Next Article in Journal
Exportin 1 as a Therapeutic Target to Overcome Drug Resistance in Lung Cancer
Previous Article in Journal
Targeting Hsp90 in Cancer for 25 Years: Failure of Previous Clinical Trials and New Hope for Future Therapeutics
Previous Article in Special Issue
From Brain to Blood: Uncovering Potential Therapeutical Targets and Biomarkers for Huntington’s Disease Using an Integrative RNA-Seq Analytical Platform (BDASeq®)
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Cells
Volume 14
Issue 24
10.3390/cells14241990
cells-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Alzheimer’s Disease Risk Variants Interact with Amyloid-Beta to Modulate Monocyte Function

by
Zena K. Chatila
1,2 and
Elizabeth M. Bradshaw
1,2,3,*
1
Division of Translational Neurobiology, Department of Neurology, Columbia University Irving Medical Center, 630 West 168th Street, New York, NY 10032, USA
2
The Carol and Gene Ludwig Center for Research on Neurodegeneration, Department of Neurology, Columbia University Irving Medical Center, 630 West 168th Street, New York, NY 10032, USA
3
Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Department of Neurology, Columbia University Irving Medical Center, 630 West 168th Street, New York, NY 10032, USA
*
Author to whom correspondence should be addressed.
Cells 2025, 14(24), 1990; https://doi.org/10.3390/cells14241990
Submission received: 2 November 2025 / Revised: 9 December 2025 / Accepted: 12 December 2025 / Published: 15 December 2025
(This article belongs to the Special Issue Role of Gene Regulation in Neurological Disorders)
Browse Figures
Versions Notes

Highlights

What are the main findings?
  • Aggregated Aβ reduces phagocytosis and decreases surface TREM2 expression in monocytes carrying CD33 rs3865444 or SPI1 rs1057233 AD-genetic risk variants.
  • Genetic variants interact with Aβ to reduce myeloid cell fitness in the periphery.
What are the implications of the main findings?
  • These findings highlight that peripheral monocytes, like brain-resident microglia, are genetically and functionally linked to AD risk.
  • These results emphasize the importance of studying peripheral myeloid cells to understand genetically influenced immune dysfunction in Alzheimer’s disease.

Abstract

While genetics implicate a central role for dysregulated innate immunity in Alzheimer’s disease (AD), the contributions of peripheral myeloid cells, such as monocytes, have been largely overlooked in favor of microglia. Here, we investigate whether AD-associated loci, specifically rs3865444 in the CD33 locus and rs1057233 in the SPI1 locus, converge on shared functional pathways in monocytes in the context of amyloid-beta peptide 1-42 (Aβ1-42) as an immune stimulus. To do so, we isolated monocytes from peripheral blood mononuclear cells (PBMCs) from healthy individuals and exposed them to aggregated Aβ1-42. In this study, we identify functional convergence of the CD33 and SPI1 AD risk variants in the context of aggregated Aβ, both resulting in reduced phagocytosis and loss of surface TREM2 expression, demonstrating an interaction between genetics and environment to reduce myeloid cell fitness. These findings highlight that peripheral monocytes, like brain-resident microglia, are genetically and functionally linked to AD risk, underscoring their importance as accessible immune cells that contribute to disease susceptibility and progression.

1. Introduction

Alzheimer’s disease (AD) is an age-related neurodegenerative disease characterized by progressive cognitive decline as well as pre-symptomatic accumulation of amyloid pathology with older age. With the discovery and validation of AD susceptibility loci, several risk factors have been identified that are solely or highly expressed in innate immune myeloid cells [1]. Such discoveries have highlighted risk genes for the disease including CD33, TREM2, and SPI1, and implicate a central role for myeloid dysfunction in AD. Importantly, many of these genetically associated proteins interact with each other and function in shared pathways [1]. However, the functional significance of these risk variants on peripheral myeloid cell activity in AD remains unclear.
While the discovery of AD genetic risk variants enriched in myeloid gene loci has led to a dedicated effort to understand microglial involvement in AD pathogenesis, myeloid cells in the periphery have been largely overlooked. Importantly, genetic data implicating myeloid cell involvement does not discriminate between those that are resident in the CNS parenchyma, such as microglia and perivascular macrophages, and those that are found in the periphery, such as monocytes and macrophages [2]. Not only can peripheral myeloid cells infiltrate the CNS, but they can also alter immune dynamics in the periphery itself and influence the adaptive immune system. Indeed, peripheral myeloid cells are even thought to impact clearance of amyloid beta (Aβ) in the CNS, both by trafficking to the CNS and participating in Aβ clearance locally, as well as through phagocytosing Aβ in the periphery, which has in turn been found to alter CNS Aβ clearance [3,4]. Increasing evidence has also implicated a role for monocyte homing to the brain and compensating for microglial dysfunction in AD [5]. While both cell types are involved in amyloid clearance, monocytes are thought to have significantly higher phagocytic capacity than microglia [6]. Thus, understanding the functional impact of AD genetic risk factors on monocyte function is valuable for identifying pathways through which genetic variants modulate disease risk and uncovering novel therapeutic approaches for this disease.
The present work aims to clarify the phenotypic consequences of the interaction between AD-associated loci enriched in myeloid cells and the response to Aβ in monocytes. CD33 is an inhibitory transmembrane receptor expressed on myeloid cells. The risk variant CD33 rs3865444C associated with AD results in increased CD33 surface expression, reduced phagocytosis, and an increased amyloid burden in the brain [7]. The CD33 rs3865444C AD risk variant not only increases CD33 surface expression but also increases TREM2 surface expression [8]. TREM2 is an excitatory surface receptor expressed exclusively on myeloid cells, which signals through the associated adaptor protein DAP12 (encoded by TYROBP), and has been associated with AD through whole-exome sequencing. Several studies have demonstrated that AD-associated genetic variants in TREM2 result in impaired signaling, suggesting that a partial loss of TREM2 function may be a major contributor to AD pathogenesis [1,9]. CD33 regulation of TREM2 surface expression is specifically mediated by membrane-bound CD33; individuals with the CD33 AD risk variant have increased surface TREM2 expression, an effect which was abrogated with a CD33 neutralizing antibody which downregulates surface CD33 [8]. TREM2 has also been found to function downstream of CD33 in mouse studies utilizing the 5xFAD model [10].
CD33 and TREM2 gene expression are both regulated by the transcription factor PU.1 (encoded by SPI1), which has also been genetically associated with AD through GWAS [11]. PU.1 functions as a master regulator transcription factor in myeloid cells [11]. The protective rs1057233G variant associated with reduced AD risk results in reduced PU.1 expression. Notably, PU.1 regulates the expression of several AD-associated genes, including CD33 and TREM2, as well as TYROBP [1,11,12,13]. Specifically, PU.1 expression is necessary for CD33 expression in myeloid cells, and silencing of SPI1 with siRNA causes reduced TREM2 expression [12,13]. Like rs3865444 in the CD33 locus, rs1057233 in the SPI1 locus has been connected to phagocytic capacity of innate immune cells. However, knockout of SPI1 in microglia surprisingly resulted in decreased phagocytosis in BV2 cells, a model of murine microglia [14].
The goal of this work is to determine whether these functionally interconnected AD-associated loci and proteins converge to alter monocyte function in the context of an AD-associated stressor, aggregated Aβ. To do so, we isolated monocytes from PBMCs from healthy individuals and exposed them to aggregated Aβ1-42. We identify convergence of AD loci on shared functional outcomes in monocytes in the context of amyloid as an immune stimulus, including reduced phagocytosis and loss of surface TREM2 expression. These findings further support a role for TREM2 loss of function in myeloid dysfunction in AD. While immune-targeting therapies in development are designed to cross the blood–brain barrier to target microglial cells within the brain, future work may clarify whether modulating TREM2, CD33, or PU.1 signaling in the periphery alone may be beneficial in AD [15].

2. Methods

2.1. Human Blood Samples

The New York Blood Center (NYBC) provided IRB-compliant access to blood samples of de-identified individuals. NYBC donors included in this study range in age from 22 to 73 years of age and include 13 females and 17 males, as detailed in Supplementary Table S1.

2.2. Genotyping

Genomic DNA (gDNA) was purified from peripheral blood mononuclear cells (PBMCs) using the Gentra Puregene Blood Kit (Qiagen, Germantown, MD, USA). DNA quantification was performed using the NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The genotypes for CD33 rs3865444 and SPI1 rs1057233 were determined using Taqman SNP Genotyping Assays (Thermo Fisher Scientific; Cat #4351379) on the QuantStudio 3 Real-Time PCR System (Thermo Fisher Scientific). Approximately 25 ng of gDNA was used in each reaction.
The CD33 rs3865444 context sequence used for genotyping was:
CTATATCCTGCTGGACTAAACACCC[C/A]ATGGATCTAGGTGAGGCTGCGACTC
The SPI1 rs1057233 context sequence used for genotyping was:
AACTTTACTTGTTTTTTGGGAGGAG[A/G]TTAATGGGTGGGAGGGGTGAGAGGG

2.3. Collection of Human Peripheral PBMCs and Monocyte Isolation

PBMCs were separated by Lymphoprep gradient centrifugation (StemCell Technologies, Vancouver, BC, Canada). PBMCs were frozen at a concentration of 2 × 107 cells mL–1 in 10% DMSO (Sigma-Aldrich, St. Louis, MO, USA)/90% fetal bovine serum (vol/vol, Corning, Corning, NY, USA). After thawing, PBMCs were washed in 10 mL of phosphate-buffered saline. Monocytes were positively selected from whole PBMCs using anti-CD14+ microbeads (Miltenyi Biotec, Gaithersburg, MD, USA) and plated at 2 × 105 cells per well in 96-well plates.

2.4. Amyloid Aggregation

Aβ1-42 (rPeptide, Watkinsville, GA, USA) was reconstituted in 1% of NH4OH in MilliQ water for 10 min at RT. Then, the solution was incubated at 37 °C for 1 week with rotation. Cells at a concentration of 2 × 105 cells per well were incubated with 250 nM aggregated Aβ1-42 in RPMI medium for 24 h at 37 °C.

2.5. Amyloid Phagocytosis Assay

Amyloid-beta 1-42 phagocytosis assays were performed according to a previously published protocol [7]. Briefly, monocytes were incubated with 15 ng mL−1 HiLyte Fluor 647–labeled Aβ1-42 (AnaSpec, Fremont, CA, USA) in 96-well polypropylene plates in RPMI media plus 5% fetal calf serum (vol/vol) for 1 h at 37 °C or 4 °C. As a negative control, cells were preincubated with 10 µM Cytochalasin D (Thermo Fisher Scientific)for 30 min before being treated with 647–labeled Aβ1-42. Cells were then washed three times with PBS with 1% fetal calf serum and labeled with Fixable LIVE/DEAD violet cell stain (Thermo Fisher Scientific) for 30 min and fixed in 4% paraformaldehyde (vol/vol) for 30 min. Flow cytometry data were collected on a NovoCyte (Agilent Technologies, Santa Clara, CA, USA) Quanteon cell analyzer and compensated and analyzed using the associated NovoExpress software v1.6.3.

2.6. TREM2 Surface Staining

Cells were stained on ice with TREM2-APC (R&D Systems, Minneapolis, MN, USA, 1:20) for 30 min protected from light, according to the manufacturer. Isotype controls (R&D Systems) were similarly stained. Cells were then washed three times with PBS with 1% fetal calf serum and labeled with Fixable LIVE/DEAD violet cell stain (Thermo Fisher Scientific) for 30 min and fixed in 4% paraformaldehyde (vol/vol) for 30 min. Flow cytometry data were collected on a NovoCyte Quanteon cell analyzer and compensated and analyzed using the associated NovoExpress software.

2.7. RNA Expression of TREM2 and TYROBP

Monocyte populations were exposed to aggregated Aβ1-42 for 24 h and were subsequently lysed for RNA isolation, which was performed using the RNAeasy isolation kit (Qiagen, cat#74134). cDNA was made from isolated RNA using the High Capacity cDNA Reverse Transcription Kit (ThermoFisher), according to the manufacturer’s protocol. Quantitative real-time PCR (qRT-PCR) was performed using Taqman probes for each of the assayed genes and Taqman Fast Advanced Mastermix (ThermoFisher). qRT-PCR was run on a QuantStudio3 (ThermoFisher) and analyzed using the associated Design and Analysis Software. A GAPDH TaqMan probe (ThermoFisher) was used as a housekeeping gene. Relative gene expression was calculated as the delta CT values between the gene of interest and GAPDH expression, times 1000.

2.8. Batch Correction

Batches were normalized in GraphPad Prism version 10 using the normalize function. Briefly, the function averaged and normalized the means of each condition. 0% was defined as the smallest mean in each dataset, and 100% was defined as the largest mean in each dataset, and the results were presented as fractions.

2.9. Statistical Analysis

Statistical analyses were performed using GraphPad Prism (GraphPad Software). Student’s t-test was performed for comparisons between individual groups. For group-wise comparison, two-way ANOVA was used with Sidak’s multiple comparison test. A p-value less than 0.05 was considered significant.

3. Results

3.1. Aggregated Aβ1-42 Impairs Phagocytosis in Monocytes Carrying CD33 and SPI1 AD Risk Variants

To examine the interaction between AD genetic risk variants and environmental stimuli associated with disease pathology in the periphery, we isolated monocytes from individuals genotyped for SPI1 rs1057233 and CD33 rs3865444 AD risk variants (Supplemental Table S1) and exposed them to aggregated Aβ1-42 in vitro for 24 h to provide a disease-associated stressor. As CD33 and PU.1 AD variants have both been associated with phagocytosis, we first examined whether phagocytic function of monocytes is altered following exposure to aggregated Aβ1-42 [7,11]. We measured phagocytosis of fluorescently labeled Aβ1-42 with flow cytometry. In aggregate (across all genotypes combined), exposure to aggregated Aβ1-42 had no significant impact on phagocytosis (Figure 1a, Supplementary Figure S1a). However, when we examined phagocytosis in a genotype-specific manner, we found that aggregated Aβ1-42 treatment resulted in impaired phagocytosis only in monocytes from individuals that were homozygous for either the CD33 or SPI1 AD risk variants, revealing a functional impairment dependent on the interaction between genetics and environment (Figure 1b,c, Supplementary Figure S2a). No differences in phagocytosis were observed in association with sex or age (Supplementary Figures S3a and S4a).
Importantly, monocytes can uptake Aβ through phagocytosis-independent mechanisms. We performed these experiments with the addition of Cytochalasin D, which inhibits phagocytosis, and found reduced Aβ1-42 internalization (Supplementary Figure S1a). These findings confirm that the uptake we observed occurs through phagocytosis.

3.2. Aggregated Aβ1-42 Reduces Monocyte TREM2 Surface Expression in CD33 and SPI1 Risk Variants

TREM2 surface expression is functionally implicated in phagocytosis [16,17,18]. CD33 regulates TREM2 surface expression [8,10], and PU.1 regulates TREM2 gene expression [12], suggesting alterations in TREM2 may be one shared factor modulating the altered phagocytosis we observed with these risk variants. Thus, we next investigated whether the interaction between CD33 or PU.1 and TREM2 surface expression is altered in the context of aggregated Aβ1-42. We exposed monocytes isolated from PBMCs of healthy individuals genotyped for CD33 and SPI1 AD risk variants to aggregated Aβ1-42 in vitro for 24 h and measured surface TREM2 expression with flow cytometry.
In aggregate, across all genotypes combined, aggregated Aβ1-42 treatment resulted in reduced TREM2 surface protein expression, regardless of the genotype of the individual (Figure 2a, Supplementary Figure S1b). However, when we analyzed these findings in a genotype-specific manner, we determined this effect was driven by those who were homozygous risk for either the CD33 or SPI1 AD variants, as only these stratified groups demonstrated significantly reduced TREM2 surface expression (Figure 2b,c). This specific reduction in surface TREM2 again demonstrates a genotype-dependent response to aggregated Aβ1-42. Importantly, the CD33 genotype has been shown to modulate TREM2, such that the rs3865444C risk variant results in increased TREM2 surface expression [8]. We observed this same effect at baseline (Supplementary Figure S2b), and found that, following aggregated Aβ1-42 treatment, monocytes homozygous for rs3865444CC no longer had increased surface TREM2 expression compared to rs3865444CA and rs3865444AA genotypes.
We next investigated whether TREM2 surface expression is mediated by age or sex. No differences in TREM2 were observed in association with age (Supplementary Figure S4b). Males had decreased surface TREM2 following treatment with aggregated Aβ1-42, while females had no difference in TREM2 expression with Aβ1-42 exposure (Supplementary Figure S3b). Larger cohorts are needed to confirm whether there are sex differences, and whether sex interacts with genotype to modulate TREM2 expression following Aβ1-42 exposure.

3.3. Aggregated Aβ1-42 Influences TREM2 and TYROBP Transcription in a Genotype-Independent Manner

As a key innate immune transcription factor, PU.1 regulates the transcription of both CD33 and TREM2, as well as TYROBP [1,11,12,13,19]. To understand whether transcriptional alterations triggered by the response to aggregated Aβ1-42 may provide a convergent mechanism underlying the shared phenotype we observed between CD33 and SPI1 risk variants, we examined RNA expression of these genes in genotyped monocytes following aggregated Aβ1-42 stimulation.
We found that treatment with aggregated Aβ1-42 altered the transcription of TREM2 and TYROBP. Specifically, both TREM2 and TYROBP gene expression were decreased following aggregated Aβ1-42 treatment in monocytes across all genotypes combined (Figure 3a,b). When stratified by CD33 and SPI1 genotype, this reduced TREM2 and TYROBP gene expression did not follow a genotype-specific pattern (Figure 3c–f). No genotype-specific differences were found in expression of either of these genes in untreated monocytes (Supplementary Figure S5). The discordance between a reduction in TREM2 gene expression in a genotype-independent manner and the specific risk genotype-dependent alterations in TREM2 surface protein levels following aggregated Aβ1-42 treatment are suggestive of differential post-translational regulation of TREM2 levels with the risk variants, such as intracellular sequestration, cleavage, or secretion of the protein.

4. Discussion

In this work, we have demonstrated that aggregated Aβ1-42 treatment exposes functional impairment associated with AD risk factors in human monocytes (Figure 4). We found that in monocytes carrying homozygous CD33 or SPI1 AD risk variants, aggregated Aβ1-42 revealed deficits in phagocytosis and reduced TREM2 surface expression. Importantly, PU.1 is a transcription factor that regulates the expression of several AD-associated myeloid genes. Thus, we further investigated this pathway at the RNA level and found that aggregated Aβ1-42 reduced gene expression of TREM2 and TYROBP, implicating altered regulation of this axis in the context of Aβ exposure. These findings implicate a functional interaction between TREM2, CD33, and PU.1 in monocytes, and suggest that modulating signaling downstream of TREM2 may provide a mechanism to alter functional deficits associated with these AD risk variants. Future work may investigate how different lengths of exposure and doses of amyloid, that monocytes may encounter in the periphery, CSF, and brain parenchyma, may further impact this interaction.
Our findings demonstrate that aggregated Aβ1-42 exposure in the context of CD33 and SPI1 AD risk variants unveils functional impairment in phagocytosis. Future work may shed light on whether genotype-dependent deficits generalize to other cargos. Importantly, CD33 rs3865444C is associated with impaired phagocytosis [7]. Our study was limited by a small sample of individuals carrying the CD33 rs3865444AA genotype, limiting our ability to replicate this finding in untreated monocytes. However, the present work clearly demonstrates a dramatic superimposed deficit in phagocytosis in CD33 rs3865444CC monocytes following Aβ1-42 treatment, highlighting an important gene-by-environment interaction in the context of human monocytes. SPI1 has also been found to regulate phagocytosis, surprisingly such that knockout of SPI1 in BV2 cells resulted in decreased phagocytosis [14]. Our findings demonstrate that the SPI1 rs1057233AA risk genotype, which results in increased PU.1 expression [11], results in decreased phagocytosis in the context of aggregated Aβ1-42 exposure. These findings highlight the importance of reconciling genotype and cell type specific effects of SPI1 rs1057233 with knockout studies, to better understand the functional implications of this risk variant in AD.
We observed different effects of aggregated Aβ1-42 treatment on TREM2 regulation at the RNA and the surface protein level, such that while TREM2 RNA expression was reduced in a genotype-independent manner, TREM2 surface expression was only reduced in monocytes carrying homozygous CD33 or SPI1 risk variants. The discordance between the mRNA TREM2 levels and surface protein expression suggests that those with homozygous risk variants alone are unable to maintain stable TREM2 surface levels in the face of reduced TREM2 gene expression following aggregated Aβ1-42 exposure, which may have functional implications such as the impaired phagocytosis we observed. These findings also suggest that post-transcriptional regulation of TREM2 may differ in the risk variants following aggregated Aβ1-42 exposure. Importantly, TREM2 does not only exist as a surface protein but also in soluble forms, either secreted or cleaved, and can be sequestered intracellularly. The risk variants may have altered regulation between these different forms of TREM2, which may contribute to impaired phagocytosis. Interestingly, soluble TREM2 has recently been linked to phagocytosis of amyloid [20]. Future work may investigate differences in secreted or cleaved TREM2 across genotypes to better understand the consequences of CD33 and SPI1 genotypes on the regulation of this protein in the context of Aβ. Importantly, while PU.1 is known to regulate TREM2 and TYROBP gene expression, we did not observe differences in gene expression in untreated monocytes across SPI1 rs1057233 genotypes. The body of literature surrounding PU.1 regulation of these genes is based on knockout studies, and further work specifically investigating the impact of SPI1 rs1057233 genotype as well as PU.1 expression levels on the regulation of these genes in all myeloid cells is needed.
Interestingly, the CD33 rs3865444C risk variant is associated with increased surface TREM2 expression, an effect which we also observed in our data [8]. This CD33 risk variant is also associated with increased CD33 surface expression, with 7-fold increased expression reported in the rs3865444CC genotype compared to rs3865444AA [7]. As CD33 is an inhibitory receptor, it is possible that increased expression of the activating TREM2 receptor in the rs3865444CC risk genotype may be a compensatory mechanism to balance myeloid cell activation. The reduction in surface TREM2 levels we observed with CD33 homozygous risk following Aβ1-42 treatment importantly occurs in the context of this increased CD33 surface expression, likely resulting in a skewed balance of myeloid cell activating receptors compared to the rs3865444AA protective genotype. Alternatively, CD33 rs3865444CC may limit cleavage of TREM2 into its soluble form, which has been suggested to be protective [21]. While we did not investigate the AD-associated TREM2R47H variant because of its low frequency in the population, this mutation also reduces both TREM2 function and SYK activation, which aligns with the functional outcomes detailed in our findings. As the direction of effect of several AD genetic risk factors results in reduced myeloid activating tone [1], these findings suggest that aggregated Aβ1-42 may interact with genetic background to exacerbate reduced myeloid efficacy associated with disease variants, as we observed here.
Activating molecules downstream in the TREM2 signaling pathway may reduce the phagocytosis impairment we observed associated with risk variants. TREM2, TREM1, as well as other innate immune receptors including CLEC7A, activate shared downstream signaling molecules, including SYK. Indeed, other studies have found that activating SYK through CLEC7A in mice expressing the AD-associated human TREM2R47H rescued microglial activation [22]. Future work is needed to evaluate whether SYK activation is sufficient for this effect. It is possible that SYK activation may differ by genotype, and future work may evaluate SYK activation across genotypes by measuring phospho-SYK to better understand how this signaling pathway is impacted following Aβ exposure. SYK inhibitors and direct SYK activators may also be employed to elucidate the role of this signaling molecule in modulating myeloid cell phenotype in the context of Aβ.
Our work raises questions as to the role of monocyte dysfunction in AD. Monocytes have been implicated in AD through several other lines of evidence in addition to the genetic studies which suggest myeloid involvement in disease [1]. Monocytes in the CSF have been found to be dysregulated in aging [23]. Furthermore, peripheral monocytes from individuals with AD were found to have impaired Aβ phagocytosis, which aligns with the phenotype we found associated with CD33 and SPI1 risk variants here [24,25]. Importantly, amyloid clearance in the periphery has been linked to amyloid clearance in the brain, such that elevated amyloid in the periphery reduces clearance in the brain [4]. In addition to their role in the periphery, patrolling monocytes have been found to infiltrate the brain and differentiate into activated macrophages [26]. Furthermore, monocytes in a mouse model of AD were found to traffic to the brain to eliminate amyloid aggregates from amyloid-laden brain vasculature and then traffic back to the periphery [3]. In this model, the removal of monocytes resulted in increased amyloid burden in the cortex and hippocampus [3]. Together, this evidence suggests that monocytes may not only impact CNS amyloid dynamics both locally and from the periphery but also that their phagocytic function and phenotype is also altered in AD. Recent work has also highlighted the importance of monocytes in rescuing exhausted microglia in AD by infiltrating the CNS and acting as a new local brain myeloid population [5].
New immunotherapies targeting TREM2 on microglia in the CNS with activating antibodies are currently being developed [15,27]. However, adverse outcomes have been reported with both anti-amyloid and anti-TREM2 antibody therapies in the CNS, including amyloid-related imaging abnormalities [28,29]. Our findings bring into question whether modulating peripheral myeloid cell activation alone may provide therapeutic benefit in AD and contribute to a foundation for future work to investigate novel therapeutic approaches that modulate the peripheral immune system.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cells14241990/s1, Figure S1. Phagocytosis and Surface Staining Controls. Figure S2. Aβ Phagocytosis and TREM2 Surface Expression by CD33 rs3865444 Genotype. Figure S3. Aβ Phagocytosis and TREM2 Surface Expression Stratified by Sex. Figure S4. Aβ Phagocytosis and TREM2 Surface Expression by Age. Figure S5. Baseline Gene Expression of TREM2 and TYROBP by CD33 rs3865444 and SPI1 rs1057233 Genotypes. Table S1. NYBC Age, Sex, and Genotype.

Author Contributions

Conceptualization, Z.K.C. and E.M.B.; methodology, Z.K.C. and EMB; formal analysis, Z.K.C. and E.M.B.; investigation, Z.K.C.; writing—original draft preparation, Z.K.C. and E.M.B.; writing—review and editing, Z.K.C. and E.M.B. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the US National Institutes of Health grants RF1AG058852, F30AG074618, and R21AG073882, as well as Alzheimer’s Association grant DVT-14-321148. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Institutional Review Board Statement

All blood samples were obtained from the New York Blood Center (NYBC), where donors provided informed consent, and samples were de-identified prior to release. Columbia University holds IRB approval to receive and use these de-identified NYBC specimens, and no identifiable donor information is accessible to investigators.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Acknowledgments

EMB is currently a Ludwig Scholar at the Carol and Gene Ludwig Center for Research in Neurodegeneration. Flow cytometry was performed in the Columbia Stem Cell Initiative Flow Cytometry core facility at Columbia University Irving Medical Center under the leadership of Michael Kissner.

Conflicts of Interest

The authors declare no conflicts of interest. The funding agencies had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Chatila, Z.K.; Bradshaw, E.M. Alzheimer’s Disease Genetics: A Dampened Microglial Response? Neuroscientist 2023, 29, 245–263. [Google Scholar] [CrossRef]
  2. Novikova, G.; Kapoor, M.; Tcw, J.; Abud, E.M.; Efthymiou, A.G.; Chen, S.X.; Cheng, H.; Fullard, J.F.; Bendl, J.; Liu, Y.; et al. Integration of Alzheimer’s disease genetics and myeloid genomics identifies disease risk regulatory elements and genes. Nat. Commun. 2021, 12, 1610. [Google Scholar] [CrossRef]
  3. Michaud, J.P.; Bellavance, M.A.; Préfontaine, P.; Rivest, S. Real-time in vivo imaging reveals the ability of monocytes to clear vascular amyloid beta. Cell Rep. 2013, 5, 646–653. [Google Scholar] [CrossRef]
  4. Marques, M.A.; Kulstad, J.J.; Savard, C.E.; Green, P.S.; Lee, S.P.; Craft, S.; Watson, G.S.; Cook, D.G. Peripheral amyloid-beta levels regulate amyloid-beta clearance from the central nervous system. J. Alzheimers Dis. 2009, 16, 325–329. [Google Scholar] [CrossRef]
  5. Abellanas, M.A.; Purnapatre, M.; Burgaletto, C.; Schwartz, M. Monocyte-derived macrophages act as reinforcements when microglia fall short in Alzheimer’s disease. Nat. Neurosci. 2025, 28, 436–445. [Google Scholar] [CrossRef] [PubMed]
  6. Ritzel, R.; Patel, A.R.; Grenier, J.M.; Crapser, J.; Verma, R.; Jellison, E.R.; McCullough, L.D. Functional differences between microglia and monocytes after ischemic stroke. J. Neuroinflamm. 2015, 12, 106. [Google Scholar] [CrossRef] [PubMed]
  7. Bradshaw, E.M.; Chibnik, L.B.; Keenan, B.T.; Ottoboni, L.; Raj, T.; Tang, A.; Rosenkrantz, L.L.; Imboywa, S.; Lee, M.; Korff, A.V.; et al. CD33 Alzheimer’s disease locus: Altered monocyte function and amyloid biology. Nat. Neurosci. 2013, 16, 848–850. [Google Scholar] [CrossRef] [PubMed]
  8. Chan, G.; White, C.C.; A Winn, P.; Cimpean, M.; Replogle, J.M.; Glick, L.R.; E Cuerdon, N.; Ryan, K.J.; A Johnson, K.; A Schneider, J.; et al. CD33 modulates TREM2: Convergence of Alzheimer loci. Nat. Neurosci. 2015, 18, 1556–1558. [Google Scholar] [CrossRef]
  9. Guerreiro, R.; Wojtas, A.; Bras, J.; Carrasquillo, M.; Rogaeva, E.; Majounie, E.; Cruchaga, C.; Sassi, C.; Kauwe, J.S.; Younkin, S.; et al. TREM2 variants in Alzheimer’s disease. N. Engl. J. Med. 2013, 368, 117–127. [Google Scholar] [CrossRef]
  10. Griciuc, A.; Patel, S.; Federico, A.N.; Choi, S.H.; Innes, B.J.; Oram, M.K.; Cereghetti, G.; McGinty, D.; Anselmo, A.; Sadreyev, R.I.; et al. TREM2 Acts Downstream of CD33 in Modulating Microglial Pathology in Alzheimer’s Disease. Neuron 2019, 103, 820–835.e827. [Google Scholar] [CrossRef]
  11. Huang, K.L.; Initiative, T.A.D.N.; Marcora, E.; A Pimenova, A.; Di Narzo, A.F.; Kapoor, M.; Jin, S.C.; Harari, O.; Bertelsen, S.; Fairfax, B.P.; et al. A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer’s disease. Nat. Neurosci. 2017, 20, 1052–1061. [Google Scholar] [CrossRef]
  12. Rustenhoven, J.; Smith, A.M.; Smyth, L.C.; Jansson, D.; Scotter, E.L.; Swanson, M.E.V.; Aalderink, M.; Coppieters, N.; Narayan, P.; Handley, R.; et al. PU.1 regulates Alzheimer’s disease-associated genes in primary human microglia. Molecular. Neurodegener. 2018, 13, 44. [Google Scholar] [CrossRef]
  13. Bodger, M.P.; Hart, D.N. Molecular cloning and functional analysis of the CD33 promoter. Br. J. Haematol. 1998, 102, 986–995. [Google Scholar] [CrossRef]
  14. Pimenova, A.A.; Herbinet, M.; Gupta, I.; Machlovi, S.I.; Bowles, K.R.; Marcora, E.; Goate, A.M. Alzheimer’s-associated PU.1 expression levels regulate microglial inflammatory response. Neurobiol. Dis. 2021, 148, 105217. [Google Scholar] [CrossRef]
  15. van Lengerich, B.; Zhan, L.; Xia, D.; Chan, D.; Joy, D.; Park, J.I.; Tatarakis, D.; Calvert, M.; Hummel, S.; Lianoglou, S.; et al. A TREM2-activating antibody with a blood-brain barrier transport vehicle enhances microglial metabolism in Alzheimer’s disease models. Nat. Neurosci. 2023, 26, 416–429. [Google Scholar] [CrossRef]
  16. Kleinberger, G.; Yamanishi, Y.; Suárez-Calvet, M.; Czirr, E.; Lohmann, E.; Cuyvers, E.; Struyfs, H.; Pettkus, N.; Wenninger-Weinzierl, A.; Mazaheri, F.; et al. TREM2 mutations implicated in neurodegeneration impair cell surface transport and phagocytosis. Sci. Transl. Med. 2014, 6, 243ra286. [Google Scholar] [CrossRef]
  17. Colonna, M. TREMs in the immune system and beyond. Nat. Rev. Immunol. 2003, 3, 445–453. [Google Scholar] [CrossRef]
  18. Colonna, M. The biology of TREM receptors. Nat. Rev. Immunol. 2023, 23, 580–594. [Google Scholar] [CrossRef] [PubMed]
  19. Zeng, H.; Ornatowska, M.; Joo, M.S.; Sadikot, R.T. TREM-1 expression in macrophages is regulated at transcriptional level by NF-kappaB and PU.1. Eur. J. Immunol. 2007, 37, 2300–2308. [Google Scholar] [CrossRef] [PubMed]
  20. Hovde, M.J.; Maaser-Hecker, A.; Bae, J.S.; Tanzi, R.E. Inhibition of Acyl-CoenzymeA: Cholesterol Acyltransferase 1 promotes shedding of soluble triggering receptor on myeloid cells 2 (TREM2) and low-density lipoprotein receptor 1 (LRP1)-dependent phagocytosis of amyloid beta protein in microglia. Alzheimers Dement. 2025, 21, e70879. [Google Scholar] [CrossRef] [PubMed]
  21. Brown, G.C.; George-Hyslop, P.S. Does Soluble TREM2 Protect Against Alzheimer’s Disease? Front. Aging Neurosci. 2021, 13, 834697. [Google Scholar] [CrossRef]
  22. Wang, S.; Sudan, R.; Peng, V.; Zhou, Y.; Du, S.; Yuede, C.M.; Lei, T.; Hou, J.; Cai, Z.; Cella, M.; et al. TREM2 drives microglia response to amyloid-β via SYK-dependent and -independent pathways. Cell 2022, 185, 4153–4169.e4119. [Google Scholar] [CrossRef]
  23. Piehl, N.; van Olst, L.; Ramakrishnan, A.; Teregulova, V.; Simonton, B.; Zhang, Z.; Tapp, E.; Channappa, D.; Oh, H.; Losada, P.M.; et al. Cerebrospinal fluid immune dysregulation during healthy brain aging and cognitive impairment. Cell 2022, 185, 5028–5039.e5013. [Google Scholar] [CrossRef] [PubMed]
  24. Fiala, M.; Lin, J.; Ringman, J.; Kermani-Arab, V.; Tsao, G.; Patel, A.; Lossinsky, A.S.; Graves, M.C.; Gustavson, A.; Sayre, J.; et al. Ineffective phagocytosis of amyloid-β by macrophages of Alzheimer’s disease patients. J. Alzheimer’s Dis. 2005, 7, 221–232. [Google Scholar] [CrossRef]
  25. Thériault, P.; ElAli, A.; Rivest, S. The dynamics of monocytes and microglia in Alzheimer’s disease. Alzheimers Res. Ther. 2015, 7, 41. [Google Scholar] [CrossRef]
  26. Prinz, M.; Priller, J. Tickets to the brain: Role of CCR2 and CX3CR1 in myeloid cell entry in the CNS. J. Neuroimmunol. 2010, 224, 80–84. [Google Scholar] [CrossRef]
  27. Schlepckow, K.; Monroe, K.M.; Kleinberger, G.; Cantuti-Castelvetri, L.; Parhizkar, S.; Xia, D.; Willem, M.; Werner, G.; Pettkus, N.; Brunner, B.; et al. Enhancing protective microglial activities with a dual function TREM2 antibody to the stalk region. EMBO Mol. Med. 2020, 12, e11227. [Google Scholar] [CrossRef]
  28. Sperling, R.A.; Jack, C.R., Jr.; Black, S.E.; Frosch, M.P.; Greenberg, S.M.; Hyman, B.T.; Scheltens, P.; Carrillo, M.C.; Thies, W.; Bednar, M.M.; et al. Amyloid-related imaging abnormalities in amyloid-modifying therapeutic trials: Recommendations from the Alzheimer’s Association Research Roundtable Workgroup. Alzheimers Dement. 2011, 7, 367–385. [Google Scholar] [CrossRef] [PubMed]
  29. Salloway, S.; Chalkias, S.; Barkhof, F.; Burkett, P.; Barakos, J.; Purcell, D.; Suhy, J.; Forrestal, F.; Tian, Y.; Umans, K.; et al. Amyloid-Related Imaging Abnormalities in 2 Phase 3 Studies Evaluating Aducanumab in Patients with Early Alzheimer Disease. JAMA Neurol. 2022, 79, 13–21. [Google Scholar] [CrossRef]
Cells 14 01990 g001
Figure 1. Aggregated Aβ1-42 Treatment Reduces Phagocytosis of Amyloid Beta in Monocytes Carrying CD33 and SPI1 Alzheimer’s Disease Risk Variants. (a). Human monocyte phagocytosis of fluorescently labeled amyloid beta peptide 1-42 (Aβ1-42) following exposure to aggregated Aβ1-42 (Agg. Aβ) for 24 h, measured with flow cytometry. Data are represented as geometric mean fluorescence intensity and normalized for batch correction using the GraphPad Prism normalize function. (b). Monocyte phagocytosis of fluorescently labeled Aβ1-42 as in (a), stratified by CD33 rs3865444 genotype. CD33 rs3865444C is associated with increased AD risk. (c). Monocyte phagocytosis of fluorescently labeled Aβ1-42 as in (a), stratified by SPI1 rs1057233 genotype. SPI1 rs1057233A is associated with increased AD risk. (a–c): Data is presented as the mean ± SEM, and each dot represents an individual donor. (a): Paired Student’s t-test, (b,c): two-way ANOVA with Sidak’s multiple comparison test, * p < 0.05, ** p < 0.01, ns = not significant.
Figure 1. Aggregated Aβ1-42 Treatment Reduces Phagocytosis of Amyloid Beta in Monocytes Carrying CD33 and SPI1 Alzheimer’s Disease Risk Variants. (a). Human monocyte phagocytosis of fluorescently labeled amyloid beta peptide 1-42 (Aβ1-42) following exposure to aggregated Aβ1-42 (Agg. Aβ) for 24 h, measured with flow cytometry. Data are represented as geometric mean fluorescence intensity and normalized for batch correction using the GraphPad Prism normalize function. (b). Monocyte phagocytosis of fluorescently labeled Aβ1-42 as in (a), stratified by CD33 rs3865444 genotype. CD33 rs3865444C is associated with increased AD risk. (c). Monocyte phagocytosis of fluorescently labeled Aβ1-42 as in (a), stratified by SPI1 rs1057233 genotype. SPI1 rs1057233A is associated with increased AD risk. (a–c): Data is presented as the mean ± SEM, and each dot represents an individual donor. (a): Paired Student’s t-test, (b,c): two-way ANOVA with Sidak’s multiple comparison test, * p < 0.05, ** p < 0.01, ns = not significant.
Cells 14 01990 g001
Cells 14 01990 g002
Figure 2. Aggregated Aβ1-42 Treatment Reduces TREM2 Surface Expression in Monocytes Carrying CD33 and SPI1 Alzheimer’s Disease Risk Variants. (a). Human monocyte surface expression of TREM2 following exposure to aggregated amyloid beta peptide 1-42 (Agg. Aβ) for 24 h, measured with flow cytometry. Data are represented as geometric mean fluorescence intensity and normalized for batch correction using the GraphPad Prism normalize function. (b). Monocyte TREM2 surface expression as in (a), stratified by CD33 rs3865444 genotype. rs3865444C is associated with increased AD risk. (c). Monocyte TREM2 surface expression as in (a), stratified by SPI1 rs1057233 genotype. rs1057233A is associated with increased AD risk. (a–c): Data is presented as the mean ± SEM, and each dot represents an individual donor. (a): Paired Student’s t-test, (b,c): two-way ANOVA with Sidak’s multiple comparison test, * p < 0.05, ns = not significant.
Figure 2. Aggregated Aβ1-42 Treatment Reduces TREM2 Surface Expression in Monocytes Carrying CD33 and SPI1 Alzheimer’s Disease Risk Variants. (a). Human monocyte surface expression of TREM2 following exposure to aggregated amyloid beta peptide 1-42 (Agg. Aβ) for 24 h, measured with flow cytometry. Data are represented as geometric mean fluorescence intensity and normalized for batch correction using the GraphPad Prism normalize function. (b). Monocyte TREM2 surface expression as in (a), stratified by CD33 rs3865444 genotype. rs3865444C is associated with increased AD risk. (c). Monocyte TREM2 surface expression as in (a), stratified by SPI1 rs1057233 genotype. rs1057233A is associated with increased AD risk. (a–c): Data is presented as the mean ± SEM, and each dot represents an individual donor. (a): Paired Student’s t-test, (b,c): two-way ANOVA with Sidak’s multiple comparison test, * p < 0.05, ns = not significant.
Cells 14 01990 g002
Cells 14 01990 g003
Figure 3. Aggregated Aβ1-42 Treatment Influences TREM2 and TYROBP Transcription in a Genotype-Independent Manner. (a,b): Human monocyte RNA expression of TREM2 (a) and TYROBP (b) following exposure to aggregated amyloid beta peptide 1-42 (Agg. Aβ) for 24 h across all genotypes. (c,d): RNA expression of TREM2 (c) and TYROBP (d) as in (a,b), stratified by CD33 rs3865444 genotype. CD33 rs3865444C is associated with increased AD risk. (e,f): RNA expression of TREM2 (e) and TYROBP (f) as in (a,b), stratified by SPI1 rs1057233 genotype. SPI1 rs1057233A is associated with increased AD risk. Gene expression results were measured using quantitative real-time PCR and were normalized to GAPDH expression. Results are depicted as the delta Ct value between the gene of interest and GAPDH times 1000. (a–f): Data is presented as the mean ± SEM, and each dot represents an individual donor. (a,b): Paired Student’s t-test, (c–f): two-way ANOVA with Sidak’s multiple comparison test * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, ns = not significant.
Figure 3. Aggregated Aβ1-42 Treatment Influences TREM2 and TYROBP Transcription in a Genotype-Independent Manner. (a,b): Human monocyte RNA expression of TREM2 (a) and TYROBP (b) following exposure to aggregated amyloid beta peptide 1-42 (Agg. Aβ) for 24 h across all genotypes. (c,d): RNA expression of TREM2 (c) and TYROBP (d) as in (a,b), stratified by CD33 rs3865444 genotype. CD33 rs3865444C is associated with increased AD risk. (e,f): RNA expression of TREM2 (e) and TYROBP (f) as in (a,b), stratified by SPI1 rs1057233 genotype. SPI1 rs1057233A is associated with increased AD risk. Gene expression results were measured using quantitative real-time PCR and were normalized to GAPDH expression. Results are depicted as the delta Ct value between the gene of interest and GAPDH times 1000. (a–f): Data is presented as the mean ± SEM, and each dot represents an individual donor. (a,b): Paired Student’s t-test, (c–f): two-way ANOVA with Sidak’s multiple comparison test * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, ns = not significant.
Cells 14 01990 g003
Cells 14 01990 g004
Figure 4. Working Model. Genetic risk variants enriched in myeloid cells impact the phenotype of microglia as well as myeloid cells in the periphery, including monocytes. Exposure to amyloid beta (Aβ) in peripheral monocytes carrying CD33 and SPI1 AD risk variants results in impaired phagocytosis of Aβ and reduced TREM2 surface expression. Figure made in BioRender (biorender.com).
Figure 4. Working Model. Genetic risk variants enriched in myeloid cells impact the phenotype of microglia as well as myeloid cells in the periphery, including monocytes. Exposure to amyloid beta (Aβ) in peripheral monocytes carrying CD33 and SPI1 AD risk variants results in impaired phagocytosis of Aβ and reduced TREM2 surface expression. Figure made in BioRender (biorender.com).
Cells 14 01990 g004
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Chatila, Z.K.; Bradshaw, E.M. Alzheimer’s Disease Risk Variants Interact with Amyloid-Beta to Modulate Monocyte Function. Cells 2025, 14, 1990. https://doi.org/10.3390/cells14241990

AMA Style

Chatila ZK, Bradshaw EM. Alzheimer’s Disease Risk Variants Interact with Amyloid-Beta to Modulate Monocyte Function. Cells. 2025; 14(24):1990. https://doi.org/10.3390/cells14241990

Chicago/Turabian Style

Chatila, Zena K., and Elizabeth M. Bradshaw. 2025. "Alzheimer’s Disease Risk Variants Interact with Amyloid-Beta to Modulate Monocyte Function" Cells 14, no. 24: 1990. https://doi.org/10.3390/cells14241990

APA Style

Chatila, Z. K., & Bradshaw, E. M. (2025). Alzheimer’s Disease Risk Variants Interact with Amyloid-Beta to Modulate Monocyte Function. Cells, 14(24), 1990. https://doi.org/10.3390/cells14241990

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop