Biologia plantarum 68:1-11, 2024 | DOI: 10.32615/bp.2023.038
Comparative analysis of bioinformatic tools to predict and quantify active circular RNAs during grape cluster development
- 1 Department of Plant Breeding and Biotechnology (PBB), Faculty of Agriculture, University of Zabol, Zabol, 9861335856, Iran
- 2 Laboratory of Computational Biotechnology and Bioinformatics (CBB), Department of Bioinformatics, Faculty of Basic Sciences, University of Zabol, Zabol, 9861335856, Iran
- 3 Department of Biology, School of Basic Science, University of Qom, Qom, 3716146611, Iran
- 4 School of Animal and Veterinary Sciences, University of Adelaide, Adelaide, SA 5371, Australia
- 5 Nuclear Agriculture Research School, Nuclear Science and Technology Research Institute (NSTRI), Karaj, 1439951113, Iran
- 6 Genomics Research Platform, School of Agriculture, Biomedicine and Environment, La Trobe University, Melbourne, 3083, Australia
- 7 School of BioSciences, University of Melbourne, Melbourne, 3052, Australia
- 8 Agronomy and Plant Breeding Department, Agriculture Research Center, Zabol Research Institute, Zabol, 9861673831, Iran
- 9 Department of Horticulture, Faculty of Agriculture and Natural Resources, Mohaghegh Ardabili University, Ardabil, 5619911367, Iran
Circular RNAs (circRNAs) are relatively new members of the RNA world and can contribute to crucial biological functions. CircRNAs have tissue-specific expression profiles depending on cell type and developmental stage. In Sistan region cultivated grapes are seedless but have small berries. The compact clusters are another notable characteristic of these grape cultivars, which negatively impacts their marketability. In this study, we aimed to identify the circRNAs that are active in cluster formation and investigated the effects of gibberellin treatment on their expression. Eight detection tools were used to predict the expressed circRNAs. Reliable circRNAs were used to identify potential functions of differentially expressed circRNAs by gene ontology (GO) analysis and prediction of target microRNAs (miRNAs). Of the 28 157 circRNAs detected, 3 715 were reliable. 900 differently expressed circRNAs were identified in the three developmental stages of the cluster under gibberellin treatment. Among the 503 target miRNAs found, 12 miRNAs were selected based on the number and expression of their circRNA sponges. Of the 29 circRNAs in the circRNAs-miRNAs-mRNAs interaction network, 12 circRNAs are highly conserved. Our results suggest that circRNAs in grape may play a key role in developmental and environmental adaptation in perennial plants.
Keywords: circular RNAs, gene ontology, gibberellin, micro RNAs.
Received: June 21, 2023; Revised: November 9, 2023; Accepted: November 22, 2023; Published online: January 12, 2024 Show citation
| ACS | AIP | APA | ASA | Harvard | Chicago | Chicago Notes | IEEE | ISO690 | MLA | NLM | Turabian | Vancouver |
Supplementary files
| Download file | 7043_Ranjbar_Suppl.pdf File size: 187.04 kB |
References
- Amkha S., Saengkai K., Rungcharoenthong P.: Gibberellin application and potash fertilizer on yield and quality of 'White Malaga' grape. - Acta Hortic. 1206: 51-56, 2017.
Go to original source... - Assenov Y., Ramírez F., Schelhorn S.-E. et al.: Computing topological parameters of biological networks. - Bioinformatics 24: 282-284, 2008.
Go to original source... - Babaei S., Singh M.B., Bhalla P.L.:Circular RNAs modulate the floral fate acquisition in soybean shoot apical meristem. - BMC Plant Biol. 23: 322, 2023.
Go to original source... - Babaei S., Singh M.B., Bhalla P.L.:Circular RNAs repertoire and expression profile during Brassica rapa pollen development. - Int. J. Mol. Sci. 22: 10297, 2021.
Go to original source... - Benjamini Y., Hochberg Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. - J. R. Stat. Soc. B 57: 289-300, 1995.
Go to original source... - Bolger A.M., Lohse M., Usadel B.: Trimmomatic: a flexible trimmer for Illumina sequence data. - Bioinformatics 30: 2114-2120, 2014.
Go to original source... - Bolser D., Staines D.M., Pritchard E., Kersey P.: Ensembl Plants: integrating tools for visualizing, mining, and analyzing plant genomics data. - In: Edwards D. (ed.): Plant Bioinformatics. Methods in Molecular Biology. Vol. 1374. Pp. 115-140. Humana Press, New York 2016.
Go to original source... - Casanova L., Casanova R., Moret A., Agustí M.: The application of gibberellic acid increases berry size of "Emperatriz" seedless grape. - Span. J. Agric. Res. 7: 919-927, 2009.
Go to original source... - Chen H., Wang T., Gong Z. et al.: Low light conditions alter genome-wide profiles of circular RNAs in rice grains during grain filling. - Plants-Basel 11: 1272, 2022.
Go to original source... - Chen L., Wang C., Sun H. et al.: The bioinformatics toolbox for circRNA discovery and analysis. - Brief. Bioinform. 22: 1706-1728, 2021.
Go to original source... - Cheng J., Metge F., Dieterich C.: Specific identification and quantification of circular RNAs from sequencing data. - Bioinformatics 32: 1094-1096, 2016.
Go to original source... - Chung M.-Y., Nath U.K., Vrebalov J. et al.: Ectopic expression of miRNA172 in tomato (Solanum lycopersicum) reveals novel function in fruit development through regulation of an AP2 transcription factor. - BMC Plant Biol. 20: 283, 2020.
Go to original source... - Dai X., Zhao P.X.: psRNATarget: a plant small RNA target analysis server. - Nucleic Acids Res. 39: W155-W159, 2011.
Go to original source... - Ding J., Zhou S., Guan J.: Finding microRNA targets in plants: current status and perspectives. - Genom. Proteom. Bioinform. 10: 264-275, 2012.
Go to original source... - Dobin A., Davis C.A., Schlesinger F. et al.: STAR: ultrafast universal RNA-seq aligner. - Bioinformatics 29: 15-21, 2013.
Go to original source... - Gaffo E., Bonizzato A., Te Kronnie G., Bortoluzzi S.: CirComPara: A multi-method comparative bioinformatics pipeline to detect and study circRNAs from RNA-seq data. - Non-coding RNA 3: 8, 2017.
Go to original source... - Gaffo E., Buratin A., Dal Molin A., Bortoluzzi S.: Sensitive, reliable and robust circRNA detection from RNA-seq with CirComPara2. - Brief. Bioinform. 23: bbab418, 2022.
Go to original source... - Gao Y., Wang J., Zhao F.: CIRI: an efficient and unbiased algorithm for de novo circular RNA identification. - Genome Biol. 16: 4, 2015.
Go to original source... - Gao Z., Li J., Luo M. et al.: Characterization and cloning of grape circular RNAs identified the cold resistance-related Vv-circATS1. - Plant Physiol. 180: 966-985, 2019.
Go to original source... - Ghorbani A., Izadpanah K., Peters J.R. et al.: Detection and profiling of circular RNAs in uninfected and maize Iranian mosaic virus-infected maize. - Plant Sci. 274: 402-409, 2018.
Go to original source... - Ghorbani A., Izadpanah K., Tahmasebi A. et al.: Characterization of maize miRNAs responsive to maize Iranian mosaic virus infection. - 3 Biotech 12: 69, 2022.
Go to original source... - He X., Guo S., Wang Y. et al.: Systematic identification and analysis of heat-stress-responsive lncRNAs, circRNAs and miRNAs with associated co-expression and ceRNA networks in cucumber (Cucumis sativus L.). - Physiol. Plantarum 168: 736-754, 2020.
Go to original source... - Hernández Y., Sanan-Mishra N.: miRNA mediated regulation of NAC transcription factors in plant development and environment stress response. - Plant Gene 11: 190-198, 2017.
Go to original source... - Hoffmann S., Otto C., Doose G. et al.: A multi-split mapping algorithm for circular RNA, splicing, trans-splicing and fusion detection. - Genome Biol. 15: R34, 2014.
Go to original source... - Hoffmann S., Otto C., Kurtz S. et al.: Fast mapping of short sequences with mismatches, insertions and deletions using index structures. - PLoS Comput. Biol. 5: e1000502, 2009.
Go to original source... - Houtgast E.J., Sima V.-M., Bertels K., Al-Ars Z.: Hardware acceleration of BWA-MEM genomic short read mapping for longer read lengths. - Comput. Biol. Chem. 75: 54-64, 2018.
Go to original source... - Jakobi T., Dieterich C.: Computational approaches for circular RNA analysis. - WIREs RNA 10: e1528, 2019.
Go to original source... - Japelaghi R.H., Haddad R., Garoosi G.-A.: Rapid and efficient isolation of high quality nucleic acids from plant tissues rich in polyphenols and polysaccharides. - Mol. Biotechnol. 49: 129-137, 2011.
Go to original source... - Jung J.-H., Lee S., Yun J. et al.: The miR172 target TOE3 represses AGAMOUS expression during Arabidopsis floral patterning. - Plant Sci. 215-216: 29-38, 2014.
Go to original source... - Kalwan G., Gill S.S., Pariyadarshni P. et al.: Approaches for identification and analysis of plant circular RNAs and their role in stress responses. - Environ. Exp. Bot. 205: 105099, 2023.
Go to original source... - Kozomara A., Griffiths-Jones S.: miRBase: annotating high confidence microRNAs using deep sequencing data. - Nucleic Acids Res. 42: D68-D73, 2014.
Go to original source... - Langmead B., Salzberg S.L.: Fast gapped-read alignment with Bowtie 2. - Nat. Methods 9: 357-359, 2012.
Go to original source... - Li C., Qin S., Bao L. et al.: Identification and functional prediction of circRNAs in Populus euphratica Oliv. heteromorphic leaves. - Genomics 112: 92-98, 2020.
Go to original source... - Li X., Yang L., Chen L.-L.: The biogenesis, functions, and challenges of circular RNAs. - Mol. Cell 71: 428-442, 2018.
Go to original source... - Liu J., Liu X., Zhang S. et al.: TarDB: an online database for plant miRNA targets and miRNA-triggered phased siRNAs. - BMC Genomics 22: 348, 2021.
Go to original source... - Ma P., Gao S., Zhang H.Y. et al.: Identification and characterization of circRNAs in maize seedlings under deficient nitrogen. - Plant Biol. 23: 850-860, 2021.
Go to original source... - Maere S., Heymans K., Kuiper M.: BiNGO: a Cytoscape plugin to assess overrepresentation of Gene Ontology categories in Biological Networks. - Bioinformatics 21: 3448-3449, 2005.
Go to original source... - Memczak S., Jens M., Elefsinioti A. et al.: Circular RNAs are a large class of animal RNAs with regulatory potency. - Nature 495: 333-338, 2013.
Go to original source... - Meng J., Shi L., Luan Y.: Plant microRNA-target interaction identification model based on the integration of prediction tools and support vector machine. - PLoS ONE 9: e103181, 2014.
Go to original source... - Rivals I., Personnaz L., Taing L., Potier M.-C.: Enrichment or depletion of a GO category within a class of genes: which test? - Bioinformatics 23: 401-407, 2007.
Go to original source... - Shannon P., Markiel A., Ozier O. et al.: Cytoscape: a software environment for integrated models of biomolecular interaction networks. - Genome Res. 13: 2498-2504, 2003.
Go to original source... - Shiri Y., Solouki M., Ebrahimie E. et al.: Unraveling the transcriptional complexity of compactness in sistan grape cluster. - Plant Sci. 270: 198-208, 2018.
Go to original source... - Shiri Y., Solouki M., Ebrahimie E. et al.: Gibberellin causes wide transcriptional modifications in the early stage of grape cluster development. - Genomics 112: 820-830, 2020.
Go to original source... - Szcze¶niak M.W., Maka³owska I.: miRNEST 2.0: a database of plant and animal microRNAs. - Nucleic Acids Res. 42: D74-D77, 2014.
Go to original source... - Szklarczyk D., Gable A.L., Nastou K.C.: The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. - Nucleic Acids Res. 49: D605-D612, 2021.
Go to original source... - Tong W., Yu J., Hou Y. et al.: Circular RNA architecture and differentiation during leaf bud to young leaf development in tea (Camellia sinensis). - Planta 248: 1417-1429, 2018.
Go to original source... - Trapnell C., Pachter L., Salzberg S.L.: TopHat: discovering splice junctions with RNA-Seq. - Bioinformatics 25: 1105-1111, 2009.
Go to original source... - Waititu J.K., Zhang C., Liu J., Wang H.: Plant non-coding RNAs: origin, biogenesis, mode of action and their roles in abiotic stress. - Int. J. Mol. Sci. 21: 8401, 2020.
Go to original source... - Wang T., Ping X., Cao Y. et al.: Genome-wide exploration and characterization of miR172/euAP2 genes in Brassica napus L. for likely role in flower organ development. - BMC Plant Biol. 19: 336, 2019.
Go to original source... - Westholm J.O., Miura P., Olson S. et al.: Genome-wide analysis of Drosophila circular RNAs reveals their structural and sequence properties and age-dependent neural accumulation. - Cell Rep. 9: 1966-1980, 2014.
Go to original source... - Wu H.-J., Ma Y.-K., Chen T. et al.: PsRobot: a web-based plant small RNA meta-analysis toolbox. - Nucleic Acids Res. 40: W22-W28, 2012.
Go to original source... - Xu X., Du T., Mao W. et al.: PlantcircBase 7.0: Full-length transcripts and conservation of plant circRNAs. - Plant Commun. 3: 100343, 2022.
Go to original source... - Ye C.-Y., Chen L., Liu C. et al.: Widespread noncoding circular RNAs in plants. - New Phytol. 208: 88-95, 2015.
Go to original source... - Yin J., Liu M., Ma D. et al.: Identification of circular RNAs and their targets during tomato fruit ripening. - Postharvest Biol. Tec. 136: 90-98, 2018.
Go to original source... - Zeng R.-F., Zhou J.-J., Hu C.-G., Zhang J.-Z.: Transcriptome-wide identification and functional prediction of novel and flowering-related circular RNAs from trifoliate orange (Poncirus trifoliata L. Raf.). - Planta 247: 1191-1202, 2018.
Go to original source... - Zhang J.-Z., Ai X.-Y., Guo W.-W. et al.: Identification of miRNAs and their target genes using deep sequencing and degradome analysis in trifoliate orange [Poncirus trifoliate (L.) Raf]. - Mol. Biotechnol. 51: 44-57, 2012.
Go to original source... - Zhang P., Fan Y., Sun X. et al.: A large-scale circular RNA profiling reveals universal molecular mechanisms responsive to drought stress in maize and Arabidopsis. - Plant J. 98: 697-713, 2019.
Go to original source... - Zhang X.-O., Wang H.-B., Zhang Y. et al.: Complementary sequence-mediated exon circularization. - Cell 159: 134-147, 2014.
Go to original source... - Zheng G., Wei W., Li Y. et al.: Conserved and novel roles of miR164-CUC2 regulatory module in specifying leaf and floral organ morphology in strawberry. - New Phytol. 224: 480-492, 2019.
Go to original source... - Zhu W., Miao Q., Sun D. et al.: The mitochondrial phosphate transporters modulate plant responses to salt stress via affecting ATP and gibberellin metabolism in Arabidopsis thaliana. - PLoS ONE 7: e43530, 2012.
Go to original source... - Zhu Y.-X., Jia J.-H., Yang L. et al.: Identification of cucumber circular RNAs responsive to salt stress. - BMC Plant Biol. 19: 164, 2019.
Go to original source... - Zuo J., Wang Q., Zhu B. et al.: Deciphering the roles of circRNAs on chilling injury in tomato. - Biochem. Bioph. Res. Co. 479: 132-138, 2016.
Go to original source... - Zuo J., Wang Y., Zhu B. et al.: Analysis of the coding and non-coding RNA transcriptomes in response to bell pepper chilling. - Int. J. Mol. Sci. 19: 2001, 2018.
Go to original source...



