Molecular Characterization and Target Prediction of Candidate miRNAs Related to Abiotic Stress Responses and/or Storage Root Development in Sweet Potato

Lee, Sun; Yang, Yiyu; Pan, Hong; Zhu, Jiahao; Zhu, Mingku; Xu, Tao; Li, Zongyun; Dong, Tingting

doi:10.3390/genes13010110

Cited by 16 publications

(7 citation statements)

References 48 publications

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“…The “sponge effect” [ 8 ] describes how lncRNA might operate as a miRNA decoy, competing with mRNA for miRNA binding and thereby regulating the expression of miRNA target genes. In tomato yellow mosaic virus (TYMV), two lncRNAs, slylnc1077 and slylnc0195, were discovered to behave as decoys for miRNAs [ 9 ]. Slylnc0195 expression is up-regulated in tomatoes that are infected with TYMV; however, miR166a expression is downregulated.…”

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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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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“…They confer gene-silencing abilities by cleaving messenger RNA (mRNA) and restricting the translation of transcripts [ 15 , 16 ]. miRNA has been found to play essential roles in various biological processes, including plant growth and development as well as biotic and abiotic stress responses [ 17 , 18 , 19 , 20 ]. Many miRNAs have been found to influence plant performance and yield-related agronomic traits, making them attractive targets for crop improvement [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Overview of Repressive miRNA Regulation by Short Tandem Target Mimic (STTM): Applications and Impact on Plant Biology

Othman

Mustaffa

Che-Othman

et al. 2023

Plants

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The application of miRNA mimic technology for silencing mature miRNA began in 2007. This technique originated from the discovery of the INDUCED BY PHOSPHATE STARVATION 1 (IPS1) gene, which was found to be a competitive mimic that prevents the cleavage of the targeted mRNA by miRNA inhibition at the post-transcriptional level. To date, various studies have been conducted to understand the molecular mimic mechanism and to improve the efficiency of this technology. As a result, several mimic tools have been developed: target mimicry (TM), short tandem target mimic (STTM), and molecular sponges (SPs). STTM is the most-developed tool due to its stability and effectiveness in decoying miRNA. This review discusses the application of STTM technology on the loss-of-function studies of miRNA and members from diverse plant species. A modified STTM approach for studying the function of miRNA with spatial–temporal expression under the control of specific promoters is further explored. STTM technology will enhance our understanding of the miRNA activity in plant-tissue-specific development and stress responses for applications in improving plant traits via miRNA regulation.

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“…Then, we observed the transcriptome and proteome in these two cultivars ( Meng et al, 2020 ). In addition, candidate miRNAs related to the storage root development of sweetpotato were identified in salt-tolerant cultivar “Xushu 22” ( Tang et al, 2020 ; Sun L. et al, 2022 ). Recently, we identified hundreds miRNAs in salt-sensitive cultivar “Xushu 32” by high-throughput deep sequencing and demonstrated that miRNAs play an important role in sweetpotato under salt stress adaptation ( Yang et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Salt Responsive MicroRNAs in Two Sweetpotato [Ipomoea batatas (L.) Lam.] Cultivars With Different Salt Stress Resistance

et al. 2022

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Sweetpotato [Ipomoea batatas (L.) Lam.] is an important food, vegetable and economic crop, but its productivity is remarkably affected by soil salinity. MiRNAs are a class of endogenous non-coding small RNAs that play an important role in plant resistance to salt stress. However, the function of miRNAs still remains largely unknown in sweetpotato under salt stress. Previously, we identified salt-responsive miRNAs in one salt-sensitive sweetpotato cultivar “Xushu 32.” In this study, we identified miRNAs in another salt-tolerant cultivar “Xushu 22” by high-throughput deep sequencing and compared the salt-responsive miRNAs between these two cultivars with different salt sensitivity. We identified 687 miRNAs in “Xushu 22,” including 514 known miRNAs and 173 novel miRNAs. Among the 759 miRNAs from the two cultivars, 72 and 109 miRNAs were specifically expressed in “Xushu 32” and “Xushu 22,” respectively, and 578 miRNAs were co-expressed. The comparison of “Xushu 32” and “Xushu 22” genotypes showed a total of 235 miRNAs with obvious differential expression and 177 salt-responsive miRNAs that were obviously differently expressed between “Xushu 32” and “Xushu 22” under salt stress. The target genes of the miRNAs were predicted and identified using the Target Finder tool and degradome sequencing. The results showed that most of the targets were transcription factors and proteins related to metabolism and stress response. Gene Ontology analysis revealed that these target genes are involved in key pathways related to salt stress response and secondary redox metabolism. The comparative analysis of salt-responsive miRNAs in sweetpotato cultivars with different salt sensitivity is helpful for understanding the regulatory pattern of miRNA in different sweetpotato genotypes and improving the agronomic traits of sweetpotato by miRNA manipulation in the future.

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Molecular Characterization and Target Prediction of Candidate miRNAs Related to Abiotic Stress Responses and/or Storage Root Development in Sweet Potato

Cited by 16 publications

References 48 publications

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

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

Overview of Repressive miRNA Regulation by Short Tandem Target Mimic (STTM): Applications and Impact on Plant Biology

Comparative Analysis of Salt Responsive MicroRNAs in Two Sweetpotato [Ipomoea batatas (L.) Lam.] Cultivars With Different Salt Stress Resistance

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