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Rooney MR, Chen J, Echouffo-Tcheugui JB, Walker KA, Schlosser P, Surapaneni A, Tang O, Chen J, Ballantyne CM, Boerwinkle E, Ndumele CE, Demmer RT, Pankow JS, Lutsey PL, Wagenknecht LE, Liang Y, Sim X, van Dam R, Tai ES, Grams ME, Selvin E, Coresh J. Proteomic Predictors of Incident Diabetes: Results From the Atherosclerosis Risk in Communities (ARIC) Study. Diabetes Care 2023; 46:733-741. [PMID: 36706097 PMCID: PMC10090896 DOI: 10.2337/dc22-1830] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 12/29/2022] [Indexed: 01/28/2023]
Abstract
OBJECTIVE The plasma proteome preceding diabetes can improve our understanding of diabetes pathogenesis. RESEARCH DESIGN AND METHODS In 8,923 Atherosclerosis Risk in Communities (ARIC) Study participants (aged 47-70 years, 57% women, 19% Black), we conducted discovery and internal validation for associations of 4,955 plasma proteins with incident diabetes. We externally validated results in the Singapore Multi-Ethnic Cohort (MEC) nested case-control (624 case subjects, 1,214 control subjects). We used Cox regression to discover and validate protein associations and risk-prediction models (elastic net regression with cardiometabolic risk factors and proteins) for incident diabetes. We conducted a pathway analysis and examined causality using genetic instruments. RESULTS There were 2,147 new diabetes cases over a median of 19 years. In the discovery sample (n = 6,010), 140 proteins were associated with incident diabetes after adjustment for 11 risk factors (P < 10-5). Internal validation (n = 2,913) showed 64 of the 140 proteins remained significant (P < 0.05/140). Of the 63 available proteins, 47 (75%) were validated in MEC. Novel associations with diabetes were found for 22 the 47 proteins. Prediction models (27 proteins selected by elastic net) developed in discovery had a C statistic of 0.731 in internal validation, with ΔC statistic of 0.011 (P = 0.04) beyond 13 risk factors, including fasting glucose and HbA1c. Inflammation and lipid metabolism pathways were overrepresented among the diabetes-associated proteins. Genetic instrument analyses suggested plasma SHBG, ATP1B2, and GSTA1 play causal roles in diabetes risk. CONCLUSIONS We identified 47 plasma proteins predictive of incident diabetes, established causal effects for 3 proteins, and identified diabetes-associated inflammation and lipid pathways with potential implications for diagnosis and therapy.
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Affiliation(s)
- Mary R. Rooney
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Jingsha Chen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Justin B. Echouffo-Tcheugui
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- Division of Endocrinology, Diabetes and Metabolism, Johns Hopkins University, Baltimore, MD
| | - Keenan A. Walker
- Laboratory of Behavioral Neuroscience, Intramural Research Program, National Institute on Aging, Baltimore, MD
| | - Pascal Schlosser
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Aditya Surapaneni
- Division of Precision Medicine, New York University Grossman School of Medicine, New York, NY
| | - Olive Tang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Jinyu Chen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | | | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics and Environmental Science, University of Texas Health Science Center, Houston, TX
| | | | - Ryan T. Demmer
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN
| | - James S. Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Pamela L. Lutsey
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Lynne E. Wagenknecht
- Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Yujian Liang
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Rob van Dam
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, George Washington University, Washington DC
| | - E. Shyong Tai
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Morgan E. Grams
- Division of Precision Medicine, New York University Grossman School of Medicine, New York, NY
| | - Elizabeth Selvin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
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Chung CH, Murphy CM, Wingate VP, Pavlicek JW, Nakashima R, Wei W, McCarty D, Rabinowitz J, Barton E. Production of rAAV by plasmid transfection induces antiviral and inflammatory responses in suspension HEK293 cells. Mol Ther Methods Clin Dev 2023; 28:272-283. [PMID: 36819978 PMCID: PMC9937832 DOI: 10.1016/j.omtm.2023.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 01/13/2023] [Indexed: 01/18/2023]
Abstract
Recombinant adeno-associated virus (rAAV) is a clinically proven viral vector for delivery of therapeutic genes to treat rare diseases. Improving rAAV manufacturing productivity and vector quality is necessary to meet clinical and commercial demand. These goals will require an improved understanding of the cellular response to rAAV production, which is poorly defined. We interrogated the kinetic transcriptional response of HEK293 cells to rAAV production following transient plasmid transfection, under manufacturing-relevant conditions, using RNA-seq. Time-series analyses identified a robust cellular response to transfection and rAAV production, with 1,850 transcripts differentially expressed. Gene Ontology analysis determined upregulated pathways, including inflammatory and antiviral responses, with several interferon-stimulated cytokines and chemokines being upregulated at the protein level. Literature-based pathway prediction implicated multiple pathogen pattern sensors and signal transducers in up-regulation of inflammatory and antiviral responses in response to transfection and rAAV replication. Systematic analysis of the cellular transcriptional response to rAAV production indicates that host cells actively sense vector manufacture as an infectious insult. This dataset may therefore illuminate genes and pathways that influence rAAV production, thereby enabling the rational design of next-generation manufacturing platforms to support safe, effective, and affordable AAV-based gene therapies.
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Affiliation(s)
- Cheng-Han Chung
- Pfizer Inc., Worldwide Research, Development and Medical, Bioprocess Research and Development, Morrisville, NC 27560, USA
| | - Christopher M. Murphy
- Pfizer Inc., Worldwide Research, Development and Medical, Bioprocess Research and Development, Morrisville, NC 27560, USA
| | - Vincent P. Wingate
- Pfizer Inc., Worldwide Research, Development and Medical, Bioprocess Research and Development, Morrisville, NC 27560, USA
| | - Jeffrey W. Pavlicek
- Pfizer Inc., Worldwide Research, Development and Medical, Bioprocess Research and Development, Morrisville, NC 27560, USA
| | - Reiko Nakashima
- Pfizer Inc., Worldwide Research, Development and Medical, Simulation and Modeling Sciences, Cambridge, MA 02139, USA
| | - Wei Wei
- Pfizer Inc., Worldwide Research, Development and Medical, Bioprocess Research and Development, Morrisville, NC 27560, USA
| | - Douglas McCarty
- Pfizer Inc., Worldwide Research, Development and Medical, Rare Disease Research Unit, Morrisville, NC 27560, USA
| | - Joseph Rabinowitz
- Pfizer Inc., Worldwide Research, Development and Medical, Rare Disease Research Unit, Morrisville, NC 27560, USA
| | - Erik Barton
- Pfizer Inc., Worldwide Research, Development and Medical, Bioprocess Research and Development, Morrisville, NC 27560, USA,Corresponding author: Erik Barton, Pfizer Inc., Worldwide Research, Development and Medical, Bioprocess Research and Development, Morrisville, NC 27560, USA.
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Zhong H, Sun X. Contribution of Interleukin-17A to Retinal Degenerative Diseases. Front Immunol 2022; 13:847937. [PMID: 35392087 PMCID: PMC8980477 DOI: 10.3389/fimmu.2022.847937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 02/25/2022] [Indexed: 12/26/2022] Open
Abstract
Retinal degenerative diseases are a leading cause of vision loss and blindness throughout the world, characterized by chronic and progressive loss of neurons and/or myelin. One of the common features of retinal degenerative diseases and central neurodegenerative diseases is chronic neuroinflammation. Interleukin-17A (IL-17A) is the cytokine most closely related to disease in its family. Accumulating evidence suggests that IL-17A plays a key role in human retinal degenerative diseases, including age-related macular degeneration, diabetic retinopathy and glaucoma. This review aims to provide an overview of the role of IL-17A participating in the pathogenesis of retinal degenerative diseases, which may open new avenues for potential therapeutic interventions.
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Affiliation(s)
- Huimin Zhong
- Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,National Clinical Research Center for Eye Diseases, Shanghai, China.,Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai, China.,Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China.,Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Xiaodong Sun
- Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,National Clinical Research Center for Eye Diseases, Shanghai, China.,Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai, China.,Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China.,Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
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