Wellcome Sanger Institute

Moyamoya disease (MMD) has a strong genetic basis, with the rare RNF213 p.Arg4810Lys variant (rs112735431) representing a major risk factor, while the broader genetic architecture and disease-relevant vascular cell types remain incompletely understood. We conducted a genome-wide association study in Japanese individuals (n=47 656; 401 MMD cases and 47 255 controls). Population-level features at MMD risk loci were examined by regional allele frequency and haplotype analyses. We performed single-nucleus RNA-seq of superficial temporal arteries from patients with MMD (n=3). Cell type-specific enrichment of genome-wide association study signals was assessed using the Single-Cell Disease Relevance Score. Endothelial signatures were validated by integration with publicly available single-cell data sets from controls (n=5) and immunohistochemistry for candidate markers (n=1). Beyond rs112735431, we identified a genome-wide significant signal in the HDAC9-TWIST1 region (rs12530920; P=3.3×10-14; odds ratio, 1.77). Conditional analysis on rs112735431 revealed a protective RNF213 missense variant, p.Asp1331Gly (rs8074015; P=3.7×10-9; odds ratio, 0.53), whose minor allele was mutually exclusive with rs112735431-A on haplotypes. Population analysis revealed geographic variation and extended haplotype structure of the rs112735431-A allele in Japan. Single-nucleus RNA-seq identified a mesenchymal-like endothelial cell (MEC) population with selective FN1 expression. Genome-wide association study-prioritized disease genes were strongly enriched in MECs. MECs showed mesenchymal pathway activation with a regulatory program distinct from canonical endothelial states. The proportion of MECs was markedly increased in MMD (72% versus 28% in controls), and fibronectin 1 (FN1) expression in endothelial regions was confirmed by immunohistochemistry. Our findings identify a protective RNF213 p.Asp1331Gly variant (rs8074015) that is mutually exclusive with the known rs112735431-A allele. Genetic risk converges on an MEC state markedly expanded in MMD.

Mutant phenotypes often vary across genetically distinct individuals. To identify the causes of such genetic background effects, we studied differences in gene essentiality across 18 genetically diverse natural yeast strains. We identified 39 genes that were essential in the laboratory reference strain but not in at least one other genetic background, and we mapped and validated the genetic variants that were responsible for the differences in gene essentiality. These variants typically occurred in single modifier genes that tended to differ between genetic backgrounds. The affected genes often indirectly compensated for the loss of the essential gene and identified naturally occurring evolutionary trajectories. Overall, our results highlight the prevalence of changes in gene essentiality in natural populations, as well as the underlying mechanisms. A thorough understanding of the causes of genetic background effects is crucial for the interpretation of genotype-to-phenotype relationships, including those associated with human disease.

Near-extinction events impose severe genomic bottlenecks that can have lasting fitness consequences, yet the specific mechanisms involved remain poorly understood. 'Alalā, the endemic Hawaiian crow, narrowly avoided extinction when a conservation breeding program was founded from just nine individuals. Although the breeding program has since recovered to ~120 birds, it remains plagued by egg hatching failure rates >50%. To investigate the impacts of this bottleneck on hatching failure and other fitness components, we generated a chromosome-level reference genome and resequenced 175 individuals, including 78 deceased embryos. Although long runs of homozygosity (ROH) >1Mb are abundant in 'Alalā (mean FROH=0.32), associations between FROH and measures of survival and reproduction, including egg failure, were weak or nonexistent. Instead, we identify two recessive lethal haplotypes that together account for ~20% of all hatching failures and have persisted in the population at high frequency (15-25%), hinting at impaired purifying selection. Eco-evolutionary simulation models demonstrate that these limited impacts of ROH and elevated recessive lethal allele frequencies are expected for a species that has endured a severe population bottleneck and exhibits modest levels of non-ROH heterozygosity. Our findings suggest that elevated recessive allele frequencies may be a broadly important consequence of population bottlenecks.

Celcomen leverages a mathematical causality framework to disentangle intra- and inter-cellular gene regulation programs in spatial transcriptomics data through a generative graph neural network. It is a first step towards perturbation models of Virtual Tissues and can generate post-perturbation counterfactual spatial transcriptomics, thereby offering access to experimentally inaccessible samples. We validated its disentanglement, identifiability of causal structure, and counterfactual prediction capabilities through simulations and in clinically relevant human glioblastoma, human fetal spleen, and mouse lung cancer samples. Celcomen provides the means to model disease- and therapy-induced changes allowing for new insights into single-cell spatially resolved tissue responses.

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously known as NAFLD (non-alcoholic fatty liver disease), is a growing global concern, affecting nearly a third of the world’s population. This umbrella term covers a range of liver pathologies, from reversible disease stages like simple steatosis, to irreversible conditions such as cirrhosis and hepatocellular carcinoma (HCC). MASLD, which may result from metabolic risk factors like obesity and type 2 diabetes, is projected to increase due to a rise in sedentary lifestyles. This review addresses genetic influences that predispose to disease development, including the role of risk-conferring and protective/preventive alleles. The epistatic relationships between genetic variants can significantly influence the development and progression of MASLD. Key genetic variants, such as those located in the PNPLA3, TM6SF2, and MBOAT7 genes, often interact to exacerbate MASLD severity and play key roles in lipid metabolism and liver inflammation. For example, the co-expression of certain PNPLA3 and TM6SF2 variants increases the risk of advanced fibrosis and HCC. Some variants located in HSD17B13 and MTARC1 offer protective effects, reducing the risk of severe liver disease despite comorbidities such as obesity, and can mitigate the harmful effects of these risk alleles. Additionally, the potential of polygenic risk scores (PRS) to predict MASLD development and its complications is also discussed, although challenges remain, particularly in underrepresented populations due to the lack of comprehensive catalogues of genetic variation. Understanding these complex gene-gene interactions and the role of the environment underscores the importance of considering epistatic relationships when assessing MASLD risk and developing personalized therapeutic strategies, which could ease the future burden on healthcare systems. · Metabolic dysfunction-associated steatotic liver disease (MASLD) affects nearly one-third of the global population, with rising incidence due to sedentary lifestyles, obesity, and type 2 diabetes. · It is estimated that nearly 50% of MASLD patients have heritable predisposition genetic variants in genes such as PNPLA3 and TM6SF2, which play significant roles in MASLD development. · Several protective alleles have also been identified, which may mitigate MASLD progression despite genetic variants and environmental risk factors. · Epistatic relationship between predisposing and protective genes can influence Polygenic Risk Score and improve the precision detection and early diagnosis of liver metabolic diseases. · Risk-modifying variants have different allelic frequencies according to the population considered.

<ns7:p>The well-being of farmed and companion animals is increasingly recognised as integral to sustainable agroecosystems, companionship, and the One Health approach, which emphasises the interconnected health of people, animals, and the environment. The ELIXIR Domestic Animals Genome and Phenome (DAGP) Community supports genome-to-phenome analyses for farmed and companion animal species. Its aim is to coordinate, discuss, and explore the potential of data technology solutions to address key issues in animal welfare, behaviour, health, infectious diseases, metabolism, nutritional efficiency, and the preservation of genetic diversity and the environment. Through consolidating efforts to develop data standards, coordination, workflows, and visualisation, it will enhance the science underpinning rapidly growing fields in domestic animal genomics, including genome-enabled breeding, population genomics, pangenome analysis, functional genomics, genome editing, paleogenomics, phenotyping, and bio-banking. These standards will adhere to the FAIR data principles and leverage established ontologies to promote best practices in data coordination and archiving. This white paper, prepared by the ELIXIR DAGP Focus Group, summarises the current data infrastructure, resources, and tools available for domestic animal genomics and phenomics, and presents community-led plans and priorities to be implemented to meet the requirements of ELIXIR services and the animal science community. We describe how ELIXIR services can be applied in the domestic animal genomics and phenomics fields, and how we can connect projects and infrastructures that are active in the animal sciences domain. We also discuss three key priority areas: i) expanding the FAANG Data Portal for phenotype data with ELIXIR Data Platforms; ii) supporting submissions of new data types across ELIXIR Core Data Resources , including proprietary data from industry partners; and iii) strengthening connections to existing ELIXIR Communities and international consortia. This article provides a set of priorities for a Domestic Animals Genome and Phenome Community in ELIXIR and outlines the next steps to engage across stakeholders and to consolidate data for domestic animal science in Europe.</ns7:p>

Bifidobacterium longum and B. infantis are pioneer colonizers of the neonatal gut and are widely used as probiotics to support infant growth, development, and disease resistance. However, commercial strains derived largely from high-income countries (HICs) may be suboptimal for infants in low- and middle-income countries (LMICs). We assembled a global genomic atlas of more than 4,000 genomes from 48 countries, increasing representation from LMICs by 12- to 17-fold. High-resolution phylogenomic and functional analyses support delineating B. longum and B. infantis as distinct species with divergent functions and epidemiological patterns. B. infantis dominates early-life microbiota in LMICs but is rarely detected in HICs. Natural B. infantis strains show extreme biogeographic stratification and predicted adaptations to local plant-glycan-rich diets and breast-milk-derived substrates, including urea and B vitamins. This genomic resource enables genome-guided selection of geographically matched strains to inform more effective probiotics and precision microbiome therapeutics for diverse infant populations.

Sepsis is a heterogeneous clinical syndrome with a high mortality, requiring personalised stratification strategies. Here, we characterise genetic variation that modulates MTOR, a critical regulator of metabolism and immune responses in sepsis. The effects are context specific, involving a regulatory element that affects MTOR expression in activated T cells with opposite effect in neutrophils. We show that the G-allele of the lead variant, rs4845987, which is associated with decreased risk of type 2 diabetes, reduces MTOR expression in T cells and improves survival in sepsis due to pneumonia, with effects specific to sepsis endotype. Using ex vivo models, we demonstrate that activated T cells promote immunosuppressive neutrophils through released cytokines, a process dampened by hypoxia and the mTOR inhibitor rapamycin. Our work demonstrates an epigenetic mechanism fine-tuning MTOR transcription and T cell activity via the variant-containing regulatory element, which further exhibits an allelic effect upon vitamin C treatment. These findings reveal how genetic variation interacts with disease state to modulate immune cell-cell communication, providing a framework for stratified therapy in sepsis.

Abstract Chemical probes are indispensable tools for validating therapeutic hypotheses, yet their broader impact on early-stage drug discovery remains unquantified. To our knowledge, this study represents the first systematic, large-scale investigation of the chemical probe literature. By screening over 18 million articles using a high-quality dictionary of 561 chemical probes, we identified 20,000 articles mentioning a chemical probe which resulted in 5,558 unique target-disease (T-D) associations. Our analysis yields four principal findings that redefine the utility of these chemicals: First, we show that chemical probe evidence typically precedes the appearance of structured data in major knowledge bases by 1–7 years, providing a crucial lead time for target prioritisation. Second, we identified 353 T-D pairs (6.4%) with no prior evidence in the Open Targets Platform, highlighting the approach’s discovery potential. Third, the application of strict novelty filters uncovered 135 new high-confidence associations between targets and diseases, revealing distinct opportunities for therapeutic repurposing in non-oncological, rare autoimmune diseases, and diseases without effective therapies due to complex biology or high treatment resistance. Finally, we demonstrate that chemical probes are essential for strengthening evidence, providing functional validation for associations previously supported only by weaker, correlative data such as RNA expression or animal models. Collectively, these findings illustrate that chemical probes catalyse early therapeutic discovery, emphasising the importance of cataloguing existing probes and identifying new ones.

Klebsiella pneumoniae is a major cause of human infections and is frequently associated with antimicrobial resistance (AMR). Carriage of K. pneumoniae in the gut is a major risk factor for infection and a reservoir for the spread of high-risk clonal lineages and associated AMR determinants. Accurate detection of K. pneumoniae at the subspecies level is therefore essential to better understand K. pneumoniae gut colonization ecology and clonal dissemination. We analysed two recently developed bioinformatic tools, mSWEEP and StrainGE, for sequence type (ST) detection of K. pneumoniae in culture-enriched sweep metagenomes compared to single-colony whole-genome sequencing (WGS). We show that both mSWEEP and StrainGE perform highly accurate ST detection, concordant with culture in 46/49 and 44/49 samples with WGS-detected single STs, respectively, as well as in 2/3 samples with two WGS-detected STs. Within-sample ST diversity was detected in 19 and 15 samples by mSWEEP and StrainGE, respectively, highlighting a major advantage of these tools over conventional single-colony WGS. StrainGE could also reconstruct accurate phylogenetic relationships between strains of the same ST for 2/3 different STs tested. Additionally, assembly of the genomes provides better resolution of ST detection by mSWEEP. Together, our results show that both mSWEEP and StrainGE are accurate tools for the detection and analysis of K. pneumoniae STs from mixed bacterial samples.