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. Author manuscript; available in PMC: 2024 Feb 1.
Published in final edited form as: Mol Immunol. 2022 Dec 24;154:1–10. doi: 10.1016/j.molimm.2022.12.006

Aggregated Alpha-Synuclein Transcriptionally Activates Pro-Inflammatory Canonical and Non-canonical NFκB Signaling Pathways in Peripheral Monocytic Cells

Frank Bearoff 1, Dhruva Dhavale 2, Paul Kotzbauer 2, Sandhya Kortagere 1,*
PMCID: PMC9905308  NIHMSID: NIHMS1861983  PMID: 36571978

Abstract

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by chronic neuroinflammation, loss of dopaminergic neurons in the substantia nigra, and in several cases accumulation of alpha-synuclein fibril (α-syn) containing Lewy-bodies (LBs). Peripheral inflammation may play a causal role in inducing and perpetuating neuroinflammation in PD and accumulation of fibrillar α-syn has been reported at several peripheral sites including the gut and liver. Peripheral fibrillar α-syn may induce activation of monocytes via recognition by toll-like receptors (TLRs) and stimulation of downstream NF-κB signaling; however, the specific mechanism by which this occurs is not defined. In this study we utilized the THP-1 monocytic cell line to model the peripheral transcriptional response to preformed fibrillar (PFF) α-syn. Compared to monomeric α-syn, PFF α-syn displays overt inflammatory gene upregulation and pathway activation including broad pan-TLR signaling pathway activation and increases in TNF and IL1B gene expression. Notably, the non-canonical NF-κB signaling pathway gene and PD genome wide association study (GWAS) candidate NFKB2 was upregulated. Additionally, non-canonical NF-κB activation-associated RANK and CD40 pathways were also upregulated. Transcriptional-phenotype analysis suggests PFFs induce transcriptional programs associated with differentiation of monocytes towards macrophages and osteoclasts via non-canonical NF-κB signaling as a potential mechanism in which myeloid/monocyte cells may contribute to peripheral inflammation and pathogenesis in PD.

Keywords: alpha-synuclein, monocytes, non-canonical NF-κB signaling, peripheral inflammation, preformed fibrils

1. Introduction

Parkinson’s disease (PD) is the second most common neurodegenerative disease with over 1 million in the US and 7–10 million people globally affected by PD and this number is expected to rise to 13M by 2040 (Marras et al., 2018). The etiology of idiopathic PD is currently unknown; however, current knowledge suggests risk is multifactorial with both genetics and environment as factors (Kalia and Lang, 2015). The pathophysiology of dopaminergic neuron neurodegeneration during PD starts within the substantia nigra pars compacta (SN) during early stages of disease, spreading to other regions of the brain during mid and late-stage disease (Swiatkiewicz et al., 2013). The ensuing dopamine deficiency in the basal ganglia results in dysfunction of the nigrostriatal pathway and development of classical parkinsonian motor syndrome characterized by tremors, bradykinesia, gait impairment, and rigidity (Kalia and Lang, 2015). Non-motor symptoms are present in those with PD, some of which may precede motor onset by 10 years, including cognitive impairments, psychiatric disorders such as depression and anxiety, sleep disorders, sexual dysfunction, orthostatic hypotension, and gastrointestinal issues (Chaudhuri and Schapira, 2009, Langston, 2006, Shulman et al., 2011). Currently there are no vaccines or therapeutics that can halt or cure PD. Dopamine agonists, including the gold standard levodopa (L-DOPA), treat motor symptoms through replenishment of extracellular dopamine; however, this approach does not address the neurodegeneration that leads to disease progression (Verschuur et al., 2019).

The gene SNCA encoding alpha-synuclein (α-syn), a short (140 aa) presynaptic protein is widely expressed in the brain and other tissues and polymorphisms associated with this gene are known to increase susceptibility to early on-set PD (Braak and Del Tredici, 2009, Dickson, 2012, Hoenen et al., 2016, Siddiqui et al., 2016, Spillantini and Goedert, 2000, Zhang et al., 2018). In healthy individuals, α-syn is involved in the formation of SNARE complexes in the presynaptic terminal (Burré et al., 2010), however under disease conditions, α-syn can polymerize into β-sheet rich conformational aggregates much like the prion protein (Fujiwara et al., 2002, Lashuel et al., 2013). These aggregated α-syn fibrils are also known to be phosphorylated at residue Serine 129 which likely promotes conformations that can cause cell-cell progression and clumpy inclusion bodies in neurons known as Lewy bodies (LB) (Spillantini and Goedert, 2000). The deposition and location of these α-syn aggregates determine the type of synucleinopathy and hence the disease caused by them. In PD and dementia associated LB, these aggregates are predominantly localized to LB in the neurites while in multiple system atrophy (MSA) the α-syn aggregates are found in cytoplasm of oligodendrocytes (Lau et al., 2020). Preformed fibrils (PFFs) and oligomers of α-syn have been demonstrated to bind toll-like receptor 2 (TLR2) and TLR4, potentially initiating inflammatory signaling cascades which may be responsible for the neuroinflammatory aspects of PD (Codolo et al., 2013, Dzamko et al., 2017, Kim et al., 2013, Lee et al., 2010b, Marques and Outeiro, 2012). CNS exosomal shedding (Shi et al., 2014, Si et al., 2019) of α-syn may initiate these cascades in the periphery as the presence of extracellular α-syn has been reported in plasma (Lin et al., 2017) and CSF (Parnetti et al., 2019). Additionally, genetic associations and clinical presence of IL-1β within LBs further establishes a role of inflammation as a driver of PD susceptibility and progression (Leal et al., 2013). Animal studies indicate peripheral IL-1β expression can exacerbate neuronal injury, suggesting a model of disease in which sustained peripheral inflammation drives neuroinflammation (Gao et al., 2011, Henry et al., 2009, Pitossi et al., 1997). Our studies have focused on the synergy of the TLR:IL-1β signaling axis in driving SN directed neuroinflammation and neurodegeneration. While these preliminary studies have laid a foundation for understanding the increase in proinflammatory profile in PD, there are several gaps in our knowledge to parse out specific cell types in the periphery and in the brain, timing of the inflammatory response, and molecular players that may be developed as therapeutic targets for PD. In this study, we explored the role of monocytes in response to monomers and PFFs of α-syn as a first step towards understanding inflammatory responses in PD.

Monocytes are mononuclear cells of the myeloid lineage found in the periphery with a high degree of plasticity for differentiation to macrophages, dendritic cells, and osteoclasts upon activation via signaling pathways including TLR, NF-κB, and RANK. While not typically found in the CNS, their trafficking across the blood-brain barrier (BBB) via chemokine ligand 2 (CCL2) gradients has been described in neurological disorders including multiple sclerosis (MS), Alzheimer’s disease (AD), amyotrophic lateral sclerosis (ALS), and PD (Savinetti et al., 2021). In the CNS, these monocytes differentiate into macrophages distinct from CNS resident microglia which are of non-hematopoietic origin (Ginhoux et al., 2013). In the context of PD, monocytes isolated from patients are in a hyperactivated pre-conditioned state with a proclivity towards production of inflammatory cytokines including IL-1β, IL-6, IL-8, and CCL2 that correlates with disease severity after stimulation with lipopolysaccharide (LPS) (Grozdanov et al., 2014). This finding suggests the increasing peripheral presence of monocyte activation factors with disease progression. In this study, we evaluated the hypothesis that α-syn PFFs are a monocyte activating factor via global transcriptional profiling of THP-1 monocytic cells exposed to α-syn PFFs.

2. Materials and methods

2.1. Generation of α-synuclein monomeric and α-synuclein PFFs

α-syn monomers and fibrils were prepared as previously described (Bagchi et al., 2013, Chu et al., 2015, Dhavale et al., 2017). Briefly, E. Coli were transformed with a pRK172 plasmid containing the human α-syn construct and grown in sterilized TB broth with 50 μg/ml ampicillin overnight. The human α-syn monomer protein was then extracted using osmotic shock, purified using heat denaturation to precipitate heat sensitive proteins, followed by ion-exchange chromatography. Monomer preparations were dialyzed in 1X Dulbecco’s PBS without calcium and magnesium (Invitrogen 14190-136) and frozen at −80 °C. The concentration of LPS was measured at 2.5 EU/ml in a representative sample of 11.8 mg/ml purified a-syn monomer, using the Endosafe® nexgen-PTS™ testing kit (Charles River Laboratories) with cartridge sensitivity of 5–0.05 EU/mL. Effective concentration of LPS in 10 μg/ml and 1 μg/ml α-syn culture conditions were 0.21 pg/ml and 0.021 pg/ml respectively.

To prepare recombinant α-syn PFFs, purified recombinant α-syn monomer (2 mg/ml) was incubated in 20 mM Tris-HCl, pH 8.0, 100 mM NaCl for 72 h at 37°C with shaking at 1000 rpm in an Eppendorf thermomixer and concentration determined by BCA protein concentration assay. The PFFs were then aliquoted into 1.5 ml tubes with each containing 100 μL of the PFFs at a concentration of 0.96 mg/ml optimal for storage in a solution of 20 mM Tris-HCl, pH 8.0, 100 mM NaCl. Both monomers and PFFs were stored at −80 °C until further use. Prior to use in cell culture experiments, fibrils were centrifuged at 18,000 × g for 15 min and then resuspended in sterile 1X Dulbecco’s PBS without calcium and magnesium (Invitrogen 14190-136) to separate monomers from PFFs.

2.2. Characterization of amplified fibrils via negative stain transmission electron microscopy (TEM).

Negative staining of the fibrillar alpha-synuclein (PFF-Syn) was performed by applying a PFF solution to Ultrathin Carbon 300 mesh Gold grids (01824G, Ted Pella). The grids were negatively glow discharged (13mA, 45sec) using GloQube glow discharge system (Model #025235 EMS). A 10 μL fibril sample at appropriate dilution was applied to the glow discharged grid for 5 minutes with carbon side facing the sample drop. Post-sample incubation, the grid was washed (6 times) with 50 μL of H20, washed once with 50uL of 0.75% uranyl formate and stained with 50uL of 0.75% uranyl formate for 3 minutes. The grids were blotted using filter paper, leaving a small amount of stain to air dry on the grid surface. Grids were imaged on a JEOL 1400 TEM operating at 120kV to visualize negatively staining fibrils.

2.3. Cell Culture

THP-1 cells were cultured in suspension and maintained in T-175 culture flasks between 1×105 and 1×106 cell/ml in RPMI 1640 medium containing 10% FBS, 1% GlutaMAX (Thermo-Fisher), and 1% Penicillin-Streptomycin (Thermo-Fisher). For α-syn assays, 5×105 THP-1 cells were exposed to α-syn monomers or PFFs for 24-hrs in a total volume of 2 ml in 6-well plates. Resuspended α-syn monomers and PFFs were triturated vigorously before addition to cells to ensure no large clumps were present (Supplemental Figure 1). Confirmatory experiments indicate trace LPS (0.21 pg/ml) present from α-syn production has no effect on expression of inflammatory cytokine expression compared to PBS vehicle via qRT-PCR (Supplemental Figure 2).

2.4. RNASeq

RNA was purified from cultured cells using TRIzol reagent and cDNA libraries were prepared using PolyA selection. The average RIN score was 7.7 and DV200 was 82.2%. Libraries were then sequenced on an Illumina HiSeq instrument with 2×150bp reads. Raw reads were mapped and quantified against the GRCh38 reference transcriptome using Salmon (Patro et al., 2017), imported into R via tximport (Soneson et al., 2016), and assessed for differential expression (DE) with DESeq2 (Love et al., 2014), correcting for multiple hypothesis testing by controlling the false discovery rate (Benjamini and Hochberg, 1995). Transcripts without a HGNC symbol were discarded and genes meeting the following criteria were defined as DE: padj < 0.05, |log2FC| > 1, baseMean > 10. Sample information and QC parameters are indicated in Supplemental Table 1.

2.5. GO Enrichment Analysis

DE gene sets were submitted to g:Profiler g:GOSt (Raudvere et al., 2019) with a threshold of 0.05 for retrieval of GO:MF, GO:BP, and GO:CC terms.

2.6. Pathway Analysis

Independent up and downregulated DE gene lists were used as input to g:Profiler g:GOSt (Raudvere, Kolberg, 2019) against the MSigDB canonical pathways v7.5.1 gene set (2982 genes) (Liberzon et al., 2011) using the gprofiler2 package for R (Kolberg et al., 2020) with a p-value threshold of 0.05. Pathway core gene networks and GO process enrichment were created in Cytoscape (Shannon et al., 2003) using the STRING protein-protein interaction database (Szklarczyk et al., 2021). Separate up and down regulated networks were constructed, filtered for largest subnetwork, and then connected using a maximum of 10 interactors directly connected to the main networks. Code used for analysis is deposited at https://github.com/fbearoff/PD_AS_RNASeq.

2.7. Cell Enrichment Analysis

Normalized transcript abundances were compared to the LM22 dataset over 1000 permutations using CIBERSORT (Newman et al., 2015). Transcriptional proportions were analyzed in Graphpad Prism 8.4.3 using multiple t-tests corrected using a false discovery rate value of 5% (Benjamini et al., 2006).

2.8. GWAS Comparison

DE genes were compared against the list of nearest genes to SNPs of interest from a list of genes reported in a study by the International Parkinson’s Disease Genomics Consortium (IPDGC) (Grenn et al., 2020).

3. Results

THP-1 cells were cultured for 24-hrs in the presence of α-syn monomers (10μg/ml), PFFs (1 and 10μg/ml), or PBS which was used as vehicle control. RNA was collected for RNAseq and sequencing was performed as described in Methods. Principal component analysis (PCA) was first performed for data quality assessment and detection of outliers on the top 500 most variable genes (Supplemental Figure 3, Supplemental Table 1). The first principal component axis (PC1) accounts for 72% of variation and corresponds to the treatment condition, with the 10 μg/ml dose of α-syn PFFs having the greatest separation from the vehicle/α-syn monomer groups. Data for the 1 μg/ml dose of α-syn PFFs falls in between the control and 10 μg/ml PFF condition as expected. The second principal component axis (PC2) accounted for 6% of variation and within-group variation suggested a potential outliers in the vehicle (FB38-443) and 10 μg/ml PFF (FB38-451) groups; however, plotting of the Cook’s distance (D) (Cook, 1977) indicated no overt outliers with a mean D > 1 (Supplemental Figure 4, Supplemental Table 1). Differential expression (DE) analysis was performed to determine genes that were significantly affected by the monomer and PFF treatments using the DESeq2 package (Love, Huber, 2014) with treatment condition as a contrast. DE genes were defined as padj < 0.05, |log2FC| > 1, and baseMean > 10 to capture transcripts with sufficient expression. Due to the strong overlap via PCA (Supplemental Figure 3), the α-syn monomer and vehicle groups were first compared and found to only differ in expression of 25 transcripts (Supplemental Table 2). Because of the minimal influence of α-syn monomer on THP-1 gene expression, the α-syn monomer group was selected as the basis of comparison for PFF treatment groups. In comparison to α-syn monomer, treatment with the 1 μg/ml dose of α-syn PFF altered expression of 221 genes (Supplemental Table 4) while the 10 μg/ml dose altered 910 genes (Figure 3, Supplemental Table 6). Expression of a majority of the 1 μg/ml dose genes (207) was also altered in the higher 10 μg/ml α-syn PFF condition and 12 of those 14 genes unique to 1 μg/ml PFF treatment were downregulated. Due to the dose dependency of transcriptional responses, the higher dose was selected for further analysis to ensure the greatest amount of signaling alteration was captured.

Figure 3. Top biological pathways enriched by α-syn PFFs.

Figure 3

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In the central bar graph strength of pathway association is plotted on the x-axis for upregulated (right) and downregulated (left) DE gene lists. Pathway data source is indicated by bar color and number of DE genes as a proportion of total pathway genes is indicated in bold parenthesis. The inset histograms indicate the top 10 most frequently occurring terms in downregulated (i) and upregulated (ii) DE lists.

At the 10 μg/ml α-syn PFF dosage there is genome wide expressional alteration of 910 genes, a majority of which are upregulated (668, 73%) compared to downregulated (242, 27%) (Figure 1). The baseMean expression values of these 910 DE genes ranged from 10-62463, with a median of 173 and mean of 912. The most upregulated transcript was CLEC4E (C-type lectin domain family 4 member E) with a log2FC of 7.2 while SMDT1 (single-pass membrane protein with aspartate rich tail 1) was the most downregulated at −23.1. Ordered by significance (adjusted p-value, padj), the top three DE genes were ICAM1 (intercellular adhesion molecule 1), MS4A3 (Membrane-spanning 4-domains subfamily A member 3), and CYBB (cytochrome b-245 beta chain, NADPH oxidase 2) (Figure 2, Supplemental Table 6). Amongst the most significant DE genes there is an even greater proportion of upregulation (42 of 50, 84%) (Figure 2).

Figure 1. α-syn PFFs induce induces differential expression of 910 genes in monocytes.

Figure 1

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RNA expression is upregulated in 668 transcripts and downregulated in 242 by α-syn PFFs in monocytes. Differentially expressed (DE) genes are denoted as orange circles. The top 12 genes in each direction are indicated in bold, fold change (FC) in parenthesis.

Figure 2. Top 50 DE genes sorted by adjusted p-value.

Figure 2

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Color intensity represents scaled transcript abundance, blue is lower and yellow is higher expression. Ranking by adjusted p-value indicated in bold parenthesis.

To further understand the effect of PFF treatment on signaling pathways in monocytes, a functional pathway enrichment analysis was performed. The curated canonical pathways MSigDB gene set consisting of 2982 genes was chosen as a reference and the lists of upregulated and downregulated DE genes were analyzed using g:Profiler g:GOst (Raudvere, Kolberg, 2019). In total there were 214 pathways enriched, all but three of which were derived from the list of upregulated DE genes and found in the Reactome pathway database (Gillespie et al., 2022) (Supplemental Table 8, Figure 3 left horizontal bars). “Hemostasis” is the only pathway unique to the downregulated gene list since “neutrophil degranulation” and “innate immune system” were also enriched by the upregulated gene set. In total, 19 downregulated genes were constituents of enriched pathways and the most frequently occurring were CEACAM6 (carcinoembryonic antigen-related cell adhesion molecule 6) and PRTN3 (proteinase 3) which appeared in all three pathways (Figure 3i). The upregulated DE gene set was enriched for 211 total biological pathways (Supplemental Table 8, Figure 3 right horizontal bars), of which 58 are from Reactome, 88 from WikiPathways (WP) (Martens et al., 2021), 14 from KEGG (Kanehisa et al., 2017), 18 from BioCarta (Nishimura, 2001), 31 from PID (Schaefer et al., 2009), three from MatrisomeDB (NABA) (Shao et al., 2020), and one from Signaling Gateway (Dinasarapu et al., 2011) pathway data sources. Most of these upregulated pathways are pro-inflammatory in nature and involve processes including cytokine signaling, innate immune activation, and pattern recognition receptor signaling among others. Within these upregulated pathways the most frequently occurring genes were members of the NF-κB and AP-1 transcription factor complexes including NFKB1 (nuclear factor kappa B subunit 1), NFKB2 (nuclear factor kappa B subunit 2), NFKBIA (NFKB inhibitor alpha), JUN (Jun proto-oncogene, AP-1 transcription factor subunit), and FOS (Fos proto-oncogene, AP-1 transcription factor subunit) (Figure 3ii). Other highly represented upregulated pathway genes include the cytokines interleukin-8 (IL-8, CXCL8), IL-1β (IL1B), and C-C motif chemokine ligand 2 (CCL2), matrix metalloproteinase 9 (MMP9), and ICAM1.

The STRING protein interaction database (Szklarczyk, Gable, 2021) was next used to construct interaction networks from the most represented downregulated (Figure 4 left) and upregulated (Figure 4 right) pathway genes to determine the relationship of protein products within enriched pathways. The disparate networks were then connected by known and predicted interactors from STRING that had direct connectivity to both networks (Figure 4 middle). Interactor terms include DE genes C-X-C motif chemokine ligand 2 (CXCL2), interleukin 1 alpha (IL1A), and advanced glycosylation end-product specific receptor (AGER) as well as the transcriptionally unaltered genes NLR family pyrin domain containing 3 (NLRP3), IL12B, IL18, and cathelicidin antimicrobial peptide (CAMP). GO enrichment analysis of this master network includes biological processes “Response to lipopolysaccharide” (GO:0032496), “Cell activation” (GO:0001775), “Positive regulation of nf-kappa b transcription factor activity” (GO:0051092), “Positive regulation of leukocyte differentiation” (GO:1902107), and “Cellular response to interleukin-1” (GO:0071347) among others (Supplemental Table 9).

Figure 4. Gene interaction network of α-syn PFF-induced enriched biological pathways.

Figure 4

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Core gene network diagrams were constructed from the most frequently occurring terms in downregulated (left) and upregulated (right) pathways. Known interactors for both networks are indicated (middle). Log2FC of each term is indicated by color of the central circle. Top enriched GO biological processes are indicated in the donut charts.

Further, to determine the cellular phenotype alterations brought about by PFFs, cell enrichment analysis was performed against known transcriptional profiles of 22 other immune cells found in the periphery including T and B lymphocytes, monocytes, and macrophages. The predominant change induced by PFFs was a shift away from a transcriptional profile associated with monocytes (3.7x decrease in PFFs, padj=3.9×10−5) towards one associated with resting macrophages (M0) (13.9x increase in PFFs, padj=2.3×10−5) (Figure 5, Supplemental Table 10). Other macrophage associated transcriptional profiles were also increased by PFFs including M1 macrophages (0.7% vs 0%, padj=9.4×10−4) and M2 macrophages (1.4x increase in PFFs, padj=0.04). Interestingly, PFF signaling was also associated with a shift away from resting mast cells (6.4% in monomers, 0% in PFFs, padj=1.7×10−3) towards a transcriptional profile associated with activated mast cells (0% in monomers, 15.7% in PFFs, padj=1.1×10−4).

Figure 5. α-syn PFFs Induce Macrophage-like Transcriptional profiles.

Figure 5

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The transcriptional signature of α-syn monomer- (left) and PFF-treated (right) monocytes were compared against the LM22 dataset comprised of 22 immune cell subsets using CIBERSORT (Newman, Liu, 2015). Size of block indicates the relative fraction of transcripts mapping to the reference cell type.

Finally, to understand the ramifications of PFF-mediated transcriptional alterations in the context of PD, we compared our results to the recent PD genome wide association study (GWAS) (Grenn, Kim, 2020, Nalls et al., 2019). In total, 13 DE genes were found in the list of nearest genes (301, supplemental table 2 in (Nalls, Blauwendraat, 2019)) to SNPs of interest from the PD-GWAS study (Figure 6). Functional enrichment of this gene list via g:Profiler g:GOSt yielded the GO biological process term “regulation of gamma-delta T cell activation”, the KEGG pathway “C-type lectin receptor signaling pathway”, and Reactome pathway “Nucleotide-binding domain, leucine rich repeat containing receptor (NLR) signaling pathways” (Supplemental Table 11). In total, four genes were contained in these enriched terms including CYLD lysine 63 deubiquitinase (CYLD), early growth response 3 (EGR3), NFKB2, and nucleotide binding oligomerization domain containing 2 (NOD2).

Figure 6. α-syn PFFs Alter PD-GWAS Candidate Expression.

Figure 6

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Comparison of PD GWAS candidates and DE lists reveals 13 common genes, all of which are upregulated (blue) by PFFs except ELOVL3 (orange). Bolded genes are members of enriched terms and pathways.

Discussion

In this study, we treated THP-1 monocytic cells with monomeric and aggregated PFFs of α-syn as an in vitro model system to understand peripheral inflammation in PD. α-syn is primarily expressed in the synaptic terminals of neurons where it is involved in assembly of the SNARE complex and in synaptic transmission and recycling. α-syn is also expressed in other tissues, especially myeloid lineage cells of the immune system including monocytes (Kasen et al., 2022). The function of non-neuronal α-syn is less understood but recent findings indicate monomeric α-syn can solely induce production of inflammatory cytokines including IL-1β, IL-6, and TNF in rat brain pericytes (Dohgu et al., 2019) and IL-6 in human PBMCs (Piancone et al., 2021). Under conditions of PD, extracellular α-syn can be detected in neurons and other CNS cells and its levels are correlative with cognitive decline and CNS toxicity in PD (Lin, Yang, 2017). In our system we found little evidence that monomeric α-syn induces inflammatory gene signaling as few transcripts were altered compared to vehicle control and those the altered transcripts did not include any described inflammatory mediators (Supplemental Table 2) or GO terms related to inflammation (Supplemental Table 3). Indeed, the mechanism of monomeric α-syn proinflammatory signaling in microglia involves Protease-Activated Receptor-1 (PAR-1, F2R) (Lee et al., 2010a), a transcript which had unchanged low levels of expression in all our experimental conditions (Supplemental Table 12).

In contrast to monomer, α-syn PFFs induced strong proinflammatory responses at low (Supplemental Table 4 and 5) and high doses (Supplemental Table 6–7, Figures 1–2) typified by increased expression of TLR and NF-κB signaling pathway members and downstream IL-1β cytokine expression (Figure 3). Other studies have reported similar α-syn induced proinflammatory IL-1β secretion in THP-1 cells (Klegeris et al., 2008), microglia (Pike et al., 2021), and primary human monocytes (Codolo, Plotegher, 2013) through TLR- NF-κB signaling. The specific TLR-dependency of this signaling axis is complicated with contradictory reports of TLR2 (Codolo, Plotegher, 2013, Dutta et al., 2021) and TLR4 (Pike, Varanita, 2021) as necessary for IL-1β secretion, and in another case TLR4 deficiency exacerbating disease in a mouse model of prodromal PD (Venezia et al., 2021). In our study, there was no change in expression of TLR2 or TLR4 receptors with the PFF treatment; however, there was an increase in factors associated with TLR2/4 signaling including CD14 and MD-2 (LY96) (da Silva et al., 2017). Additionally, there was increased expression of TLRs 6–8 and to our knowledge this is the first report of TLR6 alteration by α-syn. The upregulation of these TLRs may be a consequence of IL-1β- and TLR2/4-induced canonical NF-κB upregulation (Lee et al., 2009) and not a direct α-syn interaction or binding. A recent report in a toxin-induced mouse model of PD indicates roles of TLR 7/8 in disease progression via modulation of T cell activation and migration to the SN (Campolo et al., 2020). In another report α-syn was found to induce TLR7 expression in primary microglia (Béraud et al., 2011). Interestingly, TLR7/8-mediated proinflammatory cytokine production by PBMCs was reduced in PD patients suggesting tolerized responses to chronic TLR ligand exposure during active disease (da Silva et al., 2016).

Activation of TLRs and proinflammatory cytokines including IL-1β and TNF leading to engagement of the canonical NF-κB pathway for signaling and dysregulation of the canonical NF-κB pathway pathway has been implicated in the onset of PD. The mechanism by which the canonical NF-κB pathway is activated is dependent on the balance of neuroprotective factor c-REL (REL) and anti-neural resilience factor RelA (Bellucci et al., 2020) binding to p50 (NFKB1) to form the active canonical NF-κB heterodimer which may be altered in PD. In contrast, the non-canonical NF-κB pathway reacts over longer durations and is comprised of a heterodimer of p52(NFKB2) and RelB (RELB) activated by signaling of TNFR superfamily members including RANK (TNFRSF11A) and CD40 (Sun, 2017). Upon PFF treatment, we find upregulation of NFKB1 but no changes in the expressional alteration of canonical pathway members REL and RELA despite increased IL1B and a trend towards an increase of TNF (log2FC=4.7, padj=0.3) expression levels. In contrast, non-canonical members NFKB2 (GWAS candidate), RELB and upstream activators TNFRSF11A and CD40 were upregulated by PFFs. Unaltered REL and RELA expression may be due to increased expression of IκBα (NFKBIA) which is a regulator of the canonical pathway (Yu et al., 2020) and upregulation of the non-canonical pathway may be influenced by earlier activation of the canonical pathway (Basak et al., 2008) that may not be captured at the 24-hour timepoint in our study.

Interestingly, these results support clinical evidence of increased prevalence of osteoporosis in PD that is independent of aging (Van Den Bos et al., 2013) and may be due to brain-bone signaling degradation from the loss of dopaminergic neurons and stimulation of osteoclastogenesis (Handa et al., 2019). Additionally, other studies in animal models of PD have shown that loss of α-syn expression results in a marked reduction in spinal bone loss implicating α-syn as a strong inducer of osteoclastogenesis (Calabrese et al., 2016). Our results show that the colony stimulating factor 1 receptor (CSF1R), a factor associated with non-canonical pathway-mediated differentiation of macrophages and osteoclasts (Sun, 2017) is also upregulated by PFFs. Osteoclasts are myeloid/monocyte lineage cells responsible for bone resorption and wound healing that derive from osteoclast precursor cells (OCPs) which themselves can derive from either monocytes or tissue resident macrophages (Yao et al., 2021). OCPs differentiate into osteoclasts through RANKL (also known as osteoclast differentiation factor), IL-1, and TNF signaling (Boyce et al., 2009). Signaling via these cytokines stimulates c-FOS (FOS), a member of the AP-1 transcription factor along with c-JUN (JUN) which were both upregulated and highly represented among the enriched pathways (Figure 3ii). Downstream of this pathway is MMP9, a signature secretory factor of osteoclasts, was also greatly upregulated by PFFs and highly represented among enriched pathways (Figure 3ii). MMP9 production is inducible in monocytes via exposure to the TLR2/6 synthetic ligand FSL-1 suggesting interplay between these signaling axes (Ahmad et al., 2014).

TNF may modulate non-canonical pathway signaling via RELB and enhance osteoclastogenesis through promoting a proinflammatory shift in the macrophage pool (Zhao et al., 2015). Upon treatment with PFFs the monocyte transcriptional profile primarily shifted towards an increase in M0, M1, and M2 macrophage phenotypes compared to monomeric α-syn treated cells (Figure 5). The largest increase was noted for M0 macrophages which can differentiate into either proinflammatory M1 or anti-inflammatory M2 macrophages dependent on the local cytokine milieu (Bhattacharya and Aggarwal, 2019, Laria et al., 2016). In PFF treated cells there was an increase in M1 associated IL1B and TNF (trending) concomitant with an increase in M2 associated IL10 and TGFB1 indicating no overall dominant macrophage phenotype but an increase in conversion of monocyte to a macrophage-like gene transcriptional profile. The increase in M2 transcriptional profile in response to PFF may also exacerbate disease due to IL-10 (Cockey et al., 2021). α-syn PFFs treatment was also associated with an increase in activated mast cell phenotype as reported by others (Kempuraj et al., 2015) likely due to increases in factors including CCL2 and CXCL8 which are both produced by these myeloid lineage cells. PFFs also induced upregulation of other myeloid/monocyte associated factors that have known roles in migration towards (CD11b, ITGAM) and uptake (CD163) of α-syn (Nissen et al., 2021, Stolzenberg et al., 2017).

Despite the upregulation of IL1B there was no change in NLRP3 expression mirroring results from PBMCs of PD patients (Piancone, Saresella, 2021). NLRP3 is a central interactor connecting upregulated pathway genes with the downregulated catalase (CAT) and myeloperoxidase (MPO) genes (Figure 4). Both CAT and MPO were found in the downregulated “Neutrophil degranulation” pathway along with other azurophilic granule components including cathepsin G (CTSG) and proteinase 3 (PRTN3) suggesting a loss of these granules (van der Veen et al., 2009) during PFF stimulation independent of NLRP3 processing of IL-1β. Notably, the serine proteinase inhibitor (SERPINB10) was also downregulated suggesting a NLRP3-independent serine protease-dependent pathway for processing IL-1β that has been described in THP-1 cells (Mizushina et al., 2019, Netea et al., 2010). The PFF-upregulated GWAS candidate NOD2 may also contribute to this increase in IL1B via synergy with TLR7/8 (Schwarz et al., 2013) potentially regulating gamma-delta T cell activation (Serrano et al., 2020), a biological process we also found to be enriched in PFF-induced monocyte GWAS candidates (Supplemental Table 11). The levels of gamma-delta T cells are altered during PD with decreased levels in the blood and higher activated cells in the CSF suggesting CNS migration upon peripheral activation, potentially via factors produced by monocytes (Fiszer et al., 1994, Li et al., 2021, Zhou et al., 2020).

A limitation of this study is that the 10 μg/ml dosage of α-syn chosen may not represent the physiological concentration encountered by monocytes in peripheral tissue. The concentration of α-syn species in various anatomical locations is highly debated and study-dependent ranging from pg-μg/ml quantities in CSF, plasma, and red blood cells (Magalhães and Lashuel, 2022). There was a strong overlap of DE genes and pathway utilization at the lower 1 μg/ml concentration, although the effect was not as exaggerated as at the higher concentration. Contextually, many of the pathways we found altered by PFFs are perturbed in other chronic inflammatory disorders, especially non-canonical NF-κB signaling. Further validation of prolonged exposure to lower concentrations of α-syn PFFs, as may be the case in PD, may reveal similar transcriptional changes as to our findings at 24-hrs. Validation of these results in primary human monocytes may reveal other PFF-induced transcriptional alterations as THP-1 cells, while of human origin, may not completely encapsulate the physiological response to α-syn PFFs. Lastly, while this study is confined to transcriptional alterations induced by PFFs, protein-level alterations will need to be confirmed as many of these pathways contain signaling proteins with significant post-transcriptional regulation.

4. Conclusion

In summary, we have shown that α-syn PFFs are sufficient to induce an inflammatory gene expression phenotype in monocytes absent pathogenic stimulation through TLR-mediated pathway utilization. This phenotype is characterized by a relative shift towards non-canonical NF-κB signaling, potentially through crosstalk from the canonical NF-κB pathway, typified by RANK signaling. The non-canonical NF-κB complex in concert with AP-1 induces expressional programs resulting in a shift towards a macrophage/osteoclast gene signature including MMP9. Several genes involved in this response to α-syn PFFs are known PD GWAS candidates including NFKB2, a major constituent of the non-canonical NF-κB pathway, as well as NOD2 which may modulate monocyte-mediated gamma-delta T cell activation. The transcriptional phenotype elicited by α-syn PFFs suggests several mechanisms in which myeloid/monocytes may contribute to peripheral inflammation and pathogenesis in PD.

Supplementary Material

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Highlights.

  • α-synuclein pre-formed fibrils (PFFs) induces inflammatory signaling in monocytes

  • Non-canonical NFKB signaling is induced by α-synuclein PFFs

  • α-synuclein PFFs activate RANK pathway signaling

  • Macrophage and osteoclast transcriptional profiles are induced by α-synuclein PFFs

Acknowledgements:

This study was supported in part by funding from Drexel Coulter translational fund to SK and by a grant from the National Institutes of Health (NS110436) to PK.

Footnotes

Competing Interests: The authors have no competing interests to declare that are relevant to the content of this article.

Financial interests: SK is the co-founder of PolyCore Therapeutics Inc and holds equity in the company. PolyCore Therapeutics Inc did not fund the study and had no role in the study. All other authors declare they have no financial interests.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Supplementary Materials

1
NIHMS1861983-supplement-1.xlsx (10KB, xlsx)
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NIHMS1861983-supplement-2.xlsx (14.6KB, xlsx)
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NIHMS1861983-supplement-3.xlsx (10.6KB, xlsx)
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NIHMS1861983-supplement-6.xlsx (211KB, xlsx)
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NIHMS1861983-supplement-7.xlsx (168.9KB, xlsx)
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NIHMS1861983-supplement-8.xlsx (33.2KB, xlsx)
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NIHMS1861983-supplement-11.xlsx (9.2KB, xlsx)
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NIHMS1861983-supplement-12.xlsx (4.8MB, xlsx)
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NIHMS1861983-supplement-13.docx (644KB, docx)

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