Biology subjects

Berking, C.

Publications and source records attributed to Berking, C..

3 recordsLinked to original sources

bioRxiv2024

SOX10 and microRNAs: decoding their interplay in regulating melanoma plasticity

Recent studies show that the dysregulation of the transcription factor SOX10 is essential for development and progression of melanoma. MicroRNAs (miRNAs) can regulate the expression of transcription factors at the post-transcriptional level. The interactions between SOX10 and its targeting miRNAs form network motifs such as feedforward and feedback loops. Such motifs can result in non-linear dynamics in gene expression levels, therefore playing a crucial role in regulating tumor proliferation and metastasis as well as tumors responses to therapies. Here, we reviewed and discussed the intricate interplay between SOX10 and miRNAs in melanoma biology including melanogenesis, phenotype switch, and therapy resistance. Additionally, we investigated the gene regulatory interactions in melanoma, identifying crucial network motifs that involve both SOX10 and miRNAs. We also analyzed the expression levels of the components within these motifs. From a control theory perspective, we explained how these dynamics are linked to the phenotypic plasticity of melanoma cells. In summary, we underscored the importance of employing a data-driven network biology approach to elucidate the complex regulatory mechanisms and identify driver network motifs within the melanoma network. This methodology facilitates a deeper understanding of the regulation of SOX10 by miRNAs in melanoma. The insight gained could potentially contribute to the development of miRNA-based treatments for SOX10, thereby enhancing the clinical management of this malignancy.

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↗

bioRxiv2023

Integration of transcriptomics data into agent-based models of solid tumor metastasis

Most of the recent progress in our understanding of cancer relies in the systematic profiling of patient samples with high throughput techniques like transcriptomics. This approach has helped in finding gene signatures and networks underlying cancer aggressiveness and therapy resistance. However, -omics data alone is not sufficient to generate insights into the spatiotemporal aspects of tumor progression. Here, multi-level computational models are promising approaches, which would benefit from the possibility to integrate in their characterization the data and knowledge generated by the high throughput profiling of patient samples. We present a computational workflow to integrate transcriptomics data from tumor patients into hybrid, multi-scale models of cancer. In the method, we employ transcriptomics analysis to select key differentially regulated pathways in therapy responders and non-responders and link them to agent-based model parameters. We next utilize global and local sensitivity together with systematic model simulations to assess the relevance of variations in the selected parameters in triggering cancer progression and therapy resistance. We illustrate the methodology with a de novo generated agent-based model accounting for the interplay between tumor and immune cells in melanoma micrometastasis. Application of the workflow identifies three different scenarios of therapy resistance.

systems biology↗