Search

• Analyzed the taxonomic and functional dynamics of microbial community to algal blooms. • Constructed 161 high-quality metagenome-assembled genomes by binning analysis. • Cyanobacterial strains showed diverse energy pathways, transporters and prokaryotic defense systems. • Nitrogen and phosphorus influence the microbial structure by affecting co-pathways. Freshwater lakes are threatened by harmful cyanobacterial blooms, whose basic unit is Cyanobacterial Aggregate (CA). CA-attached bacteria play a significant role through different blooming stages with substantial variation of their taxonomic structure. However, little is known about their functional variations and functional links with cyanobacteria due to the lack of reference genomes. In this longitudinal study, we collected 16 CA samples from Lake Taihu, one of China's largest freshwater lakes, from April 2015 to February 2016, and sequenced their V4 region of 16S rRNA genes, full metagenomes (MG), and metatranscriptomes (MT). The analysis of these data revealed the dynamics of microbial taxonomic and functional structure in CAs, influenced by both external environmental factors and internal metabolism. 55 OTUs, 456 genes, and 37 transcripts showed significantly differential abundance across the early, middle, and late blooming stages (ANOVA test, P < 0.05). Total nitrogen and total phosphorus were proved to be the most important environmental drivers of microbial taxonomic and functional variations in CAs (Mantel's r > 0.25, P < 0.05). We constructed 161 high-quality metagenome-assembled genomes (MAGs), out of which 22 were cyanobacterial strains with diverse energy pathways, transporters and prokaryotic defense systems. Based on these MAGs, we constructed a cyanobacteria-bacteria co-nitrogen-pathway and a cyanobacteria-bacteria co-phosphorus-pathway, by which we demonstrated how nitrogen and phosphorus influence the dynamics of the microbial structure to a certain extent by affecting these co-pathways. Overall, these results characterized the taxonomic, functional, and transcriptional variations of microbes in CAs through different blooming stages. Genome assembly and metabolic analysis of cyanobacteria and their attached bacteria suggested that the material exchange and signal transduction do, indeed, exist among them. Our understanding of the underlying molecular pathways for cyanobacterial blooms could lead to the control of blooms by interventional strategies to disrupt critical microbes' expression. [Display omitted] [ABSTRACT FROM AUTHOR]

Cyanobacterial blooms represent a significant environmental problem, threatening aquatic ecosystems worldwide. Caused by the eutrophication of water bodies and global climate change, these blooms have altered freshwater ecosystems worldwide during recent decades. Although cyanobacterial blooms are typically caused by blue-green cyanobacteria, which derive their color from the phycocyanin pigment, other pigmented cyanobacterial blooms have been frequently observed in water bodies. These blooms pose a serious environmental threat to inland waters, endangering global public health and aquatic ecosystems. Therefore, comprehending the mechanism of color variation in cyanobacterial blooms is crucial for revealing the outbreak mechanism and implementing effective prevention and control measures. This study developed a human–machine interactive workflow for extracting cyanobacterial blooms and recognizing their colors based on the Forel-Ule index and Sentinel-2 MultiSpectral Instrument data. Using this workflow, the authors conducted spatiotemporal analysis and statistical analysis of bloom color for cyanobacterial blooms in four typical eutrophic lakes from 2019 to 2022. The findings indicated a declining trend in cyanobacterial blooms across the four studied lakes over the years, among which Hulun Lake experienced an annual increase in cyanobacterial blooms and emerged as the lake with the most severe outbreak of such blooms in 2022. The yellowing status of cyanobacterial blooms varied among the four lakes, with Taihu Lake and Dianchi Lake exhibiting a relatively high proportion of green-yellow and yellow cyanobacterial blooms, followed by Chaohu Lake, whereas Hulun Lake had the lowest occurrence. The workflow developed in this study was implemented in Google Earth Engine and provided an automated, integrated, and rapid monitoring solution for the long-term monitoring and color recognition of cyanobacterial blooms. [ABSTRACT FROM AUTHOR]