Biology subjects

Sieber, A.

Publications and source records attributed to Sieber, A..

13 recordsLinked to original sources

bioRxiv2026

Reprogramming a Protein Ligase for Genetic Code Expansion

The ribosomes DNA-encoded production of defined polymer sequences is naturally limited to 22 amino acids. Although the translation machinery has the latent capacity to polymerize backbone-modified substrates, including {beta}-amino acids, this potential is constrained by the intrinsic -selectivity of native aminoacyl-tRNA synthetases. Here, we address this limitation by "reverse engineering" the Escherichia coli protein ligase EpmA. Naturally activating (R)-{beta}-lysine, EpmA evolved to discard its tRNA-binding domain in favor of protein recognition. By grafting the anticodon-binding domain of the canonical lysyl-tRNA synthetase, LysRS, onto EpmA, we created the chimeric enzyme chEpmA. To our knowledge, this represents the first successful reprogramming of a protein ligase into a functional aminoacyl-tRNA synthetase. We demonstrate that chEpmA serves as a versatile dual-specificity platform: it efficiently charges tRNAs with the non-canonical backbone (R)-{beta}-lysine, and a single substitution unlocks the scaffold for -substrates, thereby enabling a broad spectrum of post-translational modifications previously inaccessible to genetic code expansion. This repertoire ranges from acylated lysines such as N{varepsilon}-succinyl-(S)- lysine (Ksucc) and bulky modifications such as biocytin to advanced glycation end products (AGEs) including N{varepsilon}-carboxymethyl-(S)- lysine (CML). Our work establishes a structural blueprint for mobilizing non-canonical substrates, paving the way for the biosynthesis of protease-resistant peptidomimetics and next-generation therapeutics.

synthetic biology↗

bioRxiv2025

Perturb-seq identifies TCF7 as a central nexus linking MAPK- and Wnt-driven gene expression

The MAPK pathway is a central signaling cascade whose dysregulation contributes to numerous diseases. While its upstream regulation is well studied, the mechanisms by which MAPK activation leads to diverse transcriptional outcomes remain incompletely understood. To address this shortcoming, we mapped the target gene sets controlled by 22 RAF-inducible transcription factors using targeted Perturb-seq and integrated these data with time-resolved transcriptional profiling. Network reconstruction revealed a topology dominated by two central hubs, EGR1 and FOS, which co-regulate partially overlapping target gene sets. In addition, we uncovered a positive feedback loop between EGR1, a canonical RAF-MAPK effector, and TCF7, a transcription factor typically linked to Wnt signaling. Through this interaction, TCF7 emerges as a nexus that integrates MAPK and Wnt pathway inputs. Together, these findings define the architecture of RAF-MAPK-driven transcriptional regulation and demonstrate how cross-talk between oncogenic signaling pathways can be encoded in transcriptional networks. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/694273v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@53d6d5org.highwire.dtl.DTLVardef@3d6b5borg.highwire.dtl.DTLVardef@5333e6org.highwire.dtl.DTLVardef@74519e_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2024

RUCova: Removal of Unwanted Covariance in mass cytometry data

High dimensional mass cytometry is confounded by unwanted covariance due to variations in cell size and staining efficiency, making analysis and interpretation challenging. We present RUCova, a novel method designed to address confounding factors in mass cytometry data. RUCova removes unwanted covariance using multivariate linear regression on Surrogates of Unwanted Covariance (SUCs), and Principal Component Analysis (PCA). We exemplify the use of RUCova and show that it effectively removes unwanted covariance while preserving genuine biological signals. Our results demonstrate the efficacy of RUCova in elucidating complex data patterns, facilitating the identification of activated signalling pathways, and improving the classification of important cell populations. By providing a robust framework for data normalization and interpretation, RUCova enhances the accuracy and reliability of mass cytometry analyses, contributing to advancements in our understanding of cellular biology and disease mechanisms. The R package is available on https://github.com/molsysbio/RUCova. Detailed documentation, data, and the code required to reproduce the results are available on https://doi.org/10.5281/zenodo.10913464. Supplementary material: Available at bioRxiv.

bioinformatics↗

bioRxiv2024

Spike-in enhanced phosphoproteomics uncovers synergistic signaling responses to MEK inhibition in colon cancer cells

Targeted kinase inhibitors are a cornerstone of cancer therapy, but their success is often hindered by the complexity of cellular signaling networks that can lead to resistance. Overcoming this challenge necessitates a deep understanding of cellular signaling responses. While standard global phosphoproteomics offers extensive insights, lengthy processing times, the complexity of data interpretation, and frequent omission of crucial phosphorylation sites limit its utility. Here, we combine data-independent acquisition (DIA) with spike-in of synthetic heavy stable isotope-labeled phosphopeptides to facilitate the targeted detection of particularly informative phosphorylation sites. Our spike-in enhanced detection in DIA (SPIED-DIA) approach integrates the improved sensitivity of spike-in-based targeted detection with the discovery potential of global phosphoproteomics into a simple workflow. We employed this method to investigate synergistic signaling responses in colorectal cancer cell lines following MEK inhibition. Our findings highlight that combining MEK inhibition with growth factor stimulation synergistically activates JNK signaling in HCT116 cells. This synergy emphasizes the therapeutic potential of concurrently targeting MEK and JNK pathways, as evidenced by the significantly impaired growth of HCT116 cells when treated with both inhibitors. Our results demonstrate that SPIED-DIA effectively identifies synergistic signaling responses in colorectal cancer cells, presenting a valuable tool for uncovering new therapeutic targets and strategies in cancer treatment.

systems biology↗

bioRxiv2024

Quantitative modelling of signaling in aggressive B cell lymphoma unveils conserved core network

B cell receptor (BCR) signaling is required for the survival and maturation of B cells and is deregulated in B cell lymphomas. While proximal BCR signaling is well studied, little is known about the crosstalk of downstream effector pathways, and a comprehensive quantitative network analysis of BCR signaling is missing. Here, we semi-quantitatively modelled BCR signaling in Burkitt lymphoma (BL) cells using systematically perturbed phosphorylation data of BL-2 and BL-41 cells. The models unveiled feedback and crosstalk structures in the BCR signaling network, including a negative crosstalk from p38 to MEK/ERK. The relevance of the crosstalk was verified for BCR and CD40 signaling in different BL cells and confirmed by global phosphoproteomics on ERK itself and known ERK target sites. Compared to the starting network, the trained network for BL-2 cells was better transferable to BL-41 cells. Moreover, the BL-2 network was also suited to model BCR signaling in Diffuse large B cell lymphoma cells lines with aberrant BCR signaling (HBL-1, OCI-LY3), indicating that BCR aberration does not cause a major downstream rewiring.

systems biology↗

bioRxiv2024

Targeted Perturb-seq Reveals EGR1 and FOS as Key Regulators of the Transcriptional RAF-MAPK Response

The MAPK pathway is an important cellular signaling cascade whose dysregulation causes a variety of diseases. While the upstream regulators of this cascade have been extensively characterized, the understanding of how its activation translates into different transcriptional responses remains poorly understood. This study attempts to fill this knowledge gap by using targeted Perturb-seq against 22 transcription factors in an inducible model system for RAF-MAPK signaling. A topology-based modeling approach is applied to the obtained data to construct a directional interaction network. By removing coherent feed-forward loops and integrating the expression kinetics of transcription factors, a parsimonious network structure is derived that distinguishes direct from indirect interactions between the investigated transcription factors and their targets. In particular, EGR1 and FOS are found to act as orthogonal upstream regulators of the RAF-MAPK response. The results presented here provide valuable insights into the organization of the transcriptional network downstream of RAF-MAPK signaling and thus provide a basis for a better understanding of this complex process.

systems biology↗

bioRxiv2021

Neuroblastoma signalling models unveil combination therapies targeting feedback-mediated resistance

Very high risk neuroblastoma is characterised by increased MAPK signalling, and targeting MAPK signalling is a promising therapeutic strategy. We used a deeply characterised panel of neuroblastoma cell lines and found that the sensitivity to MEK inhibitors varied drastically between these cell lines. By generating quantitative perturbation data and mathematical modelling, we determined potential resistance mechanisms. We found that negative feedbacks within MAPK signalling and to the IGF receptor mediate re-activation of MAPK signalling upon treatment in resistant cell lines. By using cell-line specific models, we predict that combinations of MEK inhibitors with RAF or IGFR inhibitors can overcome resistance, and tested these predictions experimentally. In addition, phospo-proteomics profiles confirm the cell-specific feedback effects and synergy of MEK and IGFR targeted treatements. Our study shows that a quantitative understanding of signalling and feedback mechanisms facilitated by models can help to develop and optimise therapeutic strategies, and our findings should be considered for the planning of future clinical trials introducing MEKi in the treatment of neuroblastoma.

cancer biology↗

bioRxiv2021

A Bioinformatic and Empiric Exploration of Prokaryotic Argonautes as Novel Programmable Endonuclease Systems

Argonautes are nucleases that can be programmed by short oligonucleotides to cleave complementary sequences. Here, we performed an unbiased bioinformatic search to mine bacterial genomes for prokaryotic Argonautes (pAgos) harboring a PIWI domain. Our search identified 3,033 pAgos in total, of which 1,464 portend to the subgroup of long pAgos with more than 600 amino acids. We purified a subset of 49 pAgos which were found in proximity to helicases and tested their nuclease activity in vitro. Ten of these were active towards single-stranded DNA substrates and this activity could be programmed by exogenous guide DNAs or RNAs. Cleavage of double-stranded plasmid DNA was much less readily observed and was fostered by elevated temperatures or exogenous addition of a DNA single-strand binding protein (ET-SSB). The efficiency of pAgo-mediated plasmid cleavage was dependent on the DNA target sequence as well as the surrounding sequence, suggesting that unwinding of the DNA double helix was a limiting factor. Intriguingly, we identified a cluster of pAgos from the Clostridial clade which was active at 37{degrees}C and activity was enhanced by exogenous ET-SSB. This suggests that Clostridial pAgos may be particularly suited to catalyze DNA double-strand cleavage and implies that such pAgos may be repurposed as gene editing tools in future.

molecular biology↗

bioRxiv2020

Identifying selective drug combinations using Comparative Network Reconstruction

Inhibition of aberrant signaling with target inhibitors is an important treatment strategy in cancer, but unfortunately responses are often short-lived. Multi-drug combinations have the potential to mitigate this, but to avoid toxicity such combinations must be selective and the dosage of the individual drugs should be as low as possible. Since the search space of multi-drug combinations is enormous, an efficient approach to identify the most promising drug combinations and dosages is needed. Here, we present a pipeline to prioritize promising multi-drug combinations. We performed a limited set of drug perturbations in an isogenic cell line pair with and without an activating PI3K mutation, and recorded their signaling states and cell viability. We used these data to reconstruct mutant specific signaling networks and map the short term signaling response to longer term changes in cell viability. The resulting models then allowed us to predict the effect of unseen multi-drug combinations, at arbitrary drug-concentrations, on cell viability. Our initial aim was to find combinations that selectively reduce the viability of the PI3K mutant cells, but our models indicated that such combinations do not exist for this cell line pair. However, we were able to validate 25 of the 30 low-dose multi-drug combinations that we predicted to be anti-selective. Our pipeline thus enables a powerful strategy to rapidly map the efficacy and possible selectivity of drug combinations, hence significantly speeding up the pace at which we can explore the vast space of combination therapies.

systems biology↗

bioRxiv2020

A census of cell types and paracrine interactions in colorectal cancer

In colorectal cancer, oncogenic mutations transform a hierarchically organized and homeostatic epithelium into invasive cancer tissue lacking visible organization. We sought to define colorectal cancer cell types and signals controlling their development. More than 30,000 epithelial single cell transcriptomes of tumors and matched non-cancerous tissues of twelve colorectal cancer patients were clustered into six patient-overarching groups defined by differential activities of oncogenic signaling pathways such as mitogen-activated protein kinase and oncogenic traits such as replication stress. RNA metabolic labeling and assessment of RNA velocity in patient-derived organoids revealed developmental trajectories of colorectal cancer cells organized along a mitogen-activated protein kinase activity gradient. This was in contrast to normal colon organoid cells developing along graded Wnt activity. Experimental targeting of EGFR-BRAF-MEK in cancer organoids affected signaling and gene expression contingent on predictive KRAS/BRAF mutations and induced cell plasticity overriding default developmental trajectories, providing a basis for non-genetic resistance to targeted therapies.

cancer biology↗

bioRxiv2018

Modelling Signalling Networks from Perturbation Data

MotivationIntracellular signalling is realized by complex signalling networks which are almost impossible to understand without network models, especially if feedbacks are involved. Modular Response Analysis (MRA) is a convenient modelling method to study signalling networks in various contexts.\n\nResultsWe developed a derivative of MRA that is suited to model signalling networks from incomplete perturbation schemes and multi-perturbation data. We applied the method to study the effect of SHP2, a protein that has been implicated in resistance to targeted therapy in colon cancer, using data from a knock out and parental colon cancer cell line. We find that SHP2 is required for MAPK signalling, whereas AKT signalling only partially depends on SHP2.\n\nAvailabilityAn R-package is available at https://github.com/MathurinD/STASNet\n\nContactnils.bluethgen@charite.de

bioinformatics↗

bioRxiv2018

Comparative Network Reconstruction using Mixed Integer Programming

New anti-cancer drugs that specifically target oncogenes involved in signalling show great clinical promise. However, the effectiveness of such targeted treatments is often hampered by innate or acquired resistance due to feedbacks, crosstalks or network adaptations in response to drug treatment. Addressing this problem requires an understanding of these networks and how they differ between cells with different oncogenic mutations or between sensitive and resistant cells. Here, we present Comparative Network Reconstruction (CNR), a computational method to reconstruct signaling networks based on incomplete perturbation data, and to identify which edges differ quantitatively between two or more signalling networks. Prior knowledge about network topology is not required but can straightforwardly be incorporated. We extensively tested our approach using simulated data and applied it to perturbation data from a BRAF mutant cell line that developed resistance to BRAF inhibition. Comparing the reconstructed networks of sensitive and resistant cells suggests that the resistance mechanism involves re-establishing wildtype MAPK signaling, possibly through an alternative RAF-isoform.

systems biology↗

bioRxiv2016

Dissecting cancer resistance to therapies with cell-type-specific dynamic logic models

Therapies targeting specific molecular processes, in particular kinases, are major strategies to treat cancer. Genomic features are commonly used as biomarkers for drug sensitivity, but our ability to stratify patients based on these features is still limited. As response to kinase inhibitors is a dynamic process affecting largely signal transduction, we investigated the association between cell-specific dynamic signaling pathways and drug sensitivity. We measured 14 phosphoproteins under 43 different perturbed conditions (combination of 5 stimuli and 7 inhibitors) for 14 colorectal cancer cell-lines, and built cell-line-specific dynamic logic models of the underlying signaling network. Model parameters, representing pathway dynamics, were used as features to predict sensitivity to a panel of 27 drugs. This analysis revealed associations between cell-specific signaling pathways and drug sensitivity for 14 of the drugs, 9 of which have no genomic biomarker. Following one of these associations, we validated a drug combination predicted to overcome resistance to MEK inhibitors by co-blockade of GSK3. These results underscore the value of perturbation-based studies to find biomarkers and combination therapies complementing those based on a static genomic characterization.

cancer biology↗