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Dynamic balance of pro- and anti-inflammatory signals controls disease and limits pathology.
Cicchese, Joseph M; Evans, Stephanie; Hult, Caitlin; Joslyn, Louis R; Wessler, Timothy; Millar, Jess A; Marino, Simeone; Cilfone, Nicholas A; Mattila, Joshua T; Linderman, Jennifer J; Kirschner, Denise E.
Afiliação
  • Cicchese JM; Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA.
  • Evans S; Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA.
  • Hult C; Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA.
  • Joslyn LR; Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA.
  • Wessler T; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Millar JA; Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA.
  • Marino S; Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA.
  • Cilfone NA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Mattila JT; Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA.
  • Linderman JJ; Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA.
  • Kirschner DE; Department of Infectious Diseases and Microbiology, University of Pittsburgh, Pittsburgh, PA, USA.
Immunol Rev ; 285(1): 147-167, 2018 09.
Article em En | MEDLINE | ID: mdl-30129209
ABSTRACT
Immune responses to pathogens are complex and not well understood in many diseases, and this is especially true for infections by persistent pathogens. One mechanism that allows for long-term control of infection while also preventing an over-zealous inflammatory response from causing extensive tissue damage is for the immune system to balance pro- and anti-inflammatory cells and signals. This balance is dynamic and the immune system responds to cues from both host and pathogen, maintaining a steady state across multiple scales through continuous feedback. Identifying the signals, cells, cytokines, and other immune response factors that mediate this balance over time has been difficult using traditional research strategies. Computational modeling studies based on data from traditional systems can identify how this balance contributes to immunity. Here we provide evidence from both experimental and mathematical/computational studies to support the concept of a dynamic balance operating during persistent and other infection scenarios. We focus mainly on tuberculosis, currently the leading cause of death due to infectious disease in the world, and also provide evidence for other infections. A better understanding of the dynamically balanced immune response can help shape treatment strategies that utilize both drugs and host-directed therapies.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Tuberculose / Modelos Imunológicos / Biologia Computacional / Inflamação / Pulmão / Mycobacterium tuberculosis Tipo de estudo: Prognostic_studies Limite: Animals / Humans Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Tuberculose / Modelos Imunológicos / Biologia Computacional / Inflamação / Pulmão / Mycobacterium tuberculosis Tipo de estudo: Prognostic_studies Limite: Animals / Humans Idioma: En Ano de publicação: 2018 Tipo de documento: Article