Case study C · Investigate the molecules

Genetic information and mutation

DNA stores the sequence of every protein in three-letter codons. Changing a single letter can do nothing, change one amino acid, or stop the protein short. What mutations do, and how often each kind happens, is measurable.

NCBI RefSeq NM_000518.5, coding sequence. Click any base to change, insert or delete it. Changed codons are outlined; amino acids are coloured by side-chain class.

nonpolarpolarpositive (basic)negative (acidic)stop
1Met
2Val
3His
4Leu
5Thr
6Pro
7Glu
8Glu
9Lys
10Ser
11Ala
12Val
13Thr
14Ala
15Leu
16Trp
17Gly
18Lys
19Val
20Asn
21Val
22Asp
23Glu
24Val
25Gly
26Gly
27Glu
28Ala
29Leu
30Gly
31Arg
32Leu
33Leu
34Val
35Val
36Tyr
37Pro
38Trp
39Thr
40Gln
41Arg
42Phe
43Phe
44Glu
45Ser
46Phe
47Gly
48Asp
49Leu
50Ser
51Thr
52Pro
53Asp
54Ala
55Val
56Met
57Gly
58Asn
59Pro
60Lys
61Val
62Lys
63Ala
64His
65Gly
66Lys
67Lys
68Val
69Leu
70Gly
71Ala
72Phe
73Ser
74Asp
75Gly
76Leu
77Ala
78His
79Leu
80Asp
81Asn
82Leu
83Lys
84Gly
85Thr
86Phe
87Ala
88Thr
89Leu
90Ser
91Glu
92Leu
93His
94Cys
95Asp
96Lys
97Leu
98His
99Val
100Asp
101Pro
102Glu
103Asn
104Phe
105Arg
106Leu
107Leu
108Gly
109Asn
110Val
111Leu
112Val
113Cys
114Val
115Leu
116Ala
117His
118His
119Phe
120Gly
121Lys
122Glu
123Phe
124Thr
125Pro
126Pro
127Val
128Gln
129Ala
130Ala
131Tyr
132Gln
133Lys
134Val
135Val
136Ala
137Gly
138Val
139Ala
140Asn
141Ala
142Leu
143Ala
144His
145Lys
146Tyr
147His
148Stop

The protein: before and after

OriginalMVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH*
MutantMVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH*
Length
147 → 147 amino acids
Identical residues
100%
Stop codon
at codon 148

Mutation log

No mutations yet. Click a base, mutate at random, or apply a known variant.

Compare the four kinds, side by side

One example of each kind, chosen automatically from the original sequence:

Synonymousc.10C>T: codon 4 CTG → TTG (Leu → Leu)
Protein length
147 aa
First change
none
Identical residues
100%

The codon changes but still codes for the same amino acid. Often called 'silent', though synonymous changes can affect splicing, mRNA stability and translation speed (Chamary et al. 2006).

Missense (different side chain)c.11T>A: codon 4 CTG → CAG (Leu → Gln)
Protein length
147 aa
First change
residue 4
Identical residues
99.3%

One amino acid is replaced by one with a different side-chain class, e.g. charged to non-polar, as in sickle-cell haemoglobin (Glu → Val).

Nonsensec.19G>T: codon 7 GAG → TAG (Glu → Stop)
Protein length
6 aa (was 147)
First change
residue 7
Identical residues
4.1%

A codon becomes a stop codon, so the protein is cut short. In living cells such mRNAs are often destroyed by nonsense-mediated decay.

Frameshiftc.31delG: 1 base deleted
Protein length
18 aa (was 147)
First change
residue 11
Identical residues
6.8%

An insertion or deletion that is not a multiple of three shifts the reading frame: every codon after it is read differently, usually until an early stop.

Every possible single-base substitution in this gene

All 1,332 possible substitutions in the coding region, sorted by what they do to the protein. This is a property of the genetic code and of the sequence, before any selection. The code's structure means that many errors are synonymous or swap similar amino acids (Freeland & Hurst 1998).

Real mutation spectra favour transitions. κ = 1 treats every change as equally likely.

Which kinds are tolerated is a different question: it depends on where each change falls in the folded protein, and on the organism and environment.

Methodology and limitations

How this works, and what it leaves out

Method

  • Translation uses the standard genetic code (NCBI table 1), reading from the first base until the first stop codon.
  • A substitution is synonymous if the amino acid is unchanged, nonsense if it creates a stop, stop-loss or start-loss if it removes one, and otherwise missense. Missense changes count as "similar" when both amino acids fall in the same one of four side-chain classes (non-polar, polar, positive, negative). An insertion or deletion is a frameshift unless its length is a multiple of three.
  • The β-globin sequence is the RefSeq coding sequence NM_000518.5, fetched from NCBI; the known variants use standard HGVS c. numbering.
  • The survey counts all 3 × (coding length) possible substitutions; κ weights transitions relative to transversions.

Limitations

  • The coding sequence is shown already spliced; the real HBB gene has two introns, and some variants (HbE, for example) act partly through splicing.
  • "Similar side chain" is a coarse four-class rule. Real effects depend on the position in the folded protein, and substitution matrices or experiments are needed to judge them.
  • Cellular responses such as nonsense-mediated decay, and effects on regulation, are not modelled.
Evidence and debate

Weighing the evidence

Addresses: The origin of biological informationBiological evolutionThe origin of life

01 What is observed?

  • EstablishedThe standard genetic code assigns 61 codons to 20 amino acids and 3 to 'stop'. It is nearly universal, with a few known variants. (Koonin & Novozhilov 2009)
  • EstablishedThe code limits the damage from errors: a mistaken codon usually codes for the same or a similar amino acid. Under one weighting, only about one in a million random alternative codes did better. (Freeland & Hurst 1998)
  • EstablishedMutation rates have been measured directly: about 1 × 10⁻³ per genome per generation in E. coli, and 1.2 × 10⁻⁸ per nucleotide per generation in humans. (Lee et al. 2012; Kong et al. 2012)
  • EstablishedMost mutations that affect fitness are harmful, and the proportions vary by organism and gene. In one RNA virus up to 40% of random single-base changes were lethal. (Eyre-Walker & Keightley 2007; Sanjuán et al. 2004)
  • EstablishedSickle-cell haemoglobin is a single-base change (Glu → Val at position 6). One copy protects against severe malaria; two cause disease. (Ingram 1957; Allison 1954)
  • Established'Silent' synonymous changes are not always neutral: they can affect splicing, mRNA stability and translation. (Chamary et al. 2006)
  • EstablishedIn Lenski's long-term experiment, one population evolved the ability to grow on citrate in oxygen after tens of thousands of generations, through earlier 'potentiating' mutations followed by a later one. (Blount et al. 2008)

02 What does evolutionary theory propose?

03 What are the unresolved questions and criticisms?

  • SpeculativeHow the genetic code arose. Stereochemical, coevolution and error-minimisation hypotheses each explain part of the pattern, and none is established. This is an origin-of-life question, not one that natural selection among organisms can answer. (Koonin & Novozhilov 2009)
  • Active researchBehe reviewed experimental-evolution studies and argued that most adaptive mutations break or blunt existing genes ('the first rule of adaptive evolution'). Others point to gains such as the citrate innovation and de novo genes. How common gain-of-function adaptation is remains debated. (Behe 2010; Blount et al. 2008; Carvunis et al. 2012)
  • Active researchHow long it takes to acquire changes that only work together. Durrett & Schmidt modelled two coordinated changes in a regulatory sequence and found a few million years enough for Drosophila but more than 100 million years for humans. Behe and the authors disagreed in an exchange of letters about what the result implies. (Durrett & Schmidt 2008; Behe 2009)

04 What evidence would help distinguish competing explanations?

  • Across many experimental-evolution studies, measured rates of gain-of-function versus loss-of-function adaptive mutations.
  • Laboratory evolution of new functions that need several coordinated changes, timed and compared with population-genetics predictions.
  • Experimental tests of proposed routes to the genetic code using prebiotic chemistry.

Established Measured or observed repeatedly; not in serious dispute.Active research An open research question, or a claim that specialists still dispute.Speculative A proposed explanation that has not yet been tested directly.

Your conclusions

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References

  1. Allison AC (1954). Protection afforded by sickle-cell trait against subtertian malarial infection. BMJ 1:290–294. doi:10.1136/bmj.1.4857.290
  2. Behe MJ (2009). Waiting longer for two mutations. Genetics 181:819–820 (letter; with the authors' reply). doi:10.1534/genetics.108.098905
  3. Behe MJ (2010). Experimental evolution, loss-of-function mutations, and “the first rule of adaptive evolution”. The Quarterly Review of Biology 85:419–445. doi:10.1086/656902
  4. Blount ZD, Borland CZ, Lenski RE (2008). Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli. PNAS 105:7899–7906. doi:10.1073/pnas.0803151105
  5. Carvunis AR, Rolland T, Wapinski I, et al. (2012). Proto-genes and de novo gene birth. Nature 487:370–374. doi:10.1038/nature11184
  6. Chamary JV, Parmley JL, Hurst LD (2006). Hearing silence: non-neutral evolution at synonymous sites in mammals. Nature Reviews Genetics 7:98–108. doi:10.1038/nrg1770
  7. Durrett R, Schmidt D (2008). Waiting for two mutations: with applications to regulatory sequence evolution and the limits of Darwinian evolution. Genetics 180:1501–1509. doi:10.1534/genetics.107.082610
  8. Eyre-Walker A, Keightley PD (2007). The distribution of fitness effects of new mutations. Nature Reviews Genetics 8:610–618. doi:10.1038/nrg2146
  9. Freeland SJ, Hurst LD (1998). The genetic code is one in a million. Journal of Molecular Evolution 47:238–248. doi:10.1007/pl00006381
  10. Ingram VM (1957). Gene mutations in human haemoglobin: the chemical difference between normal and sickle cell haemoglobin. Nature 180:326–328. doi:10.1038/180326a0
  11. Kimura M (1968). Evolutionary rate at the molecular level. Nature 217:624–626. doi:10.1038/217624a0
  12. Kong A, Frigge ML, Masson G, et al. (2012). Rate of de novo mutations and the importance of father's age to disease risk. Nature 488:471–475. doi:10.1038/nature11396
  13. Koonin EV, Novozhilov AS (2009). Origin and evolution of the genetic code: the universal enigma. IUBMB Life 61:99–111. doi:10.1002/iub.146
  14. Lee H, Popodi E, Tang H, Foster PL (2012). Rate and molecular spectrum of spontaneous mutations in the bacterium Escherichia coli as determined by whole-genome sequencing. PNAS 109:E2774–E2783. doi:10.1073/pnas.1210309109
  15. Modiano D, Luoni G, Sirima BS, et al. (2001). Haemoglobin C protects against clinical Plasmodium falciparum malaria. Nature 414:305–308. doi:10.1038/35104556
  16. Sanjuán R, Moya A, Elena SF (2004). The distribution of fitness effects caused by single-nucleotide substitutions in an RNA virus. PNAS 101:8396–8401. doi:10.1073/pnas.0400146101
  17. Thein SL (2013). The molecular basis of β-thalassemia. Cold Spring Harbor Perspectives in Medicine 3:a011700. doi:10.1101/cshperspect.a011700
  18. Trecartin RF, Liebhaber SA, Chang JC, et al. (1981). β⁰ thalassemia in Sardinia is caused by a nonsense mutation. Journal of Clinical Investigation 68:1012–1017. doi:10.1172/jci110323