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He J, Huo X, Pei G, Jia Z, Yan Y, Yu J, Qu H, Xie Y, Yuan J, Zheng Y, Hu Y, Shi M, You K, Li T, Ma T, Zhang MQ, Ding S, Li P, Li Y. Dual-role transcription factors stabilize intermediate expression levels. Cell 2024:S0092-8674(24)00314-3. [PMID: 38631355 DOI: 10.1016/j.cell.2024.03.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 12/08/2023] [Accepted: 03/18/2024] [Indexed: 04/19/2024]
Abstract
Precise control of gene expression levels is essential for normal cell functions, yet how they are defined and tightly maintained, particularly at intermediate levels, remains elusive. Here, using a series of newly developed sequencing, imaging, and functional assays, we uncover a class of transcription factors with dual roles as activators and repressors, referred to as condensate-forming level-regulating dual-action transcription factors (TFs). They reduce high expression but increase low expression to achieve stable intermediate levels. Dual-action TFs directly exert activating and repressing functions via condensate-forming domains that compartmentalize core transcriptional unit selectively. Clinically relevant mutations in these domains, which are linked to a range of developmental disorders, impair condensate selectivity and dual-action TF activity. These results collectively address a fundamental question in expression regulation and demonstrate the potential of level-regulating dual-action TFs as powerful effectors for engineering controlled expression levels.
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Affiliation(s)
- Jinnan He
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Xiangru Huo
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Gaofeng Pei
- State Key Laboratory of Membrane Biology, Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua University-Peking University Joint Center for Life Sciences, Beijing 100084, China
| | - Zeran Jia
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Yiming Yan
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Jiawei Yu
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Haozhi Qu
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Yunxin Xie
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Junsong Yuan
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Yuan Zheng
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Yanyan Hu
- School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China; Tsinghua University-Peking University Joint Center for Life Sciences, Beijing 100084, China
| | - Minglei Shi
- Bioinformatics Division, National Research Center for Information Science and Technology, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Kaiqiang You
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China
| | - Tingting Li
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China
| | - Tianhua Ma
- School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China; Tsinghua University-Peking University Joint Center for Life Sciences, Beijing 100084, China
| | - Michael Q Zhang
- Bioinformatics Division, National Research Center for Information Science and Technology, School of Medicine, Tsinghua University, Beijing 100084, China; Department of Biological Sciences, Center for Systems Biology, The University of Texas, Dallas, TX 75080-3021, USA
| | - Sheng Ding
- School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China; Tsinghua University-Peking University Joint Center for Life Sciences, Beijing 100084, China
| | - Pilong Li
- State Key Laboratory of Membrane Biology, Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua University-Peking University Joint Center for Life Sciences, Beijing 100084, China.
| | - Yinqing Li
- The IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Bioinformatics, State Key Lab of Molecular Oncology, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China; School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China.
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2
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Tong X, Patel AS, Kim E, Li H, Chen Y, Li S, Liu S, Dilly J, Kapner KS, Zhang N, Xue Y, Hover L, Mukhopadhyay S, Sherman F, Myndzar K, Sahu P, Gao Y, Li F, Li F, Fang Z, Jin Y, Gao J, Shi M, Sinha S, Chen L, Chen Y, Kheoh T, Yang W, Yanai I, Moreira AL, Velcheti V, Neel BG, Hu L, Christensen JG, Olson P, Gao D, Zhang MQ, Aguirre AJ, Wong KK, Ji H. Adeno-to-squamous transition drives resistance to KRAS inhibition in LKB1 mutant lung cancer. Cancer Cell 2024; 42:413-428.e7. [PMID: 38402609 DOI: 10.1016/j.ccell.2024.01.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 11/07/2023] [Accepted: 01/29/2024] [Indexed: 02/27/2024]
Abstract
KRASG12C inhibitors (adagrasib and sotorasib) have shown clinical promise in targeting KRASG12C-mutated lung cancers; however, most patients eventually develop resistance. In lung patients with adenocarcinoma with KRASG12C and STK11/LKB1 co-mutations, we find an enrichment of the squamous cell carcinoma gene signature in pre-treatment biopsies correlates with a poor response to adagrasib. Studies of Lkb1-deficient KRASG12C and KrasG12D lung cancer mouse models and organoids treated with KRAS inhibitors reveal tumors invoke a lineage plasticity program, adeno-to-squamous transition (AST), that enables resistance to KRAS inhibition. Transcriptomic and epigenomic analyses reveal ΔNp63 drives AST and modulates response to KRAS inhibition. We identify an intermediate high-plastic cell state marked by expression of an AST plasticity signature and Krt6a. Notably, expression of the AST plasticity signature and KRT6A at baseline correlates with poor adagrasib responses. These data indicate the role of AST in KRAS inhibitor resistance and provide predictive biomarkers for KRAS-targeted therapies in lung cancer.
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Affiliation(s)
- Xinyuan Tong
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ayushi S Patel
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY 10016, USA
| | - Eejung Kim
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Hongjun Li
- MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, BNRist, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Yueqing Chen
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shuai Li
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY 10016, USA
| | - Shengwu Liu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Julien Dilly
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Biological and biomedical sciences program, Harvard Medical School, Boston, MA 02115, USA
| | - Kevin S Kapner
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Ningxia Zhang
- Department of Respiratory and Critical Care Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu 322000, China
| | - Yun Xue
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China; School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
| | - Laura Hover
- Monoceros Biosystems, LLC, San Diego, CA 92129, USA
| | - Suman Mukhopadhyay
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY 10016, USA
| | - Fiona Sherman
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY 10016, USA
| | - Khrystyna Myndzar
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY 10016, USA
| | - Priyanka Sahu
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY 10016, USA
| | - Yijun Gao
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Fei Li
- Department of Pathology, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China
| | - Fuming Li
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai 200438, China
| | - Zhaoyuan Fang
- Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Haining 314400, China; The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Yujuan Jin
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Juntao Gao
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, BNRist, Tsinghua University, Beijing 100084, China
| | - Minglei Shi
- Institute of Medical Innovation, Peking University Third Hospital, Beijing 100191, China
| | - Satrajit Sinha
- Department of Biochemistry, State University of New York at Buffalo, Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY 14203, USA
| | - Luonan Chen
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China; School of Life Science and Technology, Shanghai Tech University, Shanghai 200120, China; Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China; West China Biomedical Big Data Center, Med-X Center for Informatics, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yang Chen
- State Key Laboratory of Common Mechanism Research for Major Diseases, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing 100005, China
| | - Thian Kheoh
- Mirati Therapeutics, San Diego, CA 92121, USA
| | | | - Itai Yanai
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY 10016, USA; Institute of Systems Genetics, New York University Langone Health, New York, NY 10016, USA
| | - Andre L Moreira
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY 10016, USA
| | - Vamsidhar Velcheti
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY 10016, USA
| | - Benjamin G Neel
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY 10016, USA
| | - Liang Hu
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Peter Olson
- Mirati Therapeutics, San Diego, CA 92121, USA
| | - Dong Gao
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Michael Q Zhang
- Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX 75080, USA.
| | - Andrew J Aguirre
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
| | - Kwok-Kin Wong
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY 10016, USA.
| | - Hongbin Ji
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China; School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China; School of Life Science and Technology, Shanghai Tech University, Shanghai 200120, China.
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3
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Abbas A, Chandratre K, Gao Y, Yuan J, Zhang MQ, Mani RS. ChIPr: accurate prediction of cohesin-mediated 3D genome organization from 2D chromatin features. Genome Biol 2024; 25:15. [PMID: 38217027 PMCID: PMC10785520 DOI: 10.1186/s13059-023-03158-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 12/22/2023] [Indexed: 01/14/2024] Open
Abstract
The three-dimensional genome organization influences diverse nuclear processes. Here we present Chromatin Interaction Predictor (ChIPr), a suite of regression models based on deep neural networks, random forest, and gradient boosting to predict cohesin-mediated chromatin interaction strength between any two loci in the genome. The predictions of ChIPr correlate well with ChIA-PET data in four cell lines. The standard ChIPr model requires three experimental inputs: ChIP-Seq signals for RAD21, H3K27ac, and H3K27me3 but works well with just RAD21 signal. Integrative analysis reveals novel insights into the role of CTCF motif, its orientation, and CTCF binding on cohesin-mediated chromatin interactions.
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Affiliation(s)
- Ahmed Abbas
- Department of Pathology, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Khyati Chandratre
- Department of Biological Sciences, Center for Systems Biology, The University of Texas at Dallas, Richardson, TX, 75080, USA
| | - Yunpeng Gao
- Department of Pathology, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Jiapei Yuan
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, 300020, China
| | - Michael Q Zhang
- Department of Biological Sciences, Center for Systems Biology, The University of Texas at Dallas, Richardson, TX, 75080, USA.
| | - Ram S Mani
- Department of Pathology, UT Southwestern Medical Center, Dallas, TX, 75390, USA.
- Department of Urology, UT Southwestern Medical Center, Dallas, TX, 75390, USA.
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, 75390, USA.
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4
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Tang B, Wang X, He H, Chen R, Qiao G, Yang Y, Xu Z, Wang L, Dong Q, Yu J, Zhang MQ, Shi M, Wang J. Aging-disturbed FUS phase transition impairs hematopoietic stem cells by altering chromatin structure. Blood 2024; 143:124-138. [PMID: 37748139 DOI: 10.1182/blood.2023020539] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 09/08/2023] [Accepted: 09/09/2023] [Indexed: 09/27/2023] Open
Abstract
ABSTRACT Aged hematopoietic stem cells (HSCs) exhibit compromised reconstitution capacity. The molecular mechanisms behind this phenomenon are not fully understood. Here, we observed that the expression of FUS is increased in aged HSCs, and enforced FUS recapitulates the phenotype of aged HSCs through arginine-glycine-glycine-mediated aberrant FUS phase transition. By using Fus-gfp mice, we observed that FUShigh HSCs exhibit compromised FUS mobility and resemble aged HSCs both functionally and transcriptionally. The percentage of FUShigh HSCs is increased upon physiological aging and replication stress, and FUSlow HSCs of aged mice exhibit youthful function. Mechanistically, FUShigh HSCs exhibit a different global chromatin organization compared with FUSlow HSCs, which is observed in aged HSCs. Many topologically associating domains (TADs) are merged in aged HSCs because of the compromised binding of CCCTC-binding factor with chromatin, which is invoked by aberrant FUS condensates. It is notable that the transcriptional alteration between FUShigh and FUSlow HSCs originates from the merged TADs and is enriched in HSC aging-related genes. Collectively, this study reveals for the first time that aberrant FUS mobility promotes HSC aging by altering chromatin structure.
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Affiliation(s)
- Baixue Tang
- Department of Biology, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Xinming Wang
- Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing, China
| | - Hanqing He
- Department of Biology, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Ruiqing Chen
- Department of Biology, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Guofeng Qiao
- Department of Biology, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Yang Yang
- Department of Biology, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Zihan Xu
- School of Life Sciences, Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Sciences, Peking University, Beijing, China
| | - Longteng Wang
- School of Life Sciences, Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Sciences, Peking University, Beijing, China
| | - Qiongye Dong
- Institute of Precision of Medicine, Peking University Shenzhen Hospital, Shenzhen, China
| | - Jia Yu
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Haihe Laboratory of Cell Ecosystem, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Michael Q Zhang
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, Beijing National Research Center for Information Science and Technology, School of Medicine, Tsinghua University, Beijing, China
- Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX
| | - Minglei Shi
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, Beijing National Research Center for Information Science and Technology, School of Medicine, Tsinghua University, Beijing, China
- Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing, China
| | - Jianwei Wang
- Department of Biology, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
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Wang YM, Zhang MQ, Chen ZP, Ji R, Cai J, Qiao T. [Correlation between C-reactive protein to albumin ratio and restenosis after femoral popliteal stenting in patients with lower extremity arteriosclerotic obliterans]. Zhonghua Wai Ke Za Zhi 2023; 61:1058-1064. [PMID: 37932141 DOI: 10.3760/cma.j.cn112139-20230815-00047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/08/2023]
Abstract
Objective: To investigate the study of the correlation between C-reactive protein to albumin ratio (CAR) and restenosis after stenting in patients with lower extremity atherosclerotic occlusive disease(LEASO). Methods: The clinical data of 95 patients with LEASO admitted to the Department of Vascular Surgery of Nanjing Drum Tower Hospital from June 2020 to December 2022 were retrospectively analyzed. There were 67 males and 28 females,aged (73.1±9.4) years (range:51 to 92 years). The patients were classified into the restenosis group (n=61) and the patency group (n=34) according to the CT angiography results. Independent sample t test,Mann-Whitney U test and χ2 test were used to compare the data between two groups. Risk factors for restenosis after femoropopliteal artery stenting in patients with LEASO were analyzed using multivariate Cox regression. The relationship between preoperative CAR level and restenosis after stent placement was analyzed. Subject operating characteristic(ROC) curves of CAR were plotted to assess the predictive value of CAR for restenosis after stenting,and the results were expressed as area under the curve (AUC). Results: The aortoiliac calcification grade,number of stents,length of stents,C-reactive protein and CAR levels in restenosis group were higher than those in the patency group,and the serum albumin level was lower than that in the patency group(all P<0.05). And the results of multifactorial Cox regression analysis showed that higher pre-procedure CAR level and lower ABI value was an independent risk factor for in-stent restenosis. The AUC of the ROC curve for restenosis was 0.737(95%CI:0.617 to 0.856),the AUC of the ROC curve for 12-month restenosis was 0.709(95%CI:0.602 to 0.815), and the AUC of the ROC curve for 24-month restenosis was 0.702(95%CI:0.594 to 0.811). Conclusion: Higher pre-procedural CAR levels in patients with LEASO is risk factor for in-stent restenosis,and CAR has a predictive value for restenosis after lower extremity arterial stent dilatation and angioplasty.
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Affiliation(s)
- Y M Wang
- Department of Vascular Surgery,Nanjing Drum Tower Hospital Clinical College of Nanjing University of Traditional Chinese Medicine,Nanjing 210008,China
| | - M Q Zhang
- Department of Vascular Surgery,Nanjing Drum Tower Hospital,the Affiliated Hospital of Nanjing University Medical School,Nanjing 210008,China
| | - Z P Chen
- Department of Vascular Surgery,Nanjing Drum Tower Hospital,the Affiliated Hospital of Nanjing University Medical School,Nanjing 210008,China
| | - R Ji
- Department of Vascular Surgery,Nanjing Drum Tower Hospital Clinical College of Nanjing University of Traditional Chinese Medicine,Nanjing 210008,China
| | - J Cai
- Department of Vascular Surgery,Nanjing Drum Tower Hospital,the Affiliated Hospital of Nanjing University Medical School,Nanjing 210008,China
| | - T Qiao
- Department of Vascular Surgery,Nanjing Drum Tower Hospital Clinical College of Nanjing University of Traditional Chinese Medicine,Nanjing 210008,China
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Guo T, Yuan Z, Pan Y, Wang J, Chen F, Zhang MQ, Li X. SPIRAL: integrating and aligning spatially resolved transcriptomics data across different experiments, conditions, and technologies. Genome Biol 2023; 24:241. [PMID: 37864231 PMCID: PMC10590036 DOI: 10.1186/s13059-023-03078-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 09/29/2023] [Indexed: 10/22/2023] Open
Abstract
Properly integrating spatially resolved transcriptomics (SRT) generated from different batches into a unified gene-spatial coordinate system could enable the construction of a comprehensive spatial transcriptome atlas. Here, we propose SPIRAL, consisting of two consecutive modules: SPIRAL-integration, with graph domain adaptation-based data integration, and SPIRAL-alignment, with cluster-aware optimal transport-based coordination alignment. We verify SPIRAL with both synthetic and real SRT datasets. By encoding spatial correlations to gene expressions, SPIRAL-integration surpasses state-of-the-art methods in both batch effect removal and joint spatial domain identification. By aligning spots cluster-wise, SPIRAL-alignment achieves more accurate coordinate alignments than existing methods.
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Affiliation(s)
- Tiantian Guo
- School of Software Engineering, Beijing Jiaotong University, Beijing, 100044, China
- MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Zhiyuan Yuan
- Institute of Science and Technology for Brain-Inspired Intelligence, Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
| | - Yan Pan
- School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Jiakang Wang
- School of Software Engineering, Beijing Jiaotong University, Beijing, 100044, China
| | - Fengling Chen
- Center for Stem Cell Biology and Regenerative Medicine, MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Michael Q Zhang
- Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX, 75080-3021, USA.
| | - Xiangyu Li
- School of Software Engineering, Beijing Jiaotong University, Beijing, 100044, China.
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7
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Wang Y, Zhang Z, He H, Song J, Cui Y, Chen Y, Zhuang Y, Zhang X, Li M, Zhang X, Zhang MQ, Shi M, Yi C, Wang J. Aging-induced pseudouridine synthase 10 impairs hematopoietic stem cells. Haematologica 2023; 108:2677-2689. [PMID: 37165848 PMCID: PMC10542847 DOI: 10.3324/haematol.2022.282211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 05/04/2023] [Indexed: 05/12/2023] Open
Abstract
Aged hematopoietic stem cells (HSC) exhibit compromised reconstitution capacity and differentiation-bias towards myeloid lineage, however, the molecular mechanism behind it remains not fully understood. In this study, we observed that the expression of pseudouridine (Ψ) synthase 10 is increased in aged hematopoietic stem and progenitor cells (HSPC) and enforced protein of Ψ synthase 10 (PUS10) recapitulates the phenotype of aged HSC, which is not achieved by its Ψ synthase activity. Consistently, we observed no difference of transcribed RNA pseudouridylation profile between young and aged HSPC. No significant alteration of hematopoietic homeostasis and HSC function is observed in young Pus10-/- mice, while aged Pus10-/- mice exhibit mild alteration of hematopoietic homeostasis and HSC function. Moreover, we observed that PUS10 is ubiquitinated by E3 ubiquitin ligase CRL4DCAF1 complex and the increase of PUS10 in aged HSPC is due to aging-declined CRL4DCAF1- mediated ubiquitination degradation signaling. Taken together, this study for the first time evaluated the role of PUS10 in HSC aging and function, and provided a novel insight into HSC rejuvenation and its clinical application.
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Affiliation(s)
- Yuqian Wang
- School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084
| | | | - Hanqing He
- School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084
| | - Jinghui Song
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093
| | - Yang Cui
- School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084
| | - Yunan Chen
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing
| | - Yuan Zhuang
- Department of Basic Medical Sciences, School of Medicine, Institute for Immunology, Beijing Key Lab for Immunological Research on Chronic Diseases, THU-PKU Center for Life Sciences, Tsinghua University, Beijing
| | - Xiaoting Zhang
- Department of Basic Medical Sciences, School of Medicine, Institute for Immunology, Beijing Key Lab for Immunological Research on Chronic Diseases, THU-PKU Center for Life Sciences, Tsinghua University, Beijing
| | - Mo Li
- Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, 100191
| | - Xinxiang Zhang
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing
| | - Michael Q Zhang
- School of Medicine, Tsinghua University, Beijing 100084, China; MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic and Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing 100084, China; Department of Biological Sciences, Center for Systems Biology, the University of Texas, Richardson, TX 75080-3021.
| | - Minglei Shi
- School of Medicine, Tsinghua University, Beijing 100084.
| | - Chengqi Yi
- Department of Basic Medical Sciences, School of Medicine, Institute for Immunology, Beijing Key Lab for Immunological Research on Chronic Diseases, THU-PKU Center for Life Sciences, Tsinghua University, Beijing.
| | - Jianwei Wang
- School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084.
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8
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Guo X, Guo Y, Chen H, Liu X, He P, Li W, Zhang MQ, Dai Q. Systematic comparison of genome information processing and boundary recognition tools used for genomic island detection. Comput Biol Med 2023; 166:107550. [PMID: 37826950 DOI: 10.1016/j.compbiomed.2023.107550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 09/12/2023] [Accepted: 09/28/2023] [Indexed: 10/14/2023]
Abstract
Genomic islands are fragments of foreign DNA that are found in bacterial and archaeal genomes, and are typically associated with symbiosis or pathogenesis. While numerous genomic island detection methods have been proposed, there has been limited evaluation of the efficiency of the genome information processing and boundary recognition tools. In this study, we conducted a review of the statistical methods involved in genomic signatures, host signature extraction, informative signature selection, divergence measures, and boundary detection steps in genomic island prediction. We compared the performances of these methods on simulated experiments using alien fragments obtained from both artificial and real genomes. Our results indicate that among the nine genomic signatures evaluated, genomic signature frequency and full probability performed the best. However, their performance declined when normalized to their expectations and variances, such as Z-score and composition vector. Based on our experiments of the E. coli genome, we found that the confidence intervals of the window variances achieved the best performance in the signature extraction of the host, with the best confidence interval being 1.5-2 times the standard error. Ordered kurtosis was most effective in selecting informative signatures from a single genome, without requiring prior knowledge from other datasets. Among the three divergence measures evaluated, the two-sample t-test was the most successful, and a non-overlapping window with a small eye window (size 2) was best suited for identifying compositionally distinct regions. Finally, the maximum of the Markovian Jensen-Shannon divergence score, in terms of GC-content bias, was found to make boundary detection faster while maintaining a similar error rate.
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Affiliation(s)
- Xiangting Guo
- Zhejiang Sci-Tech University, Hangzhou, 310018, China
| | - Yichu Guo
- Zhejiang Sci-Tech University, Hangzhou, 310018, China
| | - Hu Chen
- Zhejiang Sci-Tech University, Hangzhou, 310018, China
| | - Xiaoqing Liu
- College of Sciences, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Pingan He
- Zhejiang Sci-Tech University, Hangzhou, 310018, China
| | - Wenshu Li
- Zhejiang Sci-Tech University, Hangzhou, 310018, China
| | - Michael Q Zhang
- Center for Systems Biology, University of Texas at Dallas, Richardson, TX, 75080, USA; Center for Synthetic and Systems Biology, TNLIST, Tsinghua University, Beijing, 100084, China
| | - Qi Dai
- Zhejiang Sci-Tech University, Hangzhou, 310018, China; Center for Systems Biology, University of Texas at Dallas, Richardson, TX, 75080, USA.
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9
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Zhang MQ, Subinuer M, Chen ZP, Cai J, Liu C, Li XQ, Liu Z, Qiao T. [Clinical analysis of surgical treatment of infection after interventional operation for major iliac artery disease in 6 cases]. Zhonghua Wai Ke Za Zhi 2023; 61:1007-1013. [PMID: 37767668 DOI: 10.3760/cma.j.cn112139-20230228-00087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
Objective: To explore the surgical treatment strategy of stent graft infection after interventional treatment of major iliac artery related diseases. Methods: Retrospective analysis was performed on the clinical data of 6 patients with secondary stent graft infection after interventional treatment for major iliac artery related diseases admitted to the Department of Vascular Surgery,Affiliated Drum Tower Hospital,Medical School of Nanjing University from November 2021 to August 2022.There were 5 males and 1 female,with a mean age of 64 years (range:49 to 79 years).The infection time was 53 to 3 165 days.All the 6 patients received surgical treatment,including 3 patients who underwent anatomic bypass grafting (axillary arterial-femoral artery bypass,femoral arterial-femoral artery bypass) using artificial vessels,and 3 patients who underwent in situ abdominal aorta reconstruction using bovine pericardium.The perioperative situation,postoperative infection and the occurrence of serious adverse events were recorded,and the safety of different treatment methods and materials was evaluated. Results: All patients successfully completed the operation and no death occurred during hospitalization.Intraoperative blood loss was 2 000~5 000 ml,and intraoperative blood transfusion was 1 600 to 5 350 ml.All the patients were followed up for 81 to 395 days after surgery,and the incision healed well,and no reinfection occurred.Postoperative gastrointestinal bleeding occurred in 1 patient,secondary surgery (retroperitoneal hematoma removal) was performed in 1 patient due to postoperative bleeding at the vascular anastomosis,both lower limb amputations were performed in 1 patient due to postoperative lower limb ischemia,and intermittent claudication occurred in 2 patients.All patients were alive at the last follow-up. Conclusion: For patients with aortic stent graft infection,when the infection is not serious and there is enough space to block the proximal and distal aorta,in situ aortic reconstruction is an effective treatment,and different materials can achieve satisfactory results in a short period of time.
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Affiliation(s)
- M Q Zhang
- Department of Vascular Surgery,Affiliated Drum Tower Hospital,Medical School of Nanjing University,Nanjing 210008,China
| | - Maimaitiaili Subinuer
- Department of Vascular Surgery,Affiliated Drum Tower Hospital,Medical School of Nanjing University,Nanjing 210008,China
| | - Z P Chen
- Department of Vascular Surgery,Affiliated Drum Tower Hospital,Medical School of Nanjing University,Nanjing 210008,China
| | - J Cai
- Department of Vascular Surgery,Affiliated Drum Tower Hospital,Medical School of Nanjing University,Nanjing 210008,China
| | - C Liu
- Department of Vascular Surgery,Affiliated Drum Tower Hospital,Medical School of Nanjing University,Nanjing 210008,China
| | - X Q Li
- Department of Vascular Surgery,Affiliated Drum Tower Hospital,Medical School of Nanjing University,Nanjing 210008,China
| | - Z Liu
- Department of Vascular Surgery,Affiliated Drum Tower Hospital,Medical School of Nanjing University,Nanjing 210008,China
| | - T Qiao
- Department of Vascular Surgery,Affiliated Drum Tower Hospital,Medical School of Nanjing University,Nanjing 210008,China
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10
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Zhang MQ, Heirbaut S, Jing XP, Stefańska B, Vandaele L, De Neve N, Fievez V. Transition cow clusters with distinctive antioxidant ability and their relation to performance and metabolic status in early lactation. J Dairy Sci 2023; 106:5723-5739. [PMID: 37331874 DOI: 10.3168/jds.2022-22865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 02/17/2023] [Indexed: 06/20/2023]
Abstract
Metabolic and oxidative stress have been characterized as risk factors during the transition period from pregnancy to lactation. Although mutual relations between both types of stress have been suggested, they rarely have been studied concomitantly. For this, a total of 99 individual transition dairy cows (117 cases, 18 cows sampled during 2 consecutive lactations) were included in this experiment. Blood samples were taken at -7, 3, 6, 9, and 21 d relative to calving and concentrations of metabolic parameters (glucose, β-hydroxybutyric acid (BHBA), nonesterified fatty acids, insulin, insulin-like growth factor 1, and fructosamine) were determined. In the blood samples of d 21, biochemical profiles related to liver function and parameters related to oxidative status were determined. First, cases were allocated to 2 different BHBA groups (ketotic vs. nonketotic, N:n = 20:33) consisting of animals with an average postpartum BHBA concentration and at least 2 out of 4 postpartum sampling points exceeding 1.2 mmol/L or remaining below 0.8 mmol/L, respectively. Second, oxidative parameters [proportion of oxidized glutathione to total glutathione in red blood cells (%)], activity of glutathione peroxidase, and of superoxide dismutase, concentrations of malondialdehyde and oxygen radical absorbance capacity were used to perform a fuzzy C-means clustering. From this, 2 groups were obtained [i.e., lower antioxidant ability (LAA80%, n = 31) and higher antioxidant ability (HAA80%, n = 19)], with 80% referring to the cutoff value for cluster membership. Increased concentrations of malondialdehyde, decreased superoxide dismutase activity, and impaired oxygen radical absorbance capacity were observed in the ketotic group compared with the nonketotic group, and inversely, the LAA80% group showed increased concentrations of BHBA. In addition, the concentration of aspartate transaminase was higher in the LAA80% group compared with the HAA80% group. Both the ketotic and LAA80% groups showed lower dry matter intake. However, a lower milk yield was observed in the LAA80% group but not in the ketotic group. Only 1 out of 19 (5.3%) and 3 out of 31 (9.7%) cases from the HAA80% and LAA80% clusters belong to the ketotic and nonketotic group, respectively. These findings suggested that dairy cows vary in oxidative status at the beginning of the lactation, and fuzzy C-means clustering allows to classify observations with distinctive oxidative status. Dairy cows with higher antioxidant capacity in early lactation rarely develop ketosis.
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Affiliation(s)
- M Q Zhang
- Laboratory for Animal Nutrition and Animal Product Quality, Department of Animal Sciences and Aquatic Ecology, Faculty of Bioscience Engineering, Ghent University, 9000 Gent, Belgium
| | - S Heirbaut
- Laboratory for Animal Nutrition and Animal Product Quality, Department of Animal Sciences and Aquatic Ecology, Faculty of Bioscience Engineering, Ghent University, 9000 Gent, Belgium
| | - X P Jing
- Laboratory for Animal Nutrition and Animal Product Quality, Department of Animal Sciences and Aquatic Ecology, Faculty of Bioscience Engineering, Ghent University, 9000 Gent, Belgium; State Key Laboratory of Grassland and Agro-Ecosystems, International Centre for Tibetan Plateau Ecosystem Management, School of Life Sciences, Lanzhou University, Lanzhou 730000, China
| | - B Stefańska
- Department of Grassland and Natural Landscape Sciences, Poznań University of Life Sciences, 60-632 Poznań, Poland
| | - L Vandaele
- Animal Sciences Unit, ILVO, 9090 Melle, Belgium
| | - N De Neve
- Laboratory for Animal Nutrition and Animal Product Quality, Department of Animal Sciences and Aquatic Ecology, Faculty of Bioscience Engineering, Ghent University, 9000 Gent, Belgium
| | - V Fievez
- Laboratory for Animal Nutrition and Animal Product Quality, Department of Animal Sciences and Aquatic Ecology, Faculty of Bioscience Engineering, Ghent University, 9000 Gent, Belgium.
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11
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Liang Z, Gilbreath C, Liu W, Wang Y, Zhang MQ, Zhang DE, Wu S, Fu XD. Chromatin-associated RNA Dictates the ecDNA Interactome in the Nucleus. bioRxiv 2023:2023.07.27.550855. [PMID: 37547001 PMCID: PMC10402128 DOI: 10.1101/2023.07.27.550855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Extrachromosomal DNA (ecDNA) promotes cancer by driving copy number heterogeneity and amplifying oncogenes along with functional enhancers. More recent studies suggest two additional mechanisms for further enhancing their oncogenic potential, one via forming ecDNA hubs to augment oncogene expression 1 and the other through acting as portable enhancers to trans-activate target genes 2. However, it has remained entirely elusive about how ecDNA explores the three-dimensional space of the nucleus and whether different ecDNA have distinct interacting mechanisms. Here, by profiling the DNA-DNA and DNA-RNA interactomes in tumor cells harboring different types of ecDNAs in comparison with similarly amplified homogenously staining regions (HSRs) in the chromosome, we show that specific ecDNA interactome is dictated by ecDNA-borne nascent RNA. We demonstrate that the ecDNA co-amplifying PVT1 and MYC utilize nascent noncoding PVT1 transcripts to mediate specific trans-activation of both ecDNA and chromosomal genes. In contrast, the ecDNA amplifying EGFR is weak in this property because of more efficient splicing to remove chromatin-associated nascent RNA. These findings reveal a noncoding RNA-orchestrated program hijacked by cancer cells to enhance the functional impact of amplified oncogenes and associated regulatory elements.
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Affiliation(s)
- Zhengyu Liang
- Department of System Biology, School of Life Science, Southern University of Science and Technology, Shenzhen, 518055, China
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Collin Gilbreath
- Children’s Medical Center Research Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Wenyue Liu
- Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Yan Wang
- Samara Inc., San Francisco, CA, USA
| | - Michael Q. Zhang
- Department of Biological Sciences, Center for Systems Biology, University of Texas, Dallas, TX 75080, USA
| | - Dong-Er Zhang
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Sihan Wu
- Children’s Medical Center Research Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Xiang-Dong Fu
- Department Westlake Laboratory of Life Sciences and Biomedicine, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Lead contact
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12
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Huo W, Price VJ, Sharifi A, Zhang MQ, Palmer KL. Enterococcus faecalis Strains with Compromised CRISPR-Cas Defense Emerge under Antibiotic Selection for a CRISPR-Targeted Plasmid. Appl Environ Microbiol 2023; 89:e0012423. [PMID: 37278656 PMCID: PMC10304774 DOI: 10.1128/aem.00124-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 05/12/2023] [Indexed: 06/07/2023] Open
Abstract
Enterococcus faecalis is a Gram-positive bacterium that natively colonizes the human gastrointestinal tract and opportunistically causes life-threatening infections. Multidrug-resistant (MDR) E. faecalis strains have emerged that are replete with mobile genetic elements (MGEs). Non-MDR E. faecalis strains frequently possess CRISPR-Cas systems, which reduce the frequency of MGE acquisition. We demonstrated in previous studies that E. faecalis populations can transiently maintain both a functional CRISPR-Cas system and a CRISPR-Cas target. In this study, we used serial passage and deep sequencing to analyze these populations. In the presence of antibiotic selection for the plasmid, mutants with compromised CRISPR-Cas defense and enhanced ability to acquire a second antibiotic resistance plasmid emerged. Conversely, in the absence of selection, the plasmid was lost from wild-type E. faecalis populations but not E. faecalis populations that lacked the cas9 gene. Our results indicate that E. faecalis CRISPR-Cas can become compromised under antibiotic selection, generating populations with enhanced abilities to undergo horizontal gene transfer. IMPORTANCE Enterococcus faecalis is a leading cause of hospital-acquired infections and disseminator of antibiotic resistance plasmids among Gram-positive bacteria. We have previously shown that E. faecalis strains with an active CRISPR-Cas system can prevent plasmid acquisition and thus limit the transmission of antibiotic resistance determinants. However, CRISPR-Cas is not a perfect barrier. In this study, we observed populations of E. faecalis with transient coexistence of CRISPR-Cas and one of its plasmid targets. Our experimental data demonstrate that antibiotic selection results in compromised E. faecalis CRISPR-Cas function, thereby facilitating the acquisition of additional resistance plasmids by E. faecalis.
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Affiliation(s)
- Wenwen Huo
- Department of Biological Sciences, The University of Texas at Dallas, Richardson, Texas, USA
| | - Valerie J. Price
- Department of Biological Sciences, The University of Texas at Dallas, Richardson, Texas, USA
| | - Ardalan Sharifi
- Department of Biological Sciences, The University of Texas at Dallas, Richardson, Texas, USA
| | - Michael Q. Zhang
- Department of Biological Sciences, The University of Texas at Dallas, Richardson, Texas, USA
| | - Kelli L. Palmer
- Department of Biological Sciences, The University of Texas at Dallas, Richardson, Texas, USA
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13
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Ma YF, Ma YL, Chen X, Zhang PA, Zhang MQ. [Development in diagnostic criteria for allergic bronchopulmonary aspergillosis]. Zhonghua Jie He He Hu Xi Za Zhi 2023; 46:624-631. [PMID: 37278181 DOI: 10.3760/cma.j.cn112147-20221124-00925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Allergic bronchopulmonary aspergillosis (ABPA) is an allergic lung disease caused by the sensitization of Aspergillus fumigatus. In recent years, the research into ABPA has progressed, the testing methods have improved and the diagnostic criteria have been continuously updated. There is no gold standard for the diagnosis of the disease. The diagnostic criteria for ABPA include predisposing diseases, fungal-related immunoassay and pathological examination. Understanding the clinical significance of ABPA diagnostic criteria may help to prevent irreversible bronchopulmonary injury, improve respiratory function and improve the prognosis of patients.
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Affiliation(s)
- Y F Ma
- Peking University People's Hospital, Department of Respiratory and Critical Care Medicine,Beijing 100044, China
| | - Y L Ma
- Peking University People's Hospital, Department of Respiratory and Critical Care Medicine,Beijing 100044, China
| | - X Chen
- Peking University People's Hospital, Department of Respiratory and Critical Care Medicine,Beijing 100044, China
| | - P A Zhang
- Peking University People's Hospital, Department of Respiratory and Critical Care Medicine,Beijing 100044, China
| | - M Q Zhang
- Peking University People's Hospital, Department of Respiratory and Critical Care Medicine,Beijing 100044, China
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14
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Zhang W, Zhang MQ, Gong PH, Pan F, Sun KK, Bao J, Li YQ, Gao ZC. [Clinical features of IgG4-related lung disease]. Zhonghua Yi Xue Za Zhi 2023; 103:1417-1422. [PMID: 37150695 DOI: 10.3760/cma.j.cn112137-20221025-02226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Objective: To explore the clinical features of IgG4-related lung disease. Methods: The clinical data of 60 patients diagnosed with IgG4-related lung disease in Peking University People's Hospital from February 2012 to May 2021 were retrospectively collected. Analysis was made to explore the features of clinical manifestation, laboratory, imaging, prognosis and other characteristics of the disease. Results: A total of 60 patients were included, with 40 males, age of (58.2±12.9) years, an age of onset of (57.1±13.2) years, and 31.7% (19 cases) of the patients had a history of allergic disease. 36.7% (22 cases) of the patients had respiratory symptoms during the disease. 94.6% (53/56) of patients had serum IgG4>1.35 g/L, 24.1% (14/58) of patients had increased eosinophils, 79.2% (38/48) of patients had increased IgE level, and 53.7% (29/54) of patients had decreased C3 or C4. Common imaging findings included nodular changes (38 cases, 63.3%), mediastinal and/or hilar lymphadenopathy (34 cases, 56.7%), and ground glass opacities (31 cases, 51.7%). Fifty-three cases (88.3%) showed two or more imaging changes. The pathological examination of the patient was mainly characterized by lymphoplasmacytic infiltration and fibrosis, with only one case of phlebitis obliterans. Compared with the asymptomatic group (38 cases), patients with respiratory symptoms (22 cases) showed higher level of serum total IgG and eosinophils (43.2 vs 17.8 g/L, 0.30×109/L vs 0.14×109/L, P<0.05), lower proportion of allergic diseases, and higher proportion of consolidation shadows on chest CT (P<0.05). There were no significant differences in serum IgG4, IgE, complement levels, and imaging outcomes after treatment between the two groups (P>0.05). Conclusions: The clinical manifestations of IgG4-related lung disease are atypical, and asymptomatic patients account for a high proportion. The imaging of the disease is highly heterogeneous, and patients are prone to show coexisted multiple imaging changes. The main clinical features and imaging outcomes of patients with and without respiratory symptoms are not significantly different.
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Affiliation(s)
- W Zhang
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China
| | - M Q Zhang
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China
| | - P H Gong
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China
| | - F Pan
- Department of Radiology, Peking University People's Hospital, Beijing100044, China
| | - K K Sun
- Department of Pathology, Peking University People's Hospital, Beijing 100044, China
| | - J Bao
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China
| | - Y Q Li
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China
| | - Z C Gao
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China
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15
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Yuan Z, Pan W, Zhao X, Zhao F, Xu Z, Li X, Zhao Y, Zhang MQ, Yao J. Publisher Correction: SODB facilitates comprehensive exploration of spatial omics data. Nat Methods 2023; 20:623. [PMID: 36932185 DOI: 10.1038/s41592-023-01844-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023]
Affiliation(s)
- Zhiyuan Yuan
- Institute of Science and Technology for Brain-Inspired Intelligence; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China. .,Tencent AI Lab, Shenzhen, China.
| | - Wentao Pan
- Tencent AI Lab, Shenzhen, China.,Shenzhen International Graduate School, Tsinghua University, Shenzen, China
| | | | - Fangyuan Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | | | - Xiu Li
- Shenzhen International Graduate School, Tsinghua University, Shenzen, China
| | - Yi Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Michael Q Zhang
- Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX, USA.
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16
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Yuan Z, Pan W, Zhao X, Zhao F, Xu Z, Li X, Zhao Y, Zhang MQ, Yao J. SODB facilitates comprehensive exploration of spatial omics data. Nat Methods 2023; 20:387-399. [PMID: 36797409 DOI: 10.1038/s41592-023-01773-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 01/06/2023] [Indexed: 02/18/2023]
Abstract
Spatial omics technologies generate wealthy but highly complex datasets. Here we present Spatial Omics DataBase (SODB), a web-based platform providing both rich data resources and a suite of interactive data analytical modules. SODB currently maintains >2,400 experiments from >25 spatial omics technologies, which are freely accessible as a unified data format compatible with various computational packages. SODB also provides multiple interactive data analytical modules, especially a unique module, Spatial Omics View (SOView). We conduct comprehensive statistical analyses and illustrate the utility of both basic and advanced analytical modules using multiple spatial omics datasets. We demonstrate SOView utility with brain spatial transcriptomics data and recover known anatomical structures. We further delineate functional tissue domains with associated marker genes that were obscured when analyzed using previous methods. We finally show how SODB may efficiently facilitate computational method development. The SODB website is https://gene.ai.tencent.com/SpatialOmics/ . The command-line package is available at https://pysodb.readthedocs.io/en/latest/ .
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Affiliation(s)
- Zhiyuan Yuan
- Institute of Science and Technology for Brain-Inspired Intelligence; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Tencent AI Lab, Shenzhen, China.
| | - Wentao Pan
- Tencent AI Lab, Shenzhen, China
- Shenzhen International Graduate School, Tsinghua University, Shenzen, China
| | | | - Fangyuan Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | | | - Xiu Li
- Shenzhen International Graduate School, Tsinghua University, Shenzen, China
| | - Yi Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Michael Q Zhang
- Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX, USA.
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17
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Zhang MQ, Qiao T. [Application of meandering mesenteric artery in vascular surgery]. Zhonghua Wai Ke Za Zhi 2023; 61:349-352. [PMID: 36822592 DOI: 10.3760/cma.j.cn112139-20221017-00445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
In recent years, the therapeutic concepts and surgical methods of the meandering mesenteric artery involved in atherosclerosis have been reported in the literature, and the importance of this lateral branch circulation in the field of vascular surgery has received more attention. With the improvement of imaging techniques, the discovery rate of this collateral circulation increased. In the presence of major iliac artery occlusion, the meandering mesenteric artery may serve as an important collateral circulation to relieve ischemia in the visceral or lower extremities. The meandering mesenteric artery may also lead to type Ⅱ internal leakage after endovascular repair, while the embolization of inferior mesenteric artery may be performed through the meandering mesenteric artery. The role of meandering mesenteric arteries in vascular surgery needs further study.
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Affiliation(s)
- M Q Zhang
- Department of Vascular Surgery, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
| | - T Qiao
- Department of Vascular Surgery, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
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18
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Yuan J, Houlahan KE, Ramanand SG, Lee S, Baek G, Yang Y, Chen Y, Strand DW, Zhang MQ, Boutros PC, Mani RS. Prostate Cancer Transcriptomic Regulation by the Interplay of Germline Risk Alleles, Somatic Mutations, and 3D Genomic Architecture. Cancer Discov 2022; 12:2838-2855. [PMID: 36108240 PMCID: PMC9722594 DOI: 10.1158/2159-8290.cd-22-0027] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 07/18/2022] [Accepted: 09/15/2022] [Indexed: 01/12/2023]
Abstract
Prostate cancer is one of the most heritable human cancers. Genome-wide association studies have identified at least 185 prostate cancer germline risk alleles, most noncoding. We used integrative three-dimensional (3D) spatial genomics to identify the chromatin interaction targets of 45 prostate cancer risk alleles, 31 of which were associated with the transcriptional regulation of target genes in 565 localized prostate tumors. To supplement these 31, we verified transcriptional targets for 56 additional risk alleles using linear proximity and linkage disequilibrium analysis in localized prostate tumors. Some individual risk alleles influenced multiple target genes; others specifically influenced only distal genes while leaving proximal ones unaffected. Several risk alleles exhibited widespread germline-somatic interactions in transcriptional regulation, having different effects in tumors with loss of PTEN or RB1 relative to those without. These data clarify functional prostate cancer risk alleles in large linkage blocks and outline a strategy to model multidimensional transcriptional regulation. SIGNIFICANCE Many prostate cancer germline risk alleles are enriched in the noncoding regions of the genome and are hypothesized to regulate transcription. We present a 3D genomics framework to unravel risk SNP function and describe the widespread germline-somatic interplay in transcription control. This article is highlighted in the In This Issue feature, p. 2711.
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Affiliation(s)
- Jiapei Yuan
- Department of Pathology, UT Southwestern Medical Center, Dallas, Texas,State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College., Tianjin, China
| | - Kathleen E Houlahan
- Department of Human Genetics, University of California, Los Angeles, California,Jonsson Comprehensive Cancer Centre, University of California, Los Angeles, California,Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada,Vector Institute, Toronto, ON M5G 1M1, Canada,Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada
| | | | - Sora Lee
- Department of Pathology, UT Southwestern Medical Center, Dallas, Texas
| | - GuemHee Baek
- Department of Pathology, UT Southwestern Medical Center, Dallas, Texas
| | - Yang Yang
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammation Biology, Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China,Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China
| | - Yong Chen
- Department of Molecular and Cellular Biosciences, Rowan University, Glassboro, New Jersey
| | - Douglas W. Strand
- Department of Urology, UT Southwestern Medical Center, Dallas, Texas
| | - Michael Q. Zhang
- Department of Biological Sciences, Center for Systems Biology, The University of Texas at Dallas, Richardson, Texas,MOE Key Laboratory of Bioinformatics and Bioinformatics Division, Center for Synthetic and System Biology, TNLIST/Department Automation, Tsinghua University, Beijing 100084, China
| | - Paul C. Boutros
- Department of Human Genetics, University of California, Los Angeles, California,Jonsson Comprehensive Cancer Centre, University of California, Los Angeles, California,Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada,Vector Institute, Toronto, ON M5G 1M1, Canada,Department of Urology, University of California, Los Angeles, California,Institute for Precision Health, University of California, Los Angeles, California
| | - Ram S. Mani
- Department of Pathology, UT Southwestern Medical Center, Dallas, Texas,Department of Urology, UT Southwestern Medical Center, Dallas, Texas,Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas
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19
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Yuan Z, Li Y, Shi M, Yang F, Gao J, Yao J, Zhang MQ. SOTIP is a versatile method for microenvironment modeling with spatial omics data. Nat Commun 2022; 13:7330. [PMID: 36443314 PMCID: PMC9705407 DOI: 10.1038/s41467-022-34867-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 11/10/2022] [Indexed: 11/29/2022] Open
Abstract
The rapidly developing spatial omics generated datasets with diverse scales and modalities. However, most existing methods focus on modeling dynamics of single cells while ignore microenvironments (MEs). Here we present SOTIP (Spatial Omics mulTIPle-task analysis), a versatile method incorporating MEs and their interrelationships into a unified graph. Based on this graph, spatial heterogeneity quantification, spatial domain identification, differential microenvironment analysis, and other downstream tasks can be performed. We validate each module's accuracy, robustness, scalability and interpretability on various spatial omics datasets. In two independent mouse cerebral cortex spatial transcriptomics datasets, we reveal a gradient spatial heterogeneity pattern strongly correlated with the cortical depth. In human triple-negative breast cancer spatial proteomics datasets, we identify molecular polarizations and MEs associated with different patient survivals. Overall, by modeling biologically explainable MEs, SOTIP outperforms state-of-art methods and provides some perspectives for spatial omics data exploration and interpretation.
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Affiliation(s)
- Zhiyuan Yuan
- Institute of Science and Technology for Brain-Inspired Intelligence; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.
- Tencent AI Lab, Shenzhen, China.
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Yisi Li
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Minglei Shi
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, School of Medicine, Tsinghua University, Beijing, 100084, China
| | | | - Juntao Gao
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing, 100084, China
| | | | - Michael Q Zhang
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing, 100084, China.
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, School of Medicine, Tsinghua University, Beijing, 100084, China.
- Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX, 75080-3021, USA.
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20
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Shen W, Zhang Y, Shi M, Ye B, Yin M, Li P, Shi S, Jin Y, Zhang Z, Zhang MQ, Chen Y, Zhao Z. Profiling and characterization of constitutive chromatin-enriched RNAs. iScience 2022; 25:105349. [DOI: 10.1016/j.isci.2022.105349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 08/29/2022] [Accepted: 10/11/2022] [Indexed: 10/31/2022] Open
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21
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Yan A, Xiong J, Zhu J, Li X, Xu S, Feng X, Ke X, Wang Z, Chen Y, Wang HW, Zhang MQ, Kee K. DAZL regulates proliferation of human primordial germ cells by direct binding to precursor miRNAs and enhances DICER processing activity. Nucleic Acids Res 2022; 50:11255-11272. [DOI: 10.1093/nar/gkac856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 09/14/2022] [Accepted: 09/23/2022] [Indexed: 11/14/2022] Open
Abstract
Abstract
Understanding the molecular and cellular mechanisms of human primordial germ cells (hPGCs) is essential in studying infertility and germ cell tumorigenesis. Many RNA-binding proteins (RBPs) and non-coding RNAs are specifically expressed and functional during hPGC developments. However, the roles and regulatory mechanisms of these RBPs and non-coding RNAs, such as microRNAs (miRNAs), in hPGCs remain elusive. In this study, we reported a new regulatory function of DAZL, a germ cell-specific RBP, in miRNA biogenesis and cell proliferation. First, DAZL co-localized with miRNA let-7a in human PGCs and up-regulated the levels of >100 mature miRNAs, including eight out of nine let-7 family, miR21, miR22, miR125, miR10 and miR199. Purified DAZL directly bound to the loops of precursor miRNAs with sequence specificity of GUU. The binding of DAZL to the precursor miRNA increased the maturation of miRNA by enhancing the cleavage activity of DICER. Furthermore, cell proliferation assay and cell cycle analysis confirmed that DAZL inhibited the proliferation of in vitro PGCs by promoting the maturation of these miRNAs. Evidently, the mature miRNAs up-regulated by DAZL silenced cell proliferation regulators including TRIM71. Moreover, DAZL inhibited germline tumor cell proliferation and teratoma formation. These results demonstrate that DAZL regulates hPGC proliferation by enhancing miRNA processing.
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Affiliation(s)
- An Yan
- Center for Stem Cell Biology and Regenerative Medicine, Department of Basic Medical Sciences, School of Medicine, Tsinghua University , Beijing 100084 , China
| | - Jie Xiong
- Center for Stem Cell Biology and Regenerative Medicine, Department of Basic Medical Sciences, School of Medicine, Tsinghua University , Beijing 100084 , China
- Tsinghua University-–Peking University Joint Center for Life Sciences, Tsinghua University , Beijing 100084 , China
| | - Jiadong Zhu
- Center for Stem Cell Biology and Regenerative Medicine, Department of Basic Medical Sciences, School of Medicine, Tsinghua University , Beijing 100084 , China
| | - Xiangyu Li
- School of Software Engineering, Beijing Jiaotong University , Beijing 100044 , China
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University , Beijing 100084 , China
| | - Shuting Xu
- Center for Stem Cell Biology and Regenerative Medicine, Department of Basic Medical Sciences, School of Medicine, Tsinghua University , Beijing 100084 , China
- Tsinghua University-–Peking University Joint Center for Life Sciences, Tsinghua University , Beijing 100084 , China
| | - Xiaoyu Feng
- Center for Stem Cell Biology and Regenerative Medicine, Department of Basic Medical Sciences, School of Medicine, Tsinghua University , Beijing 100084 , China
| | - Xin Ke
- Ministry of Education Key Laboratory of Protein Sciences, Tsinghua–Peking Joint Center for Life Sciences, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University , Beijing 100084, China
| | - Zhenyi Wang
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University , Beijing 100084 , China
| | - Yang Chen
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University , Beijing 100084 , China
- School of Medicine, Tsinghua University , Beijing 100084 , China
| | - Hong-Wei Wang
- Ministry of Education Key Laboratory of Protein Sciences, Tsinghua–Peking Joint Center for Life Sciences, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University , Beijing 100084, China
| | - Michael Q Zhang
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University , Beijing 100084 , China
- School of Medicine, Tsinghua University , Beijing 100084 , China
- Department of Biological Sciences, Center for Systems Biology, The University of Texas at Dallas, 800 West Campbell Road, RL11, Richardson , TX 75080-3021, USA
| | - Kehkooi Kee
- Center for Stem Cell Biology and Regenerative Medicine, Department of Basic Medical Sciences, School of Medicine, Tsinghua University , Beijing 100084 , China
- Tsinghua University-–Peking University Joint Center for Life Sciences, Tsinghua University , Beijing 100084 , China
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22
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Wang XX, Zhang MQ, Cao ZG. [Periodontitis and systemic diseases: an evidence-based interrelationship and treatment strategies for periodontitis with systemic diseases]. Zhonghua Kou Qiang Yi Xue Za Zhi 2022; 57:874-879. [PMID: 35970785 DOI: 10.3760/cma.j.cn112144-20220413-00178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Periodontitis is a common chronic infectious disease, so as to be the leading cause of tooth loss in adults. Numerous studies have confirmed the interrelationship between periodontitis and systemic diseases. However, evidence-based reviews reporting the interrelationship between them and treatment strategies for periodontitis with systemic diseases were still absent currently. Therefore, based on evidence-based medical researches in recent years, this article will summarize the interrelationship between periodontitis and systemic diseases, and briefly state the treatment strategies for periodontitis with systemic diseases.
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Affiliation(s)
- X X Wang
- The State Key Laboratory Breeding Base of Basic Science of Stomatology & Key Laboratory of Oral Biomedicine Ministry of Education, School of Stomatology, Wuhan University, Wuhan 430079, China
| | - M Q Zhang
- The State Key Laboratory Breeding Base of Basic Science of Stomatology & Key Laboratory of Oral Biomedicine Ministry of Education, School of Stomatology, Wuhan University, Wuhan 430079, China
| | - Z G Cao
- Department of Periodontology, School of Stomatology, Wuhan University, Wuhan 430079, China
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23
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Zhao Y, Huang F, Zou Z, Bi Y, Yang Y, Zhang C, Liu Q, Shang D, Yan Y, Ju X, Mei S, Xie P, Li X, Tian M, Tan S, Lu H, Han Z, Liu K, Zhang Y, Liang J, Liang Z, Zhang Q, Chang J, Liu WJ, Feng C, Li T, Zhang MQ, Wang X, Gao GF, Liu Y, Jin N, Jiang C. Avian influenza viruses suppress innate immunity by inducing trans-transcriptional readthrough via SSU72. Cell Mol Immunol 2022; 19:702-714. [PMID: 35332300 PMCID: PMC9151799 DOI: 10.1038/s41423-022-00843-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 01/29/2022] [Indexed: 11/29/2022] Open
Abstract
Innate immunity plays critical antiviral roles. The highly virulent avian influenza viruses (AIVs) H5N1, H7N9, and H5N6 can better escape host innate immune responses than the less virulent seasonal H1N1 virus. Here, we report a mechanism by which transcriptional readthrough (TRT)-mediated suppression of innate immunity occurs post AIV infection. By using cell lines, mouse lungs, and patient PBMCs, we showed that genes on the complementary strand (“trans” genes) influenced by TRT were involved in the disruption of host antiviral responses during AIV infection. The trans-TRT enhanced viral lethality, and TRT abolishment increased cell viability and STAT1/2 expression. The viral NS1 protein directly bound to SSU72, and degradation of SSU72 induced TRT. SSU72 overexpression reduced TRT and alleviated mouse lung injury. Our results suggest that AIVs infection induce TRT by reducing SSU72 expression, thereby impairing host immune responses, a molecular mechanism acting through the NS1-SSU72-trans-TRT-STAT1/2 axis. Thus, restoration of SSU72 expression might be a potential strategy for preventing AIV pandemics.
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24
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Song Y, Liang Z, Zhang J, Hu G, Wang J, Li Y, Guo R, Dong X, Babarinde IA, Ping W, Sheng YL, Li H, Chen Z, Gao M, Chen Y, Shan G, Zhang MQ, Hutchins AP, Fu XD, Yao H. CTCF functions as an insulator for somatic genes and a chromatin remodeler for pluripotency genes during reprogramming. Cell Rep 2022; 39:110626. [PMID: 35385732 DOI: 10.1016/j.celrep.2022.110626] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 01/13/2022] [Accepted: 03/14/2022] [Indexed: 12/15/2022] Open
Abstract
CTCF mediates chromatin insulation and long-distance enhancer-promoter (EP) interactions; however, little is known about how these regulatory functions are partitioned among target genes in key biological processes. Here, we show that Ctcf expression is progressively increased during induced pluripotency. In this process, CTCF first functions as a chromatin insulator responsible for direct silencing of the somatic gene expression program and, interestingly, elevated Ctcf expression next ensures chromatin accessibility and contributes to increased EP interactions for a fraction of pluripotency-associated genes. Therefore, CTCF functions in a context-specific manner to modulate the 3D genome to enable cellular reprogramming. We further discover that these context-specific CTCF functions also enlist SMARCA5, an imitation switch (ISWI) chromatin remodeler, together rewiring the epigenome to facilitate cell-fate switch. These findings reveal the dual functions of CTCF in conjunction with a key chromatin remodeler to drive reprogramming toward pluripotency.
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Affiliation(s)
- Yawei Song
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China; Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China; Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhengyu Liang
- Department of Cellular and Molecular Medicine, Institute of Genomic Medicine, University of California, San Diego, La Jolla, CA 92093-0651, USA
| | - Jie Zhang
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China; Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China; Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Gongcheng Hu
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China; Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China; Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
| | - Juehan Wang
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China; Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China; Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yaoyi Li
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China; Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China; Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
| | - Rong Guo
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China; Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China; Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaotao Dong
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China; Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China; Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
| | - Isaac A Babarinde
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Wangfang Ping
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China; Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China; Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ying-Liang Sheng
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China; Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China; Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China; Department of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Huanhuan Li
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China
| | - Zhaoming Chen
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China; Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China
| | - Minghui Gao
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China
| | - Yang Chen
- MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Ge Shan
- Department of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Michael Q Zhang
- MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, School of Medicine, Tsinghua University, Beijing 100084, China; Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX 75080-3021, USA
| | - Andrew P Hutchins
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Xiang-Dong Fu
- Department of Cellular and Molecular Medicine, Institute of Genomic Medicine, University of California, San Diego, La Jolla, CA 92093-0651, USA.
| | - Hongjie Yao
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China; Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China; Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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25
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Yang Z, Yi W, Tao J, Liu X, Zhang MQ, Chen G, Dai Q. HPVMD-C: a disease-based mutation database of human papillomavirus in China. Database (Oxford) 2022; 2022:6554601. [PMID: 35348640 PMCID: PMC9216535 DOI: 10.1093/database/baac018] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 02/28/2022] [Accepted: 03/06/2022] [Indexed: 11/14/2022]
Abstract
Human papillomavirus (HPV) can cause condyloma acuminatum and cervical cancer. Some mutations of these viruses are closely related to the persistent infection of cervical cancer and are ideal cancer vaccine targets. Several databases have been developed to collect HPV sequences, but no HPV mutation database has been published. This paper reports a Chinese HPV mutation database (HPVMD-C), which contains 149 HPV genotypes, 468 HPV mutations, 3409 protein sequences, 4727 domains and 236 epitopes. We analyzed the mutation distribution among HPV genotypes, domains and epitopes. We designed a visualization tool to display these mutations, domains and epitopes and provided more detailed information about the disease, region and related literature. We also proposed an HPV genotype prediction tool, which can predict HPV carcinogenic or non-carcinogenic risk genotypes. We expect that HPVMD-C will complement the existing database and provide valuable resources for HPV vaccine research and cervical cancer treatment. HPVMD-C is freely available at Database URL: http://bioinfo.zstu.edu.cn/hpv.
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Affiliation(s)
- Zhenyu Yang
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China
| | - Wenjing Yi
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China
| | - Jin Tao
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China
| | - Xiaoqing Liu
- College of Sciences, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Michael Q Zhang
- Department of Biological Sciences, Center for Systems Biology, University of Texas at Dallas, Richardson, TX 75080, USA.,Division of Bioinformatics, Center for Synthetic and Systems Biology, TNLIST, Tsinghua University, Beijing 100084, China
| | - Guiqian Chen
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China
| | - Qi Dai
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China.,Department of Biological Sciences, Center for Systems Biology, University of Texas at Dallas, Richardson, TX 75080, USA
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26
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Guo T, Chen Y, Shi M, Li X, Zhang MQ. Integration of single cell data by disentangled representation learning. Nucleic Acids Res 2022; 50:e8. [PMID: 34850092 PMCID: PMC8788944 DOI: 10.1093/nar/gkab978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 10/04/2021] [Accepted: 10/11/2021] [Indexed: 11/22/2022] Open
Abstract
Recent developments of single cell RNA-sequencing technologies lead to the exponential growth of single cell sequencing datasets across different conditions. Combining these datasets helps to better understand cellular identity and function. However, it is challenging to integrate different datasets from different laboratories or technologies due to batch effect, which are interspersed with biological variances. To overcome this problem, we have proposed Single Cell Integration by Disentangled Representation Learning (SCIDRL), a domain adaption-based method, to learn low-dimensional representations invariant to batch effect. This method can efficiently remove batch effect while retaining cell type purity. We applied it to thirteen diverse simulated and real datasets. Benchmark results show that SCIDRL outperforms other methods in most cases and exhibits excellent performances in two common situations: (i) effective integration of batch-shared rare cell types and preservation of batch-specific rare cell types; (ii) reliable integration of datasets with different cell compositions. This demonstrates SCIDRL will offer a valuable tool for researchers to decode the enigma of cell heterogeneity.
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Affiliation(s)
- Tiantian Guo
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing 100084, China
| | - Yang Chen
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing 100084, China
| | - Minglei Shi
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; School of Medicine, Tsinghua University, Beijing 100084, China
| | - Xiangyu Li
- School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China
| | - Michael Q Zhang
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing 100084, China
- Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX 75080-3021, USA
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27
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Wang Z, Zhong Y, Ye Z, Zeng L, Chen Y, Shi M, Yuan Z, Zhou Q, Qian M, Zhang MQ. MarkovHC: Markov hierarchical clustering for the topological structure of high-dimensional single-cell omics data with transition pathway and critical point detection. Nucleic Acids Res 2022; 50:46-56. [PMID: 34850940 PMCID: PMC8754642 DOI: 10.1093/nar/gkab1132] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 10/19/2021] [Accepted: 10/27/2021] [Indexed: 11/13/2022] Open
Abstract
Clustering cells and depicting the lineage relationship among cell subpopulations are fundamental tasks in single-cell omics studies. However, existing analytical methods face challenges in stratifying cells, tracking cellular trajectories, and identifying critical points of cell transitions. To overcome these, we proposed a novel Markov hierarchical clustering algorithm (MarkovHC), a topological clustering method that leverages the metastability of exponentially perturbed Markov chains for systematically reconstructing the cellular landscape. Briefly, MarkovHC starts with local connectivity and density derived from the input and outputs a hierarchical structure for the data. We firstly benchmarked MarkovHC on five simulated datasets and ten public single-cell datasets with known labels. Then, we used MarkovHC to investigate the multi-level architectures and transition processes during human embryo preimplantation development and gastric cancer procession. MarkovHC found heterogeneous cell states and sub-cell types in lineage-specific progenitor cells and revealed the most possible transition paths and critical points in the cellular processes. These results demonstrated MarkovHC's effectiveness in facilitating the stratification of cells, identification of cell populations, and characterization of cellular trajectories and critical points.
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Affiliation(s)
- Zhenyi Wang
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing 100084, China
| | - Yanjie Zhong
- School of Mathematical Sciences, Peking University, Beijing 100871, China
- Department of Mathematics and Statistics, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Zhaofeng Ye
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; School of Medicine, Tsinghua University, Beijing 100084, China
| | - Lang Zeng
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yang Chen
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing 100084, China
| | - Minglei Shi
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; School of Medicine, Tsinghua University, Beijing 100084, China
| | - Zhiyuan Yuan
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing 100084, China
| | - Qiming Zhou
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Minping Qian
- School of Mathematical Sciences, Peking University, Beijing 100871, China
| | - Michael Q Zhang
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing 100084, China
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; School of Medicine, Tsinghua University, Beijing 100084, China
- Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX 75080-3021, USA
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28
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Wang R, Chen F, Chen Q, Wan X, Shi M, Chen AK, Ma Z, Li G, Wang M, Ying Y, Liu Q, Li H, Zhang X, Ma J, Zhong J, Chen M, Zhang MQ, Zhang Y, Chen Y, Zhu D. MyoD is a 3D genome structure organizer for muscle cell identity. Nat Commun 2022; 13:205. [PMID: 35017543 PMCID: PMC8752600 DOI: 10.1038/s41467-021-27865-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 12/15/2021] [Indexed: 12/15/2022] Open
Abstract
The genome exists as an organized, three-dimensional (3D) dynamic architecture, and each cell type has a unique 3D genome organization that determines its cell identity. An unresolved question is how cell type-specific 3D genome structures are established during development. Here, we analyzed 3D genome structures in muscle cells from mice lacking the muscle lineage transcription factor (TF), MyoD, versus wild-type mice. We show that MyoD functions as a “genome organizer” that specifies 3D genome architecture unique to muscle cell development, and that H3K27ac is insufficient for the establishment of MyoD-induced chromatin loops in muscle cells. Moreover, we present evidence that other cell lineage-specific TFs might also exert functional roles in orchestrating lineage-specific 3D genome organization during development. Pioneer transcription factors (TFs) have been proposed to act as protein anchors to orchestrate cell type-specific 3D genome architecture. MyoD is a pioneer TF for myogenic lineage specification. Here the authors provide further support for the role of MyoD in 3D genome architecture in muscle stem cells by comparing MyoD knockout and wild-type mice.
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Affiliation(s)
- Ruiting Wang
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, 5 Dong Dan San Tiao, 100005, Beijing, China
| | - Fengling Chen
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Bioinformatics Division, BNRist, Department of Automation, Tsinghua University, 100084, Beijing, China
| | - Qian Chen
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, 5 Dong Dan San Tiao, 100005, Beijing, China
| | - Xin Wan
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, 5 Dong Dan San Tiao, 100005, Beijing, China
| | - Minglei Shi
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Medicine, Tsinghua University, 100084, Beijing, China
| | - Antony K Chen
- Department of Biomedical Engineering, College of Future Technology, Peking University, 100871, Beijing, China
| | - Zhao Ma
- Department of Biomedical Engineering, College of Future Technology, Peking University, 100871, Beijing, China.,Department of Biomedical Engineering, College of Engineering, Peking University, 100871, Beijing, China
| | - Guohong Li
- National Laboratory of Biomacromolecules, CAS Center for Excellent in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101, Beijing, China.,University of Chinese Academy of Science, 100049, Beijing, China
| | - Min Wang
- National Laboratory of Biomacromolecules, CAS Center for Excellent in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101, Beijing, China.,University of Chinese Academy of Science, 100049, Beijing, China
| | - Yachen Ying
- Department of Biomedical Engineering, College of Future Technology, Peking University, 100871, Beijing, China.,Department of Biomedical Engineering, College of Engineering, Peking University, 100871, Beijing, China
| | - Qinyao Liu
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, 5 Dong Dan San Tiao, 100005, Beijing, China
| | - Hu Li
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, 5 Dong Dan San Tiao, 100005, Beijing, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), 510320, Guangzhou, China
| | - Xu Zhang
- Beijing institute of collaborative innovation, 100094, Beijing, China
| | - Jinbiao Ma
- State Key Laboratory of Genetic Engineering, Department of Biochemistry, School of Life Sciences, Fudan University, 200438, Shanghai, China
| | - Jiayun Zhong
- State Key Laboratory of Genetic Engineering, Department of Biochemistry, School of Life Sciences, Fudan University, 200438, Shanghai, China
| | - Meihong Chen
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, 5 Dong Dan San Tiao, 100005, Beijing, China
| | - Michael Q Zhang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Bioinformatics Division, BNRist, Department of Automation, Tsinghua University, 100084, Beijing, China. .,MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Medicine, Tsinghua University, 100084, Beijing, China. .,Department of Biological Sciences, Center for Systems Biology, The University of Texas, Dallas 800 West Campbell Road, RL11, Richardson, TX, 75080-3021, USA.
| | - Yong Zhang
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, 5 Dong Dan San Tiao, 100005, Beijing, China.
| | - Yang Chen
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, 5 Dong Dan San Tiao, 100005, Beijing, China. .,MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Bioinformatics Division, BNRist, Department of Automation, Tsinghua University, 100084, Beijing, China. .,MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Medicine, Tsinghua University, 100084, Beijing, China.
| | - Dahai Zhu
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, 5 Dong Dan San Tiao, 100005, Beijing, China. .,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), 510320, Guangzhou, China.
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29
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Gao JX, Wang ZY, Zhang MQ, Qian MP, Jiang DQ. A data-driven method to learn a jump diffusion process from aggregate biological gene expression data. J Theor Biol 2022; 532:110923. [PMID: 34606876 DOI: 10.1016/j.jtbi.2021.110923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 08/15/2021] [Accepted: 08/29/2021] [Indexed: 10/20/2022]
Abstract
Dynamic models of gene expression are urgently required. In this paper, we describe the time evolution of gene expression by learning a jump diffusion process to model the biological process directly. Our algorithm needs aggregate gene expression data as input and outputs the parameters of the jump diffusion process. The learned jump diffusion process can predict population distributions of gene expression at any developmental stage, obtain long-time trajectories for individual cells, and offer a novel approach to computing RNA velocity. Moreover, it studies biological systems from a stochastic dynamic perspective. Gene expression data at a time point, which is a snapshot of a cellular process, is treated as an empirical marginal distribution of a stochastic process. The Wasserstein distance between the empirical distribution and predicted distribution by the jump diffusion process is minimized to learn the dynamics. For the learned jump diffusion process, its trajectories correspond to the development process of cells, the stochasticity determines the heterogeneity of cells, its instantaneous rate of state change can be taken as "RNA velocity", and the changes in scales and orientations of clusters can be noticed too. We demonstrate that our method can recover the underlying nonlinear dynamics better compared to previous parametric models and the diffusion processes driven by Brownian motion for both synthetic and real world datasets. Our method is also robust to perturbations of data because the computation involves only population expectations.
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Affiliation(s)
- Jia-Xing Gao
- LMAM, School of Mathematical Sciences, Peking University, Beijing 100871, China
| | - Zhen-Yi Wang
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic and Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing 100084, China
| | - Michael Q Zhang
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic and Systems Biology, BNRist; School of Medicine, Tsinghua University, Beijing 100084, China; Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX 75080-3021, USA
| | - Min-Ping Qian
- LMAM, School of Mathematical Sciences, Peking University, Beijing 100871, China
| | - Da-Quan Jiang
- LMAM, School of Mathematical Sciences, Peking University, Beijing 100871, China; Center for Statistical Science, Peking University, Beijing 100871, China.
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30
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Ye Z, Chen F, Zeng J, Gao J, Zhang MQ. ScaffComb: A Phenotype-Based Framework for Drug Combination Virtual Screening in Large-Scale Chemical Datasets. Adv Sci (Weinh) 2021; 8:e2102092. [PMID: 34723439 PMCID: PMC8693048 DOI: 10.1002/advs.202102092] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 07/29/2021] [Indexed: 06/13/2023]
Abstract
Combinational therapy is used for a long time in cancer treatment to overcome drug resistance related to monotherapy. Increased pharmacological data and the rapid development of deep learning methods have enabled the construction of models to predict and screen drug pairs. However, the size of drug libraries is restricted to hundreds to thousands of compounds. The ScaffComb framework, which aims to bridge the gaps in the virtual screening of drug combinations in large-scale databases, is proposed here. Inspired by phenotype-based drug design, ScaffComb integrates phenotypic information into molecular scaffolds, which can be used to screen the drug library and identify potent drug combinations. First, ScaffComb is validated using the US food and drug administration dataset and known drug combinations are successfully reidentified. Then, ScaffComb is applied to screen the ZINC and ChEMBL databases, which yield novel drug combinations and reveal an ability to discover new synergistic mechanisms. To our knowledge, ScaffComb is the first method to use phenotype-based virtual screening of drug combinations in large-scale chemical datasets.
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Affiliation(s)
- Zhaofeng Ye
- MOE Key Laboratory of BioinformaticsBioinformatics DivisionCenter for Synthetic and Systems BiologyBNRistDepartment of AutomationTsinghua UniversityBeijing100084China
- School of MedicineTsinghua UniversityBeijing100084China
| | - Fengling Chen
- Center for Stem Cell Biology and Regenerative MedicineMOE Key Laboratory of BioinformaticsTsinghua UniversityBeijing100084China
- Tsinghua‐Peking Center for Life SciencesBeijing100084China
| | - Jiangyang Zeng
- MOE Key Laboratory of BioinformaticsBioinformatics DivisionCenter for Synthetic and Systems BiologyBNRistDepartment of AutomationTsinghua UniversityBeijing100084China
- Institute for Interdisciplinary Information SciencesTsinghua UniversityBeijing100084China
| | - Juntao Gao
- MOE Key Laboratory of BioinformaticsBioinformatics DivisionCenter for Synthetic and Systems BiologyBNRistDepartment of AutomationTsinghua UniversityBeijing100084China
| | - Michael Q. Zhang
- MOE Key Laboratory of BioinformaticsBioinformatics DivisionCenter for Synthetic and Systems BiologyBNRistDepartment of AutomationTsinghua UniversityBeijing100084China
- School of MedicineTsinghua UniversityBeijing100084China
- Department of Biological SciencesCenter for Systems BiologyThe University of Texas at DallasRichardsonTX75080‐3021USA
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31
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Zhang MQ, Ma YL, Gao ZC. [Transient spontaneous regression of pulmonary lymphoma: two case report and review of the literature]. Zhonghua Jie He He Hu Xi Za Zhi 2021; 44:902-908. [PMID: 34565118 DOI: 10.3760/cma.j.cn112147-20210307-00151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To investigate the clinical features and pathogenesis of spontaneous regression of lymphoma involving the respiratory system. Methods: Two cases of pulmonary lymphoma which regressed spontaneously were reported. Literatures with"spontaneous regression, lymphoma""spontaneous remission, lymphoma"published before February 2020 were searched in Wanfang, CNKI and Pubmed database. And cases with respiratory system involvement of lymphoma which regressed spontaneously were analyzed. A total of 22 cases were finally retrieved. There were 6 males and 16 females, with an average age of (62.5±16.8) years. Results: Common symptoms included dyspnea, cough, expectoration, fever and weakness. Imaging examination showed that any parts in respiratory system could be involved. The proportion of invasive and indolent lymphomas was approximately similar. The time interval between diagnosis of lymphoma and first sign of spontaneous regression of the disease was from 2 weeks to 1 year. Spontaneous regression of the disease could sustain from 50 days to 60 months. Moreover, 78.6% of patients achieved complete remission in their courses of treatment, and some patients showed wax and wane phenomena. Conclusions: Spontaneous regression of lymphoma may occur in any part of the respiratory system. When a patient exhibits spontaneous regression of pulmonary lesions, lymphoma should be considered in the differential diagnoses. Pathology study is needed to achieve a definitive diagnosis. Misdiagnosis and delayed diagnosis related to empirical treatment of antibiotics and corticosteroid should be avoided.
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Affiliation(s)
- M Q Zhang
- Department of Respiratory and Critical Care Medicine, Peking Universiry People's Hospital, Beijing 100044, China
| | - Y L Ma
- Department of Respiratory and Critical Care Medicine, Peking Universiry People's Hospital, Beijing 100044, China
| | - Z C Gao
- Department of Respiratory and Critical Care Medicine, Peking Universiry People's Hospital, Beijing 100044, China
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32
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Yuan Z, Zhou Q, Cai L, Pan L, Sun W, Qumu S, Yu S, Feng J, Zhao H, Zheng Y, Shi M, Li S, Chen Y, Zhang X, Zhang MQ. SEAM is a spatial single nuclear metabolomics method for dissecting tissue microenvironment. Nat Methods 2021; 18:1223-1232. [PMID: 34608315 DOI: 10.1038/s41592-021-01276-3] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Accepted: 08/18/2021] [Indexed: 02/08/2023]
Abstract
Spatial metabolomics can reveal intercellular heterogeneity and tissue organization. Here we report on the spatial single nuclear metabolomics (SEAM) method, a flexible platform combining high-spatial-resolution imaging mass spectrometry and a set of computational algorithms that can display multiscale and multicolor tissue tomography together with identification and clustering of single nuclei by their in situ metabolic fingerprints. We first applied SEAM to a range of wild-type mouse tissues, then delineated a consistent pattern of metabolic zonation in mouse liver. We further studied the spatial metabolic profile in the human fibrotic liver. We discovered subpopulations of hepatocytes with special metabolic features associated with their proximity to the fibrotic niche, and validated this finding by spatial transcriptomics with Geo-seq. These demonstrations highlighted SEAM's ability to explore the spatial metabolic profile and tissue histology at the single-cell level, leading to a deeper understanding of tissue metabolic organization.
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Affiliation(s)
- Zhiyuan Yuan
- MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, BNRist, Institute of TCM-X, Department of Automation, Tsinghua University, Beijing, China
| | - Qiming Zhou
- MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, BNRist, School of Life Sciences, Tsinghua University, Beijing, China
| | - Lesi Cai
- Department of Chemistry, Tsinghua University, Beijing, China
| | - Lin Pan
- Institute of Clinical Medicine, China-Japan Friendship Hospital, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Science, Beijing, China
| | - Weiliang Sun
- Institute of Clinical Medicine, China-Japan Friendship Hospital, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Science, Beijing, China
| | - Shiwei Qumu
- Department of Pulmonary and Critical Care Medicine, China-Japan Friend Hospital, National Clinical Research Center for Respiratory Diseases, Beijing, China
| | - Si Yu
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiaxin Feng
- Department of Chemistry, Tsinghua University, Beijing, China
| | - Hansen Zhao
- Department of Chemistry, Tsinghua University, Beijing, China
| | - Yongchang Zheng
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Minglei Shi
- MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, BNRist, Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China
| | - Shao Li
- MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, BNRist, Institute of TCM-X, Department of Automation, Tsinghua University, Beijing, China
| | - Yang Chen
- MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, BNRist, Institute of TCM-X, Department of Automation, Tsinghua University, Beijing, China. .,The State Key Laboratory of Medical Molecular Biology, Department of Molecular Biology and Biochemistry, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, China.
| | - Xinrong Zhang
- Department of Chemistry, Tsinghua University, Beijing, China.
| | - Michael Q Zhang
- MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, BNRist, Institute of TCM-X, Department of Automation, Tsinghua University, Beijing, China. .,MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, BNRist, Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China. .,Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX, USA.
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33
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Wei L, Li S, Zhang P, Hu T, Zhang MQ, Xie Z, Wang X. Characterizing microRNA-mediated modulation of gene expression noise and its effect on synthetic gene circuits. Cell Rep 2021; 36:109573. [PMID: 34433047 DOI: 10.1016/j.celrep.2021.109573] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 07/13/2021] [Accepted: 07/28/2021] [Indexed: 10/20/2022] Open
Abstract
MicroRNAs (miRNAs) have been shown to modulate gene expression noise, but less is known about how miRNAs with different properties may regulate noise differently. Here, we investigate the role of competing RNAs and the composition of miRNA response elements (MREs) in modulating noise. We find that weak competing RNAs could introduce lower noise than strong competing RNAs. In comparison with a single MRE, both repetitive and composite MREs can reduce the noise at low expression, but repetitive MREs can elevate the noise remarkably at high expression. We further observed the behavior of a synthetic cell-type classifier with miRNAs as inputs and find that miRNAs and MREs that could introduce higher noise tend to enhance cell state transition. These results provide a systematic and quantitative understanding of the function of miRNAs in controlling gene expression noise and the utilization of miRNAs to modulate the behavior of synthetic gene circuits.
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Affiliation(s)
- Lei Wei
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing 100084, China
| | - Shuailin Li
- School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Pengcheng Zhang
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing 100084, China
| | - Tao Hu
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing 100084, China
| | - Michael Q Zhang
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing 100084, China; Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing 100084, China; Department of Molecular and Cell Biology, Center for Systems Biology, The University of Texas, Richardson, TX 75080-3021, USA
| | - Zhen Xie
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xiaowo Wang
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing 100084, China.
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34
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Shi M, You K, Chen T, Hou C, Liang Z, Liu M, Wang J, Wei T, Qin J, Chen Y, Zhang MQ, Li T. Quantifying the phase separation property of chromatin-associated proteins under physiological conditions using an anti-1,6-hexanediol index. Genome Biol 2021; 22:229. [PMID: 34404448 PMCID: PMC8369651 DOI: 10.1186/s13059-021-02456-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 07/30/2021] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Liquid-liquid phase separation (LLPS) is an important organizing principle for biomolecular condensation and chromosome compartmentalization. However, while many proteins have been reported to undergo LLPS, quantitative and global analysis of chromatin LLPS property remains absent. RESULTS Here, by combining chromatin-associated protein pull-down, quantitative proteomics and 1,6-hexanediol (1,6-HD) treatment, we develop Hi-MS and define an anti-1,6-HD index of chromatin-associated proteins (AICAP) to quantify 1,6-HD sensitivity of chromatin-associated proteins under physiological conditions. Compared with known physicochemical properties involved in phase separation, we find that proteins with lower AICAP are associated with higher content of disordered regions, higher hydrophobic residue preference, higher mobility and higher predicted LLPS potential. We also construct BL-Hi-C libraries following 1,6-HD treatment to study the sensitivity of chromatin conformation to 1,6-HD treatment. We find that the active chromatin and high-order structures, as well as the proteins enriched in corresponding regions, are more sensitive to 1,6-HD treatment. CONCLUSIONS Our work provides a global quantitative measurement of LLPS properties of chromatin-associated proteins and higher-order chromatin structure. Hi-MS and AICAP data provide an experimental tool and quantitative resources valuable for future studies of biomolecular condensates.
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Affiliation(s)
- Minglei Shi
- MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, School of Medicine, Tsinghua University, Beijing, 100084, China.
| | - Kaiqiang You
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, 100191, China
| | - Taoyu Chen
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, 100191, China
| | - Chao Hou
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, 100191, China
| | - Zhengyu Liang
- Department of Cellular and Molecular Medicine, Institute of Genomic Medicine, University of California, La Jolla, San Diego, CA, 92093, USA
| | - Mingwei Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Jifeng Wang
- Laboratory of Proteomics, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Taotao Wei
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Jun Qin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Yang Chen
- MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, School of Medicine, Tsinghua University, Beijing, 100084, China.
- The State Key Laboratory of Medical Molecular Biology, Department of Molecular Biology and Biochemistry, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Michael Q Zhang
- MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, School of Medicine, Tsinghua University, Beijing, 100084, China.
- Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX, 75080-3021, USA.
| | - Tingting Li
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, 100191, China.
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Yang Y, Paul A, Bach TN, Huang ZJ, Zhang MQ. Single-cell alternative polyadenylation analysis delineates GABAergic neuron types. BMC Biol 2021; 19:144. [PMID: 34301239 PMCID: PMC8299648 DOI: 10.1186/s12915-021-01076-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Accepted: 06/17/2021] [Indexed: 01/10/2023] Open
Abstract
Background Alternative polyadenylation (APA) is emerging as an important mechanism in the post-transcriptional regulation of gene expression across eukaryotic species. Recent studies have shown that APA plays key roles in biological processes, such as cell proliferation and differentiation. Single-cell RNA-seq technologies are widely used in gene expression heterogeneity studies; however, systematic studies of APA at the single-cell level are still lacking. Results Here, we described a novel computational framework, SAPAS, that utilizes 3′-tag-based scRNA-seq data to identify novel poly(A) sites and quantify APA at the single-cell level. Applying SAPAS to the scRNA-seq data of phenotype characterized GABAergic interneurons, we identified cell type-specific APA events for different GABAergic neuron types. Genes with cell type-specific APA events are enriched for synaptic architecture and communications. In further, we observed a strong enrichment of heritability for several psychiatric disorders and brain traits in altered 3′ UTRs and coding sequences of cell type-specific APA events. Finally, by exploring the modalities of APA, we discovered that the bimodal APA pattern of Pak3 could classify chandelier cells into different subpopulations that are from different laminar positions. Conclusions We established a method to characterize APA at the single-cell level. When applied to a scRNA-seq dataset of GABAergic interneurons, the single-cell APA analysis not only identified cell type-specific APA events but also revealed that the modality of APA could classify cell subpopulations. Thus, SAPAS will expand our understanding of cellular heterogeneity. Supplementary Information The online version contains supplementary material available at 10.1186/s12915-021-01076-3.
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Affiliation(s)
- Yang Yang
- Present Address: Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China.,Department of Biological Sciences, Center for Systems Biology, The University of Texas at Dallas, Richardson, TX, 75080, USA
| | - Anirban Paul
- Cold Spring Harbor Laboratory, Harbor, Cold Spring, NY, 11724, USA.,Deparment of Neural and Behavioral Sciences, Penn State College of Medicine, Hershey, PA, 17033, USA
| | - Thao Nguyen Bach
- Department of Biological Sciences, Center for Systems Biology, The University of Texas at Dallas, Richardson, TX, 75080, USA
| | - Z Josh Huang
- Cold Spring Harbor Laboratory, Harbor, Cold Spring, NY, 11724, USA.,Deparment of Neurobiology, Duke University Medical Center, Durham, NC, USA
| | - Michael Q Zhang
- Department of Biological Sciences, Center for Systems Biology, The University of Texas at Dallas, Richardson, TX, 75080, USA.
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36
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Yang M, Fan Y, Wu ZY, Gu J, Feng Z, Zhang Q, Han S, Zhang Z, Li X, Hsueh YC, Ni Y, Li X, Li J, Hu M, Li W, Gao H, Yang C, Zhang C, Zhang L, Zhu T, Cheng M, Ji F, Xu J, Cui H, Tan G, Zhang MQ, Liang C, Liu Z, Song YQ, Niu G, Wang K. DAGM: A novel modelling framework to assess the risk of HER2-negative breast cancer based on germline rare coding mutations. EBioMedicine 2021; 69:103446. [PMID: 34157485 PMCID: PMC8220579 DOI: 10.1016/j.ebiom.2021.103446] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 05/20/2021] [Accepted: 06/03/2021] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Breast cancers can be divided into HER2-negative and HER2-positive subtypes according to different status of HER2 gene. Despite extensive studies connecting germline mutations with possible risk of HER2-negative breast cancer, the main category of breast cancer, it remains challenging to obtain accurate risk assessment and to understand the potential underlying mechanisms. METHODS We developed a novel framework named Damage Assessment of Genomic Mutations (DAGM), which projects rare coding mutations and gene expressions into Activity Profiles of Signalling Pathways (APSPs). FINDINGS We characterized and validated DAGM framework at multiple levels. Based on an input of germline rare coding mutations, we obtained the corresponding APSP spectrum to calculate the APSP risk score, which was capable of distinguish HER2-negative from HER2-positive cases. These findings were validated using breast cancer data from TCGA (AUC = 0.7). DAGM revealed that HER2 signalling pathway was up-regulated in germline of HER2-negative patients, and those with high APSP risk scores had exhibited immune suppression. These findings were validated using RNA sequencing, phosphoproteome analysis, and CyTOF. Moreover, using germline mutations, DAGM could evaluate the risk for HER2-negative breast cancer, not only in women carrying BRCA1/2 mutations, but also in those without known disease-associated mutations. INTERPRETATION The DAGM can facilitate the screening of subjects at high risk of HER2-negative breast cancer for primary prevention. This study also provides new insights into the potential mechanisms of developing HER2-negative breast cancer. The DAGM has the potential to be applied in the prevention, diagnosis, and treatment of HER2-negative breast cancer. FUNDING This work was supported by the National Key Research and Development Program of China (grant no. 2018YFC0910406 and 2018AAA0103302 to CZ); the National Natural Science Foundation of China (grant no. 81202076 and 82072939 to MY, 81871513 to KW); the Guangzhou Science and Technology Program key projects (grant no. 2014J2200007 to MY, 202002030236 to KW); the National Key R&D Program of China (grant no. 2017YFC1309100 to CL); Shenzhen Science and Technology Planning Project (grant no. JCYJ20170817095211560 574 to YN); and the Natural Science Foundation of Guangdong Province (grant no. 2017A030313882 to KW and S2013010012048 to MY); Hefei National Laboratory for Physical Sciences at the Microscale (grant no. KF2020009 to GN); and RGC General Research Fund (grant no. 17114519 to YQS).
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Affiliation(s)
- Mei Yang
- Department of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Yanhui Fan
- Phil Rivers Technology, Beijing, China; Phil Rivers Technology, Shenzhen, China
| | - Zhi-Yong Wu
- Diagnosis and Treatment Centre of Breast Diseases, Shantou Central Hospital, Shantou, Guangdong, China
| | - Jin Gu
- BNRIST Bioinformatics Division, Department of Automation, Tsinghua University, Beijing, China
| | | | | | - Shunhua Han
- Phil Rivers Technology, Beijing, China; Institute of Bioinformatics, University of Georgia, Athens, GA, USA
| | - Zhonghai Zhang
- State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Xu Li
- State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | | | - Yanxiang Ni
- Nanophotonics Research Center, Shenzhen Key Laboratory of Micro-Scale Optical Information Technology & Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, China
| | - Xiaoling Li
- Department of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Jieqing Li
- Department of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Meixia Hu
- Department of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Weiping Li
- Department of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Hongfei Gao
- Department of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Ciqiu Yang
- Department of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Chunming Zhang
- Phil Rivers Technology, Beijing, China; State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Liulu Zhang
- Department of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Teng Zhu
- Department of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Minyi Cheng
- Department of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Fei Ji
- Department of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Juntao Xu
- Phil Rivers Technology, Beijing, China
| | | | - Guangming Tan
- State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Michael Q Zhang
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Centre for Synthetic & Systems Biology, TNLIST; School of Medicine, Tsinghua University, Beijing, China
| | - Changhong Liang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - You-Qiang Song
- School of Biomedical Sciences, The University of Hong Kong, Hong Kong, China
| | - Gang Niu
- Phil Rivers Technology, Beijing, China; Western Institute of Advanced Technology, Chinese Academy of Science, Chongqing, China.
| | - Kun Wang
- Department of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China.
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Zhang X, Liu X, Du Z, Wei L, Fang H, Dong Q, Niu J, Li Y, Gao J, Zhang MQ, Xie W, Wang X. The loss of heterochromatin is associated with multiscale three-dimensional genome reorganization and aberrant transcription during cellular senescence. Genome Res 2021; 31:1121-1135. [PMID: 34140314 PMCID: PMC8256869 DOI: 10.1101/gr.275235.121] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 04/27/2021] [Indexed: 01/10/2023]
Abstract
Heterochromatin remodeling is critical for various cell processes. In particular, the "loss of heterochromatin" phenotype in cellular senescence is associated with the process of aging and age-related disorders. Although biological processes of senescent cells, including senescence-associated heterochromatin foci (SAHF) formation, chromosome compaction, and redistribution of key proteins, have been closely associated with high-order chromatin structure, the relationship between the high-order chromatin reorganization and the loss of heterochromatin phenotype during senescence has not been fully understood. By using senescent and deep senescent fibroblasts induced by DNA damage harboring the "loss of heterochromatin" phenotype, we observed progressive 3D reorganization of heterochromatin during senescence. Facultative and constitutive heterochromatin marked by H3K27me3 and H3K9me3, respectively, show different alterations. Facultative heterochromatin tends to switch from the repressive B-compartment to the active A-compartment, whereas constitutive heterochromatin shows no significant changes at the compartment level but enhanced interactions between themselves. Both types of heterochromatin show increased chromatin accessibility and gene expression leakage during senescence. Furthermore, increased chromatin accessibility in potential CTCF binding sites accompanies the establishment of novel loops in constitutive heterochromatin. Finally, we also observed aberrant expression of repetitive elements, including LTR (long terminal repeat) and satellite classes. Overall, facultative and constitutive heterochromatin show both similar and distinct multiscale alterations in the 3D map, chromatin accessibility, and gene expression leakage. This study provides an epigenomic map of heterochromatin reorganization during senescence.
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Affiliation(s)
- Xianglin Zhang
- MOE Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Department of Automation, Tsinghua University, Beijing 100084, China
- Bioinformatics Division, Beijing National Research Center for Information Science and Technology, Beijing 100084, China
| | - Xuehui Liu
- MOE Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Department of Automation, Tsinghua University, Beijing 100084, China
- Bioinformatics Division, Beijing National Research Center for Information Science and Technology, Beijing 100084, China
- State Key Laboratory of Medical Molecular Biology, Department of Pathophysiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Zhenhai Du
- Center for Stem Cell Biology and Regenerative Medicine, MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China
- THU-PKU Center for Life Sciences, Beijing 100084, China
- School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Lei Wei
- MOE Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Department of Automation, Tsinghua University, Beijing 100084, China
- Bioinformatics Division, Beijing National Research Center for Information Science and Technology, Beijing 100084, China
| | - Huan Fang
- MOE Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Department of Automation, Tsinghua University, Beijing 100084, China
- Bioinformatics Division, Beijing National Research Center for Information Science and Technology, Beijing 100084, China
| | - Qiongye Dong
- MOE Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Department of Automation, Tsinghua University, Beijing 100084, China
- Bioinformatics Division, Beijing National Research Center for Information Science and Technology, Beijing 100084, China
| | - Jing Niu
- Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Yanda Li
- MOE Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Department of Automation, Tsinghua University, Beijing 100084, China
- Bioinformatics Division, Beijing National Research Center for Information Science and Technology, Beijing 100084, China
| | - Juntao Gao
- MOE Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Department of Automation, Tsinghua University, Beijing 100084, China
- Bioinformatics Division, Beijing National Research Center for Information Science and Technology, Beijing 100084, China
| | - Michael Q Zhang
- Department of Molecular and Cell Biology, Center for Systems Biology, The University of Texas, Richardson, Texas 75080-3021, USA
| | - Wei Xie
- Center for Stem Cell Biology and Regenerative Medicine, MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China
- THU-PKU Center for Life Sciences, Beijing 100084, China
- School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Xiaowo Wang
- MOE Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Department of Automation, Tsinghua University, Beijing 100084, China
- Bioinformatics Division, Beijing National Research Center for Information Science and Technology, Beijing 100084, China
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Xing H, Wu Y, Zhang MQ, Chen Y. Deciphering hierarchical organization of topologically associated domains through change-point testing. BMC Bioinformatics 2021; 22:183. [PMID: 33838653 PMCID: PMC8037919 DOI: 10.1186/s12859-021-04113-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 03/30/2021] [Indexed: 12/20/2022] Open
Abstract
Background The nucleus of eukaryotic cells spatially packages chromosomes into a hierarchical and distinct segregation that plays critical roles in maintaining transcription regulation. High-throughput methods of chromosome conformation capture, such as Hi-C, have revealed topologically associating domains (TADs) that are defined by biased chromatin interactions within them. Results We introduce a novel method, HiCKey, to decipher hierarchical TAD structures in Hi-C data and compare them across samples. We first derive a generalized likelihood-ratio (GLR) test for detecting change-points in an interaction matrix that follows a negative binomial distribution or general mixture distribution. We then employ several optimal search strategies to decipher hierarchical TADs with p values calculated by the GLR test. Large-scale validations of simulation data show that HiCKey has good precision in recalling known TADs and is robust against random collisions of chromatin interactions. By applying HiCKey to Hi-C data of seven human cell lines, we identified multiple layers of TAD organization among them, but the vast majority had no more than four layers. In particular, we found that TAD boundaries are significantly enriched in active chromosomal regions compared to repressed regions. Conclusions HiCKey is optimized for processing large matrices constructed from high-resolution Hi-C experiments. The method and theoretical result of the GLR test provide a general framework for significance testing of similar experimental chromatin interaction data that may not fully follow negative binomial distributions but rather more general mixture distributions. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04113-8.
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Affiliation(s)
- Haipeng Xing
- Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, 100 Nicolls Rd, Stony Brook, NY, 11794, USA
| | - Yingru Wu
- Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, 100 Nicolls Rd, Stony Brook, NY, 11794, USA
| | - Michael Q Zhang
- Center for System Biology, University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX, 75080, USA
| | - Yong Chen
- Department of Molecular and Cellular Biosciences, Rowan University, 201 Mullica Hill Rd, Glassboro, NJ, 08028, USA.
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Yang G, Guan W, Cao Z, Guo W, Xiong G, Zhao F, Feng M, Qiu J, Liu Y, Zhang MQ, You L, Zhang T, Zhao Y, Gu J. Integrative Genomic Analysis of Gemcitabine Resistance in Pancreatic Cancer by Patient-derived Xenograft Models. Clin Cancer Res 2021; 27:3383-3396. [PMID: 33674273 DOI: 10.1158/1078-0432.ccr-19-3975] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 12/21/2020] [Accepted: 03/02/2021] [Indexed: 11/16/2022]
Abstract
PURPOSE Gemcitabine is most commonly used for pancreatic cancer. However, the molecular features and mechanisms of the frequently occurring resistance remain unclear. This work aims at exploring the molecular features of gemcitabine resistance and identifying candidate biomarkers and combinatorial targets for the treatment. EXPERIMENTAL DESIGN In this study, we established 66 patient-derived xenografts (PDXs) on the basis of clinical pancreatic cancer specimens and treated them with gemcitabine. We generated multiomics data (including whole-exome sequencing, RNA sequencing, miRNA sequencing, and DNA methylation array) of 15 drug-sensitive and 13 -resistant PDXs before and after the gemcitabine treatment. We performed integrative computational analysis to identify the molecular networks related to gemcitabine intrinsic and acquired resistance. Then, short hairpin RNA-based high-content screening was implemented to validate the function of the deregulated genes. RESULTS The comprehensive multiomics analysis and functional experiment revealed that MRPS5 and GSPT1 had strong effects on cell proliferation, and CD55 and DHTKD1 contributed to gemcitabine resistance in pancreatic cancer cells. Moreover, we found miR-135a-5p was significantly associated with the prognosis of patients with pancreatic cancer and could be a candidate biomarker to predict gemcitabine response. Comparing the molecular features before and after the treatment, we found that PI3K-Akt, p53, and hypoxia-inducible factor-1 pathways were significantly altered in multiple patients, providing candidate target pathways for reducing the acquired resistance. CONCLUSIONS This integrative genomic study systematically investigated the predictive markers and molecular mechanisms of chemoresistance in pancreatic cancer and provides potential therapy targets for overcoming gemcitabine resistance.
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Affiliation(s)
- Gang Yang
- Department of General Surgery, State Key Laboratory of Complex Severe and, Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P.R. China
| | - Wenfang Guan
- MOE Key Laboratory of Bioinformatics, Division of BNRist Bioinformatics, Department of Automation, Tsinghua University, Beijing, P.R. China
| | - Zhe Cao
- Department of General Surgery, State Key Laboratory of Complex Severe and, Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P.R. China
| | - Wenbo Guo
- MOE Key Laboratory of Bioinformatics, Division of BNRist Bioinformatics, Department of Automation, Tsinghua University, Beijing, P.R. China
| | - Guangbing Xiong
- Department of General Surgery, State Key Laboratory of Complex Severe and, Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P.R. China
| | - Fangyu Zhao
- Department of General Surgery, State Key Laboratory of Complex Severe and, Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P.R. China
| | - Mengyu Feng
- Department of General Surgery, State Key Laboratory of Complex Severe and, Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P.R. China
| | - Jiangdong Qiu
- Department of General Surgery, State Key Laboratory of Complex Severe and, Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P.R. China
| | - Yueze Liu
- Department of General Surgery, State Key Laboratory of Complex Severe and, Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P.R. China
| | - Michael Q Zhang
- MOE Key Laboratory of Bioinformatics, Division of BNRist Bioinformatics, Department of Automation, Tsinghua University, Beijing, P.R. China
- Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, P.R. China
- Department of Biological Sciences, Center for Systems Biology, the University of Texas at Dallas, Richardson, Texas
| | - Lei You
- Department of General Surgery, State Key Laboratory of Complex Severe and, Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P.R. China.
| | - Taiping Zhang
- Department of General Surgery, State Key Laboratory of Complex Severe and, Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P.R. China.
| | - Yupei Zhao
- Department of General Surgery, State Key Laboratory of Complex Severe and, Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P.R. China.
| | - Jin Gu
- MOE Key Laboratory of Bioinformatics, Division of BNRist Bioinformatics, Department of Automation, Tsinghua University, Beijing, P.R. China.
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Yang M, Fan Y, Wu ZY, Feng Z, Zhang Q, Han S, Zhang Z, Li X, Xue Y, Li X, Hu M, Li J, Li W, Gao H, Yang C, Zhang C, Zhang L, Zhu T, Cheng M, Ji F, Xu J, Cui H, Tan G, Zhang MQ, Liang C, Liu Z, Song YQ, Niu G, Wang K. Abstract PS8-29: Rare variants in the germline genome holistically determine receptor-independent Her2 signaling pathway activation and immune suppression, shaping pathological type and risk of HER2-negative breast cancer. Cancer Res 2021. [DOI: 10.1158/1538-7445.sabcs20-ps8-29] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background: Pathogenic factors embedded in the germline genome are widely recognized as being crucial to breast cancer development. However, current knowledge is either concentrated on the pathogenic variants of a few individual genes or SNPs distributed sparsely across the genome in non-coding regions. Methods: We developed a multi-layered framework, DAGG, which converts somatic mutations or germline rare coding variants (gRCVs) into a functional spectrum of dozens of cellular functions and signaling pathways to identify potential pathogenic factors.Findings: We analyzed whole-exome sequencing (WES) data of 726 germline DNA samples and 169 breast tumor DNA samples from breast cancer patients with various pathological types and cancer-free female subjects, we found that germline pathogens of breast cancers were (1) mainly distributed in HER2-negative subtypes, and (2) involved Her2 signaling pathway activation and immune suppression. These computational discoveries were experimentally validated and can provide digital features to explain the germline differences between diseased and healthy genome (AUC = 0.76). Furthermore, an individual’s risk for breast cancer can be estimated by calculating the combined effects of these identified germline pathogens. Carriers of BRCA1/2 pathogenic variants were found to have a significantly higher average risk (p = 0.02). Interpretation: The results demonstrated that the identified pathogenic mechanisms by DAGG were compatible with our current understanding of the causes of breast cancer. Moreover, DAGG provides improved performance over currently used polygenic risk score method of measuring complex disease risks. Our framework promises possible future applications for the prevention, diagnosis, and treatment of breast cancer.
Citation Format: Mei Yang, Yanhui Fan, Zhi-Yong Wu, Zhendong Feng, Qiangzu Zhang, Shunhua Han, Zhonghai Zhang, Xu Li, Yiqing Xue, Xiaoling Li, Meixia Hu, Jieqing Li, Weiping Li, Hongfei Gao, Ciqiu Yang, Chunming Zhang, Liulu Zhang, Teng Zhu, Minyi Cheng, Fei Ji, Juntao Xu, Hening Cui, Guangming Tan, Michael Q Zhang, Changhong Liang, Zaiyi Liu, You-Qiang Song, Gang Niu, Kun Wang. Rare variants in the germline genome holistically determine receptor-independent Her2 signaling pathway activation and immune suppression, shaping pathological type and risk of HER2-negative breast cancer [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS8-29.
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Affiliation(s)
- Mei Yang
- 1Guangdong Provincial People's Hospital, Guangzhou, China
| | | | - Zhi-Yong Wu
- 3Shantou Affiliated Hospital, Sun Yat-sen University, Shantou, China
| | | | | | | | - Zhonghai Zhang
- 4Institute of Computing Technology, Chinese Academy of Sciences, Beijing, Beijing, China
| | - Xu Li
- 4Institute of Computing Technology, Chinese Academy of Sciences, Beijing, Beijing, China
| | | | - Xiaoling Li
- 1Guangdong Provincial People's Hospital, Guangzhou, China
| | - Meixia Hu
- 1Guangdong Provincial People's Hospital, Guangzhou, China
| | - Jieqing Li
- 1Guangdong Provincial People's Hospital, Guangzhou, China
| | - Weiping Li
- 1Guangdong Provincial People's Hospital, Guangzhou, China
| | - Hongfei Gao
- 1Guangdong Provincial People's Hospital, Guangzhou, China
| | - Ciqiu Yang
- 1Guangdong Provincial People's Hospital, Guangzhou, China
| | | | - Liulu Zhang
- 1Guangdong Provincial People's Hospital, Guangzhou, China
| | - Teng Zhu
- 1Guangdong Provincial People's Hospital, Guangzhou, China
| | - Minyi Cheng
- 1Guangdong Provincial People's Hospital, Guangzhou, China
| | - Fei Ji
- 1Guangdong Provincial People's Hospital, Guangzhou, China
| | - Juntao Xu
- 2Phil Rivers Technology, Beijing, China
| | | | - Guangming Tan
- 4Institute of Computing Technology, Chinese Academy of Sciences, Beijing, Beijing, China
| | | | | | - Zaiyi Liu
- 1Guangdong Provincial People's Hospital, Guangzhou, China
| | - You-Qiang Song
- 6School of Biomedical Sciences, The University of Hong Kong, Hong Kong, China
| | - Gang Niu
- 2Phil Rivers Technology, Beijing, China
| | - Kun Wang
- 1Guangdong Provincial People's Hospital, Guangzhou, China
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Ramanand SG, Chen Y, Yuan J, Daescu K, Lambros MB, Houlahan KE, Carreira S, Yuan W, Baek G, Sharp A, Paschalis A, Kanchwala M, Gao Y, Aslam A, Safdar N, Zhan X, Raj GV, Xing C, Boutros PC, de Bono J, Zhang MQ, Mani RS. The landscape of RNA polymerase II-associated chromatin interactions in prostate cancer. J Clin Invest 2021; 130:3987-4005. [PMID: 32343676 DOI: 10.1172/jci134260] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 04/23/2020] [Indexed: 12/15/2022] Open
Abstract
Transcriptional dysregulation is a hallmark of prostate cancer (PCa). We mapped the RNA polymerase II-associated (RNA Pol II-associated) chromatin interactions in normal prostate cells and PCa cells. We discovered thousands of enhancer-promoter, enhancer-enhancer, as well as promoter-promoter chromatin interactions. These transcriptional hubs operate within the framework set by structural proteins - CTCF and cohesins - and are regulated by the cooperative action of master transcription factors, such as the androgen receptor (AR) and FOXA1. By combining analyses from metastatic castration-resistant PCa (mCRPC) specimens, we show that AR locus amplification contributes to the transcriptional upregulation of the AR gene by increasing the total number of chromatin interaction modules comprising the AR gene and its distal enhancer. We deconvoluted the transcription control modules of several PCa genes, notably the biomarker KLK3, lineage-restricted genes (KRT8, KRT18, HOXB13, FOXA1, ZBTB16), the drug target EZH2, and the oncogene MYC. By integrating clinical PCa data, we defined a germline-somatic interplay between the PCa risk allele rs684232 and the somatically acquired TMPRSS2-ERG gene fusion in the transcriptional regulation of multiple target genes - VPS53, FAM57A, and GEMIN4. Our studies implicate changes in genome organization as a critical determinant of aberrant transcriptional regulation in PCa.
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Affiliation(s)
- Susmita G Ramanand
- Department of Pathology, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Yong Chen
- Department of Biological Sciences, Center for Systems Biology, University of Texas at Dallas, Richardson, Texas, USA.,Department of Molecular and Cellular Biosciences, Rowan University, Glassboro, New Jersey, USA
| | - Jiapei Yuan
- Department of Pathology, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Kelly Daescu
- Department of Biological Sciences, Center for Systems Biology, University of Texas at Dallas, Richardson, Texas, USA
| | - Maryou Bk Lambros
- Prostate Cancer Targeted Therapy and Cancer Biomarkers Group, Institute of Cancer Research (ICR) and Royal Marsden NHS Foundation Trust, Sutton, United Kingdom
| | - Kathleen E Houlahan
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Vector Institute, Toronto, Ontario, Canada.,Department of Urology.,Department of Human Genetics, and.,Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, California, USA
| | - Suzanne Carreira
- Prostate Cancer Targeted Therapy and Cancer Biomarkers Group, Institute of Cancer Research (ICR) and Royal Marsden NHS Foundation Trust, Sutton, United Kingdom
| | - Wei Yuan
- Prostate Cancer Targeted Therapy and Cancer Biomarkers Group, Institute of Cancer Research (ICR) and Royal Marsden NHS Foundation Trust, Sutton, United Kingdom
| | - GuemHee Baek
- Department of Pathology, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Adam Sharp
- Prostate Cancer Targeted Therapy and Cancer Biomarkers Group, Institute of Cancer Research (ICR) and Royal Marsden NHS Foundation Trust, Sutton, United Kingdom
| | - Alec Paschalis
- Prostate Cancer Targeted Therapy and Cancer Biomarkers Group, Institute of Cancer Research (ICR) and Royal Marsden NHS Foundation Trust, Sutton, United Kingdom
| | | | - Yunpeng Gao
- Department of Pathology, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Adam Aslam
- Department of Pathology, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Nida Safdar
- Department of Pathology, UT Southwestern Medical Center, Dallas, Texas, USA
| | | | | | - Chao Xing
- Department of Urology.,Department of Human Genetics, and.,Department of Bioinformatics, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Paul C Boutros
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Vector Institute, Toronto, Ontario, Canada.,Department of Urology.,Department of Human Genetics, and.,Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, California, USA
| | - Johann de Bono
- Prostate Cancer Targeted Therapy and Cancer Biomarkers Group, Institute of Cancer Research (ICR) and Royal Marsden NHS Foundation Trust, Sutton, United Kingdom
| | - Michael Q Zhang
- Department of Biological Sciences, Center for Systems Biology, University of Texas at Dallas, Richardson, Texas, USA.,MOE Key Laboratory of Bioinformatics and Bioinformatics Division, Center for Synthetic and Systems Biology, TNLIST/Department of Automation, Tsinghua University, Beijing, China
| | - Ram S Mani
- Department of Pathology, UT Southwestern Medical Center, Dallas, Texas, USA.,Department of Urology, and.,Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas, USA
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42
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Zhang MQ. A personal journey on cracking the genomic codes. Quant Biol 2021. [DOI: 10.15302/j-qb-021-0245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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43
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Niu J, Zhang X, Li G, Yan P, Yan Q, Dai Q, Jin D, Shen X, Wang J, Zhang MQ, Gao J. A novel cytogenetic method to image chromatin interactions at subkilobase resolution: Tn5 transposase-based fluorescence in situ hybridization. J Genet Genomics 2020; 47:727-735. [PMID: 33750643 DOI: 10.1016/j.jgg.2020.04.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 03/18/2020] [Accepted: 04/17/2020] [Indexed: 01/02/2023]
Abstract
There is an increasing interest in understanding how three-dimensional organization of the genome is regulated. Different strategies have been used to identify genome-wide chromatin interactions. However, owing to current limitations in resolving genomic contacts, visualization and validation of these genomic loci at subkilobase resolution remain unsolved to date. Here, we describe Tn5 transposase-based fluorescence in situ hybridization (Tn5-FISH), a polymerase chain reaction-based, cost-effective imaging method, which can colocalize the genomic loci at subkilobase resolution, dissect genome architecture, and verify chromatin interactions detected by chromatin configuration capture-derived methods. To validate this method, short-range interactions in the keratin-encoding gene (KRT) locus in the topologically associated domain were imaged by triple-color Tn5-FISH, indicating that Tn5-FISH is very useful to verify short-range chromatin interactions inside the contact domain and TAD. Therefore, Tn5-FISH can be a powerful molecular tool for clinical detection of cytogenetic changes in numerous genetic diseases such as cancers.
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Affiliation(s)
- Jing Niu
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Xu Zhang
- Department of Automation, Tsinghua University, Beijing, 100084, China; Beijing Institute of Collaborative Innovation, Beijing 100094, China
| | - Guipeng Li
- Medi-X Institute, SUSTech Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, Shenzhen 518055, China; Department of Biology, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Pixi Yan
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Qing Yan
- Department of Automation, Tsinghua University, Beijing, 100084, China; MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRist, Center for Synthetic & Systems Biology, Tsinghua University, Beijing, 100084, China
| | - Qionghai Dai
- Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Dayong Jin
- Institute for Biomedical Materials and Devices (IBMD), Faculty of Science, University of Technology, Sydney, NSW, 2007, Australia; UTS-SUStech Joint Research Centre for Biomedical Materials and Devices, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Xiaohua Shen
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Jichang Wang
- Key Laboratory for Stem Cells and Tissue Engineering (Sun Yat-sen University), Ministry of Education, Guangzhou, 510080, China; Department of Histology and Embryology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Michael Q Zhang
- School of Medicine, Tsinghua University, Beijing, 100084, China; Department of Automation, Tsinghua University, Beijing, 100084, China; MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRist, Center for Synthetic & Systems Biology, Tsinghua University, Beijing, 100084, China; Department of Biological Sciences, Center for Systems Biology, The University of Texas, Dallas, 800 West Campbell Road, RL11, Richardson, TX, 75080-3021, USA
| | - Juntao Gao
- Department of Automation, Tsinghua University, Beijing, 100084, China; MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRist, Center for Synthetic & Systems Biology, Tsinghua University, Beijing, 100084, China.
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44
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Zhao F, Liu Y, Su X, Lee JE, Song Y, Wang D, Ge K, Gao J, Zhang MQ, Li H. Molecular basis for histone H3 "K4me3-K9me3/2" methylation pattern readout by Spindlin1. J Biol Chem 2020; 295:16877-16887. [PMID: 32994220 PMCID: PMC7864079 DOI: 10.1074/jbc.ra120.013649] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 09/16/2020] [Indexed: 12/11/2022] Open
Abstract
Histone recognition by "reader" modules serves as a fundamental mechanism in epigenetic regulation. Previous studies have shown that Spindlin1 is a reader of histone H3K4me3 as well as "K4me3-R8me2a" and promotes transcription of rDNA or Wnt/TCF4 target genes. Here we show that Spindlin1 also acts as a potent reader of histone H3 "K4me3-K9me3/2" bivalent methylation pattern. Calorimetric titration revealed a binding affinity of 16 nm between Spindlin1 and H3 "K4me3-K9me3" peptide, which is one to three orders of magnitude stronger than most other histone readout events at peptide level. Structural studies revealed concurrent recognition of H3K4me3 and H3K9me3/2 by aromatic pockets 2 and 1 of Spindlin1, respectively. Epigenomic profiling studies showed that Spindlin1 colocalizes with both H3K4me3 and H3K9me3 peaks in a subset of genes enriched in biological processes of transcription and its regulation. Moreover, the distribution of Spindlin1 peaks is primarily associated with H3K4me3 but not H3K9me3, which suggests that Spindlin1 is a downstream effector of H3K4me3 generated in heterochromatic regions. Collectively, our work calls attention to an intriguing function of Spindlin1 as a potent H3 "K4me3-K9me3/2" bivalent mark reader, thereby balancing gene expression and silencing in H3K9me3/2-enriched regions.
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Affiliation(s)
- Fan Zhao
- MOE Key Laboratory of Protein Sciences, Beijing Advanced Innovation Center for Structural Biology, Beijing Frontier Research Center for Biological Structure, Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China
| | - Yunan Liu
- MOE Key Laboratory of Bioinformatics, Bioinformatics Division, Center for Synthetic and Systems Biology, BNRist, Tsinghua University, Beijing, China
| | - Xiaonan Su
- MOE Key Laboratory of Protein Sciences, Beijing Advanced Innovation Center for Structural Biology, Beijing Frontier Research Center for Biological Structure, Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China
| | - Ji-Eun Lee
- Adipocyte Biology and Gene Regulation Section, Laboratory of Endocrinology and Receptor Biology, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland USA
| | - Yutong Song
- MOE Key Laboratory of Protein Sciences, Beijing Advanced Innovation Center for Structural Biology, Beijing Frontier Research Center for Biological Structure, Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China
| | - Daliang Wang
- MOE Key Laboratory of Protein Sciences, Beijing Advanced Innovation Center for Structural Biology, Beijing Frontier Research Center for Biological Structure, Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China
| | - Kai Ge
- Adipocyte Biology and Gene Regulation Section, Laboratory of Endocrinology and Receptor Biology, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland USA
| | - Juntao Gao
- MOE Key Laboratory of Bioinformatics, Bioinformatics Division, Center for Synthetic and Systems Biology, BNRist, Tsinghua University, Beijing, China
| | - Michael Q Zhang
- MOE Key Laboratory of Bioinformatics, Bioinformatics Division, Center for Synthetic and Systems Biology, BNRist, Tsinghua University, Beijing, China; Department of Biological Sciences Center for Systems Biology, University of Texas at Dallas, Richardson, Texas, USA
| | - Haitao Li
- MOE Key Laboratory of Protein Sciences, Beijing Advanced Innovation Center for Structural Biology, Beijing Frontier Research Center for Biological Structure, Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China; Tsinghua-Peking Center for Life Sciences, Beijing, China.
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45
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Gupta H, Chandratre K, Sinha S, Huang T, Wu X, Cui J, Zhang MQ, Wang SM. Highly diversified core promoters in the human genome and their effects on gene expression and disease predisposition. BMC Genomics 2020; 21:842. [PMID: 33256598 PMCID: PMC7706239 DOI: 10.1186/s12864-020-07222-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 11/09/2020] [Indexed: 12/14/2022] Open
Abstract
Background Core promoter controls transcription initiation. However, little is known for core promoter diversity in the human genome and its relationship with diseases. We hypothesized that as a functional important component in the genome, the core promoter in the human genome could be under evolutionary selection, as reflected by its highly diversification in order to adjust gene expression for better adaptation to the different environment. Results Applying the “Exome-based Variant Detection in Core-promoters” method, we analyzed human core-promoter diversity by using the 2682 exome data sets of 25 worldwide human populations sequenced by the 1000 Genome Project. Collectively, we identified 31,996 variants in the core promoter region (− 100 to + 100) of 12,509 human genes (https://dbhcpd.fhs.um.edu.mo). Analyzing the rich variation data identified highly ethnic-specific patterns of core promoter variation between different ethnic populations, the genes with highly variable core promoters, the motifs affected by the variants, and their involved functional pathways. eQTL test revealed that 12% of core promoter variants can significantly alter gene expression level. Comparison with GWAS data we located 163 variants as the GWAS identified traits associated with multiple diseases, half of these variants can alter gene expression. Conclusion Data from our study reals the highly diversified nature of core promoter in the human genome, and highlights that core promoter variation could play important roles not only in gene expression regulation but also in disease predisposition. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-020-07222-5.
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Affiliation(s)
- Hemant Gupta
- Cancer Centre and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, SAR, China
| | - Khyati Chandratre
- Cancer Centre and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, SAR, China
| | - Siddharth Sinha
- Cancer Centre and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, SAR, China
| | - Teng Huang
- Cancer Centre and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, SAR, China
| | - Xiaobing Wu
- Cancer Centre and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, SAR, China
| | - Jian Cui
- Eppley Institute for Cancer Research, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Michael Q Zhang
- Department of Biological Sciences, Center for Systems Biology, University of Texas at Dallas, Richardson, TX, 75080, USA
| | - San Ming Wang
- Cancer Centre and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, SAR, China.
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46
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Yan P, Lu JY, Niu J, Gao J, Zhang MQ, Yin Y, Shen X. LncRNA Platr22 promotes super-enhancer activity and stem cell pluripotency. J Mol Cell Biol 2020; 13:295-313. [PMID: 33049031 PMCID: PMC8339366 DOI: 10.1093/jmcb/mjaa056] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 08/17/2020] [Accepted: 09/14/2020] [Indexed: 12/13/2022] Open
Abstract
Super-enhancers (SEs) comprise large clusters of enhancers, which are co-occupied by multiple lineage-specific and master transcription factors, and play pivotal roles in regulating gene expression and cell fate determination. However, it is still largely unknown whether and how SEs are regulated by the noncoding portion of the genome. Here, through genome-wide analysis, we found that long noncoding RNA (lncRNA) genes preferentially lie next to SEs. In mouse embryonic stem cells (mESCs), depletion of SE-associated lncRNA transcripts dysregulated the activity of their nearby SEs. Specifically, we revealed a critical regulatory role of the lncRNA gene Platr22 in modulating the activity of a nearby SE and the expression of the nearby pluripotency regulator ZFP281. Through these regulatory events, Platr22 contributes to pluripotency maintenance and proper differentiation of mESCs. Mechanistically, Platr22 transcripts coat chromatin near the SE region and interact with DDX5 and hnRNP-L. DDX5 further recruits p300 and other factors related to active transcription. We propose that these factors assemble into a transcription hub, thus promoting an open and active epigenetic chromatin state. Our study highlights an unanticipated role for a class of lncRNAs in epigenetically controlling the activity and vulnerability to perturbation of nearby SEs for cell fate determination.
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Affiliation(s)
- Pixi Yan
- Tsinghua-Peking Joint Center for Life Sciences, School of Medicine and School of Life Sciences, Tsinghua University, Beijing, China
| | - J Yuyang Lu
- Tsinghua-Peking Joint Center for Life Sciences, School of Medicine and School of Life Sciences, Tsinghua University, Beijing, China
| | - Jing Niu
- Tsinghua-Peking Joint Center for Life Sciences, School of Medicine and School of Life Sciences, Tsinghua University, Beijing, China
| | - Juntao Gao
- Tsinghua-Peking Joint Center for Life Sciences, School of Medicine and School of Life Sciences, Tsinghua University, Beijing, China
| | - Michael Q Zhang
- Tsinghua-Peking Joint Center for Life Sciences, School of Medicine and School of Life Sciences, Tsinghua University, Beijing, China
| | - Yafei Yin
- Tsinghua-Peking Joint Center for Life Sciences, School of Medicine and School of Life Sciences, Tsinghua University, Beijing, China
| | - Xiaohua Shen
- Tsinghua-Peking Joint Center for Life Sciences, School of Medicine and School of Life Sciences, Tsinghua University, Beijing, China
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47
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Chen Y, Wang Y, Liu X, Xu J, Zhang MQ. Model-based analysis of chromatin interactions from dCas9-Based CAPTURE-3C-seq. PLoS One 2020; 15:e0236666. [PMID: 32735574 PMCID: PMC7394367 DOI: 10.1371/journal.pone.0236666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 07/10/2020] [Indexed: 11/18/2022] Open
Abstract
Deciphering long-range chromatin interactions is critical for understanding temporal and tissue-specific gene expression regulated by cis- and trans-acting factors. By combining the chromosome conformation capture (3C) and biotinylated dCas9 system, we previously established a method CAPTURE-3C-seq to unbiasedly identify high-resolution and locus-specific long-range DNA interactions. Here we present the statistical model and a flexible pipeline, C3S, for analysing CAPTURE-3C-seq or similar experimental data from raw sequencing reads to significantly interacting chromatin loci. C3S provides all steps for data processing, quality control and result illustration. It can automatically define the bin size based on the binding peak of the dCas9-targeted regions. Furthermore, it supports the analysis of intra- and inter-chromosomal interactions for different mammalian cell types. We successfully applied C3S across multiple datasets in human K562 cells and mouse embryonic stem cells (mESC) for detecting known and new chromatin interactions at multiple scales. Integrative and topological analysis of the interacted loci at the human β-globin gene cluster provides new insights into mechanisms in developmental gene regulation and network structure in local chromosomal architecture. Furthermore, computational results in mESCs reveal a role for chromatin interacting loops between enhancers and promoters in regulating alternative transcripts of the pluripotency gene OCT4.
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Affiliation(s)
- Yong Chen
- Department of Molecular and Cellular Biosciences, Rowan University, Glassboro, New Jersey, United States of America
- Department of Biological Sciences, Center for Systems Biology, University of Texas, Dallas, Richardson, Texas, United States of America
- * E-mail: (YC); (MZ)
| | - Yunfei Wang
- Department of Melanoma Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Xin Liu
- Children’s Medical Center Research Institute, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Jian Xu
- Children’s Medical Center Research Institute, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Michael Q. Zhang
- Department of Biological Sciences, Center for Systems Biology, University of Texas, Dallas, Richardson, Texas, United States of America
- MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China
- Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, Tsinghua University, Beijing, China
- Department of Automation, Tsinghua University, Beijing, China
- * E-mail: (YC); (MZ)
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48
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Liu X, Wei L, Dong Q, Liu L, Zhang MQ, Xie Z, Wang X. A large-scale CRISPR screen and identification of essential genes in cellular senescence bypass. Aging (Albany NY) 2020; 11:4011-4031. [PMID: 31219803 PMCID: PMC6628988 DOI: 10.18632/aging.102034] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2019] [Accepted: 06/13/2019] [Indexed: 12/24/2022]
Abstract
Cellular senescence is an important mechanism of autonomous tumor suppression, while its consequence such as the senescence-associated secretory phenotype (SASP) may drive tumorigenesis and age-related diseases. Therefore, controlling the cell fate optimally when encountering senescence stress is helpful for anti-cancer or anti-aging treatments. To identify genes essential for senescence establishment or maintenance, we carried out a CRISPR-based screen with a deliberately designed single-guide RNA (sgRNA) library. The library comprised of about 12,000 kinds of sgRNAs targeting 1378 senescence-associated genes selected by integrating the information of literature mining, protein-protein interaction network, and differential gene expression. We successfully detected a dozen gene deficiencies potentially causing senescence bypass, and their phenotypes were further validated with a high true positive rate. RNA-seq analysis showed distinct transcriptome patterns of these bypass cells. Interestingly, in the bypass cells, the expression of SASP genes was maintained or elevated with CHEK2, HAS1, or MDK deficiency; but neutralized with MTOR, CRISPLD2, or MORF4L1 deficiency. Pathways of some age-related neurodegenerative disorders were also downregulated with MTOR, CRISPLD2, or MORF4L1 deficiency. The results demonstrated that disturbing these genes could lead to distinct cell fates as a consequence of senescence bypass, suggesting that they may play essential roles in cellular senescence.
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Affiliation(s)
- Xuehui Liu
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic and Systems Biology, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, China.,Present address: State Key Laboratory of Medical Molecular Biology, Department of Pathophysiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Lei Wei
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic and Systems Biology, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Qiongye Dong
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic and Systems Biology, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, China.,Present address: Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Liyang Liu
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic and Systems Biology, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Michael Q Zhang
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic and Systems Biology, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, China.,Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing 100084, China.,Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX 75080, USA
| | - Zhen Xie
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic and Systems Biology, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xiaowo Wang
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic and Systems Biology, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, China
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Zhang MQ, Wang XH, Chen YL, Zhao KL, Cai YQ, An CL, Lin MG, Mu XD. [Clinical features of 2019 novel coronavirus pneumonia in the early stage from a fever clinic in Beijing]. Zhonghua Jie He He Hu Xi Za Zhi 2020; 43:215-218. [PMID: 32164091 DOI: 10.3760/cma.j.issn.1001-0939.2020.03.015] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To summarize and analyze the clinical and imaging characteristics of patients with 2019 novel coronavirus pneumonia in the early stage in Beijing. Methods: A retrospective analysis of clinical and imaging data of 9 patients with 2019 novel coronavirus infection diagnosed in one fever clinicic in Beijing from January 18, 2020 to February 3, 2020. Results: 5 male and 4 female was included in those 9 patients, whose median age was 36 years, and the age range from 15 to 49 years. 8 of these patients had no underlying disease and one suffered from diabetes. 7 patients had a history of travel to Wuhan City or Hubei Province, and one patient was a medical staff. Two family clustered was found. The incubation period was 1 to 6 days. The clinical manifestations were fever in 8 cases (8/9) , dry cough in 5 cases (5/9) , pharyngalgia in 4 cases (4/9) , fatigue in 4 cases (4/9) , body soreness in 4 cases (4/9) , and blocked or watery nose in 1 case (1/9) . Six patients (6/9) had abnormal cell peripheral blood, of which 3 (3/9) had an increased monocyte count, 2 (2/9) had a reduced lymphocyte, and 1 (1/9) had an increased leukocyte count, while the 3 patients had normal cell blood routines. The median of CRP was 16.3 mg/L, including 5 patients with slightly elevated (5/9) , 4 patients with normal values (4/9) . the results of procalcitonin test were negative in5 patients. Three patients were examined by chest X-ray examination, one of which was normal, one case showed infiltrates of right upper lung, and another showed in right lower lung. All patients underwent chest HRCT. And 7 cases (7/9) showed multiple ground glass exudation, including 5 cases (5/7) involved bilateral lungs, 2 cases (2/7) involved unilateral lung, 3 cases (3/7) with patchy consolidation, and 2 cases (2/9) showed no abnormality. Conclusions: The patents with 2019 novel coronavirus pneumonia in this study generally have an epidemiological history. The clinical manifestations are fever and cough. Peripheral white blood cell counts were most normal And PCT were all negative. Chest HRCT manifested as multiple ground-glass opacities with partly consolidation. Some patients had normal chest radiographs but HRCT showed pneumonia. Some patients had no pneumonia on chest HRCT.
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Affiliation(s)
- M Q Zhang
- Department of Respiratory Medicine, Peking University First Hospital, Beijing 100034, China
| | - X H Wang
- Department of kidney surgery, Peking University First Hospital, Beijing 100034, China
| | - Y L Chen
- Department of kidney surgery, Peking University First Hospital, Beijing 100034, China
| | - K L Zhao
- Department of Respiratory Medicine, Peking University First Hospital, Beijing 100034, China
| | - Y Q Cai
- Department of Respiratory Medicine, Peking University First Hospital, Beijing 100034, China
| | - C L An
- Department of Respiratory Medicine, Peking University First Hospital, Beijing 100034, China
| | - M G Lin
- Department of kidney surgery, Peking University First Hospital, Beijing 100034, China
| | - X D Mu
- Department of Respiratory Medicine, Peking University First Hospital, Beijing 100034, China
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Xiong Q, Huang S, Li YH, Lv N, Lv C, Ding Y, Liu WW, Wang LL, Chen Y, Sun L, Zhao Y, Liao SY, Zhang MQ, Zhu BL, Yu L. Single‑cell RNA sequencing of t(8;21) acute myeloid leukemia for risk prediction. Oncol Rep 2020; 43:1278-1288. [PMID: 32323795 PMCID: PMC7057921 DOI: 10.3892/or.2020.7507] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Accepted: 01/22/2020] [Indexed: 12/12/2022] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) of bone marrow or peripheral blood samples from patients with acute myeloid leukemia (AML) enables the characterization of heterogeneous malignant cells. A total of 87 cells from two patients with t(8;21) AML were analyzed using scRNA-seq. Clustering methods were used to separate leukemia cells into different sub-populations, and the expression patterns of specific marker genes were used to annotate these populations. Among the 31 differentially expressed genes in the cells of a patient who relapsed after hematopoietic stem cell transplantation, 13 genes were identified to be associated with leukemia. Furthermore, three genes, namely AT-rich interaction domain 2, lysine methyltransferase 2A and synaptotagmin binding cytoplasmic RNA interacting protein were validated as possible prognostic biomarkers using two bulk expression datasets. Taking advantage of scRNA-seq, the results of the present study may provide clinicians with several possible biomarkers to predict the prognostic outcomes of t(8;21) AML.
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Affiliation(s)
- Qian Xiong
- Department of Hematology and BMT Center, Chinese PLA General Hospital, Beijing 100853, P.R. China
| | - Sai Huang
- Department of Hematology and BMT Center, Chinese PLA General Hospital, Beijing 100853, P.R. China
| | - Yong-Hui Li
- Department of Hematology and BMT Center, Chinese PLA General Hospital, Beijing 100853, P.R. China
| | - Na Lv
- Department of Hematology and BMT Center, Chinese PLA General Hospital, Beijing 100853, P.R. China
| | - Chao Lv
- Department of Hematology and BMT Center, Chinese PLA General Hospital, Beijing 100853, P.R. China
| | - Yi Ding
- Department of Hematology and BMT Center, Chinese PLA General Hospital, Beijing 100853, P.R. China
| | - Wen-Wen Liu
- Department of Hematology and BMT Center, Chinese PLA General Hospital, Beijing 100853, P.R. China
| | - Li-Li Wang
- Department of Hematology and BMT Center, Chinese PLA General Hospital, Beijing 100853, P.R. China
| | - Yang Chen
- School of Medicine, MOE Key Laboratory of Bioinformatics and Bioinformatics Division, Center for Synthetic and System Biology, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, P.R. China
| | - Liang Sun
- School of Medicine, MOE Key Laboratory of Bioinformatics and Bioinformatics Division, Center for Synthetic and System Biology, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, P.R. China
| | - Yi Zhao
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R. China
| | - Sheng-You Liao
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R. China
| | - Michael Q Zhang
- School of Medicine, MOE Key Laboratory of Bioinformatics and Bioinformatics Division, Center for Synthetic and System Biology, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, P.R. China
| | - Bao-Li Zhu
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, P.R. China
| | - Li Yu
- Department of Hematology and BMT Center, Chinese PLA General Hospital, Beijing 100853, P.R. China
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