cefotaxime resistance. We report the collection of a library of 487 observed evolutionary trajectories for TEM-1 and determine the epistasis status based on cefotaxime resistance phenotype for 206 combinations of 2-3 TEM-1 mutations involving 17 positions under adaptive selective pressure. Gain-of-function (GOF) mutations are gatekeepers for adaptation. To see if GOF phenotypes can be inferred based solely on sequence data, we calculated the enrichment of GOF mutations in the different categories of epistatic pairs. Our results suggest that this is possible because GOF mutations are particularly enriched in sign and reciprocal sign epistasis, which leave a major imprint on the sequence space accessible to evolution. We also used FoldX to explore the relationship between thermodynamic stability and epistasis. We found that mutations in observed evolutionary trajectories tend to destabilize the folded structure of the protein, albeit their cumulative effects are consistently below the protein's free energy of folding. The destabilizing effect is stronger for epistatic pairs, suggesting that modest or local alterations in folding stability can modulate catalysis. Finally, we report a significant relationship between epistasis and the degree to which two protein positions are structurally and dynamically coupled, even in the absence of ligand.

Stochastic establishment of β-lactam-resistant Escherichia coli mutants reveals conditions for collective resistance

For antibiotic resistance to arise, new resistant mutants must establish in a bacterial population before they can spread via natural selection. Comprehending the stochastic factors that influence mutant establishment is crucial for a quantitative understanding of antibiotic resistance emergence. Here, we quantify the single-cell establishment probability of four Escherichia coli strains expressing β-lactamase alleles with different activity against the antibiotic cefotaxime, as a function of antibiotic concentration in both unstructured (liquid) and structured (agar) environments. We show that concentrations well below the minimum inhibitory concentration (MIC) can substantially hamper establishment, particularly for highly resistant mutants. While the pattern of establishment suppression is comparable in both tested environments, we find greater variability in establishment probability on agar. Using a simple branching model, we investigate possible sources of this stochasticity, including environment-dependent lineage variability, but cannot reject other possible causes. Lastly, we use the single-cell establishment probability to predict each strain's MIC in the absence of social interactions. We observe substantially higher measured than predicted MIC values, particularly for highly resistant strains, which indicates cooperative effects among resistant cells at large cell numbers, such as in standard MIC assays.

Natural and Synthetic Suppressor Mutations Defy Stability-Activity Tradeoffs

Thermodynamic stability represents one important constraint on protein evolution, but the molecular basis for how mutations that change stability impact fitness remains unclear. Here, we demonstrate that a prevalent global suppressor mutation in TEM β-lactamase, M182T, increases fitness by reducing proteolysis in vivo. We also show that a synthetic mutation, M182S, can act as a global suppressor and suggest that its absence from natural populations is due to genetic inaccessibility rather than fundamental differences in the protein's stability or activity.

Metallo-β-lactamases in the Age of Multidrug Resistance: From Structure and Mechanism to Evolution, Dissemination, and Inhibitor Design

Antimicrobial resistance is one of the major problems in current practical medicine. The spread of genes coding for resistance determinants among bacteria challenges the use of approved antibiotics, narrowing the options for treatment. Resistance to carbapenems, last resort antibiotics, is a major concern. Metallo-β-lactamases (MBLs) hydrolyze carbapenems, penicillins, and cephalosporins, becoming central to this problem. These enzymes diverge with respect to serine-β-lactamases by exhibiting a different fold, active site, and catalytic features. Elucidating their catalytic mechanism has been a big challenge in the field that has limited the development of useful inhibitors. This review covers exhaustively the details of the active-site chemistries, the diversity of MBL alleles, the catalytic mechanism against different substrates, and how this information has helped developing inhibitors. We also discuss here different aspects critical to understand the success of MBLs in conferring resistance: the molecular determinants of their dissemination, their cell physiology, from the biogenesis to the processing involved in the transit to the periplasm, and the uptake of the Zn(II) ions upon metal starvation conditions, such as those encountered during an infection. In this regard, the chemical, biochemical and microbiological aspects provide an integrative view of the current knowledge of MBLs.

Avoidance of protein unfolding constrains protein stability in long-term evolution

Every amino acid residue can influence a protein's overall stability, making stability highly susceptible to change throughout evolution. We consider the distribution of protein stabilities evolutionarily permittable under two previously reported protein fitness functions: flux dynamics and misfolding avoidance. We develop an evolutionary dynamics theory and find that it agrees better with an extensive protein stability data set for dihydrofolate reductase orthologs under the misfolding avoidance fitness function rather than the flux dynamics fitness function. Further investigation with ribonuclease H data demonstrates that not any misfolded state is avoided; rather, it is only the unfolded state. At the end, we discuss how our work pertains to the universal protein abundance-evolutionary rate correlation seen across organisms' proteomes. We derive a closed-form expression relating protein abundance to evolutionary rate that captures Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens experimental trends without fitted parameters.

Antibiotic-Resistant Bacteria in Aquaculture and Climate Change: A Challenge for Health in the Mediterranean Area

Aquaculture is the productive activity that will play a crucial role in the challenges of the millennium, such as the need for proteins that support humans and the respect for the environment. Aquaculture is an important economic activity in the Mediterranean basin. A great impact is presented, however, by aquaculture practices as they involve the use of antibiotics for treatment and prophylaxis. As a consequence of the use of antibiotics in aquaculture, antibiotic resistance is induced in the surrounding bacteria in the column water, sediment, and fish-associated bacterial strains. Through horizontal gene transfer, bacteria can diffuse antibiotic-resistance genes and mobile resistance genes further spreading genetic determinants. Once triggered, antibiotic resistance easily spreads among aquatic microbial communities and, from there, can reach human pathogenic bacteria, making vain the use of antibiotics for human health. Climate change claims a significant role in this context, as rising temperatures can affect cell physiology in bacteria in the same way as antibiotics, causing antibiotic resistance to begin with. The Mediterranean Sea represents a 'hot spot' in terms of climate change and aspects of antibiotic resistance in aquaculture in this area can be significantly amplified, thus increasing threats to human health. Practices must be adopted to counteract negative impacts on human health, with a reduction in the use of antibiotics as a pivotal point. In the meantime, it is necessary to act against climate change by reducing anthropogenic impacts, for example by reducing CO2 emissions into the atmosphere. The One Health type approach, which involves the intervention of different skills, such as veterinary, ecology, and medicine in compliance with the principles of sustainability, is necessary and strongly recommended to face these important challenges for human and animal health, and for environmental safety in the Mediterranean area.

The Role of Rigid Residues in Modulating TEM-1 β-Lactamase Function and Thermostability

The relationship between protein motions (i.e., dynamics) and enzymatic function has begun to be explored in β-lactamases as a way to advance our understanding of these proteins. In a recent study, we analyzed the dynamic profiles of TEM-1 (a ubiquitous class A β-lactamase) and several ancestrally reconstructed homologues. A chief finding of this work was that rigid residues that were allosterically coupled to the active site appeared to have profound effects on enzyme function, even when separated from the active site by many angstroms. In the present work, our aim was to further explore the implications of protein dynamics on β-lactamase function by altering the dynamic profile of TEM-1 using computational protein design methods. The Rosetta software suite was used to mutate amino acids surrounding either rigid residues that are highly coupled to the active site or to flexible residues with no apparent communication with the active site. Experimental characterization of ten designed proteins indicated that alteration of residues surrounding rigid, highly coupled residues, substantially affected both enzymatic activity and stability; in contrast, native-like activities and stabilities were maintained when flexible, uncoupled residues, were targeted. Our results provide additional insight into the structure-function relationship present in the TEM family of β-lactamases. Furthermore, the integration of computational protein design methods with analyses of protein dynamics represents a general approach that could be used to extend our understanding of the relationship between dynamics and function in other enzyme classes.

The Relationship between the Misfolding Avoidance Hypothesis and Protein Evolutionary Rates in the Light of Empirical Evidence

For more than a decade, the misfolding avoidance hypothesis (MAH) and related theories have dominated evolutionary discussions aimed at explaining the variance of the molecular clock across cellular proteins. In this study, we use various experimental data to further investigate the consistency of the MAH predictions with empirical evidence. We also critically discuss experimental results that motivated the MAH development and that are often viewed as evidence of its major contribution to the variability of protein evolutionary rates. We demonstrate, in Escherichia coli and Homo sapiens, the lack of a substantial negative correlation between protein evolutionary rates and Gibbs free energies of unfolding, a direct measure of protein stability. We then analyze multiple new genome-scale data sets characterizing protein aggregation and interaction propensities, the properties that are likely optimized in evolution to alleviate deleterious effects associated with toxic protein misfolding and misinteractions. Our results demonstrate that the propensity of proteins to aggregate, the fraction of charged amino acids, and protein stickiness do correlate with protein abundances. Nevertheless, across multiple organisms and various data sets we do not observe substantial correlations between proteins’ aggregation- and stability-related properties and evolutionary rates. Therefore, diverse empirical data support the conclusion that the MAH and similar hypotheses do not play a major role in mediating a strong negative correlation between protein expression and the molecular clock, and thus in explaining the variability of evolutionary rates across cellular proteins.

Can We Exploit β-Lactamases Intrinsic Dynamics for Designing More Effective Inhibitors?

β-lactamases (BLs) represent the most frequent cause of antimicrobial resistance in Gram-negative bacteria. Despite the continuous efforts in the development of BL inhibitors (BLIs), new BLs able to hydrolyze the last developed antibiotics rapidly emerge. Moreover, the insurgence rate of effective mutations is far higher than the release of BLIs able to counteract them. This results in a shortage of antibiotics that is menacing the effective treating of infectious diseases. The situation is made even worse by the co-expression in bacteria of BLs with different mechanisms and hydrolysis spectra, and by the lack of inhibitors able to hit them all. Differently from other targets, BL flexibility has not been deeply exploited for drug design, possibly because of the small protein size, for their apparent rigidity and their high fold conservation. In this mini-review, we discuss the evidence for BL binding site dynamics being crucial for catalytic efficiency, mutation effect, and for the design of new inhibitors. Then, we report on identified allosteric sites in BLs and on possible allosteric inhibitors, as a strategy to overcome the frequent occurrence of mutations in BLs and the difficulty of competing efficaciously with substrates. Nevertheless, allosteric inhibitors could work synergistically with traditional inhibitors, increasing the chances of restoring bacterial susceptibility towards available antibiotics.

Known Evolutionary Paths Are Accessible to Engineered ß-Lactamases Having Altered Protein Motions at the Timescale of Catalytic Turnover

The evolution of new protein functions is dependent upon inherent biophysical features of proteins. Whereas, it has been shown that changes in protein dynamics can occur in the course of directed molecular evolution trajectories and contribute to new function, it is not known whether varying protein dynamics modify the course of evolution. We investigate this question using three related ß-lactamases displaying dynamics that differ broadly at the slow timescale that corresponds to catalytic turnover yet have similar fast dynamics, thermal stability, catalytic, and substrate recognition profiles. Introduction of substitutions E104K and G238S, that are known to have a synergistic effect on function in the parent ß-lactamase, showed similar increases in catalytic efficiency toward cefotaxime in the related ß-lactamases. Molecular simulations using Protein Energy Landscape Exploration reveal that this results from stabilizing the catalytically-productive conformations, demonstrating the dominance of the synergistic effect of the E014K and G238S substitutions in vitro in contexts that vary in terms of sequence and dynamics. Furthermore, three rounds of directed molecular evolution demonstrated that known cefotaximase-enhancing mutations were accessible regardless of the differences in dynamics. Interestingly, specific sequence differences between the related ß-lactamases were shown to have a higher effect in evolutionary outcomes than did differences in dynamics. Overall, these ß-lactamase models show tolerance to protein dynamics at the timescale of catalytic turnover in the evolution of a new function.

Synthetic group A streptogramin antibiotics that overcome Vat resistance

Natural products serve as chemical blueprints for the majority of antibiotics in our clinical arsenal. The evolutionary process by which these molecules arise is inherently accompanied by the co-evolution of resistance mechanisms that shorten the clinical lifetime of any given class¹. Virginiamycin acetyltransferases (Vats) are resistance proteins that provide protection against streptogramins², potent Gram-positive antibiotics that inhibit the bacterial ribosome³. Due to the challenge of selectively modifying the chemically complex, 23-membered macrocyclic scaffold of group A streptogramins, analogs that overcome Vat resistance have not been previously accessed². Here we report the design, synthesis, and antibacterial evaluation of group A streptogramin antibiotics with unprecedented structural variability. Using cryo-electron microscopy and forcefield-based refinement, we characterize the binding of eight analogs to the bacterial ribosome at high resolution, revealing new binding interactions that extend into the peptidyl tRNA binding site and towards synergistic binders that occupy the nascent peptide exit tunnel (NPET). One of these analogs has excellent activity against several streptogramin-resistant strains of S. aureus, exhibits decreased acetylation rates in vitro, and is effective at lowering bacterial load in a mouse model of infection. Our results demonstrate that the combination of rational design and modular chemical synthesis can revitalize classes of antibiotics that are limited by naturally arising resistance mechanisms.

Allostery and Epistasis: Emergent Properties of Anisotropic Networks

Understanding the underlying mechanisms behind protein allostery and non-additivity of substitution outcomes (i.e., epistasis) is critical when attempting to predict the functional impact of mutations, particularly at non-conserved sites. In an effort to model these two biological properties, we extend the framework of our metric to calculate dynamic coupling between residues, the Dynamic Coupling Index (DCI) to two new metrics: (i) EpiScore, which quantifies the difference between the residue fluctuation response of a functional site when two other positions are perturbed with random Brownian kicks simultaneously versus individually to capture the degree of cooperativity of these two other positions in modulating the dynamics of the functional site and (ii) DCI_asym, which measures the degree of asymmetry between the residue fluctuation response of two sites when one or the other is perturbed with a random force. Applied to four independent systems, we successfully show that EpiScore and DCI_asym can capture important biophysical properties in dual mutant substitution outcomes. We propose that allosteric regulation and the mechanisms underlying non-additive amino acid substitution outcomes (i.e., epistasis) can be understood as emergent properties of an anisotropic network of interactions where the inclusion of the full network of interactions is critical for accurate modeling. Consequently, mutations which drive towards a new function may require a fine balance between functional site asymmetry and strength of dynamic coupling with the functional sites. These two tools will provide mechanistic insight into both understanding and predicting the outcome of dual mutations.

Predicting the viability of beta-lactamase: How folding and binding free energies correlate with beta-lactamase fitness

One of the long-standing holy grails of molecular evolution has been the ability to predict an organism's fitness directly from its genotype. With such predictive abilities in hand, researchers would be able to more accurately forecast how organisms will evolve and how proteins with novel functions could be engineered, leading to revolutionary advances in medicine and biotechnology. In this work, we assemble the largest reported set of experimental TEM-1 β-lactamase folding free energies and use this data in conjunction with previously acquired fitness data and computational free energy predictions to determine how much of the fitness of β-lactamase can be directly predicted by thermodynamic folding and binding free energies. We focus upon β-lactamase because of its long history as a model enzyme and its central role in antibiotic resistance. Based upon a set of 21 β-lactamase single and double mutants expressly designed to influence protein folding, we first demonstrate that modeling software designed to compute folding free energies such as FoldX and PyRosetta can meaningfully, although not perfectly, predict the experimental folding free energies of single mutants. Interestingly, while these techniques also yield sensible double mutant free energies, we show that they do so for the wrong physical reasons. We then go on to assess how well both experimental and computational folding free energies explain single mutant fitness. We find that folding free energies account for, at most, 24% of the variance in β-lactamase fitness values according to linear models and, somewhat surprisingly, complementing folding free energies with computationally-predicted binding free energies of residues near the active site only increases the folding-only figure by a few percent. This strongly suggests that the majority of β-lactamase's fitness is controlled by factors other than free energies. Overall, our results shed a bright light on to what extent the community is justified in using thermodynamic measures to infer protein fitness as well as how applicable modern computational techniques for predicting free energies will be to the large data sets of multiply-mutated proteins forthcoming.

Compounding Effects of Climate Warming and Antibiotic Resistance

Bacteria have evolved diverse mechanisms to survive environments with antibiotics. Temperature is both a key factor that affects the survival of bacteria in the presence of antibiotics and an environmental trait that is drastically increasing due to climate change. Therefore, it is timely and important to understand links between temperature changes and selection of antibiotic resistance. This review examines these links by synthesizing results from laboratories, hospitals, and environmental studies. First, we describe the transient physiological responses to temperature that alter cellular behavior and lead to antibiotic tolerance and persistence. Second, we focus on the link between thermal stress and the evolution and maintenance of antibiotic resistance mutations. Finally, we explore how local and global changes in temperature are associated with increases in antibiotic resistance and its spread. We suggest that a multidisciplinary, multiscale approach is critical to fully understand how temperature changes are contributing to the antibiotic crisis.

The fitness challenge of studying molecular adaptation

Advances in bioinformatics and high-throughput genetic analysis increasingly allow us to predict the genetic basis of adaptive traits. These predictions can be tested and confirmed, but the molecular-level changes - i.e. the molecular adaptation - that link genetic differences to organism fitness remain generally unknown. In recent years, a series of studies have started to unpick the mechanisms of adaptation at the molecular level. In particular, this work has examined how changes in protein function, activity, and regulation cause improved organismal fitness. Key to addressing molecular adaptations is identifying systems and designing experiments that integrate changes in the genome, protein chemistry (molecular phenotype), and fitness. Knowledge of the molecular changes underpinning adaptations allow new insight into the constraints on, and repeatability of adaptations, and of the basis of non-additive interactions between adaptive mutations. Here we critically discuss a series of studies that examine the molecular-level adaptations that connect genetic changes and fitness.

Stochastic Gene Expression Influences the Selection of Antibiotic Resistance Mutations

Bacteria can resist antibiotics by expressing enzymes that remove or deactivate drug molecules. Here, we study the effects of gene expression stochasticity on efflux and enzymatic resistance. We construct an agent-based model that stochastically simulates multiple biochemical processes in the cell and we observe the growth and survival dynamics of the cell population. Resistance-enhancing mutations are introduced by varying parameters that control the enzyme expression or efficacy. We find that stochastic gene expression can cause complex dynamics in terms of survival and extinction for these mutants. Regulatory mutations, which augment the frequency and duration of resistance gene transcription, can provide limited resistance by increasing mean expression. Structural mutations, which modify the enzyme or efflux efficacy, provide most resistance by improving the binding affinity of the resistance protein to the antibiotic; increasing the enzyme's catalytic rate alone may contribute to resistance if drug binding is not rate limiting. Overall, we identify conditions where regulatory mutations are selected over structural mutations, and vice versa. Our findings show that stochastic gene expression is a key factor underlying efflux and enzymatic resistances and should be taken into consideration in future antibiotic research.

Structure-Based Analysis of Cryptic-Site Opening

Many proteins in their unbound structures have cryptic sites that are not appropriately sized for drug binding. We consider here 32 proteins from the recently published CryptoSite set with validated cryptic sites, and study whether the sites remain cryptic in all available X-ray structures of the proteins solved without any ligand bound near the sites. It was shown that only few of these proteins have binding pockets that never form without ligand binding. Sites that are cryptic in some structures but spontaneously form in others are also rare. In most proteins the forming of pockets is affected by mutations or ligand binding at locations far from the cryptic site. To further explore these mechanisms, we applied adiabatic biased molecular dynamics simulations to guide the proteins from their ligand-free structures to ligand-bound conformations, and studied the distribution of druggability scores of the pockets located at the cryptic sites.

Fitness effects but no temperature-mediated balancing selection at the polymorphic Adh gene of Drosophila melanogaster

What factors maintain genetic variation in natural populations? Opposing selection pressures on protein stability and catalytic activity are thought to maintain variation along thermal gradients in many enzymes. We examined a classic hypothesis of temperature-mediated balancing selection, the alcohol dehydrogenase enzyme of Drosophila melanogaster, in which 2 latitudinally distributed variants are thought to be maintained by an activity/stability trade-off. Using in vitro and in vivo assays and population genetic analyses, we found no evidence of the predicted biochemical or fitness trade-offs and no signature of balancing selection. Rather, one variant confers greater activity and survival in the presence of ethanol, irrespective of temperature. Variation in Adh, and possibly other enzymes, must therefore be caused by other factors correlated with temperature.

Polymorphism in the alcohol dehydrogenase (ADH) protein of Drosophila melanogaster, like genetic variation in many other enzymes, has long been hypothesized to be maintained by a selective trade-off between thermostability and enzyme activity. Two major Adh variants, named Fast and Slow, are distributed along latitudinal clines on several continents. The balancing selection trade-off hypothesis posits that Fast is favored at high latitudes because it metabolizes alcohol faster, whereas Slow is favored at low latitudes because it is more stable at high temperatures. Here we use biochemical and physiological assays of precisely engineered genetic variants to directly test this hypothesis. As predicted, the Fast protein has higher catalytic activity than Slow, and both the Fast protein and regulatory variants linked to it confer greater ethanol tolerance on transgenic animals. But we found no evidence of a temperature-mediated trade-off: The Fast protein is not less stable or active at high temperatures, and Fast alleles increase ethanol tolerance and survivorship at all temperatures tested. Further, analysis of a population genomic dataset reveals no signature of balancing selection in the Adh gene. These results provide strong evidence against balancing selection driven by a stability/activity trade-off in Adh, and they justify caution about this hypothesis for other enzymes except those for which it has been directly tested. Our findings tentatively suggest that environment-specific selection for the Fast allele, coupled with demographic history, may have produced the observed pattern of Adh variation.

Chimeric dihydrofolate reductases display properties of modularity and biophysical diversity

While reverse genetics and functional genomics have long affirmed the role of individual mutations in determining protein function, there have been fewer studies addressing how large-scale changes in protein sequences, such as in entire modular segments, influence protein function and evolution. Given how recombination can reassort protein sequences, these types of changes may play an underappreciated role in how novel protein functions evolve in nature. Such studies could aid our understanding of whether certain organismal phenotypes related to protein function-such as growth in the presence or absence of an antibiotic-are robust with respect to the identity of certain modular segments. In this study, we combine molecular genetics with biochemical and biophysical methods to gain a better understanding of protein modularity in dihydrofolate reductase (DHFR), an enzyme target of antibiotics also widely used as a model for protein evolution. We replace an integral α-helical segment of Escherichia coli DHFR with segments from a number of different organisms (many nonmicrobial) and examine how these chimeric enzymes affect organismal phenotypes (e.g., resistance to an antibiotic) as well as biophysical properties of the enzyme (e.g., thermostability). We find that organismal phenotypes and enzyme properties are highly sensitive to the identity of DHFR modules, and that this chimeric approach can create enzymes with diverse biophysical characteristics.

Mutations Utilize Dynamic Allostery to Confer Resistance in TEM-1 β-lactamase

β-lactamases are enzymes produced by bacteria to hydrolyze β-lactam antibiotics as a common mechanism of resistance. Evolution in such enzymes has been rendering a wide variety of antibiotics impotent, therefore posing a major threat. Clinical and in vitro studies of evolution in TEM-1 β-lactamase have revealed a large number of single point mutations that are responsible for driving resistance to antibiotics and/or inhibitors. The distal locations of these mutations from the active sites suggest that these allosterically modulate the antibiotic resistance. We investigated the effects of resistance driver mutations on the conformational dynamics of the enzyme to provide insights about the mechanism of their long-distance interactions. Through all-atom molecular dynamics (MD) simulations, we obtained the dynamic flexibility profiles of the variants and compared those with that of the wild type TEM-1. While the mutational sites in the variants did not have any direct van der Waals interactions with the active site position S70 and E166, we observed a change in the flexibility of these sites, which play a very critical role in hydrolysis. Such long distance dynamic interactions were further confirmed by dynamic coupling index (DCI) analysis as the sites involved in resistance driving mutations exhibited high dynamic coupling with the active sites. A more exhaustive dynamic analysis, using a selection pressure for ampicillin and cefotaxime resistance on all possible types of substitutions in the amino acid sequence of TEM-1, further demonstrated the observed mechanism. Mutational positions that play a crucial role for the emergence of resistance to new antibiotics exhibited high dynamic coupling with the active site irrespective of their locations. These dynamically coupled positions were neither particularly rigid nor particularly flexible, making them more evolvable positions. Nature utilizes these sites to modulate the dynamics of the catalytic sites instead of mutating the highly rigid positions around the catalytic site.

Predicting allostery and microbial drug resistance with molecular simulations

Beta-lactamase enzymes mediate the most common forms of gram-negative antibiotic resistance affecting clinical treatment. They also constitute an excellent model system for the difficult problem of understanding how allosteric mutations can augment catalytic activity of already-competent enzymes. Multiple allosteric mutations have been identified that alter catalytic activity or drug-resistance spectrum in class A beta lactamases, but predicting these in advance continues to be challenging. Here, we review computational techniques based on structure and/or molecular simulation to predict such mutations. Structure-based techniques have been particularly helpful in developing graph algorithms for analyzing critical residues in beta-lactamase function, while classical molecular simulation has recently shown the ability to prospectively predict allosteric mutations increasing beta-lactamase activity and drug resistance. These will ultimately achieve the greatest power when combined with simulation methods that model reactive chemistry to calculate activation free energies directly.

Ceftazidime Is the Key Diversification and Selection Driver of VIM-Type Carbapenemases

In recent decades, carbapenems have been considered the last line of antibiotic therapy for Gram-negative bacterial infections. Unfortunately, strains carrying a high diversity of β-lactamases able to hydrolyze carbapenems have emerged in the clinical setting. Among them, VIM β-lactamases have diversified in a bloom of variants. The evolutionary reconstructions performed in this work revealed that, at the end of the 1980s, two independent events involving diversification from VIM-2 and VIM-4 produced at least 25 VIM variants. Later, a third event involving diversification from VIM-1 occurred in the mid-1990s. In a second approach to understanding the emergence of VIM carbapenemases, 44 mutants derived from VIM-2 and VIM-4 were obtained by site-directed mutagenesis based on those positions predicted to be under positive selection. These variants were expressed in an isogenic context. The more-evolved variants yielded increased levels of hydrolytic efficiency toward ceftazidime to a higher degree than toward carbapenems. In fact, an antagonist effect was frequently observed. These results led us to develop an experimental-evolution step. When Escherichia coli strains carrying VIM-2 or VIM-4 were submitted to serial passages at increasing concentrations of carbapenems or ceftazidime, more-efficient new variants (such as VIM-11 and VIM-1, with N165S [bearing a change from N to S at position 165] and R228S mutations, respectively) were only obtained when ceftazidime was present. Therefore, the observed effect of ceftazidime in the diversification and selection of VIM variants might help to explain the recent bloom of carbapenemase diversity, and it also represents another example of the potential universal effect exerted by ceftazidime in the selection of more-efficient β-lactamase variants, as in TEM, CTX-M, or KPC enzymes.

One of the objectives recently proposed by the World Health Organization (WHO) Assembly in the global plan on antimicrobial resistance was to improve the understanding and knowledge of antimicrobial resistance. In the present work, we paid attention to the drivers of diversification and selection of new carbapenemases in Gram-negative bacteria, which occupy one of the most critical places in the WHO priority list of antibiotic-resistant microorganisms. Based on evolutionary-reconstruction, site-directed-mutagenesis, and experimental-evolution approaches, we proposed a critical role of ceftazidime exposure in the selection of VIM carbapenemase variants. This surprising finding is also applicable to other β-lactamases, indicating that ceftazidime, and not other antibiotics, might have a universal effect in the diversification of β-lactamases. Our results might help to define future strategies to reconsider the extended use of ceftazidime.

Structural and Mechanistic Basis for Extended-Spectrum Drug-Resistance Mutations in Altering the Specificity of TEM, CTX-M, and KPC β-lactamases

The most common mechanism of resistance to β-lactam antibiotics in Gram-negative bacteria is the production of β-lactamases that hydrolyze the drugs. Class A β-lactamases are serine active-site hydrolases that include the common TEM, CTX-M, and KPC enzymes. The TEM enzymes readily hydrolyze penicillins and older cephalosporins. Oxyimino-cephalosporins, such as cefotaxime and ceftazidime, however, are poor substrates for TEM-1 and were introduced, in part, to circumvent β-lactamase-mediated resistance. Nevertheless, the use of these antibiotics has lead to evolution of numerous variants of TEM with mutations that significantly increase the hydrolysis of the newer cephalosporins. The CTX-M enzymes emerged in the late 1980s and hydrolyze penicillins and older cephalosporins and derive their name from the ability to also hydrolyze cefotaxime. The CTX-M enzymes, however, do not efficiently hydrolyze ceftazidime. Variants of CTX-M enzymes, however, have evolved that exhibit increased hydrolysis of ceftazidime. Finally, the KPC enzyme emerged in the 1990s and is characterized by its broad specificity that includes penicillins, most cephalosporins, and carbapenems. The KPC enzyme, however, does not efficiently hydrolyze ceftazidime. As with the TEM and CTX-M enzymes, variants have recently evolved that extend the spectrum of KPC β-lactamase to include ceftazidime. This review discusses the structural and mechanistic basis for the expanded substrate specificity of each of these enzymes that result from natural mutations that confer oxyimino-cephalosporin resistance. For the TEM enzyme, extended-spectrum mutations act by establishing new interactions with the cephalosporin. These mutations increase the conformational heterogeneity of the active site to create sub-states that better accommodate the larger drugs. The mutations expanding the spectrum of CTX-M enzymes also affect the flexibility and conformation of the active site to accommodate ceftazidime. Although structural data are limited, extended-spectrum mutations in KPC may act by mediating new, direct interactions with substrate and/or altering conformations of the active site. In many cases, mutations that expand the substrate profile of these enzymes simultaneously decrease the thermodynamic stability. This leads to the emergence of additional global suppressor mutations that help correct the stability defects leading to increased protein expression and increased antibiotic resistance.

The Influence of Higher-Order Epistasis on Biological Fitness Landscape Topography

The effect of a mutation on the organism often depends on what other mutations are already present in its genome. Geneticists refer to such mutational interactions as epistasis. Pairwise epistatic effects have been recognized for over a century, and their evolutionary implications have received theoretical attention for nearly as long. However, pairwise epistatic interactions themselves can vary with genomic background. This is called higher-order epistasis, and its consequences for evolution are much less well understood. Here, we assess the influence that higher-order epistasis has on the topography of 16 published, biological fitness landscapes. We find that on average, their effects on fitness landscape declines with order, and suggest that notable exceptions to this trend may deserve experimental scrutiny. We conclude by highlighting opportunities for further theoretical and experimental work dissecting the influence that epistasis of all orders has on fitness landscape topography and on the efficiency of evolution by natural selection.

Clinical Evolution of New Delhi Metallo-β-Lactamase (NDM) Optimizes Resistance under Zn(II) Deprivation

Carbapenem-resistant Enterobacteriaceae (CRE) are rapidly spreading and taking a staggering toll on all health care systems, largely due to the dissemination of genes coding for potent carbapenemases. An important family of carbapenemases are the Zn(II)-dependent β-lactamases, known as metallo-β-lactamases (MBLs). Among them, the New Delhi metallo-β-lactamase (NDM) has experienced the fastest and widest geographical spread. While other clinically important MBLs are soluble periplasmic enzymes, NDMs are lipoproteins anchored to the outer membrane in Gram-negative bacteria. This unique cellular localization endows NDMs with enhanced stability upon the Zn(II) starvation elicited by the immune system response at the sites of infection. Since the first report of NDM-1, new allelic variants (16 in total) have been identified in clinical isolates differing by a limited number of substitutions. Here, we show that these variants have evolved by accumulating mutations that enhance their stability or the Zn(II) binding affinity in vivo, overriding the most common evolutionary pressure acting on catalytic efficiency. We identified the ubiquitous substitution M154L as responsible for improving the Zn(II) binding capabilities of the NDM variants. These results also reveal that Zn(II) deprivation imposes a strict constraint on the evolution of this MBL, overriding the most common pressures acting on catalytic performance, and shed light on possible inhibitory strategies.

Inferring genetic interactions from comparative fitness data

Darwinian fitness is a central concept in evolutionary biology. In practice, however, it is hardly possible to measure fitness for all genotypes in a natural population. Here, we present quantitative tools to make inferences about epistatic gene interactions when the fitness landscape is only incompletely determined due to imprecise measurements or missing observations. We demonstrate that genetic interactions can often be inferred from fitness rank orders, where all genotypes are ordered according to fitness, and even from partial fitness orders. We provide a complete characterization of rank orders that imply higher order epistasis. Our theory applies to all common types of gene interactions and facilitates comprehensive investigations of diverse genetic interactions. We analyzed various genetic systems comprising HIV-1, the malaria-causing parasite Plasmodium vivax, the fungus Aspergillus niger, and the TEM-family of β-lactamase associated with antibiotic resistance. For all systems, our approach revealed higher order interactions among mutations.

Adaptive benefits from small mutation supplies in an antibiotic resistance enzyme

Populations with large mutation supplies adapt via the "greedy" substitution of the fittest genotype available, leading to fast and repeatable short-term responses. At longer time scales, smaller mutation supplies may in theory lead to larger improvements when distant high-fitness genotypes more readily evolve from lower-fitness intermediates. Here we test for long-term adaptive benefits from small mutation supplies using in vitro evolution of an antibiotic-degrading enzyme in the presence of a novel antibiotic. Consistent with predictions, large mutant libraries cause rapid initial adaptation via the substitution of cohorts of mutations, but show later deceleration and convergence. Smaller libraries show on average smaller initial, but also more variable, improvements, with two lines yielding alleles with exceptionally high resistance levels. These two alleles share three mutations with the large-library alleles, which are known from previous work, but also have unique mutations. Replay evolution experiments and analyses of the adaptive landscape of the enzyme suggest that the benefit resulted from a combination of avoiding mutational cohorts leading to local peaks and chance. Our results demonstrate adaptive benefits from limited mutation supplies on a rugged fitness landscape, which has implications for artificial selection protocols in biotechnology and argues for a better understanding of mutation supplies in clinical settings.