Biology subjects

Ludwig, J.

Publications and source records attributed to Ludwig, J..

3 recordsLinked to original sources

bioRxiv2022

Targeting plasma membrane and mitochondrial instability in breast cancer cells and breast epithelial to mesenchymal transition-model cells by adamantyl diaza-crown ether ZG613

The adamantane derived diaza-crown ether ZG613 was assessed as a potential breast cancer cells and breast epithelial to mesenchymal transition (EMT)-model cells targeting agent. We postulated that ZG613 activity relies on its plasma/mitochondria membrane disruption ability based on adamantane hydrophobicity and/or crown ether related ionophoric properties. We performed molecular dynamics (MD) simulations and next generation sequencing, followed by in vitro study of cell death, membrane perturbations and ionophoric ability, as well as in vivo study of effects on the tumour growth. MD simulation and RNA sequencing pointed toward physical disruption of plasma membrane by ZG613, corroborated by measured increase in membrane permeability leading to cell death. Measurements of ion fluxes confirmed ZG613 inability to transport Na+ and K+, as predicted by MD simulation. EMT-model cells exhibit changes in mitochondrial morphology and ATP levels, successfully targeted by ZG613. ZG613 caused mild retardation of tumour growth in vivo. In conclusion, ZG613 kills breast cancer cells and breast EMT-model cells by physical disruption of plasma membrane and impairments of mitochondrial functions. Breast EMT cells represent good potential targets within the breast tumour, due to their plasma membrane and mitochondrial instability.

cancer biology↗

bioRxiv2022

A conserved isoleucine in the binding pocket of RIG-I controls immune tolerance to mitochondrial RNA

RIG-I is a cytosolic receptor of viral RNA essential for the immune response to numerous RNA viruses. Accordingly, RIG-I must sensitively detect viral RNA yet tolerate abundant self-RNA species. The basic binding cleft and an aromatic amino acid of the RIG-I C-terminal domain(CTD) mediate high-affinity recognition of 5triphosphorylated and 5base-paired RNA(dsRNA). Here, we found that, while 5unmodified hydroxyl(OH)-dsRNA demonstrated residual activation potential, 5-monophosphate(5p)-termini, present on most cellular RNAs, prevented RIG-I activation. Determination of CTD/dsRNA co-crystal structures and mutant activation studies revealed that the evolutionarily conserved I875 within the CTD sterically inhibits 5p-dsRNA binding. RIG-I(I875A) was activated by both synthetic 5p-dsRNA and endogenous long dsRNA within the polyA-rich fraction of total cellular RNA. RIG-I(I875A) specifically interacted with a long, highly structured, polyA-bearing, non-coding mitochondrial(mt) RNA, and depletion of mtRNA from total RNA abolished its activation. Altogether, our study demonstrates that avoidance of 5p-RNA recognition is crucial to preventing mtRNA-triggered RIG-I-mediated autoinflammation.

immunology↗

bioRxiv2019

flowEMMi: An automated model-based clustering tool for microbial cytometric data

BackgroundFlow cytometry (FCM) is a powerful single-cell based measurement method to ascertain multidimensional optical properties of millions of cells. FCM is widely used in medical diagnostics and health research. There is also a broad range of applications in the analysis of complex microbial communities. The main concern in microbial community analyses is to track the dynamics of microbial subcommunities. So far, this can be achieved with the help of time-consuming manual clustering procedures that require extensive user-dependent input. In addition, several tools have recently been developed by using different approaches which, however, focus mainly on the clustering of medical FCM data or of microbial samples with a well-known background, while much less work has been done on high-throughput, online algorithms for two-channel FCM.\n\nResultsWe bridge this gap with flowEMMi, a model-based clustering tool based on multivariate Gaussian mixture models with subsampling and foreground/background separation. These extensions provide a fast and accurate identification of cell clusters in FCM data, in particular for microbial community FCM data that are often affected by irrelevant information like technical noise, beads or cell debris. flowEMMi outperforms other available tools with regard to running time and information content of the clustering results and provides near-online results and optional heuristics to reduce the running-time further.\n\nConclusionsflowEMMi is a useful tool for the automated cluster analysis of microbial FCM data. It overcomes the user-dependent and time-consuming manual clustering procedure and provides consistent results with ancillary information and statistical proof.

bioinformatics↗