Biology subjects

Steinheuer, L. M.

Publications and source records attributed to Steinheuer, L. M..

4 recordsLinked to original sources

bioRxiv2026

Mechanical stress-induced release of mitochondrial double-stranded RNA activates RIG-I to drive osteogenic differentiation in aortic disease

Background: Gain-of-function mutations in RIG-I and MDA5 (RIG-I-like receptors, RLR) cause Singleton-Merten syndrome, an interferonopathy with early aortic calcification. Here we set out to study a possible involvement of RIG-I-like receptor signaling in the formation of aortic valve stenosis in general. Methods and results: We investigated whether mechanical stress-induced RLR activation promotes osteogenic differentiation and calcification of primary human aortic cells, whether such RLR activation drives the development of aortic valve stenosis in vivo, and whether inhibition of downstream JAK/STAT activation ameliorates aortic valve stenosis. We found that activation of primary human aortic interstitial cells by exogenous RIG-I- and MDA5 agonists induced a type I interferon response and an osteogenic program including increased expression of BMP-2 and BSP2, and promoted calcification. These effects were RIG-I/MDA5 or MAVS-dependent, as assessed by CRISPR/Cas9 knockouts. The stress-granule protein G3BP1 and the Wnt receptor FZD5 were induced downstream of RLR activation and were required for osteogenic gene expression and calcification. To assess a potential endogenous source of RIG-I activation, mechanical stretch mimicking valvular tissue stress was applied in cell cuture, which induced a significant RIG-I-dependent interferon response. Sequencing of highly purified preparations of double-stranded RNA from stretched cells demonstrated a predominant mitochondrial origin of double-stranded RNA, and depleting mitochondrial RNA abolished the stretch-dependent response. The JAK/STAT inhibitor ruxolitinib suppressed the RIG-I-induced response including the expression of osteogenic factors in vitro. In a murine wire-injury model of aortic stenosis, MAVS deficiency attenuated valvular morphological changes and improved hemodynamic parameters. Ruxolitinib attenuated hemodynamic progression, prevented aortic stenosis development, and improved survival. Conclusions: Altogether, our results demonstrate that the mechanical-stress-induced release of mitochondrial double-stranded RNA activates RIG-I signaling in aortic interstitial cells, thereby driving osteogenic differentiation and calcification via a MAVS- and G3BP1-dependent pathway. JAK/STAT inhibition with ruxolitinib mitigates this process in a mouse model of aortic valve stenosis, identifying a candidate therapeutic strategy.

cell biology↗

bioRxiv2024

Type I interferon drives a cellular state inert to TCR-stimulation and could impede effective T-cell differentiation in cancer

Head and neck squamous cell carcinoma (HNSCC) arises from the mucosal epithelium of the oral cavity, pharynx, or larynx and is linked to exposure to classical carcinogens and human papillomavirus (HPV) infection. Due to molecular, immunological, and clinical disparities between HPV+ and HPV-HNSCC, they are recognized as distinct cancer types. While immune checkpoint inhibition (ICI) has demonstrated efficacy in recurrent/metastatic HNSCC, response variability persists irrespective of HPV status. To gain insights into the CD8+ T-cell landscape of HPV-HNSCC, we performed multimodal sequencing (RNA and TCR) of CD8+ tumor-infiltrating lymphocytes (TILs) from treatment-naive HPV-HNSCC patients. Additionally, we subjected cells to ex vivo TCR-stimulation, facilitating the tracing of clonal transcriptomic responses. Our analysis revealed a subset of CD8+ TILs highly enriched for interferon-stimulated genes (ISG), which were found to be clonally related to a subset of granzyme K (GZMK)-expressing cells. Trajectory inference suggests ISG transition via GZMK cells towards terminal effector states. However, unlike GZMK cells, which rapidly an effector-like phenotype in response to TCR stimulation, ISG cells remain transcriptionally inert. Consequently, ISG cells may impede effective T-cell differentiation within the TME. Although, the functional consequences of ISG cells are poorly understood, we revealed that they possess receptors and ligands enabling cell-cell communication networks with key TME immunomodulators such as dendritic cells. Additionally, ISG cells were found to be a core feature across various tumor entities and were specifically enriched within tumor tissue. Thus, our findings illuminate the complexity of T-cell heterogeneity in HPV-HNSCC and reveal an overlooked population of IFN-stimulated CD8+ TILs. Further exploration of their functional significance may offer insights into therapeutic strategies for HPV-HNSCC and other cancer types.

immunology↗

bioRxiv2024

BBSome-dependent ciliary Hedgehog signaling governs cell fate in the white adipose tissue

The primary cilium has emerged as critical in regulating whole-body energy metabolism, as reflected in the Bardet-Biedl syndrome (BBS), where primary cilia dysfunction leads to obesity due to hyperphagia and white adipose tissue (WAT) remodeling. The regulation of cell fate and differentiation of adipocyte precursor cells (APCs) is key to maintaining WAT homeostasis during obesity. Using mice that recapitulated the BBS patient phenotype (Bbs8-/-), we demonstrate that primary cilia dysfunction reduces the stem-cell-like P1 APC subpopulation by inducing a phenotypic switch into a fibrogenic progenitor state, characterized by extracellular matrix (ECM) remodeling and upregulation of CD9. Single-cell RNA sequencing revealed a direct transition of stem-cell-like P1 cells into fibrogenic progenitors, bypassing the committed P2 cells. Ectopic ciliary Hedgehog signaling upon loss of BBS8 emerged as a central driver of the molecular changes in Bbs8-/- APCs, altering differentiation into adipocytes and lipid uptake. These findings unravel a novel role for primary cilia in governing APC fate, determining the delicate balance between adipogenesis and fibrogenesis. The identified molecular mechanisms provide insights into potential therapeutic targets for obesity.

cell biology↗

bioRxiv2021

Benchmarking scRNA-seq imputation tools with respect to network inference highlights deficits in performance at high levels of sparsity

Gene correlation network inference from single-cell transcriptomics data potentially allows to gain unprecendented insights into cell type-specific regulatory programs. ScRNA-seq data is severely affected by dropout, which significantly hampers and restrains current downstream analysis. Although newly developed tools are capable to deal with sparse data, no appropriate single-cell network inference workflow has been established. A potential way to end this deadlock is the application of data imputation methods, which already proofed to be useful in specific contexts of single-cell data analysis, e.g., recovering cell clusters. In order to infer cell-type specific networks, two prerequisites must be met: the identification of cluster-specific cell-types and the network inference itself. Here, we propose a benchmarking framework to investigate both objections. By using suitable reference data with inherent correlation structure, six representative imputation tools and appropriate evaluation measures, we were able to systematically infer the impact of data imputation on network inference. Major network structures were found to be preserved in low dropout data sets. For moderately sparse data sets, DCA was able to recover gene correlation structures, although systematically introducing higher correlation values. No imputation tool was able to recover true signals from high dropout data. However, by using an additional biological data set we could show that cell-cell correlation by means of specific marker gene expression was not compromised through data imputation. Our analysis showed that network inference is feasible for low and moderately sparse data sets by using the unimputed and DCA-prepared data, respectively. High sparsity data, on the other side, still pose a major problem since current imputation techniques are not able to facilitate network inference. The annotation of cluster-specific cell-types as a prerequisite is not hampered by data imputation but their power to restore the deeply hidden correlation structures is still not sufficient enough.

bioinformatics↗