Genome Medicine最新文献

{"title":"Cell-type-specific subtyping of epigenomes improves prognostic stratification of cancer.","authors":"Qi Luo, Andrew E Teschendorff","doi":"10.1186/s13073-025-01453-5","DOIUrl":"10.1186/s13073-025-01453-5","url":null,"abstract":"<p><strong>Background: </strong>Most molecular classifications of cancer are based on bulk-tissue profiles that measure an average over many distinct cell types. As such, cancer subtypes inferred from transcriptomic or epigenetic data are strongly influenced by cell-type composition and do not necessarily reflect subtypes defined by cell-type-specific cancer-associated alterations, which could lead to suboptimal cancer classifications.</p><p><strong>Methods: </strong>To address this problem, we here propose the novel concept of cell-type-specific combinatorial clustering (CELTYC), which aims to group cancer samples by the molecular alterations they display in specific cell types. We illustrate this concept in the context of DNA methylation data of liver and kidney cancer, deriving in each case novel cancer subtypes and assessing their prognostic relevance against current state-of-the-art prognostic models.</p><p><strong>Results: </strong>In both liver and kidney cancer, we reveal improved cell-type-specific prognostic models, not discoverable using standard methods. In the case of kidney cancer, we show how combinatorial indexing of epithelial and immune-cell clusters define improved prognostic models driven by synergy of high mitotic age and altered cytokine signaling. We validate the improved prognostic models in independent datasets and identify underlying cytokine-immune-cell signatures driving poor outcome.</p><p><strong>Conclusions: </strong>In summary, cell-type-specific combinatorial clustering is a valuable strategy to help dissect and improve current prognostic classifications of cancer in terms of the underlying cell-type-specific epigenetic and transcriptomic alterations.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"34"},"PeriodicalIF":10.4,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11967111/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143779480","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":"STopover captures spatial colocalization and interaction in the tumor microenvironment using topological analysis in spatial transcriptomics data.","authors":"Sungwoo Bae, Hyekyoung Lee, Kwon Joong Na, Dong Soo Lee, Hongyoon Choi, Young Tae Kim","doi":"10.1186/s13073-025-01457-1","DOIUrl":"10.1186/s13073-025-01457-1","url":null,"abstract":"<p><p>Unraveling the spatial configuration of the tumor microenvironment (TME) is crucial for elucidating tumor-immune interactions based on immuno-oncology. We present STopover, a novel approach utilizing spatially resolved transcriptomics (SRT) data and topological analysis to investigate the TME. By gradually lowering the feature threshold, connected components (CCs) are extracted based on spatial distance and persistence, with Jaccard indices quantifying their spatial overlap, and transcriptomic profiles are permutated to assess statistical significance. Applied to lung and breast cancer SRT, STopover revealed immune and stromal cell infiltration patterns, predicted key cell-cell communication, and identified relevant regions, shedding light on cancer pathophysiology (URL: https://github.com/bsungwoo/STopover ).</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"33"},"PeriodicalIF":10.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11963362/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763690","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":"DNA methylation memory of pancreatic acinar-ductal metaplasia transition state altering Kras-downstream PI3K and Rho GTPase signaling in the absence of Kras mutation.","authors":"Emily K W Lo, Adrian Idrizi, Rakel Tryggvadottir, Weiqiang Zhou, Wenpin Hou, Hongkai Ji, Patrick Cahan, Andrew P Feinberg","doi":"10.1186/s13073-025-01452-6","DOIUrl":"10.1186/s13073-025-01452-6","url":null,"abstract":"<p><strong>Background: </strong>A critical area of recent cancer research is the emergence of transition states between normal and cancer that exhibit increased cell plasticity which underlies tumor cell heterogeneity. Pancreatic ductal adenocarcinoma (PDAC) can arise from the combination of a transition state termed acinar-to-ductal metaplasia (ADM) and a gain-of-function mutation in the proto-oncogene KRAS. During ADM, digestive enzyme-producing acinar cells acquire a transient ductal epithelium-like phenotype while maintaining their geographical acinar organization. One route of ADM initiation is the overexpression of the Krüppel-like factor 4 gene (KLF4) in the absence of oncogenic driver mutations. Here, we asked to what extent cells acquire and retain an epigenetic memory of the ADM transition state in the absence of oncogene mutation.</p><p><strong>Methods: </strong>We profiled the DNA methylome and transcriptome of KLF4-induced ADM in transgenic mice at various timepoints during and after recovery from ADM. We validated the identified DNA methylation and transcriptomic signatures in the widely used caerulein model of inducible pancreatitis.</p><p><strong>Results: </strong>We identified differential DNA methylation at Kras-downstream PI3K and Rho/Rac/Cdc42 GTPase pathway genes during ADM, as well as a corresponding gene expression increase in these pathways. Importantly, differential methylation persisted after gene expression returned to normal. Caerulein exposure, which induces widespread digestive system changes in addition to ADM, showed similar changes in DNA methylation in ADM cells. Regions of differential methylation were enriched for motifs of KLF and AP-1 family transcription factors, as were those of human pancreatic intraepithelial neoplasia (PanIN) samples, demonstrating the relevance of this epigenetic transition state memory in human carcinogenesis. Finally, single-cell spatial transcriptomics revealed that these ADM transition cells were enriched for PI3K pathway and AP1 family members.</p><p><strong>Conclusions: </strong>Our comprehensive study of DNA methylation in the acinar-ductal metaplasia transition state links epigenetic memory to cancer-related cell plasticity even in the absence of oncogenic mutation.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"32"},"PeriodicalIF":10.4,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143742771","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 Chinese gut virus catalogue reveals gut virome diversity and disease-related viral signatures.","authors":"Qiulong Yan, Liansha Huang, Shenghui Li, Yue Zhang, Ruochun Guo, Pan Zhang, Zhixin Lei, Qingbo Lv, Fang Chen, Zhiming Li, Jinxin Meng, Jing Li, Guangyang Wang, Changming Chen, Hayan Ullah, Lin Cheng, Shao Fan, Wei You, Yan Zhang, Jie Ma, Shanshan Sha, Wen Sun","doi":"10.1186/s13073-025-01460-6","DOIUrl":"10.1186/s13073-025-01460-6","url":null,"abstract":"<p><strong>Background: </strong>The gut viral community has been increasingly recognized for its role in human physiology and health; however, our understanding of its genetic makeup, functional potential, and disease associations remains incomplete.</p><p><strong>Methods: </strong>In this study, we collected 11,286 bulk or viral metagenomes from fecal samples across large-scale Chinese populations to establish a Chinese Gut Virus Catalogue (cnGVC) using a de novo virus identification approach. We then examined the diversity and compositional patterns of the gut virome in relation to common diseases by analyzing 6311 bulk metagenomes representing 28 disease or unhealthy states.</p><p><strong>Results: </strong>The cnGVC contains 93,462 nonredundant viral genomes, with over 70% of these being novel viruses not included in existing gut viral databases. This resource enabled us to characterize the functional diversity and specificity of the gut virome. Using cnGVC, we profiled the gut virome in large-scale populations, assessed sex- and age-related variations, and identified 4238 universal viral signatures of diseases. A random forest classifier based on these signatures achieved high accuracy in distinguishing diseased individuals from controls (AUC = 0.698) and high-risk patients from controls (AUC = 0.761), and its predictive ability was also validated in external cohorts.</p><p><strong>Conclusions: </strong>Our resources and findings significantly expand the current understanding of the human gut virome and provide a comprehensive view of the associations between gut viruses and common diseases. This will pave the way for novel strategies in the treatment and prevention of these diseases.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"30"},"PeriodicalIF":10.4,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11938785/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143718151","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":"STRchive: a dynamic resource detailing population-level and locus-specific insights at tandem repeat disease loci.","authors":"Laurel Hiatt, Ben Weisburd, Egor Dolzhenko, Vincent Rubinetti, Akshay K Avvaru, Grace E VanNoy, Nehir Edibe Kurtas, Heidi L Rehm, Aaron R Quinlan, Harriet Dashnow","doi":"10.1186/s13073-025-01454-4","DOIUrl":"10.1186/s13073-025-01454-4","url":null,"abstract":"<p><p>Approximately 8% of the human genome consists of repetitive elements called tandem repeats (TRs): short tandem repeats (STRs) of 1-6 bp motifs and variable number tandem repeats (VNTRs) of 7 + bp motifs. TR variants contribute to several dozen monogenic diseases but remain understudied and enigmatic. It remains comparatively challenging to interpret the clinical significance of TR variants, particularly relative to single nucleotide variants. We present STRchive ( http://strchive.org/ ), a dynamic resource consolidating information on TR disease loci from the research literature, up-to-date clinical resources, and large-scale genomic databases, streamlining TR variant interpretation at disease-associated loci.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"29"},"PeriodicalIF":10.4,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11938676/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143718148","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":"Advantages of updated WHO mutation catalog combined with existing whole-genome sequencing-based approaches for Mycobacterium tuberculosis resistance prediction.","authors":"Yiwang Chen, Xuecong Zhang, Jialei Liang, Qi Jiang, Mijiti Peierdun, Peng Xu, Howard E Takiff, Qian Gao","doi":"10.1186/s13073-025-01458-0","DOIUrl":"10.1186/s13073-025-01458-0","url":null,"abstract":"<p><strong>Background: </strong>The WHO recently released a second edition of the mutation catalog for predicting drug resistance in Mycobacterium tuberculosis (MTB). This study evaluated its effectiveness compared to existing whole-genome sequencing (WGS)-based prediction methods and proposes a novel approach for its optimization.</p><p><strong>Methods: </strong>We tested the accuracy of five tools-the WHO catalog, TB Profiler, SAM-TB, GenTB, and MD-CNN-for predicting drug susceptibility on a global dataset of 36,385 MTB isolates with high-quality phenotypic drug susceptibility testing (DST) and WGS data. By integrating the genotypic DST predictions of these five tools in an ensemble machine learning framework, we developed an improved computational model for MTB drug susceptibility prediction. We then validated the ensemble model on 860 MTB isolates with phenotypic and WGS data collected in Shenzhen, China (2013-2019) and Valencia, Spain (2014-2016).</p><p><strong>Results: </strong>Among the five genotypic DST tools for predicting susceptibility to ten drugs, MD-CNN exhibited the highest overall performance (AUC 92.1%; 95% CI 89.8-94.4%). The WHO catalog demonstrated the highest specificity of 97.3% (95% CI 95.8-98.4%), while TB Profiler had the best sensitivity at 79.5% (95% CI 71.8-86.2%). The ensemble machine learning model (AUC 93.4%; 95% CI 91.4-95.4%) outperformed all of the five individual tools, with a specificity of 95.4% (95% CI 93.0-97.6%) and a sensitivity of 84.1% (95% CI 78.8-88.8%), principally due to considerable improvements in second-line drug resistance predictions (AUC 91.8%; 95% CI 89.6-94.0%).</p><p><strong>Conclusions: </strong>The second edition of the WHO MTB mutation catalog does not, by itself, perform better than existing tools for predicting MTB drug resistance. An integrative approach combining the WHO catalog with other genotypic DST methods significantly enhances prediction accuracy.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"31"},"PeriodicalIF":10.4,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11938600/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143718586","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":"Metronidazole response profiles of Gardnerella species are congruent with phylogenetic and comparative genomic analyses.","authors":"Katherine A Innamorati, Joshua P Earl, Shirley C Barrera, Rachel L Ehrlich, Josephine Aiyeku, Ari Gordon, Evan Powell, Adam C Retchless, Azad Ahmed, Bhaswati Sen, Sergey Balashov, Joshua Chang Mell, Sharon L Hillier, Garth D Ehrlich","doi":"10.1186/s13073-025-01446-4","DOIUrl":"10.1186/s13073-025-01446-4","url":null,"abstract":"<p><strong>Background: </strong>Bacterial vaginosis (BV) affects 20-50% of reproductive-age female patients annually, arising when opportunistic pathogens outcompete healthy vaginal flora. Many patients fail to resolve symptoms with a course of metronidazole, the current first-line treatment for BV. Our study was designed to identify genomic variation associated with metronidazole resistance among strains of Gardnerella vaginalis spp. (GV), a genus of biogenic-amine-producing bacteria closely associated with BV pathogenesis, for the development of a companion molecular diagnostic.</p><p><strong>Methods: </strong>Whole-genome sequencing and comparative genomic metrics, including average nucleotide identity and GC content, were performed on a diverse set of 129 GV genomes to generate data for detailed taxonomic analyses. Pangenomic analyses were employed to construct a phylogenetic tree and cluster highly related strains within genospecies. G. vaginalis spp. clinical isolates within our collection were subjected to plate-based minimum inhibitory concentration (MIC) testing of metronidazole (n = 60) and clindamycin (n = 63). DECIPHER and MAFFT were used to identify genospecies-specific primers associated with antibiotic-resistance phenotypes. PCR-based analyses with these primers were used to confirm their specificity for the relevant genospecies.</p><p><strong>Results: </strong>Eleven distinct genospecies based on standard ANI criteria were identified among the GV strains in our collection. Metronidazole MIC testing revealed six genospecies within a closely related phylogenetic clade contained only highly metronidazole-resistant strains (MIC ≥ 32 µg/mL) and suggested at least two mechanisms of metronidazole resistance within the eleven GV genospecies. All strains within the six highly metronidazole-resistant genospecies displayed susceptibility to clinically relevant clindamycin concentrations (MIC ≤ 2 µg/mL). A PCR-based molecular diagnostic assay was developed to distinguish between members of the metronidazole-resistant and mixed-response genospecies, which should be useful for determining the clade membership of various GV strains and could assist in the selection of appropriate antibiotic therapies for BV cases.</p><p><strong>Conclusions: </strong>This study provides comparative genomic and phylogenetic evidence for eleven distinct genospecies within the genus Gardnerella vaginalis spp., and identifies genospecies-specific responses to metronidazole, the first-line treatment for BV. A companion molecular diagnostic assay was developed that is capable of identifying essentially all highly metronidazole-resistant strains that phylogenetically cluster together within the GV genospecies, which is informative for antibiotic treatment options.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"28"},"PeriodicalIF":10.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11934483/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143708153","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":"Rare damaging CCR2 variants are associated with lower lifetime cardiovascular risk.","authors":"Marios K Georgakis, Rainer Malik, Omar El Bounkari, Natalie R Hasbani, Jiang Li, Jennifer E Huffman, Gabrielle Shakt, Reinier W P Tack, Tamara N Kimball, Yaw Asare, Alanna C Morrison, Noah L Tsao, Renae Judy, Braxton D Mitchell, Huichun Xu, May E Montasser, Ron Do, Eimear E Kenny, Ruth J F Loos, James G Terry, John Jeffrey Carr, Joshua C Bis, Bruce M Psaty, W T Longstreth, Kendra A Young, Sharon M Lutz, Michael H Cho, Jai Broome, Alyna T Khan, Fei Fei Wang, Nancy Heard-Costa, Sudha Seshadri, Ramachandran S Vasan, Nicholette D Palmer, Barry I Freedman, Donald W Bowden, Lisa R Yanek, Brian G Kral, Lewis C Becker, Patricia A Peyser, Lawrence F Bielak, Farah Ammous, April P Carson, Michael E Hall, Laura M Raffield, Stephen S Rich, Wendy S Post, Russel P Tracy, Kent D Taylor, Xiuqing Guo, Michael C Mahaney, Joanne E Curran, John Blangero, Shoa L Clarke, Jeffrey W Haessler, Yao Hu, Themistocles L Assimes, Charles Kooperberg, Jürgen Bernhagen, Christopher D Anderson, Scott M Damrauer, Ramin Zand, Jerome I Rotter, Paul S de Vries, Martin Dichgans","doi":"10.1186/s13073-025-01456-2","DOIUrl":"10.1186/s13073-025-01456-2","url":null,"abstract":"<p><strong>Background: </strong>Previous work has shown a role of CCL2, a key chemokine governing monocyte trafficking, in atherosclerosis. However, it remains unknown whether targeting CCR2, the cognate receptor of CCL2, provides protection against human atherosclerotic cardiovascular disease.</p><p><strong>Methods: </strong>Computationally predicted damaging or loss-of-function (REVEL > 0.5) variants within CCR2 were detected in whole-exome-sequencing data from 454,775 UK Biobank participants and tested for association with cardiovascular endpoints in gene-burden tests. Given the key role of CCR2 in monocyte mobilization, variants associated with lower monocyte count were prioritized for experimental validation. The response to CCL2 of human cells transfected with these variants was tested in migration and cAMP assays. Validated damaging variants were tested for association with cardiovascular endpoints, atherosclerosis burden, and vascular risk factors. Significant associations were replicated in six independent datasets (n = 1,062,595).</p><p><strong>Results: </strong>Carriers of 45 predicted damaging or loss-of-function CCR2 variants (n = 787 individuals) were at lower risk of myocardial infarction and coronary artery disease. One of these variants (M249K, n = 585, 0.15% of European ancestry individuals) was associated with lower monocyte count and with both decreased downstream signaling and chemoattraction in response to CCL2. While M249K showed no association with conventional vascular risk factors, it was consistently associated with a lower risk of myocardial infarction (odds ratio [OR]: 0.66, 95% confidence interval [CI]: 0.54-0.81, p = 6.1 × 10^-5) and coronary artery disease (OR: 0.74, 95%CI: 0.63-0.87, p = 2.9 × 10^-4) in the UK Biobank and in six replication cohorts. In a phenome-wide association study, there was no evidence of a higher risk of infections among M249K carriers.</p><p><strong>Conclusions: </strong>Carriers of an experimentally confirmed damaging CCR2 variant are at a lower lifetime risk of myocardial infarction and coronary artery disease without carrying a higher risk of infections. Our findings provide genetic support for the translational potential of CCR2-targeting as an atheroprotective approach.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"27"},"PeriodicalIF":10.4,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11929344/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143676886","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":"Long-read sequencing identifies copy-specific markers of SMN gene conversion in spinal muscular atrophy.","authors":"M M Zwartkruis, M G Elferink, D Gommers, I Signoria, L Blasco-Pérez, M Costa-Roger, J van der Sel, I J Renkens, J W Green, J V Kortooms, C Vermeulen, R Straver, H W M van Deutekom, J H Veldink, F Asselman, E F Tizzano, R I Wadman, W L van der Pol, G W van Haaften, E J N Groen","doi":"10.1186/s13073-025-01448-2","DOIUrl":"10.1186/s13073-025-01448-2","url":null,"abstract":"<p><strong>Background: </strong>The complex 2 Mb survival motor neuron (SMN) locus on chromosome 5q13, including the spinal muscular atrophy (SMA)-causing gene SMN1 and modifier SMN2, remains incompletely resolved due to numerous segmental duplications. Variation in SMN2 copy number, presumably influenced by SMN1 to SMN2 gene conversion, affects disease severity, though SMN2 copy number alone has insufficient prognostic value due to limited genotype-phenotype correlations. With advancements in newborn screening and SMN-targeted therapies, identifying genetic markers to predict disease progression and treatment response is crucial. Progress has thus far been limited by methodological constraints.</p><p><strong>Methods: </strong>To address this, we developed HapSMA, a method to perform polyploid phasing of the SMN locus to enable copy-specific analysis of SMN and its surrounding genes. We used HapSMA on publicly available Oxford Nanopore Technologies (ONT) sequencing data of 29 healthy controls and performed long-read, targeted ONT sequencing of the SMN locus of 31 patients with SMA.</p><p><strong>Results: </strong>In healthy controls, we identified single nucleotide variants (SNVs) specific to SMN1 and SMN2 haplotypes that could serve as gene conversion markers. Broad phasing including the NAIP gene allowed for a more complete view of SMN locus variation. Genetic variation in SMN2 haplotypes was larger in SMA patients. Forty-two percent of SMN2 haplotypes of SMA patients showed varying SMN1 to SMN2 gene conversion breakpoints, serving as direct evidence of gene conversion as a common genetic characteristic in SMA and highlighting the importance of inclusion of SMA patients when investigating the SMN locus.</p><p><strong>Conclusions: </strong>Our findings illustrate that both methodological advances and the analysis of patient samples are required to advance our understanding of complex genetic loci and address critical clinical challenges.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"26"},"PeriodicalIF":10.4,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11927269/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143676883","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":"Predicting MHC-I ligands across alleles and species: how far can we go?","authors":"Daniel M Tadros, Julien Racle, David Gfeller","doi":"10.1186/s13073-025-01450-8","DOIUrl":"10.1186/s13073-025-01450-8","url":null,"abstract":"<p><strong>Background: </strong>CD8⁺ T-cell activation is initiated by the recognition of epitopes presented on class I major histocompatibility complex (MHC-I) molecules. Identifying such epitopes is useful for molecular understanding of cellular immune responses and can guide the development of personalized vaccines for various diseases including cancer. For a few hundred common human and mouse MHC-I alleles, large datasets of ligands are available and machine learning MHC-I ligand predictors trained on such data reach high prediction accuracy. However, for the vast majority of other MHC-I alleles, no ligand is known.</p><p><strong>Methods: </strong>We capitalize on an expanded architecture of our MHC-I ligand predictor (MixMHCpred3.0) to systematically assess the extent to which predictions of MHC-I ligands can be applied to MHC-I alleles that currently lack known ligand data.</p><p><strong>Results: </strong>Our results reveal high prediction accuracy for most MHC-I alleles in human and in laboratory mouse strains, but significantly lower accuracy in other species. Our work further outlines some of the molecular determinants of MHC-I ligand prediction accuracy across alleles and species. Robust benchmarking on external data shows that our MHC-I ligand predictor demonstrates competitive performance relative to other state-of-the-art MHC-I ligand predictors and can be used for CD8⁺ T-cell epitope predictions.</p><p><strong>Conclusions: </strong>Our work provides a valuable tool for predicting antigen presentation across all human and mouse MHC-I alleles. MixMHCpred3.0 tool is available at https://github.com/GfellerLab/MixMHCpred .</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"25"},"PeriodicalIF":10.4,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11927126/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669522","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}