1
|
Zhou X, Zhou L, Qian F, Chen J, Zhang Y, Yu Z, Zhang J, Yang Y, Li Y, Song C, Wang Y, Shang D, Dong L, Zhu J, Li C, Wang Q. TFTG: A comprehensive database for human transcription factors and their targets. Comput Struct Biotechnol J 2024; 23:1877-1885. [PMID: 38707542 PMCID: PMC11068477 DOI: 10.1016/j.csbj.2024.04.036] [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: 01/27/2024] [Revised: 04/15/2024] [Accepted: 04/15/2024] [Indexed: 05/07/2024] Open
Abstract
Transcription factors (TFs) are major contributors to gene transcription, especially in controlling cell-specific gene expression and disease occurrence and development. Uncovering the relationship between TFs and their target genes is critical to understanding the mechanism of action of TFs. With the development of high-throughput sequencing techniques, a large amount of TF-related data has accumulated, which can be used to identify their target genes. In this study, we developed TFTG (Transcription Factor and Target Genes) database (http://tf.liclab.net/TFTG), which aimed to provide a large number of available human TF-target gene resources by multiple strategies, besides performing a comprehensive functional and epigenetic annotations and regulatory analyses of TFs. We identified extensive available TF-target genes by collecting and processing TF-associated ChIP-seq datasets, perturbation RNA-seq datasets and motifs. We also obtained experimentally confirmed relationships between TF and target genes from available resources. Overall, the target genes of TFs were obtained through integrating the relevant data of various TFs as well as fourteen identification strategies. Meanwhile, TFTG was embedded with user-friendly search, analysis, browsing, downloading and visualization functions. TFTG is designed to be a convenient resource for exploring human TF-target gene regulations, which will be useful for most users in the TF and gene expression regulation research.
Collapse
Affiliation(s)
- Xinyuan Zhou
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- College of Artificial Intelligence and Big Data For Medical Sciences, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
| | - Liwei Zhou
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Fengcui Qian
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Jiaxin Chen
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Yuexin Zhang
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Zhengmin Yu
- School of Computer, University of South China, Hengyang, Hunan 421001, China
| | - Jian Zhang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Yongsan Yang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Yanyu Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Chao Song
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Yuezhu Wang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Desi Shang
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Longlong Dong
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Jiang Zhu
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Chunquan Li
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Maternal and Child Health Care Hospital, National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Qiuyu Wang
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| |
Collapse
|
2
|
Yang B, Zhang M, Shi Y, Zheng BQ, Shi C, Lu D, Yang ZZ, Dong YM, Zhu L, Ma X, Zhang J, He J, Zhang Y, Hu K, Lin H, Liao JY, Yin D. PerturbDB for unraveling gene functions and regulatory networks. Nucleic Acids Res 2024:gkae777. [PMID: 39265120 DOI: 10.1093/nar/gkae777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2024] [Revised: 07/26/2024] [Accepted: 09/05/2024] [Indexed: 09/14/2024] Open
Abstract
Perturb-Seq combines CRISPR (clustered regularly interspaced short palindromic repeats)-based genetic screens with single-cell RNA sequencing readouts for high-content phenotypic screens. Despite the rapid accumulation of Perturb-Seq datasets, there remains a lack of a user-friendly platform for their efficient reuse. Here, we developed PerturbDB (http://research.gzsys.org.cn/perturbdb), a platform to help users unveil gene functions using Perturb-Seq datasets. PerturbDB hosts 66 Perturb-Seq datasets, which encompass 4 518 521 single-cell transcriptomes derived from the knockdown of 10 194 genes across 19 different cell lines. All datasets were uniformly processed using the Mixscape algorithm. Genes were clustered by their perturbed transcriptomic phenotypes derived from Perturb-Seq data, resulting in 421 gene clusters, 157 of which were stable across different cellular contexts. Through integrating chemically perturbed transcriptomes with Perturb-Seq data, we identified 552 potential inhibitors targeting 1409 genes, including an mammalian target of rapamycin (mTOR) signaling inhibitor, retinol, which was experimentally verified. Moreover, we developed a 'Cancer' module to facilitate the understanding of the regulatory role of genes in cancer using Perturb-Seq data. An interactive web interface has also been developed, enabling users to visualize, analyze and download all the comprehensive datasets available in PerturbDB. PerturbDB will greatly drive gene functional studies and enhance our understanding of the regulatory roles of genes in diseases such as cancer.
Collapse
Affiliation(s)
- Bing Yang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Man Zhang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Yanmei Shi
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Bing-Qi Zheng
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Chuanping Shi
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Daning Lu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Zhi-Zhi Yang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Yi-Ming Dong
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Liwen Zhu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Xingyu Ma
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Jingyuan Zhang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Jiehua He
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Yin Zhang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Kaishun Hu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Haoming Lin
- HBP Surgery Department, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Jian-You Liao
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
- Center for Precision Medicine, Shenshan Central Hospital, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 1 Heng Er Road, Dongyong Town, Shanwei, Guangdong, 516621, China
| | - Dong Yin
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| |
Collapse
|
3
|
Hanaki S, Habara M, Tomiyasu H, Sato Y, Miki Y, Masaki T, Shibutani S, Shimada M. NFAT activation by FKBP52 promotes cancer cell proliferation by suppressing p53. Life Sci Alliance 2024; 7:e202302426. [PMID: 38803221 PMCID: PMC11109481 DOI: 10.26508/lsa.202302426] [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: 10/09/2023] [Revised: 05/07/2024] [Accepted: 05/07/2024] [Indexed: 05/29/2024] Open
Abstract
FK506-binding protein 52 (FKBP52) is a member of the FKBP family of proline isomerases. FKBP52 is up-regulated in various cancers and functions as a positive regulator of steroid hormone receptors. Depletion of FKBP52 is known to inhibit cell proliferation; however, the detailed mechanism remains poorly understood. In this study, we found that FKBP52 depletion decreased MDM2 transcription, leading to stabilization of p53, and suppressed cell proliferation. We identified NFATc1 and NFATc3 as transcription factors that regulate MDM2 We also found that FKBP52 associated with NFATc3 and facilitated its nuclear translocation. In addition, calcineurin, a well-known Ca2+ phosphatase essential for activation of NFAT, plays a role in MDM2 transcription. Supporting this notion, MDM2 expression was found to be regulated by intracellular Ca2+ Taken together, these findings reveal a new role of FKBP52 in promoting cell proliferation via the NFAT-MDM2-p53 axis, and indicate that inhibition of FKBP52 could be a new therapeutic tool to activate p53 and inhibit cell proliferation.
Collapse
Affiliation(s)
- Shunsuke Hanaki
- https://ror.org/03cxys317 Department of Veterinary Biochemistry, Yamaguchi University, Yamaguchi, Japan
| | - Makoto Habara
- https://ror.org/03cxys317 Department of Veterinary Biochemistry, Yamaguchi University, Yamaguchi, Japan
| | - Haruki Tomiyasu
- https://ror.org/03cxys317 Department of Veterinary Biochemistry, Yamaguchi University, Yamaguchi, Japan
| | - Yuki Sato
- https://ror.org/03cxys317 Department of Veterinary Biochemistry, Yamaguchi University, Yamaguchi, Japan
| | - Yosei Miki
- https://ror.org/03cxys317 Department of Veterinary Biochemistry, Yamaguchi University, Yamaguchi, Japan
| | - Takahiro Masaki
- https://ror.org/03cxys317 Department of Veterinary Biochemistry, Yamaguchi University, Yamaguchi, Japan
| | - Shusaku Shibutani
- https://ror.org/03cxys317 Department of Veterinary Hygiene, Yamaguchi University, Yamaguchi, Japan
| | - Midori Shimada
- https://ror.org/03cxys317 Department of Veterinary Biochemistry, Yamaguchi University, Yamaguchi, Japan
- https://ror.org/04chrp450 Department of Molecular Biology, Nagoya University, Graduate School of Medicine, Nagoya, Japan
| |
Collapse
|
4
|
Liu Z, Yu K, Chen K, Liu J, Dai K, Zhao P. HAS2 facilitates glioma cell malignancy and suppresses ferroptosis in an FZD7-dependent manner. Cancer Sci 2024; 115:2602-2616. [PMID: 38816349 PMCID: PMC11309948 DOI: 10.1111/cas.16232] [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: 12/25/2023] [Revised: 05/07/2024] [Accepted: 05/14/2024] [Indexed: 06/01/2024] Open
Abstract
Glioma is the most common malignant tumor in the central nervous system, and it is crucial to uncover the factors that influence prognosis. In this study, we utilized Mfuzz to identify a gene set that showed a negative correlation with overall survival in patients with glioma. Gene Ontology (GO) enrichment analyses were then undertaken to gain insights into the functional characteristics and pathways associated with these genes. The expression distribution of Hyaluronan Synthase 2 (HAS2) was explored across multiple datasets, revealing its expression patterns. In vitro and in vivo experiments were carried out through gene knockdown and overexpression to validate the functionality of HAS2. Potential upstream transcription factors of HAS2 were predicted using transcriptional regulatory databases, and these predictions were experimentally validated using ChIP-PCR and dual-luciferase reporter gene assays. The results showed that elevated expression of HAS2 in glioma indicates poor prognosis. HAS2 was found to play a role in activating an antiferroptosis pathway in glioma cells. Inhibiting HAS2 significantly increased cellular sensitivity to ferroptosis-inducing agents. Finally, we determined that the oncogenic effect of HAS2 is mediated by the key receptor of the WNT pathway, FZD7.
Collapse
Affiliation(s)
- Zhiyuan Liu
- Department of NeurosurgeryThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Kuo Yu
- Department of NeurosurgeryThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Kaile Chen
- Department of NeurosurgeryThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Jinlai Liu
- Department of NeurosurgeryThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
- Department of Neurosurgery, Yang ZhongJiangsu Province People's HospitalYangzhouChina
| | - Kexiang Dai
- Department of NeurosugeryEmergency General HospitalBeijingChina
| | - Peng Zhao
- Department of NeurosurgeryThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| |
Collapse
|
5
|
Ma H, Qu J, Pang Z, Luo J, Yan M, Xu W, Zhuang H, Liu L, Qu Q. Super-enhancer omics in stem cell. Mol Cancer 2024; 23:153. [PMID: 39090713 PMCID: PMC11293198 DOI: 10.1186/s12943-024-02066-z] [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: 04/19/2024] [Accepted: 07/12/2024] [Indexed: 08/04/2024] Open
Abstract
The hallmarks of stem cells, such as proliferation, self-renewal, development, differentiation, and regeneration, are critical to maintain stem cell identity which is sustained by genetic and epigenetic factors. Super-enhancers (SEs), which consist of clusters of active enhancers, play a central role in maintaining stemness hallmarks by specifically transcriptional model. The SE-navigated transcriptional complex, including SEs, non-coding RNAs, master transcriptional factors, Mediators and other co-activators, forms phase-separated condensates, which offers a toggle for directing diverse stem cell fate. With the burgeoning technologies of multiple-omics applied to examine different aspects of SE, we firstly raise the concept of "super-enhancer omics", inextricably linking to Pan-omics. In the review, we discuss the spatiotemporal organization and concepts of SEs, and describe links between SE-navigated transcriptional complex and stem cell features, such as stem cell identity, self-renewal, pluripotency, differentiation and development. We also elucidate the mechanism of stemness and oncogenic SEs modulating cancer stem cells via genomic and epigenetic alterations hijack in cancer stem cell. Additionally, we discuss the potential of targeting components of the SE complex using small molecule compounds, genome editing, and antisense oligonucleotides to treat SE-associated organ dysfunction and diseases, including cancer. This review also provides insights into the future of stem cell research through the paradigm of SEs.
Collapse
Affiliation(s)
- Hongying Ma
- Department of Pharmacy, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, 410008, People's Republic of China
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, People's Republic of China
| | - Jian Qu
- Department of Pharmacy, the Second Xiangya Hospital, Institute of Clinical Pharmacy, Central South University, Changsha, 410011, People's Republic of China
- Hunan key laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, 410219, China
| | - Zicheng Pang
- Department of Pharmacy, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, 410008, People's Republic of China
- Department of Medical Genetics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China
| | - Jian Luo
- Department of Pharmacy, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, 410008, People's Republic of China
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, People's Republic of China
| | - Min Yan
- Department of Pharmacy, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, 410008, People's Republic of China
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, People's Republic of China
| | - Weixin Xu
- Department of Pharmacy, the Second Xiangya Hospital, Institute of Clinical Pharmacy, Central South University, Changsha, 410011, People's Republic of China
| | - Haihui Zhuang
- Department of Pharmacy, the Second Xiangya Hospital, Institute of Clinical Pharmacy, Central South University, Changsha, 410011, People's Republic of China
| | - Linxin Liu
- Department of Hematology, Xiangya Hospital, Central South University, Changsha, 410011, People's Republic of China
| | - Qiang Qu
- Department of Pharmacy, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, 410008, People's Republic of China.
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, People's Republic of China.
- Department of Hematology, Xiangya Hospital, Central South University, Changsha, 410011, People's Republic of China.
| |
Collapse
|
6
|
Lu Z, Xiao X, Zheng Q, Wang X, Xu L. Assessing next-generation sequencing-based computational methods for predicting transcriptional regulators with query gene sets. Brief Bioinform 2024; 25:bbae366. [PMID: 39082650 PMCID: PMC11289684 DOI: 10.1093/bib/bbae366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 06/21/2024] [Accepted: 07/18/2024] [Indexed: 08/03/2024] Open
Abstract
This article provides an in-depth review of computational methods for predicting transcriptional regulators (TRs) with query gene sets. Identification of TRs is of utmost importance in many biological applications, including but not limited to elucidating biological development mechanisms, identifying key disease genes, and predicting therapeutic targets. Various computational methods based on next-generation sequencing (NGS) data have been developed in the past decade, yet no systematic evaluation of NGS-based methods has been offered. We classified these methods into two categories based on shared characteristics, namely library-based and region-based methods. We further conducted benchmark studies to evaluate the accuracy, sensitivity, coverage, and usability of NGS-based methods with molecular experimental datasets. Results show that BART, ChIP-Atlas, and Lisa have relatively better performance. Besides, we point out the limitations of NGS-based methods and explore potential directions for further improvement.
Collapse
Affiliation(s)
- Zeyu Lu
- Department of Statistics and Data Science, Moody School of Graduate and Advanced Studies, Southern Methodist University, 3225 Daniel Ave., P.O. Box 750332, Dallas, TX, United States
| | - Xue Xiao
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, United States
| | - Qiang Zheng
- Division of Data Science, College of Science, University of Texas at Arlington, 501 S. Nedderman Dr., Arlington, TX 76019, United States
| | - Xinlei Wang
- Division of Data Science, College of Science, University of Texas at Arlington, 501 S. Nedderman Dr., Arlington, TX 76019, United States
- Department of Mathematics, University of Texas at Arlington, 411 S. Nedderman Dr., Arlington, TX 76019, United States
| | - Lin Xu
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, United States
- Department of Pediatrics, Division of Hematology/Oncology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, United States
| |
Collapse
|
7
|
Wang J. TFTF: An R-Based Integrative Tool for Decoding Human Transcription Factor-Target Interactions. Biomolecules 2024; 14:749. [PMID: 39062464 PMCID: PMC11274450 DOI: 10.3390/biom14070749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 06/20/2024] [Accepted: 06/21/2024] [Indexed: 07/28/2024] Open
Abstract
Transcription factors (TFs) are crucial in modulating gene expression and sculpting cellular and organismal phenotypes. The identification of TF-target gene interactions is pivotal for comprehending molecular pathways and disease etiologies but has been hindered by the demanding nature of traditional experimental approaches. This paper introduces a novel web application and package utilizing the R program, which predicts TF-target gene relationships and vice versa. Our application integrates the predictive power of various bioinformatic tools, leveraging their combined strengths to provide robust predictions. It merges databases for enhanced precision, incorporates gene expression correlation for accuracy, and employs pan-tissue correlation analysis for context-specific insights. The application also enables the integration of user data with established resources to analyze TF-target gene networks. Despite its current limitation to human data, it provides a platform to explore gene regulatory mechanisms comprehensively. This integrated, systematic approach offers researchers an invaluable tool for dissecting the complexities of gene regulation, with the potential for future expansions to include a broader range of species.
Collapse
Affiliation(s)
- Jin Wang
- School of Public Health, Suzhou Medical College, Soochow University, Suzhou 215123, China
| |
Collapse
|
8
|
Tabe-Bordbar S, Song YJ, Lunt BJ, Alavi Z, Prasanth KV, Sinha S. Mechanistic analysis of enhancer sequences in the estrogen receptor transcriptional program. Commun Biol 2024; 7:719. [PMID: 38862711 PMCID: PMC11167054 DOI: 10.1038/s42003-024-06400-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 05/30/2024] [Indexed: 06/13/2024] Open
Abstract
Estrogen Receptor α (ERα) is a major lineage determining transcription factor (TF) in mammary gland development. Dysregulation of ERα-mediated transcriptional program results in cancer. Transcriptomic and epigenomic profiling of breast cancer cell lines has revealed large numbers of enhancers involved in this regulatory program, but how these enhancers encode function in their sequence remains poorly understood. A subset of ERα-bound enhancers are transcribed into short bidirectional RNA (enhancer RNA or eRNA), and this property is believed to be a reliable marker of active enhancers. We therefore analyze thousands of ERα-bound enhancers and build quantitative, mechanism-aware models to discriminate eRNAs from non-transcribing enhancers based on their sequence. Our thermodynamics-based models provide insights into the roles of specific TFs in ERα-mediated transcriptional program, many of which are supported by the literature. We use in silico perturbations to predict TF-enhancer regulatory relationships and integrate these findings with experimentally determined enhancer-promoter interactions to construct a gene regulatory network. We also demonstrate that the model can prioritize breast cancer-related sequence variants while providing mechanistic explanations for their function. Finally, we experimentally validate the model-proposed mechanisms underlying three such variants.
Collapse
Affiliation(s)
- Shayan Tabe-Bordbar
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - You Jin Song
- Department of Cell and Developmental Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Bryan J Lunt
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Zahra Alavi
- Department of Physics, Loyola Marymount University, Los Angeles, CA, USA
| | - Kannanganattu V Prasanth
- Department of Cell and Developmental Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Saurabh Sinha
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
| |
Collapse
|
9
|
Liu JJ, Zhang X, Cai BL, Qi MM, Chi YB, Peng B, Zhang DH. Ferroptosis inhibitors reduce celastrol toxicity and preserve its insulin sensitizing effects in insulin resistant HepG2 cells. JOURNAL OF INTEGRATIVE MEDICINE 2024; 22:286-294. [PMID: 38565435 DOI: 10.1016/j.joim.2024.03.007] [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: 05/05/2023] [Accepted: 11/27/2023] [Indexed: 04/04/2024]
Abstract
OBJECTIVE Research has shown that celastrol can effectively treat a variety of diseases, yet when passing a certain dosage threshold, celastrol becomes toxic, causing complications such as liver and kidney damage and erythrocytopenia, among others. With this dichotomy in mind, it is extremely important to find ways to preserve celastrol's efficacy while reducing or preventing its toxicity. METHODS In this study, insulin-resistant HepG2 (IR-HepG2) cells were prepared using palmitic acid and used for in vitro experiments. IR-HepG2 cells were treated with celastrol alone or in combination with N-acetylcysteine (NAC) or ferrostatin-1 (Fer-1) for 12, 24 or 48 h, at a range of doses. Cell counting kit-8 assay, Western blotting, quantitative reverse transcription-polymerase chain reaction, glucose consumption assessment, and flow cytometry were performed to measure celastrol's cytotoxicity and whether the cell death was linked to ferroptosis. RESULTS Celastrol treatment increased lipid oxidation and decreased expression of anti-ferroptosis proteins in IR-HepG2 cells. Celastrol downregulated glutathione peroxidase 4 (GPX4) mRNA. Molecular docking models predicted that solute carrier family 7 member 11 (SLC7A11) and GPX4 were covalently bound by celastrol. Importantly, we found for the first time that the application of ferroptosis inhibitors (especially NAC) was able to reduce celastrol's toxicity while preserving its ability to improve insulin sensitivity in IR-HepG2 cells. CONCLUSION One potential mechanism of celastrol's cytotoxicity is the induction of ferroptosis, which can be alleviated by treatment with ferroptosis inhibitors. These findings provide a new strategy to block celastrol's toxicity while preserving its therapeutic effects. Please cite this article as: Liu JJ, Zhang X, Qi MM, Chi YB, Cai BL, Peng B, Zhang DH. Ferroptosis inhibitors reduce celastrol toxicity and preserve its insulin sensitizing effects in insulin resistant HepG2 cells. J Integr Med. 2024; 22(3): 286-294.
Collapse
Affiliation(s)
- Jia-Jia Liu
- School of Medicine, Shanghai University, Shanghai 200444, China; Shanghai Health Commission Key Lab of Artificial Intelligence-Based Management of Inflammation and Chronic Diseases, Shanghai Pudong Gongli Hospital, Secondary Military Medical University, Shanghai 200135, China
| | - Xue Zhang
- Shanghai Health Commission Key Lab of Artificial Intelligence-Based Management of Inflammation and Chronic Diseases, Shanghai Pudong Gongli Hospital, Secondary Military Medical University, Shanghai 200135, China; School of Basic Medicine, Ningxia Medical University, Yinchuan 750004, Ningxia Hui Autonomous Region, China
| | - Bang-Lan Cai
- Shanghai Health Commission Key Lab of Artificial Intelligence-Based Management of Inflammation and Chronic Diseases, Shanghai Pudong Gongli Hospital, Secondary Military Medical University, Shanghai 200135, China; School of Basic Medicine, Ningxia Medical University, Yinchuan 750004, Ningxia Hui Autonomous Region, China
| | - Man-Man Qi
- School of Medicine, Shanghai University, Shanghai 200444, China; Shanghai Health Commission Key Lab of Artificial Intelligence-Based Management of Inflammation and Chronic Diseases, Shanghai Pudong Gongli Hospital, Secondary Military Medical University, Shanghai 200135, China
| | - Yong-Bin Chi
- Shanghai Health Commission Key Lab of Artificial Intelligence-Based Management of Inflammation and Chronic Diseases, Shanghai Pudong Gongli Hospital, Secondary Military Medical University, Shanghai 200135, China
| | - Bin Peng
- School of Medicine, Shanghai University, Shanghai 200444, China; Shanghai Health Commission Key Lab of Artificial Intelligence-Based Management of Inflammation and Chronic Diseases, Shanghai Pudong Gongli Hospital, Secondary Military Medical University, Shanghai 200135, China.
| | - Deng-Hai Zhang
- School of Medicine, Shanghai University, Shanghai 200444, China; Shanghai Health Commission Key Lab of Artificial Intelligence-Based Management of Inflammation and Chronic Diseases, Shanghai Pudong Gongli Hospital, Secondary Military Medical University, Shanghai 200135, China; School of Basic Medicine, Ningxia Medical University, Yinchuan 750004, Ningxia Hui Autonomous Region, China.
| |
Collapse
|
10
|
Yuan Q, Duren Z. Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data. Nat Biotechnol 2024:10.1038/s41587-024-02182-7. [PMID: 38609714 DOI: 10.1038/s41587-024-02182-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 02/26/2024] [Indexed: 04/14/2024]
Abstract
Existing methods for gene regulatory network (GRN) inference rely on gene expression data alone or on lower resolution bulk data. Despite the recent integration of chromatin accessibility and RNA sequencing data, learning complex mechanisms from limited independent data points still presents a daunting challenge. Here we present LINGER (Lifelong neural network for gene regulation), a machine-learning method to infer GRNs from single-cell paired gene expression and chromatin accessibility data. LINGER incorporates atlas-scale external bulk data across diverse cellular contexts and prior knowledge of transcription factor motifs as a manifold regularization. LINGER achieves a fourfold to sevenfold relative increase in accuracy over existing methods and reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Following the GRN inference from reference single-cell multiome data, LINGER enables the estimation of transcription factor activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies.
Collapse
Affiliation(s)
- Qiuyue Yuan
- Center for Human Genetics, Department of Genetics and Biochemistry, Clemson University, Greenwood, SC, USA
| | - Zhana Duren
- Center for Human Genetics, Department of Genetics and Biochemistry, Clemson University, Greenwood, SC, USA.
| |
Collapse
|
11
|
Wu X, Cao C, Li Z, Xie Y, Zhang S, Sun W, Guo J. Circular RNA CircSLC22A23 Promotes Gastric Cancer Progression by Activating HNRNPU Expression. Dig Dis Sci 2024; 69:1200-1213. [PMID: 38400886 DOI: 10.1007/s10620-024-08291-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 01/09/2024] [Indexed: 02/26/2024]
Abstract
BACKGROUND Circular RNAs (CircRNAs) play essential roles in cancer occurrence as regulatory RNAs. However, circRNA-mediated regulation of gastric cancer (GC) remains poorly understood. AIM The purpose of this study was to investigate the molecular mechanism of circSLC22A23 (hsa_circ_0075504) underlying GC occurrence. METHODS CircSLC22A23 levels were first quantified by quantitative real-time reverse transcription-polymerase chain reaction in GC cell lines, 80 paired GC tissues and adjacent normal tissues, and 27 pairs of plasma samples from preoperative and postoperative patients with GC. Then circSLC22A23 was knocked-down with short hairpin RNA to analyze its oncogenic effects on the proliferation, migration, and invasion of GC cells. Finally, circRNA-binding proteins and their downstream target genes were identified by RNA pulldown, mass spectrometry, RNA immunoprecipitation, quantitative real-time reverse transcription-polymerase chain reaction, and Western blot assays. RESULTS CircSLC22A23 was found to be highly expressed in GC cells, GC tissues, and plasma from GC patients. Knockdown of circSLC22A23 inhibited GC cell proliferation, migration and invasion. RNA pulldown and RNA immunoprecipitation assays verified the interaction between circSLC22A23 and heterogeneous nuclear ribonucleoprotein U (HNRNPU). Knockdown of circSLC22A23 decreased HNRNPU protein levels. Moreover, rescue assays showed that the tumor suppressive effect of circSLC22A23 knockdown was reversed by HNRNPU overexpression. Finally, epidermal growth factor receptor (EGFR) was found to be one of the downstream target genes of HNRNPU that was up regulated by circSLC22A23. CONCLUSION CircSLC22A23 regulated the transcription of EGFR through activation of HNRNPU in GC cells, suggesting that circSLC22A23 may serve as a potential therapeutic target for the treatment of GC.
Collapse
Affiliation(s)
- Xinxin Wu
- Department of Biochemistry and Molecular Biology and Zhejiang Key Laboratory of Pathophysiology, School of Basic Medical Sciences, Health Science Center, Ningbo University, Ningbo, 315211, China
- Department of Gastroenterology, The Affiliated No. 1 Hospital, Ningbo University, Ningbo, 315211, China
| | - Chunli Cao
- Department of Biochemistry and Molecular Biology and Zhejiang Key Laboratory of Pathophysiology, School of Basic Medical Sciences, Health Science Center, Ningbo University, Ningbo, 315211, China
- The Affiliated People's Hospital, Ningbo University, Ningbo, 315040, China
| | - Zhe Li
- Department of Gastroenterology, The Affiliated No. 1 Hospital, Ningbo University, Ningbo, 315211, China
| | - Yaoyao Xie
- Department of Biochemistry and Molecular Biology and Zhejiang Key Laboratory of Pathophysiology, School of Basic Medical Sciences, Health Science Center, Ningbo University, Ningbo, 315211, China
- Department of Gastroenterology, The Affiliated No. 1 Hospital, Ningbo University, Ningbo, 315211, China
| | - Shuangshuang Zhang
- Department of Biochemistry and Molecular Biology and Zhejiang Key Laboratory of Pathophysiology, School of Basic Medical Sciences, Health Science Center, Ningbo University, Ningbo, 315211, China
- Department of Gastroenterology, The Affiliated No. 1 Hospital, Ningbo University, Ningbo, 315211, China
| | - Weiliang Sun
- The Affiliated People's Hospital, Ningbo University, Ningbo, 315040, China
| | - Junming Guo
- Department of Biochemistry and Molecular Biology and Zhejiang Key Laboratory of Pathophysiology, School of Basic Medical Sciences, Health Science Center, Ningbo University, Ningbo, 315211, China.
- Department of Gastroenterology, The Affiliated No. 1 Hospital, Ningbo University, Ningbo, 315211, China.
- Institute of Digestive Diseases of Ningbo University, Ningbo, 315211, China.
| |
Collapse
|
12
|
Lu Z, Xiao X, Zheng Q, Wang X, Xu L. Assessing NGS-based computational methods for predicting transcriptional regulators with query gene sets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.01.578316. [PMID: 38562775 PMCID: PMC10983863 DOI: 10.1101/2024.02.01.578316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
This article provides an in-depth review of computational methods for predicting transcriptional regulators with query gene sets. Identification of transcriptional regulators is of utmost importance in many biological applications, including but not limited to elucidating biological development mechanisms, identifying key disease genes, and predicting therapeutic targets. Various computational methods based on next-generation sequencing (NGS) data have been developed in the past decade, yet no systematic evaluation of NGS-based methods has been offered. We classified these methods into two categories based on shared characteristics, namely library-based and region-based methods. We further conducted benchmark studies to evaluate the accuracy, sensitivity, coverage, and usability of NGS-based methods with molecular experimental datasets. Results show that BART, ChIP-Atlas, and Lisa have relatively better performance. Besides, we point out the limitations of NGS-based methods and explore potential directions for further improvement. Key points An introduction to available computational methods for predicting functional TRs from a query gene set.A detailed walk-through along with practical concerns and limitations.A systematic benchmark of NGS-based methods in terms of accuracy, sensitivity, coverage, and usability, using 570 TR perturbation-derived gene sets.NGS-based methods outperform motif-based methods. Among NGS methods, those utilizing larger databases and adopting region-centric approaches demonstrate favorable performance. BART, ChIP-Atlas, and Lisa are recommended as these methods have overall better performance in evaluated scenarios.
Collapse
|
13
|
Peng G, Liu B, Zheng M, Zhang L, Li H, Liu M, Liang Y, Chen T, Luo X, Shi X, Ren J, Zheng Y. TSCRE: a comprehensive database for tumor-specific cis-regulatory elements. NAR Cancer 2024; 6:zcad063. [PMID: 38213995 PMCID: PMC10782923 DOI: 10.1093/narcan/zcad063] [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: 08/28/2023] [Revised: 11/18/2023] [Accepted: 12/31/2023] [Indexed: 01/13/2024] Open
Abstract
Cis-regulatory elements (CREs) and super cis-regulatory elements (SCREs) are non-coding DNA regions which influence the transcription of nearby genes and play critical roles in development. Dysregulated CRE and SCRE activities have been reported to alter the expression of oncogenes and tumor suppressors, thereby regulating cancer hallmarks. To address the strong need for a comprehensive catalogue of dysregulated CREs and SCREs in human cancers, we present TSCRE (http://tscre.zsqylab.com/), an open resource providing tumor-specific and cell type-specific CREs and SCREs derived from the re-analysis of publicly available histone modification profiles. Currently, TSCRE contains 1 864 941 dysregulated CREs and 68 253 dysregulated SCREs identified from 1366 human patient samples spanning 17 different cancer types and 9 histone marks. Over 95% of these elements have been validated in public resources. TSCRE offers comprehensive annotations for each element, including associated genes, expression patterns, clinical prognosis, somatic mutations, transcript factor binding sites, cancer-type specificity, and drug response. Additionally, TSCRE integrates pathway and transcript factor enrichment analyses for each study, enabling in-depth functional and mechanistic investigations. Furthermore, TSCRE provides an interactive interface for users to explore any CRE and SCRE of interest. We believe TSCRE will be a highly valuable platform for the community to discover candidate cancer biomarkers.
Collapse
Affiliation(s)
- Guanjie Peng
- Clinical Big Data Research Center, Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou 510060, China
- Guangzhou Municipal and Guangdong Provincial Key Laboratory of Protein Modification and Degradation, Affiliated Cancer Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou 510120, China
| | - Bingyuan Liu
- Clinical Big Data Research Center, Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou 510060, China
- Guangzhou Municipal and Guangdong Provincial Key Laboratory of Protein Modification and Degradation, Affiliated Cancer Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou 510120, China
| | - Mohan Zheng
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou 510060, China
| | - Luowanyue Zhang
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou 510060, China
| | - Huiqin Li
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou 510060, China
| | - Mengni Liu
- Clinical Big Data Research Center, Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China
| | - Yuan Liang
- Clinical Big Data Research Center, Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China
| | - Tianjian Chen
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou 510060, China
| | - Xiaotong Luo
- Guangdong Institute of Gastroenterology, Department of General Surgery, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510060, China
| | - Xianping Shi
- Guangzhou Municipal and Guangdong Provincial Key Laboratory of Protein Modification and Degradation, Affiliated Cancer Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou 510120, China
| | - Jian Ren
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou 510060, China
| | - Yueyuan Zheng
- Clinical Big Data Research Center, Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China
| |
Collapse
|
14
|
Li J, Meng Z, Cao Z, Lu W, Yang Y, Li Z, Lu S. ADGRE5-centered Tsurv model in T cells recognizes responders to neoadjuvant cancer immunotherapy. Front Immunol 2024; 15:1304183. [PMID: 38343549 PMCID: PMC10853338 DOI: 10.3389/fimmu.2024.1304183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 01/02/2024] [Indexed: 02/15/2024] Open
Abstract
Background Neoadjuvant immunotherapy with anti-programmed death-1 (neo-antiPD1) has revolutionized perioperative methods for improvement of overall survival (OS), while approaches for major pathologic response patients' (MPR) recognition along with methods for overcoming non-MPR resistance are still in urgent need. Methods We utilized and integrated publicly-available immune checkpoint inhibitors regimens (ICIs) single-cell (sc) data as the discovery datasets, and innovatively developed a cell-communication analysis pipeline, along with a VIPER-based-SCENIC process, to thoroughly dissect MPR-responding subsets. Besides, we further employed our own non-small cell lung cancer (NSCLC) ICIs cohort's sc data for validation in-silico. Afterward, we resorted to ICIs-resistant murine models developed by us with multimodal investigation, including bulk-RNA-sequencing, Chip-sequencing and high-dimensional cytometry by time of flight (CYTOF) to consolidate our findings in-vivo. To comprehensively explore mechanisms, we adopted 3D ex-vivo hydrogel models for analysis. Furthermore, we constructed an ADGRE5-centered Tsurv model from our discovery dataset by machine learning (ML) algorithms for a wide range of tumor types (NSCLC, melanoma, urothelial cancer, etc.) and verified it in peripheral blood mononuclear cells (PBMCs) sc datasets. Results Through a meta-analysis of multimodal sequential sc sequencing data from pre-ICIs and post-ICIs, we identified an MPR-expanding T cells meta-cluster (MPR-E) in the tumor microenvironment (TME), characterized by a stem-like CD8+ T cluster (survT) with STAT5-ADGRE5 axis enhancement compared to non-MPR or pre-ICIs TME. Through multi-omics analysis of murine TME, we further confirmed the existence of survT with silenced function and immune checkpoints (ICs) in MPR-E. After verification of the STAT5-ADGRE5 axis of survT in independent ICIs cohorts, an ADGRE5-centered Tsurv model was then developed through ML for identification of MPR patients pre-ICIs and post-ICIs, both in TME and PBMCs, which was further verified in pan-cancer immunotherapy cohorts. Mechanistically, we unveiled ICIs stimulated ADGRE5 upregulation in a STAT5-IL32 dependent manner in a 3D ex-vivo system (3D-HYGTIC) developed by us previously, which marked Tsurv with better survival flexibility, enhanced stemness and potential cytotoxicity within TME. Conclusion Our research provides insights into mechanisms underlying MPR in neo-antiPD1 and a well-performed model for the identification of non-MPR.
Collapse
Affiliation(s)
| | | | | | | | | | - Ziming Li
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China
| | - Shun Lu
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China
| |
Collapse
|
15
|
Feng C, Song C, Song S, Zhang G, Yin M, Zhang Y, Qian F, Wang Q, Guo M, Li C. KnockTF 2.0: a comprehensive gene expression profile database with knockdown/knockout of transcription (co-)factors in multiple species. Nucleic Acids Res 2024; 52:D183-D193. [PMID: 37956336 PMCID: PMC10767813 DOI: 10.1093/nar/gkad1016] [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: 09/15/2023] [Revised: 10/17/2023] [Accepted: 10/28/2023] [Indexed: 11/15/2023] Open
Abstract
Transcription factors (TFs), transcription co-factors (TcoFs) and their target genes perform essential functions in diseases and biological processes. KnockTF 2.0 (http://www.licpathway.net/KnockTF/index.html) aims to provide comprehensive gene expression profile datasets before/after T(co)F knockdown/knockout across multiple tissue/cell types of different species. Compared with KnockTF 1.0, KnockTF 2.0 has the following improvements: (i) Newly added T(co)F knockdown/knockout datasets in mice, Arabidopsis thaliana and Zea mays and also an expanded scale of datasets in humans. Currently, KnockTF 2.0 stores 1468 manually curated RNA-seq and microarray datasets associated with 612 TFs and 172 TcoFs disrupted by different knockdown/knockout techniques, which are 2.5 times larger than those of KnockTF 1.0. (ii) Newly added (epi)genetic annotations for T(co)F target genes in humans and mice, such as super-enhancers, common SNPs, methylation sites and chromatin interactions. (iii) Newly embedded and updated search and analysis tools, including T(co)F Enrichment (GSEA), Pathway Downstream Analysis and Search by Target Gene (BLAST). KnockTF 2.0 is a comprehensive update of KnockTF 1.0, which provides more T(co)F knockdown/knockout datasets and (epi)genetic annotations across multiple species than KnockTF 1.0. KnockTF 2.0 facilitates not only the identification of functional T(co)Fs and target genes but also the investigation of their roles in the physiological and pathological processes.
Collapse
Affiliation(s)
- Chenchen Feng
- National Health Commission Key Laboratory of Birth Defect Research and Prevention & School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan, 421001, China
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China
| | - Chao Song
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Shuang Song
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Guorui Zhang
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Mingxue Yin
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Yuexin Zhang
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Fengcui Qian
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Qiuyu Wang
- National Health Commission Key Laboratory of Birth Defect Research and Prevention & School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Maozu Guo
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
| | - Chunquan Li
- National Health Commission Key Laboratory of Birth Defect Research and Prevention & School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan, 421001, China
- MOE Key Lab of Rare Pediatric Diseases, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| |
Collapse
|
16
|
Huang X, Song C, Zhang G, Li Y, Zhao Y, Zhang Q, Zhang Y, Fan S, Zhao J, Xie L, Li C. scGRN: a comprehensive single-cell gene regulatory network platform of human and mouse. Nucleic Acids Res 2024; 52:D293-D303. [PMID: 37889053 PMCID: PMC10767939 DOI: 10.1093/nar/gkad885] [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: 08/15/2023] [Revised: 09/19/2023] [Accepted: 10/12/2023] [Indexed: 10/28/2023] Open
Abstract
Gene regulatory networks (GRNs) are interpretable graph models encompassing the regulatory interactions between transcription factors (TFs) and their downstream target genes. Making sense of the topology and dynamics of GRNs is fundamental to interpreting the mechanisms of disease etiology and translating corresponding findings into novel therapies. Recent advances in single-cell multi-omics techniques have prompted the computational inference of GRNs from single-cell transcriptomic and epigenomic data at an unprecedented resolution. Here, we present scGRN (https://bio.liclab.net/scGRN/), a comprehensive single-cell multi-omics gene regulatory network platform of human and mouse. The current version of scGRN catalogs 237 051 cell type-specific GRNs (62 999 692 TF-target gene pairs), covering 160 tissues/cell lines and 1324 single-cell samples. scGRN is the first resource documenting large-scale cell type-specific GRN information of diverse human and mouse conditions inferred from single-cell multi-omics data. We have implemented multiple online tools for effective GRN analysis, including differential TF-target network analysis, TF enrichment analysis, and pathway downstream analysis. We also provided details about TF binding to promoters, super-enhancers and typical enhancers of target genes in GRNs. Taken together, scGRN is an integrative and useful platform for searching, browsing, analyzing, visualizing and downloading GRNs of interest, enabling insight into the differences in regulatory mechanisms across diverse conditions.
Collapse
Affiliation(s)
- Xuemei Huang
- The First Affiliated Hospital & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases & College of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Chao Song
- The First Affiliated Hospital & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases & College of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Guorui Zhang
- The First Affiliated Hospital & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases & College of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Ye Li
- The First Affiliated Hospital & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases & College of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Yu Zhao
- The First Affiliated Hospital & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases & College of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Qinyi Zhang
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases & College of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Yuexin Zhang
- The First Affiliated Hospital & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases & College of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Shifan Fan
- The First Affiliated Hospital & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases & College of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Jun Zhao
- The First Affiliated Hospital & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Liyuan Xie
- The First Affiliated Hospital & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases & College of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Chunquan Li
- The First Affiliated Hospital & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases & College of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Maternal and Child Health Care Hospital, National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| |
Collapse
|
17
|
Song C, Zhang G, Mu X, Feng C, Zhang Q, Song S, Zhang Y, Yin M, Zhang H, Tang H, Li C. eRNAbase: a comprehensive database for decoding the regulatory eRNAs in human and mouse. Nucleic Acids Res 2024; 52:D81-D91. [PMID: 37889077 PMCID: PMC10767853 DOI: 10.1093/nar/gkad925] [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: 08/14/2023] [Revised: 09/26/2023] [Accepted: 10/12/2023] [Indexed: 10/28/2023] Open
Abstract
Enhancer RNAs (eRNAs) transcribed from distal active enhancers serve as key regulators in gene transcriptional regulation. The accumulation of eRNAs from multiple sequencing assays has led to an urgent need to comprehensively collect and process these data to illustrate the regulatory landscape of eRNAs. To address this need, we developed the eRNAbase (http://bio.liclab.net/eRNAbase/index.php) to store the massive available resources of human and mouse eRNAs and provide comprehensive annotation and analyses for eRNAs. The current version of eRNAbase cataloged 10 399 928 eRNAs from 1012 samples, including 858 human samples and 154 mouse samples. These eRNAs were first identified and uniformly processed from 14 eRNA-related experiment types manually collected from GEO/SRA and ENCODE. Importantly, the eRNAbase provides detailed and abundant (epi)genetic annotations in eRNA regions, such as super enhancers, enhancers, common single nucleotide polymorphisms, expression quantitative trait loci, transcription factor binding sites, CRISPR/Cas9 target sites, DNase I hypersensitivity sites, chromatin accessibility regions, methylation sites, chromatin interactions regions, topologically associating domains and RNA spatial interactions. Furthermore, the eRNAbase provides users with three novel analyses including eRNA-mediated pathway regulatory analysis, eRNA-based variation interpretation analysis and eRNA-mediated TF-target gene analysis. Hence, eRNAbase is a powerful platform to query, browse and visualize regulatory cues associated with eRNAs.
Collapse
Affiliation(s)
- Chao Song
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Guorui Zhang
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Xinxin Mu
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Chenchen Feng
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
| | - Qinyi Zhang
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Shuang Song
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Yuexin Zhang
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Mingxue Yin
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Hang Zhang
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Huifang Tang
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Clinical Research Center for Myocardial Injury in Hunan Province, Hengyang, Hunan, 421001, China
| | - Chunquan Li
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Maternal and Child Health Care Hospital, National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| |
Collapse
|
18
|
Zhang G, Song C, Fan S, Yin M, Wang X, Zhang Y, Huang X, Li Y, Shang D, Li C, Wang Q. LncSEA 2.0: an updated platform for long non-coding RNA related sets and enrichment analysis. Nucleic Acids Res 2024; 52:D919-D928. [PMID: 37986229 PMCID: PMC10767924 DOI: 10.1093/nar/gkad1008] [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: 09/15/2023] [Revised: 10/12/2023] [Accepted: 10/19/2023] [Indexed: 11/22/2023] Open
Abstract
Long non-coding RNAs (lncRNAs) possess a wide range of biological functions, and research has demonstrated their significance in regulating major biological processes such as development, differentiation, and immune response. The accelerating accumulation of lncRNA research has greatly expanded our understanding of lncRNA functions. Here, we introduce LncSEA 2.0 (http://bio.liclab.net/LncSEA/index.php), aiming to provide a more comprehensive set of functional lncRNAs and enhanced enrichment analysis capabilities. Compared with LncSEA 1.0, we have made the following improvements: (i) We updated the lncRNA sets for 11 categories and extremely expanded the lncRNA scopes for each set. (ii) We newly introduced 15 functional lncRNA categories from multiple resources. This update not only included a significant amount of downstream regulatory data for lncRNAs, but also covered numerous epigenetic regulatory data sets, including lncRNA-related transcription co-factor binding, chromatin regulator binding, and chromatin interaction data. (iii) We incorporated two new lncRNA set enrichment analysis functions based on GSEA and GSVA. (iv) We adopted the snakemake analysis pipeline to track data processing and analysis. In summary, LncSEA 2.0 offers a more comprehensive collection of lncRNA sets and a greater variety of enrichment analysis modules, assisting researchers in a more comprehensive study of the functional mechanisms of lncRNAs.
Collapse
Affiliation(s)
- Guorui Zhang
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Chao Song
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Shifan Fan
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
| | - Mingxue Yin
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Xinyue Wang
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing, 163319, China
| | - Yuexin Zhang
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Xuemei Huang
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
| | - Ye Li
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Desi Shang
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Chunquan Li
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
- MOE Key Lab of Rare Pediatric Diseases, University of South China, Hengyang, Hunan 421001, China
| | - Qiuyu Wang
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| |
Collapse
|
19
|
Zhu X, Li C, Gao Y, Zhang Q, Wang T, Zhou H, Bu F, Chen J, Mao X, He Y, Wu K, Li N, Luo H. The feedback loop of EFTUD2/c-MYC impedes chemotherapeutic efficacy by enhancing EFTUD2 transcription and stabilizing c-MYC protein in colorectal cancer. J Exp Clin Cancer Res 2024; 43:7. [PMID: 38163859 PMCID: PMC10759692 DOI: 10.1186/s13046-023-02873-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 10/27/2023] [Indexed: 01/03/2024] Open
Abstract
BACKGROUND Chemoresistance presents a significant obstacle in the treatment of colorectal cancer (CRC), yet the molecular basis underlying CRC chemoresistance remains poorly understood, impeding the development of new therapeutic interventions. Elongation factor Tu GTP binding domain containing 2 (EFTUD2) has emerged as a potential oncogenic factor implicated in various cancer types, where it fosters tumor growth and survival. However, its specific role in modulating the sensitivity of CRC cells to chemotherapy is still unclear. METHODS Public dataset analysis and in-house sample validation were conducted to assess the expression of EFTUD2 in 5-fluorouracil (5-FU) chemotherapy-resistant CRC cells and the potential of EFTUD2 as a prognostic indicator for CRC. Experiments both in vitro, including MTT assay, EdU cell proliferation assay, TUNEL assay, and clone formation assay and in vivo, using cell-derived xenograft models, were performed to elucidate the function of EFTUD2 in sensitivity of CRC cells to 5-FU treatment. The molecular mechanism on the reciprocal regulation between EFTUD2 and the oncogenic transcription factor c-MYC was investigated through molecular docking, ubiquitination assay, chromatin immunoprecipitation (ChIP), dual luciferase reporter assay, and co-immunoprecipitation (Co-IP). RESULTS We found that EFTUD2 expression was positively correlated with 5-FU resistance, higher pathological grade, and poor prognosis in CRC patients. We also demonstrated both in vitro and in vivo that knockdown of EFTUD2 sensitized CRC cells to 5-FU treatment, whereas overexpression of EFTUD2 impaired such sensitivity. Mechanistically, we uncovered that EFTUD2 physically interacted with and stabilized c-MYC protein by preventing its ubiquitin-mediated proteasomal degradation. Intriguingly, we found that c-MYC directly bound to the promoter region of EFTUD2 gene, activating its transcription. Leveraging rescue experiments, we further confirmed that the effect of EFTUD2 on 5-FU resistance was dependent on c-MYC stabilization. CONCLUSION Our findings revealed a positive feedback loop involving an EFTUD2/c-MYC axis that hampers the efficacy of 5-FU chemotherapy in CRC cells by increasing EFTUD2 transcription and stabilizing c-MYC oncoprotein. This study highlights the potential of EFTUD2 as a promising therapeutic target to surmount chemotherapy resistance in CRC patients.
Collapse
Affiliation(s)
- Xiaojian Zhu
- Tomas Lindahl Nobel Laureate Laboratory, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, 518107, China
| | - Changxue Li
- Tomas Lindahl Nobel Laureate Laboratory, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, 518107, China
- Digestive Diseases Center, Guangdong Provincial Key Laboratory of Digestive Cancer Research, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, 518107, China
| | - Yunfei Gao
- Tomas Lindahl Nobel Laureate Laboratory, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, 518107, China
- Department of Otolaryngology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, 518107, China
| | - Qingyuan Zhang
- Tomas Lindahl Nobel Laureate Laboratory, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, 518107, China
- Digestive Diseases Center, Guangdong Provincial Key Laboratory of Digestive Cancer Research, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, 518107, China
| | - Tao Wang
- Tomas Lindahl Nobel Laureate Laboratory, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, 518107, China
| | - Huaixiang Zhou
- Tomas Lindahl Nobel Laureate Laboratory, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, 518107, China
| | - Fanqin Bu
- Department of Gastroenterology, Beijing Friendship Hospital, National Clinical Research Center for Digestive Disease, Capital Medical University, Beijing, 100050, China
| | - Jia Chen
- Tomas Lindahl Nobel Laureate Laboratory, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, 518107, China
- Digestive Diseases Center, Guangdong Provincial Key Laboratory of Digestive Cancer Research, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, 518107, China
| | - Xinjun Mao
- Department of Anesthesiology, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, 533000, China
| | - Yulong He
- Tomas Lindahl Nobel Laureate Laboratory, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, 518107, China.
- Digestive Diseases Center, Guangdong Provincial Key Laboratory of Digestive Cancer Research, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, 518107, China.
| | - Kaiming Wu
- Tomas Lindahl Nobel Laureate Laboratory, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, 518107, China.
- Digestive Diseases Center, Guangdong Provincial Key Laboratory of Digestive Cancer Research, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, 518107, China.
| | - Ningning Li
- Tomas Lindahl Nobel Laureate Laboratory, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, 518107, China.
- China-UK Institute for Frontier Science, Shenzhen, 518107, China.
| | - Hongliang Luo
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, China.
| |
Collapse
|
20
|
Minaeva M, Domingo J, Rentzsch P, Lappalainen T. Specifying cellular context of transcription factor regulons for exploring context-specific gene regulation programs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.31.573765. [PMID: 38260658 PMCID: PMC10802353 DOI: 10.1101/2023.12.31.573765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Understanding the role of transcription and transcription factors in cellular identity and disease, such as cancer and autoimmunity, is essential. However, comprehensive data resources for cell line-specific transcription factor-to-target gene annotations are currently limited. To address this, we developed a straightforward method to define regulons that capture the cell-specific aspects of TF binding and transcript expression levels. By integrating cellular transcriptome and transcription factor binding data, we generated regulons for four common cell lines comprising both proximal and distal cell line-specific regulatory events. Through systematic benchmarking involving transcription factor knockout experiments, we demonstrated performance on par with state-of-the-art methods, with our method being easily applicable to other cell types of interest. We present case studies using three cancer single-cell datasets to showcase the utility of these cell-type-specific regulons in exploring transcriptional dysregulation. In summary, this study provides a valuable tool and a resource for systematically exploring cell line-specific transcriptional regulations, emphasizing the utility of network analysis in deciphering disease mechanisms.
Collapse
Affiliation(s)
- Mariia Minaeva
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Solna, 17165, Sweden
| | | | - Philipp Rentzsch
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Solna, 17165, Sweden
| | - Tuuli Lappalainen
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Solna, 17165, Sweden
- New York Genome Center, New York, NY 10013, USA
| |
Collapse
|
21
|
Sanchez-Lopez JM, Juarez-Mancera MA, Bustamante B, Ruiz-Silvestre A, Espinosa M, Mendoza-Almanza G, Ceballos-Cancino G, Melendez-Zajgla J, Maldonado V, Lizarraga F. Decoding LINC00052 role in breast cancer by bioinformatic and experimental analyses. RNA Biol 2024; 21:1-11. [PMID: 38832821 PMCID: PMC11152094 DOI: 10.1080/15476286.2024.2355393] [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] [Accepted: 05/09/2024] [Indexed: 06/06/2024] Open
Abstract
LncRNA is a group of transcripts with a length exceeding 200 nucleotides that contribute to tumour development. Our research group found that LINC00052 expression was repressed during the formation of breast cancer (BC) multicellular spheroids. Intriguingly, LINC00052 precise role in BC remains uncertain. We explored LINC00052 expression in BC patients` RNA samples (TCGA) in silico, as well as in an in-house patient cohort, and inferred its cellular and molecular mechanisms. In vitro studies evaluated LINC00052 relevance in BC cells viability, cell cycle and DNA damage. Results. Bioinformatic RNAseq analysis of BC patients showed that LINC00052 is overexpressed in samples from all BC molecular subtypes. A similar LINC00052 expression pattern was observed in an in-house patient cohort. In addition, higher LINC00052 levels are related to better BC patient´s overall survival. Remarkably, MCF-7 and ZR-75-1 cells treated with estradiol showed increased LINC00052 expression compared to control, while these changes were not observed in MDA-MB-231 cells. In parallel, bioinformatic analyses indicated that LINC00052 influences DNA damage and cell cycle. MCF-7 cells with low LINC00052 levels exhibited increased cellular protection against DNA damage and diminished growth capacity. Furthermore, in cisplatin-resistant MCF-7 cells, LINC00052 expression was downregulated. Conclusion. This work shows that LINC00052 expression is associated with better BC patient survival. Remarkably, LINC00052 expression can be regulated by Estradiol. Additionally, assays suggest that LINC00052 could modulate MCF-7 cells growth and DNA damage repair. Overall, this study highlights the need for further research to unravel LINC00052 molecular mechanisms and potential clinical applications in BC.
Collapse
Affiliation(s)
- Jose Manuel Sanchez-Lopez
- Laboratorio de Epigenetica, Instituto Nacional de Medicina Genomica (INMEGEN), Ciudad de México, Mexico
| | | | - Benjamin Bustamante
- Laboratorio de Genomica Funcional del Cancer, Instituto Nacional de Medicina Genomica (INMEGEN), Ciudad de México, Mexico
| | - Araceli Ruiz-Silvestre
- Laboratorio de Epigenetica, Instituto Nacional de Medicina Genomica (INMEGEN), Ciudad de México, Mexico
| | - Magali Espinosa
- Laboratorio de Genomica Funcional del Cancer, Instituto Nacional de Medicina Genomica (INMEGEN), Ciudad de México, Mexico
| | - Gretel Mendoza-Almanza
- Laboratorio de Epigenetica, Instituto Nacional de Medicina Genomica (INMEGEN), Ciudad de México, Mexico
| | - Gisela Ceballos-Cancino
- Laboratorio de Genomica Funcional del Cancer, Instituto Nacional de Medicina Genomica (INMEGEN), Ciudad de México, Mexico
| | - Jorge Melendez-Zajgla
- Laboratorio de Genomica Funcional del Cancer, Instituto Nacional de Medicina Genomica (INMEGEN), Ciudad de México, Mexico
| | - Vilma Maldonado
- Laboratorio de Epigenetica, Instituto Nacional de Medicina Genomica (INMEGEN), Ciudad de México, Mexico
| | - Floria Lizarraga
- Laboratorio de Epigenetica, Instituto Nacional de Medicina Genomica (INMEGEN), Ciudad de México, Mexico
| |
Collapse
|
22
|
Xiang X, Gao LM, Zhang Y, Zhu Q, Zhao S, Liu W, Ye Y, Tang Y, Zhang W. Identifying CD1c as a potential biomarker by the comprehensive exploration of tumor mutational burden and immune infiltration in diffuse large B cell lymphoma. PeerJ 2023; 11:e16618. [PMID: 38099311 PMCID: PMC10720422 DOI: 10.7717/peerj.16618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 11/16/2023] [Indexed: 12/17/2023] Open
Abstract
Background Tumor mutational burden (TMB) is a valuable prognostic biomarker. This study explored the predictive value of TMB and the potential association between TMB and immune infiltration in diffuse large B-cell lymphoma (DLBCL). Methods We downloaded the gene expression profile, somatic mutation, and clinical data of DLBCL patients from The Cancer Genome Atlas (TCGA) database. We classified the samples into high-and low-TMB groups to identify differentially expressed genes (DEGs). Functional enrichment analyses were performed to determine the biological functions of the DEGs. We utilized the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm to estimate the abundance of 22 immune cells, and the significant difference was determined by the Wilcoxon rank-sum test between the high- and low-TMB group. Hub gene had been screened as the prognostic TMB-related immune biomarker by the combination of the Immunology Database and Analysis Portal (ImmPort) database and the univariate Cox analysis from the Gene Expression Omnibus (GEO) database including six DLBCL datasets. Various database applications such as Tumor Immune Estimation Resource (TIMER), CellMiner, konckTF, and Genotype-Tissue Expression (GTEx) verified the functions of the target gene. Wet assay confirmed the target gene expression at RNA and protein levels in DLBCL tissue and cell samples. Results Single nucleotide polymorphism (SNP) occurred more frequently than insertion and deletion, and C > T was the most common single nucleotide variant (SNV) in DLBCL. Survival analysis showed that the high-TMB group conferred poor survival outcomes. A total of 62 DEGs were obtained, and 13 TMB-related immune genes were identified. Univariate Cox analysis results illustrated that CD1c mutation was associated with lower TMB and manifested a satisfactory clinical prognosis by analysis of large samples from the GEO database. In addition, infiltration levels of immune cells in the high-TMB group were lower. Using the TIMER database, we systematically analyzed that the expression of CD1c was positively correlated with B cells, neutrophils, and dendritic cells and negatively correlated with CD8+ T cells, CD4+ T cells, and macrophages. Drug sensitivity showed a significant positive correlation between CD1c expression level and clinical drug sensitivity from the CellMiner database. CREB1, AHR, and TOX were used to comprehensively explore the regulation of CD1c-related transcription factors and signaling pathways by the KnockTF database. We searched the GETx database to compare the mRNA expression levels of CD1c between DLBCL and normal tissues, and the results suggested a significant difference between them. Moreover, wet experiments were conducted to verify the high expression of CD1c in DLBCL at the RNA and protein levels. Conclusions Higher TMB correlated with poor survival outcomes and inhibited the immune infiltrates in DLBCL. Our results suggest that CD1c is a TMB-related prognostic biomarker.
Collapse
Affiliation(s)
- Xiaoyu Xiang
- Department of Pathology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Li-Min Gao
- Department of Pathology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Yuehua Zhang
- Department of Pathology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Qiqi Zhu
- Department of Pathology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Sha Zhao
- Department of Pathology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Weiping Liu
- Department of Pathology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Yunxia Ye
- Department of Pathology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Yuan Tang
- Department of Pathology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Wenyan Zhang
- Department of Pathology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| |
Collapse
|
23
|
Hecker D, Lauber M, Behjati Ardakani F, Ashrafiyan S, Manz Q, Kersting J, Hoffmann M, Schulz MH, List M. Computational tools for inferring transcription factor activity. Proteomics 2023; 23:e2200462. [PMID: 37706624 DOI: 10.1002/pmic.202200462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 08/11/2023] [Accepted: 08/22/2023] [Indexed: 09/15/2023]
Abstract
Transcription factors (TFs) are essential players in orchestrating the regulatory landscape in cells. Still, their exact modes of action and dependencies on other regulatory aspects remain elusive. Since TFs act cell type-specific and each TF has its own characteristics, untangling their regulatory interactions from an experimental point of view is laborious and convoluted. Thus, there is an ongoing development of computational tools that estimate transcription factor activity (TFA) from a variety of data modalities, either based on a mapping of TFs to their putative target genes or in a genome-wide, gene-unspecific fashion. These tools can help to gain insights into TF regulation and to prioritize candidates for experimental validation. We want to give an overview of available computational tools that estimate TFA, illustrate examples of their application, debate common result validation strategies, and discuss assumptions and concomitant limitations.
Collapse
Affiliation(s)
- Dennis Hecker
- Goethe University Frankfurt, Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner site Rhein-Main, Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, Frankfurt am Main, Germany
| | - Michael Lauber
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Fatemeh Behjati Ardakani
- Goethe University Frankfurt, Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner site Rhein-Main, Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, Frankfurt am Main, Germany
| | - Shamim Ashrafiyan
- Goethe University Frankfurt, Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner site Rhein-Main, Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, Frankfurt am Main, Germany
| | - Quirin Manz
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Johannes Kersting
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
- GeneSurge GmbH, München, Germany
| | - Markus Hoffmann
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
- Institute for Advanced Study, Technical University of Munich, Garching, Germany
- National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Marcel H Schulz
- Goethe University Frankfurt, Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner site Rhein-Main, Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, Frankfurt am Main, Germany
| | - Markus List
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| |
Collapse
|
24
|
Müller-Dott S, Tsirvouli E, Vazquez M, Ramirez Flores R, Badia-i-Mompel P, Fallegger R, Türei D, Lægreid A, Saez-Rodriguez J. Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities. Nucleic Acids Res 2023; 51:10934-10949. [PMID: 37843125 PMCID: PMC10639077 DOI: 10.1093/nar/gkad841] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 08/08/2023] [Accepted: 09/22/2023] [Indexed: 10/17/2023] Open
Abstract
Gene regulation plays a critical role in the cellular processes that underlie human health and disease. The regulatory relationship between transcription factors (TFs), key regulators of gene expression, and their target genes, the so called TF regulons, can be coupled with computational algorithms to estimate the activity of TFs. However, to interpret these findings accurately, regulons of high reliability and coverage are needed. In this study, we present and evaluate a collection of regulons created using the CollecTRI meta-resource containing signed TF-gene interactions for 1186 TFs. In this context, we introduce a workflow to integrate information from multiple resources and assign the sign of regulation to TF-gene interactions that could be applied to other comprehensive knowledge bases. We find that the signed CollecTRI-derived regulons outperform other public collections of regulatory interactions in accurately inferring changes in TF activities in perturbation experiments. Furthermore, we showcase the value of the regulons by examining TF activity profiles in three different cancer types and exploring TF activities at the level of single-cells. Overall, the CollecTRI-derived TF regulons enable the accurate and comprehensive estimation of TF activities and thereby help to interpret transcriptomics data.
Collapse
Affiliation(s)
- Sophia Müller-Dott
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Eirini Tsirvouli
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | | | - Ricardo O Ramirez Flores
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Pau Badia-i-Mompel
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Robin Fallegger
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Dénes Türei
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Astrid Lægreid
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| |
Collapse
|
25
|
Song C, Zhang Y, Huang H, Wang Y, Zhao X, Zhang G, Yin M, Feng C, Wang Q, Qian F, Shang D, Zhang J, Liu J, Li C, Tang H. Cis-Cardio: A comprehensive analysis platform for cardiovascular-relavant cis-regulation in human and mouse. MOLECULAR THERAPY. NUCLEIC ACIDS 2023; 33:655-667. [PMID: 37637211 PMCID: PMC10458290 DOI: 10.1016/j.omtn.2023.07.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 07/25/2023] [Indexed: 08/29/2023]
Abstract
Cis-regulatory elements are important molecular switches in controlling gene expression and are regarded as determinant hubs in the transcriptional regulatory network. Collection and processing of large-scale cis-regulatory data are urgent to decipher the potential mechanisms of cardiovascular diseases from a cis-regulatory element aspect. Here, we developed a novel web server, Cis-Cardio, which aims to document a large number of available cardiovascular-related cis-regulatory data and to provide analysis for unveiling the comprehensive mechanisms at a cis-regulation level. The current version of Cis-Cardio catalogs a total of 45,382,361 genomic regions from 1,013 human and mouse epigenetic datasets, including ATAC-seq, DNase-seq, Histone ChIP-seq, TF/TcoF ChIP-seq, RNA polymerase ChIP-seq, and Cohesin ChIP-seq. Importantly, Cis-Cardio provides six analysis tools, including region overlap analysis, element upstream/downstream analysis, transcription regulator enrichment analysis, variant interpretation, and protein-protein interaction-based co-regulatory analysis. Additionally, Cis-Cardio provides detailed and abundant (epi-) genetic annotations in cis-regulatory regions, such as super-enhancers, enhancers, transcription factor binding sites (TFBSs), methylation sites, common SNPs, risk SNPs, expression quantitative trait loci (eQTLs), motifs, DNase I hypersensitive sites (DHSs), and 3D chromatin interactions. In summary, Cis-Cardio is a valuable resource for elucidating and analyzing regulatory cues of cardiovascular-specific cis-regulatory elements. The platform is freely available at http://www.licpathway.net/Cis-Cardio/index.html.
Collapse
Affiliation(s)
- Chao Song
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Yuexin Zhang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Hong Huang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
- Clinical Research Center for Myocardial Injury in Hunan Province, Hengyang, Hunan 421001, China
| | - Yuezhu Wang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Xilong Zhao
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Guorui Zhang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
| | - Mingxue Yin
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
| | - Chenchen Feng
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Qiuyu Wang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Fengcui Qian
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Desi Shang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Jian Zhang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Jiaqi Liu
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Chunquan Li
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, Hunan 410008, China
- Key Laboratory of Rare Pediatric Diseases, Ministry of Education, University of South China, Hengyang, Hunan 421001, China
| | - Huifang Tang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
- Clinical Research Center for Myocardial Injury in Hunan Province, Hengyang, Hunan 421001, China
| |
Collapse
|
26
|
Liu Z, Xia Q, Zhao X, Zheng F, Xiao J, Ge F, Wang D, Gao X. The Landscape of m6A Regulators in Multiple Brain Regions of Alzheimer's Disease. Mol Neurobiol 2023; 60:5184-5198. [PMID: 37273154 DOI: 10.1007/s12035-023-03409-5] [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: 08/22/2022] [Accepted: 05/25/2023] [Indexed: 06/06/2023]
Abstract
Alzheimer's disease research has been conducted for many years, yet no effective cure methods have been found. N6-methyladenosine (m6A) RNA methylation, an essential post-transcriptional regulation mechanism, has been discovered to affect essential neurobiological processes, such as brain cell development and aging, which are closely related to neurodegenerative diseases such as Alzheimer's disease. The relationship between Alzheimer's disease and the m6A mechanism still needs further investigation. Our work evaluated the alteration profile of m6A regulators and their influences on Alzheimer's disease in 4 brain regions: the postcentral gyrus, superior frontal gyrus, hippocampus, and entorhinal cortex. We found that the expression levels of the m6A regulators FTO, ELAVL1, and YTHDF2 were altered in Alzheimer's disease and were related to pathological development and cognitive levels. We also assessed AD-related biological processes influenced by m6A regulators via GSEA and GSVA method. Biological Processes Gene Ontology terms including memory, cognition, and synapse-signaling were found to potentially be affected by m6A regulators in AD. We also found different m6A modification patterns in AD samples among different brain regions, mainly due to differences in m6A readers. Finally, we further evaluated the importance of AD-related regulators based on the WGCNA method, assessed their potential targets based on correlation relationships, and constructed diagnostic models in 3 of all 4 regions using hub regulators, including FTO, YTHDC1, YTHDC2, etc., and their potential targets. This work aims to provide a reference for the follow-up study of m6A and Alzheimer's disease.
Collapse
Affiliation(s)
- ZiJie Liu
- Department of Biochemistry and Molecular Biology, Harbin Medical University, No. 157 Harbin health care road, Nangang District, Harbin, China
| | - Qing Xia
- Department of Biochemistry and Molecular Biology, Harbin Medical University, No. 157 Harbin health care road, Nangang District, Harbin, China
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Xue Zhao
- Department of Biochemistry and Molecular Biology, Harbin Medical University, No. 157 Harbin health care road, Nangang District, Harbin, China
| | - FeiFei Zheng
- Department of Biochemistry and Molecular Biology, Harbin Medical University, No. 157 Harbin health care road, Nangang District, Harbin, China
| | - JiaYing Xiao
- Department of Biochemistry and Molecular Biology, Harbin Medical University, No. 157 Harbin health care road, Nangang District, Harbin, China
| | - FangLiang Ge
- Department of Biochemistry and Molecular Biology, Harbin Medical University, No. 157 Harbin health care road, Nangang District, Harbin, China
| | - DaYong Wang
- Department of Biochemistry and Molecular Biology, Harbin Medical University, No. 157 Harbin health care road, Nangang District, Harbin, China.
- Basic Medical Institute, Heilongjiang Medical Science Academy, No. 157 Harbin health care road, Nangang District, Harbin, China.
- Translational Medicine Center of Northern China, No. 157 Harbin health care road, Nangang District, Harbin, China.
- Key Laboratory of Heilongjiang Province for Genetically Modified Animals, No. 157 Harbin health care road, Nangang District, Harbin, China.
| | - Xu Gao
- Department of Biochemistry and Molecular Biology, Harbin Medical University, No. 157 Harbin health care road, Nangang District, Harbin, China.
- Basic Medical Institute, Heilongjiang Medical Science Academy, No. 157 Harbin health care road, Nangang District, Harbin, China.
- Translational Medicine Center of Northern China, No. 157 Harbin health care road, Nangang District, Harbin, China.
- Key Laboratory of Heilongjiang Province for Genetically Modified Animals, No. 157 Harbin health care road, Nangang District, Harbin, China.
| |
Collapse
|
27
|
Wang L, Trasanidis N, Wu T, Dong G, Hu M, Bauer DE, Pinello L. Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics. Nat Methods 2023; 20:1368-1378. [PMID: 37537351 DOI: 10.1038/s41592-023-01971-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 07/05/2023] [Indexed: 08/05/2023]
Abstract
Gene regulatory networks (GRNs) are key determinants of cell function and identity and are dynamically rewired during development and disease. Despite decades of advancement, challenges remain in GRN inference, including dynamic rewiring, causal inference, feedback loop modeling and context specificity. To address these challenges, we develop Dictys, a dynamic GRN inference and analysis method that leverages multiomic single-cell assays of chromatin accessibility and gene expression, context-specific transcription factor footprinting, stochastic process network and efficient probabilistic modeling of single-cell RNA-sequencing read counts. Dictys improves GRN reconstruction accuracy and reproducibility and enables the inference and comparative analysis of context-specific and dynamic GRNs across developmental contexts. Dictys' network analyses recover unique insights in human blood and mouse skin development with cell-type-specific and dynamic GRNs. Its dynamic network visualizations enable time-resolved discovery and investigation of developmental driver transcription factors and their regulated targets. Dictys is available as a free, open-source and user-friendly Python package.
Collapse
Affiliation(s)
- Lingfei Wang
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Department of Pathology, Harvard Medical School, Boston, MA, USA
- Gene Regulation Observatory, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nikolaos Trasanidis
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Department of Pathology, Harvard Medical School, Boston, MA, USA
- Hugh and Josseline Langmuir Centre for Myeloma Research, Centre for Haematology, Department of Immunology and Inflammation, Imperial College London, London, UK
| | - Ting Wu
- Division of Hematology/Oncology, Boston Children's Hospital, Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Stem Cell Institute, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Guanlan Dong
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Department of Pathology, Harvard Medical School, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Bioinformatics and Integrative Genomics PhD Program, Harvard Medical School, Boston, MA, USA
| | - Michael Hu
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Daniel E Bauer
- Gene Regulation Observatory, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Stem Cell Institute, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Luca Pinello
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Department of Pathology, Harvard Medical School, Boston, MA, USA.
- Gene Regulation Observatory, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| |
Collapse
|
28
|
Kumar A, He S, Mali P. Systematic discovery of transcription factors that improve hPSC-derived cardiomyocyte maturation via temporal analysis of bioengineered cardiac tissues. APL Bioeng 2023; 7:026109. [PMID: 37252678 PMCID: PMC10219684 DOI: 10.1063/5.0137458] [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: 12/02/2022] [Accepted: 05/09/2023] [Indexed: 05/31/2023] Open
Abstract
Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) have the potential to become powerful tools for disease modeling, drug testing, and transplantation; however, their immaturity limits their applications. Transcription factor (TF) overexpression can improve hPSC-CM maturity, but identifying these TFs has been elusive. Toward this, we establish here an experimental framework for systematic identification of maturation enhancing factors. Specifically, we performed temporal transcriptome RNAseq analyses of progressively matured hPSC-derived cardiomyocytes across 2D and 3D differentiation systems and further compared these bioengineered tissues to native fetal and adult-derived tissues. These analyses revealed 22 TFs whose expression did not increase in 2D differentiation systems but progressively increased in 3D culture systems and adult mature cell types. Individually overexpressing each of these TFs in immature hPSC-CMs identified five TFs (KLF15, ZBTB20, ESRRA, HOPX, and CAMTA2) as regulators of calcium handling, metabolic function, and hypertrophy. Notably, the combinatorial overexpression of KLF15, ESRRA, and HOPX improved all three maturation parameters simultaneously. Taken together, we introduce a new TF cocktail that can be used in solo or in conjunction with other strategies to improve hPSC-CM maturation and anticipate that our generalizable methodology can also be implemented to identify maturation-associated TFs for other stem cell progenies.
Collapse
Affiliation(s)
- Aditya Kumar
- Department of Bioengineering, University of California, San Diego, California 92093, USA
| | - Starry He
- Department of Bioengineering, University of California, San Diego, California 92093, USA
| | - Prashant Mali
- Department of Bioengineering, University of California, San Diego, California 92093, USA
| |
Collapse
|
29
|
Morin A, Chu ECP, Sharma A, Adrian-Hamazaki A, Pavlidis P. Characterizing the targets of transcription regulators by aggregating ChIP-seq and perturbation expression data sets. Genome Res 2023; 33:763-778. [PMID: 37308292 PMCID: PMC10317128 DOI: 10.1101/gr.277273.122] [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: 08/31/2022] [Accepted: 04/26/2023] [Indexed: 06/14/2023]
Abstract
Mapping the gene targets of chromatin-associated transcription regulators (TRs) is a major goal of genomics research. ChIP-seq of TRs and experiments that perturb a TR and measure the differential abundance of gene transcripts are a primary means by which direct relationships are tested on a genomic scale. It has been reported that there is a poor overlap in the evidence across gene regulation strategies, emphasizing the need for integrating results from multiple experiments. Although research consortia interested in gene regulation have produced a valuable trove of high-quality data, there is an even greater volume of TR-specific data throughout the literature. In this study, we show a workflow for the identification, uniform processing, and aggregation of ChIP-seq and TR perturbation experiments for the ultimate purpose of ranking human and mouse TR-target interactions. Focusing on an initial set of eight regulators (ASCL1, HES1, MECP2, MEF2C, NEUROD1, PAX6, RUNX1, and TCF4), we identified 497 experiments suitable for analysis. We used this corpus to examine data concordance, to identify systematic patterns of the two data types, and to identify putative orthologous interactions between human and mouse. We build upon commonly used strategies to forward a procedure for aggregating and combining these two genomic methodologies, assessing these rankings against independent literature-curated evidence. Beyond a framework extensible to other TRs, our work also provides empirically ranked TR-target listings, as well as transparent experiment-level gene summaries for community use.
Collapse
Affiliation(s)
- Alexander Morin
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
- Graduate Program in Bioinformatics, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - Eric Ching-Pan Chu
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
- Graduate Program in Bioinformatics, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - Aman Sharma
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - Alex Adrian-Hamazaki
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
- Graduate Program in Bioinformatics, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - Paul Pavlidis
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada;
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| |
Collapse
|
30
|
Ahmed M, Kim HJ, Kim DR. Maximizing the utility of public data. Front Genet 2023; 14:1106631. [PMID: 37065493 PMCID: PMC10102460 DOI: 10.3389/fgene.2023.1106631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 03/21/2023] [Indexed: 04/03/2023] Open
Abstract
The human genome project galvanized the scientific community around an ambitious goal. Upon completion, the project delivered several discoveries, and a new era of research commenced. More importantly, novel technologies and analysis methods materialized during the project period. The cost reduction allowed many more labs to generate high-throughput datasets. The project also served as a model for other extensive collaborations that generated large datasets. These datasets were made public and continue to accumulate in repositories. As a result, the scientific community should consider how these data can be utilized effectively for the purposes of research and the public good. A dataset can be re-analyzed, curated, or integrated with other forms of data to enhance its utility. We highlight three important areas to achieve this goal in this brief perspective. We also emphasize the critical requirements for these strategies to be successful. We draw on our own experience and others in using publicly available datasets to support, develop, and extend our research interest. Finally, we underline the beneficiaries and discuss some risks involved in data reuse.
Collapse
Affiliation(s)
- Mahmoud Ahmed
- Department of Biochemistry and Convergence Medical Sciences, Institute of Health Sciences, College of Medicine, Gyeongsang National University, Jinju, Republic of Korea
| | - Hyun Joon Kim
- Department of Anatomy and Convergence Medical Sciences, Institute of Health Sciences, College of Medicine, Gyeongsang National University, Jinju, Republic of Korea
| | - Deok Ryong Kim
- Department of Biochemistry and Convergence Medical Sciences, Institute of Health Sciences, College of Medicine, Gyeongsang National University, Jinju, Republic of Korea
- *Correspondence: Deok Ryong Kim,
| |
Collapse
|
31
|
Fajiculay E, Hsu C. Noise response in monomolecular closed systems. J CHIN CHEM SOC-TAIP 2023. [DOI: 10.1002/jccs.202200526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2023]
Affiliation(s)
- Erickson Fajiculay
- Institute of Chemistry Academia Sinica Taipei Taiwan
- Bioinformatics Program, Institute of Statistical Science, Taiwan International Graduate Program Academia Sinica Taipei Taiwan
- Institute of Bioinformatics and Structure Biology National Tsinghua University Hsinchu City Taiwan
| | - Chao‐Ping Hsu
- Institute of Chemistry Academia Sinica Taipei Taiwan
- Bioinformatics Program, Institute of Statistical Science, Taiwan International Graduate Program Academia Sinica Taipei Taiwan
- Physics Division National Center for Theoretical Sciences Taipei Taiwan
- Genome and Systems Biology Degree Program National Taiwan University Taipei Taiwan
| |
Collapse
|
32
|
Zhong Y, Zhao J, Deng H, Wu Y, Zhu L, Yang M, Liu Q, Luo G, Ma W, Li H. Integrative bioinformatics analysis to identify novel biomarkers associated with non-obstructive azoospermia. Front Immunol 2023; 14:1088261. [PMID: 36969237 PMCID: PMC10031032 DOI: 10.3389/fimmu.2023.1088261] [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: 11/03/2022] [Accepted: 02/22/2023] [Indexed: 03/11/2023] Open
Abstract
AimThis study aimed to identify autophagy-related genes (ARGs) associated with non-obstructive azoospermia and explore the underlying molecular mechanisms.MethodsTwo datasets associated with azoospermia were downloaded from the Gene Expression Omnibus database, and ARGs were obtained from the Human Autophagy-dedicated Database. Autophagy-related differentially expressed genes were identified in the azoospermia and control groups. These genes were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, protein–protein interaction (PPI) network, and functional similarity analyses. After identifying the hub genes, immune infiltration and hub gene–RNA-binding protein (RBP)–transcription factor (TF)–miRNA–drug interactions were analyzed.ResultsA total 46 differentially expressed ARGs were identified between the azoospermia and control groups. These genes were enriched in autophagy-associated functions and pathways. Eight hub genes were selected from the PPI network. Functional similarity analysis revealed that HSPA5 may play a key role in azoospermia. Immune cell infiltration analysis revealed that activated dendritic cells were significantly decreased in the azoospermia group compared to those in the control groups. Hub genes, especially ATG3, KIAA0652, MAPK1, and EGFR were strongly correlated with immune cell infiltration. Finally, a hub gene–miRNA–TF–RBP–drug network was constructed.ConclusionThe eight hub genes, including EGFR, HSPA5, ATG3, KIAA0652, and MAPK1, may serve as biomarkers for the diagnosis and treatment of azoospermia. The study findings suggest potential targets and mechanisms for the occurrence and development of this disease.
Collapse
Affiliation(s)
- Yucheng Zhong
- Assisted Reproductive Technology Center, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, China
| | - Jun Zhao
- Assisted Reproductive Technology Center, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, China
| | - Hao Deng
- Assisted Reproductive Technology Center, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, China
| | - Yaqin Wu
- Assisted Reproductive Technology Center, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, China
| | - Li Zhu
- Assisted Reproductive Technology Center, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, China
| | - Meiqiong Yang
- Assisted Reproductive Technology Center, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, China
| | - Qianru Liu
- Assisted Reproductive Technology Center, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, China
| | - Guoqun Luo
- Assisted Reproductive Technology Center, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, China
| | - Wenmin Ma
- Assisted Reproductive Technology Center, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, China
- Assist Reproductive Medical Center, Zhaoqing West River Hospital, Zhaoqing, Guangdong, China
- *Correspondence: Wenmin Ma, ; Huan Li,
| | - Huan Li
- Assisted Reproductive Technology Center, Southern Medical University Affiliated Maternal and Child Health Hospital of Foshan, Foshan, Guangdong, China
- *Correspondence: Wenmin Ma, ; Huan Li,
| |
Collapse
|
33
|
Sahu A, Wang X, Munson P, Klomp JP, Wang X, Gu SS, Han Y, Qian G, Nicol P, Zeng Z, Wang C, Tokheim C, Zhang W, Fu J, Wang J, Nair NU, Rens JA, Bourajjaj M, Jansen B, Leenders I, Lemmers J, Musters M, van Zanten S, van Zelst L, Worthington J, Liu JS, Juric D, Meyer CA, Oubrie A, Liu XS, Fisher DE, Flaherty KT. Discovery of Targets for Immune-Metabolic Antitumor Drugs Identifies Estrogen-Related Receptor Alpha. Cancer Discov 2023; 13:672-701. [PMID: 36745048 PMCID: PMC9975674 DOI: 10.1158/2159-8290.cd-22-0244] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 09/13/2022] [Accepted: 11/23/2022] [Indexed: 02/07/2023]
Abstract
Drugs that kill tumors through multiple mechanisms have the potential for broad clinical benefits. Here, we first developed an in silico multiomics approach (BipotentR) to find cancer cell-specific regulators that simultaneously modulate tumor immunity and another oncogenic pathway and then used it to identify 38 candidate immune-metabolic regulators. We show the tumor activities of these regulators stratify patients with melanoma by their response to anti-PD-1 using machine learning and deep neural approaches, which improve the predictive power of current biomarkers. The topmost identified regulator, ESRRA, is activated in immunotherapy-resistant tumors. Its inhibition killed tumors by suppressing energy metabolism and activating two immune mechanisms: (i) cytokine induction, causing proinflammatory macrophage polarization, and (ii) antigen-presentation stimulation, recruiting CD8+ T cells into tumors. We also demonstrate a wide utility of BipotentR by applying it to angiogenesis and growth suppressor evasion pathways. BipotentR (http://bipotentr.dfci.harvard.edu/) provides a resource for evaluating patient response and discovering drug targets that act simultaneously through multiple mechanisms. SIGNIFICANCE BipotentR presents resources for evaluating patient response and identifying targets for drugs that can kill tumors through multiple mechanisms concurrently. Inhibition of the topmost candidate target killed tumors by suppressing energy metabolism and effects on two immune mechanisms. This article is highlighted in the In This Issue feature, p. 517.
Collapse
Affiliation(s)
- Avinash Sahu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Obstetrics and Gynecology, University of Colorado School of Medicine, Aurora, Colorado
| | - Xiaoman Wang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Phillip Munson
- Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, Massachusetts
| | | | - Xiaoqing Wang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Shengqing Stan Gu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Ya Han
- School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Gege Qian
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Phillip Nicol
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Zexian Zeng
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Chenfei Wang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Collin Tokheim
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Wubing Zhang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Jingxin Fu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Jin Wang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Nishanth Ulhas Nair
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | | | | | - Bas Jansen
- Lead Pharma, Kloosterstraat, Oss, the Netherlands
| | | | - Jaap Lemmers
- Lead Pharma, Kloosterstraat, Oss, the Netherlands
| | - Mark Musters
- Lead Pharma, Kloosterstraat, Oss, the Netherlands
| | | | | | | | - Jun S. Liu
- Department of Statistics, Harvard University, Cambridge, Massachusetts
| | - Dejan Juric
- Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, Massachusetts
| | - Clifford A. Meyer
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | | | - X. Shirley Liu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - David E. Fisher
- Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, Massachusetts
- Department of Dermatology, Massachusetts General Hospital, Boston, Massachusetts
| | - Keith T. Flaherty
- Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, Massachusetts
| |
Collapse
|
34
|
Wang H, Chen S, Kang W, Ding B, Cui S, Zhou L, Zhang N, Luo H, Wang M, Zhang F, Zhao Z, Guo Z, Wang C, Li L, Wang Z, Chen X, Wang Y. High dose isoleucine stabilizes nuclear PTEN to suppress the proliferation of lung cancer. Discov Oncol 2023; 14:25. [PMID: 36820928 PMCID: PMC9950318 DOI: 10.1007/s12672-023-00634-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 02/20/2023] [Indexed: 02/24/2023] Open
Abstract
PURPOSE Cancer cells require a supply of amino acids, particularly essential amino acids such as branched-chain amino acids (BCAAs, i.e., valine, leucine, and isoleucine), to meet the increased nutrient demands of malignant tumors. The cell-autonomous and non-autonomous roles of altered BCAA supply have been implicated in cancer progression. The critical proteins involved in BCAA uptake, transport, metabolism, etc. serve as potential therapeutic biomarkers in human cancers. Here, we summarize the potential anti-tumor mechanism of BCAA by exploring the chain reaction triggered by increased BCAA supply in the tumor. METHOD A system-wide strategy was employed to provide a generic solution to establish the links between BCAA and cancer based on comprehensive omics, molecular experimentation, and data analysis. RESULTS BCAA over-supplementation (900 mg/kg) significantly inhibited tumor growth and reduced tumor burden, with isoleucine having the most pronounced effect. Surprisingly, isoleucine inhibited tumor growth independently of mTORC1 activation, a classical amino acid sensor. Exploratory transcriptome analysis revealed that Phosphatase and tensin homolog (PTEN) is the critical factor in the anti-tumor effect of isoleucine. By inhibiting PTEN ubiquitination, isoleucine can promote PTEN nuclear import and maintain PTEN nuclear stability. Interestingly, this process was regulated by isoleucine-tRNA ligase, cytoplasmic (IARS), a direct target of isoleucine. We demonstrated the enhanced interaction between IARS and PTEN in the presence of excess isoleucine. At the same time, IARS knockout leads to loss of isoleucine tumor suppressor ability. CONCLUSION Overall, our results provide insights into the regulation of the IARS-PTEN anti-tumor axis by isoleucine and reveal a unique therapeutic approach based on enhancing cellular isoleucine supply.
Collapse
Affiliation(s)
- Haiqing Wang
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Life Sciences, Northwest University, No. 229 Taibai North Road, Xi'an, 710069, Shaanxi, China
| | - Sen Chen
- Immune Cells and Antibody Engineering Research Center in University of Guizhou Province, Key Laboratory of Biology and Medical Engineering, School of Biology and Engineering (School of Modern Industry for Health and Medicine), Guizhou Medical University, Guiyang, 550025, China
| | - Wenhui Kang
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Life Sciences, Northwest University, No. 229 Taibai North Road, Xi'an, 710069, Shaanxi, China
| | - Bojiao Ding
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Life Sciences, Northwest University, No. 229 Taibai North Road, Xi'an, 710069, Shaanxi, China
| | - Shulan Cui
- School of Traditional Chinese Medicine, Baoji Vocational Technology College, Baoji, 721000, Shaanxi, China
| | - Li Zhou
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Life Sciences, Northwest University, No. 229 Taibai North Road, Xi'an, 710069, Shaanxi, China
| | - Na Zhang
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Life Sciences, Northwest University, No. 229 Taibai North Road, Xi'an, 710069, Shaanxi, China
| | - Huiying Luo
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Life Sciences, Northwest University, No. 229 Taibai North Road, Xi'an, 710069, Shaanxi, China
| | - Mingjuan Wang
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Life Sciences, Northwest University, No. 229 Taibai North Road, Xi'an, 710069, Shaanxi, China
| | - Fan Zhang
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Life Sciences, Northwest University, No. 229 Taibai North Road, Xi'an, 710069, Shaanxi, China
| | - Zezhou Zhao
- Collaborative Innovation Center of Qiyao in Mt. Qinling, Yangling, 712100, Shaanxi, China
| | - Zihu Guo
- College of Pharmacy, Heze University, Heze, 274015, Shandong, China
| | - Chao Wang
- State Key Laboratory of New-Tech for Chinese Medicine Pharmaceutical Process, Jiangsu Kanion Parmaceutical Co. Ltd., Lianyungang, 222002, Jiangsu, China
| | - Liang Li
- State Key Laboratory of New-Tech for Chinese Medicine Pharmaceutical Process, Jiangsu Kanion Parmaceutical Co. Ltd., Lianyungang, 222002, Jiangsu, China
| | - Zhengzhong Wang
- State Key Laboratory of New-Tech for Chinese Medicine Pharmaceutical Process, Jiangsu Kanion Parmaceutical Co. Ltd., Lianyungang, 222002, Jiangsu, China
| | - Xuetong Chen
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Life Sciences, Northwest University, No. 229 Taibai North Road, Xi'an, 710069, Shaanxi, China.
| | - Yonghua Wang
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Life Sciences, Northwest University, No. 229 Taibai North Road, Xi'an, 710069, Shaanxi, China.
| |
Collapse
|
35
|
Zhuang HH, Qu Q, Teng XQ, Dai YH, Qu J. Superenhancers as master gene regulators and novel therapeutic targets in brain tumors. Exp Mol Med 2023; 55:290-303. [PMID: 36720920 PMCID: PMC9981748 DOI: 10.1038/s12276-023-00934-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 11/27/2022] [Accepted: 12/04/2022] [Indexed: 02/02/2023] Open
Abstract
Transcriptional deregulation, a cancer cell hallmark, is driven by epigenetic abnormalities in the majority of brain tumors, including adult glioblastoma and pediatric brain tumors. Epigenetic abnormalities can activate epigenetic regulatory elements to regulate the expression of oncogenes. Superenhancers (SEs), identified as novel epigenetic regulatory elements, are clusters of enhancers with cell-type specificity that can drive the aberrant transcription of oncogenes and promote tumor initiation and progression. As gene regulators, SEs are involved in tumorigenesis in a variety of tumors, including brain tumors. SEs are susceptible to inhibition by their key components, such as bromodomain protein 4 and cyclin-dependent kinase 7, providing new opportunities for antitumor therapy. In this review, we summarized the characteristics and identification, unique organizational structures, and activation mechanisms of SEs in tumors, as well as the clinical applications related to SEs in tumor therapy and prognostication. Based on a review of the literature, we discussed the relationship between SEs and different brain tumors and potential therapeutic targets, focusing on glioblastoma.
Collapse
Affiliation(s)
- Hai-Hui Zhuang
- Department of Pharmacy, the Second Xiangya Hospital, Central South University, Institute of Clinical Pharmacy, Central South University, Changsha, 410011, PR China
| | - Qiang Qu
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410007, PR China
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410007, PR China
| | - Xin-Qi Teng
- Department of Pharmacy, the Second Xiangya Hospital, Central South University, Institute of Clinical Pharmacy, Central South University, Changsha, 410011, PR China
| | - Ying-Huan Dai
- Department of Pathology, the Second Xiangya Hospital, Central South University, Changsha, 410011, PR China
| | - Jian Qu
- Department of Pharmacy, the Second Xiangya Hospital, Central South University, Institute of Clinical Pharmacy, Central South University, Changsha, 410011, PR China.
| |
Collapse
|
36
|
Fajiculay E, Hsu CP. Localization of Noise in Biochemical Networks. ACS OMEGA 2023; 8:3043-3056. [PMID: 36713703 PMCID: PMC9878546 DOI: 10.1021/acsomega.2c06113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 12/27/2022] [Indexed: 06/18/2023]
Abstract
Noise, or uncertainty in biochemical networks, has become an important aspect of many biological problems. Noise can arise and propagate from external factors and probabilistic chemical reactions occurring in small cellular compartments. For species survival, it is important to regulate such uncertainties in executing vital cell functions. Regulated noise can improve adaptability, whereas uncontrolled noise can cause diseases. Simulation can provide a detailed analysis of uncertainties, but parameters such as rate constants and initial conditions are usually unknown. A general understanding of noise dynamics from the perspective of network structure is highly desirable. In this study, we extended the previously developed law of localization for characterizing noise in terms of (co)variances and developed noise localization theory. With linear noise approximation, we can expand a biochemical network into an extended set of differential equations representing a fictitious network for pseudo-components consisting of variances and covariances, together with chemical species. Through localization analysis, perturbation responses at the steady state of pseudo-components can be summarized into a sensitivity matrix that only requires knowledge of network topology. Our work allows identification of buffering structures at the level of species, variances, and covariances and can provide insights into noise flow under non-steady-state conditions in the form of a pseudo-chemical reaction. We tested noise localization in various systems, and here we discuss its implications and potential applications. Results show that this theory is potentially applicable in discriminating models, scanning network topologies with interesting noise behavior, and designing and perturbing networks with the desired response.
Collapse
Affiliation(s)
- Erickson Fajiculay
- Institute
of Chemistry, Academia Sinica, Taipei115201, Taiwan
- Bioinformatics
Program, Institute of Information Science, Taiwan International Graduate
Program, Academia Sinica, Taipei115201, Taiwan
- Institute
of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu300044, Taiwan
| | - Chao-Ping Hsu
- Institute
of Chemistry, Academia Sinica, Taipei115201, Taiwan
- Bioinformatics
Program, Institute of Information Science, Taiwan International Graduate
Program, Academia Sinica, Taipei115201, Taiwan
- Physics
Division, National Center for Theoretical
Sciences, Taipei106319, Taiwan
- Genome
and Systems Biology Degree Program, National
Taiwan University, Taipei106319, Taiwan
| |
Collapse
|
37
|
Zhang Y, Zhang Y, Song C, Zhao X, Ai B, Wang Y, Zhou L, Zhu J, Feng C, Xu L, Wang Q, Sun H, Fang Q, Xu X, Li E, Li C. CRdb: a comprehensive resource for deciphering chromatin regulators in human. Nucleic Acids Res 2023; 51:D88-D100. [PMID: 36318256 PMCID: PMC9825595 DOI: 10.1093/nar/gkac960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 10/04/2022] [Accepted: 10/12/2022] [Indexed: 11/05/2022] Open
Abstract
Chromatin regulators (CRs) regulate epigenetic patterns on a partial or global scale, playing a critical role in affecting multi-target gene expression. As chromatin immunoprecipitation sequencing (ChIP-seq) data associated with CRs are rapidly accumulating, a comprehensive resource of CRs needs to be built urgently for collecting, integrating, and processing these data, which can provide abundant annotated information on CR upstream and downstream regulatory analyses as well as CR-related analysis functions. This study established an integrative CR resource, named CRdb (http://cr.liclab.net/crdb/), with the aim of curating a large number of available resources for CRs and providing extensive annotations and analyses of CRs to help biological researchers clarify the regulation mechanism and function of CRs. The CRdb database comprised a total of 647 CRs and 2,591 ChIP-seq samples from more than 300 human tissues and cell types. These samples have been manually curated from NCBI GEO/SRA and ENCODE. Importantly, CRdb provided the abundant and detailed genetic annotations in CR-binding regions based on ChIP-seq. Furthermore, CRdb supported various functional annotations and upstream regulatory information on CRs. In particular, it embedded four types of CR regulatory analyses: CR gene set enrichment, CR-binding genomic region annotation, CR-TF co-occupancy analysis, and CR regulatory axis analysis. CRdb is a useful and powerful resource that can help in exploring the potential functions of CRs and their regulatory mechanism in diseases and biological processes.
Collapse
Affiliation(s)
- Yimeng Zhang
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou 515041, China
| | | | | | - Xilong Zhao
- School of Medical Informatics, Daqing Campus, Harbin Medical University.Daqing 163319, China
| | - Bo Ai
- School of Medical Informatics, Daqing Campus, Harbin Medical University.Daqing 163319, China
| | - Yuezhu Wang
- School of Medical Informatics, Daqing Campus, Harbin Medical University.Daqing 163319, China
| | - Liwei Zhou
- School of Medical Informatics, Daqing Campus, Harbin Medical University.Daqing 163319, China
| | - Jiang Zhu
- School of Medical Informatics, Daqing Campus, Harbin Medical University.Daqing 163319, China
| | - Chenchen Feng
- School of Medical Informatics, Daqing Campus, Harbin Medical University.Daqing 163319, China
| | - Liyan Xu
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou 515041, China
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Institute of Oncologic Pathology, Cancer Research Center, Shantou University Medical College, Shantou 515041, China
| | - Qiuyu Wang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Base for Scientific and Technological Innovation Cooperation, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Hong Sun
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou 515041, China
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou 515041, China
| | - Qiaoli Fang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
| | - Xiaozheng Xu
- School of Medical Informatics, Daqing Campus, Harbin Medical University.Daqing 163319, China
| | - Enmin Li
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou 515041, China
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou 515041, China
| | - Chunquan Li
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Base for Scientific and Technological Innovation Cooperation, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South
| |
Collapse
|
38
|
Wang Y, Song C, Zhao J, Zhang Y, Zhao X, Feng C, Zhang G, Zhu J, Wang F, Qian F, Zhou L, Zhang J, Bai X, Ai B, Liu X, Wang Q, Li C. SEdb 2.0: a comprehensive super-enhancer database of human and mouse. Nucleic Acids Res 2023; 51:D280-D290. [PMID: 36318264 PMCID: PMC9825585 DOI: 10.1093/nar/gkac968] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 10/09/2022] [Accepted: 10/13/2022] [Indexed: 01/09/2023] Open
Abstract
Super-enhancers (SEs) are cell-specific DNA cis-regulatory elements that can supervise the transcriptional regulation processes of downstream genes. SEdb 2.0 (http://www.licpathway.net/sedb) aims to provide a comprehensive SE resource and annotate their potential roles in gene transcriptions. Compared with SEdb 1.0, we have made the following improvements: (i) Newly added the mouse SEs and expanded the scale of human SEs. SEdb 2.0 contained 1 167 518 SEs from 1739 human H3K27ac chromatin immunoprecipitation sequencing (ChIP-seq) samples and 550 226 SEs from 931 mouse H3K27ac ChIP-seq samples, which was five times that of SEdb 1.0. (ii) Newly added transcription factor binding sites (TFBSs) in SEs identified by TF motifs and TF ChIP-seq data. (iii) Added comprehensive (epi)genetic annotations of SEs, including chromatin accessibility regions, methylation sites, chromatin interaction regions and topologically associating domains (TADs). (iv) Newly embedded and updated search and analysis tools, including 'Search SE by TF-based', 'Differential-Overlapping-SE analysis' and 'SE-based TF-Gene analysis'. (v) Newly provided quality control (QC) metrics for ChIP-seq processing. In summary, SEdb 2.0 is a comprehensive update of SEdb 1.0, which curates more SEs and annotation information than SEdb 1.0. SEdb 2.0 provides a friendly platform for researchers to more comprehensively clarify the important role of SEs in the biological process.
Collapse
Affiliation(s)
- Yuezhu Wang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Chao Song
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Jun Zhao
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Yuexin Zhang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Xilong Zhao
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Chenchen Feng
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Guorui Zhang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
| | - Jiang Zhu
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Fan Wang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Fengcui Qian
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Liwei Zhou
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Jian Zhang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Xuefeng Bai
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Bo Ai
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Xinyu Liu
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Qiuyu Wang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Base for Scientific and Technological Innovation Cooperation, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China,Hengyang, Hunan 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China,Hengyang, Hunan 421001, China
| | - Chunquan Li
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Base for Scientific and Technological Innovation Cooperation, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China,Hengyang, Hunan 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China,Hengyang, Hunan 421001, China
| |
Collapse
|
39
|
Xu L. Identification of Autophagy-Related Targets of Berberine against Non-Small Cell Lung Cancer and Their Correlation with Immune Cell Infiltration By Combining Network Pharmacology, Molecular Docking, and Experimental Verification. Crit Rev Immunol 2023; 43:27-47. [PMID: 37938194 DOI: 10.1615/critrevimmunol.2023049923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2023]
Abstract
OBJECTIVE Non-small cell lung cancer (NSCLC) is the most common lung cancer type with high incidence. This study aimed to reveal the anti-NSCLC mechanisms of berberine and identify novel therapeutic targets. METHODS Berberine-related targets were acquired from SuperPred, SwissTargetPrediction, and GeneCards. NSCLC-re-lated targets were collected from GeneCards and DisGeNET. Differentially expressed genes (DEGs) were identified GEO database, UCSC Xena, and limma. GO and KEGG analyses were performed using clusterProfiler. Autophagy-related genes and transcriptional factors were collected from HADb and KnockTF, respectively. STRING and Cytoscape were used for PPI network analysis. Immune cell infiltration in NSCLC was assessed using CIBERSORT, and its correlation with autophagy-related targets was evaluated. Molecular docking was conducted using PyMOL and AutoDock. qRT-PCR and CCK-8 assay was used for in vitro verification. RESULTS Thirty intersecting targets of berberine-related targets, NSCLC-related targets, and DEGs were obtained. GO and KEGG analyses revealed that the intersecting targets were mainly implicated in oxidative stress, focal adhesion, and cell-substrate junction, as well as AGE-RAGE, relaxin, FoxO, and estrogen signaling pathways. Significantly, CAPN1, IKBKB, and SIRT2 were identified as the foremost autophagy-related targets, and 21 corresponding transcriptional factors were obtained. PPI network analysis showed that CAPN1, IKBKB, and SIRT2 interacted with 50 other genes. Fifty immune cell types, such as B cells naive, T cells CD8, T cells CD4 naive, T cells follicular helper, and monocytes, were implicated in NSCLC pathogenesis, and CAPN1, IKBKB, and SIRT2 were related to immune cells. Molecular docking revealed the favorable binding activity of berberine with CAPN1, IKBKB, and SIRT2. In vitro assays showed lower CAPN1, IKBKB, and SIRT2 expression in NSCLC cells than that in normal cells. Notably, berberine inhibited the viability and elevated CAPN1, IKBKB, and SIRT2 expression in NSCLC cells. CONCLUSIONS Berberine might treat NSCLC mainly by targeting CAPN1, IKBKB, and SIRT2.
Collapse
Affiliation(s)
- Liang Xu
- Respiratory Medicine, Affiliated Hospital of Shaoxing University (The Shaoxing Municipal Hospital), No. 999, Zhongxing South Road, Shaoxing 312000, China
| |
Collapse
|
40
|
He Z, Gao K, Dong L, Liu L, Qu X, Zou Z, Wu Y, Bu D, Guo JC, Zhao Y. Drug screening and biomarker gene investigation in cancer therapy through the human transcriptional regulatory network. Comput Struct Biotechnol J 2023; 21:1557-1572. [PMID: 36879883 PMCID: PMC9984461 DOI: 10.1016/j.csbj.2023.02.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 01/19/2023] [Accepted: 02/04/2023] [Indexed: 02/10/2023] Open
Abstract
A complex and vast biological network regulates all biological functions in the human body in a sophisticated manner, and abnormalities in this network can lead to disease and even cancer. The construction of a high-quality human molecular interaction network is possible with the development of experimental techniques that facilitate the interpretation of the mechanisms of drug treatment for cancer. We collected 11 molecular interaction databases based on experimental sources and constructed a human protein-protein interaction (PPI) network and a human transcriptional regulatory network (HTRN). A random walk-based graph embedding method was used to calculate the diffusion profiles of drugs and cancers, and a pipeline was constructed by using five similarity comparison metrics combined with a rank aggregation algorithm, which can be implemented for drug screening and biomarker gene prediction. Taking NSCLC as an example, curcumin was identified as a potentially promising anticancer drug from 5450 natural small molecules, and combined with differentially expressed genes, survival analysis, and topological ranking, we obtained BIRC5 (survivin), which is both a biomarker for NSCLC and a key target for curcumin. Finally, the binding mode of curcumin and survivin was explored using molecular docking. This work has a guiding significance for antitumor drug screening and the identification of tumor markers.
Collapse
Affiliation(s)
- Zihao He
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Kai Gao
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Lei Dong
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Liu Liu
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Xinchi Qu
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Zhengkai Zou
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yang Wu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Dechao Bu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Jin-Cheng Guo
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yi Zhao
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China.,Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| |
Collapse
|
41
|
Singh A, Rajeevan A, Gopalan V, Agrawal P, Day CP, Hannenhalli S. Broad misappropriation of developmental splicing profile by cancer in multiple organs. Nat Commun 2022; 13:7664. [PMID: 36509773 PMCID: PMC9744839 DOI: 10.1038/s41467-022-35322-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 11/29/2022] [Indexed: 12/14/2022] Open
Abstract
Oncogenesis mimics key aspects of embryonic development. However, the underlying mechanisms are incompletely understood. Here, we demonstrate that the splicing events specifically active during human organogenesis, are broadly reactivated in the organ-specific tumor. Such events are associated with key oncogenic processes and predict proliferation rates in cancer cell lines as well as patient survival. Such events preferentially target nitrosylation and transmembrane-region domains, whose coordinated splicing in multiple genes respectively affect intracellular transport and N-linked glycosylation. We infer critical splicing factors potentially regulating embryonic splicing events and show that such factors are potential oncogenic drivers and are upregulated specifically in malignant cells. Multiple complementary analyses point to MYC and FOXM1 as potential transcriptional regulators of critical splicing factors in brain and liver. Our study provides a comprehensive demonstration of a splicing-mediated link between development and cancer, and suggest anti-cancer targets including splicing events, and their upstream splicing and transcriptional regulators.
Collapse
Affiliation(s)
- Arashdeep Singh
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Arati Rajeevan
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Vishaka Gopalan
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Piyush Agrawal
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Chi-Ping Day
- Laboratory of Cancer Biology and Genetics National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sridhar Hannenhalli
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
| |
Collapse
|
42
|
Su M, Pan T, Chen QZ, Zhou WW, Gong Y, Xu G, Yan HY, Li S, Shi QZ, Zhang Y, He X, Jiang CJ, Fan SC, Li X, Cairns MJ, Wang X, Li YS. Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications. Mil Med Res 2022; 9:68. [PMID: 36461064 PMCID: PMC9716519 DOI: 10.1186/s40779-022-00434-8] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 11/18/2022] [Indexed: 12/03/2022] Open
Abstract
The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.
Collapse
Affiliation(s)
- Min Su
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
| | - Tao Pan
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199 Hainan China
| | - Qiu-Zhen Chen
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
| | - Wei-Wei Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081 Heilongjiang China
| | - Yi Gong
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
- Department of Immunology, Nanjing Medical University, Nanjing, 211166 China
| | - Gang Xu
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199 Hainan China
| | - Huan-Yu Yan
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
| | - Si Li
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199 Hainan China
| | - Qiao-Zhen Shi
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
| | - Ya Zhang
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199 Hainan China
| | - Xiao He
- Department of Laboratory Medicine, Women and Children’s Hospital of Chongqing Medical University, Chongqing, 401174 China
| | | | - Shi-Cai Fan
- Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, 518110 Guangdong China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081 Heilongjiang China
| | - Murray J. Cairns
- School of Biomedical Sciences and Pharmacy, Faculty of Health and Medicine, the University of Newcastle, University Drive, Callaghan, NSW 2308 Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW 2305 Australia
| | - Xi Wang
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
| | - Yong-Sheng Li
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199 Hainan China
| |
Collapse
|
43
|
Guo S, Xu Z, Dong X, Hu D, Jiang Y, Wang Q, Zhang J, Zhou Q, Liu S, Song W. GPSAdb: a comprehensive web resource for interactive exploration of genetic perturbation RNA-seq datasets. Nucleic Acids Res 2022; 51:D964-D968. [PMID: 36416261 PMCID: PMC9825484 DOI: 10.1093/nar/gkac1066] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/21/2022] [Accepted: 10/26/2022] [Indexed: 11/24/2022] Open
Abstract
Gene knock-out/down methods are commonly used to explore the functions of genes of interest, but a database that systematically collects perturbed data is not available currently. Manual curation of all the available human cell line perturbed RNA-seq datasets enabled us to develop a comprehensive human perturbation database (GPSAdb, https://www.gpsadb.com/). The current version of GPSAdb collected 3048 RNA-seq datasets associated with 1458 genes, which were knocked out/down by siRNA, shRNA, CRISPR/Cas9, or CRISPRi. The database provides full exploration of these datasets and generated 6096 new perturbed gene sets (up and down separately). GPSAdb integrated the gene sets and developed an online tool, genetic perturbation similarity analysis (GPSA), to identify candidate causal perturbations from differential gene expression data. In summary, GPSAdb is a powerful platform that aims to assist life science researchers to easily access and analyze public perturbed data and explore differential gene expression data in depth.
Collapse
Affiliation(s)
- Shipeng Guo
- To whom correspondence should be addressed. Tel: +86 23 63637851;
| | - Zhougeng Xu
- National Key Laboratory of Plant Molecular Genetics (NKLPMG), CAS Center for Excellence in Molecular Plant Sciences (CEMPS), Institute of Plant Physiology and Ecology (SIPPE), Chinese Academy of Sciences (CAS), Shanghai, China
| | - Xiangjun Dong
- Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Dongjie Hu
- Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Yanshuang Jiang
- Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Qunxian Wang
- Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Jie Zhang
- Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Qian Zhou
- Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Shengchun Liu
- Correspondence may also be addressed to Shengchun Liu.
| | - Weihong Song
- Correspondence may also be addressed to Weihong Song.
| |
Collapse
|
44
|
Jiang J, Lyu P, Li J, Huang S, Tao J, Blackshaw S, Qian J, Wang J. IReNA: Integrated regulatory network analysis of single-cell transcriptomes and chromatin accessibility profiles. iScience 2022; 25:105359. [PMID: 36325073 PMCID: PMC9619378 DOI: 10.1016/j.isci.2022.105359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 09/19/2022] [Accepted: 10/12/2022] [Indexed: 11/16/2022] Open
Abstract
Recently, single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) have been developed to separately measure transcriptomes and chromatin accessibility profiles at the single-cell resolution. However, few methods can reliably integrate these data to perform regulatory network analysis. Here, we developed integrated regulatory network analysis (IReNA) for network inference through the integrated analysis of scRNA-seq and scATAC-seq data, network modularization, transcription factor enrichment, and construction of simplified intermodular regulatory networks. Using public datasets, we showed that integrated network analysis of scRNA-seq data with scATAC-seq data is more precise to identify known regulators than scRNA-seq data analysis alone. Moreover, IReNA outperformed currently available methods in identifying known regulators. IReNA facilitates the systems-level understanding of biological regulatory mechanisms and is available at https://github.com/jiang-junyao/IReNA.
Collapse
Affiliation(s)
- Junyao Jiang
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Pin Lyu
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Jinlian Li
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Sunan Huang
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Jiawang Tao
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Seth Blackshaw
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Jiang Qian
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Jie Wang
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
- State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
- China-New Zealand Joint Laboratory on Biomedicine and Health, Guangzhou 510530, China
- Corresponding author
| |
Collapse
|
45
|
Zhai Z, Zhang X, Zhou L, Lin Z, Kuang N, Li Q, Ma Q, Tao H, Gao J, Ma S, Pan J. PertOrg 1.0: a comprehensive resource of multilevel alterations induced in model organisms by in vivo genetic perturbation. Nucleic Acids Res 2022; 51:D1094-D1101. [PMID: 36243973 PMCID: PMC9825601 DOI: 10.1093/nar/gkac872] [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: 07/21/2022] [Revised: 09/04/2022] [Accepted: 09/28/2022] [Indexed: 01/30/2023] Open
Abstract
Genetically modified organisms (GMOs) can be generated to model human genetic disease or plant disease resistance, and they have contributed to the exploration and understanding of gene function, physiology, disease onset and drug target discovery. Here, PertOrg (http://www.inbirg.com/pertorg/) was introduced to provide multilevel alterations in GMOs. Raw data of 58 707 transcriptome profiles and associated information, such as phenotypic alterations, were collected and curated from studies involving in vivo genetic perturbation (e.g. knockdown, knockout and overexpression) in eight model organisms, including mouse, rat and zebrafish. The transcriptome profiles from before and after perturbation were organized into 10 116 comparison datasets, including 122 single-cell RNA-seq datasets. The raw data were checked and analysed using widely accepted and standardized pipelines to identify differentially expressed genes (DEGs) in perturbed organisms. As a result, 8 644 148 DEGs were identified and deposited as signatures of gene perturbations. Downstream functional enrichment analysis, cell type analysis and phenotypic alterations were also provided when available. Multiple search methods and analytical tools were created and implemented. Furthermore, case studies were presented to demonstrate how users can utilize the database. PertOrg 1.0 will be a valuable resource aiding in the exploration of gene functions, biological processes and disease models.
Collapse
Affiliation(s)
| | | | | | - Zhewei Lin
- Center for Novel Target and Therapeutic Intervention, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Ni Kuang
- Center for Novel Target and Therapeutic Intervention, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Qiang Li
- Center for Novel Target and Therapeutic Intervention, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Qinfeng Ma
- Center for Novel Target and Therapeutic Intervention, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Haodong Tao
- Center for Novel Target and Therapeutic Intervention, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Jieya Gao
- Center for Novel Target and Therapeutic Intervention, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Shiyong Ma
- Center for Novel Target and Therapeutic Intervention, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Jianbo Pan
- To whom correspondence should be addressed. Tel: +86 23 684 80209; Fax: +86 23 684 80209;
| |
Collapse
|
46
|
Luo Y, Liu L, He Z, Zhang S, Huo P, Wang Z, Jiaxin Q, Zhao L, Wu Y, Zhang D, Bu D, Chen R, Zhao Y. TREAT: Therapeutic RNAs exploration inspired by artificial intelligence technology. Comput Struct Biotechnol J 2022; 20:5680-5689. [PMID: 36320935 PMCID: PMC9589171 DOI: 10.1016/j.csbj.2022.10.011] [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: 06/30/2022] [Revised: 10/04/2022] [Accepted: 10/05/2022] [Indexed: 11/08/2022] Open
Abstract
Recent advances in RNA engineering have enabled the development of RNA-based therapeutics for a broad spectrum of applications. Developing RNA therapeutics start with targeted RNA screening and move to the drug design and optimization. However, existing target screening tools ignore noncoding RNAs and their disease-relevant regulatory relationships. And designing therapeutic RNAs encounters high computational complexity of multi-objective optimization to overcome the immunogenicity, instability and inefficient translational production. To unlock the therapeutic potential of noncoding RNAs and enable one-stop screening and design of therapeutic RNAs, we have built the platform TREAT. It incorporates 43,087,953 regulatory relationships between coding and noncoding genes from 81 biological networks under different physiological conditions. TREAT introduces graph representation learning with Random Walk Diffusions to perform disease-relevant target screening, in addition to the commonly utilized Topological Degree and PageRank algorithms. Design and optimization of large RNAs or interfering RNAs are both available. To reduce the computational complexity of multi-objective optimization for large RNA, we stratified the features into local and global features. The local features are evaluated on the fixed-length or dynamic-length local bins, whereas the latter are inspired by AI language models of protein sequence. Then the global assessment is performed on refined candidates, thus reducing the enormous search space. Overall, TREAT is a one-stop platform for the screening and designing of therapeutic RNAs, with particular attention to noncoding RNAs and cutting-edge AI technology embedded, leading the progress of innovative therapeutics for challenging diseases. TREAT is freely accessible at https://rna.org.cn/treat.
Collapse
Affiliation(s)
- Yufan Luo
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Liu Liu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Zihao He
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Shanshan Zhang
- Luoyang Zhongke Information Industry Research Institute, Luoyang, China
| | - Peipei Huo
- Luoyang Zhongke Information Industry Research Institute, Luoyang, China
| | - Zhihao Wang
- Luoyang Zhongke Information Industry Research Institute, Luoyang, China
| | - Qin Jiaxin
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Lianhe Zhao
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Yang Wu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Dongdong Zhang
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Dechao Bu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China,Hwa Mei Hospital, University of Chinese Academy of Sciences, China,Correspondence authors at: Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China (Y. Zhao).
| | - Runsheng Chen
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China,Shenzhen Institute of Nucleic Acid Drug Research, Shenzhen Bay Laboratory Pingshan Translational Medicine Center, Shenzhen 510800, China,Correspondence authors at: Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China (Y. Zhao).
| | - Yi Zhao
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China,Correspondence authors at: Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China (Y. Zhao).
| |
Collapse
|
47
|
Wang J, Nakato R. CohesinDB: a comprehensive database for decoding cohesin-related epigenomes, 3D genomes and transcriptomes in human cells. Nucleic Acids Res 2022; 51:D70-D79. [PMID: 36162821 PMCID: PMC9825609 DOI: 10.1093/nar/gkac795] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 08/29/2022] [Accepted: 09/03/2022] [Indexed: 01/29/2023] Open
Abstract
Cohesin is a multifunctional protein responsible for transcriptional regulation and chromatin organization. Cohesin binds to chromatin at tens of thousands of distinct sites in a conserved or tissue-specific manner, whereas the function of cohesin varies greatly depending on the epigenetic properties of specific chromatin loci. Cohesin also extensively mediates cis-regulatory modules (CRMs) and chromatin loops. Even though next-generation sequencing technologies have provided a wealth of information on different aspects of cohesin, the integration and exploration of the resultant massive cohesin datasets are not straightforward. Here, we present CohesinDB (https://cohesindb.iqb.u-tokyo.ac.jp), a comprehensive multiomics cohesin database in human cells. CohesinDB includes 2043 epigenomics, transcriptomics and 3D genomics datasets from 530 studies involving 176 cell types. By integrating these large-scale data, CohesinDB summarizes three types of 'cohesin objects': 751 590 cohesin binding sites, 957 868 cohesin-related chromatin loops and 2 229 500 cohesin-related CRMs. Each cohesin object is annotated with locus, cell type, classification, function, 3D genomics and cis-regulatory information. CohesinDB features a user-friendly interface for browsing, searching, analyzing, visualizing and downloading the desired information. CohesinDB contributes a valuable resource for all researchers studying cohesin, epigenomics, transcriptional regulation and chromatin organization.
Collapse
Affiliation(s)
- Jiankang Wang
- Institute for Quantitative Biosciences, The University of Tokyo, Bunkyo-ku, Tokyo, Yayoi 1-1-1, Japan,Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Hongo 7-3-1, Japan
| | - Ryuichiro Nakato
- To whom correspondence should be addressed. Tel: +81 3 5841 1471; Fax: +81 3 5841 7308;
| |
Collapse
|
48
|
Jiang Y, Harigaya Y, Zhang Z, Zhang H, Zang C, Zhang NR. Nonparametric single-cell multiomic characterization of trio relationships between transcription factors, target genes, and cis-regulatory regions. Cell Syst 2022; 13:737-751.e4. [PMID: 36055233 PMCID: PMC9509445 DOI: 10.1016/j.cels.2022.08.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 06/23/2022] [Accepted: 08/11/2022] [Indexed: 01/26/2023]
Abstract
The epigenetic control of gene expression is highly cell-type and context specific. Yet, despite its complexity, gene regulatory logic can be broken down into modular components consisting of a transcription factor (TF) activating or repressing the target gene expression through its binding to a cis-regulatory region. We propose a nonparametric approach, TRIPOD, to detect and characterize the three-way relationships between a TF, its target gene, and the accessibility of the TF's binding site using single-cell RNA and ATAC multiomic data. We apply TRIPOD to interrogate the cell-type-specific regulatory logic in peripheral blood mononuclear cells and contrast our results to detections from enhancer databases, cis-eQTL studies, ChIP-seq experiments, and TF knockdown/knockout studies. We then apply TRIPOD to mouse embryonic brain data and identify regulatory relationships, validated by ChIP-seq and PLAC-seq. Finally, we demonstrate TRIPOD on the SHARE-seq data of differentiating mouse hair follicle cells and identify lineage-specific regulation supported by histone marks and super-enhancer annotations. A record of this paper's transparent peer review process is included in the supplemental information.
Collapse
Affiliation(s)
- Yuchao Jiang
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Genetics, School of Medicine, University of North Carolina, Chapel Hill, NC 27599, USA; Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA.
| | - Yuriko Harigaya
- Curriculum in Bioinformatics and Computational Biology, School of Medicine, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Zhaojun Zhang
- Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hongpan Zhang
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA
| | - Chongzhi Zang
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA; Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
| | - Nancy R Zhang
- Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA.
| |
Collapse
|
49
|
Yang Y, Qian F, Li X, Li Y, Zhou L, Wang Q, Zhou X, Zhang J, Song C, Yu Z, Cui T, Feng C, Zhu J, Shang D, Liu J, Sun M, Zhang Y, Tang H, Li C. GREAP: a comprehensive enrichment analysis software for human genomic regions. Brief Bioinform 2022; 23:6663640. [PMID: 35959979 DOI: 10.1093/bib/bbac329] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 07/05/2022] [Accepted: 07/20/2022] [Indexed: 12/12/2022] Open
Abstract
The rapid development of genomic high-throughput sequencing has identified a large number of DNA regulatory elements with abundant epigenetics markers, which promotes the rapid accumulation of functional genomic region data. The comprehensively understanding and research of human functional genomic regions is still a relatively urgent work at present. However, the existing analysis tools lack extensive annotation and enrichment analytical abilities for these regions. Here, we designed a novel software, Genomic Region sets Enrichment Analysis Platform (GREAP), which provides comprehensive region annotation and enrichment analysis capabilities. Currently, GREAP supports 85 370 genomic region reference sets, which cover 634 681 107 regions across 11 different data types, including super enhancers, transcription factors, accessible chromatins, etc. GREAP provides widespread annotation and enrichment analysis of genomic regions. To reflect the significance of enrichment analysis, we used the hypergeometric test and also provided a Locus Overlap Analysis. In summary, GREAP is a powerful platform that provides many types of genomic region sets for users and supports genomic region annotations and enrichment analyses. In addition, we developed a customizable genome browser containing >400 000 000 customizable tracks for visualization. The platform is freely available at http://www.liclab.net/Greap/view/index.
Collapse
Affiliation(s)
- Yongsan Yang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China.,West China Biomedical Big Data Center, West China Hospital, Sichuan University, China
| | - Fengcui Qian
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,School of Computer, University of South China, Hengyang, Hunan, 421001, China.,Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,Hunan Provincial Base for Scientific and Technological Innovation Cooperation, University of South China, Hengyang, Hunan, 421001, China.,The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Xuecang Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China
| | - Yanyu Li
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China
| | - Liwei Zhou
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China
| | - Qiuyu Wang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,School of Computer, University of South China, Hengyang, Hunan, 421001, China.,Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,Hunan Provincial Base for Scientific and Technological Innovation Cooperation, University of South China, Hengyang, Hunan, 421001, China.,The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Xinyuan Zhou
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China
| | - Jian Zhang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China
| | - Chao Song
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,School of Computer, University of South China, Hengyang, Hunan, 421001, China.,Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,Hunan Provincial Base for Scientific and Technological Innovation Cooperation, University of South China, Hengyang, Hunan, 421001, China.,The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Zhengmin Yu
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Ting Cui
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Chenchen Feng
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China
| | - Jiang Zhu
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China
| | - Desi Shang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,School of Computer, University of South China, Hengyang, Hunan, 421001, China.,Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,Hunan Provincial Base for Scientific and Technological Innovation Cooperation, University of South China, Hengyang, Hunan, 421001, China.,The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Jiaqi Liu
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,School of Computer, University of South China, Hengyang, Hunan, 421001, China.,Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,Hunan Provincial Base for Scientific and Technological Innovation Cooperation, University of South China, Hengyang, Hunan, 421001, China.,The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Mengfei Sun
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China
| | - Yuexin Zhang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China
| | - Huifang Tang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Chunquan Li
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China.,School of Computer, University of South China, Hengyang, Hunan, 421001, China.,Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.,Hunan Provincial Base for Scientific and Technological Innovation Cooperation, University of South China, Hengyang, Hunan, 421001, China.,The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| |
Collapse
|
50
|
Identification and Validation of Autophagy-Related Genes in Primary Ovarian Insufficiency by Gene Expression Profile and Bioinformatic Analysis. Anal Cell Pathol 2022; 2022:9042380. [PMID: 35837294 PMCID: PMC9273469 DOI: 10.1155/2022/9042380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/20/2022] [Accepted: 05/27/2022] [Indexed: 11/18/2022] Open
Abstract
Background To investigate the relationship between primary ovarian insufficiency and autophagy, we detected and got the expression profile of human granulosa cell line SVOG, which was with or without LPS induced. The expression profile was analyzed with the focus on the autophagy genes, among which hub genes were identified. Results Totally, 6 genes were selected as candidate hub genes which might correlate with the process of primary ovarian insufficiency. The expression of hub genes was then validated by quantitative real-time PCR and two of them had significant expression change. Bioinformatics analysis was performed to observe the features of hub genes, including hub gene-RBP/TF/miRNA/drug network construction, functional analysis, and protein-protein interaction network. Pearson's correlation analysis was also performed to identify the correlation between hub genes and autophagy genes, among which there were four autophagy genes significantly correlated with hub genes, including ATG4B, ATG3, ATG13, and ULK1. Conclusion The results indicated that autophagy might play an essential role in the process and underlying molecular mechanism of primary ovarian insufficiency, which was revealed for the first time and may help to provide a molecular foundation for the development of diagnostic and therapeutic approaches for primary ovarian insufficiency.
Collapse
|