Biology subjects

Kopp, J.

Publications and source records attributed to Kopp, J..

11 recordsLinked to original sources

bioRxiv2026

Small-molecule activators of the Staphylococcus aureus ClpC/ClpP AAA+ protease

Summary/AbstractThe central AAA+ ClpC/ClpP protease in Gram-positive bacteria is crucial for virulence and stress resistance and has been recognized as drug target. Natural cyclic peptides deregulate the essential Mycobacterium tuberculosis ClpC1 and cause cell death. Similarly, overactivated mutants of the non-essential Staphylococcus aureus ClpC homologue cause uncontrolled proteolysis and severe toxicity in vivo. However, no chemical modulators of S. aureus ClpC have been described. Here, using a biochemical high-throughput screen we identify eight chemically distinct bona fide small molecules that robustly stimulate ClpC ATPase and proteolytic activity in vitro. Structural, computational, and mutational analyses define two ligandable regulatory sites within the ClpC N-terminal domain (NTD) as compound targets: a conserved hydrophobic groove and an allosteric pArg1 pocket, both engaged in substrate recognition. These findings establish S. aureus ClpC as chemically targetable and provide mechanistic insight into its regulatory architecture, enabling future development and optimization of chemical probes to deregulate AAA+ protease control.

biochemistry↗

bioRxiv2025

TU_MyCo-Vision: A Deep Learning Tool for Detection of Cell Morphologies in Fungal Microscopic Images

Morphological switching in response to environmental stimuli is a well-known phenomenon in fungi, leading to diverse morphotypes. Microscopic observation remains a widely used approach to study these phenotypes. However, variation in sample preparation and operators skill can limit the scale of sample processing or introduce bias. Although several image-based cell detection tools have been developed, most are tailored to specific applications or limited to a particular taxon. To address the need for a tool applicable to the polymorphic, yeast-like fungus Aureobasidium pullulans, and with potential applicability to other taxa, we developed TU_MyCo-Vision, an Ultralytics YOLO (You Only Look Once) based object detection tool for identifying 13 fungal morphotypes in bright-field microscopic images. The tool integrates a YOLOv11m-based object detector trained on a custom dataset of 1,504 annotated images and a standalone graphical user interface that enables downstream data analysis and visualization of results. The best-performing model (Zulu_s3) achieved a mean precision of 73.4%, a recall of 66.5%, a mean average precision at 50% IoU (mAP@50) of 73.5%, and a mean average precision at varying IoU thresholds between 50 to 90% IoU (mAP@50-95) of 54.5% across all 13 classes. The single-group analysis pipeline was validated on a 90-image test set, generating six quantitative summaries, including absolute counts, relative and mean relative abundance plots, stacked bar plots, and clustered heatmaps. Multi-group evaluation on previously unseen datasets comprising Candida albicans, Komagataella phaffii, and Aspergillus niger spores demonstrated the tools potential applicability to other genera. TU_MyCo-Vision is distributed as a fully packaged, cross-platform executable, eliminating the need for environment setup or manual installation of dependencies. Built entirely on open-source frameworks, it provides a foundational and potentially extensible solution for automated fungal morphology detection and analysis. Author SummaryWe developed TU_MyCo-Vision to address challenges in fungal microscopic imaging. Fungi, such as Aureobasidium pullulans, display a remarkable ability to switch cell shapes (up to thirteen in this species alone) depending on their environment. While microscopy remains a popular method for observing these changes, manual analysis is limited by individual expertise and the number of images that can be processed, often making results subjective and difficult to scale. To overcome these challenges, we built an Ultralytics YOLOv11-based cell detector that can automatically detect and categorize thirteen fungal cell shapes from brightfield microscopic images. We designed TU_MyCo-Vision to be accessible, with a simple graphical user interface, integrated data analysis suite, and distribution as a standalone application for both Windows and macOS, so it can be used even by those with limited computational skills. Our tool demonstrated strong performance, achieving over 73% precision. Importantly, it also worked well on images from other fungal species, showing potential to be further developed as a general fungal cell morphology tool. We hope TU_MyCo-Vision will contribute to making standardized, high-throughput phenotyping of fungi accessible to a broader community.

microbiology↗

bioRxiv2025

Identification and Implications for Tumor Heterogeneity of a DNA Methylation-Based Signature Classifying Pancreatic Ductal Adenocarcinoma Based on their Cellular Origin

BackgroundPancreatic ductal adenocarcinoma (PDAC) arises from distinct cellular origins, yet the extent to which DNA methylation patterns from normal pancreatic cells are preserved in tumor cells remains unclear. Identifying cell-of-origin signatures may enhance PDAC classification and therapeutic stratification. ObjectiveTo determine whether DNA methylation signatures in normal acinar and ductal pancreatic cells are retained in PDAC cell lines and to develop a robust classifier for distinguishing tumor origins. DesignWe performed DNA methylation profiling using the Illumina Infinium Mouse MethylationEPIC array on normal acinar and ductal cells and their PDAC derivatives in genetically engineered mouse models (GEMMs). Differential methylation analysis, and hierarchical clustering were used to identify and validate a conserved cell-of-origin DNA methylation signature. A logistic regression model was developed for classification. ResultsWe identified 178 CpG sites that remain preserved during tumorigenesis and effectively distinguished acinar- and ductal-derived PDAC cell lines. This signature was validated across independent sample sets, primary tumors, and orthotopic allografts. It successfully classified cell lines of unknown origin, including PDAC samples from KPC mice, and revealed the impact of oncogenic mutations on tumor fate. A logistic regression model supported these findings, confirming the robustness of the classification approach. Furthermore, the cell of origin influenced key PDAC characteristics, including treatment response, highlighting its potential role in molecular subtyping and patient stratification. ConclusionA preserved DNA methylation signature during pancreatic carcinogenesis distinguishes PDAC origins and influences tumor behavior. These results highlight the potential of DNA methylation profiling for tumor classification and personalized treatment strategies. They also raise important questions about the relevance of KPC mice as a preclinical model and the mechanisms driving PDAC heterogeneity. What is already known on this topicO_LIHuman PDAC exhibits significant heterogeneity, with molecular subtyping (classical vs basal-like) providing some insights into tumor behavior and clinical outcomes. C_LIO_LIMouse acinar and ductal cells can give rise to PDAC, influencing tumor characteristics and survival outcomes. C_LIO_LIDNA methylation is a powerful tool for tracing cellular identity and distinguishing cancer subtypes based on epigenetic profiles. C_LI What this study addsO_LIA cell-of-origin methylation signature is preserved during mouse carcinogenesis and across diverse experimental settings, providing a reliable tool for tumor classification. C_LIO_LIA DNA methylation-based classification system reliably distinguishes between acinar- and ductal-origin PDAC, filling a critical gap in methods to trace tumor lineage. C_LIO_LIAcinar- and ductal-derived PDACs exhibit distinct methylation patterns that correlate with differences in tumor behavior, such as chemoresistance, highlighting the biological relevance of cellular origin in PDAC. C_LIO_LIThe study provides new insights into how cell-of-origin influences PDAC heterogeneity and could lead to more precise therapeutic strategies tailored to the tumors lineage. C_LI How this study might affect research, practice or policyO_LIThis study provides a new, reliable method for classifying PDAC based on its cellular origin, which could significantly improve tumor classification in both preclinical and clinical settings, aiding in more accurate diagnoses and prognostic predictions. C_LIO_LIThe identification of distinct methylation patterns linked to tumor behavior offers valuable insights for developing personalized treatment strategies, as therapies could be tailored based on the tumors cellular origin and associated molecular characteristics. C_LIO_LIThe preservation of cell-of-origin methylation signature suggests the potential for developing a universal biomarker for PDAC classification, which could guide future clinical trials, therapeutic targeting, and patient stratification in PDAC care. C_LI

cancer biology↗

bioRxiv2025

DNA-encoded Library Screening Uncovers Potent DNMT2 Inhibitors Targeting a Cryptic Allosteric Binding Site

The human RNA methyltransferase DNMT2 is thought to be involved in various pathophysiological processes, yet, a major challenge in drug targeting DNMT2 is given by the fact that current SAH-derived inhibitors have poor target selectivity and limited cellular permeability. In this study, we have performed a DNA-encoded library (DEL) screening on DNMT2 yielding five non-SAH-like hit structures, three of which feature a peptidomimetic scaffold. All DEL hits could be validated by orthogonal biophysical and biochemical assays for DNMT2 binding. At the same time, the lead structure did not interact with related methyltransferases from the DNMT and NSUN families highlighting an unmatched DNMT2-targeting selectivity profile. Subsequent crystallographic studies revealed the unique ligand binding mode including an active site loop rearrangement and the formation of a cryptic allosteric binding pocket able to modulate the enzymatic activity by non-covalent DNMT2 dimerization. Based on the crystallographic results, we performed a structure-activity relationship study around the inhibitor lead structure resulting in an optimized DNMT2 inhibitor (KD=3.04 {micro}M), which was able to reduce m5C levels in MOLM-13 tRNA. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=64 SRC="FIGDIR/small/632061v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@1bf92b3org.highwire.dtl.DTLVardef@38498borg.highwire.dtl.DTLVardef@1645740org.highwire.dtl.DTLVardef@9c61d5_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2024

Lowering the switching cost related to the activation of burdensome gene circuits promotes cell population homogeneity and productivity

The activation of gene circuits can impose a significant burden on cells, leading to heterogeneous expression and reduced productivity. In this work, we focused on the T7 production system in E. coli BL21, a prime example of a burdensome gene circuit, to investigate the main cause for this gene expression heterogeneity and methods to mitigate it. Based on continuous cultivation analyzed and control by automated flow cytometry, we quantified the trade-off between cellular growth and gene expression and tracked the cell-to-cell heterogeneity in gene expression (measured as entropy). We concluded that the growth reduction associated to the activation of the burdensome gene circuit, i.e., the switching cost, is at the origin of the population heterogeneity. The loss of growth rate imposed by the burdensome activation of the gene is compensated at the population level by the overgrowth of less induced cells that safeguard the population by generating entropy. We tried to homogenize the population by pulsing the inducer with increasing frequency but found that the population escapes control through promoter mutation, leading to a genotype exhibiting reduced gene expression, but also, reduced entropy. To engineer a more homogeneous population without sacrificing gene expression, we decreased the switching cost associated to the induction by lowering the quality of the main carbon source. This strategy successfully led to a more homogeneous and productive population. Our approach allows for a precise quantification of the trade-off between growth and gene expression in cell population cultivated under dynamic conditions and highlights the importance of the switching cost for designing efficient approaches of cell population control.

systems biology↗

bioRxiv2024

Optimizing Bioprocessing Efficiency with OptFed: Dynamic Nonlinear Modeling Improves Product-to-Biomass Yield by 19%

Biotechnological production of recombinant molecules relies heavily on fed-batch processes. However, as the cells growth, substrate uptake, and production kinetics are often unclear, the fed-batches are frequently operated under sub-optimal conditions. Process design is based on simple feed profiles (e.g., constant or exponential), operator experience, and basic statistical tools (e.g., response surface methodology), which are unable to harvest the full potential of production. To address this challenge, we propose a general modeling framework, OptFed, which utilizes experimental data from non-optimal fed-batch processes to predict an optimal one. In detail, we assume that cell-specific rates depend on several state variables and their derivatives. Using measurements of bioreactor volume, biomass, and product, we fit the kinetic constants of ordinary differential equations. A regression model avoids overfitting by reducing the number of parameters. Thereafter, OptFed predicts optimal process conditions by solving an optimal control problem using orthogonal collocation and nonlinear programming. In a case study, we apply OptFed to a recombinant protein L fed-batch production process. We determine optimal controls for feed rate and reactor temperature to maximize the product-to-biomass yield and successfully validate our predictions experimentally. Notably, our framework outperforms RSM in both simulation and experiments, capturing an optimum previously missed. We improve the experimental product-to-biomass ratio by 19 % and showcase OptFeds potential for enhancing process optimization in biotechnology.

bioengineering↗

bioRxiv2024

Structure of fungal tRNA ligase with RNA reveals conserved substrate binding principles

RNA ligases play a vital role in RNA processing and maturation including tRNA splicing, RNA repair and the unfolded protein response (UPR). In fungi and plants, the tripartite tRNA ligase Trl1 catalyzes the joining of TSEN-cleaved pre-tRNA exon halves. Trl1 also functions as ligase in the non-conventional HAC1 mRNA splicing during the UPR. The final ligation step is performed by the N-terminal adenylyltransferase domain (LIG). The spatial arrangement of the exon ends during the ligation reaction has remained elusive. Here, we report the crystal structure of Trl1-LIG in complex with a tRNA-derived substrate. Our structure represents a snapshot of the activated RNA intermediate and defines the conserved substrate binding interface. The underlying enzyme-substrate interplay reveals a general substrate binding principle shared by adenylyltransferases. Moreover, we identify the determinants of RNA end specificity as well as the specific roles of Trl1-LIGs subdomains during ligase activation, substrate binding and phosphoryltransfer.

biochemistry↗

bioRxiv2023

Structural and mechanistic insights into activation of the human RNA ligase RTCB by Archease

RNA ligases of the RTCB-type play an essential role in tRNA splicing, the unfolded protein response and RNA repair. RTCB is the catalytic subunit of the pentameric human tRNA ligase complex. RNA ligation by the tRNA-ligase complex requires GTP-dependent activation of RTCB. This active site guanylylation reaction relies on the activation factor Archease. The mechanistic interplay between both proteins has remained unknown. Here, we report a biochemical and structural analysis of the human RTCB-Archease complex in the pre- and post-activation state. Archease reaches into the active site of RTCB and promotes the formation of a covalent RTCB-GMP intermediate through coordination of GTP and metal ions. During the activation reaction, Archease prevents futile RNA substrate binding to RTCB. Moreover, monomer structures of Archease and RTCB reveal additional states within the RNA ligation mechanism. Taken together, we present structural snapshots along the reaction cycle of the tRNA ligase complex.

biochemistry↗

bioRxiv2022

Insulin receptors in pancreatic acinar cells contribute to KrasG12D-driven cancer initiation in the context of diet-induced obesity

The rising pancreatic cancer incidence due to obesity and type 2 diabetes is closely tied to hyperinsulinemia, an independent cancer risk factor. Previous studies demonstrated reducing insulin production suppressed pancreatic intraepithelial neoplasia (PanIN) pre-cancerous lesions in Kras-mutant mice. However, the pathophysiological and molecular mechanisms remained unknown and in particular it was unclear whether hyperinsulinemia affected PanIN precursor cells directly or indirectly. Here, we demonstrate that insulin receptors (Insr) in KrasG12D-expressing pancreatic acinar cells are dispensable for glucose homeostasis, but necessary for hyperinsulinemia-driven PanIN formation in the context of diet-induced hyperinsulinemia and obesity. Mechanistically, this was attributed to amplified digestive enzyme protein translation, triggering of local inflammation and PanIN metaplasia in vivo. In vitro, insulin dose-dependently increased acinar-to-ductal metaplasia formation in a trypsin- and Insr-dependent manner. Collectively, our data shed light on the mechanisms connecting obesity-driven hyperinsulinemia and pancreatic cancer development.

cancer biology↗

bioRxiv2022

Coordination between ECM and cell-cell adhesion regulates the development of islet aggregation, architecture, and functional maturation

Pancreatic islets are 3-dimensional cell aggregates consisting of unique cellular composition, cell-to-cell contacts, and interactions with blood vessels. Cell aggregation is essential for islet endocrine function; however, it remains unclear how developing islets establish aggregation. By combining genetic animal models, imaging tools, and gene expression profiling, we demonstrate that islet aggregation is regulated by extracellular matrix signaling and cell-cell adhesion. Islet endocrine cell-specific inactivation of extracellular matrix receptor Integrin {beta}1 disrupted blood vessel interactions but promoted cell-cell adhesion and the formation of larger islets. In contrast, ablation of cell-cell adhesion molecule -Catenin promoted blood vessel interactions yet compromised islet clustering. Simultaneous removal of Integrin {beta}1 and -Catenin disrupts islet aggregation and the endocrine cell maturation process, demonstrating that establishment of islet aggregates is essential for functional maturation. Our study provides new insights into understanding the fundamental self-organizing mechanism for islet aggregation, architecture, and functional maturation. HIGHLIGHTSO_LIIslet vascularization and aggregation are regulated via ECM-Itgb1 signaling C_LIO_LIECM-Itgb1 signaling negatively controls islet aggregation via regulation of cell-cell adhesion during development C_LIO_LICell-cell adhesion negatively regulates the interaction of endocrine cell-vasculature in islets C_LIO_LIDifferential cell adhesion regulates the establishment of islet architecture C_LIO_LIEndocrine functional maturation depends on islet aggregation regulated by the coordination of ECM-Itgb1 signaling and cell-cell adhesion C_LI

developmental biology↗

bioRxiv2021

Reducing phenotypic and genotypic instabilities of microbial population during continuous cultivation based on stochastic switching dynamics

Predicting the fate of a microbial population (i.e., growth, gene expression...) remains a challenge, especially when this population is exposed to very dynamic environmental conditions, such as those encountered during continuous cultivation. Indeed, the dynamic nature of continuous cultivation process implies the potential deviation of the microbial population involving genotypic and phenotypic diversification. This work has been focused on the induction of the arabinose operon in Escherichia coli as a model system. As a preliminary step, the GFP level triggered by an arabinose-inducible ParaBAD promoter has been tracked by flow cytometry in chemostat with glucose-arabinose co-feeding. For a large range of glucose-arabinose co-feeding, the simultaneous occurrence of GFP positive and negative subpopulation was observed. In a second set of experiments, continuous cultivation was performed by adding either glucose or arabinose, based on the ability of individual cells for switching from low GFP to high GFP states, according to a technology called segregostat. In segregostat mode of cultivation, on-line flow cytometry analysis was used for adjusting the arabinose/glucose transitions based on the phenotypic switching capabilities of the microbial population. This strategy allowed finding an appropriate arabinose pulsing frequency, leading to a prolonged maintenance of the induction level with limited impact on phenotypic diversity for more than 60 generations. This result suggests that constraining individual cells into a given phenotypic trajectory is maybe not the best strategy for directing cell population. Instead, allowing individual cells switching around a predefined threshold seems to be a robust strategy leading to oscillating, but predictable, cell population behavior.

microbiology↗