Evolutionary stability of developmental commitment.

Evolution of unicellular to multicellular organisms must resolve conflicts in reproductive interests between individual cells and the group. The social amoeba Dictyostelium discoideum is a soil-living eukaryote with facultative sociality. While cells grow in the presence of nutrients, cells aggregate under starvation to form fruiting bodies containing spores and altruistic stalk cells. Once cells socially committed, they complete formation of fruiting bodies, even if a new source of nutrients becomes available. The persistence of this social commitment raises questions as it inhibits individual cells from swiftly returning to solitary growth. I hypothesize that traits enabling premature de-commitment are hindered from being selected. Recent work has revealed outcomes of the premature de-commitment through forced refeeding; The de-committed cells take an altruistic prestalk-like position due to their reduced cohesiveness through interactions with socially committed cells. I constructed an evolutionary model assuming their division of labor. The results revealed a valley in the fitness landscape that prevented invasion of de-committing mutants, indicating evolutionary stability of the social commitment. The findings provide a general scheme that maintains multicellularity by evolving a specific division of labor, in which less cohesive individuals become altruists.

Fitness Landscapes and Evolution of Catalytic RNA.

The relationship between genotype and phenotype, or the fitness landscape, is the foundation of genetic engineering and evolution. However, mapping fitness landscapes poses a major technical challenge due to the amount of quantifiable data that is required. Catalytic RNA is a special topic in the study of fitness landscapes due to its relatively small sequence space combined with its importance in synthetic biology. The combination of in vitro selection and high-throughput sequencing has recently provided empirical maps of both complete and local RNA fitness landscapes, but the astronomical size of sequence space limits purely experimental investigations. Next steps are likely to involve data-driven interpolation and extrapolation over sequence space using various machine learning techniques. We discuss recent progress in understanding RNA fitness landscapes, particularly with respect to protocells and machine representations of RNA. The confluence of technical advances may significantly impact synthetic biology in the near future.

Increased Positive Selection in Highly Recombining Genes Does not Necessarily Reflect an Evolutionary Advantage of Recombination.

It is commonly thought that the long-term advantage of meiotic recombination is to dissipate genetic linkage, allowing natural selection to act independently on different loci. It is thus theoretically expected that genes with higher recombination rates evolve under more effective selection. On the other hand, recombination is often associated with GC-biased gene conversion (gBGC), which theoretically interferes with selection by promoting the fixation of deleterious GC alleles. To test these predictions, several studies assessed whether selection was more effective in highly recombining genes (due to dissipation of genetic linkage) or less effective (due to gBGC), assuming a fixed distribution of fitness effects (DFE) for all genes. In this study, I directly derive the DFE from a gene's evolutionary history (shaped by mutation, selection, drift, and gBGC) under empirical fitness landscapes. I show that genes that have experienced high levels of gBGC are less fit and thus have more opportunities for beneficial mutations. Only a small decrease in the genome-wide intensity of gBGC leads to the fixation of these beneficial mutations, particularly in highly recombining genes. This results in increased positive selection in highly recombining genes that is not caused by more effective selection. Additionally, I show that the death of a recombination hotspot can lead to a higher dN/dS than its birth, but with substitution patterns biased towards AT, and only at selected positions. This shows that controlling for a substitution bias towards GC is therefore not sufficient to rule out the contribution of gBGC to signatures of accelerated evolution. Finally, although gBGC does not affect the fixation probability of GC-conservative mutations, I show that by altering the DFE, gBGC can also significantly affect nonsynonymous GC-conservative substitution patterns.

Modulation of biophysical properties of nucleocapsid protein in the mutant spectrum of SARS-CoV-2.

Genetic diversity is a hallmark of RNA viruses and the basis for their evolutionary success. Taking advantage of the uniquely large genomic database of SARS-CoV-2, we examine the impact of mutations across the spectrum of viable amino acid sequences on the biophysical phenotypes of the highly expressed and multifunctional nucleocapsid protein. We find variation in the physicochemical parameters of its extended intrinsically disordered regions (IDRs) sufficient to allow local plasticity, but also observe functional constraints that similarly occur in related coronaviruses. In biophysical experiments with several N-protein species carrying mutations associated with major variants, we find that point mutations in the IDRs can have nonlocal impact and modulate thermodynamic stability, secondary structure, protein oligomeric state, particle formation, and liquid-liquid phase separation. In the Omicron variant, distant mutations in different IDRs have compensatory effects in shifting a delicate balance of interactions controlling protein assembly properties, and include the creation of a new protein-protein interaction interface in the N-terminal IDR through the defining P13L mutation. A picture emerges where genetic diversity is accompanied by significant variation in biophysical characteristics of functional N-protein species, in particular in the IDRs.

Like other types of RNA viruses, the genetic material of SARS-CoV-2 (the agent responsible for COVID-19) is formed of an RNA molecule which is prone to accumulating mutations. This gives SARS-CoV-2 the ability to evolve quickly, and often to remain one step ahead of treatments. Understanding how these mutations shape the behavior of RNA viruses is therefore crucial to keep diseases such as COVID-19 under control. The gene that codes for the protein that 'packages' the genetic information inside SARS-CoV-2 is particularly prone to mutations. This nucleocapsid (N) protein participates in many key processes during the life cycle of the virus, including potentially interfering with the immune response. Exactly how the physical properties of the N-Protein are impacted by the mutations in its genetic sequence remains unclear. To investigate this question, Nguyen et al. predicted the various biophysical properties of different regions of the N-protein based on a computer-based analysis of SARS-CoV-2 genetic databases. This allowed them to determine if specific protein regions were positively or negatively charged in different mutants. The analyses showed that some domains exhibited great variability in their charge between protein variants ­ reflecting the fact that the corresponding genetic sequences showed high levels of plasticity. Other regions remained conserved, however, including across related coronaviruses. Nguyen et al. also conducted biochemical experiments on a range of N-proteins obtained from clinically relevant SARS-CoV-2 variants. Their results highlighted the importance of protein segments with no fixed three-dimensional structure. Mutations in the related sequences created high levels of variation in the physical properties of these 'intrinsically disordered' regions, which had wide-ranging consequences. Some of these genetic changes even gave individual N-proteins the ability to interact with each other in a completely new way. These results shed new light on the relationship between genetic mutations and the variable physical properties of RNA virus proteins. Nguyen et al. hope that this knowledge will eventually help to develop more effective treatments for viral infections.

Sampling Strategies for Experimentally Mapping Molecular Fitness Landscapes Using High-Throughput Methods.

Empirical studies of genotype-phenotype-fitness maps of proteins are fundamental to understanding the evolutionary process, in elucidating the space of possible genotypes accessible through mutations in a landscape of phenotypes and fitness effects. Yet, comprehensively mapping molecular fitness landscapes remains challenging since all possible combinations of amino acid substitutions for even a few protein sites are encoded by an enormous genotype space. High-throughput mapping of genotype space can be achieved using large-scale screening experiments known as multiplexed assays of variant effect (MAVEs). However, to accommodate such multi-mutational studies, the size of MAVEs has grown to the point where a priori determination of sampling requirements is needed. To address this problem, we propose calculations and simulation methods to approximate minimum sampling requirements for multi-mutational MAVEs, which we combine with a new library construction protocol to experimentally validate our approximation approaches. Analysis of our simulated data reveals how sampling trajectories differ between simulations of nucleotide versus amino acid variants and among mutagenesis schemes. For this, we show quantitatively that marginal gains in sampling efficiency demand increasingly greater sampling effort when sampling for nucleotide sequences over their encoded amino acid equivalents. We present a new library construction protocol that efficiently maximizes sequence variation, and demonstrate using ultradeep sequencing that the library encodes virtually all possible combinations of mutations within the experimental design. Insights learned from our analyses together with the methodological advances reported herein are immediately applicable toward pooled experimental screens of arbitrary design, enabling further assay upscaling and expanded testing of genotype space.

Mutations in yeast are deleterious on average regardless of the degree of adaptation to the testing environment.

The role of spontaneous mutations in evolution depends on the distribution of their effects on fitness. Despite a general consensus that new mutations are deleterious on average, a handful of mutation accumulation experiments in diverse organisms instead suggest that beneficial and deleterious mutations can have comparable fitness impacts, i.e. the product of their respective rates and effects can be roughly equal. We currently lack a general framework for predicting when such a pattern will occur. One idea is that beneficial mutations will be more evident in genotypes that are not well adapted to the testing environment. We tested this prediction experimentally in the laboratory yeast Saccharomyces cerevisiae by allowing nine replicate populations to adapt to novel environments with complex sets of stressors. After >1000 asexual generations interspersed with 41 rounds of sexual reproduction, we assessed the mean effect of induced mutations on yeast growth in both the environment to which they had been adapting and the alternative novel environment. The mutations were deleterious on average, with the severity depending on the testing environment. However, we found no evidence that the adaptive match between genotype and environment is predictive of mutational fitness effects.

How mutations affect function.

A new study reveals how naturally occurring mutations affect the biophysical properties of nucleocapsid proteins in SARS-CoV-2.

Natural variation in neuraminidase activity influences the evolutionary potential of the seasonal H1N1 lineage hemagglutinin.

The antigenic evolution of the influenza A virus hemagglutinin (HA) gene poses a major challenge for the development of vaccines capable of eliciting long-term protection. Prior efforts to understand the mechanisms that govern viral antigenic evolution mainly focus on HA in isolation, ignoring the fact that HA must act in concert with the viral neuraminidase (NA) during replication and spread. Numerous studies have demonstrated that the degree to which the receptor-binding avidity of HA and receptor-cleaving activity of NA are balanced with each other influences overall viral fitness. We recently showed that changes in NA activity can significantly alter the mutational fitness landscape of HA in the context of a lab-adapted virus strain. Here, we test whether natural variation in relative NA activity can influence the evolutionary potential of HA in the context of the seasonal H1N1 lineage (pdmH1N1) that has circulated in humans since the 2009 pandemic. We observed substantial variation in the relative activities of NA proteins encoded by a panel of H1N1 vaccine strains isolated between 2009 and 2019. We comprehensively assessed the effect of NA background on the HA mutational fitness landscape in the circulating pdmH1N1 lineage using deep mutational scanning and observed pronounced epistasis between NA and residues in or near the receptor-binding site of HA. To determine whether NA variation could influence the antigenic evolution of HA, we performed neutralizing antibody selection experiments using a panel of monoclonal antibodies targeting different HA epitopes. We found that the specific antibody escape profiles of HA were highly contingent upon NA background. Overall, our results indicate that natural variation in NA activity plays a significant role in governing the evolutionary potential of HA in the currently circulating pdmH1N1 lineage.

Genotypic-phenotypic landscape computation based on first principle and deep learning.

The relationship between genotype and fitness is fundamental to evolution, but quantitatively mapping genotypes to fitness has remained challenging. We propose the Phenotypic-Embedding theorem (P-E theorem) that bridges genotype-phenotype through an encoder-decoder deep learning framework. Inspired by this, we proposed a more general first principle for correlating genotype-phenotype, and the P-E theorem provides a computable basis for the application of first principle. As an application example of the P-E theorem, we developed the Co-attention based Transformer model to bridge Genotype and Fitness model, a Transformer-based pre-train foundation model with downstream supervised fine-tuning that can accurately simulate the neutral evolution of viruses and predict immune escape mutations. Accordingly, following the calculation path of the P-E theorem, we accurately obtained the basic reproduction number (${R}_0$) of SARS-CoV-2 from first principles, quantitatively linked immune escape to viral fitness and plotted the genotype-fitness landscape. The theoretical system we established provides a general and interpretable method to construct genotype-phenotype landscapes, providing a new paradigm for studying theoretical and computational biology.

Changing selection on amino acid substitutions in Gag protein between major HIV-1 subtypes.

Amino acid preferences at a protein site depend on the role of this site in protein function and structure as well as on external constraints. All these factors can change in the course of evolution, making amino acid propensities of a site time-dependent. When viral subtypes divergently evolve in different host subpopulations, such changes may depend on genetic, medical, and sociocultural differences between these subpopulations. Here, using our previously developed phylogenetic approach, we describe sixty-nine amino acid sites of the Gag protein of human immunodeficiency virus type 1 (HIV-1) where amino acids have different impact on viral fitness in six major subtypes of the type M. These changes in preferences trigger adaptive evolution; indeed, 32 (46 per cent) of these sites experienced strong positive selection at least in one of the subtypes. At some of the sites, changes in amino acid preferences may be associated with differences in immune escape between subtypes. The prevalence of an amino acid in a protein site within a subtype is only a poor predictor for whether this amino acid is preferred in this subtype according to the phylogenetic analysis. Therefore, attempts to identify the factors of viral evolution from comparative genomics data should integrate across multiple sources of information.

Biophysical principles predict fitness of SARS-CoV-2 variants.

SARS-CoV-2 employs its spike protein's receptor binding domain (RBD) to enter host cells. The RBD is constantly subjected to immune responses, while requiring efficient binding to host cell receptors for successful infection. However, our understanding of how RBD's biophysical properties contribute to SARS-CoV-2's epidemiological fitness remains largely incomplete. Through a comprehensive approach, comprising large-scale sequence analysis of SARS-CoV-2 variants and the identification of a fitness function based on binding thermodynamics, we unravel the relationship between the biophysical properties of RBD variants and their contribution to viral fitness. We developed a biophysical model that uses statistical mechanics to map the molecular phenotype space, characterized by dissociation constants of RBD to ACE2, LY-CoV016, LY-CoV555, REGN10987, and S309, onto an epistatic fitness landscape. We validate our findings through experimentally measured and machine learning (ML) estimated binding affinities, coupled with infectivity data derived from population-level sequencing. Our analysis reveals that this model effectively predicts the fitness of novel RBD variants and can account for the epistatic interactions among mutations, including explaining the later reversal of Q493R. Our study sheds light on the impact of specific mutations on viral fitness and delivers a tool for predicting the future epidemiological trajectory of previously unseen or emerging low-frequency variants. These insights offer not only greater understanding of viral evolution but also potentially aid in guiding public health decisions in the battle against COVID-19 and future pandemics.

FLIGHTED: Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data.

Machine learning (ML) for protein design requires large protein fitness datasets generated by high-throughput experiments for training, fine-tuning, and benchmarking models. However, most models do not account for experimental noise inherent in these datasets, harming model performance and changing model rankings in benchmarking studies. Here, we develop FLIGHTED, a Bayesian method for generating fitness landscapes with calibrated errors from noisy high-throughput experimental data. We apply FLIGHTED to single-step selection assays such as phage display and to a novel high-throughput assay DHARMA that ties fitness to base editing activity. Our results show that FLIGHTED robustly generates fitness landscapes with accurate errors. We demonstrate that FLIGHTED improves model performance and enables the generation of protein fitness datasets of up to 106 variants with DHARMA. FLIGHTED can be used on any high-throughput assay and makes it easy for ML scientists to account for experimental noise when modeling protein fitness.

A Long-Lived Alpine Perennial Advances Flowering under Warmer Conditions but Not Enough to Maintain Reproductive Success.

AbstractAssessing whether phenological shifts in response to climate change confer a fitness advantage requires investigating the relationships among phenology, fitness, and environmental drivers of selection. Despite widely documented advancements in phenology with warming climate, we lack empirical estimates of how selection on phenology varies in response to continuous climate drivers or how phenological shifts in response to warming conditions affect fitness. We leverage an unusual long-term dataset with repeated, individual measurements of phenology and reproduction in a long-lived alpine plant. We analyze phenotypic plasticity in flowering phenology in relation to two climate drivers, snowmelt timing and growing degree days (GDDs). Plants flower earlier with increased GDDs and earlier snowmelt, and directional selection also favors earlier flowering under these conditions. However, reproduction still declines with warming and early snowmelt, even when flowering is early. Furthermore, the steepness of this reproductive decline increases dramatically with warming conditions, resulting in very little fruit production regardless of flowering time once GDDs exceed approximately 225 degree days or snowmelt occurs before May 15. Even though advancing phenology confers a fitness advantage relative to stasis, these shifts are insufficient to maintain reproduction under warming, highlighting limits to the potential benefits of phenological plasticity under climate change.

A stochastic programming approach to the antibiotics time machine problem.

Antibiotics Time Machine is an important problem to understand antibiotic resistance and how it can be reversed. Mathematically, it can be modeled as follows: Consider a set of genotypes, each of which contain a set of mutated and unmutated genes. Suppose that a set of growth rate measurements of each genotype under a set of antibiotics is given. The transition probabilities of a 'realization' of a Markov chain associated with each arc under each antibiotic are computable via a predefined function given the growth rate realizations. The aim is to maximize the expected probability of reaching to the genotype with all unmutated genes given the initial genotype in a predetermined number of transitions, considering the following two sources of uncertainties: (i) the randomness in growth rates, (ii) the randomness in transition probabilities, which are functions of growth rates. We develop stochastic mixed-integer linear programming and dynamic programming approaches to solve static and dynamic versions of the Antibiotics Time Machine Problem under the aforementioned uncertainties. We adapt a Sample Average Approximation approach that exploits the special structure of the problem and provide accurate solutions that perform very well in an out-of-sample analysis.

Elevated [CO2] and temperature augment gas exchange and shift the fitness landscape in a montane forb.

Climate change is simultaneously increasing carbon dioxide concentrations ([CO2]) and temperature. These factors could interact to influence plant physiology and performance. Alternatively, increased [CO2] may offset costs associated with elevated temperatures. Furthermore, the interaction between elevated temperature and [CO2] may differentially affect populations from along an elevational gradient and disrupt local adaptation. We conducted a multifactorial growth chamber experiment to examine the interactive effects of temperature and [CO2] on fitness and ecophysiology of diverse accessions of Boechera stricta (Brassicaceae) sourced from a broad elevational gradient in Colorado. We tested whether increased [CO2] would enhance photosynthesis across accessions, and whether warmer conditions would depress the fitness of high-elevation accessions owing to steep reductions in temperature with increasing elevation in this system. Elevational clines in [CO2] are not as evident, making it challenging to predict how locally adapted ecotypes will respond to elevated [CO2]. This experiment revealed that elevated [CO2] increased photosynthesis and intrinsic water use efficiency across all accessions. However, these instantaneous responses to treatments did not translate to changes in fitness. Instead, increased temperatures reduced the probability of reproduction for all accessions. Elevated [CO2] and increased temperatures interacted to shift the adaptive landscape, favoring lower elevation accessions for the probability of survival and fecundity. Our results suggest that elevated temperatures and [CO2] associated with climate change could have severe negative consequences, especially for high-elevation populations.

Dominance and multi-locus interaction.

Dominance is usually considered a constant value that describes the relative difference in fitness or phenotype between heterozygotes and the average of homozygotes at a focal polymorphic locus. However, the observed dominance can vary with the genetic background of the focal locus. Here, alleles at other loci modify the observed phenotype through position effects or dominance modifiers that are sometimes associated with pathogen resistance, lineage, sex, or mating type. Theoretical models have illustrated how variable dominance appears in the context of multi-locus interaction (epistasis). Here, we review empirical evidence for variable dominance and how the observed patterns may be captured by proposed epistatic models. We highlight how integrating epistasis and dominance is crucial for comprehensively understanding adaptation and speciation.

Rugged fitness landscapes minimize promiscuity in the evolution of transcriptional repressors.

How a protein's function influences the shape of its fitness landscape, smooth or rugged, is a fundamental question in evolutionary biochemistry. Smooth landscapes arise when incremental mutational steps lead to a progressive change in function, as commonly seen in enzymes and binding proteins. On the other hand, rugged landscapes are poorly understood because of the inherent unpredictability of how sequence changes affect function. Here, we experimentally characterize the entire sequence phylogeny, comprising 1,158 extant and ancestral sequences, of the DNA-binding domain (DBD) of the LacI/GalR transcriptional repressor family. Our analysis revealed an extremely rugged landscape with rapid switching of specificity, even between adjacent nodes. Further, the ruggedness arises due to the necessity of the repressor to simultaneously evolve specificity for asymmetric operators and disfavors potentially adverse regulatory crosstalk. Our study provides fundamental insight into evolutionary, molecular, and biophysical rules of genetic regulation through the lens of fitness landscapes.

Dosage optimization for reducing tumor burden using a phenotype-structured population model with a drug-resistance continuum.

Drug resistance is a significant obstacle to effective cancer treatment. To gain insights into how drug resistance develops, we adopted a concept called fitness landscape and employed a phenotype-structured population model by fitting to a set of experimental data on a drug used for ovarian cancer, olaparib. Our modeling approach allowed us to understand how a drug affects the fitness landscape and track the evolution of a population of cancer cells structured with a spectrum of drug resistance. We also incorporated pharmacokinetic (PK) modeling to identify the optimal dosages of the drug that could lead to long-term tumor reduction. We derived a formula that indicates that maximizing variation in plasma drug concentration over a dosing interval could be important in reducing drug resistance. Our findings suggest that it may be possible to achieve better treatment outcomes with a drug dose lower than the levels recommended by the drug label. Acknowledging the current limitations of our work, we believe that our approach, which combines modeling of both PK and drug resistance evolution, could contribute to a new direction for better designing drug treatment regimens to improve cancer treatment.

A continuous epistasis model for predicting growth rate given combinatorial variation in gene expression and environment.

Quantifying and predicting growth rate phenotype given variation in gene expression and environment is complicated by epistatic interactions and the vast combinatorial space of possible perturbations. We developed an approach for mapping expression-growth rate landscapes that integrates sparsely sampled experimental measurements with an interpretable machine learning model. We used mismatch CRISPRi across pairs and triples of genes to create over 8,000 titrated changes in E. coli gene expression under varied environmental contexts, exploring epistasis in up to 22 distinct environments. Our results show that a pairwise model previously used to describe drug interactions well-described these data. The model yielded interpretable parameters related to pathway architecture and generalized to predict the combined effect of up to four perturbations when trained solely on pairwise perturbation data. We anticipate this approach will be broadly applicable in optimizing bacterial growth conditions, generating pharmacogenomic models, and understanding the fundamental constraints on bacterial gene expression. A record of this paper's transparent peer review process is included in the supplemental information.

A synthetic differentiation circuit in Escherichia coli for suppressing mutant takeover.

Differentiation is crucial for multicellularity. However, it is inherently susceptible to mutant cells that fail to differentiate. These mutants outcompete normal cells by excessive self-renewal. It remains unclear what mechanisms can resist such mutant expansion. Here, we demonstrate a solution by engineering a synthetic differentiation circuit in Escherichia coli that selects against these mutants via a biphasic fitness strategy. The circuit provides tunable production of synthetic analogs of stem, progenitor, and differentiated cells. It resists mutations by coupling differentiation to the production of an essential enzyme, thereby disadvantaging non-differentiating mutants. The circuit selected for and maintained a positive differentiation rate in long-term evolution. Surprisingly, this rate remained constant across vast changes in growth conditions. We found that transit-amplifying cells (fast-growing progenitors) underlie this environmental robustness. Our results provide insight into the stability of differentiation and demonstrate a powerful method for engineering evolutionarily stable multicellular consortia.