RNA interference:

By M A R T GunaratnaIndex No: 8060

Special Degree in Bioinformatics

A molecular evolutionary perspective towards strengthening its blossoming applications

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Why the emphasis on RNA?• Molecules involved in the mechanism of

RNAi are non-protein - coded RNAs transcribed

from ‘junk DNA’• Biogenesis of miRNAs (microRNAs) that

are involved in:- Cell proliferation- Cell differentiation- Cell development- Programmed cell death- Interactions between viruses and host cells- Regulates activity of 1/3 of human protein-encoding genes

• Solution for C-value paradox / enigma

JUNK DNA TURNED OUT TO BE GOLD!!!!

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Nuclear Membrane

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C. elegansPetunias

Short hairpin RNA

siRNA with 3’ overhangs D. melanogaster

Craig Mellow & Andrew Fire Nobel prize 2006

Physiology or Medicine

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Plasma Membrane

RNAi blossoming applicationsDrug discovery

Therapeutic applications

Cancer

Neurodegenerative diseases

Duchenne Muscular Dystrophy & Haemophilia

Age-related macular degeneration (AMD)

• Research purposes• Stempeutics • Medical diagnostics

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Neurodegenerative Diseases

AMD

Cancer

http://stemcell.taragana.net/wp-content/uploads/2008/10/stem-cell-therapy-india1.jpg 20.06.2009http://www-tc.pbs.org/wgbh/nova/sciencenow/3210/images/02-cure-cancer.jpg 20.06.2009http://www.myvisiontest.com/img/upload/Macular_degeneration.jpg20.06.2009

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Organs for which RNAi proof of concept has been demonstrated

Challenges in RNAi Efficacious delivery

Stability

Specificity

Potency

1010http://www.nature.com/gt/journal/v13/n6/fig_tab/3302654f2.html#figure-title 20.06.2009

Spurious specificity & lack of potency: causative agents

Erroneous siRNA designing algorithms leads to Brute-Force

search of siRNAs ● Activation of immune responses

Constraints in developing

longer siRNAs● Complicated

synthesis

Target sequen

ce diversit

y

● Dive

rsify

ing

or

posi

tive

● Purif

ying

or

nega

tive

● Neut

ralSelection pressure

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Analysis of Darwinian selection pressure

dN - Number of sites with non-synonymous substitutions

dS - Number of sites with synonymous substitutions

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ω = dN / dS

ω > 1 – Positive selectionω = 1 – Neutral selectionω < 1 – Negative selection Charles Darwin at the

age of 31…http://upload.wikimedia.org/wikipedia/commons/e/ed/Charles-Darwin-31.jpg20.06.2009

PAML - Phylogenetic Analysis by Maximum Likelihood

Rich repertoire of advance substitution models

Utility of maximum likelihood method

Facilitates understanding of molecular evolution

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Getting started…

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Translation

Alignment of In-frame

Removal of identical sequences

Tree generation

Codon substitution modelsCODEML program package

ü ω not averaged throughout the phylogenetic tree

ü Codon is considered the evolutionary unit

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Branch models

Site models

Branch – Site models

Facts to be considered before proceeding…Number of sequences > 6Tree length > 0.11Number of Sequences should not be highly similar or highly diverged

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Parameters in site models

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Model NSsites

p Parameters

M0 (one ratio) 0 1 ωM1a (neutral) [N]

1 2 p0 (p1 = 1 – p0), ω0 < 1, ω1 = 1

M2a (selection) [A]

2 4 p0, p1 (p2 = 1 – p0 – p1), ω0 < 1, ω1 = 1, ω2 > 1

M3 (discrete) 3 5 p0, p1 (p2 = 1 – p0 – p1) ω0, ω1, ω2 M7 (beta) [N] 7 2 p, q

M8 (beta&ω) [A] 8 4 p0 (p1 = 1 – p0), p, q, ωs > 1

NOTE: M0 and M3 are used only to measure the variability of ω among sitesp is the number of free parameters in the ω distribution.[A] – Alternative model [N] – Null model

Parameters in Branch-site models

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Site class

Parameters Background

ωForeground

ω

0 p0 0 < ω0 < 1 0 < ω0 < 1

1 p1 ω1 = 1 ω1 = 1

2a (1 – p0 – p1) * p0 / (p0 + p1) 0 < ω0 < 1 ω2 ≥ 1

2b (1 – p0 – p1) * p1 / (p0 + p1) ω1 = 1 ω2 ≥ 1

NOTE: Branch-site model A is specified using model = 2 NSsites = 2. This is the alternative model in the branch-site test of positive selection. The null model fixes ω2 = 1. The likelihood ratio test has df = 1 At a particular instance;Foreground branch – Branch analyzed for positive evolutionBackground branch – Branch not analyzed for positive evolution

Control file in CODEML

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seqfile = xxxxx.nuc

treefile = xxxxx.trees

outfile = mlcxxxxx * main result file name

model = 2 * models for codons:

* 0:one, 1:b, 2:2 or more dN/dS ratios for branches

NSsites = 8

fix_kappa = 0 * 1: kappa fixed, 0: kappa to be estimated

kappa = 1.6 * initial or fixed kappa

fix_omega = 0 * 1: omega or omega_1 fixed, 0: estimate

omega = .8 * initial or fixed omega, for codons or codon-based

AAs

ncatG = 10 * # of categories in dG of NSsites models

getSE = 1 * 0: don't want them, 1: want S.E.s of estimates

cleandata = 0 * remove sites with ambiguity data (1:yes,

0:no)?

method = 0 * 0: simultaneous; 1: one branch at a time

CODEML results

2020

tree length = 0.52544

Model 7: beta (10 categories) lnL(ntime: 17 np: 20): -2001.404646 +0.000000 dN/dS (w) for site classes (K=10)p: 0.10000 0.10000 0.10000 0.10000 0.10000 0.10000 0.10000 0.10000 0.10000 0.10000w: 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.20940 1.00000 1.00000 Model 8: beta&w>1 (11 categories) lnL(ntime: 17 np: 22): -2000.054824 +0.000000 w= 1.88981 dN/dS (w) for site classes (K=11) p: 0.08895 0.08895 0.08895 0.08895 0.08895 0.08895 0.08895 0.08895 0.08895 0.08895 0.11047w: 0.03624 0.04694 0.05420 0.06050 0.06653 0.07272 0.07947 0.08741 0.09800 0.11736 1.88981

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CODEML results contd…

Significance of results

Hypothesis Testing

●LRT = 2● Chi – square (

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Posterior probabilities of

● NEB

● BEB

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Local optima and the global optimum

Result

MEC – Mechanistic Empirical Combination Model

Assimilates empirical amino acid substitution probabilities

Selecton server at http://selecton.tau.ac.il/

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Commercial potential of RNAi

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Global market estimates for RNAi;

Year 2010: $ 3.5 billion

Year 2015: $ 10.5 billion• Major areas of RNAi applications in the expected

market;

• Drug discovery and research• Potential therapeutics• miRNA mediated diagnostics

Impact of molecular evolutionary analysis on advancement of RNAi

2626http://www.nature.com/nrn/journal/v6/n6/images/nrn1688-i1.gif 20.06.2009http://img.medscape.com/fullsize/migrated/548/891/ncpn548891.fig2.jpg 20.06.2009http://www.pbs.org/wgbh/nova/sciencenow/3210/images/02-cure-hepatitis.jpg 20.06.2009

Clues for universal therapeutics and diagnostics

Assurance of efficacious activity

Accelerate extensive researches on RNAi

Saves time, resources, labor and money in designing siRNAs & identifying potent miRNAs

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development of blue rosesSuccess: observed…

First brain stem cell therapySUCCESS: PENDING…???

http://www.stem-cells-news.com/1/2009/05/first-stem-cell-brain-surgery/ 20.06.2009http://3.bp.blogspot.com/_Ha_cQO3YwFc/R6kGRjT5LiI/AAAAAAAADoQ/oi80O5KGIy4/s400/blueroses.jpg 20.06.2009

References1. Anisimova A, Yang Z. (2007). Multiple hypothesis testing to detect lineages under positive

selection that affects only a few sites. Mol Biol Evol. 24:1219–1228.

2. Anisimova M, Bielawski JP, Yang Z. (2001). The accuracy and power of likelihood ratio tests to detect positive selection at amino acid sites. Mol Biol Evol. 18:1585–1592.

3. Anisimova M, Nielsen R, Yang Z. (2003). Effect of recombination on the accuracy of the likelihood method for detecting positive selection at amino acid sites. Genetics. 164:1229–1236.

4. Bahadori M. (2008). New Advances in RNAs. Archives of Iranian Medicine. 11 (4), 435-43.

5. Bumcrot D, Manoharan M, Koteliansky V, & Sah DW. (2006). RNAi therapeutics: a potential new class of pharmaceutical drugs. Nature Chemical Biology.2 (12), 711-9.

6. Campbell TN, & Choy FY. (2005). RNA interference: past, present and future. Current Issues in Molecular Biology. 7 (1), 1-6.

7. Ding, S. W. (2005). RNAi: Mechanisms, biology and applications. FEBS LETTERS. 579 (26), 5821.

8. Doron-Faigenboim, A., & Pupko, T. (2007). A Combined Empirical and Mechanistic Codon Model. MOLECULAR BIOLOGY AND EVOLUTION. 24 (2), 388-397.

9. Felsenstein, J. (2004). Inferring phylogenies. Sunderland, Mass: Sinauer Associates.

10. Grimm D, & Kay MA. (2007). Therapeutic application of RNAi: is mRNA targeting finally ready for prime time? The Journal of Clinical Investigation. 117 (12), 3633-41.

11. Jain KK. (2006). Commercial potential of RNAi. Molecular BioSystems. 2 (11), 523-6.

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12. Jin, G., Nakhleh,L., Snir, S., Tuller,T. (2006).Maximum likelihood of phylogenetic networks. 22 ,21,2604–2611.

13. Kim D, & Rossi J. (2008). RNAi mechanisms and applications. BioTechniques. 44 (5), 613-6.

14. Press release by the Nobel Assembly on the 2006 Nobel Prize in Physiology or Medicine to Andrew Z. Fire and Craig C. Mello for their discovery of RNAi: http://nobelprize.org/nobel_prizes/medicine/laureates/2006/press.html

15. Rayburn ER, & Zhang R. (2008). Antisense, RNAi, and gene silencing strategies for therapy: mission possible or impossible? Drug Discovery Today. 13 (11-12), 11-12.

16. Shabalina, S. A., & Koonin, E. V. (2008). Origins and evolution of eukaryotic RNA interference. Trends in Ecology & Evolution. 23 (10), 578-587.

17. Shrivastava N, & Srivastava A. (2008). RNA interference: an emerging generation of biologicals. Biotechnology Journal. 3 (3), 339-53.

18. Sledz CA, & Williams BR. (2005). RNA interference in biology and disease. Blood. 106 (3), 787-94.

19. Tan YL, & Yin JQ. (2005). [Application of RNAi to cancer therapy]. Yao Xue Xue Bao = Acta Pharmaceutica Sinica. 40 (3), 193-8.

20. Wong WS, Yang Z, Goldman N, & Nielsen R. (2004). Accuracy and power of statistical methods for detecting adaptive evolution in protein coding sequences and for identifying positively selected sites. Genetics. 168 (2), 1041-51.

21. Yang, Z. (2007). PAML 4: Phylogenetic Analysis by Maximum Likelihood .Mol. Biol. Evol. 24(8):1586–1591.

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THANK YOU

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