Biology subjects

Posch, S.

Publications and source records attributed to Posch, S..

4 recordsLinked to original sources

bioRxiv2025

A conserved cytoplasmic glutamic acid mediates pH-dependent gating in bacterial urea channel UreI

The bacterial UreI channel family enables rapid urea uptake, essential for urease activity and acid resistance. While pH-dependent gating in Helicobacter pylori UreI (HpUreI) is attributed to periplasmic histidines, the role of cytoplasmic residues remains unexplored. The UreI homolog from Streptococcus salivarius (SsUreI), lacking periplasmic histidines, serves as a simplified model to identify gating determinants. Here, we combine yeast complementation assays, in vitro studies, and MD simulations to show that cytoplasmic glutamic acid E136 mediates pH-dependent gating in SsUreI. Mutagenesis in H. pylori/H. hepaticus homologs confirm E136 as a conserved pH sensor across UreI channels. Protonation of E136 disrupts its salt bridge with R20, increasing cytoplasmic-loop flexibility and urea-permeable filter conformations. These findings challenge the paradigm of exclusive periplasmic pH sensing, supporting a Gram-negative-specific dual-sensor model (E136 + histidines). By elucidating this molecular mechanism, we identify E136 as a therapeutic target to disrupt UreI-mediated acid resistance in pathogenic bacteria.

biophysics↗

bioRxiv2023

YidC from Escherichia coli forms an ion-conducting pore upon activation by ribosomes.

The universally conserved protein YidC aids the insertion and folding of transmembrane polypeptides independently or as a part of the SecYEG translocon complex. In the former scenario, the lipid-exposed YidC surface equipped with a highly conserved positively charged arginine is thought to facilitate membrane insertion of the nascent chain by providing a countercharge for the acidic residues at the polypeptides N-terminal region. Here we show that the purified and reconstituted E. coli YidC forms an ion-conducting transmembrane pore upon binding a ribosome or ribosome-nascent chain complex (RNC). This pore is closed in the absence of ribosomes. As this pore is not visible in the published monomeric YidC structure, we used AlphaFold to construct the model of a parallel YidC dimer. Experimental evidence for a dimeric assembly comes from our BN-PAGE analysis of native vesicles, fluorescence correlation spectroscopy studies, and single-molecule fluorescence microscopy. In the dimeric model, the conserved positively charged arginine and many residues interacting with nascent chains point into the putative pore. This result suggests the possibility of an alternative mode of YidC-assisted membrane protein insertion.

biophysics↗

bioRxiv2019

rhizoTrak: A flexible open source Fiji plugin for user-friendly manual annotation of time-series images from minirhizotrons

Background and aimsMinirhizotrons are commonly used to study root turnover which is essential for understanding ecosystem carbon and nutrient cycling. Yet, extracting data from minirhizotron images requires intensive annotation effort. Existing annotation tools often lack flexibility and provide only a subset of the required functionality. To facilitate efficient root annotation in minirhizotrons, we present the user-friendly open source tool rhizoTrak. Methods and resultsrhizoTrak builds on TrakEM2 and is publically available as Fiji plugin. It uses treelines to represent branching structures in roots and assigns customizable status labels per root segment. rhizoTrak offers configuration options for visualization and various functions for root annotation mostly accessible via keyboard shortcuts. rhizoTrak allows time-series data import and particularly supports easy handling and annotation of time series images. This is facilitated via explicit temporal links (connectors) between roots which are automatically generated when copying annotations from one image to the next. rhizoTrak includes automatic consistency checks and guided procedures for resolving conflicts. It facilitates easy data exchange with other software by supporting open data formats. ConclusionsrhizoTrak covers the full range of functions required for user-friendly and efficient annotation of time-series images. Its flexibility and open source nature will foster efficient data acquisition procedures in root studies using minirhizotrons.

plant biology↗

bioRxiv2017

Learning from mistakes: Accurate prediction of cell type-specific transcription factor binding

Computational prediction of cell type-specific, in-vivo transcription factor binding sites is still one of the central challenges in regulatory genomics, and a variety of approaches has been proposed for this purpose.\n\nHere, we present our approach that earned a shared first rank in the \"ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge\" in 2017. This approach employs features derived from chromatin accessibility, binding motifs, gene expression, genomic sequence and annotation to train classifiers using a supervised, discriminative learning principle. Two further key aspects of this approach are learning classifier parameters in an iterative training procedure that successively adds additional negative examples to the training set, and creating an ensemble prediction by averaging over classifiers obtained for different training cell types.\n\nIn post-challenge analyses, we benchmark the influence of different feature sets and find that chromatin accessiblity and binding motifs are sufficient to yield state-of-the-art performance for in-vivo binding site predictions. We also show that the iterative training procedure and the ensemble prediction are pivotal for the final prediction performance.\n\nTo make predictions of this approach readily accessible, we predict 682 peak lists for a total of 31 transcription factors in 22 primary cell types and tissues, which are available for download at https://www.synapse.org/#!Synapse:syn11526239, and we demonstrate that these may help to yield biological conclusions. Finally, we provide a user-friendly version of our approach as open source software at http://jstacs.de/index.php/Catchitt.\n\nContactgrau@informatik.uni-halle.de

bioinformatics↗