{"doi":"10.1073/pnas.1719375115","title":"Codon usage of highly expressed genes affects proteome-wide translation efficiency","abstract":"<jats:title>Significance</jats:title>\n                  <jats:p>\n                    Highly expressed genes are encoded by codons that correspond to abundant tRNAs, a phenomenon thought to ensure high expression levels. An alternative interpretation is that highly expressed genes are codon-biased to support efficient translation of the rest of the proteome. Until recently, it was impossible to examine these alternatives, since statistical analyses provided correlations but not causal mechanistic explanations. Massive genome engineering now allows recoding genes and examining effects on cellular physiology and protein translation. We engineered the\n                    <jats:italic>Escherichia coli</jats:italic>\n                    genome by changing the codon bias of highly expressed genes. The perturbation affected the translation of other genes, depending on their codon demand, suggesting that codon bias of highly expressed genes ensures translation integrity of the rest of the proteome.\n                  </jats:p>","journal":"Proceedings of the National Academy of Sciences","year":2018,"id":613977,"datarank":0.8515130703402424,"base_score":5.676753802268282,"endowment":5.676753802268282,"self_citation_contribution":0.8515130703402424,"citation_network_contribution":0.0,"self_endowment_contribution":0.8515130703402424,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":291,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":245708,"name":"Marc J. Lajoie","orcid":"0000-0002-0477-5511","position":1,"is_corresponding":false},{"id":1581965,"name":"Christopher J. Gregg","orcid":null,"position":2,"is_corresponding":false},{"id":1581966,"name":"Gil Hornung","orcid":null,"position":3,"is_corresponding":false},{"id":275622,"name":"George M. Church","orcid":"0000-0003-3535-2076","position":4,"is_corresponding":false},{"id":839314,"name":"Yitzhak Pilpel","orcid":"0000-0003-3200-9344","position":5,"is_corresponding":false},{"id":1393649,"name":"Idan Frumkin","orcid":"0000-0003-4944-3374","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Codon usage of highly expressed genes affects proteome-wide translation efficiency","abstract":"<jats:title>Significance</jats:title>\n                  <jats:p>\n                    Highly expressed genes are encoded by codons that correspond to abundant tRNAs, a phenomenon thought to ensure high expression levels. An alternative interpretation is that highly expressed genes are codon-biased to support efficient translation of the rest of the proteome. Until recently, it was impossible to examine these alternatives, since statistical analyses provided correlations but not causal mechanistic explanations. Massive genome engineering now allows recoding genes and examining effects on cellular physiology and protein translation. We engineered the\n                    <jats:italic>Escherichia coli</jats:italic>\n                    genome by changing the codon bias of highly expressed genes. The perturbation affected the translation of other genes, depending on their codon demand, suggesting that codon bias of highly expressed genes ensures translation integrity of the rest of the proteome.\n                  </jats:p>","is_dataset_classified":null,"base_score":5.676753802268282,"endowment":5.676753802268282,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"29735666","pmcid":"PMC6003480","openalex_id":"https://openalex.org/W2803275252","authors":[],"funders":[{"funder_name":"Minerva Foundation","grant_id":"0","title":null}],"total_grants":1,"fwci":10.3105,"citation_percentile":0.99032636,"influential_citations":0,"citation_trend":[{"year":2018,"count":5},{"year":2019,"count":23},{"year":2020,"count":34},{"year":2021,"count":53},{"year":2022,"count":41},{"year":2023,"count":43},{"year":2024,"count":45},{"year":2025,"count":25},{"year":2026,"count":22}],"oa_status":"bronze","license":"http://www.pnas.org/site/aboutpnas/licenses.xhtml","oa_locations":[{"url":"https://www.pnas.org/content/pnas/115/21/E4940.full.pdf","host_type":"journal"},{"url":"https://www.pnas.org/content/pnas/115/21/E4940.full.pdf","host_type":"publisher"},{"url":"http://www.pnas.org/syndication/doi/10.1073/pnas.1719375115","host_type":"publisher"},{"url":"https://pnas.org/doi/pdf/10.1073/pnas.1719375115","host_type":"publisher"},{"url":"https://doi.org/10.1073/pnas.1719375115","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/29735666","host_type":"repository"},{"url":"http://europepmc.org/pmc/articles/PMC6003480","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6003480","host_type":"repository"}],"fields_of_study":["RNA and protein synthesis mechanisms","RNA modifications and cancer","Genomics and Phylogenetic Studies"],"mesh_terms":["Codon","Escherichia coli","RNA, Transfer","Protein Biosynthesis","Open Reading Frames","Evolution, Molecular","Proteome","Escherichia coli Proteins","Transcriptome"],"keywords":["Codon usage bias","Gene","Biology","Genetics","Synonymous substitution","Genetic code","Translational efficiency","Silent mutation","Translation (biology)","Transfer RNA","Open reading frame","Coding region","Start codon","Genome","Stop codon","Proteome","Computational biology","Phenotype","RNA","Messenger RNA","Peptide sequence","tRNA","Translation Efficiency","Genome Engineering","Codon Usage Evolution","Codon-to-trna Balance"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[{"name":"gen"}],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-02T10:00:00.398319Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}