Biochemistry, computing in biology

1. Biochemistry, computing in biology

2. Course outline

3. Recombination

4. Recombination and crossover

5. Recombination and crossover

6. Recombination and crossover If no exchange of genes (i.e. phenotypic marker) occurs, recombination event can not be detected

7. Recombination and crossover

8. Introduction to ciliates

9. literature • Genome Gymnastics: Unique Modes  of DNA Evolution and Processing in Ciliates. David M. Prescott, Nature Reviews Genetics • Computational power of gene rearrangement. Lila Kari and Laura Landweber, DIMACS series in discreet mathematics and theoretical computer science

10. The ciliate • Very ancient ( ~ 2 . 109 years ago) • Very rich group ( ~ 10000 genetically different organisms) • Very important from the evolutionary point of view

11. The ciliate • DNA molecules in micronucleus are very long (hundreds of kilo bps) • DNA molecules in macronucleus are gene-size, short (average ~ 2000 bps)

12. The ciliate

13. The ciliate tree Baldauf et al. 2000. Science 290:972.

14. Urostyla grandis Holosticha kessleri Bar: 50 mm Bar: 100 mm Eschaneustyla sp. Scrambled Genes Found S. lemnae O. trifallax O. nova Uroleptus sp. Bar: 25 mm Bar: 100 mm

15. The ciliate

16. The ciliate

17. Dapi staining of the ciliate

18. Nuclei • Micronucleus the small nucleus containing a single copy of the genome that is used for sexual reproduction • Macronucleus the large nucleus that carries up to several hundred copies of the genome and controls metabolism and asexual reproduction

19. Lifecycle of a ciliate Micronucleus Macronucleus Cutting, splicing, elimination, reordering, and amplification of DNA Prescott, 2000

20. The ciliate, meiosis

21. The ciliate, reproduction MIC MAC Meiosis and Nuclear Exchange Cell Pairing Nuclear Fusion and Duplication of the Zygotic Nucleus Macronuclear Development and Nuclear Degeneration Polytenization Chromatid breakage De novo telomere formation Modified from Larry Klobutcher & Carolyn Jahn Ann. Review Microbiology, 2002

22. Computing in ciliates

23. The ciliate Astounding feats of ‘DNA computing’ are routine in this ‘simple’ single -celled organism— a protozoan. In initial micronucleus, DNA is‘junky’ and scrambled, but…. ….it reassembles itself in proper sequence by means of computer-like acrobatics (unscrambling, throwing out genetic ‘junk’)—in macronucleus

24. The complexity of spirotrich biology Telomere Pointers MAC MIC MDS: macronuclear destined sequences IES: internal eliminated segments

25. Splicing

26. Fractioned genes

27. The complexity of gene scrambling • Intervening non-coding DNA regions (IES: internal eliminated segments) interrupt protein-coding sequences (MDS macronuclear destined sequences) • IESs are removed during macronuclear development • MDSs are unscrambled Prescott, 2000

28. Scramble genes -TBP, actin I, DNA pol  -TBP Prescott et al., 1998 Actin I Oxytricha nova Hogan et al., 2001 DNA polymerase  Landweber et al., 2000

29. Degree of scrambling in -TBP Prescott et al, 1998

30. Unscrambling of actin I Hogan et al, 2001

31. Degree of scrambling in DNA pol  Landweber et al, 2000

32. DNA folding and recombination DNA pol 

33. DNA folding and recombination

34. DNA folding and recombination DNA pol  DNA pol : Hairpin loop Prescott, 2000

35. Recombination -TBP Prescott et al, 1998

36. Tracing evolutionary scrambling • Isolate the micronuclear and macronuclear forms of the -TBP gene • Compare the micronuclear and macronuclear gene structures (MDS and IESs) to determine whether the gene is scrambled • Compare homologous MDSs and scrambling patterns in various stichotrich species (earlier diverging species vs later diverging species) • Trace a parsimonious evolutionary scrambling pathway

37. Comparisons of scrambling complexity Oxytrichidaeand Paraurostyla weissei Uroleptus sp.

38. The evolution of recombination Paraurostyla weissei Uroleptus sp. Stylonychia mytilus 100 100 Oxytricha nova 100 Oxytricha trifallax

39. Evolutionary scrambling pathway Holosticha sp. S. mytilus O. nova O. trifallax Uroleptus sp. P. weissei

40. Formal theory

41. Ciliate computing • The process of gene unscrambling in hypotrichous ciliates represents one of nature’s ingenious solutions to the computational problem of gene assembly. • With some essential genes fragmented in as many as 50 pieces, these organisms rely on a set of sequence and structural clues to detangle their coding regions. • For example, pointer sequences present at the junctions between coding and non-coding sequences permit reassembly of the functional copy. As the process of gene unscrambling appears to follow a precise algorithm or set of algorithms, the question remains: what is the actual problem being solved?

42. The problem in the cell • Genomic Copies of some Protein-coding genes are obscured by intervening non-protein-coding DNA sequence elements (internally eliminated sequences, IES) • Protein-coding sequences (macronuclear destined sequences, MDS) are present in a permuted order, and must be rearranged.

43. Assumption • By clever structural alignment…, the cell decides which sequences are IES and MDS, as well as which are guides. • After this decision, the process is simply sorting, O(n). • Decision process unknown, but amounts to finding the correct path. Most Costly.

44. Ciliate computing • there is some as yet undiscovered “oracle”mechanism within the cell, • or the cell simulates non-determinism • the former solution lacks biological credibility and the latter implies exponential time and space explosion. • What we want is a deterministic algorithm for applying the inter- and intra-molecular recombination operations to descramble an arbitrary gene.

45. Ciliate computing The first proposed step in gene unscrambling—alignment or combinatorial pattern matching—may involve searches through several possible matches, via either intra-molecular or intermolecular strand associations. This part could be similar to Adleman’s (1994) DNA solution of a directed Hamiltonian path problem.

46. Ciliate computing The second step—homologous recombination at aligned repeats—involves the choice of whether to retain the coding or the non-coding segment between each pair of recombination junctions. This decision process could even be equivalent to solving an n-bit instance of a satisfiability problem, where n is the number of scrambled segments.

47. Ciliate computing We use our knowledge of the first step to develop a model for the guided homologous recombinations and prove that such a model has the computational power of a Turing machine, the accepted formal model of computation. This indicates that, in principle, these unicellular organisms may have the capacity to perform at least any computation carried out by an electronic computer.

48. Ciliate computing, the naïve model • Assume the cell simply reconstructs the genes by matching up pointers. • Just one problem... pointer sequences are not unique. In fact, may have multiplicities greater than 13. • The proposed solution to this was that the cell would simply try every possible combination of pointers until it found the right two.

49. How the cell computes • Relies on short repeat sequences to act as guides in homologous recombination events • Splints analogous to edges in Adleman • One example represents solution of 50 city HP (50 pieces reordered)

50. Formal model • Guided recombination system