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Tools to minimize interlaboratory variability in vitellogenin gene expression monitoring programs

Overview of attention for article published in Environmental Toxicology & Chemistry, August 2017
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  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

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Title
Tools to minimize interlaboratory variability in vitellogenin gene expression monitoring programs
Published in
Environmental Toxicology & Chemistry, August 2017
DOI 10.1002/etc.3885
Pubmed ID
Authors

Aaron Jastrow, Denise A. Gordon, Kasie M. Auger, Elizabeth C. Punska, Kathleen F. Arcaro, Kristen Keteles, Dana Winkelman, David Lattier, Adam Biales, James M. Lazorchak

Abstract

The egg yolk precursor protein vitellogenin is widely used as a biomarker of estrogen exposure in male fish. However, standardized methodology is lacking and little is known regarding the reproducibility of results among laboratories using different equipment, reagents, protocols, and employing different data analysis programs. To address this data gap we tested the reproducibility across labs to evaluate vitellogenin gene (vtg) expression and assessed the value of using a freely available software data analysis program. Samples collected from studies of male fathead minnows (Pimephales promelas) exposed to 17α-ethinylestradiol (EE2) and minnows exposed to processed wastewater effluent were evaluated for vtg expression in four laboratories. Our results indicate reasonable consistency among laboratories if the free software for expression analysis, LinRegPCR, is used; with three out of four laboratories detecting vtg in fish exposed to 5 ng/L EE2 (n = 5). All four laboratories detected significantly increased vtg levels in 15 male fish exposed to wastewater effluent as compared to 15 male fish held in a control stream. Finally, we were able to determine that the source of high inter-laboratory variability from cDNA to qPCR analyses was the expression analysis software unique to each real-time quantitative polymerase chain reaction (qPCR) machine. We successfully eliminated the inter-laboratory variability by reanalyzing raw fluorescence data with independent freeware, which yielded cycle thresholds and PCR efficiencies that calculated results independently of proprietary software. Our results suggest that laboratories engaged in monitoring programs validate their PCR protocols and analyze their gene expression data following the guidelines established herein for all gene expression biomarkers. This article is protected by copyright. All rights reserved.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 26%
Student > Ph. D. Student 3 16%
Student > Doctoral Student 2 11%
Other 2 11%
Professor 2 11%
Other 5 26%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 32%
Environmental Science 3 16%
Biochemistry, Genetics and Molecular Biology 3 16%
Medicine and Dentistry 1 5%
Chemistry 1 5%
Other 1 5%
Unknown 4 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 October 2017.
All research outputs
#14,393,794
of 25,382,440 outputs
Outputs from Environmental Toxicology & Chemistry
#3,513
of 5,612 outputs
Outputs of similar age
#155,328
of 327,198 outputs
Outputs of similar age from Environmental Toxicology & Chemistry
#17
of 76 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,612 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.9. This one is in the 37th percentile – i.e., 37% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 327,198 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 76 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.