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Computational prediction of mirnas in banana (musa spp.) and evaluation of their role in virus infection

By: Kokila Sajeev Anurag Mathew.
Contributor(s): Sony, K B (Guide).
Material type: materialTypeLabelBookPublisher: Vellayani Department of Plant Biotechnology, College of Agriculture 2018Description: 120p.Subject(s): Plant BiotechnologyDDC classification: 660.6 Online resources: Click here to access online Dissertation note: MSc Abstract: The study entitled "Computational prediction of miRNAs in banana (Musa spp.) and evaluation of their role in virus infection" was conducted at the Department of Plant Biotechnology, College of Agriculture, Vellayani, Thiruvananthapuram and Central Tuber Crops Research Institute (ICAR-CTCRI), Sreekariyam, Thiruvananthapuram during 2016–2018. The objective of the study was to predict miRNAs in banana using bioinformatics tools and to validate and analyze their expression during BBrMV infection. Computational miRNA prediction tool NovoMIR was used for the prediction of miRNAs in banana genome. Analysis was performed with all the gene coding or nucleotide sequences of banana and 85 pre-miRNAs were predicted from 11 chromosomes. Most of the pre-miRNAs ranged from 140–180 nt. G+C% content of pre-miRNAs ranged from 24-77% and A+U% content ranged from 23-76%. MFE of pre-miRNAs were found by using an online application RNAfold web server. MFE of pre-miRNAs ranged from -20 Kcal/mol to -194.4 Kcal/mol. AMFE of pre-miRNAs ranged from -18.9 Kcal/mol to -85.4 Kcal/mol. All the predicted pre-miRNAs by NOVOMIR had MFE ≤ -20 Kcal/mol. MFEI of pre-miRNAs ranged from 0.85 to 1.82. BLAST analysis of 85 pre-miRNAs against annotated mature miRNAs in miRBase resulted in 52 mature miRNAs. The targets for the 52 mature miRNAs were predicted using the web tool psRNATarget and were functionally annotated by Blast2Go analysis server. Targets were identified for 40 miRNAs in banana genome. A total of 124 targets were found, with each miRNA having more than one target. Validation of the predicted miRNAs were done in in vitro banana plants of variety Nendran. Three months old tissue culture plants were infected with BBrMV virus by using aphids. Twenty healthy aphids were released on BBrMV infected sucker for 30 min for acquisition feeding, after starving for 10 min. These aphids were further released on tissue culture plants for 12-14 h for infection. The infection process was repeated twice a day for 7 days. For experimental validation, five miRNAs and their target genes having role in biological process were selected and stem-loop/gene specific primers were designed. RNA was isolated from healthy and infected leaf samples and reverse transcribed to cDNA. Expression analysis using RT-qPCR showed the presence of all the five miRNAs in healthy and BBrMV infected leaf samples. Out of them, miR-3900-5p, miR-9112 and miR-5417 were found up-regulated, miR-6928-5p downregulated and miR-3900-5p remain unchanged in infected samples and there was a positive correlation with the expression of their corresponding target genes. In the present study, 52 mature miRNAs have been predicted using bioinformatics tools in banana genome. Targets for 40 miRNAs were identified in banana genome. The five miRNAs selected were validated along with their targets. Expression analysis showed regulation of four of them during virus infection, indicating the possibility of their role in stress response in banana. The remaining miRNAs predicted need validation.
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Reference Book 660.6 KOK/CO (Browse shelf) Not For Loan 174394

MSc

The study entitled "Computational prediction of miRNAs in banana (Musa spp.) and evaluation of their role in virus infection" was conducted at the
Department of Plant Biotechnology, College of Agriculture, Vellayani,
Thiruvananthapuram and Central Tuber Crops Research Institute (ICAR-CTCRI),
Sreekariyam, Thiruvananthapuram during 2016–2018. The objective of the study
was to predict miRNAs in banana using bioinformatics tools and to validate and
analyze their expression during BBrMV infection.
Computational miRNA prediction tool NovoMIR was used for the prediction
of miRNAs in banana genome. Analysis was performed with all the gene coding or
nucleotide sequences of banana and 85 pre-miRNAs were predicted from 11
chromosomes. Most of the pre-miRNAs ranged from 140–180 nt. G+C% content
of pre-miRNAs ranged from 24-77% and A+U% content ranged from 23-76%.
MFE of pre-miRNAs were found by using an online application RNAfold web
server. MFE of pre-miRNAs ranged from -20 Kcal/mol to -194.4 Kcal/mol. AMFE
of pre-miRNAs ranged from -18.9 Kcal/mol to -85.4 Kcal/mol. All the predicted
pre-miRNAs by NOVOMIR had MFE ≤ -20 Kcal/mol. MFEI of pre-miRNAs
ranged from 0.85 to 1.82.
BLAST analysis of 85 pre-miRNAs against annotated mature miRNAs in
miRBase resulted in 52 mature miRNAs. The targets for the 52 mature miRNAs
were predicted using the web tool psRNATarget and were functionally annotated
by Blast2Go analysis server.
Targets were identified for 40 miRNAs in banana genome. A total of 124
targets were found, with each miRNA having more than one target.
Validation of the predicted miRNAs were done in in vitro banana plants of
variety Nendran. Three months old tissue culture plants were infected with BBrMV
virus by using aphids. Twenty healthy aphids were released on BBrMV infected
sucker for 30 min for acquisition feeding, after starving for 10 min. These aphids
were further released on tissue culture plants for 12-14 h for infection. The infection
process was repeated twice a day for 7 days.
For experimental validation, five miRNAs and their target genes having role
in biological process were selected and stem-loop/gene specific primers were
designed. RNA was isolated from healthy and infected leaf samples and reverse
transcribed to cDNA. Expression analysis using RT-qPCR showed the presence of
all the five miRNAs in healthy and BBrMV infected leaf samples. Out of them,
miR-3900-5p, miR-9112 and miR-5417 were found up-regulated, miR-6928-5p
downregulated and miR-3900-5p remain unchanged in infected samples and there
was a positive correlation with the expression of their corresponding target genes.
In the present study, 52 mature miRNAs have been predicted using
bioinformatics tools in banana genome. Targets for 40 miRNAs were identified in
banana genome. The five miRNAs selected were validated along with their targets.
Expression analysis showed regulation of four of them during virus infection,
indicating the possibility of their role in stress response in banana. The remaining
miRNAs predicted need validation.

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