miRNAs and its Target gene
Predictions
-Shruthi & Surajit
Outline
Introduction to Neurons
 miRNA
 miRNA- mRNA interaction
 Results
 Computational algorithms for predicting the
miRNA targets
 Novel methods
 Conclusions & Future directions
 References
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NEURON
• Basis of the nervous system
• Send signals throughout the
body
• 3 main parts
1. Dendrites
2. Cell body
3. Axon
Dendrite Morphology & Neurological Disease
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The key functional role dendrites play in the establishment and maintenance
of proper neuronal circuitry is illustrated in a number of neuropathological
disease states including:
Down Syndrome, Rett Syndrome, Fragile-X Syndrome, Autism,
Fronto-Temporal Dementia, Alzheimer’s, Parkinson’s, Huntington’s,
Schizophrenia & Muscular Dystrophies.
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In each of these disease states there exists strong neuro-anatomical
correlation between specific dendritic abnormalities and cognitive
impairments.
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Dendritic arbor complexity reduced in aging brains, including branches,
synapses, and spines
Normal
Fragile X
Drosophila dendritic arborization (da) neurons provide an excellent
model for investigating class specific dendrite morphogenesis

Powerful genetic tool and relevance to higher organism

da neurons display stereotypical dendrite branching patterns with well characterized
neuronal morphology and invariant spatial distributions.
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(4) distinct morphological subclasses; ability to analyze how neuronal diversity arises;
how target fields are innervated; dendritic tiling and self-avoidance
MIRNAS – ESSENTIAL POST-TRANSCRIPTIONAL
REGULATORS OF GENE EXPRESSION
miRNAs are ~22 nucleotides in length
 The human genome encodes about 1000s miRNAs which may
target 60% of mammalian genes and are abundant in human cell
types
 Regulators of maintenance, development, cell proliferation,
differentiation
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• While miRNAs have emerged as critical post-transcriptional
modulators of gene expression in neuronal development, very
little is known regarding the roles of miRNA-mediated regulation of
dendritic morphogenesis
Why is it Important?
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miRNAs regulate the target mRNA and manage the
production of the final protein output.
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It causes various functions like cell differentiation,
proliferation, growth, mobility or apoptosis.
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Deregulation of the miRNA, plays critical role in the
pathogenesis of genetics and multifactorial diseases, and is
responsible for most human cancers.
HOW DO MIRNAS REGULATE CLASSSPECIFIC DENDRITIC PATTERNING?
1.
Which miRNAs may contribute to the specification of unique
dendritic morphologies of da neuron subclasses?
1.
What are the downstream
dendritic arborization?
targets of these miRNAs that control
miR expression profiling
To facilitate functional analyses of miRNA
regulation in Drosophila dendrite
morphogenesis, we conducted wholegenome miRNA expression profiling in
class I, III, and IV da neurons via magnetic
bead-based cell sorting.
 These analyses revealed 75 significantly expressed
miRs in da neuron subclasses and differential
expression for many of these miRs.
 Presented at right, are the top 30 differentially
expressed miRs
Gain of Function Results
miR-mediated promotion of dendritic branching complexity in Class I da neurons
M-7002
M-7159
M-7160
M-7101
M-7201
miR-mediated decrease of dendritic branching complexity in Class I da neurons
M-7247
WT
M-7127
M-7251
M-7106
M-7053
Target Prediction Methods
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The identification of this targets for the miRNA can be
identified by biochemical process or computational
process.
Although the computational method is in wide use for the
identification of the targets, the biochemical process of
identification is the most important as it would give us
more real life result.
Here we will discuss few of the Target Prediction
Algorithms and also discuss a method which we are trying
to implement.
Target ScaN
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Based on Sequence Search Similarity
Developed by Lewis et.al. 2003.
TargetScan predicts biological targets of miRNAs by searching for
the presence of conserved 8mer ( a region of exact match to
position 2-8 of mature miRNA, followed by Adeonisine) sites in
the UTR, (in mRNA;also called seed matches) that match the seed
region ( positions 2-7 of a matured miRNA) of each miRNA.
Then it extends each seed match with additional base pairs to the
miRNA as far as possible in each direction, allowing G:U pairs,
but stopping at mismatches
Using the RNAfold program (Hofacker et al., 1994), optimizes
base pairing of the remaining 3’ portion of the miRNA to the 35
bases of the UTR (in the mRNA) immediately 5’ of each seed
match thus extending each seed match to a longer “target site”
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using RNAeval (Hofacker et al., 1994) assigns a folding free
energy G to each such miRNA : target site interaction
(ignoring initiation free energy)
assigns a Z score to each UTR where
Z=∑e-Gk /T ,from k=1 to n
Then the algorithm sorts the UTRs by the z-score and give a
rank (Ri), and repeats the process for all UTRs.
The target prediction is done for targets having Z score
greater than or equal to a threshold(4.5)and Ri less than
threshold (200-350)
Miranda
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a)
b)
c)
Based on Sequence Search Similarity
Developed by Enright et.al. (2003)
In this method along 3 criterions are taken into consideration to predict
the Targets:
Sequence matching to assess whether 2 sequences are complimentary
and bind.
Free energy calculation to estimate the energetic of physical
interaction.
Evolutionary conservation as an informational filter. This can be
verified by 3 methods:
a specific miRNA independently matches orthologous UTRs in both
species
sequences of detected target sites in both species exhibit more than a
specified threshold of nucleotide identity (ID) with each other
(threshold >=80% in D.pseudoobscura and >=60% in A.Gambiae)
the positions of both target sites are equivalent according to a crossspecies UTR alignment
Miranda Scores
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Miranda scores are the addition of complementarity scores
and Free Energy
All miRNA sequences are scanned against the 3' UTR
datasets of D.Melanogaster,D.Pseudoobscura and
A.gambiae.
The thresholds used for hit detection are: initial SmithWaterman hybridization alignments must have S ≥ 80, and
the minimum energy of the duplex structure ∆G ≤ -14
kcal/mol.
Each hit between a miRNA and a UTR sequence is then
scored according to the total energy and total score of all hits
between those two sequences (complementary scores).
Then a scan is done to detect whether the sequence of the
targets are conserved or not.
Then the conserved target sites are sorted and ranked
according to their scores.
For each miRNA 10 highest ranking genes are considered as
targets.
PITA
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Algorithm based on RNA-RNA duplex considering free
energy minimization.
Devoloped by Kertescz et.al. 2007
In this paper the authors, experimentally proved that
mutations reducing target accessibility, reduce miRNA
mediated translational repression. They also deduced site
accessibility was an important method in miRNA-mRNA
interaction.
They deduced a Computational method to predict the same,
PITA( Possibility of interaction by Target Acessibility).
In this method the difference between the free energy gained
to form the miRNA-Target duplex and energy lost, to unpair
target to make it accessible to miRNA is considered as a
parameter for scoring.
CONS
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a.
b.
Most of the software are not accurate due to significant
fraction of false positivity, which are caused by
Limited comprehension of molecular basis of miRNAtarget pairing
Changes during of post transcriptional regulation
Solution
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With advanced experimental evidence of miRNA mechanism
of target degradation, the target prediction with miRNA and
gene expression profiles (obtained from microarrays) have
been proposed to predict functional mRNA-miRNA
relationship.
Also it is experimentally observed that miRNA tend to down
regulate the activity or expression of the target mRNA, it can
be very well proposed that mRNA and miRNA are anticorrelated in nature.
Some of the tools
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Web tools like mirGator and Diana-micro T web server, helps
in clarification of biological pathways, processes and
functions, through integration of target predictions with
information from different genes, functional and protein
database.
Another web tool MMIA , integrates miRNA and mRNA data
using significantly up-regulated and down-regulated data
only, not taking into consideration whole expression profile,
making it inefficient in the calculation of the whole genome
expression anti correlation degree.
MAGIA
A novel tool which integrates target prediction and gene
expression profile using Statistical tests like correlations and
Bayesian methods, for matched or un-matched expression profiles,
using miRNA-mRNA bipartite network reconstruction, gene
functional enrichment and pathway annotation for results
browsing.
Snapshot 1
Snapshot2
SnapShot3
SnAPsHOT 5
snapshot 6
MIAMI
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Our tool MIAMI, is based on miRNA-mRNA target prediction
tool, MAGIA.
The web tool can be broadly classified into two parts: Query
Section and Analysis Section.
The Query Section allows users to retrieve and browse different
target prediction databases including PITA, Miranda and
TargetScan.
The second part of the tool is dedicated on the analysis part of
the tool which is the statistical analysis of the expression data
which is given as input. It uses different statistical methods, like
correlation and Distance correlation methods to generate
miRNA-mRNA networks.
hOMe pAGE
Statistical Method
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We use Intragenic genes as the first step in understanding the
Target-miRNA relation.
We choose all the microarray results for Drosophilla
Melanogaster from Gene Expression Omnibus(GEO) and then
divide it in lieu of the platforms used(Agilent, Affymetrix, etc.)
Then we use 3 statistical tests to determine the targets: Pearson,
Distance correlation and Brownian Covariance.
Results
Conclusion
The statistical methods used in this cases helps to
identify the linear relationship (Pearson) as well as the
nonlinear relationship (Distance Correlation and
Brownian Covariance) between the Host-Target
relationship.
 Our tool uses expression value to understand the
Target-Host relationship and then uses the various
Target prediction software in understanding whether
the Targets hold good in other algorithms too, making
it a more robust approach.
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References
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