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Record W2332641935 · doi:10.1021/es103122g

Hepatic Transcriptomics and Protein Expression in Rainbow Trout Exposed to Municipal Wastewater Effluent

2011· article· en· W2332641935 on OpenAlexaff
Jennifer Ings, Mark R. Servos, Mathilakath M. Vijayan

Bibliographic record

VenueEnvironmental Science & Technology · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRainbow troutVitellogeninBiologyTranscriptomeTroutGene expressionGlucocorticoid receptorHeat shock proteinHsp70MicroarrayGeneGeneticsFisheryFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Municipal wastewater effluents (MWWEs) represent one of the largest point sources of contamination, but few studies have addressed the impact on fish populations. We tested the hypothesis that MWWEs disrupt multiple stress-related pathways by examining expression of genes and proteins in rainbow trout (Oncorhynchus mykiss). A caging study was undertaken by placing juvenile trout for 14 d either at an upstream control or 100%, 50%, and 10% MWWE sites. A custom-made low-density rainbow trout cDNA microarray was utilized for transcriptomics, and select gene expression was confirmed with quantitative real-time PCR. MWWE exposure significantly elevated plasma cortisol, glucose, and vitellogenin levels, and altered the expression of a number of hepatic genes. Notably, expression of stress-related genes, hormone receptors, glucose transporter 2, and genes related to immune function were altered. The gene and protein expression of glucocorticoid receptor, heat shock proteins 70 and 90, and cytochrome P4501A1 were also impacted by MWWE exposure. Our results demonstrate that tertiary-treated MWWEs elicit an organismal and cellular stress response in trout and may lead to an enhanced energy demand in the exposed fish. The disruption in multiple stress-related pathways suggests that tertiary-treated MWWEs exposure may reduce fish performance to subsequent stressors.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.243

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.191
Teacher spread0.176 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations81
Published2011
Admission routes1
Has abstractyes

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