Browsing by Author "Lu, Xuemei"

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    Dynamic Alu Methylation during Normal Development, Aging, and Tumorigenesis
    Luo, Yanting; Lu, Xuemei; Xie, Hehuang David (Hindawi, 2014-08-27)
    DNA methylation primarily occurs on CpG dinucleotides and plays an important role in transcriptional regulations during tissue development and cell differentiation. Over 25% of CpG dinucleotides in the human genome reside within Alu elements, the most abundant human repeats. The methylation of Alu elements is an important mechanism to suppress Alu transcription and subsequent retrotransposition. Decades of studies revealed that Alu methylation is highly dynamic during early development and aging. Recently, many environmental factors were shown to have a great impact on Alu methylation. In addition, aberrant Alu methylation has been documented to be an early event in many tumors and Alu methylation levels have been associated with tumor aggressiveness. The assessment of the Alu methylation has become an important approach for early diagnosis and/or prognosis of cancer. This review focuses on the dynamic Alu methylation during development, aging, and tumor genesis. The cause and consequence of Alu methylation changes will be discussed.
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    Epigenetic regulation of neuronal cell specification inferred with single cell “Omics” data
    Yin, Liduo; Banerjee, Sharmi; Fan, Jiayi; He, Jianlin; Lu, Xuemei; Xie, Hehuang (Elsevier, 2020-01-01)
    The brain is a highly complex organ consisting of numerous types of cells with ample diversity at the epigenetic level to achieve distinct gene expression profiles. During neuronal cell specification, transcription factors (TFs) form regulatory modules with chromatin remodeling proteins to initiate the cascade of epigenetic changes. Currently, little is known about brain epigenetic regulatory modules and how they regulate gene expression in a cell-type specific manner. To infer TFs involved in neuronal specification, we applied a recursive motif search approach on the differentially methylated regions identified from single-cell methylomes. The epigenetic transcription regulatory modules (ETRM), including EGR1 and MEF2C, were predicted and the co-expression of TFs in ETRMs were examined with RNA-seq data from single or sorted brain cells using a conditional probability matrix. Lastly, computational predications were validated with EGR1 ChIP-seq data. In addition, methylome and RNA-seq data generated from Egr1 knockout mice supported the essential role of EGR1 in brain epigenome programming, in particular for excitatory neurons. In summary, we demonstrated that brain single cell methylome and RNA-seq data can be integrated to gain a better understanding of how ETRMs control cell specification. The analytical pipeline implemented in this study is freely accessible in the Github repository (https://github.com/Gavin-Yinld/brain_TF).
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    Integrative single-cell omics analyses reveal epigenetic heterogeneity in mouse embryonic stem cells
    Luo, Yanting; He, Jianlin; Xu, Xiguang; Sun, Ming-an; Wu, Xiaowei; Lu, Xuemei; Xie, Hehuang David (PLOS, 2018-03)
    Embryonic stem cells (ESCs) consist of a population of self-renewing cells displaying extensive phenotypic and functional heterogeneity. Research towards the understanding of the epigenetic mechanisms underlying the heterogeneity among ESCs is still in its initial stage. Key issues, such as how to identify cell-subset specifically methylated loci and how to interpret the biological meanings of methylation variations remain largely unexplored. To fill in the research gap, we implemented a computational pipeline to analyze single-cell methylome and to perform an integrative analysis with single-cell transcriptome data. According to the origins of variation in DNA methylation, we determined the genomic loci associated with allelic-specific methylation or asymmetric DNA methylation, and explored a beta mixture model to infer the genomic loci exhibiting cell-subset specific methylation (CSM). We observed that the putative CSM loci in ESCs are significantly enriched in CpG island (CGI) shelves and regions with histone marks for promoter and enhancer, and the genes hosting putative CSM loci show wide-ranging expression among ESCs. More interestingly, the putative CSM loci may be clustered into co-methylated modules enriching the binding motifs of distinct sets of transcription factors. Taken together, our study provided a novel tool to explore single-cell methylome and transcriptome to reveal the underlying transcriptional regulatory networks associated with epigenetic heterogeneity of ESCs.
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    Virtual methylome dissection facilitated by single-cell analyses
    Yin, Liduo; Luo, Yanting; Xu, Xiguang; Wen, Shiyu; Wu, Xiaowei; Lu, Xuemei; Xie, Hehuang David (2019-11-11)
    Background Numerous cell types can be identified within plant tissues and animal organs, and the epigenetic modifications underlying such enormous cellular heterogeneity are just beginning to be understood. It remains a challenge to infer cellular composition using DNA methylomes generated for mixed cell populations. Here, we propose a semi-reference-free procedure to perform virtual methylome dissection using the nonnegative matrix factorization (NMF) algorithm. Results In the pipeline that we implemented to predict cell-subtype percentages, putative cell-type-specific methylated (pCSM) loci were first determined according to their DNA methylation patterns in bulk methylomes and clustered into groups based on their correlations in methylation profiles. A representative set of pCSM loci was then chosen to decompose target methylomes into multiple latent DNA methylation components (LMCs). To test the performance of this pipeline, we made use of single-cell brain methylomes to create synthetic methylomes of known cell composition. Compared with highly variable CpG sites, pCSM loci achieved a higher prediction accuracy in the virtual methylome dissection of synthetic methylomes. In addition, pCSM loci were shown to be good predictors of the cell type of the sorted brain cells. The software package developed in this study is available in the GitHub repository (https://github.com/Gavin-Yinld). Conclusions We anticipate that the pipeline implemented in this study will be an innovative and valuable tool for the decoding of cellular heterogeneity.