Biology subjects

Bantscheff, M.

Publications and source records attributed to Bantscheff, M..

11 recordsLinked to original sources

bioRxiv2024

Pharmacoproteomic profiling identifies secreted markers for aberrant drug action

Adverse drug reactions (ADRs) contribute significantly to late-stage attrition in drug discovery due to their unpredictability and enigmatic underlying mechanisms. Here we applied mass spectrometry-based proteomics to assess the effects of 46 approved or retracted drugs with various levels of concerns for drug-induced liver injury and annotated for mitochondrial mechanisms, along with 8 tool compounds, on the secretome of a hepatocyte liver model. We observed distinct clusters of non-canonical secretion, and intracellular thermal proteome profiling linked dysregulated mechanisms to extracellular markers. Functional follow-up confirmed lysosomal alterations by cationic-amphiphilic drugs, connected damage of the respiratory chain to Rab7-dependent secretion of mitochondrial proteins, and linked drug-induced endoplasmic reticulum stress to reduced basal secretion. Perturbation of sphingolipid biosynthesis pathways specifically induced secretion of the cargo sorting protein SDF4 whilst suppressing secretion of its cargo proteins. Thermal stability changes of clusters of membrane proteins in distinct subcellular compartments suggest local accumulation as important driver for unexpected drug effects through direct and indirect interactions.

pharmacology and toxicology↗

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↗

bioRxiv2022

PeptideForest: Semisupervised machine learning integrating multiple search engines for peptide identification

The first step in bottom-up proteomics is the assignment of measured fragmentation mass spectra to peptide sequences, also known as peptide spectrum matches. In recent years novel algorithms have pushed the assignment to new heights, unfortunately, different algorithms come with different strengths and weaknesses and choosing the appropriate algorithm poses a challenge for the user. Here we introduce PeptideForest, a semi-supervised machine learning approach that integrates the assignments of multiple algorithms to train a random forest classifier to elevate that issue. Additionally, PeptideForest increases the number of peptide-to-spectrum matches that exhibit a q-value lower than 1% by 25.2 {+/-} 1.6% compared to MS-GF+ data on samples containing mixed HEK and E. coli proteomes. However, an increase in quantity does not necessarily reflect an increase in quality and this is why we devised a novel approach to determine the quality of the assigned spectra through TMT quantification of samples with known ground truths. Thereby, we could show that the increase in PSMs below 1% q-value does not come with a decrease in quantification quality and as such PeptideForest offers a possibility to gain deeper insights into bottom-up proteomics. PeptideForest has been integrated into our pipeline framework Ursgal and can therefore be combined with a wide array of algorithms.

bioinformatics↗

bioRxiv2021

Patient-derived gene and protein expression signatures of NGLY1 deficiency

N-Glycanase 1 (NGLY1) deficiency is a rare and complex genetic disorder. Although recent studies have shed light on the molecular underpinnings of NGLY1 deficiency, a systematic characterization of gene and protein expression changes in patient-derived cells has been lacking. Here, we performed RNA-sequencing and mass spectrometry to determine the transcriptomes and proteomes of 66 cell lines representing 4 different cell types derived from 14 NGLY1 deficient patients and 17 controls. While gene and protein expression levels agreed well with each other, expression differences were more pronounced at the protein level. Although NGLY1 protein levels were up to 9.5-fold downregulated in patients compared to parent controls, depending on the genotype, NGLY1 protein was still detectable in all patient-derived lymphoblastoid cell lines. Consistent with the role of NGLY1 as a regulator of the transcription factor Nrf1, we observed a cell type-independent downregulation of proteasomal genes in NGLY1 deficient cells. In contrast, genes involved in ribosomal mRNA processing were upregulated in multiple cell types. In addition, we observed cell type-specific effects. For example, genes and proteins involved in glutathione synthesis, such as the glutamate-cystein ligase subunits GCLC and GCLM, were downregulated specifically in lymphoblastoid cells. We provide a web application that enables access to all results generated in this study at https://apps.embl.de/ngly1browser. This resource will guide future studies of NGLY1 deficiency in directions that are most relevant to patients.

systems biology↗

bioRxiv2020

A Bayesian semi-parametric model for thermal proteome profiling

The thermal stability of proteins can be altered when they interact with small molecules, other biomolecules or are subject to post-translation modifications. Thus monitoring the thermal stability of proteins under various cellular perturbations can provide insights into protein function, as well as potentially determine drug targets and off-targets. Thermal proteome profiling is a highly multiplexed mass-spectrommetry method for monitoring the melting behaviour of thousands of proteins in a single experiment. In essence, thermal proteome profiling assumes that proteins denature upon heating and hence become insoluble. Thus, by tracking the relative solubility of proteins at sequentially increasing temperatures, one can report on the thermal stability of a protein. Standard thermodynamics predicts a sigmoidal relationship between temperature and relative solubility and this is the basis of current robust statistical procedures. However, current methods do not model deviations from this behaviour and they do not quantify uncertainty in the melting profiles. To overcome these challenges, we propose the application of Bayesian functional data analysis tools which allow complex temperature-solubility behaviours. Our methods have improved sensitivity over the state-of-the art, identify new drug-protein associations and have less restrictive assumptions than current approaches. Our methods allows for comprehensive analysis of proteins that deviate from the predicted sigmoid behaviour and we uncover potentially biphasic phenomena with a series of published datasets.

systems biology↗

bioRxiv2020

A multipass membrane protein interacts with the cGMP-dependent protein kinase to regulate critical calcium signals in malaria parasites

In malaria parasites, all cGMP-dependent signalling is mediated through a single cGMP-dependent protein kinase (PKG), a major function of which is to control essential calcium signals. However, how PKG transmits these signals in the absence of known second messenger-dependent calcium channels or scaffolding proteins is unknown. Here we identify a polytopic membrane protein, ICM1, with homology to transporters and calcium channels that is tightly-associated with PKG in both Plasmodium falciparum asexual blood stages and P. berghei gametocytes. Phosphoproteomic analyses in both Plasmodium species reveal multiple ICM1 phosphorylation events dependent upon PKG activity. Stage-specific depletion of P. berghei ICM1 blocks gametogenesis due to the inability of mutant parasites to mobilise intracellular calcium upon PKG activation, whilst conditional loss of P. falciparum ICM1 results in reduced calcium mobilisation, defective egress and lack of invasion. Our findings provide new insights into atypical calcium homeostasis in malaria parasites essential for pathology and disease transmission.

microbiology↗

bioRxiv2020

Computational analysis of ligand dose range thermal proteome profiles

Detecting ligand-protein interactions in living cells is a fundamental challenge in molecular biology and drug research. Proteome-wide profiling of thermal stability as a function of ligand concentration promises to tackle this challenge. We present a statistical analysis method with reliable control of the false discovery rate and apply it to several datasets probing epigenetic drugs. We detect off-target drug engagement in unrelated protein families.

bioinformatics↗

bioRxiv2019

Biological Plasticity Rescues Target Activity in CRISPR Knockouts

Gene knockouts (KOs) are efficiently engineered through CRISPR-Cas9-induced frameshift mutations. While DNA editing efficiency is readily verified by DNA sequencing, a systematic understanding of the efficiency of protein elimination has been lacking. Here, we devised an experimental strategy combining RNA-seq and triple-stage mass spectrometry (SPS-MS3) to characterize 193 genetically verified deletions targeting 136 distinct genes generated by CRISPR-induced frameshifts in HAP1 cells. We observed residual protein expression for about one third of the quantified targets, at variable levels from low to original, and identified two causal mechanisms, translation reinitiation leading to N-terminally truncated target proteins, or skipping of the edited exon leading to protein isoforms with internal sequence deletions. Detailed analysis of three truncated targets, BRD4, DNMT1 and NGLY1, revealed partial preservation of protein function. Our results imply that systematic characterization of residual protein expression or function in CRISPR-Cas9 generated KO lines is necessary for phenotype interpretation.

bioengineering↗

bioRxiv2018

Proteome thermal stability reflects organ physiology and identifies drug-target engagement in vivo

Studying biological processes at a molecular level and monitoring drug-target interactions is established for simple cell systems but challenging in vivo. We introduce and apply a methodology for proteome-wide thermal stability measurements to characterize organ physiology and activity of many fundamental biological processes across tissues, such as energy metabolism and protein homeostasis. This method, termed tissue thermal proteome profiling (tissue-TPP), also enabled target and off-target identification and occupancy measurements in tissues derived from animals dosed with the non-covalent histone deacetylase inhibitor, panobinostat. Finally, we devised blood-CETSA, a thermal stability-based method to monitor target engagement in whole blood. Our study generates the first proteome-wide map of protein thermal stability in tissue and provides tools that will be of great impact for translational research.

cell biology↗

bioRxiv2018

Proteome-wide solubility and thermal stability profiling reveals distinct regulatory roles for ATP

Nucleotide triphosphates (NTPs) regulate numerous biochemical processes in cells as (co-)substrates, allosteric modulators, biosynthetic precursors, and signaling molecules1-3. Apart from its roles as energy source fueling cellular biochemistry, adenosine triphosphate (ATP), the most abundant NTP in cells, has been reported to affect macromolecular assemblies, such as protein complexes4,5 and membrane-less organelles6-8. Moreover, both ATP and guanosine triphosphate (GTP) have recently been shown to dissolve protein aggregates9. However, system-wide studies to characterize NTP-interactions under conditions approximating the native cellular environment are lacking, which limits our perspective of the diverse physiological roles of NTPs. Here, we have mapped and quantified proteome-wide NTP-interactions by assessing thermal stability and solubility of proteins using mechanically disrupted cells. Our results reveal diverse biological roles of ATP depending on its concentration. We found that ATP specifically interacts with proteins that utilize it as substrate or allosteric modulator at doses lower than 500 M, while it affects protein-protein interactions of protein complexes at mildly higher concentrations (between 1-2 mM). At high concentrations (> 2 mM), ATP modulates the solubility state of a quarter of the insoluble proteome, consisting of positively charged, intrinsically disordered, nucleic acid binding proteins, which are part of membrane-less organelles. The extent of solubilization depends on the localization of proteins to different membrane-less organelles. Furthermore, we uncover that ATP regulates protein-DNA interactions of the Barrier to autointegration factor (BANF1). Our data provides the first quantitative proteome-wide map of ATP affecting protein structure and protein complex stability and solubility, providing unique clues on its role in protein phase transitions.

systems biology↗

bioRxiv2018

Non-Parametric Analysis of Thermal Proteome Profiles Reveals Novel Drug-Binding Proteins

Detecting the targets of drugs and other molecules in intact cellular contexts is a major objective in drug discovery and in biology more broadly. Thermal proteome profiling (TPP) pursues this aim at proteome-wide scale by inferring target engagement from its effects on temperature-dependent protein denaturation. However, a key challenge of TPP is the statistical analysis of the measured melting curves with controlled false discovery rates at high proteome coverage and detection power. We present non-parametric analysis of response curves (NPARC), a statistical method for TPP based on functional data analysis and nonlinear regression. We evaluate NPARC on five independent TPP datasets and observe that it is able to detect subtle changes in any region of the melting curves, reliably detects the known targets, and outperforms a melting point-centric, single-parameter fitting approach in terms of specificity and sensitivity. NPARC can be combined with established analysis of variance (ANOVA) statistics and enables flexible, factorial experimental designs and replication levels. To facilitate access to a wide range of users, a freely available software implementation of NPARC is provided.

bioinformatics↗