Pavitra Roychoudhury1, Harshana S De Silva Feelixge2, Harlan L Pietz3, Daniel Stone2, Keith R Jerome4, Joshua T Schiffer5. 1. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA proychou@fredhutch.org. 2. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. 3. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA Department of Microbiology, University of Washington, Seattle, WA, USA Department of Biochemistry, University of Washington, Seattle, WA, USA. 4. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA Department of Microbiology, University of Washington, Seattle, WA, USA Department of Laboratory Medicine, University of Washington, Seattle, WA, USA. 5. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA Department of Medicine, University of Washington, Seattle, WA, USA.
Abstract
OBJECTIVES: A promising curative approach for HIV is to use designer endonucleases that bind and cleave specific target sequences within latent genomes, resulting in mutations that render the virus replication incompetent. We developed a mathematical model to describe the expression and activity of endonucleases delivered to HIV-infected cells using engineered viral vectors in order to guide dose selection and predict therapeutic outcomes. METHODS: We developed a mechanistic model that predicts the number of transgene copies expressed at a given dose in individual target cells from fluorescence of a reporter gene. We fitted the model to flow cytometry datasets to determine the optimal vector serotype, promoter and dose required to achieve maximum expression. RESULTS: We showed that our model provides a more accurate measure of transduction efficiency compared with gating-based methods, which underestimate the percentage of cells expressing reporter genes. We identified that gene expression follows a sigmoid dose-response relationship and that the level of gene expression saturation depends on vector serotype and promoter. We also demonstrated that significant bottlenecks exist at the level of viral uptake and gene expression: only ∼1 in 220 added vectors enter a cell and, of these, depending on the dose and promoter used, between 1 in 15 and 1 in 1500 express transgene. CONCLUSIONS: Our model provides a quantitative method of dose selection and optimization that can be readily applied to a wide range of other gene therapy applications. Reducing bottlenecks in delivery will be key to reducing the number of doses required for a functional cure.
OBJECTIVES: A promising curative approach for HIV is to use designer endonucleases that bind and cleave specific target sequences within latent genomes, resulting in mutations that render the virus replication incompetent. We developed a mathematical model to describe the expression and activity of endonucleases delivered to HIV-infected cells using engineered viral vectors in order to guide dose selection and predict therapeutic outcomes. METHODS: We developed a mechanistic model that predicts the number of transgene copies expressed at a given dose in individual target cells from fluorescence of a reporter gene. We fitted the model to flow cytometry datasets to determine the optimal vector serotype, promoter and dose required to achieve maximum expression. RESULTS: We showed that our model provides a more accurate measure of transduction efficiency compared with gating-based methods, which underestimate the percentage of cells expressing reporter genes. We identified that gene expression follows a sigmoid dose-response relationship and that the level of gene expression saturation depends on vector serotype and promoter. We also demonstrated that significant bottlenecks exist at the level of viral uptake and gene expression: only ∼1 in 220 added vectors enter a cell and, of these, depending on the dose and promoter used, between 1 in 15 and 1 in 1500 express transgene. CONCLUSIONS: Our model provides a quantitative method of dose selection and optimization that can be readily applied to a wide range of other gene therapy applications. Reducing bottlenecks in delivery will be key to reducing the number of doses required for a functional cure.
Authors: Janet D Siliciano; Joleen Kajdas; Diana Finzi; Thomas C Quinn; Karen Chadwick; Joseph B Margolick; Colin Kovacs; Stephen J Gange; Robert F Siliciano Journal: Nat Med Date: 2003-05-18 Impact factor: 53.440
Authors: Amit C Nathwani; Cecilia Rosales; Jenny McIntosh; Ghasem Rastegarlari; Devhrut Nathwani; Deepak Raj; Sushmita Nawathe; Simon N Waddington; Roderick Bronson; Scott Jackson; Robert E Donahue; Katherine A High; Federico Mingozzi; Catherine Y C Ng; Junfang Zhou; Yunyu Spence; M Beth McCarville; Marc Valentine; James Allay; John Coleman; Susan Sleep; John T Gray; Arthur W Nienhuis; Andrew M Davidoff Journal: Mol Ther Date: 2011-01-18 Impact factor: 11.454
Authors: Mark L Brantly; Jeffrey D Chulay; Lili Wang; Christian Mueller; Margaret Humphries; L Terry Spencer; Farshid Rouhani; Thomas J Conlon; Roberto Calcedo; Michael R Betts; Carolyn Spencer; Barry J Byrne; James M Wilson; Terence R Flotte Journal: Proc Natl Acad Sci U S A Date: 2009-08-12 Impact factor: 11.205
Authors: Haiyan Jiang; David Lillicrap; Susannah Patarroyo-White; Tongyao Liu; Xiaobing Qian; Ciaran D Scallan; Sandra Powell; Tracey Keller; Morag McMurray; Andrea Labelle; Dea Nagy; Joseph A Vargas; Shangzhen Zhou; Linda B Couto; Glenn F Pierce Journal: Blood Date: 2006-03-07 Impact factor: 22.113
Authors: Michele Di Mascio; Chang H Paik; Jorge A Carrasquillo; Jin-Soo Maeng; Beom-Su Jang; In Soo Shin; Sharat Srinivasula; Russ Byrum; Achilles Neria; William Kopp; Marta Catalfamo; Yoshiaki Nishimura; Keith Reimann; Malcolm Martin; H Clifford Lane Journal: Blood Date: 2009-05-05 Impact factor: 22.113
Authors: Christian Mueller; Jeffrey D Chulay; Bruce C Trapnell; Margaret Humphries; Brenna Carey; Robert A Sandhaus; Noel G McElvaney; Louis Messina; Qiushi Tang; Farshid N Rouhani; Martha Campbell-Thompson; Ann Dongtao Fu; Anthony Yachnis; David R Knop; Guo-Jie Ye; Mark Brantly; Roberto Calcedo; Suryanarayan Somanathan; Lee P Richman; Robert H Vonderheide; Maigan A Hulme; Todd M Brusko; James M Wilson; Terence R Flotte Journal: J Clin Invest Date: 2013-11-15 Impact factor: 19.456
Authors: Jianbin Wang; Colin M Exline; Joshua J DeClercq; G Nicholas Llewellyn; Samuel B Hayward; Patrick Wai-Lun Li; David A Shivak; Richard T Surosky; Philip D Gregory; Michael C Holmes; Paula M Cannon Journal: Nat Biotechnol Date: 2015-11-09 Impact factor: 54.908
Authors: Chung H Dang; Martine Aubert; Harshana S De Silva Feelixge; Kurt Diem; Michelle A Loprieno; Pavitra Roychoudhury; Daniel Stone; Keith R Jerome Journal: Sci Rep Date: 2017-04-19 Impact factor: 4.379
Authors: Danielle A Griffin; Eric R Pozsgai; Kristin N Heller; Rachael A Potter; Ellyn L Peterson; Louise R Rodino-Klapac Journal: Hum Gene Ther Date: 2021-02-18 Impact factor: 5.695
Authors: Pavitra Roychoudhury; Harshana De Silva Feelixge; Daniel Reeves; Bryan T Mayer; Daniel Stone; Joshua T Schiffer; Keith R Jerome Journal: BMC Biol Date: 2018-07-11 Impact factor: 7.364