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Role of phasiRNAs in plant-pathogen interactions: molecular perspectives and bioinformatics tools
The genome of an organism is regulated in concert with the organized action of various genetic regulators at different hierarchical levels. Small non-coding RNAs are one of these regulators, among which microRNAs (miRNAs), a distinguished sRNA group with decisive functions in the development, growth...
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Published in: | Physiology and molecular biology of plants 2022-05, Vol.28 (5), p.947-961 |
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Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | The genome of an organism is regulated in concert with the organized action of various genetic regulators at different hierarchical levels. Small non-coding RNAs are one of these regulators, among which microRNAs (miRNAs), a distinguished sRNA group with decisive functions in the development, growth and stress-responsive activities of both plants as well as animals, are keenly explored over a good number of years. Recent studies in plants revealed that apart from the silencing activity exhibited by miRNAs on their targets, miRNAs of specific size and structural features can direct the phasing pattern of their target loci to form phased secondary small interfering RNAs (phasiRNAs). These trigger-miRNAs were identified to target both coding and long non-coding RNAs that act as potent phasiRNA precursors or
PHAS
loci. The phasiRNAs produced thereby exhibit a role in enhancing further downstream regulation either on their own precursors or on those transcripts that are distinct from their genetic source of origin. Hence, these tiny regulators can stimulate an elaborative cascade of interacting RNA networks via
cis
and
trans
-regulatory mechanisms. Our review focuses on the comprehensive understanding of phasiRNAs and their trigger miRNAs, by giving much emphasis on their role in the regulation of plant defense responses, together with a summary of the computational tools available for the prediction of the same. |
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ISSN: | 0971-5894 0974-0430 |
DOI: | 10.1007/s12298-022-01189-1 |