1. Microarray analysis for research of Trans-Spicing process in Trypanosoma brucei Roy Azran Or Garfunkel Project advisors: Prof Shula Michaeli Dr Shai Carmi

2. Topics Scientific Background Scientific Background Problem overview Problem overview Solution Overview Solution Overview

3. Scientific Backgroung Trypanosoma Brucei Eukaryotic parasite developed during the past 200-500 million years. Eukaryotic parasite developed during the past 200-500 million years. Live in the gut of the Tse-Tse fly which is widespread mostly in Africa, then transfer to the bloodstream of humans and cuttle. Live in the gut of the Tse-Tse fly which is widespread mostly in Africa, then transfer to the bloodstream of humans and cuttle. Causing the African Trypanosomiasis disease. Causing the African Trypanosomiasis disease.

4. African Trypanosomiasis disease. ( מחלת השינה ) Phase I – haemolymphatic phase: Phase I – haemolymphatic phase: fever, headaches, and joint pains, and itching Phase II - neurological phase: Phase II - neurological phase: parasite invades the central nervous system by passing through the blood-brain barrier. The symptoms include confusion, reduced coordination, and disruption of the sleep cycle. The disease is endemic in some regions of sub-Saharan Africa, covering about 36 countries and 60 million people. It is estimated that 50,000 to 70,000 people are currently infected, the number having declined somewhat in recent years. The disease is endemic in some regions of sub-Saharan Africa, covering about 36 countries and 60 million people. It is estimated that 50,000 to 70,000 people are currently infected, the number having declined somewhat in recent years. Less than 50,000 people die every year. Less than 50,000 people die every year.

5. Trypanosoma Brucei Some Uniqe Properties: Kinetoplast – disk shaped mass of circular DNA inside a large mitochondrion that contains many copies of the mitochondrial genome. Kinetoplast – disk shaped mass of circular DNA inside a large mitochondrion that contains many copies of the mitochondrial genome. RNA editing with Guide RNA - RNAs that guide the insertion or deletion of uridine residues into mitochondrial mRNAs in kinetoplastid protists in a process known as RNA editing. RNA editing with Guide RNA - RNAs that guide the insertion or deletion of uridine residues into mitochondrial mRNAs in kinetoplastid protists in a process known as RNA editing. Variable surface glycoproteins Variable surface glycoproteins mRNA processing mRNA processing

6. Trans - Splicing Splicing occurs in most of the eukaryotic organisms after the Transcription.

7. Problem Overview Starting point In the lab several kinds of knockouts where made in the genome, and their expressions were checked by microarray technology. In the lab several kinds of knockouts where made in the genome, and their expressions were checked by microarray technology. We started with ~100 experiments outputs (~9000 genes each) and filtered the experiments which weren’t made in the splice- site, resulting 27 microarray data tables. We started with ~100 experiments outputs (~9000 genes each) and filtered the experiments which weren’t made in the splice- site, resulting 27 microarray data tables.

8. Problem Overview (1) (1) Finding the genes in the which changed significantly which are “suspected” in splicing regulation. (2) (2) Are there secondary structured motifs for the non-coding areas of genes that changed significantly because of these knockouts?

9. Solution overview finding the genes that changed significantly Normalization Normalization log2 for all the values. log2 for all the values. values less than 500 and NaN’s weren’t taken, their complimentary values were dropped too. values less than 500 and NaN’s weren’t taken, their complimentary values were dropped too.

10. Solution overview finding the genes that changed significantly Normalization Normalization log2 for all the values. log2 for all the values. values less than 500 and NaN’s weren’t taken, their complimentary values were dropped too. values less than 500 and NaN’s weren’t taken, their complimentary values were dropped too.

11. Solution overview finding the genes that changed significantly Normalization Normalization log2 for all the values. log2 for all the values. values less than 500 and NaN’s weren’t taken, their complimentary values were dropped too. values less than 500 and NaN’s weren’t taken, their complimentary values were dropped too.

12. Solution overview finding genes that significantly changed Comparison between genes expression in each experiment was made by 15 short programs in Matlab and Perl. the outputs where sorted lists of the genes by the formula:

13. Solution overview finding genes that significantly changed We took manually the genes that changed the most. For each kind of knockout we took ~50 of the most decreasing genes and ~50 of the most increasing genes. We took manually the genes that changed the most. For each kind of knockout we took ~50 of the most decreasing genes and ~50 of the most increasing genes. these are the genes that will be compared to the whole genome for searching structural motifs, these are the “suspected genes”. these are the genes that will be compared to the whole genome for searching structural motifs, these are the “suspected genes”.

14. Are there secondary structured motifs for the non-coding areas of genes that changed significantly because of these knockouts?

15. Non-Coding Sequences Found in 3’UTR, 5’UTR, Introns Using a script including bio-perl functions in order to extract these areas into separate lists.

16. Solution overview RNA structures representation We used both Vienna RNA package and mfold. Both using algorithm of Zuker & Stiegler 1981, which yields a single optimal structure. Folding representation (Standard format):. is uncoupled base. ( is coupled base that we haven’t seen it coupling. ) is coupled base that we already seen it coupling.

17. Example:

18. The Computation’s Limit Problem All folding algorithms are based mostly on minimal free energy ΔG. Other input parameters such as whether the sequence is linear or circular can affect ΔG. Hence several structures are possible for each sequence, this may cause a very high complexity. Each sequence will probably have a different number of structures. Therefore we chose to consider only the minimal energetic structures.

19. What are secondary structures motifs? What are motifs?

20. Solution overview ideas for comparing RNA structures Comparing the percent of the coupling in the genes Comparing the percent of the coupling in the genes Comparing the average number of loops for 100 bp’s. Comparing the average number of loops for 100 bp’s.

21. Solution overview ideas for comparing RNA structures Sub-sequences which have more than K uncoupled segments. Sub-sequences which have more than K uncoupled segments. Example: Sub-sequences which are rich of uncoupled bp’s. Sub-sequences which are rich of uncoupled bp’s.

22. Project Status Currently writing scripts using Vienna program for Windows platform, and scripts using mFold program on a Linux server platform in order to cross results and take the minimal energy results. Currently writing scripts using Vienna program for Windows platform, and scripts using mFold program on a Linux server platform in order to cross results and take the minimal energy results. Once the algorithms are done we’ll search for the motifs previously described and others which are already written in pseudo-code. Once the algorithms are done we’ll search for the motifs previously described and others which are already written in pseudo-code.

24. BIBLIOGRAPHY [1] Xue-haiLiang, Asaf Haritan, Shai Uliel, and Prof Shulamit Michaeli. 2003,trans and cis Splicing in Trypanosomatids: Mechanism, Factors, and Regulation. Eukarote Cell. [2] Corinna Benz,Daniel Nilsson,Bjorn Andersson Christine Clayton, D.Lys Guilbride. 2005, Messenger RNA processing sites in Trypanosoma brucei. Molecular & Biochemical Parasitology. [3]Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 31 (13), 3406-15, (2003) [3]Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 31 (13), 3406-15, (2003) [4]D.H. Mathews, J. Sabina, M. Zuker & D.H. Turner Expanded Sequence Dependence of Thermodynamic Parameters Improves Prediction of RNA Secondary Structure J. Mol. Biol. 288, 911-940 (1999) [5] T.Nicolai Siegel, Kevin S.W.Tan, and George A.M. 2005, Cross Systematic Study of Sequence Motifs for RNA trans Splicing in Trypanosoma brucei. Molecular And Cellular Biology.