Pregnancy is a distinct physiological context in which the mother hosts and nurtures the development of a genetically distinct fetus. Healthy pregnancy outcomes rely on successful placental development, during which specialized placental cells with fetal genotype, known as trophoblasts, drive the establishment of the maternal-fetal interface, a balanced tissue ecosystem that supports fetal development while safeguarding maternal well-being. Advances in single-cell, single-nuclei and spatial genomics technologies, along with data science, are now providing an unprecedented view of trophoblast regulation, which drives their differentiation into diverse cell subtypes that dynamically transform the maternal niche. More specifically, these cells modulate the immune system and remodel the vasculature to facilitate communication with maternal cells, leading to the successful formation of the maternal-fetal interface. These advances, when combined with in vitro modelling, hold promise for inferring causality in mechanisms that govern genome regulation, cellular behaviour and ultimately pregnancy homeostasis and maladaptive responses.

Epithelial-mesenchymal transition (EMT) is a highly dynamic and plastic cellular programme essential for embryonic development, tissue repair and ageing, and it is co-opted in pathological conditions such as cancer and fibrosis. The continuum of intermediate states and pronounced cellular heterogeneity that characterize EMT have historically limited systematic investigation across regulatory layers, including the transcriptome, epigenome, proteome and metabolome. Recent advances in high-throughput omics technologies, particularly single-cell and spatially resolved platforms, have enabled comprehensive profiling of cellular states at unprecedented resolution. These approaches, in parallel with computational methodologies such as machine learning frameworks, network-based modelling and integrative multi-omics, have been instrumental in extracting biological insight from intrinsically complex datasets. In this Review, we discuss how the convergence of emerging omics technologies and computational frameworks has advanced EMT research, outline key analytical considerations and discuss current challenges and future opportunities for biomarker discovery and translational applications.

The functioning mitochondrial genome is essential for cellular energy production. Being strictly maternally inherited and possessing limited DNA repair capacity, mitochondrial DNA (mtDNA) replication errors tend to accumulate over time. If left unchecked, these errors can accumulate through the female germline over successive generations, potentially leading to species extinction. However, this outcome is not observed in most species, including humans, which implies the existence of mechanisms that counteract the progressive accumulation of deleterious mtDNA mutations. Recent technological advances are building a deeper understanding of the processes that preserve mtDNA integrity, including the molecular and cellular basis and timing of purifying selection. This new knowledge helps to explain how mtDNA can change rapidly over just a few generations, whilst remaining compatible with the independently inherited, evolving nuclear genome.

An important goal of biology is to understand how genetic variation translates into molecular and then broader phenotypic variation. Quantitative trait locus (QTL) mapping studies are designed to statistically test the relationship between genetic and phenotypic variation, with molecular QTLs (molQTLs) capturing genetic effects on molecular traits, such as gene expression or chromatin accessibility, as the variable phenotypes of interest. Technological advances have provided molQTL mapping methods with increased molecular phenotypes to test, as well as larger cohorts, the latter of which provides greater statistical power to associate genetic variants with these phenotypes. With these advances, statistical models - and, increasingly, machine learning methods - are being developed to improve standard molQTL mapping approaches, downstream analyses and other aspects of molQTL studies to gain additional insights into the mechanisms connecting genetic and phenotypic variation, especially in a gene regulatory context.

Cytosine DNA methylation is a conserved epigenetic modification that regulates gene expression, represses transposable elements and maintains genome stability across diverse eukaryotes. Although major advances have uncovered the pathways involved in the establishment, maintenance and removal of DNA methylation, the downstream mechanisms by which this mark influences transcriptional programmes and shapes chromatin structure are less well understood. Here, we review how specialized reader proteins and transcription factors interpret DNA methylation to preserve methylation patterns, recruit effector complexes, regulate chromatin accessibility and interact with parallel epigenetic systems to mediate transcriptional silencing and activation across mammals and plants. We highlight that robust transcriptional and epigenetic states emerge from overlapping, layered and partially redundant DNA methylation-dependent mechanisms. Together, these insights provide a framework for understanding how DNA methylation shapes the epigenome to regulate development, differentiation and disease progression.

Our understanding of evolutionary genetics is based largely on chromosomes, but many organisms harbour extrachromosomal forms of DNA that exist in multiple copies per cell and segregate unequally on division. These alternative carriers of genetic information possess unique evolutionary capacities. Recent research on high-copy bacterial plasmids has established important principles about their role in gene amplification, increased mutational supply and the balance between selection and segregational drift. In human cancer, many tumours contain molecules known as extrachromosomal DNA (ecDNA) that exhibit strikingly similar dynamics. From an evolutionary perspective, cancer resembles a unicellular organism under strong selective pressure; because asymmetric segregation of ecDNA produces population-level heterogeneity, ecDNA can fulfil a similar evolutionary role to that of plasmids in bacteria. The analogy between plasmids and ecDNA has been noted previously but not explored in depth. Here, we compare their formation, expression, segregation and evolution, elucidate common principles that apply across both, and identify open questions. We propose that both plasmids and ecDNA function as 'contingency loci' that permit rapid adaptation. Despite important differences, we argue that these extrachromosomal genetic elements can be viewed as an example of convergent evolution.