bioRxivPharmacology & Toxicology
Temporal Dynamics of Cyanobacterial Bloom Community Composition and Toxin Production from Urban Lakes
J. A. Maurer, A. M. Kim, N. Oblie, S. Hefferan, H. Xie, A. Slitt, B. D. Jenkins, M. J. Bertin
Abstract
With a long evolutionary history and a need to adapt to a changing environment, cyanobacteria in freshwater systems use specialized metabolites for communication, defense, and physiological processes. However, the role that these metabolites play in differentiating species, maintaining microbial communities, and generating niche persistence and expansion is poorly understood. Furthermore, many cyanobacterial specialized metabolites and toxins present significant human health concerns due to their liver toxicity and their potential impact to drinking water. Gaps in knowledge exist with respect to changes in species diversity and toxin production during a cyanobacterial bloom (cyanoHAB) event; addressing these gaps will improve understanding of impacts to public and ecological health. In the current project, we utilized a multi-omics strategy (DNA metabarcoding and metabolomics) to determine the cyanobacterial community composition, toxin profile, and the specialized metabolite pool at three freshwater lakes in Providence, RI during summer-fall cyanoHABs. Species diversity decreased at all study sites over the course of the bloom event, and toxin production reached a maximum at the midpoint of the event. Additionally, LC-MS/MS-based molecular networking identified new toxin congeners. This work provokes intriguing questions with respect to the use of allelopathy by organisms in these systems and the presence of emerging toxic compounds that can impact public health. SYNOPSISThis study reports on cyanobacterial community succession and toxin dynamics during cyanobacterial bloom events. Results show relationships and temporal dynamics that are relevant to public health.
- assay: metagenomics
- assay: metabolomics
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
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