Pub Date : 2026-01-15DOI: 10.1038/s41576-026-00930-w
Isabel K Schuurmans,Janine F Felix,Matthew Suderman,Paul Yousefi,Charlotte A M Cecil
{"title":"Bringing methylation profile scores to early life.","authors":"Isabel K Schuurmans,Janine F Felix,Matthew Suderman,Paul Yousefi,Charlotte A M Cecil","doi":"10.1038/s41576-026-00930-w","DOIUrl":"https://doi.org/10.1038/s41576-026-00930-w","url":null,"abstract":"","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"58 1","pages":""},"PeriodicalIF":42.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145986468","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 : 2026-01-15DOI: 10.1038/s41576-025-00928-w
Bin Tian,Shan Yu,Qiang Zhang
More than half of the human protein-coding genes display alternative polyadenylation (APA), whereby 3'-end processing of the nascent RNA takes place at different sites. APA leads to mRNA isoforms containing different 3' untranslated regions (3'UTRs), which generally modulate mRNA metabolism in cis but can also exert cellular functions in trans. In addition, intronic APA alters protein sequences at the carboxy-terminal region or inhibits gene expression through premature transcription termination. APA is increasingly recognized as a key layer of transcriptomic regulation that defines cell identity and proliferation and/or differentiation states, as well as controlling cellular responses to environmental cues. The relevance of APA for human health is highlighted by the many pathological conditions that are associated with APA dysregulation, including cancer, developmental disorders and neurodegeneration, as well as the disease risks associated with a growing number of genetic variations shown to affect APA. Here, we discuss physiological and pathological APA dynamics, the human mutations and genetic variants that are associated with changes in APA, and our current understanding of the functional effects and regulatory mechanisms of APA.
{"title":"Regulation of gene expression by alternative polyadenylation in health and disease.","authors":"Bin Tian,Shan Yu,Qiang Zhang","doi":"10.1038/s41576-025-00928-w","DOIUrl":"https://doi.org/10.1038/s41576-025-00928-w","url":null,"abstract":"More than half of the human protein-coding genes display alternative polyadenylation (APA), whereby 3'-end processing of the nascent RNA takes place at different sites. APA leads to mRNA isoforms containing different 3' untranslated regions (3'UTRs), which generally modulate mRNA metabolism in cis but can also exert cellular functions in trans. In addition, intronic APA alters protein sequences at the carboxy-terminal region or inhibits gene expression through premature transcription termination. APA is increasingly recognized as a key layer of transcriptomic regulation that defines cell identity and proliferation and/or differentiation states, as well as controlling cellular responses to environmental cues. The relevance of APA for human health is highlighted by the many pathological conditions that are associated with APA dysregulation, including cancer, developmental disorders and neurodegeneration, as well as the disease risks associated with a growing number of genetic variations shown to affect APA. Here, we discuss physiological and pathological APA dynamics, the human mutations and genetic variants that are associated with changes in APA, and our current understanding of the functional effects and regulatory mechanisms of APA.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"269 1","pages":""},"PeriodicalIF":42.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145986469","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 : 2026-01-13DOI: 10.1038/s41576-025-00916-0
Benjamin S Freedman,Jeff W M Bulte,Bruce R Conklin,Luke M Judge,Melinda R Dwinell,Aron M Geurts,Madeleine J Sitton,Vineet Mahajan,Samira Kiani,Charles A Gersbach,Mo R Ebrahimkhani,John J Kelly,John A Ronald,Ryuji Morizane,Navin Gupta,Ali Shakeri-Zadeh,Nicole Vo,Krishanu Saha,Shivani Saxena,David M Gamm,Divya Sinha,Alice F Tarantal,Moriel Vandsburger,Azusa Matsubara,Hongxia Fu,Shengdar Q Tsai, ,
CRISPR-based genome editing therapeutics are entering the clinic, offering transformative potential but also presenting potential risks. Preclinical-to-clinical toolkits are needed to assess the safety and efficacy of these new therapies and accelerate progress. Emerging technologies to monitor the biological effects of genome editors cover a range of biological scales, from the direct measurement of editing outcomes in DNA, to human microphysiological systems, and non-invasive in vivo imaging. Measurements of on-target and off-target editing outcomes, including sequences unique to humans, provide essential benchmarks to understand functional responses. Microphysiological systems, including organoids and organs-on-chips, enable phenotypic evaluations of editing strategies in varied organ lineages and disease states. Non-invasive imaging modalities can track the biodistribution and activities of genome editors and edited cells in vivo. Collectively, these technologies provide complementary insights across different scales, from the single nucleotide to the whole organism, bridging preclinical therapeutics development with clinical trials.
{"title":"Monitoring biological effects of somatic cell genome editing.","authors":"Benjamin S Freedman,Jeff W M Bulte,Bruce R Conklin,Luke M Judge,Melinda R Dwinell,Aron M Geurts,Madeleine J Sitton,Vineet Mahajan,Samira Kiani,Charles A Gersbach,Mo R Ebrahimkhani,John J Kelly,John A Ronald,Ryuji Morizane,Navin Gupta,Ali Shakeri-Zadeh,Nicole Vo,Krishanu Saha,Shivani Saxena,David M Gamm,Divya Sinha,Alice F Tarantal,Moriel Vandsburger,Azusa Matsubara,Hongxia Fu,Shengdar Q Tsai, , ","doi":"10.1038/s41576-025-00916-0","DOIUrl":"https://doi.org/10.1038/s41576-025-00916-0","url":null,"abstract":"CRISPR-based genome editing therapeutics are entering the clinic, offering transformative potential but also presenting potential risks. Preclinical-to-clinical toolkits are needed to assess the safety and efficacy of these new therapies and accelerate progress. Emerging technologies to monitor the biological effects of genome editors cover a range of biological scales, from the direct measurement of editing outcomes in DNA, to human microphysiological systems, and non-invasive in vivo imaging. Measurements of on-target and off-target editing outcomes, including sequences unique to humans, provide essential benchmarks to understand functional responses. Microphysiological systems, including organoids and organs-on-chips, enable phenotypic evaluations of editing strategies in varied organ lineages and disease states. Non-invasive imaging modalities can track the biodistribution and activities of genome editors and edited cells in vivo. Collectively, these technologies provide complementary insights across different scales, from the single nucleotide to the whole organism, bridging preclinical therapeutics development with clinical trials.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"25 1","pages":""},"PeriodicalIF":42.7,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961374","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 : 2026-01-06DOI: 10.1038/s41576-025-00923-1
Kate E. Jaggi, Savannah J. Hoyt, Rachel J. O’Neill, Beth A. Sullivan
{"title":"A genomic and epigenomic view of human centromeres","authors":"Kate E. Jaggi, Savannah J. Hoyt, Rachel J. O’Neill, Beth A. Sullivan","doi":"10.1038/s41576-025-00923-1","DOIUrl":"https://doi.org/10.1038/s41576-025-00923-1","url":null,"abstract":"","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"41 1","pages":""},"PeriodicalIF":42.7,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902693","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 : 2026-01-05DOI: 10.1038/s41576-025-00915-1
Jennifer A Korchak,S Stephen Yi,Neil L Kelleher,Nidhi Sahni,Gloria M Sheynkman
Proteoforms are the diverse molecular protein species produced from a single gene through genetic variation, alternative splicing and post-translational modifications. They are the crucial link between genotype and phenotype. There are estimated to be more than one million distinct protein variants produced from ~20,000 protein-coding genes in a given cell, making these proteoforms a vast and largely uncharacterized dimension in biomedical research. This Review focuses on the role of proteoforms in human genetic diseases. We highlight cutting-edge technologies for the identification and characterization of proteoforms, including long-read transcriptomics and emerging methods for direct protein sequencing, and we present a network biology framework to explain how proteoforms can perturb the molecular interactions and cellular pathways underlying disease phenotypes. We believe that precision medicine will require precision proteomics. An increasing knowledge of proteoform biology from molecular, systems and clinical perspectives will guide future research, ultimately contributing to a more precise understanding of the molecular basis of disease and refined therapeutic interventions.
{"title":"Proteoform medicine: characterizing and targeting protein forms in human disease.","authors":"Jennifer A Korchak,S Stephen Yi,Neil L Kelleher,Nidhi Sahni,Gloria M Sheynkman","doi":"10.1038/s41576-025-00915-1","DOIUrl":"https://doi.org/10.1038/s41576-025-00915-1","url":null,"abstract":"Proteoforms are the diverse molecular protein species produced from a single gene through genetic variation, alternative splicing and post-translational modifications. They are the crucial link between genotype and phenotype. There are estimated to be more than one million distinct protein variants produced from ~20,000 protein-coding genes in a given cell, making these proteoforms a vast and largely uncharacterized dimension in biomedical research. This Review focuses on the role of proteoforms in human genetic diseases. We highlight cutting-edge technologies for the identification and characterization of proteoforms, including long-read transcriptomics and emerging methods for direct protein sequencing, and we present a network biology framework to explain how proteoforms can perturb the molecular interactions and cellular pathways underlying disease phenotypes. We believe that precision medicine will require precision proteomics. An increasing knowledge of proteoform biology from molecular, systems and clinical perspectives will guide future research, ultimately contributing to a more precise understanding of the molecular basis of disease and refined therapeutic interventions.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"1 1","pages":""},"PeriodicalIF":42.7,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145897392","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 : 2026-01-05DOI: 10.1038/s41576-025-00925-z
David B Brückner
{"title":"From models to molecules: self-organized and instructed modes of developmental patterning.","authors":"David B Brückner","doi":"10.1038/s41576-025-00925-z","DOIUrl":"https://doi.org/10.1038/s41576-025-00925-z","url":null,"abstract":"","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"83 1","pages":""},"PeriodicalIF":42.7,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145897394","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 : 2026-01-02DOI: 10.1038/s41576-025-00920-4
Daniel Dimitrov, Stefan Schrod, Martin Rohbeck, Oliver Stegle
Single-cell analyses have transitioned from descriptive atlasing towards inferring causal effects and mechanistic relationships that capture cellular logic. Technological advances and the growing scale of observational and interventional datasets have fuelled the development of machine learning methods aimed at identifying such dependencies and extrapolating perturbation effects. Here, we review and connect these approaches according to their modelling concepts (including representation learning, causal inference, mechanistic discovery, disentanglement and population tracing), underlying assumptions and downstream tasks. We propose a unifying ontology to guide practitioners in selecting the most suitable methods for a given biological question, with detailed technical descriptions provided in an online resource . Finally, we identify promising computational directions and underexplored data properties that could pave the way for future developments.
{"title":"Interpretation, extrapolation and perturbation of single cells.","authors":"Daniel Dimitrov, Stefan Schrod, Martin Rohbeck, Oliver Stegle","doi":"10.1038/s41576-025-00920-4","DOIUrl":"https://doi.org/10.1038/s41576-025-00920-4","url":null,"abstract":"<p><p>Single-cell analyses have transitioned from descriptive atlasing towards inferring causal effects and mechanistic relationships that capture cellular logic. Technological advances and the growing scale of observational and interventional datasets have fuelled the development of machine learning methods aimed at identifying such dependencies and extrapolating perturbation effects. Here, we review and connect these approaches according to their modelling concepts (including representation learning, causal inference, mechanistic discovery, disentanglement and population tracing), underlying assumptions and downstream tasks. We propose a unifying ontology to guide practitioners in selecting the most suitable methods for a given biological question, with detailed technical descriptions provided in an online resource . Finally, we identify promising computational directions and underexplored data properties that could pave the way for future developments.</p>","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":" ","pages":""},"PeriodicalIF":52.0,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145889741","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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