Selection of Reference Genes for QRT-PCR and Expression Analysis of High-altitude-related Genes in Grassland Caterpillars (Lepidoptera: Erebidae: ) Along an Altitude Gradient
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Changes in gene expression patterns can reflect the adaptation of organisms to divergent environments. Quantitative real-time PCR (qRT-PCR) is an important tool for ecological adaptation studies at the gene expression level. The quality of the results of qRT-PCR analysis largely depends on the availability of reliable reference genes (RGs). To date, reliable RGs have not been determined for adaptive evolution studies in insects using a standard approach. Here, we evaluated the reliability of 17 candidate RGs for five populations inhabiting various altitudes of the Tibetan Plateau (TP) using four independent (geNorm, NormFinder, BestKeeper, and the deltaCt method) and one comprehensive (RefFinder) algorithms. Our results showed that α, , and were the top three most suitable RGs, and a combination of these three RGs was the most optimal for normalization. Conversely, ,, and were the most unstable RGs. The expression profiles of two target genes ( and ) were used to confirm the reliability of the chosen RGs. Additionally, the expression patterns of four other genes (,,, and ) associated with adaptation to extreme environments were assessed to explore the adaptive mechanisms of TP species to divergent environments. Each of these six target genes showed discrepant expression patterns among the five populations, suggesting that the observed expression differences may be associated with the local adaptation of to divergent altitudinal environments. This study is a useful resource for studying the adaptive evolution of TP to divergent environments using qRT-PCR, and it also acts as a guide for selecting suitable RGs for ecological and evolutionary studies in insects.
Ngaki M, Srivastava S, Feifei W, Bhattacharyya M Sci Rep. 2024; 14(1):12253.
PMID: 38806545 PMC: 11133457. DOI: 10.1038/s41598-024-62332-4.
Zhang Y, Cao T, Wang Y, Yang R, Han Y, Li S Foods. 2024; 13(6).
PMID: 38540948 PMC: 10969745. DOI: 10.3390/foods13060958.
Zhao J, Hu S, Zhang L, Zhang L, Yang X, Yuan M Front Genet. 2023; 14:1137618.
PMID: 37144120 PMC: 10151491. DOI: 10.3389/fgene.2023.1137618.
Maashi M, Al-Mualm M, Al-Awsi G, Catalan Opulencia M, Al-Gazally M, Abdullaev B Mol Biol Rep. 2022; 49(9):8777-8784.
PMID: 35804214 DOI: 10.1007/s11033-022-07727-0.
Choudhury A, Verma S, Muthamilarasan M, Rajam M Mol Biol Rep. 2021; 48(11):7477-7485.
PMID: 34637095 DOI: 10.1007/s11033-021-06766-3.