Biology subjects

Wolkenhauer, O.

Publications and source records attributed to Wolkenhauer, O..

14 recordsLinked to original sources

bioRxiv2025

Integrative network modeling of colorectal cancer reveals diagnostic signatures and therapeutic targets

Emerging evidence suggests that the interplay between multiple signaling pathways and the immune microenvironment influences tumorigenesis in cancers such as colorectal cancer (CRC). To gain an in-depth understanding of CRC mechanisms and identify novel therapeutic targets, we constructed a molecular interaction map (MIM) integrating key signaling pathways from cancer cells and immune cells within the tumor microenvironment. This map comprises 218 molecules and 328 interactions, curated using PubMed references and official gene symbols. We dynamically simulated the MIM for individual pathways and combinations via stimulus-response and perturbation analyses, calibrating the model with two CRC datasets: GSE1323 (primary tumor-to-metastasis progression) and GSE8671 (normal mucosa-to-adenoma progression), which served as experimental conditions. Simulations revealed distinct disease signatures for GSE1323, including (1) simultaneous activation of TNF/TNFRSF1A,B and EGF/EGFR with inactivation of ERE/ESR, and (2) simultaneous activation of TNF/TNFRSF1A,B and TLR4 with inactivation of ERE/ESR. For GSE8671, the signature was simultaneous activation of TNF/TNFRSF1A,B and TLR4. In silico perturbation analysis identified potent anti-cancer effects from concurrent inhibition of MAPK3 and STAT3 (GSE1323), and ELK1/ATF2 and STAT3 or MAPK14 and STAT3 (GSE8671), significantly reducing epithelial-mesenchymal transition (EMT), proliferation, and inflammation while increasing apoptosis. Disease signatures and therapeutic targets were validated using patient data through Kaplan-Meier survival analysis and machine learning. This integrative model recapitulates cancer biology, predicts biomarkers and therapeutic targets, and is extensible to other immunogenic cancers.

systems biology↗

bioRxiv2024

The integration of network biology and pharmacophore modeling suggests repurposing Clindamycin as an inhibitor of pyroptosis via Caspase-1 blockage in tumor-associated macrophages

BackgroundUveal melanoma (UM) is a highly malignant intraocular tumor with a poor prognosis and response to therapy, including immune checkpoint inhibitors (ICIs), after the onset of liver metastasis. The metastatic microenvironment contains high levels of tumor-associated macrophages (TAMs) that correlate positively with a worse patient prognosis. We hypothesized that one could increase the efficacy of ICIs in UM metastases by immunomodulating UM-associated macrophages. MethodsTo identify potential targets for the immunomodulation, we created a network-based representation of the biology of TAMs and employed (bulk and single-cell) differential gene expression analysis to obtain a regulatory core of UM macrophages-associated genes. We utilized selected targets for pharmacophore-based virtual screening against a library of FDA-approved chemical compounds, followed by refined flexible docking analysis. Finally, we ranked the interactions and selected one novel drug-target combination for in vitro validation. ResultsBased on the generated TAM-specific interaction network (3863 nodes, 9073 edges), we derived a UM macrophages-associated regulatory core (74 nodes, 286 edges). From the regulatory core genes, we selected eight potential targets for pharmacophore-based virtual screening (YBX1, GSTP1, NLRP3, ISG15, MYC, PTGS2, NFKB1, CASP1). Of 266 drug-target interactions screened, we identified the interaction between the antibiotic Clindamycin and Caspase-1 as a priority for experimental validation. Our in vitro validation experiments showed that Clindamycin specifically interferes with activated Caspase-1 and inhibits the secretion of IL-1{beta}, IL-18, and lactate dehydrogenase (LDH) in macrophages after stimulation. Our results suggest that repurposed Clindamycin could reduce pyroptosis in TAMs, a pro-inflammatory form of programmed immune cell death favouring tumor progression. ConclusionWe were able to predict a novel Clindamycin-Caspase-1 interaction that effectively blocks Caspase-1-mediated inflammasome activity and pyroptosis in vitro in macrophages. This interaction is a promising clinical immunomodulator of the tumor microenvironment for improving ICI responsivenss. This work demonstrates the power of combining network-based transcriptomic analysis with pharmacophore-guided screening for de novo drug-target repurposing. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/576201v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@310a9aorg.highwire.dtl.DTLVardef@1af0dcaorg.highwire.dtl.DTLVardef@1b272aborg.highwire.dtl.DTLVardef@86572b_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2022

In silico Investigation of Molecular Networks Linking Gastrointestinal Diseases, Malnutrition, and Sarcopenia

Malnutrition is a common primary or secondary complication in gastrointestinal diseases. The patients nutritional status also influences muscle mass and function, which can be impaired up to the degree of sarcopenia. The molecular interactions in diseases leading to sarcopenia are complex and multifaceted, affecting muscle physiology, the intestine (nutrition), and the liver at different levels. Although extensive knowledge of individual molecular factors is available, their regulatory interplay is not yet fully understood. A comprehensive overall picture of pathological mechanisms and resulting phenotypes is lacking. In silico approaches that convert existing knowledge into computationally readable formats can help to unravel such complex systems. We compiled available experimental evidence for molecular interactions involved in the development of sarcopenia into a knowledge base, referred to as the Sarcopenia Map. By including specific diseases, namely liver cirrhosis, and intestinal dysfunction, and considering their effects on nutritional status and blood secretome, we investigated their contribution to the development of sarcopenia. The Sarcopenia Map is publicly available as an open-source, interactive online resource, providing tools that allow users to explore the information on the map and perform in silico perturbation experiments.

systems biology↗

bioRxiv2021

Network- and Enrichment-based Inference of Phenotypes and Targets from large-scale Disease Maps

Disease maps have emerged as computational knowledge bases for exploring and modeling diseasespecific molecular processes. By capturing molecular interactions, disease-associated processes, and phenotypes in standardized representations, disease maps provide a platform for applying bioinformatics and systems biology approaches. Applications range from simple map exploration to algorithm-driven target discovery and network perturbation. The web-based MINERVA environment for disease maps provides a platform to develop tools not only for mapping experimental data but also to identify, analyze and simulate disease-specific regulatory networks. We have developed a MINERVA plugin suite based on network topology and enrichment analyses that facilitate multi-omics data integration and enable in silico perturbation experiments on disease maps. We demonstrate workflows by analyzing two RNA-seq datasets on the Atlas of Inflammation Resolution (AIR). Our approach improves usability and increases the functionality of disease maps by providing easy access to available data and integration of selfgenerated data. It supports efficient and intuitive analysis of omics data, with a focus on disease maps.

systems biology↗

bioRxiv2021

AMES: Automated evaluation of sarcomere structures in cardiomyocytes

BackgroundArrhythmias are severe cardiac diseases and lethal if untreated. To serve as an in vitro drug testing option for anti-arrhythmic agents, cardiomyocytes are being generated in vitro from induced pluripotent stem cells (iPSCs). Unfortunately, these generated cardiomyocytes resemble fetal cardiac tissue rather than adult cardiomyocytes. An automated tool for an unbiased evaluation of cardiomyocytes would highly facilitate the establishment of new differentiation protocols to increase cellular maturity. ResultsIn this work, a novel deep learning-based approach for this task is presented and evaluated. Different convolutional neural networks (CNNs) including 2D and 3D models were trained on fluorescence images of human iPSC-derived cardiomyocytes, which were rated based on their sarcomere content (sarcomerisation) and the orientation of sarcomere filaments (directionality) beforehand by a domain expert. The CNNs were trained to perform classifications on sarcomerisation, directionality ratings, and cell source, including primary adult and differentiated cardiomyocytes. The best accuracies are reached by a 3D model with a classification accuracy of about 90 % for sarcomerisation classification, 63 % for directionality classification, and 80 % for cell source classification. The trained models were additionally evaluated using two explanatory algorithms, IGrad and Grad-CAM. The heatmaps computed by those explainability algorithms show that the important regions in the image occur inside the cell and at the cellular borders for the classifier, and, therefore, validate the calculated regions. ConclusionIn summary, we showed that cellular fluorescence images can be analyzed with CNNs and subsequently used to predict different states of sarcomere maturation. Our developed prediction tool AMES (https://github.com/maxhillemanns/AMES) can be used to make trustworthy predictions on the quality of a cardiomyocyte, which ultimately facilitates the optimized generation of cardiomyocytes from iPSCs and improves the quality control in an automated, unbiased manner. The applied workflow of testing different CNN models, adjusting parameters, and using a variety of explanatory algorithms can be easily transferred to further image based quality control, stratification, or analysis setups.

bioinformatics↗

bioRxiv2021

Automated annotation of rare-cell types from single-cell RNA-sequencing data through synthetic oversampling

The research landscape of single-cell and single-nuclei RNA sequencing is evolving rapidly, and one area that is enabled by this technology, is the detection of rare cells. An automated, unbiased and accurate annotation of rare subpopulations is challenging. Once rare cells are identified in one dataset, it will usually be necessary to generate other datasets to enrich the analysis (e.g., with samples from other tissues). From a machine learning perspective, the challenge arises from the fact that rare cell subpopulations constitute an imbalanced classification problem. We here introduce a Machine Learning (ML)-based oversampling method that uses gene expression counts of already identified rare cells as an input to generate synthetic cells to then identify similar (rare) cells in other publicly available experiments. We utilize single-cell synthetic oversampling (sc-SynO), which is based on the Localized Random Affine Shadowsampling (LoRAS) algorithm. The algorithm corrects for the overall imbalance ratio of the minority and majority class. We demonstrate the effectiveness of the method for two independent use cases, each consisting of two published datasets. The first use case identifies cardiac glial cells in snRNA-Seq data (17 nuclei out of 8,635). This use case was designed to take a larger imbalance ratio ([~]1 to 500) into account and only uses single-nuclei data. The second use case was designed to jointly use snRNA-Seq data and scRNA-Seq on a lower imbalance ratio ([~]1 to 26) for the training step to likewise investigate the potential of the algorithm to consider both single cell capture procedures and the impact of "less" rare-cell types. For validation purposes, all datasets have also been analyzed in a traditional manner using common data analysis approaches, such as the Seurat3 workflow. Our algorithm identifies rare-cell populations with a high accuracy and low false positive detection rate. A striking benefit of our algorithm is that it can be readily implemented in other and existing workflows. The code basis is publicly available at FairdomHub (https://fairdomhub.org/assays/1368) and can easily be transferred to train other customized approaches.

bioinformatics↗

bioRxiv2021

Somatic mutations in CTCF zinc fingers produce cellular phenotypes explained by structure-function relationships

BackgroundHuman cancers commonly contain mutations in transcription factors that lead to aberrant DNA binding or altered effector function at target sites. One such factor significantly mutated in cancer is the evolutionarily-conserved CCCTC-binding factor (CTCF), which has fundamental roles in maintaining chromatin architecture and transcriptional regulation. Numerous cancer genome sequencing and functional studies have revealed CTCFs role as a haploinsufficient tumour suppressor gene. However, to date, structure-function relationships of somatic CTCF mutations have not been examined. MethodsWe collated somatic CTCF mutations from cancer genome portals and published studies to determine their nature, frequency, distribution and potential functional impact. We undertook an in-depth examination of 5 CTCF missense zinc finger (ZF) mutations occurring within key intra- and inter-ZF residues. We performed functional analyses including cell growth, colony-formation, chromatin immunoprecipitation and transcriptional reporter assays. Based on their homology, each ZF mutation was then modelled on CTCFs ZF domain crystal structure and its structural impact analysed using molecular dynamics simulations. ResultsWe observed an enrichment of somatic missense mutations occurring in the ZF region of CTCF, compared to the unstructured N- and C-termini. Functional characterisation of CTCF ZF mutations revealed a complete (L309P, R339W, R377H) or intermediate (R339Q) abrogation as well as an enhancement (G420D) of the anti-proliferative effects of CTCF. DNA binding at select sites was disrupted and transcriptional regulatory activities abrogated. In silico mutagenesis revealed that L309P had the highest mutation energy and thus most severe impact on protein stability. Molecular docking and molecular dynamics simulations confirmed that mutations in residues specifically contacting DNA bases or backbone exhibited loss of DNA binding (R339Q, R339W, R377H). Remarkably, R339Q and G420D were stabilised by the formation of new primary DNA bonds. All mutations exhibited some loss or gain of bonds at neighbouring residues, often in adjacent zinc fingers. ConclusionsOur data confirm the significant negative impact haploinsufficient CTCF ZF mutations have on its tumour suppressor function. A spectrum of loss-, change- and gain-of-function impacts in CTCF zinc fingers are observed in cell growth regulation and gene regulatory activities. We have established that diverse cellular phenotypes in CTCF are explained by examining structure-function relationships.

cancer biology↗

bioRxiv2020

Omics integration analyses reveal the early evolution of malignancy in breast cancer

BackgroundThe majority of cancer evolution studies are done on individual-based approaches that neglect population dynamics necessity for the global picture of cancer evolution in each cancer type. Here, we conducted a population-based study in breast cancer to understand the timing of malignancy evolution and its correlation to the genetic evolution of pathological stages. ResultsIn an omics integrative approach, we integrated gene expression and genomic aberration data for pre-invasive (DCIS, early-stage) and post-invasive (IDC, late-stage) samples and investigated the evolutionary role of further genetic changes in late stages compared to the early ones. We found that single genetic alterations (SGAs) and copy number alterations (CNAs) conspire together for the fine-tuning of the operating signaling pathways of tumors in forward and backward evolution manners. The forward evolution applies to new genetic changes that boost the efficiency of selected signaling pathways. The backward evolution, which we detected for CNAs, is a mean to reverse unwanted SGAs of earlier stages. Analyses of the integrated point mutation and gene expression data show that (i) our proposed fine-tuning concept is also applicable in metastasis, and (ii) metastasis diverges from primary tumor sometimes at the DCIS stage. ConclusionsOur results indicate that malignant potency of breast tumors is constant over pre and post invasive pathological stages. Indeed, further genetic alterations in later stages do not establish de novo malignancy routes; however, they serve to fine-tune antecedent signaling pathways.

evolutionary biology↗

bioRxiv2020

The Atlas of Inflammation-Resolution (AIR)

Acute inflammation is a protective reaction by the immune system in response to invading pathogens or tissue damage. Ideally, the response should be localized, self-limited, and returning to homeostasis. If not resolved, acute inflammation can result in organ pathologies leading to chronic inflammatory phenotypes. Acute inflammation and inflammation resolution are complex coordinated processes, involving a number of cell types, interacting in space and time. The biomolecular complexity and the fact that several biomedical fields are involved, make a multi and interdisciplinary approach necessary. This Atlas of Inflammation Resolution (AIR) is a web-based resource capturing the state-of-the-art in acute inflammation and inflammation resolution research. The AIR provides an interface for users to search thousands of interactions, arranged in inter-connected multi-layers of process diagrams, covering a wide range of clinically relevant phenotypes. The AIR serves as an open access knowledgebase, including a gateway to numerous public databases. It is furthermore possible for the user to map experimental data onto the molecular interaction maps of the AIR, providing the basis for bioinformatics analyses and systems biology approaches. By mapping experimental data onto the Atlas, it can be used to elucidate drug action as well as molecular mechanisms underlying different disease phenotypes. For the visualization and exploration of information, the AIR uses the Minerva platform, which is a well-established tool for the presentation of disease maps. The molecular details of the AIR are encoded using international standards. The Atlas of Inflammation Resolution was created as a freely accessible resource, supporting research and education in the fields of acute inflammation and inflammation resolution. The AIR connects research communities, facilitates clinical decision making, and supports research scientists in the formulation and validation of hypotheses.

immunology↗

bioRxiv2019

GEMtractor: Extracting Views into Genome-scale Metabolic Models

SummaryComputational metabolic models typically encode for graphs of species, reactions, and enzymes. Comparing genome-scale models through topological analysis of multipartite graphs is challenging. However, in many practical cases it is not necessary to compare the full networks. The GEMtractor is a web-based tool to trim models encoded in SBML. It can be used to extract subnetworks, for example focusing on reaction- and enzyme-centric views into the model.\n\nAvailability and ImplementationThe GEMtractor is licensed under the terms of GPLv3 and developed at github.com/binfalse/GEMtractor - a public version is available at sbi.uni-rostock.de/gemtractor.\n\nContactmartin.scharm@uni-rostock.de and ali.salehzadeh-yazdi@uni-rostock.de

systems biology↗

bioRxiv2018

Pathway maps enable straightforward yet customized and semi-automated yet insightful analyses of omics data

To explore the molecular processes underlying some biological theme of interest based on public data, gene lists are used herein as input for the construction of annotated pathway maps, employing Cytoscape apps, and then high-throughput (\"omics\") gene expression data are overlaid onto these maps. Seeded with a published set of marker genes of the senescence-associated secretory phenotype and the genes of the cellular senescence KEGG pathway, a gene/protein interaction network and annotated clusters (a \"pathway map\") of cellular senescence are derived. The map can be amended, by adding some application-specific genes, and overlaid with gene expression data describing cellular senescence of fibroblasts and with disease-related gene expression data associated with prostate and pancreatic cancer, and with ischemic stroke, allowing insights into the role of cellular senescence in disease. Some gene expression data are derived from the \"Biomarker Benchmark repository\". The pathway map approach can be followed in principle for any biological theme of interest, fostering much-needed independence from the investigator-biased expert networks usually used for overlaying gene expression data.

genomics↗

bioRxiv2018

Label-free assessment of red blood cell storage lesions by deep learning

Blood transfusion is a life-saving clinical procedure. With millions of units needed globally each year, it is a growing concern to improve product quality and recipient outcomes.\n\nStored red blood cells (RBCs) undergo continuous degradation, leading to structural and biochemical changes. To analyze RBC storage lesions, complex biochemical and biophysical assays are often employed.\n\nWe demonstrate that label-free imaging flow cytometry and deep learning can characterize RBC morphologies during 42-day storage, replacing the current practice of manually quantifying a blood smear from stored blood units. Based only on bright field and dark field images, our model achieved 90% accuracy in classifying six different RBC morphologies associated with storage lesions versus human-curated manual examination. A model fitted to the deep learning-extracted features revealed a pattern of morphological changes within the aging blood unit that allowed predicting the expiration date of stored blood using solely morphological assessment.\n\nDeep learning and label-free imaging flow cytometry could therefore be applied to reduce complex laboratory procedures and facilitate robust and objective characterization of blood samples.

bioinformatics↗

bioRxiv2018

Harmonizing semantic annotations for computational models in biology

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition, and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current semantic annotation practices among the COmputational Modeling in BIology NEtwork (COMBINE) community and provide a set of recommendations for building a consensus approach to semantic annotation.

scientific communication and education↗

bioRxiv2017

Revisiting the General Concept of Network Centralities: A Propose for Centrality Analysis in Network Science

BackgroundNumerous centrality measures have been introduced to identify \"central\" nodes in large networks. The availability of a wide range of measures for ranking influential nodes leaves the user to decide which measure may best suit the analysis of a given network. The choice of a suitable measure is furthermore complicated by the impact of the network topology on ranking influential nodes by centrality measures. To approach this problem systematically, we examined the centrality profile of nodes of yeast protein-protein interaction networks (PPINs) in order to detect which centrality measure is succeeding in predicting influential proteins. We studied how different topological network features are reflected in a large set of commonly used centrality measures.\n\nResultsWe used yeast PPINs to compare 27 common of centrality measures. The measures characterize and assort influential nodes of the networks. We applied principal component analysis (PCA) and hierarchical clustering and found that the most informative measures depend on the networks topology. Interestingly, some measures had a high level of contribution in comparison to others in all PPINs, namely Latora closeness, Decay, Lin, Freeman closeness, Diffusion, Residual closeness and Average distance centralities.\n\nConclusionsThe choice of a suitable set of centrality measures is crucial for inferring important functional properties of a network. We concluded that undertaking data reduction using unsupervised machine learning methods helps to choose appropriate variables (centrality measures). Hence, we proposed identifying the contribution proportions of the centrality measures with PCA as a prerequisite step of network analysis before inferring functional consequences, e.g., essentiality of a node.

systems biology↗