Geographic patterns of microbial traits of river basins in China

ITS amplicon sequencing revealed that rare taxa of tea rhizosphere fungi are closely related to the environment and provide feedback on tea tree diseases

The rhizospheres of plants and soil microorganisms are intricately interconnected. Tea trees are cultivated extensively on the karst plateau of Guizhou Province, China; however, the understanding of the interactions among fungal communities, community taxa, and diseases impacting tea tree in the soil rhizosphere is limited. Our aim is to offer insights for the advancement of modern agriculture in ecologically fragile karst tea gardens, as well as microbiomics concepts for green and sustainable environmental development. This study utilized the internal transcribed spacer high-throughput sequencing technology to explore the symbiotic relationship between rhizosphere fungi and plant disease feedback in multiple tea estates across the Guizhou Plateau. The ecological preferences and environmental thresholds of fungi were investigated via environmental variables. Furthermore, a correlation was established between different taxa and individual soil functions. Research has indicated that tea leaf blight disrupts symbiotic connections among fungal groups. For various taxa, we found that numerous taxa consistently maintained core positions within the community, whereas rare taxa were able to stabilize due to a high proportion of positive effects. Additionally, abundant taxa presented a wider range of environmental feedback, whereas the rare taxon diversity presented a stronger positive association with the soil Z score. This study contributes to our understanding of the importance of rare taxa in plant rhizosphere soil processes. Emphasis should be placed on the role of rare taxa in pest and disease control within green agriculture while also strengthening systematic development and biogeographical research related to rare taxa in this region.IMPORTANCEIn this study, based on internal transcribed spacer high-throughput sequencing, fungal communities in the rhizosphere soil of tea trees and their interactions with the environment in karst areas were reported, and the symbiotic relationships of different fungal taxa and their feedback to the environment were described in detail by using the knowledge of microbial ecology. On this basis, it was found that tea tree diseases affect the symbiotic relationships of fungal taxa. At the same time, we found that rare taxa have stronger cooperative relationships in response to environmental changes and explored their participation in soil processes based on fungal trait sets. This study will provide basic data for the development of modern agriculture in tea gardens and theoretical basis for the sustainable prevention and control of tea tree diseases.

Identifying human activities causing water pollution based on microbial community sequencing and source classifier machine learning

Identifying and differentiating human activities is crucial for effectively preventing the threats posed by environmental pollution to aquatic ecosystems and human health. Machine learning (ML) is a powerful analytical tool for tracking human impacts on river ecosystems based on high-through datasets. This study employed an ML framework and 16S rRNA sequencing data to reveal microbial dynamics and trace human activities across China. The results revealed that the microbial assembly was mainly dominated by deterministic factors (environmental factors and interactions between species), and the metacommunity partition was significantly associated with human activities in both water and sediment (Chi-square testwP = 1.93 × 10-22; Chi-square testsP = 6.00 × 10-6). Human activities increased the vulnerability of interspecific occurrence networks and the influence of environmental factors on the OTUs similarity and phylogenetic distance. Combined of microbiological indices (MBIs), microbial relative abundance (MRA), and environmental and geographical indices (EGIs), the source classifier machine learning (SCML) algorithm was used to categorize five human activities (i.e., low human-impact, agricultural inputs, domestic inputs, industrial inputs, and dam construction). The SCML optimal configuration is (MBIs + MRA + EGIs) exhibited strong performance with TestW R2 of 0.882 and TestS R2 of 0.924. This study provides valuable insights for improving ecosystem management, supporting sustainable water resource management and advancing pollution mitigation efforts.

Temporal profiling of sediment microbial communities in the Three Gorges Reservoir Area discovered time-dissimilarity patterns and multiple stable states

Microbial communities play vital roles in cycling nutrients and maintaining water quality in aquatic ecosystems. To better understand the dynamics of microbial communities and to pave way to effective ecological remediation, it's essential to reveal the temporal patterns of the communities and to identify their states. However, research exploring the dynamic changes of microbial communities needs a large amount of time-series data, which could be an extravagant requirement for a single study. In this research, we overcame this challenge by conducting a meta-analysis of years of accumulations of 16S rRNA high-throughput sequencing data from the Three Gorges Reservoir Area (TGRA), an ecological and environmental hotspot. For better understanding the microbial communities time-dissimilarity dynamics, three microbial communities time-dissimilarity patterns were hypothesized, and the linear pattern in the TGRA was validated. In addition, to explore the stability of microbial communities in the TGRA, two alternative stable states were revealed, and their differences in community richness, alpha diversity indices, community composition, ecological network topological properties, and metabolic functions were demonstrated. In short, two states of microbial communities showed distinct richness and alpha diversity indices, and the communities in one state were more dominated by Halomonas and Nitrosopumilaceae genera, facilitating nitrogen cycling metabolic processes; whilst the main genera of the other state were Bathyarchaeia and Methanosaeta, which favored methane-related metabolism. Moreover, different studies and environmental differences between mainstream and tributaries were attributed as the potential inducing factors of the state division. Our study provides a comprehensive insight into the dynamics and stability of microbial communities in the TGRA, and a reference for future studies on microbial community dynamics.

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. Proteobacteria and Actinobacteria were the main potential hosts of ARGs and viruses, and the richness of ARGs carried by the hosts was positively correlated with viral abundance, suggesting that the transmission of viruses in the hosts could disseminate ARGs. Source tracking indicated that >18 % of the ARGs and >25 % of the viruses detected in downstream habitats originated from glaciers, demonstrating that glaciers could be one of the potential sources of ARGs and viruses in downstream habitats. Increased solar radiation and emission of carbon dioxide mainly influenced the release of the ARGs and viruses from glaciers into downstream habitats. This study provides a systematic insight demonstrating the release of ARGs and viruses from the melting glaciers, potentially increasing ecological pressure.

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.

Global distribution of marine microplastics and potential for biodegradation

Microplastics are a growing marine environmental concern globally due to their high abundance and persistent degradation. We created a global map for predicting marine microplastic pollution using a machine-learning model based on 9445 samples and found that microplastics converged in zones of accumulation in subtropical gyres and near polar seas. The predicted global potential for the biodegradation of microplastics in 1112 metagenome-assembled genomes from 485 marine metagenomes indicated high potential in areas of high microplastic pollution, such as the northern Atlantic Ocean and the Mediterranean Sea. However, the limited number of samples hindered our prediction, a priority issue that needs to be addressed in the future. We further identified hosts with microplastic degradation genes (MDGs) and found that Proteobacteria accounted for a high proportion of MDG hosts, mainly Alphaproteobacteria and Gammaproteobacteria, with host-specific patterns. Our study is essential for raising awareness, identifying areas with microplastic pollution, providing a prediction method of machine learning to prioritize surveillance, and identifying the global potential of marine microbiomes to degrade microplastics, providing a reference for selecting bacteria that have the potential to degrade microplastics for further applied research.

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.