Pub Date : 2024-10-25DOI: 10.1186/s13059-024-03425-1
Nicola De Bernardini, Guido Zampieri, Stefano Campanaro, Johannes Zimmermann, Silvio Waschina, Laura Treu
The accurate reconstruction of genome-scale metabolic models (GEMs) for unculturable species poses challenges due to the incomplete and fragmented genetic information typical of metagenome-assembled genomes (MAGs). While existing tools leverage sequence homology from single genomes, this study introduces pan-Draft, a pan-reactome-based approach exploiting recurrent genetic evidence to determine the solid core structure of species-level GEMs. By comparing MAGs clustered at the species-level, pan-Draft addresses the issues due to the incompleteness and contamination of individual genomes, providing high-quality draft models and an accessory reactions catalog supporting the gapfilling step. This approach will improve our comprehension of metabolic functions of uncultured species.
{"title":"pan-Draft: automated reconstruction of species-representative metabolic models from multiple genomes","authors":"Nicola De Bernardini, Guido Zampieri, Stefano Campanaro, Johannes Zimmermann, Silvio Waschina, Laura Treu","doi":"10.1186/s13059-024-03425-1","DOIUrl":"https://doi.org/10.1186/s13059-024-03425-1","url":null,"abstract":"The accurate reconstruction of genome-scale metabolic models (GEMs) for unculturable species poses challenges due to the incomplete and fragmented genetic information typical of metagenome-assembled genomes (MAGs). While existing tools leverage sequence homology from single genomes, this study introduces pan-Draft, a pan-reactome-based approach exploiting recurrent genetic evidence to determine the solid core structure of species-level GEMs. By comparing MAGs clustered at the species-level, pan-Draft addresses the issues due to the incompleteness and contamination of individual genomes, providing high-quality draft models and an accessory reactions catalog supporting the gapfilling step. This approach will improve our comprehension of metabolic functions of uncultured species.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"15 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142489403","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 : 2024-10-24DOI: 10.1186/s13059-024-03399-0
Kangquan Yin, Mi Yoon Chung, Bo Lan, Fang K. Du, Myong Gi Chung
Numerous plant taxa are threatened by habitat destruction or overexploitation. To overcome these threats, new methods are urgently needed for rescuing threatened and endangered plant species. Here, we review the genetic consequences of threats to species populations. We highlight potential advantages of genome editing for mitigating negative effects caused by new pathogens and pests or climate change where other approaches have failed. We propose solutions to protect threatened plants using genome editing technology unless absolutely necessary. We further discuss the challenges associated with genome editing in plant conservation to mitigate the decline of plant diversity.
{"title":"Plant conservation in the age of genome editing: opportunities and challenges","authors":"Kangquan Yin, Mi Yoon Chung, Bo Lan, Fang K. Du, Myong Gi Chung","doi":"10.1186/s13059-024-03399-0","DOIUrl":"https://doi.org/10.1186/s13059-024-03399-0","url":null,"abstract":"Numerous plant taxa are threatened by habitat destruction or overexploitation. To overcome these threats, new methods are urgently needed for rescuing threatened and endangered plant species. Here, we review the genetic consequences of threats to species populations. We highlight potential advantages of genome editing for mitigating negative effects caused by new pathogens and pests or climate change where other approaches have failed. We propose solutions to protect threatened plants using genome editing technology unless absolutely necessary. We further discuss the challenges associated with genome editing in plant conservation to mitigate the decline of plant diversity.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"110 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142488635","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}
Spatial transcriptomics technologies have been widely applied to decode cellular distribution by resolving gene expression profiles in tissue. However, sequencing techniques still limit the ability to create a fine-resolved spatial cell-type map. To this end, we develop a novel deep-learning-based approach, STASCAN, to predict the spatial cellular distribution of captured or uncharted areas where only histology images are available by cell feature learning integrating gene expression profiles and histology images. STASCAN is successfully applied across diverse datasets from different spatial transcriptomics technologies and displays significant advantages in deciphering higher-resolution cellular distribution and resolving enhanced organizational structures.
{"title":"STASCAN deciphers fine-resolution cell distribution maps in spatial transcriptomics by deep learning","authors":"Ying Wu, Jia-Yi Zhou, Bofei Yao, Guanshen Cui, Yong-Liang Zhao, Chun-Chun Gao, Ying Yang, Shihua Zhang, Yun-Gui Yang","doi":"10.1186/s13059-024-03421-5","DOIUrl":"https://doi.org/10.1186/s13059-024-03421-5","url":null,"abstract":"Spatial transcriptomics technologies have been widely applied to decode cellular distribution by resolving gene expression profiles in tissue. However, sequencing techniques still limit the ability to create a fine-resolved spatial cell-type map. To this end, we develop a novel deep-learning-based approach, STASCAN, to predict the spatial cellular distribution of captured or uncharted areas where only histology images are available by cell feature learning integrating gene expression profiles and histology images. STASCAN is successfully applied across diverse datasets from different spatial transcriptomics technologies and displays significant advantages in deciphering higher-resolution cellular distribution and resolving enhanced organizational structures.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"13 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142486679","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 : 2024-10-21DOI: 10.1186/s13059-024-03420-6
Rima Mustafa, Michelle M. J. Mens, Arno van Hilten, Jian Huang, Gennady Roshchupkin, Tianxiao Huan, Linda Broer, Joyce B. J. van Meurs, Paul Elliott, Daniel Levy, M. Arfan Ikram, Marina Evangelou, Abbas Dehghan, Mohsen Ghanbari
MicroRNAs (miRNAs) are small non-coding RNAs that post-transcriptionally regulate gene expression. Perturbations in plasma miRNA levels are known to impact disease risk and have potential as disease biomarkers. Exploring the genetic regulation of miRNAs may yield new insights into their important role in governing gene expression and disease mechanisms. We present genome-wide association studies of 2083 plasma circulating miRNAs in 2178 participants of the Rotterdam Study to identify miRNA-expression quantitative trait loci (miR-eQTLs). We identify 3292 associations between 1289 SNPs and 63 miRNAs, of which 65% are replicated in two independent cohorts. We demonstrate that plasma miR-eQTLs co-localise with gene expression, protein, and metabolite-QTLs, which help in identifying miRNA-regulated pathways. We investigate consequences of alteration in circulating miRNA levels on a wide range of clinical conditions in phenome-wide association studies and Mendelian randomisation using the UK Biobank data (N = 423,419), revealing the pleiotropic and causal effects of several miRNAs on various clinical conditions. In the Mendelian randomisation analysis, we find a protective causal effect of miR-1908-5p on the risk of benign colon neoplasm and show that this effect is independent of its host gene (FADS1). This study enriches our understanding of the genetic architecture of plasma miRNAs and explores the signatures of miRNAs across a wide range of clinical conditions. The integration of population-based genomics, other omics layers, and clinical data presents opportunities to unravel potential clinical significance of miRNAs and provides tools for novel miRNA-based therapeutic target discovery.
{"title":"A comprehensive study of genetic regulation and disease associations of plasma circulatory microRNAs using population-level data","authors":"Rima Mustafa, Michelle M. J. Mens, Arno van Hilten, Jian Huang, Gennady Roshchupkin, Tianxiao Huan, Linda Broer, Joyce B. J. van Meurs, Paul Elliott, Daniel Levy, M. Arfan Ikram, Marina Evangelou, Abbas Dehghan, Mohsen Ghanbari","doi":"10.1186/s13059-024-03420-6","DOIUrl":"https://doi.org/10.1186/s13059-024-03420-6","url":null,"abstract":"MicroRNAs (miRNAs) are small non-coding RNAs that post-transcriptionally regulate gene expression. Perturbations in plasma miRNA levels are known to impact disease risk and have potential as disease biomarkers. Exploring the genetic regulation of miRNAs may yield new insights into their important role in governing gene expression and disease mechanisms. We present genome-wide association studies of 2083 plasma circulating miRNAs in 2178 participants of the Rotterdam Study to identify miRNA-expression quantitative trait loci (miR-eQTLs). We identify 3292 associations between 1289 SNPs and 63 miRNAs, of which 65% are replicated in two independent cohorts. We demonstrate that plasma miR-eQTLs co-localise with gene expression, protein, and metabolite-QTLs, which help in identifying miRNA-regulated pathways. We investigate consequences of alteration in circulating miRNA levels on a wide range of clinical conditions in phenome-wide association studies and Mendelian randomisation using the UK Biobank data (N = 423,419), revealing the pleiotropic and causal effects of several miRNAs on various clinical conditions. In the Mendelian randomisation analysis, we find a protective causal effect of miR-1908-5p on the risk of benign colon neoplasm and show that this effect is independent of its host gene (FADS1). This study enriches our understanding of the genetic architecture of plasma miRNAs and explores the signatures of miRNAs across a wide range of clinical conditions. The integration of population-based genomics, other omics layers, and clinical data presents opportunities to unravel potential clinical significance of miRNAs and provides tools for novel miRNA-based therapeutic target discovery.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"33 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452047","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 : 2024-10-21DOI: 10.1186/s13059-024-03422-4
Marius Lange, Zoe Piran, Michal Klein, Bastiaan Spanjaard, Dominik Klein, Jan Philipp Junker, Fabian J. Theis, Mor Nitzan
Simultaneous profiling of single-cell gene expression and lineage history holds enormous potential for studying cellular decision-making. Recent computational approaches combine both modalities into cellular trajectories; however, they cannot make use of all available lineage information in destructive time-series experiments. Here, we present moslin, a Gromov-Wasserstein-based model to couple cellular profiles across time points based on lineage and gene expression information. We validate our approach in simulations and demonstrate on Caenorhabditis elegans embryonic development how moslin predicts fate probabilities and putative decision driver genes. Finally, we use moslin to delineate lineage relationships among transiently activated fibroblast states during zebrafish heart regeneration.
{"title":"Mapping lineage-traced cells across time points with moslin","authors":"Marius Lange, Zoe Piran, Michal Klein, Bastiaan Spanjaard, Dominik Klein, Jan Philipp Junker, Fabian J. Theis, Mor Nitzan","doi":"10.1186/s13059-024-03422-4","DOIUrl":"https://doi.org/10.1186/s13059-024-03422-4","url":null,"abstract":"Simultaneous profiling of single-cell gene expression and lineage history holds enormous potential for studying cellular decision-making. Recent computational approaches combine both modalities into cellular trajectories; however, they cannot make use of all available lineage information in destructive time-series experiments. Here, we present moslin, a Gromov-Wasserstein-based model to couple cellular profiles across time points based on lineage and gene expression information. We validate our approach in simulations and demonstrate on Caenorhabditis elegans embryonic development how moslin predicts fate probabilities and putative decision driver genes. Finally, we use moslin to delineate lineage relationships among transiently activated fibroblast states during zebrafish heart regeneration.\u0000","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"79 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452394","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 : 2024-10-18DOI: 10.1186/s13059-024-03418-0
Weixu Wang, Yichen Wang, Ruiqi Lyu, Dominic Grün
The identification of gene regulatory networks (GRNs) is crucial for understanding cellular differentiation. Single-cell RNA sequencing data encode gene-level covariations at high resolution, yet data sparsity and high dimensionality hamper accurate and scalable GRN reconstruction. To overcome these challenges, we introduce NetID leveraging homogenous metacells while avoiding spurious gene–gene correlations. Benchmarking demonstrates superior performance of NetID compared to imputation-based methods. By incorporating cell fate probability information, NetID facilitates the prediction of lineage-specific GRNs and recovers known network motifs governing bone marrow hematopoiesis, making it a powerful toolkit for deciphering gene regulatory control of cellular differentiation from large-scale single-cell transcriptome data.
{"title":"Scalable identification of lineage-specific gene regulatory networks from metacells with NetID","authors":"Weixu Wang, Yichen Wang, Ruiqi Lyu, Dominic Grün","doi":"10.1186/s13059-024-03418-0","DOIUrl":"https://doi.org/10.1186/s13059-024-03418-0","url":null,"abstract":"The identification of gene regulatory networks (GRNs) is crucial for understanding cellular differentiation. Single-cell RNA sequencing data encode gene-level covariations at high resolution, yet data sparsity and high dimensionality hamper accurate and scalable GRN reconstruction. To overcome these challenges, we introduce NetID leveraging homogenous metacells while avoiding spurious gene–gene correlations. Benchmarking demonstrates superior performance of NetID compared to imputation-based methods. By incorporating cell fate probability information, NetID facilitates the prediction of lineage-specific GRNs and recovers known network motifs governing bone marrow hematopoiesis, making it a powerful toolkit for deciphering gene regulatory control of cellular differentiation from large-scale single-cell transcriptome data.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"2 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142448825","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 : 2024-10-17DOI: 10.1186/s13059-024-03412-6
Kristen J. Wade, Rayo Suseno, Kerry Kizer, Jacqueline Williams, Juliano Boquett, Stacy Caillier, Nicholas R. Pollock, Adam Renschen, Adam Santaniello, Jorge R. Oksenberg, Paul J. Norman, Danillo G. Augusto, Jill A. Hollenbach
The extremely high levels of genetic polymorphism within the human major histocompatibility complex (MHC) limit the usefulness of reference-based alignment methods for sequence assembly. We incorporate a short-read, de novo assembly algorithm into a workflow for novel application to the MHC. MHConstructor is a containerized pipeline designed for high-throughput, haplotype-informed, reproducible assembly of both whole genome sequencing and target capture short-read data in large, population cohorts. To-date, no other self-contained tool exists for the generation of de novo MHC assemblies from short-read data. MHConstructor facilitates wide-spread access to high-quality, alignment-free MHC sequence analysis.
{"title":"MHConstructor: a high-throughput, haplotype-informed solution to the MHC assembly challenge","authors":"Kristen J. Wade, Rayo Suseno, Kerry Kizer, Jacqueline Williams, Juliano Boquett, Stacy Caillier, Nicholas R. Pollock, Adam Renschen, Adam Santaniello, Jorge R. Oksenberg, Paul J. Norman, Danillo G. Augusto, Jill A. Hollenbach","doi":"10.1186/s13059-024-03412-6","DOIUrl":"https://doi.org/10.1186/s13059-024-03412-6","url":null,"abstract":"The extremely high levels of genetic polymorphism within the human major histocompatibility complex (MHC) limit the usefulness of reference-based alignment methods for sequence assembly. We incorporate a short-read, de novo assembly algorithm into a workflow for novel application to the MHC. MHConstructor is a containerized pipeline designed for high-throughput, haplotype-informed, reproducible assembly of both whole genome sequencing and target capture short-read data in large, population cohorts. To-date, no other self-contained tool exists for the generation of de novo MHC assemblies from short-read data. MHConstructor facilitates wide-spread access to high-quality, alignment-free MHC sequence analysis.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"11 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443865","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 : 2024-10-15DOI: 10.1186/s13059-024-03411-7
Youshu Cheng, Biao Cai, Hongyu Li, Xinyu Zhang, Gypsyamber D’Souza, Sadeep Shrestha, Andrew Edmonds, Jacquelyn Meyers, Margaret Fischl, Seble Kassaye, Kathryn Anastos, Mardge Cohen, Bradley E. Aouizerat, Ke Xu, Hongyu Zhao
Methylation quantitative trait loci (meQTLs) quantify the effects of genetic variants on DNA methylation levels. However, most published studies utilize bulk methylation datasets composed of different cell types and limit our understanding of cell-type-specific methylation regulation. We propose a hierarchical Bayesian interaction (HBI) model to infer cell-type-specific meQTLs, which integrates a large-scale bulk methylation data and a small-scale cell-type-specific methylation data. Through simulations, we show that HBI enhances the estimation of cell-type-specific meQTLs. In real data analyses, we demonstrate that HBI can further improve the functional annotation of genetic variants and identify biologically relevant cell types for complex traits.
{"title":"HBI: a hierarchical Bayesian interaction model to estimate cell-type-specific methylation quantitative trait loci incorporating priors from cell-sorted bisulfite sequencing data","authors":"Youshu Cheng, Biao Cai, Hongyu Li, Xinyu Zhang, Gypsyamber D’Souza, Sadeep Shrestha, Andrew Edmonds, Jacquelyn Meyers, Margaret Fischl, Seble Kassaye, Kathryn Anastos, Mardge Cohen, Bradley E. Aouizerat, Ke Xu, Hongyu Zhao","doi":"10.1186/s13059-024-03411-7","DOIUrl":"https://doi.org/10.1186/s13059-024-03411-7","url":null,"abstract":"Methylation quantitative trait loci (meQTLs) quantify the effects of genetic variants on DNA methylation levels. However, most published studies utilize bulk methylation datasets composed of different cell types and limit our understanding of cell-type-specific methylation regulation. We propose a hierarchical Bayesian interaction (HBI) model to infer cell-type-specific meQTLs, which integrates a large-scale bulk methylation data and a small-scale cell-type-specific methylation data. Through simulations, we show that HBI enhances the estimation of cell-type-specific meQTLs. In real data analyses, we demonstrate that HBI can further improve the functional annotation of genetic variants and identify biologically relevant cell types for complex traits.\u0000","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"32 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142440204","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 : 2024-10-15DOI: 10.1186/s13059-024-03383-8
Fengyang Jing, Lijing Zhu, Jianyun Zhang, Xuan Zhou, Jiaying Bai, Xuefen Li, Heyu Zhang, Tiejun Li
Lactylation, a post-translational modification, is increasingly recognized for its role in cancer progression. This study investigates its prevalence and impact in oral squamous cell carcinoma (OSCC). Immunohistochemical staining of 81 OSCC cases shows lactylation levels correlate with malignancy grading. Proteomic analyses of six OSCC tissue pairs reveal 2765 lactylation sites on 1033 proteins, highlighting its extensive presence. These modifications influence metabolic processes, molecular synthesis, and transport. CAL27 cells are subjected to cleavage under targets and tagmentation assay for accessible-chromatin with high-throughput sequencing, and transcriptomic sequencing pre- and post-lactate treatment, with 217 genes upregulated due to lactylation. Chromatin immunoprecipitation-quantitative PCR and real-time fluorescence quantitative PCR confirm the regulatory role of lactylation at the K146 site of dexh-box helicase 9 (DHX9), a key factor in OSCC progression. CCK8, colony formation, scratch healing, and Transwell assays demonstrate that lactylation mitigates the inhibitory effect of DHX9 on OSCC, thereby promoting its occurrence and development. Lactylation actively modulates gene expression in OSCC, with significant effects on chromatin structure and cellular processes. This study provides a foundation for developing targeted therapies against OSCC, leveraging the role of lactylation in disease pathogenesis.
{"title":"Multi-omics reveals lactylation-driven regulatory mechanisms promoting tumor progression in oral squamous cell carcinoma","authors":"Fengyang Jing, Lijing Zhu, Jianyun Zhang, Xuan Zhou, Jiaying Bai, Xuefen Li, Heyu Zhang, Tiejun Li","doi":"10.1186/s13059-024-03383-8","DOIUrl":"https://doi.org/10.1186/s13059-024-03383-8","url":null,"abstract":"Lactylation, a post-translational modification, is increasingly recognized for its role in cancer progression. This study investigates its prevalence and impact in oral squamous cell carcinoma (OSCC). Immunohistochemical staining of 81 OSCC cases shows lactylation levels correlate with malignancy grading. Proteomic analyses of six OSCC tissue pairs reveal 2765 lactylation sites on 1033 proteins, highlighting its extensive presence. These modifications influence metabolic processes, molecular synthesis, and transport. CAL27 cells are subjected to cleavage under targets and tagmentation assay for accessible-chromatin with high-throughput sequencing, and transcriptomic sequencing pre- and post-lactate treatment, with 217 genes upregulated due to lactylation. Chromatin immunoprecipitation-quantitative PCR and real-time fluorescence quantitative PCR confirm the regulatory role of lactylation at the K146 site of dexh-box helicase 9 (DHX9), a key factor in OSCC progression. CCK8, colony formation, scratch healing, and Transwell assays demonstrate that lactylation mitigates the inhibitory effect of DHX9 on OSCC, thereby promoting its occurrence and development. Lactylation actively modulates gene expression in OSCC, with significant effects on chromatin structure and cellular processes. This study provides a foundation for developing targeted therapies against OSCC, leveraging the role of lactylation in disease pathogenesis.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"4 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142440205","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}
Isobaric labeling-based mass spectrometry (ILMS) has been widely used to quantify, on a proteome-wide scale, the relative protein abundance in different biological conditions. However, large-scale ILMS data sets typically involve multiple runs of mass spectrometry, bringing great computational difficulty to the integration of ILMS samples. We present zMAP, a toolset that makes ILMS intensities comparable across mass spectrometry runs by modeling the associated mean-variance dependence and accordingly applying a variance stabilizing z-transformation. The practical utility of zMAP is demonstrated in several case studies involving the dynamics of cell differentiation and the heterogeneity across cancer patients.
{"title":"zMAP toolset: model-based analysis of large-scale proteomic data via a variance stabilizing z-transformation","authors":"Xiuqi Gui, Jing Huang, Linjie Ruan, Yanjun Wu, Xuan Guo, Ruifang Cao, Shuhan Zhou, Fengxiang Tan, Hongwen Zhu, Mushan Li, Guoqing Zhang, Hu Zhou, Lixing Zhan, Xin Liu, Shiqi Tu, Zhen Shao","doi":"10.1186/s13059-024-03382-9","DOIUrl":"https://doi.org/10.1186/s13059-024-03382-9","url":null,"abstract":"Isobaric labeling-based mass spectrometry (ILMS) has been widely used to quantify, on a proteome-wide scale, the relative protein abundance in different biological conditions. However, large-scale ILMS data sets typically involve multiple runs of mass spectrometry, bringing great computational difficulty to the integration of ILMS samples. We present zMAP, a toolset that makes ILMS intensities comparable across mass spectrometry runs by modeling the associated mean-variance dependence and accordingly applying a variance stabilizing z-transformation. The practical utility of zMAP is demonstrated in several case studies involving the dynamics of cell differentiation and the heterogeneity across cancer patients.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"23 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142431678","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}
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