RATES OF NUCLEOTIDE SUBSTITUTION

rate = # substitutions per site per unit time (year or generation)

Ancestral sequence

Sequence 1 Sequence 2

If r = rateK = # sub between 2 homologous sequencesT = time of divergence

r = K / 2T

Why is there 2T in the denominator of equation?

Then

KA = amino acid-altering substitution rate

KS = synonymous substitution rate

# non-synonymous sub. / non-syn. site / year

# synonymous sub. / syn. site / year

What are rates of nt substitutions between wheat and maize nucleargenes (i) at synonymous sites and (ii) at non-synonymous sites?

p. 159-160

In the literature, K or d (rather than r) is often used for rate,(eg. Table 4.17, p.163)

…so be sure to check context and units in figures

Within coding sequences, can separate the rates into:

dN/dS term often used when comparing rates of non-syn sub vs. syn sub

Fig. 4.3

Rates of nt substitution in different parts of genes

1. Non-degenerate sites under greatest functional constraint

- any change will alter protein

2. Sequences that are “selectively neutral” evolve rapidly

eg pseudogenes, 4-fold degenerate, introns…

- can be useful in “molecular clock” studies

3. Flanking (5’) & untranslated regions somewhat constrained

- because contain gene regulatory signals

Fig. 4.4

Similarity profile for 2 aligned DNA sequences

Table legend: “Rates are in units of substitutions per site per 109 years”Non-synonymous rates vary greatly among genes (~100-fold range),

but synonymous rates are relatively similar

KS usually much higher than KA

Ribosomal proteins- fundamental role in protein synthesis- interact with rRNA and/or other rib. proteins

Histones- fundamental role in DNA packing- basic, compact proteins that interact with DNAand/or other histones

Immunoglobulins- antibody diversity important for immune response& recognition of foreign antigens

Fig. 4.6

For non-synonymous sites, the stronger the functional constrainton aa sequence, the slower the rate of evolution

On log scale, plot values of KS and KA for:

S14 ribosomal proteininsulinglobingrowth hormoneIg interferon 1

Convert Table 4.1 information into Figure format

10 102 103 104 105

Need to choose appropriate range for scale (and units)…

Non-synonymous substitutions rates within genes

Various functional (or structural) domains can be subject todifferent constraints and evolve at different rates

Fig. 4.5

Rate of nt substitution depends on:

1. Functional constraints- usually strong selective pressure against changes thatalter the protein, but … cases of positive selection

2. Mutational rate

Y chromosome sequences evolve ~ 4 - 6 fold faster thanhomologues on other chromosomes

- certain regions of genome may evolve at different rates

DNA replication errors, lack of recombinational repair…

Number of germ cell divisions for egg vs. sperm production

males ~ 200 females ~ 33

Number of germ cell divisions for egg vs. sperm production

Female ~ 24

Male ~ 35 + 23(age in years -15)

Spermatogenesis

If average reproductive age is 20,then ~ 195 divisions

Strachan & Read, Fig. 10.4

DAX – sex-determination “anti-testis” genes on chr3 & Y

Zfx, Zfy – zinc finger genes on chr X & Y

Note: errors not repaired during DNA replication are also not removed by homologous recombination during meiosis (since Y chromosome is effectively unpaired, unlike autosomes)

Oogenesis

Positive selection (Adaptive evolution)

- rate of nonsynonymous substitution exceeds rate ofsynonymous substitution

Examples: - some immunoglobulin genes (antibody diversity)

- surface antigen genes of parasites and viruses (evade host)

- some sex-related genes (for speciation, reproductive barriersto restrict gene flow?)

But may be difficult to detect if- number of substitutions is low- only one part of protein under positive selection

KA > KS

abalone sperm cell protein: KA / KS = 5.15 !!

Parallelism or molecular convergence

- independent occurrence of identical substitutions at homologous sites in different evolutionary lineages resulting in same phenotypic outcome

Example – lysozymes in certain mammals (cow, langur) andbirds (hoatzin) adapted for activity at low pH in posterior chamberof stomach

Fig. 4.8

Is ability of crocodile to stay underwater for long times relatedto hemoglobin structure evolution?

Comparison of crocodile (NC), alligator (MA), caiman (SC) and human (HS) globin sequences

Komiyama et al. Nature 373:244, 1995

By genetic engineering, changed several specific codons inhuman globin gene, so amino acids identical to crocodilian ones

- then measured O2 affinity of modified Hb

Oxygen-binding curves of crocodile Hb (circles) and human Hb (squares) determined in the absence (empty symbols) and presence (filled symbols) of 5% CO2.

(a) Wild-type Hb proteins(b) Human beta-chain modified at positions 29, 31, 38, 39, & 41

(a) (b)

Komiyama et al. Nature 373:244, 1995

“These results indicate that an entirely new function which enables species to adapt to a new environment could evolve in a protein by a relatively small number of amino acid substitutions in key positions, rather than by gradual accumulation of minor mutations.”

Komiyama et al. Nature 373:244, 1995

Possible reasons for variation in Ks among genes?

- if functional constraint at levels other than amino acid sequence?

2. codon usage bias?

1. due to RNA folding? or cis-elements important for RNA processing … ?

If all codons specifying a particular amino acid are functionally equivalent (selectively neutral), expect similar frequencies

but … observe non-random distribution, with differentpatterns among organisms

Fig. 4.13

Correlation between codon usage pattern and:

1. tRNA availability in cell (E. coli and yeast)

translational efficiency?

2. bias against certain dinucleotides (mutational hotspots)

eg CpG in animal DNA

3. GC content of tightly-packed genomes

eg organellar, bacterial

- deamination of C to U can be repaired by uracil-DNA glycolyase

and 5-methyl C to T mutation escapes repair

… so shift from C to T base pair G A

- but in animal DNA cytosine of CpG is often methylated

Comparison of human and chimpanzee DNA sequences (over ~ 1.9 Mbp) to assess behaviour of CpG sites

Ebersberger Am. J. Hum. Genet. 70:1490 (2002)

of changes

Non-random mutation at CpG sites in animal DNA

Griffiths Fig. 7.16

Fig. 8.29