Parallelized multidimensional analytic framework applied to mammary epithelial cells uncovers regulatory principles in EMT.

A proper understanding of disease etiology will require longitudinal systems-scale reconstruction of the multitiered architecture of eukaryotic signaling. Here we combine state-of-the-art data acquisition platforms and bioinformatics tools to devise PAMAF, a workflow that simultaneously examines twelve omics modalities, i.e., protein abundance from whole-cells, nucleus, exosomes, secretome and membrane; N-glycosylation, phosphorylation; metabolites; mRNA, miRNA; and, in parallel, single-cell transcriptomes. We apply PAMAF in an established in vitro model of TGFß-induced epithelial to mesenchymal transition (EMT) to quantify >61,000 molecules from 12 omics and 10 timepoints over 12 days. Bioinformatics analysis of this EMT-ExMap resource allowed us to identify; -topological coupling between omics, -four distinct cell states during EMT, -omics-specific kinetic paths, -stage-specific multi-omics characteristics, -distinct regulatory classes of genes, -ligand-receptor mediated intercellular crosstalk by integrating scRNAseq and subcellular proteomics, and -combinatorial drug targets (e.g., Hedgehog signaling and CAMK-II) to inhibit EMT, which we validate using a 3D mammary duct-on-a-chip platform. Overall, this study provides a resource on TGFß signaling and EMT.

Widespread perturbation of ETS factor binding sites in cancer.

Although >90% of somatic mutations reside in non-coding regions, few have been reported as cancer drivers. To predict driver non-coding variants (NCVs), we present a transcription factor (TF)-aware burden test based on a model of coherent TF function in promoters. We apply this test to NCVs from the Pan-Cancer Analysis of Whole Genomes cohort and predict 2555 driver NCVs in the promoters of 813 genes across 20 cancer types. These genes are enriched in cancer-related gene ontologies, essential genes, and genes associated with cancer prognosis. We find that 765 candidate driver NCVs alter transcriptional activity, 510 lead to differential binding of TF-cofactor regulatory complexes, and that they primarily impact the binding of ETS factors. Finally, we show that different NCVs within a promoter often affect transcriptional activity through shared mechanisms. Our integrated computational and experimental approach shows that cancer NCVs are widespread and that ETS factors are commonly disrupted.

CASCADE: high-throughput characterization of regulatory complex binding altered by non-coding variants.

Non-coding DNA variants (NCVs) impact gene expression by altering binding sites for regulatory complexes. New high-throughput methods are needed to characterize the impact of NCVs on regulatory complexes. We developed CASCADE (Customizable Approach to Survey Complex Assembly at DNA Elements), an array-based high-throughput method to profile cofactor (COF) recruitment. CASCADE identifies DNA-bound transcription factor-cofactor (TF-COF) complexes in nuclear extracts and quantifies the impact of NCVs on their binding. We demonstrate CASCADE sensitivity in characterizing condition-specific recruitment of COFs p300 and RBBP5 (MLL subunit) to the CXCL10 promoter in lipopolysaccharide (LPS)-stimulated human macrophages and quantify the impact of all possible NCVs. To demonstrate applicability to NCV screens, we profile TF-COF binding to ~1,700 single-nucleotide polymorphism quantitative trait loci (SNP-QTLs) in human macrophages and identify perturbed ETS domain-containing complexes. CASCADE will facilitate high-throughput testing of molecular mechanisms of NCVs for diverse biological applications.

High-Throughput Analysis of the Cell and DNA Site-Specific Binding of Native NF-κB Dimers Using Nuclear Extract Protein-Binding Microarrays (NextPBMs).

Nuclear factor-kappa B (NF-κB) transcription factors coordinate gene expression in response to a broad array of cellular signals. In vertebrates, there are five NF-κB proteins (c-Rel, RelA/p65, RelB, p50, and p52) that can form various dimeric combinations exhibiting both common and dimer-specific DNA-binding specificity. In this chapter, we describe the use of the nuclear extract protein-binding microarray (nextPBM), a high-throughput method to characterize the DNA binding of transcription factors present in cell nuclear extracts. NextPBMs allow for sensitive analysis of the DNA binding of NF-κB dimers and their interactions with cell-specific cofactors.