Identification and Characterization of circRNAs Responsive to Methyl Jasmonate in Arabidopsis thaliana

Zhang, Jingjing; Liu, Ruiqi; Zhu, Yanfeng; Gong, Jiaxin; Yin, Shuwei; Sun, Peisen; Feng, Hao; Wang, Qi; Zhao, Shuaijing; Wang, Zhongyuan; Li, Guanglin

doi:10.3390/ijms21030792

Cited by 25 publications

(19 citation statements)

References 74 publications

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“…Our results show a strong interaction between JA-Ile and COI1 with three hydrogen bonds at a bond distance of less than 4Å. The MYC transcription factors by bind to JAZ protein and activates JA-Ile-COI1 complex and degrade of JAZ with the help of 26S proteasome 39 . Three dimensional structure COI1 and JAZ complex is evident of its high binding a nity with JA-Ile compound 56 .…”

Section: Discussionmentioning

confidence: 69%

“…The rst and foremost event in initiation of plant defense mechanics is the recognition of the stimulus by plant receptor cells. Insect oral secretions induces several internal signals from the wounded tissues, including jasmonate signaling 39,40 . Although many oral secretions might not serve a speci c function to the insect, salivary proteins play a more active role in modulating the interactions between plant and insect herbivore [41][42][43] .…”

Section: Discussionmentioning

confidence: 99%

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A Mechanistic insight into chemical cues and interactions involved in herbivory induced jasmonate mediated plant defense mechanism

Yousafi

Hafeez

Khan

et al. 2021

Preprint

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The first step in plant defense mechanism is to sense the insect attack stimulus. Plant sensitivity of an insect attack is the first step of defense. Molecules generated by the oral secretion of the insect interact with the plant receptors to trigger plant defense mechanisms. We selected some highly cited insect elicitors molecules, volicitin, caeliferin, bruchin which interact with plant defense by interacting with plant elicitors (systemin, inceptin and peps) located on the plant cell surface. This interaction activates plant receptors SYR1, LRR, PEPR and triggers downstream defense signaling. The octadecanoid pathways, involving enzymes allene oxide synthase (AOS) and Hydroxyperoxide lyase (HPL) are activated. These enzymes mediate production of green leafy volatiles and Jasmonic acid by interacting with hydroxperoxide molecules. We docked the elicitors with receptors and enzymes with substrates in the pathway of JA production. Phe was found to be an important amino acid that interacts with 13- hydroxyperoxides in the case of AOS to produce JA but not in the case of HPL. JA is converted to JA-Ile which shows strong binding with COI1 and COI1-JA-Ile complex docked with JAZ which showed strong interaction with five hydrogens and one salt bridge bond. AOS and HPL showed less than 40% identity for sequence and structure alignment. AOS and HPL had shown an interaction between each other and showed a common interaction partner of the Lipoxygenase family. HPL shows interaction with ADH2 (Alcohol dehydrogenase) involved in GLVs production. AOS also showed interaction partner AOC, COI1 and OPR1 which are involved in JA-induced plant defense mechanism.

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Section: Discussionmentioning

confidence: 69%

Section: Discussionmentioning

confidence: 99%

A Mechanistic insight into chemical cues and interactions involved in herbivory induced jasmonate mediated plant defense mechanism

Yousafi

Hafeez

Khan

et al. 2021

Preprint

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“…One of the important common foundation for the ability of these three software programs to predict circRNAs from transcriptome data is the supporting number of reads covering circRNA back-splicing junctions (BSJs). However, the empirical standard used for supporting the number of reads is different in each prediction software, which leads to a great difference in the number of predicted circRNAs, and only a small number of overlapping circRNAs are obtained by different software programs [ 15 ]. At the same time, because the expression of circRNAs varies in different stages and tissues, it is easy to lose some circRNAs by using the number of junction reads as a vital standard to predict circRNAs.…”

Section: Introductionmentioning

confidence: 99%

PCirc: random forest-based plant circRNA identification software

et al. 2021

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Background Circular RNA (circRNA) is a novel type of RNA with a closed-loop structure. Increasing numbers of circRNAs are being identified in plants and animals, and recent studies have shown that circRNAs play an important role in gene regulation. Therefore, identifying circRNAs from increasing amounts of RNA-seq data is very important. However, traditional circRNA recognition methods have limitations. In recent years, emerging machine learning techniques have provided a good approach for the identification of circRNAs in animals. However, using these features to identify plant circRNAs is infeasible because the characteristics of plant circRNA sequences are different from those of animal circRNAs. For example, plants are extremely rich in splicing signals and transposable elements, and their sequence conservation in rice, for example is far less than that in mammals. To solve these problems and better identify circRNAs in plants, it is urgent to develop circRNA recognition software using machine learning based on the characteristics of plant circRNAs. Results In this study, we built a software program named PCirc using a machine learning method to predict plant circRNAs from RNA-seq data. First, we extracted different features, including open reading frames, numbers of k-mers, and splicing junction sequence coding, from rice circRNA and lncRNA data. Second, we trained a machine learning model by the random forest algorithm with tenfold cross-validation in the training set. Third, we evaluated our classification according to accuracy, precision, and F1 score, and all scores on the model test data were above 0.99. Fourth, we tested our model by other plant tests, and obtained good results, with accuracy scores above 0.8. Finally, we packaged the machine learning model built and the programming script used into a locally run circular RNA prediction software, Pcirc (https://github.com/Lilab-SNNU/Pcirc). Conclusion Based on rice circRNA and lncRNA data, a machine learning model for plant circRNA recognition was constructed in this study using random forest algorithm, and the model can also be applied to plant circRNA recognition such as Arabidopsis thaliana and maize. At the same time, after the completion of model construction, the machine learning model constructed and the programming scripts used in this study are packaged into a localized circRNA prediction software Pcirc, which is convenient for plant circRNA researchers to use.

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“…Subsequently, Hsu and Coca-Prados (6) first identified the existence of circRNAs in the cytoplasm of eukaryotic cells using electron microscopy in 1979. In the 21st century, benefiting from the breakthroughs in next generation sequencing technologies and rapid development of bioinformatics, numerous circRNAs have been identified in various organisms (7)(8)(9).…”

Section: Introductionmentioning

confidence: 99%

Emerging landscape of circHIPK3 and its role in cancer and other diseases (Review)

et al. 2021

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Circular RNAs (circRNAs) are a special class of recently re-discovered RNAs, which are covalently closed ring RNA molecules. circRNAs have been reported to possess multiple functions and are considered crucial regulators of several processes, and are therefore gaining increasing attention. In recent years, increasing evidence has shown that circRNAs are implicated in several crucial biological processes via regulation of gene expression, and their dysregulation is also associated with the development of numerous diseases, particularly acting as oncogenic or tumor-suppressor molecules in cancer. Furthermore, circRNAs are involved in cell proliferation, differentiation, apoptosis, invasion and metastasis. In the present review, the biogenesis and functions of circRNAs are described, with a focus on the most recent research advances and the emerging roles of circular homeodomain-interacting protein kinase 3 (circHIPK3) in human diseases. The present review may provide novel avenues for research on the roles of circHIPK3 as a clinical diagnostic and prognostic biomarker, as well as highlighting promising therapeutic targets for certain diseases and cancer. Contents 1. Introduction 2. Biogenesis of circRNAs 3. Biological functions of circRNAs 4. circHIPK3 and human diseases 5. Conclusions and future perspectives

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Identification and Characterization of circRNAs Responsive to Methyl Jasmonate in Arabidopsis thaliana

Cited by 25 publications

References 74 publications

A Mechanistic insight into chemical cues and interactions involved in herbivory induced jasmonate mediated plant defense mechanism

A Mechanistic insight into chemical cues and interactions involved in herbivory induced jasmonate mediated plant defense mechanism

PCirc: random forest-based plant circRNA identification software

Emerging landscape of circHIPK3 and its role in cancer and other diseases (Review)

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