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Uncovering leaf rust responsive miRNAs in wheat (Triticum aestivum L.) using high-throughput sequencing and prediction of their targets through degradome analysis

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Abstract

Main conclusion

Deep sequencing identified 497 conserved and 559 novel miRNAs in wheat, while degradome analysis revealed 701 targets genes. QRT-PCR demonstrated differential expression of miRNAs during stages of leaf rust progression.

Bread wheat (Triticum aestivum L.) is an important cereal food crop feeding 30 % of the world population. Major threat to wheat production is the rust epidemics. This study was targeted towards identification and functional characterizations of micro(mi)RNAs and their target genes in wheat in response to leaf rust ingression. High-throughput sequencing was used for transcriptome-wide identification of miRNAs and their expression profiling in retort to leaf rust using mock and pathogen-inoculated resistant and susceptible near-isogenic wheat plants. A total of 1056 mature miRNAs were identified, of which 497 miRNAs were conserved and 559 miRNAs were novel. The pathogen-inoculated resistant plants manifested more miRNAs compared with the pathogen infected susceptible plants. The miRNA counts increased in susceptible isoline due to leaf rust, conversely, the counts decreased in the resistant isoline in response to pathogenesis illustrating precise spatial tuning of miRNAs during compatible and incompatible interaction. Stem-loop quantitative real-time PCR was used to profile 10 highly differentially expressed miRNAs obtained from high-throughput sequencing data. The spatio-temporal profiling validated the differential expression of miRNAs between the isolines as well as in retort to pathogen infection. Degradome analysis provided 701 predicted target genes associated with defense response, signal transduction, development, metabolism, and transcriptional regulation. The obtained results indicate that wheat isolines employ diverse arrays of miRNAs that modulate their target genes during compatible and incompatible interaction. Our findings contribute to increase knowledge on roles of microRNA in wheat–leaf rust interactions and could help in rust resistance breeding programs.

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Acknowledgments

We are thankful to Centre of Excellence, Technical Education Quality Improvement Program-II, (Grant No. NPIU/TEQIP II/FIN/31/158) for providing financial support and BTISNet SubDIC (BT/BI/04/065/04) for providing facilities for bioinformatics analyses. D. K. is grateful to the Council of Scientific and Industrial Research [9/554 (0026) 2010-EMR-I] and SD to DST INSPIRE (IF140725) for fellowships. We thank Mr. Saket Chandra of BIT, Mesra, and Dr. Fritz Thümmler, CEO, vertis Biotechnolgie AG, Germany, for helpful discussions and Dr Sunil K Mukherjee of ICGEB New Delhi for critical suggestions on the manuscript.

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  1. Dharmendra Singh

    Present address: QAAFI, Centre of Plant Science, The University of Queensland, Brisbane, QLD, 4072, Australia

Authors and Affiliations

  1. Department of Bio-Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835215, India

    Dhananjay Kumar, Summi Dutta, Dharmendra Singh, Manish Kumar & Kunal Mukhopadhyay

  2. Division of Genetics, Indian Agricultural Research Institute, New Delhi, 110012, India

    Kumble Vinod Prabhu

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Correspondence to Kunal Mukhopadhyay.

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Figure S1 Analysis of sRNA samples on a Shimadzu MultiNA Microchip Electrophoresis System. a small RNA samples, M = RNA ladder. b PCR-amplified cDNA, M = 100 bp ladder. c Fractionated and amplified cDNA pool prepared for Illumina sequencing, M = 100 bp ladder. The miRNA library names are mentioned on top of respective lanes

Figure S2 Analysis of RNA samples for degradome preparation on a Shimadzu MultiNA Microchip Electrophoresis System. a total RNA samples, M = RNA ladder. b PCR-amplified cDNA, M = 100 bp ladder. The degradome library names are mentioned on top of respective lanes

Figure S3 Polyacrylamide (6 %) Gel analysis of the MmeI-digested cDNA. The 42-bp-long-released 5′ cDNA fragments are marked. M = 20 bp ladder. The library names are mentioned on top of the respective lanes

Figure S4 Analysis of the PCR-amplified 131-bp-long MmeI fragments. M = 100 bp ladder. The library names are mentioned on top of the respective lanes

Figure S5 Target ‘t’-plots. a Degradome D1. b Degradome D2. c Degradome D3. d Degradome D1

Figure S6 GO terms of the target genes identified in D1 and their enrichment analysis. (a) Analysis of the targets within the molecular function category. (b) Analysis of the targets within the biological process category. (c) Analysis of the targets within the cellular component category. This analysis was performed using the online tool Blast2GO.

Figure S7 GO terms of the target genes identified in D2 and their enrichment analysis. (a) Analysis of the targets within the molecular function category. (b) Analysis of the targets within the biological process category. (c) Analysis of the targets within the cellular component category. This analysis was performed using the online tool Blast2GO.

Figure S8 GO terms of the target genes identified in D3 and their enrichment analysis. (a) Analysis of the targets within the molecular function category. (b) Analysis of the targets within the biological process category. (c) Analysis of the targets within the cellular component category. This analysis was performed using the online tool Blast2GO.

Figure S9 GO terms of the target genes identified in D4 and their enrichment analysis. (a) Analysis of the targets within the molecular function category. (b) Analysis of the targets within the biological process category. (c) Analysis of the targets within the cellular component category. This analysis was performed using the online tool Blast2GO.

Figure S10 KEGG pathway map of oxidative phosphorylation showing the identified conserved miRNA bdi-miR168 targeting the enzyme NADH dehydrogenase (ID 1.6.5.3, shown in Red arrow)

Table S1 Descriptions of cDNA samples for preparation of small RNA libraries

Table S2 Descriptions of cDNA samples for preparation of degradome libraries

Table S3 List of primers used for Real-Time PCR-based expression profiling of selected miRNAs

Table S4 List of conserved miRNAs and their expression profiles

Table S5a List of novel miRNAs and their expression profiles

Table S5b Novel miRNAs identified using wheat draft chromosome

Table S5c Novel miRNAs identified using ESTs as reference

Table S5d List of differentially expressed miRNAs between S-Mmi and S-PImi

Table S5e List of differentially expressed miRNAs between R-Mmi and R-PImi

Table S6a Identified target genes and their characteristics in S-Mmi libraries vs. D1 libraries

Table S6b Identified target genes and their characteristics in S-PImi libraries vs. D2 libraries

Table S6c Identified target genes and their characteristics in R-Mmi libraries vs. D3 libraries

Table S6d Identified target genes and their characteristics in R-PImi libraries vs. D4 libraries

Table S7 List of pathways identified through the KEGG analysis

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Kumar, D., Dutta, S., Singh, D. et al. Uncovering leaf rust responsive miRNAs in wheat (Triticum aestivum L.) using high-throughput sequencing and prediction of their targets through degradome analysis. Planta 245, 161–182 (2017). https://doi.org/10.1007/s00425-016-2600-9

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