{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T04:19:01Z","timestamp":1777522741303,"version":"3.51.4"},"reference-count":27,"publisher":"SAGE Publications","issue":"8","license":[{"start":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T00:00:00Z","timestamp":1659312000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational Biology"],"published-print":{"date-parts":[[2022,8,1]]},"abstract":"<jats:p>Tissue specificity of gene expression sheds light on the tissue-selective manifestation of hereditary disease despite the same DNA across all tissues. The evolutionary path of such tissue specificity provides essential information about the tissue-specific function of genes and the validity of disease animal models. With recent improvements of the sequencing technology, more and more large-scale transcriptomics studies have been conducted among different species across multiple tissues. In this study, we exploit existing transcriptomics resources of humans, cynomolgus macaques, rats, mice, and dogs across 13 tissues. We find that although tissue specificity of homologous gene expression is largely well conserved across species, a total of 380 genes shift or are in the process of shifting their tissue specificity. The tissue-specificity-shifting genes are less conserved than those preserving their tissue specificity or housekeeping genes. Interestingly, tissue-specificity-shifting genes tend to be less conserved at the third codon positions, likely due to their relaxed synonymous codon usage bias. Moreover, compared with genes, cassette exons are more likely to shift their tissue specificity of splicing across the five species.<\/jats:p>","DOI":"10.1089\/cmb.2021.0592","type":"journal-article","created":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T11:56:06Z","timestamp":1656676566000},"page":"880-891","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":15,"title":["Tissue Specificity of Gene Expression Evolves Across Mammal Species"],"prefix":"10.1177","volume":"29","author":[{"given":"Wei","family":"Jiang","sequence":"first","affiliation":[{"name":"Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California, USA."}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6164-4553","authenticated-orcid":false,"given":"Liang","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California, USA."}]}],"member":"179","published-online":{"date-parts":[[2022,8,10]]},"reference":[{"key":"e_1_3_4_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-2836(05)80360-2"},{"key":"e_1_3_4_3_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1230612"},{"key":"e_1_3_4_4_1","first-page":"e1003632","article-title":"Comparative analysis of human tissue interactomes reveals factors leading to tissue-specific manifestation of hereditary diseases. PLoS Comput","volume":"10","author":"Barshir R.","year":"2014","unstructured":"Barshir R., , Shwartz O., , Smoly I.Y., et al. 2014. Comparative analysis of human tissue interactomes reveals factors leading to tissue-specific manifestation of hereditary diseases. PLoS Comput. Biol. 10, e1003632.","journal-title":"Biol"},{"key":"e_1_3_4_5_1","first-page":"7323508","article-title":"Comparative analysis and classification of cassette exons and constitutive exons. Biomed. Res","volume":"2017","author":"Cui Y.","year":"2017","unstructured":"Cui Y., , Cai M., , and Stanley H.E. 2017. Comparative analysis and classification of cassette exons and constitutive exons. Biomed. Res. Int. 2017, 7323508.","journal-title":"Int"},{"key":"e_1_3_4_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bts635"},{"key":"e_1_3_4_7_1","first-page":"4459","article-title":"Amalgamated cross-species transcriptomes reveal organ-specific propensity in gene expression evolution. Nat","volume":"11","author":"Fukushima K.","year":"2020","unstructured":"Fukushima K., , and Pollock D.D. 2020. Amalgamated cross-species transcriptomes reveal organ-specific propensity in gene expression evolution. Nat. Commun. 11, 4459.","journal-title":"Commun"},{"key":"e_1_3_4_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.2653"},{"key":"e_1_3_4_9_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2164-13-163"},{"key":"e_1_3_4_10_1","first-page":"35241","article-title":"Tissue specificity of Human Disease Module. Sci","volume":"6","author":"Kitsak M.","year":"2016","unstructured":"Kitsak M., , Sharma A., , Menche J., et al. 2016. Tissue specificity of Human Disease Module. Sci. Rep. 6, 35241.","journal-title":"Rep"},{"key":"e_1_3_4_11_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1405528111"},{"key":"e_1_3_4_12_1","first-page":"205","article-title":"A benchmark of gene expression tissue-specificity metrics","volume":"18","author":"Kryuchkova-Mostacci N.","year":"2017","unstructured":"Kryuchkova-Mostacci N., , and Robinson-Rechavi M. 2017. A benchmark of gene expression tissue-specificity metrics. Brief Bioinform. 18, 205\u2013214","journal-title":"Brief Bioinform"},{"key":"e_1_3_4_13_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0810772105"},{"key":"e_1_3_4_14_1","doi-asserted-by":"publisher","DOI":"10.1093\/molbev\/msj054"},{"key":"e_1_3_4_15_1","first-page":"1184","volume":"22","author":"Marquez Y.","year":"2012","unstructured":"Marquez Y., , Brown J.W., , Simpson C., et al. 2012. Transcriptome survey reveals increased complexity of the alternative splicing landscape in Arabidopsis. Genome Res., 22:1184\u20131195.","journal-title":"Transcriptome survey reveals increased complexity of the alternative splicing landscape in Arabidopsis. Genome Res."},{"key":"e_1_3_4_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.gde.2009.10.006"},{"key":"e_1_3_4_17_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aaw7317"},{"key":"e_1_3_4_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.259"},{"key":"e_1_3_4_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.4197"},{"key":"e_1_3_4_20_1","doi-asserted-by":"publisher","DOI":"10.1093\/sysbio\/syv042"},{"key":"e_1_3_4_21_1","doi-asserted-by":"publisher","DOI":"10.1002\/jez.b.22618"},{"key":"e_1_3_4_22_1","first-page":"16","article-title":"Deciphering targeting rules of splicing modulator compounds: Case of TG003. BMC Mol","volume":"16","author":"Sakuma M.","year":"2015","unstructured":"Sakuma M., , Iida K., , and Hagiwara M. 2015. Deciphering targeting rules of splicing modulator compounds: Case of TG003. BMC Mol. Biol. 16, 16.","journal-title":"Biol"},{"key":"e_1_3_4_23_1","first-page":"175","article-title":"Cross-species analysis of single-cell transcriptomic data. Front Cell Dev","volume":"7","author":"Shafer M.E.R.","year":"2019","unstructured":"Shafer M.E.R. 2019. Cross-species analysis of single-cell transcriptomic data. Front Cell Dev. Biol. 7, 175.","journal-title":"Biol"},{"key":"e_1_3_4_24_1","first-page":"862","article-title":"Transcriptomics resources of human tissues and organs. Mol. Syst","volume":"12","author":"Uhl\u00e9n M.","year":"2016","unstructured":"Uhl\u00e9n M., , Hallstr\u00f6m B.M., , Lindskog C., et al. 2016. Transcriptomics resources of human tissues and organs. Mol. Syst. Biol. 12, 862.","journal-title":"Biol"},{"key":"e_1_3_4_25_1","doi-asserted-by":"publisher","DOI":"10.1101\/gr.1924004"},{"key":"e_1_3_4_26_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrg3095"},{"key":"e_1_3_4_27_1","first-page":"3230","article-title":"A rat RNA-Seq transcriptomic BodyMap across 11 organs and 4 developmental stages. Nat","volume":"5","author":"Yu Y.","year":"2014","unstructured":"Yu Y., , Fuscoe J.C., , Zhao C., et al. 2014. A rat RNA-Seq transcriptomic BodyMap across 11 organs and 4 developmental stages. Nat. Commun. 5, 3230.","journal-title":"Commun"},{"key":"e_1_3_4_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tig.2008.08.004"}],"container-title":["Journal of Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1089\/cmb.2021.0592","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1089\/cmb.2021.0592","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1089\/cmb.2021.0592","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T16:18:32Z","timestamp":1777393112000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1089\/cmb.2021.0592"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,1]]},"references-count":27,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2022,8,1]]}},"alternative-id":["10.1089\/cmb.2021.0592"],"URL":"https:\/\/doi.org\/10.1089\/cmb.2021.0592","relation":{},"ISSN":["1066-5277","1557-8666"],"issn-type":[{"value":"1066-5277","type":"print"},{"value":"1557-8666","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,1]]}}}