Diurnal variation in opsin expression and common housekeeping genes necessitates comprehensive normalization methods for quantitative real‐time PCR analyses. (7th August 2019)

• Record Type: Journal Article
• Authors: Yourick, Miranda R.; Sandkam, Benjamin A.; Gammerdinger, William J.; Escobar‐Camacho, Daniel; Nandamuri, Sri Pratima; Clark, Frances E.; Joyce, Brendan; Conte, Matthew A.; Kocher, Thomas D.; Carleton, Karen L.
• Is Part Of: Molecular ecology resources. Volume 19:Number 6(2019:Nov.)
• Page Start: 1447
• Page End: 1460
• Publication Date: 2019-08-07
• Subjects: cichlid -- circadian -- colour vision -- qRT–PCR -- vision -- visual ecology; Molecular ecology -- Periodicals; 572.8
• DOI: 10.1111/1755-0998.13062
• Languages: English
• ISSNs: 1755-098X

Abstract

Abstract: To determine the visual sensitivities of an organism of interest, quantitative reverse transcription–polymerase chain reaction (qRT–PCR) is often used to quantify expression of the light‐sensitive opsins in the retina. While qRT–PCR is an affordable, high‐throughput method for measuring expression, it comes with inherent normalization issues that affect the interpretation of results, especially as opsin expression can vary greatly based on developmental stage, light environment or diurnal cycles. We tested for diurnal cycles of opsin expression over a period of 24 hr at 1‐hr increments and examined how normalization affects a data set with fluctuating expression levels using qRT–PCR and transcriptome data from the retinae of the cichlid Pelmatolapia mariae . We compared five methods of normalizing opsin expression relative to (a) the average of three stably expressed housekeeping genes ( Ube2z, EF1‐α and β‐actin ), (b) total RNA concentration, (c) GNAT2, (the cone‐specific subunit of transducin), (d) total opsin expression and (e) only opsins expressed in the same cone type. Normalizing by proportion of cone type produced the least variation and would be best for removing time‐of‐day variation. In contrast, normalizing by housekeeping genes produced the highest daily variation in expression and demonstrated that the peak of cone opsin expression was in the late afternoon. A weighted correlation network analysis showed that the expression of different cone opsins follows a very similar daily cycle. With the knowledge of how these normalization methods affect opsin expression data, we make recommendations for designing sampling approaches and quantification methods based upon the scientific question being examined.