Chronic ambient fine particulate matter (PM₂.₅) exposure correlates with various adverse health outcomes. Its impact on the circulating metabolome−a comprehensive functional readout of the interaction between an organism's genome and environment−has not however been fully understood. This study thus performed metabolomics analyses using a chronic PM₂.₅ exposure mouse model. C57Bl/6J mice (female) were subjected to inhalational concentrated ambient PM₂.₅ (CAP) or filtered air (FA) exposure for 10 months. Their sera were then analyzed by liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS). These analyses identified 2570 metabolites in total, and 148 of them were significantly different between FA- and CAP-exposed mice. The orthogonal partial least-squares discriminant analysis (OPLS-DA) and heatmap analyses displayed evident clustering of FA- and CAP-exposed samples. Pathway analyses identified 6 perturbed metabolic pathways related to amino acid metabolism. In contrast, biological characterization revealed that 71 differential metabolites were related to lipid metabolism. Furthermore, our results showed that CAP exposure increased stress hormone metabolites, 18-oxocortisol and 5a-tetrahydrocortisol, and altered the levels of circadian rhythm biomarkers including melatonin, retinal and 5-methoxytryptophol.

To evaluate the seasonal distribution of antibiotic resistance genes (ARGs) and explore the reason for their patterns in different seasons and different systems, two wastewater treatment systems were selected and analyzed using high-throughput qPCR. Linear discriminant analysis (LDA) effect size (LEfSe) was used to discover the differential ARGs (biomarkers) and estimate the biomarkers’ effect size. We found that the total absolute abundances of ARGs in inflows and excess sludge samples had no obvious seasonal fluctuations, while those in winter outflow samples decreased in comparison with the inflow samples. Eleven differentially abundant ARGs (biomarker genes, BmGs) (aadA5-02, aac-6-II, cmlA1-01, cmlA1-02, blaOXA10-02, aadA-02, tetX, aadA1, ereA, qacEΔ1-01, and blaTEM) in summer samples and 10 BmGs (tet-32, tetA-02, aacC2, vanC-03, aac-6-I1, tetE, ermB, mefA, tnpA - 07, and sul2) in winter samples were validated. According to 16S rRNA gene sequencing, the relative abundance of bacteria at the phylum level exhibited significant seasonal changes in outflow water (OW), and biomarker bacteria (BmB) were discovered at the family (or genus) level. Synechococcus and vadinCA02 are BmB in summer, and Trichococcus, Lactococcus, Pelosinus, Janthinobacterium, Nitrosomonadaceae and Sterolibacterium are BmB in winter. In addition, BmB have good correlations with BmGs in the same season, which indicates that bacterial community changes drive different distributions of ARGs during seasonal changes and that LEfSe is an acute and effective method for finding significantly different ARGs and bacteria between two or more classes.In conclusion, this study demonstrated the seasonal changes of BmGs and BmB at two wastewater treatment systems.

Antibiotics resistance genes (ARGs) are considered as an emerging pollutant among various environments. As a sink of ARGs, a comprehensive study on the spatial and temporal distribution of ARGs in the estuarine sediments is needed. In the present study, six ARGs were determined in sediments taken along the Yangtze Estuary temporally and spatially. The sulfonamides, tetracyclines and fluoroquinolones resistance genes including sul1, sul2, tetA, tetW, aac(6’)-Ib, and qnrS, were ubiquitous, and the average abundances of most ARGs showed significant seasonal differences, with relative low abundances in winter and high abundances in summer. Moreover, the relative high abundances of ARGs were found at Shidongkou (SDK) and Wusongkou (WSK), which indicated that the effluents from the wastewater treatment plant upstream and inland river discharge could influence the abundance of ARGs in sediments. The positive correlation between intI1 and sul1 implied intI1 may be related to the occurrence and propagation of sulfonamides resistance genes. Correlation analysis and redundancy discriminant analysis showed that antibiotic concentrations had no significant correlation to their corresponding ARGs, while the total extractable metal, especially the bioavailable metals, as well as other environmental factors including temperature, clay, total organic carbon and total nitrogen, could regulate the occurrence and distribution of ARGs temporally and spatially. Our findings suggested the comprehensive effects of multiple pressures on the distribution of ARGs in the sediments, providing new insight into the distribution and dissemination of ARGs in estuarine sediments, spatially and temporally.

In China, the cadmium (Cd) levels in paddy fields have increased, which has led to the excessive uptake of Cd into rice grains. In this study, we determined the physicochemical properties of soil samples, including the pH, soil organic matter (SOM) content, cation exchange capacity (CEC), and total Cd content (Cdsoil) in order to establish a quadratic discriminant analysis (QDA) model for assessing the risk of Cd in rice and to calculate its prior probability. Decision tree and logistic regression models were also established for comparison. The results showed that the accuracy rate was 74% with QDA, which was significantly higher than that obtained using the decision tree (67%) and logistic regression (68%) models. The correlation coefficients between the soil pH and the other three factors (CEC, SOM, and Cdsoil) were higher in the inaccurate set than the accurate set, whereas the correlation coefficients were smaller in the inaccurate set than the accurate set.

Bacterial communities in rivers receiving untreated domestic wastewater may show specific spatial assemblage patterns due to a wide range of physicochemical conditions created by periodic algal bloom. However, there are significant gaps in understanding environmental forces that drive changes in microbial assemblages in polluted rivers. In this study, we applied high-throughput sequencing of 16S rRNA gene amplicons to perform comprehensive spatio-temporal profiling of bacterial community structure in a local river segment receiving domestic wastewater discharge in southeast China. Multivariate statistics were then used to analyse links between bacterial community structure and environmental factors. Non-metric multidimensional scaling (NMDS) plots showed that the bacterial community structure was different between upstream and downstream sections of the river. While the upstream water contained a high proportion of bacteria degrading xenobiotic aromatic compounds, the downstream water experiencing stronger algal bloom had a more diverse bacterial community which included the genus Aeromonas comprising 14 species, most of which are human pathogens. Least discriminant analysis (LDA) effect size revealed that the surface water was mainly inhabited by aerobic microorganisms capable of degrading aromatic compounds, and also contained bacterial genera including pathogenic species. In contrast, in the bottom water we found, along with aromatic compound-degrading species, anaerobic denitrifiers and Fe3+-reducing and fermentative bacteria. Variance partitioning canonical correspondence analysis (VPA) showed that nutrient ratios had a stronger contribution to bacterial dissimilarities than other major physicochemical factors (temperature, pH, dissolved oxygen, total organic carbon, and chlorophyll a). These results show that microbial communities in rivers continuously receiving domestic wastewater have specific longitudinal and vertical assemblage patterns and may contain pathogenic species presenting a high threat to public health. These factors should be taken into consideration while developing pollution management strategies.

Colon microenvironment and microbiota dysbiosis are closely related to various human metabolic diseases. In this study, a total of 72 healthy female mice were exposed to fluoride (F) (0, 25, 50 and 100 mg/L F⁻) in drinking water for 70 days. The effect of F on intestinal barrier and the diversity and composition in colon microbiota have been evaluated. Meanwhile, the relationship among F-induced colon microbiota alterations and antimicrobial peptides (AMPs) expression and short-chain fatty acids (SCFAs) level also been assessed. The results suggested that F decreased the goblet cells number and glycoprotein expression in colon. And further high-throughput 16S rRNA gene sequencing result demonstrated that F exposure induced the diversity and community composition of colonic microbiota significantly changes. Linear Discriminant Analysis Effect Size (LEfSe) analysis identified 11 predominantly characteristic taxa which may be the biomarker in response to F exposure. F-induced intestinal microbiota perturbations lead to the significantly decreased SCFAs levels in colon. Immunofluorescence results showed that F increased the protein expression of interleukin-17A (IL-17A) and IL-22 (P < 0.01) and disturbed the expression of interleukin-17 receptor A (IL-17RA) and IL-22R (P < 0.05 or P < 0.01). In addition, the increased expression of IL-17A and IL-22 cooperatively enhanced the mRNA expression of AMPs which response to F-induced microbiota perturbations. Collectively, destroyed microenvironment and disturbed AMPs are the primary reason of microbiota dysbiosis in colon after F exposure. Colonic homoeostasis imbalance would be helpful for finding the source of F-induced chronic systemic diseases.

The Sürgü Stream, located in the Euphrates River basin of Turkey, is used for drinking water source, agricultural irrigation and rainbow trout production. Therefore, water quality of the stream is of great importance. In this study, multivariate statistical techniques (MSTs) and water quality index (WQI) were applied to assess water quality of the stream affected by multiple stressors such as untreated domestic sewage, effluents from fish farms, agricultural runoff and streambank erosion. For this, 16 water quality parameters at five sites along the stream were monitored monthly during one year. Most of parameters showed significant spatial variations, indicating the influence of anthropogenic activities. All parameters except TN (total nitrogen) showed significant seasonal differences due to high seasonality in WT (water temperature) and water flow. The spatial variations in the WQI were significant (p < 0.05) and the mean WQI values ranged from 87.6 to 95.3, indicating “good” to “excellent” water quality in the stream. Cluster analysis classified five sites into three groups, that is, clean region, low polluted region and very clean region. Stepwise temporal discriminant analysis (DA) identified that pH, WT, Cl⁻, SO₄²⁻, COD (chemical oxygen demand), TSS (total suspended solids) and Ca²⁺ are the parameters responsible for variations between seasons, and stepwise spatial DA identified that DO (dissolved oxygen), EC (electrical conductivity), NH₄–N, TN (total nitrogen) and TSS are the parameters responsible for variations between the regions. Principal component analysis/factor analysis revealed that the parameters responsible for water quality variations were mainly associated with suspended solids (both natural and anthropogenic), soluble salts (natural) and nutrients and organic matter (anthropogenic).

Humans are exposed to various anthropogenic chemicals in daily life, including endocrine-disrupting chemicals (EDCs). However, there are limited data on chronic, low-level exposure to such contaminants among the general population. Here hair analysis was used to investigate the occurrence of four polychlorinated biphenyls (PCBs), seven polybrominated diphenyl ethers (PBDEs) and two bisphenols (BPs) in 204 Chinese women living in the urban areas of Baoding and Dalian and 311 pregnant French women. All the PCBs and PBDEs tested here were more frequently detected in the hair samples of the French women than in those of the Chinese women. In both cohorts, PCB 180 and BDE 47 were the dominant PCB and PBDE congener, respectively. PCB 180 was found in 82% of the French women and 44% of the Chinese women, while the corresponding values of BDE 47 were 54% and 11%, respectively. A discriminant analysis further demonstrated the difference in PCBs and PBDEs exposure profile between the two cohorts. These results demonstrate that hair analysis is sufficiently sensitive to detect exposure to these pollutants and highlight differences in exposure between populations even at environmental levels. Although BPA and BPS were found in 100% of the hair samples in both cohorts, the French women had significantly higher levels of BPA and BPS than the Chinese women. The median concentrations of BPA were one order of magnitude higher than BPS in both the Chinese (34.9 versus 2.84 pg/mg) and the French women (118 versus 8.01 pg/mg) respectively. Our results suggest that both French and Chinese populations were extensively exposed to BPA and BPS.

The relationship between cadmium (Cd) concentration in rice grains and the soil that they are cultivated in is highly uncertain due to the influence of soil properties, rice varieties, and other undetermined factors. In this study, we introduce the probability of exceeding the threshold to characterize this uncertainty and then, build a probabilistic forewarning model. Additionally, a number of associated factors have been used as parameters to improve model performance. Considering that the physicochemical properties and Cd concentration in the soil (Cdₛₒᵢₗ) do not follow a normal distribution, and are not independent of each other, a discriminative algorithm, represented by a logistic regression (LR), performed better than generative algorithms, such as the naive Bayes and quadratic discriminant analysis models. The performance of the LR based model was found to be 0.5% better in the case of the univariate model (Cdₛₒᵢₗ) and 4.1% better with a multivariate model (soil properties used as additional factors) (p < 0.01). The output of the LR based model predicted probabilities that were positively correlated to the true exceedance rate (R² = 0.949,p < 0.01), within an exceedance threshold range of 0.1–0.4 mg kg⁻¹ and a mean deviation of 5.75%. A sensitivity analysis showed that the effect of soil properties on the exceedance probability weakens with an increase in Cd concentration in rice grains. When the threshold is below 0.15 mg kg⁻¹, soil pH strongly influences the exceedance probability. As the threshold increases, the influence of pH on the exceedance probability is gradually superseded. By quantifying the uncertainty regarding the relationship between Cd concentration in rice grains and soil, the discriminative algorithm-based probabilistic forecasting model offers a new way to assess Cd pollution in rice grown in contaminated paddy fields.

Environmental reservoirs of antibiotic resistant bacteria are poorly understood. Understanding how the environment selects for resistance traits in the absence of antibiotics is critical in developing strategies to mitigate this growing menace. Indirect or co-selection of resistance by environmental pollution has been shown to increase antibiotic resistance. However no attention has been given to the effects of low-level ionizing radiation or the interactions between radiation and heavy metals on the maintenance or selection for antibiotic resistance (AR) traits. Here we explore the effect of radiation and copper on antibiotic resistance. Bacteria were collected from biofilms in two ponds – one impacted by low-level radiocesium and the other an abandoned farm pond. Through laboratory controlled experiments we examined the effects of increasing concentrations of copper on the incidence of antibiotic resistance. Differences were detected in the resistance profiles of the controls from each pond. Low levels (0.01 mM) of copper sulfate increased resistance but 0.5 mM concentrations of copper sulfate depressed the AR response in both ponds. A similar pattern was observed for levels of multiple antibiotic resistance per isolate. The first principal component response of isolate exposure to multiple antibiotics showed significant differences among the six isolate treatment combinations. These differences were clearly visualized through a discriminant function analysis, which showed distinct antibiotic resistance response patterns based on the six treatment groups.