Limits...
Natural variation in cross-talk between glucosinolates and onset of flowering in Arabidopsis.

Jensen LM, Jepsen HS, Halkier BA, Kliebenstein DJ, Burow M - Front Plant Sci (2015)

Bottom Line: We have introduced the two highly similar enzymes into two different AOP () accessions, Col-0 and Cph-0, and found that the genes differ in their ability to affect glucosinolate levels and flowering time across the accessions.This indicated that the different glucosinolates produced by AOP2 and AOP3 serve specific regulatory roles in controlling these phenotypes.This variation likely reflects an adaptation to survival in different environments.

View Article: PubMed Central - PubMed

Affiliation: Department of Plant and Environmental Sciences, Faculty of Science, DNRF Center DynaMo, University of Copenhagen Frederiksberg, Denmark ; Department of Plant and Environmental Sciences, Faculty of Science, Copenhagen Plant Science Centre, University of Copenhagen Frederiksberg, Denmark.

ABSTRACT
Naturally variable regulatory networks control different biological processes including reproduction and defense. This variation within regulatory networks enables plants to optimize defense and reproduction in different environments. In this study we investigate the ability of two enzyme-encoding genes in the glucosinolate pathway, AOP2 and AOP3, to affect glucosinolate accumulation and flowering time. We have introduced the two highly similar enzymes into two different AOP () accessions, Col-0 and Cph-0, and found that the genes differ in their ability to affect glucosinolate levels and flowering time across the accessions. This indicated that the different glucosinolates produced by AOP2 and AOP3 serve specific regulatory roles in controlling these phenotypes. While the changes in glucosinolate levels were similar in both accessions, the effect on flowering time was dependent on the genetic background pointing to natural variation in cross-talk between defense chemistry and onset of flowering. This variation likely reflects an adaptation to survival in different environments.

No MeSH data available.


Related in: MedlinePlus

Effects of AOP2 and AOP3 on flowering. (A) Average (+ standard error) of flowering time in days relative to Col-0 WT (28.4 days ± 5.3). Black Col-0 WT, n = 110, light gray Col-0 AOP2, n = 50, (2 independent insertion lines), and dark gray Col-0 AOP3, n = 32, (1 line). ANOVA with nesting and experiment interaction (min 2 repeats) shows that Col-0 AOP2 is significantly different from Col-0 WT and Col-0 AOP3, P < 0.001, whereas P = 0.45 for the Col-0 WT and Col-0 AOP3 comparison. (B) Flowering time relative to Cph-0 WT (41.6 days ±5.3). Cph-0 WT (black), n = 60, Cph-0 AOP2 (light gray), n = 73 (3 independent insertion lines), and Cph-0 AOP3 (dark gray), n = 60 (3 independent insertion lines). ANOVA with nesting of the different insertion lines and experiment interaction (two repeats) showed no significant difference between Cph-0 WT and the insertion lines; Cph-0 WT and Cph-0 AOP2 (P = 0.06) and Cph-0 WT and Cph-0 AOP3 (P = 0.22). Cph-0 AOP2 and Cph-0 AOP3 showed a significant difference (P < 0.01).
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4561820&req=5

Figure 5: Effects of AOP2 and AOP3 on flowering. (A) Average (+ standard error) of flowering time in days relative to Col-0 WT (28.4 days ± 5.3). Black Col-0 WT, n = 110, light gray Col-0 AOP2, n = 50, (2 independent insertion lines), and dark gray Col-0 AOP3, n = 32, (1 line). ANOVA with nesting and experiment interaction (min 2 repeats) shows that Col-0 AOP2 is significantly different from Col-0 WT and Col-0 AOP3, P < 0.001, whereas P = 0.45 for the Col-0 WT and Col-0 AOP3 comparison. (B) Flowering time relative to Cph-0 WT (41.6 days ±5.3). Cph-0 WT (black), n = 60, Cph-0 AOP2 (light gray), n = 73 (3 independent insertion lines), and Cph-0 AOP3 (dark gray), n = 60 (3 independent insertion lines). ANOVA with nesting of the different insertion lines and experiment interaction (two repeats) showed no significant difference between Cph-0 WT and the insertion lines; Cph-0 WT and Cph-0 AOP2 (P = 0.06) and Cph-0 WT and Cph-0 AOP3 (P = 0.22). Cph-0 AOP2 and Cph-0 AOP3 showed a significant difference (P < 0.01).

Mentions: The glucosinolate biosynthetic genes AOP2 and AOP3 represent candidate genes for the integration of defense with reproduction as they are associated with the control of flowering time in both the laboratory and the field (Atwell et al., 2010; Kerwin et al., 2011, 2015). To test the ability of AOP2 and AOP3 to link glucosinolates and flowering time in different backgrounds, we measured flowering time in all of our lines (Figure 5). AOP2 has been identified as a QTL for altering circadian clock parameters and thereby flowering time (Kerwin et al., 2011). Accordingly, introduction of a functional AOP2 into Col-0 under 16 h light delayed flowering time by several days (Figure 5A). AOP3 has been associated with natural variation in flowering time and gene expression level of the MADS-box transcription factor FLC (Flowering Locus C), which is one of the major determinants of flowering (Shindo et al., 2005; Atwell et al., 2010). Analysis of the Col-0 AOP3 line showed no significant difference between Col-0 WT and Col-0 AOP3 lines (Figure 5A). Thus, AOP2 but not AOP3 seems to influence onset of flowering in Col-0.


Natural variation in cross-talk between glucosinolates and onset of flowering in Arabidopsis.

Jensen LM, Jepsen HS, Halkier BA, Kliebenstein DJ, Burow M - Front Plant Sci (2015)

Effects of AOP2 and AOP3 on flowering. (A) Average (+ standard error) of flowering time in days relative to Col-0 WT (28.4 days ± 5.3). Black Col-0 WT, n = 110, light gray Col-0 AOP2, n = 50, (2 independent insertion lines), and dark gray Col-0 AOP3, n = 32, (1 line). ANOVA with nesting and experiment interaction (min 2 repeats) shows that Col-0 AOP2 is significantly different from Col-0 WT and Col-0 AOP3, P < 0.001, whereas P = 0.45 for the Col-0 WT and Col-0 AOP3 comparison. (B) Flowering time relative to Cph-0 WT (41.6 days ±5.3). Cph-0 WT (black), n = 60, Cph-0 AOP2 (light gray), n = 73 (3 independent insertion lines), and Cph-0 AOP3 (dark gray), n = 60 (3 independent insertion lines). ANOVA with nesting of the different insertion lines and experiment interaction (two repeats) showed no significant difference between Cph-0 WT and the insertion lines; Cph-0 WT and Cph-0 AOP2 (P = 0.06) and Cph-0 WT and Cph-0 AOP3 (P = 0.22). Cph-0 AOP2 and Cph-0 AOP3 showed a significant difference (P < 0.01).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4561820&req=5

Figure 5: Effects of AOP2 and AOP3 on flowering. (A) Average (+ standard error) of flowering time in days relative to Col-0 WT (28.4 days ± 5.3). Black Col-0 WT, n = 110, light gray Col-0 AOP2, n = 50, (2 independent insertion lines), and dark gray Col-0 AOP3, n = 32, (1 line). ANOVA with nesting and experiment interaction (min 2 repeats) shows that Col-0 AOP2 is significantly different from Col-0 WT and Col-0 AOP3, P < 0.001, whereas P = 0.45 for the Col-0 WT and Col-0 AOP3 comparison. (B) Flowering time relative to Cph-0 WT (41.6 days ±5.3). Cph-0 WT (black), n = 60, Cph-0 AOP2 (light gray), n = 73 (3 independent insertion lines), and Cph-0 AOP3 (dark gray), n = 60 (3 independent insertion lines). ANOVA with nesting of the different insertion lines and experiment interaction (two repeats) showed no significant difference between Cph-0 WT and the insertion lines; Cph-0 WT and Cph-0 AOP2 (P = 0.06) and Cph-0 WT and Cph-0 AOP3 (P = 0.22). Cph-0 AOP2 and Cph-0 AOP3 showed a significant difference (P < 0.01).
Mentions: The glucosinolate biosynthetic genes AOP2 and AOP3 represent candidate genes for the integration of defense with reproduction as they are associated with the control of flowering time in both the laboratory and the field (Atwell et al., 2010; Kerwin et al., 2011, 2015). To test the ability of AOP2 and AOP3 to link glucosinolates and flowering time in different backgrounds, we measured flowering time in all of our lines (Figure 5). AOP2 has been identified as a QTL for altering circadian clock parameters and thereby flowering time (Kerwin et al., 2011). Accordingly, introduction of a functional AOP2 into Col-0 under 16 h light delayed flowering time by several days (Figure 5A). AOP3 has been associated with natural variation in flowering time and gene expression level of the MADS-box transcription factor FLC (Flowering Locus C), which is one of the major determinants of flowering (Shindo et al., 2005; Atwell et al., 2010). Analysis of the Col-0 AOP3 line showed no significant difference between Col-0 WT and Col-0 AOP3 lines (Figure 5A). Thus, AOP2 but not AOP3 seems to influence onset of flowering in Col-0.

Bottom Line: We have introduced the two highly similar enzymes into two different AOP () accessions, Col-0 and Cph-0, and found that the genes differ in their ability to affect glucosinolate levels and flowering time across the accessions.This indicated that the different glucosinolates produced by AOP2 and AOP3 serve specific regulatory roles in controlling these phenotypes.This variation likely reflects an adaptation to survival in different environments.

View Article: PubMed Central - PubMed

Affiliation: Department of Plant and Environmental Sciences, Faculty of Science, DNRF Center DynaMo, University of Copenhagen Frederiksberg, Denmark ; Department of Plant and Environmental Sciences, Faculty of Science, Copenhagen Plant Science Centre, University of Copenhagen Frederiksberg, Denmark.

ABSTRACT
Naturally variable regulatory networks control different biological processes including reproduction and defense. This variation within regulatory networks enables plants to optimize defense and reproduction in different environments. In this study we investigate the ability of two enzyme-encoding genes in the glucosinolate pathway, AOP2 and AOP3, to affect glucosinolate accumulation and flowering time. We have introduced the two highly similar enzymes into two different AOP () accessions, Col-0 and Cph-0, and found that the genes differ in their ability to affect glucosinolate levels and flowering time across the accessions. This indicated that the different glucosinolates produced by AOP2 and AOP3 serve specific regulatory roles in controlling these phenotypes. While the changes in glucosinolate levels were similar in both accessions, the effect on flowering time was dependent on the genetic background pointing to natural variation in cross-talk between defense chemistry and onset of flowering. This variation likely reflects an adaptation to survival in different environments.

No MeSH data available.


Related in: MedlinePlus