Distinct neuroinflammatory profiles in progressive supranuclear palsy associated with HLA haplotypes

TL;DR

This study shows that different HLA gene variations are linked to unique immune and brain changes in a type of brain disease called progressive supranuclear palsy.

Contribution

The study identifies distinct neuroinflammatory profiles associated with specific HLA haplotypes in progressive supranuclear palsy.

Findings

HLA haplotypes correlate with differences in microglia, T cells, and p-Tau pathology in progressive supranuclear palsy.

Machine learning identified specific neuroinflammatory marker ratios that distinguish HLA-defined groups.

Symptom progression sequences vary by HLA haplotype, suggesting a role of immune profiles in disease trajectory.

Abstract

Progressive supranuclear palsy is a neurodegenerative four-repeat tauopathy characterized by atypical parkinsonism and cognitive behavioural changes and a relatively uniform neuropathology. Building on prior identification of rare HLA (human leukocyte antigen) haplotypes in progressive supranuclear palsy, this study investigates whether these haplotypes correlate with distinct clinical and immunopathological phenotypes. In addition to retrospective collection of clinical data, we evaluated T and B cells, microglia and phosphorylated-tau (p-Tau) cytopathologies in 32 progressive supranuclear palsy cases. Machine learning was used to analyse whether pathological variables and their ratios, or the sequence of clinical symptoms are different HLA-defined groups, including one linked to narcolepsy (DRB1*15:01-DQB1*06:02). Neuropathology revealed regional differences in the severity of microglia load, density of cytotoxic T cells and p-Tau cytopathologies between groups. Machine learning revealed that specific ratios of neuroinflammatory markers reliably distinguished HLA haplotypes. Additionally, symptom progression sequences varied by HLA haplotype, suggesting a potential effect of neuroinflammatory profiles on disease trajectory. These findings support the notion that progressive supranuclear palsy pathology might be associated with various aetiological–pathogenic events rarely including targetable autoimmune mechanisms as well. The HLA haplotype-dependent diversity of neuroinflammatory markers should be evaluated in clinical and biomarker studies in, and beyond, progressive supranuclear palsy to understand its relevance for patient stratification in disease modifying therapy trials.

Progressive supranuclear palsy is a four-repeat tauopathy with atypical parkinsonism and cognitive changes. Forrest et al. report that rare haplotypes of human leukocyte antigen (HLA) associate with distinct clinical and neuropathological profiles of tau and immune cell markers. Machine learning revealed that specific ratios of neuroinflammatory markers reliably distinguished haplotypes.

Graphical Abstract

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Full text

Introduction

Progressive supranuclear palsy is a neurodegenerative four-repeat (4R) tauopathy associated with the accumulation of phosphorylated-tau (p-tau) in neurons, oligodendrocytes and astrocytes.^1^ Progressive supranuclear palsy is considered a homogeneous disorder based on hallmark neuropathological features^1^ and similar filament structure of misfolded tau reported in six progressive supranuclear palsy cases examined to date.^2^ However, clinical phenotypes associated with progressive supranuclear palsy pathology vary and include syndromes in the spectrum of atypical parkinsonism and cognitive behavioural changes.^3^ Clinical variability is associated with differences in the distribution patterns of p-tau cytopathologies^4^ and in cases with Richardson syndrome, a six-tiered neuropathological staging system was introduced.^4^

Traditionally, neurodegenerative diseases are defined by neuronal loss not explained by infection, neoplasia or toxins.^5^ However, neuroinflammation, defined as microglial and astrocytic activation and leukocyte infiltration, is increasingly recognized as a key component in disease progression and therapeutic targeting.^6^ Progressive supranuclear palsy-type pathology overlaps with some neuropathological features described in IgLON5 autoimmune encephalitis-related tauopathy^7^ and the 3R and 4R tauopathy, postencephalitic parkinsonism.^8^ Interestingly, recent studies using immunostainings for lymphocytes have shown that CD8 cytotoxic T cells are consistently present in the midbrain in progressive supranuclear palsy and are more prominent than observed in other neurogenerative parkinsonisms.^9,10^ These autopsy studies were complemented by bodily fluid studies such as cytokine profiles^11,12^ or shift in peripheral CD4- and CD8-positive T-cell populations.^12^

Despite these insights, variability in neuroinflammatory markers across progressive supranuclear palsy cases remains unexplained. Given the role of the human leukocyte antigen (HLA) locus in autoimmune diseases,^13^ we hypothesized that HLA haplotypes might be associated with distinct neuroinflammatory responses in progressive supranuclear palsy. Our recent study identified the DRB115:01-DQB106:02 haplotype and DQB1*06:01/06:02 alleles as strongly associated with progressive supranuclear palsy.^14^ In the present study, we mapped p-tau pathology and inflammatory markers in progressive supranuclear palsy brains stratified by HLA haplotype to explore our hypothesis.

Material and methods

Clinical information

Clinical data was analysed from a cohort of 32 patients clinically diagnosed with progressive supranuclear palsy, comprising 11 retrospective cases and 21 prospective cases obtained from the Rossy PSP Centre at the University Health Network (UHN). Demographic variables included sex and age at death. Disease duration was calculated from the time of onset of the first reported symptom to the time of death. We focused on symptoms explicitly documented in the clinical records; for details, see online Supplementary file Methods.

Neuropathology and immunohistochemistry

The brain autopsy tissue was collected with informed consent according to the Declaration of Helsinki and with approval from the UHN Research Ethics Board (Protocol Nr. 20-5258). A systematic neuropathological examination was conducted following established diagnostic criteria, including assessment of vascular pathology.^15-17^ For all cases, HLA genotyping was available as reported.^14^ To evaluate inflammatory markers, we focused on brain regions typically affected in early to moderate stages of progressive supranuclear palsy; details on these and on antibodies and procedures used for digital pathology and morphometry analysis are provided in the online Supplementary file Methods and Tables S1 and S2 and Figs. S1–S3.

Statistical analysis

Chi^2^ test was used to compare the frequency of presenting clinical symptoms and the level of vascular pathology using the vascular cognitive impairment neuropathology guidelines (VCING).^17^ Kruskal–Wallis and Mann–Whitney U tests were performed to compare total p-tau load; microglia load and CD8, CD4, CD3 and CD20 cell densities; semiquantitative p-tau cytopathology scores; grades of small vessel pathology; ratios of neuropathological variables; duration of illness; and age at death between groups and between regions in the same groups. The Spearman correlation test was used to compare the association between pathological variables, duration of illness and age at death. To correct for the effect of progressive supranuclear palsy neuropathological stage,^4^ Alzheimer’s disease neuropathologic change (ADNC) level,^18^ Lewy body disease (LDB) stage,^19^ duration of illness and age at death on the differences between groups, we used the non-parametric analysis of covariance (ANCOVA) and linear regression for cases with progressive supranuclear palsy stage ≥3.

Machine learning analysis

Four separate datasets were analysed: quantification of inflammatory marker-based pathology, cytopathology, sequence of clinical symptoms and the ratios of the inflammatory marker-based pathology. For all datasets except the sequence of clinical symptoms, permutation importance and treeSHapley Additive exPlanations (SHAP) were analysed following random forest classifier with train-test split on normalized, raw data; for details see online Supplementary file Methods.

Supplementary Material

fcag084_Supplementary_Data