Literature DB >> 24478442

Effects of aging on influenza virus infection dynamics.

Esteban A Hernandez-Vargas1, Esther Wilk, Laetitia Canini, Franklin R Toapanta, Sebastian C Binder, Alexey Uvarovskii, Ted M Ross, Carlos A Guzmán, Alan S Perelson, Michael Meyer-Hermann.   

Abstract

UNLABELLED: The consequences of influenza virus infection are generally more severe in individuals over 65 years of age (the elderly). Immunosenescence enhances the susceptibility to viral infections and renders vaccination less effective. Understanding age-related changes in the immune system is crucial in order to design prophylactic and immunomodulatory strategies to reduce morbidity and mortality in the elderly. Here, we propose different mathematical models to provide a quantitative understanding of the immune strategies in the course of influenza virus infection using experimental data from young and aged mice. Simulation results suggested a central role of CD8(+) T cells for adequate viral clearance kinetics in young and aged mice. Adding the removal of infected cells by natural killer cells did not improve the model fit in either young or aged animals. We separately examined the infection-resistant state of cells promoted by the cytokines alpha/beta interferon (IFN-α/β), IFN-γ, and tumor necrosis factor alpha (TNF-α). The combination of activated CD8(+) T cells with any of the cytokines provided the best fits in young and aged animals. During the first 3 days after infection, the basic reproductive number for aged mice was 1.5-fold lower than that for young mice (P < 0.05). IMPORTANCE: The fits of our models to the experimental data suggest that the increased levels of IFN-α/β, IFN-γ, and TNF-α (the "inflammaging" state) promote slower viral growth in aged mice, which consequently limits the stimulation of immune cells and contributes to the reported impaired responses in the elderly. A quantitative understanding of influenza virus pathogenesis and its shift in the elderly is the key contribution of this work.

Entities:  

Mesh:

Substances:

Year:  2014        PMID: 24478442      PMCID: PMC3993746          DOI: 10.1128/JVI.03644-13

Source DB:  PubMed          Journal:  J Virol        ISSN: 0022-538X            Impact factor:   5.103


  58 in total

Review 1.  The process of aging changes the interplay of the immune, endocrine and nervous systems.

Authors:  R H Straub; M Cutolo; B Zietz; J Schölmerich
Journal:  Mech Ageing Dev       Date:  2001-09-30       Impact factor: 5.432

2.  On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations.

Authors:  O Diekmann; J A Heesterbeek; J A Metz
Journal:  J Math Biol       Date:  1990       Impact factor: 2.259

3.  Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood.

Authors:  A Raue; C Kreutz; T Maiwald; J Bachmann; M Schilling; U Klingmüller; J Timmer
Journal:  Bioinformatics       Date:  2009-06-08       Impact factor: 6.937

4.  A dynamical model of human immune response to influenza A virus infection.

Authors:  Baris Hancioglu; David Swigon; Gilles Clermont
Journal:  J Theor Biol       Date:  2006-12-19       Impact factor: 2.691

5.  Sustained viral load and late death in Rag2-/- mice after influenza A virus infection.

Authors:  Haiya Wu; Verena Haist; Wolfgang Baumgärtner; Klaus Schughart
Journal:  Virol J       Date:  2010-07-28       Impact factor: 4.099

6.  Fewer CTL, not enhanced NK cells, are sufficient for viral clearance from the lungs of immunocompromised mice.

Authors:  Haley D Neff-LaFord; Beth A Vorderstrasse; B Paige Lawrence
Journal:  Cell Immunol       Date:  2003-11       Impact factor: 4.868

7.  The aging immune system: challenges for the 21st century.

Authors:  Janko Nikolich-Žugich
Journal:  Semin Immunol       Date:  2012-10       Impact factor: 11.130

8.  Estimation of dynamical model parameters taking into account undetectable marker values.

Authors:  Rodolphe Thiébaut; Jérémie Guedj; Hélène Jacqmin-Gadda; Geneviève Chêne; Pascale Trimoulet; Didier Neau; Daniel Commenges
Journal:  BMC Med Res Methodol       Date:  2006-08-01       Impact factor: 4.615

Review 9.  Neurological complications of pandemic influenza A H1N1 2009 infection: European case series and review.

Authors:  Pinki Surana; Shan Tang; Marilyn McDougall; Cheuk Yan William Tong; Esse Menson; Ming Lim
Journal:  Eur J Pediatr       Date:  2011-01-14       Impact factor: 3.183

10.  Assessing mathematical models of influenza infections using features of the immune response.

Authors:  Hana M Dobrovolny; Micaela B Reddy; Mohamed A Kamal; Craig R Rayner; Catherine A A Beauchemin
Journal:  PLoS One       Date:  2013-02-28       Impact factor: 3.240

View more
  40 in total

1.  Multiscale model within-host and between-host for viral infectious diseases.

Authors:  Alexis Erich S Almocera; Van Kinh Nguyen; Esteban A Hernandez-Vargas
Journal:  J Math Biol       Date:  2018-05-08       Impact factor: 2.259

2.  The inflammatory response to influenza A virus (H1N1): An experimental and mathematical study.

Authors:  Ian Price; Ericka D Mochan-Keef; David Swigon; G Bard Ermentrout; Sarah Lukens; Franklin R Toapanta; Ted M Ross; Gilles Clermont
Journal:  J Theor Biol       Date:  2015-04-03       Impact factor: 2.691

Review 3.  Role of Aging and the Immune Response to Respiratory Viral Infections: Potential Implications for COVID-19.

Authors:  Judy Chen; William J Kelley; Daniel R Goldstein
Journal:  J Immunol       Date:  2020-06-03       Impact factor: 5.422

4.  A theoretical framework for transitioning from patient-level to population-scale epidemiological dynamics: influenza A as a case study.

Authors:  W S Hart; P K Maini; C A Yates; R N Thompson
Journal:  J R Soc Interface       Date:  2020-05-13       Impact factor: 4.118

5.  Testosterone treatment of aged male mice improves some but not all aspects of age-associated increases in influenza severity.

Authors:  Landon G Vom Steeg; Sarah E Attreed; Barry Zirkin; Sabra L Klein
Journal:  Cell Immunol       Date:  2019-09-14       Impact factor: 4.868

6.  Discrete Dynamical Modeling of Influenza Virus Infection Suggests Age-Dependent Differences in Immunity.

Authors:  Ericka Keef; Li Ang Zhang; David Swigon; Alisa Urbano; G Bard Ermentrout; Michael Matuszewski; Franklin R Toapanta; Ted M Ross; Robert S Parker; Gilles Clermont
Journal:  J Virol       Date:  2017-11-14       Impact factor: 5.103

Review 7.  Viral kinetic modeling: state of the art.

Authors:  Laetitia Canini; Alan S Perelson
Journal:  J Pharmacokinet Pharmacodyn       Date:  2014-06-25       Impact factor: 2.745

8.  A Single Intramuscular Dose of a Plant-Made Virus-Like Particle Vaccine Elicits a Balanced Humoral and Cellular Response and Protects Young and Aged Mice from Influenza H1N1 Virus Challenge despite a Modest/Absent Humoral Response.

Authors:  Breanna Hodgins; Karen K Yam; Kaitlin Winter; Stephane Pillet; Nathalie Landry; Brian J Ward
Journal:  Clin Vaccine Immunol       Date:  2017-12-05

9.  A switching model for the impact of toxins on the spread of infectious diseases.

Authors:  Lulu Wang; Zhen Jin; Hao Wang
Journal:  J Math Biol       Date:  2018-05-09       Impact factor: 2.259

10.  Controlling of pandemic COVID-19 using optimal control theory.

Authors:  Shahriar Seddighi Chaharborj; Sarkhosh Seddighi Chaharborj; Jalal Hassanzadeh Asl; Pei See Phang
Journal:  Results Phys       Date:  2021-05-19       Impact factor: 4.476

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.