{"title":"非整倍体的隐性成本：来自酵母的新见解","authors":"Yuerong Wang, Xian Fu, Yue Shen","doi":"10.1016/j.xgen.2024.100673","DOIUrl":null,"url":null,"abstract":"<p><p>The molecular mechanisms underlying the paradoxical effects<sup>1</sup> of aneuploidy are still not completely understood. In this issue, Rojas et al.<sup>2</sup> systematically analyzed the associated costs of aneuploidy and the molecular drivers involved, which revealed that aneuploidy stress is primarily driven by the cumulative effects of genes per chromosome. Notably, gene length was predicted as the most significant indicator of aneuploidy toxicity by machine learning.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":null,"pages":null},"PeriodicalIF":11.1000,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The hidden costs of aneuploidy: New insights from yeast.\",\"authors\":\"Yuerong Wang, Xian Fu, Yue Shen\",\"doi\":\"10.1016/j.xgen.2024.100673\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The molecular mechanisms underlying the paradoxical effects<sup>1</sup> of aneuploidy are still not completely understood. In this issue, Rojas et al.<sup>2</sup> systematically analyzed the associated costs of aneuploidy and the molecular drivers involved, which revealed that aneuploidy stress is primarily driven by the cumulative effects of genes per chromosome. Notably, gene length was predicted as the most significant indicator of aneuploidy toxicity by machine learning.</p>\",\"PeriodicalId\":72539,\"journal\":{\"name\":\"Cell genomics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":11.1000,\"publicationDate\":\"2024-10-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cell genomics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.xgen.2024.100673\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CELL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell genomics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.xgen.2024.100673","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
The hidden costs of aneuploidy: New insights from yeast.
The molecular mechanisms underlying the paradoxical effects1 of aneuploidy are still not completely understood. In this issue, Rojas et al.2 systematically analyzed the associated costs of aneuploidy and the molecular drivers involved, which revealed that aneuploidy stress is primarily driven by the cumulative effects of genes per chromosome. Notably, gene length was predicted as the most significant indicator of aneuploidy toxicity by machine learning.
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