Enzyme Efficiency but Not Thermostability Drives Cefotaxime Resistance Evolution in TEM-1 ß-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 ß-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.

Real versus Artificial Variation in the Thermal Sensitivity of Biological Traits.

Whether the thermal sensitivity of an organism's traits follows the simple Boltzmann-Arrhenius model remains a contentious issue that centers around consideration of its operational temperature range and whether the sensitivity corresponds to one or a few underlying rate-limiting enzymes. Resolving this issue is crucial, because mechanistic models for temperature dependence of traits are required to predict the biological effects of climate change. Here, by combining theory with data on 1,085 thermal responses from a wide range of traits and organisms, we show that substantial variation in thermal sensitivity (activation energy) estimates can arise simply because of variation in the range of measured temperatures. Furthermore, when thermal responses deviate systematically from the Boltzmann-Arrhenius model, variation in measured temperature ranges across studies can bias estimated activation energy distributions toward higher mean, median, variance, and skewness. Remarkably, this bias alone can yield activation energies that encompass the range expected from biochemical reactions (from ~0.2 to 1.2 eV), making it difficult to establish whether a single activation energy appropriately captures thermal sensitivity. We provide guidelines and a simple equation for partially correcting for such artifacts. Our results have important implications for understanding the mechanistic basis of thermal responses of biological traits and for accurately modeling effects of variation in thermal sensitivity on responses of individuals, populations, and ecological communities to changing climatic temperatures.

Genetic variation, simplicity, and evolutionary constraints for function-valued traits.

Understanding the patterns of genetic variation and constraint for continuous reaction norms, growth trajectories, and other function-valued traits is challenging. We describe and illustrate a recent analytical method, simple basis analysis (SBA), that uses the genetic variance-covariance (G) matrix to identify "simple" directions of genetic variation and genetic constraints that have straightforward biological interpretations. We discuss the parallels between the eigenvectors (principal components) identified by principal components analysis (PCA) and the simple basis (SB) vectors identified by SBA. We apply these methods to estimated G matrices obtained from 10 studies of thermal performance curves and growth curves. Our results suggest that variation in overall size across all ages represented most of the genetic variance in growth curves. In contrast, variation in overall performance across all temperatures represented less than one-third of the genetic variance in thermal performance curves in all cases, and genetic trade-offs between performance at higher versus lower temperatures were often important. The analyses also identify potential genetic constraints on patterns of early and later growth in growth curves. We suggest that SBA can be a useful complement or alternative to PCA for identifying biologically interpretable directions of genetic variation and constraint in function-valued traits.

Fisher's geometric model of adaptation meets the functional synthesis: data on pairwise epistasis for fitness yields insights into the shape and size of phenotype space.

The functional synthesis uses experimental methods from molecular biology, biochemistry and structural biology to decompose evolutionarily important mutations into their more proximal mechanistic determinants. However these methods are technically challenging and expensive. Noting strong formal parallels between R.A. Fisher's geometric model of adaptation and a recent model for the phenotypic basis of protein evolution, we sought to use the former to make inferences into the latter using data on pairwise fitness epistasis between mutations. We present an analytic framework for classifying pairs of mutations with respect to similarity of underlying mechanism on this basis, and also show that these data can yield an estimate of the number of mutationally labile phenotypes underlying fitness effects. We use computer simulations to explore the robustness of our approach to violations of analytic assumptions and analyze several recently published datasets. This work provides a theoretical complement to the functional synthesis as well as a novel test of Fisher's geometric model.

Erroneous Arrhenius: modified arrhenius model best explains the temperature dependence of ectotherm fitness.

The initial rise of fitness that occurs with increasing temperature is attributed to Arrhenius kinetics, in which rates of reaction increase exponentially with increasing temperature. Models based on Arrhenius typically assume single rate-limiting reactions over some physiological temperature range for which all the rate-limiting enzymes are in 100% active conformation. We test this assumption using data sets for microbes that have measurements of fitness (intrinsic rate of population growth) at many temperatures and over a broad temperature range and for diverse ectotherms that have measurements at fewer temperatures. When measurements are available at many temperatures, strictly Arrhenius kinetics are rejected over the physiological temperature range. However, over a narrower temperature range, we cannot reject strictly Arrhenius kinetics. The temperature range also affects estimates of the temperature dependence of fitness. These results indicate that Arrhenius kinetics only apply over a narrow range of temperatures for ectotherms, complicating attempts to identify general patterns of temperature dependence.

Hotter is better and broader: thermal sensitivity of fitness in a population of bacteriophages.

Hotter is better is a hypothesis of thermal adaptation that posits that the rate-depressing effects of low temperature on biochemical reactions cannot be overcome by physiological plasticity or genetic adaptation. If so, then genotypes or populations adapted to warmer temperatures will have higher maximum growth rates than those adapted to low temperatures. Here we test hotter is better by measuring thermal reaction norms for intrinsic rate of population growth among an intraspecific collection of bacteriophages recently isolated from nature. Consistent with hotter is better, we find that phage genotypes with higher optimal temperatures have higher maximum growth rates. Unexpectedly, we also found that hotter is broader, meaning that the phages with the highest optimal temperatures also have the greatest temperature ranges. We found that the temperature sensitivity of fitness for phages is similar to that for insects.

Compensatory evolution in RNA secondary structures increases substitution rate variation among sites.

There is growing evidence that interactions between biological molecules (e.g., RNA-RNA, protein-protein, RNA-protein) place limits on the rate and trajectory of molecular evolution. Here, by extending Kimura's model of compensatory evolution at interacting sites, we show that the ratio of transition to transversion substitutions (kappa) at interacting sites should be equal to the square of the ratio at independent sites. Because transition mutations generally occur at a higher rate than transversions, the model predicts that kappa should be higher at interacting sites than at independent sites. We tested this prediction in 10 RNA secondary structures by comparing phylogenetically derived estimates of kappa in paired sites within stems (kappa(p)) and unpaired sites within loops (kappa(u)). Eight of the 10 structures showed an excellent match to the quantitative predictions of the model, and 9 of the 10 structures matched the qualitative prediction kappa(p) > kappa(u). Only the Rev response element from the human immunovirus (HIV) genome showed the reverse pattern, with kappa(p) < kappa(u). Although a variety of evolutionary forces could produce quantitative deviations from the model predictions, the reversal in magnitude of kappa(p) and kappa(u) could be achieved only by violating the model assumption that the underlying transition (or transversion) mutation rates were identical in paired and unpaired regions of the molecule. We explore the ability of the APOBEC3 enzymes, host defense mechanisms against retroviruses, which induce transition mutations preferentially in single-stranded regions of the HIV genome, to explain this exception to the rule. Taken as a whole, our findings suggest that kappa may have utility as a simple diagnostic to evaluate proposed secondary structures.

The genetic basis of thermal reaction norm evolution in lab and natural phage populations.

Two major goals of laboratory evolution experiments are to integrate from genotype to phenotype to fitness, and to understand the genetic basis of adaptation in natural populations. Here we demonstrate that both goals are possible by re-examining the outcome of a previous laboratory evolution experiment in which the bacteriophage G4 was adapted to high temperatures. We quantified the evolutionary changes in the thermal reaction norms--the curves that describe the effect of temperature on the growth rate of the phages--and decomposed the changes into modes of biological interest. Our analysis indicated that changes in optimal temperature accounted for almost half of the evolutionary changes in thermal reaction norm shape, and made the largest contribution toward adaptation at high temperatures. Genome sequencing allowed us to associate reaction norm shape changes with particular nucleotide mutations, and several of the identified mutations were found to be polymorphic in natural populations. Growth rate measures of natural phage that differed at a site that contributed substantially to adaptation in the lab indicated that this mutation also underlies thermal reaction norm shape variation in nature. In combination, our results suggest that laboratory evolution experiments may successfully predict the genetic bases of evolutionary responses to temperature in nature. The implications of this work for viral evolution arise from the fact that shifts in the thermal optimum are characterized by tradeoffs in performance between high and low temperatures. Optimum shifts, if characteristic of viral adaptation to novel temperatures, would ensure the success of vaccine development strategies that adapt viruses to low temperatures in an attempt to reduce virulence at higher (body) temperatures.

Pdcd4 suppresses tumor phenotype in JB6 cells by inhibiting AP-1 transactivation.

Transformation suppressor Pdcd4 is downregulated in transformed (Tx) mouse epidermal JB6 RT101 cells relative to transformation-resistant (P-) and susceptible (P+) variants. Whether Pdcd4 downregulation is necessary not only to induce transformation but also to maintain tumor phenotypes has not been determined previously. In the present study, overexpression of Pdcd4 cDNA in stably transfected RT101 cells resulted in 40% fewer anchorage-independent colonies that were smaller in size than the vector control colonies, indicating that elevated Pdcd4 expression is sufficient to suppress tumor phenotype. Transient transfection of Pdcd4 expression plasmid and 4 x AP-1 reporter gene showed that activation of AP-1-dependent transcription was inhibited by Pdcd4 expression in a concentration-dependent manner. In contrast, Pdcd4 did not inhibit serum response element-dependent transcription, indicating specificity. In a Gal4 fusion assay, Pdcd4 specifically inhibited activation of c-Jun and c-Fos activation domains, but did not inhibit activation of JunB, JunD, Fra-1, or Fra-2. Gel mobility shift assay demonstrated that c-Jun is the major component detected in the AP-1 complex in RT101 cells. Previous studies suggested that AP-1 activity is required for maintaining the transformed phenotype in RT101 cells. Thus, Pdcd4 suppresses tumor phenotype by inhibiting AP-1-dependent transcription, possibly through inhibiting c-Jun and c-Fos activation.