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.