Systematic Identification and Validation of Housekeeping and Tissue-Specific Genes in Allotetraploid Chenopodium quinoa

管家基因 生物 基因 候选基因 藜藜 参考基因 家务 遗传学 基因表达 植物
作者
Bing He,Hui Chen,Pibiao Shi,Fengqin Hu,Wenjing Song,Lin Meng,Yuanda Lv
出处
期刊:Horticulturae [Multidisciplinary Digital Publishing Institute]
卷期号:7 (8): 235-235 被引量:3
标识
DOI:10.3390/horticulturae7080235
摘要

Quinoa is a gluten-free food crop that contains all the essential amino acids and vitamins. The selection of proper housekeeping and tissue-specific genes is the crucial prerequisite for gene expression analysis using the common approach, real-time quantitative PCR (RT-qPCR). In this study, we identified 40 novel candidate housekeeping genes by the minimum transcript per million (TPM), coefficient of variation (CV) and maximum fold change (MFC) methods and 19 candidate tissue-specific genes by the co-expression network method based on an RNA-seq dataset that included 53 stem, leaf, flower and seed samples, as well as additional shoot and root samples under different stresses. The expression stability of 12 housekeeping and tissue-specific genes, as well as that of another two traditionally used housekeeping genes, was further evaluated using qPCR and ranked using NormFinder, BestKeeper and the comparative delta-Ct method. The results demonstrated that MIF, RGGA, VATE and UBA2B were ranked as the top four most stable candidate housekeeping genes. qPCR analysis also revealed three leaf-specific genes and five root-specific genes, but no stem-specific gene was identified. Gene Ontology (GO) enrichment analysis identified that housekeeping genes were mainly enriched in the small molecule metabolic process, organonitrogen compound metabolic process, NAD binding and ligase activity. In addition, tissue-specific genes are closely associated with the major functions of a specific tissue. Specifically, GO terms “photosynthesis” and “thylakoid” were most significantly overrepresented in candidate leaf-specific genes. The novel housekeeping and tissue-specific genes in our study will enable better normalization and quantification of transcript levels in quinoa.
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