Biology subjects

Bluthgen, N.

Publications and source records attributed to Bluthgen, N..

At least 19 recordsLinked to original sources

bioRxiv2025

The Establishment of Cell-Type Specific Gene Regulation in the Sea Urchin Embryo

Cell-fate commitment in metazoan development relies on precise gene regulatory programs. This study presents a comprehensive single-cell atlas of gene expression (scRNA-seq), nascent transcription (scSLAM-seq), and chromatin accessibility (scATAC-seq) in the purple sea urchin, Strongylocentrotus purpuratus, from early cleavage to pluteus larva stages. Our findings reveal a dynamic regulatory landscape with extensive usage of distal and intronic regulatory elements, which often exhibit cell-type-specific motifs and accessibility profiles that closely track gene expression. We identify a major wave of zygotic genome activation (ZGA) at the 128-cell stage, coinciding with the loss of developmental plasticity, alongside evidence of restricted, lineage-specific gene activation preceding widespread ZGA. Motif analysis highlights distinct regulatory grammars for these early accessible regions. Regulatory element usage largely clusters by germ layer, indicating shared accessibility among related cell types. We delve into the regulatory intricacies of neurons and skeletogenic cells. Sea urchin neurodevelopment proceeds through three distinct lineages, utilizing transcription factors with conserved roles in mammalian neurogenesis. Surprisingly, skeletogenic cells show significant transcriptional and regulatory diversity across their subpopulations, and we identify novel genes associated with calcification. This research offers unprecedented insights into the dynamic regulatory genome of a non-chordate deuterostome, highlighting both conserved principles of gene regulation and unique features that underscore the sea urchins importance as a model for understanding developmental and evolutionary genomics in ecologically critical marine species.

developmental biology↗

bioRxiv2024

Reporter-based screening identifies RAS-RAF stabilizing mutations as drivers of resistance to broad-spectrum RAS inhibition in colorectal cancer

Therapy-induced acquired resistance limits the clinical effectiveness of mutation-specific RAS inhibitors in colorectal cancer. It is unknown whether broad-spectrum active-state RAS inhibitors meet similar limitations. Here, we identify and categorize mechanisms of resistance to the broad-spectrum active-state RAS inhibitor RMC-7977 in colorectal cancer cell lines. We found that KRAS-mutant colorectal cancer cell lines are universally sensitive to RMC-7977, inhibiting the RAS-RAF-MEK-ERK axis, halting proliferation and in some cases inducing apoptosis. To monitor KRAS downstream effector pathway activity, we developed a compartment-specific dual-color ERK activity reporter. RMC-7977 treatment reduced reporter activity. However, long-term dose escalation with RMC-7977 revealed multiple patterns of reporter reactivation in emerging resistant cell populations that correlated with phosphorylation states of compartment-specific ERK targets. Cells sorted for high, low, or cytoplasmic reporter activity exhibited distinct patterns of genomic mutations, phospho-protein, and transcriptional activities. Notably, all resistant subpopulations showed dynamic ERK regulation in the presence of the RAS inhibitor, unlike the parental sensitive cell lines. High levels of RAS downstream activities were observed in cells characterized by a KRAS Y71H resistance mutation. In contrast, RAS inhibitor-resistant populations with low, or cytoplasmic ERK reporter reactivation displayed different genetic alterations, among them RAF1 S257L and S259P mutations. Colorectal cancer cells resistant to RMC-7977 and harboring the RAF1 mutation specifically exhibited synergistic sensitivity to concurrent RAS and RAF inhibition. Our findings endorse reporter-assisted screening together with single-cell analyses as a powerful approach for dissecting the complex landscape of therapy resistance. The strategy offers opportunities to develop clinically relevant combinatorial treatments to counteract emergence of resistant cancer cells.

cancer biology↗

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

High-confidence calling of normal epithelial cells allows identification of a novel stem-like cell state in the colorectal cancer microenvironment

Single-cell analyses can be confounded by assigning unrelated groups of cells to common developmental trajectories. For instance, cancer cells and admixed normal epithelial cells could potentially adopt similar cell states thus complicating analyses of their developmental potential. Here, we develop and benchmark CCISM (for Cancer Cell Identification using Somatic Mutations) to exploit genomic single nucleotide variants for the disambiguation of cancer cells from genomically normal non-cancer epithelial cells in single-cell data. In colorectal cancer datasets, we find that our method and others based on gene expression or allelic imbalances identify overlapping sets of cancer versus normal epithelial cells, depending on molecular characteristics of individual cancers. Further, we define consensus cell identities of normal and cancer epithelial cells with higher transcriptome cluster homogeneity than those derived using existing tools. Using the consensus identities, we identify significant shifts of cell state distributions in genomically normal epithelial cells developing in the cancer microenvironment, with immature states increased at the expense of terminal differentiation throughout the colon, and a novel stem-like cell state arising in the left colon. Trajectory analyses show that the new cell state extends the pseudo-time range of normal colon stem-like cells in a cancer context. We identify cancer-associated fibroblasts as sources of WNT and BMP ligands potentially contributing to increased plasticity of stem cells in the cancer microenvironment. Our analyses advocate careful interpretation of cell heterogeneity and plasticity in the cancer context and the consideration of genomic information in addition to gene expression data when possible. Novelty and ImpactSingle-cell analyses have become standard to assess cell heterogeneity and developmental hierarchies in cancer tissues. However, these datasets are complex and contain cancer and non-cancer lineage cells. Here, we develop and systematically benchmark tools to distinguish between cancer and non-cancer single-cell transcriptomes, based on gene expression or different levels of genomic information. We provide strategies to combine results of different tools into consensus calls tailored to the biology and genetic characteristics of the individual cancer.

cancer biology↗

bioRxiv2022

scPerturb: Information Resource for Harmonized Single-Cell Perturbation Data

Recent biotechnological advances led to growing numbers of single-cell perturbation studies, which reveal molecular and phenotypic responses to large numbers of perturbations. However, analysis across diverse datasets is typically hampered by differences in format, naming conventions, and data filtering. In order to facilitate development and benchmarking of computational methods in systems biology, we collect a set of 44 publicly available single-cell perturbation-response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform pre-processing and quality control pipelines and harmonize feature annotations. The resulting information resource enables efficient development and testing of computational analysis methods, and facilitates direct comparison and integration across datasets. In addition, we introduce E-statistics for perturbation effect quantification and significance testing, and demonstrate E-distance as a general distance measure for single cell data. Using these datasets, we illustrate the application of E-statistics for quantifying perturbation similarity and efficacy. The data and a package for computing E-statistics is publicly available at scperturb.org. This work provides an information resource and guide for researchers working with single-cell perturbation data, highlights conceptual considerations for new experiments, and makes concrete recommendations for optimal cell counts and read depth.

systems biology↗

bioRxiv2022

Oncogenic signalling is coupled to colorectal cancer cell differentiation state

Colorectal cancer progression is intrinsically linked to stepwise deregulation of the intestinal differentiation trajectory. In this process, sequential mutations of APC/Wnt, KRAS, TP53 and SMAD4 stepwisely enable an oncogenic signalling network. Here, we developed a novel mass cytometry antibody panel to analyse colorectal cancer cell differentiation and signalling in human isogenic colorectal cancer organoids and in patient-derived cultures. We define a differentiation axis following EphrinB2 abundance in all tumour progression states from normal to cancer. We show that during colorectal cancer progression, oncogenes decrease dependence on external factors and shape distribution of cells along the differentiation axis. In this regard, subsequent mutations can have stem cell-promoting or restricting effects. Individual nodes of the signalling network remain coupled to the differentiation state, regardless of the presence of oncogenic signals. Our work underscores the key role of cell plasticity as a hallmark of cancer that is gradually unlocked during colorectal cancer progression.

cancer biology↗

bioRxiv2021

A 3D gene expression atlas of the floral meristem based on spatial reconstruction of single nucleus RNA sequencing data

Identity and functions of plant cells are influenced by their precise cellular location within the plant body. Cellular heterogeneity in growth and differentiation trajectories results in organ patterning. Therefore, assessing this heterogeneity at molecular scale is a major question in developmental biology. Single-cell transcriptomics (scRNA-seq) allows to characterize and quantify gene expression heterogeneity in developing organs at unprecedented resolution. However, the original physical location of the cell is lost during the scRNA-seq procedure. To recover the original location of cells is essential to link gene activity with cellular function and morphology. Here, we reconstruct genome-wide gene expression patterns of individual cells in a floral meristem by combining single-nuclei RNA-seq with 3D spatial reconstruction. By this, gene expression differences among meristematic domains giving rise to different tissue and organ types can be determined. As a proof of principle, the data are used to trace the initiation of vascular identity within the floral meristem. Our work demonstrates the power of spatially reconstructed single cell transcriptome atlases to understand plant morphogenesis. The floral meristem 3D gene expression atlas can be accessed at http://threed-flower-meristem.herokuapp.com

plant 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↗

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

Identifiability and experimental design in perturbation studies

MotivationA common strategy to infer and quantify interactions between components of a biological system is to deduce them from the networks response to targeted perturbations. Such perturbation experiments are often challenging and costly. Therefore, optimising the experimental design is essential to achieve a meaningful characterisation of biological networks. However, it remains difficult to predict which combination of perturbations allows to infer specific interaction strengths in a given network topology. Yet, such a description of identifiability is necessary to select perturbations that maximize the number of inferable parameters. ResultsWe show analytically that the identifiability of network parameters can be determined by an intuitive maximum flow problem. Furthermore, we used the theory of matroids to describe identifiability relationships between sets of parameters in order to build identifiable effective network models. Collectively, these results allowed to device strategies for an optimal design of the perturbation experiments. We benchmarked these strategies on a database of human pathways. Remarkably, full network identifiability was achieved with on average less than a third of the perturbations that are needed in a random experimental design. Moreover, we determined perturbation combinations that additionally decreased experimental effort compared to single-target perturbations. In summary, we provide a framework that allows to infer a maximal number of interaction strengths with a minimal number of perturbation experiments. AvailabilityIdentiFlow is available at github.com/GrossTor/IdentiFlow. Contactnils.bluethgen@charite.de

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↗

bioRxiv2019

Impact of herbivore preference on the benefit of plant trait variability

We explore the hypothesis that intraspecific trait variability can be per se beneficial for the plant when the curvature of the herbivore response to this trait is concave downwards. This hypothesis is based on a mathematical relation for non-linear averaging (Jensens inequality), leading to reduced herbivory when the trait distribution becomes broader. Our study introduces and investigates a model for plants and their insect herbivores that includes an unequal distribution of nutrient content between leaves. In contrast to earlier publications, we take into account the ability of herbivores to choose leaves, and the associated costs. By performing computer simulations and analytic calculations, we find that this herbivore preference can considerably alter the conclusion cited above. In particular, we demonstrate that herbivore populations that show preference for leaves on which they grow well can benefit from large nutrient level variability independently of the curvature of the herbivore response function, and despite the cost for preference.

ecology↗

bioRxiv2019

Robust network inference using response logic

MotivationA major challenge in molecular and cellular biology is to map out the regulatory networks of cells. As regulatory interactions can typically not be directly observed experimentally, various computational methods have been proposed to disentangling direct and indirect effects. Most of these rely on assumptions that are rarely met or cannot be adapted to a given context. ResultsWe present a network inference method that is based on a simple response logic with minimal presumptions. It requires that we can experimentally observe whether or not some of the systems components respond to perturbations of some other components, and then identifies the directed networks that most accurately account for the observed propagation of the signal. To cope with the intractable number of possible networks, we developed a logic programming approach that can infer networks of hundreds of nodes, while being robust to noisy, heterogeneous or missing data. This allows to directly integrate prior network knowledge and additional constraints such as sparsity. We systematically benchmark our method on KEGG pathways, and show that it outperforms existing approaches in DREAM3 and DREAM4-challenges. Applied to a perturbation data set on PI3K and MAPK pathways in isogenic models of a colon cancer cell line, it generates plausible network hypotheses that explain distinct sensitivities towards EGFR inhibitors by different PI3K mutants. Availability and ImplementationA Python/Answer Set Programming implementation can be accessed at github.com/GrossTor/response-logic. Contactnils.bluethgen@charite.de

systems biology↗

bioRxiv2018

When more is less: Dual phosphorylation protects signaling off-state against overexpression

Kinases in signaling pathways are commonly activated by multisite phosphorylation. For example, the mitogen-activated protein kinase Erk is activated by its kinase Mek by two consecutive phosphorylations within its activation loop. In this article, we use kinetic models to study how the activation of Erk is coupled to its abundance. Intuitively, Erk activity should rise with increasing amounts of Erk protein. However, a mathematical model shows that the signaling off-state is robust to increasing amounts of Erk, and Erk activity may even decline with increasing amounts of Erk. This counter-intuitive, bell-shaped response of Erk activity to increasing amounts of Erk arises from the competition of the unmodified and single phosphorylated form of Erk for access to its kinase Mek. This shows that phosphorylation cycles can contain an intrinsic robustness mechanism that protects signaling from aberrant activation e.g. by gene expression noise or kinase overexpression following gene duplication events in diseases like cancer.

biophysics↗

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↗

bioRxiv2017

Noise control is a primary function of microRNAs and post-transcriptional regulation

microRNAs are pervasive post-transcriptional regulators of protein-coding genes in multicellular organisms. Two fundamentally different models have been proposed for the function of microRNAs in gene regulation. In the first model, microRNAs act as repressors, reducing protein concentrations by accelerating mRNA decay and inhibiting translation. In the second model, in contrast, the role of microRNAs is not to reduce protein concentrations per se but to reduce fluctuations in these concentrations. Here we present genome-wide evidence that mammalian microRNAs frequently function as noise controllers rather than repressors. Moreover, we show that post-transcriptional noise control has been widely adopted across species from bacteria to animals, with microRNAs specifically employed to reduce noise in regulatory and context-specific processes in animals. Our results substantiate the detrimental nature of expression noise, reveal a universal strategy to control it, and suggest that microRNAs represent an evolutionary innovation for adaptive noise control in animals.\n\nHighlightsO_LIGenome-wide evidence that microRNAs function as noise controllers for genes with context-specific functions\nC_LIO_LIPost-transcriptional noise control is universal from bacteria to animals\nC_LIO_LIAnimals have evolved noise control for regulatory and context-specific processes\nC_LI

genetics↗