Biology subjects

von Stillfried, S.

Publications and source records attributed to von Stillfried, S..

5 recordsLinked to original sources

bioRxiv2025

The role of Platelet-derived growth factor (PDGF) in uremic vascular calcification.

Vascular calcification is common in chronic kidney disease (CKD), contributing to increased cardiovascular morbidity and mortality. One of the proposed mechanisms of driving vascular calcification is a phenotypic switch of vascular smooth muscle cells (VSMCs). The platelet-derived growth factors (PDGFs) and their receptors (PDGFRs), particularly PDGFR-{beta}, were shown to modulate the VSMC phenotype. However, their role in uremic vascular calcification remained unclear. We adapted an ex vivo calcification model using murine aortas to simulate uremic conditions. Compared to control conditions, incubation with hemodialysate from CKD patients or using aortas from CKD animals both resulted in significantly increased PDGFR-{beta} phosphorylation and vascular calcification. Inhibition of PDGF signaling using soluble PDGFR-{beta} or the small molecule tyrosine kinase inhibitor imatinib significantly reduced uremic calcification and enhanced vascular elasticity. Next, we generated transgenic mice with a VSMC-specific, inducible expression of constitutively active PDGFR-{beta}. The aortas of these mice exhibited significantly increased vascular calcification ex vivo, which was further aggravated by uremic conditions. We established an in vivo model of accelerated vascular calcification and CKD in the transgenic mice, showing significantly aggravated vascular calcification and phenotypic switching of VSMCs compared to non-transgenic littermates. Finally, increased expression of phosphorylated PDGFR-{beta} and a VSMC phenotypic switching were detected in human arteries from patients with CKD compared to those without CKD. In conclusion, PDGFR-{beta} contributes to CKD-associated vascular calcification, representing a potential novel therapeutic target.

systems biology↗

bioRxiv2024

Rewired type I IFN signaling is linked to age-dependent differences in COVID-19

Advanced age is the most important risk factor for severe disease or death from COVID-19, but a thorough mechanistic understanding of the molecular and cellular underpinnings is lacking. Multi-omics analysis of samples from SARS-CoV-2 infected persons aged 1 to 84 years, revealed a rewiring of type I interferon (IFN) signaling with a gradual shift from signal transducer and activator of transcription 1 (STAT1) to STAT3 activation in monocytes, CD4+ T cells and B cells with increasing age. Diversion of interferon IFN signaling was associated with increased expression of inflammatory markers, enhanced release of inflammatory cytokines, and delayed contraction of infection-induced CD4+ T cells. A shift from IFN-responsive germinal center B (GCB) cells towards CD69high GCB and atypical B cells corresponded to the formation of IgA in children while complement fixing IgG was dominant in adults. Our data provide a mechanistic basis for inflammation-prone responses to infections and associated pathology during aging. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=144 SRC="FIGDIR/small/619479v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@723896org.highwire.dtl.DTLVardef@e12b2aorg.highwire.dtl.DTLVardef@d8de22org.highwire.dtl.DTLVardef@1df96ec_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2022

Plasmacytoid dendritic cells regulate megakaryocyte and platelet homeostasis

Platelet homeostasis is essential for vascular integrity and immune defense. While the process of platelet formation by fragmenting megakaryocytes (thrombopoiesis) has been extensively studied, the cellular and molecular mechanisms required to constantly replenish the pool of megakaryocytes by their progenitor cells (megakaryopoiesis) remains unclear. Here we use intravital 2 photon microscopy to track individual megakaryopoiesis over days. We identify plasmacytoid dendritic cells (pDCs) as crucial bone marrow niche cells that regulate megakaryopoiesis. pDCs monitor the bone marrow for platelet-producing megakaryocytes and deliver IFN- to the megakaryocytic niche to trigger local on-demand proliferation of megakaryocyte progenitors. This fine-tuned coordination between thrombopoiesis and megakaryopoiesis is crucial for megakaryocyte and platelet homeostasis in steady state and stress. However, uncontrolled pDC function within the megakaryocytic niche is detrimental. Accordingly, we show that pDCs activated by SARS-CoV2 drive inappropriate megakaryopoiesis associated with thrombotic complications. Together, we uncover a hitherto unknown megakaryocytic bone marrow niche maintained by the constitutive delivery of pDC-derived IFN-.

immunology↗

bioRxiv2019

Deep learning detects virus presence in cancer histology

Oncogenic viruses like human papilloma virus (HPV) or Epstein Barr virus (EBV) are a major cause of human cancer. Viral oncogenesis has a direct impact on treatment decisions because virus-associated tumors can demand a lower intensity of chemotherapy and radiation or can be more susceptible to immune check-point inhibition. However, molecular tests for HPV and EBV are not ubiquitously available.\n\nWe hypothesized that the histopathological features of virus-driven and non-virus driven cancers are sufficiently different to be detectable by artificial intelligence (AI) through deep learning-based analysis of images from routine hematoxylin and eosin (HE) stained slides. We show that deep transfer learning can predict presence of HPV in head and neck cancer with a patient-level 3-fold cross validated area-under-the-curve (AUC) of 0.89 [0.82; 0.94]. The same workflow was used for Epstein-Barr virus (EBV) driven gastric cancer achieving a cross-validated AUC of 0.80 [0.70; 0.92] and a similar performance in external validation sets. Reverse-engineering our deep neural networks, we show that the key morphological features can be made understandable to humans.\n\nThis workflow could enable a fast and low-cost method to identify virus-induced cancer in clinical trials or clinical routine. At the same time, our approach for feature visualization allows pathologists to look into the black box of deep learning, enabling them to check the plausibility of computer-based image classification.

cancer biology↗

bioRxiv2017

Detection of small breast tumors using tumor penetrating-polymersomes engineered to target p32 protein

Triple negative breast cancer (TNBC) is the deadliest form of breast cancer and its successful treatment critically depends on early diagnosis and therapy. The multi-compartment protein p32 is overexpressed and present at cell surfaces in a variety of tumors, including TNBC, specifically in the malignant cells and endothelial cells, and in macrophages localized in hypoxic areas of the tumor. Herein we used polyethylene glycol-polycaprolactone polymersomes that were affinity targeted with the p32-binding tumor penetrating peptide LinTT1 (AKRGARSTA) for imaging of TNBC lesions. A tyrosine residue was added to the peptide to allow for 124I labeling and PET imaging. In a TNBC model in mice, systemic LinTT1-targeted polymersomes accumulated in early tumor lesions more than twice as efficiently as untargeted polymersomes with up to 20% ID/cc at 24 h after administration. The PET-imaging was very sensitive, allowing detection of tumors as small as [~]20mm3. Confocal imaging of tumor tissue sections revealed a high degree of vascular exit and stromal penetration of LinTT1-targeted polymersomes and co-localization with tumor-associated macrophages. Our studies show that systemic LinTT1-targeted polymersomes can be potentially used for precision-guided tumor imaging and treatment of TNBC.

cancer biology↗