Molecular basis of evolution.
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Transcript Molecular basis of evolution.
Molecular basis of evolution.
Goal – to reconstruct the evolutionary history of all
organisms in the form of phylogenetic trees.
Classical approach: phylogenetic trees were
constructed based on the comparative morphology
and physiology.
Molecular phylogenetics: phylogenetic trees are
constructed by comparing DNA/protein sequences
between organisms.
Evolution of mankind.
Analysis of mitochondrial DNA proposes that Homo sapiens
evolved from one group of Homo erectus in Africa (African
Eve) 100,000 – 200,000 years ago.
American indians I,
25-35,000
Europeans
40-50,000
American indians II,
7-9,000
Africans
100,000
Asians
55-75,000
Adam appeared 250,000 years ago, much earlier!
Mechanisms of evolution.
- Evolution is caused by mutations of genes.
- Mutations spread through the population via
genetic drift and/or natural selection.
- If mutant gene produces an advantage (new
morphological character), this feature will be
inherited by all descendant species.
Mutational changes of DNA sequences.
1. Substitution.
Thr Tyr Leu Leu
ACC TAT TTG CTG
3. Insertion.
Thr Tyr Leu Leu
ACC TAT TTG CTG
ACC TCT TTG CTG
Thr Tyr Leu Leu
ACC TAC TTT GCT G—
Thr Tyr Phe Ala
2. Deletion.
Thr Tyr Leu Leu
ACC TAT TTG CTG
4. Inversion.
Thr Tyr Leu Leu
ACC TAT TTG CTG
ACC TAT TGC TGThr Tyr Cys
ACC TTT ATG CTG
Thr Phe Met Leu
Gene duplication and recombination.
New genes/proteins occur through the gene duplication and
recombination.
Ancestral globin
duplication
Gene 1
+
Gene 2
globin
globin
hemoglobin
myoglobin
New gene
Duplication
Recombination
Codon usage.
Phe UUU
UUC
Leu UUA
UUG
Ser UCU
UCC
UCA
UCG
Tyr UAU
UAC
Cys UGU
UGC
Frequencies of different codons for the same amino acid are
different. Codon usage bias is caused:
- Translationary machinery tends to use abundant tRNA
(and codons corresponding to these tRNA). Codon usage
bias is the same for all highly expressed genes in the
same organism.
- Mutation pressure. Difference between mutation rates
between GC AT and AT GC. GC-content is different
in different organisms.
Synonymous and nonsynonymous
nucleotide substitutions.
Synonymous substitutions in codons do not change
the encoding amino acid, occur in the first and
third codon positions.
Nonsynonymous occur in the second position.
ds/dn < 1 indicates positive natural selection.
ds, dn - # of (non)synonymous substitutions per
(non)synonymous site
Measures of evolutionary distance
between amino acid sequences.
Evolutionary distance is usually measures by the
number of amino acid substitutions.
1. P-distance.
p nd / n
nd – number of amino acid differences between
two sequences; n – number of aligned amino
acids.
Poisson correction for evolutionary
distance.
Takes into account multiple substitutions and
therefore is proportional to divergence time.
PC-distance – total # of substitutions per site for two
sequences
d ln( 1 p )
Gamma-distance.
Substitution rate varies from site to site according to
gamma-distribution.
a – gamma-parameter, describing the shape of the
distribution, =0.2-3.5.
When P<0.2, there is no need to use gammadistance.
d G a (1 p )
1 / a
1
Estimation of evolutionary rates in
hemoglobin alpha-chains.
P-distance
PC-distance
Gamma-distance
Human/cow
0.121
0.129
0.134
Human/kangaroo
0.186
0.205
0.216
Human/carp
0.486
0.665
0.789
To estimate the evolutionary rate of divergence between
human and cow (time of divergence between these groups is
~90 millions years), r = 0.129 / (2*90*10^6) = 0.717*10^-9
per site per year.
Another method to estimate evolutionary
distances: amino acid substitution
matrices.
Substitutions occur more often between amino acids of
similar properties.
Dayhoff (1978) derived first matrices from multiple
alignments of close homologs.
The number of aa substitutions is measured in terms of
accepted point mutations (PAM) – one aa substitution
per 100 sites.
Dayhoff-distance can be approximated by gamma-distance
with a=2.25.
Fixation of mutations.
Not all mutations are spread through population. Fixation –
when a mutation is incorporated into a genome of species.
Majority of mutations are neutral (Kimura), do not effect the
fitness of organism.
Fixation rate will depend on the size of population (N), fitness
(s) and mutation rate (μ):
r 4 Ns
Phylogenetic analysis.
- Phylogenetic trees are derived from multiple sequence
alignments. Each column describes the evolution of one
site.
- Each position/site in proteins/nucleic acids changes in
evolution independently from each other.
- Insertions/deletions are ususally ignored and trees are
constructed only from the aligned regions.
Evolutionary tree constructed from rRNA
analysis.
The concept of evolutionary trees.
- Trees show relationships between organisms.
- Trees consist of nodes and branches, topology branching pattern.
- The length of each branch represents the number of
substitutions occurred between two nodes. If rate of
evolution is constant, branches will have the same length
(molecular clock hypothesis).
- Trees can be binary or bifurcating.
- Trees can be rooted and unrooted. The root is placed by
including a taxon which is known to branch off earlier
than others.
Accuracies of phylogenetic trees.
Two types of errors:
- Topological error
- Branch length error
Bootstrap test:
Resampling of alignment columns with replacement;
recalculating the tree; counting how many times
this topology occurred – “bootstrap confidence
value”. If it is >0.95 – reliable topology/interior
branch.
Methods for phylogenetic trees
construction.
Set of
related
sequences
Multiple
sequence
alignments
Strong
sequence
similarity?
Yes
Maximum
parsimony
methods
No
Recognizable
sequence
similarity?
Yes
Distance
methods
No
Maximum
likelihood
methods
Analyze
reliability of
prediction
Calculating branch lengths from
distances.
A
B
C
a b 20;
A
-----
20
30
B
-----
-----
44
a c 40;
b c 44;
C
-----
-----
-----
a 8; b 12; c 32.
a
c
b
1. Distance methods: Neighbor-joining
method.
NJ is based on minimum evolution principle (sum of branch length
should be minimized).
Given the distance matrix between all sequences, NJ joins sequences
in a tree so that to give the estimate of branch lengths.
1. Starts with the star tree, calculates the sum of branch lengths.
C
d AB a b;
B
b
d AC a c;
c
a
d
D
d AD a d ;
d AE a e;
S abcd e
e
A
(d AB d AC d AD d AE d BC d BD d BE d CD d CE d DE ) /( N 1)
E
Neighbor-joining method.
2. Combine two sequences in a pair, modify the tree. Recalculate the
sum of branch lengths, S for each possible pair, choose the lowest S.
C
B
c
b
d AX (d AC d AD d AE ) / 3;
d
a
A
D
e
d BX (d BC d BD d BE ) / 3;
a b d AB ; a x d AX ; b x d BX .
E
3. Treat cluster CDE as one sequence “X”, calculate average distances
between “A” and “X”, “B” and “X”, calculate “a” and “b”.
4. Treat AB as a single sequence, recalculate the distance matrix.
5. Repeat the cycle and calculate the next pair of branch lengths.
Classwork I
Given a multiple sequence, construct distance matrix (p-distance) and
calculate the branch lengths.
APTHASTRLKHHDDHH
ALTKKSTRIRHIPD-H
DLTPSSTIIR-YPDLH
Classwork II: NJ tree using MEGA.
1. Go to CDD webpage and retrieve alignment of cd00157 in
FASTA format.
2. Import this alignment into MEGA and convert it to MEGA
format http://www.megasoftware.net/mega3/mega.html .
http://bioweb.pasteur.fr/seqanal/interfaces/protdistsimple.html
3. Construct NJ tree using different distance measures with
bootstrap.
4. Analyze obtained trees.