Microglia modulate Aβ-dependent astrocyte reactivity in Alzheimer’s disease

doi:10.21203/rs.3.rs-5184011/v1

Download PDF

Article

Microglia modulate Aβ-dependent astrocyte reactivity in Alzheimer’s disease

https://doi.org/10.21203/rs.3.rs-5184011/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Experimental evidence suggests that activated microglia induce astrocyte reactivity in neurodegenerative disorders, such as Alzheimer’s disease (AD). Here, we investigated the association between microglial activation and amyloid-β (Aβ) with reactive astrogliosis in the living AD human brain. We studied 101 individuals across the AD spectrum with positron emission tomography (PET) for Aβ aggregation ([¹⁸F]AZD4694) and translocator protein (TSPO) microglial activation ([¹¹C]PBR28), along with the plasma biomarker for astrocyte reactivity glial fibrillary acidic protein (GFAP). We further assessed tau phosphorylation by plasma phosphorylated tau at threonine 217 (p-tau217) and tau aggregation using [¹⁸F]MK-6240 PET. We found that Aβ pathology was associated with increased astrocyte reactivity across cortical brain regions only in the presence of elevated levels of microglial activation. Importantly, the microglia-dependent effects of Aβ on astrocyte reactivity were further related to cognitive impairment through tau phosphorylation and aggregation. Postmortem data from the Allen Human Brain Atlas revealed that TSPO mRNA expression patterns reflected the in-vivo Aβ-glia relationships, indicating that the interplay between AD pathophysiology and glial reactivity might be regulated at the gene expression level. Altogether, our results provide biomarker-based clinical evidence that microglial activation plays a key role in Aβ-related astrocyte reactivity, which, in turn, contributes to downstream pathological features of AD. These findings shed light on the intricate crosstalk between microglia and astrocytes in the AD brain, offering insights for the development of glia-targeting therapies.

Biological sciences/Neuroscience/Glial biology/Microglia

Health sciences/Diseases/Neurological disorders/Dementia/Alzheimer's disease

Biological sciences/Neuroscience/Diseases of the nervous system/Alzheimer's disease

Health sciences/Medical research/Biomarkers

Biological sciences/Neuroscience/Glial biology/Astrocyte

Alzheimer’s disease

amyloid-β

microglia

astrocytes

neuroinflammation

biomarkers

Alzheimer’s disease (AD) is defined by the brain accumulation of amyloid-β (Aβ) and tau proteins¹. However, a growing body of evidence indicates that these proteinopathies alone do not fully explain the clinical progression of AD^2,3. This suggests that additional pathological mechanisms are involved in the AD pathological cascade. In fact, it has been proposed that neuroinflammation plays a pivotal role in determining patients’ susceptibility to dementia, with microglia and astrocytes orchestrating the repertoire of immune responses^4,5.

In pathological conditions, astrocytes undergo molecular, functional, and morphological changes, a process referred to as reactive astrogliosis⁶. Despite the well-established involvement of astrocytes in AD progression^3,7, the biological underpinnings of reactive astrogliosis in this neurodegenerative condition remain elusive. Although Aβ pathology is an important trigger of astrocyte reactivity^8–11, other pathological components of AD have also been implicated in promoting reactive astrogliosis, notably microglial activation³. Instead of solely being a response to Aβ deposition^12–14, compelling experimental data demonstrate that activated microglia can induce astrocyte reactivity through the secretion of inflammatory cytokines¹⁵, highlighting a close relationship between Aβ and glial cells. This raises the possibility that microglial activation may account for the fact that Aβ drives astrocyte reactivity in some individuals but not in others¹⁶. However, no previous clinical study has addressed whether microglial activation can modulate astrocyte reactivity in response to Aβ pathology in the living human brain. Better understanding the microglia-astrocyte crosstalk in AD may provide valuable insights for developing therapies targeting glial cells¹⁷.

Using imaging and fluid biomarkers for the quantification of glial reactivity and AD hallmark proteinopathies, we tested the hypothesis that microglia impact the effects of Aβ pathology on reactive astrogliosis in individuals across the aging and AD clinical spectrum. In addition, we assessed whether gene expression patterns reflect the in-vivo relationship between Aβ pathology and glial reactivity. Lastly, we investigated whether glial reactivity could aid in explaining the relationships between Aβ, tau, and cognitive impairment.

The selection of study participants is displayed in Supplementary Fig. 1. We genotyped 606 individuals for the rs6971 single nucleotide polymorphism (SNP) in the translocator protein (TSPO) gene, with 314 identified as TSPO high-affinity binders. From those, we studied 101 individuals aged 50 years or older who had complete data for positron emission tomography (PET) for Aβ ([¹⁸F]AZD4694) and TSPO microglial activation ([¹¹C]PBR28), as well as clinical assessments and plasma glial fibrillary acidic protein (GFAP) measurements. Participants were also assessed for plasma plasma phosphorylated tau at threonine 217 (p-tau217) and [¹⁸F]MK-6240 tau PET imaging. Among our study population, a total of 62 were cognitively unimpaired (CU), and 39 were cognitively impaired (26 with mild cognitive impairment [MCI] and 13 with AD dementia). Table 1 displays demographic and clinical information of our population.

Table 1

Study participant characteristics.
	Overall	Cognitively unimpaired	Cognitively impaired^a
No.	101	62	39
Age, years	71.9 (6.5)	72.3 (5.8)	71.2 (7.5)
Male, No. (%)	33 (32.7)	14 (22.6)	19 (48.7)
Education, years	15.3 (3.6)	15.6 (3.8)	14.8 (3.3)
APOEε4 carrier, No. (%)	35 (34.7)	15 (24.2)	20 (51.3)
Plasma GFAP, pg/mL	242.3 (119.8)	222.8 (115.8)	273.2 (120.8)
Plasma p-tau217, pg/mL^b	0.09 (0.08)	0.06 (0.06)	0.14 (0.09)
Aβ PET SUVR	1.74 (0.60)	1.48 (0.39)	2.16 (0.65)
Tau PET SUVR	1.13 (0.71)	0.86 (0.10)	1.57 (0.99)
TSPO PET SUVR	1.31 (0.14)	1.28 (0.13)	1.34 (0.15)
Microglial activation positivity, No. (%)	51 (50.5)	29 (46.8)	22 (56.4)
MMSE score	28.2 (2.8)	29.3 (0.9)	26.4 (3.7)
Continuous variables are presented as mean (SD). Aβ PET was measured with global [¹⁸F]AZD4694 SUVR, microglial activation TSPO PET with posterior cingulate [¹¹C]PBR28 SUVR, and tau PET with temporal meta-ROI [¹⁸F]MK6240 SUVR. ^aComposed of 26 individuals with MCI and 13 individuals with AD dementia. ^bAssessed in a subset of 93 individuals.

Aβ pathology associates with astrocyte reactivity only in the presence of elevated levels of microglial activation

We first investigated the association of TSPO PET and Aβ PET with plasma GFAP. Regression analyses revealed a significant positive association between global Aβ PET SUVR and plasma GFAP levels in TSPO microglial activation-positive (MA + _TSPO) individuals (β = 0.618, 95% confidence interval [CI] 0.310 to 0.926, P < 0.001) but not in TSPO microglial activation-negative (MA-_TSPO) individuals (β = 0.184, 95% CI -0.133 to 0.502, P = 0.247), as well as a significant interaction of Aβ PET SUVR and MA_TSPO status with plasma GFAP levels (β = 0.528, 95% CI 0.172 to 0.883, P = 0.004; Fig. 1a). Additionally, a significant interaction between continuous values of Aβ PET burden and TSPO PET uptake on plasma GFAP levels (β = 0.302, 95% CI 0.116 to 0.489, P = 0.002; Fig. 1b) further supported that microglial activation affects the association of Aβ pathology with reactive astrogliosis, independently of the threshold used to define MA + _TSPO. Analysis of variance corroborated the adequacy of the interaction model compared to the reduced models (including only Aβ PET, only TSPO PET, or their additive effects; all P < 0.05). Similar findings were observed in sensitivity analyses including outliers (Supplementary Table 1). Region-wise linear regressions showed that higher plasma GFAP levels were associated with higher Aβ PET burden only in the presence of MA + _TSPO across cortical areas, including the frontal, parietal, temporal, and cingulate cortices (Fig. 1c and Supplementary Fig. 2). In a subsequent analysis assessing continuous values for the topographical distribution of microglial activation, we found that the relationship between Aβ PET burden and plasma GFAP levels was influenced by TSPO PET uptake in the cingulate and frontal brain regions (Fig. 1d and Supplementary Fig. 3). Using microarray-based postmortem data from the Allen Human Brain Atlas (AHBA), we found that the cerebral distribution of TSPO mRNA expression predicted the topography of microglia-related effects of Aβ on astrocyte reactivity which was observed in our study population (P = 0.002; Fig. 1e).

Microglial and astrocyte reactivity jointly relate to tau phosphorylation and aggregation

Next, we assessed the association of TSPO PET and plasma GFAP with tau biomarkers. Regression analysis showed that higher plasma GFAP levels were significantly associated with higher plasma p-tau217 levels in MA + _TSPO individuals (β = 0.658, 95% CI 0.323 to 0.993, P < 0.001) but not in MA-_TSPO individuals (β = 0.110, 95% CI -0.091 to 0.312, P = 0.276), and that the interaction between plasma GFAP and MA_TSPO status was significantly associated with higher plasma p-tau217 levels (β = 0.470, 95% CI 0.125 to 0.816, P = 0.008; Fig. 2a). Moreover, a significant interaction was also observed between continuous levels of plasma GFAP and TSPO PET uptake on plasma p-tau217 levels (β = 0.201, 95% CI 0.024 to 0.377, P = 0.026; Fig. 2b), reinforcing that the relationship between reactive astrogliosis and tau phosphorylation depends on microglial activation levels, irrespective of the threshold used to determine MA + _TSPO. The adequacy of the interaction model compared to the reduced models (including only plasma GFAP, only TSPO PET, or their additive effects) was supported by analysis of variance (all P < 0.05). Similar results were observed in sensitivity analyses including outliers (Supplementary Table 2). In region-wise linear regressions, we observed that higher plasma GFAP levels were associated with tau PET accumulation only in the presence of MA + _TSPO mainly in neocortical brain regions, comprising temporal structures, as well as association and sensorimotor cortices (Fig. 2c and Supplementary Fig. 4).

Glial reactivity contributes to AD-related cognitive impairment

Lastly, we applied structural equation modeling to test the associations between Aβ, astrocyte reactivity, tau phosphorylation, tau tangles, and cognition according to MA_TSPO status. In MA-_TSPO individuals, we only observed direct effects of Aβ PET load on higher plasma p-tau217 levels and tau PET burden, with no significant associations with cognitive impairment (Fig. 2d). This model explained 32% of the variance in cognitive impairment and fit the data poorly (root mean squared error of approximation [RMSEA] = 0.257, standardized root mean square residual [SRMR] = 0.069, comparative fit index [CFI] = 0.888). Conversely, in MA + _TSPO individuals, we found that plasma GFAP levels partially mediated the effects of Aβ PET load on higher plasma p-tau217, which was further associated with cognitive impairment through increased tau PET accumulation. The model also demonstrated pathways by which Aβ PET was related to cognitive deterioration via direct associations with tau pathology biomarkers (plasma p-tau217 and tau PET; Fig. 2e). This construct explained 76% of the variance in cognitive impairment and fit the data well (RMSEA = 0.000, SRMR = 0.018, CFI = 1.000). Supplementary Tables 3 and 4 report detailed coefficients and associated statistics for structural equation models.

In the present study, we observed that microglial activation determines Aβ effects on astrocyte reactivity in the living human brain. We also found that the physiological distribution of TSPO gene expression in the postmortem brain resembles this in-vivo relationship between Aβ pathology and glial reactivity. Lastly, we showed that the microglia-dependent impact of Aβ on astrocyte reactivity was further associated with cognitive impairment through tau phosphorylation and aggregation.

We found that Aβ pathology was associated with GFAP levels across cortical brain regions only in the presence of increased TSPO PET signal. It is well-established that Aβ pathology is closely related to astrocyte reactivity, with experimental studies revealing that Aβ aggregates increase markers of astrocyte reactivity^18–20. Accordingly, neuropathological studies showed that GFAP-positive reactive astrocytes surround Aβ plaques and mirror the topographical distribution of Aβ deposition in the AD brain ^11,21,22. This is further supported by an increasing number of clinical studies showing that cerebrospinal fluid (CSF) and, more prominently, plasma levels of GFAP are closely associated with Aβ biomarkers^23–30. Beyond Aβ accumulation, an animal study demonstrated that microglial activation also triggers astrocyte reactivity by secreting interleukin-1α (Il-α), tumor necrosis factor (TNF), and complement component 1, subcomponent q (C1q) cytokines¹⁵. Microglial reactivity is an important pathological feature of AD and has an intimate communication with reactive astrocytes in coordinating the innate immune response in the brain^4,5,31. Here, we build on previous experimental data by showing the first clinical evidence that microglial activation modulates the Aβ-induced astrocyte reactivity in the living AD human brain. Analyzing postmortem data from the AHBA, we also demonstrated that the physiological brain distribution of TSPO mRNA expression resembled the topographical patterns of microglia-driven effects of Aβ on astrocyte reactivity observed in our study population. Altogether, these findings support the notion that a dysregulated cellular crosstalk between microglia and astrocytes may be a major neuroinflammatory phenomenon involved in AD pathogenesis, which might be at least partially explained at the level of gene expression.

We observed that TSPO PET and plasma GFAP levels were jointly associated with tau biomarkers. Rather than merely being a response to the accumulation of AD hallmark proteins, a growing body of work supports the notion that both microglia and astrocytes are involved in tau pathogenesis through the exacerbation of tau spread and hyperphosphorylation, as well as tau-driven neurodegeneration^32–38. Accordingly, recent biomarker-based clinical studies have suggested that glial reactivity might help to explain the link between Aβ and tau pathology. More specifically, it has been consistently demonstrated that astrocyte reactivity, indexed by plasma GFAP, determines Aβ downstream effects on tau pathology in individuals across the AD continuum^16,39,40. Similarly, neuroimaging data demonstrated that the interaction between Aβ and activated microglia is crucial for tau pathology progression⁴¹. In this study, we propose a model in which the interplay between Aβ and reactive astrogliosis contributes to tau pathology in the presence of activated microglia. Noteworthy, tau biomarkers are closely related to AD-specific cognitive deterioration, but they assess distinct aspects of tau pathology: fluid p-tau detects soluble hyperphosphorylated tau, serving as an early measure of tau pathophysiology; while tau PET targets insoluble tau tangles, offering a later measure of tau aggregation^42–44. Our results align with these concepts by revealing glial-mediated effects of Aβ on pathological tau phosphorylation, which were further associated with subsequent tau aggregation, ultimately leading to the manifestation of cognitive symptoms. Altogether, these observations reinforce the importance of microglial activation and astrocyte reactivity in AD progression, suggesting glial cells as potential therapeutic targets for future disease-modifying clinical trials.

Methodological strengths of our study include the evaluation of a large cohort genetically enriched to increase the reliability of the microglial activation imaging agent. Moreover, data for multiple imaging and fluid biomarkers using the most advanced methodologies for the brain quantification of Aβ, tau, and glial reactivity were available. This study also has methodological limitations. TRIAD participants are volunteers who were motivated to participate in a study about dementia, which might limit the generalizability of our findings due to self-selection bias. Here, we used [¹¹C]PBR28 TSPO PET as an index of microglial activation^45,46 and plasma GFAP as an index of astrocyte reactivity⁴⁷. Neuroinflammation involves various glial phenotypes during AD progression^4,6,48; however, this heterogeneity could not be captured in our biomarker-based study. Although TSPO is predominantly expressed in the AD brain by microglia^49–52, it has also been detected in other cell types (e.g., astrocytes and endothelial cells)^51–54. Thus, further investigation is needed to clarify the extent to which each cell type contributes to the TSPO PET signal in AD. In addition, there is an ongoing discussion about whether the TSPO PET signal more accurately reflects microglial activation or density/recruitment⁵⁵. To elucidate the biological mechanisms underlying our findings, future research focusing on phenotypically characterizing the glial cells involved in AD neuroinflammatory response is needed. Lastly, given that this study has a cross-sectional design, our results should be expanded with multimodal longitudinal data.

In conclusion, our results support the construct that activated microglia is a key player in Aβ-dependent reactive astrogliosis, which further contributes to cognitive impairment via the aggregation of phosphorylated tau in the living AD human brain.

Participants

The present work included individuals enrolled in the Translational Biomarkers in Aging and Dementia (TRIAD) cohort (https://triad.tnl-mcgill.com), a biomarker-based study launched in 2017 which comprises participants from the community or outpatients at the McGill University Research Centre for Studies in Aging, Canada. Exclusion criteria encompassed inability to speak English or French, inadequate auditory and visual capacities for neuropsychologic testing, contraindications for PET or magnetic resonance imaging (MRI), recent head trauma or major surgery, inadequately treated conditions, current enrollment in other studies or active substance abuse. Written informed consent was obtained from all study participants. The study was approved by the Montreal Neurological Institute PET Working Committee and the Douglas Mental Health University Institute Research Ethics Board.

TRIAD participants underwent genotyping for the Ala147Thr SNP of the TSPO gene (rs6971, www.ncbi.nlm.nih.gov/snp/rs6971), which affects the binding affinity of the [¹¹C]PBR28 radiotracer. Depending on this genotype, individuals can be low-, mixed- or high-affinity binders. Since the rs6971 SNP is a methodological caveat that does not influence TSPO levels, glial reactivity, or AD pathophysiology⁵⁴, our study only included high-affinity binders to mitigate noise related to artificial uptake variations, as previously done^41,56. Study participants had available Aβ PET, TSPO microglial activation PET, magnetic resonance imaging (MRI), plasma GFAP, and neuropsychological testing. We also assessed plasma p-tau217 and tau tangle PET data. Two individuals that had plasma GFAP levels three standard deviations (SD) above the mean of the population were considered outliers and excluded from the analyses, as previously done^57–59. CU individuals had no objective cognitive impairment and a global Clinical Dementia Rating (CDR) of 0. Participants with MCI had preserved activities of daily living, subjective and/or objective cognitive impairment, and a global CDR of 0.5⁶⁰. Patients with mild-to-moderate AD dementia met the National Institute on Aging and the Alzheimer’s Association (NIA-AA) criteria for probable AD⁶¹ and had a global CDR score between 0.5 and 2.

Plasma biomarkers

Blood collection followed previously described procedures⁶². Plasma biomarkers were quantified using Single molecule array (Simoa) methods on the HD-X platform (Quanterix). Plasma GFAP concentration was measured with a commercial single-plex assay at the Clinical Neurochemistry Laboratory, University of Gothenburg⁶³. Plasma p-tau217 concentration was measured at Johnson and Johnson Innovative Medicine with a previously described assay⁶⁴.

Brain gene expression

We obtained TSPO gene expression data in the entire brain from the open-source AHBA (http://www.brain-map.org)⁶⁵. Briefly, mRNA expression intensity values were derived from microarray data of 3702 samples from 6 healthy postmortem human brains (Supplementary Table 5). Microarray-based TSPO mRNA brain expression map, generated using Gaussian process regression⁶⁶, was retrieved from www.meduniwien.ac.at/neuroimaging/mRNA.html.

Neuroimaging

All participants had a T1-weighted MRI that was used for coregistration. Detailed information concerning MRI acquisition and processing has already been described⁵⁷. Of note, structural MRI data were acquired at the Montreal Neurological Institute (MNI) on a 3T Siemens Magnetom scanner using a standard head coil. High-resolution structural images of the whole brain were obtained with the magnetization prepared rapid acquisition gradient echo MRI sequence. All PET scans were acquired at the MNI on the same brain-dedicated Siemens High-Resolution Research Tomograph. Aβ PET with [¹⁸F]AZD4694 (acquired 40–70 min post-injection), tau tangle PET with [¹⁸F]MK-6240 (acquired 90–110 min post-injection), and TSPO microglial activation PET with [¹¹C]PBR28 (acquired 60–90 min post-injection) were reconstructed with the ordered subset expectation maximization (OSEM) algorithm on a four-dimensional (4D) volume with three frames (3 × 600 s)⁶⁷, four frames (4 × 300 s)⁶⁷, and six frames (6 × 300 s)⁴¹, respectively. After acquisition, PET images were corrected for attenuation, motion, dead time, decay, and random and scattered coincidences. T1-weighted MRI images were corrected for non-uniformity and field distortions in accordance with an in-house pipeline. Then, imaging co-registration and spatial normalization to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) template space was performed. To this end, PET images were automatically registered to the native T1-weighted MRI with linear transformations, and T1-weighted MRI images were linearly and nonlinearly registered to the ADNI template space. Subsequently, PET images were registered to the ADNI template space by applying the linear and nonlinear transformation parameters from PET to native MRI and native MRI to the ADNI template space. Aβ PET SUVR and TSPO PET SUVR were calculated using the whole cerebellar gray matter as reference region^41,68. Tau PET SUVR was calculated using the inferior cerebellar gray matter as reference region⁶⁹. PET images were spatially smoothed to achieve a final resolution of 8 mm full width at half-maximum. The Desikan-Killiany-Tourville atlas was used to determine the anatomical regions of interest (ROIs)⁷⁰. The global Aβ PET SUVR composite was estimated from the following brain regions: precuneus, prefrontal, orbitofrontal, parietal, temporal, and cingulate⁷¹. The temporal meta-ROI tau PET SUVR composite was estimated from the following brain regions: entorhinal, hippocampus, fusiform, parahippocampal, inferior temporal, and middle temporal⁷¹. We identified higher [¹¹C]PBR28 uptake in the posterior cingulate as a cortical neuroinflammatory signature of AD (Supplementary Fig. 5), similar to previous work⁴¹. Thus, we used posterior cingulate [¹¹C]PBR28 SUVR as a summary measure of TSPO PET. Microglial activation positivity (MA + _TSPO) was defined as SUVR values 2 SD above the mean from a separate population of 17 CU Aβ- young adults (Supplementary Table 6), as previously reported⁷².

Statistical analysis

Statistical analyses were carried out using the R software (version 4.0.2, http://www.r-project.org/). The associations between biomarkers were tested using regression models accounting for age, sex, and cognitive status. Regression models including interaction terms also accounted for the main effects of the variables involved in the interaction. Continuous variables were standardized to facilitate comparison across estimates. Multiple comparisons correction at P < 0.05 was performed using the false discovery rate (FDR) method for region-wise analysis. Analysis of variance was used to test the adequacy of models with the interaction term compared to reduced models. Regression analysis also tested whether TSPO mRNA expression intensity resembles Aβ-glia relationships across cortical brain regions in our population. Structural equation modeling was applied to test the associations between Aβ, tau, glial reactivity, and cognition. Associations were adjusted for age, sex, and (if involving cognition) years of education. Noteworthy, structural equation models were constructed to assess specific hypotheses represented in the figure’s meta-models, and the following thresholds were used for considering a good fit: CFI > 0.97; RMSEA < 0.05; SRMR < 0.05 ^73,74. For all analyses, we considered a two-tailed P-value < 0.05 as statistically significant.

Acknowledgments:

We acknowledge all study participants and the McGill Center for Studies in Aging staff. We thank Cerveau Technologies for the use of [¹⁸F]MK-6240. We also thank Dean Jolly, Alexey Kostikov, Monica Samoila-Lactatus, Karen Ross, Mehdi Boudjemeline, and Sandy Li for assisting in the radiochemistry production, as well as Richard Strauss, Edith Strauss, Guylaine Gagne, Carley Mayhew, Tasha Vinet-Celluci, Karen Wan, Sarah Sbeiti, Meong Jin Joung, Miloudza Olmand, Rim Nazar, Hung-Hsin Hsiao, Reda Bouhachi, and Arturo Aliaga for helping with data acquisition.

Funding:

This research was supported by the Alzheimer’s Association (NIRG-12-92090 and NIRP-12-259245; PR-N), Brain Canada Foundation (CFI Project 34874 and 33397 to PR-N), CIHR-CCNA Canadian Consortium of Neurodegeneration in Aging (MOP-11-51-31; RFN 152985, 159815, and 162303 to PR-N), Fonds de Recherche du Québec – Santé (Chercheur Boursier, 2020-VICO-279314; PR-N), Weston Brain Institute (8400707, 8401154 and 8401103 to PR-N), and Colin Adair Charitable Foundation (PR-N). ERZ is funded by the Alzheimer’s Association (AARGD-21-850670), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq; 312306/2021-0), National Academy of Neuropsychology (ALZ-NAN-22-928381), Fundação de Amparo à Pesquisa do Rio Grande do Sul (FAPERGS; 21/2551-0000673-0), and Instituto Serrapilheira (Serra-1912-31365). BB receives financial support from the Alzheimer’s Association (AARFD-22-974627). PCLF receives financial support from the Alzheimer’s Association (AARFD-22-923814). CA is supported by Global Brain Health Institute, Alzheimer’s Association, and Alzheimer’s Society (GBHI ALZ UK-23-971089). MADB is supported by Fundação de Amparo à pesquisa do Estado do RS (FAPERGS) and Alzheimer’s Association (AARFD-23-1148735). WVB is funded by the Alzheimer’s Association (AACSF-D 22-928689). HZ is a Wallenberg Scholar and a Distinguished Professor at the Swedish Research Council supported by grants from the Swedish Research Council (2023-00356; 2022-01018 and 2019-02397), the European Union’s Horizon Europe research and Innovation Programme under grant agreement No 101053962, Swedish State Support for Clinical Research (ALFGBG-71320), the Alzheimer Drug Discovery Foundation (ADDF), USA (201809-2016862), the AD Strategic Fund and the Alzheimer's Association (ADSF-21-831376-C, ADSF-21-831381-C, ADSF-21-831377-C, and ADSF-24-1284328-C), the European Partnership on Metrology, co-financed from the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States (NEuroBioStand; 22HLT07), the Bluefield Project, Cure Alzheimer’s Fund, the Olav Thon Foundation, the Erling-Persson Family Foundation, Familjen Rönströms Stiftelse, Stiftelsen för Gamla Tjänarinnor, Hjärnfonden, Sweden (FO2022-0270), the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement No 860197 (MIRIADE), the European Union Joint Programme – Neurodegenerative Disease Research (JPND2021-00694), the National Institute for Health and Care Research University College London Hospitals Biomedical Research Centre, and the UK Dementia Research Institute at UCL (UKDRI-1003). KB is supported by the Swedish Research Council (2017-00915 and 2022-00732), the Alzheimer Drug Discovery Foundation (ADDF; RDAPB-201809-2016615), Swedish Alzheimer Foundation (AF-930351, AF-939721 and AF-968270), Hjärnfonden (FO2017-0243 and ALZ2022-0006), the Swedish state under the agreement between the Swedish government and the County Councils, the ALF-agreement (ALFGBG-715986 and ALFGBG-965240), the European Union Joint Program - Neurodegenerative Disease Research (JPND2019-466-236) the National Institute of Health (NIH; R01AG068398), Alzheimer’s Association 2021 Zenith Award (ZEN-21-848495), and Alzheimer’s Association 2022-2025 Grant (SG-23-1038904 QC). TAP is supported by the National Institute of Health (R01AG075336 and R01AG073267) and the Alzheimer’s Association (AACSF-20-648075).

Competing Interests

WVB served as a speaker for Novo Nordisk. SG has served as a scientific advisor to Cerveau Technologies. GT-B and HCK are employees of Johnson and Johnson Innovative Medicine and receive salary and stock from its parent company, Johnson & Johnson. NJA has given lectures in symposia sponsored by Lilly and Quanterix. HZ has served on the scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, Alzinova, ALZPath, Amylyx, Annexon, Apellis, Artery Therapeutics, AZTherapies, Cognito Therapeutics, CogRx, Denali, Eisai, LabCorp, Merry Life, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures in symposia sponsored by Alzecure, Biogen, Cellectricon, Fujirebio, Lilly, Novo Nordisk, and Roche, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). KB has served as a consultant and at advisory boards for Abbvie, AC Immune, ALZPath, AriBio, BioArctic, Biogen, Eisai, Lilly, Moleac Pte. Ltd, Neurimmune, Novartis, Ono Pharma, Prothena, Roche Diagnostics, and Siemens Healthineers; has served at data monitoring committees for Julius Clinical and Novartis; has given lectures, produced educational materials and participated in educational programs for AC Immune, Biogen, Celdara Medical, Eisai and Roche Diagnostics; and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program, outside the work presented in this paper. ERZ serves on the scientific advisory board of Next Innovative Therapeutics. All other authors declare that they have no competing interests.

Knopman, D.S., et al. Alzheimer disease. Nat Rev Dis Primers 7, 33 (2021).
Herrup, K. The case for rejecting the amyloid cascade hypothesis. Nat Neurosci 18, 794–799 (2015).
Carter, S.F., et al. Astrocyte Biomarkers in Alzheimer's Disease. Trends Mol Med 25, 77–95 (2019).
Leng, F. & Edison, P. Neuroinflammation and microglial activation in Alzheimer disease: where do we go from here? Nat Rev Neurol 17, 157–172 (2021).
Heneka, M.T., et al. Neuroinflammation in Alzheimer's disease. Lancet Neurol 14, 388–405 (2015).
Escartin, C., et al. Reactive astrocyte nomenclature, definitions, and future directions. Nat Neurosci 24, 312–325 (2021).
Kumar, A., Fontana, I.C. & Nordberg, A. Reactive astrogliosis: A friend or foe in the pathogenesis of Alzheimer's disease. J Neurochem 164, 309–324 (2023).
Hu, J., Akama, K.T., Krafft, G.A., Chromy, B.A. & Van Eldik, L.J. Amyloid-beta peptide activates cultured astrocytes: morphological alterations, cytokine induction and nitric oxide release. Brain Res 785, 195–206 (1998).
Johnstone, M., Gearing, A.J. & Miller, K.M. A central role for astrocytes in the inflammatory response to beta-amyloid; chemokines, cytokines and reactive oxygen species are produced. J Neuroimmunol 93, 182–193 (1999).
Carrero, I., et al. Oligomers of beta-amyloid protein (Abeta1-42) induce the activation of cyclooxygenase-2 in astrocytes via an interaction with interleukin-1beta, tumour necrosis factor-alpha, and a nuclear factor kappa-B mechanism in the rat brain. Exp Neurol 236, 215–227 (2012).
Osborn, L.M., Kamphuis, W., Wadman, W.J. & Hol, E.M. Astrogliosis: An integral player in the pathogenesis of Alzheimer's disease. Prog Neurobiol 144, 121–141 (2016).
Hayes, A., Thaker, U., Iwatsubo, T., Pickering-Brown, S.M. & Mann, D.M. Pathological relationships between microglial cell activity and tau and amyloid beta protein in patients with Alzheimer's disease. Neurosci Lett 331, 171–174 (2002).
Kitazawa, M., Yamasaki, T.R. & LaFerla, F.M. Microglia as a potential bridge between the amyloid beta-peptide and tau. Ann N Y Acad Sci 1035, 85–103 (2004).
McGeer, P.L. & McGeer, E.G. The amyloid cascade-inflammatory hypothesis of Alzheimer disease: implications for therapy. Acta Neuropathol 126, 479–497 (2013).
Liddelow, S.A., et al. Neurotoxic reactive astrocytes are induced by activated microglia. Nature 541, 481–487 (2017).
Bellaver, B., et al. Astrocyte reactivity influences amyloid-beta effects on tau pathology in preclinical Alzheimer's disease. Nat Med 29, 1775–1781 (2023).
Liddelow, S.A. & Barres, B.A. Reactive Astrocytes: Production, Function, and Therapeutic Potential. Immunity 46, 957–967 (2017).
Diniz, L.P., et al. Astrocyte Transforming Growth Factor Beta 1 Protects Synapses against Abeta Oligomers in Alzheimer's Disease Model. J Neurosci 37, 6797–6809 (2017).
Baglietto-Vargas, D., et al. Generation of a humanized Abeta expressing mouse demonstrating aspects of Alzheimer's disease-like pathology. Nat Commun 12, 2421 (2021).
De Bastiani, M.A., et al. Cross-species comparative hippocampal transcriptomics in Alzheimer's disease. iScience 27, 108671 (2024).
Serrano-Pozo, A., et al. Reactive glia not only associates with plaques but also parallels tangles in Alzheimer's disease. Am J Pathol 179, 1373–1384 (2011).
Perez-Nievas, B.G. & Serrano-Pozo, A. Deciphering the Astrocyte Reaction in Alzheimer's Disease. Front Aging Neurosci 10, 114 (2018).
Mila-Aloma, M., et al. Amyloid beta, tau, synaptic, neurodegeneration, and glial biomarkers in the preclinical stage of the Alzheimer's continuum. Alzheimers Dement 16, 1358–1371 (2020).
Pereira, J.B., et al. Plasma GFAP is an early marker of amyloid-beta but not tau pathology in Alzheimer's disease. Brain (2021).
Benedet, A.L., et al. Differences Between Plasma and Cerebrospinal Fluid Glial Fibrillary Acidic Protein Levels Across the Alzheimer Disease Continuum. JAMA Neurol (2021).
Oeckl, P., et al. Glial Fibrillary Acidic Protein in Serum is Increased in Alzheimer's Disease and Correlates with Cognitive Impairment. J Alzheimers Dis 67, 481–488 (2019).
Simren, J., et al. The diagnostic and prognostic capabilities of plasma biomarkers in Alzheimer's disease. Alzheimers Dement 17, 1145–1156 (2021).
Schulz, I., et al. Systematic Assessment of 10 Biomarker Candidates Focusing on alpha-Synuclein-Related Disorders. Mov Disord (2021).
Ferrari-Souza, J.P., et al. Astrocyte biomarker signatures of amyloid-beta and tau pathologies in Alzheimer's disease. Mol Psychiatry (2022).
Simren, J., et al. Differences between blood and cerebrospinal fluid glial fibrillary Acidic protein levels: The effect of sample stability. Alzheimers Dement 18, 1988–1992 (2022).
Matejuk, A. & Ransohoff, R.M. Crosstalk Between Astrocytes and Microglia: An Overview. Front Immunol 11, 1416 (2020).
Garwood, C.J., Pooler, A.M., Atherton, J., Hanger, D.P. & Noble, W. Astrocytes are important mediators of Abeta-induced neurotoxicity and tau phosphorylation in primary culture. Cell Death Dis 2, e167 (2011).
Litvinchuk, A., et al. Complement C3aR Inactivation Attenuates Tau Pathology and Reverses an Immune Network Deregulated in Tauopathy Models and Alzheimer's Disease. Neuron 100, 1337–1353 e1335 (2018).
Richetin, K., et al. Tau accumulation in astrocytes of the dentate gyrus induces neuronal dysfunction and memory deficits in Alzheimer's disease. Nat Neurosci 23, 1567–1579 (2020).
Mann, C.N., et al. Astrocytic alpha2-Na(+)/K(+) ATPase inhibition suppresses astrocyte reactivity and reduces neurodegeneration in a tauopathy mouse model. Sci Transl Med 14, eabm4107 (2022).
Hopp, S.C., et al. The role of microglia in processing and spreading of bioactive tau seeds in Alzheimer's disease. J Neuroinflammation 15, 269 (2018).
Ising, C., et al. NLRP3 inflammasome activation drives tau pathology. Nature 575, 669–673 (2019).
Mancuso, R., et al. CSF1R inhibitor JNJ-40346527 attenuates microglial proliferation and neurodegeneration in P301S mice. Brain 142, 3243–3264 (2019).
Pereira, J.B., et al. Plasma GFAP is an early marker of amyloid-beta but not tau pathology in Alzheimer's disease. Brain 144, 3505–3516 (2021).
Cogswell, P.M., et al. Modeling the temporal evolution of plasma p-tau in relation to amyloid beta and tau PET. Alzheimers Dement 20, 1225–1238 (2024).
Pascoal, T.A., et al. Microglial activation and tau propagate jointly across Braak stages. Nat Med 27, 1592–1599 (2021).
Karikari, T.K., et al. Blood phospho-tau in Alzheimer disease: analysis, interpretation, and clinical utility. Nat Rev Neurol 18, 400–418 (2022).
Pichet Binette, A., et al. Amyloid-associated increases in soluble tau relate to tau aggregation rates and cognitive decline in early Alzheimer's disease. Nat Commun 13, 6635 (2022).
Hansson, O. Biomarkers for neurodegenerative diseases. Nat Med 27, 954–963 (2021).
Kreisl, W.C., et al. In vivo radioligand binding to translocator protein correlates with severity of Alzheimer's disease. Brain 136, 2228–2238 (2013).
Dani, M., et al. Microglial activation correlates in vivo with both tau and amyloid in Alzheimer's disease. Brain 141, 2740–2754 (2018).
Limberger, C. & Zimmer, E.R. Blood GFAP reflects astrocyte reactivity to Alzheimer's pathology in post-mortem brain tissue. Brain 147, 1598–1600 (2024).
Galea, E., et al. Multi-transcriptomic analysis points to early organelle dysfunction in human astrocytes in Alzheimer's disease. Neurobiol Dis 166, 105655 (2022).
Cosenza-Nashat, M., et al. Expression of the translocator protein of 18 kDa by microglia, macrophages and astrocytes based on immunohistochemical localization in abnormal human brain. Neuropathol Appl Neurobiol 35, 306–328 (2009).
Venneti, S., Wang, G., Nguyen, J. & Wiley, C.A. The positron emission tomography ligand DAA1106 binds with high affinity to activated microglia in human neurological disorders. J Neuropathol Exp Neurol 67, 1001–1010 (2008).
Ji, B., et al. Detection of Alzheimer's disease-related neuroinflammation by a PET ligand selective for glial versus vascular translocator protein. J Cereb Blood Flow Metab 41, 2076–2089 (2021).
Tournier, B.B., et al. TSPO and amyloid deposits in sub-regions of the hippocampus in the 3xTgAD mouse model of Alzheimer's disease. Neurobiol Dis 121, 95–105 (2019).
Tournier, B.B., et al. Fluorescence-activated cell sorting to reveal the cell origin of radioligand binding. J Cereb Blood Flow Metab 40, 1242–1255 (2020).
Gui, Y., Marks, J.D., Das, S., Hyman, B.T. & Serrano-Pozo, A. Characterization of the 18 kDa translocator protein (TSPO) expression in post-mortem normal and Alzheimer's disease brains. Brain Pathol 30, 151–164 (2020).
Kreisl, W.C., et al. PET imaging of neuroinflammation in neurological disorders. Lancet Neurol 19, 940–950 (2020).
Ferrari-Souza, J.P., et al. APOEepsilon4 associates with microglial activation independently of Abeta plaques and tau tangles. Sci Adv 9, eade1474 (2023).
Ferrari-Souza, J.P., et al. Astrocyte biomarker signatures of amyloid-β and tau pathologies in Alzheimer’s disease. Molecular Psychiatry (2022).
Karikari, T.K., et al. Diagnostic performance and prediction of clinical progression of plasma phospho-tau181 in the Alzheimer's Disease Neuroimaging Initiative. Mol Psychiatry 26, 429–442 (2021).
Mattsson-Carlgren, N., et al. Longitudinal plasma p-tau217 is increased in early stages of Alzheimer's disease. Brain 143, 3234–3241 (2020).
Petersen, R.C. Mild cognitive impairment as a diagnostic entity. J Intern Med 256, 183–194 (2004).
McKhann, G.M., et al. The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement 7, 263–269 (2011).
Karikari, T.K., et al. Blood phosphorylated tau 181 as a biomarker for Alzheimer's disease: a diagnostic performance and prediction modelling study using data from four prospective cohorts. Lancet Neurol 19, 422–433 (2020).
Benedet, A.L., et al. Differences Between Plasma and Cerebrospinal Fluid Glial Fibrillary Acidic Protein Levels Across the Alzheimer Disease Continuum. JAMA Neurol 78, 1471–1483 (2021).
Triana-Baltzer, G., et al. Development and validation of a high-sensitivity assay for measuring p217 + tau in plasma. Alzheimers Dement (Amst) 13, e12204 (2021).
Hawrylycz, M.J., et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).
Gryglewski, G., et al. Spatial analysis and high resolution mapping of the human whole-brain transcriptome for integrative analysis in neuroimaging. Neuroimage 176, 259–267 (2018).
Pascoal, T.A., et al. In vivo quantification of neurofibrillary tangles with [(18)F]MK-6240. Alzheimers Res Ther 10, 74 (2018).
Cselenyi, Z., et al. Clinical validation of 18F-AZD4694, an amyloid-beta-specific PET radioligand. J Nucl Med 53, 415–424 (2012).
Pascoal, T.A., et al. 18F-MK-6240 PET for early and late detection of neurofibrillary tangles. Brain 143, 2818–2830 (2020).
Klein, A. & Tourville, J. 101 labeled brain images and a consistent human cortical labeling protocol. Front Neurosci 6, 171 (2012).
Jack, C.R., Jr., et al. Defining imaging biomarker cut points for brain aging and Alzheimer's disease. Alzheimers Dement 13, 205–216 (2017).
Therriault, J., et al. Frequency of Biologically Defined Alzheimer Disease in Relation to Age, Sex, APOE epsilon4, and Cognitive Impairment. Neurology 96, e975-e985 (2021).
Mueller, R.O. & Hancock, G.R. Best Practices in Structural Equation Modeling. in Best Practices in Quantitative Methods (ed. Osborne, J.) 488–508 (SAGE, 2008).
Schermelleh-Engel, K., Moosbrugger, H. & Müller, H. Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness-of-Fit Measures. Methods of Psychological Research 8, 23–74 (2003).

Yes there is potential Competing Interest. WVB served as a speaker for Novo Nordisk. SG has served as a scientific advisor to Cerveau Technologies. GT-B and HCK are employees of Johnson and Johnson Innovative Medicine and receive salary and stock from its parent company, Johnson & Johnson. NJA has given lectures in symposia sponsored by Lilly and Quanterix. HZ has served on the scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, Alzinova, ALZPath, Amylyx, Annexon, Apellis, Artery Therapeutics, AZTherapies, Cognito Therapeutics, CogRx, Denali, Eisai, LabCorp, Merry Life, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures in symposia sponsored by Alzecure, Biogen, Cellectricon, Fujirebio, Lilly, Novo Nordisk, and Roche, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). KB has served as a consultant and at advisory boards for Abbvie, AC Immune, ALZPath, AriBio, BioArctic, Biogen, Eisai, Lilly, Moleac Pte. Ltd, Neurimmune, Novartis, Ono Pharma, Prothena, Roche Diagnostics, and Siemens Healthineers; has served at data monitoring committees for Julius Clinical and Novartis; has given lectures, produced educational materials and participated in educational programs for AC Immune, Biogen, Celdara Medical, Eisai and Roche Diagnostics; and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program, outside the work presented in this paper. ERZ serves on the scientific advisory board of Next Innovative Therapeutics. All other authors declare that they have no competing interests.

SuppInfoJPFS.pdf

Download PDF

Version 1

posted

You are reading this latest preprint version

Microglia modulate Aβ-dependent astrocyte reactivity in Alzheimer’s disease

Status:

Version 1

Abstract

Figures

Introduction

Results

Microglial and astrocyte reactivity jointly relate to tau phosphorylation and aggregation

Glial reactivity contributes to AD-related cognitive impairment

Discussion

Methods

Participants

Plasma biomarkers

Brain gene expression

Neuroimaging

Statistical analysis

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1