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Sokolova K, Chen KM, Hao Y, Zhou J, Troyanskaya OG. Deep Learning Sequence Models for Transcriptional Regulation. Annu Rev Genomics Hum Genet 2024; 25:105-122. [PMID: 38594933 DOI: 10.1146/annurev-genom-021623-024727] [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] [Indexed: 04/11/2024]
Abstract
Deciphering the regulatory code of gene expression and interpreting the transcriptional effects of genome variation are critical challenges in human genetics. Modern experimental technologies have resulted in an abundance of data, enabling the development of sequence-based deep learning models that link patterns embedded in DNA to the biochemical and regulatory properties contributing to transcriptional regulation, including modeling epigenetic marks, 3D genome organization, and gene expression, with tissue and cell-type specificity. Such methods can predict the functional consequences of any noncoding variant in the human genome, even rare or never-before-observed variants, and systematically characterize their consequences beyond what is tractable from experiments or quantitative genetics studies alone. Recently, the development and application of interpretability approaches have led to the identification of key sequence patterns contributing to the predicted tasks, providing insights into the underlying biological mechanisms learned and revealing opportunities for improvement in future models.
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Affiliation(s)
- Ksenia Sokolova
- Department of Computer Science and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA; , ,
| | - Kathleen M Chen
- Department of Computer Science and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA; , ,
| | - Yun Hao
- Flatiron Institute, Simons Foundation, New York, NY, USA;
| | - Jian Zhou
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, Texas, USA;
| | - Olga G Troyanskaya
- Princeton Precision Health, Princeton University, Princeton, New Jersey, USA
- Flatiron Institute, Simons Foundation, New York, NY, USA;
- Department of Computer Science and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA; , ,
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2
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Romero R, Menichelli C, Vroland C, Marin JM, Lèbre S, Lecellier CH, Bréhélin L. TFscope: systematic analysis of the sequence features involved in the binding preferences of transcription factors. Genome Biol 2024; 25:187. [PMID: 38987807 DOI: 10.1186/s13059-024-03321-8] [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: 09/09/2022] [Accepted: 06/24/2024] [Indexed: 07/12/2024] Open
Abstract
Characterizing the binding preferences of transcription factors (TFs) in different cell types and conditions is key to understand how they orchestrate gene expression. Here, we develop TFscope, a machine learning approach that identifies sequence features explaining the binding differences observed between two ChIP-seq experiments targeting either the same TF in two conditions or two TFs with similar motifs (paralogous TFs). TFscope systematically investigates differences in the core motif, nucleotide environment and co-factor motifs, and provides the contribution of each key feature in the two experiments. TFscope was applied to > 305 ChIP-seq pairs, and several examples are discussed.
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Affiliation(s)
- Raphaël Romero
- LIRMM, Univ Montpellier, CNRS, Montpellier, France
- IMAG, Univ Montpellier, CNRS, Montpellier, France
| | | | - Christophe Vroland
- LIRMM, Univ Montpellier, CNRS, Montpellier, France
- Institut de Génétique Moléculaire de Montpellier, University of Montpellier, CNRS, Montpellier, France
| | | | - Sophie Lèbre
- IMAG, Univ Montpellier, CNRS, Montpellier, France.
- AMIS, Université Paul-Valéry-Montpellier 3, Montpellier, France.
| | - Charles-Henri Lecellier
- LIRMM, Univ Montpellier, CNRS, Montpellier, France.
- Institut de Génétique Moléculaire de Montpellier, University of Montpellier, CNRS, Montpellier, France.
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3
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Khoa LTP, Yang W, Shan M, Zhang L, Mao F, Zhou B, Li Q, Malcore R, Harris C, Zhao L, Rao RC, Iwase S, Kalantry S, Bielas SL, Lyssiotis CA, Dou Y. Quiescence enables unrestricted cell fate in naive embryonic stem cells. Nat Commun 2024; 15:1721. [PMID: 38409226 PMCID: PMC10897426 DOI: 10.1038/s41467-024-46121-1] [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: 12/08/2023] [Accepted: 02/14/2024] [Indexed: 02/28/2024] Open
Abstract
Quiescence in stem cells is traditionally considered as a state of inactive dormancy or with poised potential. Naive mouse embryonic stem cells (ESCs) can enter quiescence spontaneously or upon inhibition of MYC or fatty acid oxidation, mimicking embryonic diapause in vivo. The molecular underpinning and developmental potential of quiescent ESCs (qESCs) are relatively unexplored. Here we show that qESCs possess an expanded or unrestricted cell fate, capable of generating both embryonic and extraembryonic cell types (e.g., trophoblast stem cells). These cells have a divergent metabolic landscape comparing to the cycling ESCs, with a notable decrease of the one-carbon metabolite S-adenosylmethionine. The metabolic changes are accompanied by a global reduction of H3K27me3, an increase of chromatin accessibility, as well as the de-repression of endogenous retrovirus MERVL and trophoblast master regulators. Depletion of methionine adenosyltransferase Mat2a or deletion of Eed in the polycomb repressive complex 2 results in removal of the developmental constraints towards the extraembryonic lineages. Our findings suggest that quiescent ESCs are not dormant but rather undergo an active transition towards an unrestricted cell fate.
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Affiliation(s)
- Le Tran Phuc Khoa
- Department of Medicine, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90033, USA
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Wentao Yang
- Department of Medicine, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90033, USA
| | - Mengrou Shan
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Li Zhang
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Fengbiao Mao
- Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing, China
| | - Bo Zhou
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Qiang Li
- Department of Ophthalmology & Visual Sciences, W.K. Kellogg Eye Center, University of Michigan, 1000 Wall St., Ann Arbor, MI, 48105, USA
| | - Rebecca Malcore
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Clair Harris
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Lili Zhao
- Beaumont Hospital, Wayne, 33155 Annapolis St., Wayne, MI, 48184, USA
| | - Rajesh C Rao
- Department of Ophthalmology & Visual Sciences, W.K. Kellogg Eye Center, University of Michigan, 1000 Wall St., Ann Arbor, MI, 48105, USA
| | - Shigeki Iwase
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Sundeep Kalantry
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Stephanie L Bielas
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Costas A Lyssiotis
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Yali Dou
- Department of Medicine, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90033, USA.
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Zhang ACY, Rosa A, Sanguinetti G. Bottom-up data integration in polymer models of chromatin organization. Biophys J 2024; 123:184-194. [PMID: 38087781 PMCID: PMC10808044 DOI: 10.1016/j.bpj.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 11/20/2023] [Accepted: 12/07/2023] [Indexed: 12/24/2023] Open
Abstract
Cellular functions crucially depend on the precise execution of complex biochemical reactions taking place on the chromatin fiber in the tightly packed environment of the cell nucleus. Despite the availability of large datasets probing this process from multiple angles, bottom-up frameworks that allow the incorporation of the sequence-specific nature of biochemistry in a unified model of 3D chromatin structure remain scarce. Here, we propose Sequence-Enhanced Magnetic Polymer (SEMPER), a novel stochastic polymer model that naturally incorporates observational data about sequence-driven biochemical processes, such as binding of transcription factor proteins, in a 3D model of chromatin structure. We introduce a novel approximate Bayesian algorithm to quantify a posteriori the relative importance of various factors, including the polymeric nature of DNA, in determining chromatin epigenetic state, thus providing a transparent way to generate biological hypotheses. Although accurate prediction of contact frequencies (a problem already extensively studied in the literature) is not our main aim, as a by-product of the inference procedure and without additional input from the genome 3D structure, our model can predict with reasonable accuracy some notable and nontrivial conformational features of chromatin folding within the nucleus. Our work highlights the importance of introducing physically realistic statistical models for predicting chromatin states from epigenetic data and opens the way to a new class of more systematic approaches to interpreting epigenomic data.
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Affiliation(s)
- Alex Chen Yi Zhang
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Italy.
| | - Angelo Rosa
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Italy.
| | - Guido Sanguinetti
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Italy.
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Grau J, Schmidt F, Schulz MH. Widespread effects of DNA methylation and intra-motif dependencies revealed by novel transcription factor binding models. Nucleic Acids Res 2023; 51:e95. [PMID: 37650641 PMCID: PMC10570048 DOI: 10.1093/nar/gkad693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 07/20/2023] [Accepted: 08/10/2023] [Indexed: 09/01/2023] Open
Abstract
Several studies suggested that transcription factor (TF) binding to DNA may be impaired or enhanced by DNA methylation. We present MeDeMo, a toolbox for TF motif analysis that combines information about DNA methylation with models capturing intra-motif dependencies. In a large-scale study using ChIP-seq data for 335 TFs, we identify novel TFs that show a binding behaviour associated with DNA methylation. Overall, we find that the presence of CpG methylation decreases the likelihood of binding for the majority of methylation-associated TFs. For a considerable subset of TFs, we show that intra-motif dependencies are pivotal for accurately modelling the impact of DNA methylation on TF binding. We illustrate that the novel methylation-aware TF binding models allow to predict differential ChIP-seq peaks and improve the genome-wide analysis of TF binding. Our work indicates that simplistic models that neglect the effect of DNA methylation on DNA binding may lead to systematic underperformance for methylation-associated TFs.
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Affiliation(s)
- Jan Grau
- Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle 06120, Germany
| | - Florian Schmidt
- Goethe-University Frankfurt, Institute for Cardiovascular Regeneration, Theodor-Stern-Kai 7, 60590 Frankfurt, Germany
- Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken 66123, Germany
- Systems Biology and Data Analytics, Genome Institute of Singapore, Singapore 13862, Singapore
- ImmunoScape Pte Ltd, Singapore 228208, Singapore
| | - Marcel H Schulz
- Goethe-University Frankfurt, Institute for Cardiovascular Regeneration, Theodor-Stern-Kai 7, 60590 Frankfurt, Germany
- Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken 66123, Germany
- German Center for Cardiovascular Research, Partner site Rhein-Main, 60590 Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Goethe University, Frankfurt am Main, Germany
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6
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Liu C, Wang Z, Wang J, Liu C, Wang M, Ngo V, Wang W. Predicting regional somatic mutation rates using DNA motifs. PLoS Comput Biol 2023; 19:e1011536. [PMID: 37782656 PMCID: PMC10569533 DOI: 10.1371/journal.pcbi.1011536] [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: 04/02/2023] [Revised: 10/12/2023] [Accepted: 09/20/2023] [Indexed: 10/04/2023] Open
Abstract
How the locus-specificity of epigenetic modifications is regulated remains an unanswered question. A contributing mechanism is that epigenetic enzymes are recruited to specific loci by DNA binding factors recognizing particular sequence motifs (referred to as epi-motifs). Using these motifs to predict biological outputs depending on local epigenetic state such as somatic mutation rates would confirm their functionality. Here, we used DNA motifs including known TF motifs and epi-motifs as a surrogate of epigenetic signals to predict somatic mutation rates in 13 cancers at an average 23kbp resolution. We implemented an interpretable neural network model, called contextual regression, to successfully learn the universal relationship between mutations and DNA motifs, and uncovered motifs that are most impactful on the regional mutation rates such as TP53 and epi-motifs associated with H3K9me3. Furthermore, we identified genomic regions with significantly higher mutation rates than the expected values in each individual tumor and demonstrated that such cancer-related regions can accurately predict cancer types. Interestingly, we found that the same mutation signatures often have different contributions to cancer-related and cancer-independent regions, and we also identified the motifs with the most contribution to each mutation signature.
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Affiliation(s)
- Cong Liu
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California, United States of America
| | - Zengmiao Wang
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Jun Wang
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California, United States of America
| | - Chengyu Liu
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California, United States of America
| | - Mengchi Wang
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, California, United States of America
| | - Vu Ngo
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, California, United States of America
| | - Wei Wang
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California, United States of America
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, California, United States of America
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, California, United States of America
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Choi S, Prabhakar PK, Chowdhury R, Pendergast TH, Urbanowicz BR, Maranas C, Devos KM. A single amino acid change led to structural and functional differentiation of PvHd1 to control flowering in switchgrass. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:5532-5546. [PMID: 37402629 PMCID: PMC10540729 DOI: 10.1093/jxb/erad255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 07/03/2023] [Indexed: 07/06/2023]
Abstract
Switchgrass, a forage and bioenergy crop, occurs as two main ecotypes with different but overlapping ranges of adaptation. The two ecotypes differ in a range of characteristics, including flowering time. Flowering time determines the duration of vegetative development and therefore biomass accumulation, a key trait in bioenergy crops. No causal variants for flowering time differences between switchgrass ecotypes have, as yet, been identified. In this study, we mapped a robust flowering time quantitative trait locus (QTL) on chromosome 4K in a biparental F2 population and characterized the flowering-associated transcription factor gene PvHd1, an ortholog of CONSTANS in Arabidopsis and Heading date 1 in rice, as the underlying causal gene. Protein modeling predicted that a serine to glycine substitution at position 35 (p.S35G) in B-Box domain 1 greatly altered the global structure of the PvHd1 protein. The predicted variation in protein compactness was supported in vitro by a 4 °C shift in denaturation temperature. Overexpressing the PvHd1-p.35S allele in a late-flowering CONSTANS-null Arabidopsis mutant rescued earlier flowering, whereas PvHd1-p.35G had a reduced ability to promote flowering, demonstrating that the structural variation led to functional divergence. Our findings provide us with a tool to manipulate the timing of floral transition in switchgrass cultivars and, potentially, expand their cultivation range.
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Affiliation(s)
- Soyeon Choi
- Department of Genetics, University of Georgia, Athens, GA 30602, USA
| | - Pradeep K Prabhakar
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA
| | - Ratul Chowdhury
- Chemical Engineering, Penn State University, State College, PA 16801, USA
| | - Thomas H Pendergast
- Department of Plant Biology, University of Georgia, Athens, GA 30602, USA
- Department of Crop and Soil Sciences, University of Georgia, Athens, GA 30602, USA
- Institute of Plant Breeding, Genetics and Genomics, University of Georgia, Athens, GA 30602, USA
| | - Breeanna R Urbanowicz
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA
| | - Costas Maranas
- Chemical Engineering, Penn State University, State College, PA 16801, USA
| | - Katrien M Devos
- Department of Plant Biology, University of Georgia, Athens, GA 30602, USA
- Department of Crop and Soil Sciences, University of Georgia, Athens, GA 30602, USA
- Institute of Plant Breeding, Genetics and Genomics, University of Georgia, Athens, GA 30602, USA
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Ni P, Wu S, Su Z. Underlying causes for prevalent false positives and false negatives in STARR-seq data. NAR Genom Bioinform 2023; 5:lqad085. [PMID: 37745976 PMCID: PMC10516709 DOI: 10.1093/nargab/lqad085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 08/23/2023] [Accepted: 09/12/2023] [Indexed: 09/26/2023] Open
Abstract
Self-transcribing active regulatory region sequencing (STARR-seq) and its variants have been widely used to characterize enhancers. However, it has been reported that up to 87% of STARR-seq peaks are located in repressive chromatin and are not functional in the tested cells. While some of the STARR-seq peaks in repressive chromatin might be active in other cell/tissue types, some others might be false positives. Meanwhile, many active enhancers may not be identified by the current STARR-seq methods. Although methods have been proposed to mitigate systematic errors caused by the use of plasmid vectors, the artifacts due to the intrinsic limitations of current STARR-seq methods are still prevalent and the underlying causes are not fully understood. Based on predicted cis-regulatory modules (CRMs) and non-CRMs in the human genome as well as predicted active CRMs and non-active CRMs in a few human cell lines/tissues with STARR-seq data available, we reveal prevalent false positives and false negatives in STARR-seq peaks generated by major variants of STARR-seq methods and possible underlying causes. Our results will help design strategies to improve STARR-seq methods and interpret the results.
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Affiliation(s)
- Pengyu Ni
- Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Siwen Wu
- Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Zhengchang Su
- Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, Charlotte, NC 28223, USA
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Park S, Rehman MU, Ullah F, Tayara H, Chong KT. iCpG-Pos: an accurate computational approach for identification of CpG sites using positional features on single-cell whole genome sequence data. Bioinformatics 2023; 39:btad474. [PMID: 37555812 PMCID: PMC10444964 DOI: 10.1093/bioinformatics/btad474] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/11/2023] [Accepted: 08/08/2023] [Indexed: 08/10/2023] Open
Abstract
MOTIVATION The investigation of DNA methylation can shed light on the processes underlying human well-being and help determine overall human health. However, insufficient coverage makes it challenging to implement single-stranded DNA methylation sequencing technologies, highlighting the need for an efficient prediction model. Models are required to create an understanding of the underlying biological systems and to project single-cell (methylated) data accurately. RESULTS In this study, we developed positional features for predicting CpG sites. Positional characteristics of the sequence are derived using data from CpG regions and the separation between nearby CpG sites. Multiple optimized classifiers and different ensemble learning approaches are evaluated. The OPTUNA framework is used to optimize the algorithms. The CatBoost algorithm followed by the stacking algorithm outperformed existing DNA methylation identifiers. AVAILABILITY AND IMPLEMENTATION The data and methodologies used in this study are openly accessible to the research community. Researchers can access the positional features and algorithms used for predicting CpG site methylation patterns. To achieve superior performance, we employed the CatBoost algorithm followed by the stacking algorithm, which outperformed existing DNA methylation identifiers. The proposed iCpG-Pos approach utilizes only positional features, resulting in a substantial reduction in computational complexity compared to other known approaches for detecting CpG site methylation patterns. In conclusion, our study introduces a novel approach, iCpG-Pos, for predicting CpG site methylation patterns. By focusing on positional features, our model offers both accuracy and efficiency, making it a promising tool for advancing DNA methylation research and its applications in human health and well-being.
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Affiliation(s)
- Sehi Park
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea
| | - Mobeen Ur Rehman
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea
| | - Farman Ullah
- College of Information Technology in the United Arab Emirates University (UAEU), Abu Dhabi 15551, UAE
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, South Korea
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea
- Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, South Korea
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Zhao X, Song L, Yang A, Zhang Z, Zhang J, Yang YT, Zhao XM. Prioritizing genes associated with brain disorders by leveraging enhancer-promoter interactions in diverse neural cells and tissues. Genome Med 2023; 15:56. [PMID: 37488639 PMCID: PMC10364416 DOI: 10.1186/s13073-023-01210-6] [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: 02/07/2023] [Accepted: 07/10/2023] [Indexed: 07/26/2023] Open
Abstract
BACKGROUND Prioritizing genes that underlie complex brain disorders poses a considerable challenge. Despite previous studies have found that they shared symptoms and heterogeneity, it remained difficult to systematically identify the risk genes associated with them. METHODS By using the CAGE (Cap Analysis of Gene Expression) read alignment files for 439 human cell and tissue types (including primary cells, tissues and cell lines) from FANTOM5 project, we predicted enhancer-promoter interactions (EPIs) of 439 cell and tissue types in human, and examined their reliability. Then we evaluated the genetic heritability of 17 diverse brain disorders and behavioral-cognitive phenotypes in each neural cell type, brain region, and developmental stage. Furthermore, we prioritized genes associated with brain disorders and phenotypes by leveraging the EPIs in each neural cell and tissue type, and analyzed their pleiotropy and functionality for different categories of disorders and phenotypes. Finally, we characterized the spatiotemporal expression dynamics of these associated genes in cells and tissues. RESULTS We found that identified EPIs showed activity specificity and network aggregation in cell and tissue types, and enriched TF binding in neural cells played key roles in synaptic plasticity and nerve cell development, i.e., EGR1 and SOX family. We also discovered that most neurological disorders exhibit heritability enrichment in neural stem cells and astrocytes, while psychiatric disorders and behavioral-cognitive phenotypes exhibit enrichment in neurons. Furthermore, our identified genes recapitulated well-known risk genes, which exhibited widespread pleiotropy between psychiatric disorders and behavioral-cognitive phenotypes (i.e., FOXP2), and indicated expression specificity in neural cell types, brain regions, and developmental stages associated with disorders and phenotypes. Importantly, we showed the potential associations of brain disorders with brain regions and developmental stages that have not been well studied. CONCLUSIONS Overall, our study characterized the gene-enhancer regulatory networks and genetic mechanisms in the human neural cells and tissues, and illustrated the value of reanalysis of publicly available genomic datasets.
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Affiliation(s)
- Xingzhong Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai, 200433, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China
| | - Liting Song
- Institute of Science and Technology for Brain-Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai, 200433, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China
| | - Anyi Yang
- Institute of Science and Technology for Brain-Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai, 200433, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China
| | - Zichao Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai, 200433, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China
| | - Jinglong Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai, 200433, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China
| | - Yucheng T Yang
- Institute of Science and Technology for Brain-Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai, 200433, China.
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai, 200433, China.
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.
- State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, 200032, China.
- Internatioal Human Phenome Institutes (Shanghai), Shanghai, 200433, China.
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Tabe-Bordbar S, Sinha S. Integrative modeling of lncRNA-chromatin interaction maps reveals diverse mechanisms of nuclear retention. BMC Genomics 2023; 24:395. [PMID: 37442953 DOI: 10.1186/s12864-023-09498-9] [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: 03/15/2023] [Accepted: 06/30/2023] [Indexed: 07/15/2023] Open
Abstract
BACKGROUND Many long non-coding RNAs, known to be involved in transcriptional regulation, are enriched in the nucleus and interact with chromatin. However, their mechanisms of chromatin interaction and the served cellular functions are poorly understood. We sought to characterize the mechanisms of lncRNA nuclear retention by systematically mapping the sequence and chromatin features that distinguish lncRNA-interacting genomic segments. RESULTS We found DNA 5-mer frequencies to be predictive of chromatin interactions for all lncRNAs, suggesting sequence-specificity as a global theme in the interactome. Sequence features representing protein-DNA and protein-RNA binding motifs revealed potential mechanisms for specific lncRNAs. Complementary to these global themes, transcription-related features and DNA-RNA triplex formation potential were noted to be highly predictive for two mutually exclusive sets of lncRNAs. DNA methylation was also noted to be a significant predictor, but only when combined with other epigenomic features. CONCLUSIONS Taken together, our statistical findings suggest that a group of lncRNAs interacts with transcriptionally inactive chromatin through triplex formation, whereas another group interacts with transcriptionally active regions and is involved in DNA Damage Response (DDR) through formation of R-loops. Curiously, we observed a strong pattern of enrichment of 5-mers in four potentially interacting entities: lncRNA-bound DNA tiles, lncRNAs, miRNA seed sequences, and repeat elements. This finding points to a broad sequence-based network of interactions that may underlie regulation of fundamental cellular functions. Overall, this study reveals diverse sequence and chromatin features related to lncRNA-chromatin interactions, suggesting potential mechanisms of nuclear retention and regulatory function.
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Affiliation(s)
- Shayan Tabe-Bordbar
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Saurabh Sinha
- Department of Biomedical Engineering, Georgia Institute of Technology, UAW 3108, 313 Ferst Drive NW, Atlanta, GA, 30332, USA.
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12
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Luo X, Wang Y, Zou Q, Xu L. Recall DNA methylation levels at low coverage sites using a CNN model in WGBS. PLoS Comput Biol 2023; 19:e1011205. [PMID: 37315069 DOI: 10.1371/journal.pcbi.1011205] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 05/22/2023] [Indexed: 06/16/2023] Open
Abstract
DNA methylation is an important regulator of gene transcription. WGBS is the gold-standard approach for base-pair resolution quantitative of DNA methylation. It requires high sequencing depth. Many CpG sites with insufficient coverage in the WGBS data, resulting in inaccurate DNA methylation levels of individual sites. Many state-of-arts computation methods were proposed to predict the missing value. However, many methods required either other omics datasets or other cross-sample data. And most of them only predicted the state of DNA methylation. In this study, we proposed the RcWGBS, which can impute the missing (or low coverage) values from the DNA methylation levels on the adjacent sides. Deep learning techniques were employed for the accurate prediction. The WGBS datasets of H1-hESC and GM12878 were down-sampled. The average difference between the DNA methylation level at 12× depth predicted by RcWGBS and that at >50× depth in the H1-hESC and GM2878 cells are less than 0.03 and 0.01, respectively. RcWGBS performed better than METHimpute even though the sequencing depth was as low as 12×. Our work would help to process methylation data of low sequencing depth. It is beneficial for researchers to save sequencing costs and improve data utilization through computational methods.
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Affiliation(s)
- Ximei Luo
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, Guangdong, China
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Yansu Wang
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, Guangdong, China
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, Guangdong, China
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13
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Yao Z, Zhang W, Song P, Hu Y, Liu J. DeepFormer: a hybrid network based on convolutional neural network and flow-attention mechanism for identifying the function of DNA sequences. Brief Bioinform 2023; 24:bbad095. [PMID: 36917472 DOI: 10.1093/bib/bbad095] [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: 11/30/2022] [Revised: 01/19/2023] [Accepted: 02/21/2023] [Indexed: 03/16/2023] Open
Abstract
Identifying the function of DNA sequences accurately is an essential and challenging task in the genomic field. Until now, deep learning has been widely used in the functional analysis of DNA sequences, including DeepSEA, DanQ, DeepATT and TBiNet. However, these methods have the problems of high computational complexity and not fully considering the distant interactions among chromatin features, thus affecting the prediction accuracy. In this work, we propose a hybrid deep neural network model, called DeepFormer, based on convolutional neural network (CNN) and flow-attention mechanism for DNA sequence function prediction. In DeepFormer, the CNN is used to capture the local features of DNA sequences as well as important motifs. Based on the conservation law of flow network, the flow-attention mechanism can capture more distal interactions among sequence features with linear time complexity. We compare DeepFormer with the above four kinds of classical methods using the commonly used dataset of 919 chromatin features of nearly 4.9 million noncoding DNA sequences. Experimental results show that DeepFormer significantly outperforms four kinds of methods, with an average recall rate at least 7.058% higher than other methods. Furthermore, we confirmed the effectiveness of DeepFormer in capturing functional variation using Alzheimer's disease, pathogenic mutations in alpha-thalassemia and modification in CCCTC-binding factor (CTCF) activity. We further predicted the maize chromatin accessibility of five tissues and validated the generalization of DeepFormer. The average recall rate of DeepFormer exceeds the classical methods by at least 1.54%, demonstrating strong robustness.
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Affiliation(s)
- Zhou Yao
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan 430070, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Wenjing Zhang
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Peng Song
- College of Plant Science & Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Yuxue Hu
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Jianxiao Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan 430070, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
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14
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Li Z, Li D, Li Y, Guo X, Yang R. Deciphering the regulatory code of histone modifications in plants. J Genet Genomics 2022; 49:1064-1067. [PMID: 35850435 DOI: 10.1016/j.jgg.2022.07.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/08/2022] [Accepted: 07/10/2022] [Indexed: 12/29/2022]
Affiliation(s)
- Zhaohong Li
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Life Sciences, Institute of Future Agriculture, Northwest Agriculture & Forestry University, Yangling, Shaanxi 712100, China
| | - Dongwei Li
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Life Sciences, Institute of Future Agriculture, Northwest Agriculture & Forestry University, Yangling, Shaanxi 712100, China
| | - Ye Li
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Life Sciences, Institute of Future Agriculture, Northwest Agriculture & Forestry University, Yangling, Shaanxi 712100, China
| | - Xiaoping Guo
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Life Sciences, Institute of Future Agriculture, Northwest Agriculture & Forestry University, Yangling, Shaanxi 712100, China
| | - Ruolin Yang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Life Sciences, Institute of Future Agriculture, Northwest Agriculture & Forestry University, Yangling, Shaanxi 712100, China.
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15
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Ma W, Cui S, Lu Z, Yan X, Cai L, Lu Y, Cai K, Zhou H, Ma R, Zhou S, Wang X. YTH Domain Proteins Play an Essential Role in Rice Growth and Stress Response. PLANTS 2022; 11:plants11172206. [PMID: 36079588 PMCID: PMC9460353 DOI: 10.3390/plants11172206] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/19/2022] [Accepted: 08/22/2022] [Indexed: 11/16/2022]
Abstract
As the most prevalent epi-transcriptional modification, m6A modifications play essential roles in regulating RNA fate. The molecular functions of YTH521-B homology (YTH) domain proteins, the most known READER proteins of m6A modifications, have been well-studied in animals. Although plants contain more YTH domain proteins than other eukaryotes, little is known about their biological importance. In dicot species Arabidopsis thaliana, the YTHDFA clade members ECT2/3/4 and CPSF30-L are well-studied and important for cell proliferation, plant organogenesis, and nitrate transport. More emphasis is needed on the biological functions of plant YTH proteins, especially monocot YTHs. Here we presented a detailed phylogenetic relationship of eukaryotic YTH proteins and clustered plant YTHDFC clade into three subclades. To determine the importance of monocot YTH proteins, YTH knockout mutants and RNAi-induced knockdown plants were constructed and used for phenotyping, transcriptomic analysis, and stress treatments. Knocking out or knocking down OsYTHs led to the downregulation of multicellular organismal regulation genes and resulted in growth defects. In addition, loss-of-function ythdfa mutants led to better salinity tolerance whereas ythdfc mutants were more sensitive to abiotic stress. Overall, our study establishes the functional relevance of rice YTH genes in plant growth regulation and stress response.
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Affiliation(s)
- Weiwei Ma
- Institute of Crop Sciences, Ningbo Academy of Agricultural Sciences, Ningbo 315000, China
| | - Song Cui
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Jiangsu Plant Gene Engineering Research Center, Nanjing Agricultural University, Nanjing 210095, China
| | - Zhenfei Lu
- Institute of Crop Sciences, Ningbo Academy of Agricultural Sciences, Ningbo 315000, China
| | - Xiaofeng Yan
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Jiangsu Plant Gene Engineering Research Center, Nanjing Agricultural University, Nanjing 210095, China
| | - Long Cai
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Jiangsu Plant Gene Engineering Research Center, Nanjing Agricultural University, Nanjing 210095, China
| | - Yongfa Lu
- Institute of Crop Sciences, Ningbo Academy of Agricultural Sciences, Ningbo 315000, China
| | - Kefeng Cai
- Institute of Crop Sciences, Ningbo Academy of Agricultural Sciences, Ningbo 315000, China
| | - Huacheng Zhou
- Institute of Crop Sciences, Ningbo Academy of Agricultural Sciences, Ningbo 315000, China
| | - Rongrong Ma
- Institute of Crop Sciences, Ningbo Academy of Agricultural Sciences, Ningbo 315000, China
| | - Shirong Zhou
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Jiangsu Plant Gene Engineering Research Center, Nanjing Agricultural University, Nanjing 210095, China
- Correspondence: (S.Z.); (X.W.)
| | - Xiaole Wang
- Institute of Crop Sciences, Ningbo Academy of Agricultural Sciences, Ningbo 315000, China
- Correspondence: (S.Z.); (X.W.)
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16
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Pelinski Y, Hidaoui D, Stolz A, Hermetet F, Chelbi R, Diop MK, Chioukh AM, Porteu F, Elvira-Matelot E. NF-κB signaling controls H3K9me3 levels at intronic LINE-1 and hematopoietic stem cell genes in cis. J Exp Med 2022; 219:213343. [PMID: 35802137 PMCID: PMC9274146 DOI: 10.1084/jem.20211356] [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: 06/23/2021] [Revised: 12/23/2021] [Accepted: 06/21/2022] [Indexed: 01/11/2023] Open
Abstract
Ionizing radiations (IR) alter hematopoietic stem cell (HSC) function on the long term, but the mechanisms underlying these effects are still poorly understood. We recently showed that IR induces the derepression of L1Md, the mouse young subfamilies of LINE-1/L1 retroelements. L1 contributes to gene regulatory networks. However, how L1Md are derepressed and impact HSC gene expression are not known. Here, we show that IR triggers genome-wide H3K9me3 decrease that occurs mainly at L1Md. Loss of H3K9me3 at intronic L1Md harboring NF-κB binding sites motifs but not at promoters is associated with the repression of HSC-specific genes. This is correlated with reduced NFKB1 repressor expression. TNF-α treatment rescued all these effects and prevented IR-induced HSC loss of function in vivo. This TNF-α/NF-κB/H3K9me3/L1Md axis might be important to maintain HSCs while allowing expression of immune genes during myeloid regeneration or damage-induced bone marrow ablation.
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Affiliation(s)
- Yanis Pelinski
- INSERM UMR1287, Gustave Roussy, Villejuif, France,Université Paris-Saclay, Gif-sur-Yvette, France
| | - Donia Hidaoui
- INSERM UMR1287, Gustave Roussy, Villejuif, France,Université Paris-Saclay, Gif-sur-Yvette, France
| | - Anne Stolz
- INSERM UMR1287, Gustave Roussy, Villejuif, France,Université Paris-Saclay, Gif-sur-Yvette, France
| | - François Hermetet
- INSERM UMR1287, Gustave Roussy, Villejuif, France,Université Paris-Saclay, Gif-sur-Yvette, France
| | - Rabie Chelbi
- INSERM UMR1287, Gustave Roussy, Villejuif, France,Université Paris-Saclay, Gif-sur-Yvette, France
| | - M’boyba Khadija Diop
- Université Paris-Saclay, Gif-sur-Yvette, France,Bioinformatics Platform UMS AMMICa INSERM US23/CNRS 3655, Gustave Roussy, Villejuif, France
| | - Amir M. Chioukh
- INSERM UMR1287, Gustave Roussy, Villejuif, France,Université Paris-Saclay, Gif-sur-Yvette, France
| | - Françoise Porteu
- INSERM UMR1287, Gustave Roussy, Villejuif, France,Université Paris-Saclay, Gif-sur-Yvette, France
| | - Emilie Elvira-Matelot
- INSERM UMR1287, Gustave Roussy, Villejuif, France,Université Paris-Saclay, Gif-sur-Yvette, France
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17
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Ferla MP, Pagnamenta AT, Koukouflis L, Taylor JC, Marsden BD. Venus: Elucidating the Impact of Amino Acid Variants on Protein Function Beyond Structure Destabilisation. J Mol Biol 2022; 434:167567. [PMID: 35662467 PMCID: PMC9742853 DOI: 10.1016/j.jmb.2022.167567] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 03/11/2022] [Accepted: 03/22/2022] [Indexed: 12/15/2022]
Abstract
Exploring the functional effect of a non-synonymous coding variant at the protein level requires multiple pieces of information to be interpreted appropriately. This is particularly important when embarking on the study of a potentially pathogenic variant linked to a rare or monogenic disease. Whereas accurate protein stability predictions alone are generally informative, other effects, such as disruption of post-translational modifications or weakened ligand binding, may also contribute to the disease phenotype. Furthermore, consideration of nearby variants that are found in the healthy population may strengthen or refute a given mechanistic hypothesis. Whilst there are several bioinformatics tools available that score a genetic variant in terms of deleteriousness, there is no single tool that assembles multiple effects of a variant on the encoded protein, beyond structural stability, and presents them on the structure for inspection. Venus is a web application which, given a protein substitution, rapidly estimates the predicted effect on protein stability of the variant, flags if the variant affects a post-translational modification site, a predicted linear motif or known annotation, and determines the effect on protein stability of variants which affect nearby residues and have been identified in healthy populations. Venus is built upon Michelanglo and the results can be exported to it, allowing them to be annotated and shared with other researchers. Venus is freely accessible at https://venus.cmd.ox.ac.uk and its source code is openly available at https://github.com/CMD-Oxford/Michelanglo-and-Venus.
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Affiliation(s)
- Matteo P Ferla
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK; Oxford NIHR Biomedical Research Centre, Oxford, UK.
| | - Alistair T Pagnamenta
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK; Oxford NIHR Biomedical Research Centre, Oxford, UK. https://twitter.com/@alistairp2011
| | - Leonidas Koukouflis
- Centre for Medicines Discovery, University of Oxford, Old Road Campus Research Building, Oxford OX3 7DQ, UK
| | - Jenny C Taylor
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK; Oxford NIHR Biomedical Research Centre, Oxford, UK
| | - Brian D Marsden
- Centre for Medicines Discovery, University of Oxford, Old Road Campus Research Building, Oxford OX3 7DQ, UK; Kennedy Institute of Rheumatology, University of Oxford, Oxford OX3 7FY, UK. https://twitter.com/@bmarsden19
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18
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Hesami M, Alizadeh M, Jones AMP, Torkamaneh D. Machine learning: its challenges and opportunities in plant system biology. Appl Microbiol Biotechnol 2022; 106:3507-3530. [PMID: 35575915 DOI: 10.1007/s00253-022-11963-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 03/14/2022] [Accepted: 05/07/2022] [Indexed: 12/25/2022]
Abstract
Sequencing technologies are evolving at a rapid pace, enabling the generation of massive amounts of data in multiple dimensions (e.g., genomics, epigenomics, transcriptomic, metabolomics, proteomics, and single-cell omics) in plants. To provide comprehensive insights into the complexity of plant biological systems, it is important to integrate different omics datasets. Although recent advances in computational analytical pipelines have enabled efficient and high-quality exploration and exploitation of single omics data, the integration of multidimensional, heterogenous, and large datasets (i.e., multi-omics) remains a challenge. In this regard, machine learning (ML) offers promising approaches to integrate large datasets and to recognize fine-grained patterns and relationships. Nevertheless, they require rigorous optimizations to process multi-omics-derived datasets. In this review, we discuss the main concepts of machine learning as well as the key challenges and solutions related to the big data derived from plant system biology. We also provide in-depth insight into the principles of data integration using ML, as well as challenges and opportunities in different contexts including multi-omics, single-cell omics, protein function, and protein-protein interaction. KEY POINTS: • The key challenges and solutions related to the big data derived from plant system biology have been highlighted. • Different methods of data integration have been discussed. • Challenges and opportunities of the application of machine learning in plant system biology have been highlighted and discussed.
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Affiliation(s)
- Mohsen Hesami
- Department of Plant Agriculture, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | - Milad Alizadeh
- Department of Botany, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | | | - Davoud Torkamaneh
- Département de Phytologie, Université Laval, Québec City, QC, G1V 0A6, Canada. .,Institut de Biologie Intégrative Et Des Systèmes (IBIS), Université Laval, Québec City, QC, G1V 0A6, Canada.
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19
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Lai B, Qian S, Zhang H, Zhang S, Kozlova A, Duan J, Xu J, He X. Annotating functional effects of non-coding variants in neuropsychiatric cell types by deep transfer learning. PLoS Comput Biol 2022; 18:e1010011. [PMID: 35576194 PMCID: PMC9135341 DOI: 10.1371/journal.pcbi.1010011] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 05/26/2022] [Accepted: 03/11/2022] [Indexed: 12/02/2022] Open
Abstract
Genomewide association studies (GWAS) have identified a large number of loci associated with neuropsychiatric traits, however, understanding the molecular mechanisms underlying these loci remains difficult. To help prioritize causal variants and interpret their functions, computational methods have been developed to predict regulatory effects of non-coding variants. An emerging approach to variant annotation is deep learning models that predict regulatory functions from DNA sequences alone. While such models have been trained on large publicly available dataset such as ENCODE, neuropsychiatric trait-related cell types are under-represented in these datasets, thus there is an urgent need of better tools and resources to annotate variant functions in such cellular contexts. To fill this gap, we collected a large collection of neurodevelopment-related cell/tissue types, and trained deep Convolutional Neural Networks (ResNet) using such data. Furthermore, our model, called MetaChrom, borrows information from public epigenomic consortium to improve the accuracy via transfer learning. We show that MetaChrom is substantially better in predicting experimentally determined chromatin accessibility variants than popular variant annotation tools such as CADD and delta-SVM. By combining GWAS data with MetaChrom predictions, we prioritized 31 SNPs for Schizophrenia, suggesting potential risk genes and the biological contexts where they act. In summary, MetaChrom provides functional annotations of any DNA variants in the neuro-development context and the general method of MetaChrom can also be extended to other disease-related cell or tissue types.
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Affiliation(s)
- Boqiao Lai
- Toyota Technological Institute at Chicago, Chicago, Illinois, United States of America
| | - Sheng Qian
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
| | - Hanwei Zhang
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, Illinois, United States of America
| | - Siwei Zhang
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, Illinois, United States of America
| | - Alena Kozlova
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, Illinois, United States of America
| | - Jubao Duan
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, Illinois, United States of America
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, Illinois, United States of America
| | - Jinbo Xu
- Toyota Technological Institute at Chicago, Chicago, Illinois, United States of America
| | - Xin He
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
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20
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Li R, Li L, Xu Y, Yang J. Machine learning meets omics: applications and perspectives. Brief Bioinform 2021; 23:6425809. [PMID: 34791021 DOI: 10.1093/bib/bbab460] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 09/29/2021] [Accepted: 10/07/2021] [Indexed: 02/07/2023] Open
Abstract
The innovation of biotechnologies has allowed the accumulation of omics data at an alarming rate, thus introducing the era of 'big data'. Extracting inherent valuable knowledge from various omics data remains a daunting problem in bioinformatics. Better solutions often need some kind of more innovative methods for efficient handlings and effective results. Recent advancements in integrated analysis and computational modeling of multi-omics data helped address such needs in an increasingly harmonious manner. The development and application of machine learning have largely advanced our insights into biology and biomedicine and greatly promoted the development of therapeutic strategies, especially for precision medicine. Here, we propose a comprehensive survey and discussion on what happened, is happening and will happen when machine learning meets omics. Specifically, we describe how artificial intelligence can be applied to omics studies and review recent advancements at the interface between machine learning and the ever-widest range of omics including genomics, transcriptomics, proteomics, metabolomics, radiomics, as well as those at the single-cell resolution. We also discuss and provide a synthesis of ideas, new insights, current challenges and perspectives of machine learning in omics.
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Affiliation(s)
- Rufeng Li
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi'an Jiaotong University Health Science Center, Xi'an 710061, P. R. China
| | - Lixin Li
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi'an Jiaotong University Health Science Center, Xi'an 710061, P. R. China
| | - Yungang Xu
- School of Electronics and Information, Northwestern Polytechnical University, Xi'an, 710129, China
| | - Juan Yang
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi'an Jiaotong University Health Science Center, Xi'an 710061, P. R. China.,Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education of China, Xi'an 710061, P. R. China
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21
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Huang K, Xiao C, Glass LM, Critchlow CW, Gibson G, Sun J. Machine learning applications for therapeutic tasks with genomics data. PATTERNS (NEW YORK, N.Y.) 2021; 2:100328. [PMID: 34693370 PMCID: PMC8515011 DOI: 10.1016/j.patter.2021.100328] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Thanks to the increasing availability of genomics and other biomedical data, many machine learning algorithms have been proposed for a wide range of therapeutic discovery and development tasks. In this survey, we review the literature on machine learning applications for genomics through the lens of therapeutic development. We investigate the interplay among genomics, compounds, proteins, electronic health records, cellular images, and clinical texts. We identify 22 machine learning in genomics applications that span the whole therapeutics pipeline, from discovering novel targets, personalizing medicine, developing gene-editing tools, all the way to facilitating clinical trials and post-market studies. We also pinpoint seven key challenges in this field with potentials for expansion and impact. This survey examines recent research at the intersection of machine learning, genomics, and therapeutic development.
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Affiliation(s)
- Kexin Huang
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Cao Xiao
- Amplitude, San Francisco, CA 94105, USA
| | - Lucas M. Glass
- Analytics Center of Excellence, IQVIA, Cambridge, MA 02139, USA
| | | | - Greg Gibson
- Center for Integrative Genomics, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Jimeng Sun
- Computer Science Department and Carle's Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL 61820, USA
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22
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Wei S, Tao J, Xu J, Chen X, Wang Z, Zhang N, Zuo L, Jia Z, Chen H, Sun H, Yan Y, Zhang M, Lv H, Kong F, Duan L, Ma Y, Liao M, Xu L, Feng R, Liu G, Project TEWAS, Jiang Y. Ten Years of EWAS. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2021; 8:e2100727. [PMID: 34382344 PMCID: PMC8529436 DOI: 10.1002/advs.202100727] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/11/2021] [Indexed: 06/13/2023]
Abstract
Epigenome-wide association study (EWAS) has been applied to analyze DNA methylation variation in complex diseases for a decade, and epigenome as a research target has gradually become a hot topic of current studies. The DNA methylation microarrays, next-generation, and third-generation sequencing technologies have prepared a high-quality platform for EWAS. Here, the progress of EWAS research is reviewed, its contributions to clinical applications, and mainly describe the achievements of four typical diseases. Finally, the challenges encountered by EWAS and make bold predictions for its future development are presented.
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Affiliation(s)
- Siyu Wei
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
- The EWAS ProjectHarbinChina
| | - Junxian Tao
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
- The EWAS ProjectHarbinChina
| | - Jing Xu
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
- The EWAS ProjectHarbinChina
| | - Xingyu Chen
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Zhaoyang Wang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Nan Zhang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Lijiao Zuo
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Zhe Jia
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Haiyan Chen
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Hongmei Sun
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Yubo Yan
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Mingming Zhang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Hongchao Lv
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Fanwu Kong
- The EWAS ProjectHarbinChina
- Department of NephrologyThe Second Affiliated HospitalHarbin Medical UniversityHarbin150001China
| | - Lian Duan
- The EWAS ProjectHarbinChina
- The First Affiliated Hospital of Wenzhou Medical UniversityWenzhou325000China
| | - Ye Ma
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
- The EWAS ProjectHarbinChina
| | - Mingzhi Liao
- The EWAS ProjectHarbinChina
- College of Life SciencesNorthwest A&F UniversityYanglingShanxi712100China
| | - Liangde Xu
- The EWAS ProjectHarbinChina
- School of Biomedical EngineeringWenzhou Medical UniversityWenzhou325035China
| | - Rennan Feng
- The EWAS ProjectHarbinChina
- Department of Nutrition and Food HygienePublic Health CollegeHarbin Medical UniversityHarbin150081China
| | - Guiyou Liu
- The EWAS ProjectHarbinChina
- Beijing Institute for Brain DisordersCapital Medical UniversityBeijing100069China
| | | | - Yongshuai Jiang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
- The EWAS ProjectHarbinChina
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23
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Ni P, Su Z. Accurate prediction of cis-regulatory modules reveals a prevalent regulatory genome of humans. NAR Genom Bioinform 2021; 3:lqab052. [PMID: 34159315 PMCID: PMC8210889 DOI: 10.1093/nargab/lqab052] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 05/01/2021] [Accepted: 06/14/2021] [Indexed: 02/07/2023] Open
Abstract
cis-regulatory modules(CRMs) formed by clusters of transcription factor (TF) binding sites (TFBSs) are as important as coding sequences in specifying phenotypes of humans. It is essential to categorize all CRMs and constituent TFBSs in the genome. In contrast to most existing methods that predict CRMs in specific cell types using epigenetic marks, we predict a largely cell type agonistic but more comprehensive map of CRMs and constituent TFBSs in the gnome by integrating all available TF ChIP-seq datasets. Our method is able to partition 77.47% of genome regions covered by available 6092 datasets into a CRM candidate (CRMC) set (56.84%) and a non-CRMC set (43.16%). Intriguingly, the predicted CRMCs are under strong evolutionary constraints, while the non-CRMCs are largely selectively neutral, strongly suggesting that the CRMCs are likely cis-regulatory, while the non-CRMCs are not. Our predicted CRMs are under stronger evolutionary constraints than three state-of-the-art predictions (GeneHancer, EnhancerAtlas and ENCODE phase 3) and substantially outperform them for recalling VISTA enhancers and non-coding ClinVar variants. We estimated that the human genome might encode about 1.47M CRMs and 68M TFBSs, comprising about 55% and 22% of the genome, respectively; for both of which, we predicted 80%. Therefore, the cis-regulatory genome appears to be more prevalent than originally thought.
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Affiliation(s)
- Pengyu Ni
- Department of Bioinformatics and Genomics, the University of North Carolina at Charlotte, 9201 University City Boulevard, Charlotte, NC 28223, USA
| | - Zhengchang Su
- Department of Bioinformatics and Genomics, the University of North Carolina at Charlotte, 9201 University City Boulevard, Charlotte, NC 28223, USA
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24
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Zrimec J, Buric F, Kokina M, Garcia V, Zelezniak A. Learning the Regulatory Code of Gene Expression. Front Mol Biosci 2021; 8:673363. [PMID: 34179082 PMCID: PMC8223075 DOI: 10.3389/fmolb.2021.673363] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 05/24/2021] [Indexed: 11/13/2022] Open
Abstract
Data-driven machine learning is the method of choice for predicting molecular phenotypes from nucleotide sequence, modeling gene expression events including protein-DNA binding, chromatin states as well as mRNA and protein levels. Deep neural networks automatically learn informative sequence representations and interpreting them enables us to improve our understanding of the regulatory code governing gene expression. Here, we review the latest developments that apply shallow or deep learning to quantify molecular phenotypes and decode the cis-regulatory grammar from prokaryotic and eukaryotic sequencing data. Our approach is to build from the ground up, first focusing on the initiating protein-DNA interactions, then specific coding and non-coding regions, and finally on advances that combine multiple parts of the gene and mRNA regulatory structures, achieving unprecedented performance. We thus provide a quantitative view of gene expression regulation from nucleotide sequence, concluding with an information-centric overview of the central dogma of molecular biology.
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Affiliation(s)
- Jan Zrimec
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Filip Buric
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Mariia Kokina
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Victor Garcia
- School of Life Sciences and Facility Management, Zurich University of Applied Sciences, Wädenswil, Switzerland
| | - Aleksej Zelezniak
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Science for Life Laboratory, Stockholm, Sweden
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25
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Salimi D, Moeini A. Incorporating K-mers Highly Correlated to Epigenetic Modifications for Bayesian Inference of Gene Interactions. Curr Bioinform 2021. [DOI: 10.2174/1574893615999200728193621] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Objective::
A gene interaction network, along with its related biological features, has an
important role in computational biology. Bayesian network, as an efficient model, based on
probabilistic concepts is able to exploit known and novel biological casual relationships between
genes. The success of Bayesian networks in predicting the relationships greatly depends on
selecting priors.
Methods::
K-mers have been applied as the prominent features to uncover the similarity between
genes in a specific pathway, suggesting that this feature can be applied to study genes
dependencies. In this study, we propose k-mers (4,5 and 6-mers) highly correlated with epigenetic
modifications, including 17 modifications, as a new prior for Bayesian inference in the gene
interaction network.
Result::
Employing this model on a network of 23 human genes and on a network based on 27
genes related to yeast resulted in F-measure improvements in different biological networks.
Conclusion::
The improvements in the best case are 12%, 36%, and 10% in the pathway, coexpression,
and physical interaction, respectively.
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Affiliation(s)
- Dariush Salimi
- Department of Animal Science, Faculty of Agriculture, University of Zanjan, Zanjan, Iran
| | - Ali Moeini
- Department of Algorithms and Computation, Faculty of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran
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26
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Fischer J, Ardakani FB, Kattler K, Walter J, Schulz MH. CpG content-dependent associations between transcription factors and histone modifications. PLoS One 2021; 16:e0249985. [PMID: 33857234 PMCID: PMC8049299 DOI: 10.1371/journal.pone.0249985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 03/30/2021] [Indexed: 11/18/2022] Open
Abstract
Understanding the factors that underlie the epigenetic regulation of genes is crucial to understand the gene regulatory machinery as a whole. Several experimental and computational studies examined the relationship between different factors involved. Here we investigate the relationship between transcription factors (TFs) and histone modifications (HMs), based on ChIP-seq data in cell lines. As it was shown that gene regulation by TFs differs depending on the CpG class of a promoter, we study the impact of the CpG content in promoters on the associations between TFs and HMs. We suggest an approach based on sparse linear regression models to infer associations between TFs and HMs with respect to CpG content. A study of the partial correlation of HMs for the two classes of high and low CpG content reveals possible CpG dependence and potential candidates for confounding factors in our models. We show that the models are accurate, inferred associations reflect known biological relationships, and we give new insight into associations with respect to CpG content. Moreover, analysis of a ChIP-seq dataset in HepG2 cells of the HM H3K122ac, an HM about little is known, reveals novel TF associations and supports a previously established link to active transcription.
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Affiliation(s)
- Jonas Fischer
- Max Planck Institute for Informatics, Databases and Information Systems, Saarbrücken, Germany
- Cluster of Excellence for Multimodal Computing and Interaction, High Throughput Genomics and Systems Biology, Saarbrücken, Germany
- * E-mail:
| | - Fatemeh Behjati Ardakani
- Max Planck Institute for Informatics, Computational Biology and Applied Algorithmics, Saarbrücken, Germany
- Cluster of Excellence for Multimodal Computing and Interaction, High Throughput Genomics and Systems Biology, Saarbrücken, Germany
- Institute of Cardiovascular Regeneration, Goethe University, Frankfurt, Germany
| | - Kathrin Kattler
- Department of Genetics, University of Saarland, Saarbrücken, Germany
| | - Jörn Walter
- Department of Genetics, University of Saarland, Saarbrücken, Germany
| | - Marcel H. Schulz
- Max Planck Institute for Informatics, Computational Biology and Applied Algorithmics, Saarbrücken, Germany
- Cluster of Excellence for Multimodal Computing and Interaction, High Throughput Genomics and Systems Biology, Saarbrücken, Germany
- Institute of Cardiovascular Regeneration, Goethe University, Frankfurt, Germany
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27
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Santoni D. The impact of flanking sequence features on DNA CpG methylation. Comput Biol Chem 2021; 92:107480. [PMID: 33826970 DOI: 10.1016/j.compbiolchem.2021.107480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Revised: 01/18/2021] [Accepted: 03/24/2021] [Indexed: 10/21/2022]
Abstract
Epigenetics and DNA methylation play a pivotal role in many processes of the cell and we often observe that an aberrant methylation pattern characterizes pathologies. In this work we investigate the role that the flanking sequences of CGs play in the methylation process in human. We built four different CG datasets: methylated, unmethylated, and two randomly extracted ones. We evaluated features associated to the flanking sequences of those CG sets, for different size around the CG, through five measures accounting for different aspects of sequence composition complexity and structure. The analysis performed through those measures revealed evident different behaviors between methylated and unmethylated probe sets. Major differences were observed for GC content and CG dinucleotide frequency in a window size of 300-400 bp and for CG self-attraction in 3K bp. It is remarkable as the effect of methylated CG lasts much more than expected far from the CG.
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Affiliation(s)
- Daniele Santoni
- Institute for System Analysis and Computer Science "Antonio Ruberti", National Research Council of Italy, Via dei Taurini 19, 00185 Rome, Italy.
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28
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Liang L, Wang Z, Qu L, Huang W, Guo S, Guan X, Zhang W, Sun F, Yuan H, Zou H, Liu H, Yu Z. Single-molecule multiplexed profiling of protein-DNA complexes using magnetic tweezers. J Biol Chem 2021; 296:100327. [PMID: 33493518 PMCID: PMC7949110 DOI: 10.1016/j.jbc.2021.100327] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 01/10/2021] [Accepted: 01/21/2021] [Indexed: 01/14/2023] Open
Abstract
Epigenetics, such as the dynamic interplay between DNA methylation and demethylation, play diverse roles in critical cellular events. Enzymatic activity at CpG sites, where cytosines are methylated or demethylated, is known to be influenced by the density of CpGs, methylation states, and the flanking sequences of a CpG site. However, how the relevant enzymes are recruited to and recognize their target DNA is less clear. Moreover, although DNA-binding epigenetic enzymes are ideal targets for therapeutic intervention, these targets have been rarely exploited. Single-molecule techniques offer excellent capabilities to probe site-specific protein-DNA interactions and unravel the dynamics. Here, we develop a single-molecule approach that allows multiplexed profiling of protein-DNA complexes using magnetic tweezers. When a DNA hairpin with multiple binding sites is unzipping, strand separation pauses at the positions bound by a protein. We can thus measure site-specific binding probabilities and dissociation time directly. Taking the TET1 CXXC domain as an example, we show that TET1 CXXC binds multiple CpG motifs with various flanking nucleotides or different methylation patterns in an AT-rich DNA. We are able to establish for the first time, at nanometer resolution, that TET1 CXXC prefers G/C flanked CpG motif over C/G, A/T, or T/A flanked ones. CpG methylation strengthens TET1 CXXC recruitment but has little effect on dissociation time. Finally, we demonstrate that TET1 CXXC can distinguish five CpG clusters in a CpG island with crowded binding motifs. We anticipate that the feasibility of single-molecule multiplexed profiling assays will contribute to the understanding of protein-DNA interactions.
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Affiliation(s)
- Lin Liang
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy, Nankai University, Tianjin, China
| | - Zeyu Wang
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy, Nankai University, Tianjin, China
| | - Lihua Qu
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy, Nankai University, Tianjin, China
| | - Wei Huang
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy, Nankai University, Tianjin, China
| | - Shuang Guo
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy, Nankai University, Tianjin, China
| | - Xiangchen Guan
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy, Nankai University, Tianjin, China
| | - Wei Zhang
- Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Institute of Modern Optics, College of Electronic Information and Optical Engineering, Nankai University, Tianjin, China
| | - Fuping Sun
- Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Institute of Modern Optics, College of Electronic Information and Optical Engineering, Nankai University, Tianjin, China
| | - Hongrui Yuan
- Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Institute of Modern Optics, College of Electronic Information and Optical Engineering, Nankai University, Tianjin, China
| | - Huiru Zou
- Central Laboratory of Tianjin Stomatological Hospital, The Affiliated Stomatological Hospital of Nankai University, Tianjin, China
| | - Haitao Liu
- Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Institute of Modern Optics, College of Electronic Information and Optical Engineering, Nankai University, Tianjin, China
| | - Zhongbo Yu
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy, Nankai University, Tianjin, China.
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29
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Wang M, Ngo V, Wang W. Deciphering the genetic code of DNA methylation. Brief Bioinform 2021; 22:6082840. [PMID: 33432324 DOI: 10.1093/bib/bbaa424] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 12/03/2020] [Accepted: 12/22/2020] [Indexed: 12/17/2022] Open
Abstract
DNA methylation plays crucial roles in many biological processes and abnormal DNA methylation patterns are often observed in diseases. Recent studies have shed light on cis-acting DNA elements that regulate locus-specific DNA methylation, which involves transcription factors, histone modification and DNA secondary structures. In addition, several recent studies have surveyed DNA motifs that regulate DNA methylation and suggest potential applications in diagnosis and prognosis. Here, we discuss the current biological foundation for the cis-acting genetic code that regulates DNA methylation. We review the computational models that predict DNA methylation with genetic features and discuss the biological insights revealed from these models. We also provide an in-depth discussion on how to leverage such knowledge in clinical applications, particularly in the context of liquid biopsy for early cancer diagnosis and treatment.
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Affiliation(s)
- Mengchi Wang
- Bioinformatics and Systems Biology at University of California, USA
| | - Vu Ngo
- Bioinformatics and Systems Biology at University of California, USA
| | - Wei Wang
- Bioinformatics and Systems Biology, Department of Chemistry and Biochemistry, and Department of Cellular and Molecular Medicine at University of California, USA
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30
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Powell SK, O'Shea C, Brennand KJ, Akbarian S. Parsing the Functional Impact of Noncoding Genetic Variants in the Brain Epigenome. Biol Psychiatry 2021; 89:65-75. [PMID: 33131715 PMCID: PMC7718420 DOI: 10.1016/j.biopsych.2020.06.033] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 05/29/2020] [Accepted: 06/01/2020] [Indexed: 12/31/2022]
Abstract
The heritability of common psychiatric disorders has motivated global efforts to identify risk-associated genetic variants and elucidate molecular pathways connecting DNA sequence to disease-associated brain dysfunction. The overrepresentation of risk variants among gene regulatory loci instead of protein-coding loci, however, poses a unique challenge in discerning which among the many thousands of variants identified contribute functionally to disease etiology. Defined broadly, psychiatric epigenomics seeks to understand the effects of disease-associated genetic variation on functional readouts of chromatin in an effort to prioritize variants in terms of their impact on gene expression in the brain. Here, we provide an overview of epigenomic mapping in the human brain and highlight findings of particular relevance to psychiatric genetics. Computational methods, including convolutional neuronal networks, and other machine learning approaches hold great promise for elucidating the functional impact of both common and rare genetic variants, thereby refining the epigenomic architecture of psychiatric disorders and enabling integrative analyses of regulatory noncoding variants in the context of large population-level genome and phenome databases.
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Affiliation(s)
- Samuel K Powell
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Callan O'Shea
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Kristen J Brennand
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Schahram Akbarian
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York.
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31
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Chen L, Capra JA. Learning and interpreting the gene regulatory grammar in a deep learning framework. PLoS Comput Biol 2020; 16:e1008334. [PMID: 33137083 PMCID: PMC7660921 DOI: 10.1371/journal.pcbi.1008334] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 11/12/2020] [Accepted: 09/12/2020] [Indexed: 12/12/2022] Open
Abstract
Deep neural networks (DNNs) have achieved state-of-the-art performance in identifying gene regulatory sequences, but they have provided limited insight into the biology of regulatory elements due to the difficulty of interpreting the complex features they learn. Several models of how combinatorial binding of transcription factors, i.e. the regulatory grammar, drives enhancer activity have been proposed, ranging from the flexible TF billboard model to the stringent enhanceosome model. However, there is limited knowledge of the prevalence of these (or other) sequence architectures across enhancers. Here we perform several hypothesis-driven analyses to explore the ability of DNNs to learn the regulatory grammar of enhancers. We created synthetic datasets based on existing hypotheses about combinatorial transcription factor binding site (TFBS) patterns, including homotypic clusters, heterotypic clusters, and enhanceosomes, from real TF binding motifs from diverse TF families. We then trained deep residual neural networks (ResNets) to model the sequences under a range of scenarios that reflect real-world multi-label regulatory sequence prediction tasks. We developed a gradient-based unsupervised clustering method to extract the patterns learned by the ResNet models. We demonstrated that simulated regulatory grammars are best learned in the penultimate layer of the ResNets, and the proposed method can accurately retrieve the regulatory grammar even when there is heterogeneity in the enhancer categories and a large fraction of TFBS outside of the regulatory grammar. However, we also identify common scenarios where ResNets fail to learn simulated regulatory grammars. Finally, we applied the proposed method to mouse developmental enhancers and were able to identify the components of a known heterotypic TF cluster. Our results provide a framework for interpreting the regulatory rules learned by ResNets, and they demonstrate that the ability and efficiency of ResNets in learning the regulatory grammar depends on the nature of the prediction task.
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Affiliation(s)
- Ling Chen
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, United States of America
| | - John A. Capra
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, United States of America
- Vanderbilt Genetics Institute and Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Department of Computer Science, Vanderbilt University, Nashville, TN, United States of America
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32
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Wang M, Wang D, Zhang K, Ngo V, Fan S, Wang W. Motto: Representing Motifs in Consensus Sequences with Minimum Information Loss. Genetics 2020; 216:353-358. [PMID: 32816922 PMCID: PMC7536857 DOI: 10.1534/genetics.120.303597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 08/17/2020] [Indexed: 11/21/2022] Open
Abstract
Sequence analysis frequently requires intuitive understanding and convenient representation of motifs. Typically, motifs are represented as position weight matrices (PWMs) and visualized using sequence logos. However, in many scenarios, in order to interpret the motif information or search for motif matches, it is compact and sufficient to represent motifs by wildcard-style consensus sequences (such as [GC][AT]GATAAG[GAC]). Based on mutual information theory and Jensen-Shannon divergence, we propose a mathematical framework to minimize the information loss in converting PWMs to consensus sequences. We name this representation as sequence Motto and have implemented an efficient algorithm with flexible options for converting motif PWMs into Motto from nucleotides, amino acids, and customized characters. We show that this representation provides a simple and efficient way to identify the binding sites of 1156 common transcription factors (TFs) in the human genome. The effectiveness of the method was benchmarked by comparing sequence matches found by Motto with PWM scanning results found by FIMO. On average, our method achieves a 0.81 area under the precision-recall curve, significantly (P-value < 0.01) outperforming all existing methods, including maximal positional weight, Cavener's method, and minimal mean square error. We believe this representation provides a distilled summary of a motif, as well as the statistical justification.
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Affiliation(s)
- Mengchi Wang
- Bioinformatics and Systems Biology, University of California at San Diego, La Jolla, California 92093
| | - David Wang
- Department of Chemistry and Biochemistry, University of California at San Diego, La Jolla, California 92093
| | - Kai Zhang
- Bioinformatics and Systems Biology, University of California at San Diego, La Jolla, California 92093
| | - Vu Ngo
- Bioinformatics and Systems Biology, University of California at San Diego, La Jolla, California 92093
| | - Shicai Fan
- Department of Chemistry and Biochemistry, University of California at San Diego, La Jolla, California 92093
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China 610054
| | - Wei Wang
- Bioinformatics and Systems Biology, University of California at San Diego, La Jolla, California 92093
- Department of Chemistry and Biochemistry, University of California at San Diego, La Jolla, California 92093
- Department of Cellular and Molecular Medicine, University of California at San Diego, La Jolla, California 92093
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33
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Evaluating the informativeness of deep learning annotations for human complex diseases. Nat Commun 2020; 11:4703. [PMID: 32943643 PMCID: PMC7499261 DOI: 10.1038/s41467-020-18515-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 08/25/2020] [Indexed: 12/12/2022] Open
Abstract
Deep learning models have shown great promise in predicting regulatory effects from DNA sequence, but their informativeness for human complex diseases is not fully understood. Here, we evaluate genome-wide SNP annotations from two previous deep learning models, DeepSEA and Basenji, by applying stratified LD score regression to 41 diseases and traits (average N = 320K), conditioning on a broad set of coding, conserved and regulatory annotations. We aggregated annotations across all (respectively blood or brain) tissues/cell-types in meta-analyses across all (respectively 11 blood or 8 brain) traits. The annotations were highly enriched for disease heritability, but produced only limited conditionally significant results: non-tissue-specific and brain-specific Basenji-H3K4me3 for all traits and brain traits respectively. We conclude that deep learning models have yet to achieve their full potential to provide considerable unique information for complex disease, and that their conditional informativeness for disease cannot be inferred from their accuracy in predicting regulatory annotations. Deep learning models have shown great promise in predicting regulatory effects from DNA sequence. Here the authors evaluate sequence-based epigenomic deep learning models and conclude that these models are not yet ready to inform our knowledge of human disease.
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34
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Fu L, Peng Q, Chai L. Predicting DNA Methylation States with Hybrid Information Based Deep-Learning Model. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1721-1728. [PMID: 30951477 DOI: 10.1109/tcbb.2019.2909237] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
DNA methylation plays an important role in the regulation of some biological processes. Up to now, with the development of machine learning models, there are several sequence-based deep learning models designed to predict DNA methylation states, which gain better performance than traditional methods like random forest and SVM. However, convolutional network based deep learning models that use one-hot encoding DNA sequence as input may discover limited information and cause unsatisfactory prediction performance, so more data and model structures of diverse angles should be considered. In this work, we proposed a hybrid sequence-based deep learning model with both MeDIP-seq data and Histone information to predict DNA methylated CpG states (MHCpG). We combined both MeDIP-seq data and histone modification data with sequence information and implemented convolutional network to discover sequence patterns. In addition, we used statistical data gained from previous three input data and adopted a 3-layer feedforward neuron network to extract more high-level features. We compared our method with traditional predicting methods using random forest and other previous methods like CpGenie and DeepCpG, the result showed that MHCpG exceeded the other approaches and gained more satisfactory performance.
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Li J, Pu Y, Tang J, Zou Q, Guo F. DeepATT: a hybrid category attention neural network for identifying functional effects of DNA sequences. Brief Bioinform 2020; 22:5890498. [PMID: 32778871 DOI: 10.1093/bib/bbaa159] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 06/05/2020] [Accepted: 06/19/2020] [Indexed: 12/23/2022] Open
Abstract
Quantifying DNA properties is a challenging task in the broad field of human genomics. Since the vast majority of non-coding DNA is still poorly understood in terms of function, this task is particularly important to have enormous benefit for biology research. Various DNA sequences should have a great variety of representations, and specific functions may focus on corresponding features in the front part of learning model. Currently, however, for multi-class prediction of non-coding DNA regulatory functions, most powerful predictive models do not have appropriate feature extraction and selection approaches for specific functional effects, so that it is difficult to gain a better insight into their internal correlations. Hence, we design a category attention layer and category dense layer in order to select efficient features and distinguish different DNA functions. In this study, we propose a hybrid deep neural network method, called DeepATT, for identifying $919$ regulatory functions on nearly $5$ million DNA sequences. Our model has four built-in neural network constructions: convolution layer captures regulatory motifs, recurrent layer captures a regulatory grammar, category attention layer selects corresponding valid features for different functions and category dense layer classifies predictive labels with selected features of regulatory functions. Importantly, we compare our novel method, DeepATT, with existing outstanding prediction tools, DeepSEA and DanQ. DeepATT performs significantly better than other existing tools for identifying DNA functions, at least increasing $1.6\%$ area under precision recall. Furthermore, we can mine the important correlation among different DNA functions according to the category attention module. Moreover, our novel model can greatly reduce the number of parameters by the mechanism of attention and locally connected, on the basis of ensuring accuracy.
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Affiliation(s)
| | | | | | - Quan Zou
- University of Electronic Science and Technology of China
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36
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Xue H, Wei Z, Chen K, Tang Y, Wu X, Su J, Meng J. Prediction of RNA Methylation Status From Gene Expression Data Using Classification and Regression Methods. Evol Bioinform Online 2020; 16:1176934320915707. [PMID: 32733123 PMCID: PMC7372605 DOI: 10.1177/1176934320915707] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Accepted: 02/28/2020] [Indexed: 12/18/2022] Open
Abstract
RNA N6-methyladenosine (m6A) has emerged
as an important epigenetic modification for its role in regulating the
stability, structure, processing, and translation of RNA. Instability of
m6A homeostasis may result in flaws in stem cell regulation,
decrease in fertility, and risk of cancer. To this day, experimental detection
and quantification of RNA m6A modification are still time-consuming
and labor-intensive. There is only a limited number of epitranscriptome samples
in existing databases, and a matched RNA methylation profile is not often
available for a biological problem of interests. As gene expression data are
usually readily available for most biological problems, it could be appealing if
we can estimate the RNA methylation status from gene expression data using
in silico methods. In this study, we explored the
possibility of computational prediction of RNA methylation status from gene
expression data using classification and regression methods based on mouse RNA
methylation data collected from 73 experimental conditions. Elastic
Net-regularized Logistic Regression (ENLR), Support Vector Machine (SVM), and
Random Forests (RF) were constructed for classification. Both SVM and RF
achieved the best performance with the mean area under the curve (AUC) = 0.84
across samples; SVM had a narrower AUC spread. Gene Site Enrichment Analysis was
conducted on those sites selected by ENLR as predictors to access the biological
significance of the model. Three functional annotation terms were found
statistically significant: phosphoprotein, SRC Homology 3 (SH3) domain, and
endoplasmic reticulum. All 3 terms were found to be closely related to
m6A pathway. For regression analysis, Elastic Net was
implemented, which yielded a mean Pearson correlation coefficient = 0.68 and a
mean Spearman correlation coefficient = 0.64. Our exploratory study suggested
that gene expression data could be used to construct predictors for
m6A methylation status with adequate accuracy. Our work showed
for the first time that RNA methylation status may be predicted from the matched
gene expression data. This finding may facilitate RNA modification research in
various biological contexts when a matched RNA methylation profile is not
available, especially in the very early stage of the study.
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Affiliation(s)
- Hao Xue
- Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China.,Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Zhen Wei
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China.,Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK
| | - Kunqi Chen
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China.,Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK
| | - Yujiao Tang
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China.,Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK
| | - Xiangyu Wu
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China.,Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK
| | - Jionglong Su
- Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Jia Meng
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China.,Institute of Integrative Biology, University of Liverpool, Liverpool, UK
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Li X, Shi L, Wang Y, Zhong J, Zhao X, Teng H, Shi X, Yang H, Ruan S, Li M, Sun ZS, Zhan Q, Mao F. OncoBase: a platform for decoding regulatory somatic mutations in human cancers. Nucleic Acids Res 2020; 47:D1044-D1055. [PMID: 30445567 PMCID: PMC6323961 DOI: 10.1093/nar/gky1139] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Accepted: 11/11/2018] [Indexed: 12/16/2022] Open
Abstract
Whole-exome and whole-genome sequencing have revealed millions of somatic mutations associated with different human cancers, and the vast majority of them are located outside of coding sequences, making it challenging to directly interpret their functional effects. With the rapid advances in high-throughput sequencing technologies, genome-scale long-range chromatin interactions were detected, and distal target genes of regulatory elements were determined using three-dimensional (3D) chromatin looping. Herein, we present OncoBase (http://www.oncobase.biols.ac.cn/), an integrated database for annotating 81 385 242 somatic mutations in 68 cancer types from more than 120 cancer projects by exploring their roles in distal interactions between target genes and regulatory elements. OncoBase integrates local chromatin signatures, 3D chromatin interactions in different cell types and reconstruction of enhancer-target networks using state-of-the-art algorithms. It employs informative visualization tools to display the integrated local and 3D chromatin signatures and effects of somatic mutations on regulatory elements. Enhancer-promoter interactions estimated from chromatin interactions are integrated into a network diffusion system that quantitatively prioritizes somatic mutations and target genes from a large pool. Thus, OncoBase is a useful resource for the functional annotation of regulatory noncoding regions and systematically benchmarking the regulatory effects of embedded noncoding somatic mutations in human carcinogenesis.
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Affiliation(s)
- Xianfeng Li
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Laboratory of Molecular Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China.,Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Leisheng Shi
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yan Wang
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Laboratory of Molecular Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Jianing Zhong
- Key Laboratory of Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases of Ministry of Education, Gannan Medical University, Ganzhou 341000,China
| | - Xiaolu Zhao
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Huajing Teng
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Xiaohui Shi
- Sino-Danish college, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haonan Yang
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Shasha Ruan
- Department of Clinical Oncology, Renmin Hospital of Wuhan University, Wuhan, Hubei 430072, China
| | - MingKun Li
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhong Sheng Sun
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Qimin Zhan
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Laboratory of Molecular Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Fengbiao Mao
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
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Tang J, Wu Z, Tian Y, Yang R. ICGEC: a comparative method for measuring epigenetic conservation of genes via the integrated signal from multiple histone modifications between cell types. BMC Genomics 2020; 21:356. [PMID: 32398001 PMCID: PMC7216622 DOI: 10.1186/s12864-020-6771-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2019] [Accepted: 05/04/2020] [Indexed: 11/17/2022] Open
Abstract
Background Histone post-translational modifications play crucial roles in epigenetic regulation of gene expression and are known to be associated with the phenotypic differences of different cell types. Therefore, it is of fundamental importance to dissect the genes and pathways involved in such a phenotypic variation at the level of epigenetics. However, the existing comparative approaches are largely based on the differences, especially the absolute difference in the levels of individual histone modifications of genes under contrasting conditions. Thus, a method for measuring the overall change in the epigenetic circumstance of each gene underpinned by multiple types of histone modifications between cell types is lacking. Results To address this challenge, we developed ICGEC, a new method for estimating the degree of epigenetic conservation of genes between two cell lines. Different from existing comparative methods, ICGEC provides a reliable score for measuring the relative change in the epigenetic context of corresponding gene between two conditions and simultaneously produces a score for each histone mark. The application of ICGEC to the human embryonic stem cell line H1 and four H1-derived cell lines with available epigenomic data for the same 16 types of histone modifications indicated high robustness and reliability of ICGEC. Furthermore, the analysis of the epigenetically dynamic and conserved genes which were defined based on the ICGEC output results demonstrated that ICGEC can deepen our understanding of the biological processes of cell differentiation to overcome the limitations of traditional expression analysis. Specifically, the ICGEC-derived differentiation-direction-specific genes were shown to have putative functions that are well-matched with cell identity. Additionally, H3K79me1 and H3K27ac were found to be the main histone marks accounting for whether an epigenetically dynamic gene was differentially expressed between two cell lines. Conclusions The use of ICGEC creates a convenient and robust way to measure the overall epigenetic conservation of individual genes and marks between two conditions. Thus, it provides a basis for exploring the epigenotype-phenotype relationship. ICGEC can be deemed a state-of-the-art method tailored for comparative epigenomic analysis of changes in cell dynamics.
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Affiliation(s)
- Jing Tang
- College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Zefeng Wu
- College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Yuhan Tian
- College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Ruolin Yang
- College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China.
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Cao N, Yu Y, Zhu H, Chen M, Chen P, Zhuo M, Mao Y, Li L, Zhao Q, Wu M, Ye M. SETDB1 promotes the progression of colorectal cancer via epigenetically silencing p21 expression. Cell Death Dis 2020; 11:351. [PMID: 32393761 PMCID: PMC7214465 DOI: 10.1038/s41419-020-2561-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 04/22/2020] [Accepted: 04/23/2020] [Indexed: 02/06/2023]
Abstract
SETDB1, a histone H3K9 methyltransferase, has been reported to be upregulated in a variety of tumors and promotes cancer development. However, the exact pathogenesis of SETDB1 in human colorectal cancer (CRC) is hitherto unknown. Here, we showed that SETDB1 expression was highly amplified in CRC. Functionally, SETDB1 downregulation in SW480 and HCT116 cells reduced cell proliferation, migration, invasion, and increased CRC cells apoptosis. In contrast, SETDB1 overexpression promoted CRC cells proliferation, migration, and invasion. High expression of SETDB1 was associated with a more aggressive phenotype in vitro. Flow cytometry showed that cell cycle was arrested in G1 phase after SETDB1 silencing. Furthermore, depletion of SETDB1 in vivo suppressed CRC cells proliferation. Mechanistically, p21 was identified as the target of SETDB1. After transfected with siSETDB1, expression of p21 was distinctly increased. In contrast, expression of p21 was significantly decreased after overexpression SETDB1. We also showed that SETDB1 could be involved in the regulation of epithelial–mesenchymal transition (EMT) in HCT116 cells. Moreover, we confirmed that SETDB1 could regulate the activity of p21 promoter by dual-luciferase repoter assay, and proved that SETDB1 could bind to the promoter of p21 and regulate its H3K9me3 enrichment level by ChIP-PCR experiment. Finally, we verified that silencing of SETDB1 inhibited CRC tumorigenesis in vivo. In conclusion, our results indicate that SETDB1 is a major driver of CRC development and might provide a new therapeutic target for the clinical treatment of CRC.
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Affiliation(s)
- Nan Cao
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, 430071, China.,Hubei Clinical Center and Key Lab of Intestinal and Colorectal Diseases, Wuhan, Hubei, 430071, China
| | - Yali Yu
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, 430071, China.,Hubei Clinical Center and Key Lab of Intestinal and Colorectal Diseases, Wuhan, Hubei, 430071, China
| | - Hua Zhu
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, 430071, China.,Hubei Clinical Center and Key Lab of Intestinal and Colorectal Diseases, Wuhan, Hubei, 430071, China
| | - Meng Chen
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, 430071, China.,Hubei Clinical Center and Key Lab of Intestinal and Colorectal Diseases, Wuhan, Hubei, 430071, China
| | - Ping Chen
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, 430071, China.,Hubei Clinical Center and Key Lab of Intestinal and Colorectal Diseases, Wuhan, Hubei, 430071, China
| | - Mingxing Zhuo
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, 430071, China.,Hubei Clinical Center and Key Lab of Intestinal and Colorectal Diseases, Wuhan, Hubei, 430071, China
| | - Yujuan Mao
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, 430071, China.,Hubei Clinical Center and Key Lab of Intestinal and Colorectal Diseases, Wuhan, Hubei, 430071, China
| | - Lianyun Li
- College of Life Sciences, Wuhan University, Wuhan, Hubei, 430072, China
| | - Qiu Zhao
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, 430071, China.,Hubei Clinical Center and Key Lab of Intestinal and Colorectal Diseases, Wuhan, Hubei, 430071, China
| | - Min Wu
- College of Life Sciences, Wuhan University, Wuhan, Hubei, 430072, China
| | - Mei Ye
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, 430071, China. .,Hubei Clinical Center and Key Lab of Intestinal and Colorectal Diseases, Wuhan, Hubei, 430071, China.
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Hu S, Huo D, Yu Z, Chen Y, Liu J, Liu L, Wu X, Zhang Y. ncHMR detector: a computational framework to systematically reveal non-classical functions of histone modification regulators. Genome Biol 2020; 21:48. [PMID: 32093739 PMCID: PMC7038559 DOI: 10.1186/s13059-020-01953-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 02/06/2020] [Indexed: 01/02/2023] Open
Abstract
Recently, several non-classical functions of histone modification regulators (HMRs), independent of their known histone modification substrates and products, have been reported to be essential for specific cellular processes. However, there is no framework designed for identifying such functions systematically. Here, we develop ncHMR detector, the first computational framework to predict non-classical functions and cofactors of a given HMR, based on ChIP-seq data mining. We apply ncHMR detector in ChIP-seq data-rich cell types and predict non-classical functions of HMRs. Finally, we experimentally reveal that the predicted non-classical function of CBX7 is biologically significant for the maintenance of pluripotency.
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Affiliation(s)
- Shengen Hu
- Institute for Regenerative Medicine, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092 China
| | - Dawei Huo
- Department of Cell Biology, Tianjin Medical University, 2011 Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Key Laboratory of Medical Epigenetics, Qixiangtai Road 22, Tianjin, China
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Zhaowei Yu
- Institute for Regenerative Medicine, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092 China
| | - Yujie Chen
- Institute for Regenerative Medicine, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092 China
| | - Jing Liu
- Institute for Regenerative Medicine, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092 China
- Present address: Key Laboratory of Forensic Genetics, National Engineering Laboratory for Forensic Science, Institute of Forensic Science, Beijing, China
| | - Lin Liu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Xudong Wu
- Department of Cell Biology, Tianjin Medical University, 2011 Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Key Laboratory of Medical Epigenetics, Qixiangtai Road 22, Tianjin, China
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- State Key Laboratory of Experimental Hematology, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, 300020 China
| | - Yong Zhang
- Institute for Regenerative Medicine, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092 China
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Xu T, Zheng X, Li B, Jin P, Qin Z, Wu H. A comprehensive review of computational prediction of genome-wide features. Brief Bioinform 2020; 21:120-134. [PMID: 30462144 PMCID: PMC10233247 DOI: 10.1093/bib/bby110] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Revised: 10/15/2018] [Accepted: 10/16/2018] [Indexed: 12/15/2022] Open
Abstract
There are significant correlations among different types of genetic, genomic and epigenomic features within the genome. These correlations make the in silico feature prediction possible through statistical or machine learning models. With the accumulation of a vast amount of high-throughput data, feature prediction has gained significant interest lately, and a plethora of papers have been published in the past few years. Here we provide a comprehensive review on these published works, categorized by the prediction targets, including protein binding site, enhancer, DNA methylation, chromatin structure and gene expression. We also provide discussions on some important points and possible future directions.
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Affiliation(s)
- Tianlei Xu
- Department of Mathematics and Computer Science, Emory University, Atlanta, GA, USA
| | - Xiaoqi Zheng
- Department of Mathematics, Shanghai Normal University, Shanghai, China
| | - Ben Li
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Peng Jin
- Department of Human Genetics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Zhaohui Qin
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Hao Wu
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
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Prediction in Cancer Genomics Using Topological Signatures and Machine Learning. TOPOLOGICAL DATA ANALYSIS 2020. [DOI: 10.1007/978-3-030-43408-3_10] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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43
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Li T, Zhang X, Luo F, Wu FX, Wang J. MultiMotifMaker: A Multi-Thread Tool for Identifying DNA Methylation Motifs from Pacbio Reads. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:220-225. [PMID: 30059318 DOI: 10.1109/tcbb.2018.2861399] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The methylation of DNA is an important mechanism to control biological processes. Recently, the Pacbio SMRT technology provides a new way to identify base methylation in the genome. MotifMaker is a tool developed by Pacbio for discovering DNA methylation motifs from methylated DNA sequences. However, MotifMaker is single-threaded and computational expensive for identifying methylation motifs from large genomes. Here, we present an efficient motif finding algorithm (MultiMotifMaker) by implementing multi threads of the MotifMaker. The MultiMotifMaker speeds up the motif search about 8-9 times on a 32 core computer comparing to MotifMaker. MultiMotifMaker makes it possible to identify methylation motifs from Pacbio reads for large genomes.
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Zitnik M, Nguyen F, Wang B, Leskovec J, Goldenberg A, Hoffman MM. Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2019; 50:71-91. [PMID: 30467459 PMCID: PMC6242341 DOI: 10.1016/j.inffus.2018.09.012] [Citation(s) in RCA: 222] [Impact Index Per Article: 44.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include myriad properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field.
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Affiliation(s)
- Marinka Zitnik
- Department of Computer Science, Stanford University,
Stanford, CA, USA
| | - Francis Nguyen
- Department of Medical Biophysics, University of Toronto,
Toronto, ON, Canada
- Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Bo Wang
- Hikvision Research Institute, Santa Clara, CA, USA
| | - Jure Leskovec
- Department of Computer Science, Stanford University,
Stanford, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Anna Goldenberg
- Genetics & Genome Biology, SickKids Research Institute,
Toronto, ON, Canada
- Department of Computer Science, University of Toronto,
Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
| | - Michael M. Hoffman
- Department of Medical Biophysics, University of Toronto,
Toronto, ON, Canada
- Princess Margaret Cancer Centre, Toronto, ON, Canada
- Department of Computer Science, University of Toronto,
Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
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45
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Ngo V, Wang M, Wang W. Finding de novo methylated DNA motifs. Bioinformatics 2019; 35:3287-3293. [PMID: 30726880 PMCID: PMC6748772 DOI: 10.1093/bioinformatics/btz079] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 01/14/2019] [Accepted: 02/04/2019] [Indexed: 01/24/2023] Open
Abstract
MOTIVATION Increasing evidence has shown that nucleotide modifications such as methylation and hydroxymethylation on cytosine would greatly impact the binding of transcription factors (TFs). However, there is a lack of motif finding algorithms with the function to search for motifs with modified bases. In this study, we expand on our previous motif finding pipeline Epigram to provide systematic de novo motif discovery and performance evaluation on methylated DNA motifs. RESULTS mEpigram outperforms both MEME and DREME on finding modified motifs in simulated data that mimics various motif enrichment scenarios. Furthermore we were able to identify methylated motifs in Arabidopsis DNA affinity purification sequencing (DAP-seq) data that were previously demonstrated to contain such motifs. When applied to TF ChIP-seq and DNA methylome data in H1 and GM12878, our method successfully identified novel methylated motifs that can be recognized by the TFs or their co-factors. We also observed spacing constraint between the canonical motif of the TF and the newly discovered methylated motifs, which suggests operative recognition of these cis-elements by collaborative proteins. AVAILABILITY AND IMPLEMENTATION The mEpigram program is available at http://wanglab.ucsd.edu/star/mEpigram. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Vu Ngo
- Graduate Program of Bioinformatics and Systems Biology, University of California at San Diego, La Jolla, CA, USA
| | - Mengchi Wang
- Graduate Program of Bioinformatics and Systems Biology, University of California at San Diego, La Jolla, CA, USA
| | - Wei Wang
- Graduate Program of Bioinformatics and Systems Biology, University of California at San Diego, La Jolla, CA, USA
- Department of Chemistry and Biochemistry, University of California at San Diego, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, University of California at San Diego, La Jolla, CA, USA
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Ni P, Su Z. Deciphering epigenomic code for cell differentiation using deep learning. BMC Genomics 2019; 20:709. [PMID: 31510916 PMCID: PMC6739944 DOI: 10.1186/s12864-019-6072-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 08/29/2019] [Indexed: 12/31/2022] Open
Abstract
Background Although DNA sequence plays a crucial role in establishing the unique epigenome of a cell type, little is known about the sequence determinants that lead to the unique epigenomes of different cell types produced during cell differentiation. To fill this gap, we employed two types of deep convolutional neural networks (CNNs) constructed for each of differentially related cell types and for each of histone marks measured in the cells, to learn the sequence determinants of various histone modification patterns in each cell type. Results We applied our models to four differentially related human CD4+ T cell types and six histone marks measured in each cell type. The cell models can accurately predict the histone marks in each cell type, while the mark models can also accurately predict the cell types based on a single mark. Sequence motifs learned by both the cell or mark models are highly similar to known binding motifs of transcription factors known to play important roles in CD4+ T cell differentiation. Both the unique histone mark patterns in each cell type and the different patterns of the same histone mark in different cell types are determined by a set of motifs with unique combinations. Interestingly, the level of sharing motifs learned in the different cell models reflects the lineage relationships of the cells, while the level of sharing motifs learned in the different histone mark models reflects their functional relationships. These models can also enable the prediction of the importance of learned motifs and their interactions in determining specific histone mark patterns in the cell types. Conclusion Sequence determinants of various histone modification patterns in different cell types can be revealed by comparative analysis of motifs learned in the CNN models for multiple cell types and histone marks. The learned motifs are interpretable and may provide insights into the underlying molecular mechanisms of establishing the unique epigenomes in different cell types. Thus, our results support the hypothesis that DNA sequences ultimately determine the unique epigenomes of different cell types through their interactions with transcriptional factors, epigenome remodeling system and extracellular cues during cell differentiation. Electronic supplementary material The online version of this article (10.1186/s12864-019-6072-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Pengyu Ni
- Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, 9201 University City Boulevard, Charlotte, NC, 28223, USA
| | - Zhengchang Su
- Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, 9201 University City Boulevard, Charlotte, NC, 28223, USA.
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Wang M, Zhang K, Ngo V, Liu C, Fan S, Whitaker JW, Chen Y, Ai R, Chen Z, Wang J, Zheng L, Wang W. Identification of DNA motifs that regulate DNA methylation. Nucleic Acids Res 2019; 47:6753-6768. [PMID: 31334813 PMCID: PMC6649826 DOI: 10.1093/nar/gkz483] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 05/14/2019] [Accepted: 06/20/2019] [Indexed: 01/11/2023] Open
Abstract
DNA methylation is an important epigenetic mark but how its locus-specificity is decided in relation to DNA sequence is not fully understood. Here, we have analyzed 34 diverse whole-genome bisulfite sequencing datasets in human and identified 313 motifs, including 92 and 221 associated with methylation (methylation motifs, MMs) and unmethylation (unmethylation motifs, UMs), respectively. The functionality of these motifs is supported by multiple lines of evidence. First, the methylation levels at the MM and UM motifs are respectively higher and lower than the genomic background. Second, these motifs are enriched at the binding sites of methylation modifying enzymes including DNMT3A and TET1, indicating their possible roles of recruiting these enzymes. Third, these motifs significantly overlap with "somatic QTLs" (quantitative trait loci) of methylation and expression. Fourth, disruption of these motifs by mutation is associated with significantly altered methylation level of the CpGs in the neighbor regions. Furthermore, these motifs together with somatic mutations are predictive of cancer subtypes and patient survival. We revealed some of these motifs were also associated with histone modifications, suggesting a possible interplay between the two types of epigenetic modifications. We also found some motifs form feed forward loops to contribute to DNA methylation dynamics.
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Affiliation(s)
- Mengchi Wang
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA
| | - Kai Zhang
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA
| | - Vu Ngo
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA
| | - Chengyu Liu
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA
| | - Shicai Fan
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - John W Whitaker
- Department of Genomics, Denovo Biopharma, 10240 Science Center Dr., San Diego, CA, USA
| | - Yue Chen
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Rizi Ai
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA
| | - Zhao Chen
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA
| | - Jun Wang
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA
| | - Lina Zheng
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA
| | - Wei Wang
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
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48
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Wu Z, Tang J, Zhuo J, Tian Y, Zhao F, Li Z, Yan Y, Yang R. Chromatin Signature and Transcription Factor Binding Provide a Predictive Basis for Understanding Plant Gene Expression. PLANT & CELL PHYSIOLOGY 2019; 60:1471-1486. [PMID: 31038680 DOI: 10.1093/pcp/pcz051] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2019] [Accepted: 03/26/2019] [Indexed: 06/09/2023]
Abstract
Chromatin accessibility and post-transcriptional histone modifications play important roles in gene expression regulation. However, little is known about the joint effect of multiple chromatin modifications on the gene expression level in plants, despite that the regulatory roles of individual histone marks such as H3K4me3 in gene expression have been well-documented. By using machine-learning methods, we systematically performed gene expression level prediction based on multiple chromatin modifications data in Arabidopsis and rice. We found that as few as four histone modifications were sufficient to yield good prediction performance, and H3K4me3 and H3K36me3 being the top two predictors with known functions related to transcriptional initiation and elongation, respectively. We demonstrated that the predictive powers differed between protein-coding and non-coding genes as well as between CpG-enriched and CpG-depleted genes. We also showed that the predictive model trained in one tissue or species could be applied to another tissue or species, suggesting shared underlying mechanisms. More interestingly, the gene expression levels of conserved orthologs are easier to predict than the species-specific genes. In addition, chromatin state of distal enhancers was moderately correlated to gene expression but was dispensable if given the chromatin features of the proximal regions of genes. We further extended the analysis to transcription factor (TF) binding data. Strikingly, the combinatorial effects of only a few TFs were roughly fit to gene expression levels in Arabidopsis. Overall, by using quantitative modeling, we provide a comprehensive and unbiased perspective on the epigenetic and TF-mediated regulation of gene expression in plants.
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Affiliation(s)
- Zefeng Wu
- College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Jing Tang
- College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Junjie Zhuo
- College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Yuhan Tian
- College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Feiyang Zhao
- College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Zhaohong Li
- College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Yubin Yan
- College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Ruolin Yang
- College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
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49
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Lin Y, Li Y, Zhu X, Huang Y, Li Y, Li M. Genetic Contexts Characterize Dynamic Histone Modification Patterns Among Cell Types. Interdiscip Sci 2019; 11:698-710. [PMID: 31165438 DOI: 10.1007/s12539-019-00338-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 05/18/2019] [Accepted: 05/27/2019] [Indexed: 11/29/2022]
Abstract
Histone modifications play critical roles in mammalian development, regulating chromatin structure and gene expression. Dynamic histone modifications among cell types have been shown to associate with changes in mammalian development. However, how to quantitatively measure the histone modification alterations and how histone modifications vary across cell types under different genetic contexts remain largely unexplored and whether these changes are related to the primary DNA sequence remains limited. Here, we employed an entropy-based method to measure histone modification alterations in six definite genomic regions across five cell types and identified lineage-specific histone modification genes. We observed that histone modification alterations prefer to enrich in 5'-UTR exons, and also in 3'-UTR exons and its downstream. Then we built a model to predict the histone modification patterns from the primary DNA sequence. We found that the frequencies of k-mer sequence compositions are predictive of histone modification patterns, suggesting that the primary DNA sequence correlated with the histone modification alterations among cell types. Additionally, the lineage-specific histone modification genes display a higher conservation and lower GC-content. Together, we performed a systematic analysis for histone modification alterations and demonstrated how to identify genomic region-specific elements of epigenetic and genetic regulation and histone modification patterns across different cell types.
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Affiliation(s)
- Yanmei Lin
- College of Chemistry, Sichuan University, Chengdu, 610064, China
| | - Yan Li
- College of Chemistry, Sichuan University, Chengdu, 610064, China
| | - Xingyong Zhu
- College of Chemistry, Sichuan University, Chengdu, 610064, China
| | - Yuyao Huang
- College of Chemistry, Sichuan University, Chengdu, 610064, China
| | - Yizhou Li
- College of Chemistry, Sichuan University, Chengdu, 610064, China. .,College of Cybersecurity, Sichuan University, Chengdu, 610064, Sichuan, China.
| | - Menglong Li
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
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50
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Hujoel MLA, Gazal S, Hormozdiari F, van de Geijn B, Price AL. Disease Heritability Enrichment of Regulatory Elements Is Concentrated in Elements with Ancient Sequence Age and Conserved Function across Species. Am J Hum Genet 2019; 104:611-624. [PMID: 30905396 PMCID: PMC6451699 DOI: 10.1016/j.ajhg.2019.02.008] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 02/05/2019] [Indexed: 02/06/2023] Open
Abstract
Regulatory elements, e.g., enhancers and promoters, have been widely reported to be enriched for disease and complex trait heritability. We investigated how this enrichment varies with the age of the underlying genome sequence, the conservation of regulatory function across species, and the target gene of the regulatory element. We estimated heritability enrichment by applying stratified LD score regression to summary statistics from 41 independent diseases and complex traits (average N = 320K) and meta-analyzing results across traits. Enrichment of human putative enhancers and promoters was larger in elements with older sequence age, assessed via alignment with other species irrespective of conserved functionality: putative enhancer elements with ancient sequence age (older than the split between marsupial and placental mammals) were 8.8× enriched (versus 2.5× for all putative enhancers; p = 3e-14), and promoter elements with ancient sequence age were 13.5× enriched (versus 5.1× for all promoters; p = 5e-16). Enrichment of human putative enhancers and promoters was also larger in elements whose regulatory function was conserved across species, e.g., human putative enhancers that were enhancers in ≥5 of 9 other mammals were 4.6× enriched (p = 5e-12 versus all putative enhancers). Enrichment of human promoters was larger in promoters of loss-of-function intolerant genes: 12.0× enrichment (p = 8e-15 versus all promoters). The mean value of several measures of negative selection within these genomic annotations mirrored all of these findings. Notably, the annotations with these excess heritability enrichments were jointly significant conditional on each other and on our baseline-LD model, which includes a broad set of coding, conserved, regulatory, and LD-related annotations.
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Affiliation(s)
- Margaux L A Hujoel
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Division of Biostatistics, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
| | - Steven Gazal
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Farhad Hormozdiari
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Bryce van de Geijn
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alkes L Price
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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