Genetic determinants that regulate molecular phenotypes and complex traits often act in a highly context-dependent manner, and the underlying cell-type-specific regulatory mechanisms remain incompletely understood. In this study, we analyzed 42 single-cell and single-nucleus RNA sequencing (sc/snRNA-seq) datasets from pig skeletal muscle. Through systematic benchmarking, we optimized a robust workflow for SNP calling and genotype imputation tailored to sc/snRNA-seq data, achieving high accuracy and computational efficiency. We constructed a comprehensive single-cell atlas of skeletal muscle that delineates cellular components and developmental trajectories, and identified 5,020 significant single-cell expression quantitative trait loci (eQTLs). By integrating phenotypic data from the PigGTEx project and performing phenome-wide association studies (PheWAS), we further pinpointed three candidate loci significantly associated with meat quality and growth traits whose effects were dependent on cell type. This work offers a scalable computational framework for single-cell eQTL mapping and characterizes cell-type-specific associations between genetic variation and gene expression relevant to economically important traits in livestock, thereby providing functional context in particular for non-coding variants implicated by GWAS and helping to inform genomic selection and precision genome editing.
Polycystic ovary syndrome (PCOS) is a highly prevalent and heterogeneous endocrine disorder affecting women of reproductive age, with substantial reproductive, metabolic, and long-term health consequences. While genome-wide association studies (GWAS) have identified multiple PCOS-associated loci across diverse populations, the functional interpretation of these predominantly non-coding variants and their translation into clinically actionable targets remain unresolved. Here, we present an integrative population-aware framework that systematically combines regulatory functional genomics, long-range chromatin interactions, genome-wide quantitative trait loci, and protein-protein interaction networks to prioritize effector genes underlying PCOS susceptibility. Applying this framework to East Asian and European populations, we demonstrate robust performance relative to existing approaches and uncover both shared and population-specific functions. Notably, our analyses reveal a predominant enrichment of metabolic dysregulation-associated pathways in East Asian PCOS, whereas European PCOS exhibits a stronger inflammatory and immune-related signature. These population-specific molecular phenotypes were further supported by transcriptomic data from PCOS patient samples. Importantly, integration of genetic evidence with a network-based approach enabled the identification of druggable targets lacking direct genetic cues. Collectively, our study provides mechanistic insight into the ethnic heterogeneity of PCOS and establishes a scalable strategy for genetically informed, population-specific therapeutic prioritization, advancing precision medicine approaches for women's health.
Systematic analysis of copy number variants (CNVs) in large datasets is challenging, and there are limited studies of homozygous copy number losses in rare disease exomes. Here, we leveraged the genomic uniqueness and relative under-representation of the Indian population in the current public genomic databases and identified 42,386 possible homozygous losses (median 20 per individual) in a heterogeneous cohort of 2021 individuals with suspected Mendelian disorders, who had undergone exome sequencing using 12 different capture kits in a resource-limited setting. Employing a genomic position loss-count-based approach, we filtered 1224 rare homozygous loss calls in 718 individuals (median 1 per individual) for further analysis, thus significantly reducing the analysis burden. Clinical correlation and validation of these rare calls enabled 10 new diagnoses in 240 unsolved individuals. This led to a two-fold increase in diagnosis owing to homozygous deletions. Further analysis of the data and identification of additional affected individuals through collaboration led to identification of biallelic FILIP1 and FAM177A1 variants as causes of a syndromic arthrogryposis and a neuromuscular disorder respectively. Both conditions were recently reported as ultra-rare recessive disorders, thus validating our approach. We also show that biallelic loss-of-function TFCP2L1 variants cause chronic kidney disease and VPS36 variants cause a severe recessive neurodevelopmental disorder characterised by microcephaly, motor delay, agenesis of the corpus callosum, cerebellar atrophy, seizures, hypotonia, spasticity and early death. Overall, these results demonstrate a scalable approach to screen homozygous losses for improving diagnostic yield and discovering disease-genes in large exome cohorts.
CircRNAs have emerged as critical regulators in various types of cancer. Neural invasion (NI) refers to a process whereby cancer cells infiltrate into the surrounding nerves and has been shown to predict poor prognosis in gastric cancer (GC). Accumulating evidence has suggested that tumor NI is a symbiotic relationship between cancer and nerves, which leads to the growth advantage for both. However, the involvement of circRNAs in the nerve-cancer cell crosstalk remains to be elucidated. In this study, downregulation of circSLIT2 expression was validated in GC tissues, especially in NI-positive GC tissues. Reduced circSLIT2 predicts poor prognosis in GC patients. CircSLIT2 inhibits the migration and neural invasion of GC cells both in vitro and in vivo. Mechanically, a novel peptide (SLIT2-284aa) translated by circSLIT2 was identified. SLIT2-284aa facilitates SLIT2/ROBO1 signaling via binding and inducing the ROBO1 membrane localization. Moreover, SLIT2-284aa enhances the association between ROBO1 and RhoGDI1, which suppresses RhoGDI1 phosphorylation and further reduces the release and activation of RhoA. Our work uncovers a novel circRNA-encoding peptide and a previously unknown SLIT2-284aa/ROBO1/RhoGDI1/RhoA signaling pathway that suppresses cell migration and neural invasion in GC, which provides a new prospect to understand the underlying biological mechanism of the nervous system in GC.
Alternative polyadenylation (APA) generates transcript isoforms with distinct 3' ends, yet the repertoire of its protein regulators remains poorly defined. Using a large-scale tethered function screen, we profiled 879 human RNA-binding proteins (RBPs) and identified 63 high-confidence activators of poly(A) site (PAS) selection, most of which were not previously linked to APA. We validated these factors by knockdown PAS-seq, RNA sequencing (RNA-seq), and enhanced cross-linking and immunoprecipitation (eCLIP) analyses and developed a fine-tuned protein language model that predicts PAS selection activators and their key functional domains. We then mechanistically dissected two unexpected hits: GRB2, a signaling adaptor protein, and RNPS1, a peripheral component of the exon junction complex (EJC). Both regulate APA, at least in part, through direct interactions with distinct subunits of the cleavage and polyadenylation (CPA) machinery. Together, our study provides a comprehensive resource of APA-regulating RBPs and uncovers unexpected roles of signaling and EJC factors in APA regulation.
The rational design of high-performance semiconductors-particularly those with tunable bandgaps, high carrier mobility, and stability-remains a fundamental challenge in materials science, as traditional trial-and-error approaches struggle to explore vast chemical spaces efficiently. Guided by insights from band-inverted topological materials, we present an integrated high-throughput calculation and machine learning-based workflow that rapidly uncovers previously uncharted semiconductors. The workflow homes in on the non-parabolic band structures produced by inversion to reveal compounds with high mobility and defect tolerance. Starting from topological materials containing heavy IVA-VIIA elements, we generated thousands of crystal structures via light-element substitution-a strategy designed to engineer new semiconductors beyond conventional compositions. Using high-throughput calculations, we computed key electronic and stability properties, training a classification machine learning model on a 50% subset of the data to predict formation energies for the remaining candidates. The developed workflow enabled us to identify 14 new stable semiconductor candidates with promising potential for photovoltaic and thermoelectric applications. These findings demonstrate that the data-driven approach to light-element substitution in topological materials enables the design of promising semiconductors beyond conventional chemical spaces.
Modern quantum physics now enables control of quantum systems at the level of individual trajectories, opening a new frontier that links quantum information theory, quantum many-body physics, and quantum thermodynamics, and uncovers novel non-equilibrium phenomena such as deep thermalization and measurement-induced entanglement. However, a central challenge remains: their characterization relies on measuring nonlinear properties of individual quantum states, a task tantamount to fine-grained cloning of a quantum ensemble. Here, the fundamental laws governing the cloning of quantum ensembles are investigated. First, a general no-cloning theorem for arbitrary ensembles is established from an information-theoretic perspective, even assuming multiple copies of the ensemble's purification. It is then shown that this barrier can be unexpectedly circumvented for physical ensembles generated by finite-time evolutions. Nevertheless, these tasks are proven to remain computationally intractable, even when the full circuit description of state preparation is known. This stands in sharp contrast to the conventional no-cloning theorem, which relies on the state being unknown. Together, these results establish new fundamental principles of quantum mechanics, reveal intrinsic trade-offs among sample complexity, computational complexity, and quantum measurements, and highlight the necessity of problem-specific strategies for probing measurement-induced quantum phenomena.
The dynamics of tumor-immune interactions within a complex tumor microenvironment are typically modeled using a system of ordinary differential equations or partial differential equations. These models introduce some unknown parameters that need to be estimated accurately and efficiently from the limited, noisy experimental data. Moreover, due to the intricate biological complexity and limitations in experimental measurements, tumor-immune dynamics are not fully understood, and therefore, only partial knowledge of the underlying physics may be available, resulting in unknown or missing terms within the system of equations. Thus, there are twofold challenges in modeling tumor dynamics: (i) accurate estimation of model parameters and (ii) discovery of the mathematical equations governing the physical and biological systems. These types of problems are referred to as gray-box identification areas, where both experimental data and partial system knowledge are used to recover unknown parameters and missing components. In this study, we develop a cancer biology-informed neural network model (CBINN) to infer the unknown parameters in the system of equations as well as to discover the missing mechanisms from sparse and noisy measurements. We test the performance of the CBINN model on three distinct nonlinear compartmental tumour-immune models and evaluate its robustness across multiple synthetic noise levels. By harnessing these highly nonlinear dynamics, our CBINN framework effectively estimates the unknown model parameters and uncovers the underlying physical laws or mathematical structures that govern these biological systems, from scattered and noisy measurements. The models chosen here represent the dynamic patterns commonly observed in compartmental models of tumor-immune interactions, thereby validating the generalizability and efficacy of our methodology. Structural and practical identifiablility of the model parameters are also discussed using computational and Fisher information matrix based analysis. This work provides valuable guidance for researchers addressing inverse problems and gray-box identification challenges in complex dynamical systems.
Flexoelectricity is a ubiquitous electromechanical coupling mechanism that produces a polarization response to strain gradients and requires no material symmetry constraints. Here, we investigated flexoelectricity in the La0.24Sr0.76Al0.62Ta0.38O3 (LSAT) grain boundaries using atomic-resolution scanning transmission electron microscopy, including high-angle annular dark-field (HAADF) imaging, energy-dispersive x-ray spectroscopy (EDX), and electron energy-loss spectroscopy (EELS). Our results reveal that tantalum (Ta) segregates in the grain boundaries, forming unique chemically ordered structures. EELS uncovers pronounced distortions of the aluminum (Al)/Ta─oxygen (O) octahedra in the grain boundaries. We exploited the pronounced structural inhomogeneity of the 36.8° grain boundary to achieve a large strain gradient (∼2.0 per nanometer) within two to three unit cells, resulting in an atomic-scale flexoelectric displacement of up to ∼114.8 picometers. Quantitative analysis indicates that the flexoelectric displacement correlates with local nonstoichiometry induced by Ta segregation. We further demonstrated that the segregation-enhanced strain gradient exists generally in both symmetric and asymmetric grain boundaries. Our atomic-scale findings provide insight into tunable giant flexoelectricity in electroceramic grain boundaries.
While herbivorous insects often secrete effector proteins to suppress plant immunity, alternative adaptation strategies remain less explored. Here, we report a counterintuitive approach in the small brown planthopper, Laodelphax striatellus. SBPH downregulated a salivary gland-specific elicitor gene, LsG40, when transferred from the optimal host rice to the suboptimal host corn. When expressed in plants, including the model organism tobacco and the two host plants corn and rice, LsG40 acts as a defense elicitor triggering calcium-dependent reactive oxygen species burst, cell death, callose deposition, and jasmonic acid signaling. Interestingly, LsG40 induced the emission of insect-resistant but enemy-attractive volatiles in corn seedlings, including heptadecane to repel SBPH and Spodoptera frugiperda, and (E)-β-farnesene to attract natural enemy Hippodamia variegata, thereby coordinating direct and indirect defenses. Meanwhile, between the two hosts, LsG40-induced rice plants exhibited weaker defense. In further RNA interference, down-regulating LsG40 enhanced SBPH feeding and fecundity by attenuating plant defenses. In contrast to the well-documented role of insect effectors in suppressing plant defense, we demonstrate that SBPH feeding on suboptimal host corn downregulated LsG40, a salivary protein that triggers plant defense. This discovery uncovers a novel adaptive strategy whereby insects fine-tune plant immunity by modulating the expression of a defense elicitor within itself, rather than by suppressing it through secreted effectors. The differential expression of salivary protein gene LsG40 may reflect that SBPH actively manages host defense, including optimal and suboptimal host plants.
Immunodeficiency can precede and directly contribute to cancer development, particularly in B-cell lymphoproliferative disorders (B-CLPDs), where immune dysfunction is often intrinsic to the disease. A subset of such patients initially classified as having secondary immunodeficiency (SID) resulting from the BCLP may harbour underlying primary immunodeficiencies (PIDs). Recognizing the pattern of these hidden PID patients not only refines disease classification but also expands our understanding of the genetic determinants of cancer-associated immunodeficiency. Identification of tumour somatic variants that overlap with germline genes causative of PID uncovers novel mechanisms of immune dysfunction in B-CLPD, thereby providing new avenues for precision oncohaematology. This evolving host-centred perspective supports individualized, anticipatory care; enhances early detection of immune-mediated complications enabling tailored treatment responses; provides informed family counselling; and improves long-term outcomes for patients.
Crop wild relatives are used to improve cultivated plants and precise tracking of genetic introgression requires high-quality genome assemblies. Here we present de novo genome assemblies of two wild tomato species - the broadly stress-resistant Solanum pennellii (LA0716) and the salt-resistant Solanum cheesmaniae (LA1039). The improved S. pennellii genome adds 146 Mbp to the twelve chromosomes compared with the original reference. The alignment of the new assemblies with multiple gold-standard assemblies identified shared and species-specific structural variants. Analysis of repeat content demonstrates independent explosions of Tekay retrotransposons in S. pennellii and S. peruvianum. Genome sequencing of 709 recombinant plants derived from male and female backcrosses of three different hybrids reveals higher crossover rate in female meiosis. Conserved female-enhanced recombination regions were discovered and coldspots were attributed to megabase-scale inversions and insertion-deletion polymorphisms. Our S. pennellii and S. cheesmaniae genome assemblies reveal how repeat content diverged in nature and during breeding, and uncovers how both reproductive gender and structural variants dictate recombination landscapes in tomato hybrids.
In this work, we systematically investigate the structural evolution and stability of lanthanum-doped copper cluster anions LaCun- (n = 1-16) via unbiased global structure search and density functional theory (DFT) calculations. Among these clusters, species at multiple sizes exhibit enhanced energetic stability, whereas LaCu12- is structurally distinguished by its compact half-cage geometry. Complementary Mulliken population analysis and energy decomposition analysis (EDA) reveal that the central La atom donates its valence electrons to the surrounding Cu cage, giving rise to the 16-electron superatomic closed-shell configuration that underpins the cluster stability. Adaptive natural density partitioning (AdNDP) analysis confirms that this exceptional stability originates from multicenter delocalized bonds spanning the entire Cu cage; meanwhile, isochronous chemical shielding surface (ICSSzz) analysis uncovers strong spherical aromaticity, as evidenced by a pronounced shielding peak at 193 ppm. These findings elucidate the synergistic effect between electrostatic attraction and superatomic electronic shell closure and provide theoretical guidance for the rational design of rare-earth-doped superatomic clusters toward functional material applications.
Host-parasite interactions arise from a complex interplay of evolutionary history, ecological context, and community structure, yet these dimensions are rarely examined together. Here, we introduce a unified framework that links the macroevolutionary processes shaping host-parasite associations with the microevolutionary dynamics driving intraspecific viral and host diversity. This approach reveals how evolutionary and ecological forces jointly structure parasite and viral diversity across a host's range. We used the hantavirus host Oligoryzomys longicaudatus in South America as a model system to explore this analytical framework. The objective of this study was to uncover potential factors contributing to parasite and viral diversity in this system in a framework that can be applied to other disease systems. Our data suggest that parasite richness peaks in environmentally optimal, central regions of the host's range, while hantavirus diversity peaks toward environmental and geographic margins. This dynamic connection among host ecology, parasite community turnover, and viral evolution illustrates how geographic and environmental variation influence host-parasite evolution. By bridging micro- and macroevolutionary signals, our analytical framework provides a biologically sound approach for describing host, parasite, and viral diversification in a changing world, and a foundation to explain how diseases emerge across changing landscapes.
Extracellular vesicles (EVs) have emerged as promising biomarkers for monitoring physiological homeostasis and pathological progression. However, current analytic methods face limitations in preserving spatial information about EVs and their intricate connections to parental and recipient cells. Here, we present Spatial-EV-seq, a method for in situ spatial profiling of EVs within their native microenvironmental context. Spatial-EV-seq uses an antibody-engineered capture interface to preserve EVs' spatial distribution, followed by rolling circle amplification with EV surface-binding aptamers, enabling fluorescence imaging and molecular profiling of individual EVs. The method integrates ultrasensitive EV profiling, molecular subtyping and high-resolution spatial mapping with transcriptomics to resolve location-specific EV-cell communication networks. In an anti-PD1-treated breast cancer mouse model, we uncover a spatially orchestrated immunosuppressive axis: PDL1+ EV-enriched zones drive CD8+ T cell dysfunction, establishing immune-privileged niches, whereas PDL1+ EV-depleted regions preserve immunocompetence and therapeutic sensitivity. Spatial-EV-seq offers insights into EV-mediated mechanisms and unlocks avenues for precision diagnostics and therapeutics.
The clinical overlap between major psychiatric disorders (MPDs) and Alzheimer's disease (AD) implicates complex shared etiology. Previous studies demonstrated that both diseases are genetically complex and highly heritable, suggesting that more endeavors are necessary to be made from the very bottom to understand their genetic basis. With the advance of post-genomic analysis, multi-ancestry meta-analysis allows the generalizability of the genetic architecture across different populations to uncover ancestry-specific variants, while multi-trait analysis enables the discovery of the co-colocalized risk genomic regions across diseases. Therefore, in this study, we leveraged published GWAS summary statistics from European, East Asian, Hispanic and African American populations to report schizophrenia, major depressive disorders, and Alzheimer's disease risk loci and further fine-mapping to credible sets with >95% PP inclusion of the causal variant. We distilled 2871 potential traits from publicly available and found 134 traits significantly genetically correlated with both MPDs and AD using batch LD score regression. We then prioritized the identified loci from multi-ancestry results for cross-trait colocalization analysis to assess shared genetic etiology and further nominated 2 colocalized loci across both conditions, including rs2532240 and rs6504163. In the end, we finalized our analysis by validation and functional inference of the underlying susceptibility genes as well as putative mechanisms using evidence from multiple resources, including FIVEx, Open Targets, and scQTLbase.
Three previously undescribed GABA-containing heptapeptides, named unguisins P-R (1-3), were obtained from the deep-sea-derived fungus Aspergillus candidus SCSIO 41234. Their structures were elucidated by extensive spectroscopic analysis, including 1D and 2D NMR, HR-ESI-MS, and Marfey's method. Bioassay results indicated that all three compounds exerted significant pancreatic lipase (PL) inhibitory activity, while compounds 2 and 3 exhibited weak antioxidant activity. In silico docking studies further revealed the promise of these molecules as PL inhibitors, with the analysis uncovering distinctive interaction modes between the compounds and their protein targets.
Glucocorticoids(GCs) are widely used to treat erythropoietin-resistant anemias, yet the precise mechanisms underlying their erythropoiesis-promoting effects remain incompletely understood. This study used single-cell RNA sequencing, ATAC-seq, ChIP-seq, RNA-seq,quantitative PCR (qPCR), enzyme-linked immunosorbent assay (ELISA) and flow cytometry in vivo models (AIHA patients, CD163-/- mice, Gypa-eGFP-cremice, Epor-tdtomato-cre mice, and Epor-eGFP-cre rats) and in vitro human erythroblastic island(EBI) formation and EBI enrichment and cytospins, Giemsa and Prussian blue staining, quantification and co-culture systems to delineate CD163+ macrophages coordinating erythroblastic island formation and iron metabolism. GC promote erythropoiesis by regulating CD163-mediated EBI formation and modulating iron metabolism within EBI macrophages, a phenomenon conserved across humans, rats, and mice. We demonstrated that CD163+macrophages-but not their CD163- counterparts-exhibit heightened iron metabolism in the bone marrow, and that GC-induced erythropoiesis is markedly attenuated in CD163-deficient mice due to disrupted EBI architecture and impaired iron handling. Importantly, GC therapy restores iron metabolism and mitigates inflammatory responses in BM CD163+macrophages, likely contributing to improved erythropoiesis in patients with autoimmune hemolytic anemia. CD163⁺macrophages support GC-induced erythropoiesis by coordinating erythroblastic island formation and iron metabolism. These findings uncover a previously unrecognized GC-CD163-EBI axis that governs erythropoiesis and highlight the potential of targeting EBI macrophage function as a novel therapeutic strategy for anemia.
Recent epidemiological datasets have associated viral encephalitis exposure (i.e., viral-induced neuroinflammation) with increased risk of Alzheimer's disease (AD) and dementia, highlighting the need to uncover how it may impact AD neuropathology. Aged 5xFAD and wild-type (WT) mice were infected with the John Howard Mueller strain of murine hepatitis virus (JHMV), a neurotropic strain of murine coronavirus to comprehensively determine how coronavirus-induced encephalitis may induce molecular and cellular changes that impact beta-amyloid (Aβ) neuropathology. JHMV-induced encephalitis at 12 days post-infection resulted in minimal changes to overall Aβ protein, despite increased CD4+ and CD8+ T-cell infiltration and Lgals3/MAC2-expressing macrophages surrounding more compact Aβ plaques in the brain. Spatial transcriptomic imaging and pathway analysis of differentially expressed genes (DEGs) within myeloid cells demonstrate down-regulated disease-associated (DAM) pathways involving Aβ clearance, response to lipids, and macrophage activation within infected 5xFAD brains. JHMV encephalitis induces dysregulated gene expression and myeloid cell responses to Aβ plaque burden in 5xFAD mouse brains.
In Ca²⁺-triggered exocytosis such as synaptic neurotransmitter release, vesicle fusion is tightly regulated by synaptotagmin (Syt) and complexin (Cpx), which together clamp partially assembled SNARE complexes to prevent premature fusion. However, Cpx is evolutionarily more ancient than Syt, suggesting that it may also regulate exocytosis independently of Syt. To test this possibility, we sought to identify an extant exocytic pathway that requires Cpx but naturally lacks Syt. Here, we uncovered such a pathway - hormone-triggered exocytosis of glucose transporters in adipocytes. In this pathway, Cpx acts exclusively as a positive regulator, accelerating the evoked phase of exocytosis without affecting basal fusion. Mechanistically, this Syt-independent activity depends on the central helix of Cpx for SNARE binding and on its C-terminal membrane-binding peptide, which remodels the lipid bilayer to promote exocytosis. Our findings support a model in which Cpx originally evolved to accelerate exocytosis independently of Ca²⁺, enabling rapid mobilization of exocytic cargoes in response to environmental cues. With the later emergence of Syt, Cpx acquired an additional role - acting in concert with Syt to regulate Ca²⁺-triggered exocytosis.