Genome Medicine最新文献

{"title":"Evaluation of familial phenotype deviation to measure the impact of de novo mutations in autism.","authors":"Soo-Whee Kim, Hyeji Lee, Da Yea Song, Gang-Hee Lee, Jae Hyun Han, Jee Won Lee, Hee Jung Byun, Ji Hyun Son, Ye Rim Kim, Yoojeong Lee, Eunjoon Kim, Donna M Werling, So Hyun Kim, Stephan J Sanders, Hee Jeong Yoo, Joon-Yong An","doi":"10.1186/s13073-025-01532-7","DOIUrl":"10.1186/s13073-025-01532-7","url":null,"abstract":"<p><strong>Background: </strong>The phenotypic outcomes of de novo variants (DNVs) in autism spectrum disorder (ASD) exhibit wide variability. To date, no study has comprehensively estimated DNV effects accounting for familial phenotypic background.</p><p><strong>Methods: </strong>To evaluate DNV effects in a family-relative context, we defined within-family standardized deviations (WFSD) by subtracting phenotype scores of unaffected family members and standardizing the result. We applied this approach to 78,685 individuals from 21,735 families from ASD cohorts of diverse ancestries. We compared the distribution, associations with disruptive DNVs, and gene discovery results between WFSD and raw phenotype scores. We further performed outlier analysis based on WFSDs per gene to detect genes with high variability between families.</p><p><strong>Results: </strong>We observed that ASD probands with disruptive DNVs exhibited greater behavioral symptoms and lower adaptive functioning relative to their within-family unaffected members. Compared to raw phenotype scores, WFSD provided clearer associations with DNVs and enabled greater yield in DNV-enriched gene discovery, including 18 novel ASD-associated genes. Outlier analysis identified 11 genes with high intrafamilial variability in phenotypic effects, influenced by mutation sites within functional domains or exons.</p><p><strong>Conclusions: </strong>Familial DNV analysis provides accurate effect estimates, a reliable basis for predicting clinical outcomes, and precise support while minimizing confounding from family background. This approach improves the identification of ASD-associated genes with true phenotypic effects by reducing variability, as well as genes with genuine phenotypic heterogeneity across families driven by mutation site. These findings enhance our understanding of ASD phenotype variability and inform potential targets for intervention.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"93"},"PeriodicalIF":10.4,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12366145/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144951106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single-nucleus transcriptomics reveals a distinct microglial state and increased MSR1-mediated phagocytosis as common features across dementia subtypes.","authors":"Sook-Yoong Chia, Mengwei Li, Zhihong Li, Haitao Tu, Jolene Wei Ling Lee, Lifeng Qiu, Jingjing Ling, Richard Reynolds, Salvatore Albani, Eng-King Tan, Adeline Su Lyn Ng, Jinmiao Chen, Li Zeng","doi":"10.1186/s13073-025-01519-4","DOIUrl":"10.1186/s13073-025-01519-4","url":null,"abstract":"<p><strong>Background: </strong>Alzheimer's disease (AD), dementia with Lewy bodies (DLB), and Parkinson's disease dementia (PDD) collectively represent the majority of dementia cases worldwide. While these subtypes share clinical, genetic, and pathological features, their transcriptomic similarities and differences remain poorly understood.</p><p><strong>Methods: </strong>We applied single-nucleus RNA-sequencing (snRNA-seq) to prefrontal cortex samples from individuals with non-cognitive impairment control (NCI), and dementia subtypes (AD, DLB, and PDD) to investigate cell type-specific gene expression patterns and pathways underlying pathological similarities and differences across dementia subtypes. SnRNA-seq findings were validated through RNAscope, immunohistochemistry, and additional biochemical analyses in human tissues and cellular models.</p><p><strong>Results: </strong>SnRNA-seq analysis revealed elevated microglial proportions across all dementia subtypes compared to NCI. Further analysis of cell type-specific transcriptomes identified overlapping differentially expressed genes (DEGs) between microglia and oligodendrocytes across all dementia subtypes. While AD showed molecular similarities to NCI, PDD and DLB were clustered more closely together, sharing a greater number of DEGs and related pathways, predominantly associated with microglia. Investigation of interactions between microglia and oligodendrocytes revealed a distinct microglial state in all dementia subtypes. MSR1, a gene encoding a scavenger receptor, was upregulated in microglia across all dementia subtypes, along with its associated gene HSPA1A in oligodendrocytes. RNAscope supported the potential interaction between microglia and oligodendrocytes, where these cells were in closer proximity to each other in human cortical tissues of PDD compared to NCI. MSR1 expression was significantly increased in cortical primary microglia from PD mice compared with non-transgenic (NTg) mice. Additionally, the expression of myelin-associated genes (MBP, MOBP, and PLP1) was significantly upregulated in PD microglia compared to NTg, supporting the presence of the distinct microglia. Furthermore, MSR1-positive microglia colocalised with MBP in cortical tissue of PDD patients, suggesting a functional role of MSR1 in myelin debris clearance. Overexpression of MSR1 in microglial cells enhanced their phagocytic activity toward myelin, and reciprocally, myelin treatment upregulated MSR1 protein levels, indicating enhanced MSR1-mediated myelin phagocytosis.</p><p><strong>Conclusions: </strong>Our findings provide novel insights into the cell type-specific role of microglial MSR1 in AD, DLB, and PDD, linking its increased phagocytic capacity to myelin defects as a common feature of neurodegenerative dementias.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"92"},"PeriodicalIF":10.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12359983/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144872870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving automated deep phenotyping through large language models using retrieval-augmented generation.","authors":"Brandon T Garcia, Lauren Westerfield, Priya Yelemali, Nikhita Gogate, E Andres Rivera-Munoz, Haowei Du, Moez Dawood, Angad Jolly, James R Lupski, Jennifer E Posey","doi":"10.1186/s13073-025-01521-w","DOIUrl":"10.1186/s13073-025-01521-w","url":null,"abstract":"<p><strong>Background: </strong>Diagnosing rare genetic disorders relies on precise phenotypic and genotypic analysis, with the Human Phenotype Ontology (HPO) providing a standardized language for capturing clinical phenotypes. Rule-based HPO extraction tools use concept recognition to automatically identify phenotypes, but they often struggle with incomplete phenotype assignment, requiring significant manual review. While large language models (LLMs) hold promise for more context-driven phenotype extraction, they are prone to errors and \"hallucinations,\" making them less reliable without further refinement. We present RAG-HPO, a Python-based tool that leverages retrieval-augmented generation (RAG) to elevate accuracy of HPO term assignment by LLM. This approach bypasses the limitations of baseline models and eliminates the need for time- and resource-intensive fine-tuning. RAG-HPO integrates a dynamic vector database, containing > 54,000 phenotypic phrases mapped to HPO IDs, which allows real-time retrieval and contextual matching. The RAG-HPO workflow begins by extracting phenotypic phrases from clinical text via an LLM and then matching them via semantic similarity to entries within the database. The best term matches are returned to the LLM as context for final HPO term assignment of each phrase.</p><p><strong>Results: </strong>Performance was benchmarked on 112 published case reports with 1792 manually assigned HPO terms and compared to Doc2HPO, ClinPhen, and FastHPOCR. In evaluations, RAG-HPO + LLaMa-3.1 70B achieved a mean precision of 0.81, recall of 0.76, and an F1 score of 0.78-significantly surpassing conventional tools (p < 0.00001). RAG-HPO returned 1648 terms, of which 19.1% (315) were false positives that did not exactly match our manually annotated standard. Among these, < 1% (1/315) represented hallucinations, and 1.3% (4/315) represented terms with no ontological relationship to the desired target; the remaining false positives (95.2%, 300/315) were broader ancestor terms of the target term, which may still be relevant to users in many contexts.</p><p><strong>Conclusions: </strong>RAG-HPO is a user-friendly, adaptable tool designed for secure evaluation of clinical text and outperforms standard HPO-matching tools in precision, recall, and F1. Its enhanced precision and recall represent a substantial advancement in phenotypic analysis, accelerating the identification of genetic mechanisms underlying rare diseases and driving progress in genetic research and clinical genomics. RAG-HPO is available at https://github.com/PoseyPod/RAG-HPO .</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"91"},"PeriodicalIF":10.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12359922/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144872869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aging increases susceptibility to liver fibrosis through enhanced NAT10-mediated ac4C modification of TGFβ1 mRNA.","authors":"Xuyun Peng, Panlong Li, Ying Zhang, Qi Zhang, Weicheng Liang","doi":"10.1186/s13073-025-01520-x","DOIUrl":"10.1186/s13073-025-01520-x","url":null,"abstract":"<p><strong>Background: </strong>The epidemiological observational studies unveiled that aging is one of the risk factors for liver fibrosis, and the hepatic tissues in the elderly harbor more fibrotic lesions when compared to those in young people. Previous investigations found that TGFβ1 was elevated with aging and promoted liver fibrosis. However, the underlying mechanisms of aging and liver fibrosis remain largely unknown.</p><p><strong>Methods: </strong>CCl₄-induced liver fibrosis animal models were used in this study. The impact of NAT10 on liver fibrosis and cellular senescence was analyzed by using NAT10 overexpression or knockout hepatic stellate cell lines. The distribution of ac4C RNA modification was monitored by the acRIP-seq. The RNA-protein interaction was examined by the RNA immunoprecipitation.</p><p><strong>Results: </strong>We demonstrated that the middle-aged mice were more susceptible to the CCl₄-induced liver fibrosis when compared to the young mice. Then, we found that RNA ac4C-modifying enzyme NAT10 was transcriptionally activated by TGFβ1/SMAD2/3 axis and highly expressed in the aging liver as well as liver fibrosis mouse model. Suppression of NAT10 by its inhibitor Remodelin or specific shRNA attenuated senescence and activation of hepatic stellate cells. Subsequent studies found that NAT10 directly triggered the ac4C RNA modification of TGFβ1 mRNA by physically interacting with the RNA-binding protein PTBP1, enhancing the stabilization of TGFβ1 mRNA and subsequent activation of TGFβ/SMAD signaling pathway. Animal studies demonstrated that inhibition of NAT10 by Remodelin significantly alleviated liver fibrosis and cellular senescence.</p><p><strong>Conclusions: </strong>Our study identified a previously unknown mechanism of how TGFβ1 drives cellular senescence and liver fibrosis through NAT10-mediated ac4C mRNA modification.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"90"},"PeriodicalIF":10.4,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12355746/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144859098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SCAN-ACT: adoptive T cell therapy target discovery through single-cell transcriptomics.","authors":"Stefano Testa, Aastha Pal, Ajay Subramanian, Sushama Varma, Jack Pengfei Tang, Danielle Graham, Sara Arfan, Minggui Pan, Nam Q Bui, Kristen N Ganjoo, Sarah Dry, Paul Huang, Matt van de Rijn, Wei Jiang, Anusha Kalbasi, Everett J Moding","doi":"10.1186/s13073-025-01514-9","DOIUrl":"10.1186/s13073-025-01514-9","url":null,"abstract":"<p><strong>Background: </strong>The FDA approval of T cell receptor-engineered T cells (TCR-T) for synovial sarcoma demonstrates the potential for adoptive T cell therapies (ACTs) in solid tumors. However, the paucity of tumor-associated targets without expression in normal tissues remains a major bottleneck, especially in rare cancer subtypes.</p><p><strong>Methods: </strong>We developed a comprehensive computational pipeline called SCAN-ACT that leverages single-cell RNA sequencing and multi-omics data from tumor and normal tissues to nominate and prioritize putative targets for both chimeric antigen receptor (CAR)- and TCR-T cells. For surface membrane targets, SCAN-ACT proposes monospecific targets and potential target pairs for bispecific Boolean logic-gated CAR T cells. For peptide-MHC targets, SCAN-ACT proposes intracellular peptides bound to a diverse set of human leukocyte antigens. Selected targets were validated experimentally by protein expression and for peptide-MHC binding.</p><p><strong>Results: </strong>We applied the SCAN-ACT pipeline to soft tissue sarcoma (STS), analyzing 986,749 single cells to identify and prioritize 395 monospecific CAR-T targets, 14,192 bispecific CAR-T targets, and 5020 peptide-MHC targets for TCR-T cells. Proposed targets and target pairs reflected the mesenchymal, neuronal, and hematopoietic ontogeny of STS. We further validated SCAN-ACT in glioblastoma revealing its versatility.</p><p><strong>Conclusions: </strong>This work provides a robust data repository along with a web-based and user-friendly set of analysis tools to accelerate ACT development for solid tumors ( https://scanact.stanford.edu/ ).</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"89"},"PeriodicalIF":10.4,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12351953/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144855086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The quest for a complete understanding of the human genome.","authors":"Ahmad Abou Tayoun, Hamad Ali, Younes Mokrab","doi":"10.1186/s13073-025-01523-8","DOIUrl":"10.1186/s13073-025-01523-8","url":null,"abstract":"<p><p>An integrated roadmap toward clinical interpretation of the complete human genome is in dire need. We discuss approaches to meet this goal, including integrating data from diverse, well-phenotyped populations with enhanced long-read genome assemblies, variant calling as well as improved predictive models and scalable functional assays.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"88"},"PeriodicalIF":10.4,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12333148/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144798833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building digital histology models of transcriptional tumor programs with generative deep learning for pathology-based precision medicine.","authors":"Hanna M Hieromnimon, James Dolezal, Kristina Doytcheva, Frederick M Howard, Sara Kochanny, Zhenyu Zhang, Robert L Grossman, Kevin Tanager, Cindy Wang, Jakob Nikolas Kather, Evgeny Izumchenko, Nicole A Cipriani, Elana J Fertig, Alexander T Pearson, Samantha J Riesenfeld","doi":"10.1186/s13073-025-01502-z","DOIUrl":"10.1186/s13073-025-01502-z","url":null,"abstract":"<p><strong>Background: </strong>Precision oncology depends on identifying the biological vulnerabilities of a tumor. Molecular assays, like transcriptomics, provide an information-rich view of the tumor that can be leveraged to inform therapeutic selection. However, the costs of such assays can be prohibitive for clinical translation at scale. Histology-based imaging remains a predominant means of diagnosis that is widely accessible. To more broadly leverage limited molecular datasets, models have been trained to use histology to infer the expression of individual genes or pathways, with varying levels of accuracy and explainability.</p><p><strong>Methods: </strong>Our approach detects expression of transcriptional programs from tumor histology and interprets the image features supporting program detection. Specifically, we used RNA-seq data from squamous cell carcinoma (SCC) patients to infer cohesive expression patterns of multiple genes. Then, we used deep learning techniques to train a computational model to predict the activity levels of the transcriptional programs directly from histology images. We exploited that predictive capability to generate synthetic digital models of the cellular histology of each transcriptional program, using generative adversarial networks to isolate image features supporting specific transcriptional predictions and pathologist review to interpret the images.</p><p><strong>Results: </strong>Applying our histologically integrated latent space analysis to SCCs revealed sets of genes associated with both pathologist-interpretable image features and clinically relevant processes, including immune response, collagen remodeling, and fibrosis, going beyond predictions of individual molecular features.</p><p><strong>Conclusions: </strong>Our results demonstrate an approach for discovering clinically interpretable histological features that indicate molecular, potentially treatment-informing, biological processes. These features are detectable in widely available histology slides, allowing a standard microscope to deliver complex, patient-specific molecular information.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"87"},"PeriodicalIF":10.4,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12329968/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144798832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contribution of rare coding variants to microcephaly in individuals with neurodevelopmental disorders.","authors":"Jihoon G Yoon, Hyunsoo Jang, Seungbok Lee, Se Song Jang, Soojin Park, Jaeso Cho, Minji Kim, Jiye Han, Hyounji Yun, Man Jin Kim, Soo Yeon Kim, Woo Joong Kim, Anna Cho, Jin Sook Lee, Murim Choi, Alberto Fernandez-Jaen, Sebastian Silva, Reinaldo Uribe-San-Martín, Christian Cantillano, Noriko Miyake, Byung Chan Lim, Jung Min Ko, Ki Joong Kim, Ki-Jun Yoon, Jong-Hee Chae","doi":"10.1186/s13073-025-01513-w","DOIUrl":"10.1186/s13073-025-01513-w","url":null,"abstract":"<p><strong>Background: </strong>Microcephaly, characterized by an abnormally small head size, frequently co-occurs with neurodevelopmental disorders (NDDs). While the genetic basis of NDDs has been widely investigated, the contribution of rare coding variants to microcephaly remains poorly understood.</p><p><strong>Methods: </strong>We investigated the relationships between head circumference and rare coding variants in 418 individuals with microcephaly, analyzing data from 1050 exomes (312 trios and 106 proband-only samples). Participants were classified into primary microcephaly (PM) and secondary microcephaly (SM) groups, and their clinical and genetic characteristics were systematically assessed. The functional impact of high-priority candidate genes, RTF1 and ASAP2, was further validated using neural progenitor cells (NPCs) and human forebrain organoid models.</p><p><strong>Results: </strong>Exome sequencing revealed 142 causative and 12 candidate genes associated with microcephaly. Pathway analyses indicated that PM genes are linked to early phases of brain development, whereas SM genes are more associated with later stages of neuronal maturation. In addition, the PM group had a significantly higher proportion of autosomal recessive disorders and exhibited more severe microcephaly than the SM group. Notably, females displayed greater microcephaly severity than males, primarily attributable to differences in the origin of the allele and inheritance patterns on the X chromosome. Functional experiments using CRISPR-Cas9 knockout in NPCs and brain organoids demonstrated reduced NPC proliferation, supporting the essential role of RTF1 and ASAP2 in brain development.</p><p><strong>Conclusions: </strong>This study sheds light on the complex genetic architecture of microcephaly, emphasizing the impact of rare coding variants on brain development and delineating distinct clinical and molecular profiles underlying PM and SM.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"86"},"PeriodicalIF":10.4,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12326769/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144794214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blocking CXCR4⁺ CD4⁺ T cells reprograms T_reg-mediated immunosuppression via modulating the Rho-GTPase/NF-κB signaling axis.","authors":"Canhui Cao, Miaochun Xu, Ting Peng, Xiaojie Liu, Shitong Lin, Yashi Xu, Tian Chu, Shiyi Liu, Ping Wu, Bai Hu, Wencheng Ding, Li Li, Ding Ma, Peng Wu","doi":"10.1186/s13073-025-01515-8","DOIUrl":"10.1186/s13073-025-01515-8","url":null,"abstract":"<p><strong>Background: </strong>While clinical trials have shown that CXCR4 antagonists can enhance the efficacy of cancer immunotherapy, the molecular mechanisms by which CXCR4 modulates the tumor microenvironment remain poorly understood. We recently identified CXCR4 as a regulator of exhausted CD8⁺ T cell phenotypes in cancer. Here, we investigate its role in orchestrating regulatory T (T_reg) cell-mediated immunosuppression within tumors.</p><p><strong>Methods: </strong>We conducted meta-analyses of single-cell RNA-seq datasets from pan-cancer tissues to characterize CXCR4 expression patterns in CD4⁺ T cells. Using CXCR4 antagonists and conditional knockout mice (Cxcr4^flox/flox, Lck^Cre), we inhibited T_reg phenotypes in vivo. Through single-cell transcriptomics and single-cell ATAC-seq of the cervical cancer mouse model, phosphoproteomics, and ChIP-seq analyses, we elucidated how CXCR4 blockade in CD4⁺ T cells suppresses activated T_reg phenotypes by modulating the Rho-GTPase/NF-κB signaling axis. We further integrated RNA-seq data, clinical trial datasets (NCT02826486 and NCT04516616), and human organoid models to validate the therapeutic potential of CXCR4 inhibition in enhancing antitumor immunotherapy.</p><p><strong>Results: </strong>Single-cell transcriptomics of CD4⁺ T cells across multiple cancers revealed CXCR4 expression was associated with T_reg cell developmental trajectories. Pharmacological and genetic inhibition of CXCR4 inhibited T_reg phenotypes in cervical cancer and breast cancer. Mechanistically, phosphoproteomics and ChIP-seq analyses unveiled that blocking CXCR4⁺ CD4⁺ T cells reduced activated T_reg phenotypes by modulating the Rho-GTPase/NF-κB signaling axis. Single-cell transcriptomic and multi-omic analyses demonstrated that blocking CXCR4⁺ CD4⁺ T cells promoted immunotherapy via reprogramming T_reg-mediated immunosuppression. Furthermore, clinical trial data and human cervical cancer organoids confirmed that blocking CXCR4 enhances antitumor immunotherapy by reducing T_reg phenotypes.</p><p><strong>Conclusions: </strong>Our study highlights the crucial role of CXCR4 in deriving T_reg-mediated immunosuppression via regulating the Rho-GTPase/NF-κB signaling axis, informing the potential of combining CXCR4 blockades with T cell-targeted immunotherapies.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"85"},"PeriodicalIF":10.4,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12323159/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144784131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"varCADD: large sets of standing genetic variation enable genome-wide pathogenicity prediction.","authors":"Lusiné Nazaretyan, Philipp Rentzsch, Martin Kircher","doi":"10.1186/s13073-025-01517-6","DOIUrl":"10.1186/s13073-025-01517-6","url":null,"abstract":"<p><strong>Background: </strong>Machine learning and artificial intelligence are increasingly being applied to identify phenotypically causal genetic variation. These data-driven methods require comprehensive training sets to deliver reliable results. However, large unbiased datasets for variant prioritization and effect predictions are rare as most of the available databases do not represent a broad ensemble of variant effects and are often biased towards the protein-coding genome, or even towards few well-studied genes.</p><p><strong>Methods: </strong>To overcome these issues, we propose several alternative training sets derived from subsets of human standing variation. Specifically, we use variants identified from whole-genome sequences of 71,156 individuals contained in gnomAD v3.0 and approximate the benign set with frequent standing variation and the deleterious set with rare or singleton variation. We apply the Combined Annotation Dependent Depletion framework (CADD) and train several alternative models using CADD v1.6.</p><p><strong>Results: </strong>Using the NCBI ClinVar validation set, we demonstrate that the alternative models have state-of-the-art accuracy, globally on par with deleteriousness scores of CADD v1.6 and v1.7, but also outperforming them in certain genomic regions. Being larger than conventional training datasets, including the evolutionary-derived training dataset of about 30 million variants in CADD, standing variation datasets cover a broader range of genomic regions and rare instances of the applied annotations. For example, they cover more recent evolutionary changes common in gene regulatory regions, which are more challenging to assess with conventional tools.</p><p><strong>Conclusions: </strong>Standing variation allows us to directly train state-of-the-art models for genome-wide variant prioritization or to augment evolutionary-derived variants in training. The proposed datasets have several advantages, like being substantially larger and potentially less biased. Datasets derived from standing variation represent natural allelic changes in the human genome and do not require extensive simulations and adaptations to annotations of evolutionary-derived sequence alterations used for CADD training. We provide datasets as well as trained models to the community for further development and application.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"84"},"PeriodicalIF":10.4,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12323237/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144784132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}