[PPT] - Quasispecies, virus evolution, and lethal mutagenesis on realistic PowerPoint Presentation

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Quasispecies, virus evolution, and lethal mutagenesis

n realistic fitness landscapes

Peter Schuster

Institut für Theoretische Chemie, Universität Wien, Austria and The Santa Fe Institute, Santa Fe, New Mexico, USA

Seminar Lecture Universität Marburg, 14.10.2011

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Web-Page for further information: http://www.tbi.univie.ac.at/~pks

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Chemical kinetics of molecular evolution Historical prologue

The work on a molecular theory of evolution started more than 40 years ago ......

1971

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Manfred Eigen 1927 -

  

  

   

n i i n i i i j i n i ji j

x x f Φ n j Φ x x W x

1 1 1

, , 2 , 1 ; dt d 

Mutation and (correct) replication as parallel chemical reactions

M. Eigen. 1971. Naturwissenschaften 58:465,
M. Eigen & P. Schuster.1977. Naturwissenschaften 64:541, 65:7 und 65:341

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Evolution in the test tube: G.F. Joyce, Angew.Chem.Int.Ed. 46 (2007), 6420-6436

Sol Spiegelman, 1914 - 1983

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Kinetics of RNA replication

C.K. Biebricher, M. Eigen, W.C. Gardiner, Jr. Biochemistry 22:2544-2559, 1983

Christof K. Biebricher, 1941-2009

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C.K. Biebricher, R. Luce. 1992. In vitro recombination and terminal recombination of RNA by Q replicase. The EMBO Journal 11:5129-5135. stable

does not replicate!

metastable

replicates!

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RNA replication by Q-replicase

C. Weissmann, The making of a phage.

FEBS Letters 40 (1974), S10-S18

Charles Weissmann 1931-

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Chemical kinetics of molecular evolution (continued)

1977 1988

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Application of quasispecies theory to the fight against viruses Esteban Domingo 1943 -

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Error threshold versus lethal mutagenesis Vol.1(6), e61, 2005, pp.450 – 460.

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1. Complexity in molecular evolution 2. The error threshold 3. Simple landscapes and error thresholds 4. ‚Realistic‘ fitness landscapes 5. Quasispecies on realistic landscapes 6. Neutrality and consensus sequences 7. Error thresholds and lethal mutagenesis

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1. Complexity in molecular evolution

2. The error threshold 3. Simple landscapes and error thresholds 4. ‚Realistic‘ fitness landscapes 5. Quasispecies on realistic landscapes 6. Neutrality and consensus sequences 7. Error thresholds and lethal mutagenesis

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Chemical kinetics of replication and mutation as parallel reactions

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Factorization of the value matrix W separates mutation and fitness effects.

   

   

     

n i i n i i i j i i n i ji j i n i ji j

x x f Φ n j Φ x x f Q Φ x x W x

1 1 1 1

, , 2 , 1 ; dt d 

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Mutation-selection equation: [Ii] = xi  0, fi  0, Qij  0 solutions are obtained after integrating factor transformation by means

f an eigenvalue problem

f x f x n i x x f Q dt dx

n j j j n i i i j j n j ij i

     

  

   1 1 1

; 1 ; , , 2 , 1 ,   

         

) ( ) ( ; , , 2 , 1 ; exp exp

1 1 1 1

   

     

      

n i i ki k n j k k n k jk k k n k ik i

x h c n i t c t c t x     

     

n j i h H L n j i L n j i Q f W

ij ij ij i

, , 2 , 1 , ; ; , , 2 , 1 , ; ; , , 2 , 1 , ;

1

          



 

1 , , 1 , ;

1

      



n k L W L

k

 

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Perron-Frobenius theorem applied to the value matrix W

W is primitive: (i) is real and strictly positive (ii) (iii) is associated with strictly positive eigenvectors (iv) is a simple root of the characteristic equation of W (v-vi) etc. W is irreducible: (i), (iii), (iv), etc. as above (ii)

all for   k

k

 

  

all for   k

k

 

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Complexity in molecular evolution

W = G  F 0 , 0  largest eigenvalue and eigenvector

diagonalization of matrix W „ complicated but not complex “ fitness landscape mutation matrix „ complex “

genotype  phenotype mutation selection

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1. Complexity in molecular evolution

2. The error threshold

3. Simple landscapes and error thresholds 4. ‚Realistic‘ fitness landscapes 5. Quasispecies on realistic landscapes 6. Neutrality and consensus sequences 7. Error thresholds and lethal mutagenesis

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The no-mutational backflow or zeroth order approximation

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The no-mutational backflow or zeroth order approximation

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Uniform error rate model

n jj X X d X X d n ji

p Q p p Q

j i H j i H

) 1 ( and ) 1 (

) , ( ) , (

   



p ... mutation rate per site and replication n ... chain length of the polynucleotide dH(Xi,Xj) ... Hamming distance

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 



       

                   

N m i i i i m m m m m n m m n m n m m m m mm m m mm m mm m m

f x x f f f p p x p Q x f Q t t f Q x x

, 1 / 1 cr 1 ) ( 1 1 ) ( ) ( ) (

) 1 ( 1 and ) ( 1 and ) 1 ( 1 ) 1 ( 1 1 1 ) ( and ) ( dt d         

The ‚no-mutational-backflow‘ or zeroth order approximation

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Chain length and error threshold

n p n p n p p n p Q

m m m m n m mm

     ln : constant ln : constant ln ) 1 ( ln 1 ) 1 (

max max

             

sequence master

f

y superiorit ) 1 ( length chain rate error accuracy n replicatio ) 1 (    

 

   

m j m j j m m n mm

x f x f σ n p p Q

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quasispecies

The error threshold in replication and mutation

driving virus populations through threshold

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1. Complexity in molecular evolution 2. The error threshold

3. Simple landscapes and error thresholds

4. ‚Realistic‘ fitness landscapes 5. Quasispecies on realistic landscapes 6. Neutrality and consensus sequences 7. Error thresholds and lethal mutagenesis

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Sewall Wright. 1931. Evolution in Mendelian populations. Genetics 16:97-159.

- --. 1932. The roles of mutation, inbreeding, crossbreeding,

and selection in evolution. In: D.F.Jones, ed. Proceedings of the Sixth International Congress on Genetics, Vol.I. Brooklyn Botanical Garden. Ithaca, NY, pp. 356-366.

- --. 1988. Surfaces of selective value revisited.

The American Naturalist 131:115-131.

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Sewall Wrights fitness landscape as metaphor for Darwinian evolution

Sewall Wright. 1932. The roles of mutation, inbreeding, crossbreeding and selection in evolution. In: D.F.Jones, ed. Int. Proceedings of the Sixth International Congress on Genetics. Vol.1, 356-366. Ithaca, NY.

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The simplified model

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Model fitness landscapes I

single peak landscape step linear landscape

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Error threshold on the single peak landscape

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Error threshold on the step linear landscape

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Model fitness landscapes II linear and multiplicative hyperbolic

both are often used in population genetics

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The linear fitness landscape shows no error threshold

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Error threshold on the hyperbolic landscape

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Make things as simple as possible, but not simpler !

Albert Einstein

Albert Einstein‘s razor, precise refence is unknown.

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1. Complexity in molecular evolution 2. The error threshold 3. Simple landscapes and error thresholds

4. ‚Realistic‘ fitness landscapes

5. Quasispecies on realistic landscapes 6. Neutrality and consensus sequences 7. Error thresholds and lethal mutagenesis

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Realistic fitness landscapes 1.Ruggedness: nearby lying genotypes may develop into very different phenotypes 2.Neutrality: many different genotypes give rise to phenotypes with identical selection behavior 3.Combinatorial explosion: the number of possible genomes is prohibitive for systematic searches

Facit: Any successful and applicable theory of molecular evolution must be able to predict evolutionary dynamics from a small or at least in practice measurable number of fitness values.

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Rugged fitness landscapes

ver individual binary sequences

with n = 10

single peak landscape „realistic“ landscape

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Random distribution of fitness values: d = 0.5 and s = 919

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Random distribution of fitness values: d = 1.0 and s = 919

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Random distribution of fitness values: d = 1.0 and s = 637

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1. Complexity in molecular evolution 2. The error threshold 3. Simple landscapes and error thresholds 4. ‚Realistic‘ fitness landscapes

5. Quasispecies on realistic landscapes

6. Neutrality and consensus sequences 7. Error thresholds and lethal mutagenesis

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Error threshold: Individual sequences n = 10,  = 2, s = 491 and d = 0, 0.5, 0.9375

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Error threshold on ‚realistic‘ landscapes n = 10, f0 = 1.1, fn = 1.0, d = 0.5

s = 541 s = 919 s = 637

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Error threshold on ‚realistic‘ landscapes n = 10, f0 = 1.1, fn = 1.0, d = 0.5

s = 541 s = 637 s = 919

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s = 541 s = 919 s = 637

Error threshold on ‚realistic‘ landscapes n = 10, f0 = 1.1, fn = 1.0, d = 0.995

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s = 919 s = 541 s = 637

Error threshold on ‚realistic‘ landscapes n = 10, f0 = 1.1, fn = 1.0, d = 1.0

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