1. Codon usage bias Ref: Chapter 9
Xuhua Xia
dambe.bio.uottawa.ca

2. Objectives
Understand how codon usage bias affect translation efficiency and gene expression
Biomedical relevance
Protein drugs in pharmaceutical industry
Transgenic experiments in agriculture
Factors affecting codon usage bias
Indices measuring codon usage bias
Develop bioinformatic skills to study the genomic codon usage.
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Xuhua Xia

3. Codon Usage Bias
Observation: Strongly biased codon usage in a variety of species ranging from viruses, mitochondria, plastids, prokaryotes and eukaryotes.
Hypotheses:
Differential mutation hypothesis, e.g., Transcriptional hypothesis of codon usage (Xia 1996 Genetics 144: )
Different selection hypothesis, e.g., (Xia 1998 Genetics 149: 37-44)
Predictions:
From mutation hypothesis: Concordance between codon usage and mutation pressure
From Selection hypothesis:
Concordance between differential availability of tRNA and differential codon usage.
The concordance is stronger in highly expressed genes than lowly expressed genes (CAI is positively correlated with gene expression).
UCC~tRNA~Gly
GCC~tRNA~Gly
Polycistronic mRNA
Ribosome
Gene Gene Gene 3
RNA polymerase
Protein
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Xuhua Xia

4. Codon usage of HEGs in yeast
You may be wondering about Cys codon family which has 4 tRNAs matching UGC, but none matching UGU. We would have predicted that UGC should be preferred, but the opposite is true. Why? One might think that, because Cys is rarely used, the codon family is not under selection, so that codon usage will be at the mercy of mutation bias. Because the yeast genome is AT-biased, we expect U-ending codon to be more than C-ending codon. Unfortunately, the explanation is wrong because 1) the mutation bias is not sufficient for the 3/39 ratio, and 2) the lowly expressed genes, which should be even more affected by mutation bias, did not exhibit a strong bias comparable to 3/9. This criticism is also applicable to another explanation stating that the GCA anticodon can decode C-ending and U-ending codons equally well.
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Xuhua Xia
Xia Bioinformatics and the cell.

5. Calculation of RSCU RSCU and proportion: Different scaling.
RSCU (Sharp et al. 1986) is codon-specific
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Xuhua Xia

6. RSCU (HIV-1 vs Human) (a)E G I K L P Q R S T V
0.5 1
1.5 2
2.5
RSCU (Human)
RSCU (HIV-1)
A-ending
C-ending
G-ending
U-ending
Fig. 1. Relative synonymous codon usage (RSCU) of HIV-1 compared to RSCU of highly expressed human genes. Data points for codons ending with A, C, G or U are annotated with different combinations of colors and symbols. A-ending codons exhibit strong discordance in their usage between HIV-1 and human and are annotated with their coded amino acids.
van Weringh et al MBE.
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Xuhua Xia

7. RSCU (HTLV-1 vs Human)
Relative synonymous codon usage (RSCU) of HTLV-1 compared to RSCU of highly expressed human genes. Data points for codons ending with A, C, G or U are annotated with different combinations of colors and symbols. A-ending codons exhibit strong discordance in their usage between HIV-1 and human and are annotated with their coded amino acids.
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Xuhua Xia

8. Calculation of CAI N2,3,4: Number of 2-, 3-, 4-fold codon families
Compound 6- or 8-fold codon families should be broken into two codon families
CAI is gene-specific.
0  CAI  1
CAI computed with different reference sets are not comparable.
Problem with computing w as Fi/Fi.max: Suppose an amino acid is rarely used in highly expressed genes, then there is little selection on it, and the codon usage might be close to even, with wi  1. Now if we have a lowly expressed gene that happen to be made of entire of this amino acid, then the CAI for this lowly expressed gene would be 1, which is misleading.
There has been no good alternative. Further research is needed.
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Xuhua Xia

9. Weak mRNA predictive power
y = x R 2 = 10 20 30 40 50 60 70 80
0.5
1.5
2.5
3.5
4.5
mRNA abundance
Protein abundance
ENO1
FRS2
Slide 9
Xuhua Xia

10. Effect of Codon Usage Bias
y = x R 2 = 10 20 30 40 50 60 70 80
0.05
0.25
0.45
0.65
0.85
Codon usage bias
Protein abundance
ENO1
FRS2
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Xuhua Xia

11. Any problem with the mutation hypothesis?
Table 2. Frequency of A residues, length and codon adaptation index (CAI) for the three HIV-1 early (tat, rev and nef) and five late (gag-pol, vif, vpu, vpr, and env) coding sequences (CDS).
Gene
CDS (bp)
CAI
tat
261
rev
351
nef
621
gag
1503
pol
3012
vif
579
vpr
291
vpu
249
env
2571
van Weringh et al MBE.

12. Problem with CAI and a new ITEAA
Codon
Cfnon-HEG
CFHEG
tRNA A
GCA 20 40 3
GCG 80 60
CAI
ITE AA
Codon
CFnon-HEG
CFHEG w
pHEG
pnon-HEG s A
GCA 20 40
2/3
0.4
0.2 2 1
GCG 80 60
0.6
0.8
0.75
0.375 50
0.5
0.3
CAI is a special case of ITE (when there is no background codon usage bias)
Slide 12
Xuhua Xia

13. Problem with CAI and a new ITEAA
Codon
CFnon-HEG
CFHEG w
Gene1
Gene2 A
GCA 20 40
2/3 10
GCG 80 60 1 30
𝐶𝐴𝐼= 𝑒 𝐹 𝑖 ln⁡( 𝑤 𝑖 ) 𝐹 𝑖
CAI1 = ; CAI2 =
Wrong conclusions: 1. Excellent codon adaptation in the codon family (high CAI values)
2. Gene 1 has better codon adaptation than Gene2. AA
Codon
CFnon-HEG
CFHEG
pHEG
pnon-HEG s w
Gene1
Gene2 A
GCA 20 40
0.4
0.2 2 1 10
GCG 80 60
0.6
0.8
0.75
0.375 30
E. coli data
𝐼 𝑇𝐸 = 𝑒 𝐹 𝑖 ln⁡( 𝑤 𝑖 ) 𝐹 𝑖
ITE.1 = ；ITE.2 =
Correct conclusions: 1. Poor codon adaptation in the codon family (low ITE values)
2. Gene 2 has better codon adaptation than Gene1.
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Xuhua Xia

14. Problem with CAI and a new ITEAA
Codon
CFOther
CFHEG
tRNA A
GCA
25511
1973 3
GCG
43261
2654
CAI
ITE AA
Codon
CFOther
CFHEG w
pHEG
pOther s A
GCA
25511
1973
0.7434
0.4264
0.3710
1.1495 1
GCG
43261
2654
0.5736
0.6290
0.9118
0.7932
0.5
0.8528
1.1472
E. coli data
CAI is a special case of ITE (when there is no background codon usage bias)
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Xuhua Xia

15. Contrast between CAI and ITE
Kudla et al. (2009) engineered a synthetic library of 154 genes, all encoding the same protein but differing in degrees of codon adaptation, to quantify the effect of differential codon usage on protein production in E. coli. They concluded that “codon bias did not correlate with gene expression” and that “translation initiation, not elongation, is rate-limiting for gene expression”
ITE reveals that
Low protein production with low ITE, regardless of translation initiation efficiency
If translation initiation is efficient, protein production increases with ITE.
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16. Hypothesis and Predictions
Met
Leu
Glu
Lys
Gln
Arg
Trp
tRNAMet/CAU
tRNALeu/UAA
tRNAGlu/UUC
tRNALys/UUU
tRNAGln/UUG
tRNAArg/UCU
tRNATrp/UCA
AUG
UUG
GAG
AAG
CAG
AGG
UGG
AUA
UUA
GAA
AAA
CAA
AGA
UGA
A-ending codons are favoured by both mutation and tRNA-mediated selection.
AUA is favoured by mutation, but not by tRNA-mediated selection
Predictions:
1. Proportion of A-ending codons (or RSCU) should be smaller in the Met codon family than in other R-ending codon families:
PNNA = NNNA/NNNG
2. Availability of tRNAMet/UAU should increase PAUA.
Xuhua Xia
Xia et al. 2007

17. Testing prediction 1
Carullo, M. and Xia, X J Mol Evol 66:484–493.
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Xuhua Xia

18. Testing prediction 2
Fig. 5. Relationship between PAUA and PUUA, highlighting the observation that PAUA is greater when both a tRNAMet/CAU and a tRNAMet/UAU are present than when only tRNAMet/CAU is present in the mtDNA, for bivalve species (a) and chordate species (b). The filled squares are for mtDNA containing both tRNAMet/CAU and tRNAMet/UAU genes, and the open triangles are for mtDNA without a tRNAMet/UAU gene.
Xia, X In: RS Singh et al.. Evolution in the fast lane: Rapidly evolving genes and genetic systems. Oxford University Press.