• Media type: E-Article
  • Title: Predicting the response to intravenous immunoglobulins in an animal model of chronic neuritis
  • Contributor: Meyer zu Hörste, Gerd [VerfasserIn]; Pfaff, Johannes [VerfasserIn]; Bendszus, Martin [VerfasserIn]; Pham, Mirko [VerfasserIn]
  • imprint: 6 October 2016
  • Published in: PLOS ONE ; 11(2016) Artikel-Nummer e0164099, 16 Seiten
  • Language: English
  • DOI: 10.1371/journal.pone.0164099
  • ISSN: 1932-6203
  • Identifier:
  • Keywords: Magnetic resonance imaging ; Cell staining ; Electrophysiology ; Histology ; Inflammation ; Mouse models ; Muscle electrophysiology ; Sciatic nerves
  • Origination:
  • Footnote:
  • Description: Chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) is a disabling autoimmune disorder of the peripheral nervous system (PNS). Intravenous immunoglobulins (IVIg) are effective in CIDP, but the treatment response varies greatly between individual patients. Understanding this interindividual variability and predicting the response to IVIg constitute major clinical challenges in CIDP. We previously established intercellular adhesion molecule (ICAM)-1 deficient non-obese diabetic (NOD) mice as a novel animal model of CIDP. Here, we demonstrate that similar to human CIDP patients, ICAM-1 deficient NOD mice respond to IVIg treatment by clinical and histological measures. Nerve magnetic resonance imaging and histology demonstrated that IVIg ameliorates abnormalities preferentially in distal parts of the sciatic nerve branches. The IVIg treatment response also featured great heterogeneity allowing us to identify IVIg responders and non-responders. An increased production of interleukin (IL)-17 positively predicted IVIg treatment responses. In human sural nerve biopsy sections, high numbers of IL-17 producing cells were associated with younger age and shorter disease duration. Thus, our novel animal model can be utilized to identify prognostic markers of treatment responses in chronic inflammatory neuropathies and we identify IL-17 production as one potential such prognostic marker.
  • Access State: Open Access