This document discusses codon bias and how it can be used to fine-tune gene expression. It explains that the genetic code is degenerate, with multiple codons encoding the same amino acid. Organisms exhibit codon bias, preferring certain codons. Codon bias correlates with tRNA abundance and affects translation efficiency. Rare codons can slow translation to regulate co-translational folding and membrane translocation. Codon landscapes within genes also influence expression. Exploiting codon bias through tRNA supplementation or gene optimization improves heterologous protein production. Further integrating experimental data will advance understanding and synthetic biology applications of codon bias.

1. Codon Bias as a means to fine-tune
Gene Expression
Review article by Quax et al., 2015
Published in Molecular cell

2. General principle of protein expression

3. A ribosome and translation Orientation of Codon and
Anticodon

4. Genetic Code degeneracy
• 20 amino acids are encoded by multiple synonymous codons (61
codons and 3 stop codons).
• Overabundance of codons allows many amino acids to be encoded
by more than one codon.
• For this reason genetic code is said to be degenerated.

5. Synonymous and non synonymous mutations
• Miscopied DNA nucleotide
change only one nucleotide.
• Results in coding for same
amino acid
• Insertion or deletion of single
nucleotide.
• Cause frame shift mutation.

6. Codon bias
• A non random distribution of synonymous codons in genes of
different organisms.
• Each organism prefer a different set of codons over other.

7. General codon bias variants
• Codon Adaptation Index (CAI):
 The CAI for a specific gene can be determined by comparing its
codon usage frequency with reference set of highly expressed genes.
• Wobbling and tRNA modification:
 Translation of multiple synonymous codons by single tRNA .
 It can be G-U, I-U, I-A or I-C

8. Translation of mRNA with a wobbling
codon anticodon base pairing

9. Correlation of codon bias with tRNA pool
• tRNA adaptation index (tAI):
 The copy number of tRNA genes are assumed to be correlated with
tRNA abundance in cells.
 The organisms with larger genomes have higher tRNA gene
redundancy which would decrease selection for specific codons.
 Codon frequency are correlated with total supply of tRNA.
• Normalized translation efficiency (nTE):
 More frequently used codon recognized by more abundant tRNA
species, the codon will compete for this tRNA with other codons

10. Cont’d….
 nTE provides the information for supply as well as demand rates of
tRNAs.
 Highly expressed proteins are encoded by genes that contain
relatively high proportions of codons recognized by abundant,
charged tRNAs with kinetically efficient codon-anticodon base
pairing.

11. Types of Codon Bias
Synonymous Codon Co-occurrence Non synonymous Pair Base
• Synonymous codons of a
coding sequence are not
randomly distributed rather
they are biased to cluster those
codons that are recognized by
same tRNA.
• The codon resides under
selective constraint
• Nucleotides neighboring a
particular codon are
distributed in non random
manner.

12. Is Translation efficiency correlated with codon
bias?
• The translation efficiency and resultant protein is determined by
both translation initiation and elongation rates.
 Translation initiation rate controls how often the transcript is
translated.
 Translation elongation rate controls the speed of translation
process.

13. Influence of coding sequence on translation
initiation
• Translation starts when the ribosome is sequestered on mRNA
 In prokaryotes the shine-Dalgarno sequence in mRNA binds with
the anti shine-dalgarno sequence in 16S rRNA gene.
 In eukaryotes the Kozak sequence around the start codon is involved
in interaction with pre initiation complex for translation.
 The strength of mRNA folding around the initiation sequence and
start codon can influence translation initiation efficiency.

14. Is Translation elongation rate controlled by codon
bias?
• High translation initiation and elongation rate is required for
optimal expression.
• Low translational initiation and elongation rate will give low
expression.
• More frequently used codons recognized by abundant tRNA results
in faster translation and higher translation efficiency.
• Ribosome density profiling is also used to analyze the distribution of
ribosomes on mRNA.

15. Intragenic fluctuations in translation
efficiency
Intergenic fluctuations in translation
efficiency

16. Intragenic codon landscape and expression
• Besides codon frequency bias on genome level, local codon bias
within gene is also observed.
• Many genes possess locally biased distributions of rare and frequent
codons resulting in Codon Landscape.
• Variable local translations can:
 Regulate even distribution of ribosomes on mRNA
 Tuning of protein co-translational folding process
 Facilitate protein translocation across membranes

17. Cont’d…
• Rare codon ramps to reduce ribosome jamming:
 It is immediately downstream of the start codon (30-50 rare
codons).
 It is usually translated with high efficiency to support fast release of
initiator tRNA met then slows down until it reaches elongation.
 Slow start of elongation process facilitate evenly spacing of
ribosomes on mRNA to reduce jamming during further elongation
of highly expressed proteins.

18. Codon landscape for protein translation across
membrane
• Rare codon clusters were identified in genes for membrane and
secretory proteins.
• In yeast 35-40 codons downstream of binding sites for the signal
recognition particle is present.

20. Codon landscape and co-translational protein
folding
• Clusters of rare codons may helps in co-translational protein folding
• nTE metric suggested frequent codons are depleted from the regions
that encode coils in protein structures.
 Rare codons are present in alpha helix at position 1 & 4 whereas
frequent codons are present at position 2 &3 suggesting complex co-
translational folding.
 Beta sheets are encoded by frequent codons suggesting a correlation
between codon bias and co-translational folding.

21. Intergenic codon bias and differential expression
• Difference in codon bias is not only between regions within
individual genes but also between sets of genes either clustered in
operons or scattered in a genome.
 Starvation condition and codon bias:
• Rare codon clusters encodes amino acid biosynthesis pathway.
• Amino acids starvation results in selective charging .
• Charging levels of some tRNA will be low & some will remain high.
• Rare Codons read highly charged tRNA , used for efficient
translation of genes essential during starvation.

22. Cont’d…
Cell cycle, differentiation and stress regulation by codon usage:
• In humans and other vertebrates tRNA conc. differ in
differentiating cell types.
• Cyanobacterium uses codons to adjust protein production
during fluctuating environmental conditions.
• tRNA modifications can alter codon-anticodon binding
affinities and translations of certain condons can be favored.

23. Selection pressure on codon usage
• Codon usage is biased in majority of living organism
• Two explanations on the evolution of this bias are:
 Non-random mutation:
• Codon bias is related to non random mutation caused by GC
content.
 Selection for codon bias:
• Codon usage bias related to translational efficiencies.
• Codon bias must be under selective pressure during evolution and
mutations rate cannot explain alone the various observations.

24. Applying codon bias as a means to improve
protein
• Codon usage bias has been studied to be used as a strategy in
BIOTECHNOLOGY to optimize gene expression for improving
protein production and yield.
 Expressing additional tRNA genes in the production host:
• Heterologous expression of gene
• Consisting of additional copy number of tRNA genes
• Enhances tRNA levels
• Examples: E.coli Rosetta (pRARE)
• E.coli BL21-CodonPlus (pRIL)

25. Cont’d…
 Designing codon optimized genes:
• DNA synthesis companies offer codon optimization services
• Optimization is done by maximizing gene’s CAI that matches with
host’s expression
• Sequencing features that are taken into account are:
 GC content
 Avoidance of repeats and RNase recognition sites
 Transcriptional terminator site
 Shine-Dalgarno sequences
 mRNA folding sequence sites

26. Challenges ahead: unraveling codon bias and
other factors influencing expression
• Comparative analysis of coding sequences and protein productions that
influence translation process.
• Improvements, integration of experimental approaches and statistical
analysis.
• Analysis of experimental RNA sequencing data that determines mRNA
and tRNA abundance, ribosome density profiling and proteomics.
• Designing synthetic gene variants will be more efficient than generating
and testing random reported gene variants.

27. Further improving synthetic gene design
• Synthetic genes should contain as much as possible frequent codons
in order to achieve high protein production in heterologous
production system.
• These features have hardly used in synthetic gene design therefore,
deserve more attention in future attempts.
• Codon optimization can be obtained by analysis of aminoacyl tRNA
abundance ensuring balanced tRNa supply needed for protein
production.
• nTE for predicting optimal codon bias should be considered.

28. Applying codon bias as a tool in synthetic biology
• Improved gene designs may lead to
 Synthetic gene circuits, biosynthesis pathways, new genomes
• Engineering at genome level
• Replacement of rare codons by frequent codons on genome wide
scale level
• Rare codons reassigned to encode non natural amino acids