Pub Date : 2024-10-22DOI: 10.1038/s41422-024-01043-x
Ines Tomaskovic, Cristian Prieto-Garcia, Ivan Dikic
In a recent study inCell, Lascaux et al. revealed a novel pathway to repair toxic DNA lesions, providing a direct link between nucleophagy, a type of selective autophagy, and the resolution of damaged DNA.
{"title":"Nucleophagy repairs toxic DNA lesions","authors":"Ines Tomaskovic, Cristian Prieto-Garcia, Ivan Dikic","doi":"10.1038/s41422-024-01043-x","DOIUrl":"https://doi.org/10.1038/s41422-024-01043-x","url":null,"abstract":"<p><b>In a recent study in</b> <b><i>Cell</i></b><b>, Lascaux et al. revealed a novel pathway to repair toxic DNA lesions, providing a direct link between nucleophagy, a type of selective autophagy, and the resolution of damaged DNA.</b></p>","PeriodicalId":9926,"journal":{"name":"Cell Research","volume":"6 1","pages":""},"PeriodicalIF":44.1,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-17DOI: 10.1038/s41422-024-01027-x
Ting Zhao, Xueying Guan, Yan Hu, Ziqian Zhang, Han Yang, Xiaowen Shi, Jin Han, Huan Mei, Luyao Wang, Lei Shao, Hongyu Wu, Qianqian Chen, Yongyan Zhao, Jiaying Pan, Yupeng Hao, Zeyu Dong, Xuan Long, Qian Deng, Shengjun Zhao, Mengke Zhang, Yumeng Zhu, Xiaowei Ma, Zequan Chen, Yayuan Deng, Zhanfeng Si, Xin Li, Tianzhen Zhang, Fei Gu, Xiaofeng Gu, Lei Fang
DNA methylation plays multiple regulatory roles in crop development. However, the relationships of methylation polymorphisms with genetic polymorphisms, gene expression, and phenotypic variation in natural crop populations remain largely unknown. Here, we surveyed high-quality methylomes, transcriptomes, and genomes obtained from the 20-days-post-anthesis (DPA) cotton fibers of 207 accessions and extended the classical framework of population genetics to epigenetics. Over 287 million single methylation polymorphisms (SMPs) were identified, 100 times more than the number of single nucleotide polymorphisms (SNPs). These SMPs were significantly enriched in intragenic regions while depleted in transposable elements. Association analysis further identified a total of 5,426,782 cis-methylation quantitative trait loci (cis-meQTLs), 5078 cis-expression quantitative trait methylation (cis-eQTMs), and 9157 expression quantitative trait loci (eQTLs). Notably, 36.39% of cis-eQTM genes were not associated with genetic variation, indicating that a large number of SMPs associated with gene expression variation are independent of SNPs. In addition, out of the 1715 epigenetic loci associated with yield and fiber quality traits, only 36 (2.10%) were shared with genome-wide association study (GWAS) loci. The construction of multi-omics regulatory networks revealed 43 cis-eQTM genes potentially involved in fiber development, which cannot be identified by GWAS alone. Among these genes, the role of one encoding CBL-interacting protein kinase 10 in fiber length regulation was successfully validated through gene editing. Taken together, our findings prove that DNA methylation data can serve as an additional resource for breeding purposes and can offer opportunities to enhance and expedite the crop improvement process.
{"title":"Population-wide DNA methylation polymorphisms at single-nucleotide resolution in 207 cotton accessions reveal epigenomic contributions to complex traits","authors":"Ting Zhao, Xueying Guan, Yan Hu, Ziqian Zhang, Han Yang, Xiaowen Shi, Jin Han, Huan Mei, Luyao Wang, Lei Shao, Hongyu Wu, Qianqian Chen, Yongyan Zhao, Jiaying Pan, Yupeng Hao, Zeyu Dong, Xuan Long, Qian Deng, Shengjun Zhao, Mengke Zhang, Yumeng Zhu, Xiaowei Ma, Zequan Chen, Yayuan Deng, Zhanfeng Si, Xin Li, Tianzhen Zhang, Fei Gu, Xiaofeng Gu, Lei Fang","doi":"10.1038/s41422-024-01027-x","DOIUrl":"10.1038/s41422-024-01027-x","url":null,"abstract":"DNA methylation plays multiple regulatory roles in crop development. However, the relationships of methylation polymorphisms with genetic polymorphisms, gene expression, and phenotypic variation in natural crop populations remain largely unknown. Here, we surveyed high-quality methylomes, transcriptomes, and genomes obtained from the 20-days-post-anthesis (DPA) cotton fibers of 207 accessions and extended the classical framework of population genetics to epigenetics. Over 287 million single methylation polymorphisms (SMPs) were identified, 100 times more than the number of single nucleotide polymorphisms (SNPs). These SMPs were significantly enriched in intragenic regions while depleted in transposable elements. Association analysis further identified a total of 5,426,782 cis-methylation quantitative trait loci (cis-meQTLs), 5078 cis-expression quantitative trait methylation (cis-eQTMs), and 9157 expression quantitative trait loci (eQTLs). Notably, 36.39% of cis-eQTM genes were not associated with genetic variation, indicating that a large number of SMPs associated with gene expression variation are independent of SNPs. In addition, out of the 1715 epigenetic loci associated with yield and fiber quality traits, only 36 (2.10%) were shared with genome-wide association study (GWAS) loci. The construction of multi-omics regulatory networks revealed 43 cis-eQTM genes potentially involved in fiber development, which cannot be identified by GWAS alone. Among these genes, the role of one encoding CBL-interacting protein kinase 10 in fiber length regulation was successfully validated through gene editing. Taken together, our findings prove that DNA methylation data can serve as an additional resource for breeding purposes and can offer opportunities to enhance and expedite the crop improvement process.","PeriodicalId":9926,"journal":{"name":"Cell Research","volume":"34 12","pages":"859-872"},"PeriodicalIF":28.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41422-024-01027-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142440826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-14DOI: 10.1038/s41422-024-01037-9
Hanjoo Brian Shim, Justin François Deniset, Paul Kubes
In a recent study published inNature,Malamud et al. identified how neutrophil MICL recognizes neutrophil extracellular traps (NETs). This recognition suppresses further neutrophil activation and NET production, thereby preventing a vicious cycle of inflammation.
在最近发表于《自然》（Nature）的一项研究中，Malamud 等人确定了中性粒细胞 MICL 如何识别中性粒细胞胞外捕获物（NET）。这种识别抑制了中性粒细胞的进一步活化和 NET 的产生，从而防止了炎症的恶性循环。
{"title":"Knowing when to stop: MICL self-regulates neutrophil NETosis","authors":"Hanjoo Brian Shim, Justin François Deniset, Paul Kubes","doi":"10.1038/s41422-024-01037-9","DOIUrl":"https://doi.org/10.1038/s41422-024-01037-9","url":null,"abstract":"<p><b>In a recent study published in</b> <b><i>Nature</i></b><i>,</i> <b>Malamud et al. identified how neutrophil MICL recognizes neutrophil extracellular traps (NETs). This recognition suppresses further neutrophil activation and NET production, thereby preventing a vicious cycle of inflammation</b>.</p>","PeriodicalId":9926,"journal":{"name":"Cell Research","volume":"31 1","pages":""},"PeriodicalIF":44.1,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-14DOI: 10.1038/s41422-024-01039-7
Yangkyun Oh, Won-Jae Lee
While a balanced intake of macronutrients — carbohydrates, fats, and proteins — is essential for metabolic homeostasis, animals need higher protein intake during critical life stages like pregnancy. A recent paper inCellby Wu et al. introduces the novel concept of adjusting protein intake setpoints based on sex and mating status, using two opposing G protein-coupled receptor (GPCR) signaling pathways that regulate protein appetite-controlling neurons in the fruit fly,Drosophila melanogaster.
{"title":"Fine-tuning protein hunger: sex- and mating-dependent setpoint control","authors":"Yangkyun Oh, Won-Jae Lee","doi":"10.1038/s41422-024-01039-7","DOIUrl":"https://doi.org/10.1038/s41422-024-01039-7","url":null,"abstract":"<p><b>While a balanced intake of macronutrients — carbohydrates, fats, and proteins — is essential for metabolic homeostasis, animals need higher protein intake during critical life stages like pregnancy. A recent paper in</b> <b><i>Cell</i></b> <b>by Wu et al. introduces the novel concept of adjusting protein intake setpoints based on sex and mating status, using two opposing G protein-coupled receptor (GPCR) signaling pathways that regulate protein appetite-controlling neurons in the fruit fly,</b> <b><i>Drosophila melanogaster</i></b>.</p>","PeriodicalId":9926,"journal":{"name":"Cell Research","volume":"193 1","pages":""},"PeriodicalIF":44.1,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deciphering universal gene regulatory mechanisms in diverse organisms holds great potential for advancing our knowledge of fundamental life processes and facilitating clinical applications. However, the traditional research paradigm primarily focuses on individual model organisms and does not integrate various cell types across species. Recent breakthroughs in single-cell sequencing and deep learning techniques present an unprecedented opportunity to address this challenge. In this study, we built an extensive dataset of over 120 million human and mouse single-cell transcriptomes. After data preprocessing, we obtained 101,768,420 single-cell transcriptomes and developed a knowledge-informed cross-species foundation model, named GeneCompass. During pre-training, GeneCompass effectively integrated four types of prior biological knowledge to enhance our understanding of gene regulatory mechanisms in a self-supervised manner. By fine-tuning for multiple downstream tasks, GeneCompass outperformed state-of-the-art models in diverse applications for a single species and unlocked new realms of cross-species biological investigations. We also employed GeneCompass to search for key factors associated with cell fate transition and showed that the predicted candidate genes could successfully induce the differentiation of human embryonic stem cells into the gonadal fate. Overall, GeneCompass demonstrates the advantages of using artificial intelligence technology to decipher universal gene regulatory mechanisms and shows tremendous potential for accelerating the discovery of critical cell fate regulators and candidate drug targets.
{"title":"GeneCompass: deciphering universal gene regulatory mechanisms with a knowledge-informed cross-species foundation model","authors":"Xiaodong Yang, Guole Liu, Guihai Feng, Dechao Bu, Pengfei Wang, Jie Jiang, Shubai Chen, Qinmeng Yang, Hefan Miao, Yiyang Zhang, Zhenpeng Man, Zhongming Liang, Zichen Wang, Yaning Li, Zheng Li, Yana Liu, Yao Tian, Wenhao Liu, Cong Li, Ao Li, Jingxi Dong, Zhilong Hu, Chen Fang, Lina Cui, Zixu Deng, Haiping Jiang, Wentao Cui, Jiahao Zhang, Zhaohui Yang, Handong Li, Xingjian He, Liqun Zhong, Jiaheng Zhou, Zijian Wang, Qingqing Long, Ping Xu, The X-Compass Consortium, Hongmei Wang, Zhen Meng, Xuezhi Wang, Yangang Wang, Yong Wang, Shihua Zhang, Jingtao Guo, Yi Zhao, Yuanchun Zhou, Fei Li, Jing Liu, Yiqiang Chen, Ge Yang, Xin Li","doi":"10.1038/s41422-024-01034-y","DOIUrl":"10.1038/s41422-024-01034-y","url":null,"abstract":"Deciphering universal gene regulatory mechanisms in diverse organisms holds great potential for advancing our knowledge of fundamental life processes and facilitating clinical applications. However, the traditional research paradigm primarily focuses on individual model organisms and does not integrate various cell types across species. Recent breakthroughs in single-cell sequencing and deep learning techniques present an unprecedented opportunity to address this challenge. In this study, we built an extensive dataset of over 120 million human and mouse single-cell transcriptomes. After data preprocessing, we obtained 101,768,420 single-cell transcriptomes and developed a knowledge-informed cross-species foundation model, named GeneCompass. During pre-training, GeneCompass effectively integrated four types of prior biological knowledge to enhance our understanding of gene regulatory mechanisms in a self-supervised manner. By fine-tuning for multiple downstream tasks, GeneCompass outperformed state-of-the-art models in diverse applications for a single species and unlocked new realms of cross-species biological investigations. We also employed GeneCompass to search for key factors associated with cell fate transition and showed that the predicted candidate genes could successfully induce the differentiation of human embryonic stem cells into the gonadal fate. Overall, GeneCompass demonstrates the advantages of using artificial intelligence technology to decipher universal gene regulatory mechanisms and shows tremendous potential for accelerating the discovery of critical cell fate regulators and candidate drug targets.","PeriodicalId":9926,"journal":{"name":"Cell Research","volume":"34 12","pages":"830-845"},"PeriodicalIF":28.1,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41422-024-01034-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142383954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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