miRNAs and its Target gene Predictions

-Shruthi & Surajit

Introduction to Neurons miRNA miRNA- mRNA interaction Results Computational algorithms for predicting

the miRNA targets Novel methods Conclusions & Future directions References

Outline

• Basis of the nervous system

• Send signals throughout the body

• 3 main parts

1. Dendrites

2. Cell body

3. Axon

NEURON

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.

In each of these disease states there exists strong neuro-anatomical correlation between specific dendritic abnormalities and cognitive impairments.

Dendritic arbor complexity reduced in aging brains, including branches, synapses, and spines

Dendrite Morphology & Neurological Disease

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.

(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

• 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?

miRNAs regulate the target mRNA and manage the production of the final protein output.

It causes various functions like cell differentiation, proliferation, growth, mobility or apoptosis.

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 CLASS-SPECIFIC DENDRITIC PATTERNING?

1. Which miRNAs may contribute to the specification of unique dendritic morphologies of da neuron subclasses?

2. What are the downstream targets of these miRNAs that control dendritic arborization?

miR expression profiling

To facilitate functional analyses of miRNA regulation in Drosophila dendrite morphogenesis, we conducted whole-genome 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-7101

M-7159 M-7160 M-7201

WT M-7247 M-7106

M-7053M-7251M-7127

miR-mediated decrease of dendritic branching complexity in Class I da neurons

Target Prediction Methods

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

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”

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-G

k /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

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) a specific miRNA independently matches orthologous UTRs in

both speciesb) 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)

c) the positions of both target sites are equivalent according to a cross-species UTR alignment

Miranda Scores 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 Smith-Waterman 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

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

Most of the software are not accurate due to significant fraction of false positivity, which are caused by

a. Limited comprehension of molecular basis of miRNA- target pairing

b. Changes during of post transcriptional regulation

Solution

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 anti-correlated in nature.

Some of the tools

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 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

MIAMI miRNA query

single miRNA query – step 1

single miRNA query – step 2

single miRNA query – step 2

single miRNA query – step 2

single miRNA query Results

Statistical Method

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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