1. Lecture 5 Post-genomics

4. Functional genomics (A) Identifying genes from the sequence (B) Gene expression profiling (transcriptome) (C) Model systems Proteomics Systems biology Post-genomics

5. (A) Hunting genes from the sequence 2 broad approaches 1) Ab initio method (computational) 2) Experimental method

6. 1) Ab initio method (computational) Scanning ORFs (open reading frames)

7. Ab initio method (computational)  initiation or termination codons  Codon bias found in specific species Not all codons used at same frequency e.g.human leucine mainly coded by CTG and rarely by TTA or CTA  Exon-intron boundaries (splice sites) 5’-AG GTAAGT-3’ hit and miss affair  Upstream control sequences – e.g conserved motifs in transcription factor binding regions  CpG islands

8. 2) experimental method Experimental evaluation based on the use of transcribed RNA to locate exons and entire genes from DNA fragment.

9. experimental method Some strategies  Hybridisation approaches – Northern Blots, cDNA capture / cDNA select, Zoo blots  Transcript mapping: RT-PCR, exon trapping etc In this method, known DNA databases are searched to find out whether the test sequence is similar to any other known genes, suggesting an evolutionary relationship.

10. Northern BlotZoo Blot

11. Transcriptome complete collection of transcribed elements of the genome (global mRNA profiling) transcriptome maps will provide clues on Regions of transcription Transcription factor binding sites Sites of chromatin modification Sites of DNA methylation Chromosomal origins of replication (B) Gene expression profiling

12. Homology searches (BLAST searches) - Orthologous genes (homologues in different organisms with common ancestor) – comparative genomics - Paralogous genes (genes in the same organism, e.g. multigene families) - orphan genes / families COMPUTATIONAL APPROACH

13. The transcriptome Analysis can be done by either Microarray technology SAGE (serial analysis of gene expression) technology

14. (a)Schematic drawing of a DNA chip. Microarray (chip)

15. (a)Schematic drawing of a DNA chip. Microarray (chip)

16. (a)Schematic drawing of a DNA chip. Microarray (chip) Segment of a chip

17. (a)Schematic drawing of a DNA chip. Microarray (chip) Segment of a chip Spot containing copies of a single DNA molecule

18. (a)Schematic drawing of a DNA chip. Microarray (chip) Segment of a chip Spot containing copies of a single DNA molecule Part of one DNA strand A G G A C G T DNA bases

19. (b) The analysis of the hybridization process identifies genes that respond in specific ways. Cell samples are stabilized and fluorescent labels are added.

20. Examples of reactions A A T T C G C A A T T C G C A G G A C G T G G G A C T A chip DNA

21. Examples of reactions A A T T C G C chip DNA T T A A G C G cDNA from treated cells Pair of complementary bases G G G A C T A A A T T C G C C C C G G A T A G G A C G T

22. A A T T C G C Examples of reactions T T A A G C G A A T T C G C cCNA from untreated cells chip DNA T T A A G C G cDNA from treated cells Pair of complementary bases A G G A C G T G G G A C T A C C C G G A T T C C T G C A

23. (c) Computer analysis of the binding of complementary sequences can identify genes that respond to drug treatment.

24. Gene that strongly increased activity in treated cells (c) Computer analysis of the binding of complementary sequences can identify genes that respond to drug treatment.

25. Gene that strongly increased activity in treated cells Gene that strongly decreased activity in treated cells (c) Computer analysis of the binding of complementary sequences can identify genes that respond to drug treatment.

26. Gene that strongly increased activity in treated cells Gene that strongly decreased activity in treated cells Gene that was equally active in treated and untreated cells (c) Computer analysis of the binding of complementary sequences can identify genes that respond to drug treatment.

27. Gene that strongly increased activity in treated cells Gene that strongly decreased activity in treated cells Gene that was equally active in treated and untreated cells Gene that was inactive in both groups (c) Computer analysis of the binding of complementary sequences can identify genes that respond to drug treatment.

28. High-throughput microarrays

29. gene inactivation methods (knockouts, RNAi, site- directed mutagenesis, transposon tagging, genetic footprinting etc) Gene overexpression methods (knock-ins, transgenics, reporter genes) MODEL SYSTEMS

30. RNAi RNAi mimics loss-of- function mutations Non-inheritable Lack of reproducibility

31. How does RNAi work? http://www.nature.com/focus/rnai/animations/ index.html

32. Gene overexpression methods (knock-ins, transgenics, reporter genes etc) MODEL SYSTEMS

33. Proteomics Nature (2003) March 13: Insight articles from pg 194 Analysis of protein expression Protein structure and function Protein-protein interactions

34. Proteomics Proteome projects - co-ordinated by the HUPO (Human Protein Organisation) Involve protein biochemistry on a high- throughput scale Problems  limited and variable sample material,  sample degradation,  abundance,  post-translational modifications,  huge tissue, developmental and temporal specificity as well as disease and drug influences. Nature (2003) March 13: Insight articles from pgs 191-197.

35. Approaches in proteomics Nature (2003) March 13: Insight articles from pgs 191-197. High throughput approach 1)Mass- spectrometry 2) Array based proteomics 3)Structural proteomics

36. High throughput approaches in proteomics 1) Mass spectrometry-based proteomics: relies on the discovery of protein ionisation techniques. used for  protein identification and quantification,  profiling,  protein interactions and  modifications. Nature (2003) March 13: Insight articles from pgs 191-197

37. two dimensional gels and mass spectrometry Identification of proteins in complex mixtures

38. 19_09.jpg two dimensional gels

39. Mass spectrometry (MS) Nature (2003) March 13: Insight articles from pgs 191-197

40. ionizer source: converts analyte to gaseous ions mass analyser: measures mass-to-charge ratio (m/z) detector: registers the number of ions at each m/z Principle of MS

41. Types of ionizer sources Nature (2003) March 13: Insight articles from pgs 191-197. Electrospray ionisation (ESI) matrix-assisted laser desortion/ionisation (MALDI) MALDI-MS - simple peptide mixtures whereas ESI-MS - for complex samples.

42. 2) Array-based proteomics Nature (2003) March 13: Insight articles from pgs 191-197. Based on the cloning and amplification of identified ORFs into homologous (ideally used for bacterial and yeast proteins) or sometimes heterologous systems (insect cells which result in post-translational modifications similar to mammalian cells). A fusion tag (short peptide or protein domain that is linked to each protein member e.g. GST) is incorporated into the plasmid construct.

43. Array based proteomics…. Nature (2003) March 13: Insight articles from pgs 191-197. a. Protein expression and purification b. Protein activity: Analysis can be done using biochemical genomics or functional protein microarrays. c. Protein interaction analysis two-hybrid analysis (yeast 2-hybrid), FRET (Fluorescence resonance energy transfer), phage display etc d. Protein localisation: immunolocalisation of epitope-tagged products. E.g the use of GFP or luciferase tags

44. Array based proteomics…. Nature (2003) March 13: Insight articles from pgs 191-197. Protein chips Antibody chips – arrayed antibodies Antigen chips – arrayed antigens Functional arrays – arrayed proteins Protein capture chips – arrayed capture agents that interact with proteins e.g. BIAcore Solution arrays – nanoparticles

45. 19_14.jpg 3) Structural proteomics

46. 19_14.jpg 3) Structural proteomics

47. Identification of protein- protein interactions affinity capture/mass spectrometry Fig. 10. 31

48. Identification of protein-protein interactions Phage display Fig. 10.32

49. Systems Biology – the global study of multiple components of biological systems and their interactions New approach to studying biological systems has made possible –Sequencing genomes –High-throughput platform development –Development of powerful computational tools –The use of model organisms –Comparative genomics

50. Six steps in systems approach Formulate computer based model for the system Discovery science to define as many of the system’s elements as possible Perturb the system genetically or environmentally Integrating levels of information form perturbations Formulate hypothesis to explain disparities between model and experimental data Refine the model after integrating data

51. 19_20.jpg

52. Nitin S. Baliga et al. Genome Res. 2004; 14: 1025-1035 Systems biology approach to studying how Halobacterium NRC-1 transcriptome responds to uv radiation

53. Challenges for the future – ‘physiome’? Nature Reviews Molecular Cell Biology 4; 237-243 (2003)