Combined effects of azoxystrobin and oxytetracycline on rhizosphere microbiota of Arabidopsis thaliana.

The rhizosphere is one of the key determinants of plant health and productivity. Mixtures of pesticides are commonly used in intensified agriculture. However, the combined mechanisms underlying their impacts on soil microbiota remain unknown. The present study revealed that the rhizosphere microbiota was more sensitive to azoxystrobin and oxytetracycline, two commonly used pesticides, than was the microbiota present in bulk soil. Moreover, the rhizosphere microbiota enhanced network complexity and stability and increased carbohydrate metabolism and xenobiotic biodegradation as well as the expression of metabolic genes involved in defence against pesticide stress. Co-exposure to azoxystrobin and oxytetracycline had antagonistic effects on Arabidopsis thaliana growth and soil microbial variation by recruiting organic-degrading bacteria and regulating ABC transporters to reduce pesticide uptake. Our study explored the composition and function of soil microorganisms through amplicon sequencing and metagenomic approaches, providing comprehensive insights into the synergistic effect of plants and rhizosphere microbiota on pesticides and contributing to our understanding of the ecological risks associated with pesticide use.

Deciphering Microbial Community and Nitrogen Fixation in the Legume Rhizosphere.

Nitrogen is the most limiting factor in crop production. Legumes establish a symbiotic relationship with rhizobia and enhance nitrogen fixation. We analyzed 1,624 rhizosphere 16S rRNA gene samples and 113 rhizosphere metagenomic samples from three typical legumes and three non-legumes. The rhizosphere microbial community of the legumes had low diversity and was enriched with nitrogen-cycling bacteria (Sphingomonadaceae, Xanthobacteraceae, Rhizobiaceae, and Bacillaceae). Furthermore, the rhizosphere microbiota of legumes exhibited a high abundance of nitrogen-fixing genes, reflecting a stronger nitrogen-fixing potential, and Streptomycetaceae and Nocardioidaceae were the predominant nitrogen-fixing bacteria. We also identified helper bacteria and confirmed through metadata analysis and a pot experiment that the synthesis of riboflavin by helper bacteria is the key factor in promoting nitrogen fixation. Our study emphasizes that the construction of synthetic communities of nitrogen-fixing bacteria and helper bacteria is crucial for the development of efficient nitrogen-fixing microbial fertilizers.

A risk entropy approach for linking pesticides and soil bacterial communities.

Pesticides play a vital role in ensuring modern agricultural production, but also adversely affecting soil health. Microorganisms are the cornerstone of soil ecology, however, to date, there are few unified standards to measure the risk of soil pesticide residues to soil microbial community. To compensate for this gap, we collected soil samples from 55 orchards and monitored and risk-assessed 165 pesticides to microbial community in the soil. Results showed that a total of 137 pesticides were detected in all samples. Pesticide residues significantly influenced the microbial diversity and community structure in orchard soils, particularly fungicides and herbicides. The risk entropy of each pesticide was calculated in all samples and it was found that 60% of the samples had a "pesticide risk" (Risk quotient > 0.01), where the relative abundance significantly increased in 43 genera and significantly decreased in 111 genera (p < 0.05). Through multiple screens, we finally identified Bacillus and Sphingomonas as the most abundant sensitive genera under pesticide perturbation. The results showed that despite the complexity of the effects of pesticide residues on soils health, we could reveal them by identifying changes in soil bacterial, especially by the differences of microbial biomarkers abundance. The present study could provide new insights into the research strategy for pesticide pollution on soil microbial communities. ENVIRONMENTAL IMPLICATION: The risk of pesticide residues in soil needs to be quantified and standardized. We believe that microorganisms can be used as a marker to indicate soil pesticide residue risk. For this end, we investigated the residues of 165 pesticides in 55 orchard soil samples, calculated pesticide risk entropy and their effects on the soil microbial community. Through multiple analyzing and screening, we ultimately identified that, out of the 154 detected biomarkers, Bacillus and Sphingomonas were the most abundant sensitive genera under pesticide perturbation, which have the potential to be used as key biomarkers of soil microbiomes induced by pesticide perturbation.

Glyphosate Disorders Soil Enchytraeid Gut Microbiota and Increases Its Antibiotic Resistance Risk.

Pesticides promote the stable development of intensive global agriculture. Nevertheless, their residues in the soil can cause ecological and human health risks. Glyphosate is a popular herbicide and is generally thought to be ecologically safe and nontoxic, but this conclusion has been questioned. Herein, we investigated the interaction among soil fauna (Enchytraeus crypticus) exposed to glyphosate and found that glyphosate induced oxidative stress and detoxification responses in E. crypticus and disturbed their lipid metabolism and digestive systems. We further demonstrated that glyphosate disordered the gut microbiota of E. crypticus and increased the abundance of resistance determinants with significant human health risks. Empirical tests and structural equation models were then used to confirm that glyphosate could cause E. crypticus to generate reactive oxygen species, indirectly interfering with their gut microbiota. Our study provides important implications for deciphering the mechanisms of the ecotoxicity of pesticides under the challenge of worldwide pesticide contamination.

Metagenomic Insight into The Global Dissemination of The Antibiotic Resistome.

The global crisis in antimicrobial resistance continues to grow. Estimating the risks of antibiotic resistance transmission across habitats is hindered by the lack of data on mobility and habitat-specificity. Metagenomic samples of 6092 are analyzed to delineate the unique core resistomes from human feces and seven other habitats. This is found that most resistance genes (≈85%) are transmitted between external habitats and human feces. This suggests that human feces are broadly representative of the global resistome and are potentially a hub for accumulating and disseminating resistance genes. The analysis found that resistance genes with ancient horizontal gene transfer (HGT) events have a higher efficiency of transfer across habitats, suggesting that HGT may be the main driver for forming unique but partly shared resistomes in all habitats. Importantly, the human fecal resistome is historically different and influenced by HGT and age. The most important routes of cross-transmission of resistance are from the atmosphere, buildings, and animals to humans. These habitats should receive more attention for future prevention of antimicrobial resistance. The study will disentangle transmission routes of resistance genes between humans and other habitats in a One Health framework and can identify strategies for controlling the ongoing dissemination and antibiotic resistance.

Atmospheric antibiotic resistome driven by air pollutants.

The atmosphere is an important reservoir and habitat for antibiotic resistance genes (ARGs) and is a main pathway to cause potential health risks through inhalation and ingestion. However, the distribution characteristics of ARGs in the atmosphere and whether they were driven by atmospheric pollutants remain unclear. We annotated 392 public air metagenomic data worldwide and identified 1863 ARGs, mainly conferring to tetracycline, MLS, and multidrug resistance. We quantified these ARG's risk to human health and identified their principal pathogenic hosts, Burkholderia and Staphylococcus. Additionally, we found that bacteria in particulate contaminated air carry more ARGs than in chemically polluted air. This study revealed the influence of typical pollutants in the global atmosphere on the dissemination and risk of ARGs, providing a theoretical basis for the prevention and mitigation of the global risks associated with ARGs.

Metatranscriptome deciphers the effects of non-antibiotic antimicrobial agents on antibiotic resistance and virulence factors in freshwater microcosms.

The emergence and transmission of antibiotic resistance genes (ARGs) and virulence factors (VFs) pose health risks to the ecosystem and humans. Understanding how non-antibiotic antimicrobial agents drive the expression of ARGs and VFs in freshwater ecosystems, however, remains large challenges. Here, we employed freshwater microcosms and performed metatranscriptomic analysis to investigate the expression profiles of ARGs and VFs in response to pollutants of non-antibiotic antimicrobial agents, including silver nanoparticles (AgNPs) and azoxystrobin. Results showed that AgNPs significantly inhibited the total expression of ARGs and VFs and decreased the number of pathogenic microorganisms expressing these genes. Azoxystrobin increased the total expression of ARGs and VFs, as well as the number of pathogens expressing VFs, but concomitantly reduced the number of pathogens expressing ARGs. Two tested pollutants dramatically changed the expression profiles of ARGs and VFs, with distinct patterns: AgNPs displayed a negative effect, while azoxystrobin showed a positive effect on their expression profiles. Our findings provided a systematical insight to demonstrate that non-antibiotic antimicrobial agents with different mechanisms of action showed various effects on ARGs and VFs, and therefore represented different ecological risks.

Compare the performance of multiple binary classification models in microbial high-throughput sequencing datasets.

The development of machine learning and deep learning provided solutions for predicting microbiota response on environmental change based on microbial high-throughput sequencing. However, there were few studies specifically clarifying the performance and practical of two types of binary classification models to find a better algorithm for the microbiota data analysis. Here, for the first time, we evaluated the performance, accuracy and running time of the binary classification models built by three machine learning methods - random forest (RF), support vector machine (SVM), logistic regression (LR), and one deep learning method - back propagation neural network (BPNN). The built models were based on the microbiota datasets that removed low-quality variables and solved the class imbalance problem. Additionally, we optimized the models by tuning. Our study demonstrated that dataset pre-processing was a necessary process for model construction. Among these 4 binary classification models, BPNN and RF were the most suitable methods for constructing microbiota binary classification models. Using these 4 models to predict multiple microbial datasets, BPNN showed the highest accuracy and the most robust performance, while the RF method was ranked second. We also constructed the optimal models by adjusting the epochs of BPNN and the n_estimators of RF for six times. The evaluation related to performances of models provided a road map for the application of artificial intelligence to assess microbial ecology.

Xenobiotic pollution affects transcription of antibiotic resistance and virulence factors in aquatic microcosms.

Antibiotic resistance genes (ARGs) and virulence factors (VFs) are critical threats to human health. Their abundance in aquatic ecosystems is maintained and enhanced via selection driven by environmental xenobiotics. However, their activity and expression in these environments under xenobiotic stress remains unknown. Here ARG and VF expression profiles were examined in aquatic microcosms under ciprofloxacin, glyphosate and sertraline hydrochloride treatment. Ciprofloxacin increased total expression of ARGs, particularly multidrug resistance genes. Total expression of ARGs and VFs decreased significantly under glyphosate and sertraline treatments. However, in opportunistic human pathogens, these agents increased expression of both ARGs and VFs. Xenobiotic pollutants, such as the compounds we tested here, have the potential to disrupt microbial ecology, promote resistance, and increase risk to human health. This study systematically evaluated the effects of environmental xenobiotics on transcription of ARGs and VFs, both of which have direct relevance to human health. Transcription of such genes has been overlooked in previous studies.