Dynamic formation and transcriptional regulation mediated by phytohormones during chalkiness formation in rice

Abstract

Background: Rice (Oryza sativa L.) Chalkiness, the opaque part in the kernel endosperm formed by loosely piled starch and protein bodies. Chalkiness is a complex quantitative trait regulated by multiple genes and various environmental factors. Phytohormones play important roles in the regulation of chalkiness formation but the underlying molecular mechanism is still unclear at present.

Results: In this research, Xiangzaoxian24 (X24, pure line of indica rice with high-chalkiness) and its origin parents Xiangzaoxian11 (X11, female parent, pure line of indica rice with high-chalkiness) and Xiangzaoxian7 (X7, male parent, pure line of indica rice with low-chalkiness) were used as materials. The phenotype, physiological and biochemical traits combined with transcriptome analysis were conducted to illustrate the dynamic process and transcriptional regulation of rice chalkiness formation. Impressively, phytohormonal contents and multiple phytohormonal signals were significantly different in chalky caryopsis, suggesting the involvement of phytohormones, particularly ABA and auxin, in the regulation of rice chalkiness formation, through the interaction of multiple transcription factors and their downstream regulators.

Conclusion: These results indicated that chalkiness formation is a dynamic process associated with multiple genes, forming a complex regulatory network in which phytohormones play important roles. These results provided informative clues for illustrating the regulatory mechanisms of chalkiness formation in rice.

Keywords: Chalkiness; Dynamic formation; Phytohormones; Rice; Transcriptional regulation.

Fig. 1

The phenotype of mature grains (A), chalkiness trait (B), chalky rice rate (C), chalkiness degree (D), grain length (E), grain width (F) and 1000-grains weight (G) of X11, X7 and X24. Data shown as means ± SD of three biological replicates (n = 30). Asterisks indicate a significant difference based on a Dunnett’s test. * significant difference at 5% level (P < 0.05); ** significant difference at 1% level (P < 0.01)

Fig. 2

The dynamics of grain filling and chalkiness formation traits (A and B). The starch granules in endosperms examined by SEM at 8 DAH, 12 DAH, 16 DAH, 20 DAH and 24 DAH (Scale bars: 10 μm) (C)

Fig. 3

DEGs obtained by X11 vs. X7, X24 vs. X7 and X11 vs. X24 at (A) 8 DAH, (B) 12 DAH and (C) 16 DAH. Red column represents up-regulated of genes, blue column represents down-regulated of genes. Venn diagram of DEGs among X11, X7 and X24 at (D) 8 DAH, (E) 12 DAH and (F) 16 DAH. DEGs were differentially expressed with statistical significance (P-value ≤ 0.05 and |Log₂foldchange(FC)|≥ 1)

Fig. 4

Comparison of Gene Ontology (GO) classifications of DEGs at (A) 8 DAH, (B) 12 DAH and (C) 16 DAH. (D) KEGG pathway assignments of DEGs at 8 DAH, 12 DAH and 16 DAH, the top 10 categories are shown. DEGs were differentially expressed with statistical significance (P-value ≤ 0.05 and |Log₂foldchange(FC)|≥ 1)

Fig. 5

A The starch content in of mature grain in X11, X7 and X24. B The starch and soluble protein content in of mature grain in X11, X7 and X24, data shown as means ± SD of three biological replicates, asterisks indicate a significant difference based on a Dunnett’s test. * significant difference at 5% level (P < 0.05); ** significant difference at 1% level (P < 0.01). C DEG_HL involved in starch and sucrose metabolism at 8 DAH, 12 DAH and 16 DAH, which are shown as log₂Foldchange levels. DEG_HL were differentially expressed with statistical significance (P-value ≤ 0.05 and |Log₂foldchange(FC)|≥ 1)

Fig. 6

The phytohormonal contents (A) ABA, (B) IAA and (C) ZR at 8 DAH, 12 DAH and 16 DAH, data shown as means ± SD of three biological replicates, asterisks indicate a significant difference based on a Dunnett’s test. * significant difference at 5% level (P < 0.05); ** significant difference at 1% level (P < 0.01). (D) Expression patterns of phytohormone-related DEG_HL at 8 DAH, 12 DAH and 16 DAH, which are shown as log₂Foldchange levels. DEG_HL were differentially expressed with statistical significance (P-value ≤ 0.05 and |Log₂foldchange(FC)|≥ 1)

Fig. 7

Expression patterns of DEG_HL encoding TFs obtained at 8 DAH, 12 DAH and 16 DAH, which are shown as log₂Foldchange levels. DEG_HL were differentially expressed with statistical significance (P-value ≤ 0.05 and |Log₂foldchange(FC)|≥ 1)

Fig. 8

Expression patterns of DEG_HL encoding PKs obtained at 8 DAH, 12 DAH and 16 DAH, which are shown as log₂Foldchange levels. DEG_HL were differentially expressed with statistical significance (P-value ≤ 0.05 and |Log₂foldchange(FC)|≥ 1)

Fig. 9

Expression patterns of 12 DEG_HL confirmed by real-time PCR, which are shown as log₂Foldchange levels. DEG_HL were differentially expressed with statistical significance (P-value ≤ 0.05 and |Log₂foldchange(FC)|≥ 1). Data shown as means ± SD of three biological replicates. Asterisks indicate a significant difference based on a Dunnett’s test. * significant difference at 5% level (P < 0.05); ** significant difference at 1% level (P < 0.01)