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Record W6998594466

Analysis of gene expression in endosulfan exposed Homarus Americanus larvae using an oligonucleotide microarray

2012· article· en· W6998594466 on OpenAlexaboutno aff

Bibliographic record

VenueIslandScholar (University of Prince Edward Island) · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Toxicology and Ecotoxicology
Canadian institutionsnot available
Fundersnot available
KeywordsHomarusAmerican lobsterEndosulfanMetamorphosisLarvaMoultingPesticideShellfishAquatic animal
DOInot available

Abstract

fetched live from OpenAlex

Potato farming and lobster fishing are two important industries on Prince Edward Island. However for both industries to be sustainable, they must be managed responsibly. Recently, agricultural runoff has been suspected to have caused numerous fish kills in rivers throughout the island. Although a pesticide research and monitoring program was implemented for freshwater areas in Canada by Environment Canada, there is insufficient information about pesticide levels and impacts in estuarine and marine environments. One agricultural pesticide of concern is the organochlorine endosulfan that is used to combat the Colorado potato beetle. Endosulfan is a potent neurotoxin and moult inhibitor that can have serious effects on non-target organisms such as the larvae of the American lobster, Homarus americanus.\nThe lobster life cycle consists of a larval pelagic phase followed by the migration of postlarvae to a benthic habitat. For the lobster to undergo normal development, they must moult their hardened exoskeleton to allow for growth and tissue expansion. One specific moult event is linked to the crucial developmental stage of metamorphosis, wherein the lobster transitions from a larva to a postlarva. Metamorphosis is a sensitive developmental period during which the lobster experiences significant morphological, physiological, biochemical, behavioural and ecological changes. Since this is a critical stage of development, exposure to endosulfan could have deleterious effects on development and survival of lobster larvae.\nThe focus of this study was to determine the effects of environmentally relevant concentrations of endosulfan on gene expression during metamorphosis of lobster larvae. Endosulfan causes serious developmental delays and deformities in an array of\nvi\nspecies, however very little is known about the regulation of gene expression during pesticide exposure. The use of a custom made high throughput lobster, H. americanus, microarray allowed for monitoring of 14,592 genes based on unique lobster expressed sequence tags (EST). A pooled reference design was used to identify changes in gene expression between 5 endosulfan concentrations and a control. Genes with >1.5 fold change and identified as being significant at p < 0.05 using one-way ANOVA were selected for further analysis. There were 707 genes identified as being significantly differentiated. However with only ~40% annotation of the array, the majority of these genes were unknown. Annotated genes were involved in many processes: development, metabolism, immune and oxidative stress response and gene regulation.\nNine genes of interest (GOI) were selected for reverse transcription quantitative PCR (RT-qPCR) analysis to validate the microarray results. For optimal RT-qPCR normalization, 5 housekeeping genes were identified and validated using geNorm. Although the RT-qPCR detected a similar expression pattern as the microarray, the microarray results were often greatly under expressed. Due to discrepancies between expression levels in the microarray compared to the RT-qPCR method, the correlation values between the two were low.\nEndosulfan had a serious effect on survival, development and gene expression during metamorphosis. The long term objective of this research will be to use microarray gene expression profiles as screening tools for identifying what contaminants are present in the environment.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.331
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.221
Teacher spread0.207 · 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.

Study designObservational
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

Citations0
Published2012
Admission routes1
Has abstractyes

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