codon bias examination measuring the effect of codon usage deviations on protein expression level

Codon Bias Examinationmeasuring the effect of codon usage deviations on protein

expression level

Emmanuel Levi Research Group 2013

Brief introduction• Almost all amino acids found in eukaryotes are encoded by 2-

6 codon variations.

• Synonymous mutations, resulting in different coding for same amino acids, do not alter the encoded protein sequence.

• Deviation from uniform codon usage have been observed in eukaryotes but also in species from all taxa.

• May such a deviation affect cellular processes?

• It is believed that synonymous sites undergo weak selection and that codon bias is maintained by random/sequence specific mutation followed by selection.

• Codon usage variability was described already in 1965 (2), but is yet poorly understood.

• Open questions remained:

What determines the identity of the major codons?

What is the exact nature of selection on codon usage?

• May codon bias be explained only by looking at the amount of the particular codons in the genomic sequence?

• Today we know that the answer to the this question is no. Codon translation efficiency highly depend on the level of tRNA decoding and tRNA post-transcriptional modifications.

Codon bias within and between genomes (1)

Synonymous codons are translated with different efficiencies thus affecting protein abundance.

• Generally, among a group of synonymous codons recognized by several tRNAs, codons recognized by the most abundant tRNA are used more often than those recognized by rare tRNAs (3).

• Among codons recognized by the same tRNA, those making a medium-strength (optimal energy) interaction with the tRNA are usually performed (4).

• Positive correlation was found between a genes expression level and the degree of its codon bias (3,4,5).

• Possibly, codon bias is stronger in highly expressed genes, to allow increased translation efficiency and/or accuracy of protein synthesis.

• Indeed, there is a broad correspondence between the preferred codons used in highly expressed genes and measures of relative tRNA abundances (6).

• If this is true, one question remains:

• Does high codon adaptation induces strong protein expression, since rapid and/or accurate elongation increases synthesis rate, or does strong expression selects for high codon adaptation

• The hypothesis that codon adaptation induces high protein levels, clashes with the notion that initiation is generally rate-limiting for endogenous protein production (3,7).

• When initiation is limiting, the elongation rate should not influence the amount of protein produced from a given mRNA.

• On the other hand, why select for codon adaptation on hundreds of synonymous mutations, instead of tuning a promoter?

• Moreover, the use of poorly adapted codons in order to slow the translation of genes expressed at low levels would seem wasteful compared to simply reducing transcription or slowing initiation.

• Examples of codon bias affect on protein levels:

• Experiments with a synthetic library of genes that differ in codon usage (8), all encoding for GFP protein, show 250-fold variations in protein levels (E.coli).

• Up to ~ 1,000 fold variations in gene expression was induced by codon usage in synthetic genes of different organisms (9)

Interestingly, it was suggested (10,11) that genes that share similarity in codon usage may

participate in the same physiological pathways.

• First, Genomic sequencing project show a role of translation in shaping bacterial chromosomes(10).

• The authors of this study have analyzed the usage of synonymous codons for protein encoding and its geography over bacterial chromosomes and have found that genes sharing similar codon bias tend to be close to each other on the chromosome, in coherent patches more extended than transcriptional units.

• Their hypothesis is that those correlations in codon bias enable the cell to locally recycle tRNAs employed during translation, reducing stalling of the ribosomes due to rare tRNAs.

• This also entails a dependence of expression rates of a gene on its chromosomal context.

• This analysis also imply that genes involved in anabolic pathways, mainly active when the cell is starving, have a similar codon usage, and that they are encoded on the lagging strand of DNA.

• Farthermore, analysis of S.cerevisiae, E.coli and P. Falciparum genomes show that similarity in codon usage is a strong predictor of protein-protein interactions (11), again suggesting an implication of codon bias to cellular processes.

• Also, preliminary data (Elevy Weizmann - Jefferson Collaborative Program grant application) show significant correlation between tRNA adaptation index and Protein abundance (A)

• Codon usage is a strong predictive power for gene co-expression (B) and gene function is an efficient predictor for gene codon usage.

• These results were obtained with yeast genes. tRNA adaptatoin index was calculated as described before (12,13) and plotted against known protein abundances as listed in pax-db (14). ΔtAI is the calculated absolute tAI difference between two genes. Functional network was described before (15).Protein interactions are as listed in BIOGRID (16). Co-expression is obtained from pairwise Pearson correlation coefficients between expression profiles characterized in (17).

• 

Introduction Summary• Codon bias exist in species from all taxa.

• Codon bias affect mRNA and protein level.

• Codon bias undergo selection.

• Highly expressed genes tend to use abundant codons.

• Using specific codons for gene transcription is dependent on codon abundance in its genetic sequence, tRNA abundance and codon-anticodon interaction.

• Codon usage similarity is correlated with pathway dependent protein activity

Research question

• The underlying hypothesis driving this work is that codon bias selection is not random, but rather serves as a tool to control gene expression.

• We ask whether eukaryotic cells may tune codon/tRNA expression and tRNA post translational modifications in order to regulate processes needed for cellular functions under specific conditions, by regulating a large subset of genes all at once.

• Construction of a library of fluorescent proteins where hydrophilic “tail” sequences containing 7/10 copies of each of the 61 codons are added at the N’.

Research Plan

.1GAC TCT TCT AGA GAC AGA TCT TCT AGA GAC AGA TCT AGA GAC AGA TCT TCT AGA GAC AGA TCT AGA GAC AGA TCT TCT TCT TCT

.2GAC TCT TCT AGG GAC AGG TCT TCT AGG GAC AGG TCT AGG GAC AGG TCT TCT AGG GAC AGG TCT AGG GAC AGG TCT TCT TCT TCT

………………………………………

GAC TCT TCT GTT GAC GTT TCT TCT GTT GAC GTT TCT GTT GAC GTT TCT TCT GTT GAC GTT TCT GTT GAC GTT TCT TCT TCT TCT .61

• Transfection of the 61 constructs to yeast cells

• Examination of fluorescence intensity under different condition (a.a deficiency, UV irradiation, etc.)

• Examination of tRNA abundance/modifications and of proteins involved in corresponding pathways under conditions in which fluorescence was altered.

This research will be executed with the collaboration of Ya-ming Hou research group, Thomas Jefferson University, Jefferson Medical College Department of Biochemistry & Molecular Pharmacology

References1. Plotkin J B, Kudla G; Synonymous but not the same: the causes and consequences of codon bias. Nat Rev Genet. 2010 12(1):32-42.

2. Zuckerkandl E, Pauling L; Molecules as documents of evolutionary history. J Theor Biol. 1965 8(2):357-66.

3. Bulmer M; The selection-mutation-drift theory of synonymous codon usage. Genetics. 1991 129(3):897-907.

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5. M Gouy, C Gautier; Codon usage in bacteria: correlation with gene expressivity. Nucleic Acids Res. 1982 25; 10(22):7055–7074.

6. Ikemura T; Correlation between the abundance of yeast transfer RNAs and the occurrence of the respective codons in protein genes. Differences in synonymous codon choice patterns of yeast and Escherichia coli with reference to the abundance of isoaccepting transfer RNAs. J Mol Biol. 1982 158:573–97.

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10. Bailly-Bechet M, Danchin A, Iqbal M, Marsili M, Vergassola M; Codon Usage Domains over Bacterial Chromosomes. PLoS Comput Biol. 2006 2(4): e37.

11. Hamed S Najafabadi, Reza Salavati; Sequence-based prediction of protein-protein interactions by means of codon usage. Genome Biology. 2008 9:R87

12. Sharp PM, Li WH; The codon Adaptation Index--a measure of directional synonymous codon usage bias, and its potential applications. Nucleic acids research 1987 15(3):1281-1295.

13. Reis M, Savva R, Wernisch L; Solving the riddle of codon usage preferences: a test for translational selection. Nucleic acids research 2004 32(17):5036-5044.

14. Bauer F, et al; Translational control of cell division by elongator. 2012 Cell reports 1(5):424-433.http://pax-db.org/#!home

15. Jansen R et.al; A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 32003 02(5644):449-453.

16. Stark C, et al; The BioGRID Interaction Database: 2011 update. Nucleic Acids Res. 2011 39(Database issue):D698-704.http://thebiogrid.org/

17. Gasch AP, et al; Genomic expression programs in the response of yeast cells to environmental changes. Molecular biology of the cell 2000 11(12):4241-4257.

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