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Reference gene selection for cross-species and cross-ploidy level comparisons in Chrysanthemum spp.

Wang H, Chen S, Jiang J, Zhang F, Chen F - Sci Rep (2015)

Bottom Line: Here, ten candidate reference genes are compared in the context of gene transcription in the genus Chrysanthemum.EF-1α and PGK (phosphoglycerate kinase) was the best combination for the complete set of four taxa.These results suggest that when making cross-species comparison of transcript abundance involving different ploidy levels, care needs to be taken in the selection of reference gene(s).

View Article: PubMed Central - PubMed

Affiliation: 1] College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China [2] Jiangsu Province Engineering Lab for Modern Facility Agriculture Technology &Equipment, Nanjing 210095, China.

ABSTRACT
The establishment of a (set of) stably expressed reference gene(s) is required to normalize transcription data. Polyploidy is very common in the plant kingdom, but it is not necessarily the case that a reference gene which works well at the diploid level will also work well at the polyploid level. Here, ten candidate reference genes are compared in the context of gene transcription in the genus Chrysanthemum. The robustness of some, but not all, of these was shown to be high across ploidy levels. MTP (metalloprotease) and ACTIN (actin) were the most stable in diploid and tetraploid C. nankingense, while PSAA (photosynthesis-related plastid gene representing photosystem I) and EF-1α (elongation factor-1α) were the most stable in tetraploid and hexaploid C. zawadskii. EF-1α and PGK (phosphoglycerate kinase) was the best combination for the complete set of four taxa. These results suggest that when making cross-species comparison of transcript abundance involving different ploidy levels, care needs to be taken in the selection of reference gene(s).

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Relative transcript abundances of ACTIN, EF-1α and PGK across species and ploidy levels.Normalization performed using single either stable or unstable reference genes in the contrasts. (a) Relative abundance of ACTIN transcript in 2xvs 4xC. nankingense (reference genes PSAA/MTP), (b) relative abundance of EF-1α transcript in 4xvs 6xC. zawadskii (SKIP16/PSAA), and (c) relative abundance of PGK transcript between all four taxa (PSAA/EF-1α).
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f6: Relative transcript abundances of ACTIN, EF-1α and PGK across species and ploidy levels.Normalization performed using single either stable or unstable reference genes in the contrasts. (a) Relative abundance of ACTIN transcript in 2xvs 4xC. nankingense (reference genes PSAA/MTP), (b) relative abundance of EF-1α transcript in 4xvs 6xC. zawadskii (SKIP16/PSAA), and (c) relative abundance of PGK transcript between all four taxa (PSAA/EF-1α).

Mentions: A comparison was made between transcript abundances in 2xC. nankingense as estimated from qPCR (2−ΔΔCt method24) with those derived from RNA-Seq analysis (NCBI SRA accession: SRP049642, be measured by reads per kilobase of exon model per million mapped reads, RPKM25). Two methods produced the comparable transcript abundances, especially for ACTIN, EF-1α and GAPDH (Fig. 5). Hence, the Ct value could represent the relative transcript abundance in some extent. Indubitably, the transcript abundance (as well as the Ct) of ACTIN, EF-1α and PGK was similar (the second stable) in the contrasts 2x and 4xC. nankingense, 4x and 6xC. zawadskii and all four taxa, respectively (Fig. 2). To test the importance of the choice of reference gene(s), the relative abundances of ACTIN, EF-1α and PGK transcript were quantified, based on normalization carried out with either a stable or an unstable reference gene. When normalized on the basis of the three most stable reference genes, transcript abundance proved to be stable in the contrasts 2xvs 4xC. nankingense (MTP), 4xvs 6xC. zawadskii (PSAA) and all four taxa (EF-1α); conversely, when normalized on the basis of the least stable reference genes, this was no longer the case (Fig. 6).


Reference gene selection for cross-species and cross-ploidy level comparisons in Chrysanthemum spp.

Wang H, Chen S, Jiang J, Zhang F, Chen F - Sci Rep (2015)

Relative transcript abundances of ACTIN, EF-1α and PGK across species and ploidy levels.Normalization performed using single either stable or unstable reference genes in the contrasts. (a) Relative abundance of ACTIN transcript in 2xvs 4xC. nankingense (reference genes PSAA/MTP), (b) relative abundance of EF-1α transcript in 4xvs 6xC. zawadskii (SKIP16/PSAA), and (c) relative abundance of PGK transcript between all four taxa (PSAA/EF-1α).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4308696&req=5

f6: Relative transcript abundances of ACTIN, EF-1α and PGK across species and ploidy levels.Normalization performed using single either stable or unstable reference genes in the contrasts. (a) Relative abundance of ACTIN transcript in 2xvs 4xC. nankingense (reference genes PSAA/MTP), (b) relative abundance of EF-1α transcript in 4xvs 6xC. zawadskii (SKIP16/PSAA), and (c) relative abundance of PGK transcript between all four taxa (PSAA/EF-1α).
Mentions: A comparison was made between transcript abundances in 2xC. nankingense as estimated from qPCR (2−ΔΔCt method24) with those derived from RNA-Seq analysis (NCBI SRA accession: SRP049642, be measured by reads per kilobase of exon model per million mapped reads, RPKM25). Two methods produced the comparable transcript abundances, especially for ACTIN, EF-1α and GAPDH (Fig. 5). Hence, the Ct value could represent the relative transcript abundance in some extent. Indubitably, the transcript abundance (as well as the Ct) of ACTIN, EF-1α and PGK was similar (the second stable) in the contrasts 2x and 4xC. nankingense, 4x and 6xC. zawadskii and all four taxa, respectively (Fig. 2). To test the importance of the choice of reference gene(s), the relative abundances of ACTIN, EF-1α and PGK transcript were quantified, based on normalization carried out with either a stable or an unstable reference gene. When normalized on the basis of the three most stable reference genes, transcript abundance proved to be stable in the contrasts 2xvs 4xC. nankingense (MTP), 4xvs 6xC. zawadskii (PSAA) and all four taxa (EF-1α); conversely, when normalized on the basis of the least stable reference genes, this was no longer the case (Fig. 6).

Bottom Line: Here, ten candidate reference genes are compared in the context of gene transcription in the genus Chrysanthemum.EF-1α and PGK (phosphoglycerate kinase) was the best combination for the complete set of four taxa.These results suggest that when making cross-species comparison of transcript abundance involving different ploidy levels, care needs to be taken in the selection of reference gene(s).

View Article: PubMed Central - PubMed

Affiliation: 1] College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China [2] Jiangsu Province Engineering Lab for Modern Facility Agriculture Technology &Equipment, Nanjing 210095, China.

ABSTRACT
The establishment of a (set of) stably expressed reference gene(s) is required to normalize transcription data. Polyploidy is very common in the plant kingdom, but it is not necessarily the case that a reference gene which works well at the diploid level will also work well at the polyploid level. Here, ten candidate reference genes are compared in the context of gene transcription in the genus Chrysanthemum. The robustness of some, but not all, of these was shown to be high across ploidy levels. MTP (metalloprotease) and ACTIN (actin) were the most stable in diploid and tetraploid C. nankingense, while PSAA (photosynthesis-related plastid gene representing photosystem I) and EF-1α (elongation factor-1α) were the most stable in tetraploid and hexaploid C. zawadskii. EF-1α and PGK (phosphoglycerate kinase) was the best combination for the complete set of four taxa. These results suggest that when making cross-species comparison of transcript abundance involving different ploidy levels, care needs to be taken in the selection of reference gene(s).

Show MeSH