Biology subjects

Emdal, K. B.

Publications and source records attributed to Emdal, K. B..

5 recordsLinked to original sources

bioRxiv2026

Tethering-mediated recruitment of pioneer factors by NuRD enables signal dependent gene activation and protects cell identity

Cell identity relies on precise control of transcription factor (TF) activity, localization and interpretation of signaling. Mitogen-activated protein kinase (MAPK) signaling regulates transcription via the ERK kinase, phosphorylating components of the Mediator complex to regulate its association with TF-bound enhancers. Here we describe two distinct forms of TF association related to signaling response, stable DNA recognition and tethering to active sites of transcription. We find that the tethering of TFs at these active sites is both essential for signaling response and cell identity. To identify tethering substrates, we focused on signaling dependent associations with the transcriptional coregulator, the Mediator, and find the NuRD complex is engaged with the Mediator at active promoters and enhancers. Here, NuRD functions as a molecular scaffold recruiting pioneer TFs like SOX2 independently of DNA sequence, enhancing ERK dependent transcription while sequestering these factors away from context inappropriate binding sites elsewhere in the genome. Balancing the recognition of a restricted spectrum of canonical DNA sites by pioneer factors while retaining them at high concentrations in the vicinity of transcription supports robust transcriptional response, ensures plasticity in differentiation and explains why these factors bind a fraction of their naturally occurring sites in any given cell type.

molecular biology↗

bioRxiv2025

The Human Cell Line Phosphoproteome Atlas: A Deep Empirical Resource Revealing Kinase Activity Landscapes

Protein phosphorylation orchestrates cellular signaling and controls diverse biological processes, with its dysregulation driving diseases, notably cancer. Comprehensive, high-throughput phosphoproteomics remains limited by detection sensitivity, data completeness, and computational bottlenecks, especially in low-input settings. We present the deepest empirical human phosphoproteome resource to date, regrouping over 200,000 class I phosphosites across 33 diverse human cell lines, and demonstrate that this spectral library dramatically improves single-shot phosphoproteomics with 30-fold faster data processing compared to library-free approaches and enhanced confidence in phosphosite localization even from minimal sample input. Integrating proteome and phosphoproteome data, we developed a combined kinase activity score, revealing cell line- and cancer-specific signaling vulnerabilities, many correlating with drug sensitivity. This resource accelerates deep, reproducible phosphoproteomics, enabling systematic functional mapping of cellular signaling networks, and empowers precision oncology by highlighting actionable kinase targets in diverse cell states.

biochemistry↗

bioRxiv2023

Multi-layered proteomics identifies insulin-induced upregulation of the EphA2 receptor via the ERK pathway and dependent on low IGF1R level

Insulin resistance impairs the cellular insulin response and frequently precedes metabolic disorders, like type 2 diabetes, which are affecting an increasing number of people globally. Given the critical role of the liver in glucose and lipid metabolism, understanding the molecular mechanisms in hepatic insulin resistance is essential for early preventive treatments. To elucidate changes in insulin signal transduction associated with hepatocellular resistance, we employed a multi-layered mass spectrometry-based proteomics approach focusing on insulin receptor (IR) signaling at the interactome, phosphoproteome, and proteome levels.in a long-term hyperinsulinemia-induced insulin-resistant HepG2 cell line with a knockout of the insulin-like growth factor 1 receptor (IGF1R KO). Analysis of the dynamic insulin-induced IR interactome revealed recruitment of the PI3K complex in both insulin-sensitive and -resistant cells. From the phosphoproteomics dataset, a change in insulin-stimulated signaling responses in insulin resistance was observed and showed attenuated signaling via the metabolic PI3K-AKT pathway but sustained extracellular signal-regulated kinase (ERK) activity. At the proteome level, the ephrin type-A receptor 2 (EphA2) showed an insulin-induced increase in expression. This receptor belongs to the Eph receptor family and participates in various cellular processes, such as cell adhesion, migration, and tissue development. The protein abundance regulation of EphA2 occurred through the ERK signaling pathway and was concordantly independent of insulin resistance. Induction of EphA2 by insulin was confirmed in other cell lines and observed uniquely in cells with high levels of IR compared to IGF1R. The multi-layered proteomics dataset provided insights into insulin signaling in general and in the context of insulin resistance, and it can going forward serve as a resource to generate and test hypotheses, leading to an improved understanding of insulin resistance.

cell biology↗

bioRxiv2023

Spatiotemporal Proteomics Deciphers Functional Selectivity of EGFR ligands

The epidermal growth factor receptor (EGFR) induces different signaling outputs depending on ligand identity and biological context. This phenomenon is known as functional selectivity, but the underlying molecular mechanisms remain elusive. Here, we investigated this on a global scale and time-resolved by high-throughput multilayered proteomics integrating dynamic changes in the EGFR interaction network by proximity biotinylation using EGFR- TurboID, phosphoproteome, and proteome in response to stimulation with the six highest-affinity EGFR ligands. We obtained comprehensive temporal profiles of protein recruitment and phosphosite changes pinpointing signaling proteins differentially regulated by the six ligands with key impact on EGFR endocytic fate, e.g. degradation or recycling. Specifically, the Epsin family protein, Clint1 was identified to control the endocytic trafficking of EGFR towards degradation. Moreover, we characterized the protein interaction selectivity of EGFR C-terminally phosphorylated tyrosine residues using a panel of tyrosine mutated constructs showing STAT5 specificity for EGFR Y1173. These data provide a comprehensive resource deciphering functional selectivity of EGFR signaling to support discovery of novel drug targets.

molecular biology↗

bioRxiv2020

Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses

Multi-omics datasets can provide molecular insights beyond the sum of individual omics. Diverse tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from nine renal cell carcinoma patients. We used COSMOS to generate novel hypotheses such as the impact of Androgen Receptor on nucleoside metabolism and the influence of the JAK-STAT pathway on propionyl coenzyme A production. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omics studies. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=86 SRC="FIGDIR/small/057893v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@37d2d7org.highwire.dtl.DTLVardef@f333d4org.highwire.dtl.DTLVardef@cfcac3org.highwire.dtl.DTLVardef@90318_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗