Genome Biology最新文献

{"title":"Immune evasion impacts the landscape of driver genes during cancer evolution","authors":"Lucie Gourmet, Andrea Sottoriva, Simon Walker-Samuel, Maria Secrier, Luis Zapata","doi":"10.1186/s13059-024-03302-x","DOIUrl":"https://doi.org/10.1186/s13059-024-03302-x","url":null,"abstract":"Carcinogenesis is driven by interactions between genetic mutations and the local tumor microenvironment. Recent research has identified hundreds of cancer driver genes; however, these studies often include a mixture of different molecular subtypes and ecological niches and ignore the impact of the immune system. In this study, we compare the landscape of driver genes in tumors that escaped the immune system (escape +) versus those that did not (escape −). We analyze 9896 primary tumors from The Cancer Genome Atlas using the ratio of non-synonymous to synonymous mutations (dN/dS) and find 85 driver genes, including 27 and 16 novel genes, in escape − and escape + tumors, respectively. The dN/dS of driver genes in immune escaped tumors is significantly lower and closer to neutrality than in non-escaped tumors, suggesting selection buffering in driver genes fueled by immune escape. Additionally, we find that immune evasion leads to more mutated sites, a diverse array of mutational signatures and is linked to tumor prognosis. Our findings highlight the need for improved patient stratification to identify new therapeutic targets for cancer treatment.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141452956","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":"A feedback loop driven by H3K9 lactylation and HDAC2 in endothelial cells regulates VEGF-induced angiogenesis","authors":"Wei Fan, Shuhao Zeng, Xiaotang Wang, Guoqing Wang, Dan Liao, Ruonan Li, Siyuan He, Wanqian Li, Jiaxing Huang, Xingran Li, Jiangyi Liu, Na Li, Shengping Hou","doi":"10.1186/s13059-024-03308-5","DOIUrl":"https://doi.org/10.1186/s13059-024-03308-5","url":null,"abstract":"Vascular endothelial growth factor (VEGF) is one of the most powerful proangiogenic factors and plays an important role in multiple diseases. Increased glycolytic rates and lactate accumulation are associated with pathological angiogenesis. Here, we show that a feedback loop between H3K9 lactylation (H3K9la) and histone deacetylase 2 (HDAC2) in endothelial cells drives VEGF-induced angiogenesis. We find that the H3K9la levels are upregulated in endothelial cells in response to VEGF stimulation. Pharmacological inhibition of glycolysis decreases H3K9 lactylation and attenuates neovascularization. CUT& Tag analysis reveals that H3K9la is enriched at the promoters of a set of angiogenic genes and promotes their transcription. Interestingly, we find that hyperlactylation of H3K9 inhibits expression of the lactylation eraser HDAC2, whereas overexpression of HDAC2 decreases H3K9 lactylation and suppresses angiogenesis. Collectively, our study illustrates that H3K9la is important for VEGF-induced angiogenesis, and interruption of the H3K9la/HDAC2 feedback loop may represent a novel therapeutic method for treating pathological neovascularization.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141448179","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":"Splice_sim: a nucleotide conversion-enabled RNA-seq simulation and evaluation framework","authors":"Niko Popitsch, Tobias Neumann, Arndt von Haeseler, Stefan L. Ameres","doi":"10.1186/s13059-024-03313-8","DOIUrl":"https://doi.org/10.1186/s13059-024-03313-8","url":null,"abstract":"Nucleotide conversion RNA sequencing techniques interrogate chemical RNA modifications in cellular transcripts, resulting in mismatch-containing reads. Biases in mapping the resulting reads to reference genomes remain poorly understood. We present splice_sim, a splice-aware RNA-seq simulation and evaluation pipeline that introduces user-defined nucleotide conversions at set frequencies, creates mixture models of converted and unconverted reads, and calculates mapping accuracies per genomic annotation. By simulating nucleotide conversion RNA-seq datasets under realistic experimental conditions, including metabolic RNA labeling and RNA bisulfite sequencing, we measure mapping accuracies of state-of-the-art spliced-read mappers for mouse and human transcripts and derive strategies to prevent biases in the data interpretation.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141448366","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":"scHolography: a computational method for single-cell spatial neighborhood reconstruction and analysis","authors":"Yuheng C. Fu, Arpan Das, Dongmei Wang, Rosemary Braun, Rui Yi","doi":"10.1186/s13059-024-03299-3","DOIUrl":"https://doi.org/10.1186/s13059-024-03299-3","url":null,"abstract":"Spatial transcriptomics has transformed our ability to study tissue complexity. However, it remains challenging to accurately dissect tissue organization at single-cell resolution. Here we introduce scHolography, a machine learning-based method designed to reconstruct single-cell spatial neighborhoods and facilitate 3D tissue visualization using spatial and single-cell RNA sequencing data. scHolography employs a high-dimensional transcriptome-to-space projection that infers spatial relationships among cells, defining spatial neighborhoods and enhancing analyses of cell–cell communication. When applied to both human and mouse datasets, scHolography enables quantitative assessments of spatial cell neighborhoods, cell–cell interactions, and tumor-immune microenvironment. Together, scHolography offers a robust computational framework for elucidating 3D tissue organization and analyzing spatial dynamics at the cellular level.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141444953","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":"Evaluation of somatic copy number variation detection by NGS technologies and bioinformatics tools on a hyper-diploid cancer genome","authors":"Daniall Masood, Luyao Ren, Cu Nguyen, Francesco G. Brundu, Lily Zheng, Yongmei Zhao, Erich Jaeger, Yong Li, Seong Won Cha, Aaron Halpern, Sean Truong, Michael Virata, Chunhua Yan, Qingrong Chen, Andy Pang, Reyes Alberto, Chunlin Xiao, Zhaowei Yang, Wanqiu Chen, Charles Wang, Frank Cross, Severine Catreux, Leming Shi, Julia A. Beaver, Wenming Xiao, Daoud M. Meerzaman","doi":"10.1186/s13059-024-03294-8","DOIUrl":"https://doi.org/10.1186/s13059-024-03294-8","url":null,"abstract":"Copy number variation (CNV) is a key genetic characteristic for cancer diagnostics and can be used as a biomarker for the selection of therapeutic treatments. Using data sets established in our previous study, we benchmark the performance of cancer CNV calling by six most recent and commonly used software tools on their detection accuracy, sensitivity, and reproducibility. In comparison to other orthogonal methods, such as microarray and Bionano, we also explore the consistency of CNV calling across different technologies on a challenging genome. While consistent results are observed for copy gain, loss, and loss of heterozygosity (LOH) calls across sequencing centers, CNV callers, and different technologies, variation of CNV calls are mostly affected by the determination of genome ploidy. Using consensus results from six CNV callers and confirmation from three orthogonal methods, we establish a high confident CNV call set for the reference cancer cell line (HCC1395). NGS technologies and current bioinformatics tools can offer reliable results for detection of copy gain, loss, and LOH. However, when working with a hyper-diploid genome, some software tools can call excessive copy gain or loss due to inaccurate assessment of genome ploidy. With performance matrices on various experimental conditions, this study raises awareness within the cancer research community for the selection of sequencing platforms, sample preparation, sequencing coverage, and the choice of CNV detection tools.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141430487","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":"Alternative splicing coupled to nonsense-mediated decay coordinates downregulation of non-neuronal genes in developing mouse neurons","authors":"Anna Zhuravskaya, Karen Yap, Fursham Hamid, Eugene V. Makeyev","doi":"10.1186/s13059-024-03305-8","DOIUrl":"https://doi.org/10.1186/s13059-024-03305-8","url":null,"abstract":"The functional coupling between alternative pre-mRNA splicing (AS) and the mRNA quality control mechanism called nonsense-mediated decay (NMD) can modulate transcript abundance. Previous studies have identified several examples of such a regulation in developing neurons. However, the systems-level effects of AS-NMD in this context are poorly understood. We developed an R package, factR2, which offers a comprehensive suite of AS-NMD analysis functions. Using this tool, we conducted a longitudinal analysis of gene expression in pluripotent stem cells undergoing induced neuronal differentiation. Our analysis uncovers hundreds of AS-NMD events with significant potential to regulate gene expression. Notably, this regulation is significantly overrepresented in specific functional groups of developmentally downregulated genes. Particularly strong association with gene downregulation is detected for alternative cassette exons stimulating NMD upon their inclusion into mature mRNA. By combining bioinformatic analyses with CRISPR/Cas9 genome editing and other experimental approaches we show that NMD-stimulating cassette exons regulated by the RNA-binding protein PTBP1 dampen the expression of their genes in developing neurons. We also provided evidence that the inclusion of NMD-stimulating cassette exons into mature mRNAs is temporally coordinated with NMD-independent gene repression mechanisms. Our study provides an accessible workflow for the discovery and prioritization of AS-NMD targets. It further argues that the AS-NMD pathway plays a widespread role in developing neurons by facilitating the downregulation of functionally related non-neuronal genes.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141430565","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":"An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs","authors":"Richard H. Chapple, Xueying Liu, Sivaraman Natarajan, Margaret I. M. Alexander, Yuna Kim, Anand G. Patel, Christy W. LaFlamme, Min Pan, William C. Wright, Hyeong-Min Lee, Yinwen Zhang, Meifen Lu, Selene C. Koo, Courtney Long, John Harper, Chandra Savage, Melissa D. Johnson, Thomas Confer, Walter J. Akers, Michael A. Dyer, Heather Sheppard, John Easton, Paul Geeleher","doi":"10.1186/s13059-024-03309-4","DOIUrl":"https://doi.org/10.1186/s13059-024-03309-4","url":null,"abstract":"Neuroblastoma is a common pediatric cancer, where preclinical studies suggest that a mesenchymal-like gene expression program contributes to chemotherapy resistance. However, clinical outcomes remain poor, implying we need a better understanding of the relationship between patient tumor heterogeneity and preclinical models. Here, we generate single-cell RNA-seq maps of neuroblastoma cell lines, patient-derived xenograft models (PDX), and a genetically engineered mouse model (GEMM). We develop an unsupervised machine learning approach (“automatic consensus nonnegative matrix factorization” (acNMF)) to compare the gene expression programs found in preclinical models to a large cohort of patient tumors. We confirm a weakly expressed, mesenchymal-like program in otherwise adrenergic cancer cells in some pre-treated high-risk patient tumors, but this appears distinct from the presumptive drug-resistance mesenchymal programs evident in cell lines. Surprisingly, however, this weak-mesenchymal-like program is maintained in PDX and could be chemotherapy-induced in our GEMM after only 24 h, suggesting an uncharacterized therapy-escape mechanism. Collectively, our findings improve the understanding of how neuroblastoma patient tumor heterogeneity is reflected in preclinical models, provides a comprehensive integrated resource, and a generalizable set of computational methodologies for the joint analysis of clinical and pre-clinical single-cell RNA-seq datasets.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141425542","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":"Author Correction: Legal aspects of privacy-enhancing technologies in genome-wide association studies and their impact on performance and feasibility","authors":"Alissa Brauneck, Louisa Schmalhorst, Stefan Weiss, Linda Baumbach, Uwe Völker, David Ellinghaus, Jan Baumbach, Gabriele Buchholtz","doi":"10.1186/s13059-024-03311-w","DOIUrl":"https://doi.org/10.1186/s13059-024-03311-w","url":null,"abstract":"<p><b>Correction</b><b>: </b><b>Genome Biol 25, 154 (2024)</b></p><p><b>https://doi.org/10.1186/s13059-024-03296-6</b></p><br/><p>Following publication of the original article [1], the authors reported an error in the second equal contribution statement of their article. David Ellinghaus, Jan Baumbach and Gabriele Buchholtz are shared last authors. David Ellinghaus was erroneously omitted from this statement.</p><p>The original article [1] has been corrected.</p><ol data-track-component=\"outbound reference\" data-track-context=\"references section\"><li data-counter=\"1.\"><p>Brauneck A, Schmalhorst L, Weiss S, et al. Legal aspects of privacy-enhancing technologies in genome-wide association studies and their impact on performance and feasibility. Genome Biol. 2024;25:154. https://doi.org/10.1186/s13059-024-03296-6.</p><p>Article PubMed PubMed Central Google Scholar </p></li></ol><p>Download references<svg aria-hidden=\"true\" focusable=\"false\" height=\"16\" role=\"img\" width=\"16\"><use xlink:href=\"#icon-eds-i-download-medium\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"></use></svg></p><span>Author notes</span><ol><li><p>Alissa Brauneck and Louisa Schmalhorst shared first authors.</p></li><li><p>David Ellinghaus, Jan Baumbach and Gabriele Buchholtz shared last authors.</p></li></ol><h3>Authors and Affiliations</h3><ol><li><p>Hamburg University Faculty of Law, University of Hamburg, Hamburg, Germany</p><p>Alissa Brauneck, Louisa Schmalhorst &amp; Gabriele Buchholtz</p></li><li><p>Interfaculty Institute of Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany</p><p>Stefan Weiss &amp; Uwe Völker</p></li><li><p>Department of Health Economics and Health Services Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany</p><p>Linda Baumbach</p></li><li><p>Institute of Clinical Molecular Biology (IKMB), Kiel University and University Medical Center Schleswig-Holstein, Kiel, Germany</p><p>David Ellinghaus</p></li><li><p>Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany</p><p>Jan Baumbach</p></li></ol><span>Authors</span><ol><li><span>Alissa Brauneck</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Louisa Schmalhorst</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Stefan Weiss</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Linda Baumbach</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Uwe Völker</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>David Ellinghaus</span>View author publications<p>You can also search ","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141334426","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":"Beyond benchmarking and towards predictive models of dataset-specific single-cell RNA-seq pipeline performance","authors":"Cindy Fang, Alina Selega, Kieran R. Campbell","doi":"10.1186/s13059-024-03304-9","DOIUrl":"https://doi.org/10.1186/s13059-024-03304-9","url":null,"abstract":"The advent of single-cell RNA-sequencing (scRNA-seq) has driven significant computational methods development for all steps in the scRNA-seq data analysis pipeline, including filtering, normalization, and clustering. The large number of methods and their resulting parameter combinations has created a combinatorial set of possible pipelines to analyze scRNA-seq data, which leads to the obvious question: which is best? Several benchmarking studies compare methods but frequently find variable performance depending on dataset and pipeline characteristics. Alternatively, the large number of scRNA-seq datasets along with advances in supervised machine learning raise a tantalizing possibility: could the optimal pipeline be predicted for a given dataset? Here, we begin to answer this question by applying 288 scRNA-seq analysis pipelines to 86 datasets and quantifying pipeline success via a range of measures evaluating cluster purity and biological plausibility. We build supervised machine learning models to predict pipeline success given a range of dataset and pipeline characteristics. We find that prediction performance is significantly better than random and that in many cases pipelines predicted to perform well provide clustering outputs similar to expert-annotated cell type labels. We identify characteristics of datasets that correlate with strong prediction performance that could guide when such prediction models may be useful. Supervised machine learning models have utility for recommending analysis pipelines and therefore the potential to alleviate the burden of choosing from the near-infinite number of possibilities. Different aspects of datasets influence the predictive performance of such models which will further guide users.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141334419","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":"DNA methylation variations underlie lettuce domestication and divergence","authors":"Shuai Cao, Nunchanoke Sawettalake, Ping Li, Sheng Fan, Lisha Shen","doi":"10.1186/s13059-024-03310-x","DOIUrl":"https://doi.org/10.1186/s13059-024-03310-x","url":null,"abstract":"Lettuce (Lactuca sativa L.) is an economically important vegetable crop worldwide. Lettuce is believed to be domesticated from a single wild ancestor Lactuca serriola and subsequently diverged into two major morphologically distinct vegetable types: leafy lettuce and stem lettuce. However, the role of epigenetic variation in lettuce domestication and divergence remains largely unknown. To understand the genetic and epigenetic basis underlying lettuce domestication and divergence, we generate single-base resolution DNA methylomes from 52 Lactuca accessions, including major lettuce cultivars and wild relatives. We find a significant increase of DNA methylation during lettuce domestication and uncover abundant epigenetic variations associated with lettuce domestication and divergence. Interestingly, DNA methylation variations specifically associated with leafy and stem lettuce are related to regulation and metabolic processes, respectively, while those associated with both types are enriched in stress responses. Moreover, we reveal that domestication-induced DNA methylation changes could influence expression levels of nearby and distal genes possibly through affecting chromatin accessibility and chromatin loop. Our study provides population epigenomic insights into crop domestication and divergence and valuable resources for further domestication for diversity and epigenetic breeding to boost crop improvement.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141333718","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}