A monocyte gene expression signature in the early clinical course of Parkinson’s disease

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Study population

The study cohort consisted of ten male PD patients and ten male controls (Table 1). Average age between both groups was not significantly different, although the mean of the control group (61.8 years) was slightly greater than the mean of the PD group (58.6 years). We included only PD patients with a disease duration of three years or fewer. All together, eight patients were categorized to be in an early stage of the disease (Hoehn and Yahr stages 1 and 2) and two patients were classified as Hoehn and Yahr stage 3, indicating an intermediate stage of the disease. The short disease duration was also reflected by a low motor score on part III of the Unified Parkinson’s Disease Rating Scale (UPDRS-III, 15.7 ± 2.2) and a low levodopa equivalent daily dose (LEDD, 309.5 ± 64.6).

Table 1 Study population.

Gene expression signature of human monocytes

We first determined the transcriptome profile of human monocytes isolated from 10 male control individuals (Fig. 1). To this aim, total monocytes were isolated and enriched from peripheral blood mononuclear cells (PBMCs) by density-gradient centrifugation and further purified by untouched magnetic bead isolation. By choosing this strategy, we excluded non-monocyte cell subpopulations such as T cells, NK cells, B cells, dendritic cells, eosinophils, and basophils without directly labeling and potentially activating monocytes. Gene expression signature of human control monocytes was analyzed using edgeR36. Monocytic transcriptomic data indicated low to absent expression levels of non-monocyte cell markers (Fig. 1A). Relative expression values for the 30 most highly expressed genes in monocytes across each of control individual are illustrated in Fig. 1B, indicating substantial individual variation for some transcripts. This analysis showed that human monocytes expressed very high levels of numerous genes associated with antigen processing and presentation (IFI30, HLA-DRA, HLA-A, HLA-B, HLA-C) and regulation of the innate immune response (LYZ, S100A9, ACTB, S100A8, S100A4, TYROBP, CD14). In addition, FOS and JUNB as members of the AP-1 transcription factor family were highly expressed in monocytes.

Figure 1
Comparison of control human monocyte and microglia transcriptomes. (A) Gene expression of selected genes associated with various immune cell types in monocytes isolated from healthy controls revealed by RNA-seq. (B) Gene expression of the 30 most highly expressed genes in human control monocytes from individual control patients. (C) Heat map of mRNA expression values determined in monocytes from 10 controls and in microglia from 19 controls14. 3054 genes exhibited > two-fold higher average expression in monocytes compared to microglia. (D) Gene Ontology enrichment analysis of the top 1000 genes most preferentially expressed in monocytes supports their role as effector cells in immune response. (E) Heat map of mean mRNA expression levels of selected monocyte and microglia genes. RBC – red blood cells; HPC – hematopoietic precursor cells; CD – cluster of differentiation; TPM – transcripts per kilobase million.

Monocytes and microglia are both classified as myeloid cells, but reside in very distinct compartments. To test the impact of the respective niche, we compared the gene expression pattern of monocytes and microglia from control individuals. We applied a cutoff of two-fold change in expression in monocytes relative to microglia at a false discovery rate (FDR) of 0.05. A total of 3054 genes were up-regulated and 3461 genes were down-regulated in monocytes compared to microglia (Fig. 1C). Gene enrichment analysis for the top 1000 genes most preferentially expressed in monocytes compared to microglia showed a strong association to processes involved in blood coagulation and wound healing, regulation of immune response, including leukocyte migration (Fig. 1D). Gene ontology enrichment analysis for the top 1000 genes most preferentially expressed in microglia compared to monocytes included synaptic transmission (logP −8.4) and developmental growth involved in morphogenesis (logP −7.2). Genes that were highly expressed in monocytes compared to microglia included S100A8, S100A9, S100A12, LYZ, CCR2, CD36, FCN1, and VCAN (Fig. 1E). In contrast, TREM2, SALL1, TMEM119, MERTK, MLXIPL, and CX3CR1 were increased in microglia compared to monocytes.

Routine peripheral laboratory tests and distribution of monocyte subpopulation

Next, we compared standard laboratory parameters and distribution of monocyte subpopulations within the present cohort (Fig. 2). Total monocyte and lymphocyte numbers, as well as C-reactive protein (CRP) and tumor necrosis factor (TNF)-α, interleukin (IL)-6, IL-8 cytokine levels in the peripheral blood were unchanged between early course PD patients and controls (Student’s unpaired t-test, p-value > 0.05; Fig. 2). After untouched selection with magnetic beads, flow cytometry analysis based on CD14 and CD16 expression (Fig. 3A,B) revealed no significant differences in the distribution of classical (CD14+CD16), intermediate (CD14+CD16+), and non-classical (CD14dimCD16+) monocyte subpopulations between PD and controls (Fig. 3C).

Figure 2
Figure 2
Results of the routine laboratory tests of peripheral blood samples. The routine laboratory tests for monocytes (A) and leukocytes (B) were not changed between PD and control peripheral blood samples. Whereas there were no statistically significant differences in CRP (C), TNF- α (D), IL-6 (E), and IL-8 (F) levels, variability was increased in the PD cohort. Results are depicted as mean ± SEM. To test for significant differences, Student t-test was performed.
Figure 3
Figure 3
Flow cytometry data analysis of human monocyte subsets in early-course PD and control individuals. (A) After density gradient, PBMCs were visualized using Forward Scatter (FSC) vs. Side Scatter (SSC). Monocytes were enriched from PBMCs by negative isolation using magnetic beads. More than 90% of isolated monocytes were viable as determined by gating in FSC vs. SSC (B) Gating strategy for monocyte characterization and separation in classical CD14+CD16, intermediate CD14+CD16+, and non-classical CD14dimCD16+ subpopulations. (C) Quantification of monocyte subpopulation based on CD14 and CD16 expression revealed no significant differences between PD and control individuals. Mean ± SD, Student t-test, p > 0.05.

Comparison of transcriptome profiles of monocytes between PD and control individuals

Next, we asked whether monocytes isolated from individuals in the early clinical stage of PD show a distinct disease-associated gene expression signature compared to control subjects. Hierarchical clustering of the expression of genes expressed at 10 transcripts per kilobase million (TPM) or greater in at least one sample in monocytes from PD patients and control individuals is illustrated in Fig. 4A (expression values are normalized to fall between 0 and 1). Notably, the expression level of each gene varied considerably both across all individuals and within the control and PD groups. Next, we sought to identify differentially expressed genes in monocytes isolated from PD and control patients using edgeR. EdgeR is a conventional RNA-seq analysis software tool that models expected noise and variability in RNA-seq replicate experiments in order to identify differentially expressed genes. With a fold-change of two as a cut-off and FDR set at 0.05, edgeR identified a total of 52 genes (lower right corner, Fig. 4B). Given the considerable variability in gene expression we had observed within each group of patients, we considered the robustness of the edgeR to outlier gene expression values. To assess the robustness of the edgeR results, we created five cross validation sets by randomly excluding two PD patients and two controls from each cross validation set, and identified differentially expressed genes for each cross validation set using edgeR by selecting genes that had a FDR less than 0.05. The heat map depicted in Fig. 4B shows how many differentially expressed genes reported by edgeR overlap between cross validation sets and the entire cohort. For example, of the 13 differentially expressed genes found for cross validation set 1 and the 39 for cross validation set 3, we observed an overlap of 12 genes. Thus, edgeR results are not robust when examined under cross validation conditions.

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