Genome Biology and Evolution最新文献

Previously proposed chronologies of amino acid incorporation into the genetic code rely on consensus rankings derived from prebiotic synthesis experiments, biosynthetic pathways, or genomic trends. However, the role of intrinsic molecular properties in shaping amino acid recruitment remains largely underexplored. In this study, we reconstruct a complexity-based amino acid chronology by integrating sixteen molecular complexity metrics from chemical graph and information theory. Unlike approaches influenced by environmental variability, detection biases, or the evolutionary constraints of genome-based chronologies, our method provides a perspective on amino acid incorporation independent of these factors. Instead of imposing a linear ranking, we derive a minimum spanning tree capturing complexity-based relationships between amino acids. The resulting hierarchy places structurally simple amino acids in basal positions, while biosynthetically complex residues appear later, aligning with existing prebiotic and genomic chronologies. Furthermore, amino acids positioned closer in the complexity space exhibit significantly greater mutational connectivity than expected by chance, suggesting that molecular complexity reflects underlying structural considerations that constrained the genetic code's evolutionary pathways. This supports the idea that the code evolved not only to maintain biochemical stability but also to facilitate complexity-preserving substitutions, ensuring smooth adaptive transitions while minimizing energetic cost differences. Additionally, molecular complexity significantly correlates with amino acid enrichment in LUCA's inferred proteome, reinforcing its role as a fundamental constraint on early protein evolution. Our approach, rooted in intrinsic molecular properties rather than external contingencies, offers new insights into the constraints shaping the genetic code and expands the scope for identifying universal principles of biochemical evolution.

Understanding genomic function has historically relied on sequence conservation across evolutionary time. However, advances in genomics have revealed that functional innovations often arise from rapidly evolving, nonconserved elements that are frequently overlooked by conservation-based approaches. Among these, variable number tandem repeats (VNTRs) act as engines of both functional innovation and phenotypic consequence. VNTRs are repetitive genomic sequences whose copy numbers can vary significantly between individuals and species, influencing gene regulation, protein structure, and eventually, phenotypic diversity. Recent long-read assemblies and pangenomes now resolve VNTR loci accurately, enabling robust evolutionary reconstruction and functional associations. Here, we synthesize emerging insights into the functional and evolutionary impact of VNTRs in mammals. Specifically, we outline pressing questions on the mutational mechanisms driving VNTR evolution in humans, the selective forces maintaining their structural heterogeneity, and propose a theoretical framework for their persistence through evolutionary tradeoffs.

The de-extinction of species using genome-editing approaches depends on acquiring high-quality genomic information from the extinct target. However, the degraded nature of the ancient DNA (aDNA) that is typical for most extinct species, poses significant challenges to achieving comprehensive genome reconstruction. A systematic evaluation of the minimum sequencing effort that is required to reliably map the genome under varying DNA quality conditions to different reference genome remains lacking across different extinct species. Here, we systematically assess the impact of sequencing depth on genome coverage, heterozygosity estimation, and variant calling accuracy, when mapping both true aDNA data generated from the extinct Christmas Island rat (Rattus macleari), as well as in silico simulated modern- and ancient-like data generated from a modern relation (the brown rat, Rattus norvegicus), to the black rat (Rattus rattus) reference genomes. Our results demonstrate that even sequencing depths of 100× fail to yield stable heterozygosity estimates, and leave approximately 3.38% to 4.03% of its genome uncovered. These uncovered regions contained functionally relevant SNPs and indels, highlighting the limitations of reconstructing extinct genomes using reference sequences from extant relatives. Furthermore, simulations using computationally generated "degraded haploid and diploid" data based on the high-quality brown rat genome, revealed that false-positive SNPs primarily arise from insufficient coverage and low data quality, rather than aDNA damage (e.g. miscoding lesions, size of fragments, etc.) per se. These findings underscore the need to tailor sequencing depth standards by considering sample type, degradation level, and sequencing error profiles. This study provides a theoretical framework and methodological support for optimizing data strategies in aDNA research, and ultimately informing de-extinction efforts.

Genomes contain the mutational footprint of an organism's evolutionary history, shaped by diverse forces including ecological factors, selective pressures, and life history traits. The sequentially Markovian coalescent (SMC) is a versatile and tractable model for the genetic genealogy of a sample of genomes, which captures this shared history. Methods that utilize the SMC, such as PSMC and MSMC, have been widely used in evolution and ecology to infer demographic histories. However, these methods ignore common biological features, such as gene flow events and structural variation. Recently, there have been several advancements that widen the applicability of SMC-based methods: inclusion of an isolation with migration model, integration with the multi-species coalescent, incorporation of ecological life history traits (such as selfing and dormancy), and many computational advances in applying these models to data. We give an overview of the SMC model and its various recent extensions, discuss examples of biological discoveries through SMC-based inference, and comment on the assumptions, benefits and drawbacks of various methods.

Insects ally with microbial symbionts for a diversity of services. The range of these interactions is wide, spanning from beneficial to pathogenic and facultative to obligate. In many cases, such insect-microbial interactions veer towards mutual dependency with integrated physiologies. This evolutionary outcome is relatively common in insects that depend on microbes to fill gaps in their nutritional ecologies (e.g. plant-sap feeding). However, the initiation and transition towards such dependent symbiotic interactions are difficult to observe in nature. Identifying these events can provide key insights into the origins and evolutionary processes that shape symbiotic interactions. Here, we report on a novel interaction between a leafhopper (Typhlocybinae: Empoasca mexicana) and a bacterium, Symbiopectobacterium purcellii MEX strain (S-MEX). To characterize this symbiont, we assembled and annotated its complete genome. We compared its content and structure to the genomes of other Symbiopectobacterium. The S-MEX genome is unique among members of this genus. It is the largest yet sequenced at 5.3 Mb, encoding 6,838 genes (∼25% more than other strains). S-MEX's genome has significantly expanded due to the proliferation of insertion sequences and 2,723 identifiable pseudogenes-processes generally seen as accelerators of genome reduction and emerging host dependence. S-MEX and other Symbiopectobacterium strains have a core set of 818 genes shared in >90% of strains, of which S-MEX has uniquely lost 36 genes. Taken together, we hypothesize that due to expansion of IS elements, extensive pseudogenization, and loss of genes in important free-living functions, S-MEX is in the early stages of establishing a host-dependent symbiosis.

Using temporarily spaced high-coverage ancient genomes, we can assess population decline prior to extinction. However, finding suitable ancient remains for recovering this type of data is challenging. Here, we sequenced a high-coverage genome from muscle tissue of a 14,400-year-old woolly rhinoceros (Coelodonta antiquitatis)-a cold-adapted herbivore that went extinct ∼14,000-years ago-found inside a permafrost-preserved wolf's stomach. We compared genome-wide diversity, inbreeding, genetic load, and population size changes in this sample with two other Late Pleistocene Siberian woolly rhinoceros. We found no evidence of population size decline, nor any genomic erosion, shortly prior to the species' demise. Given the few long homozygous segments, typically indicative of recent inbreeding, we infer a stable population size only a few centuries before extinction. Thus, the woolly rhinoceros' extinction likely happened rapidly, during the Bølling-Allerød interstadial. This study demonstrates the ability to recover high-quality DNA from unlikely sources to elucidate species' extinction dynamics.

Protein structure-based comparison provides a framework for uncovering deep evolutionary relationships that can escape conventional sequence-based approaches. Encoding three-dimensional protein structures using a simplified structure-aware alphabet can lead to compact, comparable strings that retain key spatial relationships. Although this enables comparison, structure-aware alignments can experience misaligned regions, particularly when comparing proteins with substantial divergence in fold architecture. To address this, a web-based resource, Structome-AlignViewer, is introduced in this work for evaluating the quality of structure-aware alignments through both spatial mapping of alignment columns to protein structures and quantitative confidence scoring. Confidence is computed from pairwise structural substitutions between adjacent inputs and normalized within each alignment to highlight relatively well-supported columns. To provide broader context, thousands of alignments from established structural classification systems were analyzed, allowing for an empirical comparative statistic to be derived to assess alignment quality. The option to exclude gap-rich regions enables users to refine alignments and focus on conserved structural cores. This approach provides an interpretable method for assessing structural alignment quality and supports more robust comparative and evolutionary analyses. Structome-AlignViewer is freely available at https://biosig.lab.uq.edu.au/structome_alignviewer/.

Bees are important global pollinators that play a vital role in maintaining ecosystems and supporting global food production. They also exhibit a diversity of social organization, making them ideal model organisms for studying the evolution of sociality in animals. Recent advancements in genome sequencing have enabled researchers to address longstanding questions about the evolution of social behaviour in bees, particularly in the relatively few species that exhibit complex social structures, such as Apis. Whole genome phylogenies have enhanced our understanding of the complex evolutionary history of bees, providing a foundation for studying the evolution of specific traits, including eusociality. Recent transcriptomic and alternative splicing studies have advanced our understanding of how gene regulation and expression patterns contribute to behavioural plasticity, caste differentiation, and the emergence of social complexity. Comparative genomics across a range of bees with varying social behaviours has aided our understanding of the genomic features associated with social evolution and has shed light on its molecular underpinnings. Genomic approaches like GWAS and population genomic comparisons, combined with advanced sequencing technologies, have revolutionized the study of bee evolution, social behaviour, and environmental interactions. Pollen metabarcoding and environmental DNA (eDNA) techniques are now being used to quantify the intricate and complex interactions between bees and the plants they visit, and to identify other environmental factors, including pathogens that impact bee health. Additionally, techniques like museomics (using DNA from museum specimens) and broader genomic approaches have been instrumental in revealing how bees have been affected by anthropogenic changes. These tools offer valuable insights into population genetics, conservation biology, and the impact of environmental changes on bee populations. These advancements both provide critical insights into the molecular basis of eusociality and species adaptation and offer valuable tools for addressing the urgent challenges facing bee conservation due to anthropogenic change. By leveraging these genomic approaches, researchers can inform strategies for the preservation and sustainable management of bee populations worldwide.

We present a high-quality, chromosome-scale genome assembly for the redlip blenny Ophioblennius macclurei (family Blenniidae). The assembly was generated using a combination of Oxford Nanopore long-read sequencing, Illumina short-read data, and Hi-C scaffolding technology. The assembled genome is 529.6 Mb in size, with a scaffold N50 of 23.7 Mb and a GC content of 43.49%. BUSCO analysis recovered 97.06% of expected genes in the scaffolded assembly and 89.2% from the annotated proteome. Automatic genome annotation identified a total of 18,927 protein-coding genes. This genome provides a valuable resource for understanding the diversity and evolutionary history of Ophioblennius and the fish family Blenniidae.