Biology subjects

Backhaus, T.

Publications and source records attributed to Backhaus, T..

8 recordsLinked to original sources

bioRxiv2025

Temperature-dependence of Early Development of Zebrafish and the Consequences for Laboratory Use and Animal Welfare

Zebrafish (Danio rerio) are widely used in biological research, but the impact of incubation temperatures on developmental endpoints is still insufficiently studied. This study quantifies developmental differences in zebrafish embryos incubated at 26{degrees}C and 28{degrees}C, focusing on key endpoints (heartbeat onset, hatching time, eye size, yolk sac consumption, and body length). For this purpose, we recorded a high-resolution time series comprising hourly observations of early developmental stages and key events and bi-hourly observations of body length until 120 hours post fertilization. Additionally, we recorded a low-resolution time series at 72, 96, and 119 hours post fertilization for detailed measurements of eye size, yolk sac area, and body length. Embryos incubated at 26{degrees}C showed consistent delays in developmental stages compared to those at 28{degrees}C, with delays becoming more pronounced at later stages. Yolk sac consumption was delayed by about 19.8 hours at 26{degrees}C by 119 hours post fertilization, suggesting a delayed onset of independent feeding. These findings suggest that time-based regulatory limits for rearing zebrafish, such as the 120-hour threshold in German regulations (TierSchVerV), do not fully account for temperature-dependent development. The results emphasize the need for guidelines linking incubation temperatures to developmental progress. Summary StatementThis study highlights the impact of differences in incubation temperatures around the optimum (26 and 28{degrees}C) on zebrafish development. Results suggest reevaluation of animal welfare guidelines for temperature dependency.

developmental biology↗

bioRxiv2025

Dataset on Temperature Dependency of Zebrafish Early Development

Zebrafish (Danio rerio) early development stages that do not feed independently, are classified as non-protected life stages under EU Directive 2010/63. Zebrafish reach the independently feeding stage not earlier than 120 hours post fertilization, depending on the incubation temperature. This paper presents a dataset documenting zebrafish early development at two commonly used temperatures 26 {degrees}C and 28 {degrees}C. We recorded onset of heartbeat and hatching as well as body length, eye size, yolk sac consumption, and swim bladder inflation. Additionally, locomotor activity was tracked after 96 and 119 hours post fertilization. The dataset serves as a baseline for selecting appropriate experimental conditions and optimizing toxicological study designs. They also facilitate the comparison of experimental results that were recorded at different temperatures. Furthermore, the data provide empirical evidence for amending current guidelines for tests with zebrafish embryos, in particular moving away from a rigid 120 hours post fertilization maximum test duration towards a temperature-dependent maximum test duration that is still in line with the aims of the German Animal Welfare Act.

developmental biology↗

bioRxiv2024

AI-aided chronic mixture risk assessment along a small European river reveals multiple sites at risk and pharmaceuticals being the main risk drivers

The vast amount of registered chemicals leads to a high diversity of substances occurring in the environment and the creation of new substances outpaces chemical risk assessment as well as monitoring strategies. Hence, risk assessment strategies need to be modified ensuring that they remain aligned with the rapid development and marketing of new substances. Here we performed a longitudinal chronic mixture risk assessment considering a real-world case study scenario with diverse anthropogenic impact types characterised by different land uses along a river in Central Germany. We sampled river water using large-volume solid phase extraction at six selected sampling sites. Following chemical analysis using liquid chromatography-high resolution mass spectrometry, we quantified 192 substances. For 34% of them, we obtained empirical chronic effect data for freshwater organisms. Furthermore, we used the open-source artificial intelligence (AI) model TRIDENT to predict chronic toxicity for all substances. A multi-scenario mixture risk assessment was conducted for three taxonomic groups, using the concentration-addition concept and considering various hazard and exposure scenarios. The results showed that the chronic risk estimates for all taxonomic groups were considerably higher when the empirical data was amended with data from in silico modelling. We identified hot spots of chemical pollution and our analysis indicated that fish were the most vulnerable taxonomic group, with pharmaceuticals being the most relevant risk drivers. Our study exemplifies the application of an AI model to predict chronic risk for aquatic organisms in combination with the consideration of multiple risk scenarios, that may complement future risk assessment strategies. HighlightsO_LI192 organic chemicals were quantified in six surface water samples along a river. C_LIO_LIMultiple hazard and exposure scenarios were considered in mixture risk assessment. C_LIO_LIArtificial intelligence was used to fill data gaps and predict chronic ecotoxicity. C_LIO_LIFish were identified as the most vulnerable taxonomic group for chronic toxicity. C_LIO_LIPharmaceuticals were the most prevalent mixture risk drivers. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/623722v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1836b85org.highwire.dtl.DTLVardef@107eeaaorg.highwire.dtl.DTLVardef@1c60caaorg.highwire.dtl.DTLVardef@16995d0_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2024

Dataset of emerging contaminants in surface water, bottom water, porewater, and sediment: Urban and aquaculture impacts in the central and southern coast of Chile

Synthetic organic chemicals, including pesticides, pharmaceuticals, and industrial compounds, pose a growing threat to marine ecosystems as they enter through a variety of pathways, including direct discharges of wastewater (untreated or treated) from industrial, agricultural, and urban sources. Additionally, runoff from residential and agricultural land, as well as inland waterways, transport these chemicals to coastal zones. Despite their potential impact, data on the co-occurrence of these contaminants in the marine environment remains limited. Such information is critical for assessing coastal chemical status, establishing environmental quality benchmarks, and conducting comprehensive environmental risk assessments. In this study, we describe a multifaceted monitoring campaign targeting pesticides, pharmaceuticals, and industrial chemicals along the central-south coast and in northern Patagonia, Chile. Surface water, bottom water, porewater, and adjacent sediment samples were collected for analysis. Our results show the detection of up to 83 chemicals in surface water, 71 in bottom water, 101 in porewater, and 244 in sediments. To enhance data utility, we provide valuable information on the mode of action and molecular targets of the identified chemicals. This comprehensive dataset contributes to defining pollution fingerprints in coastal areas of the Global South, including remote regions in Patagonia. It serves as a critical resource for future research, policymaking, and the advancement of environmental protection in these regions.

pharmacology and toxicology↗

bioRxiv2024

Multi-compartment impact of micropollutants and particularly antibiotics on bacterial communities using environmental DNA at river basin-level

Microbial communities, in particular bacterial assemblies, play pivotal roles in sustaining biogeochemical processes within ecosystems. They are also responsible for the degradation of toxic chemicals, while the development of resistance against antimicrobial drugs jeopardises human health. Bacterial communities respond to environmental conditions with diverse structural and functional changes depending on their compartment (water, biofilm or sediment), type of environmental stress, and type of pollution to which they are exposed. In this study, we combined amplicon sequencing of bacterial 16S rRNA genes from water, biofilm, and sediment samples collected in the anthropogenically impacted River Aconcagua basin (Central Chile, South America), in order to evaluate whether micropollutants alter bacterial community structure and functioning based on the type and degree of chemical pollution. Furthermore, we evaluated the potential of bacterial communities from differently polluted sites to degrade contaminants. Our results show a lower diversity at sites impacted by agriculture and urban areas, featuring high loads of micropollution with pesticides, pharmaceuticals and personal care products as well as industrial chemicals. Nutrients, antibiotic stress, and micropollutant loads explain most of the variability in the sediment and biofilm bacterial community, showing a significant increase of bacterial groups known for their capabilities to degrade various organic pollutants, such as Nistrospira and also selecting for taxa known for antibiotic resistance such as Exiguobacterium and Planomicrobium. Moreover, potential ecological functions linked to the biodegradation of toxic chemicals at the basing level revealed significant reductions in ecosystem-related services in sites affected by agriculture and wastewater treatment plant (WWTP) discharges across all investigated environmental compartments. Finally, we suggest transitioning from simple concentration-based assessments of environmental pollution to more meaningful toxic pressure values in order to comprehensively evaluate the role of micropollutants at the ecological (biodiversity) level. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/587215v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@4dfdc9org.highwire.dtl.DTLVardef@50c884org.highwire.dtl.DTLVardef@19c62aforg.highwire.dtl.DTLVardef@12db45_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIMicropollutant mixtures altered bacterial community structure and functioning C_LIO_LIAntibiotic stress correlated significantly with changes in community structure C_LIO_LIReduction of ecological functions related to the degradation of contaminants C_LIO_LIWater, biofilm, and sediments relevant for microbial ecotoxicology C_LI

microbiology↗

bioRxiv2023

A multi-scenario risk assessment strategy applied to mixtures of chemicals of emerging concern in the River Aconcagua basin in Central Chile

Streams and rivers are characterised by the presence of various chemicals of emerging concern (CECs), including pesticides, pharmaceuticals, personal care products, and industrial chemicals. While these chemicals are found usually only in low (ng/L) concentrations, they might still harm aquatic life and disrupt the ecological balance of aquatic ecosystems due to their high ecotoxicological potency. Environmental risk assessments that account for the complexity of exposures are needed in order to evaluate the toxic pressure of these chemicals, which also provide suggestions for risk mitigation and management, if necessary. Currently, most studies on the co-occurrence and environmental impacts of CECs are conducted in countries of the Global North, leaving massive knowledge gaps in countries of the Global South. In this study, we implement a multi-scenario risk assessment strategy to improve the assessment of both the exposure and hazard components in the chemical risk assessment process. Our strategy incorporates a systematic consideration and weighting of CECs that were not detected, as well as an evaluation of the uncertainties associated with Quantitative Structure-Activity Relationships (QSARs) predictions for chronic ecotoxicity. Furthermore, we present a novel approach to identifying mixture risk drivers. To expand our knowledge beyond well-studied aquatic ecosystems, we applied this multi-scenario strategy to the River Aconcagua basin of Central Chile. The analysis revealed that the concentrations of CECs exceeded acceptable risk thresholds for selected organism groups and the most vulnerable taxonomic groups. Streams flowing through agricultural areas and sites near the river mouth exhibited the highest risks. Notably, the eight risk drivers among the 153 co-occurring chemicals accounted for 66-92% of the observed risks in the river basin. Six of them are pesticides and pharmaceuticals, chemical classes known for their high biological activity in specific target organisms. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/554257v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@179dc1dorg.highwire.dtl.DTLVardef@162082aorg.highwire.dtl.DTLVardef@10616baorg.highwire.dtl.DTLVardef@be1b32_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LI153 chemicals of emerging concern detected in complex multi-component mixtures. C_LIO_LI108 possible mixture risk assessment scenarios were investigated. C_LIO_LINon-detects, QSARs, and experimental ecotoxicological data were integrated for risk assessment. C_LIO_LI8 chemicals of emerging concern were responsible for driving chronic environmental risks. C_LI

pharmacology and toxicology↗

bioRxiv2023

Defining the data gap: what do we know about environmental exposure, hazards and risks of pharmaceuticals in the European aquatic environment?

Active pharmaceutical ingredients (APIs) and their transformation products inevitably enter waterways where they might cause adverse effects to aquatic organisms. Identifying the potential risks of APIs in the environment is therefore a goal and current strategic direction of environmental management described in the EU Strategic Approach to Pharmaceuticals in the Environment and the Green Deal. This is challenged by a paucity of monitoring and ecotoxicity data to adequately describe risks. In this study we analyze measured environmental concentrations (MECs) of APIs from 5933 sites in 25 European countries as documented in the EMPODAT database or collected by the German Environment Agency for the time period between 1997 to 2020. These data were compared with empirical data on the ecotoxicity of APIs from the U.S. EPA ECOTOX database. Although 1763 uniquely identifiable APIs are registered with the European Medicines Agency (EMA) for sale in the European Economic Area (EEA), only 312 (17.7%) of these are included in publicly available monitoring data, and only 36 (1.8%) compounds have sufficient ecotoxicological data to perform an EMA-compliant ERA. Among the 27 compounds with sufficient exposure and hazard data to conduct a single substance risk assessment according to EMA guidelines, four compounds (14.8%) had a median risk quotient (RQ) > 1. Endocrine disruptors had the highest median RQ, with 7.0 and 5.6 for 17-ethinyl-estradiol and 17{beta}-estradiol respectively. A comparison of in-silico and empirical data for 72 APIs demonstrated the high protectiveness of the current EMA guidelines, with predicted environmental concentrations (PECs) exceeding median MECs in 98.6% of cases, with a 100-fold median increase. This study describes the data shortfalls hindering an accurate assessment of the risk posed to European waterways by APIs, and identifies 68 APIs for prioritized inclusion in monitoring programs, and 66 APIs requiring ecotoxicity testing to fill current data gaps. HighlightsO_LI1763 medicines are EMA-approved for sale in the EEA C_LIO_LIThe data gap is 1201 APIs (68%) that have no ecotoxicity or public monitoring data C_LIO_LIOnly 27 APIs (1.5%) have sufficient empirical data for risk assessment. C_LIO_LIERA using 23 years of EU monitoring data shows four compounds with a median RQ > 1 C_LIO_LIData gap APIs prioritized for monitoring programs (68) and ecotoxicity testing (66) C_LI

pharmacology and toxicology↗

bioRxiv2023

Transformers enable accurate prediction of acute and chronic chemical toxicity in aquatic organisms

Environmental safety assessments, as mandated by many regulations, require that toxicity data is generated for up to three trophic levels, algae, aquatic invertebrates, and fish. Conducting these tests in vivo is resource-intensive, time-consuming, and causes undue suffering. Computational methods are fast and cost-efficient alternatives, however, their adaptation in regulatory settings has been slow, both due to low accuracy and narrow applicability domains. Here we present a new method for predicting chemical toxicity based on molecular structure. The method is based on a transformer, capturing structural features associated with toxicity, followed by a deep neural network that predicts the corresponding effect concentrations. After training on data from tens of thousands of exposure experiments, the model shows high predictive performance for each of the three trophic levels. Compared to commonly used QSAR methods, the model has both a larger applicability domain and a considerably lower error. In addition, training the model on data that combines multiple types of effect concentrations further improves the performance. We conclude that transformer-based models have the potential to significantly advance computational predictions of chemical toxicity and make in silico approaches a more attractive alternative when compared to animal-based exposure experiments.

pharmacology and toxicology↗