Temporal Dynamics of Cyanobacterial Bloom Community Composition and Toxin Production from Urban Lakes.


Journal

bioRxiv : the preprint server for biology
Titre abrégé: bioRxiv
Pays: United States
ID NLM: 101680187

Informations de publication

Date de publication:
10 Feb 2024
Historique:
medline: 19 2 2024
pubmed: 19 2 2024
entrez: 19 2 2024
Statut: epublish

Résumé

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. This 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.

Identifiants

pubmed: 38370816
doi: 10.1101/2024.02.07.579333
pmc: PMC10871351
pii:
doi:

Types de publication

Preprint

Langues

eng

Auteurs

Classifications MeSH