The climate is changing. The average temperature in Wuhan, China, is forecast to increase by at least 4.5 °C over the next century. Shallow lakes are important components of the biosphere, but they are sensitive to climate change and nutrient pollution. We hypothesized that nutrient concentration is the key determinant of nutrient fluxes at the water-sediment interface, and that increased temperature increases nutrient movement to the water column because warming stimulates shifts in microbial composition and function. Here, twenty-four mesocosms, mimicking shallow lake ecosystems, were used to study the effects of warming by 4.5 °C above ambient temperature at two levels of nutrients relevant to current degrees of lake eutrophication levels. This study lasted for 7 months (April–October) under conditions of near-natural light. Intact sediments from two different trophic lakes (hypertrophic and mesotrophic) were used, separately. Environmental factors and bacterial community compositions of overlying water and sediment were measured at monthly intervals (including nutrient fluxes, chlorophyll a [chl a], water conductivity, pH, sediment characteristics, and sediment-water et al.). In low nutrient treatment, warming significantly increased chl a in the overlying waters and bottom water conductivity, it also drives a shift in microbial functional composition towards more conducive sediment carbon and nitrogen emissions. In addition, summer warming significantly accelerates the release of inorganic nutrients from the sediment, to which microorganisms make an important contribution. In high nutrient treatment, by contrast, the chl a was significantly decreased by warming, and the nutrient fluxes of sediment were significantly enhanced, warming had considerably smaller effects on benthic nutrient fluxes. Our results suggest that the process of eutrophication could be significantly accelerated in current projections of global warming, especially in shallow unstratified clear-water lakes dominated by macrophytes.

As the most important component of ecosystems, microbial communities play a significant role in global biogeochemical cycles. Geographical barriers created by topographic differences are proposed as one of the main factors to shape microbial diversity, functional composition and their evolution across aquatic ecosystem. There are few studies that compare the microbial community structure and functional potential of lakes with different terrain ladders (terrains of contrasting elevation levels), especially those involving Qinghai-Tibet Plateau. This study compared microbial 16S rRNA data from 51 lakes with different terrain ladders, showing that the composition of the dominant microbial community was similar, but the microbial abundance was quite different in different terrain ladders. Actinobacteria, Proteobacteria, Cyanobacteria, Planctomycete, Verrucomicrobia and Bacteroidetes were the dominant taxa. Through the correlation analysis between environmental factors and the microbial community structure, it was found that two environmental factors (elevation and salinity) had significant contributions to the microbial composition. Metagenomics of six representative lakes revealed the relationship between microbial composition and metabolic processes related to carbon, nitrogen and sulfur cycles. The comparative analysis of genes related to metabolism cycles showed that functional processes such as aerobic respiration, nitrogen assimilation, the mineralization of nitrogen and sulfur had highest metabolic potential in the ecosystems. The analysis of indicator species based on the metabolic process in the carbon, nitrogen and sulfur cycles showed that the microbial community structure is related to the dominant metabolic functions within biogeochemical cycles. Different dominant species play an important and distinct role in lakes with different gradients. Actinobacteria, Cyanobacteria and Proteobacteria were the most important indicator species on the Qinghai-Tibet Plateau, Yunnan-Guizhou Plateau and the middle and lower reaches of the Yangtze River Plain, respectively. In general, this study explored the functional distribution patterns of the dominant microbial communities in the lakes in biogeocycles.

Ralstonia spp. are highly adaptable bacteria that are widely distributed across diverse environments. Here, we isolated four Ralstonia pickettii (R. pickettii) genomes from cultures of Dolichospermum spp., and using a comparative genomic framework of 228 Ralstonia genomes. We performed phylogenetic analyses that grouped them into water, soil, plant, and human-associated clades based on their predominant isolation habitats. Fluorescence in situ hybridization revealed minimal physical interactions between R. pickettii and cyanobacterial cells, indicating a commensal or independent ecological relationship. Distinct differences in carbohydrate-active enzymes (CAZymes) and secondary metabolite profiles were observed between water and human-associated dominant groups compared to plant-associated dominant groups, highlighting potential niche-specific adaptations. The water-associated dominant groups harbored antibiotic resistance genes, including CeoB and OXA-type β-lactamase genes. These genes are typically linked to human-associated strains, suggesting potential horizontal gene transfer or shared selective pressures, and the gene content of T3SS is reduced. Notably, water-associated dominant groups exhibited a unique pyrimidine degradation pathway, potentially enabling the utilization of exogenous pyrimidines to support survival in nutrient-limited aquatic environments. We propose that the gene content loss of T3SS and the acquisition of specialized metabolic pathways reflect adaptive strategies of Ralstonia spp. for thriving in aquatic free-living niches.

Visible light emission exceeding purely thermal radiation has been imaged at deep-sea hydrothermal vents, yet the underlying mechanisms remain unexplained. Here, we show that visible light can be produced from geothermal infrared radiation via nonlinear frequency-doubling up-conversion in sulfide minerals that are abundant in hydrothermal vents. Chalcopyrite exhibits significant second harmonic generation, which is further amplified under high pressure, yielding a 400–700 nm photon flux three orders of magnitude greater than blackbody emission. When exposed to 1064 nm of irradiation, chalcopyrite induces fluorescence responses in the cyanobacterium Synechococcus sp. PCC 7002 at 656 and 685 nm, suggesting that the up-converted 532 nm light is absorbed by phycobilisomes and transferred to photosystem II. Metagenomic analysis reveals a strong correlation between cyanobacteria and high-temperature, chalcopyrite-rich vents. Similar up-conversion processes have also been observed in other sulfide minerals, emitting wavelengths covering the entire visible spectrum. These findings unveil a novel mineral-mediated photonic mechanism that generates biologically relevant visible light at hydrothermal vents, which can be harnessed by oxygenic photosynthetic cyanobacteria in Earth's deep biosphere and possibly beyond.

With the rapid development of sequencing technology, completed genomes of microbes have explosively emerged. For a newly sequenced prokaryotic genome, gene functional annotation and metabolism pathway assignment are important foundations for all subsequent research work. However, the assignment rate for gene metabolism pathways is lower than 48% on the whole. It is even lower for newly sequenced prokaryotic genomes, which has become a bottleneck for subsequent research. Thus, the development of a high-precision metabolic pathway assignment framework is urgently needed. Here, we developed PPA-GCN, a prokaryotic pathways assignment framework based on graph convolutional network, to assist functional pathway assignments using KEGG information and genomic characteristics. In the framework, genomic gene synteny information was used to construct a network, and ideas of self-supervised learning were inspired to enhance the framework’s learning ability. Our framework is applicable to the genera of microbe with sufficient whole genome sequences. To evaluate the assignment rate, genomes from three different genera (Flavobacterium (65 genomes) and Pseudomonas (100 genomes), Staphylococcus (500 genomes)) were used. The initial functional pathway assignment rate of the three test genera were 27.7% (Flavobacterium), 49.5% (Pseudomonas) and 30.1% (Staphylococcus). PPA-GCN achieved excellence performance of 84.8% (Flavobacterium), 77.0% (Pseudomonas) and 71.0% (Staphylococcus) for assignment rate. At the same time, PPA-GCN was proved to have strong fault tolerance. The framework provides novel insights into assignment for metabolism pathways and is likely to inform future deep learning applications for interpreting functional annotations and extends to all prokaryotic genera with sufficient genomes.