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.

Marine coastal contamination caused by human activity is a major issue worldwide. The implementation of effective pollution monitoring programs, especially in coastal areas, is important and urgent. The use of biological, physiological, or biochemical measurements to monitor the impacts of pollution has garnered increasing interest, particularly for the development of new non-invasive tools to assess water pollution. Fish skin mucus is in direct contact with the marine environment, making it a favourable microenvironment for the formation of biofilm bacterial communities. In this study, we developed a non-invasive technique, sampling fish skin mucus to determine and analyse bacterial community composition using next-generation sequencing. We hypothesised that bacterial communities associated with the skin mucus of a common harbour benthic blennioid triplefin fish, Forsterygion capito, would reflect conditions of different marine environments. We detected clear differences in bacterial community alpha-diversity between contaminated and reference sites. Beta-diversity analysis also revealed differences in the bacterial community structure of the skin mucus of fish inhabiting different geographical areas. The relative abundance of different bacterial orders varied among sites, as determined by linear discriminant analysis (LDA) and effect size (LEfSe) analyses. The observed variation in bacterial community compositions correlated more strongly with variation in hydrocarbons than to various metal concentrations. Using advanced DNA sequencing technologies, we have developed a novel non-invasive, low-cost and effective tool to monitor the impacts of pollution through analysis of the bacterial communities associated with fish skin mucus.

The Keban Dam Reservoir, located on the Euphrates River, is the second largest reservoir of Turkey. Water quality of this reservoir is of great importance because it is widely used for recreation, aquaculture production, fishing, and irrigation. In this study, discriminant analysis, principal component analysis (PCA), factor analysis (FA) and cluster analysis (CA) were conducted to evaluate the seasonal and spatial variations in surface water quality of the reservoir. Also, total phosphorus (TP) content in sediments, water type and trophic status of the reservoir were determined. For this, 19 water quality variables and TP in sediments were monitored seasonally at 11 sampling stations on the reservoir during one year. Hierarchical CA classified 11 stations into three groups, i.e., upstream (moderate polluted), midstream (low polluted) and downstream (clean) regions. PCA/FA allowed to group the variables responsible for variations in water quality, which are mainly related to mineral dissolution (natural), organic matter and nutrients (anthropogenic), and physical parameters (natural). Discriminant analysis (DA) gave better results for both data reduction and spatio-temporal analysis. Stepwise temporal DA identified eight variables: water temperature (WT), chemical oxygen demand (COD), nitrate nitrogen (NO₃–N), soluble reactive phosphorus (SRP), chlorophyll-a (Chl-a), potassium (K⁺), magnesium (Mg²⁺), and calcium (Ca²⁺), which are the most significant variables responsible for temporal variations in water quality of the reservoir, while stepwise spatial DA identified three variables: K⁺, chloride (Cl⁻), and sulphate (SO₄⁻²), which are the most significant variables responsible for spatial variations. According to Ontario sediment-quality guidelines, sediments of the reservoir can be considered as unpolluted in terms of mean TP content. The water type of the reservoir was calcium-bicarbonate. According to trophic state index values based on TP and Chl-a, upstream region (moderate polluted) of the reservoir was in the eutrophic status, whereas other regions were in the mesotrophic status.

Contamination of soil with heavy metals seriously harms the growth of crops. Silicon fertilizer is known to promote growth of crops and alleviate heavy metals stresses in vegetables. However, little is known about the effects of silicon fertilizer on pakchoi vegetable growth and soil microbial community in soil contaminated with multiple heavy metals. In order to elucidate this question, current study was designed to analyze the impact of different silicon fertilizer doses on the growth of pakchoi, heavy metals accumulation in pakchoi, and diversity and composition of bacterial community in heavy metals contaminated soil. Results of the study showed that, silicon fertilizer application significantly improved the yield of pakchoi and reduced the content of heavy metals in pakchoi. Moreover, the silicon fertilizer led to the heterogeneity of bacterial community structure in soil. Linear discriminant analysis (LDA) effect size (LEfSe) test showed the change of soil bacterial community structures under the higher silicon fertilizer doses (0.8–3.2%). Similarly, soil bacteria associated with heavy metal resistance and carbon/nitrogen metabolism showed a more active response to medium fertilizer dose (0.8% w/w). In addition, Mantel test and Redundancy analysis (RDA) showed that both the soil bacterial community structures and pakchoi growth were significantly correlated with soil EC, available K and pH. Study suggested that the application of silicon fertilizer provided richer bacteria associated with heavy metal resistance and plant growth, and more favorable soil physicochemical environment for the growth of pakchoi under multiple heavy metal contamination, and the impact was dependent on fertilizing dose.

Endometriosis is a gynaecological disease characterised by the presence of endometriotic tissue outside of the uterus impacting a significant fraction of women of childbearing age. Evidence from epidemiological studies suggests a relationship between risk of endometriosis and exposure to some organochlorine persistent organic pollutants (POPs). However, these chemicals are numerous and occur in complex and highly correlated mixtures, and to date, most studies have not accounted for this simultaneous exposure. Linear and logistic regression models are constrained to adjusting for multiple exposures when variables are highly intercorrelated, resulting in unstable coefficients and arbitrary findings. Advanced machine learning models, of emerging use in epidemiology, today appear as a promising option to address these limitations. In this study, different machine learning techniques were compared on a dataset from a case-control study conducted in France to explore associations between mixtures of POPs and deep endometriosis. The battery of models encompassed regularised logistic regression, artificial neural network, support vector machine, adaptive boosting, and partial least-squares discriminant analysis with some additional sparsity constraints. These techniques were applied to identify the biomarkers of internal exposure in adipose tissue most associated with endometriosis and to compare model classification performance. The five tested models revealed a consistent selection of most associated POPs with deep endometriosis, including octachlorodibenzofuran, cis-heptachlor epoxide, polychlorinated biphenyl 77 or trans-nonachlor, among others. The high classification performance of all five models confirmed that machine learning may be a promising complementary approach in modelling highly correlated exposure biomarkers and their associations with health outcomes. Regularised logistic regression provided a good compromise between the interpretability of traditional statistical approaches and the classification capacity of machine learning approaches. Applying a battery of complementary algorithms may be a strategic approach to decipher complex exposome-health associations when the underlying structure is unknown.

Bacterial communities inhabiting sediments in coastal areas endure the effect of strong anthropogenic pressure characterized by the presence of multiple contaminants. Understanding the effect of pollutants on the organization of bacterial communities is of paramount importance in order to unravel bacterial assemblages colonizing specific ecological niches. Here, chemical and molecular approaches were combined to investigate the bacterial communities inhabiting the sediments of the Ichkeul Lake/Bizerte Lagoon, a hydrological system under anthropogenic pressure. Although the microbial community of the Ichkeul Lake sediment was different to that of the Bizerte Lagoon, common bacterial genera were identified suggesting a lake-lagoon continuum probably due to the hydrology of the system exchanging waters according to the season. These genera represent bacterial "generalists" maintaining probably general biogeochemical functions. Linear discriminant analysis effect size (LEfSe) showed significant differential abundance distribution of bacterial genera according to the habitat, the pollution type and level. Further, correlation analyses identified specific bacterial genera which abundance was linked with pesticides concentrations in the lake, while in the lagoon the abundance of specific bacterial genera was found linked with the concentrations of PAHs (Polycyclic aromatic hydrocarbons) and organic forms of Sn. As well, bacterial genera which abundance was not correlated with the concentrations of pollutants were identified in both lake and lagoon. These findings represent valuable information, pointing out specific bacterial genera associated with pollutants, which represent assets for developing bacterial tools for the implementation, the management, and monitoring of bioremediation processes to mitigate the effect of pollutants in aquatic ecosystems.