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. 2025 Aug 9;28(9):113337.
doi: 10.1016/j.isci.2025.113337. eCollection 2025 Sep 19.

Proteomics and metabolomics analyses of tobacco protoplasts under Cd stress

Chun Wang  1   2 , Huina Zhou  2 , Niu Zhai  2 , Qiansi Chen  2 , Zefeng Li  2 , Chen Wang  2 , Qingxia Zheng  2 , Ang Li  1 , Qi Shen  1 , Weimin Shi  1 , Pingping Liu  2
Affiliations

Proteomics and metabolomics analyses of tobacco protoplasts under Cd stress

Chun Wang et al. iScience. .

Abstract

Cadmium (Cd), a highly toxic non-essential heavy metal, disrupts cellular processes even at low concentrations. The current studies of Cd stress rely on whole-plant models with prolonged treatments, obscuring early response signals. Tobacco, a typical model plant, lacks protoplast-level investigations under Cd stress. Here, we integrated SWATH-based LC-MS proteomics and GC-MS metabolomics to resolve early Cd-responsive mechanisms in tobacco protoplasts, uniting bioinformatics analyses (WGCNA, k-means, PPI networks). 3262 proteins and 131 primary metabolites were identified. The hub proteins (ROS scavengers, protein synthesis) and differentially expressed metabolites, transient monosaccharide depletion (15 min) followed by recovery, and sustained organic acid accumulation-were characterized. The results indicate rapid energy and some metabolites were supplied via glycolysis and the TCA cycle to alleviate oxidative damage and enhance stress protein synthesis. This study results deciphers tobacco's multi-omics defense strategy against Cd stress, filling knowledge gaps in early stress signaling. In the future, we anticipate that our study will contribute to advancing protoplast omics.

Keywords: Natural sciences; Plant Biology; Plant biochemistry; Plant bioinformatics; Plant physiology; Plant systematics.

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Conflict of interest statement

The authors have applied for a patent only covering the protoplast Proteomics detection method described in this article. In addition, the authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.

Figures

None
Graphical abstract
Figure 1
Figure 1
Protoplasts and analysis of proteome data Fresh prepared protoplasts (A) DEPs (B) and PCA analysis (C) of proteome data upon Cd exposure. Scale bar, 50 μm. Inlet is the trajectory curve of PCA result.
Figure 2
Figure 2
Weighted gene coexpression analysis (WGCNA) diagram Module-trait relationships between consensus module eigengenes and the trait time. Each row corresponds to a module eigengene, and each column corresponds to a time point. Each cell includes the correlation coefficient and p value. Red represents a positive correlation, while green indicates a negative correlation between a specific module and the trait time.
Figure 3
Figure 3
PPI networks The structured PPI network of the orange, white, royal blue, pink and gray 60. As the color deepened from yellow to purple, the score of the protein was increase.
Figure 4
Figure 4
Analysis of proteome data via K-means method and GO Clustering analysis of proteins expression profiles (A, B) and GO analysis of proteins included in B (C). Proteins number in each cluster was shown.
Figure 5
Figure 5
PPI network of proteins in cluster 1 and cluster 2 in functional categories As the color deepened from yellow to purple, the degree score of the protein increase.
Figure 6
Figure 6
Changes of metabolites associated with the Cd exposure The line chart of sugars (A) and organic acids (B). Intensity response of chromatographic (Y axis). The Cd treatment time points (X axis). Error bars indicate SD (n = 3).
Figure 7
Figure 7
Analysis of proteins associated with early responses to Cd stress Intensity response of chromatographic (Y axis). The Cd treatment time points (X axis). Error bars indicate SD (n = 3).
See this image and copyright information in PMC

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