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Khoroshkin M, Buyan A, Dodel M, Navickas A, Yu J, Trejo F, Doty A, Baratam R, Zhou S, Lee SB, Joshi T, Garcia K, Choi B, Miglani S, Subramanyam V, Modi H, Carpenter C, Markett D, Corces MR, Mardakheh FK, Kulakovskiy IV, Goodarzi H. Systematic identification of post-transcriptional regulatory modules. Nat Commun 2024; 15:7872. [PMID: 39251607 DOI: 10.1038/s41467-024-52215-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Accepted: 08/27/2024] [Indexed: 09/11/2024] Open
Abstract
In our cells, a limited number of RNA binding proteins (RBPs) are responsible for all aspects of RNA metabolism across the entire transcriptome. To accomplish this, RBPs form regulatory units that act on specific target regulons. However, the landscape of RBP combinatorial interactions remains poorly explored. Here, we perform a systematic annotation of RBP combinatorial interactions via multimodal data integration. We build a large-scale map of RBP protein neighborhoods by generating in vivo proximity-dependent biotinylation datasets of 50 human RBPs. In parallel, we use CRISPR interference with single-cell readout to capture transcriptomic changes upon RBP knockdowns. By combining these physical and functional interaction readouts, along with the atlas of RBP mRNA targets from eCLIP assays, we generate an integrated map of functional RBP interactions. We then use this map to match RBPs to their context-specific functions and validate the predicted functions biochemically for four RBPs. This study provides a detailed map of RBP interactions and deconvolves them into distinct regulatory modules with annotated functions and target regulons. This multimodal and integrative framework provides a principled approach for studying post-transcriptional regulatory processes and enriches our understanding of their underlying mechanisms.
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Affiliation(s)
- Matvei Khoroshkin
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Andrey Buyan
- Institute of Protein Research, Russian Academy of Sciences, Pushchino, Russia
| | - Martin Dodel
- Centre for Cancer Cell and Molecular Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
- Department of Biochemistry, University of Oxford, Oxford, UK
| | - Albertas Navickas
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Institut Curie, UMR3348 CNRS, Inserm, Orsay, France
| | - Johnny Yu
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Fathima Trejo
- College of Arts and Sciences, University of San Francisco, San Francisco, CA, USA
| | - Anthony Doty
- College of Arts and Sciences, University of San Francisco, San Francisco, CA, USA
| | - Rithvik Baratam
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Shaopu Zhou
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Sean B Lee
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Tanvi Joshi
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Kristle Garcia
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Benedict Choi
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Sohit Miglani
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Vishvak Subramanyam
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Hailey Modi
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Christopher Carpenter
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Daniel Markett
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - M Ryan Corces
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Faraz K Mardakheh
- Centre for Cancer Cell and Molecular Biology, Barts Cancer Institute, Queen Mary University of London, London, UK.
- Department of Biochemistry, University of Oxford, Oxford, UK.
| | - Ivan V Kulakovskiy
- Institute of Protein Research, Russian Academy of Sciences, Pushchino, Russia.
- Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia.
| | - Hani Goodarzi
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA.
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA.
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
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2
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Kahn RA, Virk H, Laflamme C, Houston DW, Polinski NK, Meijers R, Levey AI, Saper CB, Errington TM, Turn RE, Bandrowski A, Trimmer JS, Rego M, Freedman LP, Ferrara F, Bradbury ARM, Cable H, Longworth S. Antibody characterization is critical to enhance reproducibility in biomedical research. eLife 2024; 13:e100211. [PMID: 39140332 PMCID: PMC11324233 DOI: 10.7554/elife.100211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Accepted: 08/06/2024] [Indexed: 08/15/2024] Open
Abstract
Antibodies are used in many areas of biomedical and clinical research, but many of these antibodies have not been adequately characterized, which casts doubt on the results reported in many scientific papers. This problem is compounded by a lack of suitable control experiments in many studies. In this article we review the history of the 'antibody characterization crisis', and we document efforts and initiatives to address the problem, notably for antibodies that target human proteins. We also present recommendations for a range of stakeholders - researchers, universities, journals, antibody vendors and repositories, scientific societies and funders - to increase the reproducibility of studies that rely on antibodies.
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Affiliation(s)
- Richard A Kahn
- Department of Biochemistry, Emory University School of MedicineAtlantaUnited States
| | - Harvinder Virk
- Department of Respiratory Sciences, University of LeicesterLeicesterUnited Kingdom
| | - Carl Laflamme
- Department of Neurology and Neurosurgery, Structural Genomics Consortium, The Montreal Neurological Institute, McGill UniversityMontrealCanada
| | - Douglas W Houston
- The Development Studies Hybridoma Databank, University of IowaIowa CityUnited States
| | - Nicole K Polinski
- The Michael J Fox Foundation for Parkinson’s ResearchNew YorkUnited States
| | - Rob Meijers
- Institute for Protein InnovationBostonUnited States
| | - Allan I Levey
- Department of Neurology, Emory University School of MedicineAtlantaUnited States
| | - Clifford B Saper
- Department of Neurology and Program in Neuroscience, Harvard Medical School and Beth Israel Deaconess Medical CenterBostonUnited States
| | | | - Rachel E Turn
- Department of Microbiology and Immunology, Stanford University School of MedicineStanfordUnited States
| | - Anita Bandrowski
- Department of Neuroscience, University of California, San DiegoLa JollaUnited States
| | - James S Trimmer
- Department of Physiology and Membrane Biology, University of California, Davis School of MedicineDavisUnited States
| | | | | | | | | | - Hannah Cable
- Department of Research and Development, AbcamCambridgeUnited Kingdom
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Li Y, Wang Y, Wang C, Ma A, Ma Q, Liu B. A weighted two-stage sequence alignment framework to identify motifs from ChIP-exo data. PATTERNS (NEW YORK, N.Y.) 2024; 5:100927. [PMID: 38487805 PMCID: PMC10935504 DOI: 10.1016/j.patter.2024.100927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/18/2023] [Accepted: 01/10/2024] [Indexed: 03/17/2024]
Abstract
In this study, we introduce TESA (weighted two-stage alignment), an innovative motif prediction tool that refines the identification of DNA-binding protein motifs, essential for deciphering transcriptional regulatory mechanisms. Unlike traditional algorithms that rely solely on sequence data, TESA integrates the high-resolution chromatin immunoprecipitation (ChIP) signal, specifically from ChIP-exonuclease (ChIP-exo), by assigning weights to sequence positions, thereby enhancing motif discovery. TESA employs a nuanced approach combining a binomial distribution model with a graph model, further supported by a "bookend" model, to improve the accuracy of predicting motifs of varying lengths. Our evaluation, utilizing an extensive compilation of 90 prokaryotic ChIP-exo datasets from proChIPdb and 167 H. sapiens datasets, compared TESA's performance against seven established tools. The results indicate TESA's improved precision in motif identification, suggesting its valuable contribution to the field of genomic research.
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Affiliation(s)
- Yang Li
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Yizhong Wang
- School of Mathematics, Shandong University, Jinan, Shandong 250100, China
| | - Cankun Wang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Anjun Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Bingqiang Liu
- School of Mathematics, Shandong University, Jinan, Shandong 250100, China
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Li Y, Gong J, Sun Q, Vong EG, Cheng X, Wang B, Yuan Y, Jin L, Gamazon ER, Zhou D, Lai M, Zhang D. Alternative polyadenylation quantitative trait methylation mapping in human cancers provides clues into the molecular mechanisms of APA. Am J Hum Genet 2024; 111:562-583. [PMID: 38367620 PMCID: PMC10940021 DOI: 10.1016/j.ajhg.2024.01.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 02/19/2024] Open
Abstract
Genetic variants are involved in the orchestration of alternative polyadenylation (APA) events, while the role of DNA methylation in regulating APA remains unclear. We generated a comprehensive atlas of APA quantitative trait methylation sites (apaQTMs) across 21 different types of cancer (1,612 to 60,219 acting in cis and 4,448 to 142,349 in trans). Potential causal apaQTMs in non-cancer samples were also identified. Mechanistically, we observed a strong enrichment of cis-apaQTMs near polyadenylation sites (PASs) and both cis- and trans-apaQTMs in proximity to transcription factor (TF) binding regions. Through the integration of ChIP-signals and RNA-seq data from cell lines, we have identified several regulators of APA events, acting either directly or indirectly, implicating novel functions of some important genes, such as TCF7L2, which is known for its involvement in type 2 diabetes and cancers. Furthermore, we have identified a vast number of QTMs that share the same putative causal CpG sites with five different cancer types, underscoring the roles of QTMs, including apaQTMs, in the process of tumorigenesis. DNA methylation is extensively involved in the regulation of APA events in human cancers. In an attempt to elucidate the potential underlying molecular mechanisms of APA by DNA methylation, our study paves the way for subsequent experimental validations into the intricate biological functions of DNA methylation in APA regulation and the pathogenesis of human cancers. To present a comprehensive catalog of apaQTM patterns, we introduce the Pancan-apaQTM database, available at https://pancan-apaqtm-zju.shinyapps.io/pancanaQTM/.
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Affiliation(s)
- Yige Li
- Department of Pathology, and Department of Medical Oncology of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China; Department of Pathology, Key Laboratory of Disease Proteomics of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China; Department of Pathology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Jingwen Gong
- Department of Pathology, and Department of Medical Oncology of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China; Department of Pathology, Key Laboratory of Disease Proteomics of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China; Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, Zhejiang Province, China
| | - Qingrong Sun
- Department of Pathology, Key Laboratory of Disease Proteomics of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China; Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, Zhejiang Province, China; Liangzhu Laboratory, Zhejiang University Medical Center, 1369 West Wenyi Road, Hangzhou, Zhejiang Province, China; College of Information Science and Technology, ZheJiang Shuren University, Hangzhou 310015, ZheJiang, China
| | - Eu Gene Vong
- Department of Pathology, Key Laboratory of Disease Proteomics of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China; Department of Biochemistry and Genetics, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China; The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Xiaoqing Cheng
- Department of Pathology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Binghong Wang
- Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Ying Yuan
- Department of Medical Oncology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China; Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, Chinese National Ministry of Education), the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China; Research Unit of Dissecting the Population Genetics and Developing New Technologies for Treatment and Prevention of Skin Phenotypes and Dermatological Diseases (2019RU058), Chinese Academy of Medical Sciences, Shanghai, China
| | - Eric R Gamazon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Data Science Institute, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dan Zhou
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA; School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Maode Lai
- Department of Pathology, Key Laboratory of Disease Proteomics of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China; Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, Zhejiang Province, China; Department of Pathology, Research Unit of Intelligence Classification of Tumor Pathology and Precision Therapy, Chinese Academy of Medical Sciences (2019RU042), Key Laboratory of Disease Proteomics of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China.
| | - Dandan Zhang
- Department of Pathology, and Department of Medical Oncology of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China; Department of Pathology, Key Laboratory of Disease Proteomics of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China; Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, Zhejiang Province, China; Department of Pathology, Research Unit of Intelligence Classification of Tumor Pathology and Precision Therapy, Chinese Academy of Medical Sciences (2019RU042), Key Laboratory of Disease Proteomics of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China.
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de Mello FN, Tahira AC, Berzoti-Coelho MG, Verjovski-Almeida S. The CUT&RUN greenlist: genomic regions of consistent noise are effective normalizing factors for quantitative epigenome mapping. Brief Bioinform 2024; 25:bbad538. [PMID: 38279652 PMCID: PMC10818165 DOI: 10.1093/bib/bbad538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/14/2023] [Accepted: 12/25/2023] [Indexed: 01/28/2024] Open
Abstract
Cleavage Under Targets and Release Using Nuclease (CUT&RUN) is a recent development for epigenome mapping, but its unique methodology can hamper proper quantitative analyses. As traditional normalization approaches have been shown to be inaccurate, we sought to determine endogenous normalization factors based on the human genome regions of constant nonspecific signal. This constancy was determined by applying Shannon's information entropy, and the set of normalizer regions, which we named the 'Greenlist', was extensively validated using publicly available datasets. We demonstrate here that the greenlist normalization outperforms the current top standards, and remains consistent across different experimental setups, cell lines and antibodies; the approach can even be applied to different species or to CUT&Tag. Requiring no additional experimental steps and no added cost, this approach can be universally applied to CUT&RUN experiments to greatly minimize the interference of technical variation over the biological epigenome changes of interest.
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Affiliation(s)
- Fabio N de Mello
- Cell Cycle Laboratory, Instituto Butantan, São Paulo, Brazil
- Interunit Bioinformatics Graduate Program, Universidade de São Paulo, São Paulo, Brazil
| | - Ana C Tahira
- Cell Cycle Laboratory, Instituto Butantan, São Paulo, Brazil
| | - Maria Gabriela Berzoti-Coelho
- Cell Cycle Laboratory, Instituto Butantan, São Paulo, Brazil
- Instituto de Química, Universidade de São Paulo, São Paulo, Brazil
| | - Sergio Verjovski-Almeida
- Cell Cycle Laboratory, Instituto Butantan, São Paulo, Brazil
- Instituto de Química, Universidade de São Paulo, São Paulo, Brazil
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Rauluseviciute I, Riudavets-Puig R, Blanc-Mathieu R, Castro-Mondragon J, Ferenc K, Kumar V, Lemma RB, Lucas J, Chèneby J, Baranasic D, Khan A, Fornes O, Gundersen S, Johansen M, Hovig E, Lenhard B, Sandelin A, Wasserman W, Parcy F, Mathelier A. JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles. Nucleic Acids Res 2024; 52:D174-D182. [PMID: 37962376 PMCID: PMC10767809 DOI: 10.1093/nar/gkad1059] [Citation(s) in RCA: 65] [Impact Index Per Article: 65.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/20/2023] [Accepted: 10/31/2023] [Indexed: 11/15/2023] Open
Abstract
JASPAR (https://jaspar.elixir.no/) is a widely-used open-access database presenting manually curated high-quality and non-redundant DNA-binding profiles for transcription factors (TFs) across taxa. In this 10th release and 20th-anniversary update, the CORE collection has expanded with 329 new profiles. We updated three existing profiles and provided orthogonal support for 72 profiles from the previous release's UNVALIDATED collection. Altogether, the JASPAR 2024 update provides a 20% increase in CORE profiles from the previous release. A trimming algorithm enhanced profiles by removing low information content flanking base pairs, which were likely uninformative (within the capacity of the PFM models) for TFBS predictions and modelling TF-DNA interactions. This release includes enhanced metadata, featuring a refined classification for plant TFs' structural DNA-binding domains. The new JASPAR collections prompt updates to the genomic tracks of predicted TF binding sites (TFBSs) in 8 organisms, with human and mouse tracks available as native tracks in the UCSC Genome browser. All data are available through the JASPAR web interface and programmatically through its API and the updated Bioconductor and pyJASPAR packages. Finally, a new TFBS extraction tool enables users to retrieve predicted JASPAR TFBSs intersecting their genomic regions of interest.
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Affiliation(s)
- Ieva Rauluseviciute
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0318 Oslo, Norway
| | - Rafael Riudavets-Puig
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0318 Oslo, Norway
| | - Romain Blanc-Mathieu
- Laboratoire Physiologie Cellulaire et Végétale, Univ. Grenoble Alpes, CNRS, CEA, INRAE, IRIG-DBSCI-LPCV, 17 avenue des martyrs, F-38054, Grenoble, France
| | - Jaime A Castro-Mondragon
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0318 Oslo, Norway
| | - Katalin Ferenc
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0318 Oslo, Norway
| | - Vipin Kumar
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0318 Oslo, Norway
| | - Roza Berhanu Lemma
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0318 Oslo, Norway
| | - Jérémy Lucas
- Laboratoire Physiologie Cellulaire et Végétale, Univ. Grenoble Alpes, CNRS, CEA, INRAE, IRIG-DBSCI-LPCV, 17 avenue des martyrs, F-38054, Grenoble, France
| | - Jeanne Chèneby
- Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Damir Baranasic
- MRC London Institute of Medical Sciences, Du Cane Road, London W12 0NN, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W12 0NN, UK
- Division of Electronics, Ruđer Bošković Institute, Bijenička cesta, 10000 Zagreb, Croatia
| | - Aziz Khan
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0318 Oslo, Norway
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Oriol Fornes
- Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, BC Children's Hospital Research Institute, University of British Columbia, 950 W 28th Ave, Vancouver, BC V5Z 4H4, Canada
| | - Sveinung Gundersen
- Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Morten Johansen
- Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Eivind Hovig
- Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
- Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, 0424 Oslo, Norway
| | - Boris Lenhard
- MRC London Institute of Medical Sciences, Du Cane Road, London W12 0NN, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W12 0NN, UK
| | - Albin Sandelin
- Department of Biology and Biotech Research and Innovation Centre, University of Copenhagen, Ole Maaløes Vej 5, DK2200 Copenhagen N, Denmark
| | - Wyeth W Wasserman
- Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, BC Children's Hospital Research Institute, University of British Columbia, 950 W 28th Ave, Vancouver, BC V5Z 4H4, Canada
| | - François Parcy
- Laboratoire Physiologie Cellulaire et Végétale, Univ. Grenoble Alpes, CNRS, CEA, INRAE, IRIG-DBSCI-LPCV, 17 avenue des martyrs, F-38054, Grenoble, France
| | - Anthony Mathelier
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0318 Oslo, Norway
- Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
- Department of Medical Genetics, Institute of Clinical Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway
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7
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Kołat D, Zhao LY, Kciuk M, Płuciennik E, Kałuzińska-Kołat Ż. AP-2δ Is the Most Relevant Target of AP-2 Family-Focused Cancer Therapy and Affects Genome Organization. Cells 2022; 11:cells11244124. [PMID: 36552887 PMCID: PMC9776946 DOI: 10.3390/cells11244124] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/26/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022] Open
Abstract
Formerly hailed as "undruggable" proteins, transcription factors (TFs) are now under investigation for targeted therapy. In cancer, this may alter, inter alia, immune evasion or replicative immortality, which are implicated in genome organization, a process that accompanies multi-step tumorigenesis and which frequently develops in a non-random manner. Still, targeting-related research on some TFs is scarce, e.g., among AP-2 proteins, which are known for their altered functionality in cancer and prognostic importance. Using public repositories, bioinformatics tools, and RNA-seq data, the present study examined the ligandability of all AP-2 members, selecting the best one, which was investigated in terms of mutations, targets, co-activators, correlated genes, and impact on genome organization. AP-2 proteins were found to have the conserved "TF_AP-2" domain, but manifested different binding characteristics and evolution. Among them, AP-2δ has not only the highest number of post-translational modifications and extended strands but also contains a specific histidine-rich region and cleft that can receive a ligand. Uterine, colon, lung, and stomach tumors are most susceptible to AP-2δ mutations, which also co-depend with cancer hallmark genes and drug targets. Considering AP-2δ targets, some of them were located proximally in the spatial genome or served as co-factors of the genes regulated by AP-2δ. Correlation and functional analyses suggested that AP-2δ affects various processes, including genome organization, via its targets; this has been eventually verified in lung adenocarcinoma using expression and immunohistochemistry data of chromosomal conformation-related genes. In conclusion, AP-2δ affects chromosomal conformation and is the most appropriate target for cancer therapy focused on the AP-2 family.
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Affiliation(s)
- Damian Kołat
- Department of Experimental Surgery, Medical University of Lodz, 90-136 Lodz, Poland
- Correspondence:
| | - Lin-Yong Zhao
- Gastric Cancer Center and Laboratory of Gastric Cancer, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, and Collaborative Innovation Centre for Biotherapy, Chengdu 610041, China
| | - Mateusz Kciuk
- Department of Molecular Biotechnology and Genetics, University of Lodz, 90-237 Lodz, Poland
- Doctoral School of Exact and Natural Sciences, University of Lodz, 90-237 Lodz, Poland
| | - Elżbieta Płuciennik
- Department of Functional Genomics, Medical University of Lodz, 90-752 Lodz, Poland
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Shao D, Kellogg GD, Nematbakhsh A, Kuntala PK, Mahony S, Pugh BF, Lai WKM. PEGR: a flexible management platform for reproducible epigenomic and genomic research. Genome Biol 2022; 23:99. [PMID: 35440038 PMCID: PMC9016988 DOI: 10.1186/s13059-022-02671-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 04/07/2022] [Indexed: 11/27/2022] Open
Abstract
Reproducibility is a significant challenge in (epi)genomic research due to the complexity of experiments composed of traditional biochemistry and informatics. Recent advances have exacerbated this as high-throughput sequencing data is generated at an unprecedented pace. Here, we report the development of a Platform for Epi-Genomic Research (PEGR), a web-based project management platform that tracks and quality controls experiments from conception to publication-ready figures, compatible with multiple assays and bioinformatic pipelines. It supports rigor and reproducibility for biochemists working at the bench, while fully supporting reproducibility and reliability for bioinformaticians through integration with the Galaxy platform.
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Affiliation(s)
- Danying Shao
- Institute for Computational and Data Sciences, Pennsylvania State University, University Park, PA, 16802, USA
| | - Gretta D Kellogg
- Cornell Institute of Biotechnology, Cornell University, Ithaca, NY, 14850, USA
| | - Ali Nematbakhsh
- Cornell Institute of Biotechnology, Cornell University, Ithaca, NY, 14850, USA
| | - Prashant K Kuntala
- Department of Biochemistry & Molecular Biology, Pennsylvania State University, University Park, PA, 16802, USA
| | - Shaun Mahony
- Department of Biochemistry & Molecular Biology, Pennsylvania State University, University Park, PA, 16802, USA
| | - B Franklin Pugh
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, 14850, USA
| | - William K M Lai
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, 14850, USA. .,Department of Computational Biology, Cornell University, Ithaca, NY, 14850, USA.
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Roy AL, Wilder EL, Anderson JM. Validation of antibodies: Lessons learned from the Common Fund Protein Capture Reagents Program. SCIENCE ADVANCES 2021; 7:eabl7148. [PMID: 34757791 PMCID: PMC8580312 DOI: 10.1126/sciadv.abl7148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 09/22/2021] [Indexed: 06/13/2023]
Abstract
Large-scale generation of protein capture reagents remains a technical challenge, but their generation is just the beginning. Validation is a critical, iterative process that yields different results for different uses. Independent, community-based validation offers the possibility of transparent data sharing, with use case–specific results made broadly available. This type of resource, which can grow as new validation data are obtained for an expanding group of reagents, provides a community resource that should accompany future reagent-generating efforts. To address a pressing need for antibodies or other reagents that recognize human proteins, the National Institutes of Health Common Fund launched the Protein Capture Reagents Program in 2010 as a pilot to target human transcription factors. Here, we describe lessons learned from this program concerning generation and validation of research reagents, which we believe are generally applicable for future research endeavors working in a similar space.
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Affiliation(s)
- Ananda L. Roy
- Office of Strategic Coordination, National Institutes of Health, Bethesda, MD 20892, USA
- Division of Program Coordination, Planning and Strategic Initiatives, Office of the Director, National Institutes of Health, Bethesda, MD 20892, USA
| | - Elizabeth L. Wilder
- Office of Strategic Coordination, National Institutes of Health, Bethesda, MD 20892, USA
- Division of Program Coordination, Planning and Strategic Initiatives, Office of the Director, National Institutes of Health, Bethesda, MD 20892, USA
| | - James M. Anderson
- Division of Program Coordination, Planning and Strategic Initiatives, Office of the Director, National Institutes of Health, Bethesda, MD 20892, USA
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