Collateral fitness effects of mutations

Deep mutational scanning quantifies DNA binding and predicts clinical outcomes of PAX6 variants

Nonsense and missense mutations in the transcription factor PAX6 cause a wide range of eye development defects, including aniridia, microphthalmia and coloboma. To understand how changes of PAX6:DNA binding cause these phenotypes, we combined saturation mutagenesis of the paired domain of PAX6 with a yeast one-hybrid (Y1H) assay in which expression of a PAX6-GAL4 fusion gene drives antibiotic resistance. We quantified binding of more than 2700 single amino-acid variants to two DNA sequence elements. Mutations in DNA-facing residues of the N-terminal subdomain and linker region were most detrimental, as were mutations to prolines and to negatively charged residues. Many variants caused sequence-specific molecular gain-of-function effects, including variants in position 71 that increased binding to the LE9 enhancer but decreased binding to a SELEX-derived binding site. In the absence of antibiotic selection, variants that retained DNA binding slowed yeast growth, likely because such variants perturbed the yeast transcriptome. Benchmarking against known patient variants and applying ACMG/AMP guidelines to variant classification, we obtained supporting-to-moderate evidence that 977 variants are likely pathogenic and 1306 are likely benign. Our analysis shows that most pathogenic mutations in the paired domain of PAX6 can be explained simply by the effects of these mutations on PAX6:DNA association, and establishes Y1H as a generalisable assay for the interpretation of variant effects in transcription factors.

Deep mutational scanning reveals the molecular determinants of RNA polymerase-mediated adaptation and tradeoffs

RNA polymerase (RNAP) is emblematic of complex biological systems that control multiple traits involving trade-offs such as growth versus maintenance. Laboratory evolution has revealed that mutations in RNAP subunits, including RpoB, are frequently selected. However, we lack a systems view of how mutations alter the RNAP molecular functions to promote adaptation. We, therefore, measured the fitness of thousands of mutations within a region of rpoB under multiple conditions and genetic backgrounds, to find that adaptive mutations cluster in two modules. Mutations in one module favor growth over maintenance through a partial loss of an interaction associated with faster elongation. Mutations in the other favor maintenance over growth through a destabilized RNAP-DNA complex. The two molecular handles capture the versatile RNAP-mediated adaptations. Combining both interaction losses simultaneously improved maintenance and growth, challenging the idea that growth-maintenance tradeoff resorts only from limited resources, and revealing how compensatory evolution operates within RNAP.

Systematic profiling of dominant ubiquitin variants reveals key functional nodes contributing to evolutionary selection

Dominant-negative mutations can help to investigate the biological mechanisms and to understand the selective pressures for multifunctional proteins. However, most studies have focused on recessive mutant effects that occur in the absence of a second functional gene copy, which overlooks the fact that most eukaryotic genomes contain more than one copy of many genes. We have identified dominant effects on yeast growth rate among all possible point mutations in ubiquitin expressed alongside a wild-type allele. Our results reveal more than 400 dominant-negative mutations, indicating that dominant-negative effects make a sizable contribution to selection acting on ubiquitin. Cellular and biochemical analyses of individual ubiquitin variants show that dominant-negative effects are explained by varied accumulation of polyubiquitinated cellular proteins and/or defects in conjugation of ubiquitin variants to ubiquitin ligases. Our approach to identify dominant-negative mutations is general and can be applied to other proteins of interest.

Population transcriptogenomics highlights impaired metabolism and small population sizes in tree frogs living in the Chernobyl Exclusion Zone

BACKGROUND

Individual functional modifications shape the ability of wildlife populations to cope with anthropogenic environmental changes. But instead of adaptive response, human-altered environments can generate a succession of deleterious functional changes leading to the extinction of the population. To study how persistent anthropogenic changes impacted local species' population status, we characterised population structure, genetic diversity and individual response of gene expression in the tree frog Hyla orientalis along a gradient of radioactive contamination around the Chernobyl nuclear power plant.

RESULTS

We detected lower effective population size in populations most exposed to ionizing radiation in the Chernobyl Exclusion Zone that is not compensated by migrations from surrounding areas. We also highlighted a decreased body condition of frogs living in the most contaminated area, a distinctive transcriptomics signature and stop-gained mutations in genes involved in energy metabolism. While the association with dose will remain correlational until further experiments, a body of evidence suggests the direct or indirect involvement of radiation exposure in these changes.

CONCLUSIONS

Despite ongoing migration and lower total dose rates absorbed than at the time of the accident, our results demonstrate that Hyla orientalis specimens living in the Chernobyl Exclusion Zone are still undergoing deleterious changes, emphasizing the long-term impacts of the nuclear disaster.

A null model for the distribution of fitness effects of mutations

The distribution of fitness effects (DFE) of new mutations is key to our understanding of many evolutionary processes. Theoreticians have developed several models to help understand the patterns seen in empirical DFEs. Many such models reproduce the broad patterns seen in empirical DFEs but these models often rely on structural assumptions that cannot be tested empirically. Here, we investigate how much of the underlying "microscopic" biological processes involved in the mapping of new mutations to fitness can be inferred from "macroscopic" observations of the DFE. We develop a null model by generating random genotype-to-fitness maps and show that the null DFE is that with the largest possible information entropy. We further show that, subject to one simple constraint, this null DFE is a Gompertz distribution. Finally, we illustrate how the predictions of this null DFE match empirically measured DFEs from several datasets, as well as DFEs simulated from Fisher's geometric model. This suggests that a match between models and empirical data is often not a very strong indication of the mechanisms underlying the mapping of mutation to fitness.

Genes Vary Greatly in Their Propensity for Collateral Fitness Effects of Mutations

Mutations can have deleterious fitness effects when they decrease protein specific activity or decrease active protein abundance. Mutations will also be deleterious when they cause misfolding or misinteractions that are toxic to the cell (i.e., independent of whether the mutations affect specific activity and abundance). The extent to which protein evolution is shaped by these and other collateral fitness effects is unclear in part because little is known of their frequency and magnitude. Using deep mutational scanning (DMS), we previously found at least 42% of missense mutations in the TEM-1 β-lactamase antibiotic resistance gene cause deleterious collateral fitness effects. Here, we used DMS to comprehensively determine the collateral fitness effects of missense mutations in three genes encoding the antibiotic resistance proteins New Delhi metallo-β-lactamase (NDM-1), chloramphenicol acetyltransferase I (CAT-I), and 2″-aminoglycoside nucleotidyltransferase (AadB). AadB (20%), CAT-I (0.9%), and NDM-1 (0.2%) were less susceptible to deleterious collateral fitness effects than TEM-1 (42%) indicating that genes have different propensities for these effects. As was observed with TEM-1, all the studied deleterious aadB mutants increased aggregation. However, aggregation did not correlate with collateral fitness effects for many of the deleterious mutants of CAT-I and NDM-1. Select deleterious mutants caused unexpected phenotypes to emerge. The introduction of internal start codons in CAT-1 caused loss of the episome and a mutation in aadB made its cognate antibiotic essential for growth. Our study illustrates how the complexity of the cell provides a rich environment for collateral fitness effects and new phenotypes to emerge.

Mutational fitness landscape and drug resistance

Robust technology has been developed to systematically quantify fitness landscapes that provide valuable opportunities to improve our understanding of drug resistance and define new avenues to develop drugs with reduced resistance susceptibility. We outline the critical importance of drug resistance studies and the potential for fitness landscape approaches to contribute to this effort. We describe the major technical advancements in mutational scanning, which is the primary approach used to quantify protein fitness landscapes. There are many complex steps to consider in planning and executing mutational scanning projects including developing a selection scheme, generating mutant libraries, tracking the frequency of variants using next-generation sequencing, and processing and interpreting the data. Key experimental parameters impacting each of these steps are discussed to aid in planning fitness landscape studies. There is a strong need for improved understanding of drug resistance, and fitness landscapes provide a promising new approach.

In silico study on miRNA regulation and NSs protein interactome characterization of the SFTS virus

Severe fever with thrombocytopenia syndrome causing virus i.e. SFTS virus has increased in the last few years. The underlying cause and mechanism of disease progression and development of symptoms is not well known. Many viruses including Hepatitis B, Hepatitis C, HIV-1, Herpes virus, Dengue virus and many others have been seen to regulate their functions at the miRNA level. This study aimed to find out those cellular miRNAs, which can be mimicked or antagonized by the viral genome and analyze the effect of these miRNAs on various gene functions. Investigations in this study suggest a correlation between miRNA regulation with the disease symptoms and progression. By exhaustive literature survey we have tried to identify the interacting partners of the Non Structural S (NSs) protein and characterized the protein-protein interactions. The binding interface that can serve as target for therapeutic studies involving the interfacial residues was analyzed. This study would serve as an avenue to design therapeutics making use of not only protein-protein interactions but also miRNA based regulation as well.

Johansen H, Molin S, Vila AJ, Smania AM. Longitudinal Evolution of the Pseudomonas-Derived Cephalosporinase (PDC) Structure and Activity in a Cystic Fibrosis Patient Treated with β-Lactams

Traditional studies on the evolution of antibiotic resistance development use approaches that can range from laboratory-based experimental studies, to epidemiological surveillance, to sequencing of clinical isolates. However, evolutionary trajectories also depend on the environment in which selection takes place, compelling the need to more deeply investigate the impact of environmental complexities and their dynamics over time. Herein, we explored the within-patient adaptive long-term evolution of a Pseudomonas aeruginosa hypermutator lineage in the airways of a cystic fibrosis (CF) patient by performing a chronological tracking of mutations that occurred in different subpopulations; our results demonstrated parallel evolution events in the chromosomally encoded class C β-lactamase (blaPDC). These multiple mutations within blaPDC shaped diverse coexisting alleles, whose frequency dynamics responded to the changing antibiotic selective pressures for more than 26 years of chronic infection. Importantly, the combination of the cumulative mutations in blaPDC provided structural and functional protein changes that resulted in a continuous enhancement of its catalytic efficiency and high level of cephalosporin resistance. This evolution was linked to the persistent treatment with ceftazidime, which we demonstrated selected for variants with robust catalytic activity against this expanded-spectrum cephalosporin. A "gain of function" of collateral resistance toward ceftolozane, a more recently introduced cephalosporin that was not prescribed to this patient, was also observed, and the biochemical basis of this cross-resistance phenomenon was elucidated. This work unveils the evolutionary trajectories paved by bacteria toward a multidrug-resistant phenotype, driven by decades of antibiotic treatment in the natural CF environmental setting. IMPORTANCE Antibiotics are becoming increasingly ineffective to treat bacterial infections. It has been consequently predicted that infectious diseases will become the biggest challenge to human health in the near future. Pseudomonas aeruginosa is considered a paradigm in antimicrobial resistance as it exploits intrinsic and acquired resistance mechanisms to resist virtually all antibiotics known. AmpC β-lactamase is the main mechanism driving resistance in this notorious pathogen to β-lactams, one of the most widely used classes of antibiotics for cystic fibrosis infections. Here, we focus on the β-lactamase gene as a model resistance determinant and unveil the trajectory P. aeruginosa undertakes on the path toward a multidrug-resistant phenotype during the course of two and a half decades of chronic infection in the airways of a cystic fibrosis patient. Integrating genetic and biochemical studies in the natural environment where evolution occurs, we provide a unique perspective on this challenging landscape, addressing fundamental molecular mechanisms of resistance.

Dan Tawfik's Lessons for Protein Engineers about Enzymes Adapting to New Substrates

Natural evolution has been creating new complex systems for billions of years. The process is spontaneous and requires neither intelligence nor moral purpose but is nevertheless difficult to understand. The late Dan Tawfik spent years studying enzymes as they adapted to recognize new substrates. Much of his work focused on gaining fundamental insights, so the practical utility of his experiments may not be obvious even to accomplished protein engineers. Here we focus on two questions fundamental to any directed evolution experiment. Which proteins are the best starting points for such experiments? Which trait(s) of the chosen parental protein should be evolved to achieve the desired outcome? We summarize Tawfik's contributions to our understanding of these problems, to honor his memory and encourage those unfamiliar with his ideas to read his publications.

Determinants of Multiheme Cytochrome Extracellular Electron Transfer Uncovered by Systematic Peptide Insertion

The multiheme cytochrome MtrA enables microbial respiration by transferring electrons across the outer membrane to extracellular electron acceptors. While structural studies have identified residues that mediate the binding of MtrA to hemes and to other cytochromes that facilitate extracellular electron transfer (EET), the relative importance of these interactions for EET is not known. To better understand EET, we evaluated how insertion of an octapeptide across all MtrA backbone locations affects Shewanella oneidensis MR-1 respiration on Fe(III). The EET efficiency was found to be inversely correlated with the proximity of the insertion to the heme prosthetic groups. Mutants with decreased EET efficiencies also arose from insertions in a subset of the regions that make residue-residue contacts with the porin MtrB, while all sites contacting the extracellular cytochrome MtrC presented high peptide insertion tolerance. MtrA variants having peptide insertions within the CXXCH motifs that coordinate heme cofactors retained some ability to support respiration on Fe(III), although these variants presented significantly decreased EET efficiencies. Furthermore, the fitness of cells expressing different MtrA variants under Fe(III) respiration conditions correlated with anode reduction. The peptide insertion profile, which represents the first comprehensive sequence-structure-function map for a multiheme cytochrome, implicates MtrA as a strategic protein engineering target for the regulation of EET.

Fragment-Directed Random Mutagenesis by the Reverse Kunkel Method

Two fundamentally different approaches are routinely used for protein engineering: user-defined mutagenesis and random mutagenesis, each with its own strengths and weaknesses. Here, we invent a unique mutagenesis protocol, which combines the advantages of user-defined mutagenesis and random mutagenesis. The new method, termed the reverse Kunkel method, allows the user to create random mutations at multiple specified regions in a one-pot reaction. We demonstrated the reverse Kunkel method by mimicking the somatic hypermutation in antibodies that introduces random mutations concentrated in complementarity-determining regions. Coupling with the phage display and yeast display selections, we successfully generated dramatically improved antibodies against a model protein and a neurotransmitter peptide in terms of affinity and immunostaining performance. The reverse Kunkel method is especially suitable for engineering proteins whose activities are determined by multiple variable regions, such as antibodies and adeno-associated virus capsids, or whose functional domains are composed of several discontinuous sequences, such as Cas9 and Cas12a.

Environmental selection and epistasis in an empirical phenotype-environment-fitness landscape

Fitness landscapes, mappings of genotype/phenotype to their effects on fitness, are invaluable concepts in evolutionary biochemistry. Although widely discussed, measurements of phenotype-fitness landscapes in proteins remain scarce. Here, we quantify all single mutational effects on fitness and phenotype (EC₅₀) of VIM-2 β-lactamase across a 64-fold range of ampicillin concentrations. We then construct a phenotype-fitness landscape that takes variations in environmental selection pressure into account. We found that a simple, empirical landscape accurately models the ~39,000 mutational data points, suggesting that the evolution of VIM-2 can be predicted on the basis of the selection environment. Our landscape provides new quantitative knowledge on the evolution of the β-lactamases and proteins in general, particularly their evolutionary dynamics under subinhibitory antibiotic concentrations, as well as the mechanisms and environmental dependence of non-specific epistasis.

Non-Darwinian Molecular Biology

With the discovery of the double helical structure of DNA, a shift occurred in how biologists investigated questions surrounding cellular processes, such as protein synthesis. Instead of viewing biological activity through the lens of chemical reactions, this new field used biological information to gain a new profound view of how biological systems work. Molecular biologists asked new types of questions that would have been inconceivable to the older generation of researchers, such as how cellular machineries convert inherited biological information into functional molecules like proteins. This new focus on biological information also gave molecular biologists a way to link their findings to concepts developed by genetics and the modern synthesis. However, by the late 1960s this all changed. Elevated rates of mutation, unsustainable genetic loads, and high levels of variation in populations, challenged Darwinian evolution, a central tenant of the modern synthesis, where adaptation was the main driver of evolutionary change. Building on these findings, Motoo Kimura advanced the neutral theory of molecular evolution, which advocates that selection in multicellular eukaryotes is weak and that most genomic changes are neutral and due to random drift. This was further elaborated by Jack King and Thomas Jukes, in their paper “Non-Darwinian Evolution”, where they pointed out that the observed changes seen in proteins and the types of polymorphisms observed in populations only become understandable when we take into account biochemistry and Kimura’s new theory. Fifty years later, most molecular biologists remain unaware of these fundamental advances. Their adaptionist viewpoint fails to explain data collected from new powerful technologies which can detect exceedingly rare biochemical events. For example, high throughput sequencing routinely detects RNA transcripts being produced from almost the entire genome yet are present less than one copy per thousand cells and appear to lack any function. Molecular biologists must now reincorporate ideas from classical biochemistry and absorb modern concepts from molecular evolution, to craft a new lens through which they can evaluate the functionality of transcriptional units, and make sense of our messy, intricate, and complicated genome.

Deciphering the evolution of metallo-β-lactamases: a journey from the test tube to the bacterial periplasm

Understanding the evolution of metallo-β-lactamases (MBLs) is fundamental to deciphering the mechanistic basis of resistance to carbapenems in pathogenic and opportunistic bacteria. Presently, these MBL producing pathogens are linked to high rates of morbidity and mortality worldwide. However, the study of the biochemical and biophysical features of MBLs in vitro provides an incomplete picture of their evolutionary potential, since this limited and artificial environment disregards the physiological context where evolution and selection take place. Herein, we describe recent efforts aimed to address the evolutionary traits acquired by different clinical variants of MBLs in conditions mimicking their native environment (the bacterial periplasm) and considering whether they are soluble or membrane-bound proteins. This includes addressing the metal content of MBLs within the cell under zinc starvation conditions, and the context provided by different bacterial hosts that result in particular resistance phenotypes. Our analysis highlights recent progress bridging the gap between in vitro and in-cell studies.

Analyses of HIV proteases variants at the threshold of viability reveals relationships between processing efficiency and fitness

Investigating the relationships between protein function and fitness provides keys for understanding biochemical mechanisms that underly evolution. Mutations with partial fitness defects can delineate the threshold of biochemical function required for viability. We utilized a previous deep mutational scan of HIV-1 protease (PR) to identify variants with 15–45 per cent defects in replication and analysed the biochemical function of eight variants (L10M, L10S, V32C, V32I, A71V, A71S, Q92I, Q92N). We purified each variant and assessed the efficiency of peptide cleavage for three cut sites (MA-CA, TF-PR, and PR-RT) as well as gel-based analyses of processing of purified Gag. The cutting activity of at least one site was perturbed relative to WT protease for all variants, consistent with cutting activity being a primary determinant of fitness effects. We examined the correlation of fitness defects with cutting activity of different sites. MA-CA showed the weakest correlation (R² = 0.02) with fitness, suggesting relatively weak coupling with viral replication. In contrast, cutting of the TF-PR site showed the strongest correlation with fitness (R² = 0.53). Cutting at the TF-PR site creates a new PR protein with a free N-terminus that is critical for activity. Our findings indicate that increasing the pool of active PR is rate limiting for viral replication, making this an ideal step to target with inhibitors.

Mutant alleles differentially shape fitness and other complex traits in cattle

Mutant alleles (MAs) that have been classically recognised have large effects on phenotype and tend to be deleterious to traits and fitness. Is this the case for mutations with small effects? We infer MAs for 8 million sequence variants in 113k cattle and quantify the effects of MA on 37 complex traits. Heterozygosity for variants at genomic sites conserved across 100 vertebrate species increase fertility, stature, and milk production, positively associating these traits with fitness. MAs decrease stature and fat and protein concentration in milk, but increase gestation length and somatic cell count in milk (the latter indicative of mastitis). However, the frequency of MAs decreasing stature and fat and protein concentration, increasing gestation length and somatic cell count were lower than the frequency of MAs with the opposite effect. These results suggest bias in the mutations direction of effect (e.g. towards reduced protein in milk), but selection operating to reduce the frequency of these MAs. Taken together, our results imply two classes of genomic sites subject to long-term selection: sites conserved across vertebrates show hybrid vigour while sites subject to less long-term selection show a bias in mutation towards undesirable alleles.

High throughput and quantitative enzymology in the genomic era

Accurate predictions from models based on physical principles are the ultimate metric of our biophysical understanding. Although there has been stunning progress toward structure prediction, quantitative prediction of enzyme function has remained challenging. Realizing this goal will require large numbers of quantitative measurements of rate and binding constants and the use of these ground-truth data sets to guide the development and testing of these quantitative models. Ground truth data more closely linked to the underlying physical forces are also desired. Here, we describe technological advances that enable both types of ground truth measurements. These advances allow classic models to be tested, provide novel mechanistic insights, and place us on the path toward a predictive understanding of enzyme structure and function.

Expression level is a major modifier of the fitness landscape of a protein coding gene

The phenotypic consequence of a genetic mutation depends on many factors including the expression level of a gene. However, a comprehensive quantification of this expression effect is still lacking, as is a further general mechanistic understanding of the effect. Here, we measured the fitness effect of almost all (>97.5%) single-nucleotide mutations in GFP, an exogenous gene with no physiological function, and URA3, a conditionally essential gene. Both genes were driven by two promoters whose expression levels differed by around tenfold. The resulting fitness landscapes revealed that the fitness effects of at least 42% of all single-nucleotide mutations within the genes were expression dependent. Although only a small fraction of variation in fitness effects among different mutations can be explained by biophysical properties of the protein and messenger RNA of the gene, our analyses revealed that the avoidance of stochastic molecular errors generally underlies the expression dependency of mutational effects and suggested protein misfolding as the most important type of molecular error among those examined. Our results therefore directly explained the slower evolution of highly expressed genes and highlighted cytotoxicity due to stochastic molecular errors as a non-negligible component for understanding the phenotypic consequence of mutations.

Abundance Imparts Evolutionary Constraints of Similar Magnitude on the Buried, Surface, and Disordered Regions of Proteins

An understanding of the forces shaping protein conservation is key, both for the fundamental knowledge it represents and to allow for optimal use of evolutionary information in practical applications. Sequence conservation is typically examined at one of two levels. The first is a residue-level, where intra-protein differences are analyzed and the second is a protein-level, where inter-protein differences are studied. At a residue level, we know that solvent-accessibility is a prime determinant of conservation. By inverting this logic, we inferred that disordered regions are slightly more solvent-accessible on average than the most exposed surface residues in domains. By integrating abundance information with evolutionary data within and across proteins, we confirmed a previously reported strong surface-core association in the evolution of structured regions, but we found a comparatively weak association between disordered and structured regions. The facts that disordered and structured regions experience different structural constraints and evolve independently provide a unique setup to examine an outstanding question: why is a protein’s abundance the main determinant of its sequence conservation? Indeed, any structural or biophysical property linked to the abundance-conservation relationship should increase the relative conservation of regions concerned with that property (e.g., disordered residues with mis-interactions, domain residues with misfolding). Surprisingly, however, we found the conservation of disordered and structured regions to increase in equal proportion with abundance. This observation implies that either abundance-related constraints are structure-independent, or multiple constraints apply to different regions and perfectly balance each other.

Structure and function of naturally evolved de novo proteins

Comparative evolutionary genomics has revealed that novel protein coding genes can emerge randomly from non-coding DNA. While most of the myriad of transcripts which continuously emerge vanish rapidly, some attain regulatory regions, become translated and survive. More surprisingly, sequence properties of de novo proteins are almost indistinguishable from randomly obtained sequences, yet de novo proteins may gain functions and integrate into eukaryotic cellular networks quite easily. We here discuss current knowledge on de novo proteins, their structures, functions and evolution. Since the existence of de novo proteins seems at odds with decade-long attempts to construct proteins with novel structures and functions from scratch, we suggest that a better understanding of de novo protein evolution may fuel new strategies for protein design.

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

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

Fitness variation across subtle environmental perturbations reveals local modularity and global pleiotropy of adaptation

Building a genotype-phenotype-fitness map of adaptation is a central goal in evolutionary biology. It is difficult even when adaptive mutations are known because it is hard to enumerate which phenotypes make these mutations adaptive. We address this problem by first quantifying how the fitness of hundreds of adaptive yeast mutants responds to subtle environmental shifts. We then model the number of phenotypes these mutations collectively influence by decomposing these patterns of fitness variation. We find that a small number of inferred phenotypes can predict fitness of the adaptive mutations near their original glucose-limited evolution condition. Importantly, inferred phenotypes that matter little to fitness at or near the evolution condition can matter strongly in distant environments. This suggests that adaptive mutations are locally modular - affecting a small number of phenotypes that matter to fitness in the environment where they evolved - yet globally pleiotropic - affecting additional phenotypes that may reduce or improve fitness in new environments.

Comprehensive exploration of the translocation, stability and substrate recognition requirements in VIM-2 lactamase

Metallo-β-lactamases (MBLs) degrade a broad spectrum of β-lactam antibiotics, and are a major disseminating source for multidrug resistant bacteria. Despite many biochemical studies in diverse MBLs, molecular understanding of the roles of residues in the enzyme's stability and function, and especially substrate specificity, is lacking. Here, we employ deep mutational scanning (DMS) to generate comprehensive single amino acid variant data on a major clinical MBL, VIM-2, by measuring the effect of thousands of VIM-2 mutants on the degradation of three representative classes of β-lactams (ampicillin, cefotaxime, and meropenem) and at two different temperatures (25°C and 37°C). We revealed residues responsible for expression and translocation, and mutations that increase resistance and/or alter substrate specificity. The distribution of specificity-altering mutations unveiled distinct molecular recognition of the three substrates. Moreover, these function-altering mutations are frequently observed among naturally occurring variants, suggesting that the enzymes have continuously evolved to become more potent resistance genes.