Biology subjects

Imrichova, H.

Publications and source records attributed to Imrichova, H..

9 recordsLinked to original sources

bioRxiv2026

Induced Estrogen Receptor SUMOylation drives SERD activity

The ligand dependent transcription factor estrogen receptor (ER) is a key driver of and important drug target in breast cancer. Patients with advanced disease are typically treated with selective ER degraders (SERDs), whose therapeutic activity is commonly attributed to induced ER protein degradation. Yet the exact mechanism and relevance of degradation for clinical efficacy remain unclear. We show that SERDs directly induce ER SUMOylation, thereby triggering degradation via SUMO-targeted ubiquitin ligases (STUbLs). Inactivation of STUbLs prevents ER degradation and counterintuitively further sensitizes breast cancer cells to SERDs, rather than conferring resistance. SERD efficacy is independent of ER degradation, challenging the degradation-centric model of SERD action. Instead, SUMOylation recruits transcriptional co-repressors, turning SUMOylated ER into a dominant-negative repressor. Thus, SUMOylation rather than degradation is the direct consequence and driver of SERD activity. Our findings position SUMO-inducing drugs as a hitherto underappreciated yet clinically validated therapeutic modality with broad applicability.

Cancer Biology↗

bioRxiv2024

Monovalent Pseudo-Natural Product Degraders Supercharge the Native Degradation of IDO1 by KLHDC3

Targeted protein degradation (TPD) modulates protein function beyond inhibition of enzyme activity or protein-protein interactions. Most degrader drugs function by directly mediating proximity between a neosubstrate and hijacked E3 ligase. Here, we identified pseudo-natural products derived from (-)-myrtanol, termed iDegs that inhibit and induce degradation of the immunomodulatory enzyme indoleamine-2,3-dioxygenase 1 (IDO1) by a distinct mechanism. iDegs boost IDO1 ubiquitination and degradation by the cullin-RING E3 ligase CRL2KLHDC3, which we identified to natively mediate ubiquitin-mediated degradation of IDO1. Therefore, iDegs increase IDO1 turnover using the native proteolytic pathway. In contrast to clinically explored IDO1 inhibitors, iDegs reduce formation of kynurenine by both inhibition and induced degradation of the enzyme and, thus, would also modulate non-enzymatic functions of IDO1. This unique mechanism of action may open up new therapeutic opportunities for the treatment of cancer beyond classical inhibition of IDO1.

cell biology↗

bioRxiv2024

Inhibitor-induced supercharging of kinase turnover via endogenous proteolytic circuits

Targeted protein degradation has emerged as a promising new pharmacological strategy. Traditionally, it relies on small molecules that induce proximity between a target protein and an E3 ubiquitin ligase to prompt target ubiquitination and degradation by the proteasome. Sporadic reports indicated that ligands designed to inhibit a target can also induce its destabilization. Among others, this has repeatedly been observed for kinase inhibitors. However, we lack an understanding of the frequency, generalizability, and mechanistic underpinnings of these phenomena. To address this knowledge gap, we generated dynamic abundance profiles of 98 kinases after cellular perturbations with 1570 kinase inhibitors, revealing 160 selective instances of inhibitor-induced kinase destabilization. Kinases prone to degradation are frequently annotated as HSP90 clients, thus affirming chaperone deprivation as an important route of destabilization. However, detailed investigation of inhibitor-induced degradation of LYN, BLK and RIPK2 revealed a differentiated, common mechanistic logic where inhibitors function by inducing a kinase state that is more efficiently cleared by endogenous degradation mechanisms. Mechanistically, effects can manifest by ligand-induced changes in cellular activity, localization, or multimerization which may be triggered by direct target engagement or network effects. Collectively, our data suggest that inhibitor-induced kinase degradation is a common event and positions supercharging of endogenous degradation circuits as an alternative to classical proximity-inducing degraders.

molecular biology↗

bioRxiv2023

Orpinolide disrupts a leukemic dependency on cholesterol transport by inhibiting the oxysterol-binding protein OSBP

Metabolic alterations in cancer precipitate in associated dependencies that can be therapeutically exploited. To meet this goal, natural product inspired small molecules can provide a resource of invaluable chemotypes. Here, we identify orpinolide, a synthetic withanolide analog with pronounced anti-leukemic properties via orthogonal chemical screening. Through multi-omics profiling and genome-scale CRISPR/Cas9 screens, we identify that orpinolide disrupts Golgi homeostasis via a mechanism that requires active phosphatidylinositol 4-phosphate (PI4P) signaling at the endoplasmic reticulum (ER)-Golgi membrane interface. Thermal proteome profiling and genetic validation studies reveal the oxysterol-binding protein OSBP as the direct and phenotypically relevant target of orpinolide. Collectively, these data reaffirm sterol transport as a therapeutically actionable dependency in leukemia and motivate ensuing translational investigation via the probe-like compound orpinolide.

pharmacology and toxicology↗

bioRxiv2022

Charting functional E3 ligase hotspots and resistance mechanisms to small-molecule degraders

Targeted protein degradation is a new pharmacologic paradigm established by drugs that recruit target proteins to E3 ubiquitin ligases via a ternary ligase-degrader-target complex. Based on the structure of the degrader and the neosubstrate, different E3 ligase interfaces are critically involved in this process, thus forming defined "functional hotspots". Understanding disruptive mutations in functional hotspots informs on the architecture of the underlying assembly, and highlights residues prone to cause drug resistance. Until now, their identification was driven by structural methods with limited scalability. Here, we employ haploid genetics to show that hotspot mutations cluster in the substrate receptors of the hijacked ligases and find that type and frequency of mutations are shaped by the essentiality of the harnessed ligase. Intersection with deep mutational scanning data revealed hotspots that are either conserved, or specific for chemically distinct degraders or recruited neosubstrates. Biophysical and structural validation suggest that hotspot mutations frequently converge on altered ternary complex assembly. Moreover, we identified and validated hotspots mutated in patients that relapse from degrader treatment. In sum, we present a fast and experimentally widely accessible methodology that empowers the characterization of small-molecule degraders and informs on associated resistance mechanisms.

molecular biology↗

bioRxiv2019

Breast cancer classification based on proteotypes obtained by SWATH mass spectrometry

Accurate breast cancer classification is vital for patient management decisions, and better tumour classification is expected to enable more precise and eventually personalized treatment to improve patient outcomes. Here, we present a novel quantitative proteotyping approach based on SWATH mass spectrometry and establish key proteins for breast tumour classification derived from proteotype data. The study was based on 96 tissue samples representing five breast cancer subtypes according to conventional classification. Correlation of SWATH proteotype patterns indicated groups that largely recapitulate these subtypes. However, the proteotype-based classification also revealed varying degrees of heterogeneity within the conventional subtypes, with triple negative tumours being the most heterogeneous. Proteins that contributed most strongly to the proteotype-based classification include INPP4B, CDK1, and ERBB2, which are associated with oestrogen receptor status, tumour grade, and HER2 status, respectively. While these three key proteins exhibited high levels of correlation between protein and transcript levels (R>0.67), general correlation did not exceed R=0.29, indicating the value of protein-level measurements of biomarkers and disease-regulated genes. Overall, our data shows how large-scale protein-level measurements by next-generation proteomics can lead to improved patient stratification for precision medicine.

cancer biology↗

bioRxiv2018

ChIP-seq meta-analysis yields high quality training sets for enhancer classification

Genome-wide prediction of enhancers depends on high-quality positive and negative training sets. The use of ChIP-seq peaks as positive training data can be problematic due to high degrees of indirectly bound regions, and often poor overlap between experimental conditions.\n\nHere we explore meta-analysis of ChIP-seq data to generate high-quality training data for enhancer modeling. Our method is based on rank aggregation and identifies a core set of directly bound regions per transcription factor, exploiting between five and twenty ChIP-seq data sets per factor. We applied this method to six different transcription factors, namely TP53, REST, SOX2, GRHL2, HIF1A and PPARG. Sequence analysis and modeling of recurrently bound enhancers yielded distinct enhancer features for the different factors, whereby binding sites of REST and TP53 are strongly determined by their motif; binding of GRHL2 and SOX2 is determined by nucleosome positioning; and binding of PPARG and HIF1A depends on other transcription factors. In conclusion, meta-analysis of ChIP-seq peaks, and centering on motifs, allowed discovering new properties of transcription factor binding.

bioinformatics↗

bioRxiv2017

SCENIC: Single-Cell Regulatory Network Inference And Clustering

Single-cell RNA-seq allows building cell atlases of any given tissue and infer the dynamics of cellular state transitions during developmental or disease trajectories. Both the maintenance and transitions of cell states are encoded by regulatory programs in the genome sequence. However, this regulatory code has not yet been exploited to guide the identification of cellular states from single-cell RNA-seq data. Here we describe a computational resource, called SCENIC (Single Cell rEgulatory Network Inference and Clustering), for the simultaneous reconstruction of gene regulatory networks (GRNs) and the identification of stable cell states, using single-cell RNA-seq data. SCENIC outperforms existing approaches at the level of cell clustering and transcription factor identification. Importantly, we show that cell state identification based on GRNs is robust towards batch-effects and technical-biases. We applied SCENIC to a compendium of single-cell data from the mouse and human brain and demonstrate that the proper combinations of transcription factors, target genes, enhancers, and cell types can be identified. Moreover, we used SCENIC to map the cell state landscape in melanoma and identified a gene regulatory network underlying a proliferative melanoma state driven by MITF and STAT and a contrasting network controlling an invasive state governed by NFATC2 and NFIB. We further validated these predictions by showing that two transcription factors are predominantly expressed in early metastatic sentinel lymph nodes. In summary, SCENIC is the first method to analyze scRNA-seq data using a network-centric, rather than cell-centric approach. SCENIC is generic, easy to use, and flexible, and allows for the simultaneous tracing of genomic regulatory programs and the mapping of cellular identities emerging from these programs. Availability: SCENIC is available as an R workflow based on three new R/Bioconductor packages: GENIE3, RcisTarget and AUCell. As scalable alternative to GENIE3, we also provide GRNboost, paving the way towards the network analysis across millions of single cells.

bioinformatics↗

bioRxiv2017

Identification Of cis-Regulatory Mutations Generating De Novo Edges In Personalized Cancer Gene Regulatory Networks

The identification of functional non-coding mutations is a key challenge in the field of genomics, where whole-genome re-sequencing can swiftly generate a set of all genomic variants in a sample, such as a tumor biopsy. The size of the human regulatory landscape places a challenge on finding recurrent cis-regulatory mutations across samples of the same cancer type. Therefore, powerful computational approaches are required to sift through the tens of thousands of non-coding variants, to identify potentially functional variants that have an impact on the gene expression profile of the sample. Here we introduce an integrative analysis pipeline, called - cisTarget, to filter, annotate and prioritize non-coding variants based on their putative effect on the underlying 'personal' gene regulatory network. We first validate -cisTarget by re-analyzing three cases of oncogenic non-coding mutations, namely the TAL1 and LMO1 enhancer mutations in T-ALL, and the TERT promoter mutation in melanoma. Next, we re-sequenced the full genome of ten cancer cell lines of six different cancer types, and used matched transcriptome data and motif discovery to infer master regulators for each sample. We identified candidate functional non-coding mutations that generate de novo binding sites for these master regulators, and that result in the up-regulation of nearby oncogenic drivers. We finally validated the predictions using tertiary data including matched epigenome data. Our approach is generally applicable to re-sequenced cancer genomes, or other genomes, when a disease- or sample-specific gene signature is available for network inference. -cisTarget is available from http://mucistarget.aertslab.org.

genomics↗