Molecular biology and evolution最新文献

The heterogeneous landscape of genomic variation has been well documented in population genomic studies. However, disentangling the intricate interplay of evolutionary forces influencing the genetic variation landscape over time remains challenging. In this study, we assembled a chromosome-level genome for Castanopsis eyrei and sequenced the whole genomes of 276 individuals from 12 Castanopsis species, spanning a broad divergence continuum. We found highly correlated genomic variation landscapes across these species. Furthermore, variations in genetic diversity and differentiation along the genome were strongly associated with recombination rates and gene density. These results suggest that long-term linked selection and conserved genomic features have contributed to the formation of a common genomic variation landscape. By examining how correlations between population summary statistics change throughout the species divergence continuum, we determined that background selection alone does not fully explain the observed patterns of genomic variation; the effects of recurrent selective sweeps must be considered. We further revealed that extensive gene flow has significantly influenced patterns of genomic variation in Castanopsis species. The estimated admixture proportion correlated positively with recombination rate and negatively with gene density, supporting a scenario of selection against gene flow. Additionally, putative introgression regions exhibited strong signals of positive selection, an enrichment of functional genes, and reduced genetic burdens, indicating that adaptive introgression has played a role in shaping the genomes of hybridizing species. This study provides insights into how different evolutionary forces have interacted in driving the evolution of the genomic variation landscape.

Polyploidization drives regulatory and phenotypic innovation. How the merger of different genomes contributes to polyploid development is a fundamental issue in evolutionary developmental biology and breeding research. Clarifying this issue is challenging because of genome complexity and the difficulty in tracking stochastic subgenome divergence during development. Recent single-cell sequencing techniques enabled probing subgenome-divergent regulation in the context of cellular differentiation. However, analyzing single-cell data suffers from high error rates due to high dimensionality, noise, and sparsity, and the errors stack up in polyploid analysis due to the increased dimensionality of comparisons between subgenomes of each cell, hindering deeper mechanistic understandings. In this study, we develop a quantitative computational framework, called "pseudo-genome divergence quantification" (pgDQ), for quantifying and tracking subgenome divergence directly at the cellular level. Further comparing with cellular differentiation trajectories derived from single-cell RNA sequencing data allows for an examination of the relationship between subgenome divergence and the progression of development. pgDQ produces robust results and is insensitive to data dropout and noise, avoiding high error rates due to multiple comparisons of genes, cells, and subgenomes. A statistical diagnostic approach is proposed to identify genes that are central to subgenome divergence during development, which facilitates the integration of different data modalities, enabling the identification of factors and pathways that mediate subgenome-divergent activity during development. Case studies have demonstrated that applying pgDQ to single-cell and bulk tissue transcriptomic data promotes a systematic and deeper understanding of how dynamic subgenome divergence contributes to developmental trajectories in polyploid evolution.

Modern humans have experienced explosive population growth in the past thousand years. We hypothesized that recent human populations have inhabited environments with relaxation of selective constraints, possibly due to the more abundant food supply after the Last Glacial Maximum. The ratio of nonsynonymous to synonymous mutations (N/S ratio) is a useful and common statistic for measuring selective constraints. In this study, we reconstructed a high-resolution phylogenetic tree using a total of 26,419 East Eurasian mitochondrial DNA genomes, which were further classified into expansion and nonexpansion groups on the basis of the frequencies of their founder lineages. We observed a much higher N/S ratio in the expansion group, especially for nonsynonymous mutations with moderately deleterious effects, indicating a weaker effect of purifying selection in the expanded clades. However, this observation on N/S ratio was unlikely in computer simulations where all individuals were under the same selective constraints. Thus, we argue that the expanded populations were subjected to weaker selective constraints than the nonexpanded populations were. The mildly deleterious mutations were retained during population expansion, which could have a profound impact on present-day disease patterns.

Host-pathogen coevolution is defined as the reciprocal evolutionary changes in both species due to genotype × genotype (G×G) interactions at the genetic level determining the outcome and severity of infection. While co-analyses of hosts and pathogen genomes (co-genome-wide association studies) allow us to pinpoint the interacting genes, these do not reveal which host genotype(s) is/are resistant to which pathogen genotype(s). The knowledge of this so-called infection matrix is important for agriculture and medicine. Building on established theories of host-pathogen interactions, we here derive four novel indices capturing the characteristics of the infection matrix. These indices can be computed from full genome polymorphism data of randomly sampled uninfected hosts, as well as infected hosts and their pathogen strains. We use these indices in an approximate Bayesian computation method to pinpoint loci with relevant G×G interactions and to infer their underlying interaction matrix. In a combined single nucleotide polymorphism dataset of 451 European humans and their infecting hepatitis C virus (HCV) strains and 503 uninfected individuals, we reveal a new human candidate gene for resistance to HCV and new virus mutations matching human genes. For two groups of significant human-HCV (G×G) associations, we infer a gene-for-gene infection matrix, which is commonly assumed to be typical of plant-pathogen interactions. Our model-based inference framework bridges theoretical models of G×G interactions with host and pathogen genomic data. It, therefore, paves the way for understanding the evolution of key G×G interactions underpinning HCV adaptation to the European human population after a recent expansion.

Protein space is characterized by extensive recurrence, or "reuse," of parts, suggesting that new proteins and domains can evolve by mixing-and-matching of existing segments. From an evolutionary perspective, for a given combination to persist, the protein segments should presumably not only match geometrically but also dynamically communicate with each other to allow concerted motions that are key to function. Evidence from protein space supports the premise that domains indeed combine in this manner; we explore whether a similar phenomenon can be observed at the sub-domain level. To this end, we use Gaussian Network Models (GNMs) to calculate the so-called soft modes, or low-frequency modes of motion for a dataset of 150 protein domains. Modes of motion can be used to decompose a domain into segments of consecutive amino acids that we call "dynamic elements", each of which belongs to one of two parts that move in opposite senses. We find that, in many cases, the dynamic elements, detected based on GNM analysis, correspond to established "themes": Sub-domain-level segments that have been shown to recur in protein space, and which were detected in previous research using sequence similarity alone (i.e. completely independently of the GNM analysis). This statistically significant correlation hints at the importance of dynamics in evolution. Overall, the results are consistent with an evolutionary scenario where proteins have emerged from themes that need to match each other both geometrically and dynamically, e.g. to facilitate allosteric regulation.

The Roman period saw the empire expand across Europe and the Mediterranean, including much of what is today Great Britain. While there is written evidence of high mobility into and out of Britain for administrators, traders, and the military, the impact of imperialism on local, rural population structure, kinship, and mobility is invisible in the textual record. The extent of genetic change that occurred in Britain during the Roman military occupation remains underexplored. Here, using genome-wide data from 52 ancient individuals from eight sites in Cambridgeshire covering the period of Roman occupation, we show low levels of genetic ancestry differentiation between Romano-British sites and indications of larger populations than in the Bronze Age and Neolithic. We find no evidence of long-distance migration from elsewhere in the Empire, though we do find one case of possible temporary mobility within a family unit during the Late Romano-British period. We also show that the present-day patterns of genetic ancestry composition in Britain emerged after the Roman period.

The organization of genomes into chromosomes is critical for processes such as genetic recombination, environmental adaptation, and speciation. All animals with bilateral symmetry inherited a genome structure from their last common ancestor that has been highly conserved in some taxa but seemingly unconstrained in others. However, the evolutionary forces driving these differences and the processes by which they emerge have remained largely uncharacterized. Here, we analyze genome organization across the phylum Annelida using 23 chromosome-level annelid genomes. We find that while many annelid lineages have maintained the conserved bilaterian genome structure, the Clitellata, a group containing leeches and earthworms, possesses completely scrambled genomes. We develop a rearrangement index to quantify the extent of genome structure evolution and show that, compared to the last common ancestor of bilaterians, leeches and earthworms have among the most highly rearranged genomes of any currently sampled species. We further show that bilaterian genomes can be classified into two distinct categories-high and low rearrangement-largely influenced by the presence or absence, respectively, of chromosome fission events. Our findings demonstrate that animal genome structure can be highly variable within a phylum and reveal that genome rearrangement can occur both in a gradual, stepwise fashion, or rapid, all-encompassing changes over short evolutionary timescales.

Variability in expression levels in response to random genomic mutations varies among genes, influencing both the facilitation and constraint of phenotypic evolution in organisms. Despite its importance, both the underlying mechanisms and evolutionary origins of this variability remain largely unknown due to the mixed contributions of cis- and trans-acting elements. To address this issue, we focused on the mutational variability of cis-acting elements, that is, promoter regions, in Escherichia coli. Random mutations were introduced into the natural and synthetic promoters to generate mutant promoter libraries. By comparing the variance in promoter activity of these mutant libraries, we found no significant difference in mutational variability in promoter activity between promoter groups, suggesting the absence of a signature of natural selection for mutational robustness. In contrast, the promoters controlling essential genes exhibited a remarkable bias in mutational variability, with mutants displaying higher activities than the wild types being relatively rare compared to those with lower activities. Our evolutionary simulation on a rugged fitness landscape provided a rationale for this vulnerability. These findings suggest that past selection created nonuniform mutational variability in promoters biased toward lower activities of random mutants, which now constrains the future evolution of downstream essential genes toward higher expression levels.

Pleiotropy, the phenomenon in which a single gene influences multiple traits, is a fundamental concept in genetics. However, the evolutionary mechanisms underlying pleiotropy require further investigation. In this study, we conducted parallel gene knockouts targeting 100 transcription factors in 2 strains of Saccharomyces cerevisiae. We systematically examined and quantified the pleiotropic effects of these knockouts on gene expression levels for each transcription factor. Our results showed that the knockout of a single gene generally affected the expression levels of multiple genes in both strains, indicating various degrees of pleiotropic effects. Strikingly, the pleiotropic effects of the knockouts change rapidly between strains in different genetic backgrounds, and ∼85% of them were nonconserved. Further analysis revealed that the conserved effects tended to be functionally associated with the deleted transcription factors, while the nonconserved effects appeared to be more ad hoc responses. In addition, we measured 184 yeast cell morphological traits in these knockouts and found consistent patterns. In order to investigate the evolutionary processes underlying pleiotropy, we examined the pleiotropic effects of standing genetic variations in a population consisting of ∼1,000 hybrid progenies of the 2 strains. We observed that newly evolved expression quantitative trait loci impacted the expression of a greater number of genes than did old expression quantitative trait loci, suggesting that natural selection is gradually eliminating maladaptive or slightly deleterious pleiotropic responses. Overall, our results show that, although being prevalent for new mutations, the majority of pleiotropic effects observed are evolutionarily transient, which explains how evolution proceeds despite complicated pleiotropic effects.

Insect herbivores frequently cospeciate with symbionts that enable them to survive on nutritionally unbalanced diets. While ancient symbiont gain and loss events have been pivotal for insect diversification and feeding niche specialization, evidence of recent events is scarce. We examine the recent loss of nutritional symbionts (in as little as 1 MY) in sap-feeding Pariaconus, an endemic Hawaiian insect genus that has undergone adaptive radiation, evolving various galling and free-living ecologies on a single host-plant species, Metrosideros polymorpha within the last ∼5 MY. Using 16S rRNA sequencing, we investigated the bacterial microbiomes of 19 Pariaconus species and identified distinct symbiont profiles associated with specific host-plant ecologies. Phylogenetic analyses and metagenomic reconstructions revealed significant differences in microbial diversity and functions among psyllids with different host-plant ecologies. Within a few millions of years, Pariaconus species convergently evolved the closed-gall habit twice. This shift to enclosed galls coincided with the loss of the Morganella-like symbiont that provides the essential amino acid arginine to free-living and open-gall sister species. After the Pariaconus lineage left Kauai and colonized younger islands, both open- and closed-gall species lost the Dickeya-like symbiont. This symbiont is crucial for synthesizing essential amino acids (phenylalanine, tyrosine, and lysine) as well as B vitamins in free-living species. The recurrent loss of these symbionts in galling species reinforces evidence that galls are nutrient sinks and, combined with the rapidity of the evolutionary timeline, highlights the dynamic role of insect-symbiont relationships during the diversification of feeding ecologies. We propose new Candidatus names for the novel Morganella-like and Dickeya-like symbionts.