Plant Bioinformatics and Functional Epigenomics Group
Epigenetic modifications of the genome allows 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 focus on the mechanism controlling the specificity of different Polycomb Group (PcG) members, with integrative approach combining molecular, genetic and computational tools to address the following questions on global level.
note: *, Co-first author; #, Corresponding author ; Lab members’ name are in bold
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
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
Wang H*, Liu C*, Cheng J*, Liu J, Zhang L, He C, Shen WH, Jin H#, Xu L#, Zhang Y#. (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 Genet.12(1):e1005771. doi: 10.1371/journal.pgen.1005771
http://www.plosgenetics.org/article/comments/info:doi/10.1371/journal.pgen.1005771 PDF Data Browse
Li G*, Liu S*, Wang J, He J, Zhang Y#, Xu L #. (2014) ISWI proteins participate in the genome-wide nucleosome distribution in Arabidopsis. Plant J. 78: 706-714 http://onlinelibrary.wiley.com/doi/10.1111/tpj.12499/abstract PDF
Zhou P, Zhang Y, Ma Q, Gu F, Day DS, He A, Zhou B, Li J, Stevens SM, Romo D, Pu WT#. (2013) Interrogating translational efficiency and lineage-specific transcriptomes using ribosome affinity purification. Proc. Natl. Acad. Sci. U S A. 110: 15395-15400 http://www.pnas.org/content/110/38/15395.long PDF
Zhang G*, Zhang Y*, Su Z#. (2012) CYPSI: a structure-based interface for cytochrome P450s and ligands in Arabidopsis thaliana. BMC Bioinformatics. 13: 332 http://www.biomedcentral.com/1471-2105/13/332 PDF
Conforto TL, Zhang Y, Sherman J, Waxman DJ#. (2012) Impact of CUX2 on the Female Mouse Liver Transcriptome: Activation of Female-Biased Genes and Repression of Male-Biased Genes. Mol. Cell. Biol. 32: 4611-4627 http://mcb.asm.org/content/32/22/4611.long PDF
Shao Z*, Zhang Y*, Yuan GC, Orkin SH, Waxman DJ#. (2012) MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets. Genome Biology. 13: R16 http://genomebiology.com/content/13/3/R16 PDF
Zhang Y, Laz EV, Waxman DJ#. (2011) Dynamic, Sex-Differential STAT5 and BCL6 Binding to Sex-Biased, Growth Hormone-Regulated Genes in Adult Mouse Liver. Mol. Cell. Biol. 32: 880-896 http://mcb.asm.org/content/32/4/880.lon PDF
Zhang Y, Klein K, Sugathan A, Nassery N, Dombkowski A, Zanger UM, Waxman DJ#. (2011) Transcriptional Profiling of Human Liver Identifies Sex- with Polygenic Biased Genes Associated Dyslipidemia and Coronary Artery Disease. PLos One. 6: e23506 http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0023506 PDF
Bagchi G*, Zhang Y*, Stanley KA, Waxman DJ#. (2011) Complex modulation of androgen responsive gene expression by methoxyacetic acid. Reprod. Biol. Endocrinol. 9: 42 http://www.rbej.com/content/9/1/42g PDF
Dixit E, Boulant S, Zhang Y, Lee AS, Odendall C, Shum B, Hacohen N, Chen ZJ, Whelan SP, Fransen M, Nibert ML, Superti-Furga G, Kagan JC#. (2010) Peroxisomes are signaling platforms for antiviral innate immunity. Cell. 141: 668-681 http://www.sciencedirect.com/science/article/pii/S0092867410004356 PDF
Xue Y*, Zhang Y*, Yang Y*, Li Q, Cheng Z, Dickinson HG#. (2009) Genetic features of a pollen-part mutation suggest an inhibitory role for the Antirrhinum pollen self-incompatibility determinant. Plant Mol. Biol. 70: 499-509 http://link.springer.com/article/10.1007%2Fs11103-009-9487-9 PDF
Zhang Y, Zhao Z, and Xue Y#. (2009) Roles of Proteolysis in Plant Self-Incompatibility. Annu. Rev. Plant Biol. 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
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.