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.2017 Jul 25;12(7):e0182002.
doi: 10.1371/journal.pone.0182002. eCollection 2017.

Comprehensive immune profiling reveals substantial immune system alterations in a subset of patients with amyotrophic lateral sclerosis

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Comprehensive immune profiling reveals substantial immune system alterations in a subset of patients with amyotrophic lateral sclerosis

Michael P Gustafson et al. PLoS One..

Abstract

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a median lifespan of 2-3 years after diagnosis. There are few meaningful treatments that alter progression in this disease. Preclinical and clinical studies have demonstrated that neuroinflammation may play a key role in the progression rate of ALS. Despite this, there are no validated biomarkers of neuroinflammation for use in clinical practice or clinical trials. Biomarkers of neuroinflammation could improve patient management, provide new therapeutic targets, and possibly help stratify clinical trial selection and monitoring. However, attempts to identify a singular cause of neuroinflammation have not been successful. Here, we performed multi-parameter flow cytometry to comprehensively assess 116 leukocyte populations and phenotypes from lymphocytes, monocytes, and granulocytes in a cohort of 80 ALS patients. We identified 32 leukocyte phenotypes that were altered in ALS patients compared to age and gender matched healthy volunteers (HV) that included phenotypes of both inflammation and immune suppression. Unsupervised hierarchical clustering and principle component analysis of ALS and HV immunophenotypes revealed two distinct immune profiles of ALS patients. ALS patients were clustered into a profile distinct from HVs primarily due to differences in a multiple T cell phenotypes, CD3+CD56+ T cells and HLA-DR on monocytes. Patients clustered into an abnormal immune profile were younger, more likely to have a familial form of the disease, and survived longer than those patients who clustered similarly with healthy volunteers (344 weeks versus 184 weeks; p = 0.012). The data set generated from this study establishes an extensive accounting of immunophenotypic changes readily suitable for biomarker validation studies. The extensive immune system changes measured in this study indicate that normal immune homeostatic mechanisms are disrupted in ALS patients, and that multiple immune states likely exist within a population of patients with ALS.

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Conflict of interest statement

Competing Interests:MPG and ABD are inventors of technology used as a tool in this research (Methods and materials for assessing immune system profiles, #WO2014078272 A1). While this invention is not the target of these studies, value may be brought to this invention by demonstrating new properties to the invention. MPG, ABD, and the Mayo Clinic have rights to this invention and in the future the invention may be licensed or sold to the benefit of the investigators or Mayo Clinic. This technology is not currently licensed. We confirm that this does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. ALS patients exhibit elevated cell counts of granulocytes, NK cells and T cells.
Major leukocyte populations from 80 ALS patients and 50 healthy volunteers were assessed by flow cytometry.A. Comparisons of granulocytes, NK cells, T cells, CD4+ T cells, and CD8+ T cells between healthy volunteers (dark circles), and ALS patients (open circles).B. CD3+CD56+ T cells shown as cell counts (cells/μl), percentage of total T cells, and a representative dot plot.C. Correlations of CD4+ T cells and CD4:CD8 ratios are altered in ALS patients.D. NK cells and granulocyte counts inversely correlate with ALSFRS-R score.
Fig 2
Fig 2. Hierarchical clustering of immunophenotypes reveals subgroups of ALS patients with distinct clinical characteristics.
Immunophenotypes of ALS patients and HVs were analyzed by unsupervised hierarchical clustering and principal component analysis as outlined in the methods.A. Dendrogram of hierarchical clustering of ALS patients (blue) and HVs (yellow) were grouped into Profile 1 (purple lines) and Profile 2 (green lines). 1 HV and 3 ALS patients were not clustered into a defined profile and 1 ALS patient was excluded due to insufficient data.B. Table of clinical characteristics of ALS patients subgrouped by profile. Familial ALS patients included only definite or probable for this analysis.C. Survival curves of patients in the two different profiles (Profile 1 with purple line and Profile 2 in green line). HR = hazard ratio and the 95% CI represents the confidence interval of the ratio.
Fig 3
Fig 3. Two immune profiles of ALS patients reveal significant phenotypic differences.
Leukocyte phenotypes from HVs and ALS patients sub-grouped into Profiles 1 and 2 were compared.A. Box-and-whisker plots of total white blood cells (WBCs) and mononuclear cells (MNCs) are shown. The boxes indicate the 25-75th percentile, the horizontal line indicates the median, and the whiskers represent minimum and maximum values.B. Pie graphs depicting the peripheral blood leukocyte compartment of the three groups. The HV pie graph was set to 100% and the other pie graphs were sized in relation to the HV.C. Box-and-whisker plots of immunophenotypes associated with principal components. *** = p value <0.001, and * = p value <0.05.
Fig 4
Fig 4. Different immunophenotypic biomarkers associate with clinical parameters in the two ALS immune profiles.
Immune phenotypes were plotted against ALSFRS-R score or slope (ALSFRS-R points/month). XY graphs of correlations show p-value and Spearman r value. Lines represent the best fit resulting from linear regression analysis. Closed circles represent healthy volunteers, open squares represent ALS patients in Profile 1, and open diamonds represent ALS patients in Profile 2.A. Correlations of selected immunophenotypes to ALSFRS-R score in Profile 1 patients (top row) versus Profile 2 (bottom row).B. Correlations of selected immunophenotypes to slope in ALS Profile 1 patients.C. Survival curves of patients in each profile sub-grouped by cut-off values for PD-1+ CD4+ T cells. 19.7% was the cut-off value representing the median value for Profile 1 patients (Hi PD-1≥ 19.7; Lo PD-1< 19.7) and 19.5% was the cut-off value for Profile 2 patients. For CD3+CD56+ T cells, 104.62 cells/μl was used as a cut-off value for Profile 2 patients and 28.21 cells/μl was used for Profile 1.D. CD4+CD45RA+ naïve T cells show dissimilar age related associations between Profile 2 ALS patients, Profile 1 ALS patients, and healthy volunteers.
Fig 5
Fig 5. Additional phenotypic characterizations of CD3+CD56+ T cells.
A. CD3+CD56+ T cell counts plotted against age (years) for sub-grouped ALS patients and healthy volunteers.B. Correlation between CD8+ T cell counts and CD3+CD56+ T cell counts (left) and the CD4:CD8 ratios are lower in CD56+ than CD56- T cells.C. Comparison of the percentage of CD28 negative cells in CD56- (filled shapes) and CD56+ cells (open shapes) for both CD8 (circles) and CD4 (squares) subsets.D. Examples of CD28/CD56 dot plots gated from CD4 and CD8 subsets from two ALS patients.
Fig 6
Fig 6. CD14+HLA-DRlo/neg monocytes in ALS patients.
A. Comparison of the percentages and cell counts of CD14+HLA-DRlo/neg monocytes in sub-grouped ALS patients and healthy volunteers.B. CD14+HLA-DRlo/neg monocytes associate with slope in Profile 2 patients but not Profile 1 patients.C. Correlations of CD14+HLA-DRlo/neg monocytes with other phenotypes in sub-grouped ALS patients (Profile 1: squares; Profile 2: diamonds). XY graphs of correlations show p-value and Spearman r value.
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