Extracellular sodium regulates fibroblast growth factor 23 (FGF23) formation.

The bone-derived hormone fibroblast growth factor-23 (FGF23) has recently received much attention due to its association with chronic kidney disease and cardiovascular disease progression. Extracellular sodium concentration ([Na+]) plays a significant role in bone metabolism. Hyponatremia (lower serum [Na+]) has recently been shown to be independently associated with FGF23 levels in patients with chronic systolic heart failure. However, nothing is known about the direct impact of [Na+] on FGF23 production. Here, we show that an elevated [Na+] (+20 mM) suppressed FGF23 formation, whereas low [Na+] (-20 mM) increased FGF23 synthesis in the osteoblast-like cell lines UMR-106 and MC3T3-E1. Similar bidirectional changes in FGF23 abundance were observed when osmolality was altered by mannitol but not by urea, suggesting a role of tonicity in FGF23 formation. Moreover, these changes in FGF23 were inversely proportional to the expression of NFAT5 (nuclear factor of activated T cells-5), a transcription factor responsible for tonicity-mediated cellular adaptations. Furthermore, arginine vasopressin, which is often responsible for hyponatremia, did not affect FGF23 production. Next, we performed a comprehensive and unbiased RNA-seq analysis of UMR-106 cells exposed to low versus high [Na+], which revealed several novel genes involved in cellular adaptation to altered tonicity. Additional analysis of cells with Crisp-Cas9-mediated NFAT5 deletion indicated that NFAT5 controls numerous genes associated with FGF23 synthesis, thereby confirming its role in [Na+]-mediated FGF23 regulation. In line with these in vitro observations, we found that hyponatremia patients have higher FGF23 levels. Our results suggest that [Na+] is a critical regulator of FGF23 synthesis.

Escape from NK cell tumor surveillance by NGFR-induced lipid remodeling in melanoma.

Metastatic disease is a major cause of death for patients with melanoma. Melanoma cells can become metastatic not only due to cell-intrinsic plasticity but also due to cancer-induced protumorigenic remodeling of the immune microenvironment. Here, we report that innate immune surveillance by natural killer (NK) cells is bypassed by human melanoma cells expressing the stem cell marker NGFR. Using in vitro and in vivo cytotoxic assays, we show that NGFR protects melanoma cells from NK cell-mediated killing and, furthermore, boosts metastasis formation in a mouse model with adoptively transferred human NK cells. Mechanistically, NGFR leads to down-regulation of NK cell activating ligands and simultaneous up-regulation of the fatty acid stearoyl-coenzyme A desaturase (SCD) in melanoma cells. Notably, pharmacological and small interfering RNA-mediated inhibition of SCD reverted NGFR-induced NK cell evasion in vitro and in vivo. Hence, NGFR orchestrates immune control antagonizing pathways to protect melanoma cells from NK cell clearance, which ultimately favors metastatic disease.

Synthetic single cell RNA sequencing data from small pilot studies using deep generative models.

Deep generative models, such as variational autoencoders (VAEs) or deep Boltzmann machines (DBMs), can generate an arbitrary number of synthetic observations after being trained on an initial set of samples. This has mainly been investigated for imaging data but could also be useful for single-cell transcriptomics (scRNA-seq). A small pilot study could be used for planning a full-scale experiment by investigating planned analysis strategies on synthetic data with different sample sizes. It is unclear whether synthetic observations generated based on a small scRNA-seq dataset reflect the properties relevant for subsequent data analysis steps. We specifically investigated two deep generative modeling approaches, VAEs and DBMs. First, we considered single-cell variational inference (scVI) in two variants, generating samples from the posterior distribution, the standard approach, or the prior distribution. Second, we propose single-cell deep Boltzmann machines (scDBMs). When considering the similarity of clustering results on synthetic data to ground-truth clustering, we find that the [Formula: see text] variant resulted in high variability, most likely due to amplifying artifacts of small datasets. All approaches showed mixed results for cell types with different abundance by overrepresenting highly abundant cell types and missing less abundant cell types. With increasing pilot dataset sizes, the proportions of the cells in each cluster became more similar to that of ground-truth data. We also showed that all approaches learn the univariate distribution of most genes, but problems occurred with bimodality. Across all analyses, in comparing 10[Formula: see text] Genomics and Smart-seq2 technologies, we could show that for 10[Formula: see text] datasets, which have higher sparsity, it is more challenging to make inference from small to larger datasets. Overall, the results show that generative deep learning approaches might be valuable for supporting the design of scRNA-seq experiments.