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FUNDAMENTALS OF MOLECULAR EVOLUTION. The evolutionary thinking. Russel Wallace writes to Charles Darwin (June 17 th 1858). Ernst Haeckel (mid-19 th Century): the tree of life. The neo-synthesis (Fisher, Heldane, and Wright, 1930-1950). The molecular REvolution.

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the evolutionary thinking
The evolutionary thinking
  • Russel Wallace writes to Charles Darwin (June 17th 1858)
  • Ernst Haeckel (mid-19th Century): the tree of life
  • The neo-synthesis (Fisher, Heldane, and Wright, 1930-1950)
the molecular revolution
The molecular REvolution
  • Nuttal, 1904: Serological cross-reactions to study phylogenetic relationships among various group of animals.
  • Watson and Crick beautiful helix!
  • Zuckerland and Pauling, 1965: molecular clocks.
  • Fitch & Margoliash, 1967: Construction of phylogenetic trees.A method based on mutation distances as estimated from cytochrome c sequences is of general applicability (Science, 155:279-284).
  • Kimura, 1968: Evolutionary rate at the molecular level (Nature, 217:624-626).

The birth of molecular evolution

slide5

Transitions and transversions

A

C

G

T

  • Transitions () are purine (A, G) or pyrimidine (C, T) mutations: Pu-Pu, Py-Py
  • Transversions () are purine to pyrimidine mutations or the reverse: (Pu-Py, or Py-Pu).
slide6

Point mutations and the genetic code

  • 4 possible transitions: AG, CT
  • 8 possible transversions: AC, AT, GC, GT
  • Thus if mutations were random, transversions are 2 times more likely than transitions.
  • Due to steric hindrance (as well as negative selection!), the opposite is true, transitions occur in general more often than transversions[2-15 times more, depending on the gene region and the species].
slide8

Other mutations

  • Insertions and deletions (indels). Usually by 3 nucleotides in coding regions.
  • Recombination. Often in viruses.
  • Gene (or chromosome) duplication
  • Lateral gene transfer
slide9

Genetic variation in populations

  • Polymorphism: 2 (or more) mutations co-exist (alleles) in a population of organisms.
  • Diploid organisms can be homozygous (2 identical alleles) or heterozygous (2 different alleles) at a particular locus.
  • For viruses, the term quasispecies is often used.
  • The variation in a population can be described in allele frequencies or gene frequencies
slide10

Evolution and fixation of mutations

Evolutionary forces work at the level of populations

  • Evolution is the consecutive fixation of mutations
  • The fixation rate of such a polymorphism is in fact the evolutionary rate. This is dependant on:
    • Mutation rate
    • Generation time
    • Evolutionary forces, such as fitness, selective pressure, population size
population genetics
Population genetics
  • Selective Pressure
  • Random Genetic drift
  • Effective Population size (Ne)
  • Mutation rate and evolutionary rate
slide12

Selective pressure

  • Positive selective pressure: mutant is more fit
  • Negative selective pressure: mutant is less fit
  • Balancing selection: heterozygote is more fit
  • Most synonymous mutations can be considered neutral
  • Non-synonymous mutations are always subject to selective pressure (?)
slide13

Population dynamics

1

fixed mutation

polymorphism maintained

ALLELE FREQUENCY

lost mutation

0

TIME

slide14

Effective population size

1st generation

N=10, Ne=5

2nd generation

N=10, Ne=4

3rd generation

N=10, Ne=3

4th generation

N=5, Ne=2

Bottleneck event

5th generation

N=3, Ne=2

Mutation event

6th generation

N=9, Ne=4

7th generation

N=11

slide15

Mutation and evolutionary rate

  • New mutation in a diploid population of N individuals
  • Fixation time t (Kimura and Otha 1969):
  • t = 2/s ln (2N) (s = selective advantage)
  • t = 4N for neutral mutations
  • Evolutionary Rate (or substitution rate), r:
  • number of mutants reaching fixation per unit time
  • Mutation Rate, m:
  • rate of mutation at the DNA level (biochemical concept)
molecular clocks
Molecular clocks
      • In general the evolutionary rate r can be expressed as
        • r = f 
        • f fraction on neutral mutation
        •  mutation rate
      • If f is constant

and

      •  is constant

The rate of evolution is constant (molecular clock)

  • Under neutral evolution (f = 1)
        • r = 

mutation rate = evolutionary rate (Kimura 1968)

a global molecular clock
A global molecular clock?

The hypothesis known as global clock was based on the observation that a linear relation seems to exist between the number of amino acid substitutions between homologous proteins of different species, and the species divergence times estimated from archaeological data.

slide18

Evolutionary rates of organisms

nucleotide substitutions per site per year

10 - 9

10 - 8

10 - 7

10 - 6

10 - 5

10 - 4

10 - 3

10 - 2

10 - 1

cellular genes

RNA viruses

DNA viruses

Human mtDNA

slide19

Why is the molecular clock attractive ?

  • If macromolecules evolve at constant rates, they can be used to date species-divergence times and other types of evolutionary events, similar to the dating of geological time using radioactive elements
  • Phylogenetic reconstruction is much simpler under constant rates that under nonconstant rates
  • The degree of rate variation among lineages may provide much insight into the mechanisms of molecular evolution (e.g. Kimura 1983; Gillespie 1991; Salemi et al., 1999).
slide20

Deterministic or stochastic model of evolution

  • Deterministic: fixation of mutations is entirely dependent on selective pressure. Alleles do not get lost or fixed by chance (by accident).
  • Stochastic: fixation is dependent on chance events. Chance effect is much larger than selective pressure, random genetic drift plays a big role.
  • Whether or not selective pressure plays a role can be tested by comparing synonymous with non-synonymous rates of substitution.
slide21

Neo-Darwinism - Neutral evolution

  • Neo-Darwinism:
    • Random mutations are source of variation.
    • A majority of non-synonymous mutations are deleterious, there is a strong negative selective pressure.
    • Most non-synonymous mutations become fixed because of positive selection
    • Most synonymous mutations become fixed because of random genetic drift
  • Neutral evolution (Kimura):
    • Random mutations are source of variation.
    • A majority of non-synonymous mutations are deleterious, there is a strong negative selective pressure.
    • Most mutations that become fixed are neutral, rarely positive selective pressure is strong enough to fix adaptive mutations.
slide23

The data used for phylogenetic analysis

  • Morphological characters
  • Fossils (not for viruses)
  • Genetic data:
    • AA or NT sequences
    • RFLP
    • Allele frequencies
    • ...
  • A combination of these data
slide24

Rooted phylogenetic tree

Branches can rotate freely. Branching order is called topology

External node or

Operational Taxonomic Units

OTU

(or Taxon)

A

G

node

H

B

J

K

C

Internal node or

Hypothetical Taxonomic Units

HTU

(or Ancestor)

D

I

root

E

branch

F

TIME

slide25

Unrooted phylogenetic tree

F

D

I

J

E

C

H

G

A

B

  • Root node K disappeared
  • To root an unrooted tree:
    • root by outgroup, e.g. use F as outgroup
    • midpoint rooting

Monophyletic taxa

slide26

Coalescence time on a rooted tree

A

G

B

H

J

C

D

I

E

F

Most recent common ancestor of all taxa (MRCA)

O

r = OF/T1

T2 = IE/r = ID/r

T1 T2

TIME

Coalescence time of all taxa

slide27

Evolutionary rate estimates using viral strains of known isolation time

  • Fast evolving viruses (e.g. HIV, HCV) can be sampled over time from the same patients or from different patients at different time points (longitudinal sampling)
  • The evolutionary rate can be calculated by using the difference in evolutionary distance and the time interval of isolation
  • ML and Bayesian methods can estimate simultaneously branch lengths and evolutionary rate from a tree with longitudinally sampled sequences (Rambaut, 2000; Drummond et al., 2006)

1983

d

T

1995

1997

slide29

The retroviruses

  • small RNA genome (9-10 Kb)
  • Unique replication cycle
  • extremely fast evolutionary rate (10-5 - 10-2)
  • Isolated from most vertebrate species
  • Associated with human and animal diseases
slide32

R

U5

R

U3

R

U5

U3

R

U5

Retroviral genome

gag

pol

env

pX

ssRNA genome

U3

reverse transcription

LTR

LTR

gag

pol

env

pX

dsDNA

TM

SU

Protease

Polymerase

major encoded proteins

Tax/Tat

Rex/Rev

gag-pol

env

mRNAs

Rev /Rex

Tat / Tax

global hiv 1 pandemic 1996 vs 2005
Global HIV-1 Pandemic: 1996 vs. 2005

300,000

470,000

780,000

450,000

270,000

200,000

4.8 million

1.3 million

14 million

< 35,000

2005 (~ 40 million people infected)

1996 (~ 20 million people infected)

no hiv 1 came from chimpanzees
No, HIV-1 came from chimpanzees…

Pan troglodytes

(Gao et al., Science 1999)

where did the pandemic originate
Where did the pandemic originate?

(Gao et al., Science 1999)

slide40

The River: the “interesting” journey of E. Hooper

  • A controversial theory, described by Edward Hooper in his book "The River: a journey to the source of HIV and AIDS" (1999), claims that HIV-1 originated at the end of the 1950s when live oral polio vaccines (OPV), contaminated with SIV, were administered to African children.
  • Testing the Hooper’s hypothesis by dating the the most recent common ancestor (MRCA) of HIV-1/SIVcpz and of HIV-1 group M by molecular clock analysis (Korber et al. 2000, Salemi et al., 2001).
slide41

HIV-1 group M: 41 pol strains

2DLn(l)

Date

140

1940

120

1930

100

1920

80

1910

60

1900

40

1890

20

1880

0

1870

SSCD Pol

2Ln(L) )

Date

HIV-1 group M common ancestor

# sites removed

HIV-1 group M common ancestor: 1931 (1921 - 1941)

0.1

(nucleotide substitutions per site)

Salemi et al., 2001

slide42

2DLn(l)

Date

HIV-1 group M: 61 env strains

SSCD Env

SIVCPZ

A

140

1940

G

C

120

D

1930

100

1920

80

Date

2Ln(L)

1910

HIV-1 group M common ancestor

60

1900

40

B

1890

20

0

1880

0

5

50

55

20

25

30

45

40

10

15

35

# sites removed

HIV-1 group M common ancestor: 1933 (1918 - 1948)

0.1

(nucleotide substitutions per site)

Salemi et al., 2001

slide43

Is the OPV campaign in Africa during the late 1950s to blame for the beginning of the AIDS epidemic?

  • In contrast with Hooper’s theory, our method do not support the “OPV scenario” since the radiation date for HIV-1 group M was around 1930.
  • An even older time for the separation of SIVcpz and HIV-1 can be calculated (~1700 A.D.) [Salemi et al. 2001]
hiv infection in benghazi libya
HIV Infection in Benghazi, Libya…

In May 1998, the Al-Fateh Children’s Hospital (AFH) in Benghazi, Libya1 noted their first case of HIV-1 infection. In September 1998, another 111 children who had been admitted to the hospital were found to be HIV-1 positive.

In total 418 children resulted HIV-1 positive and 300 HCV positive…

slide45

HIV Infection in Benghazi, Libya…

The outbreak was reported by local hospital authorities and representatives from the World Health Organization(WHO) were sent to AFH in December 1998 to examine the cause of the infections.

WHO report suggests that there were multiple nosocomial HIV-1/HCV

It also noted the lack of medical material in the hospital

libyan families pressure
Libyan families pressure.

Benghazi is the second biggest city and rebellious about Gaddafi…

trial begins medics in jail
Trial begins… Medics in Jail.

In March 1998 six foreign medics (five Bulgarian nurses and a doctor from Palestine) joined the medical staff at AFH. One year later (March 1999), these individuals were accused of purposefully infecting more than 400 children with HIV-1.

libyan court
Libyan court

However, the Libyan court found this report to be un-precise and lacking in evidence and therefore decided not to consider its findings in the trial4.

In December 2003, a second scientific report produced by Libyan researchers was written for the court5.

In May 2004, the foreign medical staff were sentenced to death.

Libyan court.

scientists and nature involvement
Scientists and Nature involvement…

Nature magazine reporter Declan Butler becomes involved in the case.

6 Nature editorials and news are published between September and November 2006.

  • A shocking lack of evidence (Nature 443, 888-889, 26 October 2006)
  • Protests mount against Libyan trial (Nature 443, 612-613, 12 October 2006)
  • Forgotten plights (Nature 443, 605-606, 12 October 2006)
  • Dirty needles, dirty dealings (Nature 443, 2 October 2006)
  • Libya\'s travesty (Nature 443, 245, 21 September 2006)
  • Lawyers call for science to clear AIDS nurses in Libya (Nature 443, 254 21 September 2006)
scientists around the world ask to fair trial and scientific facts
Scientists around the world ask to fair trial and scientific facts.

A new trial begins in October 2006, sentence by December 2006

october 2007 setup of an international group to analyze the data
October 2007: Setup of an International group to analyze the data

defined together an analysis plan…

  • Marco Salemi (UF) on Bayesian and ML trees for HIV.
  • Oliver Pybus (Oxford, UK) to focus on HCV sequences.
  • Tulio de Oliveira (Cape Town, South Africa) and Andrew Rambaut (Edinburgh, UK) on timing the epidemic.
tree conclusions
Tree Conclusions
  • The AFH HIV sequences form a well-supported monophyletic cluster within the CRF02_AG clade.
  • Indicating that the outbreak arose from a single CRF02_AG lineage. The cluster is closest to three West African (Blue) reference sequences
  • The branch leading to the AFH cluster is perfectly typical; hence the AFH strain is not unusually divergent as previously suggested

HIV-1 ML phylogenetic tree

hcv ml phylogenetic trees
HCV ML phylogenetic trees

Cluster 1 origin from Egypt (green)

Cluster 2 from West Africa (Blue)

Cluster 3 from worldwide subtype 1a

Genotype 1 HCV ML phylogenetic tree. Bootstrap and Bayesian probability values is shown in the internal branches.

Libyan sequences are colored red. The clusters are annotated based on their subtype classification.

i date of most recent ancestor
(i) Date of Most Recent Ancestor

No matter which model was used, the estimated TMRCA date of each cluster predated March 1998, sometimes by many years.

ii probability that mrca post dates 1 3 98
(ii) Probability that MRCA post-dates 1/3/98

In most analyses the probability that the AFH clusters originated after then was practically zero.

95% HPD confidence limits are shown in parenthesis

HKY = Hasegawa-Kishino-Yano model (1985); HKY+G = HKY model with gamma. SRD06 = One HKY+G model for codon positions 1 & 2, another HKY+G model for codon position 3

Const = constant size; Expo = exponential growth; BSP = Bayesian Skyline Plot

iii the percentage of viral lineages that already existed before 1 3 98
(iii) The percentage of viral lineages that already existed before 1/3/98.

For the three HCV clusters, the percentage of lineages already present before March 1998 was ~70%.

The percentage of HIV cluster lineages in existence before then was estimated at ~40%

95% HPD confidence limits are shown in parenthesis

HKY = Hasegawa-Kishino-Yano model (1985); HKY+G = HKY model with gamma. SRD06 = One HKY+G model for codon positions 1 & 2, another HKY+G model for codon position 3

Const = constant size; Expo = exponential growth; BSP = Bayesian Skyline Plot

nature fast track review
Nature Fast Track Review

Giving the sensitivity of the paper Nature utilized nine (9) reviewers.

4 anonymousand

5 openreviewers

All reviewers were very positive about the results.

timeline of paper
Timeline of Paper…

23 of October 2006 6 December 2006.

Initial analysis (23 Oct 2006)

Submit paper to Nature (6 of Nov 2006)

Reviewers comments (14 Nov 2006)

Submit second version as Brief Communication (17 Nov 2006)

Paper Accepted (21 Nov 2006)

Paper Published (6 Dec 2006)

biggest prize for a scientist
Biggest prize for a Scientist !

To get a kiss from the medics and to see the happiness in their face !

collaborators friends
Collaborators & friends

Maureen M. Goodenow, Rebecca Gray

UF Gainesville, FL, USA

Tulio de Oliveira

Africa Centre for Health and Population Studies

Cape Town, South Africa

Oliver Pybus

University of Oxford

Oxford, UK

Andrew Rambaut

University of Edinburgh

Edinburgh, UK

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