Pub Date : 2024-12-18DOI: 10.1186/s13059-024-03459-5
Riccardo Pianezza, Anna Haider, Robert Kofler
We present GenomeDelta, a novel tool for identifying sample-specific sequences, such as recent transposable element (TE) invasions, without requiring a repeat library. GenomeDelta compares high-quality assemblies with short-read data to detect sequences absent from the short reads. It is applicable to both model and non-model organisms and can identify recent TE invasions, spatially heterogeneous sequences, viral insertions, and hotizontal gene transfers. GenomeDelta was validated with simulated and real data and used to discover three recent TE invasions in Drosophila melanogaster and a novel TE with geographic variation in Zymoseptoria tritici.
{"title":"GenomeDelta: detecting recent transposable element invasions without repeat library","authors":"Riccardo Pianezza, Anna Haider, Robert Kofler","doi":"10.1186/s13059-024-03459-5","DOIUrl":"https://doi.org/10.1186/s13059-024-03459-5","url":null,"abstract":"We present GenomeDelta, a novel tool for identifying sample-specific sequences, such as recent transposable element (TE) invasions, without requiring a repeat library. GenomeDelta compares high-quality assemblies with short-read data to detect sequences absent from the short reads. It is applicable to both model and non-model organisms and can identify recent TE invasions, spatially heterogeneous sequences, viral insertions, and hotizontal gene transfers. GenomeDelta was validated with simulated and real data and used to discover three recent TE invasions in Drosophila melanogaster and a novel TE with geographic variation in Zymoseptoria tritici.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"22 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849045","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}
Pub Date : 2024-12-18DOI: 10.1186/s13059-024-03452-y
Prasad Sarashetti, Josipa Lipovac, Filip Tomas, Mile Šikić, Jianjun Liu
Long-read technologies from Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) have transformed genomics research by providing diverse data types like HiFi, Duplex, and ultra-long ONT. Despite recent strides in achieving haplotype-phased gapless genome assemblies using long-read technologies, concerns persist regarding the representation of genetic diversity, prompting the development of pangenome references. However, pangenome studies face challenges related to data types, volumes, and cost considerations for each assembled genome, while striving to maintain sensitivity. The absence of comprehensive guidance on optimal data selection exacerbates these challenges. Our study evaluates recommended data types and volumes required to establish a robust de novo genome assembly pipeline for population-level pangenome projects, extensively examining performance between ONT’s Duplex and PacBio HiFi datasets in the context of achieving high-quality phased genomes with enhanced contiguity and completeness. The results show that achieving chromosome-level haplotype-resolved assembly requires 20 × high-quality long reads such as PacBio HiFi or ONT Duplex, combined with 15–20 × of ultra-long ONT per haplotype and 10 × of long-range data such as Omni-C or Hi-C. High-quality long reads from both platforms yield assemblies with comparable contiguity, with HiFi excelling in phasing accuracies, while Duplex generates more T2T contigs. Our study provides insights into optimal data types and volumes for robust de novo genome assembly in population-level pangenome projects. Reassessing the recommended data types and volumes in this study and aligning them with practical economic limitations are vital to the pangenome research community, contributing to their efforts and pushing genomic studies with broader impacts.
{"title":"Evaluating data requirements for high-quality haplotype-resolved genomes for creating robust pangenome references","authors":"Prasad Sarashetti, Josipa Lipovac, Filip Tomas, Mile Šikić, Jianjun Liu","doi":"10.1186/s13059-024-03452-y","DOIUrl":"https://doi.org/10.1186/s13059-024-03452-y","url":null,"abstract":"Long-read technologies from Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) have transformed genomics research by providing diverse data types like HiFi, Duplex, and ultra-long ONT. Despite recent strides in achieving haplotype-phased gapless genome assemblies using long-read technologies, concerns persist regarding the representation of genetic diversity, prompting the development of pangenome references. However, pangenome studies face challenges related to data types, volumes, and cost considerations for each assembled genome, while striving to maintain sensitivity. The absence of comprehensive guidance on optimal data selection exacerbates these challenges. Our study evaluates recommended data types and volumes required to establish a robust de novo genome assembly pipeline for population-level pangenome projects, extensively examining performance between ONT’s Duplex and PacBio HiFi datasets in the context of achieving high-quality phased genomes with enhanced contiguity and completeness. The results show that achieving chromosome-level haplotype-resolved assembly requires 20 × high-quality long reads such as PacBio HiFi or ONT Duplex, combined with 15–20 × of ultra-long ONT per haplotype and 10 × of long-range data such as Omni-C or Hi-C. High-quality long reads from both platforms yield assemblies with comparable contiguity, with HiFi excelling in phasing accuracies, while Duplex generates more T2T contigs. Our study provides insights into optimal data types and volumes for robust de novo genome assembly in population-level pangenome projects. Reassessing the recommended data types and volumes in this study and aligning them with practical economic limitations are vital to the pangenome research community, contributing to their efforts and pushing genomic studies with broader impacts.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"1 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849131","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}
Pub Date : 2024-12-18DOI: 10.1186/s13059-024-03431-3
Brian Karlberg, Raphael Kirchgaessner, Jordan Lee, Matthew Peterkort, Liam Beckman, Jeremy Goecks, Kyle Ellrott
The accuracy of machine learning methods is often limited by the amount of training data that is available. We proposed to improve machine learning training regimes by augmenting datasets with synthetically generated samples. We present a method for synthesizing gene expression samples and test the system’s capabilities for improving the accuracy of categorical prediction of cancer subtypes. We developed SyntheVAEiser, a variational autoencoder based tool that was trained and tested on over 8000 cancer samples. We have shown that this technique can be used to augment machine learning tasks and increase performance of recognition of underrepresented cohorts.
{"title":"SyntheVAEiser: augmenting traditional machine learning methods with VAE-based gene expression sample generation for improved cancer subtype predictions","authors":"Brian Karlberg, Raphael Kirchgaessner, Jordan Lee, Matthew Peterkort, Liam Beckman, Jeremy Goecks, Kyle Ellrott","doi":"10.1186/s13059-024-03431-3","DOIUrl":"https://doi.org/10.1186/s13059-024-03431-3","url":null,"abstract":"The accuracy of machine learning methods is often limited by the amount of training data that is available. We proposed to improve machine learning training regimes by augmenting datasets with synthetically generated samples. We present a method for synthesizing gene expression samples and test the system’s capabilities for improving the accuracy of categorical prediction of cancer subtypes. We developed SyntheVAEiser, a variational autoencoder based tool that was trained and tested on over 8000 cancer samples. We have shown that this technique can be used to augment machine learning tasks and increase performance of recognition of underrepresented cohorts.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"23 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849136","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}
The inherent similarities between natural language and biological sequences have inspired the use of large language models in genomics, but current models struggle to incorporate chromatin interactions or predict in unseen cellular contexts. To address this, we propose EpiGePT, a transformer-based model designed for predicting context-specific human epigenomic signals. By incorporating transcription factor activities and 3D genome interactions, EpiGePT outperforms existing methods in epigenomic signal prediction tasks, especially in cell-type-specific long-range interaction predictions and genetic variant impacts, advancing our understanding of gene regulation. A free online prediction service is available at http://health.tsinghua.edu.cn/epigept .
{"title":"EpiGePT: a pretrained transformer-based language model for context-specific human epigenomics","authors":"Zijing Gao, Qiao Liu, Wanwen Zeng, Rui Jiang, Wing Hung Wong","doi":"10.1186/s13059-024-03449-7","DOIUrl":"https://doi.org/10.1186/s13059-024-03449-7","url":null,"abstract":"The inherent similarities between natural language and biological sequences have inspired the use of large language models in genomics, but current models struggle to incorporate chromatin interactions or predict in unseen cellular contexts. To address this, we propose EpiGePT, a transformer-based model designed for predicting context-specific human epigenomic signals. By incorporating transcription factor activities and 3D genome interactions, EpiGePT outperforms existing methods in epigenomic signal prediction tasks, especially in cell-type-specific long-range interaction predictions and genetic variant impacts, advancing our understanding of gene regulation. A free online prediction service is available at http://health.tsinghua.edu.cn/epigept .","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"38 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849133","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}
Pub Date : 2024-12-18DOI: 10.1186/s13059-024-03447-9
Jacob Blindenbach, Jiayi Kang, Seungwan Hong, Caline Karam, Thomas Lehner, Gamze Gürsoy
Cloud computing allows storing the ever-growing genotype-phenotype datasets crucial for precision medicine. Due to the sensitive nature of this data and varied laws and regulations, additional security measures are needed to ensure data privacy. We develop SQUiD, a secure queryable database for storing and analyzing genotype-phenotype data. SQUiD allows storage and secure querying of data in a low-security, low-cost public cloud using homomorphic encryption in a multi-client setting. We demonstrate SQUiD’s practical usability and scalability using synthetic and UK Biobank data.
{"title":"SQUiD: ultra-secure storage and analysis of genetic data for the advancement of precision medicine","authors":"Jacob Blindenbach, Jiayi Kang, Seungwan Hong, Caline Karam, Thomas Lehner, Gamze Gürsoy","doi":"10.1186/s13059-024-03447-9","DOIUrl":"https://doi.org/10.1186/s13059-024-03447-9","url":null,"abstract":"Cloud computing allows storing the ever-growing genotype-phenotype datasets crucial for precision medicine. Due to the sensitive nature of this data and varied laws and regulations, additional security measures are needed to ensure data privacy. We develop SQUiD, a secure queryable database for storing and analyzing genotype-phenotype data. SQUiD allows storage and secure querying of data in a low-security, low-cost public cloud using homomorphic encryption in a multi-client setting. We demonstrate SQUiD’s practical usability and scalability using synthetic and UK Biobank data.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"58 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849046","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}
Pub Date : 2024-12-05DOI: 10.1186/s13059-024-03435-z
Alex C. Soupir, Mitchell T. Hayes, Taylor C. Peak, Oscar Ospina, Nicholas H. Chakiryan, Anders E. Berglund, Paul A. Stewart, Jonathan Nguyen, Carlos Moran Segura, Natasha L. Francis, Paola M. Ramos Echevarria, Jad Chahoud, Roger Li, Kenneth Y. Tsai, Jodi A. Balasi, Yamila Caraballo Peres, Jasreman Dhillon, Lindsey A. Martinez, Warren E. Gloria, Nathan Schurman, Sean Kim, Mark Gregory, James Mulé, Brooke L. Fridley, Brandon J. Manley
Immunotherapy has improved survival for patients with advanced clear cell renal cell carcinoma (ccRCC), but resistance to therapy develops in most patients. We use cellular-resolution spatial transcriptomics in patients with immunotherapy naïve and exposed primary ccRCC tumors to better understand immunotherapy resistance. Spatial molecular imaging of tumor and adjacent stroma samples from 21 tumors suggests that viable tumors following immunotherapy harbor more stromal CD8 + T cells and neutrophils than immunotherapy naïve tumors. YES1 is significantly upregulated in immunotherapy exposed tumor cells. Spatial GSEA shows that the epithelial-mesenchymal transition pathway is spatially enriched and the associated ligand-receptor transcript pair COL4A1-ITGAV has significantly higher autocorrelation in the stroma after exposure to immunotherapy. More integrin αV + cells are observed in immunotherapy exposed stroma on multiplex immunofluorescence validation. Compared to other cancers in TCGA, ccRCC tumors have the highest expression of both COL4A1 and ITGAV. Assessing bulk RNA expression and proteomic correlates in CPTAC databases reveals that collagen IV protein is more abundant in advanced stages of disease. Spatial transcriptomics of samples of 3 patient cohorts with cRCC tumors indicates that COL4A1 and ITGAV are more autocorrelated in immunotherapy-exposed stroma compared to immunotherapy-naïve tumors, with high expression among fibroblasts, tumor cells, and endothelium. Further research is needed to understand changes in the ccRCC tumor immune microenvironment and explore potential therapeutic role of integrin after immunotherapy treatment.
{"title":"Increased spatial coupling of integrin and collagen IV in the immunoresistant clear-cell renal-cell carcinoma tumor microenvironment","authors":"Alex C. Soupir, Mitchell T. Hayes, Taylor C. Peak, Oscar Ospina, Nicholas H. Chakiryan, Anders E. Berglund, Paul A. Stewart, Jonathan Nguyen, Carlos Moran Segura, Natasha L. Francis, Paola M. Ramos Echevarria, Jad Chahoud, Roger Li, Kenneth Y. Tsai, Jodi A. Balasi, Yamila Caraballo Peres, Jasreman Dhillon, Lindsey A. Martinez, Warren E. Gloria, Nathan Schurman, Sean Kim, Mark Gregory, James Mulé, Brooke L. Fridley, Brandon J. Manley","doi":"10.1186/s13059-024-03435-z","DOIUrl":"https://doi.org/10.1186/s13059-024-03435-z","url":null,"abstract":"Immunotherapy has improved survival for patients with advanced clear cell renal cell carcinoma (ccRCC), but resistance to therapy develops in most patients. We use cellular-resolution spatial transcriptomics in patients with immunotherapy naïve and exposed primary ccRCC tumors to better understand immunotherapy resistance. Spatial molecular imaging of tumor and adjacent stroma samples from 21 tumors suggests that viable tumors following immunotherapy harbor more stromal CD8 + T cells and neutrophils than immunotherapy naïve tumors. YES1 is significantly upregulated in immunotherapy exposed tumor cells. Spatial GSEA shows that the epithelial-mesenchymal transition pathway is spatially enriched and the associated ligand-receptor transcript pair COL4A1-ITGAV has significantly higher autocorrelation in the stroma after exposure to immunotherapy. More integrin αV + cells are observed in immunotherapy exposed stroma on multiplex immunofluorescence validation. Compared to other cancers in TCGA, ccRCC tumors have the highest expression of both COL4A1 and ITGAV. Assessing bulk RNA expression and proteomic correlates in CPTAC databases reveals that collagen IV protein is more abundant in advanced stages of disease. Spatial transcriptomics of samples of 3 patient cohorts with cRCC tumors indicates that COL4A1 and ITGAV are more autocorrelated in immunotherapy-exposed stroma compared to immunotherapy-naïve tumors, with high expression among fibroblasts, tumor cells, and endothelium. Further research is needed to understand changes in the ccRCC tumor immune microenvironment and explore potential therapeutic role of integrin after immunotherapy treatment.\u0000","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"53 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142776470","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}
Pub Date : 2024-12-05DOI: 10.1186/s13059-024-03448-8
Lu Yu, Jiawei Zou, Amjad Hussain, Ruoyu Jia, Yibo Fan, Jinhang Liu, Xinhui Nie, Xianlong Zhang, Shuangxia Jin
CRISPR/Cas13 system, recognized for its compact size and specificity in targeting RNA, is currently employed for RNA degradation. However, the potential of various CRISPR/Cas13 subtypes, particularly concerning the knockdown of endogenous transcripts, remains to be comprehensively characterized in plants. Here we present a full spectrum of editing profiles for seven Cas13 orthologs from five distinct subtypes: VI-A (LwaCas13a), VI-B (PbuCas13b), VI-D (RfxCas13d), VI-X (Cas13x.1 and Cas13x.2), and VI-Y (Cas13y.1 and Cas13y.2). A systematic evaluation of the knockdown effects on two endogenous transcripts (GhCLA and GhPGF in cotton) as well as an RNA virus (TMV in tobacco) reveals that RfxCas13d, Cas13x.1, and Cas13x.2 exhibit enhanced stability with editing efficiencies ranging from 58 to 80%, closely followed by Cas13y.1 and Cas13y.2. Notably, both Cas13x.1 and Cas13y.1 can simultaneously degrade two endogenous transcripts through a tRNA-crRNA cassette approach, achieving editing efficiencies of up to 50%. Furthermore, different Cas13 orthologs enable varying degrees of endogenous transcript knockdown with minimal off-target effects, generating germplasms that exhibit a diverse spectrum of mutant phenotypes. Transgenic tobacco plants show significant reductions in damage, along with mild oxidative stress and minimal accumulation of viral particles after TMV infection. In conclusion, our study presents an efficient and reliable platform for transcriptome editing that holds promise for plant functional research and future crop improvement.
{"title":"Systemic evaluation of various CRISPR/Cas13 orthologs for knockdown of targeted transcripts in plants","authors":"Lu Yu, Jiawei Zou, Amjad Hussain, Ruoyu Jia, Yibo Fan, Jinhang Liu, Xinhui Nie, Xianlong Zhang, Shuangxia Jin","doi":"10.1186/s13059-024-03448-8","DOIUrl":"https://doi.org/10.1186/s13059-024-03448-8","url":null,"abstract":"CRISPR/Cas13 system, recognized for its compact size and specificity in targeting RNA, is currently employed for RNA degradation. However, the potential of various CRISPR/Cas13 subtypes, particularly concerning the knockdown of endogenous transcripts, remains to be comprehensively characterized in plants. Here we present a full spectrum of editing profiles for seven Cas13 orthologs from five distinct subtypes: VI-A (LwaCas13a), VI-B (PbuCas13b), VI-D (RfxCas13d), VI-X (Cas13x.1 and Cas13x.2), and VI-Y (Cas13y.1 and Cas13y.2). A systematic evaluation of the knockdown effects on two endogenous transcripts (GhCLA and GhPGF in cotton) as well as an RNA virus (TMV in tobacco) reveals that RfxCas13d, Cas13x.1, and Cas13x.2 exhibit enhanced stability with editing efficiencies ranging from 58 to 80%, closely followed by Cas13y.1 and Cas13y.2. Notably, both Cas13x.1 and Cas13y.1 can simultaneously degrade two endogenous transcripts through a tRNA-crRNA cassette approach, achieving editing efficiencies of up to 50%. Furthermore, different Cas13 orthologs enable varying degrees of endogenous transcript knockdown with minimal off-target effects, generating germplasms that exhibit a diverse spectrum of mutant phenotypes. Transgenic tobacco plants show significant reductions in damage, along with mild oxidative stress and minimal accumulation of viral particles after TMV infection. In conclusion, our study presents an efficient and reliable platform for transcriptome editing that holds promise for plant functional research and future crop improvement.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"10 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142776471","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}
Pub Date : 2024-12-03DOI: 10.1186/s13059-024-03437-x
Yanyan Liu, Xintong Xu, Chao He, Liujie Jin, Ziru Zhou, Jie Gao, Minrong Guo, Xin Wang, Chuanye Chen, Mohammed H. Ayaad, Xingwang Li, Wenhao Yan
Plants respond to environmental stimuli by altering gene transcription that is highly related with chromatin status, including histone modification, chromatin accessibility, and three-dimensional chromatin interaction. Vernalization is essential for the transition to reproductive growth for winter wheat. How wheat reshapes its chromatin features, especially chromatin interaction during vernalization, remains unknown. Combinatory analysis of gene transcription and histone modifications in winter wheat under different vernalization conditions identifies 17,669 differential expressed genes and thousands of differentially enriched peaks of H3K4me3, H3K27me3, and H3K9ac. We find dynamic gene expression across the vernalization process is highly associated with H3K4me3. More importantly, the dynamic H3K4me3- and H3K9ac-associated chromatin-chromatin interactions demonstrate that vernalization leads to increased chromatin interactions and gene activation. Remarkably, spatially distant targets of master regulators like VRN1 and VRT2 are gathered together by chromatin loops to achieve efficient transcription regulation, which is designated as a “shepherd” model. Furthermore, by integrating gene regulatory network for vernalization and natural variation of flowering time, TaZNF10 is identified as a negative regulator for vernalization-related flowering time in wheat. We reveal dynamic gene transcription network during vernalization and find that the spatially distant genes can be recruited together via chromatin loops associated with active histone mark thus to be more efficiently found and bound by upstream regulator. It provides new insights into understanding vernalization and response to environmental stimuli in wheat and other plants.
{"title":"Chromatin loops gather targets of upstream regulators together for efficient gene transcription regulation during vernalization in wheat","authors":"Yanyan Liu, Xintong Xu, Chao He, Liujie Jin, Ziru Zhou, Jie Gao, Minrong Guo, Xin Wang, Chuanye Chen, Mohammed H. Ayaad, Xingwang Li, Wenhao Yan","doi":"10.1186/s13059-024-03437-x","DOIUrl":"https://doi.org/10.1186/s13059-024-03437-x","url":null,"abstract":"Plants respond to environmental stimuli by altering gene transcription that is highly related with chromatin status, including histone modification, chromatin accessibility, and three-dimensional chromatin interaction. Vernalization is essential for the transition to reproductive growth for winter wheat. How wheat reshapes its chromatin features, especially chromatin interaction during vernalization, remains unknown. Combinatory analysis of gene transcription and histone modifications in winter wheat under different vernalization conditions identifies 17,669 differential expressed genes and thousands of differentially enriched peaks of H3K4me3, H3K27me3, and H3K9ac. We find dynamic gene expression across the vernalization process is highly associated with H3K4me3. More importantly, the dynamic H3K4me3- and H3K9ac-associated chromatin-chromatin interactions demonstrate that vernalization leads to increased chromatin interactions and gene activation. Remarkably, spatially distant targets of master regulators like VRN1 and VRT2 are gathered together by chromatin loops to achieve efficient transcription regulation, which is designated as a “shepherd” model. Furthermore, by integrating gene regulatory network for vernalization and natural variation of flowering time, TaZNF10 is identified as a negative regulator for vernalization-related flowering time in wheat. We reveal dynamic gene transcription network during vernalization and find that the spatially distant genes can be recruited together via chromatin loops associated with active histone mark thus to be more efficiently found and bound by upstream regulator. It provides new insights into understanding vernalization and response to environmental stimuli in wheat and other plants.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"25 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142760480","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}
Pub Date : 2024-12-03DOI: 10.1186/s13059-024-03446-w
Katherine Kelly, Michael Scherer, Martina Maria Braun, Pavlo Lutsik, Christoph Plass
Epigenetic heterogeneity is a fundamental property of biological systems and is recognized as a potential driver of tumor plasticity and therapy resistance. Single-cell epigenomics technologies have been widely employed to study epigenetic variation between—but not within—cellular clusters. We introduce epiCHAOS: a quantitative metric of cell-to-cell heterogeneity, applicable to any single-cell epigenomics data type. After validation in synthetic datasets, we apply epiCHAOS to investigate global and region-specific patterns of epigenetic heterogeneity across diverse biological systems. EpiCHAOS provides an excellent approximation of stemness and plasticity in development and malignancy, making it a valuable addition to single-cell cancer epigenomics analyses.
{"title":"EpiCHAOS: a metric to quantify epigenomic heterogeneity in single-cell data","authors":"Katherine Kelly, Michael Scherer, Martina Maria Braun, Pavlo Lutsik, Christoph Plass","doi":"10.1186/s13059-024-03446-w","DOIUrl":"https://doi.org/10.1186/s13059-024-03446-w","url":null,"abstract":"Epigenetic heterogeneity is a fundamental property of biological systems and is recognized as a potential driver of tumor plasticity and therapy resistance. Single-cell epigenomics technologies have been widely employed to study epigenetic variation between—but not within—cellular clusters. We introduce epiCHAOS: a quantitative metric of cell-to-cell heterogeneity, applicable to any single-cell epigenomics data type. After validation in synthetic datasets, we apply epiCHAOS to investigate global and region-specific patterns of epigenetic heterogeneity across diverse biological systems. EpiCHAOS provides an excellent approximation of stemness and plasticity in development and malignancy, making it a valuable addition to single-cell cancer epigenomics analyses.\u0000","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"103 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142760533","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}
Pub Date : 2024-12-02DOI: 10.1186/s13059-024-03438-w
Daniel J. Merk, Foteini Tsiami, Sophie Hirsch, Bianca Walter, Lara A. Haeusser, Jens D. Maile, Aaron Stahl, Mohamed A. Jarboui, Anna Lechado-Terradas, Franziska Klose, Sepideh Babaei, Jakob Admard, Nicolas Casadei, Cristiana Roggia, Michael Spohn, Jens Schittenhelm, Stephan Singer, Ulrich Schüller, Federica Piccioni, Nicole S. Persky, Manfred Claassen, Marcos Tatagiba, Philipp J. Kahle, David E. Root, Markus Templin, Ghazaleh Tabatabai
Atypical teratoid rhabdoid tumors (ATRT) are incurable high-grade pediatric brain tumors. Despite intensive research efforts, the prognosis for ATRT patients under currently established treatment protocols is poor. While novel therapeutic strategies are urgently needed, the generation of molecular-driven treatment concepts is a challenge mainly due to the absence of actionable genetic alterations. We here use a functional genomics approach to identify genetic dependencies in ATRT, validate selected hits using a functionally instructed small molecule drug library, and observe preferential activity in ATRT cells without subgroup-specific selectivity. CDK4/6 inhibitors are among the most potent drugs and display anti-tumor efficacy due to mutual exclusive dependency on CDK4 or CDK6. Chemogenetic interactor screens reveal a broad spectrum of G1 phase cell cycle regulators that differentially enable cell cycle progression and modulate response to CDK4/6 inhibition in ATRT cells. In this regard, we find that the ubiquitin ligase substrate receptor AMBRA1 acts as a context-specific inhibitor of cell cycle progression by regulating key components of mitosis including aurora kinases. Our data provide a comprehensive resource of genetic and chemical dependencies in ATRTs, which will inform further preclinical evaluation of novel targeted therapies for this tumor entity. Furthermore, this study reveals a unique mechanism of cell cycle inhibition as the basis for tumor suppressive functions of AMBRA1.
{"title":"Functional screening reveals genetic dependencies and diverging cell cycle control in atypical teratoid rhabdoid tumors","authors":"Daniel J. Merk, Foteini Tsiami, Sophie Hirsch, Bianca Walter, Lara A. Haeusser, Jens D. Maile, Aaron Stahl, Mohamed A. Jarboui, Anna Lechado-Terradas, Franziska Klose, Sepideh Babaei, Jakob Admard, Nicolas Casadei, Cristiana Roggia, Michael Spohn, Jens Schittenhelm, Stephan Singer, Ulrich Schüller, Federica Piccioni, Nicole S. Persky, Manfred Claassen, Marcos Tatagiba, Philipp J. Kahle, David E. Root, Markus Templin, Ghazaleh Tabatabai","doi":"10.1186/s13059-024-03438-w","DOIUrl":"https://doi.org/10.1186/s13059-024-03438-w","url":null,"abstract":"Atypical teratoid rhabdoid tumors (ATRT) are incurable high-grade pediatric brain tumors. Despite intensive research efforts, the prognosis for ATRT patients under currently established treatment protocols is poor. While novel therapeutic strategies are urgently needed, the generation of molecular-driven treatment concepts is a challenge mainly due to the absence of actionable genetic alterations. We here use a functional genomics approach to identify genetic dependencies in ATRT, validate selected hits using a functionally instructed small molecule drug library, and observe preferential activity in ATRT cells without subgroup-specific selectivity. CDK4/6 inhibitors are among the most potent drugs and display anti-tumor efficacy due to mutual exclusive dependency on CDK4 or CDK6. Chemogenetic interactor screens reveal a broad spectrum of G1 phase cell cycle regulators that differentially enable cell cycle progression and modulate response to CDK4/6 inhibition in ATRT cells. In this regard, we find that the ubiquitin ligase substrate receptor AMBRA1 acts as a context-specific inhibitor of cell cycle progression by regulating key components of mitosis including aurora kinases. Our data provide a comprehensive resource of genetic and chemical dependencies in ATRTs, which will inform further preclinical evaluation of novel targeted therapies for this tumor entity. Furthermore, this study reveals a unique mechanism of cell cycle inhibition as the basis for tumor suppressive functions of AMBRA1.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"79 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142758451","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：