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. 2023 Sep 12:14:1227507.
doi: 10.3389/fpls.2023.1227507. eCollection 2023.

Analysis of the response mechanisms of Pinellia ternata to terahertz wave stresses using transcriptome and metabolic data

Affiliations

Analysis of the response mechanisms of Pinellia ternata to terahertz wave stresses using transcriptome and metabolic data

Dongdong Wang et al. Front Plant Sci. .

Abstract

Pinellia ternata (Thunb.) Breit. (Araceae), a significant medicinal plant, has been used to treat various diseases for centuries. Terahertz radiation (THZ) is located between microwaves and infrared rays on the electromagnetic spectrum. THZ possesses low single-photon energy and a spectral fingerprint, but its effects on plant growth have not yet been investigated. The study's primary objective was to examine the transcriptome and metabolome databases of the SY line to provide a new perspective for identifying genes associated with resistance and growth promotion and comprehending the underlying molecular mechanism. Variations in the biological characteristics of P. ternata grown under control and experimental conditions were analyzed to determine the effect of THZ. Compared with the control group, phenotypic variables such as leaf length, petiole length, number of leaves, leaf petiole diameter, and proliferation coefficient exhibited significant differences. P. ternata response to THZ was analyzed regarding the effects of various coercions on root exudation. The experimental group contained considerably more sugar alcohol than the control group. The transcriptome analysis revealed 1,695 differentially expressed genes (DEGs), including 509 upregulated and 1,186 downregulated genes. In the KEGG-enriched plant hormone signaling pathway, there were 19 differentially expressed genes, 13 of which were downregulated and six of which were upregulated. In the metabolomic analysis, approximately 416 metabolites were uncovered. There were 112 DEMs that were downregulated, whereas 148 were upregulated. The P. ternata leaves displayed significant differences in phytohormone metabolites, specifically in brassinolide (BR) and abscisic acid (ABA). The rise in BR triggers alterations in internal plant hormones, resulting in faster growth and development of P. ternata. Our findings demonstrated a link between THZ and several metabolic pathway processes, which will enhance our understanding of P. ternata mechanisms.

Keywords: Pinellia ternata; metabonomic; plant hormones; terahertz; transcriptome.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Schematic diagram of growth interactions of THZ of P. ternata.
Figure 2
Figure 2
Plots of the effect of THZ growth promotion on TIBS Pinellia ternata SY clump seedlings. (A, C): experimental groups; (B, D): control groups.
Figure 3
Figure 3
(A) Plot of single P. ternata clumped seedlings grown in terahertz energy rings for 60 days. (B) Analysis of root exudates at CK and THZ treatment levels in Pinellia ternata. *** indicates a significant difference (P < 0.01). **** indicates a significant difference (P < 0.001).
Figure 4
Figure 4
(A) Length of transcripts and genes of P. ternata. (B) Unigene annotated summary tables in various databases. (C) Heat map of correlations between samples of the treatment and control groups of P. ternata. (D) Analysis of genetic significance of differences between the treatment and control groups of P. ternata. Volcano plot.
Figure 5
Figure 5
(A) Heat map clustering analysis of differentially expressed genes. (B) Histogram of GO functional annotation analysis of differential genes. (C) Histogram of functional annotation analysis of the differential gene KEGG. (D) Statistical chart of transcription factor families.
Figure 6
Figure 6
(A) Graph of GO enrichment analysis results. (B) KEGG enrichment analysis results graph. (C) GO-enriched string diagram. (D) KEGG-enriched string diagram.
Figure 7
Figure 7
(A) Analysis of differential gene expression in the P. ternata growth pathway. (B) Analysis of differential gene expression in the cytokinin pathway. (C) Analysis of differential gene expression in the ethylene pathway. (D) Differential gene expression analysis of the rapeseed in lactone pathway. Asterisks indicate significant differences; ****P<0.0001.
Figure 8
Figure 8
(A) MAPK signaling pathway: plant pathway. (B) Plant–pathogen interaction pathway.
Figure 9
Figure 9
(A) Differential metabolite volcano map. (B) HCA (hierarchical-clustering analysis) dendrogram. A PCA dendrogram (6PCs). 1 Control group of P. ternata. 2 THZ Experimental group of P. ternata. Each point in the graph represents a metabolite. The horizontal coordinates represent the log2 value of the multiple of the quantitative difference between the two samples for a metabolite; the vertical coordinates indicate the log10 value of the P value. The higher the absolute value of the horizontal coordinate, the more significant the difference in expression multiplicity between the two samples for a metabolite; the larger the vertical coordinate value, the more significant the differential expression and the more reliable the differentially expressed metabolites obtained from the screening. The red points in the graph represent an upregulated differential expression. The red dots represent upregulated differentially expressed metabolites, and the blue dots represent downregulated metabolites. The gray dots represent metabolites detected but did not meet the filtering parameters.
Figure 10
Figure 10
(A) Percentage of peak area of phytohormones in the control group. (B) Percentage of peak area of phytohormones in the THZ group. (C) Significant difference analysis of phytohormones in the experimental group THZ and control group CK. (D) Pathway of the effect of THZ on BR and IAA in of P. ternata. The indicated statistical differences are calculated using the one-way ANOVA and Bonferroni’s post-hoc test: *P < 0.05; ***P < 0.001; ****P < 0.0001.
Figure 11
Figure 11
(A) ABA biosynthetic pathway (Taylor et al., 2000). (B) Analysis of differential gene expression in the abscisic acid pathway.
Figure 12
Figure 12
The validation of transcriptome using qRT-PCR. (A) The 11 genes’ expression pattern of transcriptome and qRT-PCR. The columns in black and gray denote the expression value of transcriptome and qRT-PCR, respectively. The value represents the log2 fold change in the THZ group compared with the control group. (B) Correlation of transcriptome (x-axis) and qRT-PCR (y-axis) data. Values are means of three biological replicates—error bars indicate ± SE. Data are presented as means ± SE (n = 3). T-tests determined statistical significance.

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