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m icro RNA Target Prediction

m icro RNA Target Prediction. Nadav Rappoport & Dvir Aran Research methods in computational biology ‘08. Guidance: Prof. Michal Linial. microRNA. MicroRNAs ( miRNAs ) are endogenous, small, non-coding RNAs, ~22 nucleotides in length.

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m icro RNA Target Prediction

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  1. microRNATarget Prediction NadavRappoport & Dvir Aran Research methods in computational biology ‘08 Guidance: Prof. Michal Linial

  2. microRNA • MicroRNAs (miRNAs) are endogenous, small, non-coding RNAs, ~22 nucleotides in length. • They bind target messenger RNAs (mRNAs), block the target's expression by inhibiting translation. • In humans, they are predicted to potentially target up to one third of protein coding genes. • Seems to be a “Super-Regulator”.

  3. Overview • Background • Motivation • miRNA in cell development • miRNA in disease diagnostic & therapeutics • Computational approaches • miRScan • TargetScanS • Biological approaches • IP-FLAG • Conclusions

  4. miRNA History miRNAs in therapeutics miRNAs for disease diagnosis miRNAs implicated in leukemia

  5. miRNA Mechanism

  6. Mechanism • Key Players • Pri-miRna • Drosha • Dicer • RISC – RNA Induced Silencing Complex (AGO1, AGO2) • mRNA, Ribosomes

  7. Motivation • miRNA-mediated gene regulation • miRNAs in early embryonic development • miRNAs in neuronal development • miRNAs in muscle development • miRNAlympohcytedevolopment

  8. miRNA in neuronal development • PTB – Polypyrimidine tract-binding protein

  9. Disease diagnostic • High expression of miR-155 can cause leukemia and lung cancer

  10. Disease diagnostic

  11. Disease analysis • Oncology • Bladder • Breast • Cervical • Colon • Leukemia • Lung • Prostate • Thyroid • Brain • Alzheimers • Multiple Sclerosis • Prion Disease • Stroke • Miscellaneous • Atherosclerosis • Cardiac Hypertrophy • Diabetes • HIV/HPV Infection • Lupua • Cellular Process • Angiogenesis • Cell Cycle • Neuronal Differentiation • Proliferation • Telomerase Activity • T-cell Activation • Varius Cell Treatment

  12. Therapeutics

  13. Researching • Now that we know that miRNA are important, we have to problems: • Problem 1 - finding miRNAs. • Problem 2 - finding the targets of a miRNA. Computational Biology!!!

  14. Algorithms • Problem 1 - finding miRNAs • mirScanhttp://genes.mit.edu/mirscan/ Lim et al. (2003) • miSeekerhttp://www.fruitfly.org/seq_tools/miRseeker.htm Lai et al. (2003) • mirAlignhttp://bioinfo.au.tsinghua.edu.cn/miralign Wang et al. (2005) • More… • Problem 2 - finding the targets of a miRNA • TargetScanhttp://genes.mit.edu/targetscan Lewis et al. (2003) • TargetScanShttp://genes.mit.edu/targetscan Lewis et al. (2005) • PicTarhttp://pictar.bio.nyu.eduKrek et al. (2005) • miRandahttp://www.microrna.orgEnright et al. (2003) • More…

  15. Lin-4 – first miRNA discovered • Lee et al. (1993) – Discovery • lin-4 inhibits lin-14, but no lin-4 protein was found Victor Ambros

  16. miRScan – Predicting miRNAs • Examines hairpin features. • Frequency is determined by the training set.

  17. miRScan – PredicitngmiRNAs

  18. miRScan

  19. miRNA Target prediction • ~500 miRNA found in the human genome to date. • Now we have a miRNA, how do we find what mRNAs are regulated by it? • What we know (or think we know) – • miRNAs are short (~22 nt). • In plants, targets have high degree of sequence complementarity. • In Animals - G-U pairs, mismatches and bulges.

  20. TargetScan - Assumptions • Based on the criterions that where determined at the research of lin-4 and its target sites. • miRNA targets are located in the 3’ UTRs. • A consecutive W-C base pairing is concentrated in the 5’ of the miRNA – the “Seed”. • Phylogenetic conservation of the orthologous target sites of the mRNAs.

  21. TargetScan - Algorithm Input – miRNA sequence, genome DB Output - ranked list of candidate target genes • Stage 1: Search 3’ UTRs in one organism • Bases 2-8 from miRNA, the“miRNA seed” • Perfect Watson-Crick complementarity

  22. TargetScan - Algorithm • Stage 2: Extend seed matches • Allow G-U (wobble) pairs in both directions • Stop at mismatches • Stage 3: Optimize base pairing • Remaining 3’ region of miRNA • 35 bases of UTR 5’ to each seed match • RNAfold program (Hofackeret al 1994)

  23. TargetScan - Algorithm • 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

  24. TargetScan - Algorithm • 79 miRNA’s searched against human, mouse, and rat orthologous 3’ UTRs. • Results - 451 miRNA-target interactions predicted. • Average 5.7 targets per miRNA • Signal:noise ratio of 3.2:1

  25. TargetScan - results • Black – Predicted target per miRNA • White – Estimation of false positives 1.9 3.2 4.8

  26. TargetScan - results • Moving “Seed region”

  27. TargetScanTargetScanS* • TargetScan • 30% noise, too much. • 2003 – the “criterions” are some what dubious. • TargetScanS • 2005 – the genome of the dog and chicken. • Examination of old assumptions. *Lewis et al (2005), Conserved seed pairing, often flanked by adenosines, Indicates that thousands of human genes are microRNA targets, Cell 120, 15-20.

  28. TargetScanS

  29. TargetScanS

  30. TargetScanS – what’s new?

  31. TargetScanS - results

  32. TargetScanS - results t1A + m1 notA t9A + m9 notU

  33. TargetScanS - results

  34. TargetScanS - results • Biological process of the vertebrate miRNA target in the Seed + t1A + m8M

  35. TargetScanS - criticism • Phylogenetic conservation reduces the ability of the algorithm to predict targets • The miR-290-295 cluster that was found to be expressed in mouse stem cells has no known orthologs. • 3D structure not considered. • Coding & 5’ UTR of the mRNA not considered.

  36. Biological approach • Systematic Identification of mRNAs Recruited to Argonaute 2 by Specific microRNAs and Corresponding Changes in Transcript Abundance, Hendrickson et al. PLoS ONE (2008) • A direct approach for finding mRNA-miRNA connections Pat Brown

  37. Mechanism - again

  38. Immunopurification

  39. Method • Target: Isolating and identifying miRNAs and mRNAs associated with Ago2 • Expressing a FLAG-tagged Ago2 protein. • Transfecting HEK293T cells with FLAG-Ago2 • Lyse • Mix with FLAG antibody • Wash • Microarray analysis

  40. Method

  41. Control • No significant change • in transfected cells • IP of FLAG

  42. TranfectingmiRNAs

  43. Results analysis • Enrichment of seed match sites

  44. Results analysis • miR-1 – Seed match

  45. Results analysis • miR-124 – Seed match

  46. Results analysis • miR-1 – Enrichment/Expression • Note:

  47. Results analysis • miR-124 – Enrichment/Expression

  48. Results analysis

  49. Results analysis

  50. Results analysis • Comparision between IP results and different algorithms

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