1
|
Gibbs CS, Jackson CA, Saldi GA, Tjärnberg A, Shah A, Watters A, De Veaux N, Tchourine K, Yi R, Hamamsy T, Castro DM, Carriero N, Gorissen BL, Gresham D, Miraldi ER, Bonneau R. High performance single-cell gene regulatory network inference at scale: The Inferelator 3.0. Bioinformatics 2022; 38:2519-2528. [PMID: 35188184 PMCID: PMC9048651 DOI: 10.1093/bioinformatics/btac117] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 12/08/2021] [Accepted: 02/17/2022] [Indexed: 12/04/2022] Open
Abstract
Motivation Gene regulatory networks define regulatory relationships between transcription factors and target genes within a biological system, and reconstructing them is essential for understanding cellular growth and function. Methods for inferring and reconstructing networks from genomics data have evolved rapidly over the last decade in response to advances in sequencing technology and machine learning. The scale of data collection has increased dramatically; the largest genome-wide gene expression datasets have grown from thousands of measurements to millions of single cells, and new technologies are on the horizon to increase to tens of millions of cells and above. Results In this work, we present the Inferelator 3.0, which has been significantly updated to integrate data from distinct cell types to learn context-specific regulatory networks and aggregate them into a shared regulatory network, while retaining the functionality of the previous versions. The Inferelator is able to integrate the largest single-cell datasets and learn cell-type-specific gene regulatory networks. Compared to other network inference methods, the Inferelator learns new and informative Saccharomyces cerevisiae networks from single-cell gene expression data, measured by recovery of a known gold standard. We demonstrate its scaling capabilities by learning networks for multiple distinct neuronal and glial cell types in the developing Mus musculus brain at E18 from a large (1.3 million) single-cell gene expression dataset with paired single-cell chromatin accessibility data. Availability and implementation The inferelator software is available on GitHub (https://github.com/flatironinstitute/inferelator) under the MIT license and has been released as python packages with associated documentation (https://inferelator.readthedocs.io/). Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Claudia Skok Gibbs
- Flatiron Institute, Center for Computational Biology, Simons Foundation, New York, NY, USA.,Center For Data Science, NYU, New York, NY, USA
| | - Christopher A Jackson
- Center For Genomics and Systems Biology, NYU, New York, NY, USA.,Department of Biology, NYU, New York, NY, USA
| | - Giuseppe-Antonio Saldi
- Center For Genomics and Systems Biology, NYU, New York, NY, USA.,Department of Biology, NYU, New York, NY, USA
| | - Andreas Tjärnberg
- Center For Genomics and Systems Biology, NYU, New York, NY, USA.,Department of Biology, NYU, New York, NY, USA
| | - Aashna Shah
- Flatiron Institute, Center for Computational Biology, Simons Foundation, New York, NY, USA
| | - Aaron Watters
- Flatiron Institute, Center for Computational Biology, Simons Foundation, New York, NY, USA
| | - Nicholas De Veaux
- Flatiron Institute, Center for Computational Biology, Simons Foundation, New York, NY, USA
| | | | - Ren Yi
- Courant Institute of Mathematical Sciences, Computer Science Department, NYU, New York, NY, USA
| | | | - Dayanne M Castro
- Center For Genomics and Systems Biology, NYU, New York, NY, USA.,Department of Biology, NYU, New York, NY, USA
| | - Nicholas Carriero
- Flatiron Institute, Scientific Computing Core, Simons Foundation, New York, NY, USA
| | - Bram L Gorissen
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - David Gresham
- Center For Genomics and Systems Biology, NYU, New York, NY, USA.,Department of Biology, NYU, New York, NY, USA
| | - Emily R Miraldi
- Divisions of Immunobiology and Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Richard Bonneau
- Flatiron Institute, Center for Computational Biology, Simons Foundation, New York, NY, USA.,Center For Data Science, NYU, New York, NY, USA.,Center For Genomics and Systems Biology, NYU, New York, NY, USA.,Department of Biology, NYU, New York, NY, USA.,Courant Institute of Mathematical Sciences, Computer Science Department, NYU, New York, NY, USA
| |
Collapse
|
2
|
Johnson JS, De Veaux N, Rives AW, Lahaye X, Lucas SY, Perot BP, Luka M, Garcia-Paredes V, Amon LM, Watters A, Abdessalem G, Aderem A, Manel N, Littman DR, Bonneau R, Ménager MM. A Comprehensive Map of the Monocyte-Derived Dendritic Cell Transcriptional Network Engaged upon Innate Sensing of HIV. Cell Rep 2021; 30:914-931.e9. [PMID: 31968263 PMCID: PMC7039998 DOI: 10.1016/j.celrep.2019.12.054] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 06/25/2019] [Accepted: 12/13/2019] [Indexed: 01/12/2023] Open
Abstract
Transcriptional programming of the innate immune response is pivotal for host protection. However, the transcriptional mechanisms that link pathogen sensing with innate activation remain poorly under-stood. During HIV-1 infection, human dendritic cells (DCs) can detect the virus through an innate sensing pathway, leading to antiviral interferon and DC maturation. Here, we develop an iterative experimental and computational approach to map the HIV-1 innate response circuitry in monocyte-derived DCs (MDDCs). By integrating genome-wide chromatin accessibility with expression kinetics, we infer a gene regulatory network that links 542 transcription factors with 21,862 target genes. We observe that an interferon response is required, yet insufficient, to drive MDDC maturation and identify PRDM1 and RARA as essential regulators of the interferon response and MDDC maturation, respectively. Our work provides a resource for interrogation of regulators of HIV replication and innate immunity, highlighting complexity and cooperativity in the regulatory circuit controlling the response to infection. Pathogen sensing leads to host transcriptional reprogramming to protect against infection. However, it is unclear how transcription factor activity is coordinated across the genome. Johnson et al. integrate chromatin accessibility and gene expression data to infer and validate a gene regulatory network that directs the innate immune response to HIV.
Collapse
Affiliation(s)
- Jarrod S Johnson
- Department of Biochemistry, University of Utah, Salt Lake City, UT 84112, USA; Center for Infectious Disease Research, Seattle, WA 98109, USA.
| | - Nicholas De Veaux
- Flatiron Institute, Center for Computational Biology, Simons Foundation, New York, NY 10010, USA
| | - Alexander W Rives
- Flatiron Institute, Center for Computational Biology, Simons Foundation, New York, NY 10010, USA
| | - Xavier Lahaye
- Immunity and Cancer Department, Institut Curie, PSL Research University, INSERM U932, 75005 Paris, France
| | - Sasha Y Lucas
- Center for Infectious Disease Research, Seattle, WA 98109, USA
| | - Brieuc P Perot
- Laboratory of Inflammatory Responses and Transcriptomic Networks in Diseases, Imagine Institute, INSERM UMR 1163, ATIP-Avenir Team, Université de Paris, 24 Boulevard du Montparnasse, 75015 Paris, France
| | - Marine Luka
- Laboratory of Inflammatory Responses and Transcriptomic Networks in Diseases, Imagine Institute, INSERM UMR 1163, ATIP-Avenir Team, Université de Paris, 24 Boulevard du Montparnasse, 75015 Paris, France
| | - Victor Garcia-Paredes
- Laboratory of Inflammatory Responses and Transcriptomic Networks in Diseases, Imagine Institute, INSERM UMR 1163, ATIP-Avenir Team, Université de Paris, 24 Boulevard du Montparnasse, 75015 Paris, France
| | - Lynn M Amon
- Center for Infectious Disease Research, Seattle, WA 98109, USA
| | - Aaron Watters
- Flatiron Institute, Center for Computational Biology, Simons Foundation, New York, NY 10010, USA
| | - Ghaith Abdessalem
- Laboratory of Inflammatory Responses and Transcriptomic Networks in Diseases, Imagine Institute, INSERM UMR 1163, ATIP-Avenir Team, Université de Paris, 24 Boulevard du Montparnasse, 75015 Paris, France
| | - Alan Aderem
- Center for Infectious Disease Research, Seattle, WA 98109, USA; Department of Immunology, University of Washington School of Medicine, Seattle, WA 98109, USA
| | - Nicolas Manel
- Immunity and Cancer Department, Institut Curie, PSL Research University, INSERM U932, 75005 Paris, France
| | - Dan R Littman
- The Kimmel Center for Biology and Medicine of the Skirball Institute, New York University School of Medicine, New York, NY 10016, USA; Howard Hughes Medical Institute, New York University School of Medicine, New York, NY 10016, USA
| | - Richard Bonneau
- Flatiron Institute, Center for Computational Biology, Simons Foundation, New York, NY 10010, USA; Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY 10003, USA; Center for Data Science, New York University, New York, NY 10011, USA
| | - Mickaël M Ménager
- Laboratory of Inflammatory Responses and Transcriptomic Networks in Diseases, Imagine Institute, INSERM UMR 1163, ATIP-Avenir Team, Université de Paris, 24 Boulevard du Montparnasse, 75015 Paris, France; The Kimmel Center for Biology and Medicine of the Skirball Institute, New York University School of Medicine, New York, NY 10016, USA.
| |
Collapse
|
3
|
Johnson JS, De Veaux N, Rives AW, Lahaye X, Lucas SY, Pérot B, Luka M, Amon LM, Watters A, Aderem A, Manel N, Littman DR, Bonneau R, Ménager MM. A comprehensive map of the human dendritic cell HIV-response transcriptional network. The Journal of Immunology 2019. [DOI: 10.4049/jimmunol.202.supp.75.11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Abstract
Transcriptional programming of the innate immune response is pivotal for host protection. However, the transcriptional mechanisms that link pathogen sensing with innate activation remain poorly understood. During infection with HIV-1, human dendritic cells (DCs) can detect the virus through an innate sensing pathway leading to antiviral type I interferon and DC maturation. Here, we have developed an iterative experimental and computational approach to map the innate response circuitry during HIV-1 infection. By integrating genome-wide chromatin accessibility with expression kinetics, we have inferred a gene regulatory network that links 542 transcription factors (TFs) with 21,862 target genes. Through genetic perturbation and drug treatments we identify PRDM1 and RARA as essential regulators of the interferon response and DC maturation, respectively. Our work provides a resource for interrogation of regulators of HIV replication and innate immunity, highlighting the complexity and cooperativity in the regulatory circuit controlling the DC response to HIV-1 infection.
Collapse
Affiliation(s)
- Jarrod S Johnson
- 1University of Utah, Department of Biochemistry, Salt Lake City, UT
- 2Center for Infectious Disease Research, Seattle, WA
| | - Nicholas De Veaux
- 3Flatiron Institute, Center for Computational Biology, Simons Foundation, New York, NY
| | - Alexander W Rives
- 3Flatiron Institute, Center for Computational Biology, Simons Foundation, New York, NY
| | - Xavier Lahaye
- 4Immunity and Cancer Department, Institut Curie, PSL Research University, INSERM U932, 75005 Paris, France
| | - Sasha Y Lucas
- 2Center for Infectious Disease Research, Seattle, WA
| | - Brieuc Pérot
- 5Laboratory of Inflammatory Responses and Transcriptomic Networks in Diseases, ATIP-Avenir team, INSERM UMR 1163, 75015 Paris, France
- 6Imagine Institute, Paris Descartes-Sorbonne Paris Cité University, Paris, France
| | - Marine Luka
- 5Laboratory of Inflammatory Responses and Transcriptomic Networks in Diseases, ATIP-Avenir team, INSERM UMR 1163, 75015 Paris, France
- 6Imagine Institute, Paris Descartes-Sorbonne Paris Cité University, Paris, France
| | - Lynn M Amon
- 2Center for Infectious Disease Research, Seattle, WA
| | - Aaron Watters
- 3Flatiron Institute, Center for Computational Biology, Simons Foundation, New York, NY
| | - Alan Aderem
- 2Center for Infectious Disease Research, Seattle, WA
- 7Department of Immunology, University of Washington School of Medicine, Seattle, WA
| | - Nicolas Manel
- 4Immunity and Cancer Department, Institut Curie, PSL Research University, INSERM U932, 75005 Paris, France
| | - Dan R Littman
- 8Howard Hughes Medical Institute, New York University School of Medicine, New York, NY
- 9The Kimmel Center for Biology and Medicine of the Skirball Institute, New York University School of Medicine, New York, NY
| | - Richard Bonneau
- 3Flatiron Institute, Center for Computational Biology, Simons Foundation, New York, NY
- 10Center for Data Science, New York University, New York, NY
- 11Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY
| | - Mickaël M Ménager
- 5Laboratory of Inflammatory Responses and Transcriptomic Networks in Diseases, ATIP-Avenir team, INSERM UMR 1163, 75015 Paris, France
- 6Imagine Institute, Paris Descartes-Sorbonne Paris Cité University, Paris, France
- 9The Kimmel Center for Biology and Medicine of the Skirball Institute, New York University School of Medicine, New York, NY
| |
Collapse
|
4
|
Miraldi ER, Pokrovskii M, Watters A, Castro DM, De Veaux N, Hall JA, Lee JY, Ciofani M, Madar A, Carriero N, Littman DR, Bonneau R. Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells. Genome Res 2019; 29:449-463. [PMID: 30696696 PMCID: PMC6396413 DOI: 10.1101/gr.238253.118] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2018] [Accepted: 01/15/2019] [Indexed: 12/13/2022]
Abstract
Transcriptional regulatory networks (TRNs) provide insight into cellular behavior by describing interactions between transcription factors (TFs) and their gene targets. The assay for transposase-accessible chromatin (ATAC)–seq, coupled with TF motif analysis, provides indirect evidence of chromatin binding for hundreds of TFs genome-wide. Here, we propose methods for TRN inference in a mammalian setting, using ATAC-seq data to improve gene expression modeling. We test our methods in the context of T Helper Cell Type 17 (Th17) differentiation, generating new ATAC-seq data to complement existing Th17 genomic resources. In this resource-rich mammalian setting, our extensive benchmarking provides quantitative, genome-scale evaluation of TRN inference, combining ATAC-seq and RNA-seq data. We refine and extend our previous Th17 TRN, using our new TRN inference methods to integrate all Th17 data (gene expression, ATAC-seq, TF knockouts, and ChIP-seq). We highlight newly discovered roles for individual TFs and groups of TFs (“TF–TF modules”) in Th17 gene regulation. Given the popularity of ATAC-seq, which provides high-resolution with low sample input requirements, we anticipate that our methods will improve TRN inference in new mammalian systems, especially in vivo, for cells directly from humans and animal models.
Collapse
Affiliation(s)
- Emily R Miraldi
- Divisions of Immunobiology and Biomedical Informatics, Cincinnati Children's Hospital, Cincinnati, Ohio 45229, USA.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio 45257, USA
| | - Maria Pokrovskii
- Molecular Pathogenesis Program, The Kimmel Center for Biology and Medicine of the Skirball Institute, New York, New York 10016, USA
| | - Aaron Watters
- Center for Computational Biology, Flatiron Institute, New York, New York 10010, USA
| | - Dayanne M Castro
- Department of Biology, New York University, New York, New York 10012, USA
| | - Nicholas De Veaux
- Center for Computational Biology, Flatiron Institute, New York, New York 10010, USA
| | - Jason A Hall
- Molecular Pathogenesis Program, The Kimmel Center for Biology and Medicine of the Skirball Institute, New York, New York 10016, USA
| | - June-Yong Lee
- Molecular Pathogenesis Program, The Kimmel Center for Biology and Medicine of the Skirball Institute, New York, New York 10016, USA
| | - Maria Ciofani
- Department of Immunology, Duke University School of Medicine, Durham, North Carolina 27710, USA
| | - Aviv Madar
- Department of Biology, New York University, New York, New York 10012, USA
| | - Nick Carriero
- Center for Computational Biology, Flatiron Institute, New York, New York 10010, USA
| | - Dan R Littman
- Molecular Pathogenesis Program, The Kimmel Center for Biology and Medicine of the Skirball Institute, New York, New York 10016, USA.,The Howard Hughes Medical Institute
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, New York, New York 10010, USA.,Department of Biology, New York University, New York, New York 10012, USA.,Center for Data Science, New York University, New York, New York 10010, USA
| |
Collapse
|
5
|
Rivera-Garcia C, Bristow SL, Yarnall S, Kumar N, Rodriguez S, De Veaux N, Bisignano A, Chu B, Prates R, Munne S. Retracted: Validation of a multiplex genotyping platform using a novel genomic database approach. Genet Med 2015. [DOI: 10.1038/gim.2015.101] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
|