1
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Lopez VK, Cramer EY, Pagano R, Drake JM, O’Dea EB, Adee M, Ayer T, Chhatwal J, Dalgic OO, Ladd MA, Linas BP, Mueller PP, Xiao J, Bracher J, Castro Rivadeneira AJ, Gerding A, Gneiting T, Huang Y, Jayawardena D, Kanji AH, Le K, Mühlemann A, Niemi J, Ray EL, Stark A, Wang Y, Wattanachit N, Zorn MW, Pei S, Shaman J, Yamana TK, Tarasewicz SR, Wilson DJ, Baccam S, Gurung H, Stage S, Suchoski B, Gao L, Gu Z, Kim M, Li X, Wang G, Wang L, Wang Y, Yu S, Gardner L, Jindal S, Marshall M, Nixon K, Dent J, Hill AL, Kaminsky J, Lee EC, Lemaitre JC, Lessler J, Smith CP, Truelove S, Kinsey M, Mullany LC, Rainwater-Lovett K, Shin L, Tallaksen K, Wilson S, Karlen D, Castro L, Fairchild G, Michaud I, Osthus D, Bian J, Cao W, Gao Z, Lavista Ferres J, Li C, Liu TY, Xie X, Zhang S, Zheng S, Chinazzi M, Davis JT, Mu K, Pastore y Piontti A, Vespignani A, Xiong X, Walraven R, Chen J, Gu Q, Wang L, Xu P, Zhang W, Zou D, Gibson GC, Sheldon D, Srivastava A, Adiga A, Hurt B, Kaur G, Lewis B, Marathe M, Peddireddy AS, Porebski P, Venkatramanan S, Wang L, Prasad PV, Walker JW, Webber AE, Slayton RB, Biggerstaff M, Reich NG, Johansson MA. Challenges of COVID-19 Case Forecasting in the US, 2020-2021. PLoS Comput Biol 2024; 20:e1011200. [PMID: 38709852 PMCID: PMC11098513 DOI: 10.1371/journal.pcbi.1011200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 05/16/2024] [Accepted: 04/01/2024] [Indexed: 05/08/2024] Open
Abstract
During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.
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Affiliation(s)
- Velma K. Lopez
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Estee Y. Cramer
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Robert Pagano
- Unaffiliated, Tucson, Arizona, United States of America
| | - John M. Drake
- University of Georgia, Athens, Georgia, United States of America
| | - Eamon B. O’Dea
- University of Georgia, Athens, Georgia, United States of America
| | - Madeline Adee
- Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Turgay Ayer
- Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Jagpreet Chhatwal
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Ozden O. Dalgic
- Value Analytics Labs, Boston, Massachusetts, United States of America
| | - Mary A. Ladd
- Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Benjamin P. Linas
- Boston University School of Medicine, Boston, Massachusetts, United States of America
| | - Peter P. Mueller
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Jade Xiao
- Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Johannes Bracher
- Chair of Econometrics and Statistics, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | | | - Aaron Gerding
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Tilmann Gneiting
- Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
| | - Yuxin Huang
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Dasuni Jayawardena
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Abdul H. Kanji
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Khoa Le
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Anja Mühlemann
- Institute of Mathematical Statistics and Actuarial Science, University of Bern, Bern, Switzerland
| | - Jarad Niemi
- Iowa State University, Ames, Iowa, United States of America
| | - Evan L. Ray
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Ariane Stark
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Yijin Wang
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Nutcha Wattanachit
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Martha W. Zorn
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Sen Pei
- Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Jeffrey Shaman
- Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Teresa K. Yamana
- Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Samuel R. Tarasewicz
- Federal Reserve Bank of San Francisco, San Francisco, California, United States of America
| | - Daniel J. Wilson
- Federal Reserve Bank of San Francisco, San Francisco, California, United States of America
| | - Sid Baccam
- IEM, Bel Air, Maryland, United States of America
| | - Heidi Gurung
- IEM, Bel Air, Maryland, United States of America
| | - Steve Stage
- IEM, Baton Rouge, Louisiana, United States of America
| | | | - Lei Gao
- George Mason University, Fairfax, Virginia, United States of America
| | - Zhiling Gu
- Iowa State University, Ames, Iowa, United States of America
| | - Myungjin Kim
- Kyungpook National University, Bukgu, Daegu, Republic of Korea
| | - Xinyi Li
- Clemson University, Clemson, South Carolina, United States of America
| | - Guannan Wang
- College of William & Mary, Williamsburg, Virginia, United States of America
| | - Lily Wang
- George Mason University, Fairfax, Virginia, United States of America
| | - Yueying Wang
- Amazon, Seattle, Washington, United States of America
| | - Shan Yu
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Lauren Gardner
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Sonia Jindal
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | | | - Kristen Nixon
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Juan Dent
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Alison L. Hill
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Joshua Kaminsky
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Elizabeth C. Lee
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | | | - Justin Lessler
- Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Claire P. Smith
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Shaun Truelove
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Matt Kinsey
- Johns Hopkins University Applied Physics Lab, Baltimore, Maryland, United States of America
| | - Luke C. Mullany
- Johns Hopkins University Applied Physics Lab, Baltimore, Maryland, United States of America
| | | | - Lauren Shin
- Johns Hopkins University Applied Physics Lab, Baltimore, Maryland, United States of America
| | - Katharine Tallaksen
- Johns Hopkins University Applied Physics Lab, Baltimore, Maryland, United States of America
| | - Shelby Wilson
- Johns Hopkins University Applied Physics Lab, Baltimore, Maryland, United States of America
| | - Dean Karlen
- University of Victoria and TRIUMF, Victoria, British Columbia, Canada
| | - Lauren Castro
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Geoffrey Fairchild
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Isaac Michaud
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Dave Osthus
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Jiang Bian
- Microsoft, Redmond, Washington, United States of America
| | - Wei Cao
- Microsoft, Redmond, Washington, United States of America
| | - Zhifeng Gao
- Microsoft, Redmond, Washington, United States of America
| | | | - Chaozhuo Li
- Microsoft, Redmond, Washington, United States of America
| | - Tie-Yan Liu
- Microsoft, Redmond, Washington, United States of America
| | - Xing Xie
- Microsoft, Redmond, Washington, United States of America
| | - Shun Zhang
- Microsoft, Redmond, Washington, United States of America
| | - Shun Zheng
- Microsoft, Redmond, Washington, United States of America
| | - Matteo Chinazzi
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Jessica T. Davis
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Kunpeng Mu
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Ana Pastore y Piontti
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Xinyue Xiong
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | | | - Jinghui Chen
- University of California, Los Angeles, Los Angeles, California, United States of America
| | - Quanquan Gu
- University of California, Los Angeles, Los Angeles, California, United States of America
| | - Lingxiao Wang
- University of California, Los Angeles, Los Angeles, California, United States of America
| | - Pan Xu
- University of California, Los Angeles, Los Angeles, California, United States of America
| | - Weitong Zhang
- University of California, Los Angeles, Los Angeles, California, United States of America
| | - Difan Zou
- University of California, Los Angeles, Los Angeles, California, United States of America
| | - Graham Casey Gibson
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Daniel Sheldon
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Ajitesh Srivastava
- University of Southern California, Los Angeles, California, United States of America
| | - Aniruddha Adiga
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Benjamin Hurt
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Gursharn Kaur
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Bryan Lewis
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Madhav Marathe
- University of Virginia, Charlottesville, Virginia, United States of America
| | | | | | | | - Lijing Wang
- New Jersey Institute of Technology, Newark, New Jersey, United States of America
| | - Pragati V. Prasad
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Jo W. Walker
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Alexander E. Webber
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Rachel B. Slayton
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Matthew Biggerstaff
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Nicholas G. Reich
- University of Massachusetts, Amherst, Amherst, Massachusetts, United States of America
| | - Michael A. Johansson
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
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Jung SM, Loo SL, Howerton E, Contamin L, Smith CP, Carcelén EC, Yan K, Bents SJ, Levander J, Espino J, Lemaitre JC, Sato K, McKee CD, Hill AL, Chinazzi M, Davis JT, Mu K, Vespignani A, Rosenstrom ET, Rodriguez-Cartes SA, Ivy JS, Mayorga ME, Swann JL, España G, Cavany S, Moore SM, Perkins TA, Chen S, Paul R, Janies D, Thill JC, Srivastava A, Aawar MA, Bi K, Bandekar SR, Bouchnita A, Fox SJ, Meyers LA, Porebski P, Venkatramanan S, Adiga A, Hurt B, Klahn B, Outten J, Chen J, Mortveit H, Wilson A, Hoops S, Bhattacharya P, Machi D, Vullikanti A, Lewis B, Marathe M, Hochheiser H, Runge MC, Shea K, Truelove S, Viboud C, Lessler J. Potential impact of annual vaccination with reformulated COVID-19 vaccines: Lessons from the US COVID-19 scenario modeling hub. PLoS Med 2024; 21:e1004387. [PMID: 38630802 PMCID: PMC11062554 DOI: 10.1371/journal.pmed.1004387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 05/01/2024] [Accepted: 03/27/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Coronavirus Disease 2019 (COVID-19) continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Here, we present projections of COVID-19 hospitalizations and deaths in the United States for the next 2 years under 2 plausible assumptions about immune escape (20% per year and 50% per year) and 3 possible CDC recommendations for the use of annually reformulated vaccines (no recommendation, vaccination for those aged 65 years and over, vaccination for all eligible age groups based on FDA approval). METHODS AND FINDINGS The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023 and April 15, 2025 under 6 scenarios representing the intersection of considered levels of immune escape and vaccination. Annually reformulated vaccines are assumed to be 65% effective against symptomatic infection with strains circulating on June 15 of each year and to become available on September 1. Age- and state-specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. State and national projections from 8 modeling teams were ensembled to produce projections for each scenario and expected reductions in disease outcomes due to vaccination over the projection period. From April 15, 2023 to April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November to January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% projection interval (PI) [1,438,000, 4,270,000]) hospitalizations and 209,000 (90% PI [139,000, 461,000]) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% confidence interval (CI) [104,000, 355,000]) fewer hospitalizations and 33,000 (95% CI [12,000, 54,000]) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI [29,000, 69,000]) fewer deaths. CONCLUSIONS COVID-19 is projected to be a significant public health threat over the coming 2 years. Broad vaccination has the potential to substantially reduce the burden of this disease, saving tens of thousands of lives each year.
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Affiliation(s)
- Sung-mok Jung
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Sara L. Loo
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Emily Howerton
- The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Lucie Contamin
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Claire P. Smith
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Erica C. Carcelén
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Katie Yan
- The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Samantha J. Bents
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - John Levander
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Jessi Espino
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Joseph C. Lemaitre
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Koji Sato
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Clifton D. McKee
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Alison L. Hill
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Matteo Chinazzi
- Northeastern University, Boston, Massachusetts, United States of America
| | - Jessica T. Davis
- Northeastern University, Boston, Massachusetts, United States of America
| | - Kunpeng Mu
- Northeastern University, Boston, Massachusetts, United States of America
| | | | - Erik T. Rosenstrom
- North Carolina State University, Raleigh, North Carolina, United States of America
| | | | - Julie S. Ivy
- North Carolina State University, Raleigh, North Carolina, United States of America
| | - Maria E. Mayorga
- North Carolina State University, Raleigh, North Carolina, United States of America
| | - Julie L. Swann
- North Carolina State University, Raleigh, North Carolina, United States of America
| | - Guido España
- University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Sean Cavany
- University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Sean M. Moore
- University of Notre Dame, Notre Dame, Indiana, United States of America
| | - T. Alex Perkins
- University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Shi Chen
- University of North Carolina at Charlotte, Charlotte, North Carolina, United States of America
| | - Rajib Paul
- University of North Carolina at Charlotte, Charlotte, North Carolina, United States of America
| | - Daniel Janies
- University of North Carolina at Charlotte, Charlotte, North Carolina, United States of America
| | - Jean-Claude Thill
- University of North Carolina at Charlotte, Charlotte, North Carolina, United States of America
| | - Ajitesh Srivastava
- University of Southern California, Los Angeles, California, United States of America
| | - Majd Al Aawar
- University of Southern California, Los Angeles, California, United States of America
| | - Kaiming Bi
- University of Texas at Austin, Austin, Texas, United States of America
| | | | - Anass Bouchnita
- University of Texas at El Paso, El Paso, Texas, United States of America
| | - Spencer J. Fox
- University of Georgia, Athens, Georgia, United States of America
| | | | | | | | - Aniruddha Adiga
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Benjamin Hurt
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Brian Klahn
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Joseph Outten
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Jiangzhuo Chen
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Henning Mortveit
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Amanda Wilson
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Stefan Hoops
- University of Virginia, Charlottesville, Virginia, United States of America
| | | | - Dustin Machi
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Anil Vullikanti
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Bryan Lewis
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Madhav Marathe
- University of Virginia, Charlottesville, Virginia, United States of America
| | - Harry Hochheiser
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Michael C. Runge
- U.S. Geological Survey, Laurel, Maryland, United States of America
| | - Katriona Shea
- The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Shaun Truelove
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Justin Lessler
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
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3
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Loo SL, Howerton E, Contamin L, Smith CP, Borchering RK, Mullany LC, Bents S, Carcelen E, Jung SM, Bogich T, van Panhuis WG, Kerr J, Espino J, Yan K, Hochheiser H, Runge MC, Shea K, Lessler J, Viboud C, Truelove S. The US COVID-19 and Influenza Scenario Modeling Hubs: Delivering long-term projections to guide policy. Epidemics 2024; 46:100738. [PMID: 38184954 DOI: 10.1016/j.epidem.2023.100738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 10/02/2023] [Accepted: 12/18/2023] [Indexed: 01/09/2024] Open
Abstract
Between December 2020 and April 2023, the COVID-19 Scenario Modeling Hub (SMH) generated operational multi-month projections of COVID-19 burden in the US to guide pandemic planning and decision-making in the context of high uncertainty. This effort was born out of an attempt to coordinate, synthesize and effectively use the unprecedented amount of predictive modeling that emerged throughout the COVID-19 pandemic. Here we describe the history of this massive collective research effort, the process of convening and maintaining an open modeling hub active over multiple years, and attempt to provide a blueprint for future efforts. We detail the process of generating 17 rounds of scenarios and projections at different stages of the COVID-19 pandemic, and disseminating results to the public health community and lay public. We also highlight how SMH was expanded to generate influenza projections during the 2022-23 season. We identify key impacts of SMH results on public health and draw lessons to improve future collaborative modeling efforts, research on scenario projections, and the interface between models and policy.
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Affiliation(s)
- Sara L Loo
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; International Vaccine Access Center, Johns Hopkins, Baltimore, MD, USA.
| | - Emily Howerton
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Lucie Contamin
- Public Health Dynamics Lab, University of Pittsburgh, Pittsburgh, PA, USA
| | - Claire P Smith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Rebecca K Borchering
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Luke C Mullany
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA
| | - Samantha Bents
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Erica Carcelen
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; International Vaccine Access Center, Johns Hopkins, Baltimore, MD, USA
| | - Sung-Mok Jung
- UNC Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tiffany Bogich
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Willem G van Panhuis
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jessica Kerr
- Public Health Dynamics Lab, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jessi Espino
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA
| | - Katie Yan
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Harry Hochheiser
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA
| | - Michael C Runge
- Eastern Ecological Science Center at the Patuxent Research Refuge, US Geological Survey, Laurel, MD, USA
| | - Katriona Shea
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; UNC Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Shaun Truelove
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; International Vaccine Access Center, Johns Hopkins, Baltimore, MD, USA
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4
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Howerton E, Contamin L, Mullany LC, Qin M, Reich NG, Bents S, Borchering RK, Jung SM, Loo SL, Smith CP, Levander J, Kerr J, Espino J, van Panhuis WG, Hochheiser H, Galanti M, Yamana T, Pei S, Shaman J, Rainwater-Lovett K, Kinsey M, Tallaksen K, Wilson S, Shin L, Lemaitre JC, Kaminsky J, Hulse JD, Lee EC, McKee CD, Hill A, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Pastore Y Piontti A, Vespignani A, Rosenstrom ET, Ivy JS, Mayorga ME, Swann JL, España G, Cavany S, Moore S, Perkins A, Hladish T, Pillai A, Ben Toh K, Longini I, Chen S, Paul R, Janies D, Thill JC, Bouchnita A, Bi K, Lachmann M, Fox SJ, Meyers LA, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Hurt B, Chen J, Mortveit H, Wilson A, Marathe M, Hoops S, Bhattacharya P, Machi D, Cadwell BL, Healy JM, Slayton RB, Johansson MA, Biggerstaff M, Truelove S, Runge MC, Shea K, Viboud C, Lessler J. Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty. Nat Commun 2023; 14:7260. [PMID: 37985664 PMCID: PMC10661184 DOI: 10.1038/s41467-023-42680-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 10/17/2023] [Indexed: 11/22/2023] Open
Abstract
Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections.
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Affiliation(s)
- Emily Howerton
- The Pennsylvania State University, University Park, PA, USA.
| | | | - Luke C Mullany
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | | | | | - Samantha Bents
- National Institutes of Health Fogarty International Center, Bethesda, MD, USA
| | - Rebecca K Borchering
- The Pennsylvania State University, University Park, PA, USA
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Sung-Mok Jung
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Sara L Loo
- Johns Hopkins University, Baltimore, MD, USA
| | | | | | | | - J Espino
- University of Pittsburgh, Pittsburgh, PA, USA
| | | | | | | | | | - Sen Pei
- Columbia University, New York, NY, USA
| | | | | | - Matt Kinsey
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | - Kate Tallaksen
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | - Shelby Wilson
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | - Lauren Shin
- Johns Hopkins University Applied Physics Lab, Laurel, MD, USA
| | | | | | | | | | | | - Alison Hill
- Johns Hopkins University, Baltimore, MD, USA
| | - Dean Karlen
- University of Victoria, Victoria, BC, Canada
| | | | | | - Kunpeng Mu
- Northeastern University, Boston, MA, USA
| | | | | | | | | | - Julie S Ivy
- North Carolina State University, Raleigh, NC, USA
| | | | | | | | - Sean Cavany
- University of Notre Dame, Notre Dame, IN, USA
| | - Sean Moore
- University of Notre Dame, Notre Dame, IN, USA
| | | | | | | | | | | | - Shi Chen
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Rajib Paul
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Daniel Janies
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | | | | | - Kaiming Bi
- University of Texas at Austin, Austin, TX, USA
| | | | | | | | | | | | | | | | - Bryan Lewis
- University of Virginia, Charlottesville, VA, USA
| | - Brian Klahn
- University of Virginia, Charlottesville, VA, USA
| | | | | | | | | | | | | | - Stefan Hoops
- University of Virginia, Charlottesville, VA, USA
| | | | - Dustin Machi
- University of Virginia, Charlottesville, VA, USA
| | - Betsy L Cadwell
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Jessica M Healy
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | | | | | | | | | - Michael C Runge
- U.S. Geological Survey Eastern Ecological Science Center, Laurel, MD, USA
| | - Katriona Shea
- The Pennsylvania State University, University Park, PA, USA
| | - Cécile Viboud
- National Institutes of Health Fogarty International Center, Bethesda, MD, USA.
| | - Justin Lessler
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Johns Hopkins University, Baltimore, MD, USA.
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5
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Jung SM, Loo SL, Howerton E, Contamin L, Smith CP, Carcelén EC, Yan K, Bents SJ, Levander J, Espino J, Lemaitre JC, Sato K, McKee CD, Hill AL, Chinazzi M, Davis JT, Mu K, Vespignani A, Rosenstrom ET, Rodriguez-Cartes SA, Ivy JS, Mayorga ME, Swann JL, España G, Cavany S, Moore SM, Perkins A, Chen S, Paul R, Janies D, Thill JC, Srivastava A, Al Aawar M, Bi K, Bandekar SR, Bouchnita A, Fox SJ, Meyers LA, Porebski P, Venkatramanan S, Adiga A, Hurt B, Klahn B, Outten J, Chen J, Mortveit H, Wilson A, Hoops S, Bhattacharya P, Machi D, Vullikanti A, Lewis B, Marathe M, Hochheiser H, Runge MC, Shea K, Truelove S, Viboud C, Lessler J. Potential impact of annual vaccination with reformulated COVID-19 vaccines: lessons from the U.S. COVID-19 Scenario Modeling Hub. medRxiv 2023:2023.10.26.23297581. [PMID: 37961207 PMCID: PMC10635209 DOI: 10.1101/2023.10.26.23297581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Importance COVID-19 continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Objective To project COVID-19 hospitalizations and deaths from April 2023-April 2025 under two plausible assumptions about immune escape (20% per year and 50% per year) and three possible CDC recommendations for the use of annually reformulated vaccines (no vaccine recommendation, vaccination for those aged 65+, vaccination for all eligible groups). Design The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023-April 15, 2025 under six scenarios representing the intersection of considered levels of immune escape and vaccination. State and national projections from eight modeling teams were ensembled to produce projections for each scenario. Setting The entire United States. Participants None. Exposure Annually reformulated vaccines assumed to be 65% effective against strains circulating on June 15 of each year and to become available on September 1. Age and state specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. Main outcomes and measures Ensemble estimates of weekly and cumulative COVID-19 hospitalizations and deaths. Expected relative and absolute reductions in hospitalizations and deaths due to vaccination over the projection period. Results From April 15, 2023-April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November-January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% PI: 1,438,000-4,270,000) hospitalizations and 209,000 (90% PI: 139,000-461,000) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% CI: 104,000-355,000) fewer hospitalizations and 33,000 (95% CI: 12,000-54,000) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI: 29,000-69,000) fewer deaths. Conclusion and Relevance COVID-19 is projected to be a significant public health threat over the coming two years. Broad vaccination has the potential to substantially reduce the burden of this disease.
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Affiliation(s)
- Sung-mok Jung
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Sara L. Loo
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Emily Howerton
- The Pennsylvania State University, State College, Pennsylvania
| | | | - Claire P. Smith
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Erica C. Carcelén
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Katie Yan
- The Pennsylvania State University, State College, Pennsylvania
| | - Samantha J. Bents
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland
| | | | - Jessi Espino
- University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Joseph C. Lemaitre
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Koji Sato
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Clif D. McKee
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Alison L. Hill
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | | | | | - Kunpeng Mu
- University of Massachusetts Amherst, Amherst, Massachusetts
| | | | | | | | - Julie S. Ivy
- North Carolina State University, Raleigh, North Carolina
| | | | - Julie L. Swann
- North Carolina State University, Raleigh, North Carolina
| | | | - Sean Cavany
- University of Notre Dame, Notre Dame, Indiana
| | | | | | - Shi Chen
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Rajib Paul
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Daniel Janies
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Jean-Claude Thill
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | | | - Majd Al Aawar
- University of Southern California, Los Angeles, California
| | - Kaiming Bi
- University of Texas at Austin, Austin, Texas
| | | | | | | | | | | | | | | | | | - Brian Klahn
- University of Virginia, Charlottesville, Virginia
| | | | | | | | | | - Stefan Hoops
- University of Virginia, Charlottesville, Virginia
| | | | - Dustin Machi
- University of Virginia, Charlottesville, Virginia
| | | | - Bryan Lewis
- University of Virginia, Charlottesville, Virginia
| | | | | | | | - Katriona Shea
- The Pennsylvania State University, State College, Pennsylvania
| | - Shaun Truelove
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland
| | - Justin Lessler
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
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Smith CP, Armstrong WR, Clark K, Moore J, Roberts M, Farolfi A, Reiter RE, Rettig M, Shen J, Valle L, Nickols NG, Steinberg ML, Czernin J, Kishan AU, Calais J. PSMA PET Guided Salvage Radiotherapy Among Prostate Cancer Patients in the Post-Prostatectomy Setting: A Single Center Post-Hoc Analysis. Int J Radiat Oncol Biol Phys 2023; 117:e438. [PMID: 37785423 DOI: 10.1016/j.ijrobp.2023.06.1612] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Prostate-specific membrane antigen (PSMA) positron emission tomography (PET) shows improved sensitivity and specificity for detection of locoregional and distant metastatic prostate cancer (PCa) compared to conventional imaging, especially at lower PSA levels as is often the case in the biochemically recurrent (BCR), post radical prostatectomy (RP) setting. Providers are now utilizing PSMA PET findings to guide their salvage radiotherapy (sRT) treatment fields and doses, although it is not well understood how PSMA PET guided sRT impacts patient outcomes. MATERIALS/METHODS This was a post-hoc analysis of 5 prospective studies of PSMA PET conducted at UCLA from 2016 to 2021 that included patients with recurrent PCa following RP. Patients were included in this retrospective study if they initiated sRT within 3 months of PSMA PET, had at least 12 months of follow up after sRT completion, had available sRT treatment details, and did not have distant metastases (DM) by conventional imaging on upfront staging. Patients treated with palliative RT were excluded. BCR following sRT was defined as an increase in PSA of 0.2 ng/ml above the post sRT nadir. Metastasis directed therapy (MDT) was defined as sRT to all PSMA+ N1 and M1 lesions. Baseline patient demographics, PSMA PET findings, sRT & ADT treatment details, and patient outcome data were collected. RESULTS A total of 176 patients were included in this study. Median time between RP and PSMA PET was 38 months (range 1-329). Median PSA at the time of the PSMA PET was 0.625 ng/mL (range 0.063-35). PSMA PET was positive in 128 patients (73%): 21 (12%) miT+N0M0, 55 (31%) miTxN1M0 and 52 (30%) miTxNxM1 with 19 (11%) miTxNxM1a, 31 (18%) miTxNxM1b, and 2 (1%) miTxNxM1c. Median number of lesions seen on positive PSMA scans was 1 (range 1-8). 39 (22%) patients were subsequently treated with sRT to the prostate bed (PB) only, 59 (34%) to PB + pelvic lymph nodes (PLNs), 33 (19%) to PLNs only, 7 (4%) to PB + PLNs + DM, 7 (4%) to PLNs + DM, and 31 (18%) to DM only. 59 (34%) patients were treated with concurrent ADT at a median duration of 6 months (range 1-39). At a median follow-up of 32 months (range 12-70) after sRT, 80 patients (45%) did not develop BCR or imaging relapse (IR) following sRT, 24 patients (14%) developed BCR but not IR, 1 patient (<1%) developed IR only, and 70 patients (40%) developed both BCR and IR. The median time to BCR and IR following sRT was 15 months (range 1-48) and 19 months (range 6-61), respectively. 1 year post sRT biochemical recurrence free survival was 77%. Of the 83 patients treated with MDT, 32 (39%) did not develop subsequent disease relapse. CONCLUSION This post hoc analysis assessed the outcomes of 176 patients treated with PSMA PET guided salvage RT, proving it to be an effective method for treating both pelvic and extrapelvic recurrent PCa. Further investigation is needed to assess the full extent of patient outcomes in this population.
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Affiliation(s)
- C P Smith
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA
| | - W R Armstrong
- Ahmanson Translational Theranostics Division, UCLA Nuclear Medicine, Los Angeles, CA
| | - K Clark
- Ahmanson Translational Theranostics Division, Los Angeles, CA
| | - J Moore
- Ahmanson Translational Theranostics Division, UCLA Nuclear Medicine, Los Angeles, CA
| | - M Roberts
- Ahmanson Translational Theranostics Division, UCLA Nuclear Medicine, Los Angeles, CA
| | - A Farolfi
- Ahmanson Translational Theranostics Division, UCLA Nuclear Medicine, Los Angeles, CA
| | - R E Reiter
- Department of Urology, University of California, Los Angeles, Los Angeles, CA
| | - M Rettig
- Department of Medical Oncology, University of California, Los Angeles, Los Angeles, CA
| | - J Shen
- Department of Medical Oncology, University of California, Los Angeles, Los Angeles, CA
| | - L Valle
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA
| | - N G Nickols
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA
| | - M L Steinberg
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA
| | - J Czernin
- Ahmanson Translational Theranostics Division, UCLA Nuclear Medicine, Los Angeles, CA
| | - A U Kishan
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA
| | - J Calais
- Ahmanson Translational Theranostics Division, UCLA Nuclear Medicine, Los Angeles, CA
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7
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Nikitas J, Subramanian K, Proudfoot J, Davicioni E, Ricaurte-Fajardo A, Armstrong WR, Czernin J, Osborne JR, Marciscano AE, Smith CP, Valle L, Steinberg ML, Boutros P, Rettig M, Reiter RE, Weiner A, Barbieri CE, Calais J, Nagar H, Kishan AU. Predictive Value of Genomic Classifier Scores and Transcriptomic Data for Prostate Cancer Distant Metastasis Risk: A Multicenter Retrospective Study. Int J Radiat Oncol Biol Phys 2023; 117:e423-e424. [PMID: 37785390 DOI: 10.1016/j.ijrobp.2023.06.1581] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Prostate-specific membrane antigen (PSMA) positron emission tomography (PET) has a greater specificity and sensitivity for detection of extraprostatic prostate cancer than conventional imaging. The Decipher genomic classifier is an established prognostic biomarker being evaluated for its ability to predict systemic treatment intensification. The relationship between Decipher scores and PSMA-based spread remains unknown, as do differences in transcriptomic patterns of PSMA PET-based spread in the de novo vs. recurrent setting. MATERIALS/METHODS We retrospectively identified patients who (a) had undergone staging with a PSMA PET prior to treatment or for evaluation of recurrence post-radical prostatectomy (RP) at two institutions and (b) had transcriptomic data available from the Genomics Resource for Intelligent Discovery (GRID) database from either biopsy or RP specimens. We classified the PSMA PET pattern of spread using molecular imaging (mi) staging as localized (miT+N0M0), node-positive (miN1M0), distant metastasis (miM1a-c), or negative/non-diagnostic. We used logistic regression to calculate the odds ratios (OR) with 95% confidence intervals (CI) for distant metastasis risk based on Decipher score both pre-treatment and post-RP. As an exploratory analysis, we compared each of the staging groups for differences in important transcriptomic signatures. Kruskal-Wallis and Pearson chi-squared tests were used for continuous and categorical variables, respectively. RESULTS A total of 315 patients were included in this analysis (n = 164 pre-treatment, n = 151 post-RP). Eighty PSMA PET scans were negative, while 147 were miT+N0M0, 45 were miN1M0, and 43 were miM1a-c. A higher Decipher score was associated with distant metastasis (miM1a-c) on PSMA PET both pre-treatment (OR 1.3 [95% CI: 1.0-1.7] per 0.1 increase in Decipher score, P = 0.05) and post-RP (OR 1.2 [1.0-1.4] per 0.1 increase in Decipher score, P = 0.04). There were higher TP53 mutation (P = 0.01) and cell cycle progression (P = 0.04) signature scores in miM1a-c patients compared to miN1M0 or miT+N0M0 patients. Basal subtype was more prevalent per PAM50 in miM1a-c or miN1M0 patients (36%) than miT+N0M0 patients (19%, P=0.01). Patients with de novo miN1M0 or miM1a disease (n = 19) had higher Decipher scores (0.85 vs 0.57, P = 0.10) and IFNa response (P = 0.08) than patients with recurrent miN1M0 or miM1a disease (n = 35). CONCLUSION Higher Decipher scores were associated with distant metastasis on PSMA PET in both the de novo and recurrent setting. Transcriptomic differences in pathways related to proliferation, p53 status, and PAM50 classification were seen when comparing localized, node-positive, and distant metastatic disease. Patients with de novo miN1M0 or miM1a disease may harbor more aggressive disease than those with miN1M0 or miM1a disease at recurrence.
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Affiliation(s)
- J Nikitas
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA
| | - K Subramanian
- Department of Nuclear Medicine, New York-Presbyterian/Weill Cornell Hospital, New York, NY
| | | | | | - A Ricaurte-Fajardo
- Department of Radiology, New York-Presbyterian/Weill Cornell Hospital, New York, NY
| | - W R Armstrong
- Ahmanson Translational Theranostics Division, UCLA Nuclear Medicine, Los Angeles, CA
| | - J Czernin
- Ahmanson Translational Theranostics Division, UCLA Nuclear Medicine, Los Angeles, CA
| | - J R Osborne
- Department of Radiology, New York-Presbyterian/Weill Cornell Medical Center, New York, NY
| | - A E Marciscano
- Department of Radiation Oncology, New York-Presbyterian Hospital / Weill Cornell Medical College, New York, NY
| | - C P Smith
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA
| | - L Valle
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA
| | - M L Steinberg
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA
| | - P Boutros
- Department of Urology, University of California, Los Angeles, Los Angeles, CA
| | - M Rettig
- Department of Urology, University of California, Los Angeles, Los Angeles, CA
| | - R E Reiter
- Department of Urology, University of California, Los Angeles, Los Angeles, CA
| | - A Weiner
- Department of Urology, University of California, Los Angeles, Los Angeles, CA
| | - C E Barbieri
- Department of Urology, New York-Presbyterian/Weill Cornell Medical Center, New York, NY
| | - J Calais
- Ahmanson Translational Theranostics Division, UCLA Nuclear Medicine, Los Angeles, CA
| | - H Nagar
- Department of Radiation Oncology, New York-Presbyterian/Weill Cornell Hospital, New York, NY
| | - A U Kishan
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA
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Howerton E, Contamin L, Mullany LC, Qin M, Reich NG, Bents S, Borchering RK, Jung SM, Loo SL, Smith CP, Levander J, Kerr J, Espino J, van Panhuis WG, Hochheiser H, Galanti M, Yamana T, Pei S, Shaman J, Rainwater-Lovett K, Kinsey M, Tallaksen K, Wilson S, Shin L, Lemaitre JC, Kaminsky J, Hulse JD, Lee EC, McKee C, Hill A, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Piontti APY, Vespignani A, Rosenstrom ET, Ivy JS, Mayorga ME, Swann JL, España G, Cavany S, Moore S, Perkins A, Hladish T, Pillai A, Toh KB, Longini I, Chen S, Paul R, Janies D, Thill JC, Bouchnita A, Bi K, Lachmann M, Fox S, Meyers LA, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Hurt B, Chen J, Mortveit H, Wilson A, Marathe M, Hoops S, Bhattacharya P, Machi D, Cadwell BL, Healy JM, Slayton RB, Johansson MA, Biggerstaff M, Truelove S, Runge MC, Shea K, Viboud C, Lessler J. Informing pandemic response in the face of uncertainty. An evaluation of the U.S. COVID-19 Scenario Modeling Hub. medRxiv 2023:2023.06.28.23291998. [PMID: 37461674 PMCID: PMC10350156 DOI: 10.1101/2023.06.28.23291998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/24/2023]
Abstract
Our ability to forecast epidemics more than a few weeks into the future is constrained by the complexity of disease systems, our limited ability to measure the current state of an epidemic, and uncertainties in how human action will affect transmission. Realistic longer-term projections (spanning more than a few weeks) may, however, be possible under defined scenarios that specify the future state of critical epidemic drivers, with the additional benefit that such scenarios can be used to anticipate the comparative effect of control measures. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make 6-month ahead projections of the number of SARS-CoV-2 cases, hospitalizations and deaths. The SMH released nearly 1.8 million national and state-level projections between February 2021 and November 2022. SMH performance varied widely as a function of both scenario validity and model calibration. Scenario assumptions were periodically invalidated by the arrival of unanticipated SARS-CoV-2 variants, but SMH still provided projections on average 22 weeks before changes in assumptions (such as virus transmissibility) invalidated scenarios and their corresponding projections. During these periods, before emergence of a novel variant, a linear opinion pool ensemble of contributed models was consistently more reliable than any single model, and projection interval coverage was near target levels for the most plausible scenarios (e.g., 79% coverage for 95% projection interval). SMH projections were used operationally to guide planning and policy at different stages of the pandemic, illustrating the value of the hub approach for long-term scenario projections.
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Affiliation(s)
| | | | | | | | | | - Samantha Bents
- National Institutes of Health Fogarty International Center (NIH)
| | | | | | - Sara L Loo
- Johns Hopkins University Infectious Disease Dynamics (JHU-IDD)
| | - Claire P Smith
- Johns Hopkins University Infectious Disease Dynamics (JHU-IDD)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Shi Chen
- University of North Carolina at Charlotte (UNCC)
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9
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Howerton E, Runge MC, Bogich TL, Borchering RK, Inamine H, Lessler J, Mullany LC, Probert WJM, Smith CP, Truelove S, Viboud C, Shea K. Context-dependent representation of within- and between-model uncertainty: aggregating probabilistic predictions in infectious disease epidemiology. J R Soc Interface 2023; 20:20220659. [PMID: 36695018 PMCID: PMC9874266 DOI: 10.1098/rsif.2022.0659] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 01/03/2023] [Indexed: 01/26/2023] Open
Abstract
Probabilistic predictions support public health planning and decision making, especially in infectious disease emergencies. Aggregating outputs from multiple models yields more robust predictions of outcomes and associated uncertainty. While the selection of an aggregation method can be guided by retrospective performance evaluations, this is not always possible. For example, if predictions are conditional on assumptions about how the future will unfold (e.g. possible interventions), these assumptions may never materialize, precluding any direct comparison between predictions and observations. Here, we summarize literature on aggregating probabilistic predictions, illustrate various methods for infectious disease predictions via simulation, and present a strategy for choosing an aggregation method when empirical validation cannot be used. We focus on the linear opinion pool (LOP) and Vincent average, common methods that make different assumptions about between-prediction uncertainty. We contend that assumptions of the aggregation method should align with a hypothesis about how uncertainty is expressed within and between predictions from different sources. The LOP assumes that between-prediction uncertainty is meaningful and should be retained, while the Vincent average assumes that between-prediction uncertainty is akin to sampling error and should not be preserved. We provide an R package for implementation. Given the rising importance of multi-model infectious disease hubs, our work provides useful guidance on aggregation and a deeper understanding of the benefits and risks of different approaches.
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Affiliation(s)
- Emily Howerton
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Michael C. Runge
- Eastern Ecological Science Center at the Patuxent Research Refuge, U.S. Geological Survey, Laurel, MD, USA
| | - Tiffany L. Bogich
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Rebecca K. Borchering
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Hidetoshi Inamine
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
| | - Justin Lessler
- Department of Epidemiology and Carolina Population Center, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Luke C. Mullany
- Applied Physics Laboratory, Johns Hopkins University, Baltimore, MD, USA
| | - William J. M. Probert
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
| | - Claire P. Smith
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Shaun Truelove
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Katriona Shea
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA
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10
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Borchering RK, Mullany LC, Howerton E, Chinazzi M, Smith CP, Qin M, Reich NG, Contamin L, Levander J, Kerr J, Espino J, Hochheiser H, Lovett K, Kinsey M, Tallaksen K, Wilson S, Shin L, Lemaitre JC, Hulse JD, Kaminsky J, Lee EC, Hill AL, Davis JT, Mu K, Xiong X, Pastore y Piontti A, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Hurt B, Chen J, Mortveit H, Wilson A, Marathe M, Hoops S, Bhattacharya P, Machi D, Chen S, Paul R, Janies D, Thill JC, Galanti M, Yamana T, Pei S, Shaman J, España G, Cavany S, Moore S, Perkins A, Healy JM, Slayton RB, Johansson MA, Biggerstaff M, Shea K, Truelove SA, Runge MC, Viboud C, Lessler J. Impact of SARS-CoV-2 vaccination of children ages 5-11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021-March 2022: A multi-model study. Lancet Reg Health Am 2023; 17:100398. [PMID: 36437905 PMCID: PMC9679449 DOI: 10.1016/j.lana.2022.100398] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 09/21/2022] [Accepted: 10/27/2022] [Indexed: 11/23/2022]
Abstract
Background The COVID-19 Scenario Modeling Hub convened nine modeling teams to project the impact of expanding SARS-CoV-2 vaccination to children aged 5-11 years on COVID-19 burden and resilience against variant strains. Methods Teams contributed state- and national-level weekly projections of cases, hospitalizations, and deaths in the United States from September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of 1) vaccination (or not) of children aged 5-11 years (starting November 1, 2021), and 2) emergence (or not) of a variant more transmissible than the Delta variant (emerging November 15, 2021). Individual team projections were linearly pooled. The effect of childhood vaccination on overall and age-specific outcomes was estimated using meta-analyses. Findings Assuming that a new variant would not emerge, all-age COVID-19 outcomes were projected to decrease nationally through mid-March 2022. In this setting, vaccination of children 5-11 years old was associated with reductions in projections for all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios without childhood vaccination. Vaccine benefits increased for scenarios including a hypothesized more transmissible variant, assuming similar vaccine effectiveness. Projected relative reductions in cumulative outcomes were larger for children than for the entire population. State-level variation was observed. Interpretation Given the scenario assumptions (defined before the emergence of Omicron), expanding vaccination to children 5-11 years old would provide measurable direct benefits, as well as indirect benefits to the all-age U.S. population, including resilience to more transmissible variants. Funding Various (see acknowledgments).
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Affiliation(s)
| | - Luke C. Mullany
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | - Emily Howerton
- The Pennsylvania State University, University Park, PA, USA
| | | | | | | | | | | | | | | | - J. Espino
- University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Kaitlin Lovett
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | - Matt Kinsey
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | - Kate Tallaksen
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | - Shelby Wilson
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | - Lauren Shin
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | | | | | | | | | | | | | - Kunpeng Mu
- Northeastern University, Boston, MA, USA
| | | | | | | | | | | | | | | | - Bryan Lewis
- University of Virginia, Charlottesville, VA, USA
| | - Brian Klahn
- University of Virginia, Charlottesville, VA, USA
| | | | | | | | | | | | | | - Stefan Hoops
- University of Virginia, Charlottesville, VA, USA
| | | | - Dustin Machi
- University of Virginia, Charlottesville, VA, USA
| | - Shi Chen
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Rajib Paul
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Daniel Janies
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | | | | | | | - Sen Pei
- Columbia University, New York, NY, USA
| | | | | | - Sean Cavany
- University of Notre Dame, Notre Dame, IN, USA
| | - Sean Moore
- University of Notre Dame, Notre Dame, IN, USA
| | | | - Jessica M. Healy
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Rachel B. Slayton
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Michael A. Johansson
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Matthew Biggerstaff
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Katriona Shea
- The Pennsylvania State University, University Park, PA, USA
| | | | | | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Justin Lessler
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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11
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Truelove S, Smith CP, Qin M, Mullany LC, Borchering RK, Lessler J, Shea K, Howerton E, Contamin L, Levander J, Kerr J, Hochheiser H, Kinsey M, Tallaksen K, Wilson S, Shin L, Rainwater-Lovett K, Lemairtre JC, Dent J, Kaminsky J, Lee EC, Perez-Saez J, Hill A, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Pastore y Piontti A, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Orr M, Harrison G, Hurt B, Chen J, Vullikanti A, Marathe M, Hoops S, Bhattacharya P, Machi D, Chen S, Paul R, Janies D, Thill JC, Galanti M, Yamana TK, Pei S, Shaman JL, Healy JM, Slayton RB, Biggerstaff M, Johansson MA, Runge MC, Viboud C. Projected resurgence of COVID-19 in the United States in July-December 2021 resulting from the increased transmissibility of the Delta variant and faltering vaccination. eLife 2022; 11:e73584. [PMID: 35726851 PMCID: PMC9232215 DOI: 10.7554/elife.73584] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 06/03/2022] [Indexed: 01/01/2023] Open
Abstract
In Spring 2021, the highly transmissible SARS-CoV-2 Delta variant began to cause increases in cases, hospitalizations, and deaths in parts of the United States. At the time, with slowed vaccination uptake, this novel variant was expected to increase the risk of pandemic resurgence in the US in summer and fall 2021. As part of the COVID-19 Scenario Modeling Hub, an ensemble of nine mechanistic models produced 6-month scenario projections for July-December 2021 for the United States. These projections estimated substantial resurgences of COVID-19 across the US resulting from the more transmissible Delta variant, projected to occur across most of the US, coinciding with school and business reopening. The scenarios revealed that reaching higher vaccine coverage in July-December 2021 reduced the size and duration of the projected resurgence substantially, with the expected impacts was largely concentrated in a subset of states with lower vaccination coverage. Despite accurate projection of COVID-19 surges occurring and timing, the magnitude was substantially underestimated 2021 by the models compared with the of the reported cases, hospitalizations, and deaths occurring during July-December, highlighting the continued challenges to predict the evolving COVID-19 pandemic. Vaccination uptake remains critical to limiting transmission and disease, particularly in states with lower vaccination coverage. Higher vaccination goals at the onset of the surge of the new variant were estimated to avert over 1.5 million cases and 21,000 deaths, although may have had even greater impacts, considering the underestimated resurgence magnitude from the model.
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Affiliation(s)
- Shaun Truelove
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | - Claire P Smith
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | - Michelle Qin
- Harvard UniversityCambridge, MassachusettsUnited States
| | - Luke C Mullany
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
- Johns Hopkins University Applied Physics LaboratoryLaurelUnited States
| | | | - Justin Lessler
- University of North Carolina at Chapel HillChapel HillUnited States
| | - Katriona Shea
- Pennsylvania State UniversityUniversity ParkUnited States
| | - Emily Howerton
- Pennsylvania State UniversityUniversity ParkUnited States
| | | | | | | | | | - Matt Kinsey
- Johns Hopkins University Applied Physics LaboratoryLaurelUnited States
| | - Kate Tallaksen
- Johns Hopkins University Applied Physics LaboratoryLaurelUnited States
| | - Shelby Wilson
- Johns Hopkins University Applied Physics LaboratoryLaurelUnited States
| | - Lauren Shin
- Johns Hopkins University Applied Physics LaboratoryLaurelUnited States
| | | | | | - Juan Dent
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | - Joshua Kaminsky
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | - Elizabeth C Lee
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | - Javier Perez-Saez
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | - Alison Hill
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins UniversityBaltimoreUnited States
| | | | | | | | - Kunpeng Mu
- Northeastern UniversityBostonUnited States
| | | | | | | | | | | | | | | | - Bryan Lewis
- University of VirginiaCharlottesvilleUnited States
| | - Brian Klahn
- University of VirginiaCharlottesvilleUnited States
| | | | - Mark Orr
- University of VirginiaCharlottesvilleUnited States
| | | | | | | | | | | | - Stefan Hoops
- University of VirginiaCharlottesvilleUnited States
| | | | - Dustin Machi
- University of VirginiaCharlottesvilleUnited States
| | - Shi Chen
- University of North Carolina at CharlotteCharlotteUnited States
| | - Rajib Paul
- University of North Carolina at CharlotteCharlotteUnited States
| | - Daniel Janies
- University of North Carolina at CharlotteCharlotteUnited States
| | | | | | | | - Sen Pei
- Columbia UniversityNew YorkUnited States
| | | | | | | | | | | | | | - Cecile Viboud
- Fogarty International Center, National Institutes of HealthBethesdaUnited States
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12
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Wiens KE, Smith CP, Badillo-Goicoechea E, Grantz KH, Grabowski MK, Azman AS, Stuart EA, Lessler J. In-person schooling and associated COVID-19 risk in the United States over spring semester 2021. Sci Adv 2022; 8:eabm9128. [PMID: 35442740 PMCID: PMC9020776 DOI: 10.1126/sciadv.abm9128] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Because of the importance of schools to childhood development, the relationship between in-person schooling and COVID-19 risk has been one of the most important questions of this pandemic. Previous work in the United States during winter 2020-2021 showed that in-person schooling carried some risk for household members and that mitigation measures reduced this risk. Schooling and the COVID-19 landscape changed radically over spring semester 2021. Here, we use data from a massive online survey to characterize changes in in-person schooling behavior and associated risks over that period. We find increases in in-person schooling and reductions in mitigations over time. In-person schooling is associated with increased reporting of COVID-19 outcomes even among vaccinated individuals (although the absolute risk among the vaccinated is greatly reduced). Vaccinated teachers working outside the home were less likely to report COVID-19-related outcomes than unvaccinated teachers working exclusively from home. Adequate mitigation measures appear to eliminate the excess risk associated with in-person schooling.
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Affiliation(s)
- Kirsten E. Wiens
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Claire P. Smith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Elena Badillo-Goicoechea
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Kyra H. Grantz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - M. Kate Grabowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Andrew S. Azman
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Elizabeth A. Stuart
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Corresponding author.
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13
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Cramer EY, Ray EL, Lopez VK, Bracher J, Brennen A, Castro Rivadeneira AJ, Gerding A, Gneiting T, House KH, Huang Y, Jayawardena D, Kanji AH, Khandelwal A, Le K, Mühlemann A, Niemi J, Shah A, Stark A, Wang Y, Wattanachit N, Zorn MW, Gu Y, Jain S, Bannur N, Deva A, Kulkarni M, Merugu S, Raval A, Shingi S, Tiwari A, White J, Abernethy NF, Woody S, Dahan M, Fox S, Gaither K, Lachmann M, Meyers LA, Scott JG, Tec M, Srivastava A, George GE, Cegan JC, Dettwiller ID, England WP, Farthing MW, Hunter RH, Lafferty B, Linkov I, Mayo ML, Parno MD, Rowland MA, Trump BD, Zhang-James Y, Chen S, Faraone SV, Hess J, Morley CP, Salekin A, Wang D, Corsetti SM, Baer TM, Eisenberg MC, Falb K, Huang Y, Martin ET, McCauley E, Myers RL, Schwarz T, Sheldon D, Gibson GC, Yu R, Gao L, Ma Y, Wu D, Yan X, Jin X, Wang YX, Chen Y, Guo L, Zhao Y, Gu Q, Chen J, Wang L, Xu P, Zhang W, Zou D, Biegel H, Lega J, McConnell S, Nagraj VP, Guertin SL, Hulme-Lowe C, Turner SD, Shi Y, Ban X, Walraven R, Hong QJ, Kong S, van de Walle A, Turtle JA, Ben-Nun M, Riley S, Riley P, Koyluoglu U, DesRoches D, Forli P, Hamory B, Kyriakides C, Leis H, Milliken J, Moloney M, Morgan J, Nirgudkar N, Ozcan G, Piwonka N, Ravi M, Schrader C, Shakhnovich E, Siegel D, Spatz R, Stiefeling C, Wilkinson B, Wong A, Cavany S, España G, Moore S, Oidtman R, Perkins A, Kraus D, Kraus A, Gao Z, Bian J, Cao W, Ferres JL, Li C, Liu TY, Xie X, Zhang S, Zheng S, Vespignani A, Chinazzi M, Davis JT, Mu K, Pastore y Piontti A, Xiong X, Zheng A, Baek J, Farias V, Georgescu A, Levi R, Sinha D, Wilde J, Perakis G, Bennouna MA, Nze-Ndong D, Singhvi D, Spantidakis I, Thayaparan L, Tsiourvas A, Sarker A, Jadbabaie A, Shah D, Della Penna N, Celi LA, Sundar S, Wolfinger R, Osthus D, Castro L, Fairchild G, Michaud I, Karlen D, Kinsey M, Mullany LC, Rainwater-Lovett K, Shin L, Tallaksen K, Wilson S, Lee EC, Dent J, Grantz KH, Hill AL, Kaminsky J, Kaminsky K, Keegan LT, Lauer SA, Lemaitre JC, Lessler J, Meredith HR, Perez-Saez J, Shah S, Smith CP, Truelove SA, Wills J, Marshall M, Gardner L, Nixon K, Burant JC, Wang L, Gao L, Gu Z, Kim M, Li X, Wang G, Wang Y, Yu S, Reiner RC, Barber R, Gakidou E, Hay SI, Lim S, Murray C, Pigott D, Gurung HL, Baccam P, Stage SA, Suchoski BT, Prakash BA, Adhikari B, Cui J, Rodríguez A, Tabassum A, Xie J, Keskinocak P, Asplund J, Baxter A, Oruc BE, Serban N, Arik SO, Dusenberry M, Epshteyn A, Kanal E, Le LT, Li CL, Pfister T, Sava D, Sinha R, Tsai T, Yoder N, Yoon J, Zhang L, Abbott S, Bosse NI, Funk S, Hellewell J, Meakin SR, Sherratt K, Zhou M, Kalantari R, Yamana TK, Pei S, Shaman J, Li ML, Bertsimas D, Lami OS, Soni S, Bouardi HT, Ayer T, Adee M, Chhatwal J, Dalgic OO, Ladd MA, Linas BP, Mueller P, Xiao J, Wang Y, Wang Q, Xie S, Zeng D, Green A, Bien J, Brooks L, Hu AJ, Jahja M, McDonald D, Narasimhan B, Politsch C, Rajanala S, Rumack A, Simon N, Tibshirani RJ, Tibshirani R, Ventura V, Wasserman L, O’Dea EB, Drake JM, Pagano R, Tran QT, Ho LST, Huynh H, Walker JW, Slayton RB, Johansson MA, Biggerstaff M, Reich NG. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. Proc Natl Acad Sci U S A 2022; 119:e2113561119. [PMID: 35394862 PMCID: PMC9169655 DOI: 10.1073/pnas.2113561119] [Citation(s) in RCA: 87] [Impact Index Per Article: 43.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 01/24/2022] [Indexed: 01/15/2023] Open
Abstract
Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.
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Affiliation(s)
- Estee Y. Cramer
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Evan L. Ray
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Velma K. Lopez
- COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333
| | - Johannes Bracher
- Chair of Econometrics and Statistics, Karlsruhe Institute of Technology, 76185 Karlsruhe, Germany
- Computational Statistics Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany
| | | | | | - Aaron Gerding
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Tilmann Gneiting
- Computational Statistics Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany
- Institute of Stochastics, Karlsruhe Institute of Technology, 69118 Karlsruhe, Germany
| | - Katie H. House
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Yuxin Huang
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Dasuni Jayawardena
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Abdul H. Kanji
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Ayush Khandelwal
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Khoa Le
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Anja Mühlemann
- Institute of Mathematical Statistics and Actuarial Science, University of Bern, CH-3012 Bern, Switzerland
| | - Jarad Niemi
- Department of Statistics, Iowa State University, Ames, IA 50011
| | - Apurv Shah
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Ariane Stark
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Yijin Wang
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Nutcha Wattanachit
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | - Martha W. Zorn
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
| | | | - Sansiddh Jain
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Nayana Bannur
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Ayush Deva
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Mihir Kulkarni
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Srujana Merugu
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Alpan Raval
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Siddhant Shingi
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Avtansh Tiwari
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | - Jerome White
- Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India
| | | | - Spencer Woody
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712
| | - Maytal Dahan
- Texas Advanced Computing Center, Austin, TX 78758
| | - Spencer Fox
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712
| | | | | | - Lauren Ancel Meyers
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712
| | - James G. Scott
- Department of Information, Risk, and Operations Management, University of Texas at Austin, Austin, TX 78712
| | - Mauricio Tec
- Department of Statistics and Data Sciences, University of Texas at Austin, Austin, TX 78712
| | - Ajitesh Srivastava
- Ming Hsieh Department of Computer and Electrical Engineering, University of Southern California, Los Angeles, CA 90089
| | - Glover E. George
- US Army Engineer Research and Development Center, Vicksburg, MS 39180
| | - Jeffrey C. Cegan
- US Army Engineer Research and Development Center, Concord, MA 01742
| | - Ian D. Dettwiller
- US Army Engineer Research and Development Center, Vicksburg, MS 39180
| | | | | | - Robert H. Hunter
- US Army Engineer Research and Development Center, Vicksburg, MS 39180
| | - Brandon Lafferty
- US Army Engineer Research and Development Center, Vicksburg, MS 39180
| | - Igor Linkov
- US Army Engineer Research and Development Center, Concord, MA 01742
| | - Michael L. Mayo
- US Army Engineer Research and Development Center, Vicksburg, MS 39180
| | - Matthew D. Parno
- US Army Engineer Research and Development Center, Hanover, NH 03755
| | | | | | - Yanli Zhang-James
- Department of Psychiatry and Behavioral Sciences, State University of New York Upstate Medical University, Syracuse, NY 13210
| | - Samuel Chen
- School of Medicine, State University of New York Upstate Medical University, Syracuse, NY 13210
| | - Stephen V. Faraone
- Department of Psychiatry and Behavioral Sciences, State University of New York Upstate Medical University, Syracuse, NY 13210
| | - Jonathan Hess
- Department of Psychiatry and Behavioral Sciences, State University of New York Upstate Medical University, Syracuse, NY 13210
| | - Christopher P. Morley
- Department of Public Health & Preventive Medicine, State University of New York Upstate Medical University, Syracuse, NY 13210
| | - Asif Salekin
- Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13207
| | - Dongliang Wang
- Department of Public Health & Preventive Medicine, State University of New York Upstate Medical University, Syracuse, NY 13210
| | | | - Thomas M. Baer
- Department of Physics, Trinity University, San Antonio, TX 78212
| | - Marisa C. Eisenberg
- Department of Complex Systems, University of Michigan, Ann Arbor, MI 48109
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109
- School of Public Health, Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109
| | - Karl Falb
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109
| | - Yitao Huang
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109
| | - Emily T. Martin
- School of Public Health, Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109
| | - Ella McCauley
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109
| | - Robert L. Myers
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109
| | - Tom Schwarz
- Department of Physics, University of Michigan, Ann Arbor, MI, 48109
| | - Daniel Sheldon
- College of Information and Computer Sciences, University of Massachusetts, Amherst, MA 01003
| | - Graham Casey Gibson
- School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA 01003
| | - Rose Yu
- Department of Computer Science and Engineering, University of California, San Diego, CA 92093
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115
| | - Liyao Gao
- Department of Statistics, University of Washington, Seattle, WA 98185
| | - Yian Ma
- Halıcıoğlu Data Science Institute, University of California, San Diego, CA 92093
| | - Dongxia Wu
- Department of Computer Science and Engineering, University of California, San Diego, CA 92093
| | - Xifeng Yan
- Department of Computer Science, University of California, Santa Barbara, CA 93106
| | - Xiaoyong Jin
- Department of Computer Science, University of California, Santa Barbara, CA 93106
| | - Yu-Xiang Wang
- Department of Computer Science, University of California, Santa Barbara, CA 93106
| | - YangQuan Chen
- Mechatronics, Embedded Systems and Automation Lab, Department of Mechanical Engineering, University of California, Merced, CA 95301
| | - Lihong Guo
- Jilin University, Changchun City, Jilin Province, 130012, People's Republic of China
| | - Yanting Zhao
- University of Science and Technology of China, Heifei, Anhui, 230027, People's Republic of China
| | - Quanquan Gu
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Jinghui Chen
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Lingxiao Wang
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Pan Xu
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Weitong Zhang
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Difan Zou
- Department of Computer Science, University of California, Los Angeles, CA 90095
| | - Hannah Biegel
- Department of Mathematics, University of Arizona, Tucson, AZ 85721
| | - Joceline Lega
- Department of Mathematics, University of Arizona, Tucson, AZ 85721
| | | | - V. P. Nagraj
- Quality Assurance and Data Science, Signature Science, LLC, Charlottesville, VA 22911
| | - Stephanie L. Guertin
- Quality Assurance and Data Science, Signature Science, LLC, Charlottesville, VA 22911
| | | | - Stephen D. Turner
- Quality Assurance and Data Science, Signature Science, LLC, Charlottesville, VA 22911
| | - Yunfeng Shi
- Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, Troy, NY 12309
| | - Xuegang Ban
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195
| | | | - Qi-Jun Hong
- School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ 85287
- School of Engineering, Brown University, Providence, RI 02912
| | | | | | - James A. Turtle
- Infectious Disease Group, Predictive Science, Inc, San Diego, CA 92121
| | - Michal Ben-Nun
- Infectious Disease Group, Predictive Science, Inc, San Diego, CA 92121
| | - Steven Riley
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College, W2 1PG London, United Kingdom
| | - Pete Riley
- Infectious Disease Group, Predictive Science, Inc, San Diego, CA 92121
| | | | | | - Pedro Forli
- Oliver Wyman Digital, Oliver Wyman, Sao Paolo, Brazil 04711-904
| | - Bruce Hamory
- Health & Life Sciences, Oliver Wyman, Boston, MA 02110
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- Health & Life Sciences, Oliver Wyman, New York, NY 10036
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- Financial Services, Oliver Wyman, New York, NY 10036
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- Financial Services, Oliver Wyman, New York, NY 10036
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- Financial Services, Oliver Wyman, New York, NY 10036
| | - Noah Piwonka
- Health & Life Sciences, Oliver Wyman, New York, NY 10036
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- Core Consultant Group, Oliver Wyman, New York, NY 10036
| | - Chris Schrader
- Health & Life Sciences, Oliver Wyman, New York, NY 10036
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- Financial Services, Oliver Wyman, New York, NY 10036
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- Core Consultant Group, Oliver Wyman, New York, NY 10036
| | - Chris Stiefeling
- Financial Services, Oliver Wyman Digital, Toronto, ON, Canada M5J 0A1
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- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556
| | - Guido España
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556
| | - Sean Moore
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556
| | - Rachel Oidtman
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556
- Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637
| | - Alex Perkins
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556
| | - David Kraus
- Department of Mathematics and Statistics, Masaryk University, 61137 Brno, Czech Republic
| | - Andrea Kraus
- Department of Mathematics and Statistics, Masaryk University, 61137 Brno, Czech Republic
| | | | | | - Wei Cao
- Microsoft, Redmond, WA 98029
| | | | | | | | | | | | | | - Alessandro Vespignani
- Institute for Scientific Interchange Foundation, Turin, 10133, Italy
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Matteo Chinazzi
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Jessica T. Davis
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Kunpeng Mu
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Ana Pastore y Piontti
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Xinyue Xiong
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Andrew Zheng
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Jackie Baek
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Vivek Farias
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142
| | - Andreea Georgescu
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Retsef Levi
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142
| | - Deeksha Sinha
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Joshua Wilde
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
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- Technology, Operations and Statistics (TOPS) group, Stern School of Business, New York University, New York, NY 10012
| | | | | | | | - Arnab Sarker
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Ali Jadbabaie
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Devavrat Shah
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Nicolas Della Penna
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Leo A. Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA 02139
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- Statistical Sciences Group, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - Lauren Castro
- Information Systems and Modeling Group, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - Geoffrey Fairchild
- Information Systems and Modeling Group, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - Isaac Michaud
- Statistical Sciences Group, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - Dean Karlen
- Department of Physics and Astronomy, University of Victoria, Victoria, BC, V8W 2Y2, Canada
- Physical Sciences Division, TRIUMF, Vancouver, BC, V8W 2Y2, Canada
| | - Matt Kinsey
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723
| | - Luke C. Mullany
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723
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- Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723
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- Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723
| | - Elizabeth C. Lee
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
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- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
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- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
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- Institute for Computational Medicine, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21218
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- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
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- Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, UT 84108
| | - Stephen A. Lauer
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
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- Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Hannah R. Meredith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Javier Perez-Saez
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Sam Shah
- Unaffiliated, San Francisco, CA 94122
| | - Claire P. Smith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
| | - Shaun A. Truelove
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21215
- International Vaccine Access Center, Johns Hopkins University, Baltimore, MD 21231
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21231
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- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Lauren Gardner
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Kristen Nixon
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218
| | | | - Lily Wang
- Department of Statistics, Iowa State University, Ames, IA 50011
| | - Lei Gao
- Department of Finance, Iowa State University, Ames, IA 50011
| | - Zhiling Gu
- Department of Statistics, Iowa State University, Ames, IA 50011
| | - Myungjin Kim
- Department of Statistics, Iowa State University, Ames, IA 50011
| | - Xinyi Li
- School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634
| | - Guannan Wang
- Department of Mathematics, College of William & Mary, Williamsburg, VA 23187
| | - Yueying Wang
- Department of Statistics, Iowa State University, Ames, IA 50011
| | - Shan Yu
- Department of Statistics, University of Virginia, Charlottesville, VA 22904
| | - Robert C. Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - Ryan Barber
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - Emmanuela Gakidou
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - Simon I. Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - Steve Lim
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - Chris Murray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | - David Pigott
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98195
| | | | | | | | | | - B. Aditya Prakash
- College of Computing, Georgia Institute of Technology, Atlanta, GA 30308
| | - Bijaya Adhikari
- Department of Computer Science, University of Iowa, Iowa City, IA 52242
| | - Jiaming Cui
- College of Computing, Georgia Institute of Technology, Atlanta, GA 30308
| | | | - Anika Tabassum
- Department of Computer Science, Virginia Tech, Falls Church, VA 22043
| | - Jiajia Xie
- College of Computing, Georgia Institute of Technology, Atlanta, GA 30308
| | - Pinar Keskinocak
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
| | - John Asplund
- Advanced Data Analytics, Metron, Inc., Reston, VA 20190
| | - Arden Baxter
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
| | - Buse Eylul Oruc
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
| | - Nicoleta Serban
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
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- Department of Health Policy and Management, Harvard University, Cambridge, MA 02138
| | | | | | | | - Sam Abbott
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Nikos I. Bosse
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Sebastian Funk
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Joel Hellewell
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Sophie R. Meakin
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Katharine Sherratt
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom
| | - Mingyuan Zhou
- McCombs School of Business, The University of Texas at Austin, Austin, TX 78712
| | - Rahi Kalantari
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712
| | - Teresa K. Yamana
- Department of Environmental Health Sciences, Columbia University, New York, NY 10032
| | - Sen Pei
- Department of Environmental Health Sciences, Columbia University, New York, NY 10032
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Columbia University, New York, NY 10032
| | - Michael L. Li
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Dimitris Bertsimas
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142
| | - Omar Skali Lami
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Saksham Soni
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Hamza Tazi Bouardi
- Operations Research Center, Massachusetts Institute of Technology; Cambridge, MA 02139
| | - Turgay Ayer
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
- Winship Cancer Institute, Emory University Medical School, Atlanta, GA 30322
| | - Madeline Adee
- Radiology-Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114
| | - Jagpreet Chhatwal
- Radiology-Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114
| | - Ozden O. Dalgic
- Health Economic Modeling, Value Analytics Labs, 34776 İstanbul, Turkey
| | - Mary A. Ladd
- Radiology-Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114
| | - Benjamin P. Linas
- Department of Medicine, Section of Infectious Diseases, Boston University School of Medicine, Boston, MA 02118
| | - Peter Mueller
- Radiology-Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA 02114
| | - Jade Xiao
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332
| | - Yuanjia Wang
- Department of Biostatistics, Columbia University, New York, NY 10032
- Department of Psychiatry, Columbia University, New York, NY 10032
| | - Qinxia Wang
- Department of Biostatistics, Columbia University, New York, NY 10032
| | - Shanghong Xie
- Department of Biostatistics, Columbia University, New York, NY 10032
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Alden Green
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Jacob Bien
- Marshall School of Business, Department of Data Sciences and Operations (DSO), University of Southern California, Los Angeles, CA 90089
| | - Logan Brooks
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Addison J. Hu
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Maria Jahja
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Daniel McDonald
- Department of Statistics, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Balasubramanian Narasimhan
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA 94305
- Department of Statistics, Stanford University, Stanford, CA 94305
| | - Collin Politsch
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Samyak Rajanala
- Department of Statistics, Stanford University, Stanford, CA 94305
| | - Aaron Rumack
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Noah Simon
- Department of Biostatistics, University of Washington, Seattle, WA 98195
| | - Ryan J. Tibshirani
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Rob Tibshirani
- Department of Statistics, Stanford University, Stanford, CA 94305
| | - Valerie Ventura
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Larry Wasserman
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Eamon B. O’Dea
- Odum School of Ecology, University of Georgia, Athens, GA 30602
| | - John M. Drake
- Odum School of Ecology, University of Georgia, Athens, GA 30602
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- Catalog Data Science, Walmart Inc., Sunnyvale, CA 94085
| | - Lam Si Tung Ho
- Department of Mathematics and Statistics, Dalhousie University, Halifax, NS, B3H 4R2, Canada
| | - Huong Huynh
- Virtual Power System Inc, Milpitas, CA 95035
| | - Jo W. Walker
- COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333
| | - Rachel B. Slayton
- COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333
| | - Michael A. Johansson
- COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333
| | - Matthew Biggerstaff
- COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333
| | - Nicholas G. Reich
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003
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14
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Borchering RK, Mullany LC, Howerton E, Chinazzi M, Smith CP, Qin M, Reich NG, Contamin L, Levander J, Kerr J, Espino J, Hochheiser H, Lovett K, Kinsey M, Tallaksen K, Wilson S, Shin L, Lemaitre JC, Hulse JD, Kaminsky J, Lee EC, Davis JT, Mu K, Xiong X, Piontti APY, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Hurt B, Chen J, Mortveit H, Wilson A, Marathe M, Hoops S, Bhattacharya P, Machi D, Chen S, Paul R, Janies D, Thill JC, Galanti M, Yamana T, Pei S, Shaman J, Espana G, Cavany S, Moore S, Perkins A, Healy JM, Slayton RB, Johansson MA, Biggerstaff M, Shea K, Truelove SA, Runge MC, Viboud C, Lessler J. Impact of SARS-CoV-2 vaccination of children ages 5-11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021-March 2022: a multi-model study. medRxiv 2022:2022.03.08.22271905. [PMID: 35313593 PMCID: PMC8936106 DOI: 10.1101/2022.03.08.22271905] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Background SARS-CoV-2 vaccination of persons aged 12 years and older has reduced disease burden in the United States. The COVID-19 Scenario Modeling Hub convened multiple modeling teams in September 2021 to project the impact of expanding vaccine administration to children 5-11 years old on anticipated COVID-19 burden and resilience against variant strains. Methods Nine modeling teams contributed state- and national-level projections for weekly counts of cases, hospitalizations, and deaths in the United States for the period September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of: 1) presence vs. absence of vaccination of children ages 5-11 years starting on November 1, 2021; and 2) continued dominance of the Delta variant vs. emergence of a hypothetical more transmissible variant on November 15, 2021. Individual team projections were combined using linear pooling. The effect of childhood vaccination on overall and age-specific outcomes was estimated by meta-analysis approaches. Findings Absent a new variant, COVID-19 cases, hospitalizations, and deaths among all ages were projected to decrease nationally through mid-March 2022. Under a set of specific assumptions, models projected that vaccination of children 5-11 years old was associated with reductions in all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios where children were not vaccinated. This projected effect of vaccinating children 5-11 years old increased in the presence of a more transmissible variant, assuming no change in vaccine effectiveness by variant. Larger relative reductions in cumulative cases, hospitalizations, and deaths were observed for children than for the entire U.S. population. Substantial state-level variation was projected in epidemic trajectories, vaccine benefits, and variant impacts. Conclusions Results from this multi-model aggregation study suggest that, under a specific set of scenario assumptions, expanding vaccination to children 5-11 years old would provide measurable direct benefits to this age group and indirect benefits to the all-age U.S. population, including resilience to more transmissible variants.
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Affiliation(s)
| | | | | | | | - Claire P Smith
- Johns Hopkins University Infectious Disease Dynamics (JHU-IDD)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Shi Chen
- University of North Carolina at Charlotte (UNCC)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Cécile Viboud
- National Institutes of Health Fogarty International Center
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15
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Truelove S, Smith CP, Qin M, Mullany LC, Borchering RK, Lessler J, Shea K, Howerton E, Contamin L, Levander J, Salerno J, Hochheiser H, Kinsey M, Tallaksen K, Wilson S, Shin L, Rainwater-Lovett K, Lemaitre JC, Dent J, Kaminsky J, Lee EC, Perez-Saez J, Hill A, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Piontti APY, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Schlitt J, Corbett P, Telionis PA, Wang L, Peddireddy AS, Hurt B, Chen J, Vullikanti A, Marathe M, Hoops S, Bhattacharya P, Machi D, Chen S, Paul R, Janies D, Thill JC, Galanti M, Yamana T, Pei S, Shaman J, Reich NG, Healy JM, Slayton RB, Biggerstaff M, Johansson MA, Runge MC, Viboud C. Projected resurgence of COVID-19 in the United States in July-December 2021 resulting from the increased transmissibility of the Delta variant and faltering vaccination. medRxiv 2021:2021.08.28.21262748. [PMID: 34494030 PMCID: PMC8423228 DOI: 10.1101/2021.08.28.21262748] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
WHAT IS ALREADY KNOWN ABOUT THIS TOPIC? The highly transmissible SARS-CoV-2 Delta variant has begun to cause increases in cases, hospitalizations, and deaths in parts of the United States. With slowed vaccination uptake, this novel variant is expected to increase the risk of pandemic resurgence in the US in July-December 2021. WHAT IS ADDED BY THIS REPORT? Data from nine mechanistic models project substantial resurgences of COVID-19 across the US resulting from the more transmissible Delta variant. These resurgences, which have now been observed in most states, were projected to occur across most of the US, coinciding with school and business reopening. Reaching higher vaccine coverage in July-December 2021 reduces the size and duration of the projected resurgence substantially. The expected impact of the outbreak is largely concentrated in a subset of states with lower vaccination coverage. WHAT ARE THE IMPLICATIONS FOR PUBLIC HEALTH PRACTICE? Renewed efforts to increase vaccination uptake are critical to limiting transmission and disease, particularly in states with lower current vaccination coverage. Reaching higher vaccination goals in the coming months can potentially avert 1.5 million cases and 21,000 deaths and improve the ability to safely resume social contacts, and educational and business activities. Continued or renewed non-pharmaceutical interventions, including masking, can also help limit transmission, particularly as schools and businesses reopen.
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Affiliation(s)
- Shaun Truelove
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Claire P Smith
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | | | - Luke C Mullany
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Johns Hopkins University Applied Physics Laboratories, Laurel, Maryland
| | | | | | - Katriona Shea
- The Pennsylvania State University, State College, Pennsylvania
| | - Emily Howerton
- The Pennsylvania State University, State College, Pennsylvania
| | | | | | | | | | - Matt Kinsey
- Johns Hopkins University Applied Physics Laboratories, Laurel, Maryland
| | - Kate Tallaksen
- Johns Hopkins University Applied Physics Laboratories, Laurel, Maryland
| | - Shelby Wilson
- Johns Hopkins University Applied Physics Laboratories, Laurel, Maryland
| | - Lauren Shin
- Johns Hopkins University Applied Physics Laboratories, Laurel, Maryland
| | | | | | - Juan Dent
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Joshua Kaminsky
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Elizabeth C Lee
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Javier Perez-Saez
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Alison Hill
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Dean Karlen
- University of Victoria, Victoria, British Columbia, Canada
| | | | | | - Kunpeng Mu
- Northeastern University, Boston, Massachusetts
| | | | | | | | | | | | | | | | - Bryan Lewis
- University of Virginia, Charlottesville, Virginia
| | - Brian Klahn
- University of Virginia, Charlottesville, Virginia
| | | | | | | | | | - Lijing Wang
- University of Virginia, Charlottesville, Virginia
| | | | | | | | | | | | - Stefan Hoops
- University of Virginia, Charlottesville, Virginia
| | | | - Dustin Machi
- University of Virginia, Charlottesville, Virginia
| | - Shi Chen
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Rajib Paul
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Daniel Janies
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | - Jean-Claude Thill
- University of North Carolina at Charlotte, Charlotte, North Carolina
| | | | | | | | | | | | | | | | | | | | | | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland
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16
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Grosso S, Marini A, Gyuraszova K, Voorde JV, Sfakianos A, Garland GD, Tenor AR, Mordue R, Chernova T, Morone N, Sereno M, Smith CP, Officer L, Farahmand P, Rooney C, Sumpton D, Das M, Teodósio A, Ficken C, Martin MG, Spriggs RV, Sun XM, Bushell M, Sansom OJ, Murphy D, MacFarlane M, Le Quesne JPC, Willis AE. The pathogenesis of mesothelioma is driven by a dysregulated translatome. Nat Commun 2021; 12:4920. [PMID: 34389715 PMCID: PMC8363647 DOI: 10.1038/s41467-021-25173-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 07/25/2021] [Indexed: 12/22/2022] Open
Abstract
Malignant mesothelioma (MpM) is an aggressive, invariably fatal tumour that is causally linked with asbestos exposure. The disease primarily results from loss of tumour suppressor gene function and there are no 'druggable' driver oncogenes associated with MpM. To identify opportunities for management of this disease we have carried out polysome profiling to define the MpM translatome. We show that in MpM there is a selective increase in the translation of mRNAs encoding proteins required for ribosome assembly and mitochondrial biogenesis. This results in an enhanced rate of mRNA translation, abnormal mitochondrial morphology and oxygen consumption, and a reprogramming of metabolic outputs. These alterations delimit the cellular capacity for protein biosynthesis, accelerate growth and drive disease progression. Importantly, we show that inhibition of mRNA translation, particularly through combined pharmacological targeting of mTORC1 and 2, reverses these changes and inhibits malignant cell growth in vitro and in ex-vivo tumour tissue from patients with end-stage disease. Critically, we show that these pharmacological interventions prolong survival in animal models of asbestos-induced mesothelioma, providing the basis for a targeted, viable therapeutic option for patients with this incurable disease.
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Affiliation(s)
- Stefano Grosso
- MRC Toxicology Unit, Gleeson Building, University of Cambridge, Cambridge, UK
| | - Alberto Marini
- MRC Toxicology Unit, Gleeson Building, University of Cambridge, Cambridge, UK
| | - Katarina Gyuraszova
- Institute of Cancer Sciences, University of Glasgow, Glasgow, UK
- Cancer Research UK Beatson Institute, Garscube Estate, Bearsden, UK
| | | | | | - Gavin D Garland
- MRC Toxicology Unit, Gleeson Building, University of Cambridge, Cambridge, UK
| | - Angela Rubio Tenor
- MRC Toxicology Unit, Gleeson Building, University of Cambridge, Cambridge, UK
| | - Ryan Mordue
- MRC Toxicology Unit, Gleeson Building, University of Cambridge, Cambridge, UK
| | - Tanya Chernova
- MRC Toxicology Unit, Gleeson Building, University of Cambridge, Cambridge, UK
| | - Nobu Morone
- MRC Toxicology Unit, Gleeson Building, University of Cambridge, Cambridge, UK
| | - Marco Sereno
- MRC Toxicology Unit, Gleeson Building, University of Cambridge, Cambridge, UK
- Leicester Cancer Research Centre, University of Leicester, Leicester, UK
| | - Claire P Smith
- MRC Toxicology Unit, Gleeson Building, University of Cambridge, Cambridge, UK
| | - Leah Officer
- MRC Toxicology Unit, Gleeson Building, University of Cambridge, Cambridge, UK
| | - Pooyeh Farahmand
- Institute of Cancer Sciences, University of Glasgow, Glasgow, UK
- Cancer Research UK Beatson Institute, Garscube Estate, Bearsden, UK
| | - Claire Rooney
- Institute of Cancer Sciences, University of Glasgow, Glasgow, UK
- Cancer Research UK Beatson Institute, Garscube Estate, Bearsden, UK
| | - David Sumpton
- Cancer Research UK Beatson Institute, Garscube Estate, Bearsden, UK
| | - Madhumita Das
- MRC Toxicology Unit, Gleeson Building, University of Cambridge, Cambridge, UK
| | - Ana Teodósio
- MRC Toxicology Unit, Gleeson Building, University of Cambridge, Cambridge, UK
| | - Catherine Ficken
- MRC Toxicology Unit, Gleeson Building, University of Cambridge, Cambridge, UK
| | - Maria Guerra Martin
- MRC Toxicology Unit, Gleeson Building, University of Cambridge, Cambridge, UK
| | - Ruth V Spriggs
- MRC Toxicology Unit, Gleeson Building, University of Cambridge, Cambridge, UK
| | - Xiao-Ming Sun
- MRC Toxicology Unit, Gleeson Building, University of Cambridge, Cambridge, UK
| | - Martin Bushell
- Institute of Cancer Sciences, University of Glasgow, Glasgow, UK
- Cancer Research UK Beatson Institute, Garscube Estate, Bearsden, UK
| | - Owen J Sansom
- Institute of Cancer Sciences, University of Glasgow, Glasgow, UK
- Cancer Research UK Beatson Institute, Garscube Estate, Bearsden, UK
| | - Daniel Murphy
- Institute of Cancer Sciences, University of Glasgow, Glasgow, UK.
| | - Marion MacFarlane
- MRC Toxicology Unit, Gleeson Building, University of Cambridge, Cambridge, UK.
| | - John P C Le Quesne
- MRC Toxicology Unit, Gleeson Building, University of Cambridge, Cambridge, UK.
- Institute of Cancer Sciences, University of Glasgow, Glasgow, UK.
- Cancer Research UK Beatson Institute, Garscube Estate, Bearsden, UK.
- Leicester Cancer Research Centre, University of Leicester, Leicester, UK.
- Glenfield Hospital, Groby Road, University Hospitals Leicester NHS Trust Leicester, Leicester, UK.
| | - Anne E Willis
- MRC Toxicology Unit, Gleeson Building, University of Cambridge, Cambridge, UK.
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17
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Borchering RK, Viboud C, Howerton E, Smith CP, Truelove S, Runge MC, Reich NG, Contamin L, Levander J, Salerno J, van Panhuis W, Kinsey M, Tallaksen K, Obrecht RF, Asher L, Costello C, Kelbaugh M, Wilson S, Shin L, Gallagher ME, Mullany LC, Rainwater-Lovett K, Lemaitre JC, Dent J, Grantz KH, Kaminsky J, Lauer SA, Lee EC, Meredith HR, Perez-Saez J, Keegan LT, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Pastore Y Piontti A, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Schlitt J, Corbett P, Telionis PA, Wang L, Peddireddy AS, Hurt B, Chen J, Vullikanti A, Marathe M, Healy JM, Slayton RB, Biggerstaff M, Johansson MA, Shea K, Lessler J. Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios - United States, April-September 2021. MMWR Morb Mortal Wkly Rep 2021. [PMID: 33988185 DOI: 10.15585/mmwr.mm7019e3externalicon] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/27/2023]
Abstract
After a period of rapidly declining U.S. COVID-19 incidence during January-March 2021, increases occurred in several jurisdictions (1,2) despite the rapid rollout of a large-scale vaccination program. This increase coincided with the spread of more transmissible variants of SARS-CoV-2, the virus that causes COVID-19, including B.1.1.7 (1,3) and relaxation of COVID-19 prevention strategies such as those for businesses, large-scale gatherings, and educational activities. To provide long-term projections of potential trends in COVID-19 cases, hospitalizations, and deaths, COVID-19 Scenario Modeling Hub teams used a multiple-model approach comprising six models to assess the potential course of COVID-19 in the United States across four scenarios with different vaccination coverage rates and effectiveness estimates and strength and implementation of nonpharmaceutical interventions (NPIs) (public health policies, such as physical distancing and masking) over a 6-month period (April-September 2021) using data available through March 27, 2021 (4). Among the four scenarios, an accelerated decline in NPI adherence (which encapsulates NPI mandates and population behavior) was shown to undermine vaccination-related gains over the subsequent 2-3 months and, in combination with increased transmissibility of new variants, could lead to surges in cases, hospitalizations, and deaths. A sharp decline in cases was projected by July 2021, with a faster decline in the high-vaccination scenarios. High vaccination rates and compliance with public health prevention measures are essential to control the COVID-19 pandemic and to prevent surges in hospitalizations and deaths in the coming months.
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18
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Borchering RK, Viboud C, Howerton E, Smith CP, Truelove S, Runge MC, Reich NG, Contamin L, Levander J, Salerno J, van Panhuis W, Kinsey M, Tallaksen K, Obrecht RF, Asher L, Costello C, Kelbaugh M, Wilson S, Shin L, Gallagher ME, Mullany LC, Rainwater-Lovett K, Lemaitre JC, Dent J, Grantz KH, Kaminsky J, Lauer SA, Lee EC, Meredith HR, Perez-Saez J, Keegan LT, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Pastore y Piontti A, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Schlitt J, Corbett P, Telionis PA, Wang L, Peddireddy AS, Hurt B, Chen J, Vullikanti A, Marathe M, Healy JM, Slayton RB, Biggerstaff M, Johansson MA, Shea K, Lessler J. Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios - United States, April-September 2021. MMWR Morb Mortal Wkly Rep 2021; 70:719-724. [PMID: 33988185 PMCID: PMC8118153 DOI: 10.15585/mmwr.mm7019e3] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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19
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Nguyen TV, Reuter JM, Gaikwad NW, Rotroff DM, Kucera HR, Motsinger-Reif A, Smith CP, Nieman LK, Rubinow DR, Kaddurah-Daouk R, Schmidt PJ. The steroid metabolome in women with premenstrual dysphoric disorder during GnRH agonist-induced ovarian suppression: effects of estradiol and progesterone addback. Transl Psychiatry 2017; 7:e1193. [PMID: 28786978 PMCID: PMC5611719 DOI: 10.1038/tp.2017.146] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Revised: 05/05/2017] [Accepted: 05/26/2017] [Indexed: 01/08/2023] Open
Abstract
Clinical evidence suggests that symptoms in premenstrual dysphoric disorder (PMDD) reflect abnormal responsivity to ovarian steroids. This differential steroid sensitivity could be underpinned by abnormal processing of the steroid signal. We used a pharmacometabolomics approach in women with prospectively confirmed PMDD (n=15) and controls without menstrual cycle-related affective symptoms (n=15). All were medication-free with normal menstrual cycle lengths. Notably, women with PMDD were required to show hormone sensitivity in an ovarian suppression protocol. Ovarian suppression was induced for 6 months with gonadotropin-releasing hormone (GnRH)-agonist (Lupron); after 3 months all were randomized to 4 weeks of estradiol (E2) or progesterone (P4). After a 2-week washout, a crossover was performed. Liquid chromatography/tandem mass spectrometry measured 49 steroid metabolites in serum. Values were excluded if >40% were below the limit of detectability (n=21). Analyses were performed with Wilcoxon rank-sum tests using false-discovery rate (q<0.2) for multiple comparisons. PMDD and controls had similar basal levels of metabolites during Lupron and P4-derived neurosteroids during Lupron or E2/P4 conditions. Both groups had significant increases in several steroid metabolites compared with the Lupron alone condition after treatment with E2 (that is, estrone-SO4 (q=0.039 and q=0.002, respectively) and estradiol-3-SO4 (q=0.166 and q=0.001, respectively)) and after treatment with P4 (that is, allopregnanolone (q=0.001 for both PMDD and controls), pregnanediol (q=0.077 and q=0.030, respectively) and cortexone (q=0.118 and q=0.157, respectively). Only sulfated steroid metabolites showed significant diagnosis-related differences. During Lupron plus E2 treatment, women with PMDD had a significantly attenuated increase in E2-3-sulfate (q=0.035) compared with control women, and during Lupron plus P4 treatment a decrease in DHEA-sulfate (q=0.07) compared with an increase in controls. Significant effects of E2 addback compared with Lupron were observed in women with PMDD who had significant decreases in DHEA-sulfate (q=0.065) and pregnenolone sulfate (q=0.076), whereas controls had nonsignificant increases (however, these differences did not meet statistical significance for a between diagnosis effect). Alterations of sulfotransferase activity could contribute to the differential steroid sensitivity in PMDD. Importantly, no differences in the formation of P4-derived neurosteroids were observed in this otherwise highly selected sample of women studied under controlled hormone exposures.
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Affiliation(s)
- T V Nguyen
- Behavioral Endocrinology Branch, NIMH IRP/NIH/HHS, Bethesda, MD, USA
- Department of Psychiatry and Obstetrics-Gynecology, McGill University Health Center, Montreal, QC, Canada
| | - J M Reuter
- Behavioral Endocrinology Branch, NIMH IRP/NIH/HHS, Bethesda, MD, USA
| | - N W Gaikwad
- Department of Nutrition and Environmental Toxicology, West Coast Metabolomics Center, University of California, Davis, Davis, CA, USA
| | - D M Rotroff
- Department of Biostatistics, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
- Department of Statistics, North Carolina State University, Raleigh, NC, USA
| | - H R Kucera
- Department of Nutrition and Environmental Toxicology, West Coast Metabolomics Center, University of California, Davis, Davis, CA, USA
| | - A Motsinger-Reif
- Department of Biostatistics, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
- Department of Statistics, North Carolina State University, Raleigh, NC, USA
| | - C P Smith
- Department of Biostatistics, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
- Department of Statistics, North Carolina State University, Raleigh, NC, USA
| | - L K Nieman
- Diabetes, Endocrine and Obesity Branch, NIDDK, NIH, DHSS, Bethesda, MD, USA
| | - D R Rubinow
- Department of Psychiatry, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - R Kaddurah-Daouk
- Department of Psychiatry, Duke University Medical Center, Durham, NC, USA
- Duke Institute for Brain Sciences, Duke University, Durham, NC, USA
| | - P J Schmidt
- Behavioral Endocrinology Branch, NIMH IRP/NIH/HHS, Bethesda, MD, USA
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20
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Ellerby GEC, Smith CP, Zou F, Scott P, Soller BR. Validation of a spectroscopic sensor for the continuous, noninvasive measurement of muscle oxygen saturation and pH. Physiol Meas 2013; 34:859-71. [PMID: 23859848 DOI: 10.1088/0967-3334/34/8/859] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
New patient monitoring technologies can noninvasively and directly provide an assessment of the adequacy of tissue perfusion through the simultaneous determination of muscle oxygen saturation (SmO2) and muscle pH (pHm). Non-pulsatile near infrared spectroscopy is used to determine these microvascular parameters. Two separate studies were conducted using an isolated perfused swine limb preparation to widely vary venous blood oxygen saturation (SviO2) and pH (pHvi) to assess the accuracy of a noninvasive sensor with the capability to simultaneously measure both parameters. The isolated limb model is necessary to establish equilibrium between the venous output of the perfusion circuit and the venule measurement of the spectroscopic sensor. The average absolute difference between SmO2 and SviO2 determined over 50 conditions of SviO2 between 13% and 83% on 3 pig limbs was 3.8% and the coefficient of determination (R(2)) was 0.95. The average absolute difference between pHm and pHvi determined over 69 conditions of pHvi between pHvi 6.9 and pHvi 7.5 on 3 pig limbs was 0.045 pH units with an R(2) of 0.92. Measured accuracy was acceptable to support clinically relevant decision making for the assessment of impaired tissue perfusion and acidosis. Sensors were also evaluated on human subjects. There was no statistical difference in SmO2 by gender or location when multiple sensors were evaluated on the right and left calf, deltoid, and thigh of resting men and women (N = 33). SmO2 precision for subjects at rest was 5.6% over the six locations with four different sensors.
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Affiliation(s)
- G E C Ellerby
- Reflectance Medical Inc., 116 Flanders Road, Suite 1000, Westborough, MA 01581, USA
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21
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Simmons NL, Chaudhry AS, Graham C, Scriven ES, Thistlethwaite A, Smith CP, Stewart GS. Dietary regulation of ruminal bovine UT-B urea transporter expression and localization. J Anim Sci 2009; 87:3288-99. [PMID: 19574570 DOI: 10.2527/jas.2008-1710] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2023] Open
Abstract
Facilitative UT-B urea transporters have been located in the gastrointestinal tract of numerous mammalian species. We have previously identified UT-B urea transporters within the epithelial layers of the bovine (b) rumen. The aim of this study was to test the hypothesis that ruminal bUT-B urea transporters are regulated by dietary intake. Six Limousine-cross steers (initial BW = 690 +/- 51 kg) were separated into 2 groups fed a basic silage-based diet (RS) or a concentrate-based diet (RC) for 37 d and compared for ruminal morphology, content, and bUT-B expression. Analysis by reverse transcription-PCR showed that ruminal bUT-B2 mRNA expression was greater in RC-fed than RS-fed animals. Utilizing an anti-bUT-B antibody, we also detected a significant increase in bUT-B2 protein expression in RC-fed rumen (P < 0.05, n = 3). In agreement with these findings, immunolocalization studies of RC-fed ruminal tissue showed strong bUT-B signals throughout all epithelial layers, in contrast to weaker staining in RS-fed rumen that was more localized to the stratum basale. This study therefore confirmed that ruminal bUT-B urea transporter expression and localization were indeed altered by changes in dietary intake. We conclude that UT-B transporters play a significant role in the dietary regulation of bovine nitrogen balance.
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Affiliation(s)
- N L Simmons
- School of Cellular and Molecular Biosciences, Newcastle University, Newcastle-upon-Tyne, UK
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22
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Abstract
Our previous studies have detailed a novel facilitative UT-B urea transporter isoform, bUT-B2. Despite the existence of mouse and human orthologs, the functional characteristics of UT-B2 remain undefined. In this report, we produced a stable MDCK cell line that expressed bUT-B2 protein and investigated the transepithelial urea flux across cultured cell monolayers. We observed a large basal urea flux that was significantly reduced by known inhibitors of facilitative urea transporters; 1,3 dimethylurea (P < 0.001, n = 17), thionicotinamide (P < 0.05, n = 11), and phloretin (P < 0.05, n = 9). Pre-exposure for 1 h to the antidiuretic hormone vasopressin had no effect on bUT-B2-mediated urea transport (NS, n = 3). Acute vasopressin exposure for up to 30 min also failed to elicit any transient response (NS, n = 9). Further investigation confirmed that bUT-B2 function was not affected by alteration of intracellular cAMP (NS, n = 4), intracellular calcium (NS, n = 3), or protein kinase activity (NS, n = 4). Finally, immunoblot data suggested a possible role for glycosylation in regulating bUT-B2 function. In conclusion, this study showed that bUT-B2-mediated transepithelial urea transport was constitutively activated and unaffected by known regulators of renal UT-A urea transporters.
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Affiliation(s)
- P Tickle
- Faculty of Life Sciences, The University of Manchester, Manchester, UK
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23
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Sooriakumaran P, Macanas-Pirard P, Bucca G, Henderson A, Langley SEM, Laing RW, Smith CP, Laing EE, Coley HM. A gene expression profiling approach assessing celecoxib in a randomized controlled trial in prostate cancer. Cancer Genomics Proteomics 2009; 6:93-99. [PMID: 19451093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/27/2023] Open
Abstract
BACKGROUND We performed a pilot study, looking at the COX-2 inhibitor celecoxib, on newly diagnosed prostate cancer patients in the neo-adjuvant setting using DNA microarray analysis. PATIENTS AND METHODS This was a single-blinded, randomized controlled phase II presurgical (radical prostatectomy) 28-day trial of celecoxib versus no drug in patients with localized T1-2 N0 M0 prostate cancer. cDNA microarray analysis was carried out on prostate cancer biopsies taken from freshly obtained radical prostatectomy samples. Results were confirmed by qPCR analysis of a selection of genes. RESULTS Multiple genes were differentially expressed in response to celecoxib treatment. Statistical analysis of microarray data indicated 24 genes were up-regulated and 4 genes down-regulated as a consequence of celecoxib treatment. Gene changes e.g. survivin, SRP72kDa, were associated with promoting apoptotic cell death, enhancement of antioxidant processes and tumour suppressor function (p73 and cyclin B1 up-regulation). CONCLUSION Celecoxib at 400 mg b.i.d. for 4 weeks perioperatively gave rise to changes in gene expression in prostate cancer tissue consistent with enhancement of apoptosis and tumour suppressor function. Given the short time interval for the duration of this study, the data are encouraging and provide a good rationale for conducting further trials of celecoxib in prostate cancer.
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Affiliation(s)
- P Sooriakumaran
- Room 14AY04, Faculty of Health and Medical Sciences, University of Surrey GU2 7XH, UK
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24
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Abstract
The basic building block of a gene regulatory network consists of a gene encoding a transcription factor (TF) and the gene(s) it regulates. Considerable efforts have been directed recently at devising experiments and algorithms to determine TFs and their corresponding target genes using gene expression and other types of data. The underlying problem is that the expression of a gene coding for the TF provides only limited information about the activity of the TF, which can also be controlled posttranscriptionally. In the absence of a reliable technology to routinely measure the activity of regulators, it is of great importance to understand whether this activity can be inferred from gene expression data. We here develop a statistical framework to reconstruct the activity of a TF from gene expression data of the target genes in its regulatory module. The novelty of our approach is that we embed the deterministic Michaelis-Menten model of gene regulation in this statistical framework. The kinetic parameters of the gene regulation model are inferred together with the profile of the TF regulator. We also obtain a goodness-of-fit test to verify the fit of the model. The model is applied to a time series involving the Streptomyces coelicolor bacterium. We focus on the transcriptional activator cdaR, which is partly responsible for the production of a particular type of antibiotic. The aim is to reconstruct the activity profile of this regulator. Our approach can be extended to include more complex regulatory relationships, such as multiple regulatory factors, competition, and cooperativity.
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Affiliation(s)
- R Khanin
- Department of Statistics, University of Glasgow, Glasgow G12 8QW, UK.
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25
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Abstract
Urea movement across plasma membranes is modulated by specialized urea transporter proteins. These proteins are proposed to play key roles in the urinary concentrating mechanism and fluid homeostasis. To date, two urea-transporter genes have been cloned; UT-A (Slc14a2), encoding at least five proteins and UT-B (Slc14a1) encoding a single protein isoform. Recently we engineered mice that lack the inner medullary collecting duct (IMCD) urea transporters, UT-A1 and UT-A3 (UT-A1/3 -/- mice). This article includes 1) a historical review of the role of renal urea transporters in renal function; 2) a review of our studies utilizing the UT-A1/3 -/- mice; 3) description of an additional line of transgenic mice in which beta-galactosidase expression is driven by the alpha-promoter of the UT-A gene, which is allowing better physiological definition of control mechanisms for UT-A expression; and 4) a discussion of the implications of the studies in transgenic mice for the teaching of kidney physiology.
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Affiliation(s)
- R A Fenton
- The Water and Salt Research Center, Institute of Anatomy, Building 1233, University of Aarhus, DK-8000, Aarhus, Denmark.
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26
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Abstract
Urea transporters encoded by the UT-A gene play fundamental roles in the kidney and possibly other tissues. Knowledge of the genomic organization of the mouse, rat and human UT-A genes has enabled the engineering of transgenic and knockout animals and these have helped refine our understanding of the role of UT-A proteins. This review summarizes the published work that has accrued on the structure and regulation of these genes. It also documents a novel cDNA, human UT-A3, which has enabled a major refinement of the human UT-A gene structure. This and other information contained in this review should prove useful for future comparative genomic analysis, studies addressing gene regulation and for the engineering of transgenic and knockout animal strains.
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Affiliation(s)
- C P Smith
- Faculty of Life Sciences, The University of Manchester, Oxford Road, Manchester, M13 9NT, UK.
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27
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Abstract
This review summarizes what is currently known about urea transporters in fishes in the context of their physiology and evolution within the vertebrates. The existence of urea transporters has been investigated in red blood cells and hepatocytes of fish as well as in renal and branchial cells. Little is known about urea transport in red blood cells and hepatocytes, in fact, urea transporters are not believed to be present in the erythrocytes of elasmobranchs nor in teleost fish. What little physiological evidence there is for urea transport across fish hepatocytes is not supported by molecular evidence and could be explained by other transporters. In contrast, early findings on elasmobranch renal urea transporters were the impetus for research in other organisms. Urea transport in both the elasmobranch kidney and gill functions to retain urea within the animal against a massive concentration gradient with the environment. Information on branchial and renal urea transporters in teleost fish is recent in comparison but in teleosts urea transporters appear to function for excretion and not retention as in elasmobranchs. The presence of urea transporters in fish that produce a copious amount of urea, such as elasmobranchs and ureotelic teleosts, is reasonable. However, the existence of urea transporters in ammoniotelic fish is curious and could likely be due to their ability to manufacture urea early in life as a means to avoid ammonia toxicity. It is believed that the facilitated diffusion urea transporter (UT) gene family has undergone major evolutionary changes, likely in association with the role of urea transport in the evolution of terrestriality in the vertebrates.
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Affiliation(s)
- M D McDonald
- NIEHS Marine and Freshwater Biomedical Sciences Center, Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida 33149-1098, USA.
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28
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Baker BY, Lin L, Kim CJ, Raza J, Smith CP, Miller WL, Achermann JC. Nonclassic congenital lipoid adrenal hyperplasia: a new disorder of the steroidogenic acute regulatory protein with very late presentation and normal male genitalia. J Clin Endocrinol Metab 2006; 91:4781-4785. [PMID: 16968793 PMCID: PMC1865081 DOI: 10.1210/jc.2006-1565] [Citation(s) in RCA: 109] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
CONTEXT Lipoid congenital adrenal hyperplasia is a severe disorder of adrenal and gonadal steroidogenesis caused by mutations in the steroidogenic acute regulatory protein (StAR). Affected children typically present with life-threatening adrenal insufficiency in early infancy due to a failure of glucocorticoid (cortisol) and mineralocorticoid (aldosterone) biosynthesis, and 46,XY genetic males have complete lack of androgenization and appear phenotypically female due to impaired testicular androgen secretion in utero. OBJECTIVE The objective of this study was to investigate whether nonclassic forms of this condition exist. PATIENTS AND METHODS Sequence analysis of the gene encoding StAR was undertaken in three children from two families who presented with primary adrenal insufficiency at 2-4 yr of age; the males had normal genital development. Identified mutants were tested in a series of biochemical assays. RESULTS DNA sequencing identified homozygous StAR mutations Val187Met and Arg188Cys in these two families. Functional studies of StAR activity in cells and in vitro and cholesterol-binding assays showed these mutants retained approximately 20% of wild-type activity. CONCLUSIONS These patients define a new disorder, nonclassic lipoid congenital adrenal hyperplasia, and represent a new cause of nonautoimmune Addison disease (primary adrenal failure).
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Affiliation(s)
- Bo Yang Baker
- Department of Pediatrics (B.Y.B., C.J.K., W.L.M.), University of California, San Francisco, CA 94143; UCL Institute of Child Health & Department of Medicine (L.L., J.C.A.), University College London, London WC1N 1EH, UK; Endocrinology (J.R.), National Institute of Child Health, Karachi 75520, Pakistan; Department of Paediatrics (C.P.S.), East Lancashire Hospitals NHS Trust, Blackburn BB2 3HH, UK
| | - Lin Lin
- Department of Pediatrics (B.Y.B., C.J.K., W.L.M.), University of California, San Francisco, CA 94143; UCL Institute of Child Health & Department of Medicine (L.L., J.C.A.), University College London, London WC1N 1EH, UK; Endocrinology (J.R.), National Institute of Child Health, Karachi 75520, Pakistan; Department of Paediatrics (C.P.S.), East Lancashire Hospitals NHS Trust, Blackburn BB2 3HH, UK
| | - Chan Jong Kim
- Department of Pediatrics (B.Y.B., C.J.K., W.L.M.), University of California, San Francisco, CA 94143; UCL Institute of Child Health & Department of Medicine (L.L., J.C.A.), University College London, London WC1N 1EH, UK; Endocrinology (J.R.), National Institute of Child Health, Karachi 75520, Pakistan; Department of Paediatrics (C.P.S.), East Lancashire Hospitals NHS Trust, Blackburn BB2 3HH, UK
| | - Jamal Raza
- Department of Pediatrics (B.Y.B., C.J.K., W.L.M.), University of California, San Francisco, CA 94143; UCL Institute of Child Health & Department of Medicine (L.L., J.C.A.), University College London, London WC1N 1EH, UK; Endocrinology (J.R.), National Institute of Child Health, Karachi 75520, Pakistan; Department of Paediatrics (C.P.S.), East Lancashire Hospitals NHS Trust, Blackburn BB2 3HH, UK
| | - Claire P Smith
- Department of Pediatrics (B.Y.B., C.J.K., W.L.M.), University of California, San Francisco, CA 94143; UCL Institute of Child Health & Department of Medicine (L.L., J.C.A.), University College London, London WC1N 1EH, UK; Endocrinology (J.R.), National Institute of Child Health, Karachi 75520, Pakistan; Department of Paediatrics (C.P.S.), East Lancashire Hospitals NHS Trust, Blackburn BB2 3HH, UK
| | - Walter L Miller
- Department of Pediatrics (B.Y.B., C.J.K., W.L.M.), University of California, San Francisco, CA 94143; UCL Institute of Child Health & Department of Medicine (L.L., J.C.A.), University College London, London WC1N 1EH, UK; Endocrinology (J.R.), National Institute of Child Health, Karachi 75520, Pakistan; Department of Paediatrics (C.P.S.), East Lancashire Hospitals NHS Trust, Blackburn BB2 3HH, UK
| | - John C Achermann
- Department of Pediatrics (B.Y.B., C.J.K., W.L.M.), University of California, San Francisco, CA 94143; UCL Institute of Child Health & Department of Medicine (L.L., J.C.A.), University College London, London WC1N 1EH, UK; Endocrinology (J.R.), National Institute of Child Health, Karachi 75520, Pakistan; Department of Paediatrics (C.P.S.), East Lancashire Hospitals NHS Trust, Blackburn BB2 3HH, UK
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Scarff RW, Smith CP. Proliferative and other lesions of the male breast. With notes on 2 cases of proliferative mastitis in stilbœstrol workers. Br J Surg 2005. [DOI: 10.1002/bjs.18002911608] [Citation(s) in RCA: 16] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- R W Scarff
- The Bland-Sutton Institute of Pathology, The Middlesex Hospital, London, W.I
| | - C P Smith
- The Bland-Sutton Institute of Pathology, The Middlesex Hospital, London, W.I
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Affiliation(s)
| | | | - C P Smith
- Bland-Sutton Institute of Pathology, Middlesex Hospital
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Stewart GS, Graham C, Cattell S, Smith TPL, Simmons NL, Smith CP. UT-B is expressed in bovine rumen: potential role in ruminal urea transport. Am J Physiol Regul Integr Comp Physiol 2005; 289:R605-R612. [PMID: 15845882 DOI: 10.1152/ajpregu.00127.2005] [Citation(s) in RCA: 71] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
The UT-A (SLC14a2) and UT-B (SLC14a1) genes encode a family of specialized urea transporter proteins that regulate urea movement across plasma membranes. In this report, we describe the structure of the bovine UT-B (bUT-B) gene and characterize UT-B expression in bovine rumen. Northern analysis using a full-length bUT-B probe detected a 3.7-kb UT-B signal in rumen. RT-PCR of bovine mRNA revealed the presence of two UT-B splice variants, bUT-B1 and bUT-B2, with bUT-B2 the predominant variant in rumen. Immunoblotting studies of bovine rumen tissue, using an antibody targeted to the NH2-terminus of mouse UT-B, confirmed the presence of 43- to 54-kDa UT-B proteins. Immunolocalization studies showed that UT-B was mainly located on cell plasma membranes in epithelial layers of the bovine rumen. Ussing chamber measurements of ruminal transepithelial transport of (14)C-labeled urea indicated that urea flux was characteristically inhibited by phloretin. We conclude that bUT-B is expressed in the bovine rumen and may function to transport urea into the rumen as part of the ruminant urea nitrogen salvaging process.
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Affiliation(s)
- G S Stewart
- Faculty of Life Sciences, Medical School, The University of Manchester, Manchester M13 9PT, UK
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32
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Woods-Kettelberger A, Kongsamut S, Smith CP, Winslow JT, Corbett R. Animal models with potential applications for screening compounds for the treatment of obsessive-compulsive disorder. Expert Opin Investig Drugs 2005; 6:1369-81. [PMID: 15989507 DOI: 10.1517/13543784.6.10.1369] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The availability of an animal model for obsessive-compulsive disorder (OCD) is necessary for the development of novel pharmacological treatments. To be useful, the model must be predictive of clinical performance, possess characteristic criteria and distinguish anti-OCD from antidepressant compounds. Due to the lack of OCD models useful for drug discovery, all compounds currently used for OCD were developed first as antidepressants. In this article, we discuss the relative merits of: stereotypic behaviours (canine acral lick, feather picking, amphetamine- and 5-HT-induced stereotypy); adjunctive and displacement behaviours (schedule-induced polydipsia, wheel running, resident-intruder grooming); anxiolytic tests (separation and shock-induced ultrasonic vocalisation and marble burying); and depression tests (inescapable shock-induced escape and immobility in forced swim) as potential OCD models. We conclude that adjunctive and displacement behaviours, and in particular schedule-induced polydipsia, may prove to be the best models for compulsive behaviour in animals that can be used for the discovery of novel anti-OCD agents.
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Affiliation(s)
- A Woods-Kettelberger
- Department of Neuroscience Research, Hoechst Marion Roussel, Route 202-206 North, PO Box 6800, Bridgewater, NJ 08807, USA
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33
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Ward DT, Hamilton K, Burnand R, Smith CP, Tomlinson DR, Riccardi D. Altered expression of iron transport proteins in streptozotocin-induced diabetic rat kidney. Biochim Biophys Acta Mol Basis Dis 2005; 1740:79-84. [PMID: 15878745 DOI: 10.1016/j.bbadis.2005.01.008] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2004] [Revised: 01/17/2005] [Accepted: 01/31/2005] [Indexed: 01/10/2023]
Abstract
Diabetes mellitus is associated with altered iron homeostasis in both human and animal diabetic models. Iron is a metal oxidant capable of generating reactive oxygen species (ROS) and has been postulated to contribute to diabetic nephropathy. Two proteins involved in iron metabolism that are expressed in the kidney are the divalent metal transporter, DMT1 (Slc11a2), and the Transferrin Receptor (TfR). Thus, we investigated whether renal DMT1 or TfR expression is altered in diabetes, as this could potentially affect ROS generation and contribute to diabetic nephropathy. Rats were rendered diabetic with streptozotocin (STZ-diabetes) and renal DMT1 and TfR expression studied using semi-quantitative immunoblotting and immunofluorescence. In STZ-diabetic Sprague-Dawley rats, renal DMT1 expression was significantly reduced and TfR expression increased after 2 weeks. DMT1 downregulation was observed in both proximal tubules and collecting ducts. Renal DMT1 expression was also decreased in Wistar rats following 12 weeks of STZ-diabetes, an effect that was fully corrected by insulin-replacement but not by cotreatment with the aldose reductase inhibitor, sorbinil. Increased renal TfR expression was also observed in STZ-diabetic Wistar rats together with elevated cellular iron accumulation. Together these data demonstrate renal DMT1 downregulation and TfR upregulation in STZ-diabetes. Whilst the consequence of altered DMT1 expression on renal iron handling and oxidant damage remains to be determined, the attenuation of the putative lysosomal iron exit pathway in proximal tubules could potentially explain lysosomal iron accumulation reported in human diabetes and STZ-diabetic animals.
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Affiliation(s)
- D T Ward
- Faculty of Life Sciences, G38 Stopford Building, The University of Manchester, Oxford Road, Manchester, M13 9PT, UK.
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Vinciotti V, Khanin R, D'Alimonte D, Liu X, Cattini N, Hotchkiss G, Bucca G, de Jesus O, Rasaiyaah J, Smith CP, Kellam P, Wit E. An experimental evaluation of a loop versus a reference design for two-channel microarrays. Bioinformatics 2004; 21:492-501. [PMID: 15374872 DOI: 10.1093/bioinformatics/bti022] [Citation(s) in RCA: 71] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Despite theoretical arguments that so-called 'loop designs' for two-channel DNA microarray experiments are more efficient, biologists continue to use 'reference designs'. We describe two sets of microarray experiments with RNA from two different biological systems (TPA-stimulated mammalian cells and Streptomyces coelicolor). In each case, both a loop and a reference design were used with the same RNA preparations with the aim of studying their relative efficiency. RESULTS The results of these experiments show that (1) the loop design attains a much higher precision than the reference design, (2) multiplicative spot effects are a large source of variability, and if they are not accounted for in the mathematical model, for example, by taking log-ratios or including spot effects, then the model will perform poorly. The first result is reinforced by a simulation study. Practical recommendations are given on how simple loop designs can be extended to more realistic experimental designs and how standard statistical methods allow the experimentalist to use and interpret the results from loop designs in practice. AVAILABILITY The data and R code are available at http://exgen.ma.umist.ac.uk CONTACT veronica.vinciotti@brunel.ac.uk.
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Affiliation(s)
- V Vinciotti
- Department of Information Systems and Computing, Brunel University Uxbridge UB8 3PH, UK.
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Abstract
AIMS To review the process and outcome of education and training visits to paediatric departments by the RCPCH. METHODS Retrospective audit of visits reports (1997-2001) against the RCPCH criteria for general professional training. Hospital and/or community child health departments who were responsible for training paediatric senior house officers were visited to assess whether RCPCH criteria of education were being met. Follow up visits were undertaken where limited education and training approval was given. Reports were received from 214 of 242 (88%) hospital and/or community based departments in England, Wales, and Northern Ireland. RESULTS Satisfactory achievement of the 12 training criteria by departments varied widely: 39-95% (median 66%) achieved. Follow up visits reported significant improvements in most departments. Criteria which departments struggled to achieve reasonable standards were: (1) ensuring SHOs were performing educationally appropriate duties (39% achieved); and (2) satisfactory outpatient experience (41% achieved). Twenty four per cent of hospital based departments did not have a paediatrician with 12 months or more experience of paediatrics resident on call. CONCLUSIONS The visiting process highlighted areas of good practice, encouraged change to meet the criteria, and recommended increased resources and staffing where necessary to improve training and hence the service. The need for continuing approval for education and training in these departments encouraged significant efforts on the part of trainers and managers to meet the requirements, and consequently the quality of service to children has been enhanced.
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Affiliation(s)
- C P Smith
- Royal College of Paediatrics and Child Health, Education and Training Division, 50 Hallam Street, London, UK.
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Abstract
This is the first report of neurovesical dysfunction in a woman with postural tachycardia syndrome (POTS). The patient had both symptoms and urodynamic findings diagnostic of detrusor hyperreflexia. Management consisted of anticholinergic medication and timed voiding. Lower urinary tract dysfunction may be underrecognized in POTS.
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Affiliation(s)
- M L O'Leary
- University of Pittsburgh School of Medicine, Pennsylvania, USA
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37
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Smith CP, O'Leary M, Erickson J, Somogyi GT, Chancellor MB. Botulinum toxin urethral sphincter injection resolves urinary retention after pubovaginal sling operation. Int Urogynecol J 2002; 13:185-6. [PMID: 12140713 DOI: 10.1007/s192-002-8350-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The management of prolonged urinary retention following pubovaginal sling surgery typically involves transvaginal urethrolysis for anatomical urethral obstruction. Brubaker [1] recently reported on urethral sphincter abnormalities as a cause of postoperative urinary retention following either Burch suspension or pubovaginal sling procedure. We report a case of functional urethral obstruction and detrusor acontractility following pubovaginal sling surgery that was successfully treated by botulinum A toxin urethral sphincter injection.
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Affiliation(s)
- C P Smith
- University of Pittsburgh School of Medicine, 3471 Fifth Avenue, Pittsburgh, PA 15213, USA
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Fenton RA, Stewart GS, Carpenter B, Howorth A, Potter EA, Cooper GJ, Smith CP. Characterization of mouse urea transporters UT-A1 and UT-A2. Am J Physiol Renal Physiol 2002; 283:F817-25. [PMID: 12217874 DOI: 10.1152/ajprenal.00263.2001] [Citation(s) in RCA: 63] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Specialized transporter proteins that are the products of two closely related genes, UT-A (Slc14a2) and UT-B (Slc14a1), modulate the movement of urea across cell membranes. The purpose of this study was to characterize the mouse variants of two major products of the UT-A gene, UT-A1 and UT-A2. Screening a mouse kidney inner medulla cDNA library yielded 4,047- and 2,876-bp cDNAs, the mouse homologues of UT-A1 and UT-A2. Northern blot analysis showed high levels of UT-A mRNAs in kidney medulla. UT-A transcripts were also present in testes, heart, brain, and liver. Immunoblots with an antiserum raised to the 19 COOH-terminal amino acids of rat UT-A1 (L194) identified immunoreactive proteins in kidney, testes, heart, brain, and liver and showed a complex pattern of differential expression. Relative to other tissues, kidney and brain had the highest levels of UT-A protein expression. In kidney sections, immunostaining with L194 revealed immunoreactive proteins in type 1 (short) and type 3 (long) thin descending limbs of the loop of Henle and in the middle and terminal inner medullary collecting ducts. Expression in Xenopus laevis oocytes showed that, characteristic of UT-A family members, the cDNAs encoded phloretin-inhibitable urea transporters. Acute application of PKA agonists (cAMP/forskolin/IBMX) caused a significant increase in UT-A1- and UT-A3-, but not UT-A2-mediated, urea transport.
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Affiliation(s)
- R A Fenton
- School of Biological Sciences, University of Manchester, United Kingdom
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39
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Smith CP, O'Leary M, Erickson J, Somogyi GT, Chancellor MB. Botulinum toxin urethral sphincter injection resolves urinary retention after pubovaginal sling operation. Int Urogynecol J 2002; 13:55-6. [PMID: 11999210 DOI: 10.1007/s001920200013] [Citation(s) in RCA: 16] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
The management of prolonged urinary retention following pubovaginal sling surgery typically involves transvaginal urethrolysis for anatomical urethral obstruction. Brubaker recently reported on urethral sphincter abnormalities as a cause of postoperative urinary retention following either Burch suspension or a pubovaginal sling procedure. We report a case of functional urethral obstruction and detrusor acontractility following pubovaginal sling surgery that was successfully treated by botulinum A toxin urethral sphincter injection.
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Affiliation(s)
- C P Smith
- University of Pittsburgh School of Medicine, Pennsylvania, USA
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40
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Abstract
The blood-seminiferous tubule barrier is responsible for maintaining the unique microenvironment conducive to spermatogenesis. A key feature of the blood-testis barrier is selective permeability to solutes and water transport, conferred by the Sertoli cells of the seminiferous tubules (SMTs). Movement of fluid into the lumen of the seminiferous tubule is crucial to spermatogenesis. By Northern analysis, we have shown that 4.0-, 3.3-, 2.8-, and ~1.7-kb UT-A mRNA transcripts and a 3.8-kb UT-B mRNA transcript are detected within rat testis. Western analysis revealed the expression of both characterized and novel UT-A and UT-B proteins within the testis. Immunolocalization studies determined that UT-A and UT-B protein expression are coordinated with the developmental stage of the SMT. UT-A proteins were detected in Sertoli cell nuclei at all stages of tubule development and in residual bodies of stage VIII tubules. UT-B protein was expressed on Sertoli cell membranes of stage II-III tubules. Using in vitro perfusion, we determined that a phloretin-inhibitable urea pathway exists across the SMTs of rat testis and conclude that UT-B is likely to participate in this pathway.
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Affiliation(s)
- R A Fenton
- School of Biological Sciences, University of Manchester, Manchester M13 9PT, United Kingdom
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41
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Fenton RA, Cottingham CA, Stewart GS, Howorth A, Hewitt JA, Smith CP. Structure and characterization of the mouse UT-A gene (Slc14a2). Am J Physiol Renal Physiol 2002; 282:F630-8. [PMID: 11880324 DOI: 10.1152/ajprenal.00264.2001] [Citation(s) in RCA: 63] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
The movement of urea across plasma membranes is modulated by facilitated urea transporter proteins. These proteins are the products of two closely related genes, termed UT-A (Slc14a2) and UT-B (Slc14a1). By genomic library screening and P1 artificial chromosome "shotgun" sequencing, we have determined the structure of the mouse UT-A gene. The gene is >300 kb in length, contains 24 exons, and has 2 distinct promoters. Flanking the 5'-region of the gene is the UT-Aalpha promoter that regulates transcription of UT-A1 and UT-A3. The second promoter, termed UT-Abeta, is present in intron 13 and regulates transcription of UT-A2. cAMP agonists (100 microM dibutryl cAMP, 25 microM forskolin, 0.5 mM IBMX) increased the activity of a 2.2-kb UT-Aalpha promoter construct 6.2-fold [from 0.026 +/- 0.003 to 0.160 +/- 0.004, relative light units (RLU)/microg protein] and a 2.4-kb UT-Abeta promoter construct 9.5-fold (from 0.020 +/- 0.002 to 0.190 +/- 0.043 RLU/microg protein) above that in untreated controls. Interestingly, only the UT-Abeta promoter contained consensus sequences for CREs and deletion of these elements abolished cAMP sensitivity. Increasing the tonicity of culture medium from 300 to 600 mosmol/kg H(2)O with NaCl caused a significant increase (from 0.060 +/- 0.004 to 0.095 +/- 0.010 RLU/microg protein) in UT-Aalpha promoter activity but had no effect on the UT-Abeta promoter. A tonicity-responsive enhancer was identified in UT-Aalpha and is suggested to be responsible for mediating this effect. Levels of UT-A2 and UT-A3 mRNA were increased in thirsted mice compared with control animals, indicating that the activities of both promoters are likely to be elevated during prolonged antidiuresis.
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Affiliation(s)
- R A Fenton
- School of Biological Sciences, University of Manchester, Manchester M13 9PT, United Kingdom
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42
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Park K, Hurley PT, Roussa E, Cooper GJ, Smith CP, Thévenod F, Steward MC, Case RM. Expression of a sodium bicarbonate cotransporter in human parotid salivary glands. Arch Oral Biol 2002; 47:1-9. [PMID: 11743927 DOI: 10.1016/s0003-9969(01)00098-x] [Citation(s) in RCA: 33] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The human parotid gland secretes much of the bicarbonate that enters the mouth. Prompted by studies of animal models, this study sought evidence for the expression of a functional Na(+)-HCO(3)(-) cotransporter (NBC) in human parotid acinar cells. Microfluorometric measurements of intracellular pH in isolated acini showed that the recovery from an acid load was achieved in part by HCO(3)(-) uptake via a Na(+)-dependent, DIDS-sensitive mechanism. By reverse transcriptase-polymerase chain reaction, a full-length NBC1 clone was obtained showing more than 99% homology with the human pancreatic isoform hpNBC1. Expressed in Xenopus oocytes, the electrogenicity of the transporter was detected as an inwardly directed, Na(+)- and HCO(3)(-)-dependent flux of negative charge. Immunohistochemistry using antibodies raised to NBC1 showed strong staining of the basolateral membrane of the acinar cells. Therefore, it was concluded that a functional electrogenic Na(+)-HCO(3)(-) cotransporter is expressed in the human parotid gland, and that it contributes to pH regulation in the acinar cells and could play a significant part in salivary secretion.
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Affiliation(s)
- K Park
- School of Biological Sciences, University of Manchester, G.38 Stopford Building, M13 9PT, Manchester, UK
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43
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Affiliation(s)
- C P Smith
- School of Biological Science, University of Manchester, G38 Stopford Building, Manchester, M13 9PT, UK.
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44
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Miller PR, Croce MA, Bee TK, Qaisi WG, Smith CP, Collins GL, Fabian TC. ARDS after pulmonary contusion: accurate measurement of contusion volume identifies high-risk patients. J Trauma 2001; 51:223-8; discussion 229-30. [PMID: 11493778 DOI: 10.1097/00005373-200108000-00003] [Citation(s) in RCA: 182] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
BACKGROUND The pathophysiology of pulmonary contusion (PC) is poorly understood, and only minimal advances have been made in management of this entity over the past 20 years. Improvement in understanding of PC has been hindered by the fact that there has been no accurate way to quantitate the amount of pulmonary injury. With this project, we examine a method of accurately measuring degree of PC by quantifying contusion volume relative to pulmonary function and outcome. METHODS Patients with PC from isolated chest trauma who had admission chest computed tomographic scan were identified from the registry of a Level I trauma center over a 1.5-year period. Subsequently, prospective data on all patients admitted to the intensive care unit with PC during a 5-month period were collected and added to the retrospective database. Using computer-generated three-dimensional reconstruction from admission chest computed tomographic scan, contusion volume was measured and expressed as a percentage of total lung volume. Admission pulmonary function variables (Pao2/FiO2, static compliance), injury descriptors (chest Abbreviated Injury Score, Injury Severity Score, injury distribution), and indicators of degree of shock (admission systolic blood pressure, admission base deficit) were documented. Outcomes included maximum positive end-expiratory pressure, ventilator days, pneumonia, and acute respiratory distress syndrome (ARDS). RESULTS Forty-nine patients with PC (35 bilateral) were identified. The average severity of contusion was 18% (range, 5-55%). Patients were classified using contusion volume as severe PC (> or =20%, n = 17) and moderate PC (< 20%, n = 32). Injury Severity Score was similar in the severe and moderate groups (23.3 vs. 26.5, p = 0.33), as were admission Glasgow Coma Scale score (12 vs. 13, p = 0.30), admission blood pressure (131 vs. 129 mm Hg, p = 0.90), and admission Pao2/Fio2 (197 vs. 255, p = 0.14). However, there was a much higher rate of ARDS in the severe group as compared with the moderate group (82% vs. 22%, p < 0.001). There was a trend toward higher pneumonia rate in the severe group, with 50% of patients in the severe group developing pneumonia as compared with 28% in the moderate group (p = 0.20). CONCLUSION Extent of contusion volumes measured using three-dimensional reconstruction allows identification of patients at high risk of pulmonary dysfunction as characterized by development of ARDS. This method of measurement may provide a useful tool for the further study of PC as well as for the identification of patients at high risk of complications at whom future advances in therapy may be directed.
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Affiliation(s)
- P R Miller
- Department of Surgery, University of Tennessee at Memphis, 38163, USA.
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Ferguson CJ, Wareing M, Ward DT, Green R, Smith CP, Riccardi D. Cellular localization of divalent metal transporter DMT-1 in rat kidney. Am J Physiol Renal Physiol 2001; 280:F803-14. [PMID: 11292622 DOI: 10.1152/ajprenal.2001.280.5.f803] [Citation(s) in RCA: 110] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
We have demonstrated that the kidney plays an important role in iron balance and that metabolically significant reabsorption of this ion occurs in the loop of Henle and the collecting ducts [Wareing M, Ferguson CJ, Green R, Riccardi D, and Smith CP. J Physiol (Lond) 524: 581-586, 2000]. To test the possibility that the divalent metal transporter DMT1 (Gunshin H, Mackenzie B, Berger UV, Gunshin Y, Romero MF, Boron WF, Nussberger S, Gollan JL, and Hediger MA. Nature 388: 482-488, 1997) could represent the apical route for iron entry in the kidney, we raised and affinity-purified an anti-DMT-1 polyclonal antibody and determined DMT-1 distribution in rat kidney by Western analysis, immunofluorescence, and confocal microscopy. The strongest DMT1-specific (i.e., peptide-protectable) immunoreactivity was found in the collecting ducts, in both principal and intercalated cells. Thick ascending limbs of Henle's loop and, more intensely, distal convoluted tubules exhibited apical immunostaining. Considerable intracellular DMT-1 immunoreactivity was seen throughout the nephron, particularly in S3 segments. The described distribution of DMT-1 protein is in agreement with our previous identification of nephron sites of iron reabsorption, suggesting that DMT-1 provides the molecular mechanism for apical iron entry in the distal nephron but not in the proximal tubule. Basolateral iron exit may be facilitated by a different system.
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Affiliation(s)
- C J Ferguson
- School of Biological Sciences, University of Manchester, Manchester M13 9PT, United Kingdom
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46
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Sasaki K, Smith CP, Chuang YC, Lee JY, Kim JC, Chancellor MB. Oral gabapentin (neurontin) treatment of refractory genitourinary tract pain. Tech Urol 2001; 7:47-9. [PMID: 11272678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
PURPOSE Refractory genitourinary pain is a common but difficult condition to treat. Examples of chronic genitourinary pain include orchalgia, interstitial cystitis, pain after bladder suspension surgery, nonbacterial prostatitis, and genital pain related to lumbosacral neuropathy. We report our experience with oral gabapentin treatment for this condition. Gabapentin is an anticonvulsant with unclear but therapeutic effects on neurologic pain. MATERIALS AND METHODS Twenty-one patients referred with refractory genitourinary pain were treated with oral gabapentin. There were 9 men and 12 women. In the male patients, the location of pain was testicle (4), bladder (2), penis (1), or prostate (2). In female patients, the pain was located in the urethra (4), bladder (6), vulva (1), or vagina (1). The dose of gabapentin was titrated from 300 up to 2,100 mg/day. Subjective pain severity and 10-cm visual pain scale was used before and 6 months after therapy. RESULTS The mean dose of gabapentin was 1,200 mg/day (range 300-2,100 mg). Ten of 21 patients reported subjective improvement of their pain. The remaining patients did not perceive any improvement. Gabapentin was well tolerated; only 4 patients dropped out due to side effects. The most common adverse effects were dizziness and drowsiness. Five of 8 patients with interstitial cystitis reported improvement. CONCLUSIONS Although only 10 of 21 patients improved with gabapentin, this cohort included only patients with refractory genitourinary pain that failed a wide range of prior treatments. Gabapentin belongs in the armaterium of the urologist who treats genitourinary pain.
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Affiliation(s)
- K Sasaki
- Department of Urology, University of Pittsburgh School of Medicine, Pennsylvania, USA
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47
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Walsh PJ, Grosell M, Goss GG, Bergman HL, Bergman AN, Wilson P, Laurent P, Alper SL, Smith CP, Kamunde C, Wood CM. Physiological and molecular characterization of urea transport by the gills of the Lake Magadi tilapia (Alcolapia grahami). J Exp Biol 2001; 204:509-20. [PMID: 11171302 DOI: 10.1242/jeb.204.3.509] [Citation(s) in RCA: 53] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
The Lake Magadi tilapia (Alcolapia grahami) is an unusual fish, excreting all its nitrogenous waste as urea because of its highly alkaline and buffered aquatic habitat. Here, using both physiological and molecular studies, we describe the mechanism of branchial urea excretion in this species. In vivo, repeated short-interval sampling revealed that urea excretion is continuous. The computed urea permeability of A. grahami gill is 4.74×10(−)(5)+/−0.38×10(−)(5)cm s(−)(1) (mean +/− s.e.m., N=11), some 10 times higher than passive permeability through a lipid bilayer and some five times higher than that of even the most urea-permeable teleosts studied to date (e.g. the gulf toadfish). Transport of urea was bidirectional, as demonstrated by experiments in which external [urea] was elevated. Furthermore, urea transport was inhibited by classic inhibitors of mammalian and piscine urea transporters in the order thiourea>N-methylurea>acetamide. A 1700 base pair cDNA for a putative Magadi tilapia urea transporter (mtUT) was cloned, sequenced and found to display high homology with urea transporters from mammals, amphibians and other fishes. When cRNA transcribed from mtUT cDNA was injected into Xenopus laevis oocytes, phloretin-inhibitable urea uptake was enhanced 3.4-fold relative to water-injected controls. Northern analysis of gill, red blood cells, liver, muscle and brain using a portion of mtUT as a probe revealed that gill is the only tissue in which mtUT RNA is expressed. Magadi tilapia gill pavement cells exhibited a trafficking of dense-cored vesicles between the well-developed Golgi cisternae and the apical membrane. The absence of this trafficking and the poor development of the Golgi system in a non-ureotelic relative (Oreochromis niloticus) suggest that vesicle trafficking could be related to urea excretion in Alcolapia grahami. Taken together, the above findings suggest that the gills of this alkaline-lake-adapted species excrete urea constitutively via the specific facilitated urea transporter mtUT.
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Affiliation(s)
- P J Walsh
- Division of Marine Biology and Fisheries, Rosenstiel School of Marine and Atmospheric Science, NIEHS Marine and Freshwater Biomedical Sciences Center, University of Miami, Miami, FL 33149, USA.
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48
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Duchene DA, Smith CP, Goldfarb RA. Allopurinol induced meningitis. J Urol 2000; 164:2028. [PMID: 11061913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Affiliation(s)
- D A Duchene
- Department of Urology, Baylor College of Medicine, Houston, Texas, USA
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49
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Bucca G, Brassington AM, Schönfeld HJ, Smith CP. The HspR regulon of Streptomyces coelicolor: a role for the DnaK chaperone as a transcriptional co-repressordagger. Mol Microbiol 2000; 38:1093-103. [PMID: 11123682 DOI: 10.1046/j.1365-2958.2000.02194.x] [Citation(s) in RCA: 60] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
The dnaK operon of Streptomyces coelicolor encodes the DnaK chaperone machine and HspR, the transcriptional repressor of the operon; HspR confers repression by binding to several inverted repeat sequences in the promoter region, dnaKp. Here, we demonstrate that HspR specifically requires the presence of DnaK protein to retard a dnaKp fragment in gel-shift assays. This requirement is independent of the co-chaperones, DnaJ and GrpE, and it is ATP independent. Furthermore the retarded protein-DNA complex can be 'supershifted' by anti-DnaK monoclonal antibody, demonstrating that DnaK forms an integral component of the complex. It was shown in DNase I footprinting experiments that refolding and specific binding of HspR to its DNA target does not require DnaK. We conclude that the formation of the stable DnaK-HspR-DNA ternary complex does not depend on the chaperoning activity of DnaK. In affinity chromatography experiments using whole-cell extracts, DnaK was shown to co-purify with HspR, providing additional evidence that the two proteins interact in vivo; it was not possible to purify HspR away from DnaK in any experiments unless a powerful denaturant was used. The level of heat shock induction of chromosomal DnaK could be partially suppressed by expressing dnaK extrachromosomally from a heterologous promoter. In addition, it is shown that DnaK confers enhanced HspR-mediated repression of transcription in vitro. Taken together, these results suggest that DnaK functions as a transcriptional co-repressor by binding to HspR at its operator sites. In this model, the DnaK-HspR system would represent a novel example of feedback regulation of gene expression by a molecular chaperone, in which DnaK directly activates a repressor, rather than inactivates an activator (as is the case in the DnaK-sigma32 and Hsp70-HSF systems of other organisms).
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Affiliation(s)
- G Bucca
- Department of Biomolecular Sciences, UMIST, PO Box 88, Manchester, M60 1QD, UK
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50
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Yoshimura N, Smith CP, Chancellor MB, de Groat WC. Pharmacologic and potential biologic interventions to restore bladder function after spinal cord injury. Curr Opin Neurol 2000; 13:677-81. [PMID: 11148669 DOI: 10.1097/00019052-200012000-00011] [Citation(s) in RCA: 19] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Spinal cord injury disrupts voluntary control of voiding and the normal reflex pathways that coordinate bladder and urethral sphincter function. The present review addresses studies in animals and humans that have evaluated various therapeutic approaches for normalizing lower urinary tract function after spinal cord injury.
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Affiliation(s)
- N Yoshimura
- Department of Pharmacology, University of Pittsburgh School of Medicine, Pennsylvania 15261, USA. nyos+@pitt.edu
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