Contribution of influent rivers affected by different types of pollution to the changes of benthic microbial community structure in a large lake

Microbial and environmental medium-driven responses to phosphorus fraction changes in the sediments of different lake types during the freezing period

The comparative study of the transformation among sediment phosphorus (P) fractions in different lake types is a global issue in lake ecosystems. However, interactions between sediment P fractions, environmental factors, and microorganisms vary with the nutrient status of lakes. In this study, we combine sequential extraction and metagenomics sequencing to assess the characteristics of P fractions and transformation in sediments from different lake types in the Inner Mongolian section of the Yellow River Basin. We then further explore the response of relevant microbial and environmental drivers to P fraction transformation and bioavailability in sediments. The sediments of all three lakes exhibited strong exogenous pollution input characteristics, and higher nutritional conditions led to enhanced sediment P fraction transformation ability. The transformation capacity of the sediment P fractions also differed among the different lake types at the same latitudes, which is affected by many factors such as lake environmental factors and microorganisms. Different drivers reflected the mutual control of weakly adsorbed phosphorus (WA-P), potential active phosphorus (PA-P), Fe/Al-bound phosphorus (NaOH-P), and Ca-bound phosphorus (HCl-P) with the bio-directly available phosphorus (Bio-P). The transformation of NaOH-P in reducing environments can improve P bioavailability, while HCl-P is not easily bioavailable in weakly alkaline environments. There were significant differences in the bacterial community diversity and composition between the different lake types at the same latitude (p < 0.05), and the role of P fractions was stronger in the sediments of lakes with rich biodiversity than in poor biodiversity. Lake eutrophication recovery was somewhat hindered by the microbial interactions of P cycling and P fractions within the sediment. This study provides data and theoretical support for exploring the commonalities and differences among different lake types in the Inner Mongolian section of the Yellow River Basin. Besides, it is representative and typical for promoting the optimization of ecological security patterns in ecologically fragile watersheds.

Characteristics of microbial community structure and influencing factors of Yangcheng Lake and rivers entering Yangcheng Lake during the wet season

Yangcheng Lake, a typical fishery lake in the middle and lower reaches of the Yangtze River, is threatened by eutrophication. As the main performers of biogeochemical cycles, microorganisms affect the ecological stability of the lake. To study the structural characteristics of the microbial community in Yangcheng Lake and rivers entering Yangcheng Lake and the response relationship with environmental factors, the microbial community was categorized based on the contour of Yangcheng Lake, the major rivers entering Yangcheng Lake, and the pollution sources. The distribution characteristics of seven physicochemical indices were analyzed, including total organic carbon (TOC), total nitrogen (TN), total phosphorus (TP), water temperature (WT), pH, dissolved oxygen (DO), and ratio of total nitrogen to total phosphorus (TN/TP). Characterization of microbial community structure based on 16S rRNA high-flux sequencing technology and ANOSIM analysis were used to explore the differences in the relative abundance of microorganisms at each sampling point in the lake and rivers, and redundancy analysis (RDA) was used to analyze the relationship between the microbial community and physicochemical factors. The results showed that the dominant phyla, genera of microorganisms, and the total number of OTUs in the lake and rivers were similar. The dominant phyla included Proteobacteria, Actinobacteria, Bacteroidetes, Cyanobacteria, and Verrucomicrobia; the dominant genera included the hgcI clade, CL500-29 marine group, Microcystis PCC-7914, Chloroplast_norank, Clade III_norank, and Flavobacterium. ANOSIM analyses revealed that the microbial community of Yangcheng Lake exhibited an association with geographical space, while the microbial community in the rivers that was linked to the type of pollution source. Redundancy analysis (RDA) indicated that dissolved oxygen (DO), total nitrogen (TN), and pH were significantly correlated with the dominant phyla in Yangcheng Lake (p < 0.05), while total nitrogen (TN), water temperature(WT), and the ratio of total nitrogen to total phosphorus (TN/TP) were significantly related with the dominant genera in Yangcheng Lake (p < 0.05). Total nitrogen (TN) was also significantly linked to the dominant phyla and genera of the tributaries (p < 0.05). Despite the structural similarities in microbial communities between Yangcheng Lake and its inflowing rivers, environmental factors demonstrated significant associations with these communities, providing crucial data support for pollution prevention and the ecological restoration of Yangcheng Lake.

Differences in pathogenic community assembly processes and their interactions with bacterial communities in river and lake ecosystems

Pathogenic bacterial infections caused by water quality degradation are one of the most widespread environmental problems. Clarifying the structure of pathogens and their assembly mechanisms in lake ecosystems is vital to prevent the infestation of waterborne pathogens and maintain human health. However, the composition and assembly mechanisms of pathogenic bacterial communities in river and lake ecosystems are still poorly understood. In this study, we collected 17 water and 17 sediment samples from Lake Chaohu and its 11 inflow rivers. Sequencing of 16S rRNA genes was used to study bacterial pathogen communities. The results of the study showed that there was a significant difference (P < 0.05) in the composition of the pathogen community between riverine and lake habitats. Acinetobacter (36.49%) was the dominant bacterium in the river, whereas Flavobacterium (21.6%) was the most abundant bacterium in the lake. Deterministic processes (i.e., environmental filtering and species interaction) drove the assembly of pathogenic bacterial communities in the lake habitat, while stochastic processes shaped river pathogenic bacterial communities. Spearman correlation analysis showed that the α-diversity of bacterial communities was linearly and negatively linked to the relative abundance of pathogens. Having a higher bacterial community diversity had a suppressive effect on pathogen abundance. In addition, co-occurrence network analysis showed that bacterial communities were tightly linked to pathogenic bacteria. Pseudomonas aeruginosa and Salmonella enterica were identified as keystone species in an inflow water sampling network (W_FR), reducing the complexity of the network. These results provide a reference for assessments of water quality safety and pathogenic bacteria posing risks to human health in large freshwater lakes.

Land-Water Transport and Sources of Nitrogen Pollution Affecting the Structure and Function of Riverine Microbial Communities

The characterization of variations in riverine microbiota that stem from contaminant sources and transport modes is important for understanding biogeochemical processes. However, the association between complex anthropogenic nitrogen pollution and bacteria has not been extensively investigated owing to the difficulties faced while determining the distribution of nitrogen contaminants in watersheds. Here, we employed the Soil and Water Assessment Tool alongside microbiological analysis to explore microbial characteristics and their responses to complex nitrogen pollution patterns. Significant variations in microbial communities were observed in sub-basins with distinct land-water pollution transport modes. Point source-dominated areas (PSDAs) exhibited reduced microbial diversity, high number of denitrification groups, and increased nitrogen cycling compared with others. The negative relative deviations (-3.38) between the measured and simulated nitrate concentrations in PSDAs indicated that nitrate removal was more effective in PSDAs. Pollution sources were also closely associated with microbiota. Effluents from concentrated animal feeding operations were the primary factors relating to the microbiota compositions in PSDAs and balanced areas. In nonpoint source-dominated areas, contaminants from septic tanks become the most relevant sources to microbial community structures. Overall, this study expands our knowledge regarding microbial biogeochemistry in catchments and beyond by linking specific nitrogen pollution scenarios to microorganisms.

Spatiotemporal correlations between water quality and microbial community of typical inflow river into Taihu Lake, China

Changxing River, which is a typical inflow river into Taihu Lake and occurs severe algae invasion, is selected to study the effect of different pollution sources on the water quality and ecological system. Four types of pollution sources, including the estuary of Taihu Lake, discharge outlets of urban wastewater treatment plants, stormwater outlets, and nonpoint source agricultural drainage areas, were chosen, and next-generation sequencing and multivariate statistical analyses were used to characterize the microbial communities and reveal their relationship with water physicochemical properties. The results showed that ammonia nitrogen (NH₄⁺-N), total nitrogen (TN), and total phosphorus (TP) were the main pollutants in Changxing River, especially at stormwater outlets. At the same time, the diversity of microbial communities was the highest in the summer, and dominant microbes included Proteobacteria (40.9%), Bacteroidetes (21.0%), and Euryarchaeota (6.1%). The results of BIOENV analysis showed that the major seasonal differences in the diversity of microbial community of Changxing river were explained by the combination of water temperature (T), air pressure (P), TP, and COD_Mn. From the perspective of different pollution types, relative abundances of Microcystis and Nostocaceae at the estuary of Taihu Lake were correlated positively with dissolved oxygen (DO) and pH, and relative abundances of Pseudomonas and Arcobacter were correlated positively with concentrations of TN and nitrate nitrogen (NO₃^--N) at stormwater outlets. This study provided a reference for the impact of pollution types on river microbial ecosystem under complex hydrological conditions and guidance for the selection of restoration techniques for polluted rivers entering the important lake.

Ecological insights into the disturbances in bacterioplankton communities due to emerging organic pollutants from different anthropogenic activities along an urban river

Emerging organic pollutants (EOPs) in urban rivers have raised concerns regarding their eco-toxicological effects. However, the bacterioplankton community disturbances caused by EOPs in urban rivers and the associated ecological mechanisms remain unclear. This study provided profiles of the spatial distribution of a bacterioplankton community disturbed by human activity along an urban river. The results showed that EOP concentration and composition were differently distributed in residential and industrial areas, which significantly influenced bacterioplankton community structure. Based on redundancy analysis, parabens (methylparaben and propylparaben) were the major factors driving bacterioplankton community changes. Parabens inhibited gram-positive bacteria and promoted oxidative stress-tolerant bacteria in the river ecosystem. Parabens also disturbed ecological processes of bacterioplankton community assembly, shifting from a homogeneous selection (consistent selection pressure under similar environmental condition) to stochastic processes (random changes due to birth, death, immigration, and emigration) with changing in paraben concentrations. Heterogeneous selection was predicted to dominate microbial community assembly with paraben concentration changes exceeding 61.6 ng/L, which could deteriorate the river ecosystem. Furthermore, specific bacterial genera were identified as potential bioindicators to assess the condition of EOP contaminants in the river. Overall, this study highlights significant disturbances in bacterioplankton communities by EOPs at environmental concentrations, and our results could facilitate generation of appropriate management strategies aimed at EOPs in urban rivers.

Elevated pCO2 alters the interaction patterns and functional potentials of rearing seawater microbiota

Mean oceanic CO₂ values have already risen and are expected to rise further on a global scale. Elevated pCO₂ (eCO₂) changes the bacterial community in seawater. However, the ecological association of seawater microbiota and related geochemical functions are largely unknown. We provide the first evidence that eCO₂ alters the interaction patterns and functional potentials of microbiota in rearing seawater of the swimming crab, Portunus trituberculatus. Network analysis showed that eCO₂ induced a simpler and more modular bacterial network in rearing seawater, with increased negative associations and distinct keystone taxa. Using the quantitative microbial element cycling method, nitrogen (N) and phosphorus (P) cycling genes exhibited the highest increase after one week of eCO₂ stress and were significantly associated with keystone taxa. However, the functional potential of seawater bacteria was decoupled from their taxonomic composition and strongly coupled with eCO₂ levels. The changed functional potential of seawater bacteria contributed to seawater N and P chemistry, which was highlighted by markedly decreased NH₃, NH₄⁺-N, and PO₄^3--P levels and increased NO₂^--N and NO₃^--N levels. This study suggests that eCO₂ alters the interaction patterns and functional potentials of seawater microbiota, which lead to the changes of seawater chemical parameters. Our findings provide new insights into the mechanisms underlying the effects of eCO₂ on marine animals from the microbial ecological perspective.

Advancement in Benthic Microbial Fuel Cells toward Sustainable Bioremediation and Renewable Energy Production

Anthropogenic activities are largely responsible for the vast amounts of pollutants such as polycyclic aromatic hydrocarbons, cyanides, phenols, metal derivatives, sulphides, and other chemicals in wastewater. The excess benzene, toluene and xylene (BTX) can cause severe toxicity to living organisms in wastewater. A novel approach to mitigate this problem is the benthic microbial fuel cell (BMFC) setup to produce renewable energy and bio-remediate wastewater aromatic hydrocarbons. Several mechanisms of electrogens have been utilized for the bioremediation of BTX through BMFCs. In the future, BMFCs may be significant for chemical and petrochemical industry wastewater treatment. The distinct factors are considered to evaluate the performance of BMFCs, such as pollutant removal efficiency, power density, and current density, which are discussed by using operating parameters such as, pH, temperature and internal resistance. To further upgrade the BMFC technology, this review summarizes prototype electrode materials, the bioremediation of BTX, and their applications.

Application of machine learning in prediction of Chemotherapy resistant of Ovarian Cancer based on Gut Microbiota

Background: Ovarian cancer (OC) has the highest mortality among gynecological malignancies, and resistance to chemotherapy drugs is common. We aim to develop a machine learning approach based on gut microbiota to predict the chemotherapy resistance of OC.

Methods: The study included patients diagnosed with OC by pathology and treated with platinum and paclitaxel in Shengjing Hospital of China Medical University between 2017 and 2018. Fecal samples were collected from patients, and 16S rRNA sequencing was used to analyze the differences in gut microbiota between OC patients with and without chemotherapy resistance. Nine machine learning classifiers were used to derive the chemotherapy resistance of OC from gut microbiota.

Results: A total of 77 chemoresistant OC patients and 97 chemosensitive OC patients were enrolled. The gut microbiota diversity was higher in OC patients with chemotherapy resistance. There were statistically significant differences between the two groups in Shannon indexes (P <0.05) and Simpson indexes (P <0.05). Machine learning techniques can predict the chemoresistance of OC, and the random forest showed the best performance among all models. The area under the ROC curve for RF model was 0.909.

Conclusions: The diversity of gut microbiota was higher in OC patients with chemotherapy resistance. Further studies are warranted to validate our findings based on machine learning techniques.