Pub Date : 2025-01-03DOI: 10.1038/s41588-024-02014-z
John J. Y. Lee, Ran Tao, Zhen You, Parthiv Haldipur, Anders W. Erickson, Hamza Farooq, Liam D. Hendriske, Namal Abeysundara, Cory M. Richman, Evan Y. Wang, Neha Das Gupta, Jennifer Hadley, Melissa Batts, Christopher W. Mount, Xiaochong Wu, Alex Rasnitsyn, Swneke Bailey, Florence M. G. Cavalli, Sorana Morrissy, Livia Garzia, Kulandaimanuvel Antony Michealraj, Abhi Visvanathan, Vernon Fong, Jonelle Palotta, Raul Suarez, Bryn G. Livingston, Miao Liu, Betty Luu, Craig Daniels, James Loukides, Anne Bendel, Pim J. French, Johan M. Kros, Andrey Korshunov, Marcel Kool, Fernando Chico Ponce de León, Mario Perezpeña-Diazconti, Boleslaw Lach, Sheila K. Singh, Sarah E. S. Leary, Byung-Kyu Cho, Seung-Ki Kim, Kyu-Chang Wang, Ji-Yeoun Lee, Teiji Tominaga, William A. Weiss, Joanna J. Phillips, Shizhong Dai, Gelareh Zadeh, Ali G. Saad, László Bognár, Almos Klekner, Ian F. Pollack, Ronald L. Hamilton, Young-shin Ra, Wieslawa A. Grajkowska, Marta Perek-Polnik, Reid C. Thompson, Anna M. Kenney, Michael K. Cooper, Stephen C. Mack, Nada Jabado, Mathieu Lupien, Marco Gallo, Vijay Ramaswamy, Mario L. Suva, Hiromichi Suzuki, Kathleen J. Millen, L. Frank Huang, Paul A. Northcott, Michael D. Taylor
Transcription factors are frequent cancer driver genes, exhibiting noted specificity based on the precise cell of origin. We demonstrate that ZIC1 exhibits loss-of-function (LOF) somatic events in group 4 (G4) medulloblastoma through recurrent point mutations, subchromosomal deletions and mono-allelic epigenetic repression (60% of G4 medulloblastoma). In contrast, highly similar SHH medulloblastoma exhibits distinct and diametrically opposed gain-of-function mutations and copy number gains (20% of SHH medulloblastoma). Overexpression of ZIC1 suppresses the growth of group 3 medulloblastoma models, whereas it promotes the proliferation of SHH medulloblastoma precursor cells. SHH medulloblastoma ZIC1 mutants show increased activity versus wild-type ZIC1, whereas G4 medulloblastoma ZIC1 mutants exhibit LOF phenotypes. Distinct ZIC1 mutations affect cells of the rhombic lip in diametrically opposed ways, suggesting that ZIC1 is a critical developmental transcriptional regulator in both the normal and transformed rhombic lip and identifying ZIC1 as an exquisitely context-dependent driver gene in medulloblastoma. Analysis of medulloblastomas in humans and mice shows that the functional consequences of ZIC1 mutations are exquisitely dependent on the cells of origin that give rise to different subgroups of medulloblastoma.
{"title":"ZIC1 is a context-dependent medulloblastoma driver in the rhombic lip","authors":"John J. Y. Lee, Ran Tao, Zhen You, Parthiv Haldipur, Anders W. Erickson, Hamza Farooq, Liam D. Hendriske, Namal Abeysundara, Cory M. Richman, Evan Y. Wang, Neha Das Gupta, Jennifer Hadley, Melissa Batts, Christopher W. Mount, Xiaochong Wu, Alex Rasnitsyn, Swneke Bailey, Florence M. G. Cavalli, Sorana Morrissy, Livia Garzia, Kulandaimanuvel Antony Michealraj, Abhi Visvanathan, Vernon Fong, Jonelle Palotta, Raul Suarez, Bryn G. Livingston, Miao Liu, Betty Luu, Craig Daniels, James Loukides, Anne Bendel, Pim J. French, Johan M. Kros, Andrey Korshunov, Marcel Kool, Fernando Chico Ponce de León, Mario Perezpeña-Diazconti, Boleslaw Lach, Sheila K. Singh, Sarah E. S. Leary, Byung-Kyu Cho, Seung-Ki Kim, Kyu-Chang Wang, Ji-Yeoun Lee, Teiji Tominaga, William A. Weiss, Joanna J. Phillips, Shizhong Dai, Gelareh Zadeh, Ali G. Saad, László Bognár, Almos Klekner, Ian F. Pollack, Ronald L. Hamilton, Young-shin Ra, Wieslawa A. Grajkowska, Marta Perek-Polnik, Reid C. Thompson, Anna M. Kenney, Michael K. Cooper, Stephen C. Mack, Nada Jabado, Mathieu Lupien, Marco Gallo, Vijay Ramaswamy, Mario L. Suva, Hiromichi Suzuki, Kathleen J. Millen, L. Frank Huang, Paul A. Northcott, Michael D. Taylor","doi":"10.1038/s41588-024-02014-z","DOIUrl":"10.1038/s41588-024-02014-z","url":null,"abstract":"Transcription factors are frequent cancer driver genes, exhibiting noted specificity based on the precise cell of origin. We demonstrate that ZIC1 exhibits loss-of-function (LOF) somatic events in group 4 (G4) medulloblastoma through recurrent point mutations, subchromosomal deletions and mono-allelic epigenetic repression (60% of G4 medulloblastoma). In contrast, highly similar SHH medulloblastoma exhibits distinct and diametrically opposed gain-of-function mutations and copy number gains (20% of SHH medulloblastoma). Overexpression of ZIC1 suppresses the growth of group 3 medulloblastoma models, whereas it promotes the proliferation of SHH medulloblastoma precursor cells. SHH medulloblastoma ZIC1 mutants show increased activity versus wild-type ZIC1, whereas G4 medulloblastoma ZIC1 mutants exhibit LOF phenotypes. Distinct ZIC1 mutations affect cells of the rhombic lip in diametrically opposed ways, suggesting that ZIC1 is a critical developmental transcriptional regulator in both the normal and transformed rhombic lip and identifying ZIC1 as an exquisitely context-dependent driver gene in medulloblastoma. Analysis of medulloblastomas in humans and mice shows that the functional consequences of ZIC1 mutations are exquisitely dependent on the cells of origin that give rise to different subgroups of medulloblastoma.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 1","pages":"88-102"},"PeriodicalIF":31.7,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41588-024-02014-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142917044","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}
Modern sugarcane, a highly allo-autopolyploid organism, has a very complex genome. In the present study, the karyotype and genome architecture of modern sugarcane were investigated, resulting in a genome assembly of 97 chromosomes (8.84 Gb). The allopolyploid genome was divided into subgenomes from Saccharum officinarum (Soh) and S. spontaneum (Ssh), with Soh dominance in the Saccharum hybrid (S. hybrid). Genome shock affected transcriptome dynamics during allopolyploidization. Analysis of an inbreeding population with 192 individuals revealed the underlying genetic basis of transgressive segregation. Population genomics of 310 Saccharum accessions clarified the breeding history of modern sugarcane. Using the haplotype-resolved S. hybrid genome as a reference, genome-wide association studies identified a potential candidate gene for sugar content from S. spontaneum. These findings illuminate the complex genome evolution of allopolyploids, offering opportunities for genomic enhancements and innovative breeding strategies for sugarcane. A haplotype-resolved genome of hybrid sugarcane cultivar XTT22 and population analyses of Saccharum accessions highlight the genome evolution of allopolyploids and provide opportunities for sugarcane breeding.
{"title":"The highly allo-autopolyploid modern sugarcane genome and very recent allopolyploidization in Saccharum","authors":"Jisen Zhang, Yiying Qi, Xiuting Hua, Yongjun Wang, Baiyu Wang, Yongwen Qi, Yumin Huang, Zehuai Yu, Ruiting Gao, Yixing Zhang, Tianyou Wang, Yuhao Wang, Jing Mei, Qing Zhang, Gang Wang, Haoran Pan, Zhen Li, Shuangyu Li, Jia Liu, Nameng Qi, Xiaoxi Feng, Mingxing Wu, Shuqi Chen, Cuicui Du, Yihan Li, Yi Xu, Yaxue Fang, Panpan Ma, Qingyun Li, Yuanchang Sun, Xiaomin Feng, Wei Yao, Muqing Zhang, Baoshan Chen, Xinlong Liu, Ray Ming, Jianping Wang, Zuhu Deng, Haibao Tang","doi":"10.1038/s41588-024-02033-w","DOIUrl":"10.1038/s41588-024-02033-w","url":null,"abstract":"Modern sugarcane, a highly allo-autopolyploid organism, has a very complex genome. In the present study, the karyotype and genome architecture of modern sugarcane were investigated, resulting in a genome assembly of 97 chromosomes (8.84 Gb). The allopolyploid genome was divided into subgenomes from Saccharum officinarum (Soh) and S. spontaneum (Ssh), with Soh dominance in the Saccharum hybrid (S. hybrid). Genome shock affected transcriptome dynamics during allopolyploidization. Analysis of an inbreeding population with 192 individuals revealed the underlying genetic basis of transgressive segregation. Population genomics of 310 Saccharum accessions clarified the breeding history of modern sugarcane. Using the haplotype-resolved S. hybrid genome as a reference, genome-wide association studies identified a potential candidate gene for sugar content from S. spontaneum. These findings illuminate the complex genome evolution of allopolyploids, offering opportunities for genomic enhancements and innovative breeding strategies for sugarcane. A haplotype-resolved genome of hybrid sugarcane cultivar XTT22 and population analyses of Saccharum accessions highlight the genome evolution of allopolyploids and provide opportunities for sugarcane breeding.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 1","pages":"242-253"},"PeriodicalIF":31.7,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142917045","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 : 2025-01-03DOI: 10.1038/s41588-024-02031-y
Guochong Jia, Zhishan Chen, Jie Ping, Qiuyin Cai, Ran Tao, Chao Li, Joshua A. Bauer, Yuhan Xie, Stefan Ambs, Mollie E. Barnard, Yu Chen, Ji-Yeob Choi, Yu-Tang Gao, Montserrat Garcia-Closas, Jian Gu, Jennifer J. Hu, Motoki Iwasaki, Esther M. John, Sun-Seog Kweon, Christopher I. Li, Koichi Matsuda, Keitaro Matsuo, Katherine L. Nathanson, Barbara Nemesure, Olufunmilayo I. Olopade, Tuya Pal, Sue K. Park, Boyoung Park, Michael F. Press, Maureen Sanderson, Dale P. Sandler, Chen-Yang Shen, Melissa A. Troester, Song Yao, Ying Zheng, Thomas Ahearn, Abenaa M. Brewster, Adeyinka Falusi, Anselm J. M. Hennis, Hidemi Ito, Michiaki Kubo, Eun-Sook Lee, Timothy Makumbi, Paul Ndom, Dong-Young Noh, Katie M. O’Brien, Oladosu Ojengbede, Andrew F. Olshan, Min-Ho Park, Sonya Reid, Taiki Yamaji, Gary Zirpoli, Ebonee N. Butler, Maosheng Huang, Siew-Kee Low, John Obafunwa, Clarice R. Weinberg, Haoyu Zhang, Hongyu Zhao, Michelle L. Cote, Christine B. Ambrosone, Dezheng Huo, Bingshan Li, Daehee Kang, Julie R. Palmer, Xiao-Ou Shu, Christopher A. Haiman, Xingyi Guo, Jirong Long, Wei Zheng
Genome-wide association studies have identified approximately 200 genetic risk loci for breast cancer, but the causal variants and target genes are mostly unknown. We sought to fine-map all known breast cancer risk loci using genome-wide association study data from 172,737 female breast cancer cases and 242,009 controls of African, Asian and European ancestry. We identified 332 independent association signals for breast cancer risk, including 131 signals not reported previously, and for 50 of them, we narrowed the credible causal variants down to a single variant. Analyses integrating functional genomics data identified 195 putative susceptibility genes, enriched in PI3K/AKT, TNF/NF-κB, p53 and Wnt/β-catenin pathways. Single-cell RNA sequencing or in vitro experiment data provided additional functional evidence for 105 genes. Our study uncovered large numbers of association signals and candidate susceptibility genes for breast cancer, uncovered breast cancer genetics and biology, and supported the value of including multi-ancestry data in fine-mapping analyses. Multi-ancestry fine-mapping of breast cancer susceptibility regions identifies candidate causal variants and prioritizes likely effector genes supported by functional genomic evidence.
{"title":"Refining breast cancer genetic risk and biology through multi-ancestry fine-mapping analyses of 192 risk regions","authors":"Guochong Jia, Zhishan Chen, Jie Ping, Qiuyin Cai, Ran Tao, Chao Li, Joshua A. Bauer, Yuhan Xie, Stefan Ambs, Mollie E. Barnard, Yu Chen, Ji-Yeob Choi, Yu-Tang Gao, Montserrat Garcia-Closas, Jian Gu, Jennifer J. Hu, Motoki Iwasaki, Esther M. John, Sun-Seog Kweon, Christopher I. Li, Koichi Matsuda, Keitaro Matsuo, Katherine L. Nathanson, Barbara Nemesure, Olufunmilayo I. Olopade, Tuya Pal, Sue K. Park, Boyoung Park, Michael F. Press, Maureen Sanderson, Dale P. Sandler, Chen-Yang Shen, Melissa A. Troester, Song Yao, Ying Zheng, Thomas Ahearn, Abenaa M. Brewster, Adeyinka Falusi, Anselm J. M. Hennis, Hidemi Ito, Michiaki Kubo, Eun-Sook Lee, Timothy Makumbi, Paul Ndom, Dong-Young Noh, Katie M. O’Brien, Oladosu Ojengbede, Andrew F. Olshan, Min-Ho Park, Sonya Reid, Taiki Yamaji, Gary Zirpoli, Ebonee N. Butler, Maosheng Huang, Siew-Kee Low, John Obafunwa, Clarice R. Weinberg, Haoyu Zhang, Hongyu Zhao, Michelle L. Cote, Christine B. Ambrosone, Dezheng Huo, Bingshan Li, Daehee Kang, Julie R. Palmer, Xiao-Ou Shu, Christopher A. Haiman, Xingyi Guo, Jirong Long, Wei Zheng","doi":"10.1038/s41588-024-02031-y","DOIUrl":"10.1038/s41588-024-02031-y","url":null,"abstract":"Genome-wide association studies have identified approximately 200 genetic risk loci for breast cancer, but the causal variants and target genes are mostly unknown. We sought to fine-map all known breast cancer risk loci using genome-wide association study data from 172,737 female breast cancer cases and 242,009 controls of African, Asian and European ancestry. We identified 332 independent association signals for breast cancer risk, including 131 signals not reported previously, and for 50 of them, we narrowed the credible causal variants down to a single variant. Analyses integrating functional genomics data identified 195 putative susceptibility genes, enriched in PI3K/AKT, TNF/NF-κB, p53 and Wnt/β-catenin pathways. Single-cell RNA sequencing or in vitro experiment data provided additional functional evidence for 105 genes. Our study uncovered large numbers of association signals and candidate susceptibility genes for breast cancer, uncovered breast cancer genetics and biology, and supported the value of including multi-ancestry data in fine-mapping analyses. Multi-ancestry fine-mapping of breast cancer susceptibility regions identifies candidate causal variants and prioritizes likely effector genes supported by functional genomic evidence.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 1","pages":"80-87"},"PeriodicalIF":31.7,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142917047","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}
Aberrant immune responses to viral pathogens contribute to pathogenesis, but our understanding of pathological immune responses caused by viruses within the human virome, especially at a population scale, remains limited. We analyzed whole-genome sequencing datasets of 6,321 Japanese individuals, including patients with autoimmune diseases (psoriasis vulgaris, rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), pulmonary alveolar proteinosis (PAP) or multiple sclerosis) and coronavirus disease 2019 (COVID-19), or healthy controls. We systematically quantified two constituents of the blood DNA virome, endogenous HHV-6 (eHHV-6) and anellovirus. Participants with eHHV-6B had higher risks of SLE and PAP; the former was validated in All of Us. eHHV-6B-positivity and high SLE disease activity index scores had strong correlations. Genome-wide association study and long-read sequencing mapped the integration of the HHV-6B genome to a locus on chromosome 22q. Epitope mapping and single-cell RNA sequencing revealed distinctive immune induction by eHHV-6B in patients with SLE. In addition, high anellovirus load correlated strongly with SLE, RA and COVID-19 status. Our analyses unveil relationships between the human virome and autoimmune and infectious diseases. Analysis of the blood DNA virome in patients with COVID-19 and autoimmune disease associates endogenous HHV-6 (eHHV-6) and high anellovirus load with increased disease risk, most notably for systemic lupus erythematosus. eHHV-6 carriers show a distinct immune response.
{"title":"Blood DNA virome associates with autoimmune diseases and COVID-19","authors":"Noah Sasa, Shohei Kojima, Rie Koide, Takanori Hasegawa, Ho Namkoong, Tomomitsu Hirota, Rei Watanabe, Yuumi Nakamura, Eri Oguro-Igashira, Kotaro Ogawa, Tomohiro Yata, Kyuto Sonehara, Kenichi Yamamoto, Toshihiro Kishikawa, Saori Sakaue, Ryuya Edahiro, Yuya Shirai, Yuichi Maeda, Takuro Nii, Shotaro Chubachi, Hiromu Tanaka, Haruka Yabukami, Akari Suzuki, Kimiko Nakajima, Noriko Arase, Takashi Okamoto, Rika Nishikawa, Shinichi Namba, Tatsuhiko Naito, Ippei Miyagawa, Hiroaki Tanaka, Masanobu Ueno, Yosuke Ishitsuka, Junichi Furuta, Kayo Kunimoto, Ikko Kajihara, Satoshi Fukushima, Hideaki Miyachi, Hiroyuki Matsue, Masahiro Kamata, Mami Momose, Toshinori Bito, Hiroshi Nagai, Tetsuya Ikeda, Tatsuya Horikawa, Atsuko Adachi, Tsukasa Matsubara, Kyoko Ikumi, Emi Nishida, Ikuma Nakagawa, Mayu Yagita-Sakamaki, Maiko Yoshimura, Shiro Ohshima, Makoto Kinoshita, Satoru Ito, Toru Arai, Masaki Hirose, Yoshinori Tanino, Takefumi Nikaido, Toshio Ichiwata, Shinya Ohkouchi, Taizou Hirano, Toshinori Takada, Ryushi Tazawa, Konosuke Morimoto, Masahiro Takaki, Satoshi Konno, Masaru Suzuki, Keisuke Tomii, Atsushi Nakagawa, Tomohiro Handa, Kiminobu Tanizawa, Haruyuki Ishii, Manabu Ishida, Toshiyuki Kato, Naoya Takeda, Koshi Yokomura, Takashi Matsui, Akifumi Uchida, Hiromasa Inoue, Kazuyoshi Imaizumi, Yasuhiro Goto, Hiroshi Kida, Tomoyuki Fujisawa, Takafumi Suda, Takashi Yamada, Yasuomi Satake, Hidenori Ibata, Mika Saigusa, Toshihiro Shirai, Nobuyuki Hizawa, Koh Nakata, Japan COVID-19 Task Force, Shinichi Imafuku, Yayoi Tada, Yoshihide Asano, Shinichi Sato, Chikako Nishigori, Masatoshi Jinnin, Hironobu Ihn, Akihiko Asahina, Hidehisa Saeki, Tatsuyoshi Kawamura, Shinji Shimada, Ichiro Katayama, Hannah M. Poisner, Taralynn M. Mack, Alexander G. Bick, Koichiro Higasa, Tatsusada Okuno, Hideki Mochizuki, Makoto Ishii, Ryuji Koike, Akinori Kimura, Emiko Noguchi, Shigetoshi Sano, Hidenori Inohara, Manabu Fujimoto, Yoshikazu Inoue, Etsuro Yamaguchi, Seishi Ogawa, Takanori Kanai, Akimichi Morita, Fumihiko Matsuda, Mayumi Tamari, Atsushi Kumanogoh, Yoshiya Tanaka, Koichiro Ohmura, Koichi Fukunaga, Seiya Imoto, Satoru Miyano, Nicholas F. Parrish, Yukinori Okada","doi":"10.1038/s41588-024-02022-z","DOIUrl":"10.1038/s41588-024-02022-z","url":null,"abstract":"Aberrant immune responses to viral pathogens contribute to pathogenesis, but our understanding of pathological immune responses caused by viruses within the human virome, especially at a population scale, remains limited. We analyzed whole-genome sequencing datasets of 6,321 Japanese individuals, including patients with autoimmune diseases (psoriasis vulgaris, rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), pulmonary alveolar proteinosis (PAP) or multiple sclerosis) and coronavirus disease 2019 (COVID-19), or healthy controls. We systematically quantified two constituents of the blood DNA virome, endogenous HHV-6 (eHHV-6) and anellovirus. Participants with eHHV-6B had higher risks of SLE and PAP; the former was validated in All of Us. eHHV-6B-positivity and high SLE disease activity index scores had strong correlations. Genome-wide association study and long-read sequencing mapped the integration of the HHV-6B genome to a locus on chromosome 22q. Epitope mapping and single-cell RNA sequencing revealed distinctive immune induction by eHHV-6B in patients with SLE. In addition, high anellovirus load correlated strongly with SLE, RA and COVID-19 status. Our analyses unveil relationships between the human virome and autoimmune and infectious diseases. Analysis of the blood DNA virome in patients with COVID-19 and autoimmune disease associates endogenous HHV-6 (eHHV-6) and high anellovirus load with increased disease risk, most notably for systemic lupus erythematosus. eHHV-6 carriers show a distinct immune response.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 1","pages":"65-79"},"PeriodicalIF":31.7,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41588-024-02022-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142917049","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 : 2025-01-03DOI: 10.1038/s41588-024-01996-0
Constantin Ahlmann-Eltze, Wolfgang Huber
Identifying gene expression differences in heterogeneous tissues across conditions is a fundamental biological task, enabled by multi-condition single-cell RNA sequencing (RNA-seq). Current data analysis approaches divide the constituent cells into clusters meant to represent cell types, but such discrete categorization tends to be an unsatisfactory model of the underlying biology. Here, we introduce latent embedding multivariate regression (LEMUR), a model that operates without, or before, commitment to discrete categorization. LEMUR (1) integrates data from different conditions, (2) predicts each cell’s gene expression changes as a function of the conditions and its position in latent space and (3) for each gene, identifies a compact neighborhood of cells with consistent differential expression. We apply LEMUR to cancer, zebrafish development and spatial gradients in Alzheimer’s disease, demonstrating its broad applicability.
{"title":"Analysis of multi-condition single-cell data with latent embedding multivariate regression","authors":"Constantin Ahlmann-Eltze, Wolfgang Huber","doi":"10.1038/s41588-024-01996-0","DOIUrl":"https://doi.org/10.1038/s41588-024-01996-0","url":null,"abstract":"<p>Identifying gene expression differences in heterogeneous tissues across conditions is a fundamental biological task, enabled by multi-condition single-cell RNA sequencing (RNA-seq). Current data analysis approaches divide the constituent cells into clusters meant to represent cell types, but such discrete categorization tends to be an unsatisfactory model of the underlying biology. Here, we introduce latent embedding multivariate regression (LEMUR), a model that operates without, or before, commitment to discrete categorization. LEMUR (1) integrates data from different conditions, (2) predicts each cell’s gene expression changes as a function of the conditions and its position in latent space and (3) for each gene, identifies a compact neighborhood of cells with consistent differential expression. We apply LEMUR to cancer, zebrafish development and spatial gradients in Alzheimer’s disease, demonstrating its broad applicability.</p>","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"1 1","pages":""},"PeriodicalIF":30.8,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142917048","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 : 2025-01-02DOI: 10.1038/s41588-024-02026-9
Anna N. Glaus, Marion Brechet, Gwen Swinnen, Ludivine Lebeigle, Justyna Iwaszkiewicz, Giovanna Ambrosini, Irene Julca, Jing Zhang, Robyn Roberts, Christian Iseli, Nicolas Guex, José Jiménez-Gómez, Natasha Glover, Gregory B. Martin, Susan Strickler, Sebastian Soyk
Crop genomes accumulate deleterious mutations—a phenomenon known as the cost of domestication. Precision genome editing has been proposed to eliminate such potentially harmful mutations; however, experimental demonstration is lacking. Here we identified a deleterious mutation in the tomato transcription factor SUPPRESSOR OF SP2 (SSP2), which became prevalent in the domesticated germplasm and diminished DNA binding to genome-wide targets. We found that the action of SSP2 is partially redundant with that of its paralog SSP in regulating shoot and inflorescence architecture. However, redundancy was compromised during tomato domestication and lost completely in the closely related species Physalis grisea, in which a single ortholog regulates shoot branching. We applied base editing to directly repair the deleterious mutation in cultivated tomato and obtained plants with compact growth that provide an early fruit yield. Our work shows how deleterious variants have sensitized modern genotypes for phenotypic tuning and illustrates how repairing deleterious mutations with genome editing may allow predictable crop improvement. A deleterious mutation in the tomato transcription factor SSP2 was enriched during domestication. Repairing the deleterious mutation in cultivated tomato by base editing leads to compact growth and early fruit yield.
{"title":"Repairing a deleterious domestication variant in a floral regulator gene of tomato by base editing","authors":"Anna N. Glaus, Marion Brechet, Gwen Swinnen, Ludivine Lebeigle, Justyna Iwaszkiewicz, Giovanna Ambrosini, Irene Julca, Jing Zhang, Robyn Roberts, Christian Iseli, Nicolas Guex, José Jiménez-Gómez, Natasha Glover, Gregory B. Martin, Susan Strickler, Sebastian Soyk","doi":"10.1038/s41588-024-02026-9","DOIUrl":"10.1038/s41588-024-02026-9","url":null,"abstract":"Crop genomes accumulate deleterious mutations—a phenomenon known as the cost of domestication. Precision genome editing has been proposed to eliminate such potentially harmful mutations; however, experimental demonstration is lacking. Here we identified a deleterious mutation in the tomato transcription factor SUPPRESSOR OF SP2 (SSP2), which became prevalent in the domesticated germplasm and diminished DNA binding to genome-wide targets. We found that the action of SSP2 is partially redundant with that of its paralog SSP in regulating shoot and inflorescence architecture. However, redundancy was compromised during tomato domestication and lost completely in the closely related species Physalis grisea, in which a single ortholog regulates shoot branching. We applied base editing to directly repair the deleterious mutation in cultivated tomato and obtained plants with compact growth that provide an early fruit yield. Our work shows how deleterious variants have sensitized modern genotypes for phenotypic tuning and illustrates how repairing deleterious mutations with genome editing may allow predictable crop improvement. A deleterious mutation in the tomato transcription factor SSP2 was enriched during domestication. Repairing the deleterious mutation in cultivated tomato by base editing leads to compact growth and early fruit yield.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 1","pages":"231-241"},"PeriodicalIF":31.7,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911695","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 : 2025-01-02DOI: 10.1038/s41588-024-01994-2
Benjamin J. Strober, Martin Jinye Zhang, Tiffany Amariuta, Jordan Rossen, Alkes L. Price
Complex diseases often have distinct mechanisms spanning multiple tissues. We propose tissue–gene fine-mapping (TGFM), which infers the posterior inclusion probability (PIP) for each gene–tissue pair to mediate a disease locus by analyzing summary statistics and expression quantitative trait loci (eQTL) data; TGFM also assigns PIPs to non-mediated variants. TGFM accounts for co-regulation across genes and tissues and models uncertainty in cis-predicted expression models, enabling correct calibration. We applied TGFM to 45 UK Biobank diseases or traits using eQTL data from 38 Genotype–Tissue Expression (GTEx) tissues. TGFM identified an average of 147 PIP > 0.5 causal genetic elements per disease or trait, of which 11% were gene–tissue pairs. Causal gene–tissue pairs identified by TGFM reflected both known biology (for example, TPO–thyroid for hypothyroidism) and biologically plausible findings (for example, SLC20A2–artery aorta for diastolic blood pressure). Application of TGFM to single-cell eQTL data from nine cell types in peripheral blood mononuclear cells (PBMCs), analyzed jointly with GTEx tissues, identified 30 additional causal gene–PBMC cell type pairs. Tissue–gene fine-mapping (TGFM) generalizes the SuSiE method to fine-map causal tissues and genes at disease loci using external eQTL data, offering improved calibration owing to modeling of cis-predicted expression uncertainty.
{"title":"Fine-mapping causal tissues and genes at disease-associated loci","authors":"Benjamin J. Strober, Martin Jinye Zhang, Tiffany Amariuta, Jordan Rossen, Alkes L. Price","doi":"10.1038/s41588-024-01994-2","DOIUrl":"10.1038/s41588-024-01994-2","url":null,"abstract":"Complex diseases often have distinct mechanisms spanning multiple tissues. We propose tissue–gene fine-mapping (TGFM), which infers the posterior inclusion probability (PIP) for each gene–tissue pair to mediate a disease locus by analyzing summary statistics and expression quantitative trait loci (eQTL) data; TGFM also assigns PIPs to non-mediated variants. TGFM accounts for co-regulation across genes and tissues and models uncertainty in cis-predicted expression models, enabling correct calibration. We applied TGFM to 45 UK Biobank diseases or traits using eQTL data from 38 Genotype–Tissue Expression (GTEx) tissues. TGFM identified an average of 147 PIP > 0.5 causal genetic elements per disease or trait, of which 11% were gene–tissue pairs. Causal gene–tissue pairs identified by TGFM reflected both known biology (for example, TPO–thyroid for hypothyroidism) and biologically plausible findings (for example, SLC20A2–artery aorta for diastolic blood pressure). Application of TGFM to single-cell eQTL data from nine cell types in peripheral blood mononuclear cells (PBMCs), analyzed jointly with GTEx tissues, identified 30 additional causal gene–PBMC cell type pairs. Tissue–gene fine-mapping (TGFM) generalizes the SuSiE method to fine-map causal tissues and genes at disease loci using external eQTL data, offering improved calibration owing to modeling of cis-predicted expression uncertainty.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 1","pages":"42-52"},"PeriodicalIF":31.7,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911634","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 : 2025-01-02DOI: 10.1038/s41588-024-01997-z
Yusha Liu, Peter Carbonetto, Jason Willwerscheid, Scott A. Oakes, Kay F. Macleod, Matthew Stephens
Profiling tumors with single-cell RNA sequencing has the potential to identify recurrent patterns of transcription variation related to cancer progression, and to produce therapeutically relevant insights. However, strong intertumor heterogeneity can obscure more subtle patterns that are shared across tumors. Here we introduce a statistical method, generalized binary covariance decomposition (GBCD), to address this problem. We show that GBCD can decompose transcriptional heterogeneity into interpretable components—including patient-specific, dataset-specific and shared components relevant to disease subtypes—and that, in the presence of strong intertumor heterogeneity, it can produce more interpretable results than existing methods. Applied to data on pancreatic ductal adenocarcinoma, GBCD produced a refined characterization of existing tumor subtypes, and identified a gene expression program prognostic of poor survival independent of tumor stage and subtype. The gene expression program is enriched for genes involved in stress responses, and suggests a role for the integrated stress response in pancreatic ductal adenocarcinoma. Generalized binary covariance decomposition (GBCD) applies empirical Bayes matrix factorization to identify shared and sample-specific gene expression signatures in single-cell RNA sequencing data, and can more accurately capture inter- and intrasample heterogeneity than existing methods.
{"title":"Dissecting tumor transcriptional heterogeneity from single-cell RNA-seq data by generalized binary covariance decomposition","authors":"Yusha Liu, Peter Carbonetto, Jason Willwerscheid, Scott A. Oakes, Kay F. Macleod, Matthew Stephens","doi":"10.1038/s41588-024-01997-z","DOIUrl":"10.1038/s41588-024-01997-z","url":null,"abstract":"Profiling tumors with single-cell RNA sequencing has the potential to identify recurrent patterns of transcription variation related to cancer progression, and to produce therapeutically relevant insights. However, strong intertumor heterogeneity can obscure more subtle patterns that are shared across tumors. Here we introduce a statistical method, generalized binary covariance decomposition (GBCD), to address this problem. We show that GBCD can decompose transcriptional heterogeneity into interpretable components—including patient-specific, dataset-specific and shared components relevant to disease subtypes—and that, in the presence of strong intertumor heterogeneity, it can produce more interpretable results than existing methods. Applied to data on pancreatic ductal adenocarcinoma, GBCD produced a refined characterization of existing tumor subtypes, and identified a gene expression program prognostic of poor survival independent of tumor stage and subtype. The gene expression program is enriched for genes involved in stress responses, and suggests a role for the integrated stress response in pancreatic ductal adenocarcinoma. Generalized binary covariance decomposition (GBCD) applies empirical Bayes matrix factorization to identify shared and sample-specific gene expression signatures in single-cell RNA sequencing data, and can more accurately capture inter- and intrasample heterogeneity than existing methods.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 1","pages":"263-273"},"PeriodicalIF":31.7,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911694","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 : 2025-01-02DOI: 10.1038/s41588-024-01965-7
Nora Scherer, Daniel Fässler, Oleg Borisov, Yurong Cheng, Pascal Schlosser, Matthias Wuttke, Stefan Haug, Yong Li, Fabian Telkämper, Suraj Patil, Heike Meiselbach, Casper Wong, Urs Berger, Peggy Sekula, Anselm Hoppmann, Ulla T. Schultheiss, Sahar Mozaffari, Yannan Xi, Robert Graham, Miriam Schmidts, Michael Köttgen, Peter J. Oefner, Felix Knauf, Kai-Uwe Eckardt, Sarah C. Grünert, Karol Estrada, Ines Thiele, Johannes Hertel, Anna Köttgen
Genetic studies of the metabolome can uncover enzymatic and transport processes shaping human metabolism. Using rare variant aggregation testing based on whole-exome sequencing data to detect genes associated with levels of 1,294 plasma and 1,396 urine metabolites, we discovered 235 gene–metabolite associations, many previously unreported. Complementary approaches (genetic, computational (in silico gene knockouts in whole-body models of human metabolism) and one experimental proof of principle) provided orthogonal evidence that studies of rare, damaging variants in the heterozygous state permit inferences concordant with those from inborn errors of metabolism. Allelic series of functional variants in transporters responsible for transcellular sulfate reabsorption (SLC13A1, SLC26A1) exhibited graded effects on plasma sulfate and human height and pinpointed alleles associated with increased odds of diverse musculoskeletal traits and diseases in the population. This integrative approach can identify new players in incompletely characterized human metabolic reactions and reveal metabolic readouts informative of human traits and diseases. Gene-based rare variant aggregation study with the levels of 1,294 plasma and 1,396 urine metabolites from paired specimens of 4,737 participants reveals graded effects of rare, putatively damaging variants on gene function and human traits.
{"title":"Coupling metabolomics and exome sequencing reveals graded effects of rare damaging heterozygous variants on gene function and human traits","authors":"Nora Scherer, Daniel Fässler, Oleg Borisov, Yurong Cheng, Pascal Schlosser, Matthias Wuttke, Stefan Haug, Yong Li, Fabian Telkämper, Suraj Patil, Heike Meiselbach, Casper Wong, Urs Berger, Peggy Sekula, Anselm Hoppmann, Ulla T. Schultheiss, Sahar Mozaffari, Yannan Xi, Robert Graham, Miriam Schmidts, Michael Köttgen, Peter J. Oefner, Felix Knauf, Kai-Uwe Eckardt, Sarah C. Grünert, Karol Estrada, Ines Thiele, Johannes Hertel, Anna Köttgen","doi":"10.1038/s41588-024-01965-7","DOIUrl":"10.1038/s41588-024-01965-7","url":null,"abstract":"Genetic studies of the metabolome can uncover enzymatic and transport processes shaping human metabolism. Using rare variant aggregation testing based on whole-exome sequencing data to detect genes associated with levels of 1,294 plasma and 1,396 urine metabolites, we discovered 235 gene–metabolite associations, many previously unreported. Complementary approaches (genetic, computational (in silico gene knockouts in whole-body models of human metabolism) and one experimental proof of principle) provided orthogonal evidence that studies of rare, damaging variants in the heterozygous state permit inferences concordant with those from inborn errors of metabolism. Allelic series of functional variants in transporters responsible for transcellular sulfate reabsorption (SLC13A1, SLC26A1) exhibited graded effects on plasma sulfate and human height and pinpointed alleles associated with increased odds of diverse musculoskeletal traits and diseases in the population. This integrative approach can identify new players in incompletely characterized human metabolic reactions and reveal metabolic readouts informative of human traits and diseases. Gene-based rare variant aggregation study with the levels of 1,294 plasma and 1,396 urine metabolites from paired specimens of 4,737 participants reveals graded effects of rare, putatively damaging variants on gene function and human traits.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 1","pages":"193-205"},"PeriodicalIF":31.7,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41588-024-01965-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911725","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 : 2025-01-02DOI: 10.1038/s41588-024-01982-6
Sarah M. Brotman, Julia S. El-Sayed Moustafa, Li Guan, K. Alaine Broadaway, Dongmeng Wang, Anne U. Jackson, Ryan Welch, Kevin W. Currin, Max Tomlinson, Swarooparani Vadlamudi, Heather M. Stringham, Amy L. Roberts, Timo A. Lakka, Anniina Oravilahti, Lilian Fernandes Silva, Narisu Narisu, Michael R. Erdos, Tingfen Yan, Lori L. Bonnycastle, Chelsea K. Raulerson, Yasrab Raza, Xinyu Yan, Stephen C. J. Parker, Johanna Kuusisto, Päivi Pajukanta, Jaakko Tuomilehto, Francis S. Collins, Michael Boehnke, Michael I. Love, Heikki A. Koistinen, Markku Laakso, Karen L. Mohlke, Kerrin S. Small, Laura J. Scott
Complete characterization of the genetic effects on gene expression is needed to elucidate tissue biology and the etiology of complex traits. In the present study, we analyzed 2,344 subcutaneous adipose tissue samples and identified 34,774 conditionally distinct expression quantitative trait locus (eQTL) signals at 18,476 genes. Over half of eQTL genes exhibited at least two eQTL signals. Compared with primary eQTL signals, nonprimary eQTL signals had lower effect sizes, lower minor allele frequencies and less promoter enrichment; they corresponded to genes with higher heritability and higher tolerance for loss of function. Colocalization of eQTLs with genome-wide association study (GWAS) signals for 28 cardiometabolic traits identified 1,835 genes. Inclusion of nonprimary eQTL signals increased discovery of colocalized GWAS–eQTL signals by 46%. Furthermore, 21 genes with ≥2 colocalized GWAS–eQTL signals showed a mediating gene dosage effect on the GWAS trait. Thus, expanded eQTL identification reveals more mechanisms underlying complex traits and improves understanding of the complexity of gene expression regulation. This study introduces AdipoExpress, an eQTL meta-analysis of 2,344 subcutaneous adipose tissue samples, which triples the size of previous studies and expands the discovery of eQTLs colocalized with GWAS signals for cardiometabolic traits.
{"title":"Adipose tissue eQTL meta-analysis highlights the contribution of allelic heterogeneity to gene expression regulation and cardiometabolic traits","authors":"Sarah M. Brotman, Julia S. El-Sayed Moustafa, Li Guan, K. Alaine Broadaway, Dongmeng Wang, Anne U. Jackson, Ryan Welch, Kevin W. Currin, Max Tomlinson, Swarooparani Vadlamudi, Heather M. Stringham, Amy L. Roberts, Timo A. Lakka, Anniina Oravilahti, Lilian Fernandes Silva, Narisu Narisu, Michael R. Erdos, Tingfen Yan, Lori L. Bonnycastle, Chelsea K. Raulerson, Yasrab Raza, Xinyu Yan, Stephen C. J. Parker, Johanna Kuusisto, Päivi Pajukanta, Jaakko Tuomilehto, Francis S. Collins, Michael Boehnke, Michael I. Love, Heikki A. Koistinen, Markku Laakso, Karen L. Mohlke, Kerrin S. Small, Laura J. Scott","doi":"10.1038/s41588-024-01982-6","DOIUrl":"10.1038/s41588-024-01982-6","url":null,"abstract":"Complete characterization of the genetic effects on gene expression is needed to elucidate tissue biology and the etiology of complex traits. In the present study, we analyzed 2,344 subcutaneous adipose tissue samples and identified 34,774 conditionally distinct expression quantitative trait locus (eQTL) signals at 18,476 genes. Over half of eQTL genes exhibited at least two eQTL signals. Compared with primary eQTL signals, nonprimary eQTL signals had lower effect sizes, lower minor allele frequencies and less promoter enrichment; they corresponded to genes with higher heritability and higher tolerance for loss of function. Colocalization of eQTLs with genome-wide association study (GWAS) signals for 28 cardiometabolic traits identified 1,835 genes. Inclusion of nonprimary eQTL signals increased discovery of colocalized GWAS–eQTL signals by 46%. Furthermore, 21 genes with ≥2 colocalized GWAS–eQTL signals showed a mediating gene dosage effect on the GWAS trait. Thus, expanded eQTL identification reveals more mechanisms underlying complex traits and improves understanding of the complexity of gene expression regulation. This study introduces AdipoExpress, an eQTL meta-analysis of 2,344 subcutaneous adipose tissue samples, which triples the size of previous studies and expands the discovery of eQTLs colocalized with GWAS signals for cardiometabolic traits.","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"57 1","pages":"180-192"},"PeriodicalIF":31.7,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911635","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}
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