Browsing by Author "Chen, Jian"

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    Dynamic changes in P300 enhancers and enhancer-promoter contacts control mouse cardiomyocyte maturation
    (Elsevier, 2023) Zhou, Pingzhu; VanDusen, Nathan J.; Zhang, Yanchun; Cao, Yangpo; Sethi, Isha; Hu, Rong; Zhang, Shuo; Wang, Guangyu; Ye, Lincai; Mazumdar, Neil; Chen, Jian; Zhang, Xiaoran; Guo, Yuxuan; Li, Bin; Ma, Qing; Lee, Julianna Y.; Gu, Weiliang; Gupta, Weiliang; Yuan, Guo-Cheng; Ren, Bing; Chen, Kaifu; Pu, William T.; Pediatrics, School of Medicine
    Cardiomyocyte differentiation continues throughout murine gestation and into the postnatal period, driven by temporally regulated expression changes in the transcriptome. The mechanisms that regulate these developmental changes remain incompletely defined. Here, we used cardiomyocyte-specific ChIP-seq of the activate enhancer marker P300 to identify 54,920 cardiomyocyte enhancers at seven stages of murine heart development. These data were matched to cardiomyocyte gene expression profiles at the same stages and to Hi-C and H3K27ac HiChIP chromatin conformation data at fetal, neonatal, and adult stages. Regions with dynamic P300 occupancy exhibited developmentally regulated enhancer activity, as measured by massively parallel reporter assays in cardiomyocytes in vivo, and identified key transcription factor-binding motifs. These dynamic enhancers interacted with temporal changes of the 3D genome architecture to specify developmentally regulated cardiomyocyte gene expressions. Our work provides a 3D genome-mediated enhancer activity landscape of murine cardiomyocyte development.
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    In Vivo Dissection of Chamber-Selective Enhancers Reveals Estrogen-Related Receptor as a Regulator of Ventricular Cardiomyocyte Identity
    (Wolters Kluwer, 2023) Cao, Yangpo; Zhang, Xiaoran; Akerberg, Brynn N.; Yuan, Haiyun; Sakamoto, Tomoya; Xiao, Feng; VanDusen, Nathan J.; Zhou, Pingzhu; Sweat, Mason E.; Wang, Yi; Prondzynski, Maksymilian; Chen, Jian; Zhang, Yan; Wang, Peizhe; Kelly, Daniel P.; Pu, William T.; Pediatrics, School of Medicine
    Background: Cardiac chamber-selective transcriptional programs underpin the structural and functional differences between atrial and ventricular cardiomyocytes (aCMs and vCMs). The mechanisms responsible for these chamber-selective transcriptional programs remain largely undefined. Methods: We nominated candidate chamber-selective enhancers (CSEs) by determining the genome-wide occupancy of 7 key cardiac transcription factors (GATA4, MEF2A, MEF2C, NKX2-5, SRF, TBX5, TEAD1) and transcriptional coactivator P300 in atria and ventricles. Candidate enhancers were tested using an adeno-associated virus-mediated massively parallel reporter assay. Chromatin features of CSEs were evaluated by performing assay of transposase accessible chromatin sequencing and acetylation of histone H3 at lysine 27-HiChIP on aCMs and vCMs. CSE sequence requirements were determined by systematic tiling mutagenesis of 29 CSEs at 5 bp resolution. Estrogen-related receptor (ERR) function in cardiomyocytes was evaluated by Cre-loxP-mediated inactivation of ERRα and ERRγ in cardiomyocytes. Results: We identified 134 066 and 97 506 regions reproducibly occupied by at least 1 transcription factor or P300, in atria or ventricles, respectively. Enhancer activities of 2639 regions bound by transcription factors or P300 were tested in aCMs and vCMs by adeno-associated virus-mediated massively parallel reporter assay. This identified 1092 active enhancers in aCMs or vCMs. Several overlapped loci associated with cardiovascular disease through genome-wide association studies, and 229 exhibited chamber-selective activity in aCMs or vCMs. Many CSEs exhibited differential chromatin accessibility between aCMs and vCMs, and CSEs were enriched for aCM- or vCM-selective acetylation of histone H3 at lysine 27-anchored loops. Tiling mutagenesis of 29 CSEs identified the binding motif of ERRα/γ as important for ventricular enhancer activity. The requirement of ERRα/γ to activate ventricular CSEs and promote vCM identity was confirmed by loss of the vCM gene profile in ERRα/γ knockout vCMs. Conclusions: We identified 229 CSEs that could be useful research tools or direct therapeutic gene expression. We showed that chamber-selective multi-transcription factor, P300 occupancy, open chromatin, and chromatin looping are predictive features of CSEs. We found that ERRα/γ are essential for maintenance of ventricular identity. Finally, our gene expression, epigenetic, 3-dimensional genome, and enhancer activity atlas provide key resources for future studies of chamber-selective gene regulation.
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    M3S: a comprehensive model selection for multi-modal single-cell RNA sequencing data
    (BMC, 2019-12-20) Zhang, Yu; Wan, Changlin; Wang, Pengcheng; Chang, Wennan; Huo, Yan; Chen, Jian; Ma, Qin; Cao, Sha; Zhang, Chi; Medical and Molecular Genetics, School of Medicine
    Background Various statistical models have been developed to model the single cell RNA-seq expression profiles, capture its multimodality, and conduct differential gene expression test. However, for expression data generated by different experimental design and platforms, there is currently lack of capability to determine the most proper statistical model. Results We developed an R package, namely Multi-Modal Model Selection (M3S), for gene-wise selection of the most proper multi-modality statistical model and downstream analysis, useful in a single-cell or large scale bulk tissue transcriptomic data. M3S is featured with (1) gene-wise selection of the most parsimonious model among 11 most commonly utilized ones, that can best fit the expression distribution of the gene, (2) parameter estimation of a selected model, and (3) differential gene expression test based on the selected model. Conclusion A comprehensive evaluation suggested that M3S can accurately capture the multimodality on simulated and real single cell data. An open source package and is available through GitHub at https://github.com/zy26/M3S.
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    Pipeline for characterizing alternative mechanisms (PCAM) based on bi-clustering to study colorectal cancer heterogeneity
    (Elsevier, 2023-03-17) Cao, Sha; Chang, Wennan; Wan, Changlin; Lu, Xiaoyu; Dang, Pengtao; Zhou, Xinyu; Zhu, Haiqi; Chen, Jian; Li, Bo; Zang, Yong; Wang, Yijie; Zhang, Chi; Biostatistics and Health Data Science, School of Medicine
    The cells of colorectal cancer (CRC) in their microenvironment experience constant stress, leading to dysregulated activity in the tumor niche. As a result, cancer cells acquire alternative pathways in response to the changing microenvironment, posing significant challenges for the design of effective cancer treatment strategies. While computational studies on high-throughput omics data have advanced our understanding of CRC subtypes, characterizing the heterogeneity of this disease remains remarkably complex. Here, we present a novel computational Pipeline for Characterizing Alternative Mechanisms (PCAM) based on biclustering to gain a more detailed understanding of cancer heterogeneity. Our application of PCAM to large-scale CRC transcriptomics datasets suggests that PCAM can generate a wealth of information leading to new biological understanding and predictive markers of alternative mechanisms. Our key findings include: 1) A comprehensive collection of alternative pathways in CRC, associated with biological and clinical factors. 2) Full annotation of detected alternative mechanisms, including their enrichment in known pathways and associations with various clinical outcomes. 3) A mechanistic relationship between known clinical subtypes and outcomes on a consensus map, visualized by the presence of alternative mechanisms. 4) Several potential novel alternative drug resistance mechanisms for Oxaliplatin, 5-Fluorouracil, and FOLFOX, some of which were validated on independent datasets. We believe that gaining a deeper understanding of alternative mechanisms is a critical step towards characterizing the heterogeneity of CRC. The hypotheses generated by PCAM, along with the comprehensive collection of biologically and clinically associated alternative pathways in CRC, could provide valuable insights into the underlying mechanisms driving cancer progression and drug resistance, which could aid in the development of more effective cancer therapies and guide experimental design towards more targeted and personalized treatment strategies. The computational pipeline of PCAM is available in GitHub (https://github.com/changwn/BC-CRC).
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    Single-cell transcriptome and antigen-immunoglobin analysis reveals the diversity of B cells in non-small cell lung cancer
    (BMC, 2020-06-24) Chen, Jian; Tan, Yun; Sun, Fenghuan; Hou, Likun; Zhang, Chi; Ge, Tao; Yu, Huansha; Wu, Chunxiao; Zhu, Yuming; Duan, Liang; Wu, Liang; Song, Nan; Zhang, Liping; Zhang, Wei; Wang, Di; Chen, Chang; Wu, Chunyan; Jiang, Gening; Zhang, Peng; Medical and Molecular Genetics, School of Medicine
    Background Malignant transformation and progression of cancer are driven by the co-evolution of cancer cells and their dysregulated tumor microenvironment (TME). Recent studies on immunotherapy demonstrate the efficacy in reverting the anti-tumoral function of T cells, highlighting the therapeutic potential in targeting certain cell types in TME. However, the functions of other immune cell types remain largely unexplored. Results We conduct a single-cell RNA-seq analysis of cells isolated from tumor tissue samples of non-small cell lung cancer (NSCLC) patients, and identify subtypes of tumor-infiltrated B cells and their diverse functions in the progression of NSCLC. Flow cytometry and immunohistochemistry experiments on two independent cohorts confirm the co-existence of the two major subtypes of B cells, namely the naïve-like and plasma-like B cells. The naïve-like B cells are decreased in advanced NSCLC, and their lower level is associated with poor prognosis. Co-culture of isolated naïve-like B cells from NSCLC patients with two lung cancer cell lines demonstrate that the naïve-like B cells suppress the growth of lung cancer cells by secreting four factors negatively regulating the cell growth. We also demonstrate that the plasma-like B cells inhibit cancer cell growth in the early stage of NSCLC, but promote cell growth in the advanced stage of NSCLC. The roles of the plasma-like B cell produced immunoglobulins, and their interacting proteins in the progression of NSCLC are further validated by proteomics data. Conclusion Our analysis reveals versatile functions of tumor-infiltrating B cells and their potential clinical implications in NSCLC.