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Abstract: Antibiotic overuse and the subsequent environmental contamination of residual antibiotics poses a public health crisis via an acceleration in the spread of antibiotic resistance genes (ARGs) through horizontal gene transfer. Although the occurrence, distribution, and driving factors of ARGs in soils have been widely investigated, little is known about the antibiotic resistance of soilborne pathogens at a global scale. To explore this gap, contigs from 1643 globally sourced metagnomes are assembled, yielding 407 ARG‐carrying pathogens (APs) with at least one ARG; APs are detected in 1443 samples (sample detection rate of 87.8%). The richness of APs is greater in agricultural soils (with a median of 20) than in non‐agricultural ecosystems. Agricultural soils possess a high prevalence of clinical APs affiliated with Escherichia, Enterobacter, Streptococcus, and Enterococcus. The APs detected in agricultural soils tend to coexist with multidrug resistance genes and bacA. A global map of soil AP richness is generated, where anthropogenic and climatic factors explained AP hot spots in East Asia, South Asia, and the eastern United States. The results herein advance this understanding of the global distribution of soil APs and determine regions prioritized to control soilborne APs worldwide.

BackgroundIt is well-known that global warming has effects on high-latitude tundra underlain with permafrost. This leads to a severe concern that decomposition of soil organic carbon (SOC) previously stored in this region, which accounts for about 50% of the world's SOC storage, will cause positive feedback that accelerates climate warming. We have previously shown that short-term warming (1.5 years) stimulates rapid, microbe-mediated decomposition of tundra soil carbon without affecting the composition of the soil microbial community (based on the depth of 42684 sequence reads of 16S rRNA gene amplicons per 3 g of soil sample).ResultsWe show that longer-term (5 years) experimental winter warming at the same site altered microbial communities (p < 0.040). Thaw depth correlated the strongest with community assembly and interaction networks, implying that warming-accelerated tundra thaw fundamentally restructured the microbial communities. Both carbon decomposition and methanogenesis genes increased in relative abundance under warming, and their functional structures strongly correlated (R2 > 0.725, p < 0.001) with ecosystem respiration or CH4 flux.ConclusionsOur results demonstrate that microbial responses associated with carbon cycling could lead to positive feedbacks that accelerate SOC decomposition in tundra regions, which is alarming because SOC loss is unlikely to subside owing to changes in microbial community composition. Video Abstract.

Atmospheric CO2 concentration is increasing, largely due to anthropogenic activities. Previous studies of individual free-air CO2 enrichment (FACE) experimental sites have shown significant impacts of elevated CO2 (eCO2) on soil microbial communities; however, no common microbial response patterns have yet emerged, challenging our ability to predict ecosystem functioning and sustainability in the future eCO2 environment. Here we analyzed 66 soil microbial communities from five FACE sites, and showed common microbial response patterns to eCO2, especially for key functional genes involved in carbon and nitrogen fixation (e.g., pcc/acc for carbon fixation, nifH for nitrogen fixation), carbon decomposition (e.g., amyA and pulA for labile carbon decomposition, mnp and lcc for recalcitrant carbon decomposition), and greenhouse gas emissions (e.g., mcrA for methane production, norB for nitrous oxide production) across five FACE sites. Also, the relative abundance of those key genes was generally increased and directionally associated with increased biomass, soil carbon decomposition, and soil moisture. In addition, a further literature survey of more disparate FACE experimental sites indicated increased biomass, soil carbon decay, nitrogen fixation, methane and nitrous oxide emissions, plant and soil carbon and nitrogen under eCO2. A conceptual framework was developed to link commonly responsive functional genes with ecosystem processes, such as pcc/acc vs. soil carbon storage, amyA/pulA/mnp/lcc vs. soil carbon decomposition, and nifH vs. nitrogen availability, suggesting that such common responses of microbial functional genes may have the potential to predict ecosystem functioning and sustainability in the future eCO2 environment.

Elevated nitrate in the environment inhibits sulfate reduction by important microorganisms of sulfate-reducing bacteria (SRB). Several SRB may respire nitrate to survive under elevated nitrate, but how SRB that lack nitrate reductase survive to elevated nitrate remains elusive. To understand nitrate adaptation mechanisms, we evolved 12 populations of a model SRB (i.e., Desulfovibrio vulgaris Hildenborough, DvH) under elevated NaNO3 for 1000 generations, analyzed growth and acquired mutations, and linked their genotypes with phenotypes. Nitrate-evolved (EN) populations significantly (p

Sea ice plays a critical role in the Earth's climate system, and its variations affect ecosystem stability. This study introduces a novel method for detecting sea ice in the Arctic Ocean using bidirectional radar reflections from the Global Navigation Satellite System (GNSS). Utilizing delay-Doppler maps (DDM) from the UK TechDemoSat-1 (TDS-1) satellite mission and surface data from the U.S. National Oceanic and Atmospheric Administration (NOAA), we employ the local linear embedding (LLE) algorithm for feature extraction. This approach notably reduces training costs and enhances real-time performance, while maintaining a high accuracy and robust noise immunity level. Focusing on the region above 70° north latitude throughout 2018, we aimed to distinguish between sea ice and seawater. The extracted DDM features via LLE are input into a support vector machine (SVM) for classification. The results indicate that our method achieves an accuracy of over 99% for selected low-noise data and a monthly average accuracy of 92.74% for data containing noise, while the CNN method has a monthly average accuracy of only 77.31% for noisy data. A comparative analysis between the LLE-SVM approach and the convolutional neural network (CNN) method demonstrated the superior anti-interference capabilities of the former. Additionally, the impact of the sea ice melting period on detection accuracy was analyzed. [ABSTRACT FROM AUTHOR]

Contamination from anthropogenic activities has significantly impacted Earth's biosphere. However, knowledge about how environmental contamination affects the biodiversity of groundwater microbiomes and ecosystem functioning remains very limited. Here, we used a comprehensive functional gene array to analyze groundwater microbiomes from 69 wells at the Oak Ridge Field Research Center (Oak Ridge, TN), representing a wide pH range and uranium, nitrate, and other contaminants. We hypothesized that the functional diversity of groundwater microbiomes would decrease as environmental contamination (e.g., uranium or nitrate) increased or at low or high pH, while some specific populations capable of utilizing or resistant to those contaminants would increase, and thus, such key microbial functional genes and/or populations could be used to predict groundwater contamination and ecosystem functioning. Our results indicated that functional richness/diversity decreased as uranium (but not nitrate) increased in groundwater. In addition, about 5.9% of specific key functional populations targeted by a comprehensive functional gene array (GeoChip 5) increased significantly (P < 0.05) as uranium or nitrate increased, and their changes could be used to successfully predict uranium and nitrate contamination and ecosystem functioning. This study indicates great potential for using microbial functional genes to predict environmental contamination and ecosystem functioning.IMPORTANCE Disentangling the relationships between biodiversity and ecosystem functioning is an important but poorly understood topic in ecology. Predicting ecosystem functioning on the basis of biodiversity is even more difficult, particularly with microbial biomarkers. As an exploratory effort, this study used key microbial functional genes as biomarkers to provide predictive understanding of environmental contamination and ecosystem functioning. The results indicated that the overall functional gene richness/diversity decreased as uranium increased in groundwater, while specific key microbial guilds increased significantly as uranium or nitrate increased. These key microbial functional genes could be used to successfully predict environmental contamination and ecosystem functioning. This study represents a significant advance in using functional gene markers to predict the spatial distribution of environmental contaminants and ecosystem functioning toward predictive microbial ecology, which is an ultimate goal of microbial ecology.

Background and aims: Exploring biodiversity linkages between aboveground and belowground biota is a core topic in ecology, and can have implications on our understanding of ecosystem process stability. Yet, this topic still remains underexplored. Here, we explored diversity linkages, in terms of both alpha- and beta- diversity, between plant and top soil microbial communities in a semi-arid grassland ecosystem. Methods: Soil microbial community structure was assessed based on both 16S rRNA and functional genes, and plant community composition was evaluated by traditional “species composition” and a newly-defined “biomass composition”, which includes the information on the biomass of each species. Results: The bacterial alpha-diversity, expressed as the richness and Shannon diversity of 16S rRNA genes, was significantly correlated with plant species richness and Shannon diversity, whereas the alpha-diversity of microbial functional genes showed marginal association with total plant biomass. Microbial beta-diversity, evaluated by 16S rRNA genes, showed close relationship with plant beta-diversity estimated by both “species composition” and “biomass composition”, while the microbial beta-diversity based on functional genes was only associated with the compositional variation in aboveground plant biomass. Conclusions: These results showed that the differences in metabolic potential of soil microbial communities, which is closely related with ecosystem functions, can be better predicted by the variation of plant-derived resources returned to soil, than merely by the species composition of the macro-organism communities.