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Systematic Identification of Housekeeping Genes Possibly Used as References in Caenorhabditis elegans by Large-Scale Data Integration

Authors
  • Tao, Jingxin1
  • Hao, Youjin1
  • Li, Xudong1
  • Yin, Huachun1
  • Nie, Xiner1
  • Zhang, Jie1
  • Xu, Boying1
  • Chen, Qiao
  • Li, Bo1
  • 1 (B.X.)
Type
Published Article
Journal
Cells
Publisher
MDPI AG
Publication Date
Mar 24, 2020
Volume
9
Issue
3
Identifiers
DOI: 10.3390/cells9030786
PMID: 32213971
PMCID: PMC7140892
Source
PubMed Central
Keywords
License
Green

Abstract

For accurate gene expression quantification, normalization of gene expression data against reliable reference genes is required. It is known that the expression levels of commonly used reference genes vary considerably under different experimental conditions, and therefore, their use for data normalization is limited. In this study, an unbiased identification of reference genes in Caenorhabditis elegans was performed based on 145 microarray datasets (2296 gene array samples) covering different developmental stages, different tissues, drug treatments, lifestyle, and various stresses. As a result, thirteen housekeeping genes ( rps-23 , rps-26 , rps-27 , rps-16 , rps-2 , rps-4 , rps-17 , rpl-24.1 , rpl-27 , rpl-33 , rpl-36 , rpl-35 , and rpl-15 ) with enhanced stability were comprehensively identified by using six popular normalization algorithms and RankAggreg method. Functional enrichment analysis revealed that these genes were significantly overrepresented in GO terms or KEGG pathways related to ribosomes. Validation analysis using recently published datasets revealed that the expressions of newly identified candidate reference genes were more stable than the commonly used reference genes. Based on the results, we recommended using rpl-33 and rps-26 as the optimal reference genes for microarray and rps-2 and rps-4 for RNA-sequencing data validation. More importantly, the most stable rps-23 should be a promising reference gene for both data types. This study, for the first time, successfully displays a large-scale microarray data driven genome-wide identification of stable reference genes for normalizing gene expression data and provides a potential guideline on the selection of universal internal reference genes in C. elegans , for quantitative gene expression analysis.

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