Finding Sequence Motifs in Alu Transposons that Enhance the Expression of Nearby Genes Kendra Baughman York Marahrens’ Lab UCLA.

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Finding Sequence Motifs in Alu Transposons that Enhance the Expression of Nearby Genes Kendra Baughman York Marahrens’ Lab UCLA

Overview GoalBackground Prior Studies StrategyResults Remaining Tasks Future Directions

Goal Determine if there are motifs present among Alu elements near highly expressed genes, and missing from Alu elements near poorly expressed genes, that might contribute to gene expression

Background – Alu Elements Repetitive sequence Transposons (DNA sequences that make copies of themselves and insert elsewhere in the genome) Over 1 million in human genome ~50 subfamilies categorized by sequence differences

Prior Studies “Repetitive sequence environment distinguishes housekeeping genes” Eller, Daniel et al. submitted “Alu abundance positively correlates with gene expression level” C.D. Eller et. al. submitted

Higher Alu concentration near widely expressed genes

Higher Alu concentration near highly expressed genes

Alu Subfamilies Subfamily # Alu in the Subfamily

Data Human gene expression levels from microarray data (Stan Nelson’s lab, UCLA) Alu information from UCSC Genome Browser, Repeat masker tracks

Goal, reiterated Determine if there are motifs present among Alu elements near highly expressed genes, and missing from Alu elements near poorly expressed genes, that might contribute to gene expression

Strategy Find Alu “near” high and low expression genes (within 20kb) Perform multiple sequence alignment on Alu sequences Identify motifs preferentially conserved around highly expressed genes (these motifs could help the genes be highly expressed)

Strategy Find Alu “near” high and low expression genes (within 20kb) Perform multiple sequence alignment on Alu sequences Identify motifs preferentially conserved around highly expressed genes (these motifs could help the genes be highly expressed)

Used Perl scripts to extract information from MySQL databases Grouped genes by expression level in R Chose genes in top and bottom 20% Genes Expression Level Screening the genes…

 Used MySQL queries to determine flanking region  Used Perl scripts to screen Alu located within 20kb of genes  Omitted Alu in overlapping flanking regions PERCENTAGES OF ALU THROWNOUT 50%11%17%50kb 28%7% 20kb 20%6%3%10kb Chrom19 1st 20mb Chrom10Chrom1 1st 20mb HI-gene LO-gene HI-Alu??-AluLO-Alu Screening the Alu…

Strategy Find Alu “near” high and low expression genes (within 20kb) Perform multiple sequence alignment on Alu sequences Identify motifs preferentially conserved around highly expressed genes (these motifs could help the genes be highly expressed)

Alignment Process… First alignment tool: Clustalw –Slow, inaccurate Second alignment tool: T-COFFEE –Can’t handle hundreds of sequences Third alignment tool: MUSCLE Aligning thousands of sequences = big gaps and processing limitations Chose to analyze by subfamily (S, Sp/q) –Aligned elements around highly expressed genes –Aligned elements around poorly expressed genes –Profile high/low alignment –Consensus sequence alignment

 Alignment viewed in Jalview

AluS AluSp-q EPS AluSp/q Alignments of Alu Sp/q and AluS Elements High Alu High conserv. Low conserv.

Strategy Find Alu “near” high and low expression genes (within 20kb) Perform multiple sequence alignment on Alu sequences Identify motifs preferentially conserved around highly expressed genes (these motifs could help the genes be highly expressed)

AluS * * Alu w/ a base: All Alu: Frequency of consensus base Alu w/ a base: All Alu: Frequency of consensus base High Alu: TATCCACGCCTGCAAAATCTCAGCCACTCCCAAAGTTGCTGCG CANCC-CGCCT-CGTAATCCCAA AATGTT--TG-G Low Alu Alu consensus sequence All Alu: All Alu: Alu w/ a base: 596** ****** Alu w/ a base: High Alu: TGCTCAGAAATTTCTCGGCTCACTGCAACCTCCGTATCACCCC Low Alu: CG---A-AA CTCCGT--T---CT AluSp/q Alu consensus sequence Frequency of consensus base

Remaining Tasks Analyze the remaining sub-families Determine whether identified motifs agree across subfamilies BLAST motifs against all Alu sequences and correlate alignment scores with expression level

Future Directions Cluster alignments into a relationship tree to see if HI and LO Alu groups cluster differently from each other –Create a matrix of pairwise alignments and cluster these into a tree using nearest neighbour clustering Use Hidden Markov Models or Gibbs sampling to identify sequence motifs (non- multiple sequence alignment method of motif finding)

Acknowledgements Danny Eller York Marahrens Marc Suchard Chiara Sabatti SoCalBSINIH/NSF