Silica nanoparticles promote wheat growth by mediating hormones and sugar metabolism

Abstract

Background: Silica nanoparticles (SiNPs) have been demonstrated to have beneficial effects on plant growth and development, especially under biotic and abiotic stresses. However, the mechanisms of SiNPs-mediated plant growth strengthening are still unclear, especially under field condition. In this study, we evaluated the effect of SiNPs on the growth and sugar and hormone metabolisms of wheat in the field.

Results: SiNPs increased tillers and elongated internodes by 66.7% and 27.4%, respectively, resulting in a larger biomass. SiNPs can increase the net photosynthetic rate by increasing total chlorophyll contents. We speculated that SiNPs can regulate the growth of leaves and stems, partly by regulating the metabolisms of plant hormones and soluble sugar. Specifically, SiNPs can increase auxin (IAA) and fructose contents, which can promote wheat growth directly or indirectly. Furthermore, SiNPs increased the expression levels of key pathway genes related to soluble sugars (SPS, SUS, and α-glucosidase), chlorophyll (CHLH, CAO, and POR), IAA (TIR1), and abscisic acid (ABA) (PYR/PYL, PP2C, SnRK2, and ABF), whereas the expression levels of genes related to CTKs (IPT) was decreased after SiNPs treatment.

Conclusions: This study shows that SiNPs can promote wheat growth and provides a theoretical foundation for the application of SiNPs in field conditions.

Keywords: Chlorophyll; Growth; Photosynthesis; Plant hormone; Soluble sugar; Wheat.

Fig. 1

The SEM (A), TEM (B) micrographs and FTIR spectra (C) of SiNPs used in this study. (D) Deposition of silica nanoparticles on wheat leaves (E) and leaf sheaths (F) respectively. The white arrow represents the deposition position of SiNPs

Fig. 2

Effects of SiNP200 on wheat at jointing stage. Shoot length (A), stem length (B), and leaf length and width (C). Diagram of tillers (D), jointing (E), dry weight (F), fresh weight (G), shoot length (H), leaf length (I), leaf width (J), and leaf area (K). Values are means ± SE; n = 20 plants. **indicate the significant difference between the control and SiNP200 treatment at p < 0.01 (t-test)

Fig. 3

Effects of SiNP200 on the contents of chlorophyll a (A), chlorophyll b (B), carotenoid (C), total chlorophyll (D), and net photosynthetic rate (E) analysis. Values are means ± SE; n = 15. **indicate the significant difference between the control and SiNP200 treatment at p < 0.01 (t-test)

Fig. 4

Effects of SiNP200 on the contents of CTK (A, E), GA₃ (B, F), IAA (C, G) and ABA (D, H). (A–D) is wheat leaf sample; (E–H) is wheat stem sample. Values are means ± SE; n = 10. *, ** indicated that the significant difference between the control and SiNP200 treatment at p < 0.05 and p < 0.01, respectively (t-test)

Fig. 5

Effects of SiNP200 on the contents of glucose (A, D), sucrose (B, E), and fructose (C, F). (A–C) is wheat leaf sample; (D–F) is wheat stem sample. Values are means ± SE; n = 10. **indicate the significant difference between the control and SiNP200 treatment at p < 0.01 (t-test)

Fig. 6

Soluble sugar (A), chlorophyll (B), CTKs (C), IAA (D), and ABA (E) synthesis and hydrolysis pathway and relative expression levels of related genes in wheat leaves. Values are mean ± SE of 3 replicates. **indicate the significant difference between the control and SiNP200 treatment at p < 0.01 (t-test)

Fig. 7

A possible model of the effect of SiNP200 treatment on wheat growth. Red arrows represent upregulation, and dashed lines represent possible but unconfirmed routes

Fig. 8

Correlation analysis and principal component analysis. A The heat map shows the correlation analysis between the observed parameters processed by SiNP200. B The score plot and loading plot shows the PCA analysis of the observation parameters processed by SiNP200. “L” represents leaf and “S” represents stem. * and ** denote correlation coefficients that are significant at p < 0.05 and 0.01 level, respectively (t-test)