1
|
Suita Y, Bright H, Pu Y, Toruner MD, Idehen J, Tapinos N, Singh R. Machine learning on multiple epigenetic features reveals H3K27Ac as a driver of gene expression prediction across patients with glioblastoma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.25.600585. [PMID: 38979226 PMCID: PMC11230286 DOI: 10.1101/2024.06.25.600585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Cancer cells show remarkable plasticity and can switch lineages in response to the tumor microenvironment. Cellular plasticity drives invasiveness and metastasis and helps cancer cells to evade therapy by developing resistance to radiation and cytotoxic chemotherapy. Increased understanding of cell fate determination through epigenetic reprogramming is critical to discover how cancer cells achieve transcriptomic and phenotypic plasticity. Glioblastoma is a perfect example of cancer evolution where cells retain an inherent level of plasticity through activation or maintenance of progenitor developmental programs. However, the principles governing epigenetic drivers of cellular plasticity in glioblastoma remain poorly understood. Here, using machine learning (ML) we employ cross-patient prediction of transcript expression using a combination of epigenetic features (ATAC-seq, CTCF ChIP-seq, RNAPII ChIP-seq, H3K27Ac ChIP-seq, and RNA-seq) of glioblastoma stem cells (GSCs). We investigate different ML and deep learning (DL) models for this task and build our final pipeline using XGBoost. The model trained on one patient generalizes to another one suggesting that the epigenetic signals governing gene transcription are consistent across patients even if GSCs can be very different. We demonstrate that H3K27Ac is the epigenetic feature providing the most significant contribution to cross-patient prediction of gene expression. In addition, using H3K27Ac signals from patients-derived GSCs, we can predict gene expression of human neural crest stem cells suggesting a shared developmental epigenetic trajectory between subpopulations of these malignant and benign stem cells. Our cross-patient ML/DL models determine weighted patterns of influence of epigenetic marks on gene expression across patients with glioblastoma and between GSCs and neural crest stem cells. We propose that broader application of this analysis could reshape our view of glioblastoma tumor evolution and inform the design of new epigenetic targeting therapies.
Collapse
Affiliation(s)
- Yusuke Suita
- Laboratory of Cancer Epigenetics and Plasticity, Department of Neurosurgery, Brown University, Providence, RI 02903, USA
| | - Hardy Bright
- Data Science Institute, Brown University, Providence, RI 02903, USA
| | - Yuan Pu
- Center for Computational Molecular Biology, Brown University, Providence, RI 02903, USA
| | - Merih Deniz Toruner
- Laboratory of Cancer Epigenetics and Plasticity, Department of Neurosurgery, Brown University, Providence, RI 02903, USA
- Center for Computational Molecular Biology, Brown University, Providence, RI 02903, USA
| | - Jordan Idehen
- Department of Computer Science, Brown University, Providence, RI 02903, USA
| | - Nikos Tapinos
- Laboratory of Cancer Epigenetics and Plasticity, Department of Neurosurgery, Brown University, Providence, RI 02903, USA
- Brown RNA Center, Brown University, Providence, RI 02903, USA
| | - Ritambhara Singh
- Department of Computer Science, Brown University, Providence, RI 02903, USA
- Center for Computational Molecular Biology, Brown University, Providence, RI 02903, USA
| |
Collapse
|
2
|
Chen Y, Tan J, Yang C, Ling Z, Xu J, Sun D, Luo F. Dynamic chromatin accessibility landscapes of osteoblast differentiation and mineralization. Biochim Biophys Acta Mol Basis Dis 2024; 1870:166938. [PMID: 37931716 DOI: 10.1016/j.bbadis.2023.166938] [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: 04/05/2023] [Revised: 10/12/2023] [Accepted: 10/29/2023] [Indexed: 11/08/2023]
Abstract
Bone acts as a self-healing organ, which undergoes continuous regeneration process that is tightly regulated by the cooperation of osteoclasts with the capability of bone resorption and osteoblasts with the capability of bone formation. Generally, bone marrow derived mesenchymal stem cells (BMSCs) differentiated to final osteoblasts have been considered as critical role in bone remodeling. In this regard, several transcription factors (TFs) whose binding sites are initially hidden deep within accessible chromatin that participate in modulating osteoblast differentiation and bone matrix mineralization. Then, it is necessary to explore further the dynamic changes about the epigenetic transcription machinery during osteoblastogenesis. Here, we performed the chromatin accessibility and transcriptomic landscape of osteoblast differentiation and mineralization by using transposase-accessible chromatin sequencing (ATAC-seq) and RNA sequencing (RNA-Seq). Our data found that global chromatin accessibility during osteoblastogenesis was extensively improved. Above this, it is shown that key target genes including Col6a3, Serpina3n, Ms4a4d, Lyz2, Phf11b and Grin3a were enriched in differential loci RNA-seq and ATAC-Seq peaks with continuous changed tendency during osteoblasts differentiation and mineralization. In addition, Analysis of Motif Enrichment (AME) was used to elucidate TFs which modulated these target genes. In this study, it was shown for the first time that these important TFs including MEF2A, PRRX1, Shox2 and HOXB13 could alter promoter accessibility of target genes during osteoblastogenesis. This helps us understand how TF binding motif accessibility influences osteoblast differentiation. In addition, it also suggests that modulating the chromatin accessibility of osteogenesis could be developed as the promising strategies to regulate bone regeneration.
Collapse
Affiliation(s)
- Yueqi Chen
- Department of Orthopedics, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, People's Republic of China; Department of Orthopedics, 76nd Group Army Hospital, Xining, People's Republic of China.
| | - Jiulin Tan
- Department of Orthopedics, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, People's Republic of China
| | - Chuan Yang
- Department of Biomedical Materials Science, Third Military Medical University (Army Medical University), Chongqing, People's Republic of China
| | - Zhiguo Ling
- Institute of Immunology, Third Military Medical University (Army Medical University), Chongqing, People's Republic of China
| | - Jianzhong Xu
- Department of Orthopedics, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, People's Republic of China
| | - Dong Sun
- Department of Orthopedics, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, People's Republic of China.
| | - Fei Luo
- Department of Orthopedics, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, People's Republic of China.
| |
Collapse
|
3
|
Batra SS, Cabrera A, Spence JP, Hilton IB, Song YS. Predicting the effect of CRISPR-Cas9-based epigenome editing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.03.560674. [PMID: 37873127 PMCID: PMC10592942 DOI: 10.1101/2023.10.03.560674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Epigenetic regulation orchestrates mammalian transcription, but functional links between them remain elusive. To tackle this problem, we here use epigenomic and transcriptomic data from 13 ENCODE cell types to train machine learning models to predict gene expression from histone post-translational modifications (PTMs), achieving transcriptome-wide correlations of ~ 0.70 - 0.79 for most samples. In addition to recapitulating known associations between histone PTMs and expression patterns, our models predict that acetylation of histone subunit H3 lysine residue 27 (H3K27ac) near the transcription start site (TSS) significantly increases expression levels. To validate this prediction experimentally and investigate how engineered vs. natural deposition of H3K27ac might differentially affect expression, we apply the synthetic dCas9-p300 histone acetyltransferase system to 8 genes in the HEK293T cell line. Further, to facilitate model building, we perform MNase-seq to map genome-wide nucleosome occupancy levels in HEK293T. We observe that our models perform well in accurately ranking relative fold changes among genes in response to the dCas9-p300 system; however, their ability to rank fold changes within individual genes is noticeably diminished compared to predicting expression across cell types from their native epigenetic signatures. Our findings highlight the need for more comprehensive genome-scale epigenome editing datasets, better understanding of the actual modifications made by epigenome editing tools, and improved causal models that transfer better from endogenous cellular measurements to perturbation experiments. Together these improvements would facilitate the ability to understand and predictably control the dynamic human epigenome with consequences for human health.
Collapse
Affiliation(s)
| | | | | | - Isaac B. Hilton
- Department of Bioengineering, Rice University
- Department of BioSciences, Rice University
| | - Yun S. Song
- Computer Science Division, University of California, Berkeley
- Department of Statistics, University of California, Berkeley
| |
Collapse
|
4
|
González-Ramírez M, Blanco E, Di Croce L. A computational pipeline to learn gene expression predictive models from epigenetic information at enhancers or promoters. STAR Protoc 2023; 4:101948. [PMID: 36583961 PMCID: PMC9816966 DOI: 10.1016/j.xpro.2022.101948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/01/2022] [Accepted: 11/28/2022] [Indexed: 12/31/2022] Open
Abstract
Here, we present a computational pipeline to obtain quantitative models that characterize the relationship of gene expression with the epigenetic marking at enhancers or promoters in mouse embryonic stem cells. Our protocol consists of (i) generating predictive models of gene expression from epigenetic information (such as histone modification ChIP-seq) at enhancers or promoters and (ii) assessing the performance of these predictive models. This protocol could be applied to other biological scenarios or other types of epigenetic data. For complete details on the use and execution of this protocol, please refer to Gonzalez-Ramirez et al. (2021).1.
Collapse
Affiliation(s)
- Mar González-Ramírez
- Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Dr. Aiguader 88, 08003 Barcelona, Spain.
| | - Enrique Blanco
- Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Luciano Di Croce
- Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Dr. Aiguader 88, 08003 Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Pg. Lluis Companys 23, 08010 Barcelona, Spain.
| |
Collapse
|
5
|
Hecker D, Behjati Ardakani F, Karollus A, Gagneur J, Schulz MH. The adapted Activity-By-Contact model for enhancer-gene assignment and its application to single-cell data. Bioinformatics 2023; 39:7008325. [PMID: 36708003 PMCID: PMC9931646 DOI: 10.1093/bioinformatics/btad062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 12/05/2022] [Accepted: 01/26/2023] [Indexed: 01/29/2023] Open
Abstract
MOTIVATION Identifying regulatory regions in the genome is of great interest for understanding the epigenomic landscape in cells. One fundamental challenge in this context is to find the target genes whose expression is affected by the regulatory regions. A recent successful method is the Activity-By-Contact (ABC) model which scores enhancer-gene interactions based on enhancer activity and the contact frequency of an enhancer to its target gene. However, it describes regulatory interactions entirely from a gene's perspective, and does not account for all the candidate target genes of an enhancer. In addition, the ABC model requires two types of assays to measure enhancer activity, which limits the applicability. Moreover, there is neither implementation available that could allow for an integration with transcription factor (TF) binding information nor an efficient analysis of single-cell data. RESULTS We demonstrate that the ABC score can yield a higher accuracy by adapting the enhancer activity according to the number of contacts the enhancer has to its candidate target genes and also by considering all annotated transcription start sites of a gene. Further, we show that the model is comparably accurate with only one assay to measure enhancer activity. We combined our generalized ABC model with TF binding information and illustrated an analysis of a single-cell ATAC-seq dataset of the human heart, where we were able to characterize cell type-specific regulatory interactions and predict gene expression based on TF affinities. All executed processing steps are incorporated into our new computational pipeline STARE. AVAILABILITY AND IMPLEMENTATION The software is available at https://github.com/schulzlab/STARE. CONTACT marcel.schulz@em.uni-frankfurt.de. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Dennis Hecker
- Institute of Cardiovascular Regeneration, Goethe University Hospital.,Cardio-Pulmonary Institute, Goethe University.,German Centre for Cardiovascular Research, Partner site Rhine-Main, Frankfurt am Main 60590
| | - Fatemeh Behjati Ardakani
- Institute of Cardiovascular Regeneration, Goethe University Hospital.,Cardio-Pulmonary Institute, Goethe University.,German Centre for Cardiovascular Research, Partner site Rhine-Main, Frankfurt am Main 60590
| | - Alexander Karollus
- School of Computation, Information and Technology, Technical University of Munich, Garching 85748
| | - Julien Gagneur
- School of Computation, Information and Technology, Technical University of Munich, Garching 85748.,Institute of Human Genetics, Technical University of Munich, Munich 81675.,Computational Health Center, Helmholtz Center Munich, Neuherberg 85764.,Munich Data Science Institute, Technical University of Munich, Garching 85748, Germany
| | - Marcel H Schulz
- Institute of Cardiovascular Regeneration, Goethe University Hospital.,Cardio-Pulmonary Institute, Goethe University.,German Centre for Cardiovascular Research, Partner site Rhine-Main, Frankfurt am Main 60590
| |
Collapse
|
6
|
Abstract
Gene therapy is poised to revolutionize modern medicine, with seemingly unlimited potential for treating and curing genetic disorders. For otherwise incurable indications, including most inherited metabolic liver disorders, gene therapy provides a realistic therapeutic option. In this Review, we discuss gene supplementation and gene editing involving the use of recombinant adeno-associated virus (rAAV) vectors for the treatment of inherited liver diseases, including updates on several ongoing clinical trials that are producing promising results. Clinical testing has been essential in highlighting many key translational challenges associated with this transformative therapy. In particular, the interaction of a patient's immune system with the vector raises issues of safety and the duration of treatment efficacy. Furthermore, several serious adverse events after the administration of high doses of rAAVs suggest greater involvement of innate immune responses and pre-existing hepatic conditions than initially anticipated. Finally, permanent modification of the host genome associated with rAAV genome integration and gene editing raises concerns about the risk of oncogenicity that require careful evaluation. We summarize the main progress, challenges and pathways forward for gene therapy for liver diseases.
Collapse
|
7
|
Cho KT, Sen TZ, Andorf CM. Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach. Front Artif Intell 2022; 5:830170. [PMID: 35719692 PMCID: PMC9204276 DOI: 10.3389/frai.2022.830170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 04/26/2022] [Indexed: 11/13/2022] Open
Abstract
Machine learning and modeling approaches have been used to classify protein sequences for a broad set of tasks including predicting protein function, structure, expression, and localization. Some recent studies have successfully predicted whether a given gene is expressed as mRNA or even translated to proteins potentially, but given that not all genes are expressed in every condition and tissue, the challenge remains to predict condition-specific expression. To address this gap, we developed a machine learning approach to predict tissue-specific gene expression across 23 different tissues in maize, solely based on DNA promoter and protein sequences. For class labels, we defined high and low expression levels for mRNA and protein abundance and optimized classifiers by systematically exploring various methods and combinations of k-mer sequences in a two-phase approach. In the first phase, we developed Markov model classifiers for each tissue and built a feature vector based on the predictions. In the second phase, the feature vector was used as an input to a Bayesian network for final classification. Our results show that these methods can achieve high classification accuracy of up to 95% for predicting gene expression for individual tissues. By relying on sequence alone, our method works in settings where costly experimental data are unavailable and reveals useful insights into the functional, evolutionary, and regulatory characteristics of genes.
Collapse
Affiliation(s)
- Kyoung Tak Cho
- Department of Computer Science, Iowa State University, Ames, IA, United States
| | - Taner Z. Sen
- USDA-ARS, Crop Improvement and Genetics Research Unit, Albany, CA, United States
| | - Carson M. Andorf
- USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA, United States
- *Correspondence: Carson M. Andorf
| |
Collapse
|
8
|
Eslami M, Borujeni AE, Eramian H, Weston M, Zheng G, Urrutia J, Corbet C, Becker D, Maschhoff P, Clowers K, Cristofaro A, Hosseini HD, Gordon DB, Dorfan Y, Singer J, Vaughn M, Gaffney N, Fonner J, Stubbs J, Voigt CA, Yeung E. Prediction of whole-cell transcriptional response with machine learning. Bioinformatics 2022; 38:404-409. [PMID: 34570169 DOI: 10.1093/bioinformatics/btab676] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 08/29/2021] [Accepted: 09/22/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we present the host response model (HRM), a machine learning approach that maps response of single perturbations to transcriptional response of the combination of perturbations. RESULTS The HRM combines high-throughput sequencing with machine learning to infer links between experimental context, prior knowledge of cell regulatory networks, and RNASeq data to predict a gene's dysregulation. We find that the HRM can predict the directionality of dysregulation to a combination of inducers with an accuracy of >90% using data from single inducers. We further find that the use of prior, known cell regulatory networks doubles the predictive performance of the HRM (an R2 from 0.3 to 0.65). The model was validated in two organisms, Escherichia coli and Bacillus subtilis, using new experiments conducted after training. Finally, while the HRM is trained with gene expression data, the direct prediction of differential expression makes it possible to also conduct enrichment analyses using its predictions. We show that the HRM can accurately classify >95% of the pathway regulations. The HRM reduces the number of RNASeq experiments needed as responses can be tested in silico prior to the experiment. AVAILABILITY AND IMPLEMENTATION The HRM software and tutorial are available at https://github.com/sd2e/CDM and the configurable differential expression analysis tools and tutorials are available at https://github.com/SD2E/omics_tools. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
| | - Amin Espah Borujeni
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Hamed Eramian
- Data Science, Netrias, LLC, Annapolis, MD 21409, USA
| | - Mark Weston
- Data Science, Netrias, LLC, Annapolis, MD 21409, USA
| | - George Zheng
- Data Science, Netrias, LLC, Annapolis, MD 21409, USA
| | - Joshua Urrutia
- Life Sciences and Computing, Texas Advanced Computing Center, Austin, TX 78758, USA
| | | | | | | | | | - Alexander Cristofaro
- TScan Therapeutics, Inc., Waltham, MA 02451, USA.,Foundry for Synthetic Biology, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Hamid Doost Hosseini
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - D Benjamin Gordon
- Foundry for Synthetic Biology, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Yuval Dorfan
- Foundry for Synthetic Biology, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Matthew Vaughn
- Life Sciences and Computing, Texas Advanced Computing Center, Austin, TX 78758, USA
| | - Niall Gaffney
- Life Sciences and Computing, Texas Advanced Computing Center, Austin, TX 78758, USA
| | - John Fonner
- Life Sciences and Computing, Texas Advanced Computing Center, Austin, TX 78758, USA
| | - Joe Stubbs
- Life Sciences and Computing, Texas Advanced Computing Center, Austin, TX 78758, USA
| | - Christopher A Voigt
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Enoch Yeung
- Bioengineering Center, University of California Santa Barbara, Santa Barbara, CA 93106, USA
| |
Collapse
|
9
|
Orozco G, Schoenfelder S, Walker N, Eyre S, Fraser P. 3D genome organization links non-coding disease-associated variants to genes. Front Cell Dev Biol 2022; 10:995388. [PMID: 36340032 PMCID: PMC9631826 DOI: 10.3389/fcell.2022.995388] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 09/27/2022] [Indexed: 11/13/2022] Open
Abstract
Genome sequencing has revealed over 300 million genetic variations in human populations. Over 90% of variants are single nucleotide polymorphisms (SNPs), the remainder include short deletions or insertions, and small numbers of structural variants. Hundreds of thousands of these variants have been associated with specific phenotypic traits and diseases through genome wide association studies which link significant differences in variant frequencies with specific phenotypes among large groups of individuals. Only 5% of disease-associated SNPs are located in gene coding sequences, with the potential to disrupt gene expression or alter of the function of encoded proteins. The remaining 95% of disease-associated SNPs are located in non-coding DNA sequences which make up 98% of the genome. The role of non-coding, disease-associated SNPs, many of which are located at considerable distances from any gene, was at first a mystery until the discovery that gene promoters regularly interact with distal regulatory elements to control gene expression. Disease-associated SNPs are enriched at the millions of gene regulatory elements that are dispersed throughout the non-coding sequences of the genome, suggesting they function as gene regulation variants. Assigning specific regulatory elements to the genes they control is not straightforward since they can be millions of base pairs apart. In this review we describe how understanding 3D genome organization can identify specific interactions between gene promoters and distal regulatory elements and how 3D genomics can link disease-associated SNPs to their target genes. Understanding which gene or genes contribute to a specific disease is the first step in designing rational therapeutic interventions.
Collapse
Affiliation(s)
- Gisela Orozco
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom.,NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, United Kingdom
| | - Stefan Schoenfelder
- Enhanc3D Genomics Ltd., Cambridge, United Kingdom.,Epigenetics Programme, The Babraham Institute, Babraham Research Campus, CB22 3AT Cambridge, Cambridge, United Kingdom
| | | | - Stephan Eyre
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom.,NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, United Kingdom
| | - Peter Fraser
- Enhanc3D Genomics Ltd., Cambridge, United Kingdom.,Department of Biological Science, Florida State University, Tallahassee, FL, United States
| |
Collapse
|
10
|
Schmidt F, Marx A, Baumgarten N, Hebel M, Wegner M, Kaulich M, Leisegang M, Brandes R, Göke J, Vreeken J, Schulz M. Integrative analysis of epigenetics data identifies gene-specific regulatory elements. Nucleic Acids Res 2021; 49:10397-10418. [PMID: 34508352 PMCID: PMC8501997 DOI: 10.1093/nar/gkab798] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 08/01/2021] [Accepted: 09/07/2021] [Indexed: 12/19/2022] Open
Abstract
Understanding how epigenetic variation in non-coding regions is involved in distal gene-expression regulation is an important problem. Regulatory regions can be associated to genes using large-scale datasets of epigenetic and expression data. However, for regions of complex epigenomic signals and enhancers that regulate many genes, it is difficult to understand these associations. We present StitchIt, an approach to dissect epigenetic variation in a gene-specific manner for the detection of regulatory elements (REMs) without relying on peak calls in individual samples. StitchIt segments epigenetic signal tracks over many samples to generate the location and the target genes of a REM simultaneously. We show that this approach leads to a more accurate and refined REM detection compared to standard methods even on heterogeneous datasets, which are challenging to model. Also, StitchIt REMs are highly enriched in experimentally determined chromatin interactions and expression quantitative trait loci. We validated several newly predicted REMs using CRISPR-Cas9 experiments, thereby demonstrating the reliability of StitchIt. StitchIt is able to dissect regulation in superenhancers and predicts thousands of putative REMs that go unnoticed using peak-based approaches suggesting that a large part of the regulome might be uncharted water.
Collapse
Affiliation(s)
- Florian Schmidt
- Cluster of Excellence for Multimodal Computing and Interaction, Saarland University, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Graduate School of Computer Science, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, 60 Biopolis Street, 138672 Singapore, Singapore
| | - Alexander Marx
- Cluster of Excellence for Multimodal Computing and Interaction, Saarland University, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Graduate School of Computer Science, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- International Max Planck Research School for Computer Science, Saarland Informatics Campus, 66123 Saarbrücken, Germany
| | - Nina Baumgarten
- Institute for Cardiovascular Regeneration, Goethe University, 60590 Frankfurt am Main, Germany
- German Center for Cardiovascular Research (DZHK), Partner site RheinMain, 60590 Frankfurt am Main, Germany
| | - Marie Hebel
- Institute of Biochemistry II, Goethe University Frankfurt - Medical Faculty, University Hospital, 60590 Frankfurt am Main, Germany
| | - Martin Wegner
- Institute of Biochemistry II, Goethe University Frankfurt - Medical Faculty, University Hospital, 60590 Frankfurt am Main, Germany
| | - Manuel Kaulich
- Institute of Biochemistry II, Goethe University Frankfurt - Medical Faculty, University Hospital, 60590 Frankfurt am Main, Germany
- Frankfurt Cancer Institute, Goethe University, 60590 Frankfurt am Main, Germany
| | - Matthias S Leisegang
- German Center for Cardiovascular Research (DZHK), Partner site RheinMain, 60590 Frankfurt am Main, Germany
- Institute for Cardiovascular Physiology, Goethe University, 60590 Frankfurt am Main, Germany
| | - Ralf P Brandes
- German Center for Cardiovascular Research (DZHK), Partner site RheinMain, 60590 Frankfurt am Main, Germany
- Institute for Cardiovascular Physiology, Goethe University, 60590 Frankfurt am Main, Germany
| | - Jonathan Göke
- Laboratory of Computational Transcriptomics, Genome Institute of Singapore, 60 Biopolis Street, 138672 Singapore, Singapore
| | - Jilles Vreeken
- CISPA Helmholtz Center for Information Security, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Cluster of Excellence for Multimodal Computing and Interaction, Saarland University, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany
| | - Marcel H Schulz
- Cluster of Excellence for Multimodal Computing and Interaction, Saarland University, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Institute for Cardiovascular Regeneration, Goethe University, 60590 Frankfurt am Main, Germany
- German Center for Cardiovascular Research (DZHK), Partner site RheinMain, 60590 Frankfurt am Main, Germany
| |
Collapse
|
11
|
González-Ramírez M, Ballaré C, Mugianesi F, Beringer M, Santanach A, Blanco E, Di Croce L. Differential contribution to gene expression prediction of histone modifications at enhancers or promoters. PLoS Comput Biol 2021; 17:e1009368. [PMID: 34473698 PMCID: PMC8443064 DOI: 10.1371/journal.pcbi.1009368] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 09/15/2021] [Accepted: 08/21/2021] [Indexed: 12/31/2022] Open
Abstract
The ChIP-seq signal of histone modifications at promoters is a good predictor of gene expression in different cellular contexts, but whether this is also true at enhancers is not clear. To address this issue, we develop quantitative models to characterize the relationship of gene expression with histone modifications at enhancers or promoters. We use embryonic stem cells (ESCs), which contain a full spectrum of active and repressed (poised) enhancers, to train predictive models. As many poised enhancers in ESCs switch towards an active state during differentiation, predictive models can also be trained on poised enhancers throughout differentiation and in development. Remarkably, we determine that histone modifications at enhancers, as well as promoters, are predictive of gene expression in ESCs and throughout differentiation and development. Importantly, we demonstrate that their contribution to the predictive models varies depending on their location in enhancers or promoters. Moreover, we use a local regression (LOESS) to normalize sequencing data from different sources, which allows us to apply predictive models trained in a specific cellular context to a different one. We conclude that the relationship between gene expression and histone modifications at enhancers is universal and different from promoters. Our study provides new insight into how histone modifications relate to gene expression based on their location in enhancers or promoters. Gene expression can be properly predicted by the ChIP-seq signal of histone modifications at promoters, but whether this is also true at enhancers is unclear. In this study we develop predictive models of gene expression that demonstrate the predictive power of histone modifications at enhancers in the context of mouse embryonic stem cells, during differentiation, and in animal development. Moreover, by assessing the contribution of each histone modification, we found that enhancer predictive models and promoter predictive models have different histone modification requirement. Therefore, different histone modifications relate better to enhancer or promoter function(s). Finally, by applying predictive models trained in a specific cellular context to a different one, we concluded that the relationship between gene expression and histone modifications at enhancers is universal.
Collapse
Affiliation(s)
- Mar González-Ramírez
- Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Cecilia Ballaré
- Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Francesca Mugianesi
- Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Malte Beringer
- Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Alexandra Santanach
- Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Enrique Blanco
- Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Luciano Di Croce
- Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- ICREA, Pg. Barcelona, Spain
- * E-mail:
| |
Collapse
|
12
|
Su Z, Liu G, Zhang B, Lin Z, Huang D. Natural Antisense Transcript PEBP1P3 Regulates the RNA Expression, DNA Methylation and Histone Modification of CD45 Gene. Genes (Basel) 2021; 12:genes12050759. [PMID: 34067766 PMCID: PMC8156488 DOI: 10.3390/genes12050759] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 05/15/2021] [Indexed: 02/05/2023] Open
Abstract
The leukocyte common antigen CD45 is a transmembrane phosphatase expressed on all nucleated hemopoietic cells, and the expression levels of its splicing isoforms are closely related to the development and function of lymphocytes. PEBP1P3 is a natural antisense transcript from the opposite strand of CD45 intron 2 and is predicted to be a noncoding RNA. The genotype-tissue expression and quantitative PCR data suggested that PEBP1P3 might be involved in the regulation of expression of CD45 splicing isoforms. To explore the regulatory mechanism of PEBP1P3 in CD45 expression, DNA methylation and histone modification were detected by bisulfate sequencing PCR and chromatin immunoprecipitation assays, respectively. The results showed that after the antisense RNA PEBP1P3 was knocked down by RNA interference, the DNA methylation of CD45 intron 2 was decreased and histone H3K9 and H3K36 trimethylation at the alternative splicing exons of CD45 DNA was increased. Knockdown of PEBP1P3 also increased the binding levels of chromatin conformation organizer CTCF at intron 2 and the alternative splicing exons of CD45. The present results indicate that the natural antisense RNA PEBP1P3 regulated the alternative splicing of CD45 RNA, and that might be correlated with the regulation of histone modification and DNA methylation.
Collapse
Affiliation(s)
- Zhongjing Su
- Department of Histology and Embryology, Shantou University Medical College, No. 22, Xinling Road, Shantou 515041, China;
- Correspondence: (Z.S.); (D.H.)
| | - Guangyu Liu
- Department of Cell Biology, Shantou University Medical College, No. 22, Xinling Road, Shantou 515041, China;
| | - Bin Zhang
- Department of Histology and Embryology, Shantou University Medical College, No. 22, Xinling Road, Shantou 515041, China;
| | - Ze Lin
- Department of Central Laboratory, Shantou University Medical College, No. 22, Xinling Road, Shantou 515041, China;
| | - Dongyang Huang
- Department of Cell Biology, Shantou University Medical College, No. 22, Xinling Road, Shantou 515041, China;
- Correspondence: (Z.S.); (D.H.)
| |
Collapse
|
13
|
Patel N, Bush WS. Modeling transcriptional regulation using gene regulatory networks based on multi-omics data sources. BMC Bioinformatics 2021; 22:200. [PMID: 33874910 PMCID: PMC8056605 DOI: 10.1186/s12859-021-04126-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 04/09/2021] [Indexed: 11/17/2022] Open
Abstract
Background Transcriptional regulation is complex, requiring multiple cis (local) and trans acting mechanisms working in concert to drive gene expression, with disruption of these processes linked to multiple diseases. Previous computational attempts to understand the influence of regulatory mechanisms on gene expression have used prediction models containing input features derived from cis regulatory factors. However, local chromatin looping and trans-acting mechanisms are known to also influence transcriptional regulation, and their inclusion may improve model accuracy and interpretation. In this study, we create a general model of transcription factor influence on gene expression by incorporating both cis and trans gene regulatory features. Results We describe a computational framework to model gene expression for GM12878 and K562 cell lines. This framework weights the impact of transcription factor-based regulatory data using multi-omics gene regulatory networks to account for both cis and trans acting mechanisms, and measures of the local chromatin context. These prediction models perform significantly better compared to models containing cis-regulatory features alone. Models that additionally integrate long distance chromatin interactions (or chromatin looping) between distal transcription factor binding regions and gene promoters also show improved accuracy. As a demonstration of their utility, effect estimates from these models were used to weight cis-regulatory rare variants for sequence kernel association test analyses of gene expression. Conclusions Our models generate refined effect estimates for the influence of individual transcription factors on gene expression, allowing characterization of their roles across the genome. This work also provides a framework for integrating multiple data types into a single model of transcriptional regulation. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04126-3.
Collapse
Affiliation(s)
- Neel Patel
- Department of Nutrition, Case Western Reserve University, Cleveland, OH, USA.,Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - William S Bush
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.
| |
Collapse
|
14
|
A hierarchical regulatory network analysis of the vitamin D induced transcriptome reveals novel regulators and complete VDR dependency in monocytes. Sci Rep 2021; 11:6518. [PMID: 33753848 PMCID: PMC7985518 DOI: 10.1038/s41598-021-86032-5] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 02/04/2021] [Indexed: 11/11/2022] Open
Abstract
The transcription factor vitamin D receptor (VDR) is the high affinity nuclear target of the biologically active form of vitamin D3 (1,25(OH)2D3). In order to identify pure genomic transcriptional effects of 1,25(OH)2D3, we used VDR cistrome, transcriptome and open chromatin data, obtained from the human monocytic cell line THP-1, for a novel hierarchical analysis applying three bioinformatics approaches. We predicted 75.6% of all early 1,25(OH)2D3-responding (2.5 or 4 h) and 57.4% of the late differentially expressed genes (24 h) to be primary VDR target genes. VDR knockout led to a complete loss of 1,25(OH)2D3–induced genome-wide gene regulation. Thus, there was no indication of any VDR-independent non-genomic actions of 1,25(OH)2D3 modulating its transcriptional response. Among the predicted primary VDR target genes, 47 were coding for transcription factors and thus may mediate secondary 1,25(OH)2D3 responses. CEBPA and ETS1 ChIP-seq data and RNA-seq following CEBPA knockdown were used to validate the predicted regulation of secondary vitamin D target genes by both transcription factors. In conclusion, a directional network containing 47 partly novel primary VDR target transcription factors describes secondary responses in a highly complex vitamin D signaling cascade. The central transcription factor VDR is indispensable for all transcriptome-wide effects of the nuclear hormone.
Collapse
|
15
|
Scherer M, Schmidt F, Lazareva O, Walter J, Baumbach J, Schulz MH, List M. Machine learning for deciphering cell heterogeneity and gene regulation. NATURE COMPUTATIONAL SCIENCE 2021; 1:183-191. [PMID: 38183187 DOI: 10.1038/s43588-021-00038-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 02/08/2021] [Indexed: 12/14/2022]
Abstract
Epigenetics studies inheritable and reversible modifications of DNA that allow cells to control gene expression throughout their development and in response to environmental conditions. In computational epigenomics, machine learning is applied to study various epigenetic mechanisms genome wide. Its aim is to expand our understanding of cell differentiation, that is their specialization, in health and disease. Thus far, most efforts focus on understanding the functional encoding of the genome and on unraveling cell-type heterogeneity. Here, we provide an overview of state-of-the-art computational methods and their underlying statistical concepts, which range from matrix factorization and regularized linear regression to deep learning methods. We further show how the rise of single-cell technology leads to new computational challenges and creates opportunities to further our understanding of epigenetic regulation.
Collapse
Affiliation(s)
- Michael Scherer
- Department of Genetics/Epigenetics, Saarland University, Saarbrücken, Germany
- Computational Biology Group, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany
- Graduate School of Computer Science, Saarland Informatics Campus, Saarbrücken, Germany
| | | | - Olga Lazareva
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Jörn Walter
- Computational Biology Group, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
- Computational BioMedicine Lab, Institute of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
- Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Marcel H Schulz
- Institute of Cardiovascular Regeneration, University Hospital and Goethe University Frankfurt, Frankfurt, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
| |
Collapse
|
16
|
Behjati Ardakani F, Kattler K, Heinen T, Schmidt F, Feuerborn D, Gasparoni G, Lepikhov K, Nell P, Hengstler J, Walter J, Schulz MH. Prediction of single-cell gene expression for transcription factor analysis. Gigascience 2020; 9:giaa113. [PMID: 33124660 PMCID: PMC7596801 DOI: 10.1093/gigascience/giaa113] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 08/20/2020] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Single-cell RNA sequencing is a powerful technology to discover new cell types and study biological processes in complex biological samples. A current challenge is to predict transcription factor (TF) regulation from single-cell RNA data. RESULTS Here, we propose a novel approach for predicting gene expression at the single-cell level using cis-regulatory motifs, as well as epigenetic features. We designed a tree-guided multi-task learning framework that considers each cell as a task. Through this framework we were able to explain the single-cell gene expression values using either TF binding affinities or TF ChIP-seq data measured at specific genomic regions. TFs identified using these models could be validated by the literature. CONCLUSION Our proposed method allows us to identify distinct TFs that show cell type-specific regulation. This approach is not limited to TFs but can use any type of data that can potentially be used in explaining gene expression at the single-cell level to study factors that drive differentiation or show abnormal regulation in disease. The implementation of our workflow can be accessed under an MIT license via https://github.com/SchulzLab/Triangulate.
Collapse
Affiliation(s)
- Fatemeh Behjati Ardakani
- Institute for Cardiovascular Regeneration, Goethe University, 60590 Frankfurt am Main, Germany; Theodor-Stern-Kai 7
- Cluster of Excellence MMCI, Saarland University, Campus E1 7, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Max Planck Institute for Informatics, Campus E1 4, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Graduate School of Computer Science, Saarland University, Campus E1 3, Saarbrücken, Germany
| | - Kathrin Kattler
- Department of Genetics, Saarland University, Campus A2 4, 66123 Saarbrücken, Germany
| | - Tobias Heinen
- Cluster of Excellence MMCI, Saarland University, Campus E1 7, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Max Planck Institute for Informatics, Campus E1 4, Saarland Informatics Campus, 66123 Saarbrücken, Germany
| | - Florian Schmidt
- Institute for Cardiovascular Regeneration, Goethe University, 60590 Frankfurt am Main, Germany; Theodor-Stern-Kai 7
- Cluster of Excellence MMCI, Saarland University, Campus E1 7, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Max Planck Institute for Informatics, Campus E1 4, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Graduate School of Computer Science, Saarland University, Campus E1 3, Saarbrücken, Germany
| | - David Feuerborn
- Leibniz Research Centre for Working Environment and Human Factors (IfADo), Ardeystraße 67, 44139 Dortmund, Germany
| | - Gilles Gasparoni
- Department of Genetics, Saarland University, Campus A2 4, 66123 Saarbrücken, Germany
| | - Konstantin Lepikhov
- Department of Genetics, Saarland University, Campus A2 4, 66123 Saarbrücken, Germany
| | - Patrick Nell
- Leibniz Research Centre for Working Environment and Human Factors (IfADo), Ardeystraße 67, 44139 Dortmund, Germany
| | - Jan Hengstler
- Leibniz Research Centre for Working Environment and Human Factors (IfADo), Ardeystraße 67, 44139 Dortmund, Germany
| | - Jörn Walter
- Department of Genetics, Saarland University, Campus A2 4, 66123 Saarbrücken, Germany
| | - Marcel H Schulz
- Institute for Cardiovascular Regeneration, Goethe University, 60590 Frankfurt am Main, Germany; Theodor-Stern-Kai 7
- Cluster of Excellence MMCI, Saarland University, Campus E1 7, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Max Planck Institute for Informatics, Campus E1 4, Saarland Informatics Campus, 66123 Saarbrücken, Germany
| |
Collapse
|
17
|
Abstract
The hierarchical three-dimensional folding of the mammalian genome constitutes an important regulatory layer of gene expression and cell fate control during processes such as development and tumorigenesis. Accumulating evidence supports the existence of complex topological assemblies in which multiple genes and regulatory elements are frequently interacting with each other in the 3D nucleus. Here, we will discuss the nature, organizational principles, and potential function of such assemblies, including the recently reported enhancer “hubs,” “cliques,” and FIREs (frequently interacting regions) as well as multi-contact hubs. We will also review recent studies that investigate the role of transcription factors (TFs) in driving the topological genome reorganization and hub formation in the context of cell fate transitions and cancer. Finally, we will highlight technological advances that enabled these studies, current limitations, and future directions necessary to advance our understating in the field.
Collapse
Affiliation(s)
- Dafne Campigli Di Giammartino
- Sanford I. Weill Department of Medicine, Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine , New York, NY, USA
| | - Alexander Polyzos
- Sanford I. Weill Department of Medicine, Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine , New York, NY, USA
| | - Effie Apostolou
- Sanford I. Weill Department of Medicine, Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine , New York, NY, USA
| |
Collapse
|
18
|
Baumgarten N, Hecker D, Karunanithi S, Schmidt F, List M, Schulz MH. EpiRegio: analysis and retrieval of regulatory elements linked to genes. Nucleic Acids Res 2020; 48:W193-W199. [PMID: 32459338 PMCID: PMC7319550 DOI: 10.1093/nar/gkaa382] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 04/21/2020] [Accepted: 05/04/2020] [Indexed: 12/26/2022] Open
Abstract
A current challenge in genomics is to interpret non-coding regions and their role in transcriptional regulation of possibly distant target genes. Genome-wide association studies show that a large part of genomic variants are found in those non-coding regions, but their mechanisms of gene regulation are often unknown. An additional challenge is to reliably identify the target genes of the regulatory regions, which is an essential step in understanding their impact on gene expression. Here we present the EpiRegio web server, a resource of regulatory elements (REMs). REMs are genomic regions that exhibit variations in their chromatin accessibility profile associated with changes in expression of their target genes. EpiRegio incorporates both epigenomic and gene expression data for various human primary cell types and tissues, providing an integrated view of REMs in the genome. Our web server allows the analysis of genes and their associated REMs, including the REM's activity and its estimated cell type-specific contribution to its target gene's expression. Further, it is possible to explore genomic regions for their regulatory potential, investigate overlapping REMs and by that the dissection of regions of large epigenomic complexity. EpiRegio allows programmatic access through a REST API and is freely available at https://epiregio.de/.
Collapse
Affiliation(s)
- Nina Baumgarten
- Institute for Cardiovascular Regeneration, Goethe University Hospital, 60590 Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, 60590 Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner site Rhein-Main, 60590 Frankfurt am Main, Germany
- Cluster of Excellence, Multimodal Computing and Interaction, Saarland Informatics Campus, 66123 Saarbrücken, Germany
| | - Dennis Hecker
- Institute for Cardiovascular Regeneration, Goethe University Hospital, 60590 Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, 60590 Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner site Rhein-Main, 60590 Frankfurt am Main, Germany
| | - Sivarajan Karunanithi
- Institute for Cardiovascular Regeneration, Goethe University Hospital, 60590 Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, 60590 Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner site Rhein-Main, 60590 Frankfurt am Main, Germany
| | - Florian Schmidt
- German Center for Cardiovascular Research, Partner site Rhein-Main, 60590 Frankfurt am Main, Germany
- Cluster of Excellence, Multimodal Computing and Interaction, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Genome Institute of Singapore, 60 Biopolis Street, Genome, 02-01, 138672, Singapore
| | - Markus List
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Maximus-von-Imhof-Forum 3, 85354 Freising, Germany
| | - Marcel H Schulz
- Institute for Cardiovascular Regeneration, Goethe University Hospital, 60590 Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, 60590 Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner site Rhein-Main, 60590 Frankfurt am Main, Germany
- Cluster of Excellence, Multimodal Computing and Interaction, Saarland Informatics Campus, 66123 Saarbrücken, Germany
| |
Collapse
|
19
|
Chen T, Tyagi S. Integrative computational epigenomics to build data-driven gene regulation hypotheses. Gigascience 2020; 9:giaa064. [PMID: 32543653 PMCID: PMC7297091 DOI: 10.1093/gigascience/giaa064] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 05/25/2020] [Accepted: 05/26/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Diseases are complex phenotypes often arising as an emergent property of a non-linear network of genetic and epigenetic interactions. To translate this resulting state into a causal relationship with a subset of regulatory features, many experiments deploy an array of laboratory assays from multiple modalities. Often, each of these resulting datasets is large, heterogeneous, and noisy. Thus, it is non-trivial to unify these complex datasets into an interpretable phenotype. Although recent methods address this problem with varying degrees of success, they are constrained by their scopes or limitations. Therefore, an important gap in the field is the lack of a universal data harmonizer with the capability to arbitrarily integrate multi-modal datasets. RESULTS In this review, we perform a critical analysis of methods with the explicit aim of harmonizing data, as opposed to case-specific integration. This revealed that matrix factorization, latent variable analysis, and deep learning are potent strategies. Finally, we describe the properties of an ideal universal data harmonization framework. CONCLUSIONS A sufficiently advanced universal harmonizer has major medical implications, such as (i) identifying dysregulated biological pathways responsible for a disease is a powerful diagnostic tool; (2) investigating these pathways further allows the biological community to better understand a disease's mechanisms; and (3) precision medicine also benefits from developments in this area, particularly in the context of the growing field of selective epigenome editing, which can suppress or induce a desired phenotype.
Collapse
Affiliation(s)
- Tyrone Chen
- 25 Rainforest Walk, School of Biological Sciences, Monash University, Clayton, VIC 3800, Australia
| | - Sonika Tyagi
- 25 Rainforest Walk, School of Biological Sciences, Monash University, Clayton, VIC 3800, Australia
| |
Collapse
|