Pleiotropy alleviates the fitness costs associated with resource allocation trade-offs in immune signalling networks.

Many genes and signalling pathways within plant and animal taxa drive the expression of multiple organismal traits. This form of genetic pleiotropy instigates trade-offs among life-history traits if a mutation in the pleiotropic gene improves the fitness contribution of one trait at the expense of another. Whether or not pleiotropy gives rise to conflict among traits, however, likely depends on the resource costs and timing of trait deployment during organismal development. To investigate factors that could influence the evolutionary maintenance of pleiotropy in gene networks, we developed an agent-based model of co-evolution between parasites and hosts. Hosts comprise signalling networks that must faithfully complete a developmental programme while also defending against parasites, and trait signalling networks could be independent or share a pleiotropic component as they evolved to improve host fitness. We found that hosts with independent developmental and immune networks were significantly more fit than hosts with pleiotropic networks when traits were deployed asynchronously during development. When host genotypes directly competed against each other, however, pleiotropic hosts were victorious regardless of trait synchrony because the pleiotropic networks were more robust to parasite manipulation, potentially explaining the abundance of pleiotropy in immune systems despite its contribution to life history trade-offs.

Juvenile Hormone as a contributing factor in establishing midgut microbiota for fecundity and fitness enhancement in adult female Aedes aegypti.

Understanding the factors influencing mosquitoes' fecundity and longevity is important for designing better and more sustainable vector control strategies, as these parameters can impact their vectorial capacity. Here, we address how mating affects midgut growth in Aedes aegypti, what role Juvenile Hormone (JH) plays in this process, and how it impacts the mosquito's immune response and microbiota. Our findings reveal that mating and JH induce midgut growth. Additionally, the establishment of a native bacterial population in the midgut due to JH-dependent suppression of the immune response has important reproductive outcomes. Specific downregulation of AMPs with an increase in bacteria abundance in the gut results in increased egg counts and longer lifespans. Overall, these findings provide evidence of a cross-talk between JH response, gut epithelial tissue, cell cycle regulation, and the mechanisms governing the trade-offs between nutrition, immunity, and reproduction at the cellular level in the mosquito gut.

Evolutionary druggability for low-dimensional fitness landscapes toward new metrics for antimicrobial applications.

The term 'druggability' describes the molecular properties of drugs or targets in pharmacological interventions and is commonly used in work involving drug development for clinical applications. There are no current analogues for this notion that quantify the drug-target interaction with respect to a given target variant's sensitivity across a breadth of drugs in a panel, or a given drug's range of effectiveness across alleles of a target protein. Using data from low-dimensional empirical fitness landscapes composed of 16 ß-lactamase alleles and 7 ß-lactam drugs, we introduce two metrics that capture (i) the average susceptibility of an allelic variant of a drug target to any available drug in a given panel ('variant vulnerability'), and (ii) the average applicability of a drug (or mixture) across allelic variants of a drug target ('drug applicability'). Finally, we (iii) disentangle the quality and magnitude of interactions between loci in the drug target and the seven drug environments in terms of their mutation by mutation by environment (G x G x E) interactions, offering mechanistic insight into the variant variability and drug applicability metrics. Summarizing, we propose that our framework can be applied to other datasets and pathogen-drug systems to understand which pathogen variants in a clinical setting are the most concerning (low variant vulnerability), and which drugs in a panel are most likely to be effective in an infection defined by standing genetic variation in the pathogen drug target (high drug applicability).

Experimental evolution for the recovery of growth loss due to genome reduction.

As the genome encodes the information crucial for cell growth, a sizeable genomic deficiency often causes a significant decrease in growth fitness. Whether and how the decreased growth fitness caused by genome reduction could be compensated by evolution was investigated here. Experimental evolution with an Escherichia coli strain carrying a reduced genome was conducted in multiple lineages for approximately 1000 generations. The growth rate, which largely declined due to genome reduction, was considerably recovered, associated with the improved carrying capacity. Genome mutations accumulated during evolution were significantly varied across the evolutionary lineages and were randomly localized on the reduced genome. Transcriptome reorganization showed a common evolutionary direction and conserved the chromosomal periodicity, regardless of highly diversified gene categories, regulons, and pathways enriched in the differentially expressed genes. Genome mutations and transcriptome reorganization caused by evolution, which were found to be dissimilar to those caused by genome reduction, must have followed divergent mechanisms in individual evolutionary lineages. Gene network reconstruction successfully identified three gene modules functionally differentiated, which were responsible for the evolutionary changes of the reduced genome in growth fitness, genome mutation, and gene expression, respectively. The diversity in evolutionary approaches improved the growth fitness associated with the homeostatic transcriptome architecture as if the evolutionary compensation for genome reduction was like all roads leading to Rome.

Quantifying the fitness effects of resistance alleles with and without anthelmintic selection pressure using Caenorhabditis elegans.

Albendazole (a benzimidazole) and ivermectin (a macrocyclic lactone) are the two most commonly co-administered anthelmintic drugs in mass-drug administration programs worldwide. Despite emerging resistance, we do not fully understand the mechanisms of resistance to these drugs nor the consequences of delivering them in combination. Albendazole resistance has primarily been attributed to variation in the drug target, a beta-tubulin gene. Ivermectin targets glutamate-gated chloride channels (GluCls), but it is unknown whether GluCl genes are involved in ivermectin resistance in nature. Using Caenorhabditis elegans, we defined the fitness costs associated with loss of the drug target genes singly or in combinations of the genes that encode GluCl subunits. We quantified the loss-of-function effects on three traits: (i) multi-generational competitive fitness, (ii) fecundity, and (iii) development. In competitive fitness and development assays, we found that a deletion of the beta-tubulin gene ben-1 conferred albendazole resistance, but ivermectin resistance required the loss of two GluCl genes (avr-14 and avr-15). The fecundity assays revealed that loss of ben-1 did not provide any fitness benefit in albendazole conditions and that no GluCl deletion mutants were resistant to ivermectin. Next, we searched for evidence of multi-drug resistance across the three traits. Loss of ben-1 did not confer resistance to ivermectin, nor did loss of any single GluCl subunit or combination confer resistance to albendazole. Finally, we assessed the development of 124 C. elegans wild strains across six benzimidazoles and seven macrocyclic lactones to identify evidence of multi-drug resistance between the two drug classes and found a strong phenotypic correlation within a drug class but not across drug classes. Because each gene affects various aspects of nematode physiology, these results suggest that it is necessary to assess multiple fitness traits to evaluate how each gene contributes to anthelmintic resistance.

Living fast, dying young: Anthropogenic habitat modification influences the fitness and life history traits of a cooperative breeder.

Anthropogenic habitat modification can indirectly effect reproduction and survival in social species by changing the group structure and social interactions. We assessed the impact of habitat modification on the fitness and life history traits of a cooperative breeder, the Arabian babbler (Argya squamiceps). We collected spatial, reproductive and social data on 572 individuals belonging to 21 social groups over 6 years and combined it with remote sensing to characterize group territories in an arid landscape. In modified resource-rich habitats, groups bred more and had greater productivity, but individuals lived shorter lives than in natural habitats. Habitat modification favoured a faster pace-of-life with lower dispersal and dominance acquisition ages, which might be driven by higher mortality providing opportunities for the dominant breeding positions. Thus, habitat modification might indirectly impact fitness through changes in social structures. This study shows that trade-offs in novel anthropogenic opportunities might offset survival costs by increased productivity.

High Juvenile Mortality Overwhelms Benefits of Mating Potential for Reproductive Fitness.

AbstractAn individual's access to mates (i.e., its "mating potential") can constrain its reproduction but may also influence its fitness through effects on offspring survival. For instance, mate proximity may correspond with relatedness and lead to inbreeding depression in offspring. While offspring production and survival might respond differently to mating potential, previous studies have not considered the simultaneous effects of mating potential on these fitness components. We investigated the relationship of mating potential with both production and survival of offspring in populations of a long-lived herbaceous perennial, Echinacea angustifolia. Across 7 years and 14 sites, we quantified the mating potential of maternal plants in 1,278 mating bouts and followed the offspring from these bouts over 8 years. We used aster models to evaluate the relationship of mating potential with the number of offspring that emerged and that were alive after 8 years. Seedling emergence increased with mating potential. Despite this, the number of offspring surviving after 8 years showed no relationship to mating potential. Our results support the broader conclusion that the effect of mating potential on fitness erodes over time because of demographic stochasticity at the maternal level.

Genome-scale analysis of interactions between genetic perturbations and natural variation.

Interactions between genetic perturbations and segregating loci can cause perturbations to show different phenotypic effects across genetically distinct individuals. To study these interactions on a genome scale in many individuals, we used combinatorial DNA barcode sequencing to measure the fitness effects of 8046 CRISPRi perturbations targeting 1721 distinct genes in 169 yeast cross progeny (or segregants). We identified 460 genes whose perturbation has different effects across segregants. Several factors caused perturbations to show variable effects, including baseline segregant fitness, the mean effect of a perturbation across segregants, and interacting loci. We mapped 234 interacting loci and found four hub loci that interact with many different perturbations. Perturbations that interact with a given hub exhibit similar epistatic relationships with the hub and show enrichment for cellular processes that may mediate these interactions. These results suggest that an individual's response to perturbations is shaped by a network of perturbation-locus interactions that cannot be measured by approaches that examine perturbations or natural variation alone.

Outbreeding reduces survival during metamorphosis in a headwater stream salamander.

Assessing direct fitness effects of individual genetic diversity is challenging due to the intensive and long-term data needed to quantify survival and reproduction in the wild. But resolving these effects is necessary to determine how inbreeding and outbreeding influence eco-evolutionary processes. We used 8 years of capture-recapture data and single nucleotide polymorphism genotypes for 1906 individuals to test for effects of individual heterozygosity on stage-specific survival probabilities in the salamander Gyrinophilus porphyriticus. The life cycle of G. porphyriticus includes an aquatic larval stage followed by metamorphosis into a semi-aquatic adult stage. In our study populations, the larval stage lasts 6-10 years, metamorphosis takes several months, and lifespan can reach 20 years. Previous studies showed that metamorphosis is a sensitive life stage, leading us to predict that fitness effects of individual heterozygosity would occur during metamorphosis. Consistent with this prediction, monthly probability of survival during metamorphosis declined with multi-locus heterozygosity (MLH), from 0.38 at the lowest MLH (0.10) to 0.06 at the highest MLH (0.38), a reduction of 84%. Body condition of larvae also declined significantly with increasing MLH. These relationships were consistent in the three study streams. With evidence of localised inbreeding within streams, these results suggest that outbreeding disrupts adaptations in pre-metamorphic and metamorphic individuals to environmental gradients along streams, adding to evidence that headwater streams are hotspots of microgeographic adaptation. Our results also underscore the importance of incorporating life history in analyses of the fitness effects of individual genetic diversity and suggest that metamorphosis and similar discrete life stage transitions may be critical periods of viability selection.

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.

Variation and plasticity in life-history traits and fitness of wild Arabidopsis thaliana populations are not related to their genotypic and ecological diversity.

BACKGROUND: Despite its implications for population dynamics and evolution, the relationship between genetic and phenotypic variation in wild populations remains unclear. Here, we estimated variation and plasticity in life-history traits and fitness of the annual plant Arabidopsis thaliana in two common garden experiments that differed in environmental conditions. We used up to 306 maternal inbred lines from six Iberian populations characterized by low and high genotypic (based on whole-genome sequences) and ecological (vegetation type) diversity. RESULTS: Low and high genotypic and ecological diversity was found in edge and core Iberian environments, respectively. Given that selection is expected to be stronger in edge environments and that ecological diversity may enhance both phenotypic variation and plasticity, we expected genotypic diversity to be positively associated with phenotypic variation and plasticity. However, maternal lines, irrespective of the genotypic and ecological diversity of their population of origin, exhibited a substantial amount of phenotypic variation and plasticity for all traits. Furthermore, all populations harbored maternal lines with canalization (robustness) or sensitivity in response to harsher environmental conditions in one of the two experiments. CONCLUSIONS: Overall, we conclude that the environmental attributes of each population probably determine their genotypic diversity, but all populations maintain substantial phenotypic variation and plasticity for all traits, which represents an asset to endure in changing environments.

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

MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: 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.

Effects of overwintering on the transcriptome and fitness traits in a damselfly with variable voltinism across two latitudes.

Winter diapause consists of cessation of development that allows individuals to survive unfavourable conditions. Winter diapause may bear various costs and questions have been raised about the evolutionary mechanisms maintaining facultative diapause. Here, we explored to what extent a facultative winter diapause affects life-history traits and the transcriptome in the damselfly Ischnura elegans, and whether these effects were latitude-specific. We collected adult females at central and high latitudes and raised their larvae in growth chambers. Larvae were split into a non-diapausing and post-winter (diapausing) cohort, were phenotyped and collected for a gene expression analysis. At the phenotypic level, we found no difference in survival between the two cohorts, and the post-winter cohort was larger and heavier than the non-winter cohort. These effects were mostly independent of the latitude of origin. At the transcriptomic level, wintering affected gene expression with a small fraction of genes significantly overlapping across latitudes, especially those related to morphogenesis. In conclusion, we found clear effects of diapause on the phenotype but little evidence for latitudinal-specific effects of diapause. Our results showed a shared transcriptomic basis underpinning diapause demonstrated, here, at the intraspecific level and supported the idea of evolutionary convergence of the response to diapause across organisms.

Modelling daisy quorum drive: A short-term bridge across engineered fitness valleys.

Engineered gene-drive techniques for population modification and/or suppression have the potential for tackling complex challenges, including reducing the spread of diseases and invasive species. Gene-drive systems with low threshold frequencies for invasion, such as homing-based gene drive, require initially few transgenic individuals to spread and are therefore easy to introduce. The self-propelled behavior of such drives presents a double-edged sword, however, as the low threshold can allow transgenic elements to expand beyond a target population. By contrast, systems where a high threshold frequency must be reached before alleles can spread-above a fitness valley-are less susceptible to spillover but require introduction at a high frequency. We model a proposed drive system, called "daisy quorum drive," that transitions over time from a low-threshold daisy-chain system (involving homing-based gene drive such as CRISPR-Cas9) to a high-threshold fitness-valley system (requiring a high frequency-a "quorum"-to spread). The daisy-chain construct temporarily lowers the high thresholds required for spread of the fitness-valley construct, facilitating use in a wide variety of species that are challenging to breed and release in large numbers. Because elements in the daisy chain only drive subsequent elements in the chain and not themselves and also carry deleterious alleles ("drive load"), the daisy chain is expected to exhaust itself, removing all CRISPR elements and leaving only the high-threshold fitness-valley construct, whose spread is more spatially restricted. Developing and analyzing both discrete patch and continuous space models, we explore how various attributes of daisy quorum drive affect the chance of modifying local population characteristics and the risk that transgenic elements expand beyond a target area. We also briefly explore daisy quorum drive when population suppression is the goal. We find that daisy quorum drive can provide a promising bridge between gene-drive and fitness-valley constructs, allowing spread from a low frequency in the short term and better containment in the long term, without requiring repeated introductions or persistence of CRISPR elements.

Physiological Measurements and Transcriptomics Reveal the Fitness Costs of Monochamus saltuarius to Bursaphelenchus xylophilus.

The pine wood nematode (PWN) uses several Monochamus species as vehicles, through a temporary hitchhiking process known as phoresy, enabling it to access new host plant resources. Monochamus saltuarius acts as a new and major vector of the PWN in Northeastern China, showing lower PWN carrying capacity and a shorter transmission cycle compared to established vectors. The apparently altered symbiotic relationship offers an interesting area for researching the costs and adaptions involved in nematode-beetle, a specialized phoresy. We analyzed the response and fitness costs of M. saltuarius through physiological measurements and transcriptomics. The PWN exerted adverse repercussions on the growth and development of M. saltuarius. The PWN accelerated larval development into pupae, while beetle adults carrying the PWN exhibited an elevated abnormality rate and mortality, and reduced starvation resistance. During the pupal stage, the expression of growth-related genes, including ecdysone-inducible genes (E74EA), cuticle proteins, and chitin genes (CHTs), markedly increased. Meanwhile, the induced immune response, mainly by the IMD and Toll signaling pathways, could be a contributing factor to adult abnormality and mortality. Adult gonads and trachea exhibited enrichment in pathways related to fatty acid elongation, biosynthesis, and metabolism. FASN, ELOVL, and SCD possibly contributed to resistance against PWN. Our research indicated that phoretic interactions between vector beetles and PWN vary throughout the vector's lifespan, particularly before and after entry into the trachea. This study highlighted the fitness costs of immunity and metabolism on the vector beetle, indicating the adaptation mechanisms and evolutionary trade-offs to PWN.

A multidimensional selective landscape drives adaptive divergence between and within closely related Phlox species.

Selection causes local adaptation across populations within species and simultaneously divergence between species. However, it is unclear if either the force of or the response to selection is similar across these scales. We show that natural selection drives divergence between closely related species in a pattern that is distinct from local adaptation within species. We use reciprocal transplant experiments across three species of Phlox wildflowers to characterize widespread adaptive divergence. Using provenance trials, we also find strong local adaptation between populations within a species. Comparing divergence and selection between these two scales of diversity we discover that one suite of traits predicts fitness differences between species and that an independent suite of traits predicts fitness variation within species. Selection drives divergence between species, contributing to speciation, while simultaneously favoring extensive diversity that is maintained across populations within a species. Our work demonstrates how the selection landscape is complex and multidimensional.

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.

The distribution of fitness effects during adaptive walks using a simple genetic network.

The tempo and mode of adaptation depends on the availability of beneficial alleles. Genetic interactions arising from gene networks can restrict this availability. However, the extent to which networks affect adaptation remains largely unknown. Current models of evolution consider additive genotype-phenotype relationships while often ignoring the contribution of gene interactions to phenotypic variance. In this study, we model a quantitative trait as the product of a simple gene regulatory network, the negative autoregulation motif. Using forward-time genetic simulations, we measure adaptive walks towards a phenotypic optimum in both additive and network models. A key expectation from adaptive walk theory is that the distribution of fitness effects of new beneficial mutations is exponential. We found that both models instead harbored distributions with fewer large-effect beneficial alleles than expected. The network model also had a complex and bimodal distribution of fitness effects among all mutations, with a considerable density at deleterious selection coefficients. This behavior is reminiscent of the cost of complexity, where correlations among traits constrain adaptation. Our results suggest that the interactions emerging from genetic networks can generate complex and multimodal distributions of fitness effects.

Experimental Evaluation of a Direct Fitness Effect of the De Novo Evolved Mouse Gene Pldi.

De novo evolved genes emerge from random parts of noncoding sequences and have, therefore, no homologs from which a function could be inferred. While expression analysis and knockout experiments can provide insights into the function, they do not directly test whether the gene is beneficial for its carrier. Here, we have used a seminatural environment experiment to test the fitness of the previously identified de novo evolved mouse gene Pldi, which has been implicated to have a role in sperm differentiation. We used a knockout mouse strain for this gene and competed it against its parental wildtype strain for several generations of free reproduction. We found that the knockout (ko) allele frequency decreased consistently across three replicates of the experiment. Using an approximate Bayesian computation framework that simulated the data under a demographic scenario mimicking the experiment's demography, we could estimate a selection coefficient ranging between 0.21 and 0.61 for the wildtype allele compared to the ko allele in males, under various models. This implies a relatively strong selective advantage, which would fix the new gene in less than hundred generations after its emergence.

Fitness surfaces and local thermal adaptation in Drosophila along a latitudinal gradient.

Local adaptation is commonly cited to explain species distribution, but how fitness varies along continuous geographical gradients is not well understood. Here, we combine thermal biology and life-history theory to demonstrate that Drosophila populations along a 2500 km latitudinal cline are adapted to local conditions. We measured how heat tolerance and viability rate across eight populations varied with temperature in the laboratory and then simulated their expected cumulative Darwinian fitness employing high-resolution temperature data from their eight collection sites. Simulations indicate a trade-off between annual survival and cumulative viability, as both mortality and the recruitment of new flies are predicted to increase in warmer regions. Importantly, populations are locally adapted and exhibit the optimal combination of both traits to maximize fitness where they live. In conclusion, our method is able to reconstruct fitness surfaces employing empirical life-history estimates and reconstructs peaks representing locally adapted populations, allowing us to study geographic adaptation in silico.