Data-Driven Approach for Designing Eco-Friendly Heterocyclic Compounds for the Soil Microbiome.

Soil microbiota plays crucial roles in maintaining the health, productivity, and nutrient cycling of terrestrial ecosystems. The persistence and prevalence of heterocyclic compounds in soil pose significant risks to soil health. However, understanding the links between heterocyclic compounds and microbial responses remains challenging due to the complexity of microbial communities and their various chemical structures.

Integrating Anthropogenic-Pesticide Interactions Into a Soil Health-Microbial Index for Sustainable Agriculture at Global Scale.

Soil microbiota in intensive agriculture are threatened by pesticides, economic activities, and land-use changes. However, the interactions among these anthropogenic factors remain underexplored. By analyzing 2356 soil metagenomes from around the world, we developed a comprehensive soil health-microbial index that integrates microbial diversity, nutrient cycling potential, metabolic potential, primary productivity, and health risks to assess how the soil microbiota respond to anthropogenic factors.

Global biogeography of microbes driving ocean ecological status under climate change.

Microbial communities play a crucial role in ocean ecology and global biogeochemical processes. However, understanding the intricate interactions among diversity, taxonomical composition, functional traits, and how these factors respond to climate change remains a significant challenge. Here, we propose seven distinct ecological statuses by systematically considering the diversity, structure, and biogeochemical potential of the ocean microbiome to delineate their biogeography.

Effects of different assembly strategies on gene annotation in activated sludge.

Activated sludge comprises diverse bacteria, fungi, and other microorganisms, featuring a rich repertoire of genes involved in antibiotic resistance, pollutant degradation, and elemental cycling. In this regard, hybrid assembly technology can revolutionize metagenomics by detecting greater gene diversity in environmental samples. Nonetheless, the optimal utilization and comparability of genomic information between hybrid assembly and short- or long-read technology remain unclear.

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.

Lipid Metabolites as Potential Regulators of the Antibiotic Resistome in .

Antibiotic resistance genes (ARGs) are ancient but have become a modern critical threat to health. Gut microbiota, a dynamic reservoir for ARGs, transfer resistance between individuals. Surveillance of the antibiotic resistome in the gut during different host growth phases is critical to understanding the dynamics of the resistome in this ecosystem.

Effects of organic fertilizers on plant growth and the rhizosphere microbiome.

Application of organic fertilizers is an important strategy for sustainable agriculture. The biological source of organic fertilizers determines their specific functional characteristics, but few studies have systematically examined these functions or assessed their health risk to soil ecology. To fill this gap, we analyzed 16S rRNA gene amplicon sequencing data from 637 soil samples amended with plant- and animal-derived organic fertilizers (hereafter plant fertilizers and animal fertilizers).

Metagenomic analysis of antibiotic-resistance genes and viruses released from glaciers into downstream habitats.

Glaciers serve as effective reservoirs of antibiotic resistance genes (ARGs) and viruses for millions of years. Climate change and anthropogenic activity have accelerated the melting of glaciers, but the patterns of release of ARGs and viruses from melting glaciers into downstream habitats remain unknown. We analyzed 171 metagenomic samples from glaciers and their downstream habitats and found that the abundance and diversity of ARGs were higher in glaciers (polar and plateau glaciers) than downstream habitats (Arctic Ocean, Qinghai Lake, and Yangtze River Basin), with the diversity of viruses having the opposite pattern.

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.

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.

Effect of chlorpyrifos on freshwater microbial community and metabolic capacity of zebrafish.

Chlorpyrifos is a widely used organophosphorus insecticide because of its high efficiency and overall effectiveness, and it is commonly detected in aquatic ecosystems. However, at present, the impact of chlorpyrifos on the aquatic micro-ecological environment is still poorly understood. Here, we established aquatic microcosm systems treated with 0.

Antimicrobial Peptides in the Global Microbiome: Biosynthetic Genes and Resistance Determinants.

Antimicrobial peptides are a promising new class of antimicrobials that could address the antibiotic resistance crisis, which poses a major threat to human health. These peptides are present in all kingdoms of life, but especially in microorganisms, having multiple origins in diverse taxa. To date, there has been no global study on the diversity of antimicrobial peptides, the hosts in which these occur, and the potential for resistance to these agents.

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.

Geographic patterns of microbial traits of river basins in China.

River microbiotas contribute to critical geochemical processes and ecological functions of rivers but are sensitive to variations of environmental drivers. Understanding the geographic pattern of river microbial traits in biogeochemical processes can provide important insights into river health. Many studies have characterized river microbial traits in specific situations, but the geographic patterns of these traits and environmental drivers at a large scale are unknown.

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.

Shaping effects of rice, wheat, maize, and soybean seedlings on their rhizosphere microbial community.

The rhizosphere microbiome plays critical roles in plant growth and is an important interface for resource exchange between plants and the soil environment. Crops at various growing stages, especially the seedling stage, have strong shaping effects on the rhizosphere microbial community, and such community reconstruction will positively feed back to the plant growth. In the present study, we analyzed the variations of bacterial and fungal communities in the rhizosphere of four crop species: rice, soybean, maize, and wheat during successive cultivations (three repeats for the seedling stages) using 16S rRNA gene and internal transcribed spacer (ITS) high-throughput sequencing.

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.

Machine learning predicts ecological risks of nanoparticles to soil microbial communities.

With the rapid development of nanotechnology in agriculture, there is increasing urgency to assess the impacts of nanoparticles (NPs) on the soil environment. This study merged raw high-throughput sequencing (HTS) data sets generated from 365 soil samples to reveal the potential ecological effects of NPs on soil microbial community by means of metadata analysis and machine learning methods. Metadata analysis showed that treatment with nanoparticles did not have a significant impact on the alpha diversity of the microbial community, but significantly altered the beta diversity.

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).

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.

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.

Assessment of global health risk of antibiotic resistance genes.

Antibiotic resistance genes (ARGs) have accelerated microbial threats to human health in the last decade. Many genes can confer resistance, but evaluating the relative health risks of ARGs is complex. Factors such as the abundance, propensity for lateral transmission and ability of ARGs to be expressed in pathogens are all important.

Machine learning predicts the impact of antibiotic properties on the composition and functioning of bacterial community in aquatic habitats.

In the past decades, hundreds of antibiotics have been isolated from microbial metabolites or have been artificially synthesized for protecting humans, animals and crops from microbial infections. Their everlasting usage results in impacts on the microbial community composition and causes well-known collateral damage to the functioning of microbial communities. Nevertheless, the impact of different antibiotic properties on aquatic microbial communities have so far only poorly been disentangled.

Composition identification and functional verification of bacterial community in disease-suppressive soils by machine learning.

It has been widely reported that probiotic consortia in the rhizosphere can enhance the plant resistance to pathogens. However, the general composition and functional profiles of bacterial community in soils which suppress multiple diseases for various plants remain largely unknown. Here, we combined metadata analysis with machine learning to identify the general patterns of bacterial-community composition in disease-suppressive soils.