Biology subjects

Varga, E.

Publications and source records attributed to Varga, E..

9 recordsLinked to original sources

bioRxiv2026

Species-dependent clearance of alternariol monomethyl ether, tenuazonic acid and altertoxin II in rat and human primary hepatocytes

The ubiquitously occurring food contaminants alternariol monomethyl ether (AME), tenuazonic acid (TeA) and altertoxin II (ATX-II) are recognized as emerging Alternaria mycotoxins, yet substantial data gaps remain regarding their toxicokinetic characteristics. The hepatic metabolism of these three substances was investigated in primary rat (PRH) and human (PHH) hepatocytes by monitoring parent compound depletion and, where applicable, metabolite formation. AME was initially evaluated at 5 {micro}M and subsequently investigated over a concentration range of 0.75-8 {micro}M (0.75, 1.5, 3, and 8 {micro}M), whereas TeA was assessed at 5 {micro}M, and ATX-II was assessed at 0.22 and 1.11 {micro}M, with additional short-term experiments conducted over 30 min for AME and ATX-II, and over 10 min for concentration-dependent AME depletion. For AME, time-dependent clearance was further evaluated in PRHs at two hepatocyte densities, 0.25 and 0.5 million cells/mL. In PRHs, AME metabolism followed Michaelis-Menten kinetics (Vmax = 150.9 pmol{middle dot}min-{superscript 1}{middle dot}10- cells, Km = 1.18 {micro}M), whereas no reliable kinetic model could be established for PHHs. In contrast, TeA exhibited high metabolic stability, with only 9-10% depletion after 4 h, indicating negligible hepatic clearance in both species. ATX-II was also rapidly depleted and became undetectable within 30 min, accompanied by transient formation of altertoxin I (ATX-I), which was more pronounced in PHHs than in PRHs. Substrate depletion revealed pronounced interspecies differences in hepatic clearance capacity and stability. Overall, these findings provide comparative insights in primary human and rat hepatocyte systems, offering a foundation for future studies on their toxicological relevance and impact on human health.

pharmacology and toxicology↗

bioRxiv2026

Human internal exposures to alternariol and its monomethyl ether are predicted below thresholds of in vitro toxicity by physiologically based kinetic modeling

The foodborne mycotoxins alternariol (AOH) and alternariol monomethyl ether (AME) have been associated with several adverse effects, including cytotoxicity, genotoxicity, endocrine disruption, and immunomodulation. As these endpoints are typically observed in vitro at micromolar concentrations, the question arises whether such levels are attainable in exposed humans. To address this data gap in chemical risk assessment, a physiologically based kinetic (PBK) model was developed to predict internal exposure doses to AOH and AME in humans. As input parameters, kinetic constants for hepatic glucuronidation were obtained in vitro by incubating Sprague Dawley rat and human liver S9 fractions with 0.5-50 {micro}M AOH and 0.5-20 {micro}M AME, demonstrating rapid biotransformation in both species. Intestinal absorption of AME and physicochemical parameters were estimated using quantitative structure-activity relationship (QSAR) models. Sensitivity analysis identified parameters describing hepatic glucuronidation and gastrointestinal uptake as among the most influential, confirming the importance of their reliable estimation. The PBK model was evaluated against available rodent toxicokinetic data and subsequently extrapolated to humans. Ultimately, the currently available exposure estimates published by EFSA in 2016 were applied to predict target tissue concentrations, which were compared to points of departure (PoDs) for relevant toxicological endpoints. Even in the most susceptible group of male toddlers, predicted internal concentrations (10{square}{square} {micro}M range) were approximately four orders of magnitude below the respective PoDs. Consequently, under the applied exposure assumptions and considering the compounds as isolated chemicals, AOH and AME are not expected to reach systemic or tissue concentrations associated with the investigated effects.

pharmacology and toxicology↗

bioRxiv2026

Comparative metabolism of the Alternaria toxins altenuene and tentoxin in rat and human primary hepatocytes

The ubiquitously occurring food contaminants altenuene (ALT) and tentoxin (TEN) are recognized as emerging Alternaria mycotoxins, yet substantial data gaps remain when it comes to their toxicological behavior and toxicokinetic characteristics. This study aimed to compare and generate quantitative data on their hepatic metabolism and to obtain semi-quantitative insights into their metabolite profiles. To this end, primary rat and human hepatocytes were incubated with 10 {micro}M ALT or TEN over multiple time points up to 4 h. Both substrate depletion and metabolite identification revealed pronounced interspecies differences. The extent of ALT metabolism was significant, with an 88% and 57% decrease in rat and human hepatocytes after 4 h, respectively. In contrast, TEN showed extensive biotransformation in rats (67%) but only modest turnover in humans (27%) over the same period. Hepatocellular clearances were consistently higher for ALT than TEN, with hepatic extraction ratios indicating intermediate extraction for ALT and low extraction for TEN. High-resolution mass spectrometry combined with targeted analysis of selected metabolites annotated phase II conjugation as the predominant metabolic pathway for ALT and phase I oxidative metabolism for TEN, including mono- and double-metabolized species for the latter. Overall, these results provide a comprehensive characterization of ALT- and TEN-metabolism in hepatocytes, offering a foundation for future studies on their toxicological relevance and impact on human health.

pharmacology and toxicology↗

bioRxiv2024

RIDDEN: Data-driven inference of receptor activity from transcriptomic data

Intracellular signaling initiated from ligand bound receptors plays a fundamental role in both physiological regulation and development of disease states, making receptors one of the most frequent drug targets. Systems level analysis of receptor activity can help to identify cell and disease type specific receptor activity alterations. While recently several computational methods have been developed to analyse ligand -receptor interactions based on transcriptomics data, none of them focuses directly on the receptor side of these interactions. Also, most of the methods use directly the expression of ligands and receptors to infer active interaction, while co-expression of genes does not necessarily indicate functional interactions or activated state. To address these problems, we developed RIDDEN (Receptor actIvity Data Driven inferENce), a computational tool, which predicts receptor activities from the receptor-regulated gene expression profiles, and not from the expressions of ligand and receptor genes. We collected 14463 perturbation gene expression profiles for 229 different receptors. Using these data, we trained the RIDDEN model, which can effectively predict receptor activity for new bulk and single-cell transcriptomics datasets. We validated RIDDENs performance on independent in vitro and in vivo receptor perturbation data, showing that RIDDENs model weights correspond to known regulatory interactions between receptors and transcription factors, and that predicted receptor activities correlate with receptor and ligand expressions in in vivo datasets. We also show that RIDDEN can be used to identify mechanistic biomarkers in an immune checkpoint blockade-treated cancer patient cohort. RIDDEN, the largest transcriptomics-based receptor activity inference model, can be used to identify cell populations with altered receptor activity and, in turn, foster the study of cell-cell communication using transcriptomics data. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=157 SRC="FIGDIR/small/626558v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@155d87dorg.highwire.dtl.DTLVardef@f80ed2org.highwire.dtl.DTLVardef@195140eorg.highwire.dtl.DTLVardef@57ccfa_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Integrating Machine Learning with Flow-Imaging Microscopy for Automated Monitoring of Algal Blooms

Real-time monitoring of phytoplankton in freshwater systems is critical for early detection of harmful algal blooms so as to enable efficient response by water management agencies. This paper presents an image processing pipeline developed to adapt ARTiMiS, a low-cost automated flow-imaging device, for real-time algal monitoring specifically in freshwater and environmental systems. This pipeline addresses several challenges associated with autonomous imaging of aquatic samples such as flow-imaging artifacts (i.e., out-of-focus and background objects), as well as specific challenges associated with monitoring of open environmental systems (i.e., identification of novel objects). The pipeline leverages a Random Forest model to identify out- of-focus particles with an accuracy of 89% and a custom background particle detection algorithm to identify and remove particles that erroneously appear in consecutive images with >97{+/-}2.8% accuracy. Furthermore, a convolutional neural network (CNN), trained to classify distinct classes comprising both taxonomical and morphological categories, achieved 94% accuracy in a closed dataset. Nonetheless, the supervised closed-set classifiers struggled with the accurate classification of objects when challenged with debris and novel particles which are common in complex open environments; this limits real-time monitoring applications by requiring extensive manual oversight. To mitigate this, three methods incorporating classification with rejection were tested to improve model precision by excluding irrelevant or unknown classes. Combined, these advances present a fully integrated, end-to-end solution for real-time HAB monitoring in open environmental systems thus enhancing the scalability of automated detection in dynamic aquatic environments. HighlightsO_LIRandom Forest model is more generalizable than Convolutional Neural Networks to remove out-of-focus particles. C_LIO_LIA two-stage clustering algorithm is effective at removing background particles in flow imaging microscopy. C_LIO_LIClosed-set CNN classifier performance deteriorates when challenged with unknown particles. C_LIO_LIClassification with rejection improves both precision and accuracy for environmental samples. C_LI

ecology↗

bioRxiv2024

The haplotype-resolved Prymnesium parvum (type B) microalga genome reveals the genetic basis of its fish-killing toxins

The catastrophic loss of aquatic life in the Central European Oder River in 2022, caused by a toxic bloom of the haptophyte microalga Prymnesium parvum (in a wide sense, s.l.), underscores the need to improve our understanding of the genomic basis of the toxin. Previous morphological, phylogenetic, and genomic studies have revealed cryptic diversity within P. parvum s.l. and uncovered three clade-specific (types A, B, C) prymnesin toxins. Here, we used state-of-the-art long-read sequencing and assembled the first haplotype-resolved diploid genome of a P. parvum type B, the strain responsible for the Oder disaster. Comparative analyses with type A genomes uncovered a genome-size expansion driven by repetitive elements in type B. We also found conserved chromosomal synteny but divergent evolution in several polyketide synthase (PKS) genes, which are known to underlie toxin production in combination with environmental cues. We identified a specific, approximately 20 kilobase pair comprising deletion in the largest PKS gene of type B that we link to differences of the chemical structure of types A and B prymnesins. Electron-microscopy and flow cytometry confirmed diploidy in the Oder River strain and differences to closely related strains in morphology and ploidy. Our results provide unprecedented resolution of strain diversity in P. parvum and a better understanding of the genomic basis of toxin variability in haptophytes. The reference-quality genome will help to understand changes to microbial diversity in the face of increasing environmental pressures, and provides a basis for strain-level monitoring of invasive Prymnesium in the future.

genomics↗

bioRxiv2021

PeakBot: Machine learning based chromatographic peak picking

MotivationChromatographic peak picking is among the first steps in data processing workflows of raw LC-HRMS datasets in untargeted metabolomics applications. Its performance is crucial for the holistic detection of all metabolic features as well as their relative quantification for statistical analysis and metabolite identification. Random noise, non-baseline separated compounds and unspecific background signals complicate this task. ResultsA machine-learning framework entitled PeakBot was developed for detecting chromatographic peaks in LC-HRMS profile-mode data. It first detects all local signal maxima in a chromatogram, which are then extracted as super-sampled standardized areas (retention-time vs. m/z). These are subsequently inspected by a custom-trained convolutional neural network that forms the basis of PeakBots architecture. The model reports if the respective local maximum is the apex of a chromatographic peak or not as well as its peak center and bounding box. In training and independent validation datasets used for development, PeakBot achieved a high performance with respect to discriminating between chromatographic peaks and background signals (accuracy of 0.99). For training the machine-learning model a minimum of 100 reference features are needed to learn their characteristics to achieve high-quality peak-picking results for detecting such chromatographic peaks in an untargeted fashion. PeakBot is implemented in python (3.8) and uses the TensorFlow (2.5.0) package for machine-learning related tasks. It has been tested on Linux and Windows OSs. AvailabilityThe package is available free of charge for non-commercial use (CC BY-NC-SA). It is available at https://github.com/christophuv/PeakBot. Contactchristoph.bueschl@univie.ac.at Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2019

Large-scale in-vitro production of red blood cells from human peripheral blood mononuclear cells

Transfusion of donor-derived red blood cells is the most common form of cellular therapy. Donor availability and the potential risk of alloimmunization and other transfusion-related complications may, however, limit the availability of transfusion units especially for chronically transfused patients. In-vitro cultured, customizable red blood cells would negate these concerns and introduce precision medicine. Large-scale, cost effective production depends on optimization of culture conditions. We developed a defined medium and adapted our protocols to GMP culture requirements, which reproducibly provided pure erythroid cultures from peripheral blood mononuclear cells without prior CD34+ isolation, and a 3x107-fold increase in erythroblasts in 25 days. Expanded erythroblast cultures could be differentiated to CD71dimCD235a+CD44+CD117-DRAQ5- red blood cells in 12 days. More than 90% of the cells enucleated and expressed adult hemoglobin as well as the correct blood group antigens. Deformability and oxygen binding capacity of cultured red blood cells was comparable to in-vivo reticulocytes. Daily RNA sampling during differentiation followed by RNA-seq provided a high-resolution map/resource of changes occurring during terminal erythropoiesis. The culture process was compatible with upscaling using a G-Rex bioreactor with a capacity of 1L per reactor, allowing transition towards clinical studies and small-scale applications.

cell biology↗

bioRxiv2018

Efficient production of erythroid, megakaryoid and myeloid cells, using a single cell-derived iPSC colony differentiation

Hematopoietic differentiation of human induced pluripotent stem cells (iPSCs) provide opportunities not only for fundamental research and disease modelling/drug testing but also for large-scale production of blood effector cells for future clinical application. Although there are multiple ways to differentiate human iPSCs towards hematopoietic lineages, there is a need to develop reproducible and robust protocols. Here we introduce an efficient way to produce three major blood cell types using a standardized differentiation protocol that starts with a single hematopoietic initiation step. This system is feeder-free, avoids EB-formation, starts with a hematopoietic initiation step based on a novel single cell-derived iPSC colony differentiation and produces multi-potential progenitors within 8-10 days. Followed by lineage-specific growth factor supplementation these cells can be matured into well characterized erythroid, megakaryoid and myeloid cells with high-purity, without transcription factor overexpression or any kind of pre-purification step. This standardized differentiation system provides a simple platform to produce specific blood cells in a reproducible manner for hematopoietic development studies, disease modelling, drug testing and the potential for future therapeutic applications.\n\nHighlightsO_LIEfficient hematopoietic differentiation from single cell-derived iPSC colonies\nC_LIO_LIReproducible feeder-free, monolayer differentiation system independent of iPSC line\nC_LIO_LIProduction of erythroid, megakaryoid and myeloid cells with high-purity\nC_LIO_LIPlatform for hematopoietic developmental research and future clinical application\nC_LI

developmental biology↗