Biology subjects

Klimentova, E.

Publications and source records attributed to Klimentova, E..

6 recordsLinked to original sources

bioRxiv2025

Molecular mechanisms regulating PDE11A4 age-related liquid-liquid phase separation (LLPS) and its reversal by selective, potent and orally-available PDE11A4 small molecule inhibitors both in vitro and in vivo

PDE11A is a little-studied phosphodiesterase family that breaks down cAMP and cGMP, with the PDE11A4 isoform enriched in the memory-related hippocampus. Age-related increases in hippocampal PDE11A expression occur in human and rodents, causing age-related cognitive decline of social memories. Interestingly, this age-related increase triggers PDE11A4 liquid-liquid phase separation (LLPS), causing the enzyme to accumulate in the brain in filamentous structures termed "ghost axons". Here we sought to identify molecular mechanisms regulating PDE11A4 LLPS and therapeutic approaches capable of reversing it. PDE11A4 LLPS was reduced by phosphorylation of PDE11A4-S163 or-S239 and the D355A mutation that blocks the effect of cGMP binding the PDE11A4 GAF-A domain. PDE11A4 LLPS was increased by inhibiting kinases with staurosporine or stimulating packaging/repacking via the trans-Golgi network by overexpressing TGN38 or RhoB. 8 PDE11 inhibitors (MLG-122, MLG-185, MLG-199, SMQ-02-57, SMQ-03-30, SMQ-03-20, tadalafil, and BC11-38) across 3 scaffolds reverse overexpression-related PDE11A4 LLPS in HT22 mouse hippocampal neuronal cells. This effect of PDE11A4 inhibitors occurs within minutes, is reversed upon washout of lower but not higher concentrations, and occurs in part by reducing PDE11A4 homodimerization. PDE11A4 inhibitors also rescued exacerbated PDE11A4 LLPS triggered by aging-like S117D/S124D phosphomimic mutations, staurosporine, or TGN38/RhoB overexpression. In vivo, orally-administered 30mg/kg SMQ-03-20 reversed age-related increases in PDE11A4 ghost axons and neuroinflammation in old mice. Thus, PDE11A inhibitors that reverse age-related PDE11A4 LLPS in HT22 hippocampal cells also reduce PDE11A4 ghost axons and neuroinflammation in the aged mouse brain, indicating therapeutical potential.

neuroscience↗

bioRxiv2023

Knot or Not? Sequence-Based Identification of Knotted Proteins With Machine Learning

Knotted proteins, although scarce, are crucial structural components of certain protein families, and their roles remain a topic of intense research. Capitalizing on the vast collection of protein structure predictions offered by AlphaFold, this study computationally examines the entire UniProt database to create a robust dataset of knotted and unknotted proteins. Utilizing this dataset, we develop a machine learning model capable of accurately predicting the presence of knots in protein structures solely from their amino acid sequences, with our best-performing model demonstrating a 98.5% overall accuracy. Unveiling the sequence factors that contribute to knot formation, we discover that proteins predicted to be unknotted from known knotted families are typically non-functional fragments missing a significant portion of the knot core. The study further explores the significance of the substrate binding site in knot formation, particularly within the SPOUT protein family. Our findings spotlight the potential of machine learning in enhancing our understanding of protein topology and propose further investigation into the role of knotted structures across other protein families. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/556468v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1466077org.highwire.dtl.DTLVardef@1674829org.highwire.dtl.DTLVardef@1b283a5org.highwire.dtl.DTLVardef@e0e962_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Beyond microRNAs: Analysis of chimeric reads characterises the diverse targetome of AGO2-mediated regulation.

Argonaute proteins are instrumental in regulating RNA stability and translation. AGO2, the major mammalian Argonaute protein, is known to primarily associate with microRNAs, a family of small RNA driver sequences, and identifies its targets primarily via a seed mediated partial complementarity process Despite numerous studies, a definitive experimental dataset of AGO2 driver-target interactions remains elusive. Our study employs two experimental methods - AGO2 CLASH and AGO2 eCLIP, to generate thousands of AGO2 target sites verified by chimeric reads. These chimeric reads contain both the AGO2 loaded small RNA driver and the target sequence, providing a robust resource for modeling AGO2 binding preferences. Our novel analysis pipeline reveals thousands of AGO2 target sites driven by microRNAs and a significant number of AGO2 drivers derived from fragments of other small RNAs such as tRNAs, YRNAs, snoRNAs, rRNAs, and more. We utilize convolutional neural networks to train machine learning models that accurately predict the binding potential for each driver class and experimentally validate several interactions. In conclusion, our comprehensive analysis of the AGO2 targetome broadens our understanding of its driver repertoire and potential function in development and disease. Moreover, we offer practical bioinformatic tools for future experiments and the prediction of AGO2 targets. All data and code from this study are freely available at https://github.com/ML-Bioinfo-CEITEC/HybriDetector/ Contactpanagiotis.alexiou@um.edu.mt, stepanka.vanacova@ceitec.muni.cz

molecular biology↗

bioRxiv2023

SAP domain facilitates efficient loading of Ku onto DNA ends

Recognition and processing of DNA ends play a central role in maintaining genome integrity. The evolutionarily conserved DNA repair complex Ku serves as the primary sensor of free DNA ends in eukaryotic cells. Its rapid association with DNA ends is crucial for several cellular processes, including non-homologous end joining (NHEJ) DNA repair and telomere protection. In this study, we conducted a transient kinetic analysis to investigate the impact of the SAP domain on individual phases of the Ku-DNA interaction. Specifically, we examined the initial binding, the subsequent docking of Ku onto DNA, and the sliding of Ku along DNA. Our findings revealed that the C-terminal domain of Ku70, known as SAP ((SAF-A/B, Acinus and PIAS), facilitates the initial phases of Ku-DNA interaction, but does not affect the sliding process. This suggests that SAP may either establish the first interactions with DNA, or stabilize these initial interactions during loading. To assess the biological role of SAP, we generated Arabidopsis plants expressing Ku lacking the SAP domain ({Delta}SAP). Intriguingly, despite the decreased efficiency of the {Delta}SAP Ku complex in loading onto DNA, the mutant plants exhibited full proficiency in classical NHEJ and telomere maintenance. This indicates that the speed of Ku loading onto telomeres or DNA double-strand breaks (DSBs) is not the decisive factor in stabilizing these DNA structures.

molecular biology↗

bioRxiv2021

Endothelial Pannexin 1-TRPV4 channel signaling lowers pulmonary arterial pressure

Pannexin 1 (Panx1) is an ATP-efflux channel that controls endothelial function in the systemic circulation. However, the roles of endothelial Panx1 in resistance-sized pulmonary arteries (PAs) are unknown. Extracellular ATP dilates PAs through activation of endothelial TRPV4 (transient receptor potential vanilloid 4) ion channels. We hypothesized that endothelial Panx1-ATP- TRPV4 channel signaling promotes vasodilation and lowers pulmonary arterial pressure (PAP). Endothelial, but not smooth muscle, knockout of Panx1 or TRPV4 increased PA contractility and raised PAP. Panx1-effluxed extracellular ATP signaled through purinergic P2Y2 receptor (P2Y2R) to activate protein kinase C (PKC), which in turn activated endothelial TRPV4 channels. Finally, caveolin-1 provided a signaling scaffold for endothelial Panx1, P2Y2R, PKC, and TRPV4 channels in PAs, promoting their spatial proximity and enabling signaling interactions. These results indicate that endothelial Panx1-P2Y2R-TRPV4 channel signaling, facilitated by caveolin-1, reduces PA contractility and lowers PAP.

biochemistry↗

bioRxiv2020

PENGUINN: Precise Exploration of Nuclear G-quadruplexes Using Interpretable Neural Networks

G-quadruplexes (G4s) are a class of stable structural nucleic acid motifs that are known to play a role in a wide spectrum of genomic functions, such as DNA replication and transcription. The classical understanding of G4 structure points to four variable length guanine strands joined by variable length stretches of other nucleotides. Experiments using G4 immunoprecipitation and sequencing experiments have produced a high number of highly probable G4 forming genomic sequences. The expense and technical difficulty of experimental techniques highlights the need for computational approaches of G4 identification. Here, we present PENGUINN, a machine learning method based on Convolutional Neural Networks, that learns the characteristics of G4 sequences and accurately predicts G4s outperforming the state-of-the-art. We provide both a standalone implementation of the trained model, and a web application that can be used to evaluate sequences for their G4 potential.

bioinformatics↗