Panomics Approaches for Deciphering Salinity Stress Tolerance in Plants
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: 31 July 2026 | Viewed by 168
Special Issue Editors
Interests: climate change; abiotic stresses; emerging pollutants; PFAS; nanoparticles; nanoenabled agriculture; micorobiology
Interests: environmental microbiology; microbial biotechnology; soil microbiome and soil microbes; microbial biofilms and novel microorganisms; fungal biology; biodegradation and biocatalysis; xenobiotic biotransformation of environmental pollutants; cytochrome p450 and dehalogenases; laccases; extremophiles and extremozymes; protein and enzyme engineering; rational mutagenesis; enzyme immobilization and nanobiotechnology; industrial enzymes; computational and structural biology
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Special Issue Information
Dear Colleagues,
We are pleased to invite you to contribute to this Special Issue on "Panomics Approaches for Deciphering Salinity Stress Tolerance in Plants." Salinity stress affects over 1 billion hectares globally, expanding at 1–2% annually due to climate change, critically threatening agricultural productivity and food security. While traditional bulk-tissue studies have provided valuable insights but face inherent limitations, likewise, single-omics approaches have identified key tolerance mechanisms (SOS pathway, compatible solutes, transcription factors), they provide limited perspectives on the complex, interconnected molecular networks governing plant stress responses. Panomics integration—combining genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics with AI/machine learning, spatial omics, and single-cell technologies—represents a transformative approach for understanding salinity tolerance and accelerating development of climate-smart crops.
This Special Issue aims to compile cutting-edge research on panomics approaches for understanding and improving plant salinity stress tolerance. Aligned with IJMS's Molecular Plant Sciences scope, this collection will showcase multi-omics integration revealing regulatory networks, novel spatial/single-cell technologies, AI-driven data analysis, and translational applications through CRISPR/Cas editing and marker-assisted breeding. We anticipate high-quality articles providing mechanistic insights and actionable strategies for developing salt-tolerant crops essential for sustainable agriculture.
Original research articles and reviews are welcome on topics including multi-omics integration and systems biology; spatial and single-cell omics; genomics, transcriptomics, and GWAS; proteomics and post-translational modifications; metabolomics and epigenomics; phenomics and high-throughput screening; CRISPR/Cas genome editing; marker-assisted breeding; halophytes and wild relatives; plant-microbiome interactions; combined stress responses; and methodological innovations.
We look forward to receiving your contributions.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- Spatial transcriptomics revealing tissue-specific salt stress responses in roots, shoots, and reproductive organs;
- Spatially resolved metabolomics mapping ion distribution and osmo-protectant accumulation;
- Single-cell approaches (transcriptome + chromatin accessibility + proteome) decoding gene regulatory networks underlying stress memory and transgenerational adaptation;
- Phenomics-panomics integration for high-throughput screening and breeding applications;
- Microbiome-plant interactions at the root-soil interface under saline conditions.
We look forward to receiving your contributions.
Dr. Hafiz Abdul Kareem
Dr. Mohd Faheem Khan
Guest Editors
Manuscript Submission Information
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Keywords
- salinity stress
- panomics integration
- multi-omics
- salt tolerance
- spatial transcriptomics
- AI-driven breeding
- systems biology
- climate-smart crops
- molecular breeding
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