1. Rosalind Elsie Franklin  Biophysicist and crystallographer  X-ray diffraction images of DNA  Tobacco mosaic and polio viruses  1920-1958 (source: wikipedia)

2. A Structural Split in the Human Genome Clara S. M. Tang and Richard J. Epstein PLoS One (2007) 7:e603 February 13, 2007 I. Elizabeth Cha

3. Introduction  PCIs Promoter-associated CpG islands  Mediate methylation-dependent gene silencing  Co-locate to transcriptionally active genes  60% of human genes contains PCIs

4. CpG Islands  Genomic regions containing high frequency of CG dinucleotides  CpG cytidine-phosphodiester-guanosine  Formal definition At least 200bp GC percentage >50% CpG ratio >60%

5. DNA Methylation

6. Materials and Methods  Sequence data and annotations  Determination of CpG island overlapping transcription start site  Housekeeping genes and paralogs of pseudogenes  Bimodal distribution of GC content  Gene expression data  Evolutionary rate determination  Principal component analysis

7. Sequence Data and Annotations  UCSC genomic assemblies, RefSeq dataset, Emsembl gene dataset Human (hg18, 3/2006) Mouse (mm6, 3/2006) Fugu (fr1, 8/2002) Fruit fly (dm2, 4/2004) Worm (ce2, 3/2004)

8. Data Preprocessing  RepeatMask – Alu  Discard sequences Not commencing with ATG codons Not terminating with canonical stop codons  Retain the longest genomic sequences containing identical exonic sequences

9. Determination of CpG Island Overlapping Transcription Start Site  Download CpG islands annotation (cpgIslandExt) from UCSC  Identify CpG islands overlapping with promoter regions  Map with RefGene annotation (200bp upstream and 500bp downstream)

10. Data and Tools  502 Housekeeping genes  1220 pseudogene paralogs  NOCOM program  SAGEmap  Homologue data  XSTAT

11. Results – PCI+ Genes  Housekeeping gene higher GC content lower intron length/number  Pseudogene paralog lower GC content higher intron length/number  Functional distinguishable

12. Table 1

13. Results – PCI- Genes  Higher evolutionary rate  Narrower expression breadth than PCI+ genes  More frequent tissue-specific inactivation

14. Figure 1 Biphasic GC/AT Distribution of PCI+ Genes A. Distribution of GC content among different regions of genes 3’ UTR 5’ UTR coding region intronic

15. Figure 1 Biphasic GC/AT Distribution of PCI+ Genes (cont’d) With ‘start’ CpG islands (CGI+) Without ‘start’ CpG islands (CGI+) B&C Proportion of genes among different GC groups.

16. Figure 2 GC Content of Promoter vs. Non-promoter CpG Island Overlapping Genes All genes Genes with medium total intron size (10- 50kb) Intronless genes Genes with short total intron size ( 50kb) PCI+: solid line; PCI-: dash line

17. Figure 3 Distribution of Coding GC% of RefGenes with PCIs pseudogenes House- keeping genes

18. Figure 4 Quantitative Comparison of Gene Subsets L: low, GC 65%; double dark, <0.001; single dark, <0.01; open, < 0.05

19. Figure 4 Quantitative Comparison of Gene Subsets (cont’d) L: low, GC 65%; double dark, <0.001; single dark, <0.01; open, < 0.05

20. Figure 4 Quantitative Comparison of Gene Subsets (cont’d) L: low, GC 65%; double dark, <0.001; single dark, <0.01; open, < 0.05

21. Figure 6 Model of human genomic evolution

22. Conclusions  PCIs Transcriptional regulators Evolutionary accelerators to facilitate intron insertion  Mthylated PCIs on transcription and chromatin accelerate adaptive evolution towards biological complexity

23. Conclusions  Adaptive evolution of human genome Declining transcription of a subset of PCI+ genes Predisposing to both CpG  TpA mutation and intron insertion  Biological complexity model Environmentally selected gains/losses of PCI methylation (+/-) Polarizing PCI+ gene structures arounda genomic core of ancestral PCI- genes

24. Discussion  AT-rich, PCI+ gene vs. GC-rich PCI+ housekeeping gene Lower transcriptional activity Higher intron number Higher evolutionary rate  Loss of negative selection pressure

25. Discussion (cont’d)  PCI- genes vs. PCI+ genes Higher evolutionary rate Lower expression breadth  Intron number relates more directly to PCI positivity

26. Figure 5 Principal component analysis (PCA) A. PCA analysis using six variables at either 53% (left) or 59% (right) variance

27. Figure 5 Principal component analysis (PCA) (cont’d) B. 2D dot plots C. 3D dot plots GC-rich, blue; GC-poor, red