Impact of the antidepressant Bupropion on the Dynamic Energy Budget of Daphnia magna.

Psychiatric drugs are considered among the emerging contaminants of concern in ecological risk assessment, due to their potential to disrupt homeostasis in aquatic organisms. Bupropion is an antidepressant that acts by selective reuptake inhibition of norepinephrine and dopamine. Little is known about this compound's effects on aquatic organisms, despite being detected in significant concentrations in both water and biota close to waste-water treatment plants and densely populated areas. Dynamic Energy Budget (DEB) models are flexible mechanistic tools that can be applied to understand toxic effects and extrapolate individual responses to higher biological levels and under untested environmental conditions. In this work, we used the stdDEB-TKTD (an application of the DEB theory to ecotoxicology) approach to investigate the possible physiological mode of action of Bupropion on the model organism Daphnia magna. Next, Dynamic Energy Budget Individual-Based Models (DEB-IBM) were used to extrapolate the results to the population level and to predict the combined effects of Bupropion exposure and food availability on the daphnids. Our results revealed an increasing negative effect of this antidepressant on the reproduction and survival of the animals with increasing concentration (0.004, 0.058, 0.58 and 58 µM). At the population level, we found that even the lowest used doses of Bupropion could reduce the population density and its reproductive output. The impacts are predicted to be stronger under limited food conditions.

Connecting Suborganismal Data to Bioenergetic Processes: Killifish Embryos Exposed to a Dioxin-Like Compound.

A core challenge for ecological risk assessment is to integrate molecular responses into a chain of causality to organismal or population-level outcomes. Bioenergetic theory may be a useful approach for integrating suborganismal responses to predict organismal responses that influence population dynamics. We describe a novel application of dynamic energy budget (DEB) theory in the context of a toxicity framework (adverse outcome pathways [AOPs]) to make quantitative predictions of chemical exposures to individuals, starting from suborganismal data. We use early-life stage exposure of Fundulus heteroclitus to dioxin-like chemicals (DLCs) and connect AOP key events to DEB processes through "damage" that is produced at a rate proportional to the internal toxicant concentration. We use transcriptomic data of fish embryos exposed to DLCs to translate molecular indicators of damage into changes in DEB parameters (damage increases somatic maintenance costs) and DEB models to predict sublethal and lethal effects on young fish. By changing a small subset of model parameters, we predict the evolved tolerance to DLCs in some wild F. heteroclitus populations, a data set not used in model parameterization. The differences in model parameters point to reduced sensitivity and altered damage repair dynamics as contributing to this evolved resistance. Our methodology has potential extrapolation to untested chemicals of ecological concern. Environ Toxicol Chem 2023;42:2040-2053. © 2023 Oak Ridge National Laboratory and The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Dynamic Energy Budget models: fertile ground for understanding resource allocation in plants in a changing world.

Climate change is having dramatic effects on the diversity and distribution of species. Many of these effects are mediated by how an organism's physiological patterns of resource allocation translate into fitness through effects on growth, survival and reproduction. Empirically, resource allocation is challenging to measure directly and so has often been approached using mathematical models, such as Dynamic Energy Budget (DEB) models. The fact that all plants require a very similar set of exogenous resources, namely light, water and nutrients, integrates well with the DEB framework in which a small number of variables and processes linked through pathways represent an organism's state as it changes through time. Most DEB theory has been developed in reference to animals and microorganisms. However, terrestrial vascular plants differ from these organisms in fundamental ways that make resource allocation, and the trade-offs and feedbacks arising from it, particularly fundamental to their life histories, but also challenging to represent using existing DEB theory. Here, we describe key features of the anatomy, morphology, physiology, biochemistry, and ecology of terrestrial vascular plants that should be considered in the development of a generic DEB model for plants. We then describe possible approaches to doing so using existing DEB theory and point out features that may require significant development for DEB theory to accommodate them. We end by presenting a generic DEB model for plants that accounts for many of these key features and describing gaps that would need to be addressed for DEB theory to predict the responses of plants to climate change. DEB models offer a powerful and generalizable framework for modelling resource allocation in terrestrial vascular plants, and our review contributes a framework for expansion and development of DEB theory to address how plants respond to anthropogenic change.

Comparison of the performance and reliability between improved sampling strategies for polynomial chaos expansion.

As uncertainty and sensitivity analysis of complex models grows ever more important, the difficulty of their timely realizations highlights a need for more efficient numerical operations. Non-intrusive Polynomial Chaos methods are highly efficient and accurate methods of mapping input-output relationships to investigate complex models. There is substantial potential to increase the efficacy of the method regarding the selected sampling scheme. We examine state-of-the-art sampling schemes categorized in space-filling-optimal designs such as Latin Hypercube sampling and L1-optimal sampling and compare their empirical performance against standard random sampling. The analysis was performed in the context of L1 minimization using the least-angle regression algorithm to fit the GPCE regression models. Due to the random nature of the sampling schemes, we compared different sampling approaches using statistical stability measures and evaluated the success rates to construct a surrogate model with relative errors of <0.1%, <1%, and <10%, respectively. The sampling schemes are thoroughly investigated by evaluating the y of surrogate models constructed for various distinct test cases, which represent different problem classes covering low, medium and high dimensional problems. Finally, the sampling schemes are tested on an application example to estimate the sensitivity of the self-impedance of a probe that is used to measure the impedance of biological tissues at different frequencies. We observed strong differences in the convergence properties of the methods between the analyzed test functions.

E3-ligase knock down revealed differential titin degradation by autopagy and the ubiquitin proteasome system.

The sarcomere protein titin is a major determinant of cardiomyocyte stiffness and ventricular distensibility. The constant mechanical stress on titin requires well-controlled protein quality control, the exact mechanisms of which have not yet been fully elucidated. Here, we analyzed E3-ligases potentially responsible for cardiac titin ubiquitination and specifically studied the involvement of the autophagosomal system in titin degradation. Pharmacological inhibition of autophagy and the proteasome in cultured primary rat cardiomyocytes significantly elevated titin ubiquitination and increased titin degradation. Using in-vitro pull down assays we identified binding of E3-ligases MuRF1-3, CHIP and Fbx32 to several titin domains. Immunofluorescence analysis showed sarcomeric localization of the E3-ligases. siRNA-mediated knock-down of the E3-ligases MuRF-1, -3 and a combination of CHIP/Fbx32 significantly reduced autophagy-related titin ubiquitination, whereas knock-down of MuRF-2 and -3 reduced proteasome-related titin ubiquitination. We demonstrated that the proteasomal and the autophagosomal-lysosomal system participate in degradation of the titin filament. We found that ubiquitination and degradation of titin are partially regulated by E3-ligases of the MuRF family. We further identified CHIP and Fbx32 as E3-ligases involved in titin ubiquitination.

Improving the Screening Analysis of Pesticide Metabolites in Human Biomonitoring by Combining High-Throughput In Vitro Incubation and Automated LC-HRMS Data Processing.

There is a current need to monitor human exposure to a large number of pesticides and other chemicals of emerging concern (CECs). This requires screening analysis with high confidence for these compounds and their metabolites in complex matrices, which is hampered by the fact that no reference standards are available for most metabolites. We address this challenge by a high-throughput workflow based on incubation of pesticides (or other CECs) with human liver S9, followed by solid-phase extraction, liquid chromatography-high-resolution mass spectrometry (LC-HRMS) analysis, and automated data processing to generate a database (retention time, precursor m/z, and MS2 spectral library) for the annotation in human samples. The metabolite prioritization consists of statistical comparisons and mass defect and m/z range filtering to obtain a subset of probable phase I metabolites, for which molecular formulas and likely metabolic transformation are retrieved. We tested the workflow on 22 pesticides, for which we could determine 91 metabolite molecular formulas which are only partly covered by the literature and/or predicted by in silico metabolization. Our workflow allows for an efficient generation of metabolite reference information, which can be used directly for annotating LC-HRMS data from human samples. A full structure elucidation of individual metabolites can be limited to those being actually present in human samples.

Synthesis of (R)-mandelic acid and (R)-mandelic acid amide by recombinant E. coli strains expressing a (R)-specific oxynitrilase and an arylacetonitrilase.

OBJECTIVES: Chiral 2-hydroxycarboxylic acids and 2-hydroxycarboxamides are valuable synthons for the chemical industry. RESULTS: The biocatalytic syntheses of (R)-mandelic acid and (R)-mandelic acid amide by recombinant Escherichia coli clones were studied. Strains were constructed which simultaneously expressed a (R)-specific oxynitrilase (hydroxynitrile lyase) from the plant Arabidopsis thaliana together with the arylacetonitrilase from the bacterium Pseudomonas fluorescens EBC191. In addition, recombinant strains were constructed which expressed a previously described acid tolerant variant of the oxynitrilase and an amide forming variant of the nitrilase. The whole cell catalysts which simultaneously expressed the (R)-specific oxynitrilase and the wild-type nitrilase transformed in slightly acidic buffer systems benzaldehyde plus cyanide preferentially to (R)-mandelic acid with ee-values > 95%. The combination of the (R)-specific oxynitrilase with the amide forming nitrilase variant gave whole cell catalysts which converted at pH-values ≤ pH 5 benzaldehyde plus cyanide with a high degree of enantioselectivity (ee > 90%) to (R)-mandelic acid amide. The acid and the amide forming catalysts also converted chlorinated benzaldehydes with cyanide to chlorinated mandelic acid or chlorinated mandelic acid amides. CONCLUSIONS: Efficient systems for the biocatalytic production of (R)-2-hydroxycarboxylic acids and (R)-2-hydroxycarboxamides were generated.

Symbolic Aggregate Approximation Improves Gap Filling in High-Resolution Mass Spectrometry Data Processing.

Nontargeted mass spectrometry (MS) is widely used in life sciences and environmental chemistry to investigate large sets of samples. A major problem for larger-scale MS studies is data gaps or missing values in aligned data sets. The main causes for these data gaps are the absence of the compound from the sample, issues related to chromatography or mass spectrometry (for example, broad peaks, early eluting peaks, ion suppression, low ionization efficiency), and issues related to software (mainly limitations of peak detection algorithms). While those algorithms are heuristic by necessity and should be used with strict settings to minimize the number of false positive and negative peaks in a data set, gap filling may be used to reduce missing data in single samples remaining after peak detection. In this study, we present a new gap filling algorithm. The method is based on the symbolic aggregation approximation (SAX) algorithm that was developed for the evaluation and classification of time series in data mining studies. We adopted SAX for liquid chromatography high-resolution MS nontarget screening to support the detection of missing peaks in aligned mass spectral data sets. The SAX-based algorithm improves the detection efficiency considerably compared to existing gap filling methods including the Peak Finder algorithm provided in MZmine.

Conventional and nano-copper pesticides are equally toxic to the estuarine amphipod Leptocheirus plumulosus.

Modern nano-engineered pesticides have great promise for agriculture due to their extended, low dose release profiles that are intended to increase effectiveness but reduce environmental harm. Whether nanopesticides, including copper (Cu) formulations, cause reduced levels of toxicity to non-target aquatic organisms is unclear but important to assess. Predicting how aquatic species respond to incidental exposure to Cu-based nanopesticides is challenging because of the expected very low concentrations in the environment, and the two forms of exposure that may occur, namely to Cu ions and Cu nanoparticles. We conducted Cu speciation, tissue uptake, and 7-day toxicity laboratory experiments to test how a model estuarine organism, the amphipod Leptocheirus plumulosus, responded to two popular Cu-based nanopesticides, CuPRO and Kocide, and conventional CuCl2. Exposure concentrations ranged from 0 to 2.5 ppm, which were similar to those found in estuarine water located downstream of agricultural fields. Cu dissolution rates were much slower for the nanopesticides than the ionic formula, and Cu body burden in amphipods increased approximately linearly with the nominal exposure concentration. Amphipod survival declined in a normal dose-response manner with no difference among Cu formulations. Growth and movement rates after 7 days revealed no difference among exposure levels when analyzed with conventional statistical methods. By contrast, analysis of respiration rates, inferred from biomass measurements, with a bioenergetic toxicodynamic model indicated potential for population-level effects of exposure to very low-levels of the two nanopesticides, as well as the control contaminant CuCl2. Our results indicate that toxicity assessment of environmental trace pollutant concentrations may go undetected with traditional ecotoxicological tests. We present a process integrating toxicity test results and toxicodynamic modeling that can improve our capacity to detect and predict environmental impacts of very low levels of nanomaterials released into the environment.

A Data Set of 255,000 Randomly Selected and Manually Classified Extracted Ion Chromatograms for Evaluation of Peak Detection Methods.

Non-targeted mass spectrometry (MS) has become an important method over recent years in the fields of metabolomics and environmental research. While more and more algorithms and workflows become available to process a large number of non-targeted data sets, there still exist few manually evaluated universal test data sets for refining and evaluating these methods. The first step of non-targeted screening, peak detection and refinement of it is arguably the most important step for non-targeted screening. However, the absence of a model data set makes it harder for researchers to evaluate peak detection methods. In this Data Descriptor, we provide a manually checked data set consisting of 255,000 EICs (5000 peaks randomly sampled from across 51 samples) for the evaluation on peak detection and gap-filling algorithms. The data set was created from a previous real-world study, of which a subset was used to extract and manually classify ion chromatograms by three mass spectrometry experts. The data set consists of the converted mass spectrometry files, intermediate processing files and the central file containing a table with all important information for the classified peaks.

Unraveling longitudinal pollution patterns of organic micropollutants in a river by non-target screening and cluster analysis.

The pollution of aquatic ecosystems with complex and largely unknown mixtures of organic micropollutants is not sufficiently addressed with current monitoring strategies based on target screening methods. In this study, we implemented an open-source workflow based on non-target screening to unravel longitudinal pollution patterns of organic micropollutants along a river course. The 47 km long Holtemme River, a tributary of the Bode River (both Saxony-Anhalt, Germany), was used as a case study. Sixteen grab samples were taken along the river and analyzed by liquid chromatography coupled to high-resolution mass spectrometry. We applied a cluster analysis specifically designed for longitudinal data sets to identify spatial pollutant patterns and prioritize peaks for compound identification. Three main pollution patterns were identified representing pollutants entering a) from wastewater treatment plants, b) at the confluence with the Bode River and c) from diffuse and random inputs via small point sources and groundwater input. By further sub-clustering of the main patterns, source-related fingerprints were revealed. The main patterns were characterized by specific isotopologue signatures and the abundance of peaks in homologue series representing the major (pollution) sources. Furthermore, we identified 25 out of 38 representative compounds for the patterns by structure elucidation. The workflow represents an important contribution to the ongoing attempts to understand, monitor, prioritize and manage complex environmental mixtures and may be applied to other settings.

Proton radiation effects on carrier transport in diamond radiation detectors.

Diamond, a highly radiation-resistant material, is considered a nearly ideal material for radiation detection, particularly in high-energy physics. In this study, radiation damage from high-energy proton beams was induced in diamond crystals to determine exposure lifetime in detectors made from this material; the effects were investigated using non-destructive x-ray techniques and through the FLUKA simulation package. Two diamond detectors were irradiated by an 800 MeV proton beam at different fluence rates, and the real-time current response was recorded to observe degradation in the signal over time. It was determined that the proton fluence rate had a significant effect on the device degradation. The detector performance from the irradiated detectors was characterized using x-ray beam-induced current measurements, and the mechanism of proton radiation damage to diamond sensors, especially the radiation effects on carrier transport, was studied. The vacancies generated from proton irradiation were considered the major source of detector degradation by trapping holes and inducing an internal electric field. Simulation results from the FLUKA package revealed an uneven distribution of the radiation-induced vacancies along the beam path, and the corresponding detector signals calculated from the simulation results displayed a good match to the experimental results.

Non-targeted mercapturic acid screening in urine using LC-MS/MS with matrix effect compensation by postcolumn infusion of internal standard (PCI-IS).

While the targeted analysis of mercapturic acid (MA) metabolites in human urine is used to assess exposure to selected chemicals, this compound class has only rarely been addressed in non-target screening utilizing diagnostic neutral loss liquid chromatography tandem mass spectrometry (LC-MS/MS). Additionally, this type of analysis is severely affected by matrix effects (MEs) causing poor comparability of samples and distortion of signal intensities. However, MEs have been neglected in urinary MA non-target screening so far. Therefore, we developed a non-target screening method relying on neutral loss scanning for MAs using post column infusion of an isotope-labelled standard. For signal correction, we synthesized a structural analogue to MAs, N-acetyl-S-methyl-homocysteine-D3, lacking the characteristic neutral loss of the MAs. For method development, 16 structurally different model MA compounds and 20 spiked urine samples were used. Twelve out of the 16 model compounds could be analysed by the developed method. We found severe matrix effects (largely signal suppression) for the spiked model compounds, with only 34% of all peaks' intensities changing by less than a factor of two. This could be compensated by the post column internal standard infusion with now 68% of all peaks' intensities changing by less than a factor of two. For three compounds, an over-compensation was observed resulting in an increase of signal of up to a factor of 16. In the 20 urine samples, altogether 558 native MAs (between 74 and 175 per sample) could be detected after ME compensation. These results indicate that a large number of so far uncharacterized MAs are present in urine, which yield a potential for biomarker discovery and pattern characterisation. Graphical Abstract.

REGULATION OF REPRODUCTIVE PROCESSES WITH DYNAMIC ENERGY BUDGETS.

1. The simple bioenergetic models in the family of Dynamic Energy Budget (DEB) consist of a small number of state equations quantifying universal processes, such as feeding, maintenance, development, reproduction and growth. Linking these organismal level processes to underlying suborganismal mechanisms at the molecular, cellular and organ level constitutes a major challenge for predictive ecological risk assessments. 2. Motivated by the need for process-based models to evaluate the impact of endocrine disruptors on ecologically relevant endpoints, this paper develops and evaluates two general modeling modules describing demand-driven feedback mechanisms exerted by gonads on the allocation of resources to production of reproductive matter within the DEB modeling framework. 3. These modules describe iteroparous, semelparous and batch-mode reproductive strategies. The modules have a generic form with both positive and negative feedback components; species and sex specific attributes of endocrine regulation can be added without changing the core of the modules. 4. We demonstrate that these modules successfully describe time-resolved measurements of wet weight of body, ovaries and liver, egg diameter and plasma content of vitellogenin and estradiol in rainbow trout (Oncorynchus mykiss) by fitting these models to published and new data, which require the estimation of less than two parameters per data type. 5. We illustrate the general applicability of the concept of demand-driven allocation of resources to reproduction as worked out in this paper by evaluating one of the modules with data on growth and seed production of an annual plant, the common bean (Phaseolis vulgaris).

Towards a holistic and solution-oriented monitoring of chemical status of European water bodies: how to support the EU strategy for a non-toxic environment?

The definition of priority substances (PS) according to the Water Framework Directive (WFD) helped to remove many of these chemicals from the market and to reduce their concentrations in the European water bodies. However, it could not prevent that many of these chemicals have been replaced by others with similar risks. Today, monitoring of the PS-based chemical status according to WFD covers only a tiny fraction of toxic risks, extensively ignores mixture effects and lacks incentives and guidance for abatement. Thus, we suggest complement this purely status-related approach with more holistic and solution-oriented monitoring, which at the same time helps to provide links to the ecological status. Major elements include (1) advanced chemical screening techniques supporting mixture risk assessment and unraveling of source-related patterns in complex mixtures, (2) effect-based monitoring for the detection of groups of chemicals with similar effects and the establishment of toxicity fingerprints, (3) effect-directed analysis of drivers of toxicity and (4) to translate chemical and toxicological fingerprints into chemical footprints for prioritization of management measures. The requirement of more holistic and solution-oriented monitoring of chemical contamination is supported by the significant advancement of appropriate monitoring tools within the last years. Non-target screening technology, effect-based monitoring and basic understanding of mixture assessment are available conceptually and in research but also increasingly find their way into practical monitoring. Substantial progress in the development, evaluation and demonstration of these tools, for example, in the SOLUTIONS project enhanced their acceptability. Further advancement, integration and demonstration, extensive data exchange and closure of remaining knowledge gaps are suggested as high priority research needs for the next future to bridge the gap between insufficient ecological status and cost-efficient abatement measures.

An all-diamond X-ray position and flux monitor using nitrogen-incorporated ultra-nanocrystalline diamond contacts.

Diamond X-ray detectors with conducting nitrogen-incorporated ultra-nanocrystalline diamond (N-UNCD) films as electrodes were fabricated to measure X-ray beam flux and position. Structural characterization and functionality tests were performed for these devices. The N-UNCD films grown on unseeded diamond substrates were compared with N-UNCD films grown on a seeded silicon substrate. The feasibility of the N-UNCD films acting as electrodes for X-ray detectors was confirmed by the stable performance in a monochromatic X-ray beam. The fabrication process is able to change the surface status which may influence the signal uniformity under low bias, but this effect can be neglected under full collection bias.

Incorporating Suborganismal Processes into Dynamic Energy Budget Models for Ecological Risk Assessment.

A working group at the National Institute for Mathematical and Biological Synthesis (NIMBioS) explored the feasibility of integrating 2 complementary approaches relevant to ecological risk assessment. Adverse outcome pathway (AOP) models provide "bottom-up" mechanisms to predict specific toxicological effects that could affect an individual's ability to grow, reproduce, and/or survive from a molecular initiating event. Dynamic energy budget (DEB) models offer a "top-down" approach that reverse engineers stressor effects on growth, reproduction, and/or survival into modular characterizations related to the acquisition and processing of energy resources. Thus, AOP models quantify linkages between measurable molecular, cellular, or organ-level events, but they do not offer an explicit route to integratively characterize stressor effects at higher levels of organization. While DEB models provide the inherent basis to link effects on individuals to those at the population and ecosystem levels, their use of abstract variables obscures mechanistic connections to suborganismal biology. To take advantage of both approaches, we developed a conceptual model to link DEB and AOP models by interpreting AOP key events as measures of damage-inducing processes affecting DEB variables and rates. We report on the type and structure of data that are generated for AOP models that may also be useful for DEB models. We also report on case studies under development that merge information collected for AOPs with DEB models and highlight some of the challenges. Finally, we discuss how the linkage of these 2 approaches can improve ecological risk assessment, with possibilities for progress in predicting population responses to toxicant exposures within realistic environments. Integr Environ Assess Manag 2018;14:615-624. © 2018 SETAC.

Water transport through tall trees: A vertically explicit, analytical model of xylem hydraulic conductance in stems.

Trees grow by vertically extending their stems, so accurate stem hydraulic models are fundamental to understanding the hydraulic challenges faced by tall trees. Using a literature survey, we showed that many tree species exhibit continuous vertical variation in hydraulic traits. To examine the effects of this variation on hydraulic function, we developed a spatially explicit, analytical water transport model for stems. Our model allows Huber ratio, stem-saturated conductivity, pressure at 50% loss of conductivity, leaf area, and transpiration rate to vary continuously along the hydraulic path. Predictions from our model differ from a matric flux potential model parameterized with uniform traits. Analyses show that cavitation is a whole-stem emergent property resulting from non-linear pressure-conductivity feedbacks that, with gravity, cause impaired water transport to accumulate along the path. Because of the compounding effects of vertical trait variation on hydraulic function, growing proportionally more sapwood and building tapered xylem with height, as well as reducing xylem vulnerability only at branch tips while maintaining transport capacity at the stem base, can compensate for these effects. We therefore conclude that the adaptive significance of vertical variation in stem hydraulic traits is to allow trees to grow tall and tolerate operating near their hydraulic limits.