Trem2 promotes anti-inflammatory responses in microglia and is suppressed under pro-inflammatory conditions.

Genome-wide association studies have reported that, amongst other microglial genes, variants in TREM2 can profoundly increase the incidence of developing Alzheimer's disease (AD). We have investigated the role of TREM2 in primary microglial cultures from wild type mice by using siRNA to decrease Trem2 expression, and in parallel from knock-in mice heterozygous or homozygous for the Trem2 R47H AD risk variant. The prevailing phenotype of Trem2 R47H knock-in mice was decreased expression levels of Trem2 in microglia, which resulted in decreased density of microglia in the hippocampus. Overall, primary microglia with reduced Trem2 expression, either by siRNA or from the R47H knock-in mice, displayed a similar phenotype. Comparison of the effects of decreased Trem2 expression under conditions of lipopolysaccharide (LPS) pro-inflammatory or IL-4 anti-inflammatory stimulation revealed the importance of Trem2 in driving a number of the genes up-regulated in the anti-inflammatory phenotype. RNA-seq analysis showed that IL-4 induced the expression of a program of genes including Arg1 and Ap1b1 in microglia, which showed an attenuated response to IL-4 when Trem2 expression was decreased. Genes showing a similar expression profile to Arg1 were enriched for STAT6 transcription factor recognition elements in their promoter, and Trem2 knockdown decreased levels of STAT6. LPS-induced pro-inflammatory stimulation suppressed Trem2 expression, thus preventing TREM2's anti-inflammatory drive. Given that anti-inflammatory signaling is associated with tissue repair, understanding the signaling mechanisms downstream of Trem2 in coordinating the pro- and anti-inflammatory balance of microglia, particularly mediating effects of the IL-4-regulated anti-inflammatory pathway, has important implications for fighting neurodegenerative disease.

A genetic link between risk for Alzheimer's disease and severe COVID-19 outcomes via the OAS1 gene.

Recently, we reported oligoadenylate synthetase 1 (OAS1) contributed to the risk of Alzheimer's disease, by its enrichment in transcriptional networks expressed by microglia. However, the function of OAS1 within microglia was not known. Using genotyping from 1313 individuals with sporadic Alzheimer's disease and 1234 control individuals, we confirm the OAS1 variant, rs1131454, is associated with increased risk for Alzheimer's disease. The same OAS1 locus has been recently associated with severe coronavirus disease 2019 (COVID-19) outcomes, linking risk for both diseases. The single nucleotide polymorphisms rs1131454(A) and rs4766676(T) are associated with Alzheimer's disease, and rs10735079(A) and rs6489867(T) are associated with severe COVID-19, where the risk alleles are linked with decreased OAS1 expression. Analysing single-cell RNA-sequencing data of myeloid cells from Alzheimer's disease and COVID-19 patients, we identify co-expression networks containing interferon (IFN)-responsive genes, including OAS1, which are significantly upregulated with age and both diseases. In human induced pluripotent stem cell-derived microglia with lowered OAS1 expression, we show exaggerated production of TNF-α with IFN-γ stimulation, indicating OAS1 is required to limit the pro-inflammatory response of myeloid cells. Collectively, our data support a link between genetic risk for Alzheimer's disease and susceptibility to critical illness with COVID-19 centred on OAS1, a finding with potential implications for future treatments of Alzheimer's disease and COVID-19, and development of biomarkers to track disease progression.

First effects of rising amyloid-ß in transgenic mouse brain: synaptic transmission and gene expression.

Detecting and treating Alzheimer's disease, before cognitive deficits occur, has become the health challenge of our time. The earliest known event in Alzheimer's disease is rising amyloid-ß. Previous studies have suggested that effects on synaptic transmission may precede plaque deposition. Here we report how relative levels of different soluble amyloid-ß peptides in hippocampus, preceding plaque deposition, relate to synaptic and genomic changes. Immunoprecipitation-mass spectrometry was used to measure the early rise of different amyloid-ß peptides in a mouse model of increasing amyloid-ß ('TASTPM', transgenic for familial Alzheimer's disease genes APP/PSEN1). In the third postnatal week, several amyloid-ß peptides were above the limit of detection, including amyloid-ß40, amyloid-ß38 and amyloid-ß42 with an intensity ratio of 6:3:2, respectively. By 2 months amyloid-ß levels had only increased by 50% and although the ratio of the different peptides remained constant, the first changes in synaptic currents, compared to wild-type mice could be detected with patch-clamp recordings. Between 2 and 4 months old, levels of amyloid-ß40 rose by ∼7-fold, but amyloid-ß42 rose by 25-fold, increasing the amyloid-ß42:amyloid-ß40 ratio to 1:1. Only at 4 months did plaque deposition become detectable and only in some mice; however, synaptic changes were evident in all hippocampal fields. These changes included increased glutamate release probability (P < 0.001, n = 7-9; consistent with the proposed physiological effect of amyloid-ß) and loss of spontaneous action potential-mediated activity in the cornu ammonis 1 (CA1) and dentate gyrus regions of the hippocampus (P < 0.001, n = 7). Hence synaptic changes occur when the amyloid-ß levels and amyloid-ß42:amyloid-ß40 ratio are still low compared to those necessary for plaque deposition. Genome-wide microarray analysis revealed changes in gene expression at 2-4 months including synaptic genes being strongly affected but often showing significant changes only by 4 months. We thus demonstrate that, in a mouse model of rising amyloid-ß, the initial deposition of plaques does not occur until several months after the first amyloid-ß becomes detectable but coincides with a rapid acceleration in the rise of amyloid-ß levels and the amyloid-ß42:amyloid-ß40 ratio. Prior to acceleration, however, there is already a pronounced synaptic dysfunction, reflected as changes in synaptic transmission and altered gene expression, indicating that restoring synaptic function early in the disease progression may represent the earliest possible target for intervention in the onset of Alzheimer's disease.

Genetic variability in response to amyloid beta deposition influences Alzheimer's disease risk.

Genome-wide association studies of late-onset Alzheimer's disease risk have previously identified genes primarily expressed in microglia that form a transcriptional network. Using transgenic mouse models of amyloid deposition, we previously showed that many of the mouse orthologues of these risk genes are co-expressed and associated with amyloid pathology. In this new study, we generate an improved RNA-seq-derived network that is expressed in amyloid-responsive mouse microglia and we statistically compare this with gene-level variation in previous human Alzheimer's disease genome-wide association studies to predict at least four new risk genes for the disease (OAS1, LAPTM5, ITGAM/CD11b and LILRB4). Of the mouse orthologues of these genes Oas1a is likely to respond directly to amyloid at the transcriptional level, similarly to established risk gene Trem2, because the increase in Oas1a and Trem2 transcripts in response to amyloid deposition in transgenic mice is significantly higher than both the increase of the average microglial transcript and the increase in microglial number. In contrast, the mouse orthologues of LAPTM5, ITGAM/CD11b and LILRB4 (Laptm5, Itgam/CD11b and Lilra5) show increased transcripts in the presence of amyloid plaques similar in magnitude to the increase of the average microglial transcript and the increase in microglia number, except that Laptm5 and Lilra5 transcripts increase significantly quicker than the average microglial transcript as the plaque load becomes dense. This work suggests that genetic variability in the microglial response to amyloid deposition is a major determinant for Alzheimer's disease risk, and identification of these genes may help to predict the risk of developing Alzheimer's disease. These findings also provide further insights into the mechanisms underlying Alzheimer's disease for potential drug discovery.

Muscle artifacts in multichannel EEG: characteristics and reduction.

OBJECTIVE: To study the characteristics of unintentional muscle activities in clinical EEG, and to develop a high-throughput method to reduce them for better revealing drug or biological effects on EEG. METHODS: Two clinical EEG datasets are involved. Pure muscle signals are extracted from EEG using Independent Component Analysis (ICA) for studying their characteristics. A high-throughput method called ICA-SR is introduced based on a new feature named Spectral Ratio (SR). RESULTS: The spectral and temporal characteristics of the muscle artifacts are illustrated using representative muscle signals. The spatial characteristics are presented at both the group- and the subject-level, and are consistent under three different electrode reference methodologies. Objectively compared with an existing method, ICA-SR is shown to reduce more artifacts, while introduce less distortion to EEG. Its effectiveness is further demonstrated in real clinical EEG with the help of a CO(2)-inhalation EEG recording session. CONCLUSION: The characteristics of unintentional muscle activities align with the reported characteristics of controlled muscle activities. Artifact spatial characteristics can be EEG equipment dependent. The ICA-SR method can effectively and efficiently process clinical EEG. SIGNIFICANCE: Armed with advanced signal processing algorithms, this study expands our knowledge of muscle activities in EEG from muscle-controlled experiments to general clinical trials. The ICA-SR method provides an urgently needed solution with validated performance for efficiently processing large volumes of clinical EEG.

High-throughput ocular artifact reduction in multichannel electroencephalography (EEG) using component subspace projection.

After a review of the ocular artifact reduction literature, a high-throughput method designed to reduce the ocular artifacts in multichannel continuous EEG recordings acquired at clinical EEG laboratories worldwide is proposed. The proposed method belongs to the category of component-based methods, and does not rely on any electrooculography (EOG) signals. Based on a concept that all ocular artifact components exist in a signal component subspace, the method can uniformly handle all types of ocular artifacts, including eye-blinks, saccades, and other eye movements, by automatically identifying ocular components from decomposed signal components. This study also proposes an improved strategy to objectively and quantitatively evaluate artifact reduction methods. The evaluation strategy uses real EEG signals to synthesize realistic simulated datasets with different amounts of ocular artifacts. The simulated datasets enable us to objectively demonstrate that the proposed method outperforms some existing methods when no high-quality EOG signals are available. Moreover, the results of the simulated datasets improve our understanding of the involved signal decomposition algorithms, and provide us with insights into the inconsistency regarding the performance of different methods in the literature. The proposed method was also applied to two independent clinical EEG datasets involving 28 volunteers and over 1000 EEG recordings. This effort further confirms that the proposed method can effectively reduce ocular artifacts in large clinical EEG datasets in a high-throughput fashion.