Biology subjects

Kokot, B.

Publications and source records attributed to Kokot, B..

5 recordsLinked to original sources

bioRxiv2026

Report on pre-validation of an animal-free alternative method (NAM) for regulatory safety testing: InfiniteLungDT, an in-vitro-learned digital twin for the prediction of material-triggered chronic neutrophilic lung inflammation

Until now, there has been no animal-free alternative method for predicting chronic inflammation and delivering the associated dose responses, the timing of onset, and the duration of inflammation, as required by regulatory agencies. We present the results of pre-validation of an in-vitro-learned digital twin (InFiniteLungDT) capable of predicting chronic neutrophilic lung inflammation for regulatory use. The method is based on measuring the dynamics of early biological effects in vitro induced by respirable materials or their mixtures, without the need to know their intrinsic properties. We constructed the digital twin(s) for each of the material, for which we have in vivo exposure data. The instillation data set, comprising 49 different nanomaterials, was used as the primary anchor to calibrate the model. Inhalation data set, comprising 7 different nanomaterials, compliant with OECD TG 412, was used to show the general applicability of the method across species and for different exposure scenaria. In total, about 3094 single mouse exposures and 364 rat exposures (and approx. 775/225 non-exposed mouse/rat controls) were used to predict concentration-dependent time-evolved neutrophil influx into the lung. The accuracy (predictive capacity) of LOAEL determination is 93% for instillation and 84% for inhalation exposure. Taking into account the time-to-deliver-result being less than 1 week, this proves that the effect of inhaled material from acute to chronic conditions can be assessed orders of magnitude faster and cheaper than in a reference animal study.

pharmacology and toxicology↗

bioRxiv2025

HCR-Proxy resolves site-specific proximal RNA proteomes at subcompartmental nanoscale resolution

The spatial organization of RNA condensates is fundamental for understanding of basic cellular functions, but may also provide pivotal insights into diseases. One of the major challenges to understanding the role of condensates is the lack of technologies to map condensate-scale protein architecture at subcompartmental or nanoscale resolution. To address this, we introduce HCR-Proxy, a proximity labelling technique that couples Hybridization Chain Reaction (HCR)-based signal amplification with in situ proximity biotinylation (Proxy), enabling proteomic profiling of RNA-proximal proteomes at subcompartmental resolution. We benchmarked HCR-Proxy using nascent pre-rRNA targets to investigate the distinct proteomic signatures of the nucleolar subcompartments and to uncover a spatial logic of protein partitioning shaped by RNA sequence. Our results demonstrate HCR-Proxys ability to provide spatially-resolved maps of RNA interactomes within the nucleolus, offering new insights into the molecular organisation and compartmentalisation of condensates. This subcompartment-specific nucleolar proteome profiling enabled integration with deep learning frameworks, which effectively confirmed a sequence-encoded basis for protein partitioning across nested condensate subcompartments, characterised by antagonistic gradients in charge, length, and RNA-binding domains. HCR-Proxy thus provides a scalable platform for spatially resolved RNA interactome discovery, bridging transcript localisation with proteomic context in native cellular environments. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=196 SRC="FIGDIR/small/656369v1_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@116015forg.highwire.dtl.DTLVardef@1b414f1org.highwire.dtl.DTLVardef@1caf0a1org.highwire.dtl.DTLVardef@7bf8b2_HPS_FORMAT_FIGEXP M_FIG C_FIG Bullet points- HCR-Proxy enables the first nanoscale-resolution mapping of RNA-proximal proteomes in situ. - HCR-Proxy establishes a broadly applicable modular platform for spatially resolved RNA- interactomics. - Subcompartmental proteomes are resolved across nucleolar subdomains by targeting specific pre-rRNA regions. - Deep learning confirms a sequence-encoded logic of protein partitioning within condensate subcompartments.

molecular biology↗

bioRxiv2024

Integrative analysis of higher-order transcriptome organisation and RNA condensation principles

Complex RNA-protein networks play a pivotal role in the formation of many types of biomolecular condensates. How intrinsic RNA features contribute to condensate formation however remains unclear. Here, we integrate tailored transcriptomics assays to identify a distinct class of developmental condensation-prone RNAs termed smOOPs (semi-extractable, orthogonal organic phase separation-enriched RNAs). These transcripts are localised to larger intracellular foci, form denser RNA-RNA interaction subnetworks than expected and are heavily bound by RNA binding proteins (RBPs). Using an explainable deep learning framework, we reveal that smOOPs harbor characteristic sequence composition with lower sequence complexity, increased intramolecular folding and specific RBP binding patterns. Intriguingly, these RNAs encode proteins bearing extensive intrinsically disordered regions and are markedly predicted to be involved in biomolecular condensates, indicating an interplay between RNA- and protein-based features in phase separation. This work advances our understanding of condensation-prone RNAs and provides a versatile resource to further investigate RNA-driven condensation principles.

molecular biology↗

bioRxiv2021

Controlled Fluorescent Labelling of Metal Oxide Nanoparticles for Artefact-free Live Cell Microscopy

Nanotechnologies hold great promise for various applications. To predict and guarantee the safety of novel nanomaterials, it is essential to understand their mechanism of action in an organism, causally connecting adverse outcomes with early molecular events. They are best investigated using non-invasive advanced optical methods, such as high-resolution live-cell fluorescence microscopy, which require stable labelling of nanoparticles with fluorescent dyes. When performed inadequately, unbound fluorophores and inadvertently altered chemical and physical properties of the nanoparticles can, however, result in experimental artefacts and erroneous conclusions. To prevent such unintentional errors, we here describe a minimal combination of experimental methods to enable artefact-free fluorescent labelling of metal-oxide nanoparticles - the largest subpopulation of nanoparticles by industrial production and applications - and demonstrate its application in the case of TiO2 nanotubes. We 1) characterize potential changes of the nanoparticles surface charge and morphology that might occur during labelling, and 2) assess stable binding of the fluorescent dye to nanomaterial, which ensures correct nanoparticle localization. Together, these steps warrant the reliability and reproducibility of advanced optical tracking, which is necessary to explore nanomaterials mechanism of action and will foster widespread and safe use of new nanomaterials. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=182 SRC="FIGDIR/small/440400v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@358ffborg.highwire.dtl.DTLVardef@7faaecorg.highwire.dtl.DTLVardef@1ae7fb3org.highwire.dtl.DTLVardef@141c4b5_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2020

Towards predictive nanotoxicology: from roundabout of molecular events to chronic inflammation prediction

Nanomaterial-induced diseases cannot be reliably predicted because of the lack of clearly identified causal relationships, in particular between acute exposures and chronic symptoms. By applying advanced microscopies and omics to in vitro and in vivo systems, together with in silico molecular modelling, we have here determined that the long-lasting response to a single exposure originates in the counteracting of a newly discovered nanomaterial quarantining and nanomaterial cycling among different lung cell types. This allows us to predict the nanomaterial-induced spectrum of lung inflammation using only in vitro measurements and in silico modelling. Besides its profound implications for cost-efficient animal-free predictive toxicology, our work also paves the way to a better mechanistic understanding of nanomaterial- induced cancer, fibrosis, and other chronic diseases.

molecular biology↗