Transcriptomic analysis reveals responses to Cycloastragenol in Arabidopsis thaliana

PLC1 mediated Cycloastragenol-induced stomatal movement by regulating the production of NO in Arabidopsis thaliana

BACKGROUND

Astragalus grows mainly in drought areas. Cycloastragenol (CAG) is a tetracyclic triterpenoid allelochemical extracted from traditional Chinese medicine Astragalus root. Phospholipase C (PLC) and Gα-submit of the heterotrimeric G-protein (GPA1) are involved in many biotic or abiotic stresses. Nitric oxide (NO) is a crucial gas signal molecule in plants.

RESULTS

In this study, using the seedlings of Arabidopsis thaliana (A. thaliana), the results showed that low concentrations of CAG induced stomatal closure, and high concentrations inhibited stomatal closure. 30 µmol·L-1 CAG significantly increased the relative expression levels of PLC1 and GPA1 and the activities of PLC and GTP hydrolysis. The stomatal aperture of plc1, gpa1, and plc1/gpa1 was higher than that of WT under CAG treatment. CAG increased the fluorescence intensity of NO in guard cells. Exogenous application of c-PTIO to WT significantly induced stomatal aperture under CAG treatment. CAG significantly increased the relative expression levels of NIA1 and NOA1. Mutants of noa1, nia1, and nia2 showed that NO production was mainly from NOA1 and NIA1 by CAG treatment. The fluorescence intensity of NO in guard cells of plc1, gpa1, and plc1/gpa1 was lower than WT, indicating that PLC1 and GPA1 were involved in the NO production in guard cells. There was no significant difference in the gene expression of PLC1 in WT, nia1, and noa1 under CAG treatment. The gene expression levels of NIA1 and NOA1 in plc1, gpa1, and plc1/gpa1 were significantly lower than WT, indicating that PLC1 and GPA1 were positively regulating NO production by regulating the expression of NIA1 and NOA1 under CAG treatment.

CONCLUSIONS

These results suggested that the NO accumulation was essential to induce stomatal closure under CAG treatment, and GPA1 and PLC1 acted upstream of NO.

PLDδ, auxin, and H2O2 mediated the allelopathic effect of cycloastragenol on root growth in Arabidopsis

Cycloastragenol (CAG) is a tetra-cyclic triterpenoid allelochemical. It has been widely studied in animals but rarely in plants. Here, we reported that a model allelochemical CAG inhibited primary root elongation of Arabidopsis by reducing the sizes of both the meristem and elongation zones. Phospholipase Dδ(PLDδ), hydrogen peroxide (H₂O₂), and auxin affected this process. After treatment with CAG, the expression of PLDδ and the activity of the Phospholipase D(PLD) enzyme increased in WT. Mutants analysis demonstrated that PLDδ negatively regulated the primary root elongation by CAG treatment. CAG treatment stimulated the accumulation of H₂O₂ in roots. The production of H₂O₂ was derived from cell wall peroxidase. Mutants analysis showed that PLDδ positively regulated the production of H₂O₂ by CAG treatment. CAG also decreased auxin content in the root tip by affecting the expression of auxin synthesis-related genes. PLDδ was involved in the auxin reduction mediated by CAG, but H₂O₂ did not participate in this process. In conclusion, PLDδ, auxin, and H₂O₂ mediated the inhibition of primary root growth by CAG in Arabidopsis.

Modeling and cleaning RNA-seq data significantly improve detection of differentially expressed genes

Background

RNA-seq has become a standard technology to quantify mRNA. The measured values usually vary by several orders of magnitude, and while the detection of differences at high values is statistically well grounded, the significance of the differences for rare mRNAs can be weakened by the presence of biological and technical noise.

Results

We have developed a method for cleaning RNA-seq data, which improves the detection of differentially expressed genes and specifically genes with low to moderate transcription. Using a data modeling approach, parameters of randomly distributed mRNA counts are identified and reads, most probably originating from technical noise, are removed. We demonstrate that the removal of this random component leads to the significant increase in the number of detected differentially expressed genes, more significant pvalues and no bias towards low-count genes.

Conclusion

Application of RNAdeNoise to our RNA-seq data on polysome profiling and several published RNA-seq datasets reveals its suitability for different organisms and sequencing technologies such as Illumina and BGI, shows improved detection of differentially expressed genes, and excludes the subjective setting of thresholds for minimal RNA counts. The program, RNA-seq data, resulted gene lists and examples of use are in the supplementary data and at https://github.com/Deyneko/RNAdeNoise.