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Somatic variant calling, the identification of mutations in non-germline cells acquired over an individual's lifetime, is critical for studying diseases, including cancer, and for developing precision oncology strategies. Traditional somatic variant calling methods rely on linear reference genomes, which do not adequately capture human genetic diversity and result in reference bias, compromising the accuracy of somatic variant detection. Recently developed graph-based human pangenome reference represents diverse genetic variants across human populations and has promised to drive advances in many genetics and genomics studies. In this study, we introduced Pansoma, a novel pangenome-native and machine learning-based tool specifically designed for somatic variant calling using a pangenome graph reference. Pansoma performs somatic variant detection from both short- and long-read sequencing data by learning tensor representations of alignment on graph nodes rather than on a linear reference. Pansoma outputs variant representations anchored to the pangenome graph paths and conventional somatic variant calls remapped to the linear reference. Additionally, we provide accompanying bioinformatics tools tailored for graph-based genomic data management and variant calling results analysis. Benchmarking shows that Pansoma not only improves tumor-only somatic variant detection but also preserves graph-specific variant representations that are not directly recoverable from linear- reference outputs.

Genome evolution in eukaryotes is predominantly driven by the dynamics of repetitive sequences, which vary widely in both copy number and sequence composition. Rates of repeat evolution differ between and within species and are likely modulated by both genetics and environment. To uncover factors shaping the rate of genome content evolution, we analyzed 1,142 resequenced Arabidopsis thaliana genomes using a novel K-mer based approach to characterize genome content variation and identify hypervariable regions underlying differences in repeat abundance. We next treated repeat abundance as a quantitative trait and performed genome-wide association analyses across more than 400 repeat families to identify the genetic basis of copy number variation. Integrating these results through a meta-GWAS approach revealed both cis-acting variants and more than 50 trans-acting loci that regulate repeat abundance genome-wide. Cis-acting variation was predominantly localized to pericentromeric and centromeric regions, whereas trans-acting loci were enriched for candidate genes involved in DNA replication, DNA repair, DNA methylation regulation. Finally, we found evidence that purifying selection acts against mutations that accelerate genome content divergence, favoring alleles that constrain repeat expansion. Together, these findings provide new insights into the genetic architecture and evolutionary forces shaping genome evolution in A. thaliana and establish a framework for investigating these processes in other plant species.

Interferon-γ safeguards humans against intracellular pathogens, yet how most interferon-stimulated genes protect host cells, and how human-adapted pathogens evade these defenses is unclear 1,2 . Here, we discover a potent immune surveillance and effector circuit executed by an intracellularly acting cytokine, IL32, that targets and restricts phylogenetically distinct vacuolar pathogens, including the bacterium Chlamydia and the microsporidian Encephalitozoon . Quantitative proteomics coupled to a tailored CRISPR screen, uncovered components of the cysteine/Arg N-degron pathway 3 that modify IL32 through oxidation-dependent arginylation, thereby enabling the recruitment of the autophagy machinery to pathogen-containing vacuoles. A forward genetics screen in Chlamydia trachomatis , the leading cause of sexually transmitted bacterial infection, identified the secreted virulence factor IncS as an evasion factor that blocks IL32 targeting and shields this human pathogen from xenophagy. These findings establish an IL32-dependent intracellular sensing mechanism linking IFNγ signaling to N-degron-mediated xenophagy, revealing a broadly relevant axis of human host-pathogen conflict.

The Dietary Approaches to Stop Hypertension (DASH) diet reduces blood pressure and cholesterol. However, the mechanisms underlying these effects are unclear, and no randomized studies have evaluated the long-term benefits on health outcomes such as coronary artery disease (CAD) or type 2 diabetes (T2D). We performed a series of Mendelian randomization analyses of 71 serum proteins perturbed by the DASH diet in previous randomized controlled feeding studies to understand their potential mechanistic role on health outcomes. Four proteins (ANGPTL3, INHBC, PCOLCE, PLXNB2) had causal evidence of beneficial effects on risk factors that aligned with diet-induced changes in protein levels. Missense variants in INHBC and PLXNB were associated with lower risks of CAD and T2D respectively, and these effects are directionally concordant with DASH diet-induced changes in protein levels, providing causal evidence that these proteins influence disease risk. Combining molecular phenotyping in randomized interventional studies with human genetics evidence identified molecular regulators of the long-term cardioprotective effects of the DASH diet.

Aortic stenosis (AS) is a heterogeneous disease of aging characterized by valvular calcification and distinct structural, electrical, and hemodynamic remodeling that are incompletely captured by any single diagnostic measure. Here we show that three AI-derived digital biomarkers resolve AS-related remodeling into complementary structural (cine-CMR Digital AS Severity Index, DASSi), electrical (AI-ECG), and hemodynamic (phase-contrast CMR peak aortic velocity) axes. Among 68,714 UK Biobank participants, all three biomarkers were independently associated with prevalent AS and prospectively predicted aortic valve replacement. Genetic and transcriptomic analyses of the digital phenotypes revealed partially distinct, heritable architectures: peak aortic velocity aligned closely with clinical AS genetics, whereas DASSi and AI-ECG defined a shared myocardial-remodeling axis largely independent of clinical AS susceptibility. These findings support AS as a multidimensional remodeling syndrome and establish a novel digital phenotyping framework for dissecting complex cardiovascular disease into complementary, biologically informative axes.

Cranial neural crest (CNC) cells are essential developmental contributors to the remarkable diversity of vertebrate skull shapes, yet how underlying gene regulatory networks (GRNs) evolve to produce highly derived morphologies remains a challenging question. Syngnathid fishes (seahorses, pipefishes, pipehorses, and seadragons) are an opportune family of species in which to address this problem because of their unusual and extensive cranial diversity and their loss of craniofacial patterning genes, fgf3 and fgf4 . Here we investigated whether syngnathid craniofacial evolution experienced only a few localized network changes or required global rewiring of CNC GRNs. Using comparative single-cell RNA sequencing, ATAC-seq and whole genome alignments across Gulf pipefish, threespine stickleback, and zebrafish, we found that the core pharyngeal arch CNC gene network is notably conserved in syngnathids despite their derived morphology. However, we identified key local changes including expression of fgf22 in percomorph CNC-derived pharyngeal arch cells that is not shared with more basally diverging zebrafish, as well as syngnathid-specific changes in conserved regulatory elements associated with the genes ece1 and spry2 . We propose that, while loss of fgf3 expression causes severe craniofacial defects in zebrafish, pharyngeal CNC expression of fgf22 in the percomorph fish lineage provided functional redundancy and relaxed constraint on fgf3/4 , and that altered regulation of Fgf pathway modulators could contribute to craniofacial elaboration. Our findings support a model in which local GRN modifications, rather than widespread network rewiring, underlie the evolution of derived syngnathid craniofacial structures. Understanding how developmental genetic changes drive the evolution of unique traits remains a long-standing challenge in biology. In the case of syngnathid fishes (pipefishes, seahorses, and seadragons), previous genomic studies identified candidate craniofacial gene losses which are proposed to relate to their elongate and derived heads, but the developmental impact of these losses is unknown. Through gene expression and comparative genomics analyses, we find that these fishes have distinct changes to craniofacial gene regulatory networks including gene content losses and gene expression gains and losses. Our study suggests that morphological adaptations may arise from multiple key changes within largely conserved developmental regulatory networks.

Our modern environment - with its artificial lighting, irregular work hours, and frequent travel - often disrupts our circadian rhythms, which can lead to health problems, particularly in learning and memory. This is especially concerning given the aging population and the rising prevalence of dementia. Yet, the biological mechanisms linking circadian disruption to cognitive impairment remain poorly understood. At the molecular level, genetic techniques have been used to attenuate or abolish expression of key genes involved in circadian rhythms and these manipulations have detrimental effects on memory function. However, whether environmentally induced circadian disruption, impairs memory via changes in overall gene expression levels in the hippocampus or rather via changes in the coordinated rhythmic patterns of circadian expression across groups of genes is less known. Here, we examined how environmental circadian disruption affects the expression of genes involved in the circadian clock and memory in the hippocampus of rats using a forced desynchrony model. Circadian disruption changed the rhythmic properties of gene expression in most genes assessed but had no measurable effect on average expression levels across the day. These findings suggest that the inability to maintain circadian synchrony rather than overall expression may underlie the cognitive deficits observed in circadian-related disorders.

Epistasis, the non-additive effects of mutations, shapes fitness landscapes and evolutionary trajectories. Temporal genetic data reveal evolutionary dynamics and could be used to infer epistatic interactions, especially through linkage disequilibrium (LD) between interacting mutations. However, other evolutionary forces can also generate LD, challenging inference. Here, we systematically evaluated the accuracy of a variety of epistasis inference approaches across a range of selective pressures, recombination rates, and population sizes. In general, we found that inference accuracy depends on the evolutionary regime: methods based on marginal path likelihood (MPL) performed best under strong selection and low recombination, whereas quasi-linkage equilibrium (QLE) approaches were more accurate when recombination is frequent. We further showed that the strength of genetic drift can influence inference accuracy for approaches that learn from changes in allele frequencies over time. Collectively, our results show that the detectability of epistasis from temporal genetic data depends on the interplay between selection, recombination, and genetic drift, providing guidance for method selection across evolutionary contexts.

Internal tandem duplication mutations in FLT3 ( FLT3 ITD ) occur in approximately 30% of patients with acute myeloid leukemia (AML) and are among the most common genetic alterations in this disease. FLT3 ITD is a major driver of AML and is associated with poor clinical outcomes. Although FLT3 inhibitors (FLT3is) have significantly improved outcomes for patients with FLT3 ITD + AML, acquired resistance remains a major barrier to durable clinical benefit. Reactivation of RAS/MAPK signaling, often driven by activating NRAS mutations, is a major mechanism of FLT3i resistance in AML; however, effective strategies to overcome this resistance remain lacking. Here, we identify ribonucleotide reductase (RNR) as a critical therapeutic vulnerability in NRAS -driven FLT3i-resistant FLT3 ITD + AML. Activation of RAS signaling through SPRY3 loss or oncogenic NRAS mutations confers robust resistance to FLT3is, whereas pharmacologic inhibition of RNR with multiple inhibitors, as well as siRNA-mediated RNR suppression, reverses FLT3i resistance and restores FLT3i sensitivity across multiple FLT3 ITD + AML models in vitro . In vivo , clofarabine, an FDA-approved RNR inhibitor (RNRi), significantly overcomes NRAS mutation-driven FLT3i resistance. In combination with FLT3 inhibition, clofarabine markedly suppresses the progression of FLT3i-resistant AML and significantly prolongs survival in cell line-derived xenograft (CDX) models. Importantly, the therapeutic efficacy of the gilteritinib/clofarabine combination was independently validated in two genetically distinct patient-derived xenograft (PDX) models harboring different NRAS mutations, demonstrating robust reduction of leukemia burden and confirming the generalizability of RNR inhibition in primary FLT3i-resistant AML. Together, these findings identify a previously unrecognized therapeutic vulnerability in FLT3i-resistant FLT3 mut + AML and establish RNR inhibition as an effective strategy to overcome FLT3i resistance, providing a strong rationale for the clinical evaluation of RNRis in combination with FLT3is in patients with resistant AML. Although FLT3 inhibitors (FLT3i) are an important therapeutic advance in FLT3 ITD + AML, resistance commonly develops. We identified ribonucleotide reductase (RNR) as a new key vulnerability in NRAS -driven FLT3i-resistant AML and demonstrated that multiple RNRis, including the FDA-approved agent clofarabine, restore FLT3i sensitivity and enhance antileukemic activity, supporting a clinically actionable combination strategy.

Many urgent medical and agricultural challenges are driven by resistance evolution via soft selective sweeps of multiple simultaneous mutations. Standard approaches to detect these mutations involve genome scans for regions with reduced diversity and increased haplotype lengths. However, it is unknown the extent to which those signatures persist as the number of mutations driving resistance grows. Here, we analyzed longitudinal linkage-resolved data from 10 intra-host HIV populations treated with broadly neutralizing antibody 10-1074. We found that HIV escapes 10-1074 with minimal perturbations to diversity and haplotype homozygosity in the region surrounding the sweep in the majority (8/10) of treated individuals. We matched these in vivo escape trajectories to forward simulations and found that adaptive mutations conferring escape must have been present on 20 or more genetic backgrounds to generate these signatures. These "ultra-soft" sweep signatures more closely resemble genetic patterns in a treatment non-responder without an adaptive response to 10-1074 than those of two other trial participants where adaptation occurred via harder selective sweeps. Our results demonstrate that HIV can adapt to a broadly neutralizing antibody treatment while retaining nearly all of its standing genetic diversity and that selection scans dependent on regional diversity and haplotype homozygosity signatures fail in this "ultra-soft" regime.

Linking genetic variation to functional phenotype remains a major barrier to assessing the cross-host potential of emerging viruses. Here, we reconstruct the evolution of predicted hemagglutinin (HA) phenotypic traits across ~13,000 highly pathogenic avian influenza A H5N1 clade 2.3.4.4b viruses circulating in North America. The wide geographic spread of avian influenza within North America triggered a wave of broad HA phenotypic diversity that was later refined by the selective sweeps in avian hosts. Following establishment in dairy cattle, however, viral populations exhibited renewed phenotypic diversification in HA, including increased permissiveness for α2,6-linked sialoside engagement despite high conservation of the receptor-binding domain. These patterns indicate that cattle-associated HA phenotypes can draw from standing predicted HA phenotypic breadth within circulating viral populations while continuing to diversify during cattle-associated circulation, rather than following only a simple stepwise adaptive path through canonical receptor-binding substitutions. By linking viral sequence variation to predicted protein properties across naturally evolving populations, this framework provides a scalable strategy for prioritizing H5N1 variants with cross-host-relevant features for targeted surveillance and experimental follow-up.

Metformin remains the primary treatment for type 2 diabetes, yet over 40% of patients fail to maintain glycaemic control. We aimed to identify patients unlikely to respond to metformin prior to treatment initiation and to evaluate whether on-treatment management can improve glycaemic outcomes in suboptimal responders, informing early treatment decisions. We analyzed 59,881 longitudinal HbA1c measurements from 7,105 patients with type 2 diabetes receiving metformin monotherapy using real-world electronic health records from Kaiser Permanente Northern California with up to six years of follow-up. We integrated demographic, clinical, genetic, and pharmacological factors to characterize metformin responder phenotypes and quantify the impact of adherence and weight control on time to glycaemic failure. Three distinct trajectory-based phenotypes were identified: good (63.6%), poor (8.9%), and non-responders (27.5%). Poor responders initially achieved glycaemic targets but lost control within 2.5 years, while non-responders showed minimal HbA1c reduction and failed within 1 year. Five baseline factors-HbA1c, age at diagnosis, body mass index, sex, and estimated glomerular filtration rate-classified phenotypes with good discrimination (area under the receiver operating characteristic curve = 0.84). Incorporating on-treatment HbA1c further enhanced identification of non-responders. Among suboptimal responders, weight control and improved adherence delayed glycaemic failure by approximately 7 months; however, eventual glycaemic failure remained likely. We characterized three clinically relevant metformin responder phenotypes and showed that suboptimal responders can be identified early using baseline features. Poor and non-responders are unlikely to achieve durable glycaemic control with metformin alone and may require alternative treatment strategies.

Cranial motor neurons ( cMNs) , form discrete nuclei that control diverse behaviors such as eye movement, feeding, facial expression, and regulation of visceral organ function. However, the developmental programs that drive cMN target choices and functional specialization have not been comprehensively studied in any vertebrate. Here, we present an integrated single-cell RNA-sequencing atlas of zebrafish cMN development and perform extensive validation by HCR in situ hybridization. We find that each cranial motor nucleus expresses a distinct transcriptional signature, and in many cases, we identify transcriptional correlates to functional subtypes within individual nuclei. We find that identity often precedes axon targeting, indicating that cranial motor neuron fate is genetically specified early in development. These distinct identities are shaped by the intersection of shared function, rhombomere origin, and developmental time. A cross-species comparison between zebrafish and mouse reveals that these genetic programs are conserved.

Neurodevelopmental disorders are genetically heterogeneous and often remain unresolved despite extensive clinical evaluation and genomic testing. Here, we report a proband with a progressive neurodevelopmental disorder evaluated through the Undiagnosed Diseases Network who harbored heterozygous de novo missense variants in two genes, DCLK1 (p.(S228L)) and SFPQ (p.(P623R)). To determine the clinical significance of these candidate variants, we employed an integrative pipeline combining structural modeling, cross-species functional genomics, and patient-derived neuronal analyses. While the SFPQ variant yielded no detectable phenotype in Drosophila melanogaster, modeling the DCLK1 p.S228L variant in Caenorhabditis elegans induced severe locomotor deficits and aberrant neuronal morphology, including neurite blebbing. Parallel analyses of directly reprogrammed patient-derived neurons recapitulated these neurite defects, characterized by neurite beading, swelling and fragmentation, and elevated apoptosis. Transcriptomic profiling revealed dysregulation of neurodevelopmental and axon-guidance pathways alongside molecular signatures of neurodegeneration. Crucially, exogenous expression of wild-type DCLK1 or pharmacological targeting of a downstream dysregulated pathway partially rescued the neurite defects. Collectively, our findings implicate DCLK1 in a previously unrecognized progressive neurodevelopmental disorder and demonstrate the power of integrative cross-species functional genomics in resolving ultra-rare disease variants.

Highly pathogenic avian influenza (HPAI) H5N1 clade 2.3.4.4b viruses are currently responsible for a multi-species outbreak affecting wild birds, poultry, numerous mammalian species, and humans. Influenza A viruses typically initiate infection through binding to sialic acid, although select bat and human influenza viruses can also exploit class II major histocompatibility complex (MHC-II) molecules for cell entry. Here we show that emerging H5N1 clade 2.3.4.4b viruses, but not historical H5 lineages, bind human MHC-II HLA-DR and mediate sialic acid-independent cell entry. Hemagglutinin binding to primary human immune cells varies with MHC-II expression and is further shaped by HLA-DR allelic variation, identifying host genetic determinants that may influence susceptibility to infection. Mammalian-adaptive substitutions within the hemagglutinin sialic acid receptor-binding domain reduce MHC-II binding, suggesting this interaction is remodeled during clade 2.3.4.4b H5 adaptation to a human host. Lastly, cross-reactive monoclonal antibodies isolated from clade 2.3.4.4b H5-naive humans can block the hemagglutinin-MHC-II interaction. These findings identify a previously unrecognized receptor pathway in contemporary H5N1 viruses and reveal that both human genetic variation and pre-existing humoral immunity can modulate this interaction, with implications for host range, cellular tropism, spillover risk, and therapeutic intervention.

The outbreak of clade 2.3.4.4b H5N1 viruses among U.S. dairy cattle has raised concerns that sustained circulation among agricultural mammals could facilitate viral adaptation toward efficient human transmission. However, the evolutionary dynamics governing such adaptation remain poorly understood. Here we investigated the evolution of two bovine-derived H5N1 B3.13 genotype viruses during infection and airborne transmission in ferrets, building on prior characterization of their robust replication and inefficient airborne transmission. Within hosts, viral genetic diversity was limited and viruses were subject to genetic drift and weak purifying selection. Transmission, when it occurred, was characterized by stringent bottlenecks that sharply reduced viral genetic diversity. We found no evidence of mammalian adaptation during infection or transmission. Together, these findings indicate that bovine-derived H5N1 viruses face evolutionary constraints during acute mammalian infection and transmission, limiting movement toward enhanced airborne spread. These constraints may help explain why efficient mammalian replication does not necessarily coincide with efficient transmission. Continued circulation of HA clade 2.3.4.4b viruses nevertheless creates repeated opportunities for rare but consequential evolutionary events, underscoring the importance of sustained surveillance and risk mitigation.

Language is a defining trait of our species, and disruptions in language acquisition can have profound consequences to the individuals affected. Uncovering the neurodevelopmental basis of this complex trait requires detailed molecular and cellular insights into the neocortical areas that support linguistic abilities. Here we performed joint gene expression and chromatin accessibility profiling at single-nucleus resolution (10x Genomics Single cell Multiome) and spatial transcriptomic profiling (Xenium high-plex in situ spatial transcriptomics) of Broca's area alongside adjacent motor cortical areas. We profiled individuals from different ancestries (European and African) and developmental stages (infancy, childhood, adolescence, and adulthood). We provide a high-resolution dissection of the cellular and molecular architecture of Broca's and motor cortical areas across early life stages and anchor the trajectories to the cellular states found in the adult human brain. We identify distinct area- and stage-specific cellular signatures, including a prominent role of glia populations and interneuron subtypes contributing to cytoarchitectonic specializations. Using longitudinal single cell spatial transcriptomic profiling, we orthogonally validate our consensus cell taxonomy and spatially resolve layer enrichment of neuronal and astrocyte subtypes that distinguish Broca's area and motor cortex. We also uncover cell type-specific molecular signatures that distinguish cell developmental trajectories in these cortical areas, including an early molecular code established by differential expression of cadherin genes that might contribute to area-specific intercellular communication. We also identify cell type-specific vulnerabilities to language- related neurodevelopmental and neuropsychiatric disorders, with selective susceptibility of particular somatostatin-positive interneuron subtypes to ASD/ADHD. Finally, evolutionary analysis of differentially accessible regions between Broca's area and motor cortex suggests that genetic mutations that might have contributed to the emergence of linguistic abilities accumulated over the course of million years following the divergence of human and chimpanzee lineages. Together, our study provides a comprehensive molecular, cellular and spatial definition of Broca's area and motor cortex, laying the groundwork for investigations into unique aspects of human cognition and related neurodevelopmental and neuropsychiatric disorders.

Evaluating machine learning in scientific domains requires separating correct predictions from correct reasons under realistic distribution shifts. We introduce PertReason, a knowledge-grounded benchmark and framework suite for cell-state--conditioned reasoning about perturbation effects. At its core, PertReasonQA is a benchmark that tests whether models can generate mechanistically faithful explanations while remaining robust to complex shifts, such as new cells and unseen perturbations. PertReasonQA combines single-cell genetic and chemical perturbation data across multiple cellular contexts with knowledge graphs, and dynamically conditions pathways on cell-specific basal states to avoid generic memorization. Evaluations on state-of-the-art models reveal systematic gaps between predictive accuracy and mechanistic reasoning. Specifically, these models exhibit failure modes largely invisible to standard benchmarks, such as deriving correct answers through flawed logic, ignoring cellular context, and generating directionally inconsistent mechanisms. As a reference probe of the benchmark, we present PertReasonLM, a large language model trained to align outcome predictions with context-specific mechanistic reasoning. Our model targets the identified failure modes by grounding rationales in context-specific pathways and tightening agreement between outcomes and mechanisms. Together, we provide a diagnostic framework for exposing and mitigating failures in faithful reasoning in data-rich scientific systems.