Toxic Relationships: Prediction of TBT's Affinity to the Ecdysteroid Receptor of Triops longicaudatus.

Tributyltin (TBT) is a biocide introduced in the 1960s in antifouling paints. Despite legislation banning its use, its persistence in the environment still causes significant harm to organisms. Tributyltin is a ligand of retinoid X receptors (RXR) and ecdysteroid receptors (EcRs), which in arthropods act as homologs of RXR. Focusing on Metazoan species, this study used genomic and proteomic information from different sources to compare their three-dimensional structure, phylogenetic distribution, and amino acid sequence alterations. The objective was to identify possible patterns that relate organisms' sensitivity to TBT using the species Triops longicaudatus as the basis for the comparisons. The results showed great conservation of this protein across several species when comparing the interaction amino acids described to RXR (an EcR analog) in Homo sapiens. The three-dimensional comparison of RXR showed little conformational variation between different sequences by maintaining the interaction pocket. As for the Species Sensitivity Distribution (SSD) curve, an HC05 = 0.2649 [0.0789-0.7082] µg/L was obtained with no specific distribution between the different taxa. Protein-ligand docking analysis was then used to confirm the SSD curve ranking of species. Still, the results showed an opposite trend that may be related, for example, to differences in the LC50 values used in the calculations. This study serves as the first step for applying bioinformatics techniques to produce information that can be used as an alternative to animal or cellular experimentation. These techniques could be adapted to various chemicals and proteins, allowing for observations in a shorter timeframe and providing information on a broader spectrum.

Insights into environmental caffeine contamination in ecotoxicological biomarkers and potential health effects of Danio rerio.

Caffeine (CAF) exposures have been shown to cause several pharmacological and biological effects in target and non-target organisms. Although there are already several ecotoxicological studies with CAF in non-target organisms, they are focused on marine organisms, with relevant concentrations in these ecosystems, therefore, less ecologically relevant to freshwater ecosystems (the main ecoreceptor of this type of anthropogenic contaminant). The present study aimed to assess the chronic effects (28 days) of sub-lethal and environmentally relevant concentrations of CAF (0.16, 0.42, 1.09, 2.84, 7.40, 19.23, and 50 µg/L) in Danio rerio. Biochemical endpoints as biomarkers of antioxidant defense, biotransformation, lipid peroxidation, energy sources, and neurotransmission were assessed. CAF exposure induced alterations in antioxidant defenses (superoxide dismutase and glutathione reductase activities, and glutathione content) preventing lipid peroxidation. Lactate dehydrogenase activity decreased in all the concentrations tested, while acetylcholinesterase activity was only affected by the highest concentrations tested (19.23 and 50 µg/L). We also utilized a multi-biomarker approach (Integrated Biomarker Response version 2, IBRv2) to investigate the effects of CAF in the dispersion scope of individual biochemical responses of D. rerio. IBRv2 showed that the concentration of 50 µg/L promotes the highest stress. However, the results showed that CAF induced disturbances in the metabolic pathways studied in D. rerio. These results demonstrated the toxic effects of CAF on freshwater fish, compromising their physiological functions and evidencing the need for monitoring the residues of CAF released into the inland aquatic environments. Furthermore, this research evidence that phylogenetically and physiologically different species may present different biological responses with concern for ecologically relevant environmental conditions. In this sense, the present study generated ecotoxicologically relevant data, that can be considered by environment regulators, since the here-endpoints evaluated showed sensitivity and consistency in the evaluation of caffeine risks in freshwater environments.

Subcellular compartmentalization of aluminum reduced its hazardous impact on rye photosynthesis.

Aluminum (Al) toxicity limits crops growth and production in acidic soils. Compared to roots, less is known about the toxic effects of Al in leaves. Al subcellular compartmentalization is also largely unknown. Using rye (Secale cereale L.) Beira (more tolerant) and RioDeva (more sensitive to Al) genotypes, we evaluated the patterns of Al accumulation in leaf cell organelles and the photosynthetic and metabolic changes to cope with Al toxicity. The tolerant genotype accumulated less Al in all organelles, except the vacuoles. This suggests that Al compartmentalization plays a role in Al tolerance of Beira genotype. PSII efficiency, stomatal conductance, pigment biosynthesis, and photosynthesis metabolism were less affected in the tolerant genotype. In the Calvin cycle, carboxylation was compromised by Al exposure in the tolerant genotype. Other Calvin cycle-related enzymes, phoshoglycerate kinase (PGK), glyceraldehyde 3-phosphate dehydrogenase (GAPDH), triose-phosphate isomerase (TPI), and fructose 1,6-bisphosphatase (FBPase) activities decreased in the sensitive line after 48 h of Al exposure. Consequentially, carbohydrate and organic acid metabolism were affected in a genotype-specific manner, where sugar levels increased only in the tolerant genotype. In conclusion, Al transport to the leaf and compartmentalization in the vacuoles tolerant genotype's leaf cells provide complementary mechanisms of Al tolerance, protecting the photosynthetic apparatus and thereby sustaining growth.

Dietary tryptophan supplementation does not affect growth but increases brain serotonin level and modulates the expression of some liver genes in zebrafish (Danio rerio).

This study aimed at assessing the effects of the dietary tryptophan (Trp) supplementation on growth and feed utilization, brain serotonin content, and expression of selected liver genes (involved in the liver serotonin pathway, protein synthesis degradation, and antioxidant activity) in zebrafish. A growth trial was conducted with zebrafish juveniles fed five experimental isoproteic (40%DM) and isolipidic (8%DM) fishmeal-based diets containing graded levels of Trp: a Trp-non-supplemented diet (diet Trp0, with 0.22% Trp) and four Trp-supplemented diets containing 2-16 times higher Trp content (diets Trp2, Trp4, Trp8, and Trp16 with 0.40, 0.91, 2.02, and 3.34% Trp, respectively). Diets were tested in quadruplicate, with fish being fed twice a day, 6 days a week for 6 weeks to apparent visual satiation. At the end of the trial, growth performance and feed utilization were assessed, and fish from all experimental groups were sampled for whole-body composition analysis. In addition, fish fed low (Trp0), medium (Trp4), and high (Trp16) Trp diets were also sampled for analysis of brain serotonin content and liver gene expression. Tested tryptophan levels did not influence growth performance nor feed intake. However, values of energy and nitrogen retention as well as body energy content indicate a better feed utilization with diets containing around 0.9% and 2.0% DM Trp. Brain serotonin content increased with increasing dietary tryptophan levels. In addition, regarding liver genes, dietary treatment had a modulatory effect on the expression of Htr1aa and Htr2cl1 genes (encoding for serotonin receptors), TPH1a gene (encoding for tryptophan hydroxylase, the rate-limiting enzyme in the synthesis of serotonin from tryptophan), TOR gene (involved in protein synthesis), and Keap1 gene (involved in antioxidant responses).

Resilience assessment of a biological early warning system based on the locomotor behavior of zebrafish (Danio rerio).

With the development of new tools such as biological early warning systems, it becomes extremely important to test their reliability and detection capability. This work aimed at testing the sturdiness of a video tracking system by determining whether the detection capability does not deteriorate over time, after successive exposures of the zebrafish to three different toxicants, namely sodium hypochlorite, bisphenol A, and ethanol. Zebrafish were exposed to the three tested compounds separately (one fish, one toxicant) once a day, for 1 h and 30 m over 9 days, to 9 % of the 96 h LC50 of the respective toxicant. The behavior analysis was based on nine movement descriptor parameters of the fish, namely: angular velocity; linear velocity; spatial dispersion; linear acceleration; and angular acceleration. A statistical method was developed using self-organizing map (SOM), correspondence analysis, and linear and orthogonal multiple regression models. The results indicated that the system was able to successfully detect the three toxicants. With ethanol, the detection capability was maintained, but in the case of the sodium hypochlorite and bisphenol A, a deterioration of the detection capability occurred over the 9 days. This effect may be due to the induction of detoxification mechanisms and physiological acclimation, or due to the accumulation of adverse effects caused by the repeated exposure to the toxicants. Future works, especially those focusing on the application of similar early warning systems in real-world scenarios, should regularly exchange the sentinel organisms, to avoid degradation of the detection capability, as verified with two of the three tested compounds.

Forecasting of cyanobacterial density in Torrão reservoir using artificial neural networks.

The ability of general regression neural networks (GRNN) to forecast the density of cyanobacteria in the Torrão reservoir (Tâmega river, Portugal), in a period of 15 days, based on three years of collected physical and chemical data, was assessed. Several models were developed and 176 were selected based on their correlation values for the verification series. A time lag of 11 was used, equivalent to one sample (periods of 15 days in the summer and 30 days in the winter). Several combinations of the series were used. Input and output data collected from three depths of the reservoir were applied (surface, euphotic zone limit and bottom). The model that presented a higher average correlation value presented the correlations 0.991; 0.843; 0.978 for training, verification and test series. This model had the three series independent in time: first test series, then verification series and, finally, training series. Only six input variables were considered significant to the performance of this model: ammonia, phosphates, dissolved oxygen, water temperature, pH and water evaporation, physical and chemical parameters referring to the three depths of the reservoir. These variables are common to the next four best models produced and, although these included other input variables, their performance was not better than the selected best model.

Time series forecasting of cyanobacteria blooms in the Crestuma Reservoir (Douro River, Portugal) using artificial neural networks.

In this work, time series neural networks were used to predict the occurrence of toxic cyanobacterial blooms in Crestuma Reservoir, which is an important potable water supply for the Porto region, located in the north of Portugal. These models can potentially be used to provide water treatment plant operators with an early warning for developing cyanobacteria blooms. Physical, chemical, and biological parameters were collected at Crestuma Reservoir from 1999 to 2002. The data set was then divided into three independent time series, each with a fortnightly periodicity. One training series was used to "teach" the neural networks to predict results. Another series was used to verify the results, and to avoid over-fitting of the data. An additional independently collected data series was then used to test the efficacy of the model for predicting the abundance of cyanobacteria. All of the models tested in this study incorporated a prediction time (look-ahead parameter) equal to the sampling interval (two weeks). Various lag periods, from 2 to 52 weeks, were also investigated. The best model produced in this study provided the following correlations between the target and forecast values in the training, verification, and validation series: 1.000 (P = 0.000), 0.802 (P = 0.000), and 0.773 (P = 0.001), respectively. By applying this model to the three-year data set, we were able to predict fluctuations in cyanobacteria abundance in the Crestuma Reservoir, with a high level of precision. By incorporating a lag-period of eight weeks, we were able to detect secondary fluctuations in cyanobacterial abundance over the annual cycle.