Molecular biology and evolution最新文献

The insulin and insulin-like growth factor (IGF) system regulates essential biological functions such as growth, metabolism, and development. While its physiological roles are well characterized, the evolutionary origins and molecular diversification of its ligands and receptors remain incompletely defined. Here, we present the most comprehensive phylogenetic and sequence conservation analysis of this system to date, using over 1,000 sequences from vertebrates, invertebrates, and viruses. Our analyses reveal that insulin, IGF-1, and IGF-2 form distinct monophyletic clades that diverged after the emergence of vertebrates, with IGF-1 being the most conserved ligand. We show that IGF1R-binding residues, especially in the A- and B-domains of IGF-1, are highly conserved across vertebrates, while insulin's Site 2 residues, which overlap with its dimerization and hexamerization surface, are more variable-correlating with the loss of hexamer formation in hystricomorphs, reptiles, and jawless fish. Unexpectedly, we identify a 12-amino acid insert in the insulin receptor (IR) of turtles and tortoises, previously thought to be unique to mammalian IR-B isoform, challenging the view that receptor isoform diversity is a mammalian innovation. We also show that marsupials and monotremes retain ancestral receptor domain features shared with reptiles and birds and that avian insulins, particularly A-chain residues, are unusually conserved. Viral insulin/IGF-like peptides fall into two distinct clades that resemble either IGFs or insulin. Together, these findings illuminate the evolutionary architecture of the insulin/IGF system, highlight unexpected lineage-specific adaptations, and provide a framework for understanding hormone-receptor function across biology and therapeutic design.

MicrobeTrace is a free, secure, browser-based bioinformatics tool to integrate and visualize epidemiologic, laboratory, and molecular data for outbreak investigations, with over 14,000 users from 127 countries. Regular testing, user feedback, and comparison with other bioinformatics tools identified areas for improvement, prompting major architectural and functional upgrades. In MicrobeTrace 2.0, we refactored the codebase using Angular to improve scalability, performance, and usability. We also replaced the D3.js visualization engine with Cytoscape.js for faster, more efficient rendering of large networks. The update adds enhanced visualizations, new analytical tools, and expanded functionality within existing views. It also supports seamless integration with external phylogenetic platforms, such as Nextstrain and UShER (Ultrafast Sample Placement on Existing Trees), enabling users to import phylogenetic trees, visualize them as genetic networks, and securely enrich them with epidemiological and demographic metadata. These enhancements position MicrobeTrace as a next-generation, interoperable tool for genomic epidemiology and data-driven public health response.

The Central Plain of China, the cradle of Chinese civilization, experienced major demographic upheavals during the Qing era (1644-1912 AD), yet its population's genetic history during this critical period remains largely uncharacterized. Here, we generated genome-wide data for 46 Qing Dynasty individuals from the Sanzhiyuan cemetery in Sanmenxia, Henan province. The Sanzhiyuan population exhibits remarkable genetic homogeneity and shows substantial genetic continuity with preceding populations of the Yellow River region dating back to the Late Neolithic. We successfully model them as direct descendants of Tang Dynasty populations. Notably, despite the Qing being ruled by the ethnically distinct Manchu elite, we detected no evidence of large-scale genetic admixture with Manchu, Mongol, or other northern or West Eurasian groups. Furthermore, we demonstrate that these Qing-era individuals are direct ancestors of the modern Han population in Henan, completing an unbroken multi-millennial genetic lineage. Our findings further demonstrate the stability of the genetic profile of the Central Plains population-a stability that has persisted for millennia and remained profoundly unaffected by major historical upheaval.

Endogenous retroviral envelope (ERV env) genes, notably syncytins, are known for driving placental development in mammals and lizards. However, their broader contributions to non-mammalian vertebrates, particularly viviparous fish, remain largely unexplored. Here, we present the discovery and functional characterization of three co-opted/captured ERV env gene clades, including a Percom-env clade comprising previously reported percomORF in the viviparous teleost Sebastes schlegelii. Our findings reveal that each gene clade plays a distinct and critical role in neural regulation, reproductive maturation, and viviparity. Notably, Percom-env percomORF retains fusogenic activity and its brain-specific expression is driven by conserved regulatory elements across Percomorpha. Meanwhile, Seb-env4 env genes, expressed uniquely in the testis, support seasonal gonadal maturation and Sertoli cell maintenance. Lastly, Seb-env3 env genes, localized at the maternofetal interface, retain a robust fusion capacity essential for follicular placentation, and have persisted for approximately 15 million years in the Sebastes genus, thus identified as candidate syncytin-Seb, likely underpinning the emergence of viviparity. These findings demonstrate env co-option/capture drives teleost adaptations, extending retroviral env-mediated placentation beyond mammals and lizards, and highlight conserved mechanisms in vertebrate reproductive evolution.

Evolutionary simulations of multiple chromosomes, even up to the scale of full-genome simulations, are becoming increasingly important in population genetics and evolutionary ecology. Unfortunately, the popular simulation framework SLiM has always been intrinsically limited to simulations of a single diploid chromosome. Modeling multiple chromosomes of different types, such as sex chromosomes, has always been cumbersome, even with scripting, presenting a substantial barrier to the development of full-genome simulations. Here we present SLiM 5, a major extension of SLiM's capabilities for simulating multiple chromosomes. Modeling up to 256 chromosomes is now possible, and each chromosome may belong to any of a wide variety of types-not just autosomes (diploid and haploid), but also sex chromosomes (X, Y, Z, and W), haploid mitochondrial and chloroplast DNA, and more. This new functionality is integrated across all of SLiM, including not only the mechanics of reproduction and inheritance, but also input and output of multi-chromosome data in formats like VCF, and tree-sequence recording across multiple chromosomes. New recipes in the SLiM manual demonstrate these new features, and SLiM's graphical modeling environment, SLiMgui, has been extended in many ways for the visualization of multi-chromosome models. These new features will open new horizons and enable a heightened level of realism for full-genome simulations.

Our understanding of the vertebrate immune system is dominated by a few model organisms such as mice. This use of a few model systems is reasonable if major features of the immune systems evolve slowly and are conserved across most vertebrates, but may be problematic if there is substantial macroevolutionary change in immune responses. Here, we present a test of the macroevolutionary stability, across 14 species of ray-finned fishes, of the transcriptomic response to a standardized immune challenge. Intraperitoneal injection of an immune adjuvant (alum) induces a fibrosis response in nearly all jawed fishes, which in some species contributes to anti-helminth protection. Despite this conserved phenotypic response, the underlying transcriptomic response is highly inconsistent across species. Although many gene orthogroups exhibit differential expression between saline versus alum-injected fish in at least one species, few orthogroups exhibit consistent differential expression across species. This result suggests that although the phenotypic response to alum (fibrosis) is highly conserved, the underlying gene regulatory architecture is very flexible and cannot readily be extrapolated from any one species to fishes (or vertebrates) more broadly. The vertebrate immune response is remarkably changeable over macroevolutionary time, requiring a diversity of model organisms to describe effectively.

The growing availability of genomes from non-model organisms offers new opportunities to identify functional loci underlying trait variation through comparative genomics. While cis-regulatory regions drive much of phenotypic evolution, linking them to specific functions remains challenging. We identified 514 cis-regulatory motifs enriched in regulatory regions of five diverse grass species, with 73% consistently enriched across all, suggesting a deeply conserved regulatory code. Leveraging 57 new contig-level genome assemblies, we then quantified shared occupancy of specific motif instances within gene-proximal regions across 589 grass species, revealing widespread gain and loss over evolutionary time. Shared occupancy declined rapidly over the first few million years of divergence, yet ∼50% of motif instances were shared back to the origin of grasses ∼100 million years ago. We used phylogenetic mixed models to identify motif gains and losses associated with ecological niche transitions. Our models revealed significant environmental associations across 1282 motif-orthogroup combinations, including convergent gains of HSF/GARP motifs at an alpha-N-acetylglucosaminidase gene associated with occurrence in temperate environments. Our findings support a "stable motifs, variable binding sites" model in which cis-regulatory evolution involves turnover of thousands of individual binding site instances while largely preserving transcription factors' binding preferences. Our results highlight the potential of comparative genomics and phylogenetic mixed models to reveal the genetic basis of complex traits.

Ancient DNA and archaeological studies indicate the Central Plain's pivotal role in the cultural and genetic evolution of ancient China. However, limited genome-wide data have constrained our understanding of this region's population history during the Bronze Age Shang Dynasty (around 1600 to 1046 BCE). Here, we present genome-wide data from 11 individuals from the Xisima Cemetery in Central Plain, a site exhibiting clear burial evidence of social stratification dating to the Late Shang Dynasty (around 1300 to 1046 BCE). Genetic analyses reveal that all Xisima individuals can be modeled as direct, unadmixed descendants of Late Neolithic Central Plain-related people. We found no systematic genetic differentiation between individuals buried in high-grade (south-to-north) and low-grade (east-to-west) tombs, indicating genetic homogeneity across social strata. These results demonstrate that social stratification at Xisima occurred without corresponding genetic distinction, supporting the decoupling of social hierarchy from significant genetic differentiation in this Shang community.

The multispecies coalescent (MSC) model provides a framework for detecting gene flow using genomic data, including between sister species. However, the robustness of the inference to violations of model assumptions are poorly understood. Here, we use simulation to study the false positive rate of a Bayesian test of gene flow under the MSC with multiple influencing factors including recombination, natural selection, discrete versus continuous gene flow, variable species divergence time, and gene flow involving sister versus nonsister lineages. We find that in almost all scenarios examined the test has very low false positives. However, the test of gene flow between sister lineages may be prone to high false positives in cases of very recent species divergence and very high recombination rate. At low recombination rates, the test is robust to selective sweeps, background selection and balancing selection, although prolonged balancing selection can lead to false signals of gene flow between sister lineages. The impact of excessive recombination on the test of gene flow between sisters may be assessed by using a smaller number of sequences for each species and by considering shorter sequences at each locus. Recent species divergence alone (with no recombination) does not cause false positives in tests of gene flow, contrary to previous claims. The test of gene flow between nonsister lineages is robust to recombination at all divergence levels. Our findings provide guidance for reliable inference of gene flow using coalescent methods and highlight the need for care in conducting and interpreting simulation experiments.

Aggression is an essential animal behavior for survival, particularly in situations where fighting cannot be avoided. In such situations, the choice of fighting strategy (eg biting, charging, or defending) is critical. Although the molecular bases of fighting and aggressiveness have been previously studied, how genetic, transcriptional, and neurobiological mechanisms contribute to the choice of fighting strategy remains largely unknown. Here, we use two subpopulations of chickens bred for cockfighting that show markedly different fighting strategies: offensive and defensive attack. A genome-wide screen comparing individuals from the two subpopulations indicated a polygenic background and we identified 15 candidate genes, five of which are implicated in neuronal development. Among these, the transcription factor gene FOXP1 was notable. FOXP1 is essential for neuronal development in the brain and has been implicated in the regulation of motor circuits. Transcriptomic analysis of the diencephalon also revealed differential expressions of genes involved in neurodevelopment, as well as in the synthesis and release of neurotransmitters. RNA-sequencing and immunohistochemistry suggested that activation of the indirect pathway of the brain motor circuit promotes the defensive fighting strategy. This was further supported by behavioral pharmacological experiments targeting dopaminergic signaling. Taken together, our results indicate that genomic variation and altered expression of neurodevelopment-related genes underlie differences in fighting strategies, and that the neuroendocrine changes in brain circuits further modulate these behavioral outcomes.