Molecular biology and evolution最新文献

Protein synthesis, while central to cellular function, is error-prone. The resulting mistranslation is generally costly, but we do not know how these costs compare or interact with the costs imposed by external selection pressures such as antibiotics. We also do not know whether and how these costs are compensated during evolution. It is important to answer these questions, since mistranslation is ubiquitous and antibiotic exposure is widespread. We quantified the growth cost of genetically increasing and decreasing mistranslation rates and exposure to low antibiotic concentrations in Escherichia coli. Mistranslation costs were generally lower than the cost imposed by antibiotics and exacerbated in a strain-specific manner under antibiotic exposure. All strains quickly compensated for the antibiotic cost during experimental evolution, via antibiotic- and genotype- specific mutations. In contrast, mistranslation costs were significantly reduced only in some cases, without clear causal mutations. Control populations that evolved without antibiotics consistently compensated for the cost of accuracy and evolved increased antibiotic resistance as a by-product. Our work demonstrates that even when the cost of mistranslation is weak, altered translation accuracy can shape adaptive outcomes and underlying genetic mechanisms, with strong collateral fitness effects for apparently unrelated phenotypes such as antibiotic resistance.

Gene duplication and transposable elements (TEs) are major drivers of genomic innovation that can fuel adaptation. While the roles of duplication and TE-driven diversification are documented in plant pathogens, they remain insufficiently explored in insect pests such as aphids, where olfactory (OR) and gustatory receptor (GR) genes are key to host recognition. We analyzed 521 OR and 399 GR genes, alongside TEs, across 12 aphid genomes with varying host ranges. Aphid lineages with broader host ranges exhibited higher evolutionary rates, driven by gene family expansions linked to host interaction, including lipid metabolism, immune function, and transposase activity. OR and GR genes evolved through proximal and tandem duplications and were shaped by diversifying selection, with bursts of positive selection followed by prolonged purifying selection, consistent with adaptation to novel hosts. Younger TEs were significantly enriched near OR genes compared to GRs and other genomic regions, suggesting a catalytic role of TEs in their diversification. However, OR proteins encoded by TE-associated ORs exhibited reduced functional potential. In contrast, GR proteins encoded by TE-associated GRs retained signatures of adaptation, as inferred from deep learning models predicting functionally important protein regions. These findings suggest that TE activity may facilitate functional innovation in GRs while alleviating constraints or pseudogenization in ORs. This study reveals how duplication, selection, and TE dynamics shape gene evolution in insect pests. It also provides the first chromosome-scale genome assembly of Dysaphis plantaginea, with comprehensive annotations and functional predictions of OR/GR genes, bridging adaptive evolution with mechanistic insights.

Adaptive introgression is an important evolutionary process, which can be identified with widely used summary statistics, such as the number of uniquely shared sites and the quantile of the derived allele frequencies in such sites. However, these as well as more recently developed statistics such as D+ and Danc, still lack accessible implementations. Here, we present SAI, a Python package for computing these statistics along with a new statistic, DD, and demonstrate its application on 2 datasets. First, using the 1000 Genomes Project data, we replicated previously reported candidate regions and identified additional ones, including a region detected by studies using supervised deep learning. Second, we investigated bonobo introgression into central chimpanzees and identified candidate genes, finding one region that overlaps a high-frequency Denisovan-introgressed haplotype block reported in modern Papuans. This is an intriguing co-occurrence across divergent lineages, underscoring the role of adaptive introgression in evolution.

Transitions toward simplified life cycles can reshape evolutionary trajectories, yet their impact on the rate of molecular evolution remains poorly understood. In aphids, host alternation (heteroecy) entails obligate seasonal migration between highly distinct plant hosts-typically woody and herbaceous species-and has been repeatedly lost, giving rise to monoecious species with simplified life cycles. Using comparative genomics across 46 aphid species, we tested whether transitions from heteroecy to monoecy alter evolutionary dynamics at the gene level. We identified 9,304 orthologs and estimated evolutionary rates (dN/dS) and shifts in selection regimes in the diverse Aphidinae subfamily. We found that 715 orthologs evolved faster in monoecious species, primarily due to relaxed selection, while heteroecious species showed signatures of intensified selection. Genes under relaxed selection in monoecious species were enriched for functions related to environmental sensing, signaling, nutritional adjustments, morph determination, and migration related-traits likely central for host alternation. These results suggest that the loss of a complex life cycle leads to reduced selective constraints as a consequence of ecological simplification. This study provides a robust evolutionary framework for understanding how life cycle transitions shape molecular evolution and drive gene decay following trait loss.

Coevolutionary dynamics in host-parasite systems are shaped by reciprocal selection and environmental context. When hosts and parasites share ancestry and ecological overlap, selection can act on similar traits, but population structure and geography may generate adaptive mosaics. Here, we present the first study to investigate genome-wide signatures of selection in response to a broad climatic gradient and a geographic mosaic of coevolution between a social parasite and its host. We examined these processes in the dulotic ant Temnothorax americanus and its congeneric host Temnothorax longispinosus using population genomics, genome-wide association analyses, and transcriptomics. Host populations showed very weak and parasites stronger population structure, enabling geographic mosaic dynamics. Genomic responses to parasite prevalence were divergent: Hosts showed signatures of selection on immune genes, whereas regulatory genes associated with raiding were under selection in parasites. Both species displayed convergent signatures of climate adaptation, including loci related to desiccation resistance, stress response, and parasite prevalence, with signals in communication and recognition genes involved in hydrocarbon biosynthesis, chemosensory perception, circadian rhythms, and venom production. Transcriptome analyses revealed contrasting patterns, with host gene expression linked to parasite prevalence and parasite expression more strongly shaped by climate. Together, our results reveal a genomic mosaic of coadaptation, in which population structure, asymmetric selection, and ecological variation interact to generate divergent yet interconnected evolutionary trajectories. Our findings highlight communication and recognition as recurrent arenas of antagonistic coevolution, underscore climate as a pervasive selective force, and establish a framework for investigating molecular coevolution in social parasite systems.

Due to their remarkable diversity and rapid evolution, conotoxins-peptide toxins from predatory marine cone snails-provide a powerful system for exploring how gene diversification may contribute to the development of lineage-specific adaptations. We previously demonstrated that 2-loop Tau conotoxins represent an evolutionary innovation associated with mollusk-hunting behaviors in cone snails. Here, we investigate the evolutionary history of these toxins as a model to understand the mechanism of ancestral gene neofunctionalization, which may have contributed to the emergence of mollusk-hunting in cone snails. Using ancestral sequence reconstruction, we present a model in which ancestral T-superfamily conotoxins neofunctionalized into the 2-loop Tau conotoxins. Predicted ancestral sequences reveal an intermediate structure between the classic T-superfamily conotoxins and the derived 2-loop Tau forms. Notably, these ancestral intermediates acquired a new cysteine scaffold that facilitated a structural transition from a globular to a ribbon fold. This conformational shift was followed by sequence-level changes that presumably enhanced activity against molecular targets in mollusks. We propose that the emergence of 2-loop Tau conotoxins may have been one factor that contributed to the emergence of molluscivory, providing insight into how gene innovation may underlie ecological diversification.

Shared fusions between ancestral chromosomal linkage groups have previously been used to support phylogenetic groupings, notably sponges with cnidarians and bilaterians to the exclusion of ctenophores, rendering ctenophores the sister group to all other animals. The linkage groups used to identify these fusions were assessed for statistical significance relative to a model of randomly shuffled genes. I argue that the method of random shuffling treated all species as equally distant from each other and so overestimated the significance of the observed linkages. I calculate alternative statistics and further argue that there are likely to be real linkage groups that are not identified as significant. If linkage groups are not supported statistically, they cannot reliably be used to identify shared derived chromosomal rearrangements, and hence phylogenetic hypotheses derived from them are suspect.

Whole-genome duplication (WGD), a widespread macromutation across eukaryotes, is predicted to affect the tempo and modes of evolutionary processes. By theory, the additional set(s) of chromosomes present in polyploid organisms may reduce the efficiency of selection while, simultaneously, increasing heterozygosity and buffering deleterious mutations. Despite the theoretical significance of WGD, empirical genomic evidence from natural polyploid populations is scarce and direct comparisons of selection footprints between autopolyploids and closely related diploids remains completely unexplored. We therefore combined locally sampled soil data with resequenced genomes of 76 populations of diploid-autotetraploid Arabidopsis arenosa and tested whether the genomic signatures of adaptation to distinct siliceous and calcareous soils differ between the ploidies. Leveraging multiple independent transitions between these soil types in each ploidy, we identified a set of genes associated with ion transport and homeostasis that were repeatedly selected for across the species' range. Notably, polyploid populations have consistently retained greater variation at candidate loci compared with diploids, reflecting lower fixation rates. In tetraploids, positive selection predominantly acts on such a large pool of standing genetic variation, rather than targeting de novo mutations. Finally, selection in tetraploids targets genes that are more central within the protein-protein interaction network, potentially impacting a greater number of downstream fitness-related traits. In conclusion, both ploidies thrive across a broad gradient of substrate conditions, but WGD fundamentally alters the ploidies adaptive strategies: tetraploids leverage their greater genetic variation and redundancy to compensate for the predicted constraints on the efficacy of positive selection.

Bayesian phylodynamic models have become essential for reconstructing population history from genetic data, yet their accuracy depends crucially on choosing appropriate demographic models. To address uncertainty in model choice, we introduce a Bayesian model averaging (BMA) framework that integrates multiple parametric coalescent models-including constant, exponential, logistic, and Gompertz growth-along with their "expansion" variants that account for non-zero ancestral populations. Implemented in a Bayesian setting with Metropolis-coupled Markov chain Monte Carlo, this approach allows the sampler to switch among candidate growth functions, thereby capturing demographic histories without having to pre-specify a single model. Simulation studies verify that the logistic and Gompertz models may require specialized sampling strategies such as adaptive multivariate proposals to achieve robust mixing. We demonstrate the performance of these models on datasets simulated under different substitution models, and show that joint inference of genealogy and population parameters is well-calibrated when properly incorporating correlated-move operators and BMA. We then apply this method to two real-world datasets. Analysis of Egyptian Hepatitis C virus sequences indicates that models with a founder population followed by a rapid expansion are well supported, with a slight preference for Gompertz-like expansions. Our analysis of a metastatic colorectal cancer single-cell dataset suggests that exponential-like growth is plausible even for an advanced stage cancer patient. We believe this highlights that tumor subclones may retain substantial proliferative capacity into the later stages of the disease. Overall, our unified BMA framework reduces the need for restrictive model selection procedures and can also provide deeper biological insights into epidemic spread and tumor evolution. By systematically integrating multiple growth hypotheses within a standard Bayesian setting, this approach naturally avoids overfitting and offers a powerful tool for inferring population histories across diverse biological domains.