Renal cell carcinoma (RCC) is the most common type of kidney cancer, but its genetic architecture has not been fully characterized, particularly in Asian populations. Here, we perform a multi-ancestry meta-analysis of 33,712 RCC cases and 845,786 controls, including individuals of East Asian (5,313 cases and 96,912 controls), European (25,890 cases and 743,585 controls), African American (897 cases and 3,109 controls), and Latin American ancestry (1,612 cases and 2,180 controls), which unveils 10 novel RCC-associated loci and a Chinese-specific locus at 12p13.33. Leveraging genome-wide association study (GWAS) data and cross-ancestry expression quantitative trait loci (eQTLs) mapping from 266 kidney tissues, we refine the identification of putative causal variants and genes implicated in RCC. These findings are substantiated through CRISPR-based screenings and multiplexed single-cell perturbations. Additionally, we functionally validate a novel association between rs28684409 and the oncogene RPL4 at the complex genetic locus 15q22.31. This comprehensive genetic investigation underscores the utility of integrating cross-ancestry GWASs, QTLs, and functional screens to elucidate the genetic underpinnings of complex diseases.

The complexity of disease-causing signaling networks is indicative of the failure of single-target therapeutics to work, particularly because of feedback, redundancy and activation of compensatory responses. The review describes the recent movement to network pharmacology and purposeful polypharmacology facilitated by the emergence of artificial intelligence (AI) and massive biological knowledge graphs. This review explains how machine learning and graph neural networks can be used to characterize molecular interactions systematically, predict targets that are of disease relevance, as well as priorities on multi-target intervention strategies. Generative models and reinforcement-based learning strategies are addressed to create compounds and combinations of drugs designed to modulate networks, and not individual protein inhibition. It describes the experimental validation processes, such as CETSA, NanoBRET, and Perturb-seq, and patient-derived models and MIDD systems to aid the translational evidence. Data quality, bias, interpretability, and reproducibility are taken into consideration. In sum, this review presents a feasible and combined model of AI-assisted network-mediated drug discovery.

Genes impacting the bioaccumulation of perfluoroalkyl and polyfluoroalkyl substances (PFASs)and their neurotoxic effects on the brain and behavior remain unclear. Here,we examined genome-wide associations with serum accumulation of 13 PFASs in 6,823 Chinese adults. We revealed that perfluoroheptanoic acid (PFHpA) accumulation was associated with genetic variants at two loci (3q29: P = 5.20 ×10-19; 6p22.2: P = 3.69 ×10-23), mapping to 56 genes.Blood expression of 27 of these genes was associated with PFHpA accumulation in 573 subsamples. Eight genes showed potential causal effects on PFHpA accumulation,functionally linked to innate immunity (TRIM38, ZDHHC19, MUC20)and organic solute transport (SLC51A and SLC17A3). We assessed the impact of PFASs on cortical thickness and surface area, white matter fractional anisotropy and mean diffusivity,along with 25 behavioral phenotypes. We identified that seven PFASs were correlated with reduced cortical morphology, primarily in the prefrontal cortex. We also found a statistical causal effect of PFHpA accumulation on the surface area in the right frontomarginal cortex, which mediated the effect of PFHpA on anxiety. These findings indicate that serum PFHpA accumulation may be regulated by genes related to innate immunity and solute transport, heightening anxiety by impairing the prefrontal cortex.

Proper crossover (CO) formation in meiosis serves dual roles in ensuring accurate chromosome segregation and generating genetic diversity. However, the molecular mechanisms underlying CO number and distribution remain incompletely understood. Previous studies have implicated the ubiquitin-proteasome system in CO regulation, but specific regulators and mechanisms are poorly defined. Here, we identify the E3 ubiquitin ligase Ufd2p as a key regulator promoting efficient CO formation through a focused genetic screen in Saccharomyces cerevisiae. Deletion of UFD2 significantly reduces CO frequency by enhancing the strength of CO interference. Integrated multiomics analysis indicates that Ufd2p targets Topoisomerase II (Top2p) for ubiquitination and subsequent proteasomal degradation during meiosis. Deletion of UFD2 results in Top2p accumulation, which resolves DNA negative supercoils excessively and enhances CO interference in the nucleus, ultimately reducing CO numbers. We further show that the mammalian homolog of Ufd2p, UBE4B, plays a conserved role in promoting efficient CO formation by regulating TOP2A-dependent DNA negative supercoils dynamics. Notably, expression of mouse or human UBE4B in yeast restores CO formation and meiotic progression in UFD2 deletion cells, demonstrating functional conservation across species. Together, our work identifies Ufd2p as a previously uncharacterized regulator of CO formation and provides important insights into the conserved molecular mechanism, which operates through Top2p-mediated supercoils homeostasis.

BACKGROUND AND AIMS:An overactive inflammatory response and immune cell infiltration following myocardial infarction (MI) impair cardiac tissue repair. This study investigates the mechanistic role of the arachidonic acid (AA) metabolic cascade in mediating post-MI inflammation. METHODS:Single-cell RNA-sequencing analysis was performed to characterize cardiac macrophage heterogeneity in post-MI mice. Metabolomic analyses were conducted to profile polyunsaturated fatty acid metabolites in both plasma from MI patients and cardiac tissue from infarcted mice to identify key factors influencing MI progression. RESULTS:Leukotriene B4 (LTB4), an AA metabolite, was consistently elevated in MI patients and mouse models, demonstrating significantly higher plasma concentrations in recurrent MI cases. Mechanistically, AA promotes nuclear translocation of protein phosphatase 5 (PP5), which subsequently dephosphorylates 5-lipoxygenase at Thr218, driving sustained LTB4 production. This process enhances CXCL13-mediated B-cell recruitment and amplifies inflammation through macrophage-B-cell crosstalk. Disruption of PP5 in mouse macrophages prevents these adverse changes. CONCLUSIONS:The findings elucidate the conserved role of 5-lipoxygenase phosphorylation regulated LTB4 levels in MI and identify PP5 as a potential therapeutic target for the treatment of MI.

Acquired resistance to both targeted therapies and immunotherapies in cancer presents major clinical challenges, yet the molecular mechanisms underlying cross-resistance remain poorly understood. We hypothesized that loss of specific microRNAs (miRNAs) could potentiate melanoma resistance to both targeted drugs and CD8+ T cell-mediated cytotoxicity. Through genome-wide miRNA CRISPR knockout screening integrated with cellular models, longitudinal clinical samples, and in vivo experiments, we identified miR-18a as a pivotal upstream regulator of pleiotropic resistance in melanoma. We show that miR-18a deficiency drives resistance through two distinct mechanisms: derepressing AJUBA-regulated Hippo signaling during MAPK inhibition, and enhancing THBS1-CD47 interactions that impair the immunological synapse between tumor cells and CD8+ T cells. Furthermore, hnRNP A1 plays an essential role in modulating miR-18a expression, thereby mediating cross-resistance. These findings suggest that targeting non-coding RNA vulnerabilities may represent a promising therapeutic strategy to overcome complex resistance mechanisms and improve clinical outcomes in melanoma.

BACKGROUND:As an extension of diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI) quantifies non-Gaussian water diffusion and has been applied to explore brain disorders. However, the genetic architecture of brain DKI phenotypes remains unknown. METHODS:Here, we estimated heritability and conducted genome-wide association studies (GWASs) for 804 DKI phenotypes across 188 brain structures in 4183 participants. To determine whether DKI-GWASs provides genetic insights beyond DTI-GWASs, we compared results from 804 DKI-GWASs and 752 DTI-GWASs in the same cohort. To clarify the biological significance of DKI phenotypes, we examined associations between DKI phenotypes and brain health-related outcomes within the CHIMGEN, and explored associations between polygenic risk scores (PRSs) of DKI phenotypes and mental disorders in the UK Biobank. FINDINGS:Of 804 DKI phenotypes, 275 showed significant heritability (P < 0.05; h2 range: 0.143-0.602). We detected 280 significant associations (P < 5 × 10-8), with 38 surviving Bonferroni correction (P < 1.54 × 10-10). These associations were unevenly distributed across chromosomes, DKI phenotype subgroups, and brain structures. Among 229 independent variant-structure associations for DKI, 175 (76.4%) were DKI-specific. We observed 930 associations between DKI phenotypes and brain health-related outcomes (P < 0.05; ten Bonferroni-significant with P < 1.02 × 10-5), and 200 between PRSs and mental disorders (P < 0.05; one Bonferroni-significant with P < 9.61 × 10-5). INTERPRETATION:This study delineates the genetic architecture of brain DKI phenotypes, identifies complementary genetic insights into brain microstructure, and provides biologically relevant endophenotypes for investigating neural mechanisms underlying brain disorders. FUNDING:National Natural Science Foundation of China, National Key Research and Development Program of China, Tianjin Key Medical Discipline Construction Project, and Tianjin Natural Science Foundation.

Most genetic loci linked to polygenic traits are in non-coding regions, with complex regulation and linkage disequilibrium (LD), complicating causal variant and gene prioritization. We used multiplexed single-cell CRISPR interference and activation perturbations to investigate cis-regulatory element (CRE) and gene expression relationships within tight LD in the endogenous chromatin context. We demonstrated the prevalence of multiple causality in perfect LD (pLD) for independent expression quantitative trait loci (eQTLs) and uncovered fine-grained genetic effects on gene expression within pLD, which are difficult to decipher using traditional eQTL fine-mapping or existing computational methods. We found that over one-third of the causal CREs lack classical epigenetic markers prior to perturbation, and we functionally validated one of these hidden regulatory mechanisms. Leveraging Multiome single-cell epigenetic and sequence perturbations, we highlighted the regulatory plasticity of the human genome. Our study will guide the exploration of missing causal mechanisms underlying molecular trait regulation and disease development.

Advances in cancer genomics have significantly expanded our understanding of cancer biology. However, the high cost of drug development limits our ability to translate this knowledge into precise treatments. Approved non-oncology drugs, comprising a large repository of chemical entities, offer a promising avenue for repurposing in cancer therapy. Herein we present CHANCE, a supervised machine learning model designed to predict the anticancer activities of non-oncology drugs for specific patients by simultaneously considering personalized coding and non-coding mutations. Utilizing protein-protein interaction networks, CHANCE harmonizes multilevel mutation annotations and integrates pharmacological information across different drugs into a single model. We systematically benchmarked the performance of CHANCE and show its predictions are better than previous model and highly interpretable. Applying CHANCE to approximately 5000 cancer samples indicated that >30% might respond to at least one non-oncology drug, with 11% non-oncology drugs predicted to have anticancer activities. Moreover, CHANCE predictions suggested an association between SMAD7 mutations and aspirin treatment response. Experimental validation using tumor cells derived from seven patients with pancreatic or esophageal cancer confirmed the potential anticancer activity of at least one non-oncology drug for five of these patients. To summarize, CHANCE offers a personalized and interpretable approach, serving as a valuable tool for mining non-oncology drugs in the precision oncology era.

The brainstem houses numerous nuclei and tracts that serve vital functions. Genome-wide associations with brainstem substructure volumes have been explored in European individuals, yet other ancestries remain under-represented. Here, we conduct cross-ancestry genome-wide association meta-analyses in 103,098 individuals for brainstem and 78,062 individuals for eight substructure volumes, including 7094 Chinese Han individuals. We identify 713 locus-trait associations with brainstem and substructure volumes at P < 5.56 ×10−9, comprising 569 new associations. Two associations show different effect sizes, while 496 associations have similar effect sizes between ancestries. We prioritize 186 genes associated with brainstem volumetric traits. We find both shared and distinct genetic loci, genes, and pathways for midbrain, pons, and medulla volumes, along with the shared genetic architectures related to disease phenotypes and physiological functions. The results provide new insights into the genetic architectures of brainstem and substructure volumes and their genetic associations with brainstem physiologies and pathologies. A cross-ancestry GWAS meta-analyses of brainstem structures identify 713 associations. It reveals shared/distinct genetic architectures across ancestries/substructures and overlaps with neuropsychiatric disorders and physiological functions.

Combinations of cancer drugs have the potential to overcome resistance, improve the response rate of existing drugs and reduce dose-limiting toxicity associated with single agents. Existing drug combination databases only provide response data, such as synergy scores between two drugs, without important contextual information to assist oncologists in matching their patients with these combinations in an evidence-based way. To address this gap, we developed a cancer drug combination database (named as OncoDrug+) by manually collecting and integrating drug combinations and corresponding evidences from FDA databases, clinical guidelines, clinical trials, clinical case reports, patient-derived tumor xenograft models, cell line models and bioinformatics predictions. OncoDrug+ includes 7895 data entries, covering 77 cancer types, including unique 2201 drug combination therapies, involving 1200 biomarkers, 763 published reports and seven types of evidence. Unlike many previous databases only include treatment regime and drug response data, OncoDrug+ provides detailed genetic evidences, pharmacological target information and evidence scores supporting each combination strategy, making evidence-based experimental or clinical applications of cancer drug combinations be possible.

Transcriptome-wide association study (TWAS) has successfully identified numerous complex disease susceptibility genes in the post-genome-wide association study (GWAS) era. Over the past 3 years, the focus of TWAS algorithms has shifted from merely identifying associations to understanding how single nucleotide polymorphisms (SNPs) regulate gene expression, with a growing emphasis on incorporating fine-mapping techniques. Additionally, the rapid increase in GWAS summary statistics, driven largely by the UK Biobank and other consortia, has made it essential to update our webTWAS resource. To address these challenges and meet the growing needs of researchers, we developed webTWAS 2.0, an updated platform for identifying susceptibility genes for human complex diseases using TWAS. Additionally, webTWAS 2.0 provides an online TWAS analysis tool that simplifies conducting TWAS analyses. The updated resource includes 7247 GWAS summary statistics covering 1588 complex human diseases from 192 publications. It also incorporates multiple TWAS methods, such as sTF-TWAS, 3'aTWAS and GIFT, along with an updated interactive visualization tool that allows users to easily explore significant associations across different methods. Other upgrades include a personalized online analysis tool for user-submitted GWAS data and a refined search function that makes it easier to identify relevant associations and meet diverse user needs more efficiently. webTWAS 2.0 is freely accessible at http://www.webtwas.net.

The amygdala is a small but critical multi-nucleus structure for emotion, cognition and neuropsychiatric disorders. Although genetic associations with amygdala volumetric traits have been investigated in sex-combined European populations, cross-ancestry and sex-stratified analyses are lacking. Here we conducted cross-ancestry and sex-stratified genome-wide association analyses for 21 amygdala volumetric traits in 6,923 Chinese and 48,634 European individuals. We identified 191 variant-trait associations (P < 2.38 × 10-9), including 47 new associations (12 new loci) in sex-combined univariate analyses and seven additional new loci in sex-combined and sex-stratified multivariate analyses. We identified 12 ancestry-specific and two sex-specific associations. The identified genetic variants include 16 fine-mapped causal variants and regulate amygdala and fetal brain gene expression. The variants were enriched for brain development and colocalized with mood, cognition and neuropsychiatric disorders. These results indicate that cross-ancestry and sex-stratified genetic association analyses may provide insight into the genetic architectures of amygdala and subnucleus volumes.

Existing methods to distinguish deleterious/pathogenic from neutral variants still inadequately capture the full spectrum of genetic variant impact on fitness and disease susceptibility. We introduce the FIND model, which stratifies genetic variants into refined categories based on fitness spectrum and derived allele frequency. FIND demonstrates enhanced resolution in differentiating trait-modulating alleles from those that are deleterious or neutral, delivering higher performance over existing genome-wide methods. Applying FIND to the interpretation of clinical variants demonstrates its substantial potential in reclassifying variants of unknown significance, providing a new tool to explore the complexities of genetic contributions to health.

The genetic architecture of renal cell carcinoma (RCC) has not been fully characterized, particularly in Asian populations. Here, we conducted a genome-wide association study (GWAS) of 4, 692 RCC patients and 10, 116 controls of Chinese ancestry, extending to a multi-ancestry cohort totaling 879, 498 participants. Our approach unveiled 10 novel RCC-associated loci and a Chinese-specific locus at 12p13.33 with genome-wide significance. Utilizing GWAS results and cross-ancestry expression quantitative trait loci (eQTLs), we achieved a refined identification of RCC-related candidate causal variants and genes. These findings were substantiated through CRISPR-based screenings and multiplexed single-cell perturbations. Functionally, we established a novel association between rs28684409 and the oncogene RPL4 at the complex genetic locus 15q22.31. This top-down genetic study of RCC underscores the utility of integrating cross-ancestry GWASs, QTLs, and functional screens to deeply elucidate complex diseases and identify potential therapeutic targets. Hongji Dai, Xinlei Chu, Han Du, Shutong Yang, Yanxin Yao, Xinru Yu, Yanrui Zhao, Zhangyan Lyu, Wei Wang, Chao Sheng, Hong Zheng, Fangfang Song, Fengju Song, Mulin Jun Li, Kexin Chen. Genetic landscape and functional exploration of kidney cancer predisposition in cross-ancestral populations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1018.

Rare diseases affect hundreds of millions worldwide, yet diagnosis often spans years. Convectional pipelines decouple noisy evidence extraction from downstream inferential diagnosis, and general/medical large language models (LLMs) face scarce real world electronic health records (EHRs), stale domain knowledge, and hallucinations. We assemble a large, domain specialized clinical corpus and a clinician validated reasoning set, and develop RareSeek R1 via staged instruction tuning, chain of thought learning, and graph grounded retrieval. Across multicenter EHR narratives and public benchmarks, RareSeek R1 attains state of the art accuracy, robust generalization, and stability under noisy or overlapping phenotypes. Augmented retrieval yields the largest gains when narratives pair with prioritized variants by resolving ambiguity and aligning candidates to mechanisms. Human studies show performance on par with experienced physicians and consistent gains in assistive use. Notably, transparent reasoning highlights decisive non phenotypic evidence (median 23.1

As the third most common cause of cancer-related mortality, hepatocellular carcinoma (HCC) is a global health concern. Despite its prevalence, treatment options are limited, underscoring the need to identify potential therapeutic targets and strategies. In this study, we identified amplification of cAMP response element-binding protein-regulated transcription coactivator 2 (CRTC2), situated in the 1q21.3 region, due to copy-number alterations in HCC. In a cohort of patients with HCC, CRTC2 protein levels were frequently elevated and correlated with poor prognosis. Genetic deletion of Crtc2 significantly impeded the onset and progression of HCC in mouse models. CRTC2 formed cytoplasmic condensates that recruited the N6-methyladenosine (m6A) reader YTHDF2. Furthermore, CRTC2 promoted the translocation of m6A-modified mRNAs from decay sites to polyribosomes by interacting with PABP1. The activities of CRTC2 counteracted YTHDF2-mediated mRNA degradation to enhance the translational efficiency of specific mRNAs, including those encoding LRP5 and c-Jun. Targeting Crtc2 in hepatocytes using AAV8.sgCrtc2 elicited substantial therapeutic benefits in HCC mouse models and significantly enhanced the sensitivity to lenvatinib. Together, this research elucidates the pivotal role and underlying molecular mechanisms of CRTC2 in hepatocarcinogenesis and lenvatinib resistance, highlighting its potential clinical and therapeutic applications.Significance: CRTC2 hijacks the YTHDF2-m6A pathway to increase translation of c-Jun and promote hepatocellular carcinoma development and lenvatinib resistance, indicating that CRTC2 is a promising biomarker and therapeutic target.

Purpose:Depression and glaucoma are globally prevalent disorders with emerging evidence suggesting a potential inter-relation. This study aims to gain an in-depth understanding of their association and shared mechanisms. Methods:We investigated the association between depression and glaucoma using data from 348,537 Caucasian patients in the UK Biobank, including 7544 with glaucoma and 22,153 with depression. We performed Cox regression, logistic regression, mediation analyses, genetic association analysis, and Mendelian randomization. Results:Logistic regression indicated that depression increased the odds of glaucoma by 1.63 times (95% confidence interval [CI] = 1.51-1.77) and glaucoma increased the odds of depression by 1.61 times (95% CI = 1.49-1.74). Cox regression showed a hazard ratio of 1.35 for glaucoma incidence in individuals with depression (95% CI = 1.07-1.70). The risk of glaucoma was consistently approximately 60% higher across various genetic components of depression. We examined 1463 protein markers, identifying 200 markers associated with depression, some of which are linked to lipid metabolism. Mediation analysis suggested lipid metabolism as a mediator among these two diseases, with proteins like ANPEP, CCL3, and VWA1 playing significant roles. Genetic correlation analysis revealed a substantial genetic connection among depression, glaucoma, and lipid metabolism traits. Conclusions:Significant associations between depression and glaucoma were observed, with lipid metabolism playing a crucial role in their diagnosis and treatment. Translational Relevance:Our research underscores the inter-relation between glaucoma and depression, highlighting the importance of lipid metabolism in their clinical management.

Polycystic ovary syndrome (PCOS) can result in female infertility, menstrual irregularities, metabolic disturbances, hormonal imbalances, and significantly impact the reproductive health of women of childbearing age. Hyperandrogenism and insulin resistance are typical primary endocrine features of PCOS, which are also regarded as its core pathogenesis. In this study, IGFBP7 expression in granulosa cells (GCs) from women with and without PCOS was analyzed using bulk RNA-seq. A PCOS-like mouse model was constructed using dehydroepiandrosterone in IGFBP7 knockout and wild-type mice to explore the role of IGFBP7 in PCOS. Primary GCs from mice were cultured and transfected with IGFBP7 overexpression plasmid and siRNA fragments. Proliferation, apoptosis, and steroid hormone levels were measured to investigate the effects of IGFBP7 on granulosa cells. IGFBP7 expression was found to be elevated in patients with PCOS. Following IGFBP7 knockdown in mouse GC, there was a significant increase in GC proliferation, a decrease in GC apoptosis, and a notable decrease in testosterone secretion by GC. Conversely, overexpression of IGFBP7 in mouse granulosa cells significantly inhibited GC proliferation, significantly increased GC apoptosis, and led to a marked increase in testosterone secretion by GCs. With mouse model, a reduction in PCOS symptoms in mice after IGFBP7 deletion was observed. Elevated IGFBP7 expression in PCOS granulosa cells may induce apoptosis, hinder insulin signaling, and enhance androgen synthesis. These insights offer novel avenues for understanding and treating PCOS.

Gengnianchun (GNC) is a traditional remedy used for diminished ovarian reserve, but its underlying mechanisms remain unclear. This study aimed to explore these mechanisms in human granulosa-like cancer (KGN) cells pretreated with medicated rat serum (MRS) before H2O2 exposure. MRS pretreatment significantly alleviated H2O2-induced cell damage, including improvements in cell viability, superoxide dismutase and GSH-Px activities, and Bcl-2 expression. Conversely, H2O2 treatment increased apoptosis, autophagosomes, IL-1β, TNF-α, reactive oxygen species, malondialdehyde levels, and the expression of LC-II/LC3-I, Bax, and Beclin-1. GEO database analysis revealed significant differential expression of several miRNAs, including miR-548m. qPCR confirmed that MRS upregulated miR-548m expression, which was downregulated by H2O2 in a dose-dependent manner. Preincubation with MRS prevented the decline in miR-548m expression and mitigated H2O2-induced damage, including improvements in cell viability, apoptosis, autophagy, and oxidative stress. miR-548m suppressed FOXO3 3'-UTR luciferase activity, and anti-miR-548m enhanced it. Transfection with miR-548m reduced FOXO3 mRNA and protein levels, while anti-miR-548m increased them. These findings suggest that GNC protects against H2O2-induced ovarian damage by modulating the miR-548m/FOXO3 axis, triggering autophagy and apoptosis.