Genome-Wide Mining, Characterization and Development of miRNA-SSRs in<i>Arabidopsis thaliana</i>

Kumar, Anuj; Chauhan, Aditi Aditi; Kompelli, Sai Kumar; Gahlaut, Vijay; Ijaq, Johny; Singh, K. P.; Gajula, Mnv Prasad; Suravajhala, Prashanth; Mishra, A. K.; Balyan, H. S.; Gupta, P. K.

doi:10.1101/203851

Cited by 12 publications

(14 citation statements)

References 52 publications

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“…Genic SSR markers designed from heat-stress-associated genes were screened over heat-tolerant and heat-susceptible wheat genotypes to study the genetic diversity and association between SSR markers and traits of heat stress tolerance ( Sharma et al, 2020 ; Manjunatha et al, 2021 ). Previous reports confirmed the presence of SSRs in pre-miRNA sequences of many plant species including Arabidopsis , rice, wheat, and Brassica in response to different abiotic stresses ( Ganie and Mondal, 2015 ; Kumar A. et al, 2017 ; Singh et al, 2017 ; Tabkhkar et al, 2020 ). There are very few reports for genetic diversity studies in heat-tolerant and heat-responsive wheat genotypes that used miRNA-SSR as molecular markers.…”

Section: Introductionsupporting

confidence: 57%

Discovery of miRNAs and Development of Heat-Responsive miRNA-SSR Markers for Characterization of Wheat Germplasm for Terminal Heat Tolerance Breeding

et al. 2021

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A large proportion of the Asian population fulfills their energy requirements from wheat (Triticum aestivum L.). Wheat quality and yield are critically affected by the terminal heat stress across the globe. It affects approximately 40% of the wheat-cultivating regions of the world. Therefore, there is a critical need to develop improved terminal heat-tolerant wheat varieties. Marker-assisted breeding with genic simple sequence repeats (SSR) markers have been used for developing terminal heat-tolerant wheat varieties; however, only few studies involved the use of microRNA (miRNA)-based SSR markers (miRNA-SSRs) in wheat, which were found as key players in various abiotic stresses. In the present study, we identified 104 heat-stress-responsive miRNAs reported in various crops. Out of these, 70 miRNA-SSR markers have been validated on a set of 20 terminal heat-tolerant and heat-susceptible wheat genotypes. Among these, only 19 miRNA-SSR markers were found to be polymorphic, which were further used to study the genetic diversity and population structure. The polymorphic miRNA-SSRs amplified 61 SSR loci with an average of 2.9 alleles per locus. The polymorphic information content (PIC) value of polymorphic miRNA-SSRs ranged from 0.10 to 0.87 with a mean value of 0.48. The dendrogram constructed using unweighted neighbor-joining method and population structure analysis clustered these 20 wheat genotypes into 3 clusters. The target genes of these miRNAs are involved either directly or indirectly in providing tolerance to heat stress. Furthermore, two polymorphic markers miR159c and miR165b were declared as very promising diagnostic markers, since these markers showed specific alleles and discriminated terminal heat-tolerant genotypes from the susceptible genotypes. Thus, these identified miRNA-SSR markers will prove useful in the characterization of wheat germplasm through the study of genetic diversity and population structural analysis and in wheat molecular breeding programs aimed at terminal heat tolerance of wheat varieties.

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Section: Introductionsupporting

confidence: 57%

Discovery of miRNAs and Development of Heat-Responsive miRNA-SSR Markers for Characterization of Wheat Germplasm for Terminal Heat Tolerance Breeding

et al. 2021

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“…The study suggested that a complex biological process mediated by miRNA-mRNA networks controls the seed type in pomegranate. The presence of genetic variations in pre-miRNAs and miRNAs are known to affect quantitative trait expression ( Ferrao et al, 2015 ), hence DNA markers may be developed from the miRNA regions to assist the procedure of trait improvement ( Kumar et al, 2017 ). Study by Joy et al (2018) showed presence of SSRs in pre-miRNAs and the authors proposed a role for alternative splicing in creating mature RNA isoforms in response to stress.…”

Section: Introductionmentioning

confidence: 99%

Novel miRNA-SSRs for Improving Seed Hardness Trait of Pomegranate (Punica granatum L.)

et al. 2022

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Present research discovered novel miRNA-SSRs for seed type trait from 761 potential precursor miRNA sequences of pomegranate. SSR mining and BLASTx of the unique sequences identified 69 non-coding pre-miRNA sequences, which were then searched for BLASTn homology against Dabenzi genome. Sixty three true pri-miRNA contigs encoding 213 pre-miRNAs were predicted. Analysis of the resulting sequences enabled discovery of SSRs within pri-miRNA (227) and pre-miRNA sequences (79). A total of 132 miRNA-SSRs were developed for seed type trait from 63 true pri-miRNAs, of which 46 were specific to pre-miRNAs. Through ePCR, 123 primers were validated and mapped on eight Tunisia chromosomes. Further, 80 SSRs producing specific amplicons were ePCR-confirmed on multiple genomes i.e. Dabenzi, Taishanhong, AG2017 and Tunisia, yielding a set of 63 polymorphic SSRs (polymorphism information content ≥0.5). Of these, 32 miRNA-SSRs revealed higher polymorphism level (89.29%) when assayed on six pomegranate genotypes. Furthermore, target prediction and network analysis suggested a possible association of miRNA-SSRs i.e. miRNA_SH_SSR69, miRNA_SH_SSR36, miRNA_SH_SSR103, miRNA_SH_SSR35 and miRNA_SH_SSR53 with seed type trait. These miRNA-SSRs would serve as important genomic resource for rapid and targeted improvement of seed type trait of pomegranate.

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“…Plants experience several environmental stresses that adversely affect their growth and development [71,72]. Abiotic stresses such as cold, drought, heat, light, oxidation, and salt are the major ones that limit the growth and productivity of crop plants to a large extent [73,74].…”

Section: Discussionmentioning

confidence: 99%

ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo K-Tuple Nucleotide Compositional Features

Meher

Begam

Sahu

et al. 2022

IJMS

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MicroRNAs (miRNAs) play a significant role in plant response to different abiotic stresses. Thus, identification of abiotic stress-responsive miRNAs holds immense importance in crop breeding programmes to develop cultivars resistant to abiotic stresses. In this study, we developed a machine learning-based computational method for prediction of miRNAs associated with abiotic stresses. Three types of datasets were used for prediction, i.e., miRNA, Pre-miRNA, and Pre-miRNA + miRNA. The pseudo K-tuple nucleotide compositional features were generated for each sequence to transform the sequence data into numeric feature vectors. Support vector machine (SVM) was employed for prediction. The area under receiver operating characteristics curve (auROC) of 70.21, 69.71, 77.94 and area under precision-recall curve (auPRC) of 69.96, 65.64, 77.32 percentages were obtained for miRNA, Pre-miRNA, and Pre-miRNA + miRNA datasets, respectively. Overall prediction accuracies for the independent test set were 62.33, 64.85, 69.21 percentages, respectively, for the three datasets. The SVM also achieved higher accuracy than other learning methods such as random forest, extreme gradient boosting, and adaptive boosting. To implement our method with ease, an online prediction server “ASRmiRNA” has been developed. The proposed approach is believed to supplement the existing effort for identification of abiotic stress-responsive miRNAs and Pre-miRNAs.

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Genome-Wide Mining, Characterization and Development of miRNA-SSRs inArabidopsis thaliana

Cited by 12 publications

References 52 publications

Discovery of miRNAs and Development of Heat-Responsive miRNA-SSR Markers for Characterization of Wheat Germplasm for Terminal Heat Tolerance Breeding

Discovery of miRNAs and Development of Heat-Responsive miRNA-SSR Markers for Characterization of Wheat Germplasm for Terminal Heat Tolerance Breeding

Novel miRNA-SSRs for Improving Seed Hardness Trait of Pomegranate (Punica granatum L.)

ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo K-Tuple Nucleotide Compositional Features

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