Age-related nigral downregulation of the Parkinson’s risk factor FAM49B primes human microglia for inflammaging

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Park, Colista West, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6925731/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Dec, 2025 Read the published version in npj Aging → Version 1 posted 9 You are reading this latest preprint version Abstract Parkinson’s Disease (PD) is characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc), which is associated with changes in microglia function. While age remains the biggest risk factor, the underlying molecular cause of PD onset and its concurrent neuroinflammation are not well understood. Many identified PD risk genes have been directly linked to dopamine neuron impairment, while others are linked to immune cell function. In this study, we found that the PD risk gene FAM49B is critically expressed in microglia of the human SNpc and is downregulated with age. We utilized human and murine microglia cells to demonstrate the role of FAM49B in regulating fundamental microglial functions such as cytoskeletal maintenance, migration, surface adherence, energy homeostasis, endocytosis, and, importantly, inflammatory response. Downregulation of microglial FAM49B, as observed in the SNpc of aging individuals, led to significant alterations in these cellular functions, ultimately resulting in microglia impairment and over-responsiveness. Thus, our study highlights novel cell type-specific roles of FAM49B and provides a potential mechanism for susceptibility to neuroinflammation, and reactive gliosis observed in both PD and normal aging. Biological sciences/Neuroscience/Diseases of the nervous system/Parkinsons disease Biological sciences/Physiology/Ageing Biological sciences/Cell biology/Senescence Biological sciences/Cell biology/Organelles/Mitochondria Biological sciences/Neuroscience/Neural ageing Health sciences/Pathogenesis/Inflammation Parkinson’s Disease Microglia FAM49B Gene Expression Regulation Neuroinflammation Mitochondria Senescence Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Parkinson’s disease (PD) is the second most common neurodegenerative disorder, primarily characterized by the progressive degeneration of dopaminergic (DA) neurons in the substantia nigra pars compacta (SNpc) which leads to characteristic motor impairments (Dauer and Przedborski, 2003 ; Moore et al., 2005 ). Aging is the most relevant risk factor for PD, with its incidence increasing strongly after the age of 60 (De Lau and Breteler, 2006 ). While rare monogenic forms of PD are caused by mutations in genes that impair DA neurons, such as SNCA , PRKN , PINK1 , LRRK2 , and PARK7 (Martin et al., 2011 ), genes identified in genome-wide association studies (GWAS) suggest that cells of both the adaptive and innate immune system also play crucial roles in PD pathogenesis (Chang et al., 2017 ; Hamza et al., 2010 ; Holmans et al., 2013 ; Kam et al., 2020 ; Pierce and Coetzee, 2017 ). Accordingly, neuroinflammation, including microgliosis, has been described in PD for many decades (McGeer et al., 1988 ). A causal relationship between glial activity and PD has been proposed such that, inflammaging, a low-grade inflammation resulting from an imbalance between pro- and anti-inflammatory signals and cellular senescence, can drive DA neuron loss in the SNpc (Calabrese et al., 2018 ; Riessland et al., 2019 ; Russo and Riessland, 2022 ). Accordingly, recent studies indicate that microglial activation in PD is not a secondary response to neuronal loss but may actively contribute to neurodegeneration as a primary trigger. For example, α-synuclein, a protein central to PD pathology, can be taken up by microglia, triggering a TLR-Fyn-PKCδ-NFκB-mediated inflammatory response that exacerbates neuronal damage (Gordon et al., 2016 ; Panicker et al., 2019 ). While not completely understood, it has been shown that microglial phenotypes vary across brain regions and can shift in response to aging and disease states (Hammond et al., 2019 ). Relevant to PD, microglia in the vulnerable SNpc exhibit a transcriptional profile distinct from those in other CNS regions, with a predisposition toward a pro-inflammatory state (De Biase et al., 2017 ; Olah et al., 2018 ). This regional specificity has been proposed to contribute to the selective vulnerability of SNpc DA neurons in PD (Surmeier et al., 2017 ). Interestingly, due to their close interaction, inflammatory signals from microglia can drive astrocytes toward a neurotoxic phenotype, further amplifying neuronal damage (Liddelow et al., 2017 ). Given the growing evidence linking microglial dysfunction to PD, targeting neuroinflammation has emerged as a potential therapeutic strategy for the disease. Anti-inflammatory treatments, such as PKCδ and NFκB inhibitors, have shown promise in preclinical PD models by reducing microglial activation and slowing disease progression (Gordon et al., 2016 ; Singh et al., 2020 ). Further understanding of the complex interplay between aging, microglia, neuroinflammation, and PD pathology may provide new insights into disease mechanisms and therapeutic targets for the disease. In the present study, we investigated the genetic risk factor FAM49B, which was previously identified by a GWAS (Nalls et al., 2014 ), and found that it contributes to the development of inflammaging in PD by regulating microglial activation. FAM49B (family with sequence similarity 49 member B; also known as CYRIB (CYFIP-Related Rac1 Interactor B)) has been characterized as a mitochondria-localized protein regulating mitochondrial function and cancer progression (Chattaragada et al., 2018 ) and inhibiting T cell activation by repressing Rac activity and modulating cytoskeleton reorganization (Shang et al., 2018 ). While FAM49B is critical for T cell function (Park et al., 2024 ) and has been suggested to be a marker of homeostatic microglia (Trainor et al., 2024 ), neither its cellular function in microglia cells nor its role in brain inflammaging has been evaluated. In brief, using computational analysis based on human brain gene expression data, we identified an age-related downregulation of FAM49B, specifically in microglia in the human SNpc. To further evaluate whether this age-related downregulation led to increased immunological response and microgliosis, we conducted functional experiments using microglial cell lines with FAM49B knockdown/knockout. Interestingly, the reduction of FAM49B induced microglia-relevant reactivity and significant metabolic changes, including dysfunctional oxidative phosphorylation and glycolysis, as well as modifications in cytoskeletal dynamics, migratory velocity, and adhesion capacity. Importantly, compared to controls, FAM49B-deficient microglia showed noticeably higher immunological activation. Our results imply that FAM49B is essential for regulating microglial immune responses. This informs the hypothesis that age-related downregulation of FAM49B in the SNpc may predispose microglia to an overactive gliosis phenotype, aggravating dopaminergic neuron loss and accelerating PD. Crucially, the elevated immunological activity that we observed in our FAM49B-deficient microglial cells was successfully decreased by treatment with anti-inflammatory NFκB inhibitor treatment. Based on our results, FAM49B is a critical modulator of microglial immunological response. Consequently, its age-related downregulation could contribute to inflammaging and the subsequent pathophysiology of PD. Finally, anti-inflammatory treatment to target this pathway may present a viable strategy for reducing neuroinflammation and preserving dopaminergic neurons in PD. RESULTS Downregulation of FAM49B in microglia with age in the human substantia nigra FAM49B has been identified as a genetic risk factor for PD (Nalls et al., 2019) ( Figure S1b ), however, neither its role in PD pathology nor in which relevant cell type it is expressed is known. To investigate the cellular context of FAM49B expression, we analyzed bulk RNA-sequencing data from 114 human SNpc samples obtained from the Genotype-Tissue Expression (GTEx) Project (Consortium et al., 2020). Cell type proportions for each sample were computationally inferred by correlating bulk expression profiles with reference signatures of major brain cell types, including astrocytes, microglia, neurons, endothelial cells, and oligodendrocytes (see Methods). This analysis revealed that FAM49B expression was most significantly correlated with the estimated proportion of microglia (Wald test, p=4.1x10 -7 ), suggesting that FAM49B is primarily expressed in these resident immune cells of the SNpc ( Figure 1a, other cell types shown in Figure S1a ). This finding is further supported by single-cell sequencing data from human SNpc tissue, which confirmed that FAM49B expression is highest in microglia ( Figure 1b , GSE140231 (Agarwal et al., 2020)). Moreover, after correcting for potential confounders such as cell type composition, we observed a significant negative correlation between FAM49B expression and donor age, indicating an age-associated downregulation of FAM49B in microglia (Wald test, p=3.0x10 -4 , Figure 1c ). In summary, our integrative transcriptomic analysis indicates that FAM49B is predominantly expressed in microglia within the human SNpc and is significantly downregulated with aging. One hallmark of brain aging is the accumulation of senescent cells, including senescent glial cells, which contribute to neuroinflammation and inflammaging in the midbrain. Pro-inflammatory senescent brain cells have also been associated with PD (Chinta et al., 2018; Riessland et al., 2019). Hence, we sought to determine if senescence in human microglia could alter the expression of FAM49B and utilized our previously reported DNA damage-induced senescent HMC3 cells as an in vitro model of senescence (Russo et al., 2025). The senescence phenotype was validated by a battery of molecular methods (see detailed characterization: (Russo et al., 2025)). Importantly, these senescent microglia showed significant downregulation of FAM49B ( Figure 1d ), indicating a possible role of senescence in the inflammaging phenotype of SNpc microglia. Reduced expression of FAM49B in microglia results in metabolic changes To determine the function of FAM49B in human microglia, we employed the commonly used human HMC3 cell line (Dello Russo et al., 2018) and generated a CRISPR/Cas9-mediated knockout clone. After confirming the knockout, bulk RNA sequencing was performed to compare the gene expression of wild-type (WT) and knockout (KO) HMC3 cells ( Figure 2a ). Assessment of the gene expression profiles revealed significant global expression changes and substantial alterations in mitochondrial genes ( Figure 2b, c, S2a, b). These changes are of particular interest since FAM49B has recently been reported to be involved in the regulation of mitochondrial function and integrity in pancreatic ductal adenocarcinoma cells (Chattaragada et al., 2018). However, the role of FAM49B in microglia in the context of PD has yet to be characterized. Thus, we applied diverse methods to investigate the metabolic changes that occur in FAM49B-KO microglia. A Seahorse real-time cell metabolic analysis (XF Mito Stress Test) revealed that the KO of FAM49B in HMC3 microglia caused a significant decrease in basal and maximal respiration, ATP production, and spare respiratory capacity ( Figure 2d-h ) while not affecting non-mitochondrial oxygen consumption and coupling efficiency ( Figure S2c, d ) . In addition, the overall energy production was impaired as suggested by a significant change in glycolysis gene expression ( Figure S2g ). To test whether glycolysis function was impaired, we performed a 2-deoxyglucose assay, which leads to an increase in cell death if cells utilize glycolysis. Interestingly, the FAM49B-KO microglia cells were less susceptible as compared to the WT microglia (which partially depend on glycolysis (Sangineto et al., 2023)) in this assay, further suggesting a partial impairment of the glycolytic pathway ( Figure 2i ). To confirm these findings in an additional microglia model, mouse BV2 cells were used. An siRNA-mediated knockdown (KD) of Fam49b ( Figure 2j ) significantly reduced the membrane potential of mitochondria in these cells, while the overall organelle number was not affected ( Figure 2k, S2e ). Using immunogold labeling in combination with transmission electron microscopy revealed that Fam49b localized to mitochondria (~50% of all Fam49b molecules were localized to mitochondria). Interestingly, triggering an immune response in BV2 cells by lipopolysaccharide (LPS) treatment resulted in a reduction of Fam49b in mitochondria ( Figure 2l ). We also found a significant upregulation of free radicals produced in Fam49b-KD cells ( Figure S2f ), an additional indication of mitochondrial impairment. In summary, the reduction of FAM49B significantly impaired the metabolism of both mouse and human microglia cells. Interestingly, this metabolic change partially resembles microglia that face pro-inflammatory stimuli and prepare to respond (Yang et al., 2021). FAM49B-KO and KD microglia show alterations in cytoskeletal dynamics and migration Microglia exhibit significant mobility and are considered the most dynamic cells of the healthy mature brain (Paolicelli et al., 2022; Tremblay, 2011). They are constantly active and survey their environment by extending and retracting their processes to detect changes, such as neuronal damage, infection, or inflammation. When responding to inflammatory cues, microglia can adopt an amoeboid morphology, increase migratory and endocytic capacities, and move toward areas of damage or pathology (Paolicelli et al., 2022). Microglia are responsible for the elimination of microbes, dead cells/cell debris, excessive synapses, protein aggregates, and other antigens that may endanger the CNS (Colonna and Butovsky, 2017). Recently, it has been reported that microglia can use tunneling nanotubes to interact with other microglia or neurons to protect them from aggregate-induced cellular dysfunction and death (Scheiblich et al., 2024). In order to complete these tasks, cytoskeletal remodeling is essential, as microglia have to extend their processes, or nanotubes, and migrate towards the cellular debris to phagocytose it (Smolders et al., 2019). Given these critical functions and the previously published findings that FAM49B is a regulator of actin dynamics (Shang et al., 2018) and directly interacts with activated Rac Family Small GTPase 1 (RAC1), known to regulate cytoskeleton organization (Bosco et al., 2009), we investigated the impact of FAM49B loss on cytoskeletal dynamics and migration in our microglial cell lines. Indeed, gene expression profiling of genes related to cytoskeletal dynamics revealed striking rearrangements, indicating an effect on cell migration, adhesion, and other cytoskeletal functions, such as endocytosis ( Figure 3a S3a, c ). Interestingly, while WT microglia displayed a ramified morphology consistent with a surveillant state, FAM49B-KO induced a transition to an amoeboid ( Figure 3b ), reactive phenotype, additionally marked by upregulation of IL-8 ( Figure 5b ). In line with this finding, it was previously reported that FAM49B-KO in human melanoma cells induced a round shape and increased migration speed (Fort et al., 2018). Employing a trans-well assay, we assessed the migration across a porous mesh. Interestingly, the HMC3 FAM49B-KO and the BV2 Fam49b-KD cells migrated more efficiently than their respective controls, suggesting an increase in migration activity ( Figure 3c ). Based on relevant gene expression changes (adhesion genes heatmap: Figure S3a ), we aimed to evaluate the differences in adhesion capabilities. To assess adhesion differences, we utilized two experimental approaches. First, we employed an impedance-based assay. BV2 and HMC3 cells were plated in equal numbers onto an impedance plate, which measures the cell index based on electrical impedance, a direct indicator of adhesion. We tracked impedance changes from the moment cells were plated until they reached confluency. Both Fam49b-KD BV2 cells and FAM49B-KO HMC3 cells adhered at a significantly slower rate than control cells ( Figure 3d, e ). A similar trend was confirmed with a counting assay performed in the HMC3 cells. Equal numbers of cells were plated overnight, and the following day, attached cells were counted and compared to live cells remaining unattached in the supernatant. FAM49B-KO cells displayed delayed adhesion to the culture plate surface ( Figure S3b ). Next, we examined microglial endocytosis, a process that requires significant cytoskeletal rearrangement. As the primary macrophage of the CNS, endocytosis is a crucial function of microglia (Li and Barres, 2018; Solé-Domènech et al., 2016). Since we observed that endocytosis-related genes were differentially expressed in FAM49B-KO HMC3 cells, we hypothesized that their endocytic capacity might be compromised (endocytosis genes heatmap: Figure S3c ). Applying fluorescence-labeled dextran and microscopy-based analysis, we determined that dextran uptake was significantly reduced in Fam49b-KD and FAM49B-KO microglia ( Figure 3f, g ). In summary, our findings indicate that microglia lacking FAM49B exhibit significantly impaired adhesion and endocytosis, both critical hallmarks of microglial function. Additionally, we observed that KO and KD cells migrated faster than WT cells, which is in line with an activation phenotype and their reduced adhesion, which may lower resistance to movement. Exposure of FAM49B-KO microglia to DA neuron cultures triggers a global immune response In the midbrain, activated microglia release molecules that can activate a response in surrounding cells. Activation of astrocytes by microglia, for example, can exacerbate neuroinflammation (Liddelow et al., 2017). In PD models, it has been suggested that α-synuclein enhances microglial motility and that microglia-derived TNF-α induces astrocyte migration (Burda and Sofroniew, 2014; Campolo et al., 2019). Since we observed a significant increase in migratory behavior in FAM49B-KO cells ( Figure 3 ), we sought to investigate their impact on the midbrain environment, including DA neurons, glial cells and fibroblasts. Using human stem cell-derived DA neuron cultures that include glial cells, epithelial cells and perivascular fibroblasts (Kim et al., 2021), we transferred either WT or FAM49B-KO HMC3 cells onto these cultures and monitored overall migratory behavior. Interestingly, we observed a significant increase in motility when FAM49B-KO cells were plated onto the cultures as measured by live imaging for 36 hours ( Figure 4 ). Additionally, we have previously found that depletion of the PD risk gene SATB1 induced a senescence phenotype in DA neurons, leading to a significant microglial immune response in the midbrain (Riessland et al., 2019), thus, we sought to determine the effect of exposure of senescent SATB1-KO neuron cultures to FAM49B-KO microglia as compared to WT neuron cultures. As expected, plating of FAM49B-KO microglia onto senescent DA neuron cultures resulted in an even higher reaction, indicated by a significant increase of overall motility as measured per cell by velocity ( Figure 4a, b ) and distance traveled ( Figure S4 ) . It has been shown that PAI-1 ( SERPINE1 ) acts as a hub both as a target and initiator of various pathways that regulate cellular motility via cues, including urokinase-type plasminogen activator (uPA ( PLAU )) and growth factors such as transforming growth factor β1 (TGF-β1( TGFB1 )) (Czekay et al., 2011). Interestingly, these migratory signaling molecules were found to be upregulated in FAM49B-KO microglia ( Figure 4c ), suggesting that these molecules stimulate an overall migratory response in the human stem cell-derived midbrain cultures. Taken together, these results suggest that the presence of FAM49B-reduced microglia (as found in the aged SNpc) causes an inflammaging reaction in diverse surrounding midbrain cell types. This could eventually contribute to the age-related vulnerability of the SNpc and the age-driven presence of inflammation markers. FAM49B-KO in microglia shows an increased immune response that is ameliorated by NF kB inhibition Microglia, the resident macrophages of the central nervous system, play a crucial role in immune surveillance and inflammatory responses. Upon encountering inflammatory stimuli, microglia can polarize towards a pro-inflammatory state and secrete cytokines such as interleukin-8 (IL-8), which mediates neuroinflammation (Colonna and Butovsky, 2017). Given their central role in neuroinflammation, dysregulation of microglial activity has been implicated in aging and neurodegenerative diseases, including PD (Heneka et al., 2014; Tansey and Goldberg, 2010). Toll like receptors (TLRs) expressed in microglia are elevated in individuals with PD, contributing to neuroinflammation and neurodegeneration. TLR-mediated inflammation has been suggested to contribute to the loss of DA neurons in PD (Heidari et al., 2022), and TLR2/6 and TLR5 have been shown to bind α-synuclein (Zhang et al., 2024). Interestingly, loss of FAM49B significantly led to a signification upregulation of TLRs, including TLR5 and 6 ( Figure 5a ). TLRs are known to regulate immune responses via NFkB activation. To further investigate the role of FAM49B in microglial immune responses, we stimulated WT and FAM49B-KO or -KD cells with inflammatory triggers, including tumor necrosis factor-alpha (TNF-α, in HMC3 cells) and lipopolysaccharides (LPS, in BV2 cells). Notably, the absence or reduction of FAM49B led to a significantly heightened pro-inflammatory response. FAM49B-KO in HMC3 cells caused a significant upregulation of IL-8 even without TNF-α stimulation ( Figure 5b ). Importantly, both FAM49B-KO and Fam49b-KD cells exhibited significantly increased expression of multiple immune response genes, following TNF-α or LPS treatment, compared to their WT or control counterparts ( Figure 5c, S5a (HMC3) and S5b (BV2)). These findings suggest that the reduction of FAM49B primes microglia towards an exaggerated inflammatory response, potentially contributing to neuroinflammation observed in aging and neurodegenerative disorders. To explore whether pharmacological intervention could mitigate this excessive immune response, we treated both WT and FAM49B-KO HMC3 microglia with NFkB inhibitor IV. Quantitative PCR analysis revealed that the inhibitor significantly reduced IL-8 expression ( Figure 5d ), suggesting that anti-inflammatory treatments may help ameliorate the detrimental hyperactivation of microglia associated with aging and PD. These findings are consistent with previous studies demonstrating that anti-inflammatory interventions can improve neuroinflammatory pathology in models of PD (Caggiu et al., 2019; Chen et al., 2003). Moreover, systemic inflammation, as modeled by LPS administration, has been shown to induce microglial overactivation and contribute to DA neuron loss in the SNpc, further establishing a link between chronic inflammation and neurodegeneration (Qin et al., 2007). Taken together, our results suggest that FAM49B serves as a key regulator of microglial activation, and its downregulation with age could be a contributing factor to increased reactivity, inflammaging, and, ultimately, neurodegenerative diseases. DISCUSSION Given the discovery of age-related downregulation of the PD risk factor FAM49B in nigral microglia, this study provides critical new insights into how aging and PD converge on the innate immune system, particularly on the resident immune cell of the brain, to contribute to neurodegeneration. While the intersection of age-related immune dysfunction and PD pathogenesis remains poorly understood, growing evidence suggests that chronic glial activation, impaired immune homeostasis, and dysregulated phagocytic clearance contribute to disease. Aging is the strongest risk factor for idiopathic PD (De Lau and Breteler, 2006 ). Using human gene expression data from different age groups, our results suggest how aging alone alters the molecular and functional landscape of microglia driven by the reduction of FAM49B. This decline leads to increased expression of inflammatory mediators, loss of homeostatic signatures, and impaired endocytic capacity. These observations support the view that age-associated dysfunction of microglia may create vulnerability of the SNpc and a permissive environment for the development of PD, even without genetic or environmental insults. The key finding of our study is the identification of FAM49B as a molecular link between aging, immune dysregulation, and PD risk. A GWAS has implicated FAM49B as a PD risk gene (Nalls et al., 2019 ), while its role in glial function and inflammaging remained elusive. Here, we show that FAM49B expression is selectively downregulated in microglia of the SNpc with age, suggesting a critical role in maintaining immune homeostasis in the brain. Our functional studies revealed that FAM49B-KO in microglia results in aberrant activation, with upregulation of pro-inflammatory cytokines, consistent with a transition toward a disease-associated-like or responding state. However, this activation was not accompanied by functional upregulation of microglial clearance mechanisms. Instead, FAM49B-deficient microglia showed impaired endocytosis, indicating a separation of their inflammatory response from phagocytic ability. This is highly relevant for PD, where a classical hallmark of pathology is the accumulation of misfolded α-synuclein, which normally can be cleared via microglial phagocytosis (Choi et al., 2020 ; Lee et al., 2008 ). We suggest that FAM49B might play a dual role, on the one hand, regulating inflammatory responses, and on the other hand, it coordinates cytoskeletal function, thereby supporting phagocytosis. Thus, it is plausible that loss of FAM49B may tip the homeostatic balance toward a maladaptive microglial phenotype that is both neuroinflammatory and ineffective at clearing debris. This aligns with age-related microglial dysfunction and thereby contributes to midbrain inflammaging. Our findings support previous research that identifies senescent or dystrophic microglia in aged and diseased brains (Streit et al., 2004 ), suggesting that immunosenescence in the brain contributes to neurodegenerative vulnerability. More concretely, our data confirm the outcome of single-cell studies showing age-associated occurrence of dysfunctional glial states with pro-inflammatory but inefficient immune profiles (Clarke et al., 2018 ; Olah et al., 2018 ). Despite the ubiquitous gene expression of PD-associated factors, the SNpc is most vulnerable to degeneration, suggesting that, in addition to the intrinsic vulnerability of DA neurons, local cellular context, including the density and state of glial cells, are critical determinants of susceptibility. Importantly, we observed that SNpc expression of FAM49B was highest in microglia, further supporting its relevance to nigral immune health. Reduction of FAM49B in microglia, which occurs with age in this vulnerable brain region, leads to a shift from surveillance to inflammaging While our study, based on human midbrain-derived sequencing data and modeled in microglia-like cell lines, showed highly reproducible results, it has some limitations. We chose cell lines (which were thoroughly characterized (Dello Russo et al., 2018 ; Henn et al., 2009 )) to ensure high reproducibility. We are aware that the cell line-based results could be reproduced in animals and/or human primary microglia. However, these approaches have their own caveats, such as inter-individual microglia differences and variation in microglia states due to isolation procedures, which could influence FAM49B expression and thus the outcome and interpretation of such functional analyses. In summary, our results establish the PD risk factor FAM49B as a novel regulator of microglial state and function with a high relevance to both PD pathogenesis and brain aging. Its downregulation with age could lead to a "second hit" cascade in PD that exacerbates inflammaging (due to basal upregulation of interleukin expression) while reducing protective clearance mechanisms (reduced endocytosis), a combination that may accelerate DA neuron loss. Our findings suggest a mechanistic rationale for why certain individuals, particularly older adults with a genetic predisposition, may develop PD in response to otherwise subthreshold stressors. Additionally, our data suggest FAM49B as a novel potential drug target, where boosting its function may offer a strategy to simultaneously suppress neurotoxic inflammation and enhance beneficial microglial clearance. Additionally, we found that FAM49B-KO cells upregulate the expression of TLRs, which have been indicated to increase in the context of PD and mediate inflammatory responses in microglia by activation of NFκB. This process of chronic activation of TLRs and neuroinflammation was suggested to lead to neurodegeneration in PD (Heidari et al., 2022 ). Both toxin-induced (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, MPTP) and pre-formed fibrils of αSyn-induced PD mouse models were ameliorated by NFκB inhibition (Dutta et al., 2021 ; Ghosh et al., 2007 ). Interestingly, binding of α-synuclein monomers or oligomers to TLR5 efficiently activated the NOD-like receptor pyrin domain containing 3 (NLRP3) inflammasome (Scheiblich et al., 2021 ) and TLR6 (which binds α-synuclein as TLR2/6 heterodimer (Zhang et al., 2024 )) induces expression of inflammatory cytokines by activating NFκB (Akira and Takeda, 2004 ). This in in line with our findings that TLR5 and 6 were significantly upregulated in FAM49B-KO cells and the proinflammatory activity in FAM49B-KO microglia was ameliorated by NFκB inhibition. The previously published findings, in combination with our results, suggest that microglia activity (partially mediated by NFκB) drives the neuroinflammatory and neurodegenerative pathology in diverse PD models. With future studies, it would be interesting to determine if FAM49B levels were reduced in these animals, possibly connecting detrimental microglia activity to genetic predisposition and age-related susceptibility. Our research establishes the PD risk gene FAM49B as a key molecular regulator of microglial activity during aging and PD, thus providing insights into genetic vulnerability and immune function failure leading to neurodegeneration. Hence, research focused on immune homeostasis pathways of the aging brain shows promise as a method to potentially prevent or delay PD and similar neurodegenerative disorders. METHODS Age-Associated Differential Gene Expression Analysis of the human SNpc . To deconvolute and estimate the relative proportions of major brain cell types in each bulk RNA-seq sample, we applied the methodology as described by Chikina et al. (Chikina et al., 2015). This method uses marker gene sets to infer latent variables representing cell-type abundance, enabling adjustment for cellular heterogeneity in bulk tissue expression data. Marker gene sets for neurons, astrocytes, microglia, oligodendrocytes, and endothelial cells were obtained from Zhang et al. (Zhang et al., 2014), who identified these markers from purified brain cell populations. Using these marker sets, we computed relative cell-type proportion estimates for each GTEx SNpc sample using normalized gene expression values provided by the GTEx consortium (version 8). These estimates were then used as covariates in a linear model framework to assess gene expression associations with donor age while controlling for differences in cell-type composition across samples. FAM49B expression was specifically evaluated for its correlation with inferred cell-type proportions and for age-associated differential expression after covariate adjustment. Significance was assessed using linear regression models, and p-values were corrected for multiple testing. We further confirmed that the age-associated change in FAM49B expression could be specifically attributed to microglia expression through a F-test, allowing for the identification of cell-type-specific expression changes. All code to replicate the analysis is available in the supplemental material. HMC3 Cell Culture . HMC3 cells were cultured according to the vendor’s instructions. Cells were grown at 37°C in 5% CO2. The medium used was Corning Minimum Essential Medium Eagle with Earle’s salts and L-glutamine (#10-010-CV) with 1% Pen-Strep, 1% Sodium pyruvate, 1% MEM Non-essential Amino Acid Solution (#M7145-100mL, SIGMA) and 10% Fetal Bovine Serum (FBS). Cells were passaged every 2-3 days with Trypsin-EDTA (ThermoFisher #25200072). HMC3 cells were stimulated with 0.3 ug/mL TNFa (#TNA-H4211, Acro Biosystems) BV2 Cell Culture . BV2 cells were cultured according to the vendor’s instructions. Cells were grown at 37°C in 5% CO2. The medium used was RPMI (#11875-093, Gibco) with 1% Pen-Strep and 10% Fetal Bovine Serum (FBS). Cells were passaged every 2-3 days with Trypsin-EDTA (#25200072, ThermoFisher) siRNA Transfection . BV2 cells were seeded at 2.5x10 5 per well on a 6-well plate using RPMI medium and left overnight. Cell medium was changed 30 min prior to transfection, which was performed using Lipofectamine RNAiMAX (ThermoFisher #13778030), OptiMEM (Gibco #31985062), 15 pmol Silencer Select siFAM49B siRNA (ThermoFisher #4390771), and 15 pmol Silencer Select Negative Control (ThermoFisher #4390843) in accordance with the ThermoFisher RNAiMAX protocol. 24 hours following transfection, cells were passaged onto respective vessels required for each experiment. Trans-well Migration Assay HMC3: WT and FAM49B KO HMC3 cells were cultured in separate T25 flasks using EMEM medium supplemented with 10% FBS and 1% penicillin/streptomycin and incubated at 37℃. Upon reaching 90% confluency, cell medium was removed and replaced with starvation medium (EMEM supplemented with 1% FBS and 1% penicillin/streptomycin) and cells were incubated at 37℃ for 24 hours. Flasks were rinsed with 2mL pre-warmed D-PBS and then passaged with 1mL trypsin and neutralized with 2 mL starvation medium. Cells were seeded at 5.0x10 4 per well in 250 𝜇L onto 12.0 𝜇m trans-well inserts (#PIXP01250, Millipore) coated with 0.1% gelatin. 500 𝜇L normal (not starvation) HMC3 media was placed in the bottom chamber of each well, and cells were incubated at 37℃ for 48 hours. Inserts were rinsed with 250 𝜇L PBS and wells were rinsed with 500 𝜇L PBS and then aspirated. 500 𝜇L 2.8 𝜇M Calcein AM (in 0.5M EDTA) as placed in each well and cells were incubated at 37℃ for 1 hour. Wells were scraped and solution was transferred into a black 96-well plate (100 𝜇L/well). Samples were excited using a wavelength of 485 nm and fluorescence at 535 nm was measured using a SpectraMax iD3 plate reader (Molecular Devices). BV2: Transfected BV2 cells were seeded at 5.0x10 5 per well on a 6-well plate using DMEM medium supplemented with 10% FBS and 1% pen/strep and incubated at 37℃ overnight. Cell medium was removed and replaced with starvation medium (DMEM supplemented with 1% FBS and 1% penicillin/streptomycin) 30 min prior to siRNA transfection. 24 hours following transfection, cells were passaged in 500 𝜇L trypsin and neutralized with starvation medium. Cells were seeded at 5.0x10 4 per well in 250 𝜇L onto 8.0 𝜇m trans-well inserts (VWR #734-2748) coated with 0.1% gelatin (Sigma #G1393). 500 𝜇L normal (not starvation) BV2 media was placed in the bottom chamber of each well, and cells were incubated at 37℃ for 24 hours. Inserts and wells were rinsed with PBS, cells were stained with Calcein AM, and fluorescence was measured as described above. Roundness Assay. To determine changes in the shape of the microglia, we applied a measurement of roundness as previously published (Riessland et al., 2019). In brief, brightfield images of living microglia cultures were taken using an Accu-Scope EXI-410 microscope with a Skye2 camera. Images were analyzed using the roundness measure in FIJI. While typically, ramified (branched) microglia indicate a surveillance state, more amoeboid cells suggest activation. Microglia co-culture with Embryonic stem cells and cell tracking. Human embryonic stem cells [ESCs; wild-type H9 (WA-09), SATB1 KO (Riessland et al., 2019)] were differentiated into midbrain dopamine (mDA) neurons according to an optimized version of previously established protocol (Riessland et al., 2019).WT and SATB1-KO ESCs were differentiated at a density of 60k/well on a 96 well plate (µ-Plate 96 well square; Ibidi). On day 50 either HMC3-WT and HMC3-KO cells were fluorescently labeled (Cell tracker Orange CMTMR; Invitrogen) according to manufacturer’s instructions and plated on top of either WT or SATB1-KO differentiated mDA neurons. Co-cultures are fluorescently imaged (586,647) every 30 minutes for 66 hours using the Agilent BioTek Lionheart FX live imaging system. Note that cell labeling is not exclusive to HMC3 cells and ESCs become fluorescently labeled within 30 minutes of co-culture. Cell tracking analysis was completed using TrackMate (Ershov et al., 2022). Statistical analysis was completed using Ordinary one-way ANOVA (prism). Western Blot. Cells were lysed for protein using RIPA buffer (ThermoFisher #89900) and protease and phosphatase inhibitors (#11836170001, Roche). Protein concentrations for each sample were determined using a BCA assay (ThermoFisher #23225) with a SpectraMax iD3 plate reader (Molecular Devices). Protein samples were boiled in 2X Tris-Glycine-SDS sample buffer (Novex #LC2676) at 95°C for 5 min before being separated with electrophoresis using 10% Tris-glycine (Invitrogen #XP04200). Samples were then electro-transferred to nitrocellulose membranes (BioRad) and blocked using 5% BSA in TBS-Tween for 60 min, before undergoing overnight incubation with the required primary antibody in blocking solution. The primary antibodies used are listed as follows: mouse monoclonal anti-FAM49B antibody (SCT #sc-390478; ThermoFisher #PA5-52647), anti-TOM20 antibody (SCT #11415). To visualize and quantify protein bands, a ChemiDocXRS+ (BioRad) was used. RNA isolation, qPCR, and RNA-seq. RNA was isolated from cells using the RNeasy Plus Mini Kit (#74136, QIAGEN) and QIAshredder (QIAGEN, #7956). Cells were lysed using 2-Mercaptoethanol. 200ng of RNA was reverse transcribed using the Applied Biosystems High-Capacity cDNA Reverse Transcription Kit (ThermoFisher # 4368814) creating an output volume of 20µL. Real time qPCR was performed using a Quant Studio 3 (Applied Biosystems) and the Applied Biosystems Power SYBR Green PCR Master Mix (ThermoFisher #4367659). Reaction specificity was confirmed via melt curve analysis. Previously described in Russo et al, 2025, for RNA-seq, 500 ng of RNA for each sample was sent to Azenta for bulk RNAseq. In brief, sample quality control and determination of concentration was performed using TapeStation Analysis by Azenta, followed by library preparation and sequencing. Computational analysis included in their standard data analysis package, was used for data interpretation. The gene expression data of the HMC3 RNA-seq is accessible on ArrayExpress (Accession number E-MTAB-15277). Transmission Electron Microscopy. For transmission electron microscopy (TEM), cells were grown on Aclar film and fixed for 30 minutes in a mixture of cold 2% paraformaldehyde and 0.1% glutaraldehyde in 0.1M phosphate buffer (PB), pH 7.4. After washing, cells were post-fixed with 1% osmium tetroxide for 30 minutes, and stained with 1% uranyl acetate for 30 minutes, dehydrated through an ascending series of ethanol, and embedded in Durcupan resin for 48 hours at 60°C. Ultrathin sections (60-90nm) were cut on an ultramicrotome (Reichert Ultracut E) and placed on formvar-coated nickel slot grids. Sections were post-embedding immunogold labeled for rabbit FAM49b within 24 hours of sectioning using a modification of the protocol of Phend et al (Phend et al., 1995). In brief, grids were rinsed in TBS containing 0.005% Tergitol NP-10, pH 7.6 (hereafter referred to as Tris-tergitol pH 7.6), incubated in saturated (10%) sodium meta-periodate for 5 seconds, rinsed, incubated in 1% sodium borohydride for 1 minute, rinsed, and incubated in primary antibody (rabbit anti-FAM49B 1: 100) overnight at room temperature. The next day, grids were rinsed in Tris-tergitol pH 7.6, followed by Tris-tergitol pH 8.2, incubated in secondary antibody (goat anti-rabbit IgG conjugated to 18nm gold particles) (Jackson Immuno Research) 1:25 in Tris-tergitol pH 8.2 for 1 hour and rinsed. Grids were then stained with 1% methanolic uranyl acetate and 0.3% aqueous lead citrate and imaged using a JEOL 1200EX transmission electron microscope. Endocytosis assay. Cells were treated with 1 mg/mL of 75kDa Fluorescein isothiocyanate-Dextran (Sigma Aldrich #60842-46-8) in their respective medium for 15 minutes at 37°C. Cells were then washed two times with cold PBS before being fixed in 4% PFA in PBS for 15 minutes at room temperature. Following fixation, cells were incubated with Phalloidin-647 (ThermoFisher # A22287) in PBS for 1 hour at room temperature. Cells were then washed 1x in PBS, mounted using DAPI Fluoromount-G (Southern Biotech #0100-20) and imaged using an Olympus confocal microscope. Cell Adhesion Assays . Cell adhesion assay was performed using the XCELLigence Agilent Impedance device. Prior to seeding of cells, the 16-well E-plate was coated using 0.1% gelatin in D-PBS. HMC3 cells and BV2 cells were seeded at 1.5x10 4 cells per well. Cell index readings were taken every 1 minute for a total of 60 minutes (BV2) and every 2 minutes for a total of 24 hours (HMC3), or until cells reached confluency. An additional cell adhesion test was performed in the HMC3 cells using a counting assay. Equal numbers of cells were plated overnight, and the following day, attached cells were counted and compared to live cells remaining unattached in the supernatant. FAM49B-KO cells displayed delayed adhesion to the culture plate surface. Mitochondrial Assays . Cell respiration was measured using the Agilent Seahorse XFe96 Analyzer and the Mito Stress Test assay (Agilent #103015-100) which delivers a series of three drugs (Oligomycin, FCCP, Rotenone+Antimycin A while the oxygen consumption rate (OCR) is recorded. OCR is used to calculate basal respiration, maximal respiration, proton leak, non-mitochondrial oxygen consumption, ATP production, spare respiratory capacity, and coupling efficiency. A BCA assay was performed in order to normalize the results based on total cell protein content. To measure mitochondrial membrane potential, a functional dye MitoTracker Red CMXRos (ThermoFisher #M7513) was diluted to 200nM in D-PBS and incubated with cells for 15 minutes at 37°C. Cells were then washed with PBS 3X and fixed before being mounted and imaged using an Olympus Confocal microscope. CTCF values were measured using Fiji. To measure mitochondrial ROS levels, MitoSOX Red superoxide indicator (ThermoFisher #M36007) was diluted in HBSS and incubated onto cells for 15 minutes at 37°C. Cells were then imaged using the Agilent BioTek LionHeartFX live imaging system. qPCR primers used Target Forward Reverse B-Actin AGCGAGCATCCCCCAAAGTT GGGCACGAAGGCTCATCATT IL8 (Wang et al., 2021) ACTGAGAGTGATTGAGAGTGGAC AACCCTCTGCACCCAGTTTTC CD11B (Wang et al., 2021) ACTTGCAGTGAGAACACGTATG TCATCCGCCGAAAGTCATGTG TMEM119 (Wang et al., 2021) CGGCCTATTACCCATCGTCC CTGGGCTAACAAGAGAGACCC CYRIB-m (Park et al., 2024) AGGAGCTGGCCACGAAATAC GGCGTACTAGTCAAGGCTCC IL7-m (Martin et al., 2017) CTGATGATCAGCATCGATGAATTGG GCAGCACGATTTAGAAAAGCAGCTT IL6-m ( Origene #MP206798) TACCACTTCACAAGTCGGAGGC CTGCAAGTGCATCATCGTTGTTC TMEM119-m ( Origene #MP217346) ACTACCCATCCTCGTTCCCTGA TAGCAGCCAGAATGTCAGCCTG Cd11b-m (Skelly et al., 2013) TCATTCGCTACGTAATTGGG GATGGTGTCGAGCTCTCTGC Declarations Author Contribution Conceptualization: JM, G-JL, CYP, MRMethodology/Investigation: JM, G-JL, CYP, CW, TVB, SA, SL, MD, TR, WA, MW, BK, MRVisualization: JM, G-JL, CYP, MR Supervision: MR, OGTWriting—original draft: MR, JM, G-JL, CYPWriting—review & editing: JM, G-JL, CYP, CW, TVB, SA, SL, MD, TR, WA, MW, BK, OGT, MR Acknowledgements This work was supported in part through NINDS grant 1R01NS124735 (M.R.) and a Starter Grant by the Thomas Hartman Foundation (M.R.). J.M. was partially supported by a Dr. W. Burghardt Turner Fellowship. Opinions, interpretations, conclusions, and recommendations are those of the author and are not necessarily endorsed by the sponsors. Data Availability The gene expression data of the HMC3 RNA-seq is accessible on ArrayExpress (Accession number E-MTAB-15277). References Agarwal, D., Sandor, C., Volpato, V., Caffrey, T.M., Monzón-Sandoval, J., Bowden, R., Alegre-Abarrategui, J., Wade-Martins, R., and Webber, C. (2020). A single-cell atlas of the human substantia nigra reveals cell-specific pathways associated with neurological disorders. Nature Communications 11 , 4183. Akira, S., and Takeda, K. (2004). Toll-like receptor signalling. Nature Reviews Immunology 4 , 499-511. Bosco, E.E., Mulloy, J.C., and Zheng, Y. (2009). Rac1 GTPase: a "Rac" of all trades. Cell Mol Life Sci 66 , 370-374. Burda, J.E., and Sofroniew, M.V. (2014). Reactive gliosis and the multicellular response to CNS damage and disease. Neuron 81 , 229-248. Caggiu, E., Arru, G., Hosseini, S., Niegowska, M., Sechi, G., Zarbo, I.R., and Sechi, L.A. (2019). Inflammation, Infectious Triggers, and Parkinson's Disease. Front Neurol 10 , 122. Calabrese, V., Santoro, A., Monti, D., Crupi, R., Di Paola, R., Latteri, S., Cuzzocrea, S., Zappia, M., Giordano, J., Calabrese, E.J. , et al. (2018). Aging and Parkinson's Disease: Inflammaging, neuroinflammation and biological remodeling as key factors in pathogenesis. Free Radic Biol Med 115 , 80-91. Campolo, M., Paterniti, I., Siracusa, R., Filippone, A., Esposito, E., and Cuzzocrea, S. (2019). TLR4 absence reduces neuroinflammation and inflammasome activation in Parkinson's diseases in vivo model. Brain Behav Immun 76 , 236-247. Chang, D., Nalls, M.A., Hallgrímsdóttir, I.B., Hunkapiller, J., Van Der Brug, M., Cai, F., Consortium, I.P.s.D.G., Team, a.R., Kerchner, G.A., and Ayalon, G. (2017). A meta-analysis of genome-wide association studies identifies 17 new Parkinson's disease risk loci. Nature genetics 49 , 1511-1516. Chattaragada, M.S., Riganti, C., Sassoe, M., Principe, M., Santamorena, M.M., Roux, C., Curcio, C., Evangelista, A., Allavena, P., Salvia, R. , et al. (2018). FAM49B, a novel regulator of mitochondrial function and integrity that suppresses tumor metastasis. Oncogene 37 , 697-709. Chen, H., Zhang, S.M., Hernán, M.A., Schwarzschild, M.A., Willett, W.C., Colditz, G.A., Speizer, F.E., and Ascherio, A. (2003). Nonsteroidal anti-inflammatory drugs and the risk of Parkinson disease. Archives of neurology 60 , 1059-1064. Chikina, M., Zaslavsky, E., and Sealfon, S.C. (2015). CellCODE: a robust latent variable approach to differential expression analysis for heterogeneous cell populations. Bioinformatics 31 , 1584-1591. Chinta, S.J., Woods, G., Demaria, M., Rane, A., Zou, Y., McQuade, A., Rajagopalan, S., Limbad, C., Madden, D.T., Campisi, J. , et al. (2018). Cellular Senescence Is Induced by the Environmental Neurotoxin Paraquat and Contributes to Neuropathology Linked to Parkinson's Disease. Cell Rep 22 , 930-940. Choi, I., Zhang, Y., Seegobin, S.P., Pruvost, M., Wang, Q., Purtell, K., Zhang, B., and Yue, Z. (2020). Microglia clear neuron-released α-synuclein via selective autophagy and prevent neurodegeneration. Nature Communications 11 , 1386. Clarke, L.E., Liddelow, S.A., Chakraborty, C., Münch, A.E., Heiman, M., and Barres, B.A. (2018). Normal aging induces A1-like astrocyte reactivity. Proceedings of the National Academy of Sciences 115 , E1896-E1905. Colonna, M., and Butovsky, O. (2017). Microglia Function in the Central Nervous System During Health and Neurodegeneration. Annu Rev Immunol 35 , 441-468. Consortium, T.G., Aguet, F., Anand, S., Ardlie, K.G., Gabriel, S., Getz, G.A., Graubert, A., Hadley, K., Handsaker, R.E., Huang, K.H. , et al. (2020). The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369 , 1318-1330. Czekay, R.P., Wilkins-Port, C.E., Higgins, S.P., Freytag, J., Overstreet, J.M., Klein, R.M., Higgins, C.E., Samarakoon, R., and Higgins, P.J. (2011). PAI-1: An Integrator of Cell Signaling and Migration. Int J Cell Biol 2011 , 562481. Dauer, W., and Przedborski, S. (2003). Parkinson's Disease: Mechanisms and Models. Neuron 39 , 889-909. De Biase, L.M., Schuebel, K.E., Fusfeld, Z.H., Jair, K., Hawes, I.A., Cimbro, R., Zhang, H.-Y., Liu, Q.-R., Shen, H., and Xi, Z.-X. (2017). Local cues establish and maintain region-specific phenotypes of basal ganglia microglia. Neuron 95 , 341-356. e346. De Lau, L.M., and Breteler, M.M. (2006). Epidemiology of Parkinson's disease. The Lancet Neurology 5 , 525-535. Dello Russo, C., Cappoli, N., Coletta, I., Mezzogori, D., Paciello, F., Pozzoli, G., Navarra, P., and Battaglia, A. (2018). The human microglial HMC3 cell line: where do we stand? A systematic literature review. Journal of Neuroinflammation 15 , 259. Dutta, D., Jana, M., Majumder, M., Mondal, S., Roy, A., and Pahan, K. (2021). Selective targeting of the TLR2/MyD88/NF-κB pathway reduces α-synuclein spreading in vitro and in vivo. Nature Communications 12 , 5382. Ershov, D., Phan, M.-S., Pylvänäinen, J.W., Rigaud, S.U., Le Blanc, L., Charles-Orszag, A., Conway, J.R.W., Laine, R.F., Roy, N.H., Bonazzi, D. , et al. (2022). TrackMate 7: integrating state-of-the-art segmentation algorithms into tracking pipelines. Nature Methods 19 , 829-832. Fort, L., Batista, J.M., Thomason, P.A., Spence, H.J., Whitelaw, J.A., Tweedy, L., Greaves, J., Martin, K.J., Anderson, K.I., Brown, P. , et al. (2018). Fam49/CYRI interacts with Rac1 and locally suppresses protrusions. Nat Cell Biol 20 , 1159-1171. Ghosh, A., Roy, A., Liu, X., Kordower, J.H., Mufson, E.J., Hartley, D.M., Ghosh, S., Mosley, R.L., Gendelman, H.E., and Pahan, K. (2007). Selective inhibition of NF-κB activation prevents dopaminergic neuronal loss in a mouse model of Parkinson's disease. Proceedings of the National Academy of Sciences 104 , 18754-18759. Gordon, R., Singh, N., Lawana, V., Ghosh, A., Harischandra, D.S., Jin, H., Hogan, C., Sarkar, S., Rokad, D., Panicker, N. , et al. (2016). Protein kinase Cδ upregulation in microglia drives neuroinflammatory responses and dopaminergic neurodegeneration in experimental models of Parkinson's disease. Neurobiol Dis 93 , 96-114. Hammond, T.R., Dufort, C., Dissing-Olesen, L., Giera, S., Young, A., Wysoker, A., Walker, A.J., Gergits, F., Segel, M., and Nemesh, J. (2019). Single-cell RNA sequencing of microglia throughout the mouse lifespan and in the injured brain reveals complex cell-state changes. Immunity 50 , 253-271. e256. Hamza, T.H., Zabetian, C.P., Tenesa, A., Laederach, A., Montimurro, J., Yearout, D., Kay, D.M., Doheny, K.F., Paschall, J., and Pugh, E. (2010). Common genetic variation in the HLA region is associated with late-onset sporadic Parkinson's disease. Nature genetics 42 , 781-785. Heidari, A., Yazdanpanah, N., and Rezaei, N. (2022). The role of Toll-like receptors and neuroinflammation in Parkinson’s disease. Journal of Neuroinflammation 19 , 135. Heneka, M.T., Kummer, M.P., and Latz, E. (2014). Innate immune activation in neurodegenerative disease. Nature Reviews Immunology 14 , 463-477. Henn, A., Lund, S., Hedtjärn, M., Schrattenholz, A., Pörzgen, P., and Leist, M. (2009). The suitability of BV2 cells as alternative model system for primary microglia cultures or for animal experiments examining brain inflammation. Altex 26 , 83-94. Holmans, P., Moskvina, V., Jones, L., Sharma, M., Consortium, I.P.s.D.G., Vedernikov, A., Buchel, F., Sadd, M., Bras, J.M., and Bettella, F. (2013). A pathway-based analysis provides additional support for an immune-related genetic susceptibility to Parkinson's disease. Human molecular genetics 22 , 1039-1049. Kam, T.-I., Hinkle, J.T., Dawson, T.M., and Dawson, V.L. (2020). Microglia and astrocyte dysfunction in parkinson's disease. Neurobiology of Disease 144 , 105028. Kim, T.W., Piao, J., Koo, S.Y., Kriks, S., Chung, S.Y., Betel, D., Socci, N.D., Choi, S.J., Zabierowski, S., Dubose, B.N. , et al. (2021). Biphasic Activation of WNT Signaling Facilitates the Derivation of Midbrain Dopamine Neurons from hESCs for Translational Use. Cell Stem Cell 28 , 343-355.e345. Lee, H.J., Suk, J.E., Bae, E.J., and Lee, S.J. (2008). Clearance and deposition of extracellular alpha-synuclein aggregates in microglia. Biochem Biophys Res Commun 372 , 423-428. Li, Q., and Barres, B.A. (2018). Microglia and macrophages in brain homeostasis and disease. Nature Reviews Immunology 18 , 225-242. Liddelow, S.A., Guttenplan, K.A., Clarke, L.E., Bennett, F.C., Bohlen, C.J., Schirmer, L., Bennett, M.L., Münch, A.E., Chung, W.-S., and Peterson, T.C. (2017). Neurotoxic reactive astrocytes are induced by activated microglia. Nature 541 , 481-487. Martin, C.E., Spasova, D.S., Frimpong-Boateng, K., Kim, H.-O., Lee, M., Kim, K.S., and Surh, C.D. (2017). Interleukin-7 Availability Is Maintained by a Hematopoietic Cytokine Sink Comprising Innate Lymphoid Cells and T Cells. Immunity 47 , 171-182.e174. Martin, I., Dawson, V.L., and Dawson, T.M. (2011). Recent advances in the genetics of Parkinson's disease. Annual review of genomics and human genetics 12 , 301-325. McGeer, P.L., Itagaki, S., Boyes, B.E., and McGeer, E. (1988). Reactive microglia are positive for HLA‐DR in the substantia nigra of Parkinson's and Alzheimer's disease brains. Neurology 38 , 1285-1285. Moore, D.J., West, A.B., Dawson, V.L., and Dawson, T.M. (2005). Molecular pathophysiology of Parkinson's disease. Annu Rev Neurosci 28 , 57-87. Nalls, M.A., Blauwendraat, C., Vallerga, C.L., Heilbron, K., Bandres-Ciga, S., Chang, D., Tan, M., Kia, D.A., Noyce, A.J., Xue, A. , et al. (2019). Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies. Lancet Neurol 18 , 1091-1102. Nalls, M.A., Pankratz, N., Lill, C.M., Do, C.B., Hernandez, D.G., Saad, M., DeStefano, A.L., Kara, E., Bras, J., Sharma, M. , et al. (2014). Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson's disease. Nat Genet 46 , 989-993. Olah, M., Patrick, E., Villani, A.C., Xu, J., White, C.C., Ryan, K.J., Piehowski, P., Kapasi, A., Nejad, P., Cimpean, M. , et al. (2018). A transcriptomic atlas of aged human microglia. Nat Commun 9 , 539. Panicker, N., Sarkar, S., Harischandra, D.S., Neal, M., Kam, T.-I., Jin, H., Saminathan, H., Langley, M., Charli, A., and Samidurai, M. (2019). Fyn kinase regulates misfolded α-synuclein uptake and NLRP3 inflammasome activation in microglia. Journal of Experimental Medicine 216 , 1411-1430. Paolicelli, R.C., Sierra, A., Stevens, B., Tremblay, M.-E., Aguzzi, A., Ajami, B., Amit, I., Audinat, E., Bechmann, I., Bennett, M. , et al. (2022). Microglia states and nomenclature: A field at its crossroads. Neuron 110 , 3458-3483. Park, C.-S., Guan, J., Rhee, P., Gonzalez, F., Lee, H.-s., Park, J.-h., Coscoy, L., Robey, E.A., Shastri, N., and Sadegh-Nasseri, S. (2024). Fam49b dampens TCR signal strength to regulate survival of positively selected thymocytes and peripheral T cells. eLife 13 , e76940. Phend, K.D., Rustioni, A., and Weinberg, R.J. (1995). An osmium-free method of epon embedment that preserves both ultrastructure and antigenicity for post-embedding immunocytochemistry. J Histochem Cytochem 43 , 283-292. Pierce, S., and Coetzee, G.A. (2017). Parkinson's disease-associated genetic variation is linked to quantitative expression of inflammatory genes. PloS one 12 , e0175882. Qin, L., Wu, X., Block, M.L., Liu, Y., Breese, G.R., Hong, J.S., Knapp, D.J., and Crews, F.T. (2007). Systemic LPS causes chronic neuroinflammation and progressive neurodegeneration. Glia 55 , 453-462. Riessland, M., Kolisnyk, B., Kim, T.W., Cheng, J., Ni, J., Pearson, J.A., Park, E.J., Dam, K., Acehan, D., Ramos-Espiritu, L.S. , et al. (2019). Loss of SATB1 Induces p21-Dependent Cellular Senescence in Post-mitotic Dopaminergic Neurons. Cell Stem Cell. Russo, T., Plessis-Belair, J., Sher, R., and Riessland, M. (2025). Regulatory Network Inference of Induced Senescent Midbrain Cell Types Reveals Cell Type-Specific Senescence-Associated Transcriptional Regulators. bioRxiv. Russo, T., and Riessland, M. (2022). Age-Related Midbrain Inflammation and Senescence in Parkinson's Disease. Front Aging Neurosci 14 , 917797. Sangineto, M., Ciarnelli, M., Cassano, T., Radesco, A., Moola, A., Bukke, V.N., Romano, A., Villani, R., Kanwal, H., Capitanio, N. , et al. (2023). Metabolic reprogramming in inflammatory microglia indicates a potential way of targeting inflammation in Alzheimer's disease. Redox Biology 66 , 102846. Scheiblich, H., Bousset, L., Schwartz, S., Griep, A., Latz, E., Melki, R., and Heneka, M.T. (2021). Microglial NLRP3 Inflammasome Activation upon TLR2 and TLR5 Ligation by Distinct α-Synuclein Assemblies. The Journal of Immunology 207 , 2143-2154. Scheiblich, H., Eikens, F., Wischhof, L., Opitz, S., Jüngling, K., Cserép, C., Schmidt, S.V., Lambertz, J., Bellande, T., Pósfai, B. , et al. (2024). Microglia rescue neurons from aggregate-induced neuronal dysfunction and death through tunneling nanotubes. Neuron 112 , 3106-3125.e3108. Shang, W., Jiang, Y., Boettcher, M., Ding, K., Mollenauer, M., Liu, Z., Wen, X., Liu, C., Hao, P., Zhao, S. , et al. (2018). Genome-wide CRISPR screen identifies FAM49B as a key regulator of actin dynamics and T cell activation. Proc Natl Acad Sci U S A 115 , E4051-e4060. Singh, S.S., Rai, S.N., Birla, H., Zahra, W., Rathore, A.S., and Singh, S.P. (2020). NF-κB-Mediated Neuroinflammation in Parkinson's Disease and Potential Therapeutic Effect of Polyphenols. Neurotox Res 37 , 491-507. Skelly, D.T., Hennessy, E., Dansereau, M.-A., and Cunningham, C. (2013). A Systematic Analysis of the Peripheral and CNS Effects of Systemic LPS, IL-1Β, TNF-α and IL-6 Challenges in C57BL/6 Mice. PLOS ONE 8 , e69123. Smolders, S.M.-T., Kessels, S., Vangansewinkel, T., Rigo, J.-M., Legendre, P., and Brône, B. (2019). Microglia: Brain cells on the move. Progress in Neurobiology 178 , 101612. Solé-Domènech, S., Cruz, D.L., Capetillo-Zarate, E., and Maxfield, F.R. (2016). The endocytic pathway in microglia during health, aging and Alzheimer's disease. Ageing Res Rev 32 , 89-103. Streit, W.J., Sammons, N.W., Kuhns, A.J., and Sparks, D.L. (2004). Dystrophic microglia in the aging human brain. Glia 45 , 208-212. Surmeier, D.J., Obeso, J.A., and Halliday, G.M. (2017). Selective neuronal vulnerability in Parkinson disease. Nature Reviews Neuroscience 18 , 101-113. Tansey, M.G., and Goldberg, M.S. (2010). Neuroinflammation in Parkinson's disease: its role in neuronal death and implications for therapeutic intervention. Neurobiol Dis 37 , 510-518. Trainor, A.R., MacDonald, D.S., and Penney, J. (2024). Microglia: roles and genetic risk in Parkinson's disease. Front Neurosci 18 , 1506358. Tremblay, M.-È. (2011). The role of microglia at synapses in the healthy CNS: novel insights from recent imaging studies. Neuron glia biology 7 , 67-76. Wang, Y.-J., Monteagudo, A., Downey, M.A., Ashton-Rickardt, P.G., and Elmaleh, D.R. (2021). Cromolyn inhibits the secretion of inflammatory cytokines by human microglia (HMC3). Scientific Reports 11 , 8054. Yang, S., Qin, C., Hu, Z.-W., Zhou, L.-Q., Yu, H.-H., Chen, M., Bosco, D.B., Wang, W., Wu, L.-J., and Tian, D.-S. (2021). Microglia reprogram metabolic profiles for phenotype and function changes in central nervous system. Neurobiology of Disease 152 , 105290. Zhang, X., Yu, H., and Feng, J. (2024). Emerging role of microglia in inter-cellular transmission of α-synuclein in Parkinson’s disease. Frontiers in Aging Neuroscience Volume 16 - 2024 . Zhang, Y., Chen, K., Sloan, S.A., Bennett, M.L., Scholze, A.R., O'Keeffe, S., Phatnani, H.P., Guarnieri, P., Caneda, C., Ruderisch, N. , et al. (2014). An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J Neurosci 34 , 11929-11947. Additional Declarations No competing interests reported. Supplementary Files SupplFigsMartinetal.docx Cite Share Download PDF Status: Published Journal Publication published 19 Dec, 2025 Read the published version in npj Aging → Version 1 posted Editorial decision: Revision requested 27 Aug, 2025 Reviews received at journal 24 Aug, 2025 Reviewers agreed at journal 06 Aug, 2025 Reviews received at journal 21 Jul, 2025 Reviewers agreed at journal 12 Jul, 2025 Reviewers invited by journal 04 Jul, 2025 Editor assigned by journal 03 Jul, 2025 Submission checks completed at journal 23 Jun, 2025 First submitted to journal 18 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Park","email":"","orcid":"","institution":"Flatiron Institute, Simons Foundation","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"Y.","lastName":"Park","suffix":""},{"id":486893382,"identity":"7a8650a2-b54b-4919-bf22-a37eb97ff535","order_by":3,"name":"Colista West","email":"","orcid":"","institution":"Stony Brook University","correspondingAuthor":false,"prefix":"","firstName":"Colista","middleName":"","lastName":"West","suffix":""},{"id":486893383,"identity":"cdd4d9da-0738-4dae-beb8-aeba565acf11","order_by":4,"name":"Trevor Van Brunt","email":"","orcid":"","institution":"Stony Brook University","correspondingAuthor":false,"prefix":"","firstName":"Trevor","middleName":"Van","lastName":"Brunt","suffix":""},{"id":486893384,"identity":"82aecf74-7b52-4100-918a-2b6204517243","order_by":5,"name":"Samarah Ahmed","email":"","orcid":"","institution":"Stony Brook University","correspondingAuthor":false,"prefix":"","firstName":"Samarah","middleName":"","lastName":"Ahmed","suffix":""},{"id":486893385,"identity":"c56ad880-2d53-4c58-bb92-a292015016ca","order_by":6,"name":"Saheed Lawal","email":"","orcid":"","institution":"Stony Brook University","correspondingAuthor":false,"prefix":"","firstName":"Saheed","middleName":"","lastName":"Lawal","suffix":""},{"id":486893386,"identity":"a6119f0a-3e61-4362-8d34-c6f0f83b3ded","order_by":7,"name":"Maya Dickson","email":"","orcid":"","institution":"Stony Brook University","correspondingAuthor":false,"prefix":"","firstName":"Maya","middleName":"","lastName":"Dickson","suffix":""},{"id":486893388,"identity":"499ceec6-a986-4c3d-a6cd-16e92f3d9d34","order_by":8,"name":"Taylor Russo","email":"","orcid":"","institution":"Stony Brook University","correspondingAuthor":false,"prefix":"","firstName":"Taylor","middleName":"","lastName":"Russo","suffix":""},{"id":486893389,"identity":"800a10a1-5257-493f-aab2-335fc46b4fc7","order_by":9,"name":"Wendy Akmentin","email":"","orcid":"","institution":"Stony Brook University","correspondingAuthor":false,"prefix":"","firstName":"Wendy","middleName":"","lastName":"Akmentin","suffix":""},{"id":486893390,"identity":"8259691a-119b-4bf3-9c0a-5a8a40b11633","order_by":10,"name":"Molly Weiner","email":"","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"Molly","middleName":"","lastName":"Weiner","suffix":""},{"id":486893391,"identity":"ea7ba6dd-b98a-446e-a067-33d86bdc6440","order_by":11,"name":"Benjamin Kolisnyk","email":"","orcid":"","institution":"The Rockefeller University","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Kolisnyk","suffix":""},{"id":486893392,"identity":"5d2c4953-3699-47f4-9e72-1ce91117c636","order_by":12,"name":"Olga G. Troyanskaya","email":"","orcid":"","institution":"Lewis–Sigler Institute for Integrative Genomics, Princeton University","correspondingAuthor":false,"prefix":"","firstName":"Olga","middleName":"G.","lastName":"Troyanskaya","suffix":""},{"id":486893393,"identity":"6b740dd5-d50e-4838-8e54-1b4f57e6da24","order_by":13,"name":"Markus Riessland","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABNUlEQVRIie2RMUvDQBTHLxxcl6uuJ1L6CYQrB4GiHyYhkCyF4pLJIdNlCWZtBr9DodA54SAuNVkzZKgE4uKQSRyimNoWpIlUN5H7DY+7x/txf94BIJH8TeBnRZuyBkDxt91w10NHFK1RAgf+UAF7ZR4eUS7ch4LhGkxPem5RaTf5YJGmUXHN8+lpj2ugssWhoq4sZvQ5GHO8UokWl2yZGZAFvBwHXhwqs6SthCYUfQdQRCYIaEjoywyi8z4XlGaWA5tDS0lLKJpgFA2fmmDvQl/44ovy1qFkJjQw2rwCKNG50OfA2CtmCJUupYSjO04owhOV6LeCzTKDMZyU9MyLtchLrHYwE5Ln+ooO3fuiql7EwPejxwLbOW02Nlq/2petLW8hB3dl9x3hN/Nd1L+YlUgkkv/OB2wScz61Wr3WAAAAAElFTkSuQmCC","orcid":"","institution":"Stony Brook University","correspondingAuthor":true,"prefix":"","firstName":"Markus","middleName":"","lastName":"Riessland","suffix":""}],"badges":[],"createdAt":"2025-06-18 20:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6925731/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6925731/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41514-025-00296-z","type":"published","date":"2025-12-19T15:58:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86996712,"identity":"7fc10cce-09bc-481d-a383-fdfb5f78d2c1","added_by":"auto","created_at":"2025-07-18 05:53:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":433364,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFAM49B is predominantly expressed in microglia in the human SNpc and is downregulated with age. a)\u003c/strong\u003e Correlation between FAM49B normalized expression and inferred relative microglia cell-type proportions across 114 human SNpc samples from the GTEx Project. FAM49B expression is most significantly associated with microglial abundance (\u003cem\u003ep\u003c/em\u003e = 4.1 × 10⁻⁷, Wald test), consistent with microglia as the primary cell type expressing FAM49B in the SNpc.\u003cstrong\u003e b)\u003c/strong\u003eSingle-cell RNA sequencing data from human SNpc tissue (GSE140231) confirm that FAM49B expression is highest in microglia (SN=SNpc).\u003cstrong\u003e c)\u003c/strong\u003e FAM49B expression negatively correlates with donor age in GTEx SNpc samples (\u003cem\u003ep\u003c/em\u003e= 3.0 × 10⁻⁴, Wald test), indicating age-associated downregulation in microglia. \u003cstrong\u003ed)\u003c/strong\u003e Bar graph of quantification of normalized counts showing decreased FAM49B expression in senescent (BrdU-treated) HMC3 cells (expression data from (Russo et al., 2025)) (Data was analyzed using an unpaired t-test. ** p\u0026lt;0.01, N=3,3).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6925731/v1/7989a3908cc15362207fba59.png"},{"id":86996899,"identity":"c5fd3278-7978-491c-b383-4513f7c60e02","added_by":"auto","created_at":"2025-07-18 06:01:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1458699,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReduced expression of FAM49B in microglia causes metabolic changes. a) \u003c/strong\u003eWestern blot analysis confirming ablation of FAM49B in HMC3 cells (n=6,6). \u003cstrong\u003eb)\u003c/strong\u003e Volcano plot shows global gene expression changes in WT vs. FAM49B-KO HMC3 cells (numbers indicate significantly up- or downregulated genes). \u003cstrong\u003ec) \u003c/strong\u003eHeatmap of oxidative phosphorylation genes significantly altered in FAM49B-KO cells\u003cstrong\u003e. d) \u003c/strong\u003eSeahorse graph showing significant reduction in oxidative phosphorylation in FAM49B-KO cells\u003cstrong\u003e, \u003c/strong\u003e(n=18 for each condition, Student’s t-test per time point, **p\u0026lt;0.01, *** p\u0026lt;0.001\u003cstrong\u003e.\u003c/strong\u003e Data are presented as mean ± SEM)\u003cstrong\u003e. \u003c/strong\u003eBar graphs show significant reduction in basal respiration \u003cstrong\u003e(e)\u003c/strong\u003e, maximal respiration \u003cstrong\u003e(f)\u003c/strong\u003e, ATP production \u003cstrong\u003e(g)\u003c/strong\u003e, and spare respiratory capacity \u003cstrong\u003e(h). \u003c/strong\u003e(n=18 for each condition, Student’s t-test per time point, **p\u0026lt;0.01, *** p\u0026lt;0.001. Data are presented as mean ± SEM).\u003cstrong\u003ei) \u003c/strong\u003eBar graph shows cell viability in HMC3 WT and FAM49BKO cells treated with 2-Deoxyglucose. (n=4(WT)/4(KO)), ** p\u0026lt;0.01, 2-way ANOVA). \u003cstrong\u003ej) \u003c/strong\u003eWestern blot analysis confirming reduction of Fam49b in BV2 cells by siRNA (n=3,3). \u003cstrong\u003ek)\u003c/strong\u003eRepresentative confocal images of BV2 cells stained with functional dye MitoTracker Red CMXRos 48 hours post siRNA transfection. Analysis shows reduced mitochondrial membrane potential measured by average fluorescence intensity (n, ctrl = 348, siFam49b = 598). \u003cstrong\u003el) \u003c/strong\u003eRepresentative TEM images using immunogold labeling for Fam49b in BV2 cells. Bar graph shows quantification of the percentage of Fam49b located in mitochondria under control or LPS treatment (N=6, 4). Scale bar 500 nm. Data are presented as mean ± SEM. Data for \u003cstrong\u003ea, e, f, g, h, i, j, \u003c/strong\u003eand\u003cstrong\u003e l \u003c/strong\u003ewere\u003cstrong\u003e \u003c/strong\u003eanalyzed using an unpaired Mann-Whitney t-test. Data for \u003cstrong\u003ek \u003c/strong\u003ewas analyzed using an unpaired t-test with Welch’s correction. * p\u0026lt;0.05, ** p\u0026lt;0.01, *** p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6925731/v1/00bbcb6bb774cb4f313a9838.png"},{"id":86995986,"identity":"a19b0a6f-ab9a-4c39-a5d7-52e2ff2a1b0b","added_by":"auto","created_at":"2025-07-18 05:45:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2131370,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFAM49B reduction causes cytoskeletal rearrangements.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea) \u003c/strong\u003eHeatmap of cytoskeletal genes that were significantly altered in FAM49B-KO cells\u003cstrong\u003e. b) \u003c/strong\u003eRepresentative brightfield images of HMC3 WT and FAM49B-KO cells. Dot plot shows quantification of roundness comparing WT vs. KO. (scale bar 50 mm, N=3, n=38(WT)/67(KO), ** p\u0026lt;0.01, Student’s unpaired t-test). \u003cstrong\u003ec) \u003c/strong\u003eAnalysis of trans-well migration assays performed in the HMC3 cells (N=3,3) and BV2 cells (N=8,8) showing increased migration in FAM49B-KO and KD cells. \u003cstrong\u003ed) \u003c/strong\u003eCell adhesion assay measured using the XCELLigence Agilent impedance device, showing impaired adhesion in Fam49b-KD BV2 cells (N=8,8) and \u003cstrong\u003ee)\u003c/strong\u003e FAM49B-KO HMC3 cells (n=4,4). Data shown as mean (±SEM) analyzed using multiple t-test. *** p\u0026lt;0.001. \u003cstrong\u003ef) \u003c/strong\u003eEndocytosis assay performed using FITC-Dextran uptake in BV2 cells (siCtrl: N=3, n=260; siFam49b: N=3, n=204) and \u003cstrong\u003e(g) \u003c/strong\u003eHMC3 cells (WT: N=3, n=701, KO: N=3, n=940). Data was analyzed using Welch’s t-test. ** p\u0026lt;0.01 *** p\u0026lt;0.001\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6925731/v1/63a7002aace8c6631ac8af60.png"},{"id":86996715,"identity":"ca87b7e8-5853-47b1-a3d5-7185078cdd9c","added_by":"auto","created_at":"2025-07-18 05:53:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2888124,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExposure of FAM49B-KO microglia induced migration response in midbrain cultures. a)\u003c/strong\u003e Scatterplot of migration velocity of global cell movement induced by plating WT and FAM49B-KO microglia (HMC3) on WT and SATB1-KO dopamine neuron cultures (DA) (HMC3\u003csup\u003eWT\u003c/sup\u003e/DA\u003csup\u003eWT\u003c/sup\u003e; HMC3\u003csup\u003eWT\u003c/sup\u003e/DA\u003csup\u003eSATB1-KO\u003c/sup\u003e; HMC3\u003csup\u003eFAM49B-KO\u003c/sup\u003e/DA\u003csup\u003eSATB1-KO\u003c/sup\u003e). Mean ± SD is shown (n\u0026gt;4.200, N=3, 3 per condition), data was analyzed using One-way ANOVA. ** p\u0026lt;0.01 **** p\u0026lt;0.001. \u003cstrong\u003eb)\u003c/strong\u003e Representative images showing individual cell migration tracks over 36h of tracking to compare different plating conditions (FIJI, tracking). \u003cstrong\u003ec)\u003c/strong\u003e Marker gene expression of migratory cues in HMC3 cells. (Data are presented as mean ± SEM and were analyzed using an unpaired Mann-Whitney t-test. * p\u0026lt;0.05, ** p\u0026lt; 0.01).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6925731/v1/0f853d78dd7b5cee8609d272.png"},{"id":86996713,"identity":"c1900170-81e4-41c5-9b6f-d0f25bd80b3f","added_by":"auto","created_at":"2025-07-18 05:53:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":195913,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInflammatory gene expression in HMC3 FAM49B-KO cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea)\u003c/strong\u003e \u003cem\u003eTLR\u003c/em\u003egenes, \u003cem\u003eJUN\u003c/em\u003e and \u003cem\u003eNFKB1\u003c/em\u003e expression in HMC3 cells. \u003cstrong\u003eb)\u003c/strong\u003e IL8 expression in HMC3 cells, \u003cstrong\u003ec)\u003c/strong\u003e IL8 expression is triggered by TNFastimulation. FAM49B-KO cells react significantly more to this stimulation. \u003cstrong\u003ed)\u003c/strong\u003eThe expression of IL8 is significantly ameliorated by NFkB inhibitor IV treatment in HMC3 FAM49B-KO cells. (Data are presented as mean ± SEM and were\u003cstrong\u003e \u003c/strong\u003eanalyzed using multiple t-tests in a) and unpaired t-test in b) and c) and two-way ANOVA (d). * p\u0026lt; 0.05, *** p\u0026lt; 0.001)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6925731/v1/02446323b2457a7cb5030f08.png"},{"id":98814165,"identity":"ede08935-6408-461d-b79f-ecc9aa0ef395","added_by":"auto","created_at":"2025-12-22 16:11:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7212123,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6925731/v1/d109df51-8932-4b63-8dad-a6c18abc521d.pdf"},{"id":86996900,"identity":"2d961c95-b868-4ff0-9486-3b6714a88e7c","added_by":"auto","created_at":"2025-07-18 06:01:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2420865,"visible":true,"origin":"","legend":"","description":"","filename":"SupplFigsMartinetal.docx","url":"https://assets-eu.researchsquare.com/files/rs-6925731/v1/3c901384f6dd46525adf70dd.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Age-related nigral downregulation of the Parkinson’s risk factor FAM49B primes human microglia for inflammaging","fulltext":[{"header":"Introduction","content":"\u003cp\u003eParkinson\u0026rsquo;s disease (PD) is the second most common neurodegenerative disorder, primarily characterized by the progressive degeneration of dopaminergic (DA) neurons in the substantia nigra pars compacta (SNpc) which leads to characteristic motor impairments (Dauer and Przedborski, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Moore et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Aging is the most relevant risk factor for PD, with its incidence increasing strongly after the age of 60 (De Lau and Breteler, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). While rare monogenic forms of PD are caused by mutations in genes that impair DA neurons, such as \u003cem\u003eSNCA\u003c/em\u003e, \u003cem\u003ePRKN\u003c/em\u003e, \u003cem\u003ePINK1\u003c/em\u003e, \u003cem\u003eLRRK2\u003c/em\u003e, and \u003cem\u003ePARK7\u003c/em\u003e (Martin et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), genes identified in genome-wide association studies (GWAS) suggest that cells of both the adaptive and innate immune system also play crucial roles in PD pathogenesis (Chang et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hamza et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Holmans et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kam et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pierce and Coetzee, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Accordingly, neuroinflammation, including microgliosis, has been described in PD for many decades (McGeer et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). A causal relationship between glial activity and PD has been proposed such that, inflammaging, a low-grade inflammation resulting from an imbalance between pro- and anti-inflammatory signals and cellular senescence, can drive DA neuron loss in the SNpc (Calabrese et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Riessland et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Russo and Riessland, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Accordingly, recent studies indicate that microglial activation in PD is not a secondary response to neuronal loss but may actively contribute to neurodegeneration as a primary trigger. For example, α-synuclein, a protein central to PD pathology, can be taken up by microglia, triggering a TLR-Fyn-PKCδ-NFκB-mediated inflammatory response that exacerbates neuronal damage (Gordon et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Panicker et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile not completely understood, it has been shown that microglial phenotypes vary across brain regions and can shift in response to aging and disease states (Hammond et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Relevant to PD, microglia in the vulnerable SNpc exhibit a transcriptional profile distinct from those in other CNS regions, with a predisposition toward a pro-inflammatory state (De Biase et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Olah et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This regional specificity has been proposed to contribute to the selective vulnerability of SNpc DA neurons in PD (Surmeier et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Interestingly, due to their close interaction, inflammatory signals from microglia can drive astrocytes toward a neurotoxic phenotype, further amplifying neuronal damage (Liddelow et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGiven the growing evidence linking microglial dysfunction to PD, targeting neuroinflammation has emerged as a potential therapeutic strategy for the disease. Anti-inflammatory treatments, such as PKCδ and NFκB inhibitors, have shown promise in preclinical PD models by reducing microglial activation and slowing disease progression (Gordon et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Further understanding of the complex interplay between aging, microglia, neuroinflammation, and PD pathology may provide new insights into disease mechanisms and therapeutic targets for the disease.\u003c/p\u003e\u003cp\u003eIn the present study, we investigated the genetic risk factor FAM49B, which was previously identified by a GWAS (Nalls et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and found that it contributes to the development of inflammaging in PD by regulating microglial activation.\u003c/p\u003e\u003cp\u003eFAM49B (family with sequence similarity 49 member B; also known as CYRIB (CYFIP-Related Rac1 Interactor B)) has been characterized as a mitochondria-localized protein regulating mitochondrial function and cancer progression (Chattaragada et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and inhibiting T cell activation by repressing Rac activity and modulating cytoskeleton reorganization (Shang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). While FAM49B is critical for T cell function (Park et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and has been suggested to be a marker of homeostatic microglia (Trainor et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), neither its cellular function in microglia cells nor its role in brain inflammaging has been evaluated.\u003c/p\u003e\u003cp\u003eIn brief, using computational analysis based on human brain gene expression data, we identified an age-related downregulation of FAM49B, specifically in microglia in the human SNpc. To further evaluate whether this age-related downregulation led to increased immunological response and microgliosis, we conducted functional experiments using microglial cell lines with FAM49B knockdown/knockout. Interestingly, the reduction of FAM49B induced microglia-relevant reactivity and significant metabolic changes, including dysfunctional oxidative phosphorylation and glycolysis, as well as modifications in cytoskeletal dynamics, migratory velocity, and adhesion capacity. Importantly, compared to controls, FAM49B-deficient microglia showed noticeably higher immunological activation. Our results imply that FAM49B is essential for regulating microglial immune responses. This informs the hypothesis that age-related downregulation of FAM49B in the SNpc may predispose microglia to an overactive gliosis phenotype, aggravating dopaminergic neuron loss and accelerating PD.\u003c/p\u003e\u003cp\u003eCrucially, the elevated immunological activity that we observed in our FAM49B-deficient microglial cells was successfully decreased by treatment with anti-inflammatory NFκB inhibitor treatment. Based on our results, FAM49B is a critical modulator of microglial immunological response. Consequently, its age-related downregulation could contribute to inflammaging and the subsequent pathophysiology of PD. Finally, anti-inflammatory treatment to target this pathway may present a viable strategy for reducing neuroinflammation and preserving dopaminergic neurons in PD.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eDownregulation of FAM49B in microglia with age in the human substantia nigra\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFAM49B has been identified as a genetic risk factor for PD (Nalls et al., 2019) (\u003cstrong\u003eFigure S1b\u003c/strong\u003e), however, neither its role in PD pathology nor in which relevant cell type it is expressed is known. To investigate the cellular context of \u003cem\u003eFAM49B\u003c/em\u003e expression, we analyzed bulk RNA-sequencing data from 114 human SNpc samples obtained from the Genotype-Tissue Expression (GTEx) Project (Consortium et al., 2020). Cell type proportions for each sample were computationally inferred by correlating bulk expression profiles with reference signatures of major brain cell types, including astrocytes, microglia, neurons, endothelial cells, and oligodendrocytes (see Methods). This analysis revealed that \u003cem\u003eFAM49B\u003c/em\u003e expression was most significantly correlated with the estimated proportion of microglia (Wald test, p=4.1x10\u003csup\u003e-7\u003c/sup\u003e), suggesting that \u003cem\u003eFAM49B\u003c/em\u003e is primarily expressed in these resident immune cells of the SNpc (\u003cstrong\u003eFigure 1a,\u0026nbsp;\u003c/strong\u003eother cell types shown in\u003cstrong\u003e\u0026nbsp;Figure S1a\u003c/strong\u003e). This finding is further supported by single-cell sequencing data from human SNpc tissue, which confirmed that \u003cem\u003eFAM49B\u003c/em\u003e expression is highest in microglia (\u003cstrong\u003eFigure 1b\u003c/strong\u003e, GSE140231 (Agarwal et al., 2020)). Moreover, after correcting for potential confounders such as cell type composition, we observed a significant negative correlation between \u003cem\u003eFAM49B\u003c/em\u003e expression and donor age, indicating an age-associated downregulation of \u003cem\u003eFAM49B\u003c/em\u003e in microglia (Wald test, p=3.0x10\u003csup\u003e-4\u003c/sup\u003e, \u003cstrong\u003eFigure 1c\u003c/strong\u003e). In summary, our integrative transcriptomic analysis indicates that \u003cem\u003eFAM49B\u003c/em\u003e is predominantly expressed in microglia within the human SNpc and is significantly downregulated with aging.\u003c/p\u003e\n\u003cp\u003eOne hallmark of brain aging is the accumulation of senescent cells, including senescent glial cells, which contribute to neuroinflammation and inflammaging in the midbrain. Pro-inflammatory senescent brain cells have also been associated with PD (Chinta et al., 2018; Riessland et al., 2019). Hence, we sought to determine if senescence in human microglia could alter the expression of \u003cem\u003eFAM49B\u003c/em\u003e and utilized our previously reported DNA damage-induced senescent HMC3 cells as an \u003cem\u003ein vitro\u003c/em\u003e model of senescence (Russo et al., 2025). The senescence phenotype was validated by a battery of molecular methods (see detailed characterization: (Russo et al., 2025)). Importantly, these senescent microglia showed significant downregulation of \u003cem\u003eFAM49B\u003c/em\u003e (\u003cstrong\u003eFigure 1d\u003c/strong\u003e), indicating a possible role of senescence in the inflammaging phenotype of SNpc microglia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReduced expression of FAM49B in microglia results in metabolic changes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine the function of FAM49B in human microglia, we employed the commonly used human HMC3 cell line (Dello Russo et al., 2018) and generated a CRISPR/Cas9-mediated knockout clone. After confirming the knockout, bulk RNA sequencing was performed to compare the gene expression of wild-type (WT) and knockout (KO) HMC3 cells (\u003cstrong\u003eFigure 2a\u003c/strong\u003e). Assessment of the gene expression profiles revealed significant global expression changes and substantial alterations in mitochondrial genes (\u003cstrong\u003eFigure 2b, c, S2a, b).\u003c/strong\u003e These changes are of particular interest since FAM49B has recently been reported to be involved in the regulation of mitochondrial function and integrity in pancreatic ductal adenocarcinoma cells (Chattaragada et al., 2018). However, the role of FAM49B in microglia in the context of PD has yet to be characterized. Thus, we applied diverse methods to investigate the metabolic changes that occur in FAM49B-KO microglia. A Seahorse real-time cell metabolic analysis (XF Mito Stress Test) revealed that the KO of FAM49B in HMC3 microglia caused a significant decrease in basal and maximal respiration, ATP production, and spare respiratory capacity (\u003cstrong\u003eFigure 2d-h\u003c/strong\u003e) while not affecting non-mitochondrial oxygen consumption and coupling efficiency (\u003cstrong\u003eFigure S2c, d\u003c/strong\u003e)\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eIn addition, the overall energy production was impaired as suggested by a significant change in glycolysis gene expression (\u003cstrong\u003eFigure S2g\u003c/strong\u003e). To test whether glycolysis function was impaired, we performed a 2-deoxyglucose assay, which leads to an increase in cell death if cells utilize glycolysis. Interestingly, the FAM49B-KO microglia cells were less susceptible as compared to the WT microglia (which partially depend on glycolysis (Sangineto et al., 2023)) in this assay, further suggesting a partial impairment of the glycolytic pathway (\u003cstrong\u003eFigure 2i\u003c/strong\u003e). To confirm these findings in an additional microglia model, mouse BV2 cells were used. An siRNA-mediated knockdown (KD) of Fam49b (\u003cstrong\u003eFigure 2j\u003c/strong\u003e) significantly reduced the membrane potential of mitochondria in these cells, while the overall organelle number was not affected (\u003cstrong\u003eFigure 2k, S2e\u003c/strong\u003e). Using immunogold labeling in combination with transmission electron microscopy revealed that Fam49b localized to mitochondria (~50% of all Fam49b molecules were localized to mitochondria). Interestingly, triggering an immune response in BV2 cells by lipopolysaccharide (LPS) treatment resulted in a reduction of Fam49b in mitochondria (\u003cstrong\u003eFigure 2l\u003c/strong\u003e). We also found a significant upregulation of free radicals produced in Fam49b-KD cells (\u003cstrong\u003eFigure S2f\u003c/strong\u003e), an additional indication of mitochondrial impairment.\u003c/p\u003e\n\u003cp\u003eIn summary, the reduction of FAM49B significantly impaired the metabolism of both mouse and human microglia cells. Interestingly, this metabolic change partially resembles microglia that face pro-inflammatory stimuli and prepare to respond (Yang et al., 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFAM49B-KO and KD microglia show alterations in cytoskeletal dynamics and migration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMicroglia exhibit significant mobility and are considered the most dynamic cells of the healthy mature brain (Paolicelli et al., 2022; Tremblay, 2011). They are constantly active and survey their environment by extending and retracting their processes to detect changes, such as neuronal damage, infection, or inflammation. When responding to inflammatory cues, microglia can adopt an amoeboid morphology, increase migratory and endocytic capacities, and move toward areas of damage or pathology (Paolicelli et al., 2022). Microglia are responsible for the elimination of microbes, dead cells/cell debris, excessive synapses, protein aggregates, and other antigens that may endanger the CNS (Colonna and Butovsky, 2017). Recently, it has been reported that microglia can use tunneling nanotubes to interact with other microglia or neurons to protect them from aggregate-induced cellular dysfunction and death (Scheiblich et al., 2024). In order to complete these tasks, cytoskeletal remodeling is essential, as microglia have to extend their processes, or nanotubes, and migrate towards the cellular debris to phagocytose it (Smolders et al., 2019). Given these critical functions and the previously published findings that FAM49B is a regulator of actin dynamics (Shang et al., 2018) and directly interacts with activated Rac Family Small GTPase 1 (RAC1), known to regulate cytoskeleton organization (Bosco et al., 2009), we investigated the impact of FAM49B loss on cytoskeletal dynamics and migration in our microglial cell lines. Indeed, gene expression profiling of genes related to cytoskeletal dynamics revealed striking rearrangements, indicating an effect on cell migration, adhesion, and other cytoskeletal functions, such as endocytosis (\u003cstrong\u003eFigure 3a S3a, c\u003c/strong\u003e). Interestingly, while WT microglia displayed a ramified morphology consistent with a surveillant state, FAM49B-KO induced a transition to an amoeboid (\u003cstrong\u003eFigure 3b\u003c/strong\u003e), reactive phenotype, additionally marked by upregulation of IL-8 (\u003cstrong\u003eFigure 5b\u003c/strong\u003e). In line with this finding, it was previously reported that FAM49B-KO in human melanoma cells induced a round shape and increased migration speed (Fort et al., 2018). Employing a trans-well assay, we assessed the migration across a porous mesh. Interestingly, the HMC3 FAM49B-KO and the BV2 Fam49b-KD cells migrated more efficiently than their respective controls, suggesting an increase in migration activity (\u003cstrong\u003eFigure 3c\u003c/strong\u003e). Based on relevant gene expression changes (adhesion genes heatmap: \u003cstrong\u003eFigure S3a\u003c/strong\u003e), we aimed to evaluate the differences in adhesion capabilities. To assess adhesion differences, we utilized two experimental approaches. First, we employed an impedance-based assay. BV2 and HMC3 cells were plated in equal numbers onto an impedance plate, which measures the cell index based on electrical impedance, a direct indicator of adhesion. We tracked impedance changes from the moment cells were plated until they reached confluency. Both Fam49b-KD BV2 cells and FAM49B-KO HMC3 cells adhered at a significantly slower rate than control cells (\u003cstrong\u003eFigure 3d, e\u003c/strong\u003e). A similar trend was confirmed with a counting assay performed in the HMC3 cells. Equal numbers of cells were plated overnight, and the following day, attached cells were counted and compared to live cells remaining unattached in the supernatant. FAM49B-KO cells displayed delayed adhesion to the culture plate surface (\u003cstrong\u003eFigure S3b\u003c/strong\u003e). Next, we examined microglial endocytosis, a process that requires significant cytoskeletal rearrangement. As the primary macrophage of the CNS, endocytosis is a crucial function of microglia (Li and Barres, 2018; Sol\u0026eacute;-Dom\u0026egrave;nech et al., 2016). Since we observed that endocytosis-related genes were differentially expressed in FAM49B-KO HMC3 cells, we hypothesized that their endocytic capacity might be compromised (endocytosis genes heatmap: \u003cstrong\u003eFigure S3c\u003c/strong\u003e). Applying fluorescence-labeled dextran and microscopy-based analysis, we determined that dextran uptake was significantly reduced in Fam49b-KD and FAM49B-KO microglia (\u003cstrong\u003eFigure 3f, g\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eIn summary, our findings indicate that microglia lacking FAM49B exhibit significantly impaired adhesion and endocytosis, both critical hallmarks of microglial function. Additionally, we observed that KO and KD cells migrated faster than WT cells, which is in line with an activation phenotype and their reduced adhesion, which may lower resistance to movement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExposure of FAM49B-KO microglia to DA neuron cultures triggers a global immune response\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the midbrain, activated microglia release molecules that can activate a response in surrounding cells. Activation of astrocytes by microglia, for example, can exacerbate neuroinflammation (Liddelow et al., 2017). In PD models, it has been suggested that \u003cstrong\u003e\u0026alpha;-synuclein\u0026nbsp;\u003c/strong\u003eenhances microglial motility and that microglia-derived TNF-\u0026alpha; induces astrocyte migration (Burda and Sofroniew, 2014; Campolo et al., 2019). Since we observed a significant increase in migratory behavior in FAM49B-KO cells (\u003cstrong\u003eFigure 3\u003c/strong\u003e), we sought to investigate their impact on the midbrain environment, including DA neurons, glial cells and fibroblasts. Using human stem cell-derived DA neuron cultures that include glial cells, epithelial cells and perivascular fibroblasts (Kim et al., 2021), we transferred either WT or FAM49B-KO HMC3 cells onto these cultures and monitored overall migratory behavior. Interestingly, we observed a significant increase in motility when FAM49B-KO cells were plated onto the cultures as measured by live imaging for 36 hours (\u003cstrong\u003eFigure 4\u003c/strong\u003e). Additionally, we have previously found that depletion of the PD risk gene \u003cem\u003eSATB1\u003c/em\u003e induced a senescence phenotype in DA neurons, leading to a significant microglial immune response in the midbrain (Riessland et al., 2019), thus, we sought to determine the effect of exposure of senescent SATB1-KO neuron cultures to FAM49B-KO microglia as compared to WT neuron cultures. As expected, plating of FAM49B-KO microglia onto senescent DA neuron cultures resulted in an even higher reaction, indicated by a significant increase of overall motility as measured per cell by velocity (\u003cstrong\u003eFigure 4a, b\u003c/strong\u003e) and distance traveled (\u003cstrong\u003eFigure S4\u003c/strong\u003e)\u003cstrong\u003e.\u003c/strong\u003e It has been shown that PAI-1 (\u003cem\u003eSERPINE1\u003c/em\u003e) acts as a hub both as a target and initiator of various pathways that regulate cellular motility via cues, including urokinase-type plasminogen activator (uPA (\u003cem\u003ePLAU\u003c/em\u003e)) and growth factors such as transforming growth factor \u0026beta;1 (TGF-\u0026beta;1(\u003cem\u003eTGFB1\u003c/em\u003e)) (Czekay et al., 2011). Interestingly, these migratory signaling molecules were found to be upregulated in FAM49B-KO microglia (\u003cstrong\u003eFigure 4c\u003c/strong\u003e), suggesting that these molecules stimulate an overall migratory response in the human stem cell-derived midbrain cultures.\u003c/p\u003e\n\u003cp\u003eTaken together, these results suggest that the presence of FAM49B-reduced microglia (as found in the aged SNpc) causes an inflammaging reaction in diverse surrounding midbrain cell types. This could eventually contribute to the age-related vulnerability of the SNpc and the age-driven presence of inflammation markers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFAM49B-KO in microglia shows an increased immune response that is ameliorated by NF\u003c/strong\u003e\u003cstrong\u003ekB inhibition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMicroglia, the resident macrophages of the central nervous system, play a crucial role in immune surveillance and inflammatory responses. Upon encountering inflammatory stimuli, microglia can polarize towards a pro-inflammatory state and secrete cytokines such as interleukin-8 (IL-8), which mediates neuroinflammation (Colonna and Butovsky, 2017). Given their central role in neuroinflammation, dysregulation of microglial activity has been implicated in aging and neurodegenerative diseases, including PD (Heneka et al., 2014; Tansey and Goldberg, 2010).\u003c/p\u003e\n\u003cp\u003eToll like receptors (TLRs) expressed in microglia are elevated in individuals with PD, contributing to neuroinflammation and neurodegeneration. TLR-mediated inflammation has been suggested to contribute to the loss of DA neurons in PD (Heidari et al., 2022), and TLR2/6 and TLR5 have been shown to bind \u0026alpha;-synuclein (Zhang et al., 2024). Interestingly, loss of FAM49B significantly led to a signification upregulation of TLRs, including TLR5 and 6 (\u003cstrong\u003eFigure 5a\u003c/strong\u003e). TLRs are known to regulate immune responses via NFkB activation. To further investigate the role of FAM49B in microglial immune responses, we stimulated WT and FAM49B-KO or -KD cells with inflammatory triggers, including tumor necrosis factor-alpha (TNF-\u0026alpha;, in HMC3 cells) and lipopolysaccharides (LPS, in BV2 cells). Notably, the absence or reduction of FAM49B led to a significantly heightened pro-inflammatory response. FAM49B-KO in HMC3 cells caused a significant upregulation of IL-8 even without TNF-\u0026alpha; stimulation (\u003cstrong\u003eFigure 5b\u003c/strong\u003e). Importantly, both FAM49B-KO and Fam49b-KD cells exhibited significantly increased expression of multiple immune response genes, following TNF-\u0026alpha; or LPS treatment, compared to their WT or control counterparts (\u003cstrong\u003eFigure 5c, S5a\u003c/strong\u003e (HMC3) and \u003cstrong\u003eS5b\u003c/strong\u003e (BV2)).\u003c/p\u003e\n\u003cp\u003eThese findings suggest that the reduction of FAM49B primes microglia towards an exaggerated inflammatory response, potentially contributing to neuroinflammation observed in aging and neurodegenerative disorders.\u003c/p\u003e\n\u003cp\u003eTo explore whether pharmacological intervention could mitigate this excessive immune response, we treated both WT and FAM49B-KO HMC3 microglia with NFkB inhibitor IV. Quantitative PCR analysis revealed that the inhibitor significantly reduced IL-8 expression (\u003cstrong\u003eFigure 5d\u003c/strong\u003e), suggesting that anti-inflammatory treatments may help ameliorate the detrimental hyperactivation of microglia associated with aging and PD. These findings are consistent with previous studies demonstrating that anti-inflammatory interventions can improve neuroinflammatory pathology in models of PD (Caggiu et al., 2019; Chen et al., 2003). Moreover, systemic inflammation, as modeled by LPS administration, has been shown to induce microglial overactivation and contribute to DA neuron loss in the SNpc, further establishing a link between chronic inflammation and neurodegeneration (Qin et al., 2007).\u003c/p\u003e\n\u003cp\u003eTaken together, our results suggest that FAM49B serves as a key regulator of microglial activation, and its downregulation with age could be a contributing factor to increased reactivity, inflammaging, and, ultimately, neurodegenerative diseases.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eGiven the discovery of age-related downregulation of the PD risk factor FAM49B in nigral microglia, this study provides critical new insights into how aging and PD converge on the innate immune system, particularly on the resident immune cell of the brain, to contribute to neurodegeneration. While the intersection of age-related immune dysfunction and PD pathogenesis remains poorly understood, growing evidence suggests that chronic glial activation, impaired immune homeostasis, and dysregulated phagocytic clearance contribute to disease. Aging is the strongest risk factor for idiopathic PD (De Lau and Breteler, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Using human gene expression data from different age groups, our results suggest how aging alone alters the molecular and functional landscape of microglia driven by the reduction of FAM49B. This decline leads to increased expression of inflammatory mediators, loss of homeostatic signatures, and impaired endocytic capacity. These observations support the view that age-associated dysfunction of microglia may create vulnerability of the SNpc and a permissive environment for the development of PD, even without genetic or environmental insults. The key finding of our study is the identification of FAM49B as a molecular link between aging, immune dysregulation, and PD risk. A GWAS has implicated FAM49B as a PD risk gene (Nalls et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), while its role in glial function and inflammaging remained elusive. Here, we show that FAM49B expression is selectively downregulated in microglia of the SNpc with age, suggesting a critical role in maintaining immune homeostasis in the brain. Our functional studies revealed that FAM49B-KO in microglia results in aberrant activation, with upregulation of pro-inflammatory cytokines, consistent with a transition toward a disease-associated-like or responding state. However, this activation was not accompanied by functional upregulation of microglial clearance mechanisms. Instead, FAM49B-deficient microglia showed impaired endocytosis, indicating a separation of their inflammatory response from phagocytic ability. This is highly relevant for PD, where a classical hallmark of pathology is the accumulation of misfolded α-synuclein, which normally can be cleared via microglial phagocytosis (Choi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). We suggest that FAM49B might play a dual role, on the one hand, regulating inflammatory responses, and on the other hand, it coordinates cytoskeletal function, thereby supporting phagocytosis. Thus, it is plausible that loss of FAM49B may tip the homeostatic balance toward a maladaptive microglial phenotype that is both neuroinflammatory and ineffective at clearing debris. This aligns with age-related microglial dysfunction and thereby contributes to midbrain inflammaging.\u003c/p\u003e\u003cp\u003eOur findings support previous research that identifies senescent or dystrophic microglia in aged and diseased brains (Streit et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), suggesting that immunosenescence in the brain contributes to neurodegenerative vulnerability. More concretely, our data confirm the outcome of single-cell studies showing age-associated occurrence of dysfunctional glial states with pro-inflammatory but inefficient immune profiles (Clarke et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Olah et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Despite the ubiquitous gene expression of PD-associated factors, the SNpc is most vulnerable to degeneration, suggesting that, in addition to the intrinsic vulnerability of DA neurons, local cellular context, including the density and state of glial cells, are critical determinants of susceptibility. Importantly, we observed that SNpc expression of FAM49B was highest in microglia, further supporting its relevance to nigral immune health. Reduction of FAM49B in microglia, which occurs with age in this vulnerable brain region, leads to a shift from surveillance to inflammaging\u003c/p\u003e\u003cp\u003eWhile our study, based on human midbrain-derived sequencing data and modeled in microglia-like cell lines, showed highly reproducible results, it has some limitations. We chose cell lines (which were thoroughly characterized (Dello Russo et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Henn et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2009\u003c/span\u003e)) to ensure high reproducibility. We are aware that the cell line-based results could be reproduced in animals and/or human primary microglia. However, these approaches have their own caveats, such as inter-individual microglia differences and variation in microglia states due to isolation procedures, which could influence FAM49B expression and thus the outcome and interpretation of such functional analyses.\u003c/p\u003e\u003cp\u003eIn summary, our results establish the PD risk factor FAM49B as a novel regulator of microglial state and function with a high relevance to both PD pathogenesis and brain aging. Its downregulation with age could lead to a \"second hit\" cascade in PD that exacerbates inflammaging (due to basal upregulation of interleukin expression) while reducing protective clearance mechanisms (reduced endocytosis), a combination that may accelerate DA neuron loss. Our findings suggest a mechanistic rationale for why certain individuals, particularly older adults with a genetic predisposition, may develop PD in response to otherwise subthreshold stressors. Additionally, our data suggest FAM49B as a novel potential drug target, where boosting its function may offer a strategy to simultaneously suppress neurotoxic inflammation and enhance beneficial microglial clearance. Additionally, we found that FAM49B-KO cells upregulate the expression of TLRs, which have been indicated to increase in the context of PD and mediate inflammatory responses in microglia by activation of NFκB. This process of chronic activation of TLRs and neuroinflammation was suggested to lead to neurodegeneration in PD (Heidari et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Both toxin-induced (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, MPTP) and pre-formed fibrils of αSyn-induced PD mouse models were ameliorated by NFκB inhibition (Dutta et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ghosh et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eInterestingly, binding of α-synuclein monomers or oligomers to TLR5 efficiently activated the NOD-like receptor pyrin domain containing 3 (NLRP3) inflammasome (Scheiblich et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and TLR6 (which binds α-synuclein as TLR2/6 heterodimer (Zhang et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)) induces expression of inflammatory cytokines by activating NFκB (Akira and Takeda, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). This in in line with our findings that TLR5 and 6 were significantly upregulated in FAM49B-KO cells and the proinflammatory activity in FAM49B-KO microglia was ameliorated by NFκB inhibition. The previously published findings, in combination with our results, suggest that microglia activity (partially mediated by NFκB) drives the neuroinflammatory and neurodegenerative pathology in diverse PD models. With future studies, it would be interesting to determine if FAM49B levels were reduced in these animals, possibly connecting detrimental microglia activity to genetic predisposition and age-related susceptibility.\u003c/p\u003e\u003cp\u003eOur research establishes the PD risk gene FAM49B as a key molecular regulator of microglial activity during aging and PD, thus providing insights into genetic vulnerability and immune function failure leading to neurodegeneration. Hence, research focused on immune homeostasis pathways of the aging brain shows promise as a method to potentially prevent or delay PD and similar neurodegenerative disorders.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAge-Associated Differential Gene Expression Analysis of the human SNpc\u003c/em\u003e.\u003c/strong\u003e To deconvolute and estimate the relative proportions of major brain cell types in each bulk RNA-seq sample, we applied the methodology as described by Chikina et al. (Chikina et al., 2015). This method uses marker gene sets to infer latent variables representing cell-type abundance, enabling adjustment for cellular heterogeneity in bulk tissue expression data. Marker gene sets for neurons, astrocytes, microglia, oligodendrocytes, and endothelial cells were obtained from Zhang et al. (Zhang et al., 2014), who identified these markers from purified brain cell populations. Using these marker sets, we computed relative cell-type proportion estimates for each GTEx SNpc sample using normalized gene expression values provided by the GTEx consortium (version 8). These estimates were then used as covariates in a linear model framework to assess gene expression associations with donor age while controlling for differences in cell-type composition across samples. FAM49B expression was specifically evaluated for its correlation with inferred cell-type proportions and for age-associated differential expression after covariate adjustment. Significance was assessed using linear regression models, and p-values were corrected for multiple testing. We further confirmed that the age-associated change in FAM49B expression could be specifically attributed to microglia expression through a F-test, allowing for the identification of cell-type-specific expression changes. All code to replicate the analysis is available in the supplemental material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHMC3 Cell Culture\u003c/em\u003e.\u003c/strong\u003e HMC3 cells were cultured according to the vendor\u0026rsquo;s instructions. Cells were grown at 37\u0026deg;C in 5% CO2. The medium used was Corning Minimum Essential Medium Eagle with Earle\u0026rsquo;s salts and L-glutamine (#10-010-CV) with 1% Pen-Strep, 1% Sodium pyruvate, 1% MEM Non-essential Amino Acid Solution (#M7145-100mL, SIGMA) and 10% Fetal Bovine Serum (FBS). Cells were passaged every 2-3 days with Trypsin-EDTA (ThermoFisher #25200072). HMC3 cells were stimulated with 0.3 ug/mL TNFa (#TNA-H4211, Acro Biosystems)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBV2 Cell Culture\u003c/em\u003e.\u003c/strong\u003e BV2 cells were cultured according to the vendor\u0026rsquo;s instructions. Cells were grown at 37\u0026deg;C in 5% CO2. The medium used was RPMI (#11875-093, Gibco) with 1% Pen-Strep and 10% Fetal Bovine Serum (FBS). Cells were passaged every 2-3 days with Trypsin-EDTA (#25200072, ThermoFisher)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003esiRNA Transfection\u003c/em\u003e.\u003c/strong\u003e BV2 cells were seeded at 2.5x10\u003csup\u003e5\u003c/sup\u003e per well on a 6-well plate using RPMI medium and left overnight. Cell medium was changed 30 min prior to transfection, which was performed using Lipofectamine RNAiMAX (ThermoFisher #13778030), OptiMEM (Gibco #31985062), 15 pmol Silencer Select siFAM49B siRNA (ThermoFisher #4390771), and 15 pmol Silencer Select Negative Control (ThermoFisher #4390843) in accordance with the ThermoFisher RNAiMAX protocol. 24 hours following transfection, cells were passaged onto respective vessels required for each experiment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTrans-well Migration Assay\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHMC3: WT and FAM49B KO HMC3 cells were cultured in separate T25 flasks using EMEM medium supplemented with 10% FBS and 1% penicillin/streptomycin and incubated at 37℃. Upon reaching 90% confluency, cell medium was removed and replaced with starvation medium (EMEM supplemented with 1% FBS and 1% penicillin/streptomycin) and cells were incubated at 37℃ for 24 hours. Flasks were rinsed with 2mL pre-warmed D-PBS and then passaged with 1mL trypsin and neutralized with 2 mL starvation medium. Cells were seeded at 5.0x10\u003csup\u003e4\u003c/sup\u003e per well in 250 𝜇L onto 12.0 𝜇m trans-well inserts (#PIXP01250, Millipore) coated with 0.1% gelatin. 500 𝜇L normal (not starvation) HMC3 media was placed in the bottom chamber of each well, and cells were incubated at 37℃ for 48 hours. Inserts were rinsed with 250 𝜇L PBS and wells were rinsed with 500 𝜇L PBS and then aspirated. 500 𝜇L 2.8 𝜇M Calcein AM (in 0.5M EDTA) as placed in each well and cells were incubated at 37℃ for 1 hour. Wells were scraped and solution was transferred into a black 96-well plate (100 𝜇L/well). Samples were excited using a wavelength of 485 nm and fluorescence at 535 nm was measured using a SpectraMax iD3 plate reader (Molecular Devices).\u003c/p\u003e\n\u003cp\u003eBV2: Transfected BV2 cells were seeded at 5.0x10\u003csup\u003e5\u0026nbsp;\u003c/sup\u003eper well on a 6-well plate using DMEM medium supplemented with 10% FBS and 1% pen/strep and incubated at 37℃ overnight. Cell medium was removed and replaced with starvation medium (DMEM supplemented with 1% FBS and 1% penicillin/streptomycin) 30 min prior to siRNA transfection. 24 hours following transfection, cells were passaged in 500 𝜇L trypsin and neutralized with starvation medium. Cells were seeded at 5.0x10\u003csup\u003e4\u003c/sup\u003e per well in 250 𝜇L onto 8.0 𝜇m trans-well inserts (VWR #734-2748) coated with 0.1% gelatin (Sigma #G1393). 500 𝜇L normal (not starvation) BV2 media was placed in the bottom chamber of each well, and cells were incubated at 37℃ for 24 hours. Inserts and wells were rinsed with PBS, cells were stained with Calcein AM, and fluorescence was measured as described above.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRoundness Assay.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eTo determine changes in the shape of the microglia, we applied a measurement of roundness as previously published (Riessland et al., 2019). In brief, brightfield images of living microglia cultures were taken using an Accu-Scope EXI-410 microscope with a Skye2 camera. Images were analyzed using the roundness measure in FIJI. While typically, ramified (branched) microglia indicate a surveillance state, more amoeboid cells suggest activation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMicroglia co-culture with Embryonic stem cells and cell tracking.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eHuman embryonic stem cells [ESCs; wild-type H9 (WA-09), SATB1\u003csup\u003eKO\u0026nbsp;\u003c/sup\u003e(Riessland et al., 2019)] were differentiated into midbrain dopamine (mDA) neurons according to an optimized version of previously established protocol (Riessland et al., 2019).WT and SATB1-KO ESCs were differentiated at a density of 60k/well on a 96 well plate (\u0026micro;-Plate 96 well square; Ibidi). On day 50 either HMC3-WT and HMC3-KO cells were fluorescently labeled (Cell tracker Orange CMTMR; Invitrogen) according to manufacturer\u0026rsquo;s instructions and plated on top of either WT or SATB1-KO differentiated mDA neurons. Co-cultures are fluorescently imaged (586,647) every 30 minutes for 66 hours using the Agilent BioTek Lionheart FX live imaging system. Note that cell labeling is not exclusive to HMC3 cells and ESCs become fluorescently labeled within 30 minutes of co-culture. Cell tracking analysis was completed using TrackMate (Ershov et al., 2022). Statistical analysis was completed using Ordinary one-way ANOVA (prism).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eWestern Blot.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eCells were lysed for protein using RIPA buffer (ThermoFisher #89900) and protease and phosphatase inhibitors (#11836170001, Roche). Protein concentrations for each sample were determined using a BCA assay (ThermoFisher #23225) with a SpectraMax iD3 plate reader (Molecular Devices). Protein samples were boiled in 2X Tris-Glycine-SDS sample buffer (Novex #LC2676) at 95\u0026deg;C for 5 min before being separated with electrophoresis using 10% Tris-glycine (Invitrogen #XP04200). Samples were then electro-transferred to nitrocellulose membranes (BioRad) and blocked using 5% BSA in TBS-Tween for 60 min, before undergoing overnight incubation with the required primary antibody in blocking solution. The primary antibodies used are listed as follows: mouse monoclonal anti-FAM49B antibody (SCT #sc-390478; ThermoFisher #PA5-52647), anti-TOM20 antibody (SCT #11415). To visualize and quantify protein bands, a ChemiDocXRS+ (BioRad) was used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRNA isolation, qPCR, and RNA-seq.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e RNA was isolated from cells using the RNeasy Plus Mini Kit (#74136, QIAGEN) and QIAshredder (QIAGEN, #7956). Cells were lysed using 2-Mercaptoethanol. 200ng of RNA was reverse transcribed using the Applied Biosystems High-Capacity cDNA Reverse Transcription Kit (ThermoFisher # 4368814) creating an output volume of 20\u0026micro;L. Real time qPCR was performed using a Quant Studio 3 (Applied Biosystems) and the Applied Biosystems Power SYBR Green PCR Master Mix (ThermoFisher #4367659). Reaction specificity was confirmed via melt curve analysis. Previously described in Russo et al, 2025, for RNA-seq, 500 ng of RNA for each sample was sent to Azenta for bulk RNAseq. In brief, sample quality control and determination of concentration was performed using TapeStation Analysis by Azenta, followed by library preparation and sequencing. Computational analysis included in their standard data analysis package, was used for data interpretation.\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThe gene expression data of the HMC3 RNA-seq is accessible on ArrayExpress (Accession number E-MTAB-15277).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTransmission Electron Microscopy.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eFor transmission electron microscopy (TEM), cells were grown on Aclar film and fixed for 30 minutes in a mixture of cold 2% paraformaldehyde and 0.1% glutaraldehyde in 0.1M phosphate buffer (PB), pH 7.4. After washing, cells were post-fixed with 1% osmium tetroxide for 30 minutes, and stained with 1% uranyl acetate for 30 minutes, dehydrated through an ascending series of ethanol, and embedded in Durcupan resin for 48 hours at 60\u0026deg;C. Ultrathin sections (60-90nm) were cut on an ultramicrotome (Reichert Ultracut E) and placed on formvar-coated nickel slot grids. Sections were post-embedding immunogold labeled for rabbit FAM49b within 24 hours of sectioning using a modification of the protocol of Phend et al (Phend et al., 1995). In brief, grids were rinsed in TBS containing 0.005% Tergitol NP-10, pH 7.6 (hereafter referred to as Tris-tergitol pH 7.6), incubated in saturated (10%) sodium meta-periodate for 5 seconds, rinsed, incubated in 1% sodium borohydride for 1 minute, rinsed, and incubated in primary antibody (rabbit anti-FAM49B 1: 100) overnight at room temperature. The next day, grids were rinsed in Tris-tergitol pH 7.6, followed by Tris-tergitol pH 8.2, incubated in secondary antibody (goat anti-rabbit IgG conjugated to 18nm gold particles) (Jackson Immuno Research) 1:25 in Tris-tergitol pH 8.2 for 1 hour and rinsed. Grids were then stained with 1% methanolic uranyl acetate and 0.3% aqueous lead citrate and imaged using a JEOL 1200EX transmission electron microscope.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEndocytosis assay.\u003c/em\u003e\u003c/strong\u003e Cells were treated with 1 mg/mL of 75kDa Fluorescein isothiocyanate-Dextran (Sigma Aldrich #60842-46-8) in their respective medium for 15 minutes at 37\u0026deg;C. Cells were then washed two times with cold PBS before being fixed in 4% PFA in PBS for 15 minutes at room temperature. Following fixation, cells were incubated with Phalloidin-647 (ThermoFisher # A22287) in PBS for 1 hour at room temperature. Cells were then washed 1x in PBS, mounted using DAPI Fluoromount-G (Southern Biotech #0100-20) and imaged using an Olympus confocal microscope.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCell Adhesion Assays\u003c/em\u003e.\u0026nbsp;\u003c/strong\u003eCell adhesion assay was performed using the XCELLigence Agilent Impedance device. Prior to seeding of cells, the 16-well E-plate was coated using 0.1% gelatin in D-PBS. HMC3 cells and BV2 cells were seeded at 1.5x10\u003csup\u003e4\u003c/sup\u003e cells per well. Cell index readings were taken every 1 minute for a total of 60 minutes (BV2) and every 2 minutes for a total of 24 hours (HMC3), or until cells reached confluency. An additional cell adhesion test was performed in the HMC3 cells using a counting assay. Equal numbers of cells were plated overnight, and the following day, attached cells were counted and compared to live cells remaining unattached in the supernatant. FAM49B-KO cells displayed delayed adhesion to the culture plate surface.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMitochondrial Assays\u003c/em\u003e.\u003c/strong\u003e Cell respiration was measured using the Agilent Seahorse XFe96 Analyzer and the Mito Stress Test assay (Agilent #103015-100) which delivers a series of three drugs (Oligomycin, FCCP, Rotenone+Antimycin A while the oxygen consumption rate (OCR) is recorded. OCR is used to calculate basal respiration, maximal respiration, proton leak, non-mitochondrial oxygen consumption, ATP production, spare respiratory capacity, and coupling efficiency. A BCA assay was performed in order to normalize the results based on total cell protein content. To measure mitochondrial membrane potential, a functional dye MitoTracker Red CMXRos (ThermoFisher #M7513) was diluted to 200nM in D-PBS and incubated with cells for 15 minutes at 37\u0026deg;C. Cells were then washed with PBS 3X and fixed before being mounted and imaged using an Olympus Confocal microscope. CTCF values were measured using Fiji. To measure mitochondrial ROS levels, MitoSOX Red superoxide indicator (ThermoFisher #M36007) was diluted in HBSS and incubated onto cells for 15 minutes at 37\u0026deg;C. Cells were then imaged using the Agilent BioTek LionHeartFX live imaging system.\u003c/p\u003e\n\u003cp\u003eqPCR primers used\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTarget\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eForward\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReverse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eB-Actin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eAGCGAGCATCCCCCAAAGTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eGGGCACGAAGGCTCATCATT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eIL8 (Wang et al., 2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eACTGAGAGTGATTGAGAGTGGAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eAACCCTCTGCACCCAGTTTTC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eCD11B (Wang et al., 2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eACTTGCAGTGAGAACACGTATG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eTCATCCGCCGAAAGTCATGTG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eTMEM119 (Wang et al., 2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eCGGCCTATTACCCATCGTCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eCTGGGCTAACAAGAGAGACCC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eCYRIB-m (Park et al., 2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eAGGAGCTGGCCACGAAATAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eGGCGTACTAGTCAAGGCTCC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eIL7-m (Martin et al., 2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eCTGATGATCAGCATCGATGAATTGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eGCAGCACGATTTAGAAAAGCAGCTT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eIL6-m (\u003cem\u003eOrigene #MP206798)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eTACCACTTCACAAGTCGGAGGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eCTGCAAGTGCATCATCGTTGTTC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eTMEM119-m (\u003cem\u003eOrigene #MP217346)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eACTACCCATCCTCGTTCCCTGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eTAGCAGCCAGAATGTCAGCCTG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eCd11b-m (Skelly et al., 2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eTCATTCGCTACGTAATTGGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eGATGGTGTCGAGCTCTCTGC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: JM, G-JL, CYP, MRMethodology/Investigation: JM, G-JL, CYP, CW, TVB, SA, SL, MD, TR, WA, MW, BK, MRVisualization: JM, G-JL, CYP, MR Supervision: MR, OGTWriting\u0026mdash;original draft: MR, JM, G-JL, CYPWriting\u0026mdash;review \u0026amp; editing: JM, G-JL, CYP, CW, TVB, SA, SL, MD, TR, WA, MW, BK, OGT, MR\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThis work was supported in part through NINDS grant 1R01NS124735 (M.R.) and a Starter Grant by the Thomas Hartman Foundation (M.R.). J.M. was partially supported by a Dr. W. Burghardt Turner Fellowship. Opinions, interpretations, conclusions, and recommendations are those of the author and are not necessarily endorsed by the sponsors.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe gene expression data of the HMC3 RNA-seq is accessible on ArrayExpress (Accession number E-MTAB-15277).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgarwal, D., Sandor, C., Volpato, V., Caffrey, T.M., Monz\u0026oacute;n-Sandoval, J., Bowden, R., Alegre-Abarrategui, J., Wade-Martins, R., and Webber, C. (2020). A single-cell atlas of the human substantia nigra reveals cell-specific pathways associated with neurological disorders. Nature Communications\u003cem\u003e 11\u003c/em\u003e, 4183.\u003c/li\u003e\n\u003cli\u003eAkira, S., and Takeda, K. (2004). Toll-like receptor signalling. Nature Reviews Immunology\u003cem\u003e 4\u003c/em\u003e, 499-511.\u003c/li\u003e\n\u003cli\u003eBosco, E.E., Mulloy, J.C., and Zheng, Y. (2009). Rac1 GTPase: a \u0026quot;Rac\u0026quot; of all trades. Cell Mol Life Sci\u003cem\u003e 66\u003c/em\u003e, 370-374.\u003c/li\u003e\n\u003cli\u003eBurda, J.E., and Sofroniew, M.V. (2014). Reactive gliosis and the multicellular response to CNS damage and disease. Neuron\u003cem\u003e 81\u003c/em\u003e, 229-248.\u003c/li\u003e\n\u003cli\u003eCaggiu, E., Arru, G., Hosseini, S., Niegowska, M., Sechi, G., Zarbo, I.R., and Sechi, L.A. (2019). Inflammation, Infectious Triggers, and Parkinson\u0026apos;s Disease. Front Neurol\u003cem\u003e 10\u003c/em\u003e, 122.\u003c/li\u003e\n\u003cli\u003eCalabrese, V., Santoro, A., Monti, D., Crupi, R., Di Paola, R., Latteri, S., Cuzzocrea, S., Zappia, M., Giordano, J., Calabrese, E.J.\u003cem\u003e, et al.\u003c/em\u003e (2018). Aging and Parkinson\u0026apos;s Disease: Inflammaging, neuroinflammation and biological remodeling as key factors in pathogenesis. Free Radic Biol Med\u003cem\u003e 115\u003c/em\u003e, 80-91.\u003c/li\u003e\n\u003cli\u003eCampolo, M., Paterniti, I., Siracusa, R., Filippone, A., Esposito, E., and Cuzzocrea, S. (2019). TLR4 absence reduces neuroinflammation and inflammasome activation in Parkinson\u0026apos;s diseases in vivo model. Brain Behav Immun\u003cem\u003e 76\u003c/em\u003e, 236-247.\u003c/li\u003e\n\u003cli\u003eChang, D., Nalls, M.A., Hallgr\u0026iacute;msd\u0026oacute;ttir, I.B., Hunkapiller, J., Van Der Brug, M., Cai, F., Consortium, I.P.s.D.G., Team, a.R., Kerchner, G.A., and Ayalon, G. (2017). A meta-analysis of genome-wide association studies identifies 17 new Parkinson\u0026apos;s disease risk loci. Nature genetics\u003cem\u003e 49\u003c/em\u003e, 1511-1516.\u003c/li\u003e\n\u003cli\u003eChattaragada, M.S., Riganti, C., Sassoe, M., Principe, M., Santamorena, M.M., Roux, C., Curcio, C., Evangelista, A., Allavena, P., Salvia, R.\u003cem\u003e, et al.\u003c/em\u003e (2018). FAM49B, a novel regulator of mitochondrial function and integrity that suppresses tumor metastasis. Oncogene\u003cem\u003e 37\u003c/em\u003e, 697-709.\u003c/li\u003e\n\u003cli\u003eChen, H., Zhang, S.M., Hern\u0026aacute;n, M.A., Schwarzschild, M.A., Willett, W.C., Colditz, G.A., Speizer, F.E., and Ascherio, A. (2003). Nonsteroidal anti-inflammatory drugs and the risk of Parkinson disease. Archives of neurology\u003cem\u003e 60\u003c/em\u003e, 1059-1064.\u003c/li\u003e\n\u003cli\u003eChikina, M., Zaslavsky, E., and Sealfon, S.C. (2015). CellCODE: a robust latent variable approach to differential expression analysis for heterogeneous cell populations. Bioinformatics\u003cem\u003e 31\u003c/em\u003e, 1584-1591.\u003c/li\u003e\n\u003cli\u003eChinta, S.J., Woods, G., Demaria, M., Rane, A., Zou, Y., McQuade, A., Rajagopalan, S., Limbad, C., Madden, D.T., Campisi, J.\u003cem\u003e, et al.\u003c/em\u003e (2018). Cellular Senescence Is Induced by the Environmental Neurotoxin Paraquat and Contributes to Neuropathology Linked to Parkinson\u0026apos;s Disease. Cell Rep\u003cem\u003e 22\u003c/em\u003e, 930-940.\u003c/li\u003e\n\u003cli\u003eChoi, I., Zhang, Y., Seegobin, S.P., Pruvost, M., Wang, Q., Purtell, K., Zhang, B., and Yue, Z. (2020). Microglia clear neuron-released \u0026alpha;-synuclein via selective autophagy and prevent neurodegeneration. Nature Communications\u003cem\u003e 11\u003c/em\u003e, 1386.\u003c/li\u003e\n\u003cli\u003eClarke, L.E., Liddelow, S.A., Chakraborty, C., M\u0026uuml;nch, A.E., Heiman, M., and Barres, B.A. (2018). Normal aging induces A1-like astrocyte reactivity. Proceedings of the National Academy of Sciences\u003cem\u003e 115\u003c/em\u003e, E1896-E1905.\u003c/li\u003e\n\u003cli\u003eColonna, M., and Butovsky, O. (2017). Microglia Function in the Central Nervous System During Health and Neurodegeneration. Annu Rev Immunol\u003cem\u003e 35\u003c/em\u003e, 441-468.\u003c/li\u003e\n\u003cli\u003eConsortium, T.G., Aguet, F., Anand, S., Ardlie, K.G., Gabriel, S., Getz, G.A., Graubert, A., Hadley, K., Handsaker, R.E., Huang, K.H.\u003cem\u003e, et al.\u003c/em\u003e (2020). The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science\u003cem\u003e 369\u003c/em\u003e, 1318-1330.\u003c/li\u003e\n\u003cli\u003eCzekay, R.P., Wilkins-Port, C.E., Higgins, S.P., Freytag, J., Overstreet, J.M., Klein, R.M., Higgins, C.E., Samarakoon, R., and Higgins, P.J. (2011). PAI-1: An Integrator of Cell Signaling and Migration. Int J Cell Biol\u003cem\u003e 2011\u003c/em\u003e, 562481.\u003c/li\u003e\n\u003cli\u003eDauer, W., and Przedborski, S. (2003). Parkinson\u0026apos;s Disease: Mechanisms and Models. Neuron\u003cem\u003e 39\u003c/em\u003e, 889-909.\u003c/li\u003e\n\u003cli\u003eDe Biase, L.M., Schuebel, K.E., Fusfeld, Z.H., Jair, K., Hawes, I.A., Cimbro, R., Zhang, H.-Y., Liu, Q.-R., Shen, H., and Xi, Z.-X. (2017). Local cues establish and maintain region-specific phenotypes of basal ganglia microglia. Neuron\u003cem\u003e 95\u003c/em\u003e, 341-356. e346.\u003c/li\u003e\n\u003cli\u003eDe Lau, L.M., and Breteler, M.M. (2006). Epidemiology of Parkinson\u0026apos;s disease. The Lancet Neurology\u003cem\u003e 5\u003c/em\u003e, 525-535.\u003c/li\u003e\n\u003cli\u003eDello Russo, C., Cappoli, N., Coletta, I., Mezzogori, D., Paciello, F., Pozzoli, G., Navarra, P., and Battaglia, A. (2018). The human microglial HMC3 cell line: where do we stand? A systematic literature review. Journal of Neuroinflammation\u003cem\u003e 15\u003c/em\u003e, 259.\u003c/li\u003e\n\u003cli\u003eDutta, D., Jana, M., Majumder, M., Mondal, S., Roy, A., and Pahan, K. (2021). Selective targeting of the TLR2/MyD88/NF-\u0026kappa;B pathway reduces \u0026alpha;-synuclein spreading in vitro and in vivo. Nature Communications\u003cem\u003e 12\u003c/em\u003e, 5382.\u003c/li\u003e\n\u003cli\u003eErshov, D., Phan, M.-S., Pylv\u0026auml;n\u0026auml;inen, J.W., Rigaud, S.U., Le Blanc, L., Charles-Orszag, A., Conway, J.R.W., Laine, R.F., Roy, N.H., Bonazzi, D.\u003cem\u003e, et al.\u003c/em\u003e (2022). TrackMate 7: integrating state-of-the-art segmentation algorithms into tracking pipelines. Nature Methods\u003cem\u003e 19\u003c/em\u003e, 829-832.\u003c/li\u003e\n\u003cli\u003eFort, L., Batista, J.M., Thomason, P.A., Spence, H.J., Whitelaw, J.A., Tweedy, L., Greaves, J., Martin, K.J., Anderson, K.I., Brown, P.\u003cem\u003e, et al.\u003c/em\u003e (2018). Fam49/CYRI interacts with Rac1 and locally suppresses protrusions. Nat Cell Biol\u003cem\u003e 20\u003c/em\u003e, 1159-1171.\u003c/li\u003e\n\u003cli\u003eGhosh, A., Roy, A., Liu, X., Kordower, J.H., Mufson, E.J., Hartley, D.M., Ghosh, S., Mosley, R.L., Gendelman, H.E., and Pahan, K. (2007). Selective inhibition of NF-\u0026kappa;B activation prevents dopaminergic neuronal loss in a mouse model of Parkinson\u0026apos;s disease. Proceedings of the National Academy of Sciences\u003cem\u003e 104\u003c/em\u003e, 18754-18759.\u003c/li\u003e\n\u003cli\u003eGordon, R., Singh, N., Lawana, V., Ghosh, A., Harischandra, D.S., Jin, H., Hogan, C., Sarkar, S., Rokad, D., Panicker, N.\u003cem\u003e, et al.\u003c/em\u003e (2016). Protein kinase C\u0026delta; upregulation in microglia drives neuroinflammatory responses and dopaminergic neurodegeneration in experimental models of Parkinson\u0026apos;s disease. Neurobiol Dis\u003cem\u003e 93\u003c/em\u003e, 96-114.\u003c/li\u003e\n\u003cli\u003eHammond, T.R., Dufort, C., Dissing-Olesen, L., Giera, S., Young, A., Wysoker, A., Walker, A.J., Gergits, F., Segel, M., and Nemesh, J. (2019). Single-cell RNA sequencing of microglia throughout the mouse lifespan and in the injured brain reveals complex cell-state changes. Immunity\u003cem\u003e 50\u003c/em\u003e, 253-271. e256.\u003c/li\u003e\n\u003cli\u003eHamza, T.H., Zabetian, C.P., Tenesa, A., Laederach, A., Montimurro, J., Yearout, D., Kay, D.M., Doheny, K.F., Paschall, J., and Pugh, E. (2010). Common genetic variation in the HLA region is associated with late-onset sporadic Parkinson\u0026apos;s disease. Nature genetics\u003cem\u003e 42\u003c/em\u003e, 781-785.\u003c/li\u003e\n\u003cli\u003eHeidari, A., Yazdanpanah, N., and Rezaei, N. (2022). The role of Toll-like receptors and neuroinflammation in Parkinson\u0026rsquo;s disease. Journal of Neuroinflammation\u003cem\u003e 19\u003c/em\u003e, 135.\u003c/li\u003e\n\u003cli\u003eHeneka, M.T., Kummer, M.P., and Latz, E. (2014). Innate immune activation in neurodegenerative disease. Nature Reviews Immunology\u003cem\u003e 14\u003c/em\u003e, 463-477.\u003c/li\u003e\n\u003cli\u003eHenn, A., Lund, S., Hedtj\u0026auml;rn, M., Schrattenholz, A., P\u0026ouml;rzgen, P., and Leist, M. (2009). The suitability of BV2 cells as alternative model system for primary microglia cultures or for animal experiments examining brain inflammation. Altex\u003cem\u003e 26\u003c/em\u003e, 83-94.\u003c/li\u003e\n\u003cli\u003eHolmans, P., Moskvina, V., Jones, L., Sharma, M., Consortium, I.P.s.D.G., Vedernikov, A., Buchel, F., Sadd, M., Bras, J.M., and Bettella, F. (2013). A pathway-based analysis provides additional support for an immune-related genetic susceptibility to Parkinson\u0026apos;s disease. Human molecular genetics\u003cem\u003e 22\u003c/em\u003e, 1039-1049.\u003c/li\u003e\n\u003cli\u003eKam, T.-I., Hinkle, J.T., Dawson, T.M., and Dawson, V.L. (2020). Microglia and astrocyte dysfunction in parkinson\u0026apos;s disease. Neurobiology of Disease\u003cem\u003e 144\u003c/em\u003e, 105028.\u003c/li\u003e\n\u003cli\u003eKim, T.W., Piao, J., Koo, S.Y., Kriks, S., Chung, S.Y., Betel, D., Socci, N.D., Choi, S.J., Zabierowski, S., Dubose, B.N.\u003cem\u003e, et al.\u003c/em\u003e (2021). Biphasic Activation of WNT Signaling Facilitates the Derivation of Midbrain Dopamine Neurons from hESCs for Translational Use. Cell Stem Cell\u003cem\u003e 28\u003c/em\u003e, 343-355.e345.\u003c/li\u003e\n\u003cli\u003eLee, H.J., Suk, J.E., Bae, E.J., and Lee, S.J. (2008). Clearance and deposition of extracellular alpha-synuclein aggregates in microglia. Biochem Biophys Res Commun\u003cem\u003e 372\u003c/em\u003e, 423-428.\u003c/li\u003e\n\u003cli\u003eLi, Q., and Barres, B.A. (2018). Microglia and macrophages in brain homeostasis and disease. Nature Reviews Immunology\u003cem\u003e 18\u003c/em\u003e, 225-242.\u003c/li\u003e\n\u003cli\u003eLiddelow, S.A., Guttenplan, K.A., Clarke, L.E., Bennett, F.C., Bohlen, C.J., Schirmer, L., Bennett, M.L., M\u0026uuml;nch, A.E., Chung, W.-S., and Peterson, T.C. (2017). Neurotoxic reactive astrocytes are induced by activated microglia. Nature\u003cem\u003e 541\u003c/em\u003e, 481-487.\u003c/li\u003e\n\u003cli\u003eMartin, C.E., Spasova, D.S., Frimpong-Boateng, K., Kim, H.-O., Lee, M., Kim, K.S., and Surh, C.D. (2017). Interleukin-7 Availability Is Maintained by a Hematopoietic Cytokine Sink Comprising Innate Lymphoid Cells and T\u0026amp;#xa0;Cells. Immunity\u003cem\u003e 47\u003c/em\u003e, 171-182.e174.\u003c/li\u003e\n\u003cli\u003eMartin, I., Dawson, V.L., and Dawson, T.M. (2011). Recent advances in the genetics of Parkinson\u0026apos;s disease. Annual review of genomics and human genetics\u003cem\u003e 12\u003c/em\u003e, 301-325.\u003c/li\u003e\n\u003cli\u003eMcGeer, P.L., Itagaki, S., Boyes, B.E., and McGeer, E. (1988). Reactive microglia are positive for HLA‐DR in the substantia nigra of Parkinson\u0026apos;s and Alzheimer\u0026apos;s disease brains. Neurology\u003cem\u003e 38\u003c/em\u003e, 1285-1285.\u003c/li\u003e\n\u003cli\u003eMoore, D.J., West, A.B., Dawson, V.L., and Dawson, T.M. (2005). Molecular pathophysiology of Parkinson\u0026apos;s disease. Annu Rev Neurosci\u003cem\u003e 28\u003c/em\u003e, 57-87.\u003c/li\u003e\n\u003cli\u003eNalls, M.A., Blauwendraat, C., Vallerga, C.L., Heilbron, K., Bandres-Ciga, S., Chang, D., Tan, M., Kia, D.A., Noyce, A.J., Xue, A.\u003cem\u003e, et al.\u003c/em\u003e (2019). Identification of novel risk loci, causal insights, and heritable risk for Parkinson\u0026apos;s disease: a meta-analysis of genome-wide association studies. Lancet Neurol\u003cem\u003e 18\u003c/em\u003e, 1091-1102.\u003c/li\u003e\n\u003cli\u003eNalls, M.A., Pankratz, N., Lill, C.M., Do, C.B., Hernandez, D.G., Saad, M., DeStefano, A.L., Kara, E., Bras, J., Sharma, M.\u003cem\u003e, et al.\u003c/em\u003e (2014). Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson\u0026apos;s disease. Nat Genet\u003cem\u003e 46\u003c/em\u003e, 989-993.\u003c/li\u003e\n\u003cli\u003eOlah, M., Patrick, E., Villani, A.C., Xu, J., White, C.C., Ryan, K.J., Piehowski, P., Kapasi, A., Nejad, P., Cimpean, M.\u003cem\u003e, et al.\u003c/em\u003e (2018). A transcriptomic atlas of aged human microglia. Nat Commun\u003cem\u003e 9\u003c/em\u003e, 539.\u003c/li\u003e\n\u003cli\u003ePanicker, N., Sarkar, S., Harischandra, D.S., Neal, M., Kam, T.-I., Jin, H., Saminathan, H., Langley, M., Charli, A., and Samidurai, M. (2019). Fyn kinase regulates misfolded \u0026alpha;-synuclein uptake and NLRP3 inflammasome activation in microglia. Journal of Experimental Medicine\u003cem\u003e 216\u003c/em\u003e, 1411-1430.\u003c/li\u003e\n\u003cli\u003ePaolicelli, R.C., Sierra, A., Stevens, B., Tremblay, M.-E., Aguzzi, A., Ajami, B., Amit, I., Audinat, E., Bechmann, I., Bennett, M.\u003cem\u003e, et al.\u003c/em\u003e (2022). Microglia states and nomenclature: A field at its crossroads. Neuron\u003cem\u003e 110\u003c/em\u003e, 3458-3483.\u003c/li\u003e\n\u003cli\u003ePark, C.-S., Guan, J., Rhee, P., Gonzalez, F., Lee, H.-s., Park, J.-h., Coscoy, L., Robey, E.A., Shastri, N., and Sadegh-Nasseri, S. (2024). Fam49b dampens TCR signal strength to regulate survival of positively selected thymocytes and peripheral T cells. eLife\u003cem\u003e 13\u003c/em\u003e, e76940.\u003c/li\u003e\n\u003cli\u003ePhend, K.D., Rustioni, A., and Weinberg, R.J. (1995). An osmium-free method of epon embedment that preserves both ultrastructure and antigenicity for post-embedding immunocytochemistry. J Histochem Cytochem\u003cem\u003e 43\u003c/em\u003e, 283-292.\u003c/li\u003e\n\u003cli\u003ePierce, S., and Coetzee, G.A. (2017). Parkinson\u0026apos;s disease-associated genetic variation is linked to quantitative expression of inflammatory genes. PloS one\u003cem\u003e 12\u003c/em\u003e, e0175882.\u003c/li\u003e\n\u003cli\u003eQin, L., Wu, X., Block, M.L., Liu, Y., Breese, G.R., Hong, J.S., Knapp, D.J., and Crews, F.T. (2007). Systemic LPS causes chronic neuroinflammation and progressive neurodegeneration. Glia\u003cem\u003e 55\u003c/em\u003e, 453-462.\u003c/li\u003e\n\u003cli\u003eRiessland, M., Kolisnyk, B., Kim, T.W., Cheng, J., Ni, J., Pearson, J.A., Park, E.J., Dam, K., Acehan, D., Ramos-Espiritu, L.S.\u003cem\u003e, et al.\u003c/em\u003e (2019). Loss of SATB1 Induces p21-Dependent Cellular Senescence in Post-mitotic Dopaminergic Neurons. Cell Stem Cell.\u003c/li\u003e\n\u003cli\u003eRusso, T., Plessis-Belair, J., Sher, R., and Riessland, M. (2025). Regulatory Network Inference of Induced Senescent Midbrain Cell Types Reveals Cell Type-Specific Senescence-Associated Transcriptional Regulators. bioRxiv.\u003c/li\u003e\n\u003cli\u003eRusso, T., and Riessland, M. (2022). Age-Related Midbrain Inflammation and Senescence in Parkinson\u0026apos;s Disease. Front Aging Neurosci\u003cem\u003e 14\u003c/em\u003e, 917797.\u003c/li\u003e\n\u003cli\u003eSangineto, M., Ciarnelli, M., Cassano, T., Radesco, A., Moola, A., Bukke, V.N., Romano, A., Villani, R., Kanwal, H., Capitanio, N.\u003cem\u003e, et al.\u003c/em\u003e (2023). Metabolic reprogramming in inflammatory microglia indicates a potential way of targeting inflammation in Alzheimer\u0026apos;s disease. Redox Biology\u003cem\u003e 66\u003c/em\u003e, 102846.\u003c/li\u003e\n\u003cli\u003eScheiblich, H., Bousset, L., Schwartz, S., Griep, A., Latz, E., Melki, R., and Heneka, M.T. (2021). Microglial NLRP3 Inflammasome Activation upon TLR2 and TLR5 Ligation by Distinct \u0026alpha;-Synuclein Assemblies. The Journal of Immunology\u003cem\u003e 207\u003c/em\u003e, 2143-2154.\u003c/li\u003e\n\u003cli\u003eScheiblich, H., Eikens, F., Wischhof, L., Opitz, S., J\u0026uuml;ngling, K., Cser\u0026eacute;p, C., Schmidt, S.V., Lambertz, J., Bellande, T., P\u0026oacute;sfai, B.\u003cem\u003e, et al.\u003c/em\u003e (2024). Microglia rescue neurons from aggregate-induced neuronal dysfunction and death through tunneling nanotubes. Neuron\u003cem\u003e 112\u003c/em\u003e, 3106-3125.e3108.\u003c/li\u003e\n\u003cli\u003eShang, W., Jiang, Y., Boettcher, M., Ding, K., Mollenauer, M., Liu, Z., Wen, X., Liu, C., Hao, P., Zhao, S.\u003cem\u003e, et al.\u003c/em\u003e (2018). Genome-wide CRISPR screen identifies FAM49B as a key regulator of actin dynamics and T cell activation. Proc Natl Acad Sci U S A\u003cem\u003e 115\u003c/em\u003e, E4051-e4060.\u003c/li\u003e\n\u003cli\u003eSingh, S.S., Rai, S.N., Birla, H., Zahra, W., Rathore, A.S., and Singh, S.P. (2020). NF-\u0026kappa;B-Mediated Neuroinflammation in Parkinson\u0026apos;s Disease and Potential Therapeutic Effect of Polyphenols. Neurotox Res\u003cem\u003e 37\u003c/em\u003e, 491-507.\u003c/li\u003e\n\u003cli\u003eSkelly, D.T., Hennessy, E., Dansereau, M.-A., and Cunningham, C. (2013). A Systematic Analysis of the Peripheral and CNS Effects of Systemic LPS, IL-1\u0026Beta;, TNF-\u0026alpha; and IL-6 Challenges in C57BL/6 Mice. PLOS ONE\u003cem\u003e 8\u003c/em\u003e, e69123.\u003c/li\u003e\n\u003cli\u003eSmolders, S.M.-T., Kessels, S., Vangansewinkel, T., Rigo, J.-M., Legendre, P., and Br\u0026ocirc;ne, B. (2019). Microglia: Brain cells on the move. Progress in Neurobiology\u003cem\u003e 178\u003c/em\u003e, 101612.\u003c/li\u003e\n\u003cli\u003eSol\u0026eacute;-Dom\u0026egrave;nech, S., Cruz, D.L., Capetillo-Zarate, E., and Maxfield, F.R. (2016). The endocytic pathway in microglia during health, aging and Alzheimer\u0026apos;s disease. Ageing Res Rev\u003cem\u003e 32\u003c/em\u003e, 89-103.\u003c/li\u003e\n\u003cli\u003eStreit, W.J., Sammons, N.W., Kuhns, A.J., and Sparks, D.L. (2004). Dystrophic microglia in the aging human brain. Glia\u003cem\u003e 45\u003c/em\u003e, 208-212.\u003c/li\u003e\n\u003cli\u003eSurmeier, D.J., Obeso, J.A., and Halliday, G.M. (2017). Selective neuronal vulnerability in Parkinson disease. Nature Reviews Neuroscience\u003cem\u003e 18\u003c/em\u003e, 101-113.\u003c/li\u003e\n\u003cli\u003eTansey, M.G., and Goldberg, M.S. (2010). Neuroinflammation in Parkinson\u0026apos;s disease: its role in neuronal death and implications for therapeutic intervention. Neurobiol Dis\u003cem\u003e 37\u003c/em\u003e, 510-518.\u003c/li\u003e\n\u003cli\u003eTrainor, A.R., MacDonald, D.S., and Penney, J. (2024). Microglia: roles and genetic risk in Parkinson\u0026apos;s disease. Front Neurosci\u003cem\u003e 18\u003c/em\u003e, 1506358.\u003c/li\u003e\n\u003cli\u003eTremblay, M.-\u0026Egrave;. (2011). The role of microglia at synapses in the healthy CNS: novel insights from recent imaging studies. Neuron glia biology\u003cem\u003e 7\u003c/em\u003e, 67-76.\u003c/li\u003e\n\u003cli\u003eWang, Y.-J., Monteagudo, A., Downey, M.A., Ashton-Rickardt, P.G., and Elmaleh, D.R. (2021). Cromolyn inhibits the secretion of inflammatory cytokines by human microglia (HMC3). Scientific Reports\u003cem\u003e 11\u003c/em\u003e, 8054.\u003c/li\u003e\n\u003cli\u003eYang, S., Qin, C., Hu, Z.-W., Zhou, L.-Q., Yu, H.-H., Chen, M., Bosco, D.B., Wang, W., Wu, L.-J., and Tian, D.-S. (2021). Microglia reprogram metabolic profiles for phenotype and function changes in central nervous system. Neurobiology of Disease\u003cem\u003e 152\u003c/em\u003e, 105290.\u003c/li\u003e\n\u003cli\u003eZhang, X., Yu, H., and Feng, J. (2024). Emerging role of microglia in inter-cellular transmission of \u0026alpha;-synuclein in Parkinson\u0026rsquo;s disease. Frontiers in Aging Neuroscience\u003cem\u003e Volume 16 - 2024\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eZhang, Y., Chen, K., Sloan, S.A., Bennett, M.L., Scholze, A.R., O\u0026apos;Keeffe, S., Phatnani, H.P., Guarnieri, P., Caneda, C., Ruderisch, N.\u003cem\u003e, et al.\u003c/em\u003e (2014). An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J Neurosci\u003cem\u003e 34\u003c/em\u003e, 11929-11947.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-aging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Aging](https://www.nature.com/npjamd/)","snPcode":"41514","submissionUrl":"https://submission.springernature.com/new-submission/41514/3","title":"npj Aging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Parkinson’s Disease, Microglia, FAM49B, Gene Expression Regulation, Neuroinflammation, Mitochondria, Senescence","lastPublishedDoi":"10.21203/rs.3.rs-6925731/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6925731/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eParkinson\u0026rsquo;s Disease (PD) is characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc), which is associated with changes in microglia function. While age remains the biggest risk factor, the underlying molecular cause of PD onset and its concurrent neuroinflammation are not well understood. Many identified PD risk genes have been directly linked to dopamine neuron impairment, while others are linked to immune cell function. In this study, we found that the PD risk gene \u003cem\u003eFAM49B\u003c/em\u003e is critically expressed in microglia of the human SNpc and is downregulated with age. We utilized human and murine microglia cells to demonstrate the role of FAM49B in regulating fundamental microglial functions such as cytoskeletal maintenance, migration, surface adherence, energy homeostasis, endocytosis, and, importantly, inflammatory response. Downregulation of microglial FAM49B, as observed in the SNpc of aging individuals, led to significant alterations in these cellular functions, ultimately resulting in microglia impairment and over-responsiveness. Thus, our study highlights novel cell type-specific roles of FAM49B and provides a potential mechanism for susceptibility to neuroinflammation, and reactive gliosis observed in both PD and normal aging.\u003c/p\u003e","manuscriptTitle":"Age-related nigral downregulation of the Parkinson’s risk factor FAM49B primes human microglia for inflammaging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 05:45:44","doi":"10.21203/rs.3.rs-6925731/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-27T18:20:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-25T01:51:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223089297503229263839546752959245557050","date":"2025-08-06T12:21:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-21T20:12:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126187006739558940435160724050087421160","date":"2025-07-12T05:11:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-04T07:11:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-03T14:32:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-23T09:02:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Aging","date":"2025-06-18T20:01:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-aging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Aging](https://www.nature.com/npjamd/)","snPcode":"41514","submissionUrl":"https://submission.springernature.com/new-submission/41514/3","title":"npj Aging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aae879e0-ab9d-4061-8a6d-22fe1c14b06e","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51692888,"name":"Biological sciences/Neuroscience/Diseases of the nervous system/Parkinsons disease"},{"id":51692889,"name":"Biological sciences/Physiology/Ageing"},{"id":51692890,"name":"Biological sciences/Cell biology/Senescence"},{"id":51692891,"name":"Biological sciences/Cell biology/Organelles/Mitochondria"},{"id":51692892,"name":"Biological sciences/Neuroscience/Neural ageing"},{"id":51692893,"name":"Health sciences/Pathogenesis/Inflammation"}],"tags":[],"updatedAt":"2025-12-22T16:06:15+00:00","versionOfRecord":{"articleIdentity":"rs-6925731","link":"https://doi.org/10.1038/s41514-025-00296-z","journal":{"identity":"npj-aging","isVorOnly":false,"title":"npj Aging"},"publishedOn":"2025-12-19 15:58:13","publishedOnDateReadable":"December 19th, 2025"},"versionCreatedAt":"2025-07-18 05:45:44","video":"","vorDoi":"10.1038/s41514-025-00296-z","vorDoiUrl":"https://doi.org/10.1038/s41514-025-00296-z","workflowStages":[]},"version":"v1","identity":"rs-6925731","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6925731","identity":"rs-6925731","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}