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Plant Molecular Regulatory Networks and Stress Responses

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Plant Sciences".

Deadline for manuscript submissions: 20 January 2026 | Viewed by 1224

Special Issue Editor


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Guest Editor
1. School of Mathematics and Statistics, Wuhan University of Technology, Wuhan 430070, China
2. Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
Interests: plant genomics; plant stress response; single-cell genomics; gene regulatory network; regulatory mechanism; multi-omics integration; next-generation sequencing; deep learning; bioinformatics; systems biology
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Special Issue Information

Dear Colleagues, 

Plants respond to environmental stresses through complex and highly coordinated molecular regulatory networks. These networks accommodate signal perception, transduction, and gene expression, enabling plants to adapt to diverse abiotic stress conditions. By deciphering these regulatory mechanisms, researchers can identify key stress-responsive genes, offering valuable targets for crop improvement.

Recent advances in multi-omics integration technologies—including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics—have provided unprecedented resolution in dissecting plant stress responses. Integrative analyses across these data layers reveal the intricate connections between the gene regulatory circuits, signaling pathways, and metabolic processes involved in stress adaptation.

This Special Issue aims to highlight the latest breakthroughs in multi-omics integration for unraveling plant molecular regulatory networks under stress. We especially welcome studies employing innovative technologies such as deep learning, artificial intelligence, single-cell RNA sequencing, spatial transcriptomics/genomics, or advanced bioinformatics pipelines to identify key regulatory hubs/genes and predict stress-responsive elements.

We invite original research and comprehensive reviews that provide novel insights into plant–environment interactions enabled by multi-omics integration and support the advancement of precision breeding approaches.

Dr. Xiujun Zhang
Guest Editor

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Keywords

  • plant stress response
  • stress response gene
  • regulatory mechanism
  • gene regulatory network
  • transcriptional regulation
  • post-transcriptional regulation
  • multi-omics integration
  • single-cell RNA-seq
  • spatial genomics
  • bioinformatics

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Published Papers (2 papers)

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21 pages, 3098 KB  
Article
Transcriptomic Identification of Long Noncoding RNAs Modulating MPK3/MPK6-Centered Immune Networks in Arabidopsis
by Tianjiao Wang, Kaifeng Zheng, Qinyue Min, Yihao Li, Xiuhua Xue, Wanjie Li and Shengcheng Han
Int. J. Mol. Sci. 2025, 26(17), 8331; https://doi.org/10.3390/ijms26178331 - 28 Aug 2025
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Abstract
Mitogen-activated protein kinases 3 and 6 (MPK3/MPK6) are central to pattern-triggered immunity (PTI) and effector-triggered immunity (ETI) in Arabidopsis, yet the involvement of long noncoding RNAs (lncRNAs, >200 nt) in these pathways is poorly understood. Here, transcriptomic analyses were performed to compare lncRNA [...] Read more.
Mitogen-activated protein kinases 3 and 6 (MPK3/MPK6) are central to pattern-triggered immunity (PTI) and effector-triggered immunity (ETI) in Arabidopsis, yet the involvement of long noncoding RNAs (lncRNAs, >200 nt) in these pathways is poorly understood. Here, transcriptomic analyses were performed to compare lncRNA and protein-coding gene (PCG) expression profiles in wild-type (WT) and MPK3/MPK6-deficient (MPK6SR) Arabidopsis plants. These plants were inoculated with either Pseudomonas syringae pv. tomato (Pst) DC3000, which elicits both PTI and ETI, or its type III secretion-deficient mutant, Pst DC3000 hrcC, which induces only PTI. RNA sequencing (RNA-seq) analysis of 18 samples identified 1388 known and 70 novel lncRNAs, among which differentially expressed lncRNAs (DElncRNAs) involved in disease resistance were further identified. Using integrative analyses, including weighted gene co-expression network analysis (WGCNA), prediction of lncRNA cis-regulatory targets for PCGs, and validation via reverse transcription-quantitative PCR (RT-qPCR), three core lncRNA-mediated regulatory modules were identified: (i) MPK3/MPK6-dependent PTI and ETI, where lncRNAs amplify signals; (ii) MPK3/MPK6-dependent PTI, where lncRNAs fine-tune basal immunity; and (iii) MPK3/MPK6-independent PTI and ETI, where lncRNAs serve as a backup regulatory network. These modules form a multi-layered immune regulatory network via cis- and trans-regulation and further enable the identification of lncRNA-PCG pairs involved in both regulatory modes. This work enhances the understanding of the molecular mechanisms underlying plant innate immunity. Full article
(This article belongs to the Special Issue Plant Molecular Regulatory Networks and Stress Responses)
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36 pages, 3107 KB  
Article
Identification of Key Differentially Expressed Genes in Arabidopsis thaliana Under Short- and Long-Term High Light Stress
by Aleksandr V. Bobrovskikh, Ulyana S. Zubairova and Alexey V. Doroshkov
Int. J. Mol. Sci. 2025, 26(16), 7790; https://doi.org/10.3390/ijms26167790 - 12 Aug 2025
Viewed by 588
Abstract
Nowadays, with the accumulation of large amounts of stress-response transcriptomic data in plants, it is possible to clarify the key genes and transcription factors (TFs) involved in these processes. Here, we present the comprehensive transcriptomic meta-analysis of the high light (HL) response in [...] Read more.
Nowadays, with the accumulation of large amounts of stress-response transcriptomic data in plants, it is possible to clarify the key genes and transcription factors (TFs) involved in these processes. Here, we present the comprehensive transcriptomic meta-analysis of the high light (HL) response in photosynthetic tissues of Arabidopsis thaliana (L.) Heynh., offering new insights into adaptation mechanisms of plants to excessive light and involved gene regulatory networks. We analyzed 21 experiments covering 58 HL conditions in total, yielding 218,000 instances of differentially expressed genes (DEGs) corresponding to 19,000 unique genes. Based on these data, we developed the publicly accessible AraLightDEGs resource, which offers multiple search filters for experimental conditions and gene characteristics, and we conducted a detailed meta-analysis using our R pipeline, AraLightMeta. Our meta-analysis highlighted distinct transcriptional programs between short- and long-term HL responses in leaves, revealing novel regulatory interactions and refining the understanding of key DEGs. In particular, long-term HL adaptation involves key TFs such as CRF3 and PTF1 regulating antioxidant and jasmonate signaling; ATWHY2, WHY3, and emb2746 coordinating chloroplast and mitochondrial gene expression; AT2G28450 governing ribosome biogenesis; and AT4G12750 controlling methyltransferase activity. We integrated these findings into a conceptual scheme illustrating transcriptional regulation and signaling processes in leaf cells responding to long-term HL stress. Full article
(This article belongs to the Special Issue Plant Molecular Regulatory Networks and Stress Responses)
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