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. Anthropogenic climate change is expected to alter the ecological status of the surface ocean by influencing environmental conditions, particularly nutrient and oxygen contents. Our predictive model, which utilizes machine learning, indicates that the ecological status of approximately 32.44% of the surface ocean may undergo changes from the present to the end of this century, assuming no policy interventions. These changes mainly include poleward shifts in the main taxa, increases in photosynthetic carbon fixation and decreases in nutrient metabolism. However, this proportion can decrease significantly with effective control of greenhouse gas emissions. Our study underscores the urgent necessity for implementing policies to mitigate climate change, particularly from an ecological perspective.

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. To address this gap, we compared the performance of the hybrid assembly, short- and long-read technologies, abundance and diversity of annotated genes, and taxonomic diversity by analysing 46, 161, and 45 activated sludge metagenomic datasets, respectively. The results revealed that hybrid assembly technology exhibited the best performance, generating the most contiguous and longest contigs but with a lower proportion of high-quality metagenome-assembled genomes than short-read technology. Compared with short- or long-read technologies, hybrid assembly technology can detect a greater diversity of microbiota and antibiotic resistance genes, as well as a wider range of potential hosts. However, this approach may yield lower gene abundance and pathogen detection. Our study revealed the specific advantages and disadvantages of hybrid assembly and short- and long-read applications in wastewater treatment plants, and our approach could serve as a blueprint to be extended to terrestrial environments.

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.

Lipid Metabolites as Potential Regulators of the Antibiotic Resistome in Tetramorium caespitum.

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. Herein, we disentangled the ARG profiles and the dynamic mechanism of ARGs in the egg and adult phases of Tetramorium caespitum. Experimental results showed a remarkable difference in both gut microbiota and gut resistome with the development of T. caespitum. Meta-based metagenomic results of gut microbiota indicated the generalizability of gut antibiotic resistome dynamics during host development. By using Raman spectroscopy and metabolomics, the metabolic phenotype and metabolites indicated that the biotic phase significantly changed lipid metabolism as T. caespitum aged. Lipid metabolites were demonstrated as the main factor driving the enrichment of ARGs in T. caespitum. Cuminaldehyde, the antibacterial lipid metabolite that displayed a remarkable increase in the adult phase, was demonstrated to strongly induce ARG abundance. Our findings show that the gut resistome is host developmental stage-dependent and likely modulated by metabolites, offering novel insights into possible steps to reduce ARG dissemination in the soil food chain.

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). Results showed that animal fertilizers increased the diversity of soil microbiome, while plant fertilizers maintained the stability of soil microbial community. Microcosm experiments verified that plant fertilizers were beneficial to plant root development and increased carbon cycle pathways, while animal fertilizers enriched nitrogen cycle pathways. Compared with animal fertilizers, plant fertilizers harbored a lower abundance of risk factors such as antibiotic resistance genes and viruses. Consequently, plant fertilizers might be more suitable for long-term application in agriculture. This work provides a guide for organic fertilizer selection from the perspective of soil microecology and promotes sustainable development of organic agriculture.IMPORTANCEThis study provides valuable guidance for use of organic fertilizers in agricultural production from the perspective of the microbiome and ecological risk.

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.

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.

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 0.2 and 2.0 µg/L chlorpyrifos, and employed omics biotechnology, including metagenomics and 16S rRNA gene sequencing, to investigate the effect of chlorpyrifos on the composition and functional potential of the aquatic and zebrafish intestinal microbiomes after 7 d and 14 d chlorpyrifos treatment. After 14 d chlorpyrifos treatment, the aquatic microbial community was adversely affected in terms of its composition, structure, and stability, while its diversity showed only a slight impact. Most functions, especially capacities for environmental information processing and metabolism, were destroyed by chlorpyrifos treatment for 14 d. We observed that chlorpyrifos increased the number of risky antibiotic resistance genes and aggravated the growth of human pathogens. Although no clear effects on the structure of the zebrafish intestinal microbial community were observed, chlorpyrifos treatment did alter the metabolic capacity of the zebrafish. Our study highlights the ecological risk of chlorpyrifos to the aquatic environment and provides a theoretical basis for the rational use of pesticides in agricultural production.

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. Here, we investigated the diversity and number of antimicrobial peptides in four main habitats (aquatic, terrestrial, human, and engineered) by analyzing 52,515 metagenome-assembled genomes. The number of antimicrobial peptides was higher in the human gut microbiome than in other habitats, and most hosts of antimicrobial peptides were habitat-specific. The relative abundance of genes that confer resistance to antimicrobial peptides varied between habitats and was generally low, except for the built environment and on human skin. The horizontal transfer of potential resistance genes among these habitats was probably constrained by ecological barriers. We systematically quantified the risk of each resistance determinant to human health and found that nearly half of them pose a threat, especially those that confer resistance to multiple AMPs and polymyxin B. Our results help identify the biosynthetic potential of antimicrobial peptides in the global microbiome, further identifying peptides with a low risk of developing resistance.

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.

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. We reanalyzed 4505 raw 16S rRNA sequences samples for microbiota from river basins in China. The results indicated differences in the diversity, composition, and structure of microbiotas across diverse river basins. Microbial diversity and functional potential in the river basins decreased over time in northern China and increased in southern China due to niche differentiation, e.g., the Yangtze River basin was the healthiest ecosystem. River microbiotas were mainly involved in the cycling of carbon and nitrogen in the river ecosystems and participated in potential organic metabolic functions. Anthropogenic pollutants discharge was the most critical environmental driver for the microbial traits, e.g., antibiotic discharge, followed by climate change. The prediction by machine-learning models indicated that the continuous discharge of antibiotics and climate change led to high ecological risks for the rivers. Our study provides guidelines for improving the health of river ecosystems and for the formulation of strategies to restore the rivers.

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. We found that the relative abundances of specific microorganisms decreased after different cultivation times, e.g., Sphingomonas, Pseudomonas, Rhodanobacter, and Caulobacter, which have been reported as plant-growth beneficial bacteria. The relative abundances of potential plant pathogenic fungi Myrothecium and Ascochyta increased with the successive cultivation times. The co-occurrence network analysis showed that the bacterial and fungal communities under maize were much more stable than those under rice, soybean, and wheat. The present study explored the characteristics of bacteria and fungi in crop seedling rhizosphere and indicated that the characteristics of indigenous soil flora might determine the plant growth status. Further study will focus on the use of the critical microorganisms to control the growth and yield of specific crops.

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.

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. Unfortunately, the abundance of several beneficial bacteria, such as Dyella, Methylophilus, Streptomyces, which promote the growth of plants, and improve pathogenic resistance, was reduced under the addition of synthetic nanoparticles. Furthermore, metadata demonstrated that nanoparticles treatment weakened the biosynthesis ability of cofactors, carriers, and vitamins, and enhanced the degradation ability of aromatic compounds, amino acids, etc. This is unfavorable for the performance of soil functions. Besides the soil heterogeneity, machine learning uncovered that a) the exposure time of nanoparticles was the most important factor to reshape the soil microbial community, and b) long-term exposure decreased the diversity of microbial community and the abundance of beneficial bacteria. This study is the first to use a machine learning model and metadata analysis to investigate the relationship between the properties of nanoparticles and the hazards to the soil microbial community from a macro perspective. This guides the rational use of nanoparticles for which the impacts on soil microbiota are minimized.

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.

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.

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. Here, an analysis at the metagenomic level from various habitats (6 types of habitats, 4572 samples) detects 2561 ARGs that collectively conferred resistance to 24 classes of antibiotics. We quantitatively evaluate the health risk to humans, defined as the risk that ARGs will confound the clinical treatment for pathogens, of these 2561 ARGs by integrating human accessibility, mobility, pathogenicity and clinical availability. Our results demonstrate that 23.78% of the ARGs pose a health risk, especially those which confer multidrug resistance. We also calculate the antibiotic resistance risks of all samples in four main habitats, and with machine learning, successfully map the antibiotic resistance threats in global marine habitats with over 75% accuracy. Our novel method for quantitatively surveilling the health risk of ARGs will help to manage one of the most important threats to human and animal health.

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. Here we characterized the environmental risk of 50 main kinds of antibiotics from 9 classes at a concentration of 10 µg/L for aquatic bacterial communities via metadata analysis combined with machine learning. Metadata analysis showed that the alpha diversity of the bacterial community increased only after treatment with aminoglycoside and ß-lactam antibiotics, while its structure was changed by almost all tested antibiotics. The antibiotic treatment also disturbed the functions of the bacterial community, especially with regard to metabolic pathways, including amino acids, cofactors, vitamins, xenobiotics and carbohydrate metabolism. The critical characteristics (atom stereocenter count, number of hydrogen atoms in the antibiotic, and the adipose water coefficient) of antibiotics affecting the composition of the bacterial community in aquatic habitats were screened by machine learning. The key characteristics of antibiotics affecting the function bacterial communities were the number of hydrogen atoms, molecular weight and complexity. In summary, by developing machine learning models and by performing metadata analysis, this study provides the relationship between the properties of antibiotics and their adverse impacts on aquatic microbial communities from a macro perspective. The study also provides guidance for the rational design of antibiotics.