Abstract

   Alzheimer’s disease (AD) is a neurodegenerative disorder characterized
   by the accumulation of amyloid-β (Aβ) plaques followed by intracellular
   neurofibrillary tangles (NFTs) composed of hyperphosphorylated tau. An
   unrestrained immune response by microglia, the resident cells of the
   central nervous system (CNS), leads to neuroinflammation which can
   amplify AD pathology. AD pathology is also driven by metabolic
   dysfunction with strong correlations between dementia and metabolic
   disorders such as diabetes, hypercholesterolemia, and
   hypertriglyceridemia. Since elevated cholesterol and triglyceride
   levels appear to be a major risk factor for developing AD, we
   investigated the lipid metabolism transcriptome in an AD versus non-AD
   state using RNA-sequencing (RNA-seq) and microarray datasets from N9
   cells and murine microglia. We identified 52 differentially expressed
   genes (DEG) linked to lipid metabolism in LPS-stimulated N9 microglia
   versus unstimulated control cells using RNA-seq, 86 lipid metabolism
   DEG in 5XFAD versus wild-type mice by microarray, with 16 DEG common
   between both datasets. Functional enrichment and network analyses
   identified several biological processes and molecular functions, such
   as cholesterol homeostasis, insulin signaling, and triglyceride
   metabolism. Furthermore, therapeutic drugs targeting lipid metabolism
   DEG found in our study were identified. Focusing on drugs that target
   genes associated with lipid metabolism and neuroinflammation could
   provide new targets for AD drug development.

   Subject terms: Neuroimmunology, Immunology

Introduction

   Alzheimer’s disease (AD) is a neurodegenerative disorder characterized
   by the accumulation of amyloid-β (Aβ) plaques followed by intracellular
   neurofibrillary tangles (NFTs) composed of hyperphosphorylated
   tau^[26]1. Microglia, the innate immune cells of the central nervous
   system (CNS), facilitate Aβ and tau clearance, but also promote
   neuroinflammation that damages neurons and exacerbates AD
   pathology^[27]2. To date, few effective treatments for AD exist, and
   most AD drug research is focused on Aβ and tau reduction. Recently, the
   FDA approved the anti-amyloid antibodies aducanumab and lecanemab for
   the treatment of AD^[28]3,[29]4. Although both antibodies reduce the
   rate of cognitive decline, legitimate questions remain regarding the
   efficacy and safety of aducanumab and lecanemab^[30]5,[31]6.
   Furthermore, since only Aβ is targeted, and as AD is a complex disease,
   it is estimated that only 8–20% of patients with AD will be eligible
   for treatment^[32]7, so there is still an urgent need for new
   therapeutic interventions for AD. Recent studies suggest AD
   neuropathology is driven by metabolic dysfunction with strong
   correlations between dementia and metabolic disorders such as
   hypertension, diabetes, hypercholesterolemia, and
   hypertriglyceridemia^[33]8. Furthermore, increased microglial lipid
   metabolism provides energy for microglial activation and effector
   functions, and alterations in microglial lipid metabolism are
   implicated in the development of several neurological disorders,
   including AD^[34]9,[35]10. Therefore, the characterization of metabolic
   networks, and identification of drugs targeting the genes in these
   networks, could be a potential treatment strategy for AD.

   Lipids account for most of the dry mass of the brain^[36]11 and lipid
   metabolism changes during the aging process^[37]12. Lipids in the brain
   can largely be classified as sterols, fatty acids, phospholipids,
   glycerolipids, and sphingolipids^[38]13. The human brain has the
   highest level of cholesterol compared to other organs and is a vital
   component of eukaryotic membranes^[39]14. Apolipoprotein E (ApoE) is
   the main cholesterol carrier in the brain^[40]15, and is considered the
   strongest genetic risk factor for the development of late-onset
   AD^[41]16. Individuals with one copy of the apoE ε4 allele increase
   their risk of developing AD approximately fourfold, while individuals
   with two copies increase their risk by approximately 12-fold^[42]16.
   Additionally, apoE ε4 allele carriers are more likely to develop
   hypercholesterolemia and hypertriglyceridemia^[43]17–[44]19. A recent
   study suggests management of blood glucose and cholesterol levels in
   early adulthood has a significant impact on AD risk later in
   life^[45]20. Based on these findings, limiting dysfunctions in lipid
   metabolism could be vital for the prevention of AD, particularly for
   apoE ε4 allele carriers.

   Since metabolic disorders appear to be a major risk factor for
   developing AD, we investigated the lipid metabolism transcriptome in an
   AD versus non-AD state using RNA-sequencing (RNA-seq) and microarray
   datasets from N9 and murine microglia. We identified 52 lipid
   metabolism differentially expressed genes (DEG) in LPS-stimulated N9
   microglia versus unstimulated control cells using RNA-seq, 86 lipid
   metabolism DEG in 5XFAD versus wild-type mice by microarray, with 16
   DEG common between both datasets. Functional enrichment and network
   analyses identified several biological processes and molecular
   functions, such as cholesterol homeostasis, insulin signaling, and
   triglyceride metabolism as being dysregulated in neuroinflammation and
   AD. Furthermore, gene–drug interactions were characterized to identify
   drugs targeting lipid metabolism genes. Overall, these data show the
   lipid metabolism transcriptional response by murine microglia during AD
   progression with the potential for new treatment strategies directed
   towards AD.

Results

AD promotes differential expression of lipid metabolism genes

   In order to identify lipid metabolism genes involved in AD-associated
   neuroinflammation, we analyzed gene expression datasets from
   LPS-stimulated N9 microglia and microglia isolated from 8-month-old
   5XFAD mice. Analysis of RNA-seq data from LPS-stimulated N9 microglia
   versus non-stimulated control cells revealed a total of 52 lipid
   metabolism DEG (log[2]FC > 0.5, FDR-adjusted p-value < 0.05). Of these
   52 DEG, 35 were up-regulated and 17 were down-regulated (Fig. [46]1A).
   Apolipoprotein L 9a (Apol9a) was the most up-regulated lipid metabolism
   gene (log[2]FC = 6.71) and transthyretin (Ttr) was the most
   down-regulated lipid metabolism gene (log[2]FC =  − 4.66)
   (Supplementary Table [47]S1).

Figure 1.

   [48]Figure 1
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   Differentially expressed lipid metabolism genes in AD. (A) Scatter plot
   of lipid metabolism DEG (log[2]FC > 0.5, FDR-adjusted P-value < 0.05)
   by RNA-seq in N9 microglia stimulated with LPS (1 µg/ml) for 6 h versus
   unstimulated control cells. (B) Scatter plot of lipid metabolism DEG
   (log[2]FC > 0.5, P < 0.05) by microarray in microglia isolated from the
   brains of 5XFAD mice versus wild-type mice (8 months old). For both
   scatter plots, up-regulated genes are shown in red and down-regulated
   genes are shown in green. Data are graphed as log[2]FC versus − log[10]
   (P-value). (C) Venn diagram demonstrating overlap in lipid metabolism
   DEG between the N9 and mouse microglia datasets.

   We identified 86 lipid metabolism DEG (log[2]FC > 0.5, p < 0.05) in
   publicly available transcriptional data from sorted microglia from
   female 8-month-old 5XFAD mice versus wild-type mice^[50]21. The 5XFAD
   mouse model of AD rapidly develops severe amyloid pathology with Aβ
   plaque accumulation beginning around 2 months of age^[51]22. Of the 86
   lipid metabolism DEG, 55 were up-regulated and 31 were down-regulated
   (Fig. [52]1B). Fatty acid binding protein 3 (Fabp3) was the most
   up-regulated lipid metabolism gene (log[2]FC = 4.58) and transthyretin
   (Ttr) was the most down-regulated lipid metabolism gene
   (log[2]FC =  − 1.97) (Supplementary Table [53]S2).

   In total, 16 lipid metabolism DEG overlapped between the datasets
   (Fig. [54]1C). A complete list of the 16 lipid metabolism genes and
   their fold change values for both datasets is shown in Table [55]1.

Table 1.

   Altered lipid metabolism genes common to both datasets.
   Gene ID^1 Description N9 Log[2]FC Mouse Log[2]FC
   Il1b 27398 Interleukin 1 beta 5.94 2.92
   Zc3h12a 42677 Zinc finger CCCH type containing 12A 3.56 0.67
   Olr1 30162 Oxidized low density lipoprotein (lectin-like) receptor 1
   1.75 1.43
   C3 24164 Complement component 3 1.59 1.09
   Plaur 46223 Plasminogen activator, urokinase receptor 1.51 1.85
   Cybb 15340 Cytochrome b-245, beta polypeptide 1.31 2.54
   Ptgs2 32487 Prostaglandin-endoperoxide synthase 2 1.22 3.03
   Cxcl16 18920 Chemokine (C-X-C motif) ligand 16 1.11 1.18
   Plscr1 32369 Phospholipid scramblase 1 0.90 0.98
   Fabp3 28773 Fatty acid binding protein 3, muscle and heart 0.74 4.58
   Stard3 18167 START domain containing 3 0.52  − 0.52
   Tlr4 39005 Toll-like receptor 4  − 0.53  − 0.99
   Pik3cd 39936 Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic
   subunit delta  − 0.63  − 0.59
   Apoe 02985 Apolipoprotein E  − 0.68 1.54
   Fdps 59743 Farnesyl diphosphate synthetase  − 0.79 1.25
   Ttr 61808 Transthyretin  − 4.66  − 1.97
   [56]Open in a new tab

   ^1All gene IDs start with ENSMUSG000000.

Pathway and enrichment analysis of altered lipid metabolism genes

   Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and
   Search Tool for the Retrieval of Interacting Genes/Proteins (STRING)
   analyses were performed on the N9 and mouse microglial lipid metabolism
   DEG. For N9 microglia, Biological Process (BP) GO indicated the DEG
   participated in lipid metabolic process, lipoprotein metabolic process,
   lipid transport, fatty acid metabolic process, triglyceride metabolic
   process, positive regulation of angiogenesis, cholesterol homeostasis,
   fatty acid biosynthetic process, and cholesterol metabolic process
   (Fig. [57]2A). Cellular Component (CC) GO indicated the N9 microglial
   lipid metabolism DEG were located in the intracellular membrane-bounded
   organelle, endoplasmic reticulum, extracellular region, membrane raft,
   extracellular space, endoplasmic reticulum membrane, membrane, and
   Golgi apparatus (Fig. [58]2B). Molecular Function (MF) GO indicated the
   N9 microglial lipid metabolism DEG were involved in lipid binding
   (Fig. [59]2C). A complete list of the GO, false discovery rates, and
   genes associated with each GO is shown in Supplementary Table [60]S3.

Figure 2.

   [61]Figure 2
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   N9 microglia GO enrichment analysis. Biological function analyses for
   the N9 microglial lipid metabolism DEG was performed using DAVID.
   Analyses were performed for biological process (BP) (A), cellular
   component (CC) (B), and molecular function (MF) (C). Pathways are shown
   in descending order based on − log[10] FDR. The number of genes
   associated with each GO term is shown above each bar. Only GO terms
   with a gene count ≥ 5 and FDR < 0.05 were considered significant.

   For mouse microglial lipid metabolism DEG, BP GO indicated the genes
   participated in lipid metabolic process, cholesterol metabolic process,
   sterol biosynthetic process, steroid biosynthetic process, fatty acid
   metabolic process, cholesterol biosynthetic process, steroid
   biosynthetic process, lipid transport, cholesterol homeostasis, fatty
   acid biosynthetic process, long-chain fatty acid transport,
   triglyceride homeostasis, positive regulation of angiogenesis,
   lipoprotein metabolic process, cellular response to insulin stimulus,
   angiogenesis, memory, and lipid catabolic process (Fig. [63]3A). CC GO
   indicated the mouse microglial lipid metabolism DEG were located in the
   endoplasmic reticulum, very-low-density lipoprotein particle,
   high-density lipoprotein particle, endoplasmic reticulum membrane,
   extracellular region, membrane, intracellular membrane-bound organelle,
   membrane raft, extracellular space, caveolae, cell surface, cytoplasm,
   nuclear envelope, and cytosol (Fig. [64]3B). MF GO indicated the mouse
   microglial lipid metabolism DEG were involved in lipid binding
   (Fig. [65]3C). A complete list of the GO, false discovery rates, and
   genes associated with each GO is shown in Supplementary Table [66]S4.

Figure 3.

   [67]Figure 3
   [68]Open in a new tab

   Mouse microglia GO enrichment analysis. Biological function analyses
   for the mouse microglial lipid metabolism DEG was performed using
   DAVID. Analyses were performed for biological process (BP) (A),
   cellular component (CC) (B), and molecular function (MF) (C). Pathways
   are shown in descending order based on − log[10] FDR. The number of
   genes associated with each GO term is shown above each bar. Only GO
   terms with a gene count ≥ 5 and FDR < 0.05 were considered significant.

   KEGG analysis was performed on the N9 and mouse microglial lipid
   metabolism DEG. For N9 microglia, KEGG identified three pathways (lipid
   and atherosclerosis, PPAR signaling pathway, cholesterol metabolism)
   associated with the DEG (Fig. [69]4A). A complete list of the KEGG
   pathways, false discovery rates, and genes associated with each pathway
   is shown in Supplementary Table [70]S5. For the mouse microglial lipid
   metabolism DEG, KEGG identified a total of 14 associated with the DEG
   (Fig. [71]4B). The pathways included cholesterol homeostasis, steroid
   biosynthesis, metabolic pathways, AGE-RAGE signaling pathway in
   diabetic complications, lipid and atherosclerosis, PPAR signaling
   pathway, regulation of lipolysis in adipocytes, VEGF signaling pathway,
   insulin resistance, sphingolipid signaling pathway, insulin signaling
   pathway, diabetic cardiomyopathy, phosphatidylinositol signaling
   system, glycerolphospholipid metabolism. A complete list of the KEGG
   pathways, false discovery rates, and genes associated with each pathway
   is shown in Supplementary Table [72]S6.

Figure 4.

   [73]Figure 4
   [74]Open in a new tab

   KEGG pathway enrichment analysis. KEGG pathway analysis was performed
   on the lipid metabolism DEG from the N9 microglia RNA-seq (A) and mouse
   microglia microarray (B) using DAVID. Pathways are shown in descending
   order based on − log[10] FDR. The number of genes associated with each
   pathway is shown above each bar. Only pathways with a gene count ≥ 5
   and FDR < 0.05 were considered significant.

   To further understand the interactions of the lipid metabolism genes,
   we performed protein–protein interaction (PPI) analysis on the N9 and
   mouse microglial DEG using STRING to identify known and predicted PPIs.
   Of the proteins encoded by the N9 microglial lipid metabolism DEG, 26
   proteins clustered in a large network with two proteins forming a
   second, small cluster, and 24 proteins not clustering (Fig. [75]5A).
   For the mouse microglial lipid metabolism DEG, 52 proteins clustered in
   a large network, with 10 proteins forming four distinct smaller
   networks, and 24 proteins not clustering (Fig. [76]5B). The results
   suggest that the given proteins were highly enriched for both the N9
   and mouse datasets (p < 1 × 10^–16) indicating that the interactions
   were significantly more than those expected for a random collection of
   input genes, and these PPI networks could be significantly altered in
   lipid metabolism and AD-associated microglial neuroinflammation.

Figure 5.

   [77]Figure 5
   [78]Open in a new tab

   PPI analysis using STRING. STRING analysis was performed on the N9 (A)
   and mouse (B) microglial lipid metabolism DEG. For the analysis, text
   mining, experiments, and databases were chosen for active interaction
   sources, and a high value of 0.700 was selected as the minimum required
   interaction score. Line colors represent known interactions from
   curated databases (blue), experimentation (purple) and text mining
   (yellow). The proteins for which there were no connections to be mapped
   are not shown.

Lipid metabolism gene targets for therapeutic drugs

   In order to determine lipid metabolism gene targets for therapeutic
   drugs, we performed gene–drug interactions in the drug–gene interaction
   database (DGIdb)^[79]23,[80]24 using the 16 lipid metabolism DEG common
   to both datasets (Table [81]1). A total of ten lipid metabolism genes
   had interactions with therapeutic drugs (Fig. [82]6).
   Prostaglandin-endoperoxide synthase 2 (Ptgs2) had the most interactions
   (88 drugs) with most of the drugs identified used to treat arthritis,
   pain, fever, and inflammation. The DGIdb gene–drug interaction tool
   also identified the hypertension drugs reserpine and atenolol as
   targets for Ptgs2. Transthyretin (Ttr) (the most down-regulated gene in
   both datasets), had nine drug interactions identified with several of
   the drugs used for transthyretin-mediated amyloidosis (tafamidis,
   tafamidis meglumine, inotersen, patisiran). Interleukin 1 beta (Il1b)
   (highly up-regulated in both datasets) had 37 drugs interactions with
   the majority of the drugs used to treat arthritis and inflammation.
   Finally, several other drugs were also identified as potentially
   interacting with our genes of interest including those targeting
   hypertension (verapamil, nicardapine), cholesterol (pravastatin),
   bipolar disorder (lithium), and microglial activation inhibition (TT
   301) drugs for Il1b. A complete list of the lipid metabolism DEG and
   their associated drugs is shown in Supplementary Table [83]S7.

Figure 6.

   Figure 6
   [84]Open in a new tab

   Gene–drug interactions. Interactions between therapeutic drugs and the
   16 lipid metabolism DEG common to both datasets. Genes identified with
   drug interactions are shown and the number of drugs associated with
   each gene is shown above each bar.

Cybb network analysis

   Since Cybb was altered in both datasets, involved in several pathways
   at the intersection of diabetes and cardiovascular disease, and was the
   target of several drugs, expression network analysis was performed to
   identify genes positively and negatively correlated with Cybb. In the
   positive correlation map, several genes involved in lipid metabolism
   and inflammation including Plaur (0.796), Irak3 (0.785), Prdx5 (0.855),
   Casp1 (0.802), and Naip2 (0.784) displayed a positive relationship with
   Cybb (Fig. [85]7A). In the negative correlation map, Mtor (− 0.786), a
   gene that controls most metabolic processes in response to nutrients,
   displayed a negative relationship with Cybb (Fig. [86]7B). Together,
   these data suggest Cybb could be a promising target for future research
   investigating gene networks for AD treatment strategies.

Figure 7.

   [87]Figure 7
   [88]Open in a new tab

   Cybb expression network analysis. Gene constellations for Cybb were
   created using ImmGen. (A) Positive expression correlation of genes to
   Cybb. (B) Negative expression correlation of genes to Cybb.

Discussion

   Investigating N9 and mouse microglial gene expression datasets, we
   identified several lipid metabolism genes which may be important in AD
   development. Diabetes, hypertension, hypercholesterolemia, and
   hypertriglyceridemia have all been identified as major risk factors of
   AD, but the mechanisms between these metabolic syndromes and AD remains
   unclear^[89]8,[90]20,[91]25. Statins are the most commonly used drugs
   for treating lipid disorders and are effective in reducing cholesterol
   and triglyceride levels. Conflicting results are reported when
   accessing statins specifically for AD, as several studies suggest
   statins slow the progression of AD^[92]26–[93]29 while others suggest
   statins provide no protective benefit against AD^[94]30–[95]32.
   Overall, the relationship between statins and cognitive function
   remains unclear, and further investigation is needed to determine if
   statins are an appropriate therapeutic strategy for AD.

   Recently, several studies have highlighted the importance of microglial
   lipid metabolism in AD. Recent review articles provide a detailed
   overview of microglial lipid metabolism in regards to altered brain
   function in AD^[96]9,[97]10. In a single-cell RNA sequencing
   (scRNA-seq) study, Keren-Shaul et al. used the 5XFAD mouse model of AD
   to demonstrate a preference for lipids as a fuel source during the
   increased metabolic energy demands of activated microglia^[98]33.
   Furthermore, their study identified a unique subset of microglia,
   disease associated microglia (DAM), which have a unique transcriptional
   profile associated with several lipid and metabolism genes, such as
   triggering receptor expressed on myeloid cells 2 (Trem2), lipoprotein
   lipase (Lpl), and ApoE^[99]33. In another study, Krasemann et al.
   described a distinct gene expression pattern associated with a TREM2-
   and APOE-dependent response by microglia to tissue damage in the
   brain^[100]34. In human AD, and the APP-PS1 mouse model of AD, they
   demonstrated the APOE pathway, driven by TREM2, mediated a switch in
   microglial phenotypes from homeostatic to neurodegenerative, indicating
   activation of the TREM2-APOE pathway leads to the inability of
   microglia to regulate brain homeostasis^[101]34. Further understanding
   of microglia-lipid interactions, along with advances in lipidomics
   technologies^[102]35,[103]36, could aide in the development of new
   treatment regimens for AD.

   Neuroinflammation drives AD pathogenesis by exacerbating both amyloid
   and tau pathologies^[104]37. Hypercholesterolemia has been linked to
   cognitive dysfunction accelerated by neuroinflammation in mice^[105]38
   and rats^[106]39 fed a high fat diet. In these studies, activated
   microglia and astrocytes in the hippocampus released several
   proinflammatory cytokines, including IL-1β^[107]38,[108]39. IL-1β was
   highly up-regulated in both our datasets, and is a vital component of
   lipid metabolism through the regulation of Lpl^[109]40,[110]41.
   Activation of the nod-like receptor family pyrin domain containing 3
   (NLRP3) inflammasome is a major source of IL-1β, and the NLRP3
   inflammasome induces chronic neuroinflammation that significantly
   increases AD pathology^[111]42. Recently, several studies have used the
   ketone metabolite, β-hydroxybutyrate, to block activation of the NLRP3
   inflammasome as a therapeutic strategy for general inflammatory
   disease^[112]43 and AD^[113]44. The gene-drug analysis performed in our
   study, identified 37 drugs targeting IL-1β. These data suggest
   therapeutic agents that block activation of the NLRP3 inflammasome and
   IL-1β as promising treatment strategies for inflammatory diseases,
   including AD.

   Cytochrome b-245, beta polypeptide (Cybb) is a subunit of nicotinamide
   adenine dinucleotide phosphate (NADPH) oxidase that produces reactive
   oxygen species (ROS) which mediate microglial inflammatory
   responses^[114]45. If not properly regulated, ROS production by
   microglia leads to neuron damage, dysfunction, and death^[115]46. In
   our study, Cybb was up-regulated in both datasets. Network analysis
   revealed a positive correlation between Cybb and the inflammatory genes
   Casp1 and Naip2. BP GO analysis identified Cybb in the positive
   regulation of angiogenesis, and KEGG pathway analysis identified Cybb
   in the lipid and atherosclerosis, AGE-RAGE signaling pathways in
   diabetic complications, and diabetic cardiomyopathy pathways.
   Additionally, our gene-drug interaction analysis identified three
   compounds that target Cybb. Taken together, these data suggest Cybb is
   at the intersection of cardiovascular disease, diabetes, and
   neuroinflammation, making it an attractive potential target for further
   investigation.

   The identification of enriched lipid metabolism DEGs in cellular
   compartments (GO CC) such as the endoplasmic reticulum (ER), caveolae,
   and Golgi apparatus, provides further insight into the role of lipid
   metabolism DEGs in microglial-mediated neuroinflammation in AD.
   Increasing evidence suggests sustained ER stress contributes to neuron
   damage, microglial polarization, and altered inflammatory responses,
   particularly in LPS-stimulated cells^[116]47–[117]49. Furthermore, ER
   stress is shown to activate microglia^[118]50 and inhibiting ER stress
   displays a neuroprotective effect in LPS-stimulated BV2
   microglia^[119]51. The Golgi apparatus is essential for the synthesis
   and modification of proteins and lipids, and Golgi fragmentation has
   been observed in AD^[120]52. Golgi fragmentation is suggested to
   promote neuronal ion channel damage and the accumulation of tau and
   Aβ^[121]53. Specialized regions of plasma membranes, such as caveolae,
   modulate reactive oxygen species (ROS) producing systems^[122]54.
   Activated microglia produce ROS, along with many other proinflammatory
   molecules implicated in AD pathogenesis^[123]2. In our study, a large
   number of lipid metabolism DEGs in N9 and mouse microglia were found in
   the ER and Golgi apparatus, including ApoE and Cybb. Additionally,
   several mouse microglial lipid metabolism DEGs localized in the
   caveolae, including low density lipoprotein receptor (Ldlr) which is a
   primary metabolic receptor responsible for ApoE lipoprotein
   clearance^[124]55,[125]56. In the study by Shi et al., a
   neuroprotective role for Ldlr was described as Ldlr overexpression
   attenuated tau pathology through preservation of myelin, inhibiting
   microglial activation, and reducing ApoE levels, suggesting drug
   discovery directed towards increasing Ldlr levels as a treatment option
   for AD and other tauopathies^[126]57.

   One of the primary limitations of our study is that only murine
   microglia were used. We examined several publicly available human AD
   transcriptome datasets in an attempt to correlate our findings to AD in
   humans. These datasets indicated most lipid metabolism genes identified
   in our study were differentially regulated in human datasets as well.
   The issue, however, was that even though the human datasets analyzed
   showed a large number of overall genes altered in AD brains versus
   non-AD controls, the level of differential gene expression was not
   nearly as robust as seen in our mouse and cell culture transcriptome
   datasets. Gene expression analysis of human brain tissue and cells is
   certainly a valuable tool for investigating AD in humans. Brain tissue,
   however, can only be collected postmortem, and peripheral tissues and
   blood, which can easily be collected from patients, may not express
   brain proteins that are central to AD development and
   progression^[127]58. RNA stability in human samples is also a concern
   as confounding factors, like differences in postmortem interval, have
   been shown to contribute to inaccuracies in human brain transcriptomic
   data^[128]59. Furthermore, comparing the mouse and human AD
   transcriptome remains unclear, as some studies indicated the
   transcriptomes are similar^[129]60,[130]61 while others suggest they
   are different^[131]62,[132]63. A strength of our study is that N9 cell
   culture and primary microglia from mice were used. By comparing these
   datasets, we were able to determine genes which are part of the
   neuroinflammatory aspect of AD development since lipopolysaccharide
   (LPS) treated N9 microglia would primarily represent the general
   neuroinflammation process. Overall, these data offer several microglial
   gene target–drug interactions for initial validation in mouse models of
   AD.

   In summary, our transcriptome profiling data offers a useful resource
   to the field for understanding the inflammatory roles of microglial
   lipid metabolism genes in AD. Furthermore, our data identified drugs
   for microglial molecular targets for future investigation to
   attenuate/eliminate the pathological progression of AD.

Methods

Datasets

   The N9 microglial RNA-seq dataset was published previously by our group
   ([133]GSE183038)^[134]64. Briefly, immortalized murine N9 microglia
   were routinely cultured^[135]65 and seeded at a cell density of 250,000
   cells/well in a 24-well tissue culture plate. Cells were stimulated
   with LPS (1 µg/ml) from Escherichia coli O111:B4 (InvivoGen) for 6 h.
   RNA was extracted using an RNeasy Plus Mini Kit (Qiagen, Cat. No.
   74134). Quality and quantity of RNA was assessed using an Agilent 2100
   Bioanalyzer (Agilent Technologies) and a Nanodrop spectrophotometer
   (Thermo Scientific). All samples had an RNA integrity number (RIN) of
   9.7 or higher. RNA library preparation and transcriptome sequencing
   were performed by Novogene using the Illumina NovaSeq 6000 Sequencing
   System. Genes with FDR-adjusted p-values < 0.5 and log[2]FC > 0.5 were
   considered differentially expressed.

   The mouse microarray has been published in a previous study^[136]21 and
   the publicly available dataset ([137]GSE65067) was used. Briefly,
   microglia from female 8 month old wild-type (n = 3) and 5XFAD (n = 5)
   mice (The Jackson Laboratory) were FACS-sorted directly into RTL-plus
   lysis buffer. RNA extraction from microglia was performed using an
   RNeasy Plus Micro Kit (Qiagen, Cat. No. 74034). Microarray
   hybridization (Affymetrix MoGene 1.0 ST array) and data processing were
   performed at the Washington University Genome Center. Genes with
   p-values < 0.05 and log[2]FC > 0.5 were considered differentially
   expressed.

Gene analyses

   In order to identify lipid metabolism genes, the datasets were searched
   for the terms “lipid”, “lipoprotein” and “cholesterol”. Additionally,
   datasets were searched for lipid metabolism genes from lipoprotein
   signaling, cholesterol metabolism, and lipid metabolism pathway
   analysis gene lists (Supplementary Table [138]S8).

   Lipid metabolism genes found to be differentially expressed were
   selected for biological function analysis. The gene list was uploaded
   into the Database for Annotation, Visualization and Integrated
   Discovery (DAVID, v. 6.8)^[139]66,[140]67 for GO and KEGG pathway
   analysis. KEGG pathways and GO BP, CC, and MF with gene counts ≥ 5 and
   FDR < 0.05 were considered significant. Additionally, PPI analysis was
   performed to identify interactions of the selected proteins based on
   their gene IDs using the STRING database^[141]68. For the analysis,
   text mining, experiments, and databases were chosen for active
   interaction sources using the high confidence (0.700) threshold
   setting.

   Scatterplots were created using Prism 9.0.0 (GraphPad). Venn diagrams
   demonstrating overlap in lipid metabolism DEG amongst the datasets were
   generated using InteractiVenn^[142]69. Gene constellations identifying
   genes in the Cybb regulatory network were created with ImmGen^[143]70
   using the “myeloid cells” reference populations option. Gene-drug
   interactions of the common lipid metabolism DEG were identified using
   DGIdb^[144]23,[145]24 using the default settings.

Supplementary Information

   [146]Supplementary Tables.^ (84.8KB, xlsx)

Acknowledgements