Disrupted balance of T cells under natalizumab treatment in multiple sclerosis

Abstract

Objective: To compare effects of natalizumab on inflammatory and regulatory T cells with regard to expression of α4-integrin (CD49d).

Methods: Twenty-seven natalizumab-naive and 8 natalizumab-treated patients with multiple sclerosis (MS), 7 patients with neuromyelitis optica (NMO) or NMO spectrum disorder, and 8 healthy controls were included. The positive rate of CD49d was analyzed and compared among T helper 1 (Th1), T helper 17 (Th17), and regulatory T (Treg) cells (CD49d+Th1, CD49d+Th17, and CD49d+Treg, respectively).

Results: Natalizumab treatment increased CD49d ratios, CD49d+Th1/CD49d+Treg, and CD49d+Th17/CD49d+Treg. This indicates larger reduction of the CD49d+ population in Treg cells than in Th1 or Th17 cells. The CD49d ratios of 2 patients who experienced exacerbation during natalizumab treatment were remarkably higher than those of the other natalizumab-treated patients. Natalizumab treatment increased the expression of TBX21, RORC, interferon (IFN)-γ, and interleukin (IL)-17A, and decreased the expression of FOXP3 in CD49d+ memory CD4 T cells. Natalizumab treatment also increased the amount of IFN-γ and IL-17A secreted by CD49d+ memory CD4 T cells.

Conclusions: The reduction rate of the CD49d+ population in Treg cells was larger than that in Th1 or Th17 cells. Although the large reduction in CD49d+ population is beneficial for MS, the proinflammatory state of residual CD49d+ cells might, in part, explain the presence of disease activity under natalizumab treatment.

Figure 1. Natalizumab induces larger reduction of CD49d+ population in regulatory T (Treg) than in T helper 1 (Th1) or T helper 17 (Th17) cells

(A) Representative dot plot and histogram by flow cytometry. CD3+ CD4+ CD45RA− mCD4 T cells were divided into Th1, Th17, and Treg cells based on the expression of each characteristic transcription factor. The CD49d+ population was defined based on the fluorescence intensity of isotype control. (B) Frequencies of Th1, Th17, and Treg cells among memory CD4 (mCD4) T cells. (C) CD49d+ population among mCD4 T, Th1, Th17, and Treg cells. (D) Ratio of CD49d-positive rate of inflammatory cells to that of Treg cells (CD49d+Th1/CD49d+Treg, CD49d+Th17/CD49d+Treg). (E) Ratio of CD49d-negative rate of inflammatory cells to that of Treg cells (CD49d-Th1/CD49d-Treg, CD49d-Th17/CD49d-Treg). (F) Ratios (as described in D) of natalizumab-treated patients during remission and during relapse. Kruskal-Wallis test with Dunn multiple comparison test was used in B, C, D, and E. Statistical data were described only between MS and MS + natalizumab groups in C and D. Unpaired t test was used in F. Error bars represent the mean ± SD. *p < 0.05, **p < 0.01, ***p < 0.001. NC = healthy controls; MS = multiple sclerosis; NMO = neuromyelitis optica.

Figure 2. Natalizumab increases expression of proinflammatory genes and cytokines by CD49d+ memory CD4 cells

(A) Relative expressions of messenger RNA (mRNA) associated with inflammatory and regulatory T cells. The expression level of each mRNA was determined with normalization to β-actin; n = 7 for each group. (B) Amount of cytokines secreted by each population under stimulation with anti-CD3 monoclonal antibodies (mAb) and anti-CD28 mAb. Cell culture was performed in triplicate for each sample. Multiple sclerosis (MS): n = 5; MS + natalizumab: n = 4. Mann-Whitney U test was used. Error bars represent the mean ± SD. *p < 0.05, **p < 0.01, ***p < 0.001. IFN = interferon; IL = interleukin.