Dementia risk genes engage gene networks poised to tune ... · Dementia risk genes engage gene...

Dementia risk genes engage gene networks poised to tune the immune response towards chronic inflammatory states Jessica Rexach1, Vivek Swarup1, Timothy Chang1, and Daniel Geschwind1,2,3 1Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA

2Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA 3Institute of Precision Health, University of California, Los Angeles, Los Angeles, CA 90095, USA Correspondence: [email protected]

.CC-BY-NC-ND 4.0 International licensecertified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprint (which was notthis version posted April 4, 2019. . https://doi.org/10.1101/597542doi: bioRxiv preprint

https://doi.org/10.1101/597542

http://creativecommons.org/licenses/by-nc-nd/4.0/

Abstract An emerging challenge in neurodegenerative dementia is understanding how immune-associated genes

and pathways contribute to disease. To achieve a refined view of neuroinflammatory signaling across

neurodegeneration, we took an integrative functional genomics approach to consider neurodegeneration

from the perspective of microglia and their interactions with other cells. Using large-scale gene expression

and perturbation data, regulatory motif analysis, and gene knockout studies, we identify and characterize

a microglial-centric network involving distinct gene co-expression modules associated with progressive

stages of neurodegeneration. These modules, which are conserved from mouse to human, differentially

incorporate specific immune sensors of cellular damage and pathways that are predicted to eventually

tune the immune response toward chronic inflammation and immune suppression. Notably, common

genetic risk for Alzheimer’s disease (AD), Frontotemporal dementia (FTD) and Progressive Supranuclear

Palsy (PSP) resides in specific modules that distinguish between the disorders, but also show convergence

on pathways related to anti-viral defense mechanisms. These results suggest a model wherein

combinatorial microglial-immune signaling integrate specific immune activators and disease genes that

lead to the establishment of chronic states of simultaneous inflammation and immunosuppression

involving type 1 interferon in these dementias.


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Introduction

Leveraging genetic discoveries to identify therapeutic targets requires understanding how disease genes

map onto cell-type specific molecular pathways. In this regard, a remarkable body of growing genetic

evidence supports a link between Alzheimer’s and associated dementias and neuroimmune functions

involving glial cells1,2 Causal genetic variation in neurodegenerative dementias affects genes with neural-

immune functions affecting two major CNS resident neural immune cells, astrocytes and microglia,

including, TREM2, GRN, HLA-DRA, CR1, C9ORF72, APOE, BIN1, CXCR4, CLU, and TBK11-6 . Multiple

functional studies in animal models of neurodegeneration support the contribution of microglial and

neural-immune genes to disease-associated phenotypes including age-associated cognitive decline,

pathological protein deposition and dyshomeostasis, and neurodegeneration7-9. The discovery that

immune-related genes contribute to AD and associated dementias has generated great enthusiasm for

the possibility of immune-based therapies10. From this perspective, defining the detailed molecular

relationship between disease-associated neuroimmune pathways and causal dementia genes has the

potential to inform disease mechanism and inspire novel therapeutic approaches.

Microglia and CNS-resident macrophages are the principle immune cells of the brain with critical roles in

detecting immunogens and coordinating the immune response11. During nervous system injury, microglia

can be directly activated by myelin, lipids, or nucleotides released from injured cells to activate pro-

inflammatory signaling, such as through the NLRP3 inflammasome complex12,13. Experimental evidence

in multiple models of AD pathology suggests that “disease-associated microglia” express dementia risk

genes, including APOE and TREM2, and contribute to synaptic injury, neurotoxic astrocyte transitions, and

neuronal dysfunction14-17. Furthermore, single-cell genomic studies have begun to delineate

heterogeneity among disease-associated microglial states and their trajectories, highlighting the need to


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better understand their specific roles in neurodegeneration18,19. This includes defining how different

microglial states relate to disease-associated immune activators and causal genes in human

neurodegenerative diseases.

We recently used a systems biology approach to integrate gene expression data from human post mortem

brain and multiple mouse models harboring human dementia causing mutations, to identify a robust

neurodegeneration-associated inflammatory module (NAI) and a closely correlated neurodegeneration-

associated synaptic module (NAS)20. The NAI module is strongly enriched for markers of both astrocytes

and microglia, both of which are known to be significantly up-regulated in multiple neurodegenerative

syndromes9,21,22. As a result of this global up-regulation within tissue, the cell-type specific expression

patterns of glial genes in the NIA module were obscured. In silico approaches for deconvoluting cell-

specific signatures are challenged by the complex dynamics among glial genes in disease16,18,19,23,24. So,

we reasoned that the optimal resource for resolving glial pathways involved in neurodegeneration would

be gene expression data from actual glial cell types isolated from disease and control samples.

Furthermore, given that neurodegeneration involves interactions between neurons and among glia25; we

reasoned that integrating data from sorted cells and intact tissue would reveal disease-relevant and cell-

specific signaling networks.

Here we present an integrative analysis of microglial-specific transcriptomic changes that are latent

components of neurodegeneration pathways at the tissue-level. Our findings parse disease genes into

distinct microglial co-expression sub-networks (modules) related to progressive stages of neuropathology

in mice that are conserved in humans. Using large-scale gene perturbation data, regulatory motif analysis,

and knockout studies, we identify strong evidence for regulatory interplay that functionally connects

different modules into a microglial-centered interactome. By incorporating genetic association data, we


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find that the genetic risk factors contributing to Alzheimer’s disease (AD), Frontotemporal dementia (FTD)

and Progressive Supranuclear Palsy (PSP) involved shared and distinct microglia-associated neuroimmune

modules. However, as disease progresses, the associated shared transcriptional and PPI networks that

are up-regulated involve chronic viral response pathways to double stranded RNA, likely driven by Type-

1 interferon, supporting a model whereby early immune activation gives way to chronic

immunosuppression in these disorders.


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Results

We performed consensus weighted gene co-expression analysis (WGCNA26; Methods) to combine gene

expression data from sorted, purified microglia from a mutant tau mouse model (rTg4510; AMP-AD

Knowledge Portal doi:10.7303/syn2580853) and whole brain tissue collected from multiple independent

transgenic mouse models of neurodegenerative tauopathy (Methods, Figure 1A) to identify conserved

modules that exist in both purified microglia and tissue. We then used the microglia-specific gene

expression data to identify up and down-regulated pathways (Figure 1A, Schema). Using this approach,

we identified 13 distinct co-expression modules varying in disease association, trajectory and time course

(Fig 1B, 1C, 1D, 2A).

Consensus microglial modules combine cell-type specificity and tissue-level neuronal-glial relationships

We first focused on the 7 modules significantly enriched for genes expressed in microglia compared to

other cell types27 (Fig. 1B, 1D; Supplementary Fig. 1A). As independent validation of cell-type trends, we

assessed module enrichment for single cell microglial signatures, previously identified from high

resolution single cell sequencing studies in mouse28 and human29 brain, and observed significantly greater

marker enrichment among these 7 candidate microglia-gene enriched modules compared to the

remaining modules (Fig. 1D, Supplementary Fig. 1A). At the same time, these modules were distinct in

that different modules overlapped with different sets of microglial signatures identified in previous single

cell analyses28,29 suggesting that they represented distinct microglia pools (Fig. 1D, 2E, Supplementary Fig.

1A, 1G). For example, M_UP1 enriched for profiles of microglia proliferative states (clusters 2a, 2b, 2c)28

and age-associated states identified previously in single cell transcriptome analyses (C8, aging_C1,

aging_C2, aging_C3)28 (Fig. 1D).


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We next tested whether our consensus modules recapitulate biological relationships present in tissue-

level neurodegeneration modules identified in prior studies (NAI and NAS)20. As expected, the seven

microglia-enriched modules were highly positively correlated with the NAI inflammatory module and

negatively correlated with the NAS synaptic module; both of which have been previously demonstrated

to be conserved across humans with tauopathies and mice harboring mutations causing dominant forms

of FTD in humans20 (Fig. 1C).

Next, we assessed each module’s relationship to pathological neuronal Tau hyperphosphorylation, a

measure of neuropathology associated with disease progression30. We found a strong positive correlation

between pathological Tau phosphorylation levels and microglia-enriched consensus module gene

connectivity (Fig. 1E, Supplementary 1B). In contrast, when we analyzed WGCNA modules generated

using only sorted microglial cell gene expression data from the rTg4510 model, rather than consensus

modules based on network edges shared between whole tissue and the sorted cell data, the correlation

with pTau was substantially reduced (Fig. 1E). This demonstrates the utility of using both cell specific and

whole tissue data to advance our understanding of cell specific contributions to disease pathology.

Finally, we observed that the NAI and combined microglial consensus modules are conserved at the level

of protein-protein interactions (PPI), which themselves coalesce into distinct molecular pathways (Fig. 1G,

Supplementary Fig. 1C). Thus, these seven co-expression modules represent a substantial refinement of

a previously identified neural immune module observed in both model systems and post mortem human

brain18-20,31,32. We label the seven new modules “microglia-associated neurodegeneration-associated

modules” (MNMs, 1-7), and further characterized them as a means to explore associated microglial

functional pathways and regulators related to neurodegeneration.


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MNMs are conserved in human disease brain and mouse models

To assess the robustness of the microglia-enriched modules and further validate their relevance to human

disease, we tested their preservation using multiple independent mouse and human disease datasets (see

Methods). Consistent with the observation of PPI conservation for all modules, all seven MNMs are

preserved in post mortem human brain tissue from AD33, FTD20,34 and PSP33 patients (Fig. 1F). Additionally,

all MNMs are preserved in three different transgenic mouse models expressing human MAPT

mutations20,35 (Supplementary Fig. 1D) and in microglial-specific datasets from mouse models expressing

PSEN36 and APP37 mutations, except M_UP3, which is only weakly preserved in one of two datasets

(Supplementary Fig. 1E). However, we do note that the differential expression patterns of three modules

(M_UP2, M_UP3, M_DOWN3) differ between microglia isolated from P301L MAPT (rTg4510) and

PSEN/APP mutant mouse models (Supplementary Fig. 1F). Together, the preservation of these modules

across multiple independent disease datasets including human disease brain from Alzheimer’s and

associated dementias and mouse models of Alzheimer’s or FTD, as well as PPI, indicates that they

represent robust biological processes. However, some modules display variability in their differential

expression in different disease models, suggesting they may be conditional on disease-stage or disease-

specific pathology.

Microglial molecular transitions along progressive epochs of neuronal pathology

In contrast to the composite whole tissue NAI module expression vector which shows a singular increasing

trajectory over time (shown in20), we were able to deconvolute the MNM modules into highly distinct

temporal trajectories with respect to progressive disease stages modeled in the rTg4510 mouse between

2 and 8 months of age35,38-40. We identified three temporal patterns of module-disease association: (1)


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changing at the earliest disease stage, prior to neuronal loss, and persistent through later stages (M_UP1,

M_DOWN1, M_DOWN2), (2) changing during early periods of neuronal loss, and more transient (M_UP2,

M_DOWN3), and (3) most significant changes during late stages of continued neuronal loss and

cumulative pathology (M_UP3) (Fig. 2A). Therefore, combined tissue-microglial cell consensus WGCNA

resulted in microglial neurodegeneration-associated modules with distinct temporal transitions across

disease progression, which were present in latent forms, but not detected in the analysis of whole tissue

alone.

To validate our MNM-disease stage trends using complementary published datasets, we compared them

to time-course, single-cell microglial differential gene expression data collected from two different mouse

models with Alzheimer’s related pathology (5xFAD18, CK-p2519), including one model with frank

neurodegeneration (CK-p2519). As expected, the early up-regulated MNM, M_UP1, is enriched for genes

that are increased in early microglial disease states relative to homeostatic microglia (Fig. 2E,

Supplementary Fig. 1G), and the later up-regulated MNMs are enriched for genes up-regulated in later

relative to early microglial disease states (Fig. 2E, Supplementary Fig. 1G). The four down-regulated MNM

are all enriched for microglial genes down-regulated in early microglia disease states (Fig. 2E,

Supplementary Fig. 1G).

We next leveraged published data on the type 2 interferon response to ask whether MNMs reproduce

the late interferon-gamma signature reported to distinguish microglia during periods of neuronal cell

death19. We were able to show that the last up-regulated MNM in disease, M_UP3, is also the only module

induced by interferon-gamma treatment of cultured microglia41 (Fig. 2F), consistent with the published

trend19. Altogether, these findings support that MNMs recapitulate stage-associated, microglia-specific

biological trends identified from recent single-cell studies using mouse models of Alzheimer’s


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pathology18,19. Moreover, MNMs further refine prior findings, separating disease-associated microglia

changes across multiple distinct modules. Therefore, we next explored these MNMs in detail to delineate

stage-associated transitions in microglia signaling including changes prior to and subsequent to cell death,

and their relationship to dementia disease genes.

Pathway analysis to expand biological insights into microglial transitions across disease

Annotation of modules for enriched biological regulators and pathways aligned specific disease genes,

signaling and functional pathways, immune receptors, transcription factors and microglia-enriched gene

co-expression modules with progressive stages of neuronal dysfunction and degeneration that are

summarized in Figure 2 (Fig. 2A, 2B, 2C, 2D, 2F, 2G, Supplementary Fig. 2A, 2B). These annotations

indicate that each of these modules represents different aspects of the microglia function that vary across

disease stage, with different microglial modules poised to sense and respond to specific damage-

associated immune activators12,42 that change over time (Fig. 2A, 2C, 2G).

For example, the earliest up-regulated module, M_UP_1, incudes sensors of peptide and lipopeptide

immune activators (TLR1, TLR2), whereas the subsequently up-regulated module, M_UP2, includes

sensors of lipid immune activators (TREM2, SCARB2) together with receptors for viral nucleotides (TLR7,

TLR9, Ifih1) that can also be activated by damaged or dysregulated endogenous DNA12,42-46. Therefore,

our microglial time-course analysis shows a prominence of DNA and RNA detecting immune receptors

within the second phase of up-regulated MNMs, suggesting that nucleic acids activate inflammatory

pathways as neuronal injury and disease progress (Fig. 2G). Additionally, we find that these sensors are

co-expressed with genes associated with specific signaling pathways as disease progresses (Fig. 2C, 2D,

Supplementary Fig. 2A, 2B). For example, M_UP1 is enriched for genes related to the IL1 signaling


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pathway and complement cascades, whereas M_UP2 is enriched for genes of the IL6 signaling pathway

and phagocytosis (Fig. 2C, 2D, Supplementary Fig. 2B).

MNMs also provide refinement of previously reported microglial changes during disease18,19. For example,

down-regulation of homeostatic microglial markers previously reported in disease18,47, is split between

two modules, M_DOWN1 and M_DOWN2, that represent distinct biological pathways (e.g. M_DOWN1

for prostaglandin synthesis and phagocytosis; M_DOWN2 for natural killer cell activation and positive cell

cycle regulation; Fig. 2C, 2G, Supplementary Fig. 2A, 2B) and respond differently to microglial stimulation

by Abeta42 and IL-4 in cell culture (Supplementary Fig. 2C ). Lastly, we note that known common and

rare disease genes occupy several different modules: M_UP1 contains APOE, CXCR4, GRN, CSF1, PRNP,

SQSTM1, TYROBP, GBA; M_DOWN1 contains BIN1, PVRL2, PLCG; M_UP2 contains TREM2, INPP5D;

M_DOWN2 contains PICALM; and M_UP3 contains PSEN1 and CD33 (Fig. 2C, upper panel). This

annotation provides a bridge between casual disease factors and microglial stage-specific disease biology

that can potentially inform our understanding of the factors that drive disease mechanisms.

These varied observations support that MNMs delineate distinct microglial transitions or states

that accompany neuropathological disease progression from early neuronal dysfunction through

progressive injury and neuronal cell death, building upon prior observations18-20 to implicate

accompanying microglial functions and candidate driver genes. These modules thus provide a detailed

framework for understanding phases of microglia transition related to early and later disease stages in

neurodegenerative tauopathies.

Overlap of module driver genes and transcription factors indicate substantial cross talk


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Having assessed the relationship of each module to disease stage, we next moved to query the

relationship of MNMs to each other. We reasoned that understanding the regulatory relationships that

bridge different neuroimmune states is critical to predicting the effects of targeting these pathways for

therapeutics. Using experimental gene perturbation data available through the Broad Institute’s

Connectivity Map48 (see Methods), we observed that the effects of disease-related changes in MNM gene

expression are not confined to the genes occupying the same MNM, but rather can effect disease-related

changes of other MNMs (Supplementary Fig 3A). For example, perturbation of genes in the earliest up-

regulated module, M_UP1, significantly upregulates genes within the subsequently up-regulated modules

(M_UP2 and M_UP3) and downregulates genes within the down-regulated modules (M_DOWN1,

M_DOWN2, M_DOWN3, M_DOWN4) (Supplementary Fig 3A; Methods). These observations that genes

within the early up-regulated MNMs can drive later MNMs suggests that MNMs capture transitions from

early microglial states that drive subsequent states as disease progresses.

To further delineate microglial transitions captured by MNMs, we assessed their gene promoters for

shared experimentally validated transcription factor binding sites (TFBS). Nearly all MNMs showed high

TFBS overlap, consistent with shared transcriptional drivers. However, two modules, M_UP1 and M_UP2

had genes with very distinct TFBS enrichments from each other (Supplementary Fig. 3B). This was despite

substantial direct PPI connections between the modules (Supplementary Fig. 3C) and evidence of positive

driver effects of M_UP1 genes on M_UP2 expression (Supplementary Fig. 3A). Therefore, while early

MNMs are poised to be highly integrated at the level of regulatory drivers with later or concurrent, MNMs,

M_UP1 and M_UP2 appeared to be driven by distinct candidate regulators.

Identification of the inflammasome and anti-inflammasome related modules


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To assess potential regulatory cross-talk among M_UP1 and M_UP2 genes in more detail, we re-clustered

their genes to highlight any co-expression relationships that may exist between them (Fig. 3A; Module A

and Module B). This resulted in two modules that are both up-regulated early in disease with nearly

identical trajectories (Fig. 3B, 3D), but with strongly anti-correlated gene-module connectivity (anti-

correlating kME, Fig. 3C), suggestive of opposing or competing pathways26. This is not an artifact of our

transcriptional analysis, as independent CMAP gene perturbation experiments validate that gene

overexpression has opposing effects on the genes clustered within these two modules (Fig. 3F; Methods).

Other independent data confirm these relationships, in single cell RNA sequencing studies of mouse28 and

human brain29 (Fig. 3E, Supplementary Fig. 4C), and at the level of PPI (Supplementary Fig. 4A).

Furthermore, both modules are reproducible in independent transcriptomic datasets from mutant MAPT

transgenic mice20,35, microglia isolated from mutant APP37/PS36 transgenic mice, and human dementia

brain20,33, verify their robustness across mouse models of dementia and their relevance to human disease

(Supplementary Fig. 4B, 4D). These findings support the identification of two highly conserved microglial-

enriched modules that are up-regulated in disease, but that include polarized signaling pathways, which

we hypothesized were poised for regulatory cross-talk.

Module annotation and pathway analysis (Methods) identified the NLRP3 inflammasome and type 1-

interferon response pathways as defining core components of these two modules (Fig. 3G, 3H), which we

accordingly named the “inflammasome” and “anti-inflammasome” modules. The NLRP3 inflammasome

is assembled downstream of cellular stressors and activated by the detection of various stimuli13, including

pathological Abeta49,50, to promote pro-inflammatory states. Similarly, pathological Abeta rapidly and

specifically stimulates the expression of the inflammasome module eigengene in microglia, both in vivo

and in vitro (Fig. 3I, Supplementary Fig. 4F). In contrast, the prominent pathway within the anti-

inflammasome module is the type-1 interferon response. Microglial isolated from mice overexpressing


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beta-interferon show both up-regulation of the anti-inflammasome module and down-regulation of the

inflammasome module, in a manner dependent on the type 1 interferon receptor, IFNAR1 (Fig. 3J).

Consistent with this finding, type 1 interferon is a known suppressor of the NLPR3 inflammasome51, and

NLPR3 inflammasome activation has recently been shown to inhibit type 1-interferon signaling52.

Furthermore, at the center of the anti-inflammasome module PPI map is MDA5 (Ifih1), a receptor of

dsRNA that can activate type 1 interferon response downstream of viral detection or chromatin

destabilization44,45,53 (Fig. 4B). These data provide multiple lines of evidence supporting that these two

early up-regulated microglial modules represent opposing states, likely orchestrated, at least in part, by

type 1 interferon signaling as a key polarizing driver.

Type 1 interferon is not only a classic activator of acute anti-viral immunity, but more recently it has been

demonstrated to be a critical driver of immunosuppression in the context of chronic viral infections54,55.

Several features of the anti-inflammasome module suggest it too may represent aspects of interferon-

mediated immunosuppression, including its anti-correlation with the inflammasome module, and its

inclusion of genes that function as immune checkpoints (Cd274 (PDL1), Il10rb, Lag3)54,56-58 and inhibitors

of immune activity (Usp1859-62, Nfkbiz, Nfkbia, Nfkbie, Tgfbr263,64). Among these genes is Usp18, an

established negative feedback suppressor of type 1 interferon anti-viral immune activity59-62,65,66 that is

also highly connected with the anti-inflammasome module in microglia treated with interferon-beta

(Supplementary Fig. 4G). Consistent with Usp18 being a critical driver of the anti-inflammasome module,

we found that gene co-expression relationships in the anti-inflammasome module are completely

disrupted by Usp18 knockout, without any effect on the inflammasome module (Fig 4C). Furthermore,

the inflammasome module is highly up-regulated in the Usp18 knockout mouse in an IFNAR1 dependent

fashion, suggestive of “hyperimmune” activation of the inflammasome module in the absence of Usp18

and the anti-inflammasome module (Fig. 4D). These data strongly support a mechanistic model, wherein


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interferon beta drives the anti-inflammasome and inhibits the inflammasome module through activation

of immune suppressors, including Usp18, implicating interferon beta as a potential suppressor of immune

activity in the chronic phase of neurodegenerative tauopathies (Fig. 4E), similar to what has been reported

in chronic viral infections62,65,66.

Viral response mechanisms link genetic risk factors across different Tau-associated dementias

Since gene expression changes on their own may represent causal, reactive or compensatory changes, we

integrated genome-wide common genetic risk using MAGMA67 to identify whether any of the identified

MNMs enrich for causal genetic factors associated with tau-related dementias. First, we identified the

earliest interconnected MNM genes present in pre-symptomatic disease tissue and named them early

MNM submodules, reasoning that casual disease pathways would enrich among the earliest MNM

components to appear in disease (Supplementary Figure 5A, 5B, 5E; see Methods). We verified that

early MNM submodules enrich for microglial signatures defined from mouse18,19,28 and human29 single cell

studies (Supplementary Fig. 5C, 5D). We next tested all MNMs, including the early submodules, for

module-wide enrichment of disease risk genes associated with FTD, AD and PSP compared to controls,

based on published GWAS studies68-70 (see Methods, Figure 5A). We found that the common genetic risk

associated with AD, FTD and PSP is not randomly distributed, but shows distinct patterns of enrichment:

AD risk with M_UP3, FTD risk with early_UP1, FTD risk with early_DOWN1, PSP risk with early_DOWN1

(Fig. 5A). Each of these modules links distinct glial immune-related genes and associated pathways to

disease causality, including increased exogenous antigen presentation and viral defense with AD,

increased microglial immune activation and phagocytosis with FTD, and suppressed anti-viral response

with both FTD and PSP (Fig. 5B, 5C).


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To independently validate these disease-module relationships, we performed confirmatory testing using

a cross-disorder exome array dataset that included AD, FTD, PSP and control cases71. The exome array

data confirmed significant associations between AD and M_UP3 (beta = 0.19 p<0.001; Supplementary Fig.

5F), FTD and early_UP1 (beta = 0.25, p<0.001) and FTD and early_DOWN2 (beta = 0.15 p<0.001)

(Supplementary Fig. 5F), but not PSP, perhaps because the exome array data set is too small and therefore

underpowered for PSP71 (Methods). Providing additional validation is the presence of transcription

factors within these modules that are capable of inducing the disease-associated microglia gene

expression patterns, including Spi1(PU.1)72 within the AD associated module (M_UP3) and Zeb2 within

the FTD and PSP associated module (early_DOWN1) (Fig. 5B, Supplementary Figure 5G).

We note that viral response is a commonality among the modules enriched for common genetic variants

contributing to susceptibility for these three dementias that involve tau pathology, albeit to different

extents (M_UP3, and early_DOWN1) (Fig. 5B, 5C). This suggested a potential causal relationship between

tauopathy and viral response. In the case of AD, the causal association is with late up-regulated viral

response pathways, whereas for FTD and PSP the causal association is with early down-regulated anti-

viral response pathways (Fig. 5B, 5C, Supplementary Figure 5E). To test whether viral response pathways

were also engaged by pathological Tau, we identified the biological pathways most correlated with Tau

hyperphosphorylation in the TPR50 mouse brain (Methods), and indeed observed that genes involved

with virus detection and anti-viral response were enriched (Supplementary Figure 5H,I,J), consistent with

the activation of viral response pathways in concert with tau pathology in disease tissue.


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Discussion

Through an integrative systems biology approach, we have identified microglia immune networks related

to specific stages of neurodegeneration modeled in mice harboring mutant Tau protein. Combining whole

tissue and cell type specific data from multiple divergent mouse transgenic lines and strains, we identified

seven conserved microglia modules that were also represented in post mortem tissue from patients and

controls. By integrating data from brain tissue with sorted cell data, we achieved a unique perspective on

neuroinflammatory signaling in neurodegeneration that we show neither can achieve on its own. Our

results delineate a detailed time-course of microglial transitions across stages of progressive disease

pathology that highlight specific immune receptors, biological pathways and regulatory factors at each

stage. Furthermore, although each of the modules captures distinct pathways, our analysis of regulatory

overlap suggest they are not entirely isolated, but rather are highly linked pathways, whose central

components and core hubs transition as disease progresses through different stages. Within this robust

framework, MNNs differentially implicate human disease genes with specific neuroimmune pathways that

both recapitulate prior known biological relationships and identify new relationships between specific

neuroimmune pathways and different disorders for further study.

Our refined analyses of microglia-associated changes across tauopathy suggest that early immune

activation gives way to chronic immunosuppression, potentially driven by activation of interferon beta

downstream of cytosolic dsRNA detection. In support of this is the observation that interferon beta acting

through IFNAR1 is a known driver of chronic inflammatory states in cancer and chronic viral infection54.

Furthermore, dsRNA detection can trigger interferon beta downstream of chromatin destabilization53.

Here we find that interferon beta activates genes in the anti-inflammasome pathway capable of blocking

hyperimmune activation, including Usp1860, and suppresses genes of the inflammasome module that


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participate in innate immunity. Furthermore, we find the cytosolic dsRNA receptor Ifih1 (MDA5) and

associated RIG1 pathway at the core of the anti-inflammasome module PPI, and both dsRNA detection

and interferon pathways to be highly correlated with pathological Tau burden (Supplementary Fig. 5H,

5I, 5J). These observations suggest that factors related to the accumulation of pathological Tau might

trigger the interferon pathway through dsRNA detection. This is particularly salient based upon the recent

observation that pathological Tau drives chromatin destabilization73,74, a known source of endogenous

dsRNA that can activate Ifih1 (MDA5) and trigger an interferon response44,53. Combined, our results

present a parsimonious model wherein dsRNA, released following chromatin destabilization in injured

neurons in response to Tau pathology, may active chronic immune activation pathways to suppress

specific immune signaling (the inflammasome module) and activate anti-inflammasome pathways to alter

cellular functions including protein ubiquitination, autophagy, exosome formation, and translation75 (Fig.

4E). These observations predict that inhibition of the anti-inflammasome module, either through blockade

of dsRNA, IFNAR1, or immune checkpoints within the module (PD-L1) would reduce progressive immune

dysregulation triggered by pathological Tau and, at least in part, restore homeostatic microglia damage

response mechanisms. These observations suggest an important causal connection between pathological

Tau, viral control and the interferon response that has not previously described. Interestingly, interferon

has also been implicated as a driver of microglial dysfunction in aging, suggesting interferon-driven

immunosuppression in aging may also contribute to age related susceptibility to neurodegeneration7.

Future functional and mechanistic studies will be needed to experimentally test and extend these

observations, but they have potential therapeutic implications.

Genetic risk factors for AD, FTD and PSP further implicate roles for viral defense mechanisms in causal

disease biology. Interestingly, the specific genes and pathways implicated differ between AD and

FTD/PSP. AD genetic risk factors causally implicate antigen presentation pathways that increase in late


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stages in tauopathy models, whereas FTD and PSP risk factors converge upon anti-viral genes that are

down-regulated in microglia very early in the mouse tauopathy models. Our findings suggest for the first

time that early loss of specific anti-viral and microglial maintenance factors may be causal contributors to

disease progression early in primary tauopathies.

Our findings predict that microglia may contribute to disease in a stage-specific manner by linking

progressively changing disease-associated stimuli into an integrated, multi-cellular signaling network that

sets the chronic course of dementia. More specifically, our observations suggest that early in tauopathy

there is loss of microglial homeostasis including specific viral defense functions that promote disease

progression. Further, as disease progresses and pathological Tau accumulates, it drives activation of

dsRNA detection pathways, possibly through chromatin destabilization53, to further suppress healthy

immune functions and contribute to cellular dysfunction and disease propagation. From this perspective,

different stages of dementia are associated with different levels of immune activation, and as disease

progresses into its clinical phase, these analyses suggest that it is likely a state of chronic immune

suppression that may promote disease progression and contribute to chronic cellular dysfunction, rather

than immune activation.


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Methods

Data Set Acquisition and Filtering

Both RNAseq datasets used as input for consensus WGCNA were previously generated. The TPR50

dataset20 includes gene expression data from frontal cortex dissected from male mice expressing P301S

MAPT or WT controls (TPR50 transgenic model76) in three different genetic backgrounds (C57BL6/J, F1

C57BL6/J x DAB, F1 C57BL6/J x FVB), and includes samples collected at 3 months of age (n=6 per group)

and 6 months of age (n=5-6 per group). The Tg4510 microglia dataset includes gene expression data

obtained from microglia purified using C11b FACS collected from mice expressing P301L MAPT and WT

controls (rTg4510 transgenic model39), pooled to include microglia from 8-10 forebrains per sample, with

n = 4 replicate samples per time points (2, 4, 6, and 8 months of age) (AMP-AD Knowledge Portal

(doi:10.7303/syn2580853). Data were filtered for low read counts (>80% of the sample with > 10 reads

with HTSeq quantification) and normalized using log2-transformation and linear regression prior to use

for consensus WGCNA and module expression trajectory analysis, as previously described20.

Additional publicly available datasets were used throughout the study for validation or comparison.

Mouse datasets consist of microarray or RNAseq transcriptomics data from a variety of transgenic mice

models – Tg451035, PS2APP36, GRN9, USP1860, IFNAR160, 5xFAD18,31, CK-p2519, Zeb277 and in vitro and in

vivo treatments – Abeta4278,79, IFN-beta-expressing AAV7, IL480, IFN-gamma41. Human postmortem data

consist of AD temporal cortex33, FTD frontal cortex20,34, and PSP temporal cortex33. IRB exemption was

obtained from the UCLA IRB to authorize use of de-identified human postmortem brain RNAseq data in

this study.

Microarray or RNAseq datasets downloaded from the Gene Expression Omnibus (GEO) were read into R

and processed as follows. Microarray data were log2-transformed and normalized by quantile

normalization. Gene counts were filtered to remove low read counts (>80% of the sample with > 10 reads


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wth HTSeq quantification), corrected for guanine-cytosine content, gene length and library size, and log2-

transformed using the CQN package in R81. The resulting data was used as an input to test module

preservation, average gene expression and/or eigengene expression.

mRNA Weighted Co-expression Network Analysis

In order to identify gene co-expression networks present both in purified microglia and frontal cortical

brain tissue, and across multiple transgenic mouse strains and genetic backgrounds, we utilized consensus

WGCNA as previously described20 using the WGCNA R package26, applied to the TPR50 dataset of forebrain

RNAseq from mice aged 6 months, and the Tg4510 dataset of purified microglia (2,4,6 and 8 months),

described above. The input data were generated from (1) microglia purified from P301L MAPT and WT

mice from the Tg4510 model39 at ages 2, 4, 6 and 8 months (n=4 mice per condition) (AMP-AD Knowledge

Portal (doi:10.7303/syn2580853), and (2) frontal cortex from P301S MAPT and WT mice from the TPR50

model with three different genetic backgrounds (C57BL6/J, F1 C57BL6/J x DAB, F1 C57BL6/J x FVB) at 6

months of age (n=5-6 per group)20, a period with extensive gliosis and neuronal Tau pathology but prior

to frank atrophy20.

Biweighted mid-correlations were calculated for all pairs of genes, and then assigned similarity matrices

were created using the Consensus WGCNA method as previously described82. In the signed network, the

similarity between genes reflects the sign of the correlation of their expression profiles. The signed

similarity matrix was then raised to power β to emphasize strong correlations and reduce the emphasis

of weak correlations on an exponential scale. A thresholding power of 14 was chosen (as it was the

smallest threshold that resulted in a scale-free R2 fit of 0.8) and the consensus network was created using

the function blockwiseConsensusModules() to calculate the component-wise minimum values for

topologic overlap (TOM), with parameters set as networkType = “signed”, deepSplit = 2, detectcutHeight

= 0.995, consensusQuantile = 0.0, minModulesize = 100, mergeCutHeight = 0.2. Using 1 − TOM (dissTOM)


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as the distance measure, genes were hierarchically clustered. The resulting modules or groups of co-

expressed genes were used to calculate module eigengenes (MEs; or the 1st principal component of the

module). Modules were annotated using the GOElite package83. We performed module preservation

analysis using consensus module definitions84. MEs were correlated with transgenic condition to find

disease-associated modules. Module hubs were defined by calculating module membership (kME) values

which are the Pearson correlations between each gene and each ME. Gene expression was correlated

with pT231 Tau levels measured by ELISA to calculate the “gene significance” relationship with pT231 Tau,

as defined by the WGCNA method26, using gene expression data from the TPR50 model (6 months, n=36),

and this was further correlated (Pearson’s) with kME to assess the relationship between pT231 Tau and

gene-module connectivity. All network plots were constructed using the Cytoscape software85. Module

definitions from the network analysis were used to create synthetic eigengenes from which to calculate

the expression trajectory of various modules in different gene expression datasets.

Clustering of gene subsets

To apply gene co-expression methods to understand co-expression relationships among subsets of

module genes in either the original consensus dataset, or in the TPR50 dataset of pre-symptomatic mice

at 3 month of age, we again used the WGCNA package26. Biweighted mid-correlations were calculated

for a subset of genes from selected consensus modules to create an adjacency matrix that was further

transformed into a topological overlap matrix (with TOMType = “unsigned”). Using 1 − TOM (dissTOM) as

the distance measure, genes were hierarchically clustered using the following parameters (deepSplit = 2,

detectcutHeight = 0.999, minModulesize = 40, dthresh=0.1, softPower =7). The resulting modules, or

groups of co-expressed genes, were used to calculate module eigengenes (MEs; or the 1st principal

component of the module). The significance of intramodular connectivity was assessed for each module

using a permutation test (10,000 permutations), and all modules were confirmed to have permuted p-


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value <0.001. “Early submodules”, described in Figure 5 and Supplementary Figure 5, were derived by re-

clustering M_UP1 and M_UP2 genes to generate “earlyUP” modules, or M_DOWN1, M_DOWN2 and

M_DOWN3 genes to generate “earlyDOWN” modules, in the 3 month of age frontal cortex TPR50 dataset

previously described20. “Inflammasome and anti-inflammasome modules”, described in Figure 3-4, were

derived from re-clustering M_UP1 and M_UP2 genes in the consensus WGCNA input datasets (purified

microglia from the Tg4510 model and frontal cortex TPR50 dataset (6 months of age)).

Module Preservation Analysis

We used module preservation analysis to validate co-expression in independent mouse and human

datasets. Module definitions from consensus network analysis were used as reference and the analysis

was used to calculate the Zsummary statistic for each module. This measure combines module density

and intramodular connectivity metrics to give a composite statistic where Z > 2 suggests moderate

preservation and Z > 10 suggests high preservation84.

Module Gene Set Enrichment Analysis

Gene set enrichment analysis was performed using a two-sided Fisher exact test with 95% confidence

intervals calculated according to the R function fisher.test(). We used p values from this two-sided

approach for the one-sided test (which is equivalent to the hypergeometric p- value) as we do not a priori

assume enrichment86. To reduce false positives, we used FDR adjusted p-values87 for multiple

hypergeometric test comparisons. For cell-type enrichment analysis we used already published mouse

brain dataset27. The background for over-representation analyses was chosen as total genes input into the

consensus analysis (overlap of genes expressed in Tg4510 microglia and TPR50 frontal cortex RNAseq

datasets).


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To test module enrichment for single cell microglial gene expression signatures, we used signatures

defined from published single-cell studies pertaining to microglia and/or neurodegenerative

disease18,19,28,29,77. Specifically, for disease-associated microglia18,19, we set cluster signatures to be the top

100 differentially expressed genes between two microglia clusters, as defined in their corresponding

publications. For microglial and macrophage clusters defined from young and aged mouse brain in28, we

defined clusters signatures as published except duplicated genes were removed among the young cluster

group (C1, C2a, C2b, C3, C4, C5, C6, C7a, C7b, C7c, C8, C9, mono_macA, mono_macB), and aged cluster

group (aging_C1a, aging_C1b, aging_C2, aging_C3, aging_C4) to increase the distinctiveness of each

cluster’s geneset. To define genesets from the single-cell microglial trends from injured mouse brain

published in28, we used the genes with fold change >1.5 in control vs injured, and injured vs control mice,

respectively, to define the injury_C1 and injury_C2 genesets. For human microglial gene clusters defined

in29, we defined cluster signatures as genes with expression fold >1.8 compared to any other clusters. For

Zeb2 knockout compared to control microglia, we used the published set of differentially expressed

genes77. The background applied for over- representation analyses was set as the genes input into the

consensus analysis (overlap of genes expressed in Tg4510 microglia and TPR50 frontal cortex RNAseq

datasets).

Gene set annotation

Genes in network modules were characterized using GO-Elite (version 1.2.5), using as background the set

of input genes used to generated the modules being annotated83. GO-Elite uses a Z-score approximation

of the hypergeometric distribution to assess term enrichment, and removes redundant GO or KEGG terms

to give a concise output. We used 10,000 permutations and required at least 3 genes to be enriched in a

given pathway at a Z score of at least 2. We report only biological process and molecular function category

output.


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Protein-Protein Interaction Analysis

To assess and visualize protein-protein interactions among module genes, we used STRING (version 10.5;

https://string-db.org)88 with the following setting (organism: Mus musculus; meaning of network edges:

confidence; active interaction sources: experiments and databases; minimal required interaction score:

medium confidence (0.400), max number of interactors to show: none). Data was exported and visualized

using the Cytoscape software85.

Transcription Factor Binding Site Enrichment Analysis

Transcription Factor Binding Site (TFBS) enrichment analysis using an in-house package that incorporates

TFBS as previously described89. Briefly, we utilized TFBS position weight matrices (PWMs) from JASPAR

and TRANSFAC databases90,91 to examine the enrichment for TFBS within each module using the Clover

algorithm92. To compute the enrichment analysis, we utilized three different background datasets (1000

bp sequences upstream of all mouse genes, mouse CpG islands, and mouse chromosome 20 sequence).

We plotted significant TFBS-module pairs (TFBS p-value < 0.05, compared to all mouse CpG islands), for

TFs shared between multiple modules, as a network plot in Cytoscape, with edges connecting TFs and

modules and edge weights proportional to the negative log10(p-value).

Connectivity Map (CMAP) Analysis

For a given module, the top 150 module genes (ranked by kME) were used as input for the QUERY app in

the Broad’s CMAP database, version CLUE (https://clue.io)93. This signature was used to query 7,494 gene

overexpression or knockdown experiments carried out across 9 cell lines for similar (positive connectivity

score) or opposite (negative connectivity score) effects on gene expression signatures, incorporating

Kolmogorov-Smirnov statistics (a nonparametric, rank-based pattern-matching strategy) as described48,93.

Mean “connectivity scores” across all cell lines was ranked by increasing order of connectivity to the input


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module gene expression signature to generate a rank ordered list of signed perturbagen-module

connectivity scores. To identify module genes whose perturbation could reproduce the differential

expression patterns of module seen in disease, we identified genes from up-regulated disease modules

whose overexpression in CLUE had positivity connectivity scores with up-regulated modules or negative

connectivity scores with down-regulated modules, and genes from down-regulated disease modules

whose down-regulation in CLUE (via shRNA) had positive connectivity scores with signatures from up-

regulated modules and negative connectivity scores with signatures from down-regulated modules, using

a connectivity score cut off of |70|. Gene perturbation-module connectivity was plotted with edge length

= -log10(|connectivity score|), using Cystoscope.

MAGMA

Summary statistics for genome-wide association studies for AD70, PSP69 and FTD68 were used as an input

for MAGMA (v1.06)67 for gene annotation to map SNPs onto genes (with annotate window = 20,20) and

the competitive gene set analysis was performed to test module associations with GWAS variants

(permutations = 100,000). All genes assigned to a given module were used as the input for each module.

Consensus modules and re-clustered modules were run as separate groups in MAGMA given that they

contain overlapping genes. Additional FDR correction was applied across all the competitive p-value

outputs from MAGMA for all modules used in the study.

Exome-based validation of MAGMA disease-module associations

Summary statistics from Alzheimer’s disease, Frontal Temporal Dementia and Progressive Supranuclear

Palsy exome array analysis were downloaded from71. To incorporate protein-protein interaction,

summary statistics were used as input to the network burden test, NetSig94. NetSig determines a gene’s


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network association with disease. Generalized least squares regression was used to determine if NetSig

results were enriched in gene modules. Regression covariates included gene length and mean protein

expression, including the log of these values. To account for linkage disequilibrium, error was correlated

for genes within 5 megabase pairs.

ELISA

Total tau and pT231 tau content were measured by commercial tau ELISA kits according to the

manufacturer's instructions (total tau - KHB0041; pT231 tau - KHB8051, Invitrogen). Briefly, standards,

RIPA-soluble or sarkosyl insoluble samples were applied to the ELISA plate. After washing, a biotin-

conjugated detection antibody was applied. The positive reaction was enhanced with streptavidin-HRP

and colored by TMB. The absorbance at 450 nm was then measured and the concentration of tau protein

was calculated from the standard curve.

Acknowledgements

The results published here are in part based on data obtained from the AMP-AD Knowledge Portal

(doi:10.7303/syn2580853). We thank Eli Lilly and Company scientists for generating the rTg4510 microglia

RNAseq data and providing us access to them. For the FTD GWAS summary statistics used for MAGMA,

we acknowledge the investigators of the original study (Ferrari et al, 2014, Lancet Neurol, PMID:

24943344)68 as well as the consortia members listed within the supplementary material. We thank Dr.

Timothy Hammond and Dr. Marta Olah for use of their microglial single cell data and discussion, and Chris

Hartl for helpful complementary analysis and discussion. Funding for this work was provided by Takeda


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Pharmaceuticals (D.H.G.), Rainwater Charitable Foundation (D.H.G.) and NIH grants to J.R (5R25

NS065723).

Author Contribution

J. Rexach and D. Geschwind designed and supervised the experiments, analyzed and interpreted results,

and wrote this manuscript. J. Rexach performed the analyses and generated figures and tables. V. Swarup

contributed to preparation of raw sequencing data including quality control and normalization, and

provided technical training and supervision of the cWGCNA experiments. T. Chang contributed exome-

based validation of FTD, AD and PSP module associations. C. Hartl contributed to annotation of

inflammasome and anti-inflammasome modules.

Competing Interests

D.H.G. has received research funding from Takeda Pharmaceuticals Company Limited.

Figure Legends

Figure 1: Purified microglia-brain tissue consensus gene co-expression network analysis. A, Experimental

schema, showing approach for microglia-tissue consensus WGNCA and module selection to achieve

multiple microglial disease modules associated with tissue-level neurodegenerative disease inflammatory

modules. B, Cell type enrichment of modules using mRNA markers for corresponding cell types from

mouse brain (Fisher’s two-tailed exact test, ***FDR<0.005, MO = myelinating oligodendrocyte27). C,

Pearson’s product-moment correlation (n = 12413 genes) of module eigengene connectivity in consensus

module compared to tissue-level neurodegeneration module (NAS or NAI)20. D, Module enrichment for

mouse microglia single cell cluster signatures (as defined in28; Fisher’s two-tailed exact test, *FDR<0.05,


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**FDR<0.001, ***FDR<0.005). E, Scatterplot showing Pearson’s correlation of gene-module connectivity

(kME) and sample-by-sample correlation of gene expression and pT231 Tau levels (n=36) in TPR50 mouse

brain (frontal cortex, 6 months of age, n=18 per group of WT or P301L MAPT; P-values obtained from

two-sided test for Pearson correlation are shown)20. F, Module preservation in human AD and control

temporal cortex (control n=308, AD n =157)33, human PSP and control temporal cortex (control n=73, PSP

n =83)33, and human FTD and control frontal cortex from two independent datasets (dataset 195 control

n=14 , FTD n=16; dataset 220: control n=8, FTD n=10). The bottom line is at the lower cut off for

preservation (Zsummary = 2) and the upper line in at the cut off for high preservation (Zsummary = 10) as

defined in84. G, Protein-protein interaction (PPI) network plot of among all genes from tissue-level NAI

(left) and combined microglia-enriched consensus modules (MNMs; right), with nodes colored by GO and

KEGG categories, as shown.

Figure 2: Microglia-tissue consensus module microglia disease time-course and pathway annotation. A,

Signed Pearson’s correlation of the module eigengene (ME) calculated in the rTg4510 microglia gene

expression dataset at each age (unpaired two-tailed T-test; n=7 modules, n=4 mice per genotype (P301L

MAPT or WT) per timepoint; *p-value<0.05, **p-value<0.01, ***p-value<0.005). Graphed with theoretical

zero plotted at time zero. B, Module PPI network enrichment p-value (p-value calculated as described

in96). C. Select module genes (with disease genes in red), enriched gene ontology terms (Z-score >2),

transcription factors (TF) with binding site enrichment (labels are bold and italic if the TF is unique to one

module, blue if the TF is a hub gene in any module, and red if the TF is a hub gene in the same module; p-

value <0.05 compared to whole genome CpG islands), and module genes that are receptors of pathogen

or damage associated molecular patterns (”immune sensors”). D. Protein- protein interactions among top

150 module genes (ranked by kME) with enriched pathway genes labeled (GO-Elite83 permuted Z score

>2). E, Module enrichment heatmap for top 100 genes differentially expressed between progressive

microglia single cell states, as indicated, Hom = homeostatic, DAM1 = type 1 disease-associated microglia


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(DAM), DAM2 = type 2 DAMs, as defined in18; n=7 modules with 4 comparisons per module, *FDR <0.05,

**FDR<0.005, ***FDR<0.001. F, Differential module expression in purified microglia following treatment

with IFN-gamma (n=8) compared to untreated controls (n=4) (two-tailed unpaired T-test, human fetal

microglia cells, IFNg at 200u/mL for 6h or 24h, GSE143241). G, Model showing microglia transitions across

progressive disease stages based on annotation of microglia-tissue consensus modules (MNMs).

Figure 3: Polarized immune signaling networks are up-regulated early in disease and include signaling

cross-talk among up-regulated microglia module genes. A, Experimental schema for identifying opposing

regulatory networks among up-regulated microglia module genes. B, Scatterplot of module A and module

B eigengenes calculated in Tg4510 purified microglia samples (n = 32). C, Scatterplot of gene-module

connectivity scores (kME) with module A and module B calculated across Tg4510 purified microglia

samples (n=32 samples, n = 899 genes). D, Signed Pearson’s correlation of the module eigengene (ME)

calculated in the rTg4510 microglia gene expression dataset at each age (n=7 modules, n=4 mice per

genotype (P301L MAPT or WT) per age, ages = 2, 4, 6 and 8 months, * two tailed p-value of Pearson’s

correlation < 0.005). E, Module enrichment heatmap of single-cell microglial gene expression signatures

from indicated published single-cell studies (Fisher’s two-tailed exact test, *FDR<0.05, **FDR<0.01,

***FDR<0.005 corrected for 2 modules and 34 total cluster signatures as defined in Hammond et al.,

201828, Keren-Shaul et al 201718, Mathys et al. 201819; Hom = homeostatic, DAM1 = type 1 disease-

associated microglia (DAM), DAM2 = type 2 DAMs, as defined in18; from Mathys et al. 201819: Hom =

homeostatic (cluster 2), Earlyc3 = early response (cluster 3), Earlyc7 = early response (cluster 7), Late =

late response (cluster 6)). F, Barplots showing CMAP connectivity scores between overexpression of a

given gene (n=2161 genes) and inflammasome (pink) and ant-inflammasome (blue) modules, ordered

from left to right by difference between anti-inflammasome and inflammasome module connectivity

scores. Top 5 highest scoring module genes shown for each module with their ranked order among 2161

CMAP overexpressed genes. G, Module assignment and module connectivity scores for components of


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NLRP3 inflammasome complex and type 1 interferon response. H, Gene ontology terms significant for

each module (using all module genes, permuted Z-score > 2). I, Module preservation and trajectory of

average module gene expression of the inflammasome and anti-inflammasome modules in cultured

microglia treated with oligomeric Abeta42 or vehicle control (n=3 per group, GSE5562779). J, Trajectory of

inflammasome and anti-inflammasome module eigengenes in mouse microglia purified from IFNAR

knockout or wild-type mice infected with IFNb expressing or control AAV (unpaired two-sample Wilcoxon

rank-sum test, WT control-virus n=3, IFNAR knockout control-virus n=7, WT IFNb-virus n=5, IFNAR

knockout IFNb-virus n=7, GSE984017).

Figure 4: Inflammasome and anti-inflammasome modules bridge microglial sensors, mediators and

checkpoints. A, Module gene co-expression plot of inflammasome (pink) and anti-inflammasome (blue)

module genes with a distributed subset of genes labeled, and the list of transcription factors with module-

wide binding site enrichment adjacent to each module (p-value relative to whole genome CpG islands <

0.05). B, PPI maps with associated gene ontology pathways highlighted for the inflammasome (pink) and

anti-inflammasome modules (blue). C, Module preservation of inflammasome and anti-inflammasome

modules, and D, module eigengene trajectory of inflammasome module in Usp18 knockout, IFNAR1

knockout, double knockout and WT mouse brain (two-tailed unpaired T-test; n=3 per group, GSE6149960).

E, Summary model of immune signaling networks represented by inflammasome and anti-inflammasome

module and their interplay.

Figure 5. Module enrichment for GWAS variants for AD, FTD or PSP. A, Module enrichment for disease

variants for AD70, FTD97, or PSP69 (FDR corrected, two-sided competitive gene-set analysis p-value from

MAGMA67; horizontal line demarcates -log10(FDR) = 1, “anti” = anti-inflammasome). B, Gene co-

expression network plots of top 25 genes, ranked by kME, from each disease variant-associated module,

with the list of transcription factors with enriched binding sites to right of each module network plot


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(“TFBS”; TFs with binding site enrichment p-value <0.05 compared to whole genome CpG islands; unique

TFs in bold). C, Gene ontology terms enrichment among corresponding module genes (permuted Z-score

>2).

Supplementary Figure 1. A, Module enrichment for human microglia single-cell cluster signatures,

clusters labeled as defined in29 with the following abbreviations: “c1” labeled = combined c1, c3, c6_1,

c11 clusters, “c13” labeled = combined c13_1 and c13_2 clusters, “c15” labeled = combined c15_1, c15_2

clusters, “c9/c14” labeled = combined c9_1, c9_2, c14_1, and C14_2 clusters (Fisher’s two-tailed exact

test, *FDR<0.05, **FDR<0.001, ***FDR<0.005). B, Scatterplot showing Pearson’s correlation of gene-

module connectivity (kME) and sample-by-sample correlation of gene expression and pT231 Tau levels

(n=36; in TPR50 mouse brain; frontal cortex, 6 months of age, n=18 per group of WT or P301L MAPT; p-

values obtained from two-sided test for Pearson correlation are shown). C, Protein-protein interaction

(PPI) networks plots of the tissue-level NAI module (left) and combined MNM modules (right) showing PPI

node that overlap between the two (in magenta: 65% of NAI PPI nodes overlap with MNM; and in

turquoise: 59% of MNM PPI nodes overlap with NAI. D, Module preservation in three independent

datasets of models expressing mutant MAPT: PS19 model (frontal cortex, n = 10 WT, n = 8 P301S MAPT)20,

JNPL3 model (spinal cord, n = 12 WT, n=12 P301L MAPT, data obtained from the AMP-AD Knowledge

Portal), and Tg4510 model (forebrain, n = 16 WT, n = 16 P301L MAPT; data obtained from the AMP-AD

Knowledge Portal). The bottom line is at the lower cut off for preservation (Zsummary = 2) and the upper

line in at the cut off for high preservation (Zsummary = 10), as defined in84. E, Module preservation

heatmap in microglia purified from mouse models of Alzheimer’s pathology (5xFAD n= 5 mice per

condition, GSE6506737; PS2APP n=5 mice per condition, GSE7543136). * Zsummary > 2 and <10 (low

preservation) and ** Zsummary = 10 (high preservations). F, Module – disease trait correlation heatmap

in microglia purified from mouse models of Alzheimer’s pathology (Tg4510 age = 6 months n=4 mice per

genotype, 5xFAD n= 5 mice per genotype, GSE6506737; PS2APP age = 13 months n=5 mice per genotype,


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GSE7543136). *FDR < 0.05. ** FDR < 0.01 (n=7 modules with 3 comparisons per module) using P values

from two-sided test for Pearson correlation. G, Module enrichment heatmap for top 100 genes

differentially expressed between progressive microglia single cell states, as defined in19, from CK-p25

mouse model (Hom = homeostatic (cluster 2), Earlyc3 = early response (cluster 3), Earlyc7 = early response

(cluster 7), Late = late response (cluster 6); n=7 modules with 9 comparisons per module, *FDR <0.05,

**FDR<0.005, ***FDR<0.001).

Supplementary Figure 2. A, Module gene co-expression plot among top 50 module genes ranked by

module eigengene connectivity (kME26). B, Extended list of gene ontology terms significant for each

module (using all module genes, permuted Z-score > 2). C, Differential module expression in purified

microglia following treatment with oligomeric Abeta42 (two-tailed unpaired T-test with FDR correction

for 7 comparisons; primary mouse microglia cells, 10uM Abeta42 for 6h n=3, or vehicle for 6h n=3,

GSE5562779) or IL-4 (two-tailed unpaired T-test with FDR correction for 7 comparisons, mouse microglia

cells, IL4 at 100U/mL for 48h, n= 3, or untreated controls n=3, GSE7706480).

Supplementary Figure 3. A, Experimental disease-associated gene perturbation-module connectivity.

Connectivity between disease-associated perturbations of module genes and all modules, based on gene

knockdown or overexpression experiments from CMAP, showing gene-module pairs with high

connectivity (edge weighted by connectivity score -absolute connectivity score ranging 70-100- and

colored by directionality of gene expression effect on module expression, as indicated). B, Transcription

factors (TF) with binding site (BS) enrichment within more than one module (line thickness is proportion

to -log10(pvalue) of TFBS enrichment within each connected module. All TFs shown have p-value < 0.05

of TFBS enrichment within module compared to genome-wide CpG islands). C, Protein-protein interaction

maps among top 100 genes ranked by module eigengene connectivity (kME) showing later modules share


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protein-protein interactions with genes from other modules, including modules with TFBS overlap. Genes

are colored by module, with colors as labeled in Supplementary Figure 3A and 3B.

Supplementary Figure 4. A, PPI network of combined inflammasome (pink) and anti-inflammasome

(blue) module genes showing interconnectivity among both modules. The height and width of each node

is scaled to the gene connectivity to the inflammasome (height) and anti-inflammasome (width) module

eigengenes (kME26). B, Module preservation in human AD and control temporal cortex (control n=308,

AD n =157)33, human PSP and control temporal cortex (control n=73, PSP n =83)33, and human FTD and

control frontal cortex (control n=8, FTD n=10)20 and in three independent datasets of models expressing

mutant MAPT: PS19 model (frontal cortex, n = 10 WT, n = 8 P301S MAPT)20, JNPL3 model (P301L MAPT,

spinal cord, n = 12 WT, n=12 P301L MAPT, data from AMP-AD Knowledge Portal), and Tg4510 model

(forebrain, n = 16 WT, n = 16 P301L MAPT, data from AMP-AD Knowledge Portal). The bottom line is at

the lower cut off for preservation (Zsummary = 2) and the upper line in at the cut off for high preservation

(Zsummary = 10)84. C, Module enrichment for human microglia single-cell cluster signatures, clusters

labeled as defined in29 with the following abbreviations: “c1” labeled = combined c1, c3, c6_1, c11 clusters,

“c13” labeled = combined c13_1 and c13_2 clusters, “c15” labeled = combined c15_1, c15_2 clusters,

“c9/c14” labeled = combined c9_1, c9_2, c14_1, and C14_2 clusters (Fisher’s two-tailed exact test,

*FDR<0.05, **FDR<0.001, ***FDR<0.005). D, Module preservation and E, trajectory of the inflammasome

and anti-inflammasome module eigengenes in microglia purified from mouse models of Alzheimer’s

pathology (unpaired two-sample Wilcoxon rank-sum test; 5xFAD n= 5 mice per condition, GSE6506737;

PS2APP n=5 mice per condition, GSE7543136). F, Module preservation and module eigengene trajectory

in microglia purified from 8 month old C57BL/6 mice 48 hours following injection I.C.V. with either Aβ

(n=7) or vehicle (n=4) (unpaired two-sample Wilcoxon rank-sum test, GSE5718178). G, Anti-inflammasome

PPI plot highlighting genes with the highest anti-inflammasome module connectivity in microglia purified


https://doi.org/10.1101/597542


from IFNb or control AAV infected mice (unpaired two-sample Wilcoxon rank-sum test, WT control-virus

n=3, IFNAR knockout control-virus n=7, WT IFNb-virus n=5, IFNAR knockout IFNb-virus n=7, GSE984017).

Supplementary Figure 5. A, Experimental schema for identifying earliest submodules from up-regulated

or down-regulated microglia module genes. B, Module eigengene trajectories of up-regulated module and

submodules in early stage frontal cortex of TPR50 mouse model (unpaired two-sample Wilcoxon rank-

sum test; age = 3 months, 3 genetic backgrounds, n=18 total P301S MAPT, n=18 WT20). C, Module

enrichment heatmap of single-cell microglial gene expression signatures from indicated published single-

cell studies (Fisher’s two-tailed exact test, *FDR<0.05, **FDR<0.01, ***FDR<0.005 corrected for 2

modules and 33 total cluster signatures as defined in Hammond et al., 201828, Keren-Shaul et al 201718,

Mathys et al. 201819; Hom = homeostatic, DAM1 = type 1 disease-associated microglia (DAM), DAM2 =

type 2 DAMs, as defined in18; from Mathys et al. 201819: Hom = homeostatic (cluster 2), Earlyc3 = early

response (cluster 3), Earlyc7 = early response (cluster 7), Late = late response (cluster 6)). D, Module

enrichment for human microglia single-cell cluster signatures, clusters labeled as defined in29 with the

following exceptions: “c1” labeled = combined c1, c3, c6_1, c11 clusters, “c13” labeled = combined c13_1

and c13_2 clusters, “c15” labeled = combined c15_1, c15_2 clusters, “c9/c14” labeled = combined c9_1,

c9_2, c14_1, and C14_2 clusters (Fisher’s two-tailed exact test, *FDR<0.05, **FDR<0.001, ***FDR<0.005).

E, Module eigengene trajectory of early_UP1 and early_DOWN1 in microglia purified from Tg4510 colony

(unpaired two-sample Wilcoxon rank-sum test applied at each age, P301L MAPT and WT controls, n=4 per

condition per age, ages = 2, 4, 6 and 8 months). F, Exome-based array71 validation of disease-module

associations identified in MAGMA (beta and p-value based on NetSig94 and generalized least-squares

regression, see Methods). G, Module enrichment for genes differentially up- ordown- regulated in Zeb2

knockout microglia compared to controls (Fisher’s two-tailed exact test, *FDR<0.05, **FDR<0.001,

***FDR<0.005, Zeb2 microglia data source77). H, PPI network, and I, enriched gene ontology terms (Z-

score >2) among the top 100 genes positively correlated with Tau phosphorylation (T231) (cor > 0.9 for all


https://doi.org/10.1101/597542


top 100 genes) and J, box and whisker plot showing distribution, median and upper and lower quartiles

of gene – pTau correlation for each gene in the GO geneset: “Interferon beta response” (GO:0035456; 74

genes), in TPR50 mouse brain (frontal cortex, 6 months of age, n=18 per group of WT or P301L MAPT).

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0

10

20

30

40

50

C1C2a

C2bC2c C3 C4 C5 C6

C7aC7b

C7c C8 C9

mono_macA

mono_macB

aging_C1a

aging_C1b

aging_C2

aging_C3

aging_C4

injury_C1

injury_C2

black

blue

brown

M_DOWN2

green

M_UP2

M_DOWN1

pink

purple

M_DOWN4

M_DOWN3

M_UP3

M_UP1

yellow

5.3

2.2***

**

3.3*

3.9 5.9 2 2.9

* * *

3.6**

3 2.7 3.4 2.4 2.4

8.3***

2.2 4.8***

5.9***

3.1 5.8 12***

5.3***

39***

2

2.1 2.2 2.8 9.2 3.8 5.1 5 2.7

5.3***

2.5 2.1 3.2*

3.1 3 6.1***

13**

3.1 11 2.4

4.7***

2.7***

2*

2.3 2.2***

6***

9.4***

29***

16***

14***

4.1***

98***

11***

5.1***

23***

20***

* * *

Dataset 2:

0

1

2

3

4

5

Astrocyte

Endothelial

MicrogliaMO

Neuron

black

blue

brown

M_DOWN2

green

M_UP2

M_DOWN1

pink

purple

M_DOWN4

M_DOWN3

M_UP3

M_UP1

yellow

2.7***

4.2*** **

8.4***

* ***

* *** **

6.4***

3.2*** ***

2.6***

5.8***

8.7*** *

***

*

2.2*

3.2***

10***

4.7***

***5.2*** ***

** **4***

A. B.

E.

G. Neurodegeneration-associated inflammatory module (NAI)

Combined microglial neurodegenration-associated modules (MNMs)

Cell type enrichment

Mouse microglia single cell-signature enrichment

Gene-module connectivity correlation

−0.85 0.85

0.83 −0.83

0.83 −0.82

−0.84 0.84

0.85 −0.84

−0.83 0.83

−0.83 0.83

0.83 −0.82

0.85 −0.85

−0.82 0.82

−0.85 0.85

−0.84 0.84

−0.81 0.81

0.81 −0.8

NAS

−1.0

−0.5

0.0

0.5

1.0

Pearson Correlation

Cholinergic synapse (KEGG: 04725)

Cell Cycle (KEGG: 4110)

Complement cascade (KEGG: 04610)

Toll-like receptor signaling (KEGG: 4620)

Cyokine receptor signaling (GO: 0004896)

Chemokine signaling (KEGG: 4062)

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Hammond et. al 2018

Figure 1


https://doi.org/10.1101/597542


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VEGF productionNF−kappaB nuclear import

negative regulation of extrinsic apoptotic signaling TNF production

IL1-beta productionIL−6 production

response to external biotic stimulusNLRP3 inflammasome complex

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Inflammasome PYCARD 0.91 -0.77

Inflammasome CASP1 0.88 -0.70

Inflammasome IL1B 0.92 -0.63

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om

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om

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Module A -Inflammasome

module

6.3***

3.7***

2.5 3.5***

6.5***

26***

49***

6.6***

3.4*

27***

20***

8.2***

8.7 9.6***

10***

8.4***

6.1*** ***

5.1***

3***

16***

10***

4.1***

2.5***

3.2** *

5.2***

11***

2.8 7.4***

3.8***

11***

3.6***

3***

13***

9.7*** **

6.8***

13***

2.6*** *** *** ***

4.4***

2.8***

9.7***

4.7***

Module B - Anti-inflammasome

module

Hammond et. al 2018 Keren-Shaul et al. 2017 Mathys et. al 2018

ORA

�

�

50 100 200 500 1000 2000

01

23

4

Module size

Module Preservation - Abeta42treated

� inflammasome

�

anti-inflammasome

Pre

serv

atio

n Zs

umm

ary

−0.3

−0.2

−0.1

0.0

0.1

0.2

−0.2

−0.1

0.0

0.1

0.2

control Abeta42 control Abeta42

Aver

age

Mod

ule

Gen

e E

xpre

sssi

on

p = 0.01

Ant

i-inf

lam

mas

ome

mod

ule

eige

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mm

asom

e m

odul

e ei

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ene

−0.2−0.1

0.0

0.1

0.2

0.3

0.4

0.5

AAV: Ctrl CtrlIFNb IFNb

WT IFNAR KO

−0.4

−0.3

−0.2

−0.1

0.0

0.1

0.2

0.3

Ctrl CtrlIFNb IFNb

WT IFNAR KO

AAV:

p = 0.04

p = 0.04 p = 0.002

p = 0.002

p = 0.02 p = 0.02

E.

F.G.

H.

I. J.

B. C.

Inflammasome Module Anti-inflammasome Module Inflammasome Module Anti-inflammasome Module

* * * * * * * *

Figure 3


https://doi.org/10.1101/597542


PSD

PTPN23

CTSZ

BMF

INPP5D

GNS

PDGFB

IQGAP1

FN1

IL3RA

LGALS3BP

TMSB4X

TNS1

FYN

ITGA5

CTNNA1

MMP14

PCYT1AMET

F2R

PRNP

ECM1

INPPL1PARVB

DOK1

SEPP1

CDC42

QSOX1

GNA12

CTNNB1

ITGB5 WDR1

GNA13

CSF2RA

MAP3K8

FURIN

BCL2

TNIP2

CTSB

CTSF

MAP4K1

NEK6

ALAS1

NEK9

ANP32B

CMPK2

GNPDA1

ASS1

CDCA3

PRKCSH

KCTD10

DHRS1

UBXN1

LAMP1NEK7

GAA

TES

UGDH

EIF5A

LPCAT1

SRI

XPO6

PDCD6IP

QARS

EPHX1

ABHD4

LIPA

GSTM1

DHDH

GPX3

GLIPR2

MGST1

LARP1

STT3A

BCAP31

BRD2

MAP4

ERGIC3

ASF1B

PTGFRN

MAOA

SLC25A1

NOP14

HSPA9

ALDH1L1

MRPS6

PNPLA7

EPRS

PPP4C

PFKFB4

PFKFB3

EEF1B2

EEF1D

RRBP1

ESD

RBMS1

EIF4B

CALU

RPS18

LAMP2

EEF2

IGBP1

GNL3

CDV3

HADHA

TXNRD1

GNL2

RPS27L

RPLP1

SDF4

TNFAIP2

MICAL1

SPECC1L

GAS2L1

DNAJC13

AGAP1

CEP192

SNX1

CAPZB

DOCK11

GSN

DEF6

LRP1

RAB13

CBFB

CD63

NLN

ZFAND3

ANXA4

SNX8

NFXL1

RNF13

PSAP

TTYH2

TSPO

USP35

CLINT1

SNAP23

GBA

SNX5

MYO1E

STX2

ANXA3

CPD

NDE1

MOB1A

PON2

MAPRE1

SPATA13

PPARD

STXBP2

WNK1

PLEKHO1

USP12

ASAH1

NUCB1

CNPPD1

MBD2

CTSA

RBPJLCHD7

SLC12A4

PLP2

CHD4

GATAD2A

SIRT2

SF1

OAT

CCNG1

YBX1

RARG

MCM6

SAT1

MCMBP

DLG5

EEF1A1

RPL36ALRPL32 EIF3F

POLE

RIOK3

EIF4A1

EIF3B

TRAM1

RPS9

SSR4

EIF3H

RPN2

MCM5

PAN3

MCM3

CDT1

CDK9

EIF3A

RPS25

SGPL1

CFB

ABCA2

CANT1

GATM

HCLS1

SLC16A1

ADSSL1

AK2

PRDX1

RRM1

WLS

ANXA5

P4HB

CAD

PDE4B

PTGER4

CNDP2

ATP1B3

VIM

LGALS1

COL5A1

MAN1C1

SCPEP1

RNH1

WDR62

GANAB

COTL1

NAMPT

DEK

PFDN5

HMGA1

CYBA

MSN

TYMS

TKT

ANXA2

PTP4A2

DDX54

TXNDC17

TRPC4AP

MOCS1

TUBB6

DOCK1

TPM3

TNNI3

NCSTN

ADCY7

TPM4

NCL

VAT1

GCH1

MYBBP1A

DDB1

LGALS3

CPNE3

OS9

PRDX6

TMOD3

BSG

TAGLN2

H2AFY

PCBP2

HNRNPF

SKI

AKR1A1

CCNL1

LIF

RNF31

SYF2

NFKBIZ

DNA2

POLR2A

ODC1

POLE3

ATP6AP1

GADD45B

IRAK4

HDAC5

NFKB2

RELB

RBPJ

CTSD

SPOP

HNRNPAB

E2F2

PSMB8

TNFRSF12A

HNRNPL

CDK4

CDKN1A

PCNA

PAF1

POLD4

GNB2L1

SF3B2

PKN1

FLNA

RHOQ

ARPC1B

RHOC

LAMC1

SH3PXD2B

ITGAL

MYH9

ACTB

GNAI3

MYO9B

GNA14

C3

SORBS3KCNJ10

CORO1C

SURF4

GNAI2

VAMP3

ARHGEF2

GNG12

CLTA

P2RY2

FMNL2

ARAP1

RHOA

APOE

VASP

ARRB2

WIPF1

ITGB2

FBLIM1

GDI2

ARPC5

S1PR2

TUBB2B

MYO1C

ARHGDIA

SVIL

CLTB

PFN1

ARHGAP25

SNX9

SCARB2

TJP3

MAG

TMED3

CLIC4

KIF1C

LDLRAP1

CRIP1

ATP7A

MPRIP

ATOX1

PSAT1

DBI

GEM

CORO1A

MOCOS

OSTF1

USE1

RGS16

CAPG

NECAP2

FTH1

RAB5C

PLXNB2

LMAN2

MKNK2

CSF1

FLT1

AXL

GABARAP

CSPG4

AMOTL2

NDST1

PKP4

AMOTL1

LSP1

MDFIC

SHC4

IQGAP3

PDGFA

IER3

MAPKAPK3

ADAMTSL4

MAPKAPK2

CD274

GFAP

SH3GLB1

ITPKB

TPT1

HOMER3

VCAN

SDC3

SDC4

LIMA1

YWHAE

SEC13

MAFF

CEBPG

MAFK

BACH1

NFE2L1

GOLGA7

TIMP2

BLNK

LAT2

CMTM3

EHBP1L1

CAMK1

PRKD3

SKAP2

MYC

GADD45A

RNF114

RNF19B

SQSTM1

FBXW8

RNF213

EZR

RALGDS

MYL12A

TJP2

MYL6

TEP1

EIF4EBP1

TRAF1

PSME1

FAT1

PIM1

TGFBR2

UBE2L6

PSD4

SLC2A1

SH3BP2

RNF122

SPSB2

SPSB1

SMURF1

ZNRF2MCL1

ZBP1

NLRC5

PSME2

BIRC3

BCL3

RIPK1IL1R1

ACVRL1

IRAK1

CRLF2

GUSB

TNFRSF10B

JUP

VCAM1

BCL10

CD44

AKT1

LAMB2

NRAS

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PAK2

SPP1

NFKBIE

IKBKE

LMNA

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DDIT3THBS1

SERP1

SEC61A1

ADD1TLN1

CREB3L1

BAX

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DDX21

PMAIP1

IRAK3NOD2

MAPK3

DAG1

CD81

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IFIT3ISG15

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BST2ISG20

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TYK2IFIT1

IRF1

MAP3K1TRIM25

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RPL30RPL6

RPL13A

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Anti-Inflammasome Module

IFNAR KO

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0.0

0.2

0.4

�

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50 100 200 500 1000 2000

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24

68

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Module Preservation in USP18 KO brain

Module size

Pre

serv

atio

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inflammasome

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Response to dsRNA

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RIG-1-like receptor signaling

Viral entry into host cell

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Extracellular exosome

Cytokine-cytokine reeptor interaction

NLRP3 Inflammasome complex

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2-5-oligoadenylate synthetase

GFAP HNRNPABNEK6

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IL1R1IRF8WIPF1

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CD81

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OAS2CXCL10

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C3AR1

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PRNPKLF6

AXL

NFKB1A

IFIH1

PABPC1CDK9PSMB8

EIF3B

Inflammasome Module

GPR84

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IL1RN

CST7

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LY86

TICAM2

GNG10

C5AR1

CD14

CD180

GNGT2

B2M

IFITM3

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PDCD1

TNFAIP3

GNG5

C3AR1

CNR2

CD48

CD22

CD86

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LILRB4

FCGR2B

ITGAX

CD244

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PTPN6CXCL10

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TLR2

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CXCR4

OAS2

CXCL13

ICAM1

IL2RG

CCL4 CSF2RB

FCER1GCCL3

PYCARDIL1B

CASP4IL1A

CASP1

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SP1 SP2 KLF5 SpI1

ERG EHF ETS1

MZF1 FLI1 MYOD1

ELF1 SMAD2 NFKB1

RELA JUN

Enriched Transcription Factor Binding Sites:

KLF4 EGR1 FOXD3

KLF1 NRF5A2 ARID3A

STAT3 TFAP2C

(

p = 0.006

A.

Simultaneous chronic inflammation

and immune suppression Lipopeptides/

DNA/mDNAdsRNA

Immunosuppressedinflammed microglia

TLR1/2

Pro-inflammatorymicroglia

IL1

exosomes

IFIT1

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Inflammasome module

Abeta42USP18IFNb

Early neuronal injury; PAMP receptors

Next stage neuronal injury; dsRNA

Activation of

Anti-inflammasome module

Suppression of

Inflammasome module

TREM2

TLR9TLR7

Immune Attack

Inflammasome Module

Anti-Inflammasome Module

B.

C.

D.

E.

Figure 4


https://doi.org/10.1101/597542


AD variant module

M_UP3 early_UP1

FTD variant module

early_DOWN1

FTD and PSP variant module

regulation of histone modificationpositive regulation of apoptotic process

regulation of cytokine−mediated signaling pathwaymyeloid leukocyte activation

antigen processing and pres.of exogenous peptide antigenendoplasmic reticulum calcium ion homeostasis

cytokine metabolic processpositive regulation of defense response to virus by host

Z−Score

0 4 8

autophagy

0 4 8

extracellular vesicle

extrinsic apoptotic signalingpeptidyl-tyrosine phosphorylation

reg. of adaptive immune response

leukocyte activation

cytokine−mediated signaling

tetrapyrrole binding

neutrophil migrationphagocytosis

lysosomeprotein oligomerization

phosphoric ester hydrolase activityregulation of defense responsepositive regulation of transport

cell cycle

cellular response to nitrogen compoundinnate immune response

regulation of lipid metabolic processdefense response to virus

0 4

Z−Score

2

Z−Score

TFBS:Insm1Nr2c2Plag1Zfp423

TFBS:Sp1Spi1ErgEhfTfap2a/cNfkb1Plag1Usf1RelaCrx

TFBS:Sp2Sp1Klf4/5ErgSpi1Ets1Elf5Fli1ZfxInsm1Brca1Myc::Max

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Pde3b

Zeb2Trim21Golm1

Nfatc3

Ccdc80

Tlr3

Lrch1

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Lbh

Sh3tc1

Cd33

Sidt2

Stat2Ptbp1 Irf9

Mfng

Runx1

AD variant module FTD variant module FTD and PSP variant module

--

-

A.

B.

p-value = 0.024

p-value = 0.028

p-value = 0.017 p-value = 0.021

Figure 5


https://doi.org/10.1101/597542


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M_DOWN4

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Module Eigengene-Trait Correlation

not preserved

Zsum

mary

Module

eige

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trait c

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tion

Supplementary Figure 1

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0.2 0.4 0.6 0.8

0.6

00.6

50.7

00.7

50

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0.8

50

.90

0.9

5

cor=0.64, p=5.3e−19

M_UP2


0

2

4

6

8

10

c1 c13

c12_

1c1

2_3

c12_

4c1

5c6

_2 c2

c9/c1

4 c5 c7 c4 c8 c10

black

blue

brown

M_DOWN2

green

M_UP2

M_DOWN1

pink

purple

M_DOWN4

M_DOWN3

M_UP3

M_UP1

yellow

18

*** * *** * *

***

*** * ***

2.2 3.3***

3.5 3**

2.2 2 2.1

* * *** ** *** *** * *** * ***

2.2 2.9

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2.8***

3.3***

2.2**

2.2*

2.2***

2.2

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2

3.1*

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*** ***2.5

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Ge

ne

mo

du

le C

on

ne

ctivity s

co

re (

kM

E)

Ge

ne

mo

du

le

Co

nn

ectivity s

co

re (

kM

E)

B.

Od

ds R

atio

C.

D.

E. F.

Purified Microglia Purified Microglia

P301L MAPT (JNPL3) spinal cord P301S MAPT (PS19) frontal cortex

0

5

10

15

20

25

Hom vs. E

arlyc

3

Hom vs. E

arlyc

7

Hom vs. L

ate

Earlyc

3 vs.

Hom

Earlyc

7 vs.

Hom

Earlyc

3 vs.

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Latevs

Hom

Latevs

Earlyc

3

*** *** ***5.4

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***

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3.3***

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4.2

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***9.9

*** ***2.4

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2.9

*** *2.8

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3***

3.5*** *** *

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M_UP1

M_UP2

M_UP3

M_DOWN1

M_DOWN2

M_DOWN3

M_DOWN4

G.

Od

ds R

atio

M_UP1

M_DOWN1

M_UP2

M_DOWN2

M_UP3

M_DOWN3

M_DOWN4


https://doi.org/10.1101/597542


A.

SLC44A1FCGR4

SDC3IL2RG

TLR1

AXL

H2-K1

B2M

CYBBC3

ANXA4

SRGN

H2-D1

APOEIFI30

LY9

CXCL16

ADIPOR1

CD14 TLR2

PSME1

ANXA3

CD274

PTPRC

LGALS3BP CD63

CTSB

CD9

CSF1

CAPG

C3AR1

COTL1

MYO1F

LAG3

CD52

SLAMF9

SLC11A1

SULF2

TGIF1

GLTP

MAN2B1CTSD

ITGAX

CTSA

CD84

LGALS3

PABPC1

M_UP1

M_UP2

GPSM3

RNF114

PFN1

TLN1PPFIA4

FAM214B

RELA

TSSC4

CLIC1

BHLHE41

MYH9

TYK2

ITGB5

PLXNB2

TCF12

PPP1R18

MAG

LCP1

PON3

NUCB1

TREM2

SLC38A10

PALD1

SKI

ELOVL1

SPSB1

LARP1 ZNFX1

TLR7

CANT1

TEX261

VASP

FHOD1

GBGT1

TLR12

ARRB2

TRIM56P2RX7

CTTNBP2NLTLR9

IFIH1

HEATR5A ZFP36

NKX6-2

UBXN1

ACTB

LHFPL2

M_UP3

M_DOWN1M_DOWN2

M_DOWN3

M_DOWN4

LPCAT2IL13RA1

CMTM6

GBP7

ALOX5AP

DENND1C

MBOAT1

PDE3BCOL27A1

ITPR2

FRMD4BSALL1

ANKRD44

ARHGAP4

CYB561A3

TMEM119

ITGAESIGLECH

PDLIM5

BC035044

ARHGEF40

SELPLG

RNASE4

SRGAP2

PLD1PDLIM4

HMHA1

SLCO2B1

GNA15 NCKAP1L

CMTM7

PLCG2

CLEC4A2

PLOD1

ECSCR

CYSLTR1

ATP6V0A2

FCHSD2TJP1USP24

GPR34

TMEM173

CCND1

PTGS1

RPS6KA1

ITGAM

P2RY13

CX3CR1

TLR3

NPEPL1

SIDT2

SYNGR2

MFNG

CD33

SEMA4D

PLD4

LY6E

SLC29A3

RGL2LAIR1

TMCO4

LAPTM4A

SBNO2

RUNX1

EBI3

PTBP1 DCXR

RRAS

LBH

CERCAM

SPI1

FLI1

FCGR3

DHX58

TRP53INP2SLC43A3

TMEM106A

CD37

LMF2

NOTCH1

STAT2

RHOG

MAVS

RAB31

ICOSL

SIGLEC1

NOD1

IFITM2

ADGRE5

PIRB

MYO1G

PYROXD2

SLC7A7

IL21R

NFAM1

LATS2

ENTPD1

TRIP6

ABCC3

UBA7

EBF3

PARP3

TSC22D4

LCP2

AMPD3

IL16

GMIP

P2RY12TLE3

1700017B05RIK

DNM2

MYD88

STAT5A

GLIS3

PDLIM2

PIEZO1

WDFY4

SIPA1

LRRK1

PXN

MXRA8

SRRT

TGFB1

TNFRSF13B

ZCCHC24

ORAI1RIN3

BMP2K

LRP5

APOBEC3

NFATC1

ARHGEF1LYL1

PREX1

PIK3CD

PLCB3

IL6RAFMNL3

ZFP579

LGMN

F11R

NISCH

MICALL1 SNX18

SYK4632428N05RIK

WASF2 MAP4K4

SS18

RCSD1

GIT2

FBRSL1 SLFN9

AKNA

MLXIPL

MYO18A

TMEM63A

JAK3

RASA4

WDR81

MAML2

IRF5

WASQK

SIRPA

KCNK6

SMARCC2

SP110 ABI3DTX4

MVB12BADORA3SH2B3

AHNAK

ZC3HAV1

CMKLR1NCF4

PBXIP1

RAP1B

SASH39930111J21RIK2

H2-T23SLC25A45PLA2G15 FCGR1 SLA

CD53

ARHGEF6

ARHGAP9RHOH

ELF1 RESTLRMP

MTA2

CD300ACFLAR

DTX3LDDX60

LGALS8

CCR5

STXBP3

EIF2AK2AIM2

CSK

MDM2

BTK

P2RY6

IL10RA

NTPCRTNFAIP8L2

HK3

VWA5A

SLC41A1

NFKB1 CD151

ARHGDIBRAC1

NPL

PARP9

DAZAP2

LACC1

positive regulation of NF−kappaB activityphagocytic cup

interleukin−1 secretionISG15−protein conjugation

peptide antigen bindingMCM complex

lipoprotein biosynthetic processextracellular matrix disassembly

phospholipase inhibitor activityregulation of antigen processing and presentation

immunological synapsereactive oxygen species metabolic process

regulation of viral processmembrane raft

vacuolar membranesuperoxide−generating NADPH oxidase activity

endocytic vesicle membraneglial cell migration

hydrolyzing O−glycosyl compoundsresponse to endogenous stimulus

aging

regulation of ribonuclease activityinterleukin−1 beta production

detoxification of copper ionganglioside catabolic process

serine−type carboxypeptidase activity2'−5'−oligoadenylate synthetase activity

pigment granuleregulation of cell proliferation

response to copper ionregulation of cell death

regulation of plasminogen activationregeneration

MHC protein complex

response to biotic stimuluslysosome

defense response

Z−Score0 5 10 15

ubiquitin protein ligase binding

regulation of JNK cascadedefense response to virus

positive regulation of transmembrane transport

regulation of interferon−gamma productionchloride transport

hormone receptor bindingresponse to lipopolysaccharide

positive regulation of ion transport

regulation of lipid transport

positive regulation of protein serine/threonine kinase activityregulation of T cell mediated immunity

cytokine productionpositive regulation of I−kappaB kinase/NF−kappaB signaling

positive regulation of ERK1 and ERK2 cascaderegulation of cytokine biosynthetic process

negative regulation of TGFb receptor signaling pathwayCOP9 signalosome

innate immune response

regulation of interleukin−1 beta productionregulation of interleukin−12 production

positive regulation of phosphatidylinositol 3−kinase activityactin cytoskeleton organization

response to peptidoglycanregulation of interleukin−6 production

negative regulation of toll−like receptor signaling pathwaymodulation by organism of defense response in symbiotic interaction

pattern recognition receptor signaling pathway

Z−Score0 2 4 6 8

cellular response to oxidative stress

lysosomeprotein tetramerization

regulation of Rho GTPase activityautophagy

glycoprotein metabolic processregulation of tumor necrosis factor production

myeloid cell differentiation

amine transport

regulation of histone modificationpositive regulation of apoptotic process

lysosome organizationregulation of cytokine−mediated signaling pathway

myeloid leukocyte activation

antigen processing and presentation of exogenous peptide

endoplasmic reticulum calcium ion homeostasiscytokine metabolic process

positive regulation of defense response to virus by host

Z−Score0 4 8 12

positive regulation of TNF productiondefense response to virus

dioxygenase activityregulation of mRNA splicing, via spliceosome

positive regulation of chemokine productionglycosaminoglycan binding

integrin complex

phagocytosisinositol phosphate−mediated signaling

positive reg. of tyrosine phosphorylation of STAT

leukocyte migration

innate immune response

interaction with symbiontchemotaxis

very long−chain fatty acid metabolic processpositive regulation of interleukin−8 production

positive regulation of interleukin−12 production Purinergic nucleotide GPCR signaling pathway

defense response to bacteriumregulation of leukocyte migration

regulation of interferon−alpha productiontransferase activity, transferring amino−acyl groups

positive regulation of B cell proliferation

icosanoid biosynthetic processdefense response to protozoan

cellular response to interferon−gamma

Z−Score0 2 4 6 8

growth factor receptor bindingGTPase activator activity

organic acid transmembrane transporter activitywound healing

positive regulation of innate immune response

myeloid leukocyte activation

positive regulation of chemotaxispositive regulation of cytokine biosynthetic processpositive regulation of I−kappaB kinase/NF−kappaB

endocytosisphosphatase regulator activity

focal adhesioncellular response to molecule of bacterial origin

transmembrane protein tyrosine kinase signalingpositive regulation of cell cycle

lymphocyte differentiation

regulation of B cell activation

lipopolysaccharide−mediated signaling pathway somatic recombination of immune receptors

protein kinase B signalinglymphocyte homeostasis

natural killer cell activation

Z−Score0 2 4 6

nitrogen compound transport

RNA splicingangiogenesis

cell adhesionnegative regulation of cytokine productionpositive regulation of leukocyte activation

cell motilityintegrin binding

peptidyl−tyrosine modificationpositive regulation of JUN kinase

Rho GTPase binding

response to bacteriumactin cytoskeleton organizationalpha−beta T cell differentiation

regulation of RNA stabilityleukotriene metabolic process

Z−Score0 2 4 6

negative regulation of leukocyte apoptosiscellular response to abiotic stimulus

p53 bindingT cell aggregation

regulation of defense response to virus

cysteine−type endopeptidase activitynon−membrane protein tyrosine kinase activity

cellular response to alkaloidlymph node development

regulation of cytokine productionlamellipodium assembly

mast cell granule

negative regulation of exocytosiscellular response to oxidative stress

cellular response to interferon−betaregulation of hippo signaling

Z−Score0 2 4 6

M_UP1

M_DOWN1 M_DOWN2

M_UP2

M_DOWN4

M_UP3

M_DOWN3


B.

-1.0-0.8

-0.6-0.4

-0.2

0.0

0.2

0.4

0.60.8

1.0 M_UP1 M_UP2 M_UP3 M_DOWN1 M_DOWN2 M_DOWN3 M_DOWN4

Abeta42

IL4

p-value = 0.02

p-value = 0.02

Nor

mal

ized

Ave

rage

Mod

ule

Gen

e Ex

pres

sion

(tre

ated

vs

cont

rol)

C.


https://doi.org/10.1101/597542


BACH1

PBXIP1

TIMP2

CCNL1

M_UP3

M_DOWN4

M_UP2

M_UP1M_DOWN1

M_DOWN3

M_DOWN2

EWSR1

FTH1

IL1R1

FGR

EPHX1

PVR

SNX6

PLAUR

PML

ST14

CD44

BAX

SNRNP70

CCND1

FLT1

ASS1ICAM1

PVRL2

MMP14

CD83

TNFSF10

TRAM2

RPL22RHOBTB1

P2RY6

PSEN1

PPFIBP2

ANAPC5

SF1

F3

TUBA1A

ZAK

MVP

MAFG

CREB3L1

SHMT2

CYP20A1

TACC3

LACC1

UBE2L6

KLF6

CORO1ABCL10

IRF5

ELF1

RELB

TIRAP

TNFRSF1A

PGD

TICAM2

HMGA1

TRAF1

OAT

NFE2L2

FURIN

LTBRFLI1

GPR35

TRAK2

ELF4

FADD

HMGB2

RPS6KA1TSSC4

NEK6

CDKN1A GNA13

LIPH

PRDM1

GADD45A

ISG20

M_UP1 gene

M_UP2 gene

M_UP3 gene

M_DOWN1 gene

M_DOWN2 gene

M_DOWN3 gene

M_DOWN4 gene

Gene in down-regulated module whose knockdown down-regulates the connected module (CMAP)

Gene in up-regulated module whose overexpressionup-regulates the connected module (CMAP)

Gene in up-regulated module whose overexpressiondown-regulates the connected down-regulated module,or gene in a down-regulated module whose knockdownup-regulates the connected up-regulated module (CMAP)

Nfatc2

Ctcf

Klf1

Mycn

Nr5a2

Tfap2cFoxd3

Spi1Klf5Insm1

Egr2

Ehf

Fli1Mzf1

Stat3

Stat5a

Erg

Stat4

Bcl6-Log10(p-value)

1.3 3.4

Ets1

A.

B.

M_UP1

M_UP2

M_UP3

M_DOWN4

M_DOWN1

M_DOWN3M_DOWN2


C.

M_UP1 M_UP2 M_UP3


https://doi.org/10.1101/597542


Human single cell enrichment heatmap

0

2

4

6

8

10

c1 c13

c12_

1c1

2_3

c12_

4c1

5c6

_2 c2

c9/c1

4 c5 c7 c4 c8 c10

2.2

*** ***

3.5 2.9

*

6.2

***

6.6

***

9.5

***

2

** ***

2.1

***

2.2

***

2.8

***

2.5

***

2.9

***

Inflammasome

Anti-inflammasome

Inflammasome Module

Anti-inflammasome Module

anti-inflammasome module kME (width)

inflammasome module kMe (height)

-1

1

1-1

A.

�

�

50 100 200 500 1000 2000

−10

12

34

56

Module size

Pre

serv

atio

n Zs

umm

ary �

�

50 100 200 500 1000 2000

−20

24

68

Module size

�

�

50 100 200 500 1000 2000

−10

12

34

5

� �

50 100 200 500 1000 2000

−10

12

34

5

Module size

��

50 100 200 500 1000 2000

02

46

Module size

�

�

50 100 200 500 1000 2000

02

46

Module size

Module size


B.

C.

P301L MAPT (Tg4510) forebrainP301L MAPT (JNPL3) spinal cord P301S MAPT (PS19) frontal cortex

D.

ORA

E.

Alzheimer’s Disease -Temporal Cortex

Progressive Supranuclear Palsy-Temporal Cortex

Frontotemporal Dementia- Frontal Cortex

inflammasome

anti-inflammasome

inflammasome

anti-inflammasome

inflammasome

anti-inflammasome

inflammasome

anti-inflammasome inflammasomeanti-inflammasome

inflammasome

anti-inflammasome

WT 5xFAD−0.4

−0.2

0.0

0.2

0.4

Mod

ule

Eig

eneg

ene

−0.2

0.0

0.2

0.4

WT PS2APP WT PS2APP

−0.2

0.0

0.2

0.4

7 months 13 months

−0.2

0.0

0.2

0.4

p = 0.02

p = 0.02 p = 0.02

WT 5xFAD

p = 0.008 p = 0.008 Inflammasome Anti-inflammasome Inflammasome Anti-inflammasome

WT PS2APP WT PS2APP

7 months 13 months

−0.3

−0.2

−0.1

0.0

0.1

0.2

0.3

Infla

mm

asom

e M

odul

e E

igen

gene

p = 0.043

G.

�

�

50 100 200 500 1000 2000

−10

12

3 inflammasome

anti-inflammasome

Module Preservation; in vivo Abeta injection

Pre

serv

atio

n Zs

umm

ary

Module size Control Abeta

F.

100

60

IFIT3IFIT2

PSMB8SP100

IFIT1

IRF7

ISG15

CSF1

BST2

STAT1

ZBP1

TRIM25

USP18

CMPK2

SDC3

LGALS3BP

SH3BP2

XAF1

Anti-inflammasome

module genes connectivity(kME)

�

�

50 100 200 500 1000 2000

02

46

�

�

50 100 200 500 1000 2000

−20

24

68

1012

Pre

serv

atio

n Zs

umm

ary

Module size Module size

inflammasomeinflammasome

anti-inflammasome anti-inflammasome

5xFAD microglia PS2APP microglia

Pre

serv

atio

n Zs

umm

ary


https://doi.org/10.1101/597542


WT Tg

−0.2

0.0

0.2

0.4

M_UP1

Mo

du

le E

ige

ng

en

e V

alu

e

−0.2

0.0

0.2

0.4

M_UP2

−0.2

0.0

0.2

0.4

WT Tg WT Tg

M_UP3

−0.2

0.0

0.2

0.4

WT Tg

early_UP2

−0.2

0.0

0.2

0.4

early_UP3

WT Tg

−0.2

0.0

0.2

0.4

WT Tg

early_UP1

*

**

*

p = 8e-06

p = 8e-10p = 0.004

p = 2e-06

Heatmap of module enrichment of microglial single-cell signatures from published studies

0

10

20

30

40

50

aging_C1

aging_C2

aging_C3

aging_C4

C1

C2a

C2b

C2c

C3

C4

C5

C6

C8

C9

injury_C1

injury_C2

mono_macA

mono_macB

HomvsDAM

DAMvsHom

DAM1vs2

DAM2vs1

HomvsEarlyc3

HomvsEarlyc7

HomvsLate

Earlyc3vsHom

Earlyc7vsHom

Earlyc3notEarlyc7

Earlyc7notEarlyc3

Earlyc3vsLate

LatevsHom

LatevsEarlyc3

LateandEarlyc3

early_down1

early_up1

7.9 6.8 15

***

4.6 54

***

3.2 20

***

7.2

***

5.6

***

2.2 2.2 2.2

9.6

*

9.3

***

9

***

6.4

*

6.4 6.3

*

4.9 5.2 9.1

***

10 3.4

*** *

3.7

***

12

***

4.7

Hammond et. al 2018 Keren-Shaul et al. 2017 Mathy’s et. al 2018

ORA


A.

Up and down sub-modules with very early connectivity

Very early stage

frontal cortex

(P301S Tau and control)

M_UP1

M_UP2

M_DOWN1

M_DOWN2

M_DOWN3

24 early-UP sub-modules

9 early-DOWN sub-modules

Assess enrichment for causal

disease gene

Human single cell enrichment heatmap

0

5

10

15

20

c1c13

c12_1

c12_3

c12_4

c15

c6_2 c2

c9/c14 c5 c7 c4 c8

c10

3.8 3.9

*

3.6 2.9

2.2 2.1 3

*

5.3

***

Olah et. al 2018

ORA

early_down1

early_up1

B.

C.

D.

F.

G. Geneset Enrichment Heatmap

Zeb2K

O_down

Zeb2K

O_up

M_DOWN2

earlyDOWN1

earlyUP1

M_UP2

M_DOWN1

Inflammasome

Anti-inflammasome

M_DOWN4

M_DOWN3

M_UP3

M_UP1

2

*

4.3*** *

5.8***

**

***

*

4.6***

0

2

4

6

8

10

ORA

E.

0.2

0.1

0.0

0.1

0.2

earlyUP1

Mo

du

le E

ige

ng

en

e V

alu

e

0.3

0.2

0.1

0.0

0.1

0.2

earlyDOWN1

2 mnths 4 mnths 6 mnths 8 mnths 2 mnths 4 mnths 6 mnths 8 mnths

P301L MAPT

WT

positive regulation of leukocyte migrationpeptide antigen processing and presentation via MHC class I

ISG15−protein conjugationcellular response to interferon−gamma

positive regulation of RIG−I signaling pathway

negative regulation of viral genome replicationresponse to interferon−alpha

double−stranded RNA bindingCCR chemokine receptor binding

2'−5'−oligoadenylate synthetase activityresponse to interferon−beta

defense response to virus

Z−Score0 5 10 15 20

Trim25

Usp18

Stat1

Rnf213

Uba7

Isg15

Eif2ak2

H2-Q4

Itgax

H2-Q7

Fcgr4

Lgals3

H2-K1

H2-Q6

Tap1

Hck

Cxcl9

Ddx58

Dhx58

Cxcl10

Stat3

Ifih1

Ccl5

H.

*

p = 0.03

*

p = 0.03 *

p = 0.03*

p = 0.03

*

p = 0.03

*

p = 0.03*

p = 0.03

Gen

e le

vel -

pTa

u co

rrel

atio

n (p

er g

ene

in g

enes

et)

I.

J.response to

interferon-beta

-1.0

1.0


https://doi.org/10.1101/597542


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