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Ginley-Hidinger M, Abewe H, Osborne K, Richey A, Kitchen N, Mortenson KL, Wissink EM, Lis J, Zhang X, Gertz J. Cis-regulatory control of transcriptional timing and noise in response to estrogen. CELL GENOMICS 2024; 4:100542. [PMID: 38663407 PMCID: PMC11099348 DOI: 10.1016/j.xgen.2024.100542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 10/26/2023] [Accepted: 03/27/2024] [Indexed: 05/07/2024]
Abstract
Cis-regulatory elements control transcription levels, temporal dynamics, and cell-cell variation or transcriptional noise. However, the combination of regulatory features that control these different attributes is not fully understood. Here, we used single-cell RNA-seq during an estrogen treatment time course and machine learning to identify predictors of expression timing and noise. We found that genes with multiple active enhancers exhibit faster temporal responses. We verified this finding by showing that manipulation of enhancer activity changes the temporal response of estrogen target genes. Analysis of transcriptional noise uncovered a relationship between promoter and enhancer activity, with active promoters associated with low noise and active enhancers linked to high noise. Finally, we observed that co-expression across single cells is an emergent property associated with chromatin looping, timing, and noise. Overall, our results indicate a fundamental tradeoff between a gene's ability to quickly respond to incoming signals and maintain low variation across cells.
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Affiliation(s)
- Matthew Ginley-Hidinger
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA
| | - Hosiana Abewe
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA; Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Kyle Osborne
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA; Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Alexandra Richey
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA
| | - Noel Kitchen
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA; Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Katelyn L Mortenson
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA; Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Erin M Wissink
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - John Lis
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - Xiaoyang Zhang
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA; Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Jason Gertz
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA; Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA.
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2
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Ginley-Hidinger M, Abewe H, Osborne K, Richey A, Kitchen N, Mortenson KL, Wissink EM, Lis J, Zhang X, Gertz J. Cis-regulatory control of transcriptional timing and noise in response to estrogen. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.03.14.532457. [PMID: 36993565 PMCID: PMC10054948 DOI: 10.1101/2023.03.14.532457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Cis-regulatory elements control transcription levels, temporal dynamics, and cell-cell variation or transcriptional noise. However, the combination of regulatory features that control these different attributes is not fully understood. Here, we used single cell RNA-seq during an estrogen treatment time course and machine learning to identify predictors of expression timing and noise. We find that genes with multiple active enhancers exhibit faster temporal responses. We verified this finding by showing that manipulation of enhancer activity changes the temporal response of estrogen target genes. Analysis of transcriptional noise uncovered a relationship between promoter and enhancer activity, with active promoters associated with low noise and active enhancers linked to high noise. Finally, we observed that co-expression across single cells is an emergent property associated with chromatin looping, timing, and noise. Overall, our results indicate a fundamental tradeoff between a gene's ability to quickly respond to incoming signals and maintain low variation across cells.
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Affiliation(s)
- Matthew Ginley-Hidinger
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA
| | - Hosiana Abewe
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Kyle Osborne
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Alexandra Richey
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA
| | - Noel Kitchen
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Katelyn L. Mortenson
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Erin M. Wissink
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - John Lis
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - Xiaoyang Zhang
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Jason Gertz
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
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3
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Marku M, Pancaldi V. From time-series transcriptomics to gene regulatory networks: A review on inference methods. PLoS Comput Biol 2023; 19:e1011254. [PMID: 37561790 PMCID: PMC10414591 DOI: 10.1371/journal.pcbi.1011254] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023] Open
Abstract
Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the ever increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. In this review, we summarize the different types of inference algorithms specifically based on time-series transcriptomics, giving an overview of the main applications of gene regulatory networks in computational biology. This review is intended to give an updated reference of regulatory networks inference tools to biologists and researchers new to the topic and guide them in selecting the appropriate inference method that best fits their questions, aims, and experimental data.
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Affiliation(s)
- Malvina Marku
- CRCT, Université de Toulouse, Inserm, CNRS, Université Toulouse III-Paul Sabatier, Centre de Recherches en Cancérologie de Toulouse, Toulouse, France
| | - Vera Pancaldi
- CRCT, Université de Toulouse, Inserm, CNRS, Université Toulouse III-Paul Sabatier, Centre de Recherches en Cancérologie de Toulouse, Toulouse, France
- Barcelona Supercomputing Center, Barcelona, Spain
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4
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Huang YQ, Sun P, Chen Y, Liu HX, Hao GF, Song BA. Bioinformatics toolbox for exploring target mutation-induced drug resistance. Brief Bioinform 2023; 24:7026012. [PMID: 36738254 DOI: 10.1093/bib/bbad033] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 12/25/2022] [Accepted: 01/14/2023] [Indexed: 02/05/2023] Open
Abstract
Drug resistance is increasingly among the main issues affecting human health and threatening agriculture and food security. In particular, developing approaches to overcome target mutation-induced drug resistance has long been an essential part of biological research. During the past decade, many bioinformatics tools have been developed to explore this type of drug resistance, and they have become popular for elucidating drug resistance mechanisms in a low cost, fast and effective way. However, these resources are scattered and underutilized, and their strengths and limitations have not been systematically analyzed and compared. Here, we systematically surveyed 59 freely available bioinformatics tools for exploring target mutation-induced drug resistance. We analyzed and summarized these resources based on their functionality, data volume, data source, operating principle, performance, etc. And we concisely discussed the strengths, limitations and application examples of these tools. Specifically, we tested some predictive tools and offered some thoughts from the clinician's perspective. Hopefully, this work will provide a useful toolbox for researchers working in the biomedical, pesticide, bioinformatics and pharmaceutical engineering fields, and a good platform for non-specialists to quickly understand drug resistance prediction.
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Affiliation(s)
- Yuan-Qin Huang
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, P. R. China
| | - Ping Sun
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, P. R. China
| | - Yi Chen
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, P. R. China
| | - Huan-Xiang Liu
- Faculty of Applied Science, Macao Polytechnic University, Macao 999078, SAR, China
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, P. R. China
| | - Bao-An Song
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, P. R. China
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5
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Patterns of Differentially Expressed circRNAs in Human Thymocytes. Noncoding RNA 2022; 8:ncrna8020026. [PMID: 35447889 PMCID: PMC9030292 DOI: 10.3390/ncrna8020026] [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: 02/25/2022] [Revised: 03/24/2022] [Accepted: 03/29/2022] [Indexed: 12/05/2022] Open
Abstract
Circular RNAs (circRNAs) are suggested to play a discriminative role between some stages of thymocyte differentiation. However, differential aspects of the stage of mature single-positive thymocytes remain to be explored. The purpose of this study is to investigate the differential expression pattern of circRNAs in three different development stages of human thymocytes, including mature single-positive cells, and perform predictions in silico regarding the ability of specific circRNAs when controlling the expression of genes involved in thymocyte differentiation. We isolate human thymocytes at three different stages of intrathymic differentiation and determine the expression of circRNAs and mRNA by RNASeq. We show that the differential expression pattern of 50 specific circRNAs serves to discriminate between the three human thymocyte populations. Interestingly, the downregulation of RAG2, a gene involved in T-cell differentiation in the thymus, could be simultaneously controlled by the downregulation of two circRNASs (hsa_circ_0031584 and hsa_circ_0019079) through the hypothetical liberation of hsa-miR-609. Our study provides, for the first time, significant insights into the usefulness of circRNAs in discriminating between different stages of thymocyte differentiation and provides new potential circRNA–miRNA–mRNA networks capable of controlling the expression of genes involved in T-cell differentiation in the thymus.
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6
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Hozhabri H, Ghasemi Dehkohneh RS, Razavi SM, Razavi SM, Salarian F, Rasouli A, Azami J, Ghasemi Shiran M, Kardan Z, Farrokhzad N, Mikaeili Namini A, Salari A. Comparative analysis of protein-protein interaction networks in metastatic breast cancer. PLoS One 2022; 17:e0260584. [PMID: 35045088 PMCID: PMC8769308 DOI: 10.1371/journal.pone.0260584] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 11/12/2021] [Indexed: 12/27/2022] Open
Abstract
Metastatic lesions leading causes of the majority of deaths in patients with the breast cancer. The present study aimed to provide a comprehensive analysis of the differentially expressed genes (DEGs) in the brain (MDA-MB-231 BrM2) and lung (MDA-MB-231 LM2) metastatic cell lines obtained from breast cancer patients compared with those who have primary breast cancer. We identified 981 and 662 DEGs for brain and lung metastasis, respectively. Protein-protein interaction (PPI) analysis revealed seven shared (PLCB1, FPR1, FPR2, CX3CL1, GABBR2, GPR37, and CXCR4) hub genes between brain and lung metastasis in breast cancer. Moreover, GNG2 and CXCL8, C3, and PTPN6 in the brain and SAA1 and CCR5 in lung metastasis were found as unique hub genes. Besides, five co-regulation of clusters via seven important co-expression genes (COL1A2, LUM, SPARC, THBS2, IL1B, CXCL8, THY1) were identified in the brain PPI network. Clusters screening followed by biological process (BP) function and pathway enrichment analysis for both metastatic cell lines showed that complement receptor signalling, acetylcholine receptor signalling, and gastric acid secretion pathways were common between these metastases, whereas other pathways were site-specific. According to our findings, there are a set of genes and functional pathways that mark and mediate breast cancer metastasis to the brain and lungs, which may enable us understand the molecular basis of breast cancer development in a deeper levele to the brain and lungs, which may help us gain a more complete understanding of the molecular underpinnings of breast cancer development.
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Affiliation(s)
- Hossein Hozhabri
- Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
- Salari Institute of Cognitive and Behavioral Disorders (SICBD), Karaj, Alborz, Iran
- Systems Biology Research Lab, Bioinformatics Group, Systems Biology of the Next Generation Company (SBNGC), Qom, Iran
| | - Roxana Sadat Ghasemi Dehkohneh
- Salari Institute of Cognitive and Behavioral Disorders (SICBD), Karaj, Alborz, Iran
- Systems Biology Research Lab, Bioinformatics Group, Systems Biology of the Next Generation Company (SBNGC), Qom, Iran
- Department of Cell and Molecular Biology and Microbiology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran
| | - Seyed Morteza Razavi
- Salari Institute of Cognitive and Behavioral Disorders (SICBD), Karaj, Alborz, Iran
- Systems Biology Research Lab, Bioinformatics Group, Systems Biology of the Next Generation Company (SBNGC), Qom, Iran
- Department of Cell and Molecular Biology, Faculty of Biological Sciences, Kharazmi University, Tehran, Iran
| | - S. Mostafa Razavi
- Department of Chemical, Biomolecular and Corrosion Engineering, The University of Akron, Akron, Ohio, United States of America
| | - Fatemeh Salarian
- Systems Biology Research Lab, Bioinformatics Group, Systems Biology of the Next Generation Company (SBNGC), Qom, Iran
- Department of Cellular Biotechnology, Cell Science Research Center, Royan Institute for Biotechnology, ACECR, Isfahan, Iran
| | - Azade Rasouli
- Systems Biology Research Lab, Bioinformatics Group, Systems Biology of the Next Generation Company (SBNGC), Qom, Iran
- Department of Toxicology and Pharmacology, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
| | - Jalil Azami
- Systems Biology Research Lab, Bioinformatics Group, Systems Biology of the Next Generation Company (SBNGC), Qom, Iran
- Faculty of Veterinary Medicine, Urmia University, Urmia, Iran
| | - Melika Ghasemi Shiran
- Salari Institute of Cognitive and Behavioral Disorders (SICBD), Karaj, Alborz, Iran
- Systems Biology Research Lab, Bioinformatics Group, Systems Biology of the Next Generation Company (SBNGC), Qom, Iran
- Department of Biology, Faculty of Sciences, Central Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Zahra Kardan
- Systems Biology Research Lab, Bioinformatics Group, Systems Biology of the Next Generation Company (SBNGC), Qom, Iran
- Department of Cellular Molecular Biology, Faculty of Life Science and Biotechnology, Shahid Beheshti University, Tehran, Iran
| | - Negar Farrokhzad
- Systems Biology Research Lab, Bioinformatics Group, Systems Biology of the Next Generation Company (SBNGC), Qom, Iran
- Department of Biology, Faculty of Sciences, University of Guilan, Rasht, Iran
| | - Arsham Mikaeili Namini
- Systems Biology Research Lab, Bioinformatics Group, Systems Biology of the Next Generation Company (SBNGC), Qom, Iran
- Department of Animal Biology, Faculty of Biological Sciences, Kharazmi University, Tehran, Iran
| | - Ali Salari
- Salari Institute of Cognitive and Behavioral Disorders (SICBD), Karaj, Alborz, Iran
- Systems Biology Research Lab, Bioinformatics Group, Systems Biology of the Next Generation Company (SBNGC), Qom, Iran
- Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
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7
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Kashima M, Shida Y, Yamashiro T, Hirata H, Kurosaka H. Intracellular and Intercellular Gene Regulatory Network Inference From Time-Course Individual RNA-Seq. FRONTIERS IN BIOINFORMATICS 2021; 1:777299. [PMID: 36303726 PMCID: PMC9580923 DOI: 10.3389/fbinf.2021.777299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 10/26/2021] [Indexed: 11/13/2022] Open
Abstract
Gene regulatory network (GRN) inference is an effective approach to understand the molecular mechanisms underlying biological events. Generally, GRN inference mainly targets intracellular regulatory relationships such as transcription factors and their associated targets. In multicellular organisms, there are both intracellular and intercellular regulatory mechanisms. Thus, we hypothesize that GRNs inferred from time-course individual (whole embryo) RNA-Seq during development can reveal intercellular regulatory relationships (signaling pathways) underlying the development. Here, we conducted time-course bulk RNA-Seq of individual mouse embryos during early development, followed by pseudo-time analysis and GRN inference. The results demonstrated that GRN inference from RNA-Seq with pseudo-time can be applied for individual bulk RNA-Seq similar to scRNA-Seq. Validation using an experimental-source-based database showed that our approach could significantly infer GRN for all transcription factors in the database. Furthermore, the inferred ligand-related and receptor-related downstream genes were significantly overlapped. Thus, the inferred GRN based on whole organism could include intercellular regulatory relationships, which cannot be inferred from scRNA-Seq based only on gene expression data. Overall, inferring GRN from time-course bulk RNA-Seq is an effective approach to understand the regulatory relationships underlying biological events in multicellular organisms.
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Affiliation(s)
- Makoto Kashima
- College of Science and Engineering, Aoyama Gakuin University, Sagamihara, Japan
- *Correspondence: Makoto Kashima,
| | - Yuki Shida
- Department of Orthodontics and Dentofacial Orthopedics, Osaka University, Suita, Japan
| | - Takashi Yamashiro
- Department of Orthodontics and Dentofacial Orthopedics, Osaka University, Suita, Japan
| | - Hiromi Hirata
- College of Science and Engineering, Aoyama Gakuin University, Sagamihara, Japan
| | - Hiroshi Kurosaka
- Department of Orthodontics and Dentofacial Orthopedics, Osaka University, Suita, Japan
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8
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Jung HD, Sung YJ, Kim HU. Omics and Computational Modeling Approaches for the Effective Treatment of Drug-Resistant Cancer Cells. Front Genet 2021; 12:742902. [PMID: 34691155 PMCID: PMC8527086 DOI: 10.3389/fgene.2021.742902] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 09/20/2021] [Indexed: 02/05/2023] Open
Abstract
Chemotherapy is a mainstream cancer treatment, but has a constant challenge of drug resistance, which consequently leads to poor prognosis in cancer treatment. For better understanding and effective treatment of drug-resistant cancer cells, omics approaches have been widely conducted in various forms. A notable use of omics data beyond routine data mining is to use them for computational modeling that allows generating useful predictions, such as drug responses and prognostic biomarkers. In particular, an increasing volume of omics data has facilitated the development of machine learning models. In this mini review, we highlight recent studies on the use of multi-omics data for studying drug-resistant cancer cells. We put a particular focus on studies that use computational models to characterize drug-resistant cancer cells, and to predict biomarkers and/or drug responses. Computational models covered in this mini review include network-based models, machine learning models and genome-scale metabolic models. We also provide perspectives on future research opportunities for combating drug-resistant cancer cells.
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Affiliation(s)
- Hae Deok Jung
- Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
| | - Yoo Jin Sung
- Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
| | - Hyun Uk Kim
- Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea.,KAIST Institute for Artificial Intelligence, KAIST, Daejeon, South Korea.,BioProcess Engineering Research Center and BioInformatics Research Center KAIST, Daejeon, South Korea
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9
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Wan S, Sun X, Tang W, Wang L, Wu Z, Sun X. Exosome-Depleted Excretory-Secretory Products of the Fourth-Stage Larval Angiostrongylus cantonensis Promotes Alternative Activation of Macrophages Through Metabolic Reprogramming by the PI3K-Akt Pathway. Front Immunol 2021; 12:685984. [PMID: 34367145 PMCID: PMC8343011 DOI: 10.3389/fimmu.2021.685984] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 07/07/2021] [Indexed: 12/19/2022] Open
Abstract
Angiostrongylus cantonensis (AC), which parasitizes in the brain of the non-permissive host, such as mouse and human, is an etiologic agent of eosinophilic meningitis. Excretory-secretory (ES) products play an important role in the interaction between parasites and hosts’ immune responses. Inflammatory macrophages are responsible for eosinophilic meningitis induced by AC, and the soluble antigens of Angiostrongylus cantonensis fourth stage larva (AC L4), a mimic of dead AC L4, aggravate eosinophilic meningitis in AC-infected mice model via promoting alternative activation of macrophages. In this study, we investigated the key molecules in the ES products of AC L4 on macrophages and observed the relationship between metabolic reprogramming and the PI3K-Akt pathway. First, a co-culture system of macrophage and AC L4 was established to define the role of AC L4 ES products on macrophage polarization. Then, AC L4 exosome and exosome-depleted excretory-secretory products (exofree) were separated from AC L4 ES products using differential centrifugation, and their distinct roles on macrophage polarization were confirmed using qPCR and ELISA experiments. Moreover, AC L4 exofree induced alternative activation of macrophages, which is partially associated with metabolic reprogramming by the PI3K-Akt pathway. Next, lectin blot and deglycosylation assay were done, suggesting the key role of N-linked glycoproteins in exofree. Then, glycoproteomic analysis of exofree and RNA-seq analysis of exofree-treated macrophage were performed. Bi-layer PPI network analysis based on these results identified macrophage-related protein Hexa as a key molecule in inducing alternative activation of macrophages. Our results indicate a great value for research of helminth-derived immunoregulatory molecules, which might contribute to drug development for immune-related diseases.
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Affiliation(s)
- Shuo Wan
- Department of Parasitology of Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.,Key Laboratory of Tropical Disease Control (SYSU), Ministry of Education, Guangzhou, China.,Provincial Engineering Technology Research Center for Biological Vector Control, Guangzhou, China.,The First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Xiaoqiang Sun
- Key Laboratory of Tropical Disease Control (SYSU), Ministry of Education, Guangzhou, China.,Zhongshan School of Mathematics, Sun Yat-Sen University, Guangzhou, China
| | - Wenyan Tang
- Department of Neonatology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Lifu Wang
- Department of Parasitology of Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.,Key Laboratory of Tropical Disease Control (SYSU), Ministry of Education, Guangzhou, China.,Provincial Engineering Technology Research Center for Biological Vector Control, Guangzhou, China
| | - Zhongdao Wu
- Department of Parasitology of Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.,Key Laboratory of Tropical Disease Control (SYSU), Ministry of Education, Guangzhou, China.,Provincial Engineering Technology Research Center for Biological Vector Control, Guangzhou, China
| | - Xi Sun
- Department of Parasitology of Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.,Key Laboratory of Tropical Disease Control (SYSU), Ministry of Education, Guangzhou, China.,Provincial Engineering Technology Research Center for Biological Vector Control, Guangzhou, China
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10
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Gui T, Yao C, Jia B, Shen K. Identification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods. PLoS One 2021; 16:e0253136. [PMID: 34143800 PMCID: PMC8213194 DOI: 10.1371/journal.pone.0253136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 05/31/2021] [Indexed: 12/24/2022] Open
Abstract
Background Though considerable efforts have been made to improve the treatment of epithelial ovarian cancer (EOC), the prognosis of patients has remained poor. Identifying differentially expressed genes (DEGs) involved in EOC progression and exploiting them as novel biomarkers or therapeutic targets is of great value. Methods Overlapping DEGs were screened out from three independent gene expression omnibus (GEO) datasets and were subjected to Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses. The protein-protein interactions (PPI) network of DEGs was constructed based on the STRING database. The expression of hub genes was validated in GEPIA and GEO. The relationship of hub genes expression with tumor stage and overall survival and progression-free survival of EOC patients was investigated using the cancer genome atlas data. Results A total of 306 DEGs were identified, including 265 up-regulated and 41 down-regulated. Through PPI network analysis, the top 20 genes were screened out, among which 4 hub genes, which were not researched in depth so far, were selected after literature retrieval, including CDC45, CDCA5, KIF4A, ESPL1. The four genes were up-regulated in EOC tissues compared with normal tissues, but their expression decreased gradually with the continuous progression of EOC. Survival curves illustrated that patients with a lower level of CDCA5 and ESPL1 had better overall survival and progression-free survival statistically. Conclusion Two hub genes, CDCA5 and ESPL1, identified as probably playing tumor-promotive roles, have great potential to be utilized as novel therapeutic targets for EOC treatment.
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Affiliation(s)
- Ting Gui
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Chenhe Yao
- Department of R&D Technology Center, Beijing Zhicheng Biomedical Technology Co, Ltd, Beijing, China
| | - Binghan Jia
- Department of R&D Technology Center, Beijing Zhicheng Biomedical Technology Co, Ltd, Beijing, China
| | - Keng Shen
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
- * E-mail:
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11
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Hu J, Tian R, Ma Y, Zhen H, Ma X, Su Q, Cao B. Risk of Cardiac Adverse Events in Patients Treated With Immune Checkpoint Inhibitor Regimens: A Systematic Review and Meta-Analysis. Front Oncol 2021; 11:645245. [PMID: 34123795 PMCID: PMC8190385 DOI: 10.3389/fonc.2021.645245] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 04/06/2021] [Indexed: 12/15/2022] Open
Abstract
Background We performed a systematic review and meta-analysis to evaluate the risks of cardiac adverse events in solid tumor patients treated with monotherapy of immune checkpoint inhibitors (ICIs) or combined therapy of ICIs plus chemotherapy. Methods Eligible studies were selected through the following databases: PubMed, Embase and clinical trials (https://clinicaltrials.gov.) and included phase III/IV randomized controlled trials (RCTs) involving patients with the solid tumor treated with ICIs. The data was analyzed by using Review Manager (version5.3), Stata (version 15.1). Results Among 2,551 studies, 25 clinical trials including 20,244 patients were qualified for the meta-analysis. Compared with PD-1 inhibitor (nivolumab) or CTLA-4 inhibitor (ipilimumab), PD-1 inhibitor (nivolumab) plus CTLA-4 inhibitor (ipilimumab) combined therapy showed significant increase in grade 5 arrhythmology (OR 3.90, 95% CI: 1.08–14.06, p = 0.603). PD-1 inhibitor plus chemotherapy show significant increase in grades 1–5 myocardial disease (OR 5.09, 95% CI: 1.11–23.32, p = 1.000). Compared with chemotherapy, PD-1 inhibitor (nivolumab) or CTLA-4 inhibitor (ipilimumab), PD-1 inhibitor (nivolumab) plus CTLA-4 inhibitor (ipilimumab) combined therapy show significant increase in grades 1–5 arrhythmology (OR 2.49, 95% CI: 1.30–4.78, p = 0.289). Conclusions Our meta-analysis demonstrated that PD-1 inhibitor plus CTLA-4 inhibitor can result in a higher risk of grade 5 arrhythmology in comparison with PD-1/CTLA-4 inhibitor alone, and a higher risk of grade 5 arrhythmology in comparison with chemotherapy. PD-1 inhibitor plus chemotherapy treatment could increase the risk of all-grade myocardial disease compared with chemotherapy. However, in most cases, there was no significant increase of risks of cardiovascular toxicity in PD-1/PD-L1 inhibitor monotherapy or PD-1/PD-L1 inhibitor plus chemotherapy compared with chemotherapy alone.
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Affiliation(s)
- Jiexuan Hu
- Department of Oncology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Ruyue Tian
- Department of Ultrasound, Aero Space Central Hospital, Beijing, China
| | - Yingjie Ma
- Department of Oncology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Hongchao Zhen
- Department of Oncology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Xiao Ma
- Department of Oncology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Qiang Su
- Department of Oncology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Bangwei Cao
- Department of Oncology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
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Gu P, Yang D, Zhu J, Zhang M, He X. Bioinformatics analysis identified hub genes in prostate cancer tumorigenesis and metastasis. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:3180-3196. [PMID: 34198380 DOI: 10.3934/mbe.2021158] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
OBJECTIVE Prostate cancer (PCa) is the most frequent cancer found in males worldwide, and its mortality rate is increasing every year. To discover key molecular changes in PCa development and metastasis, we analyzed microarray data of localized PCa, metastatic PCa and normal prostate tissue samples from clinical specimens. METHODS Gene expression profiling datasets of PCa were analyzed online. The DAVID was used to perform GO functional and KEGG pathway enrichment analyses. CytoHubba in Cytoscape software was applied to identify hub genes. Survival data were downloaded from GEPIA. Gene expression data were obtained from ONCOMINE and UALCAN. RESULTS We obtained 4 sets of differentially expressed genes (DEGs), DEGs 1: a comparison of the gene expression between 4 normal prostate and 5 localized PCa samples in GSE27616; DEGs 2: a comparison of the gene expression between 6 normal prostate and 7 localized PCa samples in GSE3325; DEGs 3: a comparison of the gene expression between 5 localized PCa and 4 metastatic PCa samples in GSE27616; DEGs 4: a comparison of the gene expression between 7 localized PCa and 6 metastatic PCa samples in GSE3325. A comparison of these 4 sets of genes revealed 51 overlapped genes. GO function analysis revealed enrichment of the 51 DEGs in functions related to the proteinaceous extracellular matrix and centrosome, protein homodimerization activity and chromatin binding were the main functions of these genes, which participated in regulating cell division, mitotic nuclear division, proteinaceous extracellular matrix, cell adhesion and apoptotic process. KEGG pathway analysis indicated that these identified DEGs were mainly enriched in progesterone-mediated oocyte maturation, oocyte meiosis and cell cycle. We defined the 16 genes with the highest degree of connectivity as the hub genes in the 51 overlapped DEGs. Cox regression revealed TOP2A, CCNB2, BUB1, CDK1 and EZH2 were related to Disease-free survival (DFS). The expression levels of the 5 genes were 2.232-, 1.786-, 2.303-, 1.699-, and 1.986-fold higher in PCa than the levels in normal tissues, respectively (P < 0.05). We obtained 20 hub genes from DEGs by the comparison of normal prostate tissue vs. localized cancer tissue. Among them, KIF20A, CDKN3, PBK and CDCA2, were expressed higher in PCa than in normal tissues, and were associated with the DFS of PCa patients. Meanwhile, we obtained 20 hub genes from DEGs by the comparison of localized cancer tissue vs. metastatic cancer tissue. Cox regression revealed PLK1, CCNA2 and CDC20, were associated with both the DFS and overall survival of PCa patients. CONCLUSIONS The results suggested that the functions of KIF20A, CDKN3, PBK and CDCA2 may contribute to PCa development and the functions of PLK1, CCNA2 and CDC20 may contribute to PCa metastasis. Meanwhile, the functions of TOP2A, CCNB2, BUB1, CDK1 and EZH2 may contribute to both PCa development and metastasis.
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Affiliation(s)
- Peng Gu
- Department of Urology, Second Affiliated Hospital of Soochow University, 1055 Sanxiang Road, Suzhou 215000, China
- Department of Urology, Wuxi Xishan People's Hospital, 1128 Dacheng Road, Wuxi 214000, China
| | - Dongrong Yang
- Department of Urology, Second Affiliated Hospital of Soochow University, 1055 Sanxiang Road, Suzhou 215000, China
| | - Jin Zhu
- Department of Urology, Second Affiliated Hospital of Soochow University, 1055 Sanxiang Road, Suzhou 215000, China
| | - Minhao Zhang
- Department of Urology, Wuxi Xishan People's Hospital, 1128 Dacheng Road, Wuxi 214000, China
| | - Xiaoliang He
- Department of Urology, Wuxi Xishan People's Hospital, 1128 Dacheng Road, Wuxi 214000, China
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ORN: Inferring patient-specific dysregulation status of pathway modules in cancer with OR-gate Network. PLoS Comput Biol 2021; 17:e1008792. [PMID: 33819263 PMCID: PMC8049496 DOI: 10.1371/journal.pcbi.1008792] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 04/15/2021] [Accepted: 02/15/2021] [Indexed: 01/26/2023] Open
Abstract
Pathway level understanding of cancer plays a key role in precision oncology. However, the current amount of high-throughput data cannot support the elucidation of full pathway topology. In this study, instead of directly learning the pathway network, we adapted the probabilistic OR gate to model the modular structure of pathways and regulon. The resulting model, OR-gate Network (ORN), can simultaneously infer pathway modules of somatic alterations, patient-specific pathway dysregulation status, and downstream regulon. In a trained ORN, the differentially expressed genes (DEGs) in each tumour can be explained by somatic mutations perturbing a pathway module. Furthermore, the ORN handles one of the most important properties of pathway perturbation in tumours, the mutual exclusivity. We have applied the ORN to lower-grade glioma (LGG) samples and liver hepatocellular carcinoma (LIHC) samples in TCGA and breast cancer samples from METABRIC. Both datasets have shown abnormal pathway activities related to immune response and cell cycles. In LGG samples, ORN identified pathway modules closely related to glioma development and revealed two pathways closely related to patient survival. We had similar results with LIHC samples. Additional results from the METABRIC datasets showed that ORN could characterize critical mechanisms of cancer and connect them to less studied somatic mutations (e.g., BAP1, MIR604, MICAL3, and telomere activities), which may generate novel hypothesis for targeted therapy. Cellular functions are carried out by a set of gene products. Mutation of a single gene is often sufficient to disrupt certain biological functions and promote tumorigenesis. Therefore, genes participating in the same function are less likely to mutate in the same sample. Such phenomenon is called “mutual exclusivity”. In this study, our algorithm (ORN) has utilized this property to identify gene-level mutations that affect similar biological functions. It also considers mutations’ impact on mRNA expression. Functional modules identified by ORN tends to be mutually exclusive while causing similar differential expression profiles. When we applied ORN to lower-grade glioma and liver cancer datasets, we have identified gene modules significantly related to patient survival. Furthermore, across different types of cancer, ORN has connected well-known cancer driver mutations with genes whose functions remain unclear. These connections, once validated, can generate novel hypothesis for biologist to further investigate cancer mechanism and develop targeted therapy.
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Oh VKS, Li RW. Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data. Genes (Basel) 2021; 12:352. [PMID: 33673721 PMCID: PMC7997275 DOI: 10.3390/genes12030352] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 02/19/2021] [Accepted: 02/22/2021] [Indexed: 02/06/2023] Open
Abstract
Dynamic studies in time course experimental designs and clinical approaches have been widely used by the biomedical community. These applications are particularly relevant in stimuli-response models under environmental conditions, characterization of gradient biological processes in developmental biology, identification of therapeutic effects in clinical trials, disease progressive models, cell-cycle, and circadian periodicity. Despite their feasibility and popularity, sophisticated dynamic methods that are well validated in large-scale comparative studies, in terms of statistical and computational rigor, are less benchmarked, comparing to their static counterparts. To date, a number of novel methods in bulk RNA-Seq data have been developed for the various time-dependent stimuli, circadian rhythms, cell-lineage in differentiation, and disease progression. Here, we comprehensively review a key set of representative dynamic strategies and discuss current issues associated with the detection of dynamically changing genes. We also provide recommendations for future directions for studying non-periodical, periodical time course data, and meta-dynamic datasets.
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Affiliation(s)
- Vera-Khlara S. Oh
- Animal Genomics and Improvement Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville, MD 20705, USA;
- Department of Computer Science and Statistics, College of Natural Sciences, Jeju National University, Jeju City 63243, Korea
| | - Robert W. Li
- Animal Genomics and Improvement Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville, MD 20705, USA;
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Oh M, Park S, Lee S, Lee D, Lim S, Jeong D, Jo K, Jung I, Kim S. DRIM: A Web-Based System for Investigating Drug Response at the Molecular Level by Condition-Specific Multi-Omics Data Integration. Front Genet 2020; 11:564792. [PMID: 33281870 PMCID: PMC7689278 DOI: 10.3389/fgene.2020.564792] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 08/14/2020] [Indexed: 12/11/2022] Open
Abstract
Pharmacogenomics is the study of how genes affect a person's response to drugs. Thus, understanding the effect of drug at the molecular level can be helpful in both drug discovery and personalized medicine. Over the years, transcriptome data upon drug treatment has been collected and several databases compiled before drug treatment cancer cell multi-omics data with drug sensitivity (IC 50, AUC) or time-series transcriptomic data after drug treatment. However, analyzing transcriptome data upon drug treatment is challenging since more than 20,000 genes interact in complex ways. In addition, due to the difficulty of both time-series analysis and multi-omics integration, current methods can hardly perform analysis of databases with different data characteristics. One effective way is to interpret transcriptome data in terms of well-characterized biological pathways. Another way is to leverage state-of-the-art methods for multi-omics data integration. In this paper, we developed Drug Response analysis Integrating Multi-omics and time-series data (DRIM), an integrative multi-omics and time-series data analysis framework that identifies perturbed sub-pathways and regulation mechanisms upon drug treatment. The system takes drug name and cell line identification numbers or user's drug control/treat time-series gene expression data as input. Then, analysis of multi-omics data upon drug treatment is performed in two perspectives. For the multi-omics perspective analysis, IC 50-related multi-omics potential mediator genes are determined by embedding multi-omics data to gene-centric vector space using a tensor decomposition method and an autoencoder deep learning model. Then, perturbed pathway analysis of potential mediator genes is performed. For the time-series perspective analysis, time-varying perturbed sub-pathways upon drug treatment are constructed. Additionally, a network involving transcription factors (TFs), multi-omics potential mediator genes, and perturbed sub-pathways is constructed, and paths to perturbed pathways from TFs are determined by an influence maximization method. To demonstrate the utility of our system, we provide analysis results of sub-pathway regulatory mechanisms in breast cancer cell lines of different drug sensitivity. DRIM is available at: http://biohealth.snu.ac.kr/software/DRIM/.
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Affiliation(s)
- Minsik Oh
- Department of Computer Science and Engineering, Seoul National University, Seoul, South Korea
| | - Sungjoon Park
- Department of Computer Science and Engineering, Seoul National University, Seoul, South Korea
| | - Sangseon Lee
- Bioinformatics Institute, Seoul National University, Seoul, South Korea
| | - Dohoon Lee
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea
| | - Sangsoo Lim
- Bioinformatics Institute, Seoul National University, Seoul, South Korea
| | - Dabin Jeong
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea
| | - Kyuri Jo
- Department of Computer Engineering, Chungbuk National University, Cheongju, South Korea
| | - Inuk Jung
- Department of Computer Science and Engineering, Kyungpook National University, Daegu, South Korea
| | - Sun Kim
- Bioinformatics Institute, Seoul National University, Seoul, South Korea
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea
- Department of Computer Science and Engineering, Institute of Engineering Research, Seoul National University, Seoul, South Korea
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Baur B, Shin J, Zhang S, Roy S. Data integration for inferring context-specific gene regulatory networks. CURRENT OPINION IN SYSTEMS BIOLOGY 2020; 23:38-46. [PMID: 33225112 PMCID: PMC7676633 DOI: 10.1016/j.coisb.2020.09.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Transcriptional regulatory networks control context-specific gene expression patterns and play important roles in normal and disease processes. Advances in genomics are rapidly increasing our ability to measure different components of the regulation machinery at the single-cell and bulk population level. An important challenge is to combine different types of regulatory genomic measurements to construct a more complete picture of gene regulatory networks across different disease, environmental, and developmental contexts. In this review, we focus on recent computational methods that integrate regulatory genomic data sets to infer context specificity and dynamics in regulatory networks.
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Affiliation(s)
- Brittany Baur
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, 53715, USA
| | - Junha Shin
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, 53715, USA
| | - Shilu Zhang
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, 53715, USA
| | - Sushmita Roy
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, 53715, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53715, USA
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Deng AC, Sun XQ. Dynamic gene regulatory network reconstruction and analysis based on clinical transcriptomic data of colorectal cancer. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2020; 17:3224-3239. [PMID: 32987526 DOI: 10.3934/mbe.2020183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Inferring dynamic regulatory networks that rewire at different stages is a reasonable way to understand the mechanisms underlying cancer development. In this study, we reconstruct the stage-specific gene regulatory networks (GRNs) for colorectal cancer to understand dynamic changes of gene regulations along different disease stages. We combined multiple sets of clinical transcriptomic data of colorectal cancer patients and employed a supervised approach to select initial gene set for network construction. We then developed a dynamical system-based optimization method to infer dynamic GRNs by incorporating mutual information-based network sparsification and a dynamic cascade technique into an ordinary differential equations model. Dynamic GRNs at four different stages of colorectal cancer were reconstructed and analyzed. Several important genes were revealed based on the rewiring of the reconstructed GRNs. Our study demonstrated that reconstructing dynamic GRNs based on clinical transcriptomic profiling allows us to detect the dynamic trend of gene regulation as well as reveal critical genes for cancer development which may be important candidates of master regulators for further experimental test.
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Affiliation(s)
- An Cheng Deng
- School of Life Science, Sun Yat-sen University, Guangzhou 510275, China
| | - Xiao Qiang Sun
- Key Laboratory of Tropical Disease Control, Chinese Ministry of Education, Zhong-Shan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
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