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.

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.

A global atlas of marine antibiotic resistance genes and their expression.

Oceans serve as global reservoirs of antibiotic-resistant bacteria and antibiotic resistance genes (ARGs). However, little is known about the traits and expression of ARGs in response to environmental factors. We analyzed 347 metagenomes and 182 metatranscriptomes to determine the distribution, hosts, and expression of ARGs in oceans. Our study found that the diversity and abundance of ARGs varied with latitude and depth. The core marine resistome mainly conferred glycopeptide and multidrug resistance. The hosts of this resistome were mainly limited to the core marine microbiome, with phylogenetic barriers to the horizontal transfer of ARGs, transfers being more frequent within species than between species. Sixty-five percent of the marine ARGs identified were expressed. More than 90% of high-risk ARGs were more likely to be expressed. Anthropogenic activity might affect the expression of ARGs by altering nitrate and phosphate concentrations and ocean temperature. Machine-learning models predict >97% of marine ARGs will change expression by 2100. High-risk ARGs will shift to low latitudes and regions with high anthropogenic activity, such as the Pacific and Atlantic Oceans. Certain ARGs serve a dual role in antibiotic resistance and potentially participate in element cycling, along with other unknown functions. Determining whether changes in ARG expression are beneficial to ecosystems and human health is challenging without comprehensive understanding of their functions. Our study identified a core resistome in the oceans and quantified the expression of ARGs for the development of future control strategies under global change.

Negative effects of abamectin on soil microbial communities in the short term.

With the widespread use of abamectin in agriculture, there is increasing urgency to assess the effects of abamectin on soil microorganisms. Here, we treated plant-soil microcosms with abamectin at concentrations of 0.1 and 1.0 mg/kg and quantified the impacts of abamectin on bulk and rhizosphere soil microbial communities by shotgun metagenomics after 7 and 21 days of exposure. Although abamectin was reported to be easily degradable, it altered the composition of the soil microbial communities, disrupted microbial interactions, and decreased community complexity and stability after 7 days of exposure. After treatment with abamectin at a concentration of 1.0 mg/kg, some opportunistic human diseases, and soil-borne pathogens like Ralstonia were enriched in the soil. However, most ecological functions in soil, particularly the metabolic capacities of microorganisms, recovered within 21 days after abamectin treatment. The horizontal and vertical gene transfer under abamectin treatments increased the levels of antibiotic resistance genes dissemination. Overall, our findings demonstrated the negative effects of abamectin on soil ecosystems in the short-term and highlight a possible long-term risk to public and soil ecosystem health associated with antibiotic resistance genes dissemination.

Development of a machine-learning model to identify the impacts of pesticides characteristics on soil microbial communities from high-throughput sequencing data.

High-throughput sequencing (HTS) of soil environmental DNA provides an advanced insight into the effects of pesticides on soil microbial systems. However, the association between the properties of the pesticide and its ecological impact remains methodically challenging. Risks associated with pesticide use can be minimized if pesticides with optimal structural traits were applied. For this purpose, we merged the 20 independent HTS studies, to reveal that pesticides significantly reduced beneficial bacteria associated with soil and plant immunity, enhanced the human pathogen and weaken the soil's ecological stability. Through the machine-learning approach, correlating these impacts with the physicochemical properties of the pesticides yielded a random forest model with good predictive capabilities. The models revealed that physical pesticide properties such as the dissociation constant (pKa), the molecular weight and water solubility, determined the ecological impact of pesticides to a large extent. Moreover, this study identified that eco-friendly pesticides should possess a value of pKa > 5 and a molecular weight in the range of 200-300 g/mol, which were found to be conducive to bacteria related to plant immunity promotion and exerted the lowest fluctuation of human opportunistic pathogen and keystone species. This guides the design of pesticides for which the impacts on soil biota are minimized.

Response of microbial antibiotic resistance to pesticides: An emerging health threat.

The spread of microbial antibiotic resistance has seriously threatened public health globally. Non-antibiotic stressors have significantly contributed to the evolution of bacterial antibiotic resistance. Although numerous studies have been conducted on the potential risk of pesticide pollution for bacterial antibiotic resistance, a systematic review of these concerns is still lacking. In the present study, we elaborate the mechanism underlying the effects of pesticides on bacterial antibiotic resistance acquisition as well as the propagation of antimicrobial resistance. Pesticide stress enhanced the acquisition of antibiotic resistance in bacteria via various mechanisms, including the activation of efflux pumps, inhibition of outer membrane pores for resistance to antibiotics, and gene mutation induction. Horizontal gene transfer is a major mechanism whereby pesticides influence the transmission of antibiotic resistance genes (ARGs) in bacteria. Pesticides promoted the conjugation transfer of ARGs by increasing cell membrane permeability and increased the proportion of bacterial mobile gene elements, which facilitate the spread of ARGs. This review can improve our understanding regarding the pesticide-induced generation and spread of ARGs and antibiotic resistant bacteria. Moreover, it can be applied to reduce the ecological risks of ARGs in the future.

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.

Phyllosphere Microorganisms: Sources, Drivers, and Their Interactions with Plant Hosts.

The leaves of plants are colonized by various microorganisms. In comparison to the rhizosphere, less is known about the characteristics and ecological functions of phyllosphere microorganisms. Phyllosphere microorganisms mainly originate from soil, air, and seeds. The composition of phyllosphere microorganisms is mainly affected by ecological and abiotic factors. Phyllosphere microorganisms execute multiple ecological functions by influencing leaf functions and longevity, seed mass, fruit development, and homeostasis of host growth. A plant can respond to phyllosphere microorganisms by secondary metabolite secretion and its immune system. Meanwhile, phyllosphere microorganisms play an important role in ecological stability and environmental safety assessment. However, as a result of the instability of the phyllosphere environment and the poor cultivability of phyllosphere microorganisms in the current research, there are still many limitations, such as the lack of insight into the mechanisms of plant-microorganism interactions, the roles of phyllosphere microorganisms in plant growth processes, the responses of phyllosphere microorganisms to plant metabolites, etc. This review summarizes the latest progress made in the research of the phyllosphere in recent years. This is beneficial for deepening our understanding of phyllosphere microorganisms and promoting the research of plant-atmosphere interactions, plant pathogens, and plant biological control.

Effects of chiral herbicide dichlorprop on Arabidopsis thaliana metabolic profile and its implications for microbial communities in the phyllosphere.

Dichlorprop (2-(2,4-dichlorophenoxy) propionic acid, DCPP), a commonly used herbicide for weed control, can be residually detected in soil. It is still unclear whether chiral DCPP exerts an enantioselective adverse effect on plant metabolism and the microbial community of the phyllosphere. In this study, we selected Arabidopsis thaliana as a model plant to explore the effects of R- and S-DCPP enantiomers on plant physiological activities, metabolism, and associated changes in the phyllosphere microbial community. Results indicated that the fresh weight of plants decreased by 37.6% after R-DCPP treatment, whereas it increased by 7.6% after S-DCPP treatment. The R-DCPP enantiomer also caused stronger disturbance to leaf morphology, mesophyll cell structure, and leaf metabolites compared with S-DCPP. GC-MS analysis of DCPP-treated Arabidopsis leaves pointed out a differential profile mostly in carbohydrates, organic acids, and fatty acids, between S-DCPP and R-DCPP treatments. The diversity of phyllospheric microorganisms decreased and the stability of microbial community in the phyllosphere increased after R-DCPP treatment, whereas the opposite result was detected after S-DCPP exposure. The correlation analysis revealed that chiral herbicides may affect microbial communities in the phyllosphere by influencing leaf metabolism, while sugars and terpenoids were considered the main factors in reshaping the microbial community structure in the phyllosphere. Our study provides a new perspective for evaluating the effect of residual DCPP enantiomers on plant physiology and corresponding phyllosphere microorganism changes via the regulation of leaf metabolism, and clarifies the ecological risk of DCPP enantiomer application in agriculture.