Biology subjects

Salas-Bastos, A.

Publications and source records attributed to Salas-Bastos, A..

3 recordsLinked to original sources

bioRxiv2023

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

Fibroblast growth factor-23 (FGF23) is a bone-derived hormone that has recently received much attention due to its association with the progression of chronic kidney disease, cardiovascular disease, and associated mortality. Extracellular sodium concentration ([Na+]) plays a significant role in bone metabolism. Hyponatremia (low 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 line UMR-106. 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. On the other hand, arginine vasopressin (AVP), which is often responsible for hyponatremia, did not affect FGF23 production. Next, comprehensive and unbiased RNA-seq analysis of UMR-106 cells exposed to low vs. high [Na+] 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 human hyponatremia patients have higher FGF23 levels. Our results suggest that [Na+] is a critical regulator of FGF23 synthesis. SIGNIFICANCE STATEMENTFibroblast growth factor 23 (FGF23) is a bone-derived hormone that controls phosphate and vitamin D metabolism. Excess FGF23 is postulated to cause left ventricular hypertrophy, while FGF23 deficiency reduces life span and mimics age-related diseases in mice. FGF23 is also a potential biomarker for chronic kidney disease and cardiovascular disorders, but its role in disease progression is unclear. Therefore, it is important to explore the regulation of FGF23 production, which is incompletely understood. Our paper identifies extracellular-sodium-NFAT5 signaling as a key regulator of FGF23 formation.

physiology↗

bioRxiv2021

Single-cell transcriptomic resolution of stem cells and their developmental trajectories in the hippocampus reveals epigenetic control of cell state perseverance

Despite conceptual research on hippocampus development and the application of single-cell-resolved technologies, the nature and maturation of its diverse progenitor populations are unexplored. The chromatin modifier DOT1L balances progenitor proliferation and differentiation, and conditional loss-of-function mice featured impaired hippocampus development. We applied single-cell RNA sequencing on DOT1L-mutant mice and explored cell trajectories in the E16.5 hippocampus. We resolved in our data five distinct neural stem cell populations with the developmental repertoire to specifically generate the cornu ammonis (CA) 1 field and the dentate gyrus (DG). Within the two developing CA1- and CA3-fields, we identified two distinct maturation states and we thus propose CA1- and CA3-differentiation along the radial axis. In the developing hippocampus, DOT1L is primarily involved in the proper development of CA3 and the DG, and it serves as a state-preserving epigenetic factor that orchestrates the expression of several important transcription factors that impact neuronal differentiation and maturation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/452775v1_ufig1.gif" ALT="Figure 1"> View larger version (70K): org.highwire.dtl.DTLVardef@d611caorg.highwire.dtl.DTLVardef@2a0e3corg.highwire.dtl.DTLVardef@10c9082org.highwire.dtl.DTLVardef@1e358c8_HPS_FORMAT_FIGEXP M_FIG C_FIG O_LIThe developing hippocampus contains distinct and spatially separated NSC populations that differ in expression of a specific set of firstly described marker genes. C_LIO_LICA pyramidal neurons mature along the radial axis and pass through distinct maturation states. C_LIO_LIDOT1L preserves the dentate granule cell lineage in the developing hippocampus and limits maturation in the CA1- and CA3-fields development. C_LIO_LIDOT1L gates cell maturation as upstream regulator of transcription factor expression that confer instrumental roles in hippocampus development. C_LI

neuroscience↗

bioRxiv2020

Generating Synthetic Single-Cell RNA-Sequencing Datafrom Small Pilot Studies using Deep Learning

Deep generative models, such as variational autoencoders (VAEs) or deep Boltzmann machines (DBM), 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 study 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 (scDBM). When considering the similarity of clustering results on synthetic data to ground-truth clustering, we find that the scVIposterior variant resulted in high variability, most likely due to amplifying artifacts of small data sets. 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. Overall, the results showed that generative deep learning approaches might be valuable for supporting the design of scRNA-seq experiments.

bioinformatics↗