Microarray analysis for Microarray analysis for research of Trans-Spicing research of Trans-Spicing process in Trypanosoma process in Trypanosoma

bruceibrucei

Roy AzranRoy Azran Or GarfunkelOr GarfunkelProject advisors: Prof Shula Michaeli Dr Shai Carmi

TopicsTopics• Scientific BackgroundScientific Background• Problem overviewProblem overview• Solution OverviewSolution Overview

Scientific BackgroungScientific BackgroungTrypanosoma BruceiTrypanosoma Brucei• Eukaryotic parasite developed during the Eukaryotic parasite developed during the

past 200-500 million years.past 200-500 million years.• Live in the gut of the Tse-Tse fly which is Live in the gut of the Tse-Tse fly which is

widespread mostly in Africa, then transfer widespread mostly in Africa, then transfer to the bloodstream of humans and cuttle.to the bloodstream of humans and cuttle.

• Causing the African Trypanosomiasis Causing the African Trypanosomiasis disease.disease.

African Trypanosomiasis African Trypanosomiasis disease.disease.

((מחלת השינהמחלת השינה))• Phase I – haemolymphatic phase:Phase I – haemolymphatic phase:

fever, headaches, and joint pains, and itching fever, headaches, and joint pains, and itching • Phase II - neurological phase:Phase II - neurological phase:

parasite invades the central nervous system by passingparasite invades the central nervous system by passingthrough the blood-brain barrier.through the blood-brain barrier.The symptoms include confusion, reduced coordination, The symptoms include confusion, reduced coordination, and disruption of the sleep cycle.and disruption of the sleep cycle.

• The disease is endemic in some regions of sub-Saharan Africa, covering about 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 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. 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.

Trypanosoma BruceiTrypanosoma BruceiSome Uniqe Properties: Some Uniqe Properties: • Kinetoplast – Kinetoplast – disk shaped mass of circular DNA disk shaped mass of circular DNA

inside a large mitochondrion that contains many copies of inside a large mitochondrion that contains many copies of the mitochondrial genome.the mitochondrial genome.

• RNA editing with Guide RNA - RNA editing with Guide RNA - RNAs that RNAs that guide the insertion or deletion of uridine residues into guide the insertion or deletion of uridine residues into mitochondrial mRNAs in kinetoplastid protists in a process known mitochondrial mRNAs in kinetoplastid protists in a process known as RNA editing. as RNA editing.

• Variable surface glycoproteinsVariable surface glycoproteins•mRNA processingmRNA processing

Trans - SplicingTrans - SplicingSplicing occurs in most of the Splicing occurs in most of the

eukaryotic organisms after the eukaryotic organisms after the Transcription.Transcription.

Problem OverviewProblem OverviewStarting pointStarting point

• In the lab several kinds of knockouts where In the lab several kinds of knockouts where made in the genome, and their expressions made in the genome, and their expressions were checked by microarray technology. were checked by microarray technology.

• We started with ~100 experiments outputs We started with ~100 experiments outputs (~9000 genes each) and filtered the (~9000 genes each) and filtered the experiments which weren’t made in the experiments which weren’t made in the splice-site, resulting 27 microarray data splice-site, resulting 27 microarray data tables.tables.

Problem OverviewProblem Overview• (1)(1)

Finding the genes in the which changed Finding the genes in the which changed significantly which are “suspected” in significantly which are “suspected” in splicing regulation.splicing regulation.

• (2)(2)Are there Are there secondary structured motifs secondary structured motifs for the non-coding areas of genes that for the non-coding areas of genes that changed significantly because of these changed significantly because of these knockouts? knockouts?

Solution overviewSolution overviewfinding the genes that changed finding the genes that changed significantlysignificantly• NormalizationNormalization log2 for all the values.log2 for all the values. values less than 500 and NaN’s values less than 500 and NaN’s

weren’t weren’t taken, their complimentary taken, their complimentary values were values were dropped too.dropped too.

Solution overviewSolution overviewfinding the genes that changed finding the genes that changed significantlysignificantly• NormalizationNormalization log2 for all the values.log2 for all the values. values less than 500 and NaN’s values less than 500 and NaN’s

weren’t weren’t taken, their complimentary taken, their complimentary values were values were dropped too.dropped too.

Solution overviewSolution overviewfinding the genes that changed finding the genes that changed significantlysignificantly• NormalizationNormalization log2 for all the values.log2 for all the values. values less than 500 and NaN’s values less than 500 and NaN’s

weren’t weren’t taken, their complimentary taken, their complimentary values were values were dropped too.dropped too.

Solution overviewSolution overviewfinding genes that significantly finding genes that significantly changed changed Comparison between genes expression in Comparison between genes expression in

each experiment was made by 15 short each experiment was made by 15 short programs in Matlab and Perl.programs in Matlab and Perl.

the outputs where sorted lists of the genes the outputs where sorted lists of the genes by the formula:by the formula:

erimentsmutwt

exp#

Solution overviewSolution overviewfinding genes that significantly finding genes that significantly changedchanged• We took manually the genes that changed We took manually the genes that changed

the most. For each kind of knockout we the most. For each kind of knockout we took ~50 of the most decreasing genes took ~50 of the most decreasing genes and ~50 of the most increasing genes.and ~50 of the most increasing genes.

• these are the genes that will be compared these are the genes that will be compared to the whole genome for searching to the whole genome for searching structural motifs, these are the structural motifs, these are the “suspected genes”.“suspected genes”.

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

Non-Coding SequencesNon-Coding SequencesFound in 3’UTR, 5’UTR, IntronsFound in 3’UTR, 5’UTR, Introns

Using a script including bio-Using a script including bio-perl functions in order to perl functions in order to extract these areas into extract these areas into separate lists.separate lists.

Solution overviewSolution overviewRNA structures representationRNA structures representationWe used both Vienna RNA package and mfold. We used both Vienna RNA package and mfold.

Both using algorithm of Zuker & Stiegler Both using algorithm of Zuker & Stiegler 1981, which yields a single optimal 1981, which yields a single optimal structure. structure.

Folding representation (Standard format):Folding representation (Standard format):.. is uncoupled base. is uncoupled base.(( is coupled base that we haven’t seen it is coupled base that we haven’t seen it coupling. coupling.)) is coupled base that we already seen it is coupled base that we already seen it coupling. coupling.

Example:Example:

The Computation’s Limit The Computation’s Limit Problem Problem

All folding algorithms are based mostly on All folding algorithms are based mostly on minimal minimal free energy free energy ΔΔG. Other input parameters G. Other input parameters such as whether the sequence is linear or such as whether the sequence is linear or circular can affect circular can affect ΔΔG.G.Hence several structures are possible for each Hence several structures are possible for each sequence, this may cause a very high sequence, this may cause a very high complexity.complexity.Each sequence will probably have a different Each sequence will probably have a different number of structures.number of structures.

Therefore we chose to consider only Therefore we chose to consider only the minimal energetic structures. the minimal energetic structures.

What are secondary What are secondary structures motifs?structures motifs?

What are motifs?What are motifs?

Solution overviewSolution overviewideas for comparing RNA ideas for comparing RNA structuresstructures• Comparing the percent of the Comparing the percent of the

coupling in the genescoupling in the genes• Comparing the average number of Comparing the average number of

loops for 100 bp’s.loops for 100 bp’s.

Solution overviewSolution overviewideas for comparing RNA ideas for comparing RNA structuresstructures

• Sub-sequences which have more Sub-sequences which have more than K uncoupled segments.than K uncoupled segments.

Example:

• Sub-sequences which are rich of Sub-sequences which are rich of uncoupled bp’s.uncoupled bp’s.

Project Project StatusStatus• Currently writing scripts using Vienna Currently writing scripts using Vienna

program for Windows platform, and program for Windows platform, and scripts using mFold program on a scripts using mFold program on a Linux server platform in order to Linux server platform in order to cross results and take the minimal cross results and take the minimal energy results.energy results.

• Once the algorithms are done we’ll Once the algorithms are done we’ll search for the motifs previously search for the motifs previously described and others which are described and others which are already written in pseudo-code. already written in pseudo-code.

BIBLIOGRAPHBIBLIOGRAPHYY

[1][1] Xue-haiLiang, Asaf Haritan, Shai Uliel, and Prof Shulamit Xue-haiLiang, Asaf Haritan, Shai Uliel, and Prof Shulamit Michaeli. Michaeli. 20032003 ,trans and cis Splicing in ,trans and cis Splicing in Trypanosomatids: Trypanosomatids: Mechanism, Factors, and RegulationMechanism, Factors, and Regulation. . Eukarote Cell.Eukarote Cell.[2] [2] Corinna Benz ,Daniel Nilsson ,Bjorn Andersson Christine Corinna Benz ,Daniel Nilsson ,Bjorn Andersson Christine Clayton , D.Lys GuilbrideClayton , D.Lys Guilbride. . 2005, Messenger RNA processing 2005, Messenger RNA processing sites in Trypanosoma brucei. Molecular & Biochemical sites in Trypanosoma brucei. Molecular & Biochemical Parasitology.Parasitology.[3][3] Mfold web server for nucleic acid folding and hybridization Mfold web server for nucleic acid folding and hybridization

prediction. prediction. Nucleic Acids Res.Nucleic Acids Res. 31 (13), 3406-15, (2003)  31 (13), 3406-15, (2003) [4][4] D.H. Mathews, J. Sabina, M. Zuker & D.H. Turner D.H. Mathews, J. Sabina, M. Zuker & D.H. Turner 

Expanded Sequence Dependence of Thermodynamic Expanded Sequence Dependence of Thermodynamic Parameters Improves Prediction of RNA Secondary Parameters Improves Prediction of RNA Secondary Structure Structure J. Mol. Biol.J. Mol. Biol. 288, 911-940 (1999) 288, 911-940 (1999)[5][5] T.Nicolai Siegel, Kevin S.W.Tan, and George A.M. 2005, T.Nicolai Siegel, Kevin S.W.Tan, and George A.M. 2005, Cross Systematic Study of Sequence Motifs for RNA trans Cross Systematic Study of Sequence Motifs for RNA trans Splicing in Trypanosoma brucei. Splicing in Trypanosoma brucei. Molecular And Cellular Molecular And Cellular Biology.Biology.