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ABSTRACT: China Wastewater & River Water Microbiomes

PROVIDER: PRJNA857474 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR20074300_1.fastq.gz	Fastqsanger.gz
	SRR20074300_2.fastq.gz	Fastqsanger.gz
	SRR20074301_1.fastq.gz	Fastqsanger.gz
	SRR20074301_2.fastq.gz	Fastqsanger.gz
	SRR20074302_1.fastq.gz	Fastqsanger.gz

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Project description:The copper redhorse (Moxostoma hubbsi) is an endangered fish endemic to Quebec, Canada that is only known to spawn in two locations within the Richelieu River, a waterway draining a significant area of agricultural land. Accordingly, concerns have been raised over the impacts that agricultural pesticide contamination of spawning grounds and nursery habitats within the Richelieu River may have on early life stage copper redhorse. We assessed the effects of contaminants on early life stages of copper redhorse and river redhorse (Moxostoma carinatum), a closely related fish that shares the copper redhorse’s habitat and spawning grounds but is distributed more widely and is not yet listed as endangered. Copper and river redhorse embryos (1000 each) were exposed to either Richelieu River water in an in-situ flow-through system or to laboratory water used as a control. We assessed embryos hatching time, incidence of deformities and survival in copper and river redhorses. We then performed RNA sequencing on copper redhorse larvae to better understand changes due to river water exposure. We identified 341 compounds in the river water that were absent from lab water. Pesticide concentrations in the river peaked following rainfall during the spawning season. Embryos exposed to river water hatched prematurely at 63.0 and 59.2 cumulative degree days (CDD) compared to 65.4 and 69.9 CDD in laboratory water for river and copper redhorse, respectively. Copper redhorse exposed to river water also had a significantly lower survival rate than laboratory water (73% vs. 93%). RNA sequencing of copper redhorse revealed 18 differentially expressed genes (DEGs) following river water exposure. Eight of the upregulated DEGs (cd44, il1b, lamb3, lamc2, tgm5, orm1, saa, acod1) are linked to immune function and injury response and 7 of the downregulated DEGs (cpa2, ctrb, cela2a, ctrl, cpa1, prss1, cel) are involved with digestion and nutrient absorption. This study provided valuable data on the effects of anthropogenic contaminants present in the Richelieu River and increased our knowledge on the individual and mixture effects they have on an endangered fish.

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.

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