ArticlePloS one2016
Normalization for Relative Quantification of mRNA and microRNA in Soybean Exposed to Various Abiotic Stresses.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 22 citations in OpenAlex.
- Validation of suitable reference microRNAs for qRT-PCR inFrontiers in plant science · 2025Article
- Photosynthetic characteristics and genetic mapping of a yellow-green leaf mutant jym165 in soybean.BMC plant biology · 2024Article
- Screening and validation of optimal miRNA reference genes in different developing stages and tissues of Lilium henryi Baker.Scientific reports · 2024Article
- Selection of suitable reference lncRNAs for gene expression analysis inFrontiers in plant science · 2024Article
- Physiological responses and transcriptome analysis of soybean under gradual water deficit.Frontiers in plant science · 2023Article
- Differential microRNA expression, microRNA arm switching, and microRNA:long noncoding RNA interaction in response to salinity stress in soybean.BMC genomics · 2022Article
- Comparative analysis of physiological variations and genetic architecture for cold stress response in soybean germplasm.Frontiers in plant science · 2022Article
- Genome-wide identification of soybean microRNA responsive to soybean cyst nematodes infection by deep sequencing.BMC genomics · 2017Article
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Authors and funding
13 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plant microRNAs are small non-coding, endogenic RNA molecule (containing 20-24 nucleotides) produced from miRNA precursors (pri-miRNA and pre-miRNA). Evidence suggests that up and down regulation of the miRNA targets the mRNA genes involved in resistance against biotic and abiotic stresses. Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) is a powerful technique to analyze variations in mRNA levels. Normalizing the data using reference genes is essential for the analysis of reliable RT-qPCR data. In this study, two groups of candidate reference mRNAs and miRNAs in soybean leaves and roots treated with various abiotic stresses (PEG-simulated drought, salinity, alkalinity, salinity+alkalinity, and abscisic acid) were analyzed by RT-qPCR. We analyzed the most appropriate reference mRNA/miRNAs using the geNorm, NormFinder, and BestKeeper algorithms. According to the results, Act and EF1b were the most suitable reference mRNAs in leaf and root samples, for mRNA and miRNA precursor data normalization. The most suitable reference miRNAs found in leaf and root samples were 166a and 167a for mature miRNA data normalization. Hence the best combinations of reference mRNAs for mRNA and miRNA precursor data normalization were EF1a + Act or EF1b + Act in leaf samples, and EF1a + EF1b or 60s + EF1b in root samples. For mature miRNA data normalization, the most suitable combinations of reference miRNAs were 166a + 167d in leaf samples, and 171a + 156a or 167a + 171a in root samples. We identified potential reference mRNA/miRNAs for accurate RT-qPCR data normalization for mature miRNA, miRNA precursors, and their targeted mRNAs. Our results promote miRNA-based studies on soybean plants exposed to abiotic stress conditions.
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