Evidence map›Paper›PMID 27939634›Full record

ArticleEnvironmental pollution (Barking, Essex : 1987)2017

Prior knowledge-based approach for associating contaminants with biological effects: A case study in the St. Croix River basin, MN, WI, USA.

Anthony L Schroeder, Dalma Martinović-Weigelt, Gerald T Ankley, Kathy E Lee, Natalia Garcia-Reyero, Edward J Perkins, Heiko L Schoenfuss, Daniel L Villeneuve

Abstract read
In one paragraph

Article in Environmental pollution (Barking, Essex : 1987), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Anthony L SchroederUniversity of Minnesota - Twin Cities, Water Resources Center, 1985 Lower Buford Circle, St. Paul, MN 55108, USA; U.S. Environmental Protection Agency, National Health and Environmental Effects Research Laboratory, Duluth, MN 55804, USA.
Dalma Martinović-WeigeltUniversity of St. Thomas, Department of Biology, Mail OWS 390, 2115 Summit Ave, Saint Paul, MN 55105, USA.
Gerald T AnkleyU.S. Environmental Protection Agency, National Health and Environmental Effects Research Laboratory, Duluth, MN 55804, USA.
Kathy E LeeU.S. Geological Survey, Toxic Substances Hydrology Program, Grand Rapids, MN 55744, USA.
Natalia Garcia-ReyeroU.S. Army Engineer Research and Development Center - Environmental Laboratory, Vicksburg, MS 39180, USA; Mississippi State University - Institute for Genomics Biocomputing and Biotechnology, Starkville, MS 39762, USA.
Edward J PerkinsU.S. Army Engineer Research and Development Center - Environmental Laboratory, Vicksburg, MS 39180, USA.
Heiko L SchoenfussAquatic Toxicology Laboratory, WSB-273, St., Cloud State University, St. Cloud, MN 56301, USA.
Daniel L VilleneuveU.S. Environmental Protection Agency, National Health and Environmental Effects Research Laboratory, Duluth, MN 55804, USA. Electronic address: Villeneuve.dan@epa.gov.

Funding

Intramural EPA EPA999999
6 · The paper itself

Abstract

Evaluating potential adverse effects of complex chemical mixtures in the environment is challenging. One way to address that challenge is through more integrated analysis of chemical monitoring and biological effects data. In the present study, water samples from five locations near two municipal wastewater treatment plants in the St. Croix River basin, on the border of MN and WI, USA, were analyzed for 127 organic contaminants. Known chemical-gene interactions were used to develop site-specific knowledge assembly models (KAMs) and formulate hypotheses concerning possible biological effects associated with chemicals detected in water samples from each location. Additionally, hepatic gene expression data were collected for fathead minnows (Pimephales promelas) exposed in situ, for 12 d, at each location. Expression data from oligonucleotide microarrays were analyzed to identify functional annotation terms enriched among the differentially-expressed probes. The general nature of many of the terms made hypothesis formulation on the basis of the transcriptome-level response alone difficult. However, integrated analysis of the transcriptome data in the context of the site-specific KAMs allowed for evaluation of the likelihood of specific chemicals contributing to observed biological responses. Thirteen chemicals (atrazine, carbamazepine, metformin, thiabendazole, diazepam, cholesterol, p-cresol, phenytoin, omeprazole, ethyromycin, 17β-estradiol, cimetidine, and estrone), for which there was statistically significant concordance between occurrence at a site and expected biological response as represented in the KAM, were identified. While not definitive, the approach provides a line of evidence for evaluating potential cause-effect relationships between components of a complex mixture of contaminants and biological effects data, which can inform subsequent monitoring and investigation.

Indexed as

AnimalsCresolsCyprinidaeEnvironmental MonitoringEstroneGene ExpressionMinnesotaOligonucleotide Array Sequence AnalysisRiversWastewaterWater Pollutants, ChemicalWisconsin4-cresolCresolsEstroneWastewaterWater Pollutants, ChemicalAdverse outcome pathwayChemical-gene interactionsChemical mixturesComparative toxicogenomics databaseContaminants

Identifiers

PMID27939634
PMCPMC6139436

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.