Evidence for convergent evolution of ALU repeats in human and mouse

Transcription

1 Evidence for convergent evolution of ALU repeats in human and mouse Aristotelis Tsirigos - IBM Computational Genomics Group May 2

2 I. ALU repeats: a class of mobile elements II. Evolution of ALUs III. Genomic properties of ALUs IV. Evidence for convergent evolution V. Conclusions 2

3 ALUs: the most abundant mobile elements in human ALU properties: retroposons SINE repeat family ~3nt long ~ million copies in human ~ duplication per 2 births 3

4 Evolution of ALUs 4

5 Recent evolutionary history of ALUs: primates 5

6 Evolutionary origin of ALUs before primate-rodent split ~8 Mya Source: Kriegs et al., Trends Genet, 27 6

7 B elements in mouse are related to ALUs 7

8 Genomic properties of ALUs 8

9 Human/mouse: general statistics region human mouse entire genome 5.7 Gbp 5.3 Gbp conserved regions 33% 36% repeat elements 24% 2% ALU elements 5% 3% ALU size ~3nt ~5nt Recent findings (ENCODE project, Nature 27): conserved non-exonic regions are not necessarily functional large fraction of non-conserved regions are functional 9

10 ALU conservation in human and mouse

11 ALU densities in transcript neighborhoods

12 B densities in transcript neighborhoods 2

13 ALU families and C+G content ALU subfamily AluYk AluYa5 AluYb8 AluYf5 AluYb9 AluYc3 AluYf4 AluYk4 AluY AluYg6 AluSq AluYc AluSg4 AluSg AluSc8 AluSg7 AluSc AluSc5 AluSx3 AluSp AluSx AluSx4 AluSq4 AluSq2 AluSz AluSq AluSx AluSz6 AluYa8 AluJb AluJo FRAM AluJr FAM FLAM_A FLAM_ AluJr G+C content 3

14 ALU biases in introns and cancer associated regions 6% 4% observed expected 2% Alu densities (%) % 8% 6% 4% 2% % conserved regions introns fragile sites amplified sites loss of heterozygocity sites 4

15 ALU properties at a glance Descendants of 7SL rrna Dimers in human, monomers in mouse Amplified independently in human and mouse Similar density distributions in human and mouse Depletion in conserved regions Enrichment in introns & upstream regions Enrichment in loss of heterozygocity & amplified sites 5

16 Questions Why were ALUs and Bs amplified independently? Why do ALUs and Bs have similar distribution profiles? Is there a link to function? 6

17 Linking ALUs to function: method 7

18 Computational method Input: reference regions per gene (upstream, introns, exons,, etc.) test regions (ALUs( ALUs,, Bs, controls, etc.) Functional annotations: a set of GO terms per gene Statistical test: enrichment in genes associated with a GO term Output: GO term p-values p at a given false discovery rate (%) 8

19 Step : reference regions (introns/upstream) gene # gene #2 gene #3 sequences... gene #n reference sequence 9

20 Step 2: map the repeats gene # gene #2 gene #3 sequences... gene #n intronic sequence repeat instances 2

21 Step 3: compute densities sequences densities gene # gene #2 gene #3 x x 2 x gene #n x n intronic sequence repeat instances 2

22 Step 4a: assign GO terms gene # gene #2 gene #3 sequences densities x x 2 x 3 GO terms A, C, F B, C, D, E C, E gene #n x n A, F, H intronic sequence repeat instances 22

23 Step 4b: assign GO terms gene # gene #2 gene #3 sequences densities x x 2 x 3 GO terms A, C, F B, C, D, E C, E gene #n x n A, F, H intronic sequence repeat instances 23

24 Step 4c: identify GO term sets densities x x 2 x 3 GO terms A, C, F B, C, D, E C, E x n A, F, H 24

25 Step 5: GO term table densities GO terms A B C D E F G H x A, C, F x 2 B, C, D, E x 3 C, E x n A, F, H 25

26 Step 6a: compute test statistic for each GO term densities GO terms A B C D E F G H x A, C, F x 2 B, C, D, E x 3 C, E x n A, F, H 26

27 Step 6b: compute test statistic for given GO term densities for each GO term T, compute t-test statistic x x 2 x 3... probability x n repeat densities in rest of genes repeat densities in genes whose annotation contains GO term T Output = a p-value p for each GO term 27

28 Step 6c: next GO term densities GO terms A B C D E F G H x A, C, F x 2 B, C, D, E x 3 C, E x n A, F, H 28

29 Algorithm output GO term A B C... p-value p A p B p C... H p H 29

30 Random permutation test permuted densities GO terms A B C D E F G H x 7 A, C, F x B, C, D, E x C, E x 2 A, F, H Permute densities and repeat previous steps The result is a random background distribution of p-valuesp 3

31 Compute false discovery rate GO term p-value permutation ()... permutation (k) A p A p () A... p (k) A B p B p () B... p (k) B C p C p () C... p (k) C H p H p () H... p (k) H FDR(p) = { p f (j) p f=a,b,...,h and j k k } /k { p f p f=a,b,...,h } 3

32 Method: summary and advantages Statistically significant enrichments in functional gene classes Distribution-free method FDR (false discovery rate) Control for chromosome/strand biases Control for self-aligning genome regions Robustness via exclusion of potential outliers Control for GO hierarchy dependencies 32

33 Linking ALUs to function: results 33

34 Enriched GO terms per region conserved human conserved mouse human ALUs mouse Bs conserved human 385 conserved mouse 46 human ALUs 96 mouse Bs 62 34

35 Pair-wise GO term overlaps conserved human conserved mouse human ALUs mouse Bs conserved human 385 conserved mouse 334 (87%) 46 human ALUs 96 mouse Bs 62 35

36 Pair-wise GO term overlaps conserved human conserved mouse human ALUs mouse Bs conserved human 385 conserved mouse 334 (87%) 46 human ALUs 96 mouse Bs 8 (83%) 62 36

37 Pair-wise GO term overlaps conserved human conserved mouse human ALUs mouse Bs conserved human 385 conserved mouse 334 (87%) 46 human ALUs ~% ~% 96 mouse Bs ~% ~% 8 (83%) 62 37

38 Enriched GO terms conserved intronic regions cellular process cell communication regulation of cellular process cell adhesion cell differentiation regulation of biological process negative regulation of biological process regulation of development regulation of physiological process positive regulation of biological process regulation of growth interaction between organisms growth development sex differentiation developmental maturation anatomical structure development embryonic development pattern specification segmentation response to stimulus defense response response to biotic stimulus response to chemical stimulus response to stress response to external stimulus behavior repeats in introns/upstream translational elongation translational initiation chromatin modification DNA repair DNA recombination DNA packaging chromatin remodeling ubiquitin cycle transcription initiation RNA splicing RNA elongation phosphorylation protein modification gene silencing 38

39 Verification: ALU densities for discovered functions 39

40 Experimental validation: evidence in the literature DNA repair: Srikanta et al. (29) RNA editing and translation regulation: Vidal et al. (993) repress transcription following heat shock: Vidal et al. (993) recombination: Ovchinnikov et al. (2) gene silencing: Aravin et al. (27) Conclusion: functional bias of ALUs reveals role of ALUs in those functions Next question: is this due to positive selection despite lack of conservation? 4

41 Our hypothesis: positive selection Why? if neutral, then no enrichment in experimentally validated functions if detrimental, then no enrichment in upstream regions full of regulatory r signals obvious case of negative selection: depletion in exons 4

42 Alternative hypothesis I: insertion bias Are these functional classes preferential targets of other mobile e elements? LINEs = ~22% (but none of the experimentally validated functions) ERVs = ~% LTRs = ~% other mobile elements = % Recent ALUs: ~5% fewer functional classes Question: is there selective retention or selective loss of ALU instances? 42

43 Alternative hypothesis II: selective loss of ALUs How many functional classes are depleted in ALUs? Zero. 43

44 Alternative hypothesis III: non-conserved regions Are non-conserved regions biased towards the same functional classes? Only ~2% (high level GO terms, such as metabolism ) 44

45 Alternative hypothesis IV: C+G content Are functional biases related to C+G content? ALUs are CG-rich to begin with, so we look at non-alu C+G content We tested non-alu C+G content and found no functional biases 45

46 Conclusions 46

47 Original observations. Evolutionary history: common origin, but independent amplification Source: Kriegs et al., Trends Genet, Properties: lack of conservation, but similar density distribution patterns 47

48 Hypothesis tested and validated. Developed statistical method for discovering functional biases 2. Proposed and validated positive selection hypothesis 3. Provided evidence for convergent evolution of ALU elements 48

49 Acknowledgements Isidore Rigoutsos (Jefferson University) Niina Haiminen (IBM Research) Tien Huynh (IBM Research) 49

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