Particulate matter exposure disturbs inflammatory cytokine homeostasis associated with changes in trace metal levels in mouse organs

Toxicity risks associated with heavy metals to fish species in the Transboundary River - Linked Ramsar Conservation Site of Tanguar Haor, Bangladesh

The presence of trace metals in aquatic ecosystems can have detrimental effects on fish survival. The Tanguar haor, a Ramsar conservation wetland, receives sediment and water from multiple transboundary rivers. However, there have been limited studies on the metal concentrations in fish species in this sediment-rich wetland. This study aimed to analyze the concentrations of iron (Fe), manganese (Mn), chromium (Cr), copper (Cu), zinc (Zn), cadmium (Cd), and lead (Pb) in water, sediment, and fish tissues. Higher concentrations of Cd and Pb were found in the water and sediment. All these metals were detected in eight fish species, including benthic and pelagic species. Among them, Systomus sarana, a pelagic fish that also consumes benthic organisms, exhibited a higher metal pollution index than other fish, particularly benthic species. The release of higher metal concentrations from sediment into the water has the potential to impact the accumulation of metals in fish. SYNOPSIS: This study on metal concentrations in fish species will aid policymaking on ecotoxicology research for transboundary river-connected wetlands.

Exposure to particulate matter may affect semen quality via trace metals: Evidence from a retrospective cohort study on fertile males

Particulate matter (PM) exposure may be associated with male semen quality. Besides, PM exposure induces up and down levels of trace metals in tissues or organs. The levels of trace metals in semen are critical for adverse male semen quality. This study aims to evaluate the concentrations of seminal-level trace metals in fertile men and assess its associations with PM exposure and to explore the mediation role of trace metals in seminal plasma plays in the relationship between PM exposure and semen quality. Total 1225 fertile men who participated in a cohort study from 2014 to 2016 were finally recruited. Multivariate linear regression was applied to explore associations between each two of PM exposure, trace metals and semen parameters. 1-year PM2.5 and PM10 exposure levels were positively associated with arsenic (As), mercury (Hg), lanthanum (La), praseodymium (Pr), neodymium (Nd) but negatively associated with vanadium (V), magnesium (Mg), strontium (Sr), barium (Ba) in semen. It was also found that most of the elements were associated with total sperm number, followed by sperm concentration. Redundancy analysis (RDA) also determined several strong positive correlations or negative correlations between 1-year PM exposure and trace metals. Mediation analysis found that trace metals had a potentially compensatory or synergetic indirect effect on the total effect of the association between 1-year PM exposure and semen quality. The retrospective cohort study provides long-term PM exposure that may cause abnormal semen quality by affecting seminal plasma element levels.

Occurrence and risk of iodinated X-ray contrast media in source and tap water from Jiangsu province, China

Microcontaminants in the water environment have received increasing attention due to their adverse effects on human health and wildlife. However, iodinated X-ray contrast media (ICM), a type of microcontaminants, have not yet been systematically documented in source and tap water. This study investigated ICM in water samples via a sampling activity from 25 drinking water sources and their corresponding 30 household taps in south-central Jiangsu Province, China. The total concentrations of ICM ranged from 14.2 to 138.5 ng/L in source water and 3.7 to 101.3 ng/L in tap water, respectively. The calculated average water treatment efficiency to remove ICM is 38.3% with large variation under different processes (ranging from 7.3% to 75.7%), which implied that ICM could not be effectively removed using current treatment technologies. By integrating other ICM into the predominant compound iohexol with relative potency factors, the health risks of total ICM through water consumption were assessed using the Monte Carlo simulation. The results concluded that the risk of ingesting ICM through tap water was not a major health concern for adults, teens, or children in the study area. Nevertheless, due to the lack of long-term toxicity data relevant for humans for ICM, this risk may be underestimated, which requires further research.

Air pollution, depressive and anxiety disorders, and brain effects: A systematic review

Accumulating data suggest that air pollution increases the risk of internalizing psychopathology, including anxiety and depressive disorders. Moreover, the link between air pollution and poor mental health may relate to neurostructural and neurofunctional changes. We systematically reviewed the MEDLINE database in September 2021 for original articles reporting effects of air pollution on 1) internalizing symptoms and behaviors (anxiety or depression) and 2) frontolimbic brain regions (i.e., hippocampus, amygdala, prefrontal cortex). One hundred and eleven articles on mental health (76% human, 24% animals) and 92 on brain structure and function (11% human, 86% animals) were identified. For literature search 1, the most common pollutants examined were PM2.5 (64.9%), NO2 (37.8%), and PM10 (33.3%). For literature search 2, the most common pollutants examined were PM2.5 (32.6%), O3 (26.1%) and Diesel Exhaust Particles (DEP) (26.1%). The majority of studies (73%) reported higher internalizing symptoms and behaviors with higher air pollution exposure. Air pollution was consistently associated (95% of articles reported significant findings) with neurostructural and neurofunctional effects (e.g., increased inflammation and oxidative stress, changes to neurotransmitters and neuromodulators and their metabolites) within multiple brain regions (24% of articles), or within the hippocampus (66%), PFC (7%), and amygdala (1%). For both literature searches, the most studied exposure time frames were adulthood (48% and 59% for literature searches 1 and 2, respectively) and the prenatal period (26% and 27% for literature searches 1 and 2, respectively). Forty-three percent and 29% of studies assessed more than one exposure window in literature search 1 and 2, respectively. The extant literature suggests that air pollution is associated with increased depressive and anxiety symptoms and behaviors, and alterations in brain regions implicated in risk of psychopathology. However, there are several gaps in the literature, including: limited studies examining the neural consequences of air pollution in humans. Further, a comprehensive developmental approach is needed to examine windows of susceptibility to exposure and track the emergence of psychopathology following air pollution exposure.

Real ambient particulate matter-induced lipid metabolism disorder: Roles of peroxisome proliferators-activated receptor alpha

A growing body of evidence associated particulate matter (PM) exposure with lipid metabolism disorders, yet, the underlying mechanism remains to be elucidated. Among the major lipid metabolism modulators, peroxisome proliferator-activated receptor (PPAR) alpha plays an important role. In the current study, an individually ventilated cage (IVC) system was used to expose C57/B6 mice to real-ambient PM for six weeks, with or without co-treatment of PPAR alpha agonist WY14,643. The general parameters, liver and adipose tissue pathology, serum lipids, metal deposition and lipid profile of liver were assessed. The results indicated that six weeks of real-ambient PM exposure induced dyslipidemia, including increased serum triglycerides (TG) and decreased high density lipoprotein cholesterol (HDL-C) level, along with steatosis in liver, increased size of adipocytes in white adipose tissue (WAT) and whitening of brown adipose tissue (BAT). ICP-MS results indicated increased Cr and As deposition in liver. Lipidomics analysis revealed that glycerophospholipids and cytochrome P450 pathway were most significantly affected by PM exposure. Several lipid metabolism-related genes, including CYP4A14 in liver and UCP1 in BAT were downregulated following PM exposure. WY14,643 treatment alleviated PM-induced dyslipidemia, liver steatosis and whitening of BAT, while enhancing CD36, SLC27A1, CYP4A14 and UCP1 expression. In conclusion, PPAR alpha pathway participates in PM-induced lipid metabolism disorder, PPAR alpha agonist WY14,643 treatment exerted protective effects on PM-induced dyslipidemia, liver steatosis and whitening of BAT, but not on increased adipocyte size of WAT.

Association between particulate matter containing EPFRs and neutrophilic asthma through AhR and Th17

BACKGROUND

Epidemiological data associate high levels of combustion-derived particulate matter (PM) with deleterious respiratory outcomes, but the mechanism underlying those outcomes remains elusive. It has been acknowledged by the World Health Organization that PM exposure contributes to more than 4.2 million all-cause mortalities worldwide each year. Current literature demonstrates that PM exacerbates respiratory diseases, impairs lung function, results in chronic respiratory illnesses, and is associated with increased mortality. The proposed mechanisms revolve around oxidative stress and inflammation promoting pulmonary physiological remodeling. However, our previous data found that PM is capable of inducing T helper cell 17 (Th17) immune responses via aryl hydrocarbon receptor (Ahr) activation, which was associated with neutrophilic invasion characteristic of steroid insensitive asthma.

METHODS

In the present study, we utilized a combination of microarray and single cell RNA sequencing data to analyze the immunological landscape in mouse lungs following acute exposure to combustion derived particulate matter.

RESULTS

We present data that suggest epithelial cells produce specific cytokines in the aryl hydrocarbon receptor (Ahr) pathway that inform dendritic cells to initiate the production of pathogenic T helper (eTh17) cells. Using single-cell RNA sequencing analysis, we observed that upon exposure epithelial cells acquire a transcriptomic profile indicative of increased Il-17 signaling, Ahr activation, Egfr signaling, and T cell receptor and co-stimulatory signaling pathways. Epithelial cells further showed, Ahr activation is brought on by Ahr/ARNT nuclear translocation and activation of tyrosine kinase c-src, Egfr, and subsequently Erk1/2 pathways.

CONCLUSIONS

Collectively, our data corroborates that PM initiates an eTh17 specific inflammatory response causing neutrophilic asthma through pathways in epithelial, dendritic, and T cells that promote eTh17 differentiation during initial PM exposure.

Rapid monitoring of heavy metal pollution in lake water using nitrogen and phosphorus nutrients and physicochemical indicators by support vector machine

A novel method of predicting heavy metal concentration in lake water by support vector machine (SVM) model was developed, combined with low-cost, easy to obtain nutrients and physicochemical indicators as input variables. 115 surface water samples were collected from 23 sites in Chaohu Lake, China, during different hydrological periods. The particulate concentrations of heavy metals in water were much higher than the dissolved concentrations. According to Nemerow pollution index (Pi), pollution degrees by Fe, V, Mn and As ranged from heavy (2 ≤ Pi < 4) to serious (Pi ≥ 4). The concentrations of most heavy metals were the highest during the medium-water period and the lowest during the dry season. Non-metric Multidimensional Scaling Analysis confirmed heavy metal concentrations had slight spatial difference but relatively large seasonal variation. Redundancy Analysis indicated the close associations of heavy metals with nutrient and physicochemical indicators. When both nutrient and physicochemical indicators were used as input variables, the simulation effects for most elements in total and particulate were relatively better than those obtained using only nutrient or only physicochemical indicators. The simulation effects for As, Ba, Fe, Ti, V and Zn were generally good, based on their training R values of 0.847, 0.828, 0.856, 0.867, 0.817 and 0.893, respectively, as well as their test R values of 0.811, 0.836, 0.843, 0.873, 0.829 and 0.826, respectively; and meanwhile, in both the training and test stages, these metals also had relatively lower errors. The spatial distribution of heavy metals in Chaohu Lake was then predicted using the fully trained SVM models.

Magnetic properties and its application in the prediction of potentially toxic elements in aquatic products by machine learning

Magnetic measurement was provided to substitute for time-consuming conventional methods for determination of potentially toxic elements. Both the concentrations of 12 elements and 9 magnetic parameters were determined in 700 muscle tissue samples from the snail Bellamya aeruginosa, shrimp species Exopalaemon modestus and Macrobrachium nipponense, and fish species Hemisalanx prognathous Regan, Coilia ectenes taihuensis, and Culer alburnus Basilewsky collected from Chaohu Lake during different hydrological periods. Spherical and irregular iron oxide particles were observed in the muscle tissues of the studied aquatic products. A field survey of the exposure parameters in humans, such as per capita intake dose of local aquatic products, found no evidence that consumption of the tested species poses a potential health risk. Redundancy analysis revealed different degrees of correlation between the magnetic parameters and concentrations of elements in aquatic products. Back-propagation artificial neural network (BP-ANN) and support vector machine (SVM) models were applied to predict elemental concentrations in aquatic products, using magnetic parameters as input. SVM models performed well in predicting the presence of Cr and Ni, with R and index of agreement values of >0.8 in both training and validation stages as well as relatively low errors. The BP-ANN and SVM models both performed relatively poorly in predicting the presence of Cd and Zn in aquatic products, with R values between 0.333 and 0.718 for Cd and between 0.454 and 0.664 for Zn in training and validation stages. For most of the elements, a better R value was obtained with the SVM than with BP-ANN model. The R of Co, Cr, Cu, Ni, and Ti in the training and validation stages of snail in the SVM model were >0.8. This study is a first step in developing a novel approach allowing the rapid monitoring of potentially toxic elements concentrations in aquatic products.

Rapid diagnosis of heavy metal pollution in lake sediments based on environmental magnetism and machine learning

Environmental magnetism in combination with machine learning can be used to monitor heavy metal pollution in sediments. Magnetic parameters and heavy metal concentrations of sediments from Chaohu Lake (China) were analyzed. The magnetic measurements, high- and low-temperature curves, and hysteresis loops showed the primary magnetic minerals were ferrimagnetic minerals in sediments. For most metals, their concentrations were highest during the wet season and lowest during the medium-water period. Cd, Hg, and Zn were moderately enriched and Cd and Hg posed a considerable ecological risk. A redundancy analysis indicated a relationship between physicochemical indexes and magnetic parameters and heavy metal concentrations. An artificial neural network (ANN) and support vector machine (SVM) were used to construct six models to predict the heavy metal concentrations and ecological risk index. The inclusion of both the physicochemical indexes and magnetic parameters as input factors in the models were significantly ameliorated the simulation accuracy for the majority of heavy metals. The training and test R, for Be, Fe, Pb, Zn, As, Cu, and Cr were > 0.8. The SVM showed better performance and hence it has potential for the efficient and economical long-term tracking and monitoring of heavy metal pollution in lake sediments.

Particulate Matter Exposure During Oocyte Maturation: Cell Cycle Arrest, ROS Generation, and Early Apoptosis in Mice

Particulate matter (PM) is a general atmospheric pollutant released into the air by an anthropogenic and naturally derived mixture of substances. Current studies indicate that fine dust can result in different health defects, including endothelial dysfunction, asthma, lung cancer, cardiovascular diseases, uterine leiomyoma, deterioration in sperm quality, and overall birth impairment. However, the most prominent effects of PM₁₀ (diameter < 10 μM) exposure on the female reproductive system, especially with respect to oocyte maturation, remain unclear. In the present study, maturing mouse oocytes were treated with PM₁₀ and the phenotypes of the resulting toxic effects were investigated. Exposure to PM₁₀ led to impairment of maturation capacity by inducing cell cycle arrest and blocking normal polar body extrusion during in vitro maturation and activation of fertilization of mouse oocytes. Additionally, defects in tubulin formation and DNA alignment were observed in PM₁₀-treated oocytes during metaphase I to anaphase/telophase I transition. Moreover, PM₁₀ induced reactive oxygen species generation, mitochondrial dysfunction, DNA damage, and early apoptosis. Taken together, these results indicate that PM₁₀ exposure leads to a decline in oocyte quality and affects the subsequent embryonic development potential of mammalian oocytes.