Genome Medicine最新文献

{"title":"HGVS Nomenclature 2024: improvements to community engagement, usability, and computability.","authors":"Reece K Hart, Ivo F A C Fokkema, Marina DiStefano, Ros Hastings, Jeroen F J Laros, Rachel Taylor, Alex H Wagner, Johan T den Dunnen","doi":"10.1186/s13073-024-01421-5","DOIUrl":"https://doi.org/10.1186/s13073-024-01421-5","url":null,"abstract":"<p><strong>Background: </strong>The Human Genome Variation Society (HGVS) Nomenclature is the global standard for describing and communicating variants in DNA, RNA, and protein sequences in clinical and research genomics. This manuscript details recent updates to the HGVS Nomenclature, highlighting improvements in governance, community engagement, website functionality, and underlying implementation of the standard.</p><p><strong>Methods: </strong>The HGVS Variant Nomenclature Committee (HVNC) now operates under the Human Genome Organization (HUGO), facilitating broader community feedback and collaboration with related standards organizations. The website has been redesigned using modern documentation tools and practices. The specification was updated to include guidance for transcript selection and to align with recent cross-consortia recommendations for the representation of gene fusions. A formal computational grammar was introduced to improve the precision and consistency of variant descriptions.</p><p><strong>Results: </strong>Major improvements in HGVS Nomenclature v. 21.1 include a redesigned website with enhanced navigation, search functionality, and mobile responsiveness; a new versioning policy aligned with software management practices; formal mechanisms for community feedback and change proposals; and adoption of Extended Backus-Naur Form (EBNF) for defining syntax. The specification now recommends MANE Select transcripts where appropriate and includes updated guidance for representing adjoined transcripts and gene fusions. All content is freely available under permissive licenses at hgvs-nomenclature.org.</p><p><strong>Conclusions: </strong>These advancements establish a more sustainable foundation for maintaining and evolving the HGVS Nomenclature while improving its accessibility and utility. The introduction of formal computational grammar marks a crucial step toward unambiguous variant descriptions that can be reliably processed by both humans and machines. Combined with enhanced community engagement mechanisms and improved guidance, these changes position the HGVS Nomenclature to better serve the evolving needs of clinical and research genomics while maintaining the stability that users require.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"16 1","pages":"149"},"PeriodicalIF":10.4,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142864021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SARS-CoV-2 introductions to the island of Ireland: a phylogenetic and geospatiotemporal study of infection dynamics.","authors":"Alan M Rice, Evan P Troendle, Stephen J Bridgett, Behnam Firoozi Nejad, Jennifer M McKinley, Declan T Bradley, Derek J Fairley, Connor G G Bamford, Timofey Skvortsov, David A Simpson","doi":"10.1186/s13073-024-01409-1","DOIUrl":"https://doi.org/10.1186/s13073-024-01409-1","url":null,"abstract":"<p><strong>Background: </strong>Ireland's COVID-19 response combined extensive SARS-CoV-2 testing to estimate incidence, with whole genome sequencing (WGS) for genome surveillance. As an island with two political jurisdictions-Northern Ireland (NI) and Republic of Ireland (RoI)-and access to detailed passenger travel data, Ireland provides a unique setting to study virus introductions and evaluate public health measures. Using a substantial Irish genomic dataset alongside global data from GISAID, this study aimed to trace the introduction and spread of SARS-CoV-2 across the island.</p><p><strong>Methods: </strong>We recursively searched for 29,518 SARS-CoV-2 genome sequences collected in Ireland from March 2020 to June 2022 within the global SARS-CoV-2 phylogenetic tree and identified clusters based on shared last common non-Irish ancestors. A maximum parsimony approach was used to assign a likely country of origin to each cluster. The geographic locations and collection dates of the samples in each introduction cluster were used to map the spread of the virus across Ireland. Downsampling was used to model the impact of varying levels of sequencing and normalisation for population permitted comparison between jurisdictions.</p><p><strong>Results: </strong>Six periods spanning the early introductions and the emergence of Alpha, Delta, and Omicron variants were studied in detail. Among 4439 SARS-CoV-2 introductions to Ireland, 2535 originated in England, with additional cases largely from the rest of Great Britain, United States of America, and Northwestern Europe. Introduction clusters ranged in size from a single to thousands of cases. Introductions were concentrated in the densely populated Dublin and Belfast areas, with many clusters spreading islandwide. Genetic phylogeny was able to effectively trace localised transmission patterns. Introduction rates were similar in NI and RoI for most variants, except for Delta, which was more frequently introduced to NI.</p><p><strong>Conclusions: </strong>Tracking individual introduction events enables detailed modelling of virus spread patterns and clearer assessment of the effectiveness of control measures. Stricter travel restrictions in RoI likely reduced Delta introductions but not infection rates, which were similar across jurisdictions. Local and global sequencing levels influence the information available from phylogenomic analyses and we describe an approach to assess the ability of a chosen WGS level to detect virus introductions.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"16 1","pages":"150"},"PeriodicalIF":10.4,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142864023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial transcriptome profiling identifies DTX3L and BST2 as key biomarkers in esophageal squamous cell carcinoma tumorigenesis.","authors":"Rutao Li, Na Li, Qianqian Yang, Xing Tong, Wei Wang, Chang Li, Jun Zhao, Dong Jiang, Haitao Huang, Chen Fang, Kai Xie, Jiamin Yuan, Shaomu Chen, Guangbin Li, Haitao Luo, Zhibo Gao, Dongfang Wu, Xiaoli Cui, Wei Jiang, Lingchuan Guo, Haitao Ma, Yu Feng","doi":"10.1186/s13073-024-01422-4","DOIUrl":"https://doi.org/10.1186/s13073-024-01422-4","url":null,"abstract":"<p><strong>Background: </strong>Understanding the stepwise progression of esophageal squamous cell carcinoma (ESCC) is crucial for developing customized strategies for early detection and optimal clinical management. Herein, we aimed to unravel the transcriptional and immunologic alterations occurring during malignant transformation and identify clinically significant biomarkers of ESCC.</p><p><strong>Methods: </strong>Digital spatial profiling (DSP) was performed on 11 patients with early-stage ESCC (pT1) to explore the transcriptional alterations in epithelial, immune cell, and non-immune cell stromal compartments across regions of distinct histology, including normal tissues, low- and high-grade dysplasia, and cancerous tissues. Furthermore, single-cell spatial transcriptomics was performed using the CosMx Spatial Molecular Imaging (SMI) system on 4 additional patients with pT1 ESCC. Immunohistochemical (IHC) analysis was performed on consecutive histological sections of 20 pT1 ESCCs. Additionally, public bulk and single-cell RNA-sequencing (scRNA-seq) datasets were analyzed, and in vitro and in vivo functional studies were conducted.</p><p><strong>Results: </strong>Spatial transcriptional reprogramming and dynamic cell signaling pathways that determined ESCC progression were delineated. Increased infiltration of macrophages from normal tissues through dysplasia to cancerous tissues occurred. Macrophage subtypes were characterized using the scRNA-seq dataset. Cell-cell communication analysis of scRNA-seq and SMI data indicated that the migration inhibitory factor (MIF)-CD74 axis may exhibit pro-tumor interactions between macrophages and epithelial cells. DSP, SMI, and IHC data demonstrated that DTX3L expression in epithelial cells and BST2 expression in stromal cells increased gradually with ESCC progression. Functional studies demonstrated that DTX3L or BST2 knockdown inhibited ESCC proliferation and migration and decreased M2 polarization of tumor-associated macrophages.</p><p><strong>Conclusions: </strong>Spatial profiling comprehensively characterized the molecular and immunological hallmarks from normal tissue to ESCC, guiding the way to a deeper understanding of the tumorigenesis and progression of this disease and contributing to the prevention of ESCC. Within this exploration, we uncovered biomarkers that exhibit a robust correlation with ESCC progression, offering potential new avenues for insightful therapeutic approaches.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"16 1","pages":"148"},"PeriodicalIF":10.4,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142853912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural variant allelic heterogeneity in MECP2 duplication syndrome provides insight into clinical severity and variability of disease expression.","authors":"Davut Pehlivan, Jesse D Bengtsson, Sameer S Bajikar, Christopher M Grochowski, Ming Yin Lun, Mira Gandhi, Angad Jolly, Alexander J Trostle, Holly K Harris, Bernhard Suter, Sukru Aras, Melissa B Ramocki, Haowei Du, Michele G Mehaffey, KyungHee Park, Ellen Wilkey, Cemal Karakas, Jesper J Eisfeldt, Maria Pettersson, Lynn Liu, Marwan S Shinawi, Virginia E Kimonis, Wojciech Wiszniewski, Kyle Mckenzie, Timo Roser, Angela M Vianna-Morgante, Alberto S Cornier, Ahmed Abdelmoity, James P Hwang, Shalini N Jhangiani, Donna M Muzny, Tadahiro Mitani, Kazuhiro Muramatsu, Shin Nabatame, Daniel G Glaze, Jawid M Fatih, Richard A Gibbs, Zhandong Liu, Anna Lindstrand, Fritz J Sedlazeck, James R Lupski, Huda Y Zoghbi, Claudia M B Carvalho","doi":"10.1186/s13073-024-01411-7","DOIUrl":"https://doi.org/10.1186/s13073-024-01411-7","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;MECP2 Duplication Syndrome, also known as X-linked intellectual developmental disorder Lubs type (MRXSL; MIM: 300260), is a neurodevelopmental disorder caused by copy number gains spanning MECP2. Despite varying genomic rearrangement structures, including duplications and triplications, and a wide range of duplication sizes, no clear correlation exists between DNA rearrangement and clinical features. We had previously demonstrated that up to 38% of MRXSL families are characterized by complex genomic rearrangements (CGRs) of intermediate complexity (2 ≤ copy number variant breakpoints &lt; 5), yet the impact of these genomic structures on regulation of gene expression and phenotypic manifestations have not been investigated.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;To study the role of the genomic rearrangement structures on an individual's clinical phenotypic variability, we employed a comprehensive genomics, transcriptomics, and deep phenotyping analysis approach on 137 individuals affected by MRXSL. Genomic structural information was correlated with transcriptomic and quantitative phenotypic analysis using Human Phenotype Ontology (HPO) semantic similarity scores.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Duplication sizes in the cohort ranging from 64.6 kb to 16.5 Mb were classified into four categories comprising of tandem duplications (48%), terminal duplications (22%), inverted triplications (20%), and other CGRs (10%). Most of the terminal duplication structures consist of translocations (65%) followed by recombinant chromosomes (23%). Notably, 65% of de novo events occurred in the Terminal duplication group in contrast with 17% observed in Tandem duplications. RNA-seq data from lymphoblastoid cell lines indicated that the MECP2 transcript quantity in MECP2 triplications is statistically different from all duplications, but not between other classes of genomic structures. We also observed a significant (p &lt; 0.05) correlation (Pearson R = 0.6, Spearman p = 0.63) between the log-transformed MECP2 RNA levels and MECP2 protein levels, demonstrating that genomic aberrations spanning MECP2 lead to altered MECP2 RNA and MECP2 protein levels. Genotype-phenotype analyses indicated a gradual worsening of phenotypic features, including overall survival, developmental levels, microcephaly, epilepsy, and genitourinary/eye abnormalities in the following order: Tandem duplications, Other complex duplications, Terminal duplications/Translocations, and Triplications encompassing MECP2.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;In aggregate, this combined analysis uncovers an interplay between MECP2 dosage, genomic rearrangement structure and phenotypic traits. Whereas the level of MECP2 is a key determinant of the phenotype, the DNA rearrangement structure can contribute to clinical severity and disease expression variability. Employing this type of analytical approach will advance our understanding of the impact of genomic rearrangements on genomic dis","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"16 1","pages":"146"},"PeriodicalIF":10.4,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142853916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Epigenome-wide association studies identify novel DNA methylation sites associated with PTSD: a meta-analysis of 23 military and civilian cohorts.","authors":"Seyma Katrinli, Agaz H Wani, Adam X Maihofer, Andrew Ratanatharathorn, Nikolaos P Daskalakis, Janitza Montalvo-Ortiz, Diana L Núñez-Ríos, Anthony S Zannas, Xiang Zhao, Allison E Aiello, Allison E Ashley-Koch, Diana Avetyan, Dewleen G Baker, Jean C Beckham, Marco P Boks, Leslie A Brick, Evelyn Bromet, Frances A Champagne, Chia-Yen Chen, Shareefa Dalvie, Michelle F Dennis, Segun Fatumo, Catherine Fortier, Sandro Galea, Melanie E Garrett, Elbert Geuze, Gerald Grant, Michael A Hauser, Jasmeet P Hayes, Sian M J Hemmings, Bertrand Russel Huber, Aarti Jajoo, Stefan Jansen, Ronald C Kessler, Nathan A Kimbrel, Anthony P King, Joel E Kleinman, Nastassja Koen, Karestan C Koenen, Pei-Fen Kuan, Israel Liberzon, Sarah D Linnstaedt, Adriana Lori, Benjamin J Luft, Jurjen J Luykx, Christine E Marx, Samuel A McLean, Divya Mehta, William Milberg, Mark W Miller, Mary S Mufford, Clarisse Musanabaganwa, Jean Mutabaruka, Leon Mutesa, Charles B Nemeroff, Nicole R Nugent, Holly K Orcutt, Xue-Jun Qin, Sheila A M Rauch, Kerry J Ressler, Victoria B Risbrough, Eugène Rutembesa, Bart P F Rutten, Soraya Seedat, Dan J Stein, Murray B Stein, Sylvanus Toikumo, Robert J Ursano, Annette Uwineza, Mieke H Verfaellie, Eric Vermetten, Christiaan H Vinkers, Erin B Ware, Derek E Wildman, Erika J Wolf, Ross McD Young, Ying Zhao, Leigh L van den Heuvel, Monica Uddin, Caroline M Nievergelt, Alicia K Smith, Mark W Logue","doi":"10.1186/s13073-024-01417-1","DOIUrl":"https://doi.org/10.1186/s13073-024-01417-1","url":null,"abstract":"<p><strong>Background: </strong>The occurrence of post-traumatic stress disorder (PTSD) following a traumatic event is associated with biological differences that can represent the susceptibility to PTSD, the impact of trauma, or the sequelae of PTSD itself. These effects include differences in DNA methylation (DNAm), an important form of epigenetic gene regulation, at multiple CpG loci across the genome. Moreover, these effects can be shared or specific to both central and peripheral tissues. Here, we aim to identify blood DNAm differences associated with PTSD and characterize the underlying biological mechanisms by examining the extent to which they mirror associations across multiple brain regions.</p><p><strong>Methods: </strong>As the Psychiatric Genomics Consortium (PGC) PTSD Epigenetics Workgroup, we conducted the largest cross-sectional meta-analysis of epigenome-wide association studies (EWASs) of PTSD to date, involving 5077 participants (2156 PTSD cases and 2921 trauma-exposed controls) from 23 civilian and military studies. PTSD diagnosis assessments were harmonized following the standardized guidelines established by the PGC-PTSD Workgroup. DNAm was assayed from blood using Illumina HumanMethylation450 or MethylationEPIC (850 K) BeadChips. Within each cohort, DNA methylation was regressed on PTSD, sex (if applicable), age, blood cell proportions, and ancestry. An inverse variance-weighted meta-analysis was performed. We conducted replication analyses in tissue from multiple brain regions, neuronal nuclei, and a cellular model of prolonged stress.</p><p><strong>Results: </strong>We identified 11 CpG sites associated with PTSD in the overall meta-analysis (1.44e - 09 < p < 5.30e - 08), as well as 14 associated in analyses of specific strata (military vs civilian cohort, sex, and ancestry), including CpGs in AHRR and CDC42BPB. Many of these loci exhibit blood-brain correlation in methylation levels and cross-tissue associations with PTSD in multiple brain regions. Out of 9 CpGs annotated to a gene expressed in blood, methylation levels at 5 CpGs showed significant correlations with the expression levels of their respective annotated genes.</p><p><strong>Conclusions: </strong>This study identifies 11 PTSD-associated CpGs and leverages data from postmortem brain samples, GWAS, and genome-wide expression data to interpret the biology underlying these associations and prioritize genes whose regulation differs in those with PTSD.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"16 1","pages":"147"},"PeriodicalIF":10.4,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142853906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patterns of genomic instability in > 2000 patients with ovarian cancer across six clinical trials evaluating olaparib.","authors":"Alan Barnicle, Isabelle Ray-Coquard, Etienne Rouleau, Karen Cadoo, Fiona Simpkins, Carol Aghajanian, Alexandra Leary, Andrés Poveda, Stephanie Lheureux, Eric Pujade-Lauraine, Benoit You, Jonathan Ledermann, Ursula Matulonis, Charlie Gourley, Kirsten M Timms, Zhongwu Lai, Darren R Hodgson, Cathy E Elks, Simon Dearden, Coumaran Egile, Pierre Lao-Sirieix, Elizabeth A Harrington, Jessica S Brown","doi":"10.1186/s13073-024-01413-5","DOIUrl":"https://doi.org/10.1186/s13073-024-01413-5","url":null,"abstract":"<p><strong>Background: </strong>The introduction of poly(ADP-ribose) polymerase (PARP) inhibitors represented a paradigm shift in the treatment of ovarian cancer. Genomic data from patients with high-grade ovarian cancer in six phase II/III trials involving the PARP inhibitor olaparib were analyzed to better understand patterns and potential causes of genomic instability.</p><p><strong>Patients and methods: </strong>Homologous recombination deficiency (HRD) was assessed in 2147 tumor samples from SOLO1, PAOLA-1, Study 19, SOLO2, OPINION, and LIGHT using next-generation sequencing technology. Genomic instability scores (GIS) were assessed in BRCA1 and/or BRCA2 (BRCA)-mutated (BRCAm), non-BRCA homologous recombination repair-mutated (non-BRCA HRRm), and non-HRRm tumors.</p><p><strong>Results: </strong>BRCAm was identified in 1021/2147 (47.6%) tumors. BRCAm tumors had significantly higher GIS than non-BRCAm tumors (P < 0.001) and high biallelic loss (815/838; 97.3%) regardless of germline (658/672; 97.9%) or somatic (101/108; 93.5%) BRCAm status. In non-BRCA HRRm tumors (n = 121) a similar proportion were HRD-positive (GIS ≥ 42: 55/121; 45.5%) relative to HRD-negative (GIS < 42: 52/121; 43.0%). GIS was highly variable in non-BRCA HRRm (median 42 [interquartile range (IQR) 29-58]) and non-HRRm (n = 1005; median 32 [IQR 20-55]) tumors. Gene mutations with high GIS included HRR genes BRIP1 (median 46 [IQR 41-58]), RAD51C (median 58 [IQR 48-66]), RAD51D (median 62 [IQR 54-69]), and PALB2 (median 64 [IQR 58-74]), and non-HRR genes NF1 (median 49 [IQR 25-60]) and RB1 (median 55 [IQR 30-71]). CCNE1-amplified and PIK3CA-mutated tumors had low GIS (CCNE1-amplified: median 24 [IQR 18-29]; PIK3CA-mutated: median 32 [IQR 14-52]) and were predominantly non-BRCAm.</p><p><strong>Conclusions: </strong>These analyses provide valuable insight into patterns of genomic instability and potential drivers of HRD, besides BRCAm, in ovarian cancer and will help guide future research into the potential clinical effectiveness of anti-cancer treatments in ovarian cancer, including PARP inhibitors as well as other precision oncology agents.</p><p><strong>Trial registration: </strong>The SOLO1 trial was registered at ClinicalTrials.gov (NCT01844986) on April 30, 2013; the PAOLA-1 trial was registered at ClinicalTrials.gov (NCT02477644) on June 18, 2015 (retrospectively registered); Study 19 was registered at ClinicalTrials.gov (NCT00753545) on September 12, 2008 (retrospectively registered); the SOLO2 trial was registered at ClinicalTrials.gov (NCT01874353) on June 7, 2013; the OPINION trial was registered at ClinicalTrials.gov (NCT03402841) on January 3, 2018; the LIGHT trial was registered at ClinicalTrials.gov (NCT02983799) on November 4, 2016.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"16 1","pages":"145"},"PeriodicalIF":10.4,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142853910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SiRCle (Signature Regulatory Clustering) model integration reveals mechanisms of phenotype regulation in renal cancer.","authors":"Ariane Mora, Christina Schmidt, Brad Balderson, Christian Frezza, Mikael Bodén","doi":"10.1186/s13073-024-01415-3","DOIUrl":"10.1186/s13073-024-01415-3","url":null,"abstract":"<p><strong>Background: </strong>Clear cell renal cell carcinoma (ccRCC) tumours develop and progress via complex remodelling of the kidney epigenome, transcriptome, proteome and metabolome. Given the subsequent tumour and inter-patient heterogeneity, drug-based treatments report limited success, calling for multi-omics studies to extract regulatory relationships, and ultimately, to develop targeted therapies. Yet, methods for multi-omics integration to reveal mechanisms of phenotype regulation are lacking.</p><p><strong>Methods: </strong>Here, we present SiRCle (Signature Regulatory Clustering), a method to integrate DNA methylation, RNA-seq and proteomics data at the gene level by following central dogma of biology, i.e. genetic information proceeds from DNA, to RNA, to protein. To identify regulatory clusters across the different omics layers, we group genes based on the layer where the gene's dysregulation first occurred. We combine the SiRCle clusters with a variational autoencoder (VAE) to reveal key features from omics' data for each SiRCle cluster and compare patient subpopulations in a ccRCC and a PanCan cohort.</p><p><strong>Results: </strong>Applying SiRCle to a ccRCC cohort, we showed that glycolysis is upregulated by DNA hypomethylation, whilst mitochondrial enzymes and respiratory chain complexes are translationally suppressed. Additionally, we identify metabolic enzymes associated with survival along with the possible molecular driver behind the gene's perturbations. By using the VAE to integrate omics' data followed by statistical comparisons between tumour stages on the integrated space, we found a stage-dependent downregulation of proximal renal tubule genes, hinting at a loss of cellular identity in cancer cells. We also identified the regulatory layers responsible for their suppression. Lastly, we applied SiRCle to a PanCan cohort and found common signatures across ccRCC and PanCan in addition to the regulatory layer that defines tissue identity.</p><p><strong>Conclusions: </strong>Our results highlight SiRCle's ability to reveal mechanisms of phenotype regulation in cancer, both specifically in ccRCC and broadly in a PanCan context. SiRCle ranks genes according to biological features. https://github.com/ArianeMora/SiRCle_multiomics_integration .</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"16 1","pages":"144"},"PeriodicalIF":10.4,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11616309/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142778966","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":"Revisiting mutational resistance to ampicillin and cefotaxime in Haemophilus influenzae.","authors":"Margo Diricks, Sabine Petersen, Lennart Bartels, Thiên-Trí Lâm, Heike Claus, Maria Paula Bajanca-Lavado, Susanne Hauswaldt, Ricardo Stolze, Omar Jiménez Vázquez, Christian Utpatel, Stefan Niemann, Jan Rupp, Inken Wohlers, Matthias Merker","doi":"10.1186/s13073-024-01406-4","DOIUrl":"10.1186/s13073-024-01406-4","url":null,"abstract":"<p><strong>Background: </strong>Haemophilus influenzae is an opportunistic bacterial pathogen that can cause severe respiratory tract and invasive infections. The emergence of β-lactamase-negative ampicillin-resistant (BLNAR) strains and unclear correlations between genotypic (i.e., gBLNAR) and phenotypic resistance are challenging empirical treatments and patient management. Thus, we sought to revisit molecular resistance mechanisms and to identify new resistance determinants of H. influenzae.</p><p><strong>Methods: </strong>We performed a systematic meta-analysis of H. influenzae isolates (n = 291) to quantify the association of phenotypic ampicillin and cefotaxime resistance with previously defined resistance groups, i.e., specific substitution patterns of the penicillin binding protein PBP3, encoded by ftsI. Using phylogenomics and a genome-wide association study (GWAS), we investigated evolutionary trajectories and novel resistance determinants in a public global cohort (n = 555) and a new clinical cohort from three European centers (n = 298), respectively.</p><p><strong>Results: </strong>Our meta-analysis confirmed that PBP3 group II- and group III-related isolates were significantly associated with phenotypic resistance to ampicillin (p < 0.001), while only group III-related isolates were associated with resistance to cefotaxime (p = 0.02). The vast majority of H. influenzae isolates not classified into a PBP3 resistance group were ampicillin and cefotaxime susceptible. However, particularly group II isolates had low specificities (< 16%) to rule in ampicillin resistance due to clinical breakpoints classifying many of them as phenotypically susceptible. We found indications for positive selection of multiple PBP3 substitutions, which evolved independently and often step-wise in different phylogenetic clades. Beyond ftsI, other possible candidate genes (e.g., oppA, ridA, and ompP2) were moderately associated with ampicillin resistance in the GWAS. The PBP3 substitutions M377I, A502V, N526K, V547I, and N569S were most strongly related to ampicillin resistance and occurred in combination in the most prevalent resistant haplotype H1 in our clinical cohort.</p><p><strong>Conclusions: </strong>Gradient agar diffusion strips and broth microdilution assays do not consistently classify isolates from PBP3 groups as phenotypically resistant. Consequently, when the minimum inhibitory concentration is close to the clinical breakpoints, and genotypic data is available, PBP3 resistance groups should be prioritized over susceptible phenotypic results for ampicillin. The implications on treatment outcome and bacterial fitness of other extended PBP3 substitution patterns and novel candidate genes need to be determined.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"16 1","pages":"140"},"PeriodicalIF":10.4,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11616347/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142780076","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":"Correction: Applicability of epigenetic age models to next-generation methylation arrays.","authors":"Leonardo D Garma, Miguel Quintela-Fandino","doi":"10.1186/s13073-024-01402-8","DOIUrl":"10.1186/s13073-024-01402-8","url":null,"abstract":"","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"16 1","pages":"142"},"PeriodicalIF":10.4,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11613460/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142768327","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":"Using multiplexed functional data to reduce variant classification inequities in underrepresented populations.","authors":"Moez Dawood, Shawn Fayer, Sriram Pendyala, Mason Post, Divya Kalra, Karynne Patterson, Eric Venner, Lara A Muffley, Douglas M Fowler, Alan F Rubin, Jennifer E Posey, Sharon E Plon, James R Lupski, Richard A Gibbs, Lea M Starita, Carla Daniela Robles-Espinoza, Willow Coyote-Maestas, Irene Gallego Romero","doi":"10.1186/s13073-024-01392-7","DOIUrl":"10.1186/s13073-024-01392-7","url":null,"abstract":"<p><strong>Background: </strong>Multiplexed Assays of Variant Effects (MAVEs) can test all possible single variants in a gene of interest. The resulting saturation-style functional data may help resolve variant classification disparities between populations, especially for Variants of Uncertain Significance (VUS).</p><p><strong>Methods: </strong>We analyzed clinical significance classifications in 213,663 individuals of European-like genetic ancestry versus 206,975 individuals of non-European-like genetic ancestry from All of Us and the Genome Aggregation Database. Then, we incorporated clinically calibrated MAVE data into the Clinical Genome Resource's Variant Curation Expert Panel rules to automate VUS reclassification for BRCA1, TP53, and PTEN.</p><p><strong>Results: </strong>Using two orthogonal statistical approaches, we show a higher prevalence (p ≤ 5.95e - 06) of VUS in individuals of non-European-like genetic ancestry across all medical specialties assessed in all three databases. Further, in the non-European-like genetic ancestry group, higher rates of Benign or Likely Benign and variants with no clinical designation (p ≤ 2.5e - 05) were found across many medical specialties, whereas Pathogenic or Likely Pathogenic assignments were increased in individuals of European-like genetic ancestry (p ≤ 2.5e - 05). Using MAVE data, we reclassified VUS in individuals of non-European-like genetic ancestry at a significantly higher rate in comparison to reclassified VUS from European-like genetic ancestry (p = 9.1e - 03) effectively compensating for the VUS disparity. Further, essential code analysis showed equitable impact of MAVE evidence codes but inequitable impact of allele frequency (p = 7.47e - 06) and computational predictor (p = 6.92e - 05) evidence codes for individuals of non-European-like genetic ancestry.</p><p><strong>Conclusions: </strong>Generation of saturation-style MAVE data should be a priority to reduce VUS disparities and produce equitable training data for future computational predictors.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"16 1","pages":"143"},"PeriodicalIF":10.4,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11616159/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142768328","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}