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Morandini F, Rechsteiner C, Perez K, Praz V, Lopez Garcia G, Hinte LC, von Meyenn F, Ocampo A. ATAC-clock: An aging clock based on chromatin accessibility. GeroScience 2024; 46:1789-1806. [PMID: 37924441 PMCID: PMC10828344 DOI: 10.1007/s11357-023-00986-0] [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: 07/15/2023] [Accepted: 10/14/2023] [Indexed: 11/06/2023] Open
Abstract
The establishment of aging clocks highlighted the strong link between changes in DNA methylation and aging. Yet, it is not known if other epigenetic features could be used to predict age accurately. Furthermore, previous studies have observed a lack of effect of age-related changes in DNA methylation on gene expression, putting the interpretability of DNA methylation-based aging clocks into question. In this study, we explore the use of chromatin accessibility to construct aging clocks. We collected blood from 159 human donors and generated chromatin accessibility, transcriptomic, and cell composition data. We investigated how chromatin accessibility changes during aging and constructed a novel aging clock with a median absolute error of 5.27 years. The changes in chromatin accessibility used by the clock were strongly related to transcriptomic alterations, aiding clock interpretation. We additionally show that our chromatin accessibility clock performs significantly better than a transcriptomic clock trained on matched samples. In conclusion, we demonstrate that the clock relies on cell-intrinsic chromatin accessibility alterations rather than changes in cell composition. Further, we present a new approach to construct epigenetic aging clocks based on chromatin accessibility, which bear a direct link to age-related transcriptional alterations, but which allow for more accurate age predictions than transcriptomic clocks.
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Affiliation(s)
- Francesco Morandini
- Department of Biomedical Sciences, University of Lausanne, Lausanne, Switzerland
| | - Cheyenne Rechsteiner
- Department of Biomedical Sciences, University of Lausanne, Lausanne, Switzerland
| | - Kevin Perez
- EPITERNA SA, Route de la Corniche 5, Epalinges, Switzerland
| | - Viviane Praz
- Department of Biomedical Sciences, University of Lausanne, Lausanne, Switzerland
| | - Guillermo Lopez Garcia
- Department of Biomedical Sciences, University of Lausanne, Lausanne, Switzerland
- Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Málaga, Spain
| | - Laura C Hinte
- Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | | | - Alejandro Ocampo
- Department of Biomedical Sciences, University of Lausanne, Lausanne, Switzerland.
- EPITERNA SA, Route de la Corniche 5, Epalinges, Switzerland.
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2
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Yu W, Luo H, Yang J, Zhang S, Jiang H, Zhao X, Hui X, Sun D, Li L, Wei XQ, Lonardi S, Pan W. Comprehensive assessment of 11 de novo HiFi assemblers on complex eukaryotic genomes and metagenomes. Genome Res 2024; 34:326-340. [PMID: 38428994 PMCID: PMC10984382 DOI: 10.1101/gr.278232.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 01/23/2024] [Indexed: 03/03/2024]
Abstract
Pacific Biosciences (PacBio) HiFi sequencing technology generates long reads (>10 kbp) with very high accuracy (<0.01% sequencing error). Although several de novo assembly tools are available for HiFi reads, there are no comprehensive studies on the evaluation of these assemblers. We evaluated the performance of 11 de novo HiFi assemblers on (1) real data for three eukaryotic genomes; (2) 34 synthetic data sets with different ploidy, sequencing coverage levels, heterozygosity rates, and sequencing error rates; (3) one real metagenomic data set; and (4) five synthetic metagenomic data sets with different composition abundance and heterozygosity rates. The 11 assemblers were evaluated using quality assessment tool (QUAST) and benchmarking universal single-copy ortholog (BUSCO). We also used several additional criteria, namely, completion rate, single-copy completion rate, duplicated completion rate, average proportion of largest category, average distance difference, quality value, run-time, and memory utilization. Results show that hifiasm and hifiasm-meta should be the first choice for assembling eukaryotic genomes and metagenomes with HiFi data. We performed a comprehensive benchmarking study of commonly used assemblers on complex eukaryotic genomes and metagenomes. Our study will help the research community to choose the most appropriate assembler for their data and identify possible improvements in assembly algorithms.
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Affiliation(s)
- Wenjuan Yu
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Haohui Luo
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Jinbao Yang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Shengchen Zhang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Heling Jiang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Xianjia Zhao
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
- School of Agricultural Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China
| | - Xingqi Hui
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
- School of Agricultural Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China
| | - Da Sun
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Liang Li
- Fruit Research Institute, Fujian Academy of Agricultural Sciences, Fuzhou, Fujian 350002, China
| | - Xiu-Qing Wei
- Fruit Research Institute, Fujian Academy of Agricultural Sciences, Fuzhou, Fujian 350002, China;
| | - Stefano Lonardi
- Department of Computer Science and Engineering, University of California, Riverside, California 92521, USA;
| | - Weihua Pan
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China;
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3
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Mukund K, Alva-Ornelas JA, Maddox AL, Murali D, Veraksa D, Saftics A, Tomsic J, Frankhouser D, Razo M, Jovanovic-Talisman T, Seewaldt VL, Subramaniam S. Molecular Atlas of HER2+ Breast Cancer Cells Treated with Endogenous Ligands: Temporal Insights into Mechanisms of Trastuzumab Resistance. Cancers (Basel) 2024; 16:553. [PMID: 38339304 PMCID: PMC10854992 DOI: 10.3390/cancers16030553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 01/22/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024] Open
Abstract
Trastuzumab therapy in HER2+ breast cancer patients has mixed success owing to acquired resistance to therapy. A detailed understanding of downstream molecular cascades resulting from trastuzumab resistance is yet to emerge. In this study, we investigate the cellular mechanisms underlying acquired resistance using trastuzumab-sensitive and -resistant cancer cells (BT474 and BT474R) treated with endogenous ligands EGF and HRG across time. We probe early receptor organization through microscopy and signaling events through multiomics measurements and assess the bioenergetic state through mitochondrial measurements. Integrative analyses of our measurements reveal significant alterations in EGF-treated BT474 HER2 membrane dynamics and robust downstream activation of PI3K/AKT/mTORC1 signaling. EGF-treated BT474R shows a sustained interferon-independent activation of the IRF1/STAT1 cascade, potentially contributing to trastuzumab resistance. Both cell lines exhibit temporally divergent metabolic demands and HIF1A-mediated stress responses. BT474R demonstrates inherently increased mitochondrial activity. HRG treatment in BT474R leads to a pronounced reduction in AR expression, affecting downstream lipid metabolism with implications for treatment response. Our results provide novel insights into mechanistic changes underlying ligand treatment in BT474 and BT474R and emphasize the pivotal role of endogenous ligands. These results can serve as a framework for furthering the understanding of trastuzumab resistance, with therapeutic implications for women with acquired resistance.
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Affiliation(s)
- Kavitha Mukund
- Department of Bioengineering, UC San Diego, Gilman Drive, La Jolla, CA 92093, USA; (K.M.); (D.M.); (D.V.)
| | - Jackelyn A. Alva-Ornelas
- City of Hope Comprehensive Cancer Center, 1500 East Duarte Road, Duarte, CA 91010, USA; (J.A.A.-O.); (J.T.); (D.F.); (M.R.)
| | - Adam L. Maddox
- Department of Cancer Biology and Molecular Medicine, Beckman Research Institute, City of Hope, 1500 East Duarte Road, Duarte, CA 91010, USA; (A.L.M.); (A.S.); (T.J.-T.)
| | - Divya Murali
- Department of Bioengineering, UC San Diego, Gilman Drive, La Jolla, CA 92093, USA; (K.M.); (D.M.); (D.V.)
| | - Darya Veraksa
- Department of Bioengineering, UC San Diego, Gilman Drive, La Jolla, CA 92093, USA; (K.M.); (D.M.); (D.V.)
| | - Andras Saftics
- Department of Cancer Biology and Molecular Medicine, Beckman Research Institute, City of Hope, 1500 East Duarte Road, Duarte, CA 91010, USA; (A.L.M.); (A.S.); (T.J.-T.)
| | - Jerneja Tomsic
- City of Hope Comprehensive Cancer Center, 1500 East Duarte Road, Duarte, CA 91010, USA; (J.A.A.-O.); (J.T.); (D.F.); (M.R.)
| | - David Frankhouser
- City of Hope Comprehensive Cancer Center, 1500 East Duarte Road, Duarte, CA 91010, USA; (J.A.A.-O.); (J.T.); (D.F.); (M.R.)
| | - Meagan Razo
- City of Hope Comprehensive Cancer Center, 1500 East Duarte Road, Duarte, CA 91010, USA; (J.A.A.-O.); (J.T.); (D.F.); (M.R.)
| | - Tijana Jovanovic-Talisman
- Department of Cancer Biology and Molecular Medicine, Beckman Research Institute, City of Hope, 1500 East Duarte Road, Duarte, CA 91010, USA; (A.L.M.); (A.S.); (T.J.-T.)
| | - Victoria L. Seewaldt
- City of Hope Comprehensive Cancer Center, 1500 East Duarte Road, Duarte, CA 91010, USA; (J.A.A.-O.); (J.T.); (D.F.); (M.R.)
| | - Shankar Subramaniam
- Department of Bioengineering, UC San Diego, Gilman Drive, La Jolla, CA 92093, USA; (K.M.); (D.M.); (D.V.)
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4
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Sigauke RF, Sanford L, Maas ZL, Jones T, Stanley JT, Townsend HA, Allen MA, Dowell RD. Atlas of nascent RNA transcripts reveals enhancer to gene linkages. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.07.570626. [PMID: 38105978 PMCID: PMC10723487 DOI: 10.1101/2023.12.07.570626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Gene transcription is controlled and modulated by regulatory regions, including enhancers and promoters. These regions are abundant in unstable, non-coding bidirectional transcription. Using nascent RNA transcription data across hundreds of human samples, we identified over 800,000 regions containing bidirectional transcription. We then identify highly correlated transcription between bidirectional and gene regions. The identified correlated pairs, a bidirectional region and a gene, are enriched for disease associated SNPs and often supported by independent 3D data. We present these resources as an SQL database which serves as a resource for future studies into gene regulation, enhancer associated RNAs, and transcription factors.
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Affiliation(s)
- Rutendo F. Sigauke
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Ave., UCB 596, Boulder, 80309, CO, USA
| | - Lynn Sanford
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Ave., UCB 596, Boulder, 80309, CO, USA
| | - Zachary L. Maas
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Ave., UCB 596, Boulder, 80309, CO, USA
- Computer Science, University of Colorado Boulder, 1111 Engineering Drive, UCB 430, Boulder, 80309, CO, USA
| | - Taylor Jones
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Ave., UCB 596, Boulder, 80309, CO, USA
| | - Jacob T. Stanley
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Ave., UCB 596, Boulder, 80309, CO, USA
| | - Hope A. Townsend
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Ave., UCB 596, Boulder, 80309, CO, USA
- Molecular, Cellular and Developmental Biology, University of Colorado Boulder, 1945 Colorado Ave, UCB 347, Boulder, 80309, CO, USA
| | - Mary A. Allen
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Ave., UCB 596, Boulder, 80309, CO, USA
| | - Robin D. Dowell
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Ave., UCB 596, Boulder, 80309, CO, USA
- Computer Science, University of Colorado Boulder, 1111 Engineering Drive, UCB 430, Boulder, 80309, CO, USA
- Molecular, Cellular and Developmental Biology, University of Colorado Boulder, 1945 Colorado Ave, UCB 347, Boulder, 80309, CO, USA
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5
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Mills C, Marconett CN, Lewinger JP, Mi H. PEACOCK: a machine learning approach to assess the validity of cell type-specific enhancer-gene regulatory relationships. NPJ Syst Biol Appl 2023; 9:9. [PMID: 37012250 PMCID: PMC10070356 DOI: 10.1038/s41540-023-00270-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 02/20/2023] [Indexed: 04/05/2023] Open
Abstract
The vast majority of disease-associated variants identified in genome-wide association studies map to enhancers, powerful regulatory elements which orchestrate the recruitment of transcriptional complexes to their target genes' promoters to upregulate transcription in a cell type- and timing-dependent manner. These variants have implicated thousands of enhancers in many common genetic diseases, including nearly all cancers. However, the etiology of most of these diseases remains unknown because the regulatory target genes of the vast majority of enhancers are unknown. Thus, identifying the target genes of as many enhancers as possible is crucial for learning how enhancer regulatory activities function and contribute to disease. Based on experimental results curated from scientific publications coupled with machine learning methods, we developed a cell type-specific score predictive of an enhancer targeting a gene. We computed the score genome-wide for every possible cis enhancer-gene pair and validated its predictive ability in four widely used cell lines. Using a pooled final model trained across multiple cell types, all possible gene-enhancer regulatory links in cis (~17 M) were scored and added to the publicly available PEREGRINE database ( www.peregrineproj.org ). These scores provide a quantitative framework for the enhancer-gene regulatory prediction that can be incorporated into downstream statistical analyses.
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Affiliation(s)
- Caitlin Mills
- Division of Bioinformatics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
| | - Crystal N Marconett
- Department of Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Biochemistry and Molecular Medicine, Keck School of Medicine USC, Los Angeles, CA, USA
- Norris Cancer Center, Keck School of Medicine USC, Los Angeles, CA, USA
| | - Juan Pablo Lewinger
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA.
| | - Huaiyu Mi
- Division of Bioinformatics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA.
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6
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Xiao Q, Xiao Y, Li LY, Chen MK, Wu M. Multifaceted regulation of enhancers in cancer. BIOCHIMICA ET BIOPHYSICA ACTA. GENE REGULATORY MECHANISMS 2022; 1865:194839. [PMID: 35750313 DOI: 10.1016/j.bbagrm.2022.194839] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/24/2022] [Accepted: 06/14/2022] [Indexed: 12/12/2022]
Abstract
Enhancer is one kind of cis-elements regulating gene transcription, whose activity is tightly controlled by epigenetic enzymes and histone modifications. Active enhancers are classified into typical enhancers, super-enhancers and over-active enhancers, according to the enrichment and location of histone modifications. Epigenetic factors control the level of histone modifications on enhancers to determine their activity, such as histone methyltransferases and acetylases. Transcription factors, cofactors and mediators co-operate together and are required for enhancer functions. In turn, abnormalities in these trans-acting factors affect enhancer activity. Recent studies have revealed enhancer dysregulation as one of the important features for cancer. Variations in enhancer regions and mutations of enhancer regulatory genes are frequently observed in cancer cells, and altering the activity of onco-enhancers is able to repress oncogene expression, and suppress tumorigenesis and metastasis. Here we summarize the recent discoveries about enhancer regulation in cancer and discuss their potential application in diagnosis and treatment.
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Affiliation(s)
- Qiong Xiao
- Frontier Science Center for Immunology and Metabolism, Hubei Key Laboratory of Cell Homeostasis, Hubei Key Laboratory of Developmentally Originated Disease, College of Life Sciences, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, Hubei 430072, China
| | - Yong Xiao
- Frontier Science Center for Immunology and Metabolism, Hubei Key Laboratory of Cell Homeostasis, Hubei Key Laboratory of Developmentally Originated Disease, College of Life Sciences, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, Hubei 430072, China
| | - Lian-Yun Li
- Frontier Science Center for Immunology and Metabolism, Hubei Key Laboratory of Cell Homeostasis, Hubei Key Laboratory of Developmentally Originated Disease, College of Life Sciences, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, Hubei 430072, China
| | - Ming-Kai Chen
- Frontier Science Center for Immunology and Metabolism, Hubei Key Laboratory of Cell Homeostasis, Hubei Key Laboratory of Developmentally Originated Disease, College of Life Sciences, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, Hubei 430072, China.
| | - Min Wu
- Frontier Science Center for Immunology and Metabolism, Hubei Key Laboratory of Cell Homeostasis, Hubei Key Laboratory of Developmentally Originated Disease, College of Life Sciences, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, Hubei 430072, China.
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7
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Liu Z, Mushayahama T, Queme B, Ebert D, Muruganujan A, Mills C, Thomas PD, Mi H. Annotation Query (AnnoQ): an integrated and interactive platform for large-scale genetic variant annotation. Nucleic Acids Res 2022; 50:W57-W65. [PMID: 35640593 PMCID: PMC9252745 DOI: 10.1093/nar/gkac418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/18/2022] [Accepted: 05/09/2022] [Indexed: 11/30/2022] Open
Abstract
The Annotation Query (AnnoQ) (http://annoq.org/) is designed to provide comprehensive and up-to-date functional annotations for human genetic variants. The system is supported by an annotation database with ∼39 million human variants from the Haplotype Reference Consortium (HRC) pre-annotated with sequence feature annotations by WGSA and functional annotations to Gene Ontology (GO) and pathways in PANTHER. The database operates on an optimized Elasticsearch framework to support real-time complex searches. This implementation enables users to annotate data with the most up-to-date functional annotations via simple queries instead of setting up individual tools. A web interface allows users to interactively browse the annotations, annotate variants and search variant data. Its easy-to-use interface and search capabilities are well-suited for scientists with fewer bioinformatics skills such as bench scientists and statisticians. AnnoQ also has an API for users to access and annotate the data programmatically. Packages for programming languages, such as the R package, are available for users to embed the annotation queries in their scripts. AnnoQ serves researchers with a wide range of backgrounds and research interests as an integrated annotation platform.
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Affiliation(s)
- Zhu Liu
- Division of Bioinformatics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Tremayne Mushayahama
- Division of Bioinformatics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Bryan Queme
- Division of Bioinformatics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Dustin Ebert
- Division of Bioinformatics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Anushya Muruganujan
- Division of Bioinformatics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Caitlin Mills
- Division of Bioinformatics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Paul D Thomas
- Division of Bioinformatics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Huaiyu Mi
- Division of Bioinformatics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
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8
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Thomas PD, Ebert D, Muruganujan A, Mushayahama T, Albou L, Mi H. PANTHER: Making genome-scale phylogenetics accessible to all. Protein Sci 2022; 31:8-22. [PMID: 34717010 PMCID: PMC8740835 DOI: 10.1002/pro.4218] [Citation(s) in RCA: 501] [Impact Index Per Article: 250.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 10/24/2021] [Accepted: 10/26/2021] [Indexed: 02/03/2023]
Abstract
Phylogenetics is a powerful tool for analyzing protein sequences, by inferring their evolutionary relationships to other proteins. However, phylogenetics analyses can be challenging: they are computationally expensive and must be performed carefully in order to avoid systematic errors and artifacts. Protein Analysis THrough Evolutionary Relationships (PANTHER; http://pantherdb.org) is a publicly available, user-focused knowledgebase that stores the results of an extensive phylogenetic reconstruction pipeline that includes computational and manual processes and quality control steps. First, fully reconciled phylogenetic trees (including ancestral protein sequences) are reconstructed for a set of "reference" protein sequences obtained from fully sequenced genomes of organisms across the tree of life. Second, the resulting phylogenetic trees are manually reviewed and annotated with function evolution events: inferred gains and losses of protein function along branches of the phylogenetic tree. Here, we describe in detail the current contents of PANTHER, how those contents are generated, and how they can be used in a variety of applications. The PANTHER knowledgebase can be downloaded or accessed via an extensive API. In addition, PANTHER provides software tools to facilitate the application of the knowledgebase to common protein sequence analysis tasks: exploring an annotated genome by gene function; performing "enrichment analysis" of lists of genes; annotating a single sequence or large batch of sequences by homology; and assessing the likelihood that a genetic variant at a particular site in a protein will have deleterious effects.
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Affiliation(s)
- Paul D. Thomas
- Division of Bioinformatics, Department of Population and Public Health SciencesUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Dustin Ebert
- Division of Bioinformatics, Department of Population and Public Health SciencesUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Anushya Muruganujan
- Division of Bioinformatics, Department of Population and Public Health SciencesUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Tremayne Mushayahama
- Division of Bioinformatics, Department of Population and Public Health SciencesUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Laurent‐Philippe Albou
- Division of Bioinformatics, Department of Population and Public Health SciencesUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Huaiyu Mi
- Division of Bioinformatics, Department of Population and Public Health SciencesUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
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9
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Zhou B, Stueve TR, Mihalakakos EA, Miao L, Mullen D, Wang Y, Liu Y, Luo J, Tran E, Siegmund KD, Lynch SK, Ryan AL, Offringa IA, Borok Z, Marconett CN. Comprehensive epigenomic profiling of human alveolar epithelial differentiation identifies key epigenetic states and transcription factor co-regulatory networks for maintenance of distal lung identity. BMC Genomics 2021; 22:906. [PMID: 34922464 PMCID: PMC8684104 DOI: 10.1186/s12864-021-08152-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 11/05/2021] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Disruption of alveolar epithelial cell (AEC) differentiation is implicated in distal lung diseases such as chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, and lung adenocarcinoma that impact morbidity and mortality worldwide. Elucidating underlying disease pathogenesis requires a mechanistic molecular understanding of AEC differentiation. Previous studies have focused on changes of individual transcription factors, and to date no study has comprehensively characterized the dynamic, global epigenomic alterations that facilitate this critical differentiation process in humans. RESULTS We comprehensively profiled the epigenomic states of human AECs during type 2 to type 1-like cell differentiation, including the methylome and chromatin functional domains, and integrated this with transcriptome-wide RNA expression data. Enhancer regions were drastically altered during AEC differentiation. Transcription factor binding analysis within enhancer regions revealed diverse interactive networks with enrichment for many transcription factors, including NKX2-1 and FOXA family members, as well as transcription factors with less well characterized roles in AEC differentiation, such as members of the MEF2, TEAD, and AP1 families. Additionally, associations among transcription factors changed during differentiation, implicating a complex network of heterotrimeric complex switching in driving differentiation. Integration of AEC enhancer states with the catalog of enhancer elements in the Roadmap Epigenomics Mapping Consortium and Encyclopedia of DNA Elements (ENCODE) revealed that AECs have similar epigenomic structures to other profiled epithelial cell types, including human mammary epithelial cells (HMECs), with NKX2-1 serving as a distinguishing feature of distal lung differentiation. CONCLUSIONS Enhancer regions are hotspots of epigenomic alteration that regulate AEC differentiation. Furthermore, the differentiation process is regulated by dynamic networks of transcription factors acting in concert, rather than individually. These findings provide a roadmap for understanding the relationship between disruption of the epigenetic state during AEC differentiation and development of lung diseases that may be therapeutically amenable.
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Affiliation(s)
- B Zhou
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
- Hastings Center for Pulmonary Research, University of Southern California, Los Angeles, CA, 90089, USA
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - T R Stueve
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
- Department of Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Biochemistry and Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
| | - E A Mihalakakos
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
- Department of Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Biochemistry and Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
| | - L Miao
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
- Department of Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Biochemistry and Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
| | - D Mullen
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
- Department of Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Biochemistry and Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
| | - Y Wang
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
- Department of Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Biochemistry and Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
| | - Y Liu
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
| | - J Luo
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
| | - E Tran
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
- Department of Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Biochemistry and Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
| | - K D Siegmund
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
| | - S K Lynch
- Department of Engineering, Test Manufacturing Group, MAXIM Integrated Products, Sunnyvale, CA, 95134, USA
| | - A L Ryan
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
- Hastings Center for Pulmonary Research, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
| | - I A Offringa
- Hastings Center for Pulmonary Research, University of Southern California, Los Angeles, CA, 90089, USA
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
- Department of Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Biochemistry and Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
| | - Z Borok
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
- Hastings Center for Pulmonary Research, University of Southern California, Los Angeles, CA, 90089, USA
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
- Department of Biochemistry and Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA, 92093, USA
| | - C N Marconett
- Hastings Center for Pulmonary Research, University of Southern California, Los Angeles, CA, 90089, USA.
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.
- Department of Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA.
- Department of Biochemistry and Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA.
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10
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Regner MJ, Wisniewska K, Garcia-Recio S, Thennavan A, Mendez-Giraldez R, Malladi VS, Hawkins G, Parker JS, Perou CM, Bae-Jump VL, Franco HL. A multi-omic single-cell landscape of human gynecologic malignancies. Mol Cell 2021; 81:4924-4941.e10. [PMID: 34739872 PMCID: PMC8642316 DOI: 10.1016/j.molcel.2021.10.013] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 08/05/2021] [Accepted: 10/13/2021] [Indexed: 01/05/2023]
Abstract
Deconvolution of regulatory mechanisms that drive transcriptional programs in cancer cells is key to understanding tumor biology. Herein, we present matched transcriptome (scRNA-seq) and chromatin accessibility (scATAC-seq) profiles at single-cell resolution from human ovarian and endometrial tumors processed immediately following surgical resection. This dataset reveals the complex cellular heterogeneity of these tumors and enabled us to quantitatively link variation in chromatin accessibility to gene expression. We show that malignant cells acquire previously unannotated regulatory elements to drive hallmark cancer pathways. Moreover, malignant cells from within the same patients show substantial variation in chromatin accessibility linked to transcriptional output, highlighting the importance of intratumoral heterogeneity. Finally, we infer the malignant cell type-specific activity of transcription factors. By defining the regulatory logic of cancer cells, this work reveals an important reliance on oncogenic regulatory elements and highlights the ability of matched scRNA-seq/scATAC-seq to uncover clinically relevant mechanisms of tumorigenesis in gynecologic cancers.
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Affiliation(s)
- Matthew J. Regner
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA,Bioinformatics and Computational Biology Graduate Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA,These authors contributed equally
| | - Kamila Wisniewska
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA,These authors contributed equally
| | - Susana Garcia-Recio
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Aatish Thennavan
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA,Oral and Craniofacial Biomedicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Raul Mendez-Giraldez
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Venkat S. Malladi
- Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
| | - Gabrielle Hawkins
- Division of Gynecology Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Joel S. Parker
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA,Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Charles M. Perou
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA,Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA,Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Victoria L. Bae-Jump
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA,Division of Gynecology Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Hector L. Franco
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA,Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA,Bioinformatics and Computational Biology Graduate Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA,Lead contact.,Correspondence:
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