In vivo molecular evolution reveals biophysical origins of organismal fitness

Associating protein sequence positions with the modulation of quantitative phenotypes

Although protein sequences encode the information for folding and function, understanding their link is not an easy task. Unluckily, the prediction of how specific amino acids contribute to these features is still considerably impaired. Here, we developed a simple algorithm that finds positions in a protein sequence with potential to modulate the studied quantitative phenotypes. From a few hundred protein sequences, we perform multiple sequence alignments, obtain the per-position pairwise differences for both the sequence and the observed phenotypes, and calculate the correlation between these last two quantities. We tested our methodology with four cases: archaeal Adenylate Kinases and the organisms optimal growth temperatures, microbial rhodopsins and their maximal absorption wavelengths, mammalian myoglobins and their muscular concentration, and inhibition of HIV protease clinical isolates by two different molecules. We found from 3 to 10 positions tightly associated with those phenotypes, depending on the studied case. We showed that these correlations appear using individual positions but an improvement is achieved when the most correlated positions are jointly analyzed. Noteworthy, we performed phenotype predictions using a simple linear model that links per-position divergences and differences in the observed phenotypes. Predictions are comparable to the state-of-art methodologies which, in most of the cases, are far more complex. All of the calculations are obtained at a very low information cost since the only input needed is a multiple sequence alignment of protein sequences with their associated quantitative phenotypes. The diversity of the explored systems makes our work a valuable tool to find sequence determinants of biological activity modulation and to predict various functional features for uncharacterized members of a protein family.

Automated in vivo enzyme engineering accelerates biocatalyst optimization

Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.

The Biochemical Impact of Extracting an Embedded Adenylate Kinase Domain Using Circular Permutation

Adenylate kinases (AKs) have evolved AMP-binding and lid domains that are encoded as continuous polypeptides embedded at different locations within the discontinuous polypeptide encoding the core domain. A prior study showed that AK homologues of different stabilities consistently retain cellular activity following circular permutation that splits a region with high energetic frustration within the AMP-binding domain into discontinuous fragments. Herein, we show that mesophilic and thermophilic AKs having this topological restructuring retain activity and substrate-binding characteristics of the parental AK. While permutation decreased the activity of both AK homologues at physiological temperatures, the catalytic activity of the thermophilic AK increased upon permutation when assayed >30 °C below the melting temperature of the native AK. The thermostabilities of the permuted AKs were uniformly lower than those of native AKs, and they exhibited multiphasic unfolding transitions, unlike the native AKs, which presented cooperative thermal unfolding. In addition, proteolytic digestion revealed that permutation destabilized each AK in differing manners, and mass spectrometry suggested that the new termini within the AMP-binding domain were responsible for the increased proteolysis sensitivity. These findings illustrate how changes in contact order can be used to tune enzyme activity and alter folding dynamics in multidomain enzymes.

Structure determination of high-energy states in a dynamic protein ensemble

Macromolecular function frequently requires that proteins change conformation into high-energy states^1-4. However, methods for solving the structures of these functionally essential, lowly populated states are lacking. Here we develop a method for high-resolution structure determination of minorly populated states by coupling NMR spectroscopy-derived pseudocontact shifts⁵ (PCSs) with Carr-Purcell-Meiboom-Gill (CPMG) relaxation dispersion⁶ (PCS-CPMG). Our approach additionally defines the corresponding kinetics and thermodynamics of high-energy excursions, thereby characterizing the entire free-energy landscape. Using a large set of simulated data for adenylate kinase (Adk), calmodulin and Src kinase, we find that high-energy PCSs accurately determine high-energy structures (with a root mean squared deviation of less than 3.5 angström). Applying our methodology to Adk during catalysis, we find that the high-energy excursion involves surprisingly small openings of the AMP and ATP lids. This previously unresolved high-energy structure solves a longstanding controversy about conformational interconversions that are rate-limiting for catalysis. Primed for either substrate binding or product release, the high-energy structure of Adk suggests a two-step mechanism combining conformational selection to this state, followed by an induced-fit step into a fully closed state for catalysis of the phosphoryl-transfer reaction. Unlike other methods for resolving high-energy states, such as cryo-electron microscopy and X-ray crystallography, our solution PCS-CPMG approach excels in cases involving domain rearrangements of smaller systems (less than 60 kDa) and populations as low as 0.5%, and enables the simultaneous determination of protein structure, kinetics and thermodynamics while proteins perform their function.

Contingency and chance erase necessity in the experimental evolution of ancestral proteins

The roles of chance, contingency, and necessity in evolution are unresolved because they have never been assessed in a single system or on timescales relevant to historical evolution. We combined ancestral protein reconstruction and a new continuous evolution technology to mutate and select proteins in the B-cell lymphoma-2 (BCL-2) family to acquire protein–protein interaction specificities that occurred during animal evolution. By replicating evolutionary trajectories from multiple ancestral proteins, we found that contingency generated over long historical timescales steadily erased necessity and overwhelmed chance as the primary cause of acquired sequence variation; trajectories launched from phylogenetically distant proteins yielded virtually no common mutations, even under strong and identical selection pressures. Chance arose because many sets of mutations could alter specificity at any timepoint; contingency arose because historical substitutions changed these sets. Our results suggest that patterns of variation in BCL-2 sequences – and likely other proteins, too – are idiosyncratic products of a particular and unpredictable course of historical events.

One of the most fundamental and unresolved questions in evolutionary biology is whether the outcomes of evolution are predictable. Is the diversity of life we see today the expected result of organisms adapting to their environment throughout history (also known as natural selection) or the product of random chance? Or did chance events early in history shape the paths that evolution could take next, determining the biological forms that emerged under natural selection much later?

These questions are hard to study because evolution happened only once, long ago. To overcome this barrier, Xie, Pu, Metzger et al. developed an experimental approach that can evolve reconstructed ancestral proteins that existed deep in the past. Using this method, it is possible to replay evolution multiple times, from various historical starting points, under conditions similar to those that existed long ago. The end products of the evolutionary trajectories can then be compared to determine how predictable evolution actually is.

Xie, Pu, Metzger et al. studied proteins belonging to the BCL-2 family, which originated some 800 million years ago. These proteins have diversified greatly over time in both their genetic sequences and their ability to bind to specific partner proteins called co-regulators. Xie, Pu, Metzger et al. synthesized BCL-2 proteins that existed at various times in the past. Each ancestral protein was then allowed to evolve repeatedly under natural selection to acquire the same co-regulator binding functions that evolved during history.

At the end of each evolutionary trajectory, the genetic sequence of the resulting BCL-2 proteins was recorded. This revealed that the outcomes of evolution were almost completely unpredictable: trajectories initiated from the same ancestral protein produced proteins with very different sequences, and proteins launched from different ancestral starting points were even more dissimilar.

Further experiments identified the mutations in each trajectory that caused changes in coregulator binding. When these mutations were introduced into other ancestral proteins, they did not yield the same change in function. This suggests that early chance events influenced each protein’s evolution in an unpredictable way by opening and closing the paths available to it in the future.

This research expands our understanding of evolution on a molecular level whilst providing a new experimental approach for studying evolutionary drivers in more detail. The results suggest that BCL-2 proteins, in all their various forms, are unique products of a particular, unpredictable course of history set in motion by ancient chance events.

High-throughput rational design of the remdesivir binding site in the RdRp of SARS-CoV-2: implications for potential resistance

The use of remdesivir to treat COVID-19 will likely continue before clinical trials are completed. Due to the lengthening pandemic and evolving nature of the virus, predicting potential residues prone to mutation is crucial for the management of remdesivir resistance. Using a rational ligand-based interface design complemented with mutational mapping, we generated a total of 100,000 mutations and provided insight into the functional outcomes of mutations in the remdesivir-binding site in nsp12 subunit of RdRp. After designing 46 residues in the remdesivir-binding site of nsp12, the designs retained 97%–98% sequence identity, suggesting that very few mutations in nsp12 are required for SARS-CoV-2 to attain remdesivir resistance. Several mutants displayed decreased binding affinity to remdesivir, suggesting drug resistance. These hotspot residues had a higher probability of undergoing selective mutation and thus conferring remdesivir resistance. Identifying the potential residues prone to mutation improves our understanding of SARS-CoV-2 drug resistance and COVID-19 pathogenesis.

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SARS-CoV-2 may acquire mutations in nsp12 to develop remdesivir resistance

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Hotspot residues that exhibited the highest potential for mutation were identified

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Virus can undergo positive selection and attain resistance with very few mutations

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Data is crucial for the understanding and management of drug resistance

Comparative genomics of the sequential Pseudomonas aeruginosa isolates obtained from the continuous imipenem stress evolution

Pseudomonas aeruginosa is a major opportunistic human pathogen that causes nosocomial infections, and the proportion of carbapenem resistance has recently dramatically increased in P. aeruginosa due to the overuse of them. In this study, strains G10 and G20, with minimum inhibitory concentration (MIC) of imipenem of 16 μg/ml and more than 32 μg/ml, were isolated during continuous subculture of cells exposed to stepwise increasing concentrations of imipenem, respectively. The genomes of G10 and G20 were sequenced and compared with parental strain (P. aeruginosa ATCC 27853, G0). There were 59, 59, and 58 genes involved in antibiotic resistance which were predicted in G0, G10, and G20, respectively, while 374, 366, and 363 genes involved in virulence factors were identified among these three strains. Due to the significantly different MICs of imipenem and highly similar profiles of antibiotic resistance and virulence factors related genes among three strains, the specific genetic variations that occurred were identified and compared, including single nucleotide polymorphisms (SNPs), insertions and deletions (InDels), and structural variations (SVs). The increase in the MIC of imipenem was proposed to be linked to mutations involved in polyamine biosynthesis, biofilm formation, OprD, and efflux pump functions. This study aims to clarify the underlying mechanism of imipenem resistance and provide alternative strategies for reducing resistance in P. aeruginosa. KEY POINTS: • Strains with different imipenem MIC were obtained via laboratory selection evolution. • Whole genomes of two strains with different MIC of imipenem were sequenced. • Underlying mechanism of imipenem resistance was clarified via comparative genomics.

Evolutionary stalling and a limit on the power of natural selection to improve a cellular module

Cells consist of molecular modules which perform vital biological functions. Cellular modules are key units of adaptive evolution because organismal fitness depends on their performance. Theory shows that in rapidly evolving populations, such as those of many microbes, adaptation is driven primarily by common beneficial mutations with large effects, while other mutations behave as if they are effectively neutral. As a consequence, if a module can be improved only by rare and/or weak beneficial mutations, its adaptive evolution would stall. However, such evolutionary stalling has not been empirically demonstrated, and it is unclear to what extent stalling may limit the power of natural selection to improve modules. Here we empirically characterize how natural selection improves the translation machinery (TM), an essential cellular module. We experimentally evolved populations of Escherichia coli with genetically perturbed TMs for 1,000 generations. Populations with severe TM defects initially adapted via mutations in the TM, but TM adaptation stalled within about 300 generations. We estimate that the genetic load in our populations incurred by residual TM defects ranges from 0.5 to 19%. Finally, we found evidence that both epistasis and the depletion of the pool of beneficial mutations contributed to evolutionary stalling. Our results suggest that cellular modules may not be fully optimized by natural selection despite the availability of adaptive mutations.

How to resurrect ancestral proteins as proxies for ancient biogeochemistry

Throughout the history of life, enzymes have served as the primary molecular mediators of biogeochemical cycles by catalyzing the metabolic pathways that interact with geochemical substrates. The byproducts of enzymatic activities have been preserved as chemical and isotopic signatures in the geologic record. However, interpretations of these signatures are limited by the assumption that such enzymes have remained functionally conserved over billions of years of molecular evolution. By reconstructing ancient genetic sequences in conjunction with laboratory enzyme resurrection, preserved biogeochemical signatures can instead be related to experimentally constrained, ancestral enzymatic properties. We may thereby investigate instances within molecular evolutionary trajectories potentially tied to significant biogeochemical transitions evidenced in the geologic record. Here, we survey recent enzyme resurrection studies to provide a reasoned assessment of areas of success and common pitfalls relevant to ancient biogeochemical applications. We conclude by considering the Great Oxidation Event, which provides a constructive example of a significant biogeochemical transition that warrants investigation with ancestral enzyme resurrection. This event also serves to highlight the pitfalls of facile interpretation of paleophenotype models and data, as applied to two examples of enzymes that likely both influenced and were influenced by the rise of atmospheric oxygen - RuBisCO and nitrogenase.

Substrate inhibition imposes fitness penalty at high protein stability

Proteins are only moderately stable. It has long been debated whether this narrow range of stabilities is solely a result of neutral drift toward lower stability or purifying selection against excess stability-for which no experimental evidence was found so far-is also at work. Here, we show that mutations outside the active site in the essential Escherichia coli enzyme adenylate kinase (Adk) result in a stability-dependent increase in substrate inhibition by AMP, thereby impairing overall enzyme activity at high stability. Such inhibition caused substantial fitness defects not only in the presence of excess substrate but also under physiological conditions. In the latter case, substrate inhibition caused differential accumulation of AMP in the stationary phase for the inhibition-prone mutants. Furthermore, we show that changes in flux through Adk could accurately describe the variation in fitness effects. Taken together, these data suggest that selection against substrate inhibition and hence excess stability may be an important factor determining stability observed for modern-day Adk.

Structural and mutational analyses of psychrophilic and mesophilic adenylate kinases highlight the role of hydrophobic interactions in protein thermal stability

Protein thermal stability is an important field since thermally stable proteins are desirable in many academic and industrial settings. Information on protein thermal stabilization can be obtained by comparing homologous proteins from organisms living at distinct temperatures. Here, we report structural and mutational analyses of adenylate kinases (AKs) from psychrophilic Bacillus globisporus (AKp) and mesophilic Bacillus subtilis (AKm). Sequence and structural comparison showed suboptimal hydrophobic packing around Thr26 in the CORE domain of AKp, which was replaced with an Ile residue in AKm. Mutations that improved hydrophobicity of the Thr residue increased the thermal stability of the psychrophilic AKp, and the largest stabilization was observed for a Thr-to-Ile substitution. Furthermore, a reverse Ile-to-Thr mutation in the mesophilic AKm significantly decreased thermal stability. We determined the crystal structures of mutant AKs to confirm the impact of the residue substitutions on the overall stability. Taken together, our results provide a structural basis for the stability difference between psychrophilic and mesophilic AK homologues and highlight the role of hydrophobic interactions in protein thermal stability.

Experimental Design, Population Dynamics, and Diversity in Microbial Experimental Evolution

In experimental evolution, laboratory-controlled conditions select for the adaptation of species, which can be monitored in real time. Despite the current popularity of such experiments, nature's most pervasive biological force was long believed to be observable only on time scales that transcend a researcher's life-span, and studying evolution by natural selection was therefore carried out solely by comparative means. Eventually, microorganisms' propensity for fast evolutionary changes proved us wrong, displaying strong evolutionary adaptations over a limited time, nowadays massively exploited in laboratory evolution experiments. Here, we formulate a guide to experimental evolution with microorganisms, explaining experimental design and discussing evolutionary dynamics and outcomes and how it is used to assess ecoevolutionary theories, improve industrially important traits, and untangle complex phenotypes. Specifically, we give a comprehensive overview of the setups used in experimental evolution. Additionally, we address population dynamics and genetic or phenotypic diversity during evolution experiments and expand upon contributing factors, such as epistasis and the consequences of (a)sexual reproduction. Dynamics and outcomes of evolution are most profoundly affected by the spatiotemporal nature of the selective environment, where changing environments might lead to generalists and structured environments could foster diversity, aided by, for example, clonal interference and negative frequency-dependent selection. We conclude with future perspectives, with an emphasis on possibilities offered by fast-paced technological progress. This work is meant to serve as an introduction to those new to the field of experimental evolution, as a guide to the budding experimentalist, and as a reference work to the seasoned expert.

Using cellular fitness to map the structure and function of a major facilitator superfamily effluxer

The major facilitator superfamily (MFS) effluxers are prominent mediators of antimicrobial resistance. The biochemical characterization of MFS proteins is hindered by their complex membrane environment that makes in vitro biochemical analysis challenging. Since the physicochemical properties of proteins drive the fitness of an organism, we posed the question of whether we could reverse that relationship and derive meaningful biochemical parameters for a single protein simply from fitness changes it confers under varying strengths of selection. Here, we present a physiological model that uses cellular fitness as a proxy to predict the biochemical properties of the MFS tetracycline efflux pump, TetB, and a family of single amino acid variants. We determined two lumped biochemical parameters roughly describing K_m and V_max for TetB and variants. Including in vivo protein levels into our model allowed for more specified prediction of pump parameters relating to substrate binding affinity and pumping efficiency for TetB and variants. We further demonstrated the general utility of our model by solely using fitness to assay a library of tet(B) variants and estimate their biochemical properties.

Structural analyses of adenylate kinases from Antarctic and tropical fishes for understanding cold adaptation of enzymes

Psychrophiles are extremophilic organisms capable of thriving in cold environments. Proteins from these cold-adapted organisms can remain physiologically functional at low temperatures, but are structurally unstable even at moderate temperatures. Here, we report the crystal structure of adenylate kinase (AK) from the Antarctic fish Notothenia coriiceps, and identify the structural basis of cold adaptation by comparison with homologues from tropical fishes including Danio rerio. The structure of N. coriiceps AK (AKNc) revealed suboptimal hydrophobic packing around three Val residues in its central CORE domain, which are replaced with Ile residues in D. rerio AK (AKDr). The Val-to-Ile mutations that improve hydrophobic CORE packing in AKNc increased stability at high temperatures but decreased activity at low temperatures, suggesting that the suboptimal hydrophobic CORE packing is important for cold adaptation. Such linkage between stability and activity was also observed in AKDr. Ile-to-Val mutations that destabilized the tropical AK resulted in increased activity at low temperatures. Our results provide the structural basis of cold adaptation of a psychrophilic enzyme from a multicellular, eukaryotic organism, and highlight the similarities and differences in the structural adjustment of vertebrate and bacterial psychrophilic AKs during cold adaptation.

Molecular ensembles make evolution unpredictable

A long-standing goal in evolutionary biology is predicting evolution. Here, we show that the architecture of macromolecules fundamentally limits evolutionary predictability. Under physiological conditions, macromolecules, like proteins, flip between multiple structures, forming an ensemble of structures. A mutation affects all of these structures in slightly different ways, redistributing the relative probabilities of structures in the ensemble. As a result, mutations that follow the first mutation have a different effect than they would if introduced before. This implies that knowing the effects of every mutation in an ancestor would be insufficient to predict evolutionary trajectories past the first few steps, leading to profound unpredictability in evolution. We, therefore, conclude that detailed evolutionary predictions are not possible given the chemistry of macromolecules.

Evolutionary prediction is of deep practical and philosophical importance. Here we show, using a simple computational protein model, that protein evolution remains unpredictable, even if one knows the effects of all mutations in an ancestral protein background. We performed a virtual deep mutational scan—revealing the individual and pairwise epistatic effects of every mutation to our model protein—and then used this information to predict evolutionary trajectories. Our predictions were poor. This is a consequence of statistical thermodynamics. Proteins exist as ensembles of similar conformations. The effect of a mutation depends on the relative probabilities of conformations in the ensemble, which in turn, depend on the exact amino acid sequence of the protein. Accumulating substitutions alter the relative probabilities of conformations, thereby changing the effects of future mutations. This manifests itself as subtle but pervasive high-order epistasis. Uncertainty in the effect of each mutation accumulates and undermines prediction. Because conformational ensembles are an inevitable feature of proteins, this is likely universal.

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

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.

Evolution of substrate specificity in a retained enzyme driven by gene loss

The connection between gene loss and the functional adaptation of retained proteins is still poorly understood. We apply phylogenomics and metabolic modeling to detect bacterial species that are evolving by gene loss, with the finding that Actinomycetaceae genomes from human cavities are undergoing sizable reductions, including loss of L-histidine and L-tryptophan biosynthesis. We observe that the dual-substrate phosphoribosyl isomerase A or priA gene, at which these pathways converge, appears to coevolve with the occurrence of trp and his genes. Characterization of a dozen PriA homologs shows that these enzymes adapt from bifunctionality in the largest genomes, to a monofunctional, yet not necessarily specialized, inefficient form in genomes undergoing reduction. These functional changes are accomplished via mutations, which result from relaxation of purifying selection, in residues structurally mapped after sequence and X-ray structural analyses. Our results show how gene loss can drive the evolution of substrate specificity from retained enzymes.

DOI:http://dx.doi.org/10.7554/eLife.22679.001

Experimental Evolution of Escherichia coli Harboring an Ancient Translation Protein

The ability to design synthetic genes and engineer biological systems at the genome scale opens new means by which to characterize phenotypic states and the responses of biological systems to perturbations. One emerging method involves inserting artificial genes into bacterial genomes and examining how the genome and its new genes adapt to each other. Here we report the development and implementation of a modified approach to this method, in which phylogenetically inferred genes are inserted into a microbial genome, and laboratory evolution is then used to examine the adaptive potential of the resulting hybrid genome. Specifically, we engineered an approximately 700-million-year-old inferred ancestral variant of tufB, an essential gene encoding elongation factor Tu, and inserted it in a modern Escherichia coli genome in place of the native tufB gene. While the ancient homolog was not lethal to the cell, it did cause a twofold decrease in organismal fitness, mainly due to reduced protein dosage. We subsequently evolved replicate hybrid bacterial populations for 2000 generations in the laboratory and examined the adaptive response via fitness assays, whole genome sequencing, proteomics, and biochemical assays. Hybrid lineages exhibit a general adaptive strategy in which the fitness cost of the ancient gene was ameliorated in part by upregulation of protein production. Our results suggest that an ancient-modern recombinant method may pave the way for the synthesis of organisms that exhibit ancient phenotypes, and that laboratory evolution of these organisms may prove useful in elucidating insights into historical adaptive processes.

Genetic toolbox for controlled expression of functional proteins in Geobacillus spp

Species of genus Geobacillus are thermophilic bacteria and play an ever increasing role as hosts for biotechnological applications both in academia and industry. Here we screened a number of Geobacillus strains to determine which industrially relevant carbon sources they can utilize. One of the strains, G. thermoglucosidasius C56-YS93, was then chosen to develop a toolbox for controlled gene expression over a wide range of levels. It includes a library of semi-synthetic constitutive promoters (76-fold difference in expression levels) and an inducible promoter from the xylA gene. A library of synthetic in silico designed ribosome binding sites was also created for further tuning of translation. The PxylA was further used to successfully express native and heterologous xylanases in G. thermoglucosidasius. This toolbox enables fine-tuning of gene expression in Geobacillus species for metabolic engineering approaches in production of biochemicals and heterologous proteins.

Evolutionary drivers of thermoadaptation in enzyme catalysis

With early life likely to have existed in a hot environment, enzymes had to cope with an inherent drop in catalytic speed caused by lowered temperature. Here we characterize the molecular mechanisms underlying thermoadaptation of enzyme catalysis in adenylate kinase using ancestral sequence reconstruction spanning 3 billion years of evolution. We show that evolution solved the enzyme's key kinetic obstacle-how to maintain catalytic speed on a cooler Earth-by exploiting transition-state heat capacity. Tracing the evolution of enzyme activity and stability from the hot-start toward modern hyperthermophilic, mesophilic, and psychrophilic organisms illustrates active pressure versus passive drift in evolution on a molecular level, refutes the debated activity/stability trade-off, and suggests that the catalytic speed of adenylate kinase is an evolutionary driver for organismal fitness.

Mapping the Dynamics Landscape of Conformational Transitions in Enzyme: The Adenylate Kinase Case

Conformational transition describes the essential dynamics and mechanism of enzymes in pursuing their various functions. The fundamental and practical challenge to researchers is to quantitatively describe the roles of large-scale dynamic transitions for regulating the catalytic processes. In this study, we tackled this challenge by exploring the pathways and free energy landscape of conformational changes in adenylate kinase (AdK), a key ubiquitous enzyme for cellular energy homeostasis. Using explicit long-timescale (up to microseconds) molecular dynamics and bias-exchange metadynamics simulations, we determined at the atomistic level the intermediate conformational states and mapped the transition pathways of AdK in the presence and absence of ligands. There is clearly chronological operation of the functional domains of AdK. Specifically in the ligand-free AdK, there is no significant energy barrier in the free energy landscape separating the open and closed states. Instead there are multiple intermediate conformational states, which facilitate the rapid transitions of AdK. In the ligand-bound AdK, the closed conformation is energetically most favored with a large energy barrier to open it up, and the conformational population prefers to shift to the closed form coupled with transitions. The results suggest a perspective for a hybrid of conformational selection and induced fit operations of ligand binding to AdK. These observations, depicted in the most comprehensive and quantitative way to date, to our knowledge, emphasize the underlying intrinsic dynamics of AdK and reveal the sophisticated conformational transitions of AdK in fulfilling its enzymatic functions. The developed methodology can also apply to other proteins and biomolecular systems.

Predicting protein thermal stability changes upon point mutations using statistical potentials: Introducing HoTMuSiC

The accurate prediction of the impact of an amino acid substitution on the thermal stability of a protein is a central issue in protein science, and is of key relevance for the rational optimization of various bioprocesses that use enzymes in unusual conditions. Here we present one of the first computational tools to predict the change in melting temperature ΔT_m upon point mutations, given the protein structure and, when available, the melting temperature T_m of the wild-type protein. The key ingredients of our model structure are standard and temperature-dependent statistical potentials, which are combined with the help of an artificial neural network. The model structure was chosen on the basis of a detailed thermodynamic analysis of the system. The parameters of the model were identified on a set of more than 1,600 mutations with experimentally measured ΔT_m. The performance of our method was tested using a strict 5-fold cross-validation procedure, and was found to be significantly superior to that of competing methods. We obtained a root mean square deviation between predicted and experimental ΔT_m values of 4.2 °C that reduces to 2.9 °C when ten percent outliers are removed. A webserver-based tool is freely available for non-commercial use at soft.dezyme.com.

How mutational epistasis impairs predictability in protein evolution and design

There has been much debate about the extent to which mutational epistasis, that is, the dependence of the outcome of a mutation on the genetic background, constrains evolutionary trajectories. The degree of unpredictability introduced by epistasis, due to the non-additivity of functional effects, strongly hinders the strategies developed in protein design and engineering. While many studies have addressed this issue through systematic characterization of evolutionary trajectories within individual enzymes, the field lacks a consensus view on this matter. In this work, we performed a comprehensive analysis of epistasis by analyzing the mutational effects from nine adaptive trajectories toward new enzymatic functions. We quantified epistasis by comparing the effect of mutations occurring between two genetic backgrounds: the starting enzyme (for example, wild type) and the intermediate variant on which the mutation occurred during the trajectory. We found that most trajectories exhibit positive epistasis, in which the mutational effect is more beneficial when it occurs later in the evolutionary trajectory. Approximately half (49%) of functional mutations were neutral or negative on the wild-type background, but became beneficial at a later stage in the trajectory, indicating that these functional mutations were not predictable from the initial starting point. While some cases of strong epistasis were associated with direct interaction between residues, many others were caused by long-range indirect interactions between mutations. Our work highlights the prevalence of epistasis in enzyme adaptive evolution, in particular positive epistasis, and suggests the necessity of incorporating mutational epistasis in protein engineering and design to create highly efficient catalysts.

Acinetobacter baumannii Repeatedly Evolves a Hypermutator Phenotype in Response to Tigecycline That Effectively Surveys Evolutionary Trajectories to Resistance

The evolution of hypermutators in response to antibiotic treatment in both clinical and laboratory settings provides a unique context for the study of adaptive evolution. With increased mutation rates, the number of hitchhiker mutations within an evolving hypermutator population is remarkably high and presents substantial challenges in determining which mutations are adaptive. Intriguingly however, hypermutators also provide an opportunity to explore deeply the accessible evolutionary trajectories that lead to increased organism fitness, in this case the evolution of antibiotic resistance to the clinically relevant antibiotic tigecycline by the hospital pathogen Acinetobacter baumannii. Using a continuous culture system, AB210M, a clinically derived strain of A. baumannii, was evolved to tigecycline resistance. Analysis of the adapted populations showed that nearly all the successful lineages became hypermutators via movement of a mobile element to inactivate mutS. In addition, metagenomic analysis of population samples revealed another 896 mutations that occurred at a frequency greater than 5% in the population, while 38 phenotypically distinct individual colonies harbored a total of 1712 mutations. These mutations were scattered throughout the genome and affected ~40% of the coding sequences. The most highly mutated gene was adeS, a known tigecycline-resistance gene; however, adeS was not solely responsible for the high level of TGC resistance. Sixteen other genes stood out as potentially relevant to increased resistance. The five most prominent candidate genes (adeS, rpsJ, rrf, msbA, and gna) consistently re-emerged in subsequent replicate population studies suggesting they are likely to play a role in adaptation to tigecycline. Interestingly, the repeated evolution of a hypermutator phenotype in response to antibiotic stress illustrates not only a highly adaptive strategy to resistance, but also a remarkably efficient survey of successful evolutionary trajectories.

Leveraging the Mechanism of Oxidative Decay for Adenylate Kinase to Design Structural and Functional Resistances

Characterization of the mechanisms underlying hypohalous acid (i.e., hypochlorous acid or hypobromous acid) degradation of proteins is important for understanding how the immune system deactivates pathogens during infections and damages human tissues during inflammatory diseases. Proteins are particularly important hypohalous acid reaction targets in pathogens and in host tissues, as evidenced by the detection of chlorinated and brominated oxidizable residues. While a significant amount of work has been conducted for reactions of hypohalous acids with a range of individual amino acids and small peptides, the assessment of oxidative decay in full-length proteins has lagged in comparison. The most rigorous test of our understanding of oxidative decay of proteins is the rational redesign of proteins with conferred resistances to the decay of structure and function. Toward this end, in this study, we experimentally determined a putative mechanism of oxidative decay using adenylate kinase as the model system. In turn, we leveraged this mechanism to rationally design new proteins and experimentally test each system for oxidative resistance to loss of structure and function. From our extensive assessment of secondary structure, protein hydrodynamics, and enzyme activity upon hypochlorous acid or hypobromous acid challenge, we have identified two key strategies for conferring structural and functional resistance, namely, the design of proteins (adenylate kinase enzymes) that are resistant to oxidation requires complementary consideration of protein stability and the modification (elimination) of certain oxidizable residues proximal to catalytic sites.

Rampant Parasexuality Evolves in a Hospital Pathogen during Antibiotic Selection

Horizontal gene transfer threatens the therapeutic success of antibiotics by facilitating the rapid dissemination of resistance alleles among bacterial species. The conjugative mobile element Tn916 provides an excellent context for examining the role of adaptive parasexuality as it carries the tetracycline-resistance allele tetM and has been identified in a wide range of pathogens. We have used a combination of experimental evolution and allelic frequency measurements to gain insights into the adaptive trajectories leading to tigecycline resistance in a hospital strain of Enterococcus faecalis and predict what mechanisms of resistance are most likely to appear in the clinical setting. Here, we show that antibiotic selection led to the near fixation of adaptive alleles that simultaneously altered TetM expression and produced remarkably increased levels of Tn916 horizontal gene transfer. In the absence of drug, approximately 1 in 120,000 of the nonadapted E. faecalis S613 cells had an excised copy of Tn916, whereas nearly 1 in 50 cells had an excised copy of Tn916 upon selection for resistance resulting in a more than 1,000-fold increase in conjugation rates. We also show that tigecycline, a translation inhibitor, selected for a mutation in the ribosomal S10 protein. Our results show the first example of mutations that concurrently confer resistance to an antibiotic and lead to constitutive conjugal-transfer of the resistance allele. Selection created a highly parasexual phenotype and high frequency of Tn916 jumping demonstrating how the use of antibiotics can lead directly to the proliferation of resistance in, and potentially among, pathogens.

Metabolism at evolutionary optimal States

Metabolism is generally required for cellular maintenance and for the generation of offspring under conditions that support growth. The rates, yields (efficiencies), adaptation time and robustness of metabolism are therefore key determinants of cellular fitness. For biotechnological applications and our understanding of the evolution of metabolism, it is necessary to figure out how the functional system properties of metabolism can be optimized, via adjustments of the kinetics and expression of enzymes, and by rewiring metabolism. The trade-offs that can occur during such optimizations then indicate fundamental limits to evolutionary innovations and bioengineering. In this paper, we review several theoretical and experimental findings about mechanisms for metabolic optimization.

Empirically founded genotype-phenotype maps from mammalian cyclic nucleotide-gated ion channels

A major barrier between evolutionary and functional biology is the difficulty of determining appropriate genotype-phenotype-fitness maps, particularly in metazoans. Concrete perspectives towards unifying these approaches are offered by studies on the physiological systems that depend on ion channel dynamics. I focus on the cyclic nucleotide-gated (CNG) channels implicated in the photoreceptor's response to light. From an evolutionary standpoint, sensory systems offers interpretative advantages, as the relation between the sensory response and environment is relatively straightforward. For CNG and other ion channels, extensive data are available about the physiological consequences of scanning mutagenesis on sensitive protein domains, such as the conduction pore. Mutant ion channels can be easily studied in living cells, so that the relation between genotypes and phenotypes is less speculative than usual. By relying on relatively simple theoretical frameworks, I used these data to relate the sequence space with phenotypes at increasing hierarchical levels. These empirical genotype-phenotype and phenotype-phenotype landscapes became smoother at higher integration levels, especially in heterozygous condition. The epistatic interaction between sites was analyzed from double mutant constructs. Magnitude epistasis was common. Moreover, evidence of reciprocal sign epistasis and the presence of permissive mutations were also observed, which suggest how adaptive regions can be connected across maladaptive valleys. The approach I describe suggests a way to better relate the evolutionary dynamics with the underlying physiology.

Thermodynamic system drift in protein evolution

Tracking the evolution of thermostability in resurrected ancestors of a heat-tolerant extremophile protein and its less heat tolerant Escherichia coli homologue shows how thermostability has probably explored different mechanisms of protein stabilization over evolutionary time.

Proteins from thermophiles are generally more thermostable than their mesophilic homologs, but little is known about the evolutionary process driving these differences. Here we attempt to understand how the diverse thermostabilities of bacterial ribonuclease H1 (RNH) proteins evolved. RNH proteins from Thermus thermophilus (ttRNH) and Escherichia coli (ecRNH) share similar structures but differ in melting temperature (T_m) by 20°C. ttRNH's greater stability is caused in part by the presence of residual structure in the unfolded state, which results in a low heat capacity of unfolding (ΔC_p) relative to ecRNH. We first characterized RNH proteins from a variety of extant bacteria and found that T_m correlates with the species' growth temperatures, consistent with environmental selection for stability. We then used ancestral sequence reconstruction to statistically infer evolutionary intermediates along lineages leading to ecRNH and ttRNH from their common ancestor, which existed approximately 3 billion years ago. Finally, we synthesized and experimentally characterized these intermediates. The shared ancestor has a melting temperature between those of ttRNH and ecRNH; the T_ms of intermediate ancestors along the ttRNH lineage increased gradually over time, while the ecRNH lineage exhibited an abrupt drop in T_m followed by relatively little change. To determine whether the underlying mechanisms for thermostability correlate with the changes in T_m, we measured the thermodynamic basis for stabilization—ΔC_p and other thermodynamic parameters—for each of the ancestors. We observed that, while the T_m changes smoothly, the mechanistic basis for stability fluctuates over evolutionary time. Thus, even while overall stability appears to be strongly driven by selection, the proteins explored a wide variety of mechanisms of stabilization, a phenomenon we call “thermodynamic system drift.” This suggests that even on lineages with strong selection to increase stability, proteins have wide latitude to explore sequence space, generating biophysical diversity and potentially opening new evolutionary pathways.

The biophysical properties of proteins must adjust to accommodate environmental temperatures because of the narrow range over which any given protein sequence can remain folded and functional. We compared the evolution of homologous bacterial enzymes (ribonucleases H1) from two lineages: one from Escherichia coli, which live at moderate temperatures, the other from Thermus thermophilus, which live at extremely high temperatures. Our aim was to investigate how these structurally homologous proteins can have such different thermostabilities, unfolding at temperatures that are 20°C apart. We used bioinformatics to reconstruct the sequences of ancestral proteins along each lineage, synthesized the proteins in the lab, and experimentally traced the evolution of ribonuclease H1 stability. While thermostability appears to have been strongly shaped by selection, the biophysical mechanisms used to tune protein stability appear to have varied throughout evolutionary history; this suggests that proteins have wide latitude to explore different mechanisms of stabilization, generating biophysical diversity and opening up new evolutionary pathways.

Stability curve prediction of homologous proteins using temperature-dependent statistical potentials

The unraveling and control of protein stability at different temperatures is a fundamental problem in biophysics that is substantially far from being quantitatively and accurately solved, as it requires a precise knowledge of the temperature dependence of amino acid interactions. In this paper we attempt to gain insight into the thermal stability of proteins by designing a tool to predict the full stability curve as a function of the temperature for a set of 45 proteins belonging to 11 homologous families, given their sequence and structure, as well as the melting temperature () and the change in heat capacity () of proteins belonging to the same family. Stability curves constitute a fundamental instrument to analyze in detail the thermal stability and its relation to the thermodynamic stability, and to estimate the enthalpic and entropic contributions to the folding free energy. In summary, our approach for predicting the protein stability curves relies on temperature-dependent statistical potentials derived from three datasets of protein structures with targeted thermal stability properties. Using these potentials, the folding free energies () at three different temperatures were computed for each protein. The Gibbs-Helmholtz equation was then used to predict the protein's stability curve as the curve that best fits these three points. The results are quite encouraging: the standard deviations between the experimental and predicted 's, 's and folding free energies at room temperature () are equal to 13 , 1.3 ) and 4.1 , respectively, in cross-validation. The main sources of error and some further improvements and perspectives are briefly discussed.

The prediction of protein stability remains one of the key goals of protein science. Despite the significant efforts of the last decades, faster and more accurate stability predictors on the proteomic-wide scale are currently demanded. The determination and control of protein stability are indeed fundamental steps on the path towards de novo design. In this paper we develop a method for predicting the stability curve of proteins. This curve encodes the temperature dependence of the folding free energy (). Its knowledge is important in the study of protein stability since all the thermodynamic parameters characterizing the folding transition can be extracted from it. Our prediction method is based on temperature-dependent mean force potentials and uses the tertiary structure of the target protein as well as the melting temperature () and the heat capacity change () of some other proteins belonging to the same family. From the predicted stability curves, the , the and the at room temperature can be inferred. The predictions obtained are compared with experimental data and show reasonable performances.

Effectiveness and limitations of local structural entropy optimization in the thermal stabilization of mesophilic and thermophilic adenylate kinases

Local structural entropy (LSE) is a descriptor for the extent of conformational heterogeneity in short protein sequences that is computed from structural information derived from the Protein Data Bank. Reducing the LSE of a protein sequence by introducing amino acid mutations can result in fewer conformational states and thus a more stable structure, indicating that LSE optimization can be used as a protein stabilization method. Here, we describe a series of LSE optimization experiments designed to stabilize mesophilic and thermophilic adenylate kinases (AKs) and report crystal structures of LSE-optimized AK variants. In the mesophilic AK, thermal stabilization by LSE reduction was effective but limited. Structural analyses of the LSE-optimized mesophilic AK variants revealed a strong correlation between LSE and the apolar buried surface area. Additional mutations designed to introduce noncovalent interactions between distant regions of the polypeptide resulted in further stabilization. Unexpectedly, optimizing the LSE of the thermophilic AK resulted in a decrease in thermal stability. This destabilization was reduced when charged residues were excluded from the possible substitutions during LSE optimization. These observations suggest that stabilization by LSE reduction may result from the optimization of local hydrophobic contacts. The limitations of this process are likely due to ignorance of other interactions that bridge distant regions in a given amino acid sequence. Our results illustrate the effectiveness and limitations of LSE optimization as a protein stabilization strategy and highlight the importance and complementarity of local conformational stability and global interactions in protein thermal stability.

Transformable facultative thermophile Geobacillus stearothermophilus NUB3621 as a host strain for metabolic engineering

Metabolic engineers develop inexpensive enantioselective syntheses of high-value compounds, but their designs are sometimes confounded by the misfolding of heterologously expressed proteins. Geobacillus stearothermophilus NUB3621 is a readily transformable facultative thermophile. It could be used to express and properly fold proteins derived from its many mesophilic or thermophilic Bacillaceae relatives or to direct the evolution of thermophilic variants of mesophilic proteins. Moreover, its capacity for high-temperature growth should accelerate chemical transformation rates in accordance with the Arrhenius equation and reduce the risks of microbial contamination. Its tendency to sporulate in response to nutrient depletion lowers the costs of storage and transportation. Here, we present a draft genome sequence of G. stearothermophilus NUB3621 and describe inducible and constitutive expression plasmids that function in this organism. These tools will help us and others to exploit the natural advantages of this system for metabolic engineering applications.

An integrated approach for thermal stabilization of a mesophilic adenylate kinase

Thermally stable proteins are desirable for research and industrial purposes, but redesigning proteins for higher thermal stability can be challenging. A number of different techniques have been used to improve the thermal stability of proteins, but the extents of stability enhancement were sometimes unpredictable and not significant. Here, we systematically tested the effects of multiple stabilization techniques including a bioinformatic method and structure-guided mutagenesis on a single protein, thereby providing an integrated approach to protein thermal stabilization. Using a mesophilic adenylate kinase (AK) as a model, we identified stabilizing mutations based on various stabilization techniques, and generated a series of AK variants by introducing mutations both individually and collectively. The redesigned proteins displayed a range of increased thermal stabilities, the most stable of which was comparable to a naturally evolved thermophilic homologue with more than a 25° increase in its thermal denaturation midpoint. We also solved crystal structures of three representative variants including the most stable variant, to confirm the structural basis for their increased stabilities. These results provide a unique opportunity for systematically analyzing the effectiveness and additivity of various stabilization mechanisms, and they represent a useful approach for improving protein stability by integrating the reduction of local structural entropy and the optimization of global noncovalent interactions such as hydrophobic contact and ion pairs.

Protein thermostability prediction within homologous families using temperature-dependent statistical potentials

The ability to rationally modify targeted physical and biological features of a protein of interest holds promise in numerous academic and industrial applications and paves the way towards de novo protein design. In particular, bioprocesses that utilize the remarkable properties of enzymes would often benefit from mutants that remain active at temperatures that are either higher or lower than the physiological temperature, while maintaining the biological activity. Many in silico methods have been developed in recent years for predicting the thermodynamic stability of mutant proteins, but very few have focused on thermostability. To bridge this gap, we developed an algorithm for predicting the best descriptor of thermostability, namely the melting temperature , from the protein's sequence and structure. Our method is applicable when the of proteins homologous to the target protein are known. It is based on the design of several temperature-dependent statistical potentials, derived from datasets consisting of either mesostable or thermostable proteins. Linear combinations of these potentials have been shown to yield an estimation of the protein folding free energies at low and high temperatures, and the difference of these energies, a prediction of the melting temperature. This particular construction, that distinguishes between the interactions that contribute more than others to the stability at high temperatures and those that are more stabilizing at low , gives better performances compared to the standard approach based on -independent potentials which predict the thermal resistance from the thermodynamic stability. Our method has been tested on 45 proteins of known that belong to 11 homologous families. The standard deviation between experimental and predicted 's is equal to 13.6°C in cross validation, and decreases to 8.3°C if the 6 worst predicted proteins are excluded. Possible extensions of our approach are discussed.

Evolutionary biochemistry: revealing the historical and physical causes of protein properties

The repertoire of proteins and nucleic acids in the living world is determined by evolution; their properties are determined by the laws of physics and chemistry. Explanations of these two kinds of causality - the purviews of evolutionary biology and biochemistry, respectively - are typically pursued in isolation, but many fundamental questions fall squarely at the interface of fields. Here we articulate the paradigm of evolutionary biochemistry, which aims to dissect the physical mechanisms and evolutionary processes by which biological molecules diversified and to reveal how their physical architecture facilitates and constrains their evolution. We show how an integration of evolution with biochemistry moves us towards a more complete understanding of why biological molecules have the properties that they do.

Structure and stability insights into tumour suppressor p53 evolutionary related proteins

The p53 family of genes and their protein products, namely, p53, p63 and p73, have over one billion years of evolutionary history. Advances in computational biology and genomics are enabling studies of the complexities of the molecular evolution of p53 protein family to decipher the underpinnings of key biological conditions spanning from cancer through to various metabolic and developmental disorders and facilitate the design of personalised medicines. However, a complete understanding of the inherent nature of the thermodynamic and structural stability of the p53 protein family is still lacking. This is due, to a degree, to the lack of comprehensive structural information for a large number of homologous proteins and to an incomplete knowledge of the intrinsic factors responsible for their stability and how these might influence function. Here we investigate the thermal stability, secondary structure and folding properties of the DNA-binding domains (DBDs) of a range of proteins from the p53 family using biophysical methods. While the N- and the C-terminal domains of the p53 family show sequence diversity and are normally targets for post-translational modifications and alternative splicing, the central DBD is highly conserved. Together with data obtained from Molecular Dynamics simulations in solution and with structure based homology modelling, our results provide further insights into the molecular properties of evolutionary related p53 proteins. We identify some marked structural differences within the p53 family, which could account for the divergence in biological functions as well as the subtleties manifested in the oligomerization properties of this family.

Experimental evolution of protein-protein interaction networks

The modern synthesis of evolutionary theory and genetics has enabled us to discover underlying molecular mechanisms of organismal evolution. We know that in order to maximize an organism's fitness in a particular environment, individual interactions among components of protein and nucleic acid networks need to be optimized by natural selection, or sometimes through random processes, as the organism responds to changes and/or challenges in the environment. Despite the significant role of molecular networks in determining an organism's adaptation to its environment, we still do not know how such inter- and intra-molecular interactions within networks change over time and contribute to an organism's evolvability while maintaining overall network functions. One way to address this challenge is to identify connections between molecular networks and their host organisms, to manipulate these connections, and then attempt to understand how such perturbations influence molecular dynamics of the network and thus influence evolutionary paths and organismal fitness. In the present review, we discuss how integrating evolutionary history with experimental systems that combine tools drawn from molecular evolution, synthetic biology and biochemistry allow us to identify the underlying mechanisms of organismal evolution, particularly from the perspective of protein interaction networks.

Adaptation of Enterococcus faecalis to daptomycin reveals an ordered progression to resistance

With increasing numbers of hospital-acquired antibiotic resistant infections each year and staggering health care costs, there is a clear need for new antimicrobial agents, as well as novel strategies to extend their clinical efficacy. While genomic studies have provided a wealth of information about the alleles associated with adaptation to antibiotics, they do not provide essential information about the relative importance of genomic changes, their order of appearance, or potential epistatic relationships between adaptive changes. Here we used quantitative experimental evolution of a single polymorphic population in continuous culture with whole-genome sequencing and allelic frequency measurements to study daptomycin (DAP) resistance in the vancomycin-resistant clinical pathogen Enterococcus faecalis S613. Importantly, we sustained both planktonic and nonplanktonic (i.e., biofilm) populations in coculture as the concentration of antibiotic was raised, facilitating the development of more ecological complexity than is typically observed in laboratory evolution. Quantitative experimental evolution revealed a clear order and hierarchy of genetic changes leading to resistance, the signaling and metabolic pathways responsible, and the relative importance of these mutations to the evolution of DAP resistance. Despite the relative simplicity of this ex vivo approach compared to the ecological complexity of the human body, we showed that experimental evolution allows for rapid identification of clinically relevant adaptive molecular pathways and new targets for drug design in pathogens.

Molecular dynamics studies on the conformational transitions of adenylate kinase: a computational evidence for the conformational selection mechanism

Escherichia coli adenylate kinase (ADK) is a monomeric phosphotransferase enzyme that catalyzes reversible transfer of phosphoryl group from ATP to AMP with a large-scale domain motion. The detailed mechanism for this conformational transition remains unknown. In the current study, we performed long time-scale molecular dynamics simulations on both open and closed states of ADK. Based on the structural analyses of the simulation trajectories, we detected over 20 times conformational transitions between the open and closed states of ADK and identified two novel conformations as intermediate states in the catalytic processes. With these findings, we proposed a possible mechanism for the large-scale domain motion of Escherichia coli ADK and its catalytic process: (1) the substrate free ADK adopted an open conformation; (2) ATP bound with LID domain closure; (3) AMP bound with NMP domain closure; (4) phosphoryl transfer occurred with ATP, and AMP converted into two ADPs, and no conformational transition was detected in the enzyme; (5) LID domain opened with one ADP released; (6) another ADP released with NMP domain open. As both open and closed states sampled a wide range of conformation transitions, our simulation strongly supported the conformational selection mechanism for Escherichia coli ADK.

Experimental interrogation of the path dependence and stochasticity of protein evolution using phage-assisted continuous evolution

To what extent are evolutionary outcomes determined by a population's recent environment, and to what extent do they depend on historical contingency and random chance? Here we apply a unique experimental system to investigate evolutionary reproducibility and path dependence at the protein level. We combined phage-assisted continuous evolution with high-throughput sequencing to analyze evolving protein populations as they adapted to divergent and then convergent selection pressures over hundreds of generations. Independent populations of T7 RNA polymerase genes were subjected to one of two selection histories ("pathways") demanding recognition of distinct intermediate promoters followed by a common final promoter. We observed distinct classes of solutions with unequal phenotypic activity and evolutionary potential evolve from the two pathways, as well as from replicate populations exposed to identical selection conditions. Mutational analysis revealed specific epistatic interactions that explained the observed path dependence and irreproducibility. Our results reveal in molecular detail how protein adaptation to different environments, as well as stochasticity among populations evolved in the same environment, can both generate evolutionary outcomes that preclude subsequent convergence.

Synchronous waves of failed soft sweeps in the laboratory: remarkably rampant clonal interference of alleles at a single locus

It has increasingly been recognized that adapting populations of microbes contain not one, but many lineages continually arising and competing at once. This process, termed "clonal interference," alters the rate and dynamics of adaptation and biases winning mutations toward those with the largest selective effect. Here we uncovered a dramatic example of clonal interference between multiple similar mutations occurring at the same locus within replicate populations of Methylobacterium extorquens AM1. Because these mutational events involved the transposition of an insertion sequence into a narrow window of a single gene, they were both readily detectable at low frequencies and could be distinguished due to differences in insertion sites. This allowed us to detect up to 17 beneficial alleles of this type coexisting in a single population. Despite conferring a large selective benefit, the majority of these alleles rose and then fell in frequency due to other lineages emerging that were more fit. By comparing allele-frequency dynamics to the trajectories of fitness gains by these populations, we estimated the fitness values of the genotypes that contained these mutations. Collectively across all populations, these alleles arose upon backgrounds with a wide range of fitness values. Within any single population, however, multiple alleles tended to rise and fall synchronously during a single wave of multiple genotypes with nearly identical fitness values. These results suggest that alleles of large benefit arose repeatedly in failed "soft sweeps" during narrow windows of adaptation due to the combined effects of epistasis and clonal interference.

Small changes in enzyme function can lead to surprisingly large fitness effects during adaptive evolution of antibiotic resistance

In principle, evolutionary outcomes could be largely predicted if all of the relevant physicochemical variants of a particular protein function under selection were known and integrated into an appropriate physiological model. We have tested this principle by generating a family of variants of the tetracycline resistance protein TetX2 and identified the physicochemical properties most correlated with organismal fitness. Surprisingly, small changes in the K(m(MCN)), less than twofold, were sufficient to produce highly successful adaptive mutants over clinically relevant drug concentrations. We then built a quantitative model directly relating the in vitro physicochemical properties of the mutant enzymes to the growth rates of bacteria carrying a single chromosomal copy of the tet(X2) variants over a wide range of minocycline (MCN) concentrations. Importantly, this model allows the prediction of enzymatic properties directly from cellular growth rates as well as the physicochemical-fitness landscape of TetX2. Using experimental evolution and deep sequencing to monitor the allelic frequencies of the seven most biochemically efficient TetX2 mutants in 10 independently evolving populations, we showed that the model correctly predicted the success of the two most beneficial variants tet(X2)(T280A) and tet(X2)(N371I). The structure of the most efficient variant, TetX2(T280A), in complex with MCN at 2.7 Å resolution suggests an indirect effect on enzyme kinetics. Taken together, these findings support an important role for readily accessible small steps in protein evolution that can, in turn, greatly increase the fitness of an organism during natural selection.

Cellular strategies for regulating functional and nonfunctional protein aggregation

Growing evidence suggests that aggregation-prone proteins are both harmful and functional for a cell. How do cellular systems balance the detrimental and beneficial effect of protein aggregation? We reveal that aggregation-prone proteins are subject to differential transcriptional, translational, and degradation control compared to nonaggregation-prone proteins, which leads to their decreased synthesis, low abundance, and high turnover. Genetic modulators that enhance the aggregation phenotype are enriched in genes that influence expression homeostasis. Moreover, genes encoding aggregation-prone proteins are more likely to be harmful when overexpressed. The trends are evolutionarily conserved and suggest a strategy whereby cellular mechanisms specifically modulate the availability of aggregation-prone proteins to (1) keep concentrations below the critical ones required for aggregation and (2) shift the equilibrium between the monomeric and oligomeric/aggregate form, as explained by Le Chatelier’s principle. This strategy may prevent formation of undesirable aggregates and keep functional assemblies/aggregates under control.

Genomics of adaptation during experimental evolution of the opportunistic pathogen Pseudomonas aeruginosa

Adaptation is likely to be an important determinant of the success of many pathogens, for example when colonizing a new host species, when challenged by antibiotic treatment, or in governing the establishment and progress of long-term chronic infection. Yet, the genomic basis of adaptation is poorly understood in general, and for pathogens in particular. We investigated the genetics of adaptation to cystic fibrosis-like culture conditions in the presence and absence of fluoroquinolone antibiotics using the opportunistic pathogen Pseudomonas aeruginosa. Whole-genome sequencing of experimentally evolved isolates revealed parallel evolution at a handful of known antibiotic resistance genes. While the level of antibiotic resistance was largely determined by these known resistance genes, the costs of resistance were instead attributable to a number of mutations that were specific to individual experimental isolates. Notably, stereotypical quinolone resistance mutations in DNA gyrase often co-occurred with other mutations that, together, conferred high levels of resistance but no consistent cost of resistance. This result may explain why these mutations are so prevalent in clinical quinolone-resistant isolates. In addition, genes involved in cyclic-di-GMP signalling were repeatedly mutated in populations evolved in viscous culture media, suggesting a shared mechanism of adaptation to this CF–like growth environment. Experimental evolutionary approaches to understanding pathogen adaptation should provide an important complement to studies of the evolution of clinical isolates.

Pathogens face a hostile and often novel environment when infecting a new host, and adaptation to this environment can be critical to a pathogen's survival. The genetic basis of pathogen adaptation is in turn important for treatment, since the consistency with which therapies succeed may depend on the extent to which a pathogen adapts via the same routes in different patients. In this study, we investigate adaptation of the bacterium Pseudomonas aeruginosa to laboratory conditions that resemble the lungs of cystic fibrosis patients and to quinolone antibiotics. We find that a handful of genes and genetic pathways are repeatedly involved in adaptation to each condition. Nonetheless, other, less common mutations can play important roles in determining fitness, complicating strategies aimed at reducing the prevalence of antibiotic resistance.

Evolutionary paths to antibiotic resistance under dynamically sustained drug selection

Antibiotic resistance can evolve through the sequential accumulation of multiple mutations. To study such gradual evolution, we developed a selection device, the 'morbidostat', that continuously monitors bacterial growth and dynamically regulates drug concentrations, such that the evolving population is constantly challenged. We analyzed the evolution of resistance in Escherichia coli under selection with single drugs, including chloramphenicol, doxycycline and trimethoprim. Over a period of ∼20 days, resistance levels increased dramatically, with parallel populations showing similar phenotypic trajectories. Whole-genome sequencing of the evolved strains identified mutations both specific to resistance to a particular drug and shared in resistance to multiple drugs. Chloramphenicol and doxycycline resistance evolved smoothly through diverse combinations of mutations in genes involved in translation, transcription and transport. In contrast, trimethoprim resistance evolved in a stepwise manner, through mutations restricted to the gene encoding the enzyme dihydrofolate reductase (DHFR). Sequencing of DHFR over the time course of the experiment showed that parallel populations evolved similar mutations and acquired them in a similar order.

Functional and metabolic effects of adaptive glycerol kinase (GLPK) mutants in Escherichia coli

Herein we measure the effect of four adaptive non-synonymous mutations to the glycerol kinase (glpK) gene on catalytic function and regulation, to identify changes that correlate to increased fitness in glycerol media. The mutations significantly reduce affinity for the allosteric inhibitor fructose-1,6-bisphosphate (FBP) and formation of the tetramer, which are structurally related, in a manner that correlates inversely with imparted fitness during growth on glycerol, which strongly suggests that these enzymatic parameters drive growth improvement. Counterintuitively, the glpK mutations also increase glycerol-induced auto-catabolite repression that reduces glpK transcription in a manner that correlates to fitness. This suggests that increased specific GlpK activity is attenuated by negative feedback on glpK expression via catabolite repression, possibly to prevent methylglyoxal toxicity. We additionally report that glpK mutations were fixed in 47 of 50 independent glycerol-adapted lineages. By far the most frequently mutated locus (nucleotide 218) was mutated in 20 lineages, strongly suggesting this position has an elevated mutation rate. This study demonstrates that fitness correlations can be used to interrogate adaptive processes at the protein level and to identify the regulatory constraints underlying selection and improved growth.