Micrornas?

MicroRNAs? • MicroRNAs are ~22nt • ~200 miRNAs are known in plants and animals • 0.5 - 1% of genome codes for miRNA • miRNAs are conserved across mammals and often extends to invertebrate homologs • Show temporal and spatial expression patterns • Function ?

Earlier attempts - Target Identification • Prediction of Plant MicroRNA Targets (Rhoades et al., 2002)

• Identification of Drosophila MicroRNA Targets (Stark et al, 2003)

Prediction of Mammalian MicroRNA Targets Cell, Vol. 115, 787–798, December 26, 2003

Benjamin P. Lewis,1,4 I-hung Shih,2,4 Matthew W. Jones-Rhoades,1,2 David P. Bartel,1,2 and Christopher B. Burge1

1

Department of Biology, Massachusetts Institute of Technology 2

Whitehead Institute for Biomedical Research

Cambridge Center, Cambridge, Massachusetts 02142

A Lexicon Seed: 2-8 nucleotides in the 5’ of the miRNA Target miRNA

GAGCCUU-----GAUAAUACUUGAC ||||||| UCGGAUAGGACCUA--AUGAACUU

Seed match: Corresponding watson &crick paired nucleotides in Target. Redundant set:

AUU AUC AUA ILE

GUU GUC GUA VAL

Nonredundant set:

AUU GUU ILE VAL

Renilla Luciferase assay: Done in order to asses the relative efficiency of regulatory sequence.

Geneontology: Provides information to which class the gene product belongs to. Primarily classifies the gene product as per molecular function, Biological process and cellular component.

Method in Brief Collecting set of miRNAs

Collecting set of 3’UTRs

Run Algorithm for miRNA:3’UTR interaction

Prediction of Targets

Target Scan 3’ UTR

1.

2.

3.

G:U G:A A:C

4. Base pair optimization by RNAfold 5. Assigns free energy by RNAeval

Steps: n

6. Assigns

Z score given by Where, Z = Σ e- G / T k

n = No. of seed matches in UTR

Gkk=1 = Free energy of miRNA:Target site interaction for the kth site ( kcal/mole)

7. Sorts UTR by Z score & assigns Rank 8. Repeats with set of UTRs from each organism 9. Predicts Targets

MicroRNA Sets Human miRNAs

Mouse miRNAs

Fugu miRNAs

Identical sequence homologs

Clear homologs

79 miRNAs

Nonredundant pan-mammalian mirna (nrMamm) Set

Human miRNAs

Mouse miRNAs

Fugu miRNAs

Identical sequence homologs

55 miRNAs

Nonredundant pan-vertebrate mirna (nrVert) Set

Target Analysis 79 perfectly conserved mammalian miRNAs

14,539 human UTRs

16,370 mouse UTRs

Find target matches to miRNAs

15,590 rat UTRs

451 mammalian miRNA target interactions

10,276 human UTRs

Identify conserved high scoring miRNA target interactions

55 perfectly conserved vertebrate miRNAs

11,865 mouse UTRs

11,537 rat UTRs

115 vertebrate miRNA target interactions

12,054 Fugu UTRs

10.0 12.0

200

• Z score cutoff

4.5

8.0

•T

20

• Targets per miRNA

5.7

6.0

• Rank cutoff

4.0

• Targets per shuffled miRNA 1.8

2.0 0.0

Predicted targets per miRNA

Mammalian Targets

human mouse

Signal : Noise 2:1

human mouse rat

3.2:1

human mouse rat Fugu

4.6:1

High signal : Noise ratio for highly conserved miRNAs

5’ Stardom

• No. of targets predicted with 2 – 8 nts 5’of miRNA as seed is greater • 2 – 8 nts 5’ of miRNA are highly conserved across the genomes

Getting wet

Dual luciferase assay

• Renilla Luciferase as internal control and Fire fly luciferase activity was normalized • 11 out of 15 target sites tested showed increase in expression after mutation. • Assay signifies the role of miRNAs as negative regulators of gene expression.

Contrast • Function of Targets was evaluated for Gene Ontology • Functions include : Transporter acitvity Portein binding Hydrolase activity Kinase activity Signal transducer activity Tanscription regulation Receptor activity

Scope for future Development • A complete orthologous gene Annotation. • Detection of less stringent targets. • Identification of targets outside 3’UTRs. • Ability to pick less conserved sequences. • Modeling simultaneous interactions of multiple miRNA species with same 3’UTR.

Thank you ……….