miRNAs

• short (20-25nt) RNA molecules• transcribed as a precursor RNA

molecule• bind mRNAs via base pairing• cause mRNA degradation or

translational repression• more than 200 in humans

(~1% of the genome)

Nucleus

miRNA

The role of miRNAin the regulation ofprotein synthesis

miRNA

How do miRNA Control mRNA and Protein expression levels?

Binding to mRNA interferes with translation.

Facilitates cleavage of mRNA

Reduces protein expression levels.

Reduces mRNA expression levels.

miRNA miRNA

Translational Inhibition• Imperfect match between siRNA or miRNA

in RISC and target mRNA• RISC usually binds 3’ UTR• Mechanism of inhibition... ????

mRNA Degradation• Perfect complementarity

between siRNA or miRNA in RISC and the target mRNA

• Cleavage by RISC ‘Slicer’ activity– Unknown protein

miRNA Function

• control and modulation of:– cell proliferation– cell death– fat metabolism – neuronal patterning – leaf and flower development – hematopoietic lineage differentiation

micro RNA – Computational Approach

• Problem 1: Finding putative microRNA from a sequence– Horesh et al

• Problem 2: Computing secondary structure of a given sequence– Zuker & Steigler, minimum free energy, using dynamic

programming• Problem 3: miRNA predicting algorithms

– Lim et al, MiRscan• Problem 4: Predicting miRNA target genes

mRNA

DHES[mRNA, microRNA] =

es(i-1,i,j-1,j) + w(i',j',i,j) + w'(i',i)+ w'(j',j)

matching base pairs loops gaps gaps

jj'

ii'

The Duplex Hybridization Energy Score (DHES)

microRNA

mRNA

D[i,j] = min{D[i-1, j-1] + es(i-1,i,j-1,j), H[i,j], V[i,j], E[i,j]}whereV[i, j] = min {D[i', j-1] + w'(i',i) }

H[i, j] = min {D[i-1, j'] + w'(j',j) }

E[i, j] = min {D[i', j'] + w(i', j', i, j) }

jj'

ii'

The RNA DP Recurrences {Waterman and Smith 1986}

microRNA

{0 < i' < i}

{0 < j' < j}

{0 < i' < i, 0 < j' < j}

base pair contribution

cost of gap in mRNA

cost of gap in microRNA

cost of loop

(i’, j’)

(i, j)

(i’, j)

(i, j’) (i-1, j-1)

Prediction of miRNA Targets• Fairly straightforward in plants [Rhoades et al.,

2002]– miRNAs almost perfectly complementary to their

targets– Methods search for near-perfect matches in 3’ UTRs– Also look for conservation of target sites

• Not so easy in animals– miRNA:target not very complementary– Method for plants tried in other organisms

Results same as would be expected by chance

Target Prediction in Vertebrates• Some assumptions to start with:

– Interaction of 5’ end of miRNA and the target most critical

– Target binding sites likely conserved– binding sites common in 3’ UTR

Prediction of Mammalian MicroRNA Targets

Benjamin P. Lewis, I-hung Shih, Matthew W. Jones-Rhoades,

David P. Bartel, and Christopher B. Burge

Example - TargetScan Algorithm by Lewis et al 2003

The Goal – a ranked list of candidate target genes• Stage 1: Search UTRs in one organism

– Bases 2-8 from miRNA = “miRNA seed”– Perfect Watson-Crick complementarity– No wobble pairs (G-U)– 7nt matches = “seed matches”

TargetScan Algorithm

• Stage 2: Extend seed matches– Allow G-U (wobble) pairs– Both directions– Stop at mismatches

TargetScan Algorithm

• Stage 3: Optimize basepairing– Remaining 3’ region of miRNA– 35 bases of UTR 5’ to each seed match– RNAfold program (Hofacker et al 1994)

• Stage 4: Folding free energy (G) assigned to each putative miRNA:target interaction

• Assign rank to each UTR• Repeat this process for each of the other

organisms with UTR datasets

TargetScan Algorithm

TargetScan Program Flow

Mean number of predicted targets per miRNA for authentic miRNAs (filled bars) and

for shuffled sequences of miRNAs (open bars)

Mean number of targets per miRNA usingalternative miRNA seed positions

for authentic miRNAs and for shuffled controls

Reporter construct used to evaluate the interaction between miR-26a and the SMAD-1 3 UTR.

* Fragment of the UTR containing two miR target sites inserted within the luciferase 3 UTR.

* Mutant construct with three point substitutions disrupting pairing to each miR seed.

Box plots showing the luciferase activity after reporter plasmids were transfected into HeLa cells.

Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that

thousands of Human Genes are MicroRNA Targets

Benjamin P. Lewis, Christopher B. Burge and David P. Bartel

What’s new?

• Chicken and dog genome assemblies available

• Updated annotations of the human, mouse and rat genomes

• Requiring conservation in all five genomes allows elimination of score cutoffs

• Using 2-7 seed instead of 2-8 still retains specificity

Alignment of orthologous segments of the HIC UTR, showing the conserved match to

the miR-23a seed

What Else?

• Next, they looked at the sequence flanking the seed matches, searching for conserved positions

• Such positions might contribute specificity to the miRNA:target interaction

Number of miRNA:target relationships predicted,

with estimates of the number of false positives, for searches based on the indicated criteria.

Sequence conservation in positions flanking conserved miRNA seeds.

The percentage of seed matches in which that position was conserved in all five vertebrates is

shown, (red - conservation of adenosine). The gray dashes indicate the same analysis for

conserved matches to control sequences.