Editing Codon usage bias (section)

{{Short description|Genetic bias in coding DNA}}
[[File:Codon_usage_bias_in_P._patens.png|thumb|upright=2|Codon usage bias in ''[[Physcomitrella patens]]'']]
'''Codon usage bias''' refers to differences in the frequency of occurrence of [[Synonymous substitution|synonymous]] [[codon]]s in [[coding DNA]]. A codon is a series of three [[nucleotide]]s (a triplet) that encodes a specific [[amino acid]] residue in a [[polypeptide]] chain or for the termination of [[Translation (biology)|translation]] ([[stop codon]]s).

There are 64 different codons (61 codons encoding for amino acids and 3 stop codons) but only 20 different translated amino acids. The overabundance in the number of codons allows many amino acids to be encoded by more than one codon. Because of such redundancy it is said that the [[genetic code]] is degenerate. The genetic codes of different organisms are often biased towards using one of the several codons that encode the same amino acid over the others—that is, a greater frequency of one will be found than expected by chance. How such biases arise is a much debated area of [[molecular evolution]]. Codon usage tables detailing genomic codon usage bias for organisms in [[GenBank]] and [[RefSeq]] can be found in the [https://dnahive.fda.gov/dna.cgi?cmd=cuts_main HIVE-Codon Usage Tables (HIVE-CUTs) project]<ref>{{cite journal |last1=Athey|first1=John |last2=Alexaki |first2=Aikaterini |last3=Osipova |first3=Ekaterina |last4=Rostovtsev |first4=Alexandre |last5=Santana-Quintero |first5=Luis V.|last6=Katneni |first6=Upendra |last7=Simonyan |first7=Vahan |last8=Kimchi-Sarfaty |first8=Chava |date=2017-09-02 |title=A new and updated resource for codon usage tables |journal=BMC Bioinformatics |volume=18 |issue=391  |pages=391 |doi=10.1186/s12859-017-1793-7 |pmc=5581930 |pmid=28865429 |doi-access=free }}</ref> which contains two distinct databases, CoCoPUTs and TissueCoCoPUTs. Together, these two databases provide comprehensive, up-to-date codon, codon pair and dinucleotide usage statistics for all organisms with available sequence information and 52 human tissues, respectively.<ref>{{cite journal |last1=Alexaki |first1=Aikaterini |last2=Kames |first2=Jacob |last3=Holcomb |first3=David D. |last4=Athey |first4=John |last5=Santana-Quintero |first5=Luis V. |last6=Lam |first6=Phuc Vihn Nguyen |last7=Hamasaki-Katagiri |first7=Nobuko |last8=Osipova |first8=Ekaterina |last9=Simonyan |first9=Vahan |last10=Bar |first10=Haim |last11=Komar |first11=Anton A. |last12=Kimchi-Sarfaty |first12=Chava |title=Codon and Codon-Pair Usage Tables (CoCoPUTs): Facilitating Genetic Variation Analyses and Recombinant Gene Design |journal=Journal of Molecular Biology |date=June 2019 |volume=431 |issue=13 |pages=2434–2441 |doi=10.1016/j.jmb.2019.04.021|pmid=31029701 |s2cid=139104807 |doi-access=free }}</ref><ref>{{cite journal |last1=Kames |first1=Jacob |last2=Alexaki |first2=Aikaterini |last3=Holcomb |first3=David D. |last4=Santana-Quintero |first4=Luis V. |last5=Athey |first5=John C. |last6=Hamasaki-Katagiri |first6=Nobuko |last7=Katneni |first7=Upendra |last8=Golikov |first8=Anton |last9=Ibla |first9=Juan C. |last10=Bar |first10=Haim |last11=Kimchi-Sarfaty |first11=Chava |title=TissueCoCoPUTs: Novel Human Tissue-Specific Codon and Codon-Pair Usage Tables Based on Differential Tissue Gene Expression |journal=Journal of Molecular Biology |date=January 2020 |volume=432 |issue=11 |pages=3369–3378 |doi=10.1016/j.jmb.2020.01.011|pmid=31982380 |doi-access=free }}</ref>

It is generally acknowledged that codon biases reflect the contributions of 3 main factors: [[Gene_conversion#GC-biased_gene_conversion|GC-biased gene conversion]] that favors GC-ending codons in diploid organisms, [[Bias in the introduction of variation|arrival biases]] reflecting mutational preferences (typically favoring AT-ending codons), and [[natural selection]] for codons that are favorable in regard to translation.<ref name=ShahGilchrist2011>{{cite journal | author=P. Shah and M. A. Gilchrist | year=2011 | title=Explaining complex codon usage patterns with selection for translational efficiency, mutation bias, and genetic drift | journal=Proceedings of the National Academy of Sciences of the United States of America | volume=108 | issue=25 | pages=10231–6 | doi=10.1073/pnas.1016719108| pmid=21646514 | pmc=3121864 | doi-access=free | bibcode=2011PNAS..10810231S }}</ref><ref name=DuretGaltier2009>{{cite journal | author=L. Duret and N. Galtier | year=2009 | title=Biased gene conversion and the evolution of mammalian genomic landscapes | journal=Annu Rev Genomics Hum Genet | volume=10 | pages=285–311 | doi=10.1146/annurev-genom-082908-150001| pmid=19630562 }}</ref><ref name=Galtier2018>{{cite journal | author=N. Galtier, C. Roux, M. Rousselle, J. Romiguier, E. Figuet, S. Glemin, N. Bierne and L. Duret | year=2018 | title=Codon Usage Bias in Animals: Disentangling the Effects of Natural Selection, Effective Population Size, and GC-Biased Gene Conversion | journal=Mol Biol Evol | volume=35 | issue=5 | pages=1092–1103 | doi=10.1093/molbev/msy015| doi-access=free | pmid=29390090 | hdl=20.500.12210/34500 | hdl-access=free }}</ref> Optimal codons in fast-growing microorganisms, like ''[[Escherichia coli]]'' or ''[[Saccharomyces cerevisiae]]'' (baker's yeast), reflect the composition of their respective genomic [[tRNA|transfer RNA]] (tRNA) pool.<ref name="Dong1996">{{Cite journal|last1=Dong|first1=Hengjiang|last2=Nilsson|first2=Lars|author3-link=Charles Kurland|last3=Kurland|first3=Charles G.|year=1996|title=Co-variation of tRNA abundance and codon usage in ''Escherichia coli'' at different growth rates|journal=Journal of Molecular Biology|volume=260|issue=5|pages=649–663|doi=10.1006/jmbi.1996.0428|pmid=8709146|issn=0022-2836}}</ref> It is thought that optimal codons help to achieve faster translation rates and high accuracy. As a result of these factors, translational selection is expected to be stronger in highly [[Gene expression|expressed genes]], as is indeed the case for the above-mentioned organisms.<ref name="Sharp1993">{{Cite journal |last1=Sharp |first1=Paul M. |last2=Stenico |first2=Michele |last3=Peden |first3=John F. |last4=Lloyd |first4=Andrew T. |s2cid=8582630 |year=1993 |title=Codon usage: mutational bias, translational selection, or both? |journal=Biochem. Soc. Trans. |volume=21 |issue= 4|pages=835–841 |doi= 10.1042/bst0210835|pmid=8132077 }}</ref><ref name="Kanaya1999">{{Cite journal|last1=Kanaya|first1=Shigehiko|last2=Yamada|first2=Yuko|last3=Kudo|first3=Yoshihiro|last4=Ikemura|first4=Toshimichi|year=1999|title=Studies of codon usage and tRNA genes of 18 unicellular organisms and quantification of ''Bacillus subtilis'' tRNAs: gene expression level and species-specific diversity of codon usage based on multivariate analysis|journal=Gene|volume=238|issue=1|pages=143–155|doi=10.1016/s0378-1119(99)00225-5|pmid=10570992|issn=0378-1119}}</ref> In other organisms that do not show high growing rates or that present small genomes, codon usage optimization is normally absent, and codon preferences are determined by the characteristic mutational biases seen in that particular genome. Examples of this are ''[[Homo sapiens]]'' (human) and ''[[Helicobacter pylori]].''<ref>{{Cite journal|last1=Atherton|first1=John C.|last2=Sharp|first2=Paul M.|last3=Lafay|first3=Bénédicte|date=2000-04-01|title=Absence of translationally selected synonymous codon usage bias in Helicobacter pylori|journal=Microbiology|language=en|volume=146|issue=4|pages=851–860|doi=10.1099/00221287-146-4-851|pmid=10784043|issn=1350-0872|doi-access=free}}</ref><ref>{{Cite journal|last1=Bornelöv|first1=Susanne|last2=Selmi|first2=Tommaso|last3=Flad|first3=Sophia|last4=Dietmann|first4=Sabine|last5=Frye|first5=Michaela|date=2019-06-07|title=Codon usage optimization in pluripotent embryonic stem cells|journal=Genome Biology|language=en|volume=20|issue=1|pages=119|doi=10.1186/s13059-019-1726-z|pmid=31174582|pmc=6555954|issn=1474-760X |doi-access=free }}</ref> Organisms that show an intermediate level of codon usage optimization include ''[[Drosophila melanogaster]]'' (fruit fly), ''[[Caenorhabditis elegans]]'' (nematode [[worm]]), ''[[Strongylocentrotus purpuratus]]'' ([[sea urchin]]), and ''[[Arabidopsis thaliana]]'' ([[thale cress]]).<ref name="Duret2000">{{Cite journal|last=Duret|first=Laurent|year=2000|title=tRNA gene number and codon usage in the ''C. elegans'' genome are co-adapted for optimal translation of highly expressed genes|journal=Trends in Genetics|volume=16|issue=7|pages=287–289|doi=10.1016/s0168-9525(00)02041-2|pmid=10858656|issn=0168-9525}}</ref> Several viral families ([[herpesvirus]], [[lentivirus]], [[papillomavirus]], [[polyomavirus]], [[adenovirus]], and [[parvovirus]]) are known to encode [[structural proteins]] that display heavily skewed codon usage compared to the [[host cell]]. The suggestion has been made that these codon biases play a role in the temporal regulation of their late proteins.<ref>{{cite journal |last1=Shin |first1=Young C. |last2=Bischof |first2=Georg F. |last3=Lauer |first3=William A. |last4=Desrosiers |first4=Ronald C. |date=2015-09-10 |title=Importance of codon usage for the temporal regulation of viral gene expression |journal=Proceedings of the National Academy of Sciences |volume=112 |issue=45 |pages=14030–14035 |doi=10.1073/pnas.1515387112 |pmc=4653223 |pmid=26504241 |bibcode=2015PNAS..11214030S |doi-access=free }}</ref>

The nature of the codon usage-tRNA optimization has been fiercely debated. It is not clear whether codon usage drives tRNA evolution or vice versa. At least one mathematical model has been developed where both codon usage and tRNA expression co-evolve in [[Feedback#Biology|feedback]] fashion (''i.e.'', codons already present in high frequencies drive up the expression of their corresponding tRNAs, and tRNAs normally expressed at high levels drive up the frequency of their corresponding codons). However, this model does not seem to yet have experimental confirmation. Another problem is that the evolution of tRNA genes has been a very inactive area of research.{{Citation needed|date=August 2019}}