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Transcriptome-wide characterization of candidate genes for improving the water use efficiency of energy crops grown on semiarid land.

Fan Y, Wang Q, Kang L, Liu W, Xu Q, Xing S, Tao C, Song Z, Zhu C, Lin C, Yan J, Li J, Sang T - J. Exp. Bot. (2015)

Bottom Line: The field measurements showed that WUE of M. lutarioriparius in the semiarid location was significantly higher than that in the wet location.It was also found that the relatively high expression variation of the WUE-related genes was affected to a larger extent by environment than by genetic variation.The study demonstrates that transcriptome-wide correlation between physiological phenotypes and expression levels offers an effective means for identifying candidate genes involved in the adaptation to environmental changes.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Plant Resources and Beijing Botanical Garden, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China University of Chinese Academy of Sciences, Beijing 100049, China.

No MeSH data available.


Expression level correlation between RNA-Seq and qPCR. Negative correlation between FPKM values of RNA-Seq and average Ct values of qPCR indicate a consistent estimation of the relative expression levels between the two methods. The graphs (A)–(H) represent the genes: MluLR17108 (psbH), MluLR17433 (psbI), MluLR14810 (psbK), MluLR17106 (ycf4), MluLR17402 (petE), MluLR5294 (OAT4), MluLR4566 (RH57), MluLR17105 (rps4), respectively. The R in the graphs indicates the correlation coefficient. ** represents the significant level (P <0.01, Spearman’s rank correlation test). Sequences of PCR primers are given in Table 4. Red and blue dots represent individuals sampled from JH and QG, respectively.
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Figure 5: Expression level correlation between RNA-Seq and qPCR. Negative correlation between FPKM values of RNA-Seq and average Ct values of qPCR indicate a consistent estimation of the relative expression levels between the two methods. The graphs (A)–(H) represent the genes: MluLR17108 (psbH), MluLR17433 (psbI), MluLR14810 (psbK), MluLR17106 (ycf4), MluLR17402 (petE), MluLR5294 (OAT4), MluLR4566 (RH57), MluLR17105 (rps4), respectively. The R in the graphs indicates the correlation coefficient. ** represents the significant level (P <0.01, Spearman’s rank correlation test). Sequences of PCR primers are given in Table 4. Red and blue dots represent individuals sampled from JH and QG, respectively.

Mentions: Quantitative real-time PCR was performed by using 15 randomly sampled individuals from each field site for eight genes, psbH, psbI, psbK, ycf4, petE, OAT4, RH57, and rps4 (Table 4). The relative expression levels determined by the two methods were significantly correlated for all eight genes (Spearman’s rank correlation test, P <0.01; Fig. 5).


Transcriptome-wide characterization of candidate genes for improving the water use efficiency of energy crops grown on semiarid land.

Fan Y, Wang Q, Kang L, Liu W, Xu Q, Xing S, Tao C, Song Z, Zhu C, Lin C, Yan J, Li J, Sang T - J. Exp. Bot. (2015)

Expression level correlation between RNA-Seq and qPCR. Negative correlation between FPKM values of RNA-Seq and average Ct values of qPCR indicate a consistent estimation of the relative expression levels between the two methods. The graphs (A)–(H) represent the genes: MluLR17108 (psbH), MluLR17433 (psbI), MluLR14810 (psbK), MluLR17106 (ycf4), MluLR17402 (petE), MluLR5294 (OAT4), MluLR4566 (RH57), MluLR17105 (rps4), respectively. The R in the graphs indicates the correlation coefficient. ** represents the significant level (P <0.01, Spearman’s rank correlation test). Sequences of PCR primers are given in Table 4. Red and blue dots represent individuals sampled from JH and QG, respectively.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4588889&req=5

Figure 5: Expression level correlation between RNA-Seq and qPCR. Negative correlation between FPKM values of RNA-Seq and average Ct values of qPCR indicate a consistent estimation of the relative expression levels between the two methods. The graphs (A)–(H) represent the genes: MluLR17108 (psbH), MluLR17433 (psbI), MluLR14810 (psbK), MluLR17106 (ycf4), MluLR17402 (petE), MluLR5294 (OAT4), MluLR4566 (RH57), MluLR17105 (rps4), respectively. The R in the graphs indicates the correlation coefficient. ** represents the significant level (P <0.01, Spearman’s rank correlation test). Sequences of PCR primers are given in Table 4. Red and blue dots represent individuals sampled from JH and QG, respectively.
Mentions: Quantitative real-time PCR was performed by using 15 randomly sampled individuals from each field site for eight genes, psbH, psbI, psbK, ycf4, petE, OAT4, RH57, and rps4 (Table 4). The relative expression levels determined by the two methods were significantly correlated for all eight genes (Spearman’s rank correlation test, P <0.01; Fig. 5).

Bottom Line: The field measurements showed that WUE of M. lutarioriparius in the semiarid location was significantly higher than that in the wet location.It was also found that the relatively high expression variation of the WUE-related genes was affected to a larger extent by environment than by genetic variation.The study demonstrates that transcriptome-wide correlation between physiological phenotypes and expression levels offers an effective means for identifying candidate genes involved in the adaptation to environmental changes.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Plant Resources and Beijing Botanical Garden, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China University of Chinese Academy of Sciences, Beijing 100049, China.

No MeSH data available.