-Shruthi & Surajit

1. miRNAs and its Target gene Predictions -Shruthi & Surajit

2. Introduction to Neurons • miRNA • miRNA- mRNA interaction • Results • Computational algorithms for predicting the miRNA targets • Novel methods • Conclusions & Future directions • References Outline

3. NEURON • Basis of the nervous system • Send signals throughout the • body • 3 main parts • Dendrites • Cell body • Axon

4. 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

5. Powerful genetic tool and relevance to higher organism • daneurons 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 Drosophiladendriticarborization (da) neurons provide an excellent model for investigating class specific dendrite morphogenesis

6. 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 miRNAs– essential post-transcriptional regulators of gene expression

8. 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. Why is it Important?

9. Which miRNAs may contribute to the specification of unique dendritic morphologies of da neuron subclasses? • What are the downstream targets of these miRNAs that control dendritic arborization? How do miRNAs regulate class-specific dendritic patterning?

10. 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 miR expression profiling

11. Gain of Function Results

12. miR-mediated promotion of dendritic branching complexity in Class I da neurons

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

14. 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 Prediction Methods

15. 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” Target ScaN

16. 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-Gk/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)

17. 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 specific miRNA independently matches orthologous UTRs in both species • 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) • the positions of both target sites are equivalent according to a cross-species UTR alignment Miranda

18. 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. Miranda Scores

19. 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. PITA

20. Most of the software are not accurate due to significant fraction of false positivity, which are caused by • Limited comprehension of molecular basis of miRNA- target pairing • Changes during of post transcriptional regulation CONS

21. 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. Solution

22. 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. Some of the tools

23. 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. MAGIA

24. Snapshot 1

25. Snapshot2

26. SnapShot3

27. SnAPsHOT 5

28. snapshot 6

29. 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. MIAMI

30. hOMepAGE

31. MIAMI miRNA query

32. single miRNA query – step 1

33. single miRNA query – step 2

34. single miRNA query – step 2

35. single miRNA query – step 2

36. single miRNA query Results

37. 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. Statistical Method

38. Results

39. 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. Conclusion

40. 1.Principles of Biochemistry,Lehninger • 2.Exploring complex miRNA-mRNA interactions with Bayesian networks by splitting-averaging strategy-Bing Liu,Jiuyong Li, Anna Tsykin,LinLiu,Arti B Gaur and Gregory J Goodall • 3.MAGIA, a web-based tool for miRNA and Genes Integrated Analysis-Gabriele Sales, Alessandro Coppe, Andrea Bisognin, Marta Biasiolo,StefaniaBortoluzzi and ChiaraRomualdi • 4.http://onlinestatbook.com/chapter4/pearson.html-for Pearson Defination • 5.Brownian Distance Covariance- Gabor J. Szekely and Maria L. Rizzo • 6. Nam,S., Kim,B., Shin,S. and Lee,S. (2008) miRGator: an integrated system for functional annotation of microRNAs. Nucleic Acids Res., 36, D159–D164. • 7. Maragkakis,M., Reczko,M., Simossis,V.A., Alexiou,P.,Papadopoulos,G.L., Dalamagas,T., Giannopoulos,G., Goumas,G.,Koukis,E., Kourtis,K. et al. (2009) DIANA-microT web server:elucidatingmicroRNA functions through target prediction. • 8.Nam,S., Li,M., Choi,K., Balch,C., Kim,S. and Nephew,K.P.(2009) MicroRNA and mRNA integrated analysis (MMIA): a web tool for examining biological functions of microRNAexpression. Nucleic Acids Res., 37, W356–W362 • Nucleic Acids Res., 37, W273–W276. • 9. R Development Core Team (2011). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/. • 10.Maria L. Rizzo and Gabor J. Szekely (2011). energy: E-statistics (energy statistics). R package version 1.4-0. http://CRAN.R-project.org/package=energy References

41. 11.Vincenzo Alessandro Gennarino,MarcoSardiello,RaffaellaAvellino,NicolaMeola,VincenzaMaselli,SantoshAnand,LuisaCutillo,AndreaBallabio,andSandroBanfi:MicroRNA target prediction by expressionanalysis of host genes • 12.J. Reimand, M. Kull, H. Peterson, J. Hansen, J. Vilo: g:Profiler -- a web-based toolset for functional profiling of gene lists from large-scale experiments (2007) • 13.J. Reimand, T. Arak, J. Vilo: g:Profiler -- a web server for functional interpretation of gene lists (2011 update) Nucleic Acids Research 2011 • 14.Christopher Brown (2011). hash: Full feature implementation of hash/associated arrays/dictionaries. R package version 2.1.0. http://CRAN.R-project.org/package=hash • 15.https://statistics.laerd.com/statistical-guides/pearson-correlation-coefficient-statistical-guide.php • 16.http://critical-numbers.group.shef.ac.uk/glossary/correlation.html • 17. J. L. Rodgers and W. A. Nicewander. Thirteen ways to look at the correlation coefficient. The American Statistician, 42(1):59–66, February 1988. • 18.http://docs.ggplot2.org/current/ • 19.http://ggplot2.org/resources/2007-vanderbilt.pdf • 20. Székely, G. J. Rizzo, M. L. and Bakirov, N. K. (2007). "Measuring and testing independence by correlation of distances" • 21.Prediction of Mammalian MicroRNATargets:Benjamin P. Lewis,I-hung Shih,Matthew W. Jones-Rhoades,David P. Bartel and Christopher B. Burge.2003.Cell • 22.The role of site accessibility in microRNA target recognition-Michael Kertesz, Nicola Iovino, Ulrich Unnerstall, Ulrike Gaul and Eran Segal.2007. Nature genetics • 23.MicroRNA targets in Drosophila-Anton J Enright, Bino John, Ulrike Gaul, Thomas Tuschl, Chris Sander and Debora S Marks