Annual periodicity in planktonic bacterial and archaeal community composition of eutrophic Lake Taihu

Abstract

Bacterioplankton plays a key role in nutrient cycling and is closely related to water eutrophication and algal bloom. We used high-throughput 16S rRNA gene sequencing to profile archaeal and bacterial community compositions in the surface water of Lake Taihu. It is one of the largest lakes in China and has suffered from recurring cyanobacterial bloom. A total of 81 water samples were collected from 9 different sites in 9 different months of 2012. We found that temporal variation of the microbial community was significantly greater than spatial variation (adonis, n = 9999, P < 1e−4). The composition of bacterial community in December was similar to that in January, and so was the archaeal community, suggesting potential annual periodicity. Unsupervised K-means clustering was used to identify the synchrony of abundance variations between different taxa. We found that the cluster consisting mostly of ACK-M1, C111 (members of acIV), Pelagibacteraceae (alfV-A) and Synechococcaceae showed relatively higher abundance in autumn. On the contrary, the cluster of Comamonadaceae and Methylophilaceae (members of lineage betI and betIV) had higher abundance in spring. The co-occurrence relationships between taxa were greatly altered during the cyanobacterial bloom according to our further network module analysis.

Characterising and predicting cyanobacterial blooms in an 8-year amplicon sequencing time course

“…As a result, these assemblages may include bacteria that are reliant on cyanobacterial metabolites and biomass. For example, we found that bloom assemblages included potential cyanobacterial predators from the order Cytophagales and the genus Flavobacterium (Supplementary Table 9), both associated with bloom termination (Rashidan and Bird, 2001;Kirchman, 2002) but also taxa such as Methylophilaceae, acI and acIV that have been previously associated with cyanobacterial blooms (Li et al, 2015;Woodhouse et al, 2016a). We found that acI was abundant in early summer, just before the Microcystis and Dolichospermum blooms of midsummer (Figure 3b).…”

“…For example, blooms can reduce carbon dioxide concentrations, increase pH and alter the distribution of biomass across the length and depth of a lake (Verspagen et al, 2014;Sandrini et al, 2016). Such bloom-induced changes in water chemistry could then impact the structure and diversity of microbial communities (Bouvy et al, 2001;Eiler and Bertilsson, 2004;Bagatini et al, 2014;Li et al, 2015;Woodhouse et al, 2016a). For example, as cyanobacteria decompose, they release metabolites that can be utilised by other taxa, such as Cytophagaceae (Rashidan and Bird, 2001;O'Neil et al, 2012), which we therefore expect to be observed in association with blooms.…”

“…Several studies have shown that many aquatic microbial communities are temporally dynamic (Pernthaler et al, 1998;Höfle et al, 1999;Lindstrom, 2000;Crump et al, 2003;Kent et al, 2004;Shade et al, 2007;Kara et al, 2013;Fuhrman et al, 2015), often with repeatable patterns of community structure (Fuhrman et al, 2006;Fuhrman et al, 2015;Cram et al, 2015). Recent studies have tracked the dynamics of microbial communities in bloom-impacted lakes using culture-independent sequencing methods (Eiler et al, 2012;Li et al, 2015;Woodhouse et al, 2016a). Li et al (2015) found that a bloom-impacted lake returned to its initial community composition after a period of one year.…”

“…Recent studies have tracked the dynamics of microbial communities in bloom-impacted lakes using culture-independent sequencing methods (Eiler et al, 2012;Li et al, 2015;Woodhouse et al, 2016a). Li et al (2015) found that a bloom-impacted lake returned to its initial community composition after a period of one year. However, all these studies were carried out over one year or less, making it difficult to generalise the results and make robust predictions.…”

The Effects of Antibiotics on Microbial Community Composition in an Estuary Reservoir during Spring and Summer Seasons

“…Results of sub-network further indicated that water temperature was the major environmental driving factor accelerated the Synechococcus growth, and chl-a concentration also significantly increased during this period ( Figure 6). In addition, several heterotrophic bacterial OTUs belonging to Actinobacteria, Gemmatimonadetes, Planctomycetes, Verrucomicrobia, β-and γ-Proteobacteria exhibited the co-occurrence patterns with Synechococcus, which indicated that mutualism mechanisms might exist between these bacterial taxa in aspect of carbon source utilization [40][41][42][43][44]. Further research is underway to confirm whether these related bacterial taxa can be bio-indicators to predict for the proliferation of Synechococcus.…”