Abstract
Knowledgebase for rice sheath blight information (KRiShI) is a manually curated user-friendly knowledgebase for rice sheath blight (SB) disease that allows users to efficiently mine, visualize, search, benchmark, download, and update meaningful data and information related to SB using its easy and interactive interface. KRiShI collects and integrates widely scattered and unstructured information from various scientific literatures, stores it under a single window, and makes it available to the community in a user-friendly manner. From basic information, best management practices, host resistance, differentially expressed genes, proteins, metabolites, resistance genes, pathways, and OMICS scale experiments, KRiShI presents these in the form of easy and comprehensive tables, diagrams, and pictures. The “Search” tab allows users to verify if their input rice gene id(s) are Rhizoctonia solani (R. solani) responsive and/or resistant. KRiShI will serve as a valuable resource for easy and quick access to data and information related to rice SB disease for both the researchers and the farmers. To encourage community curation a submission facility is made available. KRiShI can be found at http://www.tezu.ernet.in/krishi.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Arnaboldi V, Raciti D, Van Auken K, Chan JN, Müller HM, Sternberg PW (2020) Text mining meets community curation: a newly designed curation platform to improve author experience and participation at WormBase. Database. https://doi.org/10.1093/database/baaa006
Bag MK, Yadav M, Mukherjee AK (2016) Bioefficacy of strobilurin based fungicides against rice sheath blight disease. Transcriptomics 4:2. https://doi.org/10.4172/2329-8936.1000128
Chen L, Ai P, Zhang J, Deng Q, Wang S, Li S, Zhu J, Li P, Zheng A (2016) RSIADB, a collective resource for genome and transcriptome analyses in Rhizoctonia solani AG1 IA. Database. https://doi.org/10.1093/database/baw031
Das A, Moin M, Sahu A, Kshattry M, Kirti PB, Barah P (2022) Time-course transcriptome analysis identifies rewiring patterns of transcriptional regulatory networks in rice under Rhizoctonia solani infection. Gene 828:146468. https://doi.org/10.1016/j.gene.2022.146468
De Yuan P, Xu XF, Hong WJ, Wang ST, Jia XT, Liu Y, Li S, Li ZM, Sun Q, Mei Q, Li S (2020) Transcriptome analysis of rice leaves in response to Rhizoctonia solani infection and reveals a novel regulatory mechanism. Plant Biotechnol Rep 14:559–573. https://doi.org/10.1007/s11816-020-00630-9
Ghosh S, Kanwar P, Jha G (2017) Alterations in rice chloroplast integrity, photosynthesis and metabolome associated with pathogenesis of Rhizoctonia solani. Sci Rep 7:1–2. https://doi.org/10.1038/srep41610
Gomez-Cabrero D, Abugessaisa I, Maier D, Teschendorff A, Merkenschlager M, Gisel A, Ballestar E, Bongcam-Rudloff E, Conesa A, Tegnér J (2014) Data integration in the era of omics: current and future challenges. BMC Syst Biol 8:1–10. https://doi.org/10.1186/1752-0509-8-S2-I1
Howe D, Costanzo M, Fey P, Gojobori T, Hannick L, Hide W, Hill DP, Kania R, Schaeffer M, St Pierre S, Twigger S (2008) The future of biocuration. Nature 455:47–50. https://doi.org/10.1038/455047a
International Society for Biocuration (2018) Biocuration: Distilling data into knowledge. PLoS Biol 16:e2002846. https://doi.org/10.1371/journal.pbio.2002846
Kahn SD (2011) On the future of genomic data. Science 331:728–729. https://doi.org/10.1126/science.1197891
Kanehisa M, Goto S (2000) Comprehensive gene and pathway analysis of cervical cancer progression. Nucleic Acids Res 28:27–30. https://doi.org/10.1093/nar/28.1.27
Kanehisa M, Sato Y, Kawashima M (2022) KEGG mapping tools for uncovering hidden features in biological data. Protein Sci 31:47–53. https://doi.org/10.1002/pro.4172
Karmakar S, Datta K, Molla KA, Gayen D, Das K, Sarkar SN, Datta SK (2019) Proteo-metabolomic investigation of transgenic rice unravels metabolic alterations and accumulation of novel proteins potentially involved in defence against Rhizoctonia solani. Sci Rep 9:1–6. https://doi.org/10.1038/s41598-019-46885-3
Kaur P, Singh A, Chana I (2021) Computational techniques and tools for omics data analysis: state-of-the-art, challenges, and future directions. Arch Comput Methods Eng 28:4595–4631. https://doi.org/10.1007/s11831-021-09547-0
Kawahara Y, de la Bastide M, Hamilton JP, Kanamori H, McCombie WR, Ouyang S, Schwartz DC, Tanaka T, Wu J, Zhou S, Childs KL (2013) Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data. Rice 6:1–10. https://doi.org/10.1186/1939-8433-6-4
Lee B, Zhang S, Poleksic A, Xie L (2020) Heterogeneous multi-layered network model for omics data integration and analysis. Front Genet 1381. https://doi.org/10.3389/fgene.2019.01381
Lilly JJ, Subramanian B (2019) Gene network mediated by WRKY13 to regulate resistance against sheath infecting fungi in rice (Oryza sativa L.). Plant Sci 280:269–282. https://doi.org/10.1016/j.plantsci.2018.12.017
Ma H, Sheng C, Qiao L, Zhao H, Niu D (2020) A comparative proteomic approach to identify defence-related proteins between resistant and susceptible rice cultivars challenged with the fungal pathogen Rhizoctonia solani. Plant Growth Regul 90:73–88. https://doi.org/10.1007/s10725-019-00551-w
Margani R, Widadi S (2018) Utilizing Bacillus to inhibit the growth and infection by sheath blight pathogen, Rhizoctonia solani in rice. IOP Conference Series: Earth and Environmental Science. IOP Publishing, Bristol, p 012070
Mering CV, Huynen M, Jaeggi D, Schmidt S, Bork P, Snel B (2003) STRING: a database of predicted functional associations between proteins. Nucleic Acids Res 31:258–261. https://doi.org/10.1093/nar/gkg034
Molla KA, Karmakar S, Molla J, Bajaj P, Varshney RK, Datta SK, Datta K (2020) Understanding sheath blight resistance in rice: the road behind and the road ahead. Plant Biotechnol J 18:895–915. https://doi.org/10.1111/pbi.13312
Nishida K, Ono K, Kanaya S, Takahashi K (2014) KEGGscape: a Cytoscape app for pathway data integration. F1000Research 3. https://doi.org/10.12688/f1000research.4524.1
Papoutsoglou EA, Faria D, Arend D, Arnaud E, Athanasiadis IN, Chaves I, Coppens F, Cornut G, Costa BV, Ćwiek-Kupczyńska H, Droesbeke B (2020) Enabling reusability of plant phenomic datasets with MIAPPE 1.1. New Phytol 227:260–273. https://doi.org/10.1111/nph.16544
Peng X, Hu Y, Tang X, Zhou P, Deng X, Wang H, Guo Z (2012) Constitutive expression of rice WRKY30 gene increases the endogenous jasmonic acid accumulation, PR gene expression and resistance to fungal pathogens in rice. Planta 236:1485–1498. https://doi.org/10.1007/s00425-012-1698-7
Peng X, Wang H, Jang JC, Xiao T, He H, Jiang D, Tang X (2016) OsWRKY80-OsWRKY4 module as a positive regulatory circuit in rice resistance against Rhizoctonia solani. Rice 9:1–4. https://doi.org/10.1186/s12284-016-0137-y
Prathi NB, Palit P, Madhu P, Ramesh M, Laha GS, Balachandran SM, Madhav MS, Sundaram RM, Mangrauthia SK (2018) Proteomic and transcriptomic approaches to identify resistance and susceptibility related proteins in contrasting rice genotypes infected with fungal pathogen Rhizoctonia solani. Plant Physiol Biochem 130:258–266. https://doi.org/10.1016/j.plaphy.2018.07.012
Roy N, Gaikwad M, Bhattacharrya DK, Barah P (2021) Identification of systems level molecular signatures from glioblastoma multiforme derived extracellular vesicles. J Mol Neurosci 71:1156–1167. https://doi.org/10.1007/s12031-020-01738-x
Sahu A, Das A, Saikia K, Barah P (2020) Temperature differentially modulates the transcriptome response in Oryza sativa to Xanthomonas oryzae pv. oryzae infection. Genomics 112:4842–4852. https://doi.org/10.1016/j.ygeno.2020.08.028
Sakai H, Lee SS, Tanaka T, Numa H, Kim J, Kawahara Y, Wakimoto H, Yang CC, Iwamoto M, Abe T, Yamada Y (2013) Rice Annotation Project Database (RAP-DB): an integrative and interactive database for rice genomics. Plant and Cell Physiol 54:e6–e6. https://doi.org/10.1093/pcp/pcs183
Shamim MD, Sharma D, Bisht D, Hussain R, Khan NA, Pandey P, Kesari R, Singh KN (2017) Molecular tools for controlling of sheath blight disease of rice and its management. In: Biotic Stress Management in Rice. Apple Academic Press, Florida, pp 109–148
Shi W, Zhao SL, Liu K, Sun YB, Ni ZB, Zhang GY, Tang HS, Zhu JW, Wan BJ, Sun HQ, Dai JY (2020) Comparison of leaf transcriptome in response to Rhizoctonia solani infection between resistant and susceptible rice cultivars. BMC Genomics 21:1–6. https://doi.org/10.1186/s12864-020-6645-6
Silva J, Scheffler B, Sanabria Y, De Guzman C, Galam D, Farmer A, Woodward J, May G, Oard J (2012) Identification of candidate genes in rice for resistance to sheath blight disease by whole genome sequencing. Theor Appl Genet 124:63–74. https://doi.org/10.1007/s00122-011-1687-4
Singh P, Mazumdar P, Harikrishna JA, Babu S (2019) Sheath blight of rice: a review and identification of priorities for future research. Planta 250:1387–1407. https://doi.org/10.1007/s00425-019-03246-8
Suharti WS, Nose A, Zheng SH (2016) Metabolite profiling of sheath blight disease resistance in rice: in the case of positive ion mode analysis by CE/TOF-MS. Plant Prod Sci 19:279–290. https://doi.org/10.1080/1343943X.2016.1140006
Sun C, Hu Z, Zheng T, Lu K, Zhao Y, Wang W, Shi J, Wang C, Lu J, Zhang D, Li Z (2017) RPAN: rice pan-genome browser for∼ 3000 rice genomes. Nucleic Acids Res 45:597–605. https://doi.org/10.1093/nar/gkw958
UniProt Consortium (2019) UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res 47:D506–D515. https://doi.org/10.1093/nar/gky1049
Van Bel M, Diels T, Vancaester E, Kreft L, Botzki A, Van de Peer Y, Coppens F, Vandepoele K (2018) PLAZA 4.0: an integrative resource for functional, evolutionary and comparative plant genomics. Nucleic Acids Res 46:D1190–D1196. https://doi.org/10.1093/nar/gkx1002
Wang A, Shu X, Jing X, Jiao C, Chen L, Zhang J, Ma L, Jiang Y, Yamamoto N, Li S, Deng Q (2021) Identification of rice (Oryza sativa L.) genes involved in sheath blight resistance via a genome-wide association study. Plant Biotechnol J 19:1553. https://doi.org/10.1111/pbi.13569
Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten JW, da Silva Santos LB, Bourne PE, Bouwman J (2016) The FAIR guiding principles for scientific data management and stewardship. Scientific Data 3:1–9. https://doi.org/10.1038/sdata.2016.18
Yellareddygari SK, Reddy MS, Kloepper JW, Lawrence KS, Fadamiro H (2014) Rice sheath blight: a review of disease and pathogen management approaches. J Plant Pathol Microb 5:1. https://doi.org/10.4172/2157-7471.1000241
Yuan Z, Zhang Y, Xu G, Bi D, Qu H, Zou X, Gao X, Yang H, He H, Wang X, Bao J (2018) Comparative transcriptome analysis of Rhizoctonia solani-resistant and-susceptible rice cultivars reveals the importance of pathogen recognition and active immune responses in host resistance. J Plant Biol 61:143–158. https://doi.org/10.1007/s12374-017-0209-6
Zhang Y, Wang L (2005) The WRKY transcription factor superfamily: its origin in eukaryotes and expansion in plants. BMC Evol Biol 5:1–2. https://doi.org/10.1186/1471-2148-5-1
Zhang J, Chen L, Fu C, Wang L, Liu H, Cheng Y, Li S, Deng Q, Wang S, Zhu J, Liang Y (2017) Comparative transcriptome analyses of gene expression changes triggered by Rhizoctonia solani AG1 IA infection in resistant and susceptible rice varieties. Front Plant Sci 8:1422. https://doi.org/10.3389/fpls.2017.01422
Acknowledgements
We acknowledge Amit Kumar for technical support in developing the website.
Funding
This research was supported by funding received from the Department of Biotechnology (DBT) (grant id—BT/PR24757/NER/95/843/2017) for the DBT-Twinning project- “Integrative system biology approach to identify the molecular response signatures in Rice during concurrent biotic (Rhizoctonia solani) and abiotic (heat) stresses” to Dr. Pankaj Barah.
Author information
Authors and Affiliations
Contributions
Supervision: Pankaj Barah; funding acquisition: Pankaj Barah; data curation: Pankaj Barah, Akash Das, Asutosh Mishra, Anurag Kashyap, Mahantesha B.N. Naika; formal analysis: Pankaj Barah, Akash Das; methodology: Pankaj Barah, Akash Das, Asutosh Mishra; visualization: Akash Das, Asutosh Mishra; writing — original draft: Pankaj Barah, Akash Das, Asutosh Mishra; writing — review & editing: Pankaj Barah, Akash Das.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Das, A., Mishra, A., Kashyap, A. et al. “KRiShI”: a manually curated knowledgebase on rice sheath blight disease. Funct Integr Genomics 22, 1403–1410 (2022). https://doi.org/10.1007/s10142-022-00899-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10142-022-00899-9