Biology subjects

Burtscher, M. L.

Publications and source records attributed to Burtscher, M. L..

6 recordsLinked to original sources

bioRxiv2026

Multi-omics and biophysical phosphoproteomics upon BRAF inhibition uncover functional networks of BRAFV600E-driven signaling

Dysregulated kinase activity drives oncogenic signaling, disrupts cellular homeostasis, and promotes tumour progression. The BRAFV600E mutation constitutively activates the MAPK pathway and is a key therapeutic target in melanoma and other cancers, but the functional relevance of most downstream phosphorylation events and mechanisms of drug resistance remain unclear. To address this, a global multi-omic model of BRAF inhibition response was established in BRAFV600E-mutant cells by integrating time-resolved and biophysical phosphoproteomics, transcriptomics, and thermal proteome profiling. Ultradeep phosphoproteomics revealed extensive phosphorylation changes upon BRAF inhibitor treatment, while biophysical phosphoproteomics identified phosphorylation events linked to altered protein solubility and subcellular localization, suggesting changes in nucleic acid interactions and nuclear reorganisation. Network-based integration of these datasets prioritized functionally relevant phosphorylation sites and kinases. Experimental validation identified CDK9, CLK3, and TNIK as critical regulators of BRAFV600E signaling and candidate targets for combinatorial inhibition capable of re-sensitising resistant cells. The transcription factor ETV3 emerged as a previously unrecognised effector of BRAF signaling. Biophysical proteomics data confirmed that ETV3 phosphorylation modulates DNA-binding, while functional assays combining knockdown, metabolomics, and drug screening demonstrated its role in coordinating transcriptional and metabolic adaptations to BRAF inhibition. This study provides a systems-level framework linking phosphorylation dynamics to protein function and phenotype, identifies ETV3 as a new node in oncogenic BRAF signaling, and illustrates how integrated, site-resolved models can reveal mechanisms of kinase-driven oncogenesis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=43 SRC="FIGDIR/small/704793v1_ufig1.gif" ALT="Figure 1"> View larger version (13K): org.highwire.dtl.DTLVardef@1df1401org.highwire.dtl.DTLVardef@9a77a5org.highwire.dtl.DTLVardef@124f819org.highwire.dtl.DTLVardef@1c6b57_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LITime- and cell type-resolved phosphoproteomics maps BRAF inhibition dynamics C_LIO_LIBiophysical phosphoproteomics, combining quantitative phosphoproteomics with solubility profiling or nuclear fractionation, reveals phosphorylation-driven changes of protein solubility and localization C_LIO_LIIntegration of abundance and biophysical phosphoproteomics data identifies functionally relevant phosphorylation events of BRAFV600E signaling C_LIO_LINetwork integration of multimodal phosphoproteomic, transcriptomic and thermal proteome profiling data links signaling to protein function and cellular phenotypes C_LIO_LIBiophysical evidence improves models and identifies non-canonical kinases driving BRAF signaling as well as novel downstream regulators such as ETV3 C_LIO_LIFollow-up experiments reveal a ETV3-GLUT3-mediated metabolic adaptation in BRAFV600E cells C_LI

systems biology↗

bioRxiv2024

Pervasive phosphorylation by phage T7 kinase disarms bacterial defenses

Bacteria and bacteriophages are in a constant arms race to develop bacterial defense and phage counter-defense systems. Currently known phage counter-defense systems are specific to (the activity of) the targeted bacterial defense system. Here, we uncover a mechanism by which the T7 bacteriophage broadly counteracts bacterial defenses using protein phosphorylation. We show that the T7 protein kinase (T7K), which was believed to specifically redirect the function of a few host proteins, is in fact a hyper-promiscuous, dual-specificity kinase enacting a massive wave of phosphorylation on virtually all host and phage proteins during infection. The scale of phosphorylation vastly exceeds the number of previously known phosphorylation events in E. coli, has no sequence motif specificity, and results in a higher proteome-wide phosphorylation density than that of mammalian cells which encode [~] 500 kinases. Stoichiometry analysis of phosphorylation sites revealed a strong bias of T7K activity towards nucleic acid-binding substrates, which we show is mediated by its C-terminal DNA-binding domain. This specificity for highly stoichiometric phosphorylation of nucleic acid-binding proteins enables the deactivation of DNA-targeting or - containing bacterial defense systems. We provide mechanistic insight into how T7K weakens two such defense systems, Retron-Eco9 and DarTG1, through specific phosphorylation events, with single phosphomimetic mutations in key sites of the toxins abolishing defense. Finally, by screening a large collection of E. coli strains, we provide evidence of broad counter-defense capacities for T7K in nature, as strains counteracted contain diverse bacterial defense systems.

microbiology↗

bioRxiv2024

Evaluating signaling pathway inference from kinase-substrate interactions and phosphoproteomics data

Cellular signaling plays a vital role in how cells communicate and adapt to both environmental and internal cues. At the molecular level, signaling is largely driven by phosphorylation cascades controlled by kinases. Because of this, kinase-driven signaling pathways are used as a conceptual framework to interpret molecular data across biological contexts. However, signaling pathways were created using limited throughput technologies. As knowledge of kinase-substrate interactions grows through novel computational and experimental approaches, and phosphoproteomic methods improve their coverage and accuracy, traditional signaling pathways need to be revisited. In this study, we critically assess context-specific signaling pathway reconstruction using phosphoproteomics and kinase-substrate networks. We first integrate literature, protein language models, and peptide array data to create a state-of-the-art kinase-substrate network. Focusing on epidermal growth factor (EGF), we conduct a meta-analysis of recent short-term response phosphoproteomics studies, which we complement with three own datasets, representing the most comprehensive characterization of the EGF response available to date. Using three alternative computational methods, we infer kinase-driven pathways, which we compare to multiple ground truth sets, including the canonical pathway, experimentally validated interactions, and correlation supported interactions. Our findings reveal that literature-curated networks, when combined with network propagation, yield the best recovery of ground truth interactions. We found that up to 90% of data-supported direct interactions are absent from current ground truth sets, indicating many unexplored, but data supported kinase interactions. Our results challenge traditional views on signaling pathways and illustrate how to develop new mechanistic hypotheses using phosphoproteomics and network methods.

systems biology↗

bioRxiv2024

Dynamic multi-omics and mechanistic modeling approach uncovers novel mechanisms of kidney fibrosis progression

Kidney fibrosis, characterized by excessive extracellular matrix deposition, is a progressive disease that, despite affecting 10% of the population, lacks specific treatments and suitable biomarkers. This study presents a comprehensive, time-resolved multi-omics analysis of kidney fibrosis using an in vitro model system based on human kidney PDGFR{beta}+ mesenchymal cells aimed at unraveling disease mechanisms. Using transcriptomics, proteomics, phosphoproteomics, and secretomics we quantified over 14,000 biomolecules across seven time points following TGF-{beta} stimulation. This revealed distinct temporal patterns in the expression and activity of known and potential kidney fibrosis markers and modulators. Data integration resulted in time-resolved multi-omic network models which allowed us to propose mechanisms related to fibrosis progression through early transcriptional reprogramming. Using siRNA knockdowns and phenotypic assays, we validated predictions and regulatory mechanisms underlying kidney fibrosis. In particular, we show that several early-activated transcription factors, including FLI1 and E2F1, act as negative regulators of collagen deposition and propose underlying molecular mechanisms. This work advances our understanding of the pathogenesis of kidney fibrosis and provides a resource to be further leveraged by the community. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=71 SRC="FIGDIR/small/618507v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@d2bfaborg.highwire.dtl.DTLVardef@257d34org.highwire.dtl.DTLVardef@13f06f3org.highwire.dtl.DTLVardef@e16e6c_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

systems biology↗

bioRxiv2023

Deep quantitative glycoproteomics reveals gut microbiome induced remodeling of the brain glycoproteome

HighlightsO_LIHigh throughput glycoproteomics method with multiplexed quantification C_LIO_LI25-fold improvement of the mouse brain glycoproteome coverage C_LIO_LIStructural features dictate level of glycosite micro-heterogeneity C_LIO_LIGut microbiome composition extensively impacts the brain glycoproteome C_LIO_LIModulation of glycosylation is site-specific C_LI Protein glycosylation is a highly diverse post-translational modification, modulating key cellular processes such as cell signaling, adhesion and cell-cell interactions. Its deregulation has been associated with various pathologies, including cancer and neurological diseases. Methods capable of quantifying glycosylation dynamics are essential to start unraveling the biological functions of protein glycosylation. Here we present Deep Quantitative Glycoprofiling (DQGlyco), a method that combines high-throughput sample preparation, high-sensitivity detection, and precise multiplexed quantification of protein glycosylation. We used DQGlyco to profile the mouse brain glycoproteome, in which we identify 158,972 and 15,056 unique N- and O-glycopeptides localized on 3,199 and 2,365 glycoproteins, respectively - this amounts to 25-fold more glycopeptides identified compared to previous studies. We observed extensive heterogeneity of glycoforms and determined their functional and structural preferences. The presence of a defined gut microbiota resulted in extensive remodeling of the brain glycoproteome when compared to that of germ-free animals, exemplifying how the gut microbiome may affect brain protein functions.

molecular biology↗

bioRxiv2023

Integration of Thermal Proteome Profiling with phosphoproteomic and transcriptomic data via mechanistic network models decodes the molecular response to PARP inhibition

The deregulation of complex diseases often spans multiple molecular processes. A multimodal functional characterization of these processes can shed light on the disease mechanisms and the effect of drugs. Thermal Proteome Profiling (TPP) is a mass-spectrometry based technique assessing changes in thermal protein stability that can serve as proxies of functional changes of the proteome. These unique insights of TPP can complement those obtained by other omics technologies. Here, we show how TPP can be integrated with phosphoproteomics and transcriptomics in a network-based approach using COSMOS, a framework for causal integration of multi-omics, to provide an integrated view of transcription factors, kinases and proteins with altered thermal stability. This allowed us to recover known mechanistic consequences of PARP inhibition in ovarian cancer cells on cell cycle and DNA damage response in detail and to uncover new insights into drug response mechanisms related to interferon and hippo signaling. We found that TPP complements the other omics data and allowed us to obtain a network model with higher coverage of the main underlying mechanisms. These results illustrate the added value of TPP, and more generally the power of network models to integrate the information provided by different omics technologies. We anticipate that this strategy can be used to broadly integrate functional proteomics with other omics to study complex molecular processes. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=63 SRC="FIGDIR/small/553354v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@af952eorg.highwire.dtl.DTLVardef@16b1d1borg.highwire.dtl.DTLVardef@1440787org.highwire.dtl.DTLVardef@14a4227_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗