Biology subjects

Ewald, J.

Publications and source records attributed to Ewald, J..

14 recordsLinked to original sources

bioRxiv2026

Cell Painting and machine learning for mechanistic profiling of hepatotoxic botanicals and their constituents

BACKGROUND Botanicals are widely consumed as herbal remedies and plant-derived products, but their complex and variable chemical composition presents major challenges for safety assessment. Although hepatotoxicity has been reported for several botanicals, the constituent compounds responsible and the underlying mechanisms often remain poorly characterised. OBJECTIVES We aimed to determine whether Cell Painting could identify candidate constituent drivers of botanical bioactivity and generate hypotheses about potential mechanisms of action in primary human hepatocytes. METHODS We profiled 13 botanical extracts and 85 known constituents across multiple concentrations using Cell Painting alongside assays of metabolic activity and membrane integrity. To interpret morphological phenotypes, we trained machine-learning models using a Cell Painting reference atlas of >1,000 chemicals linked to ToxCast/Tox21 molecular and cellular bioactivity annotations. RESULTS Cell Painting detected bioactivity in 11/13 botanical extracts and 46/85 constituents, compared with 9/13 extracts and 40/85 constituents in the cell-health assays, and generally detected activity at lower concentrations. For 9/13 extracts, at least one constituent was active at a lower concentration than the corresponding botanical extract, supporting the contribution of a subset of potent constituents to mixture-level responses. Aristolochia and goldenseal were the most potent extracts across Cell Painting and cell-health assays and contained highly active constituents. Reference-atlas models generated specific mechanistic hypotheses, including cytotoxicity-, endocrine-, and genotoxicity-associated responses for several botanicals and constituents. CONCLUSION These results support Cell Painting combined with machine learning as a sensitive approach for characterising cellular responses to botanicals, prioritising candidate constituent drivers of hepatotoxicity-associated phenotypes, and generating testable hypotheses about potential mechanisms of action.

pharmacology and toxicology↗

bioRxiv2026

Reciprocal Environmental Decision Support (REDS): better tailored advice in return for data

1. Environmental Decision Support systems provide model-based predictions, tailored to user-inputted information about local ecosystems, which can support management decisions by citizens. Citizen Science systems accept user input to improve models. Thus, each system type emphasises automatic data transfer in one direction: model to citizen or citizen to model, respectively. 2. We introduce a new system type that combines automated data transfer in both directions. 3. Reciprocal Environmental Decision Support (REDS) systems process user-contributed information to (1) provide tailored predictions supporting management, and (2) improve the underlying predictive model. 4. For our proof-of-concept REDS system, Garden Advice, we began with a Bayesian species-habitat association model (for the House Sparrow Passer domesticus) based on existing data, and obtained new data from UK residents about habitat structure and sparrow observations in domestic gardens. The model made predictions about sparrow presence, and effects of planned garden changes. Model parameters were updated by contributed data; parameter estimates generally tightened. One notable update likely reflects reality (a positive association with grass), while another (a reduced association with roof proximity) likely reflects observation bias. 5. The updated model predicted the new data better than the original model. Thus, untrained observers can provide data of sufficient quality to refine a model of trained observer data. Notwithstanding important questions about distinguishing observation bias from ecologically meaningful information, our system demonstrates that important synergies can be obtained from the REDS approach. Later users of the system obtained better advice thanks to automatically incorporated contributions from earlier users. Lay SummaryA summary for non-specialists is available at https://bit.ly/reds_brochure.

ecology↗

bioRxiv2025

Tsa1-Mediated Regulation of PKA Tunes Trehalose Metabolism in Saccharomyces cerevisiae

Cells must continuously adapt their metabolism, growth, and division to fluctuating nutrient availability. In Saccharomyces cerevisiae, this coordination is largely governed by the Ras/cAMP/PKA pathway, which promotes fermentative growth under glucose-rich conditions and is rapidly downregulated upon glucose depletion. This downregulation enables stress responses, global metabolic rewiring, and entry into quiescence. While PKA is known to integrate multiple environmental cues with glucose availability, the crosstalk to other stress-responsive signaling mechanisms remains incompletely understood. Here, we identify the peroxiredoxin Tsa1, a central player in oxidative stress protection and redox signaling, as a key modulator of PKA activity during glucose depletion and stationary phase entry. Combining live-cell imaging of Msn2, analysis of STRE-dependent gene expression, Nth1 phosphorylation assays, and metabolic flux modelling, we show that loss of Tsa1 leads to incomplete downregulation of PKA signaling. Consequently, tsa1{Delta} cells display delayed transition to respiratory growth, reduced gluconeogenic capacity, and impaired accumulation of storage carbohydrates. Failure to properly attenuate PKA activity also disrupts accurate cell cycle control upon glucose depletion, resulting in abnormally small cells that contribute to the reduced chronological lifespan and delayed recovery upon nutrient repletion. Together, our findings establish Tsa1 as a critical link between redox regulation and nutrient signaling, ensuring proper metabolic adaptation and cellular fitness during glucose exhaustion.

molecular biology↗

bioRxiv2025

A short cut to sample coverage standardization in meta-barcoding data provides new insights into land use effects on insect diversity

The use of metabarcoding for insect species identification has grown rapidly, but the absence of abundance data hinders meaningful diversity metrics like sample coverage-standardized species richness. Additionally, the vast number of taxa often lacks a unified phylogeny or trait database. We present a framework for constructing a phylogenetic tree encompassing the majority of insect families, standardisation of sample coverage (an objective measure of sample completeness) and assessment of both taxonomic and phylogenetic diversity using the Hill series for metabarcoding data. Applied to central Europe, our framework analysed insect diversity from 400 families along a land-use gradient. Results revealed land-use intensity significantly affects sample coverage, emphasizing the need for biodiversity standardization. After standardization, taxonomic diversity declined by 27-44%, and phylogenetic diversity by 13-29% across 39,000 Operational Taxonomic Units, from forests to agricultural areas. Rare species exhibited greater phylogenetic diversity loss than taxonomic, while dominant species showed smaller phylogenetic losses but stronger declines in taxonomic diversity. Our findings underscore agricultures detrimental effects on specific insect taxa, even after adjusting for sample coverage, and provide new insights into the loss of functional diversity, as represented by phylogeny.

ecology↗

bioRxiv2025

Zero-Shot, Big-Shot, Active-Shot - How to estimate cell confluence, lazily

Mesenchymal stem cell therapy shows promising results for difficult-to-treat diseases, but standardized manufacturing requires robust quality control through automated cell confluence monitoring. While deep learning can automate confluence estimation, research on cost-effective dataset curation and the role of foundation models in this task remains limited. We systematically investigate the most effective strategies for confluence estimation, focusing on active learning-based dataset curation, goal-specific labeling, and leveraging foundation models for zero-shot inference. Here, we show that zero-shot inference with the Segment Anything Model (SAM) achieves excellent confluence estimation without any task-specific training, outperforming fine-tuned smaller models. Further, our findings demonstrate that active learning does not significantly improve model dataset curation compared to random selection in homogeneous cell datasets. We show that goal-specific, simplified labeling strategies perform comparably to precise annotations while substantially reducing annotation effort. These results challenge common assumptions about dataset curation: neither active learning nor extensive fine-tuning provided significant benefits for our specific use case. Instead, we found that leveraging SAMs zero-shot capabilities and targeted labeling strategies offers the most cost-effective approach to automated confluence estimation. Our work provides practical guidelines for implementing automated cell monitoring in MSC manufacturing, demonstrating that extensive dataset curation may be unnecessary when foundation models can effectively handle the task out of the box.

bioinformatics↗

bioRxiv2024

A generalized and versatile framework to train and evaluate autoencoders for biological representation learning and beyond: AUTOENCODIX

Insights and discoveries in complex biological systems, e.g. for personalized medicine, are gained by the combination of large, feature-rich and high-dimensional data with powerful computational methods uncovering patterns and relationships. In recent years, autoencoders, a family of deep learning-based methods for representation learning, are advancing data-driven research due to their variability and non-linear power of multi-modal data integration. Despite their success, current implementations lack standardization, versatility, comparability, and generalizability preventing a broad application. To fill the gap, we present AUTOENCODIX (https://github.com/jan-forest/autoencodix), an open-source framework, designed as a standardized and flexible pipeline for preprocessing, training, and evaluation of autoencoder architectures. These architectures, like ontology-based and cross-modal autoencoders, provide key advantages over traditional methods via explainability of embeddings or the ability to translate across data modalities. We show the value of our framework by its application to data sets from pan-cancer studies (TCGA), single-cell sequencing as well as in combination with imaging. Our studies provide important user-centric insights and recommendations to navigate through architectures, hyperparameters, and important trade-offs in representation learning. Those include reconstruction capability of input data, the quality of embedding for downstream machine learning models, or the reliability of ontology-based embeddings for explainability. In summary, our versatile and generalizable framework allows multi-modal data integration in biomedical research and any other data-driven fields of research. Hence, it can serve as a open-source platform for several major trends and research using autoencoders including architectural improvements, explainability, or training of large-scale pre-trained models.

bioinformatics↗

bioRxiv2024

Visible neural networks for multi-omics integration: a critical review

Biomarker discovery and drug response prediction is central to personalized medicine, driving demand for predictive models that also offer biological insights. Biologically informed neural networks (BINNs), also known as visible neural networks (VNNs), have recently emerged as a solution to this goal. BINNs or VNNs are neural networks whose inter-layer connections are constrained based on prior knowledge from gene on-tologies pathway databases. These sparse models enhance interpretability by embedding prior knowledge into their architecture, ideally reducing the space of learnable functions to those that are biologically meaningful. In this systematic review--the first of its kind-- we identify 86 recent papers implementing such models and highlight key trends in architectural design decisions, data sources and methods for evaluation. Growth in popularity of the approach is apparently mitigated by a lack of standardized terminology, tools and benchmarks.

bioinformatics↗

bioRxiv2024

HumanIslets: An integrated platform for human islet data access and analysis

Comprehensive molecular and cellular phenotyping of human islets can enable deep mechanistic insights for diabetes research. We established the Human Islet Data Analysis and Sharing (HI-DAS) consortium to advance goals in accessibility, usability, and integration of data from human islets isolated from donors with and without diabetes at the Alberta Diabetes Institute (ADI) IsletCore. Here we introduce HumanIslets.com, an open resource for the research community. This platform, which presently includes data on 547 human islet donors, allows users to access linked datasets describing molecular profiles, islet function and donor phenotypes, and to perform various statistical and functional analyses at the donor, islet and single-cell levels. As an example of the analytic capacity of this resource we show a dissociation between cell culture effects on transcript and protein expression, and an approach to correct for exocrine contamination found in hand-picked islets. Finally, we provide an example workflow and visualization that highlights links between type 2 diabetes status, SERCA3b Ca2+-ATPase levels at the transcript and protein level, insulin secretion and islet cell phenotypes. HumanIslets.com provides a growing and adaptable set of resources and tools to support the metabolism and diabetes research community.

systems biology↗

bioRxiv2024

A de novo variant in PAK2 detected in an individual with Knobloch type 2 syndrome

P21-activated kinase 2 (PAK2) is a serine/threonine kinase essential for a variety of cellular processes including signal transduction, cellular survival, proliferation, and migration. A recent report proposed monoallelic PAK2 variants cause Knobloch syndrome type 2 (KNO2)--a developmental disorder primarily characterized by ocular anomalies. Here, we identified a novel de novo heterozygous missense variant in PAK2, NM_002577.4:c.1273G>A, p.(D425N), by whole genome sequencing in an individual with features consistent with KNO2. Notable clinical phenotypes include global developmental delay, congenital retinal detachment, mild cerebral ventriculomegaly, hypotonia, FTT, pyloric stenosis, feeding intolerance, patent ductus arteriosus, and mild facial dysmorphism. The p.(D425N) variant lies within the protein kinase domain and is predicted to be functionally damaging by in silico analysis. Previous clinical genetic testing did not report this variant due to unknown relevance of PAK2 variants at the time of testing, highlighting the importance of reanalysis. Our findings also substantiate the candidacy of PAK2 variants in KNO2 and expand the KNO2 clinical spectrum.

genomics↗

bioRxiv2022

When good species have porous boundaries: weak reproductive isolation and extensive gene flow between Mimulus glaucescens and M. guttatus in northern California

Barriers to reproduction are often how progress in speciation is measured. Nonetheless, a key unresolved question is the extent to which reproductive barriers diminish gene flow in incipient species in nature. The Sierra Nevada foothill endemic Mimulus glaucescens and the widespread M. guttatus are considered to be distinct species based on contrasting vegetative traits, but barriers to reproduction are not readily apparent, although these species are not known to hybridize in nature. To explore boundaries between taxa, we examined 15 potential reproductive barriers between species in a Northern California area of broad sympatry. Most barriers, with the exception of ecogeographic isolation, were weak, and total isolation for each species was estimated to be incomplete. Population genomic analyses of range-wide and broadly sympatric accessions revealed that gene flow between these taxa is common across the range, and rampant within areas of sympatry. Thus, despite fairly strong ecological differentiation - which may be involved in maintenance of vegetative differences - ecological isolation is a weak barrier to gene flow in this system. This work underscores the value of combining classical measures of reproductive isolation with estimates of natural gene flow for studies of speciation in natural communities.

evolutionary biology↗

bioRxiv2022

Landscape diversity and local temperature, but not climate, affect arthropod predation among habitat types

Arthropod predators are important for ecosystem functioning by providing top-down regulation of insect herbivores. As predator communities and activity are influenced by biotic and abiotic factors on different spatial scales, the strength of top-down regulation ( arthropod predation) is also likely to vary. Understanding the combined effects of potential drivers on arthropod predation is urgently needed with regard to anthropogenic climate and land-use change. In a large-scale study, we recorded arthropod predation rates using artificial caterpillars on 113 plots of open herbaceous vegetation embedded in contrasting habitat types (forest, grassland, arable field, settlement) along climate and land-use gradients in Bavaria, Germany. As potential drivers we included habitat characteristics (habitat type, plant species richness, local mean temperature and mean relative humidity during artificial caterpillar exposure), landscape diversity (0.5-3.0-km, six scales), climate (multi-annual mean temperature, MAT) and interactive effects of habitat type with other drivers. We observed no substantial differences in arthropod predation rates between the studied habitat types, related to plant species richness and across the Bavarian-wide climatic gradient, but predation was limited when local mean temperatures were low and tended to decrease towards higher relative humidity. Arthropod predation rates increased towards more diverse landscapes at a 2-km scale. Interactive effects of habitat type with local weather conditions, plant species richness, landscape diversity and MAT were not observed. We conclude that landscape diversity favours high arthropod predation rates in open herbaceous vegetation independent of the dominant habitat in the vicinity. This finding may be harnessed to improve top-down control of herbivores, e.g. agricultural pests, but further research is needed for more specific recommendations on landscape management. The absence of MAT effects suggests that high predation rates may occur independent of moderate increases of MAT in the near future.

ecology↗

bioRxiv2021

The landscape of toxic intermediates in the metabolic networks of pathogenic fungi reveals targets for antifungal drugs

The burden of fungal infections for humans, animals and plants is widely underestimated and comprises deadly infections as well as great economic costs. Despite that, antifungal drugs are scarce and emergence of resistance in fungal strains contributes to a high mortality. To overcome this shortage, we propose toxic intermediates and their controlling enzymes in metabolic pathways as a resource for new targets and provide a web-service, FunTox-Networks to explore the landscape of toxic intermediates in the metabolic networks of fungal pathogens. The toxicity of metabolites is predicted by a new random forest regression model and is available for over one hundred fungal species. Further, for major fungal pathogens, metabolic networks from the KEGG database were enriched with data of toxicity and regulatory effort for each enzyme to support identification of targets. We determined several toxic intermediates in fungal-specific pathways like amino acid synthesis, nitrogen and sulfur assimilation, and the glyoxylate bypass. For the latter, we show experimentally that growth of the pathogen Candida albicans is inhibited when the detoxifying enzymes Mls1 and Hbr2 are deleted and toxic glyoxylate accumulates in the cell. Thus, toxic pathway intermediates and their controlling enzymes represent an untapped resource of antifungal targets.

systems biology↗

bioRxiv2021

Dynamic optimization reveals alveolar epithelial cells as key mediators of host defense in invasive aspergillosis

Aspergillus fumigatus is an important human fungal pathogen and its conidia are constantly inhaled by humans. In immunocompromised individuals, conidia can grow out as hyphae that damage lung epithelium. The resulting invasive aspergillosis is associated with devastating mortality rates. Since infection is a race between the innate immune system and the outgrowth of A. fumigatus conidia, we use dynamic optimization to obtain insight into the recruitment and depletion of alveolar macrophages and neutrophils. Using this model, we obtain key insights into major determinants of infection outcome on host and pathogen side. On the pathogen side, we predict in silico and confirm in vitro that germination speed is a key virulence trait of fungal pathogens due to the vulnerability of conidia against host defense. On the host side, we find that epithelial cells play a so far underappreciated role in fungal clearance and are potent mediators of cytokine release which we confirm ex vivo. Further, our model affirms the importance of neutrophils in invasive aspergillosis and underlines that the role of macrophages remains elusive. We expect that our model will contribute to improvement of treatment protocols by focusing on the critical components of immune response to fungi but also fungal virulence.

systems biology↗

bioRxiv2021

Disentangling effects of climate and land use on biodiversity and ecosystem services - a multi-scale experimental design

O_LIClimate and land-use change are key drivers of environmental degradation in the Anthropocene, but too little is known about their interactive effects on biodiversity and ecosystem services. Long-term data on biodiversity trends are currently lacking. Furthermore, previous ecological studies have rarely considered climate and land use in a joint design, did not achieve variable independence or lost statistical power by not covering the full range of environmental gradients. C_LIO_LIHere, we introduce a multi-scale space-for-time study design to disentangle effects of climate and land use on biodiversity and ecosystem services. The site selection approach coupled extensive GIS-based exploration and correlation heatmaps with a crossed and nested design covering regional, landscape and local scales. Its implementation in Bavaria (Germany) resulted in a set of study plots that maximizes the potential range and independence of environmental variables at different spatial scales. C_LIO_LIStratifying the state of Bavaria into five climate zones and three prevailing land-use types, i.e. near-natural, agriculture and urban, resulted in 60 study regions covering a mean annual temperature gradient of 5.6-9.8 {degrees}C and a spatial extent of 380x360 km. Within these regions, we nested 180 study plots located in contrasting local land-use types, i.e. forests, grasslands, arable land or settlement (local climate gradient 4.5-10 {degrees}C). This approach achieved low correlations between climate and land-use (proportional cover) at the regional and landscape scale with |r [≤]0.33| and |r [≤]0.29|, respectively. Furthermore, using correlation heatmaps for local plot selection reduced potentially confounding relationships between landscape composition and configuration for plots located in forests, arable land and settlements. C_LIO_LIThe suggested design expands upon previous research in covering a significant range of environmental gradients and including a diversity of dominant land-use types at different scales within different climatic contexts. It allows independent assessment of the relative contribution of multi-scale climate and land use on biodiversity and ecosystem services. Understanding potential interdependencies among global change drivers is essential to develop effective restoration and mitigation strategies against biodiversity decline, especially in expectation of future climatic changes. Importantly, this study also provides a baseline for long-term ecological monitoring programs. C_LI

ecology↗