コドン使用
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- growth rate 増殖速度
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- temperature 温度
https://pubmed.ncbi.nlm.nih.gov/36718935/ Nucleic Acids Res . 2023 Mar 21;51(5):2363-2376. doi: 10.1093/nar/gkad035. Revealing determinants of translation efficiency via whole-gene codon randomization and machine learning Thijs Nieuwkoop 1, Barbara R Terlouw 2, Katherine G Stevens 3 4, Richard A Scheltema 3 4, Dick de Ridder 2, John van der Oost 1, Nico J Claassens 1 https://academic.oup.com/nar/article/51/5/2363/7016452 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10018363/
7:51 PM · Mar 20, 2023 https://twitter.com/kfuku0502/status/1637768721098854400 Kenji Fukushima (福島 健児) on Twitter: "同義コドンを無作為化したRFPライブラリを大腸菌で発現させて、タンパク質発現レベルへの影響を検証。最初の８コドンの配列だけで大部分（ピアソンの相関係数で0.762）予測できてしまい、その肝はmRNAの二次構造。これがCodon Adaptation Indexよりも重要な要因になる。 https://t.co/aci5qigUVw" / Twitter
3:02 AM · Nov 11, 2022 https://twitter.com/Lab_LaBella/status/1590766795224670209 The Lab LaBella on Twitter: "Codon usage in Aspergillus mitochondria is gene (not genome) specific! Learn more about Aspergillus mitogenomes in this work from @annemakerofhats @RokasLab @Lab_LaBella https://t.co/VHzisVFMk2 #3rdBaseThurs @GeneticsGSA https://t.co/3s9xV1xZqL" / Twitter
https://doi.org/10.1093/g3journal/jkac285 Evolution and codon usage bias of mitochondrial and nuclear genomes in Aspergillus section Flavi | G3 Genes|Genomes|Genetics | Oxford Academic
https://pubmed.ncbi.nlm.nih.gov/34822069/ Review Mol Biol Rep . 2021 Nov 25;1-27. doi: 10.1007/s11033-021-06749-4. Online ahead of print. Codon usage bias Sujatha Thankeswaran Parvathy 1, Varatharajalu Udayasuriyan 2, Vijaipal Bhadana 3 https://link.springer.com/article/10.1007/s11033-021-06749-4 https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC8613526/
https://pubmed.ncbi.nlm.nih.gov/34276633/ Front Microbiol . 2021 Jul 2;12:699788. doi: 10.3389/fmicb.2021.699788. eCollection 2021. Edging on Mutational Bias, Induced Natural Selection From Host and Natural Reservoirs Predominates Codon Usage Evolution in Hantaan Virus Galal Ata 1, Hao Wang 1, Haoxiang Bai 1, Xiaoting Yao 2, Shiheng Tao 1 https://www.frontiersin.org/articles/10.3389/fmicb.2021.699788/full https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC8283416/
https://pubmed.ncbi.nlm.nih.gov/34025951/ Review Comput Struct Biotechnol J . 2021 Apr 22;19:2646-2663. doi: 10.1016/j.csbj.2021.04.042. eCollection 2021. Codon-based indices for modeling gene expression and transcript evolution Shir Bahiri-Elitzur 1, Tamir Tuller 1 2 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8122159/ Dmean (mean dissimilarity-based index) Ew (weighted sum of relative entropy)
https://www.biorxiv.org/content/10.1101/2021.04.10.439128v2 Plasmid fitness costs are caused by specific genetic conflicts | bioRxiv 7:11 PM · Apr 12, 2021 https://twitter.com/jpjhall/status/1381550524974137346 Jamie Hall on Twitter: "New preprint: "Plasmid fitness costs are caused by specific genetic conflicts" with @BrockhurstLab @ellieevolves @rctwright @2jamiewood @scottishwormboy" / Twitter https://twitter.com/jpjhall/status/1381550560046891009 Jamie Hall on Twitter: "Others have found similar patterns — mutations that compensate plasmid cost tend to target specific genes, rather than reducing plasmid size, ameliorating codon biases, mass silencing, etc. https://t.co/wX3lzB0TR8 ; https://t.co/B3Ccj2WxBF ; https://t.co/HNU4VRtEqQ" / Twitter
https://pubmed.ncbi.nlm.nih.gov/33723043/ Proc Natl Acad Sci U S A . 2021 Mar 23;118(12):e2016810118. doi: 10.1073/pnas.2016810118. Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns Jake L Weissman 1, Shengwei Hou 2, Jed A Fuhrman 2 https://www.pnas.org/content/118/12/e2016810118.long https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC8000110/ MATERIALS AND METHODS All scripts used to generate figures and analysis, as well as predicted growth rates for various genomic datasets and the full EGGO database, are available at https://github.com/jlw-ecoevo/eggo. The gRodon package, including documentation and a vignette, can be downloaded at https://github.com/jlw-ecoevo/gRodon.
https://pubmed.ncbi.nlm.nih.gov/32609328/ Bioinformatics . 2020 Dec 8;36(19):4876-4884. doi: 10.1093/bioinformatics/btaa603. Network analysis of synonymous codon usage Khalique Newaz 1 2 3, Gabriel Wright 1 3, Jacob Piland 1 2 3, Jun Li 4, Patricia L Clark 5, Scott J Emrich 6, Tijana Milenković 1 2 3 https://nd.edu/∼cone/CodonUsage/
https://pubmed.ncbi.nlm.nih.gov/33299178/ Nature . 2020 Dec;588(7838):503-508. doi: 10.1038/s41586-020-3021-2. Epub 2020 Dec 9. A hydrophobic ratchet entrenches molecular complexes Georg K A Hochberg 1, Yang Liu 2, Erik G Marklund 3, Brian P H Metzger 1, Arthur Laganowsky 2, Joseph W Thornton 4 5 https://www.nature.com/articles/s41586-020-3021-2
https://twitter.com/kfuku0502/status/1336938243720409088 Kenji Fukushima on Twitter: "来週のラボミーティング当番、プログレスにしようと思ってたけどこれは論文紹介不可避やな。 https://t.co/gXLiFasV90" / Twitter
- 今まで読んだ全ての論文の中で一番面白かったものを挙げろと言われたらこれなんだけど、同じラボからだ。この論文だって読み込んだら弱いところもあるけど、起こらなかった進化的帰結を網羅的に検証するという着想がそもそもすごい。アブスト一文目から最高。
- hydrophobic ratchetの論文、まだ途中なんだけど、この結果からmutational biasやgenetic codeまで議論で持っていくのか…。自分が同じ実験データを得たとして、果たしてここまでの議論ができるかというと全く自信ないな。
- multinucleotide codon substitutionsの論文もこの人たちだったのか。そう考えるとなんとなくつながるな。
- hydrophobic ratchetの論文、主張を裏付ける論理はもちろん追えるように書かれているんだけど、やはり「もし自分がこの研究をやっていたら」を考えるとFig. 4Cを捻り出せる思考回路が理解しがたい。
https://pubmed.ncbi.nlm.nih.gov/28902834/ Nature . 2017 Sep 21;549(7672):409-413. doi: 10.1038/nature23902. Epub 2017 Sep 13. Alternative evolutionary histories in the sequence space of an ancient protein Tyler N Starr 1, Lora K Picton 2, Joseph W Thornton 2 3 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6214350/ https://www.nature.com/articles/nature23902
https://pubmed.ncbi.nlm.nih.gov/29967485/ Nat Ecol Evol . 2018 Aug;2(8):1280-1288. doi: 10.1038/s41559-018-0584-5. Epub 2018 Jul 2. Multinucleotide mutations cause false inferences of lineage-specific positive selection Aarti Venkat 1, Matthew W Hahn 2 3, Joseph W Thornton 4 5 https://www.nature.com/articles/s41559-018-0584-5 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6093625/
https://pubmed.ncbi.nlm.nih.gov/32998698/ BMC Bioinformatics . 2020 Sep 30;21(Suppl 14):367. doi: 10.1186/s12859-020-03688-y. DeeplyEssential: a deep neural network for predicting essential genes in microbes Md Abid Hasan 1, Stefano Lonardi 2 https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03688-y https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7525945/
https://twitter.com/LibKot/status/1311686627077488643 Ikemen Mas Kot on Twitter: "DeeplyEssential: a deep neural network for predicting essential genes in microbes https://t.co/2Q8YJJ5byb #maskotlib" / Twitter 背景 必須遺伝子とは、生物の生存に不可欠な遺伝子のことである。細菌の必須遺伝子を予測することで、新規抗生物質の設計や抗菌戦略のターゲットを提供することができます。 研究成果 本研究では、微生物の必須遺伝子を予測するためのディープニューラルネットワークを提案する。 DEEPLYESSENTIALと呼ばれる我々のアーキテクチャは、入力データに対する仮定を最小限にして予測を実行する（すなわち、遺伝子の一次配列とそれに対応するタンパク質配列のみを使用する）ので、既存の予測器は構造的特徴やトポロジー的特徴を必要とするため、 容易に利用できないかもしれないが、実用性を最大限に高めることができる。また、従来の分類器に影響を与えていた隠れた性能バイアスを明らかにし、研究を行った。 広範囲にわたる結果は、学習セットのバランスをとるためにダウンサンプリングを採用したり、直交遺伝子の複数コピーを除外するためにクラスタリングを使用したりする既存の分類器よりもDEEPLYESSENTIALの方が優れていることを示している。 結論 ディープニューラルネットワークアーキテクチャは、微生物遺伝子の配列情報のみを用いて、微生物遺伝子が必須かどうか（必須でないかどうか）を効率的に予測することができます。
2:07 AM · Apr 13, 2019 https://twitter.com/Tyu_Shi/status/1116749787569672193 Tyu_Shi on Twitter: "DeeplyEssential: A Deep Neural Network for Predicting Essential Genes in Microbes https://t.co/GbQ0522csF 配列の特徴のみ（遺伝子のコドン使用頻度や長さ、GC含量、アミノ酸使用頻度等）を特徴量として使っているようだ。でも本当に知りたいのは典型的では無いのに必須な遺伝子なんだよね…。" https://www.biorxiv.org/content/10.1101/607085v1
ウイルスと宿主の共進化における翻訳選択による同義コドン使用バイアスの解消 https://pubmed.ncbi.nlm.nih.gov/32123323/ Nat Ecol Evol . 2020 Apr;4(4):589-600. doi: 10.1038/s41559-020-1124-7. Epub 2020 Mar 2. Dissimilation of synonymous codon usage bias in virus-host coevolution due to translational selection Feng Chen 1 2 3, Peng Wu 2, Shuyun Deng 4, Heng Zhang 5, Yutong Hou 2, Zheng Hu 6, Jianzhi Zhang 7, Xiaoshu Chen 8 9, Jian-Rong Yang 10 11 12 13 14 15 https://www.nature.com/articles/s41559-020-1124-7 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7249751/
https://pubmed.ncbi.nlm.nih.gov/31712737/ ISME J . 2020 Feb;14(2):597-608. doi: 10.1038/s41396-019-0552-3. Epub 2019 Nov 11. Unlinked rRNA genes are widespread among bacteria and archaea Tess E Brewer 1 2, Mads Albertsen 3, Arwyn Edwards 4, Rasmus H Kirkegaard 3, Eduardo P C Rocha 5, Noah Fierer 6 7 https://www.nature.com/articles/s41396-019-0552-3 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6976660/
2019年12月23日に投稿 https://qiita.com/nkjmu/items/3572c8883d8802c3edd5 【論文紹介】メタゲノムでやってよかったよね感が出た面白い結果 - Qiita
・codon usage proxy (ΔENC’)を用いると、minimum generation timeが推定できる。 仮説的には、unlinked rRNA遺伝子を持つものは、growth rateが遅いというものがあるのでそれを検証した
codon usage proxy 初めて聞いた。論文調べてみると、"codon usage bias (ΔENC’)" って出てきますね。
Nearly Neutrality and the Evolution of Codon Usage Bias in Eukaryotic Genomes (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2323827/)
Fig.1Cに真核まで含めてプロットしたものがある。たしかに、generation timeの目安になりそう？
細菌・古細菌間程度でもそれが通用するっていうのは、refを探してません。(論文中に書いてるのかな？)
linked rRNAの場合、同一オペロンで制御することは、成長の早いバクテリア・アーキアでより強く現れるはず。(細胞内での主要なエネルギー消費はリボソーム合成)
→ということでゲノムあたりのrRNAコピー数やmaximum growth rateを予測
→コピー数は4.25vs2.72で有意にunlinkedの方が低かった。 また、ΔENC'(先述)から予測したminimum generation timeはunlinkedなものが長い。
(maximumなgrowth rateと、minimumなgeneration timeという、本文中のこの段落で2つ出てきて別のものを比べたようにみえるが、最大限速く成長する=世代時間は最小になるやろうということで根本的には同じ)
→unlinkedな方がgeneration timeが長いし、rRNAのコピー数も少ない(つまり一気にたくさんribosomeを作れない？)ということで成長遅いだろう
https://www.ncbi.nlm.nih.gov/pubmed/18430960 Genetics. 2008 Apr;178(4):2429-32. doi: 10.1534/genetics.107.086405. Nearly neutrality and the evolution of codon usage bias in eukaryotic genomes. Subramanian S1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2323827/ Figure 1.— Relationship between codon usage bias and generation time of eukaryotes.
https://pubmed.ncbi.nlm.nih.gov/33154134/ Science . 2020 Nov 6;370(6517):683-687. doi: 10.1126/science.aaz9642. Resource conservation manifests in the genetic code Liat Shenhav # 1 2, David Zeevi # 3 https://science.sciencemag.org/content/370/6517/683 https://pubmed.ncbi.nlm.nih.gov/33154134/
3:42 AM · Nov 11, 2020 https://twitter.com/DaveZeevi/status/1326233738855018502 David Zeevi on Twitter: "So now that the elections are over, I’d like to tell you about @LiatShenhav and my new paper! I want to share the story behind the paper and the process leading to our discovery. It’s a story about scientific serendipity. a thread https://t.co/2QwYbX2krx" / Twitter
3:41 AM · Nov 11, 2020 https://twitter.com/LiatShenhav/status/1326233484407545858 Liat Shenhav on Twitter: "We did it, @DaveZeevi! Our paper is now published in @ScienceMagazine! https://t.co/fdnwl4Ae7u We present a data-driven, computational perspective on how selective pressures resulting from nutrient limitation shape microbial coding sequences. Thread below:" / Twitter And we were able to study all this using publicly available data on microbes!! How cool!
4:48 AM · Oct 26, 2019 https://twitter.com/LiatShenhav/status/1187818150970085376 Liat Shenhav on Twitter: "We uncover a strong purifying selective pressure across marine microbial life that is driven by resource conservation, and also demonstrate that it is a central driving force in selection processes guiding codon usage. https://t.co/S4P6DXNi03" / Twitter KEGG and eggNOG orthologies
https://twitter.com/mbiojournal/status/1288247896874168322 https://pubmed.ncbi.nlm.nih.gov/32694138/ mBio . 2020 Jul 21;11(4):e00766-20. doi: 10.1128/mBio.00766-20. Codon Usage Optimization in the Prokaryotic Tree of Life: How Synonymous Codons Are Differentially Selected in Sequence Domains with Different Expression Levels and Degrees of Conservation José Luis López # 1, Mauricio Javier Lozano # 1, María Laura Fabre 1, Antonio Lagares 2 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7374057/ https://mbio.asm.org/content/11/4/e00766-20.long
https://pubmed.ncbi.nlm.nih.gov/32123323/ Nat Ecol Evol . 2020 Apr;4(4):589-600. doi: 10.1038/s41559-020-1124-7. Epub 2020 Mar 2. Dissimilation of synonymous codon usage bias in virus-host coevolution due to translational selection Feng Chen 1 2 3, Peng Wu 2, Shuyun Deng 4, Heng Zhang 5, Yutong Hou 2, Zheng Hu 6, Jianzhi Zhang 7, Xiaoshu Chen 8 9, Jian-Rong Yang 10 11 12 13 14 15 https://www.nature.com/articles/s41559-020-1124-7 https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC7249751/ Finally, using a manually curated list of viruses, natural hosts and symptomatic hosts, we found that virus CUB tended to be more similar to that of symptomatic hosts than that of natural hosts, supporting a general deleterious effect of excessive CUB similarity between viruses and hosts.
3:38 AM · Mar 20, 2020 https://twitter.com/AbbeAtSea/status/1240709192836476930 Abigail LaBella on Twitter: "If viral codon usage (CU) is too similar to a natural host then host translation efficiency is hampered. So viral CU evolves towards a sweet-spot. Interestingly, viral CU is more similar to symptomatic v natural hosts. https://t.co/z15X3gda0J @NatureEcoEvo @rokaslab #3rdbasethurs https://t.co/Z1B0HkuXpE" / Twitter
https://pubmed.ncbi.nlm.nih.gov/31712433/ Proc Natl Acad Sci U S A . 2019 Nov 26;116(48):24075-24083. doi: 10.1073/pnas.1908052116. Epub 2019 Nov 11. mRNA structure regulates protein expression through changes in functional half-life David M Mauger 1, B Joseph Cabral 1, Vladimir Presnyak 1, Stephen V Su 1, David W Reid 1, Brooke Goodman 1, Kristian Link 1, Nikhil Khatwani 1, John Reynders 1, Melissa J Moore 2, Iain J McFadyen 1 https://www.pnas.org/content/116/48/24075.long https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC6883848/ Fig. 5. Half-life of computationally designed eGFP-degron mRNAs is determined by codon usage and mRNA structure. (A) Codon optimality (relative synonymous codon usage, y axis) versus secondary structure (energy of the predicted MFE structure, x axis) for sets of 150,000 generated eGFP sequence variants generated using codons chosen randomly (red), weighted in proportion to the human genome(blue), and using our algorithm (gray). Colored boxes show regions from which sequences were selected for further testing. (B)
2:42 AM - 1 Jun 2019 https://twitter.com/pacyc1841/status/1134711913265295360 この論文関係あるかわからないけど、この前ほんとにコドンユーセージでOligonucleotide frequencyが説明できるか話題になった
https://pubmed.ncbi.nlm.nih.gov/31138741/ mBio . 2019 May 28;10(3):e00505-19. doi: 10.1128/mBio.00505-19. Codon Usage Heterogeneity in the Multipartite Prokaryote Genome: Selection-Based Coding Bias Associated with Gene Location, Expression Level, and Ancestry J L López 1, M J Lozano 1, A Lagares Jr 2 3, M L Fabre 1, W O Draghi 1, M F Del Papa 1, M Pistorio 1, A Becker 3, D Wibberg 4, A Schlüter 4, A Pühler 4, J Blom 5, A Goesmann 5, A Lagares 6 https://mbio.asm.org/content/10/3/e00505-19.long https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6538778/ Sinorhizobium meliloti, which harbors a chromosome, a chromid (pSymB), a megaplasmid (pSymA), and, in many strains, one or more accessory plasmids. modal codon usages
https://twitter.com/astrogenomics/status/1186949418294996992 Juan C. Villada on Twitter: "Our new pre-print is up! We explored how codon usage may be associated with hydrogen bonding in dsDNA. We found signals of a ramp that smoothly increases the number of hydrogen bonds per codon position. https://t.co/K3Ouu2unkj" / Twitter 7:16 PM · Oct 23, 2019
https://pubmed.ncbi.nlm.nih.gov/31290967/ Genome Biol Evol . 2019 Sep 1;11(9):2439-2456. doi: 10.1093/gbe/evz146. The Critical Role of Codon Composition on the Translation Efficiency Robustness of the Hepatitis A Virus Capsid Lucía D'Andrea 1, Francisco-Javier Pérez-Rodríguez 1, Montserrat de Castellarnau 1, Susana Guix 1, Enric Ribes 2, Josep Quer 3 4, Josep Gregori 3 5, Albert Bosch 1, Rosa M Pintó 1 https://academic.oup.com/gbe/article/11/9/2439/5530595 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6735747/
Posted August 09, 2019. https://www.biorxiv.org/content/10.1101/730473v1 Using Machine Learning and Gene Nonhomology Features to Predict Gene Ontology | Methods Composition of the Prediction Variable Dataset For protein-coding genes, a codon usage bias score which describes the degree of bias towards the most frequently used codons for multiple encoding amino acids in a given species was calculated following the method described in (Sharp and Li, 1987) as implemented in the SeqIO module in biopython (v1.72) package (Cock et al., 2009).
Posted June 25, 2019. https://www.biorxiv.org/content/10.1101/478016v2 Hidden patterns of codon usage bias across kingdoms
6 Jun 2019 https://twitter.com/ytksai/status/1136571651133329408 放線菌のコドン最適化に関する研究がScientific Reportsに採択されました。産総研の人工知能研究センターと生物プロセス研究部門の共同研究です。https://www.nature.com/articles/s41598-019-44500-z … プレスリリースを出しました。https://www.aist.go.jp/aist_j/press_release/pr2019/pr20190606/pr20190606.html 生体分子の設計問題のうち、タンパク質発現量を向上させる目的でmRNAの塩基配列を同義置換の範囲内で設計する問題をコドン最適化と呼びます。本研究では、産総研の所有するタンパク質発現データの解析から、放線菌におけるコドン最適化に重要な設計指標を見出し、その有効性の実験検証も行いました。
https://www.nature.com/articles/s41589-020-0507-z Article Published: 06 April 2020 New codons for efficient production of unnatural proteins in a semisynthetic organism Emil C. Fischer, Koji Hashimoto, Yorke Zhang, Aaron W. Feldman, Vivian T. Dien, Rebekah J. Karadeema, Ramkrishna Adhikary, Michael P. Ledbetter, Ramanarayanan Krishnamurthy & Floyd E. Romesberg Nature Chemical Biology volume 16, pages570–576(2020)Cite this article
Apr 8, 2020 https://twitter.com/mighty_tora/status/1247547996985634821 Romesbergらによる、67種のコドンをデコードできる半合成大腸菌の構築。橋本さんおめでとうございます！ 既知の生命において、DNAは4つの塩基（ATGC）から構成されます。筆者らはこれまでに、dNaMとdTPT3を加え、6つの塩基を利用可能な大腸菌を創出していました。（1/2） 本論文では、新しく利用できるようになった非天然コドンを体系的に探索し、高効率かつ低クロストークで非天然アミノ酸を導入できるものを3つ同定しました。67種のコドンが利用できるようになったため、より多彩なprotein engineeringが可能になると期待されます。（2/2）
https://pubmed.ncbi.nlm.nih.gov/31092918/ Nature . 2019 May;569(7757):514-518. doi: 10.1038/s41586-019-1192-5. Epub 2019 May 15. Total synthesis of Escherichia coli with a recoded genome Julius Fredens 1, Kaihang Wang 1 2, Daniel de la Torre 1, Louise F H Funke 1, Wesley E Robertson 1, Yonka Christova 1, Tiongsun Chia 1, Wolfgang H Schmied 1, Daniel L Dunkelmann 1, Václav Beránek 1, Chayasith Uttamapinant 1 3, Andres Gonzalez Llamazares 1, Thomas S Elliott 1, Jason W Chin 4 https://www.nature.com/articles/s41586-019-1192-5 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7039709/
4:39 PM · Aug 20, 2019 https://twitter.com/NatureDigest/status/1163717198868705280 Nature ダイジェスト／編集部 on Twitter: "【コドンを修正した大腸菌ゲノムの全合成 | Natureダイジェスト8月号】このほど大腸菌ゲノムを鋳型に、アミノ酸をコードするコドンの種類を通常より少なくした、これまでで最大の合成ゲノムが作製された。この完全な合成ゲノムを持つ大腸菌は生存も増殖もできた。 https://t.co/PfYJ1jYYuJ" / Twitter
2019年5月16日 https://www.natureasia.com/ja-jp/nature/pr-highlights/12951 【合成生物学】大腸菌の遺伝コードを圧縮する | Nature | Nature Research
https://twitter.com/enuroi/status/1128893267402059776 Ken Kuroki on Twitter: "大腸菌の全ゲノムを合成DNAで置換することに成功。59種のセンスコドンと2種の終止コドンしか使わないよう18,000コドンを変更。増殖時間は1.6倍に長くなったのみ。 https://t.co/u8DUxSJ8AI" 1:21 AM - 16 May 2019
https://twitter.com/hornistyf/status/1128821862337695744 Y. Furuta on Twitter: "大腸菌ゲノム上のセリンのコドン2つとストップコドン1つを同義コドンに置換した話。著者らが前に開発したREXER法でゲノム全体のコドンに適用。置換が難しかったのは、隣接遺伝子のORFやUTRと被っていたケース。／Total synthesis of Escherichia coli with a recoded genome https://t.co/fSqvontp6M" 8:37 PM - 15 May 2019
https://twitter.com/torusengoku/status/1128827977498677248 torusengoku on Twitter: "Nature, DNAコドンのうちTCG, TCA（ともにSerをコード）とTAG（アンバーストップコドン）を持たない大腸菌を作成。将来、これらのコドンに新しいアミノ酸をコードさせる遺伝暗号拡張が容易に。 https://t.co/KvQSOXbjCc どこまで減らせるのかな。" 9:02 PM - 15 May 2019
26 April 2019 https://www.sciencedirect.com/science/article/pii/S0022283619302281 Codon and Codon-Pair Usage Tables (CoCoPUTs): Facilitating Genetic Variation Analyses and Recombinant Gene Design - ScienceDirect
https://www.ncbi.nlm.nih.gov/pubmed/30828719 Nucleic Acids Res. 2019 Mar 4. pii: gkz151. doi: 10.1093/nar/gkz151. [Epub ahead of print] Prediction and large-scale analysis of primary operons in plastids reveals unique genetic features in the evolution of chloroplasts. Shahar N1, Weiner I1,2, Stotsky L1, Tuller T2,3, Yacoby I1. https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkz151/5369010 For each gene pair we computed roughly 1100 features (Figure 1B-3 and Supplementary Figure S2) based on sequence analysis alone—thus, they could be computed for any sequenced plastome without requiring additional data (e.g. RNA-Seq). These features were designed to capture essential gene characteristics (e.g. coded protein hydrophobicity, RNA structure, codon usage bias, nucleotide composition) and to quantify their level of similarity within each couple of adjacent genes (Supplementary Figure S2).
https://www.ncbi.nlm.nih.gov/pubmed/30728279 mSphere. 2019 Feb 6;4(1). pii: e00011-19. doi: 10.1128/mSphereDirect.00011-19. Gene Expansion and Positive Selection as Bacterial Adaptations to Oligotrophic Conditions. Props R1,2,3, Monsieurs P2,4, Vandamme P5, Leys N2, Denef VJ6, Boon N7. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6365617/ For example, nitrogen and carbon limitations have been correlated with genome size, GC content, and carbon/nitrogen protein content (13), thermal adaptation with genome size (8), and growth rate with GC content and codon usage bias (14, 15). Patterns of (genome-wide) positive selection have revealed lineage-specific adaptations, and together with codon usage bias, are thought to mediate fine-tuning of gene expression (18,–20). The Ramlibacter sp. MAG was part of the monophyletic Ramlibacter clade, and its GC content (70.6%), codon usage bias (SCUO = 0.57 ± 0.10), and genome size (3.95 Mbp) closely matched these of other Ramlibacter genomes (Fig. 2B).
https://www.ncbi.nlm.nih.gov/pubmed/29726922 Bioinformatics. 2018 Oct 1;34(19):3396-3398. doi: 10.1093/bioinformatics/bty382. MACARON: a python framework to identify and re-annotate multi-base affected codons in whole genome/exome sequence data. Khan W1,2, Varma Saripella G1,2, Ludwig T3, Cuppens T3, Thibord F1,2, Génin E3, Deleuze JF4, Trégouët DA1,2.
https://www.ncbi.nlm.nih.gov/pubmed/30247489 Nat Biotechnol. 2018 Sep 24. doi: 10.1038/nbt.4238. [Epub ahead of print] Evaluation of 244,000 synthetic sequences reveals design principles to optimize translation in Escherichia coli. Cambray G1,2, Guimaraes JC1,3, Arkin AP3,4.
- We precisely designed 244,000 DNA sequences implementing 56 replicates of a full factorial design to evaluate nucleotide, secondary structure, codon and amino acid properties in combination.
- Codon composition has a sizable impact on translatability, but only in comparatively rare elongation-limited transcripts.
https://twitter.com/Paul_Carini/status/1023315547985870848 Paul Carini on Twitter: "https://t.co/1dN1Ku6uX4 "however, contrary to our expectations, a majority of bacteria that showed a positive response to plant growth were predicted to have longer generation times based on codon-usage bias, meaning their genomes bear signatures of slower growth rates"… https://t.co/DH7gNpnQJG" 5:13 PM - 28 Jul 2018 https://www.ncbi.nlm.nih.gov/pubmed/29556109 Nat Microbiol. 2018 Apr;3(4):470-480. doi: 10.1038/s41564-018-0129-3. Epub 2018 Mar 19. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Zhalnina K1,2, Louie KB1, Hao Z2, Mansoori N1,3, da Rocha UN2,4, Shi S5, Cho H2,6, Karaoz U2, Loqué D1,3,6,7, Bowen BP1, Firestone MK2,8, Northen TR9, Brodie EL10,11.
https://twitter.com/kfuku0502/status/1003768475871875072 Kenji Fukushima on Twitter: "高発現遺伝子だけでなく低発現遺伝子でもコドン使用頻度は選択を受けているらしい。大腸菌でそういうサイトの同義置換を入れて適応度が下がるケースを発見している。 https://t.co/nFGnUi3m2A" 6:40 PM - 4 Jun 2018 https://www.ncbi.nlm.nih.gov/pubmed/29688501 Genome Biol Evol. 2018 Apr 1;10(5):1237-1246. doi: 10.1093/gbe/evy084. The Codon Usage of Lowly Expressed Genes Is Subject to Natural Selection. Yannai A1, Katz S1, Hershberg R1.
https://www.ncbi.nlm.nih.gov/pubmed/29596640 Mol Biol Evol. 2018 Jun 1;35(6):1463-1472. doi: 10.1093/molbev/msy047. Multiple Factors Confounding Phylogenetic Detection of Selection on Codon Usage. Laurin-Lemay S1, Philippe H1,2, Rodrigue N3.
高度発現遺伝子群のコドンバイアスが、他の遺伝子群の翻訳に影響する。 https://www.ncbi.nlm.nih.gov/pubmed/29735666 Proc Natl Acad Sci U S A. 2018 May 22;115(21):E4940-E4949. doi: 10.1073/pnas.1719375115. Epub 2018 May 7. Codon usage of highly expressed genes affects proteome-wide translation efficiency. Frumkin I1, Lajoie MJ2, Gregg CJ2, Hornung G3, Church GM4, Pilpel Y5.
Interestingly, we could alleviate the observed phenotypes by increasing the supply of the tRNA for the highly demanded codon, thus demonstrating that the codon usage of highly expressed genes was selected in evolution to maintain the efficiency of global protein translation.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6003480/
https://pubmed.ncbi.nlm.nih.gov/29155926/ Review Ann Bot . 2017 Nov 28;120(6):893-909. doi: 10.1093/aob/mcx112. Codon usage and codon pair patterns in non-grass monocot genomes Purabi Mazumdar 1, RofinaYasmin Binti Othman 1 2, Katharina Mebus 1, N Ramakrishnan 3, Jennifer Ann Harikrishna 1 2 https://academic.oup.com/aob/article/120/6/893/4636133 https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC5710610/ Table 2.Online computational tools for codon analysis
https://www.ncbi.nlm.nih.gov/pubmed/28992099 DNA Res. 2017 Dec 1;24(6):623-633. doi: 10.1093/dnares/dsx030. Co-adaption of tRNA gene copy number and amino acid usage influences translation rates in three life domains. Du MZ1, Wei W1, Qin L1, Liu S1, Zhang AY1,2, Zhang Y1,2, Zhou H1,2, Guo FB1,2,3.
https://www.ncbi.nlm.nih.gov/pubmed/27540085 Genome Biol Evol. 2017 Oct 1;9(10):2560-2579. doi: 10.1093/gbe/evw201. Switches in Genomic GC Content Drive Shifts of Optimal Codons under Sustained Selection on Synonymous Sites. Sun Y1, Tamarit D1, Andersson SGE1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5629928/
- Materials and Methods
- The strength of selected codon usage bias was estimated from the S index, which is used as a proxy for translational selection on individual genomes (Sharp et al. 2005, 2010).
- Results Switches in GC Content in Lactobacillus and Bifidobacterium Species
- fig. 2.— Phylogenetic relationships and genome features of Lactobacillus species. A schematic figure illustrates the variation in GC contents, selective constraints (S-value), genome sizes and number of tRNAs in the species selected for an in-depth analysis.
- In order to quantify the relative strength of selection, we calculated the S indexes for the genomes in this study (supplementary table S2, Supplementary Material online). The S index is inferred from the relative use of C-ending codons for Asn, Ile, Phe, Tyr (Sharp et al. 2005),
https://www.ncbi.nlm.nih.gov/pubmed/27671647 Proc Natl Acad Sci U S A. 2016 Oct 11;113(41):E6117-E6125. Epub 2016 Sep 26. Codon usage is an important determinant of gene expression levels largely through its effects on transcription. Zhou Z1, Dang Y1, Zhou M2, Li L3, Yu CH1, Fu J1, Chen S3, Liu Y4. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5068308/ Codon Optimization, Plasmid Constructs, and Neurospora Transformation.
https://www.ncbi.nlm.nih.gov/pubmed/26504241 Proc Natl Acad Sci U S A. 2015 Nov 10;112(45):14030-5. doi: 10.1073/pnas.1515387112. Epub 2015 Oct 26. Importance of codon usage for the temporal regulation of viral gene expression. Shin YC1, Bischof GF2, Lauer WA1, Desrosiers RC3.
https://www.ncbi.nlm.nih.gov/pubmed/26282127 BMC Evol Biol. 2015 Aug 19;15:163. doi: 10.1186/s12862-015-0441-y. The evolutionary dynamics of tRNA-gene copy number and codon-use in E. coli. McDonald MJ1, Chou CH2,3, Swamy KB4, Huang HD5,6, Leu JY7.
https://www.ncbi.nlm.nih.gov/pubmed/25255224 PLoS One. 2014 Sep 25;9(9):e107319. doi: 10.1371/journal.pone.0107319. eCollection 2014. Analysis of the relationship between genomic GC Content and patterns of base usage, codon usage and amino acid usage in prokaryotes: similar GC content adopts similar compositional frequencies regardless of the phylogenetic lineages. Zhou HQ1, Ning LW1, Zhang HX1, Guo FB1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4177787/
https://twitter.com/copypasteusa/status/583464468820000769 遺伝子発現の最適化に利用されてきた特徴：コドンバイアス、連続コドンバイアス、RNA二次構造、リボソーム結合部位、制限部位、隠れ終止コドン、他のモチーフ回避 8:02 PM - 1 Apr 2015 REVIEW https://www.ncbi.nlm.nih.gov/pubmed/25340050 Front Bioeng Biotechnol. 2014 Oct 6;2:41. doi: 10.3389/fbioe.2014.00041. eCollection 2014. Computational tools and algorithms for designing customized synthetic genes. Gould N1, Hendy O2, Papamichail D1.
微生物群集内の翻訳効率と遺伝子水平伝播との関連 https://www.ncbi.nlm.nih.gov/pubmed/21343180 Nucleic Acids Res. 2011 Jun;39(11):4743-55. doi: 10.1093/nar/gkr054. Epub 2011 Feb 22. Association between translation efficiency and horizontal gene transfer within microbial communities. Tuller T1, Girshovich Y, Sella Y, Kreimer A, Freilich S, Kupiec M, Gophna U, Ruppin E.
なぜ細菌の第二染色体上で遺伝子が速く進化するのか https://www.ncbi.nlm.nih.gov/pubmed/20369015 PLoS Comput Biol. 2010 Apr 1;6(4):e1000732. doi: 10.1371/journal.pcbi.1000732. Why genes evolve faster on secondary chromosomes in bacteria. Cooper VS1, Vohr SH, Wrocklage SC, Hatcher PJ. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2848543/
https://pubmed.ncbi.nlm.nih.gov/30341344/ Sci Rep . 2018 Oct 19;8(1):15548. doi: 10.1038/s41598-018-33476-x. Deciphering the rationale behind specific codon usage pattern in extremophiles Mohd Faheem Khan 1, Sanjukta Patra 2 https://www.nature.com/articles/s41598-018-33476-x https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6195531/
Preference of codons in different organisms is further explained by Selection-Mutation-Drift theory3.
Zeldovich et al. (2007) revealed that the codon usage pattern creates a direct link between principles of protein stability and evolutionary mechanisms of extremophilic adaptation11.
Lobry et al. (2006) divulged thermophiles preferred GC-rich codon whereas, psychrophiles and mesophiles preferred AT-rich31. Our finding of nucleotide composition of discriminating codons corroborated with that of Lobry et al.
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Lynn DJ, Singer GAC, Hickey DA. Synonymous codon usage is subject to selection in thermophilic bacteria. Nucleic Acids Res. 2002;30:4272–4277. doi: 10.1093/nar/gkf546. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
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https://pubmed.ncbi.nlm.nih.gov/16989961/ Gene . 2006 Dec 30;385:128-36. doi: 10.1016/j.gene.2006.05.033. Epub 2006 Aug 22. Synonymous codon usage and its potential link with optimal growth temperature in prokaryotes J R Lobry 1, A Necşulea
https://www.sciencedirect.com/science/article/abs/pii/S0378111906004835
https://pubmed.ncbi.nlm.nih.gov/12364606/ Nucleic Acids Res . 2002 Oct 1;30(19):4272-7. doi: 10.1093/nar/gkf546. Synonymous codon usage is subject to selection in thermophilic bacteria David J Lynn 1, Gregory A C Singer, Donal A Hickey https://academic.oup.com/nar/article/30/19/4272/2376087 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC140546/
https://pubmed.ncbi.nlm.nih.gov/32939027/ ISME J . 2021 Jan;15(1):183-195. doi: 10.1038/s41396-020-00773-1. Epub 2020 Sep 16. Benchmarking microbial growth rate predictions from metagenomes Andrew M Long # 1, Shengwei Hou # 2, J Cesar Ignacio-Espinoza 2, Jed A Fuhrman 2 https://www.nature.com/articles/s41396-020-00773-1 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7852909/ Growth rate indices: codon usage bias and peak-to-trough ratio The maximum growth rate of each MAG was predicted using a customized growthpred v1.0.8 (available at https://hub.docker.com/r/shengwei/growthpred) in metagenome mode (-m) and with universal codons (-c 0). Blast-retrieved ribosomal protein sequences were used as the highly expressed genes (-b) and compared to all the coding sequences of each MAG (-g).
PTR indices were calculated using iRep v1.10 [5], GRiD v1.3 [6], and DEMIC v1.0.2 [7].
https://twitter.com/EvaMariaNovoa/status/1130910439817252864 Eva Maria Novoa on Twitter: "Our paper is out! :-) Codon usage signatures across species are not just consequence of GC content - and these biases can be used for metagenomic binning! #codonusage #metagenome @manoliskellis https://t.co/fKZNIcWIll" 2:57 PM - 21 May 2019 https://www.ncbi.nlm.nih.gov/pubmed/31220870 Mol Biol Evol. 2019 Oct 1;36(10):2328-2339. doi: 10.1093/molbev/msz124. Elucidation of Codon Usage Signatures across the Domains of Life. Novoa EM1,2,3,4, Jungreis I1,2, Jaillon O1,2,5, Kellis M1,2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6759073/
2019-05-02 Anamaria Elek https://www.bioconductor.org/packages/devel/bioc/vignettes/coRdon/inst/doc/coRdon.html Codon usage (CU) analysis in R
- This approach can be efficiently used to predict highly expressed genes in a single genome, but is especially useful at the higher level of an entire metagenome.
https://www.ncbi.nlm.nih.gov/pubmed/29947757 Bioinformatics. 2018 Jun 27. doi: 10.1093/bioinformatics/bty519. [Epub ahead of print] BMC3C: Binning Metagenomic Contigs using Codon usage, sequence Composition and read Coverage. Yu G1, Jiang Y1, Wang J1, Zhang H2, Luo H2.
https://www.ncbi.nlm.nih.gov/pubmed/29742107 PLoS One. 2018 May 9;13(5):e0195869. doi: 10.1371/journal.pone.0195869. eCollection 2018. Codon usage bias reveals genomic adaptations to environmental conditions in an acidophilic consortium. Hart A1, Cortés MP2,3, Latorre M2,3,4,5, Martinez S6.
review https://www.ncbi.nlm.nih.gov/pubmed/26951112 Microbiome. 2016 Mar 8;4:8. doi: 10.1186/s40168-016-0154-5. Recovering complete and draft population genomes from metagenome datasets. Sangwan N1,2, Xia F3, Gilbert JA4,5,6,7. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4782286/ However, another framework for analysis relies on variation in codon bias to determine the genome-wide influence of in situ functional constraints on individual taxa. Since percentage codon bias variation analysis is a phylogenetically independent method that directly reflects the strength of selection and the translation efficiency of expressed genes [77, 78], it circumvents the need for reference genomes and can reveal the influence of in situ functional constraints over natural selection patterns. It is important to note that for complete genome sequences, codon use patterns are influenced by nucleotide composition (mutational biases) and horizontal gene transfer. However, because each gene in a reassembled genome represents the population with an even nucleotide composition, one can assume that these clonal isolate-based limitations will not skew codon usage.
https://www.ncbi.nlm.nih.gov/pubmed/25680374 Acta Biochim Pol. 2015;62(1):161-6. Epub 2015 Feb 13. The most widespread problems in the function-based microbial metagenomics. Felczykowska A1, Krajewska A1, Zielińska S1, Łoś JM1, Bloch SK1, Nejman-Faleńczyk B1. Codon usage bias, internal cell accumulation, correct protein folding or presence of proper initiation factors are discussed and possible ways to overcome these problems are proposed. http://www.actabp.pl/pdf/1_2015/2014_917.pdf
https://www.ncbi.nlm.nih.gov/pubmed/27115650 Methods Mol Biol. 2016;1415:509-31. doi: 10.1007/978-1-4939-3572-7_26. Big Data, Evolution, and Metagenomes: Predicting Disease from Gut Microbiota Codon Usage Profiles. Fabijanić M1, Vlahoviček K2.
https://phylogenomics.blogspot.jp/2013/08/great-use-of-metagenomic-data-community.html August 09, 2013 Great use of metagenomic data: community wide adaptation signatures
https://www.ncbi.nlm.nih.gov/pubmed/23921637 Nucleic Acids Res. 2013 Oct;41(19):8842-52. Environmental shaping of codon usage and functional adaptation across microbial communities. Roller M1, Lucić V, Nagy I, Perica T, Vlahovicek K. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3799439/ Single bacterial species’ genomes and annotation Codon usage within metagenomes follows similar patterns as in single microbial genomes
データベース
https://integbio.jp/dbcatalog/record/nbdc00033 Codon Usage Database - Integbio データベースカタログ http://www.kazusa.or.jp/codon/ Codon Usage Database
https://www.ncbi.nlm.nih.gov/pubmed/28865429 BMC Bioinformatics. 2017 Sep 2;18(1):391. doi: 10.1186/s12859-017-1793-7. A new and updated resource for codon usage tables. Athey J1, Alexaki A1, Osipova E2, Rostovtsev A2, Santana-Quintero LV2, Katneni U1, Simonyan V2, Kimchi-Sarfaty C3. High-performance Integrated Virtual Environment-Codon Usage Tables (HIVE-CUTs) http://hive.biochemistry.gwu.edu/review/codon https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5581930/ However, as plasmids are located in the same area as the genome, draw from the same tRNA pools, and use the same genetic code as the genome, plasmid coding sequences are not separated from the organism’s genomic codon usage table.
https://www.ncbi.nlm.nih.gov/pubmed/22536831 BMC Bioinformatics. 2012 Apr 26;13:62. doi: 10.1186/1471-2105-13-62. CBDB: the codon bias database. Hilterbrand A1, Saelens J, Putonti C.
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https://pubmed.ncbi.nlm.nih.gov/35536198/ Genetics . 2022 Jul 4;221(3):iyac079. doi: 10.1093/genetics/iyac079. BioKIT: a versatile toolkit for processing and analyzing diverse types of sequence data Jacob L Steenwyk 1 2, Thomas J Buida 3, Carla Gonçalves 1 2 4 5, Dayna C Goltz 6, Grace Morales 7, Matthew E Mead 1 2, Abigail L LaBella 1 2, Christina M Chavez 1 2, Jonathan E Schmitz 7, Maria Hadjifrangiskou 2 7, Yuanning Li 1, Antonis Rokas 1 2 https://academic.oup.com/genetics/article-abstract/221/3/iyac079/6583183 To demonstrate the utility of BioKIT, we conducted a comprehensive examination of relative synonymous codon usage across 171 fungal genomes that use alternative genetic codes, showed that the novel metric of gene-wise relative synonymous codon usage can accurately estimate gene-wise codon optimization, evaluated the quality and characteristics of 901 eukaryotic genome assemblies, and calculated alignment summary statistics for 10 phylogenomic data matrices.
http://www.stevekellylab.com/codonmuse CodonMuSe - Steve Kelly Lab
CodonMuSe CodonMuSe is a method for inferring the strength of selection acting on nucleotide sequences. It detects the strength of selection acting on the translation efficiency of the sequence and the strength of selection acting on biosyntheic cost of that sequence.
Most amino acids are encoded by multiple synonymous codons. These codons are not equal. They cost different mount of resources to make and they have different numbers of cognate tRNAs floating about in the cell. Thus, different codons encoding the same amino acid have different biosynthetic costs and different translational efficiencies. CodonMuSe figures out how these two competing selective forces interact to shape the coding sequence of genes. It also considers background effects such as genome-wide mutation bias.
If you want to know whether your gene has been under selection be cheap, efficient or both, ask CodonMuSe! It takes less than a second.
You can download and install CodonMuSe here https://github.com/easeward/CodonMuSe
https://github.com/enovoa/codonAutocorrelation Compute codon autocorrelation (aka codon covariation, codon reuse, codon pair usage) from fasta sequences. This code was used in the analyses of the manuscript 'Elucidation of codon usage signatures across the domains of life' (Novoa et al., Mol Biol Evol 2019), available here: https://doi.org/10.1093/molbev/msz124
https://www.ncbi.nlm.nih.gov/pubmed/30738198 Genomics. 2019 Feb 6. pii: S0888-7543(18)30608-6. doi: 10.1016/j.ygeno.2019.02.002. [Epub ahead of print] CodSeqGen: A tool for generating synonymous coding sequences with desired GC-contents. Al-Ssulami AM1, Azmi AM2, Hussain M3. Availability CodSeqGen executable is available for free download at: https://github.com/Abdulrakeeb/CodSeqGen
https://www.ncbi.nlm.nih.gov/pubmed/17439967 Nucleic Acids Res. 2007 Jul;35(Web Server issue):W126-31. Epub 2007 Apr 16. OPTIMIZER: a web server for optimizing the codon usage of DNA sequences. Puigbò P1, Guzmán E, Romeu A, Garcia-Vallvé S.
https://www.ncbi.nlm.nih.gov/pubmed/15980527 Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W526-31. JCat: a novel tool to adapt codon usage of a target gene to its potential expression host. Grote A1, Hiller K, Scheer M, Münch R, Nörtemann B, Hempel DC, Jahn D.
http://codonw.sourceforge.net Correspondence Analysis of Codon Usage CodonW 15/Apr/2005
http://pbil.univ-lyon1.fr/datasets/charif04/ Online Synonymous Codon Usage Analyses with the ade4 and seqinR packages 2004
http://bioinfo.ie.niigata-u.ac.jp/?Codon%20Usage%20Generator Codon Usage Generator (CUG) † 本プログラムは，コドン組成を計算するためのソフトウェアです．
https://github.com/mariodosreis/tai tRNA adaptation index (tAI)
http://people.cryst.bbk.ac.uk/~fdosr01/tAI/
https://www.g-language.org/documentation/1.9.1/lib/G/Seq/Codon.html
https://pubmed.ncbi.nlm.nih.gov/27797757/ Bioinformatics . 2017 Feb 15;33(4):589-591. doi: 10.1093/bioinformatics/btw647. stAIcalc: tRNA adaptation index calculator based on species-specific weights Renana Sabi 1, Renana Volvovitch Daniel 1, Tamir Tuller 1 2 https://academic.oup.com/bioinformatics/article/33/4/589/2593585
https://pubmed.ncbi.nlm.nih.gov/15448185/ Nucleic Acids Res . 2004 Sep 24;32(17):5036-44. doi: 10.1093/nar/gkh834. Print 2004. Solving the riddle of codon usage preferences: a test for translational selection Mario dos Reis 1, Renos Savva, Lorenz Wernisch https://academic.oup.com/nar/article/32/17/5036/1333956 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC521650/
As expected (Figure (Figure3),3), S.cerevisiae and E.coli show the highest S-values, C.elegans shows a moderate S-value, while H.pylori and H.sapiens show no sign of translational selection acting on their genomes.
The tRNA adaptation index The tRNA adaptation index (tAI) (22) is a measure of the tRNA usage by coding sequences inspired by the codon adaptation index of Sharp and Li (23).
https://pubmed.ncbi.nlm.nih.gov/14627830/ Nucleic Acids Res . 2003 Dec 1;31(23):6976-85. doi: 10.1093/nar/gkg897. Unexpected correlations between gene expression and codon usage bias from microarray data for the whole Escherichia coli K-12 genome Mario dos Reis 1, Lorenz Wernisch, Renos Savva https://academic.oup.com/nar/article/31/23/6976/2904389 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC290265/ In order to test for translational selection, we devised an index for tRNA usage, inspired by the CAI of Sharp and Li (24).
relative synonymous codon usage
https://www.megasoftware.net/web_help_7/rh_rscu.htm Many amino acids are coded by more than one codon; thus multiple codons for a given amino acid are synonymous. However, many genes display a non-random usage of synonymous codons for specific amino acids. A measure of the extent of this non-randomness is given by the Relative Synonymous Codon Usage (RSCU) (Sharp et al. 1986).
http://www.lirmm.fr/~rivals/rscu/ RSCURS: Measuring the bias in codon usage from ribosomal activity Paulet et al. DNA Research 2017
Elucidation of Codon Usage Signatures across the Domains of Life https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6759073/ fig. 1. (A) Hierarchical clustering of the average relative synonymous codon usage (RSCU) for each species (n = 1,625).
https://www.bio.upenn.edu/sites/default/files/research/Plotkin/Plotkin_Kudla_NatRevGenetics_2011.pdf Figure 1 | Codon bias within and between genomes.
https://www.ncbi.nlm.nih.gov/pubmed/21699680 BMC Genomics. 2011 Jun 23;12:325. doi: 10.1186/1471-2164-12-325. Unresolved orthology and peculiar coding sequence properties of lamprey genes: the KCNA gene family as test case. Qiu H1, Hildebrand F, Kuraku S, Meyer A. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3141671/ Synonymous codon usage was investigated by within-group correspondence analysis (WCA) of codon counts [22,23] and by correspondence analysis of relative synonymous codon usage (CA-RSCU). The latter is the most widely used method for codon usage analysis, whereas the former has been recently demonstrated to produce more unbiased results because it takes into account both amino acid composition and codon degeneracy information [48].
最尤推定 https://www.ncbi.nlm.nih.gov/pubmed/18495752 Nucleic Acids Res. 2008 Jun;36(11):3819-27. doi: 10.1093/nar/gkn288. Epub 2008 May 21. SCUMBLE: a method for systematic and accurate detection of codon usage bias by maximum likelihood estimation. Kloster M1, Tang C. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2441815/ The algorithm's; ability to detect weak biases is well illustrated by the genome of Helicobacter pylori, which has been claimed to contain no codon bias for highly expressed genes (31).
We found that SCUMBLE performs better than WCA or CA/RSCU in detecting GC or GT biases in prokaryote genomes. SCUMBLE is also able to detect far more biases in prokaryote genomes than a variety of other approaches using PCA (37) (Supplementary Tables S1 and S2). Unlike PCA, SCUMBLE shows a clear signature for the strength of the different biases: GC bias is most often the dominant bias, followed by expression bias and GT bias.
https://www.youtube.com/watch?v=P-fjZPf3Dnw Codon optimization: Why & how to design DNA sequences for optimal soluble protein expression - YouTube Published on Nov 3, 2014
- https://ja.wikipedia.org/wiki/コドン
- http://bioinfo.ie.niigata-u.ac.jp/?遺伝子発現量予測
- http://bioinfo.ie.niigata-u.ac.jp/?コドン組成に基づくBLSOM解析
- https://twilog.org/copypasteusa/search?word=コドン
- https://github.com/haruosuz/bioinfo/blob/master/2020/CaseStudy.md#codon-usage
- https://twitter.com/search?q=大腸菌%20コドン
https://www.ncbi.nlm.nih.gov/pubmed/12952536 Genome Biol. 2003;4(9):R57. Epub 2003 Aug 21. The source of laterally transferred genes in bacterial genomes. Daubin V1, Lerat E, Perrière G. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC193657/ The codon usage of LTGs: comparison with native genes We computed four independent factorial correspondence analyses (FCA) on the genes of each type (native and transferred genes, IS, and phages) for the four species E. coli O157:H7, Helicobacter pylori, Salmonella enterica, and Streptococcus pneumoniae. Figure 4 Intraspecies FCA.
https://www.biophys.jp/dl/journal/53-1.pdf 生物物理 53（1），015-019（2013） 交通流と翻訳過程 御手洗菜美子 コペンハーゲン大学ニールス・ボーア研究所
Mobile genetic elements (MGEs) https://ja.wikipedia.org/wiki/可動遺伝因子
https://www.ncbi.nlm.nih.gov/pubmed/28206693 Mol Ecol. 2017 Apr;26(7):1832-1847. doi: 10.1111/mec.14056. Epub 2017 Mar 13. Plasmid and clonal interference during post horizontal gene transfer evolution. Bedhomme S1,2, Perez Pantoja D3, Bravo IG2. KEYWORDS: antibiotic resistance; clonal interference; codon usage preferences; multilevel selection; plasmid https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5392415/ Plasmid carriage costs are not the only costs to plasmid-mediated HGT. Another potentially important one is the mismatch in codon usage preferences between the transferred gene and the receiving genome (Baltrus 2013). Each species is indeed characterized by specific frequencies of use of the different codons within a synonymous codon family. An important evolutionary force shaping codon usage preferences is coevolution with the translation machinery: codon usage frequencies are strongly related with the copy number of corresponding tRNA genes, especially for highly expressed genes and for rapidly growing organisms (Rocha 2004). Genes transferred horizontally from a different organism can present a mismatch in codon usage preferences with the receiving genome, and such mismatch is known to affect translation accuracy and speed (Komar et al. 1999, Burgess-Brown et al. 2008).
and comparative approaches demonstrate that HGT is more likely to be successful between genomes with similar codon usage preferences (Tuller et al. 2011, Medrano-Soto 2004).
The cost of plasmid carriage was quantified and we showed that mismatches in codon usage preferences generate differences in chloramphenicol resistance.
https://www.ncbi.nlm.nih.gov/pubmed/20369015 PLoS Comput Biol. 2010 Apr 1;6(4):e1000732. doi: 10.1371/journal.pcbi.1000732. Why genes evolve faster on secondary chromosomes in bacteria. Cooper VS1, Vohr SH, Wrocklage SC, Hatcher PJ.
Further, in every bacterial genome with multiple chromosomes that we studied, genes on secondary chromosomes exhibited significantly weaker codon usage bias than those on primary chromosomes. Faster evolution and reduced codon bias
https://www.ncbi.nlm.nih.gov/pubmed/20080407 Trends Microbiol. 2010 Apr;18(4):141-8. doi: 10.1016/j.tim.2009.12.010. Epub 2010 Jan 18. Introducing the bacterial 'chromid': not a chromosome, not a plasmid. Harrison PW1, Lower RP, Kim NK, Young JP.
Chromids carry some core genes, and their nucleotide composition and codon usage are very similar to those of the chromosomes they are associated with.
https://linkinghub.elsevier.com/retrieve/pii/S0966-842X(09)00269-8
Chromid genes have a similar codon usage to chromosomal genes A large number of studies (reviewed by Lynn et al.[11]) have investigated synonymous codon usage and demonstrated that it is highly patterned. It is known that individual genomes have characteristic synonymous codon usage signatures [12]. Our analysis demonstrates that individual replicons also have distinct codon usage characteristics, and chromids are much more similar to chromosomes than to the plasmids that are found in the same organisms. For example, in the genome of Agrobacterium tumefaciens C58 the chromosome and chromid are very similar in average codon usage but rather distinct from the two plasmids (Figure 1).
https://www.ncbi.nlm.nih.gov/pubmed/19221094 DNA Res. 2009 Apr;16(2):91-104. doi: 10.1093/dnares/dsp001. Epub 2009 Feb 15. Codon usages of genes on chromosome, and surprisingly, genes in plasmid are primarily affected by strand-specific mutational biases in Lawsonia intracellularis. Guo FB1, Yuan JB.
https://www.ncbi.nlm.nih.gov/pubmed/18391244 In Silico Biol. 2007;7(4-5):547-58. Analysis of codon usage patterns and predicted highly expressed genes for six phytopathogenic Xanthomonas genomes shows a high degree of conservation. Sen G1, Sur S, Bose D, Mondal U, Furnholm T, Bothra A, Tisa L, Sen A.
https://www.ncbi.nlm.nih.gov/pubmed/14597394 Gene. 2003 Nov 27;320:109-16. The strength of translational selection for codon usage varies in the three replicons of Sinorhizobium meliloti. Peixoto L1, Zavala A, Romero H, Musto H.
https://www.ncbi.nlm.nih.gov/pubmed/9820537 J Immunol. 1998 Nov 15;161(10):5594-9. Optimization of codon usage of plasmid DNA vaccine is required for the effective MHC class I-restricted T cell responses against an intracellular bacterium. Uchijima M1, Yoshida A, Nagata T, Koide Y.
https://pubmed.ncbi.nlm.nih.gov/32670501/ Review Comput Struct Biotechnol J . 2020 Jun 19;18:1605-1612. doi: 10.1016/j.csbj.2020.06.019. eCollection 2020. Computational approaches in viral ecology Varada Khot 1, Marc Strous 1, Alyse K Hawley 1 https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC7334295/ Host prediction approaches that do not use sequence homology include abundance profiles and oligonucleotide (k-mer) frequencies.
Fig. 2 Percentage of correct hosts identified out of 820 assignments by four methods at the genus and species level. This figure was recreated using supplementary data provided by Edwards et al. [16].
https://pubmed.ncbi.nlm.nih.gov/26657537/ Review FEMS Microbiol Rev . 2016 Mar;40(2):258-72. doi: 10.1093/femsre/fuv048. Epub 2015 Dec 9. Computational approaches to predict bacteriophage-host relationships Robert A Edwards 1, Katelyn McNair 2, Karoline Faust 3, Jeroen Raes 3, Bas E Dutilh 4 https://academic.oup.com/femsre/article/40/2/258/2570202 https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC5831537/
Table 1. Computational signals to identify bacteriophage–host relationships. The column ‘Performance’ shows for how many of the 820 phages in our benchmarking dataset we could correctly predict the host species (see Fig. 4).
Over time, phages ameliorate their nucleotide composition towards that of the host. This reflects intracellular nucleotide pools, codon usage and tRNA availability, and restriction-modification systems.
Figure 1. ROC curves displaying the classification accuracy of computational phage–host prediction approaches. (A) Pearson correlation of phage and bacterial abundance profiles across environments; (B) overall alignment length of blastn hits between phage and bacterial genome sequences; (C) number of matching proteins in blastx search of phage DNA to bacterial proteins; (D) percent identity of CRISPR spacers aligned to phage genomes; (E) number of matching CRISPR spacers in phage genomes; (F) length of longest exact match between phage and bacterial genomes; (G) Pearson correlation of oligonucleotide usage profiles (tetramers, k = 4, for other lengths of k, see Fig. S2, Supporting Information); (H) similarity in codon usage profiles of phage and bacterial coding regions; (I) similarity in GC content between phage and bacterial genomes. Note that in some ROC plots, the TP and FP rates do not continue to FP rate = 1; TP rate = 1. In those cases, we used cutoffs for assignment of a hit.
https://www.ncbi.nlm.nih.gov/pubmed/31911390 J Virol Methods. 2020 Mar;277:113806. doi: 10.1016/j.jviromet.2019.113806. Epub 2020 Jan 5. Analysis of preferred codon usage in the coronavirus N genes and their implications for genome evolution and vaccine design. Sheikh A1, Al-Taher A2, Al-Nazawi M2, Al-Mubarak AI3, Kandeel M4. https://www.sciencedirect.com/science/article/pii/S0166093418305809?via%3Dihub https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC7119019/ In this study, the variations in the N proteins among 13 different coronaviruses (CoVs) were analysed at the nucleotide and amino acid levels in an attempt to reveal how these viruses adapt to their hosts relative to their preferred codon usage in the N genes.
https://www.ncbi.nlm.nih.gov/pubmed/27278133 Sci Rep. 2016 Jun 9;6:27546. doi: 10.1038/srep27546. Codon optimization of the adenoviral fiber negatively impacts structural protein expression and viral fitness. Villanueva E1, Martí-Solano M2, Fillat C1,3.
https://www.ncbi.nlm.nih.gov/pubmed/22016848 Mob Genet Elements. 2011 May;1(1):75-77. Codon bias, tRNA pools and horizontal gene transfer. Tuller T1.
https://www.ncbi.nlm.nih.gov/pubmed/19888206 Mol Syst Biol. 2009;5:311. doi: 10.1038/msb.2009.71. Epub 2009 Oct 13. Viral adaptation to host: a proteome-based analysis of codon usage and amino acid preferences. Bahir I1, Fromer M, Prat Y, Linial M.
https://www.ncbi.nlm.nih.gov/pubmed/18463708 PLoS Comput Biol. 2008 Feb 29;4(2):e1000001. doi: 10.1371/journal.pcbi.1000001. Genome landscapes and bacteriophage codon usage. Lucks JB1, Nelson DR, Kudla GR, Plotkin JB.
https://github.com/haruosuz/microbe/blob/master/references/microbe.virus.md#coronavirus
https://github.com/haruosuz/microbe/blob/master/references/microbe.COVID-19.md
https://www.biorxiv.org/content/10.1101/2020.04.01.019463v3 Insights into The Codon Usage Bias of 13 Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Isolates from Different Geo-locations | bioRxiv Posted August 07, 2020.
2.4 Relative Synonymous Codon Usage (RSCU)
2.12 Relative Codon Deoptimization Index (RCDI)
If the tested coding sequence of a virus has a similar codon usage pattern to the human genome, the RCDI value would be closer to or equal 1, which may indicate a high translation rate, as well as, a better adaptation to the host.
3.6 Codon Adaptation Index (CAI)
https://pubmed.ncbi.nlm.nih.gov/32294518/ Comparative Study Virus Res . 2020 Jul 2;283:197976. doi: 10.1016/j.virusres.2020.197976. Epub 2020 Apr 12. A comprehensive analysis of genome composition and codon usage patterns of emerging coronaviruses Fernando L Tort 1, Matías Castells 1, Juan Cristina 2 https://www.sciencedirect.com/science/article/abs/pii/S0168170220301684?via%3Dihub https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC7152894/
Fig. 2 Heatmaps of codon and amino acid usage in βCoV ORFs. Unit variance scaling was applied. Each column corresponds to a different βCoV strain, who’s host and virus type are shown in the upper part of the figures. Both rows and columns are clustered using correlation distance and average linkage. In (a) and (b) codon and amino acids usage is shown, respectively.
3.5. Codon usage adaptation in SARS-CoV-2
https://pubmed.ncbi.nlm.nih.gov/32431949/ Virus Evol . 2020 May 14;6(1):veaa032. doi: 10.1093/ve/veaa032. eCollection 2020 Jan. Multivariate analyses of codon usage of SARS-CoV-2 and other betacoronaviruses Haogao Gu 1, Daniel K W Chu 1, Malik Peiris 1, Leo L M Poon 1 https://academic.oup.com/ve/article/6/1/veaa032/5837024 https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC7223271/ Codon usage of viral genes are believed to be subjected to different selection pressures in different host environments. Previous studies on codon usage of influenza A viruses helped identify viral host origins and evolution trends, however, similar studies on coronaviruses are lacking. In this study, we compared the codon usage bias using global correspondence analysis (CA), within-group CA and between-group CA.
2.2 Global CA on codon usage
The Pearson residuals were applied in the CA as input for singular value decomposition (Suzuki et al. 2008), and resulted eigenvalues were visualized and interpreted in the study.
2.3 Within-group CA and between-group CA In contrast to the previous global CA, the within-block CA (Benzécri 1983) (WCA) can segregate the effects of different codon compositions in different amino acids, by introducing a block structure into the analysis. WCA becomes ‘model of choice’ for analysing synonymous codon usage in recent years, as it is more robust than other traditional methods (e.g. CA with relative codon frequency or CA with relative synonymous codon usage values) (Perriere 2002; Suzuki et al. 2008).
https://f1000research.com/articles/9-190 REVISED Identification Sus scrofa and Mus musculus as potential hosts of SARS-CoV-2 via phylogenetic and homologous recombination analysis [version 2; peer review: awaiting peer review] Xiaopeng Hu https://orcid.org/0000-0002-6533-80241,2, Weixin Li1, Zhendan He2, Fengxue Zhang3
Figure 1. SARS-CoV-2 phylogeny analysis based on the codon usage. (A) Heat map of relative synonymous codon usage (RSCU) derived from the complete genome of SARS-CoV-2, as well as the mitochondrial genome of diverse animals (Sus scrofa, Mus musculus, Najaatra, Mustela Pulourius, Marmota flaviventris, Rhinolophus sinicus, Homo sapiens). (B)
Latest published: 22 Apr 2020, 9:190 (https://doi.org/10.12688/f1000research.22627.2)
7:06 AM · Mar 24, 2020 https://twitter.com/wormmaps/status/1242211046443622401 Colin J. Carlson, Ph.D. on Twitter: "This F1000 preprint argues based on codon use that pigs could plausibly be a better candidate for a SARS-CoV-2 reservoir, and even speculates about the human symptoms vs. porcine epidemic diarrhea virus, another corona. Can any virologists sense check? https://t.co/q4bZXe8htE https://t.co/hUXbYs1GWf" / Twitter
https://pubmed.ncbi.nlm.nih.gov/31967321/ J Med Virol . 2020 Apr;92(4):433-440. doi: 10.1002/jmv.25682. Cross-species transmission of the newly identified coronavirus 2019-nCoV Wei Ji 1, Wei Wang 2, Xiaofang Zhao 3, Junjie Zai 4, Xingguang Li 5 https://onlinelibrary.wiley.com/doi/10.1002/jmv.25682 https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC7138088/ To investigate possible virus reservoir, we have carried out comprehensive sequence analysis and comparison in conjunction with relative synonymous codon usage (RSCU) bias among different animal species based on the 2019‐nCoV sequence.
3.3. Relative synonymous codon usage analysis As parasitic microorganism, virus codon usage pattern resembles its host to some extent. The RSCU bias shows that the 2019‐nCoV, bat‐SL‐CoVZC45, and snakes from China have similar synonymous codon usage bias (Figure 3A, Table 1).
Figure 3 Comparison of relative synonymous codon usage (RSCU) between 2019‐nCoV and its putative wildlife animal reservoir(s). A,
2020.01.25 SAT 09:00 https://wired.jp/2020/01/25/wuhan-coronavirus-snake-flu-theory/ 新型コロナウイルスの感染源は、本当に「市場のヘビ」なのか？ 新たな論文を巡り波紋｜WIRED.jp
そのひとつが、1月22日（米国時間）の夜に医学誌『Journal of Medical Virology』で発表された中国人研究者チームの論文だ。中国の国家対策委員会が公開したウイルスのゲノム配列から、今回のアウトブレイクの感染源はヘビの可能性が高いという理論を展開している。
「ヘビ説」に反論が続出
遺伝子の解析から見えてきたこと
タンパク質を構成するコドンの使用頻度の偏り（コドンバイアス）は、生物によって異なる。一部のウイルスは、新しい宿主のコドンバイアスを採用することで、新しい宿主に適応する。
研究チームは、新型コロナウイルス（2019-nCoV）が好んで使うコドンと、宿主の可能性がある少数の生物（人間、コウモリ、ニワトリ、ハリネズミ、センザンコウ、2種類のヘビ）が好むコドンを比較した。そして新型コロナウイルス（2019-nCoV）のコドンバイアスが、タイワンコブラとアマガサヘビの2種類のヘビのコドンバイアスと最も重複していることがわかったと報告したのだ。
これに対してシドニー大学のホームズは、北京大学の研究チームによるアプローチには多くの問題があると指摘する。例えば、コドンバイアスの比較は、動物の宿主を特定するには非常に間接的な手法だという。
中立
https://en.wikipedia.org/wiki/Neutral_mutation
https://en.wikipedia.org/wiki/Mutation_bias
https://en.wikipedia.org/wiki/Codon_usage_bias
- It is generally acknowledged that codon biases reflect a balance between mutational biases and natural selection (mutation–selection balance) for translational optimization.
- Mutation-selection-drift balance model To reconcile the evidence from both mutational pressures and selection, the prevailing hypothesis for codon bias can be explained by the mutation-selection-drift balance model. This hypothesis states that selection favors major codons over minor codons, but minor codons are able to persist due to mutation pressure and genetic drift. It also suggests that selection is generally weak, but that selection intensity scales to higher expression and more functional constraints of coding sequences.[15]
https://www.ncbi.nlm.nih.gov/pubmed/31365533 PLoS Genet. 2019 Jul 31;15(7):e1008304. doi: 10.1371/journal.pgen.1008304. eCollection 2019 Jul. Variation and selection on codon usage bias across an entire subphylum. LaBella AL1, Opulente DA2, Steenwyk JL1, Hittinger CT2, Rokas A1. https://www.ncbi.nlm.nih.gov/pubmed/31365533 We found that, while genome-wide relative synonymous codon usage (RSCU) for all codons was highly correlated with the GC content of the third codon position (GC3), the usage of codons for the amino acids proline, arginine, and glycine was inconsistent with the neutral expectation where mutational bias coupled with genetic drift drive codon usage. Examination between genes' effective numbers of codons and their GC3 contents in individual genomes revealed that nearly a quarter of genes (381,174/1,683,203; 23%), as well as most genomes (308/327; 94%), significantly deviate from the neutral expectation.
https://www.ncbi.nlm.nih.gov/pubmed/22039174 DNA Res. 2011 Dec;18(6):499-512. doi: 10.1093/dnares/dsr035. Epub 2011 Oct 27. Mutation bias is the driving force of codon usage in the Gallus gallus genome. Rao Y1, Wu G, Wang Z, Chai X, Nie Q, Zhang X. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3223081/
- Synonymous codons are used with different frequencies both among species and among genes within the same genome and are controlled by neutral processes (such as mutation and drift) as well as by selection.
- As G + C content is more likely determined by genome-wide processes rather than by selective forces acting specifically on coding sequence, these findings have been inferred to reflect the genome-wide patterns of codon usage by mutational biases. Based on the fact that both mutational pressures and selective forces are involved in the phenomenon of codon bias in a variety of organisms, an integrated model, known as the mutation–selection–drift balance model, has been proposed.15,23–25 This model proposes that selection favours optimal codons over minor codons, while mutational pressure and genetic drift allow the minor codons to persist.17
https://ja.wikipedia.org/wiki/カイ二乗検定
Switches in Genomic GC Content Drive Shifts of Optimal Codons under Sustained Selection on Synonymous Sites. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5629928/ Codons used significantly more or less frequently in the highly expressed gene data set compared with the whole genome data set (chi-squared test, cutoff P = 0.01) were defined as optimal (+) and nonoptimal (−) codons according to the Ribosomal Protein (RP) method.
Synonymous Codon Usage Analysis of Thirty Two Mycobacteriophage Genomes https://www.hindawi.com/journals/abi/2009/316936/ To estimate the codon usage variation between these two sets of genes we have performed Chi square tests taking as significant criterion.
Predicting gene expression level from codon usage bias https://academic.oup.com/mbe/article/24/1/10/1070854#supplementary-data Codons occurring at significantly higher frequencies (P<5%) in the High dataset are in bold; significance was assessed by chi squared tests within synonym sets, using a sequential Bonferroni approach (Rice 1989) to overcome the problem of multiple tests. These 18 codons (for 15 amino acids) are inferred to be the translationally optimal codons.
Synonymous codon usage in Pseudomonas aeruginosa PA01. https://www.sciencedirect.com/science/article/pii/S0378111902005036?via%3Dihub Codon usage in the two data sets (Table 2) was compared using chi square tests, with the sequential Bonferroni correction (Rice, 1989) to assess significance. Sixteen codons, for 12 amino acids, were identified as significantly (P , 0:05) more frequent in the High data set.
Proteome composition and codon usage in spirochaetes: species-specific and DNA strand-specific mutational biases. Lafay B1, Lloyd AT, McLean MJ, Devine KM, Sharp PM, Wolfe KH. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC148367/pdf/271642.pdf Table 1. Codon usage (RSCU values) in B.burgdorferi (Bb) and T.pallidum (Tp) Chi-squared tests: << and >> denote P < 0.001 and indicate the direction of the difference;
増殖速度
https://github.com/haruosuz/mgsa/blob/master/references/mgsa.tools.md#growth-rate
https://github.com/haruosuz/r4bioinfo/blob/master/R_codon/README.md#grodon
翻訳選択
遺伝子の発現量
https://ja.wikipedia.org/wiki/コドン コドン出現頻度の違いは遺伝子の発現量やそのコドンに対応する tRNA の量と関係があることが知られている。発現量の多い遺伝子のコドン出現頻度の偏りは大きくなり、頻出するコドンに対応する tRNA は細胞内の存在量も多い。
http://bioinfo.ie.niigata-u.ac.jp/?遺伝子発現量予測
- Z1軸の性質は長浜バイオ大学 池村淑道教授が整理した最適コドン決定規則 (tRNA量に基づいた翻訳効率と関わる最適コドンの決定法, 表1) により説明することができます[1-2,10-12]。したがって、最適コドン決定規則を用いることにより、情報科学的解析のみで種固有の最適コドンを推定することができる。
- リボソーム蛋白質遺伝子および翻訳伸長因子といういわゆる細胞内で常に発現量が高いと仮定される遺伝子のコドン使用が生物全体の遺伝子のコドン使用とは異なっていることがZ1軸における遺伝子の分布により観察することができた。
https://www.nig.ac.jp/museum/evolution/04_c.html 遺伝暗号(コドン）使用の種による多様性 2.単細胞微生物のコドン選択の生物種による方言; 大腸菌とサルモネラ菌ならびに酵母遺伝子に見られるコドン選択の偏りを例に c) 同一生物種の遺伝子間を比較した場合、多量にタンパク質を生産する遺伝子ほど方言がきつく、生産量が下がるにつれて、同質の方言を用いながらも、その方言の程度が緩くなります。図4では、生物ごとに、タンパク質生産量の高い遺伝子を左側に配置してあります。大腸菌のtufAB(ポリペプチド鎖延長因子)、ompC(外膜タンパク質)は多量にタンパク質を生産する遺伝子の例であり、trpやthrはアミノ酸合成系の遺伝子で、通常条件下では中程度ないしは少量のタンパク質しか生産していません。酵母のG3PDHやenolaseは解糖系の酵素で、最も多量に生産されるタンパク質であり、TRPやCYCは中程度ないしは比較的少量しか生産されない例に属します。生産量の高い遺伝子ほどコドン選択の偏りが顕著で(方言がきつい)、生産量の低い遺伝子では偏りの程度が緩くなります。この傾向は広範囲の遺伝子で成立することが判明しています。
https://www.ncbi.nlm.nih.gov/pubmed/18940873 DNA Res. 2008 Dec;15(6):357-65. doi: 10.1093/dnares/dsn028. Epub 2008 Oct 21. Comparison of correspondence analysis methods for synonymous codon usage in bacteria. Suzuki H1, Brown CJ, Forney LJ, Top EM. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2608848/
- Thirdly, genes expressed at high levels in fast-growing bacteria tend to preferentially use translationally optimal codons that are recognized by the most abundant tRNAs. This presumably reflects natural selection for synonymous codons that are translated more efficiently and accurately.7,8
- Secondly, to analyze the correlation between scores of each of the three axes [Equation (4)] and levels of gene expression (Expression), we tested for the distribution of the axis scores for 40 genes expected to be expressed constitutively at high levels.10 This set included the genes encoding translation elongation factors Tu (tuf), Ts (tsf) and G (fus), and 37 of the larger ribosomal proteins (encoded by genes rplA-rplF, rplI-rplT, and rpsB-rpsT). In each axis, the score for each gene was standardized by subtracting the mean and dividing by the standard deviation of scores for all protein genes. For each axis, Expression was detected as the main source of variation among genes on the axis when the mean absolute standard score for the 40 highly expressed genes was >1.644854 (an interval in which theoretically only 5% of all protein genes are included).
- The third feature, Expression, was detected as a major source of synonymous codon usage variation among genes in C. trachomatis D/UW-3/CX, C. perfringens 13, E. coli K12 MG1655 and H. influenzae Rd KW20, which is consistent with previous findings (Table 2).
適合コドン / 最適コドン
http://bioinfo.ie.niigata-u.ac.jp/?遺伝子発現量予測 表1 翻訳過程による最適コドン決定規則 規則1:コドン使用は細胞内のtRNA量による制約を受ける。最適コドンは、細胞内に多量に含まれるtRNAのアンチコドンと対応する。
https://www.nig.ac.jp/museum/evolution/04_d.html 遺伝暗号(コドン）使用の種による多様性 3.コドン使用とtRNA量との関係、遺伝子工学との係り
規則 1) 1種類のアミノ酸にアンチコドンの異なる複数種類のisoaccepting tRNAが存在する場合、最大量tRNAの解読するコドンが好まれる。
我々のグループは、規則1ー3を総合して導かれる翻訳能率を最適化すると推定されるコドンを、適合コドン(optimal codon)と呼んでいます。
https://en.wikipedia.org/wiki/Codon_usage_bias Optimal codons in fast-growing microorganisms, like Escherichia coli or Saccharomyces cerevisiae (baker's yeast), reflect the composition of their respective genomic transfer RNA (tRNA) pool.[2] It is thought that optimal codons help to achieve faster translation rates and high accuracy.
Sun et al. (2017) における「optimal codons」と「abundant codons」の定義 Switches in Genomic GC Content Drive Shifts of Optimal Codons under Sustained Selection on Synonymous Sites. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5629928/
- We define optimal codons as codons that evolve under selection for translational efficiency in the highly expressed genes, and abundant codons as codons used at high frequency in the majority of genes.
- We defined optimal codons as codons that were significantly more abundant in the RP than in the WG data set, irrespectively of their actual frequencies. Thus, it should be noted that both highly abundant (major) and lowly abundant (minor) codons might be considered optimal.
最適コドンと準最適コドン（the primary and secondary optimal codons） https://www.ncbi.nlm.nih.gov/pubmed/23315666 DNA Res. 2013 Apr;20(2):135-50. doi: 10.1093/dnares/dss039. Epub 2013 Jan 11. Evaluation of codon biology in citrus and Poncirus trifoliata based on genomic features and frame corrected expressed sequence tags. Ahmad T1, Sablok G, Tatarinova TV, Xu Q, Deng XX, Guo WW. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3628444/ We further observed that for most amino acids with 2- to 6-fold degeneracy level, there has been a general preference for the usage of two or more codons as optimal codons. For example, in Glycine, two highly distributed optimal codons GGA and GGT were identified and they could be classified as the primary and secondary optimal codons preferentially based on the RSCU (GGA, ∼1.16 and GGT, ∼1.09).
https://www.ncbi.nlm.nih.gov/pubmed/17038449 Mol Biol Evol. 2007 Jan;24(1):10-2. Epub 2006 Oct 12. Predicting gene expression level from codon usage bias. Henry I, Sharp PM. https://academic.oup.com/mbe/article/24/1/10/1070854 Translationally optimal codons can be identified as those best recognized by the most abundant tRNAs, and the frequency of these codons in a gene is highly correlated with gene expression level (Post and Nomura 1980; Ikemura 1981; Gouy and Gautier 1982).
https://www.ncbi.nlm.nih.gov/pubmed/6175758 J Mol Biol. 1981 Sep 25;151(3):389-409. Correlation between the abundance of Escherichia coli transfer RNAs and the occurrence of the respective codons in its protein genes: a proposal for a synonymous codon choice that is optimal for the E. coli translational system. Ikemura T. https://www.sciencedirect.com/science/article/pii/0022283681900036 The synonymous codon predicted in this way to be the most preferred codon was thought to be optimized for the E. coli translational system and designated as the “Optimal codon”. E. coli genes encoding abundant protein species use the optimal codons selectively, and other E. coli genes, such as amino acid synthesizing genes, use optimal and “non-optimal” codons to a roughly equal degree.
https://www.ncbi.nlm.nih.gov/pubmed/6167728 J Mol Biol. 1981 Feb 15;146(1):1-21. Correlation between the abundance of Escherichia coli transfer RNAs and the occurrence of the respective codons in its protein genes. Ikemura T.
https://www.ncbi.nlm.nih.gov/pubmed/29018283 Nat Rev Mol Cell Biol. 2017 Oct 11. Codon optimality, bias and usage in translation and mRNA decay. Hanson G1, Coller J1.
review https://www.ncbi.nlm.nih.gov/pubmed/26029354 Comput Struct Biotechnol J. 2015 May 4;13:352-7. doi: 10.1016/j.csbj.2015.04.005. eCollection 2015. Homology-independent metrics for comparative genomics. Coutinho TJ1, Franco GR1, Lobo FP2. In this review we compile several sequence metrics that do not rely on homology inference and can be used to compare nucleotide sequences and extract biologically meaningful information from them. These metrics comprise several compositional parameters calculated from sequence data alone, such as GC content, dinucleotide odds ratio, and several codon bias metrics. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4446528/
https://www.ncbi.nlm.nih.gov/pubmed/26186290 Mol Cell. 2015 Jul 16;59(2):149-61. doi: 10.1016/j.molcel.2015.05.035. Codon Bias as a Means to Fine-Tune Gene Expression. Quax TE1, Claassens NJ2, Söll D3, van der Oost J4.
https://www.ncbi.nlm.nih.gov/pubmed/22889422 Biol Rev Camb Philos Soc. 2013 Feb;88(1):49-61. doi: 10.1111/j.1469-185X.2012.00242.x. Epub 2012 Aug 14. Codon usage bias: causative factors, quantification methods and genome-wide patterns: with emphasis on insect genomes. Behura SK1, Severson DW.
https://www.ncbi.nlm.nih.gov/pubmed/22921354 Trends Genet. 2012 Nov;28(11):574-81. doi: 10.1016/j.tig.2012.07.006. Epub 2012 Aug 23. Speeding with control: codon usage, tRNAs, and ribosomes. Novoa EM1, Ribas de Pouplana L.
http://www.ncbi.nlm.nih.gov/pubmed/21102527 Nat Rev Genet. 2011 Jan;12(1):32-42. doi: 10.1038/nrg2899. Epub 2010 Nov 23. Synonymous but not the same: the causes and consequences of codon bias. Plotkin JB1, Kudla G. https://www.bio.upenn.edu/sites/default/files/research/Plotkin/Plotkin_Kudla_NatRevGenetics_2011.pdf
https://www.ncbi.nlm.nih.gov/pubmed/18983258 Annu Rev Genet. 2008;42:287-99. doi: 10.1146/annurev.genet.42.110807.091442. Selection on codon bias. Hershberg R1, Petrov DA.
https://www.ncbi.nlm.nih.gov/pubmed/11719972 Curr Issues Mol Biol. 2001 Oct;3(4):91-7. Synonymous codon usage in bacteria. Ermolaeva MD1.
異種遺伝子発現
https://www.ncbi.nlm.nih.gov/pubmed/29410400 Nat Commun. 2018 Feb 6;9(1):522. doi: 10.1038/s41467-018-02944-3. Biochemical mechanisms determine the functional compatibility of heterologous genes. Porse A1, Schou TS1, Munck C1, Ellabaan MMH1, Sommer MOA2. In contrast to previous work, we find that GC content, codon usage, and mRNA-folding energy are of minor importance for the compatibility of mechanistically diverse gene products at moderate expression.
https://galaxyproject.org/use/codon-harmonizer/ Codon Harmonizer : Easily generate codon harmonized variants of gene sequences for heterologous expression
https://www.ncbi.nlm.nih.gov/pubmed/28902855 PLoS One. 2017 Sep 13;12(9):e0184355. doi: 10.1371/journal.pone.0184355. eCollection 2017. Improving heterologous membrane protein production in Escherichia coli by combining transcriptional tuning and codon usage algorithms. Claassens NJ1, Siliakus MF1, Spaans SK1, Creutzburg SCA1, Nijsse B2, Schaap PJ2, Quax TEF3, van der Oost J1.
https://www.ncbi.nlm.nih.gov/pubmed/28855614 Sci Rep. 2017 Aug 30;7(1):9926. doi: 10.1038/s41598-017-10546-0. Predicting synonymous codon usage and optimizing the heterologous gene for expression in E. coli. Tian J1, Yan Y1,2, Yue Q1,3, Liu X1, Chu X1, Wu N4, Fan Y1.
2016年1月21日 https://www.natureasia.com/ja-jp/nature/highlights/71552 生物工学：タンパク質翻訳を改善する J Huntたちは今回、複数のタンパク質発現実験の結果の詳細な解析を行い、コドンの影響の新たな指標を導き出した。この指標を用いて遺伝子を再設計すると、転写には影響が出ないが、翻訳効率が大幅に高まることが分かった。彼らは、in vivoでのmRNA分解と翻訳の過程は競合しており、この指標を用いることでタンパク質産生を高めることができると結論付けている。
https://www.ncbi.nlm.nih.gov/pubmed/22847936 Bioinformatics. 2012 Oct 15;28(20):2683-4. doi: 10.1093/bioinformatics/bts465. Epub 2012 Jul 30. EuGene: maximizing synthetic gene design for heterologous expression. Gaspar P1, Oliveira JL, Frommlet J, Santos MA, Moura G.
https://www.ncbi.nlm.nih.gov/pubmed/19696103 Microbiology. 2009 Nov;155(Pt 11):3581-8. doi: 10.1099/mic.0.030064-0. Epub 2009 Aug 20. Overcoming codon-usage bias in heterologous protein expression in Streptococcus gordonii. Lee SF1, Li YJ, Halperin SA. tRNA genes encoding 10 of the 12 rare codons were cloned into a plasmid. The plasmid was transformed into strains of S. gordonii expressing the fusion protein SpaP/S1, the anti-complement receptor 1 (CR1) single-chain variable fragment (scFv) antibody, or the Toxoplasma gondii cyclophilin C18 protein.
2001 http://wolfson.huji.ac.il/expression/rosetta.pdf Overcoming the codon bias of E. coli for enhanced protein expression tRNA levels can be elevated by increasing the copy number of the respective tRNA gene. This is typically accomplished by inserting the wild type tRNA gene on a multiple copy plasmid. The tRNA gene is either inserted into the expression vector itself or placed on a compatible plasmid.
http://trna.ie.niigata-u.ac.jp/cgi-bin/trnadb/index.cgi Home | tRNADB-CE : tRNA gene database curated manually by experts
Details of annotation strategy written in Japanese (click here). https://www.jstage.jst.go.jp/article/cicsj/26/1/26_1_11/_pdf Silicibacter pomeroyi DSS-3 のゲノムからは51個の tRNA遺伝子が見出されていたが、フェニルアラニンのアンチコドンをもった遺伝子が不足していた。ゲノムのデータを調べてみると、この菌はプラスミドを保有しており、不足分のフェニルアラニン tRNA 遺伝子はそのプラスミド上に乗っていることが解かった。宿主ゲノムからプラスミドにその tRNA 遺伝子が移行した可能性は充分考えられる。この菌ではそのプラスミドを失う事は出来ず、tRNA 遺伝子がプラスミドの安定な保持に働いていると考えられる。
生物種リストから http://trna.ie.niigata-u.ac.jp/cgi-bin/trnadb/whole_spe_list_inc.cgi?STYPE=B&DTYPE=CMP
Shewanella oneidensis MR-1 tRNA seq.|Anticodon
https://integbio.jp/dbcatalog/record/nbdc00720 tRNADB-CE - Integbio データベースカタログ
https://dbarchive.biosciencedbc.jp/jp/trnadb-ce/data-1.html tRNA配列、アノテーション及びキュレーションのデータ - tRNADB-CE | LSDB Archive
https://www.ncbi.nlm.nih.gov/pubmed/29846694 Mol Biol Evol. 2018 Aug 1;35(8):2046-2059. doi: 10.1093/molbev/msy110. Wobbling Forth and Drifting Back: The Evolutionary History and Impact of Bacterial tRNA Modifications. Diwan GD1,2, Agashe D1.
https://www.ncbi.nlm.nih.gov/pubmed/24782525 Nucleic Acids Res. 2014 Jun;42(10):6552-66. doi: 10.1093/nar/gku245. Epub 2014 Apr 29. Auxiliary tRNAs: large-scale analysis of tRNA genes reveals patterns of tRNA repertoire dynamics. Wald N1, Margalit H2.
https://www.ncbi.nlm.nih.gov/pubmed/18058157 J Mol Evol. 2008 Jan;66(1):21-35. Epub 2007 Dec 4. The origin and evolution of tRNA inferred from phylogenetic analysis of structure. Sun FJ1, Caetano-Anollés G.
https://www.researchgate.net/post/Why_is_tRNA_not_used_for_phylogenetic_analysis Why is tRNA not used for phylogenetic analysis?
tRNA遺伝子クラスター
古細菌 https://www.ncbi.nlm.nih.gov/pubmed/30624459 Mem Inst Oswaldo Cruz. 2019 Jan 7;114:e180348. doi: 10.1590/0074-02760180348. Exploring tRNA gene cluster in archaea. Morgado SM1, Vicente ACP1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6333295/ In prokaryotes, tRNA gene clusters were characterised in some bacteria, being prevalent in Firmicutes phylum and Mycobacterium genus, 6 , 7
マイコバクテリウム属 https://www.ncbi.nlm.nih.gov/pubmed/29867913 Front Microbiol. 2018 May 17;9:1042. doi: 10.3389/fmicb.2018.01042. eCollection 2018. Beyond the Limits: tRNA Array Units in Mycobacterium Genomes. Morgado SM1, Vicente ACP1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5966550/
- In high GC content phyla, as Actinobacteria, they were found only in few genomes but none from Mycobacterium genus (Tran et al., 2016).
- Each tRNA predicted by ARAGORN provides information about its genomic coordinate and contig number, which were accessed by our in-house script. tRNA array units were defined as genomic regions with 20 or more tRNAs and a minimum density of two tRNA/kb (Tran et al., 2016).
- The Acidithiobacillus ferrooxidans ATCC 23270 genome was used as positive control to the identification of tRNA array unit (Tran et al., 2016).
- In order to identify the presence of any tRNA array unit in these genomes we used our in-house script that was based on the tRNA array unit definition: a genomic region containing at least 20 tRNA genes with a minimal density of two tRNA genes per kilobase (Tran et al., 2016).
- Types of tRNA array Tran et al. (2016), based on the tRNA amino acid isotypes and organization of each array, assigned the arrays in seven groups.
https://www.ncbi.nlm.nih.gov/pubmed/26710853 Genome Biol Evol. 2015 Dec 28;8(1):282-95. doi: 10.1093/gbe/evv254. A Comprehensive tRNA Genomic Survey Unravels the Evolutionary History of tRNA Arrays in Prokaryotes. Tran TT1, Belahbib H1, Bonnefoy V1, Talla E2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4758250/
Materials and Methods
-
tRNA Gene Prediction and Identification of tRNA Array Units tRNAscan-SE 1.3.1 program (Lowe and Eddy 1997) Aragon tool (Laslett and Canback 2004) was also used , we first identified regions of 5, 10, 15, 20, 25 and 30 kb along the genome (incremental path of 1 kb) that contain at least 5, 10, 15, 20, 25, or 30 tRNA genes, named array seeds (supplementary table S1, Supplementary Material online). Consequently, tRNA array seeds were thus defined as a genomic region of 10 kb containing at least 20 tRNA genes.
-
Structural Organization of tRNA Array Units
Results
- Occurrence and Distribution of tRNA Array Units A tRNA array unit was defined as a genomic region containing at least 20 tRNA genes with a minimal tRNA gene density of two tRNA genes per kilobase. Thi
Discussion In E. coli, tRNA arrays significantly influence global tRNA expression at high growth rates (Ardell and Kirsebom 2005). No relationship was found between the presence of the array units and the organism’s lifestyle (aerobic or anaerobic, optimal growth temperature, pathogenicity or environmental conditions) (data not shown).
ライフスタイル
https://www.ncbi.nlm.nih.gov/pubmed/23024607 Curr Genomics. 2012 Apr;13(2):153-62. Microbial lifestyle and genome signatures. Dutta C1, Paul S. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3308326/ c) Codon Usage Distinct niche-specific trends in synonymous codon usage have also been observed in microbes thriving at high salinity [24]. An analysis of synonymous codon usage patterns in bacterial and fungal genomes by Willenbrok et al. [49] demonstrated that differences in codon preferences of translational codon adaptation and dominant codon adaptation provide an environmental signature that can segregate bacteria according to their lifestyle, for instance soil bacteria and soil symbionts, spore formers, enteric bacteria, aquatic bacteria, and small intercellular and extracellular pathogens.
https://www.ncbi.nlm.nih.gov/pubmed/22032172 Genome Biol. 2011 Oct 27;12(10):R109. doi: 10.1186/gb-2011-12-10-r109. Variation in global codon usage bias among prokaryotic organisms is associated with their lifestyles. Botzman M1, Margalit H. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3333779/
コドン使用バイアスは、ライフスタイルの異なるグループ（土壌細菌、共生細菌、腸内細菌、水生細菌、胞子形成菌、病原体）に細菌を分けることで、環境の情報を提供する。 https://www.ncbi.nlm.nih.gov/pubmed/17156429 Genome Biol. 2006;7(12):R114. An environmental signature for 323 microbial genomes based on codon adaptation indices. Willenbrock H1, Friis C, Juncker AS, Ussery DW. group bacteria according to their lifestyle, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1794427/
- Background Here, we calculate and compare a translational CAI (tCAI) based on that proposed by Sharp and Li [1] with a purely mathematical dominant CAI (dCAI) [8] for 318 bacterial and 5 fungal genomes for which full sequences are deposited in Genbank and available from the Genome Atlas Database (version 19.1) [11].
- Figure 2 Two-dimensional cluster analysis of differential codon preferences for tCAI and dCAI. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1794427/figure/F2/
- Conclusion It was previously postulated that fast-growing bacteria share codon usage preferences because they have more abundant and similar tRNAs [12]. Here, we offer a biological explanation by showing a clear relationship between environment and similarities in codon usage biases. Specifically, differences in codon preferences of translational codon adaptation and dominant codon adaptation provide an environmental signature by which it is possible to divide bacteria into groups representing different lifestyles, such as soil bacteria and symbionts, enterics, aquatic bacteria, spore formers, and small intercellular and extracellular pathogens.
https://www.ncbi.nlm.nih.gov/pubmed/15537809 Mol Biol Evol. 2005 Mar;22(3):547-61. Epub 2004 Nov 10. Codon bias signatures, organization of microorganisms in codon space, and lifestyle. Carbone A1, Képès F, Zinovyev A. https://academic.oup.com/mbe/article/22/3/547/1075922
https://www.ncbi.nlm.nih.gov/pubmed/14594704 Bioinformatics. 2003 Nov 1;19(16):2005-15. Codon adaptation index as a measure of dominating codon bias. Carbone A1, Zinovyev A, Képès F.
複製
http://www.iu.a.u-tokyo.ac.jp/~kadota/JSLAB_9_kadota.pdf 環状ゲノムの場合、どこを配列の起点として表記する かは明確にルールが決まっているわけではないが、慣例 として dnaA 遺伝子が配列の先頭となるように “ 回転 ” させておくことが多い。
LH_draft2.fa を入力として再度予備的に行った DFAST アノテーション結果においては、chromosome 上 の相補鎖側の［1436009, 1437325 bp］にdnaA がコードさ れていた［W2-2］。
具体的な手順としては、まず dnaA 遺伝子を含む領 域［1, 1437425 bp］とそれ以外の領域［1437426, 2277983 bp］を入れ替えた（回転させた）後に、相補鎖変換を行 えばよい［W4-4］。
入力は、Genbank 形 式の DFAST アノテーション結果ファイル（annotation. gbk）である。①時計の 0 時に相当する部分が複製開始点 である。一番外側から、②順鎖上および相補鎖上の CDS、
実際の複製では、①の 複製開始点から時計回りに進む順鎖側、そして反時計回り に進む相補鎖側がリーディング鎖となる。
https://github.com/haruosuz/DS4GD/blob/master/2019/CaseStudy.md GC skew
- https://kotobank.jp/word/リーディング鎖-773894 DNAの複製に際し，岡崎フラグメントで複製される方でなく，その反対の連続的に複製される側の鎖．ラギング鎖（lagging strand）の対語
- https://kotobank.jp/word/ラギング鎖-773723 リーディング鎖の対語で，二本鎖のDNAのうちの1本で，DNAの複製に際して，鎖が短い断片（岡崎フラグメント）として合成される側の鎖．
- 「ゲノム DNA は二重鎖であり、片方の鎖はセンス鎖(sense strand )、もう一方はアンチセンス鎖(antisense strand )と呼ばれる。センス鎖はワトソン鎖(Watson strand )またはプラス鎖とも呼ばれ、アンチセンス鎖はクリック鎖(Crick strand )またはマイナス鎖とも呼ばれる。」
- https://ja.wikipedia.org/wiki/センス鎖
- https://ja.wikipedia.org/wiki/アンチセンス鎖
- https://www.weblio.jp/content/マイナス鎖 ２本鎖DNAの片側の鎖をプラスとした時の反対側の鎖。一本鎖ウイルスの粒子中に取り込まれる側や転写される側の鎖をプラスとすることが多い。
https://www.ncbi.nlm.nih.gov/pubmed/12036591 Gene. 2002 May 1;289(1-2):131-9. Synonymous codon usage in Pseudomonas aeruginosa PA01. Grocock RJ1, Sharp PM. https://www.sciencedirect.com/science/article/pii/S0378111902005036?via%3Dihub This discordance appears to have arisen because Gupta and Ghosh did not correctly identify which genes are located on the leading and lagging strands. They reported finding only 39% of genes on the leading strand. This would be surprising, since most bacteria have an excess of genes on the leading strand: among nine species previously examined only one (Synechocystis) had as few as 50% of genes on the leading strand (McLean et al., 1998). In fact, we found a majority of genes (56%) on the leading strand in P. aeruginosa. It appears that Gupta and Ghosh counted all genes (whether on the leading or lagging strand) between 0 and 250 kb as ‘leading strand’, and all genes in the remainder of the chromosome as being on the ‘lagging strand’. 中略 They based their conclusion about the link between axis 1 and gene expression on this correlation, but that line of reasoning is circular, and led to quite erroneous conclusions.
https://www.ncbi.nlm.nih.gov/pubmed/10075995 Nucleic Acids Res. 1999 Apr 1;27(7):1642-9. Proteome composition and codon usage in spirochaetes: species-specific and DNA strand-specific mutational biases. Lafay B1, Lloyd AT, McLean MJ, Devine KM, Sharp PM, Wolfe KH. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC148367/pdf/271642.pdf
https://www.ncbi.nlm.nih.gov/pubmed/9724767 Proc Natl Acad Sci U S A. 1998 Sep 1;95(18):10698-703. Replicational and transcriptional selection on codon usage in Borrelia burgdorferi. McInerney JO1.
https://www.ncbi.nlm.nih.gov/pubmed/9928479 Annu Rev Genet. 1998;32:185-225. Comparative DNA analysis across diverse genomes. Karlin S1, Campbell AM, Mrázek J.
- CODON BIASES IN BACTERIAL GENOMES
- Anomalies of Ribosomal Proteins
https://www.ncbi.nlm.nih.gov/pubmed/15883368 Proc Natl Acad Sci U S A. 2005 May 17;102(20):7303-8. Epub 2005 May 9. Predicted highly expressed genes in archaeal genomes. Karlin S1, Mrázek J, Ma J, Brocchieri L.
Karlin S (2001) Trends Microbiol. "Detecting anomalous gene clusters and pathogenicity islands in diverse bacterial genomes." | pdf
http://www.cmbl.uga.edu/software/ASeqH.htm PHX/PA user guide Analysis of sequence heterogeneity by sliding window plots
http://www.cmbl.uga.edu/software/PHX-PA-guide.htm CMBL- PHX/PA user guide
https://www.ncbi.nlm.nih.gov/pubmed/11904428 Proc Natl Acad Sci U S A. 2002 Mar 19;99(6):3695-700. Metabolic efficiency and amino acid composition in the proteomes of Escherichia coli and Bacillus subtilis. Akashi H1, Gojobori T.
https://www.ncbi.nlm.nih.gov/pubmed/11965430 J Mol Evol. 2002 May;54(5):563-8. Trends in codon and amino acid usage in Thermotoga maritima. Zavala A1, Naya H, Romero H, Musto H.
https://www.ncbi.nlm.nih.gov/pubmed/9461405 Gene. 1997 Dec 31;205(1-2):309-16. Influence of genomic G+C content on average amino-acid composition of proteins from 59 bacterial species. Lobry JR1.
https://www.ncbi.nlm.nih.gov/pubmed/8065933 Nucleic Acids Res. 1994 Aug 11;22(15):3174-80. Hydrophobicity, expressivity and aromaticity are the major trends of amino-acid usage in 999 Escherichia coli chromosome-encoded genes. Lobry JR1, Gautier C.
https://www.ncbi.nlm.nih.gov/pubmed/16936139 Mol Biol Evol. 2006 Dec;23(12):2303-15. Epub 2006 Aug 25. The evolution of biased codon and amino acid usage in nematode genomes. Cutter AD1, Wasmuth JD, Blaxter ML.
https://www.ncbi.nlm.nih.gov/pubmed/14672975 Genome Res. 2004 Jan;14(1):44-53. Epub 2003 Dec 12. Mutational and selective pressures on codon and amino acid usage in Buchnera, endosymbiotic bacteria of aphids. Rispe C1, Delmotte F, van Ham RC, Moya A.
https://www.ncbi.nlm.nih.gov/pubmed/12949182 Microbiology. 2003 Sep;149(Pt 9):2585-96. Gene expression level influences amino acid usage, but not codon usage, in the tsetse fly endosymbiont Wigglesworthia.
https://www.ncbi.nlm.nih.gov/pubmed/12200484 Mol Biol Evol. 2002 Sep;19(9):1575-84. A strong effect of AT mutational bias on amino acid usage in Buchnera is mitigated at high-expression genes. Palacios C1, Wernegreen JJ.
https://www.ncbi.nlm.nih.gov/pubmed/11305938 Genome Biol. 2001;2(4):RESEARCH0010. A simple model based on mutation and selection explains trends in codon and amino-acid usage and GC composition within and across genomes. Knight RD1, Freeland SJ, Landweber LF.
https://www.ncbi.nlm.nih.gov/pubmed/11070046 Mol Biol Evol. 2000 Nov;17(11):1581-8. Nucleotide bias causes a genomewide bias in the amino acid composition of proteins. Singer GA1, Hickey DA.
https://twitter.com/AbbeAtSea Abigail LaBella (@AbbeAtSea) / Twitter
2:11 AM · Apr 23, 2021 https://twitter.com/AbbeAtSea/status/1385280013017223170 Check out my latest manuscript which uses optimal codon usage to explore yeast metabolism and ecology. One of the coolest findings it that we were able to predict growth based on codon optimization alone!
Takashi Abe
https://www.ncbi.nlm.nih.gov/pubmed/11675589 J Mol Evol. 2001 Oct-Nov;53(4-5):290-8. Codon usage and tRNA genes in eukaryotes: correlation of codon usage diversity with translation efficiency and with CG-dinucleotide usage as assessed by multivariate analysis. Kanaya S1, Yamada Y, Kinouchi M, Kudo Y, Ikemura T.
https://www.ncbi.nlm.nih.gov/pubmed/11591475 Gene. 2001 Oct 3;276(1-2):89-99. Analysis of codon usage diversity of bacterial genes with a self-organizing map (SOM): characterization of horizontally transferred genes with emphasis on the E. coli O157 genome. Kanaya S1, Kinouchi M, Abe T, Kudo Y, Yamada Y, Nishi T, Mori H, Ikemura T.
https://www.ncbi.nlm.nih.gov/pubmed/10570992 Gene. 1999 Sep 30;238(1):143-55. 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. Kanaya S1, Yamada Y, Kudo Y, Ikemura T.
1989 – 1991 https://kaken.nii.ac.jp/ja/grant/KAKENHI-PROJECT-02238105/ コドン選択
https://www.ncbi.nlm.nih.gov/pubmed/3916708 Mol Biol Evol. 1985 Jan;2(1):13-34. Codon usage and tRNA content in unicellular and multicellular organisms. Ikemura T1.
https://www.ncbi.nlm.nih.gov/pubmed/23288542 Microbiology. 2013 Mar;159(Pt 3):555-64. Evolution of optimal codon choices in the family Enterobacteriaceae. Iriarte A1, Baraibar JD, Romero H, Castro-Sowinski S, Musto H. https://www.microbiologyresearch.org/content/journal/micro/10.1099/mic.0.061952-0#tab2
- These results were confirmed by means of the estimation of the strength of selection on CU as developed by Sharp et al. (2005).
- Within-group correspondence analysis (WCA) (Charif et al., 2005; Suzuki et al., 2008) was calculated as implemented in the ADE4 package of R (Thioulouse et al., 1997).
https://www.ncbi.nlm.nih.gov/pubmed/10773076 Nucleic Acids Res. 2000 May 15;28(10):2084-90. Codon usage in Chlamydia trachomatis is the result of strand-specific mutational biases and a complex pattern of selective forces. Romero H1, Zavala A, Musto H.
http://www.life.illinois.edu/gary/programs/codon_usage.html Codon Usage Programs
https://www.youtube.com/watch?v=gzYCZ80OygU The Alien-Looking Codon Usages of Recently Acquired Genes Are Not Alien - Gary Olsen - YouTube Published on Oct 7, 2015
https://www.ncbi.nlm.nih.gov/pubmed/22128332 Proc Natl Acad Sci U S A. 2011 Dec 13;108(50):20154-9. doi: 10.1073/pnas.1109451108. Epub 2011 Nov 29. Similarity of genes horizontally acquired by Escherichia coli and Salmonella enterica is evidence of a supraspecies pangenome. Karberg KA1, Olsen GJ, Davis JJ.
https://www.ncbi.nlm.nih.gov/pubmed/20679093 Mol Biol Evol. 2011 Jan;28(1):211-21. doi: 10.1093/molbev/msq185. Epub 2010 Aug 2. Characterizing the native codon usages of a genome: an axis projection approach. Davis JJ1, Olsen GJ.
https://www.ncbi.nlm.nih.gov/pubmed/20018979 Mol Biol Evol. 2010 Apr;27(4):800-10. doi: 10.1093/molbev/msp281. Epub 2009 Dec 17. Modal codon usage: assessing the typical codon usage of a genome. Davis JJ1, Olsen GJ.
https://www.ncbi.nlm.nih.gov/pubmed/31712737 ISME J. 2020 Feb;14(2):597-608. doi: 10.1038/s41396-019-0552-3. Epub 2019 Nov 11. Unlinked rRNA genes are widespread among bacteria and archaea. Brewer TE1,2, Albertsen M3, Edwards A4, Kirkegaard RH3, Rocha EPC5, Fierer N6,7. https://www.nature.com/articles/s41396-019-0552-3
rRNA/tRNA遺伝子コピー数が多いほど、 rRNA遺伝子の複製起点からの距離が小さいほど、 高発現遺伝子の同義コドン使用バイアスが強い（方言がきつい）ほど、 原核生物の最小倍加時間が小さい（増殖速度が速い）
https://www.ncbi.nlm.nih.gov/pubmed/20090831 PLoS Genet. 2010 Jan 15;6(1):e1000808. doi: 10.1371/journal.pgen.1000808. The systemic imprint of growth and its uses in ecological (meta)genomics. Vieira-Silva S1, Rocha EP. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2797632/
- Figure 1 Genomic signatures correlated to minimum generation time (d) for 214 prokaryotes. Correlation between d and (A) the number of rRNA operons in the genome, (B) the relative distance from the origin of replication to rRNA genes (excluding species with no retrievable origin), 0.5 corresponds to half the replicon, (C,D) codon usage bias indices ΔENC′ [35] and S [46]. Spearman correlations are given (ρ) with all p-values<0.0001. Dashed lines represent the trend of the correlation.
- As expected, we found an increase in copy number of rRNA (Figure 1) and tRNA genes (Figure S1) with decreasing minimal generation times (ρ = −0.59 and ρ = −0.51, all p-value<0.0001).
- As described above, gene dosage of highly expressed genes can be increased transiently when these genes are located near the origin of replication in fast growing cells. Indeed, a positive correlation was found between minimum generation time and the relative distance to the origin of replication of rRNA genes (ρ = 0.36, Figure 1), RNA polymerase genes (ρ = 0.42), ribosomal proteins coding genes (ρ = 0.42), tRNA (ρ = 0.35) and ubi-tRNA (ρ = 0.41) genes (Figure S2) (all p-values<0.0001).
- Finally, two previously proposed indices of codon usage bias in highly expressed genes ΔENC′ [35] and S [46] correlate negatively with d (respectively, ρ = −0.64 and ρ = −0.54, p-value<0.0001, Figure 1).
- Following a previous work [35], we extracted from primary literature 214 minimal generation times (d) of species of bacteria and archaea (Table S1).
- We extracted from primary literature the minimal generation times (d) for the 214 species of bacteria and archaea (Table S1).
- Table S1 List of the 214 genomes composing our dataset and their characteristics. Generation times were retrieved from the literature. We defined the minimum generation time (Column “d”) as the smallest value reported (Column “d reference”) for one species. For very few bacteria the generation times for closely related species were used. The optimum growth temperature of the species (Column “OGT”) was retrieved from DSMZ database.
Species name Ori OGT d (h) d reference
Clostridium acetobutylicum ATCC824 1808 37 0.58 [47]
Clostridium perfringens 13 1784 37 0.2 [48]
Clostridium tetani E88 50965 37 0.5 [49]
Escherichia coli MG1655 3923657 37 0.35 [64]
Geobacter sulfurreducens PCA 1368 30 6 [66]
Mycobacterium leprae 1567 37 240 [101]
Salmonella typhimurium LT2 4083788 37 0.4 [64]
Shewanella oneidensis MR-1 6419 30 0.66 [138]
- The resulting ΔENC′ and S values were then subject to principal components analysis, of which the first component (Fa) was compared with the one obtained from the whole genome.
- Codon usage bias is the best determinant of minimum generation time
- Figure 7 Average predicted minimum generation time for 3 environmental metagenomes.
- Figure 8 Average predicted minimum generation time for the gut metagenomes of humans of different age groups.
https://www.ncbi.nlm.nih.gov/pubmed/15479947 Genome Res. 2004 Nov;14(11):2279-86. Epub 2004 Oct 12. Codon usage bias from tRNA's point of view: redundancy, specialization, and efficient decoding for translation optimization. Rocha EP1.
- We show that as minimal generation times get shorter, the genomes contain more tRNA genes, but fewer anticodon species.
- It also provides new evidence that a selective force for the optimization of the translation machinery is the maximization of growth. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC525687/
- Genome and tRNA data One hundred and two genomes, corresponding to 102 bacterial species, were retrieved from GenBank (see Supplemental Table 1 for a comprehensive listing). Minimal generation times were taken from the literature or obtained by personal communication with researchers in the field. https://genome.cshlp.org/content/14/11/2279.long https://genome.cshlp.org/content/14/11/2279/suppl/DC1 Supplemental Research Data https://genome.cshlp.org/content/suppl/2004/10/18/gr.2896904.DC1/tableA1.pdf Table A1- Data table with major results. the growth rate class (μ). the optimal doubling time (1/μ).
Clostridium perfringens 13 F 0.2
Escherichia coli K12 F 0.35
Geobacter sulfurreducens PCA S 6
Mycobacterium leprae S 12
Salmonella typhimurium LT2 F 0.4
Shewanella oneidensis MR-1 F 2
2011 https://www.era.lib.ed.ac.uk/handle/1842/4893 Codon usage bias in Archaea Emery, Laura R.
https://www.ncbi.nlm.nih.gov/pubmed/20308095 Philos Trans R Soc Lond B Biol Sci. 2010 Apr 27;365(1544):1203-12. doi: 10.1098/rstb.2009.0305. Forces that influence the evolution of codon bias. Sharp PM1, Emery LR, Zeng K. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2871821/
- Here we show that the strength of selected codon usage bias is highly correlated with bacterial growth rate, suggesting that selection has favoured translational efficiency.
- For example, C. perfringens has 10 rRNA operons and 96 tRNA genes and can replicate in only 7 min under ideal conditions (Labbe & Huang 1995).
- To test this association with growth rate, we have used minimum generation time data for 76 of these 80 species, drawn from the compilations made by E.P.C. Rocha (Rocha 2004; Coutourier & Rocha 2006).
https://www.ncbi.nlm.nih.gov/pubmed/17038449 Mol Biol Evol. 2007 Jan;24(1):10-2. Epub 2006 Oct 12. Predicting gene expression level from codon usage bias. Henry I, Sharp PM. https://academic.oup.com/mbe/article/24/1/10/1070854
- We outline a simple approach, first to check whether a genome shows evidence of selected codon usage bias and then to assess the strength of bias in genes as a guide to their likely expression level; we illustrate this with an analysis of Shewanella oneidensis.
- As an example, we have analyzed Shewanella oneidensis, a member of the gamma proteobacteria (Heidelberg et al. 2002). Eighteen codons, for 15 amino acids, occur at significantly higher frequencies in highly expressed genes than in the genome as a whole (see Supplementary Material online). Importantly, these codons do not reflect any simple compositional bias, such as G + U richness due to location of the highly expressed genes on the leading strand of replication. Rather, they include many codons which would be expected to be optimal, either because they are decoded by the most abundant tRNA species (e.g., 6 of the 9 Arg tRNA genes match CGU) or because they are perfectly complementary to the only tRNA species for the amino acid (e.g., UUC, UAC, CAC, AAC, GAC, and GAA).
細菌の増殖速度、rRNAオペロン数、tRNA遺伝子コピー数、（翻訳の効率と正確度に関連する）高発現遺伝子の同義コドン使用バイアスの相関 https://www.ncbi.nlm.nih.gov/pubmed/15728743 Nucleic Acids Res. 2005 Feb 23;33(4):1141-53. Print 2005. Variation in the strength of selected codon usage bias among bacteria. Sharp PM1, Bailes E, Grocock RJ, Peden JF, Sockett RE. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC549432/
- These results are consistent with the hypothesis that species exposed to selection for rapid growth have more rRNA operons, more tRNA genes and more strongly selected codon usage bias. For example, Clostridium perfringens, the species with the highest value of S, can have a generation time as short as 7 min.
- a set of 40 genes expected to be expressed constitutively at very high levels. This set included the genes encoding translation elongation factors Tu (tufA), Ts (tsf) and G (fusA), and 37 of the larger ribosomal proteins (encoded by genes rplA-rplF, rplI-rplT and rpsB-rpsT).
- Inter-specific variation in bacterial growth rate appears to be positively correlated with the number of rRNA operons (42).
- Information regarding the growth rate of bacteria in the wild is sparse, and so we have used the number of rRNA operons as a (very approximate) guide to the growth rate of species. Remarkably, C.perfringens, the species with the highest S-value (2.65) and 10 rRNA operons, can grow with a generation time under 7 min in specific laboratory conditions (55).
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC549432/table/tbl1/ Table 1 The 80 bacterial genome sequences analysed
Species codea Gene numbersb GC contentc Sd Randome Nf Accession nosg Species
rRNA tRNA ORF i ii iii
Gamma proteobacteria
Sheone 9 100 4630 46 45 37 1.377 (0.313/−0.275) 983 AE014299 Shewenella oneidensis
https://www.ncbi.nlm.nih.gov/pubmed/10784043 Microbiology. 2000 Apr;146 ( Pt 4):851-60. Absence of translationally selected synonymous codon usage bias in Helicobacter pylori. Lafay B1, Atherton JC, Sharp PM.
https://www.ncbi.nlm.nih.gov/pubmed/8662004 J Mol Evol. 1996 May;42(5):525-36. Codon usage and base composition in Rickettsia prowazekii. Andersson SG1, Sharp PM.
https://www.ncbi.nlm.nih.gov/pubmed/3118331 Nucleic Acids Res. 1987 Oct 12;15(19):8023-40. Synonymous codon usage in Bacillus subtilis reflects both translational selection and mutational biases. Shields DC1, Sharp PM.
https://www.ncbi.nlm.nih.gov/pubmed/3526280 Nucleic Acids Res. 1986 Jul 11;14(13):5125-43. Codon usage in yeast: cluster analysis clearly differentiates highly and lowly expressed genes. Sharp PM, Tuohy TM, Mosurski KR. Cluster analysis on relative synonymous codon usage revealed two distinct groups of genes.
https://www.ncbi.nlm.nih.gov/pubmed/27915291 Nucleic Acids Res. 2016 Dec 1;44(21):10074-10090. Epub 2016 Oct 24. The landscape of microbial phenotypic traits and associated genes. Brbić M1, Piškorec M1, Vidulin V1, Kriško A2, Šmuc T1, Supek F3,4,5. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5137458/
- INTRODUCTION Statistical associations of genes to phenotypes can implicate certain proteins or pathways, providing insight into the mechanistic basis of phenotypic traits (3,4), as demonstrated for adaptation to stress (5,6), host-association (7,8), pathogenesis (9,10), drug resistance (11,12) and relevance to biotechnological applications (13,14).
- RESULTS Systematic inference of phenotypes from codon adaptation Such genomic signatures of translation efficiency within certain gene families have been used to infer the adaptive value of individual genes to various environmental niches (6) and we thus hypothesized that the overall pattern of codon adaptation across many genes of an organism can predict its phenotype (Materials and Methods).
- DISCUSSION Further generalizing this well-known gene content-based methodology, our work demonstrates how other comparative genomics approaches normally used to predict gene function—here, synteny patterns (58,59) and codon adaptation (6,70)—can be efficiently repurposed into phenotype predictors.
https://www.ncbi.nlm.nih.gov/pubmed/26538122 J Mol Evol. 2016 Jan;82(1):65-73. doi: 10.1007/s00239-015-9714-8. Epub 2015 Nov 4. The Code of Silence: Widespread Associations Between Synonymous Codon Biases and Gene Function. Supek F1,2,3.
コドン適応の変化は（過酸化水素、熱、高塩分）ストレス耐性を増強する。好気性菌で複数遺伝子のコドン適応（翻訳効率）変化が、鉄とNAD(P)Hの量を調節し、酸化ストレス耐性を付与する証拠 https://www.ncbi.nlm.nih.gov/pubmed/24580753 Genome Biol. 2014 Mar 3;15(3):R44. doi: 10.1186/gb-2014-15-3-r44. Inferring gene function from evolutionary change in signatures of translation efficiency. Krisko A, Copic T, Gabaldón T, Lehner B, Supek F. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4054840/
https://www.ncbi.nlm.nih.gov/pubmed/26451481 Cell. 2015 Oct 8;163(2):292-300. doi: 10.1016/j.cell.2015.09.041. Pausing on Polyribosomes: Make Way for Elongation in Translational Control. Richter JD1, Coller J2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4600128/
https://www.ncbi.nlm.nih.gov/pubmed/25768907 Cell. 2015 Mar 12;160(6):1111-24. doi: 10.1016/j.cell.2015.02.029. Codon optimality is a major determinant of mRNA stability. Presnyak V1, Alhusaini N1, Chen YH1, Martin S1, Morris N2, Kline N1, Olson S3, Weinberg D4, Baker KE1, Graveley BR3, Coller J5.
人工環境で同定された細菌のゲノムの特徴（ゲノムサイズ、GC含量、DNA複製鎖間の非対称度、コドン使用バイアス） https://www.ncbi.nlm.nih.gov/pubmed/30691394 BMC Genomics. 2019 Jan 28;20(1):92. doi: 10.1186/s12864-018-5389-z. Comparative genomics of Bacteria commonly identified in the built environment. Merino N1,2, Zhang S3,4, Tomita M5,6, Suzuki H7,8. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6350394/
葉緑体のコドン適応 http://www.ncbi.nlm.nih.gov/pubmed/27196606 PLoS One. 2016 May 19;11(5):e0154306. Codon Adaptation of Plastid Genes. Suzuki H, Morton BR. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4873144/
ミドリゾウリムシのトランスクリプトーム（RNA-seq）データを用いて、クロレラ共生下で発現低下した遺伝子群では発現量とアミノ酸・同義コドン使用との間の相関が高いこと（ミドリゾウリムシとクロレラの共生に関わる自然選択？）を明らかにした。 http://www.ncbi.nlm.nih.gov/pubmed/26341535 FEBS Lett. 2015 Oct 7;589(20 Pt B):3113-8. Analysis of amino acid and codon usage in Paramecium bursaria. Dohra H, Fujishima M, Suzuki H. https://febs.onlinelibrary.wiley.com/doi/full/10.1016/j.febslet.2015.08.033
同義コドン使用の多様性 http://www.ncbi.nlm.nih.gov/pubmed/19480720 BMC Bioinformatics. 2009 Jun 1;10:167. Measure of synonymous codon usage diversity among genes in bacteria. Suzuki H1, Saito R, Tomita M. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2697163/
対応分析 https://www.ncbi.nlm.nih.gov/pubmed/18940873 DNA Res. 2008 Dec;15(6):357-65. doi: 10.1093/dnares/dsn028. Epub 2008 Oct 21. Comparison of correspondence analysis methods for synonymous codon usage in bacteria. Suzuki H1, Brown CJ, Forney LJ, Top EM. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2608848/
- Thirdly, genes expressed at high levels in fast-growing bacteria tend to preferentially use translationally optimal codons that are recognized by the most abundant tRNAs. This presumably reflects natural selection for synonymous codons that are translated more efficiently and accurately.7,8
- Secondly, to analyze the correlation between scores of each of the three axes [Equation (4)] and levels of gene expression (Expression), we tested for the distribution of the axis scores for 40 genes expected to be expressed constitutively at high levels.10 This set included the genes encoding translation elongation factors Tu (tuf), Ts (tsf) and G (fus), and 37 of the larger ribosomal proteins (encoded by genes rplA-rplF, rplI-rplT, and rpsB-rpsT). In each axis, the score for each gene was standardized by subtracting the mean and dividing by the standard deviation of scores for all protein genes. For each axis, Expression was detected as the main source of variation among genes on the axis when the mean absolute standard score for the 40 highly expressed genes was >1.644854 (an interval in which theoretically only 5% of all protein genes are included).
- The third feature, Expression, was detected as a major source of synonymous codon usage variation among genes in C. trachomatis D/UW-3/CX, C. perfringens 13, E. coli K12 MG1655 and H. influenzae Rd KW20, which is consistent with previous findings (Table 2).
http://www.ncbi.nlm.nih.gov/pubmed/18350114 EURASIP J Bioinform Syst Biol. 2007:61374. Variation in the correlation of G + C composition with synonymous codon usage bias among bacteria. Suzuki H1, Saito R, Tomita M.
主成分分析 http://www.ncbi.nlm.nih.gov/pubmed/16289058 FEBS Lett. 2005 Nov 21;579(28):6499-504. A problem in multivariate analysis of codon usage data and a possible solution. Suzuki H1, Saito R, Tomita M.
同義コドン使用の均等度 http://www.ncbi.nlm.nih.gov/pubmed/15194186 Gene. 2004 Jun 23;335:19-23. The 'weighted sum of relative entropy': a new index for synonymous codon usage bias. Suzuki H1, Saito R, Tomita M.
コドン - Haruo Suzuki / Bioinformatics
論文 - Haruo Suzuki / Bioinformatics
http://www.kri.sfc.keio.ac.jp/report/mori/2005/b-13.html 研究課題名 バイオインフォマティックスによる遺伝暗号解析
https://ja.wikipedia.org/wiki/合成生物学 安価で大規模にコドンを変化させて遺伝子発現を改善したり、新たなアミノ酸を導入する手法などによる研究がなされている[3]。
https://wired.jp/2018/01/06/semi-synthetic-organism/ 6つのコードをもつ「半合成生物」で、自然界に存在しないタンパク質を生み出すことに成功：研究結果｜WIRED.jp
https://www.sciencedirect.com/science/article/pii/S2588933818300104 Synthetic biology: Recent progress, biosafety and biosecurity concerns, and possible solutions - ScienceDirect
2.1.
A “codon harmonization” algorithm has been devised to improve functional protein expression.23
2.2.
For example, the replication and infectivity of poliovirus can be reduced through genome scale changes in codon sets in the genome; this approach makes the poliovirus vaccines safer.26
4.2.
In addition, a strategy focusing on stop codon suppression has been proposed because three stop codons are responsible for terminating protein biosynthesis in organisms. When the function of a stop codon is suppressed or altered by introducing a new engineered aminoacyl-tRNA synthetase::tRNAs pairs into the cells, it will force the cell to use several noncanonical amino acid codes as stop codons, such as l-4,4′-biophenylalanine. A redesigned essential enzyme named “bipA aminoacyl-tRNA synthetase (bipARS)/tRNAbipA system” has been constructed in E. coli strain C321.ΔA, and the UAG stop codon was assigned to a nonstandard amino acid.69
2018-12-21 https://www.labinnew.net/column3_artificial_gene/ 連載 西村尚子の生命科学探訪③人工塩基でコドンを拡張し、細胞に新規タンパク質を作らせる – Lab'in New
https://seikagaku.jbsoc.or.jp/10.14952/SEIKAGAKU.2015.870101/data/index.html DNAの文字を増やす合成生物学—Xenobiologyに向けて
http://tenure5.vbl.okayama-u.ac.jp/HM_blog/?s=コドン コドン | 酵母とシステムバイオロジー
- 2015-03-20 コドンの最適度はmRNAの安定性を決める主要な要因である。
- 2011-08-31 合成遺伝子で「コドンの最適化」をどう行なうのか？
2017/1/18 http://yfuruta.sakura.ne.jp/blog/?p=824 コドン最適化
2013年 第11号 https://www.sbj.or.jp/wp-content/uploads/file/sbj/9111/9111_biomedia_2(1).pdf コドンが決め手!?組換えタンパク質の高発現法 今中 洋行
http://www.genscript.jp/codon-opt.html Codon Optimization - Increase Protein Expression http://www.genscript.jp/high-throughput-gene-synthesis.html コドン最適化無料：独自のOptimumGene™コドン最適化ツールを無料で提供します。
https://www.nig.ac.jp/museum/evolution/04.html 遺伝暗号(コドン）使用の種による多様性
2.単細胞微生物のコドン選択の生物種による方言; 大腸菌とサルモネラ菌ならびに酵母遺伝子に見られるコドン選択の偏りを例に
３．コドン使用とtRNA量との関係、遺伝子工学との係り タンパク質生産量の高い遺伝子ほどコドン選択の方言がきつく、生産量が下がるにつれて、同質の方言を用いながらも、方言の程度が緩くなることを指摘しました。
４．コドン使用とゲノム解析との係り 単細胞微生物の場合、各遺伝子のコドン使用パターンが、そのタンパク質の細胞内存在量、すなわち生産量と関係しています。各遺伝子がどの程度にtRNA量に適合した同義コドンを使用しているのかを定量化することで、タンパク質生産量を推定することが可能になっています(11-16,20,21)。タンパク質の細胞内における存在量は、その機能を推定する上で重要な知見であり、ゲノム解析においてこの面でも重要な貢献が期待できます。
浜島 聖文，金井 昭夫 著 - 2014 http://www.jbsoc.or.jp/seika/wp-content/uploads/2015/03/86-04-10.pdf 普遍遺伝暗号表に従わない tRNA
https://twitter.com/yuifu/status/210003682958389252 Haruka Ozaki on Twitter: "バイオインフォだけでCell（実験はtRNAマイクロアレイだけ）．真核生物，真正細菌，古細菌の500種のゲノムでtRNA遺伝子の数とコドン使用頻度の相関を確認．tRNA修飾酵素によるペア拡大を考慮したのが新規？ http://t.co/fialW5zx #Everydayペーパー" 9:42 AM - 5 Jun 2012
2010-08-25 http://d.hatena.ne.jp/morimori-08/20100825/1282745255 コドンバイアスはタンパク質のフォールディングのエラーを少なくする
蛋白質核酸酵素 42(11), 1815-1827, 1997-08 https://ci.nii.ac.jp/naid/40002328271/ 多義語コドンの発見--複数のアミノ酸と結合するファジ-なtRNA
本
Multivariate Analyses of Codon Usage Biases - 1st Edition https://www.sciencedirect.com/book/9781785482960/multivariate-analyses-of-codon-usage-biases https://www.elsevier.com/books/multivariate-analyses-of-codon-usage-biases/lobry/978-1-78548-296-0 http://www.iste.co.uk/book.php?id=1403
http://www.oxfordscholarship.com/view/10.1093/acprof:osobl/9780199601165.001.0001/acprof-9780199601165 Codon Evolution: Mechanisms and Models - Oxford Scholarship
https://www.oupjapan.co.jp/ja/products/detail/12976 13. Measuring Codon Usage Bias
https://search.lib.keio.ac.jp/permalink/81SOKEI_KEIO/188bto4/alma9926525584304034 Codon evolution mechanisms and models / [electronic resource] : edited by Gina M. Cannarozzi, Adrian Schneider. Cannarozzi, Gina. Schneider, Adrian. Oxford : Oxford University Press, 2012. 2012
http://bookclub.kodansha.co.jp/product?item=0000194810 『分子からみた生物進化 ＤＮＡが明かす生物の歴史』（宮田 隆）：ブルーバックス｜講談社BOOK倶楽部
第５章 分子進化の保守性
p. 98
遺伝暗号も変わり得る
大勢を壊すことなく、中立的な抜け道でなければならない。
p. 100
進化の過程で、G+CからA+Tへの偏った塩基置換がおきたためと推定される。その過程で、終止コドンUGAは別の終止コドンUAAへと中立的に置き換わっていき、
https://www.kyoritsu-pub.co.jp/bookdetail/9784320054752 遺伝暗号の起源と進化 / 大澤 省三 著 渡辺 公綱 上田 卓也 大濱 武 訳 | 共立出版
https://www.iwanami.co.jp/book/b267838.html https://blog.goo.ne.jp/eldiaquemequieras/e/d89929b19c493306b7cda716deac7140 生物進化を考える 木村資生 著
第8章 中立説と分子進化
4 中立進化に関連した他の話題
同義コドンの不均一使用
例外的な遺伝暗号
中立説による説明
対応分析
https://home.hiroshima-u.ac.jp/naka/wiki/wiki.cgi?Matrix%A4%C8%A4%B7%A4%C6%C6%C0%A4%E9%A4%EC%A4%BF%A5%C7%A1%BC%A5%BF%A4%CE%CA%AC%C0%CF%A4%CB%A4%C4%A4%A4%A4%C6 Matrixとして得られたデータの分析について - データを解析する 主成分分析、特異値分解 (SVD)、対応分析 「バイオインフォマティクスの基礎」 斎藤 輪太郎 著 数理科学SGCライブラリ-41 に、コドンバイアスの分析に適用した例が詳しく解説されている。R 言語による、対応分析を行うプログラムが111ページに掲載されている。
https://haruosuz.hatenadiary.org/entry/20090605 コドン使用データの対応分析
https://pubmed.ncbi.nlm.nih.gov/15374859/ Bioinformatics . 2005 Feb 15;21(4):545-7. doi: 10.1093/bioinformatics/bti037. Epub 2004 Sep 16. Online synonymous codon usage analyses with the ade4 and seqinR packages D Charif 1, J Thioulouse, J R Lobry, G Perrière https://academic.oup.com/bioinformatics/article/21/4/545/203393