bioRxiv Science⌕ Search

bioRxiv · 10.1101/2021.06.04.446765

Molecular evolution of the Angiotensin II receptors AT1 and AT2: Specificity of the sodium binding site in amniota

Abstract

In vertebrates, the octopeptide angiotensin II (AngII) is an important in vivo regulator of the cardiovascular system. It acts mainly through two G protein-coupled receptors, AT1 and AT2. To better understand the interplay between these receptors throughout the evolution of the renin-angiotensin system (RAS), we combined a phylogenetic study to electrostatics computations and molecular dynamics (MD) simulations of AT1 and AT2 receptors from different species. The phylogenetic analysis reveals a mirror evolution of AT1 and AT2 that are both split in two clades, separating fish from terrestrian receptors. It also indicates that the unusual allosteric sodium binding site of human AT1 is specific of amniota. Other AT1 and AT2 receptors display a canonical sodium binding site with a serine at position 7.46 (Ballesteros numbering). Electrostatics computations and MD simulations support maintained sodium binding to human AT1 with ingress from the extracellular side. Comparison of the sodium binding modes in AT1 and AT2 from humans and eels indicates that the allosteric control by sodium in both AT1 and AT2 evolved during the transition from an aqueous to a terrestrial environment. The unusual S7.46N mutation in amniota AT1 is mirrored by a L3.36M mutation in amniota AT2. The S7.46N mutation increases the specificity of AT1 for AngII relative to Ang derivatives, whereas the L3.36M mutation might have the opposite effect on AT2. Both mutations should contribute to the split of the renin-angiotensin system into the classical (AngII/AT1) and counter-regulatory (Ang1-7/AT2, Mas) arms in amniota. AUTHOR SUMMARYThe analysis of protein sequences from different species can reveal interesting trends in the structural and functional evolution of a protein family. Here, we analyze the evolution of two G protein-coupled receptors, AT1 and AT2, which bind the angiotensin II peptide and are important regulators of the cardiovascular system. We show that these receptors underwent a mirror evolution and that specific mutations of the sodium binding pocket in both AT1 and AT2 occurred in amniota. We underwent electrostatics computations and molecular dynamics simulations to decipher the details of the sodium binding mode in eel and human receptors, as prototype of fish and amniota receptors. Our results suggest that evolution favored an increased specificity of AT1 and a decreased specificity of AT2 for angiotensin II as compared to its derivatives. In turn, these data suggest that mutations in the sodium binding pocket of G protein-coupled receptors might be an efficient way to gain functional evolution.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

TISS, A., BENBOUBAKER, R., HENRION, D., GUISSOUMA, H., CHABBERT, M.. 2021-06-04. Molecular evolution of the Angiotensin II receptors AT1 and AT2: Specificity of the sodium binding site in amniota. https://doi.org/10.1101/2021.06.04.446765

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗