Biology subjects

Schirmer, K.

Publications and source records attributed to Schirmer, K..

7 recordsLinked to original sources

bioRxiv2025

Multiomics profiling of zebrafish embryonic cell line PAC2 across growth phases to assess its relevance for toxicological studies

Permanent fish cell lines offer promising alternatives to traditional animal models for environmental risk assessment of chemicals. However, to facilitate their broader uptake into toxicity testing practice, a better understanding of functional capacities and expression of toxicologically relevant molecular targets is needed. Here, we present an extensive molecular profiling of the zebrafish embryonic cell line PAC2, combining global proteomics across cell population growth phases (over 7300 protein groups) with matched transcriptomics at exponential and stationary phase (over 14500 transcripts). Proteome coverage was sufficiently deep to reveal functional insights consistent with those derived from transcriptomics data, despite differences in the total number of measured genes. Major gene expression shifts detected upon transition from exponential to stationary cell population growth phase indicated reduction in DNA replication, translation, metabolism, and cell cycle regulation, along with increased stress responses, immune system responses, and extracellular matrix remodeling. Functional annotation revealed expression of core cellular processes along with a number of toxicologically relevant pathways, including xenobiotic metabolism, stress signaling, and nuclear receptors responsive to important chemical classes, such as steroids (e.g., estrogens, glucocorticoids) and chemicals known to disrupt lipid metabolism, e.g., through interaction with peroxisome proliferation activating receptors. These findings reinforce the potential of PAC2 cells to offer a versatile in vitro model for studying fish cell biology and omics-enhanced exploration of chemical toxicity mechanisms, aided by the well-developed molecular annotation in zebrafish. Moreover, the analysis approaches developed in this work offer a blueprint for molecular baseline characterization of other fish cell lines. This work thus strengthens the mechanistic foundation supporting the use of fish cell lines as alternative models in aquatic toxicity testing and risk assessment.

molecular biology↗

bioRxiv2025

On the Comparability of Studies in Predictive Ecotoxicology

Comparability across in silico predictive ecotoxicology studies remains a significant challenge, particularly when assessing model performance. In this work, we identify key criteria necessary for meaningful comparison between independent studies: (i) the use of identical datasets that represent the same chemical and/or taxonomic space; (ii) consistent data cleaning procedures; (iii) identical train/test splits; (iv) clearly defined evaluation metrics, as subtle differences--such as alternative formulations of R2--can lead to misleading discrepancies; and (v) transparent reporting through code and dataset sharing. Our review of recent literature on fish acute toxicity prediction reveals a critical gap: no two studies fully meet these criteria, rendering cross-study comparisons unreliable. This lack of comparability hampers scientific progress in the field. To address this, we advocate for the adoption of benchmark datasets with standardized cleaning protocols, version control, and defined data splits. We further emphasize the importance of precise metric definitions and transparent reporting practices, including code availability and the use of structured reporting or data sheets, to foster reproducibility and advance the discipline.

pharmacology and toxicology↗

bioRxiv2024

Daphnids Can Safeguard the Use of Alternative Bioassays to the Acute Fish Toxicity Test: A Focus on Neurotoxicity

Assessment of potential impacts of chemicals on the environment traditionally involves regulatory standard data requirements for acute aquatic toxicity testing using algae, daphnids and fish (e.g., OECD test guidelines (TG) 201, 202, and 203, respectively), representing different trophic levels. In line with the societal goal to replace or reduce vertebrate animal testing, alternative bioassays were developed to replace testing with fish: the fish cell line RTgill-W1 acute toxicity assay (OECD TG249) and the zebrafish embryo acute toxicity test (zFET, OECD TG236). However, previous studies revealed the lower sensitivity of the RTgill-W1 cell line assay and zFET for some neurotoxic chemicals and allyl alcohol, which is presumably biotransformed in fish to the more toxic acrolein (which is predicted well through the cell line assay). To provide an additional alternative to acute fish toxicity, in this study, we analyzed historic ecotoxicity data for fish and daphnids from the EnviroTox Database. We found a considerable variability in acute fish LC50 and acute daphnids EC50 values, particularly for neurotoxic chemicals. Comparing sensitivity of these taxonomic groups according to different neurotoxicity classification schemes indicates that fish rarely represent the most sensitive trophic level of the two. Exceptions here most prominently include a few cyclodiene compounds, which are no longer marketed, and a chemical group that could be identified through structural alerts. Moreover, daphnids are more sensitive than fish to acrolein. This analysis highlights the potential of the Daphnia acute toxicity test, which is usually a standard regulatory data requirement, in safeguarding the environmental protection level provided by the RTgill-W1 cell line assay and the zFET. This research, rooted in decades of efforts to replace the fish acute toxicity test, shifts the focus from predicting fish toxicity 1-to-1 to emphasizing the protectiveness of alternative methods, paving the way for further eliminating vertebrate tests in environmental toxicology.

pharmacology and toxicology↗

bioRxiv2024

Differential biotransformation ability may alter fish biodiversity in polluted waters

Divergence in the activity of biotransformation pathways could lead to species sensitivity differences to chemical stress. To explore this hypothesis, we evaluated the biotransformation capacity of five fish species that are representatives of Swiss biodiversity assemblages and that inhabit watercourses surrounded by different land use. We report important interspecific differences regarding the presence and activity of major biotransformation pathways, such as the invasive pumpinkseed (Lepomis gibbosus) displaying micropollutant clearance between 3- and 7-fold higher than native species (e.g. Salmo trutta, Squalius cephalus) collected in the same areas. These differences were exacerbated by urban and agricultural influence, which increased biotransformation potential at the enzyme level by as much as 11-fold and micropollutant clearance by approximately 2-fold compared to biotransformation levels in areas with minimal human influence. In the context of the chemical defensome, we argue that fish with low biotransformation activity carry a greater burden on chemical stress, making them less likely to cope with additional stressors and sustain their population in competition with species with a higher biotransformation capacity.

pharmacology and toxicology↗

bioRxiv2024

Machine learning-based prediction of fish acute mortality: Implementation, interpretation, and regulatory relevance

Regulation of chemicals requires knowledge of their toxicological effects on a large number of species, which has traditionally been acquired through in vivo testing. The recent effort to find alternatives based on machine learning, however, has not focused on guaranteeing transparency, comparability and reproducibility, which makes it difficult to assess advantages and disadvantages of these methods. Also, comparable baseline performances are needed. In this study, we trained regression models on the ADORE "t-F2F" challenge proposed in [Schur et al., Nature Scientific data, 2023] to predict acute mortality, measured as LC50 (lethal concentration 50), of organic compounds on fishes. We trained LASSO, random forest (RF), XGBoost, Gaussian process (GP) regression models, and found a series of aspects that are stable across models: (i) using mass or molar concentrations does not affect performances; (ii) the performances are only weakly dependent on the molecular representations of the chemicals, but (iii) strongly on how the data is split. Overall, the tree-based models RF and XGBoost performed best and we were able to predict the log10-transformed LC50 with a root mean square error of 0.90, which corresponds to an order of magnitude on the original LC50 scale. On a local level, on the other hand, the models are not able to consistently predict the toxicity of individual chemicals accurately enough. Predictions for single chemicals are mostly influenced by a few chemical properties while taxonomic traits are not captured sufficiently by the models. We discuss technical and conceptual improvements for these challenges to enhance the suitability of in silico methods to environmental hazard assessment. Accordingly, this work showcases state-of-the-art models and contributes to the ongoing discussion on regulatory integration. Environmental significanceConventional environmental hazard assessment in its current form will not be able to adapt to the growing need for toxicity testing. Alternative methods, such as toxicity prediction through machine learning, could fulfill that need in an economically and ethically sound manner. Proper implementation, documentation, and the integration into the regulatory process are prerequisites for the usability and acceptance of these models.

bioinformatics↗

bioRxiv2023

ADORE: A benchmark dataset for machine learning in ecotoxicology

The use of machine learning for predicting ecotoxicological outcomes is promising, but underutilized. The curation of data with informative features requires both expertise in machine learning as well as a strong biological and ecotoxicological background, which we consider a barrier of entry for this kind of research. Additionally, model performances can only be compared across studies when the same dataset, cleaning, and splittings were used. Therefore, we provide ADORE, an extensive and well-described dataset on acute aquatic toxicity in three relevant taxonomic groups (fish, crustaceans, and algae). The core dataset describes ecotoxicological experiments and is expanded with phylogenetic and species-specific data on the species as well as chemical properties and molecular representations. Apart from challenging other researchers to try and achieve the best model performances across the whole dataset, we propose specific relevant challenges on subsets of the data and include datasets and splittings corresponding to each of these challenge as well as in-depth characterization and discussion of train-test splitting approaches. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/542160v2_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1a58fc3org.highwire.dtl.DTLVardef@817f57org.highwire.dtl.DTLVardef@1cf5626org.highwire.dtl.DTLVardef@120e814_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

Wastewater microorganisms impact microbial diversity and important ecological functions of stream periphyton

Wastewater treatment plant effluents can impact microbial communities in receiving streams. However, little is known about the role of microorganisms in wastewater as opposed to other wastewater constituents, such as nutrients and micropollutants. We aimed therefore at determining the impact of wastewater microorganisms on the microbial diversity and function of periphyton, key microbial communities in streams. Periphyton was grown in flow-through channels that were continuously alimented with a mixture of stream water and unfiltered or ultra-filtered wastewater. Impacts were assessed on periphyton biomass, activities and tolerance to micropollutants, as well as on microbial diversity. Our results showed that wastewater microorganisms colonized periphyton and modified its community composition, resulting for instance in an increased abundance of Chloroflexi and a decreased abundance of diatoms and green algae. This led to shifts towards heterotrophy, as suggested by the changes in nutrient stoichiometry and the increased mineralization potential of carbon substrates. An increased tolerance towards micropollutants was only found for periphyton exposed to unfiltered wastewater but not to ultra-filtered wastewater, suggesting that wastewater microorganisms were responsible for this increased tolerance. Overall, our results highlight the need to consider the role of wastewater microorganisms when studying potential impacts of wastewater on the receiving water body. Environmental implicationThe present study investigates the impact of wastewater microorganisms on periphyton, i.e. communities forming the microbial skin of streambeds. We were able to disentangle specific effects of wastewater microorganisms in the context of the complex wastewater matrix. Indeed, wastewater microorganisms induced strong changes in periphyton community composition and function, suggesting the need to consider wastewater microbial communities as a stressor per se, similarly to, e.g., nutrients and micropollutants. Moreover, since periphyton is at the basis of the food web in streams, these changes may have consequences for higher trophic levels.

ecology↗