Fitness Landscape Research Articles

Endosymbioses Have Shaped the Evolution of Biological Diversity and Complexity Time and Time Again.

Life on Earth comprises prokaryotes and a broad assemblage of endosymbioses. The pages of Molecular Biology and Evolution and Genome Biology and Evolution have provided an essential window into how these endosymbiotic interactions have evolved and shaped biological diversity. Here, we provide a current perspective on this knowledge by drawing on decades of revelatory research published in Molecular Biology and Evolution and Genome Biology and Evolution, and insights from the field at large. The accumulated work illustrates how endosymbioses provide hosts with novel phenotypes that allow them to transition between adaptive landscapes to access environmental resources. Such endosymbiotic relationships have shaped and reshaped life on Earth. The early serial establishment of mitochondria and chloroplasts through endosymbioses permitted massive upscaling of cellular energetics, multicellularity, and terrestrial planetary greening. These endosymbioses are also the foundation upon which all later ones are built, including everything from land-plant endosymbioses with fungi and bacteria to nutritional endosymbioses found in invertebrate animals. Common evolutionary mechanisms have shaped this broad range of interactions. Endosymbionts generally experience adaptive and stochastic genome streamlining, the extent of which depends on several key factors (e.g. mode of transmission). Hosts, in contrast, adapt complex mechanisms of resource exchange, cellular integration and regulation, and genetic support mechanisms to prop up degraded symbionts. However, there are significant differences between endosymbiotic interactions not only in how partners have evolved with each other but also in the scope of their influence on biological diversity. These differences are important considerations for predicting how endosymbioses will persist and adapt to a changing planet.

Adaptive responses to habitat change: Theory and tests with field experiments.

Habitat loss and change are often implicated as the primary causes of species extinction. Although any population can be instantly imperiled by catastrophe, most habitat loss occurs gradually, thus enabling affected individuals an adaptive advantage to occupy the best of their dwindling opportunities. I demonstrate how to infer the advantage between two habitats for any density and frequency-dependent strategy of habitat selection. I explore the concept of an Adaptive Dispersal Strategy Landscape to reveal the Evolutionarily Stable Strategy separately for ideal-free and ideal preemptive habitat selectors. Both solutions reveal an initially counterintuitive expectation that individuals living at high density gain insufficient adaptive advantage to disperse from a deteriorating habitat. Adaptive dispersal is constrained at high density because habitats of better quality are fully occupied. I test the theory with measures of movement and foraging in crossover experiments on a seminatural population of meadow voles. The experiment allowed the voles to choose among patches and between enclosures in which I differentially manipulated food and shelter. Although photographs from an infrared camera documented voles venturing from one habitat to the other, none became resident. Voles preferentially foraged in the richer of the two enclosures, even when I reversed treatments, and they foraged more in patches protected by mulched straw. The adaptive advantage of dispersal using a surrogate fitness proxy based on the voles' giving-up densities mirrored that generated by theory. The convergence between theory and experiment yields much-needed insight into our ability to test, predict, and hopefully resolve, the ecological, evolutionary, and conservation consequences of habitat loss.

Universal characterization of epitope immunodominance from a multiscale model of clonal competition in germinal centers.

We introduce a multiscale model for affinity maturation, which aims to capture the intraclonal, interclonal, and epitope-specific organization of the B-cell population in a germinal center. We describe the evolution of the B-cell population via a quasispecies dynamics, with species corresponding to unique B-cell receptors (BCRs), where the desired multiscale structure is reflected on the mutational connectivity of the accessible BCR space, and on the statistical properties of its fitness landscape. Within this mathematical framework, we study the competition among classes of BCRs targeting different antigen epitopes, and we construct an effective immunogenic space where epitope immunodominance relations can be universally characterized. We finally study how varying the relative composition of a mixture of antigens with variable and conserved domains allows for a parametric exploration of this space, and we identify general principles for the rational design of two-antigen cocktails.

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.

Investigation of longitudinal dynamics of vehicles in disordered traffic

Vehicles in developing countries have wide variations in their static and dynamic characteristics, and drivers tend to not follow lane discipline. Models for driving behavior under such disordered traffic conditions need to include the vehicle dynamics and their interactions with the surrounding environment. Calibration of those models is necessary to evaluate their predictive power and suitability for analyzing traffic flow under disordered traffic. The present study aims to calibrate a longitudinal dynamics model, the High-Speed Social-Force Model (HSFM) using a vehicle trajectory dataset collected from Chennai city. The HSFM was calibrated by minimizing the deviations between the simulated and observed longitudinal coordinates of vehicles using a genetic algorithm. The observed and simulated vehicle trajectories were compared using a goodness of fit function of the positions. The convergence of the objective function has been illustrated with the help of fitness landscapes. The calibration errors were found to be within the acceptable range and the optimal parameter values were found to be consistent. The outcomes of the study indicate that the model can capture the influence of non-overlapping leaders under disordered traffic conditions.

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.

Improved ACOM pattern matching in 4D-STEM through adaptive sub-pixel peak detection and image reconstruction

The technique known as 4D-STEM has recently emerged as a powerful tool for the local characterization of crystalline structures in materials, such as cathode materials for Li-ion batteries or perovskite materials for photovoltaics. However, the use of new detectors optimized for electron diffraction patterns and other advanced techniques requires constant adaptation of methodologies to address the challenges associated with crystalline materials. In this study, we present a novel image-processing method to improve pattern matching in the determination of crystalline orientations and phases. Our approach uses sub-pixel adaptive image processing to register and reconstruct electron diffraction signals in large 4D-STEM datasets. By using adaptive prominence and linear filters, we can improve the quality of the diffraction pattern registration. The resulting data compression rate of 103 is well-suited for the era of big data and provides a significant enhancement in the performance of the entire ACOM data processing method. Our approach is evaluated using dedicated metrics, which demonstrate a high improvement in phase recognition. Several features are extracted from the registered data to map properties such as the spot count, and various virtual dark fields, which are used to enhance the handling of the results maps. Our results demonstrate that this data preparation method not only enhances the quality of the resulting image but also boosts the confidence level in the analysis of the outcomes related to determining crystal orientation and phase. Additionally, it mitigates the impact of user bias that may occur during the application of the method through the manipulation of parameters.

From heterosis to outbreeding depression: genotype-by-environment interaction shifts hybrid fitness in opposite directions.

In F1 hybrids, phenotypic values are expected to be near the parental means under additive effects or close to one parent under dominance. However, F1 traits can fall outside the parental range, and outbreeding depression occurs when inferior fitness is observed in hybrids. Another possible outcome is heterosis, a phenomenon that interspecific hybrids or intraspecific crossbred F1s exhibit improved fitness compared to both parental species or strains. As an application of heterosis, hybrids between channel catfish females and blue catfish males are superior in feed conversion efficiency, carcass yield, and harvestability. Over 20 years of hybrid catfish production in experimental settings and farming practices generated abundant phenotypic data, making it an ideal system to investigate heterosis. In this study, we characterized fitness in terms of growth and survival longitudinally, revealing environment-dependent heterosis. In ponds, hybrids outgrow both parents due to an extra rapid growth phase of 2-4 months in year 2. This bimodal growth pattern is unique to F1 hybrids in pond culture environments only. In sharp contrast, the same genetic types cultured in tanks display outbreeding depression, where hybrids perform poorly, while channel catfish demonstrate superiority in growth throughout development. Our findings represent the first example, known to the authors, of opposite fitness shifts in response to environmental changes in interspecific vertebrate hybrids, suggesting a broader fitness landscape for F1 hybrids. Future genomic studies based on this experiment will help understand genome-environment interaction in shaping the F1 progeny fitness in the scenario of environment-dependent heterosis and outbreeding depression.

An extension of the Walsh-Hadamard transform to calculate and model epistasis in genetic landscapes of arbitrary shape and complexity.

Accurate models describing the relationship between genotype and phenotype are necessary in order to understand and predict how mutations to biological sequences affect the fitness and evolution of living organisms. The apparent abundance of epistasis (genetic interactions), both between and within genes, complicates this task and how to build mechanistic models that incorporate epistatic coefficients (genetic interaction terms) is an open question. The Walsh-Hadamard transform represents a rigorous computational framework for calculating and modeling epistatic interactions at the level of individual genotypic values (known as genetical, biological or physiological epistasis), and can therefore be used to address fundamental questions related to sequence-to-function encodings. However, one of its main limitations is that it can only accommodate two alleles (amino acid or nucleotide states) per sequence position. In this paper we provide an extension of the Walsh-Hadamard transform that allows the calculation and modeling of background-averaged epistasis (also known as ensemble epistasis) in genetic landscapes with an arbitrary number of states per position (20 for amino acids, 4 for nucleotides, etc.). We also provide a recursive formula for the inverse matrix and then derive formulae to directly extract any element of either matrix without having to rely on the computationally intensive task of constructing or inverting large matrices. Finally, we demonstrate the utility of our theory by using it to model epistasis within both simulated and empirical multiallelic fitness landscapes, revealing that both pairwise and higher-order genetic interactions are enriched between physically interacting positions.

Eco-Revelatory: The Missing Link in the Development of Adaptive Landscape to Industrial Heritage Sites

ABSTRACT Adaptive reuse of industrial heritage paves the way for revitalization, new use, and achievement of dynamism and sustainability. The aim of the present study is to access the missing link in developing a landscape model adaptable to industrial heritage sites. The present study was conducted within a qualitative approach framework and used the grounded theory method. Qualitative data analysis of the research was performed by MAXQDA2020 software, and based on the results, a paradigm model of adaptive industrial heritage landscape was explained. The research results include causal conditions (physical), core category (environmental), contextual conditions (historical, infrastructural), intervening conditions (economic, technology), strategies (policies and laws), and consequences (social, cultural). According to the identified components of the adaptability of industrial heritage to the landscape, the concept of Eco-Revelatory was extracted. It also states that the missing link is the adaptability of landscape and Eco-Revelatory industrial heritage, the use of which leads to the dynamism and stability of industrial heritage.

Challenges and advances in measuring phenotypic convergence.

Tests of phenotypic convergence can provide evidence of adaptive evolution, and the popularity of such studies has grown in recent years due to the development of novel, quantitative methods for identifying and measuring convergence. These methods include the commonly applied C1-C4 measures of Stayton (2015a), which measure morphological distances between lineages, and Ornstein-Uhlenbeck (OU) model-fitting analyses, which test whether lineages converged on shared adaptive peaks. We test the performance of C-measures and other convergence measures under various evolutionary scenarios and reveal a critical issue with C-measures: they often misidentify divergent lineages as convergent. We address this issue by developing novel convergence measures-Ct1-Ct4-measures-that calculate distances between lineages at specific points in time, minimizing the possibility of misidentifying divergent taxa as convergent. Ct-measures are most appropriate when focal lineages are of the same or similar geologic ages (e.g., extant taxa), meaning that the lineages' evolutionary histories include considerable overlap in time. Beyond C-measures, we find that all convergence measures are influenced by the position of focal taxa in phenotypic space, with morphological outliers often statistically more likely to be measured as strongly convergent. Further, we mimic scenarios in which researchers assess convergence using OU models with a priori regime assignments (e.g., classifying taxa by ecological traits) and find that multiple-regime OU models with phenotypically divergent lineages assigned to a shared selective regime often outperform simpler models. This highlights that model support for these multiple-regime OU models should not be assumed to always reflect convergence among focal lineages of a shared regime. Our new Ct1-Ct4-measures provide researchers with an improved comparative tool, but we emphasize that all available convergence measures are imperfect, and researchers should recognize the limitations of these methods and use multiple lines of evidence to test convergence hypotheses.

An intensification approach based on fitness landscape characteristics for job shop scheduling problem

An intensification approach based on fitness landscape characteristics for job shop scheduling problem

ST-ADPTC: a method for clustering spatiotemporal raster data based on improved density peak detection

Spatiotemporal raster (STR) data employ an array of grids to represent temporally varying and spatially distributed information, commonly utilized for recording environmental variables and socioeconomic indices. To reveal the geographic patterns embedded in STR data, the clustering by fast search and finding of density peaks (CFSFDP) algorithm is considered effective and suitable. However, this algorithm encounters limitations in identifying cluster centers, handling large data volumes, and measuring the coupled spatial-temporal-attribute distance when applied to STR data. To overcome these challenges, we propose an improved method named spatial temporal-adaptive density peak tree clustering (ST-ADPTC). This method leverages adaptive density peak tree segmentation to identify cluster centers and optimizes memory usage through the k-nearest neighbors (kNN) technique. By constructing a neighborhood that incorporates both spatiotemporal and thematic attribute similarities, ST-ADPTC computes the local density of STR data, facilitating the discovery of time-varying clusters. Based on the proposed method, we develop an open-source Python package (Geo_ADPTC). Experiments conducted using benchmarking datasets illustrate improvements in cluster identification and memory reduction. Additionally, a case study of sea surface temperature data demonstrates the feasibility and effectiveness of exploring spatial and temporal distribution patterns using the proposed method.

Is innovation failure just a dead end?

This paper explores whether failures in innovation projects at the firm level contribute to strengthening firms’ innovative activities and the odds of future innovation. Building on the literature on NK fitness landscapes, our paper focuses on technological complexity as a key factor that influences the response to innovation failures, guiding whether to discontinue the current path or, alternatively, to leverage the failure as a foundation for future innovation success. We then test the hypothesis derived from our model using a panel data set constituted by ten waves of the Community Innovation Survey held in the Netherlands from 1996 to 2014. Our findings show the fundamental relevance of the different forms of learning that are available to the firm. In particular, we highlight the positive role of learning after a previous innovation project has been abandoned. Previous failure in innovating produces some forms of learning inside the firms, and this learning positively contributes to subsequent successful innovation. Moreover, by differentiating between radical and incremental innovation, and between complex and less-complex innovation landscapes, we highlight the role of incremental innovation over the whole spectrum of landscape complexity, while the role of radical innovation appears to be more limited to less turbulent technological landscapes.

Dine and dash: how trophic ecology and migration shape functional locomotory traits in clupeiform fishes

Abstract Understanding how interactions between multiple selective forces influence traits at the macroevolutionary scale is key to understanding adaptive landscapes. Diadromy, an extreme form of migration between marine and freshwater environments, is thought to require locomotory traits conducive to long-distance migration. Yet, other selective forces, such as predator avoidance, habitat use, and prey acquisition, are also likely to shape locomotory adaptation in fishes. We examined how diadromy and trophic ecology together influenced locomotory trait diversity across Clupeiformes, a clade of fishes containing high trophic diversity and numerous transitions to diadromy. We found that both diadromy and trophic ecology influenced the pattern and pace of trait evolution. Diadromous taxa rapidly evolved traits characterized by high cruising efficiency, but the extent to which diadromous and non-diadromous taxa differed depended on their trophic ecology. Macropredators showed greater differences in locomotory traits between diadromous and non-diadromous taxa than phytodetritivores and micropredators, suggesting that traits conducive to migration might be most costly to consumers of evasive prey. This work shows that simultaneously characterizing the roles of multiple ecological or life-history factors in phenotypic evolution can bring the topography of adaptive landscapes into sharper focus and provide a more holistic view of the forces driving patterns of trait evolution.

The Untapped Potential of Comparative Biology in Aging Research: Insights From the Extraordinary-Long-Lived Naked Mole-Rat.

The search for solutions to the vagaries of aging has, historically, been akin to searching at night in the bright light under street lamps by utilizing the few preexisting and well-established animal model systems. Throughout my career as a comparative biologist, I have ventured into the darkness across 4 continents and studied over 150 different animal species, many of which have evolved remarkable adaptations to survive on the harsh and rugged fitness landscape that exists outside of the laboratory setting. In this Fellows Forum, I will discuss the main focus of my research for the last 25 years and dig deeply into the biology of the preternaturally long-lived naked mole-rat that makes it an ideal model system for the characterization of successful strategies to combat aging.

Unpredictability of the Fitness Effects of Antimicrobial Resistance Mutations Across Environments in Escherichia coli.

The evolution of antimicrobial resistance (AMR) in bacteria is a major public health concern, and antibiotic restriction is often implemented to reduce the spread of resistance. These measures rely on the existence of deleterious fitness effects (i.e. costs) imposed by AMR mutations during growth in the absence of antibiotics. According to this assumption, resistant strains will be outcompeted by susceptible strains that do not pay the cost during the period of restriction. The fitness effects of AMR mutations are generally studied in laboratory reference strains grown in standard growth environments; however, the genetic and environmental context can influence the magnitude and direction of a mutation's fitness effects. In this study, we measure how three sources of variation impact the fitness effects of Escherichia coli AMR mutations: the type of resistance mutation, the genetic background of the host, and the growth environment. We demonstrate that while AMR mutations are generally costly in antibiotic-free environments, their fitness effects vary widely and depend on complex interactions between the mutation, genetic background, and environment. We test the ability of the Rough Mount Fuji fitness landscape model to reproduce the empirical data in simulation. We identify model parameters that reasonably capture the variation in fitness effects due to genetic variation. However, the model fails to accommodate the observed variation when considering multiple growth environments. Overall, this study reveals a wealth of variation in the fitness effects of resistance mutations owing to genetic background and environmental conditions, which will ultimately impact their persistence in natural populations.

Genotype sampling for deep-learning assisted experimental mapping of a combinatorially complete fitness landscape.

Experimental characterization of fitness landscapes, which map genotypes onto fitness, is important for both evolutionary biology and protein engineering. It faces a fundamental obstacle in the astronomical number of genotypes whose fitness needs to be measured for any one protein. Deep learning may help to predict the fitness of many genotypes from a smaller neural network training sample of genotypes with experimentally measured fitness. Here I use a recently published experimentally mapped fitness landscape of more than 260 000 protein genotypes to ask how such sampling is best performed. I show that multilayer perceptrons, recurrent neural networks, convolutional networks, and transformers, can explain more than 90% of fitness variance in the data. In addition, 90% of this performance is reached with a training sample comprising merely ≈103 sequences. Generalization to unseen test data is best when training data is sampled randomly and uniformly, or sampled to minimize the number of synonymous sequences. In contrast, sampling to maximize sequence diversity or codon usage bias reduces performance substantially. These observations hold for more than one network architecture. Simple sampling strategies may perform best when training deep learning neural networks to map fitness landscapes from experimental data. The fitness landscape data analyzed here is publicly available as described previously (Papkou et al. 2023). All code used to analyze this landscape is publicly available at https://github.com/andreas-wagner-uzh/fitness_landscape_sampling.

Robust genetic codes enhance protein evolvability.

The standard genetic code defines the rules of translation for nearly every life form on Earth. It also determines the amino acid changes accessible via single-nucleotide mutations, thus influencing protein evolvability-the ability of mutation to bring forth adaptive variation in protein function. One of the most striking features of the standard genetic code is its robustness to mutation, yet it remains an open question whether such robustness facilitates or frustrates protein evolvability. To answer this question, we use data from massively parallel sequence-to-function assays to construct and analyze 6 empirical adaptive landscapes under hundreds of thousands of rewired genetic codes, including those of codon compression schemes relevant to protein engineering and synthetic biology. We find that robust genetic codes tend to enhance protein evolvability by rendering smooth adaptive landscapes with few peaks, which are readily accessible from throughout sequence space. However, the standard genetic code is rarely exceptional in this regard, because many alternative codes render smoother landscapes than the standard code. By constructing low-dimensional visualizations of these landscapes, which each comprise more than 16 million mRNA sequences, we show that such alternative codes radically alter the topological features of the network of high-fitness genotypes. Whereas the genetic codes that optimize evolvability depend to some extent on the detailed relationship between amino acid sequence and protein function, we also uncover general design principles for engineering nonstandard genetic codes for enhanced and diminished evolvability, which may facilitate directed protein evolution experiments and the bio-containment of synthetic organisms, respectively.

Bioinformatics investigation of adaptive immune-related genes in peri-implantitis and periodontitis: Characteristics and diagnostic values.

Peri-implantitis and periodontitis have similar immunological bioprocesses and inflammatory phenotypes. In the inflammatory process, the adaptive immune cells can drive the development of disease. This research investigated the differences and diagnostic significance of peri-implantitis and periodontitis in adaptive immune responses. We acquired four GEO datasets of gene expressions in surrounding tissues in healthy person, healthy implant, periodontitis, and peri-implantitis patients. The structural characteristics and enrichment analyses of differential expression genes were examined. The adaptive immune landscapes in peri-implantitis and periodontitis were then evaluated using single sample gene set enrichment analysis. The STRING database and Cytoscape were used to identify adaptive hub genes, and the ROC curve was used to verify them. Finally, qRT-PCR method was used to verify the expression level of Hub gene in activated T cells on the titanium-containing or titanium-free culture plates. At the transcriptome level, the data of healthy implant, peri-implantitis and periodontitis were highly dissimilar. The peri-implantitis and periodontitis both exhibited adaptive immune response. Except for the activated CD4+T cells, there was no significant difference in other adaptive immune cells between peri-implantitis and periodontitis. In addition, correlation analysis showed that CD53, CYBB, and PLEK were significantly positively linked with activated CD4+T cells in the immune microenvironment of peri-implantitis, making them effective biomarkers to differentiate it from periodontitis. Peri-implantitis has a uniquely immunogenomic landscape that differs from periodontitis. This study provides new insights and ideas into the activated CD4+T cells and hub genes that underpin the immunological bioprocess of peri-implantitis.