Appropriate mowing can promote the growth of Anabasis aphylla through the auxin metabolism pathway

Abstract

Anabasis aphylla (A. aphylla), a species of the Amaranthaceae family, is widely distributed in northwestern China and has high pharmacological value and ecological functions. However, the growth characteristics are poorly understood, impeding its industrial development for biopesticide development. Here, we explored the regenerative capacity of A. aphylla. To this end, different lengths of the secondary branches of perennial branches were mowed at the end of March before sprouting. The four treatments were no mowing (M0) and mowing 1/3, 2/3, and the entire length of the secondary branches of perennial branches (M1-M3, respectively). Next, to evaluate the compensatory growth after mowing, new assimilate branches' related traits were recorded every 30 days, and the final biomass was recorded. The mowed plants showed a greater growth rate of assimilation branches than un-mowed plants. Additionally, with the increasing mowing degree, the growth rate and the final biomass of assimilation branches showed a decreasing trend, with the greatest growth rate and final biomass in response to M1. To evaluate the mechanism of the compensatory growth after mowing, a combination of dynamic (0, 1, 5, and 8 days after mowing) plant hormone-targeted metabolomics and transcriptomics was performed for the M0 and M1 treatment. Overall, 26 plant hormone metabolites were detected, 6 of which significantly increased after mowing compared with control: Indole-3-acetyl-L-valine methyl ester, Indole-3-carboxylic acid, Indole-3-carboxaldehyde, Gibberellin A24, Gibberellin A4, and cis (+)-12-oxo-phytodienoic acid. Additionally, 2,402 differentially expressed genes were detected between the mowed plants and controls. By combining clustering analysis based on expression trends after mowing and gene ontology analysis of each cluster, 18 genes related to auxin metabolism were identified, 6 of which were significantly related to auxin synthesis. Our findings suggest that appropriate mowing can promote A. aphylla growth, regulated by the auxin metabolic pathway, and lays the foundation for the development of the industrial value of A. aphylla.

Keywords: Anabasis aphylla; Auxin; Compensatory growth; Hormone metabolites; Metabolomics.

Fig. 1

Impact of mowing treatments on growth and development of assimilation branches in A. aphylla. Note: A) base diameter of assimilation branches; B) length of assimilation branches; C) biomass of assimilation branches. Different lowercase letters indicate significant differences (P < 0.05); *** indicates significant correlation at 0.001 level

Fig. 2

Analysis of target metabolite accumulation patterns of A) Indole-3-acetyl-L-valine methyl ester, B) Indole-3-carboxylic acid, C) Indole-3-carboxaldehyde, D) Gibberellin A24, E) Gibberellin A4, and F) cis (+)-12-Oxophytodienoic acid. Note: *, **, and *** indicate significance at P < 0.05, P < 0.01, and P < 0.001, respectively. The vertical line represents the standard deviation

Fig. 3

Gene functional annotation and histograms of Clusters of Orthologous Groups (COG). A eukaryotic orthologous groups (KOG); B) gene ontology (GO); and C) functional annotations

Fig. 4

Comparative transcriptomics analysis between assimilation branches of mowed and natural plants. Note: Assimilation branches of mowed and natural at days 0, 1, 5, and 8 (renamed T0, T1, T5, T8, and CK0, CK1, CK5, and CK8, respectively) were used for second-generation sequencing. In A–C, blue dots represent downregulated differentially expressed genes, red dots represent upregulated differentially expressed genes, and black dots represent non-differentially expressed genes. A Differentially expressed transcript volcano map of CK1 versus T1; B) differentially expressed transcript volcano map of CK5 versus T8; C) differentially expressed transcript volcano map of CK8 versus T8; D) Venn diagram showing the number of unshared and shared DEGs through paired comparison

Fig. 5

Classification and annotation of differentially expressed genes. Note: A) Mfuzz analysis of the 2,402 DEGs identified from the Venn diagram in Fig. 4. Seven clusters were identified based on the expression profiles of the DEGs, and heatmaps were generated for gene expression based on fragments per kilobase per million fragments; B) Line plots showing the transcription trends of seven gene clusters from hierarchical clustering and the number of genes in each cluster. Natural- and mowed-type transcription trends are represented by solid red and blue lines, respectively. C) GO enrichment word cloud for each cluster

Fig. 6

Correlation analysis of target metabolites and key genes. Note: The line connecting two parameters indicates significant correlations; line thickness represents the absolute magnitude of the correlation coefficient. The black line represents negative correlations. The circles represent nodes with several connections: red circles represent target metabolites, and blue circles represent key genes