1
|
Dixit U, Bhutoria S, Wu X, Qiu L, Spira M, Mathew S, Harris R, Adams LJ, Cahill S, Pathak R, Rajesh Kumar P, Nguyen M, Acharya SA, Brenowitz M, Almo SC, Zou X, Steven AC, Cowburn D, Girvin M, Kalpana GV. INI1/SMARCB1 Rpt1 domain mimics TAR RNA in binding to integrase to facilitate HIV-1 replication. Nat Commun 2021; 12:2743. [PMID: 33980829 PMCID: PMC8115288 DOI: 10.1038/s41467-021-22733-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 03/24/2021] [Indexed: 11/09/2022] Open
Abstract
INI1/SMARCB1 binds to HIV-1 integrase (IN) through its Rpt1 domain and exhibits multifaceted role in HIV-1 replication. Determining the NMR structure of INI1-Rpt1 and modeling its interaction with the IN-C-terminal domain (IN-CTD) reveal that INI1-Rpt1/IN-CTD interface residues overlap with those required for IN/RNA interaction. Mutational analyses validate our model and indicate that the same IN residues are involved in both INI1 and RNA binding. INI1-Rpt1 and TAR RNA compete with each other for IN binding with similar IC50 values. INI1-interaction-defective IN mutant viruses are impaired for incorporation of INI1 into virions and for particle morphogenesis. Computational modeling of IN-CTD/TAR complex indicates that the TAR interface phosphates overlap with negatively charged surface residues of INI1-Rpt1 in three-dimensional space, suggesting that INI1-Rpt1 domain structurally mimics TAR. This possible mimicry between INI1-Rpt1 and TAR explains the mechanism by which INI1/SMARCB1 influences HIV-1 late events and suggests additional strategies to inhibit HIV-1 replication.
Collapse
Affiliation(s)
- Updesh Dixit
- Department of Genetics, Albert Einstein College of Medicine, New York City, NY, USA
| | - Savita Bhutoria
- Department of Genetics, Albert Einstein College of Medicine, New York City, NY, USA
| | - Xuhong Wu
- Department of Genetics, Albert Einstein College of Medicine, New York City, NY, USA
| | - Liming Qiu
- Dalton Cardiovascular Research Center, Department of Physics and Astronomy, Department of Biochemistry, and Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA
| | - Menachem Spira
- Department of Genetics, Albert Einstein College of Medicine, New York City, NY, USA
| | - Sheeba Mathew
- Department of Genetics, Albert Einstein College of Medicine, New York City, NY, USA
| | - Richard Harris
- Department of Biochemistry, Albert Einstein College of Medicine, New York City, NY, USA
| | - Lucas J Adams
- Laboratory of Structural Biology Research, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Sean Cahill
- Department of Biochemistry, Albert Einstein College of Medicine, New York City, NY, USA
| | - Rajiv Pathak
- Department of Genetics, Albert Einstein College of Medicine, New York City, NY, USA
| | - P Rajesh Kumar
- Department of Biochemistry, Albert Einstein College of Medicine, New York City, NY, USA
| | - Minh Nguyen
- Department of Genetics, Albert Einstein College of Medicine, New York City, NY, USA
| | - Seetharama A Acharya
- Department of Anatomy & Structural Biology, Albert Einstein College of Medicine, New York, NY, USA
| | - Michael Brenowitz
- Department of Biochemistry, Albert Einstein College of Medicine, New York City, NY, USA
| | - Steven C Almo
- Department of Biochemistry, Albert Einstein College of Medicine, New York City, NY, USA
| | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, Department of Physics and Astronomy, Department of Biochemistry, and Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA
| | - Alasdair C Steven
- Laboratory of Structural Biology Research, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
| | - David Cowburn
- Department of Biochemistry, Albert Einstein College of Medicine, New York City, NY, USA
| | - Mark Girvin
- Department of Biochemistry, Albert Einstein College of Medicine, New York City, NY, USA
| | - Ganjam V Kalpana
- Department of Genetics, Albert Einstein College of Medicine, New York City, NY, USA.
| |
Collapse
|
2
|
Beyond medically actionable results: an analytical pipeline for decreasing the burden of returning all clinically significant secondary findings. Hum Genet 2020; 140:493-504. [PMID: 32892247 DOI: 10.1007/s00439-020-02220-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 08/29/2020] [Indexed: 10/23/2022]
Abstract
Genomic sequencing advances have increased the potential to identify secondary findings (SFs). Current guidelines recommend the analysis of 59 medically actionable genes; however, patient preferences indicate interest in learning a broader group of SFs. We aimed to develop an analytical pipeline for the efficient analysis and return of all clinically significant SFs. We developed a pipeline consisting of comprehensive gene lists for five categories of SFs and filtration parameters for prioritization of variants in each category. We applied the pipeline to 42 exomes to assess feasibility and efficiency. Comprehensive lists of clinically significant SF genes were curated for each category: (1) 90 medically actionable genes and 28 pharmacogenomic variants; (2) 17 common disease risk variants; (3) 3166 Mendelian disease genes, (4) 7 early onset neurodegenerative disorder genes; (5) 688 carrier status results. Analysis of 42 exomes using our pipeline resulted in a significant decrease (> 98%) in variants compared to the raw analysis (13,036.56 ± 59.72 raw variants/exome vs 161.32 ± 7.68 filtered variants/exome), and aided in time and costs savings for the overall analysis process. Our pipeline represents a critical step in overcoming the analytic challenge associated with returning all clinically relevant SFs to allow for its routine implementation in clinical practice.
Collapse
|
3
|
Pandey RV, Pabinger S, Kriegner A, Weinhäusel A. DaMold: A data-mining platform for variant annotation and visualization in molecular diagnostics research. Hum Mutat 2017; 38:778-787. [PMID: 28397319 DOI: 10.1002/humu.23227] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Revised: 03/21/2017] [Accepted: 04/06/2017] [Indexed: 12/14/2022]
Abstract
Next-generation sequencing (NGS) has become a powerful and efficient tool for routine mutation screening in clinical research. As each NGS test yields hundreds of variants, the current challenge is to meaningfully interpret the data and select potential candidates. Analyzing each variant while manually investigating several relevant databases to collect specific information is a cumbersome and time-consuming process, and it requires expertise and familiarity with these databases. Thus, a tool that can seamlessly annotate variants with clinically relevant databases under one common interface would be of great help for variant annotation, cross-referencing, and visualization. This tool would allow variants to be processed in an automated and high-throughput manner and facilitate the investigation of variants in several genome browsers. Several analysis tools are available for raw sequencing-read processing and variant identification, but an automated variant filtering, annotation, cross-referencing, and visualization tool is still lacking. To fulfill these requirements, we developed DaMold, a Web-based, user-friendly tool that can filter and annotate variants and can access and compile information from 37 resources. It is easy to use, provides flexible input options, and accepts variants from NGS and Sanger sequencing as well as hotspots in VCF and BED formats. DaMold is available as an online application at http://damold.platomics.com/index.html, and as a Docker container and virtual machine at https://sourceforge.net/projects/damold/.
Collapse
Affiliation(s)
- Ram Vinay Pandey
- Health and Environment Department, Molecular Diagnostics, Austrian Institute of Technology, Vienna, Austria.,Institut für Populationsgenetik, Vetmeduni Vienna, Veterinärplatz 1, A-1210 Vienna, Austria
| | - Stephan Pabinger
- Health and Environment Department, Molecular Diagnostics, Austrian Institute of Technology, Vienna, Austria
| | - Albert Kriegner
- Health and Environment Department, Molecular Diagnostics, Austrian Institute of Technology, Vienna, Austria
| | - Andreas Weinhäusel
- Health and Environment Department, Molecular Diagnostics, Austrian Institute of Technology, Vienna, Austria
| |
Collapse
|