Biology subjects

Pleiko, K.

Publications and source records attributed to Pleiko, K..

7 recordsLinked to original sources

bioRxiv2026

Position of de novo purine biosynthesis gene disruptions shapes purine-starvation phenotypes in Saccharomyces cerevisiae

Purine starvation induces a quiescence-like and stress-resistant physiological state in Saccharomyces cerevisiae, but it remains unclear whether this response is uniform across all disruptions of the de novo adenine biosynthetic pathway. Here, we used a pathway-position series of adenine-biosynthesis deletion mutants to test how the location of the metabolic blockage shapes cellular adaptation to purine starvation, using nitrogen starvation as a reference condition. Across mutants, purine starvation induced a shared core response marked by reduced growth, repression of ribosome-biogenesis and rRNA-processing modules, and accumulation of cells in G1/G0 phase. However, the magnitude and composition of the response varied systematically with pathway position. Early-pathway mutants showed the strongest desiccation tolerance and stress-associated transcriptional remodelling, whereas late-pathway mutants showed weaker protection. The ade16{Delta}ade17{Delta} double mutant was a distinct outlier, with altered carbon-flux distribution and limited stress resilience, suggesting that late-pathway disruption produces a physiologically different starvation state. Transcriptome analysis showed partial overlap with nitrogen starvation, but also pathway-position-dependent regulation of metabolic, ribosome-related, mitochondrial, and stress-response modules. Together, these results indicate that purine starvation is not a single physiological condition, but a set of related starvation states shaped by the position of the metabolic block within the purine biosynthetic pathway.

microbiology↗

bioRxiv2025

Proximity labelling-based identification of vascular homing peptide receptors

Identifying the receptors of vascular homing peptides (VHPs) is critical for mechanistic understanding and development of peptide-guided precision therapeutics. Conventional receptor discovery methods, such as affinity chromatography, require cell disruption and often expose intracellular proteins, resulting in high background and low specificity. To overcome these limitations, we developed a proximity labelling approach that tags proteins near VHP receptors on intact live cells. Cells were incubated with VHP-horseradish peroxidase (HRP) complexes, which, upon hydrogen peroxide treatment, activate biotin-tyramide to produce short-lived radicals that covalently label nearby membrane proteins. Using the prototypic C-end Rule peptide RPARPAR and its known receptor neuropilin-1 (NRP-1), we validated this method by successful receptor tagging and mass spectrometric identification. Using RPARPAR-HRP conjugates, we achieved selective proximity labeling of membrane proteins in NRP-1-positive PPC1 cells, with a 3- to 5-fold increase in fluorescence intensity over controls by flow cytometry. Affinity purification and Western blotting identified a strong [~]130 kDa band corresponding to NRP-1 exclusively in labeled PPC1 cells. Mass spectrometry analysis revealed a [~]20-fold enrichment of NRP-1 and significant enrichment of integrins (ITGAV, ITGB1, ITGA3), ALCAM, EPHA2, CD109, and PLXNB2 in RPARPAR-labeled samples compared to controls. This approach could be broadly used for molecular mapping of the homing peptide interactome and its spatial proximity in live cells, streamlining the discovery of homing peptide receptors and their associated partners.

cell biology↗

bioRxiv2022

Targeting triple-negative breast cancer with β1-integrin binding aptamer

Targeted therapies have increased the treatment options for triple-negative breast cancer patients. However, the paucity of targetable biomarkers and tumour heterogeneity have limited the ability of precision-guided interventions to live up to their full potential. As affinity targeting ligands, aptamers show high selectivity towards target molecules. Compared to antibodies, aptamers have lower molecular weight, increased stability during transportation, reduced immunogenicity, and increased tissue uptake. Recently, we reported the discovery of GreenB1 aptamer that is internalized in cultured triple-negative MDA-MB-231 human breast cancer cells. We show that the GreenB1 aptamer specifically targets {beta}1-integrin, a protein previously linked to breast cancer cell invasiveness and migration. Aptamer binds to {beta}1-integrin with low nanomolar affinity. GreenB1 homes in the orthotopic 4T1 triple-negative breast cancer lesions modelled in mice. Our findings suggest potential applications for the GreenB1-guided precision agents for the diagnosis and therapy of triple-negative breast cancer.

molecular biology↗

bioRxiv2021

Integrative Gene Expression and Metabolic Analysis tool IgemRNA

Genome scale metabolic modelling is widely used technique to research metabolism impacts on organisms properties. Additional omics data integration enables a more precise genotype-phenotype analysis for biotechnology, medicine and life sciences. Transcriptome data amounts rapidly increase each year. Many transcriptome analysis tools with integrated genome scale metabolic modelling are proposed. But these tools have own restrictions, compatibility issues and the necessity of previous experience and advanced user skills. We have analysed and classified published tools, summarized possible transcriptome pre-processing, and analysis methods and implemented them in the new transcriptome analysis tool IgemRNA. Tool novelty is the possibility of transcriptomics data pre-processing approach, analysis of transcriptome with or without genome scale metabolic models and different thresholding and gene mapping approach availability. In comparison with usual Gene set enrichment analysis methods, IgemRNA options provide additional transcriptome data validation, where minimal metabolic network connectivity and flux requirements are met. IgemRNA allows to process transcriptome datasets, compare data between different phenotypes, execute multiple analysis and data filtering functions. All this is done via graphical user interface. IgemRNA is compatible with Cobra Toolbox 3.0 and uses some of its functions for genome scale metabolic model optimization tasks. IgemRNA is open access software available at https://github.com/BigDataInSilicoBiologyGroup/IgemRNA.

systems biology↗

bioRxiv2021

Purine auxotrophic starvation evokes quiescence like phenotype in the budding yeast

Purine auxotrophy is a typical marker for many laboratory yeast strains. Supplementation of additional purine source (like adenine) is necessary to cultivate these strains. If not supplied in adequate amounts, purine starvation sets in. We tested purine starvation effects in budding yeast Saccharomyces cerevisiae ade8 knockout. We explored effects brought by purine starvation in cellular, central carbon metabolism and in the global transcriptome level. We observed that cells cultivated in purine depleted media became significantly more tolerant to severe thermal, oxidative and desiccation stresses when compared to the cells cultivated in media with all necessary supplements. When starved for purine, cells stop their cell cycle in G1 or G0 state; intracellular concentration of ATP, ADP and AMP decreases, but adenylate charge remains stable. Intracellular RNA concentration decreases and massive downregulation of ribosomal RNA occurs. We think that purine auxotrophic starvation in a way mimics "natural" nitrogen or carbon starvations and therefore initiates elements of a transcriptional program typical for stationary phase cells (cell cycle arrest, increased stress resistance). Therefore our results demonstrate that organised metabolic response is initiated not only via "natural starvations", but also when starving for metabolic intermediates, like purines.

cell biology↗

bioRxiv2020

In vivo phage display: identification of organ-specific peptides using deep sequencing and differential profiling across tissues

In vivo phage display is widely used for identification of organ- or disease-specific homing peptides. However, the current in vivo phage biopanning approaches fail to assess biodistribution of specific peptide phages across tissues during the screen, thus necessitating laborious and time-consuming post-screening validation studies on individual peptide phages. Here, we adopted bioinformatics tools used for RNA sequencing for analysis of high throughput sequencing (HTS) data to estimate the representation of individual peptides during biopanning in vivo. The data from in vivo phage screen were analyzed using differential binding - relative representation of each peptide in the target organ vs. in a panel of control organs. Application of this approach in a model study using low-diversity peptide T7 phage library with spiked-in brain homing phage, demonstrated brain-specific differential binding of brain homing phage and resulted in identification of novel lung and brain specific homing peptides. Our study provides a broadly applicable approach to streamline in vivo peptide phage biopanning and to increase its reproducibility and success rate. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=90 SRC="FIGDIR/small/181974v2_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@1853e75org.highwire.dtl.DTLVardef@c7ad8corg.highwire.dtl.DTLVardef@152f4feorg.highwire.dtl.DTLVardef@15261d0_HPS_FORMAT_FIGEXP M_FIG C_FIG In vivo phage display using differential binding approach

molecular biology↗

bioRxiv2018

Differential binding cell-SELEX: method for identification of cell specific aptamers using high throughput sequencing

Aptamers have evolved as a viable alternative to antibodies in recent years. High throughput sequencing (HTS) has revolutionized the aptamer research by increasing the number of reads from few using Sanger sequencing to millions of reads using HTS approach. Despite the availability and advantages of HTS compared to Sanger sequencing there are only 50 aptamer HTS sequencing samples available on public databases. HTS data for aptamer research are mostly used to compare sequence enrichment between subsequent selection cycles. This approach does not take full advantage of HTS because enrichment of sequences during selection can be due to inefficient negative selection when using live cells. Here we present differential binding cell-SELEX (systematic evolution of ligands by exponential enrichment) workflow that adapts FASTAptamer toolbox and bioinformatics tool edgeR that is mainly used for functional genomics to achieve more informative metrics about the selection process. We propose fast and practical high throughput aptamer identification method to be used with cell-SELEX technique to increase successful aptamer selection rate against live cells. The feasibility of our approach is demonstrated by performing aptamer selection against clear cell renal cell carcinoma (ccRCC) RCC-MF cell line using RC-124 cell line from healthy kidney tissue for negative selection.

molecular biology↗