Comparative transcriptomics and targeted metabolomics reveal mechanisms of leaf greenness retention during tobacco (Nicotiana tabacum L.) air-curing

Tobacco (Nicotiana tabacum L.) leaf curing is a critical postharvest process that fundamentally determines the quality, aroma, and combustibility of the final product. During air-curing and storage, persistent leaf greenness (stay-green phenotype) significantly compromises color quality and reduces the commercial value of cigar tobacco. However, the underlying mechanisms of this phenomenon remain largely elusive. Here, we employed an integrated approach combining physiological, transcriptomic, and metabolomic analyses to elucidate the physiochemical and molecular mechanisms governing leaf greenness retention during tobacco air-curing. Compared with normal yellow leaves, green-retained leaves presented a well-preserved chloroplast ultrastructure, significantly elevated chlorophyll (Chl) and anthocyanin contents, decreased carotenoid-to-Chl ratios, and attenuated expression of oxidative stress markers. Transcriptomic analysis revealed 4,304 differentially expressed genes (DEGs) (1,408 upregulated and 2,896 downregulated) in green leaves. These DEGs were significantly enriched in pathways associated with leaf senescence, autophagy, phytohormone signal transduction, and Chl metabolism. Notably, genes encoding key enzymes involved in Chl biosynthesis (HEMY, CHLD, CHLM, CHLE, DVR, and POR) were upregulated, whereas those involved in Chl degradation (SGR1, NYC1/NOL, and CLH) were downregulated in green leaves. Metabolomic profiling revealed 504 differentially accumulated metabolites (DAMs), predominantly terpenoids, amino acids, and alkaloids. Analysis of volatile compounds revealed substantial alterations in flavor-related metabolites, characterized by decreased levels of sweet-associated compounds (e.g., geraniol), which are crucial for desirable tobacco aroma, and increased levels of green-note compounds (e.g., (Z)-4-heptenal) in green leaves. Integrated transcriptome–metabolome analysis revealed coordinated regulation of porphyrin metabolism, carotenoid biosynthesis, and phenylpropanoid pathways. Collectively, our findings suggest that the stay-green phenotype is driven by a molecular circuitry characterized by the synchronized upregulation of Chl and anthocyanin biosynthesis and the suppression of pigment degradation pathways. These molecular alterations maintain physiological traits but ultimately result in modified metabolite profiles that adversely affect tobacco quality and commercial value, providing crucial insights for developing strategies to optimize tobacco curing processes.

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Journal
BMC Plant Biology
Published
2026-04-21
DOI
https://doi.org/10.1186/s12870-026-08763-9
Primary Topic
Plant Gene Expression Analysis
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article
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article

Comparative transcriptomics and targeted metabolomics reveal mechanisms of leaf greenness retention during tobacco (Nicotiana tabacum L.) air-curing

Pengfei Chang, Jinge Du, Mengnan Guo, Y F LIU et al.
BMC Plant Biology
Plant Gene Expression Analysis
article

Comparative transcriptomics and targeted metabolomics reveal mechanisms of leaf greenness retention during tobacco (Nicotiana tabacum L.) air-curing

Pengfei Chang, Jinge Du, Mengnan Guo, Y F LIU, Fajian Luo, Lu Meng, Xiangdeng Chen, Hai Hu
article en

Abstract

Tobacco (Nicotiana tabacum L.) leaf curing is a critical postharvest process that fundamentally determines the quality, aroma, and combustibility of the final product. During air-curing and storage, persistent leaf greenness (stay-green phenotype) significantly compromises color quality and reduces the commercial value of cigar tobacco. However, the underlying mechanisms of this phenomenon remain largely elusive. Here, we employed an integrated approach combining physiological, transcriptomic, and metabolomic analyses to elucidate the physiochemical and molecular mechanisms governing leaf greenness retention during tobacco air-curing. Compared with normal yellow leaves, green-retained leaves presented a well-preserved chloroplast ultrastructure, significantly elevated chlorophyll (Chl) and anthocyanin contents, decreased carotenoid-to-Chl ratios, and attenuated expression of oxidative stress markers. Transcriptomic analysis revealed 4,304 differentially expressed genes (DEGs) (1,408 upregulated and 2,896 downregulated) in green leaves. These DEGs were significantly enriched in pathways associated with leaf senescence, autophagy, phytohormone signal transduction, and Chl metabolism. Notably, genes encoding key enzymes involved in Chl biosynthesis (HEMY, CHLD, CHLM, CHLE, DVR, and POR) were upregulated, whereas those involved in Chl degradation (SGR1, NYC1/NOL, and CLH) were downregulated in green leaves. Metabolomic profiling revealed 504 differentially accumulated metabolites (DAMs), predominantly terpenoids, amino acids, and alkaloids. Analysis of volatile compounds revealed substantial alterations in flavor-related metabolites, characterized by decreased levels of sweet-associated compounds (e.g., geraniol), which are crucial for desirable tobacco aroma, and increased levels of green-note compounds (e.g., (Z)-4-heptenal) in green leaves. Integrated transcriptome–metabolome analysis revealed coordinated regulation of porphyrin metabolism, carotenoid biosynthesis, and phenylpropanoid pathways. Collectively, our findings suggest that the stay-green phenotype is driven by a molecular circuitry characterized by the synchronized upregulation of Chl and anthocyanin biosynthesis and the suppression of pigment degradation pathways. These molecular alterations maintain physiological traits but ultimately result in modified metabolite profiles that adversely affect tobacco quality and commercial value, providing crucial insights for developing strategies to optimize tobacco curing processes.

BMC Plant Biology
Longyan University (CN), China Tobacco (CN), Tobacco Research Institute (CN), Chinese Academy of Agricultural Sciences (CN), Ministry of Agriculture and Rural Affairs (CN)
Good health and well-being
Openalex Percentile: Top 9%
Plant Gene Expression Analysis
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