搜索结果：Trends in genetics : TIG

Medaka is an established vertebrate model system for biological and biomedical research. It possesses unique features that make it particularly suitable for studying genome-environment interactions. Endemic to habitats spanning from 4 to 40°C and varying salinities, it combines broad ecological adaptability with experimental tractability. Its exceptional tolerance to inbreeding enabled the creation of the Medaka Inbred Kiyosu-Karlsruhe panel-80 near-isogenic, fully sequenced lines derived from a single wild population. More than 100 wild-derived, fully sequenced strains, collected throughout East Asia for more than 40 years, show relatively low intra-strain variation (inbreeding coefficient of >0.75) but high inter-strain variability (SNP rates >4%). Advanced quantification methods facilitate genome-wide association studies and quantitative trait locus mapping. The system's amenability to clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 editing and emerging epigenomic profiling enables causal validation and regulatory-mechanism discovery. Collectively, medaka offers an unparalleled vertebrate framework for integrating genetics, environment, and epigenetics-bridging evolutionary, biomedical, and population-level perspectives.

With the advent of sequencing technologies in recent years, hundreds of high-confidence risk genes have been implicated in neurodevelopmental disorders (NDDs). However, individuals carrying pathogenic variants in the same gene frequently exhibit diverse clinical presentations, including varied symptoms and diagnoses. We propose that this heterogeneity arises from different interacting factors that modulate the phenotypic outcomes of pathogenic variants, including variant-level features, modifying variation across the genome, prenatal and early-life environmental exposures, and developmental noise. Resolving these factors requires integrative approaches that combine population-scale genetics and functional genomics with environmental monitoring and quantitative assessments of stochastic developmental variation. Advancing our understanding of these factors is critical to elucidating the etiology of NDDs and improving diagnostic and personalized therapeutic strategies.

Germ cell development involves extensive remodeling of the 3D genome architecture, which is tightly coupled to transcriptional programs, meiotic chromosome dynamics, and re-establishment of totipotency in the next generation. Recent advances in chromosome conformation capture methods have uncovered stage-specific alterations in chromosome organization during spermatogenesis and oogenesis, including germline-specific 3D genome features. These distinctive nuclear configurations orchestrate gene expression programs essential for each developmental stage and meiosis, contribute to epigenetic inheritance, and shape genome evolution. In this review, we synthesize recent progress in understanding 3D genome organization in male and female germlines, and highlight emerging principles, unresolved questions, and innovative approaches that will advance our understanding of germline biology and the principles of genome architecture.

Abiotic stress severely restricts plant growth and crop yield, potentially impacting food security during climate shifts. Alternative splicing (AS), a widely conserved gene regulatory mechanism tightly coupled to transcription, impacts stress responses by altering protein levels and function. Such molecular plasticity supports rapid environmental responses. Advances in high-throughput sequencing technologies have enabled genome-wide AS profiling, revealing that abiotic stresses extensively reshape splicing landscapes, affecting transcripts encoding heat shock transcription factors, calcium signaling components, and splicing regulators. Here, we synthesize current knowledge on plant AS mechanisms, advances in AS detection, and stress-induced AS regulation under temperature fluctuations, drought, and salinity. We further discuss prospects for manipulating AS in breeding stress-resistant crops, providing a paradigm for genetic improvement with relevance beyond stress resistance.

Adopting a safety-centric approach, this article explores how generative artificial intelligence (AI), and more specifically, foundation models for biological sequences, can exacerbate data quality issues, technical biases, and dual-use potential, particularly in critical applications such as clinical genetics, precision medicine, and pathogen engineering. This work centres on how misuse risks emerge throughout the innovation pipeline and how these intersect with the growing accessibility of generative genomic models. Particular attention is given to dual-use governance and infrastructure hardening in sequence analysis workflows. The work aims to provide scientists, regulators, and policymakers with a toolkit to discuss beneficial innovation in genomic AI while maintaining robust safeguards against harm and misuse.

The human ASXL gene family consists of ASXL1, ASXL2, and ASXL3, first described as the additional sex combs (Asx) in Drosophila. The encoded proteins scaffold BAP1-mediated histone H2A deubiquitination. ASXL genes are implicated in pre-cancerous, cancerous, and neurodevelopmental conditions. Truncating mutations predominate and were originally predicted to result in protein loss of function (LOF); however, mounting evidence from population genetics and in vitro studies supports gain-of-function (GOF) mechanisms. Sequence analysis suggests that such mechanisms require both escape from nonsense-mediated mRNA decay and removal of a putative C-terminal degron signal within ASXL proteins. We propose GOF as a generalized mechanism for ASXL mutations, resulting in increased protein stability and altered histone modifications, with implications for diagnosis and therapy for these medical conditions.

Linker histone H1 is a fundamental chromatin component, essential for higher-order chromatin compaction and transcriptional regulation. Chromatin regulator associated with M phase protein 1 (CRAMP1) was recently identified as a highly conserved factor that promotes the transcription of both replication-dependent and replication-independent H1 variants. This review synthesizes evidence that CRAMP1-mediated H1 production is critical for development via epigenetic regulation. We further summarize the multifaceted roles of H1 in maintaining genome integrity by facilitating heterochromatin formation and by serving as a key suppressor of transposable elements from Drosophila to mammals. Finally, we discuss how post-translational modifications on H1 dynamically regulate its function in chromatin dynamics and the DNA damage response. Collectively, this overview positions H1 and its master regulator CRAMP1 as important players in chromatin organization, with emerging roles in development, genome defense, and disease.

Adaptation in rapidly changing environments entails rapid shifts in phenotypic distributions or allele frequencies. This process hinges on the amount of genetic variation. Recent empirical work suggests that balancing selection can potentiate such adaptation, while theoretical studies highlight storage effects as plausible mechanisms underlying balanced polymorphism. Polygenic theory recently proposed that genome-wide shifts in allelic frequency distributions occur in response to sudden environmental changes. However, balanced polymorphism across numerous loci remains largely unexplored in polygenic models, despite being observed in nature. Bridging the gap between population genetics of balancing selection and polygenic models remains a critical challenge and is essential for uncovering mechanisms of adaptation in an increasingly variable world.

Machine learning (ML) is developing into an inherent part of genomic research due to the ever-increasing amounts of genomic data. However, data-driven algorithms are strongly dependent on good quality and representative data, which can be problematic in genomics due to various reasons. One of these reasons is data biases-flawed or incomplete data often containing systematic errors that compromise its representativeness. In this review, we examine different categories of data biases in genomics and translate them into the framework of general ML. We give examples of different types of biases present in widely used databases such as NCBI ClinVar and gnomAD and illustrate how data biases can influence model performance in assorted studies.

Genome-wide association studies (GWASs) have uncovered many SNPs associated with complex diseases, but identifying the causal genetic variants remains very difficult. This review focuses on allele-specific chromatin accessibility (CA) variants (ASCAVs)-which are SNPs that influence CA, binding of transcription factors, and gene expression-by integrating allelic imbalance analysis with assay for transposase-accessible chromatin using sequencing (ATAC-seq) data. We discuss the underlying biological mechanisms, methodologies, and benefits of ASCAV detection by ATAC-seq, emphasizing how integrating ASCAVs with GWAS and multiomics datasets enhances the prioritization of putative causal SNPs for functional studies. By leveraging allelic imbalance in ATAC-seq, researchers can bridge the gap between GWAS signals and molecular mechanisms, advancing our understanding of gene regulation.

Gene expression noise underlies cell-to-cell variability in RNA and protein levels of a seemingly homogeneous population of cells. Emerging evidence suggests a functional role for this variability in the specification of cell fates during mammalian development. Advances in genome-wide and single-cell technologies now enable the quantification and deciphering of transcriptome variability with increasing precision. In this review, we highlight recent insights into the significance of gene expression noise during early embryogenesis, focusing on RNA variability. We discuss new approaches to further quantify and unravel different sources of gene expression noise and how this yields insights into early mammalian development.

Small RNAs are fundamental to gene expression regulation, with specialized classes playing critical roles in reproduction. This review compares animal PIWI-interacting RNAs (piRNAs) and plant reproductive phased small interfering RNAs (phasiRNAs), which show remarkable similarities. Both originate from Pol II-transcribed precursors but have distinct biogenesis pathways. piRNA processing in metazoans is Dicer-independent, involving PIWI-clade proteins for amplification via 'ping-pong' and phased cleavage. Reproductive phasiRNAs are Dicer-dependent and are initiated by miRNA-guided cleavage to generate phased sRNAs. A well-defined piRNA function is transposon silencing, but roles for nontransposon-targeting piRNAs and most reproductive phasiRNAs remain unresolved. Comparing these independently evolved systems reveals common strategies for reproductive success and highlights key unresolved questions regarding their molecular targets, functions, and evolutionary pressures that shaped them.

The genomes of organisms across the tree of life are structurally and functionally organized into chromatin. In eukaryotes, within an organelle called the nucleus, chromatin is shaped by histones and structural maintenance of chromosomes (SMC) complexes, among other factors. The closest prokaryotic relatives of eukaryotes, the Asgard archaea, lack a nucleus, but their genomes encode multiple histones and SMC complexes. Understanding chromatin organization in Asgard archaea is key to understanding how eukaryotic chromatin evolved. However, to date, experimental information on the mechanisms of action of these proteins is largely lacking, particularly in vivo. In this review, we discuss the remarkable diversity of Asgard archaeal histones, the characteristics of SMC complexes, and their potential structural and regulatory roles in Asgard archaea.

Alternative splicing drives molecular diversity, yet livestock spliceopathies remain underrecognised despite their major economic impact. By synthesising evidence across major livestock species, we reveal how splicing defects disrupt production through recurrent patterns: splice variants in dosage-sensitive genes affect growth and fertility, breed-specific splice-regulatory changes drive disease susceptibility, and epigenetic modifications enable environmental adaptation. These patterns reflect evolutionary constraints and domestication pressures driving aberrant splicing in modern breeds. Recent technological advances enable systematic investigation and treatment: long-read sequencing uncovers hidden splicing complexity, while clustered regularly interspaced short palindromic repeats (CRISPR) and antisense oligonucleotides offer precision interventions. However, critical gaps persist in functional validation and population-scale mapping. Addressing these within the One Health framework will advance animal welfare, food security, and comparative medicine, positioning alternative splicing as a fundamental driver of phenotypic diversity.

Over the past decade, the rapid expansion of large-scale data and advances in computational power have allowed machine learning (ML), especially deep learning, to reshape many areas of biological research. Evolutionary genetics and molecular evolution are also poised for a similar transformation. In this review, we discuss key advances and ongoing challenges in applying ML to the study of genetics and evolution, and we highlight the potential of artificial intelligence to connect genotype, phenotype, and evolutionary history.

Investigative genetic genealogy (IGG) has helped to resolve over 1300 cases since its advent in 2018. To continue providing justice and answers in the nearly 700 000 cases that could benefit from IGG in the USA, training, funding, and better public understanding of IGG are necessary.

Carnivorous plants are a paradigm of convergent evolution, yet their genomes reveal even deeper layers of complexity. Recent work has revealed widespread polyploidy in carnivorous plants, including the decaploid East Asian pitcher plant Nepenthes gracilis and hybrid origins for the tetraploid Venus flytrap (Dionaea muscipula), the hexaploid queen sundew (Drosera regia), and the dodecaploid Cape sundew (Drosera capensis). A chromosome-scale genome for the American pitcher plant (Sarracenia purpurea) extends these insights to an independently evolved pitcher lineage. The humped bladderwort (Utricularia gibba) experienced extreme genome compaction while retaining an otherwise typical gene number, challenging assumptions about the evolution of genome size reduction. Molecular convergence is conspicuous among carnivores, from digestive enzyme recruitment to repeated amino acid substitutions under functional constraints. Drosera species further illustrate how centromere type (monocentric vs. holocentric) shapes genome architecture. These discoveries position carnivorous plants as models for studying the plasticity and adaptive landscapes of plant genomes, including tradeoffs between local and global gene duplication and intergenic DNA deletion.

The phenomenon of genome gigantism, defined as an organism's haploid nuclear DNA content (C-value) exceeding 10 gigabases (Gb), has been demonstrated to exhibit a substantial degree of variability in mutational and selective processes across diverse lineages. Recent studies of the genome and epigenome have highlighted the interplay between the proliferation of transposable elements, DNA loss dynamics, and coevolutionary host-silencing mechanisms in shaping and maintaining these massive genomes. In this review, we synthesize emerging insights into how mutation, drift, and selection collectively determine genomic scale and explore the ecological and evolutionary contexts that facilitate or impede the phenomenon of genome gigantism.

The genetic code is nearly universal across life. Yet, The National Center for Biotechnology Information (NCBI) genetic code table recognises 27 distinct variants, most of which are confined to eukaryotic nuclei and organelles. Comparative genomics and synthetic recoding studies reveal that the code is far more flexible than once believed, but why has the standard code remained so remarkably conserved among prokaryotes? Here, we propose that horizontal gene transfer (HGT) acts as a stabilising evolutionary force by enforcing translational compatibility among gene-sharing organisms. In prokaryotes, extensive HGT among prokaryotes creates strong selection for code uniformity, whereas genetic isolation in eukaryotes, driven by sexual reproduction, compartmentalisation, and reduced DNA exchange, has permitted divergence. This dynamic parallels human languages: communities that communicate frequently maintain a shared language, while isolated groups develop distinct ones. Although mobile genetic elements can locally perturb decoding through recoding and translational hijacking, these effects rarely propagate across microbial communities. We argue that the near universality of the genetic code is not a frozen historical accident but an emergent property of dense microbial connectivity shaped by HGT.