Computational Analyses of the AtTPC1 (Arabidopsis Two-Pore Channel 1) Permeation Pathway.

Int J Mol Sci

Centro de Bioinformática, Simulación y Modelado (CBSM), Facultad de Ingeniería, Campus Talca, Universidad de Talca, Talca 346000, Chile.

Published: September 2021


Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Two Pore Channels (TPCs) are cation-selective voltage- and ligand-gated ion channels in membranes of intracellular organelles of eukaryotic cells. In plants, the TPC1 subtype forms the slowly activating vacuolar (SV) channel, the most dominant ion channel in the vacuolar membrane. Controversial reports about the permeability properties of plant SV channels fueled speculations about the physiological roles of this channel type. TPC1 is thought to have high Ca permeability, a conclusion derived from relative permeability analyses using the Goldman-Hodgkin-Katz (GHK) equation. Here, we investigated in computational analyses the properties of the permeation pathway of TPC1 from . Using the crystal structure of AtTPC1, protein modeling, molecular dynamics (MD) simulations, and free energy calculations, we identified a free energy minimum for Ca, but not for K, at the luminal side next to the selectivity filter. Residues D269 and E637 coordinate in particular Ca as demonstrated in in silico mutagenesis experiments. Such a Ca-specific coordination site in the pore explains contradicting data for the relative Ca/K permeability and strongly suggests that the Ca permeability of SV channels is largely overestimated from relative permeability analyses. This conclusion was further supported by in silico electrophysiological studies showing a remarkable permeation of K but not Ca through the open channel.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8508871PMC
http://dx.doi.org/10.3390/ijms221910345DOI Listing

Publication Analysis

Top Keywords

computational analyses
8
permeation pathway
8
relative permeability
8
permeability analyses
8
free energy
8
permeability
6
channel
5
analyses attpc1
4
attpc1 arabidopsis
4
arabidopsis two-pore
4

Similar Publications

Background: Recent advances in high-throughput sequencing technologies have enabled the collection and sharing of a massive amount of omics data, along with its associated metadata-descriptive information that contextualizes the data, including phenotypic traits and experimental design. Enhancing metadata availability is critical to ensure data reusability and reproducibility and to facilitate novel biomedical discoveries through effective data reuse. Yet, incomplete metadata accompanying public omics data may hinder reproducibility and reusability and limit secondary analyses.

View Article and Find Full Text PDF

Simulated metabolic profiles reveal biases in pathway analysis methods.

Metabolomics

September 2025

Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France.

Introduction: Initially developed for transcriptomics data, pathway analysis (PA) methods can introduce biases when applied to metabolomics data, especially if input parameters are not chosen with care. This is particularly true for exometabolomics data, where there can be many metabolic steps between the measured exported metabolites in the profile and internal disruptions in the organism. However, evaluating PA methods experimentally is practically impossible when the sample's "true" metabolic disruption is unknown.

View Article and Find Full Text PDF

Background: Fetal MRI is increasingly used to investigate fetal lung pathologies, and super-resolution (SR) algorithms could be a powerful clinical tool for this assessment. Our goal was to investigate whether SR reconstructions result in an improved agreement in lung volume measurements determined by different raters, also known as inter-rater reliability.

Materials And Methods: In this single-center retrospective study, fetal lung volumes calculated from both SR reconstructions and the original images were analyzed.

View Article and Find Full Text PDF

Although dynamical systems models are a powerful tool for analysing microbial ecosystems, challenges in learning these models from complex microbiome datasets and interpreting their outputs limit use. We introduce the Microbial Dynamical Systems Inference Engine 2 (MDSINE2), a Bayesian method that learns compact and interpretable ecosystems-scale dynamical systems models from microbiome timeseries data. Microbial dynamics are modelled as stochastic processes driven by interaction modules, or groups of microbes with similar interaction structure and responses to perturbations, and additionally, noise characteristics of data are modelled.

View Article and Find Full Text PDF

Unravelling novel microbial players in the breast tissue of TNBC patients: a meta-analytic perspective.

NPJ Biofilms Microbiomes

September 2025

Bioinformatics Group, Centre for Informatics Science (CIS), School of Information Technology and Computer Science (ITCS), Nile University, Giza, Egypt.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer (BC), accounting for nearly 40% of BC-related deaths. Emerging evidence suggests that the breast tissue microbiome harbors distinct microbial communities; however, the microbiome specific to TNBC remains largely unexplored. This study presents the first comprehensive meta-analysis of the TNBC tissue microbiome, consolidating 16S rRNA amplicon sequencing data from 200 BC samples across four independent cohorts.

View Article and Find Full Text PDF