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 TGFβ-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 TGFβ signaling and EMT.
TGFβ mediated epithelial to mesenchymal transition (EMT) proceeds through hybrid "E/M" states. A deeper understanding of these states and events which regulate entry to and exit from the E/M states is needed for therapeutic exploitation. We quantified >60,000 molecules across ten time points and twelve omic layers in mammary epithelial cells. Proteomes of whole cells, phosphoproteins, nucleus, extracellular vesicles, secretome and membrane resolved major shifts, E→E/M and E/M→M during EMT, and defined state-specific signatures. Metabolomics identified early activation of arachidonic acid pathway and an enzyme-mediated switch from Cytochrome P450 to Cyclooxygenase / Lipoxygenase branches during E→E/M. Single-cell transcriptomics identified GLIS2 as an early modulator of EMT. Integrative modeling-predicted combinatorial inhibition of AURKB, PP2A and SRC exposed vulnerabilities at E→E/M juncture. Covariance analysis revealed remarkable discordance between proteins and transcripts, and between proteomic layers, implying insufficiency of current approaches. Overall, this dataset provides an unprecedented resource on TGFβ signaling, EMT and 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.
Determining how DNA variants affect the binding of regulatory complexes to cisregulatory elements (CREs) and non-coding single-nucleotide polymorphisms (ncSNPs) is a challenge in genomics. To address this challenge, we have developed CASCADE (Comprehensive ASsessment of Complex Assembly at DNA Elements), which is a proteinbinding microarray (PBM)-based approach that allows for the high-throughput profiling of cofactor (COF) recruitment to DNA sequence variants. The method also enables one to infer the identity of the transcription factor-cofactor (TF-COF) complexes involved in COF recruitment.We use CASCADE to characterize regulatory complexes binding to CREs and SNP quantitative trait loci (SNP-QTLs) in resting and stimulated human macrophages. By profiling the recruitment of the acetyltransferase p300 and MLL methyltransferase component RBBP5, we identify key regulators of the chemokine CXCL10, and by profiling a set of five functionally diverse COFs we identify a prevalence of ETS sites mediating COF recruitment at SNP-QTLs in macrophages.Our results demonstrate that CASCADE is a customizable, high-throughput platform to link DNA variants with the biophysical complexes that mediate functions such as chromatin modification or remodeling in a cell state-specific manner. MainDetermining the impact of genetic variation on cis-regulatory elements (CREs), such as enhancers and promoters that control gene expression, remains a challenge in modern genomics. Genome-wide association studies (GWAS) have identified thousands of singlenucleotide polymorphisms (SNPs) associated with human diseases, but the causal variants and their biological effects remain largely unknown 1-3 . Variants underlying disease risk often function by altering CRE function and gene expression. For example, >50% of causal SNPs for autoimmune diseases are non-coding SNPs (ncSNPs) mapping to immune gene enhancers 4 . Therefore, a major challenge in understanding disease susceptibility is to determine how non-
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