The increasing frequency of heat-waves within the current climate change scenarios represents a major emerging stressor for aquatic organisms, yet the adaptive responses at molecular level during early life stages remain poorly understood. Here, we investigated the impact of an experimentally induced heat-wave (H-W) during early development of rainbow trout (Oncorhynchus mykiss) on survival, incidence of deformities, gill histomorphology, and epigenetic regulation. Fertilized eggs were reared under control conditions (11–12 °C) until hatching. A subset of hatched embryos was exposed to a 15 days H-W (+6.8 °C maximum anomaly) while others remained at 13 ± 1 °C. Fish exposed to the H-W showed lower survival and a higher incidence of deformities, particularly craniofacial abnormalities, while juvenile growth was similar between groups. Exposure to the H-W did not induce the expression of classical heat shock genes, whereas antioxidant defense genes were differentially regulated, suggesting a redox imbalance response. Using enzymatic methyl-sequencing (EM Seq) at single-base resolution, we identified widespread and context-dependent DNA methylation differences in juvenile gills, with hundreds of differentially methylated regions predominantly in HCG and exhaustive contexts. Functional enrichment analyses revealed significant alterations in pathways related to ion transport and osmoregulation, including IgSF cell adhesion signaling. At the juvenile stage, histological analyses showed increased chloride cell area and filament thickness in exposed fish, consistent with a potential enhancement of osmoregulatory capacity. These findings suggest that early-life thermal stress may result in epigenetic alterations linked to physiological traits, highlighting DNA methylation as a potential mechanism underlying developmental plasticity to climate-related stressors in fish.
Citrus seedlings are often simultaneously exposed to excessive aluminum (Al) and copper (Cu) under acidic conditions. To investigate co-exposure of Al and Cu on citrus, seedlings of ‘Xuegan’ [Citrus sinensis (L.) Osbeck] were treated with three levels of Al-Cu stress: low (500 μM Al + 150 μM Cu), moderate (500 μM Al + 225 μM Cu), and severe (1000 μM Al + 300 μM Cu). Each stress level included Al alone, Cu alone, and combined Al-Cu treatments. After 24 weeks of treatment, we examined biomass accumulation, photosynthetic inhibition, nutrient balance, and membrane injury in C. sinensis seedlings. The results indicated that Al-Cu co-exposure reduced biomass accumulation in C. sinensis seedlings and decreased N, P, K, Ca, Mg, S, and B, while increasing Al, Cu, Mn, and Zn content in C. sinensis leaves, stems, and roots. Differentially, Fe accumulation increased in the stems but decreased in the leaves and roots under Al-Cu co-exposure. Severe Al-Cu co-exposure inhibited electron transfer and induced nonstomatal-limited photosynthetic depression in C. sinensis leaves. Moreover, Al-Cu co-exposure increased the electrolyte leakage and soluble sugar content, whereas it decreased the relative water content and starch accumulation in the leaves and roots of C. sinensis. Conclusively, the Al-Cu coexistence triggers greater damage compared with the mono-exposure of Al or Cu in C. sinensis seedlings under acidic conditions. These findings provide new insights into the heightened toxicity of Al-Cu in the citrus rhizosphere and will serve as references for integrated management in acidic citrus orchards.
Uranium (U) released from mining activities poses a threat to human and environmental health. Bioleaching bacteria are common in mining environments and play a significant role in uranium mobility. However, the relationship between the Fe/S-oxidizing bacterium Sulfobacillus thermosulfidooxidans and uranium remains poorly understood. In this study, we investigated the interaction between U(VI) and S. thermosulfidooxidans under sulfate-rich and aerobic conditions. The strain demonstrated the capacity to immobilize uranium under acidic conditions. S. thermosulfidooxidans grown on S0 exhibited a higher uranium immobilization capacity than on FeS2 (34.8 mg g- 1 at pH 4.2), with a uranium loading of 70.1 mg g- 1 under pH 3.9 and initial uranium of 20 ppm. Spectroscopic analyses confirmed that U(VI) was reduced to U(IV) during the aerobic immobilization process, and intracellular uranium accumulation occurred in association with phosphorus. The uranium reduction process and sulfur oxidation occurred concurrently, as indicated by a decrease in reduced sulfur species when uranium was present. The dynamic analysis of uranium reduction revealed competition between the bioadsorption and U (VI) reduction processes. These findings provide evidence that S. thermosulfidooxidans can immobilize uranium through biosorption, bioaccumulation, and partial U(VI) reduction under acidic and aerobic conditions. The results provide new insights into uranium biotransformation in mining environments and have important implications for land management and environmental remediation.
Sodium benzoate (SB), a common food preservative, has raised concerns regarding multi-organ toxicity, but its cardiovascular effects remain unclear. Here, we combined network toxicology, molecular docking, and experimental validation to investigate SB-induced cardiotoxicity. The toxicity profile of SB was predicted using ProTox 3.0 and ADMETlab 3.0, which identified potential cardiotoxicity signals. Network analysis identified AKT1 as a hub target, with functional enrichment implicating the PI3K-AKT apoptotic cascade, and molecular dynamics (MD) simulations confirmed stable SB-AKT1 binding. To experimentally validate these predictions, we assessed cardiac function and PI3K/AKT signaling in SB-exposed mice and AC16 cardiomyocytes. In vivo, SB impaired cardiac function and disrupted myocardial morphology, accompanied by elevated serum LDH, CK-MB, and DHE levels. In vitro, SB reduced cardiomyocyte viability, with equimolar NaCl controls confirming specific toxicity of the benzoate moiety. Mechanistically, SB suppressed PI3K/AKT phosphorylation and its downstream GSK3(3 prosurvival axis, leading to mitochondrial depolarization, BAX/BCL2 imbalance, and Cleaved-Caspase3 activation, collectively indicating mitochondrial apoptosis. mTOR phosphorylation remained unchanged, suggesting that mTOR may not be a functional downstream effector in this model. Notably, although SB elevated ROS production, ROS scavenging failed to restore AKT signaling or rescue cell death, whereas direct AKT activation reversed mitochondrial depolarization and apoptosis, confirming that AKT inhibition, rather than oxidative stress, is the primary driver. Collectively, these findings identify ROS-independent suppression of the PI3K/AKT/ GSK3(3 axis as a key mechanism underlying SB-induced cardiotoxicity, extending its toxicological profile to the cardiovascular system.
The increasing release of persistent and emerging chemicals into the environment has intensified the need for robust frameworks capable of predicting their transport, fate, and environmental behavior across multiple spatial and temporal scales. Over the past decades, environmental multimedia mass-balance models have played a key role in mechanistic simulations of chemical fate. Rapid advances in machine learning (ML) have introduced data-driven alternatives with enhanced capacity for handling high-dimensional, nonlinear datasets. The relative strengths, limitations, and appropriate application domains of these two paradigms remain insufficiently discussed. We examine classical and emerging numerical environmental models for chemical transport and fate, with a focus on multimedia mass-balance models and machine learning. We review the theoretical foundations, structures, and applications of widely used mass-balance models at local, regional, and global scales. We evaluate recent ML applications in parameter estimation, concentration prediction, source identification, and degradation modeling. We synthesize these models according to their thermodynamic basis, spatial segmentation, temporal treatment, and chemical applicability. We highlight the differences in model interpretability, data dependence, uncertainty propagation, and policy relevance. We identify key limitations that affect model reliability, including limited availability and uncertainty of parameters, incomplete characterization and propagation of uncertainty, and restricted generalization beyond the model assumptions or training data. We further discuss emerging hybrid strategies that integrate machine learning with mechanistic mass-balance models. When appropriately designed, independently validated, and accompanied by uncertainty analysis, these approaches can potentially improve parameterization, reduce systematic bias, and support scenario-based projections; however, they may also introduce additional uncertainty associated with ML predictions and model coupling. This review provides a methodological reference for future environmental fate modeling, supporting the development of more reliable tools for predicting the environmental fate of chemicals and for decision-making.
