Biology subjects

Beltman, J. B.

Publications and source records attributed to Beltman, J. B..

6 recordsLinked to original sources

bioRxiv2026

Towards Bayesian-based quantitative adverse outcome pathways using in vitro data from open literature and continuous variables: a case example for liver fibrosis.

As toxicology shifts towards non-animal testing, quantitative models are essential to predict adverse health effects from molecular or cellular perturbations. Quantitative Adverse Outcome Pathways (qAOPs) represent such models, building on mechanistic knowledge and quantifying the Key Event Relationships (KERs) described in AOPs. Despite the recognized need, the number of qAOPs remains limited. Bayesian-based approaches are often chosen for developing qAOP for their flexibility, but most use discretized variables, limiting their predictive power. In addition, these models are mainly built from newly generated data, underexploiting the large amount of information available. This study successfully leverages data from public literature and presents an innovative framework based on continuous variables to develop a Bayesian-based quantitative model for a central KER towards liver fibrosis. The model predicts the probability of the expression fold change for two key markers of hepatic stellate cell activation (aSMA and COL1A1), given the effects on tissue injury, using in vitro data from 9 chemicals. We propose a newly developed workflow to assist in knowledge identification, organization, and extraction from scientific literature and chemical databases. Based on in vitro data and in vivo information from the Open TG-GATEs (Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System) database, we estimate a biologically relevant range in COL1A1 fold change that indicates an activated state of stellate cells and high liver fibrosis odds ratios. Our study provides a case example of integrating published data and continuous variables to build a Bayesian-based model, which constitutes an essential step for predicting liver fibrosis from in vitro data.

pharmacology and toxicology↗

bioRxiv2025

Data-driven dynamic modelling identifies polyploidisation as key process in cell cycle progression upon DNA damage

Chemotherapeutic agents often cause DNA damage in order to kill fast-dividing cancer cells or disrupt their proliferation. Therefore, understanding the interplay between DNA damage and cell cycle progression is highly relevant for understanding cancer cell behaviour. An important regulator is transcription factor p53, primarily known for its function to maintain genomic stability, regulate transient and permanent cell cycle arrest and apoptosis. Activated p53 transcriptionally regulates the expression of many proteins, among which are MDM2, p21 and BTG2. MDM2 functions as a direct inhibitor of p53 by targeting it for ubiquitination. The proteins p21 and BTG2 are known for their regulatory function in G1 and G2 cell cycle arrest. Using HepG2-FUCCI cells, we showed that exposure to cisplatin or etoposide caused a temporary G2 arrest. To study the link between protein expression and cell cycle arrest, we developed a mathematical model in which we integrated a previously established model for the protein expression dynamics of p53, MDM2, p21 and BTG2 with a cell cycle model. This allowed us to determine the importance of p21 and BTG2 in their stimulation of G1 and G2 cell cycle arrest. We found that the protein dynamics could predict the G2 cell cycle arrest in exposed cells, but only in combination with endoreplication, i.e., the alternation of S and G phases without mitosis, resulting in polyploid cells. Our model predicted that the majority of cells endoreplicate upon exposure to high concentrations of cisplatin and most concentrations of etoposide, which we validated with additional time-lapse imaging data in which we could track individual cells. In conclusion, polyploidisation is a generic response of HepG2 cells after treatment with DNA-damaging compounds.

systems biology↗

bioRxiv2022

User-friendly high-content imaging analysis on a single desktop: R package H5CellProfiler

Technological development has led to ever-increasing amounts of data in high-content screening. For utilizing such data in an efficient, thorough, and user-friendly manner we developed the R package H5CellProfiler. H5CellProfiler is based on R packages data.table, ggvis, ggplot2 and shiny. H5CellProfiler launches a browser that allows scientists to analyze large single-cell datasets and make statistical summaries and graphs on their local desktop in a fast and memory-efficient manner. In addition, single-cell track labels are calculated and broken tracks are re-connected based on user-defined thresholds resulting in unique sets of annotated tracks.

bioinformatics↗

bioRxiv2022

Bidirectional crosstalk between epithelial-mesenchymal plasticity and IFNγ-induced PD-L1 expression promotes tumor progression

Epithelial-Mesenchymal Transition (EMT) and immunoevasion through upregulation of Programmed Death-Ligand 1 (PD-L1) are important drivers of cancer progression. While EMT has been proposed to facilitate PD-L1-mediated immunosuppression, the molecular mechanisms of their interaction remain obscure. Here we provide insight into these mechanisms by proposing a mathematical model that describes the crosstalk between EMT and Interferon gamma (IFN{gamma})-induced PD-L1 expression. Our model shows that via interaction with microRNA-200 (miR-200), the multistability of the EMT regulatory circuit is mirrored in the PD-L1 levels, which are further amplified by IFN{gamma} stimulation. This IFN{gamma}-mediated effect is most prominent for cells in a fully mesenchymal state, and less strong for those in an epithelial or partially mesenchymal state. Additionally, bi-directional crosstalk between miR-200 and PD-L1 implies that IFN{gamma} stimulation allows cells to undergo EMT for lower amounts of inducing signal, and that IFN{gamma} presence accelerates EMT and decelerates Mesenchymal-Epithelial Transition (MET). Overall, our model agrees with published findings and provides insight into possible mechanisms behind EMT-mediated immune-evasion; and primary, adaptive, or acquired resistance to immunotherapy. Our model can be used as a starting point to explore additional crosstalk mechanisms, as an improved understanding of these mechanisms is indispensable for developing better diagnostic and therapeutic options for cancer patients. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/478950v1_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@1e5fde0org.highwire.dtl.DTLVardef@7e929dorg.highwire.dtl.DTLVardef@1cb9f6borg.highwire.dtl.DTLVardef@14f50ad_HPS_FORMAT_FIGEXP M_FIG C_FIG Schematic overview of the crosstalk between Epithelial-Mesenchymal Transition (EMT) and Interferon gamma (IFN{gamma})-induced Programmed Death-Ligand 1 (PD-L1) expression. IFN{gamma}-induced PD-L1 expression promotes the occurrence of EMT. EMT, also induced by, for example, Transforming Growth Factor Beta (TGF{beta}), increases PD-L1 expression levels, facilitating tumor immunoevasion.

systems biology↗

bioRxiv2021

Density-dependent migration characteristics of cancer cells driven by pseudopod coordination

The ability of cancer cells to invade neighboring tissue from primary tumors is an important determinant of metastatic behavior. Quantification of cell migration characteristics such as migration speed and persistence helps to understand the requirements for such invasiveness. One factor that may influence invasion is how local tumor cell density shapes cell migration characteristics, which we here investigate with a combined experimental and computational modeling approach. First, we generated and analyzed time-lapse imaging data on two aggressive Triple-Negative Breast Cancer (TNBC) cell lines, HCC38 and Hs578T, during 2D migration assays at various cell densities. HCC38 cells exhibited a counter-intuitive increase in speed and persistence with increasing density, whereas Hs578T did not exhibit such an increase. Moreover, HCC38 cells exhibited strong cluster formation with active pseudopod-driven migration, especially at low densities, whereas Hs578T cells maintained a dispersed positioning. In order to obtain a mechanistic understanding of the density-dependent cell migration characteristics and cluster formation, we developed realistic spatial simulations using a Cellular Potts Model (CPM) with an explicit description of pseudopod dynamics. Model analysis demonstrated that strong coordination between pseudopods within single cells could explain the experimentally observed increase in speed and persistence with increasing density in HCC38 cells. Thus, the density-dependent migratory behavior could be an emergent property of single-cell characteristics without the need for additional mechanisms. This implies that coordination amongst pseudopods may play a role in the aggressive nature of cancers through mediating dispersal.

systems biology↗

bioRxiv2017

Tumor cell division rate heterogeneity explains in vitro clonal dominance

Tumors consist of a hierarchical population of cells that differ in their phenotype and genotype. This hierarchical organization of cells means that a few clones (i.e., cells and several generations of offspring) are abundant while most are rare, which is called clonal dominance. Such dominance also occurred in published in vitro iterated growth and passage experiments with tumor cells in which genetic barcodes were used for lineage tracing. A potential source for such heterogeneity is that dominant clones derive from cancer stem cells with an unlimited self-renewal capacity. Furthermore, ongoing evolution within the growing population may also induce clonal dominance. To understand how clonal dominance developed in the iterated growth and passage experiments, we built a computational model that accurately simulates these experiments. The model simulations reproduced the clonal dominance that developed in in vitro iterated growth and passage experiments when the division rates vary between cells, due to a combination of initial variation and of ongoing mutational processes. In contrast, the experimental results can neither be reproduced with a model that considers random growth and passage, nor with a model based on cancer stem cells. Altogether, our model suggests that in vitro clonal dominance develops due to selection of fast-dividing clones.

bioinformatics↗