Biology subjects

Zang, C.

Publications and source records attributed to Zang, C..

40 records · Page 3Linked to original sources

bioRxiv2019

Specific inhibition of DPY30 activity by ASH2L-derived peptides suppresses blood cancer cell growth

DPY30 facilitates H3K4 methylation by directly binding to ASH2L in the SET1/MLL complexes and plays an important role in hematologic malignancies. However, the domain on DPY30 that regulates cancer growth is not evident, and the potential of pharmacologically targeting this chromatin modulator to inhibit cancer has not been explored. Here we have developed a peptide-based strategy to specifically target DPY30 activity. We have designed cell-penetrating peptides derived from ASH2L that can either bind to DPY30 or show defective or enhanced binding to DPY30. The DPY30-binding peptides specifically inhibit its activity in interacting with ASH2L and enhancing H3K4 methylation. Treatment with the DPY30-binding peptides significantly inhibited the growth of MLL-rearranged leukemia and other MYC-dependent hematologic cancer cells. We also revealed subsets of genes that may mediate the effect of the peptides on cancer cell growth, and showed that the DPY30-binding peptide sensitized leukemia to other types of epigenetic inhibitors. These results strongly support a critical role of the ASH2L-binding groove of DPY30 in promoting blood cancers, and demonstrate a proof-of-principle for the feasibility of pharmacologically targeting the ASH2L-binding groove of DPY30 for potential cancer inhibition.

cancer biology↗

bioRxiv2018

Integrative data analysis predicts YY1 as a Cis-regulator in the 3D Cell Culture Models of MCF10A at the Stiffness Level of High Mammographic Density

Previous studies have shown that in 3D cell culture models of human mammary cells (HMEC) (i) colony organizations are heterogeneous, and (ii) ERBB2 is overexpressed in MCF10A when the stiffness of the microenvironment is increased to that of high mammographic density (MD). The goal of the current study is to identify transcription factors that regulate processes associated with the increased stiffness of the microenvironment. Two HMEC premalignant lines of MCF7 and 184A1 are cultured in 3D, colonies are imaged using confocal microscopy, and colony organizations and heterogeneity are quantified as a function of the stiffness of the microenvironment. In parallel and surrogate assays, colony organizations are profiled by transcriptomics. Transcriptome data are enriched by correlative analysis with the computed morphometric indices, from 3D culture, and a subset of transcriptome data is selected. This subset is then processed with Model-based Analysis of Regulation of Gene Expression (MARGE) and publicly available ChIP-seq data to predict regulatory transcription factors. The integrative analysis indicated that YY1 regulates ERBB2 in the 3D cell culture of MCF10A when the stiffness of the microenvironment is increased to that of high MD. Subsequent experimental validation confirmed that YY1 is only expressed at the high stiffness value of the microenvironment concomitant with the overexpression of ERBB2 in MCF10A. Furthermore, using ERBB2 positive SKBR3 cell line, co-expression of YY1 and ERBB2 is absent, which indicates that YY1 regulates tumorigenicity through multiple pathways.\n\nAuthors summaryMCF10A is a premalignant immortalized human mammary cell that has been isolated from a patient with fibrocystic and lost several barriers toward transformation. In an earlier study, we showed that ERBB2 is upregulated in 3D cultures of MCF10A when the stiffness of the microenvironment is increased to that of high mammographic density. Here, we leverage publicly available ChIP-seq data to predict and validate the cis-regulator of ERBB2. Our integrated experimental and computation protocol provides a pathway for elucidating regulators that can potentially be targeted for intervention.

cancer biology↗

bioRxiv2018

BART: a transcription factor prediction tool with query gene sets or epigenomic profiles

SummaryIdentification of functional transcription factors that regulate a given gene set is an important problem in gene regulation studies. Conventional approaches for identifying transcription factors, such as DNA sequence motif analysis, are unable to predict functional binding of specific factors and not sensitive to detect factors binding at distal enhancers. Here we present Binding Analysis for Regulation of Transcription (BART), a novel computational method and software package for predicting functional transcription factors that regulate a query gene set or associate with a query genomic profile, based on more than 6,000 existing ChIP-seq datasets for over 400 factors in human or mouse. This method demonstrates the advantage of utilizing publicly available data for functional genomics research.\n\nAvailabilityBART is implemented in Python and available at http://faculty.virginia.edu/zanglab/bart\n\nContact: zang@virginia.edu

bioinformatics↗

bioRxiv2017

Universal correction of enzymatic sequence bias

Coupling molecular biology to high throughput sequencing has revolutionized the study of biology. Molecular genomics techniques are continually refined to provide higher resolution mapping of nucleic acid interactions and structure. Sequence preferences of enzymes can interfere with the accurate interpretation of these data. We developed seqOutBias to characterize enzymatic sequence bias from experimental data and scale individual sequence reads to correct intrinsic enzymatic sequence biases. SeqOutBias efficiently corrects DNase-seq, TACh-seq, ATAC-seq, MNase-seq, and PRO-seq data. We show that seqOutBias correction facilitates identification of true molecular signatures resulting from transcription factors and RNA polymerase interacting with DNA.

genomics↗