Biogenesis, Functions, Interactions, and Resources of Non-Coding RNAs in Plants

Chao, Haoyu; Hu, Yueming; Zhao, Liang; Xin, Saige; Ni, Qingyang; Zhang, Peijing; Chen, Ming

doi:10.3390/ijms23073695

Cited by 21 publications

(10 citation statements)

References 250 publications

(198 reference statements)

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“…As reviewed by Chao et al [18], the biogenesis of miRNAs is a multi-step procedure which involves transcription, processing, alterations, and then RNA-induced silencing (RISC) complex assembly. First, a pri-miRNA (primary miRNA) is transcribed from RNA Polymerase II containing a hairpin RNA secondary structure.…”

Section: Genesis Of Non-coding Rnas and Classificationmentioning

confidence: 99%

Role of Non-Coding RNAs in Plant Nutrition through Mycorrhizal Interactions

Verma¹,

Singh²,

Patra³

et al. 2023

Arbuscular Mycorrhizal Fungi in Agriculture - New Insights

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In nature, many plants rely on symbiotic interaction with mycorrhizae for their nutrition and survival. For instance, nitrogen-fixing nodules and mycorrhizae are well established mutualistic biotic interactions between plants and bacterial/fungal partners under nitrogen limiting environment. Many small regulatory components of RNA like micro-RNAs play a critical role in establishment of these symbioses. These regulatory components are also crucial for balancing hormone levels, and synchronization of plant defenses and development pathways. However, functions of various sRNAs are still need to be addressed. This chapter will detailed out various important parts these regulatory components (sRNA, miRNA and siRNA) are playing during mycorrhizal interactions for plant growth, development and nutrition.

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Section: Genesis Of Non-coding Rnas and Classificationmentioning

confidence: 99%

Role of Non-Coding RNAs in Plant Nutrition through Mycorrhizal Interactions

Verma¹,

Singh²,

Patra³

et al. 2023

Arbuscular Mycorrhizal Fungi in Agriculture - New Insights

View full text Add to dashboard Cite

show abstract

“…These RNAs are mostly transcribed in the nucleus with RNA polymerase II in an mRNA-like way. The mechanisms by which plant lncRNAs function are numerous, and some of them are better understood in animals [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Recent studies have shown that the short open reading frames (sORFs) of some plant lncRNAs encode functional small peptides (micropeptides) with a size of around 5 to 100 amino acids [ 10 , 12 , 17 , 18 , 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Biological Activity of Artificial Plant Peptides Corresponding to the Translational Products of Small ORFs in Primary miRNAs and Other Long “Non-Coding” RNAs

Erokhina,

Ryazantsev,

Zavriev

et al. 2024

Plants

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Generally, lncPEPs (peptides encoded by long non-coding RNAs) have been identified in many plant species of several families and in some animal species. Importantly, molecular mechanisms of the miPEPs (peptides encoded by primary microRNAs, pri-miRNAs) are often poorly understood in different flowering plants. Requirement for the additional studies in these directions is highlighted by alternative findings concerning positive regulation of pri-miRNA/miRNA expression by synthetic miPEPs in plants. Further extensive studies are also needed to understand the full set of their roles in eukaryotic organisms. This review mainly aims to consider the available data on the regulatory functions of the synthetic miPEPs. Studies of chemically synthesized miPEPs and analyzing the fine molecular mechanisms of their functional activities are reviewed. Brief description of the studies to identify lncORFs (open reading frames of long non-coding RNAs) and the encoded protein products is also provided.

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“…(1) In addition to performing a direct role as a miRNA precursor, lncRNA can be spliced into shorter miRNA. For example, MiR869a and miR160c may be spliced into lncRNAs npc521 and npc83 [ 6 ]. (2) lncRNA can be cleaved by miRNA as a target [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Ensemble Deep Learning Model for Predicting Plant miRNA–IncRNA Based on Artificial Gorilla Troops Algorithm

Hamdy

Ismail

Awad

et al. 2023

Sensors

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MicroRNAs (miRNA) are small, non-coding regulatory molecules whose effective alteration might result in abnormal gene manifestation in the downstream pathway of their target. miRNA gene variants can impact miRNA transcription, maturation, or target selectivity, impairing their usefulness in plant growth and stress responses. Simple Sequence Repeat (SSR) based on miRNA is a newly introduced functional marker that has recently been used in plant breeding. MicroRNA and long non-coding RNA (lncRNA) are two examples of non-coding RNA (ncRNA) that play a vital role in controlling the biological processes of animals and plants. According to recent studies, the major objective for decoding their functional activities is predicting the relationship between lncRNA and miRNA. Traditional feature-based classification systems’ prediction accuracy and reliability are frequently harmed because of the small data size, human factors’ limits, and huge quantity of noise. This paper proposes an optimized deep learning model built with Independently Recurrent Neural Networks (IndRNNs) and Convolutional Neural Networks (CNNs) to predict the interaction in plants between lncRNA and miRNA. The deep learning ensemble model automatically investigates the function characteristics of genetic sequences. The proposed model’s main advantage is the enhanced accuracy in plant miRNA–IncRNA prediction due to optimal hyperparameter tuning, which is performed by the artificial Gorilla Troops Algorithm and the proposed intelligent preying algorithm. IndRNN is adapted to derive the representation of learned sequence dependencies and sequence features by overcoming the inaccuracies of natural factors in traditional feature architecture. Working with large-scale data, the suggested model outperforms the current deep learning model and shallow machine learning, notably for extended sequences, according to the findings of the experiments, where we obtained an accuracy of 97.7% in the proposed method.

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Biogenesis, Functions, Interactions, and Resources of Non-Coding RNAs in Plants

Cited by 21 publications

References 250 publications

Role of Non-Coding RNAs in Plant Nutrition through Mycorrhizal Interactions

Role of Non-Coding RNAs in Plant Nutrition through Mycorrhizal Interactions

Biological Activity of Artificial Plant Peptides Corresponding to the Translational Products of Small ORFs in Primary miRNAs and Other Long “Non-Coding” RNAs

An Optimized Ensemble Deep Learning Model for Predicting Plant miRNA–IncRNA Based on Artificial Gorilla Troops Algorithm

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