1
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Schrod S, Zacharias HU, Beißbarth T, Hauschild AC, Altenbuchinger M. CODEX: COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations. Bioinformatics 2024; 40:i91-i99. [PMID: 38940173 DOI: 10.1093/bioinformatics/btae261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION High-throughput screens (HTS) provide a powerful tool to decipher the causal effects of chemical and genetic perturbations on cancer cell lines. Their ability to evaluate a wide spectrum of interventions, from single drugs to intricate drug combinations and CRISPR-interference, has established them as an invaluable resource for the development of novel therapeutic approaches. Nevertheless, the combinatorial complexity of potential interventions makes a comprehensive exploration intractable. Hence, prioritizing interventions for further experimental investigation becomes of utmost importance. RESULTS We propose CODEX (COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations) as a general framework for the causal modeling of HTS data, linking perturbations to their downstream consequences. CODEX relies on a stringent causal modeling strategy based on counterfactual reasoning. As such, CODEX predicts drug-specific cellular responses, comprising cell survival and molecular alterations, and facilitates the in silico exploration of drug combinations. This is achieved for both bulk and single-cell HTS. We further show that CODEX provides a rationale to explore complex genetic modifications from CRISPR-interference in silico in single cells. AVAILABILITY AND IMPLEMENTATION Our implementation of CODEX is publicly available at https://github.com/sschrod/CODEX. All data used in this article are publicly available.
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Affiliation(s)
- Stefan Schrod
- Department of Medical Bioinformatics, University Medical Center Göttingen, 37077 Niedersachsen, Germany
| | - Helena U Zacharias
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover Medical School, 30625 Hannover, Germany
| | - Tim Beißbarth
- Department of Medical Bioinformatics, University Medical Center Göttingen, 37077 Niedersachsen, Germany
| | - Anne-Christin Hauschild
- Department of Medical Informatics, University Medical Center Göttingen, 37075 Niedersachsen, Germany
| | - Michael Altenbuchinger
- Department of Medical Bioinformatics, University Medical Center Göttingen, 37077 Niedersachsen, Germany
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2
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Bai D, Ellington CN, Mo S, Song L, Xing EP. AttentionPert: accurately modeling multiplexed genetic perturbations with multi-scale effects. Bioinformatics 2024; 40:i453-i461. [PMID: 38940174 DOI: 10.1093/bioinformatics/btae244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION Genetic perturbations (e.g. knockouts, variants) have laid the foundation for our understanding of many diseases, implicating pathogenic mechanisms and indicating therapeutic targets. However, experimental assays are fundamentally limited by the number of measurable perturbations. Computational methods can fill this gap by predicting perturbation effects under novel conditions, but accurately predicting the transcriptional responses of cells to unseen perturbations remains a significant challenge. RESULTS We address this by developing a novel attention-based neural network, AttentionPert, which accurately predicts gene expression under multiplexed perturbations and generalizes to unseen conditions. AttentionPert integrates global and local effects in a multi-scale model, representing both the nonuniform system-wide impact of the genetic perturbation and the localized disturbance in a network of gene-gene similarities, enhancing its ability to predict nuanced transcriptional responses to both single and multi-gene perturbations. In comprehensive experiments, AttentionPert demonstrates superior performance across multiple datasets outperforming the state-of-the-art method in predicting differential gene expressions and revealing novel gene regulations. AttentionPert marks a significant improvement over current methods, particularly in handling the diversity of gene perturbations and in predicting out-of-distribution scenarios. AVAILABILITY AND IMPLEMENTATION Code is available at https://github.com/BaiDing1234/AttentionPert.
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Affiliation(s)
- Ding Bai
- Machine Learning Department, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, 00000, United Arabic Emirates
| | - Caleb N Ellington
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 15213, United States
| | - Shentong Mo
- Machine Learning Department, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, 00000, United Arabic Emirates
| | - Le Song
- Machine Learning Department, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, 00000, United Arabic Emirates
| | - Eric P Xing
- Machine Learning Department, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, 00000, United Arabic Emirates
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, 15213, United States
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3
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Huang W, Liu H. Predicting single-cell cellular responses to perturbations using cycle consistency learning. Bioinformatics 2024; 40:i462-i470. [PMID: 38940153 DOI: 10.1093/bioinformatics/btae248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
SUMMARY Phenotype-based drug screening emerges as a powerful approach for identifying compounds that actively interact with cells. Transcriptional and proteomic profiling of cell lines and individual cells provide insights into the cellular state alterations that occur at the molecular level in response to external perturbations, such as drugs or genetic manipulations. In this paper, we propose cycleCDR, a novel deep learning framework to predict cellular response to external perturbations. We leverage the autoencoder to map the unperturbed cellular states to a latent space, in which we postulate the effects of drug perturbations on cellular states follow a linear additive model. Next, we introduce the cycle consistency constraints to ensure that unperturbed cellular state subjected to drug perturbation in the latent space would produces the perturbed cellular state through the decoder. Conversely, removal of perturbations from the perturbed cellular states can restore the unperturbed cellular state. The cycle consistency constraints and linear modeling in the latent space enable to learn transferable representations of external perturbations, so that our model can generalize well to unseen drugs during training stage. We validate our model on four different types of datasets, including bulk transcriptional responses, bulk proteomic responses, and single-cell transcriptional responses to drug/gene perturbations. The experimental results demonstrate that our model consistently outperforms existing state-of-the-art methods, indicating our method is highly versatile and applicable to a wide range of scenarios. AVAILABILITY AND IMPLEMENTATION The source code is available at: https://github.com/hliulab/cycleCDR.
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Affiliation(s)
- Wei Huang
- College of Computer and Information Engineering, Nanjing Tech University, Nanjing, Jiangsu 211816, China
| | - Hui Liu
- College of Computer and Information Engineering, Nanjing Tech University, Nanjing, Jiangsu 211816, China
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4
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Kowalski MH, Wessels HH, Linder J, Dalgarno C, Mascio I, Choudhary S, Hartman A, Hao Y, Kundaje A, Satija R. Multiplexed single-cell characterization of alternative polyadenylation regulators. Cell 2024:S0092-8674(24)00645-7. [PMID: 38925112 DOI: 10.1016/j.cell.2024.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/12/2024] [Accepted: 06/05/2024] [Indexed: 06/28/2024]
Abstract
Most mammalian genes have multiple polyA sites, representing a substantial source of transcript diversity regulated by the cleavage and polyadenylation (CPA) machinery. To better understand how these proteins govern polyA site choice, we introduce CPA-Perturb-seq, a multiplexed perturbation screen dataset of 42 CPA regulators with a 3' scRNA-seq readout that enables transcriptome-wide inference of polyA site usage. We develop a framework to detect perturbation-dependent changes in polyadenylation and characterize modules of co-regulated polyA sites. We find groups of intronic polyA sites regulated by distinct components of the nuclear RNA life cycle, including elongation, splicing, termination, and surveillance. We train and validate a deep neural network (APARENT-Perturb) for tandem polyA site usage, delineating a cis-regulatory code that predicts perturbation response and reveals interactions between regulatory complexes. Our work highlights the potential for multiplexed single-cell perturbation screens to further our understanding of post-transcriptional regulation.
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Affiliation(s)
- Madeline H Kowalski
- New York Genome Center, New York, NY, USA; Center for Genomics and Systems Biology, New York University, New York, NY, USA; New York University Grossman School of Medicine, New York, NY, USA
| | - Hans-Hermann Wessels
- New York Genome Center, New York, NY, USA; Center for Genomics and Systems Biology, New York University, New York, NY, USA.
| | - Johannes Linder
- Department of Genetics, Stanford University, Stanford, CA, USA; Department of Computer Science, Stanford University, Stanford, CA, USA
| | | | - Isabella Mascio
- New York Genome Center, New York, NY, USA; Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Saket Choudhary
- New York Genome Center, New York, NY, USA; Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | | | - Yuhan Hao
- New York Genome Center, New York, NY, USA; Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, CA, USA; Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Rahul Satija
- New York Genome Center, New York, NY, USA; Center for Genomics and Systems Biology, New York University, New York, NY, USA; New York University Grossman School of Medicine, New York, NY, USA.
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5
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Kwon JJ, Pan J, Gonzalez G, Hahn WC, Zitnik M. On knowing a gene: A distributional hypothesis of gene function. Cell Syst 2024; 15:488-496. [PMID: 38810640 PMCID: PMC11189734 DOI: 10.1016/j.cels.2024.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 02/25/2024] [Accepted: 04/30/2024] [Indexed: 05/31/2024]
Abstract
As words can have multiple meanings that depend on sentence context, genes can have various functions that depend on the surrounding biological system. This pleiotropic nature of gene function is limited by ontologies, which annotate gene functions without considering biological contexts. We contend that the gene function problem in genetics may be informed by recent technological leaps in natural language processing, in which representations of word semantics can be automatically learned from diverse language contexts. In contrast to efforts to model semantics as "is-a" relationships in the 1990s, modern distributional semantics represents words as vectors in a learned semantic space and fuels current advances in transformer-based models such as large language models and generative pre-trained transformers. A similar shift in thinking of gene functions as distributions over cellular contexts may enable a similar breakthrough in data-driven learning from large biological datasets to inform gene function.
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Affiliation(s)
- Jason J Kwon
- Dana-Farber Cancer Institute and Harvard Medical School, Department of Medical Oncology, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Joshua Pan
- Dana-Farber Cancer Institute and Harvard Medical School, Department of Medical Oncology, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Guadalupe Gonzalez
- Department of Computing, Faculty of Engineering, Imperial College, London SW7 2AZ, UK
| | - William C Hahn
- Dana-Farber Cancer Institute and Harvard Medical School, Department of Medical Oncology, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
| | - Marinka Zitnik
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Department of Biomedical Informatics, Boston, MA 02115, USA; Harvard Data Science Initiative, Harvard University, Cambridge, MA 02138, USA; Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Allston, MA 02134, USA.
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6
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Sabath K, Nabih A, Arnold C, Moussa R, Domjan D, Zaugg JB, Jonas S. Basis of gene-specific transcription regulation by the Integrator complex. Mol Cell 2024:S1097-2765(24)00474-X. [PMID: 38906142 DOI: 10.1016/j.molcel.2024.05.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 03/04/2024] [Accepted: 05/29/2024] [Indexed: 06/23/2024]
Abstract
The Integrator complex attenuates gene expression via the premature termination of RNA polymerase II (RNAP2) at promoter-proximal pausing sites. It is required for stimulus response, cell differentiation, and neurodevelopment, but how gene-specific and adaptive regulation by Integrator is achieved remains unclear. Here, we identify two sites on human Integrator subunits 13/14 that serve as binding hubs for sequence-specific transcription factors (TFs) and other transcription effector complexes. When Integrator is attached to paused RNAP2, these hubs are positioned upstream of the transcription bubble, consistent with simultaneous TF-promoter tethering. The TFs co-localize with Integrator genome-wide, increase Integrator abundance on target genes, and co-regulate responsive transcriptional programs. For instance, sensory cilia formation induced by glucose starvation depends on Integrator-TF contacts. Our data suggest TF-mediated promoter recruitment of Integrator as a widespread mechanism for targeted transcription regulation.
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Affiliation(s)
- Kevin Sabath
- Department of Biology, Institute of Molecular Biology and Biophysics, ETH Zurich, 8093 Zurich, Switzerland.
| | - Amena Nabih
- Department of Biology, Institute of Molecular Biology and Biophysics, ETH Zurich, 8093 Zurich, Switzerland
| | - Christian Arnold
- European Molecular Biology Laboratory, Structural and Computational Biology Unit, 69117 Heidelberg, Germany
| | - Rim Moussa
- European Molecular Biology Laboratory, Structural and Computational Biology Unit, 69117 Heidelberg, Germany
| | - David Domjan
- Department of Biology, Institute of Molecular Biology and Biophysics, ETH Zurich, 8093 Zurich, Switzerland
| | - Judith B Zaugg
- European Molecular Biology Laboratory, Structural and Computational Biology Unit, 69117 Heidelberg, Germany
| | - Stefanie Jonas
- Department of Biology, Institute of Molecular Biology and Biophysics, ETH Zurich, 8093 Zurich, Switzerland.
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7
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Yao Y, Zhou Z, Wang X, Liu Z, Zhai Y, Chi X, Du J, Luo L, Zhao Z, Wang X, Xue C, Rao S. SpRY-mediated screens facilitate functional dissection of non-coding sequences at single-base resolution. CELL GENOMICS 2024:100583. [PMID: 38889719 DOI: 10.1016/j.xgen.2024.100583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 01/28/2024] [Accepted: 05/16/2024] [Indexed: 06/20/2024]
Abstract
CRISPR mutagenesis screens conducted with SpCas9 and other nucleases have identified certain cis-regulatory elements and genetic variants but at a limited resolution due to the absence of protospacer adjacent motif (PAM) sequences. Here, leveraging the broad targeting scope of the near-PAMless SpRY variant, we have demonstrated that saturated SpRY mutagenesis and base editing screens can faithfully identify functional regulatory elements and essential genetic variants for target gene expression at single-base resolution. We further extended this methodology to investigate a genome-wide association study (GWAS) locus at 10q22.1 associated with a red blood cell trait, where we identified potential enhancers regulating HK1 gene expression, despite not all of these enhancers exhibiting typical chromatin signatures. More importantly, our saturated base editing screens pinpoint multiple causal variants within this locus that would otherwise be missed by Bayesian statistical fine-mapping. Our approach is generally applicable to functional interrogation of all non-coding genomic elements while complementing other high-coverage CRISPR screens.
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Affiliation(s)
- Yao Yao
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600, China.
| | - Zhiwei Zhou
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600, China
| | - Xiaoling Wang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600, China
| | - Zhirui Liu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600, China
| | - Yixin Zhai
- Department of Oncology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
| | - Xiaolin Chi
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600, China
| | - Jingyi Du
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600, China
| | - Liheng Luo
- Center for Bioinformatics, National Infrastructures for Translational Medicine, Institute of Clinical Medicine & Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China
| | - Zhigang Zhao
- Department of Medical Oncology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin 300192, China
| | - Xiaoyue Wang
- Center for Bioinformatics, National Infrastructures for Translational Medicine, Institute of Clinical Medicine & Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China
| | - Chaoyou Xue
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.
| | - Shuquan Rao
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600, China.
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8
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Roohani Y, Huang K, Leskovec J. Predicting transcriptional outcomes of novel multigene perturbations with GEARS. Nat Biotechnol 2024; 42:927-935. [PMID: 37592036 PMCID: PMC11180609 DOI: 10.1038/s41587-023-01905-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 07/12/2023] [Indexed: 08/19/2023]
Abstract
Understanding cellular responses to genetic perturbation is central to numerous biomedical applications, from identifying genetic interactions involved in cancer to developing methods for regenerative medicine. However, the combinatorial explosion in the number of possible multigene perturbations severely limits experimental interrogation. Here, we present graph-enhanced gene activation and repression simulator (GEARS), a method that integrates deep learning with a knowledge graph of gene-gene relationships to predict transcriptional responses to both single and multigene perturbations using single-cell RNA-sequencing data from perturbational screens. GEARS is able to predict outcomes of perturbing combinations consisting of genes that were never experimentally perturbed. GEARS exhibited 40% higher precision than existing approaches in predicting four distinct genetic interaction subtypes in a combinatorial perturbation screen and identified the strongest interactions twice as well as prior approaches. Overall, GEARS can predict phenotypically distinct effects of multigene perturbations and thus guide the design of perturbational experiments.
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Affiliation(s)
- Yusuf Roohani
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Kexin Huang
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Jure Leskovec
- Department of Computer Science, Stanford University, Stanford, CA, USA.
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9
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Bell CC, Balic JJ, Talarmain L, Gillespie A, Scolamiero L, Lam EYN, Ang CS, Faulkner GJ, Gilan O, Dawson MA. Comparative cofactor screens show the influence of transactivation domains and core promoters on the mechanisms of transcription. Nat Genet 2024; 56:1181-1192. [PMID: 38769457 DOI: 10.1038/s41588-024-01749-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 04/09/2024] [Indexed: 05/22/2024]
Abstract
Eukaryotic transcription factors (TFs) activate gene expression by recruiting cofactors to promoters. However, the relationships between TFs, promoters and their associated cofactors remain poorly understood. Here we combine GAL4-transactivation assays with comparative CRISPR-Cas9 screens to identify the cofactors used by nine different TFs and core promoters in human cells. Using this dataset, we associate TFs with cofactors, classify cofactors as ubiquitous or specific and discover transcriptional co-dependencies. Through a reductionistic, comparative approach, we demonstrate that TFs do not display discrete mechanisms of activation. Instead, each TF depends on a unique combination of cofactors, which influences distinct steps in transcription. By contrast, the influence of core promoters appears relatively discrete. Different promoter classes are constrained by either initiation or pause-release, which influences their dynamic range and compatibility with cofactors. Overall, our comparative cofactor screens characterize the interplay between TFs, cofactors and core promoters, identifying general principles by which they influence transcription.
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Affiliation(s)
- Charles C Bell
- Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia.
- Mater Research Institute, University of Queensland, TRI Building, Woolloongabba, Queensland, Australia.
| | - Jesse J Balic
- Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Laure Talarmain
- Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Andrea Gillespie
- Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Laura Scolamiero
- Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Enid Y N Lam
- Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Ching-Seng Ang
- Bio21 Mass Spectrometry and Proteomics Facility, The University of Melbourne, Parkville, Victoria, Australia
| | - Geoffrey J Faulkner
- Mater Research Institute, University of Queensland, TRI Building, Woolloongabba, Queensland, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
| | - Omer Gilan
- Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Australian Centre for Blood Diseases, Monash University, Melbourne, Victoria, Australia
| | - Mark A Dawson
- Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia.
- Department of Haematology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
- Centre for Cancer Research, University of Melbourne, Melbourne, Victoria, Australia.
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10
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Maraslioglu-Sperber A, Blanc F, Heller S. Murine cochlear damage models in the context of hair cell regeneration research. Hear Res 2024; 447:109021. [PMID: 38703432 DOI: 10.1016/j.heares.2024.109021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 04/16/2024] [Accepted: 04/26/2024] [Indexed: 05/06/2024]
Abstract
Understanding the complex pathologies associated with hearing loss is a significant motivation for conducting inner ear research. Lifelong exposure to loud noise, ototoxic drugs, genetic diversity, sex, and aging collectively contribute to human hearing loss. Replicating this pathology in research animals is challenging because hearing impairment has varied causes and different manifestations. A central aspect, however, is the loss of sensory hair cells and the inability of the mammalian cochlea to replace them. Researching therapeutic strategies to rekindle regenerative cochlear capacity, therefore, requires the generation of animal models in which cochlear hair cells are eliminated. This review discusses different approaches to ablate cochlear hair cells in adult mice. We inventoried the cochlear cyto- and histo-pathology caused by acoustic overstimulation, systemic and locally applied drugs, and various genetic tools. The focus is not to prescribe a perfect damage model but to highlight the limitations and advantages of existing approaches and identify areas for further refinement of damage models for use in regenerative studies.
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Affiliation(s)
- Ayse Maraslioglu-Sperber
- Department of Otolaryngology - Head and Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Fabian Blanc
- Department of Otolaryngology - Head and Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Otolaryngology - Head & Neck Surgery, University Hospital Gui de Chauliac, University of Montpellier, Montpellier, France
| | - Stefan Heller
- Department of Otolaryngology - Head and Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
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11
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Villiger L, Joung J, Koblan L, Weissman J, Abudayyeh OO, Gootenberg JS. CRISPR technologies for genome, epigenome and transcriptome editing. Nat Rev Mol Cell Biol 2024; 25:464-487. [PMID: 38308006 DOI: 10.1038/s41580-023-00697-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/18/2023] [Indexed: 02/04/2024]
Abstract
Our ability to edit genomes lags behind our capacity to sequence them, but the growing understanding of CRISPR biology and its application to genome, epigenome and transcriptome engineering is narrowing this gap. In this Review, we discuss recent developments of various CRISPR-based systems that can transiently or permanently modify the genome and the transcriptome. The discovery of further CRISPR enzymes and systems through functional metagenomics has meaningfully broadened the applicability of CRISPR-based editing. Engineered Cas variants offer diverse capabilities such as base editing, prime editing, gene insertion and gene regulation, thereby providing a panoply of tools for the scientific community. We highlight the strengths and weaknesses of current CRISPR tools, considering their efficiency, precision, specificity, reliance on cellular DNA repair mechanisms and their applications in both fundamental biology and therapeutics. Finally, we discuss ongoing clinical trials that illustrate the potential impact of CRISPR systems on human health.
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Affiliation(s)
- Lukas Villiger
- McGovern Institute for Brain Research, Massachusetts Institute of Technology Cambridge, Cambridge, MA, USA
| | - Julia Joung
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Luke Koblan
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jonathan Weissman
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Omar O Abudayyeh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology Cambridge, Cambridge, MA, USA.
| | - Jonathan S Gootenberg
- McGovern Institute for Brain Research, Massachusetts Institute of Technology Cambridge, Cambridge, MA, USA.
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12
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Machine learning predicts cellular response to genetic perturbation. Nat Biotechnol 2024; 42:858-859. [PMID: 37592037 DOI: 10.1038/s41587-023-01907-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/19/2023]
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13
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Do BT, Hsu PP, Vermeulen SY, Wang Z, Hirz T, Abbott KL, Aziz N, Replogle JM, Bjelosevic S, Paolino J, Nelson SA, Block S, Darnell AM, Ferreira R, Zhang H, Milosevic J, Schmidt DR, Chidley C, Harris IS, Weissman JS, Pikman Y, Stegmaier K, Cheloufi S, Su XA, Sykes DB, Vander Heiden MG. Nucleotide depletion promotes cell fate transitions by inducing DNA replication stress. Dev Cell 2024:S1534-5807(24)00327-7. [PMID: 38823395 DOI: 10.1016/j.devcel.2024.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 04/14/2024] [Accepted: 05/09/2024] [Indexed: 06/03/2024]
Abstract
Control of cellular identity requires coordination of developmental programs with environmental factors such as nutrient availability, suggesting that perturbing metabolism can alter cell state. Here, we find that nucleotide depletion and DNA replication stress drive differentiation in human and murine normal and transformed hematopoietic systems, including patient-derived acute myeloid leukemia (AML) xenografts. These cell state transitions begin during S phase and are independent of ATR/ATM checkpoint signaling, double-stranded DNA break formation, and changes in cell cycle length. In systems where differentiation is blocked by oncogenic transcription factor expression, replication stress activates primed regulatory loci and induces lineage-appropriate maturation genes despite the persistence of progenitor programs. Altering the baseline cell state by manipulating transcription factor expression causes replication stress to induce genes specific for alternative lineages. The ability of replication stress to selectively activate primed maturation programs across different contexts suggests a general mechanism by which changes in metabolism can promote lineage-appropriate cell state transitions.
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Affiliation(s)
- Brian T Do
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Harvard-MIT Health Sciences and Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Peggy P Hsu
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Dana-Farber Cancer Institute, Boston, MA 02115, USA; Massachusetts General Hospital Cancer Center, Boston, MA 02113, USA; Rogel Cancer Center and Division of Hematology and Oncology, Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Sidney Y Vermeulen
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Zhishan Wang
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Taghreed Hirz
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA 02113, USA; Harvard Stem Cell Institute, Cambridge, MA 02139, USA
| | - Keene L Abbott
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Najihah Aziz
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA 02113, USA; Harvard Stem Cell Institute, Cambridge, MA 02139, USA
| | - Joseph M Replogle
- Whitehead Institute for Biomedical Research, Cambridge, MA 02139, USA; Medical Scientist Training Program, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Stefan Bjelosevic
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Pediatric Oncology, Dana-Farber Cancer Institute and Boston Children's Hospital, Boston, MA 02115, USA; Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jonathan Paolino
- Department of Pediatric Oncology, Dana-Farber Cancer Institute and Boston Children's Hospital, Boston, MA 02115, USA; Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Samantha A Nelson
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Samuel Block
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Alicia M Darnell
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Raphael Ferreira
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Hanyu Zhang
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA 02113, USA; Harvard Stem Cell Institute, Cambridge, MA 02139, USA
| | - Jelena Milosevic
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA 02113, USA; Harvard Stem Cell Institute, Cambridge, MA 02139, USA
| | - Daniel R Schmidt
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Radiation Oncology, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Christopher Chidley
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Isaac S Harris
- Wilmot Cancer Institute, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Jonathan S Weissman
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Whitehead Institute for Biomedical Research, Cambridge, MA 02139, USA; Howard Hughes Medical Institute, Cambridge, MA 02139, USA
| | - Yana Pikman
- Department of Pediatric Oncology, Dana-Farber Cancer Institute and Boston Children's Hospital, Boston, MA 02115, USA; Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Kimberly Stegmaier
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Pediatric Oncology, Dana-Farber Cancer Institute and Boston Children's Hospital, Boston, MA 02115, USA; Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Sihem Cheloufi
- Department of Biochemistry, University of California, Riverside, Riverside, CA 92521, USA; Stem Cell Center, University of California, Riverside, Riverside, CA 92521, USA; Center for RNA Biology and Medicine, Riverside, CA 92521, USA
| | - Xiaofeng A Su
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - David B Sykes
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA 02113, USA; Harvard Stem Cell Institute, Cambridge, MA 02139, USA
| | - Matthew G Vander Heiden
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Dana-Farber Cancer Institute, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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14
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Bilous M, Hérault L, Gabriel AA, Teleman M, Gfeller D. Building and analyzing metacells in single-cell genomics data. Mol Syst Biol 2024:10.1038/s44320-024-00045-6. [PMID: 38811801 DOI: 10.1038/s44320-024-00045-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 05/03/2024] [Accepted: 05/08/2024] [Indexed: 05/31/2024] Open
Abstract
The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells-and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data ( https://github.com/GfellerLab/MetacellAnalysisTutorial ) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods ( https://github.com/GfellerLab/MetacellAnalysisToolkit ).
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Affiliation(s)
- Mariia Bilous
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Léonard Hérault
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Aurélie Ag Gabriel
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Matei Teleman
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - David Gfeller
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland.
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland.
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland.
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15
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Lazar NH, Celik S, Chen L, Fay MM, Irish JC, Jensen J, Tillinghast CA, Urbanik J, Bone WP, Gibson CC, Haque IS. High-resolution genome-wide mapping of chromosome-arm-scale truncations induced by CRISPR-Cas9 editing. Nat Genet 2024:10.1038/s41588-024-01758-y. [PMID: 38811841 DOI: 10.1038/s41588-024-01758-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 04/18/2024] [Indexed: 05/31/2024]
Abstract
Clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9) is a powerful tool for introducing targeted mutations in DNA, but recent studies have shown that it can have unintended effects such as structural changes. However, these studies have not yet looked genome wide or across data types. Here we performed a phenotypic CRISPR-Cas9 scan targeting 17,065 genes in primary human cells, revealing a 'proximity bias' in which CRISPR knockouts show unexpected similarities to unrelated genes on the same chromosome arm. This bias was found to be consistent across cell types, laboratories, Cas9 delivery methods and assay modalities, and the data suggest that it is caused by telomeric truncations of chromosome arms, with cell cycle and apoptotic pathways playing a mediating role. Additionally, a simple correction is demonstrated to mitigate this pervasive bias while preserving biological relationships. This previously uncharacterized effect has implications for functional genomic studies using CRISPR-Cas9, with applications in discovery biology, drug-target identification, cell therapies and genetic therapeutics.
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Affiliation(s)
| | | | - Lu Chen
- Recursion, Salt Lake City, UT, USA
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16
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Razew M, Fraudeau A, Pfleiderer MM, Linares R, Galej WP. Structural basis of the Integrator complex assembly and association with transcription factors. Mol Cell 2024:S1097-2765(24)00431-3. [PMID: 38823386 DOI: 10.1016/j.molcel.2024.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 03/18/2024] [Accepted: 05/09/2024] [Indexed: 06/03/2024]
Abstract
Integrator is a multi-subunit protein complex responsible for premature transcription termination of coding and non-coding RNAs. This is achieved via two enzymatic activities, RNA endonuclease and protein phosphatase, acting on the promoter-proximally paused RNA polymerase Ⅱ (RNAPⅡ). Yet, it remains unclear how Integrator assembly and recruitment are regulated and what the functions of many of its core subunits are. Here, we report the structures of two human Integrator sub-complexes: INTS10/13/14/15 and INTS5/8/10/15, and an integrative model of the fully assembled Integrator bound to the RNAPⅡ paused elongating complex (PEC). An in silico protein-protein interaction screen of over 1,500 human transcription factors (TFs) identified ZNF655 as a direct interacting partner of INTS13 within the fully assembled Integrator. We propose a model wherein INTS13 acts as a platform for the recruitment of TFs that could modulate the stability of the Integrator's association at specific loci and regulate transcription attenuation of the target genes.
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Affiliation(s)
- Michal Razew
- European Molecular Biology Laboratory, EMBL Grenoble, 71 Avenue des Martyrs, 38042 Grenoble, France
| | - Angelique Fraudeau
- European Molecular Biology Laboratory, EMBL Grenoble, 71 Avenue des Martyrs, 38042 Grenoble, France
| | - Moritz M Pfleiderer
- European Molecular Biology Laboratory, EMBL Grenoble, 71 Avenue des Martyrs, 38042 Grenoble, France
| | - Romain Linares
- European Molecular Biology Laboratory, EMBL Grenoble, 71 Avenue des Martyrs, 38042 Grenoble, France
| | - Wojciech P Galej
- European Molecular Biology Laboratory, EMBL Grenoble, 71 Avenue des Martyrs, 38042 Grenoble, France.
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17
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Tang Q, Ratnayake R, Seabra G, Jiang Z, Fang R, Cui L, Ding Y, Kahveci T, Bian J, Li C, Luesch H, Li Y. Morphological profiling for drug discovery in the era of deep learning. Brief Bioinform 2024; 25:bbae284. [PMID: 38886164 PMCID: PMC11182685 DOI: 10.1093/bib/bbae284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Revised: 05/13/2024] [Accepted: 06/03/2024] [Indexed: 06/20/2024] Open
Abstract
Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high throughput. These efforts have facilitated understanding of compound mechanism of action, drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering- and deep learning-based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.
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Affiliation(s)
- Qiaosi Tang
- Calico Life Sciences, South San Francisco, CA 94080, United States
| | - Ranjala Ratnayake
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
| | - Gustavo Seabra
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
| | - Zhe Jiang
- Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL 32611, United States
| | - Ruogu Fang
- Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL 32611, United States
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL 32611, United States
| | - Lina Cui
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
| | - Yousong Ding
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
| | - Tamer Kahveci
- Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL 32611, United States
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32611, United States
| | - Chenglong Li
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
| | - Hendrik Luesch
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
| | - Yanjun Li
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
- Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL 32611, United States
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18
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Rafelski SM, Theriot JA. Establishing a conceptual framework for holistic cell states and state transitions. Cell 2024; 187:2633-2651. [PMID: 38788687 DOI: 10.1016/j.cell.2024.04.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 04/10/2024] [Accepted: 04/24/2024] [Indexed: 05/26/2024]
Abstract
Cell states were traditionally defined by how they looked, where they were located, and what functions they performed. In this post-genomic era, the field is largely focused on a molecular view of cell state. Moving forward, we anticipate that the observables used to define cell states will evolve again as single-cell imaging and analytics are advancing at a breakneck pace via the collection of large-scale, systematic cell image datasets and the application of quantitative image-based data science methods. This is, therefore, a key moment in the arc of cell biological research to develop approaches that integrate the spatiotemporal observables of the physical structure and organization of the cell with molecular observables toward the concept of a holistic cell state. In this perspective, we propose a conceptual framework for holistic cell states and state transitions that is data-driven, practical, and useful to enable integrative analyses and modeling across many data types.
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Affiliation(s)
- Susanne M Rafelski
- Allen Institute for Cell Science, 615 Westlake Avenue N, Seattle, WA 98125, USA.
| | - Julie A Theriot
- Department of Biology and Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA.
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19
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McShane E, Churchman LS. Central dogma rates in human mitochondria. Hum Mol Genet 2024; 33:R34-R41. [PMID: 38779776 PMCID: PMC11112385 DOI: 10.1093/hmg/ddae036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 12/28/2023] [Accepted: 02/29/2024] [Indexed: 05/25/2024] Open
Abstract
In human cells, the nuclear and mitochondrial genomes engage in a complex interplay to produce dual-encoded oxidative phosphorylation (OXPHOS) complexes. The coordination of these dynamic gene expression processes is essential for producing matched amounts of OXPHOS protein subunits. This review focuses on our current understanding of the mitochondrial central dogma rates, highlighting the striking differences in gene expression rates between mitochondrial and nuclear genes. We synthesize a coherent model of mitochondrial gene expression kinetics, highlighting the emerging principles and emphasizing where more precise measurements would be beneficial. Such an understanding is pivotal for grasping the unique aspects of mitochondrial function and its role in cellular energetics, and it has profound implications for aging, metabolic disorders, and neurodegenerative diseases.
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Affiliation(s)
- Erik McShane
- Department of Genetics, Blavatnik Institute, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States
| | - L Stirling Churchman
- Department of Genetics, Blavatnik Institute, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States
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20
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Cooper S, Obolenski S, Waters AJ, Bassett AR, Coelho MA. Analyzing the functional effects of DNA variants with gene editing. CELL REPORTS METHODS 2024; 4:100776. [PMID: 38744287 PMCID: PMC11133854 DOI: 10.1016/j.crmeth.2024.100776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/01/2024] [Accepted: 04/22/2024] [Indexed: 05/16/2024]
Abstract
Continual advancements in genomics have led to an ever-widening disparity between the rate of discovery of genetic variants and our current understanding of their functions and potential roles in disease. Systematic methods for phenotyping DNA variants are required to effectively translate genomics data into improved outcomes for patients with genetic diseases. To make the biggest impact, these approaches must be scalable and accurate, faithfully reflect disease biology, and define complex disease mechanisms. We compare current methods to analyze the function of variants in their endogenous DNA context using genome editing strategies, such as saturation genome editing, base editing and prime editing. We discuss how these technologies can be linked to high-content readouts to gain deep mechanistic insights into variant effects. Finally, we highlight key challenges that need to be addressed to bridge the genotype to phenotype gap, and ultimately improve the diagnosis and treatment of genetic diseases.
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Affiliation(s)
- Sarah Cooper
- Cellular and Gene Editing Research, Wellcome Sanger Institute, Hinxton, UK
| | - Sofia Obolenski
- Experimental Cancer Genetics, Wellcome Sanger Institute, Hinxton, UK; Department of Dermatology, Leiden University Medical Center, Leiden, the Netherlands
| | - Andrew J Waters
- Experimental Cancer Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Andrew R Bassett
- Cellular and Gene Editing Research, Wellcome Sanger Institute, Hinxton, UK.
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21
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Gao ZJ, Fang H, Sun S, Liu SQ, Fang Z, Liu Z, Li B, Wang P, Sun SR, Meng XY, Wu Q, Chen CS. Single-cell analyses reveal evolution mimicry during the specification of breast cancer subtype. Theranostics 2024; 14:3104-3126. [PMID: 38855191 PMCID: PMC11155410 DOI: 10.7150/thno.96163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 05/12/2024] [Indexed: 06/11/2024] Open
Abstract
Background: The stem or progenitor antecedents confer developmental plasticity and unique cell identities to cancer cells via genetic and epigenetic programs. A comprehensive characterization and mapping of the cell-of-origin of breast cancer using novel technologies to unveil novel subtype-specific therapeutic targets is still absent. Methods: We integrated 195,144 high-quality cells from normal breast tissues and 406,501 high-quality cells from primary breast cancer samples to create a large-scale single-cell atlas of human normal and cancerous breasts. Potential heterogeneous origin of malignant cells was explored by contrasting cancer cells against reference normal epithelial cells. Multi-omics analyses and both in vitro and in vivo experiments were performed to screen and validate potential subtype-specific treatment targets. Novel biomarkers of identified immune and stromal cell subpopulations were validated by immunohistochemistry in our cohort. Results: Tumor stratification based on cancer cell-of-origin patterns correlated with clinical outcomes, genomic aberrations and diverse microenvironment constitutions. We found that the luminal progenitor (LP) subtype was robustly associated with poor prognosis, genomic instability and dysfunctional immune microenvironment. However, the LP subtype patients were sensitive to neoadjuvant chemotherapy (NAC), PARP inhibitors (PARPi) and immunotherapy. The LP subtype-specific target PLK1 was investigated by both in vitro and in vivo experiments. Besides, large-scale single-cell profiling of breast cancer inspired us to identify a range of clinically relevant immune and stromal cell subpopulations, including subsets of innate lymphoid cells (ILCs), macrophages and endothelial cells. Conclusion: The present single-cell study revealed the cellular repertoire and cell-of-origin patterns of breast cancer. Combining single-cell and bulk transcriptome data, we elucidated the evolution mimicry from normal to malignant subtypes and expounded the LP subtype with vital clinical implications. Novel immune and stromal cell subpopulations of breast cancer identified in our study could be potential therapeutic targets. Taken together, Our findings lay the foundation for the precise prognostic and therapeutic stratification of breast cancer.
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Affiliation(s)
- Zhi-Jie Gao
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Huan Fang
- Kunming Institute of Zoology, Chinese Academy of Sciences. Kunming, Yunnan, China
- Kunming College of Life Sciences, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Si Sun
- Department of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Si-Qing Liu
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Zhou Fang
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Zhou Liu
- Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Bei Li
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, Hubei. China
| | - Ping Wang
- Medical College, Anhui University of Science and Technology, Huainan, AnHui. China
- Tongji University Cancer Center, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Sheng-Rong Sun
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Xiang-Yu Meng
- Health Science Center, Hubei Minzu University, Enshi, Hubei, China
| | - Qi Wu
- Tongji University Cancer Center, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Ce-Shi Chen
- Kunming Institute of Zoology, Chinese Academy of Sciences. Kunming, Yunnan, China
- Academy of Biomedical Engineering, Kunming Medical University, Kunming, Yunnan, China
- The Third Affiliated Hospital, Kunming Medical University, Kunming, Yunnan, China
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22
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Pacalin NM, Steinhart Z, Shi Q, Belk JA, Dorovskyi D, Kraft K, Parker KR, Shy BR, Marson A, Chang HY. Bidirectional epigenetic editing reveals hierarchies in gene regulation. Nat Biotechnol 2024:10.1038/s41587-024-02213-3. [PMID: 38760566 DOI: 10.1038/s41587-024-02213-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 03/19/2024] [Indexed: 05/19/2024]
Abstract
CRISPR perturbation methods are limited in their ability to study non-coding elements and genetic interactions. In this study, we developed a system for bidirectional epigenetic editing, called CRISPRai, in which we apply activating (CRISPRa) and repressive (CRISPRi) perturbations to two loci simultaneously in the same cell. We developed CRISPRai Perturb-seq by coupling dual perturbation gRNA detection with single-cell RNA sequencing, enabling study of pooled perturbations in a mixed single-cell population. We applied this platform to study the genetic interaction between two hematopoietic lineage transcription factors, SPI1 and GATA1, and discovered novel characteristics of their co-regulation on downstream target genes, including differences in SPI1 and GATA1 occupancy at genes that are regulated through different modes. We also studied the regulatory landscape of IL2 (interleukin-2) in Jurkat T cells, primary T cells and chimeric antigen receptor (CAR) T cells and elucidated mechanisms of enhancer-mediated IL2 gene regulation. CRISPRai facilitates investigation of context-specific genetic interactions, provides new insights into gene regulation and will enable exploration of non-coding disease-associated variants.
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Affiliation(s)
- Naomi M Pacalin
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Zachary Steinhart
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
| | - Quanming Shi
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
| | - Julia A Belk
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
| | - Dmytro Dorovskyi
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Katerina Kraft
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
| | - Kevin R Parker
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA
- Cartography Biosciences, Inc., South San Francisco, CA, USA
| | - Brian R Shy
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
- UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Alexander Marson
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA
- Diabetes Center, University of California, San Francisco, San Francisco, CA, USA
- Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Howard Y Chang
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
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23
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Barry T, Mason K, Roeder K, Katsevich E. Robust differential expression testing for single-cell CRISPR screens at low multiplicity of infection. Genome Biol 2024; 25:124. [PMID: 38760839 PMCID: PMC11100084 DOI: 10.1186/s13059-024-03254-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 04/19/2024] [Indexed: 05/19/2024] Open
Abstract
Single-cell CRISPR screens (perturb-seq) link genetic perturbations to phenotypic changes in individual cells. The most fundamental task in perturb-seq analysis is to test for association between a perturbation and a count outcome, such as gene expression. We conduct the first-ever comprehensive benchmarking study of association testing methods for low multiplicity-of-infection (MOI) perturb-seq data, finding that existing methods produce excess false positives. We conduct an extensive empirical investigation of the data, identifying three core analysis challenges: sparsity, confounding, and model misspecification. Finally, we develop an association testing method - SCEPTRE low-MOI - that resolves these analysis challenges and demonstrates improved calibration and power.
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Affiliation(s)
- Timothy Barry
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, USA.
| | - Kaishu Mason
- Department of Statistics and Data Science, Wharton School, University of Pennsylvania, Philadelphia, USA
| | - Kathryn Roeder
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, USA
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, USA
| | - Eugene Katsevich
- Department of Statistics and Data Science, Wharton School, University of Pennsylvania, Philadelphia, USA.
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24
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Khodaee F, Zandie R, Edelman ER. Multimodal Learning for Mapping the Genotype-Phenotype Dynamics. RESEARCH SQUARE 2024:rs.3.rs-4355413. [PMID: 38798675 PMCID: PMC11118704 DOI: 10.21203/rs.3.rs-4355413/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
How complex phenotypes emerge from intricate gene expression patterns is a fundamental question in biology. Quantitative characterization of this relationship, however, is challenging due to the vast combinatorial possibilities and dynamic interplay between genotype and phenotype landscapes. Integrating high-content genotyping approaches such as single-cell RNA sequencing and advanced learning methods such as language models offers an opportunity for dissecting this complex relationship. Here, we present a computational integrated genetics framework designed to analyze and interpret the high-dimensional landscape of genotypes and their associated phenotypes simultaneously. We applied this approach to develop a multimodal foundation model to explore the genotype-phenotype relationship manifold for human transcriptomics at the cellular level. Analyzing this joint manifold showed a refined resolution of cellular heterogeneity, enhanced precision in phenotype annotating, and uncovered potential cross-tissue biomarkers that are undetectable through conventional gene expression analysis alone. Moreover, our results revealed that the gene networks are characterized by scale-free patterns and show context-dependent gene-gene interactions, both of which result in significant variations in the topology of the gene network, particularly evident during aging. Finally, utilizing contextualized embeddings, we investigated gene polyfunctionality which illustrates the multifaceted roles that genes play in different biological processes, and demonstrated that for VWF gene in endothelial cells. Overall, this study advances our understanding of the dynamic interplay between gene expression and phenotypic manifestation and demonstrates the potential of integrated genetics in uncovering new dimensions of cellular function and complexity.
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Affiliation(s)
- Farhan Khodaee
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, 02139, MA, USA
| | - Rohola Zandie
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, 02139, MA, USA
| | - Elazer R. Edelman
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, 02139, MA, USA
- Department of Medicine (Cardiovascular Medicine), Brigham and Women’s Hospital, Boston, 02115, MA, USA
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25
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Fansler MM, Mitschka S, Mayr C. Quantifying 3'UTR length from scRNA-seq data reveals changes independent of gene expression. Nat Commun 2024; 15:4050. [PMID: 38744866 PMCID: PMC11094166 DOI: 10.1038/s41467-024-48254-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 04/22/2024] [Indexed: 05/16/2024] Open
Abstract
Although more than half of all genes generate transcripts that differ in 3'UTR length, current analysis pipelines only quantify the amount but not the length of mRNA transcripts. 3'UTR length is determined by 3' end cleavage sites (CS). We map CS in more than 200 primary human and mouse cell types and increase CS annotations relative to the GENCODE database by 40%. Approximately half of all CS are used in few cell types, revealing that most genes only have one or two major 3' ends. We incorporate the CS annotations into a computational pipeline, called scUTRquant, for rapid, accurate, and simultaneous quantification of gene and 3'UTR isoform expression from single-cell RNA sequencing (scRNA-seq) data. When applying scUTRquant to data from 474 cell types and 2134 perturbations, we discover extensive 3'UTR length changes across cell types that are as widespread and coordinately regulated as gene expression changes but affect mostly different genes. Our data indicate that mRNA abundance and mRNA length are two largely independent axes of gene regulation that together determine the amount and spatial organization of protein synthesis.
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Affiliation(s)
- Mervin M Fansler
- Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Graduate College, New York, NY, 10021, USA
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Sibylle Mitschka
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Christine Mayr
- Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Graduate College, New York, NY, 10021, USA.
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
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26
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Lotfollahi M, Yuhan Hao, Theis FJ, Satija R. The future of rapid and automated single-cell data analysis using reference mapping. Cell 2024; 187:2343-2358. [PMID: 38729109 PMCID: PMC11184658 DOI: 10.1016/j.cell.2024.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 03/05/2024] [Accepted: 03/08/2024] [Indexed: 05/12/2024]
Abstract
As the number of single-cell datasets continues to grow rapidly, workflows that map new data to well-curated reference atlases offer enormous promise for the biological community. In this perspective, we discuss key computational challenges and opportunities for single-cell reference-mapping algorithms. We discuss how mapping algorithms will enable the integration of diverse datasets across disease states, molecular modalities, genetic perturbations, and diverse species and will eventually replace manual and laborious unsupervised clustering pipelines.
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Affiliation(s)
- Mohammad Lotfollahi
- Institute of Computational Biology, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Germany; Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Yuhan Hao
- Center for Genomics and Systems Biology, New York University, New York, NY, USA; New York Genome Center, New York, NY, USA
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Germany; Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK; Department of Mathematics, Technical University of Munich, Garching, Germany.
| | - Rahul Satija
- Center for Genomics and Systems Biology, New York University, New York, NY, USA; New York Genome Center, New York, NY, USA.
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27
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Tao W, Yu Z, Han JDJ. Single-cell senescence identification reveals senescence heterogeneity, trajectory, and modulators. Cell Metab 2024; 36:1126-1143.e5. [PMID: 38604170 DOI: 10.1016/j.cmet.2024.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 12/15/2023] [Accepted: 03/13/2024] [Indexed: 04/13/2024]
Abstract
Cellular senescence underlies many aging-related pathologies, but its heterogeneity poses challenges for studying and targeting senescent cells. We present here a machine learning program senescent cell identification (SenCID), which accurately identifies senescent cells in both bulk and single-cell transcriptome. Trained on 602 samples from 52 senescence transcriptome datasets spanning 30 cell types, SenCID identifies six major senescence identities (SIDs). Different SIDs exhibit different senescence baselines, stemness, gene functions, and responses to senolytics. SenCID enables the reconstruction of senescent trajectories under normal aging, chronic diseases, and COVID-19. Additionally, when applied to single-cell Perturb-seq data, SenCID helps reveal a hierarchy of senescence modulators. Overall, SenCID is an essential tool for precise single-cell analysis of cellular senescence, enabling targeted interventions against senescent cells.
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Affiliation(s)
- Wanyu Tao
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, China
| | - Zhengqing Yu
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, China
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, China; Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies, Chengdu, China.
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28
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Madrazo N, Khattar Z, Powers ET, Rosarda JD, Wiseman RL. Mapping stress-responsive signaling pathways induced by mitochondrial proteostasis perturbations. Mol Biol Cell 2024; 35:ar74. [PMID: 38536439 PMCID: PMC11151107 DOI: 10.1091/mbc.e24-01-0041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 03/12/2024] [Accepted: 03/22/2024] [Indexed: 04/09/2024] Open
Abstract
Imbalances in mitochondrial proteostasis are associated with pathologic mitochondrial dysfunction implicated in etiologically diverse diseases. This has led to considerable interest in defining the mechanisms responsible for regulating mitochondria in response to mitochondrial stress. Numerous stress-responsive signaling pathways have been suggested to regulate mitochondria in response to proteotoxic stress. These include the integrated stress response (ISR), the heat shock response (HSR), and the oxidative stress response (OSR). Here, we define the stress signaling pathways activated in response to chronic mitochondrial proteostasis perturbations by monitoring the expression of sets of genes regulated downstream of each of these signaling pathways in published Perturb-seq datasets from K562 cells CRISPRi-depleted of mitochondrial proteostasis factors. Interestingly, we find that the ISR is preferentially activated in response to chronic, genetically-induced mitochondrial proteostasis stress, with no other pathway showing significant activation. Further, we demonstrate that CRISPRi depletion of other mitochondria-localized proteins similarly shows preferential activation of the ISR relative to other stress-responsive signaling pathways. These results both establish our gene set profiling approach as a viable strategy to probe stress responsive signaling pathways induced by perturbations to specific organelles and identify the ISR as the predominant stress-responsive signaling pathway activated in response to chronic disruption of mitochondrial proteostasis.
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Affiliation(s)
- Nicole Madrazo
- Department of Molecular and Cellular Biology, Scripps Research, La Jolla, CA 92037
| | - Zinia Khattar
- Department of Molecular and Cellular Biology, Scripps Research, La Jolla, CA 92037
- Del Norte High School, San Diego, CA 92127
| | - Evan T. Powers
- Department of Chemistry, Scripps Research, La Jolla, CA 92037
| | - Jessica D. Rosarda
- Department of Anatomy, Physiology, and Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD 20814
| | - R. Luke Wiseman
- Department of Molecular and Cellular Biology, Scripps Research, La Jolla, CA 92037
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29
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Fianu I, Ochmann M, Walshe JL, Dybkov O, Cruz JN, Urlaub H, Cramer P. Structural basis of Integrator-dependent RNA polymerase II termination. Nature 2024; 629:219-227. [PMID: 38570683 PMCID: PMC11062913 DOI: 10.1038/s41586-024-07269-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 03/05/2024] [Indexed: 04/05/2024]
Abstract
The Integrator complex can terminate RNA polymerase II (Pol II) in the promoter-proximal region of genes. Previous work has shed light on how Integrator binds to the paused elongation complex consisting of Pol II, the DRB sensitivity-inducing factor (DSIF) and the negative elongation factor (NELF) and how it cleaves the nascent RNA transcript1, but has not explained how Integrator removes Pol II from the DNA template. Here we present three cryo-electron microscopy structures of the complete Integrator-PP2A complex in different functional states. The structure of the pre-termination complex reveals a previously unresolved, scorpion-tail-shaped INTS10-INTS13-INTS14-INTS15 module that may use its 'sting' to open the DSIF DNA clamp and facilitate termination. The structure of the post-termination complex shows that the previously unresolved subunit INTS3 and associated sensor of single-stranded DNA complex (SOSS) factors prevent Pol II rebinding to Integrator after termination. The structure of the free Integrator-PP2A complex in an inactive closed conformation2 reveals that INTS6 blocks the PP2A phosphatase active site. These results lead to a model for how Integrator terminates Pol II transcription in three steps that involve major rearrangements.
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Affiliation(s)
- Isaac Fianu
- Department of Molecular Biology, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany.
| | - Moritz Ochmann
- Department of Molecular Biology, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - James L Walshe
- Department of Molecular Biology, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Olexandr Dybkov
- Bioanalytical Mass Spectrometry, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Joseph Neos Cruz
- Department of Molecular Biology, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Henning Urlaub
- Bioanalytical Mass Spectrometry, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
- Institute of Clinical Chemistry, Bioanalytics Group, University Medical Center Göttingen, Göttingen, Germany
- Cluster of Excellence 'Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells' (MBExC), University of Göttingen, Göttingen, Germany
| | - Patrick Cramer
- Department of Molecular Biology, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany.
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30
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Xue G, Zhang X, Li W, Zhang L, Zhang Z, Zhou X, Zhang D, Zhang L, Li Z. A logic-incorporated gene regulatory network deciphers principles in cell fate decisions. eLife 2024; 12:RP88742. [PMID: 38652107 PMCID: PMC11037919 DOI: 10.7554/elife.88742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024] Open
Abstract
Organisms utilize gene regulatory networks (GRN) to make fate decisions, but the regulatory mechanisms of transcription factors (TF) in GRNs are exceedingly intricate. A longstanding question in this field is how these tangled interactions synergistically contribute to decision-making procedures. To comprehensively understand the role of regulatory logic in cell fate decisions, we constructed a logic-incorporated GRN model and examined its behavior under two distinct driving forces (noise-driven and signal-driven). Under the noise-driven mode, we distilled the relationship among fate bias, regulatory logic, and noise profile. Under the signal-driven mode, we bridged regulatory logic and progression-accuracy trade-off, and uncovered distinctive trajectories of reprogramming influenced by logic motifs. In differentiation, we characterized a special logic-dependent priming stage by the solution landscape. Finally, we applied our findings to decipher three biological instances: hematopoiesis, embryogenesis, and trans-differentiation. Orthogonal to the classical analysis of expression profile, we harnessed noise patterns to construct the GRN corresponding to fate transition. Our work presents a generalizable framework for top-down fate-decision studies and a practical approach to the taxonomy of cell fate decisions.
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Affiliation(s)
- Gang Xue
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Xiaoyi Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Wanqi Li
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Lu Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Zongxu Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Xiaolin Zhou
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Di Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Lei Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
- Beijing International Center for Mathematical Research, Center for Machine Learning Research, Peking UniversityBeijingChina
| | - Zhiyuan Li
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
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31
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Frenkel M, Raman S. Discovering mechanisms of human genetic variation and controlling cell states at scale. Trends Genet 2024:S0168-9525(24)00074-X. [PMID: 38658256 DOI: 10.1016/j.tig.2024.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Revised: 03/29/2024] [Accepted: 03/29/2024] [Indexed: 04/26/2024]
Abstract
Population-scale sequencing efforts have catalogued substantial genetic variation in humans such that variant discovery dramatically outpaces interpretation. We discuss how single-cell sequencing is poised to reveal genetic mechanisms at a rate that may soon approach that of variant discovery. The functional genomics toolkit is sufficiently modular to systematically profile almost any type of variation within increasingly diverse contexts and with molecularly comprehensive and unbiased readouts. As a result, we can construct deep phenotypic atlases of variant effects that span the entire regulatory cascade. The same conceptual approach to interpreting genetic variation should be applied to engineering therapeutic cell states. In this way, variant mechanism discovery and cell state engineering will become reciprocating and iterative processes towards genomic medicine.
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Affiliation(s)
- Max Frenkel
- Cellular and Molecular Biology Graduate Program, University of Wisconsin, Madison, WI, USA; Medical Scientist Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Biochemistry, University of Wisconsin, Madison, WI, USA.
| | - Srivatsan Raman
- Department of Biochemistry, University of Wisconsin, Madison, WI, USA; Department of Bacteriology, University of Wisconsin, Madison, WI, USA; Department of Chemical and Biological Engineering, University of Wisconsin, Madison, WI, USA.
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32
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Zhou J, Chng WJ. Unveiling novel insights in acute myeloid leukemia through single-cell RNA sequencing. Front Oncol 2024; 14:1365330. [PMID: 38711849 PMCID: PMC11070491 DOI: 10.3389/fonc.2024.1365330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 04/09/2024] [Indexed: 05/08/2024] Open
Abstract
Acute myeloid leukemia (AML) is a complex and heterogeneous group of aggressive hematopoietic stem cell disease. The presence of diverse and functionally distinct populations of leukemia cells within the same patient's bone marrow or blood poses a significant challenge in diagnosing and treating AML. A substantial proportion of AML patients demonstrate resistance to induction chemotherapy and a grim prognosis upon relapse. The rapid advance in next generation sequencing technologies, such as single-cell RNA-sequencing (scRNA-seq), has revolutionized our understanding of AML pathogenesis by enabling high-resolution interrogation of the cellular heterogeneity in the AML ecosystem, and their transcriptional signatures at a single-cell level. New studies have successfully characterized the inextricably intertwined interactions among AML cells, immune cells and bone marrow microenvironment and their contributions to the AML development, therapeutic resistance and relapse. These findings have deepened and broadened our understanding the complexity and heterogeneity of AML, which are difficult to detect with bulk RNA-seq. This review encapsulates the burgeoning body of knowledge generated through scRNA-seq, providing the novel insights and discoveries it has unveiled in AML biology. Furthermore, we discuss the potential implications of scRNA-seq in therapeutic opportunities, focusing on immunotherapy. Finally, we highlight the current limitations and future direction of scRNA-seq in the field.
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Affiliation(s)
- Jianbiao Zhou
- Cancer Science Institute of Singapore, Center for Translational Medicine, National University of Singapore, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- NUS Center for Cancer Research, Center for Translational Medicine, Singapore, Singapore
| | - Wee-Joo Chng
- Cancer Science Institute of Singapore, Center for Translational Medicine, National University of Singapore, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- NUS Center for Cancer Research, Center for Translational Medicine, Singapore, Singapore
- Department of Hematology-Oncology, National University Cancer Institute of Singapore (NCIS), The National University Health System (NUHS), Singapore, Singapore
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33
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Barry T, Mason K, Roeder K, Katsevich E. Robust differential expression testing for single-cell CRISPR screens at low multiplicity of infection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.15.540875. [PMID: 38659821 PMCID: PMC11042176 DOI: 10.1101/2023.05.15.540875] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Single-cell CRISPR screens (perturb-seq) link genetic perturbations to phenotypic changes in individual cells. The most fundamental task in perturb-seq analysis is to test for association between a perturbation and a count outcome, such as gene expression. We conduct the first-ever comprehensive benchmarking study of association testing methods for low multiplicity-of-infection (MOI) perturb-seq data, finding that existing methods produce excess false positives. We conduct an extensive empirical investigation of the data, identifying three core analysis challenges: sparsity, confounding, and model misspecification. Finally, we develop an association testing method - SCEPTRE low-MOI - that resolves these analysis challenges and demonstrates improved calibration and power.
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34
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Luo T, Pan J, Zhu Y, Wang X, Li K, Zhao G, Li B, Hu Z, Xia K, Li J. Association between de novo variants of nuclear-encoded mitochondrial-related genes and undiagnosed developmental disorder and autism. QJM 2024; 117:269-276. [PMID: 37930872 PMCID: PMC11014680 DOI: 10.1093/qjmed/hcad249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 10/24/2023] [Indexed: 11/08/2023] Open
Abstract
BACKGROUND Evidence suggests that mitochondrial abnormalities increase the risk of two neurodevelopmental disorders: undiagnosed developmental disorder (UDD) and autism spectrum disorder (ASD). However, which nuclear-encoded mitochondrial-related genes (NEMGs) were associated with UDD-ASD is unclear. AIM To explore the association between de novo variants (DNVs) of NEMGs and UDD-ASD. DESIGN Comprehensive analysis based on DNVs of NEMGs identified in patients (31 058 UDD probands and 10 318 ASD probands) and 4262 controls. METHODS By curating NEMGs and cataloging publicly published DNVs in NEMGs, we compared the frequency of DNVs in cases and controls. We also applied a TADA-denovo model to highlight disease-associated NEMGs and characterized them based on gene intolerance, functional networks and expression patterns. RESULTS Compared with levels in 4262 controls, an excess of protein-truncating variants and deleterious missense variants in 1421 cataloged NEMGs from 41 376 patients (31 058 UDD and 10 318 ASD probands) was observed. Overall, 3.23% of de novo deleterious missense variants and 3.20% of de novo protein-truncating variants contributed to 1.1% and 0.39% of UDD-ASD cases, respectively. We prioritized 130 disease-associated NEMGs and showed distinct expression patterns in the developing human brain. Disease-associated NEMGs expression was enriched in both excitatory and inhibitory neuronal lineages from the developing human cortex. CONCLUSIONS Rare genetic alterations of disease-associated NEMGs may play a role in UDD-ASD development and lay the groundwork for a better understanding of the biology of UDD-ASD.
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Affiliation(s)
- T Luo
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
| | - J Pan
- Department of Birth Health and Genetics, The Reproductive Hospital of Guangxi Zhuang Autonomous Region, Nanning 530022, China
| | - Y Zhu
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
| | - X Wang
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
| | - K Li
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, Anhui, China
| | - G Zhao
- 4National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital & Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008,China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - B Li
- 4National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital & Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008,China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Z Hu
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
| | - K Xia
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
- MOE Key Lab of Rare Pediatric Diseases & School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 410008, China
| | - J Li
- 4National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital & Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008,China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
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35
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Schmid EW, Walter JC. Predictomes: A classifier-curated database of AlphaFold-modeled protein-protein interactions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.09.588596. [PMID: 38645019 PMCID: PMC11030396 DOI: 10.1101/2024.04.09.588596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Protein-protein interactions (PPIs) are ubiquitous in biology, yet a comprehensive structural characterization of the PPIs underlying biochemical processes is lacking. Although AlphaFold-Multimer (AF-M) has the potential to fill this knowledge gap, standard AF-M confidence metrics do not reliably separate relevant PPIs from an abundance of false positive predictions. To address this limitation, we used machine learning on well curated datasets to train a Structure Prediction and Omics informed Classifier called SPOC that shows excellent performance in separating true and false PPIs, including in proteome-wide screens. We applied SPOC to an all-by-all matrix of nearly 300 human genome maintenance proteins, generating ~40,000 predictions that can be viewed at predictomes.org, where users can also score their own predictions with SPOC. High confidence PPIs discovered using our approach suggest novel hypotheses in genome maintenance. Our results provide a framework for interpreting large scale AF-M screens and help lay the foundation for a proteome-wide structural interactome.
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Affiliation(s)
- Ernst W. Schmid
- Department of Biological Chemistry & Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Johannes C. Walter
- Department of Biological Chemistry & Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Boston, MA 02115, USA
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36
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Ansari M, Faour KNW, Shimamura A, Grimes G, Kao EM, Denhoff ER, Blatnik A, Ben-Isvy D, Wang L, Helm BM, Firth H, Breman AM, Bijlsma EK, Iwata-Otsubo A, de Ravel TJL, Fusaro V, Fryer A, Nykamp K, Stühn LG, Haack TB, Korenke GC, Constantinou P, Bujakowska KM, Low KJ, Place E, Humberson J, Napier MP, Hoffman J, Juusola J, Deardorff MA, Shao W, Rockowitz S, Krantz I, Kaur M, Raible S, Dortenzio V, Kliesch S, Singer-Berk M, Groopman E, DiTroia S, Ballal S, Srivastava S, Rothfelder K, Biskup S, Rzasa J, Kerkhof J, McConkey H, Sadikovic B, Hilton S, Banka S, Tüttelmann F, Conrad DF, O'Donnell-Luria A, Talkowski ME, FitzPatrick DR, Boone PM. Heterozygous loss-of-function SMC3 variants are associated with variable growth and developmental features. HGG ADVANCES 2024; 5:100273. [PMID: 38297832 PMCID: PMC10876629 DOI: 10.1016/j.xhgg.2024.100273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 01/23/2024] [Accepted: 01/23/2024] [Indexed: 02/02/2024] Open
Abstract
Heterozygous missense variants and in-frame indels in SMC3 are a cause of Cornelia de Lange syndrome (CdLS), marked by intellectual disability, growth deficiency, and dysmorphism, via an apparent dominant-negative mechanism. However, the spectrum of manifestations associated with SMC3 loss-of-function variants has not been reported, leading to hypotheses of alternative phenotypes or even developmental lethality. We used matchmaking servers, patient registries, and other resources to identify individuals with heterozygous, predicted loss-of-function (pLoF) variants in SMC3, and analyzed population databases to characterize mutational intolerance in this gene. Here, we show that SMC3 behaves as an archetypal haploinsufficient gene: it is highly constrained against pLoF variants, strongly depleted for missense variants, and pLoF variants are associated with a range of developmental phenotypes. Among 14 individuals with SMC3 pLoF variants, phenotypes were variable but coalesced on low growth parameters, developmental delay/intellectual disability, and dysmorphism, reminiscent of atypical CdLS. Comparisons to individuals with SMC3 missense/in-frame indel variants demonstrated an overall milder presentation in pLoF carriers. Furthermore, several individuals harboring pLoF variants in SMC3 were nonpenetrant for growth, developmental, and/or dysmorphic features, and some had alternative symptomatologies with rational biological links to SMC3. Analyses of tumor and model system transcriptomic data and epigenetic data in a subset of cases suggest that SMC3 pLoF variants reduce SMC3 expression but do not strongly support clustering with functional genomic signatures of typical CdLS. Our finding of substantial population-scale LoF intolerance in concert with variable growth and developmental features in subjects with SMC3 pLoF variants expands the scope of cohesinopathies, informs on their allelic architecture, and suggests the existence of additional clearly LoF-constrained genes whose disease links will be confirmed only by multilayered genomic data paired with careful phenotyping.
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Affiliation(s)
- Morad Ansari
- South East Scotland Genetic Service, Western General Hospital, Edinburgh, UK; MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Kamli N W Faour
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA; Cornelia de Lange Syndrome and Related Disorders Clinic, Boston Children's Hospital, Boston, MA, USA
| | - Akiko Shimamura
- Division of Hematology and Oncology, Boston Children's Hospital, Boston, MA, USA
| | - Graeme Grimes
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Emeline M Kao
- Institutional Centers for Clinical and Translational Research, Boston Children's Hospital, Boston, MA, USA
| | - Erica R Denhoff
- Institutional Centers for Clinical and Translational Research, Boston Children's Hospital, Boston, MA, USA
| | - Ana Blatnik
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK; Department of Clinical Cancer Genetics, Institute of Oncology Ljubljana, Ljubljana, Slovenia
| | - Daniel Ben-Isvy
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Medical Sciences, Harvard Medical School, Boston, MA, USA
| | - Lily Wang
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Medical Sciences, Harvard Medical School, Boston, MA, USA
| | - Benjamin M Helm
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Helen Firth
- Clinical Genetics, Addenbrooke's Hospital, Cambridge University Hospitals, Cambridge, UK
| | - Amy M Breman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Emilia K Bijlsma
- Department of Clinical Genetics, Leiden University Medical Centre, Leiden, the Netherlands
| | - Aiko Iwata-Otsubo
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Thomy J L de Ravel
- Centre for Human Genetics, UZ Leuven/Leuven University Hospitals, Leuven, Belgium
| | | | - Alan Fryer
- Department of Clinical Genetics, Alder Hey Children's Hospital Liverpool, Liverpool, UK
| | | | - Lara G Stühn
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Tobias B Haack
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - G Christoph Korenke
- Department of Neuropaediatric and Metabolic Diseases, University Children's Hospital Oldenburg, Oldenburg, Germany
| | - Panayiotis Constantinou
- West of Scotland Centre for Genomic Medicine, Queen Elizabeth University Hospital, Glasgow, UK
| | | | - Karen J Low
- University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK; University of Bristol, Bristol, UK
| | - Emily Place
- Massachusetts Eye and Ear Infirmary, Boston, MA, USA
| | | | | | | | | | - Matthew A Deardorff
- Departments of Pathology and Pediatrics, Children's Hospital Los Angeles and University of Southern California, Los Angeles, CA, USA
| | - Wanqing Shao
- Research Computing, Information Technology, Boston Children's Hospital, Boston, MA, USA
| | - Shira Rockowitz
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA; Research Computing, Information Technology, Boston Children's Hospital, Boston, MA, USA; The Manton Center for Orphan Disease Research, Boston Children's Hospital, Boston, MA, USA
| | - Ian Krantz
- Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Maninder Kaur
- Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Sarah Raible
- Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Sabine Kliesch
- Department of Clinical and Surgical Andrology, Centre of Reproductive Medicine and Andrology, University Hospital Münster, Münster, Germany
| | - Moriel Singer-Berk
- Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Emily Groopman
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA; Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Stephanie DiTroia
- Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sonia Ballal
- Cornelia de Lange Syndrome and Related Disorders Clinic, Boston Children's Hospital, Boston, MA, USA; Division of Gastroenterology, Boston Children's Hospital, Boston, MA, USA
| | - Siddharth Srivastava
- Cornelia de Lange Syndrome and Related Disorders Clinic, Boston Children's Hospital, Boston, MA, USA; Divison of Neurology, Boston Children's Hospital, Boston, MA, USA
| | | | - Saskia Biskup
- Zentrum für Humangenetik, Tübingen, Germany; Center for Genomics and Transcriptomics (CeGaT), Tübingen, Germany
| | - Jessica Rzasa
- Molecular Diagnostics Program and Verspeeten Clinical Genome Centre, London Health Sciences Centre, London, ON, Canada
| | - Jennifer Kerkhof
- Molecular Diagnostics Program and Verspeeten Clinical Genome Centre, London Health Sciences Centre, London, ON, Canada
| | - Haley McConkey
- Molecular Diagnostics Program and Verspeeten Clinical Genome Centre, London Health Sciences Centre, London, ON, Canada
| | - Bekim Sadikovic
- Molecular Diagnostics Program and Verspeeten Clinical Genome Centre, London Health Sciences Centre, London, ON, Canada
| | - Sarah Hilton
- Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Health Innovation Manchester, Manchester, UK
| | - Siddharth Banka
- Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Health Innovation Manchester, Manchester, UK; Division of Evolution, Infection, and Genomics, School of Biological Sciences, Faculty of Biology, Medicine, and Health, University of Manchester, Manchester, UK
| | - Frank Tüttelmann
- Institute of Reproductive Genetics, University of Münster, Münster, Germany
| | - Donald F Conrad
- Division of Genetics, Oregon National Primate Research Center, Oregon Health and Science University, Portland, OR, USA; Center for Embryonic Cell and Gene Therapy, Oregon Health and Science University, Portland, OR, USA
| | - Anne O'Donnell-Luria
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Michael E Talkowski
- Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - David R FitzPatrick
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Philip M Boone
- Cornelia de Lange Syndrome and Related Disorders Clinic, Boston Children's Hospital, Boston, MA, USA; Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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37
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Gentili M, Carlson RJ, Liu B, Hellier Q, Andrews J, Qin Y, Blainey PC, Hacohen N. Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.07.588166. [PMID: 38645119 PMCID: PMC11030420 DOI: 10.1101/2024.04.07.588166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
STING is an innate immune sensor that traffics across many cellular compartments to carry out its function of detecting cyclic di-nucleotides and triggering defense processes. Mutations in factors that regulate this process are often linked to STING-dependent human inflammatory disorders. To systematically identify factors involved in STING trafficking, we performed a genome-wide optical pooled screen and examined the impact of genetic perturbations on intracellular STING localization. Based on subcellular imaging of STING protein and trafficking markers in 45 million cells perturbed with sgRNAs, we defined 464 clusters of gene perturbations with similar cellular phenotypes. A higher-dimensional focused optical pooled screen on 262 perturbed genes which assayed 11 imaging channels identified 73 finer phenotypic clusters. In a cluster containing USE1, a protein that mediates Golgi to ER transport, we found a gene of unknown function, C19orf25. Consistent with the known role of USE1, loss of C19orf25 enhanced STING signaling. Other clusters contained subunits of the HOPS, GARP and RIC1-RGP1 complexes. We show that HOPS deficiency delayed STING degradation and consequently increased signaling. Similarly, GARP/RIC1-RGP1 loss increased STING signaling by delaying STING exit from the Golgi. Our findings demonstrate that genome-wide genotype-phenotype maps based on high-content cell imaging outperform other screening approaches, and provide a community resource for mining for factors that impact STING trafficking as well as other cellular processes observable in our dataset.
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38
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Carlson RJ, Patten JJ, Stefanakis G, Soong BY, Radhakrishnan A, Singh A, Thakur N, Amarasinghe GK, Hacohen N, Basler CF, Leung D, Uhler C, Davey RA, Blainey PC. Single-cell image-based genetic screens systematically identify regulators of Ebola virus subcellular infection dynamics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.06.588168. [PMID: 38617272 PMCID: PMC11014611 DOI: 10.1101/2024.04.06.588168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Ebola virus (EBOV) is a high-consequence filovirus that gives rise to frequent epidemics with high case fatality rates and few therapeutic options. Here, we applied image-based screening of a genome-wide CRISPR library to systematically identify host cell regulators of Ebola virus infection in 39,085,093 million single cells. Measuring viral RNA and protein levels together with their localization in cells identified over 998 related host factors and provided detailed information about the role of each gene across the virus replication cycle. We trained a deep learning model on single-cell images to associate each host factor with predicted replication steps, and confirmed the predicted relationship for select host factors. Among the findings, we showed that the mitochondrial complex III subunit UQCRB is a post-entry regulator of Ebola virus RNA replication, and demonstrated that UQCRB inhibition with a small molecule reduced overall Ebola virus infection with an IC50 of 5 μM. Using a random forest model, we also identified perturbations that reduced infection by disrupting the equilibrium between viral RNA and protein. One such protein, STRAP, is a spliceosome-associated factor that was found to be closely associated with VP35, a viral protein required for RNA processing. Loss of STRAP expression resulted in a reduction in full-length viral genome production and subsequent production of non-infectious virus particles. Overall, the data produced in this genome-wide high-content single-cell screen and secondary screens in additional cell lines and related filoviruses (MARV and SUDV) revealed new insights about the role of host factors in virus replication and potential new targets for therapeutic intervention.
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Affiliation(s)
- Rebecca J Carlson
- Massachusetts Institute of Technology, Department of Health Sciences and Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - J J Patten
- Department of Virology, Immunology, and Microbiology, Boston University School of Medicine, Boston, MA, USA
- National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA
| | - George Stefanakis
- Laboratory for Information & Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Brian Y Soong
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Adityanarayanan Radhakrishnan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard School of Engineering and Applied Sciences, Cambridge, MA, USA
| | - Avtar Singh
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Naveen Thakur
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gaya K Amarasinghe
- Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA
| | - Nir Hacohen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts General Hospital, Cancer Center, Boston, MA, USA
| | - Christopher F Basler
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Daisy Leung
- Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA
| | - Caroline Uhler
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Laboratory for Information & Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Robert A Davey
- Department of Virology, Immunology, and Microbiology, Boston University School of Medicine, Boston, MA, USA
- National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA
| | - Paul C Blainey
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts Institute of Technology, Department of Biological Engineering, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
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39
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Cortes DE, Escudero M, Korgan AC, Mitra A, Edwards A, Aydin SC, Munger SC, Charland K, Zhang ZW, O'Connell KMS, Reinholdt LG, Pera MF. An in vitro neurogenetics platform for precision disease modeling in the mouse. SCIENCE ADVANCES 2024; 10:eadj9305. [PMID: 38569042 PMCID: PMC10990289 DOI: 10.1126/sciadv.adj9305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 02/27/2024] [Indexed: 04/05/2024]
Abstract
The power and scope of disease modeling can be markedly enhanced through the incorporation of broad genetic diversity. The introduction of pathogenic mutations into a single inbred mouse strain sometimes fails to mimic human disease. We describe a cross-species precision disease modeling platform that exploits mouse genetic diversity to bridge cell-based modeling with whole organism analysis. We developed a universal protocol that permitted robust and reproducible neural differentiation of genetically diverse human and mouse pluripotent stem cell lines and then carried out a proof-of-concept study of the neurodevelopmental gene DYRK1A. Results in vitro reliably predicted the effects of genetic background on Dyrk1a loss-of-function phenotypes in vivo. Transcriptomic comparison of responsive and unresponsive strains identified molecular pathways conferring sensitivity or resilience to Dyrk1a1A loss and highlighted differential messenger RNA isoform usage as an important determinant of response. This cross-species strategy provides a powerful tool in the functional analysis of candidate disease variants identified through human genetic studies.
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Affiliation(s)
| | | | | | - Arojit Mitra
- The Jackson Laboratory, Bar Harbor, ME 04660, USA
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40
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Mackay TFC, Anholt RRH. Pleiotropy, epistasis and the genetic architecture of quantitative traits. Nat Rev Genet 2024:10.1038/s41576-024-00711-3. [PMID: 38565962 DOI: 10.1038/s41576-024-00711-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2024] [Indexed: 04/04/2024]
Abstract
Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.
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Affiliation(s)
- Trudy F C Mackay
- Center for Human Genetics, Clemson University, Greenwood, SC, USA.
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA.
| | - Robert R H Anholt
- Center for Human Genetics, Clemson University, Greenwood, SC, USA.
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA.
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41
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Pepin ME, Gupta RM. The Role of Endothelial Cells in Atherosclerosis: Insights from Genetic Association Studies. THE AMERICAN JOURNAL OF PATHOLOGY 2024; 194:499-509. [PMID: 37827214 PMCID: PMC10988759 DOI: 10.1016/j.ajpath.2023.09.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 08/21/2023] [Accepted: 09/01/2023] [Indexed: 10/14/2023]
Abstract
Endothelial cells (ECs) mediate several biological functions that are relevant to atherosclerosis and coronary artery disease (CAD), regulating an array of vital processes including vascular tone, wound healing, reactive oxygen species, shear stress response, and inflammation. Although which of these functions is linked causally with CAD development and/or progression is not yet known, genome-wide association studies have implicated more than 400 loci associated with CAD risk, among which several have shown EC-relevant functions. Given the arduous process of mechanistically interrogating single loci to CAD, high-throughput variant characterization methods, including pooled Clustered Regularly Interspaced Short Palindromic Repeats screens, offer exciting potential to rapidly accelerate the discovery of bona fide EC-relevant genetic loci. These discoveries in turn will broaden the therapeutic avenues for CAD beyond lipid lowering and behavioral risk modification to include EC-centric modalities of risk prevention and treatment.
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Affiliation(s)
- Mark E Pepin
- Cardiovascular Disease Initiative, The Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts; Divisions of Genetics and Cardiovascular Medicine, Brigham & Women's Hospital, Boston, Massachusetts
| | - Rajat M Gupta
- Cardiovascular Disease Initiative, The Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts; Divisions of Genetics and Cardiovascular Medicine, Brigham & Women's Hospital, Boston, Massachusetts.
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42
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Dreyer J, Ricci G, van den Berg J, Bhardwaj V, Funk J, Armstrong C, van Batenburg V, Sine C, VanInsberghe MA, Marsman R, Mandemaker IK, di Sanzo S, Costantini J, Manzo SG, Biran A, Burny C, Völker-Albert M, Groth A, Spencer SL, van Oudenaarden A, Mattiroli F. Acute multi-level response to defective de novo chromatin assembly in S-phase. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.22.586291. [PMID: 38585916 PMCID: PMC10996472 DOI: 10.1101/2024.03.22.586291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Long-term perturbation of de novo chromatin assembly during DNA replication has profound effects on epigenome maintenance and cell fate. The early mechanistic origin of these defects is unknown. Here, we combine acute degradation of Chromatin Assembly Factor 1 (CAF-1), a key player in de novo chromatin assembly, with single-cell genomics, quantitative proteomics, and live-microscopy to uncover these initiating mechanisms in human cells. CAF-1 loss immediately slows down DNA replication speed and renders nascent DNA hyperaccessible. A rapid cellular response, distinct from canonical DNA damage signaling, is triggered and lowers histone mRNAs. As a result, histone variants usage and their modifications are altered, limiting transcriptional fidelity and delaying chromatin maturation within a single S-phase. This multi-level response induces a cell-cycle arrest after mitosis. Our work reveals the immediate consequences of defective de novo chromatin assembly during DNA replication, explaining how at later times the epigenome and cell fate can be altered.
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Affiliation(s)
- Jan Dreyer
- Hubrecht Institute-KNAW & University Medical Center Utrecht, Uppsalalaan 8, 3584 CT Utrecht, The Netherlands
| | - Giulia Ricci
- Hubrecht Institute-KNAW & University Medical Center Utrecht, Uppsalalaan 8, 3584 CT Utrecht, The Netherlands
| | - Jeroen van den Berg
- Hubrecht Institute-KNAW & University Medical Center Utrecht, Uppsalalaan 8, 3584 CT Utrecht, The Netherlands
- Oncode Institute, The Netherlands
| | - Vivek Bhardwaj
- Hubrecht Institute-KNAW & University Medical Center Utrecht, Uppsalalaan 8, 3584 CT Utrecht, The Netherlands
- Oncode Institute, The Netherlands
| | - Janina Funk
- Hubrecht Institute-KNAW & University Medical Center Utrecht, Uppsalalaan 8, 3584 CT Utrecht, The Netherlands
| | - Claire Armstrong
- Department of Biochemistry, University of Colorado Boulder, Boulder, CO 80303, USA
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO 80303, USA
| | - Vincent van Batenburg
- Hubrecht Institute-KNAW & University Medical Center Utrecht, Uppsalalaan 8, 3584 CT Utrecht, The Netherlands
- Oncode Institute, The Netherlands
| | - Chance Sine
- Department of Biochemistry, University of Colorado Boulder, Boulder, CO 80303, USA
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO 80303, USA
| | - Michael A. VanInsberghe
- Hubrecht Institute-KNAW & University Medical Center Utrecht, Uppsalalaan 8, 3584 CT Utrecht, The Netherlands
- Oncode Institute, The Netherlands
| | - Richard Marsman
- Hubrecht Institute-KNAW & University Medical Center Utrecht, Uppsalalaan 8, 3584 CT Utrecht, The Netherlands
| | - Imke K. Mandemaker
- Hubrecht Institute-KNAW & University Medical Center Utrecht, Uppsalalaan 8, 3584 CT Utrecht, The Netherlands
| | - Simone di Sanzo
- MOLEQLAR Analytics GmbH, Rosenheimer Street 141 h, 81671 Munich, Germany
| | - Juliette Costantini
- Hubrecht Institute-KNAW & University Medical Center Utrecht, Uppsalalaan 8, 3584 CT Utrecht, The Netherlands
| | - Stefano G. Manzo
- Oncode Institute, The Netherlands
- Division of Gene Regulation, Netherlands Cancer Institute, The Netherlands
- Department of Biosciences, Università degli Studi di Milano, Via Celoria 26, 20133 Milan, Italy
| | - Alva Biran
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen 2200, Denmark
- Biotech Research & Innovation Centre, University of Copenhagen, Copenhagen 2200, Denmark
- Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen 2200, Denmark
| | - Claire Burny
- MOLEQLAR Analytics GmbH, Rosenheimer Street 141 h, 81671 Munich, Germany
| | | | - Anja Groth
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen 2200, Denmark
- Biotech Research & Innovation Centre, University of Copenhagen, Copenhagen 2200, Denmark
- Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen 2200, Denmark
| | - Sabrina L. Spencer
- Department of Biochemistry, University of Colorado Boulder, Boulder, CO 80303, USA
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO 80303, USA
| | - Alexander van Oudenaarden
- Hubrecht Institute-KNAW & University Medical Center Utrecht, Uppsalalaan 8, 3584 CT Utrecht, The Netherlands
- Oncode Institute, The Netherlands
| | - Francesca Mattiroli
- Hubrecht Institute-KNAW & University Medical Center Utrecht, Uppsalalaan 8, 3584 CT Utrecht, The Netherlands
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43
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Muthukumar G, Stevens TA, Inglis AJ, Esantsi TK, Saunders RA, Schulte F, Voorhees RM, Guna A, Weissman JS. Triaging of α-helical proteins to the mitochondrial outer membrane by distinct chaperone machinery based on substrate topology. Mol Cell 2024; 84:1101-1119.e9. [PMID: 38428433 DOI: 10.1016/j.molcel.2024.01.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 12/08/2023] [Accepted: 01/31/2024] [Indexed: 03/03/2024]
Abstract
Mitochondrial outer membrane ⍺-helical proteins play critical roles in mitochondrial-cytoplasmic communication, but the rules governing the targeting and insertion of these biophysically diverse proteins remain unknown. Here, we first defined the complement of required mammalian biogenesis machinery through genome-wide CRISPRi screens using topologically distinct membrane proteins. Systematic analysis of nine identified factors across 21 diverse ⍺-helical substrates reveals that these components are organized into distinct targeting pathways that act on substrates based on their topology. NAC is required for the efficient targeting of polytopic proteins, whereas signal-anchored proteins require TTC1, a cytosolic chaperone that physically engages substrates. Biochemical and mutational studies reveal that TTC1 employs a conserved TPR domain and a hydrophobic groove in its C-terminal domain to support substrate solubilization and insertion into mitochondria. Thus, the targeting of diverse mitochondrial membrane proteins is achieved through topological triaging in the cytosol using principles with similarities to ER membrane protein biogenesis systems.
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Affiliation(s)
- Gayathri Muthukumar
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Taylor A Stevens
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Avenue, Pasadena, CA 91125, USA
| | - Alison J Inglis
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Avenue, Pasadena, CA 91125, USA
| | - Theodore K Esantsi
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Reuben A Saunders
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Tetrad Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Fabian Schulte
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
| | - Rebecca M Voorhees
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Avenue, Pasadena, CA 91125, USA
| | - Alina Guna
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA; Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Avenue, Pasadena, CA 91125, USA.
| | - Jonathan S Weissman
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
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44
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Cheng C, Hu J, Mannan R, Bhattacharyya R, Rossiter NJ, Magnuson B, Wisniewski JP, Zheng Y, Xiao L, Li C, Awad D, He T, Bao Y, Zhang Y, Cao X, Wang Z, Mehra R, Morlacchi P, Sahai V, di Magliano MP, Shah YM, Ding K, Qiao Y, Lyssiotis CA, Chinnaiyan AM. Targeting PIKfyve-driven lipid homeostasis as a metabolic vulnerability in pancreatic cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.18.585580. [PMID: 38562800 PMCID: PMC10983929 DOI: 10.1101/2024.03.18.585580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) subsists in a nutrient-deregulated microenvironment, making it particularly susceptible to treatments that interfere with cancer metabolism12. For example, PDAC utilizes and is dependent on high levels of autophagy and other lysosomal processes3-5. Although targeting these pathways has shown potential in preclinical studies, progress has been hampered by the challenge of identifying and characterizing favorable targets for drug development6. Here, we characterize PIKfyve, a lipid kinase integral to lysosomal functioning7, as a novel and targetable vulnerability in PDAC. In human patient and murine PDAC samples, we discovered that PIKFYVE is overexpressed in PDAC cells compared to adjacent normal cells. Employing a genetically engineered mouse model, we established the essential role of PIKfyve in PDAC progression. Further, through comprehensive metabolic analyses, we found that PIKfyve inhibition obligated PDAC to upregulate de novo lipid synthesis, a relationship previously undescribed. PIKfyve inhibition triggered a distinct lipogenic gene expression and metabolic program, creating a dependency on de novo lipid metabolism pathways, by upregulating genes such as FASN and ACACA. In PDAC, the KRAS-MAPK signaling pathway is a primary driver of de novo lipid synthesis, specifically enhancing FASN and ACACA levels. Accordingly, the simultaneous targeting of PIKfyve and KRAS-MAPK resulted in the elimination of tumor burden in a syngeneic orthotopic model and tumor regression in a xenograft model of PDAC. Taken together, these studies suggest that disrupting lipid metabolism through PIKfyve inhibition induces synthetic lethality in conjunction with KRAS-MAPK-directed therapies for PDAC.
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Affiliation(s)
- Caleb Cheng
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA
- Medical Scientist Training Program, University of Michigan, Ann Arbor, MI, USA
- Cellular and Molecular Biology Program, University of Michigan, Ann Arbor, MI, USA
| | - Jing Hu
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Pathology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, PRC
| | - Rahul Mannan
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Rupam Bhattacharyya
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Nicholas J Rossiter
- Cellular and Molecular Biology Program, University of Michigan, Ann Arbor, MI, USA
| | - Brian Magnuson
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Jasmine P Wisniewski
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Yang Zheng
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Lanbo Xiao
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
| | - Chungen Li
- State Key Laboratory of Chemical Biology, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, PRC
| | - Dominik Awad
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
| | - Tongchen He
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA
- Medical Scientist Training Program, University of Michigan, Ann Arbor, MI, USA
- Department of Urology, Xiangya Hospital, Central South University, Changsha, Hunan, PRC
| | - Yi Bao
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Yuping Zhang
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Xuhong Cao
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
- Howard Hughes Medical Institute, University of Michigan, Ann Arbor, MI, USA
| | - Zhen Wang
- State Key Laboratory of Chemical Biology, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, PRC
| | - Rohit Mehra
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | | | - Vaibhav Sahai
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
- Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Marina Pasca di Magliano
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
- Department of Surgery, University of Michigan, Ann Arbor, MI, USA
| | - Yatrik M Shah
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, Division of Gastroenterology, University of Michigan, Ann Arbor, MI, USA
| | - Ke Ding
- State Key Laboratory of Chemical Biology, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, PRC
| | - Yuanyuan Qiao
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
| | - Costas A Lyssiotis
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, Division of Gastroenterology, University of Michigan, Ann Arbor, MI, USA
| | - Arul M Chinnaiyan
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
- Howard Hughes Medical Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Urology, University of Michigan, Ann Arbor, MI, USA
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45
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Walton RT, Qin Y, Blainey PC. CROPseq-multi: a versatile solution for multiplexed perturbation and decoding in pooled CRISPR screens. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.17.585235. [PMID: 38558968 PMCID: PMC10979941 DOI: 10.1101/2024.03.17.585235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Forward genetic screens seek to dissect complex biological systems by systematically perturbing genetic elements and observing the resulting phenotypes. While standard screening methodologies introduce individual perturbations, multiplexing perturbations improves the performance of single-target screens and enables combinatorial screens for the study of genetic interactions. Current tools for multiplexing perturbations are incompatible with pooled screening methodologies that require mRNA-embedded barcodes, including some microscopy and single cell sequencing approaches. Here, we report the development of CROPseq-multi, a CROPseq1-inspired lentiviral system to multiplex Streptococcus pyogenes (Sp) Cas9-based perturbations with mRNA-embedded barcodes. CROPseq-multi has equivalent per-guide activity to CROPseq and low lentiviral recombination frequencies. CROPseq-multi is compatible with enrichment screening methodologies and optical pooled screens, and is extensible to screens with single-cell sequencing readouts. For optical pooled screens, an optimized and multiplexed in situ detection protocol improves barcode detection efficiency 10-fold, enables detection of recombination events, and increases decoding efficiency 3-fold relative to CROPseq. CROPseq-multi is a widely applicable multiplexing solution for diverse SpCas9-based genetic screening approaches.
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Affiliation(s)
- Russell T. Walton
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
| | - Yue Qin
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Paul C. Blainey
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
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46
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Strobl EV, Gamazon ER. Discovering Root Causal Genes with High Throughput Perturbations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.13.574491. [PMID: 38260506 PMCID: PMC10802597 DOI: 10.1101/2024.01.13.574491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Root causal gene expression levels - or root causal genes for short - correspond to the initial changes to gene expression that generate patient symptoms as a downstream effect. Identifying root causal genes is critical towards developing treatments that modify disease near its onset, but no existing algorithms attempt to identify root causal genes from data. RNA-sequencing (RNA-seq) data introduces challenges such as measurement error, high dimensionality and non-linearity that compromise accurate estimation of root causal effects even with state-of-the-art approaches. We therefore instead leverage Perturb-seq, or high throughput perturbations with single cell RNA-seq readout, to learn the causal order between the genes. We then transfer the causal order to bulk RNA-seq and identify root causal genes specific to a given patient for the first time using a novel statistic. Experiments demonstrate large improvements in performance. Applications to macular degeneration and multiple sclerosis also reveal root causal genes that lie on known pathogenic pathways, delineate patient subgroups and implicate a newly defined omnigenic root causal model.
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Affiliation(s)
- Eric V Strobl
- Vanderbilt University Medical Center, Nashville, United States of America
| | - Eric R Gamazon
- Vanderbilt University Medical Center, Nashville, United States of America
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47
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Domingo J, Minaeva M, Morris JA, Ziosi M, Sanjana NE, Lappalainen T. Non-linear transcriptional responses to gradual modulation of transcription factor dosage. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.01.582837. [PMID: 38464330 PMCID: PMC10925300 DOI: 10.1101/2024.03.01.582837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Genomic loci associated with common traits and diseases are typically non-coding and likely impact gene expression, sometimes coinciding with rare loss-of-function variants in the target gene. However, our understanding of how gradual changes in gene dosage affect molecular, cellular, and organismal traits is currently limited. To address this gap, we induced gradual changes in gene expression of four genes using CRISPR activation and inactivation. Downstream transcriptional consequences of dosage modulation of three master trans-regulators associated with blood cell traits (GFI1B, NFE2, and MYB) were examined using targeted single-cell multimodal sequencing. We showed that guide tiling around the TSS is the most effective way to modulate cis gene expression across a wide range of fold-changes, with further effects from chromatin accessibility and histone marks that differ between the inhibition and activation systems. Our single-cell data allowed us to precisely detect subtle to large gene expression changes in dozens of trans genes, revealing that many responses to dosage changes of these three TFs are non-linear, including non-monotonic behaviours, even when constraining the fold-changes of the master regulators to a copy number gain or loss. We found that the dosage properties are linked to gene constraint and that some of these non-linear responses are enriched for disease and GWAS genes. Overall, our study provides a straightforward and scalable method to precisely modulate gene expression and gain insights into its downstream consequences at high resolution.
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Affiliation(s)
| | - Mariia Minaeva
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - John A Morris
- New York Genome Center, New York, NY 10013, USA
- Department of Biology, New York University, New York, NY 10003, USA
| | | | - Neville E Sanjana
- New York Genome Center, New York, NY 10013, USA
- Department of Biology, New York University, New York, NY 10003, USA
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY 10013, USA
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
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48
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Peidli S, Green TD, Shen C, Gross T, Min J, Garda S, Yuan B, Schumacher LJ, Taylor-King JP, Marks DS, Luna A, Blüthgen N, Sander C. scPerturb: harmonized single-cell perturbation data. Nat Methods 2024; 21:531-540. [PMID: 38279009 DOI: 10.1038/s41592-023-02144-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 12/04/2023] [Indexed: 01/28/2024]
Abstract
Analysis across a growing number of single-cell perturbation datasets is hampered by poor data interoperability. To facilitate development and benchmarking of computational methods, we collect a set of 44 publicly available single-cell perturbation-response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform quality control pipelines and harmonize feature annotations. The resulting information resource, scPerturb, enables development and testing of computational methods, and facilitates comparison and integration across datasets. We describe energy statistics (E-statistics) for quantification of perturbation effects and significance testing, and demonstrate E-distance as a general distance measure between sets of single-cell expression profiles. We illustrate the application of E-statistics for quantifying similarity and efficacy of perturbations. The perturbation-response datasets and E-statistics computation software are publicly available at scperturb.org. This work provides an information resource for researchers working with single-cell perturbation data and recommendations for experimental design, including optimal cell counts and read depth.
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Affiliation(s)
- Stefan Peidli
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität, Berlin, Germany.
- Institute of Biology, Humboldt-Universität, Berlin, Germany.
| | - Tessa D Green
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Ciyue Shen
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | | | - Joseph Min
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Samuele Garda
- Institute of Biology, Humboldt-Universität, Berlin, Germany
- Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Bo Yuan
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Linus J Schumacher
- Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, UK
| | | | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Augustin Luna
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute, Cambridge, MA, USA.
- Computational Biology Branch, National Library of Medicine and Developmental Therapeutics Branch, National Cancer Institute, Bethesda, MD, USA.
| | - Nils Blüthgen
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität, Berlin, Germany.
- Institute of Biology, Humboldt-Universität, Berlin, Germany.
| | - Chris Sander
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute, Cambridge, MA, USA.
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49
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Proteins shaping global chromatin accessibility. Nat Genet 2024; 56:367-368. [PMID: 38418745 DOI: 10.1038/s41588-024-01667-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
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50
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Cui H, Wang C, Maan H, Pang K, Luo F, Duan N, Wang B. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat Methods 2024:10.1038/s41592-024-02201-0. [PMID: 38409223 DOI: 10.1038/s41592-024-02201-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 01/30/2024] [Indexed: 02/28/2024]
Abstract
Generative pretrained models have achieved remarkable success in various domains such as language and computer vision. Specifically, the combination of large-scale diverse datasets and pretrained transformers has emerged as a promising approach for developing foundation models. Drawing parallels between language and cellular biology (in which texts comprise words; similarly, cells are defined by genes), our study probes the applicability of foundation models to advance cellular biology and genetic research. Using burgeoning single-cell sequencing data, we have constructed a foundation model for single-cell biology, scGPT, based on a generative pretrained transformer across a repository of over 33 million cells. Our findings illustrate that scGPT effectively distills critical biological insights concerning genes and cells. Through further adaptation of transfer learning, scGPT can be optimized to achieve superior performance across diverse downstream applications. This includes tasks such as cell type annotation, multi-batch integration, multi-omic integration, perturbation response prediction and gene network inference.
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Affiliation(s)
- Haotian Cui
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontartio, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Chloe Wang
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontartio, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Hassaan Maan
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontartio, Canada
- Vector Institute, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Kuan Pang
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Fengning Luo
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Nan Duan
- Microsoft Research, Redmond, WA, USA
| | - Bo Wang
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontartio, Canada.
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
- Vector Institute, Toronto, Ontario, Canada.
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.
- AI Hub, University Health Network, Toronto, Ontario, Canada.
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