The document summarizes the process of predicting miRNAs related to late blight disease of potato using bioinformatics approaches. It discusses how miRNAs play an important role in host-pathogen interactions and regulates genes. The author proposes to identify potential pathogenic miRNAs and their targets in Solanum tuberosum in response to Phytophthora infestans infection using available EST sequences. A multi-step computational approach including screening ESTs, identifying pre-miRNAs, predicting secondary structures, and identifying targets is outlined. Relevant literature on plant miRNA prediction and P. infestans is also reviewed.

1. Prediction of miRNA related to late blight disease
of potato
1
Animesh kumar
20406
M. Sc. Bioinformatics
IASRI, New Delhi

2. Introduction
Motivation
Objectives
Review of Literature
Program of Research Work
Facilities Required
References
2

3.  Many recent studies show that miRNA (~22 nt) play a very important role in
host-pathogen interaction by silencing genes either by destructing or blocking of
translation of mRNA.
 They are often well conserved within plant and animal kingdoms (Millar and
Waterhouse, 2005), and are produced from either their own genes or from introns.
 Primary miRNA transcripts (pri-miRNA) are transcribed by RNA polymerase II
which contains self-complementary regions that fold to form imperfect double-
stranded stem-loop structures
3

4. 4
Bhatt et al. (2011). J Am Soc
Nephrology. 23(3): 400–404.
 Passenger strand associates with an AGO protein to form the miRNA induced
silencing complex (miRISC) and guides the complex to the target transcript (Bartel,
2004; Ghildiyal and Zamore, 2009).

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 Late blight of potato is the most important fungal disease in potato cultivation.
 P. infestans genome (~240 megabases), is the largest and most complex genomes in
Phytophthora family, which results from repetitive DNA accounting for ~74% of the
genome (Brian et al., 2009)
Fig. Repeat-driven genome expansion in Phytophthora infestans.
BJ Haas et al. (2009) Nature 000, 1-6 ,doi:10.1038/nature08358

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 The fast-evolving genes are localized to highly dynamic and expanded regions of the P.
infestans genome.
 Sexual reproduction in P. infestans develops genetic variation via recombination.
 This helps in rapid adaptability of the pathogen to host plants.

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 In P. infestans populations it is seen that there is constant genetic variation which is
destructive threat to a world that relies heavily on potato production (Garelik, 2002)
(Fry and Goodwin, 1997).
 Due to dynamic nature of P. infestans genome, study of miRNA in this is not very
imperative.
 So it is highly imperative to study the miRNA in counterpart of P. infestans in Solanum
tuberosum.

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 Different strategy are used to detect miRNAs such as through forward genetics, direct
cloning or using Bioinformatics approaches.
 Sometimes it becomes difficult to validate miRNAs using experimental approaches due
to the presence of degraded products of mRNAs, endogenous non-coding RNAs etc. in
the sample.
 As we know miRNAs have a unique secondary structure ranging from ~21 to 24
nucleotides in length and are conserved between different species.
 Also a large amount of data is available such as genomes and EST sequences in
database

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 On such basis researcher developed an expressed sequence tag (EST) approach to
identify miRNAs (Zhang et al., 2005)
 EST (expressed sequence tag) is a short sub-sequence of a cDNA sequence
 cDNA is complementary to mRNA, so the ESTs represent portions of expressed genes.

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 EST analysis has some substantial advantages over the other approaches such as:
(1) provides direct evidence for miRNA expression that cannot be inferred from genomic
sequence surveys, and
(2) miRNA identification can be conducted without highly specialized software

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 To predict potential pathogenic miRNAs related to late blight disease of potato
 To predict the targets of predicted miRNAs and their network.

12. Prediction of potential pathogenic miRNAs related to late blight disease of potato
12
Author Year Work done
Grad et al. 2003 Identified computationally and experimentally
microRNAs of C. elegans.
Bonnet et al. 2004 Presented a genome-wide computational approach to
detect miRNA genes in the Arabidopsis thaliana
genome
Wang et al. 2004 Predicted and identified 83 new microRNAs of
Arabidopsis thaliana and their mRNA targets.
Zhang et al. 2007 Identified 30 potential microRNAs and their targets in
cotton using bioinformatics approaches.

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Author Year Work done
Xie et al. 2007 Used previously known miRNAs from Arabidopsis, rice
and other plant species against both expressed sequence
tags (EST) and genomic survey sequence (GSS) databases
to search for potential miRNAs in B. napus.
Vetukuri et al. 2012 Gave evidence for small RNAs homologous to effector-
encoding genes and transposable elements in the oomycete,
Phytophthora infestans.
YongJun et al. 2012 Identified 43 new miRNAs using a homology search based
on expressed sequence tag (EST) analysis and miRNA
precursor secondary structure in Panicum miliaceum
Fahlgren et al. 2013 Showed Phytophthora have distinct endogenous small
RNA populations that include short interfering and
microRNAs.

14. 14
Author Year Work done
Pandey et al. 2007 Identified new stress-induced microRNA and their targets
in wheat using computational approach (which is
amalgamation of bioinformatics software and perl script).
In addition, 14 potential target genes were subsequently
predicted
Panda et al. 2014 Identified and characterized conserved miRNAs in garlic
expressed sequence tags (ESTs) through computational
means.
Cui et al. 2014 Predicted and validated potential pathogenic microRNAs
involved in Phytophthora infestans infection through
Bioinformatics approaches.

15. Prediction of the targets of the predicted miRNAs and the target network
15
Author Year Work done
Rhoades et al. 2002 Predicted regulatory targets for 14 Arabidopsis microRNAs
(miRNAs) by identifying mRNAs with near complementarity.
 This suggests that many plant miRNAs act similarly to small
interfering RNAs and direct mRNA cleavage.
 The targeting of developmental transcription factors suggests
that many plant miRNAs function during cellular differentiation
to clear key regulatory transcripts from daughter cell lineages.
Thomson et al. 2014 Summarizes and critiques the existing experimental techniques for
miRNA target identification. They laid more emphasis on
combining multiple strategies to obtain a comprehensive high-
confidence description of miRNA targeting networks.

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To search potential miRNAs, one has to go through the following steps
Contin…
Remove protein encoding sequence
Remove redundant miRNAs
Remove candidate not meeting the
criteria
Blastx
Candidate pathogenic Pre-miRNA
Novel potential miRNA related to late blight disease of potato
Protein sequence database
Prediction of secondary structure
Select sequences with 0-4 mismatch
without any gap and minimum E-value
IdentificationofmiRNAs
Screening of EST related to
Phytoplasma infection
sequences
EST sequences
EST sequences of Solanum tuberosum All known mature miRNAs
Blastn
Non- redundant miRNAs

17. Steps continue …
17
Figure – Flowchart of computational prediction of potential pathogenic miRNAs related to late
blight disease of potato
GO annotation
Refseq mRNA and assembled
EST sequences of potato
Characterization of Target Genes
miRNA Target Gene identification
PredictedmiRNAsTargetGene
Identification
Formation of miRNA- miRNA network
Identification of the key genes related to
Phytoplasma infection
Novel potential miRNA
related to late blight
disease of potato

18.  Library facility available at IARI/IASRI and computational facility available
will be utilised.
18

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Bonnet, E. et al. (2004). Detection of 91 potential conserved plant microRNAs in Arabidopsis
thaliana and Oryza sativa identifies important target genes. PNAS, 101(31), 11511–11516
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