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

Abstract

Cyanobacterial blooms occur in lakes worldwide, producing toxins that pose a serious public health threat. Eutrophication caused by human activities and warmer temperatures both contribute to blooms, but it is still difficult to predict precisely when and where blooms will occur. One reason that prediction is so difficult is that blooms can be caused by different species or genera of cyanobacteria, which may interact with other bacteria and respond to a variety of environmental cues. Here we used a deep 16S amplicon sequencing approach to profile the bacterial community in eutrophic Lake Champlain over time, to characterise the composition and repeatability of cyanobacterial blooms, and to determine the potential for blooms to be predicted based on time course sequence data. Our analysis, based on 135 samples between 2006 and 2013, spans multiple bloom events. We found that bloom events significantly alter the bacterial community without reducing overall diversity, suggesting that a distinct microbial community-including non-cyanobacteria-prospers during the bloom. We also observed that the community changes cyclically over the course of a year, with a repeatable pattern from year to year. This suggests that, in principle, bloom events are predictable. We used probabilistic assemblages of OTUs to characterise the bloom-associated community, and to classify samples into bloom or non-bloom categories, achieving up to 92% classification accuracy (86% after excluding cyanobacterial sequences). Finally, using symbolic regression, we were able to predict the start date of a bloom with 78-92% accuracy (depending on the data used for model training), and found that sequence data was a better predictor than environmental variables.

Niche separation increases with genetic distance among bloom-forming cyanobacteria

“…To investigate node-environment relationships, we used an environmental data matrix that 161 included: particulate phosphorus in µg/ L (PP, the difference between TP and DP), particulate 162 nitrogen in mg/ L (PN, the difference between TN and DN), soluble reactive phosphorus in µg/ L 163 (DP), dissolved nitrogen in mg/ L (DN), 1-week-cumulative precipitation in mm and 1-week-164 average air temperature in Celsius. The measurements of the different environment variables are 165 described in Tromas et al (2017). In this previous study, we showed that these environmental 166 variable, over the 8 years, were not correlated with one another.…”

“…we used 16S amplicon sequencing to broadly survey changes in the lake microbial community 97 over time, generally at the genus level (Tromas et al, 2017). Here, we use Minimum Entropy 98…”

Microbial diversity and metabolic potential in cyanotoxin producing cyanobacterial mats throughout a river network

“…Mou et al (2013) found a positive association between microcystin degradation rates and Burkholderiales, suggesting a role for these bacteria in toxin breakdown. Burkholderiales are important in some cyanobacterial blooms (Louati et al, 2015), although a decline in abundance of a Burkholderiales strain can be predictive of the onset of a cyanobacterial bloom (Tromas et al, 2017). In the current study, samples with the anatoxin-a operon had fewer Burkholderiales, so it possible that the bacteria from this family in the Eel River mats are negatively impacted by toxin production (unfortunately, the genes for anatoxin-a degradation are not known, so this cannot be determined genomically).…”

“…Cyanobacterial growth rates and other physiological process are often enhanced in the presence of co-occurring microbes (Allen, 1952), and few antagonisms between cyanobacteria and other bacteria have been reported (Paerl, 1996;Berg et al, 2009). The microbial assemblage of lakes shifts when cyanobacteria bloom (Woodhouse et al, 2016;Tromas et al, 2017), and the assemblage composition differs depending on the cyanobacterial species causing the bloom (Louati et al, 2015;Bagatini et al, 2014). With most interactions among Cyanobacteria and co-occurring bacteria facilitating cyanobacterial growth, identifying the diversity and metabolisms of the whole microbial consortia within toxigenic cyanobacterial mats is central to understanding the ecological processes that drive the proliferation of toxigenic mats in rivers.…”

The evolution of realized niches within freshwater Synechococcus

“…We followed the same protocol described in the study by Tromas et al, (2017) to generate a library of V4 region amplicons. Libraries for 2 × 250 bp paired-end Illumina sequencing were prepared using a two-step 16S rRNA gene amplicon PCR as described in the study by Preheim et al (2013).…”