Dataset Information

Freshwater benthic cyanobacterial biofilm LC-MSMS

ABSTRACT: In order to understand how Phormidium mats establish, and the role of associated taxa in their development, we collected biofilms over a 19-day growth period during a nitrate-induced bloom event in the Wai-iti River for proteogenomics analysis. At the onset of a late summer bloom, cobbles from the Wai-iti River (Nelson, New Zealand) were removed, cleared of incipient growth with sterile sponges, and placed back into the river. Clearing was gentle as seeding from the pre-existing rock surface is important for bloom establishment [24]. Five pre-cleared cobbles were collected at each of 3 time points to capture the first 3, 6 and 9 days of growth (Table S1). Additional cobbles that contemporaneously developed biofilms were collected at days 12 and 19.

INSTRUMENT(S): TripleTOF 6600

ORGANISM(S): Uncultured Phormidium Sp.

SUBMITTER: Hwee Sze Tee

LAB HEAD: Kim Handley

PROVIDER: PXD015625 | Pride | 2020-05-19

REPOSITORIES: Pride

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Publications

Tools for successful proliferation: diverse strategies of nutrient acquisition by a benthic cyanobacterium.

Tee H S HS Waite D D Payne L L Middleditch M M Wood S S Handley K M KM

The ISME journal 20200518 8

Freshwater cyanobacterial blooms have increased worldwide, channeling organic carbon into these systems, and threatening animal health through the production of cyanotoxins. Both toxic and nontoxic Microcoleus proliferations usually occur when there are moderate concentrations of dissolved inorganic nitrogen, but when phosphorus is scarce. In order to understand how Microcoleus establishes thick biofilms (or mats) on riverbeds under phosphorus-limiting conditions, we collected Microcoleus-domina ...[more]

PMID: 32424245

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Project description:Today, many contaminants of emerging concern can be measured in waters across the United States, including the tributaries of the Great Lakes. However, just because the chemicals can be measured does not mean that they necessarily result in harm to fish and other aquatic species. Complicating risk assessment in these waters is the fact that aquatic species are encountering the chemicals as mixtures, which may have additive or synergistic risks that cannot be calculated using single chemical hazard and concentration-response information. We developed an in vitro effects-based screening approach to help us predict potential liver toxicity and cancer in aquatic organisms using water from specific Great Lakes tributaries: St. Louis River (MN), Bad River (WI), Fox River (WI), Manitowoc River (WI), Milwaukee River (WI), Indiana Harbor Canal (IN), St. Joseph River (MI), Grand River (MI), Clinton River (MI), River Rouge (MI), Maumee River (OH), Vermilion River (OH), Cuyahoga River (OH), Genesee River (NY), and Oswego River (NY). We exposed HepG2 cells for 48hrs to medium spiked with either field collected water (final concentration of environmental samples in the exposure medium were 75% of the field-collected water samples) or purified water. Using a deep neural network we clustered our collection sites from each tributary based on water chemistry. We also performed high throughput transcriptomics on the RNA obtained from the HepG2 cells. We used the transcriptomics data with our Bayesian Inferene for Sustance and Chemical Toxicity (BISCT) Bayesian Network for Steatosis to predict the probability of the field samples yielding a gene expression pattern consistent with predicting steatosis as an outcome. Surprisingly, we found that the probability of steatosis did not correspond to the surface water chemistry clustering. Our analysis suggests that chemical signatures are not informative in predicting biological effects. Furthermore, recent reports published after we obtained our samples, suggest that chemical levels in the sediment may be more relevant for predicting potential biological effects in the fish species developing tumors in the Great Lakes basin.

Dataset Information

Freshwater benthic cyanobacterial biofilm LC-MSMS

Publications

Tools for successful proliferation: diverse strategies of nutrient acquisition by a benthic cyanobacterium.

Similar Datasets

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