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Planta
Mar 17, 2025;261 (4): 92.

Accelerating crop improvement via integration of transcriptome-based network biology and genome editing.

Izreen Izzati Razalli, Muhammad-Redha Abdullah-Zawawi, Amin-Asyraf Tamizi, Sarahani Harun, Rabiatul-Adawiah Zainal-Abidin, Muhammad Irfan Abdul Jalal, Mohammad Asad Ullah, Zamri Zainal
Author Information
  1. Izreen Izzati Razalli: Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia.
  2. Muhammad-Redha Abdullah-Zawawi: UKM Medical Molecular Biology Institute (UMBI), UKM Medical Centre, Jalan Ya'acob Latiff, Bandar Tun Razak, 56000, Cheras, Kuala Lumpur, Malaysia.
  3. Amin-Asyraf Tamizi: Malaysian Agricultural Research and Development Institute (MARDI), 43400, Serdang, Selangor, Malaysia.
  4. Sarahani Harun: Institute of Systems Biology, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia.
  5. Rabiatul-Adawiah Zainal-Abidin: Malaysian Agricultural Research and Development Institute (MARDI), 43400, Serdang, Selangor, Malaysia.
  6. Muhammad Irfan Abdul Jalal: UKM Medical Molecular Biology Institute (UMBI), UKM Medical Centre, Jalan Ya'acob Latiff, Bandar Tun Razak, 56000, Cheras, Kuala Lumpur, Malaysia.
  7. Mohammad Asad Ullah: Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia.
  8. Zamri Zainal: Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia. zz@ukm.edu.my. ORCID
PMID: 40095140 DOI: 10.1007/s00425-025-04666-5

Abstract

MAIN CONCLUSION: Big data and network biology infer functional coupling between genes. In combination with machine learning, network biology can dramatically accelerate the pace of gene discovery using modern transcriptomics approaches and be validated via genome editing technology for improving crops to stresses. Unlike other living things, plants are sessile and frequently face various environmental challenges due to climate change. The cumulative effects of combined stresses can significantly influence both plant growth and yields. In navigating the complexities of climate change, ensuring the nourishment of our growing population hinges on implementing precise agricultural systems. Conventional breeding methods have been commonly employed; however, their efficacy has been impeded by limitations in terms of time, cost, and infrastructure. Cutting-edge tools focussing on big data are being championed to usher in a new era in stress biology, aiming to cultivate crops that exhibit enhanced resilience to multifactorial stresses. Transcriptomics, combined with network biology and machine learning, is proving to be a powerful approach for identifying potential genes to target for gene editing, specifically to enhance stress tolerance. The integration of transcriptomic data with genome editing can yield significant benefits, such as gaining insights into gene function by modifying or manipulating of specific genes in the target plant. This review provides valuable insights into the use of transcriptomics platforms and the application of biological network analysis and machine learning in the discovery of novel genes, thereby enhancing the understanding of plant responses to combined or sequential stress. The transcriptomics as a forefront omics platform and how it is employed through biological networks and machine learning that lead to novel gene discoveries for producing multi-stress-tolerant crops, limitations, and future directions have also been discussed.

Keywords

Big data Crop management Genome editing Network biology Transcriptomics

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Grants

  1. GUP-2021-044/Universiti Kebangsaan Malaysia

MeSH Term

Crops, Agricultural
Gene Editing
Transcriptome
Stress, Physiological
Machine Learning
Plant Breeding
Gene Regulatory Networks
Gene Expression Profiling
Climate Change
Journal Article Review
Article has an altmetric score of 3

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