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BackgroundFreshwater lakes are threatened by harmful cyanobacterial blooms; whose basic unit is Cyanobacterial Aggregate (CA). Community variations of CA-attached bacteria are substantial during different blooming stages. However, little is known about their transcriptional and metabolic variations. Most bacterial genomes in CA were not constructed in existing database, which limits our understanding of the bacterial variations as responses to cyanobacterial blooms. ResultsIn this longitudinal study, 16 CA samples were collected from Lake Taihu, one of the largest freshwater lakes in China, from April of 2015 to February of 2016. By sequencing the V4 region of 16S rRNA genes, full metagenomes (MG) and metatranscriptomes (MT), we generated 424 Mb of 16S rRNA gene data, 122 Gb of high-quality MG data and 160 Gb of high-quality MT data. We analyzed the taxonomic, functional and transcriptional variations of microbes in CAs along three blooming stages, and constructed metagenome-assembled genomes (MAGs) by binning analysis. First, 55 OTUs, 456 genes and 37 transcripts mainly associated with pathways of transporters, photosystem and energy metabolism showed significantly different abundance among the three stages. Second, 161 high-quality MAGs in CAs were achieved, with 19 of which significantly shifted in relative abundance among three stages. The most abundant MAGs have gene capacities to synthesize flagella and divers of transporters, and participate in metabolic pathways of nitrogen, phosphorus and sulfur. Finally, 22 high-quality cyanobacterial MAGs were constructed and can be divided into four functional clusters, which showed significant differences on the energy pathways, transporters and prokaryotic defense system.ConclusionOverall, these results demonstrated the taxonomic, functional and transcriptional variations of microbes in CAs among three different blooming stages. Genome construction and metabolic analysis of cyanobacteria and their attached bacteria suggested that the material exchange and signal transmission do, indeed, exist among them. Our understanding of the underlying molecular pathways for cyanobacterial blooms could potentially lead to the control of blooms by interventional strategies to disrupt the expression of critical microbes.

In aquatic ecosystems, both phytoplankton and bacteria play pivotal roles. Based on 16S rRNA gene sequencing, considerable research focused on phytoplankton colony attached and free-living bacteria has revealed the close relationship between them, and indicated that the entire bacterial community mediates crucial biogeochemical processes in aquatic ecosystems. However, our understanding of their distribution patterns and response to environmental factors remains poor. Besides, picocyanobacteria, which were omitted from attached bacteria analysis, were reported to be important in cyanobacterial blooms. To explore the spatiotemporal variation of the entire bacterial community with their driving environmental factors and detect the relationships among them, we collected 61 water samples spanning one year and the entire Lake Taihu regions for surveying the entire bacterial community. Our results indicated: 1) seasonal variation of the bacterial community composition was stronger than spatial variation due to the clearly seasonal variation of Microcystis, Synechococcus (pico-cyanobacteria) and other bacteria (Actinomycetales, Pirellulaceae and Sphingobacteriaceae); 2) the spatial distribution of the bacterial community showed that different phyla were dominant in different regions; 3) the bacterial co-occurrence networks varied seasonally and were dominated by Microcystis, ACK-M1, Chthoniobacteraceae, Synechococcus, Pirellulaceae and Pelagibacteraceae; 4) phytoplankton density, chlorophyll a, water temperature and total nitrogen were the major factors that drove the spatiotemporal variation of bacterial community composition. This study revealed the seasonal succession and spatial distribution of the entire bacterial community in Lake Taihu, providing new insights into the relationship between water bloom-forming cyanobacterial species and other bacteria, and their response to environmental factors in eutrophic freshwater ecosystem.

Massive partial sequencing of 16S rRNA genes has become the predominant tool used for studying microbial ecology. However, determining which hypervariable regions and primer sets should be used for screening microbial communities requires extensive investigation if controversial results are to be avoided. Here, the performances of different variable regions of the 16S rRNA gene on bacterial diversity studies were evaluated in silico with respect to the SILVA non-redundant reference database (SILVA SSU Ref 123NR), and subsequently verified using samples from Lake Taihu in China, a eutrophic lake. We found that the bacterial community composition results were strongly impacted by the different V regions. The results show that V1-V2 and V1-V3 regions were the most reliable regions in the full-length 16S rRNA sequences, while most V3 to V6 regions (including V3, V4, V3-V4, V5, V4-V5, V6, V3-V6, V4-V6, and V5-V6) were more closely aligned with the SILVA SSU Ref 123NR database. Overall, V4 was the most prominent V region for achieving good domain specificity, higher coverage and a broader spectrum in the Bacteria domain, as confirmed by the validation experiments. S-D-Bact-0564-a-S-15/S-D-Bact-0785-b-A-18 is, therefore, a promising primer set for surveying bacterial diversity in eutrophic lakes.

Mining relationships between microbes and the environment they live in are crucial to understand the intrinsic mechanisms that govern cycles of carbon, nitrogen and energy in a microbial community. Building upon next-generation sequencing technology, the selective capture of 16S rRNA genes has enabled the study of co-occurrence patterns of microbial species from the viewpoint of complex networks, yielding successful descriptions of phenomena exhibited in a microbial community. However, since the effects of such environmental factors as temperature or soil conditions on microbes are complex, reliance on the analysis of co-occurrence networks alone cannot elucidate such complicated effects underlying microbial communities. In this study, we apply a statistical method, which is called Boolean implications for metagenomic studies (BIMS) for extracting Boolean implications (IF-THEN relationships) to capture the effects of environmental factors on microbial species based on 16S rRNA sequencing data. We first demonstrate the power and effectiveness of BIMS through comprehensive simulation studies and then apply it to a 16S rRNA sequencing dataset of real marine microbes. Based on a total of 6,514 pairwise relationships identified at a low false discovery rate (FDR) of 0.01, we construct a Boolean implication network between operational taxonomic units (OTUs) and environmental factors. Relationships in this network are supported by literature, and, most importantly, they bring biological insights into the effects of environmental factors on microbes. We next apply BIMS to detect three-way relationships and show the possibility of using this strategy to explain more complex relationships within a microbial community. Open image in new window