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BBW_1 BBW_4 BBW_5 BBW_9 BBW_11 BBW_12 BBW_13 BBW_17 BBW_18 BBW_20 BBW_22 BBW_24 BBW_29 YLJ_4 YLJ_5 YLJ_7 YLJ_8 YLJ_9 YLJ_11 YLJ_13 YLJ_17 YLJ_19 YLJ_20 YLJ_21 YLJ_22 YLJ_26 YLJ_27

ABSTRACT: 2020 Beibu Gulf and Yalu River sediment Metagenome samples

PROVIDER: PRJNA855291 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR20013000_1.fastq.gz	Fastqsanger.gz
	SRR20013000_2.fastq.gz	Fastqsanger.gz
	SRR20013001_1.fastq.gz	Fastqsanger.gz
	SRR20013001_2.fastq.gz	Fastqsanger.gz
	SRR20013002_1.fastq.gz	Fastqsanger.gz

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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

BBW_1 BBW_4 BBW_5 BBW_9 BBW_11 BBW_12 BBW_13 BBW_17 BBW_18 BBW_20 BBW_22 BBW_24 BBW_29 YLJ_4 YLJ_5 YLJ_7 YLJ_8 YLJ_9 YLJ_11 YLJ_13 YLJ_17 YLJ_19 YLJ_20 YLJ_21 YLJ_22 YLJ_26 YLJ_27

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