High-throughput transcriptomics of water extracts detects reductions in biological activity with water treatment processes
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE252117 | GEO | 2024/03/05
REPOSITORIES: GEO
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Project description:To assess the impact of surface water across the Hun River, several sampling sites located in the mainstream and the tributary were selected representative of pollution gradient and different pollution source. Human mesenchymal stem cells were exposed to organic extracts of surface water from six sites for 2 days. Microarrays were used to measure the gene expression. And the gene expression profiles were used to evaluate the ability of determine the potential biological effects, to differentiate different pollution source, and to identify the toxic components.
2018-06-01 | GSE110310 | GEO
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.
2022-12-01 | GSE146903 | GEO
Project description:In the search for sustainable drinking water, many countries are weighing up the benefits of advanced treatment technologies as a proactive measure to assist with the transformation of treated wastewater into a source of water used for the production of potable water. We investigated the biological effects along a pilot plant with an advanced water treatment process, using zebrafish embryos at different stages of development. The study took an innovative approach, comparing phenotypic observations with whole genome responses. This enabled us to keep an open mind about which chemicals might be influencing the biological activity. There was no evidence of acute toxicity at any stage of treatment, but distinctive abnormalities – skeletal, cardiovascular and pigmentation – occurred in a small proportion of embryos along the treatment process, and in a tap water, that were not detected in the aquarium water control. Reverse osmosis (RO) reduced the concentration of measured chemical contaminants in the water the most, whilst eliminating the occurrence of abnormalities detected in the fish. In contrast, advanced oxidation appeared to reverse the benefits of RO treatment by increasing the frequency of teratogenic and sub-lethal abnormalities seen in embryos. Genomic analysis found alterations to the retinoid system, which was consistent with the teratogenic abnormalities observed. In addition, we found evidence of changes to metabolic pathways, including tryptophan metabolism associated with the production of melatonin required for the control of normal circadian rhythms. Although we cannot extrapolate these preliminary findings in zebrafish embryos to human or environmental health, we show that underexplored forms of biological activity (that existing Test Guidelines are not designed to capture) occur in treated wastewater effluent, and/or may be created depending on the type of advanced treatment process used. Although the identity of the culprit chemicals are unknown at this time, our innovative approach highlights the need for more research into the effects of chemicals on the retinoid system (and metabolism).
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Project description:Small organisms can be used as biomonitoring tools to assess chemicals in the environment. Chemical stressors are especially hard to assess and monitor when present as complex mixtures. Here, Daphnia magna were exposed for 24 hours to five different munitions constituents 2,4,6-trinitrotoluene (TNT), 2,4-dinitrotoluene (2,4-DNT), 2,6-dinitrotoluene (2,6-DNT), trinitrobenzene (TNB), dinitrobenzene (DNB), or 1,3,5-trinitro-1,3,5-triazacyclohexane (RDX) as well as to 8 different munitions mixtures and ground water contaminated with munitions constituents. To better understand possible mixture effects, gene expression changes from all treatments were compared using high-density microarrays. While mixtures and ground water exposures had genes and gene functions in common with single chemical exposures, unique functions were also affected, which was consistent with the non-additivity of chemical effects in these mixtures.
2011-06-23 | GSE30163 | GEO
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.
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Project description:Daphnia magna is a bio-indicator organism accepted by several international water quality regulatory agencies. Current approaches for assessment of water quality rely on acute and chronic toxicity that provide no insight into the cause of toxicity. Recently, molecular approaches, such as genome wide gene expression responses, are enabling an alternative mechanism based approach to toxicity assessment. While these genomic methods are providing important mechanistic insight into toxicity, statistically robust prediction systems that allow the identification of chemical contaminants from the molecular response to exposure are needed. Here we apply advanced machine learning approaches to develop predictive models of contaminant exposure using a D. magna gene expression dataset for 36 chemical exposures. We demonstrate here that we can discriminate between chemicals belonging to different chemical classes including endocrine disruptors, metals and industrial chemicals based on gene expression. We also show that predictive models based on indices of whole pathway transcriptional activity can achieve comparable results while facilitating biological interpretability.
2013-01-17 | GSE43564 | GEO
Project description:Daphnia magna is a bio-indicator organism accepted by several international water quality regulatory agencies. Current approaches for assessment of water quality rely on acute and chronic toxicity that provide no insight into the cause of toxicity. Recently, molecular approaches, such as genome wide gene expression responses, are enabling an alternative mechanism based approach to toxicity assessment. While these genomic methods are providing important mechanistic insight into toxicity, statistically robust prediction systems that allow the identification of chemical contaminants from the molecular response to exposure are needed. Here we apply advanced machine learning approaches to develop predictive models of contaminant exposure using a D. magna gene expression dataset for 36 chemical exposures. We demonstrate here that we can discriminate between chemicals belonging to different chemical classes including endocrine disruptors, metals and industrial chemicals based on gene expression. We also show that predictive models based on indices of whole pathway transcriptional activity can achieve comparable results while facilitating biological interpretability. D. magna were exposed to 36 Chemicals and 5 control series in quadruplicate.
2013-01-17 | E-GEOD-43564 | biostudies-arrayexpress
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Project description:Small organisms can be used as biomonitoring tools to assess chemicals in the environment. Chemical stressors are especially hard to assess and monitor when present as complex mixtures. Here, Daphnia magna were exposed for 24 hours to five different munitions constituents 2,4,6-trinitrotoluene (TNT), 2,4-dinitrotoluene (2,4-DNT), 2,6-dinitrotoluene (2,6-DNT), trinitrobenzene (TNB), dinitrobenzene (DNB), or 1,3,5-trinitro-1,3,5-triazacyclohexane (RDX) as well as to 8 different munitions mixtures and ground water contaminated with munitions constituents. To better understand possible mixture effects, gene expression changes from all treatments were compared using high-density microarrays. While mixtures and ground water exposures had genes and gene functions in common with single chemical exposures, unique functions were also affected, which was consistent with the non-additivity of chemical effects in these mixtures. The study consisted of three different experiments: (1) exposure to a concentration corresponding to 70% of 1/10th of the LC50 value of six individual MCs (TNT, 2,4-DNT, 2,6-DNT, DNB, TNB, RDX) and a control; (2) exposure to eight different laboratory mixtures of the previously mentioned MCs. Different combinations of MCs including four mixtures (Mixtures 5, 6, 7 and 8) representative of field collected groundwater from LAAP (Louisiana Army Ammunition Plant) were created; and (3) exposure to MC-contaminated ground water field-collected from 3 different wells (85, 108, and 141) at the LAAP. All exposures were conducted for 24h.
2011-06-22 | E-GEOD-30163 | biostudies-arrayexpress
Project description:the venom duct extracted by pure water and analyzed by LC-MS with a Hilic column
2020-06-17 | MSV000085600 | MassIVE