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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Molecular Plant Pathology Laboratory » Research » Publications at this Location » Publication #423321
Research Project: Omics-Based Approach to Detection, Identification, and Systematics of Plant Pathogenic Phytoplasmas and Spiroplasmas

Location: Molecular Plant Pathology Laboratory

Title: Top-down and bottom-up approaches in phytoplasma research: advancing pathogenesis, taxonomy, and diagnostics

Author
item Wei, Wei

Submitted to: Journal of Taiwan Agricultural Research
Publication Type: Review Article
Publication Acceptance Date: 8/13/2025
Publication Date: 1/7/2026
Citation: Wei, W. 2026. Top-down and bottom-up approaches in phytoplasma research: advancing pathogenesis, taxonomy, and diagnostics. Journal of Taiwan Agricultural Research. 74 (4):359-376. https://doi.org/10.6156/JTAR.202512_74(4).0001.
DOI: https://doi.org/10.6156/JTAR.202512_74(4).0001

Interpretive Summary:

Technical Abstract: Phytoplasmas are minute, cell wall-less bacteria responsible for devastating plant diseases, leading to significant economic losses in agriculture. This article explores recent advancements in phytoplasma research, focusing on pathogenesis, taxonomy, and diagnostics through top-down and bottom-up approaches. Top-down multi-omics studies have provided a systems-level understanding of phytoplasma-induced disruptions, particularly in sugar metabolism and hormone signaling, revealing their extensive impact on plant physiology. Complementing this, bottom-up strategies have dissected molecular interactions, elucidating how phytoplasmas derail meristem fate, modulate plant growth patterns, alter plant architecture, and induce characteristic symptoms. Advances in taxonomy and classification have improved species differentiation, integrating 16S rRNA sequencing, multilocus sequence typing (MLST), and whole-genome sequencing (WGS) with database-guided tools refining classification accuracy. The development of cutting-edge diagnostic technologies, such as CRISPR-based detection, has significantly enhanced the sensitivity, specificity, and efficiency of phytoplasma identification and surveillance. Additionally, the integration of big data analytics and artificial intelligence (AI)-driven models has pioneered image-based symptom recognition, supporting disease surveillance and monitoring. This article consolidates key research findings and technological advancements, demonstrating how the integration of top-down systems biology with bottom-up molecular analyses is driving innovations in phytoplasma detection, classification, and sustainable disease management strategies.