Plant Bioinformatics and Functional Epigenomics Group
Our research is focused on the following two aspects:
1. The genomic and epigenomic changes and the functional consequences during crop domestication and polyploidization.
Both polyploidization and domestication are the major forces shaping current crop genomes. Many important crops are polyploid, typical examples including wheat and peanut. To characterize the major genomic and epigenomic changes during both processes, and further identify changes determining the desired traits are of great significance. This part of work is in close collaboration with wet lab colleagues.
2. The mechanism underlying specific regulation of epigenetic modifications.
Epigenetic modifications of the genome allow for a relatively stable and reversible control of gene expression state, which is essential for organisms to adapt the dynamic developmental and environmental cues. How do plants know when and where to change the epigenome is still a mystery. We are paritularly interested in exploring the mechanism controlling the specificity of different Polycomb Group (PcG) members, with integrative approach combining molecular, genetic and computational tools.
note: *, Co-first author; #, Corresponding author ; Lab members’ name are in bold
Meifang Qi#, Zijuan Li#, Chunmei Liu#, Wenyan Hu, Luhuan Ye, Yilin Xie, Yili Zhuang, Fei Zhao, Wan Teng, Qi Zheng, Zhenjun Fan, Lin Xu, Zhaobo Lang, Yiping Tong*, and Yijing Zhang* (2018) CGT-seq: epigenome-guided de novo assembly of the core genome for divergent populations with large genome, Nucleic Acid Research, gky522.
Yue Zhou, Yuejun Wang, Kristin Krause, Tingting Yang, Joram A. Dongus, Yijing Zhang and Franziska Turck. Telobox motifs recruit CLF/SWN–PRC2 for H3K27me3 deposition via TRB factors in Arabidopsis. Nature Genetics, 2018
Xiaojuan Ran, Jian Liu, Meifang Qi, Yuejun Wang, Jingfei Cheng and Yijing Zhang#. GSHR, a Web-based Platform Provides Gene Set-level Analyses of Hormone Responses in Arabidopsis[J]. Frontiers in Plant Science, 2018, 9: 23
Zhaobo Lang, Yihai Wang, Kai Tang, Dengguo Tang, Tatsiana Datsenka, Jingfei Cheng, Yijing Zhang, Avtar K. Handab, and Jian-Kang Zhu. Critical roles of DNA demethylation in the activation of ripening-induced genes and inhibition of ripening-repressed genes in tomato fruit[J]. Proceedings of the National Academy of Sciences, 2017: 201705233.
Bo Hu#, Guifang Zhang#, Wu Liu#, Jianmin Shi#, Hua Wang#, Meifang Qi, Jiqin Li, Peng Qin, Ying Ruan, Hai Huang, Yijing Zhang, Lin Xu*.(2017) Divergent regeneration-competent cells adopt a common mechanism for callus initiation in angiosperms, Regeneration, 4(3):132-139.
Jia-Shi Peng*, Yue-Jun Wang*, Ge Ding, Hai-Ling Ma, Yi-Jing Zhang#, Ji-Ming Gong#.(2016) A Pivotal Role of Cell Wall in Cadmium Accumulation in the Crassulaceae hyperaccumulator Sedum plumbizincicola. Molecular Plant , Volume 10 , Issue 5 , 771 - 774 https://doi.org/10.1016/j.molp.2016.12.007 PDF
Zhongfei Li1*, Bin Li1*, Jian Liu2, Zhihao Guo1, Yuhao Liu1, Yan Li3, Wen-Hui Shen1,4, Ying Huang3, Hai Huang2, Yijing Zhang2#and Aiwu Dong1#.(2016) Transcription factors AS1 and AS2 interact withLHP1 to repress KNOX genes in Arabidopsis. Journal of Integrative Plant Biology.DOI: 10.1111/jipb.12485.
Xiaodong Chen, Jingfei Cheng, Lyuqin Chen, Guifang Zhang, Hai Huang, Yijing Zhang and Lin Xu*.(2016) Auxin-Independent NAC Pathway Acts in Response to Explant-Specific Wounding and Promotes Root Tip Emergence during de Novo Root Organogenesis in Arabidopsis, Plant Physiology,170(4):2136-2145.
Anthony J Covarrubias1, Halil Ibrahim Aksoylar1, Jiujiu Yu1, Nathaniel W Snyder2,3, Andrew J Worth2, Shankar S Iyer4, Jiawei Wang5, Issam Ben-Sahra1, Vanessa Byles1, Tiffany Polynne-Stapornkul1, Erika C Espinosa1, Dudley Lamming6, Brendan D Manning1, Yijing Zhang5, Ian A Blair2, Tiffany Horng1*.(2016) Akt-mTORC1signaling regulates Acly to integrate metabolic input to control of macrophage activation. eLIFE.2016,5: p e11612.
Hua Wang#, Chunmei Liu*, Jingfei Cheng*, Jian Liu, Lei Zhang, Chongsheng He, Wen-Hui Shen, Hong Jin*, Lin Xu*, Yijing Zhang*.(2016) Arabidopsis Flower and Embryo Developmental Genes are Repressed in Seedlings by Different Combinations of Polycomb Group Proteins in Association with Distinct Sets of Cis-regulatory Elements, PLoS Genetics, 12(1): e1005771.
http://www.plosgenetics.org/article/comments/info:doi/10.1371/journal.pgen.1005771 PDF Data Browse
Meiqing Xing#, Yijing Zhang#, Shirong Zhou#, Wenyan Hu#, Xueting Wu, Yajin Ye, Xiaoxia Wu, Yunping Xiao, Xuan Li, and Hongwei Xue*,（2015）Global Analysis Reveals the Crucial Roles of DNA Methylation during Rice Seed Development, Plant Physiology, 168（4）：1417–1432.
Jiawei Wang#, Meifang Qi#, Jian Liu#, Yijing Zhang*.(2015) CARMO: a comprehensive annotation platform for functional exploration of rice multi-omics data, Plant Journal, 83(2):359-374.
Marta Biagioli#, Francesco Ferrari#, Eric M. Mendenhall, Yijing Zhang, Serkan Erdin, Ravi Vijayvargia, Sonia M. Vallabh, Nicole Solomos,Poornima Manavalan, Ashok Ragavendran, Fatih Ozsolak, Jong Min Lee,Michael E. Talkowski, James F. Gusella, Marcy E. Macdonald,Peter J. Park and Ihn Sik Seong*.(2015)Htt CAG repeat expansion confers pleiotropic gains of mutant huntingtin function in chromatin regulation, Human Molecular Genetics, 24( 9 ):2442–2457.
Guang Li*, Shujing Liu*, Jiawei Wang, Jianfeng He, Hai Huang, Yijing Zhang* and Lin Xu*.(2014) ISWI proteins participate in the genome-wide nucleosome distribution in Arabidopsis, Plant Journal ,78(4):706–714.
Pingzhu Zhou, Yijing Zhang, Qing Maa, Fei Gu, Daniel S. Dayb,c, Aibin He, Bin Zhou, Jing Li, Sean M. Stevens, Daniel Romo, and William T. Pu*,(2013) Interrogating translational efficiency and lineage-specific transcriptomes using ribosome affinity purification, Proceedings of the National Academy of Sciences, 110(38):15395-15400.
Gaihua Zhang#>, Yijing Zhang# and Zhen Su*.(2012) CYPSI: a structure-based interface for cytochrome P450s and ligands in Arabidopsis thaliana, BMC Bioinformatics, 13: 332 .
Tara L. Conforto, Yijing Zhang, Jennifer Sherman, and David J. Waxman*.(2012) Impact of CUX2 on the Female Mouse Liver Transcriptome: Activation of Female-Biased Genes and Repression of Male-Biased Genes, Molecular and Cellular Biology, 32(22): 4611-4627.
Zhen Shao#, Yijing Zhang#, Guo-Cheng Yuan, Stuart H Orkin* and David J Waxman*.(2012) MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets, Genome Biology, 13(3):R16. http://genomebiology.com/content/13/3/R16 PDF
Yijing Zhang, Ekaterina V. Laz, and David J. Waxman.(2011) Dynamic, Sex-Differential STAT5 and BCL6 Binding to Sex-Biased, Growth Hormone-Regulated Genes in Adult Mouse Liver, Molecular and Cellular Biology,32(4): 880-896. http://mcb.asm.org/content/32/4/880.lon PDF
Yijing Zhang, Kathrin Klein, Aarathi Sugathan, Najlla Nassery, Alan Dombkowski, Ulrich M. Zanger,David J. Waxman1*,(2011) Transcriptional Profiling of Human Liver Identifies Sex-Biased Genes Associated with Polygenic Dyslipidemia and Coronary Artery Disease, PLos One, 6(8):e23506. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0023506 PDF
Gargi Bagchi#, Yijing Zhang#, Kerri A Stanley and David J Waxman*.(2011) Complex modulation of androgen responsive gene expression by methoxyacetic acid, Reproductive Biology and Endocrinology, 9(1):42 http://www.rbej.com/content/9/1/42g PDF
Gargi Bagchi, Yijing Zhang and David J Waxman*.(2010) Impact of methoxyacetic acid on mouse Leydig cell gene expression, Reproductive Biology and Endocrinology,2010, 8:65. http://www.rbej.com/content/8/1/65 PDF
Evelyn Dixit, Steeve Boulant, Yijing Zhang, Amy S. Lee, Charlotte Odendall, Bennett Shum, Nir Hacohen, Zhijian J. Chen, Sean P. Whelan, Marc Fransen, Max L. Nibert,Giulio Superti-Furga, and Jonathan C. Kagan*.(2010) Peroxisomes are signaling platforms for antiviral innate immunity, Cell, 141(4): 668-681. http://www.sciencedirect.com/science/article/pii/S0092867410004356 PDF
Yongbiao Xue#*,Yijing Zhang#, Qiuying Yang#, Qun Li,Zhukuan Cheng, Hugh G. Dickinson.(2009) Genetic features of a pollen-part mutation suggest an inhibitory role for the Antirrhinum pollen self-incompatibility determinant, Plant Molecular Biology, 70(5): 499-509 http://link.springer.com/article/10.1007%2Fs11103-009-9487-9 PDF
Yijing Zhang, Zhonghua Zhao, and Yongbiao Xue*.(2009) Roles of Proteolysis in Plant Self-Incompatibility, Annual Review of Plant Biology, 60:21-42 http://www.annualreviews.org/doi/full/10.1146/annurev.arplant.043008.092108 PDF
Zhang Y and Xue Y#. “Molecular Biology of S-RNase-based Self-Incompatibility” in Book “Self-Incompatibility in Flowering Plants”.(2008) Berlin- Heidelberg-New York: Springer-Verlag, p193-215. http://link.springer.com/chapter/10.1007/978-3-540-68486-2_9 PDF
CGT-seq, which employed epigenomic information from both active and repressive epigenetic marks to guide the assembly of the core genome mainly composed of promoter and intragenic regions. This method was relatively easily implemented, and displayed high sensitivity and specificity for capturing the core genome of bread wheat.
95% intragenic and 89% promoter region from wheat were covered by CGT-seq read. We further demonstrated in rice that CGT-seq captured hundreds of novel genes and regulatory sequences from a previously unsequenced ecotype.
Together, with specific enrichment and sequencing of regions within and nearby genes, CGT-seq is a time- and resource-effective approach to profiling functionally relevant regions in sequenced and non-sequenced populations with large genomes.
GSHR is a web server provides analyses based on integrated hormone response gene sets in Arabidopsis thaliana. We developed this to facilitate cross-study and cross-platform comparisons of transcriptomic changes to hormones.
The GSHR is user-friendly and has several features when comparing with other similar tools:
1. The GSHR especially focuses on genes response to hormones in Arabidopsis thaliana. It supported hormone response gene sets for users to compare with their own gene lists based on Fisher's exact test.
2. Other analysis tools are provided including cluster analysis, co-expression network, enrichment analysis of KEGG, GO and InterPro to help users unearthing the underlying biological insights of their gene lists.
CARMO is a web-based platform providing comprehensive annotations for multi-omics data, including transcriptomic data sets, epi-genomic modification sites, SNPs from genome re-sequencing, and the large gene lists derived from these omics studies. Well-organized results, as well as multiple tools for interactive visualization, are available through a user-friendly web interface.
The power of CARMO lies in the comprehensive collection and integration of information from both multi-omics data and diverse functional evidence of rice, which was further curated into gene sets and higher level gene modules. In this way, the high-throughput data can easily be compared across studies and platforms, and notably, integration of multiple types of evidence provides biological interpretation from the level of modules with high confidence. Examples in the manuscripts demonstrated that CARMO not only reproduced reported evidence, but also proposed novel functional insights for further experimental exploration.
ChIP-Seq is widely used to characterize genome-wide binding patterns of transcription factors and other chromatin-associated proteins. Although comparison of ChIP-Seq data sets is critical for understanding cell type-dependent and cell state-specific binding, and thus the study of cell-specific gene regulation, few quantitative approaches have been developed. Here, we present a simple and effective method, MAnorm, for quantitative comparison of ChIP-Seq data sets describing transcription factor binding sites and epigenetic modifications. The quantitative binding differences inferred by MAnorm showed strong correlation with both the changes in expression of target genes and the binding of cell type-specific regulators.
(Under Constuction) With the accumulation of ChIP-seq data across different cell types, an effective and accurate method are essential to unravel the relationship between regulator binding and epigenetic modifications in different cell types. We present an integrative computational toolkit, MAmotif, to infer cell type specific regulators.
Based on a hypotheses that the regions with higher epigenetic changes are more likely to be directly targeted by key cell type specific regulators, we combine MAnorm’s quantitative comparison information of 2 cell types and transcription factor binding sites information to infer cell type specific regulators. Here MAnorm is a model for quantitative comparison of ChIP-seq data between 2 cell types. While TFBS are detected from the epigenetic change regions by our newly developed motif scanning package.
Our motif scan algorithm is a probabilistic model based on position weight matrix (PWM): the score of motif A is calculated as the ratio of A’s probability of occurrence on the target sequence and its probability of occurrence on the genome background. The target sequence can finally be defined as the motif A target sequence when the score is beyond the score threshold, which is from the distribution of motif A scores calculated on the whole genome sequence. When the epigenetic modification changes and TFBS information are prepared, several statistical tests and clustering methods are applied to determine the linkage between epigenetic modification changes and the motif binding affinity in specific cell type.
The CYP Structure Interface (CYPSI) is a platform for CYP studies. CYPSI integrated the 3D structures for 266 A. thaliana CYPs predicted by three TBM methods: BMCD, which we developed specifically for CYP TBM; and two well-known web-servers, MUSTER and I-TASSER. After careful template selection and optimization, the models built by BMCD were accurate enough for practical application, which we demonstrated using a docking example aimed at searching for the CYPs responsible for ABA 8′-hydroxylation. CYPSI also provides extensive resources for A. thaliana CYP structure and function studies, including 400 PDB entries for solved CYPs, 48 metabolic pathways associated with A. thaliana CYPs, 232 reported CYP ligands and 18 A. thaliana CYPs docked with ligands (61 complexes in total). In addition, CYPSI also includes the ability to search for similar sequences and chemicals.
CYPSI provides comprehensive structure and function information for A. thaliana CYPs, which should facilitate investigations into the interactions between CYPs and their substrates. CYPSI has a user-friendly interface, which is available at http://bioinfo.cau.edu.cn/CYPSI.